Identification of heat-tolerance QTLs and high-temperature stress-responsive genes through conventional QTL mapping, QTL-seq and RNA-seq in tomato

Abstract

Background High temperature is one of the major abiotic stresses in tomato and greatly reduces fruit yield and quality. Identifying high-temperature stress-responsive (HSR) genes and breeding heat-tolerant varieties is an effective way to address this issue. However, there are few reports on the fine mapping of heat-tolerance quantitative trait locus (QTL) and the identification of HSR genes in tomato. Here, we applied three heat tolerance-related physiological indexes, namely, relative electrical conductivity (REC), chlorophyll content (CC) and maximum photochemical quantum efficiency (Fv/Fm) of PSII (photosystem II), as well as the phenotypic index, the heat injury index (HII), and conventional QTL analysis combined with QTL-seq technology to comprehensively detect heat-tolerance QTLs in tomato seedlings. In addition, we integrated the QTL mapping results with RNA-seq to identify key HSR genes within the major QTLs. Results A total of five major QTLs were detected: qHII-1-1, qHII-1-2, qHII-1-3, qHII-2-1 and qCC-1-5 (qREC-1-3). qHII-1-1, qHII-1-2 and qHII-1-3 were located, respectively, in the intervals of 1.43, 1.17 and 1.19 Mb on chromosome 1, while the interval of qHII-2-1 was located in the intervals of 1.87 Mb on chromosome 2. The locations observed with conventional QTL mapping and QTL-seq were consistent. qCC-1-5 and qREC-1-3 for CC and REC, respectively, were located at the same position by conventional QTL mapping. Although qCC-1-5 was not detected in QTL-seq analysis, its phenotypic variation (16.48%) and positive additive effect (0.22) were the highest among all heat tolerance QTLs. To investigate the genes involved in heat tolerance within the major QTLs in tomato, RNA-seq analysis was performed, and four candidate genes (SlCathB2, SlGST, SlUBC5, and SlARG1) associated with heat tolerance were finally detected within the major QTLs by DEG analysis, qRT-PCR screening and biological function analysis. Conclusions In conclusion, this study demonstrated that the combination of conventional QTL mapping, QTL-seq analysis and RNA-seq can rapidly identify candidate genes within major QTLs for a complex trait of interest to replace the fine-mapping process, thus greatly shortening the breeding process and improving breeding efficiency. The results have important applications for the fine mapping and identification of HSR genes and breeding for improved thermotolerance.

Transgenic Breeding Approaches for Improving Abiotic Stress Tolerance: Recent Progress and Future Perspectives

“…Three TOF-related QTL were stable, and four TOF-QTL were detected in the corresponding genomic locations of the flowering QTL of M. truncatula, an indication of possible evolutionarily conserved regions [29]. The potential candidate genes for the SNP sequences of QTL regions were identified for all three traits, and these genes would be potential targets for further molecular studies [30].…”

“…Studies identified specific resistance loci through QTL mapping and assessed the race-specificity of resistance and interactive genes involved in the plant developmental process under specific environmental conditions [26,29]. These studies provide insights into the number of QTL involved in complex disease resistance, epistatic and environmental interactions, race-specificity of partial resistance loci, interactions between pathogen biology, plant development and biochemistry, and the relationship between qualitative and quantitative loci [30]. QTL mapping also provides a framework for the marker-assisted selection of complex disease resistance characters and the positional cloning of partial resistance genes [29].…”

“…A number of important QTL were identified to control key traits in plants. Recently, 25 QTL were identified for timing of flowering (TOF), 17 QTL for spring yield, and 6 QTL for cumulative summer biomass in Medicago sativa L [30]. Three TOF-related QTL were stable, and four TOF-QTL were detected in the corresponding genomic locations of the flowering QTL of M. truncatula, an indication of possible evolutionarily conserved regions [29].…”

“…These QTL, markers and the potential candidate genes associated with spring flowering time and the biomass yield of alfalfa constitute valuable genomic resources for improving these traits via marker-assisted selection (MAS). Another study reported five major QTL, qHII-1-1, qHII-1-2, qHII-1-3, qHII-2-1, and qCC-1-5 (qREC-1-3), in tomatoes involved in high temperature stress tolerance [30]. The authors reported that qHII-1-1, qHII-1-2, and qHII-1-3 were located, respectively, in the intervals of 1.43, 1.17, and 1.19 Mb on chromosome 1, while qHII-2-1 was located in the interval of 1.87 Mb on chromosome 2.…”

“…The findings concluded that the combination of conventional QTL mapping, QTL-seq, and RNA-seq analysis can rapidly identify candidate genes, thus greatly shortening the breeding process and improving the breeding efficiency [31]. QTL can work as an excellent tool for translational genomics and the breeding of quantitative traits in a variety of plants through comparative genomics approaches [26,27,29,30]. These studies clearly demonstrated the importance of QTL identification and hence offered new and unique markers ( Figure 3) with great capabilities to contribute to and improve abiotic stress tolerance and yields.…”