In living organisms, small heat shock proteins (sHSPs) are triggered in response to stress situations. This family of proteins is large in plants and, in the case of tomato (Solanum lycopersicum), 33 genes have been identified, most of them related to heat stress response and to the ripening process. Transcriptomic and proteomic studies have revealed complex patterns of expression for these genes. In this work, we investigate the coregulation of these genes by performing a computational analysis of their promoter architecture to find regulatory motifs known as heat shock elements (HSEs). We leverage the presence of sHSP members that originated from tandem duplication events and analyze the promoter architecture diversity of the whole sHSP family, focusing on the identification of HSEs. We performed a search for conserved genomic sequences in the promoter regions of the sHSPs of tomato, plus several other proteins (mainly HSPs) that are functionally related to heat stress situations or to ripening. Several computational analyses were performed to build multiple sequence motifs and identify transcription factor binding sites (TFBS) homologous to HSF1AE and HSF21 in Arabidopsis. We also investigated the expression and interaction of these proteins under two heat stress situations in whole tomato plants and in protoplast cells, both in the presence and in the absence of heat shock transcription factor A2 (HsfA2). The results of these analyses indicate that different sHSPs are up-regulated depending on the activation or repression of HsfA2, a key regulator of HSPs. Further, the analysis of protein-protein interaction between the sHSP protein family and other heat shock response proteins (Hsp70, Hsp90 and MBF1c) suggests that several sHSPs are mediating alternative stress response through a regulatory subnetwork that is not dependent on HsfA2. Overall, this study identifies two regulatory motifs (HSF1AE and HSF21) associated with the sHSP family in tomato which are considered genomic HSEs. The study also suggests that, despite the apparent redundancy of these proteins, which has been linked to gene duplication, tomato sHSPs showed different up-regulation and different interaction patterns when analyzed under different stress situations.

Incorporating wild germplasm such as S. pimpinellifolium is an alternative strategy to prolong tomato fruit shelf life (SL) without reducing fruit quality. A set of recombinant inbred lines with discrepant values of SL and weight (FW) were derived by antagonistic-divergent selection from an interspecific cross. The general objective of this research was to evaluate Genotype x Year (GY) and Marker x Year (MY) interaction in these new genetic materials for both traits. Genotype and year principal effects and GY interaction were statistically significant for SL. Genotype and year principal effects were significant for FW but GY interaction was not. The marker principal effect was significant for SL and FW but both year principal effect and MY interaction were not significant. Though SL was highly influenced by year conditions, some genome regions appeared to maintain a stable effect across years of evaluation. Fruit weight, instead, was more independent of year effect.

The objective of this work was to evaluate a multiple factor analysis approach to estimate heritability for quality traits in tomatoes (Solanum lycopersicum), taking as a reference the values of heritability obtained through the classical method offspring-parent regression. A total of 18 F₃ tomato families originated from the selfing of 18 individuals from a second-cycle hybrid were evaluated. In both generations, ten quantitative traits related to fruit quality were assessed. The multiple factor analysis showed that some of the evaluated traits performed differently and others similarly between generations. Heritability is low or null for some traits, such as shelf life and reflectance percentage, and high for others, as weight and shape. That analysis also enables the study of the global connection between both generations through the RV (F₃, F₂) coefficient, which measures the correlation between two matrix configurations. RV was 0.503 and may be associated with the general heritability of all data. The multiple factor analysis is a valid multivariate technique to evaluate heritability for quality traits in tomatoes.