Biology subjects

Baryshnikova, A.

Publications and source records attributed to Baryshnikova, A..

4 recordsLinked to original sources

bioRxiv2018

Yeast Genomic Screens Identify Kinesins as Potential Targets of the Pseudomonas syringae Type III Effector, HopZ1a

Gram-negative bacterial pathogens inject type III secreted effectors (T3SEs) directly into host cells to promote pathogen fitness by manipulating host cellular processes. Despite their crucial role in promoting virulence, relatively few T3SEs have well-characterized enzymatic activities or host targets. This is in part due to functional redundancy within pathogen T3SE repertoires as well as promiscuous individual T3SEs that can have multiple host targets. To overcome these challenges, we conducted heterologous genetic screens in yeast, a non-host organism, to identify T3SEs that target conserved eukaryotic processes. We screened 75 T3SEs from the plant pathogen Pseudomonas syringae and identified 16 that inhibited yeast growth on rich media and eight that inhibited growth on stress-inducing media, including the acetyltransferase HopZ1a. We focused our further analysis on HopZ1a, which interacts with plant tubulin and alters microtubule networks. We first performed a Pathogenic Genetic Array (PGA) screen of HopZ1a against ~4400 yeast carrying non-essential mutations and found 95 and 10 deletion mutants which reduced or enhanced HopZ1a toxicity, respectively. To uncover putative HopZ1a host targets, we interrogated both the genetic- and physical-interaction profiles of HopZ1a by identifying yeast genes with PGA profiles most similar (i.e. congruent) to that of HopZ1a, performing a functional enrichment analysis of these HopZ1a-congruent genes, and by analyzing previously described HopZ physical interaction datasets. Finally, we demonstrated that HopZ1a can target kinesins by acetylating the plant kinesins HINKEL and MKRP1.\n\nARTICLE SUMMARYBacterial pathogens utilize secretion systems to directly deliver effector proteins into host cells, with the ultimate goal of promoting pathogen fitness. Despite the central role that effectors play in infection, the molecular function and host targets of most effectors remain uncharacterized. We used yeast genomics and protein interaction data to identify putative virulence targets of the effector HopZ1a from the plant pathogen Pseudomonas syringae. HopZ1a is an acetyltransferase that induces plant microtubule destruction. We showed that HopZ1a acetylated plant kinesin proteins known to regulate microtubule networks. Our study emphasizes the power of yeast functional genomic screens to characterize effector functions.

microbiology↗

bioRxiv2017

Functional Annotation of Chemical Libraries across Diverse Biological Processes

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compounds mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

systems biology↗

bioRxiv2017

Comparative analysis of protein abundance studies to quantify the Saccharomyces cerevisiae proteome

Global gene expression and proteomics tools have allowed large-scale analyses of the transcriptome and proteome in eukaryotic cells. These tools have enabled studies of protein abundance changes that occur in cells under stress conditions, providing insight into regulatory programs required for cellular adaptation. While the proteome of yeast has been subjected to the most comprehensive analysis of any eukary-ote, each of the existing datasets is separate and reported in different units. A comparison of all the available protein abundance data sets is key towards developing a complete understanding of the yeast proteome. We evaluated 19 quantitative proteomic analyses performed under normal and stress conditions and normalized and converted all measurements of protein abundance into absolute molecules per cell. Our analysis yields an estimate of the cellular abundance of 97% of the proteins in the yeast proteome, as well as an assessment of the variation in each abundance measurement. We evaluate the variance and sensitivity associated with different measurement methods. We find that C-terminal tagging of proteins, and the accompanying alterations to the 3 untranslated regions of the tagged genes, has little effect on protein abundance. Finally, our normalization of diverse datasets facilitates comparisons of protein abundance remodeling of the proteome during cellular stresses.

bioinformatics↗

bioRxiv2016

Spatial Analysis of Functional Enrichment (SAFE) in Large Biological Networks

Summary/AbstractSpatial Analysis of Functional Enrichment (SAFE) is a systematic quantitative approach for annotating large biological networks. SAFE detects network regions that are statistically overrepresented for functional groups or quantitative phenotypes of interest, and provides an intuitive visual representation of their relative positioning within the network. In doing so, SAFE determines which functions are represented in a network, which parts of the network they are associated with and how they are potentially related to one another.\n\nHere, I provide a detailed stepwise description of how to perform a SAFE analysis. As an example, I use SAFE to annotate the genome-scale genetic interaction similarity network from Saccharomyces cerevisiae with Gene Ontology (GO) biological process terms. In addition, I show how integrating GO with chemical genomic data in SAFE can recapitulate known modes-of-action of chemical compounds and potentially identify novel drug mechanisms.

systems biology↗