ABSTRACT
Due to immunosuppressive cancer therapies, cancer patients diagnosed with COVID-19 have a higher chance of developing severe symptoms and present a higher mortality rate in comparison to the general population. Here we show a comparative analysis of the microbiome from naso-oropharyngeal samples of breast cancer patients with respect to SARS-CoV-2 status and identified bacteria associated with symptom severity. Total DNA of naso-oropharyngeal swabs from 74 women with or without breast cancer, positive or negative for SARS-CoV-2 were PCR-amplified for 16S-rDNA V3 and V4 regions and submitted to massive parallel sequencing. Sequencing data were analyzed with QIIME2 and taxonomic identification was performed using the q2-feature-classifier QIIME2 plugin, the Greengenes Database, and amplicon sequence variants (ASV) analysis. A total of 486 different bacteria were identified. No difference was found in taxa diversity between sample groups. Cluster analysis did not group the samples concerning SARS-CoV-2 status, breast cancer diagnosis, or symptom severity. Three taxa (Pseudomonas, Moraxella, and Klebsiella,) showed to be overrepresented in women with breast cancer and positive for SARS-CoV-2 when compared to the other women groups, and five bacterial groups were associated with COVID-19 severity among breast cancer patients: Staphylococcus, Staphylococcus epidermidis, Scardovia, Parasegitibacter luogiensis, and Thermomonas. The presence of Staphylococcus in COVID-19 breast cancer patients may possibly be a consequence of nosocomial infection.
ABSTRACT
SARS-CoV-2 promotes an imbalanced host response that underlies the development and severity of COVID-19. Infections with viruses are known to modulate transposable elements (TEs), which can exert downstream effects by modulating host gene expression, innate immune sensing, or activities encoded by their protein products. We investigated the impact of SARS-CoV-2 infection on TE expression using RNA-Seq data from cell lines and from primary patient samples. Using a bioinformatics tool, Telescope, we showed that SARS-CoV-2 infection led to upregulation or downregulation of TE transcripts, a subset of which differed from cells infected with SARS, Middle East respiratory syndrome coronavirus (MERS-CoV or MERS), influenza A virus (IAV), respiratory syncytial virus (RSV), and human parainfluenza virus type 3 (HPIV3). Differential expression of key retroelements specifically identified distinct virus families, such as Coronaviridae, with unique retroelement expression subdividing viral species. Analysis of ChIP-Seq data showed that TEs differentially expressed in SARS-CoV-2 infection were enriched for binding sites for transcription factors involved in immune responses and for pioneer transcription factors. In samples from patients with COVID-19, there was significant TE overexpression in bronchoalveolar lavage fluid and downregulation in PBMCs. Thus, although the host gene transcriptome is altered by infection with SARS-CoV-2, the retrotranscriptome may contain the most distinctive features of the cellular response to SARS-CoV-2 infection.
Subject(s)
COVID-19/genetics , Endogenous Retroviruses/genetics , Long Interspersed Nucleotide Elements/genetics , A549 Cells , Cell Line , Chromatin Immunoprecipitation Sequencing , Computational Biology , Coronavirus Infections/genetics , DNA Transposable Elements/genetics , Down-Regulation , Host Microbial Interactions/genetics , Humans , In Vitro Techniques , Influenza A virus , Influenza, Human/genetics , Middle East Respiratory Syndrome Coronavirus , Parainfluenza Virus 3, Human , RNA-Seq , Respiratory Syncytial Virus Infections/genetics , Respiratory Syncytial Viruses , Respirovirus Infections/genetics , Retroelements/genetics , Severe acute respiratory syndrome-related coronavirus , SARS-CoV-2 , Severe Acute Respiratory Syndrome/genetics , Transcriptome , Up-RegulationABSTRACT
Next-generation sequencing (NGS) offers a powerful opportunity to identify low-abundance, intra-host viral sequence variants, yet the focus of many bioinformatic tools on consensus sequence construction has precluded a thorough analysis of intra-host diversity. To take full advantage of the resolution of NGS data, we developed HAplotype PHylodynamics PIPEline (HAPHPIPE), an open-source tool for the de novo and reference-based assembly of viral NGS data, with both consensus sequence assembly and a focus on the quantification of intra-host variation through haplotype reconstruction. We validate and compare the consensus sequence assembly methods of HAPHPIPE to those of two alternative software packages, HyDRA and Geneious, using simulated HIV and empirical HIV, HCV, and SARS-CoV-2 datasets. Our validation methods included read mapping, genetic distance, and genetic diversity metrics. In simulated NGS data, HAPHPIPE generated pol consensus sequences significantly closer to the true consensus sequence than those produced by HyDRA and Geneious and performed comparably to Geneious for HIV gp120 sequences. Furthermore, using empirical data from multiple viruses, we demonstrate that HAPHPIPE can analyze larger sequence datasets due to its greater computational speed. Therefore, we contend that HAPHPIPE provides a more user-friendly platform for users with and without bioinformatics experience to implement current best practices for viral NGS assembly than other currently available options.