Lung biopsy cells transcriptional landscape from COVID-19 patient stratified lung injury in SARS-CoV-2 infection through impaired pulmonary surfactant metabolism

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

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Abstract

Clinical management of COVID-19 is still complicated due to the lack of therapeutic interventions to reduce the breathing problems, respiratory complications and acute lung injury – which are the major complications of most of the mild to critically affected patients and the molecular mechanisms behind these clinical features are still largely unknown. In this study, we have used the RNA-seq gene expression pattern in the COVID-19 affected lung biopsy cells and compared it with the effects observed in typical cell lines infected with SARS-CoV-2 and SARS-CoV. We performed functional overrepresentation analyses using these differentially expressed genes to signify the processes/pathways which could be deregulated during SARS-CoV-2 infection resulting in the symptomatic impairments observed in COVID-19. Our results showed that the significantly altered processes include inflammatory responses, antiviral cytokine signaling, interferon responses, and interleukin signaling etc. along with downmodulated processes related to lung’s functionality like-responses to hypoxia, lung development, respiratory processes, cholesterol biosynthesis and surfactant metabolism. We also found that the viral protein interacting host’s proteins involved in similar pathways like: respiratory failure, lung diseases, asthma, and hypoxia responses etc., suggesting viral proteins might be deregulating the processes related to acute lung injury/breathing complications in COVID-19 patients. Protein-protein interaction networks of these processes and map of gene expression of deregulated genes revealed that several viral proteins can directly or indirectly modulate the host genes/proteins of those lung related processes along with several host transcription factors and miRNAs. Surfactant proteins and their regulators SPD, SPC, TTF1 etc. which maintains the stability of the pulmonary tissue are found to be downregulated through viral NSP5, NSP12 that could lead to deficient gaseous exchange by the surface films. Mitochondrial dysfunction owing to the aberration of NDUFA10, NDUFAF5, SAMM50 etc. by NSP12; abnormal thrombosis in lungs through atypical PLAT, EGR1 functions by viral ORF8, NSP12; dulled hypoxia responses due to unusual shift in HIF-1 downstream signaling might be the causative elements behind the acute lung injury in COVID-19 patients. Our study put forward a distinct mechanism of probable virus induced lung damage apart from cytokine storm and advocate the need of further research for alternate therapy in this direction.

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  1. 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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  2. Version published to 10.1101/2020.05.07.082297v1 on bioRxiv