The plant disease resistance system involves a very complex regulatory network in which jasmonates play a key role in response to external biotic or abiotic stresses. As inhibitors of the jasmonic acid (JA) signaling pathway, JASMONATE ZIM domain (JAZ) proteins have been identified in many plant species, and their functions are gradually being clarified. In this study, 26 JAZ genes were identified in tomato. The physical and chemical properties, predicted subcellular localization, gene structure, cis-acting elements, and interspecies collinearity of 26 SlJAZ genes were subsequently analyzed. RNA-seq data combined with qRT-PCR analysis data showed that the expression of most SlJAZ genes were induced in response to Stemphylium lycopersici, methyl jasmonate (MeJA) and salicylic acid (SA). Tobacco rattle virus RNA2-based VIGS vector (TRV2)-SlJAZ25 plants were more resistant to tomato gray leaf spots than TRV2-00 plants. Therefore, we speculated that SlJAZ25 played a negative regulatory role in tomato resistance to gray leaf spots. Based on combining the results of previous studies and those of our experiments, we speculated that SlJAZ25 might be closely related to JA and SA hormone regulation. SlJAZ25 interacted with SlJAR1, SlCOI1, SlMYC2, and other resistance-related genes to form a regulatory network, and these genes played an important role in the regulation of tomato gray leaf spots. The subcellular localization results showed that the SlJAZ25 gene was located in the nucleus. Overall, this study is the first to identify and analyze JAZ family genes in tomato via bioinformatics approaches, clarifying the regulatory role of SlJAZ25 genes in tomato resistance to gray leaf spots and providing new ideas for improving plant disease resistance.

Understanding the genetic background of elite cultivated tomato germplasm resources in crossbreeding and revealing the genetic basis of complex traits are vital for better-targeted germplasm expansion and the creation of strong hybrids. Here, we obtained approximately 21 million single-nucleotide polymorphisms (SNPs) based on the sequencing of 212 cultivated tomato accessions and the population structure of which was revealed. More importantly, we found that target genes distributed on chromosomes 1, 5, 9, and 11 may be actively selected in breeding. In particular, the significant signals related to soluble sugar content (chr1_94170222, chr1_96273188, chr9_4167512, and chr11_55592768), fruit firmness (chr5_4384919 and chr5_5111452) and gray leaf spot resistance (chr11_8935252 and chr11_9707993) were also detected on the corresponding chromosomes, respectively. Overall, we reported 28 significant association signals for nine agronomic traits based on a mixed linear model (MLM), including 114 genes. Among these signals, 21 contained potential novel genes for six fruit traits. These novel candidate genes located in genomic regions without previously known loci or on different chromosomes explained approximately 16% of the phenotypic variance on average in cultivated tomatoes. These findings could accelerate the identification and validation of novel and known candidate genes and QTLs, improving the understanding of the genetic structure of complex quantitative traits. These results also provide a basis for tomato breeding and improvement.

The TONNEAU1 Recruited Motif (TRM) gene family is integral to the growth and development of various plants, playing a particularly critical role in regulating the shape of plant organs. While the functions of the TRM gene family in other plant species have been documented, knowledge regarding the members of the tomato (Solanum lycopersicum). SlTRM gene family remains limited, and their specific roles are not yet well understood. In this study, we identified and analyzed 28 members of the SlTRM gene family in tomato using bioinformatics approaches based on the latest whole genome data. Our analysis included the examination of protein structures, physicochemical properties, collinearity analysis, gene structures, conserved motifs, and promoter cis-acting elements of the SlTRM gene family members. The phylogenetic analysis indicated that both tomato and Arabidopsis thaliana are categorized into five distinct subfamilies. Furthermore, we conducted a three-dimensional structure prediction of 28 SlTRM genes for the first time, utilizing AlphaFold3, a deep learning architecture developed by DeepMind. Subsequently, we analyzed public transcriptome data to assess the tissue specificity of these 28 genes. Additionally, we examined the expression of SlTRM genes using RNA-seq and qRT-PCR techniques. Our analysis revealed that SlTRM25 was significantly differentially expressed, leading us to hypothesize that it may be involved in the development of lateral branches in tomatoes. Finally, we predicted the regulatory interaction network of SlTRM25 and identified that it interacts with genes such as SlFAF3/4b, SlCSR-like1, SlCSR-like2, and SlTRM19. This study serves as a reference for the investigation of the tomato TRM gene family members and introduces a novel perspective on the role of this gene family in the formation of lateral branches in tomatoes, offering both theoretical and practical significance.

To comprehensively evaluate 212 tomato accessions and gain an in-depth understanding of their genetic background, 17 major agronomic traits were assessed, and whole-genome resequencing was performed on all materials. The results indicated that the genetic diversity of quantitative traits was abundant, with the genetic diversity index (H′) ranging from 0.79 to 2.03. In contrast, the genetic diversity of qualitative traits was relatively low, with the H′ falling between 0.363 and 0.936. The relationships between phenotypes were intricate and characterized by information redundancy. The top five principal components captured 69.014% of the phenotypic information. The F and D score-based comprehensive evaluation methods demonstrated strong consistency (r = 0.955 **). Among the 17 accessions ranked in the top 20 by both scores, 12 belonged to the large-fruit yellowish late-ripening type with distinct genetic backgrounds. These preliminary efforts not only provide an idea, a framework and a dataset for the integrated analysis of phenotypic evaluation, phenotypic clustering and population structure, but also lay a foundation for future inter-data network research, as well as potential material support for subsequent breeding efforts.