MicrobiomeMicrobiologyPeer-reviewed journal article
The genetic diversity and populational specificity of the human gut virome at single nucleotide resolution
Xiuchao WangiD, Quanbin DongiD, Pan Huang, Shuai YangiD, Mengke Gao, Chengcheng ZhangiD, Chuan ZhangiD, Youpeng Deng, Zijing Huang, Beining Ma, Yuwen JiaoiD, Yan Zhou, Tingting WuiD, Huayiyang Zou, Jing ShiiD, Yanhui ShengiD, Yifeng WangiD, Liming TangiD, Shixian HuiD, Yi DuaniD, Wei SuniD, Wei CheniD, Qixiao ZhaiiD, Kong XiangqingiD, Lianmin CheniD
Abstract
Large-scale characterization of gut viral genomes provides strain-resolved insights into host-microbe interactions. However, existing viral genomes are mainly derived from Western populations, limiting our understanding of global gut viral diversity and functional variations necessary for personalized medicine and addressing regional health disparities. Here, we introduce the Chinese Gut Viral Reference (CGVR) set, consisting of 120,568 viral genomes from 3,234 deeply sequenced fecal samples collected nationwide, covering 72,751 viral species-level clusters (VSCs), nearly 90% of which are absent from current databases. Analysis of single nucleotide variations (SNVs) in 233 globally prevalent VSCs revealed that 18.9% showed significant genetic stratification between Chinese and non-Chinese populations, potentially linked to bacterial infection susceptibility. The predicted bacterial hosts of population-stratified viruses exhibit distinct genetic components associated with health-related functions, including multidrug resistance. Additionally, viral strain diversity at the SNV level correlated with human phenotypic traits, such as age and gastrointestinal issues like constipation. Our analysis also indicates that the human gut bacteriome is specifically shaped by the virome, which mediates associations with human phenotypic traits. This foundational resource underscores the unique genetic makeup of the gut virome across populations and emphasizes the importance of recognizing gut viral genetic heterogeneity for deeper insights into regional health implications.
- organism: human
- assay: metagenomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 7
- FWCI
- 3.22
- Field percentile
- top 8%
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