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Schifferli, D. M.

Publications and source records attributed to Schifferli, D. M..

2 recordsLinked to original sources

Innate detection of Salmonella replication triggers caspase-8-dependent apoptosis via TLR-driven TNF signaling and NLRC4-mediated sensing of the SPI-2 Type III secretion system

Salmonella enterica comprises over 2500 serovars that are responsible for over 90 million annual infections and 100,000 deaths worldwide. Despite this diversity, our understanding of innate immune responses to Salmonella is based on extensive study of a few serovars, primarily Typhimurium, including strains that cannot replicate within primary murine macrophages. Non-replicating Salmonella trigger caspase-1 and -11-dependent pyroptosis. Whether the innate immune system distinguishes between replicating and non-replicating intracellular Salmonella is poorly defined. Here we demonstrate that replicating Salmonella enterica induce a distinct pathway of TNF- and caspase-8-driven apoptosis via host TLR4 and Salmonella Pathogenicity Island-2 activity. This pathway is independent of gasdermin D and involves the apoptotic pore protein Pannexin-1. Combined loss of Pannexin-1 and gasdermin D resulted in defective control of systemic Salmonella, indicating that these pathways function together to promote anti-Salmonella host defense. Altogether, our findings uncover a previously unappreciated pathway by which macrophages sense intracellular replicating bacteria.

microbiology↗

HamHeat: A fast and simple package for calculating Hamming distance from multiple sequence data for heatmap visualization

The problem of fast calculation of Hamming distance inferred from many sequence datasets is still not a trivial task. Here, we present HamHeat, as a new software package to efficiently calculate Hamming distance for hundreds of aligned protein or DNA sequences of a large number of residues or nucleotides, respectively. HamHeat uses a unique algorithm with many advantages, including its ease of use and the execution of fast runs for large amounts of data. The package consists of three consecutive modules. In the first module, the software ranks the sequences from the most to the least frequent variant. The second module uses the most common variant as the reference sequence to calculate the Hamming distance of each additional sequence based on the number of residue or nucleotide changes. A final module formats all the results in a comprehensive table that displays the sequence ranks and Hamming distances. Availability and implementationHamHeat is based on Python 3 and AWK, runs on Linux system and is available under the MIT License at: https://github.com/alexeyrakov/HamHeat. Contactrakovalexey@gmail.com

bioinformatics↗