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Cagirici HB, Andorf CM, Sen TZ. Co-expression pan-network reveals genes involved in complex traits within maize pan-genome. BMC PLANT BIOLOGY 2022; 22:595. [PMID: 36529716 PMCID: PMC9762053 DOI: 10.1186/s12870-022-03985-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND With the advances in the high throughput next generation sequencing technologies, genome-wide association studies (GWAS) have identified a large set of variants associated with complex phenotypic traits at a very fine scale. Despite the progress in GWAS, identification of genotype-phenotype relationship remains challenging in maize due to its nature with dozens of variants controlling the same trait. As the causal variations results in the change in expression, gene expression analyses carry a pivotal role in unraveling the transcriptional regulatory mechanisms behind the phenotypes. RESULTS To address these challenges, we incorporated the gene expression and GWAS-driven traits to extend the knowledge of genotype-phenotype relationships and transcriptional regulatory mechanisms behind the phenotypes. We constructed a large collection of gene co-expression networks and identified more than 2 million co-expressing gene pairs in the GWAS-driven pan-network which contains all the gene-pairs in individual genomes of the nested association mapping (NAM) population. We defined four sub-categories for the pan-network: (1) core-network contains the highest represented ~ 1% of the gene-pairs, (2) near-core network contains the next highest represented 1-5% of the gene-pairs, (3) private-network contains ~ 50% of the gene pairs that are unique to individual genomes, and (4) the dispensable-network contains the remaining 50-95% of the gene-pairs in the maize pan-genome. Strikingly, the private-network contained almost all the genes in the pan-network but lacked half of the interactions. We performed gene ontology (GO) enrichment analysis for the pan-, core-, and private- networks and compared the contributions of variants overlapping with genes and promoters to the GWAS-driven pan-network. CONCLUSIONS Gene co-expression networks revealed meaningful information about groups of co-regulated genes that play a central role in regulatory processes. Pan-network approach enabled us to visualize the global view of the gene regulatory network for the studied system that could not be well inferred by the core-network alone.
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Affiliation(s)
- H Busra Cagirici
- US Department of Agriculture - Agricultural Research Service, Crop Improvement Genetics Research Unit, Western Regional Research Center, 800 Buchanan St, Albany, CA, 94710, USA
| | - Carson M Andorf
- US Department of Agriculture - Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA, 50011, USA.
- Department of Computer Science, Iowa State University, Ames, IA, 50011, USA.
| | - Taner Z Sen
- US Department of Agriculture - Agricultural Research Service, Crop Improvement Genetics Research Unit, Western Regional Research Center, 800 Buchanan St, Albany, CA, 94710, USA.
- Department of Bioengineering, University of California, Berkeley, CA, 94720, USA.
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2
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Schaefer RJ, Cullen J, Manfredi J, McCue M. Functional contexts of adipose and gluteal muscle tissue gene co-expression networks in the domestic horse. Integr Comp Biol 2020; 63:icaa134. [PMID: 32970803 DOI: 10.1093/icb/icaa134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 08/14/2020] [Accepted: 08/27/2020] [Indexed: 11/13/2022] Open
Abstract
A gene's response to an environment is tightly bound to the underlying genetic variation present in an individual's genome and varies greatly depending on the tissue it is being expressed in. Gene co-expression networks provide a mechanism to understand and interpret the collective transcriptional responses of genes. Here, we use the Camoco co-expression network framework to characterize the transcriptional landscape of adipose and gluteal muscle tissue in 83 domestic horses (Equus caballus) representing 5 different breeds. In each tissue, gene expression profiles, capturing transcriptional response due to variation across individuals, were used to build two separate, tissue-focused, genotypically-diverse gene co-expression networks. The aim of our study was to identify significantly co-expressed clusters of genes in each tissue, then compare the clusters across networks to quantify the extent that clusters were found in both networks as well as to identify clusters found in a single network. The known and unknown functions for each network were quantified using complementary, supervised and unsupervised approaches. First, supervised ontological enrichment was utilized to quantify biological functions represented by each network. Curated ontologies (GO and KEGG) were used to measure the known biological functions present in each tissue. Overall, a large percentage of terms (40.3% of GO and 41% of KEGG) were co-expressed in at least one tissue. Many terms were co-expressed in both tissues, however a small proportion of terms exhibited single tissue co-expression suggesting functional differentiation based on curated, functional annotation. To complement this, an unsupervised approach not relying on ontologies was employed. Strongly co-expressed sets of genes defined by Markov clustering identified sets of unannotated genes showing similar patterns of co-expression within a tissue. We compared gene sets across tissues and identified clusters of genes the either segregate in co-expression by tissue or exhibit high levels of co-expression in both tissues. Clusters were also integrated with GO and KEGG ontologies to identify gene sets containing previously curated annotations versus unannotated gene sets indicating potentially novel biological function. Coupling together these transcriptional datasets, we mapped the transcriptional landscape of muscle and adipose setting up a generalizable framework for interpreting gene function for additional tissues in the horse and other species.
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Affiliation(s)
- Robert J Schaefer
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN
| | - Jonah Cullen
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN
| | - Jane Manfredi
- Department of Pathobiology and Diagnostic Investigation, College of Veterinary Medicine, Michigan State University, East Lansing, MI
| | - Molly McCue
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN
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3
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Michno J, Liu J, Jeffers JR, Stupar RM, Myers CL. Identification of nodulation-related genes in Medicago truncatula using genome-wide association studies and co-expression networks. PLANT DIRECT 2020; 4:e00220. [PMID: 32426691 PMCID: PMC7229696 DOI: 10.1002/pld3.220] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 02/04/2020] [Accepted: 02/24/2020] [Indexed: 05/17/2023]
Abstract
Genome-wide association studies (GWAS) have proven to be a valuable approach for identifying genetic intervals associated with phenotypic variation in Medicago truncatula. These intervals can vary in size, depending on the historical local recombination. Typically, significant intervals span numerous gene models, limiting the ability to resolve high-confidence candidate genes underlying the trait of interest. Additional genomic data, including gene co-expression networks, can be combined with the genetic mapping information to successfully identify candidate genes. Co-expression network analysis provides information about the functional relationships of each gene through its similarity of expression patterns to other well-defined clusters of genes. In this study, we integrated data from GWAS and co-expression networks to pinpoint candidate genes that may be associated with nodule-related phenotypes in M. truncatula. We further investigated a subset of these genes and confirmed that several had existing evidence linking them nodulation, including MEDTR2G101090 (PEN3-like), a previously validated gene associated with nodule number.
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Affiliation(s)
- Jean‐Michel Michno
- Bioinformatics and Computational BiologyUniversity of MinnesotaSt. PaulMinnesota
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSt. PaulMinnesota
| | - Junqi Liu
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSt. PaulMinnesota
| | - Joseph R. Jeffers
- Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisMinnesota
| | - Robert M. Stupar
- Bioinformatics and Computational BiologyUniversity of MinnesotaSt. PaulMinnesota
- Department of Agronomy and Plant GeneticsUniversity of MinnesotaSt. PaulMinnesota
| | - Chad L. Myers
- Bioinformatics and Computational BiologyUniversity of MinnesotaSt. PaulMinnesota
- Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisMinnesota
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4
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Xiao L, Liu X, Lu W, Chen P, Quan M, Si J, Du Q, Zhang D. Genetic dissection of the gene coexpression network underlying photosynthesis in Populus. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:1015-1026. [PMID: 31584236 PMCID: PMC7061883 DOI: 10.1111/pbi.13270] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 09/09/2019] [Accepted: 09/29/2019] [Indexed: 05/06/2023]
Abstract
Photosynthesis is a key reaction that ultimately generates the carbohydrates needed to form woody tissues in trees. However, the genetic regulatory network of protein-encoding genes (PEGs) and regulatory noncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), underlying the photosynthetic pathway is unknown. Here, we integrated data from coexpression analysis, association studies (additive, dominance and epistasis), and expression quantitative trait nucleotide (eQTN) mapping to dissect the causal variants and genetic interaction network underlying photosynthesis in Populus. We initially used 30 PEGs, 6 miRNAs and 12 lncRNAs to construct a coexpression network based on the tissue-specific gene expression profiles of 15 Populus samples. Then, we performed association studies using a natural population of 435 unrelated Populus tomentosa individuals, and identified 72 significant associations (P ≤ 0.001, q ≤ 0.05) with diverse additive and dominance patterns underlying photosynthesis-related traits. Analysis of epistasis and eQTNs revealed that the complex genetic interactions in the coexpression network contribute to phenotypes at various levels. Finally, we demonstrated that heterologously expressing the most highly linked gene (PtoPsbX1) in this network significantly improved photosynthesis in Arabidopsis thaliana, pointing to the functional role of PtoPsbX1 in the photosynthetic pathway. This study provides an integrated strategy for dissecting a complex genetic interaction network, which should accelerate marker-assisted breeding efforts to genetically improve woody plants.
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Affiliation(s)
- Liang Xiao
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Xin Liu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Wenjie Lu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Panfei Chen
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Mingyang Quan
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Jingna Si
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Qingzhang Du
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
| | - Deqiang Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular DesignBeijing Forestry UniversityBeijingChina
- National Engineering Laboratory for Tree BreedingCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental PlantsMinistry of EducationCollege of Biological Sciences and TechnologyBeijing Forestry UniversityBeijingChina
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Zhang H, Wang ML, Schaefer R, Dang P, Jiang T, Chen C. GWAS and Coexpression Network Reveal Ionomic Variation in Cultivated Peanut. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2019; 67:12026-12036. [PMID: 31589432 DOI: 10.1021/acs.jafc.9b04939] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Peanut is an important legume providing products with nutrient dense including mineral nutrition. However, the genetic basis underlying the variations in the mineral composition in peanut is still unknown. Genome-wide association studies (GWAS) of the concentrations of 13 mineral elements combined with coexpression network were performed using a diverse panel of 120 genotypes mainly selected from the U.S. peanut mini core collection. A total of 36 significant quantitative trait loci (QTLs) associated with five elemental concentrations were identified with phenotypic variation explained (PVE) from 18.35% to 27.56%, in which 24 QTLs were for boron (B), 2 QTLs for copper (Cu), 6 QTLs for sodium (Na), 3 QTLs for sulfur (S), and 1 QTL for zinc (Zn). A total of 110 nonredundant candidate causal genes identified were significantly associated with elemental accumulation, which included one high-priority overlap (HPO) candidate gene arahy.KQD4NT, an important elemental/metal transporter gene located on LGB04 with position 5,413,913-5,417,353.
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Affiliation(s)
- Hui Zhang
- Department of Crop, Soil, and Environmental Sciences , Auburn University , Auburn , Alabama 36849 , United States
| | - Ming Li Wang
- USDA-ARS Plant Genetic Resources Conservation , Griffin , Georgia 30223 , United States
| | - Robert Schaefer
- Equine Genetics and Genomics Lab , University of Minnesota , Minneapolis , Minnesota 55455 , United States
| | - Phat Dang
- USDA-ARS National Peanut Research Laboratory , Dawson , Georgia 39842 , United States
| | - Tao Jiang
- Department of Crop, Soil, and Environmental Sciences , Auburn University , Auburn , Alabama 36849 , United States
| | - Charles Chen
- Department of Crop, Soil, and Environmental Sciences , Auburn University , Auburn , Alabama 36849 , United States
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6
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Pan Q, Wei J, Guo F, Huang S, Gong Y, Liu H, Liu J, Li L. Trait ontology analysis based on association mapping studies bridges the gap between crop genomics and Phenomics. BMC Genomics 2019; 20:443. [PMID: 31159731 PMCID: PMC6547493 DOI: 10.1186/s12864-019-5812-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Accepted: 05/20/2019] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Trait ontology (TO) analysis is a powerful system for functional annotation and enrichment analysis of genes. However, given the complexity of the molecular mechanisms underlying phenomes, only a few hundred gene-to-TO relationships in plants have been elucidated to date, limiting the pace of research in this "big data" era. RESULTS Here, we curated all the available trait associated sites (TAS) information from 79 association mapping studies of maize (Zea mays L.) and rice (Oryza sativa L.) lines with diverse genetic backgrounds and built a large-scale TAS-derived TO system for functional annotation of genes in various crops. Our TO system contains information for up to 18,042 genes (6345 in maize at the 25 k level and 11,697 in rice at the 50 k level), including gene-to-TO relationships, which covers over one fifth of the annotated gene sets for maize and rice. A comparison of Gene Ontology (GO) vs. TO analysis demonstrated that the TAS-derived TO system is an efficient alternative tool for gene functional annotation and enrichment analysis. We therefore combined information from the TO, GO, metabolic pathway, and co-expression network databases and constructed the TAS system, which is publicly available at http://tas.hzau.edu.cn . TAS provides a user-friendly interface for functional annotation of genes, enrichment analysis, genome-wide extraction of trait-associated genes, and crosschecking of different functional annotation databases. CONCLUSIONS TAS bridges the gap between genomic and phenomic information in crops. This easy-to-use tool will be useful for geneticists, biologists, and breeders in the agricultural community, as it facilitates the dissection of molecular mechanisms conferring agronomic traits in an easy, genome-wide manner.
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Affiliation(s)
- Qingchun Pan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Junfeng Wei
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Feng Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Suiyong Huang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yong Gong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hao Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jianxiao Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
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7
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Sircar S, Parekh N. Meta-analysis of drought-tolerant genotypes in Oryza sativa: A network-based approach. PLoS One 2019; 14:e0216068. [PMID: 31059518 PMCID: PMC6502313 DOI: 10.1371/journal.pone.0216068] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Accepted: 04/12/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Drought is a severe environmental stress. It is estimated that about 50% of the world rice production is affected mainly by drought. Apart from conventional breeding strategies to develop drought-tolerant crops, innovative computational approaches may provide insights into the underlying molecular mechanisms of stress response and identify drought-responsive markers. Here we propose a network-based computational approach involving a meta-analytic study of seven drought-tolerant rice genotypes under drought stress. RESULTS Co-expression networks enable large-scale analysis of gene-pair associations and tightly coupled clusters that may represent coordinated biological processes. Considering differentially expressed genes in the co-expressed modules and supplementing external information such as resistance/tolerance QTLs, transcription factors, network-based topological measures, we identify and prioritize drought-adaptive co-expressed gene modules and potential candidate genes. Using the candidate genes that are well-represented across the datasets as 'seed' genes, two drought-specific protein-protein interaction networks (PPINs) are constructed with up- and down-regulated genes. Cluster analysis of the up-regulated PPIN revealed ABA signalling pathway as a central process in drought response with a probable crosstalk with energy metabolic processes. Tightly coupled gene clusters representing up-regulation of core cellular respiratory processes and enhanced degradation of branched chain amino acids and cell wall metabolism are identified. Cluster analysis of down-regulated PPIN provides a snapshot of major processes associated with photosynthesis, growth, development and protein synthesis, most of which are shut down during drought. Differential regulation of phytohormones, e.g., jasmonic acid, cell wall metabolism, signalling and posttranslational modifications associated with biotic stress are elucidated. Functional characterization of topologically important, drought-responsive uncharacterized genes that may play a role in important processes such as ABA signalling, calcium signalling, photosynthesis and cell wall metabolism is discussed. Further transgenic studies on these genes may help in elucidating their biological role under stress conditions. CONCLUSION Currently, a large number of resources for rice functional genomics exist which are mostly underutilized by the scientific community. In this study, a computational approach integrating information from various resources such as gene co-expression networks, protein-protein interactions and pathway-level information is proposed to provide a systems-level view of complex drought-responsive processes across the drought-tolerant genotypes.
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Affiliation(s)
- Sanchari Sircar
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - Nita Parekh
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
- * E-mail:
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8
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Han L, Mu Z, Luo Z, Pan Q, Li L. New lncRNA annotation reveals extensive functional divergence of the transcriptome in maize. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2019; 61:394-405. [PMID: 30117291 DOI: 10.1111/jipb.12708] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 08/06/2018] [Indexed: 06/08/2023]
Abstract
Long non-coding RNAs (lncRNAs), whose sequences are approximately 200 bp or longer and unlikely to encode proteins, may play an important role in eukaryotic gene regulation. Although the latest maize (Zea mays L.) reference genome provides an essential genomic resource, genome-wide annotations of maize lncRNAs have not been updated. Here, we report on a large transcriptomic dataset collected from 749 RNA sequencing experiments across different tissues and stages of the maize reference inbred B73 line and 60 from its wild relative teosinte. We identified 18,165 high-confidence lncRNAs in maize, of which 6,873 are conserved between maize and teosinte. We uncovered distinct genomic characteristics of conserved lncRNAs, non-conserved lncRNAs, and protein-coding transcripts. Intriguingly, Shannon entropy analysis showed that conserved lncRNAs are likely to be expressed similarly to protein-coding transcripts. Co-expression network analysis revealed significant variation in the degree of co-expression. Furthermore, selection analysis indicated that conserved lncRNAs are more likely than non-conserved lncRNAs to be located in regions subject to recent selection, indicating evolutionary differentiation. Our results provide the latest genome-wide annotation and analysis of maize lncRNAs and uncover potential functional divergence between protein-coding, conserved lncRNA, and non-conserved lncRNA genes, demonstrating the high complexity of the maize transcriptome.
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Affiliation(s)
- Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Zhenna Mu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Zi Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Qingchun Pan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
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Zhou P, Hirsch CN, Briggs SP, Springer NM. Dynamic Patterns of Gene Expression Additivity and Regulatory Variation throughout Maize Development. MOLECULAR PLANT 2019; 12:410-425. [PMID: 30593858 DOI: 10.1016/j.molp.2018.12.015] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 12/14/2018] [Accepted: 12/18/2018] [Indexed: 05/26/2023]
Abstract
Gene expression variation is a key component underlying phenotypic variation and heterosis. Transcriptome profiling was performed on 23 different tissues or developmental stages of two maize inbreds, B73 and Mo17, as well as their F1 hybrid. The obtained large-scale datasets provided opportunities to monitor the developmental dynamics of differential expression, additivity for gene expression, and regulatory variation. The transcriptome can be divided into ∼30 000 genes that are expressed in at least one tissue of one inbred and an additional ∼10 000 ″silent" genes that are not expressed in any tissue of any genotype, 90% of which are non-syntenic relative to other grasses. Many (∼74%) of the expressed genes exhibit differential expression in at least one tissue. However, the majority of genes with differential expression do not exhibit consistent differential expression in different tissues. These genes often exhibit tissue-specific differential expression with equivalent expression in other tissues, and in many cases they switch the directionality of differential expression in different tissues. This suggests widespread variation for tissue-specific regulation of gene expression between the two maize inbreds B73 and Mo17. Nearly 5000 genes are expressed in only one parent in at least one tissue (single parent expression) and 97% of these genes are expressed at mid-parent levels or higher in the hybrid, providing extensive opportunities for hybrid complementation in heterosis. In general, additive expression patterns are much more common than non-additive patterns, and this trend is more pronounced for genes with strong differential expression or single parent expression. There is relatively little evidence for non-additive expression patterns that are maintained in multiple tissues. The analysis of allele-specific expression allowed classification of cis- and trans-regulatory variation. Genes with cis-regulatory variation often exhibit additive expression and tend to have more consistent regulatory variation throughout development. In contrast, genes with trans-regulatory variation are enriched for non-additive patterns and often show tissue-specific differential expression. Taken together, this study provides a deeper understanding of regulatory variation and the degree of additive gene expression throughout maize development. The dynamic nature of differential expression, additivity, and regulatory variation imply abundant variability for tissue-specific regulatory mechanisms and suggest that connections between transcriptome and phenome will require expression data from multiple tissues.
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Affiliation(s)
- Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN 55108, USA
| | - Steven P Briggs
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108, USA.
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10
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Gupta C, Pereira A. Recent advances in gene function prediction using context-specific coexpression networks in plants. F1000Res 2019; 8:F1000 Faculty Rev-153. [PMID: 30800290 PMCID: PMC6364378 DOI: 10.12688/f1000research.17207.1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/30/2019] [Indexed: 12/11/2022] Open
Abstract
Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks-created by integrating multiple expression datasets-connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional "global" to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.
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Affiliation(s)
- Chirag Gupta
- Crop, Soil and Environmental Sciences, University of Arkansas, Fayetteville, AR, USA
| | - Andy Pereira
- Crop, Soil and Environmental Sciences, University of Arkansas, Fayetteville, AR, USA
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11
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Schaefer RJ, Michno JM, Jeffers J, Hoekenga O, Dilkes B, Baxter I, Myers CL. Integrating Coexpression Networks with GWAS to Prioritize Causal Genes in Maize. THE PLANT CELL 2018; 30:2922-2942. [PMID: 30413654 PMCID: PMC6354270 DOI: 10.1105/tpc.18.00299] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 10/08/2018] [Accepted: 10/31/2018] [Indexed: 05/02/2023]
Abstract
Genome-wide association studies (GWAS) have identified loci linked to hundreds of traits in many different species. Yet, because linkage equilibrium implicates a broad region surrounding each identified locus, the causal genes often remain unknown. This problem is especially pronounced in nonhuman, nonmodel species, where functional annotations are sparse and there is frequently little information available for prioritizing candidate genes. We developed a computational approach, Camoco, that integrates loci identified by GWAS with functional information derived from gene coexpression networks. Using Camoco, we prioritized candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize (Zea mays) seeds. Strikingly, we observed a strong dependence in the performance of our approach based on the type of coexpression network used: expression variation across genetically diverse individuals in a relevant tissue context (in our case, roots that are the primary elemental uptake and delivery system) outperformed other alternative networks. Two candidate genes identified by our approach were validated using mutants. Our study demonstrates that coexpression networks provide a powerful basis for prioritizing candidate causal genes from GWAS loci but suggests that the success of such strategies can highly depend on the gene expression data context. Both the software and the lessons on integrating GWAS data with coexpression networks generalize to species beyond maize.
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Affiliation(s)
- Robert J Schaefer
- Biomedical Informatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, Minnesota 55455
| | - Jean-Michel Michno
- Biomedical Informatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, Minnesota 55455
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Joseph Jeffers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455
| | - Owen Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, New York 14850
| | - Brian Dilkes
- Department of Biochemistry, Purdue University, West Lafayette, Indiana 47907
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
- U.S. Department of Agriculture-Agricultural Research Service Plant Genetics Research Unit, St. Louis, Missouri 63132
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455
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12
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Huang J, Vendramin S, Shi L, McGinnis KM. Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data. PLANT PHYSIOLOGY 2017; 175:568-583. [PMID: 28768814 PMCID: PMC5580776 DOI: 10.1104/pp.17.00825] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 07/31/2017] [Indexed: 05/22/2023]
Abstract
With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.
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Affiliation(s)
- Ji Huang
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306
| | - Stefania Vendramin
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306
| | - Lizhen Shi
- Department of Computer Science, Florida State University, Tallahassee, Florida 32306
| | - Karen M McGinnis
- Department of Biological Science, Florida State University, Tallahassee, Florida 32306
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13
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Zhang Y, Ngu DW, Carvalho D, Liang Z, Qiu Y, Roston RL, Schnable JC. Differentially Regulated Orthologs in Sorghum and the Subgenomes of Maize. THE PLANT CELL 2017; 29:1938-1951. [PMID: 28733421 PMCID: PMC5590507 DOI: 10.1105/tpc.17.00354] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 07/05/2017] [Accepted: 07/18/2017] [Indexed: 05/19/2023]
Abstract
Identifying interspecies changes in gene regulation, one of the two primary sources of phenotypic variation, is challenging on a genome-wide scale. The use of paired time-course data on cold-responsive gene expression in maize (Zea mays) and sorghum (Sorghum bicolor) allowed us to identify differentially regulated orthologs. While the majority of cold-responsive transcriptional regulation of conserved gene pairs is species specific, the initial transcriptional responses to cold appear to be more conserved than later responses. In maize, the promoters of genes with conserved transcriptional responses to cold tend to contain more micrococcal nuclease hypersensitive sites in their promoters, a proxy for open chromatin. Genes with conserved patterns of transcriptional regulation between the two species show lower ratios of nonsynonymous to synonymous substitutions. Genes involved in lipid metabolism, known to be involved in cold acclimation, tended to show consistent regulation in both species. Genes with species-specific cold responses did not cluster in particular pathways nor were they enriched in particular functional categories. We propose that cold-responsive transcriptional regulation in individual species may not be a reliable marker for function, while a core set of genes involved in perceiving and responding to cold stress are subject to functionally constrained cold-responsive regulation across the grass tribe Andropogoneae.
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Affiliation(s)
- Yang Zhang
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
| | - Daniel W Ngu
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
| | - Daniel Carvalho
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
| | - Zhikai Liang
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
| | - Yumou Qiu
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
| | - Rebecca L Roston
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
| | - James C Schnable
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska 68588
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14
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Schaefer RJ, Michno JM, Myers CL. Unraveling gene function in agricultural species using gene co-expression networks. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2017; 1860:53-63. [DOI: 10.1016/j.bbagrm.2016.07.016] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Revised: 07/23/2016] [Accepted: 07/25/2016] [Indexed: 10/21/2022]
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15
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Musungu BM, Bhatnagar D, Brown RL, Payne GA, OBrian G, Fakhoury AM, Geisler M. A Network Approach of Gene Co-expression in the Zea mays/ Aspergillus flavus Pathosystem to Map Host/Pathogen Interaction Pathways. Front Genet 2016; 7:206. [PMID: 27917194 PMCID: PMC5116468 DOI: 10.3389/fgene.2016.00206] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 11/04/2016] [Indexed: 12/27/2022] Open
Abstract
A gene co-expression network (GEN) was generated using a dual RNA-seq study with the fungal pathogen Aspergillus flavus and its plant host Zea mays during the initial 3 days of infection. The analysis deciphered novel pathways and mapped genes of interest in both organisms during the infection. This network revealed a high degree of connectivity in many of the previously recognized pathways in Z. mays such as jasmonic acid, ethylene, and reactive oxygen species (ROS). For the pathogen A. flavus, a link between aflatoxin production and vesicular transport was identified within the network. There was significant interspecies correlation of expression between Z. mays and A. flavus for a subset of 104 Z. mays, and 1942 A. flavus genes. This resulted in an interspecies subnetwork enriched in multiple Z. mays genes involved in the production of ROS. In addition to the ROS from Z. mays, there was enrichment in the vesicular transport pathways and the aflatoxin pathway for A. flavus. Included in these genes, a key aflatoxin cluster regulator, AflS, was found to be co-regulated with multiple Z. mays ROS producing genes within the network, suggesting AflS may be monitoring host ROS levels. The entire GEN for both host and pathogen, and the subset of interspecies correlations, is presented as a tool for hypothesis generation and discovery for events in the early stages of fungal infection of Z. mays by A. flavus.
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Affiliation(s)
- Bryan M Musungu
- Department of Plant Biology, Southern Illinois University, CarbondaleIL, USA; Southern Regional Research Center, United States Department of Agriculture - Agricultural Research Service, New OrleansLA, USA
| | - Deepak Bhatnagar
- Southern Regional Research Center, United States Department of Agriculture - Agricultural Research Service, New Orleans LA, USA
| | - Robert L Brown
- Southern Regional Research Center, United States Department of Agriculture - Agricultural Research Service, New Orleans LA, USA
| | - Gary A Payne
- Department of Plant Pathology, North Carolina State University, Raleigh NC, USA
| | - Greg OBrian
- Department of Plant Pathology, North Carolina State University, Raleigh NC, USA
| | - Ahmad M Fakhoury
- Department of Plant Soil and Agriculture Systems, Southern Illinois University, Carbondale IL, USA
| | - Matt Geisler
- Department of Plant Biology, Southern Illinois University, Carbondale IL, USA
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16
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Li L, Briskine R, Schaefer R, Schnable PS, Myers CL, Flagel LE, Springer NM, Muehlbauer GJ. Co-expression network analysis of duplicate genes in maize (Zea mays L.) reveals no subgenome bias. BMC Genomics 2016; 17:875. [PMID: 27814670 PMCID: PMC5097351 DOI: 10.1186/s12864-016-3194-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 10/22/2016] [Indexed: 01/08/2023] Open
Abstract
Background Gene duplication is prevalent in many species and can result in coding and regulatory divergence. Gene duplications can be classified as whole genome duplication (WGD), tandem and inserted (non-syntenic). In maize, WGD resulted in the subgenomes maize1 and maize2, of which maize1 is considered the dominant subgenome. However, the landscape of co-expression network divergence of duplicate genes in maize is still largely uncharacterized. Results To address the consequence of gene duplication on co-expression network divergence, we developed a gene co-expression network from RNA-seq data derived from 64 different tissues/stages of the maize reference inbred-B73. WGD, tandem and inserted gene duplications exhibited distinct regulatory divergence. Inserted duplicate genes were more likely to be singletons in the co-expression networks, while WGD duplicate genes were likely to be co-expressed with other genes. Tandem duplicate genes were enriched in the co-expression pattern where co-expressed genes were nearly identical for the duplicates in the network. Older gene duplications exhibit more extensive co-expression variation than younger duplications. Overall, non-syntenic genes primarily from inserted duplications show more co-expression divergence. Also, such enlarged co-expression divergence is significantly related to duplication age. Moreover, subgenome dominance was not observed in the co-expression networks – maize1 and maize2 exhibit similar levels of intra subgenome correlations. Intriguingly, the level of inter subgenome co-expression was similar to the level of intra subgenome correlations, and genes from specific subgenomes were not likely to be the enriched in co-expression network modules and the hub genes were not predominantly from any specific subgenomes in maize. Conclusions Our work provides a comprehensive analysis of maize co-expression network divergence for three different types of gene duplications and identifies potential relationships between duplication types, duplication ages and co-expression consequences. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3194-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lin Li
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, 55108, USA.,National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Roman Briskine
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Robert Schaefer
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | | | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Lex E Flagel
- Monsanto Company, Chesterfield, MO, 63017, USA.,Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Gary J Muehlbauer
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, 55108, USA. .,Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN, 55108, USA.
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17
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Cao J. Analysis of the Prefoldin Gene Family in 14 Plant Species. FRONTIERS IN PLANT SCIENCE 2016; 7:317. [PMID: 27014333 PMCID: PMC4792155 DOI: 10.3389/fpls.2016.00317] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Accepted: 02/29/2016] [Indexed: 05/03/2023]
Abstract
Prefoldin is a hexameric molecular chaperone complex present in all eukaryotes and archaea. The evolution of this gene family in plants is unknown. Here, I identified 140 prefoldin genes in 14 plant species. These prefoldin proteins were divided into nine groups through phylogenetic analysis. Highly conserved gene organization and motif distribution exist in each prefoldin group, implying their functional conservation. I also observed the segmental duplication of maize prefoldin gene family. Moreover, a few functional divergence sites were identified within each group pairs. Functional network analyses identified 78 co-expressed genes, and most of them were involved in carrying, binding and kinase activity. Divergent expression profiles of the maize prefoldin genes were further investigated in different tissues and development periods and under auxin and some abiotic stresses. I also found a few cis-elements responding to abiotic stress and phytohormone in the upstream sequences of the maize prefoldin genes. The results provided a foundation for exploring the characterization of the prefoldin genes in plants and will offer insights for additional functional studies.
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18
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Righetti K, Vu JL, Pelletier S, Vu BL, Glaab E, Lalanne D, Pasha A, Patel RV, Provart NJ, Verdier J, Leprince O, Buitink J. Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways. THE PLANT CELL 2015; 27:2692-708. [PMID: 26410298 PMCID: PMC4682330 DOI: 10.1105/tpc.15.00632] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 08/24/2015] [Accepted: 09/09/2015] [Indexed: 05/20/2023]
Abstract
Seed longevity, the maintenance of viability during storage, is a crucial factor for preservation of genetic resources and ensuring proper seedling establishment and high crop yield. We used a systems biology approach to identify key genes regulating the acquisition of longevity during seed maturation of Medicago truncatula. Using 104 transcriptomes from seed developmental time courses obtained in five growth environments, we generated a robust, stable coexpression network (MatNet), thereby capturing the conserved backbone of maturation. Using a trait-based gene significance measure, a coexpression module related to the acquisition of longevity was inferred from MatNet. Comparative analysis of the maturation processes in M. truncatula and Arabidopsis thaliana seeds and mining Arabidopsis interaction databases revealed conserved connectivity for 87% of longevity module nodes between both species. Arabidopsis mutant screening for longevity and maturation phenotypes demonstrated high predictive power of the longevity cross-species network. Overrepresentation analysis of the network nodes indicated biological functions related to defense, light, and auxin. Characterization of defense-related wrky3 and nf-x1-like1 (nfxl1) transcription factor mutants demonstrated that these genes regulate some of the network nodes and exhibit impaired acquisition of longevity during maturation. These data suggest that seed longevity evolved by co-opting existing genetic pathways regulating the activation of defense against pathogens.
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Affiliation(s)
- Karima Righetti
- UMR 1345, Institut de Recherche en Horticulture et Semences, Institut National de la Recherche Agronomique, SFR 4207 QUASAV, Angers, France
| | - Joseph Ly Vu
- UMR 1345, Institut de Recherche en Horticulture et Semences, Institut National de la Recherche Agronomique, SFR 4207 QUASAV, Angers, France
| | - Sandra Pelletier
- UMR 1345, Institut de Recherche en Horticulture et Semences, Institut National de la Recherche Agronomique, SFR 4207 QUASAV, Angers, France
| | - Benoit Ly Vu
- UMR 1345, Institut de Recherche en Horticulture et Semences, SFR 4207 QUASAV, 49071 Beaucouzé, France
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - David Lalanne
- UMR 1345, Institut de Recherche en Horticulture et Semences, Institut National de la Recherche Agronomique, SFR 4207 QUASAV, Angers, France
| | - Asher Pasha
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario M5S 3B2, Canada
| | - Rohan V Patel
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario M5S 3B2, Canada
| | - Nicholas J Provart
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario M5S 3B2, Canada
| | - Jerome Verdier
- Shanghai Center for Plant Stress Biology, SIBS, Chinese Academy of Sciences, Shanghai 201602, P.R. China
| | - Olivier Leprince
- UMR 1345, Institut de Recherche en Horticulture et Semences, SFR 4207 QUASAV, 49071 Beaucouzé, France
| | - Julia Buitink
- UMR 1345, Institut de Recherche en Horticulture et Semences, Institut National de la Recherche Agronomique, SFR 4207 QUASAV, Angers, France
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Li D, Ono N, Sato T, Sugiura T, Altaf-Ul-Amin M, Ohta D, Suzuki H, Arita M, Tanaka K, Ma Z, Kanaya S. Targeted Integration of RNA-Seq and Metabolite Data to Elucidate Curcuminoid Biosynthesis in Four Curcuma Species. PLANT & CELL PHYSIOLOGY 2015; 56:843-51. [PMID: 25637373 DOI: 10.1093/pcp/pcv008] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2014] [Accepted: 01/19/2015] [Indexed: 05/09/2023]
Abstract
Curcuminoids, namely curcumin and its analogs, are secondary metabolites that act as the primary active constituents of turmeric (Curcuma longa). The contents of these curcuminoids vary among species in the genus Curcuma. For this reason, we compared two wild strains and two cultivars to understand the differences in the synthesis of curcuminoids. Because the fluxes of metabolic reactions depend on the amounts of their substrate and the activity of the catalysts, we analyzed the metabolite concentrations and gene expression of related enzymes. We developed a method based on RNA sequencing (RNA-Seq) analysis that focuses on a specific set of genes to detect expression differences between species in detail. We developed a 'selection-first' method for RNA-Seq analysis in which short reads are mapped to selected enzymes in the target biosynthetic pathways in order to reduce the effect of mapping errors. Using this method, we found that the difference in the contents of curcuminoids among the species, as measured by gas chromatography-mass spectrometry, could be explained by the changes in the expression of genes encoding diketide-CoA synthase, and curcumin synthase at the branching point of the curcuminoid biosynthesis pathway.
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Affiliation(s)
- Donghan Li
- Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, 630-0192 Japan
| | - Naoaki Ono
- Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, 630-0192 Japan
| | - Tetsuo Sato
- Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, 630-0192 Japan
| | - Tadao Sugiura
- Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, 630-0192 Japan
| | - Md Altaf-Ul-Amin
- Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, 630-0192 Japan
| | - Daisaku Ohta
- Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Sakai, Osaka, 599-8531 Japan
| | - Hideyuki Suzuki
- Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba, 292-0818 Japan
| | - Masanori Arita
- Center for Information Biology, National Institute of Genetics, Mishima, 411-8540 Japan RIKEN Center for Sustainable Resource Science, Kanagawa, 230-0045 Japan
| | - Ken Tanaka
- Division of Pharmacognosy, College of Pharmaceutical Science, Ritsumeikan University, Kusatsu, 525-8577 Japan
| | - Zhiqiang Ma
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China
| | - Shigehiko Kanaya
- Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, 630-0192 Japan
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Correction: Discovering functional modules across diverse maize transcriptomes using COB, the co-expression browser. PLoS One 2015; 10:e0120222. [PMID: 25769027 PMCID: PMC4359158 DOI: 10.1371/journal.pone.0120222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
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