Molecular testing of predicted bat hosts for zoonotic hemoplasmas

PROJECT SUMMARY
Most emerging infectious diseases that threaten human health originate in wildlife, such that identifying wild
species likely to harbor potentially zoonotic pathogens is critical for preempting or limiting spillover. Substantial
efforts have characterized zoonotic viruses in bats, which remain understudied for other pathogens such as
bacteria. Many bacteria are highly prevalent in bats and can cause chronic and often severe acute infections in
humans. Bats harbor diverse hemotropic mycoplasmas (hemoplasmas), including those that cause non-trivial
case-fatality rates (i.e., Candidatus Mycoplasma haemohominis; CMhh). Yet only 104 species of bats to date
have been tested for hemoplasmas, representing under 10% of bat diversity. Studies to date also suffer from
geographic and taxonomic biases, with most studies sampling bats in Latin America, Oceania, and Europe.
Molecular testing of samples from other bat species is necessary to understand the global distribution of
zoonotic hemoplasmas and identify where human risk is high. However, the broad diversity of bats makes this
goal challenging, and prioritization is needed to identify which bat species are most likely to harbor CMhh,
focusing on those that occur in human interfaces. This project will use recently validated machine learning
models to guide molecular testing of high-priority bat species for zoonotic hemoplasmas. We recently trained
boosted regression trees that classified bat species hosting any hemoplasma and CMhh-like infections with
90% and 84% accuracy, predicting 219 and 33 highly likely but yet-unsampled hosts. Under Aim 1, we will
extract DNA from blood, liver, and spleen from 50 of these predicted hosts, focusing on bat species that roost
in anthropogenic structures to capture an epidemiologically relevant interface for zoonotic spillover. We will
leverage our network of bat field sampling collaborators in the Americas, Africa, and Asia, including museum
collections, and use a multi-locus approach to uncover new hemoplasmas. We will use PCR to test DNA for
the hemoplasma 16S rRNA, 23S rRNA, and rpoB genes; for a subset of samples, we will also use our recent
success with selective whole-genome sequencing to generate bat hemoplasma genomes. Multi-locus and
genome phylogenies will identify putative hemoplasma species, including CMhh. Novel positivity data will feed
back into our machine learning models to generate revised, robust predictions to guide future surveillance. In
Aim 2, we will use our collection of vampire bat (Desmodus rotundus) samples to test if wing biopsy punches,
which are commonly collected from bats for isotopic and genomic studies, could serve as a plausible matrix for
hemoplasma testing given their high vein density. We will screen DNA from wing biopsies matched to bats
previously PCR-positive for hemoplasmas in blood to validate this tissue as a site of infection. If successful
(i.e., ≥ 50% matched positivity), we will solicit wing biopsies from our collaborators and use these to further test
predicted bat hosts for CMhh. Overall, our findings will uncover novel hemoplasma diversity and expand our
understanding of the geographic and taxonomic distribution of zoonotic lineages to inform human health risk.

Grant Number: 1R03AI188200-01A1
NIH Institute/Center: NIH
Principal Investigator: Daniel Becker