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Eshun J, Lamar NC, Aksoy SG, Akers S, Garcia B, Cunningham H, Chin G, Bilbrey JA. Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2024:ozae068. [PMID: 39083424 DOI: 10.1093/mam/ozae068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 05/06/2024] [Accepted: 07/04/2024] [Indexed: 08/02/2024]
Abstract
Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. As a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.
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Affiliation(s)
- Jasmine Eshun
- National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA
| | - Natalie C Lamar
- National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA
| | - Sinan G Aksoy
- National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA
| | - Sarah Akers
- National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA
| | - Benjamin Garcia
- National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA
| | - Heather Cunningham
- National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA
| | - George Chin
- National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA
| | - Jenna A Bilbrey
- National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA
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2
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Liu Y, Yu Y, Wu B, Qian J, Mu H, Gu L, Zhou R, Zhang H, Wu H, Bu Y. A comprehensive prediction system for silkworm acute toxicity assessment of environmental and in-silico pesticides. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 282:116759. [PMID: 39029220 DOI: 10.1016/j.ecoenv.2024.116759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/03/2024] [Accepted: 07/16/2024] [Indexed: 07/21/2024]
Abstract
The excessive application and loss of pesticides poses a great risk to the ecosystem, and the environmental safety assessment of pesticides is time-consuming and expensive using traditional animal toxicity tests. In this work, a pesticide acute toxicity dataset was created for silkworm integrating extensive experiments and various common pesticide formulations considering the sensitivity of silkworm to adverse environment, its economic value in China, and a gap in machine learning (ML) research on the toxicity prediction of this species, which addressed the previous limitation of only being able to predict toxicity classification without specific toxicity values. A new comprehensive voting model (CVR) was developed based on ML, combined with three regression algorithms, namely, Bayesian Ridge (BR), K Neighbors Regressor (KNN), Random Forest Regressor (RF) to accurately calculate lethal concentration 50 % (LC50). Three conformal models were successfully constructed, marking the first combination of conformal models with confidence intervals to predict silkworm toxicity. Further, the mechanism by analyzing structural alerts was summarized, and identified 25 warning structures, 24 positive compounds and 14 negative compounds. Importantly, a novel comprehensive prediction system was constructed that can provide LC50 and confidence intervals, structural alerts analysis, lipid-water partition coefficient (LogP) and similarity analysis, which can comprehensively evaluate the ecological toxicity risk of substances to make up for the incomplete toxicity data of new pesticides. The validity and generalization of the CVR model were verified by an external validation set. In addition, five new, low-toxic and green pesticide alternatives were designed through 50,000 cycles. Moreover, our software and ST Profiler can provide low-cost information access to accelerate environmental risk assessment, which can predict not only a single chemical, but also batches of chemicals, simply by inputting the SMILES / CAS / (Chinese / English) name of chemicals.
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Affiliation(s)
- Yutong Liu
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China; Department of Chemistry, College of Sciences, Nanjing Agricultural University, Nanjing 210095, PR China
| | - Yue Yu
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China
| | - Bing Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Jieshu Qian
- School of Environmental Engineering, Wuxi University, Jiangsu 214105, PR China
| | - Hongxin Mu
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China
| | - Luyao Gu
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China
| | - Rong Zhou
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China
| | - Houhu Zhang
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China
| | - Hua Wu
- Department of Chemistry, College of Sciences, Nanjing Agricultural University, Nanjing 210095, PR China.
| | - Yuanqing Bu
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, PR China.
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3
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Liu M, Yang J, He Y, Cao F, Li W, Han W. VmmScore: An umami peptide prediction and receptor matching program based on a deep learning approach. Comput Biol Med 2024; 179:108814. [PMID: 38944902 DOI: 10.1016/j.compbiomed.2024.108814] [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: 03/05/2024] [Revised: 05/17/2024] [Accepted: 06/24/2024] [Indexed: 07/02/2024]
Abstract
Peptides, with recognized physiological and medical implications, such as the ability to lower blood pressure and lipid levels, are central to our research on umami taste perception. This study introduces a computational strategy to tackle the challenge of identifying optimal umami receptors for these peptides. Our VmmScore algorithm includes two integral components: Mlp4Umami, a predictive module that evaluates the umami taste potential of peptides, and mm-Score, which enhances the receptor matching process through a machine learning-optimized molecular docking and scoring system. This system encompasses the optimization of docking structures, clustering of umami peptides, and a comparative analysis of docking energies across peptide clusters, streamlining the receptor identification process. Employing machine learning, our method offers a strategic approach to the intricate task of umami receptor determination. We undertook virtual screening of peptides derived from Lateolabrax japonicus, experimentally verifying the umami taste of three identified peptides and determining their corresponding receptors. This work not only advances our understanding of the mechanisms behind umami taste perception but also provides a rapid and cost-effective method for peptide screening. The source code is publicly accessible at https://github.com/heyigacu/mlp4umami/, encouraging further scientific exploration and collaborative efforts within the research community.
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Affiliation(s)
- Minghao Liu
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
| | - Jiuliang Yang
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
| | - Yi He
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
| | - Fuyan Cao
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
| | - Wannan Li
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
| | - Weiwei Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun, 130012, China.
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4
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He Y, Huang R, Zhang R, He F, Han L, Han W. PredCoffee: A binary classification approach specifically for coffee odor. iScience 2024; 27:110041. [PMID: 38868178 PMCID: PMC11167484 DOI: 10.1016/j.isci.2024.110041] [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: 01/24/2024] [Revised: 04/26/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024] Open
Abstract
Compared to traditional methods, using machine learning to assess or predict the odor of molecules can save costs in various aspects. Our research aims to collect molecules with coffee odor and summarize the regularity of these molecules, ultimately creating a binary classifier that can determine whether a molecule has a coffee odor. In this study, a total of 371 coffee-odor molecules and 9,700 non-coffee-odor molecules were collected. The Knowledge-guided Pre-training of Graph Transformer (KPGT), support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP), and message-passing neural networks (MPNN) were used to train the data. The model with the best performance was selected as the basis of the predictor. The prediction accuracy value of the KPGT model exceeded 0.84 and the predictor has been deployed as a webserver PredCoffee.
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Affiliation(s)
- Yi He
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Ruirui Huang
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Ruoyu Zhang
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Fei He
- Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Lu Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Weiwei Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
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5
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Ryzhkov FV, Ryzhkova YE, Elinson MN. Python tools for structural tasks in chemistry. Mol Divers 2024:10.1007/s11030-024-10889-7. [PMID: 38744790 DOI: 10.1007/s11030-024-10889-7] [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: 03/14/2024] [Accepted: 04/27/2024] [Indexed: 05/16/2024]
Abstract
In recent decades, the use of computational approaches and artificial intelligence in the scientific environment has become more widespread. In this regard, the popular and versatile programming language Python has attracted considerable attention from scientists in the field of chemistry. It is used to solve a variety of chemical and structural problems, including calculating descriptors, molecular fingerprints, graph construction, and computing chemical reaction networks. Python offers high-quality visualization tools for analyzing chemical spaces and compound libraries. This review is a list of tools for the above tasks, including scripts, libraries, ready-made programs, and web interfaces. Inevitably this manuscript does not claim to be an all-encompassing handbook including all the existing Python-based structural chemistry codes. The review serves as a starting point for scientists wishing to apply automatization or optimization to routine chemistry problems.
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Affiliation(s)
- Fedor V Ryzhkov
- N. D. Zelinsky Institute of Organic Chemistry Russian Academy of Sciences, 47 Leninsky Prospekt, Moscow, 119991, Russia.
| | - Yuliya E Ryzhkova
- N. D. Zelinsky Institute of Organic Chemistry Russian Academy of Sciences, 47 Leninsky Prospekt, Moscow, 119991, Russia
| | - Michail N Elinson
- N. D. Zelinsky Institute of Organic Chemistry Russian Academy of Sciences, 47 Leninsky Prospekt, Moscow, 119991, Russia
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Zhao Q, Zheng Y, Qiu Y, Yu Y, Huang M, Wu Y, Chen X, Huang Y, Cui S, Zhuang S. Graph Convolutional Network-Enhanced Model for Screening Persistent, Mobile, and Toxic and Very Persistent and Very Mobile Substances. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6149-6157. [PMID: 38556993 DOI: 10.1021/acs.est.4c01201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The global management for persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances has been further strengthened with the rapid increase of emerging contaminants. The development of a ready-to-use and publicly available tool for the high-throughput screening of PMT/vPvM substances is thus urgently needed. However, the current model building with the coupling of conventional algorithms, small-scale data set, and simplistic features hinders the development of a robust model for screening PMT/vPvM with wide application domains. Here, we construct a graph convolutional network (GCN)-enhanced model with feature fusion of a molecular graph and molecular descriptors to effectively utilize the significant correlation between critical descriptors and PMT/vPvM substances. The model is built with 213,084 substances following the latest PMT classification criteria. The application domains of the GCN-enhanced model assessed by kernel density estimation demonstrate the high suitability for high-throughput screening PMT/vPvM substances with both a high accuracy rate (86.6%) and a low false-negative rate (6.8%). An online server named PMT/vPvM profiler is further developed with a user-friendly web interface (http://www.pmt.zj.cn/). Our study facilitates a more efficient evaluation of PMT/vPvM substances with a globally accessible screening platform.
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Affiliation(s)
- Qiming Zhao
- College of Environmental and Resource Sciences, and Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yuting Zheng
- Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment of the People's Republic of China, Beijing 100029, China
| | - Yu Qiu
- College of Environmental and Resource Sciences, and Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yang Yu
- Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment of the People's Republic of China, Beijing 100029, China
| | - Meiling Huang
- College of Environmental and Resource Sciences, and Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yiqu Wu
- College of Environmental and Resource Sciences, and Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xiyu Chen
- College of Environmental and Resource Sciences, and Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yizhou Huang
- College of Environmental and Resource Sciences, and Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shixuan Cui
- College of Environmental and Resource Sciences, and Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shulin Zhuang
- College of Environmental and Resource Sciences, and Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
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7
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Amoroso N, Gambacorta N, Mastrolorito F, Togo MV, Trisciuzzi D, Monaco A, Pantaleo E, Altomare CD, Ciriaco F, Nicolotti O. Making sense of chemical space network shows signs of criticality. Sci Rep 2023; 13:21335. [PMID: 38049451 PMCID: PMC10696027 DOI: 10.1038/s41598-023-48107-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/22/2023] [Indexed: 12/06/2023] Open
Abstract
Chemical space modelling has great importance in unveiling and visualising latent information, which is critical in predictive toxicology related to drug discovery process. While the use of traditional molecular descriptors and fingerprints may suffer from the so-called curse of dimensionality, complex networks are devoid of the typical drawbacks of coordinate-based representations. Herein, we use chemical space networks (CSNs) to analyse the case of the developmental toxicity (Dev Tox), which remains a challenging endpoint for the difficulty of gathering enough reliable data despite very important for the protection of the maternal and child health. Our study proved that the Dev Tox CSN has a complex non-random organisation and can thus provide a wealth of meaningful information also for predictive purposes. At a phase transition, chemical similarities highlight well-established toxicophores, such as aryl derivatives, mostly neurotoxic hydantoins, barbiturates and amino alcohols, steroids, and volatile organic compounds ether-like chemicals, which are strongly suspected of the Dev Tox onset and can thus be employed as effective alerts for prioritising chemicals before testing.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy.
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy.
| | - Nicola Gambacorta
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
- Division of Medical Genetics, Fondazione IRCCS-Casa Sollievo della Sofferenza, San Giovanni Rotondo (Foggia), Italy
| | - Fabrizio Mastrolorito
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Maria Vittoria Togo
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari Aldo Moro, Via Giovanni Amendola, 173, 70125, Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari Aldo Moro, Via Giovanni Amendola, 173, 70125, Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy.
| | - Orazio Nicolotti
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
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Zhang J, Xu Y, Wang M, Li X, Liu Z, Kuang D, Deng Z, Ou HY, Qu J. Mobilizable plasmids drive the spread of antimicrobial resistance genes and virulence genes in Klebsiella pneumoniae. Genome Med 2023; 15:106. [PMID: 38041146 PMCID: PMC10691111 DOI: 10.1186/s13073-023-01260-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 11/15/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND Klebsiella pneumoniae is a notorious clinical pathogen and frequently carries various plasmids, which are the main carriers of antimicrobial resistance and virulence genes. In comparison to self-transmissible conjugative plasmids, mobilizable plasmids have received much less attention due to their defects in conjugative elements. However, the contribution of mobilizable plasmids to the horizontal transfer of antimicrobial resistance genes and virulence genes of K. pneumoniae remains unclear. In this study, the transfer, stability, and cargo genes of the mobilizable plasmids of K. pneumoniae were examined via genetic experiments and genomic analysis. METHODS Carbapenem-resistant (CR) plasmid pHSKP2 and multidrug-resistant (MDR) plasmid pHSKP3 of K. pneumoniae HS11286, virulence plasmid pRJF293 of K. pneumoniae RJF293 were employed in conjugation assays to assess the transfer ability of mobilizable plasmids. Mimic mobilizable plasmids and genetically modified plasmids were constructed to confirm the cotransfer models. The plasmid morphology was evaluated through XbaI and S1 nuclease pulsed-field gel electrophoresis and/or complete genome sequencing. Mobilizable plasmid stability in transconjugants was analyzed via serial passage culture. In addition, in silico genome analysis of 3923 plasmids of 1194 completely sequenced K. pneumoniae was performed to investigate the distribution of the conjugative elements, the cargo genes, and the targets of the CRISPR-Cas system. The mobilizable MDR plasmid and virulence plasmid of K. pneumoniae were investigated, which carry oriT but lack other conjugative elements. RESULTS Our results showed that mobilizable MDR and virulence plasmids carrying oriT but lacking the relaxase gene were able to cotransfer with a helper conjugative CR plasmid across various Klebsiella and Escherichia coli strains. The transfer and stability of mobilizable plasmids rather than conjugative plasmids were not interfered with by the CRISPR-Cas system of recipient strains. According to the in silico analysis, the mobilizable plasmids carry about twenty percent of acquired antimicrobial resistance genes and more than seventy-five percent of virulence genes in K. pneumoniae. CONCLUSIONS Our work observed that a mobilizable MDR or virulence plasmid that carries oriT but lacks the relaxase genes transferred with the helper CR conjugative plasmid and mobilizable plasmids escaped from CRISPR-Cas defence and remained stable in recipients. These results highlight the threats of mobilizable plasmids as vital vehicles in the dissemination of antibiotic resistance and virulence genes in K. pneumoniae.
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Affiliation(s)
- Jianfeng Zhang
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Yanping Xu
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Meng Wang
- State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Xiaobin Li
- Zhuhai Precision Medical Center, Zhuhai People's Hospital (Zhuhai Hospital affiliated with Jinan University), Zhuhai, 519000, China
| | - Zhiyuan Liu
- State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Dai Kuang
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- National Health Commission (NHC) Key Laboratory of Tropical Disease Control, School of Tropical Medicine, Hainan Medical University, Haikou, China
| | - Zixin Deng
- State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Hong-Yu Ou
- State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China.
| | - Jieming Qu
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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