Predicting Phenotypic Polymyxin Resistance in Klebsiella pneumoniae through Machine Learning Analysis of Genomic Data. - Related Documents




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494001.0000Predicting Phenotypic Polymyxin Resistance in Klebsiella pneumoniae through Machine Learning Analysis of Genomic Data. Polymyxins are used as treatments of last resort for Gram-negative bacterial infections. Their increased use has led to concerns about emerging polymyxin resistance (PR). Phenotypic polymyxin susceptibility testing is resource intensive and difficult to perform accurately. The complex polygenic nature of PR and our incomplete understanding of its genetic basis make it difficult to predict PR using detection of resistance determinants. We therefore applied machine learning (ML) to whole-genome sequencing data from >600 Klebsiella pneumoniae clonal group 258 (CG258) genomes to predict phenotypic PR. Using a reference-based representation of genomic data with ML outperformed a rule-based approach that detected variants in known PR genes (area under receiver-operator curve [AUROC], 0.894 versus 0.791, P = 0.006). We noted modest increases in performance by using a bacterial genome-wide association study to filter relevant genomic features and by integrating clinical data in the form of prior polymyxin exposure. Conversely, reference-free representation of genomic data as k-mers was associated with decreased performance (AUROC, 0.692 versus 0.894, P = 0.015). When ML models were interpreted to extract genomic features, six of seven known PR genes were correctly identified by models without prior programming and several genes involved in stress responses and maintenance of the cell membrane were identified as potential novel determinants of PR. These findings are a proof of concept that whole-genome sequencing data can accurately predict PR in K. pneumoniae CG258 and may be applicable to other forms of complex antimicrobial resistance.IMPORTANCE Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection of antibiotic resistance, but this has been difficult to apply to polymyxin resistance because of its complex polygenic nature. The significance of our research is that we successfully applied machine learning methods to predict polymyxin resistance in Klebsiella pneumoniae clonal group 258, a common health care-associated and multidrug-resistant pathogen. Our findings highlight that machine learning can be successfully applied even in complex forms of antibiotic resistance and represent a significant contribution to the literature that could be used to predict resistance in other bacteria and to other antibiotics.202032457240
475910.9997Recent advances in rapid antimicrobial susceptibility testing systems. INTRODUCTION: Until recently antimicrobial susceptibility testing (AST) methods based on the demonstration of phenotypic susceptibility in 16-24 h remained largely unchanged. AREAS COVERED: Advances in rapid phenotypic and molecular-based AST systems. EXPERT OPINION: AST has changed over the past decade, with many rapid phenotypic and molecular methods developed to demonstrate phenotypic or genotypic resistance, or biochemical markers of resistance such as β-lactamases associated with carbapenem resistance. Most methods still require isolation of bacteria from specimens before both legacy and newer methods can be used. Bacterial identification by MALDI-TOF mass spectroscopy is now widely used and is often key to the interpretation of rapid AST results. Several PCR arrays are available to detect the most frequent pathogens associated with bloodstream infections and their major antimicrobial resistance genes. Many advances in whole-genome sequencing of bacteria and fungi isolated by culture as well as directly from clinical specimens have been made but are not yet widely available. High cost and limited throughput are the major obstacles to uptake of rapid methods, but targeted use, continued development and decreasing costs are expected to result in more extensive use of these increasingly useful methods.202133926351
494320.9997Targeted sequencing of Enterobacterales bacteria using CRISPR-Cas9 enrichment and Oxford Nanopore Technologies. Sequencing DNA directly from patient samples enables faster pathogen characterization compared to traditional culture-based approaches, but often yields insufficient sequence data for effective downstream analysis. CRISPR-Cas9 enrichment is designed to improve the yield of low abundance sequences but has not been thoroughly explored with Oxford Nanopore Technologies (ONT) for use in clinical bacterial epidemiology. We designed CRISPR-Cas9 guide RNAs to enrich the human pathogen Klebsiella pneumoniae, by targeting multi-locus sequence type (MLST) and transfer RNA (tRNA) genes, as well as common antimicrobial resistance (AMR) genes and the resistance-associated integron gene intI1. We validated enrichment performance in 20 K. pneumoniae isolates, finding that guides generated successful enrichment across all conserved sites except for one AMR gene in two isolates. Enrichment of MLST genes led to a correct allele call in all seven loci for 8 out of 10 isolates that had depth of 30× or more in these regions. We then compared enriched and unenriched sequencing of three human fecal samples spiked with K. pneumoniae at varying abundance. Enriched sequencing generated 56× and 11.3× the number of AMR and MLST reads, respectively, compared to unenriched sequencing, and required approximately one-third of the computational storage space. Targeting the intI1 gene often led to detection of 10-20 proximal resistance genes due to the long reads produced by ONT sequencing. We demonstrated that CRISPR-Cas9 enrichment combined with ONT sequencing enabled improved genomic characterization outcomes over unenriched sequencing of patient samples. This method could be used to inform infection control strategies by identifying patients colonized with high-risk strains. IMPORTANCE: Understanding bacteria in complex samples can be challenging due to their low abundance, which often results in insufficient data for analysis. To improve the detection of harmful bacteria, we implemented a technique aimed at increasing the amount of data from target pathogens when combined with modern DNA sequencing technologies. Our technique uses CRISPR-Cas9 to target specific gene sequences in the bacterial pathogen Klebsiella pneumoniae and improve recovery from human stool samples. We found our enrichment method to significantly outperform traditional methods, generating far more data originating from our target genes. Additionally, we developed new computational techniques to further enhance the analysis, providing a thorough method for characterizing pathogens from complex biological samples.202539772804
992130.9997Identification of Multiple Low-Level Resistance Determinants and Coselection of Motility Impairment upon Sub-MIC Ceftriaxone Exposure in Escherichia coli. Resistance to third-generation cephalosporins among Gram-negative bacteria is a rapidly growing public health threat. Among the most commonly used third-generation cephalosporins is ceftriaxone. Bacterial exposure to sublethal or sub-MIC antibiotic concentrations occurs widely, from environmental residues to intermittently at the site of infection. Quality of ceftriaxone is also a concern, especially in low- and middle-income countries, with medicines having inappropriate active pharmaceutical ingredient (API) content or concentration. While focus has been largely on extended-spectrum β-lactamases and high-level resistance, there are limited data on specific chromosomal mutations and other pathways that contribute to ceftriaxone resistance under these conditions. In this work, Escherichia coli cells were exposed to a broad range of sub-MICs of ceftriaxone and mutants were analyzed using whole-genome sequencing. Low-level ceftriaxone resistance emerged after as low as 10% MIC exposure, with the frequency of resistance development increasing with concentration. Genomic analyses of mutants revealed multiple genetic bases. Mutations were enriched in genes associated with porins (envZ, ompF, ompC, and ompR), efflux regulation (marR), and the outer membrane and metabolism (galU and pgm), but none were associated with the ampC β-lactamase. We also observed selection of mgrB mutations. Notably, pleiotropic effects on motility and cell surface were selected for in multiple independent genes, which may have important consequences. Swift low-level resistance development after exposure to low ceftriaxone concentrations may result in reservoirs of bacteria with relevant mutations for survival and increased resistance. Thus, initiatives for broader surveillance of low-level antibiotic resistance and genomic resistance determinants should be pursued when resources are available. IMPORTANCE Ceftriaxone is a widely consumed antibiotic used to treat bacterial infections. Bacteria, however, are increasingly becoming resistant to ceftriaxone. Most work has focused on known mechanisms associated with high-level ceftriaxone resistance. However, bacteria are extensively exposed to low antibiotic concentrations, and there are limited data on the evolution of ceftriaxone resistance under these conditions. In this work, we observed that bacteria quickly developed low-level resistance due to both novel and previously described mutations in multiple different genes upon exposure to low ceftriaxone concentrations. Additionally, exposure also led to changes in motility and the cell surface, which can impact other processes associated with resistance and infection. Notably, low-level-resistant bacteria would be missed in the clinic, which uses set breakpoints. While they may require increased resources, this work supports continued initiatives for broader surveillance of low-level antibiotic resistance or their resistance determinants, which can serve as predictors of higher risk for clinical resistance.202134787446
485640.9997An Overview on Phenotypic and Genotypic Characterisation of Carbapenem-Resistant Enterobacterales. Improper use of antimicrobials has resulted in the emergence of antimicrobial resistance (AMR), including multi-drug resistance (MDR) among bacteria. Recently, a sudden increase in Carbapenem-resistant Enterobacterales (CRE) has been observed. This presents a substantial challenge in the treatment of CRE-infected individuals. Bacterial plasmids include the genes for carbapenem resistance, which can also spread to other bacteria to make them resistant. The incidence of CRE is rising significantly despite the efforts of health authorities, clinicians, and scientists. Many genotypic and phenotypic techniques are available to identify CRE. However, effective identification requires the integration of two or more methods. Whole genome sequencing (WGS), an advanced molecular approach, helps identify new strains of CRE and screening of the patient population; however, WGS is challenging to apply in clinical settings due to the complexity and high expense involved with this technique. The current review highlights the molecular mechanism of development of Carbapenem resistance, the epidemiology of CRE infections, spread of CRE, treatment options, and the phenotypic/genotypic characterisation of CRE. The potential of microorganisms to acquire resistance against Carbapenems remains high, which can lead to even more susceptible drugs such as colistin and polymyxins. Hence, the current study recommends running the antibiotic stewardship programs at an institutional level to control the use of antibiotics and to reduce the spread of CRE worldwide.202236422214
569050.9997Rapid Detection of MCR-Mediated Colistin Resistance in Escherichia coli. Colistin is one of the last-resort antibiotics for infections caused by multidrug-resistant Gram-negative bacteria. However, the wide spread of novel plasmid-carrying colistin resistance genes mcr-1 and its variants substantially compromise colistin's therapeutic effectiveness and pose a severe danger to public health. To detect colistin-resistant microorganisms induced by mcr genes, rapid and reliable antibiotic susceptibility testing (AST) is imminently needed. In this study, we identified an RNA-based AST (RBAST) to discriminate between colistin-susceptible and mcr-1-mediated colistin-resistant bacteria. After short-time colistin treatment, RBAST can detect differentially expressed RNA biomarkers in bacteria. Those candidate mRNA biomarkers were successfully verified within colistin exposure temporal shifts, concentration shifts, and other mcr-1 variants. Furthermore, a group of clinical strains were effectively distinguished by using the RBAST approach during the 3-h test duration with over 93% accuracy. Taken together, our findings imply that certain mRNA transcripts produced in response to colistin treatment might be useful indicators for the development of fast AST for mcr-positive bacteria. IMPORTANCE The emergence and prevalence of mcr-1 and its variants in humans, animals, and the environment pose a global public health threat. There is a pressing urgency to develop rapid and accurate methods to identify MCR-positive colistin-resistant bacteria in the clinical samples, providing a basis for subsequent effective antibiotic treatment. Using the specific mRNA signatures, we develop an RNA-based antibiotic susceptibility testing (RBAST) for effectively distinguishing colistin-susceptible and mcr-1-mediated colistin-resistant strains. Meanwhile, the detection efficiency of these RNA biomarkers was evidenced in other mcr variants-carrying strains. By comparing with the traditional AST method, the RBAST method was verified to successfully characterize a set of clinical isolates during 3 h assay time with over 93% accuracy. Our study provides a feasible method for the rapid detection of colistin-resistant strains in clinical practice.202235616398
511060.9997Surveillance of carbapenem-resistant organisms using next-generation sequencing. The genomic data generated from next-generation sequencing (NGS) provides nucleotide-level resolution of bacterial genomes which is critical for disease surveillance and the implementation of prevention strategies to interrupt the spread of antimicrobial resistance (AMR) bacteria. Infection with AMR bacteria, including Gram-negative Carbapenem-Resistant Organisms (CRO), may be acute and recurrent-once they have colonized a patient, they are notoriously difficult to eradicate. Through phylogenetic tools that assess the single nucleotide polymorphisms (SNPs) within a pathogen genome dataset, public health scientists can estimate the genetic identity between isolates. This information is used as an epidemiologic proxy of a putative outbreak. Pathogens with minimal to no differences in SNPs are likely to be the same strain attributable to a common source or transmission between cases. These genomic comparisons enhance public health response by prompting targeted intervention and infection control measures. This methodology overview demonstrates the utility of phenotypic and molecular assays, antimicrobial susceptibility testing (AST), NGS, publicly available genomics databases, and open-source bioinformatics pipelines for a tiered workflow to detect resistance genes and potential clusters of illness. These methods, when used in combination, facilitate a genomic surveillance workflow for detecting potential AMR bacterial outbreaks to inform epidemiologic investigations. Use of this workflow helps to target and focus epidemiologic resources to the cases with the highest likelihood of being related.202337255756
432070.9997The mobilome landscape of biocide-resistance in Brazilian ESKAPE isolates. The increasing frequency of antibiotic-resistant bacteria is a constant threat to global human health. Therefore, the pathogens of the ESKAPE group (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and Enterobacter spp.) are among the most relevant causes of hospital infections responsible for millions of deaths every year. However, little has been explored about the danger of microorganisms resistant to biocides such as antiseptics and disinfectants. Widely used in domestic, industrial, and hospital environments, these substances reach the environment and can cause selective pressure for resistance genes and induce cross-resistance to antibiotics, further aggravating the problem. Therefore, it is necessary to use innovative and efficient strategies to monitor the spread of genes related to resistance to biocides. Whole genome sequencing and bioinformatics analysis aiming to search for sequences encoding resistance mechanisms are essential to help monitor and combat these pathogens. Thus, this work describes the construction of a bioinformatics tool that integrates different databases to identify gene sequences that may confer some resistance advantage about biocides. Furthermore, the tool analyzed all the genomes of Brazilian ESKAPE isolates deposited at NCBI and found a series of different genes related to resistance to benzalkonium chloride, chlorhexidine, and triclosan, which were the focus of this work. As a result, the presence of resistance genes was identified in different types of biological samples, environments, and hosts. Regarding mobile genetic elements (MGEs), around 52% of isolates containing genes related to resistance to these compounds had their genes identified in plasmids, and 48.7% in prophages. These data show that resistance to biocides can be a silent, underestimated danger spreading across different environments and, therefore, requires greater attention.202439028534
481680.9997Sub-inhibitory concentrations of colistin and imipenem impact the expression of biofilm-associated genes in Acinetobacter baumannii. Acinetobacter baumannii is an opportunistic pathogen that is responsible for nosocomial infections. Imipenem and colistin are drugs that are commonly used to treat severe infections caused by A. baumannii, such as sepsis, ventilator-associated pneumonia, and bacteremia. However, some strains of A. baumannii have become resistant to these drugs, which is a concern for public health. Biofilms produced by A. baumannii increase their resistance to antibiotics and the cells within the inner layers of biofilm are exposed to sub-inhibitory concentrations (sub-MICs) of antibiotics. There is limited information available regarding how the genes of A. baumannii are linked to biofilm formation when the bacteria are exposed to sub-MICs of imipenem and colistin. Thus, this study's objective was to explore this relationship by examining the genes involved in biofilm formation in A. baumannii when exposed to low levels of imipenem and colistin. The study found that exposing an isolate of A. baumannii to low levels of these drugs caused changes in their drug susceptibility pattern. The relative gene expression profiles of the biofilm-associated genes exhibited a change in their expression profile during short-term and long-term exposure. This study highlights the potential consequences of overuse and misuse of antibiotics, which can help bacteria become resistant to these drugs.202438489041
474490.9997Whole-Genome Sequencing of Resistance, Virulence and Regulation Genes in Extremely Resistant Strains of Pseudomonas aeruginosa. BACKGROUND/OBJECTIVES: Pseudomonas aeruginosa is a clinically significant opportunistic pathogen, renowned for its ability to acquire and develop diverse mechanisms of antibiotic resistance. This study examines the resistance, virulence, and regulatory mechanisms in extensively drug-resistant clinical strains of P. aeruginosa. METHODS: Antibiotic susceptibility was assessed using the Minimum Inhibitory Concentration (MIC) method, and whole-genome sequencing (WGS) was performed on the Illumina NovaSeq platform. RESULTS: The analysis demonstrated a higher prevalence of virulence genes compared to resistance and regulatory genes. Key virulence factors identified included secretion systems, motility, adhesion, and biofilm formation. Resistance mechanisms observed comprised efflux pumps and beta-lactamases, while regulatory systems involved two-component systems, transcriptional regulators, and sigma factors. Additionally, phenotypic profiles were found to correlate with resistance genes identified through genotypic analysis. CONCLUSIONS: This study underscores the significant resistance and virulence of the clinical P. aeruginosa strains analyzed, highlighting the urgent need for alternative strategies to address infections caused by extensively drug-resistant bacteria.202539846701
5691100.9997Rapid and Accurate Antibiotic Susceptibility Determination of tet(X)-Positive E. coli Using RNA Biomarkers. The emergence and prevalence of novel plasmid-mediated tigecycline resistance genes, namely, tet(X) and their variants, pose a serious threat to public health worldwide. Rapid and accurate antibiotic susceptibility testing (AST) that can simultaneously detect the genotype and phenotype of tet(X)-positive bacteria may contribute to the deployment of an effective antibiotic arsenal, mortality reduction, and a decrease in the use of broad-spectrum antimicrobial agents. However, current bacterial growth-based AST methods, such as broth microdilution, are time consuming and delay the prompt treatment of infectious diseases. Here, we developed a rapid RNA-based AST (RBAST) assay to effectively distinguish tet(X)-positive and -negative strains. RBAST works by detecting specific mRNA expression signatures in bacteria after short-term tigecycline exposure. As a proof of concept, a panel of clinical isolates was characterized successfully by using the RBAST method, with a 3-h assay time and 87.9% accuracy (95% confidence interval [CI], 71.8% to 96.6%). Altogether, our findings suggest that RNA signatures upon antibiotic exposure are promising biomarkers for the development of rapid AST, which could inform early antibiotic choices. IMPORTANCE Infections caused by multidrug-resistant (MDR) Gram-negative pathogens are an increasing threat to global health. Tigecycline is one of the last-resort antibiotics for the treatment of these complicated infections; however, the emergence of plasmid-encoded tigecycline resistance genes, namely, tet(X), severely diminishes its clinical efficacy. Currently, there is a lack of rapid and accurate antibiotic susceptibility testing (AST) for the detection of tet(X)-positive bacteria. In this study, we developed a rapid and robust RNA-based antibiotic susceptibility determination (RBAST) assay to effectively distinguish tet(X)-negative and -positive strains using specific RNA biomarkers in bacteria after tigecycline exposure. Using this RBAST method, we successfully characterized a set of clinical strains in 3 h. Our data indicate that the RBAST assay is useful for identifying tet(X)-positive Escherichia coli.202134704829
4758110.9997Development of New Tools to Detect Colistin-Resistance among Enterobacteriaceae Strains. The recent discovery of the plasmid-mediated mcr-1 gene conferring resistance to colistin is of clinical concern. The worldwide screening of this resistance mechanism among samples of different origins has highlighted the urgent need to improve the detection of colistin-resistant isolates in clinical microbiology laboratories. Currently, phenotypic methods used to detect colistin resistance are not necessarily suitable as the main characteristic of the mcr genes is the low level of resistance that they confer, close to the clinical breakpoint recommended jointly by the CLSI and EUCAST expert systems (S ≤ 2 mg/L and R > 2 mg/L). In this context, susceptibility testing recommendations for polymyxins have evolved and are becoming difficult to implement in routine laboratory work. The large number of mechanisms and genes involved in colistin resistance limits the access to rapid detection by molecular biology. It is therefore necessary to implement well-defined protocols using specific tools to detect all colistin-resistant bacteria. This review aims to summarize the current clinical microbiology diagnosis techniques and their ability to detect all colistin resistance mechanisms and describe new tools specifically developed to assess plasmid-mediated colistin resistance. Phenotyping, susceptibility testing, and genotyping methods are presented, including an update on recent studies related to the development of specific techniques.201830631384
5112120.9997Genome-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
5113130.9997Identification of bacterial antibiotic resistance genes in next-generation sequencing data (review of literature). The spread of antibiotic-resistant human bacterial pathogens is a serious threat to modern medicine. Antibiotic susceptibility testing is essential for treatment regimens optimization and preventing dissemination of antibiotic resistance. Therefore, development of antibiotic susceptibility testing methods is a priority challenge of laboratory medicine. The aim of this review is to analyze the capabilities of the bioinformatics tools for bacterial whole genome sequence data processing. The PubMed database, Russian scientific electronic library eLIBRARY, information networks of World health organization and European Society of Clinical Microbiology and Infectious Diseases (ESCMID) were used during the analysis. In this review, the platforms for whole genome sequencing, which are suitable for detection of bacterial genetic resistance determinants, are described. The classic step of genetic resistance determinants searching is an alignment between the query nucleotide/protein sequence and the subject (database) nucleotide/protein sequence, which is performed using the nucleotide and protein sequence databases. The most commonly used databases are Resfinder, CARD, Bacterial Antimicrobial Resistance Reference Gene Database. The results of the resistance determinants searching in genome assemblies is more correct in comparison to results of the searching in contigs. The new resistance genes searching bioinformatics tools, such as neural networks and machine learning, are discussed in the review. After critical appraisal of the current antibiotic resistance databases we designed a protocol for predicting antibiotic resistance using whole genome sequence data. The designed protocol can be used as a basis of the algorithm for qualitative and quantitative antimicrobial susceptibility testing based on whole genome sequence data.202134882354
4870140.9997Emergent Polymyxin Resistance: End of an Era? Until recently, the polymyxin antibiotics were sparingly used due to dose limiting toxicities. However, the lack of therapeutic alternatives for infections caused by highly resistant Gram-negative bacteria has led to the increased use of the polymyxins. Unfortunately, in the last decade the world has witnessed increased rates of polymyxin resistance, which is likely in part due to its irrational use in human and veterinary medicine. The spread of polymyxin-resistance has been aided by the dissemination of the transferable polymyxin-resistance gene, mcr, in humans and the environment. The mortality of colistin-resistant bacteria infections varies in different reports. However, poor clinical outcome was associated with prior colistin treatment, illness severity, complications and multidrug resistance. Detection of polymyxin-resistance in the clinic is possible through multiple robust and practical tests including broth microdilution susceptibility testing, chromogenic agar testing, and molecular biology assays. There are multiple risk factors that increase a person's risk for infection with a polymyxin-resistant bacteria including age, prior colistin treatment, hospitalization and ventilator support. For patients that are determined to be infected by polymyxin-resistant bacteria, various antibiotic treatment options currently exist. The rising trend of polymyxin-resistance threatens patient care and warrants an effective control.201931420655
4628150.9997Genomic Analysis of Molecular Bacterial Mechanisms of Resistance to Phage Infection. To optimize phage therapy, we need to understand how bacteria evolve against phage attacks. One of the main problems of phage therapy is the appearance of bacterial resistance variants. The use of genomics to track antimicrobial resistance is increasingly developed and used in clinical laboratories. For that reason, it is important to consider, in an emerging future with phage therapy, to detect and avoid phage-resistant strains that can be overcome by the analysis of metadata provided by whole-genome sequencing. Here, we identified genes associated with phage resistance in 18 Acinetobacter baumannii clinical strains belonging to the ST-2 clonal complex during a decade (Ab2000 vs. 2010): 9 from 2000 to 9 from 2010. The presence of genes putatively associated with phage resistance was detected. Genes detected were associated with an abortive infection system, restriction-modification system, genes predicted to be associated with defense systems but with unknown function, and CRISPR-Cas system. Between 118 and 171 genes were found in the 18 clinical strains. On average, 26% of these genes were detected inside genomic islands in the 2000 strains and 32% in the 2010 strains. Furthermore, 38 potential CRISPR arrays in 17 of 18 of the strains were found, as well as 705 proteins associated with CRISPR-Cas systems. A moderately higher presence of these genes in the strains of 2010 in comparison with those of 2000 was found, especially those related to the restriction-modification system and CRISPR-Cas system. The presence of these genes in genomic islands at a higher rate in the strains of 2010 compared with those of 2000 was also detected. Whole-genome sequencing and bioinformatics could be powerful tools to avoid drawbacks when a personalized therapy is applied. In this study, it allows us to take care of the phage resistance in A. baumannii clinical strains to prevent a failure in possible phage therapy.202135250902
5111160.9997Antimicrobial Resistance Prediction for Gram-Negative Bacteria via Game Theory-Based Feature Evaluation. The increasing prevalence of antimicrobial-resistant bacteria drives the need for advanced methods to identify antimicrobial-resistance (AMR) genes in bacterial pathogens. With the availability of whole genome sequences, best-hit methods can be used to identify AMR genes by differentiating unknown sequences with known AMR sequences in existing online repositories. Nevertheless, these methods may not perform well when identifying resistance genes with sequences having low sequence identity with known sequences. We present a machine learning approach that uses protein sequences, with sequence identity ranging between 10% and 90%, as an alternative to conventional DNA sequence alignment-based approaches to identify putative AMR genes in Gram-negative bacteria. By using game theory to choose which protein characteristics to use in our machine learning model, we can predict AMR protein sequences for Gram-negative bacteria with an accuracy ranging from 93% to 99%. In order to obtain similar classification results, identity thresholds as low as 53% were required when using BLASTp.201931597945
4820170.9997Network Integrative Genomic and Transcriptomic Analysis of Carbapenem-Resistant Klebsiella pneumoniae Strains Identifies Genes for Antibiotic Resistance and Virulence. Global increases in the use of carbapenems have resulted in several strains of Gram-negative bacteria acquiring carbapenem resistance, thereby limiting treatment options. Klebsiella pneumoniae is a common carbapenem-resistant pathogenic bacterium that is widely studied to identify novel antibiotic resistance mechanisms and drug targets. Antibiotic-resistant clinical isolates generally harbor many genetic alterations, and the identification of responsible mutations would provide insights into the molecular mechanisms of antibiotic resistance. We propose a method to prioritize mutated genes responsible for antibiotic resistance on the basis of expression changes in their local subnetworks, hypothesizing that mutated genes that show significant expression changes among the corresponding functionally associated genes are more likely to be involved in the carbapenem resistance. For network-based gene prioritization, we developed KlebNet (www.inetbio.org/klebnet), a genome-scale cofunctional network of K. pneumoniae genes. Using KlebNet, we reconstructed the functional modules for carbapenem resistance and virulence and identified the functional association between antibiotic resistance and virulence. Using complementation assays with the top candidate genes, we were able to validate a novel gene that negatively regulated carbapenem resistance and four novel genes that positively regulated virulence in Galleria mellonella larvae. Therefore, our study demonstrated the feasibility of network-based identification of genes required for antibiotic resistance and virulence of human-pathogenic bacteria.IMPORTANCE Klebsiella pneumoniae is a major bacterial pathogen that causes pneumonia and urinary tract infections in human. K. pneumoniae infections are treated with carbapenem, but carbapenem-resistant K. pneumoniae has been spreading worldwide. We are able to identify antimicrobial-resistant genes among mutated genes of the antibiotic-resistant clinical isolates. However, they usually harbor many mutated genes, including those that cause weak or neutral functional effects. Therefore, we need to prioritize the mutated genes to identify the more likely candidates for the follow-up functional analysis. For this study, we present a functional network of K. pneumoniae genes and propose a network-based method of prioritizing the mutated genes of the resistant clinical isolates. We also reconstructed the network-based functional modules for carbapenem resistance and virulence and retrieved the functional association between antibiotic resistance and virulence. This study demonstrated the feasibility of network-based analysis of clinical genomics data for the study of K. pneumoniae infection.201931117026
4875180.9997An Overview of the Genetic Mechanisms of Colistin-Resistance in Bacterial Pathogens: An Indian Perspective. Colistin resistance in bacteria is a growing global issue, given its role as a critical last-resort antibiotic, particularly for treating Gram-negative bacterial infections. Pathogens adopt multiple resistance mechanisms, mediated either by plasmids or chromosomal changes. Some of the most frequently observed strategies include the occurrence of plasmid-borne mobile colistin resistance (mcr) genes, enhanced efflux pump activity, mutations in the regulatory systems, and alterations in the lipid A structure. This article provides an overview of the studies investigating the genetic mechanisms underlying colistin resistance in nosocomial Gram-negative bacteria from India. A total of 37 studies were identified through online searches across various databases, including PubMed, ScienceDirect, and Web of Science. These studies were reviewed to examine bacterial species and their mechanisms of colistin resistance. Over 26 (70.27%) studies were focused on Klebsiella pneumoniae. The most commonly reported mechanism of colistin resistance involved mutations in the two-component systems pmrAB and phoPQ. Plasmid-mediated colistin-resistant mcr genes were identified in 22 studies (18.18%). Four studies reported the overexpression of efflux pump genes as a mechanism of colistin resistance. This article provides a comprehensive summary of these studies, emphasizing the presence of diverse resistance mechanisms across various pathogens. It underscores the necessity for future genomic research on a broader range of pathogens to investigate the prevalence of different mechanisms of colistin resistance in the various regions of India.202540078264
4942190.9997Nanopore-based enrichment of antimicrobial resistance genes - a case-based study. Rapid screening of hospital admissions to detect asymptomatic carriers of resistant bacteria can prevent pathogen outbreaks. However, the resulting isolates rarely have their genome sequenced due to cost constraints and long turn-around times to get and process the data, limiting their usefulness to the practitioner. Here we used real-time, on-device target enrichment ("adaptive") sequencing as a highly multiplexed assay covering 1,147 antimicrobial resistance genes. We compared its utility against standard and metagenomic sequencing, focusing on an isolate of Raoultella ornithinolytica harbouring three carbapenemases (NDM, KPC, VIM). Based on this experimental data, we then modelled the influence of several variables on the enrichment results and predicted the large effect of nucleotide identity (higher is better) and read length (shorter is better). Lastly, we showed how all relevant resistance genes are detected using adaptive sequencing on a miniature ("Flongle") flow cell, motivating its use in a clinical setting to monitor similar cases and their surroundings.202336949817