Biology subjects

Bartholomay, L. C.

Publications and source records attributed to Bartholomay, L. C..

4 recordsLinked to original sources

bioRxiv2026

High-throughput phenotypic profiling of insecticide responses in mosquito larvae

Extensive use of insecticides is increasing resistance risks that could severely reduce the control of mosquito vectors that transmit infectious agents of Neglected Tropical Diseases. Addressing resistance threats by discovering new insecticides can be challenging because of the limited throughput and translatability of existing in vitro screening pipelines. Acquiring multiple phenotypic endpoints of larvae could alleviate these restrictions by more thoroughly profiling drug effects on whole organisms, providing leads to compounds with novel mechanisms of action, and increasing screening throughput compared to adult-stage assays. Here, we establish a pair of assays for profiling motility and development traits in larval-stage mosquitoes at a higher scale than standard larvicidal screening techniques. We optimized assay parameters and developed novel image processing approaches that enable relatively high throughput screening of chemical compounds on single larvae within a screen with condition replicates. We tested the assay with an insect growth regulator (S-methoprene), a slow-acting pyrrole (chlorfenapyr), and two microbial larvicides (Spinosad and Lysinibacillus sphaericus). These measurements aligned well with known insecticide mechanisms, and dose response curves established assay baselines for comparison in future screens. Finally, we present ways in which the assay design can be modified across different imaging technologies, showing the flexibility of the screening approach.

pharmacology and toxicology↗

bioRxiv2021

Identification of public submitted tick images: a neural network approach

Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated and by providing researchers and public health agencies with better data on tick activity and geographic range. We report the development and validation of a convolutional neural network, a type of deep learning algorithm, trained on a dataset of more than 12,000 user-generated tick images. The model, which we call "TickIDNet," is trained to identify the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. At baseline, TickIDNet scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the networks ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNets performance can be increased by using confidence thresholds to introduce an "unsure" class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.

bioinformatics↗

bioRxiv2019

Genetic and functional diversification of chemosensory pathway receptors in filarial nematode parasites

Lymphatic filariasis (LF) afflicts over 60 million people worldwide and leads to severe pathological outcomes in chronic cases. The nematode parasites (Nematoda: Filarioidea) that cause LF require both arthropod (mosquito) intermediate hosts and mammalian definitive hosts for their propagation. The invasion and migration of filarial worms through host tissues are complex and critical to survival, yet little is known about the receptors and signaling pathways that mediate directed migration in these medically important species. In order to better understand the role of chemosensory signaling in filarial worm taxis, we employ comparative genomics, transcriptomics, reverse genetics, and chemical approaches to identify putative chemosensory receptor proteins and perturb chemotaxis phenotypes in filarial worms. We find that chemoreceptor family size is correlated with the presence of environmental (extra-host) stages in nematode life cycles, and that filarial worms contain a compact and highly-diverged chemoreceptor complement and lineage-specific ion channels that are predicted to operate downstream of chemoreceptor activation. In Brugia malayi, an etiological agent of LF, chemoreceptor expression patterns correspond to distinct parasite migration events across the life cycle. To interrogate the role of chemosensation in the migration of larval worms, arthropod infectious stage (microfilariae) and mammalian infectious stage (L3) Brugia parasites were incubated in nicotinamide, an agonist of the nematode transient receptor potential (TRP) channel OSM-9. Exposure of microfilariae to nicotinamide alters intra-mosquito migration while exposure of L3s reduces chemotaxis towards host-associated cues in vitro. Nicotinamide also potently modulates thermosensory responses in L3s, suggesting a polymodal sensory role for Brugia osm-9. Reverse genetic studies implicate both Brugia osm-9 and the cyclic nucleotide-gated (CNG) channel subunit tax-4 in larval chemotaxis towards host serum, and these ion channel subunits rescue sensory defects in C. elegans osm-9 and tax-4 knock-out strains. Together, these data reveal genetic and functional diversification of chemosensory signaling proteins in filarial worms, and encourage a more thorough investigation of clade and parasite-specific facets of nematode sensory receptor biology.

microbiology↗

bioRxiv2018

Long-term surveillance defines spatial and temporal patterns implicating Culex tarsalis as the primary vector of West Nile virus in Iowa, USA

West Nile virus (WNV) has become the most epidemiologically important mosquito-borne disease in the United States, causing [~]50,000 cases since its introduction in 1999. Transmitted primarily by Culex species, WNV transmission requires the complex interplay between bird reservoirs and mosquito vectors, with human cases the result of epizootic spillover. To better understand the intrinsic factors that drive these interactions, we have compiled infection data from sentinel chickens, mosquito vectors, and human cases in Iowa over a 15 year period (2002-2016) to better understand the spatial and temporal components that drive WNV transmission. Supplementing these findings with mosquito abundance, distribution, and host preferences data, we provide strong support that Culex tarsalis is the most important vector of human WNV infections in the region. Finally, we identify underlying climatic factors (temperature and drought) that are associated with inter-annual trends in WNV intensity. Together, our analysis provides new insights into WNV infection patterns in multiple hosts and highlights the importance of long-term surveillance to understand the dynamics of mosquito-borne-disease transmission.

epidemiology↗