# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 8399 | 0 | 1.0000 | SYN-View: A Phylogeny-Based Synteny Exploration Tool for the Identification of Gene Clusters Linked to Antibiotic Resistance. The development of new antibacterial drugs has become one of the most important tasks of the century in order to overcome the posing threat of drug resistance in pathogenic bacteria. Many antibiotics originate from natural products produced by various microorganisms. Over the last decades, bioinformatical approaches have facilitated the discovery and characterization of these small compounds using genome mining methodologies. A key part of this process is the identification of the most promising biosynthetic gene clusters (BGCs), which encode novel natural products. In 2017, the Antibiotic Resistant Target Seeker (ARTS) was developed in order to enable an automated target-directed genome mining approach. ARTS identifies possible resistant target genes within antibiotic gene clusters, in order to detect promising BGCs encoding antibiotics with novel modes of action. Although ARTS can predict promising targets based on multiple criteria, it provides little information about the cluster structures of possible resistant genes. Here, we present SYN-view. Based on a phylogenetic approach, SYN-view allows for easy comparison of gene clusters of interest and distinguishing genes with regular housekeeping functions from genes functioning as antibiotic resistant targets. Our aim is to implement our proposed method into the ARTS web-server, further improving the target-directed genome mining strategy of the ARTS pipeline. | 2020 | 33396183 |
| 8398 | 1 | 0.9999 | ARTS 2.0: feature updates and expansion of the Antibiotic Resistant Target Seeker for comparative genome mining. Multi-drug resistant pathogens have become a major threat to human health and new antibiotics are urgently needed. Most antibiotics are derived from secondary metabolites produced by bacteria. In order to avoid suicide, these bacteria usually encode resistance genes, in some cases within the biosynthetic gene cluster (BGC) of the respective antibiotic compound. Modern genome mining tools enable researchers to computationally detect and predict BGCs that encode the biosynthesis of secondary metabolites. The major challenge now is the prioritization of the most promising BGCs encoding antibiotics with novel modes of action. A recently developed target-directed genome mining approach allows researchers to predict the mode of action of the encoded compound of an uncharacterized BGC based on the presence of resistant target genes. In 2017, we introduced the 'Antibiotic Resistant Target Seeker' (ARTS). ARTS allows for specific and efficient genome mining for antibiotics with interesting and novel targets by rapidly linking housekeeping and known resistance genes to BGC proximity, duplication and horizontal gene transfer (HGT) events. Here, we present ARTS 2.0 available at http://arts.ziemertlab.com. ARTS 2.0 now includes options for automated target directed genome mining in all bacterial taxa as well as metagenomic data. Furthermore, it enables comparison of similar BGCs from different genomes and their putative resistance genes. | 2020 | 32427317 |
| 9567 | 2 | 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 |
| 9671 | 3 | 0.9997 | 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 |
| 9566 | 4 | 0.9996 | Computational resources in the management of antibiotic resistance: Speeding up drug discovery. This article reviews more than 50 computational resources developed in past two decades for forecasting of antibiotic resistance (AR)-associated mutations, genes and genomes. More than 30 databases have been developed for AR-associated information, but only a fraction of them are updated regularly. A large number of methods have been developed to find AR genes, mutations and genomes, with most of them based on similarity-search tools such as BLAST and HMMER. In addition, methods have been developed to predict the inhibition potential of antibiotics against a bacterial strain from the whole-genome data of bacteria. This review also discuss computational resources that can be used to manage the treatment of AR-associated diseases. | 2021 | 33892146 |
| 9597 | 5 | 0.9996 | 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 |
| 9475 | 6 | 0.9996 | 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 |
| 9669 | 7 | 0.9996 | Genomic encyclopedia of bacteria and archaea: sequencing a myriad of type strains. Microbes hold the key to life. They hold the secrets to our past (as the descendants of the earliest forms of life) and the prospects for our future (as we mine their genes for solutions to some of the planet's most pressing problems, from global warming to antibiotic resistance). However, the piecemeal approach that has defined efforts to study microbial genetic diversity for over 20 years and in over 30,000 genome projects risks squandering that promise. These efforts have covered less than 20% of the diversity of the cultured archaeal and bacterial species, which represent just 15% of the overall known prokaryotic diversity. Here we call for the funding of a systematic effort to produce a comprehensive genomic catalog of all cultured Bacteria and Archaea by sequencing, where available, the type strain of each species with a validly published name (currently∼11,000). This effort will provide an unprecedented level of coverage of our planet's genetic diversity, allow for the large-scale discovery of novel genes and functions, and lead to an improved understanding of microbial evolution and function in the environment. | 2014 | 25093819 |
| 9565 | 8 | 0.9996 | Finding drug targets in microbial genomes. In this era of genomic science, knowledge about biological function is integrated increasingly with DNA sequence data. One area that has been significantly impacted by this accumulation of information is the discovery of drugs to treat microbial infections. Genome sequencing and bioinformatics is driving the discovery and development of novel classes of broad-spectrum antimicrobial compounds, and could enable medical science to keep pace with the increasing resistance of bacteria, fungi and parasites to current antimicrobials. This review discusses the use of genomic information in the rapid identification of target genes for antimicrobial drug discovery. | 2001 | 11522517 |
| 9670 | 9 | 0.9996 | 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 |
| 9610 | 10 | 0.9996 | 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 |
| 9598 | 11 | 0.9996 | Strategies and molecular tools to fight antimicrobial resistance: resistome, transcriptome, and antimicrobial peptides. The increasing number of antibiotic resistant bacteria motivates prospective research toward discovery of new antimicrobial active substances. There are, however, controversies concerning the cost-effectiveness of such research with regards to the description of new substances with novel cellular interactions, or description of new uses of existing substances to overcome resistance. Although examination of bacteria isolated from remote locations with limited exposure to humans has revealed an absence of antibiotic resistance genes, it is accepted that these genes were both abundant and diverse in ancient living organisms, as detected in DNA recovered from Pleistocene deposits (30,000 years ago). Indeed, even before the first clinical use of antibiotics more than 60 years ago, resistant organisms had been isolated. Bacteria can exhibit different strategies for resistance against antibiotics. New genetic information may lead to the modification of protein structure affecting the antibiotic carriage into the cell, enzymatic inactivation of drugs, or even modification of cellular structure interfering in the drug-bacteria interaction. There are still plenty of new genes out there in the environment that can be appropriated by putative pathogenic bacteria to resist antimicrobial agents. On the other hand, there are several natural compounds with antibiotic activity that may be used to oppose them. Antimicrobial peptides (AMPs) are molecules which are wide-spread in all forms of life, from multi-cellular organisms to bacterial cells used to interfere with microbial growth. Several AMPs have been shown to be effective against multi-drug resistant bacteria and have low propensity to resistance development, probably due to their unique mode of action, different from well-known antimicrobial drugs. These substances may interact in different ways with bacterial cell membrane, protein synthesis, protein modulation, and protein folding. The analysis of bacterial transcriptome may contribute to the understanding of microbial strategies under different environmental stresses and allows the understanding of their interaction with novel AMPs. | 2013 | 24427156 |
| 9744 | 12 | 0.9996 | PARGT: a software tool for predicting antimicrobial resistance in bacteria. With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called 'features' in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies. | 2020 | 32620856 |
| 4249 | 13 | 0.9996 | Detection of essential genes in Streptococcus pneumoniae using bioinformatics and allelic replacement mutagenesis. Although the emergence and spread of antimicrobial resistance in major bacterial pathogens for the past decades poses a growing challenge to public health, discovery of novel antimicrobial agents from natural products or modification of existing antibiotics cannot circumvent the problem of antimicrobial resistance. The recent development of bacterial genomics and the availability of genome sequences allow the identification of potentially novel antimicrobial agents. The cellular targets of new antimicrobial agents must be essential for the growth, replication, or survival of the bacterium. Conserved genes among different bacterial genomes often turn out to be essential (1, 2). Thus, the combination of comparative genomics and the gene knock-out procedure can provide effective ways to identify the essential genes of bacterial pathogens (3). Identification of essential genes in bacteria may be utilized for the development of new antimicrobial agents because common essential genes in diverse pathogens could constitute novel targets for broad-spectrum antimicrobial agents. | 2008 | 18392984 |
| 9406 | 14 | 0.9996 | Proteomics as the final step in the functional metagenomics study of antimicrobial resistance. The majority of clinically applied antimicrobial agents are derived from natural products generated by soil microorganisms and therefore resistance is likely to be ubiquitous in such environments. This is supported by the fact that numerous clinically important resistance mechanisms are encoded within the genomes of such bacteria. Advances in genomic sequencing have enabled the in silico identification of putative resistance genes present in these microorganisms. However, it is not sufficient to rely on the identification of putative resistance genes, we must also determine if the resultant proteins confer a resistant phenotype. This will require an analysis pipeline that extends from the extraction of environmental DNA, to the identification and analysis of potential resistance genes and their resultant proteins and phenotypes. This review focuses on the application of functional metagenomics and proteomics to study antimicrobial resistance in diverse environments. | 2015 | 25784907 |
| 9399 | 15 | 0.9996 | Inhibition of Replication Fork Formation and Progression: Targeting the Replication Initiation and Primosomal Proteins. Over 1.2 million deaths are attributed to multi-drug-resistant (MDR) bacteria each year. Persistence of MDR bacteria is primarily due to the molecular mechanisms that permit fast replication and rapid evolution. As many pathogens continue to build resistance genes, current antibiotic treatments are being rendered useless and the pool of reliable treatments for many MDR-associated diseases is thus shrinking at an alarming rate. In the development of novel antibiotics, DNA replication is still a largely underexplored target. This review summarises critical literature and synthesises our current understanding of DNA replication initiation in bacteria with a particular focus on the utility and applicability of essential initiation proteins as emerging drug targets. A critical evaluation of the specific methods available to examine and screen the most promising replication initiation proteins is provided. | 2023 | 37240152 |
| 9564 | 16 | 0.9996 | 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 |
| 9563 | 17 | 0.9996 | Do we need new antibiotics? The search for new targets and new compounds. Resistance to antibiotics and other antimicrobial compounds continues to increase. There are several possibilities for protection against pathogenic microorganisms, for instance, preparation of new vaccines against resistant bacterial strains, use of specific bacteriophages, and searching for new antibiotics. The antibiotic search includes: (1) looking for new antibiotics from nontraditional or less traditional sources, (2) sequencing microbial genomes with the aim of finding genes specifying biosynthesis of antibiotics, (3) analyzing DNA from the environment (metagenomics), (4) re-examining forgotten natural compounds and products of their transformations, and (5) investigating new antibiotic targets in pathogenic bacteria. | 2010 | 21086099 |
| 9398 | 18 | 0.9996 | Effectiveness of CRISPR-Cas in Sensitizing Bacterial Populations with Plasmid-Encoded Antimicrobial Resistance. The spread of bacteria resistant to antibiotics poses a serious threat to human health. Genes that encode antibiotic resistance are often harbored on plasmids, extra-chromosomal DNA molecules found in bacteria. The emergence of multiresistance plasmids is particularly problematic and demands the development of new antibiotics and alternative strategies. CRISPR-Cas derived tools with their sequence specificity offer a promising new approach to combating antibiotic resistance. By introducing CRISPR-Cas encoding plasmids that %specifically target antibiotic resistance genes on plasmids, the susceptibility of bacteria to conventional antibiotics can be restored. However, genetic variation within bacterial populations can hinder the effectiveness of such CRISPR-Cas tools by allowing some mutant plasmids to evade CRISPR-mediated cleaving or gene silencing. In this study, we develop a model to test the effectiveness of CRISPR-Cas in sensitizing bacterial populations carrying resistance on non-transmissible plasmids and assess the success probability of a subsequent treatment with conventional antibiotics. We evaluate this probability according to the target interference mechanism, the copy number of the resistance-encoding plasmid, and its compatibility with the CRISPR-Cas encoding plasmid. Our results identify promising approaches to revert antibiotic resistance with CRISPR-Cas encoding plasmids: A DNA-cleaving CRISPR-Cas system on a plasmid incompatible with the targeted plasmid is most effective for low copy numbers, while for resistance plasmids with higher copy numbers gene silencing by CRISPR-Cas systems encoded on compatible plasmids is the superior solution. | 2025 | 40985758 |
| 9217 | 19 | 0.9996 | Role of CRISPR-Cas systems and anti-CRISPR proteins in bacterial antibiotic resistance. The emergence and development of antibiotic resistance in bacteria is a serious threat to global public health. Antibiotic resistance genes (ARGs) are often located on mobile genetic elements (MGEs). They can be transferred among bacteria by horizontal gene transfer (HGT), leading to the spread of drug-resistant strains and antibiotic treatment failure. CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated genes) is one of the many strategies bacteria have developed under long-term selection pressure to restrict the HGT. CRISPR-Cas systems exist in about half of bacterial genomes and play a significant role in limiting the spread of antibiotic resistance. On the other hand, bacteriophages and other MGEs encode a wide range of anti-CRISPR proteins (Acrs) to counteract the immunity of the CRISPR-Cas system. The Acrs could decrease the CRISPR-Cas system's activity against phages and facilitate the acquisition of ARGs and virulence traits for bacteria. This review aimed to assess the relationship between the CRISPR-Cas systems and Acrs with bacterial antibiotic resistance. We also highlighted the CRISPR technology and Acrs to control and prevent antibacterial resistance. The CRISPR-Cas system can target nucleic acid sequences with high accuracy and reliability; therefore, it has become a novel gene editing and gene therapy tool to prevent the spread of antibiotic resistance. CRISPR-based approaches may pave the way for developing smart antibiotics, which could eliminate multidrug-resistant (MDR) bacteria and distinguish between pathogenic and beneficial microorganisms. Additionally, the engineered anti-CRISPR gene-containing phages in combination with antibiotics could be used as a cutting-edge treatment approach to reduce antibiotic resistance. | 2024 | 39149034 |