Unravelling the regulatory network involved in heat-stress tolerance of potato plants (Pot-HotNet)

点击翻译按钮获取中文摘要

Potato (Solanum tuberosum) is one of the most important crops worldwide. Its popularity increases steadily due to its relatively easy cultivation and the high nutritional value of potato tubers. However, already slightly elevated temperatures have a strong negative impact on potato tuber formation, yield and quality. Also in Central Europe a higher frequency of warm periods is expected during the growing season of potato demonstrating that there is is an urgent need to breed new heat-stress tolerant cultivars to ensure yield stability. A better understanding of underlying molecular mechanisms will build the basis for these improvements. In previous studies, we analyzed the physiological, metabolic and molecular changes in response to elevated temperatures in a sensitive cultivar and identified processes that might contribute to increased temperature adaptation. Within the proposed project we will explore the broad spectrum of European cultivars to elucidate the molecular mechanisms and regulatory networks which confer heat tolerance. To this end, we plan to grow 200 different (breeder-selected) potato cultivars under control (20°C) and elevated temperatures (30°C) and to monitor phenotypic, morphological and biochemical changes allowing to group genotypes based on their response pattern. Preliminary experiments showed that the heat-mediated increase in shoot length negatively correlates with tuber yield. Shoot length is an easy to determine parameter which will be used for a genome-wide association study (GWAS). To facilitate this, all cultivars will be sequences (GBS) to detect alleles that correspond with heat stress tolerance. So far, most GWAS were performed in diploid species. Here, we will use and develop this approach for the tetraploid potato crop. Corresponding bioinformatic tools have been established in our lab. A pilot experiment with six cultivars revealed an appropriate sequencing depth and variability. Beside the genotyping will aim at investigating transcriptional changes in contrasting genotypes by RNA sequencing to identify genes that differ in their expression in sensitive and tolerant varieties. Furthermore, the transcriptome data will permit to analyze changes in gene expression in previously identified processes. Moreover, they will allow us to prove whether allelic variance is reflected at transcriptional level and thereby to narrow down candidate genes. In order to detect genome- transcriptome-phenome interactions all data will be integrated in a network analysis. Candidate genes will be verified independently and subsequently characterized in greater detail and used to develop diagnostic markers suitable for breeding programs.