Eunize Maciel-Zambolim

In silico identification of coffee genome expressed sequences potentially associated with resistance to diseases

Sequences potentially associated with coffee resistance to diseases were identified by in silico analyses using the database of the Brazilian Coffee Genome Project (BCGP). Keywords corresponding to plant resistance mechanisms to pathogens identified in the literature were used as baits for data mining. Expressed sequence tags (ESTs) related to each of these keywords were identified with tools available in the BCGP bioinformatics platform. A total of 11,300 ESTs were mined. These ESTs were clustered and formed 979 EST-contigs with similarities to chitinases, kinases, cytochrome P450 and nucleotide binding site-leucine rich repeat (NBS-LRR) proteins, as well as with proteins related to disease resistance, pathogenesis, hypersensitivity response (HR) and plant defense responses to diseases. The 140 EST-contigs identified through the keyword NBS-LRR were classified according to function. This classification allowed association of the predicted products of EST-contigs with biological processes, including host defense and apoptosis, and with molecular functions such as nucleotide binding and signal transducer activity. Fishers exact test was used to examine the significance of differences in contig expression between libraries representing the responses to biotic stress challenges and other libraries from the BCGP. This analysis revealed seven contigs highly similar to catalase, chitinase, protein with a BURP domain and unknown proteins. The involvement of these coffee proteins in plant responses to disease is discussed.

The effects of encoding data in diversity studies and the applicability of the weighting index approach for data analysis from different molecular markers

The use of molecular markers to study genetic diversity represents a breakthrough in this area, because of the increase in polymorphism levels and phenotypic neutrality. Codominant markers, such as microsatellites (SSR), are sensitive enough to distinguish the heterozygotes in genetic studies. Despite this advantage, there are some studies that ignore this feature and work with encoded data because of the simplicity of the evaluation, existence of polyploids and need for the combined analysis of different types of molecular markers. Thus, our study aims to investigate the consequences of these encodings on simulated and real data. In addition, we suggest an alternative analysis for genetic evaluations using different molecular markers. For the simulated data, we proposed the following two scenarios: the first uses SNP markers, and the second SSR markers. For real data, we used the SSR genotyping data from Coffea canephora accessions maintained in the Embrapa Germplasm Collection. The genetic diversity was studied using cluster analysis, the dissimilarity index, and the Bayesian approach implemented in the STRUCTURE software. For the simulated data, we observed a loss of genetic information to the encoded data in both scenarios. The same result was observed in the coffee studies. This loss of information was discussed in the context of a plant-breeding program, and the consequences were weighted to germplasm evaluations and the selection of parents for hybridization. In the studies that involved different types of markers, an alternative to the combined analysis is discussed, where the informativeness, coverage and quality of markers are weighted in the genetic diversity studies.

Chapter 2 – Molecular Markers

Genetic breeding programs of plants have had a significant impact on agricultural production. Despite the success of these programs, additional gains in breeding efficiency can be obtained through the application of molecular technologies. Prominent among the available molecular technologies is the analysis of DNA polymorphism, which has become an area of active research in major agronomic species and model plants. Molecular markers defined in this context have proven to be useful in plant breeding and in increasing our understanding of crop domestication, plant evolution, and the genetic mechanisms involved in agronomical traits.