發(fā)布者：抗性基因網(wǎng) 時(shí)間：2023-06-14 瀏覽量：1704
摘要
? ? ? 2019年冠狀病毒病（新冠肺炎）的嚴(yán)重程度與腸道微生物群的改變有關(guān)。然而，腸道微生物組的改變與新冠肺炎預(yù)后之間的關(guān)系尚不明確。在這里，我們對(duì)300名住院新冠肺炎患者的糞便樣本進(jìn)行了基因組解析的宏基因組分析，這些樣本是在入院時(shí)收集的。在2568個(gè)高質(zhì)量宏基因組組裝基因組（HQMAG）中，冗余分析確定了33個(gè)HQMAG，它們?cè)谳p度、中度和重度/危重癥組中表現(xiàn)出差異分布。共豐度網(wǎng)絡(luò)分析確定，33個(gè)HQMAG被組織為兩個(gè)相互競(jìng)爭(zhēng)的行會(huì)。與Guild 2相比，Guild 1含有更多的短鏈脂肪酸生物合成基因，而毒力和抗生素耐藥性基因較少。根據(jù)兩個(gè)行會(huì)之間的平均豐度差異，行會(huì)水平的微生物組指數(shù)（GMI）對(duì)不同嚴(yán)重程度組的患者進(jìn)行了分類（平均AUROC?[接收器工作曲線下的面積]=?0.83）。此外，年齡調(diào)整后的部分Spearman相關(guān)性顯示，入院時(shí)的GMI與8個(gè)臨床參數(shù)相關(guān)，這些參數(shù)是新冠肺炎住院第7天預(yù)后的預(yù)測(cè)因素。此外，入院時(shí)的GMI與危重患者的死亡/出院結(jié)果相關(guān)。我們進(jìn)一步驗(yàn)證了GMI能夠在不同國(guó)家對(duì)不同新冠肺炎癥狀嚴(yán)重程度的患者進(jìn)行一致分類，并在四個(gè)獨(dú)立數(shù)據(jù)集中將新冠肺炎患者與健康受試者和肺炎對(duì)照者區(qū)分開(kāi)來(lái)。因此，這種基于基因組的行會(huì)級(jí)特征可能有助于早期識(shí)別住院的新冠肺炎患者，這些患者在入院時(shí)有更嚴(yán)重結(jié)局的高風(fēng)險(xiǎn)。
ABSTRACT
Coronavirus disease 2019 (COVID-19) severity has been associated with alterations of the gut microbiota. However, the relationship between gut microbiome alterations and COVID-19 prognosis remains elusive. Here, we performed a genome-resolved metagenomic analysis on fecal samples from 300 in-hospital COVID-19 patients, collected at the time of admission. Among the 2,568 high quality metagenome-assembled genomes (HQMAGs), redundancy analysis identified 33 HQMAGs which showed differential distribution among mild, moderate, and severe/critical severity groups. Co-abundance network analysis determined that the 33 HQMAGs were organized as two competing guilds. Guild 1 harbored more genes for short-chain fatty acid biosynthesis, and fewer genes for virulence and antibiotic resistance, compared with Guild 2. Based on average abundance difference between the two guilds, the guild-level microbiome index (GMI) classified patients from different severity groups (average AUROC?[area under the receiver operating curve] =?0.83). Moreover, age-adjusted partial Spearman’s correlation showed that GMIs at admission were correlated with 8 clinical parameters, which are predictors for COVID-19 prognosis, on day 7 in hospital. In addition, GMI at admission was associated with death/discharge outcome of the critical patients. We further validated that GMI was able to consistently classify patients with different COVID-19 symptom severities in different countries and differentiated COVID-19 patients from healthy subjects and pneumonia controls in four independent data sets. Thus, this genome-based guild-level signature may facilitate early identification of hospitalized COVID-19 patients with high risk of more severe outcomes at time of admission.
https://journals.asm.org/doi/full/10.1128/mbio.03519-22