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Gaur A, Jindal Y, Singh V, Tiwari R, Juliana P, Kaushik D, Kumar KJY, Ahlawat OP, Singh G, Sheoran S. GWAS elucidated grain yield genetics in Indian spring wheat under diverse water conditions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:177. [PMID: 38972024 DOI: 10.1007/s00122-024-04680-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 06/11/2024] [Indexed: 07/08/2024]
Abstract
KEY MESSAGE Underpinned natural variations and key genes associated with yield under different water regimes, and identified genomic signatures of genetic gain in the Indian wheat breeding program. A novel KASP marker for TKW under water stress was developed and validated. A comprehensive genome-wide association study was conducted on 300 spring wheat genotypes to elucidate the natural variations associated with grain yield and its eleven contributing traits under fully irrigated, restricted water, and simulated no water conditions. Utilizing the 35K Wheat Breeders' Array, we identified 1155 quantitative trait nucleotides (QTNs), with 207 QTNs exhibiting stability across diverse conditions. These QTNs were further delimited into 539 genomic regions using a genome-wide LD value of 3.0 Mbp, revealing pleiotropic control across traits and conditions. Sub-genome A was significantly associated with traits under irrigated conditions, while sub-genome B showed more QTNs under water stressed conditions. Favourable alleles with significantly associated QTNs were delineated, with a notable pyramiding effect for enhancing trait performance. Additionally, allele of only 921 QTNs significantly affected the population mean. Allele profiling highlighted C-306 as a most potential source of drought tolerance. Moreover, 762 genes overlapping significant QTNs were identified, narrowing down to 27 putative candidate genes overlapping 29 novel and functional SNPs expressing (≥ 0.5 tpm) relevance across various growth conditions. A new KASP assay was developed, targeting a gene TraesCS2A03G1123700 regulating thousand kernel weight under severe drought condition. Genomic selection models (GBLUP, BayesB, MxE, and R-Norm) demonstrated an average prediction accuracy of 0.06-0.58 across environments, indicating potential for trait selection. Retrospective analysis of the Indian wheat breeding program supported a genetic gain in GY at the rate of ca. 0.56% per breeding cycle, since 1960, supporting the identification of genomic signatures driving trait selection and genetic gain. These findings offer insight into improving the rate of genetic gain in wheat breeding programs globally.
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Affiliation(s)
- Arpit Gaur
- Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, India
- Crop Improvement, ICAR- Indian Institute of Wheat and Barley Research, Karnal, India
| | - Yogesh Jindal
- Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, India
| | - Vikram Singh
- Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, India
| | - Ratan Tiwari
- Crop Improvement, ICAR- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Deepak Kaushik
- Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar, India
| | | | - Om Parkash Ahlawat
- Crop Improvement, ICAR- Indian Institute of Wheat and Barley Research, Karnal, India
| | - Gyanendra Singh
- Crop Improvement, ICAR- Indian Institute of Wheat and Barley Research, Karnal, India
| | - Sonia Sheoran
- Crop Improvement, ICAR- Indian Institute of Wheat and Barley Research, Karnal, India.
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Zhang J, Jiang Q, Du Z, Geng Y, Hu Y, Tong Q, Song Y, Zhang HY, Yan X, Feng Z. Knowledge graph-derived feed efficiency analysis via pig gut microbiota. Sci Rep 2024; 14:13939. [PMID: 38886444 PMCID: PMC11182767 DOI: 10.1038/s41598-024-64835-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
Feed efficiency (FE) is essential for pig production, has been reported to be partially explained by gut microbiota. Despite an extensive body of research literature to this topic, studies regarding the regulation of feed efficiency by gut microbiota remain fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Meanwhile, structured databases for microbiota analysis are available, yet they often lack a comprehensive understanding of the associated biological processes. Therefore, we have devised an approach to construct a comprehensive knowledge graph by combining unstructured textual intelligence with structured database information and applied it to investigate the relationship between pig gut microbes and FE. Firstly, we created the pgmReading knowledge base and the domain ontology of pig gut microbiota by annotating, extracting, and integrating semantic information from 157 scientific publications. Secondly, we created the pgmPubtator by utilizing PubTator to expand the semantic information related to microbiota. Thirdly, we created the pgmDatabase by mapping and combining the ADDAGMA, gutMGene, and KEGG databases based on the ontology. These three knowledge bases were integrated to form the Pig Gut Microbial Knowledge Graph (PGMKG). Additionally, we created five biological query cases to validate the performance of PGMKG. These cases not only allow us to identify microbes with the most significant impact on FE but also provide insights into the metabolites produced by these microbes and the associated metabolic pathways. This study introduces PGMKG, mapping key microbes in pig feed efficiency and guiding microbiota-targeted optimization.
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Affiliation(s)
- Junmei Zhang
- National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qin Jiang
- National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
- Yazhouwan National Laboratory (YNL), Sanya, 572025, China
| | - Zhihong Du
- National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yilin Geng
- National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuren Hu
- National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qichang Tong
- National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yunfeng Song
- National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hong-Yu Zhang
- National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xianghua Yan
- National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zaiwen Feng
- National Key Laboratory of Agricultural Microbiology, College of Informatics, College of Animal Sciences and Technology, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
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Nédellec C, Sauvion C, Bossy R, Borovikova M, Deléger L. TaeC: A manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature. PLoS One 2024; 19:e0305475. [PMID: 38870159 PMCID: PMC11175518 DOI: 10.1371/journal.pone.0305475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 05/31/2024] [Indexed: 06/15/2024] Open
Abstract
Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. A growing number of plant molecular information networks provide interlinked interoperable data to support the discovery of gene-phenotype interactions. A large body of scientific literature and observational data obtained in-field and under controlled conditions document wheat breeding experiments. The cross-referencing of this complementary information is essential. Text from databases and scientific publications has been identified early on as a relevant source of information. However, the wide variety of terms used to refer to traits and phenotype values makes it difficult to find and cross-reference the textual information, e.g. simple dictionary lookup methods miss relevant terms. Corpora with manually annotated examples are thus needed to evaluate and train textual information extraction methods. While several corpora contain annotations of human and animal phenotypes, no corpus is available for plant traits. This hinders the evaluation of text mining-based crop knowledge graphs (e.g. AgroLD, KnetMiner, WheatIS-FAIDARE) and limits the ability to train machine learning methods and improve the quality of information. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 528 PubMed references that are fully annotated by trait, phenotype, and species. We address the interoperability challenge of crossing sparse assay data and publications by using the Wheat Trait and Phenotype Ontology to normalize trait mentions and the species taxonomy of the National Center for Biotechnology Information to normalize species. The paper describes the construction of the corpus. A study of the performance of state-of-the-art language models for both named entity recognition and linking tasks trained on the corpus shows that it is suitable for training and evaluation. This corpus is currently the most comprehensive manually annotated corpus for natural language processing studies on crop phenotype information from the literature.
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Affiliation(s)
- Claire Nédellec
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
| | - Clara Sauvion
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
| | - Robert Bossy
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
| | - Mariya Borovikova
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- TETIS, Univ. Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France
| | - Louise Deléger
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
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Bleker C, Ramšak Ž, Bittner A, Podpečan V, Zagorščak M, Wurzinger B, Baebler Š, Petek M, Križnik M, van Dieren A, Gruber J, Afjehi-Sadat L, Weckwerth W, Županič A, Teige M, Vothknecht UC, Gruden K. Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses. PLANT COMMUNICATIONS 2024; 5:100920. [PMID: 38616489 PMCID: PMC11211517 DOI: 10.1016/j.xplc.2024.100920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/28/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
Stress Knowledge Map (SKM; https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical, signaling, and regulatory molecular interactions in plants: a highly curated model of plant stress signaling (PSS; 543 reactions) and a large comprehensive knowledge network (488 390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entry point for investigations of plant stress response and related growth trade-offs, as well as interactive explorations of current knowledge. PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation and design of validation experiments, or to gain new insights into experimental observations in plant biology.
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Affiliation(s)
- Carissa Bleker
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia.
| | - Živa Ramšak
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Andras Bittner
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, 53115 Bonn, Germany
| | - Vid Podpečan
- Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Maja Zagorščak
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Bernhard Wurzinger
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Špela Baebler
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Marko Petek
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Maja Križnik
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Annelotte van Dieren
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, 53115 Bonn, Germany
| | - Juliane Gruber
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Leila Afjehi-Sadat
- Mass Spectrometry Unit, Core Facility Shared Services, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Wolfram Weckwerth
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Anže Županič
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Markus Teige
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Ute C Vothknecht
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, 53115 Bonn, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia.
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5
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Xu H, Wang Z, Wang F, Hu X, Ma C, Jiang H, Xie C, Gao Y, Ding G, Zhao C, Qin R, Cui D, Sun H, Cui F, Wu Y. Genome-wide association study and genomic selection of spike-related traits in bread wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:131. [PMID: 38748046 DOI: 10.1007/s00122-024-04640-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 04/27/2024] [Indexed: 06/09/2024]
Abstract
KEY MESSAGE Identification of 337 stable MTAs for wheat spike-related traits improved model accuracy, and favorable alleles of MTA259 and MTA64 increased grain weight and yield per plant. Wheat (Triticum aestivum L.) is one of the three primary global, staple crops. Improving spike-related traits in wheat is crucial for optimizing spike and plant morphology, ultimately leading to increased grain yield. Here, we performed a genome-wide association study using a dataset of 24,889 high-quality unique single-nucleotide polymorphisms (SNPs) and phenotypic data from 314 wheat accessions across eight diverse environments. In total, 337 stable and significant marker-trait associations (MTAs) related to spike-related traits were identified. MTA259 and MTA64 were consistently detected in seven and six environments, respectively. The presence of favorable alleles associated with MTA259 and MTA64 significantly reduced wheat spike exsertion length and spike length, while enhancing thousand kernel weight and yield per plant. Combined gene expression and network analyses identified TraesCS6D03G0692300 and TraesCS6D03G0692700 as candidate genes for MTA259 and TraesCS2D03G0111700 and TraesCS2D03G0112500 for MTA64. The identified MTAs significantly improved the prediction accuracy of each model compared with using all the SNPs, and the random forest model was optimal for genome selection. Additionally, the eight stable and major MTAs, including MTA259, MTA64, MTA66, MTA94, MTA110, MTA165, MTA180, and MTA164, were converted into cost-effective and efficient detection markers. This study provided valuable genetic resources and reliable molecular markers for wheat breeding programs.
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Affiliation(s)
- Huiyuan Xu
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China
| | - Zixu Wang
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China
| | - Faxiang Wang
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China
| | - Xinrong Hu
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China
| | - Chengxue Ma
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China
| | - Huijiao Jiang
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China
| | - Chang Xie
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China
| | - Yuhang Gao
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China
| | - Guangshuo Ding
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China
| | - Chunhua Zhao
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China
| | - Ran Qin
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China
| | - Dezhou Cui
- Crop Research Institute, Shandong Academy of Agricultural Sciences/National Engineering Research Center of Wheat and Maize/Key Laboratory of Wheat Biology and Genetics and Breeding in Northern Huang-Huai River Plain, Ministry of Agriculture and Rural Affairs/Shandong Technology Innovation Center of Wheat/Jinan Key Laboratory of Wheat Genetic Improvement, Jinan, Shandong, China
| | - Han Sun
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China.
| | - Fa Cui
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China.
| | - Yongzhen Wu
- College of Agriculture, Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, Ludong University, Yantai, Shandong, China.
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Afonnikova SD, Kiseleva AA, Fedyaeva AV, Komyshev EG, Koval VS, Afonnikov DA, Salina EA. Identification of Novel Loci Precisely Modulating Pre-Harvest Sprouting Resistance and Red Color Components of the Seed Coat in T. aestivum L. PLANTS (BASEL, SWITZERLAND) 2024; 13:1309. [PMID: 38794380 PMCID: PMC11126043 DOI: 10.3390/plants13101309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024]
Abstract
The association between pre-harvest sprouting (PHS) and seed coat color has long been recognized. Red-grained wheats generally exhibit greater PHS resistance compared to white-grained wheat, although variability in PHS resistance exists within red-grained varieties. Here, we conducted a genome-wide association study on a panel consisting of red-grained wheat varieties, aimed at uncovering genes that modulate PHS resistance and red color components of seed coat using digital image processing. Twelve loci associated with PHS traits were identified, nine of which were described for the first time. Genetic loci marked by SNPs AX-95172164 (chromosome 1B) and AX-158544327 (chromosome 7D) explained approximately 25% of germination index variance, highlighting their value for breeding PHS-resistant varieties. The most promising candidate gene for PHS resistance was TraesCS6B02G147900, encoding a protein involved in aleurone layer morphogenesis. Twenty-six SNPs were significantly associated with grain color, independently of the known Tamyb10 gene. Most of them were related to multiple color characteristics. Prioritization of genes within the revealed loci identified TraesCS1D03G0758600 and TraesCS7B03G1296800, involved in the regulation of pigment biosynthesis and in controlling pigment accumulation. In conclusion, our study identifies new loci associated with grain color and germination index, providing insights into the genetic mechanisms underlying these traits.
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Affiliation(s)
- Svetlana D. Afonnikova
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Antonina A. Kiseleva
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Anna V. Fedyaeva
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Evgenii G. Komyshev
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Vasily S. Koval
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Dmitry A. Afonnikov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Elena A. Salina
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
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Reiser L, Bakker E, Subramaniam S, Chen X, Sawant S, Khosa K, Prithvi T, Berardini TZ. The Arabidopsis Information Resource in 2024. Genetics 2024; 227:iyae027. [PMID: 38457127 PMCID: PMC11075553 DOI: 10.1093/genetics/iyae027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/07/2024] [Indexed: 03/09/2024] Open
Abstract
Since 1999, The Arabidopsis Information Resource (www.arabidopsis.org) has been curating data about the Arabidopsis thaliana genome. Its primary focus is integrating experimental gene function information from the peer-reviewed literature and codifying it as controlled vocabulary annotations. Our goal is to produce a "gold standard" functional annotation set that reflects the current state of knowledge about the Arabidopsis genome. At the same time, the resource serves as a nexus for community-based collaborations aimed at improving data quality, access, and reuse. For the past decade, our work has been made possible by subscriptions from our global user base. This update covers our ongoing biocuration work, some of our modernization efforts that contribute to the first major infrastructure overhaul since 2011, the introduction of JBrowse2, and the resource's role in community activities such as organizing the structural reannotation of the genome. For gene function assessment, we used gene ontology annotations as a metric to evaluate: (1) what is currently known about Arabidopsis gene function and (2) the set of "unknown" genes. Currently, 74% of the proteome has been annotated to at least one gene ontology term. Of those loci, half have experimental support for at least one of the following aspects: molecular function, biological process, or cellular component. Our work sheds light on the genes for which we have not yet identified any published experimental data and have no functional annotation. Drawing attention to these unknown genes highlights knowledge gaps and potential sources of novel discoveries.
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Zhang D, Zhao R, Xian G, Kou Y, Ma W. A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops. FRONTIERS IN PLANT SCIENCE 2024; 15:1361716. [PMID: 38571713 PMCID: PMC10987776 DOI: 10.3389/fpls.2024.1361716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 03/04/2024] [Indexed: 04/05/2024]
Abstract
Identifying polyphenotype genes that simultaneously regulate important agronomic traits (e.g., plant height, yield, and disease resistance) is critical for developing novel high-quality crop varieties. Predicting the associations between genes and traits requires the organization and analysis of multi-dimensional scientific data. The existing methods for establishing the relationships between genomic data and phenotypic data can only elucidate the associations between genes and individual traits. However, there are relatively few methods for detecting elite polyphenotype genes. In this study, a knowledge graph for traits regulating-genes was constructed by collecting data from the PubMed database and eight other databases related to the staple food crops rice, maize, and wheat as well as the model plant Arabidopsis thaliana. On the basis of the knowledge graph, a model for predicting traits regulating-genes was constructed by combining the data attributes of the gene nodes and the topological relationship attributes of the gene nodes. Additionally, a scoring method for predicting the genes regulating specific traits was developed to screen for elite polyphenotype genes. A total of 125,591 nodes and 547,224 semantic relationships were included in the knowledge graph. The accuracy of the knowledge graph-based model for predicting traits regulating-genes was 0.89, the precision rate was 0.91, the recall rate was 0.96, and the F1 value was 0.94. Moreover, 4,447 polyphenotype genes for 31 trait combinations were identified, among which the rice polyphenotype gene IPA1 and the A. thaliana polyphenotype gene CUC2 were verified via a literature search. Furthermore, the wheat gene TraesCS5A02G275900 was revealed as a potential polyphenotype gene that will need to be further characterized. Meanwhile, the result of venn diagram analysis between the polyphenotype gene datasets (consists of genes that are predicted by our model) and the transcriptome gene datasets (consists of genes that were differential expression in response to disease, drought or salt) showed approximately 70% and 54% polyphenotype genes were identified in the transcriptome datasets of Arabidopsis and rice, respectively. The application of the model driven by knowledge graph for predicting traits regulating-genes represents a novel method for detecting elite polyphenotype genes.
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Affiliation(s)
- Dandan Zhang
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
| | - Ruixue Zhao
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Integration Publishing Knowledge Mining and Knowledge Service, National Press and Publication Administration, Beijing, China
| | - Guojian Xian
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yuantao Kou
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agricultural Integration Publishing Knowledge Mining and Knowledge Service, National Press and Publication Administration, Beijing, China
| | - Weilu Ma
- Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing, China
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Harrison C, Noleto-Dias C, Ruvo G, Hughes DJ, Smith DP, Mead A, Ward JL, Heuer S, MacGregor DR. The mechanisms behind the contrasting responses to waterlogging in black-grass ( Alopecurus myosuroides) and wheat ( Triticum aestivum). FUNCTIONAL PLANT BIOLOGY : FPB 2024; 51:FP23193. [PMID: 38417910 DOI: 10.1071/fp23193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/07/2024] [Indexed: 03/01/2024]
Abstract
Black-grass (Alopecurus myosuroides ) is one of the most problematic agricultural weeds of Western Europe, causing significant yield losses in winter wheat (Triticum aestivum ) and other crops through competition for space and resources. Previous studies link black-grass patches to water-retaining soils, yet its specific adaptations to these conditions remain unclear. We designed pot-based waterlogging experiments to compare 13 biotypes of black-grass and six cultivars of wheat. These showed that wheat roots induced aerenchyma when waterlogged whereas aerenchyma-like structures were constitutively present in black-grass. Aerial biomass of waterlogged wheat was smaller, whereas waterlogged black-grass was similar or larger. Variability in waterlogging responses within and between these species was correlated with transcriptomic and metabolomic changes in leaves of control or waterlogged plants. In wheat, transcripts associated with regulation and utilisation of phosphate compounds were upregulated and sugars and amino acids concentrations were increased. Black-grass biotypes showed limited molecular responses to waterlogging. Some black-grass amino acids were decreased and one transcript commonly upregulated was previously identified in screens for genes underpinning metabolism-based resistance to herbicides. Our findings provide insights into the different waterlogging tolerances of these species and may help to explain the previously observed patchiness of this weed's distribution in wheat fields.
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Affiliation(s)
- Christian Harrison
- Rothamsted Research, Protecting Crops and the Environment, Harpenden, Hertfordshire, UK
| | - Clarice Noleto-Dias
- Rothamsted Research, Plant Sciences for the Bioeconomy, Harpenden, Hertfordshire, UK
| | - Gianluca Ruvo
- Rothamsted Research, Plant Sciences for the Bioeconomy, Harpenden, Hertfordshire, UK
| | - David J Hughes
- Rothamsted Research, Intelligent Data Ecosystems, Harpenden, Hertfordshire, UK
| | - Daniel P Smith
- Rothamsted Research, Intelligent Data Ecosystems, Harpenden, Hertfordshire, UK
| | - Andrew Mead
- Rothamsted Research, Intelligent Data Ecosystems, Harpenden, Hertfordshire, UK
| | - Jane L Ward
- Rothamsted Research, Plant Sciences for the Bioeconomy, Harpenden, Hertfordshire, UK
| | - Sigrid Heuer
- International Consultant Crop Improvement and Food Security, Harpenden, UK
| | - Dana R MacGregor
- Rothamsted Research, Protecting Crops and the Environment, Harpenden, Hertfordshire, UK
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Bazile J, Nadaud I, Lasserre-Zuber P, Kitt J, De Oliveira R, Choulet F, Sourdille P. TaRECQ4 contributes to maintain both homologous and homoeologous recombination during wheat meiosis. FRONTIERS IN PLANT SCIENCE 2024; 14:1342976. [PMID: 38348162 PMCID: PMC10859459 DOI: 10.3389/fpls.2023.1342976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 12/29/2023] [Indexed: 02/15/2024]
Abstract
Introduction Meiotic recombination (or crossover, CO) is essential for gamete fertility as well as for alleles and genes reshuffling that is at the heart of plant breeding. However, CO remains a limited event, which strongly hampers the rapid production of original and improved cultivars. RecQ4 is a gene encoding a helicase protein that, when mutated, contributes to improve recombination rate in all species where it has been evaluated so far. Methods In this study, we developed wheat (Triticum aestivum L.) triple mutant (TM) for the three homoeologous copies of TaRecQ4 as well as mutants for two copies and heterozygous for the last one (Htz-A, Htz-B, Htz-D). Results Phenotypic observation revealed a significant reduction of fertility and pollen viability in TM and Htz-B plants compared to wild type plants suggesting major defects during meiosis. Cytogenetic analyses of these plants showed that complete absence of TaRecQ4 as observed in TM plants, leads to chromosome fragmentation during the pachytene stage, resulting in problems in the segregation of chromosomes during meiosis. Htz-A and Htz-D mutants had an almost normal meiotic progression indicating that both TaRecQ4-A and TaRecQ4-D copies are functional and that there is no dosage effect for TaRecQ4 in bread wheat. On the contrary, the TaRecQ4-B copy seems knocked-out, probably because of a SNP leading to a Threonine>Alanine change at position 539 (T539A) of the protein, that occurs in the crucial helicase ATP bind/DEAD/ResIII domain which unwinds nucleic acids. Occurrence of numerous multivalents in TM plants suggests that TaRecQ4 could also play a role in the control of homoeologous recombination. Discussion These findings provide a foundation for further molecular investigations into wheat meiosis regulation to fully understand the underlying mechanisms of how TaRecQ4 affects chiasma formation, as well as to identify ways to mitigate these defects and enhance both homologous and homoeologous recombination efficiency in wheat.
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Affiliation(s)
- Jeanne Bazile
- INRAE, UMR 1095 INRAE – UCA Genetics, Diversity & Ecophysiology of Cereals, Clermont-Ferrand, France
| | - Isabelle Nadaud
- INRAE, UMR 1095 INRAE – UCA Genetics, Diversity & Ecophysiology of Cereals, Clermont-Ferrand, France
| | - Pauline Lasserre-Zuber
- INRAE, UMR 1095 INRAE – UCA Genetics, Diversity & Ecophysiology of Cereals, Clermont-Ferrand, France
| | - Jonathan Kitt
- INRAE, UMR 1095 INRAE – UCA Genetics, Diversity & Ecophysiology of Cereals, Clermont-Ferrand, France
| | - Romain De Oliveira
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Frédéric Choulet
- INRAE, UMR 1095 INRAE – UCA Genetics, Diversity & Ecophysiology of Cereals, Clermont-Ferrand, France
| | - Pierre Sourdille
- INRAE, UMR 1095 INRAE – UCA Genetics, Diversity & Ecophysiology of Cereals, Clermont-Ferrand, France
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11
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Kong X, Diao L, Jiang P, Nie S, Guo S, Li D. DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge. Brief Bioinform 2024; 25:bbae111. [PMID: 38517698 PMCID: PMC10959161 DOI: 10.1093/bib/bbae111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/24/2024] Open
Abstract
The high-throughput genomic and proteomic scanning approaches allow investigators to measure the quantification of genome-wide genes (or gene products) for certain disease conditions, which plays an essential role in promoting the discovery of disease mechanisms. The high-throughput approaches often generate a large gene list of interest (GOIs), such as differentially expressed genes/proteins. However, researchers have to perform manual triage and validation to explore the most promising, biologically plausible linkages between the known disease genes and GOIs (disease signals) for further study. Here, to address this challenge, we proposed a network-based strategy DDK-Linker to facilitate the exploration of disease signals hidden in omics data by linking GOIs to disease knowns genes. Specifically, it reconstructed gene distances in the protein-protein interaction (PPI) network through six network methods (random walk with restart, Deepwalk, Node2Vec, LINE, HOPE, Laplacian) to discover disease signals in omics data that have shorter distances to disease genes. Furthermore, benefiting from the establishment of knowledge base we established, the abundant bioinformatics annotations were provided for each candidate disease signal. To assist in omics data interpretation and facilitate the usage, we have developed this strategy into an application that users can access through a website or download the R package. We believe DDK-Linker will accelerate the exploring of disease genes and drug targets in a variety of omics data, such as genomics, transcriptomics and proteomics data, and provide clues for complex disease mechanism and pharmacological research. DDK-Linker is freely accessible at http://ddklinker.ncpsb.org.cn/.
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Affiliation(s)
- Xiangren Kong
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Lihong Diao
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Peng Jiang
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Shiyan Nie
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Shuzhen Guo
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Dong Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
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12
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Zeng V, Uauy C, Chen Y. Identification of a novel SNP in the miR172 binding site of Q homoeolog AP2L-D5 is associated with spike compactness and agronomic traits in wheat (Triticum aestivum L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 137:13. [PMID: 38142253 DOI: 10.1007/s00122-023-04514-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 11/23/2023] [Indexed: 12/25/2023]
Abstract
KEY MESSAGE This study found that the compact spike locus of ANK-15 is on chromosome 5D instead of 2B. We have identified a new allele of AP2L-D5 as the candidate causal polymorphism. Spike architecture is a key determinant of wheat yield, a crop which supports much of the human diet but whose yield gains are stagnating. Spike architecture mutants offer opportunities to identify genetic factors contributing to inflorescence development. Here, we investigate the locus underlying the compact spike phenotype of mutant line ANK-15 by conducting mRNA-sequencing and genetic mapping using ANK-15 and its non-compact spike near-isogenic line Novosibirskaya 67 (N67). Previous literature has placed the compact spike locus of ANK-15 to chromosome 2B. However, based on the single nucleotide polymorphisms (SNPs) identified using mRNA-seq data, we were unable to detect polymorphisms between N67 and ANK-15 in the putative chromosome 2B region. We performed differential expression analysis of developing rachis and found that AP2L-D5, the D homoeolog of the domestication Q gene, is upregulated in ANK-15 in comparison to N67. ANK-15 carries a SNP in the microRNA172 binding site of AP2L-D5, which is predicted to lead to higher expression of AP2L-D5 due to decreased miRNA172-mediated degradation. Furthermore, we performed genetic mapping using an ANK-15 × N67 F2 population and found a single quantitative trait locus on chromosome 5D coinciding with the position of AP2L-D5. This result suggests that AP2L-D5 is likely the underlying causal gene for the compact spike phenotype in ANK-15. We performed a field trial to investigate the effect of the AP2L-D5 allele on agronomic traits and found that the AP2L-D5 allele from ANK-15 is associated with a significant reduction in height, increased thousand grain weight (TGW), and increased grain width.
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Affiliation(s)
- Victoria Zeng
- John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK
| | - Cristobal Uauy
- John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK
| | - Yi Chen
- John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK.
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13
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Oddy J, Chhetry M, Awal R, Addy J, Wilkinson M, Smith D, King R, Hall C, Testa R, Murray E, Raffan S, Curtis TY, Wingen L, Griffiths S, Berry S, Elmore JS, Cryer N, Moreira de Almeida I, Halford NG. Genetic control of grain amino acid composition in a UK soft wheat mapping population. THE PLANT GENOME 2023; 16:e20335. [PMID: 37138544 DOI: 10.1002/tpg2.20335] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/23/2022] [Accepted: 03/13/2023] [Indexed: 05/05/2023]
Abstract
Wheat (Triticum aestivum L.) is a major source of nutrients for populations across the globe, but the amino acid composition of wheat grain does not provide optimal nutrition. The nutritional value of wheat grain is limited by low concentrations of lysine (the most limiting essential amino acid) and high concentrations of free asparagine (precursor to the processing contaminant acrylamide). There are currently few available solutions for asparagine reduction and lysine biofortification through breeding. In this study, we investigated the genetic architecture controlling grain free amino acid composition and its relationship to other traits in a Robigus × Claire doubled haploid population. Multivariate analysis of amino acids and other traits showed that the two groups are largely independent of one another, with the largest effect on amino acids being from the environment. Linkage analysis of the population allowed identification of quantitative trait loci (QTL) controlling free amino acids and other traits, and this was compared against genomic prediction methods. Following identification of a QTL controlling free lysine content, wheat pangenome resources facilitated analysis of candidate genes in this region of the genome. These findings can be used to select appropriate strategies for lysine biofortification and free asparagine reduction in wheat breeding programs.
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Affiliation(s)
| | | | - Rajani Awal
- John Innes Centre, Norwich Research Park, Norwich, UK
| | | | | | | | | | | | | | | | | | | | - Luzie Wingen
- John Innes Centre, Norwich Research Park, Norwich, UK
| | | | | | - J Stephen Elmore
- Department of Food & Nutritional Sciences, University of Reading, Reading, UK
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14
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López-Fernández M, García-Abadillo J, Uauy C, Ruiz M, Giraldo P, Pascual L. Genome wide association in Spanish bread wheat landraces identifies six key genomic regions that constitute potential targets for improving grain yield related traits. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:244. [PMID: 37957405 PMCID: PMC10643358 DOI: 10.1007/s00122-023-04492-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023]
Abstract
KEY MESSAGE Association mapping conducted in 189 Spanish bread wheat landraces revealed six key genomic regions that constitute stable QTLs for yield and include 15 candidate genes. Genetically diverse landraces provide an ideal population to conduct association analysis. In this study, association mapping was conducted in a collection of 189 Spanish bread wheat landraces whose genomic diversity had been previously assessed. These genomic data were combined with characterization for yield-related traits, including grain size and shape, and phenological traits screened across five seasons. The association analysis revealed a total of 881 significant marker trait associations, involving 434 markers across the genome, that could be grouped in 366 QTLs based on linkage disequilibrium. After accounting for days to heading, we defined 33 high density QTL genomic regions associated to at least four traits. Considering the importance of detecting stable QTLs, 6 regions associated to several grain traits and thousand kernel weight in at least three environments were selected as the most promising ones to harbour targets for breeding. To dissect the genetic cause of the observed associations, we studied the function and in silico expression of the 413 genes located inside these six regions. This identified 15 candidate genes that provide a starting point for future analysis aimed at the identification and validation of wheat yield related genes.
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Affiliation(s)
- Matilde López-Fernández
- Department of Biotechnology-Plant Biology, School of Agricultural, Food and Biosystems Engineering (ETSIAAB), Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Julián García-Abadillo
- Department of Biotechnology and Plant Biology, Centre for Biotechnology and Plant Genomics (CBGP), Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Cristobal Uauy
- John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK
| | - Magdalena Ruiz
- Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA), CSIC, Autovía A2, Km. 36.2. Finca La Canaleja, 28805, Alcalá de Henares, Madrid, Spain
| | - Patricia Giraldo
- Department of Biotechnology-Plant Biology, School of Agricultural, Food and Biosystems Engineering (ETSIAAB), Universidad Politécnica de Madrid (UPM), Madrid, Spain.
| | - Laura Pascual
- Department of Biotechnology-Plant Biology, School of Agricultural, Food and Biosystems Engineering (ETSIAAB), Universidad Politécnica de Madrid (UPM), Madrid, Spain
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15
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Kiseleva AA, Leonova IN, Ageeva EV, Likhenko IE, Salina EA. Identification of genetic loci for early maturity in spring bread wheat using the association analysis and gene dissection. PeerJ 2023; 11:e16109. [PMID: 37842052 PMCID: PMC10569184 DOI: 10.7717/peerj.16109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/25/2023] [Indexed: 10/17/2023] Open
Abstract
Background Early maturity in spring bread wheat is highly desirable in the regions where it enables the plants to evade high temperatures and plant pathogens at the end of the growing season. Methods To reveal the genetic loci responsible for the maturity time association analysis was carried out based on phenotyping for an 11-year period and high-throughput SNP genotyping of a panel of the varieties contrasting for this trait. The expression of candidate genes was verified using qPCR. The association between the SNP markers and the trait was validated using the biparental F2:3 population. Results Our data showed that under long-day conditions, the period from seedling to maturity is mostly influenced by the time from heading to maturity, rather than the heading time. The QTLs associated with the trait were located on 2A, 3B, 4A, 5B, 7A and 7B chromosomes with the 7BL locus being the most significant and promising for its SNPs accelerated the maturity time by about 9 days. Gene dissection in this locus detected a number of candidates, the best being TraesCS7B02G391800 (bZIP9) and TraesCS7B02G412200 (photosystem II reaction center). The two genes are predominantly expressed in the flag leaf while flowering. The effect of the SNPs was verified in F2:3 population and confirmed the association of the 4A, 5B and 7BL loci with the maturity time.
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Affiliation(s)
- Antonina A. Kiseleva
- Laboratory of Plant Molecular Genetics and Cytogenetics, The Federal State Budgetary Institution of Science Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | - Irina N. Leonova
- Laboratory of Plant Molecular Genetics and Cytogenetics, The Federal State Budgetary Institution of Science Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Elena V. Ageeva
- Laboratory of Field Crop Breeding and Seed Industry, Siberian Research Institute of Plant Production and Breeding, Branch of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Ivan E. Likhenko
- Laboratory of Field Crop Breeding and Seed Industry, Siberian Research Institute of Plant Production and Breeding, Branch of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Elena A. Salina
- Laboratory of Plant Molecular Genetics and Cytogenetics, The Federal State Budgetary Institution of Science Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
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Imbert B, Kreplak J, Flores RG, Aubert G, Burstin J, Tayeh N. Development of a knowledge graph framework to ease and empower translational approaches in plant research: a use-case on grain legumes. Front Artif Intell 2023; 6:1191122. [PMID: 37601035 PMCID: PMC10435283 DOI: 10.3389/frai.2023.1191122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023] Open
Abstract
While the continuing decline in genotyping and sequencing costs has largely benefited plant research, some key species for meeting the challenges of agriculture remain mostly understudied. As a result, heterogeneous datasets for different traits are available for a significant number of these species. As gene structures and functions are to some extent conserved through evolution, comparative genomics can be used to transfer available knowledge from one species to another. However, such a translational research approach is complex due to the multiplicity of data sources and the non-harmonized description of the data. Here, we provide two pipelines, referred to as structural and functional pipelines, to create a framework for a NoSQL graph-database (Neo4j) to integrate and query heterogeneous data from multiple species. We call this framework Orthology-driven knowledge base framework for translational research (Ortho_KB). The structural pipeline builds bridges across species based on orthology. The functional pipeline integrates biological information, including QTL, and RNA-sequencing datasets, and uses the backbone from the structural pipeline to connect orthologs in the database. Queries can be written using the Neo4j Cypher language and can, for instance, lead to identify genes controlling a common trait across species. To explore the possibilities offered by such a framework, we populated Ortho_KB to obtain OrthoLegKB, an instance dedicated to legumes. The proposed model was evaluated by studying the conservation of a flowering-promoting gene. Through a series of queries, we have demonstrated that our knowledge graph base provides an intuitive and powerful platform to support research and development programmes.
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Affiliation(s)
- Baptiste Imbert
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Jonathan Kreplak
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Raphaël-Gauthier Flores
- Université Paris-Saclay, INRAE, URGI, Versailles, France
- Université Paris-Saclay, INRAE, BioinfOmics, Plant Bioinformatics Facility, Versailles, France
| | - Grégoire Aubert
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Judith Burstin
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Nadim Tayeh
- Agroécologie, INRAE, Institut Agro, Univ. Bourgogne, Univ. Bourgogne Franche-Comté, Dijon, France
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17
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Cuzick A, Seager J, Wood V, Urban M, Rutherford K, Hammond-Kosack KE. A framework for community curation of interspecies interactions literature. eLife 2023; 12:e84658. [PMID: 37401199 DOI: 10.7554/elife.84658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 05/18/2023] [Indexed: 07/05/2023] Open
Abstract
The quantity and complexity of data being generated and published in biology has increased substantially, but few methods exist for capturing knowledge about phenotypes derived from molecular interactions between diverse groups of species, in such a way that is amenable to data-driven biology and research. To improve access to this knowledge, we have constructed a framework for the curation of the scientific literature studying interspecies interactions, using data curated for the Pathogen-Host Interactions database (PHI-base) as a case study. The framework provides a curation tool, phenotype ontology, and controlled vocabularies to curate pathogen-host interaction data, at the level of the host, pathogen, strain, gene, and genotype. The concept of a multispecies genotype, the 'metagenotype,' is introduced to facilitate capturing changes in the disease-causing abilities of pathogens, and host resistance or susceptibility, observed by gene alterations. We report on this framework and describe PHI-Canto, a community curation tool for use by publication authors.
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Affiliation(s)
- Alayne Cuzick
- Strategic area: Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom
| | - James Seager
- Strategic area: Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom
| | - Valerie Wood
- Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Martin Urban
- Strategic area: Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom
| | - Kim Rutherford
- Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Kim E Hammond-Kosack
- Strategic area: Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom
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18
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Thessen AE, Cooper L, Swetnam TL, Hegde H, Reese J, Elser J, Jaiswal P. Using knowledge graphs to infer gene expression in plants. Front Artif Intell 2023; 6:1201002. [PMID: 37384147 PMCID: PMC10298150 DOI: 10.3389/frai.2023.1201002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/23/2023] [Indexed: 06/30/2023] Open
Abstract
Introduction Climate change is already affecting ecosystems around the world and forcing us to adapt to meet societal needs. The speed with which climate change is progressing necessitates a massive scaling up of the number of species with understood genotype-environment-phenotype (G×E×P) dynamics in order to increase ecosystem and agriculture resilience. An important part of predicting phenotype is understanding the complex gene regulatory networks present in organisms. Previous work has demonstrated that knowledge about one species can be applied to another using ontologically-supported knowledge bases that exploit homologous structures and homologous genes. These types of structures that can apply knowledge about one species to another have the potential to enable the massive scaling up that is needed through in silico experimentation. Methods We developed one such structure, a knowledge graph (KG) using information from Planteome and the EMBL-EBI Expression Atlas that connects gene expression, molecular interactions, functions, and pathways to homology-based gene annotations. Our preliminary analysis uses data from gene expression studies in Arabidopsis thaliana and Populus trichocarpa plants exposed to drought conditions. Results A graph query identified 16 pairs of homologous genes in these two taxa, some of which show opposite patterns of gene expression in response to drought. As expected, analysis of the upstream cis-regulatory region of these genes revealed that homologs with similar expression behavior had conserved cis-regulatory regions and potential interaction with similar trans-elements, unlike homologs that changed their expression in opposite ways. Discussion This suggests that even though the homologous pairs share common ancestry and functional roles, predicting expression and phenotype through homology inference needs careful consideration of integrating cis and trans-regulatory components in the curated and inferred knowledge graph.
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Affiliation(s)
- Anne E. Thessen
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
| | - Tyson L. Swetnam
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - Harshad Hegde
- Environmental Genomics and Systems Biology Division, Berkeley Lab (DOE), Berkeley, CA, United States
| | - Justin Reese
- Environmental Genomics and Systems Biology Division, Berkeley Lab (DOE), Berkeley, CA, United States
| | - Justin Elser
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
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Mourad AM, Hamdy RM, Esmail SM. Novel genomic regions on chromosome 5B controlling wheat powdery mildew seedling resistance under Egyptian conditions. FRONTIERS IN PLANT SCIENCE 2023; 14:1160657. [PMID: 37235018 PMCID: PMC10208068 DOI: 10.3389/fpls.2023.1160657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 03/27/2023] [Indexed: 05/28/2023]
Abstract
Wheat powdery mildew (PM) causes significant yield losses worldwide. None of the Egyptian wheat cultivars was detected to be highly resistant to such a severe disease. Therefore, a diverse spring wheat panel was evaluated for PM seedling resistance using different Bgt conidiospores collected from Egyptian fields in two growing seasons. The evaluation was done in two separate experiments. Highly significant differences were found between the two experiments suggesting the presence of different isolates populations. Highly significant differences were found among the tested genotypes confirming the ability to improve PM resistance using the recent panel. Genome-wide association study (GWAS) was done for each experiment separately and a total of 71 significant markers located within 36 gene models were identified. The majority of these markers are located on chromosome 5B. Haplotype block analysis identified seven blocks containing the significant markers on chromosome 5B. Five gene models were identified on the short arm of the chromosome. Gene enrichment analysis identified five and seven pathways based on the biological process and molecular functions respectively for the detected gene models. All these pathways are associated with disease resistance in wheat. The genomic regions on 5B seem to be novel regions that are associated with PM resistance under Egyptian conditions. Selection of superior genotypes was done and Grecian genotypes seem to be a good source for improving PM resistance under Egyptian conditions.
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Affiliation(s)
- Amira M.I. Mourad
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, OT Gatersleben, Germany
- Department of Agronomy, Faculty of Agriculture, Assiut University, Assiut, Egypt
| | - Rania M. Hamdy
- Food Science and Technology Department, Faculty of Agriculture, Assiut University, Assiut, Egypt
| | - Samar M. Esmail
- Wheat Disease Research Department, Plant Pathology Research Institute, Agricultural Research Center, Giza, Egypt
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20
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Saidi MN, Mahjoubi H, Yacoubi I. Transcriptome meta-analysis of abiotic stresses-responsive genes and identification of candidate transcription factors for broad stress tolerance in wheat. PROTOPLASMA 2023; 260:707-721. [PMID: 36063229 DOI: 10.1007/s00709-022-01807-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
Under field conditions, wheat is subjected to single or multiple stress conditions. The elucidation of the molecular mechanism of stress response is a key step to identify candidate genes for stress resistance in plants. In this study, RNA-seq data analysis identified 17.324, 10.562, 5.510, and 8.653 differentially expressed genes (DEGs) under salt, drought, heat, and cold stress, respectively. Moreover, the comparison of DEGs from each stress revealed 2374 shared genes from which 40% showed highly conserved expression patterns. Moreover, co-expression network analysis and GO enrichment revealed co-expression modules enriched with genes involved in transcription regulation, protein kinase pathway, and genes responding to phytohormones or modulating hormone levels. The expression of 15 selected transcription factor encoding genes was analyzed under abiotic stresses and ABA treatment in durum wheat. The identified transcription factor genes are excellent candidates for genetic engineering of stress tolerance in wheat.
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Affiliation(s)
- Mohamed Najib Saidi
- Biotechnology and Plant Improvement Laboratory, Centre of Biotechnology of Sfax, Road Sidi Mansour 6 km, P.O. Box 1177, 3018, Sfax, Tunisia.
| | - Habib Mahjoubi
- Biotechnology and Plant Improvement Laboratory, Centre of Biotechnology of Sfax, Road Sidi Mansour 6 km, P.O. Box 1177, 3018, Sfax, Tunisia
| | - Ines Yacoubi
- Biotechnology and Plant Improvement Laboratory, Centre of Biotechnology of Sfax, Road Sidi Mansour 6 km, P.O. Box 1177, 3018, Sfax, Tunisia
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21
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Barker R, Kruse CPS, Johnson C, Saravia-Butler A, Fogle H, Chang HS, Trane RM, Kinscherf N, Villacampa A, Manzano A, Herranz R, Davin LB, Lewis NG, Perera I, Wolverton C, Gupta P, Jaiswal P, Reinsch SS, Wyatt S, Gilroy S. Meta-analysis of the space flight and microgravity response of the Arabidopsis plant transcriptome. NPJ Microgravity 2023; 9:21. [PMID: 36941263 PMCID: PMC10027818 DOI: 10.1038/s41526-023-00247-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 01/10/2023] [Indexed: 03/23/2023] Open
Abstract
Spaceflight presents a multifaceted environment for plants, combining the effects on growth of many stressors and factors including altered gravity, the influence of experiment hardware, and increased radiation exposure. To help understand the plant response to this complex suite of factors this study compared transcriptomic analysis of 15 Arabidopsis thaliana spaceflight experiments deposited in the National Aeronautics and Space Administration's GeneLab data repository. These data were reanalyzed for genes showing significant differential expression in spaceflight versus ground controls using a single common computational pipeline for either the microarray or the RNA-seq datasets. Such a standardized approach to analysis should greatly increase the robustness of comparisons made between datasets. This analysis was coupled with extensive cross-referencing to a curated matrix of metadata associated with these experiments. Our study reveals that factors such as analysis type (i.e., microarray versus RNA-seq) or environmental and hardware conditions have important confounding effects on comparisons seeking to define plant reactions to spaceflight. The metadata matrix allows selection of studies with high similarity scores, i.e., that share multiple elements of experimental design, such as plant age or flight hardware. Comparisons between these studies then helps reduce the complexity in drawing conclusions arising from comparisons made between experiments with very different designs.
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Affiliation(s)
- Richard Barker
- Department of Botany, University of Wisconsin, Madison, WI, 53706, USA
| | - Colin P S Kruse
- Los Alamos National Laboratory, Bioscience Division, Los Alamos, NM, 87545, USA
| | | | - Amanda Saravia-Butler
- Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, 94035, USA
- Logyx, LLC, Mountain View, CA, 94043, USA
| | - Homer Fogle
- Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, 94035, USA
- Bionetics, Yorktown, VA, 23693, USA
| | - Hyun-Seok Chang
- Department of Botany, University of Wisconsin, Madison, WI, 53706, USA
| | - Ralph Møller Trane
- Department of Statistics, University of Wisconsin, Madison, WI, 53706, USA
| | - Noah Kinscherf
- Department of Botany, University of Wisconsin, Madison, WI, 53706, USA
| | - Alicia Villacampa
- Centro de Investigaciones Biológicas Margarita Salas (CSIC), 28040, Madrid, Spain
| | - Aránzazu Manzano
- Centro de Investigaciones Biológicas Margarita Salas (CSIC), 28040, Madrid, Spain
| | - Raúl Herranz
- Centro de Investigaciones Biológicas Margarita Salas (CSIC), 28040, Madrid, Spain
| | - Laurence B Davin
- Institute of Biological Chemistry, Washington State University, Pullman, WA, 99164-741, USA
| | - Norman G Lewis
- Institute of Biological Chemistry, Washington State University, Pullman, WA, 99164-741, USA
| | - Imara Perera
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, 27695, USA
| | - Chris Wolverton
- Department of Botany and Microbiology, Ohio Wesleyan University, Delaware, OH, 43015, USA
| | - Parul Gupta
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, 97331, USA
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, 97331, USA
| | - Sigrid S Reinsch
- Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, 94035, USA
| | - Sarah Wyatt
- Department of Environmental and Plant Biology, Ohio University, Athens, OH, 45701, USA
| | - Simon Gilroy
- Department of Botany, University of Wisconsin, Madison, WI, 53706, USA.
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22
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Zuluaga DL, Blanco E, Mangini G, Sonnante G, Curci PL. A Survey of the Transcriptomic Resources in Durum Wheat: Stress Responses, Data Integration and Exploitation. PLANTS (BASEL, SWITZERLAND) 2023; 12:1267. [PMID: 36986956 PMCID: PMC10056183 DOI: 10.3390/plants12061267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/28/2023] [Accepted: 03/04/2023] [Indexed: 06/19/2023]
Abstract
Durum wheat (Triticum turgidum subsp. durum (Desf.) Husn.) is an allotetraploid cereal crop of worldwide importance, given its use for making pasta, couscous, and bulgur. Under climate change scenarios, abiotic (e.g., high and low temperatures, salinity, drought) and biotic (mainly exemplified by fungal pathogens) stresses represent a significant limit for durum cultivation because they can severely affect yield and grain quality. The advent of next-generation sequencing technologies has brought a huge development in transcriptomic resources with many relevant datasets now available for durum wheat, at various anatomical levels, also focusing on phenological phases and environmental conditions. In this review, we cover all the transcriptomic resources generated on durum wheat to date and focus on the corresponding scientific insights gained into abiotic and biotic stress responses. We describe relevant databases, tools and approaches, including connections with other "omics" that could assist data integration for candidate gene discovery for bio-agronomical traits. The biological knowledge summarized here will ultimately help in accelerating durum wheat breeding.
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23
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Esmail SM, Omar GE, Mourad AMI. In-Depth Understanding of the Genetic Control of Stripe Rust Resistance ( Puccinia striiformis f. sp. tritici) Induced in Wheat ( Triticum aestivum) by Trichoderma asperellum T34. PLANT DISEASE 2023; 107:457-472. [PMID: 36449539 DOI: 10.1094/pdis-07-22-1593-re] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Wheat stripe rust (caused by Puccinia striiformis f. tritici Erikss.) causes severe yield losses worldwide. Due to the continuous appearance of new stripe rust races, resistance has been broken in most of the highly resistant genotypes in Egypt and worldwide. Therefore, looking for new ways to resist such a severe disease is urgently needed. Trichoderma asperellum strain T34 has been known as an effective bioagent against many crop diseases. It exists naturally in Egyptian fields. Therefore, in our study, the effectiveness of strain T34 was tested as a bioagent against wheat stripe rust. For this purpose, 198 spring wheat genotypes were tested for their resistance against two different P. striiformis f. tritici populations collected from the Egyptian fields. The most highly aggressive P. striiformis f. tritici population was used to test the effectiveness of strain T34. Highly significant differences were found between strain T34 and stripe rust, suggesting the effectiveness of strain T34 in stripe rust resistance. A genome-wide association study identified 48 gene models controlling resistance under normal conditions and 46 gene models controlling strain T34-induced resistance. Of these gene models, only one common gene model was found, suggesting the presence of two different genetic systems controlling resistance under each condition. The pathways of the biological processes were investigated under both conditions. This study provided in-depth understanding of genetic control and, hence, will accelerate the future of wheat breeding programs for stripe rust resistance.
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Affiliation(s)
- Samar M Esmail
- Wheat Disease Research Department, Plant Pathology Research Institute, Agricultural Research Center, Giza, Egypt
| | - Ghady E Omar
- Wheat Disease Research Department, Plant Pathology Research Institute, Agricultural Research Center, Giza, Egypt
| | - Amira M I Mourad
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Germany
- Department of Agronomy, Faculty of Agriculture, Assiut University, Assiut, Egypt
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24
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Exotic alleles contribute to heat tolerance in wheat under field conditions. Commun Biol 2023; 6:21. [PMID: 36624201 PMCID: PMC9829678 DOI: 10.1038/s42003-022-04325-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 11/30/2022] [Indexed: 01/11/2023] Open
Abstract
Global warming poses a major threat to food security and necessitates the development of crop varieties that are resilient to future climatic instability. By evaluating 149 spring wheat lines in the field under yield potential and heat stressed conditions, we demonstrate how strategic integration of exotic material significantly increases yield under heat stress compared to elite lines, with no significant yield penalty under favourable conditions. Genetic analyses reveal three exotic-derived genetic loci underlying this heat tolerance which together increase yield by over 50% and reduce canopy temperature by approximately 2 °C. We identified an Ae. tauschii introgression underlying the most significant of these associations and extracted the introgressed Ae. tauschii genes, revealing candidates for further dissection. Incorporating these exotic alleles into breeding programmes could serve as a pre-emptive strategy to produce high yielding wheat cultivars that are resilient to the effects of future climatic uncertainty.
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25
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Blyth HR, Smith D, King R, Bayon C, Ashfield T, Walpole H, Venter E, Ray RV, Kanyuka K, Rudd JJ. Fungal plant pathogen "mutagenomics" reveals tagged and untagged mutations in Zymoseptoria tritici and identifies SSK2 as key morphogenesis and stress-responsive virulence factor. FRONTIERS IN PLANT SCIENCE 2023; 14:1140824. [PMID: 37206970 PMCID: PMC10190600 DOI: 10.3389/fpls.2023.1140824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/29/2023] [Indexed: 05/21/2023]
Abstract
"Mutagenomics" is the combination of random mutagenesis, phenotypic screening, and whole-genome re-sequencing to uncover all tagged and untagged mutations linked with phenotypic changes in an organism. In this study, we performed a mutagenomics screen on the wheat pathogenic fungus Zymoseptoria tritici for altered morphogenetic switching and stress sensitivity phenotypes using Agrobacterium-mediated "random" T-DNA mutagenesis (ATMT). Biological screening identified four mutants which were strongly reduced in virulence on wheat. Whole genome re-sequencing defined the positions of the T-DNA insertion events and revealed several unlinked mutations potentially affecting gene functions. Remarkably, two independent reduced virulence mutant strains, with similarly altered stress sensitivities and aberrant hyphal growth phenotypes, were found to have a distinct loss of function mutations in the ZtSSK2 MAPKKK gene. One mutant strain had a direct T-DNA insertion affecting the predicted protein's N-terminus, while the other possessed an unlinked frameshift mutation towards the C-terminus. We used genetic complementation to restore both strains' wild-type (WT) function (virulence, morphogenesis, and stress response). We demonstrated that ZtSSK2 has a non-redundant function with ZtSTE11 in virulence through the biochemical activation of the stress-activated HOG1 MAPK pathway. Moreover, we present data suggesting that SSK2 has a unique role in activating this pathway in response to specific stresses. Finally, dual RNAseq-based transcriptome profiling of WT and SSK2 mutant strains revealed many HOG1-dependent transcriptional changes in the fungus during early infection and suggested that the host response does not discriminate between WT and mutant strains during this early phase. Together these data define new genes implicated in the virulence of the pathogen and emphasise the importance of a whole genome sequencing step in mutagenomic discovery pipelines.
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Affiliation(s)
- Hannah R. Blyth
- Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom
| | - Dan Smith
- Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom
| | - Robert King
- Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom
| | - Carlos Bayon
- Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom
| | - Tom Ashfield
- Crop Health and Protection (CHAP), Rothamsted Research, Harpenden, United Kingdom
| | - Hannah Walpole
- Bioimaging Unit, Rothamsted Research, Harpenden, United Kingdom
| | - Eudri Venter
- Bioimaging Unit, Rothamsted Research, Harpenden, United Kingdom
| | - Rumiana V. Ray
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Loughborough, United Kingdom
| | - Kostya Kanyuka
- Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom
| | - Jason J. Rudd
- Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom
- *Correspondence: Jason J. Rudd,
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26
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Ng JWX, Chua SK, Mutwil M. Feature importance network reveals novel functional relationships between biological features in Arabidopsis thaliana. FRONTIERS IN PLANT SCIENCE 2022; 13:944992. [PMID: 36212273 PMCID: PMC9539877 DOI: 10.3389/fpls.2022.944992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/24/2022] [Indexed: 06/16/2023]
Abstract
Understanding how the different cellular components are working together to form a living cell requires multidisciplinary approaches combining molecular and computational biology. Machine learning shows great potential in life sciences, as it can find novel relationships between biological features. Here, we constructed a dataset of 11,801 gene features for 31,522 Arabidopsis thaliana genes and developed a machine learning workflow to identify linked features. The detected linked features are visualised as a Feature Important Network (FIN), which can be mined to reveal a variety of novel biological insights pertaining to gene function. We demonstrate how FIN can be used to generate novel insights into gene function. To make this network easily accessible to the scientific community, we present the FINder database, available at finder.plant.tools.
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27
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Morales N, Ogbonna AC, Ellerbrock BJ, Bauchet GJ, Tantikanjana T, Tecle IY, Powell AF, Lyon D, Menda N, Simoes CC, Saha S, Hosmani P, Flores M, Panitz N, Preble RS, Agbona A, Rabbi I, Kulakow P, Peteti P, Kawuki R, Esuma W, Kanaabi M, Chelangat DM, Uba E, Olojede A, Onyeka J, Shah T, Karanja M, Egesi C, Tufan H, Paterne A, Asfaw A, Jannink JL, Wolfe M, Birkett CL, Waring DJ, Hershberger JM, Gore MA, Robbins KR, Rife T, Courtney C, Poland J, Arnaud E, Laporte MA, Kulembeka H, Salum K, Mrema E, Brown A, Bayo S, Uwimana B, Akech V, Yencho C, de Boeck B, Campos H, Swennen R, Edwards JD, Mueller LA. Breedbase: a digital ecosystem for modern plant breeding. G3 GENES|GENOMES|GENETICS 2022; 12:6564228. [PMID: 35385099 PMCID: PMC9258556 DOI: 10.1093/g3journal/jkac078] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/14/2022] [Indexed: 01/17/2023]
Abstract
Modern breeding methods integrate next-generation sequencing and phenomics to identify plants with the best characteristics and greatest genetic merit for use as parents in subsequent breeding cycles to ultimately create improved cultivars able to sustain high adoption rates by farmers. This data-driven approach hinges on strong foundations in data management, quality control, and analytics. Of crucial importance is a central database able to (1) track breeding materials, (2) store experimental evaluations, (3) record phenotypic measurements using consistent ontologies, (4) store genotypic information, and (5) implement algorithms for analysis, prediction, and selection decisions. Because of the complexity of the breeding process, breeding databases also tend to be complex, difficult, and expensive to implement and maintain. Here, we present a breeding database system, Breedbase (https://breedbase.org/, last accessed 4/18/2022). Originally initiated as Cassavabase (https://cassavabase.org/, last accessed 4/18/2022) with the NextGen Cassava project (https://www.nextgencassava.org/, last accessed 4/18/2022), and later developed into a crop-agnostic system, it is presently used by dozens of different crops and projects. The system is web based and is available as open source software. It is available on GitHub (https://github.com/solgenomics/, last accessed 4/18/2022) and packaged in a Docker image for deployment (https://hub.docker.com/u/breedbase, last accessed 4/18/2022). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem.
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Affiliation(s)
- Nicolas Morales
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- Cornell University , Ithaca, NY 14853, USA
| | - Alex C Ogbonna
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- Cornell University , Ithaca, NY 14853, USA
| | | | | | | | | | | | - David Lyon
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | - Naama Menda
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | | | - Surya Saha
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Ezenwanyi Uba
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Adeyemi Olojede
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Joseph Onyeka
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | | | | | - Chiedozie Egesi
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- IITA Ibadan , 200001 Ibadan, Nigeria
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Hale Tufan
- Cornell University , Ithaca, NY 14853, USA
| | | | | | - Jean-Luc Jannink
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | | | - Clay L Birkett
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | - David J Waring
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | | | | | | | - Trevor Rife
- Kansas State University , Manhattan, KS 66506, USA
| | | | - Jesse Poland
- Kansas State University , Manhattan, KS 66506, USA
| | | | | | | | | | | | | | | | | | | | - Craig Yencho
- North Carolina State University (NCSU) , Raleigh, NC 27695, USA
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28
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El-Soda M, Aljabri M. Genome-Wide Association Mapping of Grain Metal Accumulation in Wheat. Genes (Basel) 2022; 13:genes13061052. [PMID: 35741814 PMCID: PMC9222749 DOI: 10.3390/genes13061052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/02/2022] [Accepted: 06/04/2022] [Indexed: 12/28/2022] Open
Abstract
Increasing wheat grain yield while ignoring grain quality and metal accumulation can result in metal deficiencies, particularly in countries where bread wheat accounts for the majority of daily dietary regimes. When the accumulation level exceeds a certain threshold, it becomes toxic and causes various diseases. Biofortification is an effective method of ensuring nutritional security. We screened 200 spring wheat advanced lines from the wheat association mapping initiative for Mn, Fe, Cu, Zn, Ni, and Cd concentrations. Interestingly, high-yielding genotypes had high essential metals, such as Mn, Fe, Cu, and Zn, but low levels of toxic metals, such as Ni and Cd. Positive correlations were found between all metals except Ni and Cd, where no correlation was found. We identified 142 significant SNPs, 26 of which had possible pleiotropic effects on two or more metals. Several QTLs co-located with previously mapped QTL for the same or other metals, whereas others were new. Our findings contribute to wheat genetic biofortification through marker-assisted selection, ensuring nutritional security in the long run.
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Affiliation(s)
- Mohamed El-Soda
- Department of Genetics, Faculty of Agriculture, Cairo University, Giza 12613, Egypt
- Correspondence:
| | - Maha Aljabri
- Department of Biology, Faculty of Applied Science, Umm Al-Qura University, Makkah 24231, Saudi Arabia;
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29
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Rosignoli S, Cosenza F, Moscou MJ, Civolani L, Musiani F, Forestan C, Milner SG, Savojardo C, Tuberosa R, Salvi S. Cloning the barley nec3 disease lesion mimic mutant using complementation by sequencing. THE PLANT GENOME 2022; 15:e20187. [PMID: 35302294 DOI: 10.1002/tpg2.20187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/17/2021] [Indexed: 06/14/2023]
Abstract
Disease lesion mimic (DLM) or necrotic mutants display necrotic lesions in the absence of pathogen infections. They can show improved resistance to some pathogens and their molecular dissection can contribute to revealing components of plant defense pathways. Although forward-genetics strategies to find genes causal to mutant phenotypes are available in crops, these strategies require the production of experimental cross populations, mutagenesis, or gene editing and are time- and resource-consuming or may have to deal with regulated plant materials. In this study, we described a collection of 34 DLM mutants in barley (Hordeum vulgare L.) and applied a novel method called complementation by sequencing (CBS), which enables the identification of the gene responsible for a mutant phenotype given the availability of two or more chemically mutagenized individuals showing the same phenotype. Complementation by sequencing relies on the feasibility to obtain all induced mutations present in chemical mutants and on the low probability that different individuals share the same mutated genes. By CBS, we identified a cytochrome P450 CYP71P1 gene as responsible for orange blotch DLM mutants, including the historical barley nec3 locus. By comparative phylogenetic analysis we showed that CYP71P1 gene family emerged early in angiosperm evolution but has been recurrently lost in some lineages including Arabidopsis thaliana (L.) Heynh. Complementation by sequencing is a straightforward cost-effective approach to clone genes controlling phenotypes in a chemically mutagenized collection. The TILLMore (TM) collection will be instrumental for understanding the molecular basis of DLM phenotypes and to contribute knowledge about mechanisms of host-pathogen interaction.
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Affiliation(s)
- Serena Rosignoli
- Dep. of Agricultural and Food Sciences, Univ. of Bologna, Viale G. Fanin 44, Bologna, Italy, 40127
| | - Francesco Cosenza
- Dep. of Agricultural and Food Sciences, Univ. of Bologna, Viale G. Fanin 44, Bologna, Italy, 40127
- The Sainsbury Laboratory, Univ. of East Anglia, Norwich Research Park, Norwich, NR4 7UK, UK
| | - Matthew J Moscou
- The Sainsbury Laboratory, Univ. of East Anglia, Norwich Research Park, Norwich, NR4 7UK, UK
| | - Laura Civolani
- Dep. of Agricultural and Food Sciences, Univ. of Bologna, Viale G. Fanin 44, Bologna, Italy, 40127
| | - Francesco Musiani
- Laboratory of Bioinorganic Chemistry, Dep. of Pharmacy and Biotechnology, Univ. of Bologna, Via Belmeloro 6, Bologna, Italy, 40126
| | - Cristian Forestan
- Dep. of Agricultural and Food Sciences, Univ. of Bologna, Viale G. Fanin 44, Bologna, Italy, 40127
| | - Sara Giulia Milner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Seeland, D
| | - Castrense Savojardo
- Biocomputing Group, Dep. of Pharmacy and Biotechnology, Univ. of Bologna, Via Belmeloro 6, Bologna, Italy, 40126
| | - Roberto Tuberosa
- Dep. of Agricultural and Food Sciences, Univ. of Bologna, Viale G. Fanin 44, Bologna, Italy, 40127
| | - Silvio Salvi
- Dep. of Agricultural and Food Sciences, Univ. of Bologna, Viale G. Fanin 44, Bologna, Italy, 40127
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30
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Rice SL, Lazarus E, Anderton C, Birnbaum K, Brophy J, Cole B, Dickel D, Ehrhardt D, Fahlgren N, Frank M, Haswell E, Huang SC, Leiboff S, Libault M, Otegui MS, Provart N, Uhrig RG, Rhee SY. First Plant Cell Atlas symposium report. PLANT DIRECT 2022; 6:e406. [PMID: 35774620 PMCID: PMC9219010 DOI: 10.1002/pld3.406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 05/16/2022] [Accepted: 05/18/2022] [Indexed: 06/15/2023]
Abstract
The Plant Cell Atlas (PCA) community hosted a virtual symposium on December 9 and 10, 2021 on single cell and spatial omics technologies. The conference gathered almost 500 academic, industry, and government leaders to identify the needs and directions of the PCA community and to explore how establishing a data synthesis center would address these needs and accelerate progress. This report details the presentations and discussions focused on the possibility of a data synthesis center for a PCA and the expected impacts of such a center on advancing science and technology globally. Community discussions focused on topics such as data analysis tools and annotation standards; computational expertise and cyber-infrastructure; modes of community organization and engagement; methods for ensuring a broad reach in the PCA community; recruitment, training, and nurturing of new talent; and the overall impact of the PCA initiative. These targeted discussions facilitated dialogue among the participants to gauge whether PCA might be a vehicle for formulating a data synthesis center. The conversations also explored how online tools can be leveraged to help broaden the reach of the PCA (i.e., online contests, virtual networking, and social media stakeholder engagement) and decrease costs of conducting research (e.g., virtual REU opportunities). Major recommendations for the future of the PCA included establishing standards, creating dashboards for easy and intuitive access to data, and engaging with a broad community of stakeholders. The discussions also identified the following as being essential to the PCA's success: identifying homologous cell-type markers and their biocuration, publishing datasets and computational pipelines, utilizing online tools for communication (such as Slack), and user-friendly data visualization and data sharing. In conclusion, the development of a data synthesis center will help the PCA community achieve these goals by providing a centralized repository for existing and new data, a platform for sharing tools, and new analytical approaches through collaborative, multidisciplinary efforts. A data synthesis center will help the PCA reach milestones, such as community-supported data evaluation metrics, accelerating plant research necessary for human and environmental health.
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Affiliation(s)
- Selena L. Rice
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
| | - Elena Lazarus
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
| | - Christopher Anderton
- Environmental Molecular Sciences DivisionPacific Northwest National LaboratoryRichlandWashingtonUSA
| | - Kenneth Birnbaum
- Center for Genomics and Systems BiologyNew York UniversityNew YorkNew YorkUSA
| | - Jennifer Brophy
- Department of BioengineeringStanford UniversityStanfordCaliforniaUSA
| | - Benjamin Cole
- Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | | | - David Ehrhardt
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
| | - Noah Fahlgren
- Donald Danforth Plant Science CenterSt. LouisMissouriUSA
| | - Margaret Frank
- Department of Plant BiologyCornell UniversityIthacaNew YorkUSA
| | - Elizabeth Haswell
- Department of BiologyWashington University in St. LouisSt. LouisMissouriUSA
| | | | - Samuel Leiboff
- Department of Botany and Plant PathologyOregon State UniversityCorvallisOregonUSA
| | - Marc Libault
- Department of Agronomy and HorticultureUniversity of Nebraska‐LincolnLincolnNebraskaUSA
| | - Marisa S. Otegui
- Department of BotanyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Nicholas Provart
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and FunctionUniversity of TorontoTorontoOntarioCanada
| | - R. Glen Uhrig
- Department of ScienceUniversity of AlbertaEdmontonAlbertaCanada
| | - Seung Y. Rhee
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
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Yao E, Blake VC, Cooper L, Wight CP, Michel S, Cagirici HB, Lazo GR, Birkett CL, Waring DJ, Jannink JL, Holmes I, Waters AJ, Eickholt DP, Sen TZ. GrainGenes: a data-rich repository for small grains genetics and genomics. Database (Oxford) 2022; 2022:6591224. [PMID: 35616118 PMCID: PMC9216595 DOI: 10.1093/database/baac034] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/01/2022] [Accepted: 04/26/2022] [Indexed: 05/16/2023]
Abstract
As one of the US Department of Agriculture-Agricultural Research Service flagship databases, GrainGenes (https://wheat.pw.usda.gov) serves the data and community needs of globally distributed small grains researchers for the genetic improvement of the Triticeae family and Avena species that include wheat, barley, rye and oat. GrainGenes accomplishes its mission by continually enriching its cross-linked data content following the findable, accessible, interoperable and reusable principles, enhancing and maintaining an intuitive web interface, creating tools to enable easy data access and establishing data connections within and between GrainGenes and other biological databases to facilitate knowledge discovery. GrainGenes operates within the biological database community, collaborates with curators and genome sequencing groups and contributes to the AgBioData Consortium and the International Wheat Initiative through the Wheat Information System (WheatIS). Interactive and linked content is paramount for successful biological databases and GrainGenes now has 2917 manually curated gene records, including 289 genes and 254 alleles from the Wheat Gene Catalogue (WGC). There are >4.8 million gene models in 51 genome browser assemblies, 6273 quantitative trait loci and >1.4 million genetic loci on 4756 genetic and physical maps contained within 443 mapping sets, complete with standardized metadata. Most notably, 50 new genome browsers that include outputs from the Wheat and Barley PanGenome projects have been created. We provide an example of an expression quantitative trait loci track on the International Wheat Genome Sequencing Consortium Chinese Spring wheat browser to demonstrate how genome browser tracks can be adapted for different data types. To help users benefit more from its data, GrainGenes created four tutorials available on YouTube. GrainGenes is executing its vision of service by continuously responding to the needs of the global small grains community by creating a centralized, long-term, interconnected data repository. Database URL:https://wheat.pw.usda.gov.
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Affiliation(s)
- Eric Yao
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
- Department of Bioengineering, University of California, Stanley Hall, Berkeley, CA 94720-1762, USA
| | - Victoria C Blake
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
- Department of Plant Sciences and Plant Pathology, Montana State University, 119 Plant Biosciences Building, Bozeman, MT 59717, USA
| | - Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, 1500 SW Jefferson Way, Corvallis, OR 97331, USA
| | - Charlene P Wight
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Ave., Ottawa, ON K1A 0C6, Canada
| | - Steve Michel
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
| | - H Busra Cagirici
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
| | - Gerard R Lazo
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
| | - Clay L Birkett
- United States Department of Agriculture—Agricultural Research Service, Robert Holley Center, 538 Tower Rd., Ithaca, NY 14853, USA
| | - David J Waring
- Section of Plant Breeding and Genetics, Cornell University, Bradfield Hall, 306 Tower Rd, Ithaca, NY 14853, USA
| | - Jean-Luc Jannink
- United States Department of Agriculture—Agricultural Research Service, Robert Holley Center, 538 Tower Rd., Ithaca, NY 14853, USA
- Section of Plant Breeding and Genetics, Cornell University, Bradfield Hall, 306 Tower Rd, Ithaca, NY 14853, USA
| | - Ian Holmes
- Department of Bioengineering, University of California, Stanley Hall, Berkeley, CA 94720-1762, USA
| | - Amanda J Waters
- PepsiCo R&D, 1991 Upper Buford Circle, 210 Borlaug Hall, St. Paul, MN 55108, USA
| | - David P Eickholt
- PepsiCo R&D, 1991 Upper Buford Circle, 210 Borlaug Hall, St. Paul, MN 55108, USA
| | - Taner Z Sen
- *Corresponding author: Tel: +1 (510) 559-5982; Fax: + 1 (510) 559-5963;
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Zanini SF, Bayer PE, Wells R, Snowdon RJ, Batley J, Varshney RK, Nguyen HT, Edwards D, Golicz AA. Pangenomics in crop improvement-from coding structural variations to finding regulatory variants with pangenome graphs. THE PLANT GENOME 2022; 15:e20177. [PMID: 34904403 DOI: 10.1002/tpg2.20177] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 10/07/2021] [Indexed: 05/15/2023]
Abstract
Since the first reported crop pangenome in 2014, advances in high-throughput and cost-effective DNA sequencing technologies facilitated multiple such studies including the pangenomes of oilseed rape (Brassica napus L.), soybean [Glycine max (L.) Merr.], rice (Oryza sativa L.), wheat (Triticum aestivum L.), and barley (Hordeum vulgare L.). Compared with single-reference genomes, pangenomes provide a more accurate representation of the genetic variation present in a species. By combining the genomic data of multiple accessions, pangenomes allow for the detection and annotation of complex DNA polymorphisms such as structural variations (SVs), one of the major determinants of genetic diversity within a species. In this review we summarize the current literature on crop pangenomics, focusing on their application to find candidate SVs involved in traits of agronomic interest. We then highlight the potential of pangenomes in the discovery and functional characterization of noncoding regulatory sequences and their variations. We conclude with a summary and outlook on innovative data structures representing the complete content of plant pangenomes including annotations of coding and noncoding elements and outcomes of transcriptomic and epigenomic experiments.
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Affiliation(s)
- Silvia F Zanini
- Dep. of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig Univ. Giessen, Giessen, 35392, Germany
| | - Philipp E Bayer
- School of Biological Sciences and Institute of Agriculture, Univ. of Western Australia, Perth, Western Australia, Australia
| | - Rachel Wells
- Dep. of Crop Genetics, John Innes Centre, Norwich Research Park, Norwich, NR47UH, UK
| | - Rod J Snowdon
- Dep. of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig Univ. Giessen, Giessen, 35392, Germany
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, Univ. of Western Australia, Perth, Western Australia, Australia
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India
- State Agricultural Biotechnology Centre, Centre for Crop Food Innovation, Food Futures Institute, Murdoch Univ., Murdoch, WA, Australia
| | - Henry T Nguyen
- Division of Plant Sciences, Univ. of Missouri, Columbia, MO, USA
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, Univ. of Western Australia, Perth, Western Australia, Australia
| | - Agnieszka A Golicz
- Dep. of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig Univ. Giessen, Giessen, 35392, Germany
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Genome Wide Association Study Uncovers the QTLome for Osmotic Adjustment and Related Drought Adaptive Traits in Durum Wheat. Genes (Basel) 2022; 13:genes13020293. [PMID: 35205338 PMCID: PMC8871942 DOI: 10.3390/genes13020293] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 01/25/2022] [Accepted: 01/29/2022] [Indexed: 01/27/2023] Open
Abstract
Osmotic adjustment (OA) is a major component of drought resistance in crops. The genetic basis of OA in wheat and other crops remains largely unknown. In this study, 248 field-grown durum wheat elite accessions grown under well-watered conditions, underwent a progressively severe drought treatment started at heading. Leaf samples were collected at heading and 17 days later. The following traits were considered: flowering time (FT), leaf relative water content (RWC), osmotic potential (ψs), OA, chlorophyll content (SPAD), and leaf rolling (LR). The high variability (3.89-fold) in OA among drought-stressed accessions resulted in high repeatability of the trait (h2 = 72.3%). Notably, a high positive correlation (r = 0.78) between OA and RWC was found under severe drought conditions. A genome-wide association study (GWAS) revealed 15 significant QTLs (Quantitative Trait Loci) for OA (global R2 = 63.6%), as well as eight major QTL hotspots/clusters on chromosome arms 1BL, 2BL, 4AL, 5AL, 6AL, 6BL, and 7BS, where a higher OA capacity was positively associated with RWC and/or SPAD, and negatively with LR, indicating a beneficial effect of OA on the water status of the plant. The comparative analysis with the results of 15 previous field trials conducted under varying water regimes showed concurrent effects of five OA QTL cluster hotspots on normalized difference vegetation index (NDVI), thousand-kernel weight (TKW), and/or grain yield (GY). Gene content analysis of the cluster regions revealed the presence of several candidate genes, including bidirectional sugar transporter SWEET, rhomboid-like protein, and S-adenosyl-L-methionine-dependent methyltransferases superfamily protein, as well as DREB1. Our results support OA as a valuable proxy for marker-assisted selection (MAS) aimed at enhancing drought resistance in wheat.
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34
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Bayer PE, Edwards D. Searching for Homologous Genes Using Daisychain. Methods Mol Biol 2022; 2512:95-101. [PMID: 35818002 DOI: 10.1007/978-1-0716-2429-6_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Genome assemblies have become a standard tool of genomics research and are relatively inexpensive to produce due to falling sequencing costs. For many species, there are now several reference-grade genome assemblies. However, comparing different assemblies or the same or related individuals is not an easy task, especially with different levels of quality of assembly and annotation. Tools are needed to visualise related genes with different IDs across genome assemblies. Here, we present a workflow to search and visualise related genes using Daisychain, a web-based tool aimed at researchers who wish to compare genes between assemblies.
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Affiliation(s)
- Philipp E Bayer
- Applied Bioinformatics Group, School of Biological Sciences, The University of Western Australia, Perth, WA, Australia
| | - David Edwards
- Applied Bioinformatics Group, School of Biological Sciences, The University of Western Australia, Perth, WA, Australia.
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35
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Gardiner LJ, Krishna R. Bluster or Lustre: Can AI Improve Crops and Plant Health? PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10122707. [PMID: 34961177 PMCID: PMC8707749 DOI: 10.3390/plants10122707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 11/24/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as a potential mechanism to support this. This review explores current research in the area to convey the state-of-the-art as to how AI/ML have been used to advance research, gain insights, and generally enable progress in this area. We address the question-Can AI improve crops and plant health? We further discriminate the bluster from the lustre by identifying the key challenges that AI has been shown to address, balanced with the potential issues with its usage, and the key requisites for its success. Overall, we hope to raise awareness and, as a result, promote usage, of AI related approaches where they can have appropriate impact to improve practices in agricultural and plant sciences.
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Shao Y, Feng X, Nakahara H, Irshad M, Eneji AE, Zheng Y, Fujimaki H, An P. Apical-root apoplastic acidification affects cell wall extensibility in wheat under salinity stress. PHYSIOLOGIA PLANTARUM 2021; 173:1850-1861. [PMID: 34402071 DOI: 10.1111/ppl.13527] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/24/2021] [Accepted: 08/15/2021] [Indexed: 06/13/2023]
Abstract
Plant salt tolerance is associated with a high rate of root growth. Although root growth is governed by cell wall and apoplastic pH, the relationship between these factors in the root elongation zone under salinity stress remains unclear. Herein, we assess apoplastic pH, pH- and expansin-dependent cell wall extensibility, and expansin expression in the root elongation zone of salt-sensitive (Yongliang-15) and -tolerant (JS-7) cultivars under salinity stress. A six-day 80 mM NaCl treatment significantly reduced apical root apoplastic pH in both cultivars. Using a pH-dependent cell wall extensibility experiment, we found that, under 0 mM NaCl treatment, the optimal pH for cell wall loosening was 6.0 in the salinity-tolerant cultivar and 4.6 in the salinity-sensitive cultivar. Under 80 mM treatment, a pH of 5.0 mitigated the cell wall stiffness caused by salinity stress in the salinity-tolerant cultivar but promoted cell wall stiffening in the salinity-sensitive cultivar. Salinity stress altered expansin expression and differentially affecting cell wall extensibility under pH 5.0 and 6.0. TaEXPA8 might be relative to cell wall loosening at pH 5.0, whereas TaEXPA5 relative to cell wall loosening at pH 6.0. These results elucidate the relationship between expansins and cell wall extensibility in the root elongation zone, with important implications for enhancing plant growth under salinity stress.
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Affiliation(s)
- Yang Shao
- Arid Land Research Center, Tottori University, Tottori City, Japan
| | - Xiaohui Feng
- Key Laboratory of Agricultural Water Resources, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang, Hebei, China
| | - Hiroki Nakahara
- Arid Land Research Center, Tottori University, Tottori City, Japan
| | - Muhammad Irshad
- Department of Environmental Sciences, COMSATS Institute of Information Technology, Abbottabad, Pakistan
| | - A Egrinya Eneji
- Department of Soil Science, Faculty of Agriculture, Forestry and Wildlife Resources Management, University of Calabar, Calabar, Nigeria
| | - Yuanrun Zheng
- Key Laboratory of Resource Plants, Institute of Botany, Chinese Academy of Sciences, Beijing, China
| | | | - Ping An
- Arid Land Research Center, Tottori University, Tottori City, Japan
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Bayer PE, Scheben A, Golicz AA, Yuan Y, Faure S, Lee H, Chawla HS, Anderson R, Bancroft I, Raman H, Lim YP, Robbens S, Jiang L, Liu S, Barker MS, Schranz ME, Wang X, King GJ, Pires JC, Chalhoub B, Snowdon RJ, Batley J, Edwards D. Modelling of gene loss propensity in the pangenomes of three Brassica species suggests different mechanisms between polyploids and diploids. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:2488-2500. [PMID: 34310022 PMCID: PMC8633514 DOI: 10.1111/pbi.13674] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 07/11/2021] [Accepted: 07/20/2021] [Indexed: 05/26/2023]
Abstract
Plant genomes demonstrate significant presence/absence variation (PAV) within a species; however, the factors that lead to this variation have not been studied systematically in Brassica across diploids and polyploids. Here, we developed pangenomes of polyploid Brassica napus and its two diploid progenitor genomes B. rapa and B. oleracea to infer how PAV may differ between diploids and polyploids. Modelling of gene loss suggests that loss propensity is primarily associated with transposable elements in the diploids while in B. napus, gene loss propensity is associated with homoeologous recombination. We use these results to gain insights into the different causes of gene loss, both in diploids and following polyploidization, and pave the way for the application of machine learning methods to understanding the underlying biological and physical causes of gene presence/absence.
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Affiliation(s)
- Philipp E. Bayer
- School of Biological Sciences and the Institute of AgricultureFaculty of ScienceThe University of Western AustraliaCrawleyWAAustralia
| | - Armin Scheben
- School of Biological Sciences and the Institute of AgricultureFaculty of ScienceThe University of Western AustraliaCrawleyWAAustralia
| | - Agnieszka A. Golicz
- Plant Molecular Biology and Biotechnology LaboratoryFaculty of Veterinary and Agricultural SciencesUniversity of MelbourneParkvilleVICAustralia
| | - Yuxuan Yuan
- School of Biological Sciences and the Institute of AgricultureFaculty of ScienceThe University of Western AustraliaCrawleyWAAustralia
| | | | - HueyTyng Lee
- Department of Plant BreedingIFZ Research Centre for BiosystemsLand Use and NutritionJustus Liebig University GiessenGiessenGermany
| | - Harmeet Singh Chawla
- Department of Plant BreedingIFZ Research Centre for BiosystemsLand Use and NutritionJustus Liebig University GiessenGiessenGermany
| | - Robyn Anderson
- School of Biological Sciences and the Institute of AgricultureFaculty of ScienceThe University of Western AustraliaCrawleyWAAustralia
| | | | - Harsh Raman
- NSW Department of Primary IndustriesWagga Wagga Agricultural Institute, PMBWagga WaggaNSWAustralia
| | - Yong Pyo Lim
- Department of HorticultureChungnam National UniversityDaejeonSouth Korea
| | | | - Lixi Jiang
- Institute of crop scienceDepartment of Agronomy and Plant BreedingZhejiang UniversityHangzhouChina
| | - Shengyi Liu
- Chinese Academy of Agricultural SciencesOil Crops Research InstituteWuhanChina
| | - Michael S. Barker
- Department of Ecology & Evolutionary BiologyUniversity of ArizonaTucsonAZUSA
| | - M. Eric Schranz
- Biosystematics GroupWageningen University and Research CenterWageningenThe Netherlands
| | - Xiaowu Wang
- Institute of Vegetables and FlowersChinese Academy of Agricultural Sciences (IVF, CAAS)BeijingChina
| | - Graham J. King
- Southern Cross Plant ScienceSouthern Cross UniversityLismoreNSWAustralia
| | - J. Chris Pires
- Division of Biological SciencesBond Life Sciences CenterUniversity of MissouriColumbiaMissouriUSA
| | - Boulos Chalhoub
- Institute of crop scienceDepartment of Agronomy and Plant BreedingZhejiang UniversityHangzhouChina
| | - Rod J. Snowdon
- Department of Plant BreedingIFZ Research Centre for BiosystemsLand Use and NutritionJustus Liebig University GiessenGiessenGermany
| | - Jacqueline Batley
- School of Biological Sciences and the Institute of AgricultureFaculty of ScienceThe University of Western AustraliaCrawleyWAAustralia
| | - David Edwards
- School of Biological Sciences and the Institute of AgricultureFaculty of ScienceThe University of Western AustraliaCrawleyWAAustralia
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Urban M, Cuzick A, Seager J, Wood V, Rutherford K, Venkatesh SY, Sahu J, Iyer SV, Khamari L, De Silva N, Martinez MC, Pedro H, Yates AD, Hammond-Kosack KE. PHI-base in 2022: a multi-species phenotype database for Pathogen-Host Interactions. Nucleic Acids Res 2021; 50:D837-D847. [PMID: 34788826 PMCID: PMC8728202 DOI: 10.1093/nar/gkab1037] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/13/2021] [Accepted: 11/08/2021] [Indexed: 12/28/2022] Open
Abstract
Since 2005, the Pathogen–Host Interactions Database (PHI-base) has manually curated experimentally verified pathogenicity, virulence and effector genes from fungal, bacterial and protist pathogens, which infect animal, plant, fish, insect and/or fungal hosts. PHI-base (www.phi-base.org) is devoted to the identification and presentation of phenotype information on pathogenicity and effector genes and their host interactions. Specific gene alterations that did not alter the in host interaction phenotype are also presented. PHI-base is invaluable for comparative analyses and for the discovery of candidate targets in medically and agronomically important species for intervention. Version 4.12 (September 2021) contains 4387 references, and provides information on 8411 genes from 279 pathogens, tested on 228 hosts in 18, 190 interactions. This provides a 24% increase in gene content since Version 4.8 (September 2019). Bacterial and fungal pathogens represent the majority of the interaction data, with a 54:46 split of entries, whilst protists, protozoa, nematodes and insects represent 3.6% of entries. Host species consist of approximately 54% plants and 46% others of medical, veterinary and/or environmental importance. PHI-base data is disseminated to UniProtKB, FungiDB and Ensembl Genomes. PHI-base will migrate to a new gene-centric version (version 5.0) in early 2022. This major development is briefly described.
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Affiliation(s)
- Martin Urban
- Department of Biointeractions and Crop Protection, Rothamsted Research, Harpenden AL5 2JQ, UK
| | - Alayne Cuzick
- Department of Biointeractions and Crop Protection, Rothamsted Research, Harpenden AL5 2JQ, UK
| | - James Seager
- Department of Biointeractions and Crop Protection, Rothamsted Research, Harpenden AL5 2JQ, UK
| | - Valerie Wood
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
| | - Kim Rutherford
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
| | | | - Jashobanta Sahu
- Molecular Connections, Kandala Mansions, Kariappa Road, Basavanagudi, Bengaluru 560 004, India
| | - S Vijaylakshmi Iyer
- Molecular Connections, Kandala Mansions, Kariappa Road, Basavanagudi, Bengaluru 560 004, India
| | - Lokanath Khamari
- Molecular Connections, Kandala Mansions, Kariappa Road, Basavanagudi, Bengaluru 560 004, India
| | - Nishadi De Silva
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Manuel Carbajo Martinez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Helder Pedro
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Andrew D Yates
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Kim E Hammond-Kosack
- Department of Biointeractions and Crop Protection, Rothamsted Research, Harpenden AL5 2JQ, UK
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Delmas M, Filangi O, Paulhe N, Vinson F, Duperier C, Garrier W, Saunier PE, Pitarch Y, Jourdan F, Giacomoni F, Frainay C. FORUM: Building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases. Bioinformatics 2021; 37:3896-3904. [PMID: 34478489 PMCID: PMC8570811 DOI: 10.1093/bioinformatics/btab627] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 08/16/2021] [Accepted: 09/01/2021] [Indexed: 11/22/2022] Open
Abstract
Motivation Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories. Results The use of a Semantic Web framework on biological data allows us to apply ontological-based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries. Availability and implementation A web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM KG, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- M Delmas
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
| | - O Filangi
- IGEPP, INRAE, Institut Agro, Université de Rennes, Domaine de la Motte, Le Rheu, 35653, France
| | - N Paulhe
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, F-63000, France
| | - F Vinson
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
| | - C Duperier
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, F-63000, France
| | - W Garrier
- ISIMA, Campus des Cézeaux, Aubière, 63177, France
| | - P-E Saunier
- ISIMA, Campus des Cézeaux, Aubière, 63177, France
| | - Y Pitarch
- IRIT, Université de Toulouse, Cours Rose Dieng-Kuntz, Toulouse, 31400, France
| | - F Jourdan
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
| | - F Giacomoni
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, F-63000, France
| | - C Frainay
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31300, France
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Joynson R, Molero G, Coombes B, Gardiner L, Rivera‐Amado C, Piñera‐Chávez FJ, Evans JR, Furbank RT, Reynolds MP, Hall A. Uncovering candidate genes involved in photosynthetic capacity using unexplored genetic variation in Spring Wheat. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:1537-1552. [PMID: 33638599 PMCID: PMC8384606 DOI: 10.1111/pbi.13568] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 01/26/2021] [Indexed: 05/10/2023]
Abstract
To feed an ever-increasing population we must leverage advances in genomics and phenotyping to harness the variation in wheat breeding populations for traits like photosynthetic capacity which remains unoptimized. Here we survey a diverse set of wheat germplasm containing elite, introgression and synthetic derivative lines uncovering previously uncharacterized variation. We demonstrate how strategic integration of exotic material alleviates the D genome genetic bottleneck in wheat, increasing SNP rate by 62% largely due to Ae. tauschii synthetic wheat donors. Across the panel, 67% of the Ae. tauschii donor genome is represented as introgressions in elite backgrounds. We show how observed genetic variation together with hyperspectral reflectance data can be used to identify candidate genes for traits relating to photosynthetic capacity using association analysis. This demonstrates the value of genomic methods in uncovering hidden variation in wheat and how that variation can assist breeding efforts and increase our understanding of complex traits.
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Affiliation(s)
| | - Gemma Molero
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT)TexcocoMexico
| | | | | | - Carolina Rivera‐Amado
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT)TexcocoMexico
| | | | - John R. Evans
- ARC Centre of Excellence for Translational PhotosynthesisAustralian National UniversityCanberraAustralia
| | - Robert T. Furbank
- ARC Centre of Excellence for Translational PhotosynthesisAustralian National UniversityCanberraAustralia
| | - Matthew P. Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT)TexcocoMexico
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