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Abstract Background The parasitic mite, Varroa destructor, is the most serious pest of the western honey bee, Apis mellifera, and has caused the death of millions of colonies worldwide. This mite reproduces in brood cells and parasitizes immature and adult bees. We investigated whether Varroa infestation induces changes in Apis mellifera gene expression, and whether there are genotypic differences that affect gene expression relevant to the bee's tolerance, as first steps toward unravelling mechanisms of host response and differences in susceptibility to Varroa parasitism. Results We explored the transcriptional response to mite parasitism in two genetic stocks of A. mellifera which differ in susceptibility to Varroa, comparing parasitized and non-parasitized full-sister pupae from both stocks. Bee expression profiles were analyzed using microarrays derived from honey bee ESTs whose annotation has recently been enhanced by results from the honey bee genome sequence. We measured differences in gene expression in two colonies of Varroa-susceptible and two colonies of Varroa-tolerant bees. We identified a set of 148 genes with significantly different patterns of expression: 32 varied with the presence of Varroa, 116 varied with bee genotype, and 2 with both. Varroa parasitism caused changes in the expression of genes related to embryonic development, cell metabolism and immunity. Bees tolerant to Varroa were mainly characterized by differences in the expression of genes regulating neuronal development, neuronal sensitivity and olfaction. Differences in olfaction and sensitivity to stimuli are two parameters that could, at least in part, account for bee tolerance to Varroa; differences in olfaction may be related to increased grooming and hygienic behavior, important behaviors known to be involved in Varroa tolerance. Conclusion These results suggest that differences in behavior, rather than in the immune system, underlie Varroa tolerance in honey bees, and give an indication of the specific physiological changes found in parasitized bees. They provide a first step toward better understanding molecular pathways involved in this important host-parasite relationship.

Boundary domains delimit and organize organ growth throughout plant development almost relentlessly, building plant architecture and morphogenesis. Boundary domains display reduced growth and orchestrate development of adjacent tissues in a non-cell-autonomous manner. How these two functions are achieved remains elusive despite the identification of several boundary-specific genes. Here, we show using morphometrics at the organ and cellular levels that leaf boundary domain development requires SPINDLY (SPY), an O-fucosyltransferase, to act as cell growth repressor. Furthermore, we show that SPY acts redundantly with the CUP-SHAPED COTYLEDON transcription factors (CUC2 and CUC3), which are major determinants of boundaries development. Accordingly, at the molecular level CUC2 and SPY repress a common set of genes involved in cell wall loosening, providing a molecular framework for the growth repression associated with boundary domains. Atomic force microscopy confirmed that young leaf boundary domain cells have stiffer cell walls than marginal outgrowth. This differential cell wall stiffness was reduced in spy mutant plants. Taken together, our data reveal a concealed CUC2 cell wall-associated gene network linking tissue patterning with cell growth and mechanics., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2022. Published by The Company of Biologists Ltd.)

In legumes interacting with rhizobia, the formation of symbiotic organs involved in the acquisition of atmospheric nitrogen gas (N2) is dependent on the plant nitrogen (N) demand. We used Medicago truncatula plants cultivated in split-root systems to discriminate between responses to local and systemic N signaling. We evidenced a strong control of nodule formation by systemic N signaling but obtained no clear evidence of a local control by mineral nitrogen. Systemic signaling of the plant N demand controls numerous transcripts involved in root transcriptome reprogramming associated with early rhizobia interaction and nodule formation. SUPER NUMERIC NODULES (SUNN) has an important role in this control, but we found that major systemic N signaling responses remained active in the sunn mutant. Genes involved in the activation of nitrogen fixation are regulated by systemic N signaling in the mutant, explaining why its hypernodulation phenotype is not associated with higher nitrogen fixation of the whole plant. We show that the control of transcriptome reprogramming of nodule formation by systemic N signaling requires other pathway(s) that parallel the SUNN/CLE (CLAVATA3/EMBRYO SURROUNDING REGION-LIKE PEPTIDES) pathway., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

In the context of finite mixture models one considers the problem of classifying as many observations as possible in the classes of interest while controlling the classification error rate in these same classes. Similar to what is done in the framework of statistical test theory, different type I and type II-like classification error rates can be defined, along with their associated optimal rules, where optimality is defined as minimizing type II error rate while controlling type I error rate at some nominal level. It is first shown that finding an optimal classification rule boils down to searching an optimal region in the observation space where to apply the classical Maximum A Posteriori (MAP) rule. Depending on the misclassification rate to be controlled, the shape of the optimal region is provided, along with a heuristic to compute the optimal classification rule in practice. In particular, a multiclass FDR-like optimal rule is defined and compared to the thresholded MAP rules that is used in most applications. It is shown on both simulated and real datasets that the FDR-like optimal rule may be significantly less conservative than the thresholded MAP rule., (© 2021 Walter de Gruyter GmbH, Berlin/Boston.)

The high mutational load of mitochondrial genomes combined with their uniparental inheritance and high polyploidy favors the maintenance of deleterious mutations within populations. How cells compose and adapt to the accumulation of disadvantageous mitochondrial alleles remains unclear. Most harmful changes are likely corrected by purifying selection, however, the intimate collaboration between mitochondria- and nuclear-encoded gene products offers theoretical potential for compensatory adaptive changes. In plants, cytoplasmic male sterilities are known examples of nucleo-mitochondrial coadaptation situations in which nuclear-encoded restorer of fertility (Rf) genes evolve to counteract the effect of mitochondria-encoded cytoplasmic male sterility (CMS) genes and restore fertility. Most cloned Rfs belong to a small monophyletic group, comprising 26 pentatricopeptide repeat genes in Arabidopsis, called Rf-like (RFL). In this analysis, we explored the functional diversity of RFL genes in Arabidopsis and found that the RFL8 gene is not related to CMS suppression but essential for plant embryo development. In vitro-rescued rfl8 plantlets are deficient in the production of the mitochondrial heme-lyase complex. A complete ensemble of molecular and genetic analyses allowed us to demonstrate that the RFL8 gene has been selected to permit the translation of the mitochondrial ccmFN2 gene encoding a heme-lyase complex subunit which derives from the split of the ccmFN gene, specifically in Brassicaceae plants. This study represents thus a clear case of nuclear compensation to a lineage-specific mitochondrial genomic rearrangement in plants and demonstrates that RFL genes can be selected in response to other mitochondrial deviancies than CMS suppression., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Phytoplasmas inhabit phloem sieve elements and cause abnormal growth and altered sugar partitioning. However, how they interact with phloem functions is not clearly known. The phloem responses were investigated in tomatoes infected by " Candidatus Phytoplasma solani" at the beginning of the symptomatic stage, the first symptoms appearing in the newly emerged leaf at the stem apex. Antisense lines impaired in the phloem sucrose transporters SUT1 and SUT2 were included. In symptomatic sink leaves, leaf curling was associated with higher starch accumulation and the expression of defense genes. The analysis of leaf midribs of symptomatic leaves indicated that transcript levels for genes acting in the glycolysis and peroxisome metabolism differed from these in noninfected plants. The phytoplasma also multiplied in the three lower source leaves, even if it was not associated with the symptoms. In these leaves, the rate of phloem sucrose exudation was lower for infected plants. Metabolite profiling of phloem sap-enriched exudates revealed that glycolate and aspartate levels were affected by the infection. Their levels were also affected in the noninfected SUT1 - and SUT2 -antisense lines. The findings suggest the role of sugar transporters in the responses to infection and describe the consequences of impaired sugar transport on the primary metabolism., Competing Interests: The authors declare no conflicts of interest.

Prostate cancer (PCa) immunotherapy has shown limited efficacy so far, even in advanced-stage cancers. The success rate of PCa immunotherapy might be improved by approaches more adapted to the immunobiology of the disease. The objective of this study was to perform a multi-omics analysis to identify immune genes associated with PCa progression to better characterize PCa immunobiology and propose new immunotherapeutic targets. mRNA, miRNA, methylation, copy number aberration, and single nucleotide variant datasets from The Cancer Genome Atlas PRAD cohort were analyzed after filtering for genes associated with immunity. Sparse partial least squares-discriminant analyses were performed to identify features associated with biochemical recurrence (BCR) in each type of omics data. Selected features predicted BCR with a balanced error rate (BER) of 0.20 to 0.51 in single-omics and of 0.05 in multi-omics analyses. Amongst features associated with BCR were genes from the Immunoglobulin Ig-like Receptor (LILR) family which are immune checkpoints with immunotherapeutic potential. Using Multivariate INTegrative (MINT) analysis, the association of five LILR genes with BCR was quantified in a combination of three RNA-seq datasets and confirmed with Kaplan-Meier analysis in both these and in an independent RNA-seq dataset. Finally, immunohistochemistry showed that a high number of LILRB1 positive cells within the tumors predicted long-term adverse outcomes. Thus, tumors characterized by abnormal expression of LILR genes have an elevated risk of recurring after definitive local therapy. The immunotherapeutic potential of these regulators to stimulate the immune response against PCa should be evaluated in pre-clinical models., Competing Interests: No potential conflicts of interest were disclosed., (© 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.)

Determining which treatment to provide to men with prostate cancer (PCa) is a major challenge for clinicians. Currently, the clinical risk-stratification for PCa is based on clinico-pathological variables such as Gleason grade, stage and prostate specific antigen (PSA) levels. But transcriptomic data have the potential to enable the development of more precise approaches to predict evolution of the disease. However, high quality RNA sequencing (RNA-seq) datasets along with clinical data with long follow-up allowing discovery of biochemical recurrence (BCR) biomarkers are small and rare. In this study, we propose a machine learning approach that is robust to batch effect and enables the discovery of highly predictive signatures despite using small datasets. Gene expression data were extracted from three RNA-Seq datasets cumulating a total of 171 PCa patients. Data were re-analyzed using a unique pipeline to ensure uniformity. Using a machine learning approach, a total of 14 classifiers were tested with various parameters to identify the best model and gene signature to predict BCR. Using a random forest model, we have identified a signature composed of only three genes (JUN, HES4, PPDPF) predicting BCR with better accuracy [74.2%, balanced error rate (BER) = 27%] than the clinico-pathological variables (69.2%, BER = 32%) currently in use to predict PCa evolution. This score is in the range of the studies that predicted BCR in single-cohort with a higher number of patients. We showed that it is possible to merge and analyze different small and heterogeneous datasets altogether to obtain a better signature than if they were analyzed individually, thus reducing the need for very large cohorts. This study demonstrates the feasibility to regroup different small datasets in one larger to identify a predictive genomic signature that would benefit PCa patients., (Copyright © 2020 Vittrant, Leclercq, Martin-Magniette, Collins, Bergeron, Fradet and Droit.)

In symbiotic root nodules of legumes, terminally differentiated rhizobia fix atmospheric N2 producing an NH4+ influx that is assimilated by the plant. The plant, in return, provides photosynthates that fuel the symbiotic nitrogen acquisition. Mechanisms responsible for the adjustment of the symbiotic capacity to the plant N demand remain poorly understood. We have investigated the role of systemic signaling of whole-plant N demand on the mature N2-fixing nodules of the model symbiotic association Medicago truncatula/Sinorhizobium using split-root systems. The whole-plant N-satiety signaling rapidly triggers reductions of both N2 fixation and allocation of sugars to the nodule. These responses are associated with the induction of nodule senescence and the activation of plant defenses against microbes, as well as variations in sugars transport and nodule metabolism. The whole-plant N-deficit responses mirror these changes: a rapid increase of sucrose allocation in response to N-deficit is associated with a stimulation of nodule functioning and development resulting in nodule expansion in the long term. Physiological, transcriptomic, and metabolomic data together provide evidence for strong integration of symbiotic nodules into whole-plant nitrogen demand by systemic signaling and suggest roles for sugar allocation and hormones in the signaling mechanisms., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

The control of gynecium development in Arabidopsis thaliana by the auxin response factor ETTIN (ETT) correlates with a reduction in the methylesterification of cell-wall pectins and a decrease in cell-wall stiffness in the valve tissues of the ovary. Here, we determine the list of genes rapidly regulated following the in-vivo activation of an ETT fusion protein, and show these to be significantly enriched in genes encoding cell-wall proteins, including several pectin methylesterases (PMEs) and pectin methylesterase inhibitors (PMEIs). We also perform a genome-wide scan for potential ETT-binding sites, and incorporate the results of this procedure into a comparison of datasets, derived using four distinct methods, to identify genes regulated directly or indirectly by ETT. We conclude from our combined analyses that PMEIs are likely to be key actors that mediate the regulation of gynecium development by ETT, while ETT may simultaneously regulate PMEs to prevent exaggerated developmental effects from the regulation of PMEIs. We also postulate the existence of one or more rapidly-acting intermediate factors in the transcriptional regulation of PMEs and PMEIs by ETT.

Root architecture varies widely between species; it even varies between ecotypes of the same species, despite strong conservation of the coding portion of their genomes. By contrast, noncoding RNAs evolve rapidly between ecotypes and may control their differential responses to the environment, since several long noncoding RNAs (lncRNAs) are known to quantitatively regulate gene expression. Roots from ecotypes Columbia and Landsberg erecta of Arabidopsis ( Arabidopsis thaliana ) respond differently to phosphate starvation. Here, we compared transcriptomes (mRNAs, lncRNAs, and small RNAs) of root tips from these two ecotypes during early phosphate starvation. We identified thousands of lncRNAs that were largely conserved at the DNA level in these ecotypes. In contrast to coding genes, many lncRNAs were specifically transcribed in one ecotype and/or differentially expressed between ecotypes independent of phosphate availability. We further characterized these ecotype-related lncRNAs and studied their link with small interfering RNAs. Our analysis identified 675 lncRNAs differentially expressed between the two ecotypes, including antisense RNAs targeting key regulators of root-growth responses. Misregulation of several lincRNAs showed that at least two ecotype-related lncRNAs regulate primary root growth in ecotype Columbia. RNA-sequencing analysis following deregulation of lncRNA NPC48 revealed a potential link with root growth and transport functions. This exploration of the noncoding transcriptome identified ecotype-specific lncRNA-mediated regulation in root apexes. The noncoding genome may harbor further mechanisms involved in ecotype adaptation of roots to different soil environments., (© 2020 American Society of Plant Biologists. All Rights Reserved.)

Background: Recent literature on the differential role of genes within networks distinguishes core from peripheral genes. If previous works have shown contrasting features between them, whether such categorization matters for phenotype prediction remains to be studied., Results: We measured 17 phenotypic traits for 241 cloned genotypes from a Populus nigra collection, covering growth, phenology, chemical and physical properties. We also sequenced RNA for each genotype and built co-expression networks to define core and peripheral genes. We found that cores were more differentiated between populations than peripherals while being less variable, suggesting that they have been constrained through potentially divergent selection. We also showed that while cores were overrepresented in a subset of genes statistically selected for their capacity to predict the phenotypes (by Boruta algorithm), they did not systematically predict better than peripherals or even random genes., Conclusion: Our work is the first attempt to assess the importance of co-expression network connectivity in phenotype prediction. While highly connected core genes appear to be important, they do not bear enough information to systematically predict better quantitative traits than other gene sets.

Understanding the molecular mechanisms controlling the accumulation of grain storage proteins in response to nitrogen (N) and sulfur (S) nutrition is essential to improve cereal grain nutritional and functional properties. Here, we studied the grain transcriptome and metabolome responses to postanthesis N and S supply for the diploid wheat einkorn ( Triticum monococcum ). During grain filling, 848 transcripts and 24 metabolites were differentially accumulated in response to N and S availability. The accumulation of total free amino acids per grain and the expression levels of 241 genes showed significant modifications during most of the grain filling period and were upregulated in response to S deficiency. Among them, 24 transcripts strongly responded to S deficiency and were identified in coexpression network analyses as potential coordinators of the grain response to N and S supply. Sulfate transporters and genes involved in sulfate and Met metabolism were upregulated, suggesting regulation of the pool of free amino acids and of the grain N-to-S ratio. Several genes highlighted in this study might limit the impact of S deficiency on the accumulation of grain storage proteins., (© 2020 American Society of Plant Biologists. All Rights Reserved.)

Background: RNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high number of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. More recently, statistical studies realised neutral comparison studies using benchmark datasets, shedding light on the most appropriate approaches for RNAseq data analysis., Results: DiCoExpress is a script-based tool implemented in R that includes methods chosen based on their performance in neutral comparisons studies. DiCoExpress uses pre-existing R packages including FactoMineR, edgeR and coseq, to perform quality control, differential, and co-expression analysis of RNAseq data. Users can perform the full analysis, providing a mapped read expression data file and a file containing the information on the experimental design. Following the quality control step, the user can move on to the differential expression analysis performed using generalized linear models thanks to the automated contrast writing function. A co-expression analysis is implemented using the coseq package. Lists of differentially expressed genes and identified co-expression clusters are automatically analyzed for enrichment of annotations provided by the user. We used DiCoExpress to analyze a publicly available RNAseq dataset on the transcriptional response of Brassica napus L. to silicon treatment in plant roots and mature leaves. This dataset, including two biological factors and three replicates for each condition, allowed us to demonstrate in a tutorial all the features of DiCoExpress., Conclusions: DiCoExpress is an R script-based tool allowing users to perform a full RNAseq analysis from quality controls to co-expression analysis through differential analysis based on contrasts inside generalized linear models. DiCoExpress focuses on the statistical modelling of gene expression according to the experimental design and facilitates the data analysis leading the biological interpretation of the results., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2020.)

Uncertainty is a crucial issue in statistics which can be considered from different points of view. One type of uncertainty, typically referred to as sampling uncertainty, arises through the variability of results obtained when the same analysis strategy is applied to different samples. Another type of uncertainty arises through the variability of results obtained when using the same sample but different analysis strategies addressing the same research question. We denote this latter type of uncertainty as method uncertainty. It results from all the choices to be made for an analysis, for example, decisions related to data preparation, method choice, or model selection. In medical sciences, a large part of omics research is focused on the identification of molecular biomarkers, which can either be performed through ranking or by selection from among a large number of candidates. In this paper, we introduce a general resampling-based framework to quantify and compare sampling and method uncertainty. For illustration, we apply this framework to different scenarios related to the selection and ranking of omics biomarkers in the context of acute myeloid leukemia: variable selection in multivariable regression using different types of omics markers, the ranking of biomarkers according to their predictive performance, and the identification of differentially expressed genes from RNA-seq data. For all three scenarios, our findings suggest highly unstable results when the same analysis strategy is applied to two independent samples, indicating high sampling uncertainty and a comparatively smaller, but non-negligible method uncertainty, which strongly depends on the methods being compared., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Mitochondria and chloroplasts are important actors in the plant nutritional efficiency. So, it could be expected that a disruption of the coadaptation between nuclear and organellar genomes impact plant response to nutrient stresses. We addressed this issue using two Arabidopsis accessions, namely Ct 1 and Jea , and their reciprocal cytolines possessing the nuclear genome from one parent and the organellar genomes of the other one. We measured gene expression, and quantified proteins and metabolites under N starvation and non-limiting conditions. We observed a typical response to N starvation at the phenotype and molecular levels. The phenotypical response to N starvation was similar in the cytolines compared to the parents. However, we observed an effect of the disruption of genomic coadaptation at the molecular levels, distinct from the previously described responses to organellar stresses. Strikingly, genes differentially expressed in cytolines compared to parents were mainly repressed in the cytolines. These genes encoded more mitochondrial and nuclear proteins than randomly expected, while N starvation responsive ones were enriched in genes for chloroplast and nuclear proteins. In cytolines, the non-coadapted cytonuclear genomic combination tends to modulate the response to N starvation observed in the parental lines on various biological processes., Competing Interests: The authors declare no conflict of interest.

Understanding the mechanisms triggering variation of cell wall degradability is a prerequisite to improving the energy value of lignocellulosic biomass for animal feed or biorefinery. Here, we implemented a multiscale systems approach to shed light on the genetic basis of cell wall degradability in maize. We demonstrated that allele replacement in two pairs of near-isogenic lines at a region encompassing a major quantitative trait locus (QTL) for cell wall degradability led to phenotypic variation of a similar magnitude and sign to that expected from a QTL analysis of cell wall degradability in the F271 × F288 recombinant inbred line progeny. Using DNA sequences within the QTL interval of both F271 and F288 inbred lines and Illumina RNA sequencing datasets from internodes of the selected near-isogenic lines, we annotated the genes present in the QTL interval and provided evidence that allelic variation at the introgressed QTL region gives rise to coordinated changes in gene expression. The identification of a gene co-expression network associated with cell wall-related trait variation revealed that the favorable F288 alleles exploit biological processes related to oxidation-reduction, regulation of hydrogen peroxide metabolism, protein folding and hormone responses. Nested in modules of co-expressed genes, potential new cell-wall regulators were identified, including two transcription factors of the group VII ethylene response factor family, that could be exploited to fine-tune cell wall degradability. Overall, these findings provide new insights into the regulatory mechanisms by which a major locus influences cell wall degradability, paving the way for its map-based cloning in maize., Competing Interests: The authors have declared that no competing interests exist.

Mycoheterotrophic plants have lost photosynthesis and obtain carbon through mycorrhizal fungi colonizing their roots. They are likely to have evolved from mixotrophic ancestors, which rely on both photosynthesis and fungal carbon for their development. Whereas our understanding of the ecological and genomic changes associated with the evolutionary shift to mycoheterotrophy is deepening, little information is known about the specific metabolic and physiological features driving this evolution. We investigated this issue in naturally occurring achlorophyllous variants of temperate mixotrophic orchids. We carried out an integrated transcriptomic and metabolomic analysis of the response to achlorophylly in the leaves of three mixotrophic species sampled in natura. Achlorophyllous leaves showed major impairment of their photosynthetic and mineral nutrition functions, strong accumulation of free amino acids, overexpression of enzymes and transporters related to sugars, amino acids and fatty acid catabolism, as well as induction of some autophagy-related and biotic stress genes. Such changes were reminiscent of these reported for variegated leaves and appeared to be symptomatic of a carbon starvation response. Rather than decisive metabolic innovations, we suggest that the evolution towards mycoheterotrophy in orchids is more likely to be reliant on the versatility of plant metabolism and an ability to exploit fungal organic resources, especially amino acids, to replace missing photosynthates., (© 2019 The Authors The Plant Journal © 2019 John Wiley & Sons Ltd.)

The identification of biomarker signatures in omics molecular profiling is usually performed to predict outcomes in a precision medicine context, such as patient disease susceptibility, diagnosis, prognosis, and treatment response. To identify these signatures, we have developed a biomarker discovery tool, called BioDiscML. From a collection of samples and their associated characteristics, i.e., the biomarkers (e.g., gene expression, protein levels, clinico-pathological data), BioDiscML exploits various feature selection procedures to produce signatures associated to machine learning models that will predict efficiently a specified outcome. To this purpose, BioDiscML uses a large variety of machine learning algorithms to select the best combination of biomarkers for predicting categorical or continuous outcomes from highly unbalanced datasets. The software has been implemented to automate all machine learning steps, including data pre-processing, feature selection, model selection, and performance evaluation. BioDiscML is delivered as a stand-alone program and is available for download at https://github.com/mickaelleclercq/BioDiscML.

Dormancy and germination vigor are complex traits of primary importance for adaptation and agriculture. Intraspecific variation in cytoplasmic genomes and cytonuclear interactions were previously reported to affect germination in Arabidopsis using novel cytonuclear combinations that disrupt co-adaptation between natural variants of nuclear and cytoplasmic genomes. However, specific aspects of dormancy and germination vigor were not thoroughly explored, nor the parental contributions to the genetic effects. Here, we specifically assessed dormancy, germination performance and longevity of seeds from Arabidopsis plants with natural and new genomic compositions. All three traits were modified by cytonuclear reshuffling. Both depth and release rate of dormancy could be modified by a changing of cytoplasm. Significant changes on dormancy and germination performance due to specific cytonuclear interacting combinations mainly occurred in opposite directions, consistent with the idea that a single physiological consequence of the new genetic combination affected both traits oppositely. However, this was not always the case. Interestingly, the ability of parental accessions to contribute to significant cytonuclear interactions modifying the germination phenotype was different depending on whether they provided the nuclear or cytoplasmic genetic compartment. The observed deleterious effects of novel cytonuclear combinations (in comparison with the nuclear parent) were consistent with a contribution of cytonuclear interactions to germination adaptive phenotypes. More surprisingly, we also observed favorable effects of novel cytonuclear combinations, suggesting suboptimal genetic combinations exist in natural populations for these traits. Reduced sensitivity to exogenous ABA and faster endogenous ABA decay during germination were observed in a novel cytonuclear combination that also exhibited enhanced longevity and better germination performance, compared to its natural nuclear parent. Taken together, our results strongly support that cytoplasmic genomes represent an additional resource of natural variation for breeding seed vigor traits.

In their natural environment, plants are generally confronted with multiple co-occurring stresses. However, the interaction between stresses is not well known and transcriptomic data in response to combined stresses remain scarce. This study aims at characterizing the interaction between transcriptomic responses to biotic stress and nitrogen (N) limitation. Plants were grown in low or full N, infected or not with Erwinia amylovora ( Ea ) and plant gene expression was analyzed through microarray and qRT-PCR. Most Ea -responsive genes had the same profile (induced/repressed) in response to Ea in low and full N. In response to stress combination, one third of modulated transcripts responded in a manner that could not be deduced from their response to each individual stress. Many defense-related genes showed a prioritization of their response to biotic stress over their response to N limitation, which was also observed using Pseudomonas syringae as a second pathosystem. Our results indicate an interaction between transcriptomic responses to N and biotic stress. A small fraction of transcripts was prioritized between antagonistic responses, reflecting a preservation of the plant defense program under N limitation. Furthermore, this interaction also led to a complex and specific response in terms of metabolism and cellular homeostasis-associated genes.

Despite a long history, the production of useful alien introgression lines in wheat remains difficult mainly due to linkage drag and incomplete genetic compensation. In addition, little is known about the molecular mechanisms underlying the impact of foreign chromatin on plant phenotype. Here, a comparison of the transcriptomes of barley, wheat and a wheat-barley 7HL addition line allowed the transcriptional impact both on 7HL genes of a non-native genetic background and on the wheat gene complement as a result of the presence of 7HL to be assessed. Some 42% (389/923) of the 7HL genes assayed were differentially transcribed, which was the case for only 3% (960/35 301) of the wheat gene complement. The absence of any transcript in the addition line of a suite of chromosome 7A genes implied the presence of a 36 Mbp deletion at the distal end of the 7AL arm; this deletion was found to be in common across the full set of Chinese Spring/Betzes barley addition lines. The remaining differentially transcribed wheat genes were distributed across the whole genome. The up-regulated barley genes were mostly located in the proximal part of the 7HL arm, while the down-regulated ones were concentrated in the distal part; as a result, genes encoding basal cellular functions tended to be transcribed, while those encoding specific functions were suppressed. An insight has been gained into gene transcription in an alien introgression line, thereby providing a basis for understanding the interactions between wheat and exotic genes in introgression materials., (© 2018 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)

Tapping Panel Dryness (TPD) affects latex production in Hevea brasiliensis. This physiological syndrome involves the agglutination of rubber particles, which leads to partial or complete cessation of latex flow. Latex harvesting consists in tapping soft bark. Ethephon can be applied to stimulate latex flow and its regeneration in laticifers. Several studies have reported transcriptome changes in bark tissues. This study is the first report on deep RNA sequencing of latex to compare the effect of ethephon stimulation and TPD severity. Trees were carefully selected for paired-end sequencing using an Illumina HiSeq 2000. In all, 43 to 60 million reads were sequenced for each treatment in three biological replicates (slight TPD trees without ethephon stimulation, and slight and severe TPD trees with ethephon treatment). Differentially expressed genes were identified and annotated, giving 8,111 and 728 in response to ethephon in slight TPD trees and in ethephon-induced severe TPD trees, respectively. A biological network of responses to ethephon and TPD highlighted the major influence of metabolic processes and the response to stimulus, especially wounding and jasmonate depression in TPD-affected trees induced by ethephon stimulation.

Background: Genome-wide characterization of tissue- or cell-specific gene expression is a recurrent bottleneck in biology. We have developed a sensitive approach based on ultra-low RNA sequencing coupled to laser assisted microdissection for analyzing different tissues of the small Arabidopsis embryo., Methods and Results: We first characterized the number of genes detected according to the quantity of tissue yield and total RNA extracted. Our results revealed that as low as 0.02 mm 2 of tissue and 50 pg of total RNA can be used without compromising the number of genes detected. The optimised protocol was used to compare the epidermal versus mesophyll cell transcriptomes of cotyledons at the torpedo-shaped stage of embryo development. The approach was validated by the recovery of well-known epidermal genes such AtML1 or AtPDF2 and genes involved in flavonoid and cuticular waxes pathways. Moreover, the interest and sensitivity of this approach were highlighted by the characterization of several transcription factors preferentially expressed in epidermal cells., Conclusion: This technical advance unlocks some current limitations of transcriptomic analyses and allows to investigate further and efficiently new biological questions for which only a very small amounts of cells need to be isolated. For instance, it paves the way to increasing the spatial accuracy of regulatory networks in developing small embryo of Arabidopsis or other plant tissues.

Numerous statistical pipelines are now available for the differential analysis of gene expression measured with RNA-sequencing technology. Most of them are based on similar statistical frameworks after normalization, differing primarily in the choice of data distribution, mean and variance estimation strategy and data filtering. We propose an evaluation of the impact of these choices when few biological replicates are available through the use of synthetic data sets. This framework is based on real data sets and allows the exploration of various scenarios differing in the proportion of non-differentially expressed genes. Hence, it provides an evaluation of the key ingredients of the differential analysis, free of the biases associated with the simulation of data using parametric models. Our results show the relevance of a proper modeling of the mean by using linear or generalized linear modeling. Once the mean is properly modeled, the impact of the other parameters on the performance of the test is much less important. Finally, we propose to use the simple visualization of the raw P-value histogram as a practical evaluation criterion of the performance of differential analysis methods on real data sets., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Cloning enables the generation of both clinically normal and pathological individuals from the same donor cells, and may therefore be a DNA sequence-independent driver of phenotypic variability. We took advantage of cattle clones with identical genotypes but different developmental abilities to investigate the role of epigenetic factors in perinatal mortality, a complex trait with increasing prevalence in dairy cattle. We studied livers from pathological clones dying during the perinatal period, clinically normal adult clones with the same genotypes as perinatal clones and conventional age-matched controls. The livers from deceased perinatal clones displayed histological lesions, modifications to quantitative histomorphometric and metabolic parameters such as glycogen storage and fatty acid composition, and an absence of birth-induced maturation. In a genome-wide epigenetic analysis, we identified DNA methylation patterns underlying these phenotypic alterations and targeting genes relevant to liver metabolism, including the type 2 diabetes gene TCF7L2. The adult clones were devoid of major phenotypic and epigenetic abnormalities in the liver, ruling out the effects of genotype on the phenotype observed. These results thus provide the first demonstration of a genome-wide association between DNA methylation and perinatal mortality in cattle, and highlight epigenetics as a driving force for phenotypic variability in farmed animals.

Using a metabolomic approach, we have quantified the metabolite composition of the phloem sap exudate of seventeen European and American lines of maize that had been previously classified into five main groups on the basis of molecular marker polymorphisms. In addition to sucrose, glutamate and aspartate, which are abundant in the phloem sap of many plant species, large quantities of aconitate and alanine were also found in the phloem sap exudates of maize. Genetic variability of the phloem sap composition was observed in the different maize lines, although there was no obvious relationship between the phloem sap composition and the five previously classified groups. However, following hierarchical clustering analysis there was a clear relationship between two of the subclusters of lines defined on the basis of the composition of the phloem sap exudate and the earliness of silking date. A comparison between the metabolite contents of the ear leaves and the phloem sap exudates of each genotype, revealed that the relative content of most of the carbon- and nitrogen-containing metabolites was similar. Correlation studies performed between the metabolite content of the phloem sap exudates and yield-related traits also revealed that for some carbohydrates such as arabitol and sucrose there was a negative or positive correlation with kernel yield and kernel weight respectively. A posititive correlation was also found between kernel number and soluble histidine., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Although the contribution of cytonuclear interactions to plant fitness variation is relatively well documented at the interspecific level, the prevalence of cytonuclear interactions at the intraspecific level remains poorly investigated. In this study, we set up a field experiment to explore the range of effects that cytonuclear interactions have on fitness-related traits in Arabidopsis thaliana To do so, we created a unique series of 56 cytolines resulting from cytoplasmic substitutions among eight natural accessions reflecting within-species genetic diversity. An assessment of these cytolines and their parental lines scored for 28 adaptive whole-organism phenotypes showed that a large proportion of phenotypic traits (23 of 28) were affected by cytonuclear interactions. The effects of these interactions varied from slight but frequent across cytolines to strong in some specific parental pairs. Two parental pairs accounted for half of the significant pairwise interactions. In one parental pair, Ct-1/Sha, we observed symmetrical phenotypic responses between the two nuclear backgrounds when combined with specific cytoplasms, suggesting nuclear differentiation at loci involved in cytonuclear epistasis. In contrast, asymmetrical phenotypic responses were observed in another parental pair, Cvi-0/Sha. In the Cvi-0 nuclear background, fecundity and phenology-related traits were strongly affected by the Sha cytoplasm, leading to a modified reproductive strategy without penalizing total seed production. These results indicate that natural variation in cytoplasmic and nuclear genomes interact to shape integrative traits that contribute to adaptation, thereby suggesting that cytonuclear interactions can play a major role in the evolutionary dynamics ofA. thaliana.

Motivation: In recent years, gene expression studies have increasingly made use of high-throughput sequencing technology. In turn, research concerning the appropriate statistical methods for the analysis of digital gene expression (DGE) has flourished, primarily in the context of normalization and differential analysis., Results: In this work, we focus on the question of clustering DGE profiles as a means to discover groups of co-expressed genes. We propose a Poisson mixture model using a rigorous framework for parameter estimation as well as the choice of the appropriate number of clusters. We illustrate co-expression analyses using our approach on two real RNA-seq datasets. A set of simulation studies also compares the performance of the proposed model with that of several related approaches developed to cluster RNA-seq or serial analysis of gene expression data., Availability and and Implementation: The proposed method is implemented in the open-source R package HTSCluster, available on CRAN., Contact: andrea.rau@jouy.inra.fr, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

CATdb (http://urgv.evry.inra.fr/CATdb) is a database providing a public access to a large collection of transcriptomic data, mainly for Arabidopsis but also for other plants. This resource has the rare advantage to contain several thousands of microarray experiments obtained with the same technical protocol and analyzed by the same statistical pipelines. In this paper, we present GEM2Net, a new module of CATdb that takes advantage of this homogeneous dataset to mine co-expression units and decipher Arabidopsis gene functions. GEM2Net explores 387 stress conditions organized into 18 biotic and abiotic stress categories. For each one, a model-based clustering is applied on expression differences to identify clusters of co-expressed genes. To characterize functions associated with these clusters, various resources are analyzed and integrated: Gene Ontology, subcellular localization of proteins, Hormone Families, Transcription Factor Families and a refined stress-related gene list associated to publications. Exploiting protein-protein interactions and transcription factors-targets interactions enables to display gene networks. GEM2Net presents the analysis of the 18 stress categories, in which 17,264 genes are involved and organized within 681 co-expression clusters. The meta-data analyses were stored and organized to compose a dynamic Web resource., (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The RNA exosome is the major 3'-5' RNA degradation machine of eukaryotic cells and participates in processing, surveillance and turnover of both nuclear and cytoplasmic RNA. In both yeast and human, all nuclear functions of the exosome require the RNA helicase MTR4. We show that the Arabidopsis core exosome can associate with two related RNA helicases, AtMTR4 and HEN2. Reciprocal co-immunoprecipitation shows that each of the RNA helicases co-purifies with the exosome core complex and with distinct sets of specific proteins. While AtMTR4 is a predominantly nucleolar protein, HEN2 is located in the nucleoplasm and appears to be excluded from nucleoli. We have previously shown that the major role of AtMTR4 is the degradation of rRNA precursors and rRNA maturation by-products. Here, we demonstrate that HEN2 is involved in the degradation of a large number of polyadenylated nuclear exosome substrates such as snoRNA and miRNA precursors, incompletely spliced mRNAs, and spurious transcripts produced from pseudogenes and intergenic regions. Only a weak accumulation of these exosome substrate targets is observed in mtr4 mutants, suggesting that MTR4 can contribute, but plays rather a minor role for the degradation of non-ribosomal RNAs and cryptic transcripts in Arabidopsis. Consistently, transgene post-transcriptional gene silencing (PTGS) is marginally affected in mtr4 mutants, but increased in hen2 mutants, suggesting that it is mostly the nucleoplasmic exosome that degrades aberrant transgene RNAs to limit their entry in the PTGS pathway. Interestingly, HEN2 is conserved throughout green algae, mosses and land plants but absent from metazoans and other eukaryotic lineages. Our data indicate that, in contrast to human and yeast, plants have two functionally specialized RNA helicases that assist the exosome in the degradation of specific nucleolar and nucleoplasmic RNA populations, respectively.

Background: Fusarium Head Blight (FHB) caused primarily by Fusarium graminearum (Fg) is one of the major diseases of small-grain cereals including bread wheat. This disease both reduces yields and causes quality losses due to the production of deoxynivalenol (DON), the major type B trichothecene mycotoxin. DON has been described as a virulence factor enabling efficient colonization of spikes by the fungus in wheat, but its precise role during the infection process is still elusive. Brachypodium distachyon (Bd) is a model cereal species which has been shown to be susceptible to FHB. Here, a functional genomics approach was performed in order to characterize the responses of Bd to Fg infection using a global transcriptional and metabolomic profiling of B. distachyon plants infected by two strains of F. graminearum: a wild-type strain producing DON (Fgdon+) and a mutant strain impaired in the production of the mycotoxin (Fgdon-)., Results: Histological analysis of the interaction of the Bd21 ecotype with both Fg strains showed extensive fungal tissue colonization with the Fgdon+ strain while the florets infected with the Fgdon- strain exhibited a reduced hyphal extension and cell death on palea and lemma tissues. Fungal biomass was reduced in spikes inoculated with the Fgdon- strain as compared with the wild-type strain. The transcriptional analysis showed that jasmonate and ethylene-signalling pathways are induced upon infection, together with genes encoding putative detoxification and transport proteins, antioxidant functions as well as secondary metabolite pathways. In particular, our metabolite profiling analysis showed that tryptophan-derived metabolites, tryptamine, serotonin, coumaroyl-serotonin and feruloyl-serotonin, are more induced upon infection by the Fgdon+ strain than by the Fgdon- strain. Serotonin was shown to exhibit a slight direct antimicrobial effect against Fg., Conclusion: Our results show that Bd exhibits defense hallmarks similar to those already identified in cereal crops. While the fungus uses DON as a virulence factor, the host plant preferentially induces detoxification and the phenylpropanoid and phenolamide pathways as resistance mechanisms. Together with its amenability in laboratory conditions, this makes Bd a very good model to study cereal resistance mechanisms towards the major disease FHB.

Characterizing the transcriptome of eukaryotic organisms is essential for studying gene regulation and its impact on phenotype. The realization that anti-sense (AS) and noncoding RNA transcription is pervasive in many genomes has emphasized our limited understanding of gene transcription and post-transcriptional regulation. Numerous mechanisms including convergent transcription, anti-correlated expression of sense and AS transcripts, and RNAi remain ill-defined. Here, we have combined microarray analysis and high-throughput sequencing of small RNAs (sRNAs) to unravel the complexity of transcriptional and potential post-transcriptional regulation in eight organs of apple (Malus × domestica). The percentage of AS transcript expression is higher than that identified in annual plants such as rice and Arabidopsis thaliana. Furthermore, we show that a majority of AS transcripts are transcribed beyond 3'UTR regions, and may cover a significant portion of the predicted sense transcripts. Finally we demonstrate at a genome-wide scale that anti-sense transcript expression is correlated with the presence of both short (21-23 nt) and long (> 30 nt) siRNAs, and that the sRNA coverage depth varies with the level of AS transcript expression. Our study provides a new insight on the functional role of anti-sense transcripts at the genome-wide level, and a new basis for the understanding of sRNA biogenesis in plants., (© 2014 INRA. New Phytologist © 2014 New Phytologist Trust.)

Background: Mitogen-activated protein kinases (MAPKs) are key regulators of immune responses in animals and plants. In Arabidopsis, perception of microbe-associated molecular patterns (MAMPs) activates the MAPKs MPK3, MPK4 and MPK6. Increasing information depicts the molecular events activated by MAMPs in plants, but the specific and cooperative contributions of the MAPKs in these signalling events are largely unclear., Results: In this work, we analyse the behaviour of MPK3, MPK4 and MPK6 mutants in early and late immune responses triggered by the MAMP flg22 from bacterial flagellin. A genome-wide transcriptome analysis reveals that 36% of the flg22-upregulated genes and 68% of the flg22-downregulated genes are affected in at least one MAPK mutant. So far MPK4 was considered as a negative regulator of immunity, whereas MPK3 and MPK6 were believed to play partially redundant positive functions in defence. Our work reveals that MPK4 is required for the regulation of approximately 50% of flg22-induced genes and we identify a negative role for MPK3 in regulating defence gene expression, flg22-induced salicylic acid accumulation and disease resistance to Pseudomonas syringae. Among the MAPK-dependent genes, 27% of flg22-upregulated genes and 76% of flg22-downregulated genes require two or three MAPKs for their regulation. The flg22-induced MAPK activities are differentially regulated in MPK3 and MPK6 mutants, both in amplitude and duration, revealing a highly interdependent network., Conclusions: These data reveal a new set of distinct functions for MPK3, MPK4 and MPK6 and indicate that the plant immune signalling network is choreographed through the interplay of these three interwoven MAPK pathways.

To complement N2 fixation through symbiosis, legumes can efficiently acquire soil mineral N through adapted root architecture. However, root architecture adaptation to mineral N availability has been little studied in legumes. Therefore, this study investigated the effect of nitrate availability on root architecture in Medicago truncatula and assessed the N-uptake potential of a new highly branched root mutant, TR185. The effects of varying nitrate supply on both root architecture and N uptake were characterized in the mutant and in the wild type. Surprisingly, the root architecture of the mutant was not modified by variation in nitrate supply. Moreover, despite its highly branched root architecture, TR185 had a permanently N-starved phenotype. A transcriptome analysis was performed to identify genes differentially expressed between the two genotypes. This analysis revealed differential responses related to the nitrate acquisition pathway and confirmed that N starvation occurred in TR185. Changes in amino acid content and expression of genes involved in the phenylpropanoid pathway were associated with differences in root architecture between the mutant and the wild type., (© The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Infections with Salmonella enterica belong to the most prominent causes of food poisoning and infected fruits and vegetables represent important vectors for salmonellosis. Recent evidence indicates that plants recognize S. enterica and raise defense responses. Nonetheless, the molecular mechanisms controlling the interaction of S. enterica with plants are still largely unclear. Here, we show that flagellin from S. enterica represents a prominent pathogen-associated molecular pattern (PAMP) in Arabidopsis thaliana, which induces PAMP-triggered immunity (PTI) via the recognition of the flg22 domain by the receptor kinase FLS2. The Arabidopsis fls2 mutant shows reduced though not abolished PTI activation, indicating that plants rely also on recognition of other S. enterica PAMPs. Interestingly, the S. enterica type III secretion system (T3SS) mutant prgH- induced stronger defense gene expression than wild-type bacteria in Arabidopsis, suggesting that T3SS effectors are involved in defense suppression. Furthermore, we observe that S. enterica strains show variation in the flg22 epitope, which results in proteins with reduced PTI-inducing activity. Altogether, these results show that S. enterica activates PTI in Arabidopsis and suggest that, in order to accomplish plant colonization, S. enterica evolved strategies to avoid or suppress PTI.

We compare two major approaches to variable selection in clustering: model selection and regularization. Based on previous results, we select the method of Maugis et al. (2009b), which modified the method of Raftery and Dean (2006), as a current state of the art model selection method. We select the method of Witten and Tibshirani (2010) as a current state of the art regularization method. We compared the methods by simulation in terms of their accuracy in both classification and variable selection. In the first simulation experiment all the variables were conditionally independent given cluster membership. We found that variable selection (of either kind) yielded substantial gains in classification accuracy when the clusters were well separated, but few gains when the clusters were close together. We found that the two variable selection methods had comparable classification accuracy, but that the model selection approach had substantially better accuracy in selecting variables. In our second simulation experiment, there were correlations among the variables given the cluster memberships. We found that the model selection approach was substantially more accurate in terms of both classification and variable selection than the regularization approach, and that both gave more accurate classifications than K -means without variable selection. But the model selection approach is not available in a very high dimension context.

Background: Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates., Results: MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements., Conclusion: We show the utility of MultiChIPmixHMM by analyzing histone modifications of Arabidopsis thaliana. MultiChIPmixHMM is implemented in R and including functions in C, freely available from the CRAN web site: http://cran.r-project.org.

A key innovation of flowering plants is the female reproductive organ, the carpel. Here, we show that a mechanism that regulates carpel margin development in the model flowering plant Arabidopsis thaliana was recruited from light-regulated processes. This recruitment followed the loss from the basic helix-loop-helix transcription factor SPATULA (SPT) of a domain previously responsible for its negative regulation by phytochrome. We propose that the loss of this domain was a prerequisite for the light-independent expression in female reproductive tissues of a genetic module that also promotes shade avoidance responses in vegetative organs. Striking evidence for this proposition is provided by the restoration of wild-type carpel development to spt mutants by low red/far-red light ratios, simulating vegetation shade, which we show to occur via phytochrome B, PHYTOCHROME INTERACTING FACTOR4 (PIF4), and PIF5. Our data illustrate the potential of modular evolutionary events to generate rapid morphological change and thereby provide a molecular basis for neo-Darwinian theories that describe this nongradualist phenomenon. Furthermore, the effects shown here of light quality perception on carpel development lead us to speculate on the potential role of light-regulated mechanisms in plant organs that, like the carpel, form within the shade of surrounding tissues.

Background: Expression traits can vary quantitatively between individuals and have a complex inheritance. Identification of the genetics underlying transcript variation can help in the understanding of phenotypic variation due to genetic factors regulating transcript abundance and shed light into divergence patterns. So far, only a limited number of studies have addressed this subject in Arabidopsis, with contrasting results due to dissimilar statistical power. Here, we present the transcriptome architecture in leaf tissue of two RIL sets obtained from a connected-cross design involving 3 commonly used accessions. We also present the transcriptome architecture observed in developing seeds of a third independent cross., Results: The utilisation of the novel R/eqtl package (which goal is to automatize and extend functions from the R/qtl package) allowed us to map 4,290 and 6,534 eQTLs in the Cvi-0 × Col-0 and Bur-0 × Col-0 recombinant populations respectively. In agreement with previous studies, we observed a larger phenotypic variance explained by eQTLs in linkage with the controlled gene (potentially cis-acting), compared to distant loci (acting necessarily indirectly or in trans). Distant eQTLs hotspots were essentially not conserved between crosses, but instead, cross-specific. Accounting for confounding factors using a probabilistic approach (VBQTL) increased the mapping resolution and the number of significant associations. Moreover, using local eQTLs obtained from this approach, we detected evidence for a directional allelic effect in genes with related function, where significantly more eQTLs than expected by chance were up-regulated from one of the accessions. Primary experimental data, analysis parameters, eQTL results and visualisation of LOD score curves presented here are stored and accessible through the QTLstore service database http://qtlstore.versailles.inra.fr/., Conclusions: Our results demonstrate the extensive diversity and moderately conserved eQTL landscape between crosses and validate the utilisation of expression traits to explore for candidates behind phenotypic variation among accessions. Furthermore, this stresses the need for a wider spectrum of diversity to fully understand expression trait variation within a species.

The conserved Target of Rapamycin (TOR) kinase forms high molecular mass complexes and is a major regulator of cellular adaptations to environmental cues. The Lethal with Sec Thirteen 8/G protein β subunit-like (LST8/GβL) protein is a member of the TOR complexes, and two putative LST8 genes are present in Arabidopsis thaliana, of which only one (LST8-1) is significantly expressed. The Arabidopsis LST8-1 protein is able to complement yeast lst8 mutations and interacts with the TOR kinase. Mutations in the LST8-1 gene resulted in reduced vegetative growth and apical dominance with abnormal development of flowers. Mutant plants were also highly sensitive to long days and accumulated, like TOR RNA interference lines, higher amounts of starch and amino acids, including proline and glutamine, while showing reduced concentrations of inositol and raffinose. Accordingly, transcriptomic and enzymatic analyses revealed a higher expression of genes involved in nitrate assimilation when lst8-1 mutants were shifted to long days. The transcriptome of lst8-1 mutants in long days was found to share similarities with that of a myo-inositol 1 phosphate synthase mutant that is also sensitive to the extension of the light period. It thus appears that the LST8-1 protein has an important role in regulating amino acid accumulation and the synthesis of myo-inositol and raffinose during plant adaptation to long days.

Identifying the connections between molecular and physiological processes underlying the diversity of drought stress responses in plants is key for basic and applied science. Drought stress response involves a large number of molecular pathways and subsequent physiological processes. Therefore, it constitutes an archetypical systems biology model. We first inferred a gene-phenotype network exploiting differences in drought responses of eight sunflower (Helianthus annuus) genotypes to two drought stress scenarios. Large transcriptomic data were obtained with the sunflower Affymetrix microarray, comprising 32423 probesets, and were associated to nine morpho-physiological traits (integrated transpired water, leaf transpiration rate, osmotic potential, relative water content, leaf mass per area, carbon isotope discrimination, plant height, number of leaves and collar diameter) using sPLS regression. Overall, we could associate the expression patterns of 1263 probesets to six phenotypic traits and identify if correlations were due to treatment, genotype and/or their interaction. We also identified genes whose expression is affected at moderate and/or intense drought stress together with genes whose expression variation could explain phenotypic and drought tolerance variability among our genetic material. We then used the network model to study phenotypic changes in less tractable agronomical conditions, i.e. sunflower hybrids subjected to different watering regimes in field trials. Mapping this new dataset in the gene-phenotype network allowed us to identify genes whose expression was robustly affected by water deprivation in both controlled and field conditions. The enrichment in genes correlated to relative water content and osmotic potential provides evidence of the importance of these traits in agronomical conditions.

Tiling arrays make possible a large-scale exploration of the genome thanks to probes which cover the whole genome with very high density, up to 2,000,000 probes. Biological questions usually addressed are either the expression difference between two conditions or the detection of transcribed regions. In this work, we propose to consider both questions simultaneously as an unsupervised classification problem by modeling the joint distribution of the two conditions. In contrast to previous methods, we account for all available information on the probes as well as biological knowledge such as annotation and spatial dependence between probes. Since probes are not biologically relevant units, we propose a classification rule for non-connected regions covered by several probes. Applications to transcriptomic and ChIP-chip data of Arabidopsis thaliana obtained with a NimbleGen tiling array highlight the importance of a precise modeling and of the region classification. The "TAHMMAnnot" package is implemented in R and C and is freely available from CRAN.

Background: One of the key goals of oak genomics research is to identify genes of adaptive significance. This information may help to improve the conservation of adaptive genetic variation and the management of forests to increase their health and productivity. Deep-coverage large-insert genomic libraries are a crucial tool for attaining this objective. We report herein the construction of a BAC library for Quercus robur, its characterization and an analysis of BAC end sequences., Results: The EcoRI library generated consisted of 92,160 clones, 7% of which had no insert. Levels of chloroplast and mitochondrial contamination were below 3% and 1%, respectively. Mean clone insert size was estimated at 135 kb. The library represents 12 haploid genome equivalents and, the likelihood of finding a particular oak sequence of interest is greater than 99%. Genome coverage was confirmed by PCR screening of the library with 60 unique genetic loci sampled from the genetic linkage map. In total, about 20,000 high-quality BAC end sequences (BESs) were generated by sequencing 15,000 clones. Roughly 5.88% of the combined BAC end sequence length corresponded to known retroelements while ab initio repeat detection methods identified 41 additional repeats. Collectively, characterized and novel repeats account for roughly 8.94% of the genome. Further analysis of the BESs revealed 1,823 putative genes suggesting at least 29,340 genes in the oak genome. BESs were aligned with the genome sequences of Arabidopsis thaliana, Vitis vinifera and Populus trichocarpa. One putative collinear microsyntenic region encoding an alcohol acyl transferase protein was observed between oak and chromosome 2 of V. vinifera., Conclusions: This BAC library provides a new resource for genomic studies, including SSR marker development, physical mapping, comparative genomics and genome sequencing. BES analysis provided insight into the structure of the oak genome. These sequences will be used in the assembly of a future genome sequence for oak.

Post-translational modification of histones and DNA methylation are important components of chromatin-level control of genome activity in eukaryotes. However, principles governing the combinatorial association of chromatin marks along the genome remain poorly understood. Here, we have generated epigenomic maps for eight histone modifications (H3K4me2 and 3, H3K27me1 and 2, H3K36me3, H3K56ac, H4K20me1 and H2Bub) in the model plant Arabidopsis and we have combined these maps with others, produced under identical conditions, for H3K9me2, H3K9me3, H3K27me3 and DNA methylation. Integrative analysis indicates that these 12 chromatin marks, which collectively cover ∼90% of the genome, are present at any given position in a very limited number of combinations. Moreover, we show that the distribution of the 12 marks along the genomic sequence defines four main chromatin states, which preferentially index active genes, repressed genes, silent repeat elements and intergenic regions. Given the compact nature of the Arabidopsis genome, these four indexing states typically translate into short chromatin domains interspersed with each other. This first combinatorial view of the Arabidopsis epigenome points to simple principles of organization as in metazoans and provides a framework for further studies of chromatin-based regulatory mechanisms in plants.

Cultivated for centuries, the varieties of rose have been selected based on a number of flower traits. Understanding the genetic and molecular basis that contributes to these traits will impact on future improvements for this economically important ornamental plant. In this study, we used scanning electron microscopy and sections of meristems and flowers to establish a precise morphological calendar from early rose flower development stages to senescing flowers. Global gene expression was investigated from floral meristem initiation up to flower senescence in three rose genotypes exhibiting contrasted floral traits including continuous versus once flowering and simple versus double flower architecture, using a newly developed Affymetrix microarray (Rosa1_Affyarray) tool containing sequences representing 4765 unigenes expressed during flower development. Data analyses permitted the identification of genes associated with floral transition, floral organs initiation up to flower senescence. Quantitative real time PCR analyses validated the mRNA accumulation changes observed in microarray hybridizations for a selection of 24 genes expressed at either high or low levels. Our data describe the early flower development stages in Rosa sp, the production of a rose microarray and demonstrate its usefulness and reliability to study gene expression during extensive development phases, from the vegetative meristem to the senescent flower.