Comparative in vitro transcriptomic analyses of COVID-19 candidate therapy hydroxychloroquine suggest limited immunomodulatory evidence of SARS-CoV-2 host response genes
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Abstract
Hydroxychloroquine (HCQ) has emerged as a potential and controversial antiviral candidate therapy for COVID-19. While many clinical trials are underway to test the efficacy of HCQ as a treatment for COVID-19, underlying mechanisms of HCQ in the setting of COVID-19 remain unclear. Hence, we examined differential gene expression signatures of HCQ exposure, in vitro SARS-CoV-2 infection, and host signatures of COVID-19 in blood, bronchoalveolar lavage, and postmortem lung to evaluate whether HCQ transcriptome signatures associate with restoration of SARS-CoV-2-related host transcriptional responses. Here, we show that 24 hours of in vitro treatment of peripheral blood mononuclear cells(PBMC) with HCQ significantly impacted transcription of 16 genes involved in immune regulation and lipid metabolism. Using transcriptome data from in vitro SARS-CoV-2 infected NHBE and A549 cells and PBMC derived from confirmed COVID-19 infected patients, we determined that only 0.24% of the COVID-19 PBMC differentially expressed gene set and 0.39% of the in vitro SARS-CoV-2 cells differentially expressed gene set overlapped with HCQ-related differentially expressed genes. Moreover, we observed that HCQ treatment significantly impacted transcription of 159 genes in human primary monocyte-derived macrophages involved in cholesterol biosynthetic process and chemokine activity. Notably, when we compared the macrophage HCQ-related gene lists with genes transcriptionally altered during SARS-CoV-2 infection and in bronchoalveolar lavage of COVID-19+ patients, the CXCL6 gene was impacted in all three transcriptional signatures revealing evidence in favor of chemokine modulation. HCQ-related transcriptional changes minimally overlapped with host genes altered in postmortem lung biopsies from COVID-19 participants. These results may provide insight into the immunomodulation mechanisms of HCQ treatment in the setting of COVID-19 and suggest HCQ is not a panacea to SARS-CoV-2 infection.
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SciScore for 10.1101/2020.04.13.039263: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Comparative analysis strategy: Our comparative analysis utilized Excel, R, and Graphpad Prism software. Graphpad Prismsuggested: (GraphPad Prism, RRID:SCR_002798)RNA-Seq gene expression raw FASTQ or gene count data was obtained from the Gene Expression Omnibus (GEO) and BigD database. Gene Expression Omnibussuggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)Differential Expression: RNA-Seq count data was read into R using Bioconductor and rowSums >1 were filtered to remove … SciScore for 10.1101/2020.04.13.039263: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Comparative analysis strategy: Our comparative analysis utilized Excel, R, and Graphpad Prism software. Graphpad Prismsuggested: (GraphPad Prism, RRID:SCR_002798)RNA-Seq gene expression raw FASTQ or gene count data was obtained from the Gene Expression Omnibus (GEO) and BigD database. Gene Expression Omnibussuggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)Differential Expression: RNA-Seq count data was read into R using Bioconductor and rowSums >1 were filtered to remove lowly expressed genes. Bioconductorsuggested: (Bioconductor, RRID:SCR_006442)Differential expression was conducted with edgeR and adjusted for P value using the FDR method option. edgeRsuggested: (edgeR, RRID:SCR_012802)Heatmaps and unsupervised clustering utilized the pheatmap R package. pheatmapsuggested: (pheatmap, RRID:SCR_016418)RNA-Seq Cell Deconvolution: COVID-19 and uninfected control gene expression data from bulk PBMC, alveolar lavage, and postmortem lung biopsies were analyzed using the CIBERSORT [25]analytical tool to provide an estimate of the abundances of immune cell types in the mixed cell/tissue population. CIBERSORTsuggested: (CIBERSORT, RRID:SCR_016955)SARS-CoV-2 Viral Detection in Bulk RNA-Seq Data: Reads were aligned with STAR[35] 2.7.3a in two-pass quantification mode. STAR[35suggested: NoneA custom reference was generated comprising Ensembl GrCh38 release 98 and SARS-CoV-2 2019-nCoV/USA-WA1/2020 (NCBI Nucleotide MN985325.1) to detect viral RNA. Ensemblsuggested: (Ensembl, RRID:SCR_002344)Results from OddPub: Thank you for sharing your data.
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.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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