photosynthesis journal articles pdf

REVIEW article

Photosynthesis in a changing global climate: scaling up and scaling down in crops.

• 1 Laboratory of Biochemistry, Faculty of Agriculture, Niigata University, Niigata, Japan
• 2 Graduate School of Science and Technology, Niigata University, Niigata, Japan
• 3 Institute of Plant Sciences Paris-Saclay (IPS2), CNRS, INRAE, Université Paris-Saclay, Université Evry, Université Paris Diderot, Paris, France
• 4 Institute of Biochemical Plant Pathology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
• 5 USDA-ARS Plant Physiology and Genetics Research, US Arid-Land Agricultural Research Center, Maricopa, AZ, United States
• 6 Agrobiotechnology Institute (IdAB-CSIC), Consejo Superior de Investigaciones Científicas-Gobierno de Navarra, Mutilva, Spain
• 7 Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL, United States

Photosynthesis is the major process leading to primary production in the Biosphere. There is a total of 7000bn tons of CO 2 in the atmosphere and photosynthesis fixes more than 100bn tons annually. The CO 2 assimilated by the photosynthetic apparatus is the basis of crop production and, therefore, of animal and human food. This has led to a renewed interest in photosynthesis as a target to increase plant production and there is now increasing evidence showing that the strategy of improving photosynthetic traits can increase plant yield. However, photosynthesis and the photosynthetic apparatus are both conditioned by environmental variables such as water availability, temperature, [CO 2 ], salinity, and ozone. The “omics” revolution has allowed a better understanding of the genetic mechanisms regulating stress responses including the identification of genes and proteins involved in the regulation, acclimation, and adaptation of processes that impact photosynthesis. The development of novel non-destructive high-throughput phenotyping techniques has been important to monitor crop photosynthetic responses to changing environmental conditions. This wealth of data is being incorporated into new modeling algorithms to predict plant growth and development under specific environmental constraints. This review gives a multi-perspective description of the impact of changing environmental conditions on photosynthetic performance and consequently plant growth by briefly highlighting how major technological advances including omics, high-throughput photosynthetic measurements, metabolic engineering, and whole plant photosynthetic modeling have helped to improve our understanding of how the photosynthetic machinery can be modified by different abiotic stresses and thus impact crop production.

Introduction

Owing to the expected increase in the world’s population, yields of major crops must increase by over 70% in the next 30 years to sustain human requirements ( FAO, 2009 ) and this should be attained without increasing the use of arable land and detrimental effects on nutritional quality while limiting the use of fertilizers and pesticides This means that breeders must increase crop yield at a rate of +2.4% per year, while the current rate is only +1.3% ( FAO, 2009 ). In addition, abiotic stresses such as heat, drought, and flooding among others will tend to decrease yields up to 50% by 2050, if management techniques such as precision irrigation and breeding for abiotic stress tolerance are not implemented ( Bierbaum et al., 2007 ).

Photosynthesis is a complex process that for simplification can be divided into light reactions driven by electrons passing through different protein complexes associated with chloroplast thylakoid membranes and the Calvin cycle reactions of photosynthetic CO 2 fixation taking place in the chloroplast stroma ( Renger, 2007 ). In the light, the photosynthetic electron transfer chain consisting of photosystem II (PSII), the cytochrome b 6 f complex (cytb 6 f), photosystem I (PSI), and the free electron carriers plastoquinone (PQ) and plastocyanin, lead to the production of ATP and NADPH that fuel the Calvin-Benson cycle (CBC) and other assimilatory processes ( Rochaix, 2011 ; Foyer et al., 2012 ). Three main stages operate during the CBC reactions namely carbon fixation, reduction, and regeneration. In all plants, CO 2 can be fixed by ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO), an enzyme catalyzing the carboxylation of ribulose-1,5-bisphosphate (RuBP) and leading to two molecules of 3-phosphoglycerate (3-PGA). Instead of CO 2 , RuBisCO can also add O 2 to RuBP, resulting in one molecule each of 3-PGA and 2-phosphoglycolate (2-PG). Since 2-PG is toxic, it has to be removed in a metabolic pathway called photorespiration that is not only energy demanding, but also leads to a loss of carbon in the form of CO 2 . Thus the efficiency of photosynthesis can be substantially decreased under environmental conditions favoring photorespiration ( Ehleringer et al., 1991 ) and this would be associated with factors altering CO 2 entry and diffusion within the leaf such as stomatal density and aperture.

Furthermore, photosynthesis is highly sensitive to abiotic stresses such as drought, high temperatures, and ozone, since they inactivate photosynthetic electron transfer and photophosphorylation, adversely affect photosynthetic metabolic processes, and lead to damage of thylakoid membranes and organelle ultrastructure ( Ainsworth et al., 2013 ; Lobell et al., 2014 ; Sieber et al., 2016 ). In fact, an increase in atmospheric temperature can reduce crop yields by between 6 to 25% depending on the region and the crop ( Sieber et al., 2016 ; Zhao et al., 2017 ). However, drought is the major abiotic stress that impairs crop production ( Mishra and Cherkauer, 2010 ; Lobell et al., 2014 ; Lesk et al., 2016 ; Zipper et al., 2016 ; Matiu et al., 2017 ) due to photosynthetic limitations imposed by stomatal and non-stomatal processes ( Tissue et al., 2005 ; Kohzuma et al., 2009 ; Dahal et al., 2014 ). It has been estimated that drought has caused the loss of 1820 million tons of cereal production during the last 4 decades ( Lesk et al., 2016 ). In the future, drought occurrence and severity are projected to rise, increasing the risk of yield loss by 24% in soybean, 21% in maize, 18% in rice, and 20% in wheat ( Leng and Hall, 2019 ). On the other hand, the predicted increase in atmospheric CO 2 levels, as a substrate of photosynthesis, is expected to increase yields by up to 30% depending on plant species and other environmental conditions ( Ainsworth and Long, 2005 ; Long et al., 2006 ; Sanz-Saez et al., 2017 ). It has been shown that elevated temperature and drought can negate the positive effects of elevated CO 2 on yield ( Ruiz-Vera et al., 2013 ; Gray et al., 2016 ). While plant breeders and plant biologists have worked extensively over the years to increase yields and improve plant responses to abiotic stresses, photosynthesis was often overlooked ( Long et al., 2015 ). Advances in genomics, genetics, and modeling tools have now paved the way for improving photosynthesis to increase yields within climate change scenarios ( Zhu et al., 2010 ; Long et al., 2015 ; Ort et al., 2015 ).

The effects of abiotic stresses on photosynthesis have given rise to numerous review articles ( Hikosaka et al., 2006 ; Pinheiro and Chaves, 2011 ; Ainsworth et al., 2013 ; Song et al., 2014 ; Dusenge et al., 2019 ); however, many of them only focused on specific aspects. In this review, the effects of abiotic stresses are considered from a holistic point of view. It covers the use of “omics” techniques (genomics, transcriptomics, proteomics, and hormonomics) (Section “-Omics” Analyses to Identify Novel Targets and Networks Underlying the Function of the Photosynthesis Machinery: Roads to Develop Engineered Environmental Stress-Tolerant Crops Through Photosynthesis”) to identify potential target genes that could improve photosynthesis and crop yield. Whole plant physiological responses (Section “Physiological Traits Involved in the Maintenance of Photosynthesis as Tools for Crop Improvement in a Context of Climate Change”) and the development of semi- and high-throughput phenotyping techniques (Section “Semi- and High-Throughput Phenotyping Techniques to Measure Photosynthetic Traits”) are described that allow for a better understanding of major physiological traits associating the maintenance of photosynthesis with abiotic stress tolerance. To bring together the wealth of knowledge and to extrapolate the effects of the environment on photosynthetic capacity and plant development at the whole plant land canopy levels, Section “Modeling Photosynthesis in Crop Growth Models” reviews the application of photosynthetic models to calculate carbon gain for biomass production and to estimate possible future impacts of a changing climate on global crop production and grain yield. Finally, Section “Metabolic Engineering to Improve Photosynthesis and Elevated CO 2 Acclimation” gives an overview of the application of metabolic engineering and examples of what has been successfully achieved already to improve photosynthesis and how elevated CO 2 acclimation might limit yield improvement and quality of certain C3-plant species.

“-Omics” Analyses to Identify Novel Targets and Networks Underlying the Function of the Photosynthesis Machinery: Roads to Develop Engineered Environmental Stress-Tolerant Crops Through Photosynthesis

The emergence of omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, ionomics, and hormonomics have permitted to identify components associated with photosynthesis including molecular regulatory circuitries, photosynthetic machinery and functioning, and photoprotective mechanisms, thus underpinning factors paving the way to photosynthesis efficiency-boosting and the improved productivity and quality of modern crop varieties ( Figure 1 ).

Genomics to Study the Natural Variation of Plant Photosynthetic Efficiency

This section does not intend to give a detailed account of genomics and the reader is directed to other publications to read about general genomic innovation for crop improvement ( Bevan et al., 2017 ), development of new genomic technologies ( Huang et al., 2010 ; Takagi et al., 2013 ; Schlotterer et al., 2014 ; Varshney et al., 2014 ; Pandey et al., 2016 ; Crossa et al., 2017 ; Rasheed et al., 2017 ; Scheben et al., 2017 ; Watson et al., 2018 ), and the use of genomics in crop breeding ( Varshney et al., 2012 , 2018 ).

Evolution has been continually shaping photosynthesis, so fine-tuning this rather inefficient metabolic process could help to boost crop yields under normal and adverse conditions. This could be achieved using new plant breeding technologies to target photosynthetic processes and thus to contribute substantially to improving global food security under climate change scenarios. Conventional quantitative trait locus (QTL) mapping using recombinant inbred lines (RIL) and near-isogenic lines (NIL) is an effective tool to identify quantitative traits associated with photosynthesis and the modulation of photosynthetic parameters in response to environmental cues ( Adachi et al., 2011 ; Gehan et al., 2015 ; Yan et al., 2015 ; de Oliveira Silva et al., 2018 ; Oakley et al., 2018 ). Indeed, RIL and NIL populations have been used to discover genetic variation and genes associated with photosynthetic efficiency, while some specific photosynthesis-related traits were found to be influenced by functional genetic variation in a limited number of genes ( Oakley et al., 2018 ). Indeed, putative QTLs have been detected for Single-Photon Avalanche Diode (SPAD) value, chlorophyll content, stomatal conductance, sink size, source strength, carbon isotope discrimination, and carbohydrate translocation ( Ulloa et al., 2000 ; Teng et al., 2004 ; Takai et al., 2010 , 2013 ). Potential QTLs have been revealed also for net CO 2 assimilation rate (A n ) in rice ( Ishimaru et al., 2001 ; Price et al., 2002 ; Zhao et al., 2011 ; Hirotsu et al., 2017 ; Ye et al., 2017 ; Adachi et al., 2019 ), barley ( Teulat et al., 2002 ; Cantalapiedra et al., 2015 ; Liu et al., 2017 ; Du et al., 2019 ), maize ( Fracheboud et al., 2002 ), soybean ( Jun et al., 2014 ; Lv et al., 2018 ; Liu D. et al., 2019 ), cucumber ( Zhang et al., 2004 ), and legumes ( Muchero et al., 2009 ; Kumar et al., 2014 ; Li F. et al., 2015 ). In the case of rice, several loci enhancing leaf A n have been detected on chromosomes 3, 4, 5, 6, 8, 9, and 11 ( Adachi et al., 2011 ; Gu et al., 2012 ). In addition, some backcross inbred lines (BILs) derived from an indica variety, Takanari, and an elite japonica cultivar have 20–50% higher values of leaf A n than those of the parental cultivars ( Adachi et al., 2013 ). By using BILs and chromosome segment substitution lines (CSSLs), Adachi et al. (2019) detected 10 “qHP” (high photosynthesis) QTLs linked to an increased A n during at least 2 years in the field and named qHP1a, qHP1b, qHP2, qHP3a, qHP3b, qHP4, qHP5, qHP7a, qHP7b, and qHP10. Takai et al. (2013) identified qHP4 in a chromosomal region containing the GPS (GREEN FOR PHOTOSYNTHESIS) gene by using the above-mentioned BIL mapping population. Similarly, a previous fine-mapping study revealed Carbon Assimilation Rate 8 (CAR8) as an A n -enhancing QTL ( Adachi et al., 2017 ). Whole-genome sequencing (WGS) is another genetic tool that can be used to identify genes susceptible to make photosynthesis more efficient. This requires the development of high-resolution mapping populations in the form of genotypically detailed diversity panels suitable for genome-wide association studies (GWAS). Together, natural variation associated with different traits can be determined, thereby providing breeders with marker-trait associations that can be directly exploited for crop design ( Huang and Han, 2014 ; Ogura and Busch, 2015 ; Barabaschi et al., 2016 ). The use of natural variation to understand the genetic basis of photosynthetic efficiency represents a powerful tool. Indeed, this approach has been widely used to reveal the genetic basis of photosynthesis-related traits under changing environmental conditions in several crops ( Panthee et al., 2006 ; Wang et al., 2016 ; Lv et al., 2018 ). Tsai et al. (2019) investigated photosynthetic efficiency under salinity stress and identified several chromosomal regions associated with chlorophyll fluorescence parameter variations, and identified some significant SNPs linked to genes involved in salt tolerance. It has been shown also that plants exhibit genetic variation for photosynthetic response to changing irradiance levels ( van Rooijen et al., 2015 ). Additionally, the application of GWAS as a powerful tool to identify candidate genes for the improvement of crop productivity has been validated by its role in the discovery of many genome regions and genes associated to A n and chlorophyll fluorescence under different stresses ( Strigens et al., 2013 ; Fiedler et al., 2016 ; Ortiz et al., 2017 ; Su et al., 2019 ). Recently, a multi-parent advanced generation intercross (MAGIC) strategy was proposed to promote genome intercrossing and shuffling ( Cavanagh et al., 2008 ). MAGIC populations have been developed for several plant species 1 and used to create ideotypes under climate change ( Bandillo et al., 2013 ; Lucas et al., 2013 ; Muchero et al., 2013 ; Huynh et al., 2018 ).

The functional dissection of photosynthesis can be undertaken also by forward genetic screens. Strategies, identification, insights and mutant effects have been reviewed previously ( Somerville, 1986 ; Parinov and Sundaresan, 2000 ; Page and Grossniklaus, 2002 ; Luo et al., 2018 ; Döring et al., 2019 ). Knowledge obtained from mutant screenings can reveal new chloroplast functions, including those necessary for high photosynthetic performance, and accelerate the molecular characterization required for deciphering the genetic basis of plant photosynthesis for future improvements. For instance, Döring et al. (2019) identified genomic segments that contained mutated candidate genes to create a more C4–like bundle sheath by using a mapping-by-sequencing approach. However, a successful forward genetic screen needs an easily identifiable trait followed by a validation of the identified mutated genes by state-of-the-art technologies such as T-DNA knock-out lines, RNAi lines, or by gene-editing tools ( Hahn et al., 2017 ). Indeed, genome editing approaches, such as transcription activator-like effector nucleases (TALENs) ( Bedell et al., 2012 ; Li et al., 2012 ) and the CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 RNA-guided system ( Cong et al., 2013 ; Feng et al., 2014 ), will enable precise genome engineering that could be useful to improve photosynthesis by generating targeted variations for precision breeding ( Scheben and Edwards, 2017 ; Scheben et al., 2017 ). Crop breeding programs will benefit from the integration of modern genomics approaches, and the use of high-throughput genotyping/phenotyping platforms (see section “Semi- and High-Throughput Phenotyping Techniques to Measure Photosynthetic Traits”). Indeed, within the context of modern plant breeding, several molecular breeding approaches have been applied to introgress genomic regions into elite lines ( Varshney et al., 2012 ). Marker-assisted selection (MAS), marker-assisted backcrossing (MABC), and gene pyramiding programs have been widely used in crop improvement to create desirable characters including high photosynthetic efficiency under (a) biotic stress conditions ( Singh and Singh, 2015 ; Varshney, 2016 ; Cobb et al., 2018 ). While transgenic approaches have been successful in improving plant yield through improved photosynthesis (as highlighted in section “Modeling Photosynthesis in Crop Growth Models”), the genetic mapping of desired photosynthesis-related traits will require an efficient implementation of high-throughput, non-destructive phenotyping (see section “Semi- and High-Throughput Phenotyping Techniques to Measure Photosynthetic Traits” for more details) to assess them between plant genotypes ( van Bezouw et al., 2018 ). The gap between genomes and phenotypes will be bridged by “omics” approaches, including transcriptomics, proteomics, hormononics, and metabolomics.

Photosynthesis and Transcriptional Regulation

About 3000 genes are required for a plant to carry out photosynthesis and high-throughput sequencing to quantify transcripts will help determine when and where a gene is turned on/off. The analysis of deregulated gene expression patterns controlling photosynthesis-related processes across a wide array of cellular responses, phenotypes, and conditions would help to engineer multiple aspects of photosynthesis in the future. This could be achieved by the manipulation of gene regulatory networks. For instance, genes encoding the four major multi-component complexes of the thylakoid membrane [PSII-LHC (light harvesting complex) II, cytb 6 f, PSI-LHCI, and ATP synthase] ( cf. Figure 2 ) that work together to carry out light-dependent energy-production must be co-regulated to be efficient.

Figure 2. Light energy drives mainstream electron flow used by photosystem II (PSII) to reduce plastoquinone (PQ) to plastoquinol. The reducing equivalents on plastoquinol move through the electron transfer chain (ETC) to the cytochrome b 6 f complex and plastocyanin (Pc), releasing protons (H + ) into the thylakoid lumen, while the electrons are used by photosystem I (PSI) to reduce ferredoxin (Fd), again driven by light-supported ETC through PSI. Reduced Fd is coupled to Fd:NADP(H) oxidoreductase (FNR) that catalyzes the reduction of NADP + to NADPH. The oxidation of water by PSII and the oxidation of plastoquinol by the cytochrome b 6 f complex allows the generation of a ΔpH between the thylakoid lumen and the stroma, essential for generating ATP via the ATPase and balancing the proportion of ATP:NADPH produced by electron transfer that is required to function the Calvin cycle that assimilates CO 2 and leads to sugar production, and plant growth.

Photoreceptors regulate the expression of many genes important for plant performance including the initiation of chloroplast biogenesis, chloroplast gene transcription, chlorophyll biosynthesis and photosynthetic-associated processes like chloroplast movements and stomatal opening ( Lepistö and Rintamäki, 2012 ; Legris et al., 2019 ), therefore engineering these genes could be of great interest to improve photosynthetic traits. The modulation of certain phytochrome gene families, especially PHYA and PHYB, in several crops of interest plays an important role in determining the enhancement of quality and yield as well as the development of agronomically important traits including abiotic stress tolerance ( Franklin and Whitelam, 2007 ; Abdurakhmonov et al., 2014 ; Gupta et al., 2014 ; Gururani et al., 2015 ; Martín et al., 2016 ; Shin et al., 2016 ). At least 41 transcription factors (TFs) have been described in Arabidopsis to act downstream of photoreceptor genes, the most characterized being PHYTOCHROME INTERACTING FACTOR (PIF) and PIFLIKE (PIL) families of basic helix-loop-helix (bHLH) proteins. Most plastid-encoded genes appear to be regulated by several sigma factors with overlapping functions. Stress-responsive TFs such as MYC (myelocytomatosis oncogene)/MYB (myeloblastosis oncogene), bZIP, NAC (NAM, ATAF, and CUC) and ZF-HD (zinc-finger homeodomain), CBF/DREB, and AREB/ABF (ABA-responsive element-binding protein/ABA-binding factor) are known to regulate the expression of photosynthetic genes in response to abiotic stresses. Homeobox homeodomain leucine-zipper (HD-Zip) TFs have diverse functions during plant development and stress adaptation, and some members of this family are under the control of the phytochrome system such as ARABIDOPSIS THALIANA HOMEOBOX 2 (ATHB2) ( Kunihiro et al., 2011 ). ATHB17 -overexpressing plants enhance abiotic stress tolerance by coordinating both photosynthesis-associated nuclear genes (PhANGs) involved in the light reactions and an essential nucleus-encoded Arabidopsis σ-Like Factor ( AtSig5 ) ( Zhao et al., 2017 ). The functional analysis of transgenic wheat overexpressing Nuclear Factor Y ( TaNF-YB3 ) provided evidence for the positive involvement of the TF gene TaNF-YB3 in the regulation of photosynthesis genes leading to an increase in leaf chlorophyll content and photosynthetic rate ( Stephenson et al., 2011 ). Rice plants over-expressing HARDY (HRD) , an AP2-EREF-like TF, showed drought tolerance, thicker leaves, more chloroplast-bearing mesophyll cells, and improved water use efficiency by enhancing photosynthetic assimilation and reducing transpiration thus contributing to increased biomass in a water-limiting environment ( Karaba et al., 2007 ). Genetically engineering stress-responsive TFs regulating photosynthesis-related genes to modulate stress tolerance may hold promising beneficial traits of agronomic interest including improved productivity.

Further targets for improving photosynthetic traits could be to modulate TFs known to directly control the photosynthetic machinery. GOLDEN TWO-LIKE (GLK) TFs, key mediators of developmental control, have been implicated in positively regulating both chloroplast formation and coordinating the expression of photosynthetic apparatus genes, such as LIGHT HARVESTING COMPLEX PROTEIN genes and tetrapyrrole synthesis genes HEMA1 , GUN4 , GUN5 / CHLH , and CHLOROPHYLL A / B OXIDASE ( CAO ) ( Waters et al., 2009 ; Powell et al., 2012 ; Nguyen et al., 2014 ). Furthermore, the nuclear GATA NITRATE-INDUCIBLE CARBON-METABOLISM-INVOLVED (GNC) TF is involved in the control of both chloroplast development from the proplastid and control of chloroplast growth and division ( Bastakis et al., 2018 ).

Since the ectopic overexpression of some genes might result in the overexpression of other genes (since many genes are coordinately regulated, for instance by photoreceptors) and increase the levels of associated proteins with undesired phenotypic modifications that could increase trade-offs within the agronomic characteristics and worsen productivity, even when photosynthetic performance has been improved. Therefore, it might be required to modulate only photosynthesis-related genes to accomplish the desired boosting of crops under adverse environmental conditions.

The physiological and biochemical changes in plants under specific stress conditions are related to altered gene expression, with a common set of about 750 nuclear-regulated genes responsive to changes in photosynthetic redox state ( Jung et al., 2013 ). Genes showing redox-regulated expression characteristics are either directly involved in or connected to photosynthesis ( Pfannschmidt, 2003 ). Adverse environmental conditions often lead to chloroplast damage including photoinhibition but this can be limited by acclimation mechanisms, many of which are based on ROS generation and/or the triggering of regulatory redox-reactive molecules (e.g., thioredoxins, and reduced glutathione). These redox-molecules can regulate transcription by interacting with TFs and other signaling molecules and thus deregulate the expression of photosynthetic component genes at multiple levels of signal transduction cascades and signaling pathways. Targeting such acclimation mechanisms, at the gene level, could help improve photosynthesis and plant adaptability under abiotic stresses. To achieve this, a better understanding of how triggered regulatory redox-reactive molecules deregulate the expression of photosynthetic component genes is required. Furthermore, the identification of redox signal targets and/or stress-responsive TFs could help identify unknown photosynthesis-related genes.

Although redox-associated changes in nuclear gene expression have been described, only a limited number of TFs that mediate transduction of redox signals controlling chloroplast signaling have been identified ( Pesaresi et al., 2009 ; Petrillo et al., 2014 ). The over-expression of the zinc finger transcription factor, ZAT10, altered photosynthetic rates and resulted in enhanced tolerance to light and exogenous H 2 O 2 photoinhibition, and increased expression of ROS detoxification genes whose products were targeted to multiple subcellular compartments ( Rossel et al., 2007 ). Also, three A-type heat-shock transcription factors (HSFs) -HSFA1D, HSFA2, and HSFA3- were found to be key factors regulating the gene encoding ASCORBATE PEROXIDASE 2 (APX2) in response to a redox-generated plastid stress signal ( Jung et al., 2013 ). Furthermore, RADICAL INDUCED CELL DEATH PROTEIN 1 (RCD1) stabilized the TF Rap2.4-dependent redox-regulation of genes encoding chloroplast antioxidant enzymes, although it was also found to be essential for protecting cells from photooxidative stress, in a widely redox-independent manner ( Hiltscher et al., 2014 ). Recent promising approaches targeting chloroplast energy balance via AOX, a mitochondrial terminal alternative oxidase ( Vanlerberghe, 2013 ; Vanlerberghe et al., 2016 ; Dahal and Vanlerberghe, 2017 , 2018 ) and the overexpression of CBF (C-repeat binding factor) transcription factors ( Dahal et al., 2012 ; Kurepin et al., 2013 ; Hüner et al., 2014 ) have been reported to enhance plant photosynthetic performance under stress conditions.

It can be seen that the manipulation of TF to modulate gene networks and gene expression is an avenue that could be exploited to engineer crops for enhanced photosynthesis and productivity under adverse environmental conditions. To achieve such aims, efforts are required to identify the appropriate TFs. Also, deep learning techniques that exploit large scale data set analyses (chromatin accessibility assays, microarray, RNA-seq expression, ChIP-seq data, gene expression profiles, DNA affinity purification sequencing, ampDAP-seq) to help resolve complex biological problems in transcriptomics need to be developed further.

Photosynthesis is mediated by the coordinated action of ca. 3000 nuclear-encoded preproteins synthesized in the cytosol and imported into organelles through special machineries ( Nakai, 2015 ; Baslam et al., 2016 ) in envelope membranes. About 2400 of these proteins are found in the chloroplast ( Friso et al., 2004 ), while only ca. 100 proteins are encoded by the chloroplast genome. Many environmental changes lead to an imbalance in photosynthetic electron transfer due to a modification of the redox potential of ETC components as well as functionally coupled pools of redox-active compounds (e.g., thioredoxins and glutathiones) thus affecting photosynthetic efficiency ( Roach and Krieger-Liszkay, 2014 ; Larosa et al., 2018 ; Jimbo et al., 2019 ; Takagi et al., 2019 ). This imbalance can be redressed by the photosynthetic control of LHC, PS, and cytb 6 f stoichiometry.

In order to prevent ROS generation, PSI must be induced to accept electrons when PSII is strongly active in the daytime by shorter-wavelength light. The mechanism of induction of PSI occurs through the de-phosphorylation of sigma factors by redox signals monitoring PQ status ( Shimizu et al., 2010 ). Redox proteomics has been developed to monitor the redox dynamics of cellular proteins under environmental stimuli ( Ansong et al., 2014 ; Sadler et al., 2014 ; Ameztoy et al., 2019 ). Application of this technology to plants and chloroplasts has identified novel protein targets undergoing thiol modifications [e.g., NADPH-dependent thioredoxin reductase C (NTRC), chloroplastic fructose 1,6-bisphosphatase (FBPase)] and plastid redox signaling networks to maintain a high photosynthesis efficiency which is important for the global adjustment of plant metabolism ( Lindahl and Kieselbach, 2009 ; Hall et al., 2010 ; Dietz and Pfannschmidt, 2011 ; Ameztoy et al., 2019 ). Quantitative phosphoproteomic profiling using isobaric tags for relative and absolute quantitation (iTRAQ) showed that ROS generated by an oxidative burst under drought stress could trigger NO synthesis to protect the photosynthetic apparatus by modulating the phosphorylation of diverse proteins such as LHC, thylakoid-bound Ser/Thr kinase STN7, and chloroplast post-illumination chlorophyll-fluorescence-increase protein (PIFI). ROS produced under drought conditions provoked an increase of the cellular concentration of Fe 2+ ions, resulting in an increased electron transfer to oxygen via the Fenton reaction ( de Carvalho, 2008 ). Similar effects are observed under nutrient starvation, including Mg 2+ and Fe 2+ , which are essential co-factors for several redox-active proteins in the photosynthetic ETC. More recently, Inomata et al. (2018) reported the chloroplast phosphoproteome profile of a rice nucleotide pyrophosphatase / phosphodiesterase 1 (NPP1) mutant. This study highlighted that the loss-of-function of NPP1 in rice leaves increased stomatal conductance, photosynthesis, starch, and sucrose storage while also impacting proteins involved in carbohydrate metabolism and protein synthesis system under high temperature and CO 2 conditions. Their data indicated that NPP1 plays a crucial role in carbon flux by transporting carbon taken up from starch and from cell wall polysaccharide biosynthesis to other metabolic pathways in response to the physiological needs of the cell.

Using proteomics, five new photosynthetic activity responsive transcriptional regulators were classed as redox-active in response to nutrient limitation in the photosynthetic cyanobacteria Synechococcus sp. PCC 7002. These were RbcR regulating the rbc LXS operon, Fur and Zur regulating iron and zinc homeostasis, respectively, cyAbrB regulating N and C metabolisms, and a TetR family regulator ( Sadler et al., 2014 ). Furthermore, proteomics has led to the identification of proteins that mediate redox control during RNA maturation and transcription. These RNA plastid-encoded polymerase (PEP)-associated proteins are plastid transcription kinases (PTKs) (such as STN, CSK, and cpCK2), which respond to changes in thiol/disulfide redox state mediated by glutathione ( Baginsky et al., 1999 ), and can phosphorylate the sigma-like TF, SIG6 , involved in the regulation of chloroplast gene transcription. Similarly, these PTKs are under the control of the chloroplast GSH (glutathione) pool, suggesting a GSH-mediating redox control of their activities ( Baena-González et al., 2002 ). Proteomics has identified also several heat-responsive TFs and proteins, such as MYB, WRKY, DnaJ protein (LeCDJ1), heat shock proteins (HSPs), filamentous temperature-sensitive H (ftsH11), sedoheptulose-1,7-bisphosphatase (SBPase), and constitutive or stress-inducible key enzymes ( Chen et al., 2006 ; Rushton et al., 2012 ; Yang et al., 2012 ; Grover et al., 2013 ; Kong et al., 2014 ). (Phospho)-proteomic analyses suggested that heat-responsive phosphorylation levels of some important proteins [e.g., protochlorophyllide reductase (POR), oxygen-evolving complex (OEC), RuBisCO, and phosphoenolpyruvate carboxykinase (PEPCK)] were modulated, thus indicating that post-translational modifications (PTMs) were critical processes for plant heat tolerance ( Hu et al., 2015 ; Walker et al., 2016 ). A proteomic approach has shown the role of PSII protein phosphorylation [e.g., the minor antenna polypeptide Lhcb4 (CP29)] in PSII protection and in the photoinhibition-repair cycle (reviewed in Aro et al., 2004 ).

In order to optimize leaf gas exchange under stressful environmental conditions, proteins related to stomatal development have been identified. Indeed, plants can modulate stomatal aperture, density, and placement through signaling pathways involving peptide ligands, transmembrane receptors, and mitogen-activated protein kinase (MAPK) modules. The TFs bHLH [including both MUTE and FAMA, inducer of CBF expression 1 (ICE1/SCRMI), HIGH CARBON DIOXIDE (HIC) protein, PHYTOCROME INTERACTING FACTOR (PIF), mitogen-activated protein kinases (MPKs), and their upstream MKK, YODA, SPCH, C2/H2-type zinc-finger proteins (SZT and AZF2)] have been described to regulate stomatal response to environmental perturbations and improve stress tolerance ( Gray et al., 2000 ; Chinnusamy et al., 2003 ; Sakamoto et al., 2004 ; MacAlister et al., 2007 ; Wang et al., 2007 ; Kanaoka et al., 2008 ; Casson et al., 2009 ; Pillitteri and Dong, 2013 ). EPIDERMAL PATTERNING FACTOR 1 and 2, and STOMAGEN are secreted peptides that regulate the function and development of stomata ( Hara et al., 2007 ; Hunt and Gray, 2009 ; Sugano et al., 2010 ). Furthermore, the α-subunit of the heterodimeric G protein (GPA1) and ERECTA protein are known to regulate plant transpiration efficiency by regulating stomatal density ( Masle et al., 2005 ).

Interestingly, chloroplast proteome turnover is crucial to cell homeostasis and adaptation to changing conditions. In their review, Izumi and Nakamura analyzed the influence of extra-plastidial processes on the turnover of chloroplast proteins ( Izumi and Nakamura, 2018 ). Fine-tuning protein turnover, and/or increasing the efficiency of respiratory ATP production can help “maintenance respiration” -the energy required to maintain mature tissue biomass when growth rate is zero ( Thornley, 2011 ; O’Leary et al., 2017 ; Machacova et al., 2019 )-, and hence reduce carbon loss. This process can be a primordial factor in determining growth rate and it may also impact biomass formation. Indeed, growth rate is negatively correlated with protein turnover among Arabidopsis accessions ( Ishihara et al., 2017 ). For instance, eliminating THI4 (a suicide enzyme in thiamin biosynthesis) protein turnover, increased crop biomass accumulation by 4% by essentially reducing the high energy costs and loss of photosynthetically fixed carbon to produce thiamin ( Hanson et al., 2018 ).

As thousands of different proteins make up the machinery of plant cells, proteomics and its derivatives (phosphoproteomics, redox proteomics, and peptidomics) are important tools to better understand processes that regulate protein synthesis and degradation in plants such as protein turnover, abundance, location, compartment-specific proteases/peptidases, protein interactors, and PTMs (e.g., phosphorylation, ubiquitination, nitrosylation, and carbonylation) in steady and non-steady state scenarios. Establishing an integrated understanding of the processes that underpin changes in protein expression under several physiological and developmental scenarios could define new targets to rationally engineer photosynthesis for agronomic gain.

Hormonomics

Chloroplasts synthesize hormones that are known to play a critical role in photosynthesis gene expression and to participate as signaling molecules in stress signal transduction. Phytohormones including brassinosteroids (BRs), abscisic acid (ABA), cytokinins (CKs), salicylic acid (SA), ethylene, jasmonate, and auxins have been implicated in the control of stomatal development and function in response to environmental stresses, which ultimately impact photosynthesis. The importance of ABA as a central regulator and integrator of long-term changes in stomatal behavior was revealed by Dittrich et al. (2019) . Under stress environments, such as drought, ABA induces stomatal closure through calcium-sensing receptor signaling driven by NO accumulation via H 2 O 2 production in guard cell chloroplasts leading to membrane depolarization and loss of guard cell volume and turgor ( Wang et al., 2012 ). ABA-dependent guard cell closure has been shown also to be regulated by the guard cell anion channel SLAC1, together with the protein kinase OST1 ( Hedrich and Geiger, 2017 ). Using genetic approaches, Chater et al. (2015) showed that either guard cell ABA or ABA receptors, PYR/PYL/RCAR, were sufficient to mediate a [CO 2 ]-induced stomatal density response. However, recently a model for the convergence of CO 2 and ABA signal transduction downstream of OST1 protein kinase activation has been reported ( Hsu et al., 2018 ). Transgenic rice and Arabidopsis plants overexpressing the pyrabactin resistance 1-like ( PYL ) family of ABA receptors promoted resistance to extreme drought stress by reducing transpiration rate and stomatal conductance thus enhancing the photosynthetic rate and water use efficiency ( Zhao et al., 2016 ). Efforts have been made to improve photosynthetic efficiency by engineering the photosynthesis-related transcription factor, ABA-responsive 17 ( ABR17 ) ( Grover et al., 2013 ). Constitutive expression of ABA-responsive element-binding protein ( ABP9 ) increased photosynthetic capacity, carbon use efficiency and tolerance to high temperature and water stress ( Zhang et al., 2008 ). Xiong and Zhu (2003) suggested that genotypes with putative constitutive high ABA concentrations could be more tolerant to environmental stresses. ABA can also protect the photosynthetic apparatus against photoinhibition by modulating the xanthophyll cycle and by increasing the recovery rate of photo-inactivated PSII complexes ( Saradhi et al., 2000 ). Therefore, altering stomatal sensitivity to ABA could allow plant acclimation to changing environments by optimizing gas exchange for photosynthesis.

A water deficit stimulates not only ABA synthesis but inhibits the production of CKs resulting in an imbalance between the two hormones in leaf tissues and this can control physiological responses (e.g., stomatal closure) that lead to whole plant higher adaptive fitness ( Pospisilova et al., 2005 ; Tanaka et al., 2006 ). The action of CKs is mediated mainly by AHK3 receptors and several TFs (i.e., ARR1 , ARR10 , and ARR12 ) that regulate nuclear gene expression encoding plastid proteins (e.g., LHC, RuBisCO), plastid-related protein abundance [e.g., gamma-subunit of ATP synthase, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ClpP, ribosomal protein L21], and downstream TFs (i.e., CGA1 , GNC , HY5 , GLK2 , CRF2 ). In this way, CKs modulate chloroplast development, division, and function ( Chiang et al., 2012 ; Cortleven and Schmülling, 2015 ; Okazaki et al., 2015 ). Transcriptomic responses to CKs include over 100 different photosynthesis genes ( Brenner et al., 2012 ) while a (phospho)-proteomic study identified about 50% of CK-regulated proteins to be localized in the chloroplast ( Černý et al., 2011 ). Under high light stress, CKs show a protective function by decreasing photoinhibition, mediated by AHK2 and AHK3 receptors and the TFs ARR1 and ARR12 ( Cortleven et al., 2014 ).

Genetic and pharmacological studies have implicated BRs in stomatal development and patterning. Kim et al. (2012) reported that the BR-insensitive mutants bri1-116 and bsu-q ( amiRNA-BSL2,3 bsu1 bsl1 quadruple mutant) contained only paired guard cells, and lacked other epidermal cells. Genetic analyses indicated that receptor kinase-mediated BR signaling inhibited stomatal development through glycogen synthase kinase 3 (GSK3)-like kinase BIN2, which acts upstream of the MAPKKK YODA, and mediates signaling by ERECTA family receptor kinases. Previous studies had also demonstrated key functions of BRs in inhibiting photosynthetic gene expression, and promoting cell elongation, chloroplast senescence, and floral induction ( Li et al., 1996 ). Furthermore, it was found in leaves and cotyledons that BR-promoted stomatal formation was via a cross-communication of the YDA-MKK4/5-MPK3/6 cascade and the basic helix-loop-helix transcription factor SPEECHLESS (SPCH), a regulator of the entry, amplifying and spacing divisions that occur during stomatal lineage development ( Gudesblat et al., 2012 ).

As photosynthetic gas exchange and transpiration balance are impacted by altered stomatal patterning under changing environmental conditions, auxin control of stem cell compartment size, as well as auxin depletion as the switch from unequal to equal divisions, play key roles during stomatal development. High auxin activity has been observed during unequal cell divisions in stomatal patterning, whereas a decrease in auxin activity promoted guard mother cell (GMC) fate and its subsequent equal division into two guard cells. Similarly, an auxin-resistant mutant where AUX/IAA proteins failed to interact with the auxin receptor, leading to auxin insensitivity, was defective in the suppression of stomatal development in dark-grown seedlings ( Balcerowicz et al., 2014 ). Zhang et al. (2014) reported that auxin negatively regulated stomatal development through MONOPTEROS (MP) repression of mobile peptide STOMAGEN gene expression in mesophyll cells where photosynthesis mainly takes place ( Zhang et al., 2014 ). The regulation of stomatal and vascular developments by MP indicated that MP should play a role in photosynthesis and the transpiration system for optimizing plant growth and development. Loss-of-function quadruple mutants, pin2, 3, 4, 7 and pin1, 3, 4, 7 of the PIN gene family, controlling PIN protein-mediated auxin transport, showed stomatal defects ( Le et al., 2014 ). Moreover, Ogura et al. (2019) identified a new gene, EXOCYST70A3 that directly regulated root system architecture by affecting the distribution of PIN4 and hence controlling the auxin pathway without disrupting other pathways. This study suggested that EXO70A3-dependent variation in the control of root system architecture could result in improved photosynthesis and help plants fight climate change. Taken together, such studies showed the important roles of stomata in photosynthesis and global carbon and water circulation and suggest that coordinating stomatal development with photosynthesis could be achieved by manipulating auxin signaling specifically in the mesophyll cells without disturbing whole plant development.

Salicylic acid (SA) acts as an important signaling molecule and influences various physiological and biochemical functions in plants, playing an important role in plant responses to biotic and abiotic stresses. Under chilling stress conditions, inhibition of SA biosynthesis by L-α-aminooxy-β-phenyl propionic acid (AOPP) increased PSII photooxidation, leading to the generation of ROS and impairment of photosynthesis and growth, whereas applying SA at moderate concentrations induced a stress tolerance by restoring the photosynthetic machinery ( Cheng et al., 2016 ). Other studies have shown that SA treatment alleviated carbon assimilation and several components of PSII electron transfer under heat stress by increasing proline production through the increase in γ-glutamyl kinase (GK) and a decrease in proline oxidase (PROX) activity, resulting in the promotion of both osmotic and water potentials necessary for maintaining photosynthetic activity ( Wang et al., 2010 ; Nazar et al., 2011 ). Under salt stress, it was revealed that SA could modulate photosynthetic capacity due to its interaction with metabolic signaling by ROS (including H 2 O 2 ), and glutathione ( Arfan et al., 2007 ; Nazar et al., 2011 ; Sewelam et al., 2016 ). Indeed, Miura et al. (2013) reported that SA accumulation in siz1 [small ubiquitin-like modifier (SUMO) E3 ligase] mutant plants enhanced stomatal closure and drought tolerance by controlling guard cell ROS accumulation, while the introduction of salicylate hydroxylase ( NAHG ) into siz1 , which reduced SA accumulation, restored stomatal opening ( Miura et al., 2013 ). Furthermore, other SA-accumulating mutants, cpr5 and acd6 , exhibited stomatal closure thus reducing the entry of sufficient CO 2 for optimal photosynthesis while hindering the movement of water vapor and hence leading to drought tolerance ( Miura et al., 2013 ).

In addition to the SA pathway, jasmonic acid (JA)-signaling (co)-regulates a wide-range of plant developmental processes and responses to biotic and abiotic stresses that probably involve the photosynthesis machinery. Indeed, the examination of high-throughput gene expression data for heat stress and methyl jasmonate (MeJA) responsive genes using GENEVESTIGATOR ( Zimmermann et al., 2004 ), an online tool for large-scale expression data analysis, revealed a preponderance of genes associated with protein translation and photosynthetic electron transport, which could represent features associated with cellular recovery following heat stress ( Clarke et al., 2009 ).

Ethylene receptor mutants show altered photosynthetic properties and they are sensitive to abiotic stresses. Indeed, Arabidopsis etr1 mutants have demonstrated the role of ethylene receptor ETR1 in guard cell H 2 O 2 signaling ( Desikan et al., 2005 ). Other studies showed that ethylene-insensitive mutants, etr1-1 and ein2 , had smaller stomata, possessed lower chlorophyll and CAB (chlorophyll a/b binding complex) contents, RuBisCO activities, and had a lower whole-plant and leaf photosynthetic capacity, suggesting the role of basal ethylene perception in controlling stomatal conductance and photosynthetic capacity ( Grbic and Bleecker, 1995 ; Tholen et al., 2007 ). Other seminal works suggested that ethylene-responsiveness was required for the fine regulation of PSII photochemical efficiency ( Kim et al., 2017 ) and carbon fixation by achieving maximal RuBisCO activities through ethylene-responsive factors (ERFs) ( Bracher et al., 2017 ; Xie et al., 2017 ). The control of photosynthesis by ethylene also affected plant biomass production by influencing final plant size ( Ceusters and Van de Poel, 2018 ). Ethylene was found to directly control photosynthesis in juvenile non-senescing leaves and acted indirectly in mature leaves by promoting senescence.

In conclusion, it can be seen that hormonal networks influence plant photosynthesis and therefore they could assist us to develop new strategies to improve plant productivity and to help plants tolerate severe environmental conditions.

Physiological Traits Involved in the Maintenance of Photosynthesis as Tools for Crop Improvement in a Context of Climate Change

Crop growth is linked to the assimilation of ambient CO 2 through photosynthesis, in which green plants convert sunlight, water, and CO 2 into O 2 and carbohydrates. During the last decade, different studies have highlighted that the improvement of plant photosynthetic rates can be a strategic tool to increase crop yields ( Reynolds et al., 2011 ). Several studies analyzing the impact of overexpression of proteins linked with CO 2 assimilation have shown an increase in photosynthesis and plant growth ( Driever et al., 2017 ; Kubis and Bar-Even, 2019 ; Ermakova et al., 2019 ; see section “Metabolic Engineering to Improve Photosynthesis and Elevated CO 2 Acclimation” for details). Further, as described by Parry et al. (2011) , increases in wheat yield potential during the last decades have been associated with increased photosynthesis while Flood et al. (2011) have shown that variations in either the efficiency or capacity of photosynthesis can lead to variations in growth rate and productivity. Within this context, the adaptive potential of photosynthesis to changing environments depends on the degree of genetic variation for photosynthesis that is present within a population ( Flood et al., 2011 ). Indeed, different studies ( Peng et al., 2001 ; Hubbart et al., 2007 ) show that since 1980, increases in rice yield, rather than harvest index, correlate better with increases in biomass. Furthermore, the fact that varieties released after the 1980’s show higher saturating photosynthetic rates when compared to older varieties suggest that varieties with higher biomass values would be the ones with improved photosynthesis. This suggests that breeding programs aiming to improve crop biomass production will also have an effect on photosynthetic physiology ( Flood et al., 2011 ). Supporting this observation, the increase in crop yields detected in plants grown under elevated [CO 2 ] ( Ainsworth and Long, 2005 ; Long et al., 2006 ; Sanz-Saez et al., 2017 ; Torralbo et al., 2019 ) are also associated with higher photosynthetic rates measured under such conditions.

Yield depends on many factors such as the efficiency of light interception (LI), the radiation use efficiency of light energy conversion to biomass (RUE) and the fraction of biomass that is contained in harvested organs. Leaf morphological and physiological characteristics are two target factors conditioning variation in photosynthetic properties of individual leaves that are influenced by environment and genetics ( Flood et al., 2011 ). Furthermore, genetically based differences in leaf morphology are commonly encountered at the interspecific level, and often correlate with growth ( Hikosaka, 2010 ). During the last decade, the enhancement of plant light capturing surface and conversion of light energy has been a major target of crop breeders ( Murchie et al., 2009 ). Within this context, a clear example of this strategy has been the increase in the development of erect leaves with a higher leaf area per unit ground area that enables more efficient radiation capture ( Murchie et al., 2009 ). Despite this, it should be noted that the major step that is not yet near to the maximum is light conversion efficiency to biomass which is only at 50% of its theoretical level (see Zhu et al., 2008 ; Long et al., 2015 ; Slattery and Ort, 2015 ). However, despite its potential, selection based on improving photosynthesis was not properly considered during the last decades.

The assimilation of CO 2 is a complex process that involves multiple genes, regulatory mechanisms, and different metabolic pathways and plant structures working together. The overall photosynthetic process is determined by CO 2 diffusion to the chloroplast (conditioned by stomatal opening and mesophyll conductance), the capture and conversion of light energy to make ATP and NADPH (the light reactions) required for the assimilation of CO 2 to produce sugar-phosphates used to regenerate RuBP, the molecule used to fix CO 2 by RuBisCO, and to produce complex sugars like starch and sucrose. However, as mentioned in the introduction, O 2 competes with CO 2 at the RuBisCO active site thus reducing photosynthetic CO 2 assimilation capacity and producing toxic 2-PG ( Flugel et al., 2017 ) that is removed by the photorespiratory cycle. Photorespiration has a high energetic cost and it leads to the potential loss of carbon and nitrogen in the form of CO 2 and ammonium. It has been calculated that photorespiration can reduce photosynthetic energy conversion to yield of certain important C3 grain plants by 20−50% (see South et al., 2019 ), including soybean and wheat ( Walker et al., 2016 ). Therefore, photorespiration became a target for crop improvement (see section “Metabolic Engineering to Improve Photosynthesis and Elevated CO 2 Acclimation”). However recent studies ( Betti et al., 2016 ; Eisenhut et al., 2017 ) suggest that reducing photorespiration may not always have beneficial effects since a higher photorespiratory capacity would contribute to: (1) maintaining Calvin cycle activity; (2) decreasing excess reducing power (a target under stressful growth conditions such as exposure to drought, salinity, cold, etc.); (3) improving nitrate assimilation under elevated CO 2 conditions. Similarly, it was found that under low CO 2 availability conditions, unrestricted photorespiratory metabolism favored plant performance ( Eisenhut et al., 2017 ). Therefore, modulating photorespiration would probably be important to maintain or improve crop yield under certain environmental conditions that alter the chloroplast CO 2 /O 2 ratio in favor of O 2 .

Semi- and High-Throughput Phenotyping Techniques to Measure Photosynthetic Traits

Within the context of climate change, it is crucial to identify the crops that will perform better under the current and near-future conditions in the field. However, current breeding programs are constrained by the limitations of field phenotyping methods ( Araus et al., 2018 ). During the last decade, different phenotyping platforms have emerged as a strategic tool to characterize crop performance. The light reactions can be studied by measuring chlorophyll fluorescence, whereas photosynthesis and respiration are studied by measuring CO 2 exchange between the plant and the atmosphere using infrared gas analyzers (IRGA). Depending on the type of parameter, measurements can take a few minutes, such as leaf chlorophyll fluorescence or respiration measurements, to 30−90 min, as is the case of photosynthetic parameters such as maximum rate of RuBisCO carboxylation (V cmax ) and maximal rate of electron transport (J max ) that are calculated using photosynthesis to CO 2 curves, named A-Ci curves ( Farquhar et al., 1980 ; Bernacchi et al., 2003 ).

These parameters can be used to distinguish differences of photosynthetic efficiency under different environments allowing researchers to identify better-adapted cultivars ( Aranjuelo et al., 2009 , 2013 ; Sanz-Saez et al., 2017 ); or be used as input parameters for earth systems models that predict ecosystem responses to environmental changes ( Rogers, 2014 ). However, a lack of information about V cmax and J max in some species in several ecosystems is the major source of error using earth systems models ( Rogers, 2014 ). Another parameter that can be useful for the selection of abiotic stress-tolerant cultivars is dark respiration (R d ) ( Vanlerberghe and McIntosh, 1997 ; Millar et al., 2011 ). Recently, high-throughput methodologies based on O 2 consumption have been developed ( O’Leary et al., 2017 ; Scafaro et al., 2017 ), and they can rapidly (in 1−2 min) measure precise respiration rates. However, this requires the leaf to be removed from the plant and introduced into a measuring chamber, therefore it is destructive and thus not the best option. The latest technology used to estimate this parameter is a non-destructive technique that uses leaf reflectance spectroscopy, and it will be described below.

With the rise of the genomic era, screening of entire populations for traits of interest has become paramount to associate specific genomic regions with a given plant trait (see Section “Genomics to Study the Natural Variation of Plant Photosynthetic Efficiency”). Genomic approaches need the implementation of technologies that allow the rapid measurement of photosynthetic and fluorescence traits to screen hundreds of cultivars in the shortest amount of time. Here, we will summarize semi- and high-throughput phenotyping methods to estimate parameters related to: (1) gas exchange such as V c max , J max , and R d using the latest LI-COR 6400 and LI-COR 6800 methodologies as well as hyperspectral reflectance; and (2) chlorophyll fluorescence such as solar-induced fluorescence (SIF) and stimulated fluorescence by a known source of light.

Semi- and High-Throughput Phenotyping Methods Related to Gas Exchange Parameters

In this subsection, the most recent literature focusing on two aspects of high-throughput phenotyping (HTP) of photosynthetic parameters are summarized and discussed: (1) New semi-HTP methodologies to estimate V c max and J max using the Rapid A-Ci Response (RACiR) method for LICOR IRGA equipment and the use of the leaf excision method to estimate V cmax , J max , and light-saturated photosynthesis. (2) The use of hyperspectral reflectance technology to estimate gas exchange parameters such as V c max , J max , and R d .

Semi High Throughput Phenotyping Methods to Measure Gas Exchange Parameters

In order to estimate V cmax and J max , A-Ci curves need to be performed using an IRGA system. In regular A-Ci curves, the leaf receives different CO 2 concentrations ([CO 2 ]) in the IRGA chamber containing the leaf, usually from 50 μmol CO 2 mol –1 up to 2000 μmol CO 2 mol –1 ( Long and Bernacchi, 2003 ). During this measurement, each time that [CO 2 ] is increased, leaf photosynthesis and stomatal conductance are measured after reaching a steady-state equilibrium ( Long and Bernacchi, 2003 ), which may take between 3 and 6 min per step. In this way, 30 to 90 min are needed per one A-Ci curve, which makes this method a Low Throughput Phenotyping technique.

Due to modifications in the way that the reference and sample IRGAs are placed in the new LI6800, Stinziano et al. (2017) were able to develop a Rapid A-Ci response curve protocol with a duration of approximately 12 min. The new design can minimize lags between the reference and the sample IRGAs thus generating a constant ramp rate for CO 2 control. In this method, the leaf is first stabilized at a [CO 2 ] of 500 μmol mol –1 before being reduced to 0 μmol mol –1 at a rate of 100 μmol mol –1 min –1 . Data is recorded at a rate of 0.5 Hz, which is equivalent to a measurement every 2 s, therefore assuring that changes in photosynthetic response can be recorded. In order not to miss data near the inflection point of the A-Ci response curve, Stinziano et al. (2017) added another set of measurements from 300 μmol mol –1 to 800 μmol mol –1 to complete the curve ( Figure 3 ). Plotting together these 2 curves, the authors were able to fit the data to the Farquhar-von Caemmerer-Berry (FvCB) model, thus obtaining V c max and J max estimates that were very close to those calculated from a standard A-Ci curve. However, this method has some limitations; for example, although the physical separation between the reference and the sample chamber has been reduced, it still produces a lag between the two signals that is increased when the volume of the sample chamber has to be mixed. This lag creates a differential in CO 2 concentration that if not corrected can cause very significant variations in the measurements. To correct this lag, an empty chamber rapid A-Ci curve is run for each CO 2 ramp ( Stinziano et al., 2017 ). In addition, Stinziano et al. (2019) produced a best practices guide in which they indicated under which conditions an empty chamber A-Ci curve was needed. Taylor and Long (2019) found significant offsets in R d (95% variation) and CO 2 compensation point (Γ, 11% variation). According to their published data, RACiR curves can be a good tool to perform semi-HTP measurements in plant populations, being able to perform up to 60−80 A-Ci curves per day (8-h day) and per machine. However, when starting any experiment, a set of standard A-Ci curves should be performed to test that the method is working for each species and/or environmental condition. Therefore, this RACiR methodology only appears to be worth the effort when analyzing hundreds of samples at the same environmental condition as is often the case for GWAS and/or QTL experiments ( Dhanapal et al., 2015 ; Herritt et al., 2018 ; Luo et al., 2018 ). For small experiments where only a few cultivars/species are to be analyzed, it is more reasonable to do standard A-Ci curve measurements even if it is more time consuming, as they can be used to obtain other important information such as C c , g m ( Harley et al., 1992 ), R d and Γ that can give further valuable information about the physiological state of the plant.

Figure 3. The Rapid A-Ci curve method generates a wealth of data that when corrected to reduce the lags of the empty chamber, etc., overlay well onto the “standard” A-Ci curve. In this case, a modification of an A-Ci curve from Populus deltoides is shown. The graph has been adapted from Figure 2 ( Stinziano et al., 2017 ) by the author of the manuscript. Data for the “standard” A–Ci curve are presented as the mean of the mean (s.e.m.) for three measured responses on the same leaf of a single seedling (gray dots), while RACiRs were replicated once per CO 2 range per seedling on the same leaf at the same location as the “standard” A–Ci curve. The best RACiRs were produced by ramping [CO 2 ] from 500 to 0 (blue) and from 300 to 800 (red). For statistical information about the curve fits and the calculated parameters, please check Stinziano et al. (2017) .

Other problems occurring when measuring A-Ci curves or mid-day photosynthesis under field conditions on hundreds of samples include a transient decrease in water potential, a decrease in chloroplast inorganic phosphate concentration, and a decrease in maximum PSII efficiency. These can all occur after a few hours of light exposure making it difficult to compare measurements at the beginning with those taken at the end of the day ( Ainsworth et al., 2004 ). When measurements are performed in the field, changing environmental conditions can alter the photosynthetic response of the plants thus making it difficult to determine treatment effects. With this in mind, Ainsworth et al. (2004) developed the “leave scission method” where soybean leaves were cut pre-dawn under water, stored in the dark, and stimulated at saturating light, at least 30 min before measurements were recorded. Using this method, all samples were measured under the same temperature, light intensity, and biochemical state. A-Ci curves have been performed in this way for the last 15 years with successful results with soybean ( Ainsworth et al., 2004 ; Bernacchi et al., 2005 ; Sanz-Saez et al., 2015 , 2017 ) and corn ( Leakey et al., 2006 ; Yendrek et al., 2017 ). Additionally, Choquette et al. (2019) used this methodology to phenotype light-saturated photosynthesis response to elevated ozone in a panel of 48 corn lines, measuring more than 200 plots per day. Although this technique cannot be considered high-throughput, we believe that it could be used to screen photosynthetic parameters in diversity panel populations of about 200 lines for several days to cover the different replications. The fact that all measurements are taken under the same conditions reduces the variability associated with weather changes that happen during sampling and allows differentiating between treatments and cultivars ( Choquette et al., 2019 ).

Use of Hyperspectral Reflectance Technology to Estimate Gas Exchange Parameters

Hyperspectral sensors capture electromagnetic radiation reflected from vegetation in the visible (VIS, 400–700 nm), near-infrared (NIR, 700–1300 nm), and short-wavelength infrared regions (SWIR, 1400–3000 nm), which contain information about leaf physiological status, including pigments, structural constituents of biomass, and water content ( Curran, 1989 ; Penuelas and Filella, 1998 ). Variation of foliar reflectance at different wavelengths is specific to variations in the chemical and structural characteristics of the leaf ( Serbin et al., 2012 ). With the improvement of computational methods, predictive models using partial-least square regression (PLSR) have been used to create equations that predict other physiological parameters such as leaf isotopic ratio ( Richardson and Reeves, 2005 ), specific leaf area ( Asner and Martin, 2008 ; Asner et al., 2011 ), leaf carbohydrate content ( Dreccer et al., 2014 ; Asner and Martin, 2015 ), and leaf mineral content ( Mahajan et al., 2014 ). The use of hyperspectral reflectance spectroscopy as a HTP tool has been recognized as promising in agricultural research ( Weber et al., 2012 ; Araus and Cairns, 2014 ), however, until recently its utility to differentiate between a big set of cultivars had not been tested. We will focus now on the methodologies used to produce models capable of predicting photosynthetic parameters such as V c max and J max and R d .

As measuring A-Ci curves is a tedious technique to calculate V c max and J max , Serbin et al. (2012) tested the possibility to predict these parameters using hyperspectral reflectance and PLSR models in aspen and cottonwood seedlings grown at different atmospheric temperatures. Because of large phenotypic variations in V c max and J max due to the temperature treatments and the inclusion of two different species, the correlation between the predicted data using hyperspectral reflectance measurements and the standard A-Ci curves was very high (R 2 of 0.89 and 0.93, respectively). This breakthrough publication demonstrated that it was possible to use hyperspectral reflectance data to estimate photosynthetic parameters. Following this discovery, Ainsworth et al. (2014) carried out a similar experiment with soybean grown at ambient and elevated ozone (O 3 ) in which standard A-Ci curves were combined with hyperspectral reflectance measurements using the Field Spec Hi-Res 4 (ASD technologies). Although the number of samples was not very high (59), the phenotypic variation due to the O 3 treatment resulted in a good correlation between the predicted and the standard A-Ci curve (R 2 of 0.88). More recently, similar correlations between standard and predicted V cmax estimations have been found for corn ( Yendrek et al., 2017 ; R 2 of 0.6) and wheat ( Silva-Perez et al., 2018 ; R 2 of 0.62).

These results are very promising for applications using very big sample sets however, to date, nobody has applied this technique to estimate V c max without testing its accuracy with standard A-Ci curve measurements. Although it seems risky, this is the avenue to take if we want to increase the speed of analysis and contribute to future breeding. To break this barrier, Choquette et al. (2019) tested 45 F 1 corn hybrids with a differential response to elevated O 3 under field atmospheric conditions. The effect of elevated O 3 was studied by performing photosynthesis measurements under light-saturating conditions using a LI6400, and estimations of V c max using hyperspectral reflectance data and equations developed by Yendrek et al. (2017) . In this way, Choquette et al. (2019) showed that they could detect both genotypic and O 3 effects on predicted V c max using hyperspectral data. They found good correlations between V c max and other variables estimated using the spectra such as chlorophyll content, a parameter that had a very strong correlation between predicted and measured values, and thus confirmed the quality of the general predictions.

R d measurements using an O 2 electrode can be quick, around 2 min, allowing semi-HTP screening ( O’Leary et al., 2017 ; Scafaro et al., 2017 ). However, the equipment is expensive and the technique requires destructive sampling of leaf material. To solve these problems, Coast et al. (2019) adapted a piecewise linear regression splines (PLRS) model based on equations developed by Serbin et al. (2012) to estimate R d from large and diverse sets of wheat cultivars. In their experiment, several wheat cultivars were tested under controlled and field conditions thus analyzing a total of 1,318 leaf samples using a standard R d measurement and hyperspectral reflectance measurements ( Coast et al., 2019 ). These authors found an overall R 2 between the measured and the predicted parameters of 0.50−0.63, which was higher than previous parameters used to estimate respiration such as leaf mass area ( Wright et al., 2006 ) and leaf N content ( Reich et al., 2008 ). As for V cmax and J max , it was theorized that some of the low predictability of the models could be due to low phenotypic differences for R d . Indeed, low phenotypic variation has been identified as one of the problems when producing prediction models with NIRS technology as seen for the case of isotopic ratios ( Cabrera-Bosquet et al., 2011 ). This limitation can be solved only by performing experiments with a diverse genetic background under different environmental conditions, or even better with stresses such as drought, elevated O 3 , increasing temperatures, etc. that will increase the variability of the measured phenotype. Furthermore, a collaborative database sharing phenotypes and spectroscopy data could advance this technology much quicker, as suggested by Coast et al. (2019) .

Although further validation is needed using other species and under other environmental conditions such as drought and high temperature, this could be the beginning of an era where researchers can estimate gas exchange related parameters using hyperspectral reflectance spectroscopy data that only takes 1−2 min to collect. Until then, if a researcher is thinking of performing large cultivar screenings using values estimated from hyperspectral reflectance data, it is still recommended to have a reduced set of samples that serve to undertake gold standard measurements (A-Ci curves, or R d measurements) just to test whether predictions are coherent. For example, a solution would be to measure and compare hyperspectral data with standard measurements using cultivars identified as extremes with hyperspectral data just to test that standard measurements identify them as extremes.

High-Throughput Phenotyping Methods to Estimate Chlorophyll Fluorescence Parameters

Chlorophyll fluorescence measurements are based on capturing and measuring the light re-emitted by chlorophylls during a return from an excited to a non-excited state. Researchers measure chlorophyll fluorescence using different approaches: (1) After the leaf has been stimulated by solar radiation, and called “Solar Induced Fluorescence” (SIF). (2) After stimulation of the leaf with a light beam of known intensity and wavelength, and measurement at specific wavelengths, here referred to as “chlorophyll fluorescence.”

Solar Induced Fluorescence

As previously mentioned, reflected light from vegetation can provide information about various plant traits. Light reflected from plants contains light remitted by chlorophyll that contributes to the reflectance signature. Chlorophyll remits absorbed light (fluorescence) at peak wavelengths of 690 and 740 nm associated with PSII and PSI, respectively ( Krause and Weis, 1984 ). The reflectance signature of leaves is an outcome of various parameters that influence how incoming radiation is reflected. The deconvolution of reflectance and fluorescence can be observed in absorption bands of oxygen (centered at 687 and 760.4 nm) and hydrogen (centered at 656.4 nm) where solar radiation is absorbed by the atmosphere ( Meroni et al., 2009 ). Reflectance recorded near these wavebands is from chlorophyll fluorescence and thus, it is possible to passively measure the amount of fluorescence being emitted from plant tissues while solar radiation is reaching the plants.

Multispectral measuring methods of SIF require the incident solar irradiance to be obtained along with the vegetative reflectance after which SIF is calculated by comparing the relative increase between a wavelength in and out of the absorption band ( Carter et al., 1990 ). SIF can be also calculated using hyperspectral reflectance spectroscopy as it contains more information in the multitude of wavelengths ( Alonso et al., 2008 ). However, the fluorescence measured under these conditions is a complex outcome of physiological processes. Previous studies have shown how SIF can be used to obtain information about photosynthesis ( Rosema et al., 1998 ; Flexas et al., 2002 ; Evain et al., 2004 ). SIF can be measured remotely by satellites and at nearer to ground levels using multispectral and hyperspectral platforms. These multispectral and hyperspectral methods are especially amenable to high-throughput analyses and can be incorporated into different HTP platforms such as aerial drones ( Sankaran et al., 2015 ; Kanning et al., 2018 ), tractors ( Scotford and Miller, 2004 ; Andrade-Sanchez et al., 2014 ), and carts ( Thompson et al., 2018 ). Previously, SIF had been shown to be correlated with canopy photosynthesis ( Yang et al., 2015 ) and used to estimate gross primary productivity ( Bacour et al., 2019 ). Passive measurements of photosynthetic traits like SIF can be carried out extremely rapidly and at multiple times during the growing season.

Chlorophyll Fluorescence

Chlorophyll fluorescence is an important tool used to investigate the light-dependent reactions of photosynthesis. This is achieved by removing or drastically decreasing one of the three routes of absorbed light energy. Without the addition of herbicides that inhibit PSII, this is achieved by applying a short saturating flash to the photosynthetic sample. With a short enough flash, no changes to non-photochemical quenching or photosynthetic efficiency occur and this allows the fluorescence maximum to be reached that can, with other fluorescence measurements, provide information about PSII efficiency ( Maxwell and Johnson, 2000 ).

The commercial availability of handheld fluorometers has allowed researchers to use chlorophyll fluorescence measurements to study the effects of various stresses on the light-dependent reactions including nitrogen availability ( Huang et al., 2004 ), salinity ( Belkhodja et al., 1994 ), heat ( Pastenes and Horton, 1996 ), cold ( Fracheboud et al., 1999 ), and drought ( Meyer and Genty, 1999 ; Sánchez-Rodríguez et al., 1999 ). While the use of such fluorometers in the field has yielded valuable information, throughput is limited by the time required to walk from one plant to another and to initiate a new measurement. Additionally, the time frame in which photosynthetic traits are somewhat stable limits when measurements can be collected depending on the aim of the experiment. Because chlorophyll fluorescence is changing in response to irradiance, large data collections that span several hours can be influenced by when measurements were obtained ( Huang et al., 2006 ). To avoid incorporating a large source of error, timing the measurements around solar noon, when chlorophyll fluorescence is relatively stable, produces better quality data. That said, several chlorophyll fluorescence studies involving large populations of genotypes have provided genetic information that could be used to improve photosynthesis and crop production ( Guo et al., 2008 ; Kiani et al., 2008 ; Azam et al., 2015 ; Herritt et al., 2018 ).

Imaging-based methods for measuring chlorophyll fluorescence allow spatial details of leaf and plant canopy fluorescence that handheld devices cannot provide. This approach requires that the whole imaging area is provided with a rapid, homogenous, and saturating light flash. Thus, the field of view for the imaging system will dictate the number of light sources required to saturate the leaf area being imaged. Several studies have shown the sensitivity of fluorescence imaging concerning pathogen interactions ( Meyer et al., 2001 ; Chaerle et al., 2004 , 2007 ). More recently, several companies have offered systems that can obtain chlorophyll fluorescence images. However, the deployment of these and other custom-built systems in field experiments is often difficult. To achieve a high-throughput capacity with fluorescence imaging, automated systems that move the imaging system to the plants or move the plants to the imaging system are required ( Fahlgren et al., 2015 ; Virlet et al., 2017 ). With the incorporation of such automated systems, chlorophyll fluorescence imaging can provide spatial information about the efficiency of the light-dependent reactions within large plant populations.

One emerging improvement in chlorophyll fluorometry is the use of light-emitting diodes (LEDs) to provide fast and repetitive flashes of sub-saturating light to obtain information about the primary electron acceptor of PSII as well as the reduction of the PQ pool. Previous fluorescence measurement methods relied on saturating light pulses to measure the relative changes in fluorescence required to describe biophysical and physiological aspects of photosynthesis ( Avenson and Saathoff, 2018 ). The use of LEDs has allowed the development of multiphase flash techniques that use short sub-saturating light flashes to achieve a complete reduction of PSII primary quinone acceptors and PSII acceptor pools ( Loriaux et al., 2013 ). Multiphase flash chlorophyll fluorescence allows for a more accurate measurement of light-adapted maximum fluorescence (F m ’). Despite these improvements, the multiphase flash technique has not been incorporated into HTP. The potential for high-throughput measurements has been realized with the fast repetition rate (FRR) protocol thus allowing for extremely rapid measurements of fluorescence (<0.2 s) ( Kolber et al., 1998 ). The combination of LED systems with FRR capability into laser or light-induced fluorescence transient (LIFT) instruments can provide high-throughput fluorescence data. Thus, LIFT systems have been incorporated into HTP systems and used in the field and controlled environments to collect plant fluorescence data ( Keller et al., 2018 ).

Modeling Photosynthesis in Crop Growth Models

Over the last five decades, many crop growth models have been developed and applied to simulate agricultural production systems and to forecast crop yields ( Priesack and Gayler, 2009 ). In particular, during the last years, a selection of these models have been tested and compared to characterize their ability to simulate crop production at different sites across the globe situated in different continents and representing different climatic conditions for major crops such as wheat ( Asseng et al., 2013 ), maize ( Bassu et al., 2014 ), rice ( Li T. et al., 2015 ), and potato ( Fleisher et al., 2017 ). The aim was also to apply the models to estimate possible future impacts of a changing climate on global crop production and grain yields ( Asseng et al., 2019 ; Liu Y. et al., 2019 ).

Almost all crop growth models aim to estimate the carbon gain for biomass production at the field level based on models of photosynthesis and radiation absorbed by the canopy. Many models assume a linear relationship between net primary biomass production (NPP) and the photoactive radiation absorbed by the crop canopy ( R PAR ). Models such as APSIM, CERES, EPIC, SALUS, LINTUL, Sirius, and STICS ( Asseng et al., 2013 supplement, Bassu et al., 2014 supplement) follow the so-called “big-leaf” approach, where the whole canopy is treated as if it was one big leaf and photosynthetic carbon gain is described by the light-use-efficiency model. It is defined by the following direct proportionality with the parameterε LUE , the light-use-efficiency, representing all photosynthetic and respiratory processes ( Medlyn, 1998 ):

where NPP denotes net primary production [g m –2 d –1 ], R PAR is absorbed photoactive radiation [MJ m –2 d –1 ] and ε LUE is light-use-efficiency [g MJ –1 ].

Other models such as Ecosys, ExpertN-SPASS, GECROS, HERMES, IXIM, LPJml, MCWLA, MONICA, SUCROS, WOFOST ( Asseng et al., 2013 supplement, Bassu et al., 2014 supplement) simulate the photosynthesis rate of the canopy based on single leaf photosynthesis rates. This is achieved in three major steps by calculating (i) single leaf photosynthesis per leaf area of each leaf, (ii) the instantaneous photosynthesis rate of the whole canopy at given light conditions by integration over the canopy depth and plant leaf areas at each depth, (iii) the daily canopy photosynthesis by integration over the day. In these models, a distinction is made between shaded and sunlit leaves ( Spitters, 1986 ; Goudriaan and van Laar, 1994 ; Wang and Leuning, 1998 ) and leaf photosynthesis is calculated for each leaf type separately. This modeling approach is known as the “two-leaf” model.

A further type of canopy photosynthesis model distinguishes different leaf classes depending on their height in the canopy above the soil surface and it is called the “multi-layer” model ( Leuning et al., 1995 ).

Besides the availability of light and CO 2 , the impact of air temperature and the supply of water and nitrogen on leaf photosynthesis have to be modeled depending on the modeling approach at either canopy or both at the leaf and canopy-scales.

Leaf Photosynthesis Rate Models

In the case of the “big-leaf” approach, as with the LINTUL model, the daily net gain of carbon for biomass growth is described by:

where μ B is the daily net carbon gain of the canopy biomass [g m –2 d –1 ], ε LUE the light-use-efficiency [g MJ –1 ], R PAR the absorbed photoactive radiation [MJ m –2 d –1 ], α ext the light extinction coefficient, f LAI the leaf area index, f CO 2 the impact factor of atmospheric CO 2 concentration, f S the impact factor of senescence, f T the impact factor of daily average air temperature, f ϑ the impact factor of available soil water content, and f N the impact factor of available soil nitrogen. In a similar way this approach is used in the CERES model, where only the term representing the absorbed global radiation takes an empirically derived exponential form, i.e:

In cases of the “two-leaf” or the “multi-layer” approach, the description of leaf photosynthesis rates again follows the general scheme given by a maximal rate of carbon gain and additional reduction factors representing environmental conditions which are not often in an optimal state to allow maximal photosynthesis:

where P gm denotes the gross leaf photosynthesis rate at light saturation [kg CO 2 m –2 d –1 ] and P gmax the maximal gross leaf photosynthesis rate [kg CO 2 m –2 d –1 ] with impact factors of CO 2 , senescence S, temperature T, soil water ϑ and soil nitrogen availability N .

This scheme is similar to the mechanistic description of growth rates, which change by the impact of environmental factors, formulated in analogy to mechanics, i.e., to the description of the velocity change of a moving particle due to forces acting on the particle ( Priesack et al., 2012 ).

The gross leaf photosynthesis rate P gl [kg CO 2 m – 2 d – 1 ] is then given by accounting for the absorbed photoactive radiation R PAR [MJ m – 2 d – 1 ] and by applying the light-use-efficiency parameter ε PAR [kg CO 2 MJ – 1 ] of photosynthesis and the gross photosynthesis rate at light saturation P gm [kg CO 2 m – 2 d – 1 ]:

Whole Canopy Photosynthesis Rate Models

The up-scaling calculation from leaf photosynthesis rate to whole canopy photosynthesis rate for a given time during the day often follows the method of Spitters et al. (1989) . It is assumed that light-use-efficiency of photosynthesis ε PAR and gross photosynthesis at light saturation P gm are constant within the canopy. In a first step, photosynthesis rates of shaded and sunlit leaves at each depth of the canopy are calculated separately. In the case of sunlit leaves, an additional integration over the leaf angle distribution is performed to include an averaged value of the adsorbed direct radiation for the estimation of sunlit leaf photosynthesis rates at different canopy depths. Finally, the integration over the canopy depth of the photosynthesis rates of both sunlit plus shaded leaves gives gross canopy photosynthesis at any given time during the day.

P g,l,z [g CO 2 m – 2 d – 1 ] denotes the total photosynthesis at depth z of the canopy given by the fraction of sunlit leaves f slt,z at depth z and the gross photosynthesis of sunlit Pg , slt , z or shaded leaves P g,shd,z :

Since the integration over the cumulative leaf area index f LAI,z at canopy depth z from zero at the soil surface to the total leaf area index f LAI of the canopy corresponds to the integration over the canopy height, the total gross photosynthesis of the canopy P g can be calculated using:

The daily gross photosynthesis and hence the daily amount of assimilated carbon by the canopy is then estimated by integration over the day length ( Spitters et al., 1989 ) from the time of sunrise t 0 to the time of sunset t 1 :

Gaussian integration is usually applied as a fast and accurate method to calculate both instantaneous and daily canopy photosynthesis ( Goudriaan, 1986 ; Spitters, 1986 ).

In the case of the GECROS model, upscaling from the leaf transpiration as determined by leaf stomatal conductance from either sunlit or shaded leaves to the whole canopy transpiration is achieved by the same integration procedures ( Yin and van Laar, 2005 ).

Impact Factors of Temperature, Atmospheric CO 2 Concentration, Soil Water, and Soil Nitrogen Availability

Besides the differences between “big-leaf,” “two-leaf,” and “multi-layer” approaches, crop growth models mainly differ in their choice of impact functions.

The impact functions of air temperature are well documented in the supplementary information of Wang et al. (2017) an will not be mentioned further here.

To simulate the impact of atmospheric CO 2 concentration on photosynthesis, strongly different approaches have been incorporated into crop growth models especially if CO 2 enrichment experiments are considered. In the case of the “big-leaf” approach, the CO 2 impact factor is either a linear or a curvilinear multiplier leading to an increase of light-use-efficiency ( Tubiello and Ewert, 2002 ), as in several models including CERES, Cropsyst, EPIC, Sirius and STICS ( Vanuytrecht and Thorburn, 2017 ). In the case of leaf scale photosynthesis models, common and often documented approaches are the biochemical model of leaf photosynthesis of Farquhar et al. (1980) for C 3 plants and an equivalent version by Yin and Struik (2009) for C 4 plants. Both models are based on the calculation of intercellular CO 2 concentration and require the incorporation of a stomatal conductance model. However, for both photosynthesis models, several parameters have to be determined and the application of the model can be difficult. A simpler approach for leaf-level photosynthesis is the empirically determined increase of light-saturated photosynthetic rate prescribed by the impact factor f CO_2 in eq. (4). Additionally, photosynthetic light-use-efficiency ε PAR can be modeled as influenced by atmospheric CO 2 concentrations ( Nendel et al., 2009 ) and it is increased if higher CO 2 concentrations occur ( Vanuytrecht and Thorburn, 2017 ). In contrast, the more complex structure of the GECROS model can simulate the acclimation of photosynthesis to higher CO 2 levels, which has been observed in FACE experiments. This good model performance is based on a better consideration of plant internal C−N interactions ( Thornley, 1998 , 2004 ) within the GECROS model ( Biernath et al., 2013 ; Yin, 2013 ).

In several models, the impact factor of water availability on photosynthesis is set equal to the ratio of actual to potential evapotranspiration, which then reduces canopy light-use-efficiency or the maximal leaf photosynthesis rate if the actual transpiration, i.e., the root water uptake, is less than the potential demand. It is assumed that stomatal closure is controlled by the balance between available soil water and water demand caused by atmospheric conditions ( Tubiello and Ewert, 2002 ). Less simple approaches calculate leaf stomatal conductance, which decreases under water stress, and thus limits photosynthetic rates by reducing CO 2 entry into leaves or fluxes within the canopy. This has to be based on simulations of energy balance either at the leaf or canopy level to adequately represent the impact of atmospheric conditions that determine transpiration demand.

Similar to the very simple approach to account for water availability, the impact factor of nitrogen availability can be defined as the ratio of actual nitrogen demand versus optimal nitrogen supply. This is often calculated as the ratio of the difference between actual and minimal nitrogen content about the difference between optimal and minimal nitrogen content of either the leaf in the case of leaf photosynthesis or of the aboveground canopy biomass in the case of canopy photosynthesis. It is assumed that nitrogen contents are not optimal if the mineral nitrogen uptake from the soil cannot fulfill plant nitrogen demand given as the sum of the differences between actual and optimal concentrations in the plant organs ( Priesack and Gayler, 2009 ). More complex nitrogen uptake models can also simulate the observed increase in photosynthetic nitrogen-use-efficiency and decreased tissue N concentrations at elevated [CO 2 ] ( Biernath et al., 2013 ; Vanuytrecht and Thorburn, 2017 ). This is achieved for example by incorporating a functional balance between root N acquisition and shoot C gain in GECROS ( Yin and van Laar, 2005 ; Priesack and Gayler, 2009 ), or by including an adaptation of photosynthetic N demand to atmospheric [CO 2 ] as in the growth model AgPasture of APSIM ( Cullen et al., 2009 ).

Most of the considered crop models are source driven assuming growth limitation by the supply of assimilates. Therefore, approaches to model either positive or negative environmental impacts on yields by factors increasing or reducing maximal leaf photosynthesis rate or crop light-use-efficiency strongly determine the simulation of crop growth. Determination of these factors needs numerous field experiments and extensive testing to provide a sound basis for adequate simulations of impacts on crop growth. By this rather simple and parsimonious approach, crop growth is conceived as carbon-source driven and described by balancing gains from assimilation and losses through respiration and plant tissue abscission.

Metabolic Engineering to Improve Photosynthesis and Elevated CO 2 Acclimation

As already stated in this review, improving photosynthesis has become a major aim for increasing plant yield (example: the RIPE project 2 ) ( Long et al., 2015 ; Ort et al., 2015 ; Simkin et al., 2019 ; Weber and Bar-Even, 2019 ). To date, targets to achieve this include: RuBisCO properties and activation, RuBP regeneration, photorespiration, CO 2 availability by improving mesophyll conductance and by introducing CO 2 concentrating mechanisms based on cyanobacterial, algal and C4-plant systems, photoprotection by modifying the relaxation of energy quenching processes, and by optimizing crop canopies to improve light capture (see Ort et al., 2015 ). Already, several studies have provided support by demonstrating that modifying photosynthetic processes through genetic engineering can improve photosynthetic CO 2 assimilation rates and yield potential (see reviews by Simkin et al., 2019 ; Weber and Bar-Even, 2019 ). Several major examples are highlighted below and include improving RuBP regeneration by overexpressing selected Calvin cycle enzymes and modifying photorespiration by creating artificial glycolate-metabolizing bypass pathways in the chloroplast (see Figure 4 ). These processes were found to be amongst the best targets to improve photosynthesis CO 2 assimilation efficiency after in silico modeling studies pin-pointed SBPase, fructose bisphosphate aldolase (FBPA), and photorespiration as potential limiting reactions ( Zhu et al., 2007 ).

Figure 4. A simplified scheme highlighting tested ways to increase photosynthesis and crop yield by overexpressing selected Calvin cycle enzymes and by creating synthetic chloroplastic photorespiratory bypasses. The enzymes in green circles/squares have been overexpressing either individually or together. Details of the different photorespiratory pathways can be found in the main text: 1 is based on the bacterial glycerate pathway leading to 3-PGA from glycolate ( Kebeish et al., 2007 ; Dalal et al., 2015 ), 2 is an incomplete glycerate pathway leading to glyoxylate from glycolate ( Nölke et al., 2014 ), 3 are pathways leading to the production of CO 2 from glycolate ( Maier et al., 2012 ; Shen et al., 2019 ; South et al., 2019 ). The transporter in the red circle was repressed to improve photorespiratory bypasses (see South et al., 2019 ). Abbreviations can be found in the main text.

Plants over-expressing the redox-regulated Calvin cycle enzyme SBPase show improved photosynthetic activities and increased biomass. This has been seen to occur in Arabidopsis thaliana ( Simkin et al., 2017a ), tobacco ( Lefebvre et al., 2005 ; Rosenthal et al., 2011 ; Simkin et al., 2015 ), tomato ( Ding et al., 2016 ), and wheat ( Driever et al., 2017 ). However, beneficial effects were found to be dependent on both developmental stage and growth conditions. An increase in photosynthesis was only observed in young expanding tobacco leaves but not in fully expanded ones and no effect on photosynthesis was seen when plants were grown under short days and low light ( Lefebvre et al., 2005 ). When tobacco over-expressing Arabidopsis SBPase was grown outside under elevated CO 2 (585 ppm) an increase in photosynthesis and biomass was observed when compared to wild-type plants ( Rosenthal et al., 2011 ). However, the increase in biomass was only 50% of the theoretical value due to C3-plant acclimation to elevated CO 2 (see below). Furthermore, higher CO 2 assimilation rates were variable over the growing season with no significant increase observed in August compared to July. Cyanobacterial and green algal Calvin cycle enzymes have also been used to improve plant productivity. The overexpression of either Chlamydomonas SBPase or cyanobacterial FBPase led to increases in both photosynthesis and biomass ( Tamoi et al., 2006 ). A cyanobacterial bifunctional SBPase/FBPase enzyme has been overexpressed also in tobacco ( Miyagawa et al., 2001 ), lettuce ( Ichikawa et al., 2010 ), and soybean ( Köhler et al., 2017 ) with increases in photosynthetic CO 2 fixation and biomass. In the work of Köhler et al. (2017) , soybean overexpressing cyanobacterial SBPase/FBPase was grown in the field during three seasons under elevated CO 2 (600 ppm) and elevated temperature (+3°C) and compared to normal ambient conditions. Across the different treatments, the over-expressing lines exhibited higher carbon assimilation rates. Under ambient CO 2 , elevated temperature led to seed yield reductions in both control and transgenic genotypes. However, under elevated CO 2 and high temperature, the SBPase/FBPase plants maintained higher seed yield levels, while WT plants had reduced seed yields, compared with plants grown under only high CO 2 . Therefore, perhaps Calvin cycle manipulation can offset the detrimental effects of future climate change conditions. Improved biomass has been observed also when overexpressing FBPA in tobacco ( Uematsu et al., 2012 ) and the positive effect on photosynthesis and biomass was more pronounced when plants were grown at elevated CO 2 (700 ppm). When Arabidopsis FBPA was overexpressed in the photosynthetic tissues of Arabidopsis using a RuBisCO small subunit 2A promoter, similar increases in photosynthesis, dry weight, and seed yield occurred ( Simkin et al., 2017a ). However, when overexpression of SBPase and FBPA were stacked in Arabidopsis, no significant additional increases in maximal efficiency of CO 2 assimilation rate (A max ), dry weight and seed yield were observed when compared to individual transgene overexpressing lines ( Simkin et al., 2017a ).

Another major strategy for improving photosynthesis has been a synthetic biology approach to express within the chloroplast an alternative pathway to efficiently metabolize photorespiratory glycolate and thus alleviate the potential inhibitory action of 2-PG on photosynthesis, while preventing ammonium release and limiting CO 2 release to the chloroplast where RuBisCO is located ( Figure 4 ). Since the first report ( Kebeish et al., 2007 ), several photorespiratory bypass strategies have been used to improve photosynthetic CO 2 assimilation and biomass. The initial bypass tested was based on the bacterial glycerate pathway where glyoxylate was converted to glycerate by two enzymes; glyoxylate carboligase and tartronate semialdehyde reductase ( Figure 4 , pathway 1). The conversion of glycolate to glyoxylate in the chloroplast was achieved by expressing a bacterial glycolate dehydrogenase (GlycDH). When expressed in Arabidopsis chloroplasts, transgenic lines produced bigger rosettes and more biomass while increasing A max ( Kebeish et al., 2007 ). A similar strategy was used to increase biomass and seed yield in Camelina sativa ( Dalal et al., 2015 ). Bacterial GlycDH alone was introduced into Solanum tuberosum using a single construct to produce a polyprotein to circumvent instability problems ( Nölke et al., 2014 ). These potato lines exhibited an enhanced A max at 400 ppm CO 2 (but not when measured at 2000 ppm CO 2 ) and an increase in tuber yield even though the complete glycerate pathway had not been introduced ( Figure 4 , pathway 2). This was observed also for GlycDH-expressing Arabidopsis ( Kebeish et al., 2007 ) and Camelina sativa ( Dalal et al., 2015 ). Another successful photorespiratory bypass was achieved by expressing glycolate oxidase, catalase and malate synthase in chloroplasts ( Maier et al., 2012 ). Such a bypass has the potential to completely oxidize glycolate to CO 2 and it led to increases in leaf dry weight and net CO 2 fixation rates ( Figure 4 pathway 3). More recently, another chloroplastic photorespiratory bypass (named the GOC bypass) was expressed in rice to increase photosynthetic efficiency ( Shen et al., 2019 ). It consisted of three rice enzymes; glycolate oxidase, oxalate oxidase, and catalase expressed in chloroplasts and designed to lead to the complete oxidation of glycolate to CO 2 ( Figure 4 pathway 3). Improved photosynthetic efficiency, biomass, and yield were found in both greenhouse and field experiments although there were differences according to seeding season and it was more advantageous under high light. To improve flux through chloroplastic photorespiratory bypasses, glycolate export out of the chloroplast was manipulated by down-regulating PLGG1 (a plastidial glycolate/glycerate transporter, Pick et al., 2013 ; South et al., 2019 ). The best results were obtained with a variant of the glycolate oxidation pathway where glycolate oxidase was replaced by Chlamydomonas reinhardtii GlycDH ( Figure 4 , pathway 3). Field-grown tobacco expressing this version of an alternative photorespiratory pathway exhibited a >25% increase in total vegetative biomass (without PLGG1 inhibition) and a 40% increase (with inhibited PLGG1) although the impact on net CO 2 assimilation was quite low (5−8%) and no significant increase in seed yield was observed ( South et al., 2019 ).

The over-expression of specific photorespiratory enzymes has also led to increased biomass, A max , and grain yield. This was observed when mitochondrial serine hydroxymethyltransferase (SHMT1) was overexpressed in rice ( Wu et al., 2015 ) and when individual glycine decarboxylase (GDC) subunits were overexpressed in Arabidopsis either the H-protein ( Timm et al., 2012 ) or the L-protein ( Timm et al., 2015 ; Figure 4 ). When the H-protein was overexpressed in tobacco, improved biomass was only observed when under the control of a leaf-specific promoter and this only became significant at high light intensities while constitutively overexpressed H-protein led to a detrimental growth effect ( López-Calcagno et al., 2019 ). In gene stacking experiments, the additional overexpression of the H-protein in Arabidopsis lines overexpressing SBPase and FBPA led to further improvements in seed weight per plant (under high light growth conditions) and leaf area with no further increase in A max compared to SBPase-FBPA lines alone ( Simkin et al., 2017a ).

Improved photosynthesis has also been found in plants where the light-side of photosynthesis has been manipulated. In Arabidopsis, the overexpression of the Rieske-FeS protein of the cytb 6 f complex led to plants exhibiting increased A max , dry weight, leaf area and seed yield ( Simkin et al., 2017b ). Photosynthesis was improved also under fluctuating light conditions by overexpressing violaxanthin de-epoxidase, zeaxanthin epoxidase and PsbS , all components of a photoprotection mechanism involving light energy dissipation as heat ( Kromdijk et al., 2016 ). Field-grown tobacco plants overexpressing these three proteins showed increases in dry weight, leaf area and plant height ( Kromdijk et al., 2016 ).

As mentioned above, certain strategies to improve photosynthesis and yield have already been tested under one or more climate change condition(s) such as elevated temperature and CO 2 levels. In general, increased temperatures of only several °C have been shown to cancel the beneficial effects of elevated CO 2 (see Köhler et al., 2017 ). Multiple FACE experiments (carried out at around 600 ppm CO 2 ) have consistently shown that the increase in C3-crop yield in response to long-term elevated CO 2 conditions is 50% lower than predicted due to photosynthetic acclimation (see Ainsworth and Long, 2005 ; Kant et al., 2012 ). Many C3-plant species only exhibit a small 15% increase in yield compared to a hypothetical 40% increase under predicted climate change CO 2 levels. This yield response to elevated CO 2 has been observed in both controlled growth conditions and FACE experiments (see Leakey et al., 2009 and references therein) where a significant reduction in N-content was also reported ( Ainsworth and Long, 2005 ; Vicente et al., 2015 ). This underachievement of certain C3 model plants like Arabidopsis thaliana and major C3-cereal plants including wheat and rice to elevated CO 2 is due to modifications in plant metabolism, physiology, and development where acclimation is associated with a negative impact on leaf photosynthesis ( Ainsworth and Rogers, 2007 ), root nitrate uptake and leaf nitrate assimilation ( Rachmilevitch et al., 2004 ; Bloom et al., 2010 ), thus reducing the expected benefits of elevated CO 2 . It includes a reduction in RuBisCO protein content, a reduction in stomatal conductance, and decreases in both photosynthetic and N-assimilation gene expression (e.g., Vicente et al., 2015 ) that brings about a reduction in leaf and seed N-content. In the literature, photosynthetic acclimation to elevated CO 2 has been explained by the inhibition of photosynthetic gene expression due to the accumulation of excess non-structural carbohydrates in source leaves ( Moore et al., 1999 ; Ruiz-Vera et al., 2017 ). However, it is possible that this acclimation is also driven by N-limitations when N-assimilation cannot keep up with the increased C-assimilation rates. It has also been suggested that at elevated CO 2 , a decrease in photorespiration impacts negatively both nitrate uptake and assimilation ( Bloom, 2015 ). Several factors have been proposed to influence acclimation to elevated CO 2 , such as sink strength, sugar signaling, and N-regime (see Long et al., 2004 and references therein). Several papers have suggested a link between improved sink strength and a reduction of this acclimation in tobacco ( Ruiz-Vera et al., 2017 ), rice ( Zhu et al., 2014 ), barley ( Torralbo et al., 2019 ), and Larrea tridentata ( Aranjuelo et al., 2011 ). Although a number of actors in sugar signaling and sensing are known (see Pego et al., 2000 ), a lack of information on how they are affected by elevated CO 2 , especially in roots, has been stated ( Thompson et al., 2017 ). This is similar to nitrate-signaling where actors of perception, signal transduction and even root to shoot communication have been discovered (see Wang et al., 2018 ) but little is known about how elevated CO 2 and other climate change factors affect such processes. Indeed, to date, there is no global understanding of the regulatory networks involved in the acclimation processes occurring to balance plant C and N metabolism under elevated CO 2 . That said, a recent work using correlation network analyses confirmed the tight coordination between C and N metabolisms while carbohydrate levels were linked to the down-regulation of both photosynthetic and N metabolism genes ( Vicente et al., 2018 ).

When light is saturating, photosynthesis can be limited by several factors including V cmax (amount and maximal carboxylase activity of RuBisCO), RuBP regeneration, triose-phosphate utilization/carbohydrate export (source-sink properties) and, of course, photorespiration. Under future climate conditions of elevated CO 2 and temperature, major limitations will probably shift to RuBP regeneration and source-sink properties. Under elevated CO 2 , plants reduce RuBisCO amounts since it is no longer a limiting factor but they need to improve their photosynthetic electron transport properties to produce enough NADPH and ATP to regenerate RuBP via the Calvin cycle. Less RuBisCO is a common feature of elevated CO 2 acclimation in C3-cereals while N reallocations to improve the light reactions are not adequate and there is an overall reduction in plant N-content.

Predicted climate change conditions of elevated CO 2 and temperature have been shown to affect the benefits of improved photosynthesis by current genetic manipulations, as mentioned above ( Rosenthal et al., 2011 ; Köhler et al., 2017 ). That said, strategies used to improve RuBP regeneration have often given rise to the best increases in photosynthesis and yields under either elevated CO 2 or high light or both (see above). However, strategies to reduce the negative impact of photorespiratory carbon recycling might be expected to have a lesser impact under conditions that lower photorespiration like elevated CO 2 although benefits may still occur under elevated temperatures and high light in association with CO 2 . It is probable that C3-plant acclimation to future atmospheric CO 2 and temperature levels could hamper strategies to improve photosynthesis and yield of actual plant genotypes. Therefore, perhaps additional strategies to reduce C3-plant acclimation by deregulating plant functions associated with known acclimation processes might be required. This would require extensive omics analyses to identify the regulating gene networks and proteins involved in photosynthetic acclimation to climate change conditions, this could be helped by photosynthetic performance measurements in the field using non-destructive HTP techniques and platforms while the data sets could be used to improve plant growth models to predict the benefits. In this way, the best gene targets will be identified and tested to create new crops for the future.

Author Contributions

MB and ÁS-S integrated the contributions. All authors read and approved the final version of the manuscript.

This work was supported by IRUEC project funded by EIG CONCERT-Japan 3rd Joint Call on “Food Crops and Biomass Production Technologies” under the Strategic International Research Cooperative Program of the Japan Science and Technology Agency (JST) and the Spanish Innovation and Universities Ministry (Acciones de programación conjunta Internacional, PCIN-2017-007), and by the ANR-14-CE19-0015 grant REGUL3P. A Grant for Promotion of KAAB Projects (Niigata University) from the Ministry of Education, Culture, Sports, Science, and Technology-Japan is also acknowledged.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We apologize to all colleagues whose work was not referenced due to space constraints.

• ^ https://sites.google.com/site/ijmackay/work/magic
• ^ http://ripe.illinois.edu/

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Keywords : photosynthesis, climate change, crop improvement, -omics, phenotyping, modeling

Citation: Baslam M, Mitsui T, Hodges M, Priesack E, Herritt MT, Aranjuelo I and Sanz-Sáez Á (2020) Photosynthesis in a Changing Global Climate: Scaling Up and Scaling Down in Crops. Front. Plant Sci. 11:882. doi: 10.3389/fpls.2020.00882

Received: 29 November 2019; Accepted: 29 May 2020; Published: 06 July 2020.

Reviewed by:

Copyright © 2020 Baslam, Mitsui, Hodges, Priesack, Herritt, Aranjuelo and Sanz-Sáez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Marouane Baslam, [email protected] ; Iker Aranjuelo, [email protected] ; Álvaro Sanz-Sáez, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Article Contents

Introduction, temperature response of photosynthesis within the leaf: the critical role of enzyme function, temperature impacts on stomata and plant transport systems, adding complexity: leaf interactions influence whole-plant responses to temperature, scaling from plants to ecosystem reinforces the complex relationship between temperature and photosynthesis, conclusion and future directions, acknowledgements, author contributions.

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The effect of increasing temperature on crop photosynthesis: from enzymes to ecosystems

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Caitlin E Moore, Katherine Meacham-Hensold, Pauline Lemonnier, Rebecca A Slattery, Claire Benjamin, Carl J Bernacchi, Tracy Lawson, Amanda P Cavanagh, The effect of increasing temperature on crop photosynthesis: from enzymes to ecosystems, Journal of Experimental Botany , Volume 72, Issue 8, 2 April 2021, Pages 2822–2844, https://doi.org/10.1093/jxb/erab090

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As global land surface temperature continues to rise and heatwave events increase in frequency, duration, and/or intensity, our key food and fuel cropping systems will likely face increased heat-related stress. A large volume of literature exists on exploring measured and modelled impacts of rising temperature on crop photosynthesis, from enzymatic responses within the leaf up to larger ecosystem-scale responses that reflect seasonal and interannual crop responses to heat. This review discusses (i) how crop photosynthesis changes with temperature at the enzymatic scale within the leaf; (ii) how stomata and plant transport systems are affected by temperature; (iii) what features make a plant susceptible or tolerant to elevated temperature and heat stress; and (iv) how these temperature and heat effects compound at the ecosystem scale to affect crop yields. Throughout the review, we identify current advancements and future research trajectories that are needed to make our cropping systems more resilient to rising temperature and heat stress, which are both projected to occur due to current global fossil fuel emissions.

Global land surface temperatures are increasing due to rising atmospheric CO 2 from anthropogenic emissions that are causing climate change, and with this comes the challenge of meeting food and fuel supply demands under more stressful crop growing conditions. Despite a drop in emissions associated with the coronavirus pandemic of 2020 (COVID-19; Le Quéré et al. , 2020 ), global emissions are currently tracking the worst-case ‘business as usual’ emissions scenario (RCP8.5) that will very likely equate to unprecedented warming from pre-industrial (1850–1990) levels of 3–5 °C by 2100 ( IPCC, 2014 ). A recent IPCC report indicated, with medium confidence, that crop yields will experience ‘severe and widespread impacts’ if global warming exceeds 1.5 °C above pre-industrial levels, but that these impacts can be managed below this warming threshold ( IPCC, 2018 ). Coupled with rising mean global temperature is a projected increase in the frequency, intensity, and duration of extreme heatwave events that have the potential to cripple crop yields ( Battisti and Naylor, 2009 ; Perkins et al. , 2012 ; Hatfield and Prueger, 2015 ; Hoegh-Guldberg et al. , 2018 ). Additionally, some cropping areas, such as temperate, high-latitude regions, will likely face even greater warming than tropical regions of the world ( Hoegh-Guldberg et al. , 2018 ). Therefore, there is an urgent need, first and foremost, for mitigation strategies to reduce fossil fuel emissions to cap warming at 1.5 °C ( IPCC, 2018 ), but also for development of our major cropping systems to be more resilient to hotter growing seasons and extreme temperature events that seem inevitable in the coming century.

Global yield losses in key crops, such as maize and wheat, have been attributed to higher growing season temperatures ( Lobell et al. , 2011 ; Lobell and Gourdji, 2012 ; Asseng et al. , 2015 ). Without crop improvement strategies, including genetic engineering and adaptation under carbon dioxide (CO 2 ) fertilization, substantial yield declines per °C of warming have been projected for the major cropping systems of maize (7.4%), wheat (6.0%), rice (3.2%), and soybean (3.1%) ( C. Zhao et al. , 2017 ). Yet, to keep pace with supplying food and fuel to the growing human population, agricultural production will need to double (based on average yield in 2005) over this century to meet increased caloric demand ( Long and Ort, 2010 ; Ray et al. , 2013 ). Additionally, the full theoretical extent of the CO 2 fertilization effect is unlikely to be realized due to the impact of rising temperature ( Long et al. , 2006 a ; Ainsworth and Long, 2020 ). Thus, improving crop resilience to temperature stress is a vital step towards ensuring global food and fuel demands are met.

Temperature is a critical meteorological determinant of crop development and function. Temperature alters enzyme function within a leaf ( Bernacchi et al. , 2001 ; Walker et al. , 2013 ; Florian et al. , 2014 ; Kumarathunge et al. , 2019 ; Timm et al. , 2019 ) and triggers changes in developmental growth stage that are tightly coupled with crop yield ( Ruiz-Vera et al. , 2018 ; Zhu et al. , 2018 ). Furthermore, the amount of water vapour in air at saturation increases exponentially with temperature, raising the vapour pressure deficit (VPD), and driving more potential water loss from plants ( Novick et al. , 2016 ; Grossiord et al. , 2020 ). The result of these broad crop physiological responses to temperature means that any shifts in long-term mean annual temperature and extreme temperature events will be likely to have significant impacts on crop production from the key food and fuel growing regions of the world.

Improvements in how crops function from the enzyme to ecosystem scale are required to maintain historic increases in crop yields into the future, whilst ensuring cropping systems remain resilient to rising temperatures ( Long and Ort, 2010 ). Engineering improvements to photosynthesis, including its resilience to perform under hotter temperatures at the leaf, plant, and canopy levels, is an emergent strategy that may help boost yields ( Long et al. , 2006 b ; Ainsworth and Ort, 2010 ; Ort et al. , 2015 ; Betti et al. , 2016 ; Kromdijk and Long, 2016 ; Kubis and Bar-Even, 2019 ; Posch et al. , 2019 ; Simkin et al. , 2019 ; Wu et al. , 2019 ; Furbank et al. , 2020 ). Developing better warning systems, such as early detection of crop ecosystem stress, will also improve targeted management approaches that reduce resource use (i.e. water and pesticides), expenditure, and time ( Guanter et al. , 2014 ; Chlingaryan et al. , 2018 ; Camino et al. , 2019 ).

Realizing the full impact of temperature increase on crop photosynthesis across scales is an area of ongoing investigation, particularly given the complex interactions of water availability, increasing atmospheric CO 2 concentrations ([CO 2 ]), nutrient availability, and the increased frequency and/or intensity of extreme climate events that feed back to alter annual crop photosynthesis and productivity. There have been several seminal reviews on the effect of rising temperature on crop photosynthetic performance ( Ainsworth and Ort, 2010 ), photosynthetic enzyme function ( Slattery and Ort, 2019 ), plant carbon metabolism ( Dusenge et al. , 2019 ), and plant development ( Wang et al. , 2012 ), as well as global assessments of how crop yield is likely to change as temperatures rise ( Lobell and Gourdji, 2012 ; C. Zhao et al. , 2017 ). Yet, reviews that address all these scales in one are limited.

This review focuses on synthesizing current advances in understanding the effects of temperature on cropping systems from the enzyme to ecosystem scale ( Fig. 1 ) to provide a comprehensive assessment of how crop photosynthesis changes as temperature increases. Beginning at the enzyme scale, we discuss (i) within-leaf responses to temperature, followed by (ii) stomata and plant transport system responses to heat; (iii) temperature effects on whole plants and their development; and (iv) how each of these factors scales to the crop ecosystem to impact photosynthesis and annual yield ( Fig. 1 ). Key abbreviations used throughout the review are listed and expanded in Table 1 . For each scale discussed, we identify areas for research development that are needed to ensure the major crops that feed and fuel the world are more resilient to the impacts of rising temperature that will occur without implementation of climate mitigation strategies.

Nomenclature and explanation of terms used across different scales

The spatial scale and temporal response time of photosynthetic processes in cropping systems from the enzyme to ecosystem scale.

Temperature regulation is foundational in biological systems, as chemical reaction rates are a function of the tissue temperature and the energy required to initiate the reaction—the activation energy (E a ) ( Fig. 2A ). Enzymes lower this E a barrier, enhancing the rate of enzyme-catalysed reactions driving biological metabolism ( Wolfenden and Snider, 2001 ). In theory, reaction rates are predicted to increase exponentially with temperature. In reality, most biological temperature responses increase exponentially with temperature until reaching a thermal optimum ( T opt ), after which rates decline due to enzyme deactivation and denaturation at increasingly high temperatures ( Fig. 2B , C ).

Temperature effects on enzyme-driven processes of photosynthesis. (A) Schematic energy profile of an exergonic chemical reaction. Enzymes, such as Rubisco, facilitate biochemical reaction progression by lowering the activation energy requirements of the transition state between reactants and product formation, though in the case of Rubisco this is simplified as the enzyme facilitates a multistep catalysis ( Flamholz et al. , 2019 ). (B) Modelled temperature responses of tobacco Rubisco carboxylation catalytic turnover rate (green solid) and specificity for CO 2 over O 2 (yellow dashed line), using parameters from Orr et al. (2016) and temperature responses from Bernacchi et al. (2001) . (C) Temperature response of gross photosynthesis (carbon assimilation A +mitochondrial respiration R d , green solid line) and of mitochondrial respiration ( R d , gold dotted line) for an idealized C 3 species. Data were modelled using the leaf model of photosynthesis ( Farquhar et al. , 1980 ) with temperature adjustments ( Bernacchi et al. , 2001 ).

The photosynthetic machinery within a leaf is a logical place to begin when considering the effects of temperature on crop photosynthesis, as many component processes of photosynthetic metabolism are highly temperature sensitive. At a biochemical level, net photosynthetic carbon assimilation ( A ) is largely determined by Rubisco efficiency and activation, and ribulose bisphosphate (RuBP) regeneration ( Table 1 ) ( Farquhar et al. , 1980 ). The predominant determinant varies with chloroplastic [CO 2 ]; RuBP regeneration limits A at elevated [CO 2 ], but Rubisco performance limits A at ambient and subambient [CO 2 ]. Enzyme degradation at elevated temperatures can impede the function of PSII, decrease electron transport rates, inhibit Rubisco activase (Rca), and decrease chlorophyll content ( Salvucci et al. , 2001 ; Guo et al. , 2006 ; Allakhverdiev et al. , 2008 ; Prasad and Djanaguiraman, 2011 ). Elevated temperature can also induce membrane permeability, leading to direct damage of the chloroplast thylakoid membranes, which further inhibits light harvesting, electron transport rates, and ATP generation ( Schrader et al. , 2004 ; Prasad et al. , 2008 ; Djanaguiraman et al. , 2013 ; Pokharel et al. , 2020 ). However, thermal lability of enzymes directly involved in A remains the major cause of photosynthetic inhibition of C 3 and C 4 crops grown under elevated temperatures ( Crafts-Brandner and Salvucci, 2000 ; Schrader et al. , 2004 ; Sage and Kubien, 2007 ; Perdomo et al. , 2016 ; Slattery and Ort, 2019 ).

The optimal temperature of RuBP regeneration is generally higher than that of Rubisco carboxylation ( Hikosaka et al. , 2006 ); therefore, under current atmospheric [CO 2 ] and saturating light, the temperature dependence of photosynthesis is well explained by Rubisco biochemistry ( Sage and Kubien, 2007 ). As temperatures increase, the fraction of enzyme able to meet or exceed the E a required for catalysis increases, and so Rubisco carboxylation activity increases ( Fig. 2B ). However, Rubisco is a bi-functional enzyme, also catalysing the oxygenation of RuBP ( Ogren and Bowes, 1971 ; Tcherkez, 2016 ; Bathellier et al. , 2020 ; von Caemmerer, 2020 ). The specificity of Rubisco for CO 2 versus O 2 (S C/O ) declines as temperatures increase, decreasing the ratio of carboxylation to oxygenation in vivo ( Fig. 2B ). This increased propensity for Rubisco oxygenation at elevated temperatures produces more 2-phosphoglycolate, which must be cycled through the photorespiratory pathway, resulting in a loss of previously fixed carbon at an energetic expense ( Walker et al. , 2016 ).

In C 4 photosynthesis, CO 2 is concentrated around Rubisco in bundle sheath chloroplasts. Thus, stimulation of photorespiration by elevated temperatures is minimal, and A in C 4 plants has a higher T opt than in C 3 plants ( Sage and Kubien, 2007 ). Above the T opt , C 4 photosynthesis may also be limited through inactivation of Rubisco ( Crafts-Brandner and Salvucci, 2002 ), or by rates of other C 4 bundle sheath enzymes ( Boyd et al. , 2015 ), which show species-specific temperature responses ( Sonawane et al. , 2017 ). This impact is evident in field-grown maize, where leaf-level A and yield decline with elevated temperature, even under elevated CO 2 conditions ( Ruiz-Vera et al. , 2015 ).

The duration and intensity of future warming events are both projected to change ( Hoegh-Guldberg et al. , 2018 ), resulting in significant impacts on any potential thermal acclimation of A ( Kattge and Knorr, 2007 ; Vico et al. , 2019 ). In sunlit leaves near the top of the canopy, photosynthetic acclimation through increased electron transport capacity, differential expression of Rca isoforms, and heat shock protein expression can occur with long-term growth at warmer temperatures ( Yamori et al. , 2014 ). However, short-term temperature increases can increase leaf respiration, resulting in lower A compared with those at ambient temperature, and a strong and relatively rapid acclimation response that reduces the effect as higher temperatures persist ( Way and Yamori, 2014 ; Kumarathunge et al. , 2019 ). During heatwaves or acute heat stress, defined by sudden increases in temperature ( Smith and Dukes, 2017 ) with significant but reversible effects on photosynthesis ( Siebers et al. , 2015 , 2017 ; Thomey et al. , 2019 ), the acclimation responses may be too slow or small to confer a measurable benefit. In these situations, energy balances will shift as rates of photosynthesis decline above the T opt and respiration rates increase ( Fig. 2C ). Thus, most opportunities for improving crop productivity in a warmer world focus on improving photosynthetic carbon gain above T opt .

Recent advances made at the leaf level to improve understanding on temperature effects

The response of A to a wide range of environmental conditions is well understood based on the leaf model of photosynthesis ( Farquhar et al. , 1980 ; Long, 1991 ). Despite the mechanistic understanding of modelled predictions, there remain significant uncertainties. For example, the leaf photosynthesis model ( Farquhar et al. , 1980 ) was recently parameterized using values measured from C 3 plants grown under field conditions exposed to supplemental heating ( Bagley et al. , 2015 ). The results demonstrate that growth at higher temperatures does not translate to a higher T opt but does lower photosynthetic rates at all temperatures. An interaction between warmer temperature and elevated [CO 2 ] was observed; however, acclimation of photosynthetic enzymatic activity to higher temperature negatively impacted the benefit of higher CO 2 ( Fig. 3 ) ( Bagley et al. , 2015 ). These results demonstrate the challenges associated with temperature, namely that short- and long-term responses of photosynthesis are complex and are complicated by other environmental variables.

Temperature response of C 3 leaf photosynthesis (μmol m –2 s –1 ) modelled at atmospheric [CO 2 ] of 400 (solid lines), 600 (dotted lined), and 800 (dashed lines) μmol mol -–1 . Model parameters were taken from Bernacchi et al. (2001 , 2003 , black circles) and Long (1991 , green triangles), with the symbol location on the curve representing the temperature optimum for each photosynthetic response curve. The figure has been redrawn from Bagley et al. (2015) , with permission.

Despite the complex interaction between temperature and photosynthesis, promising strategies have been identified to increase photosynthetic A at higher temperatures by either enhancing RuBP carboxylation or improving energy efficiency of photorespiration. The limitations imposed by Rubisco include a slow catalytic rate, competitive inhibition by O 2 , and activation requirement via heat-sensitive Rca. Strategies to improve our understanding of Rubisco are needed to overcome these temperature impacts.

Rubisco has long been a target for modification to improve its catalytic rate and substrate specificity ( Somerville and Ogren, 1982 ; Zhu et al. , 2004 ; Sharwood, 2017 ). For example, an apparent trade-off between catalytic rate and specificity hinders progress for exploitation ( Tcherkez et al. , 2006 ; Savir et al. , 2010 ; Flamholz et al. , 2019 ). Recently, a systematic survey of prokaryotic Rubisco has identified the fastest version of the enzyme measured to date (22 s –1 ), but it still displays characteristically poor substrate specificity ( Davidi et al. , 2020 ). Screening for natural variation in Rubisco performance has uncovered kinetic diversity among land plants that would confer a predicted benefit to crop A , particularly at elevated temperatures ( Galmés et al. , 2015 ; Orr et al. , 2016 ; Sharwood et al. , 2016 ). Unique combinations of Rubisco small and large subunits from different species also provide an opportunity to optimize kinetic performance at higher temperatures ( Lin et al. , 2020 ; Martin-Avila et al. , 2020 ; Sakoda et al. , 2020 ). Finally, the newfound ability to assemble plant Rubisco in a bacterial host will enable both structure–function comparisons and directed evolution studies to identify novel mutations to improve Rubisco performance ( Aigner et al. , 2017 ; Zhou and Whitney, 2019 ).

Rca regulates Rubisco activity by displacing inhibitory sugar phosphates from the catalytic site of Rubisco. Although Rubisco remains active up to 50 °C in vitro , Rca activity declines well below this temperature ( Salvucci and Crafts-Brandner, 2004 ; Galmés et al. , 2016 ), and thus can limit photosynthesis at high temperatures. The production of inhibitory catalytic misfire products increases with temperature, implying that the role of Rca also becomes increasingly important. However, when measured in vitro , the rate of spontaneous release of these inhibitors also increases at elevated temperatures, resulting in less inhibition of Rubisco activity, which contradicts this assumption ( Schrader et al. , 2006 ; Carmo-Silva et al. , 2015 ; Bracher et al. , 2017 ). Despite this, manipulating Rca thermostability has improved photosynthetic thermotolerance in Arabidopsis ( Kurek et al. , 2007 ; Kumar et al. , 2009 ) and rice ( Wang et al. , 2010 ; Scafaro et al. , 2016 , 2018 ; Shivhare and Mueller-Cajar, 2017 ), motivating research efforts to enhance the thermotolerance of Rca in other crops. Exploiting temperature-induced differential expression of Rca is a potential strategy to accomplish this objective. In many crops, Rca consists of multiple protein isoforms with differing heat sensitivity ( Crafts-Brandner et al. , 1997 ; Law et al. , 2001 ; Law and Crafts-Brandner, 2001 ; Carmo-Silva et al. , 2015 ; Scafaro et al. , 2019 ; Kim et al. , 2020 ). In bread wheat, altered thermal tolerance between Rca isoforms is conferred by a single amino acid substitution that acts as a thermal and regulatory switch, providing a compelling target for future genome editing efforts ( Scafaro et al. , 2019 ; Degen et al. , 2020 ).

The photorespiratory pathway recycles the inhibitory by-products of Rubisco oxygenation, which releases previously fixed carbon and ammonium that is energetically costly to re-fix. Photorespiratory CO 2 loss limits productivity in C 3 plants, reducing crop yields by >20% in soy and wheat ( Walker et al. , 2016 ). Engineering carbon-concentrating mechanisms (CCMs) to directly increase the [CO 2 ] at the site of Rubisco represents one strategy for stimulating carboxylation over oxygenation ( Long et al. , 2018 ; Atkinson et al. , 2020 ). This can be accomplished via the introduction of a biophysical CCM, such as those found in cyanobacteria and algae ( Hennacy and Jonikas, 2020 ), or via the conversion of C 3 photosynthesis to C 4 or C 2 types. Researchers have recently established a functioning C 4 pathway in rice by transformation with a single construct harbouring coding sequences for five enzymes, although expression will require optimization before any benefit is realized ( Ermakova et al. , 2020 ). Engineering C 2 photosynthesis, a simple CCM that captures, concentrates, and re-assimilates photorespired CO 2 , is a promising approach currently in its infancy. An advantage of C 2 photosynthesis is the ability to exploit native genes and alter only their regulation and expression, as all required genes are present in C 3 species ( Lundgren, 2020 ). Finally, direct manipulations of the photorespiratory pathway can lower the cost of photorespiration. Overexpression of native photorespiratory genes can enhance A and growth, probably altering the balance between photosynthesis and photorespiration ( Timm et al. , 2012 , 2015 , 2018 ; Flügel et al. , 2017 ; López-Calcagno et al. , 2019 ). Synthetic glycolate metabolic pathways using enzymes from other organisms in combination with RNAi to limit glycolate flux through the native pathway increase tobacco biomass under field-grown conditions ( South et al. , 2019 ). Similarly, an alternative photorespiratory pathway introduced into rice using three rice enzymes improved A , leading to increased aboveground biomass, but displayed inconsistent improvements in yield ( Shen et al. , 2019 ). Further carbon-conserving glycolate metabolic pathways have also been designed and tested in vitro ( Trudeau et al. , 2018 ; Ross et al. , 2020 ). While these and the previous strategies to enhance photosynthetic performance above the T opt hold potential to improve crop performance, testing in food and fuel crops over diverse environmental ranges will provide the key validation of their efficacy.

Scaling the response of plant photosynthesis, from the chloroplast to leaf or whole plant, involves CO 2 diffusion to the site of the chloroplast, as well as subsequent photosynthate transport throughout the plant. To reach the site of carboxylation within chloroplasts, CO 2 must first diffuse from the atmosphere to the substomatal cavities, then through the intercellular airspaces to the chloroplast. This gaseous diffusion imposes a restriction on CO 2 availability in the chloroplast that depends on the CO 2 conductance through the leaf boundary layer, stomata, and intercellular environment (i.e. mesophyll conductance). The temperature response of mesophyll conductance varies between species, and can impose a limitation on carbon fixation, which has been well reviewed ( Niinemets et al. , 2009 ; Flexas et al. , 2012 , 2014 ; von Caemmerer and Evans, 2015 ). In this section, we discuss temperature impacts on stomata, as well as the plant transport systems that move photosynthate from leaves to other parts of the plant for growth, maintenance, and storage.

How stomatal function links leaf to whole-plant photosynthesis

Stomata control the majority of gaseous exchange between the atmosphere and the leaf interior. Therefore, stomatal behaviour is critically important for CO 2 uptake to meet photosynthetic demand and for controlling leaf water loss that impacts evaporative cooling, nutrient uptake, and plant water status ( Lawson et al. , 2010 ; Matthews and Lawson, 2019 ; Lawson and Matthews, 2020 ). Stomata open and close in response to various environmental signals and internal leaf conditions. In general, conditions of high or increasing light intensity, low (internal) [CO 2 ], and low VPD open stomata, whilst closure is observed under opposite conditions ( Matthews and Lawson, 2019 ). Stomatal conductance ( g s ) provides a measure of the capacity for gaseous exchange of water vapour leaving the leaf ( Table 1 ), and is determined by the number of stomata per unit leaf area and the size of the pore aperture. Thus, alterations in both leaf morphological features and leaf functional responses to external meteorological forcing can influence g s , which in turn can impact photosynthesis and overall crop performance.

According to the optimization hypothesis, plants coordinate g s and A to maximize A whilst minimizing water loss ( Cowan and Farquhar, 1977 ; Lawson et al. , 2010 ; Buckley et al. , 2017 ). However, this is not always the case, as a decoupling between g s and A has been reported ( von Caemmerer and Evans, 2015 ; Urban et al. , 2017 ), whereby stomata open to increase leaf cooling despite the suppression of A ( Drake et al. , 2018 ). A positive correlation between steady-state g s and yield has been observed in the field ( Fischer et al. , 1998 ; Fischer and Rebetzke, 2018 ), reflecting the control stomata exert on CO 2 uptake for photosynthesis and on evaporative cooling. Temperature can severely limit stomatal performance and consequently yield, especially in temperature-sensitive crops such as wheat, where evaporative cooling to maintain T opt can be more important than removal of diffusional constraints for photosynthesis ( Fischer et al. , 1998 ; Lu et al. , 1998 ). The same environmental cues that stimulate changes in stomatal aperture can also induce alterations to the stomatal density (SD) per unit leaf area and their distribution across the leaf ( Weyers et al. , 1997 ; Weyers and Lawson, 1997 ), which impacts g s with implications for A . Changes in one anatomical trait (i.e. SD) are often compensated for by modifications in another (i.e. stomatal size), with many studies reporting a strong negative correlation between SD and size (e.g. Drake et al. , 2013 ). However, while this relationship appears in closely related species ( Faralli et al. , 2019 ), it does not hold across multiple diverse species ( McAusland et al. , 2016 ).

One of the most well-studied impacts of environment on stomatal numbers is atmospheric [CO 2 ], which has been demonstrated to decrease SD with increasing [CO 2 ] in a number of different species ( Hetherington and Woodward, 2003 ), including several major cropping systems ( Ainsworth and Rogers, 2007 ). Global warming associated with rising [CO 2 ] has been shown to increase SD in several crop species ( Rodrigues et al. , 2016 ; Caine et al. , 2019 ) including soybean ( Jumrani et al. , 2017 ), tobacco ( Hu et al. , 2014 ), and grape ( Rogiers et al. , 2011 ), often with concurrent decreases in stomatal size ( Rodrigues et al. , 2016 ), although no effect was reported for maize ( Zheng et al. , 2013 ). However, such changes in anatomy (i.e. SD or guard cell length) do not necessarily translate into differences in g s , and vice versa ( Rodrigues et al. , 2016 ; Kapadiya et al. , 2017 ), illustrating the importance of considering both functional responses and anatomical alterations with growth temperature.

Stomatal behavioural responses to elevated temperature

Whilst higher temperatures can disrupt a number of metabolic processes, including those that take place in the guard cells, stomatal response to high temperatures is often complicated by the fact that temperature also affects photosynthesis, VPD, transpiration, and plant water status, which all feed back on stomatal behaviour ( Urban et al. , 2017 ). Changes in temperature alter VPD (see Scaling from plants to ecosystem), which subsequently alters transpiration as stomata respond to the change in atmospheric dryness (e.g. Brodribb and McAdam, 2011 ; Merilo et al. , 2018 ). Higher VPD increases the leaf–atmosphere diffusion gradient, driving greater water loss and triggering stomatal closure to maintain plant water status ( Mott and Peak, 2013 ). The actual mechanisms for stomatal response to VPD are still not fully elucidated, except for a broad classification into two hydraulic responses: active and passive ( Xie et al. , 2006 ; Chater et al. , 2011 ; Bauer et al. , 2013 ; Franks, 2013 ; McAdam and Brodribb, 2014 ).

Studies examining stomatal responses specifically to temperature have received less attention than those focusing on other environmental factors ( Way, 2011 ; Teskey et al. , 2015 ), and the findings are highly variable between species ( Sage and Kubien, 2007 ; Matthews and Lawson, 2019 ). g s has a mixed response with rising temperature across crop species ( Schulze et al. , 1975 ; Lu et al. , 2000 ; von Caemmerer and Evans, 2015 ; Urban et al. , 2017 ), with an increase in g s of 163% observed in maize ( Zheng et al. , 2013 ), yet a decrease ( Sage and Sharkey, 1987 ; Raven et al. , 2005 ) or no effect on g s at all with increased temperature reported in other crops ( Sage and Sharkey, 1987 ; Aphalo and Jarvis, 1991 ; von Caemmerer and Evans, 2015 ). Generalizing stomatal response to changes in leaf temperature is complicated by interactions between temperature and VPD, but also by the non-linearity in responses, often described as bell shaped ( Fig. 4 ) ( Way, 2011 ; Matthews and Lawson, 2019 ). g s tends to increase with temperature up to a tipping point ( Way, 2011 ; Tricker et al. , 2018 ) before rapidly decreasing at greater temperatures ( Šantrůcek and Sage, 1996 ), and can increase again if stomata reopen at very high temperatures ( Fig. 4 ). The temperature where stomata commence closure in the bell-shaped response is species specific and dependent on the growth temperature conditions ( Sage and Sharkey, 1987 ). It is likely that this variation can be explained by differences in hydraulic conductance and temperature effects on viscosity ( Cochard et al. , 2000 ), as well as photosynthetic demand ( Šantrůcek and Sage, 1996 ).

Impact of temperature on changes in stomatal conductance and response in major cropping systems. Highlighted is a generic response of stomatal conductance ( g s ) across a temperature range (red line); optimal temperature ranges for major global crop types (two-headed arrows), including critical temperatures when biomass and yield are significantly reduced (dots). Reproduced with permission from Matthews and Lawson (2019) .

Heat stress induces responses in g s that vary genotypically ( Zhou et al. , 2017 ; Ferguson et al. , 2020 ); however, whether this variation in g s can be linked to heat sensitivity levels remains unclear. Plants can also acclimate to different growth temperatures, resulting in lower stomatal sensitivity to short-term (i.e. minutes) changes in ambient temperature (e.g. Šantrůcek and Sage, 1996 ). Under different growth temperatures, the g s response that plants exhibit can be a similar shape, though the magnitude can vary greatly ( Yamori et al. , 2006 ; Way, 2011 ).

Increased g s values at higher temperatures will benefit plant performance by removing diffusional constraints on CO 2 diffusion into the leaf, and the resulting increase in intercellular CO 2 will help to reduce the negative impact of increased photorespiration at higher leaf temperatures (see previous section). Additionally, higher g s will facilitate enhanced transpiration and evaporative cooling, which will support the maintenance of leaf temperature closer to the T opt for photosynthesis, further reducing photorespiratory processes ( Urban et al. , 2017 ). However, the increased water loss through higher g s can compromise plant water status ( Matthews and Lawson, 2019 ) which, depending on the degree of water stress, could be detrimental to plant performance and growth. Furthermore, high atmospheric temperatures often occur in conjunction with reduced water availability, so stomatal temperature responses are linked closely not only with VPD but also with drought and water potential ( Urban et al. , 2017 ). Stomata close when water becomes limiting to avoid catastrophic water loss, even when demands for photosynthesis are high, demonstrating the hierarchal response of one signal over-riding others. As g s decreases with rising temperature and/or limited water availability, leaf temperature will further increase due to reduced evaporative cooling, leading to metabolic disruptions ( Tezara et al. , 1999 ; Perdomo et al. , 2017 ), and lower photosynthesis from restricted CO 2 diffusion ( Chaves et al. , 2003 ).

Advancements needed to improve stomatal resilience to heat stress

Manipulation of stomatal anatomy and metabolism has been suggested as a potential mechanism for crop improvement under adverse environmental conditions. SD has been altered via manipulating the stomatal development pathway, which can be achieved by focusing on the epidermal patterning factor family of transcription factors (EPFs). Many studies suggest that decreasing SD will reduce water loss and improve water use efficiency ( Hughes et al. , 2017 ; Caine et al. , 2019 ), but this could also increase leaf temperatures. However, rice with reduced SD (due to increased expression of osEPF1) showed reduced water use that resulted in lower leaf temperature relative to wild-type controls under drought stress ( Caine et al. , 2019 ). Conversely, overexpression of EPF9/Stomagen results in increased g s and A , but at the expense of water use efficiency ( Tanaka et al. , 2013 ). Masle et al. (2005) demonstrated in Arabidopsis that the ERECTA gene not only influenced SD (and subsequently g s ), but also the coordination between A and g s , which offers the potential to manipulate transpiration efficiency. Thus, it would be interesting to explore the potential of these mutants under different water, temperature, and VPD stress conditions ( Lawson et al. , 2014 ).

Manipulating guard cell metabolism or signalling pathways is an alternative and mostly unexplored avenue for future consideration ( Lawson and Blatt, 2014 ; Lawson et al. , 2014 ). For example, Hettenhausen et al. (2012) manipulated a mitogen-activated protein kinase, MPK4, in tobacco that results in increased g s , whilst overexpression of aquaporins in rice and grapevine increases g s and A under both stress and non-stress conditions ( Hanba et al. , 2004 ; Sade et al. , 2010 ). There are many other examples where components of guard cell osmoregulation and/or mesophyll metabolism have altered stomatal function (see table 1 in Matthews and Lawson, 2019 ) that provide a mostly unexploited genetic reservoir of material to explore for manipulating stomatal behaviour to cope with global warming. Altogether, these studies suggest that manipulation of stomatal anatomy and function could be a promising path to increase evaporative cooling as a strategy to cope with future climate conditions, but this may increase water requirements as a consequence.

The detrimental effect of elevated temperature is often associated with impacts on leaf biochemistry; however, for some crops, the main cause of decreased yield is due to high temperature during the reproductive stage of growth ( Akter and Islam, 2017 ). Therefore, manipulating SD and stomatal function in non-foliar tissue may also be an important and overlooked route for reducing temperature stress at key times ( Simkin et al. , 2020 ). Furthermore, the function of stomata in both foliar and non-foliar tissue and the role they play in translocation of photosynthate from source to sink tissues, including grain yield, is often ignored, as bulk flow within the phloem requires bulk flow of water in the xylem, which is a direct result of transpirational water loss that is ultimately controlled by stomata. Additionally, coordination between SD and minor vein density, which is a principle determinant of leaf hydraulic capacity ( Brodribb et al. , 2007 ), has been observed in many species contributing to the balance between leaf water supply and demand ( W.-L. Zhao et al. , 2017 ). The effect of rising temperature on this relationship requires further investigation, since trends differ across species ( Hu et al. , 2014 ; Yang et al. , 2020 ).

Temperature impacts on source to sink allocation and phloem transport

Carbohydrate translocation from photosynthetic source tissues (sources) to non-photosynthetic sink tissues (sinks) via the phloem is critical for vegetative and reproductive development, and ultimately crop yield. Alterations in plant source–sink balances, often induced by environmental stress such as high temperature, can impair carbohydrate allocation and negatively impact photosynthetic capacity and yield. Generally, heat stress decreases photosynthetic efficiency while increasing respiration and photorespiration rates (see earlier) and can affect reproductive development ( Prasad et al. , 2017 ; Ferguson et al. , 2021 ), which shifts the dynamics between sources and sinks. Thus, a better understanding of these mechanisms is crucial to maintain crop productivity in a warmer world.

Alongside reduced photosynthesis, declines in leaf non-structural carbohydrate (NSC) contents have been reported in several crop species (including soybean, chickpea, castor bean, and maize) with short-term (≤7 d) exposure to heat stress ( Kaushal et al. , 2013 ; Ribeiro et al. , 2014 ; Sun et al. , 2016 ; Thomey et al. , 2019 ). In tomato, maintained or higher levels of NSC in mature leaves were associated with heat tolerance under short-term heat stress ( Zhou et al. , 2017 ), which could help fuel increased respiration ( Ferguson et al. , 2021 ). However, under longer term heat stress, NSC accumulation in leaves and stems (tomato and rice, Zhang et al. , 2012 ; Zhang et al. , 2018 ) decreases root to shoot biomass ratio (castor bean, Ribeiro et al. , 2014 ), and the reduced carbon export rate from leaves suggests a reduction in carbohydrate export towards sinks (maize, Suwa et al. , 2010 ). Carbohydrate accumulation in mesophyll cells has been linked to down-regulation of photosynthetic capacity via negative feedback on Rubisco content and activity ( Moore et al. , 1999 ; Long et al. , 2004 ). Yet any potential regulatory role for leaf carbohydrate accumulation observed during long-term heat stress remains unclear, due to the direct impact of temperature on Rubisco (see earlier).

Remobilization of NSCs stored in intermediate sinks, such as stems, contributes to grain allocation especially in cereal crops, and could help compensate for reduced A when heat stress occurs at certain development stages ( Fig. 5 ) ( Blum et al. , 1994 ; Morita and Nakano, 2011 ; Zamani et al. , 2014 ; Xu et al. , 2020 ; Zhen et al. , 2020 ; Ferguson et al. , 2021 ). However, heat stress can also reduce stem NSC translocation efficiency decreasing yield further ( Zamani et al. , 2014 ; Zhen et al. , 2020 ). Together, these studies suggest a negative impact of heat stress on carbohydrate translocation, especially towards the reproductive sinks, which highlights the importance of maintaining these functions to preserve yield in resilient crop cultivars.

Structural and functional attributes that make a crop plant more susceptible (left) or tolerant (right) to heat stress. Numbers indicate the following: (1) higher invertase activity in spike/grain to maintain or increase carbohydrate import; (ii) remobilization of non-structural carbohydrates from the stems towards the spike/grain; (iii) short/erect flag leaf avoids direct light penetration and scorching, and has higher sucrose transporter expression to help maintain phloem loading and carbohydrate allocation to non-photosynthetic tissues; (iv) short/erect leaves avoid direct heat exposure, with angled leaves allowing light penetration lower into the canopy to help keep all leaves closer to temperature optimum; waxy leaves also help reduce water loss; (5) extra tillers and leaves to help maintain green leaf area and delay senescence; (6) more roots that reach deeper to access more soil moisture; (7) concentrated chlorophyll in the ‘sweet spot’ (i.e. not all in the top leaves) to improve leaf temperature optima; and (8) increased leaf stomatal density to improve CO 2 entry into the leaves.

Various modifications in phloem structure and function, which may affect carbohydrate transport and allocation in response to elevated temperature and heat stress, have been described in several crop species ( Fig. 5 ). At a biochemical level, intraspecific variation in rice shows that maintained or increased expression of sucrose transporters in leaves, stems, and grains is related to heat tolerance ( Miyazaki et al. , 2013 ; Phan et al. , 2013 ; Zhang et al. , 2018 ; Yaliang et al. , 2020 ), particularly for transporters thought to be involved in phloem loading and apoplastic sucrose retrieval along the transport pathway ( Scofield et al. , 2007 ; Julius et al. , 2017 ). These findings suggest that sucrose transporters are promising targets to develop heat-resilient crop cultivars. Invertases and sucrose synthases may also be interesting targets for crop improvement under heat stress ( Julius et al. , 2017 ; Xu et al. , 2020 ). By catalysing sucrose degradation in sinks, they increase the amount of sucrose being unloaded from the phloem into these sinks. Increased or maintained expression and/or activity of invertases and sucrose synthases in reproductive sinks has been linked to heat tolerance in several crop species including rice, tomato, and chickpea ( Pressman et al. , 2006 ; Li et al. , 2012 ; Kaushal et al. , 2013 ; Phan et al. , 2013 ; Li et al. , 2015 ; Bahuguna et al. , 2017 ; Rezaul et al. , 2019 ; Yaliang et al. , 2020 ). With photosynthetic improvements to heat stress, the enzymes involved in sucrose transport and metabolism may become increasingly important for ensuring increased photosynthates reach vegetative and reproductive sinks.

At a structural level, deposition of callose (a polysaccharide) and protein conformational change were observed in broad bean phloem following heat shock, resulting in blocked phloem transport ( Furch et al. , 2007 ). Heat-triggered callose deposition was also found in rice leaf and sheath plasmodesmata, especially in a heat-sensitive mutant with impaired carbohydrate translocation, potentially blocking phloem loading and/or unloading ( Zhang et al. , 2018 ). The underlying mechanisms of callose deposition in phloem under heat stress still need further investigation. Additionally, phloem anatomical features, such as the number and cross-sectional area of phloem cells, are correlated with photosynthetic capacity and environmental conditions ( Cohu et al. , 2014 ; Muller et al. , 2014 ; Adams et al. , 2016 ; Stewart et al. , 2016 ). Elevated temperature decreased phloem cell number and area in an Arabidopsis ecotype from a cool climate, correlating with reduced photosynthetic capacity compared with growth at lower temperature ( Adams et al. , 2016 ; Stewart et al. , 2016 ). This highlights the need for comparative studies in major food and fuel crops to inform acclimation potential to elevated temperatures, and identify anatomical features to select for future crop varieties.

Scaling from enzymes functioning within a single leaf to a collective of leaves that make up a single plant adds a layer of complexity to the relationship between temperature and photosynthesis. The interaction of individual leaves within and among plants modifies the microclimate or phylloclimate ( Chelle, 2005 ), causing variation in individual leaf temperatures within a crop plant. Leaf temperature depends on the leaf energy balance, including radiation, convection, and transpiration processes ( Jones, 1993 ; Lambers et al. , 1998 ). Shading of lower leaves by leaves higher in the canopy drives exponential declines in light availability in crop canopies ( Monteith, 1965 ), while leaves and stems present physical barriers to wind, reducing wind speed with canopy depth ( Jacobs et al. , 1995 ). Air temperature, VPD, and [CO 2 ] profiles influence gas exchange between the plant and the atmosphere. Thus, the interactions among all of these variables influence leaf temperature profiles with canopy depth.

Improving whole-plant photosynthesis has focused on the plant ‘ideotype’ that best intercepts light for optimal photon capture and utilization by light-harvesting complexes ( Long et al. , 2006 b ; Ort and Melis, 2011 ). While temperature effects are usually secondary to optimal photon capture, work to improve light distribution within plant canopies may alleviate some of the limitations posed by plant temperature gradients ( Fig. 5 ). Modelling suggests that less light absorption by upper canopy leaves could result in cooler leaf temperatures at the top of the plant ( Drewry et al. , 2014 ), allowing those leaves to operate nearer T opt , which would be especially beneficial under heat stress conditions when g s is limited. Shifting a greater proportion of photosynthesis to the lower canopy where wind speeds are lower and humidity is higher could also increase water use efficiency ( Drewry et al. , 2014 ). However, the effects on leaf temperature remain uncertain.

How a crop plant develops under heat stress and what this means for photosynthesis and yield

While leaf temperatures higher than T opt directly affect whole-plant photosynthesis, they also have indirect impacts at plant and canopy scales across all stages of a plant’s life cycle. During the vegetative stage, deviation from a T opt alters plant development and subsequently limits A for biomass accumulation. Heat stress reduces germination, seedling vigour, and establishment in soybean and cowpea ( Covell et al. , 1986 ), and radicle elongation in rice ( Han et al. , 2009 ). In maize, extreme heat reduces, and can completely halt, coleoptile growth ( Weaich et al. , 1996 ). After plant establishment, heat stress can prevent leaf development (i.e. cassava, Burns et al. , 2010 ), thereby preventing leaf area accumulation for photosynthetic gain to the plant canopy ( Fig. 5 ). For example, daytime temperatures >33 °C and high night-time temperatures reduce leaf emergence and tillering in rice, thereby reducing plant biomass ( Chaudhary and Ghildyal, 1970 ; Fahad et al. , 2016 a ).

Heat damage to leaf photosynthetic pigments reduces photosynthetic efficiency during vegetative growth, which impacts biomass accumulation and development to reduce crop yield. For example, temperatures >35 °C negatively impact maize biomass accumulation due to degradation of chlorophyll, consequently reducing photosynthetic light absorption ( Hatfield et al. , 2011 ; Hussain et al. , 2019 ). Premature loss of leaf chlorophyll due to heat stress accelerates mobilization of photosynthate to newer leaves and triggers early maturity of the whole plant ( Nooden, 1986 ). This drives a shorter plant life cycle and reduces the grain-filling window—a critical yield determinant period for cereal plants. Heat-induced reductions in life cycle length have caused grain yield reduction in wheat ( Camp et al. , 1982 ; Nicolas et al. , 1984 ; Reynolds et al. , 1994 ; Benbella and Paulsen, 1998 ), rice ( Fahad et al. , 2016 b ), and maize ( Ruiz-Vera et al. , 2015 ).

Photosynthate availability and transport capacity from source tissues to reproductive tissues may also affect reproductive development (see above). For example, in some maize hybrids, kernel number and kernel weight correspond to source capacity during grain filling, suggesting that these yield components may be limited by photosynthate supply even under non-stressed conditions ( Cerrudo et al. , 2013 ). Therefore, detrimental effects of heat stress on leaf photosynthesis probably further impair grain development and yield where grain sink strength is high ( Fig. 5 ). As discussed above, heat stress may also impair photosynthate transport between crop source and sink tissues ( Suwa et al. , 2010 ; Bagley et al. , 2015 ). These studies emphasize the need for sufficient production of sugars through photosynthesis and maintenance of their transport, especially during heat stress. Although beyond the scope of this review, direct impacts of high temperature on reproductive structures also play a critical role in determining crop yields and will require engineering for greater tolerance to heat stress to ensure sufficient sink size for enhanced photosynthate production and transport ( Barnabás et al. , 2008 ; Ruiz-Vera et al. , 2015 ; Ferguson et al. , 2021 ).

Recent advances made at the plant level to improve understanding of temperature effects

Developing plant mechanisms to cope with heat stress is complicated by interacting climate factors and the geographical variability forecast for temperature ( Long and Ort, 2010 ; Hoegh-Guldberg et al. , 2018 ), with heat stress responses greatly influenced by region and environmental conditions. Further, a combination of traits and agronomic manipulations determine heat stress tolerance. The determination of heat-tolerant crop ‘ideotypes’ is a challenge for plant breeders, and has driven a push to locate quantitative trait loci (QTLs) and genetic markers for photosynthetic heat tolerance ( Azam et al. , 2014 ; Sharma et al. , 2017 ). While progress has been made, searching for QTLs is a substantial task, given the combination of changing variables throughout a plant life cycle and the challenges in genotyping and phenotyping large germplasm sets at different growth stages.

Plant phenotyping may provide a quicker means of detecting plant heat stress responses given recent technological advances ( Furbank et al. , 2019 ; Furbank and Tester, 2011 ; Gao et al. , 2020 ). For example, plant temperature stress causes stomatal responses detectable with thermal imaging ( Stoll and Jones, 2007 ; Prashar and Jones, 2014 ) and visible scorching and damage detectable with red–green–blue imaging ( Elazab et al. , 2016 ). Photosynthetic responses are also detectable with chlorophyll fluorescence ( Sharma et al. , 2012 ; Jedmowski and Brüggemann, 2015 ) and hyperspectral analysis ( Dobrowski et al. , 2005 ). At the plant scale, recent advancements in field phenotyping have seen hyperspectral analysis used to predict photosynthetic capacity in field trials ( Serbin et al. , 2012 ; Yendrek et al. , 2017 ; Silva-Perez et al. , 2018 ; Fu et al. , 2019 , 2020 ; Furbank et al. , 2019 ; Meacham-Hensold et al. , 2019 , 2020 ). Using these phenotyping tools to screen genetically targeted germplasm is required to target heat-tolerant traits for breeders.

Scaling from the leaf to the whole-plant level in translation of heat stress traits at a higher resolution remains an additional challenge. At the plant level, temperature responses are closely linked with irradiance profiles. Recent advances in functional and structural plant modelling (FSPM) ( Vos et al. , 2010 ; Evers et al. , 2018 ) offer scope for deconstructing the relationship between irradiance gradients on whole-plant temperature profiles to pinpoint T opt for leaves at different plant canopy layers. The greater challenge in creating heat-resistant crops is pairing whole-plant FSPM, which considers leaf-level physiology to suggest heat-tolerant plant ideotypes, with tools to phenotype for genetic heat-tolerant markers across a range of species and environmental conditions.

The effects of temperature on enzyme, leaf, and plant scales compound to impact crop photosynthesis and productivity at the ecosystem scale. This is due to the additive responses to the microclimate of all leaves and plants that make up a crop ecosystem ( Bagley et al. , 2015 ). The microclimate impacts crop productivity through the effects of atmospheric turbulence and wind changing the temperature, humidity, and light environment experienced by leaves at different heights within the canopy ( Cleugh, 1998 ). While the speed at which a cropping system can respond to changes in light can reduce ecosystem photosynthesis ( Kromdijk et al. , 2016 ; Morales and Kaiser, 2020 ), increases in temperature are a crucial driver reducing photosynthesis and yields across the major cropping varieties ( Lobell et al. , 2014 ; Asseng et al. , 2015 ; Liu et al. , 2016 ; C. Zhao et al. , 2017 ), and will be the focus of this section.

A key mechanism controlling the reduction in ecosystem photosynthesis at higher temperature is the link with atmospheric VPD ( Bernacchi and VanLoocke, 2015 ). The amount of water vapour which air can hold at saturation ( e s ) increases with temperature, while the actual water vapour of air at any given time ( e a ) remains relatively constant, resulting in increased atmospheric VPD—the difference between e s and e a ( Bernacchi and VanLoocke, 2015 ; Ficklin and Novick, 2017 ). Increasing atmospheric VPD has a feedback effect on plants, particularly on the stomata, whereby a drier atmosphere exerts a stronger pull on water from within leaves during photosynthesis ( Lawson and Vialet-Chabrand, 2019 ). As discussed earlier, crops can close their stomata to conserve water, but this comes at the cost of photosynthesis, which reduces yield at the ecosystem scale if relied upon too often during the growing season.

Early lessons from FACE ( Table 1 ) studies suggest that crop photosynthesis would be enhanced with higher [CO 2 ], and water loss would decline with lower g s ( Leakey et al. , 2009 ). A recent update of the literature has confirmed that these conclusions hold for C 3 and C 4 crops ( Ainsworth and Long, 2020 ). However, when FACE systems were coupled with increased temperature (T-FACE), canopy warming and periodic heat stress caused an acceleration in maize and soybean crop development and often decreased yield ( Siebers et al. , 2015 ; Ruiz-Vera et al. , 2018 ), particularly when higher temperatures were coupled with water deficit ( Gray et al. , 2016 ). Even without supplemental heating through experimentation, hotter and drier growing seasons reduced wheat yield grown under FACE relative to FACE-grown plants under ‘typical’ growing seasons ( Fitzgerald et al. , 2016 ; Macabuhay et al. , 2018 ). However, mixed results have been reported for rice grown at elevated temperature, probably due to latitudinal differences in average temperature maxima impacting rice grown in the tropics more than at higher latitudes ( Lesk et al. , 2016 ; Usui et al. , 2016 ).

Crops grown under well-watered conditions can afford to maintain high A under elevated temperature for longer than crops grown under water stress ( Fitzgerald et al. , 2016 ). In regions of the world where increasing temperature is coupled with increasing rainfall, drought and heat stress impacts on crop photosynthesis and productivity may be minimized ( Tesfaye et al. , 2018 ). However, the timing and duration of rainfall events will be critical for determining the effectiveness of increased moisture as a buffer to hotter temperatures. For example, in the currently rain-fed and highly productive region of the Midwest United States, DeLucia et al. (2019) project that a water limit will be reached for maize productivity due to increased atmospheric VPD that will be driven by rising global temperature. Lobell et al. (2014) have shown that while maize yields have historically been increasing, the crop is very susceptible to drought and VPD stress. This impact on maize yield was evident in the 2012 drought experienced by the Midwest US during the growing season ( Fig. 6 ). For cropping systems already reliant on irrigation, changes in mean annual rainfall associated with a warming world could be catastrophic for future yields if water resources become scarce. Shifting cropping systems that are primarily rain-fed to an irrigation-reliant system will place increased pressure on existing hydrological reserves to deliver water for agriculture in addition to metropolitan and natural systems ( DeLucia et al. , 2019 ).

The difference in gross primary productivity (GPP) and annual yield for maize across different climatic years, as indicated by air temperature and rainfall. (A–D) were produced using data from Ameriflux site Ui-C using processing protocols from Moore et al. (2020) . The years 2013 and 2016 are omitted from (D) as these years were under a soybean rotation at the site.

Changes to the by-products of photosynthesis associated with rising temperature

Rising temperature at the ecosystem scale also affects carbon consumption processes that can impact short-term annual yield of cropping systems and their long-term ecological sustainability. For ecosystem-scale carbon cycle concepts, photosynthesis is referred to as gross primary productivity (GPP; Table 1 ) ( Chapin et al. , 2006 ). Changes to ecosystem autotrophic respiration (RA) and GPP as global temperature increases will be likely to mirror that of the processes described earlier, in that photosynthesis has a clear T opt and peak thermal response, and RA increases exponentially with rising temperature until acclimation occurs. However, what is less certain is the rate at which heterotrophic respiration (RH) will change as temperatures rise, particularly that of soil microbes ( Bond-Lamberty and Thomson, 2010 ; von Haden et al. , 2019 ). It is commonly accepted that ecosystem respiration (ER; combined RA and RH) increases with temperature ( Lloyd and Taylor, 1994 ), and can acclimate under prolonged heat exposure ( Way and Yamori, 2014 ). A recent synthesis has suggested that this has predictably responded to global warming, though there still remains large uncertainty surrounding the RH contribution in particular ( Bond-Lamberty et al. , 2018 ).

Recent advancements and prospects for monitoring crop canopies and improving management responses with rising temperature

There is an inherent need for the development of strategies to ensure crop productivity with global warming. Current agronomic practices rely on weather and climate forecasts to predict when cropping systems are likely to require irrigation or nutrient application. However, these meteorological services lack information on real-time carbon uptake and water loss from the cropping system of interest. Such information could advance understanding of crop responses to the environment and, where possible, lead to informed management decisions to minimize losses.

Eddy covariance flux towers monitor ecosystem photosynthesis, along with water use and a suite of common meteorological measurements including air temperature, solar radiation, wind, soil moisture/temperature, and humidity ( Baldocchi et al. , 2001 ). Yet, the data require large amounts of post-processing to generate complete time series for each measured variable ( Isaac et al. , 2017 ; Pastorello et al. , 2020 ). Further, GPP is estimated (not measured) as the difference between the comparatively smaller net ecosystem exchange (NEE) of CO 2 as the measured variable and ER estimated using nocturnal ( Lloyd and Taylor, 1994 ) or diurnal ( Lasslop et al. , 2010 ) temperature response functions. While this approach is imperfect in many ways, it provides the most reliable and accurate means of quantifying, with high temporal precision, the rates of photosynthesis and respiration from cropping systems at the ecosystem scale.

With >900 sites registered as part of the FLUXNET community, there still remains a paucity of flux towers providing openly available long-term monitoring data (i.e. >5 years) from agricultural systems ( Baldocchi et al. , 2018 ; Cleverly et al. , 2020 ; Pastorello et al. , 2020 ). Increasing the number of flux towers operating in cropping systems in key climatic regions of the world, and making these data immediately and freely available through open-access licensing, will be an important step for improving current understanding of the wide-scale impact of rising temperature on crop ecosystem photosynthesis. The capacity to provide measurements of carbon and water fluxes in real-time is building (i.e. FluxSuite & SmartFlux from LICOR Biosciences, Lincoln, NE, USA or EasyFlux from Campbell Scientific, Logan, UT, USA), but delivering these data in real-time to land managers, as with weather forecasting, is lacking. While FLUXNET data require significant post-processing and data corrections, the end result is generally research related. Real-time output of fluxes with minimal processing may be suitable for land managers to make informed decisions. Given the link between ecosystem carbon and water fluxes, and crop photosynthetic efficiency and water stress, supplying these data in real-time would make a substantial contribution towards faster crop stress detection.

Flux tower networks also deliver important ground-truth data to validate satellite information that can be used to infer crop photosynthesis over landscape, regional, and global scales, which flux towers are incapable of completely capturing (i.e. measurement region of interest is usually between 200 m 2 and 2000 m 2 ). Satellite data products have typically relied on the calculation of vegetation indices from surface reflectance information, such as the normalized difference vegetation index (NDVI; Tucker, 1979 ), enhanced vegetation index (EVI; Huete et al. , 2002 ), and photochemical reflectance index (PRI; Gamon et al. , 1997 ) to provide indications of vegetation stress. However, these indices depend on changes in vegetation greenness to show variation in the index value, after which it can be too late to remedy vegetation stress. In addition, the indices typically measure top-of-canopy responses, so changes at lower canopy layers are missed.

Improvements in spectral sensing technology have led to the development of passive remote sensing of sun-induced chlorophyll fluorescence (SIF) as a proxy for real-time monitoring of photosynthesis ( Meroni et al. , 2009 ; Sun et al. , 2017 ; Frankenberg and Berry, 2018 ). Chlorophyll fluorescence represents one of three fates of light energy absorbed by light-harvesting complexes within leaves; the other two being photochemistry and heat dissipation ( Baker, 2008 ). Active measurement of chlorophyll fluorescence is a commonly used tool in plant physiology research, as these three light use pathways do not operate in isolation from each other. Chlorophyll fluorescence yield provides useful information on photosynthetic quantum efficiency and heat dissipation, which leads to its use in inferring A and in imaging to screen for genetic trait expression in plants ( Murchie and Lawson, 2013 ). At scales from the ecosystem to globe, passive measurement of chlorophyll fluorescence as SIF relies on the spectral emission of SIF surrounding oxygen absorption bands (O 2 -A and O 2 -B) within a narrow spectral range ( Meroni et al. , 2009 ; Frankenberg and Berry, 2018 ).

Advancements in SIF monitoring in recent years have rapidly expanded, with studies demonstrating a strong correlation between crop GPP at the ecosystem ( Miao et al. , 2018 ; Wu et al. , 2020 ), regional ( Guan et al. , 2016 ), and global scales ( Guanter et al. , 2014 ). The relationship between SIF and crop GPP has led to the use of SIF in detecting crop stress, as the two signals are inherently linked ( Zarco-Tejada et al. , 2012 ; Camino et al. , 2019 ; Peng et al. , 2020 ). Additional satellite sensing of land surface evapotranspiration (ET)—the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS)—is also being used to assess ecosystem stress on daily time scales ( Fisher et al. , 2020 ). The combination of SIF and ECOSTRESS satellite products has the potential to greatly advance our understanding of ecosystem GPP in relation to ET, and how environmental stresses, such as increased temperature and heatwaves, are likely to impact crop productivity at regional to global scales. Granted, there still remain several unanswered questions surrounding the quantity of information provided by SIF, whether the signal is primarily affected by changes in canopy architecture or if it is a direct product of biochemistry ( Magney et al. , 2020 ). As these fundamental questions are answered, and with the addition of new satellite remote-sensing platforms to monitor SIF globally at high temporal resolution (i.e. TROPOMI, OCO-2, and GOME), SIF will certainly continue to advance as an important real-time tool for monitoring crop photosynthesis and productivity as global temperature rises.

This review provides a comprehensive evaluation of current understanding on how crop photosynthesis responds to temperature from the enzyme to ecosystem scale. The key conclusions for each scale are summarized as follows.

(i)  Direct impacts of elevated temperature on photosynthetic enzymes involved in carbon assimilation are particularly damaging to C 3 crops. Enzyme rates increase with temperature, but substrate specificity declines in the carbon-fixing enzyme Rubisco, which deactivates past optimal temperatures.

(ii)    Stomata typically respond to temperature through the complex effects of heat on photosynthesis, VPD, transpiration, and plant water status. Stomatal conductance can change under temperature stress, and stomatal density and size can be altered if a plant develops under hotter conditions.

(iii)  Photosynthate allocation from sources to sinks is impacted by heat stress through differential expression and activity of enzymes involved in sucrose transport and metabolism, as well as phloem structural changes.

(iv)   At the whole-plant scale, leaf interactions create temperature gradients, and heat stress impairs plant development processes.

(v)    The factors identified in (i)–(iv) act together to impact crop ecosystem photosynthesis and its response to temperature, the effects of which are typically seen as a cumulative response through the growing season and lead to reduced yield.

Ensuring our cropping systems remain resilient to rising temperatures will require integration of knowledge and information across scales. For each scale discussed, the areas of research needed to improve resiliency of cropping systems to rising temperature and heat stress are as follows.

(i)  At the biochemical scale, most strategies for improving carbon fixation in a warmer climate involve enhancing Rubisco performance or minimizing the energy expended in photorespiration, but many remain to be tested in crop species or replicated field trials.

(ii)    Altering stomatal anatomy and metabolism may help to reduce water loss from crops whilst maintaining photosynthetic rates to ensure high crop yields are maintained. However, the relationship between stomata and leaf hydraulic capacity should also be considered to maintain a balance between leaf water supply and demand.

(iii)  At the transport system level, strategies need to be tested to help maintain photosynthate allocation from sources to sinks by increasing sucrose phloem loading in sources (e.g. increasing expression of leaf sucrose transporters) and sucrose phloem unloading in sinks (e.g. increasing invertase activity in reproductive sinks), as well as increasing remobilization of sugars stored in intermediate sinks.

(iv)  Coupling whole-plant modelling of temperature gradients with phenotyping resources will allow identification and breeding of heat-resistant crop ideotypes.

(v)    At the ecosystem scale, the implementation of faster crop stress detection systems will be critical for applying management strategies to combat temperature-related stress. These strategies may include combining ground-based measurements, such as those from flux towers, with satellite remote-sensing information, to provide closer to real-time monitoring of crop systems.

CEM and CJB acknowledge funding from the DOE Center for Advanced Bioenergy and Bioproducts Innovation (U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Number DE-SC0018420). APC, KM-H, PL, RAS, CB, TL, and CJB acknowledge funding from the research project Realizing Increased Photosynthetic Efficiency (RIPE) that is funded by the Bill & Melinda Gates Foundation, Foundation for Food and Agricultural Research (FFAR), and the UK Foreign Commonwealth & Development Office under grant no. OPP1172157. TL also acknowledges the BBSRC IWYP programme (grant no. BB/S005080/1). CJB also acknowledges support from the USDA to the Global Change and Photosynthesis Research Unit of the USDA Agricultural Research Service. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the U.S. Department of Agriculture or the U.S. Department of Energy.

CEM led manuscript preparation with contributions from all co-authors. Specifically, APC and CJB wrote the first section, TL and PL wrote the second section, KM-H and RAS wrote the third section, and CEM and CJB wrote the fourth section. CB led production of figures, with input from all other co-authors. Given the range of topics reviewed, CEM and APC share corresponding authorship.

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Photosynthesis: a multiscopic view

• JPR Symposium
• Imaging, Screening and Remote Sensing of Photosynthetic Activity and Stress Responses
• Published: 25 June 2021
• Volume 134 , pages 665–682, ( 2021 )

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• Jeffrey A. Cruz   ORCID: orcid.org/0000-0003-1098-5176 1 , 2   na1 &
• Thomas J. Avenson 3   na1  

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A recurring analogy for photosynthesis research is the fable of the blind men and the elephant. Photosynthesis has many complex working parts, which has driven the need to study each of them individually, with an inherent understanding that a more complete picture will require systematic integration of these views. However, unlike the blind men, who are limited to using their hands, researchers have developed over the past decades a repertoire of methods for studying these components, many of which capitalize on unique features intrinsic to each. More recent concerns about food security and clean, renewable energy have increased support for applied photosynthesis research, with the idea of either improving photosynthetic performance as a desired trait in select species or using photosynthetic measurements as a phenotyping tool in breeding efforts or for high precision crop management. In this review, we spotlight the migration of approaches for studying photosynthesis from the laboratory into field environments, highlight some recent advances and speculate on areas where further development would be fruitful, with an eye towards how applied photosynthesis research can have impacts at local and global scales.

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Acknowledgements

We thank Drs. J.A. Berry, J.E. Johnson, G.R. Fleming, and G. Peers for useful discussions. This work was supported by the Office of Science of the U.S. Department of Energy DE-FG02-91ER20021.

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Jeffrey A. Cruz and Thomas J. Avenson contributed equally to the preparation of this manuscript.

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Plant Research Laboratories, Michigan State University, 612 Wilson Road, MI, S-206, Lansing, USA

Jeffrey A. Cruz

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Cruz, J.A., Avenson, T.J. Photosynthesis: a multiscopic view . J Plant Res 134 , 665–682 (2021). https://doi.org/10.1007/s10265-021-01321-4

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• Published: 23 June 2022

A hybrid inorganic–biological artificial photosynthesis system for energy-efficient food production

• Elizabeth C. Hann   ORCID: orcid.org/0000-0001-6921-9412 1 , 2   na1 ,
• Sean Overa   ORCID: orcid.org/0000-0001-5164-1519 3   na1 ,
• Marcus Harland-Dunaway   ORCID: orcid.org/0000-0002-3548-1620 1 , 2   na1 ,
• Andrés F. Narvaez   ORCID: orcid.org/0000-0002-0627-1069 1 , 4 ,
• Dang N. Le 1 ,
• Martha L. Orozco-Cárdenas 4 ,
• Feng Jiao   ORCID: orcid.org/0000-0002-3335-3203 3 &
• Robert E. Jinkerson   ORCID: orcid.org/0000-0001-9399-1613 1 , 2  

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Artificial photosynthesis systems are proposed as an efficient alternative route to capture CO 2 to produce additional food for growing global demand. Here a two-step CO 2 electrolyser system was developed to produce a highly concentrated acetate stream with a 57% carbon selectivity (CO 2 to acetate), allowing its direct use for the heterotrophic cultivation of yeast, mushroom-producing fungus and a photosynthetic green alga, in the dark without inputs from biological photosynthesis. An evaluation of nine crop plants found that carbon from exogenously supplied acetate incorporates into biomass through major metabolic pathways. Coupling this approach to existing photovoltaic systems could increase solar-to-food energy conversion efficiency by about fourfold over biological photosynthesis, reducing the solar footprint required. This technology allows for a reimagination of how food can be produced in controlled environments.

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Food demand is growing globally, but food production is ultimately constrained by the energy conversion efficiency of photosynthesis. Most crop plants can convert sunlight and CO 2 into plant biomass at an energy conversion efficiency of only ~1% or less 1 . Large tracts of land are thus required for crop cultivation to capture the requisite solar energy to provide food for humanity. Recent breeding and genetic engineering efforts to increase photosynthetic efficiency have yielded only select gains in a limited number of food crops 2 , 3 , 4 . Increasing the energy efficiency of food production (solar-to-biomass conversion) would allow for more food to be produced using less resources.

Artificial photosynthesis seeks to overcome the limitations of biological photosynthesis, including low efficiency of solar energy capture and poor carbon dioxide reduction, and could provide an alternative route for food production. Recent studies have demonstrated systems that convert CO 2 and H 2 O into reduced species, such as CO, formate, methanol and H 2 , through electrolysis processes. CO 2 , CO and H 2 can be upgraded to fuels and chemicals through gas-phase fermentation by select bacteria 5 , 6 , 7 ; however, gas–liquid mass transfer limits the volumetric efficiency and results in uneconomic fermentation systems. The use of formate or methanol as a carbon source for fermentation is limited because formaldehyde, a toxic intermediate, is formed during biological metabolism of these substrates 8 , 9 , 10 . To date, electrochemically derived substrates cannot support the growth of most food-producing organisms 11 . However, acetate is a soluble, two-carbon substrate that can be electrochemically produced 12 and is more readily metabolized by a broad range of organisms. The use of acetate produced from CO 2 electrolysis to cultivate food-producing organisms could allow food production independent of biological photosynthesis but has not yet been demonstrated.

Here we describe the development of a hybrid inorganic–biological system for food production. A two-step electrochemical process converts CO 2 to acetate, which serves as a carbon and energy source for algae, yeast, mushroom-producing fungus, lettuce, rice, cowpea, green pea, canola, tomato, pepper, tobacco and Arabidopsis ( A. thaliana ) (Fig. 1 ). Coupling this system of carbon fixation to photovoltaics offers an alternative, more energy-efficient approach to food production.

a , CO 2 electrolysis uses electricity (generated by photovoltaics) to convert CO 2 and H 2 O into O 2 and acetate. This process was optimized to produce an effluent output ideal for supporting the growth of food-producing organisms. b , Chlamydomonas , Saccharomyces , mushroom-producing fungus and a variety of vascular crop plants were grown using the electrolyser-produced effluent. c , The organisms grown using the electrolyser-produced effluent serve as food or food products. This system is capable of making food independent of photosynthesis, using CO 2 , H 2 O and solar energy.

Acetate production from CO 2 electrolysis

To provide a carbon and energy source independent of biological photosynthesis that can sustain the growth of food-producing organisms, we developed an electrocatalytic process to produce acetate (as either sodium or potassium acetate depending on the electrolyte salt) from CO 2 . Acetate produced directly from electrochemical CO 2 reduction using a copper catalyst has a less than 15% carbon selectivity, which is defined as the amount of carbon in the end product(s) divided by the total amount of carbon reduced in the system 13 , 14 , 15 , 16 . However, recent studies on CO reduction have demonstrated that acetate can be produced at industrially relevant reaction rates with a carbon selectivity of greater than 50% using a nanostructured copper catalyst 12 , 17 , 18 . To achieve maximum selectivity and production of acetate from a direct CO 2 feed, a two-step electrolyser system was demonstrated to convert CO 2 to CO and then CO to acetate through a tandem process (Fig. 2a and Extended Data Fig. 1a ). More specifically, CO 2 is fed to the cathode of the first electrolyser, which utilizes a commercial silver catalyst supported on a gas diffusion layer (that is, a carbon paper) and produces a gaseous product stream containing CO, H 2 and trace CO 2 . The gas diffusion electrode improves the gaseous CO 2 transport to the electrocatalyst, achieving higher currents towards CO 2 reduction products compared with dissolved CO 2 in a typical batch reactor 16 , 19 , 20 , 21 . A solution of 1 M KHCO 3 in deionized water was used as the electrolyte on the anode side (that is, anolyte) for the CO 2 electrolyser and recirculated through the anode compartment to maintain the ionic conductivity. The presence of an aqueous electrolyte has been shown to reduce the total cell potential for anion exchange membrane-based CO 2 electrolysers 13 , 22 . An IrO 2 anode was utilized for CO 2 reduction, due to its stability in neutral pH. The gas product stream was then fed to the cathode chamber of the CO electrolyser, which contains a commercial copper catalyst for CO reduction, 1 M KOH as the anolyte and a NiFeO x anode. This design is similar to a tandem system previously reported by Romero Cuellar et al. 23 . By maximizing the conversion of the first and second electrolysers, as well as specifically targeting acetate over other multi-carbon products, this system was able to achieve a single-pass conversion of CO 2 to acetate of 25%, a large improvement over the <1% conversion previously reported (Supplementary Table 1 ). Effluents, the liquid products of electrolysis containing acetate and other by-products (Extended Data Fig. 1c,d ), were evaluated as carbon and energy sources for the cultivation of food-producing organisms. Early experiments found that effluents with an acetate-to-electrolyte ratio below 0.4 did not support the growth of algae ( Supplementary Note ). Maximizing the ratio of acetate to electrolyte was therefore crucial for integrating these carbon products with biological food production.

a – f , Early ( a – c ) and optimized ( d – f ) two-step electrolysis systems for the production of acetate. Panels a and d show overviews of the schematics. Effluent containing acetate is collected in the anode compartment of the CO electrolyser. In b and e , carbon selectivities towards specific products and area-normalized production rates are shown. Acetate, propionate, n -propanol and ethanol were collected and quantified in effluent, ethylene was quantified continuously over the course of the experiment and the average value is presented. In c and f , the electrolyser voltage stabilities of CO 2 electrolyser (gold) and CO electrolyser (silver) are shown. CO 2 electrolyser operated for the entire experiment using 1 M KHCO 3 electrolyte, a silver nanoparticle cathode and an iridium oxide anode. CO electrolyser operation began after 30 minutes using 1 M KOH electrolyte, a copper nanoparticle cathode and a nickel iron oxide anode. CO 2 flow was held constant at 7 ml min −1 . Oscillations in potential were caused by fluctuations in the back-pressure controller. Panel c shows a voltage increase over the time observed. Fresh KOH was fed to recover the electrolyser voltage (arrow) to determine whether the electrolyser voltage increase was due to the acidification of electrolyte. The CO 2 scrubber contained 5 M NaOH to capture residual CO 2 .

Operating parameters of the tandem CO 2 electrolysis system were identified that maximized the conversion of CO 2 feed to acetate. For the production of CO, the operating current density of the CO 2 electrolyser was 100 mA cm −2 at an inlet flow rate of 7 ml min −1 CO 2 , which maximized CO 2 conversion to CO at 43% and maintained a high level of performance (Extended Data Fig. 1e–h ). For the CO electrolyser, the operating current density of 150 mA cm −2 was selected to achieve greater than 80% conversion of electrochemically produced CO into C 2+ products over the entire duration of electrolysis (Extended Data Fig. 1g,h ). Initial experiments found that trace amounts of CO 2 negatively affected acetate selectivity in the CO electrolyser and led to a rapid increase in CO electrolyser voltage (Fig. 2b,c ). When a 5 M NaOH scrubber was introduced between the two electrolysers, acetate selectivity was increased by over a factor of 3, because the scrubber prevented the unreacted CO 2 from the first reactor from reaching the CO electrolyser (Fig. 2d–f and Extended Data Fig. 1b ). Overall, 57% of reacted CO 2 formed acetate at a production rate of 0.7 g d −1  cm −2 , representing the highest conversion of CO 2 feed to acetate reported to date (Fig. 2e and Supplementary Table 1 ).

The tandem CO 2 electrolysis system operated stably while producing effluent with high acetate-to-electrolyte ratios. The electrolyser voltage for the CO 2 electrolyser remained constant near 2.95 V (Fig. 2f ) with less than a 60 mV increase in voltage over the six-hour experiment. The CO reactor operated at 2.22 V with a voltage increase of 160 mV over the course of the experiment (Fig. 2f ), which is attributed to the pH shift in the electrolyte from 13.7 to 13.4 as acetate accumulated. Both catholyte (through the use of a cold-trap) and anolyte were collected and analysed for product quantification. As >99% of the produced acetate was collected in the anolyte, this was the primary effluent used for food production. The final effluent produced contained 0.75 M acetate and an acetate-to-electrolyte salt ratio of 0.75, representing the highest recorded acetate-to-electrolyte product stream to date (Supplementary Table 1 ).

Using CO 2 electrolysis to grow food heterotrophically

We used the photosynthetic alga Chlamydomonas ( C. reinhardtii ), which can grow heterotrophically on acetate in the dark, as a model to determine whether the improved effluent produced by the electrolysers could support the growth of food-producing organisms. Chlamydomonas is added to processed foods for protein fortification and has been shown to have a positive effect on human gastrointestinal health 24 , 25 , 26 , 27 , 28 . Algae can produce large amounts of starch, protein and oil. Chlamydomonas grown heterotrophically can produce over 1 g of starch, 1 g of protein and 0.7 g of lipid per litre per day 28 , 29 . To evaluate effluent as a carbon source, Chlamydomonas was grown heterotrophically in the dark with effluent diluted to match the acetate concentration of a typical liquid heterotrophic growth medium (17.5 mM acetate in Tris–acetate–phosphate (TAP) medium) and adjusted to pH 7.2. Effluent-derived acetate served as the sole carbon and energy source. Effluents from different electrolysis experiments used throughout this manuscript are noted by the acetate-to-electrolyte concentration ratio (acetate concentration: electrolyte concentration), with more details provided in Supplementary Table 2 . All effluents evaluated with an acetate-to-electrolyte ratio of greater than 0.4 M acetate: 1 M electrolyte enabled the growth of Chlamydomonas in the dark ( Supplementary Note , Fig. 3a–c , Extended Data Figs. 2 and 3a–e , and Supplementary Table 3 ).

a , Image of Chlamydomonas cultures taken after four days of growth in darkness; the cultures were grown with effluent (0.691 M acetate: 1 M KOH) or with no effluent. b , c , Growth of Chlamydomonas cultures grown in the dark, shaking, at 30 °C, with effluent (0.75 M acetate: 1 M KOH), with acetate (control) or with neither (no acetate or effluent). Panel b shows daily cell counts for eight days of growth, and c shows dry weight after two days of growth. Chlamydomonas cultures were grown in TAP media with acetate, without acetate or with effluent in place of acetate to match the acetate concentration of a typical liquid heterotrophic growth medium (17.5 mM). All Chlamydomonas media were adjusted to pH 7.2. d , e , Saccharomyces cerevisiae cultures were grown at 30 °C, shaking, in YPD media with glucose (3.45 g l −1 ), without glucose (0 g l −1 ) or with effluent (0.75 M acetate: 1 M KOH) in place of glucose with an acetate concentration to match the energetic equivalent of 3.45 g glucose per l (53.36 kJ l −1 ). All Saccharomyces media were adjusted to pH 6.0. Panel d shows optical density (OD) (600 nm) over 96 hours. Panel e shows dry weight 24 hours after inoculation. In b – e , each data point represents three biological replicates. The error bars represent standard deviations. In c and e , two-tailed unpaired t -tests showed no significant difference between Chlamydomonas growth in acetate and in effluent media ( P  = 0.1045) and no significant difference between Saccharomyces growth in glucose and in effluent media ( P  = 0.1857). NS, not significant. f , Images of pearl oyster, enokitake, elm oyster and blue oyster mushroom mycelium from colonization of a solid vermiculite substrate soaked with YPD media containing simulated effluent in place of glucose (0.0691 M acetate: 1 M KOH) to reach 0.5% (w/w) acetate as the primary carbon and energy source. The images were taken 24 days after inoculation. Scale bar, 20 mm. g , Growth seven days after inoculation of pearl oyster mushroom mycelium (white) on solid vermiculite substrate soaked with liquid YPD media containing simulated effluent in place of glucose (0.0691 M acetate: 1 M KOH) as indicated. Scale bar, 20 mm.

Chlamydomonas grown on an effluent with an improved acetate-to-electrolyte salt ratio (0.75 M acetate: 1 M KOH) had a yield of 0.28 g algae per g acetate (Fig. 3b,c ), which is comparable to yields reported from non-effluent medias 29 . Chlamydomonas utilized over 99% of acetate in the media (Extended Data Fig. 3f ). No products of photosynthesis (such as carbohydrates) or ancient photosynthesis (such as petroleum-derived carbon sources) were required for growth. Hence, our cultivation of a photosynthetic organism using carbon fixed through electrolysis is fully decoupled from biological photosynthesis.

The nutritional yeast Saccharomyces cerevisiae is used as a food source in single-cell protein spreads and in the production of breads and fermented beverages 30 , 31 . Yeast is a heterotrophic organism most commonly grown with glucose derived from photosynthesis (that is, starch-derived) as a carbon and energy source. To cultivate yeast without inputs derived from photosynthesis, we replaced the primary carbon source, glucose, in the yeast–peptone–dextrose (YPD) media with effluent from electrolysis. All media were adjusted to pH 6.0. Electrolyser-produced effluent (0.75 M acetate: 1 M KOH) supported the growth of yeast, enabling a yield of 0.19 g yeast biomass per g acetate as well as a ninefold increase in OD600 and a twofold increase in dry weight compared with yeast grown without acetate or glucose (Fig. 3d,e ). After successfully decoupling the production of yeast from photosynthesis-derived carbon and energy, we sought to achieve the same results in mushroom-producing fungus. Mushrooms are widely consumed as a food, and fungal mycelium has emerged as a high-protein meat analogue 32 . These fungi are typically cultivated on solid substrates composed of photosynthesis-derived carbohydrates, such as cellulose or rice flour. To cultivate fungal mycelium without these carbohydrates, we developed a solid-state fermentation approach that used effluent as the primary carbon and energy source. Due to the amounts needed, simulated effluents were used here, made up of the same components as electrolyser-produced effluents. Simulated effluent (0.691 M acetate: 1 M KOH) was added to YPD media without glucose to reach 0.5% (w/w) acetate; this substrate supported the growth of pearl oyster, blue oyster, elm oyster, coral tooth and enokitake mycelium (Fig. 3f and Extended Data Fig. 3g–i ). The majority of the substrate was fully colonized by all five species, indicating that effluent can serve as a carbon source for the cultivation of a variety of mushroom-producing fungus mycelia. Higher levels of effluent inhibited mycelium growth (Fig. 3g and Extended Data Fig. 3g ), probably due to the higher levels of effluent by-products, specifically propionate, which is used as an antifungal food preservative 33 . The successful growth of mycelium indicates the ability to produce mycelium-based foods and potentially mushrooms independent of carbon and energy derived from photosynthesis.

Acetate can be metabolized by crop plants

To further evaluate whether carbon fixed through CO 2 electrolysis could be used to produce plant-based food, we examined the potential of acetate to serve as a carbon and energy source for crops. We tracked acetate incorporation into plant biomass using heavy-isotope 13 C-acetate labelled at both carbon atoms to evaluate whether exogenous acetate can be metabolized by crops. Previous acetate incorporation studies have primarily focused on metabolites involved in lipid biosynthesis in only a few plant species. In these studies, acetate was used in low concentrations as a tracer, not as a carbon source (Supplementary Table 4 ) 34 , 35 , 36 . To investigate acetate utilization as a carbon source, we grew undifferentiated lettuce tissue (callus) (to avoid the carbon and energy stored in seeds) in the dark in liquid media containing effluent (0.691 M acetate: 1 M KOH) and added in 13 C-acetate to enable tracking of carbon incorporation. Calli showed extensive 13 C-labelling in multiple pathways, confirming that lettuce tissue metabolizes acetate as a carbon and energy source (Fig. 4a ). Labelling of citrate/isocitrate, succinate, α-ketoglutarate and malate indicated that exogenous acetate was converted to biologically active acetyl-CoA and entered the tricarboxylic acid cycle (TCA) cycle to produce energy (GTP, NADH and FADH 2 ) (Fig. 4a ). Amino acids were also labelled with 13 C, indicating that carbon from acetate can be used to build proteins. Products and intermediates of gluconeogenesis were also labelled, indicating that carbon from acetate can be used for carbohydrate biosynthesis (Fig. 4a and Extended Data Fig. 4 ). Carbon incorporation of 13 C-acetate into amino acids and sugars through the TCA cycle, glycolysis and gluconeogenesis provides strong evidence that exogenous acetate can be readily incorporated into the bulk biomass of lettuce tissue grown in dark conditions and could be a carbon and energy source for other crops.

a , Heat maps showing the log 2 fold enrichment of 13 C between treated and untreated whole lettuce plants and lettuce callus (undifferentiated lettuce). Whole lettuce plants were grown in the light and treated with no acetate or with 13 C-acetate ( n  = 3; each replicate was a mixture of tissue from ten plants). Lettuce callus was grown in the dark and treated with electrolyser-produced effluent (0.691 M acetate: 1 M KOH) ( n  = 4) and 13 C-acetate ( n  = 3). A variety of crop species were treated with 13 C-acetate, and incorporation was measured. 2/3-PG, 2/3-phosphoglycerate. Representative no-acetate control replicates for whole lettuce plant, lettuce callus, cowpea and Arabidopsis are included for comparison. There was broad 13 C incorporation into crop metabolism, indicating that exogenously supplied acetate can be used for energy and biomass production in these plants. b , Schematic of exogenous acetate metabolism by a typical plant cell. c , 13 C incorporation into iso/citrate and malate at multiple carbon positions in whole lettuce plants. We calculated the ratio of iso/citrate and malate molecules that were detected with 13 C incorporation compared with 12 C. M  +  X denotes the number of labelled carbons in the molecule. The error bars represent the standard deviation between the biological replicates ( n  = 3).

To test acetate uptake and utilization by whole plants, we grew lettuce plants in the light with 13 C-acetate added to the plant growth agar medium at 2 mM. In vegetative leaf tissue, we observed that amino acids, sugars and intermediates from the TCA cycle, glycolysis and gluconeogenesis were labelled with 13 C at multiple carbon positions (Fig. 4a–c ). Finding labelled metabolites in leaf tissue indicates that carbon from acetate assimilated by the roots can be distributed throughout the plant. In addition to lettuce, we discovered that acetate can be incorporated into a broad variety of crops. Rice, green pea, jalapeño pepper, canola, tomato, cowpea, tobacco and Arabidopsis seedlings grown in the light on solid agar containing 13 C-acetate all showed similar 13 C-labelling of amino acids, carbohydrates and TCA cycle intermediates (Fig. 4a and Extended Data Fig. 5 ) as was observed in lettuce and lettuce callus. This metabolite labelling pattern suggests a conserved mechanism of acetate utilization that exists across diverse plant species.

Lettuce seeds germinated normally on all concentrations of acetate evaluated (up to 10 mM) (Extended Data Fig. 6a,b ). However, plant growth was largely inhibited by acetate at concentrations that would have measurably increased plant biomass, although some growth parameters such as roots showed increased growth (Extended Data Figs. 6 and 7 ). Lettuce plants grown with electrolyser-produced effluent (0.691 M acetate: 1 M KOH or 0.648 M acetate: 1 M KHCO 3 ) added to reach a final media concentration of 1.0 mM acetate did not show additional growth inhibition in plant weight or leaf number from secondary electrolysis products (Extended Data Fig. 7b,d ). Plant tolerance and consumption of acetate as a heterotrophic energy source will need to be increased to fully decouple plants from biological photosynthesis. Together, these analyses explore acetate as a heterotrophic carbon source for crops and illustrate that many crop species can process acetate into a usable biological form and incorporate it into major energy-production cycles through TCA and biomass in the form of sugars and amino acids.

We demonstrated a hybrid inorganic–biological system that can produce food from carbon dioxide and electricity, independent of biological photosynthesis. CO 2 electrolysis for acetate production was significantly improved for the purpose of biological integration. Using a two-step process, we achieved electrochemical reduction of CO 2 to acetate with a 57% carbon selectivity towards acetate, the highest published value to date. The electrolysis system was further engineered to produce improved effluents with acetate-to-electrolyte salt ratios as high as 0.75, well above the ratio determined necessary to support biological growth. Electrochemically derived acetate was incorporated into a diverse variety of organisms grown for food, including algae, fungi and crop plants. This includes the cultivation of a photosynthetic organism, Chlamydomonas , utilizing carbon fixed through electrolysis, independent of biological photosynthesis. 13 C-labelling experiments showed that a broad range of crops can utilize exogenous acetate for energy and biomass production, suggesting that acetate has the capacity to support crop growth with further optimization.

By powering electrolysis with photovoltaics, the conversion of sunlight and CO 2 to food in our system (photovoltaics to electrolysis to acetate to yeast) is almost 18 times more solar-to-biomass energy-conversion-efficient than typical food production, which relies on biological photosynthesis (photosynthesis to glucose to yeast) (Extended Data Fig. 8 ). For algae production, our process (photovoltaics to electrolysis to acetate to algae) is almost four times more solar-to-biomass energy-conversion-efficient than biological photosynthesis of crop plants (photosynthesis to crop plants) and is equivalent to or higher than the instantaneous energy efficiency of outdoor algae production 37 (Fig. 5 and Extended Data Fig. 8 ). There is potential for continued improvement of the system through advances in photovoltaics, electrolysis and acetate utilization in food-producing organisms. Our approach to food production is ideally suited for applications where high energy efficiencies and low physical space usage are desired, such as on space flight missions or in controlled environments on Earth. Widespread adoption of this approach in conjunction with readily available solar energy could allow for the production of more food or animal feed for a given solar footprint, which will help meet the rising demand for food without the expansion of agricultural lands.

Sankey diagrams of solar energy to plant- and algae-based food production compare the efficiencies of artificial and biological photosynthesis. Losses during energy conversion from sunlight (100% solar energy) to food are represented by arrows; the width of each arrow is proportional to the energy lost, and the percentage of the total energy lost at each step is indicated. The green arrows indicate the solar energy that can be found in biomass grown from artificial and biological photosynthesis; the width of each arrow is proportional to the energy, and the percentage of the total solar energy found in the biomass is indicated. The values for biological photosynthesis are from Zhu et al. 57 . For artificial photosynthesis, the values for electrochemical CO 2 reduction to acetate and heterotrophic cultivation on acetate were determined in this work. The value for photovoltaic losses is based on a commercially available silicon solar cell 49 , 50 .

Electrolysis methods

Cathode preparations.

For the CO 2 reduction electrodes, commercial silver (Ag) catalysts (nanopowder with <100 nm particle size, 99.5%) were purchased from Sigma Aldrich. Catalyst ink was prepared by suspending 100 mg of Ag nanopowder in 20 ml of 2:1 (v:v) isopropanol/deionized water. The suspension was sonicated for five minutes to allow complete dispersion of Ag particles. Afterwards, Sustanion XA-9 ionomer (5% (w/w) in ethanol, Dioxide Materials) was added until the ionomer reached 10% (w/w) in solution (metal basis). The resulting mixture was further sonicated for an additional 30 minutes to allow complete dispersion. To prepare the electrodes, a 25 cm 2 piece of Sigracet 39BB gas carbon paper (Fuel Cell Store), used as a gas diffusion layer, was heated on a hot plate to 100 °C. Catalyst ink was airbrushed onto the carbon paper until a loading of 1.4 mg cm −2 was reached. The electrode was dried at 70 °C overnight to ensure the complete evaporation of solvents.

For the CO reduction electrodes, commercial copper (Cu) catalyst (nanopowder with 25 nm particle size (TEM)) was purchased from Sigma Aldrich. Catalyst ink was prepared by suspending 100 mg of Cu nanopowder in 20 ml of 2:1 (v:v) isopropanol/deionized water. The suspension was sonicated for five minutes to allow complete dispersion of Cu particles. Afterwards, Sustanion XA-9 ionomer (5% (w/w) in ethanol, Dioxide Materials) was added until the ionomer reached 20% (w/w) in solution (metal basis). The resulting mixture was further sonicated for an additional 30 minutes to allow complete dispersion. To prepare the electrodes, a 5 cm 2 piece of Sigracet 39BB carbon paper (Fuel Cell Store) was heated on a hot plate to 100 °C. Catalyst ink was drop-casted onto the carbon paper until a loading of 2.0 mg cm −2 was reached. The electrode was dried at 70 °C overnight to ensure the complete evaporation of solvents.

Anode preparations

For experiments using 1 M KHCO 3 , IrO 2 catalysts were prepared via a previously reported method 38 . In a typical preparation, titanium felt (Fuel Cell Store) was degreased using acetone and etched in boiling 0.5 M oxalic acid (98%, Sigma Aldrich). The titanium felt was then dip-coated in a solution of 10 ml of isopropanol, 10 vol% concentrated HCl (ACS reagent, Sigma Aldrich), containing 30 mg dissolved IrCl 3 · x H 2 O (99.8%, Alfa Aesar). This was followed by drying at 100 °C for ten minutes and calcination at 500 °C for ten minutes. This procedure was repeated until a loading of 3 mg cm −2 was achieved.

For experiments conducted in alkaline conditions, NiFeO x anodes prepared by a previously reported method 39 were used. In a typical preparation, Ni foam (>99.99%, MTI Corporation) was sonicated in a 5 M HCl solution for 30 minutes to remove the NiO x layer on the surface. The foam was then rinsed using deionized water and ethanol and dried in air. Electrodeposition was carried out in a standard three-electrode electrochemical reactor containing the nickel foam as the working electrode, a platinum wire counter electrode and a Ag/AgCl (Pine Research) reference electrode. The electrolyte bath contained 3 mM Ni(NO 3 ) 2 ·6H 2 O and 3 mM Fe(NO 3 ) 3 ·9H 2 O. A constant potential of −1.0 V versus Ag/AgCl was applied for 300 seconds. The electrode was then removed and rinsed with ethanol and deionized water, followed by drying overnight at 70 °C to fully remove solvents. A fresh cathode was used for each experiment, whereas the anodes were reused multiple times.

Flow electrolyser

Both the CO 2 and CO electrolysers were constructed as 5 cm 2 membrane electrode assemblies with serpentine flow patterns. The cathode end plates and the CO 2 electrolyser anode end plate were gold-plated stainless steel. The CO electrolyser anode end plate was not gold coated, as stainless steel is stable in alkaline conditions. The CO 2 gas flow rate into the electrolysers was controlled via a Brooks GF040 and held at 7 ml min −1 for the two-step experiments. The back pressure was controlled using a back-pressure controller (Cole-Palmer). The anolyte flow rates for both electrolysers were controlled via a peristaltic pump, with flow rates ranging between 0.5 and 1 ml min −1 . A cold trap chilled using ice was placed between the gas outlet stream of the CO electrolyser and the back-pressure controller to capture any vaporized liquid products that might exit through the gas stream.

For the CO 2 electrolyser, the Ag cathode and the IrO 2 anode were pressed into a Sustanion 37-50 Anion Exchange Membrane (Dioxide Materials) at 20 lb-in torque. Laser-cut Teflon (McMaster Carr) was used as a gasket to ensure that the electrolyser was gas-tight. 1 M KHCO 3 was used as the anolyte for all CO 2 reduction experiments. A constant current was applied using a power source (National Instruments). For the CO electrolyser, the Cu cathode and the IrO 2 or NiFeO x anode were pressed against an FAA-3-50 anion exchange membrane (Fumatech) at 20 lb-in torque. 1 M KHCO 3 or 2 M KOH was recirculated through the anode chamber. Liquid products were accumulated in the anolyte of the CO electrolyser until a target concentration was reached. The quantification of liquid products was conducted at the end of the experiment. For the CO electrolyser, the applied current was controlled using an Autolab PG128N potentiostat (Metrohm).

Gas products were quantified using a Multiple Gas Analyzer No. 5 gas chromatography system (SRI Instruments) equipped with a HaySep D and Molsieve 5 A column connected to a thermal conductivity detector. All gas products were quantified using the thermal conductivity detector data. Liquid products were quantified using a Bruker AVIII 600 MHz NMR spectrometer. Typically, 20 to 100 μl of collected electrolyte exiting the reactor was diluted to 500 μl, and then 100 μl D 2 O containing 20 ppm (m/m) dimethyl sulfoxide (≥99.9%, Alfa Aesar) was added. One-dimensional 1 H spectrum was measured with water suppression using a pre-saturation method.

Effluents containing all liquid products were used for the growth of food-producing organisms. Electrolyser-produced effluents were used when possible, but in some cases simulated effluents of the same makeup were used.

Algae methods

Chlamydomonas reinhardtii (strain 21gr+, CC-1690 from the Chlamydomonas Resource Center) was cultivated on TAP media 40 where the source of acetate was a commercial supplier (Sigma 64-19-7), simulated effluent, electrolyser-produced effluent or none at all. When effluent (electrolyser produced or simulated) was the acetate source, it was added until the desired final acetate concentration was reached (17.5 mM). Ethanol (Koptec 64-17-5), propionic acid (Sigma 79-09-4) and n -propanol (Sigma 71-23-8) were added as indicated in Supplementary Table 3 . All media were adjusted to pH 7.2 with 5 M HCl. Effluents and media concentrations are shown in Supplementary Tables 2 and 3 .

Chlamydomonas was grown in 50 ml of media in 250 ml flasks in the dark. The flasks were stationary at 22 °C except for the experiment using 0.75 M acetate: 1 M KOH (data in Fig. 3b,c ), where the flasks were at 30 °C with a shaking speed of 150 r.p.m. 29 . Aliquots were taken in a darkened biosafety cabinet and used for OD measurements at wavelength 750 nm (QuickDrop Spectrophotometer, Molecular Devices), chlorophyll extraction and quantification 41 , and cell counts using a haemocytometer or the Bio-Rad TC20 Automated Cell Counter. For dry cell weights, the entire culture was centrifuged, washed two times with deionized water to remove residual salts, dried overnight at 100 °C and then weighed. Images were taken with a Nikon 7500 DSLR camera.

Yeast and mushroom methods

Saccharomyces cerevisiae was cultured in a base medium of yeast extract (10 g l −1 ) and peptone (20 g l −1 ) with glucose, effluent or no additional primary carbon source 42 . Standardized comparisons of acetate and glucose were made on the basis of energy content, and a variety of glucose and effluent concentrations were tested, as shown in Supplementary Table 5 . The highest concentration is based on 2% sodium acetate 42 . Media with a carbon source with the energetic equivalent of 53.36 kJ l −1 (0.061 M glucose or 0.019 M acetate) were determined to be the most efficient and thus were used in the yeast experiments shown in Fig. 3d,e . All media were adjusted to pH 6.0 with 5 M HCl. Cells were grown in 5 ml of media in culture tubes at 30 °C and 251 r.p.m. Growth was monitored by measuring OD at 600 nm and dry cell weight at 96 hours. For dry weights, the pellet was washed with deionized water to remove residual salts, dried overnight at 100 °C and then weighed. Effluents and media concentrations are shown in Supplementary Tables 2 and 3 .

For the cultivation experiments with mushroom-producing fungi, the following five species were used: pearl oyster ( Pleurotus ostreatus ), elm oyster ( Hypsizygus ulmarius ), blue oyster ( Pleurotus ostreatus var. columbinus), enokitake ( Flammulina velutipes ) and coral tooth ( Hericium coralloides ). The strains were purchased from liquidfungi.com and maintained on a liquid medium of glucose, yeast extract and peptone. Fungal mycelia were grown in a solid-state fermentation approach that roughly followed the PF-Tek methodology 43 ; however, the carbon sources typically added, such as starch (rice flour), peat moss and coconut fibre, were omitted. A solid substrate of fine-grade vermiculite (30 g) was mixed with gypsum (0.5 g) and added to a 10 oz wide-mouth mason jar (Kamota). Liquid growth media (90 ml) were added to each jar, which soaked into the vermiculite. Media composition was the same as the maintenance media except that the carbon source was glucose (20 g l −1 ), acetate (as indicated), effluent (as indicated), no carbon source or a combination of these. Media pH was adjusted to 6.0 with HCl. The jars were closed with lids with four drilled holes (12.7 mm in diameter) covered with a synthetic filter disk (0.3 µm pore size) to allow gas exchange. The jars were autoclaved for 45 minutes at 121 °C. Liquid mycelium cultures were centrifuged, washed with sterilized media, resuspended and then used to inoculate the sterilized jars.

Plant methods

Plant material, media and growth conditions.

The following nine plants were used: lettuce ( Lactuca sativa L. cv. ‘Black Seeded Simpson’), rice ( Oryza sativa ssp. japonica cv. ‘Kitaake’), green pea ( Pisum sativum ), tomato ( Solanum lycopersicum cv. ‘Micro-Tom’), jalapeño pepper ( Capsicum annuum cv. ‘Jalapeño’), canola ( Brassica napus ), cowpea ( Vigna unguiculata L. cv. CB46), thale cress ( A. thaliana var. ‘Columbia’) and tobacco ( Nicotiana tabacum cv. Xanthi ).

Plants were cultivated on 50 ml of basal medium of 1/2 Murashige and Skoog salts 44 (Caisson Labs) and Gamborg’s vitamins 45 (Phytotech Labs), unless otherwise noted. Solid media were made with 0.7% in vitro growth-grade agarose (Caisson Labs). The following were added as indicated: sucrose (2%), acetic acid, 13 C 2 -acetate (Sigma Aldrich) or electrolyser-produced effluent. All media were adjusted to pH 5.8 using 1 M KOH or HCl. The plants were grown in 16:8-hour light–dark cycles under fluorescent lighting (100 µmol m −2  s −1 ) at 22 °C, unless otherwise noted.

Acetate feeding experiment

Lettuce was germinated in soil for 11 days. The plants were clipped at the base of the stem and were further trimmed so that all lettuce stems were the same length for the start of the experiment. Each plant was transferred to an 8 ml glass vial containing deionized water with acetate dissolved at various concentrations (nine plantlets per treatment: 0, 0.1, 0.3, 0.6, 1, 3, 6 and 10 mM acetate). After 29 days, the plantlets were imaged (using a Nikon 7500 DSLR with an AF-S VR Micro-Nikkor 105 mm f/2.8 G IF-ED lens) and removed from the solution for measurement of leaf number, stem length, root length and fresh weight. Stem length was measured from the first lateral root to the tip of the stem, and roots were measured on the basis of the longest root on the plantlet.

Experimental setup for plant germination and 13 C exposure

For the lettuce germination experiments, seeds were sterilized using a 15% bleach solution and a drop of Tween for every 50 ml of sterilizing solution. The seeds were incubated in the sterilizing solution for 15 minutes while shaking and then washed five times with sterile water, five minutes for each wash while shaking. Ten lettuce seeds were placed on agar basal media (25 ml) supplemented with acetate (0.1, 0.3, 0.6, 1, 2, 3, 6 or 10 mM), with 2 mM labelled 13 C 2 -acetate or with no additions to the base medium. They were allowed to germinate and grow for 28 days and were then imaged and removed to measure stem length, leaf number and fresh weight (not including the roots). Tissue samples for the 13 C-labelling experiments and the untreated controls were frozen in liquid nitrogen and stored at −80 °C until they were used for metabolomic analysis. For the other plant species, the same sterilization procedure was used for the seeds, and the plants were grown until there was at least a few hundred milligrams of tissue for metabolomic analysis (canola, 14 days; rice, 14 days; Arabidopsis , 18 days; Micro-Tom tomato, 22 days; green pea, 22 days; Nicotiana benthamiana , 32 days; cowpea, 32 days).

Lettuce callus in liquid culture

Undifferentiated lettuce callus was generated by plating pieces of lettuce leaf tissue on callus-inducing media (1/2 Murashige and Skoog salts, 0.05 mg l −1 α-naphthaleneacetic acid, 0.4 mg l −1 6-benzyl aminopurine and Gamborg’s vitamins 45 ) (Phytotech Labs). The calli were cut to the desired size and weighed to ensure that an equal amount of tissue was added to each flask. Each flask of calli was incubated in liquid 1/2 Murashige and Skoog media supplemented with acetate, 13 C 2 -acetate or sucrose (2%) as indicated and to the specified concentrations. All cultures were grown at 22 °C in the dark at 100 r.p.m. Tissue samples for the 13 C-labelling experiment and control samples were frozen in liquid nitrogen and stored at −80 °C until they were used for metabolomic analysis.

Metabolomic analysis

Sample preparation.

Plant tissue was freeze-dried, and then approximately 10 mg was weighed into a 2 ml tube and homogenized using a bead mill, using three 2.8 mm beads per tube. To each sample, 750 µl of 1:2 water:methanol was added, and the samples were then vortexed for 60 min at 4 °C. Then, 500 µl of chloroform was added, and the samples were vortexed at 4 °C for an additional 15 min. After centrifugation for 10 min (16,000  g at 4 °C), the top, polar layer was transferred to a glass vial and analysed by liquid chromatography–mass spectrometry (LC–MS).

LC–MS metabolomics analysis was performed at the University of California, Riverside Metabolomics Core Facility. The analysis was performed on a Synapt G2-Si quadrupole time-of-flight mass spectrometer (Waters). Metabolite separations were carried out on an I-class UPLC system (Waters) using a ZIC-pHILIC column (2.1 mm × 150 mm, 5 µM) (EMD Millipore). The two mobile phases used were (A) water with 15 mM ammonium bicarbonate adjusted to pH 9.6 with ammonium hydroxide and (B) acetonitrile. The column was held at 20 °C, and the flow rate was 150 µl min −1 . The sample injection volume was 2 µl. The following gradient was performed: 0 min, 10% A, 90% B; 1.5 min, 10% A, 90% B; 16 min, 80% A, 20% B; 29 min, 80% A, 20% B; 31 min, 10% A, 90% B; 32 min, 10% A, 90% B.

The mass spectrometer was operated in negative ion mode (50 to 1200  m / z ) with a 100 ms scan time. Tandem MS was acquired in a data-dependent fashion. The source temperature was 150 °C, and the desolvation temperature was 600 °C. Nitrogen was used as a desolvation gas (1,100 l h −1 ) and cone gas (150 l h −1 ). The collision gas used was argon. The capillary voltage was 2 kV. Leucine enkephalin was infused and used for mass correction.

Data processing and analysis

Data processing was performed with the open-source Skyline software 46 . Metabolites were identified by MS (less than 5 ppm) and tandem MS using the Metlin database 47 . Data for isocitrate and citrate and for 2-phosphoglycerate and 3-phosphoglycerate are included as cumulative values (iso/citrate and 2/3-phosphoglycerate) because they are not distinguishable through the LC–MS methodology used. The log 2 13 C enrichment was calculated for the heat maps using the equation:

where M is the area under the curve measured by LC–MS of molecules made up of 12 C atoms only, and M  +  X is the area under the curve measured by LC–MS of molecules with 13 C-isotope atoms incorporated into the molecule, X being the number of 13 C-isotope atoms incorporated. When multiple biological replicates were available, they were averaged before dividing treatment by control. The untreated control replicates shown are a single representative replicate normalized to the average of all replicates, which helps visualize any variation between controls.

Energy efficiency calculations

Efficiency calculations for electrocatalysis.

The following equations were used to determine the efficiency of the electrolysis process. Faradaic efficiency (FE) from gas chromatography was calculated as

where n is the number of electrons transferred, F is Faraday’s constant, x is the mole fraction of the product, V is the total molar flow rate of the gas and j Tot is the total current. Liquid Faradaic efficiency was calculated using quantitative 1 H-NMR.

Carbon selectivity was calculated as

where n i is the number of electrons transferred to product i , j i is the partial current towards product i and C i is the number of carbons in product i . This value represents the percentage of CO 2 reacted towards C 2+ products found in a given product or the molar selectivity of a given product scaled to the number of carbons contained in it.

To determine the overall efficiency of the electrolysis process, we calculated the theoretical energy required to produce the C 2+ products and divided that by the actual amount of energy it took to produce those C 2+ products. The C 2+ product distribution is shown in Supplementary Table 6 , where we assume 1 g of the C 2+ products.

Theoretical potentials were calculated using the following equation:

where E 0 is the theoretical potential, n is the number of electrons and Δ G 0 is the Gibbs free energy of reaction.

Theoretical energy was calculated as follows:

where \(E_i^0\) is the theoretical cell potential for species i , m i is the mass produced of product i and MW i is the molecular weight of product i . The values for E 0 and m can be found in Supplementary Table 6 .

The actual energy input to produce 1 g of C 2+ products is calculated using the following equation:

where m AcO is the mass of acetate produced to produce a total of 1 g of C 2+ products at the Faradaic efficiencies calculated and listed in Supplementary Table 6 , n AcO is the number of moles of electrons passed per mole of acetate produced, E CO is the measured cell potential for the CO electrolyser, MW AcO is the molecular weight of acetate and FE AcO is the measured Faradaic efficiency of acetate. For the CO 2 electrolyser:

where N CO is the moles of carbon monoxide necessary to produce a total of 1 g of C 2+ products at the 92% (measured) conversion in the CO electrolyser, n CO is the number of electrons passed per mol of carbon monoxide produced, \(E_{{\mathrm{CO}}_2}\) is the measured cell potential for the CO 2 electrolyser and FE CO is the measured Faradaic efficiency of carbon monoxide.

Using the values listed in Supplementary Table 6 , the energetic efficiency was calculated by taking the ratio of the theoretical energy and the actual energy input per 1 g of product:

Our tandem electrolysis process has an energy efficiency of 35.62% for the production of all C 2+ products, which accounts for losses due to selectivity, conversion and overpotential. If you consider ethylene as a complete loss and consider only products in the effluent, the energy efficiency of C 2+ product production is 24.26%. We next calculated the energy efficiency of the production of just acetate, as this is what is used as the energy and carbon source for algal and yeast growth. To calculate acetate efficiency, we did not include losses due to selectivity because in a commercial setting, the other C 2+ products (for example, ethylene) would be considered co-products of the process. The efficiency numbers including ethylene as a co-product were used to calculate the energy efficiency of the whole system. More details are shown below:

Efficiency calculations for food production

The efficiency values reported here are based on the statistical averages of at least three biological replicates. These calculations were conducted similarly to the approaches in Blankenship et al. 1 and Nangle et al. 48 .

Energy efficiency of the heterotrophic cultivation of Chlamydomonas without inputs from biological photosynthesis

The energy efficiency of biological photosynthesis is defined as the energy content of the biomass that can be harvested annually divided by the annual solar irradiance over the same area 1 . To be able to compare our process to biological photosynthesis, we calculated the conversion efficiency of sunlight to biomass using photovoltaics to power our process of the electrolytic production of acetate followed by the heterotrophic cultivation of Chlamydomonas in the dark. We define the energy efficiency as the increase in biomass energy content divided by the required solar energy input. The increase in biomass energy content is calculated as

where Δ X biomass is the gain of algal biomass and \(\Delta H_{{\mathrm{biomass}}}^{{\circ}}\) is the enthalpy of combustion of algal biomass, which was determined experimentally using an oxygen bomb calorimeter. The required solar energy input is calculated as

where C AcO is the acetate concentration in the algal media, MW AcO is the molecular weight of acetate, w consumed is the fraction of acetate consumed by the algae, E AcO is the energy required to generate acetate in the electrolyser (calculated above), η AM1.5 is the maximum power conversion efficiency (peak solar intensity, AM1.5 spectral distribution) of a commercially available silicon solar cell (from Canadian Solar 49 , 50 ) and η annual is the photovoltaic annual efficiency, which is about 95% of the maximum power conversion efficiency value due to the changing solar zenith angle throughout the day and year 1 . The energy efficiency is then calculated as

Comparison of this number with the ~1% annual efficiency for most crop plants 1 shows that our approach can be almost four times more energy efficient than biological photosynthesis for the cultivation of photosynthetic organisms. Improvements in photovoltaic maximum power conversion efficiency would increase the efficiency of our approach. For example, multi-junction solar cells have been shown to reach efficiencies as high as 47.1% 49 , which coupled to our system would bring the overall energy efficiency of sunlight to food to ~9%.

For Fig. 5 and Extended Data Fig. 8 , we calculated the energy efficiencies at each step within the system as follows:

where Y algae/acetate is the yield of algae grown with acetate effluent media, and \(\Delta H_{\mathrm{AcO}}^{\circ}\) is the enthalpy of combustion of acetate.

Energy efficiency of the cultivation of yeast

Nutritional yeast is heterotrophic and is typically cultivated with glucose derived from biological photosynthesis as the primary carbon and energy source. In our process, electrolysis-derived acetate is substituted for glucose. To compare these two ways to produce yeast, we calculated the amount of yeast that could be produced per area of land in Illinois, a large corn-producing state in the United States. To calculate the amount of yeast that could be produced by our process independent of biological photosynthesis, we used the annual average solar irradiance in Illinois to calculate the electricity that could be generated by photovoltaics to synthesize acetate in the CO 2 electrolyser and subsequently used to cultivate yeast, as follows:

where E e is the average annual solar irradiance from the North America Land Data Assimilation System Daily Sunlight (kJ m −2 ) dataset for Illinois from 2000 to 2011 51 ; η AM1.5 is the maximum power conversion efficiency (peak solar intensity, AM1.5 spectral distribution) of a commercially available silicon solar cell 49 ; η annual is the photovoltaic annual efficiency, which is about 95% of the maximum power conversion efficiency value due to the changing solar zenith angle throughout the day and year 2 ; E AcO is the energy required to generate acetate in the electrolyser (calculated above); and Y yeast/acetate is the yield of yeast grown with acetate effluent media.

To calculate the amount of yeast that could be produced per area of land with biological photosynthesis, we used the annual corn harvest data from Illinois to determine the glucose that could be generated by photosynthesis and subsequently used as the primary carbon and energy source to cultivate yeast, as follows:

where \(Y_{{\mathrm{corn}}\;{\mathrm{per}}\;{\mathrm{m}}^2}\) is the average corn kernel production in Illinois per square meter 52 , Y glucose/corn is the glucose produced from a bushel of corn kernels 53 and Y yeast/glucose is the yield of yeast grown with glucose as the carbon source 54 . Using our artificial photosynthesis approach, almost 18 times more yeast could be produced per area of land.

For Extended Data Fig. 8 , we calculated the energy efficiencies at each step within the systems as follows:

where \(\Delta H_{\mathrm{biomass}}^{\circ}\) is the enthalpy of combustion of yeast biomass 55 , \(\Delta H_{\mathrm{kernel}}^{\circ}\) is the enthalpy of combustion of corn kernels 56 , w glucose/corn is the mass fraction of glucose found in a corn kernel 53 and \(\Delta H_{\mathrm{glucose}}^{\circ}\) is the enthalpy of combustion of glucose.

There are many ways in which the energy of biological photosynthesis can be improved on, such as increasing CO 2 concentrations and metabolic engineering. We have included numbers for the theoretical maxima of both systems in Extended Data Fig. 8b (refs. 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ).

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

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The metabolomics data (Fig. 4a and Extended Data Figs. 4 and 5b ) and all source data can be found at https://doi.org/10.6086/D1VT2V in the Dryad Digital Repository.

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Acknowledgements

We thank J. Kirkwood (University of California, Riverside (UCR)) and the Institute of Integrative Genome Biology Metabolomics Core Facility at UCR for help with metabolomics analysis; the UCR Plant Transformation Research Center, where all plant experiments were conducted; H. Blanch (UCR) for advice on the efficiency calculations; Y. Li (UCR), S. Xu (UCR) and S. Wu (UCR) for advice and reagents for the yeast experiments; J. Hoover (UCR) for his early efforts towards acetate isolation; C. Mendoza (UCR) for help early on with the algae experiments; and M. Jouny (University of Delaware) for his efforts in developing the early concept of the two-step process. We thank T. Xiang (University of North Carolina, Charlotte), B. Velazquez Benitez (UCR), S. Frey (UCR) and J. Russo (UCR) for providing feedback on the manuscript. The following funding supported this work: Translational Research Institute for Space Health (TRISH) through NASA grant no. NNX16AO69A (R.E.J., E.H., M.H.-D., M.L.O.-C. and A.N.), Foundation for Food & Agriculture Research grant no. FF-NIA20–000000009 (R.E.J.), National Science Foundation grant no. DBI-1922642 (M.H.-D.), a Link Foundation Energy Fellowship (E.H.), Department of Energy grant no. DE-FE0029868 (F.J. and S.O.) and National Science Foundation grant no. CBET-1803200 (F.J. and S.O.). The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the Foundation for Food & Agriculture Research (FFAR).

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These authors contributed equally: Elizabeth C. Hann, Sean Overa, Marcus Harland-Dunaway.

Authors and Affiliations

Center for Industrial Biotechnology, Department of Chemical and Environmental Engineering, University of California, Riverside, CA, USA

Elizabeth C. Hann, Marcus Harland-Dunaway, Andrés F. Narvaez, Dang N. Le & Robert E. Jinkerson

Center for Plant Cell Biology, Department of Botany and Plant Sciences, University of California, Riverside, CA, USA

Elizabeth C. Hann, Marcus Harland-Dunaway & Robert E. Jinkerson

Center for Catalytic Science and Technology, Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, USA

Sean Overa & Feng Jiao

Plant Transformation Research Center, University of California, Riverside, CA, USA

Andrés F. Narvaez & Martha L. Orozco-Cárdenas

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Contributions

R.E.J. and F.J. conceived the experiments. S.O. performed the CO 2 electrolysis experiments. E.C.H. and D.N.L. performed the algae experiments. E.C.H. performed the yeast experiments. R.E.J. performed the mushroom experiments. M.H.-D., A.F.N. and M.L.O.-C. helped conceive the plant experiments, performed them and analysed the data for them. S.O. calculated the efficiencies for electrocatalysis and R.E.J. and E.C.H. calculated the efficiencies for food production. R.E.J., F.J., S.O., E.C.H. and M.H.-D. analysed the data and wrote the manuscript. All authors edited and approved the manuscript.

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Correspondence to Feng Jiao or Robert E. Jinkerson .

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Extended data

Extended data fig. 1 tandem co 2 electrochemical system for the production of acetate..

a,b , Schematic of the two-step electrolysis system without ( a ) and with ( b ) 5 M NaOH scrubber. Oxygen was allowed to vent to the atmosphere from anolytes. Primary products were used as effluents for growth. c , Electrolyser voltage of direct feed CO electrolyser using 2 M KOH and 1 M KHCO 3 . 1 M KHCO 3 was produced in two separate effluents. Electrolyte was changed at 380 minutes, causing the large spike in potential. 0.476 M acetate: 1 M KHCO 3 was produced in the first half of the KHCO 3 experiment, 0.648 M acetate: 1 M KHCO 3 in the second half, and 0.691 M acetate: 1 M KOH was produced in the 2 M KOH experiment. d , Normalized production rate of dissolved CO reduction products collected in the effluent. Liquid product production rate from KHCO 3 is cumulative from both experiments, and represents the average over the entire experiment. Data presented represents one experiment for each electrolyte. e , Outlet molar fractions of gas products and feeds from the two-step electrolyser system (left axis). Flow rate (right axis) was measured continuously and is indicated by the arrow. The 5 M NaOH scrubber introduced at 30 minutes completely removed CO 2 . The CO electrolyser operated from 45 to 345 minutes. The increase in flow rate is attributed to the increase in H 2 production over the course of experiment in the CO 2 electrolyser. f , Conversion of CO 2 and CO during the reaction. The CO 2 electrolyser was run without the scrubber at first (grey), a 5 M NaOH scrubber was introduced at 45 minutes (green), the CO electrolyser began operation at 60 minutes (blue). No CO 2 was detected on gas chromatogram after the 5 M NaOH scrubber was introduced. CO conversion is based on the average CO flow rate measured before CO electrolyser operation. g , Outlet molar concentrations of gas products and feeds (left axis) for CO 2 electrolyser and corresponding Faradaic efficiencies towards CO and H 2 (right axis) at varied inlet CO 2 flow rates. The arrow indicates the red and black scatter belong to the Faradaic efficiency of the system. h , Electrolyser voltage over the course of the flow rate experiment. Oscillations in cell voltage are due to oscillations in the back pressure controller. Step changes were caused by a decrease in inlet CO 2 flow rate.

Extended Data Fig. 2 Chlamydomonas growth varies depending on salinity and acetate:electrolyte ratio of media.

a , The fraction of growth compared to growth in standard acetate media as electrolyte salt concentrations increased. Fraction of growth was calculated as cells per ml of culture grown in effluent media divided by cells per ml of culture grown in acetate positive control media on the last day of growth. Growth was not observed for electrolyte salt concentrations above 80 mM. Media made with effluents that have lower electrolyte concentrations support higher amounts of growth. Standard acetate media is represented as 0 M. Line is a linear regression (R 2  = 0.969). b , The same data as in a but plotted against acetate: electrolyte salt ratio. Media made with effluents with higher acetate-to-electrolyte salt ratio support higher amounts of growth. The threshold for growth is between 0.2 and 0.4 acetate-to-electrolyte salt ratio. Standard Tris-acetate-phosphate (TAP) media is arbitrarily set to a ratio of 0.8 since it contains no electrolyte. These graphs do not include growth of Chlamydomonas with the most optimized effluents, as seen in Fig. 3. Chlamydomonas cultures were grown in TAP media with effluent in place of acetate to match the acetate concentration of a typical liquid heterotrophic growth medium (17.5 mM). All media was adjusted to pH 7.2.

Extended Data Fig. 3 Chlamydomonas and mushroom mycelium can grow heterotrophically with electrolyser produced effluent as the sole carbon and energy source.

a, b, c, d, e , Chlamydomonas grown in the dark with electrolyser produced effluents (0.691 M acetate: 1 M KOH, 0.476 M acetate: 1 M KHCO 3 , and 0.648 M acetate: 1 M KHCO 3 ), acetate, and no acetate. ( a ) Images taken on day 0 and 4, ( b ) cell counts, ( c ) optical density (OD) (750 nm), ( d ) chlorophyll concentration, ( e ) and dry weight after 16 days of growth. f , Percentage of acetate in media utilized by Chlamydomonas growth determined by comparing NMR measured acetate concentration of media before and after growth. Cultures were grown in Tris-acetate-phosphate (TAP) media with acetate, without acetate (TP), or with effluent in place of acetate to match the acetate concentration of a typical liquid heterotrophic growth medium (17.5 mM). All media was adjusted to pH 7.2. Each data point represents three biological replicates. Error bars indicate standard deviations. Images are representative of all replicates. g , Mushroom mycelium’s ability to colonize vermiculite substrate soaked with YPD media with no glucose (0 g l -1 ) and increasing levels of effluent as the carbon source. Full colonization (+++) represents 75% to 100% of substrate colonized, partial colonization (++) represents 15% to 75%, limited colonization (+) represents 1% to 15% of substrate colonized. Effluent added to reach 0.5% (w/w) acetate could support the growth of all fungal species evaluated. h, i , Images of pearl oyster ( h ) and enokitake ( i ) mushroom mycelium colonization of a solid vermiculite substrate soaked with YPD media containing glucose (20 g l -1 ), glucose (20 g l -1 ) and effluent (0.0691 M acetate: 1 M KOH) added to reach 0.5% (w/w) acetate, or only effluent (0.0691 M acetate: 1 M KOH) in place of glucose (0 g l -1 ) to reach 0.5% (w/w) acetate as the primary carbon and energy source. Images were taken 24 days post inoculation and are representative of at least 3 replicates. Substrates were fully colonized for all three media. The morphology of the pearl oyster mycelium on top of the effluent containing substrate was different and not as ‘fluffy’ as compared to the glucose containing media. Images are representative of at least 3 replicates. Scale bars: 20 mm.

Extended Data Fig. 4 Heat map of metabolites that had 13 C-labeling in undifferentiated lettuce cells (calli).

Heat map of all the replicates of the lettuce callus samples treated with no acetate (n = 3), 2 mM 13 C-acetate (n = 3), 5 mM 13 C-acetate + 5 mM acetate from KHCO 3 effluent (0.648 M acetate: 1 M KHCO 3 , n = 3), or 5 mM 13 C-acetate + 5 mM acetate from KOH effluent (0.691 M acetate: 1 M KOH, n = 4). Number after treatment corresponds to the replicate number. Log 2 fold enrichment of 13 C between treated and untreated samples, see methods. All samples were grown in the dark showing the ability of plant cells to incorporate 13 C from acetate into biomass without light. M+1, 2, 3 or 4 denotes the additional mass of a molecule, which corresponds to the number of carbons in a molecule that are labeled with carbon isotopes.

Extended Data Fig. 5 Heat map of labeled metabolites in crops grown on 13 C 2 -acetate.

a , Representative images showing crop plants grown with 2 mM 13 C-acetate 1/2 MS media and control 1/2 MS media. These plants were later used for metabolomic analysis seen in Fig. 4a . b , Heat map of the log 2 enrichment of 13 C-labeling in all crop replicates compared to the average value of non-treated controls used to create Fig. 4a . The number after each plant name at the top of the figure corresponds to the replicate number. Samples grown without acetate with only one replicate are not included. There is enrichment in all treated crop samples. There are lower levels of 13 C-enrichment in green pea relative to other crops. This may be due to a larger reliance for carbon and energy from the endosperm of the seed resulting in less absorption of nutrients and 13 C 2 -acetate from the growth media.

Extended Data Fig. 6 Effect of increasing levels of acetate exposure on lettuce germination and growth (0.0, 0.1, 0.3, 0.6, 1.0, 2.0, 3.0, 6.0, 10 mM).

a, b , Seed germination rate for lettuce after 28 days on ( a ) agar + 1/2 MS + sucrose + acetate ( b ) agar + 1/2 MS + acetate. 1/2 MS is a typical plant nutrient mix for growth on agar. Seed germination percent is not significantly different from controls across all treatments (Tukey’s HSD all p-values >0.15). All error bars represent the standard deviation between the germination percentage of the replicates (3 biological replicates with 10 seeds each). Images of lettuce seed germination and growth. c, d , The media was made of ( c ) 1/2 MS + acetate, and ( d ) 1/2 MS + sucrose + acetate. ( c ) 1/2 MS + acetate visually shows the effect of acetate on plant growth in a concentration dependent manner. ( d ) Agar + 1/2 MS + sucrose + acetate shows that supplementing lettuce with an additional carbon source, sucrose, does not affect the concentration dependent inhibition. All images are representative of all biological replicates for each treatment (3 biological replicates with 10 seeds each). e-l Quantify plant growth measurements: weight, root length, stem height, and leaf count for lettuce plants (~30 individuals shown, and 3 biological replicates visualized in red, grey, and blue for each treatment). e, g, i and k , show agar + 1/2 MS + acetate. There is an acetate concentration dependent inhibition of plant growth that occurs at different concentrations of acetate for different traits, height being the most sensitive and leaf count being the least sensitive. f, h, j and l , show agar + 1/2 MS + sucrose + acetate. Again, there is inhibition of growth at higher acetate concentrations of acetate (2-10 mM). It did not appear that the addition of sucrose had a strong effect on growth, positive or negative, in combination with acetate. The box plots encompasses the quartiles of the dataset and the whiskers capture the rest of the data distribution, except for points that are determined to be “outliers” based on a function of the interquartile range 60 .

Extended Data Fig. 7 Lettuce plants grown on electrolysis effluent and liquid media with acetate feeding.

a , Representative images of lettuce seeds that were germinated and grown on control 1/2 MS no acetate media, 1/2 MS media supplemented with 1 mM acetate KOH effluent (0.691 M acetate: 1 M KOH), and 1/2 MS media supplemented with 1 mM acetate Bicarbonate effluent (0.648 M acetate: 1 M KHCO 3 ) (3 biological replicates with 10 seeds each with at least 18 total seeds germinating in each treatment). White scale bar is 2 cm. All treatments had 3 biological replicates with 10 seeds each that were grown for 28 days and then growth parameters were measured and photographed. The white scale bar represents 2 cm. b, c, d, e and f , show the leaf number, height, fresh weight, root length, and germination rate, respectively, of lettuce plants germinated and grown on no acetate, 1 mM acetate KOH effluent (0.691 M acetate: 1 M KOH), and 1 mM acetate bicarbonate effluent (0.648 M acetate: 1 M KHCO 3 ). There was no statistical difference in weight between lettuce grown with (n = 21) and without (n = 18) effluent (p-value >0.05, Tukey’s HSD). However there was a significant difference between the 0.691 M acetate: 1 M KOH for root length and height, which could suggest that the potassium salt may cause some level of inhibition. g , Representative images of the acetate feeding experiment performed on lettuce seedling. Lettuce seedlings were germinated for 11 days and then cut at the base of the stem and transferred to water and acetate solutions at acetate concentrations of 0, 0.1, 0.3, 0.6, 1, 3, 6 and 10 mM (n = 9 biological replicates for each treatment). h, i, j and k , show the height, leaf count, length of the roots, and the weight of the plants respectively. Root length was significantly increased in the presence of acetate (p-value <0.001, Tukey’s HSD) (n = 9 for each treatment). Height for 6 and 10 mM is not included due to incompatibility with the method of measuring the height, which was the distance from the top of the stem to the first lateral root, at these higher levels of acetate concentration there was poor root development, which led to artificially inflated height. There is a significant increase in root length in lower levels of acetate concentration (0.1, 1, and 3 mM) based on Tukey’s HSD statistical test, before higher acetate levels start to inhibit root growth. Significant p-values from Tukey’s HSD comparing controls to treatment are denoted by an asterisk, p-value <0.05, double asterisk is a p-value <0.001, N.S. denotes no significant difference from controls (p-value > 0.05). All box plots encompass the quartiles of the dataset and the whiskers capture the rest of the data distribution, except for points that are determined to be “outliers” based on a function of the interquartile range 60 .

Extended Data Fig. 8 Energy efficiency of food production from artificial photosynthesis compared to biological photosynthesis.

The energy efficiency of sunlight to food production through artificial photosynthesis and biological photosynthesis are compared. Major steps in energy conversion from sunlight (100% solar energy) to food are represented by arrows, with the percentage of remaining energy after each step indicated. The energy efficiency of each step is noted in parentheses. a , Same data as in Fig. 5 . The current efficiency numbers for algae production through artificial photosynthesis were determined in this work through experimentation (electricity to acetate and acetate to algae efficiencies). The value for solar to electricity efficiency is based on a commercially available silicon solar cell 49 , 50 . The value for biological photosynthetic efficiency was obtained from the literature 1 . b , Theoretical maximum efficiencies for both biological photosynthesis and artificial photosynthesis are given to show the potential of these approaches. The theoretical maximum efficiency for biological photosynthesis (6%) is for C4 plants (including maize, sugarcane, and sorghum). The theoretical maximum efficiency of biological photosynthesis for C3 plants (including rice, tomato, pepper, and cowpea) is 4.6% (not shown) 57 . The value for solar to electricity efficiency (47.1%) is from a lab demonstration of a multi-junction solar cell under concentrated illumination 49 . Continued improvements to electrolysis technology and acetate utilization in plants and algae would increase the overall efficiency of this artificial photosynthetic approach. c , The current efficiency numbers for nutritional yeast production through artificial photosynthesis were determined in this work through experimentation (electricity to acetate and acetate to yeast efficiencies). The value for solar to electricity efficiency is based on a commercially available silicon solar cell 49 , 50 . Nutritional yeast is typically grown using glucose derived from plants, such as corn. The efficiency was calculated using values for solar irradiance and corn productivity in Illinois, USA 51 , 52 .

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Hann, E.C., Overa, S., Harland-Dunaway, M. et al. A hybrid inorganic–biological artificial photosynthesis system for energy-efficient food production. Nat Food 3 , 461–471 (2022). https://doi.org/10.1038/s43016-022-00530-x

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Research Article

Somatic nuclear mitochondrial DNA insertions are prevalent in the human brain and accumulate over time in fibroblasts

Contributed equally to this work with: Weichen Zhou, Kalpita R. Karan

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America

Roles Conceptualization, Data curation, Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, New York, United States of America

Roles Data curation, Formal analysis, Methodology, Visualization, Writing – review & editing

Roles Data curation, Formal analysis, Methodology, Resources, Validation, Writing – review & editing

Affiliations Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, New York, United States of America, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, New York, United States of America

Affiliations Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, New York, United States of America, Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, United States of America

Roles Funding acquisition, Resources, Writing – review & editing

Affiliation Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America

Affiliation Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, New York, United States of America

Roles Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Writing – review & editing

* E-mail: [email protected] (MP); [email protected] (REM)

Affiliations Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center, New York, New York, United States of America, Department of Neurology, H. Houston Merritt Center, Columbia University Translational Neuroscience Initiative, Columbia University Irving Medical Center, New York, New York, United States of America, New York State Psychiatric Institute, New York, New York, United States of America, Robert N Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, New York, United States of America

Roles Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Writing – original draft, Writing – review & editing

Affiliations Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America, Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America

• Weichen Zhou, 
• Kalpita R. Karan, 
• Wenjin Gu, 
• Hans-Ulrich Klein, 
• Gabriel Sturm, 
• Philip L. De Jager, 
• David A. Bennett, 
• Michio Hirano, 
• Martin Picard, 
• Ryan E. Mills

• Published: August 22, 2024
• https://doi.org/10.1371/journal.pbio.3002723
• Peer Review
• Reader Comments

The transfer of mitochondrial DNA into the nuclear genomes of eukaryotes (Numts) has been linked to lifespan in nonhuman species and recently demonstrated to occur in rare instances from one human generation to the next. Here, we investigated numtogenesis dynamics in humans in 2 ways. First, we quantified Numts in 1,187 postmortem brain and blood samples from different individuals. Compared to circulating immune cells ( n = 389), postmitotic brain tissue ( n = 798) contained more Numts, consistent with their potential somatic accumulation. Within brain samples, we observed a 5.5-fold enrichment of somatic Numt insertions in the dorsolateral prefrontal cortex (DLPFC) compared to cerebellum samples, suggesting that brain Numts arose spontaneously during development or across the lifespan. Moreover, an increase in the number of brain Numts was linked to earlier mortality. The brains of individuals with no cognitive impairment (NCI) who died at younger ages carried approximately 2 more Numts per decade of life lost than those who lived longer. Second, we tested the dynamic transfer of Numts using a repeated-measures whole-genome sequencing design in a human fibroblast model that recapitulates several molecular hallmarks of aging. These longitudinal experiments revealed a gradual accumulation of 1 Numt every ~13 days. Numtogenesis was independent of large-scale genomic instability and unlikely driven by cell clonality. Targeted pharmacological perturbations including chronic glucocorticoid signaling or impairing mitochondrial oxidative phosphorylation (OxPhos) only modestly increased the rate of numtogenesis, whereas patient-derived SURF1 -mutant cells exhibiting mtDNA instability accumulated Numts 4.7-fold faster than healthy donors. Combined, our data document spontaneous numtogenesis in human cells and demonstrate an association between brain cortical somatic Numts and human lifespan. These findings open the possibility that mito-nuclear horizontal gene transfer among human postmitotic tissues produces functionally relevant human Numts over timescales shorter than previously assumed.

Citation: Zhou W, Karan KR, Gu W, Klein H-U, Sturm G, De Jager PL, et al. (2024) Somatic nuclear mitochondrial DNA insertions are prevalent in the human brain and accumulate over time in fibroblasts. PLoS Biol 22(8): e3002723. https://doi.org/10.1371/journal.pbio.3002723

Academic Editor: Thomas B.L. Kirkwood, University of Newcastle upon Tyne, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND

Received: February 28, 2024; Accepted: June 26, 2024; Published: August 22, 2024

Copyright: © 2024 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The Whole genome sequence of ROSMAP can be obtained through the NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS, https://dss.niagads.org/ ) data set NG00067 or by contacting them directly at [email protected] . Whole genome sequences of aged primary human dermal fibroblasts can be found in a recent study at https://columbia-picard.shinyapps.io/shinyapp-Lifespan_Study/ . Illumina-sequenced 2504 independent individuals from the 1000 Genomes Project Phase 3 can be found by contacting the project at [email protected] or downloading directly from ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/. The Numt callsets by Dinumt from 1000 Genomes Project (GRCh38) and from ROSMAP and Lifespan dataset (GRCh37), the SV callsets by DELLY, Manta, and Canvas from Lifespan dataset (GRCh37), and the MEI callset by MELT from Lifespan dataset (GRCh37) can be found at GitHub page: github.com/mills-lab/numts-and-aging-in-fibroblasts-and-brains-. The raw data for generating the plots in the figures and supplementary figures can be found in Supporting Information S1 Data . Dinumt: https://github.com/mills-lab/dinumt . The scripts and command lines in the project can be found on the GitHub page: github.com/mills-lab/numts-and-aging-in-fibroblasts-and-brains, and ZENODO: https://zenodo.org/records/11625510 with DOI https://doi.org/10.5281/zenodo.11625510 .

Funding: This work was supported by NIA R01AG066828 (including salary support to K.R.K., G.S., M.H., and M.P.), 1R21HG011493-01 (including salary support to R.E.M. and W.Z.), and the BaszuckiBrain Research Fund. W.Z. was partially supported by and provided salary support from the NIH/NIA-funded Michigan Alzheimer’s Disease Research Center (P30AG072931) and the University of Michigan Alzheimer’s Disease Center Berger Endowment. ROSMAP is supported by P30AG10161, P30AG72975, R01AG15819, R01AG17917, U01AG46152, and U01AG61356. ROSMAP resources can be requested at https://www.radc.rush.edu . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: AC, anterior caudate; AD, Alzheimer’s dementia; DLPFC, dorsolateral prefrontal cortex; MAP, Memory and Aging Project; MCI, mild cognitive impairment; MEI, mobile element insertion; mtDNA, mitochondrial DNA; NCI, no cognitive impairment; OxPhos, oxidative phosphorylation; PBMC, peripheral blood mononuclear cell; PCC, posterior cingulate cortex; ROS, Religious Orders Study; SNV, single-nucleotide variant; SV, structural variation; VAF, variant allele frequency; WB, whole blood; WGS, whole genome sequencing

Introduction

The incorporation of mitochondrial DNA into the nuclear genomes of organisms is an ongoing phenomenon [ 1 – 8 ]. These nuclear mitochondrial insertions, referred to as “Numts,” have been observed in the germline of both human [ 6 , 8 – 11 ] and nonhuman [ 7 , 12 – 22 ] species. These insertions occur as part of a wider biological process termed numtogenesis [ 23 , 24 ], which has been defined as the occurrence of any mitochondrial DNA (mtDNA) components into the nucleus or nuclear genome. Once integrated, Numts are biparentally transmitted to future generations, like other types of genetic variation. While mostly benign, Numts have been implicated with cellular evolution and function [ 1 , 25 ], various cancers [ 23 , 24 ], and can confound studies of mitochondrial DNA heteroplasmy [ 26 , 27 ], maternal inheritance of mitochondria [ 10 , 28 – 31 ], and forensics [ 32 – 34 ].

Investigations of Numts have been conducted in numerous species, but yeast, in particular, has provided an excellent experimental platform as a model organism due to its smaller genome and fast replication timing. Mechanisms of Numt integration involve genome replication processes in several yeast species [ 1 , 2 ] and have further been linked to the yeast YME1 (yeast mitochondrial escape 1) gene and double-stranded break repair [ 35 – 37 ]. Interestingly, Numts have also been associated with chronological aging in Saccharomyces cerevisiae [ 3 ], suggesting a model where the accumulation of somatic mutations with aging [ 38 , 39 ], particularly structural genomic changes, could provide an opportunistic environment for somatic numtogenesis.

In humans, neural progenitor cells and cortical neurons harbor extensive tissue-specific somatic mutations, including single-nucleotide variants (SNVs) [ 40 – 42 ], transposable elements [ 43 – 45 ], and larger structural variants [ 46 – 48 ]. However, to date, no studies have investigated the extent of Numts specific in human brain regions, though several studies have now explored somatic numtogenesis in various cancers [ 23 , 49 ]. Using blood as the source of DNA, rare events of germline numtogenesis leading to a new Numt absent from either parent are estimated to occur every 4,000 human births and to be more frequent in solid tumors but not hematological cancers [ 4 ]. Using the observations in yeast as a foundation, we hypothesized that the accumulation of somatic mutations with age in the human brain also could be associated with numtogenesis and an increase in the number of somatically acquired (i.e., de novo) Numts. Mechanistically, numtogenesis requires the release of mitochondrial fragments into the cytoplasm and nucleus [ 50 , 51 ], where they can be integrated into autosomal sequences. In this context, we note that neuroendocrine, energetic, and mitochondrial DNA maintenance stressors in human and mouse cells trigger mitochondrial DNA release into the cytoplasm [ 52 ] and even in the bloodstream [ 53 , 54 ]. Thus, intrinsic genetic perturbations to mitochondrial biology or environmentally induced stressors could increase numtogenesis across the lifespan.

We investigated these scenarios through a multifaceted approach using postmortem human brain tissue and blood from large cohorts of older individuals, as well as a longitudinal analysis of cultured primary human fibroblasts from healthy donors and patients deficient for SURF1 , a gene associated with Leigh syndrome and cytochrome c oxidase deficiency [ 55 ] that alters oxidative phosphorylation (OxPhos). We further examined the potential role of environmental stress on numtogenesis through the treatment of these cells with oligomycin (OxPhos inhibitor) and dexamethasone (glucocorticoid receptor agonist).

Somatic Numt integration differs by tissue, age, and cognitive status

We applied our Numt detection approach, dinumt , to whole genome sequencing (WGS) data generated in the ROSMAP cohort [ 56 , 57 ] comprising 466 dorsolateral prefrontal cortex (DLPFC), 260 cerebella, 68 posterior cingulate cortex (PCC), and 4 anterior caudate (AC) tissue samples as well as non-brain tissue from 366 whole blood (WB) and 23 peripheral blood mononuclear cells (PBMCs) samples ( Methods , Fig 1A ). We note that the sequencing coverage of these samples was too low (~45×, S1 Table ) to confidently identify lower-level somatic mosaicism within any individual tissue sample, though some would still likely be detected by our approach. Our strategy instead was to examine whether there were any potential clonal mosaic events occurring predominantly in one or more tissues compared to others by filtering out all common germline Numts, with the expectation that any rare germline Numts that were not filtered out would be present at a constant rate across all tissues, age ranges, and cognitive categories and thus any differences we observe between them would be driven by somatic events.

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(A) Overview of approach to identify tissue-specific Numts in ROSMAP cohort. (B) Abundance of tissue-specific Numts across brain regions and blood cells from ROSMAP participants. (C) Effect size (Hedge’s g ) of average tissue-specific Numts relative to cerebellum. (D) Length of tissue-specific Numts across brain regions and whole blood. (E) Genic and intergenic distribution of all tissue-specific Numts versus the expected distribution in the whole genome for all samples. (F) Percentage of genic and intergenic distribution of tissue-specific Numts delineated by brain regions and whole blood. (G) Random genomic distributions of tissue-specific Numts across tissues (left), cognitive impairment stratifications (middle), and age groups (right), based on comparison to simulation data. The age groups were defined as less than 85 ( n = 295), between 85 and 93 ( n = 545), and greater than or equal to 93 ( n = 319). One-way ANOVA was used to test the significance in B and D. Pearson’s chi-square test was used to test the significance in E. Fisher’s exact test was employed instead of Pearson’s chi-square test when sample sizes are small. ***, **, and * represent a significant p -value less than 0.001, 0.01, and 0.05, respectively. DLPFC, dorsolateral prefrontal cortex; PCC, posterior cingulate cortex. NCI, no cognitive impairment; MCI, mild cognitive impairment; AD, Alzheimer’s dementia. Graphical artwork in Fig 1A were created with BioRender.com and are pursuant to BioRender’s Academic License Terms. The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.g001

We obtained 3,758 unique quality-pass Numt calls from ROSMAP 1,187 samples. Only 17 of these tissue samples were from the same individual, and thus typical somatic variant analysis using multiple tissues to distinguish germline and early somatic variation from tissue-specific events was prohibitive. To mitigate this, we cross-referenced all of our detected non-reference Numts against large population cohorts including the 1000 Genomes Project [ 6 ] and another recent study using the 100,000 Genomes Project in England [ 4 ]. This step identified 45 Numts shared between one of our samples and the population-level reference, which were filtered out ( Methods ). The remaining 3,713 Numts were then examined in aggregate across tissues to determine whether there were differences in Numt abundance between specific tissues. To identify and rectify any false positive calls that may have been triggered by potential bacterial mitochondria contamination [ 58 ], we further conducted an investigation into the length of paired-end read fragments mapped to the nuclear genome and supported Numt calls and found that in all cases ( Methods ), there were at least 150 bp of sequence anchored to human chromosomal sequences ( S1 Fig ), demonstrating that there is likely no microbiome interference in the data.

We identified a mean of 10.4 Numts per sample across all tissues, of which ~3 on average were found to be tissue-specific after filtering for germline Numt polymorphisms found in other samples, population-scale controls, or somatic Numts in other tissues, as described above. We observed no correlation between the number of Numts detected in each sample and its genomic sequence coverage (r 2 = 0.003, S1 Table ), indicating that our results are robust across a range of sequence depths [ 59 ]. The detected Numts ranged from 22 bp to 8,172 bp in length, with a median of 73 bp, a mean of 1,169 bp, and s.d. 1,882 bp ( S1 Table ), consistent with previous results from population-scale data in blood DNA [ 4 , 6 , 7 ]. We observed an average of 3.52 Numts in each whole blood tissue sample, closely comparable to the 4.9 Numts in blood samples reported in Wei and colleagues [ 4 ].

Interestingly, we found the majority of tissue-specific Numts fell within DLPFC regions (mean = 4.13 per person), representing a 5.5-fold ( p -value <0.001) higher frequency compared to the cerebellum (mean = 0.75), 2.4-fold higher than PCC (mean = 1.71, p -value <0.001), and 15.9-fold higher than PBMC (mean = 0.26, p -value <0.001) ( Fig 1B ). From the 17 individuals with multiple tissue samples comprising 9 pairs of the cerebellum and DLPFC samples, we consistently observed significant differences of tissue-specific Numts between the 2 tissues ( S2 Fig , p -value = 0.033, Student’s T test, paired, two-sided) which aligns with our larger analysis across the cohort. Relative to the cerebellum, DLPFC and AC also showed the highest effect sizes (Hedge’s g = 1.03 and 1.38, respectively) in Numt abundance, followed by PCC (g = 0.49) ( Fig 1C ). In addition, we observed a significant correlation between the mtDNA copy number (mtDNAcn) and somatic Numts in DLPFC (r 2 = 0.146, p < 0.001), but not in other tissues ( S3 Fig ). mtDNAcn was higher in DLPFC than in other brain regions and tissues (median copies per cell: DLPFC = 4,047, versus cerebellum = 999, PCC = 4,042, WB = 172, and PBMCs = 125). Interestingly, we found that the length of tissue-specific Numts in 3 brain regions (DLPFC, cerebellum, and PCC) were significantly longer than those observed in whole blood (median = 63 bp, one-way ANOVA, p -value <0.001), with PCC-specific Numts themselves exhibiting larger lengths (median = 2,477 bp) than DLPFC and cerebellum (median = 152 bp and 210 bp, one-way ANOVA, p -value <0.001, Fig 1D ). The absence of large Numts in blood immune cells could reflect negative selection against new Numts [ 4 ].

We next explored Numt integration using a gene-centric approach in the tissues with the largest number of samples (DLFPC, cerebellum, PCC, and WB). We focused on the Numts that were inserted in and around transcribed regions of the genome (introns, coding, UTR, and intergenic regions) and examined the proportion of our detected NUMTs that fell within each region. Surprisingly, we found that our somatic Numts integrated into introns at a significantly higher frequency compared to its overall expected proportion of the genome based on Ensembl gene annotations (44.58% v.s. 34.92%, p -value <0.001), while a negative enrichment was observed in intergenic regions (53.58% v.s. 63.92%, p -value <0.001, Methods, Fig 1E ), though these could be the result of differences of sequence mappability within these regions precluding the detection of Numts. These significant differences in genomic distributions were further observed across the various tissues ( Fig 1F ).

We lastly hypothesized that the genomic distribution of somatic Numt integration sites might differ within individual tissues, cognitive status, or age groups from an expected random distribution throughout the genome. We tested this hypothesis by iteratively assigning random positions for each of our observed Numts 50,000 times across the human genome reference and assessing whether our observed integration sites differed significantly when compared within predefined 10 Mb windows across the genome as a permutation test. After the multiple test correction (Benjamini–Hochberg procedure), we observed no significant difference from random for any of the tested tissues ( Fig 1G ). We further stratified our results by age and cognitive status ( Methods ) and likewise observed no differences in genomic distribution. This is in agreement with previous studies that suggest Numt integration is a random occurrence [ 6 , 7 , 11 ], though we note that the paucity of Numts may lead to the permutation test being underpowered and thus preclude an accurate assessment at such broad regions across the genome.

DLPFC-specific Numts are negatively associated with age at death in persons without cognitive impairment

On the basis of potential adverse genomic effects of Numts [ 4 , 50 , 51 ] and the results above, we hypothesized that tissue-specific Numts were associated with mortality and age at death, though this correlation might differ between tissues or clinical diagnoses. We first examined NUMTs in aggregate across tissues and observed almost no correlation in Numt abundance with the age of death ( Fig 2A ). Given the existence of mitochondrial DNA defects alterations in the human brain with cognitive decline [ 60 ], we next stratified individuals with tissue-specific Numts by their cognitive status into no cognitive impairment (NCI), mild cognitive impairment (MCI), and Alzheimer’s dementia (AD, COGDX score, Methods, S1 and S2 Tables). We found that in DLPFC tissues, NCI individuals that carried more Numts died earlier (r 2 = 0.094, p -value <0.001), with 2 additional Numt insertions observed per decade of life lost ( Fig 2B ). MCI individuals exhibited a similar but lower negative association (r 2 = 0.031, p -value <0.05). However, no correlation between Numts and age at death was observed in the AD group (r 2 = 0.009, p -value = 0.19). In the cerebellum, we observed similar patterns of correlation between Numts and age at death among cognitive groups, albeit with weaker correlations (NCI: r 2 = 0.044, p -value <0.05; MCI: r 2 = 0.007, p -value = 0.514; and AD: r 2 = 0.045, p -value <0.05, S4 Fig ). These results indicate that Numts are negatively associated with age at death in certain brain regions of non-AD individuals, suggesting the possibility that brain numtogenesis is deleterious and that the pathogenicity of AD may be uncoupled from age-dependent Numt integration.

(A) Correlation between age at death and abundance of DLPFC-specific, cerebellum-specific, and PCC-specific Numts, respectively. (B) DLPFC samples correlated with age at death, stratified by cognitive diagnosis status as NCI ( n = 121, left), MCI ( n = 112, middle), and AD ( n = 176, right). Data points are colored by arbitrary age groups (see Methods ) in light yellow, orange, and brown, respectively. r 2 and p -values are calculated using standard least-squares regression models. DLPFC, dorsolateral prefrontal cortex; PCC, posterior cingulate cortex. NCI, no cognitive impairment; MCI, mild cognitive impairment; AD, Alzheimer’s dementia. The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.g002

Somatic Numts accumulate over time in fibroblasts

The cross-tissue analysis of the ROSMAP cohort provided compelling results that Numts have both tissue and age-dependent characteristics of their integration in aggregate. However, the lack of relationships between individual tissue samples prohibits a direct measurement of Numt integration rates or numtogenesis. We therefore tested the dynamic transfer of Numts using a longitudinal, repeated-measures WGS study in primary human fibroblasts cultured in vitro under physiological conditions ( Table 1 ) [ 5 , 61 – 63 ]. Over time, replicating cells exhibit conserved epigenomic (hypomethylation), telomeric (shortening), transcriptional (senescence-associated markers), and secretory (pro-inflammatory) features of human aging, representing a useful model to quantify the rate of dynamic age-related molecular processes in a human system [ 5 ]. We recently showed that primary mitochondrial bioenergetic defects accelerate the rate of aging based on the telomere shortening per cell division, DNA methylation clocks, and age-related secreted proteins [ 63 ]. Therefore, using this model to monitor the accumulation of overall and donor-specific unique Numts absent in the general population, we analyzed cultured fibroblasts from 3 unrelated healthy donors, aged in culture under physiological conditions for up to 211 days [ 5 ] ( Fig 3A ). Instead of focusing on a single cell line tested in triplicates, we opted to include 3 separate donors, which provides a more robust test of our hypothesis.

https://doi.org/10.1371/journal.pbio.3002723.t001

(A) Study design of cellular aging model using primary fibroblasts from 3 healthy donors. (B) Cell line-specific Numts accumulate over time in aging fibroblasts obtained from 3 healthy donors cultured up to 211 days. (C) Heatmap of slopes based on the linear regression between days cultured and the cell line-specific calls, including Numts (from Dinumt, left) and structural variants (from DELLY, right). (D) Time-course of cell line-specific deletions in the 3 healthy donors. Graphical artwork in Fig 3A were created with BioRender.com and are pursuant to BioRender’s Academic License Terms. The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.g003

Across the 3 donors, we observed a positive correlation between time in culture and the number of unique Numts (r 2 = 0.30, 0.38, and 0.59, respectively) with a positive slope range of 0.08 to 0.13 Numts/day. Thus, on average, human fibroblasts accumulated a novel Numt every 12.6 days of culture (or 0.79 Numt per 10 days, 95% C.I. = 0.28 to 1.31). Numtogenesis was also evident when delineating our Numts per donor into total Numts observed at each time point (total Numts) and Numt insertions specific to individual cell lines with treatment within each donor (cell line-specific Numts) (Figs 3B and S5 ).

To determine if global genomic instability could account for this effect, we conducted the same analysis on multiple types of somatic structural variation (e.g., deletion, duplication, inversion, insertion, and breakends; Methods ). The accumulation rates of these variant types were significantly lower compared to the rate of numtogenesis ( Fig 3C ). For example, although we observed a positive yet more moderate increase in deletion abundance compared to Numts when considering all such variants, we did not observe the same increase in the cell line-specific deletions as with the somatic Numts ( Fig 3D ). These results indicate that Numt insertions occur at a higher rate than autosomal deletions in this system and suggest a higher rate of age-related somatic numtogenesis rate than the other genetic variants ( S6 Fig ). We further observed no significant increase in cell line-specific genomic duplications over time, thus indicating that the increase in the total number of Numts over time is likely due to novel integration events and not duplications of preexisting copies.

We questioned whether the accumulation of these apparently somatic Numts could be driven by the simple clonal expansion of few Numts-containing cells. Even within a given donor line followed longitudinally, all observed Numts were unique in their length and sequence (average 562 bp, s.d. 1,400 bp) and showed no evidence of relatedness with one another. This lack of sequence overlap is most parsimoniously explained by the random nature of our sequencing coverage (sequencing depth: 25×; the total number of genomes in each experiment 2 × 10 6 diploid genomes) and a large number of new Numts accumulating over time. Thus, the unique identity of all observed Numts in these in vitro experiments argues against the clonal origin of these events.

Impact of environmental and genetic stress on somatic Numt integration rates

We next explored whether the cellular environment could impact somatic numtogenesis by testing if chronic exposure to a stress-mimetic or an inhibitor of mitochondrial OxPhos would alter the rate of Numt accumulation in otherwise healthy and aging fibroblasts. We analyzed human fibroblasts derived from the same 3 healthy donors described above that were treated with (a) the glucocorticoid receptor agonist dexamethasone (Dex, 100 nM); and (b) the ATP synthesis inhibitor oligomycin (Oligo, 100 nM). Similar to the untreated donors (see Methods , r 2 = 0.30, p -value = 0.004 linear regression), both treatment groups exhibited an accumulation of new Numts over time (see Methods , linear regression for Dex group r 2 = 0.59, p -value <0.001; for Oligo group r 2 = 0.22, p -value = 0.052). Compared to the untreated group (0.79 Numt per 10 days, 95% C.I. = 0.28 to 1.31), Dex and Oligo treatments tended to increase the rate of numtogenesis to 1.07 Numt (95% C.I. = 0.65 to 1.49) and 2.15 Numt (95% C.I. = −0.02 to 3.27) per 10 days, respectively ( Fig 4A, 4B and 4D ). Although these differences in effects did not reach statistical significance, the accumulation of Numts over time in these biologically independent experiments from those above further document Numtogenesis in aging human cells in vitro.

(A) Time course of Numt accumulation in healthy donors 1–3 and same cells cultured in dexamethasone (Dex) mimicking chronic Dex exposure. (B) Time course of Numt accumulation in healthy donors 1–3 and same cells cultured in oligomycin (Oligo). (C) Numt accumulation time course in the patient fibroblasts with SURF1 gene defect (Patient 1–3) and the ones from 3 healthy donors (Donor 1–3). (D) Comparison of Slopes derived from all patients untreated, Dex-treated, oligo-treated, and SURF1 gene defect. Numt counts for each group were normalized by the median value. A linear regression analysis was performed to derive the rate of the Numt accumulation and calculate the slopes in each group. ANOVA test was used to test the significance between the slopes of untreated donors and the ones of treated donors or patients in the hypothesis that pharmacological or genetic perturbation would increase the accumulation of Numts. ***, **, and * represent a significant p -value less than 0.001, 0.01, and 0.05, respectively, and ns represents non-significance. The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.g004

Using a genetic approach, we further tested whether defects in mitochondrial OxPhos associated with mtDNA instability are sufficient to alter the rate of numtogenesis. We analyzed data from a similar fibroblast culture system in 3 patient-derived fibroblasts with SURF1 mutations (Patient 1–3) [ 5 ]. Mutations in SURF1 represent one of the most frequent causes of cytochrome c oxidase and OxPhos deficiency in humans [ 64 ], and we recently showed that these SURF1 -mutant fibroblasts accumulate large-scale mtDNA deletions over time in culture, demonstrating mtDNA instability in this model. In these independent donors, we again observed the accumulation of new Numts over time (regression r 2 = 0.64, p -value <0.001). Strikingly, the rate of Numtogenesis in SURF1 -mutant cells was 3.71 Numt per 10 days (95% C.I. = 2.24 to 5.18), in contrast to the rate of 0.79 Numt per 10 days in the healthy donors (4.7-fold of control, p -value = 8.20E-05, ANOVA test, Fig 4C and 4D ). As in tumors [ 23 , 24 ] and yeast [ 1 , 2 ], this result further documents Numtogenesis as an inter-genomic event occurring in human cells over relatively short timescales, and establishes, using patient-derived cells with mtDNA instability, the modifiability of the rate of numtogenesis in vitro [ 63 ].

The transfer of mitochondrial DNA into the nuclear genome of eukaryotes occurs in the germlines of various eukaryotes, suggesting that the endosymbiotic event initiated 1.5 billion years ago is still ongoing [ 4 ]. However, the extent and impact of somatic Numt insertions in specific human tissues have remained elusive outside of cancerous environments. Here, we provide some of the first evidence of the somatic nuclear accumulation of Numts in both healthy and impaired human brain tissue across different age ranges. We found that specific brain regions harbor more somatic insertions than others, in line with previous studies of other types of genomic variation [ 46 – 48 ], and that these rates do differ with the degree of cognitive impairment. We further extend these observations in a longitudinal study of primary human fibroblasts under various environmental and genetic conditions, documenting variable rates of ongoing numtogenesis in dividing human cells.

Our data provide new information concerning the rate of numtogenesis in humans. While the cross-sectional study of postmortem human brains does not allow us to draw conclusions about the rate of Numt transfer, the higher frequency of Numts in postmitotic brain tissue—relative to the commonly studied genomic material from blood—indicates a greater number of Numts. We further observed a significant correlation with mtDNA copy number in these samples, suggesting a potential biological mass action mechanism whereby higher mtDNAcn results in more potential transfers to the nuclear genome ( S3 and S7 Figs). On the other hand, our longitudinal in vitro studies allow us to measure Numts among the same cell population (i.e., “individual”) over time as cells accumulate age-related molecular hallmarks of aging. In this system, cells divide approximately every 40 h (1.7 days) when they are young (from days 0 to 80) and slow their replication rate dramatically towards the end of life when they undergo less than 1 replicative event per month (see growth curves in [ 65 ]). Although the temporal resolution of our trajectories is limited by the number of time points across the lifespan of each cell line (on average 7), our data suggests that the rate of numtogenesis is roughly linear across the wide range of replication rate, and therefore, more dependent on time rather than the rate of replication. This observation aligns with the results in cortical brain tissue (DLPFC), a postmitotic tissue where cell division is expected to be minimal to absent. This result is unlikely explained by clonality as all discovered Numts are unique. Consistent with the ROSMAP tissue data suggesting the accumulation of somatic Numts detected at very low variant allele frequency (VAF) in genomic material (median = 4.4%, mean = 5.3% ± 3.5%, with 99.7% of total number VAF <35%, S8 Fig ), the VAF distribution for somatic Numts in fibroblasts was comparably low (median = 9.1%, mean = 13.1% ± 7.4%, with 98.3% of total number VAF <35%, S8 Fig ). In cultured fibroblasts, each cell population contains between 1 and 5 million cells, meaning 2 to 10 million genomic copies. At 25× coverage, we therefore sample 0.00125% to 0.00025% of all copies. Our sampling is therefore essentially random. If the accumulation of Numts in aging fibroblasts was driven by clonality, we would see an increase in abundance of a few Numts present at each passage in the same donor, rather than an increased frequency of unique Numts at each time point, as observed here.

At least 2 main findings from our results and that of others suggest that de novo Numts may be functionally meaningful. First, strong evidence that detectable Numts are excluded from coding DNA sequences and instead preferentially integrate within intergenic regions [ 4 , 6 – 8 ] suggest that they are under some functional constraint during development. In contrast, tumors frequently contain Numts within genes, and Numts may even contribute to oncogenesis, which in this case would drive positive selection in tumors [ 4 ]. Numts have also been implicated previously in several diverse disorders [ 66 – 71 ]. Second, Numts prevalence correlates with the somatic selection pressure for adverse mitochondrial genomic changes. In the replicating blood immune compartment of the bone marrow, high selection pressure occurs and eliminates mtDNA mutations over time [ 72 , 73 ], whereas de novo mtDNA defects accumulate at high levels in postmitotic tissues such as skeletal muscle, heart, and in the brain [ 74 ]. Similarly, it is possible that Numt insertions which negatively affect cell fitness in the lymphoid or myeloid lineages of the bone marrow are outcompeted and eliminated from the cell pool (or exist at low levels and are not sampled during blood draw), compared to somatic tissues where the same potentially deleterious Numt insertions in postmitotic cells cannot readily be outcompeted and eliminated without functionally compromising the tissue.

Some limitations of our study should be noted. While our human multi-tissue study includes >1,000 individuals, given the low number of Numts per person, we are likely underpowered to draw definitive conclusions around the cartography of Numts, such as potential Numts hotspots in the nuclear genome. The report by Wei and colleagues in 66,083 human genomes robustly addresses this point [ 4 ]. The absence of WGS data from multiple tissues in the same individuals also precludes direct comparisons of the specific rate of numtogenesis between tissues. This question could be addressed in other studies (e.g., GTEx) by sequencing dozens of tissues from the same individuals, but the current lack of such data set precludes a robust analysis of this kind. In relation to our longitudinal cellular studies with genetic and pharmacological mitochondrial OxPhos defects, the marginal increase in cell death when OxPhos is disrupted [ 63 ] or with chronic glucocorticoid stress [ 75 ] could offer a route of negative selection that eliminates deleterious de novo Numts; the most compromised cells die, cleansing the cell population of new variants. If this effect was strong, it would make numtogenesis undetectable to WGS, or reduce the observed effect size of the rate of numtogenesis in both our brain and fibroblast studies. This possibility could be addressed in future studies by systematically sequencing cellular debris or dead cells from the culture medium. Thus, while our positive results conclusively establish the dynamic nature of Numts transfer in healthy and stressed human cells, the magnitude reported across donors (range of 0.79 [controls] to 3.71 [ SURF1 -mutant] Numts every 10 days) may reflect a lower bound, and the rates of numtogenesis in cells under stress should be interpreted with caution. Validating the dynamic nature of numtogenesis across the lifespan in humans would require repeated measures, longitudinal WGS of postmitotic tissues (e.g., muscle biopsies), with the caveat that the same exact cells likely cannot be repeatedly biopsied, and therefore that new Numts would be missed. For the reasons mentioned above (negative selection in the immune compartment), repeated WGS in blood would likely underestimate the true rate of in vivo mito-nuclear genomic transfer.

We also considered if the increase in Numts over time could be driven by the clonal expansion of Numt-containing cells, which would suggest that there is no active transfer of mtDNA and numtogenesis in this model. Here, our observed increase in unique Numts over time suggests that these are not the product of clonal expansion, which also is consistent with the somatic Numts in ROSMAP samples, and in Wei and colleagues [ 4 ]. Several possible theories have been advanced for numtogenesis, with several studies showing environmental effects of ionizing radiation [ 76 , 77 ] or mitochondrial reactive oxygen species [ 3 , 78 ] causing double-strand breaks associated with Numt accumulation. However, there have been few mechanistic studies in this area to conclusively determine the precise process. What is clear is that for such events to happen, whole or fragments of mtDNA must come in close proximity to the autosomal genes. There are now several reports of cytoplasmic mtDNA release where the mtDNA is released in “free form” and able to bind DNA-sensing receptors such as cGAS [ 50 , 79 – 81 ]. In fact, partial mitochondrial permeabilization that may lead to cytoplasmic mtDNA release triggers nuclear genomic instability [ 82 ], which could theoretically open the door ongoing to numtogenesis, possibly independent of cell division and genome replication.

In conclusion, our results demonstrating the high prevalence of non-germline Numts in hundreds of human brains and their negative association with age at death suggests that numtogenesis occurs across the human lifespan and that they may have deleterious health effects. Using a longitudinal in vitro human system, we establish that primary human fibroblasts accumulate Numts over time and that numtogenesis may be accelerated by some stressors, in particular, SURF1 defects associated with mtDNA instability. These findings build and extend previous evidence that numtogenesis is active in the human germline and can have deleterious genomic, cellular, and health effects on the host organism. The active transfer of mtDNA sequences to the nuclear genome adds to the vast repertoire of mito-nuclear communication mechanisms [ 83 ] that shape human health.

Materials and methods

Ethics statement.

The ROS and MAP studies were approved by the Institutional Review Board of Rush University Medical Center, protocols #L91020181 (ROS), L86121802 (MAP), and #L99032481 (RADC repository). All participants signed an informed consent, Anatomical Gift Act, and a repository consent to share data and biospecimens. Patient-derived fibroblasts were utilized from the previous study and were approved through the Columbia University Irving Medical Center IRB #AAAB0483 in a previous publication [ 5 ]. All human studies were conducted according to the principles expressed in the Declaration of Helsinki.

ROSMAP cohort

Study participants..

The Rush Memory and Aging Project (MAP) and the Religious Orders Study (ROS) [ 56 , 57 ] are 2 ongoing cohort studies of older persons, collectively referred to as ROSMAP. The ROS study enrolls older Catholic nuns, priests, and brothers, from more than 40 groups across the United States. The MAP study enrolls participants primarily from retirement communities throughout northeastern Illinois. Participants in both cohorts were without known dementia at study enrolment and agreed to annual evaluations and brain donation on death.

The clinical diagnosis of AD proximate to death was based on the review of the annual clinical diagnosis of dementia and its causes by the study neurologist blinded for postmortem data. Postmortem Alzheimer’s disease pathology was assessed as described previously [ 84 , 85 ] and Alzheimer’s disease classification was defined based on the National Institutes of Ageing-Reagan criteria [ 86 ]. Dementia status was coded as NCI, MCI, or AD from the final clinical diagnosis of dementia and the NIA Reagan criteria as previously described [ 87 – 89 ].

Data processing.

We obtained and processed WGS samples from these cohorts through the NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) data set NG00067. In brief, we obtained sequence data for 1,187 tissue samples comprising 466 DLPFC, 260 cerebella, 68 PCC, 4 AC, 366 WB samples, and 23 PBMCs. We obtained these 1,187 samples from 1,170 individuals, where only 17 participants in the data set contributed 2 tissue different samples to the sequencing data ( S1 and S3 Tables). All sequencing data were provided in CRAM format and aligned to the human genome reference (GRCh37) with an average read depth of 45×. Based on the clinical information of age, we also stratified all 1,187 samples into 3 age groups with a roughly similar sample size based on their percentiles (30%ile, 40%ile, and 30%ile) when fit to a Gaussian distribution: (a) samples died at an age younger than 85; (b) age at death older than or equal to 85, and less than 93; and (c) age at death older than or equal to 93 ( S2 Table ). All the alignment statistics are presented in S1 Table , along with the clinical characteristics of the study participants.

In vitro fibroblast aging model

Fibroblast collection and passaging..

We further made use of processed WGS generated from a recent study of aged primary human dermal fibroblasts [ 5 ]. In brief, primary human dermal fibroblasts were obtained from distributors or our local clinic from 3 healthy and 3 SURF1 -patient donors. Fibroblasts were isolated from biopsy tissue using standard procedures. Cells were passaged approximately every 5 days (+/− 1 day). Study measurements and treatment began after 15-day culture to allow for adjustment to the in vitro environment. Treatment conditions for healthy controls include the chronic addition of 1 nM oligomycin (oligo) to inhibit the OxPhos FoF1 ATP synthase and 100 nM dexamethasone (Dex) to stimulate the glucocorticoid receptor as a model of chronic stress [ 75 , 90 ]. Time points collected vary by assay, with an average sampling frequency of 15 days and 4 to 10 time points for each cell line and condition. Individual cell lines were terminated after exhibiting less than 1 population doubling over a 30-day period, as described in [ 5 ].

Whole-genome sequencing and processing.

Whole-genome sequencing data were performed in the lifespan samples at each time point (overall 85 time points). Paired-end reads were aligned to the human genome (GRCh37) using Isaac (Isaac-04.17.06.15) [ 91 ]. Samtools (Ver1.2) [ 92 ] and Picard Toolkit ( https://broadinstitute.github.io/picard/ ) were further used to process the aligned bam files and mark duplicates. The average read depth from the WGS and other alignment statistics used in this study can be found in S4 Table .

Population-scale WGS control data

We leveraged 2,504 independent individuals from the 1000 Genomes Project Phase 3 to serve as population-level controls. Samples were sequenced by 30× Illumina NovaSeq ( https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/ ) [ 93 ], and the data were archived in CRAM format with the GRCh38 reference ( https://ftp-trace.ncbi.nih.gov/1000genomes/ftp/technical/reference/GRCh38_reference_genome/ ). We also used Numts reported in the 100,000 Genomes Project in England [ 4 ] as population-level controls.

Detection of non-reference Numts

We applied an updated version of Dinumt [ 6 , 7 , 94 ] to identify non-reference Numts across the different sequenced cohorts. Dinumt is an established software that was first used in the 1000 Genomes Project and validated by orthogonal methods, including PCR and Sanger sequencing [ 6 ], and long-read sequencing [ 7 ]. Briefly, Dinumt identifies aberrant/discordant reads aligning with either the mtDNA or the reference Numts on one end and map elsewhere in the genome on the other end, read orientation, and various other filters to define insertion breakpoints. Reads are discarded if they do not align uniquely to the nuclear genome and have a mapping quality (MAPQ) of less than 10. Identified insertions are then filtered for quality using a Phred scale (≥50), a cutoff of supporting reads (≥4), and a cutoff of read depth (≥5×) around the insertion point. We built the first set of Numts as populational and polymorphic controls from the 2,504 individuals of the 1KG Project recently re-sequenced to high coverage [ 93 ]. Individual non-reference Numt callsets were resolved and merged into a single VCF file using the merging module of Dinumt. All “PASS” Numts are lifted over [ 95 , 96 ] from GRCh38 to GRCh37 for the downstream analysis. Dinumt was used to identify non-reference Numts across the individual sequences from 1,187 samples in ROSMAP or 85 cell line genomes in the lifespan model. The same criteria were conducted in the pipelines ( Fig 1A ). The VAF of each Numt call was calculated based on the number of supporting reads reported by Dinumt divided by the number of overall read coverage in the sequenced genome. In addition, the length distribution of paired-end read fragments mapped to the nuclear genome and supported Numt calls were calculated to investigate the potential bacterial mitochondria contamination.

We then cross-referenced all of our detected non-reference Numts against large population cohorts including the 1000 Genomes Project and Numts reported from 66,083 genomes in the 100,000 Genomes Project in England [ 4 ]. In each case, we considered a Numt detected in our analysis as a germline polymorphic insertion if it fell within +/− 50 bp of a Numt reported in either of these studies.

We identified tissue/cell line-specific Numt insertions using the identified non-reference Numts. Tissue-specific Numts were derived from the ROSMAP callset, and cell line-specific calls were derived from the Numt callset of fibroblast lifespan data. Numts from each sample were first merged into an aggregated set for these 2 callsets. We then extracted all non-reference Numts that were found in only 1 specific tissue across all samples or cell line, respectively ( S1 and S4 Tables). All analysis pipelines and the command lines for running Dinumt can be found at https://github.com/mills-lab/numts-and-aging-in-fibroblasts-and-brains [ 97 ].

Statistical analysis

Slopes ( β 1 ) were compared by ANOVA test separately between 2 categories. All statistical analyses were performed in R 4.0.5.

Genomic analyses for non-reference Numts

Numt hotspots across chromosomes..

We delineated the entire nuclear genome into 10 Mbp bins, resulting in an average of 10 detected Numts per bin. The frequency for tissue-specific Numts from ROSMAP in each bin was calculated. We performed a permutation analysis by randomly shuffling the genomic positions of each observed Numt 50,000 times to determine any hotspots across genome bins compared to the real data. An empirical p -value was calculated for all the bins based on the frequency of Numt ranking in simulation data. A multiple test correction (Benjamini–Hochberg) was further conducted to decrease the false discovery rate. A bin with a p -value less than 0.05 after the adjustment was defined as a significant hotspot. A Z-score is calculated for normalizing Numt count, which measures the deviation of the Numt count in each 10 Mbp bin from the genome-wide average across all the bins. We stratified the tissue-specific Numts into different tissues, cognitive impairment levels, and age groups to perform the hotspot analysis separately.

Genomic content analysis and functional annotation.

We conducted the genomic content analysis for non-reference Numt insertions. We calculated the genic distribution for the tissue-specific Numts from ROSMAP. Gene track (GRCh37) was obtained from Ensembl Genome Browser ( https://grch37.ensembl.org/ ). Parameters for protein-coding regions, transcriptomes, and exons were calculated based on a previous report [ 98 ]. Pearson’s chi-square test and Fisher’s exact test were used to assess the statistical significance of the discrepancies between the observed and expected distributions of Numts within genic or intergenic regions. We compared 2 × 2 categorical variables, specifically the proportion of Numts within a particular genomic region against the one in the rest of the genome, with respect to either the observed or expected data. GC content and repeat sequence analyses were carried out both in the set of cell line-specific Numt insertions from the lifespan model and polymorphic Numts from the 1000 Genomes project. GC content and repeat sequence were downloaded from the GC content table and RepeatMasker track in the UCSC Genome Browser ( https://genome.ucsc.edu/ ). Gene mapping was carried out by AnnotSV ( https://lbgi.fr/AnnotSV/ ) [ 99 ] to determine the genes that were potentially affected by the tissue-specific Numts from ROSMAP ( S5 Table ) or cell line-specific Numts from the lifespan model ( S6 Table ).

Detection of structural variation (SV)

Background structural variations (SVs) were detected in the data of the 1000 Genomes Project, ROSMAP, and lifespan model. We used an integrated non-reference SV callset from the 1000 Genomes Project as the control in the project to filter out potential non-somatic SVs at the population level. It was derived from 13 callers and can be obtained from http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage_SV/working/20210104_JAX_Integration_13callers/ . Delly2 (Version 0.8.5) [ 100 ] was applied to resolve non-reference SVs (including deletions, duplications, insertions, inversions, and translocations), and MELT (Version 2.1.4) [ 101 ] was used to identify a specific type of non-reference SVs, mobile element insertions (MEIs, including Alus, LINE-1s, and SVAs), in the sequenced genomes of lifespan experiments. Manta [ 102 ] and Canvas (Version 1.28.0) [ 103 ] were also applied to resolve non-reference SVs in the sequenced genomes of lifespan experiments. The same pipeline used in Numts was implemented to identify tissue-specific or cell line-specific SVs/MEIs among the ROSMAP and lifespan samples ( S1 and S4 Tables).

Mitochondrial DNA copy number

We calculated the mtDNAcn using autosomal coverage with the following formula: mtDNAcn = (cov MT / cov autosomal ) × 2 [ 60 ] in both the ROSMAP data set and lifespan study. The median sequence coverages of the autosomal chromosomes covnuc and of the mitochondrial genome covmt were calculated using R/Bioconductor (packages GenomicAlignments and GenomicRanges). We filtered out the reads with MAPQ = 0 in the analysis. Ambiguous regions were excluded using the intra-contig ambiguity mask from the BSgenome package. The mtDNAcn was z-standardized within each brain region and DNA extraction kit and then logarithmized. The normalization facilitated the combined analysis of the 2 different kits used for the DLPFC and resulted in approximately normal mtDNAcn measures [ 60 ]. To note, using either the median or mode will not have a noticeable impact on the mtDNA copy number estimates and downstream analyses ( S9 Fig ). R and shell scripts used for mtDNA analysis are deposited at GitHub: https://github.com/cu-ctcn/mtDNA [ 60 ].

Cellular mtDNA content was also quantified by qPCR on the same genomic material used for other DNA-based measurements in the ROSMAP and lifespan studies. Duplex qPCR reactions were performed to simultaneously quantify mitochondrial (mtDNA, ND1) and nuclear (nDNA, B2M) amplicons, details of which can be found in the previous studies [ 5 , 104 ]. We observed a significant correlation between qPCR and WGS method in terms of mtDNAcn. Spearman correlation analysis between mtDNAcn measures from qPCR versus WGS is presented across cell lines by treatment ( S10 Fig ).

Supporting information

S1 table. meta table for rosmap sequencing data, including alignment statistics, clinical information, and variant numbers..

https://doi.org/10.1371/journal.pbio.3002723.s001

S2 Table. Sample counts in different age groups (age at death) and cognitive status across main tissues in ROSMAP.

https://doi.org/10.1371/journal.pbio.3002723.s002

S3 Table. The list of 17 participants contributing 2 tissue samples in the ROSMAP with sample ID, information, and numbers of detected somatic Numt.

https://doi.org/10.1371/journal.pbio.3002723.s003

S4 Table. Meta table for lifespan sequencing data, including alignment statistics, experimental information, and variant numbers.

https://doi.org/10.1371/journal.pbio.3002723.s004

S5 Table. Meta table for gene annotation of tissue-specific Numts and Numt callset from ROSMAP.

https://doi.org/10.1371/journal.pbio.3002723.s005

S6 Table. Meta table for gene annotation for cell line-specific Numts and Numt callset from the lifespan model.

https://doi.org/10.1371/journal.pbio.3002723.s006

S1 Fig. Length distribution of the fragments in paired-end reads mapped to the nuclear genome.

Left, 2 × 151 bp in ROSMAP and right, 2 × 149 bp in the lifespan WGS. The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.s007

S2 Fig. Boxplots of Numt count in matched cerebellum and DLPFC samples from n = 9 individuals.

P -value = 0.033, Student’s T test, paired, two-sided. Samples are shown in jittered points. The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.s008

S3 Fig. MtDNA copy number association with Numt count and age at death by tissue and cognitive impairment in ROSMAP cohort.

Correlation between mtDNA copy number and Numt count in all ROSMAP samples (A), DLPFC (B), cerebellum (C), PCC (D), whole blood (E), and PBMC (F), respectively. (G) Correlation between mtDNA copy number and age at death in all ROSMAP samples, cerebellum, PCC, whole blood, and PBMC, respectively. (H) Correlation between mtDNA copy number and age at death in DLPFC and 3 cognitive groups in DLPFC, respectively. r 2 and p -values are calculated using standard least-squares regression models. The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.s009

S4 Fig. Cerebellum-, PCC-, and whole-blood-specific Numts are not associated with the age of death or cognitive status.

(A) Cerebellum samples correlated with age at death, stratified by cognitive diagnosis status. (B) PCC samples correlated with age at death, stratified by cognitive diagnosis status. (C) Whole-blood samples correlated with age at death, stratified by cognitive diagnosis status. Data points are colored by arbitrary age groups (see Methods ) in light yellow, orange, and brown, respectively. r 2 and p -values are calculated using standard least-squares regression models.

https://doi.org/10.1371/journal.pbio.3002723.s010

S5 Fig. Common fibroblast Numts are not abundant across the lifespan and are not associated with age.

(A) Numts shared between cell lines (donors) are not significantly correlated with aging. (B) Slopes from cell line-specific Numts and shared Numts in the lifespan model. The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.s011

S6 Fig. Background somatic SVs and MEIs during aging in primary human fibroblasts.

(A) Heatmap of slopes based on the linear regression between days cultured and the cell line-specific Numts (from Dinumt). (B) Heatmap of slopes based on the linear regression between days cultured and the cell line-specific MEIs (from MELT). (C) Heatmap of slopes based on the linear regression between days cultured and the cell line-specific SVs (from DELLY).

https://doi.org/10.1371/journal.pbio.3002723.s012

S7 Fig. MtDNA copy number (mtDNAcn) association with Numt count and days cultured in lifespan model.

(A) Correlation between mtDNA copy number and Numt count in 3 treatment groups and SURF1 defect group, respectively. (B) Correlation between mtDNA copy number and the number of days cells were cultured (Days cultured) in 3 treatment groups and SURF1 defect group, respectively. r 2 and p -values are calculated using standard least-squares regression models. The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.s013

S8 Fig. Variant allele frequency of non-reference Numts in ROSMAP and lifespan study.

The Numts were categorized into germline ones (red), which overlapped with 1KG and Wei and colleagues callset and shared between tissues (ROSMAP, left) or cell lines (lifespan study, right), and potential somatic ones (green), which are tissue-specific (ROSMAP, left) or cell line-specific (lifespan study, right). The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.s014

S9 Fig. Effect of using the mean, median, or mode of the coverage on mtDNAcn estimates.

Each dot represents one of the 455 ROSMAP samples from the dorsolateral prefrontal cortex (DLPFC). The black line indicates the diagonal and the blue line represents a linear regression line. The Pearson correlation is displayed in the top left corner. Scatterplots (A–C) depict (A) autosomal coverage, (B) MT coverage, and (C) mtDNAcn using the median (x-axis) versus the mean (y-axis). Scatterplots (D–F) depict (D) autosomal coverage, (E) MT coverage, and (F) mtDNAcn using the median (x-axis) versus the mode (y-axis). The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.s015

S10 Fig. mtDNA copy number measures by qPCR and WGS are comparable in control and stressed fibroblasts during lifespan.

Three donors in each group are merged for analysis. R-squared values and p -values are calculated using standard linear regression models. The data underlying this figure can be found in S1 Data .

https://doi.org/10.1371/journal.pbio.3002723.s016

S1 Data. Data underlying Figs 1B–1F , 2A, 2B, 2G , 3B, 3D and 4A–4D , S1 , S2 , S3 , S4A–S4C , S5 , S7 , S8 , S9A–S9F and S10A–S10D .

https://doi.org/10.1371/journal.pbio.3002723.s017

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• Front Plant Sci

Response and adaptation of photosynthesis, respiration, and antioxidant systems to elevated CO 2 with environmental stress in plants

1 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China

Yanling Jiang

Guangsheng zhou.

2 Chinese Academy of Meteorological Sciences, Beijing, China

It is well known that plant photosynthesis and respiration are two fundamental and crucial physiological processes, while the critical role of the antioxidant system in response to abiotic factors is still a focus point for investigating physiological stress. Although one key metabolic process and its response to climatic change have already been reported and reviewed, an integrative review, including several biological processes at multiple scales, has not been well reported. The current review will present a synthesis focusing on the underlying mechanisms in the responses to elevated CO 2 at multiple scales, including molecular, cellular, biochemical, physiological, and individual aspects, particularly, for these biological processes under elevated CO 2 with other key abiotic stresses, such as heat, drought, and ozone pollution, as well as nitrogen limitation. The present comprehensive review may add timely and substantial information about the topic in recent studies, while it presents what has been well established in previous reviews. First, an outline of the critical biological processes, and an overview of their roles in environmental regulation, is presented. Second, the research advances with regard to the individual subtopics are reviewed, including the response and adaptation of the photosynthetic capacity, respiration, and antioxidant system to CO 2 enrichment alone, and its combination with other climatic change factors. Finally, the potential applications for plant responses at various levels to climate change are discussed. The above issue is currently of crucial concern worldwide, and this review may help in a better understanding of how plants deal with elevated CO 2 using other mainstream abiotic factors, including molecular, cellular, biochemical, physiological, and whole individual processes, and the better management of the ecological environment, climate change, and sustainable development.

Introduction

The major components of climate change include elevated atmospheric carbon dioxide concentrations (elevated CO 2 ), warming, and altered precipitation patterns, as well as their interactions within and with other environmental factors ( IPCC, 2013 ). Based on updated information, with increases in global atmospheric CO 2 concentrations of 43% from the pre-industrial level of 280 μmol mol -1 in 1750 to the present level of 400 μmol mol -1 (an annual increase of 1.35%), the global CO 2 concentration has increased by about 1.55 ppm CO 2 per year over the past 55 years. It continues to be elevated at an unprecedented pace of ∼1.0 μmol mol -1 per year, as a result of the further increase in the cumulative emissions of CO 2 to the atmosphere during the 21st century (400 μmol mol -1 in 2011 vs. 936 μmol mol -1 in 2100; IPCC, 2013 ; NASA, 2014 ). Meanwhile, the global mean surface temperature is expected to increase by 2.6–4.8°C by the end of the 21st century (2081–2100), relative to the 1986–2005 level under RCP8.5, based on a more undisciplined management scenario with higher greenhouse gas emissions ( IPCC, 2013 ). The climate changes, such as elevated CO 2 , rising temperature, and altered precipitation, have resulted in drastic impacts on the natural ecosystems, such as in vegetation function, sustainable food production, and crop yields ( Lobell et al., 2011 ; Peñuelas et al., 2013 ; Ruiz-Vera et al., 2013 ; Xu et al., 2013a , 2014 ; Lavania et al., 2015 ), leading to more profound impacts when the climate changes are combined with other environmental constraints, such as air pollution, nutrition limitation, and their interactions ( Gillespie et al., 2012 ; Peñuelas et al., 2012 ; Xu et al., 2013a , b ; Wang et al., 2015 ).

Herein, we focus on the critical biological processes of plants with regard to climate change, including (mainly) photosynthesis, respiration, the antioxidant system, and the related metabolic activities. Photosynthesis and respiration are two fundamental physiological processes of plants, because the former involves initial carbon fixation, light energy transfer, and oxygen release, and the latter works on carbon efflux, energy production, and the relevant substrate metabolisms, such as those providing the carbon skeleton. They play a critical role in balancing the carbon budget and maintaining the carbon sink in terrestrial ecosystems, as well as in the response and feedback to climate change ( Melillo et al., 1993 ; Prentice et al., 2001 ; Sage, 2004 ; Long et al., 2006 ; Atkin et al., 2010 ; Atkin, 2015 ). The excessive accumulation of reactive oxygen species (ROS) often occurs in plants grown under abiotic stress, while an enzymatic and non-enzymatic antioxidant defense system may work to protect plants against oxidative stress-induced damage, which can be affected by climate change (such as elevated CO 2 , drought, and heat waves; Pérez-López et al., 2009 ; Gill and Tuteja, 2010 ; Xu et al., 2014 ; Zinta et al., 2014 ; Way et al., 2015 ).

Rising CO 2 has affected almost all crucial biological processes, including photosynthesis, respiration, and antioxidant systems, as well as other key secondary metabolisms in plants ( Poorter et al., 1997 ; Long et al., 2004 ; Matros et al., 2006 ; Peñuelas et al., 2013 ; Singh and Agrawal, 2015 ). All other effects of elevated CO 2 on individual plants and ecosystems may be partly derived from these fundamental biological responses ( Long et al., 2004 ; Ainsworth and Rogers, 2007 ; Peñuelas et al., 2013 ; Zinta et al., 2014 ). Genetic variations relative to the biological processes’ traits might also be impacted by elevated CO 2 , closely linking to these responses in various spatiotemporal aspects, from molecular, biochemical, and physiological, through individual levels and ecosystems, up to the entire Earth’s life system, interacting with multiple environmental factors (both biotic and abiotic) as well as human-driven disturbances at different temporal scales ( Long et al., 2004 ; Teng et al., 2009 ; Peñuelas et al., 2012 , 2013 ; Jagadish et al., 2014 ; Zinta et al., 2014 ).

As stated above, plant responses to climate change have become a hot topic in botanical research across various scales in the recent decades. Many reports have reviewed the biological responses to CO 2 enrichment, and their interactions with environmental change, including photosynthesis and stomatal behavior (e.g., Long et al., 2004 ; Ainsworth and Long, 2005 ; Ainsworth and Rogers, 2007 ). Our earlier review by Xu et al. (2013a) examined plant growth, carbon and nitrogen (N) allocations, gas exchange responses to elevated CO 2 with drought and high temperature. Although this review discussed the changes in growth and photosynthesis, and water use efficiency (WUE) in higher plants exposed to CO 2 enrichment with abiotic variables, the various underlying mechanisms of the critical biological processes that are affected, modulated, and controlled by elevated CO 2 with other abiotic environmental variables were not fully covered, particular at the molecular, organelle, cell, biochemical, physiological, organ, individual, and ecosystem scales. Actually, no systematic synthesis of these has been well reviewed, thus far. Therefore, in this review, based on correcting and synthesizing any new progress of the relevant research concerning plant biology and climatic change, we attempted to systematically summarize the considerable study results that have reported the responses of photosynthesis, respiration, and the antioxidant systems, as well as the key substrate metabolisms to elevated CO 2 with other environmental variables. Particularly, we reviewed the underlying mechanisms and the response pathways, as well as their interrelationships. Finally, the future perspectives for this study related to the possible implications are briefly presented and discussed. Thus, the present review may be of current interest in terms of its interdisciplinary and systematic synthesis, providing comprehensive information on the important historical and new experimental results, relative theoretical analysis, underlying mechanisms, and potential applications to promote further research.

Responses of Critical Biological Processes to Elevated CO 2

Photosynthetic response to elevated co 2 concentrations, response magnitude.

The responses of photosynthesis to elevated CO 2 concentrations have been reviewed in many reports [e.g., Drake et al., 1997 most for enclosure results; Long et al., 2004 ; Nowak et al., 2004 ; Ainsworth and Long, 2005 ; Ainsworth and Rogers, 2007 for free-air CO 2 enrichment (FACE)]. The stimulation of the light-saturated photosynthetic CO 2 assimilation rate ( A sat ) is a general response to CO 2 enrichment, with an average of 31% in the FACE experiments ( Ainsworth and Rogers, 2007 ), and 23–58% in the potted plant experiments from earlier reports ( Ryle et al., 1992 ; Drake et al., 1997 ). The magnitude of the stimulation by CO 2 enrichment varies with the different plant functional types (PFTs), with a maximum for trees and C 3 grasses; moderate for shrubs, C 3 and C 4 crops, and legumes; and minimum for C 4 grass (even with a negative response; Drake et al., 1997 ; Ainsworth and Long, 2005 ; Ainsworth and Rogers, 2007 ). Therefore, there is greater variation in the stimulation by elevated CO 2 , depending on the plant species, PFTs, and their surroundings, specifically environmental conditions like nutrition and water resource availability. For instance, elevated CO 2 leads to an increase in the A sat of Arabidopsis thaliana leaves by 82%, since the N availability is ample ( Markelz et al., 2014 ). However, a recent study of soybean plants indicated that elevated CO 2 did not produce significant effects on midday net photosynthetic rate ( A net ), either in the FACE or open-top chamber (OTC) studies ( Bunce, 2014 ), suggesting that the A net at high photosynthetic photon flux density (PPFD) might be limited by a low ribulose 1, 5-bisphosphate carboxylase/oxygenase (Rubisco) carboxylation capacity ( Bunce, 2014 ). Actually, other abiotic and biotic factors such as high temperature (e.g., Ruiz-Vera et al., 2013 ), drought ( Xu et al., 2014 ), N deficit ( Markelz et al., 2014 ), genetic variation ( Ainsworth et al., 2004 ), and leaf senescence ( Liu et al., 2014 ) may also diminish the photosynthetic response to elevated CO 2 .

The same results appeared in Lolium perenne and Medicago lupulina plants in controlled chambers ( Farfan-Vignolo and Asard, 2012 ). Moreover, C 4 plants may have no response to elevated CO 2 , because their CO 2 concentrating mechanism (CCM) may concentrate the CO 2 12–20 times at the site of Rubisco, which is relatively higher than in C 3 species ( von Caemmerer and Furbank, 2003 ; Ainsworth and Rogers, 2007 ). Case studies confirmed this theoretical conclusion under well-watered conditions in either enclosure (e.g., Xu et al., 2014 ) or FACE experiments (e.g., Leakey et al., 2006 ; Markelz et al., 2011 ). However, under a water deficit, the stimulation of the C 4 A sat by elevated CO 2 still appears, because the drought-induced impairment of C 4 photosynthesis might be ameliorated by elevated CO 2 ( Markelz et al., 2011 ; Meng et al., 2014 ; Xu et al., 2014 ). Moreover, C 4 plants can avoid photorespiration to promote CO 2 fixation with higher light use efficiency ( von Caemmerer and Furbank, 2003 ; Long et al., 2006 ). On the other hand, the down-regulation of the photosynthesis capacity is also more profound in C 3 species than in C 4 species ( Morgan et al., 2001 ; Duarte et al., 2014 ), due in part to the N dilution, possibly because C 3 plants need to invest more N from the leaf into Rubisco, relative to the C 4 species, so that the former may easily undergo more severe N dilution under CO 2 enrichment ( Sage et al., 1987 ; Yin, 2002 ; Luo et al., 2004 ; Sage, 2004 ), with no CCM ( von Caemmerer and Furbank, 2003 ).

In addition to N limitation, photosynthetic acclimation under higher CO 2 levels may result from high stomatal and internal resistances, higher starch levels, and diluted chlorophyll concentrations ( Delucia et al., 1985 ; Teng et al., 2009 ). Under elevated CO 2 , carbohydrate accumulations, such as starch size and number of chloroplasts ( Teng et al., 2006 , 2009 ), can be enhanced, partially due to the carbon substrate increase. However, the excessive carbohydrate accumulation may cause feedback inhibition or physical damage at the chloroplast level, reducing the photosynthetic capacity ( Delucia et al., 1985 ; Aranjuelo et al., 2011 ). More importantly, the Rubisco response, excessive sugar feedback, and the related gene expression may, together; play crucial roles in plants’ photosynthetic acclimation under higher CO 2 concentrations, particularly for long-term CO 2 enrichment under a nitrogen availability deficit (see details below).

Molecular Mechanisms: Role of Rubisco

The stimulation of photosynthesis in C 3 species by short-term elevated CO 2 has been well established, and confirmed under almost all experimental conditions, particularly with FACE (e.g., Long et al., 2004 ; Ainsworth and Rogers, 2007 ; Duarte et al., 2014 ). However, with long-term exposure to elevated CO 2 or other limitations, photosynthetic acclimation or the down-regulation of the photosynthetic capacity may occur, depending on the species, plant developmental stage, and environmental conditions ( Moore et al., 1999 ; Urban et al., 2012 ; Sanz-Sáez et al., 2013 ).

Rubisco has been identified as a controlling rate enzyme for carbon fixation ( Eichelmann et al., 2009 ). Here, we succinctly summarize the five major mechanisms that might explain the response to elevated CO 2 , involving Rubisco: (1) under current CO 2 concentration levels, although the value of the Rubisco Michaelis–Menten constant ( K m ) for CO 2 is close to the current intercellular CO 2 concentration ( C i ) (c. 190 μmol mol -1 ) at the site of carboxylation ( von Caemmerer and Evans, 1991 ; Ainsworth and Rogers, 2007 ). CO 2 , as a substrate of photosynthesis, does not have to reach saturation; therefore, the rising CO 2 can lead to an immediate increase in the Rubisco carboxylation velocity, due to an increase in the carbon substrate availability. (2) The Rubisco catalyzing function has two intrinsic side features: carboxylation and oxygenation. The carboxylation rate is ∼2.2 fold greater than the oxygenation rate at 25°C in C 3 plants; that is, about one-third of the ribulose-1,5-bisphosphate (RuBP) may be consumed in the oxygenation reaction ( Ainsworth and Rogers, 2007 ). Thus, elevated CO 2 , as a competing substrate, can competitively inhibit the oxygenation of RuBP (light-dependent photorespiration) through the down-regulation of Rubisco’s affinity for O 2 , while competitively promoting the carboxylation of RuBP via the up-regulation of Rubisco’s affinity for CO 2 ( Bowes, 1991 ; Long, 1991 ; Ainsworth and Rogers, 2007 ; Kane et al., 2013 ; Moroney et al., 2013 ). Consequently, this leads to the stimulation of photosynthesis, which may be compromised by heat and drought due to the enhancement of Rubisco’s affinity for O 2 ( Wingler et al., 1999 ; Tingey et al., 2003 ; Carmo-Silva et al., 2008 ; Moroney et al., 2013 ) ( Figure ​ Figure1 1 ). On the other hand, (3) with continually increasing CO 2 , the ATP products may not meet enough of the demand for RuBP regeneration, and a reduction in Rubisco’s activation state may occur, usually accompanied by a decrease in the capacity for RuBP regeneration, as well as in the RuBP pool, as indicated by a decline in the ATP:ADP ratio in the chloroplast ( Eichelmann et al., 2009 ; Watanabe et al., 2014 ). (4) A reduction in the Rubisco content via N dilution, particularly under long-term elevated CO 2 , may finally contribute to the reduction of carboxylation at the Rubisco active site. In addition, the nitrogen use efficiency (NUE) might be increased due to the optimization of the resource use ( Moore et al., 1999 ; Luo et al., 2004 ; Fukayama et al., 2012 ; Urban et al., 2012 ; Palmroth et al., 2013 ; Sanz-Sáez et al., 2013 ). Because the leaf N of C 3 species can be more invested in Rubisco (more than 25% vs. 10–15% of the leaf N in C 3 and C 4 plants, respectively), the former may be affected more profoundly by N dilution.

A diagrammatic outline of the Calvin–Benson–Basshan (CBB) cycle and photorespiration pathway in plants in response to elevated CO 2 with abiotic factors . Rubisco has two sites of carboxylation and oxygenation. Elevated CO 2 (eCO 2 ) may promote carboxylation, but repress oxygenation under ample environmental conditions, such as well-watering, whereas extreme abiotic stress, such as heat and drought, may repress carboxylation but promote oxygenation ( Wingler et al., 1999 ; Tingey et al., 2003 ). An energy consumption trade-off between the key cycles may occur, possibly modified by the CO 2 level, in which photorespiration may be promoted to quench reactive oxygen species (ROS), related to glutamine synthetase (GS2) to recycle ammonia, diminishing photo-oxidation and photo-inhibition (dotted orange line ellipse; Kozaki and Takeba, 1996 ; Watanabe et al., 2014 ). A low Gly:Ser ratio provides evidence that photorespiration is repressed in eCO 2 ( Kebeish et al., 2007 ). Water use efficiency (WUE) and nitrogen use efficiency (NUE), despite the N dilution, should be enhanced by elevated CO 2 , by decreasing stomatal conductance and investing relatively more N into the Rubisco protein ( Palmroth et al., 2013 ). The photorespiration process is compartmentalized into the chloroplast (red line ellipse), peroxisome (dotted green line bent rectangle), and mitochondrion (dotted green line ellipse). The green plus and red minus signs denote the stimulation and suppression via rising CO 2 , respectively (mainly referring to Kozaki and Takeba, 1996 ; Wingler et al., 1999 ; Tingey et al., 2003 ; Ainsworth and Rogers, 2007 ; Moroney et al., 2013 ; Xu et al., 2013a ; Watanabe et al., 2014 ).

Finally, (5) Hexokinase (HXK), as a sensor of excessive photosynthate, may be involved in the downregulation of the Rubisco content ( Ainsworth and Rogers, 2007 ; Kirschbaum, 2011 ; see below). In summary, with respect to the Rubisco response, parts (1) and (2) above may explain the stimulation of photosynthesis by elevated CO 2 , while the last three points may provide a mechanism for understanding the downregulation of the photosynthetic capacity under relatively long-term elevated CO 2 or other resource deficit conditions, such as a scarcity of N.

Sugar Feedback Inhibition of Photosynthesis

Under higher CO 2 concentrations, a prevailing explanation of the downregulation of photosynthesis may be ascribed to the sugar feedback inhibition hypothesis: certain reactive bioprocess activities within the Calvin–Benson–Basshan (CBB) cycle may be inhibited by elevated CO 2 , due to the overload of the chemical reaction substrates. The hypothesis of the sugar feedback mechanism suggests that excessive photosynthate in chloroplasts under elevated CO 2 may trigger the sugar signal network (HXK acting as a flux sensor) to down-regulate the Rubisco contents through the gene expression processes, affecting the subunit of Rubisco ( Drake et al., 1997 ; Stitt and Krapp, 1999 ; Long et al., 2004 ; Ainsworth and Rogers, 2007 ).

As noted above, HXK acting as a flux sensor in mesophyll cells may involve the down-regulation of the Rubisco content associated with genetic expressions under elevated CO 2 ; however, plants may prefer to reduce the Rubisco activity relative to the RuBP regeneration capacity ( Ainsworth and Rogers, 2007 ). For instance, Aranjuelo et al. (2011) found a decline in wheat Rubisco and its activase protein content accompanying a photosynthetic down-regulation. In addition, the effects of the source-sink balance in response to CO 2 enrichment may play an important role in the regulation of the photosynthetic capacity.

Based on the “source-sink” hypothesis, some plants with a strong sink can encounter photosynthetic down-regulation, to some extent, under higher CO 2 , which can generally be repressed by other limitations, such as intrinsic genetic constraints or the specific plant developmental stage (such as the flowering stage; Lewis et al., 2002 ; Ainsworth et al., 2004 ). Moreover, when enhanced carbohydrate availability exceeds the plants’ ability to fully utilize carbohydrates, due to nutrient or inherent internal growth limitations, the feedback may lead to a lower level of photosynthesis ( Kirschbaum, 2011 ), which may lead to an imbalance in the carbon sink:source ratio ( Bryant et al., 1998 ; Aranjuelo et al., 2011 ). For instance, wheat plants exposed to high atmospheric CO 2 are incapable of excessive accumulation of leaf photoassimilate, due to the lack of an increase in the carbon sink strength ( Aranjuelo et al., 2011 ).

Furthermore, the respiratory ATP may be consumed more under elevated CO 2 ( Watanabe et al., 2014 ); for example, the rate of the carbohydrate/sugar export (i.e., the cost related to the carbohydrate export) is higher under elevated CO 2 than under normal CO 2 ( Watanabe et al., 2014 ), which may cause a negative feedback effect on photosynthesis. This also highlights the close link between the photosynthetic and respiratory bioprocesses, between both the CBB and tricarboxylic acid (TCA) cycle, under climate change ( Moroney et al., 2013 ; Watanabe et al., 2014 ).

Response of Respiration to Elevated CO 2

Photorespiration.

Photorespiration enables the photosynthetic process to recycle the phosphoglycolate produced by the oxygenase reaction of Rubisco, consequently avoiding more carbon loss, with some protective regulation functions for plants, such as in the oxidative defense mechanism ( Bowes et al., 1971 ; Husic et al., 1987 ; Kozaki and Takeba, 1996 ; Wingler et al., 1999 ; Carmo-Silva et al., 2008 ; Moroney et al., 2013 ). However, as reported in the review by Ainsworth and Rogers (2007) , at room temperature (25°C), photorespiration can lead to a loss of 23–30% of the carbon fixed by photosynthesis with the rising temperature, whereas the CO 2 fixation may be increased by ∼53% if only the carboxylation reaction occurs, without the oxygenation reaction ( Monteith, 1977 ; Long et al., 2006 ).

Widely accepted results show that photorespiration can be restricted when C 3 plants are grown under high CO 2 concentrations ( Bowes, 1991 ; Tingey et al., 2003 ; Long et al., 2004 ), because in C 3 plants, the carboxylation capacity of Rubisco, with a low catalytic activity (operating below its K m for CO 2 ), is easily promoted by high CO 2 . Meanwhile, an increase in the CO 2 concentration, leading to a high CO 2 :O 2 ratio, may reduce its oxygenation reaction capacity, inhibiting photorespiration ( Bowes, 1991 ; Tingey et al., 2003 ; see above). For example, based on the earlier report by Sharkey (1988) , the photorespiration rate should fall by ∼50% when the CO 2 level is doubled. In A. thaliana plants grown under elevated CO 2 , although the accumulations of several major amino acids (including glutamate, aspartate, asparagine, and alanine) were enhanced, a lower level of glycine (Gly), an intermediate of photorespiration, was observed in the plants, leading to a decline in the Gly:Ser ratio, indicating a lower photorespiration activity ( Kebeish et al., 2007 ; Figure ​ Figure1 1 ).

Enhanced photoperoxidation in chloroplasts can induce a destruction of the chlorophyll and a disassembly of the chloroplast membranes, leading to a decline in photosynthesis ( Heath and Packer, 1968 ). Conversely, the constraints of photorespiration by elevated CO 2 may also reduce the H 2 O 2 products, weakening oxidation stress, possibly protecting the photosynthetic apparatus ( Watanabe et al., 2014 ; Zinta et al., 2014 ). Based on the fact that photorespiration has a protective function against photo-oxidation ( Kozaki and Takeba, 1996 ; Zinta et al., 2014 ), possibly via the up-regulation of glutamine synthetase (GS2) to recycle ammonia, diminishing photo-oxidation and photo-inhibition ( Kozaki and Takeba, 1996 ). This brings with it another dilemma: a decline in photorespiration under rising CO 2 levels may cancel the protective role, leading to a higher level of photo-oxidation than the higher rate of carboxylation stimulated by elevated CO 2 can maintain. In order to solve this dilemma, further research is required to cope with climate change, possibly by manipulating the modulated photorespiration bioprocess ( Moroney et al., 2013 ).

Mitochondrial Respiration

Mitochondrial respiration involves the carbon balance in the whole plant, with 20–80% of the carbon fixed in photosynthesis being released again through the respiration process. The respiration of the leaves in both the light and dark can account for ∼50% of the whole-plant respiratory CO 2 ( Ayub et al., 2014 ). The response of dark leaf respiration ( R d ) to elevated CO 2 remains debatable, with a decrease in the major reports, while increasing or remaining stable in a number of experiments (e.g., Ryle et al., 1992 ; Curtis and Wang, 1998 ; Drake et al., 1999 ; Amthor, 2000 ; Gonzalez-Meler et al., 2004 ; Loreto et al., 2007 ; Ayub et al., 2014 ). For instance, there is a 15–18% range in the reduction of foliar respiration when plants are grown under a doubled CO 2 concentration, relative to the ambient CO 2 level from one review ( Drake et al., 1999 ; references). However, no significant response to the leaf R d was observed in L. perenne plants exposed to high CO 2 ; although, the leaves grown in elevated CO 2 had a relative lower R d ( Ryle et al., 1992 ). A small response in the leaf respiration rate to a short-term CO 2 elevation (a 1.5% decrease) was obtained from the deciduous tree species used in an earlier experiment by Amthor (2000) , with similar evidence found in soybean plants from a recent report by Ayub et al. (2014) . Thus, for the plants grown under elevated CO 2 , the R d decrease response is general , not universal .

Correspondingly, the underlying mechanism has also been proposed in two contrasting hypotheses: elevated CO 2 may enhance the R d due to the great increase in the respiratory substrates, such as sugar; whereas the N dilution induced by elevated CO 2 might reduce the demand on dark respiration to support the protein turnover, leading to a decline in the R d ( Thomas et al., 1993 ; Gonzalez-Meler et al., 2004 ; Fukayama et al., 2011 ; Markelz et al., 2014 ). A recent report showed that CO 2 enrichment can accelerate the accumulation of the relevant carbohydrates, such as sugar, starch, and respiratory glycolysis intermediates like hexose-P, phosphoglycerate (PGA), and phosphoenolpyruvate (PEP) in A. thaliana plants, which may enhance the respiration potential ( Watanabe et al., 2014 ). Recent evidence has also indicated that the promotion to greater photo-assimilation availability at elevated CO 2 leads to a great transcriptional up-regulation of the genes, in association with the respiratory pathway ( Leakey et al., 2009a ; Fukayama et al., 2011 ; Markelz et al., 2014 ), supporting the first hypothesis. However, this may depend on the availability of the nutritional components, including nitrogen in the plants and/or the soil. For instance, based on a recent report by Markelz et al. (2014) , widely and greatly adaptive responses of the expression of the respiratory genes were obtained when the plants were exposed to elevated CO 2 . However, the transcriptional reprogramming with the stimulation of leaf respiration by elevated CO 2 can be suppressed by limited nitrogen availability ( Markelz et al., 2014 ).

Response of Antioxidant System to Elevated CO 2

The ROS in plants, including superoxide radicals ( O 2 ⋅ − ), hydrogen peroxide (H 2 O 2 ), the hydroxyl radical (OH ⋅ ), and the perhydroxy radical ( H ⁢ O 2 ⋅ ), often accumulate when plants are subjected to abiotic stress, while the antioxidant defense system with enzymatic and non-enzymatic machinery may work to protect the plants against damage due to oxidative stress. This occurs particularly in the face of stressful environmental changes, such as adverse climatic changes like droughts and heat waves ( Figure ​ Figure2 2 ) ( Schwanz and Polle, 1998 ; Pérez-López et al., 2009 ; Gill and Tuteja, 2010 ; Sekmen et al., 2014 ; Zinta et al., 2014 ). Generally, when plants become senesced, with some antioxidants increasing and others decreasing, the ROS may accumulate in a large amount, and the antioxidant system does not work well. This is often indicated by enhanced lipid peroxidation and decreased levels of antioxidant enzymes, such as superoxide dismutase (SOD) and catalase (CAT), leading to programmed cell death (PCD), particularly under severe abiotic stress ( Dhindsa et al., 1981 ; Hodges and Forney, 2000 ; Gill and Tuteja, 2010 ; Duarte et al., 2013 ).

A diagrammatic outline of the antioxidant defense systems and the responses to elevated CO 2 with abiotic stress . Elevated CO 2 may alleviate the damage of oxidative stress from abiotic stress factors, such as heat, drought, and ozone, by ameliorating the antioxidant defense systems of non-enzymatic compounds, potentially including ascorbate (ASC), glutathione (GSH), phenolic compounds, and alkaloids, and the relevant enzymes, possibly including superoxide dismutase (SOD), ascorbate peroxidase (APX), dehydroascorbate reductase (DHAR), glutathione reductase (GR), peroxidase (POX), catalase (CAT), and glutathione peroxidase (GPX). ROSs, including superoxide radicals ( O 2 ⋅ − ), hydrogen peroxide (H 2 O 2 ), hydroxyl radicals (OH ⋅ ), and perhydroxy radicals ( H ⁢ O 2 ⋅ ), accumulate when plants undergo abiotic stress or are senesced by the Fenton reaction and/or the Habere Weiss mechanism ( Hodges and Forney, 2000 ; Gill and Tuteja, 2010 ). Whether the rising CO 2 mitigates oxidative damage and the response magnitude, and which parts play major roles, depends on the plant species, crop varieties, developmental stage, abiotic factors, and their combinations (e.g., Hodges and Forney, 2000 ; Gill and Tuteja, 2010 ; Abd Elgawad and Asard, 2013 ; Kumari et al., 2013 ; Zinta et al., 2014 ). GSSG, oxidized glutathione; DHA, dehydroascorbate. This diagram is based mainly on the studies by Gill and Tuteja (2010) and Zinta et al. (2014) .

Elevated CO 2 may increase the levels of antioxidants, including polyphenols, ascorbate (ASC), alkaloids, and some antioxidant enzyme activities (such as CAT and SOD), with a significant enhancement in the antioxidant capacity, leading to declines in the ROS levels ( Mishra and Agrawal, 2014 ; Zinta et al., 2014 ). For example, when the plants were exposed to elevated CO 2 , increases in the ASC and phenol levels were obtained in Beta vulgaris ( Kumari et al., 2013 ), and increases in the ASC, glutathione (GSH), and ASC/GSH, as well as in their redox status, were found in L. perenne and M. lupulina ( Farfan-Vignolo and Asard, 2012 ). Ascorbate synthesis can be triggered and enhanced by excessive carbohydrate production due to elevated CO 2 ( Smirnoff and Wheeler, 2000 ; Zinta et al., 2014 ), which is closely linked to carbon metabolism ( Smirnoff and Wheeler, 2000 ), and together improve the plant-antioxidant defense system. Moreover, with a delay in the onset of senescence and/or severe stress under elevated CO 2 conditions, it is suggested that the antioxidant profiles, such as the accumulation of antioxidant compounds and antioxidant enzyme activity, may show better performance in dealing with the biological process of senescence ( Hodges and Forney, 2000 ). For instance, a reduction in the oxidative stress under elevated CO 2 was found in Zingiber officinale ( Ghasemzadeh et al., 2010 ), Catharanthus roseus ( Singh and Agrawal, 2015 ), a temperate grassland shrub, Caragana microphylla ( Xu et al., 2014 ), a bean, Vigna radiate ( Mishra and Agrawal, 2014 ), and A. thaliana plants ( Zinta et al., 2014 ).

However, with the elevated CO 2 -alleviated oxidation stress evidence coming from a number of reports (e.g., Pérez-López et al., 2009 ; Xu et al., 2014 ), these results have not been confirmed in some species, such as in Spinacia oleracea leaves ( Hodges and Forney, 2000 ). Farfan-Vignolo and Asard (2012) reported that CO 2 enrichment could exacerbate lipid peroxidation in M. lupulina , but not in L. perenne plants, with no rising-CO 2 responses in the ascorbate peroxidase (APX) and peroxidase (POX) in M. lupulina . No significant responses in the antioxidant enzyme activity, including APX, glutathione reductase (GR), POX, CAT, and SOD, were found when B. vulgaris plants were exposed to high levels of O 3 with elevated CO 2 , except in the inhibition of APX ( Kumari et al., 2013 ).

Instead, in Quercus pubescens and Q. ilex plants grown under elevated CO 2 , down-regulation of the protective systems was observed ( Schwanz and Polle, 1998 ). According to the findings of Schwanz and Polle (1998) , although the GR in oak leaves remains stable, the activities of SOD, CAT, POX, and APX, as well as the sum of dehydroascorbate and ASC, were reduced in CO 2 -elevated environments. Base on a recent report ( Singh and Agrawal, 2015 ), the activities of the SOD, CAT, and APX declined, but the GR and POX were stimulated, finally leading to a significant reduction in the O 2 ⋅ − , H 2 O 2 , and malondialdehyde (MDA) contents in C. roseus plants grown under elevated CO 2 . Recently, marked decreases in the ROS levels ( O 2 ⋅ − , H 2 O 2 ) and reductions in some antioxidant enzymes, such as CAT and SOD, were observed simultaneously in mung bean plants exposed to elevated CO 2 , suggesting that a lower level of ROS might match the lower activity of antioxidant enzymes ( Mishra and Agrawal, 2014 ).

Based on a report by AbdElgawad et al. (2015) , who used C 3 grasses ( L. perenne, Poa pratensis ) and C 3 legumes ( M. lupulina , Lotus corniculatus ) as experimental materials, elevated CO 2 can reduce the H 2 O 2 level, lipid peroxidation, and lipoxygenase (LOX) activities, while it decreased the SOD, CAT, glutathione peroxidase (GPX), and GR levels, but did not affect the ASC-GSH cycle ( AbdElgawad et al., 2015 ). Thus, the predominant form of the enzymatic antioxidant defense may strongly depend on the species and the abiotic stress ( Duarte et al., 2013 ; Singh and Agrawal, 2015 ).

The activities and gene transcription expression levels of ROS scavenging enzymes in A. thaliana at elevated CO 2 remained unchanged, particularly under well-watered conditions ( Zinta et al., 2014 ). However, the excessive gene transcriptional response related to antioxidant metabolism due to O 3 pollution was partly repressed by elevated CO 2 in soybean ( Glycine max ) plants under field conditions, again arguing the protective role of elevated CO 2 ( Gillespie et al., 2012 ). Lipid peroxidation, indicated by MDA accumulation, would be lessened by CO 2 enrichment, especially under other severe abiotic stress conditions such as drought ( Salazar-Parra et al., 2012 ; Xu et al., 2014 ; AbdElgawad et al., 2015 ), heat wave stress ( Xu et al., 2014 ; Zinta et al., 2014 ; AbdElgawad et al., 2015 ), O 3 pollution ( Yan et al., 2010 ; Kumari et al., 2013 ), and salinity ( Pérez-López et al., 2009 ), implying that oxidative stress induced by severe environmental constraints may be mitigated, generally or at least partly, by CO 2 fertilization. It is again highlighted that the positive vs. negative roles of elevated CO 2 concentrations in antioxidant enzyme regulation under severe stressful abiotic environments may depend considerably on different species ( Schwanz and Polle, 1998 , 2001 ; Guo et al., 2006 ; Kumari et al., 2013 ; Xu et al., 2014 ; Zinta et al., 2014 ).

Moreover, based on a recent report using wheat plants, with increasing sugar levels via CO 2 enrichment, sugar-derived reactive carbonyls (RCs; aggressive by-products of oxidative stress), including methylglyoxal (MG), were provoked by elevated CO 2 , which can negate the functions of multiple proteins, and impair the biological membrane, suggesting that plant diabetes may be inducible ( Takagi et al., 2014 ), supporting an earlier study by Schwanz and Polle (1998) . Thus, whether and how much elevated CO 2 affects antioxidant systems in plant tissues depends on the plant species, crop variety, developmental stage, abiotic factors, and the combination of these (e.g., Hodges and Forney, 2000 ; Gill and Tuteja, 2010 ; Mishra and Agrawal, 2014 ; Xu et al., 2014 ; Zinta et al., 2014 ). This is a debatable issue, requiring further research.

Response of Crucial Metabolites to Elevated CO 2

The metabolism changes of certain important metabolites, and the related genetic variations induced by elevated CO 2 have been found in a number of research reports. For instance, an accumulation of carbon compounds under elevated CO 2 occurs in wheat leaves accompanied by an up-regulation of phosphoglycerate mutase (PGAM) involving carbohydrate transport, but a down-regulation of the adenosine diphosphate glucose pyrophosphatase protein for synthesizing starch; thus affecting the carbon flux within the plants’ tissues, and the balance between the carbon sink and source ( Aranjuelo et al., 2011 ). Changes in the major chemical components induced by elevated CO 2 have also been reported in many studies. Generally, under elevated CO 2 , there may be a decrease in the total N and organic N compounds, which define the elevated CO 2 -induced dilution effectiveness. However, there is an increase in the total non-structural carbohydrates (TNC), including starch and sugar (e.g., glucose, fructose, sucrose; Lavola and Julkunen-Tiitto, 1994 ; Poorter et al., 1997 ; Luo et al., 2004 ; Markelz et al., 2014 ), with a mostly stable level in the total structural carbohydrates (cellulose plus hemicellulose), lignin, and lipids (review by Poorter et al., 1997 ; Markelz et al., 2014 ). Nitrogen assimilation may be enhanced by elevated CO 2 ( Ribeiro et al., 2012 ), and a recent report indicated that elevated CO 2 may promote N assimilation and transamination-related enzyme activities, such as glutamate oxoglutarate aminotransferase (GOGAT) and glutamate oxalate transaminase (GOT), and lead to an increase in the phloem amino acid content in M. truncatula ( Guo et al., 2013 ).

Elevated CO 2 can change not only the primary metabolic processes, but also the secondary metabolic composition in plant tissues ( Lavola and Julkunen-Tiitto, 1994 ; Poorter et al., 1997 ; Matros et al., 2006 ; Lavola et al., 2013 ). Here, we mainly address the secondary metabolite responses, because there are fewer studies related to the key metabolites. Plant secondary metabolites often indicate that these compounds have no primary functions in the maintenance of life processes in plants; however, they are involved in the biological processes of plants dealing with environmental stress, with regard to adaptation and defense ( Lavola and Julkunen-Tiitto, 1994 ; Ramakrishna and Ravishankar, 2011 ). Changes in the secondary metabolites with rising CO 2 have been reported in several relevant studies. For example, an alteration in the carbon allocation under elevated CO 2 has revised the carbon-nutrient balance (CNB) hypothesis ( Bryant et al., 1983 ), increasing the C:N ratio in plant tissues (e.g., Poorter et al., 1997 ; Cotrufo et al., 1998 ; Xu et al., 2007 ), while increasing the levels of the C-based secondary compounds due to easier synthesis, in plants with excess carbon relative to the other nutrients (such as N; Lavola and Julkunen-Tiitto, 1994 ; Matros et al., 2006 ). However, a contradiction may arise when elevated CO 2 -induced N dilution limits the carbohydrate reserves, leading instead to a reduction in some secondary substances ( Lavola and Julkunen-Tiitto, 1994 ). However, with rising CO 2 , a large accumulation of some secondary metabolites, including phenylpropanoids, tannins, triterpenoids, phenolic acids, and alkaloids, was observed, despite the effectiveness of the dilution ( Lavola and Julkunen-Tiitto, 1994 ; Matros et al., 2006 ; Ghasemzadeh et al., 2010 ; Lavola et al., 2013 ). For example, in tobacco leaves there was a large accumulation of phenylpropanoids, including the major carbon-rich compound chlorogenic acid (CGA), and the scopolin and scopoletin coumarins ( Matros et al., 2006 ). In the flavonoid response, although both the kaempferol and fisetin were increased by elevated CO 2 in ginger ( Zingiber officinale Roscoe; Ghasemzadeh et al., 2010 ), whether there was a decrease or increase in birch plants grown in elevated CO 2 depended on the genetic type or environmental conditions ( Lavola and Julkunen-Tiitto, 1994 ; Lavola et al., 2013 ). The glucosinolate accumulation was enhanced in Brassica plants exposed to elevated CO 2 , possibly changing the feeding behavior of specialized herbivores ( Klaiber et al., 2013 ). In other metabolites, including lignin, cell wall polysaccharides, and terpenes, no obvious response was found, depending on the compound composition, species, genotype, nutrient status (such as N availability), and other environmental factors ( Poorter et al., 1997 ; Lindroth et al., 2001 ; Matros et al., 2006 ; Lavola et al., 2013 ; AbdElgawad et al., 2014 ; Singh and Agrawal, 2015 ). For example, the levels of the condensed tannins, most flavonols, and phenolic acids in birch plants can be stimulated by elevated CO 2 and elevated UVB, but this effect disappeared at high temperatures ( Lavola et al., 2013 ).

Isoprene is a volatile hydrocarbon molecule, generally emitted by certain vegetation types, particularly tree species, protecting plants against damage from abiotic stress, and playing an important role in tropospheric chemistry and climate change due to its highly reactive molecular properties, especially in the formation processes of ozone and secondary organic aerosols ( Sharkey and Singsaas, 1995 ; Claeys et al., 2004 ; Sun et al., 2012 ). However, it has been confirmed that isoprene may have an active function in protecting the photosynthetic apparatus against oxidative stress from abiotic stress (such as heat), by quenching the ROS via the promotion of oxidative defense machinery ( Gill and Tuteja, 2010 ; Morfopoulos et al., 2014 ). In a number of related reports, elevated CO 2 has produced various effects on plant-derived isoprene emissions, including increases ( Sharkey et al., 1991 ; Tognetti et al., 1998 ), remaining unchanged ( Rosenstiel et al., 2003 ; Sun et al., 2012 ), and, most often, showing decreases (e.g., Wilkinson et al., 2009 ; Possell and Hewitt, 2011 ; Morfopoulos et al., 2014 ). The reason for the decreasing isoprene emission may be that the available reducing power captured by light may cause a large consumption, due to carbon fixation rather than isoprene synthesis, in CO 2 enrichment conditions, resulting in a reduction in isoprene emissions ( Morfopoulos et al., 2014 ). A reduction in the isoprene emission capacity may be attributable to a decrease in both the isoprene synthase activity and pool size of dimethylallyldiphosphate (DMADP), an immediate isoprene precursor ( Sun et al., 2012 ). Actually, DMADP synthesis is involved in the primary photosynthetic product of glyceraldehyde-3-phosphate (GAP), linked to a leaf’s photosynthetic carbon metabolism ( Loreto and Sharkey, 1993 ; Lichtenthaler, 1999 ; Sun et al., 2012 ; Trowbridge et al., 2012 ). Reduced ATP induced by elevated CO 2 may also diminish DMADP synthesis ( Sun et al., 2012 ). Thus, the isoprene emission capacity may be determined by the status of the balance between the primary metabolites, such as sugar, and the secondary metabolites, such as isoprene ( Loreto and Sharkey, 1993 ; Sun et al., 2012 ), again highlighting the importance of the primary-secondary metabolite balance (abbreviated by PSMB) with CO 2 enrichment. Based on the response model suggested by Morfopoulos et al. (2014) , Figure ​ Figure3 3 succinctly describes a pathway involved in the downregulation of isoprene biosynthesis in response to elevated CO 2 . Under elevated CO 2 , more electron flux may be used in the CBB cycle for photosynthesis, whereas less electrons may flow into the photorespiration cycle, xanthophyll cycle, and the methylerythritol 4-phosphate (MEP, 5) pathway to synthesize isoprene, as well as other redox reactions, such as quenching ROS (e.g., GR reaction demands of NADPH; Gill and Tuteja, 2010 ). It is worth noting that the isoprene biosynthesis and emission, in and from plants, may be tightly associated with photosynthesis, photorespiration, the xanthophyll cycle, and oxidative defense systems in response to CO 2 enrichment, with abiotic environmental changes ( Gill and Tuteja, 2010 ; Moroney et al., 2013 ; Morfopoulos et al., 2014 ; Figure ​ Figure3 3 ).

A diagrammatic representation of isoprene biosynthesis downregulation in response to elevated CO 2 . Light energy from the sun (1) is transferred into the plant metabolic bioprocesses using an electron transport chain (ETC). Under elevated CO 2 , more electron flux may be used for the CBB cycle for photosynthesis (2), while less electrons may flow into the photorespiration cycle (3), xanthophyll cycle (4), and the methylerythritol 4-phosphate pathway (MEP), (5) to synthesize isoprene (6), as well as other redox reactions, including the quenchers of ROS (7) (based mainly on Morfopoulos et al., 2014 ).

The role of hormone pathways in regulating the growth and metabolic responses to elevated CO 2 is not well known, despite there being a few reports (e.g., Li et al., 2011 ; Ribeiro et al., 2012 ; Zavala et al., 2013 ). Elevated CO 2 can promote an accumulation in the salicylic acid (SA, Zavala et al., 2013 ) and brassinosteroids (BR; Jiang et al., 2012 ), while reducing Jasmonates (JA) and ethylene concentrations ( Zavala et al., 2013 ; Vaughan et al., 2014 ). Elevated CO 2 -induced genes were associated with the metabolic processes of the BR regulator in plant tissues ( Li et al., 2006 ), which can alleviate the heat-induced inhibition of photosynthesis, by increasing the carboxylation efficiency and enhancing the antioxidant systems in Lycopersicon esculentum ( Ogweno et al., 2008 ). In one recent report, BRs were found to enhance the stimulation of plant growth and photosynthetic potential under elevated CO 2 ( Jiang et al., 2012 ). An increase in the indole-3-acetic acid (IAA), isopentenyl-adenosine (iPA), and dihydrozeatin riboside (DHZR) was found, while a decrease in the ABA and unchangeable zeatin riboside (ZR) occurred in Pinus tabulaeformis plants exposed to elevated CO 2 , which can encounter O 3 exposure effects to alleviate damage ( Li et al., 2011 ). The iPA, DHZR, and ZR are recognized as the most commonly active cytokinins (CTKs) in plants. The results of the experiment by Ribeiro et al. (2012) indicated that elevated CO 2 may play a role similar to gibberellin (GA) in the integration of carbohydrate and nitrogen metabolisms underlying the optimal biomass determination. When the Arabidopsis plants exhibited the inhibition of growth via the GA biosynthesis inhibitor (low-GA regime), the activities of the enzymes involved in photosynthesis, including the CBB cycle enzymes [phosphoglycerate kinase (PGK) and transketolase (TK)], were enhanced by elevated CO 2 , whereas the activities of the enzymes related to organic acid metabolism, such as the NAD-dependent malate dehydrogenase (MDH), were inhibited ( Ribeiro et al., 2012 ). Moreover, nitrate reductase (NR) can be stimulated by elevated CO 2 (by 31%) in plants with a low-GA content, indicating that rising CO 2 may mediate inorganic N metabolism in association with GA ( Ribeiro et al., 2012 ). This clearly indicates that elevated CO 2 can substitute for the relevant metabolic bioprocesses in the low-GA species, which may have a marked potential application for plants, particularly staple crops, to cope with future climate change in a high CO 2 concentration environment.

General Gene Expression Profile Under Elevated CO 2

The genes expressed differently between ambient and elevated CO 2 might encode great changes in their metabolic functions ( Li et al., 2006 ), including increases in the expression of a subset of genes encoding stress-related functions, and decreases in the expression of genes encoding chloroplast functions and other processes of photosynthesis ( Moore et al., 1999 ; Miyazaki et al., 2004 ; Li et al., 2006 ). The decline in the gene expression may partly lead to so-called photosynthetic acclimation to long-term elevated CO 2 , particularly under limited environmental conditions or in carbon sink limited species ( Jifon and Wolfe, 2002 ; Ainsworth et al., 2004 ; Long et al., 2004 ; Fukayama et al., 2012 ; Xu et al., 2013a ). For example, Fukayama et al. (2012) found the overexpression of Rubisco activase in rice leaves grown under elevated CO 2 , possibly leading to a decrease in the photosynthetic capacity. However, the gene expression in response to CO 2 fumigation may depend on different developmental stages at the time of sampling, and different physiological conditions of the ecotypes of A. thaliana ( Li et al., 2006 ). Moreover, limited N, a typical example of a nutrition resource deficit, may lower the stimulation of photosynthesis by elevated CO 2 , due to excess photoassimilate availability, triggering sugar-signaling feedback. This reduces the expression of the photosynthetic genes, especially in Rubisco, leading to the allocation of photosynthetic N into sinks that are more necessary for relative biosynthesis ( Moore et al., 1999 ; Leakey et al., 2009b ; Markelz et al., 2014 ). Based on a report by Duanmu et al. (2009) , the enhanced expression of the limited CO 2 -induced gene HLA3 may increase the HCO 3 - transport and photosynthetic C i affinity, which may counter the down-regulation of the photosynthetic capacity under CO 2 enrichment, if the gene can be transported to higher plants ( Price et al., 2011 ). This demonstrates the potential modified gene applications in the improvement of photosynthetic regulation traits in high CO 2 climates.

In a rice cultivar, gene expression for D1 protein (a protein of PSII gene) was down-regulated by 20% at heat stress under elevated CO 2 , but this change did not occur in another cultivar, indicated that elevated CO 2 may enhance the damage of D1 protein, depending on genotypic variation ( Gesch et al., 2003 ). Based on a recent study in poplar plants ( Liu et al., 2014 ), only eight significantly changed key genes involved in crucial metabolisms in response to elevated CO 2 were identified by a qRT-PCR test. During wheat plant senescence, up-regulation of genes related to nitrogen remobilization, and down-regulation of genes related to carbon remobilization were observed under elevated CO 2 , reflecting greater grain N-sink strength of developing grains ( Buchner et al., 2015 ). Based on a microarray analysis, the A. thaliana photosynthetic gene expression can be most adversely affected by abiotic stress, such as heat and drought, where almost all of genes were down-regulated. However, the greatest down-regulation in gene expression can be diminished by elevated CO 2 ( Zinta et al., 2014 ). From the genome-wide expression profiling of the mRNA in A. thaliana leaves ( Zinta et al., 2014 ), 3643 differentially expressed genes appeared between plants exposed to climate extremes and ambient CO 2 , whereas only 2841 genes were obtained when grown under elevated CO 2 . Specifically, both the up-regulated and down-regulated genes were remarkably lower in plants exposed to elevated CO 2 , than in ambient CO 2 . For example, under stressful conditions such as heat and drought, the down-regulations of the genes involved in the light reactions (photosystem I and II, light-harvesting complex II), pigment synthesis, and the Calvin cycle can be dampened by elevated CO 2 , being consistent with changes in photosynthetic rates. It is indicated that elevated CO 2 may repress the impact of climate extremes on gene expression in rosette leaves ( Zinta et al., 2014 ). It is worth noting that we only presented a general description here, and a detailed list of the gene expression differences may be found in the report by Zinta et al. (2014) .

Elevated CO 2 often can induce a marked decline in photorespiration (see above), suggesting that there may be an involvement of the expression of the genes related to photorespiration pathway including both transcripts and metabolite levels ( Sharkey, 1988 ; Novitskaya et al., 2002 ; Foyer et al., 2009 ; Florian et al., 2014 ; Wang et al., 2014 ). A BOUT DE SOUFFLE ( BOU ) gene encoding a mitochondrial carrier may be involved in photorespiration in Arabidopsis because of the knockout mutant bou-2 can arrest growth at ambient CO 2 , but not at high CO 2 concentration, implying BOU gene linking glycine decarboxylase (GDC) activity, may regulate the response to CO 2 concentration changes ( Eisenhut et al., 2013 ). Plants defective ( glyk1 mutants), the gene encoding glycerate kinase (GLYK), cannot grow in normal CO 2 level but fully recover at elevated CO 2 , which the reasonable reason why the mutant requires a high CO 2 concentration is unknown ( Timm and Bauwe, 2013 ). The transcript levels of some photorespiratory genes up-regulated such as plastid chaperonin proteins (CPN60B), and those down-regulated such as GDC under heat and drought stresses, were largely repressed under elevated CO 2 , but that is not universal for all genes ( Zinta et al., 2014 ). Furthermore, according to a study by Florian et al. (2014) , the transcript levels of photorespiratory genes in Arabidopsis were almost unchanged at high CO 2 concentration except a decline in transcript levels of glycine decarboxylase H-protein (GDCH1) that functions in photorespiratory carbon recovery. Thus, whether and how the photorespiratory gene expression play a major role in responses to atmospheric CO 2 concentration changes are mostly unknown ( Foyer et al., 2009 ; Timm and Bauwe, 2013 ; Florian et al., 2014 ), which needs to be tested further.

Because antioxidant defense systems would be enhanced by elevated CO 2 , the gene expression levels of antioxidant enzymes may be also promoted accordingly ( Gillespie et al., 2012 ; Mishra and Agrawal, 2014 ; Zinta et al., 2014 ). In A. thaliana plants, CO 2 enrichment can up-regulate the gene transcriptional expression of an antioxidant enzyme, dehydroascorbate reductase (DHAR), but down-regulate that of CAT, particularly under stressful environments. However, the gene expression changes in others such as APX, GR, GPX, POX, and SOD to elevated CO 2 were not significant ( Zinta et al., 2014 ). Additionally, a high transcript abundance for the majority of the genes coding antioxidant recycling enzymes enhanced by high O 3 concentration was also not affected by elevated CO 2 ( Gillespie et al., 2012 ). Elevated CO 2 did not modify the up-regulation of transcripts of oxidative-stress-related genes induced by herbivory or elevated O 3 in soybean plants ( Casteel et al., 2008 ). Kontunen-Soppela et al. (2010) indicating that CO 2 enrichment cannot alleviate harmful effects from O 3 pollution based on a gene expression test in birch plants. Thus, the authors could not conclude that CO 2 enrichment can up-regulate the gene transcriptional expression levels of the antioxidant enzymes under stressful environment. Further studies are needed urgently to elucidate the molecular responses in the diverse antioxidant systems in responses to elevated CO 2 with the key environmental factors including drought, heat, and ozone ( Gillespie et al., 2012 ; Zinta et al., 2014 ).

One recent research study described the results of a gene bioinformatics analysis of hardy winter wheat ( Triticum aestivum ), with different low temperature adaptive capacities in response to elevated CO 2 ( Kane et al., 2013 ). The genes induced by elevated CO 2 was three times higher in the non-acclimated (NA) relative to cold-acclimated (CA) conditions (1,022 vs. 372). The greatest down-regulation of genes appeared in the plant defense responses in the NA plants. On the other hand, CA can reverse this down-regulation, due to the cold-induced genes involved in the plant’s resistance to pathogenesis, and cellular and chloroplast protection ( Kane et al., 2013 ), suggesting that cold-adapted hardy winter plants may be less affected by elevated CO 2 . Conversely, the plants that are more sensitive to cold weather may be regulated both easily and drastically via CO 2 enrichment. Of note is the down-regulative interaction of high CO 2 levels with low temperature adaptations, which requires further investigation.

Another recent microarray study describes the expression of the respiratory genes in A. thaliana plants exposed to elevated CO 2 , with both limited and sufficient N availabilities ( Markelz et al., 2014 ). This analysis showed that 4439 transcripts were significantly different between the ambient and elevated CO 2 . Particularly, the transcriptional response of the genes related to protein synthesis was greatest during the day, due to elevated CO 2 induction. These genes included those related to the components of glycolysis, the TCA cycle, the mitochondrial electron transport chain (ETC), and the mitochondrial protein import complexes. The evidence of the up-regulation of the transcription of the genes, with relation to respiration under elevated CO 2 levels, has also been obtained from rice ( Fukayama et al., 2011 ) and soybean plants ( Leakey et al., 2009b ). Furthermore, 1,708 transcripts differed significantly in abundance between the limited N and ample N availabilities, while 258 transcripts differed significantly due to the interactions of the CO 2 level and N availability, again indicating that the expression of the genes related to the key physiological bioprocesses in response to elevated CO 2 may be markedly affected by other environmental factors, such as N limitation ( Markelz et al., 2014 ) and day length ( Queval et al., 2012 ). It is worth pointing out that the systematicness and complexity of the underlying molecular mechanisms may coexist in the plant response to elevated CO 2 , and its interaction with other multiple abiotic factors including nutrition condition.

In addition to the relevant studies concerning the specific gene manipulation and genome-wide transcriptional analysis, with strong selective pressure due to the novel CO 2 level, the evolutionary adaption to an atmospheric CO 2 concentration change has been found in many reports of the stomatal developmental response ( Gray et al., 2000 ; Ward and Kelly, 2004 ). Moreover, because the previous studies concerning the response to CO 2 enrichment are often limited to one generation of the plant life-cycle ( Ward and Kelly, 2004 ), to further understand the genetic variations in the plants exposed to long-term elevated CO 2 , Teng et al. (2009) found that the maternal genetic effects of elevated CO 2 cannot be retrieved in their offspring after undergoing 15 generations of A. thaliana grown in a long-term elevated CO 2 atmosphere, indicating the lack of genetic variation and specific adaptations for CO 2 -enriched responsiveness ( Teng et al., 2009 ). It is suggested that selective pressure from elevated CO 2 may be not enough to produce a genetic modification to adapt to new environmental changes. This issue should be investigated further, with long-term exposure to elevated CO 2 .

Elevated CO 2 Interactions with Multiple Abiotic Stresses

There have been several review reports concerning the interactions between elevated CO 2 and other abiotic factors, such as temperature or drought, on plant growth and physiological processes (e.g., Morison and Lawlor, 1999 ; Ainsworth and Rogers, 2007 ; Peñuelas et al., 2013 ; Ruiz-Vera et al., 2013 ). However, the underlying mechanism concerning the responses to CO 2 enrichment with multiple factors has rarely been systematically reviewed ( Xu et al., 2013a ; Jagadish et al., 2014 ; Way et al., 2015 ). Although the related descriptions have been presented in the appropriate places above, here, we present a succinct statement, particularly for the underlying mechanisms in physiological responses to elevated CO 2 , in combination with several abiotic factors, such as drought and heat waves.

Water deficits and heat waves are considered to be the most critical stress factors with markedly potential effects on plant growth, crop yield, vegetation productivity, photosynthetic capacity, promotion of ROS accumulation (such as H 2 O 2 ), and the oxidative enhancement of functional molecules, such as active proteins and DNA (e.g., Mittler, 2006 ; Xu and Zhou, 2006 ; Barnabás et al., 2008 ; Xu et al., 2009b , 2013a , 2014 ; Zinta et al., 2014 ). With the exception of a few reports (e.g., Coleman et al., 1991 ; Roden and Ball, 1996 ), most of the relevant studies have concluded that CO 2 fertilization may mitigate the adverse impacts of environmental stresses, such as heat, drought, O 3 pollution, and their combinations ( Biswas et al., 2013 ; Xu et al., 2013a , 2014 ; Zinta et al., 2014 ). These aspects of the mitigation of CO 2 enrichment include relatively increased individual growth (e.g., Xu et al., 2014 ) and community production ( Naudts et al., 2014 ), enhanced photosynthesis ( Biswas et al., 2013 ; Xu et al., 2014 ; Zinta et al., 2014 ), elevated WUE and NUE ( Palmroth et al., 2013 ), optimized chlorophyll fluorescence ( Biswas et al., 2013 ; Xu et al., 2014 ; Zinta et al., 2014 ), up-regulated antioxidant defense metabolism via increased lipophilic antioxidants and membrane-protecting enzymes ( Naudts et al., 2014 ; Xu et al., 2014 ), and decreased photorespiration with low H 2 O 2 production ( Foyer and Noctor, 2009 ; Munne-Bosch et al., 2013 ; Zinta et al., 2014 ).

Elevated CO 2 may help the leaf tissues of a dominant grass in Northern China to partly escape the negative effects of heat and drought stresses on plant growth, canopy structure, leaf development, photosynthetic potential, and antioxidant systems ( Xu et al., 2014 ). For A. thaliana plants, the combination of the heat and drought-induced inhibition of photosynthesis was 62% under ambient CO 2 , but the reduction in photosynthesis was only 40% with elevated CO 2 . Furthermore, the protein carbonyl content, a marker of protein oxidation, increased significantly during a heat wave and drought, in which the effects were repressed by increased CO 2 ( Zinta et al., 2014 ). The dramatic differences between the altered transcriptional expression of A. thaliana plants subjected to a combination of heat and drought stresses were demonstrated in the presence and absence of elevated CO 2 , with less down-regulation of the genes involved in the light reactions (photosystem I and II, light-harvesting complex II), pigment synthesis, and the CBB under elevated CO 2 . Additionally, there was less limitation to the photosynthetic parameters, such as A net , maximum photochemical efficiency ( F v / F m ), g s , and chlorophyll content ( Zinta et al., 2014 ), possibly due to the effectiveness of the mitigation of the CO 2 enrichment. Moreover, following cancelation of the extreme heat and drought stresses, the plant growth and physiological activities related to the positive responses to growth may partly resume at high CO 2 concentrations, and the oxidative stress can be greatly alleviated, although they cannot reach the control levels ( Xu et al., 2009a , 2010 ; Xu and Zhou, 2011 ; Zinta et al., 2014 ). Again, this implies that CO 2 fertilization may alleviate the damage of extreme climatic events, such as snap heat waves and droughts, compromising part of the loss and accelerating recovery in case of the elimination of severe abiotic stress. However, a recent report indicated that high temperature, with no elevated CO 2 , provokes the drought sensitivity of the leaf to gas exchange, while the latter did not affect the Eucalyptus radiata seedling response to drought, and cannot alleviate the negative effects of rising temperature on drought stress ( Duan et al., 2014 ). From another point of view, drought, warming, air pollution, and, particularly, their combination may substantially negate the elevated-CO 2 stimulation in photosynthesis, plant growth, and productivity ( Biswas et al., 2013 ; Ruiz-Vera et al., 2013 ; Xu et al., 2013a ; Duan et al., 2014 ), which is worth noting.

A hot issue has arisen, in which the photosynthetic responses to elevated CO 2 and its combination with climatic change may differ completely between plant species within their photosynthetic pathways. Because of the C 4 specific photosynthetic pathway with a CO 2 -concentrating pump ( von Caemmerer and Furbank, 2003 ), they cannot benefit from elevated CO 2 relative to C 3 plants ( Leakey et al., 2006 ; Morgan et al., 2011 ; Bütof et al., 2012 ; Xu et al., 2014 ). However, there is a practical and explicable positive response of growth and photosynthesis to elevated CO 2 , with drought and heat stress in C 4 plants. (1) Although no obvious response to CO 2 enrichment occurs under ample water availability, great stimulation in the growth and photosynthetic capacity may be obtained under water deficits, due to the marked alleviations of drought stress via g s reduction and WUE elevation, and oxidative stress mitigation under elevated CO 2 (e.g., Long et al., 2004 ; Ghasemzadeh et al., 2010 ). (2) The higher temperature might benefit the C 4 species that originate from, and currently grow under warming conditions, and because the photorespiration of C 3 plants increases with rising temperature, leading to a reduction in the A net . The C 4 plants lack photorespiration pathways, with no effect on photosynthesis ( Long et al., 2006 ; Long and Ort, 2010 ; Morgan et al., 2011 ). Actually, both hypotheses have been well tested in several reports ( Morgan et al., 2001 , 2011 ; Leakey et al., 2006 ; Xu et al., 2014 ). This positive response to a combination of CO 2 enrichment and warming, as well as water deficits, highlights the fact that C 4 plants may have a great potential advantage in future climatic change. A higher CO 2 concentration with warming and drought suggests that C 4 plants may prosper in these vulnerable ecosystems in arid and semiarid regions in the future ( Morgan et al., 2011 ; Lobell et al., 2013 ; Xu et al., 2014 ). However, elevated CO 2 induced an electron transfer rate (ETR) enhancement in one C 3 species, Halimione portulacoides , and one C 4 species, Spartina maritime , but with lower photosynthetic efficiency in the C 4 plants due to an increase in the dissipated energy flux, indicated by higher non-photochemical quenching (NPQ), suggesting that the abundance of C 3 species may increase in Mediterranean halophyte vegetation ( Duarte et al., 2014 ). Thus, the future climatic change may induce a rapid shift in some terrestrial vegetation, because of the different responses between the species with the specific photosynthetic pathways, such as C 3 and C 4 plants, depending on the combination of multiple climatic factors.

We briefly summarize several key points. (1) Elevated CO 2 generally increases the A net , in which the positive responses strongly depend on the plant functional groups and species, with the expected stimulation from rising CO 2 , for almost all of the C 3 species, but only for C 4 plants under water deficit conditions (due to the CCM). The performance of Rubisco in fixing carbon is promoted by CO 2 enrichment, because of its dual character. However, a downregulation in the photosynthetic capacity may occur because of the decreased ATP:ADP ratio, diluted N, and excessive photosynthate accumulation under continually rising CO 2 , particularly under N and/or carbon sink limitations. (2) An elevated CO 2 -induced suppression of photorespiration has been tested using a lower Gly:Ser ratio as an indicator, while a general negative response in mitochondrial respiration varies, depending on the species. The balance between the increased respiratory substrate and diluted N may play a key role in the rising CO 2 -induced response, with evidence from the expression up-regulation of the genes related to the respiratory pathway. (3) Plants may run an antioxidant defense system with both the enzymatic and non-enzymatic machinery protected from the damage of oxidative stress due to the generation of ROS under abiotic stresses (such as drought and heat), while elevated CO 2 may partly promote the accumulation of antioxidants like polyphenols and ascorbate, and enhance some antioxidant enzyme activities to diminish the oxidative stress from abiotic factors, alone or combination, depending on the genetic variations and plant developmental stage. (4) Elevated CO 2 leads to a lower N level and higher content of the total non-structural carbohydrates (TNC), including starch and sugars, while remaining mostly stable in the totals of the structural carbohydrates, lignin, and lipids. However, some secondary metabolites, such as phenylpropanoids, tannins, and phenolic acids, are enhanced by CO 2 enrichment. Isoprene emissions may be weakened by elevated CO 2 , because biosynthesis may need to balance the ATP and NADPH with photosynthetic metabolism. (5) Elevated CO 2 might mitigate the adverse effects of abiotic stresses via relatively increased individual growth, enhanced photosynthesis, increased resource use efficiency, promoted antioxidant defense metabolism, and decreased photorespiration under multiple environmental stresses. In terms of the photosynthetic pathway, CO 2 enrichment did not affect C 4 plants under ample environmental conditions, but promoted it when exposed to drought, warming and their combination, predicting a great potential advantage in future climatic change scenarios for the C 4 species, particularly in arid and semiarid areas.

Future Perspectives

Promotion of the relevant research.

In the future, we may focus on several crucial research aspects: (1) to further elucidate the underlying mechanisms of the response to CO 2 enrichment in key biological processes, including photosynthesis, antioxidant machinery, and other related critical metabolic bioprocesses, such as hormone-involved regulation, as well as the relevant biochemical signal cascades; (2) to disentangle and compare the diverse responses from different species and PFTs to elevated CO 2 or its combination with other abiotic factors; (3) to integrate various spatial-temporal scales from molecular, cellular, biochemical, physiological, individual, ecosystem, and global vegetation levels, and from instantaneous to annual or longer time-scales to elucidate the underlying genetic mechanisms in association with key biological processes under the effects of global environmental factors, including elevated CO 2 , warming, drought, and air pollution; (4) to strengthen the linkages to other relevant research subjects, including ecological, biogeoscience, environmental, climatic, and social-economic aspects, to find appropriate synthetic solutions to urgent practical issues like environmental contamination, ecosystem damage, and global warming impacts.

Potential Applications under Future Climate Change

Future climate change may impact key biological metabolic processes and their feedback. For example, environmental stresses may provoke the generation of ROS in chloroplasts, the site of photosynthesis, while future high CO 2 levels may alleviate the limitations of these stresses. We might also use biotechnological tools such as the protection function against ROS to deal with future climatic change. In addition, Rubisco properties may be improved by regulating the transgenic expression of Rubisco activase in crops such as rice, possibly enhancing the photosynthetic capacity under rising CO 2 ( Fukayama et al., 2012 ), while the high CO 2 -induced downregulation of the photosynthetic capacity might induce the modification of the photosynthetic pathway ( Price et al., 2011 ). Furthermore, the modified genetic capacity for the high utilization of photosynthate to strengthen sink storage may make plants capable of sustaining increased photosynthesis when the plants are grown in elevated atmospheric CO 2 , while additional thermo-tolerant transgenic crops may be required to cope simultaneously with climatic warming ( Lavania et al., 2015 ). Finally, research should be conducted to strengthen the feasible applications from the relevant research results in response to CO 2 enrichment, and its combination with multiple environmental factors for ecological management, climate change mitigation, sustainable development, and related policy decisions, but not at the expense of environment.

Author Contributions

YJ is co-first author, ZX and YJ collected and analyzed the data, ZX, YJ, and GZ wrote the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The study was funded by the National Natural Science Foundation of China (41330531, 31170456), and China Special Fund for Meteorological Research in the Public Interest (Major projects) (GYHY201506001-3).

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