RESUMEN
Climate changes leading to increasingly longer seasonal drought periods in large parts of the world increase the necessity for breeding drought-tolerant crops. Cultivated potato (Solanum tuberosum), the third most important vegetable crop worldwide, is regarded as drought-sensitive due to its shallow root architecture. Two German tetraploid potato cultivars differing in drought tolerance and their F1-progeny were evaluated under various drought scenarios. Bulked segregant analyses were combined with whole-genome sequencing (BSA-Seq) using contrasting bulks of drought-tolerant and drought-sensitive F1-clones. Applying QTLseqr, 15 QTLs comprising 588,983 single nucleotide polymorphisms (SNPs) in 2325 genes associated with drought stress tolerance were identified. SeqSNP analyses in an association panel of 34 mostly starch potato varieties using 1-8 SNPs for each of 188 selected genes narrowed the number of candidate genes down to 10. In addition, ent-kaurene synthase B was the only gene present under QTL 10. Eight of the identified genes (StABP1, StBRI1, StKS, StLEA, StPKSP1, StPKSP2, StYAB5, and StZOG1) address plant development, the other three genes (StFATA, StHGD and StSYP) contribute to plant protection under drought stress. Allelic variation in these genes might be explored in future breeding for drought-tolerant potato varieties.
Asunto(s)
Resistencia a la Sequía , Solanum tuberosum , Humanos , Solanum tuberosum/genética , Tetraploidía , Fitomejoramiento , SequíasRESUMEN
Drought represents a major abiotic stress factor negatively affecting growth, yield and tuber quality of potatoes. Quantitative trait locus (QTL) analyses were performed in cultivated potatoes for drought tolerance index DRYM (deviation of relative starch yield from the experimental median), tuber starch content, tuber starch yield, tuber fresh weight, selected transcripts and metabolites under control and drought stress conditions. Eight genomic regions of major interest for drought tolerance were identified, three representing standalone DRYM QTL. Candidate genes, e.g., from signaling pathways for ethylene, abscisic acid and brassinosteroids, and genes encoding cell wall remodeling enzymes were identified within DRYM QTL. Co-localizations of DRYM QTL and QTL for tuber starch content, tuber starch yield and tuber fresh weight with underlying genes of the carbohydrate metabolism were observed. Overlaps of DRYM QTL with metabolite QTL for ribitol or galactinol may indicate trade-offs between starch and compatible solute biosynthesis. Expression QTL confirmed the drought stress relevance of selected transcripts by overlaps with DRYM QTL. Bulked segregant analyses combined with next-generation sequencing (BSAseq) were used to identify mutations in genes under the DRYM QTL on linkage group 3. Future analyses of identified genes for drought tolerance will give a better insight into drought tolerance in potatoes.
Asunto(s)
Cromosomas de las Plantas/genética , Sequías , Genoma de Planta , Polimorfismo de Nucleótido Simple , Sitios de Carácter Cuantitativo , Solanum tuberosum/genética , Tetraploidía , Mapeo Cromosómico , Ligamiento Genético , Genómica , Fenotipo , Tubérculos de la Planta/genética , Solanum tuberosum/fisiologíaRESUMEN
Based on a recently introduced immunohistochemical panel (Bachmann et al. 2015) for aganglionic megacolon (AM), also known as Hirschsprung disease, histopathological diagnosis, we evaluated whether the use of digital pathology and 'machine learning' could help to obtain a reliable diagnosis. Slides were obtained from 31 specimens of 27 patients immunohistochemically stained for MAP2, calretinin, S100ß and GLUT1. Slides were digitized by whole slide scanning. We used a Definiens Developer Tissue Studios as software for analysis. We configured necessary parameters in combination with 'machine learning' to identify pathological aberrations. A significant difference between AM- and non-AM-affected tissues was found for calretinin (AM 0.55% vs. non-AM 1.44%) and MAP2 (AM 0.004% vs. non-AM 0.07%) staining measurements and software-based evaluations. In contrast, S100ß and GLUT1 staining measurements and software-based evaluations showed no significant differences between AM- and non-AM-affected tissues. However, no difference was found in comparison of suction biopsies with resections. Applying machine learning via an ensemble voting classifier, we achieved an accuracy of 87.5% on the test set. Automated diagnosis of AM by applying digital pathology on immunohistochemical panels was successful for calretinin and MAP2, whereas S100ß and GLUT1 were not effective in diagnosis. Our method suggests that software-based approaches are capable of diagnosing AM. Our future challenge will be the improvement of efficiency by reduction of the time-consuming need for large pre-labelled training data. With increasing technical improvement, especially in unsupervised training procedures, this method could be helpful in the future.