# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 8398 | 0 | 0.9968 | 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 |
| 9172 | 1 | 0.9968 | These Are the Genes You're Looking For: Finding Host Resistance Genes. Humanity's ongoing struggle with new, re-emerging and endemic infectious diseases serves as a frequent reminder of the need to understand host-pathogen interactions. Recent advances in genomics have dramatically advanced our understanding of how genetics contributes to host resistance or susceptibility to bacterial infection. Here we discuss current trends in defining host-bacterial interactions at the genome-wide level, including screens that harness CRISPR/Cas9 genome editing, natural genetic variation, proteomics, and transcriptomics. We report on the merits, limitations, and findings of these innovative screens and discuss their complementary nature. Finally, we speculate on future innovation as we continue to progress through the postgenomic era and towards deeper mechanistic insight and clinical applications. | 2021 | 33004258 |
| 8399 | 2 | 0.9967 | 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 |
| 9219 | 3 | 0.9966 | Knowing and Naming: Phage Annotation and Nomenclature for Phage Therapy. Bacteriophages, or phages, are viruses that infect bacteria shaping microbial communities and ecosystems. They have gained attention as potential agents against antibiotic resistance. In phage therapy, lytic phages are preferred for their bacteria killing ability, while temperate phages, which can transfer antibiotic resistance or toxin genes, are avoided. Selection relies on plaque morphology and genome sequencing. This review outlines annotating genomes, identifying critical genomic features, and assigning functional labels to protein-coding sequences. These annotations prevent the transfer of unwanted genes, such as antimicrobial resistance or toxin genes, during phage therapy. Additionally, it covers International Committee on Taxonomy of Viruses (ICTV)-an established phage nomenclature system for simplified classification and communication. Accurate phage genome annotation and nomenclature provide insights into phage-host interactions, replication strategies, and evolution, accelerating our understanding of the diversity and evolution of phages and facilitating the development of phage-based therapies. | 2023 | 37932119 |
| 9174 | 4 | 0.9966 | Developing Phage Therapy That Overcomes the Evolution of Bacterial Resistance. The global rise of antibiotic resistance in bacterial pathogens and the waning efficacy of antibiotics urge consideration of alternative antimicrobial strategies. Phage therapy is a classic approach where bacteriophages (bacteria-specific viruses) are used against bacterial infections, with many recent successes in personalized medicine treatment of intractable infections. However, a perpetual challenge for developing generalized phage therapy is the expectation that viruses will exert selection for target bacteria to deploy defenses against virus attack, causing evolution of phage resistance during patient treatment. Here we review the two main complementary strategies for mitigating bacterial resistance in phage therapy: minimizing the ability for bacterial populations to evolve phage resistance and driving (steering) evolution of phage-resistant bacteria toward clinically favorable outcomes. We discuss future research directions that might further address the phage-resistance problem, to foster widespread development and deployment of therapeutic phage strategies that outsmart evolved bacterial resistance in clinical settings. | 2023 | 37268007 |
| 8259 | 5 | 0.9966 | Secondary Metabolite Transcriptomic Pipeline (SeMa-Trap), an expression-based exploration tool for increased secondary metabolite production in bacteria. For decades, natural products have been used as a primary resource in drug discovery pipelines to find new antibiotics, which are mainly produced as secondary metabolites by bacteria. The biosynthesis of these compounds is encoded in co-localized genes termed biosynthetic gene clusters (BGCs). However, BGCs are often not expressed under laboratory conditions. Several genetic manipulation strategies have been developed in order to activate or overexpress silent BGCs. Significant increases in production levels of secondary metabolites were indeed achieved by modifying the expression of genes encoding regulators and transporters, as well as genes involved in resistance or precursor biosynthesis. However, the abundance of genes encoding such functions within bacterial genomes requires prioritization of the most promising ones for genetic manipulation strategies. Here, we introduce the 'Secondary Metabolite Transcriptomic Pipeline' (SeMa-Trap), a user-friendly web-server, available at https://sema-trap.ziemertlab.com. SeMa-Trap facilitates RNA-Seq based transcriptome analyses, finds co-expression patterns between certain genes and BGCs of interest, and helps optimize the design of comparative transcriptomic analyses. Finally, SeMa-Trap provides interactive result pages for each BGC, allowing the easy exploration and comparison of expression patterns. In summary, SeMa-Trap allows a straightforward prioritization of genes that could be targeted via genetic engineering approaches to (over)express BGCs of interest. | 2022 | 35580059 |
| 9375 | 6 | 0.9965 | Multistep diversification in spatiotemporal bacterial-phage coevolution. The evolutionary arms race between phages and bacteria, where bacteria evolve resistance to phages and phages retaliate with resistance-countering mutations, is a major driving force of molecular innovation and genetic diversification. Yet attempting to reproduce such ongoing retaliation dynamics in the lab has been challenging; laboratory coevolution experiments of phage and bacteria are typically performed in well-mixed environments and often lead to rapid stagnation with little genetic variability. Here, co-culturing motile E. coli with the lytic bacteriophage T7 on swimming plates, we observe complex spatiotemporal dynamics with multiple genetically diversifying adaptive cycles. Systematically quantifying over 10,000 resistance-infectivity phenotypes between evolved bacteria and phage isolates, we observe diversification into multiple coexisting ecotypes showing a complex interaction network with both host-range expansion and host-switch tradeoffs. Whole-genome sequencing of these evolved phage and bacterial isolates revealed a rich set of adaptive mutations in multiple genetic pathways including in genes not previously linked with phage-bacteria interactions. Synthetically reconstructing these new mutations, we discover phage-general and phage-specific resistance phenotypes as well as a strong synergy with the more classically known phage-resistance mutations. These results highlight the importance of spatial structure and migration for driving phage-bacteria coevolution, providing a concrete system for revealing new molecular mechanisms across diverse phage-bacterial systems. | 2022 | 36577749 |
| 9744 | 7 | 0.9965 | 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 |
| 8401 | 8 | 0.9965 | 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 |
| 9552 | 9 | 0.9965 | Addressing antibiotic resistance: computational answers to a biological problem? The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance. | 2023 | 37031568 |
| 9086 | 10 | 0.9965 | Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas. Drug resistant tuberculosis is increasing world-wide. Resistance against isoniazid (INH), rifampicin (RIF), or both (multi-drug resistant TB, MDR-TB) is of particular concern, since INH and RIF form part of the standard regimen for TB disease. While it is known that suboptimal treatment can lead to resistance, it remains unclear how host immune responses and antibiotic dynamics within granulomas (sites of infection) affect emergence and selection of drug-resistant bacteria. We take a systems pharmacology approach to explore resistance dynamics within granulomas. We integrate spatio-temporal host immunity, INH and RIF dynamics, and bacterial dynamics (including fitness costs and compensatory mutations) in a computational framework. We simulate resistance emergence in the absence of treatment, as well as resistance selection during INH and/or RIF treatment. There are four main findings. First, in the absence of treatment, the percentage of granulomas containing resistant bacteria mirrors the non-monotonic bacterial dynamics within granulomas. Second, drug-resistant bacteria are less frequently found in non-replicating states in caseum, compared to drug-sensitive bacteria. Third, due to a steeper dose response curve and faster plasma clearance of INH compared to RIF, INH-resistant bacteria have a stronger influence on treatment outcomes than RIF-resistant bacteria. Finally, under combination therapy with INH and RIF, few MDR bacteria are able to significantly affect treatment outcomes. Overall, our approach allows drug-specific prediction of drug resistance emergence and selection in the complex granuloma context. Since our predictions are based on pre-clinical data, our approach can be implemented relatively early in the treatment development process, thereby enabling pro-active rather than reactive responses to emerging drug resistance for new drugs. Furthermore, this quantitative and drug-specific approach can help identify drug-specific properties that influence resistance and use this information to design treatment regimens that minimize resistance selection and expand the useful life-span of new antibiotics. | 2018 | 29746491 |
| 9184 | 11 | 0.9965 | Unlocking the potential of phages: Innovative approaches to harnessing bacteriophages as diagnostic tools for human diseases. Phages, viruses that infect bacteria, have been explored as promising tools for the detection of human disease. By leveraging the specificity of phages for their bacterial hosts, phage-based diagnostic tools can rapidly and accurately detect bacterial infections in clinical samples. In recent years, advances in genetic engineering and biotechnology have enabled the development of more sophisticated phage-based diagnostic tools, including those that express reporter genes or enzymes, or target specific virulence factors or antibiotic resistance genes. However, despite these advancements, there are still challenges and limitations to the use of phage-based diagnostic tools, including concerns over phage safety and efficacy. This review aims to provide a comprehensive overview of the current state of phage-based diagnostic tools, including their advantages, limitations, and potential for future development. By addressing these issues, we hope to contribute to the ongoing efforts to develop safe and effective phage-based diagnostic tools for the detection of human disease. | 2023 | 37770168 |
| 9372 | 12 | 0.9965 | 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 |
| 9561 | 13 | 0.9965 | The resistance tsunami, antimicrobial stewardship, and the golden age of microbiology. Modern medicine is built on antibiotics. Antibiotics are something that we take for granted. We have however spent over 60 years educating bacteria to become resistant, and the global resistance tsunami has caught everyone unawares. Since bacteria have changed, we also have to change, and to change most of the practices of how we use antibiotics. Because the development of new antibiotics is so expensive, a stewardship approach may help to preserve those that we have now while we work to develop new antibiotics and to develop other approaches to controlling and treating infections. We need to adopt the ethic of Good Stewardship Practice (GSP) as an active and dynamic process of continuous improvement in antibiotic use, a process with many steps of different sizes involving everyone involved in antibiotic use. All antibiotic users have an important role to play in GSP. Although the resistance situation is pessimistic, and the future of antibiotics looks uncertain, we are fortunately entering what may be seen as the golden age of microbiology. This encompasses an astonishing array of technologies for rapid pathogen and resistance gene detection, for clone identification by genome sequencing, for identification of novel bacterial genes and for identification of the Achilles' heels of different pathogens. Future antibiotics may have to be far more targeted to the individual pathogen and the site of infection. A global tax on antibiotics might reduce their use while funding the cost of developing new antibiotics and new approaches to control of infectious diseases. | 2014 | 24646601 |
| 9176 | 14 | 0.9965 | Evolutionary Dynamics between Phages and Bacteria as a Possible Approach for Designing Effective Phage Therapies against Antibiotic-Resistant Bacteria. With the increasing global threat of antibiotic resistance, there is an urgent need to develop new effective therapies to tackle antibiotic-resistant bacterial infections. Bacteriophage therapy is considered as a possible alternative over antibiotics to treat antibiotic-resistant bacteria. However, bacteria can evolve resistance towards bacteriophages through antiphage defense mechanisms, which is a major limitation of phage therapy. The antiphage mechanisms target the phage life cycle, including adsorption, the injection of DNA, synthesis, the assembly of phage particles, and the release of progeny virions. The non-specific bacterial defense mechanisms include adsorption inhibition, superinfection exclusion, restriction-modification, and abortive infection systems. The antiphage defense mechanism includes a clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated (Cas) system. At the same time, phages can execute a counterstrategy against antiphage defense mechanisms. However, the antibiotic susceptibility and antibiotic resistance in bacteriophage-resistant bacteria still remain unclear in terms of evolutionary trade-offs and trade-ups between phages and bacteria. Since phage resistance has been a major barrier in phage therapy, the trade-offs can be a possible approach to design effective bacteriophage-mediated intervention strategies. Specifically, the trade-offs between phage resistance and antibiotic resistance can be used as therapeutic models for promoting antibiotic susceptibility and reducing virulence traits, known as bacteriophage steering or evolutionary medicine. Therefore, this review highlights the synergistic application of bacteriophages and antibiotics in association with the pleiotropic trade-offs of bacteriophage resistance. | 2022 | 35884169 |
| 9108 | 15 | 0.9965 | Learning from losers. Bacteria can overcome environmental challenges by killing nearby bacteria and incorporating their DNA. | 2017 | 29148975 |
| 8264 | 16 | 0.9964 | Anti-CRISPR Phages Cooperate to Overcome CRISPR-Cas Immunity. Some phages encode anti-CRISPR (acr) genes, which antagonize bacterial CRISPR-Cas immune systems by binding components of its machinery, but it is less clear how deployment of these acr genes impacts phage replication and epidemiology. Here, we demonstrate that bacteria with CRISPR-Cas resistance are still partially immune to Acr-encoding phage. As a consequence, Acr-phages often need to cooperate in order to overcome CRISPR resistance, with a first phage blocking the host CRISPR-Cas immune system to allow a second Acr-phage to successfully replicate. This cooperation leads to epidemiological tipping points in which the initial density of Acr-phage tips the balance from phage extinction to a phage epidemic. Furthermore, both higher levels of CRISPR-Cas immunity and weaker Acr activities shift the tipping points toward higher initial phage densities. Collectively, these data help elucidate how interactions between phage-encoded immune suppressors and the CRISPR systems they target shape bacteria-phage population dynamics. | 2018 | 30033365 |
| 9186 | 17 | 0.9964 | From Gene Editing to Biofilm Busting: CRISPR-CAS9 Against Antibiotic Resistance-A Review. In recent decades, the development of novel antimicrobials has significantly slowed due to the emergence of antimicrobial resistance (AMR), intensifying the global struggle against infectious diseases. Microbial populations worldwide rapidly develop resistance due to the widespread use of antibiotics, primarily targeting drug-resistant germs. A prominent manifestation of this resistance is the formation of biofilms, where bacteria create protective layers using signaling pathways such as quorum sensing. In response to this challenge, the CRISPR-Cas9 method has emerged as a ground-breaking strategy to counter biofilms. Initially identified as the "adaptive immune system" of bacteria, CRISPR-Cas9 has evolved into a state-of-the-art genetic engineering tool. Its exceptional precision in altering specific genes across diverse microorganisms positions it as a promising alternative for addressing antibiotic resistance by selectively modifying genes in diverse microorganisms. This comprehensive review concentrates on the historical background, discovery, developmental stages, and distinct components of CRISPR Cas9 technology. Emphasizing its role as a widely used genome engineering tool, the review explores how CRISPR Cas9 can significantly contribute to the targeted disruption of genes responsible for biofilm formation, highlighting its pivotal role in reshaping strategies to combat antibiotic resistance and mitigate the challenges posed by biofilm-associated infectious diseases. | 2024 | 38702575 |
| 9591 | 18 | 0.9964 | Interaction of phages, bacteria, and the human immune system: Evolutionary changes in phage therapy. Phages and bacteria are known to undergo dynamic and co-evolutionary arms race interactions in order to survive. Recent advances from in vitro and in vivo studies have improved our understanding of the complex interactions between phages, bacteria, and the human immune system. This insight is essential for the development of phage therapy to battle the growing problems of antibiotic resistance. It is also pivotal to prevent the development of phage-resistance during the implementation of phage therapy in the clinic. In this review, we discuss recent progress of the interactions between phages, bacteria, and the human immune system and its clinical application for phage therapy. Proper phage therapy design will ideally produce large burst sizes, short latent periods, broad host ranges, and a low tendency to select resistance. | 2019 | 31145517 |
| 9183 | 19 | 0.9964 | Overcoming Bacteriophage Resistance in Phage Therapy. Antibiotic resistance among pathogenic bacteria is one of the most severe global challenges. It is predicted that over ten million lives will be lost annually by 2050. Phage therapy is a promising alternative to antibiotics. However, the ease of development of phage resistance during therapy is a concern. This review focuses on the possible ways to overcome phage resistance in phage therapy. | 2024 | 37966611 |