Secretory Tissues and Volatile Components of Disc Florets in Several Wild Helianthus L. Species.

Although flower pollinator interactions are known to be mediated by floral traits, not enough attention has been paid to the research of secretory tissues and volatile components of sunflower disc florets as potentially important parameters in breeding programs. (1) To our knowledge, this is the first integrated study aimed at better understanding the attractiveness of sunflower capitula to insects. In the study, we have made a very detailed comparative analysis of secretory tissues and the characterization of the volatile components (VOCs) of disc florets in 10 wild perennial Helianthus species. (2) For anatomical analyses, cross-sections were obtained from the nectary zone of disc florets using a cryotechnique procedure. Micromorphological observation and morphological and anatomical analysis of disc florets were performed using light and scanning electron microscopy. For VOCs, we applied headspace, GC-FID, and GC/MS analyses. (3) The obtained results indicate that there is a difference between the analyzed traits among studied species. H. eggertii, H. hirsutus, H. mollis, H. resinosus, and H. tuberosus had high disc diameter values, a high cross-section area and disc floret corolla length, as well as the largest cross-section area and thickness of the disc florets nectary. In the analyzed VOCs, 30 different compounds were detected. The highest yield and quantity of α-Pinene was observed in H. mollis. (4) Inflorescence features, such as receptacle diameter, corolla and secretory tissue properties, and floret VOCs production and characterization, provided valuable information that can be used as guidelines in sunflower breeding programs to maximize pollinator attractiveness and increase seed yield.

Oil yield prediction for sunflower hybrid selection using different machine learning algorithms.

Due to the increased demand for sunflower production, its breeding assignment is the intensification of the development of highly productive oil seed hybrids to satisfy the edible oil industry. Sunflower Oil Yield Prediction (SOYP) can help breeders to identify desirable new hybrids with high oil yield and their characteristics using machine learning (ML) algorithms. In this study, we developed ML models to predict oil yield using two sets of features. Moreover, we evaluated the most relevant features for accurate SOYP. ML algorithms that were used and compared were Artificial Neural Network (ANN), Support Vector Regression, K-Nearest Neighbour, and Random Forest Regressor (RFR). The dataset consisted of samples for 1250 hybrids of which 70% were randomly selected and were used to train the model and 30% were used to test the model and assess its performance. Employing MAE, MSE, RMSE and R2 evaluation metrics, RFR consistently outperformed in all datasets, achieving a peak of 0.92 for R2 in 2019. In contrast, ANN recorded the lowest MAE, reaching 65 in 2018 The paper revealed that in addition to seed yield, the following characteristics of hybrids were important for SOYP: resistance to broomrape (Or) and downy mildew (Pl) and maturity. It was also disclosed that the locality feature could be used for the estimation of sunflower oil yield but it is highly dependable on weather conditions that affect the oil content and seed yield. Up to our knowledge, this is the first study in which ML was used for sunflower oil yield prediction. The obtained results indicate that ML has great potential for application in oil yield prediction, but also selection of parental lines for hybrid production, RFR algorithm was found to be the most effective and along with locality feature is going to be further evaluated as an alternative method for genotypic selection.

KASP Markers Specific for the Fertility Restorer Locus Rf1 and Application for Genetic Purity Testing in Sunflowers (Helianthus annuus L.).

Single nucleotide polymorphisms (SNPs) were significantly associated with fertility restoration of cytoplasmic male sterility (CMS) PET1 by the restorer gene Rf1. For these SNPs, four Kompetitive allele-specific PCR (KASP) markers were successfully designed. The KASP markers cover the fertility restorer locus Rf1, spanning about 3 Mb, and clearly differentiate restorer and maintainer lines. For genetic purity testing in sunflower hybrid production, the efficiency for detecting contaminations in samples was simulated using mixtures of hypocotyls or leaves. Contaminations of restorer lines with 1%, 3%, 5%, 10%, and 50% of maintainer lines were screened with all four KASP markers. Contaminations of 10% could be clearly detected in pools of 100 plants. Contaminations below this level require detection on a single plant level. For single plant detections, ethyl methanesulfonate-treated sunflower F1 hybrids, which had been phenotypically evaluated for male sterility (potential mutation in the Rf1 gene) were screened. Nine identified either partially male-sterile or male-sterile plants were analyzed with all four KASP markers and only one proved to be a hybrid with a mutation, seven were male-sterile contaminants in the F1 seeds used (1.6%) and one a recombinant plant. The four KASP markers should be valuable tools for marker-assisted selection (MAS) in sunflower breeding regarding the restorer locus Rf1.

Genetic Improvement in Sunflower Breeding-Integrated Omics Approach.

Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement.

Genetic and Genomic Tools in Sunflower Breeding for Broomrape Resistance.

Broomrape is a root parasitic plant causing yield losses in sunflower production. Since sunflower is an important oil crop, the development of broomrape-resistant hybrids is the prime breeding objective. Using conventional plant breeding methods, breeders have identified resistant genes and developed a number of hybrids resistant to broomrape, adapted to different growing regions worldwide. However, the spread of broomrape into new countries and the development of new and more virulent races have been noted intensively. Recent advances in sunflower genomics provide additional tools for plant breeders to improve resistance and find durable solutions for broomrape spread and virulence. This review describes the structure and distribution of new, virulent physiological broomrape races, sources of resistance for introduction into susceptible cultivated sunflower, qualitative and quantitative resistance genes along with gene pyramiding and marker assisted selection (MAS) strategies applied in the process of increasing sunflower resistance. In addition, it presents an overview of underutilized biotechnological tools, such as phenotyping, -omics, and genome editing techniques, which need to be introduced in the study of sunflower resistance to broomrape in order to achieve durable resistance.

Development and Validation of Markers for the Fertility Restorer Gene Rf1 in Sunflower.

Hybrid breeding in sunflowers based on CMS PET1 requires development of restorer lines carrying, in most cases, the restorer gene Rf1. Markers for marker-assisted selection have been developed, but there is still need for closer, more versatile, and co-dominant markers linked to Rf1. Homology searches against the reference sunflower genome using sequences of cloned markers, as well as Bacterial Artificial Chromosome (BAC)-end sequences of clones hybridizing to them, allowed the identification of two genomic regions of 30 and 3.9 Mb, respectively, as possible physical locations of the restorer gene Rf1 on linkage group 13. Nine potential candidate genes, encoding six pentatricopeptide repeat proteins, one tetratricopeptide-like helical domain, a probable aldehyde dehydrogenase 22A1, and a probable poly(A) polymerase 3 (PAPS3), were identified in these two genomic regions. Amplicon targeted next generation sequencing of these nine candidate genes for Rf1 was performed in an association panel consisting of 27 maintainer and 32 restorer lines and revealed the presence of 210 Single Nucleotide Polymorphisms (SNPs) and 67 Insertions/Deletions (INDELs). Association studies showed significant associations of 10 SNPs with fertility restoration (p-value < 10-4), narrowing Rf1 down to three candidate genes. Three new markers, one co-dominant marker 67N04_P and two dominant markers, PPR621.5R for restorer, and PPR621.5M for maintainer lines were developed and verified in the association panel of 59 sunflower lines. The versatility of the three newly developed markers, as well as of three existing markers for the restorer gene Rf1 (HRG01 and HRG02, Cleaved Amplified Polymorphic Sequence (CAPS)-marker H13), was analyzed in a large association panel consisting of 557 accessions.

Sunflower Genetics from Ancestors to Modern Hybrids-A Review.

Domestication and the first steps of sunflower breeding date back more than 4000 years. As an interesting crop to humans, sunflower underwent significant changes in the past to finally find its place as one of the most significant oil crops today. Substantial progress has already been made in understanding how sunflower was domesticated. Recent advances in molecular techniques with improved experimental designs contributed to further understanding of the genetic and molecular basis underlying the architectural and phenotypic changes that occurred during domestication and improvements in sunflower breeding. Understanding the domestication process and assessing the current situation concerning available genotypic variations are essential in order for breeders to face future challenges. A review of the tools that are used for exploring the genetic and genome changes associated with sunflower domestication is given in the paper, along with a discussion of their possible implications on classical sunflower breeding techniques and goals.