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Yin Z, Liang J, Zhang M, Chen B, Yu Z, Tian X, Deng X, Peng L. Pan-genome insights into adaptive evolution of bacterial symbionts in mixed host-microbe symbioses represented by human gut microbiota Bacteroides cellulosilyticus. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172251. [PMID: 38604355 DOI: 10.1016/j.scitotenv.2024.172251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
Animal hosts harbor diverse assemblages of microbial symbionts that play crucial roles in the host's lifestyle. The link between microbial symbiosis and host development remains poorly understood. In particular, little is known about the adaptive evolution of gut bacteria in host-microbe symbioses. Recently, symbiotic relationships have been categorized as open, closed, or mixed, reflecting their modes of inter-host transmission and resulting in distinct genomic features. Members of the genus Bacteroides are the most abundant human gut microbiota and possess both probiotic and pathogenic potential, providing an excellent model for studying pan-genome evolution in symbiotic systems. Here, we determined the complete genome of an novel clinical strain PL2022, which was isolated from a blood sample and performed pan-genome analyses on a representative set of Bacteroides cellulosilyticus strains to quantify the influence of the symbiotic relationship on the evolutionary dynamics. B. cellulosilyticus exhibited correlated genomic features with both open and closed symbioses, suggesting a mixed symbiosis. An open pan-genome is characterized by abundant accessory gene families, potential horizontal gene transfer (HGT), and diverse mobile genetic elements (MGEs), indicating an innovative gene pool, mainly associated with genomic islands and plasmids. However, massive parallel gene loss, weak purifying selection, and accumulation of positively selected mutations were the main drivers of genome reduction in B. cellulosilyticus. Metagenomic read recruitment analyses showed that B. cellulosilyticus members are globally distributed and active in human gut habitats, in line with predominant vertical transmission in the human gut. However, existence and/or high abundance were also detected in non-intestinal tissues, other animal hosts, and non-host environments, indicating occasional horizontal transmission to new niches, thereby creating arenas for the acquisition of novel genes. This case study of adaptive evolution under a mixed host-microbe symbiosis advances our understanding of symbiotic pan-genome evolution. Our results highlight the complexity of genetic evolution in this unusual intestinal symbiont.
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Affiliation(s)
- Zhiqiu Yin
- Department of Clinical Laboratory, Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 510700, Guangdong, China
| | - Jiaxin Liang
- Department of Clinical Laboratory, Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 510700, Guangdong, China
| | - Mujie Zhang
- Department of Clinical Laboratory, Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 510700, Guangdong, China
| | - Baozhu Chen
- Department of Clinical Laboratory, Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 510700, Guangdong, China
| | - Zhanpeng Yu
- Department of Clinical Laboratory, Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 510700, Guangdong, China
| | - Xiaoyan Tian
- Department of Clinical Laboratory, Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 510700, Guangdong, China
| | - Xiaoyan Deng
- Department of Clinical Laboratory, Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 510700, Guangdong, China.
| | - Liang Peng
- Department of Clinical Laboratory, Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 510700, Guangdong, China; KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou 510180, Guangdong, China.
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2
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Yu MK, Fogarty EC, Eren AM. Diverse plasmid systems and their ecology across human gut metagenomes revealed by PlasX and MobMess. Nat Microbiol 2024; 9:830-847. [PMID: 38443576 PMCID: PMC10914615 DOI: 10.1038/s41564-024-01610-3] [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: 07/21/2022] [Accepted: 01/17/2024] [Indexed: 03/07/2024]
Abstract
Plasmids alter microbial evolution and lifestyles by mobilizing genes that often confer fitness in changing environments across clades. Yet our ecological and evolutionary understanding of naturally occurring plasmids is far from complete. Here we developed a machine-learning model, PlasX, which identified 68,350 non-redundant plasmids across human gut metagenomes and organized them into 1,169 evolutionarily cohesive 'plasmid systems' using our sequence containment-aware network-partitioning algorithm, MobMess. Individual plasmids were often country specific, yet most plasmid systems spanned across geographically distinct human populations. Cargo genes in plasmid systems included well-known determinants of fitness, such as antibiotic resistance, but also many others including enzymes involved in the biosynthesis of essential nutrients and modification of transfer RNAs, revealing a wide repertoire of likely fitness determinants in complex environments. Our study introduces computational tools to recognize and organize plasmids, and uncovers the ecological and evolutionary patterns of diverse plasmids in naturally occurring habitats through plasmid systems.
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Affiliation(s)
- Michael K Yu
- Toyota Technological Institute at Chicago, Chicago, IL, USA.
| | - Emily C Fogarty
- Department of Medicine, University of Chicago, Chicago, IL, USA
- Committee On Microbiology, University of Chicago, Chicago, IL, USA
| | - A Murat Eren
- Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA, USA.
- Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany.
- Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, Oldenburg, Germany.
- Helmholtz Institute for Functional Marine Biodiversity, Oldenburg, Germany.
- Marine 'Omics Group, Max Planck Institute for Marine Microbiology, Bremen, Germany.
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3
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Fogarty EC, Schechter MS, Lolans K, Sheahan ML, Veseli I, Moore RM, Kiefl E, Moody T, Rice PA, Yu MK, Mimee M, Chang EB, Ruscheweyh HJ, Sunagawa S, Mclellan SL, Willis AD, Comstock LE, Eren AM. A cryptic plasmid is among the most numerous genetic elements in the human gut. Cell 2024; 187:1206-1222.e16. [PMID: 38428395 DOI: 10.1016/j.cell.2024.01.039] [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: 03/29/2023] [Revised: 10/03/2023] [Accepted: 01/25/2024] [Indexed: 03/03/2024]
Abstract
Plasmids are extrachromosomal genetic elements that often encode fitness-enhancing features. However, many bacteria carry "cryptic" plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes and is 14 times as numerous as crAssphage, currently established as the most abundant extrachromosomal genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales, and although it does not appear to impact bacterial host fitness in vivo, it can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an alternative approach to track human colonic inflammatory states.
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Affiliation(s)
- Emily C Fogarty
- Committee on Microbiology, University of Chicago, Chicago, IL 60637, USA; Duchossois Family Institute, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
| | - Matthew S Schechter
- Committee on Microbiology, University of Chicago, Chicago, IL 60637, USA; Duchossois Family Institute, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Karen Lolans
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Madeline L Sheahan
- Duchossois Family Institute, University of Chicago, Chicago, IL 60637, USA; Department of Microbiology, University of Chicago, Chicago, IL 60637, USA
| | - Iva Veseli
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA; Graduate Program in Biophysical Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Ryan M Moore
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Evan Kiefl
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA; Graduate Program in Biophysical Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Thomas Moody
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Phoebe A Rice
- Committee on Microbiology, University of Chicago, Chicago, IL 60637, USA; Department of Biochemistry, University of Chicago, Chicago, IL 60637, USA
| | - Michael K Yu
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA
| | - Mark Mimee
- Committee on Microbiology, University of Chicago, Chicago, IL 60637, USA; Department of Microbiology, University of Chicago, Chicago, IL 60637, USA; Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL 60637, USA
| | - Eugene B Chang
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Hans-Joachim Ruscheweyh
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
| | - Shinichi Sunagawa
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zurich, Zurich 8093, Switzerland
| | - Sandra L Mclellan
- School of Freshwater Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI 53204, USA
| | - Amy D Willis
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Laurie E Comstock
- Committee on Microbiology, University of Chicago, Chicago, IL 60637, USA; Duchossois Family Institute, University of Chicago, Chicago, IL 60637, USA; Department of Microbiology, University of Chicago, Chicago, IL 60637, USA.
| | - A Murat Eren
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA; Marine Biological Laboratory, Woods Hole, MA 02543, USA; Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, 27570 Bremerhaven, Germany; Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, 26129 Oldenburg, Germany; Max Planck Institute for Marine Microbiology, 28359 Bremen, Germany; Helmholtz Institute for Functional Marine Biodiversity, 26129 Oldenburg, Germany.
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4
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Anda M, Yamanouchi S, Cosentino S, Sakamoto M, Ohkuma M, Takashima M, Toyoda A, Iwasaki W. Bacteria can maintain rRNA operons solely on plasmids for hundreds of millions of years. Nat Commun 2023; 14:7232. [PMID: 37963895 PMCID: PMC10645730 DOI: 10.1038/s41467-023-42681-w] [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: 02/13/2023] [Accepted: 10/17/2023] [Indexed: 11/16/2023] Open
Abstract
It is generally assumed that all bacteria must have at least one rRNA operon (rrn operon) on the chromosome, but some strains of the genera Aureimonas and Oecophyllibacter carry their sole rrn operon on a plasmid. However, other related strains and species have chromosomal rrn loci, suggesting that the exclusive presence of rrn operons on a plasmid is rare and unlikely to be stably maintained over long evolutionary periods. Here, we report the results of a systematic search for additional bacteria without chromosomal rrn operons. We find that at least four bacterial clades in the phyla Bacteroidota, Spirochaetota, and Pseudomonadota (Proteobacteria) lost chromosomal rrn operons independently. Remarkably, Persicobacteraceae have apparently maintained this peculiar genome organization for hundreds of millions of years. In our study, all the rrn-carrying plasmids in bacteria lacking chromosomal rrn loci possess replication initiator genes of the Rep_3 family. Furthermore, the lack of chromosomal rrn operons is associated with differences in copy numbers of rrn operons, plasmids, and chromosomal tRNA genes. Thus, our findings indicate that the absence of rrn loci in bacterial chromosomes can be stably maintained over long evolutionary periods.
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Affiliation(s)
- Mizue Anda
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Chiba, 277-0882, Japan.
- Department of Biological Sciences, Graduate School of Science, the University of Tokyo, Bunkyo-ku, Tokyo, 113-0032, Japan.
| | - Shun Yamanouchi
- Department of Biological Sciences, Graduate School of Science, the University of Tokyo, Bunkyo-ku, Tokyo, 113-0032, Japan
| | - Salvatore Cosentino
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Chiba, 277-0882, Japan
- Department of Biological Sciences, Graduate School of Science, the University of Tokyo, Bunkyo-ku, Tokyo, 113-0032, Japan
| | - Mitsuo Sakamoto
- Microbe Division/Japan Collection of Microorganisms, RIKEN BioResource Research Center, Tsukuba, Ibaraki, 305-0074, Japan
| | - Moriya Ohkuma
- Microbe Division/Japan Collection of Microorganisms, RIKEN BioResource Research Center, Tsukuba, Ibaraki, 305-0074, Japan
| | - Masako Takashima
- Microbe Division/Japan Collection of Microorganisms, RIKEN BioResource Research Center, Tsukuba, Ibaraki, 305-0074, Japan
| | - Atsushi Toyoda
- Advanced Genomics Center, National Institute of Genetics, Mishima, Shizuoka, 411-8540, Japan
| | - Wataru Iwasaki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Chiba, 277-0882, Japan.
- Department of Biological Sciences, Graduate School of Science, the University of Tokyo, Bunkyo-ku, Tokyo, 113-0032, Japan.
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Chiba, 277-0882, Japan.
- Atmosphere and Ocean Research Institute, the University of Tokyo, Kashiwa, Chiba, 277-0882, Japan.
- Institute for Quantitative Biosciences, the University of Tokyo, Bunkyo-ku, Tokyo, 113-0032, Japan.
- Collaborative Research Institute for Innovative Microbiology, the University of Tokyo, Bunkyo-ku, Tokyo, 113-0032, Japan.
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Wang B, Finazzo M, Artsimovitch I. Machine Learning Suggests That Small Size Helps Broaden Plasmid Host Range. Genes (Basel) 2023; 14:2044. [PMID: 38002987 PMCID: PMC10670969 DOI: 10.3390/genes14112044] [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: 10/15/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
Plasmids mediate gene exchange across taxonomic barriers through conjugation, shaping bacterial evolution for billions of years. While plasmid mobility can be harnessed for genetic engineering and drug-delivery applications, rapid plasmid-mediated spread of resistance genes has rendered most clinical antibiotics useless. To solve this urgent and growing problem, we must understand how plasmids spread across bacterial communities. Here, we applied machine-learning models to identify features that are important for extending the plasmid host range. We assembled an up-to-date dataset of more than thirty thousand bacterial plasmids, separated them into 1125 clusters, and assigned each cluster a distribution possibility score, taking into account the host distribution of each taxonomic rank and the sampling bias of the existing sequencing data. Using this score and an optimized plasmid feature pool, we built a model stack consisting of DecisionTreeRegressor, EvoTreeRegressor, and LGBMRegressor as base models and LinearRegressor as a meta-learner. Our mathematical modeling revealed that sequence brevity is the most important determinant for plasmid spread, followed by P-loop NTPases, mobility factors, and β-lactamases. Ours and other recent results suggest that small plasmids may broaden their range by evading host defenses and using alternative modes of transfer instead of autonomous conjugation.
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Affiliation(s)
- Bing Wang
- Department of Microbiology and Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA;
| | | | - Irina Artsimovitch
- Department of Microbiology and Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA;
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6
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Sielemann J, Sielemann K, Brejová B, Vinař T, Chauve C. plASgraph2: using graph neural networks to detect plasmid contigs from an assembly graph. Front Microbiol 2023; 14:1267695. [PMID: 37869681 PMCID: PMC10587606 DOI: 10.3389/fmicb.2023.1267695] [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: 07/26/2023] [Accepted: 09/08/2023] [Indexed: 10/24/2023] Open
Abstract
Identification of plasmids from sequencing data is an important and challenging problem related to antimicrobial resistance spread and other One-Health issues. We provide a new architecture for identifying plasmid contigs in fragmented genome assemblies built from short-read data. We employ graph neural networks (GNNs) and the assembly graph to propagate the information from nearby nodes, which leads to more accurate classification, especially for short contigs that are difficult to classify based on sequence features or database searches alone. We trained plASgraph2 on a data set of samples from the ESKAPEE group of pathogens. plASgraph2 either outperforms or performs on par with a wide range of state-of-the-art methods on testing sets of independent ESKAPEE samples and samples from related pathogens. On one hand, our study provides a new accurate and easy to use tool for contig classification in bacterial isolates; on the other hand, it serves as a proof-of-concept for the use of GNNs in genomics. Our software is available at https://github.com/cchauve/plasgraph2 and the training and testing data sets are available at https://github.com/fmfi-compbio/plasgraph2-datasets.
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Affiliation(s)
- Janik Sielemann
- Computational Biology, Faculty of Biology, Center for Biotechnology & Graduate School Digital Infrastructures for the Life Sciences (DILS), Bielefeld Institute for Bioinformatics Infrastructure, Bielefeld University, Bielefeld, Germany
| | - Katharina Sielemann
- Genetics and Genomics of Plants, Faculty of Biology, Center for Biotechnology & Graduate School Digital Infrastructures for the Life Sciences (DILS), Bielefeld Institute for Bioinformatics Infrastructure, Bielefeld University, Bielefeld, Germany
| | - Broňa Brejová
- Department of Computer Science, Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia
| | - Tomáš Vinař
- Department of Applied Informatics, Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia
| | - Cedric Chauve
- Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada
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Tang X, Shang J, Ji Y, Sun Y. PLASMe: a tool to identify PLASMid contigs from short-read assemblies using transformer. Nucleic Acids Res 2023; 51:e83. [PMID: 37427782 PMCID: PMC10450166 DOI: 10.1093/nar/gkad578] [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: 12/16/2022] [Revised: 06/19/2023] [Accepted: 06/26/2023] [Indexed: 07/11/2023] Open
Abstract
Plasmids are mobile genetic elements that carry important accessory genes. Cataloging plasmids is a fundamental step to elucidate their roles in promoting horizontal gene transfer between bacteria. Next generation sequencing (NGS) is the main source for discovering new plasmids today. However, NGS assembly programs tend to return contigs, making plasmid detection difficult. This problem is particularly grave for metagenomic assemblies, which contain short contigs of heterogeneous origins. Available tools for plasmid contig detection still suffer from some limitations. In particular, alignment-based tools tend to miss diverged plasmids while learning-based tools often have lower precision. In this work, we develop a plasmid detection tool PLASMe that capitalizes on the strength of alignment and learning-based methods. Closely related plasmids can be easily identified using the alignment component in PLASMe while diverged plasmids can be predicted using order-specific Transformer models. By encoding plasmid sequences as a language defined on the protein cluster-based token set, Transformer can learn the importance of proteins and their correlation through positionally token embedding and the attention mechanism. We compared PLASMe and other tools on detecting complete plasmids, plasmid contigs, and contigs assembled from CAMI2 simulated data. PLASMe achieved the highest F1-score. After validating PLASMe on data with known labels, we also tested it on real metagenomic and plasmidome data. The examination of some commonly used marker genes shows that PLASMe exhibits more reliable performance than other tools.
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Affiliation(s)
- Xubo Tang
- Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
| | - Jiayu Shang
- Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
| | - Yongxin Ji
- Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
| | - Yanni Sun
- Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
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Meletis G, Malousi A, Tychala A, Kassomenaki A, Vlachodimou N, Mantzana P, Metallidis S, Skoura L, Protonotariou E. Probable Three-Species In Vivo Transfer of blaNDM-1 in a Single Patient in Greece: Occurrence of NDM-1-Producing Klebsiella pneumoniae, Proteus mirabilis, and Morganella morganii. Antibiotics (Basel) 2023; 12:1206. [PMID: 37508302 PMCID: PMC10376024 DOI: 10.3390/antibiotics12071206] [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: 06/18/2023] [Revised: 07/11/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
NDM carbapenemase-encoding genes disseminate commonly among Enterobacterales through transferable plasmids carrying additional resistance determinants. Apart from the intra-species dissemination, the inter-species exchange of plasmids seems to play an additional important role in the spread of blaNDM. We here present the genetics related to the isolation of three species (Klebsiella pneumoniae, Proteus mirabilis, and Morganella morganii) harboring the blaNDM-1 gene from a single patient in Greece. Bacterial identification and antimicrobial susceptibility testing were performed using the Vitek2. Whole genome sequencing and bioinformatic tools were used to identify resistance genes and plasmids. BlaNDM-1 harboring plasmids were found in all three isolates. Moreover, the plasmid constructs of the respective incomplete or circular contigs showed that the blaNDM-1 and its neighboring genes form a cluster that was found in all isolates. Our microbiological findings, together with the patient's history, suggest the in vivo transfer of the blaNDM-1-containing cluster through three different species in a single patient.
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Affiliation(s)
- Georgios Meletis
- Department of Microbiology, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Andigoni Malousi
- Laboratory of Biological Chemistry, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Areti Tychala
- Department of Microbiology, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Angeliki Kassomenaki
- Department of Microbiology, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Nikoletta Vlachodimou
- Department of Microbiology, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Paraskevi Mantzana
- Department of Microbiology, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Simeon Metallidis
- First Department of Internal Medicine, Infectious Diseases Division, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Lemonia Skoura
- Department of Microbiology, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
| | - Efthymia Protonotariou
- Department of Microbiology, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
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9
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Mane A, Faizrahnemoon M, Vinař T, Brejová B, Chauve C. PlasBin-flow: a flow-based MILP algorithm for plasmid contigs binning. Bioinformatics 2023; 39:i288-i296. [PMID: 37387134 DOI: 10.1093/bioinformatics/btad250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION The analysis of bacterial isolates to detect plasmids is important due to their role in the propagation of antimicrobial resistance. In short-read sequence assemblies, both plasmids and bacterial chromosomes are typically split into several contigs of various lengths, making identification of plasmids a challenging problem. In plasmid contig binning, the goal is to distinguish short-read assembly contigs based on their origin into plasmid and chromosomal contigs and subsequently sort plasmid contigs into bins, each bin corresponding to a single plasmid. Previous works on this problem consist of de novo approaches and reference-based approaches. De novo methods rely on contig features such as length, circularity, read coverage, or GC content. Reference-based approaches compare contigs to databases of known plasmids or plasmid markers from finished bacterial genomes. RESULTS Recent developments suggest that leveraging information contained in the assembly graph improves the accuracy of plasmid binning. We present PlasBin-flow, a hybrid method that defines contig bins as subgraphs of the assembly graph. PlasBin-flow identifies such plasmid subgraphs through a mixed integer linear programming model that relies on the concept of network flow to account for sequencing coverage, while also accounting for the presence of plasmid genes and the GC content that often distinguishes plasmids from chromosomes. We demonstrate the performance of PlasBin-flow on a real dataset of bacterial samples. AVAILABILITY AND IMPLEMENTATION https://github.com/cchauve/PlasBin-flow.
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Affiliation(s)
- Aniket Mane
- Department of Mathematics, Simon Fraser University, Burnaby V5A 1S6, Canada
| | | | - Tomáš Vinař
- Department of Applied Informatics, Comenius University, Bratislava 84248, Slovakia
| | - Broňa Brejová
- Department of Computer Science, Comenius University, Bratislava 84248, Slovakia
| | - Cedric Chauve
- Department of Mathematics, Simon Fraser University, Burnaby V5A 1S6, Canada
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10
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Zhu Q, Gao S, Xiao B, He Z, Hu S. Plasmer: an Accurate and Sensitive Bacterial Plasmid Prediction Tool Based on Machine Learning of Shared k-mers and Genomic Features. Microbiol Spectr 2023; 11:e0464522. [PMID: 37191574 PMCID: PMC10269668 DOI: 10.1128/spectrum.04645-22] [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: 11/14/2022] [Accepted: 04/26/2023] [Indexed: 05/17/2023] Open
Abstract
Identification of plasmids in bacterial genomes is critical for many factors, including horizontal gene transfer, antibiotic resistance genes, host-microbe interactions, cloning vectors, and industrial production. There are several in silico methods to predict plasmid sequences in assembled genomes. However, existing methods have evident shortcomings, such as unbalance in sensitivity and specificity, dependency on species-specific models, and performance reduction in sequences shorter than 10 kb, which has limited their scope of applicability. In this work, we proposed Plasmer, a novel plasmid predictor based on machine-learning of shared k-mers and genomic features. Unlike existing k-mer or genomic-feature based methods, Plasmer employs the random forest algorithm to make predictions using the percent of shared k-mers with plasmid and chromosome databases combined with other genomic features, including alignment E value and replicon distribution scores (RDS). Plasmer can predict on multiple species and has achieved an average the area under the curve (AUC) of 0.996 with accuracy of 98.4%. Compared to existing methods, tests of both sliding sequences and simulated and de novo assemblies have consistently shown that Plasmer has outperforming accuracy and stable performance across long and short contigs above 500 bp, demonstrating its applicability for fragmented assemblies. Plasmer also has excellent and balanced performance on both sensitivity and specificity (both >0.95 above 500 bp) with the highest F1-score, which has eliminated the bias on sensitivity or specificity that was common in existing methods. Plasmer also provides taxonomy classification to help identify the origin of plasmids. IMPORTANCE In this study, we proposed a novel plasmid prediction tool named Plasmer. Technically, unlike existing k-mer or genomic features-based methods, Plasmer is the first tool to combine the advantages of the percent of shared k-mers and the alignment score of genomic features. This has given Plasmer (i) evident improvement in performance compared to other methods, with the best F1-score and accuracy on sliding sequences, simulated contigs, and de novo assemblies; (ii) applicability for contigs above 500 bp with highest accuracy, enabling plasmid prediction in fragmented short-read assemblies; (iii) excellent and balanced performance between sensitivity and specificity (both >0.95 above 500 bp) with the highest F1-score, which eliminated the bias on sensitivity or specificity that commonly existed in other methods; and (iv) no dependency of species-specific training models. We believe that Plasmer provides a more reliable alternative for plasmid prediction in bacterial genome assemblies.
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Affiliation(s)
- Qianhui Zhu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Shenghan Gao
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Binghan Xiao
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China
| | - Zilong He
- School of Engineering Medicine, Beihang University, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Interdisciplinary Innovation Institute of Medicine and Engineering, Beihang University, Beijing, China
| | - Songnian Hu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China
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Li B, Yan T. Metagenomic next generation sequencing for studying antibiotic resistance genes in the environment. ADVANCES IN APPLIED MICROBIOLOGY 2023; 123:41-89. [PMID: 37400174 DOI: 10.1016/bs.aambs.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
Bacterial antimicrobial resistance (AMR) is a persisting and growing threat to human health. Characterization of antibiotic resistance genes (ARGs) in the environment is important to understand and control ARG-associated microbial risks. Numerous challenges exist in monitoring ARGs in the environment, due to the extraordinary diversity of ARGs, low abundance of ARGs with respect to the complex environmental microbiomes, difficulties in linking ARGs with bacterial hosts by molecular methods, difficulties in achieving quantification and high throughput simultaneously, difficulties in assessing mobility potential of ARGs, and difficulties in determining the specific AMR determinant genes. Advances in the next generation sequencing (NGS) technologies and related computational and bioinformatic tools are facilitating rapid identification and characterization ARGs in genomes and metagenomes from environmental samples. This chapter discusses NGS-based strategies, including amplicon-based sequencing, whole genome sequencing, bacterial population-targeted metagenome sequencing, metagenomic NGS, quantitative metagenomic sequencing, and functional/phenotypic metagenomic sequencing. Current bioinformatic tools for analyzing sequencing data for studying environmental ARGs are also discussed.
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Affiliation(s)
- Bo Li
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Tao Yan
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI, United States.
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Fogarty EC, Schechter MS, Lolans K, Sheahan ML, Veseli I, Moore R, Kiefl E, Moody T, Rice PA, Yu MK, Mimee M, Chang EB, Mclellan SL, Willis AD, Comstock LE, Eren AM. A highly conserved and globally prevalent cryptic plasmid is among the most numerous mobile genetic elements in the human gut. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.25.534219. [PMID: 36993556 PMCID: PMC10055365 DOI: 10.1101/2023.03.25.534219] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Plasmids are extrachromosomal genetic elements that often encode fitness enhancing features. However, many bacteria carry 'cryptic' plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes, and is 14 times as numerous as crAssphage, currently established as the most abundant genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one's mother. pBI143 can transfer between Bacteroidales and although it does not appear to impact bacterial host fitness in vivo, can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an inexpensive alternative for detecting human colonic inflammatory states.
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Affiliation(s)
- Emily C Fogarty
- Committee on Microbiology, University of Chicago, Chicago, IL 60637, USA
- Duchossois Family Institute, University of Chicago, Chicago, IL 60637, USA
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Matthew S Schechter
- Committee on Microbiology, University of Chicago, Chicago, IL 60637, USA
- Duchossois Family Institute, University of Chicago, Chicago, IL 60637, USA
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Karen Lolans
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Madeline L. Sheahan
- Duchossois Family Institute, University of Chicago, Chicago, IL 60637, USA
- Department of Microbiology, University of Chicago, Chicago, IL, 60637, USA
| | - Iva Veseli
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
- Graduate Program in Biophysical Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Ryan Moore
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Evan Kiefl
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
- Graduate Program in Biophysical Sciences, University of Chicago, Chicago, IL 60637, USA
| | - Thomas Moody
- Department of Systems Biology, Columbia University, New York, NY, 10032 USA
| | - Phoebe A Rice
- Committee on Microbiology, University of Chicago, Chicago, IL 60637, USA
- Department of Biochemistry, University of Chicago, Chicago, IL, 60637, USA
| | | | - Mark Mimee
- Committee on Microbiology, University of Chicago, Chicago, IL 60637, USA
- Department of Microbiology, University of Chicago, Chicago, IL, 60637, USA
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL 60637, USA
| | - Eugene B Chang
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Sandra L Mclellan
- School of Freshwater Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, 53204, USA
| | - Amy D Willis
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Laurie E Comstock
- Duchossois Family Institute, University of Chicago, Chicago, IL 60637, USA
- Department of Microbiology, University of Chicago, Chicago, IL, 60637, USA
| | - A Murat Eren
- Marine Biological Laboratory, Woods Hole, MA, 02543, USA
- Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, 27570 Bremerhaven, Germany
- Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, 26129 Oldenburg, Germany
- Helmholtz Institute for Functional Marine Biodiversity, 26129 Oldenburg, Germany
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Shang J, Tang X, Guo R, Sun Y. Accurate identification of bacteriophages from metagenomic data using Transformer. Brief Bioinform 2022; 23:6620872. [PMID: 35769000 PMCID: PMC9294416 DOI: 10.1093/bib/bbac258] [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: 03/10/2022] [Revised: 05/22/2022] [Accepted: 06/04/2022] [Indexed: 11/20/2022] Open
Abstract
Motivation Bacteriophages are viruses infecting bacteria. Being key players in microbial communities, they can regulate the composition/function of microbiome by infecting their bacterial hosts and mediating gene transfer. Recently, metagenomic sequencing, which can sequence all genetic materials from various microbiome, has become a popular means for new phage discovery. However, accurate and comprehensive detection of phages from the metagenomic data remains difficult. High diversity/abundance, and limited reference genomes pose major challenges for recruiting phage fragments from metagenomic data. Existing alignment-based or learning-based models have either low recall or precision on metagenomic data. Results In this work, we adopt the state-of-the-art language model, Transformer, to conduct contextual embedding for phage contigs. By constructing a protein-cluster vocabulary, we can feed both the protein composition and the proteins’ positions from each contig into the Transformer. The Transformer can learn the protein organization and associations using the self-attention mechanism and predicts the label for test contigs. We rigorously tested our developed tool named PhaMer on multiple datasets with increasing difficulty, including quality RefSeq genomes, short contigs, simulated metagenomic data, mock metagenomic data and the public IMG/VR dataset. All the experimental results show that PhaMer outperforms the state-of-the-art tools. In the real metagenomic data experiment, PhaMer improves the F1-score of phage detection by 27%.
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Affiliation(s)
- Jiayu Shang
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong (SAR), China
| | - Xubo Tang
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong (SAR), China
| | - Ruocheng Guo
- School of Data Science, City University of Hong Kong, Hong Kong (SAR), China
| | - Yanni Sun
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong (SAR), China
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