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Abstract Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact of design choices of DL approaches on the performance of the learned representation, including the model architecture, the training methodology and the various hyperparameters. To address this problem, we evaluate the performance of various design choices of DL representation learning methods using TCGA and DepMap pan-cancer datasets and assess their predictive power for survival and gene essentiality predictions. We demonstrate that baseline methods achieve comparable or superior performance compared to more complex models on survival predictions tasks. DL representation methods, however, are the most efficient to predict the gene essentiality of cell lines. We show that auto-encoders (AE) are consistently improved by techniques such as masking and multi-head training. Our results suggest that the impact of DL representations and of pretraining are highly task- and architecture-dependent, highlighting the need for adopting rigorous evaluation guidelines. These guidelines for robust evaluation are implemented in a pipeline made available to the research community.

Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact of design choices of DL approaches on the performance of the learned representation, including the model architecture, the training methodology and the various hyperparameters. To address this problem, we evaluate the performance of various design choices of DL representation learning methods using TCGA and DepMap pan-cancer datasets and assess their predictive power for survival and gene essentiality predictions. We demonstrate that baseline methods achieve comparable or superior performance compared to more complex models on survival predictions tasks. DL representation methods, however, are the most efficient to predict the gene essentiality of cell lines. We show that auto-encoders (AE) are consistently improved by techniques such as masking and multi-head training. Our results suggest that the impact of DL representations and of pretraining are highly task- and architecture-dependent, highlighting the need for adopting rigorous evaluation guidelines. These guidelines for robust evaluation are implemented in a pipeline made available to the research community. [ABSTRACT FROM AUTHOR]

Clostridium acetobutylicum is a solventogenic, anaerobic, gram-positive bacterium that is commonly considered the model organism for studying acetone-butanol-ethanol fermentation. The need to produce these chemicals sustainably and with a minimal impact on the environment has revived the interest in research on this bacterium. The recent development of efficient genetic tools allows to better understand the physiology of this micro-organism, aiming at improving its fermentation capacities. Knowledge about gene essentiality would guide the future genetic editing strategies and support the understanding of crucial cellular functions in this bacterium. In this work, we applied a transposon insertion site sequencing method to generate large mutant libraries containing millions of independent mutants that allowed us to identify a core group of 418 essential genes needed for in vitro development. Future research on this significant biocatalyst will be guided by the data provided in this work, which will serve as a valuable resource for the community. [ABSTRACT FROM AUTHOR]

ABSTRACT The Enterobacteriaceae are a scientifically and medically important clade of bacteria, containing the model organism Escherichia coli, as well as major human pathogens including Salmonella enterica and Klebsiella pneumoniae. Essential gene sets have been determined for several members of the Enterobacteriaceae, with the Keio E. coli single-gene deletion library often regarded as a gold standard. However, it remains unclear how gene essentiality varies between related strains and species. To investigate this, we have assembled a collection of 13 sequenced high-density transposon mutant libraries from five genera within the Enterobacteriaceae. We first assess several gene essentiality prediction approaches, investigate the effects of transposon density on essentiality prediction, and identify biases in transposon insertion sequencing data. Based on these investigations, we develop a new classifier for gene essentiality. Using this new classifier, we define a core essential genome in the Enterobacteriaceae of 201 universally essential genes. Despite the presence of a large cohort of variably essential genes, we find an absence of evidence for genus-specific essential genes. A clear example of this sporadic essentiality is given by the set of genes regulating the σE extracytoplasmic stress response, which appears to have independently acquired essentiality multiple times in the Enterobacteriaceae. Finally, we compare our essential gene sets to the natural experiment of gene loss in obligate insect endosymbionts that have emerged from within the Enterobacteriaceae. This isolates a remarkably small set of genes absolutely required for survival and identifies several instances of essential stress responses masked by redundancy in free-living bacteria.IMPORTANCEThe essential genome, that is the set of genes absolutely required to sustain life, is a core concept in genetics. Essential genes in bacteria serve as drug targets, put constraints on the engineering of biological chassis for technological or industrial purposes, and are key to constructing synthetic life. Despite decades of study, relatively little is known about how gene essentiality varies across related bacteria. In this study, we have collected gene essentiality data for 13 bacteria related to the model organism Escherichia coli, including several human pathogens, and investigated the conservation of essentiality. We find that approximately a third of the genes essential in any particular strain are non-essential in another related strain. Surprisingly, we do not find evidence for essential genes unique to specific genera; rather it appears a substantial fraction of the essential genome rapidly gains or loses essentiality during evolution. This suggests that essentiality is not an immutable characteristic but depends crucially on the genomic context. We illustrate this through a comparison of our essential genes in free-living bacteria to genes conserved in 34 insect endosymbionts with naturally reduced genomes, finding several cases where genes generally regarded as being important for specific stress responses appear to have become essential in endosymbionts due to a loss of functional redundancy in the genome.

The massive sequencing of transposon insertion mutant libraries (Tn-Seq) represents a commonly used method to determine essential genes in bacteria. Using a hypersaturated transposon mutant library consisting of 400,096 unique Tn insertions, 523 genes were classified as essential in Escherichia coli K-12 MG1655. This provided a useful genome-wide gene essentiality landscape for rapidly identifying 233 of 301 essential genes previously validated by a knockout study. However, there was a discrepancy in essential gene sets determined by conventional gene deletion methods and Tn-Seq, although different Tn-Seq studies reported different extents of discrepancy. We have elucidated two causes of this discrepancy. First, 68 essential genes not detected by Tn-Seq contain nonessential subgenic domains that are tolerant to transposon insertion, which leads to the false assignment of an essential gene as a nonessential or dispensable gene. These genes exhibited a high level of transposon insertion in their subgenic nonessential domains. In contrast, 290 genes were additionally categorized as essential by Tn-Seq, although their knockout mutants were available. The comparative analysis of Tn-Seq and high-resolution footprinting of nucleoid-associated proteins (NAPs) revealed that a protein-DNA interaction hinders transposon insertion. We identified 213 false-positive genes caused by NAP-genome interactions. These two limitations have to be considered when addressing essential bacterial genes using Tn-Seq. Furthermore, a comparative analysis of high-resolution Tn-Seq with other data sets is required for a more accurate determination of essential genes in bacteria. IMPORTANCE Transposon mutagenesis is an efficient way to explore gene essentiality of a bacterial genome. However, there was a discrepancy between the essential gene set determined by transposon mutagenesis and that determined using single-gene knockout strains. In this study, we generated a hypersaturated Escherichia coli transposon mutant library comprising approximately 400,000 different mutants. Determination of transposon insertion sites using next-generation sequencing provided a high-resolution essentiality landscape of the E. coli genome. We identified false negatives of essential gene discovery due to the permissive insertion of transposons in the C-terminal region. Comparisons between the transposon insertion landscape with binding profiles of DNA-binding proteins revealed interference of nucleoid-associated proteins to transposon insertion, generating false positives of essential gene discovery. Consideration of these findings is required to avoid the misinterpretation of transposon mutagenesis results.

This article provides an overview of microbial host selection, synthetic biology, genome annotation, metabolic modeling, and computational methods for predicting gene essentiality for developing a microbial chassis. This article focuses on lactic acid bacteria (LAB) as a microbial chassis and strategies for genome annotation of the LAB genome. As a case study, Lactococcus lactis is chosen based on its well-established therapeutic applications such as probiotics and oral vaccine development. In this article, we have delineated the strategies for genome annotations of lactic acid bacteria. These strategies also provide insights into streamlining genome reduction without compromising the functionality of the chassis and the potential for minimal genome chassis development. These insights underscore the potential for the development of efficient and sustainable synthetic biology systems using streamlined microbial chassis with minimal genomes. [ABSTRACT FROM AUTHOR]

Microbial genomics studies focusing on the dynamics of selection have often used a small number of distant genomes. As a result, they could only analyze mutations that had become fixed during the divergence between species. However, thousands of genomes of some species are now available in public databases, thanks to high-throughput sequencing. These data provide a more complete picture of the polymorphisms segregating within a species, offering a unique insight into the processes that shape the recent evolution of a species. In this study, we present GLASS (Gene-Level Amino-acid Score Shift), a selection test that is based on the predicted effects of amino acid changes. By comparing the distribution of effects of mutations observed in a gene to the expectation in the absence of selection, GLASS can quantify the intensity of selection. We applied GLASS to a dataset of 60,472 Escherichia coli strains and used this to reexamine the longstanding debate about the role of essentiality versus expression level in the rate of protein evolution. We found that selection has contrasting shortterm and long-term dynamics, with essential genes being subject to strong purifying selection in the short term, while expression level determines the rate of gene evolution in the long term. GLASS also found an overrepresentation of inactivating mutations in specific transcription factors, such as efflux pump repressors, which is consistent with selection for antibiotic resistance. These gene-inactivating polymorphisms do not reach fixation, suggesting another contrast between short-term fitness gains and long-term counterselection. [ABSTRACT FROM AUTHOR]

Abstract Background The study of gene essentiality, which measures the importance of a gene for cell division and survival, is used for the identification of cancer drug targets and understanding of tissue-specific manifestation of genetic conditions. In this work, we analyze essentiality and gene expression data from over 900 cancer lines from the DepMap project to create predictive models of gene essentiality. Methods We developed machine learning algorithms to identify those genes whose essentiality levels are explained by the expression of a small set of “modifier genes”. To identify these gene sets, we developed an ensemble of statistical tests capturing linear and non-linear dependencies. We trained several regression models predicting the essentiality of each target gene, and used an automated model selection procedure to identify the optimal model and hyperparameters. Overall, we examined linear models, gradient boosted trees, Gaussian process regression models, and deep learning networks. Results We identified nearly 3000 genes for which we accurately predict essentiality using gene expression data of a small set of modifier genes. We show that both in the number of genes we successfully make predictions for, as well as in the prediction accuracy, our model outperforms current state-of-the-art works. Conclusions Our modeling framework avoids overfitting by identifying the small set of modifier genes, which are of clinical and genetic importance, and ignores the expression of noisy and irrelevant genes. Doing so improves the accuracy of essentiality prediction in various conditions and provides interpretable models. Overall, we present an accurate computational approach, as well as interpretable modeling of essentiality in a wide range of cellular conditions, thus contributing to a better understanding of the molecular mechanisms that govern tissue-specific effects of genetic disease and cancer.

Contrary to dogma, evolutionarily young and dynamic genes can encode essential functions. We find that evolutionarily dynamic ZAD-ZNF genes, which encode the most abundant class of insect transcription factors, are more likely to encode essential functions in Drosophila melanogaster than ancient, conserved ZAD-ZNF genes. We focus on the Nicknack ZAD-ZNF gene, which is evolutionarily young, poorly retained in Drosophila species, and evolves under strong positive selection. Yet we find that it is necessary for larval development in D. melanogaster. We show that Nicknack encodes a heterochromatin-localizing protein like its paralog Oddjob, also an evolutionarily dynamic yet essential ZAD-ZNF gene. We find that the divergent D. simulans Nicknack protein can still localize to D. melanogaster heterochromatin and rescue viability of female but not male Nicknack-null D. melanogaster. Our findings suggest that innovation for rapidly changing heterochromatin functions might generally explain the essentiality of many evolutionarily dynamic ZAD-ZNF genes in insects.

ABSTRACT Mycobacterium kansasii (Mk) is an opportunistic pathogen that is frequently isolated from urban water systems, posing a health risk to susceptible individuals. Despite its ability to cause tuberculosis-like pulmonary disease, very few studies have probed the genetics of this opportunistic pathogen. Here, we report a comprehensive essentiality analysis of the Mk genome. Deep sequencing of a high-density library of Mk Himar1 transposon mutants revealed that 86.8% of the chromosomal thymine–adenine (TA) dinucleotide target sites were permissive to insertion, leaving 13.2% TA sites unoccupied. Our analysis identified 394 of the 5,350 annotated open reading frames (ORFs) as essential. The majority of these essential ORFs (84.8%) share essential mutual orthologs with Mycobacterium tuberculosis (Mtb). A comparative genomics analysis identified 139 Mk essential ORFs that share essential orthologs in four other species of mycobacteria. Thirteen Mk essential ORFs share orthologs in all four species that were identified as being not essential, while only two Mk essential ORFs are absent in all species compared. We used the essentiality data and a comparative genomics analysis reported here to highlight differences in essentiality between candidate Mtb drug targets and the corresponding Mk orthologs. Our findings suggest that the Mk genome encodes redundant or additional pathways that may confound validation of potential Mtb drugs and drug target candidates against the opportunistic pathogen. Additionally, we identified 57 intergenic regions containing four or more consecutive unoccupied TA sites. A disproportionally large number of these regions were located upstream of pe/ppe genes. Finally, we present an essentiality and orthology analysis of the Mk pRAW-like plasmid, pMK1248. IMPORTANCE Mk is one of the most common nontuberculous mycobacterial pathogens associated with tuberculosis-like pulmonary disease. Drug resistance emergence is a threat to the control of Mk infections, which already requires long-term, multidrug courses. A comprehensive understanding of Mk biology is critical to facilitate the development of new and more efficacious therapeutics against Mk. We combined transposon-based mutagenesis with analysis of insertion site identification data to uncover genes and other genomic regions required for Mk growth. We also compared the gene essentiality data set of Mk to those available for several other mycobacteria. This analysis highlighted key similarities and differences in the biology of Mk compared to these other species. Altogether, the genome-wide essentiality information generated and the results of the cross-species comparative genomics analysis represent valuable resources to assist the process of identifying and prioritizing potential Mk drug target candidates and to guide future studies on Mk biology.

Background: The study of gene essentiality, which measures the importance of a gene for cell division and survival, is used for the identification of cancer drug targets and understanding of tissue-specific manifestation of genetic conditions. In this work, we analyze essentiality and gene expression data from over 900 cancer lines from the DepMap project to create predictive models of gene essentiality. Methods: We developed machine learning algorithms to identify those genes whose essentiality levels are explained by the expression of a small set of "modifier genes". To identify these gene sets, we developed an ensemble of statistical tests capturing linear and non-linear dependencies. We trained several regression models predicting the essentiality of each target gene, and used an automated model selection procedure to identify the optimal model and hyperparameters. Overall, we examined linear models, gradient boosted trees, Gaussian process regression models, and deep learning networks. Results: We identified nearly 3000 genes for which we accurately predict essentiality using gene expression data of a small set of modifier genes. We show that both in the number of genes we successfully make predictions for, as well as in the prediction accuracy, our model outperforms current state-of-the-art works. Conclusions: Our modeling framework avoids overfitting by identifying the small set of modifier genes, which are of clinical and genetic importance, and ignores the expression of noisy and irrelevant genes. Doing so improves the accuracy of essentiality prediction in various conditions and provides interpretable models. Overall, we present an accurate computational approach, as well as interpretable modeling of essentiality in a wide range of cellular conditions, thus contributing to a better understanding of the molecular mechanisms that govern tissue-specific effects of genetic disease and cancer. [ABSTRACT FROM AUTHOR]

Modeling all the chemical reactions that take place in a minimal cell will help us understand the fundamental interactions that power life.

A gene is considered essential if its function is indispensable for the viability or reproductive success of a cell or an organism. Distinguishing essential genes from non-essential ones is a fundamental question in genetics, and it is key to understanding the minimal set of functional requirements of an organism. Knowledge of the set of essential genes is also crucial in drug discovery. Several reports in the literature show that the gene location in a protein-protein interaction network is correlated with the target gene’s essentiality. Here, we ask whether the node embeddings of a protein-protein interaction (PPI) network can help predict gene essentiality. Our results on predicting human gene essentiality show that node embeddings alone can achieve up to 88% AUC score, which is better than using topological features to characterize gene properties and other previous work’s results. We also show that, when combined with homology information across species, this performance reaches 89% AUC. Our work shows that node embeddings of a protein in the PPI network capture the network connectivity patterns of the proteins and improve the gene essentiality predictions.

ABSTRACT The massive sequencing of transposon insertion mutant libraries (Tn-Seq) represents a commonly used method to determine essential genes in bacteria. Using a hypersaturated transposon mutant library consisting of 400,096 unique Tn insertions, 523 genes were classified as essential in Escherichia coli K-12 MG1655. This provided a useful genome-wide gene essentiality landscape for rapidly identifying 233 of 301 essential genes previously validated by a knockout study. However, there was a discrepancy in essential gene sets determined by conventional gene deletion methods and Tn-Seq, although different Tn-Seq studies reported different extents of discrepancy. We have elucidated two causes of this discrepancy. First, 68 essential genes not detected by Tn-Seq contain nonessential subgenic domains that are tolerant to transposon insertion, which leads to the false assignment of an essential gene as a nonessential or dispensable gene. These genes exhibited a high level of transposon insertion in their subgenic nonessential domains. In contrast, 290 genes were additionally categorized as essential by Tn-Seq, although their knockout mutants were available. The comparative analysis of Tn-Seq and high-resolution footprinting of nucleoid-associated proteins (NAPs) revealed that a protein-DNA interaction hinders transposon insertion. We identified 213 false-positive genes caused by NAP-genome interactions. These two limitations have to be considered when addressing essential bacterial genes using Tn-Seq. Furthermore, a comparative analysis of high-resolution Tn-Seq with other data sets is required for a more accurate determination of essential genes in bacteria. IMPORTANCE Transposon mutagenesis is an efficient way to explore gene essentiality of a bacterial genome. However, there was a discrepancy between the essential gene set determined by transposon mutagenesis and that determined using single-gene knockout strains. In this study, we generated a hypersaturated Escherichia coli transposon mutant library comprising approximately 400,000 different mutants. Determination of transposon insertion sites using next-generation sequencing provided a high-resolution essentiality landscape of the E. coli genome. We identified false negatives of essential gene discovery due to the permissive insertion of transposons in the C-terminal region. Comparisons between the transposon insertion landscape with binding profiles of DNA-binding proteins revealed interference of nucleoid-associated proteins to transposon insertion, generating false positives of essential gene discovery. Consideration of these findings is required to avoid the misinterpretation of transposon mutagenesis results.

Melanoma is a heterogenous malignancy with an unpredictable clinical course. Most patients who present in the clinic are diagnosed with primary melanoma, yet large‐scale sequencing efforts have focused primarily on metastatic disease. In this study we sequence‐profiled 524 American Joint Committee on Cancer Stage I–III primary tumours. Our analysis of these data reveals recurrent driver mutations, mutually exclusive genetic interactions, where two genes were never or rarely co‐mutated, and an absence of co‐occurring genetic events. Further, we intersected copy number calls from our primary melanoma data with whole‐genome CRISPR screening data to identify the transcription factor interferon regulatory factor 4 (IRF4) as a melanoma‐associated dependency. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Plant growth-promoting (PGP) bacteria are important to the development of sustainable agricultural systems. PGP microbes that fix atmospheric nitrogen (diazotrophs) could minimize the application of industrially derived fertilizers and function as a biofertilizer. The bacterium Gluconacetobacter diazotrophicus is a nitrogen-fixing PGP microbe originally discovered in association with sugarcane plants, where it functions as an endophyte. It also forms endophyte associations with a range of other agriculturally relevant crop plants. G. diazotrophicus requires microaerobic conditions for diazotrophic growth. We generated a transposon library for G. diazotrophicus and cultured the library under various growth conditions and culture medium compositions to measure fitness defects associated with individual transposon inserts (transposon insertion sequencing [Tn-seq]). Using this library, we probed more than 3,200 genes and ascertained the importance of various genes for diazotrophic growth of this microaerobic endophyte. We also identified a set of essential genes. [ABSTRACT FROM AUTHOR]

A gene is considered essential when its loss of function results in a deleterious phenotype, hugely reducing the organism's viability or fitness. However, the link between the essentiality of a gene and its degree of polymorphism is unclear. In this review, we show that there is a place for a certain degree of variability, even for essential genes. We first study the role of infectious diseases in the prevalence of genetic disorders among humans: balancing selection has selected harmful variants due to the selective pressure of pathogens because the heterozygous carrier can resist them. Then we show that the environment can induce adaptation of species by rapidly evolving genes. We also study the role of positive selection on speciation, particularly upon genes of the immune, reproductive and nervous systems. Finally, we highlight the role of regulatory sequences in changes in morphology between species and adaptation to the environment within species. [ABSTRACT FROM AUTHOR]

CHCHD4 (MIA40) is central to the functions of the mitochondrial disulfide relay system (DRS). CHCHD4 is essential and evolutionarily conserved. Previously, we have shown CHCHD4 to be a critical regulator of tumour cell growth. Here, we use integrated analysis of our genome-wide CRISPR/Cas9 and SILAC proteomic screening data to delineate mechanisms of CHCHD4 essentiality in cancer. We identify a shortlist of common essential genes/proteins regulated by CHCHD4, including subunits of complex I that are known DRS substrates, and genes/proteins involved in key metabolic pathways. Our study highlights a range of CHCHD4-regulated nuclear encoded mitochondrial genes/proteins essential for tumour cell growth. • CHCHD4 (MIA40) is an essential, evolutionarily conserved protein, central to the mitochondrial disulfide relay system (DRS) • Previously, we have shown CHCHD4 to be a critical regulator of tumour cell growth • Here, we use genome-wide CRISPR/Cas9 and SILAC proteomic analyses to delineate mechanisms of CHCHD4 essentiality in cancer • We identify a shortlist of common essential genes/proteins involved in key metabolic pathways, including complex I subunits • Our study highlights CHCHD4-regulated nuclear encoded mitochondrial genes/proteins essential for tumour cell growth [ABSTRACT FROM AUTHOR]

Introduction: The fungal priority pathogen Cryptococcus neoformans causes cryptococcal meningoencephalitis in immunocompromised individuals and leads to hundreds of thousands of deaths per year. The undesirable side effects of existing treatments, the need for long application times to prevent the disease from recurring, the lack of resources for these treatment methods to spread over all continents necessitate the search for new treatment methods.Methods: Genome-scale models have been shown to be valuable in studying the metabolism of many organisms. Here we present the first genome-scale metabolic model for C. neoformans, iCryptococcus. This comprehensive model consists of 1,270 reactions, 1,143 metabolites, 649 genes, and eight compartments. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources and growth rate in comparison to experimental data.Results and Discussion: The compatibility of the in silico Cryptococcus metabolism under infection conditions was assessed. The steroid and amino acid metabolisms found in the essentiality analyses have the potential to be drug targets for the therapeutic strategies to be developed against Cryptococcus species. iCryptococcus model can be applied to explore new targets for antifungal drugs along with essential gene, metabolite and reaction analyses and provides a promising platform for elucidation of pathogen metabolism.

The Enterobacteriaceae are a scientifically and medically important clade of bacteria, containing the model organism Escherichia coli , as well as major human pathogens including Salmonella enterica and Klebsiella pneumoniae . Essential gene sets have been determined for several members of the Enterobacteriaceae, with the Keio E. coli single-gene deletion library often regarded as a gold standard. However, it remains unclear how gene essentiality varies between related strains and species. To investigate this, we have assembled a collection of 13 sequenced high-density transposon mutant libraries from five genera within the Enterobacteriaceae. We first assess several gene essentiality prediction approaches, investigate the effects of transposon density on essentiality prediction, and identify biases in transposon insertion sequencing data. Based on these investigations, we develop a new classifier for gene essentiality. Using this new classifier, we define a core essential genome in the Enterobacteriaceae of 201 universally essential genes. Despite the presence of a large cohort of variably essential genes, we find an absence of evidence for genus-specific essential genes. A clear example of this sporadic essentiality is given by the set of genes regulating the σE extracytoplasmic stress response, which appears to have independently acquired essentiality multiple times in the Enterobacteriaceae. Finally, we compare our essential gene sets to the natural experiment of gene loss in obligate insect endosymbionts that have emerged from within the Enterobacteriaceae. This isolates a remarkably small set of genes absolutely required for survival and identifies several instances of essential stress responses masked by redundancy in free-living bacteria.IMPORTANCEThe essential genome, that is the set of genes absolutely required to sustain life, is a core concept in genetics. Essential genes in bacteria serve as drug targets, put constraints on the engineering of biological chassis for technological or industrial purposes, and are key to constructing synthetic life. Despite decades of study, relatively little is known about how gene essentiality varies across related bacteria. In this study, we have collected gene essentiality data for 13 bacteria related to the model organism Escherichia coli , including several human pathogens, and investigated the conservation of essentiality. We find that approximately a third of the genes essential in any particular strain are non-essential in another related strain. Surprisingly, we do not find evidence for essential genes unique to specific genera; rather it appears a substantial fraction of the essential genome rapidly gains or loses essentiality during evolution. This suggests that essentiality is not an immutable characteristic but depends crucially on the genomic context. We illustrate this through a comparison of our essential genes in free-living bacteria to genes conserved in 34 insect endosymbionts with naturally reduced genomes, finding several cases where genes generally regarded as being important for specific stress responses appear to have become essential in endosymbionts due to a loss of functional redundancy in the genome., Competing Interests: The authors declare no conflict of interest.

Essential gene products carry out fundamental cellular activities in interaction with other components. However, the lack of essential gene mutants and appropriate methodologies to link essential gene functions with their partners poses significant challenges. Here, we have generated deletion mutants in 32 genes previously identified as essential, with 23 mutants showing extremely slow growth in the SK36 strain of Streptococcus sanguinis . The 23 genes corresponding to these mutants encode components of diverse pathways, are widely conserved among bacteria, and are essential in many other bacterial species. Whole-genome sequencing of 243 independently evolved populations of these mutants has identified >1000 spontaneous suppressor mutations in experimental evolution. Many of these mutations define new gene and pathway relationships, such as F1Fo-ATPase/V1Vo-ATPase/TrkA1-H1 that were demonstrated across multiple Streptococcus species. Patterns of spontaneous mutations occurring in essential gene mutants differed from those found in wildtype. While gene duplications occurred rarely and appeared most often at later stages of evolution, substitutions, deletions, and insertions were prevalent in evolved populations. These essential gene deletion mutants and spontaneous mutations fixed in the mutant populations during evolution establish a foundation for understanding gene essentiality and the interaction of essential genes in networks., Competing Interests: Declaration of interests A patent application has been submitted by P.X. and L.B. and the Virginia Commonwealth University based on these results.

Abstract Background The study of gene essentiality is fundamental to understand the basic principles of life, as well as for applications in many fields. In recent decades, dozens of sets of essential genes have been determined using different experimental and bioinformatics approaches, and this information has been useful for genome reduction of model organisms. Multiple in silico strategies have been developed to predict gene essentiality, but no optimal algorithm or set of gene features has been found yet, especially for non-model organisms with incomplete functional annotation. Results We have developed DELEAT v0.1 (DELetion design by Essentiality Analysis Tool), an easy-to-use bioinformatic tool which integrates an in silico gene essentiality classifier in a pipeline allowing automatic design of large-scale deletions in any bacterial genome. The essentiality classifier consists of a novel logistic regression model based on only six gene features which are not dependent on experimental data or functional annotation. As a proof of concept, we have applied this pipeline to the determination of dispensable regions in the genome of Bartonella quintana str. Toulouse. In this already reduced genome, 35 possible deletions have been delimited, spanning 29% of the genome. Conclusions Built on in silico gene essentiality predictions, we have developed an analysis pipeline which assists researchers throughout multiple stages of bacterial genome reduction projects, and created a novel classifier which is simple, fast, and universally applicable to any bacterial organism with a GenBank annotation file.

ABSTRACT Tuberculosis has severe impacts on both humans and animals. Understanding the genetic basis of survival of both Mycobacterium tuberculosis, the human-adapted species, and Mycobacterium bovis, the animal-adapted species, is crucial to deciphering the biology of both pathogens. There are several studies that identify the genes required for survival of M. tuberculosis in vivo using mouse models; however, there are currently no studies probing the genetic basis of survival of M. bovis in vivo. In this study, we utilize transposon insertion sequencing in M. bovis AF2122/97 to determine the genes required for survival in cattle. We identify genes encoding established mycobacterial virulence functions such as the ESX-1 secretion system, phthiocerol dimycocerosate (PDIM) synthesis, mycobactin synthesis, and cholesterol catabolism that are required in vivo. We show that, as in M. tuberculosis H37Rv, phoPR is required by M. bovis AF2122/97 in vivo despite the known defect in signaling through this system. Comparison to studies performed in species that are able to use carbohydrates as an energy source, such as M. bovis BCG and M. tuberculosis, suggests that there are differences in the requirement for genes involved in cholesterol import (mce4 operon) and oxidation (hsd). We report a good correlation with existing mycobacterial virulence functions but also find several novel virulence factors, including genes involved in protein mannosylation, aspartate metabolism, and glycerol-phosphate metabolism. These findings further extend our knowledge of the genetic basis of survival in vivo in bacteria that cause tuberculosis and provide insight for the development of novel diagnostics and therapeutics. IMPORTANCE This is the first report of the genetic requirements of an animal-adapted member of the Mycobacterium tuberculosis complex (MTBC) in a natural host. M. bovis has devastating impacts on cattle, and bovine tuberculosis is a considerable economic, animal welfare, and public health concern. The data highlight the importance of mycobacterial cholesterol catabolism and identify several new virulence factors. Additionally, the work informs the development of novel differential diagnostics and therapeutics for TB in both human and animal populations.

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The majority of the genes present in bacterial genomes remain poorly characterized, with up to one-third of those that are protein encoding having no definitive function. Transposon insertion sequencing represents a high-throughput technique that can help rectify this deficiency. The technology, however, can only be realistically applied to those species in which high rates of DNA transfer can be achieved. Here, we have developed a number of approaches that overcome this barrier in the autotrophic species Clostridium autoethanogenum by using a marinerbased transposon system. The inherent instability of such systems in the Escherichia coli conjugation donor due to transposition events was counteracted through the incorporation of a conditionally lethal codA marker on the plasmid backbone. Relatively low frequencies of transformation of the plasmid into C. autoethanogenum were circumvented through the use of a plasmid that is conditional for replication coupled with the routine implementation of an Illumina library preparation protocol that eliminates plasmid-based reads. A transposon library was then used to determine the essential genes needed for growth using carbon monoxide as the sole carbon and energy source. [ABSTRACT FROM AUTHOR]

Abstract Essential genes tend to be highly conserved across eukaryotes, but, in some cases, their critical roles can be bypassed through genetic rewiring. From a systematic analysis of 728 different essential yeast genes, we discovered that 124 (17%) were dispensable essential genes. Through whole‐genome sequencing and detailed genetic analysis, we investigated the genetic interactions and genome alterations underlying bypass suppression. Dispensable essential genes often had paralogs, were enriched for genes encoding membrane‐associated proteins, and were depleted for members of protein complexes. Functionally related genes frequently drove the bypass suppression interactions. These gene properties were predictive of essential gene dispensability and of specific suppressors among hundreds of genes on aneuploid chromosomes. Our findings identify yeast's core essential gene set and reveal that the properties of dispensable essential genes are conserved from yeast to human cells, correlating with human genes that display cell line‐specific essentiality in the Cancer Dependency Map (DepMap) project.

While high-throughput drug screening offers possibilities to profile phenotypic responses of hundreds of compounds, elucidation of the cell context-specific mechanisms of drug action requires additional analyses. To that end, we developed a computational target deconvolution pipeline that identifies the key target dependencies based on collective drug response patterns in each cell line separately. The pipeline combines quantitative drug-cell line responses with drug-target interaction networks among both intended on- and potent off-targets to identify pharmaceutically actionable and selective therapeutic targets. To demonstrate its performance, the target deconvolution pipeline was applied to 310 small molecules tested on 20 genetically and phenotypically heterogeneous triple-negative breast cancer (TNBC) cell lines to identify cell line-specific target mechanisms in terms of cytotoxic and cytostatic drug target vulnerabilities. The functional essentiality of each protein target was quantified with a target addiction score (TAS), as a measure of dependency of the cell line on the therapeutic target. The target dependency profiling was shown to capture inhibitory information that is complementary to that obtained from the structure or sensitivity of the drugs. Comparison of the TAS profiles and gene essentiality scores from CRISPR-Cas9 knockout screens revealed that certain proteins with low gene essentiality showed high target addictions, suggesting that they might be functioning as protein groups, and therefore be resistant to single gene knock-out. The comparative analysis discovered protein groups of potential multi-target synthetic lethal interactions, for instance, among histone deacetylases (HDACs). Our integrated approach also recovered a number of well-established TNBC cell line-specific drivers and known TNBC therapeutic targets, such as HDACs and cyclin-dependent kinases (CDKs). The present work provides novel insights into druggable vulnerabilities for TNBC, and opportunities to identify multi-target synthetic lethal interactions for further studies.

Abstract Background The recent reference genome assembly and annotation of the Asian malaria vector Anopheles stephensi detected only one gene encoding the leucine-rich repeat immune factor APL1, while in the Anopheles gambiae and sibling Anopheles coluzzii, APL1 factors are encoded by a family of three paralogs. The phylogeny and biological function of the unique APL1 gene in An. stephensi have not yet been specifically examined. Methods The APL1 locus was manually annotated to confirm the computationally predicted single APL1 gene in An. stephensi. APL1 evolution within Anopheles was explored by phylogenomic analysis. The single or paralogous APL1 genes were silenced in An. stephensi and An. coluzzii, respectively, followed by mosquito survival analysis, experimental infection with Plasmodium and expression analysis. Results APL1 is present as a single ancestral gene in most Anopheles including An. stephensi but has expanded to three paralogs in an African lineage that includes only the Anopheles gambiae species complex and Anopheles christyi. Silencing of the unique APL1 copy in An. stephensi results in significant mosquito mortality. Elevated mortality of APL1-depleted An. stephensi is rescued by antibiotic treatment, suggesting that pathology due to bacteria is the cause of mortality, and indicating that the unique APL1 gene is essential for host survival. Successful Plasmodium development in An. stephensi depends upon APL1 activity for protection from high host mortality due to bacteria. In contrast, silencing of all three APL1 paralogs in An. coluzzii does not result in elevated mortality, either with or without Plasmodium infection. Expression of the single An. stephensi APL1 gene is regulated by both the Imd and Toll immune pathways, while the two signaling pathways regulate different APL1 paralogs in the expanded APL1 locus. Conclusions APL1 underwent loss and gain of functions concomitant with expansion from a single ancestral gene to three paralogs in one lineage of African Anopheles. We infer that activity of the unique APL1 gene promotes longevity in An. stephensi by conferring protection from or tolerance to an effect of bacterial pathology. The evolution of an expanded APL1 gene family could be a factor contributing to the exceptional levels of malaria transmission mediated by human-feeding members of the An. gambiae species complex in Africa.

Background: The study of gene essentiality is fundamental to understand the basic principles of life, as well as for applications in many fields. In recent decades, dozens of sets of essential genes have been determined using different experimental and bioinformatics approaches, and this information has been useful for genome reduction of model organisms. Multiple in silico strategies have been developed to predict gene essentiality, but no optimal algorithm or set of gene features has been found yet, especially for non-model organisms with incomplete functional annotation. Results: We have developed DELEAT v0.1 (DELetion design by Essentiality Analysis Tool), an easy-to-use bioinformatic tool which integrates an in silico gene essentiality classifier in a pipeline allowing automatic design of large-scale deletions in any bacterial genome. The essentiality classifier consists of a novel logistic regression model based on only six gene features which are not dependent on experimental data or functional annotation. As a proof of concept, we have applied this pipeline to the determination of dispensable regions in the genome of Bartonella quintana str. Toulouse. In this already reduced genome, 35 possible deletions have been delimited, spanning 29% of the genome. Conclusions: Built on in silico gene essentiality predictions, we have developed an analysis pipeline which assists researchers throughout multiple stages of bacterial genome reduction projects, and created a novel classifier which is simple, fast, and universally applicable to any bacterial organism with a GenBank annotation file. [ABSTRACT FROM AUTHOR]

ABSTRACT TnSeq is a widely used methodology for determining gene essentiality, conditional fitness, and genetic interactions in bacteria. The Himar1 transposon is restricted to insertions at TA dinucleotides, but otherwise, few site-specific biases have been identified. As a result, most analytical approaches assume that insertions are expected to be randomly distributed among TA sites in nonessential regions. However, through analysis of Himar1 transposon libraries in Mycobacterium tuberculosis, we demonstrate that there are site-specific biases that affect the frequency of insertion of the Himar1 transposon at different TA sites. We use machine learning and statistical models to characterize patterns in the nucleotides surrounding TA sites that correlate with high or low insertion counts. We then develop a quantitative model based on these patterns that can be used to predict the expected counts at each TA site based on nucleotide context, which can explain up to half of the variance in insertion counts. We show that these insertion preferences exist in Himar1 TnSeq data sets from other mycobacterial and nonmycobacterial species. We present an improved method for identification of essential genes, called TTN-Fitness, that can better distinguish true biological fitness effects by comparing observed counts to expected counts based on our site-specific model of insertion preferences. Compared to previous essentiality methods, TTN-Fitness can make finer distinctions among genes whose disruption causes a fitness defect (or advantage), separating them out from the large pool of nonessentials, and is able to classify many smaller genes (with few TA sites) that were previously characterized as uncertain. IMPORTANCE When using the Himar1 transposon to create transposon insertion mutant libraries, it is known that the transposon is restricted to insertions at TA dinucleotide sites throughout the genome, and the absence of insertions is used to infer which genes are essential (or conditionally essential) in a bacterial organism. It is widely assumed that insertions in nonessential regions are otherwise random, and this assumption is used as the basis of several methods for statistical analysis of TnSeq data. In this paper, we show that the nucleotide sequence surrounding TA sites influences the magnitude of insertions, and these Himar1 insertion preferences (sequence biases) can partially explain why some sites have higher counts than others. We use this predictive model to make improved estimates of the fitness effects of genes, which help make finer distinctions of the phenotype and biological consequences of disruption of nonessential genes.

Abstract Background Genome-wide loss-of-function screens using the CRISPR/Cas9 system allow the efficient discovery of cancer cell vulnerabilities. While several studies have focused on correcting for DNA cleavage toxicity biases associated with copy number alterations, the effects of sgRNAs co-targeting multiple genomic loci in CRISPR screens have not been discussed. Results In this work, we analyze CRISPR essentiality screen data from 391 cancer cell lines to characterize biases induced by multi-target sgRNAs. We investigate two types of multi-targets: on-targets predicted through perfect sequence complementarity and off-targets predicted through sequence complementarity with up to two nucleotide mismatches. We find that the number of on-targets and off-targets both increase sgRNA activity in a cell line-specific manner and that existing additive models of gene knockout effects fail at capturing genetic interactions that may occur between co-targeted genes. We use synthetic lethality between paralog genes to show that genetic interactions can introduce biases in essentiality scores estimated from multi-target sgRNAs. We further show that single-mismatch tolerant sgRNAs can confound the analysis of gene essentiality and lead to incorrect co-essentiality functional networks. Lastly, we also find that single nucleotide polymorphisms located in protospacer regions can impair on-target activity as a result of mismatch tolerance. Conclusion We show the impact of multi-target effects on estimating cancer cell dependencies and the impact of off-target effects caused by mismatch tolerance in sgRNA-DNA binding.

The causal genetic variants underlying more than 50% of single gene (monogenic) disorders are yet to be discovered. Many patients with conditions likely to have a monogenic basis do not receive a confirmed molecular diagnosis which has potential impacts on clinical management. We have developed a gene-specific score, essentiality-specific pathogenicity prioritization (ESPP), to guide the recognition of genes likely to underlie monogenic disease variation to assist in filtering of genome sequence data. When a patient genome is sequenced, there are frequently several plausibly pathogenic variants identified in different genes. Recognition of the single gene most likely to include pathogenic variation can guide the identification of a causal variant. The ESPP score integrates gene-level scores which are broadly related to gene essentiality. Previous work towards the recognition of monogenic disease genes proposed a model with increasing gene essentiality from 'non-essential' to 'essential' genes (for which pathogenic variation may be incompatible with survival) with genes liable to contain disease variation positioned between these two extremes. We demonstrate that the ESPP score is useful for recognizing genes with high potential for pathogenic disease-related variation. Genes classed as essential have particularly high scores, as do genes recently recognized as strong candidates for developmental disorders. Through the integration of individual gene-specific scores, which have different properties and assumptions, we demonstrate the utility of an essentiality-based gene score to improve sequence genome filtering. [ABSTRACT FROM AUTHOR]

Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as a priori guide to researchers in the pharmaceutical field. Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.

Contrary to dogma, evolutionarily young and dynamic genes can encode essential functions. We find that evolutionarily dynamic ZAD-ZNF genes, which encode the most abundant class of insect transcription factors, are more likely to encode essential functions in Drosophila melanogaster than ancient, conserved ZAD-ZNF genes. We focus on the Nicknack ZAD-ZNF gene, which is evolutionarily young, poorly retained in Drosophila species, and evolves under strong positive selection. Yet we find that it is necessary for larval development in D. melanogaster. We show that Nicknack encodes a heterochromatin-localizing protein like its paralog Oddjob, also an evolutionarily dynamic yet essential ZAD-ZNF gene. We find that the divergent D. simulans Nicknack protein can still localize to D. melanogaster heterochromatin and rescue viability of female but not male Nicknack-null D. melanogaster. Our findings suggest that innovation for rapidly changing heterochromatin functions might generally explain the essentiality of many evolutionarily dynamic ZAD-ZNF genes in insects.

ABSTRACT Enterococcus faecalis is an opportunistic pathogen, which can cause multidrug-resistant life-threatening infections. Gaining a complete understanding of enterococcal pathogenesis is a crucial step in identifying a strategy to effectively treat enterococcal infections. However, bacterial pathogenesis is a complex process often involving a combination of genes and multilevel regulation. Compared to established knockout methodologies, CRISPR interference (CRISPRi) approaches enable the rapid and efficient silencing of genes to interrogate gene products and pathways involved in pathogenesis. As opposed to traditional gene inactivation approaches, CRISPRi can also be quickly repurposed for multiplexing or used to study essential genes. Here, we have developed a novel dual-vector nisin-inducible CRISPRi system in E. faecalis that can efficiently silence via both nontemplate and template strand targeting. Since the nisin-controlled gene expression system is functional in various Gram-positive bacteria, the developed CRISPRi tool can be extended to other genera. This system can be applied to study essential genes, genes involved in antimicrobial resistance, and genes involved in biofilm formation and persistence. The system is robust and can be scaled up for high-throughput screens or combinatorial targeting. This tool substantially enhances our ability to study enterococcal biology and pathogenesis, host-bacterium interactions, and interspecies communication. IMPORTANCE Enterococcus faecalis causes multidrug-resistant life-threatening infections and is often coisolated with other pathogenic bacteria from polymicrobial biofilm-associated infections. Genetic tools to dissect complex interactions in mixed microbial communities are largely limited to transposon mutagenesis and traditional time- and labor-intensive allelic-exchange methods. Built upon streptococcal dCas9, we developed an easily modifiable, inducible CRISPRi system for E. faecalis that can efficiently silence single and multiple genes. This system can silence genes involved in biofilm formation and antibiotic resistance and can be used to interrogate gene essentiality. Uniquely, this tool is optimized to study genes important for biofilm initiation, maturation, and maintenance and can be used to perturb preformed biofilms. This system will be valuable to rapidly and efficiently investigate a wide range of aspects of complex enterococcal biology.

Abstract Mycobacterium kansasii (Mk) is a resilient opportunistic human pathogen that causes tuberculosis‐like chronic pulmonary disease and mortality stemming from comorbidities and treatment failure. The standard treatment of Mk infections requires costly, long‐term, multidrug courses with adverse side effects. The emergence of drug‐resistant isolates further complicates the already challenging drug therapy regimens and threatens to compromise the future control of Mk infections. Despite the increasingly recognized global burden of Mk infections, the biology of this opportunistic pathogen remains essentially unexplored. In particular, studies reporting gene function or generation of defined mutants are scarce. Moreover, no transposon (Tn) mutagenesis tool has been validated for use in Mk, a situation limiting the repertoire of genetic approaches available to accelerate the dissection of gene function and the generation of gene knockout mutants in this poorly characterized pathogen. In this study, we validated the functionality of a powerful Tn mutagenesis tool in Mk and used this tool in conjunction with a forward genetic screen to establish a previously unrecognized role of a conserved mycobacterial small RNA gene of unknown function in colony morphology features and biofilm formation. We also combined Tn mutagenesis with next‐generation sequencing to identify 12,071 Tn insertions that do not compromise viability in vitro. Finally, we demonstrated the susceptibility of the Galleria mellonella larva to Mk, setting the stage for further exploration of this simple and economical infection model system to the study of this pathogen.

Different genes have their protein products localized in various subcellular compartments. The diversity in protein localization may serve as a gene characteristic, revealing gene essentiality from a subcellular perspective. To measure this diversity, we introduced a Subcellular Diversity Index (SDI) based on the Gene Ontology-Cellular Component Ontology (GO-CCO) and a semantic similarity measure of GO terms. Analyses revealed that SDI of human genes was well correlated with some known measures of gene essentiality, including protein–protein interaction (PPI) network topology measurements, dN/dS ratio, homologous gene number, expression level and tissue specificity. In addition, SDI had a good performance in predicting human essential genes (AUC = 0.702) and drug target genes (AUC = 0.704), and drug targets with higher SDI scores tended to cause more side-effects. The results suggest that SDI could be used to identify novel drug targets and to guide the filtering of drug targets with fewer potential side effects. Finally, we developed a user-friendly online database for querying SDI score for genes across eight species, and the predicted probabilities of human drug target based on SDI. The online database of SDI is available at: http://www.cuilab.cn/sdi.

Candida albicans is one of the most impactful fungal pathogens and the most common cause of invasive candidiasis, which is associated with very high mortality rates. With the rise in the frequency of multidrug-resistant clinical isolates, the identification of new drug targets and new drugs is crucial in overcoming the increase in therapeutic failure. In this study, the first validated genome-scale metabolic model for Candida albicans, iRV781, is presented. The model consists of 1221 reactions, 926 metabolites, 781 genes, and four compartments. This model was reconstructed using the open-source software tool merlin 4.0.2. It is provided in the well-established systems biology markup language (SBML) format, thus, being usable in most metabolic engineering platforms, such as OptFlux or COBRA. The model was validated, proving accurate when predicting the capability of utilizing different carbon and nitrogen sources when compared to experimental data. Finally, this genome-scale metabolic reconstruction was tested as a platform for the identification of drug targets, through the comparison between known drug targets and the prediction of gene essentiality in conditions mimicking the human host. Altogether, this model provides a promising platform for global elucidation of the metabolic potential of C. albicans, possibly guiding the identification of new drug targets to tackle human candidiasis. [ABSTRACT FROM AUTHOR]

Essential genes tend to be highly conserved across eukaryotes, but, in some cases, their critical roles can be bypassed through genetic rewiring. From a systematic analysis of 728 different essential yeast genes, we discovered that 124 (17%) were dispensable essential genes. Through whole‐genome sequencing and detailed genetic analysis, we investigated the genetic interactions and genome alterations underlying bypass suppression. Dispensable essential genes often had paralogs, were enriched for genes encoding membrane‐associated proteins, and were depleted for members of protein complexes. Functionally related genes frequently drove the bypass suppression interactions. These gene properties were predictive of essential gene dispensability and of specific suppressors among hundreds of genes on aneuploid chromosomes. Our findings identify yeast's core essential gene set and reveal that the properties of dispensable essential genes are conserved from yeast to human cells, correlating with human genes that display cell line‐specific essentiality in the Cancer Dependency Map (DepMap) project. Synopsis: A systematic analysis of 728 different essential yeast genes identifies 124 (17%) dispensable essential genes. Whole‐genome sequencing is used to identify the genome alterations underlying the bypass suppression. Dispensable essential genes show distinct properties that can be used to predict essential gene dispensability and are conserved from yeast to human cells.Bypass suppressors often show a strong functional connection to the dispensable essential gene, which can be used to predict suppressor genes.Dispensable essential genes can generally only be suppressed by a single genetic mechanism, including aneuploidies and mutations in specific suppressor genes, which involve both loss‐of-function and gain‐of-function alleles.A list of 805 core essential genes is defined that are either absolutely required for cell viability in yeast or only suppressed by highly complex genetic mechanisms. [ABSTRACT FROM AUTHOR]

Different genes have their protein products localized in various subcellular compartments. The diversity in protein localization may serve as a gene characteristic, revealing gene essentiality from a subcellular perspective. To measure this diversity, we introduced a Subcellular Diversity Index (SDI) based on the Gene Ontology-Cellular Component Ontology (GO-CCO) and a semantic similarity measure of GO terms. Analyses revealed that SDI of human genes was well correlated with some known measures of gene essentiality, including protein–protein interaction (PPI) network topology measurements, dN/dS ratio, homologous gene number, expression level and tissue specificity. In addition, SDI had a good performance in predicting human essential genes (AUC = 0.702) and drug target genes (AUC = 0.704), and drug targets with higher SDI scores tended to cause more side-effects. The results suggest that SDI could be used to identify novel drug targets and to guide the filtering of drug targets with fewer potential side effects. Finally, we developed a user-friendly online database for querying SDI score for genes across eight species, and the predicted probabilities of human drug target based on SDI. The online database of SDI is available at: http://www.cuilab.cn/sdi. [ABSTRACT FROM AUTHOR]

Background: The recent reference genome assembly and annotation of the Asian malaria vector Anopheles stephensi detected only one gene encoding the leucine-rich repeat immune factor APL1, while in the Anopheles gambiae and sibling Anopheles coluzzii, APL1 factors are encoded by a family of three paralogs. The phylogeny and biological function of the unique APL1 gene in An. stephensi have not yet been specifically examined. Methods: The APL1 locus was manually annotated to confirm the computationally predicted single APL1 gene in An. stephensi. APL1 evolution within Anopheles was explored by phylogenomic analysis. The single or paralogous APL1 genes were silenced in An. stephensi and An. coluzzii, respectively, followed by mosquito survival analysis, experimental infection with Plasmodium and expression analysis. Results: APL1 is present as a single ancestral gene in most Anopheles including An. stephensi but has expanded to three paralogs in an African lineage that includes only the Anopheles gambiae species complex and Anopheles christyi. Silencing of the unique APL1 copy in An. stephensi results in significant mosquito mortality. Elevated mortality of APL1-depleted An. stephensi is rescued by antibiotic treatment, suggesting that pathology due to bacteria is the cause of mortality, and indicating that the unique APL1 gene is essential for host survival. Successful Plasmodium development in An. stephensi depends upon APL1 activity for protection from high host mortality due to bacteria. In contrast, silencing of all three APL1 paralogs in An. coluzzii does not result in elevated mortality, either with or without Plasmodium infection. Expression of the single An. stephensi APL1 gene is regulated by both the Imd and Toll immune pathways, while the two signaling pathways regulate different APL1 paralogs in the expanded APL1 locus. Conclusions: APL1 underwent loss and gain of functions concomitant with expansion from a single ancestral gene to three paralogs in one lineage of African Anopheles. We infer that activity of the unique APL1 gene promotes longevity in An. stephensi by conferring protection from or tolerance to an effect of bacterial pathology. The evolution of an expanded APL1 gene family could be a factor contributing to the exceptional levels of malaria transmission mediated by human-feeding members of the An. gambiae species complex in Africa. [ABSTRACT FROM AUTHOR]

Streptomyces albus J1074 is recognized as an effective host for heterologous production of natural products. Its fast growth and efficient genetic toolbox due to a naturally minimized genome have contributed towards its advantage in expressing biosynthetic pathways for a diverse repertoire of products such as antibiotics and flavonoids. In order to develop precise model-driven engineering strategies for de novo production of natural products, a genome-scale metabolic model (GEM) was reconstructed for the microorganism based on protein homology to model species Streptomyces coelicolor while drawing annotated data from databases and literature for further curation. To demonstrate its capabilities, the Salb-GEM was used to predict overexpression targets for desirable compounds using flux scanning with enforced objective function (FSEOF). Salb-GEM was also utilized to investigate the effect of a minimized genome on metabolic gene essentialities in comparison to another Streptomyces species, S. coelicolor.

Abstract Background With the explosion of data comes a proportional opportunity to identify novel knowledge with the potential for application in targeted therapies. In spite of this huge amounts of data, the solutions to treating complex disease is elusive. One reason being that these diseases are driven by a network of genes that need to be targeted in order to understand and treat them effectively. Part of the solution lies in mining and integrating information from various disciplines. Here we propose a machine learning method to mining through publicly available literature on RNA interference with the goal of identifying genes essential for cell survival. Results A total of 32,164 RNA interference abstracts were identified from 10.5 million pubmed abstracts (2001 - 2015). These abstracts spanned over 1467 cancer cell lines and 4373 genes representing a total of 25,891 cell gene associations. Among the 1467 cell lines 88% of them had at least 1 or up to 25 genes studied in a given cell line. Among the 4373 genes 96% of them were studied in at least 1 or up to 25 different cell lines. Conclusions Identifying genes that are crucial for cell survival can be a critical piece of information especially in treating complex diseases, such as cancer. The efficacy of a therapeutic intervention is multifactorial in nature and in many cases the source of therapeutic disruption could be from an unsuspected source. Machine learning algorithms helps to narrow down the search and provides information about essential genes in different cancer types. It also provides the building blocks to generate a network of interconnected genes and processes. The information thus gained can be used to generate hypothesis which can be experimentally validated to improve our understanding of what triggers and maintains the growth of cancerous cells.

Abstract Background Genome-wide loss-of-function profiling is widely used for systematic identification of genetic dependencies in cancer cells; however, the poor reproducibility of RNA interference (RNAi) screens has been a major concern due to frequent off-target effects. Currently, a detailed understanding of the key factors contributing to the sub-optimal consistency is still a lacking, especially on how to improve the reliability of future RNAi screens by controlling for factors that determine their off-target propensity. Methods We performed a systematic, quantitative analysis of the consistency between two genome-wide shRNA screens conducted on a compendium of cancer cell lines, and also compared several gene summarization methods for inferring gene essentiality from shRNA level data. We then devised novel concepts of seed essentiality and shRNA family, based on seed region sequences of shRNAs, to study in-depth the contribution of seed-mediated off-target effects to the consistency of the two screens. We further investigated two seed-sequence properties, seed pairing stability, and target abundance in terms of their capability to minimize the off-target effects in post-screening data analysis. Finally, we applied this novel methodology to identify genetic interactions and synthetic lethal partners of cancer drivers, and confirmed differential essentiality phenotypes by detailed CRISPR/Cas9 experiments. Results Using the novel concepts of seed essentiality and shRNA family, we demonstrate how genome-wide loss-of-function profiling of a common set of cancer cell lines can be actually made fairly reproducible when considering seed-mediated off-target effects. Importantly, by excluding shRNAs having higher propensity for off-target effects, based on their seed-sequence properties, one can remove noise from the genome-wide shRNA datasets. As a translational application case, we demonstrate enhanced reproducibility of genetic interaction partners of common cancer drivers, as well as identify novel synthetic lethal partners of a major oncogenic driver, PIK3CA, supported by a complementary CRISPR/Cas9 experiment. Conclusions We provide practical guidelines for improved design and analysis of genome-wide loss-of-function profiling and demonstrate how this novel strategy can be applied towards improved mapping of genetic dependencies of cancer cells to aid development of targeted anticancer treatments.

ABSTRACT Bordetella pertussis is the causative agent of whooping cough, a serious respiratory illness affecting children and adults, associated with prolonged cough and potential mortality. Whooping cough has reemerged in recent years, emphasizing a need for increased knowledge of basic mechanisms of B. pertussis growth and pathogenicity. While previous studies have provided insight into in vitro gene essentiality of this organism, very little is known about in vivo gene essentiality, a critical gap in knowledge, since B. pertussis has no previously identified environmental reservoir and is isolated from human respiratory tract samples. We hypothesize that the metabolic capabilities of B. pertussis are especially tailored to the respiratory tract and that many of the genes involved in B. pertussis metabolism would be required to establish infection in vivo. In this study, we generated a diverse library of transposon mutants and then used it to probe gene essentiality in vivo in a murine model of infection. Using the CON-ARTIST pipeline, 117 genes were identified as conditionally essential at 1 day postinfection, and 169 genes were identified as conditionally essential at 3 days postinfection. Most of the identified genes were associated with metabolism, and we utilized two existing genome-scale metabolic network reconstructions to probe the effects of individual essential genes on biomass synthesis. This analysis suggested a critical role for glucose metabolism and lipooligosaccharide biosynthesis in vivo. This is the first genome-wide evaluation of in vivo gene essentiality in B. pertussis and provides tools for future exploration. IMPORTANCE Our study describes the first in vivo transposon sequencing (Tn-seq) analysis of B. pertussis and identifies genes predicted to be essential for in vivo growth in a murine model of intranasal infection, generating key resources for future investigations into B. pertussis pathogenesis and vaccine design.

Paracoccidioidomycosis (PCM) is the most prevalent endemic mycosis in Latin America. The disease is caused by fungi of the genus Paracoccidioides and mainly affects low-income rural workers after inhalation of fungal conidia suspended in the air. The current arsenal of chemotherapeutic agents requires long-term administration protocols. In addition, chemotherapy is related to a significantly increased frequency of disease relapse, high toxicity, and incomplete elimination of the fungus. Due to the limitations of current anti-PCM drugs, we developed a computational drug repurposing-chemogenomics approach to identify approved drugs or drug candidates in clinical trials with anti-PCM activity. In contrast to the one-drug-one-target paradigm, our chemogenomics approach attempts to predict interactions between drugs, and Paracoccidioides protein targets. To achieve this goal, we designed a workflow with the following steps: (a) compilation and preparation of Paracoccidioides spp. genome data; (b) identification of orthologous proteins among the isolates; (c) identification of homologous proteins in publicly available drug-target databases; (d) selection of Paracoccidioides essential targets using validated genes from Saccharomyces cerevisiae; (e) homology modeling and molecular docking studies; and (f) experimental validation of selected candidates. We prioritized 14 compounds. Two antineoplastic drug candidates (vistusertib and BGT-226) predicted to be inhibitors of phosphatidylinositol 3-kinase TOR2 showed antifungal activity at low micromolar concentrations (

Modeling all the chemical reactions that take place in a minimal cell will help us understand the fundamental interactions that power life.