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New Research Validates Autism’s Link to Gut

Researchers have identified a microbial signature for autism spectrum disorder, a critical finding that offers clarity about how the gut microbiome influences this neurological syndrome.

The data-driven study published by 43 researchers challenges the idea that autism is a primarily genetic condition and suggests that environmental factors may be behind the sharp rise in the debilitating condition.

The trillions of microbes (bacteria, viruses, fungi, and other microorganisms) that populate the gut microbiome are the basis of that microbial signature. Other research has found that having more microbes and greater diversity is associated with health and lower disease risk. Among other tasks, gut bacteria metabolize fiber and create metabolites that facilitate digestion, brain functions, and more.

The study involved reanalyzing 25 previously published datasets to find autism-specific metabolic pathways that could be linked to particular gut microbes. Originated at the Simons Foundation’s Autism Research Initiative (SFARI), the meta-analysis was published on June 26 in Nature Neuroscience and aligns with a recent long-term study of microbiome-focused treatment on 18 people with autism who exhibited improvement in both gut and brain symptoms.

“It provides further evidence that the microbiome is altered in autism and that it relates to alterations in biochemistry and that those alterations can affect GI [gastrointestinal] and neurological functioning,” James Adams , professor at Arizona State University’s Biodesign Center for Health Through Microbiomes, told The Epoch Times. He’s been studying the gut–autism link for 20 years and is co-author of the study of 18 people highlighted in the new research.

The Growing Shadow of Autism

No single cause has been found for autism spectrum disorder , which is a heterogeneous condition displaying genetic, physiological, and behavioral patterns. It’s usually diagnosed in childhood and now affects 1 in 36 children, up from 1 in 44 just two years ago .

The obstacles to studying autism include difficulty testing children who have severe cases and difficulty observing signs and symptoms in subjects. The fact that it’s a neurological condition makes it more difficult to study.

Combined with the vastness of the microbiome, that has made it difficult and controversial to quantify the role gastrointestinal problems play in autism. One goal of the study was to forge consensus on this relationship, Jamie Morton, one of the study’s corresponding authors and an independent consultant, told The Epoch Times.

Mr. Morton said researchers were surprised at the connections observed when they applied an algorithm to the data. They put autistic and neurotypical controls side by side to look for such traits as gene expression, immune system response, and diet.

“What was startling was how strong the signal was. After running our analysis, you could just see it pop off from the raw data,” Mr. Morton said. “We hadn’t seen this kind of clear overlap between gut microbial and human metabolic pathways in autism before.”

A “pathway” is a biochemical process of linked reactions whereby one molecule is processed into another, or compounds are changed in a series of processes to deliver a certain substance to a certain place in the body. For example, you may eat a certain vitamin or compound that gets digested into other molecules that get changed into other molecules through cellular processes until they eventually reach your brain as a specific neurotransmitter.

Researchers said the new information paves the way for precise treatment-focused research on manipulation of the microbiome. The ability to use stool analysis to see how patients respond to specific interventions over time can shape future studies and, ultimately, clinical care.

“What’s significant about this work is not only the identification of major signatures, but also the computational analysis that identified the need for future studies to include longitudinal, carefully designed measurements and controls to enable robust interpretation,” Kelsey Martin, executive vice president of SFARI and the Simons Foundation Neuroscience Collaborations, said in a SFARI statement .

Study Specifics

The meta-analysis compared 600 pairs of children; each pair consisted of a child with autism and a neurotypical control of the same age and sex. Each pair was analyzed and compared using novel computational methodologies so the researchers could identify microbes with differing abundances between the two groups.

There were 95 metabolic pathways differentially expressed in the brains of autistic subjects that had corresponding microbial pathways. “Pathways related to amino acid metabolism, carbohydrate metabolism and lipid metabolism were disproportionately represented among the overlapping pathways,” the study reads.

Functionally, those pathways were confirmed with microbial species in the genera of Prevotella, Bifidobacterium, Desulfovibrio, and Bacteroides. And they are associated with brain gene expression changes, restrictive dietary patterns, and pro-inflammatory cytokine profiles.

The study’s inclusion of the 2019 long-term fecal microbiota transplant study led by Mr. Adams and Rosa Krajmalnik-Brown makes the evidence more robust.

“Another set of eyes looked at this, from a different lens, and they validated our findings,” Ms. Krajmalnik-Brown said of the meta-analysis in the statement .

The Adams and Krajmalnik-Brown study was also published in Nature and noted lower overall microbial diversity and reduced Prevotella copri and Bifidobacterium in children with autism.

The original study treated 18 children with a microbial transfer therapy that included two weeks of treatment with the powerful antibiotic vancomycin, a bowel cleanse, one initial high dose and 10 weeks of daily low doses of microbial transfers along with a low-dose stomach-acid suppressant.

Essentially, subjects had their gut microbiome cleared out and received a new one from a transplant of healthy donor stool. The results included an 80-percent reduction in GI symptoms and a slow, steady improvement in autism symptoms. The two-year follow-up of the same cohort showed that children in the severe range of autism had significantly decreased symptoms and that beneficial bacteria remained high.

The meta-analysis provides large-scale confirmation of a theory that many clinicians and researchers have had for years based on studies and observational evidence.

“They’re adding credibility to gut treatment with autistic kids. We’ve been treating autistic kids for decades on the gut, and we’ve had a lot of mainstream criticism for it,” Dr. Armen Nikogosian, a medical and functional doctor who specializes in autism care, told The Epoch Times. “That being said, we certainly haven’t figured it all out, but we knew there was a clear connection between the gut and the brain of the autistic child.

“Having mainstream medicine accept this idea would open more avenues for research and treatment. More information on specific microbes that need to be eliminated or encouraged to grow is a never-ending quest for us.”

Morton said those could be topics of future studies, but so far the patterns found in autistic children are mostly indicative of the entire microbial ecosystem being dysbiotic, or out of balance.

“The gut bacteria in autism is very complex, and there has been disagreement between different studies as to which bacteria are different in autism,” Mr. Adams said. “I think the answer is it depends on where you live. There are different pathogenic bacteria that are present, and there are beneficial bacteria that are missing.”

Still, dysbiosis has been addressed in functional medicine for some time with varying degrees of success among those with autistic traits. It’s even something of a hot topic online among parents of autistic children who have attempted to alter microbial landscapes through diet.

Parental Intuition

That was the case for Ginger Taylor, whose son began behaviorally regressing in 2003 at 18 months old. Her research uncovered widespread GI issues common in autistic children. One theory was that gluten and casein were contributing to symptoms such as communication and language issues, arm flapping, and hyperactivity.

With little knowledge about nutrition, she changed her son’s diet for a few days so she could gather more information about healthy diets for brain health. Immediately, he began having normal bowel movements and maintaining eye contact. Though controversial , gluten-free and casein-free diets have been embraced by many families that claim it has alleviated symptoms. Ms. Taylor first read about it in a book called “ Children with Starving Brains .”

“GI problems have been particularly difficult, with terrible pain that’s not diagnosed or treated correctly or even acknowledged,” Ms. Taylor said. “I hope this study is accepted, and we stop having this argument about whether GI is involved with autism.”

Ms. Taylor, who maintains a website full of autism research that includes many studies about the gut–brain axis , is optimistic that perhaps this will be the research that leads to better screenings for children, as well as advancing treatment.

But she’s also skeptical, since new studies haven’t historically led to deep acceptance of the GI link that could drive systemic changes in how autism is approached. For instance, a meta-analysis in 2014 already made a definitive link between autism and GI symptoms. Published in Pediatrics , the review article examined 15 different studies.

Improving Education

The responsibility to identify gut problems tends to fall on families, who might not even be aware of them, to convey to doctors who often lack knowledge on how to proceed.

When trained, specialists can identify GI signs and symptoms if they understand autism, Dr. Arthur Krigsman , pediatric gastroenterologist specializing in the evaluation and treatment of children with autism, told The Epoch Times.

Autistic children, he said, express pain through screaming, crying, hitting, and breaking things. They don’t often use the same universal signs that are often associated with GI disorders.

“You can have a patient with severe abdominal pain, a ruptured appendix, and they won’t put their hand on their belly,” Dr. Krigsman said. “Their ability to transmit information, even non-verbally, is affected.”

Yet when intestinal tissue from autistic children is biopsied, he said there’s a commonality. Cells and molecules are uniquely inflamed—not like other inflammatory bowel diseases, such as Crohn’s disease. Autism has unique mitochondrial, metabolic, and neurological components that constitute autoimmunity, he said.

“Autism is a medical disease. It’s not a psychiatric disease. The intestine plays a role and is probably the most common comorbidity,” Dr. Krigsman said. “The good news is the autoimmune disease can be treated, just like Crohn’s is treatable … if the doctor is able to make the right diagnosis.”

Reposted from: https://www.theepochtimes.com/health/new-research-validates-autisms-link-to-gut-5394935

Resources for Parents of Children with Autism

Asperger Syndrome and High Functioning Autism Association : This New York-based organization offers great resources for those with higher functioning autism. Ahany also provides a great list of summer programs and day camps in New York, as well as useful questions to ask when choosing any camp or summer program for your special needs child.
Autism Beacon strives to supply the best resources for autism treatments. It also offers a broad range of articles on autism, including sensitive topics such as bullying and sexuality.
Autism Hwy : Autism Highway was started by Kelly Green after her son Wyatt was diagnosed with autism. It provides an extensive list of autism-related events and specialists. It also includes many fun games that children are sure to enjoy.
Autism Society has been providing information for individuals on the spectrum, their family members, and professionals for more than 50 years. It hosts an annual conference and lobbies nationally for policies to help families touched by autism.
AutismNow.Org features news, information, an easy-to-use search engine, upcoming events, and even a local agencies map to help you find services and support in your area.
Autism Learn , a site is dedicated to the process of teaching autistic children how to learn. It is jam-packed with visually stimulating activities geared toward helping develop skills with people, fine motor control, creating a connected hierarchy, learning about the seasons and weather, money, and much more.
Autism and Oughtisms : The mom of 2 autistic boys is the author of this inspirational and informative blog about autism.
The Guardian: “The biggest problem for children with special needs? Other people.” The An inspiring story about a mother and her son, who has autism. It discusses one of the biggest challenges parents and their disabled child face. At the end of the article, there are more than 175 comments from others sharing their stories and offering tips and resources.
NeedQuest : If you’re in the New Jersey area, this site is chock full of information about early intervention, therapists, camps, schools, sports programs, and more. And even if you’re not in the Garden State, it’s worth checking out the blog which has helpful articles like this one about whether to worry if your child is obsessed with trains, dinosaurs, or something else (as so many of our children on the spectrum are).
AngelSense : For parents of kids who are nonverbal or prone to wandering, nothing is scarier than not knowing where your child is. GPS trackers can be a lifesaver – literally. AngelSense is one company that offers them. It also has a blog with valuable tips for keeping kids on the spectrum safe and managing school, outings, water, and other challenges.
Sunshine Behavioral Health : A guide on how people in need can find online resources for Autism in United States. Sunshine Behavioral Health is based in California, United States.

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The Connection Between Autism And The Gut Microbiome Is Clearer Than Ever

The link between autism spectrum disorder ( ASD ) and the body's ' second brain ' is more apparent than ever before.

A new paper, authored by no less than 43 scientists of various disciplines, has found the strongest link yet between gut microbes, host immunity, genetic expression in the nervous system, and dietary patterns.

The new analysis does not confirm autism 's underlying causes , nor does it identify specific subtypes as other research has attempted , but rather reveals a more generalized gut profile that seems to be consistent among those with ASD .

If this crucial biomarker can be elucidated in further research, it could one day be used to diagnose ASD and probe potential treatments.

"Before this, we had smoke indicating the microbiome was involved in autism, and now we have fire," says microbiologist Rob Knight from the University of California San Diego.

"We can apply this approach to many other areas, from depression to Parkinson's to cancer , where we think the microbiome plays a role, but where we don't yet know exactly what the role is."

Today, scientists know that people with autism are more likely to experience gastrointestinal issues, such as constipation, diarrhea, bloating, and vomiting.

What's more, in recent years, researchers have begun to find links between the makeup of microbes that call our guts home and neurodevelopmental disorders, like ASD.

Nevertheless, this connection isn't always consistent, and some experts have argued it isn't gut bacteria that trigger ASD, necessarily; it could be that kids with autism are more likely to restrict their diets because of 'picky' eating, which in turn influences the kinds of bacteria that persist in the digestive tract.

The new study incorporates 10 existing datasets on autism and the microbiome, plus 15 other datasets regarding dietary patterns, metabolism, immune cell profiles, and gene expression profiles of the human brain.

The authors of the analysis say their findings boost "the statistical power and biological insight" into the gut-brain axis behind ASD, and provide "stronger associations among gut microbes, host immunity, brain expression and dietary patterns than previously reported".

The fundamental connection between the gut and the brain is itself a relatively new frontier in science. In 1992, a researcher named the gut "the neglected human organ", and it took until the 21st century for the term "human microbiome" to be properly conceptualized.

In the years since, research on the trillions of individual microbes found in our guts has blossomed, and yet experts still aren't really sure what to make of their results. To date, it's not yet clear what a healthy microbiome looks like, let alone an atypical one.

There are just so many variables to consider, especially because communication between the gut and the brain seems to be a two-way street, and because diet can so quickly change the mix of gut bacteria.

In 1998, a scientist by the name of ER Bolt first hypothesized abnormal gut microbiota could be involved in the development of ASD.

Those with autism, for instance, showed more species of Clostridium and Ruminococcus bacteria in their stool than that of a control group.

But these early studies were generally deemed to be of "low to moderate quality, predominantly due to small sample sizes", "inadequate or absent explanation of sources" of the stool samples, and "potential biases", according to a trio of Dutch nutrition researchers reviewing the evidence in 2014.

Even today, carefully designed, long-term studies are hard to come by, and there is little agreement from paper to paper.

The current analysis attempts to bridge that gap by comparing existing data on the gut and ASD. For each dataset, the research team designed an algorithm to match the best pairs of autistic and neurotypical individuals by age and sex, which are two common confounding factors in autism studies.

Rather than analyzing study averages, these 600 pairs were each considered a single data point, allowing researchers to simultaneously analyze the gut microbe differences across more than a thousand individuals.

In the end, the authors found major signatures of autism in certain metabolic pathways that were associated with diet, gene expression, and particular gut microbes.

What's more, these microbes matched those identified by a recent long-term study on fecal transplants among 18 children with ASD. At a 2-year follow-up, participants showed continued improvements in gastrointestinal and behavioral symptoms, based on the scale most commonly used to evaluate symptoms of ASD.

Together, the findings suggest a potential role of the microbiome in improving autism symptoms, although how those underlying gut changes might relate to actual brain changes is still not clear.

"We were able to harmonize seemingly disparate data from different studies and find a common language with which to unite them," explains Jamie Morton, who worked on the paper as a biostatistician at the Simons Foundation, a charitable organization that funds biomedical research.

"With this, we were able to identify a microbial signature that distinguishes autistic from neurotypical individuals across many studies. But the bigger point is that going forward, we need robust long-term studies that look at as many datasets as possible and understand how they change when there is a [therapeutic] intervention."

The study was published in Nature Neuroscience .

Gut Microbes Could Help Diagnose Autism, Study Suggests

While more research is needed, scientists detected specific differences between the gut microbiomes of children with and without autism

Will Sullivan

Daily Correspondent

A petri dish with bacteria in a red-colored culture medium

A new study has found a link between certain gut microbes and autism in children. The findings suggest that analyzing the gut microbiome could one day aid in diagnosing autism, potentially making the process much quicker and more straightforward, researchers reported Monday in the journal Nature Microbiology .

“Usually, it takes three to four years to make a confirmed diagnosis for suspected autism, with most children diagnosed at six years old,” Qi Su , lead author of the study and a researcher at the Chinese University of Hong Kong, says to the Guardian ’s Ian Sample. “Our microbiome biomarker panel has a high performance in children under the age of four, which may help facilitate an early diagnosis.”

Su adds that the study’s model needs to be validated in a more diverse group of children, since most of the kids in the study were from Hong Kong. “The current study is only a start in a long journey,” he says to the New York Times ’ Teddy Rosenbluth.

Autism spectrum disorder is a developmental disability that causes people to communicate, interact and learn differently from neurotypical people, according to the Centers for Disease Control and Prevention (CDC). Its cause is unknown, though it’s thought to be connected to genetic and environmental factors, per the study.

There is no medical test for autism, so diagnosing it can be difficult for doctors, who have to rely on children’s behavior and subjective reports from parents. Some people aren’t diagnosed until adolescence or adulthood and miss out on receiving care when they are younger.

Other research has shown there might be a connection between the gut microbiome and autism. Previous studies have found that fecal transplants given from neurotypical people to people with autism improved their symptoms. But most work has focused mainly on the bacterial component of the microbiome—leaving unanswered questions around whether the gut’s archaea, fungi, viruses or functions are affected by autism, the study authors write.

For the new paper, the researchers studied 1,627 children between the ages of 1 and 13, both with and without autism. They found that 14 archaea, 51 bacteria, 7 fungi, 18 viruses, 27 microbial genes and 12 metabolic pathways were altered in children with autism. The researchers then used machine learning tools to identify the children with autism based on their gut microbes with up to 82 percent accuracy, according to the Guardian .

“The results are broadly in line with previous studies that show reduced microbial diversity in autistic individuals. It also examines one of the largest samples seen in a study like this, which further strengthens the results,” Bhismadev Chakrabarti , research director of the Center for Autism at the University of Reading in England, says in a statement from the Science Media Center. “One limitation of this data is that it cannot assess any causal role for the microbiota in the development of autism.”

Early diagnosis of autism can lead to better social and behavioral outcomes, according to the paper. The new findings could pave the way to diagnostic tests for autism that use stool samples, which contain information about the gut microbiome.

Elizabeth Lund , an independent consultant in nutrition and gastrointestinal health who did not contribute to the findings, calls this idea “very exciting” in the Science Media Center statement. “The current [diagnostic] process is very lengthy, and there is a shortage of clinicians such as psychologists and psychiatrists trained to carry out a proper diagnosis,” she adds. “Clearly, the study needs to be repeated by other groups and in other populations around the world, but the approach might offer a novel and more automated route to diagnosis in the longer term.”

Some researchers have also argued that any differences in the gut microbiome in people with autism could be caused by their diets. In the new study, the team found that while diet did impact the gut microbiomes of children with autism, the differences in their microbes persisted even after accounting for dietary variation.

“There is a changing of the winds,” Gaspar Taroncher-Oldenburg , a microbiologist at New York University who has studied links between gut microbes and autism, tells the New York Times . “People are now accepting that the microbiome is not just part of this, but it might be a fundamental piece of the puzzle.”

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Will Sullivan is a science writer based in Washington, D.C. His work has appeared in Inside Science and NOVA Next .

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New multi-national study adds to evidence linking alterations of the gut microbiome to autism

Strong new evidence linking alterations of the gut microbiome to autism spectrum disorders (ASD) comes from a new multi-national study by James Morton and colleagues.

In the study, researchers in North America, South America, Europe, and Asia developed an algorithm to re-analyze 25 datasets containing information on autistic and neurotypical controls. The datasets included 10 microbiome datasets and 15 other datasets containing information on dietary patterns, products of cell metabolism, cytokine profiles, and human brain gene expression profiles. Within each dataset, the algorithm found the best-matched pairs of autistic and neurotypical individuals based on age and sex.

new research validates autism's link to gut

“Rather than comparing average cohort results within studies,” study coauthor Gaspar Taroncher-Oldenburg says, “we treated each pair as a single data point, and thus were able to simultaneously analyze over 600 ASD-control pairs corresponding to a de facto cohort of over 1,200 children.” This allowed them to reliably identify microbes that differed in abundance between individuals with ASD and neurotypical controls.

The researchers say their analysis identified autism-specific metabolic pathways associated with specific human gut microbes. These pathways correlated with brain gene expression changes, restrictive dietary patterns, and pro-inflammatory cytokine profiles seen in individuals with ASD. “We hadn’t seen this kind of clear overlap between gut microbial and human metabolic pathways in autism before,” Morton says.

He adds, “We were able to harmonize seemingly disparate data from different studies and find a common language with which to unite them. With this, we were able to identify a microbial signature that distinguishes autistic from neurotypical individuals across many studies.”

Importantly, the researchers detected an overlap between microbes associated with autism and those identified in a long-term fecal microbiota transplant study led by James Adams and Rosa Krajmalnik-Brown (see ARRI 2023, No. 1). Commenting on the findings, Krajmalnik-Brown (who was not involved in the current study) says, “Another set of eyes looked at this, from a different lens, and they validated our findings.”

Rob Knight, a co-author of the current study, says, “Before this, we had smoke indicating the microbiome was involved in autism, and now we have fire.”

“Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles,” James T. Morton, Dong-Min Jin, Robert H. Mills, Yan Shao, Gibraan Rahman, Daniel McDonald, Qiyun Zhu, Metin Balaban, Yueyu Jiang, Kalen Cantrell, Antonio Gonzalez, Julie Carmel, Linoy Mia Frankiensztajn, Sandra Martin-Brevet, Kirsten Berding, Brittany D. Needham, María Fernanda Zurita, Maude David, Olga V. Averina, Alexey S. Kovtun, Antonio Noto, Michele Mussap, Mingbang Wang, Daniel N. Frank, Ellen Li, Wenhao Zhou, Vassilios Fanos, Valery N. Danilenko, Dennis P. Wall, Paúl Cárdenas, Manuel E. Baldeón, Sébastien Jacquemont, Omry Koren, Evan Elliott, Ramnik J. Xavier, Sarkis K. Mazmanian, Rob Knight, Jack A. Gilbert, Sharon M.Donovan, Trevor D. Lawley, Bob Carpenter, Richard Bonneau, and Gaspar Taroncher-Oldenburg, Nature Neuroscience , June 26, 2023 (free online). Address: Gaspar Taroncher-Oldenburg, [email protected] .

“New research clarifies connection between autism and the microbiome,” news release, Susan Reslewic Keatley, Simons Foundation, June 26, 2023.

This article originally appeared in Autism Research Review International, Vol. 36, No. 3, 2023

This article originally appeared in ARI’s Autism Research Review International – now available online. Visit the ARRI Online to continue reading this issue and more.

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New Research Clarifies Connection Between Autism and the Microbiome

A SFARI initiated and funded reanalysis of previous studies reveals consistent biological signals in the human microbiome and other physiological signals associated with autism and highlights the need for long-term studies to determine autism’s underlying causes.

The biological roots of autism continue to perplex researchers, despite a growing body of studies looking at an increasing array of genetic, cellular and microbial data. Recently, scientists have homed in on a new and promising area of focus: the microbiome. This collection of microbes that inhabit the human gut has been shown to play a role in autism, but the mechanics of this link have remained awash in ambiguity. Taking a fresh computational approach to the problem, a study published today in Nature Neuroscience sheds new light on the relationship between the microbiome and autism. This research — which originated at the Simons Foundation’s Autism Research Initiative (SFARI) and involved an innovative reanalysis of dozens of previously published datasets — aligns with a recent, long-term study of autistic individuals that centered on a microbiome-focused treatment intervention. These findings also underscore the importance of longitudinal studies in elucidating the interplay between the microbiome and complex conditions such as autism.

“We were able to harmonize seemingly disparate data from different studies and find a common language with which to unite them. With this, we were able to identify a microbial signature that distinguishes autistic from neurotypical individuals across many studies,” says Jamie Morton, one of the study’s corresponding authors, who began this work while a postdoctoral researcher at the Simons Foundation and is now an independent consultant. “But the bigger point is that going forward, we need robust long-term studies that look at as many datasets as possible and understand how they change when there is a [therapeutic] intervention.”

With 43 authors, this study brought together leaders in computational biology, engineering, medicine, autism and the microbiome who hailed from institutions in North America, South America, Europe and Asia. “The sheer number of fields and areas of expertise in this large-scale collaboration is noteworthy and necessary to get a new and consistent picture of autism,” says Rob Knight, the director of the Center for Microbiome Innovation at the University of California San Diego and a study co-author.

“We wanted to address the constantly evolving question of how the microbiome is associated with autism, and thought, ‘let’s go back to existing datasets and see how much information we may be able to get out of them.'” - Gaspar Taroncher-Oldenburg, director of Therapeutics Alliances at New York University

Autism is inherently complex, and studies that attempt to pinpoint specific gut microbes involved in the condition have been confounded by this complexity. First, autism presents in heterogeneous ways — autistic individuals differ from each other genetically, physiologically and behaviorally. Second, the microbiome presents unique difficulties. Microbiome studies typically report simply the relative proportions of specific microbes, requiring sophisticated statistics to understand which microbial population changes are relevant to a condition of interest. This makes it challenging to find the signal amongst the noise. Making matters more complicated, most studies to date have been one-time snapshots of the microbial populations present in autistic individuals. “A single time point is only so powerful; it could be very different tomorrow or next week,” says study co-author Brittany Needham, assistant professor of anatomy, cell biology and physiology at the Indiana University School of Medicine.

“We wanted to address the constantly evolving question of how the microbiome is associated with autism, and thought, ‘let’s go back to existing datasets and see how much information we may be able to get out of them,’” says co-corresponding author Gaspar Taroncher-Oldenburg, director of Therapeutics Alliances at New York University, who initiated the work with Morton while he was a consultant-in-residence for SFARI.

In the new study, the research team developed an algorithm to re-analyze 25 previously published datasets containing microbiome and other “omic” information — such as gene expression, immune system response and diet — from both autistic and neurotypical cohorts. Within each dataset, the algorithm found the best matched pairs of autistic and neurotypical individuals in terms of age and sex, two factors that can typically confound autism studies. “Rather than comparing average cohort results within studies, we treated each pair as a single data point, and thus were able to simultaneously analyze over 600 ASD-control pairs corresponding to a de facto cohort of over 1,200 children,” says Taroncher-Oldenburg. “From a technical standpoint, this required the development of novel computational methodologies altogether,” he adds. Their new computational approach enabled them to reliably identify microbes that have differing abundances between ASD and neurotypical individuals.

To the researchers’ surprise, their analysis identified autism-specific metabolic pathways associated with particular human gut microbes. Importantly, these pathways were also seen elsewhere in autistic individuals, from their brain-associated gene expression profiles to their diets. “We hadn’t seen this kind of clear overlap between gut microbial and human metabolic pathways in autism before,” says Morton.

Even more striking was an overlap between microbes associated with autism, and those identified in a recent long-term fecal microbiota transplant study led by James Adams and Rosa Krajmalnik-Brown at Arizona State University’s Biodesign Center for Health Through Microbiomes. “Another set of eyes looked at this, from a different lens, and they validated our findings,” says Krajmalnik-Brown, who was not involved in the study published today.

“What’s significant about this work is not only the identification of major signatures, but also the computational analysis that identified the need for future studies to include longitudinal, carefully designed measurements and controls to enable robust interpretation,” says Kelsey Martin, executive vice president of SFARI and the Simons Foundation Neuroscience Collaborations, who was not involved in the study.

“Going forward, we need more long-term studies that involve interventions, so we can get at cause-and-effect,” says Morton. Taroncher-Oldenburg, who cites the compliance issues often faced by traditional long-term studies, suggests that study designs could more effectively take into account the realities of long-term microbiome sampling of autistic individuals. “Practical, clinical restrictions must inform the statistics, and that will inform the study design,” he says. Further, he points out that long-term studies can reveal insights about both the group and the individual, as well as how that individual responds to specific interventions over time.

Importantly, researchers say these findings go beyond autism. The approach set forth here could also be employed across other areas of biomedicine that have long proved challenging. “Before this, we had smoke indicating the microbiome was involved in autism, and now we have fire. We can apply this approach to many other areas, from depression to Parkinson’s to cancer, where we think the microbiome plays a role, but where we don’t yet know exactly what the role is,” says Knight.

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For more information, please contact Stacey Greenebaum at [email protected] .

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August 18, 2022

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Study sheds new light on the relationship between gut microbiome and autism

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The biological roots of autism continue to perplex researchers, despite a growing body of studies looking at an increasing array of genetic, cellular and microbial data. Recently, scientists have homed in on a new and promising area of focus: the microbiome . This collection of microbes that inhabit the human gut has been shown to play a role in autism, but the mechanics of this link have remained awash in ambiguity. Taking a fresh computational approach to the problem, a study published today in Nature Neuroscience sheds new light on the relationship between the microbiome and autism. This research -; which originated at the Simons Foundation's Autism Research Initiative (SFARI) and involved an innovative reanalysis of dozens of previously published datasets -; aligns with a recent, long-term study of autistic individuals that centered on a microbiome-focused treatment intervention. These findings also underscore the importance of longitudinal studies in elucidating the interplay between the microbiome and complex conditions such as autism.

"We were able to harmonize seemingly disparate data from different studies and find a common language with which to unite them. With this, we were able to identify a microbial signature that distinguishes autistic from neurotypical individuals across many studies," says Jamie Morton, one of the study's corresponding authors, who began this work while a postdoctoral researcher at the Simons Foundation and is now an independent consultant. "But the bigger point is that going forward, we need robust long-term studies that look at as many datasets as possible and understand how they change when there is a [therapeutic] intervention."

The sheer number of fields and areas of expertise in this large-scale collaboration is noteworthy and necessary to get a new and consistent picture of autism." Rob Knight, director of the Center for Microbiome Innovation at the University of California San Diego and study co-author

Autism is inherently complex, and studies that attempt to pinpoint specific gut microbes involved in the condition have been confounded by this complexity. First, autism presents in heterogeneous ways -; autistic individuals differ from each other genetically, physiologically and behaviorally. Second, the microbiome presents unique difficulties. Microbiome studies typically report simply the relative proportions of specific microbes, requiring sophisticated statistics to understand which microbial population changes are relevant to a condition of interest. This makes it challenging to find the signal amongst the noise. Making matters more complicated, most studies to date have been one-time snapshots of the microbial populations present in autistic individuals. "A single time point is only so powerful; it could be very different tomorrow or next week," says study co-author Brittany Needham, assistant professor of anatomy, cell biology and physiology at the Indiana University School of Medicine.

"We wanted to address the constantly evolving question of how the microbiome is associated with autism, and thought, 'let's go back to existing datasets and see how much information we may be able to get out of them,'" says co-corresponding author Gaspar Taroncher-Oldenburg, director of Therapeutics Alliances at New York University, who initiated the work with Morton while he was a consultant-in-residence for SFARI.

In the new study, the research team developed an algorithm to re-analyze 25 previously published datasets containing microbiome and other "omic" information -; such as gene expression, immune system response and diet -; from both autistic and neurotypical cohorts. Within each dataset, the algorithm found the best matched pairs of autistic and neurotypical individuals in terms of age and sex, two factors that can typically confound autism studies. "Rather than comparing average cohort results within studies, we treated each pair as a single data point, and thus were able to simultaneously analyze over 600 ASD-control pairs corresponding to a de facto cohort of over 1,200 children," says Taroncher-Oldenburg. "From a technical standpoint, this required the development of novel computational methodologies altogether," he adds. Their new computational approach enabled them to reliably identify microbes that have differing abundances between ASD and neurotypical individuals.

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Open access
Published: 26 June 2023

Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles

James T. Morton ORCID: orcid.org/0000-0003-3189-2681 1 , 2 na1 ,
Dong-Min Jin 3 ,
Robert H. Mills 4 ,
Yan Shao ORCID: orcid.org/0000-0002-8662-0504 5 ,
Gibraan Rahman 6 , 7 ,
Daniel McDonald 7 ,
Qiyun Zhu 8 , 9 ,
Metin Balaban 6 ,
Yueyu Jiang 10 ,
Kalen Cantrell 7 , 11 ,
Antonio Gonzalez 7 ,
Julie Carmel 12 ,
Linoy Mia Frankiensztajn 12 ,
Sandra Martin-Brevet 13 ,
Kirsten Berding 14 ,
Brittany D. Needham ORCID: orcid.org/0000-0002-0280-1886 15 , 16 ,
María Fernanda Zurita ORCID: orcid.org/0000-0001-7530-5624 17 ,
Maude David 18 ,
Olga V. Averina 19 ,
Alexey S. Kovtun 19 , 20 ,
Antonio Noto ORCID: orcid.org/0000-0003-3538-0050 21 ,
Michele Mussap 22 ,
Mingbang Wang ORCID: orcid.org/0000-0002-5989-5377 23 , 24 ,
Daniel N. Frank ORCID: orcid.org/0000-0001-6669-228X 25 ,
Ellen Li ORCID: orcid.org/0000-0002-1141-0406 26 ,
Wenhao Zhou ORCID: orcid.org/0000-0001-8956-7238 23 ,
Vassilios Fanos 27 ,
Valery N. Danilenko 19 ,
Dennis P. Wall ORCID: orcid.org/0000-0002-7889-9146 28 ,
Paúl Cárdenas ORCID: orcid.org/0000-0001-9626-4489 29 ,
Manuel E. Baldeón ORCID: orcid.org/0000-0002-1243-7467 30 ,
Sébastien Jacquemont ORCID: orcid.org/0000-0001-6838-8767 31 , 32 ,
Omry Koren 12 ,
Evan Elliott ORCID: orcid.org/0000-0002-1630-969X 12 , 33 ,
Ramnik J. Xavier ORCID: orcid.org/0000-0002-5630-5167 34 , 35 , 36 ,
Sarkis K. Mazmanian ORCID: orcid.org/0000-0003-2713-1513 37 ,
Rob Knight ORCID: orcid.org/0000-0002-0975-9019 7 , 11 , 38 , 39 ,
Jack A. Gilbert ORCID: orcid.org/0000-0001-7920-7001 7 , 39 , 40 ,
Sharon M. Donovan 14 ,
Trevor D. Lawley ORCID: orcid.org/0000-0002-4805-621X 5 ,
Bob Carpenter 1 ,
Richard Bonneau 1 , 3 , 41 &
Gaspar Taroncher-Oldenburg ORCID: orcid.org/0000-0003-2840-1415 42 , 43 na1

Nature Neuroscience volume 26 , pages 1208–1217 ( 2023 ) Cite this article

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Autism spectrum disorders
Data integration
Microbiology

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut–brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella , Bifidobacterium , Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.

Multi-angle meta-analysis of the gut microbiome in Autism Spectrum Disorder: a step toward understanding patient subgroups

Age and severity-dependent gut microbiota alterations in Tunisian children with autism spectrum disorder

Longitudinal study of stool-associated microbial taxa in sibling pairs with and without autism spectrum disorder

Autism spectrum disorder (ASD) encompasses a broad range of neurodevelopmental conditions defined by heterogeneous cognitive, behavioral and communication impairments that manifest early in childhood 1 . To date, over 100 genes have been identified as putatively associated with ASD, with some genotypes now having a standardized clinical diagnosis 2 . However, most of the genetic variants are still associated with heterogeneous phenotypes, making it difficult to identify molecular mechanisms that might be responsible for particular impairments 3 . Some studies have also looked at the presence of abnormalities in different brain regions in children with ASD 4 . However, whether such neuroanatomical features could mechanistically determine autism, and whether environmental factors could induce analogous ASD-like symptoms, remain unresolved 1 .

In addition to risk factors, one comorbidity that has been linked to ASD with high confidence is the occurrence of gastrointestinal (GI) symptoms, such as constipation, diarrhea or abdominal bloating, but causal insights remain elusive 5 . Mechanistically, much research has been focused on the interplay between the GI system and processes controlled by the neuroendocrine, neuroimmune and autonomous nervous systems, all of which converge around the GI tract and together modulate the gut–brain axis (GBA) 6 .

The GBA facilitates bidirectional communication between the gut and the brain, contributing to brain homeostasis and helping regulate cognitive and emotional functions 7 , 8 . Over the past decade, research on the factors modulating the GBA has revealed the central role played by the gut microbiome—the trillions of microbes that colonize the gut—in regulating neuroimmune networks, modifying neural networks and directly communicating with the brain 9 . Dysregulation of the gut microbiome and the ensuing disruption of the GBA are thought to contribute to the pathogenesis of neurodevelopmental disorders, including autism, but the underlying mechanisms and the extent to which the microbiome explains these dynamics are still unclear 10 .

Several dozen autism gut metagenomics studies have revealed many, albeit inconsistent, variations in microbial diversity in individuals with ASD compared to neurotypical individuals 10 . Similarly, metagenome-based functional reconstructions and metabolic analyses have also shown strong, albeit inconclusive, differences between individuals with ASD and neurotypical individuals 11 . Comparative analyses at other omic levels have further shown little agreement across studies 12 , raising the question of whether the results obtained so far reflect intrinsic biological differences among cohorts, insufficient statistical power or experimental biases that preclude meaningful comparisons 13 .

A wide range of factors could explain the disagreement across studies, including confounding variation due to batch effects, the application of inappropriate statistical methodologies and the vast phenotypic and genotypic heterogeneity of ASD. Batch effects can be caused by many factors, including mis-specified experimental designs, technical variability, geographical location and demographic composition, and several algorithms have been proposed to correct for them, but a lack of standardized statistical methods further complicates interpretation 14 . Microbiome datasets, like other omic datasets, are compositional, and failure to account for the compositional nature of sequencing counts can lead to high false-positive and false-negative rates when identifying differentially abundant microbes 15 . Microbiome analysis in ASD is further confounded by the phenotypic and genotypic heterogeneity of the disorder, which is known to be critical for stratifying ASD subtypes and constructing reliable diagnostics, but is typically not measured or controlled for 1 .

Understanding the functional architecture—the network of interactions among different omic levels that determines individual phenotypes—of complex neurodevelopmental disorders, such as autism, requires an accurate and comprehensive characterization of the different omic levels contributing to it 16 . Traditionally focused on the human genomic, metabolic and cellular components, mounting evidence of the role the GBA plays in phenotype determination raises the need for considering the metagenomic and metabolic contributions of the microbiome as potential key components of the functional architecture of autism 17 .

To identify autism-specific omic profiles while reducing cohort-specific confounding factors, we devised a Bayesian differential ranking algorithm to estimate a distribution of microbial differentials, or relative log fold changes 15 , across multiple potential ASD subtypes implicit in 25 omic datasets (Table 1 ). Ranking microbes by their log fold changes allows us to simultaneously (1) cancel out the compositional bias inherent in microbiome datasets and (2) minimize inflated false positives due to microbe-specific false discovery rate (FDR)-corrected statistical tests 15 . A key feature of our approach was to match individual study participants by sex and age within each study to adjust for confounders in childhood development. This setup also reduced confounding variation due to cohort-specific processing protocols, because within-study fold change calculations are insensitive to batch effects 18 (Extended Data Fig. 2 ). The preponderance of autism among males is well documented, and several potentially sex-dependent mechanisms to explain this phenomenon have been proposed. Furthermore, the development of the microbiome during childhood is a hallmark of microbiome dynamics in the human gut. Our analysis reveals strong associations among omic levels along the GBA and in particular of the microbiome in the context of ASD. Ultimately, our analysis highlights the inherent limitations of cross-sectional studies for understanding the dynamics of the functional architecture of autism and provides a framework for future studies aimed at better defining the causal relationship between the microbiome and other omic levels and ASD.

The structure of our analysis consisted of a multi-cohort and multi-omic meta-analysis framework that allowed us to combine independent and dependent omic datasets in one integrated analysis 19 . To minimize issues of compositionality and sequencing depth 20 , we modeled sequencing count data using a negative binomial distribution 21 (Extended Data Fig. 1 , ‘Study approach’). Our differential ranking approach incorporated a case–control matching component that individually paired children with ASD with age-matched and sex-matched neurotypical control children within each study cohort to adjust for confounding variation and batch effects ( Supplementary Information ). Finally, we cross-referenced the microbial differential rankings estimated from 16S rRNA gene (16S) amplicon data obtained from seven age-matched and sex-matched cohorts against 15 other omic datasets to contextualize the potential functional roles that these microbes could play in autism (Fig. 1 ).

a , Children with ASD and neurotypical children of the same gender and similar age (±6 months) were matched within studies to reduce batch effects due to experimental and other cohort-specific differences. Matched pairs were then used to compute differentials (log fold ratios) of different omic features (microbes, metabolites, etc.). Downstream analyses across studies compared the within-study differentials determined for the different pairs of matched individuals (numbers inside circles denote age in years). b , The structure of our meta-analysis across multiple omic levels. For Fig. 2 , 16S differentials computed from age-matched and sex-matched cohorts were cross-referenced against 16S differentials from sibling-matched cohorts as well as against SMS differentials from other age-matched and sex-matched cohorts. For Fig. 3 , the 16S differentials from the age-matched and sex-matched cohorts were cross-referenced against cytokine differentials and RNA-seq differentials using KEGG pathways as a reference. Figure 3 also includes a microbe–diet co-occurrence analysis. For Fig. 4 , the 16S differentials from the age-matched and sex-matched cohorts were cross-referenced against 16S differentials computed from the Kang et al. FMT trial 52 .

Age matching and sex matching enhance ASD data analysis

To establish the validity and robustness of our age-matched and sex-matched Bayesian differential ranking approach, we performed a series of benchmarking exercises and sensitivity tests.

We started by investigating the means and the standard deviations for the 16S and shotgun metagenomics sequencing (SMS) differentials from the age-matched and sex-matched cohorts compared to the total sequencing depth for each microbe (Extended Data Fig. 2a–d ). In both analyses, we observed that the models could use sequencing depth to calibrate the uncertainty estimates, giving larger standard deviations for rare taxa with fewer observed reads (Extended Data Fig. 2b,d ). Furthermore, rare taxa (with fewer than 100 reads total) were not among the most differentially abundant ASD-associated taxa (Extended Data Fig. 2a,c ).

Next, we performed a rarefaction benchmark to test whether the high frequency of rare taxa would influence the results of our log fold change calculations. A comparison of differential abundance estimates between rarefied (9,000 threshold) and non-rarefied data from our 16S cross-sectional datasets showed that rarefaction did not substantially affect our results (Extended Data Fig. 2e ). We also conducted a data-driven simulation with varying differential sequencing depths between cases and controls and showed that, despite the sequencing depth confounder, our differential abundance method could accurately recover the ground truth log fold changes (Extended Data Fig. 2f ). We then compared performance of our age-matched and sex-matched differential ranking analysis to the standard group-averaged differential ranking analysis across seven out of the 11 16S studies 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 (Extended Data Fig. 2g ). A side-by-side comparison with a commonly used differential abundance approach, ANCOM-BC (ref. 30 ), was then conducted to highlight the differences between our methodology and one of the state-of-the-art differential abundance methods (Extended Data Fig. 2i–k ).

We benchmarked the overall batch-effect-reducing power of performing within-study differential analyses with our sex-matched and age-matched Bayesian differential ranking approach. We used the MicroBiome Quality Control (MBQC) study (Sinha et al. 2017 (ref. 31 )) to evaluate the extent to which within-study differential analysis removed experimental and other study-related confounders, allowing for meaningful comparisons across independent studies. Focusing on the microbial abundance datasets (16S microbial counts) generated by four independent laboratories (Lab A, Lab B, Lab C and Lab D) processing two identical MBQC microbiome samples (samples ‘4’ and ‘6’), we calculated the differentials between microbial counts for these samples for each laboratory. An initial assessment of overall variability between the two samples (principal coordinate analysis (PCoA) plot with Bray–Curtis dissimilarity) (Extended Data Fig. 2l ) showed a reasonable separation between both samples just based on microbial counts, but a visualization of study membership revealed that a significant degree of variability was associated with the laboratory that generated the dataset (Extended Data Fig. 2n ). Given that the metagenomic samples by each laboratory were identical, the high level of variability observed among datasets could be ascribed only to experimental and laboratory-specific batch effects. Consistent with the theoretical findings of McLaren et al. 18 , our differential analysis showed a high degree of correlation among within-study differentials, clearly supporting the use of scale-equivariant log fold change calculations within studies as a way to provide a high-confidence readout of ground truth differentials and to enable cross-comparisons of independent cohort studies (Extended Data Fig. 2m ).

To determine if our analyses can generalize between 16S and SMS datasets, we focused on the Dan et al. 28 cohort that paired 16S and SMS samples. Abundances obtained after mapping reads to the Greengenes2 database 32 highlight strong agreement between 16S and SMS datasets on the genus level ( r = 0.63, P < 1 × 10 −100 ). Furthermore, the log fold changes between 16S and SMS obtained from our age-matching and sex-matching approach also show strong agreement on the genus level ( r = 0.47, P = 1 × 10 −7 ).

Differential ranking analysis reveals strong ASD–microbiome links

A global age-matched and sex-matched differential ranking analysis of the seven 16S datasets selected for this study revealed a clear partitioning of microbial differences with respect to ASD and cohort membership (Fig. 2a ). The distribution of the overall case–control differences showed a strong ASD-specific signal driven by 591 microbes more commonly found in children with ASD and 169 microbes more commonly found in their control counterparts (Supplementary Table 4) . The variability observed is most likely due to confounding factors such as cohort demographics and geographic location, with the seven cohorts originating from Asia, Europe, South America and North America. Analogous differential ranking trends could be observed for the virome, SMS and RNA sequencing (RNA-seq) datasets (Extended Data Fig. 3 ). To determine whether these highly significant microbiome signals ( P < 0.0025) could be used to distinguish children with ASD from their age-matched and sex-matched control counterparts, we trained random forest classifiers on train/validation/test splits of data derived from 16S-targeted sequencing and SMS–whole-genome sequencing of microbial communities. We fitted gradient boosting classifiers on combined microbiome datasets as well as on individual datasets and measured their performance with area under the receiver operating characteristic (AUROC) curve. Of the nine age-matched and sex-matched cohorts 22 , 23 , 24 , 25 , 27 , 28 , 29 , 33 , 34 , six of the studies had an AUC > 0.87, highlighting the strong microbial differences between children with ASD and neurotypical children within age-matched and sex-matched cohorts (Fig. 2b ). The classification performance decreased when we trained one classifier across all 1,193 samples across all of the cohorts but is still predictive of ASD (AUC = 0.78). This is consistent with previous observations in other disease meta-analyses 35 , where within-study classification performance is greater than across-study classification performance. We suspect that widespread microbial heterogeneity across diverse human populations could play a role in impeding classification performance.

a , Global microbial 16S log fold changes between age-matched and sex-matched ASD and control individuals. Error bars represent the 95% credible intervals. Heat map showing all center log ratio (CLR) transformed microbial differentials for each age-matched and sex-matched ASD–control pair across all cohorts. Microbes are binned into ASD-associated, Neutral and Control-associated groups using an age-matched and sex-matched classifier ( Methods ). K is an unknown bias due to the shift in the microbial load between the ASD and neurotypical control population. b , Sample size, male:female (M:F) ratio and average ages across all 16S and shotgun metagenomics datasets analyzed in this study and held-out gradient boosting ASD prediction performance measured by AUROC. V3–V4, V4 and V4–V5 refer to the variable region of the bacterial ribosomal RNA analyzed. c , Log ratios of microbes that are classified to be ASD associated and control associated were computed for each sample. The x axis represents the case–control differences of these log ratios, where values greater than 0 indicate that there is a separation between children with ASD and neurotypical children. The box plots show the median (line), 25–75% range (box) and 5–95% range (whiskers). d , Effect sizes of different omics levels: viral, 16S, SMS and RNA-seq.

In contrast to the age-matched and sex-matched cohorts, the AUC dropped substantially in the sibling-matched cohorts (Son et al. 36 AUC = 0.69; David et al. 37 AUC = 0.46; Elliot et al. 38 AUC = 0.38). Similarly PERMANOVA detected ASD-specific microbiome differences only in the age-matched and sex-matched cohort ( P = 0.002), whereas no such signal was found in the sibling-matched cohort ( P = 0.535). In both cohorts, age and sex were significant confounders ( P < 0.002), but only in the age-matched and sex-matched cohort could the age and sex differences between case–control pairs be minimized (Extended Data Fig. 6a–d ), where more than two times more case–control pairs are the within 1-year age difference and the same gender compared to the sibling-matched cohort (Extended Data Fig. 6e,f ). Although household has been observed to be a confounder in the sibling-matched cohort ( P < 0.001), we did see strong classifier generalization in the age-matched and sex-matched cohorts, where none of the children live in the same home. However, it is possible that unmeasured confounders, such as household diet or socioeconomic status, could artificially boost classification performance. To investigate the potential age and sex confounders in the sibling studies, we performed a data-driven simulation with a known ground truth to determine how large age differences (± >2 years) would bias modeling outcomes compared to optimized age matchings (± ≤0.5 years) using a sibling-like age distribution (obtained from David et al. 37 ) and our overall sex-matched and age-matched distribution, respectively (Extended Data Fig. 6c ). The analysis showed that, in case–controls with a sufficiently large age confounder, methods using age and sex matching or sibling matching cannot exactly recover the ground truth log fold changes. However, for sibling-like age distribution, the estimated log fold changes exhibited a large bias (mean squared error = 589.3) that was reduced by an order of magnitude in the sex-matched and age-matched group (mean squared error = 57.8) (Extended Data Fig. 6g,h ).

Age-matched and sex-matched differential analysis outperformed standard group averaging with respect to R 2 , and its overall performance strictly improved as more studies were added (Extended Data Fig. 2g,h ). This performance boost reflected a reduction in model uncertainty with larger cohorts that was indicative of overlapping differentially abundant taxa across studies and of reduced confounding variation. To aid in the interpretation of the classification results, we constructed log ratios of taxa derived from the age-matched and sex-matched differential abundance analysis that strongly separated children with ASD from the neurotypical controls within each study. From these individual analyses, we assembled a single microbial log ratio that highlighted a strong consistent enrichment of taxa in children with ASD relative to their control counterparts with log ratios greater 0 across 88% of pairs (Fig. 2c ). This pattern was consistent across all age-matched and sex-matched cohorts, including two held-out shotgun metagenomics datasets: Wang et al. 33 (log ratios > 0 in 70% of pairs) and Dan et al. 28 (log-ratios > 0 in 73% of pairs).

ASD-specific patterns are present at several omic levels

Differential ranking analysis of three omic levels—microbiome (16S and SMS) and human transcriptome (RNA-seq)—revealed strong and highly significant differences between children with ASD and their age-matched and sex-matched neurotypical counterparts ( P < 0.0025) (Fig. 2d and Supplementary Tables 5 and 6) . Two additional omic levels—the metabolome and the virome—did not show significant signals (Extended Data Fig. 4 and Supplementary Table 7) .

Host cytokines correlate with microbial abundances

Immune dysregulation, ranging from circulating ‘anti-brain’ antibodies and perturbed cytokine profiles to simply having a family history of immune disorders, has been repeatedly associated with ASD 39 . Recently, for example, Zurita et al. 24 showed that concentrations of the inflammatory cytokine transforming growth factor beta (TGF-β) are significantly elevated in children with ASD. We re-analyzed this dataset, after age matching and sex matching, and observed that 16S microbial differentials estimated from Zurita et al. 24 were associated with TGF-β and were positively correlated with the global microbial log fold changes between ASD and control pairs (TGF-β: r = 0.237, P = 2.84 × 10 −5 ) (Supplementary Tables 1 and 9 ). In contrast, the global microbial log fold changes had little correlation with interleukin (IL)-6 concentrations ( r = 0.07, P = 0.17). However, when we calculated the log ratios of the most differentiating microbial taxa, they were highly correlated with both TGF-β and IL-6 concentrations (TGF-β: r = 0.61, P = 1.84 × 10 −5 ; IL-6: r = 0.73, P = 5.74 × 10 −8 ) (Fig. 3a–d ). This highlights how IL-6 changes are linked to only a handful of taxa, whereas TGF-β is linked to a much larger number of taxa.

a , b , Comparison of microbial differentials obtained from age matching and sex matching and cytokine analysis. c , d , Microbial log ratios constructed from the 50 top and bottom most differentially abundant microbes corresponding to each cytokine. K and C represent unknown biases due to the shift in the microbial load between the ASD and neurotypical control population. e , Heat map showing the overlap of molecules between ASD-enriched pathways in the microbiome and in the brain. The microbial and human pathways are both sorted alpha-numerically; the dense diagonal is largely indicative of common pathways between microbial and human genomes. f , g , PC3 from microbe–diet co-occurrence analysis is contrasted against microbial log fold changes and dietary differences from Berding et al. 25 Dietary compounds that are depleted ( P < 0.1) in children with ASD are highlighted as ‘x’ markers. T (ASD-Control) represents the t -statistic that measures the differences between ASD and neurotypical dietary intake. conc., concentration.

Prevotella , Bacteroides and Bifidobacterium were predominantly associated with the cytokine differentials. Partial mechanistic insights on some of these cytokine–microbe associations were previously published. Bacteroides thetaiotaomicron was the second most highly elevated microbe when TGF-β was depleted and has been suggested to play a role modulating maternal immune activation-dependent metabolites that are linked to behavorial symptoms 40 . Bifidobacterium callitrichidarum was the sixth most enriched taxon when IL-6 was in lower concentration. Other Bifidobacteria species, such as Bifidobacterium longum , have been observed to downregulate IL-6 in fetal human enterocytes in vitro 41 . Prevotella copri was the second most enriched taxon when IL-6 was in lower concentration and the sixth most enriched taxon when TGF-β was in lower concentration. This was consistent with Tett et al. 42 , where P. copri associations with different cytokines were observed in multiple disease contexts. Similarly, P. copri and Bacteroides fragilis both co-occurred with phages enriched in children with ASD or in neurotypical children (Extended Data Fig. 7 and Supplementary Table 10) , but, whereas microbes were previously reported to mediate viral infections 43 , the mechanistic underpinnings of these interactions with the host’s immunity remain poorly understood 44 .

Microbiome metabolism mirrors human brain metabolism in ASD

To determine potential crosstalk between microbiome physiology and the human brain, we compared the metabolic capacities encoded by the microbial metagenome—combining the individual metabolic capacities of thousands of different microbes—and the differentially expressed human genome in the brain, two omic levels representing entirely different biological contexts. We identified 138 microbial and 1,772 human metabolic encoding genes, inferred from SMS and RNA-seq, respectively, that were linked to ASD phenotype. Ninety-five human metabolic pathways differentially expressed in the brain tissues of individuals with ASD had analogous microbial pathways differentially abundant in the microbiome of children with ASD, suggesting a potential coordination of metabolic pathways across omic levels in ASD (Fig. 3e ). Pathways related to amino acid metabolism, carbohydrate metabolism and lipid metabolism were disproportionately represented among the overlapping pathways (Extended Data Fig. 9 and Supplementary Table 14) . Cross-comparison of the ASD-associated microbial enzyme-encoding genes with the gut–brain modules (GBMs), previously defined as part of the GBM framework, also revealed an approximately 48.5% overlap (101/208), further supporting the notion of potential metabolic crosstalk across omic levels 45 (Supplementary Table 13) .

Microbiome metabolic capacity mirrors diet patterns in ASD

Autistic traits in early childhood have been shown to correlate with poor diet quality later in life; however, little is known about how diet quality is directly linked to autistic traits 46 . Here, we re-analyzed the paired microbiome and dietary survey data from Berding et al. 25 . A microbiome–diet co-occurrence analysis revealed startlingly similar amino acid, carbohydrate and lipid metabolism association patterns to those observed in the microbiome–brain metabolic capacity analysis (Supplementary Tables 2 and 15) ( Q 2 = 0.43). From the microbe–diet co-occurrence analysis, only principal component (PC) 3, which explains 3% of the microbe–diet variance, could differentiate between ASD and neurotypical diets ( r = 0.26, P = 0.004) and was strongly correlated with the microbial log fold changes between ASD and age-matched and sex-matched controls ( r = 0.22, P = 4.3 × 10 −9 ). Autistic children were less likely to consume foods high in glutamic acid, serine, choline, phenylalanine, leucine, tyrosine, valine and histidine, all compounds involved in neurotransmitter biosynthesis 47 . Interestingly, multiple Bacteroides taxa and P. copri taxa were among the top 20 taxa along MMvec PC3, highlighting how these taxa could be involved in metabolizing amino acid dietary compounds (Fig. 3f and Extended Data Fig. 8 ). Even though the metabolomic analysis did not yield statistically significant signals after FDR correction, the metabolites that showed the strongest signal included glutamate and phenylalanine, consistent with the microbiome–diet analysis 48 , 49 , 50 . Disruptions in the biosynthesis of these neurotransmitter molecules have been implicated in a wide variety of psychiatric disorders, and a recent blood metabolomics study showed the potential of using branched-chain amino acids to define autism subtypes 51 . Due to the incompatibility among the molecular features across datasets, it was not possible to combine any of the metabolomics datasets to boost the statistical power, which remains a major limitation of metabolomics technologies at present ( Methods ).

ASD microbiomes mirror behavior improvement after fecal matter transplant

Although the preceding cross-sectional analyses showed significant associations among several omic levels (virome, microbiome and immunome) or diet and ASD, insights into causality are still limited. By contrast, longitudinal intervention studies provide an opportunity to obtain stronger insights into causality. To test this, we re-analyzed data from a 2-year, open-label fecal matter transplant (FMT) study with 18 children with ASD 52 . In this study, the children were subjected to a 2-week antibiotic treatment and a bowel cleanse, followed by 2 d of high-dose FMT treatment and 8 weeks of daily maintenance FMT doses. Based on one of the most common evaluation scales for ASD, the Childhood Autism Rating Scale (CARS), significant improvements were achieved after the 10-week course of treatment. Two months later, the initial gains were largely maintained, and a 2-year follow-up showed signs of further improvement in most of the patients. The results are consistent with a potential role of the microbiome in improving autism symptoms, but how the underlying changes in microbiome composition related to those seen in other studies remains unknown.

In the present study, we re-analyzed the original raw data in the context of the ASD profiles revealed by our cross-sectional differential ranking analysis (Supplementary Tables 3 and 16) . All microbes associated with ASD in the 18 children before the FMT treatment had been identified as ASD-associated microbes in our age-matched and sex-matched cross-sectional analysis. After 2 years, 91% of these microbes that had low uncertainty (posterior standard deviation < 3) exhibited a mean decrease in abundance, and this decrease was significant (95% log fold change quantile < 0) in 57% of the microbes (Fig. 4 ). Consistent with the original analysis by Kang et al., we detected an increase in Prevotella sp . over the 2-year span of the study. In addition, we also determined an increase in Desulfovibrio piger and no significant changes in Bifidobacteria , counter to the original analysis by Kang et al. 52 Interestingly, 305 taxa remained stable throughout the duration of the study. Of these, 13 taxa belonged to the Prevotella , Bifidobacterium , Bacteroides and Desulfovibrio lineages, pointing to a potentially wide functional diversity within these genera not noted in the original study. Some of these taxa, including B. fragilis , B. thetaiotaomicron , B. longum and P. copri 42 , were previously associated with beneficial immunomodulatory properties. Also worth pointing out are multiple butyrate producers in the Butyricimonas and Anaerobutyricum genera that we detected as being stable throughout the 2 years of the study, indicating a potential role in contributing to GBA homeostasis 53 .

a , The improvement of CARS for each child with ASD over time. The children were split into three groups—non-ASD, mild/moderate and severe—based on whether their CARS score fell below 30, was between 30 and 37 or was higher than 37. b , Microbial log fold changes over time: the time series was generated by calculating log fold changes between timepoints for each microbe. ASD-specific microbes highlighted in red were determined in the cross-sectional study. c – f , Microbial log fold changes are re-colored with genera highlighted in cytokine comparisons.

The functional architecture of ASD, and in particular the potential role that the microbiome plays in modulating the GBA in the context of autism, remain poorly understood due to disagreements among existing microbiome and other omic studies. However, in contrast to recently reported findings 54 , we observed a clear separation between children with ASD and unrelated age-matched and sex-matched neurotypical controls, and this signal was validated using three distinct methodologies—namely PERMANOVA, classification and differential abundance—across multiple cohorts. Unlike the age-matched and sex-matched analysis, no ASD–microbiome signal was detected in the sibling-matched cohorts, including in the Elliott et al. 38 dataset that consists of individuals with chromosome 16p11.2 deletion, a known risk factor for ASD. One possibility is due to age and sex confounding in the sibling cohorts, because age remains a major confounding factor in early childhood microbiome development 55 . However, we cannot rule out the possibility that our classifiers are identifying differences between households rather than individuals with ASD in the age-matched and sex-matched cohorts. Previous efforts identified household-specific effects on the human microbiome 56 , and other studies raised issues with sibling controls in these studies because siblings often exhibit a higher risk of developing ASD compared to the general population 57 . However, the fact that we see a clear ASD–microbiome signal that generalizes across households within cohorts highlights the need to control these confounding factors to understand the functional role that these gut microbiota could play. Thus, a follow-up study investigating gut microbiome and genetic variation between households with and without children with ASD is needed.

Parallel analyses at the immunome, human transcriptome and dietome levels revealed strong associations among omic levels. The virome and the direct metabolome signals, although present, were markedly weaker than the other omic signals. The inferred ASD-specific metabolic profiles from the microbiome and the human transcriptome, on the other hand, showed a high and significant degree of overlap in microbial and human pathways expressed in the gut and in the brain, respectively. The metabolic connection implied by this overlap, which included differentially enriched carbohydrate and amino acid metabolic pathways in ASD, is a remarkable observation given the fundamental difference between the gut and brain physiologies, which would a priori suggest a reduced overlap in metabolic capacities. The microbiome–diet co-occurrence analysis also highlighted a reduced intake of amino acids and carbohydrates linked to specific microbiome profiles in children with ASD. These metabolic and dietary imbalances, particularly regarding glutamate levels, were further apparent, albeit weakly, in the serum, fecal and urine metabolomes that we analyzed. This multi-scale overlap that we observed along the GBA points to the existence of a functional architecture of ASD driven by the metabolic potential at the genomic and metagenomic levels.

In light of the heterogeneity across studies, our analysis identified several microorganisms consistently detected across omic levels that point to potentially interesting functional connections. The diet co-occurrence analysis also showed a strong association between P. copri and carbohydrate depletion in ASD, in addition to upregulation of IL-6. Bacteroides genera are observed to play a key role in ASD diet differentiation, with B. thetaiotaomicron associated with the depletion of TGF-β. Multiple other microbes, including P. copri and several Bacteroides , stood out in the immune and viral analyses. In the FMT study, we observed a stable core microbiome made up of Bacteroides , Prevotella , Bifidobacteria and Desulfovibrio in addition to multiple butyrate producers. The presence of this core microbiome in combination with the depletion of most ASD-associated taxa further suggests a causal role for these microorganisms in shaping autism symptoms.

Despite our inability to determine actual metabolomic profiles at this point ( Methods ), our metabolite analysis based on microbiome-derived and brain-derived metabolite inferences as well as the diet-derived metabolite data reveals a picture of a unifying and distinct ASD functional architecture. With the brain, the immunome and diet as major effectors, the multi-factorial complexity of ASD is reduced to a multi-scale set of interactions centered around human and bacterial metabolism that, in turn, determines phenotypic, genomic and metagenomic attributes via multiple feedback loops. Although we did not observe an effect on genotype to the microbiome, previous studies identified genes that are high risk for ASD 2 . The pivotal role of the immune system in mediating the communication between the gut microbiome and the human brain as well as other peripheral systems is also firmly established. Furthermore, the central role of the microbiome in mediating diet-derived nutrient mobilization has been extensively documented, and several hardwired feedback loops among these effectors, such as the hypothalamus-mediated regulation of appetite and diet, have also been described 6 .

Our understanding of how the gut microbiome is connected to dietary preferences, host immunity and GI and ASD behavioral symptoms is limited in cross-sectional studies and, thus, restricts our ability to perform causal inference. We envision that obtaining causal insights into the functional architecture of autism will require a multi-arm approach, from culturing key microbes and probing their metabolic capacity, to performing experimental interventions with model organisms and conducting longitudinal observational studies, with multi-omics data collection and extensive phenotypic profiling to observe the effects of natural interventions. Building realistic causal models of autism needs to take into account the multi-factorial complexity underlying different ASD subtypes, which will require a concerted effort to simultaneously analyze several omic levels and at clinically relevant timescales. For instance, understanding the engraftment dynamics of FMT and its functional implications on the recipients’ gut microbiomes requires frequent initial sampling of the microbiome, immunome and metabolome, but tracing any behavioral changes over time requires less frequent sampling over periods of up to several years, in combination with reliable behavioral, medical and dietary surveys 58 . Collecting and integrating such multi-scale omic datasets presents unique logistical and analytical challenges.

Managing data acquisition and access will require coordinating multiple sites and potentially centralizing some aspects of sample processing. Recent initiatives, such as The Environmental Determinants of Diabetes in the Young (TEDDY) study, an international long-term, multi-center initiative to link specific environmental triggers to particular type 1 diabetes-associated genotypes, provide a blueprint for similar approaches in ASD. A key component of such an initiative would be the establishment of standardized sampling and processing protocols that would minimize technical confounders, one of the top confounders at most omic levels. Moreover, although extensive efforts are underway to calibrate microbiome datasets, other omic levels, such as the metabolome, present even more fundamental technical issues that make it imperative to develop concerted strategies to be able to include them in an integrated analysis.

In addition to the considerable variations in statistical properties across datasets, interactions among omic levels are mostly underdetermined, making the construction of informative models a major challenge. Determining the necessary biologically relevant assumptions is a non-trivial process and can inadvertently lead to model mis-specifications, resulting in misleading conclusions. This was the likely consequence from Yap et al. 54 , where the proposed model that tested for a causal relationship among diet, microbes and the ASD phenotype implicitly assumed that there was no relationship between diet and gut microbiome, prematurely rejecting the potential role between gut microbiota and ASD. Addressing these types of model mis-specication issues will be critical to inferring causal mechanisms from population-scale studies. In addition, and given the vast heterogeneity of ASD, designing cohort studies that minimize confounding factor effects will be key to furthering understanding of autism. For example, although our analysis could not identify ASD subtypes, we determined stronger associations among gut microbes, host immunity, brain expression and dietary patterns than previously reported, highlighting the potential for boosting the statistical power and biological insight with comprehensive omic analyses.

We conclude that multi-omic longitudinal intervention studies on appropriately stratified cohorts, combined with comprehensive patient metadata, would provide the necessary entry points for advancing mechanistic studies along the GBA in ASD. The experimental framework that we propose for inferring causal mechanisms from population-scale studies will require the development of consensuated multi-disciplinary strategies. For instance, given the central role played by the metabolome in relaying information across omic levels, a unified approach to metabolomics studies will be needed to overcome current differences in data types (targeted versus untargeted and liquid chromatography–mass spectrometry (LC–MS) versus gas chromatography–mass spectrometry (GC–MS)) or origin of the specimens (blood/serum, urine or feces). Phenotyping behavorial and GI symptoms in children with ASD is another issue that is still far from being resolved, making it further challenging to stratify patient cohorts. Issues of timescales—from the molecular to the behavioral—need to be harmonized in statistically relevant ways to allow for proper causality inference. Finally, using appropriate statistical methodologies for identifying potential causal relationships will be critical to ensure the success of the proposed mechanistic studies and of efforts to advance understanding of the role that the microbiome plays in the context of the overall functional architecture of ASD.

Search strategy and inclusion criteria

We performed a systematic search for published and/or publicly deposited or not yet published and/or publicly available human microbiome, metabolome, immunome, transcriptome and autism/ASD datasets in several National Center for Biotechnology Information (NCBI) databases (PubMed, Sequence Read Archive (SRA) and BioProject), UCSD’s MassIVE resource, the PsychENCODE consortium and the American Gut Project and from individual research groups worldwide. About half of the 70+ studies that we identified were already deposited on public data repositories or were made directly available to us by the research groups.

Most studies consisted of heterogenous—no genotype or phenotype stratification—ASD and neurotypical age-matched and sex-matched cohorts and had one or two datasets (microbiome (16S, SMS), metabolome (urine/serum/fecal), immunome (cytokines), transcriptome (RNA-seq), dietary survey and behavioral survey) associated with them, with only a few studies having three or more omic datasets associated with them (Table 1 ). We adopted a multi-cohort and multi-omics meta-analysis framework that allowed us to combine independent and dependent omic datasets in one overall analysis 19 . In total, we analyzed 528 ASD–control pairs that had either age and sex information or sibling-matching information. To reduce the batch effects and noise associated with primer choice in the 16S datasets, a major confounder in microbiome analyses, we restricted the 16S datasets to include only those targeting the variable region V4 of the bacterial ribosomal RNA, a region exhibiting higher heterogeneity and lower evolution rates than other variable regions 64 . Previous studies showed how primers targeting adjacent regions in the 16S can yield similar composition estimates up to the genus level 65 . Our analysis included 16S datasets obtained targeting the V4 region exclusively, the V3–V4 region or the V4–V5 region.

The final metabolomic meta-analysis that we present here consists of the combined analysis of only four independently pre-processed, normalized and analyzed metabolomic datasets. Despite several more ASD-related datasets being available, the disparity in mass spectrometric technologies used to generate them, which results in the detection of different subsets of metabolites, precluded their side-by-side comparison (Table 1 ). For example, targeted mass spectrometry enables the precise determination of concentrations for a finite number of metabolites, whereas untargeted mass spectrometry detects up to two or three orders of magnitude more metabolites but is compositional in nature and, thus, does not yield absolute abundances. Furthermore, batch effects due to sample processing, such as differences in reagents, sample storage and mass spectrometry instruments, can introduce unwanted variation in both the abundances and the detected molecular features 66 . One additional obstacle that we encountered was the proprietary nature of many of the metabolomic datasets that made it impossible to access the raw data and run standardized workflows.

Of the 40 transcriptomic datasets that were available in recount3 (ref. 67 ), the vast majority were obtained from studies with model animals, and only four of them had been obtained from postmortem processing of brain samples from autistic and neurotypical individuals. These four datasets collected different brain tissue types, including from the amygdala, the prefrontal cortex, the anterior cingulate and the dorsolateral prefrontal cortex.

Martin-Brevet et al. cohort

Data from Martin-Brevet et al. 38 were acquired from two different cohorts: one from the Simons Variation in Individuals Project, consisting mostly of families from the United States. From this cohort, there are 24 individuals with the 16p11.2 deletion and 24 corresponding siblings from the same family who do not carry the deletion; and a second cohort consisting of individuals from the European 16p11.2 consortium (24 deletion carriers and 24 familial controls). More exact information about this cohort was previously published 38 . Deletion carriers were ascertained regardless of age or clinical diagnosis. DNA was extracted from stool samples, and 16S sequencing was performed using primers to the V4 region.

Data processing

We constructed matched reference databases for 16S and SMS data analyses. The Web of Life 2 (WoL2) reference genome database contains 15,953 bacterial and archaeal genomes sampled from the NCBI to maximize representation of biodiversity. It is a major upgrade from WoL (10,575 genomes). A reference phylogeny was reconstructed based on 387 universal marker genes using uDance, a novel phylogenomic inference workflow employing a divide-and-conquer method. Taxonomic assignments of the genomes were based on Genome Taxonomy Database (GTDB) r207 and curated according to the phylogeny using tax2tree. The Greengenes2 reference 16S rRNA database was constructed based on the WoL2 whole-genome phylogeny and updated with full-length 16S rRNA sequences from the Living Tree Project and 16S from high-quality bacterial operons, using uDance to revise the topology. Into this backbone, we inserted all 16S V4 amplicon sequencing variants from public and private samples Qiita using DEPP. A taxonomy based on GTDB r207, expanded with lineages from the Living Tree Project not present in GTDB, was decorated onto the phylogeny using tax2tree. Full details behind the construction of WoL and Greengenes2 can be found in Usyk et al. 32 .

The 16S amplicon and shotgun metagenomics samples were downloaded from the SRA. The 16S amplicon samples were processed using Deblur and subsequently mapped to Greenegenes2 using Vsearch with qiime2 (ref. 68 ). Shotgun metagenomics samples were mapped to bacterial whole genomes captured in the WoL2 using Bowtie2 followed by Woltka 69 . Viral abundances were extracted from shotgun metagenomics samples using GPD and BWA. RNA expression data were obtained directly from recount3 (ref. 67 ); the four metabolomics datasets were provided by the authors.

To enable age and sex matching, a bipartite matching between individuals with ASD and neurotypical individuals was performed using age and sex covariates. This approach has been shown to be optimal for case–control matching 70 . Individuals who could not be matched were excluded from the meta-analysis. Among the 16S and SMS datasets, there were multiple longitudinal datasets. To integrate these datasets into the cross-sectional analysis, we picked only the first timepoint for each individual.

Differential ranking analysis

One of the most common approaches to evaluating microbiome and other omic studies consists of determining differences in the abundances of microbial taxa, human metabolites or other omic features between cases and controls. Such differential abundance analysis is typically performed by computing the log fold changes between the case and control groups 21 . However, confounders, such as sex-related, age-related and geography-related batch effects, compositionality, high dimensionality, overdispersion and sparsity, prevented a reliable estimation of differential abundances and, thus, compromised the side-by-side comparison of these differential abundances across studies in the manner of a traditional meta-analysis. Here, we set out to overcome these inherent limitations of traditional meta-analyses by developing a generalizable approach for controlling for select confounders that would help reveal a comprehensive picture of ASD-specific omic signals.

To minimize confounder effects, we developed a Bayesian differential ranking algorithm that uses bipartite matching to optimize the age-based and sex-based pairing of ASD and control individuals within each dataset. This approach helped control for potential age and sex confounders while also minimizing batch effects, such as sample collection method, sample processing protocol, different primers and geographical provenance 71 . Our approach could do this by leveraging recent insights into the multiplicative nature of protocol biases 18 . Fold change calculations can be designed to be robust to bias induced by protocols, provided that the fold changes are being computed only on samples processed under the same protocol. Similar observations have been made about biases induced by differences in polymerase chain reaction (PCR) primers, with abundance-based beta diversity metrics being robust to primer biases, as long as comparisons are confined to datasets generated with the same protocol 71 . We extended this strategy to handle age and sex matching, taking advantage of the fact that most of the cohorts that we analyzed selected their participants to be age and sex matched. Most of the case–control pairings in the 16S and SMS datasets were within 1 year apart, providing an opportunity to remove age-related confounders in downstream analyses. Our Bayesian models were fitted via Markov chain Monte Carlo (MCMC) using Stan 72 . Conceptually, this allowed us to compute log fold change differences of microbes between age-matched and sex-matched individuals, but, because we did not have absolute abundance information, we could estimate this log fold change only up to a constant 73 ( Supplementary Information ).

To determine how sensitive our proposed differential abundance strategy was to sequencing depth, we conducted a rarefaction benchmark in addition to a simulation benchmark. When comparing unrarefied data (with mean sequencing depth greater than 200,000 reads per sample) and sequencing count data rarefied down to 9,000 reads per sample from the 16S cross-sectional cohort data, we still see strong agreement between the unrarefied log fold changes and the rarefied log fold changes (Extended Data Fig. 2e ). This supported the theoretical evidence that our differential abundance method was scale equivariant and that changes in sequencing depth would not markedly affect the mean log fold change estimates.

This was further validated in our simulation benchmarks, where we showed that our model could capture the ground truth log fold changes based on 16S differentials from the age-matched and sex-matched cohort (Extended Data Fig. 2f ). We compared our proposed age-matched and sex-matched differential ranking method to ANCOM-BC and our differential abundance method without age and sex matching (which we will refer to as group-averaged differential ranking) (Extended Data Fig. 2i–k ) to showcase the differences between these methods. This benchmark was performed using data-driven simulations derived from the 16S cohort analysis. For the side-by-side comparison, we ran three different configurations of ANCOM-BC: (1) case–control differences only [‘formula=disease status’]; (2) case–control differences adjusted by age and sex confounders [‘formula=disease status + age + sex’]; and (3) case–control differences by age and sex matching [‘formula=disease status + (disease status–age sex matching IDs)’]. The first configuration provided a direct comparator to our ‘standard group-averaged differential ranking’, and the third configuration provided the most direct comparator to our ‘sex- and age-matched differential ranking’. None of the three ANCOM-BC models could recover the ground truth log fold changes in our simulations ( r = 0.38, 0.37 and 0.39 for implementations 1, 2 and 3, respectively), whereas both the ‘standard group-averaged differential ranking’ and the ‘sex- and age-matched differential ranking’ models were able to recover the ground truth ( r = 0.64 and 0.79, respectively). Ultimately, this illustrates how our method could account for age and sex matching and perform as expected if the assumptions were satisfied.

Similar to other simulation-based benchmarks, this is not a rigorous benchmark showcasing the improved performance of our method; rather, it is showcasing how all three methods have different assumptions. To determine in which biological scenarios age and sex matching could be more informative than household matching, we generated another simulation incorporating both a household confounder and an age confounder. The subject ages and age differences were sampled from the age distribution observed in David et al. 37 . Similarly to our previous simulations, we simulated the ground truth log fold changes using the model from the 16S cohort analysis. Here, we observed that, with a sufficiently large age confounder, the household matching estimated log fold changes with a noticeably large bias (mean squared error = 589.3) (Extended Data Fig. 6e ). In contrast, although age and sex matching did not precisely estimate the ground truth log fold changes, we observed a 10×-fold reduction in bias (mean squared error = 57.8) (Extended Data Fig. 6f ). This simulation also showcased how cohort randomization may play a role in mitigating the bias introduced by age confounding, at the expense of increased variance of the estimator.

To determine how sensitive our proposed differential abundance strategy is to batch effects, we computed the log fold changes between two samples, ‘sample4’ and ‘sample6’, from the MBQC study 31 for each processing laboratory. These samples were replicated and processed by multiple laboratories, providing an experimental setup for validating batch removal methods. Bray–Curtis PCoA shows a weak separation in sample name but a strong separation due to batch effects induced by differences in processing protocols. However, when we compare the log fold changes for each processing laboratory, we see strong agreement ( r > 0.5, P < < 0.05) (Extended Data Fig. 6m ), which supports the claim of McLaren et al. 18 that within-study fold change calculations are insensitive to batch effect, as long as the processing protocol is consistent within the study.

To determine if there was a significant difference between the age-matched and sex-matched pairs, we constructed an effect size metric using our model’s uncertainty estimation (see Supplementary Methods for more details). A global model for each data type—16S, SMS and RNA-seq—was used to determine if there was a significant difference between age-matched and sex-matched case–control pairs across each datatype. When we evaluated our Bayesian model fit on the 16S, SMS and RNA-seq datasets, our model fits achieved Rhat values below 1.1 and ESS values above 300, indicating that the draws from the posterior distribution are reliable.

The age-matched and sex-matched classifiers were constructed to build a classifier that generalizes across cohorts, identifying microbes that consistently differentiate between age-matched and sex-matched case–control pairs. To build age-matched and sex-matched classifiers, within each age-matched and sex-matched 16S cohort, we fitted our Bayesian model and assigned taxa into three groups: ASD-associated, Control-associated and Neutral. A taxon is assigned to the ASD-associated group if 70% of their posterior samples are greater than 0; a taxon is assigned to the Control-associated group if 70% of their posterior samples is less than 0. The remaining taxa are assigned to the Neutral group. After assigning taxa to each group, for each sample, a single log ratio, or balance 15 , is computed by taking the geometric mean of all of the taxon abundances within each group. To create a single log ratio that generalizes across cohorts, we assigned taxa to the ASD-associated group if it appears to be ASD associated in at least two studies. The same procedure is applied to the Control-associated group. The differences of these log ratios across the case–control pairs are shown for the age-matched and sex-matched cohorts in Fig. 2c . Although we did not apply this approach to the shotgun metagenomics datasets, we showed that the log ratios constructed from the 16S datasets also separated more than 70% of the ASD–control samples, serving as an additional cross-validation.

To determine if a microbe in increased or decreased between two groups of samples, a reference frame that identifies which group of microbes is stable is required. To do this using our Bayesian models, the quantiles estimated from the posterior distribution of the log fold change is used. A microbe is said to be significantly increased if the log fold change is greater than 0 in 95% of the posterior samples (5% log fold change quantile > 0). Finally, a microbe is said to be stable if the 90% quantile of the posterior distribution overlaps with 0 and the standard deviation of the posterior distribution is less than 3. Similarly, a microbe is said to be significantly decreased if the log fold change is less than 0 in 95% of the posterior samples (95% log fold change quantile < 0). The reference frame in the FMT analysis used microbes that were identified to be neutral or control associated by the age-matched and sex-matched classifier, with the assumption that the average abundance of these taxa is stable throughout the entire 2-year follow-up study. The FMT analysis used the same matching strategy, but, instead of matching on age and sex, the matchings were performed on the subjects to compare different timepoints. When identifying microbes that are the core microbiome, we focused on taxa that overlapped with 0 and had a posterior standard deviation of less than 3. Similarly, when computing the overlap between the cross-sectional cohort and the microbes depleted after the FMT, we focused on taxa with low uncertainty with a posterior standard deviation of less than 3.

The heat map shown in Fig. 2b displays the log fold changes for each case–control pair. To do this, a robust center log ratio (CLR) transform was performed, and all zeros were imputed to the mean abundance for visualization purposes. The case–control log fold changes were then computed for each case–control pair.

Bayesian differential ranking

Conceptually, the goal of a differential analysis is to make a statement about change in abundance for a given feature i between conditions A and B by evaluating the following null hypothesis:

However, most omic datasets do not provide a direct observation of the absolute quantities of A i and B i , or the total microbial loads ${N}_{{A}_{i}}$ and ${N}_{{B}_{i}}$ but, rather, only an observation of their proportions ${p}_{{A}_{i}}$ and ${p}_{{B}_{i}}$ , respectively, within each dataset, which are determined by a bias term, $\frac{{N}_{A}}{{N}_{B}}$ . This bias term, given by

results in high FDRs that cannot be adjusted for in models analyzing compositional omics datasets because the overall contribution of N A and N B to change cannot be unequivocally quantified 74 . To avoid the total biomass bias without having to resort to performing traditional FDR corrections, we adopted a ranking approach that allowed us to sort omic features by their log fold change values independently of how large their change was in absolute terms 73 . Because the biomass bias impacts every species within a dataset equally, the ranking approach ignores this bias, making the approach scale invariant (Equation 1).

The overall model that we designed consisted of a customized differential abundance tool that leveraged the experimental design of each study included in the analysis to determine study-specific feature perturbation profiles that could then be combined with the normalized perturbation profiles of other studies to perform a global differential perturbation analysis. The overall model had the following structure:

where y i , j denotes the microbial counts in sample i of species j across d species; λ i , j , α i j represents the expected counts for species j ; sample i , j represents a microbe-specific overdispersion term; N i represents sequencing depth (self-normalization and preemptive of rarefaction); C k ( i ), j represents the log proportion of species j in the k ( i ) control subject (age matched and sex matched); and D j I[ i = A S D ] represents the log fold change difference between control and ASD subjects with a corrective function that equals 1 when i corresponds to the paired ASD subject and 0 when i corresponds to the control subject. Incorporating N i into the model renders the model self-normalizing and not dependent on rarefaction, and C k ( i ), j incorporates the age-matching and sex-matching component for a given pair k . The priors for these variables are given below.

Here, the overdispersion parameters are estimated for each microbe, for each batch and for the ASD and control groups. This approach is adapted from DESeq2, allowing for the overdispersion to be modeled by both linear and quadratic terms with respect to the abundance. Furthermore, this parameterization does allow for a compositional interpretation owing to the following rationale. The Poisson distribution with an offset term is known to approximate the multi-nomial distribution. Furthermore, the negative binomial can be re-parameterized as a gamma–Poisson distribution, allowing for overdispersion modeling by breaking the mean–variance relationship inherent in the Poisson distribution.

The age-matched and sex-matched differential abundance has a similar methodology to paired tests, such as the paired t -test and the Wilcoxon test. To this end, we also used this differential abundance methodology to analyze the FMT dataset. Here, instead of matching pairs of subjects, we matched pairs of timepoints and computed the differential abundance across each pair of timepoints. To make these differentials comparable, a common set of taxa that was detected to be associated with controls was selected. Specifically, taxa that had a log fold change less than 0 in the cross-sectional cohort were assigned to this reference set. The estimated log fold changes were adjusted by centering around the mean log fold in the reference dataset as follows:

where ${\bar{{{{\boldsymbol{D}}}}}}_{R}$ denotes the mean of the log fold changes of the reference taxa, and D * represents the recentered log fold changes. By doing this, all timepoints will have the same reference and will be more directly comparable.

One of the advantages of the above model is that it will cancel out any multiplicative batch effect, such as PCR amplification bias, with no impact on j . This is because D is computed only within cohorts, and, as a result, cohort-specific batch effects are mitigated. Another advantage of the proposed model is that negative binomial models can be fitted independently for each microbe; as a result, the log fold change estimates for one microbe will not affect the estimates of other microbes. This can be a benefit, because these models will be agnostic to the choice of filtering criteria—filtering certain microbes will not affect the log fold change estimates of the remaining microbes. Furthermore, this differential abundance model can be applied to different types of omics data. Moreover, because we built the differential ranking model in a Bayesian environment, we were able to fit the model using an MCMC approach to estimate uncertainty by sampling the resulting posterior distributions.

For example, to make a statement about the value of an estimated posterior probability distribution p ( D ∣ y ), we could compute an average value using the following approximation:

Using this classic application of MCMC sampling in which N samples of i are drawn from the posterior distribution p ( D ∣ y ), we were able to approximate the true mean of the posterior differential abundance distributions and the corresponding effect sizes. With this, we can compute an effect size metric that determines if there is any global difference detected. This metric is analogous to PERMANOVA but one that computes this from log fold changes using the age-matched and sex-matched design. The effect size E is measured as follows:

where μ D is the mean of the posterior distribution, and r D represents the radius of a sphere that contains all of the samples from the posterior distribution. If the effect size is greater than 1, that means that 0 is not included in the posterior distribution, and the difference is significant. Bayesian P values are computed from the number of draws of ${\hat{D}}_{i}$ that were simulated from the posterior distribution p ( D ∣ y ). For instance, if 100 draws are sampled from the posterior distribution, and 0 is not within the sphere estimated from those 100 draws, then we say that the posterior distribution is significantly not overlapping with 0 with P < 0.01.

Other methods

We fitted gradient boosting classifiers on 10 16S datasets and on three SMS datasets using q2-sample-classifier 68 . We randomly split the samples into 80/20 training and test splits, performed a fivefold cross-validation on the training datasets to obtain optimal model parameters and computed predictions on the held-out test dataset. PERMANOVA with Bray–Curtis distances was used to determine if confounding variation due to household, age and sex was statistically significant in the sibling cohorts.

We used MMvec 73 to perform the diet–microbe co-occurrence analysis. Here, microbes were used to predict dietary intake. This analysis enabled the estimation of conditional probabilities, namely the probability of observing a dietary compound given that the microbe was already observed. To estimate these conditional probabilities, MMvec performs a matrix factorization, identifying the factors that explain the most information in these interactions. We compared the MMvec microbial factors against the cross-sectional log fold changes. We then compared the MMvec dietary factors against t -statistics that measure the differences in dietary compounds between children with ASD and neurotypical children.

To identify candidate viral–microbe interactions, we ran MMvec on each of the SMS datasets. We then pulled out the top co-occurring viral taxa for each microbe that had a conditional log probability greater than 1, amounting to 78,580 microbe–viral interactions. Then, we filtered out the microbe–viral interactions that were not present in the Gut Phage Database (GPD) 44 , leaving 31,276 microbial–viral interactions. The microbe–viral interactions estimated by Dan et al. 28 and Wang et al. 33 were weakly generalizable ( Q 2 =0.0036 > 0 and Q 2 =0.0114 > 0). However, the microbe–viral interactions estimated from Averina et al. 34 were similar to random chance ( Q 2 = −0.005).

We used Songbird 15 to perform the cytokine−microbe analysis via a multinomial regression that used the cytokines to predict microbial abundances. We reported biased microbial log fold changes with respect to cytokine concentration differences. Pearson correlation was used to determine the agreement between the 16S cross-sectional microbial differentials and the microbe−cytokine differentials. To directly link these microbial abundances to the cytokine concentrations, we computed log ratios, or balances, of microbes for each sample. For example, for IL-6, the numerator consisted of the top 50 microbes that are estimated to increase the most in abundance when IL-6 concentration increased, and the denominator consisted of the bottom 50 microbes that are estimated to be the most decreased when IL-6 concentration increases. Once these partitions are defined, the balances for each sample are computed by taking the log ratio of the average abundance of the numerator group and the denominator group 15 . Pearson correlation between these balances and the cytokine concentrations is then computed to measure the agreement between the microbial abundances and the cytokine concentrations.

To identify key microbial genes, we performed a comparative genomic analysis in which we binned the microbial genomes into those associated with ASD subjects and those associated with control subjects in the shotgun metagenomics data. We focused on microbes that are strongly associated with ASD, specifically those that are significantly greater than 10% of taxa that are estimated to be enriched in ASD. Using a binomial test, we were able to determine if a particular gene was more commonly observed in ASD-associated microbes than by random chance. Altogether, we identified 2,176 statistically significant microbial genes that differentiated ASD-associated microbial genomes from neurotypical-associated microbial genomes. Similarly, we identified 1,570 human transcripts that were differentially expressed between ASD and neurotypical subjects. Significant microbial genes and RNA transcripts were subsequently mapped to KEGG pathways. To directly compare the two contrasting omics levels and gauge metabolic similarity, we retrieved all the molecules involved in both the microbial and human pathways and calculated their intersection. Because the metabolomics datasets are not discrete values like sequencing count data, we additive log ratio (ALR) transformed the metabolomics datasets using the reference frames highlighted in the original papers. We then performed Wilcoxon tests on age-matched and sex-matched metabolomics samples within each cohort separately. Although our analysis revealed multiple metabolites that were below the 0.05 P value threshold, none of these metabolites passed the FDR-corrected threshold.

Reporting summary

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

Data availability

This study is based on previously published 16S 22 , 23 , 24 , 25 , 27 , 28 , 29 , 37 , 38 , 52 , 36 , metagenomics 28 , 33 , 34 , RNA-seq 59 , 60 , 61 , 62 and metabolomics 48 , 49 , 50 data. (The 16S sequencing data in Martin-Brevet et al. 38 is available under accession number ERP147524. All processed datasets and harmonized metadata are available on Zenodo at 10.5281/zenodo.7877350 as well as on Github at https://github.com/mortonjt/asd_multiomics_analyses .

Code availability

Software implementation of our Bayesian age-matched and sex-matched differential ranking algorithm can be found at https://github.com/flatironinstitute/q2-matchmaker . Our group-averaged differential ranking algorithm can be found at https://github.com/mortonjt/q2-differential . Finally, our analysis scripts can be found at https://github.com/mortonjt/asd_multiomics_analyses . We would like to acknowledge Matplotlib, Seaborn, Scipy, Numpy, Xarray, Arviz Scikit-learn, biom-format and Scikit-bio for providing the software foundation that this work was built upon.

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Acknowledgements

J.T.M. was funded by the intramural research program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). Y.S. and T.D.L. are supported by the Wellcome Trust (WT206194). M.W. is supported by the National Natural Science Foundation of China (program no. 82071733) and Shanghai talent development funding (no. 2020115). E.E. is supported by Israel Science Foundation grant 818/17 and a research grant provided by Teva Pharmaceuticals under their support of the Azrieli Faculty of Medicine. O.K. is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program (grant agreement ERC-2020-COG no. 101001355). We would like to thank A. Packer, P. Wang, N. Volfovsky, K. Martin and J. Spiro for their critical review of the manuscript. We would like to thank S. Mirarab for feedback on the construction of the Greengenes2 and Web of Life databases. We would like to thank A. Amir for insights on processing shotgun metagenomics and 16S sequencing data using the GetData software package. We would also like to thank K. Liu, H. Sherman and X.-J. Kong for insightful discussions.

Author information

These authors contributed equally: James T. Morton, Gaspar Taroncher-Oldenburg.

Authors and Affiliations

Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA

James T. Morton, Bob Carpenter & Richard Bonneau

Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA

James T. Morton

Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA

Dong-Min Jin & Richard Bonneau

Precidiag, Inc., Watertown, MA, USA

Robert H. Mills

Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK

Yan Shao & Trevor D. Lawley

Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA

Gibraan Rahman & Metin Balaban

Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA

Gibraan Rahman, Daniel McDonald, Kalen Cantrell, Antonio Gonzalez, Rob Knight & Jack A. Gilbert

School of Life Sciences, Arizona State University, Tempe, AZ, USA

Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA

Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USA

Yueyu Jiang

Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, USA

Kalen Cantrell & Rob Knight

Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel

Julie Carmel, Linoy Mia Frankiensztajn, Omry Koren & Evan Elliott

Laboratory for Research in Neuroimaging, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland

Sandra Martin-Brevet

Division of Nutritional Sciences, University of Illinois, Urbana, IL, USA

Kirsten Berding & Sharon M. Donovan

Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA

Brittany D. Needham

Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA

Microbiology Institute and Health Science College, Universidad San Francisco de Quito, Quito, Ecuador

María Fernanda Zurita

Departments of Microbiology & Pharmaceutical Sciences, Oregon State University, Corvallis, OR, USA

Maude David

Vavilov Institute of General Genetics Russian Academy of Sciences, Moscow, Russia

Olga V. Averina, Alexey S. Kovtun & Valery N. Danilenko

Skolkovo Institute of Science and Technology, Skolkovo, Russia

Alexey S. Kovtun

Department of Biomedical Sciences, School of Medicine, University of Cagliari, Cagliari, Italy

Antonio Noto

Laboratory Medicine, Department of Surgical Sciences, School of Medicine, University of Cagliari, Cagliari, Italy

Michele Mussap

Shanghai Key Laboratory of Birth Defects, Division of Neonatology, Children’s Hospital of Fudan University, National Center for Children’s Health, Shanghai, China

Mingbang Wang & Wenhao Zhou

Microbiome Therapy Center, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, China

Mingbang Wang

Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Daniel N. Frank

Department of Medicine, Division of Gastroenterology and Hepatology, Stony Brook University, Stony Brook, NY, USA

Neonatal Intensive Care Unit and Neonatal Pathology, Department of Surgical Sciences, School of Medicine, University of Cagliari, Cagliari, Italy

Vassilios Fanos

Pediatrics (Systems Medicine), Biomedical Data Science, and Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

Dennis P. Wall

Institute of Microbiology, COCIBA, Universidad San Francisco de Quito, Quito, Ecuador

Paúl Cárdenas

Facultad de Ciencias Médicas, de la Salud y la Vida, Universidad Internacional del Ecuador, Quito, Ecuador

Manuel E. Baldeón

Sainte Justine Hospital Research Center, Montréal, QC, Canada

Sébastien Jacquemont

Department of Pediatrics, Université de Montréal, Montréal, QC, Canada

The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat Gan, Israel

Evan Elliott

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Ramnik J. Xavier

Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA

Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Boston, MA, USA

Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA, USA

Sarkis K. Mazmanian

Department of Bioengineering, University of California, San Diego, La Jolla, California, USA

Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA

Rob Knight & Jack A. Gilbert

Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA

Jack A. Gilbert

Prescient Design, a Genentech Accelerator, New York, NY, USA

Richard Bonneau

Gaspar Taroncher Consulting, Philadelphia, PA, USA

Gaspar Taroncher-Oldenburg

Simons Foundation Autism Research Initiative, Simons Foundation, New York, NY, USA

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Contributions

J.T.M. and G.T.-O. conceived and designed the study, developed the software package q2-matchmaker, analyzed the data, interpreted the results and wrote the manuscript. R.B. contributed to study design, data analysis and results interpretation. R.H.M. contributed to study design, data analysis and manuscript editing. R.J.X. and S.K.M. contributed to study design. G.R. and B.C. contributed to software development and manuscript editing. D.M., Q.Z., K.C., A.G., M.B. and Y.J. have contributed the Greengenes2 and Web of Life 2 databases. D.-M.J. and Y.S. contributed to data analysis. K.B., B.D.N., M.F.Z., M.D., O.V.A., A.S.K., A.N., M.M., M.W., J.C., S.J., S.M.-B., O.K, E.E., D.N.F., E.L., W.Z., V.F., V.N.D., D.P.W., M.E.B., R.K., J.A.G., S.M.D., T.D.L. J.C. and M.F.Z. provided access to data. All authors contributed to manuscript editing.

Corresponding author

Correspondence to Gaspar Taroncher-Oldenburg .

Ethics declarations

Competing interests.

R.H.M. is Scientific Director at Precidiag, Inc. T.D.L. is a co-founder and Chief Scientific Officer of Microbiotica. S.K.M. is a co-founder and has equity in Axial Therapeutics. R.J.X. is a co-founder of Celsius Therapeutics and Jnana Therapeutics, a member of the Scientific Advisory Board at Nestle and a member of the Board of Directors at Moonlake Immunotherapeutics. R.B. is currently Executive Director of Prescient Design, a Genentech Accelerator. J.T.M. is the founder of Gutz Analytics and a co-founder of Integrated Omics AI. G.T.-O. is a Consultant-in-Residence at the Simons Foundation. The remaining authors declare no competing interests.

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Extended data

Extended data fig. 1 study approach..

Metagenomic sequence data present unique quantification challenges due to a lack of total microbial load measurements, which precludes the determination of absolute microbe abundances, and to limitations brought about by sampling and sequencing depth limitations, which result in an incomplete representation of the metagenome. We devised a Bayesian differential ranking algorithm to address both these challenges, the compositional challenge and the zero-inflation challenge. The compositional challenge : Most sequencing count datasets lack absolute abundance information in the form of cells, colony forming units, or transcripts per volume. This limitation preempts the reliable estimation of log fold changes (LFCs) and is a defining characteristic of compositional data that can lead to excessive false positives or false negatives depending on the magnitude of the change in absolute abundances 15 . As illustrated in panels a) through c), microbial counts (a) are typically converted into proportional abundances (b) that are then used to compute log-fold ratios. Fold change calculations adopt the general formula $\frac{B}{A}=\frac{{N}_{B}{p}_{B}}{{N}_{A}{p}_{A}}=\frac{{p}_{B}}{{p}_{A}}\times \frac{{N}_{B}}{{N}_{A}}$ , where A and B represent the two samples being compared, p A and p B represent the microbial proportions in A and B , and N A and N B represent the total number of microbes in A and B , also known as the ground truth. A key limitation of sequencing count data is their lack of proportionality to the corresponding absolute abundances in the original samples due to sequencing depth constraints. Our inability to observe N A and N B introduces a bias that ultimately prevents us from performing false discovery rate (FDR) correction to identify differentially abundant microbes. This bias depends on the change in microbial population size, with large population shifts leading to increased false positive and false negative rates, and an overall skewed representation of the ground truth (c). The zero-inflation challenge : Sampling errors and shallow sequencing lead to disproportionately high numbers of zero counts, especially for microbes present in low abundances (d). Multinomial, Poisson and Negative Binomial distributions have been used to explicitly handle zero counts 21 . However, estimating log-fold differentials remains problematic when microbes are not observed in any of the samples in one group since log 0 is − ∞ and thus the true log-fold change of a zero-count microbe can not be determined (e). Bayesian inference avoids this problem by introducing a prior that prevents nonsensical log-fold change estimates (f). Specifically, this introduces a rounded-zero assumption whereby all microbes have a non-zero chance of being observed. Panel h highlights what these log-fold changes would look like using a Dirichlet prior, where every microbe has the same probability of being observed before collecting data.

Extended Data Fig. 2 Benchmarks.

(a-d) Mean and standard deviations of the per-microbe log-fold changes compared to the total sequencing depth (log10 scale) for each microbe. e) Rarefaction benchmark, showcasing how differential abundance analysis is insensitive to rarefaction. (f) Differential abundance estimation derived from a data-driven simulated 16S dataset. (g) Comparison of age- and sex-matching approach compared to standard group averaging with respect to dataset size across 7 of the 11 16S studies (excluding Kang et al 52 , David et al 37 and Son et al 36 The x-axis represents the number of aggregated datasets, the y-axis on the left panel is the average R 2 metric to measure the model error. (h) Number of samples analyzed on the y-axis, and the x-axis on the right panel is the number of aggregated dataset. (i-k) Simulated datasets with a sequencing depth differential between matched cases and controls, where matched controls always have a larger sequencing depth than their case counterparts. This benchmark investigates how well ANCOM-BC, group averaged differential ranking and age-sex matched differential ranking can recover the ground truth log-fold changes. The group averaged and age-sex matched differential ranking both use the Negative Binomial (NB) distribution to model sequencing count data.(l-m) Simulated datasets comparing household matching to age-sex matching. (l-n) Bray-Curtis PCoA of 2 samples replicated across 4 processing labs in the MBQC 31 . (m) Pairwise comparsions of log-fold change between 2 samples across all 4 labs using group-averaged differential abundance analysis.

Extended Data Fig. 3 Differential ranking trends observed for the virome, 16S, SMS, and RNAseq datasets analyzed in this study.

The top 10% most differentially abundant features are highlighted in red. The x axis for the virome, 16S and SMS datasets is equivalent to showcase the differences in feature counts; the x axes for the RNAseq dataset is larger by a factor of 10, illustrating the stark difference in number of features of this dataset compared to the other three.

Extended Data Fig. 4 Metabolomics differential ranking analysis across four studies.

Paired t -tests were performed to identify differentially abundant metabolites. The metabolites shown in Needham et al consist of both fecal and serum metabolites. None of the metabolites had significant log-fold changes after applying FDR correction.

Extended Data Fig. 5 Comparison of log-fold changes computed from 16S and SMS.

(a) Comparison of taxa proportions across all 16S and SMS samples from Dan et al 28 the cross-sectional datasets after mapping to Greengenes2. (b) Comparison of differentials obtained from 16S and SMS on the same samples from Dan et al across taxa observed in both datasets. Only log-fold changes with high confidence (std < 0.5) are shown here.

Extended Data Fig. 6 Age differences between case-control matchings.

(a) 16S age-sex matched dataset, (b) the SMS age-sex matched dataset, (c) the David et al household matched dataset (16S) 37 (d) the Son et al household matched dataset (16S) 36 all datasets, the age of the control subject is subtracted from the age of the corresponding matched ASD subject. Neither David et al 37 or Son et al 36 showed a statistical difference between ages across households. (e) Estimated microbial log-fold changes compared to ground truth microbial log-fold changes in household matched simulation. (f) Estimated microbial log-fold changes compared to ground truth microbial log-fold changes in age-sex matched simulation. (g) Percentage of case-control pairs that are within 1 year in the age-sex matched dataset and the sibling matched dataset. (h) Percentage of case-control pairs that are have the same gender in the age-sex matched dataset and the sibling matched dataset.

Extended Data Fig. 7 Microbe-viral co-occurrence network estimated using MMvec.

Microbes are colored red and viruses are colored blue. Edges are drawn between microbes and viruses if they are highly co-occurring and the interaction was annotated in GPD.

Extended Data Fig. 8 Microbe-diet co-occurrences.

Microbe-diet co-occurrence heatmaps sorted by the (a) first and (b) third principal components estimated from MMvec.

Extended Data Fig. 9 Distribution of pathways in ASD and control-associated genes detected in SMS and RNAseq data.

(a-b) Breakdown of pathways in SMS data that are associated with ASD and neurotypical controls. (c-d) Breakdown of pathways in RNAseq data that are associated with ASD and neurotypical controls. e) Overlap of ASD associated KEGG enzymes derived from the multi-cohort cross-sectional analysis and KEGG enzymes that are found to be present in the microbes that decreased in the Kang et al FMT study. f) Pathway break down of KEGG enyzmes found in both the Kang et al FMT study and ASD children in the multi-cohort cross-sectional analysis. Only microbes that were also found in the SMS data were considered in the Kang et al study.

Supplementary information

Supplementary information.

Supplementary Tables 1–3.

Reporting Summary

Supplementary tables 4–16.

Table S4: Table of statistics for 16S differentials, including mean log fold change, standard deviation log fold change, 90% credible intervals and taxonomy for each microbe. Table S5: Table of statistics for SMS differentials, including mean log fold change, standard deviation log fold change, 90% credible intervals and taxonomy for each microbe. Table S6: Table of statistics for RNA-seq differentials, including mean log fold change, standard deviation log fold change and 90% credible intervals for each transcript. Table S7: Table of statistics for viral differentials, including mean log fold change, standard deviation log fold change and 90% credible intervals for each virus. Table S8: PERMANOVA breakdown of sibling-matched cohorts looking at the confounding variation due to age, sex and household. Table S9: Microbial log fold changes due to cytokine differences, including mean log fold change for each cytokine. Table S10: Microbe virus co-occurrences estimated by MMvec, where entries represent the centered log probability of a microbe and a virus both present for a given sample. The first two columns yield the t -statistic and a P value measuring the difference between case and control dietary preferences. Table S11: A list of genes and their associated KEGG pathways that were determined to be statistically abundant in ASD-associated microbial genomes using a one-sided binomial test corrected for multiple comparisons. Table S12: A list of genes and their associated KEGG pathways that were determined to be statistically expressed in humans using a one-sided binomial test corrected for multiple comparisons. Table S13: A list of microbial pathways that were both statistically abundant in ASD-associated microbial genomes and present in GBMs. Table S14: A list of paired microbe and human pathways in addition to the number of overlapping metabolites. Table S15: Microbe diet co-occurrences estimated by MMvec, where entries represent the centered log probability of a microbe and a dietary compound both present for a given subject. Table S16: Microbial log fold changes between paired timepoints across all of the individuals in the FMT study. The reported log fold change was calculated by using the reference frame estimated in the cross-sectional analysis.

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Morton, J.T., Jin, DM., Mills, R.H. et al. Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles. Nat Neurosci 26 , 1208–1217 (2023). https://doi.org/10.1038/s41593-023-01361-0

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Science continues to suggest a link between autism and the gut. Here’s why that’s important

Researcher in Enteric Neuroscience and Autism, RMIT University

Senior Lecturer, Department of Microbiology, La Trobe University

Lecturer, Department of Anatomy & Developmental Biology, Monash University

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Many people will associate autism with traits including atypical social interactions, repetitive behaviours, and difficulties with speech and communication.

But perhaps lesser known is the fact people with autism are more likely to experience gastrointestinal disorders than the general population.

One review found children with autism were four times more likely to report gastrointestinal symptoms than children without a diagnosis. A number of studies in the review reported the prevalence of gut problems was the same among boys and girls.

These symptoms can include constipation, diarrhoea, abdominal pain, bloating, reflux and vomiting.

Gut problems like these hinder quality of life for people with autism and their families, further affecting sleep, concentration and behavioural issues.

Read more: What causes autism? What we know, don’t know and suspect

For a long time we thought this was due to the way the brain controls the gut. Think of the “butterflies” you get in your stomach, or the need to rush to the toilet when you’re really nervous.

While the brain does influence gut function, this is only part of the story. Newer research is showing gastrointestinal symptoms in autism may be due to differences in the gut itself.

The mini brain of the gut

The gut contains its own dedicated nervous system, called the enteric nervous system, which co-ordinates digestion and the absorption of food and nutrients.

The enteric nervous system is a complex integrated network of neurons that extends along the gastrointestinal tract.

While structurally quite different, it contains about the same number of cells as the spinal cord and uses many of the same neurochemical messengers, receptors and proteins as the brain.

Autism has a strong genetic component. More than 1,000 gene mutations are associated with the disorder. Many of these gene mutations alter how neurons communicate in the brain.

We hypothesised some of these gene mutations may also cause neuron wiring to go awry in the gut, resulting in gastrointestinal issues in some people with autism.

Read more: We need to stop perpetuating the myth that children grow out of autism

Our research

To test this theory, we studied patient records of two brothers with autism , who have a single gene mutation associated with autism that affects neuron communication. We also studied mice.

Mouse models with this specific mutation, called neuroligin-3, have previously shown behaviours relevant to autism, such as altered social interactions , reduced communication and repetitive behaviours .

We found this mutation also affects the enteric nervous system of the gut in mice. Mutant mice exhibited altered gut contractions, and the speed at which food moved through their small intestine was faster than the speed for mice without the mutation.

Meanwhile, both brothers have gut issues including esophagitis (inflammation of the esophagus) and diarrhoea.

So our work shows a gene mutation associated with autism, previously only studied in the brain, could affect the gut too.

The gut microbiota

We also found mice with the mutation had differences in their gut microbiota compared to normally developing mice.

The gut microbiota is the community of microorganisms (including bacteria, fungi and viruses) that live within the gastrointestinal tract. The largest amount of microbiota are found in the large intestine, where they digest some of the food we eat.

Read more: Can a gut bacteria imbalance really cause autism?

The mice we studied with the neuroligin-3 mutation had what’s called an altered Firmicutes:Bacteroidetes ratio.

Scientists have found this ratio is altered in people with a range of conditions including type 2 diabetes, obesity and inflammatory bowel disease.

Why is all this important?

Now that we’re beginning to understand more about the link between autism and the gut, scientists are investigating whether changing the gut microbiota could affect autism behaviours. One way we can alter the gut microbiota is using faecal transplants.

One recent study took faeces (microbiota) from boys with or without autism and transplanted the faeces into mice. The researchers then studied how the offspring of these mice behaved.

The offspring of mice that received microbes from boys with autism showed behaviours that could be relevant to autism (they buried more marbles in their cage bedding, potentially an indication of repetitive behaviour), compared to mice who were transplanted with microbes from typically developing children.

Another recent study assessed gut problems and behavioural traits for two years in people with autism after they received a faecal transplant. This study reported improvements in gut symptoms and behaviour. But the researchers only studied a small number of people, and didn’t control for placebo effects.

Other studies have tested if changing gut microbes by treating patients with prebiotics (food for the bacteria in your gut) or probiotics (helpful bacteria) can affect autism behaviours. But a review of these studies showed no consensus – in other words, some studies showed an effect, while others didn’t.

Read more: Essays on health: microbes aren't the enemy, they're a big part of who we are

What does this mean for people with autism?

Many of the studies looking at the gut in autism so far have been conducted using mice. We need more research in humans to confirm the results can be extrapolated.

We need to continue to build our understanding of how gene mutations in the nervous system influence gut microbes. In the future, tweaking the gut microbiota might be one way to manage behaviours in people with autism.

This would not reverse gene mutations leading to autism, but it might tone down the effects, and improve quality of life for people with autism and their families.

In the meantime, clinicians treating people with autism should consider assessing and treating gut problems alongside behavioural issues.

Gastrointestinal problems

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Scientists link autism to gut health

Researchers have linked autism to gut health.

Scientists at the Chinese University of Hong Kong believe the findings could help develop a new diagnostic tool for autism.

They found alterations in four gut microbes and highlighted 31 biomarkers that they say have “great potential” for diagnosing autism.

And they believe the findings could lead to potential treatments.

Researchers have linked autism to gut health. Scientists at the Chinese University of Hong Kong believe the findings could help develop a new diagnostic tool for autism.

Professor Siew Ng said her team’s work has shown the potential of a non-invasive biomarker for autism

Treatment alleviated anxiety

In a pilot study, the researchers showed that treating these changes in the gut alleviated the symptoms of anxiety in children on the autism spectrum.

Professor Siew Ng is the director of the university’s microbiota centre.

She said the research was the first study to “demonstrate the robustness and utility of a non-invasive biomarker to diagnose and predict ASD (autism spectrum disorder) across different ages, gender and settings”.

Development of a diagnostic tool

The scientists examined stool samples from 1,627 children with and without autism aged between one and 13 to attempt to develop a diagnostic tool.

The treatment used by the research team involved boosting levels of y-aminobutyric acid in the gut.

When this neurotransmitter is depleted it can lead to sensory hypersensitivity and anxiety.

The treatment enabled the researchers to identify autistic children with up to 82 per cent accuracy.

Improvement in quality of life

The researchers trialled the treatment on 30 autistic children aged between four and 11 years for 12 weeks.

The scientists say the children showed a reduction in sensory and anxiety symptoms of between 15 and 20 per cent.

Biomedical charity Thinking Autism said the study adds to a “large body of evidence” that points to a link between autism and gut health.

In a statement, the charity said it hopes the study leads not only to a new diagnostic tool, but also to “treatments which could potentially improve quality of life for many thousands of people”.

The study appeared in the scientific journal Nature Microbiology .

Challenging behaviours linked to gut
Stool treatment ‘helps gut issues’
Gut bacteria linked to behaviours
Study reinforces role of gut health
Research confirms gut-brain autism link
Food enzyme tested as autism treatment
Study finds faecal transplants effective

Published: 14 July 2024

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Research That Potentially Links Autism and Brain-Gut Microbiome

Summary: A new review of almost 200 publications suggests the gut microbiota may play a critical role in modulating brain function, social behavior and other symptoms of autism.

Source: USC

A new scoping review of nearly 200 publications covering the relationships between autism spectrum disorder and the brain–gut–microbiome system was published online today in Nutrients.

The review synthesizes the growing body of research suggesting that gut microbiota—the trillions of microorganisms living within the human digestive system—may serve critical roles in modulating brain functions, social behaviors and autistic symptoms.

Two of the review’s co-first authors, Michelle Chernikova and Genesis Flores, were participants in USC’s Diversity, Inclusion, and Access JumpStart program, a structured summer research program for talented undergraduates considering pursuing a Ph.D. degree, at the time of literature review and manuscript preparation.

Joining as co-first author is Emily Kilroy Ph.D. ’18, Postdoc ’22, a postdoctoral scholar in the USC Chan Division of Occupational Science and Occupational Therapy. Jennifer Labus and Emeran Mayer, microbiologists at the University of California, Los Angeles, are co-authors. Senior author is Lisa Aziz-Zadeh, associate professor at the USC Chan Division jointly appointed to the USC Dornsife College of Letters, Arts and Sciences’ Brain and Creativity Institute.

The review synthesizes current understandings about the mechanisms by which gut microbiota, metabolic substances and the brain communicate to influence behaviors, including the different social–communication and restricted or repetitive patterns that characterize autism. Gastrointestinal symptoms such as abdominal pain, constipation and diarrhea have been reported in 46 to 84 percent of autistic people, giving recent rise to a hypothesis that gut dysregulation may be especially prevalent in autistic populations.

The paper was supported in part by Aziz-Zadeh’s four-year, $506,000 grant from the U.S. Department of Defense’s (DoD) Autism Research Program Idea Development Award.

“To date, most autism studies in humans either look at the brain and behavior, or at the gut microbiome and behavior,” Aziz-Zadeh said. “Our DoD study is one of the largest autism studies to look at all three factors together—brain, gut and behavior. The current paper in Nutrients lays down the theory behind this endeavor, reviewing everything from rodent studies on the topic, potential neurotransmitter pathways that may be involved and potential brain regions that may be modified by this interaction.”

Scientists have yet to determine the exact microbial composition associated with autism, and the authors recommend several future research directions. Those include the need for more standardized sampling, collection and analyses; research studying the prenatal gut microbiome in pregnant mothers; studies comparing the microbiomes of autistic and typically-developing populations; and longitudinal tracking of metabolic states and specific biomarkers through early childhood development.

This graph shows the diversity of gut microbes across a lifespan

About this autism research news

Author: Mike McNulty Source: USC Contact: Mike McNulty – USC Image: The image is credited to the researchers

Original Research: Open access. “ The Brain-Gut-Microbiome System: Pathways and Implications for Autism Spectrum Disorder ” by Michelle A. Chernikova et al. Nutrients

The Brain-Gut-Microbiome System: Pathways and Implications for Autism Spectrum Disorder

Gastrointestinal dysfunction is one of the most prevalent physiological symptoms of autism spectrum disorder (ASD). A growing body of largely preclinical research suggests that dysbiotic gut microbiota may modulate brain function and social behavior, yet little is known about the mechanisms that underlie these relationships and how they may influence the pathogenesis or severity of ASD.

While various genetic and environmental risk factors have been implicated in ASD, this review aims to provide an overview of studies elucidating the mechanisms by which gut microbiota, associated metabolites, and the brain interact to influence behavior and ASD development, in at least a subgroup of individuals with gastrointestinal problems.

\Specifically, we review the brain-gut-microbiome system and discuss findings from current animal and human studies as they relate to social-behavioral and neurological impairments in ASD, microbiota-targeted therapies (i.e., probiotics, fecal microbiota transplantation) in ASD, and how microbiota may influence the brain at molecular, structural, and functional levels, with a particular interest in social and emotion-related brain networks.

A deeper understanding of microbiome-brain-behavior interactions has the potential to inform new therapies aimed at modulating this system and alleviating both behavioral and physiological symptomatology in individuals with ASD.

The most important thing the study left out is why kids are born with it. Does the mother’s gut bacteria affect the zygote?

I have heard of a study where cruciferous foods were easily digested by the mitochondria. This only left flatulence to be emitted upon processing. An optometrist has stated that eating cruciferous foods prevents macrodegeneration of the eyes. Your study only adds support to his advice which often goes unheeded.

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New evidence links gut bacteria alterations to autism

A new study, published in the peer-reviewed journal Science Advances , is shedding light on the potential link between autism and gut microbiome impairments. The research reveals a mechanism by which altered gut bacteria populations can lead to abnormal microbial detoxification and mitochondrial dysfunction.

The connection between gut bacteria and autism spectrum disorder (ASD) is arguably one of the most intriguing areas of microbiome research. Gastrointestinal problems are common in children with ASD and several recent, albeit small, studies have revealed behavioral and psychological symptoms of autism in children can be improved using fecal transplants from healthy subjects.

Perhaps the most challenging hurdle microbiome researchers face translating their discoveries into clinical therapies is the sheer, mind-bending diversity of gut bacteria populations from individual to individual. Put simply, while some bacterial species can be generally considered "good" and others "bad", there is no one-size-fits-all solution to microbiome therapeutics. And it is this diversity that makes it hard for researchers to home in on exactly how the microbiome influences disease.

Trying to overcome this hurdle, a large team of scientists from China developed a novel analytic strategy called a “quasi-paired cohort.” First, the researchers enrolled a traditional cohort of 79 age- and gender-matched children, half with ASD and half serving as neurotypical controls.

Initial microbiome genomic testing revealed little differences in bacterial diversity between the two groups. A small handful of differences were identified between the two groups, but these were generally in line with what had been identified in previous research.

The next step was to generate a quasi-paired cohort. This involved pairing specific ASD samples with control samples of similar metabolic backgrounds. As the researchers explain in the study, “This approach allowed us to transform the original group cohort into a paired cohort, which not only controls for individual diversity but also increases statistical power.”

This allowed the researchers to identify more than just simple differences in bacterial populations, but instead revealed the key downstream metabolic differences between ASD and neurotypical subjects.

Five specific metabolic pathway deficiencies were detected in the research. These deficiencies were linked to detoxification processes triggered by certain enzymes produced by gut bacteria. The researchers hypothesize these microbiome detoxification deficiencies influence the pathogenesis of ASD.

“One of the main pathological manifestations of ASD is the dysfunction in mitochondria, major targets of organic toxicants due to their lipophilic properties,” the researchers write in the study. “When the intestinal microbial detoxification is severely impaired in ASD, more toxicants of external and internal origins might enter circulation and injure the mitochondria of various tissues. Thus, our finding of impaired microbial detoxification helps explain why ASD children are so vulnerable to environmental toxins and suggests that impairment in microbial detoxification might be involved in the pathogenesis of ASD.”

As with most research on this subject, there are plenty of caveats limiting broader conclusions. While the study does offer a rigorous investigation showing ASD subjects may present a deficiency in microbiome detoxification pathways, any causal link to ASD onset or severity is just speculation at this stage. Further research is needed to both affirm this connection and investigate whether modulating the microbiome can prevent ASD developing in the first place.

“The impaired microbial detoxification is correlated with the clinical rating of ASD and the extent of mitochondrial dysfunction, one of the main pathological alterations of ASD, which strongly suggests that impaired microbial detoxification is deeply involved in the pathogenesis of ASD,” the researchers conclude in the study. “Such a previously unknown protective role of intestinal microbes suggests potential future therapeutic strategies of rebuilding the impaired microbial detoxification for patients with ASD.”

The new study was published in the journal Science Advances .

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Exploring the Link Between Gut Health and Autism

Discover the fascinating gut-brain connection and its impact on autism. Explore the link between gut health and autism spectrum disorder.

By Brighter Strides ABA

June 19, 2024

Understanding the Gut-Brain Connection

The gut-brain connection refers to the bidirectional communication between the gut and the brain. It involves intricate interactions between the gut microbiome, gut health, and various aspects of brain function. Understanding this connection is crucial, especially in the context of autism spectrum disorder (ASD), as emerging research has revealed a definitive association between gut microbiome and autism.

The Role of the Gut Microbiome

The gut microbiome refers to the intricate community of microorganisms residing in the gastrointestinal tract. This complex ecosystem consists of trillions of bacteria, fungi, viruses, and other microbes. The gut microbiome plays a crucial role in various physiological processes, including digestion, immune function , and nutrient absorption.

Recent research has shown that the gut microbiome also exerts a profound influence on brain development and behavior. Animal studies have demonstrated that alterations in gut bacteria can lead to changes in social behavior and brain function . Additionally, differences in the composition of gut bacteria have been observed between individuals with autism and those without the disorder, suggesting a potential link between the gut microbiome and autism spectrum disorder.

Gut Health and Autism Spectrum Disorder

The association between gut health and autism spectrum disorder has garnered significant attention in recent years. A groundbreaking study involving 615 children with autism spectrum disorder revealed distinct differences in their gut microbiomes compared to children without autism. Specific gut bacteria have been associated with the severity of autism and behavioral challenges in children on the spectrum.

Research suggests that abnormalities in gut bacteria and their metabolites may contribute to the symptoms of autism. Significant differences in the composition of gut bacteria have been observed between children with autism and those without the disorder. This growing body of evidence highlights the potential role of the gut microbiome in the development and manifestation of autism spectrum disorder.

Understanding the gut-brain connection and the impact of the gut microbiome on autism spectrum disorder opens up new possibilities for personalized and targeted interventions. Promising research is focusing on microbiota-targeted therapies and the use of probiotics to restore a healthy balance of gut bacteria in individuals with autism. By modulating the gut microbiome, researchers hope to improve the lives of individuals with autism and potentially alleviate some of the behavioral symptoms associated with the disorder.

Continued research and advancements in understanding the gut-brain connection hold great potential for developing innovative treatments and interventions for autism spectrum disorder. By unraveling the complexities of this connection, scientists aim to identify specific gut microbiota biomarkers and develop diagnostic applications that could revolutionize autism diagnosis and treatment in the future.

The Link Between Gut Microbiome and Autism

Extensive research has shed light on the connection between the gut microbiome and autism, revealing a definitive association that impacts the lives of millions of individuals on the autism spectrum. Studies have demonstrated notable differences in the gut microbiomes of children with autism compared to those without the disorder.

Research on Gut Microbiome and Autism

Research has revealed significant differences in the composition of gut bacteria between children with autism and neurotypical individuals, suggesting the involvement of the gut microbiome in the development and manifestation of autism spectrum disorder. A study involving 615 children with autism spectrum disorder demonstrated distinct variations in their gut microbiomes compared to children without autism [1]. These findings further support the notion that abnormalities in gut bacteria may contribute to the symptoms of autism.

Gut Bacteria and Autism Severity

Specific gut bacteria have been associated with autism severity and behavioral challenges in children on the spectrum. A study conducted in 2019 found that children with autism may have higher levels of potentially harmful bacteria in their gut, which could contribute to the behavioral symptoms associated with the disorder. The composition and abundance of certain bacterial strains have shown significant differences between individuals with autism and neurotypical controls, suggesting a potential role for specific gut bacteria in the development and severity of autism spectrum disorder.

Understanding the link between the gut microbiome and autism opens up new avenues for potential therapeutic interventions. Restoring a healthy balance of gut bacteria through probiotic treatments has shown promise in alleviating some of the behavioral symptoms associated with autism. Further research is needed to fully comprehend the mechanisms underlying the gut-brain connection in autism and to develop targeted therapies that can improve the lives of individuals on the spectrum.

Mechanisms of Gut-Brain Communication

Understanding the intricate relationship between the gut and the brain is essential in exploring the link between gut health and autism. The communication between these two systems occurs through various mechanisms, including the gut-brain axis and the production of neurotransmitters and metabolites.

The Gut-Brain Axis

The gut-brain axis is a bidirectional communication network that connects the central nervous system (CNS) and the enteric nervous system (ENS) of the gastrointestinal tract. This axis allows for constant communication and feedback between the gut and the brain. Research from Harvard Medical School suggests that the gut microbiome plays a crucial role in influencing brain development and behavior, with changes in gut bacteria potentially leading to alterations in social behavior and brain function.

The gut-brain axis involves complex interactions between the gut microbiota, immune system, and the nervous system. The gut microbiota , which refers to the trillions of bacteria residing in the gut, produce various chemicals and metabolites that can influence brain function. These chemicals can enter the bloodstream, cross the blood-brain barrier, and interact with the brain, affecting neural activity and behavior.

Neurotransmitters and Metabolites

One of the key mechanisms through which the gut communicates with the brain is the production and release of neurotransmitters and metabolites. Beneficial gut bacteria in the intestines have the ability to generate neurotransmitters and active metabolites by utilizing consumed foods. For example, the amino acid tryptophan found in food acts as a precursor of serotonin, a neurotransmitter that regulates emotions and behavior.

Research indicates that the gut microbiota can influence communication between the gut and the brain by producing and releasing various chemicals. These chemicals include neurotransmitters such as serotonin, dopamine, and gamma-aminobutyric acid (GABA), which play crucial roles in regulating mood, behavior, and cognition. Additionally, metabolites produced by gut bacteria, such as short-chain fatty acids (SCFAs), can also have effects on brain function and behavior.

The production and release of these neurotransmitters and metabolites by the gut microbiota highlight the significant role that gut health plays in influencing brain function and potentially impacting conditions such as autism.

Understanding the mechanisms of gut-brain communication and the role of neurotransmitters and metabolites is crucial in unraveling the complex relationship between gut health and autism. Further research in this field holds promising potential for developing innovative therapeutic interventions that target the gut microbiome to improve outcomes for individuals with autism spectrum disorder (ASD).

Implications for Autism Treatment

When considering the link between gut health and autism, the implications for autism treatment become a significant area of focus. Understanding the potential therapeutic interventions and the role of probiotics in promoting gut health can offer hope for managing the symptoms associated with autism.

Potential Therapeutic Interventions

Research has shown that probiotic treatments, which aim to restore a healthy balance of gut bacteria, have the potential to alleviate some of the behavioral symptoms of autism, providing a promising avenue for future therapeutic interventions. Microbiota-targeted therapies, such as probiotics, prebiotics, dietary supplements, fecal microbiota transplantation, and microbiota transfer therapy, have shown promise in reducing and potentially curing symptoms associated with autism spectrum disorder (ASD).

Clinical trials and animal studies have reported changes in neurological function, behavior, and comorbid symptoms of autistic children after rebalancing the composition of the gut microbiota through the use of antibiotics, prebiotics, probiotics, or microbiota transfer therapy (MMT). These interventions show potential in addressing the underlying gut dysbiosis associated with autism and improving the overall well-being of individuals with ASD.

Probiotics and Gut Health

Probiotics, live bacteria that confer health benefits when consumed in adequate amounts, have emerged as a promising intervention for improving gut health in individuals with autism. Studies have shown that probiotics can positively impact the balance of microbiota in children with ASD and have the potential to improve ASD symptoms. Probiotics have been found to alleviate neuroinflammation, restore biochemical parameters related to neurotransmission, balance energy metabolism, and reduce oxidative stress associated with autism.

While further research is needed to fully understand the specific strains and dosages that are most effective for individuals with autism, Lactobacillus plantarum has shown promise as an effective strain for probiotic treatment of ASD. It is important to note that individual responses to probiotic interventions may vary, and consulting with healthcare professionals is recommended to determine the most suitable probiotic regimen for each individual with autism.

By exploring potential therapeutic interventions, including probiotics, and their impact on gut health, researchers and healthcare professionals are paving the way for new treatment options for individuals with autism. While more research is needed to fully understand the complex gut-brain connection and its implications for autism, these advancements offer hope for improving the lives of individuals affected by autism spectrum disorder.

Gastrointestinal Symptoms in Autism

Individuals with autism spectrum disorder (ASD) often experience gastrointestinal (GI) symptoms, which can have a significant impact on their overall well-being. These symptoms include constipation, abdominal pain, diarrhea, and vomiting [4]. The prevalence of GI symptoms in individuals with ASD is notable, and research has explored the association between these symptoms and the severity of autism.

Prevalence of GI Symptoms

Gastrointestinal symptoms are prevalent in children with ASD. Symptoms commonly reported by parents and caregivers include constipation, diarrhea, abdominal bloating, and pain during bowel movements. These GI symptoms have been correlated with various maladaptive behaviors in individuals with ASD.

The exact prevalence of GI symptoms in individuals with ASD can vary due to methodological differences and a lack of standardized definitions and assessment tools. However, studies consistently demonstrate a higher prevalence of GI symptoms in individuals with ASD compared to the general population. The reported prevalence of GI symptoms in individuals with ASD ranges from 9% to 91% [4].

Association with Autism Severity

The relationship between gastrointestinal symptoms and the severity of ASD symptoms is a topic of ongoing research. While some studies suggest a correlation between GI symptoms and the severity of ASD symptoms, findings have been inconsistent due to variations in research methodologies and the lack of standardized definitions and assessment tools. However, it is important to note that certain GI symptoms have been associated with specific behaviors in individuals with ASD.

For example, gastrointestinal symptoms such as constipation, diarrhea, and abdominal pain have been correlated with self-injury, aggressive behaviors, restricted stereotypical behaviors, hyperactivity, and language regression in some individuals with ASD. Understanding the relationship between GI symptoms and the severity of ASD symptoms is crucial for developing effective interventions and improving the overall quality of life for individuals with ASD.

The association between GI symptoms and autism severity highlights the complex interplay between the gut and the brain. Further research is needed to explore the underlying mechanisms and identify potential therapeutic interventions that can address both GI symptoms and ASD symptoms simultaneously.

In conclusion, gastrointestinal symptoms are prevalent in individuals with ASD and can significantly impact their well-being. While the relationship between GI symptoms and the severity of ASD symptoms is still being explored, addressing GI symptoms is an important aspect of managing ASD. Collaborative efforts between healthcare professionals specializing in autism and gastroenterology can help provide comprehensive care for individuals with ASD and gastrointestinal concerns.

Sleep Disturbances and Gut Health

Sleep disturbances are common among children with Autism Spectrum Disorder (ASD), and these issues can have a significant impact on their overall well-being. Insomnia, increased bedtime resistance, sleep disordered breathing, early morning wakening, and daytime sleepiness are among the most frequently observed sleep problems in individuals with ASD.

Sleep Issues in Autism

Research has shown that sleep problems in children with ASD are associated with behavioral symptoms and the severity of these symptoms. The lack of quality sleep can lead to the accumulation of reactive oxygen species (ROS) and have adverse effects on the individual's health and functioning. Considering sleep disturbances , along with the core symptoms of ASD, is crucial when developing treatment approaches for individuals with ASD.

Impact on Gut Microbiota

The gut microbiota (GM) plays a vital role in various aspects of human health, including sleep regulation. Clinical trials and animal studies have demonstrated that rebalancing the composition of the GM can lead to changes in neurological function, behavior, and comorbid symptoms in children with autism. This can be achieved through interventions such as the use of antibiotics, prebiotics, probiotics, or microbiota transfer therapy (MMT).

Studies have found significant alterations in the gut microbiota of children with ASD, both in terms of species diversity and composition. Certain strains, such as Akkermansia, Coprococcus, and Ruminococcus, have been found to be elevated in children with ASD. Conversely, strains like Lactobacillus and Bifidobacterium , which have anti-inflammatory properties, are often reduced in individuals with ASD. Additionally, high levels of strains like Collinsella and Clostridium have been associated with autism-like symptoms, as these strains can produce neurotoxic short-chain fatty acids.

Probiotics, which are beneficial bacteria, have shown promise in positively impacting the balance of the gut microbiota in children with ASD. These probiotics have been found to alleviate neuroinflammation, restore biochemical parameters related to neurotransmission, balance energy metabolism, and reduce oxidative stress associated with autism. Incorporating probiotics into the treatment plan may offer potential benefits for individuals with ASD and their gut health.

Understanding the relationship between sleep disturbances and gut health is essential when considering the holistic approach to managing Autism Spectrum Disorder. By addressing both sleep issues and gut health, healthcare professionals can improve the overall well-being and quality of life for individuals with ASD. Further research in this area holds promise for developing targeted interventions and personalized treatment strategies.

The Role of Genetic and Environmental Factors

When exploring the link between gut health and autism, it's important to consider both genetic and environmental factors that contribute to the development and progression of Autism Spectrum Disorder (ASD).

Genetics and Autism

Genetic factors play a significant role in the pathogenesis and advancement of ASD. Chromosomal abnormalities, gene mutations, and variations can contribute to the risk of developing autism. However, it's important to note that not all cases of autism can be attributed solely to genetics. While certain genetic factors may increase susceptibility to ASD, they do not guarantee the development of the disorder.

Research into the genetics of autism has identified various genes and pathways that may be involved in its development. However, the genetic landscape of autism is complex and heterogeneous, with multiple genes contributing to its manifestation. Ongoing studies continue to shed light on the genetic underpinnings of autism, which may help in understanding the disorder better and developing targeted interventions.

Environmental Influences on Gut Microbiome

In addition to genetics, environmental factors also play a crucial role in the gut-brain connection and the development of autism. Studies have shown that early colonization, mode of delivery, and antibiotic usage significantly affect the gut microbiome and the onset of autism. The gut microbiome refers to the community of microorganisms residing in the gastrointestinal tract.

Environmental influences, such as diet, stress, exposure to toxins, and antibiotic use, can shape the composition and diversity of the gut microbiome. Alterations in the gut microbiome, known as dysbiosis, have been implicated in the pathogenesis of various diseases, including ASD.

The gut microbiome plays a vital role in the production of neurotransmitters and metabolites that have an impact on brain function and behavior. Beneficial gut bacteria can generate neurotransmitters and active metabolites by utilizing consumed foods. For example, the amino acid tryptophan found in food acts as a precursor of serotonin, a neurotransmitter that regulates emotions and behavior.

Understanding the interplay between genetic factors, environmental influences, and the gut microbiome is crucial in unraveling the complex relationship between gut health and autism. Further research is needed to explore the mechanisms underlying this connection and to develop targeted interventions that can positively impact both gut health and the symptoms of autism.

Promising Research and Future Directions

As research on the gut-brain connection continues to unfold, promising avenues for future interventions and advancements in understanding autism are emerging. Two key areas of focus are microbiota-targeted therapies and expanding our knowledge about autism.

Microbiota-Targeted Therapies

Microbiota-targeted therapies have gained attention in the field of autism research. These therapies aim to restore a healthy balance of gut bacteria, which may have a positive impact on autism symptoms. Probiotic treatments, in particular, have shown promise in alleviating some behavioral symptoms of autism.

Probiotics are beneficial bacteria that can be taken as supplements or found in certain foods. They have the potential to positively influence the gut microbiome and improve gut health. Clinical trials and animal studies have reported changes in neurological function, behavior, and comorbid symptoms of autistic children after rebalancing the composition of the gut microbiota through the use of antibiotics, prebiotics, probiotics, or microbiota transfer therapy .

It is important to note that further well-designed research studies with large sample sizes are needed to fully understand the potential benefits and mechanisms of microbiota-targeted therapies for autism. However, these therapies hold promise as possible therapeutic interventions for reducing and alleviating symptoms related to autism spectrum disorder (ASD).

Advancements in Understanding Autism

Advancements in understanding autism and its relationship with gut health are ongoing. Researchers are exploring various aspects, including the role of genetics, environmental influences, and the gut-brain axis. Understanding the complex interplay between these factors can provide insights into the underlying mechanisms behind autism and guide future research and treatment strategies.

Genetic studies have identified certain genes associated with autism, shedding light on the potential genetic factors contributing to the disorder. Environmental influences, such as diet, exposure to toxins, and antibiotic use, may also play a role in shaping the gut microbiome and impacting autism symptoms.

Further research is needed to uncover the specific mechanisms through which the gut microbiome and autism interact. This includes investigating the role of neurotransmitters and metabolites in the gut-brain axis, as well as identifying potential gut microbiota biomarkers that could aid in diagnosis and treatment.

As the field continues to evolve, advancements in understanding autism and the gut-brain connection hold promise for developing more targeted and effective interventions. Continued research efforts, collaborations, and clinical trials will contribute to our understanding of autism spectrum disorder and provide hope for improved outcomes for individuals with autism.

Biomarkers and Diagnostic Potential

The study of gut health in relation to autism has revealed the potential for gut microbiota biomarkers to aid in the diagnosis of autism spectrum disorder (ASD). Understanding these biomarkers and their diagnostic applications can provide valuable insights into the development of targeted interventions.

Potential Gut Microbiota Biomarkers

Several studies have investigated the composition of the gut microbiome in individuals with ASD compared to neurotypical individuals. These studies have identified potential gut microbiota biomarkers that exhibit significant differences between ASD patients and typical neurodevelopers. Prevotella, Roseburia, Ruminococcus, Megasphaera, and Streptococcus are among the potential biomarkers associated with ASD. Notably, Prevotella has shown significant differences in abundance between ASD patients and neurotypical individuals.

Diagnostic Applications

The identification of potential gut microbiota biomarkers holds promise for the development of diagnostic applications for ASD. By analyzing the composition and abundance of specific gut bacteria, it may be possible to differentiate individuals with ASD from neurotypical individuals. A case-control study conducted in China selected ten bacterial strains for clinical discrimination between ASD and neurotypical controls, achieving a high AUC value of 0.947 in the model.

While further research is needed to validate these potential biomarkers and develop standardized diagnostic tests, the prospect of utilizing gut microbiota biomarkers in diagnosing ASD offers a non-invasive and potentially early screening method. Early identification of ASD can lead to earlier interventions and support, improving outcomes for individuals on the autism spectrum.

The exploration of gut microbiota biomarkers and their diagnostic potential represents an exciting avenue for future research in the field of autism. Continued advancements in understanding the gut-brain connection and the role of the gut microbiome in ASD can contribute to the development of more accurate and efficient diagnostic tools, ultimately enhancing the lives of individuals with autism and their families.

‍ https://www.euronews.com/health/2023/07/11/groundbreaking-research-reveals-definitive-association-between-gut-microbiome-and-autism
‍ https://hms.harvard.edu/news/gut-brain-connection-autism
‍ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163862/
‍ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9355470/
‍ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8835713/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9196865/
‍ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870536/

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Autism Spectrum Disorder as a Brain‑Gut‑Microbiome Axis Disorder

Virginia Saurman

1 Department of Pediatrics, Columbia University Medical Center, 620 West 168th Street, New York, NY 10032, USA

Kara G. Margolis

Ruth Ann Luna

2 Department of Pathology and Immunology, Texas Children’s Microbiome Center, Baylor College of Medicine, Texas Children’s Hospital, Feigin Tower, 1102 Bates Avenue, Suite 955, Houston, TX 77030, USA

While there are numerous medical comorbidities associated with ASD, gastrointestinal (GI) issues have a significant impact on quality of life for these individuals. Recent findings continue to support the relationship between the gut microbiome and both GI symptoms and behavior, but the heterogeneity within the autism spectrum requires in-depth clinical characterization of these clinical cohorts. Large, diverse, well-controlled studies in this area of research are still needed. Although there is still much to discover about the brain-gut-microbiome axis in ASD, microbially mediated therapies, specifically probiotics and fecal microbiota transplantation have shown promise in the treatment of GI symptoms in ASD, with potential benefit to the core behavioral symptoms of ASD as well. Future research and clinical trials must increasingly consider complex phenotypes in ASD in stratification of large datasets as well as in design of inclusion criteria for individual therapeutic interventions.

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by persistent deficits in social communication and repetitive behavioral patterns. Current statistics suggest that ASD affects 1 in 59 children throughout the USA [ 1 ]. Although the diagnosis of ASD is exclusively neurobehavioral, ASD is accompanied by many medical comorbidities that often occur in much higher prevalence than in neurotypical children. Among these co-occurring medical conditions, gastrointestinal (GI) problems are among the most common.

GI disorders are approximately fourfold more prevalent in children with ASD than in the neurotypical population. Although all of the same GI disorders that present in neurotypical individuals can also be found in those with ASD, constipation and diarrhea tend to be the most common [ 2 , 3 ]. There have been strong correlations found between GI dysfunction and multiple other comorbidities. For example, functional constipation in children with ASD has been associated with worsened behavioral symptoms, as well as an increase in cortisol, stress, and anxiety [ 4 – 14 ]. Other CNS-based comorbidities in the ASD population, such as seizures and sleep disorders, also present more commonly in association with GI dysfunction [ 3 , 15 – 17 ].

The high prevalence of GI dysfunction in ASD and its significant correlations with challenging behaviors and psychiatric comorbidities suggest that there is a relationship between gut and brain dysfunction in a significant subset of these individuals. Insight into the mechanisms that cause dysfunction in brain-gut communication may thus lead to a greater understanding of the underlying pathophysiology of the brain-gut axis in patients with ASD and, moreover, lead to the discovery of novel therapeutic targets. There is increasing evidence to suggest that one of the key modulators of gut-brain communication in ASD is the intestinal microbiome.

The brain-gut-microbiome axis has become a compelling area of investigation in ASD, specifically in the pediatric population ( Fig. 1 ). Characterization of the gut microbiome, usually profiled by sequencing the 16S rRNA gene in bacteria isolated from stool, has been increasingly employed in pediatric GI disorders over the past decade. Unique microbial patterns have been identified in a variety of functional GI disorders like irritable bowel syndrome and recurrent abdominal pain as well as in inflammatory disorders such as inflammatory bowel disease [ 18 – 23 ]. Pilot studies with small cohorts have expanded into large multicenter studies that are now beginning to provide the power to identify robust patterns in these GI conditions, and ASD research is just beginning to benefit from these studies. In this review, we will discuss the potential role of the gut in ASD with a focus on the current state of knowledge of the gut microbiome in this population.

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The brain-gut-microbiome axis plays an important role in autism spectrum disorder. Differences exist in the gut microbiome of typically developing children compared to children with autism spectrum disorder. However, these changes to the microbial community are affected by differences in diet, medication regimens, medical comorbidities, geographic location, and both acute and chronic GI symptoms. In addition, the brain-gut-microbiome axis is bidirectional with regard to changes in both the gut (GI symptoms and microbial profile) and behavior

Gut Microbiome and ASD

The role of the gut microbiome in ASD was first questioned with the emergence of anecdotal reports of young children who developed regressive ASD after repeated exposure to antibiotics for chronic otitis media [ 24 , 25 ]. The clinical course described in this subset of children included the development of chronic diarrhea post-antibiotic administration that was quickly followed by a loss of language and social skills [ 26 , 27 ]. The initial hypothesis, which was generated in part by Ellen Bolte [ 26 ], a mother of a child who with regressive ASD, implicated anaerobic bacteria, namely Clostridiales, based on their ability to produce neurotoxins [ 28 ]. Bolte was also the first individual to report an overall improvement in ASD symptoms when her son was given additional antibiotics targeted to eradication of Clostridiales, leading to further research on the possibility that antibiotics could effect change in the core symptoms of ASD [ 29 ].

The notion that Clostridiales could be an underlying factor in ASD-associated symptoms for regressive ASD set the foundation for the first open-label clinical trial of vancomycin, an antibiotic known to treat Clostridia [ 30 ], in kids with regressive ASD. The inclusion criteria for the study created a unique subgroup of ASD, specifically those who developed ASD-like symptoms and diarrhea after antibiotic exposure in early childhood and who continued to have GI issues and significant communication difficulties post-antibiotic exposure. Each subject was given an 8 week course of vancomycin followed by 4 weeks of oral probiotics ( L. acidophilus L. bulgaricus , and B. bifidum ) [ 24 ]. Interestingly, eight of the 11 individuals who received vancomycin showed substantial behavioral improvements. The majority of subjects, however, experienced substantial behavioral deterioration within 2 weeks after vancomycin cessation [ 24 ]. Although there were significant limitations in the study, including small sample size, lack of an untreated control group, and an open-label study design [ 31 , 32 ], it was a crucial first step in support of the hypothesis that changes in the gut microbiome could elicit changes in the core symptoms of ASD.

A number of studies over the past 20 years have reconfirmed the finding that stool from some individuals with ASD may harbor distinct Clostridial species relative to neurotypical individuals [ 27 , 33 – 40 ]. Clostridium (Lachno-clostridium) bolteae has specifically been associated with ASD patients with GI problems [ 34 , 36 , 41 ]. Despite these differences in proportions, the ways in which Clostridiales impacts host physiology in ASD are not yet known. Blood samples from some ASD patients demonstrate increased levels of metabolites that are chemically similar to p-cresol (e.g., 4-Ethylphenylsulfate), a neurotoxin produced by Clostridiales [ 42 , 43 ]. This finding supports the hypothesis that Clostridiales produce neurotoxins. Studies have not been done, however, to confirm a connection between increased levels of 4-Ethylphenylsulfate and deleterious effects on brain or enteric nervous system development or function.

Extensive Diversity of Current Studies: Background and Implications

In addition to Clostridiales, multiple distinct microbiota populations have been demonstrated in individuals with ASD compared to neurotypical controls, and these studies have been done almost exclusively in children [ 27 , 33 – 36 , 39 – 41 , 44 ]. It is important to note that these studies have generated highly divergent results [ 45 ]. Underlying reasons for this discordance in findings include recruitment of small cohorts, utilization of unaffected siblings as the singular control group, and failure to control for key modulators of the microbiome, including diet, antibiotic exposure, probiotic intake, and both over-the-counter and prescription medications ( Fig. 1 ) [ 46 – 48 ]. Individuals with ASD often have highly self-restrictive diets, which have been shown to affect gut microbiota [ 49 ]. GI motility issues, including constipation and diarrhea, have also been shown to affect the microbiome (and vice versa) and, as stated above, are also common in individuals with ASD [ 2 , 3 , 12 , 50 , 51 ]. Finally, it is exceedingly difficult to account for the extensive genetic and medical heterogeneity inherent in the ASD population which may also impact microbial composition [ 2 , 3 , 52 ].

Beyond variation in study design and population, laboratory and analytic techniques employed in the study of the microbiome have differed vastly as technology has rapidly advanced. As the cost of sequencing has decreased, studies have exponentially increased average sequencing depth. This continuous advancement, paired with more complete microbially focused databases, has increased the level of resolution in the microbiome field yet has also contributed to the diversity of outcomes based on the type of technology utilized. For example, the earlier studies, that detected an increased abundance of Clostridium species in the GI tracts of ASD patients [ 27 , 36 , 39 ], utilized stool-based culture and RT-PCR. The results of this research was much more limited in that stool bacteria was examined at higher taxonomic levels rather than at the species or OTU level [ 33 , 34 , 38 , 41 ]. The next phase of studies, that incorporated next-generation sequencing, were more specific but continued to exhibit clinical study limitations, as detailed above. As the studies in the USA evolved from PCR-based or culture-enhanced characterization of the microbiome, the findings of previous studies in pediatric ASD failed to be replicated [ 33 , 38 ] with some studies unable to identify any statistically significant differences in microbiota between the ASD and control groups [ 37 , 40 ].

Geographic location is known to affect microbiome profiles, and diet is thought to be the major driver behind those differences. While the initial studies that sought to evaluate the microbiota in ASD were mainly conducted in the USA, recent publications are now offering a glimpse into how gut microbiome profiles in ASD differ across several countries including Italy [ 53 , 54 ], India [ 55 ], Spain [ 56 ], China [ 57 ], and Ecuador [ 58 ]. As expected, even though these individual studies highlight differences between the ASD and control groups, there is very little consistency of findings across these studies. For example, two recent studies both conducted in Italy failed to reach consensus at even the phylum level, with one study reporting a decrease in Bacteroidetes [ 54 ] while the other reported an increase [ 53 ]. It is worth noting that, in contrast to the former study, the latter focused on young children (ages 2–4 years) and age stratification is a crucial point of analysis in the evaluation of the developing gut microbiome in pediatric ASD. Studies with larger cohorts, including multi-site recruitment from diverse geographic locations, that are integrating a critical mass of data related to the clinical phenotype ( Fig. 1 ), with GI and behavioral components, as well as addressing dietary differences, are thus greatly needed in this population.

Stool Versus Tissue Microbiota: Is There a Difference?

Most current studies have evaluated the intestinal microbiome in ASD patients through stool. Though stool collection is a less invasive option, evaluation of microbiota that lay directly on the intestinal mucosa may be more highly indicative of the direct interactions that specific microbiota have with the gut mucosa, as well as their impact on host physiology [ 59 – 64 ]. There have been two studies published thus far that have sought to identify a mucosal microbiome in ASD [ 44 , 65 ]. The initial study utilized next-generation sequencing to evaluate ileal and cecal mucosal biopsy specimens from children with and without ASD. There were differences in the microbiota composition between the children with ASD and neurotypical children, including increases in Clostridiales, specifically Lachnospiraceaea and Rumino-coccaceae [ 44 ]. The investigators further found that these microbiota shifts were associated with lower mRNA levels of genes important for carbohydrate digestion (e.g., disac-charidases and hexose transporters). This was an interesting finding because there have been multiple anecdotal reports describing behavioral and GI improvements in children with ASD placed on gluten-free/low carbohydrate diets [ 66 ]. Lack of adequate carbohydrate breakdown and absorption has been shown to lead to fermentation, gas production and increased gut osmotic load which results in bloating, abdominal pain and diarrhea with carbohydrate intake [ 44 ]. Although the double-blind, placebo-controlled studies have not shown differences in behavior or GI symptoms after a gluten-free diet, this study was important in that it was the first to provide a potential explanation for why some children with ASD may respond well to gluten free and/or low carbohydrate diets. The current study, however, was small and included patients with a diverse array of GI motility problems (e.g., constipation versus diarrhea). If these findings were to be reconfirmed in a larger population of children whose clinical metadata is more clearly defined, this could be an important insight into how diet can be used as therapy for a subset of individuals with ASD. It is also important that the precise functional roles of the bacteria associated with these changes in hexose transporters and dissacharidases be investigated.

A more recent study that focused on the relationship between the mucosal microbiome in children with and without ASD specifically was advantageous in that included children with ASD who also met the criteria for a functional GI disorder (FGID) [ 2 , 67 ], the most common type of GI problems affecting this population. The ASD and neurotypical (NT) children evaluated were grouped based on their FGID status as assessed by the Questionnaire on Pediatric Gastrointestinal Symptoms-Rome III (QPGS-RIII) and endoscopic findings (ASD-FGID, NT-FGID, NT). The investigators found novel correlations from not only those patients with ASD compared to neurotypical children, but also those individuals with ASD and abdominal pain compared to those without pain. As seen in prior stool-derived microbiota studies, there was a significant increase in several Clostridiales species, but here it was noted specifically in the population of children with ASD who also had a diagnosis of FGID, indicating a novel association between Clostridiales, ASD, and FGIDs [ 65 ]. Several of the Clostridium sp . were also found to correlate significantly with proinflammatory cytokines (IL-6, IL-1, IL-17, and INF-gamma) as well as tryptophan and serotonin, indicative of newly identified potential links between the intestinal microbiota, serotonin and/or tryptophan secretion as well as inflammation [ 65 ]. These correlations thus yielded the first multi-omic profile specific to ASD-FGID and ASD-FGID with abdominal pain and identified novel human-relevant associations between specific microbiota, gut neurotransmitters and immunity. Finally, the data also may provide some insight into how serotonin affects gut function in these individuals. Almost a third of individuals with ASD have high blood serotonin levels [ 68 ]. Children with ASD and hyperserotonemia have also been found to exhibit higher levels of lower GI tract problems (e.g., constipation) [ 4 ]. This may be linked to bidirectional communication with the gut microbiota; gut bacteria can synthesize serotonin, increases in host serotonin levels can impact gut microbiota composition and, further, specific bacteria (i.e., Turicibacter sanguinis ) may actually possess a serotonin “sensor” that takes up serotonin with consequent effects on its colonization and host physiology [ 61 , 62 , 69 ]. These correlates have been demonstrated in several transgenic murine models, including the BTBR T + Itpr3tf/J (Black and Tan BRachyury with inserted tufted inositol 1,4,5-triphosphate receptor 3 delta as a marker) and the SERT Ala56 (serotonin reuptake transporter alanine 56 substitution) mice, that exhibit core behavioral phenotypes of ASD, as well as alterations in intestinal microbiota that correlate with delayed GI transit and impaired serotonin signaling [ 70 , 71 ] (and pilot data by Luna and Margolis).

Altogether, these data suggest that alteration of the microbiota is associated with changes in the gut-neuro-immune axis. They also provide the basis for much larger studies, both clinical and functional, to identify the roles of specific microbiota in behavioral and gut dysfunction in ASD and also the utility of serotonin-based modulators as potential therapeutic targets.

Genetic Abnormalities and Environmental Exposures Contribute to ASD Risk: Understanding the Underlying Mechanisms in Relation to the Gut Microbiome

Although there have been many studies that demonstrate that the microbiota community in individuals with ASD is different, the specific microbiota involved and their functional effects on ASD development and/or the perpetuation of ASD-associated behaviors remain largely unknown. Although still in its infancy, the examination of this field in murine studies has begun to elucidate potentially important links between environmental risk factors, the microbiota and ASD development.

A number of murine studies support the notion that genetic abnormalities and/or environmental exposures that affect brain development and function also impact gut function [ 72 – 77 ]. The microbiome has been increasingly shown to play a role in these gene-environmental interactions. Gnotobiotic mice and/or rats, for example, that are born and raised in the absence of microbial colonization, exhibit aberrations in several complex behaviors diagnostic of ASD including decreased sociability, repetitive stereotyped behaviors and impaired social behaviors, when introduced to new partners [ 78 ]. Some of these deficits, further, can be reversed by the administration of probiotics or co-housing with WT mice [ 79 – 84 ]. Underlying these observations are evidence that intestinal microbiota impact brain development in areas critical for emotion and mood. Gnotobiotic mice, for example, exhibit gene expression changes in the amygdala, a key region for emotional responses [ 85 ]. They also have increased hippocampal serotonergic neurons, in male mice, and decreased hippocampal brain-derived neurotrophic factor levels [ 80 , 86 ]. The dorsal hippocampus plays a role in spatial learning and memory, while the ventral hippocampus preferentially regulates anxiety and the stress response. These abnormalities in CNS development correlate with behavioral disturbances in ASD, including stereotypies and decreased socialization [ 80 ].

In addition to its impact on the brain and behavior, the microbiome may also impact the gut itself. Gnotobiotic mice also exhibit gut abnormalities, including increased gut permeability and slowed motility [ 79 , 87 – 91 ]. Gut dysfunction may be causative of CNS disturbance; it has been shown in prior mouse studies that increased intestinal permeability allows for the passage of proinflammatory mediators and/or hormones into the circulation, where they may be transported through the bloodstream to the brain, where they may ultimately impact CNS neurodevelopment and/or function [ 62 – 64 , 69 , 75 , 92 , 93 ]. Consequently, any genetic predisposition or maternal exposure that impacts the intestinal microbiome could have implications on both brain and intestinal function.

Two exposures that have been linked to ASD development in clinical epidemiological studies are maternal obesity and maternal inflammation [ 60 ]. Mouse models of maternal obesity and maternal infection are also linked with development of ASD in progeny and, further, have implicated the microbiome in their underlying pathology.

In the USA, ~ one-third of women are obese [ 94 , 95 ] and there has been a coincident rise in both obesity and neuropsychiatric disorders [ 96 ]. A mouse model of maternal obesity, induced by a maternal high-fat diet (MHFD), has been shown to result in ASD-like behaviors as well as changes in the CNS in resulting progeny. MHFD-exposed pups exhibit reduced vocalizations and disruptions in exploratory, cognitive, and stereotyped behaviors [ 72 , 73 , 82 ]. They also have fewer oxytocinergic neurons in the hypothalamus and impaired mesolimbic (dopaminergic) system function. Interestingly, these pups exhibit a dysbiosis characterized, in part, by lower levels of Lactobacillus reuteri , compared to controls [ 72 , 82 ]. When Lactobacillus reuteri is replenished or oxytocin is administered, however, there is a reversal of the abnormal brain and behavioral findings, implicating a potential role for the microbiome in brain oxytocin production [ 82 ]. It was further shown by these investigators that the vagal nerve stimulation that induces oxytocin release, by projecting fibers into the paraventricular nuclei (PVN) which synthesize oxytocin in the brain, is triggered by L. reuteri , a hypothesis further confirmed by the lack of oxytocin release after vagotomy. A later study by the same laboratory determined that L. reuteri supplementation also increased the number and intensity of fluorescence of oxytocinergic neurons in the PVN in Shank3BKO mice, another mouse model of ASD with a genetic mutation found in Phelan McDermid syndrome. These findings together suggest that the microbiome may alter brain activity and behavior by stimulating the vagus nerve to increase PVN production of oxytocin. These findings have a clinical correlate in that there are anecdotal reports of behavioral improvements after oxytocin administration [ 97 , 98 ].

L. reuteri is unlikely, however, to be the only microbe affected by high-fat diet exposure. A complementary study was done in which pregnant female mice were subjected to a microbiome depletion/transplantation paradigm. Antibiotics were used for microbial clearance and were followed by transplantation with microbiota isolated from donors on a high-fat diet, throughout pregnancy and breastfeeding. Male and female high-fat diet fed pups both had significantly decreased communication compared to controls while males exhibited significantly decreased amounts of exploratory time, an increase in stereotyped behaviors and impaired learning. Metagenomic R16S analysis suggested that decreased representation of particular species of the Firmicutes phylum was predictive of behavioral declines in male HFD pups [ 72 ].

The link between maternal infection and ASD originates from a large epidemiological study that showed strong associations between peripartum infections, specifically first trimester viral infections or second trimester bacterial infections, and ASD risk [ 99 ]. A subsequent prospective cohort analysis of over 100,000 women showed that maternal influenza infection or episodes of fevers lasting more than 7 days during pregnancy are associated with a two- and three-fold increased risk of ASD, respectively [ 100 ].

A highly validated mouse model of maternal inflammation is the maternal immune activation (MIA) model, in which mouse dams are injected with the viral mimetic, poly(I:C). Studies of this model have revealed connections between maternal inflammation, enteric microbiome changes and abnormalities in brain-gut development and function [ 75 , 76 , 84 , 101 , 102 ]. Resulting offspring of maternal dams exposed to poly(I:C) exhibit deficits in core communication and social behaviors as well as stereotypical movements reminiscent of ASD [ 75 ]. In addition to the neurobehavioral deficits, these mice exhibit increased gut permeability accompanied by decreased expression of gastrointestinal tight junction proteins, and microbial dysbiosis characterized by changes in the populations of Clostridia, Bacterioides and Lachnospiraceae. These differences were associated with several abnormalities in serum metabolites, including increased levels of indolpyruvate, 5-HT, and 4-ethylphenylsulfate (4-EPS). Interestingly, oral gavage of B. fragilis corrected the dysbiosis, corrected the serum abnormalities in indolpyruvate and 4-EPS, and resulted in improvement of the behavioral abnormalities [ 84 ]. Because the gut permeability was also normalized after B. fragilis exposure, the investigators suggested that this microbial supplement repaired the intestinal barrier, thereby preventing leakage of toxic molecules (e.g., 4-EPS) into the serum which could not then circulate to the brain.

The proinflammatory cytokine, IL-6 has been implicated in the pathogenesis underlying the MIA models [ 75 , 103 ]. In addition to its proinflammatory effects, Il-6 exhibits a robust inverse relationship with full scale intelligence quotient (FSIQ) scores and socialization scores in ASD patients, indicating a potential relationship between inflammation and behavior [ 5 ]. Interestingly, increases in IL-6 have also been linked to changes in intestinal tight junction expression and intestinal barrier integrity suggesting that the cytokine could be a link between inflammation, tight junction integrity and behavioral dysfunction [ 104 ]. Given the apparent microbial modulation of gut and brain function in MIA models and the repeated observation that IL-6 and the microbiome impact each others’ regulation, it is possible that the underlying connection between IL-6, inflammation, and gut-brain dysfunction lites in its ability to modulate the microbiome, or vice versa [ 105 – 107 ]. Conversely, however, enteric microbes are also known to regulate intestinal tight junction and cytokine levels, suggestive of the idea that the microbiome may be a cause of the inflammation associated with maternal infection [ 108 ].

Other environmental exposure or genetic mouse models that exhibit impaired social behaviors and microbial dysbiosis, for which there is less data available, include a valproic acid exposure model and the BTBR T + Itpr3tf/J mice [ 70 , 109 ]. Interestingly, social deficits observed in the BTBR T + Itpr3tf/J mice can be reversed by co-housing with their WT counterparts, making the microbiota a possible modulator for ASD-associated behavior in these mice as well [ 110 ].

In contrast to maternal infection/maternal immune activation and maternal obesity, an environmental exposure that may be protective against ASD is the ketogenic diet (KD). The KD has been used to manage epilepsy for nearly a century [ 111 ], including the treatment of epilepsy syndromes where ASD is a core symptom [ 112 , 113 ]. Interestingly, however, the KD has also been shown to relieve ASD-associated behaviors in some of these patients. There are several case reports of Rett syndrome patients who exhibited improvements in sociability and contact behavior after being placed on a KD [ 114 ]. These findings have been replicated in several mouse models; the KD has been shown to improve sociability in environmental exposure mouse models, including those subjected to MIA or valproic acid during development [ 77 , 111 , 115 ] and also in three genetically modified mouse models of ASD, the BTBR model [ 116 ], the EL strain (a strain used in epilepsy research) [ 117 ], and the genetic variant Engrailed 2 gene [ 118 ].

The mechanism linking the KD to seizure amelioration and sociability improvement remains unclear [ 111 ] though it has been suggested that these changes may result from microbial differences that emerge from the dietary changes. KD alters the composition of gut microbiota in mice [ 119 , 120 ], as well as in humans [ 121 , 122 ] BTBR mice have been shown to have fecal microbiota composition similar to that of some individuals with ASD, including elevated levels of Clostriadiales cluster XI [ 39 ], decreased Firmicutes and increased Bacteroidetes sp [ 33 , 34 ] as well as increased levels of Akkermansia muciniphila [ 33 , 34 , 120 ]. The fecal and cecal low Firmicutes: Bacteroidetes ratio in BTBR mice, as well as the elevated levels of Akkermansia muciniphila ,is both reversed by the KD [ 120 ]. The studies evaluating the effects of the KD on the gut microbiota, however, have not been entirely consistent; a KD has also been shown to result in an increase in Akkermansia and Parabacteroides species in a different study, and, moreover, confers the protective effects of KD against electrically induced seizures in the 6 Hz seizure-induced model [ 81 ].

It has been suggested that the link between ASD, the microbiota and seizure activity may all evolve from an underlying mitochondrial disturbance. Mitochondrial instability is found in many neurodevelopmental disorders, including ASD [ 123 ]; moreover, abnormalities in adenosine levels in ASD patients have been observed [ 124 ]. Improved mitochondrial function is thought to be a mechanism by which KD functions [ 125 ]. In accordance with this postulate, KD has been shown to increase adenosine levels in the CNS, purinergic therapies have been described as improving core symptoms of ASD [ 126 , 127 ] and KD restored mitochondrial function in the valproic acid exposure model of autism [ 77 ]. It has been shown that gut microbiota signal to the mitochondria of mucosal cells, including epithelial cells and immune cells and, further, that this signaling alters mitochondrial metabolism [ 128 ]. If this happens in ASD models, however, has not yet been explored.

Potential of Microbially Mediated Therapies in ASD

With mounting evidence for a role of the brain-gut-microbiome axis in ASD, microbially mediated therapies hold significant potential for improving the quality of life for these individuals. We are at the cusp of better understanding the effect of diet on human health and behavior; however, dietary factors are significant modulators of the gut microbiome and thus dietary interventions may be considered a microbially mediated therapy for subsets of individuals with ASD. Gluten- and casein-free (GFCF) diets are commonly utilized in the ASD population even though small yet properly controlled studies fail to show significant improvement [ 129 , 130 ]. Studies in other parts of the world, however, have shown potential benefit of a GF diet on GI symptoms and behavior in ASD [ 131 ] as well as global improvements while on a ketogenic diet [ 132 ]. The most recent study took a more comprehensive approach in randomizing children and adults with ASD to a gluten-, casein-, and soy-free (GFCFSF) diet along with a variety of supplements, and after 1 year, the treatment group showed significant improvement in the core symptoms of ASD and developmental age compared to the control group [ 133 ]. Larger, well-controlled studies that take into account the clinical heterogeneity in ASD may help to target which individuals may benefit from diets like these.

Clinical trials that evaluate the use of probiotics for the treatment of a variety of GI disorders are ongoing, with many already showing highly favorable results [ 134 ]. While probiotics containing a single beneficial bacterial strain have been successful in IBS, formulations containing multiple strains across more than one genera may be more effective in the ASD population; recent trials in ASD using a multi-strain probiotic [ 135 ] and a combination of a single strain ( B. infantis ) and colostrum [ 136 ] have provided evidence of positive benefits and support the need for larger studies.

Although the initial vancomycin trial in pediatric ASD provided compelling evidence for the potential of antibiotics for treatment of the core symptoms of ASD [ 24 ], very little progress has been made on this front. More recently, minocycline as an adjuvant to risperidone was shown to decrease irritability and hyperactivity/noncompliance, but further work is needed to determine if these improvements are sustained [ 137 ]. Conversely, a pilot study evaluating the effect of D-cycloserine on core social deficits in ASD showed no improvement in the drug treatment group but improvement in the entire cohort that was attributed to the social skills therapy program [ 138 ].

The potential for microbially mediated therapy in general may be best assessed by fecal microbiota transplantation (FMT). Vancomycin is commonly prescribed prior to FMT, as was the case in the only study to investigate the potential efficacy of FMT in children with ASD. An open-label trial of 18 children with ASD included 2 weeks of vancomycin followed by 8 weeks of FMT [ 139 ]. Improvement in GI symptoms were seen during FMT treatment and persisted at 8 weeks following treatment cessation. Improvements were also seen in behavioral symptoms of ASD, and increases in Bifidobacterium, Prevotella, and Desulfovibrio in the gut microbiome were seen in parallel. Two years following treatment, improvements in GI symptoms remain as well as improvement in behavioral symptoms and sustained abundances of Bifidobacterium and Prevotella [ 140 ].

An expanded study that includes not only a placebo group but also a vancomycin only group is needed to accurately assess the impact of FMT plus vancomycin versus vancomycin alone. It is also important to consider that individuals with ASD continue to develop albeit at a delayed pace in most cases, so the inclusion of a control group for comparison with regard to GI and behavior will be needed to evaluate long-term outcomes. It will also be important to characterize the gut microbiome in parallel with these positive changes to potentially identify microbial profiles of individuals who were responders to FMT as well as determine the functional microbiome changes that are associated with alleviation of symptoms.

Future of Brain‑Gut‑Microbiome Axis Research in ASD

There has been a rapid advancement in the knowledge base of the gut microbiome and the technologies used to study it. Despite this vast increase in information, the many factors that confound the current gut microbiome studies in ASD make them impossible to interpret as a whole. With the increased understanding that many factors that impact the microbiome are present in ASD, such medication exposure and GI comorbidities, the inclusion criteria that relies solely on a “diagnosis of ASD” is no longer acceptable. Future studies must thus incorporate the clinical characteristics that comprise complex phenotypes in ASD. These studies will likely result in the stratification of multiple cohorts rather than “one ASD.” Along these lines, it is likely that interventions that have anecdotally shown promise in individuals with ASD but have failed to show statistically significant improvement in clinical trials may have favorable outcomes when targeted “ideal groups” are identified. Significant effort is also needed in the investigation of the brain-gut-microbiome axis in adults with ASD, as almost all research in this area has been focused on the pediatric population. In conjunction with clinical studies, basic or translational studies are also critically needed to determine the effects of specific microbiota and their impact on host physiology in health and disease. Given all of the data supporting a role for microbial modulation of CNS development, behavior and cognition, it seems a worthwhile goal to pursue these aims as a way of developing novel microbially mediated treatments for this exceptionally hard to treat set of disorders.

There is significant heterogeneity within autism spectrum disorder, including varying levels of severity with regard to behavior, cognitive ability, and medical comorbidities.
Gastrointestinal symptoms can be difficult to recognize in individuals with ASD with limited communication abilities.
Preclinical models of ASD have illustrated significant crosstalk along the brain-gut-microbiome axis.
Complex phenotypes based on in-depth clinical characterization are vital to conducting robust research and clinical trials.
Microbially mediated therapies are worth further exploration in specific complex phenotypes of ASD.

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New Research Validates Autism’s Link To Gut

Authored by Amy Denny via The Epoch Times (emphasis ours),

Researchers have identified a microbial signature for autism spectrum disorder, a critical finding that offers clarity about how the gut microbiome influences this neurological syndrome.

The trillions of microbes (bacteria, viruses, fungi, and other microorganisms) that populate the gut microbiome are the basis of that microbial signature. Other research has found that having more microbes and greater diversity is associated with health and lower disease risk. Among other tasks, g ut bacteria metabolize fiber and create metabolites that facilitate digestion, brain functions, and more.

“ It provides further evidence that the microbiome is altered in autism and that it relates to alterations in biochemistry and that those alterations can affect GI [gastrointestinal] and neurological functioning, ” James Adams , professor at Arizona State University’s Biodesign Center for Health Through Microbiomes, told The Epoch Times. He’s been studying the gut–autism link for 20 years and is co-author of the study of 18 people highlighted in the new research.

The Growing Shadow of Autism

Study Specifics

The study’s inclusion of the 2019 long-term fecal microbiota transplant study led by Mr. Adams and Rosa Krajmalnik-Brown makes the evidence more robust.

“ Another set of eyes looked at this, from a different lens, and they validated our findings, ” Ms. Krajmalnik-Brown said of the meta-analysis in the statement .

The Adams and Krajmalnik-Brown study was also published in Nature and noted lower overall microbial diversity and reduced Prevotella copri and Bifidobacterium in children with autism.

The meta-analysis provides large-scale confirmation of a theory that many clinicians and researchers have had for years based on studies and observational evidence.

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UC-Irvine led study identifies therapeutic targets for veterans with Gulf War illness

Results validate and build on past pilot study linking gut microbiome with the illness

Publication date August 30, 2024

A new study has provided critical insights into Gulf War illness, a chronic condition affecting veterans deployed during the 1990–1991 Gulf War. The findings are the most comprehensive to date that establishes an association between gut microbiome imbalance and Gulf War veterans, paving the way for new treatments.

Findings are published in the International Journal Environmental Research and Public Health .

Gulf War illness is characterized by a range of symptoms, including chronic fatigue, cognitive issues, pain and gastrointestinal complaints – which persist even 30 years after deployment – impacting quality of life. This study, building on previous pilot research conducted by the same team, further validates the link between an imbalance in the gut microbiome and Gulf War illness.

Using stool samples and Multidimensional Fatigue Inventory data from 89 Gulf War veteran participants, 63 of whom have Gulf War illness as the study group and 26 who do not have the illness as the control group, Saurabh Chatterjee, MSc, PhD , corresponding author and professor at the UC Irvine Joe C. Wen School of Population & Public Health, and team discovered distinct differences in the gut bacterial composition between those affected by Gulf War illness and those who are not.

Notably, veterans with Gulf War illness had a noticeably different mix of gut bacteria, often expressed as Bray-Curtis beta diversity, compared to those without the illness. This mix is important because it has implications on gut, immune and brain health. Specifically, veterans with Gulf War illness had more of certain bacteria that are usually less beneficial, like Blautia , Streptococcus , Klebsiella , and Clostridium , and fewer of the helpful ones, such as Akkermansia and Bacteroides .

“This study offers hope for the development of new treatments for Gulf War illness that specifically target gut health, potentially improving the quality of life for veterans who have suffered for decades.” – Saurabh Chatterjee, MSc, PhD

The team using advanced machine learning algorithms to identify two bacteria called Coprococcus and Eisenbergiella as critical predictors of Gulf War illness. The results were impressively accurate, where nearly 75 percent of the time the algorithm was able to correctly distinguish between veterans with Gulf War illness and those without it using those two bacteria. Additionally, higher fatigue scores in affected veterans were associated with altered gut bacterial diversity, particularly in species like Lachnospiraceae and Blautia .

These findings not only deepen the understanding of Gulf War illness but also suggest potential therapeutic targets focusing on the gut microbiome of veterans to alleviate specific symptoms of the illness.

“This study offers hope for the development of new treatments for Gulf War illness that specifically target gut health, potentially improving the quality of life for veterans who have suffered for decades,” said Chatterjee who has parallel appointments at the UC Irvine School of Medicine and the Long Beach VA Medical Center. “We’ve only scratched the surface in our understanding of the association between the microbiome imbalance and chronic fatigue but see this as a breakthrough leading to more in-depth studies on Gulf War veterans.”

Additional authors include Ayushi Trivedi and Dipro Bose from UC Irvine Joe C. Wen School of Population & Public Health; Kelly Moffat, Elisabeth Pearson, and Dana Walsh from CosmosID; Devra Cohen from Miami VA Healthcare System; Jonathan Skupsky from VA Long Beach Health Care; Linda Chao from University of California, San Francisco; Julia Golier, J. Peters from VA Medical Center and Icahn School of Medicine at Mount Sinai; Patricia Janulewicz and Kimberly Sullivan from Boston University School of Public Health; Maxine Krengel from Boston University Chobanian and Avedisian School of Medicine; Ashok Tuteja from University of Utah; Nancy Klimas from Nova Southeastern University and Miami VA Healthcare System.

This study was supported by VA Merit Award I01CX001923-01 awarded to Saurabh Chatterjee. BBRAIN samples were provided by support from the US Department of Defense Congressionally Directed Medical Research Program (CDMRP/GWIRP) award W81XWH-18-1-0549 awarded to Kimberly Sullivan.

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UC-Irvine led study identifies therapeutic targets for veterans with
A new study has provided critical insights into Gulf War illness, a chronic condition affecting veterans deployed during the 1990-1991 Gulf War. The findings are the most comprehensive to date that establishes an association between gut microbiome imbalance and Gulf War veterans, paving the way for new treatments.

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Extended data

Extended Data Fig. 2 Benchmarks.

Extended Data Fig. 3 Differential ranking trends observed for the virome, 16S, SMS, and RNAseq datasets analyzed in this study.

Extended Data Fig. 4 Metabolomics differential ranking analysis across four studies.

Extended Data Fig. 5 Comparison of log-fold changes computed from 16S and SMS.

Extended Data Fig. 6 Age differences between case-control matchings.

Extended Data Fig. 7 Microbe-viral co-occurrence network estimated using MMvec.

Extended Data Fig. 8 Microbe-diet co-occurrences.

Extended Data Fig. 9 Distribution of pathways in ASD and control-associated genes detected in SMS and RNAseq data.

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