Potential of gut microbiome for detection of autism spectrum disorder

Contributions of Artificial Intelligence to Analysis of Gut Microbiota in Autism Spectrum Disorder: A Systematic Review

BACKGROUND

Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder whose etiology is not known today, but everything indicates that it is multifactorial. For example, genetic and epigenetic factors seem to be involved in the etiology of ASD. In recent years, there has been an increase in studies on the implications of gut microbiota (GM) on the behavior of children with ASD given that dysbiosis in GM may trigger the onset, development and progression of ASD through the microbiota-gut-brain axis. At the same time, significant progress has occurred in the development of artificial intelligence (AI).

METHODS

The aim of the present study was to perform a systematic review of articles using AI to analyze GM in individuals with ASD. In line with the PRISMA model, 12 articles using AI to analyze GM in ASD were selected.

RESULTS

Outcomes reveal that the majority of relevant studies on this topic have been conducted in China (33.3%) and Italy (25%), followed by the Netherlands (16.6%), Mexico (16.6%) and South Korea (8.3%).

CONCLUSIONS

The bacteria Bifidobacterium is the most relevant biomarker with regard to ASD. Although AI provides a very promising approach to data analysis, caution is needed to avoid the over-interpretation of preliminary findings. A first step must be taken to analyze GM in a representative general population and ASD samples in order to obtain a GM standard according to age, sex and country. Thus, more work is required to bridge the gap between AI in mental health research and clinical care in ASD.

Revealing the gut microbiome mystery: A meta-analysis revealing differences between individuals with autism spectrum disorder and neurotypical children

The brain-gut axis intricately links gut microbiota (GM) dysbiosis to the development or worsening of autism spectrum disorder (ASD). However, the precise GM composition in ASD and the effectiveness of probiotics are unclear. To address this, we performed a thorough meta-analysis of 28 studies spanning PubMed, PsycINFO, Web of Science, Scopus, and MEDLINE, involving 1,256 children with ASD and 1042 neurotypical children, up to February 2024. Using Revman 5.3, we analyzed the relative abundance of 8 phyla and 64 genera. While individuals with ASD did not exhibit significant differences in included phyla, they exhibited elevated levels of Parabacteroides, Anaerostipes, Faecalibacterium, Clostridium, Dorea, Phascolarctobacterium, Lachnoclostridium, Catenibacterium, and Collinsella along with reduced percentages of Barnesiella, Odoribacter, Paraprevotella, Blautia, Turicibacter, Lachnospira, Pseudomonas, Parasutterella, Haemophilus, and Bifidobacterium. Notably, discrepancies in Faecalibacterium, Clostridium, Dorea, Phascolarctobacterium, Catenibacterium, Odoribacter, and Bifidobacterium persisted even upon systematic exclusion of individual studies. Consequently, the GM of individuals with ASD demonstrates an imbalance, with potential increases or decreases in both beneficial and harmful bacteria. Therefore, personalized probiotic interventions tailored to ASD specifics are imperative, rather than a one-size-fits-all approach.

Neurodevelopmental Disorders Associated with Gut Microbiome Dysbiosis in Children

The formation of the human gut microbiome initiates in utero, and its maturation is established during the first 2-3 years of life. Numerous factors alter the composition of the gut microbiome and its functions, including mode of delivery, early onset of breastfeeding, exposure to antibiotics and chemicals, and maternal stress, among others. The gut microbiome-brain axis refers to the interconnection of biological networks that allow bidirectional communication between the gut microbiome and the brain, involving the nervous, endocrine, and immune systems. Evidence suggests that the gut microbiome and its metabolic byproducts are actively implicated in the regulation of the early brain development. Any disturbance during this stage may adversely affect brain functions, resulting in a variety of neurodevelopmental disorders (NDDs). In the present study, we reviewed recent evidence regarding the impact of the gut microbiome on early brain development, alongside its correlation with significant NDDs, such as autism spectrum disorder, attention-deficit/hyperactivity disorder, Tourette syndrome, cerebral palsy, fetal alcohol spectrum disorders, and genetic NDDs (Rett, Down, Angelman, and Turner syndromes). Understanding changes in the gut microbiome in NDDs may provide new chances for their treatment in the future.

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 comprehensive diversity analysis on the gut microbiomes of ASD patients: from alpha, beta to gamma diversities

Autism spectrum disorder (ASD) is estimated to influence as many as 1% children worldwide, but its etiology is still unclear. It has been suggested that gut microbiomes play an important role in regulating abnormal behaviors associated with ASD. A de facto standard analysis on the microbiome-associated diseases has been diversity analysis, and nevertheless, existing studies on ASD-microbiome relationship have not produced a consensus. Here, we perform a comprehensive analysis of the diversity changes associated with ASD involving alpha-, beta-, and gamma-diversity metrics, based on 8 published data sets consisting of 898 ASD samples and 467 healthy controls (HC) from 16S-rRNA sequencing. Our findings include: (i) In terms of alpha-diversity, in approximately 1/3 of the studies cases, ASD patients exhibited significantly higher alpha-diversity than the HC, which seems to be consistent with the "1/3 conjecture" of diversity-disease relationship (DDR). (ii) In terms of beta-diversity, the AKP (Anna Karenina principle) that predict all healthy microbiomes should be similar, and every diseased microbiome should be dissimilar in its own way seems to be true in approximately 1/2 to 3/4 studies cases. (iii) In terms of gamma-diversity, the DAR (diversity-area relationship) modeling suggests that ASD patients seem to have large diversity-area scaling parameter than the HC, which is consistent with the AKP results. However, the MAD (maximum accrual diversity) and RIP (ratio of individual to population diversity) parameters did not suggest significant differences between ASD patients and HC. Throughout the study, we adopted Hill numbers to measure diversity, which stratified the diversity measures in terms of the rarity-commonness-dominance spectrum. It appears that the differences between ASD patients and HC are more propounding on rare-species side than on dominant-species side. Finally, we discuss the apparent inconsistent diversity-ASD relationships among different case studies and postulate that the relationships are not monotonic.

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.

Machine learning and artificial intelligence in neuroscience: A primer for researchers

Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues. Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML.

Microbiota in Autism Spectrum Disorder: A Systematic Review

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by several core symptoms: restricted interests, communication difficulties, and impaired social interactions. Many ASD children experience gastrointestinal functional disorders, impacting their well-being. Emerging evidence suggests that a gut microbiota imbalance may exacerbate core and gastrointestinal symptoms. Our review assesses the gut microbiota in children with ASD and interventions targeting microbiota modulation. The analysis of forty-four studies (meta-analyses, reviews, original research) reveals insights into the gut microbiota-ASD relationship. While specific microbiota alterations are mixed, some trends emerge. ASD children exhibit increased Firmicutes (36-81%) and Pseudomonadota (78%) and decreased Bacteroidetes (56%). The Bacteroidetes to Firmicutes ratio tends to be lower (56%) compared to children without ASD, which correlates with behavioral and gastrointestinal abnormalities. Probiotics, particularly Lactobacillus, Bifidobacterium, and Streptococcus strains, show promise in alleviating behavioral and gastrointestinal symptoms (66%). Microbiota transfer therapy (MTT) seems to have lasting benefits for the microbiota and symptoms in one longitudinal study. Prebiotics can potentially help with gastrointestinal and behavioral issues, needing further research for conclusive efficacy due to different interventions being used. This review highlights the gut microbiota-ASD interplay, offering potential therapeutic avenues for the gut-brain axis. However, study heterogeneity, small sample sizes, and methodological variations emphasize the need for comprehensive, standardized research. Future investigations may unveil complex mechanisms linking the gut microbiota to ASD, ultimately enhancing the quality of life for affected individuals.

A novel approach for exploring the regional features of vaginal fluids based on microbial relative abundance and alpha diversity

Vaginal fluids are one of the most common biological samples in forensic sexual assault cases, and their characterization is vital to narrow the scope of investigation. Presently, approaches for identifying vaginal fluids in different regions are not only rare but also have certain limitations. However, the microbiome has shown the potential to identify the source of body fluids and reveal the characteristics of individuals. In this study, 16S rRNA gene high-throughput sequencing was used to characterize the vaginal microbial community from three regions, Sichuan, Hainan and Hunan. In addition, data on relative abundance and alpha diversity were used to construct a random forest model. The results revealed that the dominant genera in the three regions were Lactobacillus, followed by Gardnerella. In addition, Ureaplasma, Nitrospira, Nocardiodes, Veillonella and g-norank-f-Vicinamibacteraceae were significantly enriched genera in Sichuan, llumatobacter was enriched in Hainan, and Pseudomonas was enriched in Hunan. The random forest classifier based on combined data on relative abundance and alpha diversity had a good ability to distinguish vaginal fluids with similar dominant microbial compositions in the three regions. The study suggests that combining high-throughput sequencing data with machine learning models has good potential for application in the biogeographic inference of vaginal fluids.

Overview of data preprocessing for machine learning applications in human microbiome research

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

From-Toilet-to-Freezer: A Review on Requirements for an Automatic Protocol to Collect and Store Human Fecal Samples for Research Purposes

The composition, viability and metabolic functionality of intestinal microbiota play an important role in human health and disease. Studies on intestinal microbiota are often based on fecal samples, because these can be sampled in a non-invasive way, although procedures for sampling, processing and storage vary. This review presents factors to consider when developing an automated protocol for sampling, processing and storing fecal samples: donor inclusion criteria, urine-feces separation in smart toilets, homogenization, aliquoting, usage or type of buffer to dissolve and store fecal material, temperature and time for processing and storage and quality control. The lack of standardization and low-throughput of state-of-the-art fecal collection procedures promote a more automated protocol. Based on this review, an automated protocol is proposed. Fecal samples should be collected and immediately processed under anaerobic conditions at either room temperature (RT) for a maximum of 4 h or at 4 °C for no more than 24 h. Upon homogenization, preferably in the absence of added solvent to allow addition of a buffer of choice at a later stage, aliquots obtained should be stored at either -20 °C for up to a few months or -80 °C for a longer period-up to 2 years. Protocols for quality control should characterize microbial composition and viability as well as metabolic functionality.

Role of gastrointestinal health in managing children with autism spectrum disorder

Children with autism spectrum disorders (ASD) or autism are more prone to gastrointestinal (GI) disorders than the general population. These disorders can significantly affect their health, learning, and development due to various factors such as genetics, environment, and behavior. The causes of GI disorders in children with ASD can include gut dysbiosis, immune dysfunction, food sensitivities, digestive enzyme deficiencies, and sensory processing differences. Many studies suggest that numerous children with ASD experience GI problems, and effective management is crucial. Diagnosing autism is typically done through genetic, neurological, functional, and behavioral assessments and observations, while GI tests are not consistently reliable. Some GI tests may increase the risk of developing ASD or exacerbating symptoms. Addressing GI issues in individuals with ASD can improve their overall well-being, leading to better behavior, cognitive function, and educational abilities. Proper management can improve digestion, nutrient absorption, and appetite by relieving physical discomfort and pain. Alleviating GI symptoms can improve sleep patterns, increase energy levels, and contribute to a general sense of well-being, ultimately leading to a better quality of life for the individual and improved family dynamics. The primary goal of GI interventions is to improve nutritional status, reduce symptom severity, promote a balanced mood, and increase patient independence.

Comparing In Vitro Faecal Fermentation Methods as Surrogates for Phage Therapy Application

The human microbiome and its importance in health and disease have been the subject of numerous research articles. Most microbes reside in the digestive tract, with up to 10¹² cells per gram of faecal material found in the colon. In terms of gene number, it has been estimated that the gut microbiome harbours >100 times more genes than the human genome. Several human intestinal diseases are strongly associated with disruptions in gut microbiome composition. Less studied components of the gut microbiome are the bacterial viruses called bacteriophages that may be present in numbers equal to or greater than the prokaryotes. Their potential to lyse their bacterial hosts, or to act as agents of horizontal gene transfer makes them important research targets. In this study in vitro faecal fermentation systems were developed and compared for their ability to act as surrogates for the human colon. Changes in bacterial and viral composition occurred after introducing a high-titre single phage preparation both with and without a known bacterial host during the 24 h-long fermentation. We also show that during this timeframe 50 mL plastic tubes can provide data similar to that generated in a sophisticated faecal fermenter system. This knowledge can guide us to a better understanding of the short-term impact of bacteriophage transplants on the bacteriomes and viromes of human recipients.

Gut microbial evidence chain in high-salt diet exacerbates intestinal aging process

Although excessive salt consumption appears to hasten intestinal aging and increases susceptibility to cardiovascular disease, the molecular mechanism is unknown. In this study, mutual validation of high salt (HS) and aging fecal microbiota transplantation (FMT) in C56BL/6 mice was used to clarify the molecular mechanism by which excessive salt consumption causes intestinal aging. Firstly, we observed HS causes vascular endothelial damage and can accelerate intestinal aging associated with decreased colon and serum expression of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and increased malondialdehyde (MDA); after transplantation with HS fecal microbiota in mice, vascular endothelial damage and intestinal aging can also occur. Secondly, we also found intestinal aging and vascular endothelial damage in older mice aged 14 months; and after transplantation of the older mice fecal microbiota, the same effect was observed in mice aged 6–8 weeks. Meanwhile, HS and aging significantly changed gut microbial diversity and composition, which was transferable by FMT. Eventually, based on the core genera both in HS and the aging gut microbiota network, a machine learning model was constructed which could predict HS susceptibility to intestinal aging. Further investigation revealed that the process of HS-related intestinal aging was highly linked to the signal transduction mediated by various bacteria. In conclusion, the present study provides an experimental basis of potential microbial evidence in the process of HS related intestinal aging. Even, avoiding excessive salt consumption and actively intervening in gut microbiota alteration may assist to delay the aging state that drives HS-related intestinal aging in clinical practice.

Machine Learning Data Analysis Highlights the Role of Parasutterella and Alloprevotella in Autism Spectrum Disorders

In recent years, the involvement of the gut microbiota in disease and health has been investigated by sequencing the 16S gene from fecal samples. Dysbiotic gut microbiota was also observed in Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by gastrointestinal symptoms. However, despite the relevant number of studies, it is still difficult to identify a typical dysbiotic profile in ASD patients. The discrepancies among these studies are due to technical factors (i.e., experimental procedures) and external parameters (i.e., dietary habits). In this paper, we collected 959 samples from eight available projects (540 ASD and 419 Healthy Controls, HC) and reduced the observed bias among studies. Then, we applied a Machine Learning (ML) approach to create a predictor able to discriminate between ASD and HC. We tested and optimized three algorithms: Random Forest, Support Vector Machine and Gradient Boosting Machine. All three algorithms confirmed the importance of five different genera, including Parasutterella and Alloprevotella. Furthermore, our results show that ML algorithms could identify common taxonomic features by comparing datasets obtained from countries characterized by latent confounding variables.

Overall Rebalancing of Gut Microbiota Is Key to Autism Intervention

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with unclear etiology, and due to the lack of effective treatment, ASD patients bring enormous economic and psychological burden to families and society. In recent years, many studies have found that children with ASD are associated with gastrointestinal diseases, and the composition of intestinal microbiota (GM) is different from that of typical developing children. Thus, many researchers believe that the gut-brain axis may play an important role in the occurrence and development of ASD. Indeed, some clinical trials and animal studies have reported changes in neurological function, behavior, and comorbid symptoms of autistic children after rebalancing the composition of the GM through the use of antibiotics, prebiotics, and probiotics or microbiota transfer therapy (MMT). In view of the emergence of new therapies based on the modulation of GM, characterizing the individual gut bacterial profile evaluating the effectiveness of intervention therapies could help provide a better quality of life for subjects with ASD. This article reviews current studies on interventions to rebalance the GM in children with ASD. The results showed that Lactobacillus plantarum may be an effective strain for the probiotic treatment of ASD. However, the greater effectiveness of MMT treatment suggests that it may be more important to pay attention to the overall balance of the patient's GM. Based on these findings, a more thorough assessment of the GM is expected to contribute to personalized microbial intervention, which can be used as a supplementary treatment for ASD.

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.

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.

Gut bless you: The microbiota-gut-brain axis in irritable bowel syndrome

Irritable bowel syndrome (IBS) is a common clinical label for medically unexplained gastrointestinal symptoms, recently described as a disturbance of the microbiota-gut-brain axis. Despite decades of research, the pathophysiology of this highly heterogeneous disorder remains elusive. However, a dramatic change in the understanding of the underlying pathophysiological mechanisms surfaced when the importance of gut microbiota protruded the scientific picture. Are we getting any closer to understanding IBS' etiology, or are we drowning in unspecific, conflicting data because we possess limited tools to unravel the cluster of secrets our gut microbiota is concealing? In this comprehensive review we are discussing some of the major important features of IBS and their interaction with gut microbiota, clinical microbiota-altering treatment such as the low FODMAP diet and fecal microbiota transplantation, neuroimaging and methods in microbiota analyses, and current and future challenges with big data analysis in IBS.

Virulence factor-related gut microbiota genes and immunoglobulin A levels as novel markers for machine learning-based classification of autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition for which early identification and intervention is crucial for optimum prognosis. Our previous work showed gut Immunoglobulin A (IgA) to be significantly elevated in the gut lumen of children with ASD compared to typically developing (TD) children. Gut microbiota variations have been reported in ASD, yet not much is known about virulence factor-related gut microbiota (VFGM) genes. Upon determining the VFGM genes distinguishing ASD from TD, this study is the first to utilize VFGM genes and IgA levels for a machine learning-based classification of ASD. Sequence comparisons were performed of metagenome datasets from children with ASD (n = 43) and TD children (n = 31) against genes in the virulence factor database. VFGM gene composition was associated with ASD phenotype. VFGM gene diversity was higher in children with ASD and positively correlated with IgA content. As Group B streptococcus (GBS) genes account for the highest proportion of 24 different VFGMs between ASD and TD and positively correlate with gut IgA, GBS genes were used in combination with IgA and VFGMs diversity to distinguish ASD from TD. Given that VFGM diversity, increases in IgA, and ASD-enriched VFGM genes were independent of sex and gastrointestinal symptoms, a classification method utilizing them will not pertain only to a specific subgroup of ASD. By introducing the classification value of VFGM genes and considering that VFs can be isolated in pregnant women and newborns, these findings provide a novel machine learning-based early risk identification method for ASD.