王明帮等：肠道IgA 菌群毒力因子基因，助力自闭症早期诊断 | 热心肠日报
以毒力因子相关肠道菌群基因及IgA水平作为新型标志物，基于机器学习对自闭症谱系障碍进行分类
10.1016/j.csbj.2020.12.012
2020-12-29, Article
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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.
First Authors:
Mingbang Wang,Ceymi Doenyas
Correspondence Authors:
Mingbang Wang,Zhaoqing Yin,Wenhao Zhou
All Authors:
Mingbang Wang,Ceymi Doenyas,Jing Wan,Shujuan Zeng,Chunquan Cai,Jiaxiu Zhou,Yanqing Liu,Zhaoqing Yin,Wenhao Zhou