On-field phenotypic evaluation of sunflower populations for broad-spectrum resistance to Verticillium leaf mottle and wilt.

Sunflower Verticillium Wilt and Leaf Mottle (SVW), caused by Verticillium dahliae (Kleb.; Vd), is a soil-borne disease affecting sunflower worldwide. A single dominant locus, known as V1, was formerly effective in controlling North-American Vd races, whereas races from Argentina, Europe and an emerging race from USA overcome its resistance. This emphasizes the need for identifying broad-spectrum genetic resistance (BSR) sources. Here we characterize two sunflower mapping populations (MPs) for SVW resistance: a biparental MP and the association MP from the National Institute of Agricultural Technology (INTA), under field growing conditions. Nine field-trials (FTs) were conducted in highly infested fields in the most SVW-affected region of Argentina. Several disease descriptors (DDs), including incidence and severity, were scored across four phenological stages. Generalized linear models were fitted according to the nature of each variable, adjusting mean phenotypes for inbred lines across and within FTs. Comparison of these responses allowed the identification of novel BSR sources. Furthermore, we present the first report of SVW resistance heritability, with estimates ranging from 35 to 45% for DDs related to disease incidence and severity, respectively. This study constitutes the largest SVW resistance characterization reported to date in sunflower, identifying valuable genetic resources for BSR-breeding to cope with a pathogen of increasing importance worldwide.

Main and epistatic QTL analyses for Sclerotinia Head Rot resistance in sunflower.

Sclerotinia Head Rot (SHR), a disease caused by Sclerotinia sclerotiorum, is one of the most limiting factors in sunflower production. In this study, we identified genomic loci associated with resistance to SHR to support the development of assisted breeding strategies. We genotyped 114 Recombinant Inbred Lines (RILs) along with their parental lines (PAC2 -partially resistant-and RHA266 -susceptible-) by using a 384 single nucleotide polymorphism (SNP) Illumina Oligo Pool Assay to saturate a sunflower genetic map. Subsequently, we tested these lines for SHR resistance using assisted inoculations with S. sclerotiorum ascospores. We also conducted a randomized complete-block assays with three replicates to visually score disease incidence (DI), disease severity (DS), disease intensity (DInt) and incubation period (IP) through four field trials (2010-2014). We finally assessed main effect quantitative trait loci (M-QTLs) and epistatic QTLs (E-QTLs) by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively. As a result of this study, the improved map incorporates 61 new SNPs over candidate genes. We detected a broad range of narrow sense heritability (h2) values (1.86-59.9%) as well as 36 M-QTLs and 13 E-QTLs along 14 linkage groups (LGs). On LG1, LG10, and LG15, we repeatedly detected QTLs across field trials; which emphasizes their putative effectiveness against SHR. In all selected variables, most of the identified QTLs showed high determination coefficients, associated with moderate to high heritability values. Using markers shared with previous Sclerotinia resistance studies, we compared the QTL locations in LG1, LG2, LG8, LG10, LG11, LG15 and LG16. This study constitutes the largest report of QTLs for SHR resistance in sunflower. Further studies focusing on the regions in LG1, LG10, and LG15 harboring the detected QTLs are necessary to identify causal alleles and contribute to unraveling the complex genetic basis governing the resistance.

Association mapping in sunflower for Sclerotinia Head Rot resistance.

BACKGROUND: Sclerotinia Head Rot (SHR) is one of the most damaging diseases of sunflower in Europe, Argentina, and USA, causing average yield reductions of 10 to 20 %, but leading to total production loss under favorable environmental conditions for the pathogen. Association Mapping (AM) is a promising choice for Quantitative Trait Locus (QTL) mapping, as it detects relationships between phenotypic variation and gene polymorphisms in existing germplasm without development of mapping populations. This article reports the identification of QTL for resistance to SHR based on candidate gene AM. RESULTS: A collection of 94 sunflower inbred lines were tested for SHR under field conditions using assisted inoculation with the fungal pathogen Sclerotinia sclerotiorum. Given that no biological mechanisms or biochemical pathways have been clearly identified for SHR, 43 candidate genes were selected based on previous transcript profiling studies in sunflower and Brassica napus infected with S. sclerotiorum. Associations among SHR incidence and haplotype polymorphisms in 16 candidate genes were tested using Mixed Linear Models (MLM) that account for population structure and kinship relationships. This approach allowed detection of a significant association between the candidate gene HaRIC_B and SHR incidence (P < 0.01), accounting for a SHR incidence reduction of about 20 %. CONCLUSIONS: These results suggest that AM will be useful in dissecting other complex traits in sunflower, thus providing a valuable tool to assist in crop breeding.

Metabolic profiles of sunflower genotypes with contrasting response to Sclerotinia sclerotiorum infection.

We report a comprehensive primary metabolite profiling of sunflower (Helianthus annuus) genotypes displaying contrasting behavior to Sclerotinia sclerotiorum infection. Applying a GC-MS-based metabolite profiling approach, we were able to identify differential patterns involving a total of 63 metabolites including major and minor sugars and sugar alcohols, organic acids, amino acids, fatty acids and few soluble secondary metabolites in the sunflower capitulum, the main target organ of pathogen attack. Metabolic changes and disease incidence of the two contrasting genotypes were determined throughout the main infection period (R5.2-R6). Both point-by-point and non-parametric statistical analyses showed metabolic differences between genotypes as well as interaction effects between genotype and time after inoculation. Network correlation analyses suggested that these metabolic changes were synchronized in a time-dependent manner in response to the pathogen. Concerted differential metabolic changes were detected to a higher extent in the susceptible, rather than the resistant genotype, thereby allowing differentiation of modules composed by intermediates of the same pathway which are highly interconnected in the susceptible line but not in the resistant one. Evaluation of these data also demonstrated a genotype specific regulation of distinct metabolic pathways, suggesting the importance of detection of metabolic patterns rather than specific metabolite changes when looking for metabolic markers differentially responding to pathogen infection. In summary, the GC-MS strategy developed in this study was suitable for detection of differences in carbon primary metabolism in sunflower capitulum, a tissue which is the main entry point for this and other pathogens which cause great detrimental impact on crop yield.

Evaluation of the time of uncapping and removing dead brood from cells by hygienic and non-hygienic honey bees.

Most research on hygienic behavior has recorded the time taken by the colony to remove an experimental amount of dead brood, usually after one or two days. We evaluated the time that hygienic (H) and non-hygienic (NH) honey bees take to uncap and remove dead brood in observation hives after the brood was killed using the pin-killing assay. Four experimental colonies were selected as the extreme cases among 108 original colonies. Thirty brood cells were perforated with a pin in two H and two NH colonies and observations were made after 1, 2, 3, 4, 5, 6, and 24 h. Different stages of uncapping and removing were recorded. Differences in uncapping and removal between H and NH colonies were significant for all comparisons made at the different times after perforation. Using observation hives one obtains a better and faster discrimination between H and NH colonies than in full size colonies. It is possible to differentiate H and NH within a few hours after perforating the cells.