JOURNAL ARTICLE

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

Priscila Mary Yuyama, Osvaldo Reis Júnior, Suzana Tiemi Ivamoto, Douglas Silva Domingues, Marcelo Falsarella Carazzolle, Gonçalo Amarante Guimarães Pereira, Pierre Charmetant, Thierry Leroy, Luiz Filipe Protasio Pereira
Molecular Genetics and Genomics: MGG 2016, 291 (1): 323-36
26334613
Studies in diploid parental species of polyploid plants are important to understand their contributions to the formation of plant and species evolution. Coffea eugenioides is a diploid species that is considered to be an ancestor of allopolyploid Coffea arabica together with Coffea canephora. 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.

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