Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment (preprint)

Synergistic effects of bacteria on viral stability and transmission are widely documented but remain unclear in the context of SARS-CoV-2. We collected 972 samples from hospitalized patients with coronavirus disease 2019 (COVID-19), their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and contextualized the massive microbial diversity in this dataset through meta-analysis of over 20,000 samples. Sixteen percent of surfaces from COVID-19 patient rooms were positive, with the highest prevalence in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples increasingly resembled the patient microbiome over time, SARS-CoV-2 was detected less there (11%). Despite viral surface contamination in almost all patient rooms, no health care workers contracted the disease, suggesting that personal protective equipment was effective in preventing transmissions. SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity across human and surface samples, and higher biomass in floor samples. 16S microbial community profiles allowed for high SARS-CoV-2 classifier accuracy in not only nares, but also forehead, stool, and floor samples. Across distinct microbial profiles, a single amplicon sequence variant from the genus Rothia was highly predictive of SARS-CoV-2 across sample types and had higher prevalence in positive surface and human samples, even compared to samples from patients in another intensive care unit prior to the COVID-19 pandemic. These results suggest that bacterial communities may contribute to viral prevalence both in the host and hospital environment. One Sentence SummaryMicrobial classifier highlights specific taxa predictive of SARS-CoV-2 prevalence across diverse microbial niches in a COVID-19 hospital unit.

Analysis of the Dynamics and Distribution of SARS-CoV-2 Mutations and its Possible Structural and Functional Implications (preprint)

After eight months of the pandemic declaration, COVID-19 has not been globally controlled. Several efforts to control SARS-CoV-2 dissemination are still running including vaccines and drug treatments. The effectiveness of these procedures depends, in part, that the regions to which these treatments are directed do not vary considerably. Although, it is known that the mutation rate of SARS-CoV-2 is relatively low it is necessary to monitor the adaptation and evolution of the virus in the different stages of the pandemic. Thus, identification, analysis of the dynamics, and possible functional and structural implication of mutations are relevant. Here, we first estimate the number of COVID-19 cases with a virus with a specific mutation and then calculate its global relative frequency (NRFp). Using this approach in a dataset of 100 924 genomes from GISAID, we identified 41 mutations to be present in viruses in an estimated number of 750 000 global COVID-19 cases (0.03 NRFp). We classified these mutations into three groups: high-frequent, low-frequent non-synonymous, and low-frequent synonymous. Analysis of the dynamics of these mutations by month and continent showed that high-frequent mutations appeared early in the pandemic, all are present in all continents and some of them are almost fixed in the global population. On the other hand, low-frequent mutations (non-synonymous and synonymous) appear late in the pandemic and seems to be at least partially continent-specific. This could be due to that high-frequent mutation appeared early when lockdown policies had not yet been applied and low-frequent mutations appeared after lockdown policies. Thus, preventing global dissemination of them. Finally, we present a brief structural and functional review of the analyzed ORFs and the possible implications of the 25 identified non-synonymous mutations.

A comparison of DNA/RNA extraction protocols for high-throughput sequencing of microbial communities (preprint)

One goal among microbial ecology researchers is to capture the maximum amount of information from all organisms in a sample. The recent COVID-19 pandemic, caused by the RNA virus SARS-CoV-2, has highlighted a gap in traditional DNA-based protocols, including the high-throughput methods we previously established as field standards. To enable simultaneous SARS-CoV-2 and microbial community profiling, we compare the relative performance of two total nucleic acid extraction protocols and our previously benchmarked protocol. We included a diverse panel of environmental and host-associated sample types, including body sites commonly swabbed for COVID-19 testing. Here we present results comparing the cost, processing time, DNA and RNA yield, microbial community composition, limit of detection, and well-to-well contamination, between these protocols. Accession numbersRaw sequence data were deposited at the European Nucleotide Archive (accession#: ERP124610) and raw and processed data are available at Qiita (Study ID: 12201). All processing and analysis code is available on GitHub (github.com/justinshaffer/Extraction_test_MagMAX). Methods summaryTo allow for downstream applications involving RNA-based organisms such as SARS-CoV-2, we compared the two extraction protocols designed to extract DNA and RNA against our previously established protocol for extracting only DNA for microbial community analyses. Across 10 diverse sample types, one of the two protocols was equivalent or better than our established DNA-based protocol. Our conclusion is based on per-sample comparisons of DNA and RNA yield, the number of quality sequences generated, microbial community alpha- and beta-diversity and taxonomic composition, the limit of detection, and extent of well-to-well contamination.