Biology subjects

Thornburg, K.

Publications and source records attributed to Thornburg, K..

3 recordsLinked to original sources

bioRxiv2026

Lachesin is a mosquito receptor for multiple arthritogenic alphaviruses

Arthritogenic alphaviruses cause acute febrile illnesses associated with rash and arthritis when they are transmitted to humans through the bite of infected mosquitoes. Among these, chikungunya virus (CHIKV), transmitted primarily through the bite of infected Aedes aegypti and Aedes albopictus mosquitoes, causes explosive outbreaks involving hundreds of thousands to millions of cases, with recent re-emergence in several global regions. While several cellular receptors that mediate alphavirus entry into mammalian cells have been identified, their mosquito counterparts remained unknown, largely due to a lack of functional genomics tools for these invertebrate species. Here, we established a CRISPR-based genetic screening platform in Aedes albopictus cells and used it to identify Lachesin, a conserved invertebrate cell adhesion molecule, as a receptor for CHIKV and multiple related alphaviruses including Semliki Forest virus (SFV), onyong-nyong virus (ONNV), Mayaro virus (MAYV), and Ross River virus (RRV). Lachesin depletion using RNA interference, anti-Lachesin antibody treatment, and soluble forms of Lachesin blocked CHIKV and SFV E2-E1 glycoprotein-mediated infection of mosquito cells. We show that alphavirus E2-E1 glycoproteins bind the first immunoglobulin domain of Lachesin, facilitating attachment and internalization of virus-like particles. Orthologs from divergent mosquito genera, but not from arachnids or other arthropods, also serve as alphavirus receptors, suggesting that cellular receptor binding is not the main obstacle to arthritogenic alphavirus vector host expansion. Our findings enhance understanding of the mechanisms of alphavirus emergence and vector competence and could aid in the development of broadly active, entry-targeted therapeutics against multiple alphaviruses that threaten public health.

microbiology↗

bioRxiv2025

OocystMeter, a machine-learning algorithm to count and measure Plasmodium oocysts, reveals clustering patterns in the Anopheles midgut

We present OocystMeter, a machine learning-based software developed to automate the segmentation of malaria oocysts from images of mosquito midguts stained with mercurochrome. Existing bioimage analysis tools, including machine learning-based ones, often struggle with the unique staining patterns, complex midgut backgrounds, and variable morphology of oocysts, making the determination of oocyst size and numbers cumbersome. To overcome these challenges, we curated a high-quality dataset comprised of 11,178 Plasmodium falciparum oocysts in Anopheles gambiae midguts annotated by expert parasitologists. Using this dataset, we fine-tuned a Mask R-CNN object detection model to achieve segmentation accuracy comparable to human parasitologists (Spearmans correlation of 0.998 for oocyst counts and 0.978 for size measurements). Applying this tool in conjunction with spatial analysis, we uncovered a non-random, clustered spatial distribution of oocysts independent of the midguts anatomical regions or geometric axes, particularly in infections with fewer than 75 oocysts/midgut. Our workflow significantly accelerates malaria oocyst intensity and size analysis, reduces human bias, and provides spatial coordinates for advanced parasitology studies. OocystMeter is freely available at https://github.com/duopeng/OocystMeter, and as a web tool at http://Oocystmeter.org/, offering a valuable resource for researchers investigating the oocyst stage of malaria development.

bioinformatics↗

bioRxiv2020

Multiscale cardiac imaging to capture the whole heart and its internal cellular architecture, with applications to congenital heart disease

Efficient cardiac pumping depends on the morphological structure of the heart, but also on its sub-cellular (ultrastructural) architecture, which enables cardiac contraction. In cases of congenital heart defects, localized sub-cellular disruptions in architecture that increase the risk of heart failure are only starting to be discovered. This is in part due to a lack of technologies that can image the three dimensional (3D) heart structure, assessing malformations; and its ultrastructure, assessing disruptions. We present here a multiscale, correlative imaging procedure that achieves high-resolution images of the whole heart, using 3D micro-computed tomography (micro-CT); and its ultrastructure, using 3D scanning electron microscopy (SEM). This combination of technologies has not been possible before in imaging the same cardiac sample due to the heart large size, even when studying small fetal and neonatal animal models (~5x5x5mm3). Here, we achieved uniform fixation and staining of the whole heart, without losing ultrastructural preservation (at the nm resolution range). Our approach enables multiscale studies of cardiac architecture in models of congenital heart disease and beyond.

developmental biology↗