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For: Su Y, Dai S, Li N, Gentile A, He C, Xu J, Duan K, Wang X, Wang B, Li D. Unleashing the Potential of EIL Transcription Factors in Enhancing Sweet Orange Resistance to Bacterial Pathologies: Genome-Wide Identification and Expression Profiling. Int J Mol Sci 2023;24:12644. [PMID: 37628825 PMCID: PMC10454048 DOI: 10.3390/ijms241612644] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/02/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open

For:	Su Y, Dai S, Li N, Gentile A, He C, Xu J, Duan K, Wang X, Wang B, Li D. Unleashing the Potential of EIL Transcription Factors in Enhancing Sweet Orange Resistance to Bacterial Pathologies: Genome-Wide Identification and Expression Profiling. Int J Mol Sci 2023;24:12644. [PMID: 37628825 PMCID: PMC10454048 DOI: 10.3390/ijms241612644] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/02/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open

Number

Cited by Other Article(s)

Yu W, Ren X, Zhang J, Huang Z, Zhao Y, Zhang M, Yao S, Ji K. Identification and Characterization of EIN3/EIL Transcription Factor Family Members in Pinus massoniana Lamb. Int J Mol Sci 2024;25:11928. [PMID: 39595998 PMCID: PMC11593336 DOI: 10.3390/ijms252211928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2024] [Revised: 11/04/2024] [Accepted: 11/04/2024] [Indexed: 11/28/2024] Open

Affiliation(s)

Wenya Yu State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing 210037, China; (W.Y.); (S.Y.) Key Open Laboratory of Forest Genetics and Gene Engineering of National Forestry and Grassland Administration, Nanjing 210037, China Key Laboratory of Forestry Genetics & Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
Xingyue Ren State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing 210037, China; (W.Y.); (S.Y.) Key Open Laboratory of Forest Genetics and Gene Engineering of National Forestry and Grassland Administration, Nanjing 210037, China Key Laboratory of Forestry Genetics & Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
Jingjing Zhang State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing 210037, China; (W.Y.); (S.Y.) Key Open Laboratory of Forest Genetics and Gene Engineering of National Forestry and Grassland Administration, Nanjing 210037, China Key Laboratory of Forestry Genetics & Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
Zichen Huang State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing 210037, China; (W.Y.); (S.Y.) Key Open Laboratory of Forest Genetics and Gene Engineering of National Forestry and Grassland Administration, Nanjing 210037, China Key Laboratory of Forestry Genetics & Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
Yulu Zhao State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing 210037, China; (W.Y.); (S.Y.) Key Open Laboratory of Forest Genetics and Gene Engineering of National Forestry and Grassland Administration, Nanjing 210037, China Key Laboratory of Forestry Genetics & Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
Mengyang Zhang State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing 210037, China; (W.Y.); (S.Y.) Key Open Laboratory of Forest Genetics and Gene Engineering of National Forestry and Grassland Administration, Nanjing 210037, China Key Laboratory of Forestry Genetics & Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
Sheng Yao State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing 210037, China; (W.Y.); (S.Y.) Key Open Laboratory of Forest Genetics and Gene Engineering of National Forestry and Grassland Administration, Nanjing 210037, China Key Laboratory of Forestry Genetics & Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
Kongshu Ji State Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing 210037, China; (W.Y.); (S.Y.) Key Open Laboratory of Forest Genetics and Gene Engineering of National Forestry and Grassland Administration, Nanjing 210037, China Key Laboratory of Forestry Genetics & Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, China Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

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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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