Biology subjects

Collins, M. H.

Publications and source records attributed to Collins, M. H..

3 recordsLinked to original sources

bioRxiv2025

Evidence of dengue virus transmission and a diverse Aedes mosquito virome on the Democratic Republic of Congo-Angola border

Aedes mosquitoes are widely distributed across the Democratic Republic of Congo (DRC), and are major vectors of dengue (DENV), Zika, chikungunya (CHIKV), and yellow fever (YFV) viruses. While the high burden of malaria in the DRC receives considerable attention, arboviruses remain understudied. In the setting of recent CHIKV and YFV outbreaks in southwestern DRC, we collected Aedes mosquitoes in three areas of Kimpese, DRC, near the Angola border, to investigate their virome. Metagenomic and targeted sequencing of eight randomly selected field mosquito pools, comprising 155 mosquitoes from three collection sites, confirmed high-confidence DENV reads and human blood meals in six (75%) and eight (100%) pools, respectively. We find diverse mosquito viromes including other known and putative human and animal viruses. Our findings provide strong evidence of endemic DENV transmission along the DRC-Angola border and illustrate the potential of wild-caught mosquitoes for xenosurveillance of emerging pathogens.

microbiology↗

bioRxiv2024

Spatiotemporal dispersion of DENV1 genotype V in western Colombia

Dengue virus (DENV) is a significant public health concern in Colombia, with increased transmission of DENV type 1 (DENV-1) in the departments of Risaralda and Valle del Cauca in the Central-West region of the country following a large outbreak in 2019. However, little is known about the source, genetic diversity, and evolution of circulating viruses. We obtained plasma samples from individuals with acute DENV infection and analyzed DENV-1 genetic diversity, phylodynamics, and phylogeography. We found that most viruses belonged to DENV-1 genotype V, and phylogenetic analysis revealed three distinct clades, each of which was most closely related to viruses from neighboring departments of Colombia sampled over the last 5-10 years. Thus, the 2019 outbreak and subsequent DENV-1 circulation was not due to the introduction of a new lineage but rather reflected local DENV-1V dispersion and evolution. We identified amino acid positions under positive selection in structural proteins and NS1, which may have a role in immune evasion and pathogenesis. Overall, our analysis of DENV1 genotype V diversity, evolution and spread within Colombia highlights the important role of genomic surveillance in understanding virus dynamics during endemic circulation and outbreaks.

evolutionary biology↗

bioRxiv2023

Machine Learning Based Identification and Characterization of Mast Cells in Eosinophilic Esophagitis

BackgroundEosinophilic esophagitis (EoE) is diagnosed and monitored using esophageal eosinophil levels; however, EoE also exhibits a marked, understudied esophageal mastocytosis. ObjectiveUsing machine learning, we localized and characterized esophageal mast cells to decipher their potential role in disease pathology. MethodsEsophageal biopsy samples (EoE, control) were stained for mast cells by anti-tryptase and imaged using immunofluorescence; high-resolution whole tissue images were digitally assembled. Machine learning software was trained to identify, enumerate, and characterize mast cells, designated Mast Cell-Artificial Intelligence (MC-AI). ResultsMC-AI enumerated cell counts with high accuracy. During active EoE, epithelial mast cells increased and lamina propria (LP) mast cells decreased. In controls and EoE remission patients, papillae had the highest mast cell density and negatively correlated with epithelial mast cell density. Mast cell density in the epithelium and papillae correlated with the degree of epithelial eosinophilic inflammation, basal zone hyperplasia, and LP fibrosis. MC-AI detected greater mast cell degranulation in the epithelium, papillae, and LP in EoE patients compared with control individuals. Mast cells were localized further from the basement membrane in active EoE than EoE remission and controls individuals but were closer than eosinophils to the basement membrane in active EoE. ConclusionUsing MC-AI, we identified a distinct population of homeostatic esophageal papillae mast cells; during active EoE, this population decreases, undergoes degranulation, negatively correlates with epithelial mast cell levels, and significantly correlates with distinct histologic features. Overall, MC-AI provides a means to understand the potential involvement of mast cells in EoE and other disorders. Clinical ImplicationWe have developed a methodology for identifying, enumerating, and characterizing mast cells using artificial intelligence; this has been applied to decipher eosinophilic esophagitis and provides a platform approach for other diseases. Capsule SummaryA machine learning protocol for identifying mast cells, designated Mast Cell-Artificial Intelligence, readily identified spatially distinct and dynamic populations of mast cells in EoE, providing a platform to better understand this cell type in EoE and other diseases.

immunology↗