Biology subjects

Cummings, C. A.

Publications and source records attributed to Cummings, C. A..

3 recordsLinked to original sources

bioRxiv2026

Bartonella and hemotropic Mycoplasma species in synanthropic bats in Kenya

Identifying and characterizing zoonotic pathogens in wildlife is essential for understanding disease risk to humans. In Sub-Saharan Africa, many people live with bats in their houses and are exposed to their pathogens, yet little is known about the bacterial pathogens in Afrotropical bat species. Globally, Bartonella spp. (bartonellae) and hemotropic Mycoplasma spp. (hemoplasmas) are common bacterial pathogens in bats, and some lineages are known to spill over and cause infections in humans. To evaluate this disease risk, we screened three common synanthropic bat species in Kenya, and their ectoparasites, for hemoplasmas and bartonellae and assessed their relatedness to known human pathogens. Of 767 bats across 21 sites, 17.9% of bats were Bartonella spp. positive and 19.3% were hemoplasma positive. Bat ectoparasites had similar Bartonella prevalence (13.5-25.0%) and, for most bat species, ectoparasite loads were not associated with increased likelihood of Bartonella infection. We found that Bartonella lineages displayed phylogenetic overlap between different bat species and ectoparasites, suggesting pathogen sharing between species, while hemoplasma lineages corresponded strictly to host taxonomy. Finally, we found that 16S rRNA sequences from one heart-nosed bat (Cardioderma cor) were 97.85% similar to a human-associated hemoplasma found previously in Schreibers bats (Miniopterus schreibersii) in Spain. We show that synanthropic bats host bacteria of potential public health concern, highlighting the need to investigate the emerging impacts of these pathogens on human health in Kenya and elsewhere in Sub-Saharan Africa.

ecology↗

bioRxiv2024

Viral epidemic potential is not uniformly distributed across the bat phylogeny

Characterizing host-virus associations is critical due to the rising frequency of emerging infectious diseases originating from wildlife. Past analyses have evaluated zoonotic risk as binary, but virulence and transmissibility can vary dramatically. Recent work suggests bats harbor more viruses with high virulence in humans than other taxa. However, it remains unknown whether all bats harbor viruses of equal zoonotic potential. We used phylogenetic factorization to flexibly identify clades of mammals (at any taxonomic level) associated with low or high viral epidemic potential, and found virulence and transmissibility only cluster within bat subclades, often among cosmopolitan families. Mapping the geographic distributions of these bat clades with spatial data on anthropogenic footprint suggests high zoonotic risk in coastal South America, Southeast Asia, and equatorial Africa. Our results deepen understanding of the host- virus network and identify clades to prioritize for viral surveillance, risk mitigation, and future studies characterizing mechanisms of viral tolerance.

evolutionary biology↗

bioRxiv2020

Aggregation Tool for Genomic Concepts (ATGC): A deep learning framework for sparse genomic measures and its application to tumor mutational burden

Deep learning can extract meaningful features from data given enough training examples. Large-scale genomic data are well suited for this class of machine learning algorithms; however, for many of these data the labels are at the level of the sample instead of at the level of the individual genomic measures. Conventional approaches to this data statically featurise and aggregate the measures separately from prediction. We propose to featurise, aggregate, and predict with a single trainable end-to-end model by turning to attention-based multiple instance learning. This allows for direct modelling of instance importance to sample-level classification in addition to trainable encoding strategies of genomic descriptions, such as mutations. We first demonstrate this approach by successfully solving synthetic tasks conventional approaches fail. Subsequently we applied the approach to somatic variants and achieved best-in-class performance when classifying tumour type or microsatellite status, while simultaneously providing an improved level of model explainability. Our results suggest that this framework could lead to biological insights and improve performance on tasks that aggregate information from sets of genomic data.

bioinformatics↗