# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 9078 | 0 | 0.9959 | MetaCherchant: analyzing genomic context of antibiotic resistance genes in gut microbiota. MOTIVATION: Antibiotic resistance is an important global public health problem. Human gut microbiota is an accumulator of resistance genes potentially providing them to pathogens. It is important to develop tools for identifying the mechanisms of how resistance is transmitted between gut microbial species and pathogens. RESULTS: We developed MetaCherchant-an algorithm for extracting the genomic environment of antibiotic resistance genes from metagenomic data in the form of a graph. The algorithm was validated on a number of simulated and published datasets, as well as applied to new 'shotgun' metagenomes of gut microbiota from patients with Helicobacter pylori who underwent antibiotic therapy. Genomic context was reconstructed for several major resistance genes. Taxonomic annotation of the context suggests that within a single metagenome, the resistance genes can be contained in genomes of multiple species. MetaCherchant allows reconstruction of mobile elements with resistance genes within the genomes of bacteria using metagenomic data. Application of MetaCherchant in differential mode produced specific graph structures suggesting the evidence of possible resistance gene transmission within a mobile element that occurred as a result of the antibiotic therapy. MetaCherchant is a promising tool giving researchers an opportunity to get an insight into dynamics of resistance transmission in vivo basing on metagenomic data. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available for download at https://github.com/ctlab/metacherchant. The code is written in Java and is platform-independent. COTANCT: ulyantsev@rain.ifmo.ru. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. | 2018 | 29092015 |
| 9079 | 1 | 0.9956 | Review, Evaluation, and Directions for Gene-Targeted Assembly for Ecological Analyses of Metagenomes. Shotgun metagenomics has greatly advanced our understanding of microbial communities over the last decade. Metagenomic analyses often include assembly and genome binning, computationally daunting tasks especially for big data from complex environments such as soil and sediments. In many studies, however, only a subset of genes and pathways involved in specific functions are of interest; thus, it is not necessary to attempt global assembly. In addition, methods that target genes can be computationally more efficient and produce more accurate assembly by leveraging rich databases, especially for those genes that are of broad interest such as those involved in biogeochemical cycles, biodegradation, and antibiotic resistance or used as phylogenetic markers. Here, we review six gene-targeted assemblers with unique algorithms for extracting and/or assembling targeted genes: Xander, MegaGTA, SAT-Assembler, HMM-GRASPx, GenSeed-HMM, and MEGAN. We tested these tools using two datasets with known genomes, a synthetic community of artificial reads derived from the genomes of 17 bacteria, shotgun sequence data from a mock community with 48 bacteria and 16 archaea genomes, and a large soil shotgun metagenomic dataset. We compared assemblies of a universal single copy gene (rplB) and two N cycle genes (nifH and nirK). We measured their computational efficiency, sensitivity, specificity, and chimera rate and found Xander and MegaGTA, which both use a probabilistic graph structure to model the genes, have the best overall performance with all three datasets, although MEGAN, a reference matching assembler, had better sensitivity with synthetic and mock community members chosen from its reference collection. Also, Xander and MegaGTA are the only tools that include post-assembly scripts tuned for common molecular ecology and diversity analyses. Additionally, we provide a mathematical model for estimating the probability of assembling targeted genes in a metagenome for estimating required sequencing depth. | 2019 | 31749830 |
| 5098 | 2 | 0.9955 | Feature selection and aggregation for antibiotic resistance GWAS in Mycobacterium tuberculosis: a comparative study. INTRODUCTION: Drug resistance (DR) of pathogens remains a global healthcare concern. In contrast to other bacteria, acquiring mutations in the core genome is the main mechanism of drug resistance for Mycobacterium tuberculosis (MTB). For some antibiotics, the resistance of a particular isolate can be reliably predicted by identifying specific mutations, while for other antibiotics the knowledge of resistance mechanisms is limited. Statistical machine learning (ML) methods are used to infer new genes implicated in drug resistance leveraging large collections of isolates with known whole-genome sequences and phenotypic states for different drugs. However, high correlations between the phenotypic states for commonly used drugs complicate the inference of true associations of mutations with drug phenotypes by ML approaches. METHODS: Recently, several new methods have been developed to select a small subset of reliable predictors of the dependent variable, which may help reduce the number of spurious associations identified. In this study, we evaluated several such methods, namely, logistic regression with different regularization penalty functions, a recently introduced algorithm for solving the best-subset selection problem (ABESS) and "Hungry, Hungry SNPos" (HHS) a heuristic algorithm specifically developed to identify resistance-associated genetic variants in the presence of resistance co-occurrence. We assessed their ability to select known causal mutations for resistance to a specific drug while avoiding the selection of mutations in genes associated with resistance to other drugs, thus we compared selected ML models for their applicability for MTB genome wide association studies. RESULTS AND DISCUSSION: In our analysis, ABESS significantly outperformed the other methods, selecting more relevant sets of mutations. Additionally, we demonstrated that aggregating rare mutations within protein-coding genes into markers indicative of changes in PFAM domains improved prediction quality, and these markers were predominantly selected by ABESS, suggesting their high informativeness. However, ABESS yielded lower prediction accuracy compared to logistic regression methods with regularization. | 2025 | 40606161 |
| 9076 | 3 | 0.9955 | ResiDB: An automated database manager for sequence data. The amount of publicly available DNA sequence data is drastically increasing, making it a tedious task to create sequence databases necessary for the design of diagnostic assays. The selection of appropriate sequences is especially challenging in genes affected by frequent point mutations such as antibiotic resistance genes. To overcome this issue, we have designed the webtool resiDB, a rapid and user-friendly sequence database manager for bacteria, fungi, viruses, protozoa, invertebrates, plants, archaea, environmental and whole genome shotgun sequence data. It automatically identifies and curates sequence clusters to create custom sequence databases based on user-defined input sequences. A collection of helpful visualization tools gives the user the opportunity to easily access, evaluate, edit, and download the newly created database. Consequently, researchers do no longer have to manually manage sequence data retrieval, deal with hardware limitations, and run multiple independent software tools, each having its own requirements, input and output formats. Our tool was developed within the H2020 project FAPIC aiming to develop a single diagnostic assay targeting all sepsis-relevant pathogens and antibiotic resistance mechanisms. ResiDB is freely accessible to all users through https://residb.ait.ac.at/. | 2021 | 33495705 |
| 4342 | 4 | 0.9954 | Evolution and diversity of clonal bacteria: the paradigm of Mycobacterium tuberculosis. BACKGROUND: Mycobacterium tuberculosis complex species display relatively static genomes and 99.9% nucleotide sequence identity. Studying the evolutionary history of such monomorphic bacteria is a difficult and challenging task. PRINCIPAL FINDINGS: We found that single-nucleotide polymorphism (SNP) analysis of DNA repair, recombination and replication (3R) genes in a comprehensive selection of M. tuberculosis complex strains from across the world, yielded surprisingly high levels of polymorphisms as compared to house-keeping genes, making it possible to distinguish between 80% of clinical isolates analyzed in this study. Bioinformatics analysis suggests that a large number of these polymorphisms are potentially deleterious. Site frequency spectrum comparison of synonymous and non-synonymous variants and Ka/Ks ratio analysis suggest a general negative/purifying selection acting on these sets of genes that may lead to suboptimal 3R system activity. In turn, the relaxed fidelity of 3R genes may allow the occurrence of adaptive variants, some of which will survive. Furthermore, 3R-based phylogenetic trees are a new tool for distinguishing between M. tuberculosis complex strains. CONCLUSIONS/SIGNIFICANCE: This situation, and the consequent lack of fidelity in genome maintenance, may serve as a starting point for the evolution of antibiotic resistance, fitness for survival and pathogenicity, possibly conferring a selective advantage in certain stressful situations. These findings suggest that 3R genes may play an important role in the evolution of highly clonal bacteria, such as M. tuberculosis. They also facilitate further epidemiological studies of these bacteria, through the development of high-resolution tools. With many more microbial genomes being sequenced, our results open the door to 3R gene-based studies of adaptation and evolution of other, highly clonal bacteria. | 2008 | 18253486 |
| 8375 | 5 | 0.9954 | Genome-scale identification method applied to find cryptic aminoglycoside resistance genes in Pseudomonas aeruginosa. BACKGROUND: The ability of bacteria to rapidly evolve resistance to antibiotics is a critical public health problem. Resistance leads to increased disease severity and death rates, as well as imposes pressure towards the discovery and development of new antibiotic therapies. Improving understanding of the evolution and genetic basis of resistance is a fundamental goal in the field of microbiology. RESULTS: We have applied a new genomic method, Scalar Analysis of Library Enrichments (SCALEs), to identify genomic regions that, given increased copy number, may lead to aminoglycoside resistance in Pseudomonas aeruginosa at the genome scale. We report the result of selections on highly representative genomic libraries for three different aminoglycoside antibiotics (amikacin, gentamicin, and tobramycin). At the genome-scale, we show significant (p<0.05) overlap in genes identified for each aminoglycoside evaluated. Among the genomic segments identified, we confirmed increased resistance associated with an increased copy number of several genomic regions, including the ORF of PA5471, recently implicated in MexXY efflux pump related aminoglycoside resistance, PA4943-PA4946 (encoding a probable GTP-binding protein, a predicted host factor I protein, a delta 2-isopentenylpyrophosphate transferase, and DNA mismatch repair protein mutL), PA0960-PA0963 (encoding hypothetical proteins, a probable cold shock protein, a probable DNA-binding stress protein, and aspartyl-tRNA synthetase), a segment of PA4967 (encoding a topoisomerase IV subunit B), as well as a chimeric clone containing two inserts including the ORFs PA0547 and PA2326 (encoding a probable transcriptional regulator and a probable hypothetical protein, respectively). CONCLUSIONS: The studies reported here demonstrate the application of new a genomic method, SCALEs, which can be used to improve understanding of the evolution of antibiotic resistance in P. aeruginosa. In our demonstration studies, we identified a significant number of genomic regions that increased resistance to multiple aminoglycosides. We identified genetic regions that include open reading frames that encode for products from many functional categories, including genes related to O-antigen synthesis, DNA repair, and transcriptional and translational processes. | 2009 | 19907650 |
| 9073 | 6 | 0.9954 | EpitoCore: Mining Conserved Epitope Vaccine Candidates in the Core Proteome of Multiple Bacteria Strains. In reverse vaccinology approaches, complete proteomes of bacteria are submitted to multiple computational prediction steps in order to filter proteins that are possible vaccine candidates. Most available tools perform such analysis only in a single strain, or a very limited number of strains. But the vast amount of genomic data had shown that most bacteria contain pangenomes, i.e., their genomic information contains core, conserved genes, and random accessory genes specific to each strain. Therefore, in reverse vaccinology methods it is of the utmost importance to define core proteins and core epitopes. EpitoCore is a decision-tree pipeline developed to fulfill that need. It provides surfaceome prediction of proteins from related strains, defines core proteins within those, calculate their immunogenicity, predicts epitopes for a given set of MHC alleles defined by the user, and then reports if epitopes are located extracellularly and if they are conserved among the core homologs. Pipeline performance is illustrated by mining peptide vaccine candidates in Mycobacterium avium hominissuis strains. From a total proteome of ~4,800 proteins per strain, EpitoCore predicted 103 highly immunogenic core homologs located at cell surface, many of those related to virulence and drug resistance. Conserved epitopes identified among these homologs allows the users to define sets of peptides with potential to immunize the largest coverage of tested HLA alleles using peptide-based vaccines. Therefore, EpitoCore is able to provide automated identification of conserved epitopes in bacterial pangenomic datasets. | 2020 | 32431712 |
| 3765 | 7 | 0.9954 | An allelic exchange system for compliant genetic manipulation of the select agents Burkholderia pseudomallei and Burkholderia mallei. Burkholderia pseudomallei and B. mallei are Gram-negative bacterial pathogens that cause melioidosis in humans and glanders in horses, respectively. Both bacteria are classified as category B select agents in the United States. Due to strict select-agent regulations, the number of antibiotic selection markers approved for use in these bacteria is greatly limited. Approved markers for B. pseudomallei include genes encoding resistance to kanamycin (Km), gentamicin (Gm), and zeocin (Zeo); however, wild type B. pseudomallei is intrinsically resistant to these antibiotics. Selection markers for B. mallei are limited to Km and Zeo resistance genes. Additionally, there are few well developed counter-selection markers for use in Burkholderia. The use of SacB as a counter-selection method has been of limited success due to the presence of endogenous sacBC genes in the genomes of B. pseudomallei and B. mallei. These impediments have greatly hampered the genetic manipulation of B. pseudomallei and B. mallei and currently few reliable tools for the genetic manipulation of Burkholderia exist. To expand the repertoire of genetic tools for use in Burkholderia, we developed the suicide plasmid pMo130, which allows for the compliant genetic manipulation of the select agents B. pseudomallei and B. mallei using allelic exchange. pMo130 harbors an aphA gene which allows for Km selection, the reporter gene xylE, which allows for reliable visual detection of Burkholderia transformants, and carries a modified sacB gene that allows for the resolution of co-integrants. We employed this system to generate multiple unmarked and in-frame mutants in B. pseudomallei, and one mutant in B. mallei. This vector significantly expands the number of available tools that are select-agent compliant for the genetic manipulation of B. pseudomallei and B. mallei. | 2009 | 19010402 |
| 8399 | 8 | 0.9954 | 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 |
| 3764 | 9 | 0.9953 | Evidence for diversifying selection in a set of Mycobacterium tuberculosis genes in response to antibiotic- and nonantibiotic-related pressure. Tuberculosis (TB) is a global health problem estimated to kill 1.4 million people per year. Recent advances in the genomics of the causative agents of TB, bacteria known as the Mycobacterium tuberculosis complex (MTBC), have allowed a better comprehension of its population structure and provided the foundation for molecular evolution analyses. These studies are crucial for a better understanding of TB, including the variation of vaccine efficacy and disease outcome, together with the emergence of drug resistance. Starting from the analysis of 73 publicly available genomes from all the main MTBC lineages, we have screened for evidences of positive selection, a set of 576 genes previously associated with drug resistance or encoding membrane proteins. As expected, because antibiotics constitute strong selective pressure, some of the codons identified correspond to the position of confirmed drug-resistance-associated substitutions in the genes embB, rpoB, and katG. Furthermore, we identified diversifying selection in specific codons of the genes Rv0176 and Rv1872c coding for MCE1-associated transmembrane protein and a putative l-lactate dehydrogenase, respectively. Amino acid sequence analyses showed that in Rv0176, sites undergoing diversifying selection were in a predicted antigen region that varies between "modern" lineages and "ancient" MTBC/BCG strains. In Rv1872c, some of the sites under selection are predicted to impact protein function and thus might result from metabolic adaptation. These results illustrate that diversifying selection in MTBC is happening as a consequence of both antibiotic treatment and other evolutionary pressures. | 2013 | 23449927 |
| 8400 | 10 | 0.9953 | Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis. BACKGROUND: Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date. METHODS: In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l(2)-regularized logistic regression model. RESULTS: The computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance. CONCLUSIONS: The proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between M. tuberculosis H37Rv and Homo sapiens, provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways. | 2018 | 29954330 |
| 9237 | 11 | 0.9953 | The gossip paradox: Why do bacteria share genes? Bacteria, in contrast to eukaryotic cells, contain two types of genes: chromosomal genes that are fixed to the cell, and plasmids, smaller loops of DNA capable of being passed from one cell to another. The sharing of plasmid genes between individual bacteria and between bacterial lineages has contributed vastly to bacterial evolution, allowing specialized traits to 'jump ship' between one lineage or species and the next. The benefits of this generosity from the point of view of both recipient cell and plasmid are generally understood: plasmids receive new hosts and ride out selective sweeps across the population, recipient cells gain new traits (such as antibiotic resistance). Explaining this behavior from the point of view of donor cells is substantially more difficult. Donor cells pay a fitness cost in order to share plasmids, and run the risk of sharing advantageous genes with their competition and rendering their own lineage redundant, while seemingly receiving no benefit in return. Using both compartment based models and agent based simulations we demonstrate that 'secretive' genes which restrict horizontal gene transfer are favored over a wide range of models and parameter values, even when sharing carries no direct cost. 'Generous' chromosomal genes which are more permissive of plasmid transfer are found to have neutral fitness at best, and are generally disfavored by selection. Our findings lead to a peculiar paradox: given the obvious benefits of keeping secrets, why do bacteria share information so freely? | 2022 | 35603365 |
| 8293 | 12 | 0.9953 | Identification of Bicarbonate as a Trigger and Genes Involved with Extracellular DNA Export in Mycobacterial Biofilms. Extracellular DNA (eDNA) is an integral biofilm matrix component of numerous pathogens, including nontuberculous mycobacteria (NTM). Cell lysis is the source of eDNA in certain bacteria, but the source of eDNA remains unidentified for NTM, as well as for other eDNA-containing bacterial species. In this study, conditions affecting eDNA export were examined, and genes involved with the eDNA export mechanism were identified. After a method for monitoring eDNA in real time in undisturbed biofilms was established, different conditions affecting eDNA were investigated. Bicarbonate positively influenced eDNA export in a pH-independent manner in Mycobacterium avium, M. abscessus, and M. chelonae The surface-exposed proteome of M. avium in eDNA-containing biofilms revealed abundant carbonic anhydrases. Chemical inhibition of carbonic anhydrases with ethoxzolamide significantly reduced eDNA export. An unbiased transposon mutant library screen for eDNA export in M. avium identified many severely eDNA-attenuated mutants, including one not expressing a unique FtsK/SpoIIIE-like DNA-transporting pore, two with inactivation of carbonic anhydrases, and nine with inactivation of genes belonging to a unique genomic region, as well as numerous mutants involved in metabolism and energy production. Complementation of nine mutants that included the FtsK/SpoIIIE and carbonic anhydrase significantly restored eDNA export. Interestingly, several attenuated eDNA mutants have mutations in genes encoding proteins that were found with the surface proteomics, and many more mutations are localized in operons potentially encoding surface proteins. Collectively, our data strengthen the evidence of eDNA export being an active mechanism that is activated by the bacterium responding to bicarbonate. IMPORTANCE: Many bacteria contain extracellular DNA (eDNA) in their biofilm matrix, as it has various biological and physical functions. We recently reported that nontuberculous mycobacteria (NTM) can contain significant quantities of eDNA in their biofilms. In some bacteria, eDNA is derived from dead cells, but that does not appear to be the case for all eDNA-containing organisms, including NTM. In this study, we found that eDNA export in NTM is conditionally dependent on the molecules to which the bacteria are exposed and that bicarbonate positively influences eDNA export. We also identified genes and proteins important for eDNA export, which begins to piece together a description of a mechanism for eDNA. Better understanding of eDNA export can give new targets for the development of antivirulence drugs, which are attractive because resistance to classical antibiotics is currently a significant problem. | 2016 | 27923918 |
| 8398 | 13 | 0.9953 | 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 |
| 8378 | 14 | 0.9953 | Genome mining reveals unlocked bioactive potential of marine Gram-negative bacteria. BACKGROUND: Antibiotic resistance in bacteria spreads quickly, overtaking the pace at which new compounds are discovered and this emphasizes the immediate need to discover new compounds for control of infectious diseases. Terrestrial bacteria have for decades been investigated as a source of bioactive compounds leading to successful applications in pharmaceutical and biotech industries. Marine bacteria have so far not been exploited to the same extent; however, they are believed to harbor a multitude of novel bioactive chemistry. To explore this potential, genomes of 21 marine Alpha- and Gammaproteobacteria collected during the Galathea 3 expedition were sequenced and mined for natural product encoding gene clusters. RESULTS: Independently of genome size, bacteria of all tested genera carried a large number of clusters encoding different potential bioactivities, especially within the Vibrionaceae and Pseudoalteromonadaceae families. A very high potential was identified in pigmented pseudoalteromonads with up to 20 clusters in a single strain, mostly NRPSs and NRPS-PKS hybrids. Furthermore, regulatory elements in bioactivity-related pathways including chitin metabolism, quorum sensing and iron scavenging systems were investigated both in silico and in vitro. Genes with siderophore function were identified in 50% of the strains, however, all but one harboured the ferric-uptake-regulator gene. Genes encoding the syntethase of acylated homoserine lactones were found in Roseobacter-clade bacteria, but not in the Vibrionaceae strains and only in one Pseudoalteromonas strains. The understanding and manipulation of these elements can help in the discovery and production of new compounds never identified under regular laboratory cultivation conditions. High chitinolytic potential was demonstrated and verified for Vibrio and Pseudoalteromonas species that commonly live in close association with eukaryotic organisms in the environment. Chitin regulation by the ChiS histidine-kinase seems to be a general trait of the Vibrionaceae family, however it is absent in the Pseudomonadaceae. Hence, the degree to which chitin influences secondary metabolism in marine bacteria is not known. CONCLUSIONS: Utilizing the rapidly developing sequencing technologies and software tools in combination with phenotypic in vitro assays, we demonstrated the high bioactive potential of marine bacteria in an efficient, straightforward manner - an approach that will facilitate natural product discovery in the future. | 2015 | 25879706 |
| 9610 | 15 | 0.9953 | 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 |
| 9088 | 16 | 0.9952 | Cocrystallizing and Codelivering Complementary Drugs to Multidrugresistant Tuberculosis Bacteria in Perfecting Multidrug Therapy. Bacteria cells exhibit multidrug resistance in one of two ways: by raising the genetic expression of multidrug efflux pumps or by accumulating several drug-resistant components in many genes. Multidrug-resistive tuberculosis bacteria are treated by multidrug therapy, where a few certain antibacterial drugs are administered together to kill a bacterium jointly. A major drawback of conventional multidrug therapy is that the administration never ensures the reaching of different drug molecules to a particular bacterium cell at the same time, which promotes growing drug resistivity step-wise. As a result, it enhances the treatment time. With additional tabletability and plasticity, the formation of a cocrystal of multidrug can ensure administrating the multidrug chemically together to a target bacterium cell. With properly maintaining the basic philosophy of multidrug therapy here, the synergistic effects of drug molecules can ensure killing the bacteria, even before getting the option to raise the drug resistance against them. This can minimize the treatment span, expenditure and drug resistance. A potential threat of epidemic from tuberculosis has appeared after the Covid-19 outbreak. An unwanted loop of finding molecules with the potential to kill tuberculosis, getting their corresponding drug approvals, and abandoning the drug after facing drug resistance can be suppressed here. This perspective aims to develop the universal drug regimen by postulating the principles of drug molecule selection, cocrystallization, and subsequent harmonisation within a short period to address multidrug-resistant bacteria. | 2023 | 37150990 |
| 8401 | 17 | 0.9952 | LSTrAP-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. | 2020 | 32883264 |
| 9372 | 18 | 0.9952 | The population genetics of collateral resistance and sensitivity. Resistance mutations against one drug can elicit collateral sensitivity against other drugs. Multi-drug treatments exploiting such trade-offs can help slow down the evolution of resistance. However, if mutations with diverse collateral effects are available, a treated population may evolve either collateral sensitivity or collateral resistance. How to design treatments robust to such uncertainty is unclear. We show that many resistance mutations in Escherichia coli against various antibiotics indeed have diverse collateral effects. We propose to characterize such diversity with a joint distribution of fitness effects (JDFE) and develop a theory for describing and predicting collateral evolution based on simple statistics of the JDFE. We show how to robustly rank drug pairs to minimize the risk of collateral resistance and how to estimate JDFEs. In addition to practical applications, these results have implications for our understanding of evolution in variable environments. | 2021 | 34889185 |
| 8173 | 19 | 0.9952 | Advancing Antibacterial Strategies: CRISPR-Phage-Mediated Gene Therapy Targeting Bacterial Resistance Genes. One of the most significant issues facing the world today is antibiotic resistance, which makes it increasingly difficult to treat bacterial infections. Regular antibiotics no longer work against many bacteria, affecting millions of people. A novel approach known as CRISPR-phage therapy may be beneficial. This technique introduces a technology called CRISPR into resistant bacteria using bacteriophages. The genes that cause bacteria to become resistant to antibiotics can be identified and cut using CRISPR. This enables antibiotics to function by inhibiting the bacteria. This approach is highly precise, unlike conventional antibiotics, so it doesn't damage our bodies' beneficial bacteria. Preliminary studies and limited clinical trials suggest that this technique can effectively target drug-resistant bacteria such as Klebsiella pneumoniae and Methicillinresistant Staphylococcus aureus (MRSA). However, challenges in phage engineering, host delivery, and the growing threat of bacterial CRISPR resistance demand urgent and strategic innovation. Our perspective underscores that without proactive resolution of these hurdles, the current hopefulness could disappear. Looking ahead, integrating next-generation Cas effectors, non-DSB editors, and resistance monitoring frameworks could transform CRISPR-phage systems from an experimental novelty into a clinical mainstay. This shift will require not only scientific ingenuity but also coordinated advances in regulatory, translational, and manufacturing efforts. | 2025 | 40990280 |