Serum Proteomics Identifies Immune Pathways and Candidate Biomarkers of Coronavirus Infection in Wild Vampire Bats

  1. Daniel J. Becker
  2. Guang-Sheng Lei
  3. Michael G. Janech
  4. Alison M. Bland
  5. M. Brock Fenton
  6. Nancy B. Simmons
  7. Ryan F. Relich
  8. Benjamin A. Neely

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Abstract

The apparent ability of bats to harbor many virulent viruses without showing disease is likely driven by distinct immune responses that coevolved with mammalian flight and the exceptional longevity of this order. Yet our understanding of the immune mechanisms of viral tolerance is restricted to a small number of bat–virus relationships and remains poor for coronaviruses (CoVs), despite their relevance to human health. Proteomics holds particular promise for illuminating the immune factors involved in bat responses to infection, because it can accommodate especially low sample volumes (e.g., sera) and thus can be applied to both large and small bat species as well as in longitudinal studies where lethal sampling is necessarily limited. Further, as the serum proteome includes proteins secreted from not only blood cells but also proximal organs, it provides a more general characterization of immune proteins. Here, we expand our recent work on the serum proteome of wild vampire bats ( Desmodus rotundus ) to better understand CoV pathogenesis. Across 19 bats sampled in 2019 in northern Belize with available sera, we detected CoVs in oral or rectal swabs from four individuals (21.1% positivity). Phylogenetic analyses identified all RdRp gene sequences in vampire bats as novel α-CoVs most closely related to known human CoVs. Across 586 identified serum proteins, we found no strong differences in protein composition nor abundance between uninfected and infected bats. However, receiver operating characteristic curve analyses identified seven to 32 candidate biomarkers of CoV infection, including AHSG, C4A, F12, GPI, DSG2, GSTO1, and RNH1. Enrichment analyses using these protein classifiers identified downregulation of complement, regulation of proteolysis, immune effector processes, and humoral immunity in CoV-infected bats alongside upregulation of neutrophil immunity, overall granulocyte activation, myeloid cell responses, and glutathione processes. Such results denote a mostly cellular immune response of vampire bats to CoV infection and identify putative biomarkers that could provide new insights into CoV pathogenesis in wild and experimental populations. More broadly, applying a similar proteomic approach across diverse bat species and to distinct life history stages in target species could improve our understanding of the immune mechanisms by which wild bats tolerate viruses.

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  1. Version published to 10.3389/fviro.2022.862961
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    SciScore for 10.1101/2022.01.26.477790: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIACUC: Field protocols followed guidelines for safe and humane handling of bats from the American Society of Mammalogists (Sikes and Gannon, 2011) and were approved by the Institutional Animal Care and Use Committee of the American Museum of Natural History (AMNHIACUC-20190129).
    Field Sample Permit: Serum specimens used for proteomic analysis were approved by the National Institute of Standards and Technology Animal Care and Use Coordinator under approval MML-AR19-0018.
    Sex as a biological variablenot detected.
    RandomizationUsing the original sample randomization gave a randomized sample order, and injection volumes were determined for 0.5 μg loading (0.21–0.44 μL sample).
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Resulting sequences were aligned using Geneious (Biomatters; (Kearse et al., 2012), followed by analysis using NCBI BLAST (Altschul et al., 1990)
    NCBI BLAST
    suggested: (NCBI BLAST, RRID:SCR_004870)
    PhyML 3.0 was used to build a maximum-likelihood phylogeny of these and additional CoV sequences (Guindon et al., 2010).
    PhyML
    suggested: (PhyML, RRID:SCR_014629)
    The method file (85min_DIA_40×21mz.meth) and mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2022) partner repository with the dataset identifier PXD031075.
    PRIDE
    suggested: (Pride-asap, RRID:SCR_012052)
    To search the bat samples, we used the NCBI RefSeq Desmodus rotundus Release 100 GCF_002940915.1_ASM294091v2 FASTA (29,845 sequences).
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    To additionally search for CoV proteins (Neely et al., 2020), we performed a secondary search using the same settings and the addition of a Coronaviridae FASTA (117709 sequences) retrieved from UniProtKB (2021_03 release) using taxon identifier 1118 with all SwissProt and TrEMBL entries.
    UniProtKB
    suggested: (UniProtKB, RRID:SCR_004426)
    Our identified bat proteins were then mapped to human orthologs using BLAST+ (Camacho et al., 2009) and a series of python scripts described previously (Neely et al., 2020) to facilitate downstream analysis using human-centric databases (see Supplemental Material for full details).
    python
    suggested: (IPython, RRID:SCR_001658)
    These ratios were used in a moderated t-test with the limma package in R to evaluate protein changes within sera samples before and after heat treatment (Ritchie et al., 2015), followed by Benjamini–Hochberg (BH) correction (Benjamini and Hochberg, 1995).
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    We then used a permutation multivariate analysis of variance (PERMANOVA) with the vegan package to test for differences in protein composition between uninfected and infected bats (Dixon, 2003).
    vegan
    suggested: (vegan, RRID:SCR_011950)
    We used a modified function (https://github.com/dnafinder/roc) in MATLAB to generate the area under the ROC curve (AuROC) as a measure of classifier performance with 95% confidence intervals, which we calculated with standard error, α = 0.05, and a putative optimum threshold closest to 100% sensitivity and specificity (Hanley and McNeil, 1982; Pepe, 2003).
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)
    We also visualized the matrix of all candidate serum biomarkers with the pheatmap package, using log2-transformed protein abundances (scaled and centered around zero) and Ward’s hierarchical clustering method (Murtagh and Legendre, 2014; Kolde and Kolde, 2015).
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)
    Next, we used the gprofiler2 package as an interface to the g:Profiler tool g:GOSt for functional enrichment tests (Raudvere et al., 2019; Kolberg et al., 2020).
    g:Profiler
    suggested: (G:Profiler, RRID:SCR_006809)
    We restricted our data sources to GO biological processes, the Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways (WP).
    WikiPathways
    suggested: (WikiPathways, RRID:SCR_002134)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Additionally, our ability to detect viral proteins may have been further restricted by ongoing limitations in applying proteomics to wild species. In humans, over 3000 serum proteins can be detected by mass spectrometry after depletion of the most abundant proteins (Uhlén et al., 2019). However, using antibody-based depletion techniques is not an effective strategy in non-human mammals (Neely et al., 2014), such that undepleted serum proteomics in bats will be limited to the top 300–600 proteins, with false negatives for low abundance proteins such as those of viruses (Anderson and Anderson, 2002). Alternatively, lack of detection of CoV proteins in sera despite detection of CoV RNA in oral and rectal swabs could indicate tropism, as CoVs have been more readily detected in bat feces and saliva than in blood (Smith et al., 2016). Using our novel α-CoVs, we then tested for differential composition and abundance of serum proteins between uninfected and infected vampire bats. In both cases, we found negligible overall differences in serum proteomes with CoV infection. However, such null results should be qualified by the challenges posed to differential abundance tests by sample imbalance, given the small number of infected relative to uninfected bats (Yang et al., 2006). To partly address this imbalance, we used ROC curve analyses to identify proteins with strict (AuROC ≥ 0.9; n = 7) and less conservative (AuROC ≥ 0.8; n = 25) classifier ability for infection (Arthur et al., 201...

    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.

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

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  3. Version published to 10.1101/2022.01.26.477790v1 on bioRxiv