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

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.

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

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.

Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder

Background

Artificial intelligence (AI) has been the subject of studies in autism spectrum disorder (ASD) and may affect its identification, diagnosis, intervention, and other medical practices in the future. Although previous studies have used bibliometric techniques to analyze and investigate AI, there has been little research on the adoption of AI in ASD. This study aimed to explore the broad applications and research frontiers of AI used in ASD.

Methods

Citation data were retrieved from the Web of Science Core Collection (WoSCC) database to assess the extent to which AI is used in ASD. CiteSpace.5.8. R3 and VOSviewer, two online tools for literature metrology analysis, were used to analyze the data.

Results

A total of 776 publications from 291 countries and regions were analyzed; of these, 256 publications were from the United States and 173 publications were from China, and England had the largest centrality of 0.33; Stanford University had the highest H-index of 17; and the largest cluster label of co-cited references was machine learning. In addition, keywords with a high number of occurrences in this field were autism spectrum disorder (295), children (255), classification (156) and diagnosis (77). The burst keywords from 2021 to 2023 were infants and feature selection, and from 2022 to 2023, the burst keyword was corpus callosum.

Conclusion

This research provides a systematic analysis of the literature concerning AI used in ASD, presenting an overall demonstration in this field. In this area, the United States and China have the largest number of publications, England has the greatest influence, and Stanford University is the most influential. In addition, the research on AI used in ASD mostly focuses on classification and diagnosis, and "infants, feature selection, and corpus callosum are at the forefront, providing directions for future research. However, the use of AI technologies to identify ASD will require further research.

Correlation between gut microbiota composition, enteric infections and linear growth impairment: a case-control study in childhood stunting in Pidie, Aceh, Indonesia

BACKGROUND

Gut microbiota is pivotal in maintaining children's health and well-being. The ingestion of enteric pathogens and dysbiosis lead to Environmental Enteric Dysfunction (EED), which is essential in stunting pathogenesis. The roles of gut microbiome and enteric infections have not been explored comprehensively in relation to childhood stunting in Indonesia. This study aimed to determine the correlation between gut microbiota composition, enteric infections, and growth biomarker, Insulin-like Growth Factor 1 (IGF-1), in stunted children from Pidie, Aceh, Indonesia.

METHODS

This study was a case-control study involving 42 subjects aged 24 to 59 months, comprising 21 stunted children for the case and 21 normal children for the control group. The IGF-1 serum level was quantified using ELISA. The gut microbiome profiling was conducted using 16S rDNA amplicon sequencing. The expression of enteric pathogens virulence genes was determined using quantitative PCR (qPCR) assay. The correlations of observed variables were analysed using suitable statistical analyses.

RESULTS

The result showed that the IGF-1 sera levels in stunted were lower than those in normal children (p ≤ 0.001). The abundance of Firmicutes (50%) was higher than Bacteroidetes (34%) in stunted children. The gut microbiome profile of stunted children showed enriched genera such as Blautia, Dorea, Collinsella, Streptococcus, Clostridium sensu stricto 13, Asteroleplasma and Anaerostipes. Meanwhile the depleted genera comprised Prevotella, Lactococcus, Butyrivibrio, Muribaculaceae, Alloprevotella, Akkermansia, Enterococcus, Terrisporobacter and Turicibacter. The abundance of water biological contaminants such as Aeromonas, Stappiaceae, and Synechococcus was also higher in stunted children compared to normal children. The virulence genes expression of Enteroaggregative Escherichia coli (aaiC), Enterotoxigenic E. coli (estA), Enteropathogenic E. coli (eaeA), Shigella/Enteroinvasive E. coli (ipaH3) and Salmonella enterica (ompC) in stunted was higher than in normal children (p ≤ 0.001), which negatively correlated to height and level of IGF-1.

CONCLUSION

The present study showed the distinctive gut microbiome profile of stunted and normal children from Pidie, Aceh, Indonesia. The gut microbiota of stunted children revealed dysbiosis, comprised several pro-inflammatory, metabolic abnormalities and high-fat/low-fiber diet-related taxa, and expressed virulence genes of enteric pathogens. These findings provide evidence that it is imperative to restore dysbiosis and preserve the balance of gut microbiota to support linear growth in children.

Alteration of the Gut Microbiota in Pigs Infected with African Swine Fever Virus

The factors that influence the pathogenicity of African swine fever (ASF) are still poorly understood, and the host's immune response has been indicated as crucial. Although an increasing number of studies have shown that gut microbiota can control the progression of diseases caused by viral infections, it has not been characterized how the ASF virus (ASFV) changes a pig's gut microbiome. This study analyzed the dynamic changes in the intestinal microbiome of pigs experimentally infected with the high-virulence ASFV genotype II strain (N = 4) or mock strain (N = 3). Daily fecal samples were collected from the pigs and distributed into the four phases (before infection, primary phase, clinical phase, and terminal phase) of ASF based on the individual clinical features of the pigs. The total DNA was extracted and the V4 region of the 16 s rRNA gene was amplified and sequenced on the Illumina platform. Richness indices (ACE and Chao1) were significantly decreased in the terminal phase of ASF infection. The relative abundances of short-chain-fatty-acids-producing bacteria, such as Ruminococcaceae, Roseburia, and Blautia, were decreased during ASFV infection. On the other hand, the abundance of Proteobacteria and Spirochaetes increased. Furthermore, predicted functional analysis using PICRUSt resulted in a significantly reduced abundance of 15 immune-related pathways in the ASFV-infected pigs. This study provides evidence for further understanding the ASFV-pig interaction and suggests that changes in gut microbiome composition during ASFV infection may be associated with the status of immunosuppression.