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Echeverría-Beirute F, Murray SC, Bertrand B, Klein PE.2019. Candidate genes in coffee (Coffea arabica L.) leaves associated with rust (Hemileia vastatrix Berk. & Br) stress. PeerJ Preprints7:e27923v1https://doi.org/10.7287/peerj.preprints.27923v1
Background. Coffee leaf rust (CLR) caused by Hemileia vastatrix Berk. & Br, is one of the most threatening diseases for Coffea arabica L. It is hypothesized that host tolerance to CLR relies on non-race-specific resistance genes.
Methods. This study evaluated gene expression in leaves of two susceptible coffee cultivars (one inbred and one F1 hybrid) under different stress conditions: rust control (fungicide and untreated) and fruit thinning (thinned and un-thinned) treatments. RNA-seq analysis focused on the association of differentially expressed genes (DEGs) with CLR and associated the effect of the most significant genes into the phenotype, using regression and prediction statistical models.
Results. Gene expression and gene ontology (GO) analysis allowed identification of 100 genes associated with quantitative traits. From these, 88 were correlated with rust incidence, rust severity, and rust sporulation. The expression of genes coding for pathogenesis-related proteins increased positively with rust incidence in the inbred, while genes involved in homoeostasis and broader cell wall structuring processes were upregulated in the F1 hybrid. The enriched gene functions and associations revealed that a possible hypersensitive response (HR) in the inbred and a systemic acquired resistance (SAR) in the F1 hybrid were involved in the tolerance mechanisms to CLR stress. This is the first study to demonstrate the specific interactions between CLR and host at a molecular level, useful for identifying control targets for breeding perennial species.
Table S2. Identification of 128 DEGs at a read depth >0.5X when comparing the inbred vs hybrid that had known annotated descriptions
Gene ID and annotation are displayed according to the reference genome (Denoeud et al. 2014) . A positive fold change (FC) represents upregulated expression in the hybrid as compared to the inbred, while a negative FC represents upregulated expression in the inbred as compared to the hybrid. Average sequencing depth was calculated by averaging all samples and treatments as described by (Dugas et al. 2011) . The GO term number is presented for known genes using AgriGO 2.0 (Tian et al. 2017) . Any GO term with N/A represents unknown GO term. Statistical significance using Bonferroni and FDR are not shown since were lesser than 0.01. Additional GO description of some GO terms can be seen in Table S6.
Table S3. Gene ontology (GO) terms enriched between treatment when compared to the control
For each comparison, the treatments involved, regulation of expression (upregulated in…), number of differentially expressed genes (DEGs), and their corresponding gene ontology (GO) final branch terms, are summarized. The higher regulation indicates which treatment (C, R, T or R+T) resulted in increased expression of the gene. The final branch GO term represents the last node in the pathways were almost all other enriched gene ontologies converge. No distinction between the GO term classifications (biological process, molecular function, or cellular component) are specified.
Table S4. Genes found to be differentially expressed and associated to a trait according to the stepwise regression when compared the rust control treatment with no rust control
The regulation represents if the gene expression was upregulated (increased with an increase in the trait), or was downregulated (decreased with an increase of the trait). GO terms with several pathways, are referred to as “Several”, rather than a specific GO term code.
Table S6. Genes associated with disease-related traits when compared the rust control (R) vs no control (C) treatments in the hybrid
All the significant correlations ranged between |0.74| and |0.94|. A negative correlation indicates an increase in gene expression as the trait value increases, while positive correlations indicate an increase in gene expression as the trait value increases. Stepwise regression data is not shown.