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Zhang W, Higgins EE, Robinson SJ, Clarke WE, Boyle K, Sharpe AG, Fobert PR, Parkin IAP. A systems genomics and genetics approach to identify the genetic regulatory network for lignin content in Brassica napus seeds. FRONTIERS IN PLANT SCIENCE 2024; 15:1393621. [PMID: 38903439 PMCID: PMC11188405 DOI: 10.3389/fpls.2024.1393621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 04/29/2024] [Indexed: 06/22/2024]
Abstract
Seed quality traits of oilseed rape, Brassica napus (B. napus), exhibit quantitative inheritance determined by its genetic makeup and the environment via the mediation of a complex genetic architecture of hundreds to thousands of genes. Thus, instead of single gene analysis, network-based systems genomics and genetics approaches that combine genotype, phenotype, and molecular phenotypes offer a promising alternative to uncover this complex genetic architecture. In the current study, systems genetics approaches were used to explore the genetic regulation of lignin traits in B. napus seeds. Four QTL (qLignin_A09_1, qLignin_A09_2, qLignin_A09_3, and qLignin_C08) distributed on two chromosomes were identified for lignin content. The qLignin_A09_2 and qLignin_C08 loci were homologous QTL from the A and C subgenomes, respectively. Genome-wide gene regulatory network analysis identified eighty-three subnetworks (or modules); and three modules with 910 genes in total, were associated with lignin content, which was confirmed by network QTL analysis. eQTL (expression quantitative trait loci) analysis revealed four cis-eQTL genes including lignin and flavonoid pathway genes, cinnamoyl-CoA-reductase (CCR1), and TRANSPARENT TESTA genes TT4, TT6, TT8, as causal genes. The findings validated the power of systems genetics to identify causal regulatory networks and genes underlying complex traits. Moreover, this information may enable the research community to explore new breeding strategies, such as network selection or gene engineering, to rewire networks to develop climate resilience crops with better seed quality.
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Affiliation(s)
- Wentao Zhang
- Aquatic and Crop Resource Development, National Research Council of Canada, Saskatoon, SK, Canada
| | - Erin E. Higgins
- Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
| | - Stephen J. Robinson
- Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
| | - Wayne E. Clarke
- Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
| | - Kerry Boyle
- Aquatic and Crop Resource Development, National Research Council of Canada, Saskatoon, SK, Canada
| | - Andrew G. Sharpe
- Global Institute for Food Security (GIFS), University of Saskatchewan, Saskatoon, SK, Canada
| | - Pierre R. Fobert
- Aquatic and Crop Resource Development, National Research Council of Canada, Ottawa, ON, Canada
| | - Isobel A. P. Parkin
- Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
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Chen X, Xie L, Jiang Y, Zhang R, Wu W. LCK, FOXC1 and hsa-miR-146a-5p as potential immune effector molecules associated with rheumatoid arthritis. Biomarkers 2023; 28:130-138. [PMID: 36420648 DOI: 10.1080/1354750x.2022.2150315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Rheumatoid arthritis (RA) is a type of systemic immune disease characterized by chronic inflammatory disease of the joints. However, the aetiology and underlying molecular events of RA are unclear. Here, we applied bioinformatics analysis to identify potential immune effector molecules involved in RA. The three microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. We used the R software screen 115 overlapping differentially expressed genes (DEGs). Subsequently, we constructed a protein-protein interaction (PPI) network encoded by these DEGs and identified 10 genes closely associated with RA - LCK, GZMA, GZMB, CD2, LAG3, IL-15, TNFRSF4, CD247, CCR5 and CCR7. Furthermore, in the miRNA-hub gene networks, we screened out hsa-miR-146a-5p, which is the miRNA controlling the largest number of hub genes. Finally, we found some transcription factors that closely interact with hub genes, such as FOXC1, GATA2, YY1, RUNX2, SREBF1, CEBPB and NFIC. This study successfully predicted that LCK, FOXC1 and hsa-miR-146a-5p can be used as potential immune effector molecules of RA. Our study may have potential implications for future prediction of disease progression in patients with symptomatic RA, and has important significance for the pathogenesis and targeted therapy of RA.
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Affiliation(s)
- Xuemeng Chen
- Department of Traditional Chinese Medicine and rheumatism immunology, the First Affiliated Hospital of Army Medical University, Chongqing City, China
| | - Li Xie
- Department of Traditional Chinese Medicine, Chongqing Dadukou District People's Hospital, Chongqing City, China
| | - Yi Jiang
- Department of Traditional Chinese Medicine and rheumatism immunology, the First Affiliated Hospital of Army Medical University, Chongqing City, China
| | - Ronghua Zhang
- Department of Traditional Chinese Medicine and rheumatism immunology, the First Affiliated Hospital of Army Medical University, Chongqing City, China
| | - Wei Wu
- Department of Traditional Chinese Medicine and rheumatism immunology, the First Affiliated Hospital of Army Medical University, Chongqing City, China
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Shrestha V, Yobi A, Slaten ML, Chan YO, Holden S, Gyawali A, Flint-Garcia S, Lipka AE, Angelovici R. Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition. PLANT PHYSIOLOGY 2022; 188:111-133. [PMID: 34618082 PMCID: PMC8774818 DOI: 10.1093/plphys/kiab390] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 07/21/2021] [Indexed: 06/13/2023]
Abstract
Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.
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Affiliation(s)
- Vivek Shrestha
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Abou Yobi
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Marianne L Slaten
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Yen On Chan
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Samuel Holden
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Abiskar Gyawali
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
| | - Sherry Flint-Garcia
- U.S. Department of Agriculture-Agricultural Research Service, Columbia, Missouri 65211, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois, Urbana, Illinois 61801, USA
| | - Ruthie Angelovici
- Division of Biological Sciences and Interdisciplinary Plant Group, University of Missouri, Columbia, Missouri 65211, USA
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Blanc J, Kremling KAG, Buckler E, Josephs EB. Local adaptation contributes to gene expression divergence in maize. G3-GENES GENOMES GENETICS 2021; 11:6114460. [PMID: 33604670 PMCID: PMC8022924 DOI: 10.1093/g3journal/jkab004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/20/2020] [Indexed: 11/14/2022]
Abstract
Gene expression links genotypes to phenotypes, so identifying genes whose expression is shaped by selection will be important for understanding the traits and processes underlying local adaptation. However, detecting local adaptation for gene expression will require distinguishing between divergence due to selection and divergence due to genetic drift. Here, we adapt a QST−FST framework to detect local adaptation for transcriptome-wide gene expression levels in a population of diverse maize genotypes. We compare the number and types of selected genes across a wide range of maize populations and tissues, as well as selection on cold-response genes, drought-response genes, and coexpression clusters. We identify a number of genes whose expression levels are consistent with local adaptation and show that genes involved in stress response show enrichment for selection. Due to its history of intense selective breeding and domestication, maize evolution has long been of interest to researchers, and our study provides insight into the genes and processes important for in local adaptation of maize.
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Affiliation(s)
- Jennifer Blanc
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| | - Karl A G Kremling
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.,Inari Agriculture, Cambridge, MA 02139, USA
| | - Edward Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.,Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.,United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
| | - Emily B Josephs
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA.,Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI 48824, USA
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Zhang D, Li Z, Zhang R, Yang X, Zhang D, Li Q, Wang C, Yang X, Xiong Y. Identification of differentially expressed and methylated genes associated with rheumatoid arthritis based on network. Autoimmunity 2020; 53:303-313. [DOI: 10.1080/08916934.2020.1786069] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Di Zhang
- Institute of Endemic Diseases and Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People’s Republic of China, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, P.R. China
| | - ZhaoFang Li
- Institute of Endemic Diseases and Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People’s Republic of China, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, P.R. China
| | - RongQiang Zhang
- Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, P.R. China
| | - XiaoLi Yang
- Institute of Endemic Diseases and Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People’s Republic of China, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, P.R. China
| | - DanDan Zhang
- Institute of Endemic Diseases and Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People’s Republic of China, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, P.R. China
| | - Qiang Li
- Institute of Endemic Diseases and Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People’s Republic of China, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, P.R. China
| | - Chen Wang
- Institute of Endemic Diseases and Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People’s Republic of China, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, P.R. China
| | - Xuena Yang
- Institute of Endemic Diseases and Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People’s Republic of China, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, P.R. China
| | - YongMin Xiong
- Institute of Endemic Diseases and Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People’s Republic of China, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, P.R. China
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