Gene expression profiling gut microbiota in different races of humans

The gut microbiome is shaped and modified by the polymorphisms of microorganisms in the intestinal tract. Its composition shows strong individual specificity and may play a crucial role in the human digestive system and metabolism. Several factors can affect the composition of the gut microbiome, su...

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Bibliographic Details
Published in:Scientific reports 2016-03, Vol.6 (1), p.23075-23075, Article 23075
Main Authors: Chen, Lei, Zhang, Yu-Hang, Huang, Tao, Cai, Yu-Dong
Format: Article
Language:English
Subjects:
45/43
631/208/199
639/705/1042
Algorithms
Asian Continental Ancestry Group
China
Cluster Analysis
Digestive system
Eating behavior
Europe
European Continental Ancestry Group
Feeding Behavior - ethnology
Gastrointestinal Microbiome - genetics
Gastrointestinal tract
Gene expression
Gene Expression Profiling - methods
Humanities and Social Sciences
Humans
Intestinal microflora
Intestine
Learning algorithms
Machine Learning
Metagenome - genetics
Metagenomics - methods
Microorganisms
multidisciplinary
Oligonucleotide Array Sequence Analysis - methods
Races
Science
United States
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Summary:The gut microbiome is shaped and modified by the polymorphisms of microorganisms in the intestinal tract. Its composition shows strong individual specificity and may play a crucial role in the human digestive system and metabolism. Several factors can affect the composition of the gut microbiome, such as eating habits, living environment and antibiotic usage. Thus, various races are characterized by different gut microbiome characteristics. In this present study, we studied the gut microbiomes of three different races, including individuals of Asian, European and American races. The gut microbiome and the expression levels of gut microbiome genes were analyzed in these individuals. Advanced feature selection methods (minimum redundancy maximum relevance and incremental feature selection) and four machine-learning algorithms (random forest, nearest neighbor algorithm, sequential minimal optimization, Dagging) were employed to capture key differentially expressed genes. As a result, sequential minimal optimization was found to yield the best performance using the 454 genes, which could effectively distinguish the gut microbiomes of different races. Our analyses of extracted genes support the widely accepted hypotheses that eating habits, living environments and metabolic levels in different races can influence the characteristics of the gut microbiome.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep23075