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

Wang M., DOENYAS C., Wan J., Zeng S., Cai C., Zhou J., ...More

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, vol.19, pp.545-554, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 19
  • Publication Date: 2021
  • Doi Number: 10.1016/j.csbj.2020.12.012
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, Compendex, EMBASE, INSPEC, Veterinary Science Database, Directory of Open Access Journals
  • Page Numbers: pp.545-554
  • Keywords: Autism spectrum disorder, Classification, Early diagnosis, Genetics, Gut microbiota, Immunoglobulin A, Machine learning, Metagenome, Virulence factor, IGA, CHILDREN, BEHAVIOR, CELLS
  • Yıldız Technical University Affiliated: No


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. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.