Characterization of Microbial Co-infections in the Respiratory Tract of hospitalized COVID-19 patients (preprint)

Summary Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of Coronavirus disease 2019 (COVID-19). However, microbial composition of the respiratory tract and other infected tissues, as well as their possible pathogenic contributions to varying degrees of disease severity in COVID-19 patients remain unclear. Method Between January 27 and February 26, 2020, serial clinical specimens (sputum, nasal and throat swab, anal swab and feces) were collected from a cohort of hospitalized COVID-19 patients, including 8 mildly and 15 severely ill patients (requiring ICU admission and mechanical ventilation), in the Guangdong province, China. Total RNA was extracted and ultra-deep metatranscriptomic sequencing was performed in combination with laboratory diagnostic assays. Co-infection rates, the prevalence and abundance of microbial communities in these COVID-19 patients were determined. Findings Notably, respiratory microbial co-infections were exclusively found in 84.6% of severely ill patients (11/13), among which viral and bacterial co-infections were detected by sequencing in 30.8% (4/13) and 69.2% (9/13) of the patients, respectively. In addition, for 23.1% (3/13) of the patients, bacterial co-infections with Burkholderia cepacia complex (BCC) and Staphylococcus epidermidis were also confirmed by bacterial culture. Further, a time-dependent, secondary infection of B. cenocepacia with expressions of multiple virulence genes in one severely ill patient was demonstrated, which might be the primary cause of his disease deterioration and death one month after ICU admission. Interpretation Our findings identified distinct patterns of co-infections with SARS-CoV-2 and various respiratory pathogenic microbes in hospitalized COVID-19 patients in relation to disease severity. Detection and tracking of BCC-associated nosocomial infections are recommended to improve the pre-emptive treatment regimen and reduce fatal outcomes of hospitalized patients infected with SARS-CoV-2. Funding National Science and Technology Major Project of China, National Major Project for Control and Prevention of Infectious Disease in China, the emergency grants for prevention and control of SARS-CoV-2 of Ministry of Science and Technology and Guangdong province, Guangdong Provincial Key Laboratory of Genome Read and Write, Guangdong Provincial Academician Workstation of BGI Synthetic Genomics, and Shenzhen Engineering Laboratory for Innovative Molecular Diagnostics.

Intra-host Variation and Evolutionary Dynamics of SARS-CoV-2 Population in COVID-19 Patients (preprint)

As of middle May 2020, the causative agent of COVID-19, SARS-CoV-2, has infected over 4 million people with more than 300 thousand death as official reports1,2. The key to understanding the biology and virus-host interactions of SARS-CoV-2 requires the knowledge of mutation and evolution of this virus at both inter- and intra-host levels. However, despite quite a few polymorphic sites identified among SARS-CoV-2 populations, intra-host variant spectra and their evolutionary dynamics remain mostly unknown. Here, using deep sequencing data, we achieved and characterized consensus genomes and intra-host genomic variants from 32 serial samples collected from eight patients with COVID-19. The 32 consensus genomes revealed the coexistence of different genotypes within the same patient. We further identified 40 intra-host single nucleotide variants (iSNVs). Most (30/40) iSNVs presented in single patient, while ten iSNVs were found in at least two patients or identical to consensus variants. Comparison of allele frequencies of the iSNVs revealed genetic divergence between intra-host populations of the respiratory tract (RT) and gastrointestinal tract (GIT), mostly driven by bottleneck events among intra-host transmissions. Nonetheless, we observed a maintained viral genetic diversity within GIT, showing an increased population with accumulated mutations developed in the tissue-specific environments. The iSNVs identified here not only show spatial divergence of intra-host viral populations, but also provide new insights into the complex virus-host interactions.