bioRxiv Science⌕ Search

Biology subjects

Typas, A.

Publications and source records attributed to Typas, A..

39 records · Page 3Linked to original sources

Spatiotemporal proteomics uncovers cathepsin-dependent host cell death during bacterial infection

Immune cells need to swiftly and effectively respond to invading pathogens. This response relies heavily on rapid protein synthesis and accurate cellular targeting to ensure pathogen destruction. In return, pathogens intercept this response to ensure their survival and proliferation. To gain insight into this dynamic interface, we combined click-chemistry with pulsed stable isotope labeling of amino acids (pSILAC-AHA) in cell culture to quantify the newly synthesised host proteome during macrophage infection with the model intracellular bacterial pathogen, Salmonella enterica Typhimurium (STm). We monitored newly synthesised proteins across different host cell compartments and infection stages, and used available proteomics data in response to lipopolysaccharide to deconvolute the STm-specific response. Within this rich resource, we detected aberrant trafficking of lysosomal proteases to the extracellular space and the nucleus, the latter of which correlated with signatures of cell death. Pharmacological cathepsin inhibition suppressed Caspase-11 dependent macrophage cell death, thus demonstrating an active role for cathepsins during STm induced pyroptosis. Our study illustrates that resolving host proteome dynamics during infection can drive the discovery of biological mechanisms at the host-microbe interface.

systems biology↗

Comprehensive variant effect predictions of single nucleotide variants in model organisms

The effect of single nucleotide variants (SNVs) in coding and non-coding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this we compiled and benchmarked sequence and structure-based variant effect predictors and we analyzed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of H. sapiens, S. cerevisiae and E. coli. Studied mechanisms include protein stability, interaction interfaces, post-translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc, a tool by which users can query precomputed predictions by providing amino acid or nucleotide-level variants.

genomics↗

Phenotype prediction in an Escherichia coli strain panel

Understanding how genetic variation contributes to phenotypic differences is a fundamental question in biology. Here, we set to predict fitness defects of an individual using mechanistic models of the impact of genetic variants combined with prior knowledge of gene function. We assembled a diverse panel of 696 Escherichia coli strains for which we obtained genomes and measured growth phenotypes in 214 conditions. We integrated variant effect predictors to derive gene-level probabilities of loss of function for every gene across strains. We combined these probabilities with information on conditional gene essentiality in the reference K-12 strain to predict the strains growth defects, providing significant predictions for up to 38% of tested conditions. The putative causal variants were validated in complementation assays highlighting commonly perturbed pathways in evolution for the emergence of growth phenotypes. Altogether, our work illustrates the power of integrating high-throughput gene function assays to predict the phenotypes of individuals.\n\nHighlightsO_LIAssembled a reference panel of E. coli strains\nC_LIO_LIGenotyped and high-throughput phenotyped the E. coli reference strain panel\nC_LIO_LIReliably predicted the impact of genetic variants in up to 38% of tested conditions\nC_LIO_LIHighlighted common genetic pathways for the emergence of deleterious phenotypes\nC_LI

systems biology↗