Lung transcriptome of a COVID-19 patient and systems biology predictions suggest impaired surfactant production which may be druggable by surfactant therapy

  1. Abul Bashar Mir Md. Khademul Islam
  2. Md. Abdullah-Al-Kamran Khan

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Abstract

An incomplete understanding of the molecular mechanisms behind impairment of lung pathobiology by COVID-19 complicates its clinical management. In this study, we analyzed the gene expression pattern of cells obtained from biopsies of COVID-19-affected patient and compared to the effects observed in typical SARS-CoV-2 and SARS-CoV-infected cell-lines. We then compared gene expression patterns of COVID-19-affected lung tissues and SARS-CoV-2-infected cell-lines and mapped those to known lung-related molecular networks, including hypoxia induced responses, lung development, respiratory processes, cholesterol biosynthesis and surfactant metabolism; all of which are suspected to be downregulated following SARS-CoV-2 infection based on the observed symptomatic impairments. Network analyses suggest that SARS-CoV-2 infection might lead to acute lung injury in COVID-19 by affecting surfactant proteins and their regulators SPD, SPC, and TTF1 through NSP5 and NSP12; thrombosis regulators PLAT, and EGR1 by ORF8 and NSP12; and mitochondrial NDUFA10, NDUFAF5, and SAMM50 through NSP12. Furthermore, hypoxia response through HIF-1 signaling might also be targeted by SARS-CoV-2 proteins. Drug enrichment analysis of dysregulated genes has allowed us to propose novel therapies, including lung surfactants, respiratory stimulants, sargramostim, and oseltamivir. Our study presents a distinct mechanism of probable virus induced lung damage apart from cytokine storm.

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  1. Version published to 10.1038/s41598-020-76404-8
  2. ScreenIT

    SciScore for 10.1101/2020.05.07.082297: (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
    SentencesResources
    Analysis of microarray expression data: Microarray expression data on SARS-CoV infected 2B4 cells or uninfected controls for 24 hrs obtained from Gene Expression Omnibus (GEO), accession: GSE17400 (https://www.ncbi.nlm.nih.gov/geo) [21].
    2B4
    suggested: None
    Software and Algorithms
    SentencesResources
    Analysis of microarray expression data: Microarray expression data on SARS-CoV infected 2B4 cells or uninfected controls for 24 hrs obtained from Gene Expression Omnibus (GEO), accession: GSE17400 (https://www.ncbi.nlm.nih.gov/geo) [21].
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Quality of microarray experiment (data not shown) was verified by Bioconductor package “arrayQualityMetrics v3.44.0” [22].
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    Differentially expressed (DE) between two experimental conditions were called using Bioconductor package Limma [23].
    Limma
    suggested: (LIMMA, RRID:SCR_010943)
    Probe annotations were converted to genes using in-house python script basing the Ensembl gene model (Biomart 99) [24].
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)
    We have checked the raw sequence quality using FastQC program (v0.11.9) [26] and found that the “Per base sequence quality”, and “Per sequence quality scores” were high over threshold for all sequences (data not shown).
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    Mapping of reads was done with TopHat (tophat v2.1.1 with Bowtie v2.4.1) [27].
    tophat
    suggested: (TopHat, RRID:SCR_013035)
    Bowtie
    suggested: (Bowtie, RRID:SCR_005476)
    After mapping, we used SubRead package featureCount (v2.21) [31] to calculate absolute read abundance (read count, rc) for each transcript/gene associated to the Ensembl genes.
    SubRead
    suggested: (Subread, RRID:SCR_009803)
    For differential expression (DE) analysis we used DESeq2 (v1.26.0) with R (v3.6.2; 2019-07-05) [32] that uses a model based on the negative binomial distribution.
    DESeq2
    suggested: (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)” [22].
    R/Bioconductor
    suggested: None
    We have utilized the Gene Ontology Biological Processes (GOBP) [37], Reactome pathway [38], Bioplanet pathways [39], HumanCyc database [40], DisGeNet [41], KEGG pathway [42] modules, and a custom in house built combined module (Supplementary file 4) for the overrepresentation analysis.
    HumanCyc
    suggested: None
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Mapping of the deregulated genes in cellular pathways: We have utilized Reactome pathway browser [38] for the mapping of deregulated genes of SARS-CoV-2 infection in different cellular pathways.
    Reactome pathway browser
    suggested: None
    Obtaining the transcription factors which can modulate the differential gene expression: We have obtained the transcription factors (TFs) which bind to the given differentially expressed genes using a custom TFs module created using ENCODE [43], TRRUST [44], and ChEA [45] database.
    ENCODE
    suggested: (Encode, RRID:SCR_015482)
    ChEA
    suggested: (ChEA, RRID:SCR_005403)
    Obtaining human miRNAs target genes: We extracted the experimentally validated target genes of human miRNAs from miRTarBase database [46].
    miRTarBase
    suggested: (miRTarBase, RRID:SCR_017355)
    Extraction of transcription factors modulate human miRNA expression: We have downloaded the experimentally validated TFs which bind to miRNA promoters and module it from TransmiR (v2.0) database which provides regulatory relations between TFs and miRNAs [47].
    TransmiR
    suggested: (TransmiR, RRID:SCR_017499)
    Identification of the host epigenetic factors genes: We used EpiFactors database [48] to find human genes related to epigenetic activity.
    EpiFactors
    suggested: (EpiFactors , RRID:SCR_016956)
    Construction of biological networks: Construction, visualization and analysis of biological networks with differentially expressed genes, their associated transcription factors, associated human miRNAs, and interacting viral proteins were executed in the Cytoscape software (v3.8.0) [49].
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    We used STRING [50] database to extract highest confidences (0.9) edges only for the protein-protein interactions to reduce any false positive connection.
    STRING
    suggested: (STRING, RRID:SCR_005223)

    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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  3. Version published to 10.1101/2020.05.07.082297 on bioRxiv