Pierre Charmetant

Genetics of coffee quality

Coffee quality, in the present context of overproduction worldwide, has to be considered as a main selection criterion for coffee improvement. After a definition of quality, and an overview of the non genetic factors affecting its variation, this review focuses on the genetic factors involved in the control of coffee quality variation. Regarding the complexity of this trait, the different types of quality are first presented. Then, the great variation within and between coffee species is underlined, mainly for biochemical compounds related to quality (caffeine, sugars, chlorogenic acids, lipids). The ways for breeding quality traits for cultivated species, Coffea arabica and Coffea canephora are discussed, with specific challenges for each species. For C. arabica, maintaining a good quality in F 1 intraspecific hybrids, introgressed lines from Timor hybrid, and grafted varieties are the main challenges. For C. canephora, the improvement is mainly based on intraspecific and interspecific hybrids, using the whole genetic variability available within this species. An improvement is obtained for bean size, with significant genetic gains in current breeding programmes. The content in biochemical compounds related to cup quality is another way to improve Robusta quality. Finally, ongoing programmes towards the understanding of the molecular determinism of coffee quality, particularly using coffee ESTs, are presented.

Transcriptome analysis in Coffea eugenioides, an Arabica coffee ancestor, reveals differentially expressed genes in leaves and fruits.

Studies in diploid parental species of polyploid plants are important to understand their contributions to the formation of plant and species evolution. Coffeaeugenioides is a diploid species that is considered to be an ancestor of allopolyploid Coffeaarabica together with Coffeacanephora. Despite its importance in the evolutionary history of the main economic species of coffee, no study has focused on C. eugenioides molecular genetics. RNA-seq creates the possibility to generate reference transcriptomes and identify coding genes and potential candidates related to important agronomic traits. Therefore, the main objectives were to obtain a global overview of transcriptionally active genes in this species using next-generation sequencing and to analyze specific genes that were highly expressed in leaves and fruits with potential exploratory characteristics for breeding and understanding the evolutionary biology of coffee. A de novo assembly generated 36,935 contigs that were annotated using eight databases. We observed a total of ~5000 differentially expressed genes between leaves and fruits. Several genes exclusively expressed in fruits did not exhibit similarities with sequences in any database. We selected ten differentially expressed unigenes in leaves and fruits to evaluate transcriptional profiles using qPCR. Our study provides the first gene catalog for C. eugenioides and enhances the knowledge concerning the mechanisms involved in the C. arabica homeologous. Furthermore, this work will open new avenues for studies into specific genes and pathways in this species, especially related to fruit, and our data have potential value in assisted breeding applications.

Application of near infrared spectroscopy for green coffee biochemical phenotyping

Accessions resulting from surveys in Ethiopia (the centre of origin of Arabica coffee) can be used as a source of genetic variability in breeding coffee plants. They may contain some genes of interest for coffee breeding, specifically in relation to beverage quality. Near infrared (NIR) spectroscopy was used to develop models for predicting the major coffee constituents related to quality beverage (proteins, caffeine, lipids, chlorogenic acids, phenolic compounds, total sugars and sucrose). We selected coffee samples listed in a database containing data of chemical contents from samples of traditional and modern cultivars and of Ethiopian accessions to construct models to predict these compounds. Spectra were collected between 1100 nm and 2500 nm, and mathematical pretreatments were applied. The number of samples for the calibration step for each compound was set so as to be representative of distribution values. Cross-validation was performed on the total set of samples to select the optimal number of terms for the prediction models of each component. The prediction models were developed employing a modified partial least-squares regression. The total set of samples for each component was divided randomly into two subsets: one for developing the prediction model and the other to evaluate the predicted values. The best prediction models obtained were for chlorogenic acids (r2 = 0.94, RPD = 4.16), proteins (r2 = 0.94, RPD = 4.09) and caffeine (r2 = 0.92, RPD = 4.16). Models for lipids and phenolic compounds were not as accurate (r2 = 0.87, RPD = 2.77 and r2 = 0.86, RPD = 2.62, respectively), while models for sucrose (r2 = 0.84, RPD = 2.44) and total sugars (r2 = 0.85, RPD = 2.55) were even less accurate. All these models can be used for identifying coffee lines with more desirable traits in breeding programmes. The models were effective in discriminating Ethiopian coffee accessions from modern cultivars of coffee. Additionally, the NIR technique will make it possible to analyse a large number of samples in breeding programmes and may be used as a high-throughput analysis for green coffee phenotyping.