Metabolic dyshomeostasis induced by SARS-CoV-2 structural proteins reveals immunological insights into viral olfactory interactions

  1. Mercedes Lachén-Montes
  2. Naroa Mendizuri
  3. Karina Ausín
  4. Miriam Echaide
  5. Ester Blanco
  6. Luisa Chocarro
  7. María de Toro
  8. David Escors
  9. Joaquín Fernández-Irigoyen
  10. Grazyna Kochan
  11. Enrique Santamaría

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Abstract

One of the most common symptoms in COVID-19 is a sudden loss of smell. SARS-CoV-2 has been detected in the olfactory bulb (OB) from animal models and sporadically in COVID-19 patients. To decipher the specific role over the SARS-CoV-2 proteome at olfactory level, we characterized the in-depth molecular imbalance induced by the expression of GFP-tagged SARS-CoV-2 structural proteins (M, N, E, S) on mouse OB cells. Transcriptomic and proteomic trajectories uncovered a widespread metabolic remodeling commonly converging in extracellular matrix organization, lipid metabolism and signaling by receptor tyrosine kinases. The molecular singularities and specific interactome expression modules were also characterized for each viral structural factor. The intracellular molecular imbalance induced by each SARS-CoV-2 structural protein was accompanied by differential activation dynamics in survival and immunological routes in parallel with a differentiated secretion profile of chemokines in OB cells. Machine learning through a proteotranscriptomic data integration uncovered TGF-beta signaling as a confluent activation node by the SARS-CoV-2 structural proteome. Taken together, these data provide important avenues for understanding the multifunctional immunomodulatory properties of SARS-CoV-2 M, N, S and E proteins beyond their intrinsic role in virion formation, deciphering mechanistic clues to the olfactory inflammation observed in COVID-19 patients.

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  1. Version published to 10.3389/fimmu.2022.866564
  2. ScreenIT

    SciScore for 10.1101/2022.02.01.478724: (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

    Antibodies
    SentencesResources
    After washing, a biotinylated anti-cytokine antibody mixture was added to the membranes followed by incubation with HRP-conjugated streptavidin and then exposed to the manufacturer’s peroxidase substrate.
    anti-cytokine
    suggested: None
    Recombinant DNA
    SentencesResources
    Genes were further cloned into pDUAL-PuroR lentivectors (Escors et al., 2008;Gato-Canas et al., 2017) under the transcriptional control of the SFFV promoter.
    pDUAL-PuroR
    suggested: None
    Software and Algorithms
    SentencesResources
    Sequencing libraries were prepared by following the Illumina Stranded Total RNA Prep with Ribo-Zero Plus (Illumina Inc., San Diego, CA) from 100 ng of total RNA, that has been depleted by following the instructions.
    Ribo-Zero Plus
    suggested: None
    The quality of the RNAseq results was initially assessed using FastQC v0.11.9 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and MultiQC v1.9 (http://multiqc.info/).
    FastQC
    suggested: (FastQC, RRID:SCR_014583)
    MultiQC
    suggested: (MultiQC, RRID:SCR_014982)
    The raw reads were trimmed, filtered for those with a Phred quality score of at least 25 and all adapters were removed with TrimGalore v0.5.0 (https://www.bioinformatics.brabraham.ac.uk/projects/trim_galore/).
    Phred
    suggested: (Phred, RRID:SCR_001017)
    TrimGalore
    suggested: None
    Trimmed reads were analyzed with SortMeRNA v2.1 software (https://bioinfo.lifl.fr/RNA/sortmerna/) (Kopylova et al., 2012) to delete the 18S and 28S rRNA to eliminate the rRNA residues that could remain undepleted by the chemical treatment in the library preparation.
    SortMeRNA
    suggested: (SortMeRNA, RRID:SCR_014402)
    Clean reads were aligned versus the Mus Musculus reference genome (release GRCm38.p6/GCA_000001635.8, ftp://ftp.ensembl.org) using HISAT2 v2.2.1 (https://daehwankimlab.github.io/hisat2/) (Kim et al., 2019) with default parameters.
    HISAT2
    suggested: (HISAT2, RRID:SCR_015530)
    Resulting alignment files were quality assessed with Qualimap2 (http://qualimap.bioinfo.cipf.es) (Okonechnikov et al., 2016) and sorted and indexed with Samtools software (Li et al., 2009).
    http://qualimap.bioinfo.cipf.es
    suggested: (QualiMap, RRID:SCR_001209)
    Samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    After taking a read count on gene features with the FeatureCounts tool (http://subread.sourceforge.net) (Liao et al., 2014), quantitative differential expression analysis between conditions was performed by DESeq2 (Love et al., 2014), implemented as R Bioconductor package, performing read-count normalization by following a negative binomial distribution model.
    FeatureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    http://subread.sourceforge.net
    suggested: (Subread, RRID:SCR_009803)
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Bioconductor
    suggested: (Bioconductor, RRID:SCR_006442)
    In order to automate this process and facilitate all group combination analysis, the SARTools pipeline (Varet et al., 2016) was used.
    SARTools
    suggested: (SARTools, RRID:SCR_016533)
    For this, the MS/MS spectra of the assigned peptides was extracted by ProteinPilot, and only the proteins that fulfilled the following criteria were validated: (1) peptide mass tolerance lower than 10 ppm, (2) 99% of confidence level in peptide identification, and (3) complete b/y ions series found in the MS/MS spectrum.
    ProteinPilot
    suggested: (ProteinPilot Software, RRID:SCR_018681)
    The quantitative data obtained by PeakView® were analyzed using Perseus software 1.6.14 version (Tyanova et al., 2016) for statistical analysis and data visualization.
    Perseus
    suggested: (Perseus, RRID:SCR_015753)
    MS data and search results files were deposited in the Proteome Xchange Consortium via the JPOST partner repository (https://repository.jpostdb.org) (Okuda et al., 2017) with the identifier PXD027645 for ProteomeXchange and JPST001274 for jPOST (for reviewers: https://repository.jpostdb.org/preview/6417285016102b4b16aaa0; Access key: 3355).
    ProteomeXchange
    suggested: (ProteomeXchange, RRID:SCR_004055)
    Interactome and pathway analysis were performed using Metascape (Zhou et al., 2019) and machine learning based bioinformatic QIAGEN’s Ingenuity Pathway Analysis (IPA, QIAGEN Redwood City,
    Metascape
    suggested: (Metascape, RRID:SCR_016620)
    Ingenuity Pathway Analysis
    suggested: (Ingenuity Pathway Analysis, RRID:SCR_008653)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Although our study has uncovered the existence of commonalities and differences in the SARS-CoV-2 structural protein functionality, potential limitations exist that warrant discussion. It is important to note that SARS-CoV-2 structural and non-structural protein interactions are absent in our study (Boson et al., 2021;Xu et al., 2021). We are aware that using our multi-omic approach, the molecular dimension generated by posttranslational modifications (i.e phosphorylation) not only in host proteins but also in the SARS-CoV-2 structural proteins (Stukalov et al., 2021) has not been considered in this study, hampering the characterization of potential substrates modulated by the virus-hijacked kinase activation profiles. Due to our experimental design, the overexpression of exogenous proteins may generate drawbacks concerning protein misfolding, localization and regulation as well as intrinsic limitations associated to GFP expression systems (Jensen, 2012). Based on the olfactory cellular system used, additional experiments are needed to verify the specific role of the SARS-CoV-2 structural proteome in different human olfactory cellular contexts (Lachen-Montes et al., 2020;Hatton et al., 2021). As shown in previous reports performed at cellular and tissular levels (Nie et al., 2021;Stukalov et al., 2021), the application of proteomics in different olfactory cell layers as well as in olfactory areas directly derived from COVID-19 individuals, would increase our understanding of ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2022.02.01.478724v1 on bioRxiv