Essential Genes In Bacteria Research Articles

High-throughput transposon mutagenesis in the family Enterobacteriaceae reveals core essential genes and rapid turnover of essentiality.

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.

Differentially used codons among essential genes in bacteria identified by machine learning-based analysis.

Codon usage bias (CUB), the uneven usage of synonymous codons encoding the same amino acid, differs among genes within and across bacteria genomes. CUB is known to be influenced by gene expression and accordingly, CUB differs between the high-expression and low-expression genes in several bacteria. In this article, we have extended codon usage study considering gene essentiality as a feature. Using machine learning (ML) based approaches, we have analysed Relative Synonymous Codon Usage (RSCU) values between essential and non-essential genes in Escherichia coli and thirty-four other bacterial genomes whose gene essentiality features were available in public databases. We observed significant differences in codon usage patterns between essential and non-essential genes for majority of the bacterial genomes and accordingly, ML based classifiers achieved high area under curve (AUC) scores, with a minimum score of 70.0 across twenty-eight organisms. Further, importance of the codons towards classifying genes found to differ among the codons in each genome. Arg codon CGT and Gly codon GGT were observed to be the most preferred codons among essential genes in Escherichia coli. Interestingly, some of the codons like CGT, ATA, GGT and GGG observed to be contributing consistently towards classifying essential genes across thirty-five bacteria genomes studied. In other hand, codons TGY and CAY encoding amino acids Cys and His respectively were among the least contributing codons towards classification among all these bacteria. This study demonstrates the gene essentiality based differences in synonymous codon usage in bacteria genomes and presents a common codon usage pattern across bacteria.

DeeplyEssential: a deep neural network for predicting essential genes in microbes

BackgroundEssential genes are those genes that are critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies.ResultsWe propose a deep neural network for predicting essential genes in microbes. Our architecture called DeeplyEssential makes minimal assumptions about the input data (i.e., it only uses gene primary sequence and the corresponding protein sequence) to carry out the prediction thus maximizing its practical application compared to existing predictors that require structural or topological features which might not be readily available. We also expose and study a hidden performance bias that effected previous classifiers. Extensive results show that DeeplyEssential outperform existing classifiers that either employ down-sampling to balance the training set or use clustering to exclude multiple copies of orthologous genes.ConclusionDeep neural network architectures can efficiently predict whether a microbial gene is essential (or not) using only its sequence information.

CRISPR/Cas9 recombineering‐mediated deep mutational scanning of essential genes in Escherichia coli

Deep mutational scanning can provide significant insights into the function of essential genes in bacteria. Here, we developed a high‐throughput method for mutating essential genes of Escherichia coli in their native genetic context. We used Cas9‐mediated recombineering to introduce a library of mutations, created by error‐prone PCR, within a gene fragment on the genome using a single gRNA pre‐validated for high efficiency. Tracking mutation frequency through deep sequencing revealed biases in the position and the number of the introduced mutations. We overcame these biases by increasing the homology arm length and blocking mismatch repair to achieve a mutation efficiency of 85% for non‐essential genes and 55% for essential genes. These experiments also improved our understanding of poorly characterized recombineering process using dsDNA donors with single nucleotide changes. Finally, we applied our technology to target rpoB, the beta subunit of RNA polymerase, to study resistance against rifampicin. In a single experiment, we validate multiple biochemical and clinical observations made in the previous decades and provide insights into resistance compensation with the study of double mutants.

MSphere of Influence: Uncovering New Ways To Control Multidrug Resistance by Dissecting Essential Cell Processes.

Ana L. Flores-Mireles works in the fields of microbial pathogenesis and development of new therapeutics. In this mSphere of Influence article, she reflects on how the papers "Bacterial cell wall biogenesis is mediated by SEDS and PBP polymerase families functioning semi-autonomously" by H. Cho et al. (Nat Microbiol 1:16172, 2016, https://doi.org/10.1038/nmicrobiol.2016.172) and "A comprehensive, CRISPR-based functional analysis of essential genes in bacteria" by J. M. Peters et al. (Cell 165:1493-1506, 2016, https://doi.org/10.1016/j.cell.2016.05.003) made an impact on her approach to dissecting essential processes to understand microbial pathogenesis in catheter-associated urinary tract infections and generate an effective treatment with reduced likelihood of developing resistance.

CRISPR Interference for Rapid Knockdown of Essential Cell Cycle Genes in Lactobacillus plantarum.

Studies of essential genes in bacteria are often hampered by the lack of accessible genetic tools. This is also the case for Lactobacillus plantarum, a key species in food and health applications. Here, we develop a clustered regularly interspaced short palindromic repeat interference (CRISPRi) system for knockdown of gene expression in L. plantarum The two-plasmid CRISPRi system, in which a nuclease-inactivated Cas9 (dCas9) and a gene-specific single guide RNA (sgRNA) are expressed on separate plasmids, allows efficient knockdown of expression of any gene of interest. We utilized the CRISPRi system to gain initial insights into the functions of key cell cycle genes in L. plantarum As a proof of concept, we investigated the phenotypes resulting from knockdowns of the cell wall hydrolase-encoding acm2 gene and of the DNA replication initiator gene dnaA and of ezrA, which encodes an early cell division protein. Furthermore, we studied the phenotypes of three cell division genes which have recently been functionally characterized in ovococcal bacteria but whose functions have not yet been investigated in rod-shaped bacteria. We show that the transmembrane CozE proteins do not seem to play any major role in cell division in L. plantarum On the other hand, RNA-binding proteins KhpA and EloR are critical for proper cell elongation in this bacterium.IMPORTANCEL. plantarum is an important bacterium for applications in food and health. Deep insights into the biology and physiology of this species are therefore necessary for further strain optimization and exploitation; however, the functions of essential genes in the bacterium are mainly unknown due to the lack of accessible genetic tools. The CRISPRi system developed here is ideal to quickly screen for phenotypes of both essential and nonessential genes. Our initial insights into the function of some key cell cycle genes represent the first step toward understanding the cell cycle in this bacterium.

IEsGene-ZCPseKNC: Identify Essential Genes Based on Z Curve Pseudo $k$ -Tuple Nucleotide Composition

As an important technique for synthetic biology, computational identification of essential genes will facilitate the development of the related fields, such as genome analysis, drug design, etc. The identification of prokaryotic essential genes has been extensively studied, especially focusing on the essential genes in bacteria. Archaea as an important domain in prokaryote exists high variance of genome sizes. However, there is no predictor available for predicting essential genes in archaea. In this paper, we developed the first computational predictor for predicting essential genes in archaea called iEsGene-ZCPseKNC. With the purpose of capturing sequence patterns of the essential genes, a new feature called Z curve pseudo $k$ -tuple nucleotide composition (ZCPseKNC) was proposed, which incorporates the advantages of both Z curve and pseudo $k$ -tuple nucleotide composition (PseKNC). In order to overcome the problems caused by the imbalanced training set, the SMOTE algorithm was employed to further improve the predictive performance of iEsGene-ZCPseKNC. Evaluated by the rigorous jackknife test on a benchmark dataset, the experimental results showed that the iEsGene-ZCPseKNC predictor outperformed the predictors based on Z curve and PseKNC, indicating that iEsGene-ZCPseKNC is useful for identification of essential genes in archaea, and would be a powerful tool for genome analysis. A user friendly web server of the iEsGene-ZCPseKNC predictor was established and can be easily accessed from http://bliulab.net/iEsGene-ZCPseKNC/ .

Exploration of Using Antisense Peptide Nucleic Acid (PNA)-cell Penetrating Peptide (CPP) as a Novel Bactericide against Fire Blight Pathogen Erwinia amylovora

Erwinia amylovora is a Gram-negative bacterial plant pathogen in the family Enterobacteriaceae and is the causal agent of fire blight, a devastating disease of apple and pear. Fire blight is traditionally managed by the application of the antibiotic streptomycin during bloom, but this strategy has been challenged by the development and spread of streptomycin resistance. Thus, there is an urgent need for effective, specific, and sustainable control alternatives for fire blight. Antisense antimicrobials are oligomers of nucleic acid homologs with antisense sequence of essential genes in bacteria. The binding of these molecules to the mRNA of essential genes can result in translational repression and antimicrobial effect. Here, we explored the possibility of developing antisense antimicrobials against E. amylovora and using these compounds in fire blight control. We determined that a 10-nucleotide oligomer of peptide nucleic acid (PNA) targeting the start codon region of an essential gene acpP is able to cause complete growth inhibition of E. amylovora. We found that conjugation of cell penetrating peptide (CPP) to PNA is essential for the antimicrobial effect, with CPP1 [(KFF)3K] being the most effective against E. amylovora. The minimal inhibitory concentration (MIC) of anti-acpP-CPP1 (2.5 μM) is comparable to the MIC of streptomycin (2 μM). Examination of the antimicrobial mechanisms demonstrated that anti-acpP-CPP1 caused dose-dependent reduction of acpP mRNA in E. amylovora upon treatment and resulted in cell death (bactericidal effect). Anti-acpP-CPP1 (100 μM) is able to effectively limit the pathogen growth on stigmas of apple flowers, although less effective than streptomycin. Finally, unlike streptomycin that does not display any specificity in inhibiting pathogen growth, anti-acpP-CPP1 has more specific antimicrobial effect against E. amylovora. In summary, we demonstrated that PNA–CPP can cause an effective, specific antimicrobial effect against E. amylovora and may provide the basis for a novel approach for fire blight control.

The Genome-Wide Interaction Network of Nutrient Stress Genes in Escherichia coli.

ABSTRACTConventional efforts to describe essential genes in bacteria have typically emphasized nutrient-rich growth conditions. Of note, however, are the set of genes that become essential when bacteria are grown under nutrient stress. For example, more than 100 genes become indispensable when the model bacterium Escherichia coli is grown on nutrient-limited media, and many of these nutrient stress genes have also been shown to be important for the growth of various bacterial pathogens in vivo. To better understand the genetic network that underpins nutrient stress in E. coli, we performed a genome-scale cross of strains harboring deletions in some 82 nutrient stress genes with the entire E. coli gene deletion collection (Keio) to create 315,400 double deletion mutants. An analysis of the growth of the resulting strains on rich microbiological media revealed an average of 23 synthetic sick or lethal genetic interactions for each nutrient stress gene, suggesting that the network defining nutrient stress is surprisingly complex. A vast majority of these interactions involved genes of unknown function or genes of unrelated pathways. The most profound synthetic lethal interactions were between nutrient acquisition and biosynthesis. Further, the interaction map reveals remarkable metabolic robustness in E. coli through pathway redundancies. In all, the genetic interaction network provides a powerful tool to mine and identify missing links in nutrient synthesis and to further characterize genes of unknown function in E. coli. Moreover, understanding of bacterial growth under nutrient stress could aid in the development of novel antibiotic discovery platforms.

A Comprehensive, CRISPR-based Functional Analysis of Essential Genes in Bacteria

Essential gene functions underpin the core reactions required for cell viability, but their contributions andrelationships are poorly studied invivo. Using CRISPR interference, we created knockdowns of every essential gene in Bacillus subtilis and probed their phenotypes. Our high-confidence essential gene network, established using chemical genomics, showed extensive interconnections among distantly related processes and identified modes of action for uncharacterized antibiotics. Importantly, mild knockdown of essential gene functions significantly reduced stationary-phase survival without affecting maximal growth rate, suggesting that essential protein levels are set to maximize outgrowth from stationary phase. Finally, high-throughput microscopy indicated that cell morphology is relatively insensitive to mild knockdown but profoundly affected by depletion of gene function, revealing intimate connections between cell growth and shape. Our results provide a framework for systematic investigation of essential gene functions invivo broadly applicable to diverse microorganisms and amenable to comparative analysis.

Utilization of bromine azide to access vicinal-azidobromides from arylidene malononitrile

Abstract An efficient and facile approach for tetrachlorosilane as an in situ mediated transformation via a one-pot, synthesis of vicinal bromoazides through the generation of BrN 3 from azidochlorosilane and N -bromosuccinimide in acetonitrile as solvent at ambient temperature is achieved. This catalytic process represents a highly regioselective and high yielding method for the synthesis of 1,2-bromoazides. Thiamine pyrophosphate (TPP) riboswitches regulate essential genes in bacteria by changing conformation upon binding intracellular TPP. Molecular docking studies are conducted to understand the orientation and the interaction of each synthesized molecules with TPP riboswitches.

Uncovering major genomic features of essential genes in Bacteria and a methanogenic Archaea.

Identification of essential genes is critical to understanding the physiology of a species, proposing novel drug targets and uncovering minimal gene sets required for life. Although essential gene sets of several organisms have been determined using large-scale mutagenesis techniques, systematic studies addressing their conservation, genomic context and functions remain scant. Here we integrate 17 essential gene sets from genome-wide in vitro screenings and three gene collections required for growth in vivo, encompassing 15 Bacteria and one Archaea. We refine and generalize important theories proposed using Escherichia coli. Essential genes are typically monogenic and more conserved than nonessential genes. Genes required in vivo are less conserved than those essential in vitro, suggesting that more divergent strategies are deployed when the organism is stressed by the host immune system and unstable nutrient availability. We identified essential analogous pathways that would probably be missed by orthology-based essentiality prediction strategies. For example, Streptococcus sanguinis carries horizontally transferred isoprenoid biosynthesis genes that are widespread in Archaea. Genes specifically essential in Mycobacterium tuberculosis and Burkholderia pseudomallei are reported as potential drug targets. Moreover, essential genes are not only preferentially located in operons, but also occupy the first position therein, supporting the influence of their regulatory regions in driving transcription of whole operons. Finally, these important genomic features are shared between Bacteria and at least one Archaea, suggesting that high order properties of gene essentiality and genome architecture were probably present in the last universal common ancestor or evolved independently in the prokaryotic domains.

Capturing Uncertainty by Modeling Local Transposon Insertion Frequencies Improves Discrimination of Essential Genes.

Transposon mutagenesis experiments enable the identification of essential genes in bacteria. Deep-sequencing of mutant libraries provides a large amount of high-resolution data on essentiality. Statistical methods developed to analyze this data have traditionally assumed that the probability of observing a transposon insertion is the same across the genome. This assumption, however, is inconsistent with the observed insertion frequencies from transposon mutant libraries of M. tuberculosis. We propose a modified Binomial model of essentiality that can characterize the insertion probability of individual genes in which we allow local variation in the background insertion frequency in different non-essential regions of the genome. Using the Metropolis-Hastings algorithm, samples of the posterior insertion probabilities were obtained for each gene, and the probability of each gene being essential is estimated. We compared our predictions to those of previous methods and show that, by taking into consideration local insertion frequencies, our method is capable of making more conservative predictions that better match what is experimentally known about essential and non-essential genes.

Validating Fragment-Based Drug Discovery for Biological RNAs: Lead Fragments Bind and Remodel the TPP Riboswitch Specifically

Thiamine pyrophosphate (TPP) riboswitches regulate essential genes in bacteria by changing conformation upon binding intracellular TPP. Previous studies using fragment-based approaches identified small molecule "fragments" that bind this gene-regulatory mRNA domain. Crystallographic studies now show that, despite having micromolar Kds, four different fragments bind the TPP riboswitch site-specifically, occupying the pocket that recognizes the aminopyrimidine of TPP. Unexpectedly, the unoccupied site that would recognize the pyrophosphate of TPP rearranges into a structure distinct from that of the cognate complex. This idiosyncratic fragment-induced conformation, also characterized by small-angle X-ray scattering and chemical probing, represents a possible mechanism for adventitious ligand discrimination by the riboswitch, and suggests that off-pathway conformations of RNAs can be targeted for drug development. Our structures, together with previous screening studies, demonstrate the feasibility of fragment-based drug discovery against RNA targets.

Bayesian analysis of gene essentiality based on sequencing of transposon insertion libraries

Next-generation sequencing affords an efficient analysis of transposon insertion libraries, which can be used to identify essential genes in bacteria. To analyse this high-resolution data, we present a formal Bayesian framework for estimating the posterior probability of essentiality for each gene, using the extreme-value distribution to characterize the statistical significance of the longest region lacking insertions within a gene. We describe a sampling procedure based on the Metropolis-Hastings algorithm to calculate posterior probabilities of essentiality while simultaneously integrating over unknown internal parameters. Using a sequence dataset from a transposon library for Mycobacterium tuberculosis, we show that this Bayesian approach predicts essential genes that correspond well with genes shown to be essential in previous studies. Furthermore, we show that by using the extreme-value distribution to characterize genomic regions lacking transposon insertions, this method is capable of identifying essential domains within genes. This approach can be used for analysing transposon libraries in other organisms and augmenting essentiality predictions with statistical confidence scores.

A Mystery Unraveled: Essentiality of RNase III in Bacillus subtilis Is Caused by Resident Prophages

One of the key issues in our understanding of life is the study of the essential set of genes and proteins that make up a living cell and a living organism. Genome-wide studies have identified between 270 and 650 essential genes in bacteria and about 900 essential genes in yeast [1]. Once the essential gene set is known, questions are raised as to why these genes are essential and which important functions the encoded proteins fulfil. The issue is complicated by the fact that essential functions may be carried out by pairs of homologous genes that functionally replace each other and by convergent metabolic functions of non-homologous proteins [2], [3]. Comparative analyses of the essential gene sets of different bacteria have revealed a significant set of genes that is essential in all bacteria studied so far. These genes can be referred to as obligatory essential genes. In contrast, facultative essential genes are essential in one organism but may be non-essential or even absent in other organisms, related or unrelated. It is obvious that the obligatory essential genes encode proteins that fulfil the most important housekeeping functions, such as the processing of the genetic information. Indeed, many proteins involved in DNA replication, transcription and translation are conserved and essential in all bacteria.

Identifying essential genes in bacterial metabolic networks with machine learning methods

BackgroundIdentifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.ResultsWe developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway.ConclusionsUsing elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism.

Predicting conserved essential genes in bacteria: in silico identification of putative drug targets

Many genes have been listed as putatively essential for bacterial viability in the Database of Essential Genomes (DEG), although few have been experimentally validated. By prioritising targets according to the criteria suggested by the Research and Training in Tropical Diseases (TDR) Targets database, we have developed a modified down-selection tool to identify essential genes conserved across diverse genera. Using this approach we identified 52 proteins conserved to 7 or more of the 14 genomes in DEG. We confirmed the validity of the down-selection by attempting to make mutants of 8 of these targets in a model organism, Yersinia pseudotuberculosis, which is not closely related to any of the bacteria in DEG. Mutants were recovered for only one of the 8 targets, suggesting that the other 7 were essential in Y. pseudotuberculosis, an impressive success rate compared to other approaches of identification for such targets. Identification of essential proteins common in diverse bacterial genera can then be used to facilitate the selection of effective targets for novel broad-spectrum antibiotics.

High-Throughput Identification of Conditionally Essential Genes in Bacteria: From STM to TSM

Signature-tagged mutagenesis (STM) provided the first widely applicable high-throughput method for detecting conditionally essential genes in bacteria by using negative selection to screen large pools of transposon (Tn) mutants. STM requires no prior knowledge of the bacterium's genome sequence, and has been used to study a large number of Gram-positive and Gram-negative species, greatly expanding the repertoires of known virulence factors for these organisms. Originally, hybridization of radiolabelled probes to colony or dot blots was used to detect differences in populations of tagged mutants before and after growth under a selective condition. Modifications of the tag detection method involving polymerase chain reaction (PCR) amplification and visualisation by gel electrophoresis have been developed and can be automated through the use of robotics. Genetic footprinting is another negative selection technique that uses PCR amplification to detect loss of mutants from a pool. Unlike PCR-STM, this technique allows direct amplification of Tn-flanking sequences. However, it requires the bacterium's whole genome sequence in order to design specific primers for every gene of interest. More recently, a number of techniques have been described that combine the negative-selection principle of STM and genetic footprinting with the genome-wide screening power of DNA microarrays. These techniques, although also requiring whole genome sequences, use either a form of linker-mediated or semi-random PCR to amplify and label Tn-flanking regions for hybridization to microarrays. The superior sensitivity microarray detection allows greater numbers of mutants to be screened per pool, as well as determination of the coverage/distribution of insertions in the library prior to screening, two significant advantages over STM.