Computational prediction of gene regulatory networks in plant growth and development

Haque, Samiul; Ahmad, Jabeen; Clark, Natalie M; Williams, Cranos M.; Sozzani, Rosangela

doi:10.1016/j.pbi.2018.10.005

Cited by 68 publications

(47 citation statements)

References 59 publications

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“…Statistics, machine learning (ML), and calculus-based computational strategies have all been used to build temporal biological networks. Many of these strategies can successfully be applied to developmental and chronological time-series data [12][13][14][15]. In this section, we explore different computational strategies used by the community to generate and interpret time-resolved biological networks.…”

Section: Time Course and Dynamicsmentioning

confidence: 99%

“…Timelag Pearson correlation is a statistical approach that utilizes the time lag between cause and effect to identify potential causality. A study of temporal nitrogen (N) deprivation in Chlamydomonas used the time-lag Pearson approach to identify potential TFs or other regulators that control the expression of target genes and/or metabolites in response to N deprivation [12].…”

Section: Statistical Approachesmentioning

confidence: 99%

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Plant Networks as Traits and Hypotheses: Moving Beyond Description

Marshall‐Colón

Kliebenstein

2019

Trends in Plant Science

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Biology relies on the central thesis that the genes in an organism encode molecular mechanisms that combine with stimuli and raw materials from the environment to create a final phenotypic expression representative of the genomic programming. While conceptually simple, the genotype-to-phenotype linkage in a eukaryotic organism relies on the interactions of thousands of genes and an environment with a potentially unknowable level of complexity. Modern biology has moved to the use of networks in systems biology to try to simplify this complexity to decode how an organism's genome works. Previously, biological networks were basic ways to organize, simplify, and analyze data. However, recent advances are allowing networks to move beyond description and become phenotypes or hypotheses in their own right. This review discusses these efforts, like mapping responses across biological scales, including relationships among cellular entities, and the direct use of networks as traits or hypotheses. Biological Networks The modern molecular synthesis proposes that the genes in an organism encode molecular mechanisms that combine with signals and raw materials from the environment to create the final phenotypic expression. This genotype-to-phenotype linkage is conceptually simple, but in reality a eukaryotic organism has thousands of genes and the environment has an unknown and potentially unknowable level of complexity. This complexity has led to current efforts to utilize systems analysis to decode how an organism's genome works to create the optimal phenotype for a given environment. One simplification in the systems biology toolkit is to use biological networks to organize, simplify, and analyze data. These networks can be used to map responses across biological scales, including relationships among cellular entities, such as protein-protein interactions, gene-gene coexpression, and protein:DNA binding. The description of biological processes with networks has uncovered regulatory factors that exert control over many biological processes [1-3], regulatory motifs that shed light on signal transduction [4-7], and putative roles for genes of unknown function [8-10]. The construction of gene regulatory networks (GRNs) provides insight about the regulationdirect, indirect, or hierarchicalin a network by layering DNA-binding data (predicted or observed) onto coexpression networks. However, the predominant use of biological networks has been to describe relationships among molecules at a snapshot in time or to focus on the potential role of hub genes, yielding important insights but with significant limitations [4]. For example, downstream functional analysis of hub genes identified by network analysis can result in no obvious physiological phenotype, potentially due to functional redundancy, or the network may not translate to another experiment [11]. While some of this difficulty is caused by the available data being limiting for complete network inference, there is a key need to improve our ability to derive functional and pred...

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Section: Time Course and Dynamicsmentioning

confidence: 99%

Section: Statistical Approachesmentioning

confidence: 99%

Plant Networks as Traits and Hypotheses: Moving Beyond Description

Marshall‐Colón

Kliebenstein

2019

Trends in Plant Science

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“…There are more than a dozen other published algorithms for inference of GRNs from large-scale expression data. Several concepts in GRN inference, available algorithms, and their limitations and applications in plant studies are well summarized by others as a primer to interested researchers 61, 63, 67, 68 . An earlier meta-analysis of some of the popular approaches suggests integrating predictions from different algorithms to boost the accuracy of the consensus GRN 59 .…”

Section: The Curious Case Of Transcription Factorsmentioning

confidence: 99%

Recent advances in gene function prediction using context-specific coexpression networks in plants

Gupta

Pereira

2019

F1000Res

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Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks—created by integrating multiple expression datasets—connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional “global” to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.

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“…Using computational network inference methods, the structure of the gene regulatory interactions that makeup GRNs can be reverse-engineered. That is, causal relationships can be inferred between genes (such as those encoding TFs) directly controlling the expression of other genes [25,26]. By taking advantage of advancements in high-throughput sequencing technology, GRNs can be reconstructed utilizing genome-wide expression data, such as from RNA sequencing (RNA-seq) [27].…”

Section: Introductionmentioning

confidence: 99%

A transcriptional regulatory network of Rsv3-mediated extreme resistance against Soybean mosaic virus

et al. 2020

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Resistance genes are an effective means for disease control in plants. They predominantly function by inducing a hypersensitive reaction, which results in localized cell death restricting pathogen spread. Some resistance genes elicit an atypical response, termed extreme resistance, where resistance is not associated with a hypersensitive reaction and its standard defense responses. Unlike hypersensitive reaction, the molecular regulatory mechanism(s) underlying extreme resistance is largely unexplored. One of the few known, naturally occurring, instances of extreme resistance is resistance derived from the soybean Rsv3 gene, which confers resistance against the most virulent Soybean mosaic virus strains. To discern the regulatory mechanism underlying Rsv3-mediated extreme resistance, we generated a gene regulatory network using transcriptomic data from time course comparisons of Soybean mosaic virus-G7-inoculated resistant (L29, Rsv3-genotype) and susceptible (Williams82, rsv3-genotype) soybean cultivars. Our results show Rsv3 begins mounting a defense by 6 hpi via a complex phytohormone network, where abscisic acid, cytokinin, jasmonic acid, and salicylic acid pathways are suppressed. We identified putative regulatory interactions between transcription factors and genes in phytohormone regulatory pathways, which is consistent with the demonstrated involvement of these pathways in Rsv3-mediated resistance. One such transcription factor identified as a putative transcriptional regulator was MYC2 encoded by Glyma.07G051500. Known as a master regulator of abscisic acid and jasmonic acid signaling, MYC2 specifically recognizes the G-box motif ("CACGTG"), which was significantly enriched in our data among differentially expressed genes implicated in abscisic acidand jasmonic acid-related activities. This suggests an important role for Glyma.07G051500 in abscisic acid-and jasmonic acid-derived defense signaling in Rsv3. Resultantly, the findings from our network offer insights into genes and biological pathways underlying the molecular defense mechanism of Rsv3-mediated extreme resistance against Soybean mosaic virus. The computational pipeline used to reconstruct the gene regulatory network in this study is freely available at https://github.com/LiLabAtVT/rsv3-network.

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Computational prediction of gene regulatory networks in plant growth and development

Cited by 68 publications

References 59 publications

Plant Networks as Traits and Hypotheses: Moving Beyond Description

Plant Networks as Traits and Hypotheses: Moving Beyond Description

Recent advances in gene function prediction using context-specific coexpression networks in plants

A transcriptional regulatory network of Rsv3-mediated extreme resistance against Soybean mosaic virus

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