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Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, Vilovic M, Vrdoljak J, Martinovic D, Grahovac M, Bozic J. Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence. Microorganisms 2024; 12:842. [PMID: 38792673 PMCID: PMC11123121 DOI: 10.3390/microorganisms12050842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.
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Affiliation(s)
- Doris Rusic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marko Kumric
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Ana Seselja Perisin
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Dario Leskur
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Josipa Bukic
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Darko Modun
- Department of Pharmacy, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (D.R.); (A.S.P.); (D.L.); (J.B.); (D.M.)
| | - Marino Vilovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Josip Vrdoljak
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
| | - Dinko Martinovic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Department of Maxillofacial Surgery, University Hospital of Split, Spinciceva 1, 21000 Split, Croatia
| | - Marko Grahovac
- Department of Pharmacology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia;
| | - Josko Bozic
- Department of Pathophysiology, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia; (M.K.); (M.V.); (J.V.); (D.M.)
- Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
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Hu K, Meyer F, Deng ZL, Asgari E, Kuo TH, Münch PC, McHardy AC. Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes. Brief Bioinform 2024; 25:bbae206. [PMID: 38706320 PMCID: PMC11070729 DOI: 10.1093/bib/bbae206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.
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Affiliation(s)
- Kaixin Hu
- Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
| | - Fernando Meyer
- Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
| | - Zhi-Luo Deng
- Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
| | - Ehsaneddin Asgari
- Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
- Molecular Cell Biomechanics Laboratory, Department of Bioengineering and Mechanical Engineering, University of California, Berkeley, USA
| | - Tzu-Hao Kuo
- Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
| | - Philipp C Münch
- Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
- Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany
- German Center for Infection Research (DZIF), partner site Hannover Braunschweig, Braunschweig, Germany
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
| | - Alice C McHardy
- Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
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Gmeiner A, Njage PMK, Hansen LT, Aarestrup FM, Leekitcharoenphon P. Predicting Listeria monocytogenes virulence potential using whole genome sequencing and machine learning. Int J Food Microbiol 2024; 410:110491. [PMID: 38000216 DOI: 10.1016/j.ijfoodmicro.2023.110491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 10/06/2023] [Accepted: 11/12/2023] [Indexed: 11/26/2023]
Abstract
Contamination with food-borne pathogens, such as Listeria monocytogenes, remains a big concern for food safety. Hence, rigorous and continuous microbial surveillance is a standard procedure. At this point, however, the food industry and authorities only focus on detection of Listeria monocytogenes without characterization of individual strains into groups of more or less concern. As whole genome sequencing (WGS) gains increasing interest in the industry, this methodology presents an opportunity to obtain finer resolution of microbial traits such as virulence. Within this study, we therefore aimed to explore the use of WGS in combination with Machine Learning (ML) to predict L. monocytogenes virulence potential on a sub-species level. The WGS datasets used in this study for ML model training consisted of i) national surveillance isolates (n = 169, covering 38 MLST types) and ii) publicly available isolates acquired through the GenomeTrakr network (n = 2880, spanning 80 MLST types). We used the clinical frequency, i.e., ratio of the number of clinical isolates to total amount of isolates, as estimate for virulence potential. The predictive performance of input features from three different genomic levels (i.e., virulence genes, pan-genome genes, and single nucleotide polymorphisms (SNPs)) and six machine learning algorithms (i.e., Support Vector Machine with a linear kernel, Support Vector Machine with a radial kernel, Random Forrest, Neural Networks, LogitBoost, and Majority Voting) were compared. Our machine learning models predicted sub-species virulence potential with nested cross-validation F1-scores up to 0.88 for the majority voting classifier trained on national surveillance data and using pan-genome genes as input features. The validation of the pre-trained ML models based on 101 previously in vivo studied isolates resulted in F1-scores up to 0.76. Furthermore, we found that the more rapid and less computationally intensive raw read alignment yields comparably accurate models as de novo assembly. The results of our study suggest that a majority voting classifier trained on pan-genome genes is the best and most robust choice for the prediction of clinical frequency. Our study contributes to more rapid and precise characterization of L. monocytogenes virulence and its variation on a sub-species level. We further demonstrated a possible application of WGS data in the context of microbial hazard characterization for food safety. In the future, predictive models may assist case-specific microbial risk management in the food industry. The python code, pre-trained models, and prediction pipeline are deposited at (https://github.com/agmei/LmonoVirulenceML).
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Affiliation(s)
- Alexander Gmeiner
- National Food Institute, Technical University of Denmark, Research Group for Genomic Epidemiology, Kgs. Lyngby, Denmark.
| | - Patrick Murigu Kamau Njage
- National Food Institute, Technical University of Denmark, Research Group for Genomic Epidemiology, Kgs. Lyngby, Denmark
| | - Lisbeth Truelstrup Hansen
- National Food Institute, Technical University of Denmark, Research Group for Food Microbiology and Hygiene, Kgs. Lyngby, Denmark
| | - Frank M Aarestrup
- National Food Institute, Technical University of Denmark, Research Group for Genomic Epidemiology, Kgs. Lyngby, Denmark
| | - Pimlapas Leekitcharoenphon
- National Food Institute, Technical University of Denmark, Research Group for Genomic Epidemiology, Kgs. Lyngby, Denmark
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Van Camp PJ, Prasath VBS, Haslam DB, Porollo A. MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile. MICROBIOME 2023; 11:223. [PMID: 37833777 PMCID: PMC10571262 DOI: 10.1186/s40168-023-01674-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND Identification of pathogenic bacteria from clinical specimens and evaluating their antimicrobial resistance (AMR) are laborious tasks that involve in vitro cultivation, isolation, and susceptibility testing. Recently, a number of methods have been developed that use machine learning algorithms applied to the whole-genome sequencing data of isolates to approach this problem. However, making AMR assessments from more easily available metagenomic sequencing data remains a big challenge. RESULTS We present the Metagenomic Sequencing to Antimicrobial Resistance (MGS2AMR) pipeline, which detects antibiotic resistance genes (ARG) and their possible organism of origin within a sequenced metagenomics sample. This in silico method allows for the evaluation of bacterial AMR directly from clinical specimens, such as stool samples. We have developed two new algorithms to optimize and annotate the genomic assembly paths within the raw Graphical Fragment Assembly (GFA): the GFA Linear Optimal Path through seed segments (GLOPS) algorithm and the Adapted Dijkstra Algorithm for GFA (ADAG). These novel algorithms improve the sensitivity of ARG detection and aid in species annotation. Tests based on 1200 microbiome samples show a high ARG recall rate and correct assignment of the ARG origin. The MGS2AMR output can further be used in many downstream applications, such as evaluating AMR to specific antibiotics in samples from emerging intestinal infections. We demonstrate that the MGS2AMR-derived data is as informative for the entailing prediction models as the whole-genome sequencing (WGS) data. The performance of these models is on par with our previously published method (WGS2AMR), which is based on the sequencing data of bacterial isolates. CONCLUSIONS MGS2AMR can provide researchers with valuable insights into the AMR content of microbiome environments and may potentially improve patient care by providing faster quantification of resistance against specific antibiotics, thereby reducing the use of broad-spectrum antibiotics. The presented pipeline also has potential applications in other metagenome analyses focused on the defined sets of genes. Video Abstract.
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Affiliation(s)
- Pieter-Jan Van Camp
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - V B Surya Prasath
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, 45267, USA
| | - David B Haslam
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, 45267, USA
- Division of Infectious Diseases, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Aleksey Porollo
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, 45267, USA.
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA.
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Erdmann MB, Gardner PP, Lamont IL. The PitA protein contributes to colistin susceptibility in Pseudomonas aeruginosa. PLoS One 2023; 18:e0292818. [PMID: 37824582 PMCID: PMC10569645 DOI: 10.1371/journal.pone.0292818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
Pseudomonas aeruginosa is an opportunistic pathogen that causes a wide range of problematic infections in individuals with predisposing conditions. Infections can be treated with colistin but some isolates are resistant to this antibiotic. To better understand the genetic basis of resistance, we experimentally evolved 19 independent resistant mutants from the susceptible laboratory strain PAO1. Whole genome sequencing identified mutations in multiple genes including phoQ and pmrB that have previously been associated with resistance, pitA that encodes a phosphate transporter, and carB and eno that encode enzymes of metabolism. Individual mutations were engineered into the genome of strain PAO1. Mutations in pitA, pmrB and phoQ increased the minimum inhibitory concentration (MIC) for colistin 8-fold, making the bacteria resistant. Engineered pitA/phoQ and pitA/pmrB double mutants had higher MICs than single mutants, demonstrating additive effects on colistin susceptibility. Single carB and eno mutations did not increase the MIC suggesting that their effect is dependent on the presence of other mutations. Many of the resistant mutants had increased susceptibility to β-lactams and lower growth rates than the parental strain demonstrating that colistin resistance can impose a fitness cost. Two hundred and fourteen P. aeruginosa isolates from a range of sources were tested and 18 (7.8%) were colistin resistant. Sequence variants in genes identified by experimental evolution were present in the 18 resistant isolates and may contribute to resistance. Overall our results identify pitA mutations as novel contributors to colistin resistance and demonstrate that resistance can reduce fitness of the bacteria.
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Affiliation(s)
| | - Paul P. Gardner
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Iain L. Lamont
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
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Vieira ADA, Piccoli BC, Y Castro TR, Casarin BC, Tessele LF, Martins RCR, Schwarzbold AV, Trindade PDA. Pipeline validation for the identification of antimicrobial-resistant genes in carbapenem-resistant Klebsiella pneumoniae. Sci Rep 2023; 13:15189. [PMID: 37709838 PMCID: PMC10502106 DOI: 10.1038/s41598-023-42154-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 09/06/2023] [Indexed: 09/16/2023] Open
Abstract
Antimicrobial-resistant Klebsiella pneumoniae is a global threat to healthcare and an important cause of nosocomial infections. Antimicrobial resistance causes prolonged treatment periods, high mortality rates, and economic impacts. Whole Genome Sequencing (WGS) has been used in laboratory diagnosis, but there is limited evidence about pipeline validation to parse generated data. Thus, the present study aimed to validate a bioinformatics pipeline for the identification of antimicrobial resistance genes from carbapenem-resistant K. pneumoniae WGS. Sequences were obtained from a publicly available database, trimmed, de novo assembled, mapped to the K. pneumoniae reference genome, and annotated. Contigs were submitted to different tools for bacterial (Kraken2 and SpeciesFinder) and antimicrobial resistance gene identification (ResFinder and ABRicate). We analyzed 201 K. pneumoniae genomes. In the bacterial identification by Kraken2, all samples were correctly identified, and in SpeciesFinder, 92.54% were correctly identified as K. pneumoniae, 6.96% erroneously as Pseudomonas aeruginosa, and 0.5% erroneously as Citrobacter freundii. ResFinder found a greater number of antimicrobial resistance genes than ABRicate; however, many were identified more than once in the same sample. All tools presented 100% repeatability and reproducibility and > 75% performance in other metrics. Kraken2 was more assertive in recognizing bacterial species, and SpeciesFinder may need improvements.
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Affiliation(s)
- Andressa de Almeida Vieira
- Laboratório de Biologia Molecular e Bioinformática Aplicada à Microbiologia Clínica, Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal de Santa Maria, Santa Maria, 97105-900, Brazil
| | - Bruna Candia Piccoli
- Laboratório de Biologia Molecular e Bioinformática Aplicada à Microbiologia Clínica, Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal de Santa Maria, Santa Maria, 97105-900, Brazil
| | - Thaís Regina Y Castro
- Laboratório de Biologia Molecular e Bioinformática Aplicada à Microbiologia Clínica, Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal de Santa Maria, Santa Maria, 97105-900, Brazil
| | - Bruna Campestrini Casarin
- Laboratório de Biologia Molecular e Bioinformática Aplicada à Microbiologia Clínica, Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal de Santa Maria, Santa Maria, 97105-900, Brazil
| | - Luiza Funck Tessele
- Laboratório de Biologia Molecular e Bioinformática Aplicada à Microbiologia Clínica, Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal de Santa Maria, Santa Maria, 97105-900, Brazil
| | - Roberta Cristina Ruedas Martins
- Laboratório de Parasitologia Médica (LIM-46), Departamento de Doenças Infecciosas e Parasitárias, Instituto de Medicina Tropical da Universidade de São Paulo, Faculdade de Medicina da Universidade de São Paulo, São Paulo, 01246-903, Brazil
| | | | - Priscila de Arruda Trindade
- Laboratório de Biologia Molecular e Bioinformática Aplicada à Microbiologia Clínica, Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal de Santa Maria, Santa Maria, 97105-900, Brazil.
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Salam MA, Al-Amin MY, Salam MT, Pawar JS, Akhter N, Rabaan AA, Alqumber MAA. Antimicrobial Resistance: A Growing Serious Threat for Global Public Health. Healthcare (Basel) 2023; 11:1946. [PMID: 37444780 DOI: 10.3390/healthcare11131946] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
Antibiotics are among the most important discoveries of the 20th century, having saved millions of lives from infectious diseases. Microbes have developed acquired antimicrobial resistance (AMR) to many drugs due to high selection pressure from increasing use and misuse of antibiotics over the years. The transmission and acquisition of AMR occur primarily via a human-human interface both within and outside of healthcare facilities. A huge number of interdependent factors related to healthcare and agriculture govern the development of AMR through various drug-resistance mechanisms. The emergence and spread of AMR from the unrestricted use of antimicrobials in livestock feed has been a major contributing factor. The prevalence of antimicrobial-resistant bacteria has attained an incongruous level worldwide and threatens global public health as a silent pandemic, necessitating urgent intervention. Therapeutic options of infections caused by antimicrobial-resistant bacteria are limited, resulting in significant morbidity and mortality with high financial impact. The paucity in discovery and supply of new novel antimicrobials to treat life-threatening infections by resistant pathogens stands in sharp contrast to demand. Immediate interventions to contain AMR include surveillance and monitoring, minimizing over-the-counter antibiotics and antibiotics in food animals, access to quality and affordable medicines, vaccines and diagnostics, and enforcement of legislation. An orchestrated collaborative action within and between multiple national and international organizations is required urgently, otherwise, a postantibiotic era can be a more real possibility than an apocalyptic fantasy for the 21st century. This narrative review highlights on this basis, mechanisms and factors in microbial resistance, and key strategies to combat antimicrobial resistance.
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Affiliation(s)
- Md Abdus Salam
- Department of Basic Medical Sciences, Kulliyyah of Medicine, International Islamic University Malaysia, Kuantan 25200, Malaysia
| | - Md Yusuf Al-Amin
- Purdue University Interdisciplinary Life Sciences Graduate Program, Purdue University, West Lafayette, IN 47907, USA
- Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA
| | | | - Jogendra Singh Pawar
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN 47907, USA
- The Ohio State University Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Columbus, OH 43210, USA
| | - Naseem Akhter
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA
| | - Ali A Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran 31311, Saudi Arabia
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
| | - Mohammed A A Alqumber
- Laboratory Medicine Department, Faculty of Applied Medical Sciences, Albaha University, Al Baha 65431, Saudi Arabia
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Liao X, Deng R, Warriner K, Ding T. Antibiotic resistance mechanism and diagnosis of common foodborne pathogens based on genotypic and phenotypic biomarkers. Compr Rev Food Sci Food Saf 2023; 22:3212-3253. [PMID: 37222539 DOI: 10.1111/1541-4337.13181] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 04/22/2023] [Accepted: 05/06/2023] [Indexed: 05/25/2023]
Abstract
The emergence of antibiotic-resistant bacteria due to the overuse or inappropriate use of antibiotics has become a significant public health concern. The agri-food chain, which serves as a vital link between the environment, food, and human, contributes to the large-scale dissemination of antibiotic resistance, posing a concern to both food safety and human health. Identification and evaluation of antibiotic resistance of foodborne bacteria is a crucial priority to avoid antibiotic abuse and ensure food safety. However, the conventional approach for detecting antibiotic resistance heavily relies on culture-based methods, which are laborious and time-consuming. Therefore, there is an urgent need to develop accurate and rapid tools for diagnosing antibiotic resistance in foodborne pathogens. This review aims to provide an overview of the mechanisms of antibiotic resistance at both phenotypic and genetic levels, with a focus on identifying potential biomarkers for diagnosing antibiotic resistance in foodborne pathogens. Furthermore, an overview of advances in the strategies based on the potential biomarkers (antibiotic resistance genes, antibiotic resistance-associated mutations, antibiotic resistance phenotypes) for antibiotic resistance analysis of foodborne pathogens is systematically exhibited. This work aims to provide guidance for the advancement of efficient and accurate diagnostic techniques for antibiotic resistance analysis in the food industry.
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Affiliation(s)
- Xinyu Liao
- Department of Food Science and Nutrition, Zhejiang University, Hangzhou, Zhejiang, China
- School of Mechanical and Energy Engineering, NingboTech University, Ningbo, Zhejiang, China
- Future Food Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan, Zhejiang, China
| | - Ruijie Deng
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu, Sichuan, China
| | - Keith Warriner
- Department of Food Science, University of Guelph, Guelph, Ontario, Canada
| | - Tian Ding
- Department of Food Science and Nutrition, Zhejiang University, Hangzhou, Zhejiang, China
- Future Food Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan, Zhejiang, China
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Álvarez VE, Quiroga MP, Centrón D. Identification of a Specific Biomarker of Acinetobacter baumannii Global Clone 1 by Machine Learning and PCR Related to Metabolic Fitness of ESKAPE Pathogens. mSystems 2023:e0073422. [PMID: 37184409 DOI: 10.1128/msystems.00734-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023] Open
Abstract
Since the emergence of high-risk clones worldwide, constant investigations have been undertaken to comprehend the molecular basis that led to their prevalent dissemination in nosocomial settings over time. So far, the complex and multifactorial genetic traits of this type of epidemic clones have allowed only the identification of biomarkers with low specificity. A machine learning algorithm was able to recognize unequivocally a biomarker for early and accurate detection of Acinetobacter baumannii global clone 1 (GC1), one of the most disseminated high-risk clones. A support vector machine model identified the U1 sequence with a length of 367 nucleotides that matched a fragment of the moaCB gene, which encodes the molybdenum cofactor biosynthesis C and B proteins. U1 differentiates specifically between A. baumannii GC1 and non-GC1 strains, becoming a suitable biomarker capable of being translated into clinical settings as a molecular typing method for early diagnosis based on PCR as shown here. Since the metabolic pathways of Mo enzymes have been recognized as putative therapeutic targets for ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) pathogens, our findings highlight that machine learning can also be useful in knowledge gaps of high-risk clones and provides noteworthy support to the literature to identify relevant nosocomial biomarkers for other multidrug-resistant high-risk clones. IMPORTANCE A. baumannii GC1 is an important high-risk clone that rapidly develops extreme drug resistance in the nosocomial niche. Furthermore, several strains have been identified worldwide in environmental samples, exacerbating the risk of human interactions. Early diagnosis is mandatory to limit its dissemination and to outline appropriate antibiotic stewardship schedules. A region with a length of 367 bp (U1) within the moaCB gene that is not subjected to lateral genetic transfer or to antibiotic pressures was successfully found by a support vector machine model that predicts A. baumannii GC1 strains. At the same time, research on the group of Mo enzymes proposed this metabolic pathway related to the superbug's metabolism as a potential future drug target site for ESKAPE pathogens due to its central role in bacterial fitness during infection. These findings confirm that machine learning used for the identification of biomarkers of high-risk lineages can also serve to identify putative novel therapeutic target sites.
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Affiliation(s)
- Verónica Elizabeth Álvarez
- Laboratorio de Investigaciones en Mecanismos de Resistencia a Antibióticos (LIMRA), Instituto de Investigaciones en Microbiología y Parasitología Médica, Facultad de Medicina, Universidad de Buenos Aires-Consejo Nacional de Investigaciones Científicas y Tecnológicas (IMPaM, UBA-CONICET), Ciudad Autónoma de Buenos Aires, Argentina
| | - María Paula Quiroga
- Laboratorio de Investigaciones en Mecanismos de Resistencia a Antibióticos (LIMRA), Instituto de Investigaciones en Microbiología y Parasitología Médica, Facultad de Medicina, Universidad de Buenos Aires-Consejo Nacional de Investigaciones Científicas y Tecnológicas (IMPaM, UBA-CONICET), Ciudad Autónoma de Buenos Aires, Argentina
- Nodo de Bioinformática. Instituto de Investigaciones en Microbiología y Parasitología Médica, Facultad de Medicina, Universidad de Buenos Aires-Consejo Nacional de Investigaciones Científicas y Técnicas (IMPaM, UBA-CONICET), Ciudad Autónoma de Buenos Aires, Argentina
| | - Daniela Centrón
- Laboratorio de Investigaciones en Mecanismos de Resistencia a Antibióticos (LIMRA), Instituto de Investigaciones en Microbiología y Parasitología Médica, Facultad de Medicina, Universidad de Buenos Aires-Consejo Nacional de Investigaciones Científicas y Tecnológicas (IMPaM, UBA-CONICET), Ciudad Autónoma de Buenos Aires, Argentina
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10
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Zhang X, Liu S, Sun H, Huang K, Ye L. Impact of different organic matters on the occurrence of antibiotic resistance genes in activated sludge. J Environ Sci (China) 2023; 127:273-283. [PMID: 36522059 DOI: 10.1016/j.jes.2022.04.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/18/2022] [Accepted: 04/19/2022] [Indexed: 06/17/2023]
Abstract
The occurrence of antibiotic resistance genes (ARGs) in various environments has drawn worldwide attention due to their potential risks. Previous studies have reported that a variety of substances can enhance the occurrence and dissemination of ARGs. However, few studies have compared the response of ARGs under the stress of different organic matters in biological wastewater treatment systems. In this study, seven organic pollutants were added into wastewater treatment bioreactors to investigate their impacts on the ARG occurrence in activated sludge. Based on high-throughput sequencing, it was found that the microbial communities and ARG patterns were significantly changed in the activated sludge exposed to these organic pollutants. Compared with the non-antibiotic refractory organic matters, antibiotics not only increased the abundance of ARGs but also significantly changed the ARG compositions. The increase of Gram-negative bacteria (e.g., Archangium, Prosthecobacter and Dokdonella) carrying ARGs could be the main cause of ARG proliferation. In addition, significant co-occurrence relationships between ARGs and mobile genetic elements were also observed in the sludge samples, which may also affect the ARG diversity and abundance during the organic matter treatment in the bioreactors. Overall, these findings provide new information for better understanding the ARG occurrence and dissemination caused by organic pollutants in wastewater treatment systems.
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Affiliation(s)
- Xiuwen Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Suwan Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Haohao Sun
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Kailong Huang
- Nanjing Jiangdao Institute of Environmental Research Co., Ltd., Nanjing 210019, China
| | - Lin Ye
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
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11
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Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation. Antibiotics (Basel) 2023; 12:antibiotics12030523. [PMID: 36978390 PMCID: PMC10044311 DOI: 10.3390/antibiotics12030523] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/08/2023] Open
Abstract
Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.
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12
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Yu J, Lin YT, Chen WC, Tseng KH, Lin HH, Tien N, Cho CF, Huang JY, Liang SJ, Ho LC, Hsieh YW, Hsu KC, Ho MW, Hsueh PR, Cho DY. Direct prediction of carbapenem-resistant, carbapenemase-producing, and colistin-resistant Klebsiella pneumoniae isolates from routine MALDI-TOF mass spectra using machine learning and outcome evaluation. Int J Antimicrob Agents 2023; 61:106799. [PMID: 37004755 DOI: 10.1016/j.ijantimicag.2023.106799] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 03/14/2023] [Accepted: 03/26/2023] [Indexed: 04/03/2023]
Abstract
The objective of this study was to develop a rapid prediction method for carbapenem-resistant Klebsiella pneumoniae (CRKP) and colistin-resistant K. pneumoniae (ColRKP) based on routine MALDI-TOF mass spectrometry (MS) results in order to formulate a suitable and rapid treatment strategy. In total, 830 CRKP and 1,462 carbapenem-susceptible K. pneumoniae (CSKP) isolates were collected; 54 ColRKP isolates and 1,592 colistin-intermediate K. pneumoniae (ColIKP) isolates were also included. Routine MALDI-TOF MS, antimicrobial susceptibility testing, NG-Test CARBA 5, and resistance gene detection were followed by machine learning (ML). Using the ML model, the accuracy and area under the curve for differentiating CRKP and CSKP were 0.8869 and 0.9551, and those for ColRKP and ColIKP were 0.8361 and 0.8447, respectively. The most important MS features of CRKP and ColRKP were m/z 4520-4529 and m/z 4170-4179, respectively. Of the CRKP isolates, MS m/z 4520-4529 was a potential biomarker for distinguishing KPC from OXA, NDM, IMP, and VIM. Of the 34 patients who received preliminary CRKP ML prediction results (by texting), 24 (70.6%) were confirmed to have CRKP infection. The mortality rate was lower in patients who received antibiotic regimen adjustment based on the preliminary ML prediction (4/14, 28.6%). In conclusion, the proposed model can provide rapid results for differentiating CRKP and CSKP, as well as ColRKP and ColIKP. The combination of ML-based CRKP with preliminary reporting of results can help physicians alter the regimen approximately 24 h earlier, resulting in improved survival of patients with timely antibiotic intervention.
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13
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Anderson E, Nair B, Nizet V, Kumar G. Man vs Microbes - The Race of the Century. J Med Microbiol 2023; 72. [PMID: 36748622 DOI: 10.1099/jmm.0.001646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
The complexity of the antimicrobial resistance (AMR) crisis and its global impact on healthcare invokes an urgent need to understand the underlying forces and to conceive and implement innovative solutions. Beyond focusing on a traditional pathogen-centric approach to antibiotic discovery yielding diminishing returns, future therapeutic interventions can expand to focus more comprehensively on host-pathogen interactions. In this manner, increasing the resiliency of our innate immune system or attenuating the virulence mechanisms of the pathogens can be explored to improve therapeutic outcomes. Key pathogen survival strategies such as tolerance, persistence, aggregation, and biofilm formation can be considered and interrupted to sensitize pathogens for more efficient immune clearance. Understanding the evolution and emergence of so-called 'super clones' that drive AMR spread with rapid clonotyping assays may guide more precise antibiotic regimens. Innovative alternatives to classical antibiotics such as bacteriophage therapy, novel engineered peptide antibiotics, ionophores, nanomedicines, and repurposing drugs from other domains of medicine to boost innate immunity are beginning to be successfully implemented to combat AMR. Policy changes supporting shorter durations of antibiotic treatment, greater antibiotic stewardship, and increased surveillance measures can enhance patient safety and enable implementation of the next generation of targeted prevention and control programmes at a global level.
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Affiliation(s)
- Ericka Anderson
- Collaborative to Halt Antibiotic Resistant Microbes (CHARM), Department of Pediatrics University of California San Diego, La Jolla, CA, USA
| | - Bipin Nair
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
| | - Victor Nizet
- Collaborative to Halt Antibiotic Resistant Microbes (CHARM), Department of Pediatrics University of California San Diego, La Jolla, CA, USA.,Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego, La Jolla, CA, USA
| | - Geetha Kumar
- School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India
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14
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Sharma P, Dahiya S, Kaur P, Kapil A. Computational biology: Role and scope in taming antimicrobial resistance. Indian J Med Microbiol 2023; 41:33-38. [PMID: 36870746 DOI: 10.1016/j.ijmmb.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 12/11/2022] [Accepted: 12/16/2022] [Indexed: 01/05/2023]
Abstract
BACKGROUND Infectious diseases pose many challenges due to increasing threat of antimicrobial resistance, which necessitates continuous research to develop novel strategies for development of new molecules with antibacterial activity. In the era of computational biology there are tools and techniques available to address and solve the disease management issues in the field of clinical microbiology. The sequencing techniques, structural biology and machine learning can be implemented collectively to tackle infectious diseases e.g. for the diagnosis, epidemiological typing, pathotyping, antimicrobial resistance detection as well as the discovery of novel drugs and vaccine biomarkers. OBJECTIVES The present review is a narrative review representing a comprehensive literature-based assessment regarding the use of whole genome sequencing, structural biology and machine learning for the diagnosis, molecular typing and antibacterial drug discovery. CONTENT Here, we seek to present an overview of molecular and structural basis of resistance to antibiotics, while focusing on the recent bioinformatics approaches in whole genome sequencing and structural biology. The application of next generation sequencing in management of bacterial infections focusing on investigation of microbial population diversity, genotypic resistance testing and scope for the identification of targets for novel drug and vaccine candidates, has been addressed along with the use of structural biophysics and artificial intelligence.
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Affiliation(s)
- Priyanka Sharma
- Department of Biophysics, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India.
| | - Sushila Dahiya
- Department of Microbiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India.
| | - Punit Kaur
- Department of Biophysics, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India.
| | - Arti Kapil
- Department of Microbiology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India.
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15
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Vereecke N, Van Hoorde S, Sperling D, Theuns S, Devriendt B, Cox E. Virotyping and genetic antimicrobial susceptibility testing of porcine ETEC/STEC strains and associated plasmid types. Front Microbiol 2023; 14:1139312. [PMID: 37143544 PMCID: PMC10151945 DOI: 10.3389/fmicb.2023.1139312] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/13/2023] [Indexed: 05/06/2023] Open
Abstract
Introduction Enterotoxigenic Escherichia coli (ETEC) infections are the most common cause of secretory diarrhea in suckling and post-weaning piglets. For the latter, Shiga toxin-producing Escherichia coli (STEC) also cause edema disease. This pathogen leads to significant economic losses. ETEC/STEC strains can be distinguished from general E. coli by the presence of different host colonization factors (e.g., F4 and F18 fimbriae) and various toxins (e.g., LT, Stx2e, STa, STb, EAST-1). Increased resistance against a wide variety of antimicrobial drugs, such as paromomycin, trimethoprim, and tetracyclines, has been observed. Nowadays, diagnosing an ETEC/STEC infection requires culture-dependent antimicrobial susceptibility testing (AST) and multiplex PCRs, which are costly and time-consuming. Methods Here, nanopore sequencing was used on 94 field isolates to assess the predictive power, using the meta R package to determine sensitivity and specificity and associated credibility intervals of genotypes associated with virulence and AMR. Results Genetic markers associated with resistance for amoxicillin (plasmid-encoded TEM genes), cephalosporins (ampC promoter mutations), colistin (mcr genes), aminoglycosides (aac(3) and aph(3) genes), florfenicol (floR), tetracyclines (tet genes), and trimethoprim-sulfa (dfrA genes) could explain most acquired resistance phenotypes. Most of the genes were plasmid-encoded, of which some collocated on a multi-resistance plasmid (12 genes against 4 antimicrobial classes). For fluoroquinolones, AMR was addressed by point mutations within the ParC and GyrA proteins and the qnrS1 gene. In addition, long-read data allowed to study the genetic landscape of virulence- and AMR-carrying plasmids, highlighting a complex interplay of multi-replicon plasmids with varying host ranges. Conclusion Our results showed promising sensitivity and specificity for the detection of all common virulence factors and most resistance genotypes. The use of the identified genetic hallmarks will contribute to the simultaneous identification, pathotyping, and genetic AST within a single diagnostic test. This will revolutionize future quicker and more cost-efficient (meta)genomics-driven diagnostics in veterinary medicine and contribute to epidemiological studies, monitoring, tailored vaccination, and management.
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Affiliation(s)
- Nick Vereecke
- Laboratory of Virology, Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
- PathoSense BV, Lier, Belgium
- *Correspondence: Nick Vereecke,
| | - Sander Van Hoorde
- Laboratory of Immunology, Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | | | - Sebastiaan Theuns
- Laboratory of Virology, Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Bert Devriendt
- Laboratory of Immunology, Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - Eric Cox
- Laboratory of Immunology, Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
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16
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Geurtsen J, de Been M, Weerdenburg E, Zomer A, McNally A, Poolman J. Genomics and pathotypes of the many faces of Escherichia coli. FEMS Microbiol Rev 2022; 46:6617594. [PMID: 35749579 PMCID: PMC9629502 DOI: 10.1093/femsre/fuac031] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 06/22/2022] [Indexed: 01/09/2023] Open
Abstract
Escherichia coli is the most researched microbial organism in the world. Its varied impact on human health, consisting of commensalism, gastrointestinal disease, or extraintestinal pathologies, has generated a separation of the species into at least eleven pathotypes (also known as pathovars). These are broadly split into two groups, intestinal pathogenic E. coli (InPEC) and extraintestinal pathogenic E. coli (ExPEC). However, components of E. coli's infinite open accessory genome are horizontally transferred with substantial frequency, creating pathogenic hybrid strains that defy a clear pathotype designation. Here, we take a birds-eye view of the E. coli species, characterizing it from historical, clinical, and genetic perspectives. We examine the wide spectrum of human disease caused by E. coli, the genome content of the bacterium, and its propensity to acquire, exchange, and maintain antibiotic resistance genes and virulence traits. Our portrayal of the species also discusses elements that have shaped its overall population structure and summarizes the current state of vaccine development targeted at the most frequent E. coli pathovars. In our conclusions, we advocate streamlining efforts for clinical reporting of ExPEC, and emphasize the pathogenic potential that exists throughout the entire species.
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Affiliation(s)
- Jeroen Geurtsen
- Janssen Vaccines and Prevention B.V., 2333 Leiden, the Netherlands
| | - Mark de Been
- Janssen Vaccines and Prevention B.V., 2333 Leiden, the Netherlands
| | | | - Aldert Zomer
- Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, 3584 Utrecht, the Netherlands
| | - Alan McNally
- Institute of Microbiology and Infection, College of Medical and Dental Sciences, University of Birmingham, B15 2TT Birmingham, United Kingdom
| | - Jan Poolman
- Janssen Vaccines and Prevention B.V., 2333 Leiden, the Netherlands
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17
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Comparative genomics reveals the evolution of antimicrobial resistance in Bacteroides nordii. Microb Pathog 2022; 173:105811. [PMID: 36183960 DOI: 10.1016/j.micpath.2022.105811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/24/2022] [Accepted: 09/26/2022] [Indexed: 11/22/2022]
Abstract
Bacteroides nordii, is an understudied member of the pathogenic B. fragilis group which comprises several multidrug-resistant (MDR) strains. Thus, it is of great interest to study the genome biology of Bacteroides nordii. However, no detailed study is available that characterized B. nordii at the genetic level and explored its role as a potential pathogen. We isolated an MDR strain viz., B. nordii PGMM4098 from the pus sample and subjected it to whole genome sequencing using Illumina technology. The draft genome was de-novo assembled and annotated, followed by comprehensive comparative genomics analyses using the publicly available genome dataset of B. nordii. The pan-genome analysis revealed the open nature of B. nordii, indicating the continuous accumulation of novel genes in non-core components leading to the emergence of new strains of this species. The thirteen antimicrobial resistance (AMR) genes identified in the genomes of all B. nordii strains were part of the non-core component of the pan-genome. Of these, four AMR genes, nimE, aadS, mef(En2), and ermB/F/G were found to be acquired via the process of horizontal gene transfer (HGT) from anaerobic Bacteroidetes. Importantly, the nimE gene conferring metronidazole resistance was found to be present only in B. nordii PGMM4098, which harbors five other AMR genes encoded in its genome. Of these, nimE (metronidazole resistance), ermB/F/G (macrolide-lincosamide-streptogramin B resistance), and cfxA2/A3 (class A β-lactam resistance) genes were further validated using targeted polymerase chain reaction assay. Notably, these three genes were also found to be under the operation of positive selective pressure suggesting the diversification of these genes, which might lead to the emergence of new MDR strains of B. nordii in the near future. Our study reported and characterized the genome of the first MDR strain of B. nordii and revealed the AMR evolution in this species using a comprehensive comparative genomics approach.
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18
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Ruppé E, d'Humières C, Armand-Lefèvre L. Inferring antibiotic susceptibility from metagenomic data: dream or reality? Clin Microbiol Infect 2022; 28:1225-1229. [PMID: 35551982 DOI: 10.1016/j.cmi.2022.04.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/15/2022] [Accepted: 04/19/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND The diagnosis of bacterial infections continues to rely on culture, a slow process in which antibiotic susceptibility profiles of potential pathogens are made available to clinicians 48h after sampling, at best. Recently, clinical metagenomics (CMg), the metagenomic sequencing of samples with the purpose of identifying microorganisms and determining their susceptibility to antimicrobials, has emerged as a potential diagnostic tool that could prove faster than culture. CMg indeed has the potential to detect antibiotic resistance genes (ARGs) and mutations associated with resistance. Nevertheless, many challenges have yet to be overcome in order to make rapid phenotypic inference of antibiotic susceptibility from metagenomic data a reality. OBJECTIVES The objective of this narrative review is to discuss the challenges underlying the phenotypic inference of antibiotic susceptibility from metagenomic data. SOURCES We conducted a narrative review using published articles available in the NCBI Pubmed database. CONTENT We review the current ARG databases with a specific emphasis on those which now provide associations with phenotypic data. Next, we discuss the bioinformatic tools designed to identify ARGs in metagenomes. We then report on the performance of phenotypic inference from genomic data and the issue predicting the expression of ARGs. Finally, we address the challenge of linking an ARG to this host. IMPLICATIONS Significant improvements have recently been made in associating ARG and phenotype, and the inference of susceptibility from genomic data has been demonstrated in pathogenic bacteria such as Staphylococci and Enterobacterales. Resistance involving gene expression is more challenging however, and inferring susceptibility from species such as Pseudomonas aeruginosa remains difficult. Future research directions include the consideration of gene expression via RNA sequencing and machine learning.
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Affiliation(s)
- Etienne Ruppé
- Université de Paris Cité, INSERM UMR1137 IAME, F-75018 Paris, France; AP-HP, Hôpital Bichat, Laboratoire de Bactériologie, F-75018 Paris, France.
| | - Camille d'Humières
- Université de Paris Cité, INSERM UMR1137 IAME, F-75018 Paris, France; AP-HP, Hôpital Bichat, Laboratoire de Bactériologie, F-75018 Paris, France
| | - Laurence Armand-Lefèvre
- Université de Paris Cité, INSERM UMR1137 IAME, F-75018 Paris, France; AP-HP, Hôpital Bichat, Laboratoire de Bactériologie, F-75018 Paris, France
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19
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Smith HG, Bean DC, Clarke RH, Loyn R, Larkins JA, Hassell C, Greenhill AR. Presence and antimicrobial resistance profiles of Escherichia coli, Enterococcusspp. and Salmonellasp. in 12 species of Australian shorebirds and terns. Zoonoses Public Health 2022; 69:615-624. [PMID: 35460193 PMCID: PMC9544147 DOI: 10.1111/zph.12950] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 03/13/2022] [Accepted: 04/05/2022] [Indexed: 11/27/2022]
Abstract
Antibiotic resistance is an ongoing threat to both human and animal health. Migratory birds are a potential vector for the spread of novel pathogens and antibiotic resistance genes. To date, there has been no comprehensive study investigating the presence of antibiotic resistance (AMR) in the bacteria of Australian shorebirds or terns. In the current study, 1022 individual birds representing 12 species were sampled across three states of Australia (Victoria, South Australia, and Western Australia) and tested for the presence of phenotypically resistant strains of three bacteria with potential to be zoonotic pathogens; Escherichia coli, Enterococcusspp., and Salmonellasp. In total, 206 E. coli, 266 Enterococcusspp., and 20 Salmonellasp. isolates were recovered, with AMR detected in 42% of E. coli, 85% of Enterococcusspp., and 10% of Salmonellasp. Phenotypic resistance was commonly detected to erythromycin (79% of Enterococcusspp.), ciprofloxacin (31% of Enterococcusspp.) and streptomycin (21% of E. coli). Resident birds were more likely to carry AMR bacteria than migratory birds (p ≤ .001). Bacteria isolated from shorebirds and terns are commonly resistant to at least one antibiotic, suggesting that wild bird populations serve as a potential reservoir and vector for AMR bacteria. However, globally emerging phenotypes of multidrug‐resistant bacteria were not detected in Australian shorebirds. This study provides baseline data of the carriage of AMR bacteria in Australian shorebirds and terns.
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Affiliation(s)
- Hannah G Smith
- Institute of Innovation, Science and Sustainability, Federation University, Churchill, Australia
| | - David C Bean
- Institute of Innovation, Science and Sustainability, Federation University, Churchill, Australia
| | - Rohan H Clarke
- School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Richard Loyn
- School of Life Sciences, Centre for Freshwater Ecosystems, La Trobe University, Wodonga, Victoria, Australia.,Institute for Land, Water and Society, Charles Sturt University, Albury, New South Wales, Australia
| | - Jo-Ann Larkins
- Institute of Innovation, Science and Sustainability, Federation University, Churchill, Australia.,School of Science, Engineering and Information Technology, Federation University, Ballarat, Victoria, Australia
| | - Chris Hassell
- Global Flyway Network, Broome, Western Australia, Australia.,Australasian Wader Studies Group, Broome, Western Australia, Australia
| | - Andrew R Greenhill
- Institute of Innovation, Science and Sustainability, Federation University, Churchill, Australia
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20
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Wang S, Zhao C, Yin Y, Chen F, Chen H, Wang H. A Practical Approach for Predicting Antimicrobial Phenotype Resistance in Staphylococcus aureus Through Machine Learning Analysis of Genome Data. Front Microbiol 2022; 13:841289. [PMID: 35308374 PMCID: PMC8924536 DOI: 10.3389/fmicb.2022.841289] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 02/11/2022] [Indexed: 11/28/2022] Open
Abstract
With the reduction in sequencing price and acceleration of sequencing speed, it is particularly important to directly link the genotype and phenotype of bacteria. Here, we firstly predicted the minimum inhibitory concentrations of ten antimicrobial agents for Staphylococcus aureus using 466 isolates by directly extracting k-mer from whole genome sequencing data combined with three machine learning algorithms: random forest, support vector machine, and XGBoost. Considering one two-fold dilution, the essential agreement and the category agreement could reach >85% and >90% for most antimicrobial agents. For clindamycin, cefoxitin and trimethoprim-sulfamethoxazole, the essential agreement and the category agreement could reach >91% and >93%, providing important information for clinical treatment. The successful prediction of cefoxitin resistance showed that the model could identify methicillin-resistant S. aureus. The results suggest that small datasets available in large hospitals could bypass the existing basic research and known antimicrobial resistance genes and accurately predict the bacterial phenotype.
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Affiliation(s)
- Shuyi Wang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China.,Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Chunjiang Zhao
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Yuyao Yin
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Fengning Chen
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China.,Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Hongbin Chen
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Hui Wang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China.,Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
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21
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Iskandar K, Murugaiyan J, Hammoudi Halat D, Hage SE, Chibabhai V, Adukkadukkam S, Roques C, Molinier L, Salameh P, Van Dongen M. Antibiotic Discovery and Resistance: The Chase and the Race. Antibiotics (Basel) 2022; 11:antibiotics11020182. [PMID: 35203785 PMCID: PMC8868473 DOI: 10.3390/antibiotics11020182] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/23/2022] [Accepted: 01/26/2022] [Indexed: 12/14/2022] Open
Abstract
The history of antimicrobial resistance (AMR) evolution and the diversity of the environmental resistome indicate that AMR is an ancient natural phenomenon. Acquired resistance is a public health concern influenced by the anthropogenic use of antibiotics, leading to the selection of resistant genes. Data show that AMR is spreading globally at different rates, outpacing all efforts to mitigate this crisis. The search for new antibiotic classes is one of the key strategies in the fight against AMR. Since the 1980s, newly marketed antibiotics were either modifications or improvements of known molecules. The World Health Organization (WHO) describes the current pipeline as bleak, and warns about the scarcity of new leads. A quantitative and qualitative analysis of the pre-clinical and clinical pipeline indicates that few antibiotics may reach the market in a few years, predominantly not those that fit the innovative requirements to tackle the challenging spread of AMR. Diversity and innovation are the mainstays to cope with the rapid evolution of AMR. The discovery and development of antibiotics must address resistance to old and novel antibiotics. Here, we review the history and challenges of antibiotics discovery and describe different innovative new leads mechanisms expected to replenish the pipeline, while maintaining a promising possibility to shift the chase and the race between the spread of AMR, preserving antibiotic effectiveness, and meeting innovative leads requirements.
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Affiliation(s)
- Katia Iskandar
- Department of Mathématiques Informatique et Télécommunications, Université Toulouse III, Paul Sabatier, INSERM, UMR 1295, 31000 Toulouse, France
- INSPECT-LB: Institut National de Santé Publique, d’Épidémiologie Clinique et de Toxicologie-Liban, Beirut 6573, Lebanon;
- Faculty of Pharmacy, Lebanese University, Beirut 6573, Lebanon
- Correspondence: (K.I.); (D.H.H.)
| | - Jayaseelan Murugaiyan
- Department of Biological Sciences, SRM University–AP, Amaravati 522502, India; (J.M.); (S.A.)
| | - Dalal Hammoudi Halat
- Department of Pharmaceutical Sciences, School of Pharmacy, Lebanese International University, Bekaa Campus, Beirut 1103, Lebanon
- Correspondence: (K.I.); (D.H.H.)
| | - Said El Hage
- Faculty of Medicine, Lebanese University, Beirut 6573, Lebanon;
| | - Vindana Chibabhai
- Division of Clinical Microbiology and Infectious Diseases, School of Pathology, University of the Witwatersrand, Johannesburg 2193, South Africa;
- Microbiology Laboratory, National Health Laboratory Service, Charlotte Maxeke Johannesburg Academic Hospital, Johannesburg 2193, South Africa
| | - Saranya Adukkadukkam
- Department of Biological Sciences, SRM University–AP, Amaravati 522502, India; (J.M.); (S.A.)
| | - Christine Roques
- Laboratoire de Génie Chimique, Department of Bioprocédés et Systèmes Microbiens, Université Paul Sabtier, Toulouse III, UMR 5503, 31330 Toulouse, France;
| | - Laurent Molinier
- Department of Medical Information, Centre Hospitalier Universitaire, INSERM, UMR 1295, Université Paul Sabatier Toulouse III, 31000 Toulouse, France;
| | - Pascale Salameh
- INSPECT-LB: Institut National de Santé Publique, d’Épidémiologie Clinique et de Toxicologie-Liban, Beirut 6573, Lebanon;
- Faculty of Medicine, Lebanese University, Beirut 6573, Lebanon;
- Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia 2408, Cyprus
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22
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Singh S, Numan A, Cinti S. Point-of-Care for Evaluating Antimicrobial Resistance through the Adoption of Functional Materials. Anal Chem 2022; 94:26-40. [PMID: 34802244 PMCID: PMC8756393 DOI: 10.1021/acs.analchem.1c03856] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sima Singh
- IES
Institute of Pharmacy, IES University Campus, Kalkheda, Ratibad Main Road, Bhopal 462044, Madhya Pradesh, India
| | - Arshid Numan
- Graphene
& Advanced 2D Materials Research Group (GAMRG), School of Engineering
and Technology, Sunway University, 5, Jalan University, Bandar Sunway, 47500 Petaling
Jaya, Selangor, Malaysia
| | - Stefano Cinti
- Department
of Pharmacy, University of Naples “Federico
II”, Via D. Montesano 49, 80131 Naples, Italy
- BAT
Center−Interuniversity Center for Studies on Bioinspired Agro-Environmental
Technology, University of Napoli Federico
II, 80055 Naples, Italy
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23
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Haslam DB. Future Applications of Metagenomic Next-Generation Sequencing for Infectious Diseases Diagnostics. J Pediatric Infect Dis Soc 2021; 10:S112-S117. [PMID: 34951467 DOI: 10.1093/jpids/piab107] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Metagenomic next-generation sequencing (mNGS) has the theoretical capacity to detect any microbe present in a host. mNGS also has the potential to infer a pathogen's phenotypic characteristics, including the ability to colonize humans, cause disease, and resist treatment. Concurrent host nucleic acid sequencing can assess the infected individual's physiological state, including characterization and appropriateness of the immune response. When the pathogen cannot be identified, host RNA sequencing may help infer the organism's nature. While the full promise of mNGS remains far from realization, the potential ability to identify all microbes in a complex clinical sample, assess each organism's virulence and antibiotic susceptibility traits, and simultaneously characterize the host's response to infection provide opportunities for mNGS to supplant existing technologies and become the primary method of infectious diseases diagnostics.
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Affiliation(s)
- David B Haslam
- Microbial Genomics and Metagenomics Laboratory, Cincinnati Children's Hospital, Cincinnati, Ohio, USA.,Antimicrobial Stewardship Program, Cincinnati Children's Hospital, Cincinnati, Ohio, USA.,Division of Infectious Diseases, Cincinnati Children's Hospital, Cincinnati, Ohio, USA
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24
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Aslam B, Khurshid M, Arshad MI, Muzammil S, Rasool M, Yasmeen N, Shah T, Chaudhry TH, Rasool MH, Shahid A, Xueshan X, Baloch Z. Antibiotic Resistance: One Health One World Outlook. Front Cell Infect Microbiol 2021; 11:771510. [PMID: 34900756 PMCID: PMC8656695 DOI: 10.3389/fcimb.2021.771510] [Citation(s) in RCA: 152] [Impact Index Per Article: 50.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 10/29/2021] [Indexed: 01/07/2023] Open
Abstract
Antibiotic resistance (ABR) is a growing public health concern worldwide, and it is now regarded as a critical One Health issue. One Health’s interconnected domains contribute to the emergence, evolution, and spread of antibiotic-resistant microorganisms on a local and global scale, which is a significant risk factor for global health. The persistence and spread of resistant microbial species, and the association of determinants at the human-animal-environment interface can alter microbial genomes, resulting in resistant superbugs in various niches. ABR is motivated by a well-established link between three domains: human, animal, and environmental health. As a result, addressing ABR through the One Health approach makes sense. Several countries have implemented national action plans based on the One Health approach to combat antibiotic-resistant microbes, following the Tripartite’s Commitment Food and Agriculture Organization (FAO)-World Organization for Animal Health (OIE)-World Health Organization (WHO) guidelines. The ABR has been identified as a global health concern, and efforts are being made to mitigate this global health threat. To summarize, global interdisciplinary and unified approaches based on One Health principles are required to limit the ABR dissemination cycle, raise awareness and education about antibiotic use, and promote policy, advocacy, and antimicrobial stewardship.
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Affiliation(s)
- Bilal Aslam
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Mohsin Khurshid
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | | | - Saima Muzammil
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Maria Rasool
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Nafeesa Yasmeen
- College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
| | - Taif Shah
- Faculty of Life Science and Technology, Kunming University of Life Science and Technology, Kunming, China
| | - Tamoor Hamid Chaudhry
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan.,Public Health Laboratories Division, National Institute of Health, Islamabad, Pakistan
| | | | - Aqsa Shahid
- Faculty of Rehabilitation and Allied Health Sciences, Riphah International University, Faisalabad, Pakistan
| | - Xia Xueshan
- Faculty of Life Science and Technology, Kunming University of Life Science and Technology, Kunming, China
| | - Zulqarnain Baloch
- Faculty of Life Science and Technology, Kunming University of Life Science and Technology, Kunming, China
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25
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Chaplin AV, Korzhanova M, Korostin DO. Identification of bacterial antibiotic resistance genes in next-generation sequencing data (review of literature). Klin Lab Diagn 2021; 66:684-688. [PMID: 34882354 DOI: 10.51620/0869-2084-2021-66-11-684-688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
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.
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Affiliation(s)
- A V Chaplin
- Pirogov Russian National Research Medical University
| | - M Korzhanova
- Pirogov Russian National Research Medical University
| | - D O Korostin
- Pirogov Russian National Research Medical University
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26
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VanOeffelen M, Nguyen M, Aytan-Aktug D, Brettin T, Dietrich EM, Kenyon RW, Machi D, Mao C, Olson R, Pusch GD, Shukla M, Stevens R, Vonstein V, Warren AS, Wattam AR, Yoo H, Davis JJ. A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes. Brief Bioinform 2021; 22:bbab313. [PMID: 34379107 PMCID: PMC8575023 DOI: 10.1093/bib/bbab313] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/18/2021] [Accepted: 07/20/2021] [Indexed: 11/14/2022] Open
Abstract
Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt.
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Affiliation(s)
| | - Marcus Nguyen
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - Derya Aytan-Aktug
- National Food Institute, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Thomas Brettin
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL, USA
| | - Emily M Dietrich
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL, USA
| | - Ronald W Kenyon
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Dustin Machi
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Chunhong Mao
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Robert Olson
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - Gordon D Pusch
- Fellowship for Interpretation of Genomes, Burr Ridge, IL, USA
| | - Maulik Shukla
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - Rick Stevens
- Computing Environment and Life Sciences, Argonne National Laboratory, Argonne, IL, USA
- Department of Computer Science, University of Chicago, Chicago, IL, USA
| | | | - Andrew S Warren
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Alice R Wattam
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
- Biocomplexity Institute and Initiative, University of Virginia, Virginia, USA
| | - Hyunseung Yoo
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
| | - James J Davis
- University of Chicago Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Argonne, IL, USA
- Northwestern Argonne Institute for Science and Engineering, Evanston, IL, USA
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27
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He S, Leanse LG, Feng Y. Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases. Adv Drug Deliv Rev 2021; 178:113922. [PMID: 34461198 DOI: 10.1016/j.addr.2021.113922] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 07/14/2021] [Accepted: 08/09/2021] [Indexed: 12/23/2022]
Abstract
In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.
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Affiliation(s)
- Sheng He
- Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA.
| | - Leon G Leanse
- Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Yanfang Feng
- Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA.
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28
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Assessment of Phenotype Relevant Amino Acid Residues in TEM-β-Lactamases by Mathematical Modelling and Experimental Approval. Microorganisms 2021; 9:microorganisms9081726. [PMID: 34442804 PMCID: PMC8399295 DOI: 10.3390/microorganisms9081726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 07/26/2021] [Accepted: 08/10/2021] [Indexed: 11/24/2022] Open
Abstract
Single substitutions or combinations of them alter the hydrolytic activity towards specific β-lactam-antibiotics and β-lactamase inhibitors of TEM-β-lactamases. The sequences and phenotypic classification of allelic TEM variants, as provided by the NCBI National Database of Antibiotic Resistant Organisms, does not attribute phenotypes to all variants. Some entries are doubtful as the data assessment differs strongly between the studies or no data on the methodology are provided at all. This complicates mathematical and bioinformatic predictions of phenotypes that rely on the database. The present work aimed to prove the role of specific substitutions on the resistance phenotype of TEM variants in, to our knowledge, the most extensive mutagenesis study. In parallel, the predictive power of extrapolation algorithms was assessed. Most well-known substitutions with direct impact on the phenotype could be reproduced, both mathematically and experimentally. Most discrepancies were found for supportive substitutions, where some resulted in antagonistic effects in contrast to previously described synergism. The mathematical modelling proved to predict the strongest phenotype-relevant substitutions accurately but showed difficulties in identifying less prevalent but still phenotype transforming ones. In general, mutations increasing cephalosporin resistance resulted in increased sensitivity to β-lactamase inhibitors and vice versa. Combining substitutions related to cephalosporin and β-lactamase inhibitor resistance in almost all cases increased BLI susceptibility, indicating the rarity of the combined phenotype.
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29
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Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research. J Clin Microbiol 2021; 59:e0126020. [PMID: 33536291 DOI: 10.1128/jcm.01260-20] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Antimicrobial resistance (AMR) remains one of the most challenging phenomena of modern medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the development of algorithms that learn how to accurately predict outcome variables using large sets of predictor variables that are typically not hand selected and are minimally curated. Models are parameterized using a training data set and then applied to a test data set on which predictive performance is evaluated. The application of ML algorithms to the problem of AMR has garnered increasing interest in the past 5 years due to the exponential growth of experimental and clinical data, heavy investment in computational capacity, improvements in algorithm performance, and increasing urgency for innovative approaches to reducing the burden of disease. Here, we review the current state of research at the intersection of ML and AMR with an emphasis on three domains of work. The first is the prediction of AMR using genomic data. The second is the use of ML to gain insight into the cellular functions disrupted by antibiotics, which forms the basis for understanding mechanisms of action and developing novel anti-infectives. The third focuses on the application of ML for antimicrobial stewardship using data extracted from the electronic health record. Although the use of ML for understanding, diagnosing, treating, and preventing AMR is still in its infancy, the continued growth of data and interest ensures it will become an important tool for future translational research programs.
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30
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Marinelli F, Alifano P, Landini P, Visca P. Editorial: XXXIII SIMGBM Congress 2019 - Antimicrobials and Host-Pathogen Interactions. Front Microbiol 2021; 12:672517. [PMID: 33897678 PMCID: PMC8058205 DOI: 10.3389/fmicb.2021.672517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 03/15/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Flavia Marinelli
- Dipartimento di Biotecnologie e Scienze della Vita, Università degli Studi dell'Insubria, Varese, Italy
| | - Pietro Alifano
- Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali, Università del Salento, Lecce, Italy
| | - Paolo Landini
- Dipartimento di Bioscienze, Università degli Studi di Milano Statale, Milano, Italy
| | - Paolo Visca
- Dipartimento di Scienze, Università degli Studi Roma Tre, Roma, Italy
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31
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Overview of bioinformatic methods for analysis of antibiotic resistome from genome and metagenome data. J Microbiol 2021; 59:270-280. [DOI: 10.1007/s12275-021-0652-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 12/13/2022]
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