Dual RNA-Seq analysis of SARS-CoV-2 correlates specific human transcriptional response pathways directly to viral expression
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Abstract
The SARS-CoV-2 pandemic has challenged humankind’s ability to quickly determine the cascade of health effects caused by a novel infection. Even with the unprecedented speed at which vaccines were developed and introduced into society, identifying therapeutic interventions and drug targets for patients infected with the virus remains important as new strains of the virus may evolve, or future coronaviruses may emerge, that are resistant to current vaccines. The application of transcriptomic RNA sequencing of infected samples may shed new light on the pathways involved in viral mechanisms and host responses. We describe the application of “dual RNA-seq” analysis to consider both the host and pathogen transcriptomes simultaneously, to investigate for the first time the co -regulation of human and SARS-CoV-2 genes. Together with differential expression analysis, we describe the tissue specificity of SARS-CoV-2 expression, an inferred lipopolysaccharide response, and co-regulation of CXCL’s, SPRR’s, S100’s with SARS-CoV-2 expression. Lipopolysaccharide response pathways in particular offer promise for future therapeutic research and the prospect of subgrouping patients based on chemokine expression that may help explain the vastly different reactions patients have to infection. Taken together these findings illuminate previously unappreciated SARS-CoV-2 expression signatures, identify new therapeutic considerations, and contribute a pipeline for studying multi-transcriptome systems.
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SciScore for 10.1101/2021.02.09.430517: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Cell Lines Sentences Resources To be in these networks, an inclusion criteria was enforced where each gene and pathway displayed is found in at least two of the three SARS-CoV-2 infected sample types, namely BALF patient samples, NHBE infected cells, and A549 infected cells. A549suggested: NCI-DTP Cat# A549, RRID:CVCL_0023)Software and Algorithms Sentences Resources The human reference FASTA and ENSEMBL GTF for hg38 and the SARS-CoV-2 reference FASTA (NC_045512v2) and REFSeq GTF were downloaded from the UCSC Genome Browser 39. UCSC Genome Browsersuggested: (UCSC Genome Browser, RRID:SCR_005780)Following the creation … SciScore for 10.1101/2021.02.09.430517: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Cell Lines Sentences Resources To be in these networks, an inclusion criteria was enforced where each gene and pathway displayed is found in at least two of the three SARS-CoV-2 infected sample types, namely BALF patient samples, NHBE infected cells, and A549 infected cells. A549suggested: NCI-DTP Cat# A549, RRID:CVCL_0023)Software and Algorithms Sentences Resources The human reference FASTA and ENSEMBL GTF for hg38 and the SARS-CoV-2 reference FASTA (NC_045512v2) and REFSeq GTF were downloaded from the UCSC Genome Browser 39. UCSC Genome Browsersuggested: (UCSC Genome Browser, RRID:SCR_005780)Following the creation of the merged index, each sample was then mapped to the index using STAR to get transcription counts with the parameters outSAMtype, twopassMode, outFilterMultimapNmax, and quantMode set as ‘BAM SortedByCoordinate’, ‘Basic’, 1, and ‘GeneCounts’, respectively. STARsuggested: (STAR, RRID:SCR_015899)This generated a ‘ReadsPerGene’ for each sample, which was then used as input for DESeq2 count normalization and differential gene expression (DGE) determination 41. DESeq2suggested: (DESeq, RRID:SCR_000154)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
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