1
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Cook R, Crisci MA, Pye HV, Telatin A, Adriaenssens EM, Santini JM. Decoding huge phage diversity: a taxonomic classification of Lak megaphages. J Gen Virol 2024; 105:001997. [PMID: 38814706 PMCID: PMC11165621 DOI: 10.1099/jgv.0.001997] [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: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 05/31/2024] Open
Abstract
High-throughput sequencing for uncultivated viruses has accelerated the understanding of global viral diversity and uncovered viral genomes substantially larger than any that have so far been cultured. Notably, the Lak phages are an enigmatic group of viruses that present some of the largest known phage genomes identified in human and animal microbiomes, and are dissimilar to any cultivated viruses. Despite the wealth of viral diversity that exists within sequencing datasets, uncultivated viruses have rarely been used for taxonomic classification. We investigated the evolutionary relationships of 23 Lak phages and propose a taxonomy for their classification. Predicted protein analysis revealed the Lak phages formed a deeply branching monophyletic clade within the class Caudoviricetes which contained no other phage genomes. One of the interesting features of this clade is that all current members are characterised by an alternative genetic code. We propose the Lak phages belong to a new order, the 'Grandevirales'. Protein and nucleotide-based analyses support the creation of two families, three sub-families, and four genera within the order 'Grandevirales'. We anticipate that the proposed taxonomy of Lak megaphages will simplify the future classification of related viral genomes as they are uncovered. Continued efforts to classify divergent viruses are crucial to aid common analyses of viral genomes and metagenomes.
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Affiliation(s)
- Ryan Cook
- Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | - Marco A. Crisci
- Department of Structural and Molecular Biology, Division of Biosciences, UCL, London, UK
| | - Hannah V. Pye
- Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | - Andrea Telatin
- Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | | | - Joanne M. Santini
- Department of Structural and Molecular Biology, Division of Biosciences, UCL, London, UK
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2
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Wu Y, Peng Y. Ten computational challenges in human virome studies. Virol Sin 2024:S1995-820X(24)00068-3. [PMID: 38697263 DOI: 10.1016/j.virs.2024.04.008] [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: 01/10/2024] [Accepted: 04/25/2024] [Indexed: 05/04/2024] Open
Abstract
In recent years, substantial advancements have been achieved in understanding the diversity of the human virome and its intricate roles in human health and diseases. Despite this progress, the field of human virome research remains nascent, primarily hindered by the absence of effective methods, particularly in the domain of computational tools. This perspective systematically outlines ten computational challenges spanning various types of virome studies. These challenges arise due to the vast diversity of viromes, the absence of a universal marker gene in viral genomes, the low abundance of virus populations, the remote or minimal homology of viral proteins to known proteins, and the highly dynamic and heterogeneous nature of viromes. For each computational challenge, we discuss the underlying reasons, current research progress, and potential solutions. The resolution of these challenges necessitates ongoing collaboration among computational scientists, virologists, and multidisciplinary experts. In essence, this perspective serves as a comprehensive guide for directing computational efforts in human virome studies.
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Affiliation(s)
- Yifan Wu
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Yousong Peng
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China.
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3
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Liu X, Liu Y, Liu J, Zhang H, Shan C, Guo Y, Gong X, Cui M, Li X, Tang M. Correlation between the gut microbiome and neurodegenerative diseases: a review of metagenomics evidence. Neural Regen Res 2024; 19:833-845. [PMID: 37843219 PMCID: PMC10664138 DOI: 10.4103/1673-5374.382223] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/19/2023] [Accepted: 06/17/2023] [Indexed: 10/17/2023] Open
Abstract
A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis. As a contributing factor, microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota's diverse microorganisms, and for both neuroimmune and neuroendocrine systems. Here, we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases, with an emphasis on multi-omics studies and the gut virome. The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated. Finally, we discuss the role of diet, prebiotics, probiotics, postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.
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Affiliation(s)
- Xiaoyan Liu
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Yi Liu
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
- Institute of Animal Husbandry, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu Province, China
| | - Junlin Liu
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Hantao Zhang
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Chaofan Shan
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Yinglu Guo
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Xun Gong
- Department of Rheumatology & Immunology, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Mengmeng Cui
- Department of Neurology, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong Province, China
| | - Xiubin Li
- Department of Neurology, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong Province, China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu Province, China
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4
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Nie W, Qiu T, Wei Y, Ding H, Guo Z, Qiu J. Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies. Brief Bioinform 2024; 25:bbae117. [PMID: 38555471 PMCID: PMC10981677 DOI: 10.1093/bib/bbae117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 01/15/2024] [Accepted: 03/02/2024] [Indexed: 04/02/2024] Open
Abstract
Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.
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Affiliation(s)
- Wanchun Nie
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Tianyi Qiu
- Institute of Clinical Science, Zhongshan Hospital; Intelligent Medicine Institute, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, 200032, China
| | - Yiwen Wei
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Hao Ding
- Institute of Clinical Science, Zhongshan Hospital; Intelligent Medicine Institute, Fudan University, Shanghai, 200032, China
| | - Zhixiang Guo
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Jingxuan Qiu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
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5
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Ni Y, Xu T, Yan S, Chen L, Wang Y. Hiding in plain sight: The discovery of complete genomes of 11 hypothetical spindle-shaped viruses that putatively infect mesophilic ammonia-oxidizing archaea. ENVIRONMENTAL MICROBIOLOGY REPORTS 2024; 16:e13230. [PMID: 38263861 PMCID: PMC10866085 DOI: 10.1111/1758-2229.13230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 12/14/2023] [Indexed: 01/25/2024]
Abstract
The genome of a putative Nitrosopumilaceae virus with a hypothetical spindle-shaped particle morphology was identified in the Yangshan Harbour metavirome from the East China Sea through protein similarity comparison and structure analysis. This discovery was accompanied by a set of 10 geographically dispersed close relatives found in the environmental virus datasets from typical locations of ammonia-oxidizing archaeon distribution. Its host prediction was supported by iPHoP prediction and protein sequence similarity. The structure of the predicted major capsid protein, together with the overall N-glycosylation site, the transmembrane helices prediction, the hydrophilicity profile, and the docking simulation of the major capsid proteins, indicate that these viruses resemble spindle-shaped viruses. It suggests a similarly assembled structure and, consequently, a possibly spindle-shaped morphology of these newly discovered archaeal viruses.
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Affiliation(s)
- Yimin Ni
- College of Food Science and TechnologyShanghai Ocean UniversityShanghaiChina
| | - Tianqi Xu
- College of Food Science and TechnologyShanghai Ocean UniversityShanghaiChina
| | - Shuling Yan
- Entwicklungsgenetik und Zellbiologie der TierePhilipps‐Universität MarburgMarburgGermany
| | - Lanming Chen
- College of Food Science and TechnologyShanghai Ocean UniversityShanghaiChina
- Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai)Ministry of AgricultureShanghaiChina
| | - Yongjie Wang
- College of Food Science and TechnologyShanghai Ocean UniversityShanghaiChina
- Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai)Ministry of AgricultureShanghaiChina
- Laboratory for Marine Biology and BiotechnologyQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
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6
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Ishola OA, Kublik S, Durai Raj AC, Ohnmacht C, Schulz S, Foesel BU, Schloter M. Comparative Metagenomic Analysis of Bacteriophages and Prophages in Gnotobiotic Mouse Models. Microorganisms 2024; 12:255. [PMID: 38399658 PMCID: PMC10892684 DOI: 10.3390/microorganisms12020255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 02/25/2024] Open
Abstract
Gnotobiotic murine models are important to understand microbiota-host interactions. Despite the role of bacteriophages as drivers for microbiome structure and function, there is no information about the structure and function of the gut virome in gnotobiotic models and the link between bacterial and bacteriophage/prophage diversity. We studied the virome of gnotobiotic murine Oligo-MM12 (12 bacterial species) and reduced Altered Schaedler Flora (ASF, three bacterial species). As reference, the virome of Specific Pathogen-Free (SPF) mice was investigated. A metagenomic approach was used to assess prophages and bacteriophages in the guts of 6-week-old female mice. We identified a positive correlation between bacteria diversity, and bacteriophages and prophages. Caudoviricetes (82.4%) were the most prominent class of phages in all samples with differing relative abundance. However, the host specificity of bacteriophages belonging to class Caudoviricetes differed depending on model bacterial diversity. We further studied the role of bacteriophages in horizontal gene transfer and microbial adaptation to the host's environment. Analysis of mobile genetic elements showed the contribution of bacteriophages to the adaptation of bacterial amino acid metabolism. Overall, our results implicate virome "dark matter" and interactions with the host system as factors for microbial community structure and function which determine host health. Taking the importance of the virome in the microbiome diversity and horizontal gene transfer, reductions in the virome might be an important factor driving losses of microbial biodiversity and the subsequent dysbiosis of the gut microbiome.
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Affiliation(s)
- Oluwaseun A. Ishola
- Research Unit for Comparative Microbiome Analysis, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany; (O.A.I.)
| | - Susanne Kublik
- Research Unit for Comparative Microbiome Analysis, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany; (O.A.I.)
| | - Abilash Chakravarthy Durai Raj
- Research Unit for Comparative Microbiome Analysis, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany; (O.A.I.)
| | - Caspar Ohnmacht
- Mucosal Immunology Group, Center of Allery and Environment (ZAUM), Technical University of Munich, Helmholtz Zentrum München, 85764 München, Germany
| | - Stefanie Schulz
- Research Unit for Comparative Microbiome Analysis, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany; (O.A.I.)
| | - Bärbel U. Foesel
- Research Unit for Comparative Microbiome Analysis, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany; (O.A.I.)
| | - Michael Schloter
- Research Unit for Comparative Microbiome Analysis, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany; (O.A.I.)
- Chair for Environmental Microbiology, TUM School of Life Science, Technical University of Munich, 85354 Freising, Germany
- Central Institute for Nutrition and Health, Technical University of Munich, 85354 Freising, Germany
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7
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Qiu J, Nie W, Ding H, Dai J, Wei Y, Li D, Zhang Y, Xie J, Tian X, Wu N, Qiu T. PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy. Brief Bioinform 2024; 25:bbae010. [PMID: 38344864 PMCID: PMC10859729 DOI: 10.1093/bib/bbae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/16/2023] [Accepted: 01/05/2024] [Indexed: 02/15/2024] Open
Abstract
Bacteriophages can help the treatment of bacterial infections yet require in-silico models to deal with the great genetic diversity between phages and bacteria. Despite the tolerable prediction performance, the application scope of current approaches is limited to the prediction at the species level, which cannot accurately predict the relationship of phages across strain mutants. This has hindered the development of phage therapeutics based on the prediction of phage-bacteria relationships. In this paper, we present, PB-LKS, to predict the phage-bacteria interaction based on local K-mer strategy with higher performance and wider applicability. The utility of PB-LKS is rigorously validated through (i) large-scale historical screening, (ii) case study at the class level and (iii) in vitro simulation of bacterial antiphage resistance at the strain mutant level. The PB-LKS approach could outperform the current state-of-the-art methods and illustrate potential clinical utility in pre-optimized phage therapy design.
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Affiliation(s)
- Jingxuan Qiu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Wanchun Nie
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Hao Ding
- Institute of Clinical Science, Zhongshan Hospital, Shanghai Institute of Infectious Disease and Biosecurity, Intelligent Medicine Institute, Fudan University, Shanghai, 200032, China
| | - Jia Dai
- Shanghai Institute of Phage, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China
| | - Yiwen Wei
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Dezhi Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Yuxi Zhang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Junting Xie
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Xinxin Tian
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Nannan Wu
- Shanghai Institute of Phage, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China
| | - Tianyi Qiu
- Institute of Clinical Science, Zhongshan Hospital, Shanghai Institute of Infectious Disease and Biosecurity, Intelligent Medicine Institute, Fudan University, Shanghai, 200032, China
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8
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Yin H, Wu S, Tan J, Guo Q, Li M, Guo J, Wang Y, Jiang X, Zhu H. IPEV: identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning. Gigascience 2024; 13:giae018. [PMID: 38649300 PMCID: PMC11034026 DOI: 10.1093/gigascience/giae018] [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: 11/23/2023] [Revised: 03/14/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses. FINDINGS We present IPEV, a novel method to distinguish prokaryotic and eukaryotic viruses in viromes, with a 2-dimensional convolutional neural network combining trinucleotide pair relative distance and frequency. Cross-validation assessments of IPEV demonstrate its state-of-the-art precision, significantly improving the F1-score by approximately 22% on an independent test set compared to existing methods when query viruses share less than 30% sequence similarity with known viruses. Furthermore, IPEV outperforms other methods in accuracy on marine and gut virome samples based on annotations by sequence alignments. IPEV reduces runtime by at most 1,225 times compared to existing methods under the same computing configuration. We also utilized IPEV to analyze longitudinal samples and found that the gut virome exhibits a higher degree of temporal stability than previously observed in persistent personal viromes, providing novel insights into the resilience of the gut virome in individuals. CONCLUSIONS IPEV is a high-performance, user-friendly tool that assists biologists in identifying and classifying prokaryotic and eukaryotic viruses within viromes. The tool is available at https://github.com/basehc/IPEV.
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Affiliation(s)
- Hengchuang Yin
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Shufang Wu
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Jie Tan
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Qian Guo
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Mo Li
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
- School of Life Sciences, Peking University, Beijing 100871, China
| | - Jinyuan Guo
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Yaqi Wang
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Xiaoqing Jiang
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
- Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China
| | - Huaiqiu Zhu
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
- School of Life Sciences, Peking University, Beijing 100871, China
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
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9
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Goff JL, Lui LM, Nielsen TN, Poole FL, Smith HJ, Walker KF, Hazen TC, Fields MW, Arkin AP, Adams MWW. Mixed waste contamination selects for a mobile genetic element population enriched in multiple heavy metal resistance genes. ISME COMMUNICATIONS 2024; 4:ycae064. [PMID: 38800128 PMCID: PMC11128244 DOI: 10.1093/ismeco/ycae064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/11/2024] [Indexed: 05/29/2024]
Abstract
Mobile genetic elements (MGEs) like plasmids, viruses, and transposable elements can provide fitness benefits to their hosts for survival in the presence of environmental stressors. Heavy metal resistance genes (HMRGs) are frequently observed on MGEs, suggesting that MGEs may be an important driver of adaptive evolution in environments contaminated with heavy metals. Here, we report the meta-mobilome of the heavy metal-contaminated regions of the Oak Ridge Reservation subsurface. This meta-mobilome was compared with one derived from samples collected from unimpacted regions of the Oak Ridge Reservation subsurface. We assembled 1615 unique circularized DNA elements that we propose to be MGEs. The circular elements from the highly contaminated subsurface were enriched in HMRG clusters relative to those from the nearby unimpacted regions. Additionally, we found that these HMRGs were associated with Gamma and Betaproteobacteria hosts in the contaminated subsurface and potentially facilitate the persistence and dominance of these taxa in this region. Finally, the HMRGs were associated with conjugative elements, suggesting their potential for future lateral transfer. We demonstrate how our understanding of MGE ecology, evolution, and function can be enhanced through the genomic context provided by completed MGE assemblies.
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Affiliation(s)
- Jennifer L Goff
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, United States
- Department of Chemistry, State University of New York College of Environmental Science and Forestry, Syracuse, NY 13210, United States
| | - Lauren M Lui
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
| | - Torben N Nielsen
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
| | - Farris L Poole
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, United States
| | - Heidi J Smith
- Center for Biofilm Engineering, Montana State University, Bozeman, MT 59717, United States
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT 59717, United States
| | - Kathleen F Walker
- Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37916, United States
| | - Terry C Hazen
- Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37916, United States
- Genome Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States
| | - Matthew W Fields
- Center for Biofilm Engineering, Montana State University, Bozeman, MT 59717, United States
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT 59717, United States
| | - Adam P Arkin
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
- Department of Bioengineering, University of California, Berkeley, CA 94720, United States
| | - Michael W W Adams
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, United States
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10
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Hribovšek P, Olesin Denny E, Dahle H, Mall A, Øfstegaard Viflot T, Boonnawa C, Reeves EP, Steen IH, Stokke R. Putative novel hydrogen- and iron-oxidizing sheath-producing Zetaproteobacteria thrive at the Fåvne deep-sea hydrothermal vent field. mSystems 2023; 8:e0054323. [PMID: 37921472 PMCID: PMC10734525 DOI: 10.1128/msystems.00543-23] [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: 06/20/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023] Open
Abstract
IMPORTANCE Knowledge on microbial iron oxidation is important for understanding the cycling of iron, carbon, nitrogen, nutrients, and metals. The current study yields important insights into the niche sharing, diversification, and Fe(III) oxyhydroxide morphology of Ghiorsea, an iron- and hydrogen-oxidizing Zetaproteobacteria representative belonging to Zetaproteobacteria operational taxonomic unit 9. The study proposes that Ghiorsea exhibits a more extensive morphology of Fe(III) oxyhydroxide than previously observed. Overall, the results increase our knowledge on potential drivers of Zetaproteobacteria diversity in iron microbial mats and can eventually be used to develop strategies for the cultivation of sheath-forming Zetaproteobacteria.
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Affiliation(s)
- Petra Hribovšek
- Centre for Deep Sea Research, University of Bergen, Bergen, Norway
- Department of Earth Science, University of Bergen, Bergen, Norway
| | - Emily Olesin Denny
- Centre for Deep Sea Research, University of Bergen, Bergen, Norway
- Department of Biological Sciences, University of Bergen, Bergen, Norway
- Computational Biology Unit, University of Berge, Bergen, Norway
| | - Håkon Dahle
- Centre for Deep Sea Research, University of Bergen, Bergen, Norway
- Department of Biological Sciences, University of Bergen, Bergen, Norway
- Computational Biology Unit, University of Berge, Bergen, Norway
| | - Achim Mall
- Centre for Deep Sea Research, University of Bergen, Bergen, Norway
- Department of Biological Sciences, University of Bergen, Bergen, Norway
| | - Thomas Øfstegaard Viflot
- Centre for Deep Sea Research, University of Bergen, Bergen, Norway
- Department of Earth Science, University of Bergen, Bergen, Norway
| | - Chanakan Boonnawa
- Centre for Deep Sea Research, University of Bergen, Bergen, Norway
- Department of Earth Science, University of Bergen, Bergen, Norway
| | - Eoghan P. Reeves
- Centre for Deep Sea Research, University of Bergen, Bergen, Norway
- Department of Earth Science, University of Bergen, Bergen, Norway
| | - Ida Helene Steen
- Centre for Deep Sea Research, University of Bergen, Bergen, Norway
- Department of Biological Sciences, University of Bergen, Bergen, Norway
| | - Runar Stokke
- Centre for Deep Sea Research, University of Bergen, Bergen, Norway
- Department of Biological Sciences, University of Bergen, Bergen, Norway
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11
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Howell AA, Versoza CJ, Pfeifer SP. Computational host range prediction-The good, the bad, and the ugly. Virus Evol 2023; 10:vead083. [PMID: 38361822 PMCID: PMC10868548 DOI: 10.1093/ve/vead083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/05/2023] [Accepted: 12/19/2023] [Indexed: 02/17/2024] Open
Abstract
The rapid emergence and spread of antimicrobial resistance across the globe have prompted the usage of bacteriophages (i.e. viruses that infect bacteria) in a variety of applications ranging from agriculture to biotechnology and medicine. In order to effectively guide the application of bacteriophages in these multifaceted areas, information about their host ranges-that is the bacterial strains or species that a bacteriophage can successfully infect and kill-is essential. Utilizing sixteen broad-spectrum (polyvalent) bacteriophages with experimentally validated host ranges, we here benchmark the performance of eleven recently developed computational host range prediction tools that provide a promising and highly scalable supplement to traditional, but laborious, experimental procedures. We show that machine- and deep-learning approaches offer the highest levels of accuracy and precision-however, their predominant predictions at the species- or genus-level render them ill-suited for applications outside of an ecosystems metagenomics framework. In contrast, only moderate sensitivity (<80 per cent) could be reached at the strain-level, albeit at low levels of precision (<40 per cent). Taken together, these limitations demonstrate that there remains room for improvement in the active scientific field of in silico host prediction to combat the challenge of guiding experimental designs to identify the most promising bacteriophage candidates for any given application.
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Affiliation(s)
| | - Cyril J Versoza
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Susanne P Pfeifer
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
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12
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Wu R, Davison MR, Nelson WC, Smith ML, Lipton MS, Jansson JK, McClure RS, McDermott JE, Hofmockel KS. Hi-C metagenome sequencing reveals soil phage-host interactions. Nat Commun 2023; 14:7666. [PMID: 37996432 PMCID: PMC10667309 DOI: 10.1038/s41467-023-42967-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 10/27/2023] [Indexed: 11/25/2023] Open
Abstract
Bacteriophages are abundant in soils. However, the majority are uncharacterized, and their hosts are unknown. Here, we apply high-throughput chromosome conformation capture (Hi-C) to directly capture phage-host relationships. Some hosts have high centralities in bacterial community co-occurrence networks, suggesting phage infections have an important impact on the soil bacterial community interactions. We observe increased average viral copies per host (VPH) and decreased viral transcriptional activity following a two-week soil-drying incubation, indicating an increase in lysogenic infections. Soil drying also alters the observed phage host range. A significant negative correlation between VPH and host abundance prior to drying indicates more lytic infections result in more host death and inversely influence host abundance. This study provides empirical evidence of phage-mediated bacterial population dynamics in soil by directly capturing specific phage-host interactions.
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Affiliation(s)
- Ruonan Wu
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Michelle R Davison
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - William C Nelson
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Montana L Smith
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Mary S Lipton
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Janet K Jansson
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ryan S McClure
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Jason E McDermott
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR, USA
| | - Kirsten S Hofmockel
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
- Department of Agronomy, Iowa State University, Ames, IA, USA.
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13
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Fu P, Wu Y, Zhang Z, Qiu Y, Wang Y, Peng Y. VIGA: a one-stop tool for eukaryotic virus identification and genome assembly from next-generation-sequencing data. Brief Bioinform 2023; 25:bbad444. [PMID: 38048079 PMCID: PMC10753531 DOI: 10.1093/bib/bbad444] [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: 04/27/2023] [Revised: 10/26/2023] [Accepted: 11/11/2023] [Indexed: 12/05/2023] Open
Abstract
Identification of viruses and further assembly of viral genomes from the next-generation-sequencing data are essential steps in virome studies. This study presented a one-stop tool named VIGA (available at https://github.com/viralInformatics/VIGA) for eukaryotic virus identification and genome assembly from NGS data. It was composed of four modules, namely, identification, taxonomic annotation, assembly and novel virus discovery, which integrated several third-party tools such as BLAST, Trinity, MetaCompass and RagTag. Evaluation on multiple simulated and real virome datasets showed that VIGA assembled more complete virus genomes than its competitors on both the metatranscriptomic and metagenomic data and performed well in assembling virus genomes at the strain level. Finally, VIGA was used to investigate the virome in metatranscriptomic data from the Human Microbiome Project and revealed different composition and positive rate of viromes in diseases of prediabetes, Crohn's disease and ulcerative colitis. Overall, VIGA would help much in identification and characterization of viromes, especially the known viruses, in future studies.
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Affiliation(s)
- Ping Fu
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Yifan Wu
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Zhiyuan Zhang
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Ye Qiu
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Yirong Wang
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Yousong Peng
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
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14
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Wu Z, Cai P, Liang E, Chen Q, Sun W, Wang J. Distinct adaptive strategies and microbial interactions of soil viruses under different metal(loid) contaminations. JOURNAL OF HAZARDOUS MATERIALS 2023; 460:132347. [PMID: 37619274 DOI: 10.1016/j.jhazmat.2023.132347] [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: 06/07/2023] [Revised: 08/05/2023] [Accepted: 08/17/2023] [Indexed: 08/26/2023]
Abstract
Viruses, as the most abundant organisms, significantly influence ecological function and microbial survival in soils, yet little was known about how viruses and virus-microbe interactions respond to environmental stresses induced by metal(loid) contaminations. Here, we conducted the metagenomic analysis to investigate the adaptative mechanisms of soil viruses under different metal(loid) contamination levels. By capturing a catalogue of 23,066 viruses, we found that viral communities exhibited the increased richness, diversity, and the temperate to lytic ratio in facing the highest metal(loid) contaminations. Meanwhile, viruses displayed obvious lineage-specific infection modes to distinct dominant hosts under different pollution levels. Viral functions linking to the inhibition of transcription and the enhancement of DNA repairment as well as multiple resistance not only contributed to coping with elevated multiple metal(loid) stresses, but also facilitated the adaptation and functioning of viral hosts. Moreover, the harmonious coexistence of viruses and resistant/pathogenic bacteria under the heaviest contaminations potentially exacerbated disseminating resistance and pathogenicity, while viruses under the lightest contaminations might be natural predators of resistant/pathogenic bacteria through lysing host cells. Overall, this study highlights the ecological importance of viral adaptation and the interactions between viruses and resistant/pathogenic bacteria in contaminated environments, contributing to developing virus-based approaches to soil restoration.
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Affiliation(s)
- Zongzhi Wu
- College of Environmental Sciences and Engineering, Key Laboratory of Water and Sediment Sciences, Ministry of Education, Peking University, Beijing 100871, China; School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Pinggui Cai
- School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Enhang Liang
- College of Environmental Sciences and Engineering, Key Laboratory of Water and Sediment Sciences, Ministry of Education, Peking University, Beijing 100871, China
| | - Qian Chen
- College of Environmental Sciences and Engineering, Key Laboratory of Water and Sediment Sciences, Ministry of Education, Peking University, Beijing 100871, China
| | - Weiling Sun
- College of Environmental Sciences and Engineering, Key Laboratory of Water and Sediment Sciences, Ministry of Education, Peking University, Beijing 100871, China
| | - Jiawen Wang
- College of Environmental Sciences and Engineering, Key Laboratory of Water and Sediment Sciences, Ministry of Education, Peking University, Beijing 100871, China; School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
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15
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Peng Y, Lu Z, Pan D, Shi LD, Zhao Z, Liu Q, Zhang C, Jia K, Li J, Hubert CRJ, Dong X. Viruses in deep-sea cold seep sediments harbor diverse survival mechanisms and remain genetically conserved within species. THE ISME JOURNAL 2023; 17:1774-1784. [PMID: 37573455 PMCID: PMC10504277 DOI: 10.1038/s41396-023-01491-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 08/01/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023]
Abstract
Deep sea cold seep sediments have been discovered to harbor novel, abundant, and diverse bacterial and archaeal viruses. However, little is known about viral genetic features and evolutionary patterns in these environments. Here, we examined the evolutionary ecology of viruses across active and extinct seep stages in the area of Haima cold seeps in the South China Sea. A total of 338 viral operational taxonomic units are identified and linked to 36 bacterial and archaeal phyla. The dynamics of host-virus interactions are informed by diverse antiviral defense systems across 43 families found in 487 microbial genomes. Cold seep viruses are predicted to harbor diverse adaptive strategies to persist in this environment, including counter-defense systems, auxiliary metabolic genes, reverse transcriptases, and alternative genetic code assignments. Extremely low nucleotide diversity is observed in cold seep viral populations, being influenced by factors including microbial host, sediment depth, and cold seep stage. Most cold seep viral genes are under strong purifying selection with trajectories that differ depending on whether cold seeps are active or extinct. This work sheds light on the understanding of environmental adaptation mechanisms and evolutionary patterns of viruses in the sub-seafloor biosphere.
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Affiliation(s)
- Yongyi Peng
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
- School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
| | - Zijian Lu
- South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China
| | - Donald Pan
- School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Ling-Dong Shi
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhao Zhao
- School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China
| | - Qing Liu
- School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
| | - Chuwen Zhang
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China
| | - Kuntong Jia
- School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519082, China
| | - Jiwei Li
- Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya, 572000, China
| | - Casey R J Hubert
- Department of Biological Sciences, University of Calgary, Calgary, AB, T2N 1N4, Canada
| | - Xiyang Dong
- Key Laboratory of Marine Genetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China.
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China.
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16
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Gonzales MEM, Ureta JC, Shrestha AMS. Protein embeddings improve phage-host interaction prediction. PLoS One 2023; 18:e0289030. [PMID: 37486915 PMCID: PMC10365317 DOI: 10.1371/journal.pone.0289030] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/07/2023] [Indexed: 07/26/2023] Open
Abstract
With the growing interest in using phages to combat antimicrobial resistance, computational methods for predicting phage-host interactions have been explored to help shortlist candidate phages. Most existing models consider entire proteomes and rely on manual feature engineering, which poses difficulty in selecting the most informative sequence properties to serve as input to the model. In this paper, we framed phage-host interaction prediction as a multiclass classification problem that takes as input the embeddings of a phage's receptor-binding proteins, which are known to be the key machinery for host recognition, and predicts the host genus. We explored different protein language models to automatically encode these protein sequences into dense embeddings without the need for additional alignment or structural information. We show that the use of embeddings of receptor-binding proteins presents improvements over handcrafted genomic and protein sequence features. The highest performance was obtained using the transformer-based protein language model ProtT5, resulting in a 3% to 4% increase in weighted F1 and recall scores across different prediction confidence thresholds, compared to using selected handcrafted sequence features.
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Affiliation(s)
- Mark Edward M Gonzales
- Bioinformatics Laboratory, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, Philippines
- Department of Software Technology, College of Computer Studies, De La Salle University, Manila, Philippines
| | - Jennifer C Ureta
- Bioinformatics Laboratory, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, Philippines
- Department of Software Technology, College of Computer Studies, De La Salle University, Manila, Philippines
| | - Anish M S Shrestha
- Bioinformatics Laboratory, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, Philippines
- Systems and Computational Biology Research Unit, Center for Natural Sciences and Environmental Research, De La Salle University, Manila, Philippines
- Department of Software Technology, College of Computer Studies, De La Salle University, Manila, Philippines
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17
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Pan J, You W, Lu X, Wang S, You Z, Sun Y. GSPHI: A novel deep learning model for predicting phage-host interactions via multiple biological information. Comput Struct Biotechnol J 2023; 21:3404-3413. [PMID: 37397626 PMCID: PMC10314231 DOI: 10.1016/j.csbj.2023.06.014] [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: 02/23/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 07/04/2023] Open
Abstract
Emerging evidence suggests that due to the misuse of antibiotics, bacteriophage (phage) therapy has been recognized as one of the most promising strategies for treating human diseases infected by antibiotic-resistant bacteria. Identification of phage-host interactions (PHIs) can help to explore the mechanisms of bacterial response to phages and provide new insights into effective therapeutic approaches. Compared to conventional wet-lab experiments, computational models for predicting PHIs can not only save time and cost, but also be more efficient and economical. In this study, we developed a deep learning predictive framework called GSPHI to identify potential phage and target bacterium pairs through DNA and protein sequence information. More specifically, GSPHI first initialized the node representations of phages and target bacterial hosts via a natural language processing algorithm. Then a graph embedding algorithm structural deep network embedding (SDNE) was utilized to extract local and global information from the interaction network, and finally, a deep neural network (DNN) was applied to accurately detect the interactions between phages and their bacterial hosts. In the drug-resistant bacteria dataset ESKAPE, GSPHI achieved a prediction accuracy of 86.65 % and AUC of 0.9208 under the 5-fold cross-validation technique, significantly better than other methods. In addition, case studies in Gram-positive and negative bacterial species demonstrated that GSPHI is competent in detecting potential Phage-host interactions. Taken together, these results indicate that GSPHI can provide reasonable candidate sensitive bacteria to phages for biological experiments. The webserver of the GSPHI predictor is freely available at http://120.77.11.78/GSPHI/.
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Affiliation(s)
- Jie Pan
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, The College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Wencai You
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, The College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Xiaoliang Lu
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, The College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Shiwei Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, The College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Zhuhong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Yanmei Sun
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, The College of Life Sciences, Northwest University, Xi’an 710069, China
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18
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Simmonds P, Adriaenssens EM, Zerbini FM, Abrescia NGA, Aiewsakun P, Alfenas-Zerbini P, Bao Y, Barylski J, Drosten C, Duffy S, Duprex WP, Dutilh BE, Elena SF, García ML, Junglen S, Katzourakis A, Koonin EV, Krupovic M, Kuhn JH, Lambert AJ, Lefkowitz EJ, Łobocka M, Lood C, Mahony J, Meier-Kolthoff JP, Mushegian AR, Oksanen HM, Poranen MM, Reyes-Muñoz A, Robertson DL, Roux S, Rubino L, Sabanadzovic S, Siddell S, Skern T, Smith DB, Sullivan MB, Suzuki N, Turner D, Van Doorslaer K, Vandamme AM, Varsani A, Vasilakis N. Four principles to establish a universal virus taxonomy. PLoS Biol 2023; 21:e3001922. [PMID: 36780432 PMCID: PMC9925010 DOI: 10.1371/journal.pbio.3001922] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023] Open
Abstract
A universal taxonomy of viruses is essential for a comprehensive view of the virus world and for communicating the complicated evolutionary relationships among viruses. However, there are major differences in the conceptualisation and approaches to virus classification and nomenclature among virologists, clinicians, agronomists, and other interested parties. Here, we provide recommendations to guide the construction of a coherent and comprehensive virus taxonomy, based on expert scientific consensus. Firstly, assignments of viruses should be congruent with the best attainable reconstruction of their evolutionary histories, i.e., taxa should be monophyletic. This fundamental principle for classification of viruses is currently included in the International Committee on Taxonomy of Viruses (ICTV) code only for the rank of species. Secondly, phenotypic and ecological properties of viruses may inform, but not override, evolutionary relatedness in the placement of ranks. Thirdly, alternative classifications that consider phenotypic attributes, such as being vector-borne (e.g., "arboviruses"), infecting a certain type of host (e.g., "mycoviruses," "bacteriophages") or displaying specific pathogenicity (e.g., "human immunodeficiency viruses"), may serve important clinical and regulatory purposes but often create polyphyletic categories that do not reflect evolutionary relationships. Nevertheless, such classifications ought to be maintained if they serve the needs of specific communities or play a practical clinical or regulatory role. However, they should not be considered or called taxonomies. Finally, while an evolution-based framework enables viruses discovered by metagenomics to be incorporated into the ICTV taxonomy, there are essential requirements for quality control of the sequence data used for these assignments. Combined, these four principles will enable future development and expansion of virus taxonomy as the true evolutionary diversity of viruses becomes apparent.
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Affiliation(s)
- Peter Simmonds
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | - F. Murilo Zerbini
- Departamento de Fitopatologia/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Nicola G. A. Abrescia
- Structure and Cell Biology of Viruses Lab, Center for Cooperative Research in Biosciences—BRTA, Derio, Spain
- Basque Foundation for Science, IKERBASQUE, Bilbao, Spain
| | - Pakorn Aiewsakun
- Department of Microbiology, Faculty of Science, Mahidol University, Bangkok, Thailand
| | | | - Yiming Bao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jakub Barylski
- Department of Molecular Virology, Adam Mickiewicz University, Poznan, Poland
| | - Christian Drosten
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Free University Berlin, Humboldt University, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Siobain Duffy
- Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological Sciences, Rutgers The State University of New Jersey, New Brunswick, New Jersey, United States of America
| | - W. Paul Duprex
- The Center for Vaccine Research, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Bas E. Dutilh
- Institute of Biodiversity, Faculty of Biological Sciences, Cluster of Excellence Balance of the Microverse, Friedrich-Schiller-University, Jena, Germany
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, the Netherlands
| | - Santiago F. Elena
- Instituto de Biología Integrativa de Sistemas (I2SysBio), CSIC-Universitat de València, Valencia, Spain
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Maria Laura García
- Instituto de Biotecnología y Biología Molecular, CCT-La Plata, CONICET, UNLP, La Plata, Argentina
| | - Sandra Junglen
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Free University Berlin, Humboldt University, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Aris Katzourakis
- Department of Biology, University of Oxford, Oxford, United Kingdom
| | - Eugene V. Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Mart Krupovic
- Institut Pasteur, Université Paris Cité, CNRS UMR6047, Archaeal Virology Unit, Paris, France
| | - Jens H. Kuhn
- Integrated Research Facility at Fort Detrick (IRF-Frederick), National Institute of Allergy and Infectious Diseases, National Institutes of Health, Fort Detrick, Frederick, Maryland, United States of America
| | - Amy J. Lambert
- Division of Vector-Borne Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, United States of America
| | - Elliot J. Lefkowitz
- Department of Microbiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Małgorzata Łobocka
- Institute of Biochemistry and Biophysics of the Polish Academy of Sciences, Warsaw, Poland
| | - Cédric Lood
- Department of Biosystems, KU Leuven, Leuven, Belgium
| | - Jennifer Mahony
- School of Microbiology and APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Jan P. Meier-Kolthoff
- Department of Bioinformatics and Databases, Leibniz Institute DSMZ—German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany
| | - Arcady R. Mushegian
- Division of Molecular and Cellular Biosciences, National Science Foundation, Alexandria, Virginia, United States of America
| | - Hanna M. Oksanen
- Molecular and Integrative Biosciences Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Minna M. Poranen
- Molecular and Integrative Biosciences Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Alejandro Reyes-Muñoz
- Max Planck Tandem Group in Computational Biology, Departamento de Ciencias Biológicas, Universidad de los Andes, Bogotá, Colombia
| | - David L. Robertson
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Simon Roux
- Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Luisa Rubino
- Istituto per la Protezione Sostenibile delle Piante, CNR, UOS Bari, Bari, Italy
| | - Sead Sabanadzovic
- Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Mississippi State, Mississippi, United States of America
| | - Stuart Siddell
- School of Cellular and Molecular Medicine, Faculty of Life Sciences, University of Bristol, Bristol, United Kingdom
| | - Tim Skern
- Medical University of Vienna, Max Perutz Labs, Vienna Biocenter, Vienna, Austria
| | - Donald B. Smith
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Matthew B. Sullivan
- Departments of Microbiology and Civil, Environmental, and Geodetic Engineering, Ohio State University, Columbus, Ohio, United States of America
| | - Nobuhiro Suzuki
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Okayama, Japan
| | - Dann Turner
- School of Applied Sciences, College of Health, Science and Society, University of the West of England, Bristol, United Kingdom
| | - Koenraad Van Doorslaer
- School of Animal and Comparative Biomedical Sciences, Department of Immunobiology, BIO5 Institute, and University of Arizona Cancer Center, Tucson, Arizona, United States of America
| | - Anne-Mieke Vandamme
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Leuven, Belgium
- Center for Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Arvind Varsani
- The Biodesign Center for Fundamental and Applied Microbiomics, School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, United States of America
| | - Nikos Vasilakis
- Department of Pathology, Center of Vector-Borne and Zoonotic Diseases, Institute for Human Infection and Immunity and World Reference Center for Emerging Viruses and Arboviruses, The University of Texas Medical Branch, Galveston, Texas, United States of America
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19
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Bajiya N, Dhall A, Aggarwal S, Raghava GPS. Advances in the field of phage-based therapy with special emphasis on computational resources. Brief Bioinform 2023; 24:6961791. [PMID: 36575815 DOI: 10.1093/bib/bbac574] [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: 07/28/2022] [Revised: 11/07/2022] [Accepted: 11/25/2022] [Indexed: 12/29/2022] Open
Abstract
In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage-host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage-host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy's advantages, challenges and opportunities.
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Affiliation(s)
- Nisha Bajiya
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Suchet Aggarwal
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
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20
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Iuchi H, Kawasaki J, Kubo K, Fukunaga T, Hokao K, Yokoyama G, Ichinose A, Suga K, Hamada M. Bioinformatics approaches for unveiling virus-host interactions. Comput Struct Biotechnol J 2023; 21:1774-1784. [PMID: 36874163 PMCID: PMC9969756 DOI: 10.1016/j.csbj.2023.02.044] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.
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Affiliation(s)
- Hitoshi Iuchi
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan
| | - Junna Kawasaki
- Faculty of Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Kento Kubo
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Tsukasa Fukunaga
- Waseda Institute for Advanced Study, Waseda University, Nishi Waseda, Shinjuku-ku, Tokyo 169-0051, Japan
| | - Koki Hokao
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Gentaro Yokoyama
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Akiko Ichinose
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan
| | - Kanta Suga
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Michiaki Hamada
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan.,Graduate School of Medicine, Nippon Medical School, Tokyo 113-8602, Japan
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21
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Khan T, Raza S. Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review. Curr Top Med Chem 2023; 23:1640-1663. [PMID: 36725827 DOI: 10.2174/1568026623666230201144522] [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: 06/21/2022] [Revised: 11/14/2022] [Accepted: 12/19/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body. METHODS This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review. RESULTS Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes. CONCLUSION Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.
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Affiliation(s)
- Tahmeena Khan
- Department of Chemistry, Integral University, Lucknow, 226026, U.P., India
| | - Saman Raza
- Department of Chemistry, Isabella Thoburn College, Lucknow, 226007, U.P., India
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22
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Aggarwal S, Dhall A, Patiyal S, Choudhury S, Arora A, Raghava GPS. An ensemble method for prediction of phage-based therapy against bacterial infections. Front Microbiol 2023; 14:1148579. [PMID: 37032893 PMCID: PMC10076811 DOI: 10.3389/fmicb.2023.1148579] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/06/2023] [Indexed: 04/11/2023] Open
Abstract
Phage therapy is a viable alternative to antibiotics for treating microbial infections, particularly managing drug-resistant strains of bacteria. One of the major challenges in designing phage-based therapy is to identify the most appropriate potential phage candidate to treat bacterial infections. In this study, an attempt has been made to predict phage-host interactions with high accuracy to identify the potential bacteriophage that can be used for treating a bacterial infection. The developed models have been created using a training dataset containing 826 phage- host interactions, and have been evaluated on a validation dataset comprising 1,201 phage-host interactions. Firstly, alignment-based models have been developed using similarity between phage-phage (BLASTPhage), host-host (BLASTHost) and phage-CRISPR (CRISPRPred), where we achieved accuracy between 42.4-66.2% for BLASTPhage, 55-78.4% for BLASTHost, and 43.7-80.2% for CRISPRPred across five taxonomic levels. Secondly, alignment free models have been developed using machine learning techniques. Thirdly, hybrid models have been developed by integrating the alignment-free models and the similarity-scores where we achieved maximum performance of (60.6-93.5%). Finally, an ensemble model has been developed that combines the hybrid and alignment-based models. Our ensemble model achieved highest accuracy of 67.9, 80.6, 85.5, 90, and 93.5% at Genus, Family, Order, Class, and Phylum levels on validation dataset. In order to serve the scientific community, we have also developed a webserver named PhageTB and provided a standalone software package (https://webs.iiitd.edu.in/raghava/phagetb/) for the same.
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Affiliation(s)
- Suchet Aggarwal
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, New Delhi, India
| | - Anjali Dhall
- 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
| | - Shubham Choudhury
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Akanksha Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Gajendra P. S. Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
- *Correspondence: Gajendra P. S. Raghava,
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23
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Andrianjakarivony HF, Bettarel Y, Armougom F, Desnues C. Phage-Host Prediction Using a Computational Tool Coupled with 16S rRNA Gene Amplicon Sequencing. Viruses 2022; 15:76. [PMID: 36680116 PMCID: PMC9862649 DOI: 10.3390/v15010076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 12/29/2022] Open
Abstract
Metagenomics studies have revealed tremendous viral diversity in aquatic environments. Yet, while the genomic data they have provided is extensive, it is unannotated. For example, most phage sequences lack accurate information about their bacterial host, which prevents reliable phage identification and the investigation of phage-host interactions. This study aimed to take this knowledge further, using a viral metagenomic framework to decipher the composition and diversity of phage communities and to predict their bacterial hosts. To this end, we used water and sediment samples collected from seven sites with varying contamination levels in the Ebrié Lagoon in Abidjan, Ivory Coast. The bacterial communities were characterized using the 16S rRNA metabarcoding approach, and a framework was developed to investigate the virome datasets that: (1) identified phage contigs with VirSorter and VIBRANT; (2) classified these contigs with MetaPhinder using the phage database (taxonomic annotation); and (3) predicted the phages' bacterial hosts with a machine learning-based tool: the Prokaryotic Virus-Host Predictor. The findings showed that the taxonomic profiles of phages and bacteria were specific to sediment or water samples. Phage sequences assigned to the Microviridae family were widespread in sediment samples, whereas phage sequences assigned to the Siphoviridae, Myoviridae and Podoviridae families were predominant in water samples. In terms of bacterial communities, the phyla Latescibacteria, Zixibacteria, Bacteroidetes, Acidobacteria, Calditrichaeota, Gemmatimonadetes, Cyanobacteria and Patescibacteria were most widespread in sediment samples, while the phyla Epsilonbacteraeota, Tenericutes, Margulisbacteria, Proteobacteria, Actinobacteria, Planctomycetes and Marinimicrobia were most prevalent in water samples. Significantly, the relative abundance of bacterial communities (at major phylum level) estimated by 16S rRNA metabarcoding and phage-host prediction were significantly similar. These results demonstrate the reliability of this novel approach for predicting the bacterial hosts of phages from shotgun metagenomic sequencing data.
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Affiliation(s)
- Harilanto Felana Andrianjakarivony
- Microbes, Evolution, Phylogeny, and Infection (MEΦI), IHU—Méditerranée Infection, 19-21 Boulevard Jean Moulin, 13005 Marseille, France
- Microbiologie Environnementale Biotechnologie (MEB), Mediterranean Institute of Oceanography (MIO), 163 Avenue de Luminy, 13009 Marseille, France
| | - Yvan Bettarel
- MARBEC, Marine Biodiversity, Exploitation & Conservation, Université de Montpellier, CNRS, Ifremer, IRD, 093 Place Eugène Bataillon, 34090 Montpellier, France
| | - Fabrice Armougom
- Microbiologie Environnementale Biotechnologie (MEB), Mediterranean Institute of Oceanography (MIO), 163 Avenue de Luminy, 13009 Marseille, France
| | - Christelle Desnues
- Microbes, Evolution, Phylogeny, and Infection (MEΦI), IHU—Méditerranée Infection, 19-21 Boulevard Jean Moulin, 13005 Marseille, France
- Microbiologie Environnementale Biotechnologie (MEB), Mediterranean Institute of Oceanography (MIO), 163 Avenue de Luminy, 13009 Marseille, France
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24
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Liao M, Xie Y, Shi M, Cui J. Over two decades of research on the marine RNA virosphere. IMETA 2022; 1:e59. [PMID: 38867898 PMCID: PMC10989941 DOI: 10.1002/imt2.59] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/30/2022] [Accepted: 09/14/2022] [Indexed: 06/14/2024]
Abstract
RNA viruses (realm: Riboviria), including RNA phages and eukaryote-infecting RNA viruses, are essential components of marine ecosystems. A large number of marine RNA viruses have been discovered in the last two decades because of the rapid development of next-generation sequencing (NGS) technology. Indeed, the combination of NGS and state-of-the-art meta-omics methods (viromics, the study of all viruses in a specific environment) has led to a fundamental understanding of the taxonomy and genetic diversity of RNA viruses in the sea, suggesting the complex ecological roles played by RNA viruses in this complex ecosystem. Furthermore, comparisons of viromes in the context of highly variable marine niches reveal the biogeographic patterns and ecological impact of marine RNA viruses, whose role in global ecology is becoming increasingly clearer. In this review, we summarize the characteristics of the global marine RNA virosphere and outline the taxonomic hierarchy of RNA viruses with a specific focus on their ancient evolutionary history. We also review the development of methodology and the major progress resulting from its applications in RNA viromics. The aim of this review is not only to provide an in-depth understanding of multifaceted aspects of marine RNA viruses, but to offer future perspectives on developing a better methodology for discovery, and exploring the evolutionary origin and major ecological significance of marine RNA virosphere.
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Affiliation(s)
- Meng‐en Liao
- CAS Key Laboratory of Molecular Virology & Immunology, Institut Pasteur of Shanghai, Center for Biosafety Mega‐ScienceChinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Yunyi Xie
- CAS Key Laboratory of Molecular Virology & Immunology, Institut Pasteur of Shanghai, Center for Biosafety Mega‐ScienceChinese Academy of SciencesShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Mang Shi
- School of MedicineSun Yat‐sen UniversityShenzhen Campus of Sun Yat‐sen UniversityShenzhenChina
| | - Jie Cui
- CAS Key Laboratory of Molecular Virology & Immunology, Institut Pasteur of Shanghai, Center for Biosafety Mega‐ScienceChinese Academy of SciencesShanghaiChina
- Laboatory for Marine Biology and BiotechnologyPilot National Laboratory for Marine Science and Technology (Qingdao)QingdaoChina
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25
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Wu R, Smith CA, Buchko GW, Blaby IK, Paez-Espino D, Kyrpides NC, Yoshikuni Y, McDermott JE, Hofmockel KS, Cort JR, Jansson JK. Structural characterization of a soil viral auxiliary metabolic gene product - a functional chitosanase. Nat Commun 2022; 13:5485. [PMID: 36123347 PMCID: PMC9485262 DOI: 10.1038/s41467-022-32993-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/26/2022] [Indexed: 11/12/2022] Open
Abstract
Metagenomics is unearthing the previously hidden world of soil viruses. Many soil viral sequences in metagenomes contain putative auxiliary metabolic genes (AMGs) that are not associated with viral replication. Here, we establish that AMGs on soil viruses actually produce functional, active proteins. We focus on AMGs that potentially encode chitosanase enzymes that metabolize chitin - a common carbon polymer. We express and functionally screen several chitosanase genes identified from environmental metagenomes. One expressed protein showing endo-chitosanase activity (V-Csn) is crystalized and structurally characterized at ultra-high resolution, thus representing the structure of a soil viral AMG product. This structure provides details about the active site, and together with structure models determined using AlphaFold, facilitates understanding of substrate specificity and enzyme mechanism. Our findings support the hypothesis that soil viruses contribute auxiliary functions to their hosts.
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Affiliation(s)
- Ruonan Wu
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Clyde A Smith
- Stanford Synchrotron Radiation Light source, Stanford University, Menlo Park, CA, USA
| | - Garry W Buchko
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
- School of Molecular Biosciences, Washington State University, Pullman, WA, USA
| | - Ian K Blaby
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Nikos C Kyrpides
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Yasuo Yoshikuni
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jason E McDermott
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Molecular Microbiology and Immunology, Oregon Health & Science University, Portland, OR, USA
| | - Kirsten S Hofmockel
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - John R Cort
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
- Institute of Biological Chemistry, Washington State University, Pullman, WA, USA
| | - Janet K Jansson
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
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26
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Li J, Yang F, Xiao M, Li A. Advances and challenges in cataloging the human gut virome. Cell Host Microbe 2022; 30:908-916. [PMID: 35834962 DOI: 10.1016/j.chom.2022.06.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022]
Abstract
The human gut virome, which is often referred to as the "dark matter" of the gut microbiome, remains understudied. A better understanding of the composition and variations of the gut virome across populations is critical for exploring its impact on diseases and health. A series of advances in the characterization of human gut virome have unveiled high genetic diversity and various functional potentials of gut viruses. Here, we summarize the recently available human gut virome databases and discuss their features, procedures, and challenges with the intention to provide a reference to researchers to use while choosing a profiling database. We also propose a "best practice" for cataloging the viral population.
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Affiliation(s)
- Junhua Li
- BGI-Shenzhen, Shenzhen 518083, China; Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen 518083, China.
| | | | - Minfeng Xiao
- BGI-Shenzhen, Shenzhen 518083, China; Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen 518083, China.
| | - Aixin Li
- BGI-Shenzhen, Shenzhen 518083, China; Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen 518083, China
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27
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Zhou F, Gan R, Zhang F, Ren C, Yu L, Si Y, Huang Z. PHISDetector: A Tool to Detect Diverse In Silico Phage-host Interaction Signals for Virome Studies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:508-523. [PMID: 35272051 PMCID: PMC9801046 DOI: 10.1016/j.gpb.2022.02.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/22/2021] [Accepted: 02/28/2022] [Indexed: 01/26/2023]
Abstract
Phage-microbe interactions are appealing systems to study coevolution, and have also been increasingly emphasized due to their roles in human health, disease, and the development of novel therapeutics. Phage-microbe interactions leave diverse signals in bacterial and phage genomic sequences, defined as phage-host interaction signals (PHISs), which include clustered regularly interspaced short palindromic repeats (CRISPR) targeting, prophage, and protein-protein interaction signals. In the present study, we developed a novel tool phage-host interaction signal detector (PHISDetector) to predict phage-host interactions by detecting and integrating diverse in silico PHISs, and scoring the probability of phage-host interactions using machine learning models based on PHIS features. We evaluated the performance of PHISDetector on multiple benchmark datasets and application cases. When tested on a dataset of 758 annotated phage-host pairs, PHISDetector yields the prediction accuracies of 0.51 and 0.73 at the species and genus levels, respectively, outperforming other phage-host prediction tools. When applied to on 125,842 metagenomic viral contigs (mVCs) derived from 3042 geographically diverse samples, a detection rate of 54.54% could be achieved. Furthermore, PHISDetector could predict infecting phages for 85.6% of 368 multidrug-resistant (MDR) bacteria and 30% of 454 human gut bacteria obtained from the National Institutes of Health (NIH) Human Microbiome Project (HMP). The PHISDetector can be run either as a web server (http://www.microbiome-bigdata.com/PHISDetector/) for general users to study individual inputs or as a stand-alone version (https://github.com/HIT-ImmunologyLab/PHISDetector) to process massive phage contigs from virome studies.
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Affiliation(s)
- Fengxia Zhou
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Rui Gan
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Fan Zhang
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Chunyan Ren
- Department of Hematology/oncology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ling Yu
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Yu Si
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China
| | - Zhiwei Huang
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150080, China,Corresponding author.
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28
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Chen H, Zhu Z, Qiu Y, Ge X, Zheng H, Peng Y. Prediction of coronavirus 3C-like protease cleavage sites using machine-learning algorithms. Virol Sin 2022; 37:437-444. [PMID: 35513273 PMCID: PMC9060714 DOI: 10.1016/j.virs.2022.04.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 04/02/2022] [Indexed: 12/05/2022] Open
Abstract
The coronavirus 3C-like (3CL) protease, a cysteine protease, plays an important role in viral infection and immune escape. However, there is still a lack of effective tools for determining the cleavage sites of the 3CL protease. This study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein, and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus, Betacoronavirus and Gammacoronavirus. Strong residue preferences were observed at the neighboring positions of the cleavage sites. A random forest (RF) model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes, and the model achieved an AUC of 0.96 in cross-validations. The RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus hosts. It achieved an AUC of 0.95 and predicted correctly 80% of the cleavage sites. Then, 1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF model. These proteins were enriched in several GO terms related to the cytoskeleton, such as the microtubule, actin and tubulin. Finally, a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF model. Overall, the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses. A view of the diversity of the cleavage sites by the coronavirus 3CL protease. An accurate RF model for predicting the cleavage of coronavirus 3CL protease. A web-server named 3CLP for predicting the cleavage of coronavirus 3CL protease. The 3CLP is available at http://www.computationalbiology.cn/3CLPHost/home.html. 1352 human proteins were predicted to be cleaved by the coronavirus 3CL protease.
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Affiliation(s)
- Huiting Chen
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Zhaozhong Zhu
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Ye Qiu
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Xingyi Ge
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Heping Zheng
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China
| | - Yousong Peng
- Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha 410082, China.
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29
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Versoza CJ, Pfeifer SP. Computational Prediction of Bacteriophage Host Ranges. Microorganisms 2022; 10:149. [PMID: 35056598 PMCID: PMC8778386 DOI: 10.3390/microorganisms10010149] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/06/2022] [Accepted: 01/11/2022] [Indexed: 12/27/2022] Open
Abstract
Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a bacteriophage, i.e., the bacterial genera, species, and strains a bacteriophage is able to infect. Although experimental explorations of host ranges remain the gold standard, such investigations are inherently limited to a small number of viruses and bacteria amendable to cultivation. Here, we review recently developed bioinformatic tools that offer a promising and high-throughput alternative by computationally predicting the putative host ranges of bacteriophages, including those challenging to grow in laboratory environments.
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Affiliation(s)
- Cyril J. Versoza
- Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA;
| | - Susanne P. Pfeifer
- Center for Mechanisms of Evolution, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
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30
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Zuppi M, Hendrickson HL, O’Sullivan JM, Vatanen T. Phages in the Gut Ecosystem. Front Cell Infect Microbiol 2022; 11:822562. [PMID: 35059329 PMCID: PMC8764184 DOI: 10.3389/fcimb.2021.822562] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 12/10/2021] [Indexed: 12/23/2022] Open
Abstract
Phages, short for bacteriophages, are viruses that specifically infect bacteria and are the most abundant biological entities on earth found in every explored environment, from the deep sea to the Sahara Desert. Phages are abundant within the human biome and are gaining increasing recognition as potential modulators of the gut ecosystem. For example, they have been connected to gastrointestinal diseases and the treatment efficacy of Fecal Microbiota Transplant. The ability of phages to modulate the human gut microbiome has been attributed to the predation of bacteria or the promotion of bacterial survival by the transfer of genes that enhance bacterial fitness upon infection. In addition, phages have been shown to interact with the human immune system with variable outcomes. Despite the increasing evidence supporting the importance of phages in the gut ecosystem, the extent of their influence on the shape of the gut ecosystem is yet to be fully understood. Here, we discuss evidence for phage modulation of the gut microbiome, postulating that phages are pivotal contributors to the gut ecosystem dynamics. We therefore propose novel research questions to further elucidate the role(s) that they have within the human ecosystem and its impact on our health and well-being.
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Affiliation(s)
- Michele Zuppi
- The Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Heather L. Hendrickson
- The School of Natural and Computational Sciences, Massey University, Auckland, New Zealand
| | - Justin M. O’Sullivan
- The Liggins Institute, University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
| | - Tommi Vatanen
- The Liggins Institute, University of Auckland, Auckland, New Zealand
- The Broad Institute of MIT and Harvard, Cambridge, MA, United States
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Abstract
Motivation Phage–host associations play important roles in microbial communities. But in natural communities, as opposed to culture-based lab studies where phages are discovered and characterized metagenomically, their hosts are generally not known. Several programs have been developed for predicting which phage infects which host based on various sequence similarity measures or machine learning approaches. These are often based on whole viral and host genomes, but in metagenomics-based studies, we rarely have whole genomes but rather must rely on contigs that are sometimes as short as hundreds of bp long. Therefore, we need programs that predict hosts of phage contigs on the basis of these short contigs. Although most existing programs can be applied to metagenomic datasets for these predictions, their accuracies are generally low. Here, we develop ContigNet, a convolutional neural network-based model capable of predicting phage–host matches based on relatively short contigs, and compare it to previously published VirHostMatcher (VHM) and WIsH. Results On the validation set, ContigNet achieves 72–85% area under the receiver operating characteristic curve (AUROC) scores, compared to the maximum of 68% by VHM or WIsH for contigs of lengths between 200 bps to 50 kbps. We also apply the model to the Metagenomic Gut Virus (MGV) catalogue, a dataset containing a wide range of draft genomes from metagenomic samples and achieve 60–70% AUROC scores compared to that of VHM and WIsH of 52%. Surprisingly, ContigNet can also be used to predict plasmid-host contig associations with high accuracy, indicating a similar genetic exchange between mobile genetic elements and their hosts. Availability and implementation The source code of ContigNet and related datasets can be downloaded from https://github.com/tianqitang1/ContigNet.
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Affiliation(s)
- Tianqi Tang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Shengwei Hou
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Marine and Environmental Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Jed A Fuhrman
- Marine and Environmental Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Fengzhu Sun
- To whom correspondence should be addressed. E-mail:
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32
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Shang J, Sun Y. Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning. BMC Biol 2021; 19:250. [PMID: 34819064 PMCID: PMC8611875 DOI: 10.1186/s12915-021-01180-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/29/2021] [Indexed: 11/23/2022] Open
Abstract
Background Prokaryotic viruses, which infect bacteria and archaea, are the most abundant and diverse biological entities in the biosphere. To understand their regulatory roles in various ecosystems and to harness the potential of bacteriophages for use in therapy, more knowledge of viral-host relationships is required. High-throughput sequencing and its application to the microbiome have offered new opportunities for computational approaches for predicting which hosts particular viruses can infect. However, there are two main challenges for computational host prediction. First, the empirically known virus-host relationships are very limited. Second, although sequence similarity between viruses and their prokaryote hosts have been used as a major feature for host prediction, the alignment is either missing or ambiguous in many cases. Thus, there is still a need to improve the accuracy of host prediction. Results In this work, we present a semi-supervised learning model, named HostG, to conduct host prediction for novel viruses. We construct a knowledge graph by utilizing both virus-virus protein similarity and virus-host DNA sequence similarity. Then graph convolutional network (GCN) is adopted to exploit viruses with or without known hosts in training to enhance the learning ability. During the GCN training, we minimize the expected calibrated error (ECE) to ensure the confidence of the predictions. We tested HostG on both simulated and real sequencing data and compared its performance with other state-of-the-art methods specifically designed for virus host classification (VHM-net, WIsH, PHP, HoPhage, RaFAH, vHULK, and VPF-Class). Conclusion HostG outperforms other popular methods, demonstrating the efficacy of using a GCN-based semi-supervised learning approach. A particular advantage of HostG is its ability to predict hosts from new taxa. Supplementary Information The online version contains supplementary material available at (10.1186/s12915-021-01180-4).
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Affiliation(s)
- Jiayu Shang
- Electrical Engineering, City University of Hong Kong, Hong Kong, China
| | - Yanni Sun
- Electrical Engineering, City University of Hong Kong, Hong Kong, China.
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33
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Zielezinski A, Barylski J, Karlowski WM. Taxonomy-aware, sequence similarity ranking reliably predicts phage-host relationships. BMC Biol 2021; 19:223. [PMID: 34625070 PMCID: PMC8501573 DOI: 10.1186/s12915-021-01146-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 09/06/2021] [Indexed: 12/02/2022] Open
Abstract
Background Characterizing phage–host interactions is critical to understanding the ecological role of both partners and effective isolation of phage therapeuticals. Unfortunately, experimental methods for studying these interactions are markedly slow, low-throughput, and unsuitable for phages or hosts difficult to maintain in laboratory conditions. Therefore, a number of in silico methods emerged to predict prokaryotic hosts based on viral sequences. One of the leading approaches is the application of the BLAST tool that searches for local similarities between viral and microbial genomes. However, this prediction method has three major limitations: (i) top-scoring sequences do not always point to the actual host; (ii) mosaic virus genomes may match to many, typically related, bacteria; and (iii) viral and host sequences may diverge beyond the point where their relationship can be detected by a BLAST alignment. Results We created an extension to BLAST, named Phirbo, that improves host prediction quality beyond what is obtainable from standard BLAST searches. The tool harnesses information concerning sequence similarity and bacteria relatedness to predict phage–host interactions. Phirbo was evaluated on three benchmark sets of known virus–host pairs, and it improved precision and recall by 11–40 percentage points over currently available, state-of-the-art, alignment-based, alignment-free, and machine-learning host prediction tools. Moreover, the discriminatory power of Phirbo for the recognition of virus–host relationships surpassed the results of other tools by at least 10 percentage points (area under the curve = 0.95), yielding a mean host prediction accuracy of 57% and 68% at the genus and family levels, respectively, and drops by 12 percentage points when using only a fraction of viral genome sequences (3 kb). Finally, we provide insights into a repertoire of protein and ncRNA genes that are shared between phages and hosts and may be prone to horizontal transfer during infection. Conclusions Our results suggest that Phirbo is a simple and effective tool for predicting phage–host relationships. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-021-01146-6.
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Affiliation(s)
- Andrzej Zielezinski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University Poznan, Uniwersytetu Poznanskiego 6, 61-614, Poznan, Poland.
| | - Jakub Barylski
- Molecular Virology Research Unit, Faculty of Biology, Adam Mickiewicz University Poznan, Uniwersytetu Poznanskiego 6, 61-614, Poznan, Poland
| | - Wojciech M Karlowski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University Poznan, Uniwersytetu Poznanskiego 6, 61-614, Poznan, Poland.
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34
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Li M, Zhang W. PHIAF: prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion. Brief Bioinform 2021; 23:6362109. [PMID: 34472593 DOI: 10.1093/bib/bbab348] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 07/05/2021] [Accepted: 07/18/2021] [Indexed: 01/01/2023] Open
Abstract
Phage therapy has become one of the most promising alternatives to antibiotics in the treatment of bacterial diseases, and identifying phage-host interactions (PHIs) helps to understand the possible mechanism through which a phage infects bacteria to guide the development of phage therapy. Compared with wet experiments, computational methods of identifying PHIs can reduce costs and save time and are more effective and economic. In this paper, we propose a PHI prediction method with a generative adversarial network (GAN)-based data augmentation and sequence-based feature fusion (PHIAF). First, PHIAF applies a GAN-based data augmentation module, which generates pseudo PHIs to alleviate the data scarcity. Second, PHIAF fuses the features originated from DNA and protein sequences for better performance. Third, PHIAF utilizes an attention mechanism to consider different contributions of DNA/protein sequence-derived features, which also provides interpretability of the prediction model. In computational experiments, PHIAF outperforms other state-of-the-art PHI prediction methods when evaluated via 5-fold cross-validation (AUC and AUPR are 0.88 and 0.86, respectively). An ablation study shows that data augmentation, feature fusion and an attention mechanism are all beneficial to improve the prediction performance of PHIAF. Additionally, four new PHIs with the highest PHIAF score in the case study were verified by recent literature. In conclusion, PHIAF is a promising tool to accelerate the exploration of phage therapy.
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Affiliation(s)
- Menglu Li
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
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35
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Tan J, Fang Z, Wu S, Guo Q, Jiang X, Zhu H. HoPhage: an ab initio tool for identifying hosts of phage fragments from metaviromes. Bioinformatics 2021; 38:543-545. [PMID: 34383025 PMCID: PMC8723153 DOI: 10.1093/bioinformatics/btab585] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 07/27/2021] [Accepted: 08/10/2021] [Indexed: 02/03/2023] Open
Abstract
SUMMARY We present HoPhage (Host of Phage) to identify the host of a given phage fragment from metavirome data at the genus level. HoPhage integrates two modules using a deep learning algorithm and a Markov chain model, respectively. HoPhage achieves 47.90% and 82.47% mean accuracy at the genus and phylum levels for ∼1-kb long artificial phage fragments when predicting host among 50 genera, representing 7.54-20.22% and 13.55-24.31% improvement, respectively. By testing on three real virome samples, HoPhage yields 81.11% mean accuracy at the genus level within a much broader candidate host range. AVAILABILITY AND IMPLEMENTATION HoPhage is available at http://cqb.pku.edu.cn/ZhuLab/HoPhage/data/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jie Tan
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Zhencheng Fang
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Shufang Wu
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Qian Guo
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Xiaoqing Jiang
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering and Center for Quantitative Biology, Peking University, Beijing 100871, China
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36
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Global overview and major challenges of host prediction methods for uncultivated phages. Curr Opin Virol 2021; 49:117-126. [PMID: 34126465 DOI: 10.1016/j.coviro.2021.05.003] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/20/2021] [Accepted: 05/22/2021] [Indexed: 12/14/2022]
Abstract
Bacterial communities play critical roles across all of Earth's biomes, affecting human health and global ecosystem functioning. They do so under strong constraints exerted by viruses, that is, bacteriophages or 'phages'. Phages can reshape bacterial communities' structure, influence long-term evolution of bacterial populations, and alter host cell metabolism during infection. Metagenomics approaches, that is, shotgun sequencing of environmental DNA or RNA, recently enabled large-scale exploration of phage genomic diversity, yielding several millions of phage genomes now to be further analyzed and characterized. One major challenge however is the lack of direct host information for these phages. Several methods and tools have been proposed to bioinformatically predict the potential host(s) of uncultivated phages based only on genome sequence information. Here we review these different approaches and highlight their distinct strengths and limitations. We also outline complementary experimental assays which are being proposed to validate and refine these bioinformatic predictions.
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Computational Viromics: Applications of the Computational Biology in Viromics Studies. Virol Sin 2021; 36:1256-1260. [PMID: 34057678 PMCID: PMC8165334 DOI: 10.1007/s12250-021-00395-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 04/14/2021] [Indexed: 12/30/2022] Open
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