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Rahimian M, Panahi B. Metagenome sequence data mining for viral interaction studies: Review on progress and prospects. Virus Res 2024; 349:199450. [PMID: 39151562 PMCID: PMC11388672 DOI: 10.1016/j.virusres.2024.199450] [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: 07/13/2024] [Revised: 08/11/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
Metagenomics has been greatly accelerated by the development of next-generation sequencing (NGS) technologies, which allow scientists to discover and describe novel microorganisms without the need for conventional culture techniques. Examining integrative bioinformatics methods used in viral interaction research, this study highlights metagenomic data from various contexts. Accurate viral identification depends on high-purity genetic material extraction, appropriate NGS platform selection, and sophisticated bioinformatics tools like VirPipe and VirFinder. The efficiency and precision of metagenomic analysis are further improved with the advent of AI-based techniques. The diversity and dynamics of viral communities are demonstrated by case studies from a variety of environments, emphasizing the seasonal and geographical variations that influence viral populations. In addition to speeding up the discovery of new viruses, metagenomics offers thorough understanding of virus-host interactions and their ecological effects. This review provides a promising framework for comprehending the complexity of viral communities and their interactions with hosts, highlighting the transformational potential of metagenomics and bioinformatics in viral research.
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Affiliation(s)
- Mohammadreza Rahimian
- Department of Biology, Faculty of Basic Sciences, University of Maragheh, Maragheh, Iran
| | - Bahman Panahi
- Department of Genomics, Branch for Northwest & West Region, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran.
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3
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Johnson MA, Vinatzer BA, Li S. Reference-Free Plant Disease Detection Using Machine Learning and Long-Read Metagenomic Sequencing. Appl Environ Microbiol 2023; 89:e0026023. [PMID: 37184398 PMCID: PMC10304783 DOI: 10.1128/aem.00260-23] [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: 02/16/2023] [Accepted: 04/14/2023] [Indexed: 05/16/2023] Open
Abstract
Surveillance for early disease detection is crucial to reduce the threat of plant diseases to food security. Metagenomic sequencing and taxonomic classification have recently been used to detect and identify plant pathogens. However, for an emerging pathogen, its genome may not be similar enough to any public genome to permit reference-based tools to identify infected samples. Also, in the case of point-of care diagnosis in the field, database access may be limited. Therefore, here we explore reference-free detection of plant pathogens using metagenomic sequencing and machine learning (ML). We used long-read metagenomes from healthy and infected plants as our model system and constructed k-mer frequency tables to test eight different ML models. The accuracy in classifying individual reads as coming from a healthy or infected metagenome were compared. Of all models, random forest (RF) had the best combination of short run-time and high accuracy (over 0.90) using tomato metagenomes. We further evaluated the RF model with a different tomato sample infected with the same pathogen or a different pathogen and a grapevine sample infected with a grapevine pathogen and achieved similar performances. ML models can thus learn features to successfully perform reference-free detection of plant diseases whereby a model trained with one pathogen-host system can also be used to detect different pathogens on different hosts. Potential and challenges of applying ML to metagenomics in plant disease detection are discussed. IMPORTANCE Climate change may lead to the emergence of novel plant diseases caused by yet unknown pathogens. Surveillance for emerging plant diseases is crucial to reduce their threat to food security. However, conventional genomic based methods require knowledge of existing plant pathogens and cannot be applied to detecting newly emerged pathogens. In this work, we explored reference-free, meta-genomic sequencing-based disease detection using machine learning. By sequencing the genomes of all microbial species extracted from an infected plant sample, we were able to train machine learning models to accurately classify individual sequencing reads as coming from a healthy or an infected plant sample. This method has the potential to be integrated into a generic pipeline for a meta-genomic based plant disease surveillance approach but also has limitations that still need to be overcome.
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Affiliation(s)
- Marcela A. Johnson
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, Virginia, USA
- Graduate Program in Genetics, Bioinformatics, and Computational Biology, Virginia Tech, Blacksburg, Virginia, USA
| | - Boris A. Vinatzer
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, Virginia, USA
| | - Song Li
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, Virginia, USA
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Haegeman A, Foucart Y, De Jonghe K, Goedefroit T, Al Rwahnih M, Boonham N, Candresse T, Gaafar YZA, Hurtado-Gonzales OP, Kogej Zwitter Z, Kutnjak D, Lamovšek J, Lefebvre M, Malapi M, Mavrič Pleško I, Önder S, Reynard JS, Salavert Pamblanco F, Schumpp O, Stevens K, Pal C, Tamisier L, Ulubaş Serçe Ç, van Duivenbode I, Waite DW, Hu X, Ziebell H, Massart S. Looking beyond Virus Detection in RNA Sequencing Data: Lessons Learned from a Community-Based Effort to Detect Cellular Plant Pathogens and Pests. PLANTS (BASEL, SWITZERLAND) 2023; 12:2139. [PMID: 37299118 PMCID: PMC10255714 DOI: 10.3390/plants12112139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 05/26/2023] [Accepted: 05/27/2023] [Indexed: 06/12/2023]
Abstract
High-throughput sequencing (HTS), more specifically RNA sequencing of plant tissues, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases. In this way, they are neglecting sequences without homologies to viruses, which usually represent the majority of sequencing reads. We hypothesized that traces of other pathogens might be detected in this unused sequence data. In the present study, our goal was to investigate whether total RNA-seq data, as generated for plant virus detection, is also suitable for the detection of other plant pathogens and pests. As proof of concept, we first analyzed RNA-seq datasets of plant materials with confirmed infections by cellular pathogens in order to check whether these non-viral pathogens could be easily detected in the data. Next, we set up a community effort to re-analyze existing Illumina RNA-seq datasets used for virus detection to check for the potential presence of non-viral pathogens or pests. In total, 101 datasets from 15 participants derived from 51 different plant species were re-analyzed, of which 37 were selected for subsequent in-depth analyses. In 29 of the 37 selected samples (78%), we found convincing traces of non-viral plant pathogens or pests. The organisms most frequently detected in this way were fungi (15/37 datasets), followed by insects (13/37) and mites (9/37). The presence of some of the detected pathogens was confirmed by independent (q)PCRs analyses. After communicating the results, 6 out of the 15 participants indicated that they were unaware of the possible presence of these pathogens in their sample(s). All participants indicated that they would broaden the scope of their bioinformatic analyses in future studies and thus check for the presence of non-viral pathogens. In conclusion, we show that it is possible to detect non-viral pathogens or pests from total RNA-seq datasets, in this case primarily fungi, insects, and mites. With this study, we hope to raise awareness among plant virologists that their data might be useful for fellow plant pathologists in other disciplines (mycology, entomology, bacteriology) as well.
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Affiliation(s)
- Annelies Haegeman
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), 9820 Merelbeke, Belgium
| | - Yoika Foucart
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), 9820 Merelbeke, Belgium
| | - Kris De Jonghe
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), 9820 Merelbeke, Belgium
| | - Thomas Goedefroit
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), 9820 Merelbeke, Belgium
| | - Maher Al Rwahnih
- Foundation Plant Services, Department of Plant Pathology, University of California, Davis, CA 95616, USA
| | - Neil Boonham
- School of Natural and Environmental Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
| | - Thierry Candresse
- UMR 1332 Biologie du Fruit et Pathologie, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Bordeaux, 33882 Villenave-d’Ornon, France
| | - Yahya Z. A. Gaafar
- Centre for Plant Health, Canadian Food Inspection Agency, 8801 East Saanich Road, North Saanich, BC V8L 1H3, Canada
| | - Oscar P. Hurtado-Gonzales
- Plant Germplasm Quarantine Program, Animal and Plant Health Inspection Service, United States Department of Agriculture (USDA-APHIS), Beltsville, ML 20705, USA
| | - Zala Kogej Zwitter
- Department of Biotechnology and Systems Biology, National Institute of Biology (NIB), 1000 Ljubljana, Slovenia
- Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
| | - Denis Kutnjak
- Department of Biotechnology and Systems Biology, National Institute of Biology (NIB), 1000 Ljubljana, Slovenia
| | - Janja Lamovšek
- Plant Protection Department, Agricultural Institute of Slovenia (KIS), 1000 Ljubljana, Slovenia
| | - Marie Lefebvre
- UMR 1332 Biologie du Fruit et Pathologie, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Bordeaux, 33882 Villenave-d’Ornon, France
| | - Martha Malapi
- Biotechnology Risk Analysis Program, Animal and Plant Health Inspection Service, United States Department of Agriculture (USDA-APHIS), Riverdale, ML 20737, USA
| | - Irena Mavrič Pleško
- Plant Protection Department, Agricultural Institute of Slovenia (KIS), 1000 Ljubljana, Slovenia
| | - Serkan Önder
- Department of Plant Protection, Faculty of Agriculture, Eskişehir Osmangazi University, Odunpazarı, Eskişehir 26160, Turkey
| | | | | | - Olivier Schumpp
- Department of Plant Protection, Agroscope, 1260 Nyon, Switzerland
| | - Kristian Stevens
- Foundation Plant Services, Department of Plant Pathology, University of California, Davis, CA 95616, USA
| | - Chandan Pal
- Zespri International Limited, 400 Maunganui Road, Mount Maunganui 3116, New Zealand
| | - Lucie Tamisier
- Unités GAFL et Pathologie Végétale, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 84143 Montfavet, France
| | - Çiğdem Ulubaş Serçe
- Department of Plant Production and Technologies, Faculty of Agricultural Sciences and Technologies, Niğde Ömer Halisdemir University, 51240 Niğde, Turkey
| | - Inge van Duivenbode
- Dutch General Inspection Service for Agricultural Seed and Seed Potatoes (NAK), Randweg 14, 8304 AS Emmeloord, The Netherlands
| | - David W. Waite
- Plant Health and Environment Laboratory, Ministry for Primary Industries, Auckland 1140, New Zealand
| | - Xiaojun Hu
- Plant Germplasm Quarantine Program, Animal and Plant Health Inspection Service, United States Department of Agriculture (USDA-APHIS), Beltsville, ML 20705, USA
| | - Heiko Ziebell
- Institute for Epidemiology and Pathogen Diagnostics, Federal Research Centre for Cultivated Plants, Julius Kühn Institute (JKI), Messeweg 11-12, 38104 Braunschweig, Germany
| | - Sébastien Massart
- Plant Pathology Laboratory, University of Liège, Gembloux Agro-Bio Tech, TERRA, 5030 Gembloux, Belgium
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Venbrux M, Crauwels S, Rediers H. Current and emerging trends in techniques for plant pathogen detection. FRONTIERS IN PLANT SCIENCE 2023; 14:1120968. [PMID: 37223788 PMCID: PMC10200959 DOI: 10.3389/fpls.2023.1120968] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 03/21/2023] [Indexed: 05/25/2023]
Abstract
Plant pathogenic microorganisms cause substantial yield losses in several economically important crops, resulting in economic and social adversity. The spread of such plant pathogens and the emergence of new diseases is facilitated by human practices such as monoculture farming and global trade. Therefore, the early detection and identification of pathogens is of utmost importance to reduce the associated agricultural losses. In this review, techniques that are currently available to detect plant pathogens are discussed, including culture-based, PCR-based, sequencing-based, and immunology-based techniques. Their working principles are explained, followed by an overview of the main advantages and disadvantages, and examples of their use in plant pathogen detection. In addition to the more conventional and commonly used techniques, we also point to some recent evolutions in the field of plant pathogen detection. The potential use of point-of-care devices, including biosensors, have gained in popularity. These devices can provide fast analysis, are easy to use, and most importantly can be used for on-site diagnosis, allowing the farmers to take rapid disease management decisions.
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Affiliation(s)
- Marc Venbrux
- Centre of Microbial and Plant Genetics, Laboratory for Process Microbial Ecology and Bioinspirational Management (PME&BIM), Department of Microbial and Molecular Systems (M2S), KU Leuven, Leuven, Belgium
| | - Sam Crauwels
- Centre of Microbial and Plant Genetics, Laboratory for Process Microbial Ecology and Bioinspirational Management (PME&BIM), Department of Microbial and Molecular Systems (M2S), KU Leuven, Leuven, Belgium
- Leuven Plant Institute (LPI), KU Leuven, Leuven, Belgium
| | - Hans Rediers
- Centre of Microbial and Plant Genetics, Laboratory for Process Microbial Ecology and Bioinspirational Management (PME&BIM), Department of Microbial and Molecular Systems (M2S), KU Leuven, Leuven, Belgium
- Leuven Plant Institute (LPI), KU Leuven, Leuven, Belgium
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Gökdemir FŞ, İşeri ÖD, Sharma A, Achar PN, Eyidoğan F. Metagenomics Next Generation Sequencing (mNGS): An Exciting Tool for Early and Accurate Diagnostic of Fungal Pathogens in Plants. J Fungi (Basel) 2022; 8:1195. [PMID: 36422016 PMCID: PMC9699264 DOI: 10.3390/jof8111195] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 09/19/2023] Open
Abstract
Crop output is directly impacted by infections, with fungi as the major plant pathogens, making accurate diagnosis of these threats crucial. Developing technology and multidisciplinary approaches are turning to genomic analyses in addition to traditional culture methods in diagnostics of fungal plant pathogens. The metagenomic next-generation sequencing (mNGS) method is preferred for genotyping identification of organisms, identification at the species level, illumination of metabolic pathways, and determination of microbiota. Moreover, the data obtained so far show that this new approach is promising as an emerging new trend in fungal disease detection. Another approach covered by mNGS technologies, known as metabarcoding, enables use of specific markers specific to a genetic region and allows for genotypic identification by facilitating the sequencing of certain regions. Although the core concept of mNGS remains constant across applications, the specific sequencing methods and bioinformatics tools used to analyze the data differ. In this review, we focus on how mNGS technology, including metabarcoding, is applied for detecting fungal pathogens and its promising developments for the future.
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Affiliation(s)
- Fatma Şeyma Gökdemir
- Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
- Department of Molecular Biology and Genetics, Faculty of Science and Letters, Başkent University, Ankara 06790, Turkey
| | - Özlem Darcansoy İşeri
- Department of Molecular Biology and Genetics, Faculty of Science and Letters, Başkent University, Ankara 06790, Turkey
- Institute of Food, Agriculture and Livestock Development, Başkent University, Ankara 06790, Turkey
| | - Abhishek Sharma
- Amity Food and Agriculture Foundation, Amity University, Noida 201313, Uttar Pradesh, India
| | - Premila N. Achar
- Department of Molecular and Cellular Biology, Kennesaw State University, Kennesaw, GA 30144, USA
| | - Füsun Eyidoğan
- Department of Molecular Biology and Genetics, Faculty of Science and Letters, Başkent University, Ankara 06790, Turkey
- Institute of Food, Agriculture and Livestock Development, Başkent University, Ankara 06790, Turkey
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New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True? J Fungi (Basel) 2022; 8:jof8070737. [PMID: 35887492 PMCID: PMC9320658 DOI: 10.3390/jof8070737] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/01/2022] [Accepted: 07/11/2022] [Indexed: 02/01/2023] Open
Abstract
The fast and continued progress of high-throughput sequencing (HTS) and the drastic reduction of its costs have boosted new and unpredictable developments in the field of plant pathology. The cost of whole-genome sequencing, which, until few years ago, was prohibitive for many projects, is now so affordable that a new branch, phylogenomics, is being developed. Fungal taxonomy is being deeply influenced by genome comparison, too. It is now easier to discover new genes as potential targets for an accurate diagnosis of new or emerging pathogens, notably those of quarantine concern. Similarly, with the development of metabarcoding and metagenomics techniques, it is now possible to unravel complex diseases or answer crucial questions, such as "What's in my soil?", to a good approximation, including fungi, bacteria, nematodes, etc. The new technologies allow to redraw the approach for disease control strategies considering the pathogens within their environment and deciphering the complex interactions between microorganisms and the cultivated crops. This kind of analysis usually generates big data that need sophisticated bioinformatic tools (machine learning, artificial intelligence) for their management. Herein, examples of the use of new technologies for research in fungal diversity and diagnosis of some fungal pathogens are reported.
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