Kartierung des Resistenzgens Rpv gegen den Falschen Mehltau bei der Erbse (Pisum sativum L.)

Downy mildew causes severe yield and quality losses in pea (Pisum sativum L.). Therefore, the development of downy mildew resistant varieties is of high priority for pea breeders. Within this study in total 335 F3 families from a cross of the highly susceptible green pea variety `Topaz´ with the resistant breeding line `Gen. 27´ were tested for resistance behaviour against Peronospora viciae f. sp. pisi to determine the genotype of the corresponding F2 parental plants. The ratio 94:161:80 for homozygous resistant, heterozygous resistant and homozygous susceptible F2 plants was not significantly different from 1:2:1, expected for the effect of a single dominant resistance gene. The resistance gene Rpv was mapped to linkage group 1 of the pea genetic map. This linkage group was saturated by molecular markers available from literature. The marker AD147 was identified as nearest proximal flanking marker with 4.4 cM distance, and in distal position marker AB28 with 18.8 cM distance. Further marker saturation using the syntenic relationship of P. sativum and Medicago truncatula was not effective. The usefulness of the identified markers for marker assisted selection has been confirmed in independent pea breeding material and the results of this study should lay the basis for future fine mapping studies.

Mapping of the Rpv Resistance Gene against Downy Mildew in Pea (Pisum sativum L.)

Die Entwicklung von Sorten, welche eine Resistenz gegen den Erreger des Falschen Mehltaus (Peronospora viciae f. sp. pisi) aufweisen, hat für Erbsenzüchter einen hohen Stellenwert, da dieser Pilz deutliche Ertragsminderungen und Qualitätseinbußen verursachen kann. In der vorliegenden Arbeit wurde eine spaltende Nachkommenschaft von 335 F2-Nachkommen einer Initialkreuzung zwischen der anfälligen Hochleistunssorte `Topaz´ und der resistenten Zuchtlinie `Gen. 27´ erstellt. Jeweils 10 F3-Nachkommen dieser 335 F2-Pflanzen wurden in der Klimakammer auf Resistenz gegen den Falschen Mehltau getestet um auf den Genotyp der ursprünglichen F2-Pflanze schließen zu können. Das Verhältnis von 94 homozygot resistenten, 161 heterozygot resistenten und 80 homozygot anfälligen F2-Pflanzen weicht nicht signifikant von einer 1:2:1-Spaltung ab, die für das Vorliegen eines dominanten Resistenzgens erwartet wird. Das Resis­tenzgen Rpv wurde auf Kopplungsgruppe 1 der Erbse kartiert. Diese Kopplungsgruppe wurde anschließend mit weiteren molekularen Markern, die in der Literatur beschrieben wurden, gesättigt. Der Marker AD147 wurde dabei als proximaler Marker mit der geringsten Distanz (4,4 cM) bestimmt, während in distaler Position AB28 eine Distanz von 18,8 cM aufwies. Durch Nutzung der Synthenie zum Medicago truncatula Chromosom 5 konnten keine enger gekoppelten Marker ermittelt werden. Der Nutzen der identifizierten Marker in unabhängigem Zuchtmaterial konnte jedoch demonstriert und auch die Basis für eine zukünftige Feinkartierung des Resis­tenzgens gelegt werden.Downy mildew causes severe yield and quality losses in pea (Pisum sativum L.). Therefore, the development of downy mildew resistant varieties is of high priority for pea breeders. Within this study in total 335 F3 families from a cross of the highly susceptible green pea variety `Topaz´ with the resistant breeding line `Gen. 27´ were tested for resistance behaviour against Peronospora viciae f. sp. pisi to determine the genotype of the corresponding F2 parental plants. The ratio 94:161:80 for homozygous resistant, heterozygous resistant and homozygous susceptible F2 plants was not significantly different from 1:2:1, expected for the effect of a single dominant resistance gene. The resistance gene Rpv was mapped to linkage group 1 of the pea genetic map. This linkage group was saturated by molecular markers available from lite­rature. The marker AD147 was identified as nearest proxi­mal flanking marker with 4.4 cM distance, and in distal position marker AB28 with 18.8 cM distance. Further marker saturation using the syntenic relationship of P. sativum and Medicago truncatula was not effective. The usefulness of the identified markers for marker assisted selection has been confirmed in independent pea breeding material and the results of this study should lay the basis for future fine mapping studies

Feinkartierung und Markerentwicklung für das Resistenzgen <em>Rrs2</em> gegen <em>Rhynchosporium secalis</em> in Gerste

The Rrs2 gene confers resistance to the fungal barley pathogen Rhynchosporium secalis. The gene was fine mapped to an interval of 0.08 cM on barley chromosome 7HS within a mapping population of 9179 F2-plants derived from a cross of Atlas (resistant) x Steffi (susceptible). Through physical mapping experiments and an association study it was discovered that, whenever present, the Rrs2 gene is localized in an area of suppressed recombination. It is supposed that this is due to a structural chromosomal rearrangement like an introgression or an inversion. The association study also resulted in the discovery of highly diagnostic SNPs for the Rrs2 resistance, which were converted into CAPS and Pyrosequencing markers. Those markers can be used in the breeding process for barley cultivars which carry the Rrs2 mediated resistance against Rhynchosporium secalis. Furthermore, three possible candidate genes for Rrs2 were identified in the study.Das Gen Rrs2 vermittelt Resistenz gegen den pilzlichen Erreger Rhynchosporium secalis, Verursacher der gleichnamigen Blattfleckenkrankheit in Gerste. Mithilfe einer 9179 F2-Pflanzen umfassenden Kartierungspopulation von Atlas (resistent) x Steffi (anfällig) wurde Rrs2 in einen 0,08 cM großen Bereich auf Chromosom 7HS kartiert. Durch die physikalische Kartierung und eine Assoziationsstudie wurde herausgefunden, dass das Rrs2 Gen in einem Bereich liegt, in dem Rekombination unterdrückt ist. Es wird vermutet, dass dies durch chromosomale Veränderungen infolge eines Introgressions- oder Inversionsereignisses in den Sorten, die das Rrs2 Gen tragen, hervorgerufen wird. Weiterhin wurden diagnostische SNPs für die Rrs2 Resistenz gefunden, welche in CAPS und Pyrosequenziermarker umgewandelt werden konnten. Die Marker können zur Selektion in der Gerstenzüchtung eingesetzt werden. Auch wurden drei mögliche Kandidatengene für Rrs2 identifiziert

Phosphatidylcholine (Lecithin) and the Mucus Layer: Evidence of Therapeutic Efficacy in Ulcerative Colitis?

Colonic mucus protects against attacks from bacteria in stool. One component of mucus is phosphatidylcholine (PC) which is thought to be arranged as continuous lamellar layer in the apical mucus and to be responsible for establishing a protective hydrophobic surface. This ‘intestinal surfactant’ plays a key role in mucosal defense. Thus, a defective PC layer contributes to the development of inflammation. Analysis of rectoscopically acquired mucus aliquots revealed a 70% decrease in PC content in ulcerative colitis (UC) compared to Crohn´s disease (CD) and healthy controls – independent of disease activity. Accordingly, we propose that lack of mucus PC is a key pathogenetic factor in UC. In clinical studies a delayed-release oral PC preparation (rPC) was found to substitute the lack of PC in rectal mucus. Indeed, in non-steroid-treated active UC, 53% of rPC patients reached remission [clinical activity index (CAI) ≤3] compared to 10% of placebo patients (p ≤ 0.001). Endoscopic and histologic findings improved concomitantly. A second trial with 60 chronic-active, steroid-dependent UC patients was conducted to test for steroid-sparing effects. Complete steroid withdrawal with a concomitant achievement of remission (CAI ≤3) or clinical response (≧50% CAI improvement) was reached in 15 PC-treated patients (50%) but only in 3 (10%) placebo patients (p = 0.002). In conclusion, intrinsic reduction of PC (lecithin) in colonic mucus may be a key pathogenetic feature of UC. Topical supplement of PC by a delayed-released oral PC preparation is effective in resolving inflammatory activity of UC and may develop to a first-choice therapy for this disease.</jats:p

UAV-Based Estimation of Grain Yield for Plant Breeding: Applied Strategies for Optimizing the Use of Sensors, Vegetation Indices, Growth Stages, and Machine Learning Algorithms

Non-destructive in-season grain yield (GY) prediction would strongly facilitate the selection process in plant breeding but remains challenging for phenologically and morphologically diverse germplasm, notably under high-yielding conditions. In recent years, the application of drones (UAV) for spectral sensing has been established, but data acquisition and data processing have to be further improved with respect to efficiency and reliability. Therefore, this study evaluates the selection of measurement dates, sensors, and spectral parameters, as well as machine learning algorithms. Multispectral and RGB data were collected during all major growth stages in winter wheat trials and tested for GY prediction using six machine-learning algorithms. Trials were conducted in 2020 and 2021 in two locations in the southeast and eastern areas of Germany. In most cases, the milk ripeness stage was the most reliable growth stage for GY prediction from individual measurement dates, but the maximum prediction accuracies differed substantially between drought-affected trials in 2020 (R2 = 0.81 and R2 = 0.68 in both locations, respectively), and the wetter, pathogen-affected conditions in 2021 (R2 = 0.30 and R2 = 0.29). The combination of data from multiple dates improved the prediction (maximum R2 = 0.85, 0.81, 0.61, and 0.44 in the four-year*location combinations, respectively). Among the spectral parameters under investigation, the best RGB-based indices achieved similar predictions as the best multispectral indices, while the differences between algorithms were comparably small. However, support vector machine, together with random forest and gradient boosting machine, performed better than partial least squares, ridge, and multiple linear regression. The results indicate useful GY predictions in sparser canopies, whereas further improvements are required in dense canopies with counteracting effects of pathogens. Efforts for multiple measurements were more rewarding than enhanced spectral information (multispectral versus RGB)

TECHNISCHE UNIVERSITÄT MÜNCHEN Lehrstuhl für Pflanzenzüchtung Fine Mapping and Marker Development for the Resistance Gene Rrs2 against Rhynchosporium secalis in Barley

Erlangung des akademischen Grades eine

The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations

Grain yield (GY) prediction based on non-destructive UAV-based spectral sensing could make screening of large field trials more efficient and objective. However, the transfer of models remains challenging, and is affected by location, year-dependent weather conditions and measurement dates. Therefore, this study evaluates GY modelling across years and locations, considering the effect of measurement dates within years. Based on a previous study, we used a normalized difference red edge (NDRE1) index with PLS (partial least squares) regression, trained and tested with the data of individual dates and date combinations, respectively. While strong differences in model performance were observed between test datasets, i.e., different trials, as well as between measurement dates, the effect of the train datasets was comparably small. Generally, within-trials models achieved better predictions (max. R2 = 0.27–0.81), but R2-values for the best across-trials models were lower only by 0.03–0.13. Within train and test datasets, measurement dates had a strong influence on model performance. While measurements during flowering and early milk ripeness were confirmed for within- and across-trials models, later dates were less useful for across-trials models. For most test sets, multi-date models revealed to improve predictions compared to individual-date models