Genetic analysis of phytosterol content in sunflower seeds

Genome-wide identification and characterization of protein phosphatase 2C (PP2C) gene family in sunflower (Helianthus annuus L.) and their expression profiles in response to multiple abiotic stresses

Plant protein phosphatase 2C (PP2C) plays vital roles in responding to various stresses, stimulating growth factors, phytohormones, and metabolic activities in many important plant species. However, the PP2C gene family has not been investigated in the economically valuable plant species sunflower (Helianthus annuus L.). This study used comprehensive bioinformatics tools to identify and characterize the PP2C gene family members in the sunflower genome (H. annuus r1.2). Additionally, we analyzed the expression profiles of these genes using RNA-seq data under four different stress conditions in both leaf and root tissues. A total of 121 PP2C genes were identified in the sunflower genome distributed unevenly across the 17 chromosomes, all containing the Type-2C phosphatase domain. HanPP2C genes are divided into 15 subgroups (A-L) based on phylogenetic tree analysis. Analyses of conserved domains, gene structures, and motifs revealed higher structural and functional similarities within various subgroups. Gene duplication and collinearity analysis showed that among the 53 HanPP2C gene pairs, 48 demonstrated segmental duplications under strong purifying selection pressure, with only five gene pairs showing tandem duplications. The abundant segmental duplication was observed compared to tandem duplication, which was the major factor underlying the dispersion of the PP2C gene family in sunflowers. Most HanPP2C proteins were localized in the nucleus, cytoplasm, and chloroplast. Among the 121 HanPP2C genes, we identified 71 miRNAs targeting 86 HanPP2C genes involved in plant developmental processes and response to abiotic stresses. By analyzing cis-elements, we identified 63 cis-regulatory elements in the promoter regions of HanPP2C genes associated with light responsiveness, tissue-specificity, phytohormone, and stress responses. Based on RNA-seq data from two sunflower tissues (leaf and root), 47 HanPP2C genes exhibited varying expression levels in leaf tissue, while 49 HanPP2C genes showed differential expression patterns in root tissue across all stress conditions. Transcriptome profiling revealed that nine HanPP2C genes (HanPP2C12, HanPP2C36, HanPP2C38, HanPP2C47, HanPP2C48, HanPP2C53, HanPP2C54, HanPP2C59, and HanPP2C73) exhibited higher expression in leaf tissue, and five HanPP2C genes (HanPP2C13, HanPP2C47, HanPP2C48, HanPP2C54, and HanPP2C95) showed enhanced expression in root tissue in response to the four stress treatments, compared to the control conditions. These results suggest that these HanPP2C genes may be potential candidates for conferring tolerance to multiple stresses and further detailed characterization to elucidate their functions. From these candidates, 3D structures were predicted for six HanPP2C proteins (HanPP2C47, HanPP2C48, HanPP2C53, HanPP2C54, HanPP2C59, and HanPP2C73), which provided satisfactory models. Our findings provide valuable insights into the PP2C gene family in the sunflower genome, which could play a crucial role in responding to various stresses. This information can be exploited in sunflower breeding programs to develop improved cultivars with increased abiotic stress tolerance.

Overexpression of the OsFes1A increased the phytosterols content and enhanced drought and salt stress tolerance in Arabidopsis

MAIN CONCLUSION

Overexpression of the rice gene, OsFes1A, increased phytosterol content and drought and salt stress tolerance in Arabidopsis. Phytosterols are key components of the phospholipid bilayer membrane and regulate various processes of plant growth and response to biotic and abiotic stresses. In this study, it was demonstrated that the overexpression of OsFes1A (Hsp70 nucleotide exchange factor Fes1) increased phytosterols content and enhanced tolerance to salt and drought stress in Arabidopsis. In transgenic plants, the average content of campesterol was 17.6% higher than that of WT, and the average content of β-sitosterol reached 923.75 μg/g, with an increase of 1.33-fold. In fes1a seeds, the contents of campesterol and β-sitosterol reduced by 20% and 10.93%, respectively. In OsFes1A transgenic seeds, the contents of campesterol and β-sitosterol increased by 1.38-fold and 1.25-fold respectively. Furthermore, the germination rate of transgenic Arabidopsis was significantly higher than WT under stress (salt, ABA, and drought treatment). Under salt stress, transgenic plants accumulated a lower MDA content, higher chlorophyll content, and POD activity relative to the wild type, while the mutants showed the opposite pattern Our study found multiple other functions of OsFes1A beyond the defined role of Fes1 in regulating Hsp70, contributing to the better understanding of the essential roles of Fes1 in plants. Meanwhile, it provides the theoretical basis for developing high phytosterol crop varieties.

Genome-wide identification of DCL, AGO and RDR gene families and their associated functional regulatory element analyses in sunflower (Helianthus annuus)

RNA interference (RNAi) regulates a variety of eukaryotic gene expressions that are engaged in response to stress, growth, and the conservation of genomic stability during developmental phases. It is also intimately connected to the post-transcriptional gene silencing (PTGS) process and chromatin modification levels. The entire process of RNA interference (RNAi) pathway gene families mediates RNA silencing. The main factors of RNA silencing are the Dicer-Like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) gene families. To the best of our knowledge, genome-wide identification of RNAi gene families like DCL, AGO, and RDR in sunflower (Helianthus annuus) has not yet been studied despite being discovered in some species. So, the goal of this study is to find the RNAi gene families like DCL, AGO, and RDR in sunflower based on bioinformatics approaches. Therefore, we accomplished an inclusive in silico investigation for genome-wide identification of RNAi pathway gene families DCL, AGO, and RDR through bioinformatics approaches such as (sequence homogeneity, phylogenetic relationship, gene structure, chromosomal localization, PPIs, GO, sub-cellular localization). In this study, we have identified five DCL (HaDCLs), fifteen AGO (HaAGOs), and ten RDR (HaRDRs) in the sunflower genome database corresponding to the RNAi genes of model plant Arabidopsis thaliana based on genome-wide analysis and a phylogenetic method. The analysis of the gene structure that contains exon-intron numbers, conserved domain, and motif composition analyses for all HaDCL, HaAGO, and HaRDR gene families indicated almost homogeneity among the same gene family. The protein-protein interaction (PPI) network analysis illustrated that there exists interconnection among identified three gene families. The analysis of the Gene Ontology (GO) enrichment showed that the detected genes directly contribute to the RNA gene-silencing and were involved in crucial pathways. It was observed that the cis-acting regulatory components connected to the identified genes were shown to be responsive to hormone, light, stress, and other functions. That was found in HaDCL, HaAGO, and HaRDR genes associated with the development and growth of plants. Finally, we are able to provide some essential information about the components of sunflower RNA silencing through our genome-wide comparison and integrated bioinformatics analysis, which open the door for further research into the functional mechanisms of the identified genes and their regulatory elements.

Genotyping and lipid profiling of 601 cultivated sunflower lines reveals novel genetic determinants of oil fatty acid content

BACKGROUND

Sunflower is an important oilseed crop domesticated in North America approximately 4000 years ago. During the last century, oil content in sunflower was under strong selection. Further improvement of oil properties achieved by modulating its fatty acid composition is one of the main directions in modern oilseed crop breeding.

RESULTS

We searched for the genetic basis of fatty acid content variation by genotyping 601 inbred sunflower lines and assessing their lipid and fatty acid composition. Our genome-wide association analysis based on the genotypes for 15,483 SNPs and the concentrations of 23 fatty acids, including minor fatty acids, revealed significant genetic associations for eleven of them. Identified genomic regions included the loci involved in rare fatty acids variation on chromosomes 3 and 14, explaining up to 34.5% of the total variation of docosanoic acid (22:0) in sunflower oil.

CONCLUSIONS

This is the first large scale implementation of high-throughput lipidomic profiling to sunflower germplasm characterization. This study contributes to the genetic characterization of Russian sunflower collections, which made a substantial contribution to the development of sunflower as the oilseed crop worldwide, and provides new insights into the genetic control of oil composition that can be implemented in future studies.

Fatty acid composition and oil content during coriander fruit development

Coriander contains petroselinic acid, an isomer fatty acid of oleic acid. Coriander seed oil has been proposed as novel food ingredient in the European Union. Field experiments were performed at Auch (France) during two seasons (2010 and 2011). From flowering to maturity, fruits were harvested weekly and oil content and fatty acid (FA) compositions were determined. Fruits presented 2% more oil in 2010 than in 2011. Petroselinic acid (PA) contents was higher in 2011 than in 2010. Oil accumulation began earlier after flowering (2 DAF) in 2011. A first step in accumulation was identified between two and 21 DAF characterized by high SFA and PUFA, which decreased 21 DAF. Subsequently, PA increased to its highest concentration (30-55 DAF) and SFA and PUFA reached their lowest. These results suggest that higher concentrations of PA can be achieved by collecting fruits before full maturity.

Effect of Sowing Dates on Fatty Acids and Phytosterols Patterns of Carthamus tinctorius L

Field experiments were carried out at the Regional Centre of Experimentation in Organic agriculture at Auch (near Toulouse, South west of France). Due to the high potential applications for its oil components such as fatty acids and phytosterols, safflower (Carthamus tinctorius L.) is considered as an emerging crop. Safflower plants, as many other oil crops, are submitted to environmental stresses that modify seed composition. Nevertheless, few reports are available about the effects of environmental conditions on fatty acid and phytosterol compositions in safflower. Different rainfall supplies can be managed by delaying the sowing dates. In this study, fatty acid and phytosterol contents have been evaluated in safflower seeds cultivated at two sowing dates (conventional and late) that led to a differential of rainfall during seed development. At harvest, seeds were used for oil extraction. Fatty acid composition was performed by using GC-FID. A set of seeds was dehulled to separate the almond (embryo) and hull to release the extraction and measurement of sterol contents in the two compartments by GC-FID. A delay of sowing increased the content of all sterol categories but induced a significant decrease in fatty acids. The ratio of saturated to unsaturated fatty acids increased under a delaying sowing. The repartition of phytosterols was ¾ and ¼ of total sterols in the embryo and the hull, respectively. These results could make the use of hull (considered as waste) possible, help breeders to improve safflower oil composition and develop new industrial applications.

Pistacia Atlantica Desf., a Source of Healthy Vegetable Oil

Pistacia atlantica, which belongs to the Anacardiaceae family, is an important species for rural people in arid and semi-arid areas. The fruit, rich in oil, is used in traditional medicine for the treatment of various diseases. The oil extracted from this species growing in a northern area of Algeria and its fatty acid composition were previously studied. However, the largest areas where this species is present (traditional cultivation) is located in southern Algeria. Moreover, studies on oil fatty acid composition and essential oil were always conducted separately. This study was performed in order to assess the fatty acid and volatile organic compound composition of P. atlantica vegetable oil. The seeds were collected randomly from Djelfa (300 km South of Algiers, Algeria). Oil content and fatty acid composition were determined by Soxhlet extraction. The seeds contained high concentrations of oil (32–67%). The major fatty acids were oleic (39–49%), linoleic (23.6–31%), and palmitic (21.3–26.6%) acids. The ratio of polyunsaturated fatty acids (PUFA) to saturated fatty acids (SFA) indicated that the content of unsaturated fatty acids was approximately three times higher than that of SFA. This ratio is widely used in epidemiological studies and research on cardiovascular diseases, diabetes, and metabolic syndrome. The ratios of -acids, i.e., -9/-6 and -6/-3, were 1.3–2 and 18.5–38.3, respectively. Crushed seeds were analyzed by headspace solid-phase microextraction (SPME) coupled with gas chromatography–mass spectrometry. More than 40 compounds were identified, mainly monoterpenes (C10H16), such as α-terpinene and terpinolene, but also sesquiterpenes (C15H24) at lower levels. The value of this species as a source of healthy oil rich in -3 acid and its effects on cardiovascular disease risk are discussed.

Characterization of the APETALA2/Ethylene-responsive factor (AP2/ERF) transcription factor family in sunflower

One of the most prominent families of genes in plants is the AP2/ERF which play an important role in regulating plant growth and responses to various stresses. In this research, a genome-wide survey was conducted to recognize the AP2/ERF genes in sunflower (Helianthus annuus L.), and a total of 288 HaAP2/ERF was obtained. Phylogenetic analysis divided them into four sub-families, including 248 ERF, 4 RAV and 35 AP2, and one subgroup of the Soloist family. Localization of chromosome, gene structure, the conserved motif, gene ontology, interaction networks, homology modeling, the modeling of cis-regulatory elements and the analysis of events in the duplication of genes were carried out for HaAP2/ERF genes. Finally, 9AP2/ERF genes were chosen to confirm the gene expression of the selected genes in leaf and root tissues in various abiotic stress conditions by qPCR. The results confirmed that AP2/ERFs genes could effectively resist abiotic stress. Also, proline content was studied under drought, salinity, cold and heat stress. The results indicated that proline was increased under abiotic stress. This research has been done for the first time to determine the HaAP2/ERF family, which prepared valuable data for the evolutionary and practical research regarding AP2/ERF in sunflower.

Genomic Prediction of Sunflower Hybrids Oil Content

Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore, GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.

Molecular diversity of sunflower populations maintained as genetic resources is affected by multiplication processes and breeding for major traits

SNP genotyping of 114 cultivated sunflower populations showed that the multiplication process and the main traits selected during breeding of sunflower cultivars drove molecular diversity of the populations. The molecular diversity in a set of 114 cultivated sunflower populations was studied by single-nucleotide polymorphism genotyping. These populations were chosen as representative of the 400 entries in the INRA collection received or developed between 1962 and 2011 and made up of land races, open-pollinated varieties, and breeding pools. Mean allele number varied from 1.07 to 1.90. Intra-population variability was slightly reduced according to the number of multiplications since entry but some entries were probably largely homozygous when received. A principal component analysis was used to study inter-population variability. The first 3 axes accounted for 17% of total intra-population variability. The first axis was significantly correlated with seed oil content, more closely than just the distinction between oil and confectionary types. The second axis was related to the presence or absence of restorer genes and the third axis to flowering date and possibly to adaptation to different climates. Our results provide arguments highlighting the effect of the maintenance process on the within population genetic variability as well as on the impact of breeding for major agronomic traits on the between population variability of the collection. Propositions are made to improve sunflower population maintenance procedures to keep maximum genetic variability for future breeding.

The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution

The domesticated sunflower, Helianthus annuus L., is a global oil crop that has promise for climate change adaptation, because it can maintain stable yields across a wide variety of environmental conditions, including drought. Even greater resilience is achievable through the mining of resistance alleles from compatible wild sunflower relatives, including numerous extremophile species. Here we report a high-quality reference for the sunflower genome (3.6 gigabases), together with extensive transcriptomic data from vegetative and floral organs. The genome mostly consists of highly similar, related sequences and required single-molecule real-time sequencing technologies for successful assembly. Genome analyses enabled the reconstruction of the evolutionary history of the Asterids, further establishing the existence of a whole-genome triplication at the base of the Asterids II clade and a sunflower-specific whole-genome duplication around 29 million years ago. An integrative approach combining quantitative genetics, expression and diversity data permitted development of comprehensive gene networks for two major breeding traits, flowering time and oil metabolism, and revealed new candidate genes in these networks. We found that the genomic architecture of flowering time has been shaped by the most recent whole-genome duplication, which suggests that ancient paralogues can remain in the same regulatory networks for dozens of millions of years. This genome represents a cornerstone for future research programs aiming to exploit genetic diversity to improve biotic and abiotic stress resistance and oil production, while also considering agricultural constraints and human nutritional needs.

Progress in modification of sunflower oil to expand its industrial value

Increasing the sunflower seed oil content as well as improving its quality makes it compatible for industrial demands. This is an important breeding objective of sunflower which increases its market value and ensures high returns for the producers. The present review focuses on determining the progress of improving sunflower seed oil content and modifying its quality by empirical and advanced molecular breeding methods. It is known that the sunflower oil content and quality have been altered through empirical selection methods and mutation breeding programmes in various parts of the world. Further improvement in seed oil content and its components (such as phytosterols, tocopherols and modified fatty acid profile) has been slowed down due to low genetic variation in elite germplasm and complex of hereditary traits. Introgression from wild species can be carried out to modify the fatty acids profile and tocopherol contents with linkage drags. Different transgenes introduced through biotechnological methods may produce novel long-chain fatty acids within sunflower oil. Bio-engineering of sunflower oil could allow it to be used in diverse industrial products such as bio-diesel or bio-plastics. These results showed that past and current trends of modifying sunflower oil quality are essential for its further expansion as an oilseed crop. © 2017 Society of Chemical Industry.

Genomic Prediction of Sunflower Hybrids Oil Content

Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore, GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.

Association mapping for kernel phytosterol content in almond

Almond kernels are a rich source of phytosterols, which are important compounds for human nutrition. The genetic control of phytosterol content has not yet been documented in almond. Association mapping (AM), also known as linkage disequilibrium (LD), was applied to an almond germplasm collection in order to provide new insight into the genetic control of total and individual sterol contents in kernels. Population structure analysis grouped the accessions into two principal groups, the Mediterranean and the non-Mediterranean. There was a strong subpopulation structure with LD decaying with increasing genetic distance, resulting in lower levels of LD between more distant markers. A significant impact of population structure on LD in the almond cultivar groups was observed. The mean r(2) -value for all intra-chromosomal loci pairs was 0.040, whereas, the r(2) for the inter-chromosomal loci pairs was 0.036. For analysis of association between the markers and phenotypic traits five models were tested. The mixed linear model (MLM) approach using co-ancestry values from population structure and kinship estimates (K model) as covariates identified a maximum of 13 significant associations. Most of the associations found appeared to map within the interval where many candidate genes involved in the sterol biosynthesis pathway are predicted in the peach genome. These findings provide a valuable foundation for quality gene identification and molecular marker assisted breeding in almond.

Differential expression of seven conserved microRNAs in response to abiotic stress and their regulatory network in Helianthus annuus

Biotic and abiotic stresses affect plant development and production through alternation of the gene expression pattern. Gene expression itself is under the control of different regulators such as miRNAs and transcription factors (TFs). MiRNAs are known to play important roles in regulation of stress responses via interacting with their target mRNAs. Here, for the first time, seven conserved miRNAs, associated with drought, heat, salt and cadmium stresses were characterized in sunflower. The expression profiles of miRNAs and their targets were comparatively analyzed between leaves and roots of plants grown under the mentioned stress conditions. Gene ontology analysis of target genes revealed that they are involved in several important pathways such as auxin and ethylene signaling, RNA mediated silencing and DNA methylation processes. Gene regulatory network highlighted the existence of cross-talks between these stress-responsive miRNAs and the other stress responsive genes in sunflower. Based on network analysis, we suggest that some of these miRNAs in sunflower such as miR172 and miR403 may play critical roles in epigenetic responses to stress. It seems that depending on the stress type, theses miRNAs target several pathways and cellular processes to help sunflower to cope with drought, heat, salt and cadmium stress conditions in a tissue-associated manner.