Tryptophan-related Gut Microbes are Linked with Neural and Behavioral Autism Phenotypes

Tryptophan-related Gut Microbes are Linked with Neural and Behavioral Autism Phenotypes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Tryptophan-related Gut Microbes are Linked with Neural and Behavioral Autism Phenotypes Sofronia Ringold, Emeran Mayer, Skylar Tanartkit, Aditya Jayashankar, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8725888/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Converging evidence implicates brain activity differences in interoceptive and emotion-related brain regions (e.g., insula) in the pathophysiology of autism spectrum disorder (ASD). Given that regions such as the insula are heavily modulated by serotonin, and gut microbes influence central serotonin levels by regulating the availability of its precursor, tryptophan via microbial metabolism, studies exploring associations between such gut microbes and brain regions are much needed. However, studies integrating neural, gut microbiome, and behavioral data in humans are rare. Such studies are necessary to better understand the neurobiology of ASD from a systems perspective and develop novel treatment strategies. In 53 ASD (42 Male, Mage = 12.05 years) and 53 neurotypical (NT) (30 Male, Mage = 11.82 years) youth we tested the hypotheses that gut species involved in tryptophan metabolism: 1) significantly differ in relative abundance between ASD and NT children; 2) are associated with activity in interoceptive and emotion regulating brain regions during functional neuroimaging tasks; and 3) are linked to ASD symptoms. In addition, we tested the exploratory hypothesis that functional brain activity mediates relationships between gut bacteria and behavior. Autistic youth had significantly greater relative abundances of species of Blautia, Clostridium, Ruminococcus , and Streptococcus , which were significantly associated with distinct neural activity patterns in interoceptive and emotion-related brain regions. Activity in the right mid-insula during physical disgust processing was a significant mediator of the relationship between Lactococcus lactis and restricted and repetitive behaviors. These results support the hypothesis that differences in the relative abundance of several gut microbes involved in the metabolism of tryptophan are related to known ASD brain alterations and symptomatology. These findings highlight the potential for targeting the gut microbiome to influence neural activity and behavioral outcomes in ASD, offering a promising avenue for novel intervention strategies. Biological sciences/Microbiology/Bacteria/Metagenomics Biological sciences/Neuroscience/Cognitive neuroscience Figures Figure 1 Figure 2 Figure 3 Introduction The Brain Gut Microbiome (BGM) system encompasses the bidirectional communication network between the gut microbiome and the brain that plays a role in the modulation of gut functions and behavior via direct and indirect pathways. 1 , 2 Disruptions of the BGM system are implicated in neurological conditions though further studies are necessary to determine causality. 3 – 5 Preclinical models link autism-like behaviors to gut microbiome alterations and neurodevelopmental changes, indicating a potential role of a dysregulated BGM system in autism spectrum disorder (ASD). 6 – 11 ASD is a neurodevelopmental disorder characterized by persistent social communication difficulties, sensory sensitivities, and restricted, repetitive behaviors. 12 The pathophysiology of ASD is incompletely understood and multifactorial, involving genetic factors and the exposome. 13 – 15 In children, ASD co-occurs with anxiety (42%-79%), 16 sleep disturbances (50%-80%), 17 and gastrointestinal (GI) issues (46–84%), 18 all of which are related to alterations in the BGM system. 1 , 19 , 20 Prior studies in autistic children identified significant differences in the gut and oral microbiomes compared to controls, with specific taxa associated with core ASD symptoms. 21 – 26 ASD symptoms are linked to atypical brain structure and function. 27 , 28 However, no studies have integrated brain imaging, fecal metagenomics, and behavior, to study the BGM system within the same cohort. Given the complexity of ASD, a comprehensive, multi-system approach is essential to better understand its neurobiology. Fecal metabolomics studies implicate alterations in tryptophan metabolism including serotonin, kynurenine, indoles and their derivatives in ASD. 22 , 26 , 29 – 33 Serotonin, metabolized both in the gut and in brainstem nuclei, is a neurotransmitter involved in several vital functions, including emotion regulation, sleep, food intake, and pain processing. 3 , 34 , 35 Elevated serum serotonin has been observed in approximately 30% of autistic individuals, and increased fecal serotonin is associated with increased GI symptom severity. 31 , 36 In rodent models, hyperserotonemia is associated with ASD-like social-behavioral deficits and repetitive behaviors. 37 While serotonin in systemic circulation is rapidly taken up by platelets and cannot cross the blood brain barrier (BBB), kynurenine and indole metabolites generated by gut microbes can enter systemic circulation and cross the BBB. Alterations in these metabolites have been linked to changes in brain structure, neural activity, and behavior in autistic youth and rodent models. 26 , 33 , 38 – 42 Thus, the tryptophan pathway is a promising avenue for hypothesis-driven research in ASD. Here, we used shotgun metagenomics to study operational genomic units (OGUs), a method that provides the highest resolution of community composition, 43 involved in the metabolism of tryptophan-derived metabolites (serotonin, kynurenine, indole, and their derivatives), that belong to the following genera: Bacteroides, Bifidobacterium, Blautia, Clostridium, Enterococcus, Escheria, Klebsiella, Lactobacillus, Lactococcus, Ruminococcus, Streptococcus , and Alistipes . 44 – 59 In autistic and neurotypical (NT) children, we investigate group differences in the relative abundance of OGUs from these genera and assess associations with behavior and brain activity in tasks related to interoception and socio-emotional processing. 33 , 60 , 61 Given that these processes are associated with autonomic nervous system signaling and the gut microbiome, the brain regions involved may function as mediators of the microbiome-behavior connection. 3 , 62 – 65 We also test whether neural activity mediates associations between tryptophan-related gut microbiota and behavior in ASD, in line with our prior work. 33 Methods Participants This study was approved by the University of Southern California’s (USC) Institutional Review Board (Approval Number: UP-19-00522). Participants were recruited from healthcare clinics in Los Angeles, through advertising in the local community and social media, and by word-of-mouth. Written informed consent and assent were obtained prior to all study procedures. The sample included 106 participants aged 8–17 years: 53 ASD (42 Male, 11 Female, M age =12.05 years), and 53 NT (30 Male, 23 Female, M age =11.82 years). Inclusion/exclusion criteria were as described previously, 33,60,66 with full criteria in the supplementary materials. Briefly, all participants had an IQ ≥ 79 on either the Full-Scale Intelligence Quotient (FSIQ) or Verbal Comprehension Index (VCI) of the WASI-II 67 and were right-handed. 68 ASD diagnosis was verified with the Autism Diagnostic Observation Schedule (ADOS-2) 69 and the Autism Diagnostic Interview-Revised (ADI-R) 70 for autistic children. Participants were excluded if they had contraindications to participating in MRI (i.e., metal implants, braces, inability to remain still for 1 hour), consumed probiotics within two weeks, or antibiotics within 30 days prior to participation. Study Procedures The study took place over two visits at USC. On the first visit written informed consent and assent were obtained, eligibility was verified, and mock MRI scanning was completed. Participants brought fecal samples to the lab on their second visit and underwent fMRI scans. Behavioral Data Collection Parents completed measures to assess their child’s social difficulties (SRS-2), 71 sensory sensitivities (Sensory Experiences Questionnaire [SEQ-3]), 72 diet, and medical history including antibiotic usage (infant and prenatal), maternal illness during pregnancy, breastfeeding history, and birth mode. Participants self-reported their anxiety (Screen for Child Anxiety Related Emotional Disorder [SCARED-C]), 73 gastrointestinal symptoms (Gastrointestinal Symptom Rating Scale [GSRS]), 74 disgust propensity and sensitivity (Disgust Propensity and Sensitivity Scale-Revised [DPSS-R]), 75 sleep quality (Adolescent Sleep Wake Scale [ASWS]), 76 and alexithymia (Alexithymia Questionnaire for Children [ACQ]). 77 Measure descriptions are in the Supplementary Materials. Stool Sample Collection Fecal samples were collected as described previously. 33 Briefly, participants collected stool samples within 72 hours of their MRI with a fecal collection kit and transported them to the lab in insulated containers. Samples were stored at -80°C then aliquoted over dry ice. Functional Magnetic Resonance Imaging (fMRI) On the second visit, participants underwent task-based fMRI scans related to interoceptive and socio-emotional processing – tasks that commonly show strong differences in ASD. 33 , 60 , 61 , 78 – 80 For additional details on tasks and stimuli, and an additional exploratory somatosensory task, see the Supplementary Materials and our prior studies. 33 , 60 , 61 Face and Action Observation (ASD = 53, NT = 51) : During a 9-minute run, participants watched blocks of videos of emotional facial expressions (i.e., sad), non-emotional expressions (i.e., puffed cheeks), bimanual hand actions (i.e., peeling a banana), or control stimuli. Disgust Processing (ASD = 25, NT = 25) : During a 10-minute run, participants watched blocks of pictures of disgusting foods, disgusted facial expressions, neutral foods, and neutral facial expressions. Prior to the scan, participants were asked to rate (love, like, neutral, or dislike) foods and the video was tailored to each participant. Data Analysis Shotgun Metagenomic Analysis Shotgun metagenomic sequencing and subsequent bioinformatic processing was performed as previously described, 80,81 with full methods in the Supplementary Materials. The resulting OGU tables were converted to BIOM format, 82 filtered for 30% feature prevalence, center log-ratio transformed, and filtered against Greengenes2 (v. 2024.09), which yielded 425 OGUs. 83 Filtering to the 24 genera related to tryptophan metabolism yielded 96 OGUs listed in the Supplementary Materials. Group Differences in Behavioral Measures Group differences in behavioral measures were assessed using independent samples t-tests and Fisher’s exact tests. Effect sizes are reported with Cohen’s d and Cramer's V, respectively. Statistical significance was set at p < 0.05. Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) To identify a multivariate metagenomic signature discriminating ASD from NT, we employed a sparse partial least squares discriminant analysis (sPLS-DA) on the 96 OGUs residualized for age, sex, BMI, IQ, and diet with the mixOmics R package. 86 , 87 Although the primary goal of this analysis was to generate a parsimonious model through feature selection and data reduction, 88 we also examined the predictive accuracy of the derived microbiome signature. sPLS-DA is a latent variable approach that employs a supervised framework forming linear combinations of the predictors (OGUs) based on class membership (ASD/NT) and reduces the dimensionality of the data by finding sets of orthogonal components or metagenomic signatures each comprised by a selected set of OGU features. 86 , 89 Each feature of the metagenomic signature has an associated “loading”, reflecting the relative importance of that feature for the group discrimination. 89 Additional information and an analysis using random forest classification 90 , 91 are in the Supplementary Materials. Group Differences in Tryptophan-Related OGUs Linear contrast analysis (LCA) within the framework of the general linear model (GLM) was applied to determine group differences in the 96 OGUs. The model included group as a factor, and age, sex, body mass index (BMI), and diet as covariates. The Benjamini-Hochberg method to correct for multiple comparisons was used with the false discovery reporting (FDR) threshold (q) set at 10%. 84 Cohen’s d was calculated to provide an effect size for group differences (small:0.2–0.5, medium:0.5–0.8, and large > 0.8). 85 Neuroimaging Analysis fMRI analyses were performed using FMRIB’s Software Library (FSL) 92 – 97 as described previously. 33 , 58 , 59 Additional information is in the Supplementary Materials. Association Analyses GLMs were applied within groups to assess: 1) OGU associations with brain activity (DV=brain activity, IV = OGU) and behavior (DV = OGU, IV=behavior) with age, sex, IQ, BMI, and diet as covariates; and 2) associations with brain activity and behavior with age, sex, and IQ as covariates (DV=brain activity, IV=behavior). The q was set at 10% for the number of dependent variables. 84 Standardized betas (Std β) were calculated as a measure of effect size (small: 0.10–0.29, medium: 0.30–0.49, and large: >0.50). 98 Mediation Models Mediation analyses were performed with the lavaan package 99 in R to assess whether the brain mediates microbiome-behavior associations in autistic children. Model variables were selected based on ROIs (mediator) with both significant ROI-OGU and ROI-behavior associations. Bootstrapped 95% confidence intervals for indirect effects were obtained using the R package semhelpinghands. 100 Age, sex, BMI, diet, and IQ were included as covariates for mediator and outcome variables. Confidence intervals that do not contain zero for indirect effects suggest statistical mediation. 101 Results Group differences in behavioral variables Autistic children had significantly more males (ASD:79%; NT: 57%) and increased BMI, GI symptoms, social difficulties, sensory sensitivities, anxiety, disgust propensity and sensitivity, alexithymia, prenatal maternal illness, and more frequently followed the Modified Standard American diet (all p s < 0.05; Supplemental Table 5). NT children had significantly higher IQ and better sleep quality (all p s < 0.05). A metagenomic signature predicts ASD diagnosis with moderate accuracy The sPLS-DA indicated a one component solution discriminating ASD from NT composed of 10 features (Fig. 1 ). The top two features were increased relative abundance of Streptococcus intermedius and Blautia A 141781 hydrogenotrophica in ASD. Scores on this metagenomic signature showed a large effect size difference between groups (d = 0.80, t (107.9) = 4.21, p = 5.35 x 10 − 5 ), with the ASD group scoring higher on the signature. Classification performance accuracy was slightly better than chance (Balanced Error Rate = 42%, AUC = 59%). Autistic children have significantly increased levels of OGUs involved in tryptophan metabolism compared to NT peers Autistic children had significantly higher levels of OGUs belonging to species of Streptococcus, Blautia , and Clostridium (Table 1 ). Table 1 Significant group differences in OGUs Species OGU Estimate SE p q Cohen's d Streptococcus_intermedius G000463355 0.944 0.26 < 0.001 0.041 0.80 Blautia_A_141781_hydrogenotrophica G000157975 0.703 0.21 0.001 0.046 0.75 Streptococcus oralis_E_351036 G000344275 0.777 0.24 0.001 0.047 0.72 Clostridium_AP scindens G000154505 0.832 0.29 0.004 0.070 0.64 Clostridium_AQ innocuum G000183585 0.470 0.16 0.004 0.070 0.65 Streptococcus anginosus G000463505 0.737 0.25 0.004 0.070 0.64 Streptococcus sanguinis_H G000014205 0.686 0.27 0.012 0.076 0.56 Streptococcus mitis_AR_353295 G000027165 0.684 0.25 0.007 0.076 0.61 Streptococcus infantis G000187465 0.689 0.25 0.007 0.076 0.60 Streptococcus cristatus_B_353950 G000385925 0.627 0.25 0.012 0.076 0.56 Clostridium_AQ innocuum G000450985 0.379 0.14 0.010 0.076 0.58 Streptococcus_pneumoniae G001133125 0.715 0.27 0.009 0.076 0.59 Clostridium_AQ innocuum G001688965 0.431 0.16 0.008 0.076 0.59 Blautia_A_141780 sp001304935 G003478165 0.485 0.19 0.012 0.076 0.56 Clostridium_AQ innocuum G900114575 0.504 0.19 0.010 0.076 0.58 Clostridium_AQ innocuum G000242195 0.423 0.17 0.015 0.088 0.55 Note. Significance was set at FDR corrected p-value (q) < 0.10. The model included group as a factor, and age, sex, body mass index (BMI), and diet as covariates. SE: Standardized Error; p: p-value; q = FDR corrected p-value. fMRI tasks evoked differences in brain regions related to socio-emotional processing and interoception In the face and action observation task, NT children displayed significantly greater activity in the right inferior gyrus pars opercularis (IFGop) and mid-cingulate cortex (MCC) while viewing all stimuli. While viewing disgusted faces, autistic children had significantly increased activity in the pregenual anterior cingulate cortex (pACC), left dorsal anterior (dA) and right ventral anterior (vA) insula, and decreased activity in the right fusiform face area (FFA). While viewing disgusting foods, NT children had significantly increased activity in the right mid-insula, and the left vA insula (Supplemental Table 3). Tryptophan-related OGUs are significantly associated with fMRI task activity in ASD Moderate to large effects size associations were observed between relative abundance of OGUs and both fMRI tasks (Fig. 2 ). Results for the NT group are in the Supplementary Materials. Functional brain activity during disgust processing is associated with repetitive behaviors and autism severity Moderate to large effect size associations were observed between brain activity during the disgust processing task and repetitive behaviors and autism severity (Table 2 ). In autistic children, activity in left insular subregions (dA, vA) and the right mid-insula decreased as restricted and repetitive behaviors (RRBs; ADOS and ADI-R) and autism severity (ADOS total) increased. Increased activity in the right FFA was associated with higher ADI-R RRBs scores. There were no associations with q < 0.10 for the observation task or within the NT group for either task. Table 2 Significant Task-based ROI-Behavior Associations in the ASD group Disgust Processing ROI Stimuli Behavior β SE Std β p q L dA Insula Disgusted Faces ADOS RRBs -0.603 0.045 -0.603 0.005 0.032 R Mid-Insula Disgusting Food ADOS RRBs -0.536 0.037 -0.536 0.013 0.038 R Mid-Insula Disgusting Food ADOS Total -0.572 0.015 -0.572 0.008 0.048 L vA Insula Disgusting Food ADI-R RRBs -0.630 0.021 -0.630 0.021 0.089 R FFA Disgusted Faces ADI-R RRBs 0.486 0.044 0.486 0.030 0.089 Note. Significance was set at FDR correct p=value (q) < 0.10. ROI: Region of Interest; β: unstandardized beta, SE: Standardized Error; Std β: Standardized Beta; p: p-value, q = FDR adjusted p-value; L: Left; R: Right; MCC: mid-cingulate cortex; DPSS-R: Disgust Propensity and Sensitivity Scale- Revised; ADOS: Autism Diagnostic Observation Schedule; RRBs: Restricted and Repetitive Behaviors; ADI-R: Autism Diagnostic Interview-Revised; dA: dorsal anterior; vA: ventral anterior; FFA: fusiform face area Neural activity is a significant mediator between Lactococcus A lactis and RRBs in ASD There were no significant associations between OGUs and behavior (all qs > 0.10) in autistic children. We next investigated whether incorporating neural activity as a mediator in the OGU-behavior associations would reveal significant indirect effects. Activity in the right mid-insula while viewing disgusting foods was a significant mediator of the relationship between Lactococcus A 346120 lactis 344179 and RRBs (ADOS), while controlling for age, sex, IQ, diet, and BMI (indirect effect: Std. β = 0.46, SE = 0.13, 95% CI [0.01,0.91], Fig. 3). Discussion In line with our hypotheses, autistic youth showed significant differences in abundances of gut microbes related to tryptophan metabolism, some of which significantly related to activity in socio-emotional brain regions. Importantly, the insula mediated relationships between Lactococcus A 346120 lactis 344179 and RRBs – a core symptom of ASD. We discuss these findings below. Autistic children had significantly elevated relative abundances of OGUs belonging to Streptococcus, Blautia, Clostridium , which are involved in serotonin and indole metabolism. 44–59 Untargeted metabolomic analysis on a subset of this sample (N = 84) 33 found decreased kynurenate in autistic children. Taken together, these results suggest that in ASD, tryptophan metabolism may be shifted towards alternative pathways (e.g., serotonin and indoles) – consistent with findings of elevated serotonin in 30% autistic individuals. 36 In line with the idea that such species impact neural activity via their production of neuroactive and inflammatory molecules, we found OGUs belonging to Clostridium, Bifidobacterium, Ruminococcus, Bacteroides, Lactococcus , and Blautia , were associated with socio-emotional task-evoked activity in the MCC, IFGop, insula, pACC, and FFA. Importantly, mediation analyses revealed that right mid-insula activity during physical disgust significantly mediated a positive relationship between a Lactococcus OGU and RRBs. The mid-insula is strongly implicated in disgust and interoceptive processing 102 and commonly shows significant differences in ASD. 103 , 104 The insula receives interoceptive information from the gut via vagal signals sent to brainstem nuclei, 105 and is modulated by serotonin. 106 Further, some species of Lactococcus are involved in producing serotonin. 54 , 57 Preclinical studies show that increased Lactococcus is associated with increased atypical behaviors, including excessive self-grooming and marble burying – behaviors commonly used as proxies for human RRBs. 107 , 108 Together, these results suggest that microbial taxa and metabolites may influence core autistic behaviors via their effects on brain function, highlighting the BGM system’s role in ASD. We also found significantly elevated levels of Clostridium ( C. scindens, C. innocuum ) in autistic children compared to NT, and significant associations between C. leptum and decreased neural activity in the right MCC while processing non-emotional facial expressions. Some Clostridium species are known to produce toxins that can cause neurological impairment, 109 and prior studies in autistic youth found significantly higher levels of Clostridium that were associated with autism severity and gastrointestinal symptoms. 21 , 110 Additionally, we saw increased levels of Streptococcus in ASD, which contains both beneficial and pathogenic species. 111 – 113 Interestingly, we found increased levels of common oral Streptococcus species ( S. mitis, S. oralis , and S. sanguinis ). 114 – 117 Evidence suggests that oral bacteria can translocate to the gut via swallowing, contributing to gut dysbiosis. 118 Future studies collecting oral and fecal samples from the same individual to compare overlap between communities may support this mechanism. 119 Overall, our findings suggest that differences in tryptophan-related gut microbiota in autistic children are associated with differences in brain function related to socio-emotional and interoceptive processing, potentially through microbial metabolism of tryptophan metabolites into serotonin and indoles. This supports the growing body of evidence implicating the BGM system in the neurobiology of ASD, specifically affecting RRBs, as observed in our mediation analysis. Despite the negative impact of RRBs on quality of life for both autistic children and their families, 120–122 no treatments exist for RRBs besides behavioral interventions with modest impacts. 122 – 125 However, emerging microbiome-based approaches, such as fecal microbiota transplants and tryptophan depletion studies in autistic adults, are more targeted approaches that have been linked to changes in tryptophan metabolism, neural activity, and RRBs. 126 – 130 Large longitudinal interventional studies are needed to observe their effectiveness in children. The current study is limited by its cross-sectional observational design. Additionally, our mediation models do not establish causation. Nevertheless, our results demonstrate significant statistical mediation and lay a foundational model that can be expanded upon in interventional and longitudinal research. Future studies with larger, more heterogeneous samples should be conducted to confirm generalizability. Since some of the genera examined here are related to the production of other neuromodulators (e.g., GABA, dopamine, acetylcholine) 131 , future multi-omic work combining shotgun metagenomics and metabolomics is warranted. Collecting detailed dietary information is a common difficulty in microbiome research. Diet diaries and food frequency questionnaires are often critiqued for high participant burden and recall bias. 132 We used a less detailed, but potentially more accurate measure of general diet by collecting the participant’s habitual diet type. Future studies should utilize more detailed measures of diet 133 or focus on populations following specific diets. 134 Tools such as the foodMASST platform, a Mass Spectrometry Search Tool with a reference database of food metabolites, could supplement self-reported diet measures. 135 Nevertheless, we address critiques of prior microbiome studies in ASD 133 , 136 by defining specific a priori hypothesis, providing effect sizes for all findings, using compositionally aware techniques, applying multiple comparisons corrections, and including confounding variables (sex, age, diet, BMI) in all analyses. In conclusion, we found that neural activity in interoceptive and socio-emotional brain regions is associated with relative abundance of species involved in tryptophan metabolism. Further, our exploratory statistical mediation models suggest brain activity can act as a mediation between gut bacteria and RRBs. These findings underscore the importance of integrating neural, microbial, and behavioral data to reveal ASD’s neurobiology. Data Availability De-identified data are available via the NIMH Data Archive: The Relationship Between Brain Functioning, Behavior, and Microbiota in Autism Spectrum Disorder #4991. All sequencing data have been deposited in Zenodo. Code Availability MRI codes are available on: https://github.com/CeNEC?tab=repositories Declarations Conflicts of Interest Rob Knight is a scientific advisory board member and consultant for BiomeSense, Inc., has equity, and receives income. He is a scientific advisory board member and has equity in GenCirq. He is a consultant and scientific advisory board member for DayTwo and receives income. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a co-founder of Micronoma and has equity and is a scientific advisory board member. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. Funding This work was funded by Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD079432) and the Department of Defense through the Idea Development Award under award number AR170062. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the NIH or the Department of Defense. Additional support was provided by the Nedra Gillette Endowed Research Fellowship. Lucas Patel is supported by the University of California San Diego Medical Scientist Training Program (NIH/NIGMS T32GM154642). Acknowledgements We thank all participants, CeNEC research assistants, and the Integrative Biostatistics and Bioinformatics Core at the Goodman Luskin Microbiome Center for their contributions to this study. 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He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a co-founder of Micronoma and has equity and is a scientific advisory board member. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. Supplementary Files SupplementaryMaterials.docx Supplementary Materials Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Ringold","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYFACxgYgYcHYD+ExE61FgnFmA/FawECCccMBYrXIzz7c9vBLhYTs5hvpDz8wVFgnNhDSYnAusd1Y5oyE8bYbOcYSDGfSidDCw9gmLdkmkQjUwsbA2HaYsBb5HqiWzTPSnzEw/iNCC8MZxjbJj0AtGyQSzIChR4QWA6AWaQagX2aceWMskXAs3ZgIh7E/k/xRYSPb3w4MsQ811rKEHQYEzDwwVgIxykGA8QexKkfBKBgFo2BkAgDZfTy05C/45QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8020-7735","institution":"University of Southern California","correspondingAuthor":true,"prefix":"","firstName":"Sofronia","middleName":"","lastName":"Ringold","suffix":""},{"id":587103589,"identity":"6bda316c-1436-4993-b48d-1d43b6712f88","order_by":1,"name":"Emeran Mayer","email":"","orcid":"","institution":"University of California Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Emeran","middleName":"","lastName":"Mayer","suffix":""},{"id":587103590,"identity":"03043e75-d8d3-4261-a41a-83cd7e9da750","order_by":2,"name":"Skylar Tanartkit","email":"","orcid":"","institution":"UCLA","correspondingAuthor":false,"prefix":"","firstName":"Skylar","middleName":"","lastName":"Tanartkit","suffix":""},{"id":587103591,"identity":"923c5dfd-9bcd-4629-bd74-95c264c09979","order_by":3,"name":"Aditya Jayashankar","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Aditya","middleName":"","lastName":"Jayashankar","suffix":""},{"id":587103592,"identity":"c34ec527-61e7-47a4-82d1-b0e938b5be19","order_by":4,"name":"Emily Kilroy","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"","lastName":"Kilroy","suffix":""},{"id":587103593,"identity":"43df116e-1f1f-4dd6-ba39-e759bc5a9c6a","order_by":5,"name":"Christiana Butera","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Christiana","middleName":"","lastName":"Butera","suffix":""},{"id":587103594,"identity":"3c940c83-9335-43c7-8daa-84b19c4966db","order_by":6,"name":"Jonathan Jacobs","email":"","orcid":"https://orcid.org/0000-0003-4698-0254","institution":"UCLA","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Jacobs","suffix":""},{"id":587103595,"identity":"30cb3d86-c558-4b2d-a536-678dd350c6bf","order_by":7,"name":"Swapna Joshi","email":"","orcid":"","institution":"UCLA","correspondingAuthor":false,"prefix":"","firstName":"Swapna","middleName":"","lastName":"Joshi","suffix":""},{"id":587103596,"identity":"4e1e36ca-e6b9-4660-a426-b6c781c9e121","order_by":8,"name":"Lucas Patel","email":"","orcid":"https://orcid.org/0000-0001-8607-2782","institution":"University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Lucas","middleName":"","lastName":"Patel","suffix":""},{"id":587103599,"identity":"d4832cf7-3b4f-4f62-bd12-7cc2358ebf6f","order_by":9,"name":"Asim Wahab","email":"","orcid":"","institution":"University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Asim","middleName":"","lastName":"Wahab","suffix":""},{"id":587103601,"identity":"e61aa8dd-d21f-4212-a2c2-a2ca51a09d94","order_by":10,"name":"Rob Knight","email":"","orcid":"https://orcid.org/0000-0002-0975-9019","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Rob","middleName":"","lastName":"Knight","suffix":""},{"id":587103602,"identity":"a791abc0-9b3b-45e4-a575-352ab5039a93","order_by":11,"name":"Mirella Dapretto","email":"","orcid":"","institution":"University of California Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Mirella","middleName":"","lastName":"Dapretto","suffix":""},{"id":587103603,"identity":"59cfd31d-deae-4506-bf06-45daca0f691f","order_by":12,"name":"Jennifer Labus","email":"","orcid":"","institution":"University of California Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Labus","suffix":""},{"id":587103604,"identity":"4d561774-e681-4635-9934-455704343d13","order_by":13,"name":"Lisa Aziz-Zadeh","email":"","orcid":"https://orcid.org/0000-0003-3557-8083","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Aziz-Zadeh","suffix":""}],"badges":[],"createdAt":"2026-01-29 01:30:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8725888/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8725888/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104345161,"identity":"abc6075a-5e3e-4ece-a85c-6280b464df41","added_by":"auto","created_at":"2026-03-10 17:33:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59602,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003esPLS-DA Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote. Top: Visualization of prediction based on two components, residualized for age, sex, BMI, IQ, and diet. Background color represents prediction of the group (blue= ASD, orange=NT). Blue circles represent ASD and orange triangles represent NT samples B: loading plot of the OGUs selected by sPLS-DA on component 1. OGUs are ranked according to their loading weight (most important for the discrimination at the bottom to least important at the top). Bars in blue reflect greater abundance in ASD.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8725888/v1/04bcf80c12a1c14959fa0d03.png"},{"id":104405685,"identity":"8b2e6997-f43b-4546-a5bb-04660251ad89","added_by":"auto","created_at":"2026-03-11 12:23:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109156,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignificant OGU associations with tasked based ROIs in ASD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote. Significance was set at FDR correct p=value (q) \u0026lt;0.10. ROI: region of interest; Std β: Standardized Beta; SE: Standardized Error, p: p-value, q= FDR adjusted p-value; NA: not applicable; L: Left; R: Right; MCC: mid-cingulate cortex; IFGop: inferior gyrus pars opercularis; vA: ventral anterior; pACC: pregenual anterior cingulate cortex; FFA: fusiform face area.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8725888/v1/6da3720fc9daa292a160babc.jpg"},{"id":104345162,"identity":"2584bb84-1acf-467d-a96f-0a6e2a8b1924","added_by":"auto","created_at":"2026-03-10 17:33:57","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37381,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003esPLS-DA Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote. Top: Visualization of prediction based on two components, residualized for age, sex, BMI, IQ, and diet. Background color represents prediction of the group (blue= ASD, orange=NT). Blue circles represent ASD and orange triangles represent NT samples B: loading plot of the OGUs selected by sPLS-DA on component 1. OGUs are ranked according to their loading weight (most important for the discrimination at the bottom to least important at the top). Bars in blue reflect greater abundance in ASD.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8725888/v1/fa452ffb7d58f06d6dd9d91d.jpg"},{"id":104409506,"identity":"b18c9418-1da4-44ce-a774-541b95141ee4","added_by":"auto","created_at":"2026-03-11 12:45:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1394644,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8725888/v1/c59ae062-2c42-4acd-a393-41f348dcc708.pdf"},{"id":104405896,"identity":"6c75a44e-c2d4-4111-9de1-80040aebce31","added_by":"auto","created_at":"2026-03-11 12:24:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":728932,"visible":true,"origin":"","legend":"Supplementary Materials","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8725888/v1/90e70a65a65d6b1be1b1a35b.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nRob Knight is a scientific advisory board member and consultant for BiomeSense, Inc., has equity, and receives income. He is a scientific advisory board member and has equity in GenCirq. He is a consultant and scientific advisory board member for DayTwo and receives income. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a co-founder of Micronoma and has equity and is a scientific advisory board member. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies.","formattedTitle":"Tryptophan-related Gut Microbes are Linked with Neural and Behavioral Autism Phenotypes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Brain Gut Microbiome (BGM) system encompasses the bidirectional communication network between the gut microbiome and the brain that plays a role in the modulation of gut functions and behavior via direct and indirect pathways.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Disruptions of the BGM system are implicated in neurological conditions though further studies are necessary to determine causality.\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePreclinical models link autism-like behaviors to gut microbiome alterations and neurodevelopmental changes, indicating a potential role of a dysregulated BGM system in autism spectrum disorder (ASD).\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e ASD is a neurodevelopmental disorder characterized by persistent social communication difficulties, sensory sensitivities, and restricted, repetitive behaviors.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e The pathophysiology of ASD is incompletely understood and multifactorial, involving genetic factors and the exposome.\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e In children, ASD co-occurs with anxiety (42%-79%),\u003csup\u003e16\u003c/sup\u003e sleep disturbances (50%-80%),\u003csup\u003e17\u003c/sup\u003e and gastrointestinal (GI) issues (46\u0026ndash;84%),\u003csup\u003e18\u003c/sup\u003e all of which are related to alterations in the BGM system.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrior studies in autistic children identified significant differences in the gut and oral microbiomes compared to controls, with specific taxa associated with core ASD symptoms.\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e ASD symptoms are linked to atypical brain structure and function.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e However, no studies have integrated brain imaging, fecal metagenomics, and behavior, to study the BGM system within the same cohort. Given the complexity of ASD, a comprehensive, multi-system approach is essential to better understand its neurobiology.\u003c/p\u003e \u003cp\u003eFecal metabolomics studies implicate alterations in tryptophan metabolism including serotonin, kynurenine, indoles and their derivatives in ASD.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Serotonin, metabolized both in the gut and in brainstem nuclei, is a neurotransmitter involved in several vital functions, including emotion regulation, sleep, food intake, and pain processing.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Elevated serum serotonin has been observed in approximately 30% of autistic individuals, and increased fecal serotonin is associated with increased GI symptom severity.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e In rodent models, hyperserotonemia is associated with ASD-like social-behavioral deficits and repetitive behaviors.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e While serotonin in systemic circulation is rapidly taken up by platelets and cannot cross the blood brain barrier (BBB), kynurenine and indole metabolites generated by gut microbes can enter systemic circulation and cross the BBB. Alterations in these metabolites have been linked to changes in brain structure, neural activity, and behavior in autistic youth and rodent models.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan additionalcitationids=\"CR39 CR40 CR41\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Thus, the tryptophan pathway is a promising avenue for hypothesis-driven research in ASD.\u003c/p\u003e \u003cp\u003eHere, we used shotgun metagenomics to study operational genomic units (OGUs), a method that provides the highest resolution of community composition,\u003csup\u003e43\u003c/sup\u003e involved in the metabolism of tryptophan-derived metabolites (serotonin, kynurenine, indole, and their derivatives), that belong to the following genera: \u003cem\u003eBacteroides, Bifidobacterium, Blautia, Clostridium, Enterococcus, Escheria, Klebsiella, Lactobacillus, Lactococcus, Ruminococcus, Streptococcus\u003c/em\u003e, and \u003cem\u003eAlistipes\u003c/em\u003e.\u003csup\u003e\u003cspan additionalcitationids=\"CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56 CR57 CR58\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e In autistic and neurotypical (NT) children, we investigate group differences in the relative abundance of OGUs from these genera and assess associations with behavior and brain activity in tasks related to interoception and socio-emotional processing.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e Given that these processes are associated with autonomic nervous system signaling and the gut microbiome, the brain regions involved may function as mediators of the microbiome-behavior connection.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR63 CR64\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e We also test whether neural activity mediates associations between tryptophan-related gut microbiota and behavior in ASD, in line with our prior work.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e This study was approved by the University of Southern California\u0026rsquo;s (USC) Institutional Review Board (Approval Number: UP-19-00522). Participants were recruited from healthcare clinics in Los Angeles, through advertising in the local community and social media, and by word-of-mouth. Written informed consent and assent were obtained prior to all study procedures. The sample included 106 participants aged 8\u0026ndash;17 years: 53 ASD (42 Male, 11 Female, M\u003csub\u003eage\u003c/sub\u003e=12.05 years), and 53 NT (30 Male, 23 Female, M\u003csub\u003eage\u003c/sub\u003e=11.82 years). Inclusion/exclusion criteria were as described previously,\u003csup\u003e33,60,66\u003c/sup\u003e with full criteria in the supplementary materials. Briefly, all participants had an IQ\u0026thinsp;\u0026ge;\u0026thinsp;79 on either the Full-Scale Intelligence Quotient (FSIQ) or Verbal Comprehension Index (VCI) of the WASI-II\u003csup\u003e67\u003c/sup\u003e and were right-handed.\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e ASD diagnosis was verified with the Autism Diagnostic Observation Schedule (ADOS-2)\u003csup\u003e69\u003c/sup\u003e and the Autism Diagnostic Interview-Revised (ADI-R)\u003csup\u003e70\u003c/sup\u003e for autistic children. Participants were excluded if they had contraindications to participating in MRI (i.e., metal implants, braces, inability to remain still for 1 hour), consumed probiotics within two weeks, or antibiotics within 30 days prior to participation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Procedures\u003c/h3\u003e\n\u003cp\u003eThe study took place over two visits at USC. On the first visit written informed consent and assent were obtained, eligibility was verified, and mock MRI scanning was completed. Participants brought fecal samples to the lab on their second visit and underwent fMRI scans.\u003c/p\u003e\n\u003ch3\u003eBehavioral Data Collection\u003c/h3\u003e\n\u003cp\u003eParents completed measures to assess their child\u0026rsquo;s social difficulties (SRS-2),\u003csup\u003e71\u003c/sup\u003e sensory sensitivities (Sensory Experiences Questionnaire [SEQ-3]),\u003csup\u003e72\u003c/sup\u003e diet, and medical history including antibiotic usage (infant and prenatal), maternal illness during pregnancy, breastfeeding history, and birth mode. Participants self-reported their anxiety (Screen for Child Anxiety Related Emotional Disorder [SCARED-C]),\u003csup\u003e73\u003c/sup\u003e gastrointestinal symptoms (Gastrointestinal Symptom Rating Scale [GSRS]),\u003csup\u003e74\u003c/sup\u003e disgust propensity and sensitivity (Disgust Propensity and Sensitivity Scale-Revised [DPSS-R]),\u003csup\u003e75\u003c/sup\u003e sleep quality (Adolescent Sleep Wake Scale [ASWS]),\u003csup\u003e76\u003c/sup\u003e and alexithymia (Alexithymia Questionnaire for Children [ACQ]).\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e Measure descriptions are in the Supplementary Materials.\u003c/p\u003e\n\u003ch3\u003eStool Sample Collection\u003c/h3\u003e\n\u003cp\u003eFecal samples were collected as described previously.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Briefly, participants collected stool samples within 72 hours of their MRI with a fecal collection kit and transported them to the lab in insulated containers. Samples were stored at -80\u0026deg;C then aliquoted over dry ice.\u003c/p\u003e\n\u003ch3\u003eFunctional Magnetic Resonance Imaging (fMRI)\u003c/h3\u003e\n\u003cp\u003eOn the second visit, participants underwent task-based fMRI scans related to interoceptive and socio-emotional processing \u0026ndash; tasks that commonly show strong differences in ASD.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan additionalcitationids=\"CR79\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e For additional details on tasks and stimuli, and an additional exploratory somatosensory task, see the Supplementary Materials and our prior studies.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFace and Action Observation (ASD\u0026thinsp;=\u0026thinsp;53, NT\u0026thinsp;=\u0026thinsp;51)\u003c/span\u003e: During a 9-minute run, participants watched blocks of videos of emotional facial expressions (i.e., sad), non-emotional expressions (i.e., puffed cheeks), bimanual hand actions (i.e., peeling a banana), or control stimuli.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDisgust Processing (ASD\u0026thinsp;=\u0026thinsp;25, NT\u0026thinsp;=\u0026thinsp;25)\u003c/span\u003e: During a 10-minute run, participants watched blocks of pictures of disgusting foods, disgusted facial expressions, neutral foods, and neutral facial expressions. Prior to the scan, participants were asked to rate (love, like, neutral, or dislike) foods and the video was tailored to each participant.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eShotgun Metagenomic Analysis\u003c/h2\u003e \u003cp\u003eShotgun metagenomic sequencing and subsequent bioinformatic processing was performed as previously described,\u003csup\u003e80,81\u003c/sup\u003e with full methods in the Supplementary Materials. The resulting OGU tables were converted to BIOM format,\u003csup\u003e82\u003c/sup\u003e filtered for 30% feature prevalence, center log-ratio transformed, and filtered against Greengenes2 (v. 2024.09), which yielded 425 OGUs.\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e Filtering to the 24 genera related to tryptophan metabolism yielded 96 OGUs listed in the Supplementary Materials.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eGroup Differences in Behavioral Measures\u003c/h3\u003e\n\u003cp\u003eGroup differences in behavioral measures were assessed using independent samples t-tests and Fisher\u0026rsquo;s exact tests. Effect sizes are reported with Cohen\u0026rsquo;s d and Cramer's V, respectively. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSparse Partial Least Squares Discriminant Analysis (sPLS-DA)\u003c/h2\u003e \u003cp\u003eTo identify a multivariate metagenomic signature discriminating ASD from NT, we employed a sparse partial least squares discriminant analysis (sPLS-DA) on the 96 OGUs residualized for age, sex, BMI, IQ, and diet with the mixOmics R package.\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e Although the primary goal of this analysis was to generate a parsimonious model through feature selection and data reduction,\u003csup\u003e88\u003c/sup\u003e we also examined the predictive accuracy of the derived microbiome signature. sPLS-DA is a latent variable approach that employs a supervised framework forming linear combinations of the predictors (OGUs) based on class membership (ASD/NT) and reduces the dimensionality of the data by finding sets of orthogonal components or metagenomic signatures each comprised by a selected set of OGU features.\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e Each feature of the metagenomic signature has an associated \u0026ldquo;loading\u0026rdquo;, reflecting the relative importance of that feature for the group discrimination.\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e Additional information and an analysis using random forest classification\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e are in the Supplementary Materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGroup Differences in Tryptophan-Related OGUs\u003c/h2\u003e \u003cp\u003eLinear contrast analysis (LCA) within the framework of the general linear model (GLM) was applied to determine group differences in the 96 OGUs. The model included group as a factor, and age, sex, body mass index (BMI), and diet as covariates. The Benjamini-Hochberg method to correct for multiple comparisons was used with the false discovery reporting (FDR) threshold (q) set at 10%.\u003csup\u003e84\u003c/sup\u003e Cohen\u0026rsquo;s d was calculated to provide an effect size for group differences (small:0.2\u0026ndash;0.5, medium:0.5\u0026ndash;0.8, and large\u0026thinsp;\u0026gt;\u0026thinsp;0.8).\u003csup\u003e85\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNeuroimaging Analysis\u003c/h2\u003e \u003cp\u003efMRI analyses were performed using FMRIB\u0026rsquo;s Software Library (FSL)\u003csup\u003e\u003cspan additionalcitationids=\"CR93 CR94 CR95 CR96\" citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e as described previously.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e Additional information is in the Supplementary Materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Analyses\u003c/h2\u003e \u003cp\u003eGLMs were applied within groups to assess: 1) OGU associations with brain activity (DV=brain activity, IV\u0026thinsp;=\u0026thinsp;OGU) and behavior (DV\u0026thinsp;=\u0026thinsp;OGU, IV=behavior) with age, sex, IQ, BMI, and diet as covariates; and 2) associations with brain activity and behavior with age, sex, and IQ as covariates (DV=brain activity, IV=behavior). The q was set at 10% for the number of dependent variables.\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e Standardized betas (Std β) were calculated as a measure of effect size (small: 0.10\u0026ndash;0.29, medium: 0.30\u0026ndash;0.49, and large: \u0026gt;0.50).\u003csup\u003e98\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMediation Models\u003c/h2\u003e \u003cp\u003eMediation analyses were performed with the lavaan package\u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e in R to assess whether the brain mediates microbiome-behavior associations in autistic children. Model variables were selected based on ROIs (mediator) with both significant ROI-OGU and ROI-behavior associations. Bootstrapped 95% confidence intervals for indirect effects were obtained using the R package semhelpinghands.\u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e Age, sex, BMI, diet, and IQ were included as covariates for mediator and outcome variables. Confidence intervals that do not contain zero for indirect effects suggest statistical mediation.\u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003eGroup differences in behavioral variables\u003c/h2\u003e\n\u003cp\u003eAutistic children had significantly more males (ASD:79%; NT: 57%) and increased BMI, GI symptoms, social difficulties, sensory sensitivities, anxiety, disgust propensity and sensitivity, alexithymia, prenatal maternal illness, and more frequently followed the Modified Standard American diet (all \u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplemental Table\u0026nbsp;5). NT children had significantly higher IQ and better sleep quality (all \u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cem\u003eA metagenomic signature predicts ASD diagnosis with moderate accuracy\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe sPLS-DA indicated a one component solution discriminating ASD from NT composed of 10 features (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The top two features were increased relative abundance of \u003cem\u003eStreptococcus intermedius\u003c/em\u003e and \u003cem\u003eBlautia A 141781 hydrogenotrophica\u003c/em\u003e in ASD. Scores on this metagenomic signature showed a large effect size difference between groups (d\u0026thinsp;=\u0026thinsp;0.80, t (107.9)\u0026thinsp;=\u0026thinsp;4.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.35 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), with the ASD group scoring higher on the signature. Classification performance accuracy was slightly better than chance (Balanced Error Rate\u0026thinsp;=\u0026thinsp;42%, AUC\u0026thinsp;=\u0026thinsp;59%).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutistic children have significantly increased levels of OGUs involved in tryptophan metabolism compared to NT peers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAutistic children had significantly higher levels of OGUs belonging to species of \u003cem\u003eStreptococcus, Blautia\u003c/em\u003e, and \u003cem\u003eClostridium\u003c/em\u003e (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSignificant group differences in OGUs\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecies\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOGU\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eq\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCohen's d\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStreptococcus_intermedius\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000463355\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlautia_A_141781_hydrogenotrophica\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000157975\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.703\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStreptococcus oralis_E_351036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000344275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.777\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClostridium_AP scindens\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000154505\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.832\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClostridium_AQ innocuum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000183585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.470\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStreptococcus anginosus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000463505\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.737\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStreptococcus sanguinis_H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000014205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.686\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStreptococcus mitis_AR_353295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000027165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.684\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStreptococcus infantis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000187465\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.689\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStreptococcus cristatus_B_353950\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000385925\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.627\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClostridium_AQ innocuum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000450985\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.379\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStreptococcus_pneumoniae\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG001133125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.715\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClostridium_AQ innocuum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG001688965\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBlautia_A_141780 sp001304935\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG003478165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.485\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClostridium_AQ innocuum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG900114575\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.504\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClostridium_AQ innocuum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eG000242195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.423\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.088\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eNote. Significance was set at FDR corrected p-value (q)\u0026thinsp;\u0026lt;\u0026thinsp;0.10. The model included group as a factor, and age, sex, body mass index (BMI), and diet as covariates. SE: Standardized Error; p: p-value; q\u0026thinsp;=\u0026thinsp;FDR corrected p-value.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003efMRI tasks evoked differences in brain regions related to socio-emotional processing and interoception\u003c/h2\u003e\n\u003cp\u003eIn the face and action observation task, NT children displayed significantly greater activity in the right inferior gyrus pars opercularis (IFGop) and mid-cingulate cortex (MCC) while viewing all stimuli. While viewing disgusted faces, autistic children had significantly increased activity in the pregenual anterior cingulate cortex (pACC), left dorsal anterior (dA) and right ventral anterior (vA) insula, and decreased activity in the right fusiform face area (FFA). While viewing disgusting foods, NT children had significantly increased activity in the right mid-insula, and the left vA insula (Supplemental Table\u0026nbsp;3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003eTryptophan-related OGUs are significantly associated with fMRI task activity in ASD\u003c/h2\u003e\n\u003cp\u003eModerate to large effects size associations were observed between relative abundance of OGUs and both fMRI tasks (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Results for the NT group are in the Supplementary Materials.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003eFunctional brain activity during disgust processing is associated with repetitive behaviors and autism severity\u003c/h2\u003e\n\u003cp\u003eModerate to large effect size associations were observed between brain activity during the disgust processing task and repetitive behaviors and autism severity (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In autistic children, activity in left insular subregions (dA, vA) and the right mid-insula decreased as restricted and repetitive behaviors (RRBs; ADOS and ADI-R) and autism severity (ADOS total) increased. Increased activity in the right FFA was associated with higher ADI-R RRBs scores. There were no associations with \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10 for the observation task or within the NT group for either task.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSignificant Task-based ROI-Behavior Associations in the ASD group\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eDisgust Processing\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eROI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStimuli\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBehavior\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026beta;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStd \u0026beta;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eq\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL dA Insula\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisgusted Faces\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADOS RRBs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.603\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.603\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.032\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR Mid-Insula\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisgusting Food\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADOS RRBs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.536\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.536\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.038\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR Mid-Insula\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisgusting Food\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADOS Total\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.572\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.572\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL vA Insula\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisgusting Food\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADI-R RRBs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.630\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.630\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR FFA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDisgusted Faces\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADI-R RRBs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.486\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.044\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.486\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eNote. Significance was set at FDR correct p=value (q)\u0026thinsp;\u0026lt;\u0026thinsp;0.10. ROI: Region of Interest; \u0026beta;: unstandardized beta, SE: Standardized Error; Std \u0026beta;: Standardized Beta; p: p-value, q\u0026thinsp;=\u0026thinsp;FDR adjusted p-value; L: Left; R: Right; MCC: mid-cingulate cortex; DPSS-R: Disgust Propensity and Sensitivity Scale- Revised; ADOS: Autism Diagnostic Observation Schedule; RRBs: Restricted and Repetitive Behaviors; ADI-R: Autism Diagnostic Interview-Revised; dA: dorsal anterior; vA: ventral anterior; FFA: fusiform face area\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003ch2\u003eNeural activity is a significant mediator between Lactococcus A lactis and RRBs in ASD\u003c/h2\u003e\n\u003cp\u003eThere were no significant associations between OGUs and behavior (all \u003cem\u003eqs\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.10) in autistic children. We next investigated whether incorporating neural activity as a mediator in the OGU-behavior associations would reveal significant indirect effects. Activity in the right mid-insula while viewing disgusting foods was a significant mediator of the relationship between \u003cem\u003eLactococcus A 346120 lactis 344179\u003c/em\u003e and RRBs (ADOS), while controlling for age, sex, IQ, diet, and BMI (indirect effect: Std. \u0026beta;\u0026thinsp;=\u0026thinsp;0.46, SE\u0026thinsp;=\u0026thinsp;0.13, 95% CI [0.01,0.91], Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn line with our hypotheses, autistic youth showed significant differences in abundances of gut microbes related to tryptophan metabolism, some of which significantly related to activity in socio-emotional brain regions. Importantly, the insula mediated relationships between \u003cem\u003eLactococcus A 346120 lactis 344179\u003c/em\u003e and RRBs \u0026ndash; a core symptom of ASD. We discuss these findings below.\u003c/p\u003e \u003cp\u003eAutistic children had significantly elevated relative abundances of OGUs belonging to \u003cem\u003eStreptococcus, Blautia, Clostridium\u003c/em\u003e, which are involved in serotonin and indole metabolism. \u003csup\u003e44\u0026ndash;59\u003c/sup\u003e Untargeted metabolomic analysis on a subset of this sample (N\u0026thinsp;=\u0026thinsp;84)\u003csup\u003e33\u003c/sup\u003e found decreased kynurenate in autistic children. Taken together, these results suggest that in ASD, tryptophan metabolism may be shifted towards alternative pathways (e.g., serotonin and indoles) \u0026ndash; consistent with findings of elevated serotonin in 30% autistic individuals.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn line with the idea that such species impact neural activity via their production of neuroactive and inflammatory molecules, we found OGUs belonging to \u003cem\u003eClostridium, Bifidobacterium, Ruminococcus, Bacteroides, Lactococcus\u003c/em\u003e, and \u003cem\u003eBlautia\u003c/em\u003e, were associated with socio-emotional task-evoked activity in the MCC, IFGop, insula, pACC, and FFA. Importantly, mediation analyses revealed that right mid-insula activity during physical disgust significantly mediated a positive relationship between a \u003cem\u003eLactococcus\u003c/em\u003e OGU and RRBs. The mid-insula is strongly implicated in disgust and interoceptive processing\u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e and commonly shows significant differences in ASD.\u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e,\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e The insula receives interoceptive information from the gut via vagal signals sent to brainstem nuclei,\u003csup\u003e105\u003c/sup\u003e and is modulated by serotonin.\u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e Further, some species of \u003cem\u003eLactococcus\u003c/em\u003e are involved in producing serotonin.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e Preclinical studies show that increased \u003cem\u003eLactococcus\u003c/em\u003e is associated with increased atypical behaviors, including excessive self-grooming and marble burying \u0026ndash; behaviors commonly used as proxies for human RRBs.\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e,\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e Together, these results suggest that microbial taxa and metabolites may influence core autistic behaviors via their effects on brain function, highlighting the BGM system\u0026rsquo;s role in ASD.\u003c/p\u003e \u003cp\u003eWe also found significantly elevated levels of \u003cem\u003eClostridium\u003c/em\u003e (\u003cem\u003eC. scindens, C. innocuum\u003c/em\u003e) in autistic children compared to NT, and significant associations between \u003cem\u003eC. leptum\u003c/em\u003e and decreased neural activity in the right MCC while processing non-emotional facial expressions. Some \u003cem\u003eClostridium\u003c/em\u003e species are known to produce toxins that can cause neurological impairment,\u003csup\u003e109\u003c/sup\u003e and prior studies in autistic youth found significantly higher levels of \u003cem\u003eClostridium\u003c/em\u003e that were associated with autism severity and gastrointestinal symptoms.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAdditionally, we saw increased levels of \u003cem\u003eStreptococcus\u003c/em\u003e in ASD, which contains both beneficial and pathogenic species.\u003csup\u003e\u003cspan additionalcitationids=\"CR112\" citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e Interestingly, we found increased levels of common oral \u003cem\u003eStreptococcus\u003c/em\u003e species (\u003cem\u003eS. mitis, S. oralis\u003c/em\u003e, and \u003cem\u003eS. sanguinis\u003c/em\u003e).\u003csup\u003e\u003cem\u003e\u003cspan additionalcitationids=\"CR115 CR116\" citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e Evidence suggests that oral bacteria can translocate to the gut via swallowing, contributing to gut dysbiosis.\u003csup\u003e\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e\u003c/sup\u003e Future studies collecting oral and fecal samples from the same individual to compare overlap between communities may support this mechanism.\u003csup\u003e\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOverall, our findings suggest that differences in tryptophan-related gut microbiota in autistic children are associated with differences in brain function related to socio-emotional and interoceptive processing, potentially through microbial metabolism of tryptophan metabolites into serotonin and indoles. This supports the growing body of evidence implicating the BGM system in the neurobiology of ASD, specifically affecting RRBs, as observed in our mediation analysis. Despite the negative impact of RRBs on quality of life for both autistic children and their families,\u003csup\u003e120\u0026ndash;122\u003c/sup\u003e no treatments exist for RRBs besides behavioral interventions with modest impacts.\u003csup\u003e\u003cspan additionalcitationids=\"CR123 CR124\" citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e\u003c/sup\u003e However, emerging microbiome-based approaches, such as fecal microbiota transplants and tryptophan depletion studies in autistic adults, are more targeted approaches that have been linked to changes in tryptophan metabolism, neural activity, and RRBs.\u003csup\u003e\u003cspan additionalcitationids=\"CR127 CR128 CR129\" citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e\u003c/sup\u003e Large longitudinal interventional studies are needed to observe their effectiveness in children.\u003c/p\u003e \u003cp\u003eThe current study is limited by its cross-sectional observational design. Additionally, our mediation models do not establish causation. Nevertheless, our results demonstrate significant statistical mediation and lay a foundational model that can be expanded upon in interventional and longitudinal research. Future studies with larger, more heterogeneous samples should be conducted to confirm generalizability. Since some of the genera examined here are related to the production of other neuromodulators (e.g., GABA, dopamine, acetylcholine)\u003csup\u003e\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e\u003c/sup\u003e, future multi-omic work combining shotgun metagenomics and metabolomics is warranted.\u003c/p\u003e \u003cp\u003eCollecting detailed dietary information is a common difficulty in microbiome research. Diet diaries and food frequency questionnaires are often critiqued for high participant burden and recall bias.\u003csup\u003e\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e\u003c/sup\u003e We used a less detailed, but potentially more accurate measure of general diet by collecting the participant\u0026rsquo;s habitual diet type. Future studies should utilize more detailed measures of diet\u003csup\u003e\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e\u003c/sup\u003e or focus on populations following specific diets.\u003csup\u003e\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e\u003c/sup\u003e Tools such as the foodMASST platform, a Mass Spectrometry Search Tool with a reference database of food metabolites, could supplement self-reported diet measures.\u003csup\u003e\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e\u003c/sup\u003e Nevertheless, we address critiques of prior microbiome studies in ASD\u003csup\u003e\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e,\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e\u003c/sup\u003e by defining specific a priori hypothesis, providing effect sizes for all findings, using compositionally aware techniques, applying multiple comparisons corrections, and including confounding variables (sex, age, diet, BMI) in all analyses.\u003c/p\u003e \u003cp\u003eIn conclusion, we found that neural activity in interoceptive and socio-emotional brain regions is associated with relative abundance of species involved in tryptophan metabolism. Further, our exploratory statistical mediation models suggest brain activity can act as a mediation between gut bacteria and RRBs. These findings underscore the importance of integrating neural, microbial, and behavioral data to reveal ASD\u0026rsquo;s neurobiology.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eDe-identified data are available via the NIMH Data Archive: The Relationship Between Brain Functioning, Behavior, and Microbiota in Autism Spectrum Disorder #4991. All sequencing data have been deposited in Zenodo.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCode Availability\u003c/h2\u003e \u003cp\u003eMRI codes are available on: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/CeNEC?tab=repositories\u003c/span\u003e\u003cspan address=\"https://github.com/CeNEC?tab=repositories\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eRob Knight is a scientific advisory board member and consultant for BiomeSense, Inc., has equity, and receives income. He is a scientific advisory board member and has equity in GenCirq. He is a consultant and scientific advisory board member for DayTwo and receives income. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a co-founder of Micronoma and has equity and is a scientific advisory board member. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was funded by Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD079432) and the Department of Defense through the Idea Development Award under award number AR170062. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the NIH or the Department of Defense. Additional support was provided by the Nedra Gillette Endowed Research Fellowship. Lucas Patel is supported by the University of California San Diego Medical Scientist Training Program (NIH/NIGMS T32GM154642).\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003e We thank all participants, CeNEC research assistants, and the Integrative Biostatistics and Bioinformatics Core at the Goodman Luskin Microbiome Center for their contributions to this study. We are grateful to Ruty Mehrian-Shai and Antonio Damasio for helpful discussions and to Lora Khatib, Lauren Hanse, Jennifer Cao, Antonio Gonz\u0026aacute;lez Pe\u0026ntilde;a, Gail Ackermann, Jackson Hausrath, Sawyer Farmer, and Martin Casas Maya for their assistance with sample analysis, data management, and feedback on the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCryan JF, O'Riordan KJ, Cowan CSM et al (2019) The microbiota-gut-brain axis. 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Neuron 0(0). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuron.2025.10.006\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2025.10.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8725888/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8725888/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConverging evidence implicates brain activity differences in interoceptive and emotion-related brain regions (e.g., insula) in the pathophysiology of autism spectrum disorder (ASD). Given that regions such as the insula are heavily modulated by serotonin, and gut microbes influence central serotonin levels by regulating the availability of its precursor, tryptophan via microbial metabolism, studies exploring associations between such gut microbes and brain regions are much needed. However, studies integrating neural, gut microbiome, and behavioral data in humans are rare. Such studies are necessary to better understand the neurobiology of ASD from a systems perspective and develop novel treatment strategies. In 53 ASD (42 Male, Mage = 12.05 years) and 53 neurotypical (NT) (30 Male, Mage = 11.82 years) youth we tested the hypotheses that gut species involved in tryptophan metabolism: 1) significantly differ in relative abundance between ASD and NT children; 2) are associated with activity in interoceptive and emotion regulating brain regions during functional neuroimaging tasks; and 3) are linked to ASD symptoms. In addition, we tested the exploratory hypothesis that functional brain activity mediates relationships between gut bacteria and behavior. Autistic youth had significantly greater relative abundances of species of \u003cem\u003eBlautia, Clostridium, Ruminococcus\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e, which were significantly associated with distinct neural activity patterns in interoceptive and emotion-related brain regions. Activity in the right mid-insula during physical disgust processing was a significant mediator of the relationship between \u003cem\u003eLactococcus lactis \u003c/em\u003eand restricted and repetitive behaviors. These results support the hypothesis that differences in the relative abundance of several gut microbes involved in the metabolism of tryptophan are related to known ASD brain alterations and symptomatology. These findings highlight the potential for targeting the gut microbiome to influence neural activity and behavioral outcomes in ASD, offering a promising avenue for novel intervention strategies.\u003c/p\u003e","manuscriptTitle":"Tryptophan-related Gut Microbes are Linked with Neural and Behavioral Autism Phenotypes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 17:33:52","doi":"10.21203/rs.3.rs-8725888/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b83fc169-c31b-4599-8268-3610fa3e4281","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62469680,"name":"Biological sciences/Microbiology/Bacteria/Metagenomics"},{"id":62469681,"name":"Biological sciences/Neuroscience/Cognitive neuroscience"}],"tags":[],"updatedAt":"2026-03-10T17:33:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 17:33:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8725888","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8725888","identity":"rs-8725888","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}