Metatranscriptome of human lung microbial communities in a cohort of mechanically ventilated COVID-19 Omicron patients.

The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) infected a substantial proportion of Chinese population, and understanding the factors underlying the severity of the disease and fatality is valuable for future prevention and clinical treatment. We recruited 64 patients with invasive ventilation for COVID-19 and performed metatranscriptomic sequencing to profile host transcriptomic profiles, plus viral, bacterial, and fungal content, as well as virulence factors and examined their relationships to 28-day mortality were examined. In addition, the bronchoalveolar lavage fluid (BALF) samples from invasive ventilated hospital/community-acquired pneumonia patients (HAP/CAP) sampled in 2019 were included for comparison. Genomic analysis revealed that all Omicron strains belong to BA.5 and BF.7 sub-lineages, with no difference in 28-day mortality between them. Compared to HAP/CAP cohort, invasive ventilated COVID-19 patients have distinct host transcriptomic and microbial signatures in the lower respiratory tract; and in the COVID-19 non-survivors, we found significantly lower gene expressions in pathways related viral processes and positive regulation of protein localization to plasma membrane, higher abundance of opportunistic pathogens including bacterial Alloprevotella, Caulobacter, Escherichia-Shigella, Ralstonia and fungal Aspergillus sydowii and Penicillium rubens. Correlational analysis further revealed significant associations between host immune responses and microbial compositions, besides synergy within viral, bacterial, and fungal pathogens. Our study presents the relationships of lower respiratory tract microbiome and transcriptome in invasive ventilated COVID-19 patients, providing the basis for future clinical treatment and reduction of fatality.

[The BIG Data Center's database resources].

Omics data in life and health sciences are of fundamental significance for scientific research and biomedical technology development. However, there is yet to be a platform for biological data management and sharing in China, making it difficult to meet the development needs of biomedical and related fields and consequently leading to severe issues in big data management, sharing and translation. To address these issues, Beijing Institute of Genomics (BIG) of Chinese Academy of Sciences founded the BIG Data Center (BIGD) in 2016, which is dedicated to establish a biological big data management platform and multi-omics databases, with a particular focus on national population healthcare and important strategic biological resources. In this paper, we describe core database resources in BIGD, including GSA (Genome Sequence Archive), GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas), MethBank (Methylation Bank), BioCode and Science Wikis. Taken together, all these resources provide a series of services for data deposition, integration and sharing, laying solid foundations for enhancing national biological science data management and further promoting the construction of national bioinformatics center.