Biology subjects

Baryshnikova, A.

Publications and source records attributed to Baryshnikova, A..

9 recordsLinked to original sources

bioRxiv2025

Platform-dependent effects of genetic variants on plasma APOL1 and their implications for kidney disease

Mutations in apolipoprotein L1 (APOL1) are strongly associated with protection against parasitic infections and increased risk of kidney disease in individuals of African ancestry. To better understand the mechanisms underlying APOL1-related pathologies, we examined genetic drivers of circulating APOL1 in individuals of African and European ancestry from four independent cohorts (UK Biobank, AASK, deCODE, and Health ABC) using three proteomic technologies (Olink, SomaLogic, and mass spectrometry). We found that disease-associated APOL1 G1 and G2 variants are strong cis-pQTLs for plasma APOL1 measured by Olink and SomaLogic, but not mass spectrometry. Critically, the direction of variant effects differed between proteomic platforms, being positive with Olink and negative with SomaLogic. We identified an additional APOL1 missense variant (rs2239785), common in Europeans, exhibiting the same platform-dependent directional discrepancy. Furthermore, variants in the kallikrein-kinin system (KKS), involving KLKB1, F12, and KNG1, and their genetic interactions showed strong trans-pQTL effects on APOL1 measured by Olink, but not SomaLogic. These platform-dependent discrepancies raise the possibility that both intrinsic APOL1 mutations and extrinsic KKS activity induce conformational changes in the APOL1 protein that are differentially recognized by the proteomic platforms.

genetics↗

bioRxiv2022

Global analysis of the yeast knock-out phenome

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae have produced the largest, richest and most systematic phenotypic description of any organism. Such an achievement was enabled by the development of highly scalable phenotypic assays and construction of the yeast knock-out (YKO) collection, comprising ~5,000 isogenic strains each deleted for exactly one open reading frame. Systematic screening of the YKO collection led to ~500 publications describing ~14,500 phenotypes capturing nearly every aspect of yeast biology. Yet, integrative analyses of this rich data source have been virtually impossible due to the lack of a central repository and consistent meta-data annotations. Here, we describe the aggregation, harmonization and analysis of all published phenotypic screens of the YKO collection, which we refer to as the Yeast Phenome (www.yeastphenome.org). To demonstrate the power of data integration and illustrate how much it facilitates the generation of testable hypotheses, we present three discoveries uniquely enabled by Yeast Phenome. First, we use the variation in the number of phenotypes per gene to identify tryptophan homeostasis as a central point of vulnerability to a wide range of chemical compounds, including FDA-approved drugs. Second, using phenotypic profiles as a tool for predicting gene function, we identify and validate the role of YHR045W as a novel regulator of ergosterol biosynthesis and DNA damage response, and YGL117W as a new member of the aromatic amino acid biosynthesis pathway. Finally, we describe a surprising exponential relationship between phenotypic similarity and intergenic distance in both yeast and human genomes. This relationship, which stretches as far as 380 kb in yeast and 100 Mb in humans, suggests that gene positions are optimized for function to a much greater extent than appreciated previously. Overall, we show that Yeast Phenome enables systematic enquiries into the nature of gene-gene and gene-phenotype relationships and is an important new resource for systems biology.

systems biology↗

bioRxiv2021

Complex Mutation Profiles in Mismatch Repair and Ribonucleotide Reductase Mutations Reveal Novel Repair Substrate Specificity of MutS Homolog (MSH) Complexes

Determining mutation signatures is standard for understanding the etiology of human tumors and informing cancer treatment. Multiple determinants of DNA replication fidelity prevent mutagenesis that leads to carcinogenesis, including the regulation of free deoxyribonucleoside triphosphate (dNTP) pools by ribonucleotide reductase (RNR) and repair of replication errors by the mismatch repair (MMR) system. We identified genetic interactions between rnr1 alleles that elevate dNTP levels and MMR. We then utilized a targeted deep-sequencing approach to determine mutational signatures associated with MMR pathway defects. By combining rnr1 and msh mutations to increase dNTP levels and alter the mutational load, we uncovered previously unreported specificities of Msh2-Msh3 and Msh2-Msh6. Msh2-Msh3 is uniquely able to direct repair of G/C single base deletions in GC runs, while Msh2-Msh6 specifically directs repair of substitutions at G/C dinucleotides. We also identified broader sequence contexts that influence variant profiles in different genetic backgrounds. Finally, we observed that the mutation profiles in double mutants were not necessarily an additive relationship of mutation profiles in single mutants. Our results have implications for interpreting mutation signatures from human tumors, particularly when MMR is defective.

genetics↗

bioRxiv2020

A genome-scale yeast library with inducible expression of individual genes

The ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships, including instances of feedback control. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which 5,687 yeast genes are engineered for transcriptional inducibility with single-gene precision at their native loci and without plasmids. Each strain contains Synthetic Genetic Array (SGA) screening markers and a unique molecular barcode, enabling high-throughput yeast genetics. We characterized YETI using quantitative growth phenotyping and pooled BAR-seq screens, and we used a YETI allele to characterize the regulon of ROF1, showing that it is a transcriptional repressor. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from other eukaryotic and prokaryotic systems shows that low native expression is a critical variable that can bias promoter-perturbing screens, including CRISPRi. We engineered a second expression system, Z3EB42, that gives lower expression than Z3EV, a feature enabling both conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.

synthetic biology↗

bioRxiv2019

Regulatory kinase genetic interaction profiles differ between environmental conditions and cellular states

Cell growth and quiescence in eukaryotic cells is controlled by an evolutionarily conserved network of signaling pathways. Signal transduction networks operate to modulate a wide range of cellular processes and physiological properties when cells exit proliferative growth and initiate a quiescent state. How signaling networks function to respond to diverse signals that result in cell cycle exit and establishment of a quiescent state is poorly understood. Here, we studied the function of signaling pathways in quiescent cells using global genetic interaction mapping in the model eukaryotic cell, Saccharomyces cerevisiae (budding yeast). We performed pooled analysis of genotypes using molecular barcode sequencing to test the role of [~]3,900 gene deletion mutants and [~]11,700 pairwise interactions between all non-essential genes and the protein kinases TOR1, RIM15, PHO85 in three different nutrient-restricted conditions in both proliferative and quiescent cells. We detect nearly five-fold more genetic interactions in quiescent cells compared to proliferative cells. We find that both individual gene effects and genetic interaction profiles vary depending on the specific pro-quiescence signal. The master regulator of quiescence, RIM15 shows distinct genetic interaction profiles in response to different starvation signals. However, vacuole-related functions show consistent genetic interactions with RIM15 in response to different starvation signals suggesting that RIM15 integrates diverse signals to maintain protein homeostasis in quiescent cells. Our study expands genome-wide genetic interaction profiling to additional conditions, and phenotypes, highlighting the conditional dependence of epistasis.

genetics↗

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↗