Uncovering virulence factors in Cronobacter sakazakii: insights from genetic screening and proteomic profiling. - Related Documents




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840301.0000Uncovering virulence factors in Cronobacter sakazakii: insights from genetic screening and proteomic profiling. The increasing problem of antibiotic resistance has driven the search for virulence factors in pathogenic bacteria, which can serve as targets for the development of new antibiotics. Although whole-genome Tn5 transposon mutagenesis combined with phenotypic assays has been a widely used approach, its efficiency remains low due to labor-intensive processes. In this study, we aimed to identify specific genes and proteins associated with the virulence of Cronobacter sakazakii, a pathogenic bacterium known for causing severe infections, particularly in infants and immunocompromised individuals. By employing a combination of genetic screening, comparative proteomics, and in vivo validation using zebrafish and rat models, we rapidly screened highly virulent strains and identified two genes, rcsA and treR, as potential regulators of C. sakazakii toxicity toward zebrafish and rats. Proteomic profiling revealed upregulated proteins upon knockout of rcsA and treR, including FabH, GshA, GppA, GcvH, IhfB, RfaC, MsyB, and three unknown proteins. Knockout of their genes significantly weakened bacterial virulence, confirming their role as potential virulence factors. Our findings contribute to understanding the pathogenicity of C. sakazakii and provide insights into the development of targeted interventions and therapies against this bacterium.IMPORTANCEThe emergence of antibiotic resistance in pathogenic bacteria has become a critical global health concern, necessitating the identification of virulence factors as potential targets for the development of new antibiotics. This study addresses the limitations of conventional approaches by employing a combination of genetic screening, comparative proteomics, and in vivo validation to rapidly identify specific genes and proteins associated with the virulence of Cronobacter sakazakii, a highly pathogenic bacterium responsible for severe infections in vulnerable populations. The identification of two genes, rcsA and treR, as potential regulators of C. sakazakii toxicity toward zebrafish and rats and the proteomic profiling upon knockout of rcsA and treR provides novel insights into the mechanisms underlying bacterial virulence. The findings contribute to our understanding of C. sakazakii's pathogenicity, shed light on the regulatory pathways involved in bacterial virulence, and offer potential targets for the development of novel interventions against this highly virulent bacterium.202337750707
840210.9996Exploring phage-host interactions in Burkholderia cepacia complex bacterium to reveal host factors and phage resistance genes using CRISPRi functional genomics and transcriptomics. Complex interactions of bacteriophages with their bacterial hosts determine phage host range and infectivity. While phage defense systems and host factors have been identified in model bacteria, they remain challenging to predict in non-model bacteria. In this paper, we integrate functional genomics and transcriptomics to investigate phage-host interactions, revealing active phage resistance and host factor genes in Burkholderia cenocepacia K56-2. Burkholderia cepacia complex species are commonly found in soil and are opportunistic pathogens in immunocompromised patients. We studied infection of B. cenocepacia K56-2 with Bcep176, a temperate phage isolated from Burkholderia multivorans. A genome-wide dCas9 knockdown library targeting B. cenocepacia K56-2 was constructed, and a pooled infection experiment identified 63 novel genes or operons coding for candidate host factors or phage resistance genes. The activities of a subset of candidate host factor and resistance genes were validated via single-gene knockdowns. Transcriptomics of B. cenocepacia K56-2 during Bcep176 infection revealed that expression of genes coding for host factor and resistance candidates identified in this screen was significantly altered during infection by 4 h post-infection. Identifying which bacterial genes are involved in phage infection is important to understand the ecological niches of B. cenocepacia and its phages, and for designing phage therapies.IMPORTANCEBurkholderia cepacia complex bacteria are opportunistic pathogens inherently resistant to antibiotics, and phage therapy is a promising alternative treatment for chronically infected patients. Burkholderia bacteria are also ubiquitous in soil microbiomes. To develop improved phage therapies for pathogenic Burkholderia bacteria, or engineer phages for applications, such as microbiome editing, it's essential to know the bacterial host factors required by the phage to kill bacteria, as well as how the bacteria prevent phage infection. This work identified 65 genes involved in phage-host interactions in Burkholderia cenocepacia K56-2 and tracked their expression during infection. These findings establish a knowledge base to select and engineer phages infecting or transducing Burkholderia bacteria.202541036840
960820.9996A genome-wide atlas of antibiotic susceptibility targets and pathways to tolerance. Detailed knowledge on how bacteria evade antibiotics and eventually develop resistance could open avenues for novel therapeutics and diagnostics. It is thereby key to develop a comprehensive genome-wide understanding of how bacteria process antibiotic stress, and how modulation of the involved processes affects their ability to overcome said stress. Here we undertake a comprehensive genetic analysis of how the human pathogen Streptococcus pneumoniae responds to 20 antibiotics. We build a genome-wide atlas of drug susceptibility determinants and generated a genetic interaction network that connects cellular processes and genes of unknown function, which we show can be used as therapeutic targets. Pathway analysis reveals a genome-wide atlas of cellular processes that can make a bacterium less susceptible, and often tolerant, in an antibiotic specific manner. Importantly, modulation of these processes confers fitness benefits during active infections under antibiotic selection. Moreover, screening of sequenced clinical isolates demonstrates that mutations in genes that decrease antibiotic sensitivity and increase tolerance readily evolve and are frequently associated with resistant strains, indicating such mutations could be harbingers for the emergence of antibiotic resistance.202235672367
960630.9996Rapid identification of key antibiotic resistance genes in E. coli using high-resolution genome-scale CRISPRi screening. Bacteria possess a vast repertoire of genes to adapt to environmental challenges. Understanding the gene fitness landscape under antibiotic stress is crucial for elucidating bacterial resistance mechanisms and antibiotic action. To explore this, we conducted a genome-scale CRISPRi screen using a high-density sgRNA library in Escherichia coli exposed to various antibiotics. This screen identified essential genes under antibiotic-induced stress and offered insights into the molecular mechanisms underlying bacterial responses. We uncovered previously unrecognized genes involved in antibiotic resistance, including essential membrane proteins. The screen also underscored the importance of transcriptional modulation of essential genes in antibiotic tolerance. Our findings emphasize the utility of genome-wide CRISPRi screening in mapping the genetic landscape of antibiotic resistance. This study provides a valuable resource for identifying potential targets for antibiotics or antimicrobial strategies. Moreover, it offers a framework for exploring transcriptional regulatory networks and resistance mechanisms in E. coli and other bacterial pathogens.202540352728
955540.9996Bacteria.guru: Comparative Transcriptomics and Co-Expression Database for Bacterial Pathogens. While bacteria can be beneficial to our health, their deadly pathogenic potential has been an ever-present concern exacerbated by the emergence of drug-resistant strains. As such, there is a pressing urgency for an enhanced understanding of their gene function and regulation, which could mediate the development of novel antimicrobials. Transcriptomic analyses have been established as insightful and indispensable to the functional characterization of genes and identification of new biological pathways, but in the context of bacterial studies, they remain limited to species-specific datasets. To address this, we integrated the genomic and transcriptomic data of the 17 most notorious and researched bacterial pathogens, creating bacteria.guru, an interactive database that can identify, visualize, and compare gene expression profiles, coexpression networks, functionally enriched clusters, and gene families across species. Through illustrating antibiotic resistance mechanisms in P. aeruginosa, we demonstrate that bacteria.guru could potentially aid in discovering multi-faceted antibiotic targets and, overall, facilitate future bacterial research. AVAILABILITY: The database and coexpression networks are freely available from https://bacteria.guru/. Sample annotations can be found in the supplemental data.202234838806
922050.9995Pathogen virulence genes: Advances, challenges and future directions in infectious disease research (Review). Pathogens, including bacteria, viruses and fungi, employ virulence genes to invade their hosts, circumvent immunity and induce diseases. The present review examines the categorization and regulatory mechanisms of virulence genes and their co‑evolution with antimicrobial resistance. The present review focused on the fimbrial adhesion H adhesion gene of Escherichia coli, the spike protein gene of severe acute respiratory syndrome coronavirus 2 and the enhanced filamentous growth protein 1 (EFG1) morphological transition gene of Candida albicans, as well as their roles in host adhesion, immune evasion and tissue damage. Application of technologies, including multi‑omics integration, artificial intelligence and CRISPR‑based genome editing, is discussed in the context of precision diagnostics, targeted therapy and vaccine development. By elucidating pathogen adaptation dynamics and host‑pathogen interactions, the present review offers a basis for reducing the global burden of drug‑resistant infections through improved surveillance and personalized interventions.202540849821
954260.9995Development of quorum-based anti-virulence therapeutics targeting Gram-negative bacterial pathogens. Quorum sensing is a cell density-dependent signaling phenomenon used by bacteria for coordination of population-wide phenotypes, such as expression of virulence genes, antibiotic resistance and biofilm formation. Lately, disruption of bacterial communication has emerged as an anti-virulence strategy with enormous therapeutic potential given the increasing incidences of drug resistance in pathogenic bacteria. The quorum quenching therapeutic approach promises a lower risk of resistance development, since interference with virulence generally does not affect the growth and fitness of the bacteria and, hence, does not exert an associated selection pressure for drug-resistant strains. With better understanding of bacterial communication networks and mechanisms, many quorum quenching methods have been developed against various clinically significant bacterial pathogens. In particular, Gram-negative bacteria are an important group of pathogens, because, collectively, they are responsible for the majority of hospital-acquired infections. Here, we discuss the current understanding of existing quorum sensing mechanisms and present important inhibitory strategies that have been developed against this group of pathogenic bacteria.201323939429
897070.9995Transcriptomic Analyses to Unravel Cronobacter sakazakii Resistance Pathways. The proliferation of antibiotic usage has precipitated the emergence of drug-resistant variants of bacteria, thereby augmenting their capacity to withstand pharmaceutical interventions. Among these variants, Cronobacter sakazakii (C. sakazakii), prevalent in powdered infant formula (PIF), poses a grave threat to the well-being of infants. Presently, global contamination by C. sakazakii is being observed. Consequently, research endeavors have been initiated to explore the strain's drug resistance capabilities, alterations in virulence levels, and resistance mechanisms. The primary objective of this study is to investigate the resistance mechanisms and virulence levels of C. sakazakii induced by five distinct antibiotics, while concurrently conducting transcriptomic analyses. Compared to the susceptible strains prior to induction, the drug-resistant strains exhibited differential gene expression, resulting in modifications in the activity of relevant enzymes and biofilm secretion. Transcriptomic studies have shown that the expression of glutathione S-transferase and other genes were significantly upregulated after induction, leading to a notable enhancement in biofilm formation ability, alongside the existence of antibiotic resistance mechanisms associated with efflux pumps, cationic antimicrobial peptides, and biofilm formation pathways. These alterations significantly influence the strain's resistance profile.202439272551
961780.9995Multiplex CRISPRi System Enables the Study of Stage-Specific Biofilm Genetic Requirements in Enterococcus faecalis. 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.IMPORTANCEEnterococcus 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.202033082254
949790.9995The Complex Relationship between Virulence and Antibiotic Resistance. Antibiotic resistance, prompted by the overuse of antimicrobial agents, may arise from a variety of mechanisms, particularly horizontal gene transfer of virulence and antibiotic resistance genes, which is often facilitated by biofilm formation. The importance of phenotypic changes seen in a biofilm, which lead to genotypic alterations, cannot be overstated. Irrespective of if the biofilm is single microbe or polymicrobial, bacteria, protected within a biofilm from the external environment, communicate through signal transduction pathways (e.g., quorum sensing or two-component systems), leading to global changes in gene expression, enhancing virulence, and expediting the acquisition of antibiotic resistance. Thus, one must examine a genetic change in virulence and resistance not only in the context of the biofilm but also as inextricably linked pathologies. Observationally, it is clear that increased virulence and the advent of antibiotic resistance often arise almost simultaneously; however, their genetic connection has been relatively ignored. Although the complexities of genetic regulation in a multispecies community may obscure a causative relationship, uncovering key genetic interactions between virulence and resistance in biofilm bacteria is essential to identifying new druggable targets, ultimately providing a drug discovery and development pathway to improve treatment options for chronic and recurring infection.201728106797
9517100.9995Better together-Salmonella biofilm-associated antibiotic resistance. Salmonella poses a serious threat to public health and socioeconomic development worldwide because of its foodborne pathogenicity and antimicrobial resistance. This biofilm-planktonic lifestyle enables Salmonella to interfere with the host and become resistant to drugs, conferring inherent tolerance to antibiotics. The complex biofilm structure makes bacteria tolerant to harsh conditions due to the diversity of physiological, biochemical, environmental, and molecular factors constituting resistance mechanisms. Here, we provide an overview of the mechanisms of Salmonella biofilm formation and antibiotic resistance, with an emphasis on less-studied molecular factors and in-depth analysis of the latest knowledge about upregulated drug-resistance-associated genes in bacterial aggregates. We classified and extensively discussed each group of these genes encoding transporters, outer membrane proteins, enzymes, multiple resistance, metabolism, and stress response-associated proteins. Finally, we highlighted the missing information and studies that need to be undertaken to understand biofilm features and contribute to eliminating antibiotic-resistant and health-threatening biofilms.202337401756
8922110.9995Transitioning from Soil to Host: Comparative Transcriptome Analysis Reveals the Burkholderia pseudomallei Response to Different Niches. Burkholderia pseudomallei, a soil and water saprophyte, is responsible for the tropical human disease melioidosis. A hundred years since its discovery, there is still much to learn about B. pseudomallei proteins that are essential for the bacterium's survival in and interaction with the infected host, as well as their roles within the bacterium's natural soil habitat. To address this gap, bacteria grown under conditions mimicking the soil environment were subjected to transcriptome sequencing (RNA-seq) analysis. A dual RNA-seq approach was used on total RNA from spleens isolated from a B. pseudomallei mouse infection model at 5 days postinfection. Under these conditions, a total of 1,434 bacterial genes were induced, with 959 induced in the soil environment and 475 induced in bacteria residing within the host. Genes encoding metabolism and transporter proteins were induced when the bacteria were present in soil, while virulence factors, metabolism, and bacterial defense mechanisms were upregulated during active infection of mice. On the other hand, capsular polysaccharide and quorum-sensing pathways were inhibited during infection. In addition to virulence factors, reactive oxygen species, heat shock proteins, siderophores, and secondary metabolites were also induced to assist bacterial adaptation and survival in the host. Overall, this study provides crucial insights into the transcriptome-level adaptations which facilitate infection by soil-dwelling B. pseudomallei. Targeting novel therapeutics toward B. pseudomallei proteins required for adaptation provides an alternative treatment strategy given its intrinsic antimicrobial resistance and the absence of a vaccine. IMPORTANCE Burkholderia pseudomallei, a soil-dwelling bacterium, is the causative agent of melioidosis, a fatal infectious disease of humans and animals. The bacterium has a large genome consisting of two chromosomes carrying genes that encode proteins with important roles for survival in diverse environments as well as in the infected host. While a general mechanism of pathogenesis has been proposed, it is not clear which proteins have major roles when the bacteria are in the soil and whether the same proteins are key to successful infection and spread. To address this question, we grew the bacteria in soil medium and then in infected mice. At 5 days postinfection, bacteria were recovered from infected mouse organs and their gene expression was compared against that of bacteria grown in soil medium. The analysis revealed a list of genes expressed under soil growth conditions and a different set of genes encoding proteins which may be important for survival, replication, and dissemination in an infected host. These proteins are a potential resource for understanding the full adaptation mechanism of this pathogen. In the absence of a vaccine for melioidosis and with treatment being reliant on combinatorial antibiotic therapy, these proteins may be ideal targets for designing antimicrobials to treat melioidosis.202336856434
9379120.9995Essential phage component induces resistance of bacterial community. Despite extensive knowledge on phage resistance at bacterium level, the resistance of bacterial communities is still not well-understood. Given its ubiquity, it is essential to understand resistance at the community level. We performed quantitative investigations on the dynamics of phage infection in Klebsiella pneumoniae biofilms. We found that the biofilms quickly developed resistance and resumed growth. Instead of mutations, the resistance was caused by unassembled phage tail fibers released by the phage-lysed bacteria. The tail fibers degraded the bacterial capsule essential for infection and induced spreading of capsule loss in the biofilm, and tuning tail fiber and capsule levels altered the resistance. Latent infections sustained in the biofilm despite resistance, allowing stable phage-bacteria coexistence. Last, we showed that the resistance exposed vulnerabilities in the biofilm. Our findings indicate that phage lysate plays important roles in shaping phage-biofilm interactions and open more dimensions for the rational design of strategies to counter bacteria with phage.202439231230
9553130.9995A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains. Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased. There are several strains of bacteria lacking known resistance genes; however, they demonstrate resistance phenotype to drugs of that family. Although such strains are fewer compared to the overall population, they pose grave emerging threats to an already heavily challenged area of antimicrobial resistance (AMR), where death tolls have reached ~700 000 per year and a grim projection of ~10 million deaths per year by 2050 looms. Considering the fact that development of novel antibiotics is not keeping pace with the emergence and dissemination of resistance, there is a pressing need to decipher yet unknown genetic mechanisms of resistance, which will enable developing strategies for the best use of available interventions and show the way for the development of new drugs. In this study, we present a machine learning framework to predict novel AMR factors that are potentially responsible for resistance to specific antimicrobial drugs. The machine learning framework utilizes whole-genome sequencing AMR genetic data and antimicrobial susceptibility testing phenotypic data to predict resistance phenotypes and rank AMR genes by their importance in discriminating the resistance from the susceptible phenotypes. In summary, we present here a bioinformatics framework for training machine learning models, evaluating their performances, selecting the best performing model(s) and finally predicting the most important AMR loci for the resistance involved.202134015806
9607140.9995Transcriptome-Level Signatures in Gene Expression and Gene Expression Variability during Bacterial Adaptive Evolution. Antibiotic-resistant bacteria are an increasingly serious public health concern, as strains emerge that demonstrate resistance to almost all available treatments. One factor that contributes to the crisis is the adaptive ability of bacteria, which exhibit remarkable phenotypic and gene expression heterogeneity in order to gain a survival advantage in damaging environments. This high degree of variability in gene expression across biological populations makes it a challenging task to identify key regulators of bacterial adaptation. Here, we research the regulation of adaptive resistance by investigating transcriptome profiles of Escherichia coli upon adaptation to disparate toxins, including antibiotics and biofuels. We locate potential target genes via conventional gene expression analysis as well as using a new analysis technique examining differential gene expression variability. By investigating trends across the diverse adaptation conditions, we identify a focused set of genes with conserved behavior, including those involved in cell motility, metabolism, membrane structure, and transport, and several genes of unknown function. To validate the biological relevance of the observed changes, we synthetically perturb gene expression using clustered regularly interspaced short palindromic repeat (CRISPR)-dCas9. Manipulation of select genes in combination with antibiotic treatment promotes adaptive resistance as demonstrated by an increased degree of antibiotic tolerance and heterogeneity in MICs. We study the mechanisms by which identified genes influence adaptation and find that select differentially variable genes have the potential to impact metabolic rates, mutation rates, and motility. Overall, this work provides evidence for a complex nongenetic response, encompassing shifts in gene expression and gene expression variability, which underlies adaptive resistance. IMPORTANCE Even initially sensitive bacteria can rapidly thwart antibiotic treatment through stress response processes known as adaptive resistance. Adaptive resistance fosters transient tolerance increases and the emergence of mutations conferring heritable drug resistance. In order to extend the applicable lifetime of new antibiotics, we must seek to hinder the occurrence of bacterial adaptive resistance; however, the regulation of adaptation is difficult to identify due to immense heterogeneity emerging during evolution. This study specifically seeks to generate heterogeneity by adapting bacteria to different stresses and then examines gene expression trends across the disparate populations in order to pinpoint key genes and pathways associated with adaptive resistance. The targets identified here may eventually inform strategies for impeding adaptive resistance and prolonging the effectiveness of antibiotic treatment.201728217741
9610150.9995The evolutionary rate of antibacterial drug targets. BACKGROUND: One of the major issues in the fight against infectious diseases is the notable increase in multiple drug resistance in pathogenic species. For that reason, newly acquired high-throughput data on virulent microbial agents attract the attention of many researchers seeking potential new drug targets. Many approaches have been used to evaluate proteins from infectious pathogens, including, but not limited to, similarity analysis, reverse docking, statistical 3D structure analysis, machine learning, topological properties of interaction networks or a combination of the aforementioned methods. From a biological perspective, most essential proteins (knockout lethal for bacteria) or highly conserved proteins (broad spectrum activity) are potential drug targets. Ribosomal proteins comprise such an example. Many of them are well-known drug targets in bacteria. It is intuitive that we should learn from nature how to design good drugs. Firstly, known antibiotics are mainly originating from natural products of microorganisms targeting other microorganisms. Secondly, paleontological data suggests that antibiotics have been used by microorganisms for million years. Thus, we have hypothesized that good drug targets are evolutionary constrained and are subject of evolutionary selection. This means that mutations in such proteins are deleterious and removed by selection, which makes them less susceptible to random development of resistance. Analysis of the speed of evolution seems to be good approach to test this hypothesis. RESULTS: In this study we show that pN/pS ratio of genes coding for known drug targets is significantly lower than the genome average and also lower than that for essential genes identified by experimental methods. Similar results are observed in the case of dN/dS analysis. Both analyzes suggest that drug targets tend to evolve slowly and that the rate of evolution is a better predictor of drugability than essentiality. CONCLUSIONS: Evolutionary rate can be used to score and find potential drug targets. The results presented here may become a useful addition to a repertoire of drug target prediction methods. As a proof of concept, we analyzed GO enrichment among the slowest evolving genes. These may become the starting point in the search for antibiotics with a novel mechanism.201323374913
9596160.9995Revealing AMP mechanisms of action through resistance evolution and quantitative proteomics. Antimicrobial resistance (AMR) is a significant public health issue that threatens our ability to treat common infections. AMR often emerges in bacteria through upregulation of proteins that allow a subpopulation of resistant bacteria to proliferate through natural selection. Identifying these proteins is crucial for understanding how AMR develops in bacteria and is essential in developing novel therapeutics to combat the threat of widespread AMR. Mass spectrometry-based proteomics is a powerful tool for understanding the biochemical pathways of biological systems, lending remarkable insight into AMR mechanisms in bacteria through measuring the changing protein abundances as a result of antibiotic treatment. Here, we describe a serial passaging method for evolving resistance in bacteria that implements quantitative proteomics to reveal the differential proteomes of resistant bacteria. The focus herein is on antimicrobial peptides (AMPs), but the approach can be generalized for any antimicrobial compound. Comparative proteomics of sensitive vs. resistance strains in response to AMP treatment reveals mechanisms to survive the bioactive compound and points to the mechanism of action for novel AMPs.202235168792
9597170.9995Role of xenobiotic transporters in bacterial drug resistance and virulence. Since the discovery of antibiotic therapeutics, the battles between humans and infectious diseases have never been stopped. Humans always face the appearance of a new bacterial drug-resistant strain followed by new antibiotic development. However, as the genome sequences of infectious bacteria have been gradually determined, a completely new approach has opened. This approach can analyze the entire gene resources of bacterial drug resistance. Through analysis, it may be possible to discover the underlying mechanism of drug resistance that will appear in the future. In this review article, we will first introduce the method to analyze all the xenobiotic transporter genes by using the genomic information. Next, we will discuss the regulation of xenobiotic transporter gene expression through the two-component signal transduction system, the principal environmental sensing and response system in bacteria. Furthermore, we will also introduce the virulence roles of xenobiotic transporters, which is an ongoing research area.200818481812
8923180.9995The Genome-Wide Interaction Network of Nutrient Stress Genes in Escherichia coli. Conventional 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. IMPORTANCE: With the rise of antibiotic drug resistance, there is an urgent need for new antibacterial drugs. Here, we studied a group of genes that are essential for the growth of Escherichia coli under nutrient limitation, culture conditions that arguably better represent nutrient availability during an infection than rich microbiological media. Indeed, many such nutrient stress genes are essential for infection in a variety of pathogens. Thus, the respective proteins represent a pool of potential new targets for antibacterial drugs that have been largely unexplored. We have created all possible double deletion mutants through a genetic cross of nutrient stress genes and the E. coli deletion collection. An analysis of the growth of the resulting clones on rich media revealed a robust, dense, and complex network for nutrient acquisition and biosynthesis. Importantly, our data reveal new genetic connections to guide innovative approaches for the development of new antibacterial compounds targeting bacteria under nutrient stress.201627879333
8401190.9994LSTrAP-Crowd: prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data. BACKGROUND: Bacterial resistance to antibiotics is a growing health problem that is projected to cause more deaths than cancer by 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the structurally conserved bacterial ribosomes, factors involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. Here, we use a bioinformatics approach to identify novel components of protein synthesis. RESULTS: In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. CONCLUSIONS: We identified genes related to protein synthesis in common bacterial pathogens and thus provide a resource of potential antibiotic development targets for experimental validation. The data can be used to explore additional vulnerabilities of bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowd-sourced.202032883264