The Spring of Systems Biology-Driven Breeding

Lavarenne, Jérémy; Guyomarc’h, Soazig; Sallaud, Christophe; Gantet, Pascal; Lucas, Mikaël

doi:10.1016/j.tplants.2018.04.005

Cited by 38 publications

(24 citation statements)

References 94 publications

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“…NAM-A1 (TraesCS6A02G108300) has 111 targets, and its homeolog NAM-D1 (TraesCS6D02G096300) has 69 shared targets, shown with broken axis. development and environmental responses (Bar-Joseph et al, 2012;Lavarenne et al, 2018).…”

Section: Time-resolved Transcriptional Control Of Senescence In Wheatmentioning

confidence: 99%

Identification of Transcription Factors Regulating Senescence in Wheat through Gene Regulatory Network Modelling

et al. 2019

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Senescence is a tightly regulated developmental program coordinated by transcription factors. Identifying these transcription factors in crops will provide opportunities to tailor the senescence process to different environmental conditions and regulate the balance between yield and grain nutrient content. Here, we use ten time points of gene expression data along with gene network modeling to identify transcription factors regulating senescence in polyploid wheat (Triticum aestivum). We observe two main phases of transcriptional changes during senescence: early down-regulation of housekeeping functions and metabolic processes followed by up-regulation of transport and hormone-related genes. These two phases are largely conserved with Arabidopsis (Arabidopsis thaliana), although the individual genes underlying these changes are often not orthologous. We have identified transcription factor families associated with these early and later waves of differential expression. Using gene regulatory network modeling, we identified candidate transcription factors that may control senescence. Using independent, publicly available datasets, we found that the most highly ranked candidate genes in the network were enriched for senescencerelated functions compared with all genes in the network. We validated the function of one of these candidate transcription factors in senescence using wheat chemically induced mutants. This study lays the groundwork to understand the transcription factors that regulate senescence in polyploid wheat and exemplifies the integration of time-series data with publicly available expression atlases and networks to identify candidate regulatory genes.

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Section: Time-resolved Transcriptional Control Of Senescence In Wheatmentioning

confidence: 99%

Identification of Transcription Factors Regulating Senescence in Wheat through Gene Regulatory Network Modelling

et al. 2019

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“…Direct and indirect interactions between genes and their molecular regulators, such as transcription factors (TFs), determine the complexity of the global large-scale reprogramming of gene activity (MacNeil and Walhout, 2011). Understanding the organization of these gene regulatory networks (GRNs) and how they ultimately drive specific biological outputs are key questions in plant biology, with the resulting knowledge important for driving improvement of agronomically important traits in crops (Ferrier et al, 2011; Krouk et al, 2013; Lavarenne et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Transcriptional Dynamics of the Salicylic Acid Response and its Interplay with the Jasmonic Acid Pathway

Hickman

Mendes

Verk

et al. 2019

Preprint

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The phytohormone salicylic acid (SA) is a central regulator of plant immunity.Antagonistic and synergistic actions between SA and other defense-associated hormones like jasmonic acid (JA) play key roles in determining the outcome of the plant immune response. To obtain a deeper understanding of SA-mediated transcriptional reprogramming and SA/JA crosstalk, we generated a high-resolution time series of gene expression from Arabidopsis leaves treated with SA alone and a combination of SA and methyl JA (MeJA), sampled at 14 time points over a 16-h period. We found that approximately one-third of the Arabidopsis genome was differentially expressed in response to SA, and temporal changes in gene expression could be partitioned into 45 distinct clusters of process-specific coregulated genes, linked to specific cis-regulatory elements and binding of transcription factors (TFs).Integration of our expression data with information on TF-DNA binding allowed us to generate a dynamic gene regulatory network model of the SA response, recovering known regulators and identifying novel ones. We found that 12% of SA-responsive genes and 69% of the MeJA-responsive genes exhibited antagonistic or synergistic expression levels in the combination treatment. Multi-condition co-clustering of the single-and combined-hormone expression profiles predicted underlying regulatory mechanisms in signal integration. Finally, we identified the TFs ANAC061 and ANAC090 as negative regulators of SA pathway genes and defense against biotrophic pathogens. Collectively, our data provide an unprecedented level of detail about transcriptional changes during the SA response and SA/JA crosstalk, serving as a valuable resource for systems-level network studies and functional plant defense studies.

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“…While existing reductionist-based approaches aimed at characterizing individual significant QTLs are powerful for genetic dissection, it has become increasingly clear that complex traits, especially morphological integration between different but developmentally coordinated traits, may also be controlled by QTL-QTL interactions that coalesce into a highly intricate but coordinated network. A wealth of literature supporting network thinking has arisen from medical research (Barabási et al, 2011;Chan and Loscalzo, 2012), but in recent years a consensus has been reached on the necessity of using holistic, system-oriented approaches to study plant complex traits (Ogura and Busch, 2016;Lavarenne et al, 2018. Approaches for inferring various regulatory networks from genomic, proteomic, and transcriptomic data have been well developed and widely used as a routine approach for modern biological research (Mizrachi et al, 2017). However, the characterization of QTL interaction networks remains largely unexplored, mainly because no powerful statistical methods have been developed.…”

Section: Introductionmentioning

confidence: 99%

A Computational Model for Inferring QTL Control Networks Underlying Developmental Covariation

et al. 2019

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How one trait developmentally varies as a function of others shapes a spectrum of biological phenomena. Despite its importance to trait dissection, the understanding of whether and how genes mediate such developmental covariation is poorly understood. We integrate developmental allometry equations into the functional mapping framework to map specific QTLs that govern the correlated development of different traits. Based on evolutionary game theory, we assemble and contextualize these QTLs into an intricate but organized network coded by bidirectional, signed, and weighted QTL-QTL interactions. We use this approach to map shoot height-diameter allometry QTLs in an ornamental woody species, mei (Prunus mume). We detect "pioneering" QTLs (piQTLs) and "maintaining" QTLs (miQTLs) that determine how shoot height varies with diameter and how shoot diameter varies with height, respectively. The QTL networks inferred can visualize how each piQTL regulates others to promote height growth at a cost of diameter growth, how miQTL regulates others to benefit radial growth at a cost of height growth, and how piQTLs and miQTLs regulate each other to form a pleiotropic web of primary and secondary growth in trees. Our approach provides a unique gateway to explore the genetic architecture of developmental covariation, a widespread phenomenon in nature.

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The Spring of Systems Biology-Driven Breeding

Cited by 38 publications

References 94 publications

Identification of Transcription Factors Regulating Senescence in Wheat through Gene Regulatory Network Modelling

Identification of Transcription Factors Regulating Senescence in Wheat through Gene Regulatory Network Modelling

Transcriptional Dynamics of the Salicylic Acid Response and its Interplay with the Jasmonic Acid Pathway

A Computational Model for Inferring QTL Control Networks Underlying Developmental Covariation

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