The Chemical Genetic Interactions of Statin Drugs with Their Target Genes in Saccharomyces cerevisiae

<p>Statins, competitive inhibitors of the rate limiting cholesterol/ergosterol enzymes HMG-CoA reductase (HMG1 and HMG2), are the most widely prescribed human therapeutic drugs. They are effective in lowering cholesterol levels in atherosclerosis and related syndromes. However, statins exhibit a range of pleiotropic side effects whose mechanisms are poorly understood. This study investigates statin pleiotropy by analysis of genetic interaction networks in yeast, Saccharomyces cerevisiae, which shows high homology to mammalian pathways affected by statins. Synthetic genetic array (SGA) analysis allows elucidation of functional genetic networks of genes of interest ("query genes") by measurement of genetic epistasis in double mutants of the query gene with the genome - wide deletion mutant array of ~4800 non-essential strains. Chemicalgenetic profiling is similar where a SMP may effectively replace the query gene in genome wide epistatic analysis. The genetic interaction networks resulting from use of HMG1 and HMG2 as query genes for SGA analysis were compared to the chemical-genetic profiles of atorvastatin, cerivastatin and lovastatin. The genes ARV1, BTS1, OPI3 displaying phenotypic enhancements (i.e. their deletion caused major growth inhibition) with statins became essential in the presence of all the statins. Two mitochondrial genes, COX17 and MMM1, showed phenotypic suppressions (i.e. their deletion allowed better growth) in common to all three statin drugs. An attractive hypothesis is that major pleiotropic effects of statins could be due to variation in function or expression of these enhancing or suppressing genes. Other processes compensating statin use were also elucidated. For example, when HMG1 and its epistatically interacting genes are shut down by deletion coupled with inhibition of HMG2 with statin, there is strong evidence that the cell attempts to maintain membrane/lipid homeostasis via anterograde and retrograde transport mechanisms, including the mobilisation of lipid storage droplets. To aid refinement of genetic analysis in this and future studies, a more direct phenotypic assay was developed for quantifying ergosterol. Such an assay may be used as a phenotype to map the effect of up - and downstream - genes, or network genes affecting ergosterol levels. This assay was used to quantify ergosterol in a drug - resistant mutant developed by others aiding confirmation of the drug target.</p>

The Chemical Genetic Interactions of Statin Drugs with Their Target Genes in Saccharomyces cerevisiae

<p>Statins, competitive inhibitors of the rate limiting cholesterol/ergosterol enzymes HMG-CoA reductase (HMG1 and HMG2), are the most widely prescribed human therapeutic drugs. They are effective in lowering cholesterol levels in atherosclerosis and related syndromes. However, statins exhibit a range of pleiotropic side effects whose mechanisms are poorly understood. This study investigates statin pleiotropy by analysis of genetic interaction networks in yeast, Saccharomyces cerevisiae, which shows high homology to mammalian pathways affected by statins. Synthetic genetic array (SGA) analysis allows elucidation of functional genetic networks of genes of interest ("query genes") by measurement of genetic epistasis in double mutants of the query gene with the genome - wide deletion mutant array of ~4800 non-essential strains. Chemicalgenetic profiling is similar where a SMP may effectively replace the query gene in genome wide epistatic analysis. The genetic interaction networks resulting from use of HMG1 and HMG2 as query genes for SGA analysis were compared to the chemical-genetic profiles of atorvastatin, cerivastatin and lovastatin. The genes ARV1, BTS1, OPI3 displaying phenotypic enhancements (i.e. their deletion caused major growth inhibition) with statins became essential in the presence of all the statins. Two mitochondrial genes, COX17 and MMM1, showed phenotypic suppressions (i.e. their deletion allowed better growth) in common to all three statin drugs. An attractive hypothesis is that major pleiotropic effects of statins could be due to variation in function or expression of these enhancing or suppressing genes. Other processes compensating statin use were also elucidated. For example, when HMG1 and its epistatically interacting genes are shut down by deletion coupled with inhibition of HMG2 with statin, there is strong evidence that the cell attempts to maintain membrane/lipid homeostasis via anterograde and retrograde transport mechanisms, including the mobilisation of lipid storage droplets. To aid refinement of genetic analysis in this and future studies, a more direct phenotypic assay was developed for quantifying ergosterol. Such an assay may be used as a phenotype to map the effect of up - and downstream - genes, or network genes affecting ergosterol levels. This assay was used to quantify ergosterol in a drug - resistant mutant developed by others aiding confirmation of the drug target.</p>

Varia: Prediction, analysis and visualisation of variable genes

Assessing the diversity or expression of variable gene families in pathogens can inform about immune escape mechanisms or host interaction phenotypes of clinical relevance. However, obtaining the sequences and quantifying their expression is a challenge. Here, we present a tool, which based on unique sequence tag similarity between members of a gene family, predicts the domains encoded by the queried gene. As an example, we are using the var gene family, encoding the major virulence proteins (PfEMP1) of the human malaria parasite, Plasmodium falciparum. We developed Varia, which predicts the likely var gene sequence and encoded protein domain composition of a gene from short sequence tags. We provide a new extended annotated var genome database, in which Varia identifies genes with identical tag sequences and compares these to return the most probable domain composition of the query gene. Varia's ability to predict correct PfEMP1 domain compositions from short var sequence tags was tested in two complementary pipelines to (a) return the putative gene sequences and domain compositions of the query gene from any partial sequence provided, thereby enabling detailed assessment of specific genes putative function and experimental validation of these (b) to accommodate rapid profiling of var gene expression in complex patient samples, by compiling the overall domain prevalence among var transcripts predicted identified and quantified by next generation sequencing of so-called var DBLα-sequence tags. Availability and implementation: Varia is available on GitHub (https://github.com/GCJMackenzie/Varia) under the MIT license.

Prediction of protein architectures involved in the signaling-pathway initiating sporulation in Firmicutes

Objectives Like many other proteins, those belonging to the signal transduction cascade initiating sporulation (Spo0 pathway) have conserved protein domains (Capra and Laub in Annu Rev Microbiol 66:325–47, 2012). Improvements in bioinformatics applications to discover proteins involved in the initiation of the sporulating cascade in newly sequenced genomes is an important task that requires rigorous comparative genomic methods and manual curation to identify endospore-forming bacteria. This note aims to present a collection of predicted proteins involved in the Spo0 pathway found in the proteomes of fully sequenced and manually curated endospore-forming Firmicutes species. This collection may serve as a guide to conduct future experiments in endospore formers in genomic and metagenomic projects. Data description Similar to the report of Davidson et al. (PLoS Genet 14:1–33, 2018), we used Pfam profiles (El-Gebali et al. in Nucleic Acids Res 47:D427–32, 2019) defining each protein and the genomic context surrounding the query gene to predict probable orthologs of the Spo0 pathway in Firmicutes. We present in this note a collection of 325 Firmicutes species organized by phylogenetic class and classified as spore formers, non-spore formers or unknown spore phenotype based on published literature, for which we predicted probable orthologs defining the signal transduction pathway initiating sporulation.

octoFLU: Automated Classification for the Evolutionary Origin of Influenza A Virus Gene Sequences Detected in U.S. Swine

The diversity of the 8 genes of influenza A viruses (IAV) in swine reflects introductions from nonswine hosts and subsequent antigenic drift and shift. Here, we curated a data set and present a pipeline that assigns evolutionary lineage and genetic clade to query gene segments.

Gene Annotation Easy Viewer (GAEV): Integrating KEGG’s Gene Function Annotations and Associated Molecular Pathways

We developed a Gene Annotation Easy Viewer (GAEV) that integrates the gene annotation data from the KEGG (Kyoto Encyclopedia of Genes and Genomes) Automatic Annotation Server. GAEV generates an easy-to-read table that summarizes the query gene name, the KO (KEGG Orthology) number, name of gene orthologs, functional definition of the ortholog, and the functional pathways that query gene has been mapped to. Via links to KEGG pathway maps, users can directly examine the interaction between gene products involved in the same molecular pathway. We provide a usage example by annotating the newly published freshwater microcrustacean Daphnia pulex genome. This gene-centered view of gene function and pathways will greatly facilitate the genome annotation of non-model species and metagenomics data. GAEV runs on a Windows or Linux system equipped with Python 3 and provides easy accessibility to users with no prior Unix command line experience.

Gene Annotation Easy Viewer (GAEV): Integrating KEGG’s Gene Function Annotations and Associated Molecular Pathways

We developed a Gene Annotation Easy Viewer (GAEV) that integrates the gene annotation data from the KEGG (Kyoto Encyclopedia of Genes and Genomes) Automatic Annotation Server. GAEV generates an easy-to-read table that summarizes the query gene name, the KO (KEGG Orthology) number, name of gene orthologs, functional definition of the ortholog, and the functional pathways that query gene has been mapped to. Via links to KEGG pathway maps, users can directly examine the interaction between gene products involved in the same molecular pathway. We provide a usage example by annotating the newly published freshwater microcrustacean Daphnia pulex genome. This gene-centered view of gene function and pathways will greatly facilitate the genome annotation of non-model species and metagenomics data. GAEV runs on a Windows or Linux system equipped with Python 3 and provides easy accessibility to users with no prior Unix command line experience.

Gene Annotation Easy Viewer (GAEV): Integrating KEGG’s Gene Function Annotations and Associated Molecular Pathways

We developed a Gene Annotation Easy Viewer (GAEV) that integrates the gene annotation data from the KEGG (Kyoto Encyclopedia of Genes and Genomes) Automatic Annotation Server. GAEV generates an easy-to-read table that summarizes the query gene name, the KO (KEGG Orthology) number, name of gene orthologs, functional definition of the ortholog, and the functional pathways that query gene has been mapped to. Via links to KEGG pathway maps, users can directly examine the interaction between gene products involved in the same molecular pathway. We provide a usage example by annotating the newly published freshwater microcrustacean Daphnia pulex genome. This gene-centered view of gene function and pathways will greatly facilitate the genome annotation of non-model species and metagenomics data. GAEV runs on a Windows or Linux system equipped with Python 3 and provides easy accessibility to users with no prior Unix command line experience.

BART: a transcription factor prediction tool with query gene sets or epigenomic profiles

SummaryIdentification of functional transcription factors that regulate a given gene set is an important problem in gene regulation studies. Conventional approaches for identifying transcription factors, such as DNA sequence motif analysis, are unable to predict functional binding of specific factors and not sensitive to detect factors binding at distal enhancers. Here we present Binding Analysis for Regulation of Transcription (BART), a novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile, based on more than 6,000 existing ChIP-seq datasets for over 400 factors in human or mouse. This method demonstrates the advantage of utilizing publicly available data for functional genomics research.AvailabilityBART is implemented in Python and available at http://faculty.virginia.edu/zanglab/bartContact: [email protected]