# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 9555 | 0 | 1.0000 | Bacteria.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. | 2022 | 34838806 |
| 9606 | 1 | 0.9998 | Rapid 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. | 2025 | 40352728 |
| 9671 | 2 | 0.9998 | Genome-scale genetic manipulation methods for exploring bacterial molecular biology. Bacteria are diverse and abundant, playing key roles in human health and disease, the environment, and biotechnology. Despite progress in genome sequencing and bioengineering, much remains unknown about the functional organization of prokaryotes. For instance, roughly a third of the protein-coding genes of the best-studied model bacterium, Escherichia coli, currently lack experimental annotations. Systems-level experimental approaches for investigating the functional associations of bacterial genes and genetic structures are essential for defining the fundamental molecular biology of microbes, preventing the spread of antibacterial resistance in the clinic, and driving the development of future biotechnological applications. This review highlights recently introduced large-scale genetic manipulation and screening procedures for the systematic exploration of bacterial gene functions, molecular relationships, and the global organization of bacteria at the gene, pathway, and genome levels. | 2012 | 22517266 |
| 9596 | 3 | 0.9997 | Revealing 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. | 2022 | 35168792 |
| 9483 | 4 | 0.9997 | Ecological and evolutionary mechanisms driving within-patient emergence of antimicrobial resistance. The ecological and evolutionary mechanisms of antimicrobial resistance (AMR) emergence within patients and how these vary across bacterial infections are poorly understood. Increasingly widespread use of pathogen genome sequencing in the clinic enables a deeper understanding of these processes. In this Review, we explore the clinical evidence to support four major mechanisms of within-patient AMR emergence in bacteria: spontaneous resistance mutations; in situ horizontal gene transfer of resistance genes; selection of pre-existing resistance; and immigration of resistant lineages. Within-patient AMR emergence occurs across a wide range of host niches and bacterial species, but the importance of each mechanism varies between bacterial species and infection sites within the body. We identify potential drivers of such differences and discuss how ecological and evolutionary analysis could be embedded within clinical trials of antimicrobials, which are powerful but underused tools for understanding why these mechanisms vary between pathogens, infections and individuals. Ultimately, improving understanding of how host niche, bacterial species and antibiotic mode of action combine to govern the ecological and evolutionary mechanism of AMR emergence in patients will enable more predictive and personalized diagnosis and antimicrobial therapies. | 2024 | 38689039 |
| 9567 | 5 | 0.9997 | How to discover new antibiotic resistance genes? Antibiotic resistance (AR) is a worldwide concern and the description of AR have been discovered mainly because of their implications in human medicine. Since the recent burden of whole-genome sequencing of microorganisms, the number of new AR genes (ARGs) have dramatically increased over the last decade. Areas covered: In this review, we will describe the different methods that could be used to characterize new ARGs using classic or innovative methods. First, we will focus on the biochemical methods, then we will develop on molecular methods, next-generation sequencing and bioinformatics approaches. The use of various methods, including cloning, mutagenesis, transposon mutagenesis, functional genomics, whole genome sequencing, metagenomic and functional metagenomics will be reviewed here, outlining the advantages and drawbacks of each method. Bioinformatics softwares used for resistome analysis and protein modeling will be also described. Expert opinion: Biological experiments and bioinformatics analysis are complementary. Nowadays, the ARGs described only account for the tip of the iceberg of all existing resistance mechanisms. The multiplication of the ecosystems studied allows us to find a large reservoir of AR mechanisms. Furthermore, the adaptation ability of bacteria facing new antibiotics promises a constant discovery of new AR mechanisms. | 2019 | 30895843 |
| 4089 | 6 | 0.9997 | Genetic mechanisms of antibiotic resistance and virulence in Acinetobacter baumannii: background, challenges and future prospects. With the advent of the multidrug-resistant era, many opportunistic pathogens including the species Acinetobacter baumannii have gained prominence and pose a major global threat to clinical health care. Pathogenicity in bacteria is genetically regulated by a complex network of transcription and virulence factors and a brief overview of the major investigations on comprehending these processes over the past few decades in A. baumanni are compiled here. Many investigators have employed genome sequencing techniques to identify the regions that contribute to antibiotic resistance and comparative genomics to study sequence similarities to understand evolutionary trends of resistance gene transfers between isolates. A summary of these studies given here provides an insight into the invasion and successful colonization of the species. The individual roles played by different genes, regulators & promoters, enzymes, metal ions as well as mobile elements in influencing antibiotic resistance are briefly discussed. Precautionary measures and prospects for developing future strategies by exploring promising new research targets in effective control of multidrug resistant A. baumannii are also analyzed. | 2020 | 32303957 |
| 9597 | 7 | 0.9997 | Role 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. | 2008 | 18481812 |
| 9553 | 8 | 0.9997 | A 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. | 2021 | 34015806 |
| 9670 | 9 | 0.9997 | An Approach to In Silico Dissection of Bacterial Intelligence Through Selective Genomic Tools. All the genetic potential and the intelligence a bacteria can showcase in a given environment are embedded in its genome. In this study, we have presented systematic guidelines to understand a bacterial genome with the relevant set of in silico tools using a novel bacteria as an example. This study presents a multi-dimensional approach from genome annotation to tracing genes and their network of metabolism operating in an organism. It also shows how the sequence can be used to mine the enzymes and construction of its 3-dimensional structure so that its functional behavior can be predicted and compared. The discriminating algorithm allows analysis of the promoter region and provides the insight in the regulation of genes in spite of the similarity in its sequences. The ecological niche specific bacterial behavior and adapted altered physiology can be understood through the presence of secondary metabolite, antibiotic resistance genes, and viral genes; and it helps in the valorization of genetic information for developing new biological application/processes. This study provides an in silico work plan and necessary steps for genome analysis of novel bacteria without any rigorous wet lab experiments. | 2018 | 30013271 |
| 9475 | 10 | 0.9997 | Rapidly evolving genes in pathogens: methods for detecting positive selection and examples among fungi, bacteria, viruses and protists. The ongoing coevolutionary struggle between hosts and pathogens, with hosts evolving to escape pathogen infection and pathogens evolving to escape host defences, can generate an 'arms race', i.e., the occurrence of recurrent selective sweeps that each favours a novel resistance or virulence allele that goes to fixation. Host-pathogen coevolution can alternatively lead to a 'trench warfare', i.e., balancing selection, maintaining certain alleles at loci involved in host-pathogen recognition over long time scales. Recently, technological and methodological progress has enabled detection of footprints of selection directly on genes, which can provide useful insights into the processes of coevolution. This knowledge can also have practical applications, for instance development of vaccines or drugs. Here we review the methods for detecting genes under positive selection using divergence data (i.e., the ratio of nonsynonymous to synonymous substitution rates, d(N)/d(S)). We also review methods for detecting selection using polymorphisms, such as methods based on F(ST) measures, frequency spectrum, linkage disequilibrium and haplotype structure. In the second part, we review examples where targets of selection have been identified in pathogens using these tests. Genes under positive selection in pathogens have mostly been sought among viruses, bacteria and protists, because of their paramount importance for human health. Another focus is on fungal pathogens owing to their agronomic importance. We finally discuss promising directions in pathogen studies, such as detecting selection in non-coding regions. | 2009 | 19442589 |
| 9487 | 11 | 0.9997 | Molecular mechanisms of antibiotic resistance revisited. Antibiotic resistance is a global health emergency, with resistance detected to all antibiotics currently in clinical use and only a few novel drugs in the pipeline. Understanding the molecular mechanisms that bacteria use to resist the action of antimicrobials is critical to recognize global patterns of resistance and to improve the use of current drugs, as well as for the design of new drugs less susceptible to resistance development and novel strategies to combat resistance. In this Review, we explore recent advances in understanding how resistance genes contribute to the biology of the host, new structural details of relevant molecular events underpinning resistance, the identification of new resistance gene families and the interactions between different resistance mechanisms. Finally, we discuss how we can use this information to develop the next generation of antimicrobial therapies. | 2023 | 36411397 |
| 9674 | 12 | 0.9997 | Global epistasis in plasmid-mediated antimicrobial resistance. Antimicrobial resistance (AMR) in bacteria is a major public health threat and conjugative plasmids play a key role in the dissemination of AMR genes among bacterial pathogens. Interestingly, the association between AMR plasmids and pathogens is not random and certain associations spread successfully at a global scale. The burst of genome sequencing has increased the resolution of epidemiological programs, broadening our understanding of plasmid distribution in bacterial populations. Despite the immense value of these studies, our ability to predict future plasmid-bacteria associations remains limited. Numerous empirical studies have recently reported systematic patterns in genetic interactions that enable predictability, in a phenomenon known as global epistasis. In this perspective, we argue that global epistasis patterns hold the potential to predict interactions between plasmids and bacterial genomes, thereby facilitating the prediction of future successful associations. To assess the validity of this idea, we use previously published data to identify global epistasis patterns in clinically relevant plasmid-bacteria associations. Furthermore, using simple mechanistic models of antibiotic resistance, we illustrate how global epistasis patterns may allow us to generate new hypotheses on the mechanisms associated with successful plasmid-bacteria associations. Collectively, we aim at illustrating the relevance of exploring global epistasis in the context of plasmid biology. | 2024 | 38409539 |
| 9564 | 13 | 0.9997 | Genomic tools to profile antibiotic mode of action. The increasing emergence of antimicrobial multiresistant bacteria is of great concern to public health. While these bacteria are becoming an ever more prominent cause of nosocomial and community-acquired infections worldwide, the antibiotic discovery pipeline has been stalled in the last few years with very few efforts in the research and development of novel antibacterial therapies. Some of the root causes that have hampered current antibiotic drug development are the lack of understanding of the mode of action (MOA) of novel antibiotic molecules and the poor characterization of the bacterial physiological response to antibiotics that ultimately causes resistance. Here, we review how bacterial genetic tools can be applied at the genomic level with the goal of profiling resistance to antibiotics and elucidating antibiotic MOAs. Specifically, we highlight how chemical genomic detection of the MOA of novel antibiotic molecules and antibiotic profiling by next-generation sequencing are leveraging basic antibiotic research to unprecedented levels with great opportunities for knowledge translation. | 2015 | 24617440 |
| 9517 | 14 | 0.9997 | Better 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. | 2023 | 37401756 |
| 9610 | 15 | 0.9997 | The 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. | 2013 | 23374913 |
| 9683 | 16 | 0.9997 | Antimicrobial resistance and virulence: a successful or deleterious association in the bacterial world? Hosts and bacteria have coevolved over millions of years, during which pathogenic bacteria have modified their virulence mechanisms to adapt to host defense systems. Although the spread of pathogens has been hindered by the discovery and widespread use of antimicrobial agents, antimicrobial resistance has increased globally. The emergence of resistant bacteria has accelerated in recent years, mainly as a result of increased selective pressure. However, although antimicrobial resistance and bacterial virulence have developed on different timescales, they share some common characteristics. This review considers how bacterial virulence and fitness are affected by antibiotic resistance and also how the relationship between virulence and resistance is affected by different genetic mechanisms (e.g., coselection and compensatory mutations) and by the most prevalent global responses. The interplay between these factors and the associated biological costs depend on four main factors: the bacterial species involved, virulence and resistance mechanisms, the ecological niche, and the host. The development of new strategies involving new antimicrobials or nonantimicrobial compounds and of novel diagnostic methods that focus on high-risk clones and rapid tests to detect virulence markers may help to resolve the increasing problem of the association between virulence and resistance, which is becoming more beneficial for pathogenic bacteria. | 2013 | 23554414 |
| 9723 | 17 | 0.9997 | Deciphering the genetic network and programmed regulation of antimicrobial resistance in bacterial pathogens. Antimicrobial resistance (AMR) in bacteria is an important global health problem affecting humans, animals, and the environment. AMR is considered as one of the major components in the "global one health". Misuse/overuse of antibiotics in any one of the segments can impact the integrity of the others. In the presence of antibiotic selective pressure, bacteria tend to develop several defense mechanisms, which include structural changes of the bacterial outer membrane, enzymatic processes, gene upregulation, mutations, adaptive resistance, and biofilm formation. Several components of mobile genetic elements (MGEs) play an important role in the dissemination of AMR. Each one of these components has a specific function that lasts long, irrespective of any antibiotic pressure. Integrative and conjugative elements (ICEs), insertion sequence elements (ISs), and transposons carry the antimicrobial resistance genes (ARGs) on different genetic backbones. Successful transfer of ARGs depends on the class of plasmids, regulons, ISs proximity, and type of recombination systems. Additionally, phage-bacterial networks play a major role in the transmission of ARGs, especially in bacteria from the environment and foods of animal origin. Several other functional attributes of bacteria also get successfully modified to acquire ARGs. These include efflux pumps, toxin-antitoxin systems, regulatory small RNAs, guanosine pentaphosphate signaling, quorum sensing, two-component system, and clustered regularly interspaced short palindromic repeats (CRISPR) systems. The metabolic and virulence state of bacteria is also associated with a range of genetic and phenotypic resistance mechanisms. In spite of the availability of a considerable information on AMR, the network associations between selection pressures and several of the components mentioned above are poorly understood. Understanding how a pathogen resists and regulates the ARGs in response to antimicrobials can help in controlling the development of resistance. Here, we provide an overview of the importance of genetic network and regulation of AMR in bacterial pathogens. | 2022 | 36506027 |
| 9558 | 18 | 0.9997 | Antimicrobial Resistance: Enzymes, Proteins, and Computational Resources. Antimicrobial resistance (AMR) is an important health concern rooted in antibiotic misuse and overuse, resulting in drug-resistant bacteria. However, resistance to these antimicrobials developed as soon as they were administered. Several variables lead to the progression of antimicrobial resistance (AMR), making it a multifaceted challenge for healthcare systems worldwide, such as erroneous diagnosis, inappropriate prescription, incomplete treatment, and many more. Getting an in-depth idea about the mechanism underlying AMR development is essential to overcome this. This review aims to provide information on how various enzymes or proteins aid in the antimicrobial resistance mechanisms and also highlight the clinical perspective of AMR, emphasizing its growing impact on patient outcomes, and incorporate the latest recent data from the World Health Organisation (WHO), underscoring the global urgency of the AMR crisis, with specific attention to trends observed in recent years. Additionally, it is intended to provide ideas about inhibitors that can inhibit the mechanism of antibiotic resistance and also to provide an idea about numerous computational resources available that can be employed to predict genes and/or proteins and enzymes involved in various antibiotic resistance mechanisms. | 2025 | 40770471 |
| 9667 | 19 | 0.9997 | Novel resistance functions uncovered using functional metagenomic investigations of resistance reservoirs. Rates of infection with antibiotic-resistant bacteria have increased precipitously over the past several decades, with far-reaching healthcare and societal costs. Recent evidence has established a link between antibiotic resistance genes in human pathogens and those found in non-pathogenic, commensal, and environmental organisms, prompting deeper investigation of natural and human-associated reservoirs of antibiotic resistance. Functional metagenomic selections, in which shotgun-cloned DNA fragments are selected for their ability to confer survival to an indicator host, have been increasingly applied to the characterization of many antibiotic resistance reservoirs. These experiments have demonstrated that antibiotic resistance genes are highly diverse and widely distributed, many times bearing little to no similarity to known sequences. Through unbiased selections for survival to antibiotic exposure, functional metagenomics can improve annotations by reducing the discovery of false-positive resistance and by allowing for the identification of previously unrecognizable resistance genes. In this review, we summarize the novel resistance functions uncovered using functional metagenomic investigations of natural and human-impacted resistance reservoirs. Examples of novel antibiotic resistance genes include those highly divergent from known sequences, those for which sequence is entirely unable to predict resistance function, bifunctional resistance genes, and those with unconventional, atypical resistance mechanisms. Overcoming antibiotic resistance in the clinic will require a better understanding of existing resistance reservoirs and the dissemination networks that govern horizontal gene exchange, informing best practices to limit the spread of resistance-conferring genes to human pathogens. | 2013 | 23760651 |