The chloroplast protein HCF164 is predicted to be associated with Coffea SH9 resistance factor against Hemileia vastatrix

To explore the connection between chloroplast and coffee resistance factors, designated as SH1 to SH9, whole genomic DNA of 42 coffee genotypes was sequenced, and entire chloroplast genomes were de novo assembled. The chloroplast phylogenetic haplotype network clustered individuals per species instead of SH factors. However, for the first time, it allowed the molecular validation of Coffea arabica as the maternal parent of the spontaneous hybrid "Híbrido de Timor". Individual reads were also aligned on the C. arabica reference genome to relate SH factors with chloroplast metabolism, and an in-silico analysis of selected nuclear-encoded chloroplast proteins (132 proteins) was performed. The nuclear-encoded thioredoxin-like membrane protein HCF164 enabled the discrimination of individuals with and without the SH9 factor, due to specific DNA variants linked to chromosome 7c (from C. canephora-derived sub-genome). The absence of both the thioredoxin domain and redox-active disulphide center in the HCF164 protein, observed in SH9 individuals, raises the possibility of potential implications on redox regulation. For the first time, the identification of specific DNA variants of chloroplast proteins allows discriminating individuals according to the SH profile. This study introduces an unexplored strategy for identifying protein/genes associated with SH factors and candidate targets of H. vastatrix effectors, thereby creating new perspectives for coffee breeding programs.

Metabolic modelling in a dynamic evolutionary framework predicts adaptive diversification of bacteria in a long-term evolution experiment

BACKGROUND

Predicting adaptive trajectories is a major goal of evolutionary biology and useful for practical applications. Systems biology has enabled the development of genome-scale metabolic models. However, analysing these models via flux balance analysis (FBA) cannot predict many evolutionary outcomes including adaptive diversification, whereby an ancestral lineage diverges to fill multiple niches. Here we combine in silico evolution with FBA and apply this modelling framework, evoFBA, to a long-term evolution experiment with Escherichia coli.

RESULTS

Simulations predicted the adaptive diversification that occurred in one experimental population and generated hypotheses about the mechanisms that promoted coexistence of the diverged lineages. We experimentally tested and, on balance, verified these mechanisms, showing that diversification involved niche construction and character displacement through differential nutrient uptake and altered metabolic regulation.

CONCLUSION

The evoFBA framework represents a promising new way to model biochemical evolution, one that can generate testable predictions about evolutionary and ecosystem-level outcomes.