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Núñez-Lillo G, Lillo-Carmona V, Pérez-Donoso AG, Pedreschi R, Campos-Vargas R, Meneses C. Fruit sugar hub: gene regulatory network associated with soluble solids content (SSC) in Prunus persica. Biol Res 2024; 57:63. [PMID: 39243048 PMCID: PMC11378430 DOI: 10.1186/s40659-024-00539-5] [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: 03/16/2024] [Accepted: 08/21/2024] [Indexed: 09/09/2024] Open
Abstract
Chilean peach growers have achieved worldwide recognition for their high-quality fruit products. Among the main factors influencing peach fruit quality, sweetness is pivotal for maintaining the market's competitiveness. Numerous studies have been conducted in different peach-segregating populations to unravel SSC regulation. However, different cultivars may also have distinct genetic conformation, and other factors, such as environmental conditions, can significantly impact SSC. Using a transcriptomic approach with a gene co-expression network analysis, we aimed to identify the regulatory mechanism that controls the sugar accumulation process in an 'O × N' peach population. This population was previously studied through genomic analysis, associating LG5 with the genetic control of the SSC trait. The results obtained in this study allowed us to identify 91 differentially expressed genes located on chromosome 5 of the peach genome as putative new regulators of sugar accumulation in peach, together with a regulatory network that involves genes directly associated with sugar transport (PpSWEET15), cellulose biosynthesis (PpCSLG2), flavonoid biosynthesis (PpPAL1), pectin modifications (PpPG, PpPL and PpPMEi), expansins (PpEXPA1 and PpEXPA8) and several transcription factors (PpC3H67, PpHB7, PpRVE1 and PpCBF4) involved with the SSC phenotype. These results contribute to a better understanding of the genetic control of the SSC trait for future breeding programs in peaches.
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Affiliation(s)
- Gerardo Núñez-Lillo
- Escuela de Agronomía, Facultad de Ciencias Agronómicas y de los Alimentos, Pontificia Universidad Católica de Valparaíso, Quillota, Chile.
| | - Victoria Lillo-Carmona
- Departamento de Fruticultura y Enología, Facultad de Agronomía y Sistemas Naturales, Pontificia Universidad Católica de Chile, Santiago, Chile
- Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Alonso G Pérez-Donoso
- Departamento de Fruticultura y Enología, Facultad de Agronomía y Sistemas Naturales, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Romina Pedreschi
- Escuela de Agronomía, Facultad de Ciencias Agronómicas y de los Alimentos, Pontificia Universidad Católica de Valparaíso, Quillota, Chile
- Millennium Institute Center for Genome Regulation (CRG), Santiago, Chile
| | - Reinaldo Campos-Vargas
- Departamento de Producción Agrícola, Facultad de Ciencias Agronómicas, Universidad de Chile, Santiago, Chile
| | - Claudio Meneses
- Departamento de Fruticultura y Enología, Facultad de Agronomía y Sistemas Naturales, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Millennium Institute Center for Genome Regulation (CRG), Santiago, Chile.
- ANID-Millennium Science Initiative Program - Millennium Nucleus for the Development of Super Adaptable Plants (MN-SAP), Santiago, Chile.
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2
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Go D, Lu B, Alizadeh M, Gazzarrini S, Song L. Voice from both sides: a molecular dialogue between transcriptional activators and repressors in seed-to-seedling transition and crop adaptation. FRONTIERS IN PLANT SCIENCE 2024; 15:1416216. [PMID: 39166233 PMCID: PMC11333834 DOI: 10.3389/fpls.2024.1416216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 06/20/2024] [Indexed: 08/22/2024]
Abstract
High-quality seeds provide valuable nutrients to human society and ensure successful seedling establishment. During maturation, seeds accumulate storage compounds that are required to sustain seedling growth during germination. This review focuses on the epigenetic repression of the embryonic and seed maturation programs in seedlings. We begin with an extensive overview of mutants affecting these processes, illustrating the roles of core proteins and accessory components in the epigenetic machinery by comparing mutants at both phenotypic and molecular levels. We highlight how omics assays help uncover target-specific functional specialization and coordination among various epigenetic mechanisms. Furthermore, we provide an in-depth discussion on the Seed dormancy 4 (Sdr4) transcriptional corepressor family, comparing and contrasting their regulation of seed germination in the dicotyledonous species Arabidopsis and two monocotyledonous crops, rice and wheat. Finally, we compare the similarities in the activation and repression of the embryonic and seed maturation programs through a shared set of cis-regulatory elements and discuss the challenges in applying knowledge largely gained in model species to crops.
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Affiliation(s)
- Dongeun Go
- Department of Botany, University of British Columbia, Vancouver, BC, Canada
| | - Bailan Lu
- Department of Botany, University of British Columbia, Vancouver, BC, Canada
| | - Milad Alizadeh
- Department of Botany, University of British Columbia, Vancouver, BC, Canada
| | - Sonia Gazzarrini
- Department of Biological Science, University of Toronto Scarborough, Toronto, ON, Canada
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Liang Song
- Department of Botany, University of British Columbia, Vancouver, BC, Canada
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3
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Huo Q, Song R, Ma Z. Recent advances in exploring transcriptional regulatory landscape of crops. FRONTIERS IN PLANT SCIENCE 2024; 15:1421503. [PMID: 38903438 PMCID: PMC11188431 DOI: 10.3389/fpls.2024.1421503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 05/23/2024] [Indexed: 06/22/2024]
Abstract
Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.
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Affiliation(s)
| | | | - Zeyang Ma
- State Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Joint International Research Laboratory of Crop Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
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4
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Shen B, Coruzzi GM, Shasha D. Bipartite networks represent causality better than simple networks: evidence, algorithms, and applications. Front Genet 2024; 15:1371607. [PMID: 38798697 PMCID: PMC11120958 DOI: 10.3389/fgene.2024.1371607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 04/17/2024] [Indexed: 05/29/2024] Open
Abstract
A network, whose nodes are genes and whose directed edges represent positive or negative influences of a regulatory gene and its targets, is often used as a representation of causality. To infer a network, researchers often develop a machine learning model and then evaluate the model based on its match with experimentally verified "gold standard" edges. The desired result of such a model is a network that may extend the gold standard edges. Since networks are a form of visual representation, one can compare their utility with architectural or machine blueprints. Blueprints are clearly useful because they provide precise guidance to builders in construction. If the primary role of gene regulatory networks is to characterize causality, then such networks should be good tools of prediction because prediction is the actionable benefit of knowing causality. But are they? In this paper, we compare prediction quality based on "gold standard" regulatory edges from previous experimental work with non-linear models inferred from time series data across four different species. We show that the same non-linear machine learning models have better predictive performance, with improvements from 5.3% to 25.3% in terms of the reduction in the root mean square error (RMSE) compared with the same models based on the gold standard edges. Having established that networks fail to characterize causality properly, we suggest that causality research should focus on four goals: (i) predictive accuracy; (ii) a parsimonious enumeration of predictive regulatory genes for each target gene g; (iii) the identification of disjoint sets of predictive regulatory genes for each target g of roughly equal accuracy; and (iv) the construction of a bipartite network (whose node types are genes and models) representation of causality. We provide algorithms for all goals.
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Affiliation(s)
- Bingran Shen
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, United States
| | - Gloria M. Coruzzi
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States
| | - Dennis Shasha
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, United States
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5
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Guo C, Huang Z, Chen J, Yu G, Wang Y, Wang X. Identification of Novel Regulators of Leaf Senescence Using a Deep Learning Model. PLANTS (BASEL, SWITZERLAND) 2024; 13:1276. [PMID: 38732491 PMCID: PMC11085074 DOI: 10.3390/plants13091276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Abstract
Deep learning has emerged as a powerful tool for investigating intricate biological processes in plants by harnessing the potential of large-scale data. Gene regulation is a complex process that transcription factors (TFs), cooperating with their target genes, participate in through various aspects of biological processes. Despite its significance, the study of gene regulation has primarily focused on a limited number of notable instances, leaving numerous aspects and interactions yet to be explored comprehensively. Here, we developed DEGRN (Deep learning on Expression for Gene Regulatory Network), an innovative deep learning model designed to decipher gene interactions by leveraging high-dimensional expression data obtained from bulk RNA-Seq and scRNA-Seq data in the model plant Arabidopsis. DEGRN exhibited a compared level of predictive power when applied to various datasets. Through the utilization of DEGRN, we successfully identified an extensive set of 3,053,363 high-quality interactions, encompassing 1430 TFs and 13,739 non-TF genes. Notably, DEGRN's predictive capabilities allowed us to uncover novel regulators involved in a range of complex biological processes, including development, metabolism, and stress responses. Using leaf senescence as an example, we revealed a complex network underpinning this process composed of diverse TF families, including bHLH, ERF, and MYB. We also identified a novel TF, named MAF5, whose expression showed a strong linear regression relation during the progression of senescence. The mutant maf5 showed early leaf decay compared to the wild type, indicating a potential role in the regulation of leaf senescence. This hypothesis was further supported by the expression patterns observed across four stages of leaf development, as well as transcriptomics analysis. Overall, the comprehensive coverage provided by DEGRN expands our understanding of gene regulatory networks and paves the way for further investigations into their functional implications.
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Affiliation(s)
| | | | | | | | | | - Xu Wang
- Shanghai Collaborative Innovation Center of Agri-Seeds, Joint Center for Single Cell Biology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China; (C.G.); (Z.H.); (J.C.); (G.Y.); (Y.W.)
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6
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Shanks CM, Rothkegel K, Brooks MD, Cheng CY, Alvarez JM, Ruffel S, Krouk G, Gutiérrez RA, Coruzzi GM. Nitrogen sensing and regulatory networks: it's about time and space. THE PLANT CELL 2024; 36:1482-1503. [PMID: 38366121 PMCID: PMC11062454 DOI: 10.1093/plcell/koae038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 01/05/2024] [Accepted: 01/08/2024] [Indexed: 02/18/2024]
Abstract
A plant's response to external and internal nitrogen signals/status relies on sensing and signaling mechanisms that operate across spatial and temporal dimensions. From a comprehensive systems biology perspective, this involves integrating nitrogen responses in different cell types and over long distances to ensure organ coordination in real time and yield practical applications. In this prospective review, we focus on novel aspects of nitrogen (N) sensing/signaling uncovered using temporal and spatial systems biology approaches, largely in the model Arabidopsis. The temporal aspects span: transcriptional responses to N-dose mediated by Michaelis-Menten kinetics, the role of the master NLP7 transcription factor as a nitrate sensor, its nitrate-dependent TF nuclear retention, its "hit-and-run" mode of target gene regulation, and temporal transcriptional cascade identified by "network walking." Spatial aspects of N-sensing/signaling have been uncovered in cell type-specific studies in roots and in root-to-shoot communication. We explore new approaches using single-cell sequencing data, trajectory inference, and pseudotime analysis as well as machine learning and artificial intelligence approaches. Finally, unveiling the mechanisms underlying the spatial dynamics of nitrogen sensing/signaling networks across species from model to crop could pave the way for translational studies to improve nitrogen-use efficiency in crops. Such outcomes could potentially reduce the detrimental effects of excessive fertilizer usage on groundwater pollution and greenhouse gas emissions.
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Affiliation(s)
- Carly M Shanks
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA
| | - Karin Rothkegel
- Agencia Nacional de Investigación y Desarrollo-Millennium Science Initiative Program, Millennium Institute for Integrative Biology (iBio), 7500565 Santiago, Chile
- Center for Genome Regulation (CRG), Institute of Ecology and Biodiversity (IEB), Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, 8331010 Santiago, Chile
| | - Matthew D Brooks
- Global Change and Photosynthesis Research Unit, USDA-ARS, Urbana, IL 61801, USA
| | - Chia-Yi Cheng
- Department of Life Science, National Taiwan University, Taipei 10663, Taiwan
| | - José M Alvarez
- Agencia Nacional de Investigación y Desarrollo-Millennium Science Initiative Program, Millennium Institute for Integrative Biology (iBio), 7500565 Santiago, Chile
- Centro de Biotecnología Vegetal, Facultad de Ciencias, Universidad Andrés Bello, 8370035 Santiago, Chile
| | - Sandrine Ruffel
- Institute for Plant Sciences of Montpellier (IPSiM), Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche pour l’Agriculture, l’Alimentation, et l'Environnement (INRAE), Université de Montpellier, Montpellier 34090, France
| | - Gabriel Krouk
- Institute for Plant Sciences of Montpellier (IPSiM), Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche pour l’Agriculture, l’Alimentation, et l'Environnement (INRAE), Université de Montpellier, Montpellier 34090, France
| | - Rodrigo A Gutiérrez
- Agencia Nacional de Investigación y Desarrollo-Millennium Science Initiative Program, Millennium Institute for Integrative Biology (iBio), 7500565 Santiago, Chile
- Center for Genome Regulation (CRG), Institute of Ecology and Biodiversity (IEB), Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, 8331010 Santiago, Chile
| | - Gloria M Coruzzi
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA
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7
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Ranjan R, Srijan S, Balekuttira S, Agarwal T, Ramey M, Dobbins M, Kuhn R, Wang X, Hudson K, Li Y, Varala K. Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes. Proc Natl Acad Sci U S A 2024; 121:e2322751121. [PMID: 38652750 PMCID: PMC11066984 DOI: 10.1073/pnas.2322751121] [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: 01/04/2024] [Accepted: 03/14/2024] [Indexed: 04/25/2024] Open
Abstract
Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana. A GRN inference approach was used to determine the: i. influential transcription factors (TFs) in each organ and, ii. most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, an important food and biofuel trait. Of the top 20 predicted TFs, eight are known regulators of seed oil content, e.g., WRI1, LEC1, FUS3. Importantly, we validated our prediction of MybS2, TGA4, SPL12, AGL18, and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MybS2 and show that it induces purple acid phosphatase family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to find unique regulators of any trait-of-interest.
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Affiliation(s)
- Rajeev Ranjan
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN47907
- Center for Plant Biology, Purdue University, West Lafayette, IN47907
| | - Sonali Srijan
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN47907
| | - Somaiah Balekuttira
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN47907
| | - Tina Agarwal
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN47907
- Center for Plant Biology, Purdue University, West Lafayette, IN47907
| | - Melissa Ramey
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN47907
| | - Madison Dobbins
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN47907
| | - Rachel Kuhn
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN47907
| | - Xiaojin Wang
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN47907
- Center for Plant Biology, Purdue University, West Lafayette, IN47907
| | - Karen Hudson
- United States Department of Agriculture-Agricultural Research Service Crop Production and Pest Control Research Unit, West Lafayette, IN47907
| | - Ying Li
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN47907
- Center for Plant Biology, Purdue University, West Lafayette, IN47907
| | - Kranthi Varala
- Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN47907
- Center for Plant Biology, Purdue University, West Lafayette, IN47907
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8
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Cerda A, Alvarez JM. Insights into molecular links and transcription networks integrating drought stress and nitrogen signaling. THE NEW PHYTOLOGIST 2024; 241:560-566. [PMID: 37974513 DOI: 10.1111/nph.19403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 10/03/2023] [Indexed: 11/19/2023]
Abstract
Drought and the availability of nitrate, the predominant source of nitrogen (N) in agriculture, are major factors limiting plant growth and crop productivity. The dissection of the transcriptional networks' components integrating droght stress and nitrate responses provides valuable insights into how plants effectively balance stress response with growth programs. Recent evidence in Arabidopsis thaliana indicates that transcription factors (TFs) involved in abscisic acid (ABA) signaling affect N metabolism and nitrate responses, and reciprocally, components of nitrate signaling might affect ABA and drought gene responses. Advances in understanding regulatory circuits of nitrate and drought crosstalk in plant tissues empower targeted genetic modifications to enhance plant development and stress resistance, critical traits for optimizing crop yield and promoting sustainable agriculture.
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Affiliation(s)
- Ariel Cerda
- Centro de Biotecnología Vegetal, Facultad de Ciencias, Universidad Andrés Bello, Santiago, 8370186, Chile
- Millennium Science Initiative - Millennium Institute for Integrative Biology (iBio), Santiago, 8331150, Chile
| | - José M Alvarez
- Centro de Biotecnología Vegetal, Facultad de Ciencias, Universidad Andrés Bello, Santiago, 8370186, Chile
- Millennium Science Initiative - Millennium Institute for Integrative Biology (iBio), Santiago, 8331150, Chile
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9
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Pérez-Stuardo D, Frazão M, Ibaceta V, Brianson B, Sánchez E, Rivas-Pardo JA, Vallejos-Vidal E, Reyes-López FE, Toro-Ascuy D, Vidal EA, Reyes-Cerpa S. KLF17 is an important regulatory component of the transcriptomic response of Atlantic salmon macrophages to Piscirickettsia salmonis infection. Front Immunol 2023; 14:1264599. [PMID: 38162669 PMCID: PMC10755876 DOI: 10.3389/fimmu.2023.1264599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/07/2023] [Indexed: 01/03/2024] Open
Abstract
Piscirickettsia salmonis is the most important health problem facing Chilean Aquaculture. Previous reports suggest that P. salmonis can survive in salmonid macrophages by interfering with the host immune response. However, the relevant aspects of the molecular pathogenesis of P. salmonis have been poorly characterized. In this work, we evaluated the transcriptomic changes in macrophage-like cell line SHK-1 infected with P. salmonis at 24- and 48-hours post-infection (hpi) and generated network models of the macrophage response to the infection using co-expression analysis and regulatory transcription factor-target gene information. Transcriptomic analysis showed that 635 genes were differentially expressed after 24- and/or 48-hpi. The pattern of expression of these genes was analyzed by weighted co-expression network analysis (WGCNA), which classified genes into 4 modules of expression, comprising early responses to the bacterium. Induced genes included genes involved in metabolism and cell differentiation, intracellular transportation, and cytoskeleton reorganization, while repressed genes included genes involved in extracellular matrix organization and RNA metabolism. To understand how these expression changes are orchestrated and to pinpoint relevant transcription factors (TFs) controlling the response, we established a curated database of TF-target gene regulatory interactions in Salmo salar, SalSaDB. Using this resource, together with co-expression module data, we generated infection context-specific networks that were analyzed to determine highly connected TF nodes. We found that the most connected TF of the 24- and 48-hpi response networks is KLF17, an ortholog of the KLF4 TF involved in the polarization of macrophages to an M2-phenotype in mammals. Interestingly, while KLF17 is induced by P. salmonis infection, other TFs, such as NOTCH3 and NFATC1, whose orthologs in mammals are related to M1-like macrophages, are repressed. In sum, our results suggest the induction of early regulatory events associated with an M2-like phenotype of macrophages that drives effectors related to the lysosome, RNA metabolism, cytoskeleton organization, and extracellular matrix remodeling. Moreover, the M1-like response seems delayed in generating an effective response, suggesting a polarization towards M2-like macrophages that allows the survival of P. salmonis. This work also contributes to SalSaDB, a curated database of TF-target gene interactions that is freely available for the Atlantic salmon community.
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Affiliation(s)
- Diego Pérez-Stuardo
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Programa de Doctorado en Genómica Integrativa, Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile
| | - Mateus Frazão
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
| | - Valentina Ibaceta
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
| | - Bernardo Brianson
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
| | - Evelyn Sánchez
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Programa de Doctorado en Genómica Integrativa, Vicerrectoría de Investigación, Universidad Mayor, Santiago, Chile
- Agencia Nacional de Investigación y Desarrollo (ANID) Millennium Science Initiative Program-Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - J. Andrés Rivas-Pardo
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
| | - Eva Vallejos-Vidal
- Núcleo de Investigaciones Aplicadas en Ciencias Veterinarias y Agronómicas, Facultad de Medicina Veterinaria y Agronomía, Universidad De Las Américas, La Florida, Santiago, Chile
- Centro de Biotecnología Acuícola, Facultad de Química y Biología, Universidad de Santiago de Chile, Santiago, Chile
- Centro de Nanociencia y Nanotecnología (CEDENNA), Universidad de Santiago de Chile, Santiago, Chile
| | - Felipe E. Reyes-López
- Centro de Biotecnología Acuícola, Facultad de Química y Biología, Universidad de Santiago de Chile, Santiago, Chile
| | - Daniela Toro-Ascuy
- Laboratorio de Virología, Departamento de Biología, Facultad de Ciencias, Universidad de Chile, Santiago, Chile
| | - Elena A. Vidal
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Agencia Nacional de Investigación y Desarrollo (ANID) Millennium Science Initiative Program-Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - Sebastián Reyes-Cerpa
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
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10
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Olukayode T, Chen J, Zhao Y, Quan C, Kochian LV, Ham BK. Phloem-Mobile MYB44 Negatively Regulates Expression of PHOSPHATE TRANSPORTER 1 in Arabidopsis Roots. PLANTS (BASEL, SWITZERLAND) 2023; 12:3617. [PMID: 37896080 PMCID: PMC10610484 DOI: 10.3390/plants12203617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 10/03/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023]
Abstract
Phosphorus (P) is an essential plant macronutrient; however, its availability is often limited in soils. Plants have evolved complex mechanisms for efficient phosphate (Pi) absorption, which are responsive to changes in external and internal Pi concentration, and orchestrated through local and systemic responses. To explore these systemic Pi responses, here we identified AtMYB44 as a phloem-mobile mRNA, an Arabidopsis homolog of Cucumis sativus MYB44, that is responsive to the Pi-starvation stress. qRT-PCR assays revealed that AtMYB44 was up-regulated and expressed in both shoot and root in response to Pi-starvation stress. The atmyb44 mutant displayed higher shoot and root biomass compared to wild-type plants, under Pi-starvation conditions. Interestingly, the expression of PHOSPHATE TRANSPORTER1;2 (PHT1;2) and PHT1;4 was enhanced in atmyb44 in response to a Pi-starvation treatment. A split-root assay showed that AtMYB44 expression was systemically regulated under Pi-starvation conditions, and in atmyb44, systemic controls on PHT1;2 and PHT1;4 expression were moderately disrupted. Heterografting assays confirmed graft transmission of AtMYB44 transcripts, and PHT1;2 and PHT1;4 expression was decreased in heterografted atmyb44 rootstocks. Taken together, our findings support the hypothesis that mobile AtMYB44 mRNA serves as a long-distance Pi response signal, which negatively regulates Pi transport and utilization in Arabidopsis.
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Affiliation(s)
- Toluwase Olukayode
- Global Institute for Food Security (GIFS), University of Saskatchewan, 421 Downey Rd, Saskatoon, SK S7N 4L8, Canada; (T.O.); (J.C.); (Y.Z.); (C.Q.); (L.V.K.)
- Department of Biology, University of Saskatchewan, 112 Science Place, Saskatoon, SK S7N 5E2, Canada
| | - Jieyu Chen
- Global Institute for Food Security (GIFS), University of Saskatchewan, 421 Downey Rd, Saskatoon, SK S7N 4L8, Canada; (T.O.); (J.C.); (Y.Z.); (C.Q.); (L.V.K.)
| | - Yang Zhao
- Global Institute for Food Security (GIFS), University of Saskatchewan, 421 Downey Rd, Saskatoon, SK S7N 4L8, Canada; (T.O.); (J.C.); (Y.Z.); (C.Q.); (L.V.K.)
| | - Chuanhezi Quan
- Global Institute for Food Security (GIFS), University of Saskatchewan, 421 Downey Rd, Saskatoon, SK S7N 4L8, Canada; (T.O.); (J.C.); (Y.Z.); (C.Q.); (L.V.K.)
- Department of Biology, University of Saskatchewan, 112 Science Place, Saskatoon, SK S7N 5E2, Canada
| | - Leon V. Kochian
- Global Institute for Food Security (GIFS), University of Saskatchewan, 421 Downey Rd, Saskatoon, SK S7N 4L8, Canada; (T.O.); (J.C.); (Y.Z.); (C.Q.); (L.V.K.)
- Department of Plant Science, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada
| | - Byung-Kook Ham
- Global Institute for Food Security (GIFS), University of Saskatchewan, 421 Downey Rd, Saskatoon, SK S7N 4L8, Canada; (T.O.); (J.C.); (Y.Z.); (C.Q.); (L.V.K.)
- Department of Biology, University of Saskatchewan, 112 Science Place, Saskatoon, SK S7N 5E2, Canada
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11
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Hale B, Ratnayake S, Flory A, Wijeratne R, Schmidt C, Robertson AE, Wijeratne AJ. Gene regulatory network inference in soybean upon infection by Phytophthora sojae. PLoS One 2023; 18:e0287590. [PMID: 37418376 PMCID: PMC10328377 DOI: 10.1371/journal.pone.0287590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 06/07/2023] [Indexed: 07/09/2023] Open
Abstract
Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean (Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PRR has entailed host genetic resistance (both vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverse P. sojae pathotypes necessitates developing novel technologies to attenuate PRR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data and deep learning to elucidate molecular features in soybean following infection by P. sojae. In doing so, we generated transcriptomes to identify differentially expressed genes (DEGs) during compatible and incompatible interactions with P. sojae and a mock inoculation. The expression data were then used to select two defense-related transcription factors (TFs) belonging to WRKY and RAV families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites in the soybean genome. These bound sites were used to train Deep Neural Networks with convolutional and recurrent layers to predict new target sites of WRKY and RAV family members in the DEG set. Moreover, we leveraged publicly available Arabidopsis (Arabidopsis thaliana) DAP-seq data for five TF families enriched in our transcriptome analysis to train similar models. These Arabidopsis data-based models were used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target gene interactions that orchestrate an immune response against P. sojae. Information herein provides novel insight into molecular plant-pathogen interaction and may prove useful in developing soybean cultivars with more durable resistance to P. sojae.
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Affiliation(s)
- Brett Hale
- Molecular Biosciences Graduate Program, Arkansas State University, State University, AR, United States of America
- Arkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America
- College of Science and Mathematics, Arkansas State University, State University, AR, United States of America
| | - Sandaruwan Ratnayake
- Arkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America
- College of Science and Mathematics, Arkansas State University, State University, AR, United States of America
| | - Ashley Flory
- Arkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America
| | | | - Clarice Schmidt
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, United States of America
| | - Alison E. Robertson
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, United States of America
| | - Asela J. Wijeratne
- Arkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America
- College of Science and Mathematics, Arkansas State University, State University, AR, United States of America
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12
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A multiomics integrative analysis of color de-synchronization with softening of 'Hass' avocado fruit: A first insight into a complex physiological disorder. Food Chem 2023; 408:135215. [PMID: 36528992 DOI: 10.1016/j.foodchem.2022.135215] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 12/01/2022] [Accepted: 12/11/2022] [Indexed: 12/15/2022]
Abstract
Exocarp color de-synchronization with softening of 'Hass' avocado is a relevant recurrent problem for the avocado supply chain. This study aimed to unravel the mechanisms driving this de-synchronization integrating omics datasets from avocado exocarp of different storage conditions and color phenotypes. In addition, we propose potential biomarkers to predict color synchronized/de-synchronized fruit. Integration of transcriptomics, proteomics and metabolomics and network analysis revealed eight transcription factors associated with differentially regulated genes between regular air (RA) and controlled atmosphere (CA) and twelve transcription factors related to avocado fruit color de-synchronization control in ready-to-eat stage. CA was positively correlated to auxins, ethylene, cytokinins and brassinosteroids-related genes, while RA was characterized by enrichment of cell wall remodeling and abscisic acid content associated genes. At ready-to-eat higher contents of flavonoids, abscisic acid and brassinosteroids were associated with color-softening synchronized avocados. In contrast, de-synchronized fruit revealed increases of jasmonic acid, salicylic acid and auxin levels.
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13
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Cassan O, Pimparé LL, Dubos C, Gojon A, Bach L, Lèbre S, Martin A. A gene regulatory network in Arabidopsis roots reveals features and regulators of the plant response to elevated CO 2. THE NEW PHYTOLOGIST 2023. [PMID: 36727308 DOI: 10.1111/nph.18788] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
The elevation of CO2 in the atmosphere increases plant biomass but decreases their mineral content. The genetic and molecular bases of these effects remain mostly unknown, in particular in the root system, which is responsible for plant nutrient uptake. To gain knowledge about the effect of elevated CO2 on plant growth and physiology, and to identify its regulatory in the roots, we analyzed genome expression in Arabidopsis roots through a combinatorial design with contrasted levels of CO2 , nitrate, and iron. We demonstrated that elevated CO2 has a modest effect on root genome expression under nutrient sufficiency, but by contrast leads to massive expression changes under nitrate or iron deficiencies. We demonstrated that elevated CO2 negatively targets nitrate and iron starvation modules at the transcriptional level, associated with a reduction in high-affinity nitrate uptake. Finally, we inferred a gene regulatory network governing the root response to elevated CO2 . This network allowed us to identify candidate transcription factors including MYB15, WOX11, and EDF3 which we experimentally validated for their role in the stimulation of growth by elevated CO2 . Our approach identified key features and regulators of the plant response to elevated CO2 , with the objective of developing crops resilient to climate change.
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Affiliation(s)
- Océane Cassan
- IPSiM, Univ. Montpellier, CNRS, INRAE, Institut Agro, 34000, Montpellier, France
| | - Léa-Lou Pimparé
- IPSiM, Univ. Montpellier, CNRS, INRAE, Institut Agro, 34000, Montpellier, France
| | - Christian Dubos
- IPSiM, Univ. Montpellier, CNRS, INRAE, Institut Agro, 34000, Montpellier, France
| | - Alain Gojon
- IPSiM, Univ. Montpellier, CNRS, INRAE, Institut Agro, 34000, Montpellier, France
| | - Liên Bach
- IPSiM, Univ. Montpellier, CNRS, INRAE, Institut Agro, 34000, Montpellier, France
| | - Sophie Lèbre
- IMAG, Univ. Montpellier, CNRS, 34000, Montpellier, France
- Université Paul-Valéry-Montpellier 3, 34000, Montpellier, France
| | - Antoine Martin
- IPSiM, Univ. Montpellier, CNRS, INRAE, Institut Agro, 34000, Montpellier, France
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14
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Vandepoele K, Kaufmann K. Characterization of Gene Regulatory Networks in Plants Using New Methods and Data Types. Methods Mol Biol 2023; 2698:1-11. [PMID: 37682465 DOI: 10.1007/978-1-0716-3354-0_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
A major question in plant biology is to understand how plant growth, development, and environmental responses are controlled and coordinated by the activities of regulatory factors. Gene regulatory network (GRN) analyses require integrated approaches that combine experimental approaches with computational analyses. A wide range of experimental approaches and tools are now available, such as targeted perturbation of gene activities, quantitative and cell-type specific measurements of dynamic gene activities, and systematic analysis of the molecular 'hard-wiring' of the systems. At the computational level, different tools and databases are available to study regulatory sequences, including intuitive visualizations to explore data-driven gene regulatory networks in different plant species. Furthermore, advanced data integration approaches have recently been developed to efficiently leverage complementary regulatory data types and learn context-specific networks.
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Affiliation(s)
- Klaas Vandepoele
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium.
- VIB-UGent Center for Plant Systems Biology, Ghent, Belgium.
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium.
| | - Kerstin Kaufmann
- Institute of Biology, Humboldt-Universitaet zu Berlin, Berlin, Germany
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15
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Lau V, Provart NJ. AGENT for Exploring and Analyzing Gene Regulatory Networks from Arabidopsis. Methods Mol Biol 2023; 2698:351-360. [PMID: 37682484 DOI: 10.1007/978-1-0716-3354-0_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Gene regulatory networks (GRNs) are important for determining how an organism develops and how it responds to external stimuli. In the case of Arabidopsis thaliana, several GRNs have been identified covering many important biological processes. We present AGENT, the Arabidopsis GEne Network Tool, for exploring and analyzing published GRNs. Using tools in AGENT, regulatory motifs such as feed-forward loops can be easily identified. Nodes with high centrality-and hence importance-can likewise be identified. Gene expression data can also be overlaid onto GRNs to help discover subnetworks acting in specific tissues or under certain conditions.
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Affiliation(s)
- Vincent Lau
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada
| | - Nicholas J Provart
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada.
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16
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Chau T, Timilsena P, Li S. Gene Regulatory Network Modeling Using Single-Cell Multi-Omics in Plants. Methods Mol Biol 2023; 2698:259-275. [PMID: 37682480 DOI: 10.1007/978-1-0716-3354-0_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Single-cell multi-omics technology can be applied to plant cells to characterize gene expression and open chromatin regions in individual cells. In this chapter, we describe a computational pipeline for the analysis of single-cell data to construct gene regulatory networks. The major steps of this pipeline include the following: (1) normalize and integrate scRNA-seq and scATAC-seq data (2) identify cluster maker genes (3) perform motif finding for selected marker genes, and (4) identify regulatory networks with machine learning. The pipeline has been tested using data from the model species Arabidopsis and is generally applicable to other plant and animal species to characterize regulatory networks using single-cell multi-omics data.
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Affiliation(s)
- Tran Chau
- Graduate Program in Genetics, Bioinformatics and Computational Biology (GBCB), Blacksburg, VA, USA
| | - Prakash Timilsena
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA
| | - Song Li
- Graduate Program in Genetics, Bioinformatics and Computational Biology (GBCB), Blacksburg, VA, USA.
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA.
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17
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Huang J, Katari MS, Juang CL, Coruzzi GM, Brooks MD. Building High-Confidence Gene Regulatory Networks by Integrating Validated TF-Target Gene Interactions Using ConnecTF. Methods Mol Biol 2023; 2698:195-220. [PMID: 37682477 DOI: 10.1007/978-1-0716-3354-0_13] [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] [Indexed: 09/09/2023]
Abstract
Many methods are now available to identify or predict the target genes of transcription factors (TFs) in plants. These include experimental approaches such as in vivo or in vitro TF-target gene-binding assays and various methods for identifying regulated targets in mutants, transgenics, or isolated plant cells. In addition, computational approaches are used to infer TF-target gene interactions from the regulatory elements or gene expression changes across treatments. While each of these approaches has now been applied to a large number of TFs from many species, each method has its own limitations which necessitates that multiple data types are integrated to build the most accurate representation of the gene regulatory networks operating in plants. To make the analyses of TF-target interaction datasets available to the broader research community, we have developed the ConnecTF web platform ( https://connectf.org/ ). In this chapter, we describe how ConnecTF can be used to integrate validated and predicted TF-target gene interactions in order to dissect the regulatory role of TFs in developmental and stress response pathways. Using as our examples KN1 and RA1, two well-characterized maize TFs involved in developing floral tissue, we demonstrate how ConnecTF can be used to (1) compare the target genes between TFs, (2) identify direct vs. indirect targets by combining TF-binding and TF-regulation datasets, (3) chart and visualize network paths between TFs and their downstream targets, and (4) prune inferred user networks for high-confidence predicted interactions using validated TF-target gene data. Finally, we provide instructions for setting up a private version of ConnecTF that enables research groups to store and analyze their own TF-target gene interaction datasets.
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Affiliation(s)
- Ji Huang
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Manpreet S Katari
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Che-Lun Juang
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Gloria M Coruzzi
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Matthew D Brooks
- Global Change and Photosynthesis Research Unit, USDA ARS, Urbana, IL, USA.
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18
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Brooks MD, Reed KM, Krouk G, Coruzzi GM, Bargmann BOR. The TARGET System: Rapid Identification of Direct Targets of Transcription Factors by Gene Regulation in Plant Cells. Methods Mol Biol 2023; 2594:1-12. [PMID: 36264484 DOI: 10.1007/978-1-0716-2815-7_1] [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] [Indexed: 06/16/2023]
Abstract
The TARGET system allows for the rapid identification of direct regulated gene targets of transcription factors (TFs). It employs the transient transformation of plant protoplasts with inducible nuclear entry of the TF and subsequent transcriptomic and/or ChIP-seq analysis. The ability to separate direct TF-target gene regulatory interactions from indirect downstream responses and the significantly shorter amount of time required to perform the assay, compared to the generation of transgenics, make this plant cell-based approach a valuable tool for a higher throughput approach to identify the genome-wide targets of multiple TFs, to build validated transcriptional networks in plants. Here, we describe the use of the TARGET system in Arabidopsis seedling root protoplasts to map the gene regulatory network downstream of transcription factors-of-interest.
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Affiliation(s)
- Matthew D Brooks
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA
- USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA
| | - Kelsey M Reed
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Gabriel Krouk
- BPMP, Univ Montpellier, CNRS, INRA, SupAgro, Montpellier, France
| | - Gloria M Coruzzi
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA
| | - Bastiaan O R Bargmann
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
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19
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Núñez-Lillo G, Pérez-Reyes W, Riveros A, Lillo-Carmona V, Rothkegel K, Álvarez JM, Blanco-Herrera F, Pedreschi R, Campos-Vargas R, Meneses C. Transcriptome and Gene Regulatory Network Analyses Reveal New Transcription Factors in Mature Fruit Associated with Harvest Date in Prunus persica. PLANTS (BASEL, SWITZERLAND) 2022; 11:3473. [PMID: 36559585 PMCID: PMC9783919 DOI: 10.3390/plants11243473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 11/24/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Harvest date is a critical parameter for producers and consumers regarding agro-industrial performance. It involves a pleiotropic effect controlling the development of other fruit quality traits through finely controlling regulatory mechanisms. Fruit ripening is a process in which various signals and biological events co-occur and are regulated by hormone signaling that produces the accumulation/degradation of multiple compounds. However, the regulatory mechanisms that control the hormone signaling involved in fruit development and ripening are still unclear. To investigate the issue, we used individuals with early, middle and late harvest dates from a peach segregating population to identify regulatory candidate genes controlling fruit quality traits at the harvest stage and validate them in contrasting peach varieties for this trait. We identified 467 and 654 differentially expressed genes for early and late harvest through a transcriptomic approach. In addition, using the Arabidopsis DAP-seq database and network analysis, six transcription factors were selected. Our results suggest significant hormonal balance and cell wall composition/structure differences between early and late harvest samples. Thus, we propose that higher expression levels of the transcription factors HB7, ERF017 and WRKY70 in early harvest individuals would induce the expression of genes associated with the jasmonic acid pathway, photosynthesis and gibberellins inhibition. While on the other hand, the high expression levels of LHY, CDF3 and NAC083 in late harvest individuals would promote the induction of genes associated with abscisic acid biosynthesis, auxins and cell wall remodeling.
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Affiliation(s)
- Gerardo Núñez-Lillo
- Escuela de Agronomía, Facultad de Ciencias Agronómicas y de los Alimentos, Pontificia Universidad Católica de Valparaíso, Quillota 2260000, Chile
| | - Wellasmin Pérez-Reyes
- Centro de Biotecnología Vegetal, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago 8370186, Chile
| | - Anibal Riveros
- Departamento de Fruticultura y Enología, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
- ANID-Millennium Science Initiative Program, Millennium Nucleus for the Development of Super Adaptable Plants (MN-SAP), Santiago 8331150, Chile
| | - Victoria Lillo-Carmona
- Departamento de Fruticultura y Enología, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | - Karin Rothkegel
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | - José Miguel Álvarez
- Centro de Biotecnología Vegetal, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago 8370186, Chile
| | - Francisca Blanco-Herrera
- Centro de Biotecnología Vegetal, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago 8370186, Chile
- ANID-Millennium Science Initiative Program, Millennium Nucleus for the Development of Super Adaptable Plants (MN-SAP), Santiago 8331150, Chile
| | - Romina Pedreschi
- Escuela de Agronomía, Facultad de Ciencias Agronómicas y de los Alimentos, Pontificia Universidad Católica de Valparaíso, Quillota 2260000, Chile
- Millennium Institute Center for Genome Regulation (CRG), Santiago 8331150, Chile
| | - Reinaldo Campos-Vargas
- Departamento de Producción Agrícola, Facultad de Ciencias Agronómicas, Universidad de Chile, Santiago 8820808, Chile
| | - Claudio Meneses
- Departamento de Fruticultura y Enología, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
- ANID-Millennium Science Initiative Program, Millennium Nucleus for the Development of Super Adaptable Plants (MN-SAP), Santiago 8331150, Chile
- Millennium Institute Center for Genome Regulation (CRG), Santiago 8331150, Chile
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20
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Tu M, Zeng J, Zhang J, Fan G, Song G. Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics. FRONTIERS IN PLANT SCIENCE 2022; 13:1038109. [PMID: 36570898 PMCID: PMC9773216 DOI: 10.3389/fpls.2022.1038109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
RNA-seq has become a state-of-the-art technique for transcriptomic studies. Advances in both RNA-seq techniques and the corresponding analysis tools and pipelines have unprecedently shaped our understanding in almost every aspects of plant sciences. Notably, the integration of huge amount of RNA-seq with other omic data sets in the model plants and major crop species have facilitated plant regulomics, while the RNA-seq analysis has still been primarily used for differential expression analysis in many less-studied plant species. To unleash the analytical power of RNA-seq in plant species, especially less-studied species and biomass crops, we summarize recent achievements of RNA-seq analysis in the major plant species and representative tools in the four types of application: (1) transcriptome assembly, (2) construction of expression atlas, (3) network analysis, and (4) structural alteration. We emphasize the importance of expression atlas, coexpression networks and predictions of gene regulatory relationships in moving plant transcriptomes toward regulomics, an omic view of genome-wide transcription regulation. We highlight what can be achieved in plant research with RNA-seq by introducing a list of representative RNA-seq analysis tools and resources that are developed for certain minor species or suitable for the analysis without species limitation. In summary, we provide an updated digest on RNA-seq tools, resources and the diverse applications for plant research, and our perspective on the power and challenges of short-read RNA-seq analysis from a regulomic point view. A full utilization of these fruitful RNA-seq resources will promote plant omic research to a higher level, especially in those less studied species.
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Affiliation(s)
- Min Tu
- School of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan, China
| | - Jian Zeng
- Guangdong Provincial Key Laboratory of Utilization and Conservation of Food and Medicinal Resources in Northern Region, Shaoguan University, Shaoguan, Guangdong, China
| | - Juntao Zhang
- School of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan, China
| | - Guozhi Fan
- School of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan, China
| | - Guangsen Song
- School of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan, China
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21
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Wang G, Li X, Shen W, Li MW, Huang M, Zhang J, Li H. The chromatin accessibility landscape of pistils and anthers in rice. PLANT PHYSIOLOGY 2022; 190:2797-2811. [PMID: 36149297 PMCID: PMC9706442 DOI: 10.1093/plphys/kiac448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 09/04/2022] [Indexed: 06/16/2023]
Abstract
Transcription activation is tightly associated with the openness of chromatin and allows direct contact between transcriptional regulators and their targeted DNA for gene expression. However, there are limited studies on the annotation of open chromatin regions (OCRs) in rice (Oryza sativa), especially those in reproductive organs. Here, we characterized OCRs in rice pistils and anthers with an assay for transposase-accessible chromatin using sequencing. Despite a large overlap, we found more OCRs in pistils than in anthers. These OCRs were enriched in gene transcription start sites (TSSs) and showed tight associations with gene expression. Transcription factor (TF) binding motifs were enriched at these OCRs as validated by TF chromatin immunoprecipitation followed by sequencing. Pistil-specific OCRs provided potential regulatory networks by binding directly to the targets, indicating that pistil-specific OCRs may be indicators of cis-regulatory elements in regulating pistil development, which are absent in anthers. We also found that open chromatin of pistils and anthers responded differently to low temperature (LT). These data offer a comprehensive overview of OCRs regulating reproductive organ development and LT responses in rice.
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Affiliation(s)
- Guanqun Wang
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518000, China
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Shatin 999077, Hong Kong
| | - Xiaozheng Li
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518000, China
| | - Wei Shen
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Shatin 999077, Hong Kong
| | - Man-Wah Li
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Shatin 999077, Hong Kong
| | - Mingkun Huang
- Lushan Botanical Garden Jiangxi Province, Chinese Academy of Sciences, Jiujiang 332900, China
| | - Jianhua Zhang
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Shatin 999077, Hong Kong
- Department of Biology, Hong Kong Baptist University, Kowloon 999077, Hong Kong
| | - Haoxuan Li
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Shatin 999077, Hong Kong
- Department of Biology, Hong Kong Baptist University, Kowloon 999077, Hong Kong
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22
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Shanks CM, Huang J, Cheng CY, Shih HJS, Brooks MD, Alvarez JM, Araus V, Swift J, Henry A, Coruzzi GM. Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice. FRONTIERS IN PLANT SCIENCE 2022; 13:1006044. [PMID: 36507422 PMCID: PMC9732682 DOI: 10.3389/fpls.2022.1006044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/01/2022] [Indexed: 05/03/2023]
Abstract
Nitrogen (N) and Water (W) - two resources critical for crop productivity - are becoming increasingly limited in soils globally. To address this issue, we aim to uncover the gene regulatory networks (GRNs) that regulate nitrogen use efficiency (NUE) - as a function of water availability - in Oryza sativa, a staple for 3.5 billion people. In this study, we infer and validate GRNs that correlate with rice NUE phenotypes affected by N-by-W availability in the field. We did this by exploiting RNA-seq and crop phenotype data from 19 rice varieties grown in a 2x2 N-by-W matrix in the field. First, to identify gene-to-NUE field phenotypes, we analyzed these datasets using weighted gene co-expression network analysis (WGCNA). This identified two network modules ("skyblue" & "grey60") highly correlated with NUE grain yield (NUEg). Next, we focused on 90 TFs contained in these two NUEg modules and predicted their genome-wide targets using the N-and/or-W response datasets using a random forest network inference approach (GENIE3). Next, to validate the GENIE3 TF→target gene predictions, we performed Precision/Recall Analysis (AUPR) using nine datasets for three TFs validated in planta. This analysis sets a precision threshold of 0.31, used to "prune" the GENIE3 network for high-confidence TF→target gene edges, comprising 88 TFs and 5,716 N-and/or-W response genes. Next, we ranked these 88 TFs based on their significant influence on NUEg target genes responsive to N and/or W signaling. This resulted in a list of 18 prioritized TFs that regulate 551 NUEg target genes responsive to N and/or W signals. We validated the direct regulated targets of two of these candidate NUEg TFs in a plant cell-based TF assay called TARGET, for which we also had in planta data for comparison. Gene ontology analysis revealed that 6/18 NUEg TFs - OsbZIP23 (LOC_Os02g52780), Oshox22 (LOC_Os04g45810), LOB39 (LOC_Os03g41330), Oshox13 (LOC_Os03g08960), LOC_Os11g38870, and LOC_Os06g14670 - regulate genes annotated for N and/or W signaling. Our results show that OsbZIP23 and Oshox22, known regulators of drought tolerance, also coordinate W-responses with NUEg. This validated network can aid in developing/breeding rice with improved yield on marginal, low N-input, drought-prone soils.
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Affiliation(s)
- Carly M. Shanks
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, United States
| | - Ji Huang
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, United States
| | - Chia-Yi Cheng
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, United States
- Department of Life Science, College of Life Science, National Taiwan University, Taipei, Taiwan
| | - Hung-Jui S. Shih
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, United States
| | - Matthew D. Brooks
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, United States
- Global Change and Photosynthesis Research Unit, United States Department of Agriculture (USDA) Agricultural Research Service (ARS), Urbana, IL, United States
| | - José M. Alvarez
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, United States
- Centro de Biotecnología Vegetal, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile
- Agencia Nacional de Investigación y Desarrollo–Millennium Science Initiative Program, Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - Viviana Araus
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, United States
- Agencia Nacional de Investigación y Desarrollo–Millennium Science Initiative Program, Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- Departamento de Genética Molecular y Microbiología, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Joseph Swift
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, United States
- Plant Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, United States
| | - Amelia Henry
- Rice Breeding Innovations Platform, International Rice Research Institute, Los Baños, Laguna, Philippines
| | - Gloria M. Coruzzi
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, United States
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23
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Chakraborty S, Valdés-López O, Stonoha-Arther C, Ané JM. Transcription Factors Controlling the Rhizobium-Legume Symbiosis: Integrating Infection, Organogenesis and the Abiotic Environment. PLANT & CELL PHYSIOLOGY 2022; 63:1326-1343. [PMID: 35552446 DOI: 10.1093/pcp/pcac063] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/03/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Legume roots engage in a symbiotic relationship with rhizobia, leading to the development of nitrogen-fixing nodules. Nodule development is a sophisticated process and is under the tight regulation of the plant. The symbiosis initiates with a signal exchange between the two partners, followed by the development of a new organ colonized by rhizobia. Over two decades of study have shed light on the transcriptional regulation of rhizobium-legume symbiosis. A large number of transcription factors (TFs) have been implicated in one or more stages of this symbiosis. Legumes must monitor nodule development amidst a dynamic physical environment. Some environmental factors are conducive to nodulation, whereas others are stressful. The modulation of rhizobium-legume symbiosis by the abiotic environment adds another layer of complexity and is also transcriptionally regulated. Several symbiotic TFs act as integrators between symbiosis and the response to the abiotic environment. In this review, we trace the role of various TFs involved in rhizobium-legume symbiosis along its developmental route and highlight the ones that also act as communicators between this symbiosis and the response to the abiotic environment. Finally, we discuss contemporary approaches to study TF-target interactions in plants and probe their potential utility in the field of rhizobium-legume symbiosis.
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Affiliation(s)
- Sanhita Chakraborty
- Department of Bacteriology, University of Wisconsin, Microbial Sciences Building, 1550 Linden Dr, Madison, WI 53706, USA
| | - Oswaldo Valdés-López
- Laboratorio de Genómica Funcional de Leguminosas, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla, Estado de México 54090, México
| | - Christina Stonoha-Arther
- Department of Bacteriology, University of Wisconsin, Microbial Sciences Building, 1550 Linden Dr, Madison, WI 53706, USA
| | - Jean-Michel Ané
- Department of Bacteriology, University of Wisconsin, Microbial Sciences Building, 1550 Linden Dr, Madison, WI 53706, USA
- Department of Agronomy, University of Wisconsin, 1575 Linden Dr, Madison, WI 53706, USA
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24
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Ruengsrichaiya B, Nukoolkit C, Kalapanulak S, Saithong T. Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach. FRONTIERS IN PLANT SCIENCE 2022; 13:970018. [PMID: 36082286 PMCID: PMC9445498 DOI: 10.3389/fpls.2022.970018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/01/2022] [Indexed: 06/15/2023]
Abstract
As a sessile organism, plants hold elaborate transcriptional regulatory systems that allow them to adapt to variable surrounding environments. Current understanding of plant regulatory mechanisms is greatly constrained by limited knowledge of transcription factor (TF)-DNA interactions. To mitigate this problem, a Plant-DTI predictor (Plant DBD-TFBS Interaction) was developed here as the first machine-learning model that covered the largest experimental datasets of 30 plant TF families, including 7 plant-specific DNA binding domain (DBD) types, and their transcription factor binding sites (TFBSs). Plant-DTI introduced a novel TFBS feature construction, called TFBS base-preference, which enhanced the specificity of TFBS to DBD types. The proposed model showed better predictive performance with the TFBS base-preference than the simple binary representation. Plant-DTI was validated with 22 independent ChIP-seq datasets. It accurately predicted the measured DBD-TFBS pairs along with their TFBS motifs, and effectively predicted interactions of other TFs containing similar DBD types. Comparing to the existing state-of-art methods, Plant-DTI prediction showed a figure of merit in sensitivity and specificity with respect to the position weight matrix (PWM) and TSPTFBS methods. Finally, the proposed Plant-DTI model helped to fill the knowledge gap in the regulatory mechanisms of the cassava sucrose synthase 1 gene (MeSUS1). Plant-DTI predicted MeERF72 as a regulator of MeSUS1 in consistence with the yeast one-hybrid (Y1H) experiment. Taken together, Plant-DTI would help facilitate the prediction of TF-TFBS and TF-target gene (TG) interactions, thereby accelerating the study of transcriptional regulatory systems in plant species.
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Affiliation(s)
- Bhukrit Ruengsrichaiya
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut’s University of Technology Thonburi (Bang KhunThian), Bangkok, Thailand
| | - Chakarida Nukoolkit
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut’s University of Technology Thonburi (Bang KhunThian), Bangkok, Thailand
- School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
| | - Saowalak Kalapanulak
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut’s University of Technology Thonburi (Bang KhunThian), Bangkok, Thailand
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut’s University of Technology Thonburi (Bang KhunThian), Bangkok, Thailand
| | - Treenut Saithong
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology and School of Information Technology, King Mongkut’s University of Technology Thonburi (Bang KhunThian), Bangkok, Thailand
- Center for Agricultural Systems Biology, Systems Biology and Bioinformatics Research Group, Pilot Plant Development and Training Institute, King Mongkut’s University of Technology Thonburi (Bang KhunThian), Bangkok, Thailand
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25
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Tripathi RK, Wilkins O. Single cell gene regulatory networks in plants: Opportunities for enhancing climate change stress resilience. PLANT, CELL & ENVIRONMENT 2021; 44:2006-2017. [PMID: 33522607 PMCID: PMC8359182 DOI: 10.1111/pce.14012] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 01/21/2021] [Accepted: 01/22/2021] [Indexed: 05/05/2023]
Abstract
Global warming poses major challenges for plant survival and agricultural productivity. Thus, efforts to enhance stress resilience in plants are key strategies for protecting food security. Gene regulatory networks (GRNs) are a critical mechanism conferring stress resilience. Until recently, predicting GRNs of the individual cells that make up plants and other multicellular organisms was impeded by aggregate population scale measurements of transcriptome and other genome-scale features. With the advancement of high-throughput single cell RNA-seq and other single cell assays, learning GRNs for individual cells is now possible, in principle. In this article, we report on recent advances in experimental and analytical methodologies for single cell sequencing assays especially as they have been applied to the study of plants. We highlight recent advances and ongoing challenges for scGRN prediction, and finally, we highlight the opportunity to use scGRN discovery for studying and ultimately enhancing abiotic stress resilience in plants.
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Affiliation(s)
- Rajiv K. Tripathi
- Department of Biological SciencesUniversity of ManitobaWinnipegManitobaCanada
| | - Olivia Wilkins
- Department of Biological SciencesUniversity of ManitobaWinnipegManitobaCanada
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26
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Alvarez JM, Brooks MD, Swift J, Coruzzi GM. Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks. ANNUAL REVIEW OF PLANT BIOLOGY 2021; 72:105-131. [PMID: 33667112 PMCID: PMC9312366 DOI: 10.1146/annurev-arplant-081320-090914] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets-at both the local and genome-wide levels-and how they are used to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology.
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Affiliation(s)
- Jose M Alvarez
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
- ANID-Millennium Science Initiative Program-Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - Matthew D Brooks
- Global Change and Photosynthesis Research Unit, US Department of Agriculture Agricultural Research Service, Urbana, Illinois 61801, USA
| | - Joseph Swift
- Salk Institute for Biological Studies, La Jolla, California 92037, USA
| | - Gloria M Coruzzi
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA;
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27
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Inferring and analyzing gene regulatory networks from multi-factorial expression data: a complete and interactive suite. BMC Genomics 2021; 22:387. [PMID: 34039282 PMCID: PMC8152307 DOI: 10.1186/s12864-021-07659-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/28/2021] [Indexed: 11/29/2022] Open
Abstract
Background High-throughput transcriptomic datasets are often examined to discover new actors and regulators of a biological response. To this end, graphical interfaces have been developed and allow a broad range of users to conduct standard analyses from RNA-seq data, even with little programming experience. Although existing solutions usually provide adequate procedures for normalization, exploration or differential expression, more advanced features, such as gene clustering or regulatory network inference, often miss or do not reflect current state of the art methodologies. Results We developed here a user interface called DIANE (Dashboard for the Inference and Analysis of Networks from Expression data) designed to harness the potential of multi-factorial expression datasets from any organisms through a precise set of methods. DIANE interactive workflow provides normalization, dimensionality reduction, differential expression and ontology enrichment. Gene clustering can be performed and explored via configurable Mixture Models, and Random Forests are used to infer gene regulatory networks. DIANE also includes a novel procedure to assess the statistical significance of regulator-target influence measures based on permutations for Random Forest importance metrics. All along the pipeline, session reports and results can be downloaded to ensure clear and reproducible analyses. Conclusions We demonstrate the value and the benefits of DIANE using a recently published data set describing the transcriptional response of Arabidopsis thaliana under the combination of temperature, drought and salinity perturbations. We show that DIANE can intuitively carry out informative exploration and statistical procedures with RNA-Seq data, perform model based gene expression profiles clustering and go further into gene network reconstruction, providing relevant candidate genes or signalling pathways to explore. DIANE is available as a web service (https://diane.bpmp.inrae.fr), or can be installed and locally launched as a complete R package. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-021-07659-2).
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