A genetic assay for gene essentiality in Clostridium.
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ABSTRACT:
SUBMITTER: Walker DJF
PROVIDER: S-EPMC5154367 | biostudies-literature | 2016 Dec
REPOSITORIES: biostudies-literature
Publications
A genetic assay for gene essentiality in Clostridium.
Walker David J F DJF Heap John T JT Winzer Klaus K Minton Nigel P NP
Anaerobe 20160731
Essential genes of pathogens are potential therapeutic targets, but are difficult to verify. Here, gene essentiality was determined by targeted knockout following engineered gene duplication. Null mutants of candidate essential genes of Clostridium difficile were viable only in the presence of a stable second copy of the gene. ...[more]
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Project description:UNLABELLED:Clostridium difficile is the most common cause of antibiotic-associated intestinal infections and a significant cause of morbidity and mortality. Infection with C. difficile requires disruption of the intestinal microbiota, most commonly by antibiotic usage. Therapeutic intervention largely relies on a small number of broad-spectrum antibiotics, which further exacerbate intestinal dysbiosis and leave the patient acutely sensitive to reinfection. Development of novel targeted therapeutic interventions will require a detailed knowledge of essential cellular processes, which represent attractive targets, and species-specific processes, such as bacterial sporulation. Our knowledge of the genetic basis of C. difficile infection has been hampered by a lack of genetic tools, although recent developments have made some headway in addressing this limitation. Here we describe the development of a method for rapidly generating large numbers of transposon mutants in clinically important strains of C. difficile. We validated our transposon mutagenesis approach in a model strain of C. difficile and then generated a comprehensive transposon library in the highly virulent epidemic strain R20291 (027/BI/NAP1) containing more than 70,000 unique mutants. Using transposon-directed insertion site sequencing (TraDIS), we have identified a core set of 404 essential genes, required for growth in vitro. We then applied this technique to the process of sporulation, an absolute requirement for C. difficile transmission and pathogenesis, identifying 798 genes that are likely to impact spore production. The data generated in this study will form a valuable resource for the community and inform future research on this important human pathogen. IMPORTANCE:Clostridium difficile is a common cause of potentially fatal intestinal infections in hospital patients, particularly those who have been treated with antibiotics. Our knowledge of this bacterium has been hampered by a lack of tools for dissecting the organism. We have developed a method to study the function of every gene in the bacterium simultaneously. Using this tool, we have identified a set of 404 genes that are required for growth of the bacteria in the laboratory. C. difficile also produces a highly resistant spore that can survive in the environment for a long time and is a requirement for transmission of the bacteria between patients. We have applied our genetic tool to identify all of the genes required for production of a spore. All of these genes represent attractive targets for new drugs to treat infection.
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Project description:We lack an understanding of how the full range of genetic variants that occur in individuals can interact. To address this shortcoming, here we combine diverse mutations between genes in a model regulatory network, the galactose (GAL) switch of budding yeast. The effects of thousands of pairs of mutations fall into a limited number of phenotypic classes. While these effects are mostly predictable using simple rules that capture the 'stereotypical' genetic interactions of the network, some double mutants have unexpected outcomes including constituting alternative functional switches. Each of these 'harmonious' genetic combinations exhibits altered dependency on other regulatory genes. These cases illustrate how both pairwise and higher epistasis determines gene essentiality and how combinations of mutations rewire regulatory networks. Together, our results provide an overview of how broad spectra of mutations interact, how these interactions can be predicted, and how diverse genetic solutions can achieve 'wild-type' phenotypic behavior.
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Project description:In Escherichia coli and Bacillus subtilis, essentiality, not expressivity, drives the distribution of genes between the two replicating strands. Although essential genes tend to be coded in the leading replicating strand, the underlying selective constraints and the evolutionary extent of these findings have still not been subject to comparative studies. Here, we extend our previous analysis to the genomes of low G + C firmicutes and gamma-proteobacteria, and in a second step to all sequenced bacterial genomes. The inference of essentiality by homology allows us to show that essential genes are much more frequent in the leading strand than other genes, even when compared with non- essential highly expressed genes. Smaller biases were found in the genomes of obligatory intracellular bacteria, for which the assignment of essentiality by homology from fast growing free-living bacteria is most problematic. Cross-comparisons used to assess potential errors in the assignment of essentiality by homology revealed that, in most cases, variations in the assignment criteria have little influence on the overall results. Essential genes tend to be more conserved in the leading strand than average genes, which is consistent with selection for this positioning and may impose a strong constraint on chromosomal rearrangements. These results indicate that essentiality plays a fundamental role in the distribution of genes in most bacterial genomes.
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Project description:Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.
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Project description:Cancer metabolism is a marvellously complex topic, in part, due to the reprogramming of its pathways to self-sustain the malignant phenotype in the disease, to the detriment of its healthy counterpart. Understanding these adjustments can provide novel targeted therapies that could disrupt and impair proliferation of cancerous cells. For this very purpose, genome-scale metabolic models (GEMs) have been developed, with Human1 being the most recent reconstruction of the human metabolism. Based on GEMs, we introduced the genetic Minimal Cut Set (gMCS) approach, an uncontextualized methodology that exploits the concepts of synthetic lethality to predict metabolic vulnerabilities in cancer. gMCSs define a set of genes whose knockout would render the cell unviable by disrupting an essential metabolic task in GEMs, thus, making cellular proliferation impossible. Here, we summarize the gMCS approach and review the current state of the methodology by performing a systematic meta-analysis based on two datasets of gene essentiality in cancer. First, we assess several thresholds and distinct methodologies for discerning highly and lowly expressed genes. Then, we address the premise that gMCSs of distinct length should have the same predictive power. Finally, we question the importance of a gene partaking in multiple gMCSs and analyze the importance of all the essential metabolic tasks defined in Human1. Our meta-analysis resulted in parameter evaluation to increase the predictive power for the gMCS approach, as well as a significant reduction of computation times by only selecting the crucial gMCS lengths, proposing the pertinency of particular parameters for the peak processing of gMCS.
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