key: cord-0320200-iq2tprcc
authors: Becker, Daniel J.; Lei, Guang-Sheng; Janech, Michael G.; Brand, Alison M.; Fenton, M. Brock; Simmons, Nancy B.; Relich, Ryan F.; Neely, Benjamin A.
title: Serum proteomics identifies immune pathways and candidate biomarkers of coronavirus infection in wild vampire bats
date: 2022-01-27
journal: bioRxiv
DOI: 10.1101/2022.01.26.477790
sha: 9e8f4fdab58b3c5ab21c949c297afe6f7061fffc
doc_id: 320200
cord_uid: iq2tprcc

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 vampire bat sequences 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.

driven by distinct immune responses that coevolved with mammalian flight and the exceptional 23 longevity of this order. Yet our understanding of the immune mechanisms of viral tolerance is 24 restricted to a small number of bat-virus relationships and remains poor for coronaviruses 25 (CoVs), despite their relevance to human health. Proteomics holds particular promise for 26 illuminating the immune factors involved in bat responses to infection, because it can 27 accommodate especially low sample volumes (e.g., sera) and thus can be applied to both large 28 and small bat species as well as in longitudinal studies where lethal sampling is necessarily 29 limited. Further, as the serum proteome includes proteins secreted from not only blood cells but 30 also proximal organs, it provides a more general characterization of immune proteins. Here, we 31 expand our recent work on the serum proteome of wild vampire bats (Desmodus rotundus) to 32 better understand CoV pathogenesis. Across 19 bats sampled in 2019 in northern Belize with 33 available sera, we detected CoVs in oral or rectal swabs from four individuals (21.1% positivity). 34 Phylogenetic analyses identified all vampire bat sequences as novel α-CoVs most closely related 35 to known human CoVs. Across 586 identified serum proteins, we found no strong differences in 36 protein composition nor abundance between uninfected and infected bats. However, receiver 37 operating characteristic curve analyses identified seven to 32 candidate biomarkers of CoV 69 have also been ascribed to bats, but spillover has typically involved intermediate hosts rather shedding without substantial weight loss or pathology, supporting viral tolerance, although some 77 species seem to entirely resist infection. Tolerance appears driven by innate immune processes, 78 such as increased expression of ISGs, with little adaptive immune response. In vitro studies have 79 further supported bat receptor affinity for CoVs (i.e., susceptibility) but little host inflammatory included in this study, we used a harp trap and mist nets to capture bats upon emergence from a 125 tree roost. All individuals were issued a unique incoloy wing band (3.5 mm, Porzana Inc) and 126 identified by sex, age, and reproductive status. For sera, we collected blood by lancing the 127 propatagial vein with 23-gauge needles followed by collection with heparinized capillary tubes.

Blood was stored in serum separator tubes (BD Microtainer) for 10−20 minutes before 129 centrifugation. Following recent CDC guidelines, all sera were inactivated for importation to the 130 United States by heating at 56 °C for one hour. We also collected saliva and rectal samples using 131 sterile miniature rayon swabs (1.98 mm; Puritan) stored in DNA/RNA Shield (Zymo Inc).

Samples were held at −80 °C using a cryoshipper (LabRepCo) prior to long-term storage.

Bleeding was stopped with styptic gel, and all bats were released at their capture location. In addition to profiling serum from the 19 bats described above, we also performed another Table S1 . Peptide mixtures were analyzed using an UltiMate 3000 176 Nano LC coupled to a Fusion Lumos Orbitrap mass spectrometer (ThermoFisher Scientific). A 177 trap elute setup was used with a PepMap 100 C18 trap column (ThermoFisher Scientific) 178 followed by separation on an Acclaim PepMap RSLC 2 µm C18 column (ThermoFisher 179 Scientific) at 40 °C. Following 10 minutes of trapping, peptides were eluted along a 60 minute 180 two-step gradient of 5-30% mobile phase B (80% acetonitrile volume fraction, 0.08% formic 181 acid volume fraction) over 50 minutes, followed by a ramp to 45% mobile phase B over 10 182 minutes, ramped to 95% mobile phase B over 5 minutes, and held at 95% mobile phase B for 5 183 minutes, all at a flow rate of 300 nL per minute. The data-independent acquisition (DIA) settings 184 are briefly described here. The full scan resolution using the orbitrap was set at 120000, the mass we thus used UniProt identifiers from chimpanzee, cow, horse, mouse, and pig (Table S2) .

Proteomic data analyses 219 The final data matrix of relative protein abundance for all identified proteins was stratified into 220 two datasets for differential analysis: (i) the four 2015 samples analyzed before and after heat 221 inactivation (Table S3) (Table S4 ). Our analysis also included a pooled serum sample as a quality control between the 223 two digestion batches (Table S2) For the CoV infection analyses, we first reduced dimensionality of our protein dataset 232 using a principal components analysis (PCA) of all identified proteins, with abundances scaled 233 and centered to have unit variance. We then used a permutation multivariate analysis of variance 234 (PERMANOVA) with the vegan package to test for differences in protein composition between 235 uninfected and infected bats (Dixon, 2003) . Next, we used a two-sided Wilcoxon rank sum test 236 in MATLAB to detect differentially abundant proteins between uninfected and infected bats. We 237 again used the BH correction to adjust for the inflated false discovery rate. We also calculated 238 LFC as the difference of mean log2-transformed counts between uninfected and infected bats. To 239 next identify candidate biomarkers of CoV infection, we used receiver operating characteristic 240 8 (ROC) curve analysis. We used a modified function (https://github.com/dnafinder/roc) in 241 MATLAB to generate the area under the ROC curve (AuROC) as a measure of classifier 242 performance with 95% confidence intervals, which we calculated with standard error, α = 0.05, 243 and a putative optimum threshold closest to 100% sensitivity and specificity (Hanley and 244 McNeil, 1982; Pepe, 2003) . We considered proteins with AuROC ≥ 0.9 to be strict classifiers of 245 CoV positivity, whereas proteins with AuROC ≥ 0.8 but less than 0.9 were considered less 246 conservative; all other proteins were considered to be poor classifiers (Mallick et al., 2007) . Bottom-up proteomics using DIA identified 586 proteins in the 19 vampire bat sera samples, 282 with relative quantification covering 5.6 orders of magnitude ( Fig. 2A; Table S4 ). The overall were included in our analysis here; Fig. S1 ) and similar protein ranks (Table S5 ). Although the 288 prior and current study have low sample sizes (n = 17 and 19, respectively) and were sampled 289 across different years, the similarity in protein abundance, composition, and ranks suggest that Effects of heat inactivation 295 One key difference between analyses here and our prior proteomic study of this vampire bat 296 population is unknown technical artifacts from heat inactivation. To assess these possible effects, 297 we compared proteomes before and after treatment of four serum samples used in our prior study 298 (Neely et al., 2020). Using a moderated t-test of the four paired sera samples, 34 proteins showed 299 significant changes after heat inactivation (unadjusted p < 0.05), but no differences remained 300 after BH adjustment (even using a liberal adjusted p < 0.3). Although we found no statistically 301 significant changes in protein abundance with heat inactivation, we observed a mean 28% 302 absolute change across the proteome, with a maximum mean 500% absolute change. Most 303 proteins (52.6%) changed less than 17% in response to heat inactivation ( Fig. S2 ; Table S3 ).

Mining for CoV proteins 306 Given prior proteomic identification of putative viral proteins in undepleted serum, including 307 CoVs (Neely et al., 2020), we broadened our search space to include any CoV proteins. As 308 observing non-host proteins is a rare event, we used additional stringent criteria to verify any 309 initial CoV peptide spectral matches (see Supplemental Material). Of the 749 CoV peptide 310 spectral matches, none passed these more stringent criteria (Table S6 ). Thus we cannot firmly 311 say that viral proteins were identified in this set of undepleted sera, regardless of CoV status. To assess differences in the serum proteome between CoV-infected and uninfected bats, we first 315 used multivariate tests. Across the 586 identified proteins, the first two PCs explained 25.46 % of 316 the variance in serum proteomes (Fig. S3) . A PERMANOVA found no difference in proteome 317 composition by viral infection status (F1,17 = 0.99, R 2 = 0.05, p = 0.46), although variation was 318 greater in infected bats. Using Wilcoxon rank sum tests, we initially identified 22 proteins with 319 significantly different abundance in CoV-infected bats (unadjusted p < 0.05), but no differences 320 likewise remained after BH adjustment (even using a liberal adjusted p < 0.3; Fig. 2B ).

In contrast to multivariate and differential abundance tests, ROC curve analyses 322 identified 32 candidate protein biomarkers of CoV infection using strict (n = 7, AuROC ≥ 0.9) 323 and less-conservative (n = 25, 0.9 > AuROC ≥ 0.8) classifier cutoffs (Fig. 2) . Considering the Fig. 2C ). The total 32 candidate biomarkers provided clear discriminatory power in 332 differentiating the phenotypes of uninfected and infected vampire bats (Fig. 3) . 333 We lastly interrogated up-and down-regulated responses to CoV infection using GO We here focused this initial study on CoVs, which have been previously characterized as CoVs have been more readily detected in bat feces and saliva than in blood .

Using our novel α-CoVs, we then tested for differential composition and abundance of 417 serum proteins between uninfected and infected vampire bats. In both cases, we found negligible 418 overall differences in serum proteomes with CoV infection. However, such null results should be 419 qualified by the challenges posed to differential abundance tests by sample imbalance, given the 420 small number of infected relative to uninfected bats (Yang et al., 2006) . To partly address this 421 imbalance, we used ROC curve analyses to identify proteins with strict (AuROC ≥ 0.9; n = 7) isotypes) as another putative biomarker. In humans, lower complement C4A and C3 can signal 434 elevated autoimmunity (Walport, 2002; Wang and Liu, 2021) , and decreased complement C4 435 and C3 in COVID-19 patients also corresponds to disease severity (Zinellu and Mangoni, 2021) . 436 The processes that shape serum complement, namely complement synthesis, activation, and 437 clearance, remain poorly characterized in bats , but the identification of C4A 438 as a classifier could suggest specific explorations into how complement affects CoV infection.

Other candidate biomarkers also had more direct implications for the antiviral response in In addition to identifying candidate biomarkers, we also leveraged these proteins to more 457 generally assess broad up-and downregulated biological processes with CoV infection through 458 enrichment analyses. Using all candidate biomarkers, we found that CoV-infected bats displayed 459 downregulation of the complement system, regulation of proteolysis, immune effector processes, 

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