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Borowsky AT, Bailey-Serres J. Rewiring gene circuitry for plant improvement. Nat Genet 2024:10.1038/s41588-024-01806-7. [PMID: 39075207 DOI: 10.1038/s41588-024-01806-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 05/17/2024] [Indexed: 07/31/2024]
Abstract
Aspirations for high crop growth and yield, nutritional quality and bioproduction of materials are challenged by climate change and limited adoption of new technologies. Here, we review recent advances in approaches to profile and model gene regulatory activity over developmental and response time in specific cells, which have revealed the basis of variation in plant phenotypes: both redeployment of key regulators to new contexts and their repurposing to control different slates of genes. New synthetic biology tools allow tunable, spatiotemporal regulation of transgenes, while recent gene-editing technologies enable manipulation of the regulation of native genes. Ultimately, understanding how gene circuitry is wired to control form and function across varied plant species, combined with advanced technology to rewire that circuitry, will unlock solutions to our greatest challenges in agriculture, energy and the environment.
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Affiliation(s)
- Alexander T Borowsky
- Center for Plant Cell Biology, Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
| | - Julia Bailey-Serres
- Center for Plant Cell Biology, Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA.
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2
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Brooks MD, Szeto RC. Biological nitrogen fixation maintains carbon/nitrogen balance and photosynthesis at elevated CO 2. PLANT, CELL & ENVIRONMENT 2024; 47:2178-2191. [PMID: 38481026 DOI: 10.1111/pce.14873] [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: 11/04/2023] [Revised: 01/17/2024] [Accepted: 02/22/2024] [Indexed: 04/30/2024]
Abstract
Understanding crop responses to elevated CO2 is necessary to meet increasing agricultural demands. Crops may not achieve maximum potential yields at high CO2 due to photosynthetic downregulation, often associated with nitrogen limitation. Legumes have been proposed to have an advantage at elevated CO2 due to their ability to exchange carbon for nitrogen. Here, the effects of biological nitrogen fixation (BNF) on the physiological and gene expression responses to elevated CO2 were examined at multiple nitrogen levels by comparing alfalfa mutants incapable of nitrogen fixation to wild-type. Elemental analysis revealed a role for BNF in maintaining shoot carbon/nitrogen (C/N) balance under all nitrogen treatments at elevated CO2, whereas the effect of BNF on biomass was only observed at elevated CO2 and the lowest nitrogen dose. Lower photosynthetic rates at were associated with the imbalance in shoot C/N. Genome-wide transcriptional responses were used to identify carbon and nitrogen metabolism genes underlying the traits. Transcription factors important to C/N signalling were identified from inferred regulatory networks. This work supports the hypothesis that maintenance of C/N homoeostasis at elevated CO2 can be achieved in plants capable of BNF and revealed important regulators in the underlying networks including an alfalfa (Golden2-like) GLK ortholog.
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Affiliation(s)
- Matthew D Brooks
- Global Change and Photosynthesis Research Unit, USDA ARS, Urbana, Illinois, USA
| | - Ronnia C Szeto
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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3
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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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4
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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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5
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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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6
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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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7
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Singh KS, van der Hooft JJJ, van Wees SCM, Medema MH. Integrative omics approaches for biosynthetic pathway discovery in plants. Nat Prod Rep 2022; 39:1876-1896. [PMID: 35997060 PMCID: PMC9491492 DOI: 10.1039/d2np00032f] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Indexed: 12/13/2022]
Abstract
Covering: up to 2022With the emergence of large amounts of omics data, computational approaches for the identification of plant natural product biosynthetic pathways and their genetic regulation have become increasingly important. While genomes provide clues regarding functional associations between genes based on gene clustering, metabolome mining provides a foundational technology to chart natural product structural diversity in plants, and transcriptomics has been successfully used to identify new members of their biosynthetic pathways based on coexpression. Thus far, most approaches utilizing transcriptomics and metabolomics have been targeted towards specific pathways and use one type of omics data at a time. Recent technological advances now provide new opportunities for integration of multiple omics types and untargeted pathway discovery. Here, we review advances in plant biosynthetic pathway discovery using genomics, transcriptomics, and metabolomics, as well as recent efforts towards omics integration. We highlight how transcriptomics and metabolomics provide complementary information to link genes to metabolites, by associating temporal and spatial gene expression levels with metabolite abundance levels across samples, and by matching mass-spectral features to enzyme families. Furthermore, we suggest that elucidation of gene regulatory networks using time-series data may prove useful for efforts to unwire the complexities of biosynthetic pathway components based on regulatory interactions and events.
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Affiliation(s)
- Kumar Saurabh Singh
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
- Plant-Microbe Interactions, Institute of Environmental Biology, Utrecht University, The Netherlands.
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Saskia C M van Wees
- Plant-Microbe Interactions, Institute of Environmental Biology, Utrecht University, The Netherlands.
| | - Marnix H Medema
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
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8
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Swift J, Greenham K, Ecker JR, Coruzzi GM, McClung CR. The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 109:764-778. [PMID: 34797944 PMCID: PMC9215356 DOI: 10.1111/tpj.15589] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 11/10/2021] [Accepted: 11/15/2021] [Indexed: 05/26/2023]
Abstract
As sessile organisms, plants are finely tuned to respond dynamically to developmental, circadian and environmental cues. Genome-wide studies investigating these types of cues have uncovered the intrinsically different ways they can impact gene expression over time. Recent advances in single-cell sequencing and time-based bioinformatic algorithms are now beginning to reveal the dynamics of these time-based responses within individual cells and plant tissues. Here, we review what these techniques have revealed about the spatiotemporal nature of gene regulation, paying particular attention to the three distinct ways in which plant tissues are time sensitive. (i) First, we discuss how studying plant cell identity can reveal developmental trajectories hidden in pseudotime. (ii) Next, we present evidence that indicates that plant cell types keep their own local time through tissue-specific regulation of the circadian clock. (iii) Finally, we review what determines the speed of environmental signaling responses, and how they can be contingent on developmental and circadian time. By these means, this review sheds light on how these different scales of time-based responses can act with tissue and cell-type specificity to elicit changes in whole plant systems.
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Affiliation(s)
- Joseph Swift
- Plant Biology Laboratory, The Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA
| | - Kathleen Greenham
- Department of Plant and Microbial Biology, University of Minnesota, St Paul, MN 55108, USA
| | - Joseph R. Ecker
- Plant Biology Laboratory, The Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA
| | - Gloria M. Coruzzi
- Department of Biology, Center for Genomics and Systems Biology, New York University, NY, USA
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9
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OUP accepted manuscript. Brief Funct Genomics 2022; 21:243-269. [DOI: 10.1093/bfgp/elac007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/14/2022] Open
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10
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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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11
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Brooks MD, Juang CL, Katari MS, Alvarez JM, Pasquino A, Shih HJ, Huang J, Shanks C, Cirrone J, Coruzzi GM. ConnecTF: A platform to integrate transcription factor-gene interactions and validate regulatory networks. PLANT PHYSIOLOGY 2021; 185:49-66. [PMID: 33631799 PMCID: PMC8133578 DOI: 10.1093/plphys/kiaa012] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 10/27/2020] [Indexed: 05/08/2023]
Abstract
Deciphering gene regulatory networks (GRNs) is both a promise and challenge of systems biology. The promise lies in identifying key transcription factors (TFs) that enable an organism to react to changes in its environment. The challenge lies in validating GRNs that involve hundreds of TFs with hundreds of thousands of interactions with their genome-wide targets experimentally determined by high-throughput sequencing. To address this challenge, we developed ConnecTF, a species-independent, web-based platform that integrates genome-wide studies of TF-target binding, TF-target regulation, and other TF-centric omic datasets and uses these to build and refine validated or inferred GRNs. We demonstrate the functionality of ConnecTF by showing how integration within and across TF-target datasets uncovers biological insights. Case study 1 uses integration of TF-target gene regulation and binding datasets to uncover TF mode-of-action and identify potential TF partners for 14 TFs in abscisic acid signaling. Case study 2 demonstrates how genome-wide TF-target data and automated functions in ConnecTF are used in precision/recall analysis and pruning of an inferred GRN for nitrogen signaling. Case study 3 uses ConnecTF to chart a network path from NLP7, a master TF in nitrogen signaling, to direct secondary TF2s and to its indirect targets in a Network Walking approach. The public version of ConnecTF (https://ConnecTF.org) contains 3,738,278 TF-target interactions for 423 TFs in Arabidopsis, 839,210 TF-target interactions for 139 TFs in maize (Zea mays), and 293,094 TF-target interactions for 26 TFs in rice (Oryza sativa). The database and tools in ConnecTF will advance the exploration of GRNs in plant systems biology applications for model and crop species.
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Affiliation(s)
- Matthew D Brooks
- Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA
- USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA
| | - Che-Lun Juang
- Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA
| | - Manpreet Singh Katari
- Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA
| | - José M Alvarez
- Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
- Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - Angelo Pasquino
- Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA
| | - Hung-Jui Shih
- Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA
| | - Ji Huang
- Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA
| | - Carly Shanks
- Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA
| | - Jacopo Cirrone
- Courant Institute for Mathematical Sciences, Department of Computer Science, New York University NY, USA
| | - Gloria M Coruzzi
- Center for Genomics and Systems Biology, Department of Biology, New York University, NY, USA
- Author for communication: (G.C.)
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