Host transcriptional responses and SARS-CoV-2 isolates from the nasopharyngeal samples of Bangladeshi COVID-19 patients
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
As the COVID-19 pandemic progresses, fatality and cases of new infections are also increasing at an alarming rate. SARS-CoV-2 follows a highly variable course and it is becoming more evident that individual’s immune system has a decisive influence on the progression of the disease. However, the detailed underlying molecular mechanisms of the SARS-CoV-2 mediate disease pathogenesis are largely unknown. Only a few host transcriptional responses in COVID-19 have been reported so far from the Western world, but no such data has been generated from the South-Asian region yet to correlate the conjectured lower fatality around this part of the globe. In this context, we aimed to perform the transcriptomic profiling of the COVID-19 patients from Bangladesh along with the reporting of the SARS-CoV-2 isolates from these patients. Moreover, we performed a comparative analysis to demonstrate how differently the various SARS-CoV-2 infection systems are responding to the viral pathogen. We detected a unique missense mutation at 10329 position of ORF1ab gene, annotated to 3C like proteinase, which is found in 75% of our analyzed isolates; but is very rare globally. Upon the functional enrichment analyses of differentially modulated genes, we detected a similar host induced response reported earlier; this response was mainly mediated by the innate immune system, interferon stimulation, and upregulated cytokine expression etc. in the Bangladeshi patients. Surprisingly, we did not perceive the induction of apoptotic signaling, phagosome formation, antigen presentation and production, hypoxia response within these nasopharyngeal samples. Furthermore, while comparing with the other SARS-CoV-2 infection systems, we spotted that lung cells trigger the more versatile immune and cytokine signaling which was several folds higher compared to our reported nasopharyngeal samples. We also observed that lung cells did not express ACE2 in a very high amount as suspected, however, the nasopharyngeal cells are found overexpressing ACE2 . But the amount of DPP4 expression within the nasal samples was significantly lower compared to the other cell types. Surprisingly, we observed that lung cells express a very high amount of integrins compared to the nasopharyngeal samples, which might suggest the putative reasons for an increased amount of viral infections in the lungs. From the network analysis, we got clues on the probable viral modulation for the overexpression of these integrins. Our data will provide valuable insights in developing potential studies to elucidate the roles of ethnicity effect on the viral pathogenesis, and incorporation of further data will enrich the search of an effective therapeutics.
Article activity feed
-
-
SciScore for 10.1101/2020.07.23.218198: (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 Data processing and identification of the viral agent: Firstly, the sequencing reads were adapter and quality trimmed using the Trimmomatic program [27]. Trimmomaticsuggested: (Trimmomatic, RRID:SCR_011848)The remaining reads were mapped against the SARS-CoV-2 reference sequence (NC_045512.2) using Bowtie 2 [28]. Bowtiesuggested: (Bowtie, RRID:SCR_005476)Mapping of the RNA-seq reads onto SARS-CoV-2 reference genome: We mapped the normalized (by count per million mapped reads-CPM) … SciScore for 10.1101/2020.07.23.218198: (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 Data processing and identification of the viral agent: Firstly, the sequencing reads were adapter and quality trimmed using the Trimmomatic program [27]. Trimmomaticsuggested: (Trimmomatic, RRID:SCR_011848)The remaining reads were mapped against the SARS-CoV-2 reference sequence (NC_045512.2) using Bowtie 2 [28]. Bowtiesuggested: (Bowtie, RRID:SCR_005476)Mapping of the RNA-seq reads onto SARS-CoV-2 reference genome: We mapped the normalized (by count per million mapped reads-CPM) RNA-seq reads onto the SARS-CoV-2 genome track of the UCSC genome browser [30] using the “bamCoverage” feature of deepTools2 suite [31]. UCSC genome browsersuggested: (UCSC Genome Browser, RRID:SCR_005780)Different representations showing the information regarding the variations were produced using the Microsoft Excel program [33]. Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)The impacts of the variations were further characterized utilizing the Ensembl Variant Effect Predictor (VEP) tool [34]. Ensembl Variant Effect Predictorsuggested: NoneVariantsuggested: (VARIANT, RRID:SCR_005194)We have checked the raw sequence quality using FastQC program (v0.11.9) [36] and found that the "Per base sequence quality", and "Per sequence quality scores" were high over the threshold for all sequences (Supplementary file 2). FastQCsuggested: (FastQC, RRID:SCR_014583)The mapping of reads was done with TopHat (tophat v2.1.1 with Bowtie v2.4.1) [37]. TopHatsuggested: (TopHat, RRID:SCR_013035)After mapping, we used the SubRead package featureCount (v2.21) [41] to calculate absolute read abundance (read count, rc) for each transcript/gene associated to the Ensembl genes. SubReadsuggested: (Subread, RRID:SCR_009803)Ensemblsuggested: (Ensembl, RRID:SCR_002344)For differential expression (DE) analysis we used DESeq2 (v1.26.0) with R (v3.6.2; 2019-07-05) [42] that uses a model based on the negative binomial distribution. DESeq2suggested: (DESeq, RRID:SCR_000154)To assess the fidelity of the RNA-seq data used in this study and normalization method applied here, we checked the normalized Log2 expression data quality using R/Bioconductor package “arrayQualityMetrics (v3.44.0)” [43]. R/Bioconductorsuggested: NoneWe also performed a multifactorial differential gene expression analysis using the edgeR tool [44] following the experimental design-(Sample A/control for sample A)/(Sample B/control for sample B). edgeRsuggested: (edgeR, RRID:SCR_012802)Firstly, the genome sequences were aligned using MAFFT [46] tool using the auto-configuration. MAFFTsuggested: (MAFFT, RRID:SCR_011811)Then we used MEGA X [47] for constructing the phylogenetic tree utilizing 500 bootstrapping with substitution model/method: maximum composite likelihood, uniform rates of variation among sites, the partial deletion of gaps/missing data and site coverage cutoff 95%. MEGAsuggested: (Mega BLAST, RRID:SCR_011920)We have utilized the Gene Ontology Biological Processes (GOBP) [49], Bioplanet pathways [50], KEGG pathway [51], and Reactome pathway [52] modules for the overrepresentation analysis. KEGGsuggested: (KEGG, RRID:SCR_012773)Construction of biological networks: Construction, visualization, and analysis of biological networks with differentially expressed genes, their associated transcription factors, and interacting viral proteins were executed in the Cytoscape software (v3.8.0) [54]. Cytoscapesuggested: (Cytoscape, RRID:SCR_003032)We used the STRING [55] database to extract the highest confidences (0.9) edges only for the protein-protein interactions to reduce any false positive connection. STRINGsuggested: (STRING, RRID:SCR_005223)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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 40, 38 and 39. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
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.
-