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Julian R, Patrick RM, Li Y. Organ-specific characteristics govern the relationship between histone code dynamics and transcriptional reprogramming during nitrogen response in tomato. Commun Biol 2023; 6:1225. [PMID: 38044380 PMCID: PMC10694154 DOI: 10.1038/s42003-023-05601-8] [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: 06/16/2022] [Accepted: 11/17/2023] [Indexed: 12/05/2023] Open
Abstract
Environmental stimuli trigger rapid transcriptional reprogramming of gene networks. These responses occur in the context of the local chromatin landscape, but the contribution of organ-specific dynamic chromatin modifications in responses to external signals remains largely unexplored. We treated tomato seedlings with a supply of nitrate and measured the genome-wide changes of four histone marks, the permissive marks H3K27ac, H3K4me3, and H3K36me3 and repressive mark H3K27me3, in shoots and roots separately, as well as H3K9me2 in shoots. Dynamic and organ-specific histone acetylation and methylation were observed at functionally relevant gene loci. Integration of transcriptomic and epigenomic datasets generated from the same organ revealed largely syngenetic relations between changes in transcript levels and histone modifications, with the exception of H3K27me3 in shoots, where an increased level of this repressive mark is observed at genes activated by nitrate. Application of a machine learning approach revealed organ-specific rules regarding the importance of individual histone marks, as H3K36me3 is the most successful mark in predicting gene regulation events in shoots, while H3K4me3 is the strongest individual predictor in roots. Our integrated study substantiates a view that during plant environmental responses, the relationships between histone code dynamics and gene regulation are highly dependent on organ-specific contexts.
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Affiliation(s)
- Russell Julian
- Department of Horticulture & Landscape Architecture, Purdue University, West Lafayette, IN, 47907, USA
- Center for Plant Biology, Purdue University, West Lafayette, IN, 47907, USA
| | - Ryan M Patrick
- Department of Horticulture & Landscape Architecture, Purdue University, West Lafayette, IN, 47907, USA
- Center for Plant Biology, Purdue University, West Lafayette, IN, 47907, USA
| | - Ying Li
- Department of Horticulture & Landscape Architecture, Purdue University, West Lafayette, IN, 47907, USA.
- Center for Plant Biology, Purdue University, West Lafayette, IN, 47907, USA.
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Bulbul Ahmed M, Humayan Kabir A. Understanding of the various aspects of gene regulatory networks related to crop improvement. Gene 2022; 833:146556. [PMID: 35609798 DOI: 10.1016/j.gene.2022.146556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/14/2022] [Accepted: 05/06/2022] [Indexed: 12/30/2022]
Abstract
The hierarchical relationship between transcription factors, associated proteins, and their target genes is defined by a gene regulatory network (GRN). GRNs allow us to understand how the genotype and environment of a plant are incorporated to control the downstream physiological responses. During plant growth or environmental acclimatization, GRNs are diverse and can be differently regulated across tissue types and organs. An overview of recent advances in the development of GRN that speed up basic and applied plant research is given here. Furthermore, the overview of genome and transcriptome involving GRN research along with the exciting advancement and application are discussed. In addition, different approaches to GRN predictions were elucidated. In this review, we also describe the role of GRN in crop improvement, crop plant manipulation, stress responses, speed breeding and identifying genetic variations/locus. Finally, the challenges and prospects of GRN in plant biology are discussed.
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Affiliation(s)
- Md Bulbul Ahmed
- Plant Science Department, McGill University, 21111 lakeshore Road, Ste. Anne de Bellevue H9X3V9, Quebec, Canada; Institut de Recherche en Biologie Végétale (IRBV), University of Montreal, Montréal, Québec H1X 2B2, Canada.
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Sun Y, Oh DH, Duan L, Ramachandran P, Ramirez A, Bartlett A, Tran KN, Wang G, Dassanayake M, Dinneny JR. Divergence in the ABA gene regulatory network underlies differential growth control. NATURE PLANTS 2022; 8:549-560. [PMID: 35501452 DOI: 10.1038/s41477-022-01139-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
The phytohormone abscisic acid (ABA) is a central regulator of acclimation to environmental stress; however, its contribution to differences in stress tolerance between species is unclear. To establish a comparative framework for understanding how stress hormone signalling pathways diverge across species, we studied the growth response of four Brassicaceae species to ABA treatment and generated transcriptomic and DNA affinity purification and sequencing datasets to construct a cross-species gene regulatory network (GRN) for ABA. Comparison of genes bound directly by ABA-responsive element binding factors suggests that cis-factors are most important for determining the target loci represented in the ABA GRN of a particular species. Using this GRN, we reveal how rewiring of growth hormone subnetworks contributes to stark differences in the response to ABA in the extremophyte Schrenkiella parvula. Our study provides a model for understanding how divergence in gene regulation can lead to species-specific physiological outcomes in response to hormonal cues.
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Affiliation(s)
- Ying Sun
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Dong-Ha Oh
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Lina Duan
- Department of Biology, Stanford University, Stanford, CA, USA
| | | | - Andrea Ramirez
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Kieu-Nga Tran
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Guannan Wang
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Maheshi Dassanayake
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - José R Dinneny
- Department of Biology, Stanford University, Stanford, CA, USA.
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Applications of cell- and tissue-specific 'omics to improve plant productivity. Emerg Top Life Sci 2022; 6:163-173. [PMID: 35293572 PMCID: PMC9023014 DOI: 10.1042/etls20210286] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/21/2022] [Accepted: 02/25/2022] [Indexed: 01/05/2023]
Abstract
The individual tissues and cell types of plants each have characteristic properties that contribute to the function of the plant as a whole. These are reflected by unique patterns of gene expression, protein and metabolite content, which enable cell-type-specific patterns of growth, development and physiology. Gene regulatory networks act within the cell types to govern the production and activity of these components. For the broader organism to grow and reproduce successfully, cell-type-specific activity must also function within the context of surrounding cell types, which is achieved by coordination of signalling pathways. We can investigate how gene regulatory networks are constructed and function using integrative ‘omics technologies. Historically such experiments in plant biological research have been performed at the bulk tissue level, to organ resolution at best. In this review, we describe recent advances in cell- and tissue-specific ‘omics technologies that allow investigation at much improved resolution. We discuss the advantages of these approaches for fundamental and translational plant biology, illustrated through the examples of specialised metabolism in medicinal plants and seed germination. We also discuss the challenges that must be overcome for such approaches to be adopted widely by the community.
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Detecting drought regulators using stochastic inference in Bayesian networks. PLoS One 2021; 16:e0255486. [PMID: 34398879 PMCID: PMC8367000 DOI: 10.1371/journal.pone.0255486] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 07/18/2021] [Indexed: 11/19/2022] Open
Abstract
Drought is a natural hazard that affects crops by inducing water stress. Water stress, induced by drought accounts for more loss in crop yield than all the other causes combined. With the increasing frequency and intensity of droughts worldwide, it is essential to develop drought-resistant crops to ensure food security. In this paper, we model multiple drought signaling pathways in Arabidopsis using Bayesian networks to identify potential regulators of drought-responsive reporter genes. Genetically intervening at these regulators can help develop drought-resistant crops. We create the Bayesian network model from the biological literature and determine its parameters from publicly available data. We conduct inference on this model using a stochastic simulation technique known as likelihood weighting to determine the best regulators of drought-responsive reporter genes. Our analysis reveals that activating MYC2 or inhibiting ATAF1 are the best single node intervention strategies to regulate the drought-responsive reporter genes. Additionally, we observe simultaneously activating MYC2 and inhibiting ATAF1 is a better strategy. The Bayesian network model indicated that MYC2 and ATAF1 are possible regulators of the drought response. Validation experiments showed that ATAF1 negatively regulated the drought response. Thus intervening at ATAF1 has the potential to create drought-resistant crops.
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Abstract
Lysine is the first limiting essential amino acid in rice because it is present in the lowest quantity compared to all the other amino acids. Amino acids are the building block of proteins and play an essential role in maintaining the human body’s healthy functioning. Rice is a staple food for more than half of the global population; thus, increasing the lysine content in rice will help improve global health. In this paper, we studied the lysine biosynthesis pathway in rice (Oryza sativa) to identify the regulators of the lysine reporter gene LYSA (LOC_Os02g24354). Genetically intervening at the regulators has the potential to increase the overall lysine content in rice. We modeled the lysine biosynthesis pathway in rice seedlings under normal and saline (NaCl) stress conditions using Bayesian networks. We estimated the model parameters using experimental data and identified the gene DAPF(LOC_Os12g37960) as a positive regulator of the lysine reporter gene LYSA under both normal and saline stress conditions. Based on this analysis, we conclude that the gene DAPF is a potent candidate for genetic intervention. Upregulating DAPF using methods such as CRISPR-Cas9 gene editing strategy has the potential to upregulate the lysine reporter gene LYSA and increase the overall lysine content in rice.
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Sharma R, Upadhyay S, Bhattacharya S, Singh A. Abiotic Stress-Responsive miRNA and Transcription Factor-Mediated Gene Regulatory Network in Oryza sativa: Construction and Structural Measure Study. Front Genet 2021; 12:618089. [PMID: 33643383 PMCID: PMC7907651 DOI: 10.3389/fgene.2021.618089] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 01/19/2021] [Indexed: 11/13/2022] Open
Abstract
Climate changes and environmental stresses have a consequential association with crop plant growth and yield, meaning it is necessary to cultivate crops that have tolerance toward the changing climate and environmental disturbances such as water stress, temperature fluctuation, and salt toxicity. Recent studies have shown that trans-acting regulatory elements, including microRNAs (miRNAs) and transcription factors (TFs), are emerging as promising tools for engineering naive improved crop varieties with tolerance for multiple environmental stresses and enhanced quality as well as yield. However, the interwoven complex regulatory function of TFs and miRNAs at transcriptional and post-transcriptional levels is unexplored in Oryza sativa. To this end, we have constructed a multiple abiotic stress responsive TF-miRNA-gene regulatory network for O. sativa using a transcriptome and degradome sequencing data meta-analysis approach. The theoretical network approach has shown the networks to be dense, scale-free, and small-world, which makes the network stable. They are also invariant to scale change where an efficient, quick transmission of biological signals occurs within the network on extrinsic hindrance. The analysis also deciphered the existence of communities (cluster of TF, miRNA, and genes) working together to help plants in acclimatizing to multiple stresses. It highlighted that genes, TFs, and miRNAs shared by multiple stress conditions that work as hubs or bottlenecks for signal propagation, for example, during the interaction between stress-responsive genes (TFs/miRNAs/other genes) and genes involved in floral development pathways under multiple environmental stresses. This study further highlights how the fine-tuning feedback mechanism works for balancing stress tolerance and how timely flowering enable crops to survive in adverse conditions. This study developed the abiotic stress-responsive regulatory network, APRegNet database (http://lms.snu.edu.in/APRegNet), which may help researchers studying the roles of miRNAs and TFs. Furthermore, it advances current understanding of multiple abiotic stress tolerance mechanisms.
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Affiliation(s)
- Rinku Sharma
- Department of Life Sciences, Shiv Nadar University, Gautam Buddha Nagar, India
| | | | | | - Ashutosh Singh
- Department of Life Sciences, Shiv Nadar University, Gautam Buddha Nagar, India
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Gupta P, Singh SK. Gene Regulatory Networks: Current Updates and Applications in Plant Biology. ENERGY, ENVIRONMENT, AND SUSTAINABILITY 2019. [DOI: 10.1007/978-981-15-0690-1_18] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Mochida K, Koda S, Inoue K, Nishii R. Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets. FRONTIERS IN PLANT SCIENCE 2018; 9:1770. [PMID: 30555503 PMCID: PMC6281826 DOI: 10.3389/fpls.2018.01770] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Accepted: 11/14/2018] [Indexed: 05/20/2023]
Abstract
Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.
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Affiliation(s)
- Keiichi Mochida
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, Yokohama, Japan
- Microalgae Production Control Technology Laboratory, RIKEN Baton Zone Program, RIKEN Cluster for Science, Technology and Innovation Hub, Yokohama, Japan
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
- Kihara Institute for Biological Research, Yokohama City University, Yokohama, Japan
- *Correspondence: Keiichi Mochida, Ryuei Nishii,
| | - Satoru Koda
- Graduate School of Mathematics, Kyushu University, Fukuoka, Japan
| | - Komaki Inoue
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Ryuei Nishii
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
- *Correspondence: Keiichi Mochida, Ryuei Nishii,
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