The Plant Resistance Genes database (PRGdb; http://prgdb.org/prgdb4/) has been greatly expanded, keeping pace with the increasing amount of available knowledge and data (sequenced proteomes, cloned genes, public analysis data, etc.). The easy-to-use style of the database website has been maintained, while an updated prediction tool, more data and a new section have been added. This new section will contain plant resistance transcriptomic experiments, providing additional easy-to-access experimental information. DRAGO3, the tool for automatic annotation and prediction of plant resistance genes behind PRGdb, has been improved in both accuracy and sensitivity, leading to more reliable predictions. PRGdb offers 199 reference resistance genes and 586.652 putative resistance genes from 182 sequenced proteomes. Compared to the previous release, PRGdb 4.0 has increased the number of reference resistance genes from 153 to 199, the number of putative resistance genes from 177K from 76 proteomes to 586K from 182 sequenced proteomes. A new section has been created that collects plant-pathogen transcriptomic data for five species of agricultural interest. Thereby, with these improvements and data expansions, PRGdb 4.0 aims to serve as a reference to the plant scientific community and breeders worldwide, helping to further study plant resistance mechanisms that contribute to fighting pathogens.

Many studies showed that few degrees above tomato optimum growth temperature threshold can lead to serious loss in production. Therefore, the development of innovative strategies to obtain tomato cultivars with improved yield under high temperature conditions is a main goal both for basic genetic studies and breeding activities. In this paper, a F4 segregating population was phenotypically evaluated for quantitative and qualitative traits under heat stress conditions. Moreover, a genotyping by sequencing (GBS) approach has been employed for building up genomic selection (GS) models both for yield and soluble solid content (SCC). Several parameters, including training population size, composition and marker quality were tested to predict genotype performance under heat stress conditions. A good prediction accuracy for the two analyzed traits (0.729 for yield production and 0.715 for SCC) was obtained. The predicted models improved the genetic gain of selection in the next breeding cycles, suggesting that GS approach is a promising strategy to accelerate breeding for heat tolerance in tomato. Finally, the annotation of SNPs located in gene body regions combined with QTL analysis allowed the identification of five candidates putatively involved in high temperatures response, and the building up of a GS model based on calibrated panel of SNP markers.

Main conclusion Dissimilar patterns of variants affecting genes involved in response to herbivory, including those leading to difference in VOC production, were identified in tomato lines with contrasting response to Tuta absoluta. Abstract Tuta absoluta is one of the most destructive insect pest affecting tomato production, causing important yield losses both in open field and greenhouse. The selection of tolerant varieties to T. absoluta is one of the sustainable approaches to control this invasive leafminer. In this study, the genomic diversity of two tomato varieties, one tolerant and the other susceptible to T. absoluta infestation was explored, allowing us to identify chromosome regions with highly dissimilar pattern. Genes affected by potential functional variants were involved in several processes, including response to herbivory and secondary metabolism. A metabolic analysis for volatile organic compounds (VOCs) was also performed, highlighting a difference in several classes of chemicals in the two genotypes. Taken together, these findings can aid tomato breeding programs aiming to develop tolerant plants to T. absoluta.