摘要
      2019年冠状病毒病（新冠肺炎）的严重程度与肠道微生物群的改变有关。然而，肠道微生物组的改变与新冠肺炎预后之间的关系尚不明确。在这里，我们对300名住院新冠肺炎患者的粪便样本进行了基因组解析的宏基因组分析，这些样本是在入院时收集的。在2568个高质量宏基因组组装基因组（HQMAG）中，冗余分析确定了33个HQMAG，它们在轻度、中度和重度/危重症组中表现出差异分布。共丰度网络分析确定，33个HQMAG被组织为两个相互竞争的行会。与Guild 2相比，Guild 1含有更多的短链脂肪酸生物合成基因，而毒力和抗生素耐药性基因较少。根据两个行会之间的平均丰度差异，行会水平的微生物组指数（GMI）对不同严重程度组的患者进行了分类（平均AUROC [接收器工作曲线下的面积]= 0.83）。此外，年龄调整后的部分Spearman相关性显示，入院时的GMI与8个临床参数相关，这些参数是新冠肺炎住院第7天预后的预测因素。此外，入院时的GMI与危重患者的死亡/出院结果相关。我们进一步验证了GMI能够在不同国家对不同新冠肺炎症状严重程度的患者进行一致分类，并在四个独立数据集中将新冠肺炎患者与健康受试者和肺炎对照者区分开来。因此，这种基于基因组的行会级特征可能有助于早期识别住院的新冠肺炎患者，这些患者在入院时有更严重结局的高风险。
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