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Do VH, Nguyen VS, Nguyen SH, Le DQ, Nguyen TT, Nguyen CH, Ho TH, Vo NS, Nguyen T, Nguyen HA, Cao MD. PanKA: Leveraging population pangenome to predict antibiotic resistance. iScience 2024; 27:110623. [PMID: 39228791 PMCID: PMC11369404 DOI: 10.1016/j.isci.2024.110623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 04/14/2024] [Accepted: 07/29/2024] [Indexed: 09/05/2024] Open
Abstract
Machine learning has the potential to be a powerful tool in the fight against antimicrobial resistance (AMR), a critical global health issue. Machine learning can identify resistance mechanisms from DNA sequence data without prior knowledge. The first step in building a machine learning model is a feature extraction from sequencing data. Traditional methods like single nucleotide polymorphism (SNP) calling and k-mer counting yield numerous, often redundant features, complicating prediction and analysis. In this paper, we propose PanKA, a method using the pangenome to extract a concise set of relevant features for predicting AMR. PanKA not only enables fast model training and prediction but also improves accuracy. Applied to the Escherichia coli and Klebsiella pneumoniae bacterial species, our model is more accurate than conventional and state-of-the-art methods in predicting AMR.
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Affiliation(s)
- Van Hoan Do
- Center for Applied Mathematics and Informatics, Le Quy Don Technical University, Hanoi, Vietnam
| | - Van Sang Nguyen
- Center for Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
| | | | - Duc Quang Le
- Faculty of IT, Hanoi University of Civil Engineering, Hanoi, Vietnam
| | - Tam Thi Nguyen
- Oxford University Clinical Research Unit, Hanoi, Vietnam
| | - Canh Hao Nguyen
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | - Tho Huu Ho
- Department of Medical Microbiology, The 103 Military Hospital, Vietnam Military Medical University, Hanoi, Vietnam
- Department of Genomics & Cytogenetics, Institute of Biomedicine & Pharmacy, Vietnam Military Medical University, Hanoi, Vietnam
| | - Nam S. Vo
- Center for Biomedical Informatics, Vingroup Big Data Institute, Hanoi, Vietnam
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Ceres K, Zehr JD, Murrell C, Millet JK, Sun Q, McQueary HC, Horton A, Cazer C, Sams K, Reboul G, Andreopoulos WB, Mitchell PK, Anderson R, Franklin-Guild R, Cronk BD, Stanhope BJ, Burbick CR, Wolking R, Peak L, Zhang Y, McDowall R, Krishnamurthy A, Slavic D, Sekhon PK, Tyson GH, Ceric O, Stanhope MJ, Goodman LB. Evolutionary genomic analyses of canine E. coli infections identify a relic capsular locus associated with resistance to multiple classes of antimicrobials. Appl Environ Microbiol 2024; 90:e0035424. [PMID: 39012166 PMCID: PMC11337803 DOI: 10.1128/aem.00354-24] [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: 02/28/2024] [Accepted: 06/08/2024] [Indexed: 07/17/2024] Open
Abstract
Infections caused by antimicrobial-resistant Escherichia coli are the leading cause of death attributed to antimicrobial resistance (AMR) worldwide, and the known AMR mechanisms involve a range of functional proteins. Here, we employed a pan-genome wide association study (GWAS) approach on over 1,000 E. coli isolates from sick dogs collected across the US and Canada and identified a strong statistical association (empirical P < 0.01) of AMR, involving a range of antibiotics to a group 1 capsular (CPS) gene cluster. This cluster included genes under relaxed selection pressure, had several loci missing, and had pseudogenes for other key loci. Furthermore, this cluster is widespread in E. coli and Klebsiella clinical isolates across multiple host species. Earlier studies demonstrated that the octameric CPS polysaccharide export protein Wza can transmit macrolide antibiotics into the E. coli periplasm. We suggest that the CPS in question, and its highly divergent Wza, functions as an antibiotic trap, preventing antimicrobial penetration. We also highlight the high diversity of lineages circulating in dogs across all regions studied, the overlap with human lineages, and regional prevalence of resistance to multiple antimicrobial classes. IMPORTANCE Much of the human genomic epidemiology data available for E. coli mechanism discovery studies has been heavily biased toward shiga-toxin producing strains from humans and livestock. E. coli occupies many niches and produces a wide variety of other significant pathotypes, including some implicated in chronic disease. We hypothesized that since dogs tend to share similar strains with their owners and are treated with similar antibiotics, their pathogenic isolates will harbor unexplored AMR mechanisms of importance to humans as well as animals. By comparing over 1,000 genomes with in vitro antimicrobial susceptibility data from sick dogs across the US and Canada, we identified a strong multidrug resistance association with an operon that appears to have once conferred a type 1 capsule production system.
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Affiliation(s)
| | | | | | - Jean K. Millet
- Université Paris-Saclay, INRAE, UVSQ, Virologie et Immunologie Moléculaires, Jouy-en-Josas, Paris, France
| | - Qi Sun
- Cornell University, Ithaca, New York, USA
| | | | | | | | - Kelly Sams
- Cornell University, Ithaca, New York, USA
| | | | | | | | | | | | | | | | - Claire R. Burbick
- Washington Animal Disease Diagnostic Laboratory, Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, Washington, USA
| | - Rebecca Wolking
- Washington Animal Disease Diagnostic Laboratory, Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, Washington, USA
| | - Laura Peak
- Louisiana Animal Disease Diagnostic Laboratory, School of Veterinary Medicine, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Yan Zhang
- Ohio Department of Agriculture Animal Disease Diagnostic Laboratory, Reynoldsburg, Ohio, USA
| | - Rebeccah McDowall
- University of Guelph, Animal Health Laboratory, Guelph, Ontario, Canada
| | | | - Durda Slavic
- University of Guelph, Animal Health Laboratory, Guelph, Ontario, Canada
| | | | - Gregory H. Tyson
- US Food and Drug Administration, Veterinary Laboratory Investigation and Response Network, Laurel, Maryland, USA
| | - Olgica Ceric
- US Food and Drug Administration, Veterinary Laboratory Investigation and Response Network, Laurel, Maryland, USA
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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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Allan-Blitz LT, Klausner JD. Letter to the Editor: Differing Criteria for Phenotypic Resistance to Antimicrobials Further Complicates Identification of Molecular Determinants. Sex Transm Dis 2024; 51:84. [PMID: 37921856 DOI: 10.1097/olq.0000000000001890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
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Peters RPH, Jung H, Mitchev N, Mdingi MM, Gigi R, Shroufi A, Martinez FP, Bamford C. Antimicrobial Resistance and Molecular Typing of Neisseria gonorrhoeae Isolates From the Eastern Cape Province in South Africa. Sex Transm Dis 2023; 50:821-826. [PMID: 37820114 DOI: 10.1097/olq.0000000000001877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
BACKGROUND There is a paucity of Neisseria gonorrhoeae antimicrobial resistance data from resource-constrained settings because of the lack of diagnostic testing and limited scale of surveillance programs. This study aimed to determine the antimicrobial resistance profile of N. gonorrhoeae in the rural Eastern Cape province of South Africa. METHODS Specimens for N. gonorrhoeae culture were obtained from men with urethral discharge and women with vaginal discharge attending primary health care facilities. Direct inoculation of the agar plates was performed followed by culture and drug susceptibility testing using the Etest at the laboratory. Whole-genome sequencing of the isolates was performed to identify resistance-determining variants. RESULTS One hundred N. gonorrhoeae isolates were obtained. Most strains were nonsusceptible to ciprofloxacin (76%), tetracycline (75%), and penicillin G (72%). The gyrA S91F mutation was present in 68 of 72 ciprofloxacin-resistant isolates (94%), with concurrent parC mutations in 47 of 68 (69%); gyrA I250M was the only mutation in 4 other resistant strains. One azithromycin-resistant isolate was identified with a minimal inhibitory concentration (MIC) of 8.0 mg/L and the 23S rDNA gene mutation C2597T. The median MIC of cefixime was 0.016 mg/L (range, 0.016-0.064 mg/L), and that of ceftriaxone was 0.016 mg/L (range, 0.016 mg/L). Whole-genome sequencing showed 58 sequence types as revealed in N. gonorrhoeae sequence typing for antimicrobial resistance and 70 sequence types in N. gonorrhoeae multiantigen sequence typing. CONCLUSIONS This study confirmed high rates of N. gonorrhoeae antimicrobial resistance to ciprofloxacin, penicillin G, and tetracycline in our setting. The MICs of cephalosporins are reassuring for ceftriaxone use in syndromic treatment regimens, but the identification of azithromycin resistance warrants further attention.
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Affiliation(s)
| | - Hyunsul Jung
- Department of Medical Microbiology, University of Pretoria, Pretoria
| | - Nireshni Mitchev
- School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Mandisa M Mdingi
- From the Research Unit, Foundation for Professional Development, East London
| | | | - Amir Shroufi
- Drugs for Neglected Diseases Initiative (DNDi) & Global Antibiotic Research & Development Partnership (GARDP) Southern Africa, Cape Town, South Africa
| | - Fernando P Martinez
- Global Antibiotic Research & Development Partnership (GARDP), Geneva, Switzerland
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Perea-Jacobo R, Paredes-Gutiérrez GR, Guerrero-Chevannier MÁ, Flores DL, Muñiz-Salazar R. Machine Learning of the Whole Genome Sequence of Mycobacterium tuberculosis: A Scoping PRISMA-Based Review. Microorganisms 2023; 11:1872. [PMID: 37630431 PMCID: PMC10456961 DOI: 10.3390/microorganisms11081872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 08/27/2023] Open
Abstract
Tuberculosis (TB) remains one of the most significant global health problems, posing a significant challenge to public health systems worldwide. However, diagnosing drug-resistant tuberculosis (DR-TB) has become increasingly challenging due to the rising number of multidrug-resistant (MDR-TB) cases, despite the development of new TB diagnostic tools. Even the World Health Organization-recommended methods such as Xpert MTB/XDR or Truenat are unable to detect all the Mycobacterium tuberculosis genome mutations associated with drug resistance. While Whole Genome Sequencing offers a more precise DR profile, the lack of user-friendly bioinformatics analysis applications hinders its widespread use. This review focuses on exploring various artificial intelligence models for predicting DR-TB profiles, analyzing relevant English-language articles using the PRISMA methodology through the Covidence platform. Our findings indicate that an Artificial Neural Network is the most commonly employed method, with non-statistical dimensionality reduction techniques preferred over traditional statistical approaches such as Principal Component Analysis or t-distributed Stochastic Neighbor Embedding.
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Affiliation(s)
- Ricardo Perea-Jacobo
- Facultad de Ingeniería Arquitectura y Diseño, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22860, Mexico; (R.P.-J.); (G.R.P.-G.); (M.Á.G.-C.)
- Escuela de Ciencias de la Salud, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22890, Mexico
| | - Guillermo René Paredes-Gutiérrez
- Facultad de Ingeniería Arquitectura y Diseño, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22860, Mexico; (R.P.-J.); (G.R.P.-G.); (M.Á.G.-C.)
| | - Miguel Ángel Guerrero-Chevannier
- Facultad de Ingeniería Arquitectura y Diseño, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22860, Mexico; (R.P.-J.); (G.R.P.-G.); (M.Á.G.-C.)
| | - Dora-Luz Flores
- Facultad de Ingeniería Arquitectura y Diseño, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22860, Mexico; (R.P.-J.); (G.R.P.-G.); (M.Á.G.-C.)
| | - Raquel Muñiz-Salazar
- Escuela de Ciencias de la Salud, Universidad Autónoma de Baja California, Campus Ensenada, Ensenada 22890, Mexico
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7
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Yang MR, Su SF, Wu YW. Using bacterial pan-genome-based feature selection approach to improve the prediction of minimum inhibitory concentration (MIC). Front Genet 2023; 14:1054032. [PMID: 37323667 PMCID: PMC10267731 DOI: 10.3389/fgene.2023.1054032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 05/16/2023] [Indexed: 06/17/2023] Open
Abstract
Background: Predicting the resistance profiles of antimicrobial resistance (AMR) pathogens is becoming more and more important in treating infectious diseases. Various attempts have been made to build machine learning models to classify resistant or susceptible pathogens based on either known antimicrobial resistance genes or the entire gene set. However, the phenotypic annotations are translated from minimum inhibitory concentration (MIC), which is the lowest concentration of antibiotic drugs in inhibiting certain pathogenic strains. Since the MIC breakpoints that classify a strain to be resistant or susceptible to specific antibiotic drug may be revised by governing institutes, we refrained from translating these MIC values into the categories "susceptible" or "resistant" but instead attempted to predict the MIC values using machine learning approaches. Results: By applying a machine learning feature selection approach on a Salmonella enterica pan-genome, in which the protein sequences were clustered to identify highly similar gene families, we showed that the selected features (genes) performed better than known AMR genes, and that models built on the selected genes achieved very accurate MIC prediction. Functional analysis revealed that about half of the selected genes were annotated as hypothetical proteins (i.e., with unknown functional roles), and that only a small portion of known AMR genes were among the selected genes, indicating that applying feature selection on the entire gene set has the potential of uncovering novel genes that may be associated with and may contribute to pathogenic antimicrobial resistances. Conclusion: The application of the pan-genome-based machine learning approach was indeed capable of predicting MIC values with very high accuracy. The feature selection process may also identify novel AMR genes for inferring bacterial antimicrobial resistance phenotypes.
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Affiliation(s)
- Ming-Ren Yang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Shun-Feng Su
- Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Yu-Wei Wu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei, Taiwan
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Wang H, Jia C, Li H, Yin R, Chen J, Li Y, Yue M. Paving the way for precise diagnostics of antimicrobial resistant bacteria. Front Mol Biosci 2022; 9:976705. [PMID: 36032670 PMCID: PMC9413203 DOI: 10.3389/fmolb.2022.976705] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 07/19/2022] [Indexed: 12/26/2022] Open
Abstract
The antimicrobial resistance (AMR) crisis from bacterial pathogens is frequently emerging and rapidly disseminated during the sustained antimicrobial exposure in human-dominated communities, posing a compelling threat as one of the biggest challenges in humans. The frequent incidences of some common but untreatable infections unfold the public health catastrophe that antimicrobial-resistant pathogens have outpaced the available countermeasures, now explicitly amplified during the COVID-19 pandemic. Nowadays, biotechnology and machine learning advancements help create more fundamental knowledge of distinct spatiotemporal dynamics in AMR bacterial adaptation and evolutionary processes. Integrated with reliable diagnostic tools and powerful analytic approaches, a collaborative and systematic surveillance platform with high accuracy and predictability should be established and implemented, which is not just for an effective controlling strategy on AMR but also for protecting the longevity of valuable antimicrobials currently and in the future.
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Affiliation(s)
- Hao Wang
- Institute of Preventive Veterinary Sciences & Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou, China
| | - Chenhao Jia
- Institute of Preventive Veterinary Sciences & Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou, China
- Hainan Institute of Zhejiang University, Sanya, China
| | - Hongzhao Li
- Institute of Preventive Veterinary Sciences & Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou, China
- Hainan Institute of Zhejiang University, Sanya, China
| | - Rui Yin
- Institute of Preventive Veterinary Sciences & Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou, China
| | - Jiang Chen
- Department of Microbiology, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
- *Correspondence: Jiang Chen, ; Yan Li, ; Min Yue,
| | - Yan Li
- Institute of Preventive Veterinary Sciences & Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou, China
- Hainan Institute of Zhejiang University, Sanya, China
- Zhejiang Provincial Key Laboratory of Preventive Veterinary Medicine, Hangzhou, China
- *Correspondence: Jiang Chen, ; Yan Li, ; Min Yue,
| | - Min Yue
- Institute of Preventive Veterinary Sciences & Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou, China
- Hainan Institute of Zhejiang University, Sanya, China
- Zhejiang Provincial Key Laboratory of Preventive Veterinary Medicine, Hangzhou, China
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
- *Correspondence: Jiang Chen, ; Yan Li, ; Min Yue,
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Marini S, Mora RA, Boucher C, Robertson Noyes N, Prosperi M. Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing. Brief Bioinform 2022; 23:bbac020. [PMID: 35212354 PMCID: PMC8921637 DOI: 10.1093/bib/bbac020] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 01/13/2023] Open
Abstract
Antimicrobial resistance (AMR) is a growing threat to public health and farming at large. In clinical and veterinary practice, timely characterization of the antibiotic susceptibility profile of bacterial infections is a crucial step in optimizing treatment. High-throughput sequencing is a promising option for clinical point-of-care and ecological surveillance, opening the opportunity to develop genotyping-based AMR determination as a possibly faster alternative to phenotypic testing. In the present work, we compare the performance of state-of-the-art methods for detection of AMR using high-throughput sequencing data from clinical settings. We consider five computational approaches based on alignment (AMRPlusPlus), deep learning (DeepARG), k-mer genomic signatures (KARGA, ResFinder) or hidden Markov models (Meta-MARC). We use an extensive collection of 585 isolates with available AMR resistance profiles determined by phenotypic tests across nine antibiotic classes. We show how the prediction landscape of AMR classifiers is highly heterogeneous, with balanced accuracy varying from 0.40 to 0.92. Although some algorithms-ResFinder, KARGA and AMRPlusPlus-exhibit overall better balanced accuracy than others, the high per-AMR-class variance and related findings suggest that: (1) all algorithms might be subject to sampling bias both in data repositories used for training and experimental/clinical settings; and (2) a portion of clinical samples might contain uncharacterized AMR genes that the algorithms-mostly trained on known AMR genes-fail to generalize upon. These results lead us to formulate practical advice for software configuration and application, and give suggestions for future study designs to further develop AMR prediction tools from proof-of-concept to bedside.
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Affiliation(s)
- Simone Marini
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Rodrigo A Mora
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
| | - Christina Boucher
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA
| | - Noelle Robertson Noyes
- Department of Veterinary Population Medicine, University of Minnesota, Gainesville, FL, USA
| | - Mattia Prosperi
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
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Yasir M, Mustafa Karim A, Kausar Malik S, Bajaffer AA, Azhar EI. Prediction of Antimicrobial Minimal Inhibitory Concentrations for Neisseria gonorrhoeae using Machine Learning Models. Saudi J Biol Sci 2022; 29:3687-3693. [PMID: 35844400 PMCID: PMC9280306 DOI: 10.1016/j.sjbs.2022.02.047] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/12/2022] [Accepted: 02/27/2022] [Indexed: 11/26/2022] Open
Abstract
The lowest concentration of an antimicrobial agent that can inhibit the visible growth of a microorganism after overnight incubation is called as minimum inhibitory concentration (MIC) and the drug prescriptions are made on the basis of MIC data to ensure successful treatment outcomes. Therefore, reliable antimicrobial susceptibility data is crucial, and it will help clinicians about which drug to prescribe. Although few prediction studies based on strategies have been conducted, however, no single machine learning (ML) modelling has been carried out to predict MICs in N. gonorrhoeae. In this study, we propose a ML based approach that can predict MICs of a specific antibiotic using unitigs sequences data. We retrieved N. gonorrhoeae genomes from European Nucleotide Archive and NCBI and analysed them combined with their respective MIC data for cefixime, ciprofloxacin, and azithromycin and then we constructed unitigs by using de Brujin graphs. We built and compared 35 different ML regression models to predict MICs. Our results demonstrate that RandomForest and CATBoost models showed best performance in predicting MICs of the three antibiotics. The coefficient of determination, R2, (a statistical measure of how well the regression predictions approximate the real data points) for cefixime, ciprofloxacin, and azithromycin was 0.75787, 0.77241, and 0.79009 respectively using RandomForest. For CATBoost model, the R2 value was 0.74570, 0.77393, and 0.79317 for cefixime, ciprofloxacin, and azithromycin respectively. Lastly, using feature importance, we explore the important genomic regions identified by the models for predicting MICs. The major mutations which are responsible for resistance against these three antibiotics were chosen by ML models as a top feature in case of each antibiotics. CATBoost, DecisionTree, GradientBoosting, and RandomForest regression models chose the same unitigs which are responsible for resistance. This unitigs-based strategy for developing models for MIC prediction, clinical diagnostics, and surveillance can be applicable for other critical bacterial pathogens.
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11
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Nanayakkara AK, Boucher HW, Fowler VG, Jezek A, Outterson K, Greenberg DE. Antibiotic resistance in the patient with cancer: Escalating challenges and paths forward. CA Cancer J Clin 2021; 71:488-504. [PMID: 34546590 DOI: 10.3322/caac.21697] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/23/2021] [Accepted: 08/12/2021] [Indexed: 12/13/2022] Open
Abstract
Infection is the second leading cause of death in patients with cancer. Loss of efficacy in antibiotics due to antibiotic resistance in bacteria is an urgent threat against the continuing success of cancer therapy. In this review, the authors focus on recent updates on the impact of antibiotic resistance in the cancer setting, particularly on the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.). This review highlights the health and financial impact of antibiotic resistance in patients with cancer. Furthermore, the authors recommend measures to control the emergence of antibiotic resistance, highlighting the risk factors associated with cancer care. A lack of data in the etiology of infections, specifically in oncology patients in United States, is identified as a concern, and the authors advocate for a centralized and specialized surveillance system for patients with cancer to predict and prevent the emergence of antibiotic resistance. Finding better ways to predict, prevent, and treat antibiotic-resistant infections will have a major positive impact on the care of those with cancer.
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Affiliation(s)
- Amila K Nanayakkara
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, University of Texas Southwestern, Dallas, Texas
| | - Helen W Boucher
- Division of Geographic Medicine and Infectious Diseases, Tufts Medical Center, Boston, Massachusetts
| | - Vance G Fowler
- Division of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, North Carolina
| | - Amanda Jezek
- Infectious Diseases Society of America, Arlington, Virginia
| | - Kevin Outterson
- CARB-X, Boston, Massachusetts
- Boston University School of Law, Boston, Massachusetts
| | - David E Greenberg
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, University of Texas Southwestern, Dallas, Texas
- Department of Microbiology, University of Texas Southwestern, Dallas, Texas
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