Protein localization analysis of essential genes in prokaryotes

Recent advances in the characterization of essential genes and development of a database of essential genes

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

Genome-wide identification of the opsin protein in Leptosphaeria maculans and comparison with other fungi (pathogens of Brassica napus)

The largest family of transmembrane receptors are G-protein-coupled receptors (GPCRs). These receptors respond to perceived environmental signals and infect their host plants. Family A of the GPCR includes opsin. However, there is little known about the roles of GPCRs in phytopathogenic fungi. We studied opsin in Leptosphaeria maculans, an important pathogen of oilseed rape (Brassica napus) that causes blackleg disease, and compared it with six other fungal pathogens of oilseed rape. A phylogenetic tree analysis of 31 isoforms of the opsin protein showed six major groups and six subgroups. All three opsin isoforms of L. maculans are grouped in the same clade in the phylogenetic tree. Physicochemical analysis revealed that all studied opsin proteins are stable and hydrophobic. Subcellular localization revealed that most isoforms were localized in the endoplasmic reticulum membrane except for several isoforms in Verticillium species, which were localized in the mitochondrial membrane. Most isoforms comprise two conserved domains. One conserved motif was observed across all isoforms, consisting of the BACTERIAL_OPSIN_1 domain, which has been hypothesized to have an identical sensory function. Most studied isoforms showed seven transmembrane helices, except for one isoform of V. longisporum and four isoforms of Fusarium oxysporum. Tertiary structure prediction displayed a conformational change in four isoforms of F. oxysporum that presumed differences in binding to other proteins and sensing signals, thereby resulting in various pathogenicity strategies. Protein-protein interactions and binding site analyses demonstrated a variety of numbers of ligands and pockets across all isoforms, ranging between 0 and 13 ligands and 4 and 10 pockets. According to the phylogenetic analysis in this study and considerable physiochemically and structurally differences of opsin proteins among all studied fungi hypothesized that this protein acts in the pathogenicity, growth, sporulation, and mating of these fungi differently.

Bacterial genome reductions: Tools, applications, and challenges

Bacterial cells are widely used to produce value-added products due to their versatility, ease of manipulation, and the abundance of genome engineering tools. However, the efficiency of producing these desired biomolecules is often hindered by the cells’ own metabolism, genetic instability, and the toxicity of the product. To overcome these challenges, genome reductions have been performed, making strains with the potential of serving as chassis for downstream applications. Here we review the current technologies that enable the design and construction of such reduced-genome bacteria as well as the challenges that limit their assembly and applicability. While genomic reductions have shown improvement of many cellular characteristics, a major challenge still exists in constructing these cells efficiently and rapidly. Computational tools have been created in attempts at minimizing the time needed to design these organisms, but gaps still exist in modelling these reductions in silico. Genomic reductions are a promising avenue for improving the production of value-added products, constructing chassis cells, and for uncovering cellular function but are currently limited by their time-consuming construction methods. With improvements to and the creation of novel genome editing tools and in silico models, these approaches could be combined to expedite this process and create more streamlined and efficient cell factories.

Kin selection for cooperation in natural bacterial populations

Bacteria secrete many molecules outside the cell, where they provide benefits to other cells. One potential reason for producing these “public goods” is that they benefit closely related cells that share the gene for cooperation (kin selection). While many laboratory studies have supported this hypothesis, there is a lack of evidence that kin selection favors cooperation in natural populations. We examined bacterial genomes from the environment and used population genetics theory to analyze the DNA sequences. Our analyses suggest that public goods cooperation has indeed been favored by kin selection in natural populations.

Bacteria produce a range of molecules that are secreted from the cell and can provide a benefit to the local population of cells. Laboratory experiments have suggested that these “public goods” molecules represent a form of cooperation, favored because they benefit closely related cells (kin selection). However, there is a relative lack of data demonstrating kin selection for cooperation in natural populations of bacteria. We used molecular population genetics to test for signatures of kin selection at the genomic level in natural populations of the opportunistic pathogen Pseudomonas aeruginosa. We found consistent evidence from multiple traits that genes controlling putatively cooperative traits have higher polymorphism and greater divergence and are more likely to harbor deleterious mutations relative to genes controlling putatively private traits, which are expressed at similar rates. These patterns suggest that cooperative traits are controlled by kin selection, and we estimate that the relatedness for social interactions in P. aeruginosa is r = 0.84. More generally, our results demonstrate how molecular population genetics can be used to study the evolution of cooperation in natural populations.

Position preference of essential genes in prokaryotic operons

Essential genes, which form the basis of life activities, are crucial for the survival of organisms. Essential genes tend to be located in operons, but how they are distributed in operons is still unclear for most prokaryotes. In order to clarify the general rule of position preference of essential genes in operons, an index of the average position of genes in an operon was proposed, and the distributions of essential and non-essential genes in operons in 51 bacterial genomes and two archaeal genomes were analyzed based on this new index. Consequently, essential genes were found to preferentially occupy the front positions of the operons, which tend to be expressed at higher levels.

Machine learning approach to gene essentiality prediction: a review

 

Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions.

SHORT ABSTRACT

Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.

Elucidating Essential Genes in Plant-Associated Pseudomonas protegens Pf-5 Using Transposon Insertion Sequencing

Gene essentiality studies have been performed on numerous bacterial pathogens, but essential gene sets have been determined for only a few plant-associated bacteria. Pseudomonas protegens Pf-5 is a plant-commensal, biocontrol bacterium that can control disease-causing pathogens on a wide range of crops. Work on Pf-5 has mostly focused on secondary metabolism and biocontrol genes, but genome-wide approaches such as high-throughput transposon mutagenesis have not yet been used for this species. In this study, we generated a dense P. protegens Pf-5 transposon mutant library and used transposon-directed insertion site sequencing (TraDIS) to identify 446 genes essential for growth on rich media. Genes required for fundamental cellular machinery were enriched in the essential gene set, while genes related to nutrient biosynthesis, stress responses, and transport were underrepresented. The majority of Pf-5 essential genes were part of the P. protegens core genome. Comparison of the essential gene set of Pf-5 with those of two plant-associated pseudomonads, P. simiae and P. syringae, and the well-studied opportunistic human pathogen P. aeruginosa PA14 showed that the four species share a large number of essential genes, but each species also had uniquely essential genes. Comparison of the Pf-5 in silico-predicted and in vitro-determined essential gene sets highlighted the essential cellular functions that are over- and underestimated by each method. Expanding essentiality studies into bacteria with a range of lifestyles may improve our understanding of the biological processes important for bacterial survival and growth.IMPORTANCE Essential genes are those crucial for survival or normal growth rates in an organism. Essential gene sets have been identified in numerous bacterial pathogens but only a few plant-associated bacteria. Employing genome-wide approaches, such as transposon insertion sequencing, allows for the concurrent analyses of all genes of a bacterial species and rapid determination of essential gene sets. We have used transposon insertion sequencing to systematically analyze thousands of Pseudomonas protegens Pf-5 genes and gain insights into gene functions and interactions that are not readily available using traditional methods. Comparing Pf-5 essential genes with those of three other pseudomonads highlights how gene essentiality varies between closely related species.

Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation, Essentiality and Function

BACKGROUND

Protein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work, we relate conservation, essentiality and functional repertoire of a gene to the connectivity k (i.e. the number of interactions, links) of the corresponding protein in the PPI network.

METHODS

On a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs).

RESULTS

We show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolated a core of highly connected proteins (connectivities k≥40), which is ubiquitous among the species considered here, though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes).

CONCLUSION

The genes that support this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and the processing of genetic information.

Genome-Wide Identification and Analysis of Chitinase GH18 Gene Family in Mycogone perniciosa

Mycogone perniciosa causes wet bubble disease in Agaricus bisporus and various Agaricomycetes species. In a previous work, we identified 41 GH18 chitinase genes and other pathogenicity-related genes in the genome of M. perniciosa Hp10. Chitinases are enzymes that degrade chitin, and they have diverse functions in nutrition, morphogenesis, and pathogenesis. However, these important genes in M. perniciosa have not been fully characterized, and their functions remain unclear. Here, we performed a genome-wide analysis of M. perniciosa GH18 genes and analyzed the transcriptome profiles and GH18 expression patterns in M. perniciosa during the time course of infection in A. bisporus. Phylogenetic analysis of the 41 GH18 genes with those of 15 other species showed that the genes were clustered into three groups and eight subgroups based on their conserved domains. The GH18 genes clustered in the same group shared different gene structures but had the same protein motifs. All GH18 genes were localized in different organelles, were unevenly distributed on 11 contigs, and had orthologs in the other 13 species. Twelve duplication events were identified, and these had undergone both positive and purifying selection. The transcriptome analyses revealed that numerous genes, including transporters, cell wall degrading enzymes (CWDEs), cytochrome P450, pathogenicity-related genes, secondary metabolites, and transcription factors, were significantly upregulated at different stages of M. perniciosa Hp10 infection of A. bisporus. Twenty-three out of the 41 GH18 genes were differentially expressed. The expression patterns of the 23 GH18 genes were different and were significantly expressed from 3 days post-inoculation of M. perniciosa Hp10 in A. bisporus. Five differentially expressed GH18 genes were selected for RT-PCR and gene cloning to verify RNA-seq data accuracy. The results showed that those genes were successively expressed in different infection stages, consistent with the previous sequencing results. Our study provides a comprehensive analysis of pathogenicity-related and GH18 chitinase genes’ influence on M. perniciosa mycoparasitism of A. bisporus. Our findings may serve as a basis for further studies of M. perniciosa mycoparasitism, and the results have potential value for improving resistance in A. bisporus and developing efficient disease-management strategies to mitigate wet bubble disease.

A systematic strategy for the investigation of vaccines and drugs targeting bacteria

Infectious and epidemic diseases induced by bacteria have historically caused great distress to people, and have even resulted in a large number of deaths worldwide. At present, many researchers are working on the discovery of viable drug and vaccine targets for bacteria through multiple methods, including the analyses of comparative subtractive genome, core genome, replication-related proteins, transcriptomics and riboswitches, which plays a significant part in the treatment of infectious and pandemic diseases. The 3D structures of the desired target proteins, drugs and epitopes can be predicted and modeled through target analysis. Meanwhile, molecular dynamics (MD) analysis of the constructed drug/epitope-protein complexes is an important standard for testing the suitability of these screened drugs and vaccines. Currently, target discovery, target analysis and MD analysis are integrated into a systematic set of drug and vaccine analysis strategy for bacteria. We hope that this comprehensive strategy will help in the design of high-performance vaccines and drugs.

Quantifying Gene Essentiality Based on the Context of Cellular Components

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.

New insights on human essential genes based on integrated analysis and the construction of the HEGIAP web-based platform

Essential genes are those whose loss of function compromises organism viability or results in profound loss of fitness. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis that comprehensively and systematically elucidates the genetic and regulatory characteristics of human essential genes. First, we found that essential genes act as ‘hubs’ in protein–protein interaction networks, chromatin structure and epigenetic modification. Second, essential genes represent conserved biological processes across species, although gene essentiality changes differently among species. Third, essential genes are important for cell development due to their discriminate transcription activity in embryo development and oncogenesis. In addition, we developed an interactive web server, the Human Essential Genes Interactive Analysis Platform (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to enable global, multidimensional interpretation of gene essentiality. Our study provides new insights that improve the understanding of human essential genes.

Comparative assessment of the therapeutic drug targets of C. botulinum ATCC 3502 and C. difficile str. 630 using in silico subtractive proteomics approach

Growing antimicrobial resistance of the pathogens against multiple drugs posed a serious threat to the human health worldwide. This fueled the need of identifying the novel therapeutic targets that can be used for developing new class of the drugs. Recently, there is a substantial rise in the rate of Clostridium infections as well as in the emergence of virulent and antibiotic resistant strains. Hence, there is an urgent need for the identification of potential therapeutic targets and the development of new drugs for the treatment and prevention of Clostridium infections. In the present study, a combinatorial approach involving systems biology and comparative genomics strategy was tested against Clostridium botulinum ATCC 3502 and Clostridium difficile str. 630 pathogens, to render potential therapeutic target at qualitative and quantitative level. This resulted in the identification of five common (present in both the pathogens, 34 in C. botulinum ATCC 3502 and 42 in C. difficile str. 630) drug targets followed by virtual screening-based identification of potential inhibitors employing molecular docking and molecular dynamics simulations. The identified targets will provide a solid platform for the designing of novel wide-spectrum lead compounds capable of inhibiting their catalytic activities against multidrug-resistant Clostridium in the near future.

Essentiality, conservation, evolutionary pressure and codon bias in bacterial genomes

Essential genes constitute the core of genes which cannot be mutated too much nor lost along the evolutionary history of a species. Natural selection is expected to be stricter on essential genes and on conserved (highly shared) genes, than on genes that are either nonessential or peculiar to a single or a few species. In order to further assess this expectation, we study here how essentiality of a gene is connected with its degree of conservation among several unrelated bacterial species, each one characterised by its own codon usage bias. Confirming previous results on E. coli, we show the existence of a universal exponential relation between gene essentiality and conservation in bacteria. Moreover, we show that, within each bacterial genome, there are at least two groups of functionally distinct genes, characterised by different levels of conservation and codon bias: i) a core of essential genes, mainly related to cellular information processing; ii) a set of less conserved nonessential genes with prevalent functions related to metabolism. In particular, the genes in the first group are more retained among species, are subject to a stronger purifying conservative selection and display a more limited repertoire of synonymous codons. The core of essential genes is close to the minimal bacterial genome, which is in the focus of recent studies in synthetic biology, though we confirm that orthologs of genes that are essential in one species are not necessarily essential in other species. We also list a set of highly shared genes which, reasonably, could constitute a reservoir of targets for new anti-microbial drugs.

Identification of novel drug targets in bovine respiratory disease: an essential step in applying biotechnologic techniques to develop more effective therapeutic treatments

BACKGROUND

Bovine Respiratory Disease (BRD) is a major problem in cattle production which causes substantial economic loss. BRD has multifactorial aetiologies, is multi-microbial, and several of the causative pathogens are unknown. Consequently, primary management practices such as metaphylactic antimicrobial injections for BRD prevention are used to reduce the incidence of BRD in feedlot cattle. However, this poses a serious threat in the form of development of antimicrobial resistance and demands an urgent need to find novel interventions that could reduce the effects of BRD drastically and also delay/prevent bacterial resistance.

MATERIALS AND METHODS

We have employed a subtractive genomics approach that helps delineate essential, host-specific, and druggable targets in pathogens responsible for BRD. We also proposed antimicrobials from FDA green and orange book that could be repositioned for BRD.

RESULTS

We have identified 107 putative targets that are essential, selective and druggable. We have also confirmed the susceptibility of two BRD pathogens to one of the proposed antimicrobials - oxytetracycline.

CONCLUSION

This approach allows for repositioning drugs known for other infections to BRD, predicting novel druggable targets for BRD infection, and providing a new direction in developing more effective therapeutic treatments for BRD.

A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes

Genes critical for the survival or reproduction of an organism in certain circumstances are classified as essential genes. Essential genes play a significant role in deciphering the survival mechanism of life. They may be greatly applied to pharmaceutics and synthetic biology. The continuous progress of experimental method for essential gene identification has accelerated the accumulation of gene essentiality data which facilitates the study of essential genes in silico. In this article, we present some available online resources related to gene essentiality, including bioinformatic software tools for transposon sequencing (Tn-seq) analysis, essential gene databases and online services to predict bacterial essential genes. We review several computational approaches that have been used to predict essential genes, and summarize the features used for gene essentiality prediction. In addition, we evaluate the available online bacterial essential gene prediction servers based on the experimentally validated essential gene sets of 30 bacteria from DEG. This article is intended to be a quick reference guide for the microbiologists interested in the essential genes.

Evolutionary conservation analysis between the essential and nonessential genes in bacterial genomes

Essential genes are thought to be critical for the survival of the organisms under certain circumstances, and the natural selection acting on essential genes is expected to be stricter than on nonessential ones. Up to now, essential genes have been identified in approximately thirty bacterial organisms by experimental methods. In this paper, we performed a comprehensive comparison between the essential and nonessential genes in the genomes of 23 bacterial species based on the Ka/Ks ratio, and found that essential genes are more evolutionarily conserved than nonessential genes in most of the bacteria examined. Furthermore, we also analyzed the conservation by functional clusters with the clusters of orthologous groups (COGs), and found that the essential genes in the functional categories of G (Carbohydrate transport and metabolism), H (Coenzyme transport and metabolism), I (Transcription), J (Translation, ribosomal structure and biogenesis), K (Lipid transport and metabolism) and L (Replication, recombination and repair) tend to be more evolutionarily conserved than the corresponding nonessential genes in bacteria. The results suggest that the essential genes in these subcategories are subject to stronger selective pressure than the nonessential genes, and therefore, provide more insights of the evolutionary conservation for the essential and nonessential genes in complex biological processes.

Comparative analysis of essential genes in prokaryotic genomic islands

Essential genes are thought to encode proteins that carry out the basic functions to sustain a cellular life, and genomic islands (GIs) usually contain clusters of horizontally transferred genes. It has been assumed that essential genes are not likely to be located in GIs, but systematical analysis of essential genes in GIs has not been explored before. Here, we have analyzed the essential genes in 28 prokaryotes by statistical method and reached a conclusion that essential genes in GIs are significantly fewer than those outside GIs. The function of 362 essential genes found in GIs has been explored further by BLAST against the Virulence Factor Database (VFDB) and the phage/prophage sequence database of PHAge Search Tool (PHAST). Consequently, 64 and 60 eligible essential genes are found to share the sequence similarity with the virulence factors and phage/prophages-related genes, respectively. Meanwhile, we find several toxin-related proteins and repressors encoded by these essential genes in GIs. The comparative analysis of essential genes in genomic islands will not only shed new light on the development of the prediction algorithm of essential genes, but also give a clue to detect the functionality of essential genes in genomic islands.