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* The genomic era facilitates the understanding of how transcriptional networks are interconnected to program seed development and filling. However, to date, little information is available regarding dicot seeds with a transient perisperm and a persistent, copious endosperm. Coffea arabica is the subject of increasing genomic research and is a model for nonorthodox albuminous dicot seeds of tropical origin. * The aim of this study was to reconstruct the metabolic pathways involved in the biosynthesis of the main coffee seed storage compounds, namely cell wall polysaccharides, triacylglycerols, sucrose, and chlorogenic acids. For this purpose, we integrated transcriptomic and metabolite analyses, combining real-time RT-PCR performed on 137 selected genes (of which 79 were uncharacterized in Coffea) and metabolite profiling. * Our map-drawing approach derived from model plants enabled us to propose a rationale for the peculiar traits of the coffee endosperm, such as its unusual fatty acid composition, remarkable accumulation of chlorogenic acid and cell wall polysaccharides. * Comparison with the developmental features of exalbuminous seeds described in the literature revealed that the two seed types share important regulatory mechanisms for reserve biosynthesis, independent of the origin and ploidy level of the storage tissue.

Due to its economic importance, Coffea arabica is becoming the subject of increasing genomic research and, in particular, the genes involved in the final chemical composition of the bean and the sensorial quality of the coffee beverage. The aim of the present study was to decipher the transcriptional networks that govern the development of the C. arabica seed, a model for non-orthodox albuminous seeds of tropical origin. For this purpose, we developed a transcriptomic approach combining two techniques: targeted cDNA arrays, containing 266 selected candidate gene sequences, and real-time RT-PCR on a large subset of 111 genes. The combination of the two techniques allowed us to limit detection of false positives and to reveal the advantages of using large real-time RT-PCR screening. Multivariate analysis was conducted on both datasets and results were broadly convergent. First, principle component analysis (PCA) revealed a dramatic re-programming of the transcriptional machinery between early cell division and elongation, storage and maturation phases. Second, hierarchical clustering analysis (HCA) led to the identification of 11 distinct patterns of gene expression during seed development as well as to the detection of genes expressed at specific developmental stages that can be used as functional markers of phenological changes. In addition, this study led to the description of gene expression profiles for quality-related genes, most of them formerly uncharacterised in Coffea. Their involvement in storage compound synthesis and accumulation during endosperm development and final metabolic re-adjustments during maturation is discussed.