Gut microbiota in type 2 diabetes, China, case-control

Assessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide. To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals. We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses. MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance. An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.

Classified as human gut / stool shotgun DNA metagenome of adult and elderly by deterministic_prior with confidence 0.90. Assay shotgun_dna.

• host external_curation.infant_catalog · “infant triage reason_code age_adult_only”
• body site study.title · “association study of gut microbiota in type”
• body site study.description · “and characterization of gut microbiota has become”
• life stage external_curation.infant_catalog · “infant triage reason_code age_adult_only”
• life stage sample.attr.age · “62 (n=50/370)”

370 runs over 370 samples · mean 3.58 Gbp per run (median 3.35 Gbp, total 1,323.99 Gbp; 370 runs with a base count) · layout PAIRED 370 · platform ILLUMINA 370 · models Illumina Genome Analyzer II (370) · 92% of runs with a Sandpiper profile · first public 2012-09-15 – 2015-02-13

Qin J first · Li Y · Cai Z · Li S · Zhu J · Zhang F · Liang S · Zhang W · Guan Y · Shen D · Peng Y · Zhang D · Jie Z · Wu W · Qin Y · Xue W · Li J · Han L · Lu D · Wu P · Dai Y · Sun X · Li Z · Tang A · Zhong S · Li X · Chen W · Xu R · Wang M · Feng Q · Gong M · Yu J · Zhang Y · Zhang M · Hansen T · Sanchez G · Raes J · Falony G · Okuda S · Almeida M · LeChatelier E · Renault P · Pons N · Batto JM · Zhang Z · Chen H · Yang R · Zheng W · Yang H · Wang J · Ehrlich SD · Nielsen R · Pedersen O · Kristiansen K · Nayfach S first · Rodriguez-Mueller B · Garud N · Pollard KS last · Tisza MJ first · Buck CB last · Chen DW first · Garud NR last · Fontana F first · Alessandri G · Tarracchini C · Bianchi MG · Rizzo SM · Mancabelli L · Lugli GA · Argentini C · Vergna LM · Anzalone R · Longhi G · Viappiani A · Taurino G · Chiu M · Turroni F · Bussolati O · van Sinderen D · Milani C · Ventura M last · Xie XH first · Huang YJ · Han GS · Yu ZG · Ma YL last · Ormaasen I first · Rudi K · Diep DB · Snipen L last · De Biase R · Bocchio F · De Conto F · Nouvenne A · Ticinesi A · Meschi T · Cecchi R · Wei A first · Zhan H · Xiao Z · Zhao W · Jiang X last · Jiang MZ · Chen X · Li M · Wang YL · Liu C · Liu SJ · Chen WH last · Xu B first · Wu S · Huang J · Lin M · Liu Q · Fu M · Xia Z · Yi G last · Qian X · Li Q · Jin Z · Liu N · Zhao J · Wang S · Tian P last · Schroer HW first · Beghini F · Raygoza Garay JA · Christakis NA · Bosch DE last

PRJNA422434.csv.gz (11.1 KB) · parquet (85.5 KB) — all 370 samples, every field with route and confidence · PRJNA422434_determinations.csv.gz (23.2 KB) — 2,386 evidence rows