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Singh DP, Maurya S, Yerasu SR, Bisen MS, Farag MA, Prabha R, Shukla R, Chaturvedi KK, Farooqi MS, Srivastava S, Rai A, Sarma BK, Rai N, Behera TK. Metabolomics of early blight (Alternaria solani) susceptible tomato (Solanum lycopersicum) unfolds key biomarker metabolites and involved metabolic pathways. Sci Rep 2023; 13:21023. [PMID: 38030710 PMCID: PMC10687106 DOI: 10.1038/s41598-023-48269-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 11/24/2023] [Indexed: 12/01/2023] Open
Abstract
Tomato (Solanum lycopersicum) is among the most important commercial horticultural crops worldwide. The crop quality and production is largely hampered due to the fungal pathogen Alternaria solani causing necrotrophic foliage early blight disease. Crop plants usually respond to the biotic challenges with altered metabolic composition and physiological perturbations. We have deciphered altered metabolite composition, modulated metabolic pathways and identified metabolite biomarkers in A. solani-challenged susceptible tomato variety Kashi Aman using Liquid Chromatography-Mass Spectrometry (LC-MS) based metabolomics. Alteration in the metabolite feature composition of pathogen-challenged (m/z 9405) and non-challenged (m/z 9667) plant leaves including 8487 infection-exclusive and 8742 non-infection exclusive features was observed. Functional annotation revealed putatively annotated metabolites and pathway mapping indicated their enrichment in metabolic pathways, biosynthesis of secondary metabolites, ubiquinone and terpenoid-quinones, brassinosteroids, steroids, terpenoids, phenylpropanoids, carotenoids, oxy/sphingolipids and metabolism of biotin and porphyrin. PCA, multivariate PLS-DA and OPLS-DA analysis showed sample discrimination. Significantly up regulated 481 and down regulated 548 metabolite features were identified based on the fold change (threshold ≥ 2.0). OPLS-DA model based on variable importance in projection (VIP scores) and FC threshold (> 2.0) revealed 41 up regulated discriminant metabolite features annotated as sphingosine, fecosterol, melatonin, serotonin, glucose 6-phosphate, zeatin, dihydrozeatin and zeatin-β-D-glucoside. Similarly, 23 down regulated discriminant metabolites included histidinol, 4-aminobutyraldehyde, propanoate, tyramine and linalool. Melatonin and serotonin in the leaves were the two indoleamines being reported for the first time in tomato in response to the early blight pathogen. Receiver operating characteristic (ROC)-based biomarker analysis identified apigenin-7-glucoside, uridine, adenosyl-homocysteine, cGMP, tyrosine, pantothenic acid, riboflavin (as up regulated) and adenosine, homocyctine and azmaline (as down regulated) biomarkers. These results could aid in the development of metabolite-quantitative trait loci (mQTL). Furthermore, stress-induced biosynthetic pathways may be the potential targets for modifications through breeding programs or genetic engineering for improving crop performance in the fields.
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Affiliation(s)
| | - Sudarshan Maurya
- ICAR-Indian Institute of Vegetable Research, Varanasi, 221305, India
| | | | - Mansi Singh Bisen
- ICAR-Indian Institute of Vegetable Research, Varanasi, 221305, India
| | - Mohamed A Farag
- Pharmacognosy Department, College of Pharmacy, Cairo University, Cairo, Egypt
| | - Ratna Prabha
- ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi, India
| | - Renu Shukla
- Indian Council of Agricultural Research, New Delhi, 110012, India
| | | | - Md Samir Farooqi
- ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi, India
| | - Sudhir Srivastava
- ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi, India
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi, India
- Indian Council of Agricultural Research, New Delhi, 110012, India
| | - Birinchi Kumar Sarma
- Department of Mycology and Plant Pathology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, 221005, India
| | - Nagendra Rai
- ICAR-Indian Institute of Vegetable Research, Varanasi, 221305, India
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Kumar D, Ramkumar MK, Dutta B, Kumar A, Pandey R, Jain PK, Gaikwad K, Mishra DC, Chaturvedi KK, Rai A, Solanke AU, Sevanthi AM. Integration of miRNA dynamics and drought tolerant QTLs in rice reveals the role of miR2919 in drought stress response. BMC Genomics 2023; 24:526. [PMID: 37674140 PMCID: PMC10481553 DOI: 10.1186/s12864-023-09609-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 08/20/2023] [Indexed: 09/08/2023] Open
Abstract
To combat drought stress in rice, a major threat to global food security, three major quantitative trait loci for 'yield under drought stress' (qDTYs) were successfully exploited in the last decade. However, their molecular basis still remains unknown. To understand the role of secondary regulation by miRNA in drought stress response and their relation, if any, with the three qDTYs, the miRNA dynamics under drought stress was studied at booting stage in two drought tolerant (Sahbaghi Dhan and Vandana) and one drought sensitive (IR 20) cultivars. In total, 53 known and 40 novel differentially expressed (DE) miRNAs were identified. The primary drought responsive miRNAs were Osa-MIR2919, Osa-MIR3979, Osa-MIR159f, Osa-MIR156k, Osa-MIR528, Osa-MIR530, Osa-MIR2091, Osa-MIR531a, Osa-MIR531b as well as three novel ones. Sixty-one target genes that corresponded to 11 known and 4 novel DE miRNAs were found to be co-localized with the three qDTYs, out of the 1746 target genes identified. We could validate miRNA-mRNA expression under drought for nine known and three novel miRNAs in eight different rice genotypes showing varying degree of tolerance. From our study, Osa-MIR2919, Osa-MIR3979, Osa-MIR528, Osa-MIR2091-5p and Chr01_11911S14Astr and their target genes LOC_Os01g72000, LOC_Os01g66890, LOC_Os01g57990, LOC_Os01g56780, LOC_Os01g72834, LOC_Os01g61880 and LOC_Os01g72780 were identified as the most promising candidates for drought tolerance at booting stage. Of these, Osa-MIR2919 with 19 target genes in the qDTYs is being reported for the first time. It acts as a negative regulator of drought stress tolerance by modulating the cytokinin and brassinosteroid signalling pathway.
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Affiliation(s)
- Deepesh Kumar
- ICAR-National Institute for Plant Biotechnology, New Delhi, 110012, India
- PG School, Indian Agricultural Research Institute, Pusa Campus New Delhi, New Delhi, 110012, India
| | - M K Ramkumar
- ICAR-National Institute for Plant Biotechnology, New Delhi, 110012, India
| | - Bipratip Dutta
- ICAR-National Institute for Plant Biotechnology, New Delhi, 110012, India
- PG School, Indian Agricultural Research Institute, Pusa Campus New Delhi, New Delhi, 110012, India
| | - Ajay Kumar
- ICAR-National Institute for Plant Biotechnology, New Delhi, 110012, India
| | - Rakesh Pandey
- Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Pradeep Kumar Jain
- ICAR-National Institute for Plant Biotechnology, New Delhi, 110012, India
| | - Kishor Gaikwad
- ICAR-National Institute for Plant Biotechnology, New Delhi, 110012, India
| | - Dwijesh C Mishra
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India
| | - K K Chaturvedi
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India
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3
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Das P, Grover M, Mishra DC, Guha Majumdar S, Shree B, Kumar S, Mir ZA, Chaturvedi KK, Bhardwaj SC, Singh AK, Rai A. Genome-wide identification and characterization of Puccinia striiformis-responsive lncRNAs in Triticum aestivum. Front Plant Sci 2023; 14:1120898. [PMID: 37650000 PMCID: PMC10465180 DOI: 10.3389/fpls.2023.1120898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 07/10/2023] [Indexed: 09/01/2023]
Abstract
Wheat stripe rust (yellow rust) caused by Puccinia striiformis f. sp. tritici (Pst) is a serious biotic stress factor limiting wheat production worldwide. Emerging evidence demonstrates that long non-coding RNAs (lncRNAs) participate in various developmental processes in plants via post-transcription regulation. In this study, RNA sequencing (RNA-seq) was performed on a pair of near-isogenic lines-rust resistance line FLW29 and rust susceptible line PBW343-which differed only in the rust susceptibility trait. A total of 6,807 lncRNA transcripts were identified using bioinformatics analyses, among which 10 lncRNAs were found to be differentially expressed between resistance and susceptible lines. In order to find the target genes of the identified lncRNAs, their interactions with wheat microRNA (miRNAs) were predicted. A total of 199 lncRNAs showed interactions with 65 miRNAs, which further target 757 distinct mRNA transcripts. Moreover, detailed functional annotations of the target genes were used to identify the candidate genes, pathways, domains, families, and transcription factors that may be related to stripe rust resistance response in wheat plants. The NAC domain protein, disease resistance proteins RPP13 and RPM1, At1g58400, monodehydroascorbate reductase, NBS-LRR-like protein, rust resistance kinase Lr10-like, LRR receptor, serine/threonine-protein kinase, and cysteine proteinase are among the identified targets that are crucial for wheat stripe rust resistance. Semiquantitative PCR analysis of some of the differentially expressed lncRNAs revealed variations in expression profiles of two lncRNAs between the Pst-resistant and Pst-susceptible genotypes at least under one condition. Additionally, simple sequence repeats (SSRs) were also identified from wheat lncRNA sequences, which may be very useful for conducting targeted gene mapping studies of stripe rust resistance in wheat. These findings improved our understanding of the molecular mechanism responsible for the stripe rust disease that can be further utilized to develop wheat varieties with durable resistance to this disease.
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Affiliation(s)
- Parinita Das
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Monendra Grover
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | | | - Bharti Shree
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Sundeep Kumar
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Zahoor Ahmad Mir
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | | | | | - Amit Kumar Singh
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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4
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Kuhn JH, Abe J, Adkins S, Alkhovsky SV, Avšič-Županc T, Ayllón MA, Bahl J, Balkema-Buschmann A, Ballinger MJ, Kumar Baranwal V, Beer M, Bejerman N, Bergeron É, Biedenkopf N, Blair CD, Blasdell KR, Blouin AG, Bradfute SB, Briese T, Brown PA, Buchholz UJ, Buchmeier MJ, Bukreyev A, Burt F, Büttner C, Calisher CH, Cao M, Casas I, Chandran K, Charrel RN, Kumar Chaturvedi K, Chooi KM, Crane A, Dal Bó E, Carlos de la Torre J, de Souza WM, de Swart RL, Debat H, Dheilly NM, Di Paola N, Di Serio F, Dietzgen RG, Digiaro M, Drexler JF, Duprex WP, Dürrwald R, Easton AJ, Elbeaino T, Ergünay K, Feng G, Firth AE, Fooks AR, Formenty PBH, Freitas-Astúa J, Gago-Zachert S, Laura García M, García-Sastre A, Garrison AR, Gaskin TR, Gong W, Gonzalez JPJ, de Bellocq J, Griffiths A, Groschup MH, Günther I, Günther S, Hammond J, Hasegawa Y, Hayashi K, Hepojoki J, Higgins CM, Hongō S, Horie M, Hughes HR, Hume AJ, Hyndman TH, Ikeda K, Jiāng D, Jonson GB, Junglen S, Klempa B, Klingström J, Kondō H, Koonin EV, Krupovic M, Kubota K, Kurath G, Laenen L, Lambert AJ, Lǐ J, Li JM, Liu R, Lukashevich IS, MacDiarmid RM, Maes P, Marklewitz M, Marshall SH, Marzano SYL, McCauley JW, Mirazimi A, Mühlberger E, Nabeshima T, Naidu R, Natsuaki T, Navarro B, Navarro JA, Neriya Y, Netesov SV, Neumann G, Nowotny N, Nunes MRT, Ochoa-Corona FM, Okada T, Palacios G, Pallás V, Papa A, Paraskevopoulou S, Parrish CR, Pauvolid-Corrêa A, Pawęska JT, Pérez DR, Pfaff F, Plemper RK, Postler TS, Rabbidge LO, Radoshitzky SR, Ramos-González PL, Rehanek M, Resende RO, Reyes CA, Rodrigues TCS, Romanowski V, Rubbenstroth D, Rubino L, Runstadler JA, Sabanadzovic S, Sadiq S, Salvato MS, Sasaya T, Schwemmle M, Sharpe SR, Shi M, Shimomoto Y, Kavi Sidharthan V, Sironi M, Smither S, Song JW, Spann KM, Spengler JR, Stenglein MD, Takada A, Takeyama S, Tatara A, Tesh RB, Thornburg NJ, Tian X, Tischler ND, Tomitaka Y, Tomonaga K, Tordo N, Tu C, Turina M, Tzanetakis IE, Maria Vaira A, van den Hoogen B, Vanmechelen B, Vasilakis N, Verbeek M, von Bargen S, Wada J, Wahl V, Walker PJ, Waltzek TB, Whitfield AE, Wolf YI, Xia H, Xylogianni E, Yanagisawa H, Yano K, Ye G, Yuan Z, Zerbini FM, Zhang G, Zhang S, Zhang YZ, Zhao L, Økland AL. Annual (2023) taxonomic update of RNA-directed RNA polymerase-encoding negative-sense RNA viruses (realm Riboviria: kingdom Orthornavirae: phylum Negarnaviricota). J Gen Virol 2023; 104:001864. [PMID: 37622664 PMCID: PMC10721048 DOI: 10.1099/jgv.0.001864] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 05/26/2023] [Indexed: 08/26/2023] Open
Abstract
In April 2023, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by one new family, 14 new genera, and 140 new species. Two genera and 538 species were renamed. One species was moved, and four were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.
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Affiliation(s)
- Jens H. Kuhn
- Integrated Research Facility at Fort Detrick (IRF-Frederick), Division of Clinical Research (DCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), B-8200 Research Plaza, Fort Detrick, Frederick, MD 21702, USA
| | - Junya Abe
- Ornamental Plants and Vegetables Research Center, Agricultural Research Department, Hokkaido Research Organization, Takikawa, Hokkaido, Japan
| | - Scott Adkins
- United States Department of Agriculture, Agricultural Research Service, US Horticultural Research Laboratory, Fort Pierce, FL, USA
| | - Sergey V. Alkhovsky
- D.I. Ivanovsky Institute of Virology of N.F. Gamaleya National Center on Epidemiology and Microbiology of Ministry of Health of Russian Federation, Moscow, Russia
| | - Tatjana Avšič-Županc
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - María A. Ayllón
- Centro de Biotecnología y Genómica de Plantas; Departamento de Biotecnología-Biología Vegetal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA/CSIC), Campus de Montegancedo, Pozuelo de Alarcón; Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Justin Bahl
- Center for Ecology of Infectious Diseases, Department of Infectious Diseases, Department of Epidemiology and Biostatistics, Insitute of Bioinformatics, University of Georgia, Athens, GA, USA
| | - Anne Balkema-Buschmann
- Friedrich-Loeffler-Institut, Institute of Novel and Emerging Infectious Diseases, Greifswald-Insel Riems, Greifswald, Germany
| | - Matthew J. Ballinger
- Department of Biological Sciences, Mississippi State University, Starkville, MS,, Mississippi State, USA
| | | | - Martin Beer
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany
| | | | - Éric Bergeron
- Division of High-Consequence Pathogens and Pathology, Viral Special Pathogens Branch, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Nadine Biedenkopf
- Institute of Virology, Philipps-University Marburg, Marburg, Germany
| | - Carol D. Blair
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO, USA
| | - Kim R. Blasdell
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australian Centre for Disease Preparedness, Geelong, VIC, Australia
| | - Arnaud G. Blouin
- Virology-Phytoplasmology Laboratory, Agroscope, 1260 Nyon, Switzerland
| | | | - Thomas Briese
- Center for Infection and Immunity, and Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA
| | - Paul A. Brown
- French Agency for Food, Environmental and Occupational Heath Safety ANSES, Laboratory of Ploufragan-Plouzané-Niort, Ploufragan, France
| | - Ursula J. Buchholz
- RNA Viruses Section, Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Michael J. Buchmeier
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, USA
| | | | - Felicity Burt
- Division of Virology, National Health Laboratory Service and Division of Virology, University of the Free State, Bloemfontein, Bloemfontein, South Africa
| | - Carmen Büttner
- Division Phytomedicine, Faculty of Life Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Mengji Cao
- National Citrus Engineering and Technology Research Center, Citrus Research Institute, Southwest University, Beibei, Chongqing, PR China
| | - Inmaculada Casas
- Respiratory Virus and Influenza Unit, National Microbiology Center, Instituto de Salud Carlos III, Madrid, Spain
| | - Kartik Chandran
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Rémi N. Charrel
- Unite des Virus Emergents (Aix-Marseille Univ-IRD 190-Inserm 1207), Marseille, France
| | - Krishna Kumar Chaturvedi
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Kar Mun Chooi
- The New Zealand Institute for Plant and Food Research Limited, Auckland, New Zealand
| | - Anya Crane
- Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Elena Dal Bó
- CIDEFI. Facultad de Ciencias Agrarias y Forestales, Universidad de La Plata, La Plata, Argentina
| | - Juan Carlos de la Torre
- Department of Immunology and Microbiology IMM-6, The Scripps Research Institute, La Jolla, CA, USA
| | - William M. de Souza
- World Reference Center for Emerging Viruses and Arboviruses and Department of Microbiology and Immunology, The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Rik L. de Swart
- Department of Virology, Wageningen Bioveterinary Research, Lelystad, Netherlands
| | - Humberto Debat
- Instituto de Patología Vegetal, Centro de Investigaciones Agropecuarias, Instituto Nacional de Tecnología Agropecuaria (IPAVE-CIAP-INTA), Consejo Nacional de Investigaciones Científicas y Técnicas, Unidad de Fitopatología y Modelización Agrícola, Córdoba, Argentina
| | - Nolwenn M. Dheilly
- UMR 1161 Virology ANSES/INRAE/ENVA, ANSES Animal Health Laboratory, Maisons-Alfort, France
| | - Nicholas Di Paola
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Francesco Di Serio
- Istituto per la Protezione Sostenibile delle Piante, Consiglio Nazionale delle Ricerche, Bari, Italy
| | - Ralf G. Dietzgen
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, Australia
| | - Michele Digiaro
- CIHEAM, Istituto Agronomico Mediterraneo di Bari, Valenzano, Italy
| | - J. Felix Drexler
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - W. Paul Duprex
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | - Toufic Elbeaino
- CIHEAM, Istituto Agronomico Mediterraneo di Bari, Valenzano, Italy
| | - Koray Ergünay
- Department of Medical Microbiology, Virology Unit, Hacettepe University Faculty of Medicine, Ankara, Turkey
- Walter Reed Biosystematics Unit (WRBU), Smithsonian Institution, Museum Support Center, Suitland, MD, USA
- One Health Branch, Walter Reed Army Institute of Research (WRAIR), Silver Spring, MD, USA
- Department of Entomology, Smithsonian Institution–National Museum of Natural History (NMNH), Washington, DC, USA
| | - Guozhong Feng
- China National Rice Research Institute, Hangzhou, PR China
| | - Andrew E. Firth
- Department of Pathology, University of Cambridge, Cambridge, UK
| | | | | | | | - Selma Gago-Zachert
- Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle/Saale, Germany
| | - María Laura García
- Instituto de Biotecnología y Biología Molecular, Facultad de Ciencias Exactas, CONICET UNLP, La Plata, Argentina
| | | | - Aura R. Garrison
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, MD, USA
| | - Thomas R. Gaskin
- Brandenburg State Office of Rural Development, Agriculture and Land Consolidation (LELF), Frankfurt, Germany
- Division Phytomedicine, Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Wenjie Gong
- State Key Laboratory for Zoonotic Diseases, Key Laboratory of Zoonoses Research, Ministry of Education, College of Veterinary Medicine, Jilin University, Changchun, PR China
| | - Jean-Paul J. Gonzalez
- Department of Microbiology and Immunology, Division of Biomedical Graduate Research Organization, School of Medicine, Georgetown University, Washington, DC, USA
| | | | - Anthony Griffiths
- Department of Virology, Immunology and Microbiology, Chobanian and Avedisian School of Medicine; National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - Martin H. Groschup
- Institute of Novel and Emerging Infectious Diseases, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany
| | - Ines Günther
- Division Phytomedicine, Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität Zu Berlin, Berlin, Germany
| | - Stephan Günther
- Department of Virology, WHO Collaborating Centre for Arboviruses and Hemorrhagic Fever Reference and Research, Bernhard-Nocht Institute for Tropical Medicine, Hamburg, Germany
| | - John Hammond
- United States Department of Agriculture, Agricultural Research Service, USNA, Floral and Nursery Plants Research Unit, Beltsville, MD, USA
| | - Yusuke Hasegawa
- Department of Clinical Plant Science, Hosei University, Koganei, Tokyo, 184-8584, Japan
| | - Kazusa Hayashi
- Kochi Agricultural Research Center, Nankoku, Kochi, Japan
| | - Jussi Hepojoki
- Department of Virology, University of Helsinki, Medicum, Helsinki, Finland
- Institute of Veterinary Pathology, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland
| | - Colleen M. Higgins
- The School of Science, Auckland University of Technology, Auckland, New Zealand
| | - Seiji Hongō
- Department of Infectious Diseases, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Masayuki Horie
- Graduate School of Veterinary Science, Osaka Metropolitan University; International Research Center for Infectious Diseases, Osaka Metropolitan University, Izumisano, Osaka, Japan
| | - Holly R. Hughes
- Centers for Disease Control and Prevention, Fort Collins, CO, USA
| | - Adam J. Hume
- Department of Virology, Immunology and Microbiology, Chobanian and Avedisian School of Medicine; National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - Timothy H. Hyndman
- School of Veterinary Medicine, Murdoch University, Murdoch, WA, Australia
| | - Kenichi Ikeda
- Graduate School of Agricultural Science, Kobe University, Kobe, Hyogo, Japan
| | - Dàohóng Jiāng
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, Hubei Province, PR China
| | - Gilda B. Jonson
- International Rice Research Institute, College, Los Baños, 4032, Laguna, Philippines
| | - Sandra Junglen
- Institute of Virology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, Berlin 10117, Germany
| | - Boris Klempa
- Institute of Virology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Jonas Klingström
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Hideki Kondō
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
| | - Eugene V. Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Mart Krupovic
- Institut Pasteur, Université Paris Cité, CNRS UMR6047, Archaeal Virology Unit, Paris, France
| | - Kenji Kubota
- Institute for Plant Protection, NARO, Tsukuba, Ibaraki, Japan
| | - Gael Kurath
- US Geological Survey Western Fisheries Research Center, Seattle, Washington, USA
| | - Lies Laenen
- KU Leuven, Rega Institute, Zoonotic Infectious Diseases unit; Department of Laboratory Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Amy J. Lambert
- Centers for Disease Control and Prevention, Fort Collins, CO, USA
| | - Jiànróng Lǐ
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Jun-Min Li
- Institute of Plant Virology, Ningbo University, Ningbo, PR China
| | - Ran Liu
- Illumina (China), Beijing, PR China
| | - Igor S. Lukashevich
- Department of Pharmacology and Toxicology, School of Medicine, and the Center for Predictive Medicine for Biodefense and Emerging Infectious Diseases, University of Louisville, Louisville, KY, USA
| | - Robin M. MacDiarmid
- The New Zealand Institute for Plant and Food Research Limited; School of Biological Sciences, The University of Auckland, Auckland, New Zealand
| | - Piet Maes
- KU Leuven, Rega Institute, Zoonotic Infectious Diseases unit, Leuven, Belgium
| | | | - Sergio H. Marshall
- Instituto de Biología-Laboratorio de Genética Molecular-Pontificia Universidad Católica de ValparaísoCampus Curauma, Valparaíso, Chile
| | - Shin-Yi L. Marzano
- United States Department of Agriculture, Agricultural Research Service, Toledo, OH, USA
| | | | | | - Elke Mühlberger
- Department of Virology, Immunology and Microbiology, Chobanian and Avedisian School of Medicine; National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | | | - Rayapati Naidu
- Department of Plant Pathology, Irrigated Agricultural Research and Extension Center, Washington State University, Prosser, WA, USA
| | | | - Beatriz Navarro
- Istituto per la Protezione Sostenibile delle Piante, Consiglio Nazionale delle Ricerche, Bari, Italy
| | - José A. Navarro
- Instituto de Biología Molecular y Celular de Plantas, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas, Valencia, Spain
| | - Yutaro Neriya
- School of Agriculture, Utsunomiya University, Utsunomiya, Japan
| | | | - Gabriele Neumann
- Department of Pathobiological Sciences, Influenza Research Institute, University of Wisconsin-Madison, Madison, USA
| | - Norbert Nowotny
- Institute of Virology, University of Veterinary Medicine Vienna, Vienna, Austria
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | | | - Francisco M. Ochoa-Corona
- Institute for Biosecurity and Microbial Forensics. Stillwater, Oklahoma State University, Oklahoma, USA
| | - Tomoyuki Okada
- Kochi Agricultural Research Center, Nankoku, Kochi, Japan
| | - Gustavo Palacios
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vicente Pallás
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), Consejo Superior de Investigaciones Cientificas-Universitat Politècnica de Valencia, Valencia, Spain
| | - Anna Papa
- National Reference Centre for Arboviruses and Haemorrhagic Fever viruses, Department of Microbiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Colin R. Parrish
- College of Veterinary Medicine, Baker Institute for Animal Health, Cornell University, Ithaca, NY, USA
| | | | - Janusz T. Pawęska
- Center for Emerging Zoonotic and Parasitic Diseases, National Institute for Communicable Diseases of the National Health Laboratory Service, Sandringham-Johannesburg, Gauteng, South Africa
| | - Daniel R. Pérez
- Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
| | - Florian Pfaff
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany
| | - Richard K. Plemper
- Center for Translational Antiviral Research, Georgia State University Institute for Biomedical Sciences, Atlanta, GA, USA
| | - Thomas S. Postler
- Vaccine Design and Development Laboratory, International AIDS Vaccine Initiative, Brooklyn, NY, USA
| | - Lee O. Rabbidge
- The New Zealand Institute for Plant and Food Research Limited; The School of Science, Auckland University of Technology, Auckland, New Zealand
| | - Sheli R. Radoshitzky
- Division of Antivirals, Office of Infectious Diseases, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA
| | | | - Marius Rehanek
- Division Phytomedicine, Faculty of Life Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Renato O. Resende
- Departamento de Biologia Celular, Universidade de Brasília, Brasília, Brazil
| | - Carina A. Reyes
- Instituto de Biotecnología y Biología Molecular, CONICET-UNLP, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Buenos Aires, Argentina
| | - Thaís C. S. Rodrigues
- Department of Infectious Diseases and Immunology, College of Veterinary Medicine, University of Florida, Gainesville, Florida, USA
| | - Víctor Romanowski
- Instituto de Biotecnología y Biología Molecular, CONICET-UNLP, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Buenos Aires, Argentina
| | - Dennis Rubbenstroth
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany
| | - Luisa Rubino
- Istituto per la Protezione Sostenibile delle Piante, Consiglio Nazionale delle Ricerche, Bari, Italy
| | - Jonathan A. Runstadler
- Department of Infectious Disease & Global Health, Tufts University Cummings School of Veterinary Medicine, North Grafton, MA, USA
| | - Sead Sabanadzovic
- Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Mississippi, Mississippi State, USA
| | - Sabrina Sadiq
- Sydney Institute for Infectious Diseases, School of Medical Sciences, The University of Sydney, Sydney, Australia
| | - Maria S. Salvato
- Department of Veterinary Medicine, University of Maryland, College Park, MD 20742, USA
| | - Takahide Sasaya
- Institute for Plant Protection, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Martin Schwemmle
- Faculty of Medicine, University Medical Center-University Freiburg, Freiburg, Germany
| | - Stephen R. Sharpe
- Hawkesbury Institute for the Environment, Western Sydney University, Sydney, NSW, Australia
| | - Mang Shi
- Sun Yat-sen University, Shenzhen, PR China
| | | | | | - Manuela Sironi
- Bioinformatics Unit, Scientific Institute IRCCS “E. Medea”, Bosisio Parini, Italy
| | - Sophie Smither
- CBR Division, Dstl, Porton Down, Salisbury, Wiltshire, UK
| | - Jin-Won Song
- Department of Microbiology, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Kirsten M. Spann
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Jessica R. Spengler
- Viral Special Pathogens Branch, Division of High-Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Mark D. Stenglein
- Department of Microbiology, Immunology, and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA
| | - Ayato Takada
- Division of Global Epidemiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan
| | - Sawana Takeyama
- Institute for Plant Protection, NARO, Tsukuba, Ibaraki, Japan
| | - Akio Tatara
- Faculty of Agricultural Production and Management, Shizuoka Professional University of Agriculture, Shizuoka, Japan
| | - Robert B. Tesh
- The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | | | - Xin Tian
- National Citrus Engineering and Technology Research Center, Citrus Research Institute, Southwest University, Beibei, Chongqing, PR China
| | - Nicole D. Tischler
- Laboratorio de Virología Molecular, Centro Ciencia & Vida, Fundación Ciencia & Vida and Facultad de Medicina y Ciencia, Universidad San Sebastián, Santiago, Chile
| | - Yasuhiro Tomitaka
- Institute for Plant Protection, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Keizō Tomonaga
- Institute for Life and Medical Sciences (LiMe), Kyoto University, Kyoto, Japan
| | - Noël Tordo
- Institut Pasteur de Guinée, BP 4416, Conakry, Guinea
| | - Changchun Tu
- College of Veterinary Medicine, Jiangsu Co-innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, PR China
- Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, PR China
| | - Massimo Turina
- Institute for Sustainable Plant Protection, National Research Council of Italy (IPSP-CNR), Torino, Italy
| | - Ioannis E. Tzanetakis
- Department of Entomology and Plant Pathology, Division of Agriculture, University of Arkansas System, Fayetteville, AR, USA
| | - Anna Maria Vaira
- Institute for Sustainable Plant Protection, National Research Council of Italy (IPSP-CNR), Torino, Italy
| | | | - Bert Vanmechelen
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
| | - Nikos Vasilakis
- The University of Texas Medical Branch at Galveston, Galveston, TX,, USA
| | - Martin Verbeek
- Wageningen University and Research, Biointeractions and Plant Health, Wageningen, Netherlands
| | - Susanne von Bargen
- Division Phytomedicine, Faculty of Life Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jiro Wada
- Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA
| | - Victoria Wahl
- National Biodefense Analysis and Countermeasures Center, Fort Detrick, Frederick, MD, USA
| | - Peter J. Walker
- School of Chemistry and Molecular Biosciences, University of Queensland, St. Lucia, QLD, Australia
| | - Thomas B. Waltzek
- Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA, USA
| | - Anna E. Whitfield
- Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA
| | - Yuri I. Wolf
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Han Xia
- Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, PR China
| | - Evanthia Xylogianni
- Plant Pathology Laboratory, Department of Crop Science, School of Agricultural Production, Infrastructure and Environment, Agricultural University of Athens, Votanikos, Athens, Greece
| | | | - Kazutaka Yano
- Kochi Agricultural Research Center, Nankoku, Kochi, Japan
| | - Gongyin Ye
- Institute of Insect Sciences, Zhejiang University, Hangzhou, PR China
| | - Zhiming Yuan
- Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, PR China
| | - F. Murilo Zerbini
- Dep. de Fitopatologia/BIOAGRO, Universidade Federal de Viçosa, Viçosa, MG, Brazil
| | - Guilin Zhang
- Center for Disease Control and Prevention of Xinjiang Military Command Area, Urumqi, Xinjiang, PR China
| | - Song Zhang
- National Citrus Engineering and Technology Research Center, Citrus Research Institute, Southwest University, Beibei, Chongqing, PR China
- Guangxi Academy of Specialty Crops, Guilin, Guangxi, PR China
| | - Yong-Zhen Zhang
- School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, PR China
| | - Lu Zhao
- Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, PR China
- University of Chinese Academy of Sciences, Beijing, PR China
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Mishra DC, Bhati J, Yadav S, Avashthi H, Sikka P, Jerome A, Balhara AK, Singh I, Rai A, Chaturvedi KK. Comparative expression analysis of water buffalo ( Bubalus bubalis) to identify genes associated with economically important traits. Front Vet Sci 2023; 10:1160486. [PMID: 37252384 PMCID: PMC10213454 DOI: 10.3389/fvets.2023.1160486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/11/2023] [Indexed: 05/31/2023] Open
Abstract
The milk, meat, skins, and draft power of domestic water buffalo (Bubalus bubalis) provide substantial contributions to the global agricultural economy. The world's water buffalo population is primarily found in Asia, and the buffalo supports more people per capita than any other livestock species. For evaluating the workflow, output rate, and completeness of transcriptome assemblies within and between reference-free (RF) de novo transcriptome and reference-based (RB) datasets, abundant bioinformatics studies have been carried out to date. However, comprehensive documentation of the degree of consistency and variability of the data produced by comparing gene expression levels using these two separate techniques is lacking. In the present study, we assessed the variations in the number of differentially expressed genes (DEGs) attained with RF and RB approaches. In light of this, we conducted a study to identify, annotate, and analyze the genes associated with four economically important traits of buffalo, viz., milk volume, age at first calving, post-partum cyclicity, and feed conversion efficiency. A total of 14,201 and 279 DEGs were identified in RF and RB assemblies. Gene ontology (GO) terms associated with the identified genes were allocated to traits under study. Identified genes improve the knowledge of the underlying mechanism of trait expression in water buffalo which may support improved breeding plans for higher productivity. The empirical findings of this study using RNA-seq data-based assembly may improve the understanding of genetic diversity in relation to buffalo productivity and provide important contributions to answer biological issues regarding the transcriptome of non-model organisms.
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Affiliation(s)
- Dwijesh Chandra Mishra
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Jyotika Bhati
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Sunita Yadav
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Himanshu Avashthi
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Poonam Sikka
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Andonissamy Jerome
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Ashok Kumar Balhara
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Inderjeet Singh
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Krishna Kumar Chaturvedi
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
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6
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Yadav AK, Singh CK, Kalia RK, Mittal S, Wankhede DP, Kakani RK, Ujjainwal S, Saroha A, Nathawat NS, Rani R, Panchariya P, Choudhary M, Solanki K, Chaturvedi KK, Archak S, Singh K, Singh GP, Singh AK. Genetic diversity, population structure, and genome-wide association study for the flowering trait in a diverse panel of 428 moth bean (Vigna aconitifolia) accessions using genotyping by sequencing. BMC Plant Biol 2023; 23:228. [PMID: 37120525 PMCID: PMC10148550 DOI: 10.1186/s12870-023-04215-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/05/2023] [Indexed: 05/03/2023]
Abstract
BACKGROUND Moth bean (Vigna aconitifolia) is an underutilized, protein-rich legume that is grown in arid and semi-arid areas of south Asia and is highly resistant to abiotic stresses such as heat and drought. Despite its economic importance, the crop remains unexplored at the genomic level for genetic diversity and trait mapping studies. To date, there is no report of SNP marker discovery and association mapping of any trait in this crop. Therefore, this study aimed to dissect the genetic diversity, population structure and marker-trait association for the flowering trait in a diversity panel of 428 moth bean accessions using genotyping by sequencing (GBS) approach. RESULTS A total of 9078 high-quality single nucleotide polymorphisms (SNPs) were discovered by genotyping of 428 moth bean accessions. Model-based structure analysis and PCA grouped the moth bean accessions into two subpopulations. Cluster analysis revealed accessions belonging to the Northwestern region of India had higher variability than accessions from the other regions suggesting that this region represents its center of diversity. AMOVA revealed more variations within individuals (74%) and among the individuals (24%) than among the populations (2%). Marker-trait association analysis using seven multi-locus models including mrMLM, FASTmrEMMA FASTmrEMMA, ISIS EM-BLASSO, MLMM, BLINK and FarmCPU revealed 29 potential genomic regions for the trait days to 50% flowering, which were consistently detected in three or more models. Analysis of the allelic effect of the major genomic regions explaining phenotypic variance of more than 10% and those detected in at least 2 environments showed 4 genomic regions with significant phenotypic effect on this trait. Further, we also analyzed genetic relationships among the Vigna species using SNP markers. The genomic localization of moth bean SNPs on genomes of closely related Vigna species demonstrated that maximum numbers of SNPs were getting localized on Vigna mungo. This suggested that the moth bean is most closely related to V. mungo. CONCLUSION Our study shows that the north-western regions of India represent the center of diversity of the moth bean. Further, the study revealed flowering-related genomic regions/candidate genes which can be potentially exploited in breeding programs to develop early-maturity moth bean varieties.
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Affiliation(s)
- Arvind Kumar Yadav
- ICAR- National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, Delhi, India
| | - Chandan Kumar Singh
- ICAR- National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, Delhi, India
| | - Rajwant K Kalia
- ICAR- Central Arid Zone Research Institute, Jodhpur, Rajasthan, India
| | - Shikha Mittal
- Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Solan, Himachal Pradesh, India
| | | | - Rajesh K Kakani
- ICAR- Central Arid Zone Research Institute, Jodhpur, Rajasthan, India
| | - Shraddha Ujjainwal
- ICAR- National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, Delhi, India
| | - Ankit Saroha
- ICAR- National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, Delhi, India
| | - N S Nathawat
- ICAR- Central Arid Zone Research Institute, Regional Research Station, Bikaner, Rajasthan, India
| | - Reena Rani
- ICAR- Central Arid Zone Research Institute, Jodhpur, Rajasthan, India
| | - Pooja Panchariya
- ICAR- Central Arid Zone Research Institute, Jodhpur, Rajasthan, India
| | - Manoj Choudhary
- ICAR- Central Arid Zone Research Institute, Jodhpur, Rajasthan, India
| | - Kantilal Solanki
- ICAR- Central Arid Zone Research Institute, Jodhpur, Rajasthan, India
| | - K K Chaturvedi
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, Delhi, India
| | - Sunil Archak
- ICAR- National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, Delhi, India
| | - Kuldeep Singh
- ICAR- National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, Delhi, India
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, Telangana, India
| | | | - Amit Kumar Singh
- ICAR- National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, Delhi, India.
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Singh DP, Bisen MS, Prabha R, Maurya S, Yerasu SR, Shukla R, Tiwari JK, Chaturvedi KK, Farooqi MS, Srivastava S, Rai A, Sarma BK, Rai N, Singh PM, Behera TK, Farag MA. Untargeted Metabolomics of Alternaria solani-Challenged Wild Tomato Species Solanum cheesmaniae Revealed Key Metabolite Biomarkers and Insight into Altered Metabolic Pathways. Metabolites 2023; 13:metabo13050585. [PMID: 37233626 DOI: 10.3390/metabo13050585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 05/27/2023] Open
Abstract
Untargeted metabolomics of moderately resistant wild tomato species Solanum cheesmaniae revealed an altered metabolite profile in plant leaves in response to Alternaria solani pathogen. Leaf metabolites were significantly differentiated in non-stressed versus stressed plants. The samples were discriminated not only by the presence/absence of specific metabolites as distinguished markers of infection, but also on the basis of their relative abundance as important concluding factors. Annotation of metabolite features using the Arabidopsis thaliana (KEGG) database revealed 3371 compounds with KEGG identifiers belonging to biosynthetic pathways including secondary metabolites, cofactors, steroids, brassinosteroids, terpernoids, and fatty acids. Annotation using the Solanum lycopersicum database in PLANTCYC PMN revealed significantly upregulated (541) and downregulated (485) features distributed in metabolite classes that appeared to play a crucial role in defense, infection prevention, signaling, plant growth, and plant homeostasis to survive under stress conditions. The orthogonal partial least squares discriminant analysis (OPLS-DA), comprising a significant fold change (≥2.0) with VIP score (≥1.0), showed 34 upregulated biomarker metabolites including 5-phosphoribosylamine, kaur-16-en-18-oic acid, pantothenate, and O-acetyl-L-homoserine, along with 41 downregulated biomarkers. Downregulated metabolite biomarkers were mapped with pathways specifically known for plant defense, suggesting their prominent role in pathogen resistance. These results hold promise for identifying key biomarker metabolites that contribute to disease resistive metabolic traits/biosynthetic routes. This approach can assist in mQTL development for the stress breeding program in tomato against pathogen interactions.
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Affiliation(s)
| | | | - Ratna Prabha
- ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi 110012, India
| | - Sudarshan Maurya
- ICAR-Indian Institute of Vegetable Research, Varanasi 221305, India
| | | | - Renu Shukla
- Indian Council of Agricultural Research, New Delhi 110012, India
| | | | | | - Md Samir Farooqi
- ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi 110012, India
| | - Sudhir Srivastava
- ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi 110012, India
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi 110012, India
| | - Birinchi Kumar Sarma
- Department of Mycology and Plant Pathology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Nagendra Rai
- ICAR-Indian Institute of Vegetable Research, Varanasi 221305, India
| | | | | | - Mohamed A Farag
- Pharmacognosy Department, College of Pharmacy, Cairo University, Cairo 11562, Egypt
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Das J, Kumar S, Mishra DC, Chaturvedi KK, Paul RK, Kairi A. Machine learning in the estimation of CRISPR-Cas9 cleavage sites for plant system. Front Genet 2023; 13:1085332. [PMID: 36699447 PMCID: PMC9868961 DOI: 10.3389/fgene.2022.1085332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/12/2022] [Indexed: 01/12/2023] Open
Abstract
CRISPR-Cas9 system is one of the recent most used genome editing techniques. Despite having a high capacity to alter the precise target genes and genomic regions that the planned guide RNA (or sgRNA) complements, the off-target effect still exists. But there are already machine learning algorithms for people, animals, and a few plant species. In this paper, an effort has been made to create models based on three machine learning-based techniques [namely, artificial neural networks (ANN), support vector machines (SVM), and random forests (RF)] for the prediction of the CRISPR-Cas9 cleavage sites that will be cleaved by a particular sgRNA. The plant dataset was the sole source of inspiration for all of these machine learning-based algorithms. 70% of the on-target and off-target dataset of various plant species that was gathered was used to train the models. The remaining 30% of the data set was used to evaluate the model's performance using a variety of evaluation metrics, including specificity, sensitivity, accuracy, precision, F1 score, F2 score, and AUC. Based on the aforementioned machine learning techniques, eleven models in all were developed. Comparative analysis of these produced models suggests that the model based on the random forest technique performs better. The accuracy of the Random Forest model is 96.27%, while the AUC value was found to be 99.21%. The SVM-Linear, SVM-Polynomial, SVM-Gaussian, and SVM-Sigmoid models were trained, making a total of six ANN-based models (ANN1-Logistic, ANN1-Tanh, ANN1-ReLU, ANN2-Logistic, ANN2-Tanh, and ANN-ReLU) and Support Vector Machine models (SVM-Linear, SVM-Polynomial, SVM-Gaussian However, the overall performance of Random Forest is better among all other ML techniques. ANN1-ReLU and SVM-Linear model performance were shown to be better among Artificial Neural Network and Support Vector Machine-based models, respectively.
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Affiliation(s)
- Jutan Das
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Sanjeev Kumar
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India,*Correspondence: Sanjeev Kumar,
| | | | | | - Ranjit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Amit Kairi
- ICAR-Indian Agricultural Research Institute, New Delhi, India
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9
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Singh DP, Bisen MS, Shukla R, Prabha R, Maurya S, Reddy YS, Singh PM, Rai N, Chaubey T, Chaturvedi KK, Srivastava S, Farooqi MS, Gupta VK, Sarma BK, Rai A, Behera TK. Metabolomics-Driven Mining of Metabolite Resources: Applications and Prospects for Improving Vegetable Crops. Int J Mol Sci 2022; 23:ijms232012062. [PMID: 36292920 PMCID: PMC9603451 DOI: 10.3390/ijms232012062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/13/2022] [Accepted: 09/23/2022] [Indexed: 11/16/2022] Open
Abstract
Vegetable crops possess a prominent nutri-metabolite pool that not only contributes to the crop performance in the fields, but also offers nutritional security for humans. In the pursuit of identifying, quantifying and functionally characterizing the cellular metabolome pool, biomolecule separation technologies, data acquisition platforms, chemical libraries, bioinformatics tools, databases and visualization techniques have come to play significant role. High-throughput metabolomics unravels structurally diverse nutrition-rich metabolites and their entangled interactions in vegetable plants. It has helped to link identified phytometabolites with unique phenotypic traits, nutri-functional characters, defense mechanisms and crop productivity. In this study, we explore mining diverse metabolites, localizing cellular metabolic pathways, classifying functional biomolecules and establishing linkages between metabolic fluxes and genomic regulations, using comprehensive metabolomics deciphers of the plant’s performance in the environment. We discuss exemplary reports covering the implications of metabolomics, addressing metabolic changes in vegetable plants during crop domestication, stage-dependent growth, fruit development, nutri-metabolic capabilities, climatic impacts, plant-microbe-pest interactions and anthropogenic activities. Efforts leading to identify biomarker metabolites, candidate proteins and the genes responsible for plant health, defense mechanisms and nutri-rich crop produce are documented. With the insights on metabolite-QTL (mQTL) driven genetic architecture, molecular breeding in vegetable crops can be revolutionized for developing better nutritional capabilities, improved tolerance against diseases/pests and enhanced climate resilience in plants.
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Affiliation(s)
- Dhananjaya Pratap Singh
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
- Correspondence:
| | - Mansi Singh Bisen
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Renu Shukla
- Indian Council of Agricultural Research (ICAR), Krishi Bhawan, Dr. Rajendra Prasad Road, New Delhi 110001, India
| | - Ratna Prabha
- ICAR-Indian Agricultural Statistics Research Institute, Centre for Agricultural Bioinformatics, Library Avenue, Pusa, New Delhi 110012, India
| | - Sudarshan Maurya
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Yesaru S. Reddy
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Prabhakar Mohan Singh
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Nagendra Rai
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Tribhuwan Chaubey
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Krishna Kumar Chaturvedi
- ICAR-Indian Agricultural Statistics Research Institute, Centre for Agricultural Bioinformatics, Library Avenue, Pusa, New Delhi 110012, India
| | - Sudhir Srivastava
- ICAR-Indian Agricultural Statistics Research Institute, Centre for Agricultural Bioinformatics, Library Avenue, Pusa, New Delhi 110012, India
| | - Mohammad Samir Farooqi
- ICAR-Indian Agricultural Statistics Research Institute, Centre for Agricultural Bioinformatics, Library Avenue, Pusa, New Delhi 110012, India
| | - Vijai Kumar Gupta
- Biorefining and Advanced Materials Research Centre, Scotland’s Rural College, Kings Buildings, West Mains Road, Edinburgh EH9 3JG, UK
| | - Birinchi K. Sarma
- Department of Mycology and Plant Pathology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, Centre for Agricultural Bioinformatics, Library Avenue, Pusa, New Delhi 110012, India
| | - Tusar Kanti Behera
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
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Sinha D, Sharma A, Mishra DC, Rai A, Lal SB, Kumar S, Farooqi MS, Chaturvedi KK. MetaConClust - Unsupervised Binning of Metagenomics Data using Consensus Clustering. Curr Genomics 2022; 23:137-146. [PMID: 36778980 PMCID: PMC9878838 DOI: 10.2174/1389202923666220413114659] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/18/2022] [Accepted: 02/21/2022] [Indexed: 11/22/2022] Open
Abstract
Background: Binning of metagenomic reads is an active area of research, and many unsupervised machine learning-based techniques have been used for taxonomic independent binning of metagenomic reads. Objective: It is important to find the optimum number of the cluster as well as develop an efficient pipeline for deciphering the complexity of the microbial genome. Methods: Applying unsupervised clustering techniques for binning requires finding the optimal number of clusters beforehand and is observed to be a difficult task. This paper describes a novel method, MetaConClust, using coverage information for grouping of contigs and automatically finding the optimal number of clusters for binning of metagenomics data using a consensus-based clustering approach. The coverage of contigs in a metagenomics sample has been observed to be directly proportional to the abundance of species in the sample and is used for grouping of data in the first phase by MetaConClust. The Partitioning Around Medoid (PAM) method is used for clustering in the second phase for generating bins with the initial number of clusters determined automatically through a consensus-based method. Results: Finally, the quality of the obtained bins is tested using silhouette index, rand Index, recall, precision, and accuracy. Performance of MetaConClust is compared with recent methods and tools using benchmarked low complexity simulated and real metagenomic datasets and is found better for unsupervised and comparable for hybrid methods. Conclusion: This is suggestive of the proposition that the consensus-based clustering approach is a promising method for automatically finding the number of bins for metagenomics data.
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Affiliation(s)
- Dipro Sinha
- These authors contributed equally to this work
| | - Anu Sharma
- Address correspondence to this author at the Division of Agriculture Bioinformatics, ICAR-IASRI, New Delhi- 110012, India; E-mail:
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Kumar S, Bhati J, Saha A, Lal SB, Pandey PK, Mishra DC, Farooqi MS, Kumar A, Chaturvedi KK, Rai A. CerealESTDb: A Comprehensive Resource for Abiotic Stress-Responsive Annotated ESTs With Predicted Genes, Gene Ontology, and Metabolic Pathways in Major Cereal Crops. Front Genet 2022; 13:842868. [PMID: 35281847 PMCID: PMC8907976 DOI: 10.3389/fgene.2022.842868] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 01/21/2022] [Indexed: 11/13/2022] Open
Abstract
Cereals are the most important food crops and are considered key contributors to global food security. Loss due to abiotic stresses in cereal crops is limiting potential productivity in a significant manner. The primary reasons for abiotic stresses are abrupt temperature, variable rainfall, and declining nutrient status of the soil. Varietal development is the key to sustaining productivity under influence of multiple abiotic stresses and must be studied in context with genomics and molecular breeding. Recently, advances in a plethora of Next Generation Sequencing (NGS) based methods have accelerated the enormous genomic data generation associated with stress-induced transcripts such as microarray, RNAseq, Expressed Sequenced Tag (ESTs), etc. Many databases related to microarray and RNA-seq based transcripts have been developed and profusely utilized. However, an abundant amount of transcripts related to abiotic stresses in various cereal crops arising from EST technology are available but still remain underutilized in absence of a consolidated database. In this study, an attempt has been made with a primary goal to integrate, analyse, and characterise the available resources of ESTs responsive to abiotic stresses in major cereals. The developed CerealESTdb presents a customisable search in two different ways in the form of searchable content for easy access and potential use. This database comprises ESTs from four major cereal crops, namely rice (Oryza sativa L.), wheat (Triticum aestivum L.), sorghum (Sorghum bicolour L.), and maize (Zea mays L.), under a set of abiotic stresses. The current statistics of this cohesive database consists of 55,826 assembled EST sequences, 51,791 predicted genes models, and their 254,609 gene ontology terms including extensive information on 1,746 associated metabolic pathways. We anticipate that developed CerealESTdb will be helpful in deciphering the knowledge of complex biological phenomena under abiotic stresses to accelerate the molecular breeding programs towards the development of crop cultivars resilient to abiotic stresses. The CerealESTdb is publically available with the URL http://cabgrid.res.in/CerealESTDb.
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Budhlakoti N, Kushwaha AK, Rai A, Chaturvedi KK, Kumar A, Pradhan AK, Kumar U, Kumar RR, Juliana P, Mishra DC, Kumar S. Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops. Front Genet 2022; 13:832153. [PMID: 35222548 PMCID: PMC8864149 DOI: 10.3389/fgene.2022.832153] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/10/2022] [Indexed: 12/17/2022] Open
Abstract
Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.
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Affiliation(s)
- Neeraj Budhlakoti
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Anil Rai
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - K K Chaturvedi
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anuj Kumar
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), Ludhiana, India
| | | | | | - D C Mishra
- ICAR- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sundeep Kumar
- ICAR- National Bureau of Plant Genetic Resources, New Delhi, India
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Angadi UB, Chaturvedi KK, Srivastava S, Rai A. A Novel Way of Comparing Protein 3D Structure Using Graph Partitioning Approach. IEEE/ACM Trans Comput Biol Bioinform 2021; 18:1361-1368. [PMID: 31494554 DOI: 10.1109/tcbb.2019.2938948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Alignment and comparison of protein 3D structures is an important and fundamental task in structural biology to study evolutionary, functional and structural relatedness among proteins. Since two decades, the research on protein structure alignment has been taken up on priority and numbers of research articles are being published. There are incremental advances over previous efforts, and still these methods continue to improve over the time and still this is an open problem in structural biology. A novel methodology has been developed for comparing protein 3D structure by employing conversion of pair of protein 3D structures into 2D graphs (undirected weighted graph), partitioning of 2D graphs into sub-graphs, matching sub-graphs with main graphs and finally these sub-graphs matches calculates similarity between the pair of proteins. The proposed method has been implemented in MATLAB and R Package. The performance of the developed methodology is tested with four existing best methods such as CE, jFATCAT, TM_Align and Dali on 100 proteins benchmark dataset with SCOP database. The proposed method is efficient in terms of time complexity, accuracy, grouping of proteins in relevant structural groups and provides additional information towards non-bonded interactions and sub-graphs indicates the dominance of secondary structure.
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Sikka P, Nath A, Paul SS, Andonissamy J, Mishra DC, Rao AR, Balhara AK, Chaturvedi KK, Yadav KK, Balhara S. Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach. Front Vet Sci 2020; 7:518. [PMID: 32984408 PMCID: PMC7492607 DOI: 10.3389/fvets.2020.00518] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 07/06/2020] [Indexed: 11/13/2022] Open
Abstract
Machine learning algorithms were employed for predicting the feed conversion efficiency (FCE), using the blood parameters and average daily gain (ADG) as predictor variables in buffalo heifers. It was observed that isotonic regression outperformed other machine learning algorithms used in study. Further, we also achieved the best performance evaluation metrics model with additive regression as the meta learner and isotonic regression as the base learner on 10-fold cross-validation and leaving-one-out cross-validation tests. Further, we created three separate partial least square regression (PLSR) models using all 14 parameters of blood and ADG as independent (explanatory) variables and FCE as the dependent variable, to understand the interactions of blood parameters, ADG with FCE each by inclusion of all FCE values (i), only higher FCE values (negative RFI) (ii), and inclusion of only lower FCE (positive RFI) values (iii). The PLSR model including only the higher FCE values was concluded the best, based on performance evaluation metrics as compared to PLSR models developed by inclusion of the lower FCE values and all types of FCE values. IGF1 and its interactions with the other blood parameters were found highly influential for higher FCE measures. The strength of the estimated interaction effects of the blood parameter in relation to FCE may facilitate understanding of intricate dynamics of blood parameters for growth.
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Affiliation(s)
- Poonam Sikka
- Animal Biochemistry, Division of Genetics and Breeding, Central Institute for Research on Buffaloes (ICAR), Hisar, India
| | - Abhigyan Nath
- Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Pt. Deendayal Upadhyay Memorial Health Sciences and Ayush University of Chhatisgarh, Raipur, India
| | - Shyam Sundar Paul
- Poultry Nutrition, Directorate of Poultry Research (DPR), ICAR, Hyderabad, India
| | - Jerome Andonissamy
- Animal Biochemistry, Division of Genetics and Breeding, Central Institute for Research on Buffaloes (ICAR), Hisar, India
| | - Dwijesh Chandra Mishra
- Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research, New Delhi, India
| | - Atmakuri Ramakrishna Rao
- Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research, New Delhi, India
| | - Ashok Kumar Balhara
- Animal Biochemistry, Division of Genetics and Breeding, Central Institute for Research on Buffaloes (ICAR), Hisar, India
| | - Krishna Kumar Chaturvedi
- Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research, New Delhi, India
| | - Keerti Kumar Yadav
- Department of Bioinfromatics, School of Earth, Biological and Environmental Sciences, Central University of South Bihar, Patna, India
| | - Sunesh Balhara
- Animal Biochemistry, Division of Genetics and Breeding, Central Institute for Research on Buffaloes (ICAR), Hisar, India
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Mishra DC, Sikka P, Yadav S, Bhati J, Paul SS, Jerome A, Singh I, Nath A, Budhlakoti N, Rao AR, Rai A, Chaturvedi KK. Identification and characterization of trait-specific SNPs using ddRAD sequencing in water buffalo. Genomics 2020; 112:3571-3578. [PMID: 32320820 DOI: 10.1016/j.ygeno.2020.04.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 03/19/2020] [Accepted: 04/17/2020] [Indexed: 12/14/2022]
Abstract
Single Nucleotide Polymorphism (SNP) is one of the important molecular markers widely used in animal breeding program for improvement of any desirable genetic traits. Considering this, the present study was carried out to identify, annotate and analyze the SNPs related to four important traits of buffalo viz. milk volume, age at first calving, post-partum cyclicity and feed conversion efficiency. We identified 246,495, 168,202, 74,136 and 194,747 genome-wide SNPs related to mentioned traits, respectively using ddRAD sequencing technique based on 85 samples of Murrah Buffaloes. Distribution of these SNPs were highest (61.69%) and lowest (1.78%) in intron and exon regions, respectively. Under coding regions, the SNPs for the four traits were further classified as synonymous (4697) and non-synonymous (3827). Moreover, Gene Ontology (GO) terms of identified genes assigned to various traits. These characterized SNPs will enhance the knowledge of cellular mechanism for enhancing productivity of water buffalo through molecular breeding.
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Affiliation(s)
- D C Mishra
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Poonam Sikka
- ICAR-Central Institute for Research on Buffaloes, Hisar, India
| | - Sunita Yadav
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Jyotika Bhati
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - S S Paul
- ICAR-Central Institute for Research on Buffaloes, Hisar, India
| | - A Jerome
- ICAR-Central Institute for Research on Buffaloes, Hisar, India
| | - Inderjeet Singh
- ICAR-Central Institute for Research on Buffaloes, Hisar, India
| | - Abhigyan Nath
- ICAR-Central Institute for Research on Buffaloes, Hisar, India
| | - Neeraj Budhlakoti
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - A R Rao
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - K K Chaturvedi
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.
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Budhlakoti N, Mishra DC, Rai A, Lal SB, Chaturvedi KK, Kumar RR. A Comparative Study of Single-Trait and Multi-Trait Genomic Selection. J Comput Biol 2019; 26:1100-1112. [PMID: 30994361 DOI: 10.1089/cmb.2019.0032] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
In recent years of animal and plant breeding research, genomic selection (GS) became a choice for selection of appropriate candidate for breeding as it significantly contributes to enhance the genetic gain. Various studies related to GS have been carried out in the recent past. These studies were mostly confined to single trait. Although GS methods based on single trait have not performed very well in cases like pleiotropy, missing data and when the trait under study has low heritability. Gradually, some studies were carried out to explore the possibility of methods for GS based on multiple traits in the view of overcoming the above-mentioned problems in the method of single-trait GS (STGS). Currently, multi-trait-based GS methods are getting importance as it exploits the information of correlated structure among response. In this study, we have compared various methods related to STGS, such as stepwise regression, ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian, best linear unbiased prediction, and support vector machine, and multi-trait-based GS methods, such as multivariate regression with covariance estimation, conditional Gaussian graphical models, mixed model, and LASSO. In almost all cases, multi-trait-based methods are found to be more accurate. Based on the results of this study, it may be concluded that multi-trait-based methods have great potential to increase genetic gain as they utilize the correlation among the response variable as extra information, which contributes to estimate breeding value more precisely. This study is a comprehensive review of the methods of GS right from single trait to multiple traits and comparisons among these two classes.
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Affiliation(s)
- Neeraj Budhlakoti
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | | | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - S B Lal
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | | | - Rajeev Ranjan Kumar
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
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Sureshkumar V, Dutta B, Kumar V, Prakash G, Mishra DC, Chaturvedi KK, Rai A, Sevanthi AM, Solanke AU. RiceMetaSysB: a database of blast and bacterial blight responsive genes in rice and its utilization in identifying key blast-resistant WRKY genes. Database (Oxford) 2019; 2019:5310415. [PMID: 30753479 PMCID: PMC6369264 DOI: 10.1093/database/baz015] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 01/17/2019] [Indexed: 12/13/2022]
Abstract
Nearly two decades of revolution in the area of genomics serves as the basis of present-day molecular breeding in major food crops such as rice. Here we report an open source database on two major biotic stresses of rice, named RiceMetaSysB, which provides detailed information about rice blast and bacterial blight (BB) responsive genes (RGs). Meta-analysis of microarray data from different blast- and BB-related experiments across 241 and 186 samples identified 15135 unique genes for blast and 7475 for BB. A total of 9365 and 5375 simple sequence repeats (SSRs) in blast and BB RGs were identified for marker development. Retrieval of candidate genes using different search options like genotypes, tissue, developmental stage of the host, strain, hours/days post-inoculation, physical position and SSR marker information is facilitated in the database. Search options like 'common genes among varieties' and 'strains' have been enabled to identify robust candidate genes. A 2D representation of the data can be used to compare expression profiles across genes, genotypes and strains. To demonstrate the utility of this database, we queried for blast-responsive WRKY genes (fold change ≥5) using their gene IDs. The structural variations in the 12 WRKY genes so identified and their promoter regions were explored in two rice genotypes contrasting for their reaction to blast infection. Expression analysis of these genes in panicle tissue infected with a virulent and an avirulent strain of Magnaporthe oryzae could identify WRKY7, WRKY58, WRKY62, WRKY64 and WRKY76 as potential candidate genes for resistance to panicle blast, as they showed higher expression only in the resistant genotype against the virulent strain. Thus, we demonstrated that RiceMetaSysB can play an important role in providing robust candidate genes for rice blast and BB.
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Affiliation(s)
- V Sureshkumar
- Indian Council of Agricultural Research (ICAR)-National Research Centre on Plant Biotechnology, Pusa Campus, New Delhi, India
| | - Bipratip Dutta
- Indian Council of Agricultural Research (ICAR)-National Research Centre on Plant Biotechnology, Pusa Campus, New Delhi, India
| | - Vishesh Kumar
- Indian Council of Agricultural Research (ICAR)-National Research Centre on Plant Biotechnology, Pusa Campus, New Delhi, India.,Jamia Hamdard, Hamdard Nagar, New Delhi, India
| | - G Prakash
- ICAR-Indian Agricultural Research Institute, Pusa Campus, New Delhi, India
| | - Dwijesh C Mishra
- ICAR-Indian Agricultural Statistics Research Institute, Pusa Campus, New Delhi, India
| | - K K Chaturvedi
- ICAR-Indian Agricultural Statistics Research Institute, Pusa Campus, New Delhi, India
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, Pusa Campus, New Delhi, India
| | - Amitha Mithra Sevanthi
- Indian Council of Agricultural Research (ICAR)-National Research Centre on Plant Biotechnology, Pusa Campus, New Delhi, India
| | - Amolkumar U Solanke
- Indian Council of Agricultural Research (ICAR)-National Research Centre on Plant Biotechnology, Pusa Campus, New Delhi, India
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Kumar A, Farooqi MS, Mishra DC, Kumar S, Rai A, Chaturvedi KK, Lal SB, Sharma A. Prediction of miRNA and Identification of their Relationship Network Related to Late Blight Disease of Potato. Microrna 2017; 7:11-19. [PMID: 29237394 DOI: 10.2174/2211536607666171213123038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 09/12/2017] [Accepted: 12/04/2017] [Indexed: 11/22/2022]
Abstract
BACKGROUND Late blight is a serious disease in potato caused by Phytophthora infestans. To date only few miRNA have been discovered which are related to late blight disease of potato during host pathogen interaction. Recent studies showed that miRNA, an important gene expression regulator, plays a very important role in host-pathogen interaction by silencing genes either by destructing or blocking of translation of mRNA. METHOD Homology search was performed between non-redundant mature miRNA sequences from miRBase database and Solanum tuberosum EST sequences from NCBI database. Screening of the potential miRNA was done after secondary structure prediction. The target related to late blight disease of respective miRNA was functionally annotated. To identify the relationship between the predicted and mature miRNAs, multiple sequence alignment and evolutionary relationships were established. RESULTS AND CONCLUSION 34 Candidate miRNA related to late blight disease of potato were identified which were associated to five target genes. These miRNAs were linked with Avr3a, INF1, INF2b genes which are elicitin like protein and triggers a hypersensitive response to host cell. Mapping of target sequences showed similarity with Solanum lycopersicum NRC1 gene of chr.1, which are reported as a casual protein required for Pto-mediated cell death and resistance in N. benthamiana. NRC1 are considered as a RX-CC_like domain-containing protein which shows similarity with coiledcoil domain of the potato virus X resistance protein (RX) in Solanum tuberosum. RX recognizes pathogen effector proteins and triggers a response that may be as severe as localized cell death thereby providing resistance against potato virus X.
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Affiliation(s)
- Animesh Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110012, India
| | - Mohammad Samir Farooqi
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110012, India
| | - Dwijesh Chandra Mishra
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110012, India
| | - Sanjeev Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110012, India
| | - Anil Rai
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110012, India
| | - Krishna Kumar Chaturvedi
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110012, India
| | - Shashi Bhushan Lal
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110012, India
| | - Anu Sharma
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi 110012, India
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Abstract
Halophilic archaea/bacteria adapt to different salt concentration, namely extreme, moderate and low. These type of adaptations may occur as a result of modification of protein structure and other changes in different cell organelles. Thus proteins may play an important role in the adaptation of halophilic archaea/bacteria to saline conditions. The Halophile protein database (HProtDB) is a systematic attempt to document the biochemical and biophysical properties of proteins from halophilic archaea/bacteria which may be involved in adaptation of these organisms to saline conditions. In this database, various physicochemical properties such as molecular weight, theoretical pI, amino acid composition, atomic composition, estimated half-life, instability index, aliphatic index and grand average of hydropathicity (Gravy) have been listed. These physicochemical properties play an important role in identifying the protein structure, bonding pattern and function of the specific proteins. This database is comprehensive, manually curated, non-redundant catalogue of proteins. The database currently contains 59 897 proteins properties extracted from 21 different strains of halophilic archaea/bacteria. The database can be accessed through link. Database URL: http://webapp.cabgrid.res.in/protein/
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Affiliation(s)
- Naveen Sharma
- Center for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Pusa Campus, New Delhi 110012, India
| | - Mohammad Samir Farooqi
- Center for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Pusa Campus, New Delhi 110012, India
| | - Krishna Kumar Chaturvedi
- Center for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Pusa Campus, New Delhi 110012, India
| | - Shashi Bhushan Lal
- Center for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Pusa Campus, New Delhi 110012, India
| | - Monendra Grover
- Center for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Pusa Campus, New Delhi 110012, India
| | - Anil Rai
- Center for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Pusa Campus, New Delhi 110012, India
| | - Pankaj Pandey
- Center for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Pusa Campus, New Delhi 110012, India
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Lal SB, Pandey PK, Rai PK, Rai A, Sharma A, Chaturvedi KK. Design and development of portal for biological database in agriculture. Bioinformation 2013; 9:588-98. [PMID: 23888101 PMCID: PMC3717188 DOI: 10.6026/97320630009588] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2013] [Accepted: 03/04/2013] [Indexed: 11/23/2022] Open
Abstract
UNLABELLED The application of novel and modern techniques in genetic engineering and genomics has resulted in information explosion in genomics. Three major genome databases under International Nucleotide Sequence Database collaboration NCBI, DDBJ and EMBL have been providing a convenient platform for submission of sequences which they share among themselves. Many institutes in India under Indian Council of Agricultural Research have scientists working on biotechnology and bioinformatics research. The various studies conducted by them, generate massive data related to biological information of plants, animals, insects, microbes and fisheries. These scientists are dependent on NCBI, EMBL, DDBJ and other portals for their sequence submissions, analysis and other data mining tasks. Due to various limitations imposed on these sites and the poor connectivity problem prevents them to conduct their studies on these open domain databases. The valued information generated by them needs to be shared by the scientific communities to eliminate the duplication of efforts and expedite their knowledge extended towards new findings. A secured common submission portal system with user-friendly interfaces, integrated help and error checking facilities has been developed in such a way that the database at the backend consists of a union of the items available on the above mentioned databases. Standard database management concepts have been employed for their systematic storage management. Extensive hardware resources in the form of high performance computing facility are being installed for deployment of this portal. AVAILABILITY http://cabindb.iasri.res.in:8080/sequence_portal/
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Affiliation(s)
- Shashi Bhushan Lal
- Centre for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Pusa, New Delhi-110012, India
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Jain NK, Chaturvedi KK. Mental-sulphonamides as antibacterials. Part II. Hindustan Antibiot Bull 1975; 18:40-1. [PMID: 1236092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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