Kenneth Street

Unlocking wheat genetic resources for the molecular identification of previously undescribed functional alleles at the Pm3 resistance locus

The continuous improvement of crop plants is essential for agriculture in the coming decades and relies on the use of genetic variability through breeding. However, domestication and modern breeding have reduced diversity in the crop germplasm. Global gene banks conserve diversity, but these resources remain underexplored owing to a lack of efficient strategies to isolate important alleles. Here we describe a large-scale allele-mining project at the molecular level. We first selected a set of 1,320 bread wheat landraces from a database of 16,089 accessions, using the focused identification of germplasm strategy. On the basis of a hierarchical selection procedure on this set, we then isolated 7 resistance alleles of the powdery mildew resistance gene Pm3, doubling the known functional allelic diversity at this locus. This targeted approach for molecular utilization of gene bank accessions reveals landraces as a rich resource of new functional alleles. This strategy can be implemented for other studies on the molecular diversity of agriculturally important genes, as well as for molecular breeding.

Molecular approaches for characterization and use of natural disease resistance in wheat

Wheat production is threatened by a constantly changing population of pathogen species and races. Given the rapid ability of many pathogens to overcome genetic resistance, the identification and practical implementation of new sources of resistance is essential. Landraces and wild relatives of wheat have played an important role as genetic resources for the improvement of disease resistance. The use of molecular approaches, particularly molecular markers, has allowed better characterization of the genetic diversity in wheat germplasm. In addition, the molecular cloning of major resistance (R) genes has recently been achieved in the large, polyploid wheat genome. For the first time this allows the study and analysis of the genetic variability of wheat R loci at the molecular level and therefore, to screen for allelic variation at such loci in the gene pool. Thus, strategies such as allele mining and ecotilling are now possible for characterization of wheat disease resistance. Here, we discuss the approaches, resources and potential tools to characterize and utilize the naturally occurring resistance diversity in wheat. We also report a first step in allele mining, where we characterize the occurrence of known resistance alleles at the wheat Pm3 powdery mildew resistance locus in a set of 1,320 landraces assembled on the basis of eco-geographical criteria. From known Pm3 R alleles, only Pm3b was frequently identified (3% of the tested accessions). In the same set of landraces, we found a high frequency of a Pm3 haplotype carrying a susceptible allele of Pm3. This analysis allowed the identification of a set of resistant lines where new potentially functional alleles would be present. Newly identified resistance alleles will enrich the genetic basis of resistance in breeding programmes and contribute to wheat improvement.

The FIGS (Focused Identification of Germplasm Strategy) Approach Identifies Traits Related to Drought Adaptation in Vicia faba Genetic Resources

Efficient methods to explore plant agro-biodiversity for climate change adaptive traits are urgently required. The focused identification of germplasm strategy (FIGS) is one such approach. FIGS works on the premise that germplasm is likely to reflect the selection pressures of the environment in which it developed. Environmental parameters describing plant germplasm collection sites are used as selection criteria to improve the probability of uncovering useful variation. This study was designed to test the effectiveness of FIGS to search a large faba bean (Vicia faba L.) collection for traits related to drought adaptation. Two sets of faba bean accessions were created, one from moisture-limited environments, and the other from wetter sites. The two sets were grown under well watered conditions and leaf morpho-physiological traits related to plant water use were measured. Machine-learning algorithms split the accessions into two groups based on the evaluation data and the groups created by this process were compared to the original climate-based FIGS sets. The sets defined by trait data were in almost perfect agreement to the FIGS sets, demonstrating that ecotypic differentiation driven by moisture availability has occurred within the faba bean genepool. Leaflet and canopy temperature as well as relative water content contributed more than other traits to the discrimination between sets, indicating that their utility as drought-tolerance selection criteria for faba bean germplasm. This study supports the assertion that FIGS could be an effective tool to enhance the discovery of new genes for abiotic stress adaptation.

Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables

Recent studies have shown that novel genetic variation for resistance to pests and diseases can be detected in plant genetic resources originating from locations with an environmental profile similar to the collection sites of a reference set of accessions with known resistance, based on the Focused Identification of Germplasm Strategy (FIGS) approach. FIGS combines both the development of a priori information based on the quantification of the trait-environment relationship and the use of this information to define a best bet subset of accessions with a higher probability of containing new variation for the sought after trait(s). The present study investigates the development strategy of the a priori information using different modeling techniques including learning-based techniques as a follow up to previous work where parametric approaches were used to quantify the stem rust resistance and climate variables relationship. The results show that the predictive power, derived from the accuracy parameters and cross-validation, varies depending on whether the models are based on linear or non-linear approaches. The prediction based on learning techniques are relatively higher indicating that the non-linear approaches, in particular support vector machine and neural networks, outperform both principal component logistic regression and generalized partial least squares. Overall there are indications that the trait distribution of resistance to stem rust is confined to certain environments or areas, whereas the susceptible types appear to be limited to other areas with some degree of overlapping of the two classes. The results also point to a number of issues to consider for improving the predictive performance of the models.

Diversity analysis of Central Asia and Caucasian lentil (Lens culinaris Medik.) germplasm using SSR fingerprinting.

Diversity analysis was performed among 39 cultivated lentil (Lensculinaris Medik.) accessions of Central Asia and Caucasian origin using five highly polymorphic microsatellite markers. A total of 33 alleles determined ranging from 3 to 8 per locus. Estimated gene diversity value for 33 loci was 0.66. Genetic similarity indices among 39 accessions ranged from 0.24 to 1.0. Cluster analysis using the unweighted pair group method with arithmetic mean method classified accessions into six major groups at 0.5 similarity coefficient. More than half accessions from Tajikistan formed large cluster. On the other hand, a few accessions from each country showed unique genotypes. Overall, most of the accessions, except ones with closely related origin, were distinguished by the present high quality DNA fingerprinting. This molecular diversity information gives important basis for conservation strategy in gene bank and exotic germplasm introduction in breeding programs in Central Asia and Caucasian countries.

Do faba bean ( Vicia faba L.) accessions from environments with contrasting seasonal moisture availabilities differ in stomatal characteristics and related traits

Drought is a major constraint to faba bean (Vicia faba L.) production, and there are many mechanisms by which leaves can regulate water loss. Our primary objective was to test if the origin of the faba bean accessions, from drought-prone and non-drought-prone environments, was associated with differences in measurable aspects of stomatal morphology and physiology related to water use. Two sets, each consisting of 201 faba bean accessions, were chosen from environments with contrasting seasonal moisture profiles following the focused identification of germplasm strategy (FIGS), and then screened under well watered conditions. From these, two subsets of 10 accessions each were chosen to test for differences in response to drought. Parameters related to stomatal function and water status were measured. The dry-adapted set had bigger stomata, higher leaf relative water content (LRWC) and cooler leaves under well watered conditions. Stomatal density and stomatal area per unit area of leaflet were negatively correlated with gas exchange parameters and positively correlated with intrinsic water use efficiency. Drought caused stomatal densities to increase in the dry set while stomatal length decreased in both sets. The moisture deficit was sufficient to decrease gas exchange in both sets to similar levels, but the dry-adapted set maintained warmer leaves and a higher LRWC that showed no significant correlations with leaf morphology or gas exchange, demonstrating more effective stomatal regulation. The results also support that collection site data from the environment where genetic resources are collected can be used as indicators of adaptive traits in an herbaceous annual species.

In silico evaluation of plant genetic resources to search for traits for adaptation to climate change

Plant genetic resources display patterns resulting from ecological and co-evolutionary processes. Such patterns are instrumental in tracing the origin and diversity of crops and locating adaptive traits. With climate change and the anticipated increase in demand for food, new crop varieties will be needed to perform under unprecedented climatic conditions. In the present study, we explored genetic resources patterns to locate traits of adaptation to drought and to maximize the utilization of plant genetic resources lacking ex ante evaluation for emerging climate conditions. This approach is based on the use of mathematical models to predict traits as response variables driven by stochastic ecological and co-evolutionary processes. The high congruence of metrics between model predictions and empirical trait evaluations confirms in silico evaluation as an effective tool to manage large numbers of crop accessions lacking ex ante evaluation. This outcome will assist in developing cultivars adaptable to various climatic conditions and in the ultimate use of genetic resources to sustain agricultural productivity under conditions of climate change.