Single Nucleus Multiomic Profiling Reveals Age-Dynamic Regulation of Host Genes Associated with SARS-CoV-2 Infection

  1. Allen Wang
  2. Joshua Chiou
  3. Olivier B Poirion
  4. Justin Buchanan
  5. Michael J Valdez
  6. Jamie M Verheyden
  7. Xiaomeng Hou
  8. Minzhe Guo
  9. Jacklyn M Newsome
  10. Parul Kudtarkar
  11. Dina A Faddah
  12. Kai Zhang
  13. Randee E Young
  14. Justinn Barr
  15. Ravi Misra
  16. Heidie Huyck
  17. Lisa Rogers
  18. Cory Poole
  19. Jeffery A. Whitsett
  20. Gloria Pryhuber
  21. Yan Xu
  22. Kyle J Gaulton
  23. Sebastian Preissl
  24. Xin Sun
  25. NHLBI LungMap Consortium

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Abstract

Respiratory failure is the leading cause of COVID-19 death and disproportionately impacts adults more than children. Here, we present a large-scale snATAC-seq dataset (90,980 nuclei) of the human lung, generated in parallel with snRNA-seq (46,500 nuclei), from healthy donors of ~30 weeks, ~3 years and ~30 years of age. Focusing on genes implicated in SARS-CoV-2 cell entry, we observed an increase in the proportion of alveolar epithelial cells expressing ACE2 and TMPRSS2 in adult compared to young lungs. Consistent with expression dynamics, 10 chromatin peaks linked to TMPRSS2 exhibited significantly increased activity with age and harbored IRF and STAT binding sites. Furthermore, we identified 14 common sequence variants in age-increasing peaks with predicted regulatory function, including several associated with respiratory traits and TMPRSS2 expression. Our findings reveal a plausible contributor to why children are more resistant to COVID-19 and provide an epigenomic basis for transferring this resistance to older populations.

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    SciScore for 10.1101/2020.04.12.037580: (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
    Differential gene expression analysis between ACE2+ and ACE2- AT2 cells we used FindAllMarkers with parameters logfc = 0, min.pct = 0, test.use = “wilcox”, verbose = TRUE.
    ACE2- AT2
    suggested: None
    Experimental Models: Organisms/Strains
    SentencesResources
    The library was sequenced on a HiSeq4000 or NextSeq500 sequencer (Illumina) using custom sequencing primers with following read lengths: 50 + 10 + 12 + 50 (Read1 + Index1 + Index2 + Read2).
    Read1 + Index1 + Index2 + Read2
    suggested: None
    Software and Algorithms
    SentencesResources
    Final library concentration was assessed by Qubit dsDNA HS Assay Kit (Thermo-Fischer Scientific) and fragment size was checked using Tapestation High Sensitivity D1000 (Agilent) to ensure that fragment sizes were distributed normally about 500 bp.
    Thermo-Fischer Scientific
    suggested: None
    To analyze changes in percentage of nuclei expressing we performed One-way ANOVA (ANalysis Of VAriance) with post-hoc Tukey HSD (Honestly Significant Difference) using GraphPad Prism version 8.0.0 for Windows, GraphPad Software, San Diego, California USA, www.graphpad.com.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Raw gene expression matrices were downloaded from Gene Expression Omnibus (GEO) repository (GSE128033 and GSE122960).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    We aligned reads to the hg19 reference genome using bwa mem (v.0.7.17) (Li and Durbin, 2009) and subsequently used samtools (Li et al., 2009) to remove unmapped, low map quality (MAPQ<30), secondary, and mitochondrial reads.
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    Identification and clustering of AT2 peaks with changes in chromatin accessibility genome-wide: We used edgeR (Robinson et al., 2010) to identify differential accessible peaks between each of pair of time points.
    edgeR
    suggested: (edgeR, RRID:SCR_012802)
    Transcription factor related analyses: De novo motif enrichment analysis in genomic elements was performed using HOMER (Heinz et al., 2010) with standard parameters.
    HOMER
    suggested: (HOMER, RRID:SCR_010881)
    First, we extracted the sequences underlying AT2 sites that were promoter-distal (>±500 bp from GENCODE v19 transcript TSS for protein-coding and long non-coding RNA genes).
    GENCODE
    suggested: (GENCODE, RRID:SCR_014966)
    For each common variant, we obtained sequence surrounding each variant allele and predicted sequence motifs from the JASPAR database (Fornes et al., 2020) using FIMO (Grant et al., 2011), and focused on motifs of TF families enriched in age-dependent AT2 chromatin.
    JASPAR
    suggested: (JASPAR, RRID:SCR_003030)
    Deconvoluting the TMPRSS2 lung eQTL: We used MuSiC (v.0.1.1) (Wang et al., 2019) to estimate the proportions of lung cell types with >500 cells from our scRNA-seq dataset in lung bulk RNA-seq samples from the GTEx v8 release (Aguet et al., 2019).
    MuSiC
    suggested: (MuSiC, RRID:SCR_008792)

    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.
    • 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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  2. Version published to 10.1101/2020.04.12.037580v1 on bioRxiv