Biology subjects

Halligan, B.

Publications and source records attributed to Halligan, B..

3 recordsLinked to original sources

bioRxiv2026

A High-Content Pseudotime Framework for Morphological Profiling of Dengue Virus Inhibitors from a Drug-Repurposing Library

Dengue virus (DENV) causes dengue fever, a globally prevalent, epidemic-prone disease with no FDA-approved antivirals. Additionally, the available vaccine for DENV can increase the risk of severe dengue fever for those who have never had a DENV infection due to antibody-dependent enhancement. Thus, there is an urgent need to identify DENV antivirals. Drug repurposing, high-content screening, and machine learning were leveraged to efficiently identify antivirals likely to inhibit DENV replication. Using high-content screening in a DENV infection model, we characterized the morphological patterns of NS4B and envelope (E) protein expression over time and developed a viral pseudotime model that predicts infection progression to aid in phenotypic interpretation of drug effects. We then developed a single cell infection classifier for antiviral efficacy and performed high-throughput drug screening of 960 compounds. We identified four concentration-dependent inhibitors of DENV with nanomolar-to-submicromolar potencies: esomeprazole, GW4064, pralatrexate, and LY411575. With our pseudotime machine learning model trained on data from the time-series assay, we scored the concentration-dependent inhibitors to map their mechanism of action onto time points in the viral life cycle. Our most notable results show that LY411575, a gamma secretase inhibitor, exhibited an IC of 72 nM and reduced percent infection to near the level of the mock infection control. Pseudotime inferences generated mechanistic predictions that were supported by time-of-addition experiments, indicating that LY411575 arrests DENV infection before the development of late-stage infection associated morphologies, while retaining activity when added after inoculation.

cell biology↗

bioRxiv2023

Imaging-based screening identifies modulators of the eIF3 translation initiation factor complex in Candida albicans

Fungal pathogens like Candida albicans can cause devastating human disease. Treatment of candidemia is complicated by the high rate of resistance to common antifungal therapies. Additionally, there is host toxicity associated with many antifungal compounds due to the conservation between essential mammalian and fungal proteins. An attractive new approach for antimicrobial development is to target virulence factors: non-essential processes that are required for the organism to cause disease in human hosts. This approach expands the potential target space while reducing the selective pressure towards resistance, as these targets are not essential for viability. In C. albicans, a key virulence factor is the ability to transition to hyphal morphology. We developed a high-throughput image analysis pipeline to distinguish between yeast and filamentous growth in C. albicans at the single cell level. Based on this phenotypic assay, we screened the FDA drug repurposing library of 2,017 compounds for their ability to inhibit filamentation and identified 33 compounds that block the hyphal transition in C. albicans with IC50 values ranging from 0.2 to 150 {micro}M. Multiple compounds showed a phenyl vinyl sulfone chemotype, prompting further analysis. Of these phenyl vinyl sulfones, NSC 697923 displayed the most efficacy, and by selecting for resistant mutants, we identified eIF3 as the target of NSC 697923 in C. albicans.

microbiology↗

bioRxiv2021

Perturbation of transmembrane 6 superfamily member 2 expression alters lipid metabolism in a human liver cell line

BackgroundNonalcoholic fatty liver disease (NAFLD) is caused by accumulation of excess lipids in hepatocytes. Genome wide association studies have identified strong association of NAFLD with non-synonymous E167K amino acid mutation in transmembrane 6 superfamily member 2 (TM6SF2) protein. The E167K mutation affects TM6SF2 stability and its carriers display increased hepatic lipids levels and lower serum triglycerides. While similar phenotype is evident in mice with TM6SF2 knockdown, effects of TM6SF2 on hepatic lipid metabolism is not completely understood. MethodsHere, we overexpressed wild-type or E167K variant of TM6SF2 or knocked down TM6SF2 expression in lipid-treated Huh-7 cells and used biochemical assays, untargeted lipidomic analysis, RNAseq transcriptome analysis and high-throughput fluorescent imaging to determine changes in lipid metabolism. ResultsBoth knockdown and E167K overexpression increased acylglyceride levels which was decreased by wild-type TM6SF2 overexpression. Further, mean intensity of individual lipid droplets was increased by E167K overexpression and knockdown while wild-type TM6SF2 had no effects. We also observed lipid chain remodeling for acylglycerides by TM6SF2 knockdown leading to a relative increase in species with shorter and more saturated side chains. RNA sequencing revealed differential expression of several lipid metabolizing genes, including genes belonging to AKR1 family and lipases, primarily in cells with TM6SF2 knockdown. ConclusionTaken together, our data shows that overexpression of TM6SF2 gene or its loss-of-function changes hepatic lipid species composition and expression of lipid metabolizing genes. Further, overexpression of E167K variant and TM6SF2 knockdown similarly increased hepatic lipid accumulation and lipid droplets profile further confirming a loss-of-function effect for variant.

cell biology↗