Genomic characterization of a nematode tolerance locus in sugar beet.

BACKGROUND: Infection by beet cyst nematodes (BCN, Heterodera schachtii) causes a serious disease of sugar beet, and climatic change is expected to improve the conditions for BCN infection. Yield and yield stability under adverse conditions are among the main breeding objectives. Breeding of BCN tolerant sugar beet cultivars offering high yield in the presence of the pathogen is therefore of high relevance. RESULTS: To identify causal genes providing tolerance against BCN infection, we combined several experimental and bioinformatic approaches. Relevant genomic regions were detected through mapping-by-sequencing using a segregating F2 population. DNA sequencing of contrasting F2 pools and analyses of allele frequencies for variant positions identified a single genomic region which confers nematode tolerance. The genomic interval was confirmed and narrowed down by genotyping with newly developed molecular markers. To pinpoint the causal genes within the potential nematode tolerance locus, we generated long read-based genome sequence assemblies of the tolerant parental breeding line Strube U2Bv and the susceptible reference line 2320Bv. We analyzed continuous sequences of the potential locus with regard to functional gene annotation and differential gene expression upon BCN infection. A cluster of genes with similarity to the Arabidopsis thaliana gene encoding nodule inception protein-like protein 7 (NLP7) was identified. Gene expression analyses confirmed transcriptional activity and revealed clear differences between susceptible and tolerant genotypes. CONCLUSIONS: Our findings provide new insights into the genomic basis of plant-nematode interactions that can be used to design and accelerate novel management strategies against BCN.

Field Detection of Rhizoctonia Root Rot in Sugar Beet by Near Infrared Spectrometry.

Rhizoctonia root and crown rot (RRCR) is an important disease in sugar beet production areas, whose assessment and control are still challenging. Therefore, breeding for resistance is the most practical way to manage it. Although the use of spectroscopy methods has proven to be a useful tool to detect soil-borne pathogens through leaves reflectance, no study has been carried out so far applying near-infrared spectroscopy (NIRS) directly in the beets. We aimed to use NIRS on sugar beet root pulp to detect and quantify RRCR in the field, in parallel to the harvest process. For the construction of the calibration model, mainly beets from the field with natural RRCR infestation were used. To enrich the model, artificially inoculated beets were added. The model was developed based on Partial Least Squares Regression. The optimized model reached a Pearson correlation coefficient (R) of 0.972 and a Ratio of Prediction to Deviation (RPD) of 4.131. The prediction of the independent validation set showed a high correlation coefficient (R = 0.963) and a root mean square error of prediction (RMSEP) of 0.494. These results indicate that NIRS could be a helpful tool in the assessment of Rhizoctonia disease in the field.

The FLC-like gene BvFL1 is not a major regulator of vernalization response in biennial beets.

Many plant species in temperate climate regions require vernalization over winter to initiate flowering. Flowering Locus C (FLC) and FLC-like genes are key regulators of vernalization requirement and growth habit in winter-annual and perennial Brassicaceae. In the biennial crop species Beta vulgaris ssp. vulgaris in the evolutionarily distant Caryophyllales clade of core eudicots growth habit and bolting time are controlled by the vernalization and photoperiod response gene BTC1 and the downstream BvFT1-BvFT2 module. B. vulgaris also contains a vernalization-responsive FLC homolog (BvFL1). Here, to further elucidate the regulation of vernalization response and growth habit in beet, we functionally characterized BvFL1 by RNAi and over-expression in transgenic plants. BvFL1 RNAi neither eliminated the requirement for vernalization of biennial beets nor had a major effect on bolting time after vernalization. Over-expression of BvFL1 resulted in a moderate late-bolting phenotype, with bolting after vernalization being delayed by approximately 1 week. By contrast, RNAi-induced down-regulation of the BvFT1-BvFT2 module led to a strong delay in bolting after vernalization by several weeks. The data demonstrate for the first time that an FLC homolog does not play a major role in the control of vernalization response in a dicot species outside the Brassicaceae.

The role of a pseudo-response regulator gene in life cycle adaptation and domestication of beet.

Life cycle adaptation to latitudinal and seasonal variation in photoperiod and temperature is a major determinant of evolutionary success in flowering plants. Whereas the life cycle of the dicotyledonous model species Arabidopsis thaliana is controlled by two epistatic genes, FLOWERING LOCUS C and FRIGIDA, three unrelated loci (VERNALIZATION) determine the spring and winter habits of monocotyledonous plants such as temperate cereals. In the core eudicot species Beta vulgaris, whose lineage diverged from that leading to Arabidopsis shortly after the monocot-dicot split 140 million years ago, the bolting locus B is a master switch distinguishing annuals from biennials. Here, we isolated B and show that the pseudo-response regulator gene BOLTING TIME CONTROL 1 (BvBTC1), through regulation of the FLOWERING LOCUS T genes, is absolutely necessary for flowering and mediates the response to both long days and vernalization. Our results suggest that domestication of beets involved the selection of a rare partial loss-of-function BvBTC1 allele that imparts reduced sensitivity to photoperiod that is restored by vernalization, thus conferring bienniality, and illustrate how evolutionary plasticity at a key regulatory point can enable new life cycle strategies.

Genetic basis of agronomically important traits in sugar beet (Beta vulgaris L.) investigated with joint linkage association mapping.

Epistatic interactions may contribute substantially to the hybrid performance of sugar beet. The main goal of our study was to dissect the genetic basis of eight important physiological and agronomic traits using two different biometrical models for joint linkage association mapping. A total of 197 genotypes of an elite breeding population were evaluated in multi-location trials and fingerprinted with 194 SNP markers. Two different statistical models were used for the genome-wide scan for marker-trait associations: Model A, which corrects for the genetic background with markers as cofactors and Model B, which additionally models a population effect. Based on the extent of linkage disequilibrium in the parental population, we estimated that for a genome-wide scan at least 100 equally spaced markers are necessary. We mapped across the eight traits 39 QTL for Model A and 22 for Model B. Only 11% of the total number of QTL were identified based on Models A and B, which indicates that both models are complementary. Epistasis was detected only for two out of the eight traits, and contributed only to a minor extent to the genotypic variance. This low relevance of epistasis implies that in sugar beet breeding the prediction of performance of three-way hybrids is feasible with high accuracy based on the means of their single crosses.

Association mapping in multiple segregating populations of sugar beet (Beta vulgaris L.).

Association mapping in multiple segregating populations (AMMSP) combines high power to detect QTL in genome-wide approaches of linkage mapping with high mapping resolution of association mapping. The main objectives of this study were to (1) examine the applicability of AMMSP in a plant breeding context based on segregating populations of various size of sugar beet (Beta vulgaris L.), (2) compare different biometric approaches for AMMSP, and (3) detect markers with significant main effect across locations for nine traits in sugar beet. We used 768 F(n) (n = 2, 3, 4) sugar beet genotypes which were randomly derived from 19 crosses among diploid elite sugar beet clones. For all nine traits, the genotypic and genotype x location interaction variances were highly significant (P < 0.01). Using a one-step AMMSP approach, the total number of significant (P < 0.05) marker-phenotype associations was 44. The identification of genome regions associated with the traits under consideration indicated that not only segregating populations derived from crosses of parental genotypes in a systematic manner could be used for AMMSP but also populations routinely derived in plant breeding programs from multiple, related crosses. Furthermore, our results suggest that data sets, whose size does not permit analysis by the one-step AMMSP approach, might be analyzed using the two-step approach based on adjusted entry means for each location without losing too much power for detection of marker-phenotype associations.