# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 9552 | 0 | 1.0000 | 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 |
| 4002 | 1 | 0.9998 | Targeting bioinformatics tools to study the dissemination and spread of antibiotic resistant genes in the environment and clinical settings. Antibiotic resistance has expanded as a result of the careless use of antibiotics in the medical field, the food industry, agriculture, and other industries. By means of genetic recombination between commensal and pathogenic bacteria, the microbes obtain antibiotic resistance genes (ARGs). In bacteria, horizontal gene transfer (HGT) is the main mechanism for acquiring ARGs. With the development of high-throughput sequencing, ARG sequence analysis is now feasible and widely available. Preventing the spread of AMR in the environment requires the implementation of ARGs mapping. The metagenomic technique, in particular, has helped in identifying antibiotic resistance within microbial communities. Due to the exponential growth of experimental and clinical data, significant investments in computer capacity, and advancements in algorithmic techniques, the application of machine learning (ML) algorithms to the problem of AMR has attracted increasing attention over the past five years. The review article sheds a light on the application of bioinformatics for the antibiotic resistance monitoring. The most advanced tool currently being employed to catalog the resistome of various habitats are metagenomics and metatranscriptomics. The future lies in the hands of artificial intelligence (AI) and machine learning (ML) methods, to predict and optimize the interaction of antibiotic-resistant compounds with target proteins. | 2025 | 39552541 |
| 9557 | 2 | 0.9998 | 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 |
| 9558 | 3 | 0.9998 | Antimicrobial Resistance: Enzymes, Proteins, and Computational Resources. Antimicrobial resistance (AMR) is an important health concern rooted in antibiotic misuse and overuse, resulting in drug-resistant bacteria. However, resistance to these antimicrobials developed as soon as they were administered. Several variables lead to the progression of antimicrobial resistance (AMR), making it a multifaceted challenge for healthcare systems worldwide, such as erroneous diagnosis, inappropriate prescription, incomplete treatment, and many more. Getting an in-depth idea about the mechanism underlying AMR development is essential to overcome this. This review aims to provide information on how various enzymes or proteins aid in the antimicrobial resistance mechanisms and also highlight the clinical perspective of AMR, emphasizing its growing impact on patient outcomes, and incorporate the latest recent data from the World Health Organisation (WHO), underscoring the global urgency of the AMR crisis, with specific attention to trends observed in recent years. Additionally, it is intended to provide ideas about inhibitors that can inhibit the mechanism of antibiotic resistance and also to provide an idea about numerous computational resources available that can be employed to predict genes and/or proteins and enzymes involved in various antibiotic resistance mechanisms. | 2025 | 40770471 |
| 9567 | 4 | 0.9998 | How to discover new antibiotic resistance genes? Antibiotic resistance (AR) is a worldwide concern and the description of AR have been discovered mainly because of their implications in human medicine. Since the recent burden of whole-genome sequencing of microorganisms, the number of new AR genes (ARGs) have dramatically increased over the last decade. Areas covered: In this review, we will describe the different methods that could be used to characterize new ARGs using classic or innovative methods. First, we will focus on the biochemical methods, then we will develop on molecular methods, next-generation sequencing and bioinformatics approaches. The use of various methods, including cloning, mutagenesis, transposon mutagenesis, functional genomics, whole genome sequencing, metagenomic and functional metagenomics will be reviewed here, outlining the advantages and drawbacks of each method. Bioinformatics softwares used for resistome analysis and protein modeling will be also described. Expert opinion: Biological experiments and bioinformatics analysis are complementary. Nowadays, the ARGs described only account for the tip of the iceberg of all existing resistance mechanisms. The multiplication of the ecosystems studied allows us to find a large reservoir of AR mechanisms. Furthermore, the adaptation ability of bacteria facing new antibiotics promises a constant discovery of new AR mechanisms. | 2019 | 30895843 |
| 9565 | 5 | 0.9998 | Finding drug targets in microbial genomes. In this era of genomic science, knowledge about biological function is integrated increasingly with DNA sequence data. One area that has been significantly impacted by this accumulation of information is the discovery of drugs to treat microbial infections. Genome sequencing and bioinformatics is driving the discovery and development of novel classes of broad-spectrum antimicrobial compounds, and could enable medical science to keep pace with the increasing resistance of bacteria, fungi and parasites to current antimicrobials. This review discusses the use of genomic information in the rapid identification of target genes for antimicrobial drug discovery. | 2001 | 11522517 |
| 4086 | 6 | 0.9998 | Insights into antibiotic resistance through metagenomic approaches. The consequences of bacterial infections have been curtailed by the introduction of a wide range of antibiotics. However, infections continue to be a leading cause of mortality, in part due to the evolution and acquisition of antibiotic-resistance genes. Antibiotic misuse and overprescription have created a driving force influencing the selection of resistance. Despite the problem of antibiotic resistance in infectious bacteria, little is known about the diversity, distribution and origins of resistance genes, especially for the unculturable majority of environmental bacteria. Functional and sequence-based metagenomics have been used for the discovery of novel resistance determinants and the improved understanding of antibiotic-resistance mechanisms in clinical and natural environments. This review discusses recent findings and future challenges in the study of antibiotic resistance through metagenomic approaches. | 2012 | 22191448 |
| 9487 | 7 | 0.9998 | Molecular mechanisms of antibiotic resistance revisited. Antibiotic resistance is a global health emergency, with resistance detected to all antibiotics currently in clinical use and only a few novel drugs in the pipeline. Understanding the molecular mechanisms that bacteria use to resist the action of antimicrobials is critical to recognize global patterns of resistance and to improve the use of current drugs, as well as for the design of new drugs less susceptible to resistance development and novel strategies to combat resistance. In this Review, we explore recent advances in understanding how resistance genes contribute to the biology of the host, new structural details of relevant molecular events underpinning resistance, the identification of new resistance gene families and the interactions between different resistance mechanisms. Finally, we discuss how we can use this information to develop the next generation of antimicrobial therapies. | 2023 | 36411397 |
| 4085 | 8 | 0.9998 | 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 |
| 9488 | 9 | 0.9998 | Minimizing potential resistance: the molecular view. The major contribution of molecular biology to the study of antibiotic resistance has been the elucidation of nearly all biochemical mechanisms of resistance and the routes for dissemination of genetic information among bacteria. In this review, we consider the potential contribution of molecular biology to counteracting the evolution of resistant bacteria. In particular, we emphasize the fact that fundamental approaches have had direct practical effects on minimizing potential resistance: by improving interpretation of resistance phenotypes, by providing more adequate human therapy, by fostering more prudent use of antibiotics, and by allowing the rational design of new drugs that evade existing resistance mechanisms or address unexploited targets. | 2001 | 11524711 |
| 9566 | 10 | 0.9997 | Computational resources in the management of antibiotic resistance: Speeding up drug discovery. This article reviews more than 50 computational resources developed in past two decades for forecasting of antibiotic resistance (AR)-associated mutations, genes and genomes. More than 30 databases have been developed for AR-associated information, but only a fraction of them are updated regularly. A large number of methods have been developed to find AR genes, mutations and genomes, with most of them based on similarity-search tools such as BLAST and HMMER. In addition, methods have been developed to predict the inhibition potential of antibiotics against a bacterial strain from the whole-genome data of bacteria. This review also discuss computational resources that can be used to manage the treatment of AR-associated diseases. | 2021 | 33892146 |
| 4089 | 11 | 0.9997 | Genetic mechanisms of antibiotic resistance and virulence in Acinetobacter baumannii: background, challenges and future prospects. With the advent of the multidrug-resistant era, many opportunistic pathogens including the species Acinetobacter baumannii have gained prominence and pose a major global threat to clinical health care. Pathogenicity in bacteria is genetically regulated by a complex network of transcription and virulence factors and a brief overview of the major investigations on comprehending these processes over the past few decades in A. baumanni are compiled here. Many investigators have employed genome sequencing techniques to identify the regions that contribute to antibiotic resistance and comparative genomics to study sequence similarities to understand evolutionary trends of resistance gene transfers between isolates. A summary of these studies given here provides an insight into the invasion and successful colonization of the species. The individual roles played by different genes, regulators & promoters, enzymes, metal ions as well as mobile elements in influencing antibiotic resistance are briefly discussed. Precautionary measures and prospects for developing future strategies by exploring promising new research targets in effective control of multidrug resistant A. baumannii are also analyzed. | 2020 | 32303957 |
| 6684 | 12 | 0.9997 | An African perspective on the prevalence, fate and effects of carbapenem resistance genes in hospital effluents and wastewater treatment plant (WWTP) final effluents: A critical review. This article provides an overview of the antibiotic era and discovery of earliest antibiotics until the present day state of affairs, coupled with the emergence of carbapenem-resistant bacteria. The ways of response to challenges of antibiotic resistance (AR) such as the development of novel strategies in the search of new antibiotics, designing more effective preventive measures as well as the ecology of AR have been discussed. The applications of plant extract and chemical compounds like nanomaterials which are based on recent developments in the field of antimicrobials, antimicrobial resistance (AMR), and chemotherapy were briefly discussed. The agencies responsible for environmental protection have a role to play in dealing with the climate crisis which poses an existential threat to the planet, and contributes to ecological support towards pathogenic microorganisms. The environment serves as a reservoir and also a vehicle for transmission of antimicrobial resistance genes hence, as dominant inhabitants we have to gain a competitive advantage in the battle against AMR. | 2020 | 32420480 |
| 9553 | 13 | 0.9997 | A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains. Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased. There are several strains of bacteria lacking known resistance genes; however, they demonstrate resistance phenotype to drugs of that family. Although such strains are fewer compared to the overall population, they pose grave emerging threats to an already heavily challenged area of antimicrobial resistance (AMR), where death tolls have reached ~700 000 per year and a grim projection of ~10 million deaths per year by 2050 looms. Considering the fact that development of novel antibiotics is not keeping pace with the emergence and dissemination of resistance, there is a pressing need to decipher yet unknown genetic mechanisms of resistance, which will enable developing strategies for the best use of available interventions and show the way for the development of new drugs. In this study, we present a machine learning framework to predict novel AMR factors that are potentially responsible for resistance to specific antimicrobial drugs. The machine learning framework utilizes whole-genome sequencing AMR genetic data and antimicrobial susceptibility testing phenotypic data to predict resistance phenotypes and rank AMR genes by their importance in discriminating the resistance from the susceptible phenotypes. In summary, we present here a bioinformatics framework for training machine learning models, evaluating their performances, selecting the best performing model(s) and finally predicting the most important AMR loci for the resistance involved. | 2021 | 34015806 |
| 4298 | 14 | 0.9997 | Genomic and Metagenomic Approaches for Predictive Surveillance of Emerging Pathogens and Antibiotic Resistance. Antibiotic-resistant organisms (AROs) are a major concern to public health worldwide. While antibiotics have been naturally produced by environmental bacteria for millions of years, modern widespread use of antibiotics has enriched resistance mechanisms in human-impacted bacterial environments. Antibiotic resistance genes (ARGs) continue to emerge and spread rapidly. To combat the global threat of antibiotic resistance, researchers must develop methods to rapidly characterize AROs and ARGs, monitor their spread across space and time, and identify novel ARGs and resistance pathways. We review how high-throughput sequencing-based methods can be combined with classic culture-based assays to characterize, monitor, and track AROs and ARGs. Then, we evaluate genomic and metagenomic methods for identifying ARGs and biosynthetic pathways for novel antibiotics from genomic data sets. Together, these genomic analyses can improve surveillance and prediction of emerging resistance threats and accelerate the development of new antibiotic therapies to combat resistance. | 2019 | 31172511 |
| 4087 | 15 | 0.9997 | Next-generation approaches to understand and combat the antibiotic resistome. Antibiotic resistance is a natural feature of diverse microbial ecosystems. Although recent studies of the antibiotic resistome have highlighted barriers to the horizontal transfer of antibiotic resistance genes between habitats, the rapid global spread of genes that confer resistance to carbapenem, colistin and quinolone antibiotics illustrates the dire clinical and societal consequences of such events. Over time, the study of antibiotic resistance has grown from focusing on single pathogenic organisms in axenic culture to studying antibiotic resistance in pathogenic, commensal and environmental bacteria at the level of microbial communities. As the study of antibiotic resistance advances, it is important to incorporate this comprehensive approach to better inform global antibiotic resistance surveillance and antibiotic development. It is increasingly becoming apparent that although not all resistance genes are likely to geographically and phylogenetically disseminate, the threat presented by those that are is serious and warrants an interdisciplinary research focus. In this Review, we highlight seminal work in the resistome field, discuss recent advances in the studies of resistomes, and propose a resistome paradigm that can pave the way for the improved proactive identification and mitigation of emerging antibiotic resistance threats. | 2017 | 28392565 |
| 8181 | 16 | 0.9997 | Bacterial resistance to antibacterial agents: Mechanisms, control strategies, and implications for global health. The spread of bacterial drug resistance has posed a severe threat to public health globally. Here, we cover bacterial resistance to current antibacterial drugs, including traditional herbal medicines, conventional antibiotics, and antimicrobial peptides. We summarize the influence of bacterial drug resistance on global health and its economic burden while highlighting the resistance mechanisms developed by bacteria. Based on the One Health concept, we propose 4A strategies to combat bacterial resistance, including prudent Application of antibacterial agents, Administration, Assays, and Alternatives to antibiotics. Finally, we identify several opportunities and unsolved questions warranting future exploration for combating bacterial resistance, such as predicting genetic bacterial resistance through the use of more effective techniques, surveying both genetic determinants of bacterial resistance and the transmission dynamics of antibiotic resistance genes (ARGs). | 2023 | 36435256 |
| 4088 | 17 | 0.9997 | Expanding the soil antibiotic resistome: exploring environmental diversity. Antibiotic resistance has largely been studied in the context of failure of the drugs in clinical settings. There is now growing evidence that bacteria that live in the environment (e.g. the soil) are multi-drug-resistant. Recent functional screens and the growing accumulation of metagenomic databases are revealing an unexpected density of resistance genes in the environment: the antibiotic resistome. This challenges our current understanding of antibiotic resistance and provides both barriers and opportunities for antimicrobial drug discovery. | 2007 | 17951101 |
| 9483 | 18 | 0.9997 | Ecological and evolutionary mechanisms driving within-patient emergence of antimicrobial resistance. The ecological and evolutionary mechanisms of antimicrobial resistance (AMR) emergence within patients and how these vary across bacterial infections are poorly understood. Increasingly widespread use of pathogen genome sequencing in the clinic enables a deeper understanding of these processes. In this Review, we explore the clinical evidence to support four major mechanisms of within-patient AMR emergence in bacteria: spontaneous resistance mutations; in situ horizontal gene transfer of resistance genes; selection of pre-existing resistance; and immigration of resistant lineages. Within-patient AMR emergence occurs across a wide range of host niches and bacterial species, but the importance of each mechanism varies between bacterial species and infection sites within the body. We identify potential drivers of such differences and discuss how ecological and evolutionary analysis could be embedded within clinical trials of antimicrobials, which are powerful but underused tools for understanding why these mechanisms vary between pathogens, infections and individuals. Ultimately, improving understanding of how host niche, bacterial species and antibiotic mode of action combine to govern the ecological and evolutionary mechanism of AMR emergence in patients will enable more predictive and personalized diagnosis and antimicrobial therapies. | 2024 | 38689039 |
| 9564 | 19 | 0.9997 | Genomic tools to profile antibiotic mode of action. The increasing emergence of antimicrobial multiresistant bacteria is of great concern to public health. While these bacteria are becoming an ever more prominent cause of nosocomial and community-acquired infections worldwide, the antibiotic discovery pipeline has been stalled in the last few years with very few efforts in the research and development of novel antibacterial therapies. Some of the root causes that have hampered current antibiotic drug development are the lack of understanding of the mode of action (MOA) of novel antibiotic molecules and the poor characterization of the bacterial physiological response to antibiotics that ultimately causes resistance. Here, we review how bacterial genetic tools can be applied at the genomic level with the goal of profiling resistance to antibiotics and elucidating antibiotic MOAs. Specifically, we highlight how chemical genomic detection of the MOA of novel antibiotic molecules and antibiotic profiling by next-generation sequencing are leveraging basic antibiotic research to unprecedented levels with great opportunities for knowledge translation. | 2015 | 24617440 |