Children with Autism and Their Typically Developing Siblings Differ in Amplicon Sequence Variants and Predicted Functions of Stool-Associated Microbes

Precision synbiotics increase gut microbiome diversity and improve gastrointestinal symptoms in a pilot open-label study for autism spectrum disorder

The efficacy of prebiotics and probiotics (synbiotics when combined) to improve symptoms associated with autism spectrum disorder (ASD) has shown considerable inter-study variation, likely due to the complex, heterogeneous nature of the disorder and its associated behavioral, developmental, and gastrointestinal symptoms. Here, we present a precision synbiotic supplementation study in 296 children and adults diagnosed with ASD versus 123 age-matched neurotypical controls. One hundred seventy ASD participants completed the study. Baseline and post-synbiotic assessment of ASD and gastrointestinal (GI) symptoms and deep metagenomic sequencing were performed. Within the ASD cohort, there were significant differences in microbes between subpopulations based on the social responsiveness scale (SRS2) survey (Prevotella spp., Bacteroides, Fusicatenibacter, and others) and gluten and dairy-free diets (Bifidobacterium spp., Lactococcus, Streptococcus spp., and others). At the baseline, the ASD cohort maintained a lower taxonomic alpha diversity and significant differences in taxonomic composition, metabolic pathways, and gene families, with a greater proportion of potential pathogens, including Shigella, Klebsiella, and Clostridium, and lower proportions of beneficial microbes, including Faecalibacterium compared to controls. Following the 3-month synbiotic supplementation, the ASD cohort showed increased taxonomic alpha diversity, shifts in taxonomy and metabolic pathway potential, and improvements in some ASD-related symptoms, including a significant reduction in GI discomfort and overall improved language, comprehension, cognition, thinking, and speech. However, the open-label study design may include some placebo effects. In summary, we found that precision synbiotics modulated the gut microbiome and could be used as supplementation to improve gastrointestinal and ASD-related symptoms.

IMPORTANCE

Autism spectrum disorder (ASD) is prevalent in 1 out of 36 children in the United States and contributes to health, financial, and psychological burdens. Attempts to identify a gut microbiome signature of ASD have produced varied results. The limited pre-clinical and clinical population sizes have hampered the success of these trials. To understand the microbiome associated with ASD, we employed whole metagenomic shotgun sequencing to classify microbial composition and genetic functional potential. Despite being one of the most extensive ASD post-synbiotic assessment studies, the results highlight the complexity of performing such a case-control supplementation study in this population and the potential for a future therapeutic approach in ASD.

Systematic Review: Autism Spectrum Disorder and the Gut Microbiota

Objective

Autism spectrum disorders (ASD) are a varying group of disorders characterized by deficiency in social interaction and restrictive patterns of behavior and interests. While there are several studies focusing on the neuro-psychiatric pathogenesis of ASD, its etiology remains unclear. The role of gut-brain-axis in ASD has been studied increasingly and a correlation between symptoms and the composition of gut microbiota has been documented in various works. Despite this, the significance of individual microbes and their function is still widely unknown. This work aims to elucidate the current knowledge of the interrelations between ASD and the gut microbiota in children based on scientific evidence.

Methods

This is a systematic review done by a literature search focusing on the main findings concerning the gut microbiota composition, interventions targeting the gut microbiota, and possible mechanisms explaining the results in children aged between 2 and 18 years of age.

Results

Most studies in this review found significant differences between microbial communities, while there was notable variation in results regarding diversity indices or taxonomic level abundance. The most consistent results regarding taxa differences in ASD children's gut microbiota were higher levels of Proteobacteria, Actinobacteria and Sutterella compared to controls.

Conclusion

These results show that the gut microbiota of children with ASD is altered compared to one of neurotypically developed children. More research is needed to discover whether some of these features could be used as potential biomarkers for ASD and how the gut microbiota could be targeted in therapeutical interventions.Appeared originally in Acta Psychiatr Scand 2023;148:242-254.

Autism spectrum disorders and the gastrointestinal tract: insights into mechanisms and clinical relevance

Autism spectrum disorders (ASDs) are recognized as central neurodevelopmental disorders diagnosed by impairments in social interactions, communication and repetitive behaviours. The recognition of ASD as a central nervous system (CNS)-mediated neurobehavioural disorder has led most of the research in ASD to be focused on the CNS. However, gastrointestinal function is also likely to be affected owing to the neural mechanistic nature of ASD and the nervous system in the gastrointestinal tract (enteric nervous system). Thus, it is unsurprising that gastrointestinal disorders, particularly constipation, diarrhoea and abdominal pain, are highly comorbid in individuals with ASD. Gastrointestinal problems have also been repeatedly associated with increased severity of the core symptoms diagnostic of ASD and other centrally mediated comorbid conditions, including psychiatric issues, irritability, rigid-compulsive behaviours and aggression. Despite the high prevalence of gastrointestinal dysfunction in ASD and its associated behavioural comorbidities, the specific links between these two conditions have not been clearly delineated, and current data linking ASD to gastrointestinal dysfunction have not been extensively reviewed. This Review outlines the established and emerging clinical and preclinical evidence that emphasizes the gut as a novel mechanistic and potential therapeutic target for individuals with ASD.

Methodology for biomarker discovery with reproducibility in microbiome data using machine learning

BACKGROUND

In recent years, human microbiome studies have received increasing attention as this field is considered a potential source for clinical applications. With the advancements in omics technologies and AI, research focused on the discovery for potential biomarkers in the human microbiome using machine learning tools has produced positive outcomes. Despite the promising results, several issues can still be found in these studies such as datasets with small number of samples, inconsistent results, lack of uniform processing and methodologies, and other additional factors lead to lack of reproducibility in biomedical research. In this work, we propose a methodology that combines the DADA2 pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) in multiple datasets to increase reproducibility and obtain robust and reliable results in biomedical research.

RESULTS

Three experiments were performed analyzing microbiome data from patients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarker signature in one dataset and applied to 2 other as further validation. The effectiveness of the proposed methodology was compared with other feature selection methods such as K-Best with F-score and random selection as a base line. The Area Under the Curve (AUC) was employed as a measure of diagnostic accuracy and used as a metric for comparing the results of the proposed methodology with other feature selection methods. Additionally, we use the Matthews Correlation Coefficient (MCC) as a metric to evaluate the performance of the methodology as well as for comparison with other feature selection methods.

CONCLUSIONS

We developed a methodology for reproducible biomarker discovery for 16s rRNA microbiome sequence analysis, addressing the issues related with data dimensionality, inconsistent results and validation across independent datasets. The findings from the three experiments, across 9 different datasets, show that the proposed methodology achieved higher accuracy compared to other feature selection methods. This methodology is a first approach to increase reproducibility, to provide robust and reliable results.

A robust microbiome signature for autism spectrum disorder across different studies using machine learning

Autism spectrum disorder (ASD) is a highly complex neurodevelopmental disorder characterized by deficits in sociability and repetitive behaviour, however there is a great heterogeneity within other comorbidities that accompany ASD. Recently, gut microbiome has been pointed out as a plausible contributing factor for ASD development as individuals diagnosed with ASD often suffer from intestinal problems and show a differentiated intestinal microbial composition. Nevertheless, gut microbiome studies in ASD rarely agree on the specific bacterial taxa involved in this disorder. Regarding the potential role of gut microbiome in ASD pathophysiology, our aim is to investigate whether there is a set of bacterial taxa relevant for ASD classification by using a sibling-controlled dataset. Additionally, we aim to validate these results across two independent cohorts as several confounding factors, such as lifestyle, influence both ASD and gut microbiome studies. A machine learning approach, recursive ensemble feature selection (REFS), was applied to 16S rRNA gene sequencing data from 117 subjects (60 ASD cases and 57 siblings) identifying 26 bacterial taxa that discriminate ASD cases from controls. The average area under the curve (AUC) of this specific set of bacteria in the sibling-controlled dataset was 81.6%. Moreover, we applied the selected bacterial taxa in a tenfold cross-validation scheme using two independent cohorts (a total of 223 samples-125 ASD cases and 98 controls). We obtained average AUCs of 74.8% and 74%, respectively. Analysis of the gut microbiome using REFS identified a set of bacterial taxa that can be used to predict the ASD status of children in three distinct cohorts with AUC over 80% for the best-performing classifiers. Our results indicate that the gut microbiome has a strong association with ASD and should not be disregarded as a potential target for therapeutic interventions. Furthermore, our work can contribute to use the proposed approach for identifying microbiome signatures across other 16S rRNA gene sequencing datasets.

The Role of Short-Chain Fatty Acids and Altered Microbiota Composition in Autism Spectrum Disorder: A Comprehensive Literature Review

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by deficits in communication and social interactions, restrictive and repetitive behavior, and a wide range of cognitive impediments. The prevalence of ASD tripled in the last 20 years and now affects 1 in 44 children. Although ASD's etiology is not yet elucidated, a growing body of evidence shows that it stems from a complex interplay of genetic and environmental factors. In recent years, there has been increased focus on the role of gut microbiota and their metabolites, as studies show that ASD patients show a significant shift in their gut composition, characterized by an increase in specific bacteria and elevated levels of short-chain fatty acids (SCFAs), especially propionic acid (PPA). This review aims to provide an overview of the role of microbiota and SCFAs in the human body, as well as possible implications of microbiota shift. Also, it highlights current studies aiming to compare the composition of the gut microbiome of ASD-afflicted patients with neurotypical control. Finally, it highlights studies with rodents where ASD-like symptoms or molecular hallmarks of ASD are evoked, via the grafting of microbes obtained from ASD subjects or direct exposure to PPA.

Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism

While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processes, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed "cross-omic topics", which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis.

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

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

Alteration in Gut Microbiota Associated with Zinc Deficiency in School-Age Children

Zinc deficiency could lead to a dynamic variation in gut microbial composition and function in animals. However, how zinc deficiency affects the gut microbiome in school-age children remains unclear. The purpose of this study was to profile the dynamic shifts in the gut microbiome of school-age children with zinc deficiency, and to determine whether such shifts are associated with dietary intake. A dietary survey, anthropometric measurements, and serum tests were performed on 177 school-age children, and 67 children were selected to explore the gut microbial community using amplicon sequencing. School-age children suffered from poor dietary diversity and insufficient food and nutrient intake, and 32% of them were zinc deficient. The inflammatory cytokines significantly increased in the zinc deficiency (ZD) group compared to that in the control (CK) group (p < 0.05). There was no difference in beta diversity, while the Shannon index was much higher in the ZD group (p < 0.05). At the genus level, Coprobacter, Acetivibrio, Paraprevotella, and Clostridium_XI were more abundant in the ZD group (p < 0.05). A functional predictive analysis showed that the metabolism of xenobiotics by cytochrome P450 was significantly depleted in the ZD group (p < 0.05). In conclusion, gut microbial diversity was affected by zinc deficiency with some specific bacteria highlighted in the ZD group, which may be used as biomarkers for further clinical diagnosis of zinc deficiency.

Gut Microbiota Ecology and Inferred Functions in Children With ASD Compared to Neurotypical Subjects

Autism spectrum disorders (ASDs) is a multifactorial neurodevelopmental disorder. The communication between the gastrointestinal (GI) tract and the central nervous system seems driven by gut microbiota (GM). Herein, we provide GM profiling, considering GI functional symptoms, neurological impairment, and dietary habits. Forty-one and 35 fecal samples collected from ASD and neurotypical children (CTRLs), respectively, (age range, 3–15 years) were analyzed by 16S targeted-metagenomics (the V3–V4 region) and inflammation and permeability markers (i.e., sIgA, zonulin lysozyme), and then correlated with subjects’ metadata. Our ASD cohort was characterized as follows: 30/41 (73%) with GI functional symptoms; 24/41 (58%) picky eaters (PEs), with one or more dietary needs, including 10/41 (24%) with food selectivity (FS); 36/41 (88%) presenting high and medium autism severity symptoms (HMASSs). Among the cohort with GI symptoms, 28/30 (93%) showed HMASSs, 17/30 (57%) were picky eaters and only 8/30 (27%) with food selectivity. The remaining 11/41 (27%) ASDs without GI symptoms that were characterized by HMASS for 8/11 (72%) and 7/11 (63%) were picky eaters. GM ecology was investigated for the overall ASD cohort versus CTRLs; ASDs with GI and without GI, respectively, versus CTRLs; ASD with GI versus ASD without GI; ASDs with HMASS versus low ASSs; PEs versus no-PEs; and FS versus absence of FS. In particular, the GM of ASDs, compared to CTRLs, was characterized by the increase of Proteobacteria, Bacteroidetes, Rikenellaceae, Pasteurellaceae, Klebsiella, Bacteroides, Roseburia, Lactobacillus, Prevotella, Sutterella, Staphylococcus, and Haemophilus. Moreover, Sutterella, Roseburia and Fusobacterium were associated to ASD with GI symptoms compared to CTRLs. Interestingly, ASD with GI symptoms showed higher value of zonulin and lower levels of lysozyme, which were also characterized by differentially expressed predicted functional pathways. Multiple machine learning models classified correctly 80% overall ASDs, compared with CTRLs, based on Bacteroides, Lactobacillus, Prevotella, Staphylococcus, Sutterella, and Haemophilus features. In conclusion, in our patient cohort, regardless of the evaluation of many factors potentially modulating the GM profile, the major phenotypic determinant affecting the GM was represented by GI hallmarks and patients’ age.

GMEmbeddings: An R Package to Apply Embedding Techniques to Microbiome Data

Large-scale microbiome studies investigating disease-inducing microbial roles base their findings on differences between microbial count data in contrasting environments (e.g., stool samples between cases and controls). These microbiome survey studies are often impeded by small sample sizes and database bias. Combining data from multiple survey studies often results in obvious batch effects, even when DNA preparation and sequencing methods are identical. Relatedly, predictive models trained on one microbial DNA dataset often do not generalize to outside datasets. In this study, we address these limitations by applying word embedding algorithms (GloVe) and PCA transformation to ASV data from the American Gut Project and generating translation matrices that can be applied to any 16S rRNA V4 region gut microbiome sequencing study. Because these approaches contextualize microbial occurrences in a larger dataset while reducing dimensionality of the feature space, they can improve generalization of predictive models that predict host phenotype from stool associated gut microbiota. The GMEmbeddings R package contains GloVe and PCA embedding transformation matrices at 50, 100 and 250 dimensions, each learned using ∼15,000 samples from the American Gut Project. It currently supports the alignment, matching, and matrix multiplication to allow users to transform their V4 16S rRNA data into these embedding spaces. We show how to correlate the properties in the new embedding space to KEGG functional pathways for biological interpretation of results. Lastly, we provide benchmarking on six gut microbiome datasets describing three phenotypes to demonstrate the ability of embedding-based microbiome classifiers to generalize to independent datasets. Future iterations of GMEmbeddings will include embedding transformation matrices for other biological systems. Available at: https://github.com/MaudeDavidLab/GMEmbeddings.

Peppermint oil effects on the gut microbiome in children with functional abdominal pain

Peppermint oil (PMO) is effective in the treatment of functional abdominal pain disorders, but its mechanism of action is unclear. Evidence suggests PMO has microbicidal activity. We investigated the effect of three different doses of PMO on gut microbiome composition. Thirty children (7-12 years of age) with functional abdominal pain provided a baseline stool sample prior to randomization to 180, 360, or 540 mg of enteric coated PMO (10 participants per dose). They took their respective dose of PMO (180 mg once, 180 mg twice, or 180 mg thrice daily) for 1 week, after which the stool collection was repeated. Baseline and post-PMO stools were analyzed for microbiome composition. There was no difference in alpha diversity of the gut microbiome between the baseline and post-PMO treatment. Principal coordinate analysis revealed no significant difference in overall bacterial composition between baseline and post-PMO samples, as well as between the PMO dose groups. However, the very low abundant Collinsella genus and three operational taxonomic units (one belonging to Collinsella) were significantly different in samples before and after PMO treatment. The Firmicutes/Bacteroidetes ratio was lower in children who received 540 mg of PMO compared to the 180 mg and 360 mg dose groups (p = 0.04). Network analysis revealed separation between pre- and post-PMO fecal samples with the genus Collinsella driving the post-PMO clusters. PMO administration appeared to impact only low abundance bacteria. The 540 mg PMO dose differentially impacted the Firmicutes/Bacteroidetes ratio. A higher dose and/or longer duration of treatment might yield different results.

Different Alterations in Gut Microbiota between Bifidobacterium longum and Fecal Microbiota Transplantation Treatments in Propionic Acid Rat Model of Autism

Autism spectrum disorders (ASD) consist of a range of neurodevelopmental conditions accompanied by dysbiosis of gut microbiota. Therefore, a number of microbiota manipulation strategies were developed to restore their balance. However, a comprehensive comparison of the various methods on gut microbiota is still lacking. Here, we evaluated the effect of Bifidobacterium (BF) treatment and fecal microbiota transplantation (FT) on gut microbiota in a propionic acid (PPA) rat model of autism using 16S rRNA sequencing. Following PPA treatment, gut microbiota showed depletion of Bacteroidia and Akkermansia accompanied by a concomitant increase of Streptococcus, Lachnospiraceae, and Paraeggerthella. The dysbiosis was predicted to cause increased levels of porphyrin metabolism and impairments of acyl-CoA thioesterase and ubiquinone biosynthesis. On the contrary, BF and FT treatments resulted in a distinct increase of Clostridium, Bifidobacterium, Marvinbryantia, Butyricicoccus, and Dorea. The taxa in BF group positively correlated with vitamin B12 and flagella biosynthesis, while FT mainly enriched flagella biosynthesis. In contrast, BF and FT treatments negatively correlated with succinate biosynthesis, pyruvate metabolism, nitrogen metabolism, beta-Lactam resistance, and peptidoglycan biosynthesis. Therefore, the present study demonstrated that BF and FT treatments restored the PPA-induced dysbiosis in a treatment-specific manner.

Beneficial Effects of Repeated Washed Microbiota Transplantation in Children With Autism

OBJECTIVE

While fecal microbiota transplantation is demonstrated to improve symptoms of autism spectrum disorder (ASD), it remains unclear whether additional treatment courses yield better results. This study sought to evaluate the efficacy of repeated washed microbiota transplantation (WMT) in children with ASD.

METHODS

Retrospective data from children who were serially treated with WMT, including ASD symptoms, sleep disorders, gastrointestinal (GI) symptoms, and white blood cell (WBC) and globulin levels were obtained. The effect of WMT on children with ASD and whether additional WMT courses led to a further improvement in symptoms were assessed.

RESULTS

Aberrant Behavior Checklist (ABC), Childhood Autism Rating Scale, and Sleep Disturbance Scale for Children (SDSC) scores, the proportion of children with constipation and abnormal fecal forms, and WBC and globulin levels were all significantly lower in ASD children after WMT. More WMT treatment courses led to significantly lower scores on the ABC and SDSC.

CONCLUSION

WMT significantly improved ASD and GI symptoms and sleep disorders in children with ASD, and reduced systemic inflammation. Additional WMT courses led to more obvious improvements in ASD symptoms within three treatment courses.

Kefir ameliorates specific microbiota-gut-brain axis impairments in a mouse model relevant to autism spectrum disorder

Autism spectrum disorder (ASD) is one of the most severe developmental disorders, affecting on average 1 in 150 children worldwide. There is a great need for more effective strategies to improve quality of life in ASD subjects. The gut microbiome has emerged as a potential therapeutic target in ASD. A novel modulator of the gut microbiome, the traditionally fermented milk drink kefir, has recently been shown to modulate the microbiota and decrease repetitive behaviour, one of the hallmarks of ASD, in mice. As such, we hypothesized that kefir could ameliorate behavioural deficits in a mouse model relevant to ASD; the BTBR T⁺ Itpr3^tf/J mouse strain. To this end, adult mice were administered either kefir (UK4) or a milk control for three weeks as treatment lead-in, after which they were assessed for their behavioural phenotype using a battery of tests. In addition, we assessed systemic immunity by flow cytometry and the gut microbiome using shotgun metagenomic sequencing. We found that indeed kefir decreased repetitive behaviour in this mouse model. Furthermore, kefir prolonged stress-induced increases in corticosterone 60 min post-stress, which was accompanied by an ameliorated innate immune response as measured by LY6C^hi monocyte levels. In addition, kefir increased the levels of anti-inflammatory Treg cells in mesenteric lymph nodes (MLNs). Kefir also increased the relative abundance of Lachnospiraceae bacterium A2, which correlated with reduced repetitive behaviour and increased Treg cells in MLNs. Functionally, kefir modulated various predicted gut microbial pathways, including the gut-brain module S-Adenosylmethionine (SAM) synthesis, as well as L-valine biosynthesis and pyruvate fermentation to isobutanol, which all correlated with repetitive behaviour. Taken together our data show that kefir modulates peripheral immunoregulation, can ameliorate specific ASD behavioural dysfunctions and modulates selective aspects of the composition and function of the gut microbiome, indicating that kefir supplementation might prove a viable strategy in improving quality of life in ASD subjects.