# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 8164 | 0 | 0.9910 | Antibiotic Resistance - A Cause for Reemergence of Infections. This article can rightly be called 'the rise of the microbial phoenix'; for, all the microbial infections whose doomsday was predicted with the discovery of antibiotics, have thumbed their noses at mankind and reemerged phoenix like. The hubris generated by Sir Alexander Fleming's discovery of Penicillin in 1928, exemplified best by the comment by William H Stewart, the US Surgeon General in 1967, "It is time to close the books on infectious diseases" has been replaced by the realisation that the threat of antibiotic resistance is, in the words of the Chief Medical Officer of England, Dame Sally Davies, "just as important and deadly as climate change and international terrorism". Antimicrobial resistance threatens to negate all the major medical advances of the last century because antimicrobial use is linked to many other fields like organ transplantation and cancer chemotherapy. Antibiotic resistance genes have been there since ancient times in response to naturally occurring antibiotics. Modern medicine has only driven further evolution of antimicrobial resistance by use, misuse, overuse and abuse of antibiotics. Resistant bacteria proliferate by natural selection when their drug sensitive comrades are removed by antibiotics. In this article the authors discuss the various causes of antimicrobial resistance and dwell in some detail on antibiotic resistance in gram-positive and gram-negative organisms. Finally they stress on the important role clinicians have in limiting the development and spread of antimicrobial resistance. | 2020 | 32026301 |
| 9561 | 1 | 0.9908 | 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 |
| 9219 | 2 | 0.9906 | 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 |
| 9184 | 3 | 0.9905 | 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 |
| 9744 | 4 | 0.9904 | 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 |
| 9467 | 5 | 0.9903 | To give or not to give antibiotics is not the only question. In a 1945 Nobel Lecture, Sir Alexander Fleming warned against the overuse of antibiotics, particularly in response to public pressure. In the subsequent decades, evidence has shown that bacteria can become resistant to almost any available molecule. One key question is how the emergence and dissemination of resistant bacteria or resistance genes can be delayed. Although some clinicians remain sceptical, in this Personal View, we argue that the prescription of fewer antibiotics and shorter treatment duration is just as effective as longer regimens that remain the current guideline. Additionally, we discuss the fact that shorter antibiotic treatments exert less selective pressure on microorganisms, preventing the development of resistance. By contrast, longer treatments associated with a strong selective pressure favour the emergence of resistant clones within commensal organisms. We also emphasise that more studies are needed to identify the optimal duration of antibiotic therapy for common infections, which is important for making changes to the current guidelines, and to identify clinical biomarkers to guide antibiotic treatment in both hospital and ambulatory settings. | 2021 | 33347816 |
| 9075 | 6 | 0.9903 | CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter. BACKGROUND: The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge. RESULTS: To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation. CONCLUSIONS: The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at https://github.com/JoseBarbero/CamPype . | 2023 | 37474912 |
| 8327 | 7 | 0.9903 | 'Big things in small packages: the genetics of filamentous phage and effects on fitness of their host'. This review synthesizes recent and past observations on filamentous phages and describes how these phages contribute to host phentoypes. For example, the CTXφ phage of Vibrio cholerae encodes the cholera toxin genes, responsible for causing the epidemic disease, cholera. The CTXφ phage can transduce non-toxigenic strains, converting them into toxigenic strains, contributing to the emergence of new pathogenic strains. Other effects of filamentous phage include horizontal gene transfer, biofilm development, motility, metal resistance and the formation of host morphotypic variants, important for the biofilm stress resistance. These phages infect a wide range of Gram-negative bacteria, including deep-sea, pressure-adapted bacteria. Many filamentous phages integrate into the host genome as prophage. In some cases, filamentous phages encode their own integrase genes to facilitate this process, while others rely on host-encoded genes. These differences are mediated by different sets of 'core' and 'accessory' genes, with the latter group accounting for some of the mechanisms that alter the host behaviours in unique ways. It is increasingly clear that despite their relatively small genomes, these phages exert signficant influence on their hosts and ultimately alter the fitness and other behaviours of their hosts. | 2015 | 25670735 |
| 9550 | 8 | 0.9902 | Stereoselective Bacterial Metabolism of Antibiotics in Environmental Bacteria - A Novel Biochemical Workflow. Although molecular genetic approaches have greatly increased our understanding of the evolution and spread of antibiotic resistance genes, there are fewer studies on the dynamics of antibiotic - bacterial (A-B) interactions, especially with respect to stereochemistry. Addressing this knowledge gap requires an interdisciplinary synthesis, and the development of sensitive and selective analytical tools. Here we describe SAM (stereoselective antimicrobial metabolism) workflow, a novel interdisciplinary approach for assessing bacterial resistance mechanisms in the context of A-B interactions that utilise a combination of whole genome sequencing and mass spectrometry. Chloramphenicol was used to provide proof-of-concept to demonstrate the importance of stereoselective metabolism by resistant environmental bacteria. Our data shows that chloramphenicol can be stereoselectively transformed via microbial metabolism with R,R-(-)-CAP being subject to extensive metabolic transformation by an environmental bacterial strain. In contrast S,S-(+)-CAP is not metabolised by this bacterial strain, possibly due to the lack of previous exposure to this isomer in the absence of historical selective pressure to evolve metabolic capacity. | 2021 | 33935981 |
| 9074 | 9 | 0.9902 | BacAnt: A Combination Annotation Server for Bacterial DNA Sequences to Identify Antibiotic Resistance Genes, Integrons, and Transposable Elements. Whole genome sequencing (WGS) of bacteria has become a routine method in diagnostic laboratories. One of the clinically most useful advantages of WGS is the ability to predict antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) in bacterial sequences. This allows comprehensive investigations of such genetic features but can also be used for epidemiological studies. A plethora of software programs have been developed for the detailed annotation of bacterial DNA sequences, such as rapid annotation using subsystem technology (RAST), Resfinder, ISfinder, INTEGRALL and The Transposon Registry. Unfortunately, to this day, a reliable annotation tool of the combination of ARGs and MGEs is not available, and the generation of genbank files requires much manual input. Here, we present a new webserver which allows the annotation of ARGs, integrons and transposable elements at the same time. The pipeline generates genbank files automatically, which are compatible with Easyfig for comparative genomic analysis. Our BacAnt code and standalone software package are available at https://github.com/xthua/bacant with an accompanying web application at http://bacant.net. | 2021 | 34367079 |
| 9183 | 10 | 0.9902 | 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 |
| 9666 | 11 | 0.9901 | The comprehensive antibiotic resistance database. The field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment. | 2013 | 23650175 |
| 5118 | 12 | 0.9901 | Automated extraction of genes associated with antibiotic resistance from the biomedical literature. The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction. | 2022 | 35134132 |
| 9174 | 13 | 0.9901 | 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 |
| 9185 | 14 | 0.9901 | The Age of Phage: Friend or Foe in the New Dawn of Therapeutic and Biocontrol Applications? Extended overuse and misuse of antibiotics and other antibacterial agents has resulted in an antimicrobial resistance crisis. Bacteriophages, viruses that infect bacteria, have emerged as a legitimate alternative antibacterial agent with a wide scope of applications which continue to be discovered and refined. However, the potential of some bacteriophages to aid in the acquisition, maintenance, and dissemination of negatively associated bacterial genes, including resistance and virulence genes, through transduction is of concern and requires deeper understanding in order to be properly addressed. In particular, their ability to interact with mobile genetic elements such as plasmids, genomic islands, and integrative conjugative elements (ICEs) enables bacteriophages to contribute greatly to bacterial evolution. Nonetheless, bacteriophages have the potential to be used as therapeutic and biocontrol agents within medical, agricultural, and food processing settings, against bacteria in both planktonic and biofilm environments. Additionally, bacteriophages have been deployed in developing rapid, sensitive, and specific biosensors for various bacterial targets. Intriguingly, their bioengineering capabilities show great promise in improving their adaptability and effectiveness as biocontrol and detection tools. This review aims to provide a balanced perspective on bacteriophages by outlining advantages, challenges, and future steps needed in order to boost their therapeutic and biocontrol potential, while also providing insight on their potential role in contributing to bacterial evolution and survival. | 2021 | 33670836 |
| 9552 | 15 | 0.9901 | 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 |
| 9553 | 16 | 0.9901 | A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains. Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased. There are several strains of bacteria lacking known resistance genes; however, they demonstrate resistance phenotype to drugs of that family. Although such strains are fewer compared to the overall population, they pose grave emerging threats to an already heavily challenged area of antimicrobial resistance (AMR), where death tolls have reached ~700 000 per year and a grim projection of ~10 million deaths per year by 2050 looms. Considering the fact that development of novel antibiotics is not keeping pace with the emergence and dissemination of resistance, there is a pressing need to decipher yet unknown genetic mechanisms of resistance, which will enable developing strategies for the best use of available interventions and show the way for the development of new drugs. In this study, we present a machine learning framework to predict novel AMR factors that are potentially responsible for resistance to specific antimicrobial drugs. The machine learning framework utilizes whole-genome sequencing AMR genetic data and antimicrobial susceptibility testing phenotypic data to predict resistance phenotypes and rank AMR genes by their importance in discriminating the resistance from the susceptible phenotypes. In summary, we present here a bioinformatics framework for training machine learning models, evaluating their performances, selecting the best performing model(s) and finally predicting the most important AMR loci for the resistance involved. | 2021 | 34015806 |
| 4008 | 17 | 0.9901 | Impacts of mobile genetic elements on antimicrobial resistance genes in gram-negative pathogens: Current insights and genomic approaches. Antimicrobial resistance threatens to take 10 million lives per year by 2050. It is a recognised global health crisis and understanding the historic and current spread of resistance determinants is important for informing surveillance and control measures. The 'inheritance' of resistance is difficult to track because horizontal transfer is common. Antimicrobial resistance genes (ARGs) spread rapidly between bacteria, plasmids and chromosomes due to different mobile genetic elements (MGEs). This movement can increase the range of species carrying an ARG, simplify acquisition of multi-resistance, or otherwise alter the selective advantage associated with carriage of the ARG. MGE activity is therefore a significant factor in understanding routes of ARG dissemination. Characterising the combinations of MGEs contributing to the movement of individual ARGs is crucial. Each MGE category has unique genetic characteristics, and distinct impacts on the location and expression of associated ARGs. Here, the ways in which MGEs can meaningfully associate with ARGs are discussed. Approaches for extracting information about MGE associations from bacterial genome sequences are also considered. Accurate and informative annotations of the genetic contexts of relevant ARGs provide crucial insight into the presence of MGEs and their locations relative to ARGs. Combining this genomic information with knowledge about relevant biological processes allows more accurate conclusions to be drawn about transmission and dissemination of ARGs. | 2026 | 41005125 |
| 9557 | 18 | 0.9901 | Antimicrobial Resistance Profile by Metagenomic and Metatranscriptomic Approach in Clinical Practice: Opportunity and Challenge. The burden of bacterial resistance to antibiotics affects several key sectors in the world, including healthcare, the government, and the economic sector. Resistant bacterial infection is associated with prolonged hospital stays, direct costs, and costs due to loss of productivity, which will cause policy makers to adjust their policies. Current widely performed procedures for the identification of antibiotic-resistant bacteria rely on culture-based methodology. However, some resistance determinants, such as free-floating DNA of resistance genes, are outside the bacterial genome, which could be potentially transferred under antibiotic exposure. Metagenomic and metatranscriptomic approaches to profiling antibiotic resistance offer several advantages to overcome the limitations of the culture-based approach. These methodologies enhance the probability of detecting resistance determinant genes inside and outside the bacterial genome and novel resistance genes yet pose inherent challenges in availability, validity, expert usability, and cost. Despite these challenges, such molecular-based and bioinformatics technologies offer an exquisite advantage in improving clinicians' diagnoses and the management of resistant infectious diseases in humans. This review provides a comprehensive overview of next-generation sequencing technologies, metagenomics, and metatranscriptomics in assessing antimicrobial resistance profiles. | 2022 | 35625299 |
| 4085 | 19 | 0.9900 | The antibiotic resistome. IMPORTANCE OF THE FIELD: Antibiotics are essential for the treatment of bacterial infections and are among our most important drugs. Resistance has emerged to all classes of antibiotics in clinical use. Antibiotic resistance has, proven inevitable and very often it emerges rapidly after the introduction of a drug into the clinic. There is, therefore, a great interest in understanding the origins, scope and evolution of antibiotic resistance. AREAS COVERED IN THIS REVIEW: The review discusses the concept of the antibiotic resistome, which is the collection of all genes that directly or indirectly contribute to antibiotic resistance. WHAT THE READER WILL GAIN: The review seeks to assemble current knowledge of the resistome concept as a means of understanding the totality of resistance and not just resistance in pathogenic bacteria. TAKE HOME MESSAGE: The concept of the antibiotic resistome provides a framework for the study and understanding of how resistance emerges and evolves. Furthermore, the study of the resistome reveals strategies that can be applied in new antibiotic discoveries. | 2010 | 22827799 |