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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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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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Meyer RC, Weigelt-Fischer K, Tschiersch H, Topali G, Altschmied L, Heuermann MC, Knoch D, Kuhlmann M, Zhao Y, Altmann T. Dynamic growth QTL action in diverse light environments: characterization of light regime-specific and stable QTL in Arabidopsis. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5341-5362. [PMID: 37306093 DOI: 10.1093/jxb/erad222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 06/10/2023] [Indexed: 06/13/2023]
Abstract
Plant growth is a complex process affected by a multitude of genetic and environmental factors and their interactions. To identify genetic factors influencing plant performance under different environmental conditions, vegetative growth was assessed in Arabidopsis thaliana cultivated under constant or fluctuating light intensities, using high-throughput phenotyping and genome-wide association studies. Daily automated non-invasive phenotyping of a collection of 382 Arabidopsis accessions provided growth data during developmental progression under different light regimes at high temporal resolution. Quantitative trait loci (QTL) for projected leaf area, relative growth rate, and PSII operating efficiency detected under the two light regimes were predominantly condition-specific and displayed distinct temporal activity patterns, with active phases ranging from 2 d to 9 d. Eighteen protein-coding genes and one miRNA gene were identified as potential candidate genes at 10 QTL regions consistently found under both light regimes. Expression patterns of three candidate genes affecting projected leaf area were analysed in time-series experiments in accessions with contrasting vegetative leaf growth. These observations highlight the importance of considering both environmental and temporal patterns of QTL/allele actions and emphasize the need for detailed time-resolved analyses under diverse well-defined environmental conditions to effectively unravel the complex and stage-specific contributions of genes affecting plant growth processes.
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Affiliation(s)
- Rhonda C Meyer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Kathleen Weigelt-Fischer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Henning Tschiersch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Georgia Topali
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Lothar Altschmied
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Marc C Heuermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Dominic Knoch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Markus Kuhlmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Breeding Research, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
| | - Thomas Altmann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Department of Molecular Genetics, OT Gatersleben, Corrensstraße 3, D-06466 Seeland, Germany
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Kelly J, Berzuini C, Keavney B, Tomaszewski M, Guo H. A review of causal discovery methods for molecular network analysis. Mol Genet Genomic Med 2022; 10:e2055. [PMID: 36087049 PMCID: PMC9544222 DOI: 10.1002/mgg3.2055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/12/2022] [Accepted: 08/18/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions. METHODS We have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade. RESULTS In this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area. CONCLUSION Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.
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Affiliation(s)
- Jack Kelly
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
| | - Carlo Berzuini
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
| | - Bernard Keavney
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
- Division of Cardiology and Manchester Academic Health Science CentreManchester University NHS Foundation TrustManchesterUK
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
- Manchester Heart Centre and Manchester Academic Health Science CentreManchester University NHS Foundation TrustManchesterUK
| | - Hui Guo
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
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Lardon R, Trinh HK, Xu X, Vu LD, Van De Cotte B, Pernisová M, Vanneste S, De Smet I, Geelen D. Histidine kinase inhibitors impair shoot regeneration in Arabidopsis thaliana via cytokinin signaling and SAM patterning determinants. FRONTIERS IN PLANT SCIENCE 2022; 13:894208. [PMID: 36684719 PMCID: PMC9847488 DOI: 10.3389/fpls.2022.894208] [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: 03/11/2022] [Accepted: 07/27/2022] [Indexed: 06/17/2023]
Abstract
Reversible protein phosphorylation is a post-translational modification involved in virtually all plant processes, as it mediates protein activity and signal transduction. Here, we probe dynamic protein phosphorylation during de novo shoot organogenesis in Arabidopsis thaliana. We find that application of three kinase inhibitors in various time intervals has different effects on root explants. Short exposures to the putative histidine (His) kinase inhibitor TCSA during the initial days on shoot induction medium (SIM) are detrimental for regeneration in seven natural accessions. Investigation of cytokinin signaling mutants, as well as reporter lines for hormone responses and shoot markers, suggests that TCSA impedes cytokinin signal transduction via AHK3, AHK4, AHP3, and AHP5. A mass spectrometry-based phosphoproteome analysis further reveals profound deregulation of Ser/Thr/Tyr phosphoproteins regulating protein modification, transcription, vesicle trafficking, organ morphogenesis, and cation transport. Among TCSA-responsive factors are prior candidates with a role in shoot apical meristem patterning, such as AGO1, BAM1, PLL5, FIP37, TOP1ALPHA, and RBR1, as well as proteins involved in polar auxin transport (e.g., PIN1) and brassinosteroid signaling (e.g., BIN2). Putative novel regeneration determinants regulated by TCSA include RD2, AT1G52780, PVA11, and AVT1C, while NAIP2, OPS, ARR1, QKY, and aquaporins exhibit differential phospholevels on control SIM. LC-MS/MS data are available via ProteomeXchange with identifier PXD030754.
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Affiliation(s)
- Robin Lardon
- HortiCell, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Hoang Khai Trinh
- HortiCell, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
- Biotechnology Research and Development Institute, Can Tho University, Can Tho, Vietnam
| | - Xiangyu Xu
- Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, VIB, Ghent, Belgium
| | - Lam Dai Vu
- Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, VIB, Ghent, Belgium
| | - Brigitte Van De Cotte
- Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, VIB, Ghent, Belgium
| | - Markéta Pernisová
- Mendel Centre for Plant Genomics and Proteomics, Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
- Laboratory of Functional Genomics and Proteomics, Faculty of Science, National Centre for Biomolecular Research, Masaryk University, Brno, Czechia
| | - Steffen Vanneste
- HortiCell, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, VIB, Ghent, Belgium
- Lab of Plant Growth Analysis, Ghent University Global Campus, Incheon, South Korea
| | - Ive De Smet
- Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, VIB, Ghent, Belgium
| | - Danny Geelen
- HortiCell, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
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Deshpande A, Chu LF, Stewart R, Gitter A. Network inference with Granger causality ensembles on single-cell transcriptomics. Cell Rep 2022; 38:110333. [PMID: 35139376 PMCID: PMC9093087 DOI: 10.1016/j.celrep.2022.110333] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 02/19/2021] [Accepted: 01/12/2022] [Indexed: 12/20/2022] Open
Abstract
Cellular gene expression changes throughout a dynamic biological process, such as differentiation. Pseudotimes estimate cells' progress along a dynamic process based on their individual gene expression states. Ordering the expression data by pseudotime provides information about the underlying regulator-gene interactions. Because the pseudotime distribution is not uniform, many standard mathematical methods are inapplicable for analyzing the ordered gene expression states. Here we present single-cell inference of networks using Granger ensembles (SINGE), an algorithm for gene regulatory network inference from ordered single-cell gene expression data. SINGE uses kernel-based Granger causality regression to smooth irregular pseudotimes and missing expression values. It aggregates predictions from an ensemble of regression analyses to compile a ranked list of candidate interactions between transcriptional regulators and target genes. In two mouse embryonic stem cell differentiation datasets, SINGE outperforms other contemporary algorithms. However, a more detailed examination reveals caveats about poor performance for individual regulators and uninformative pseudotimes.
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Affiliation(s)
- Atul Deshpande
- Department of Electrical and Computer Engineering, University of Wisconsin - Madison, Madison, WI 53706, USA; Morgridge Institute for Research, Madison, WI 53715, USA
| | - Li-Fang Chu
- Morgridge Institute for Research, Madison, WI 53715, USA
| | - Ron Stewart
- Morgridge Institute for Research, Madison, WI 53715, USA
| | - Anthony Gitter
- Morgridge Institute for Research, Madison, WI 53715, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI 53792, USA.
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