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Aggarwal R, Mahajan P, Pandiya S, Bajaj A, Verma SK, Yadav P, Kharat AS, Khan AU, Dua M, Johri AK. Antibiotic resistance: a global crisis, problems and solutions. Crit Rev Microbiol 2024:1-26. [PMID: 38381581 DOI: 10.1080/1040841x.2024.2313024] [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: 10/11/2023] [Accepted: 01/28/2024] [Indexed: 02/23/2024]
Abstract
Healthy state is priority in today's world which can be achieved using effective medicines. But due to overuse and misuse of antibiotics, a menace of resistance has increased in pathogenic microbes. World Health Organization (WHO) has announced ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) as the top priority pathogens as these have developed resistance against certain antibiotics. To combat such a global issue, it is utmost important to identify novel therapeutic strategies/agents as an alternate to such antibiotics. To name certain antibiotic adjuvants including: inhibitors of beta-lactamase, efflux pumps and permeabilizers for outer membrane can potentially solve the antibiotic resistance problems. In this regard, inhibitors of lytic domain of lytic transglycosylases provide a novel way to not only act as an alternate to antibiotics but also capable of restoring the efficiency of previously resistant antibiotics. Further, use of bacteriophages is another promising strategy to deal with antibiotic resistant pathogens. Taking in consideration the alternatives of antibiotics, a green synthesis nanoparticle-based therapy exemplifies a good option to combat microbial resistance. As horizontal gene transfer (HGT) in bacteria facilitates the evolution of new resistance strains, therefore identifying the mechanism of resistance and development of inhibitors against it can be a novel approach to combat such problems. In our perspective, host-directed therapy (HDT) represents another promising strategy in combating antimicrobial resistance (AMR). This approach involves targeting specific factors within host cells that pathogens rely on for their survival, either through replication or persistence. As many new drugs are under clinical trials it is advisable that more clinical data and antimicrobial stewardship programs should be conducted to fully assess the clinical efficacy and safety of new therapeutic agents.
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Affiliation(s)
- Rupesh Aggarwal
- School of Life Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Pooja Mahajan
- School of Life Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Sameeksha Pandiya
- School of Life Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Aayushi Bajaj
- School of Life Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Shailendra Kumar Verma
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Puja Yadav
- Department of Microbiology, Central University of Haryana, Mahendergarh, India
| | - Arun S Kharat
- School of Life Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Asad Ullah Khan
- Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Meenakshi Dua
- School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Atul Kumar Johri
- School of Life Sciences, Jawaharlal Nehru University, New Delhi, India
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Lund D, Coertze RD, Parras-Moltó M, Berglund F, Flach CF, Johnning A, Larsson DGJ, Kristiansson E. Extensive screening reveals previously undiscovered aminoglycoside resistance genes in human pathogens. Commun Biol 2023; 6:812. [PMID: 37537271 PMCID: PMC10400643 DOI: 10.1038/s42003-023-05174-6] [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: 01/20/2023] [Accepted: 07/24/2023] [Indexed: 08/05/2023] Open
Abstract
Antibiotic resistance is a growing threat to human health, caused in part by pathogens accumulating antibiotic resistance genes (ARGs) through horizontal gene transfer. New ARGs are typically not recognized until they have become widely disseminated, which limits our ability to reduce their spread. In this study, we use large-scale computational screening of bacterial genomes to identify previously undiscovered mobile ARGs in pathogens. From ~1 million genomes, we predict 1,071,815 genes encoding 34,053 unique aminoglycoside-modifying enzymes (AMEs). These cluster into 7,612 families (<70% amino acid identity) of which 88 are previously described. Fifty new AME families are associated with mobile genetic elements and pathogenic hosts. From these, 24 of 28 experimentally tested AMEs confer resistance to aminoglycoside(s) in Escherichia coli, with 17 providing resistance above clinical breakpoints. This study greatly expands the range of clinically relevant aminoglycoside resistance determinants and demonstrates that computational methods enable early discovery of potentially emerging ARGs.
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Affiliation(s)
- David Lund
- Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
- Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, Sweden
| | - Roelof Dirk Coertze
- Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, Sweden
- Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Marcos Parras-Moltó
- Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
- Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, Sweden
| | - Fanny Berglund
- Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, Sweden
- Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Carl-Fredrik Flach
- Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, Sweden
- Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anna Johnning
- Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
- Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, Sweden
- Department of Systems and Data Analysis, Fraunhofer-Chalmers Centre, Gothenburg, Sweden
| | - D G Joakim Larsson
- Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, Sweden
- Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Erik Kristiansson
- Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
- Centre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, Sweden.
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Kong W, Huang W, Peng C, Zhang B, Duan G, Ma W, Huang Z. Multiple machine learning methods aided virtual screening of Na V 1.5 inhibitors. J Cell Mol Med 2022; 27:266-276. [PMID: 36573431 PMCID: PMC9843531 DOI: 10.1111/jcmm.17652] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/30/2022] [Accepted: 12/06/2022] [Indexed: 12/28/2022] Open
Abstract
Nav 1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Nav 1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF-Graph model performed best. Similarly, RF-Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Nav 1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Nav 1.5 ion channel and key privileged substructures with high affinity were also extracted.
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Affiliation(s)
- Weikaixin Kong
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina,Institute for Molecular Medicine Finland (FIMM)HiLIFE, University of HelsinkiHelsinkiFinland,Institute Sanqu Technology (Hangzhou) Co., Ltd.HangzhouChina
| | - Weiran Huang
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina
| | - Chao Peng
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina
| | - Bowen Zhang
- ComMedX (Computational Medicine Beijing Co., Ltd.)BeijingChina
| | - Guifang Duan
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina
| | - Weining Ma
- Department of NeurologyShengjing Hospital affiliated to China Medical UniversityShenyangChina
| | - Zhuo Huang
- Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina,State Key Laboratory of Natural and Biomimetic Drugs, Department of Molecular and Cellular Pharmacology, School of Pharmaceutical SciencesPeking University Health Science CenterBeijingChina
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Cui G, Liu Z, Xu W, Gao Y, Yang S, Grossart HP, Li M, Luo Z. Metagenomic exploration of antibiotic resistance genes and their hosts in aquaculture waters of the semi-closed Dongshan Bay (China). THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:155784. [PMID: 35537512 DOI: 10.1016/j.scitotenv.2022.155784] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/30/2022] [Accepted: 05/04/2022] [Indexed: 06/14/2023]
Abstract
In marine environments, increasing occurrence and numbers of microbial Antibiotic Resistance Gene (ARG) subtypes, especially of new beta-lactamases, have received lots of attention in recent years. Updated databases with novel developed tools provide new opportunities to obtain more comprehensive ARG profiles as well as ARG-carrying hosts. Yet, ARGs in human-associated marine aquaculture environments, e.g. in China, remains largely unknown. Using metagenomic data, we revealed high numbers of Multi-drug Resistance, beta-lactamase and aminoglycoside genes throughout the year. Thereby, Alpha- and Gamma-proteobacteria were assigned to the majority of beta-lactamase-carrying hosts. From Metagenome-assembled genomes, three blaF-like beta-lactamases (91.7-94.7% identity with beta-lactamase from Mycobacterium fortuitum (blaF)) were exclusively observed in an unclassified Mycobacterium genus. Notably, other new beta-lactamases, VMB-1-like (n = 3) (58.5-67.4% identity to Vibrio metallo-beta-lactamase 1 (VMB-1)), were found in Gammaproteobacteria. Additionally, 175 Multi-drug Resistant Organisms possessed at least 3 ARG subtypes, and seven of the potentially pathogenic genera (n = 17) were assigned to Gammaproteobacteria. These results, together with high-risk ARGs (e.g. tetM, dfrA14 and dfrA17), provide hosts and new beta-lactamases of ARGs in Chinese coastal aquaculture.
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Affiliation(s)
- Guojie Cui
- College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China; Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China; Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China
| | - Zongbao Liu
- Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China; Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China
| | - Wei Xu
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Yuanhao Gao
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Shuai Yang
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Hans-Peter Grossart
- Department of Plankton and Microbial Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Stechlin 16775, Germany; Institute of Biochemistry and Biology, Postdam University, Potsdam 14469, Germany
| | - Meng Li
- Archaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China; Shenzhen Key Laboratory of Marine Microbiome Engineering, Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China.
| | - Zhuhua Luo
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China; School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China; Co-Innovation Center of Jiangsu Marine Bio-industry Technology, Jiangsu Ocean University, Lianyungang 222005, China.
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Wang L, Tay ACY, Li J, Zhao Q. Editorial: Computational Predictions, Dynamic Tracking, and Evolutionary Analysis of Antibiotic Resistance Through the Mining of Microbial Genomes and Metagenomic Data. Front Microbiol 2022; 13:880967. [PMID: 35444627 PMCID: PMC9014298 DOI: 10.3389/fmicb.2022.880967] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 03/17/2022] [Indexed: 12/18/2022] Open
Affiliation(s)
- Liang Wang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China.,Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, China.,Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Alfred Chin Yen Tay
- The Marshall Centre for Infectious Diseases, Research and Training, University of Western Australia, Perth, WA, Australia
| | - Jian Li
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China
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Forde BM, De Oliveira DMP, Falconer C, Graves B, Harris PNA. Strengths and caveats of identifying resistance genes from whole genome sequencing data. Expert Rev Anti Infect Ther 2021; 20:533-547. [PMID: 34852720 DOI: 10.1080/14787210.2022.2013806] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Antimicrobial resistance (AMR) continues to present major challenges to modern healthcare. Recent advances in whole-genome sequencing (WGS) have made the rapid molecular characterization of AMR a realistic possibility for diagnostic laboratories; yet major barriers to clinical implementation exist. AREAS COVERED We describe and compare short- and long-read sequencing platforms, typical components of bioinformatics pipelines, tools for AMR gene detection and the relative merits of read- or assembly-based approaches. The challenges of characterizing mobile genetic elements from genomic data are outlined, as well as the complexities inherent to the prediction of phenotypic resistance from WGS. Practical obstacles to implementation in diagnostic laboratories, the critical role of quality control and external quality assurance, as well as standardized reporting standards are also discussed. Future directions, such as the application of machine-learning and artificial intelligence algorithms, linked to clinically meaningful outcomes, may offer a new paradigm for the clinical application of AMR prediction. EXPERT OPINION AMR prediction from WGS data presents an exciting opportunity to advance our capacity to comprehensively characterize infectious pathogens in a rapid manner, ultimately aiming to improve patient outcomes. Collaborative efforts between clinicians, scientists, regulatory bodies and healthcare administrators will be critical to achieve the full promise of this approach.
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Affiliation(s)
- Brian M Forde
- University of Queensland, Faculty of Medicine, Uq Centre for Clinical Research, Royal Brisbane and Woman's Hospital, Herston, Australia
| | - David M P De Oliveira
- University of Queensland, Faculty of Science, School of Chemistry and Molecular Biosciences, St Lucia, Australia
| | - Caitlin Falconer
- University of Queensland, Faculty of Medicine, Uq Centre for Clinical Research, Royal Brisbane and Woman's Hospital, Herston, Australia
| | - Bianca Graves
- Herston Infectious Disease Institute, Royal Brisbane & Women's Hospital, Herston, Australia
| | - Patrick N A Harris
- University of Queensland, Faculty of Medicine, Uq Centre for Clinical Research, Royal Brisbane and Woman's Hospital, Herston, Australia.,Herston Infectious Disease Institute, Royal Brisbane & Women's Hospital, Herston, Australia.,Central Microbiology, Pathology Queensland, Royal Brisbane & Women's Hospital, Herston, Australia
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Kaur D, Patiyal S, Arora C, Singh R, Lodhi G, Raghava GPS. In-Silico Tool for Predicting, Scanning, and Designing Defensins. Front Immunol 2021; 12:780610. [PMID: 34880873 PMCID: PMC8645896 DOI: 10.3389/fimmu.2021.780610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 10/28/2021] [Indexed: 12/12/2022] Open
Abstract
Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service "DefPred" to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.
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Affiliation(s)
- Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Chakit Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Ritesh Singh
- Department of Computer Science, Indraprastha Institute of Information Technology, New Delhi, India
| | - Gaurav Lodhi
- Department of Computer Science, Indraprastha Institute of Information Technology, New Delhi, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
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