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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.)

Seeds are complex biological systems comprising three genetically distinct tissues nested one inside another (embryo, endosperm, and maternal tissues). However, the complexity of the kernel makes it difficult to understand intercompartment interactions without access to spatially accurate information. Here, we took advantage of the large size of the maize ( Zea mays ) kernel to characterize genome-wide expression profiles of tissues at different embryo/endosperm interfaces. Our analysis identifies specific transcriptomic signatures in two interface tissues compared with whole seed compartments: the scutellar aleurone layer and the newly named endosperm adjacent to scutellum (EAS). The EAS, which appears around 9 d after pollination and persists for around 11 d, is confined to one to three endosperm cell layers adjacent to the embryonic scutellum. Its transcriptome is enriched in genes encoding transporters. The absence of the embryo in an embryo specific mutant can alter the expression pattern of EAS marker genes. The detection of cell death in some EAS cells together with an accumulation of crushed cell walls suggests that the EAS is a dynamic zone from which cell layers in contact with the embryo are regularly eliminated and to which additional endosperm cells are recruited as the embryo grows., (© 2020 American Society of Plant Biologists. All rights reserved.)

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.)

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.)

In the exalbuminous species Arabidopsis thaliana, seed maturation is accompanied by the deposition of oil and storage proteins and the reduction of the endosperm to one cell layer. Here, we consider reserve partitioning between embryo and endosperm compartments. The pattern of deposition, final amount, and composition of these reserves differ between the two compartments, with the embryo representing the principal storage tissue in mature seeds. Complex regulatory mechanisms are known to prevent activation of maturation-related programs during embryo morphogenesis and, later, during vegetative growth. Here, we describe a regulator that represses the expression of maturation-related genes during maturation within the endosperm. MYB118 is transcriptionally induced in the maturing endosperm, and seeds of myb118 mutants exhibit an endosperm-specific derepression of maturation-related genes associated with a partial relocation of storage compounds from the embryo to the endosperm. Moreover, MYB118 activates endosperm-induced genes through the recognition of TAACGG elements. These results demonstrate that the differential partitioning of reserves between the embryo and endosperm in exalbuminous Arabidopsis seeds does not only result from developmental programs that establish the embryo as the preponderant tissue within seeds. This differential partitioning is also regulated by MYB118, which regulates the biosynthesis of reserves at the spatial level during maturation., (© 2014 American Society of Plant Biologists. All rights reserved.)

CATdb is a free resource available at http://urgv.evry.inra.fr/CATdb that provides public access to a large collection of transcriptome data for Arabidopsis thaliana produced by a single Complete Arabidopsis Transcriptome Micro Array (CATMA) platform. CATMA probes consist of gene-specific sequence tags (GSTs) of 150-500 bp. The v2 version of CATMA contains 24 576 GST probes representing most of the predicted A. thaliana genes, and 615 probes tiling the chloroplastic and mitochondrial genomes. Data in CATdb are entirely processed with the same standardized protocol, from microarray printing to data analyses. CATdb contains the results of 53 projects including 1724 hybridized samples distributed between 13 different organs, 49 different developmental conditions, 45 mutants and 63 environmental conditions. All the data contained in CATdb can be downloaded from the web site and subsets of data can be sorted out and displayed either by keywords, by experiments, genes or lists of genes up to 100. CATdb gives an easy access to the complete description of experiments with a picture of the experiment design.

Genomic projects heavily depend on genome annotations and are limited by the current deficiencies in the published predictions of gene structure and function. It follows that, improved annotation will allow better data mining of genomes, and more secure planning and design of experiments. The purpose of the GeneFarm project is to obtain homogeneous, reliable, documented and traceable annotations for Arabidopsis nuclear genes and gene products, and to enter them into an added-value database. This re-annotation project is being performed exhaustively on every member of each gene family. Performing a family-wide annotation makes the task easier and more efficient than a gene-by-gene approach since many features obtained for one gene can be extrapolated to some or all the other genes of a family. A complete annotation procedure based on the most efficient prediction tools available is being used by 16 partner laboratories, each contributing annotated families from its field of expertise. A database, named GeneFarm, and an associated user-friendly interface to query the annotations have been developed. More than 3000 genes distributed over 300 families have been annotated and are available at http://genoplante-info.infobiogen.fr/Genefarm/. Furthermore, collaboration with the Swiss Institute of Bioinformatics is underway to integrate the GeneFarm data into the protein knowledgebase Swiss-Prot.

FLAGdb++ is dedicated to the integration and visualization of data for high-throughput functional analysis of a fully sequenced genome, as illustrated for Arabidopsis. FLAGdb++ displays the predicted or experimental data in a position-dependent way and displays correlations and relationships between different features. FLAGdb++ provides for a given genome region, summarized characteristics of experimental materials like probe lengths, locations and specificities having an impact upon the confidence we will put in the experimental results. A selected subset of the available information is linked to a locus represented on an easy-to-interpret and memorable graphical display. Data are curated, processed and formatted before their integration into FLAGdb++. FLAGdb++ contains different options for easy back and forth navigation through many loci selected at the start of a session. It includes an original two-component visualization of the data, a genome-wide and a local view, which are permanently linked and display complementary information. Density curves along the chromosomes may be displayed in parallel for suggesting correlations between different structural and functional data. FLAGdb++ is fully accessible at http://genoplante-info.infobiogen.fr/FLAGdb/.