王明帮等:肠道IgA+菌群毒力因子基因,助力自闭症早期诊断
创作:szx 审核:szx 01月08日
  • 纳入43名ASD患儿及31名正常儿童,对VFGM基因进行对比分析;
  • ASD患儿的VFGM基因多样性显著升高,且与肠道IgA含量呈显著正相关;
  • VFGM基因组成与ASD相关,17个VFGM基因在ASD患儿中富集,而7个VFGM基因在ASD患儿中减少;
  • 在上述24个差异化表达的VFGM基因中,B型链球菌(GBS)基因比例最高;
  • 结合肠道IgA水平、VFGM基因多样性及GBS基因丰度开发的机器学习算法,可准确区分ASD患儿及正常儿童。
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szx
前期研究发现,自闭症谱系障碍(ASD)患儿的肠道IgA水平显著升高,且肠道菌群发生改变。复旦大学附属儿科医院王明帮、周文浩和德宏州人民医院Zhaoqing Yin与团队,在Computational and Structural Biotechnology Journal上发表的一项最新研究,发现ASD患儿与正常儿童的毒力因子相关肠道菌群(VFGM)基因的组成及多样性有显著差异,结合VFGM基因多样性、肠道IgA水平及特定VFGM基因的丰度开发机器学习算法,可准确区分ASD患儿及正常儿童。
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Virulence factor-related gut microbiota genes and immunoglobulin A levels as novel markers for machine learning-based classification of autism spectrum disorder

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

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