A cell wall-associated gene network shapes leaf boundary domains.

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.

Error rate control for classification rules in multiclass mixture models.

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.

Systemic control of nodule formation by plant nitrogen demand requires autoregulation-dependent and independent mechanisms.

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.

A Case of Gene Fragmentation in Plant Mitochondria Fixed by the Selection of a Compensatory Restorer of Fertility-Like PPR Gene.

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.

Involvement of SUT1 and SUT2 Sugar Transporters in the Impairment of Sugar Transport and Changes in Phloem Exudate Contents in Phytoplasma-Infected Plants.

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.

Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer.

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.

Immune-focused multi-omics analysis of prostate cancer: leukocyte Ig-Like receptors are associated with disease progression.

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.

Gene expression predictions and networks in natural populations supports the omnigenic theory.

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.

Immediate targets of ETTIN suggest a key role for pectin methylesterase inhibitors in the control of Arabidopsis gynecium development.

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.

DiCoExpress: a tool to process multifactorial RNAseq experiments from quality controls to co-expression analysis through differential analysis based on contrasts inside GLM models.

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.

Responses of mature symbiotic nodules to the whole-plant systemic nitrogen signaling.

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.

Landscape of the Noncoding Transcriptome Response of Two Arabidopsis Ecotypes to Phosphate Starvation.

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.

The Consequences of a Disruption in Cyto-Nuclear Coadaptation on the Molecular Response to a Nitrate Starvation in Arabidopsis.

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 Ct1 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.

Omics Data Reveal Putative Regulators of Einkorn Grain Protein Composition under Sulfur Deficiency.

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.

Sampling uncertainty versus method uncertainty: A general framework with applications to omics biomarker selection.

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.

Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data.

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.

In situ transcriptomic and metabolomic study of the loss of photosynthesis in the leaves of mixotrophic plants exploiting fungi.

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.

Novel Cytonuclear Combinations Modify Arabidopsis thaliana Seed Physiology and Vigor.

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.

A systems biology approach uncovers a gene co-expression network associated with cell wall degradability in maize.

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.