A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains. - Related Documents




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955301.0000A 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.202134015806
955710.9998Antimicrobial 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.202235625299
967420.9998Global epistasis in plasmid-mediated antimicrobial resistance. Antimicrobial resistance (AMR) in bacteria is a major public health threat and conjugative plasmids play a key role in the dissemination of AMR genes among bacterial pathogens. Interestingly, the association between AMR plasmids and pathogens is not random and certain associations spread successfully at a global scale. The burst of genome sequencing has increased the resolution of epidemiological programs, broadening our understanding of plasmid distribution in bacterial populations. Despite the immense value of these studies, our ability to predict future plasmid-bacteria associations remains limited. Numerous empirical studies have recently reported systematic patterns in genetic interactions that enable predictability, in a phenomenon known as global epistasis. In this perspective, we argue that global epistasis patterns hold the potential to predict interactions between plasmids and bacterial genomes, thereby facilitating the prediction of future successful associations. To assess the validity of this idea, we use previously published data to identify global epistasis patterns in clinically relevant plasmid-bacteria associations. Furthermore, using simple mechanistic models of antibiotic resistance, we illustrate how global epistasis patterns may allow us to generate new hypotheses on the mechanisms associated with successful plasmid-bacteria associations. Collectively, we aim at illustrating the relevance of exploring global epistasis in the context of plasmid biology.202438409539
406130.9998Beyond serial passages: new methods for predicting the emergence of resistance to novel antibiotics. Market launching of a new antibiotic requires knowing in advance its benefits and possible risks, and among them how rapidly resistance will emerge and spread among bacterial pathogens. This information is not only useful from a public health point of view, but also for pharmaceutical industry, in order to reduce potential waste of resources in the development of a compound that might be discontinued at the short term because of resistance development. Most assays currently used for predicting the emergence of resistance are based on culturing the target bacteria by serial passages in the presence of increasing concentrations of antibiotics. Whereas these assays may be valuable for identifying mutations that might cause resistance, they are not useful to establish how fast resistance might appear, neither to address the risk of spread of resistance genes by horizontal gene transfer. In this article, we review recent information pertinent for a more accurate prediction on the emergence and dispersal of antibiotic resistance.201121835695
429240.9998The impact of different antibiotic regimens on the emergence of antimicrobial-resistant bacteria. BACKGROUND: The emergence and ongoing spread of antimicrobial-resistant bacteria is a major public health threat. Infections caused by antimicrobial-resistant bacteria are associated with substantially higher rates of morbidity and mortality compared to infections caused by antimicrobial-susceptible bacteria. The emergence and spread of these bacteria is complex and requires incorporating numerous interrelated factors which clinical studies cannot adequately address. METHODS/PRINCIPAL FINDINGS: A model is created which incorporates several key factors contributing to the emergence and spread of resistant bacteria including the effects of the immune system, acquisition of resistance genes and antimicrobial exposure. The model identifies key strategies which would limit the emergence of antimicrobial-resistant bacterial strains. Specifically, the simulations show that early initiation of antimicrobial therapy and combination therapy with two antibiotics prevents the emergence of resistant bacteria, whereas shorter courses of therapy and sequential administration of antibiotics promote the emergence of resistant strains. CONCLUSIONS/SIGNIFICANCE: The principal findings suggest that (i) shorter lengths of antibiotic therapy and early interruption of antibiotic therapy provide an advantage for the resistant strains, (ii) combination therapy with two antibiotics prevents the emergence of resistance strains in contrast to sequential antibiotic therapy, and (iii) early initiation of antibiotics is among the most important factors preventing the emergence of resistant strains. These findings provide new insights into strategies aimed at optimizing the administration of antimicrobials for the treatment of infections and the prevention of the emergence of antimicrobial resistance.200819112501
511250.9998Genome-Based Prediction of Bacterial Antibiotic Resistance. Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.201930381421
426260.9998Fitness cost of antibiotic susceptibility during bacterial infection. Advances in high-throughput DNA sequencing allow for a comprehensive analysis of bacterial genes that contribute to virulence in a specific infectious setting. Such information can yield new insights that affect decisions on how to best manage major public health issues such as the threat posed by increasing antimicrobial drug resistance. Much of the focus has been on the consequences of the selective advantage conferred on drug-resistant strains during antibiotic therapy. It is thought that the genetic and phenotypic changes that confer resistance also result in concomitant reductions in in vivo fitness, virulence, and transmission. However, experimental validation of this accepted paradigm is modest. Using a saturated transposon library of Pseudomonas aeruginosa, we identified genes across many functional categories and operons that contributed to maximal in vivo fitness during lung infections in animal models. Genes that bestowed both intrinsic and acquired antibiotic resistance provided a positive in vivo fitness advantage to P. aeruginosa during infection. We confirmed these findings in the pathogenic bacteria Acinetobacter baumannii and Vibrio cholerae using murine and rabbit infection models, respectively. Our results show that efforts to confront the worldwide increase in antibiotic resistance might be exacerbated by fitness advantages that enhance virulence in drug-resistant microbes.201526203082
382970.9998Associations among Antibiotic and Phage Resistance Phenotypes in Natural and Clinical Escherichia coli Isolates. The spread of antibiotic resistance is driving interest in new approaches to control bacterial pathogens. This includes applying multiple antibiotics strategically, using bacteriophages against antibiotic-resistant bacteria, and combining both types of antibacterial agents. All these approaches rely on or are impacted by associations among resistance phenotypes (where bacteria resistant to one antibacterial agent are also relatively susceptible or resistant to others). Experiments with laboratory strains have shown strong associations between some resistance phenotypes, but we lack a quantitative understanding of associations among antibiotic and phage resistance phenotypes in natural and clinical populations. To address this, we measured resistance to various antibiotics and bacteriophages for 94 natural and clinical Escherichia coli isolates. We found several positive associations between resistance phenotypes across isolates. Associations were on average stronger for antibacterial agents of the same type (antibiotic-antibiotic or phage-phage) than different types (antibiotic-phage). Plasmid profiles and genetic knockouts suggested that such associations can result from both colocalization of resistance genes and pleiotropic effects of individual resistance mechanisms, including one case of antibiotic-phage cross-resistance. Antibiotic resistance was predicted by core genome phylogeny and plasmid profile, but phage resistance was predicted only by core genome phylogeny. Finally, we used observed associations to predict genes involved in a previously uncharacterized phage resistance mechanism, which we verified using experimental evolution. Our data suggest that susceptibility to phages and antibiotics are evolving largely independently, and unlike in experiments with lab strains, negative associations between antibiotic resistance phenotypes in nature are rare. This is relevant for treatment scenarios where bacteria encounter multiple antibacterial agents.IMPORTANCE Rising antibiotic resistance is making it harder to treat bacterial infections. Whether resistance to a given antibiotic spreads or declines is influenced by whether it is associated with altered susceptibility to other antibiotics or other stressors that bacteria encounter in nature, such as bacteriophages (viruses that infect bacteria). We used natural and clinical isolates of Escherichia coli, an abundant species and key pathogen, to characterize associations among resistance phenotypes to various antibiotics and bacteriophages. We found associations between some resistance phenotypes, and in contrast to past work with laboratory strains, they were exclusively positive. Analysis of bacterial genome sequences and horizontally transferred genetic elements (plasmids) helped to explain this, as well as our finding that there was no overall association between antibiotic resistance and bacteriophage resistance profiles across isolates. This improves our understanding of resistance evolution in nature, potentially informing new rational therapies that combine different antibacterials, including bacteriophages.201729089428
955480.9998A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling. The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.202235272349
956990.9998The global epidemic nature of antimicrobial resistance and the need to monitor and manage it locally. An antimicrobial agent may be used for years before a gene expressing resistance to it emerges in a strain of bacteria somewhere. Progeny of that strain, or of others to which the gene is transferred, may then disseminate preferentially through global networks of bacterial populations on people or animals treated with that agent or with other agents as the gene becomes linked to genes expressing resistance to them. Over 100 resistance genes-varying in their frequency of emergence, vectors, linkages, and pathways-have thus emerged, reemerged, converged, and disseminated irregularly through the world's bacterial ecosystems over the last 60 years to reach infecting strains and block treatment of infection. We may delay emergence by using agents less and retard dissemination by good hygiene, infection control measures, and avoidance of agents that select for resistance genes in contiguous populations. Local monitoring and management of resistance appear essential because of the intricacies of tracing and targeting the problems at each place and because national or global surveillance and strategy develop from local information and understanding.19978994775
4391100.9998'To be, or not to be'-The dilemma of 'silent' antimicrobial resistance genes in bacteria. Antimicrobial resistance is a serious threat to public health that dramatically undermines our ability to treat bacterial infections. Microorganisms exhibit resistance to different drug classes by acquiring resistance determinants through multiple mechanisms including horizontal gene transfer. The presence of drug resistance genotypes is mostly associated with corresponding phenotypic resistance against the particular antibiotic. However, bacterial communities harbouring silent antimicrobial resistance genes-genes whose presence is not associated with a corresponding resistant phenotype do exist. Under suitable conditions, the expression pattern of such genes often revert and regain resistance and could potentially lead to therapeutic failure. We often miss the presence of silent genes, since the current experimental paradigms are focused on resistant strains. Therefore, the knowledge on the prevalence, importance and mechanism of silent antibiotic resistance genes in bacterial pathogens are very limited. Silent genes, therefore, provide an additional level of complexity in the war against drug-resistant bacteria, reminding us that not only phenotypically resistant strains but also susceptible strains should be carefully investigated. In this review, we discuss the presence of silent antimicrobial resistance genes in bacteria, their relevance and their importance in public health.202235882476
4089110.9998Genetic 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.202032303957
9683120.9998Antimicrobial resistance and virulence: a successful or deleterious association in the bacterial world? Hosts and bacteria have coevolved over millions of years, during which pathogenic bacteria have modified their virulence mechanisms to adapt to host defense systems. Although the spread of pathogens has been hindered by the discovery and widespread use of antimicrobial agents, antimicrobial resistance has increased globally. The emergence of resistant bacteria has accelerated in recent years, mainly as a result of increased selective pressure. However, although antimicrobial resistance and bacterial virulence have developed on different timescales, they share some common characteristics. This review considers how bacterial virulence and fitness are affected by antibiotic resistance and also how the relationship between virulence and resistance is affected by different genetic mechanisms (e.g., coselection and compensatory mutations) and by the most prevalent global responses. The interplay between these factors and the associated biological costs depend on four main factors: the bacterial species involved, virulence and resistance mechanisms, the ecological niche, and the host. The development of new strategies involving new antimicrobials or nonantimicrobial compounds and of novel diagnostic methods that focus on high-risk clones and rapid tests to detect virulence markers may help to resolve the increasing problem of the association between virulence and resistance, which is becoming more beneficial for pathogenic bacteria.201323554414
9565130.9998Finding 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.200111522517
4340140.9998Predicting antimicrobial susceptibility from the bacterial genome: A new paradigm for one health resistance monitoring. The laboratory identification of antibacterial resistance is a cornerstone of infectious disease medicine. In vitro antimicrobial susceptibility testing has long been based on the growth response of organisms in pure culture to a defined concentration of antimicrobial agents. By comparing individual isolates to wild-type susceptibility patterns, strains with acquired resistance can be identified. Acquired resistance can also be detected genetically. After many decades of research, the inventory of genes underlying antimicrobial resistance is well known for several pathogenic genera including zoonotic enteric organisms such as Salmonella and Campylobacter and continues to grow substantially for others. With the decline in costs for large scale DNA sequencing, it is now practicable to characterize bacteria using whole genome sequencing, including the carriage of resistance genes in individual microorganisms and those present in complex biological samples. With genomics, we can generate comprehensive, detailed information on the bacterium, the mechanisms of antibiotic resistance, clues to its source, and the nature of mobile DNA elements by which resistance spreads. These developments point to a new paradigm for antimicrobial resistance detection and tracking for both clinical and public health purposes.202133010049
4298150.9998Genomic 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.201931172511
4300160.9998A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria. Antimicrobials have paved the way for medical and social development over the last century and are indispensable for treating infections in humans and animals. The dramatic spread and diversity of antibiotic-resistant pathogens have significantly reduced the efficacy of essentially all antibiotic classes and is a global problem affecting human and animal health. Antimicrobial resistance is influenced by complex factors such as resistance genes and dosing, which are highly nonlinear, time-lagged and multivariate coupled, and the amount of resistance data is large and redundant, making it difficult to predict and analyze. Based on machine learning methods and data mining techniques, this paper reviews (1) antimicrobial resistance data storage and analysis techniques, (2) antimicrobial resistance assessment methods and the associated risk assessment methods for antimicrobial resistance, and (3) antimicrobial resistance prediction methods. Finally, the current research results on antimicrobial resistance and the development trend are summarized to provide a systematic and comprehensive reference for the research on antimicrobial resistance.202134522024
9558170.9998Antimicrobial 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.202540770471
9560180.9998The History of Colistin Resistance Mechanisms in Bacteria: Progress and Challenges. Since 2015, the discovery of colistin resistance genes has been limited to the characterization of new mobile colistin resistance (mcr) gene variants. However, given the complexity of the mechanisms involved, there are many colistin-resistant bacterial strains whose mechanism remains unknown and whose exploitation requires complementary technologies. In this review, through the history of colistin, we underline the methods used over the last decades, both old and recent, to facilitate the discovery of the main colistin resistance mechanisms and how new technological approaches may help to improve the rapid and efficient exploration of new target genes. To accomplish this, a systematic search was carried out via PubMed and Google Scholar on published data concerning polymyxin resistance from 1950 to 2020 using terms most related to colistin. This review first explores the history of the discovery of the mechanisms of action and resistance to colistin, based on the technologies deployed. Then we focus on the most advanced technologies used, such as MALDI-TOF-MS, high throughput sequencing or the genetic toolbox. Finally, we outline promising new approaches, such as omics tools and CRISPR-Cas9, as well as the challenges they face. Much has been achieved since the discovery of polymyxins, through several innovative technologies. Nevertheless, colistin resistance mechanisms remains very complex.202133672663
9483190.9998Ecological 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.202438689039