Serum proteomics of coronavirus shedding in vampire bats (Desmodus rotundus)
Bibliographic record
Abstract
Introduction Bats can harbor many pathogens without showing disease. However, the mechanisms by which bats resolve these infections or limit pathology remain unclear. To illuminate the bat immune response to coronaviruses, viruses with high public health significance, we will use serum proteomics to assess broad differences in immune proteins of uninfected and infected vampire bats (Desmodus rotundus). In contrast to global profiling techniques of blood such as transcriptomics, proteomics provides a unique perspective into immunology, as the serum proteome includes proteins from not only blood but also those secreted from proximal tissues. We will use enrichment analyses alongside generalized linear models of targeted serum proteins involved in important immune pathways to identify up- and downregulated bat immune responses to coronavirus shedding. Methods We sampled vampire bats in April 2019 in the Lamanai Archeological Reserve of northern Belize. For the 19 individuals included in this study, we used a harp trap and mist nets to capture bats upon leaving a single roost site. All individuals were identified by sex, age, and reproductive status. For serum proteomics, we collected blood by lancing the propatagial vein followed by collection into serum separator tubes prior to centrifugation and storage at −80 °C. Saliva and rectal swab samples were also collected and stored in DNA/RNA Shield for coronavirus detection. We used a semi-nested pan-coronavirus PCR targeting the RNA-dependent RNA polymerase gene (RdRp), followed by amplicon sequencing and NCBI BLAST to identify virus genera. Preliminary data Because CDC regulatory guidelines require any vampire bat samples to undergo heat inactivation (56 °C for 1 h) prior to importation into the U.S., we used four serum samples previously analyzed to evaluate the effects of this treatment on proteomic analysis. Since these prior samples had not been heat inactivated, a paired analysis of heat inactivation was possible (results not discussed in poster, will be in forthcoming paper). Next, using the 19 samples in the current coronavirus study, we used the S-trap method for tryptic digestion, followed by data-independent acquisition bottom-up proteomics at 300 nL/min with an Ultimate 3000 interfaced to a Fusion Lumos Orbitrap mass spectrometer. Data will be processed in a library-free manner with DIA-NN using the RefSeq annotated vampire bat genome. We identified 586 proteins over five orders of magnitude. Similar to our previous study, we converted the vampire bat protein identifications to human orthologs to aid in downstream analysis. Of our included 19 vampire bats, four were infected with coronaviruses, and phylogenetic analyses identified all as alphacoronaviruses specifically. We used receiver operator characteristic curves to identify candidate markers of alphacoronaviruses shedding status. Though this is a small sample set, it will provide invaluable information of the proteome changes, specifically immune related, in a natural host in response to alphacoronavirus. Novel aspect These results will provide much needed insight into changes in the bat serum proteome in response to coronavirus infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".