Motivation Inferring gene regulatory networks in non-independent genetically related panels is a methodological challenge. This hampers evolutionary and biological studies using heterozygote individuals such as in wild sunflower populations or cultivated hybrids. Results First, we simulated 100 datasets of gene expressions and polymorphisms, displaying the same gene expression distributions, heterozygosities and heritabilities as in our dataset including 173 genes and 353 genotypes measured in sunflower hybrids. Secondly, we performed a meta-analysis based on six inference methods [least absolute shrinkage and selection operator (Lasso), Random Forests, Bayesian Networks, Markov Random Fields, Ordinary Least Square and fast inference of networks from directed regulation (Findr)] and selected the minimal density networks for better accuracy with 64 edges connecting 79 genes and 0.35 area under precision and recall (AUPR) score on average. We identified that triangles and mutual edges are prone to errors in the inferred networks. Applied on classical datasets without heterozygotes, our strategy produced a 0.65 AUPR score for one dataset of the DREAM5 Systems Genetics Challenge. Finally, we applied our method to an experimental dataset from sunflower hybrids. We successfully inferred a network composed of 105 genes connected by 106 putative regulations with a major connected component. Availability and implementation Our inference methodology dedicated to genomic and transcriptomic data is available at https://forgemia.inra.fr/sunrise/inference_methods. Supplementary information Supplementary data are available at Bioinformatics online.

In meat-producing animals, preslaughter operations (e.g., transportation, mixing unfamiliar animals, food and water deprivation) may be a source of stress with detrimental effects on meat quality. The objective of this work was to study the effect of emotional and physical stress by comparing the transcriptomes of two muscles (M. longissimus thoracis, LT and M. semitendinosus, ST) in Normand cows exposed to stress (n = 16) vs. cows handled with limited stress (n = 16). Using a microarray, we showed that exposure to stress resulted in differentially expressed genes (DEGs) in both muscles (62 DEGs in LT and 32 DEGs in ST, of which eight were common transcription factors (TFs)). Promoter analysis of the DEGs showed that 25 cis transcriptional modules were overrepresented, of which nine were detected in both muscles. Molecular interaction networks of the DEGs targeted by the most represented cis modules helped identify common regulators and common targets involved in the response to stress. They provided elements showing that the transcriptional response to stress is likely to (i) be controlled by regulators of energy metabolism, factors involved in the response to hypoxia, and inflammatory cytokines; and (ii) initiate metabolic processes, angiogenesis, corticosteroid response, immune system processes, and satellite cell activation/quiescence. The results of this study demonstrate that exposure to stress induced a core response to stress in both muscles, including changes in the expression of TFs. These factors could relay the physiological adaptive response of cattle muscles to cope with emotional and physical stress. The study provides information to further understand the consequences of these molecular processes on meat quality and find strategies to attenuate them.

This article describes how the transcriptomic data were produced on sunflower plants subjected to water deficit. Twenty-four sunflower (Helianthus annuus) genotypes were selected to represent genetic diversity within cultivated sunflower and included both inbred lines and their hybrids. Drought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen. Here, we provide transcriptomic data from sunflower leaves. These data differentiate both plant water status and the different genotypes. They constitute a valuable resource to the community to study adaptation of crops to drought and the transcriptomic basis of heterosis.

This article describes how RNA expression data of 173 genes were produced on 384 sunflower hybrids grown in field conditions. Sunflower hybrids were selected to represent genetic diversity within cultivated sunflower. The RNA was extracted from mature leaves at one time seven days after anthesis. These data allow to differentiate the different genotype behaviours and constitute a valuable resource to the community to study the adaptation of crops to field conditions and the molecular basis of heterosis. It is available on data.inra.fr repository.

Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA) – Institut National de la Recherche Agronomique : UR875 – Chemin de Borde Rouge, 31320 Castanet Tolosan, France Laboratoire des interactions plantes micro-organismes (LIPM) – Institut National de la Recherche Agronomique, Centre National de la Recherche Scientifique : UMR2594 – Chemin de Borde-Rouge BP 27 31326 CASTANET TOLOSAN CEDEX, France

Background: Trees experience mechanical stimuli -like wind- that trigger thigmomorphogenetic syndrome, leading to modifications of plant growth and wood quality. This syndrome affects tree productivity but is also believed to improve tree acclimation to chronic wind. Wind is particularly challenging for trees, because of their stature and perenniality. Climate change forecasts are predicting that the occurrence of high wind will worsen, making it increasingly vital to understand the mechanisms regulating thigmomorphogenesis, especially in perennial plants. By extension, this also implies factoring in the recurring nature of wind episodes. However, data on the molecular processes underpinning mechanoperception and transduction of mechanical signals, and their dynamics, are still dramatically lacking in trees.Results: Here we performed a genome-wide and time-series analysis of poplar transcriptional responsiveness to transitory and recurring controlled stem bending, mimicking wind. The study revealed that 6% of the poplar genome is differentially expressed after a single transient bending. The combination of clustering, Gene Ontology categorization and time-series expression approaches revealed the diversity of gene expression patterns and biological processes affected by stem bending. Short-term transcriptomic responses entailed a rapid stimulation of plant defence and abiotic stress signalling pathways, including ethylene and jasmonic acid signalling but also photosynthesis process regulation. Late transcriptomic responses affected genes involved in cell wall organization and/or wood development. An analysis of the molecular impact of recurring bending found that the vast majority (96%) of the genes differentially expressed after a first bending presented reduced or even net-zero amplitude regulation after the second exposure to bending.Conclusion: This study constitutes the first dynamic characterization of the molecular processes affected by single or repeated stem bending in poplar. Moreover, the global attenuation of the transcriptional responses, observed from as early as after a second bending, indicates the existence of a mechanism governing a fine tuning of plant responsiveness. This points toward several mechanistic pathways that can now be targeted to elucidate the complex dynamics of wind acclimation.

List of genes differentially expressed probset in response to one or two bendings, as identified by microarray analysis. Affymetrix 61 k poplar microarrays (composed of 61,251 probesets) were used to detect differences in expression. Four comparisons between bent plants (b) vs. control plants (c) at different time points after the bending were realized: 0.5 h PB (1b0.5 h vs. C0.5 h), 2 h PB (1b2h vs. C2h), 24 h PB (1b24h vs. C24h) and 72 h PB (1b72h vs. C72h). To understand the effect of a second bending, we ran two comparisons (i) 0.5 h after the 2nd bending (with a time-lapse of 24 h between the two bendings) compared to 24 h after the 1st bending (2b0.5 h vs. 1b24h) and (ii) 0.5 h after the 2nd bending compared to 0.5 h after the 1st bending (2b0.5 h vs. 1b0.5 h). Expression was considered significantly different if the P-value from the Benjamini–Hochberg test (BH P-value) is below 0.05. (CSV 694 kb)