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Biology subjects

Sturmach, C.

Publications and source records attributed to Sturmach, C..

2 recordsLinked to original sources

Affinity-enhanced peptides delivered by mRNA lipid nanoparticles inhibit influenza A virus replication by disrupting PA-PB1 interaction

Seasonal influenza causes up to 650,000 deaths annually and remains a persistent pandemic threat due to zoonotic strains crossing the species barrier. Current antivirals, which target neuraminidase or the viral polymerase, have limited efficacy and rapidly select for resistant variants, highlighting the urgent need for new therapeutic strategies. The heterotrimeric influenza polymerase (FluPol), comprising PA, PB1, and PB2 subunits, is essential for viral replication and harbors virus-specific protein-protein interfaces that are potential drug targets. We focused on the highly conserved PA-PB1 interface, where the N-terminal peptide of PB1 binds the PA C-terminal domain with high affinity. Disrupting this interaction abrogates polymerase function, halting viral replication. Using phage display, we identified PB1-derived peptides with enhanced affinity for PA and characterized their binding via biophysical methods and X-ray crystallography. Lead peptides efficiently disrupted the PB1-PA interaction and inhibited polymerase activity in cell-based assays. To address peptide delivery challenges, we expressed these inhibitors intracellularly from synthetic mRNA formulated in lipid nanoparticles, achieving robust inhibition of viral replication in cultured cells. This work establishes intracellularly expressed peptide inhibitors as a viable antiviral strategy and provides a generalizable framework for targeting essential protein-protein interactions of influenza and other RNA viruses.

microbiology↗

IMAGENE: Single-cell association of live cell imaging and gene expression profiles of non-adherent cells through photoactivatable adhesives

Live cell imaging is uniquely placed to study cell behavior as it preserves spatial context and enables non-destructive observations over time. Integrating live cell imaging and molecular phenotypes with single-cell resolution is key to uncovering the relationship between the behavioral and morphological signatures of cells, and their molecular states. Non-adherent cells - as are most immune cells - however, present unique challenges in linking live cell imaging and fixed cell assays with single-cell resolution due to the difficulty of identifying individual cells across experimental modalities. To overcome this issue, we developed IMAGENE, an experimental and computational pipeline that leverages previously reported photoactivatable biocompatible adhesive material (PA-BAM) coatings for on-the-fly cell immobilization. We demonstrate the IMAGENE experimental and computational pipeline by generating a dataset of label-free time-lapse videos of primary human naive CD8+ T cells following 24 hours of polyclonal stimulation. Individual cells, including highly motile cells, can be matched to expression profiles of genes of interest obtained through KrakenFISH, a modified version of the previously reported autoFISH setup for automated, single-molecule fluorescence in situ hybridization (smFISH) experiments that supports sample parallelization. We use this data to train explainable machine learning models that predict expression levels of individual genes, with variable performance, from hand-crafted dynamic and spatial features obtained from live cell imaging.

bioengineering↗