Transcriptome analysis reveals the potential molecular mechanism involved in fatty acids biosynthesis of Sunflower

doi:10.21203/rs.3.rs-4974760/v1

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Transcriptome analysis reveals the potential molecular mechanism involved in fatty acids biosynthesis of Sunflower

https://doi.org/10.21203/rs.3.rs-4974760/v1

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Background

Sunflower (Helianthus annuus) is one of the most important oilseed crops worldwide. To reveal the molecular mechanism of biosynthesis and accumulation of major fatty acid components in sunflower, and to clarify the primary and secondary relationships between temperature, genes, and fatty acid synthesis in sunflower. In this study, as our study materials, we chose the sunflower inbred lines J9 and P50 with high and low oleic acids, respectively. Sampling at the same period of sowing at different periods (S-test) and sampling at different periods of sowing at the same time (A-test) were used to determine the fatty acid fractions and contents of different samples of seeds after pollination, respectively; and RNA-Seq technology was utilized to sequence the different samples to obtain the gene expression data related to fatty acid synthesis for each sample.

Results

Five main fatty acids were detected, including Oleic acid, linoleic acid, stearic acid, linolenic acid, palmitic acid, and the relative contents of oleic acid and linoleic acid were negatively correlated, while the relative contents of linolenic acid and palmitic acid were lower and showed a continuous decreasing trend during seed development. The crucial turning point in sunflower fatty acid synthesis occurs at 20 days after flowering (DAF), and fatty acid fractions and contents of high oleic sunflower lines were little affected by temperature, while low oleic lines were susceptible to temperature. A total of 81,676 unigenes were obtained by transcriptome sequencing. There were 15885 and 18220 genes obtained in the S dataset and A dataset, respectively, were annotated in the GO and KEGG functional databases. Based on the KEGG database, a total of 19 pathways associated with lipid metabolism, with a cumulative enrichment of 29 DEGs. Through the analysis of metabolic pathways, a total of 42 differentially expressed genes (DEGs) directly related to sunflower fatty acid metabolism were identified. These DEGs belong to 22 different types of enzymes, including PIGT, KCS, ACC, SAD, MFP, ACX, ACP, and FAB, which are key genes involved in the biosynthesis of unsaturated fatty acids and oil accumulation in sunflowers.

Conclusions

We predicted that PIGT, KCS, ACC, SAD, MFP, ACX, ACP, and FAB were the key enzymes, regulating their genes in the fatty acid biosynthesis and oil accumulation in sunflower seed. This study provides the first comprehensive genomic resources characterizing sunflowers seed gene expression at the transcriptional level. These data lay the foundation for elucidating the molecular mechanisms of fatty acid biosynthesis and oil accumulation for sunflower.

Helianthus annuus L.

Fatty acid synthesis

Transcriptomics

Differentially expressed gene

Sunflower (Helianthus annuus L.) is one of the four major oil crops in the world[1, 2]. Sunflower contains 52 species and many subspecies, all of which originated in North America[3]. Sunflower has recently been gaining substantial attention because of its highly edible oil production and ornamental values[4].

Vegetable oil plays an irreplaceable role in providing essential nutrients and maintaining specific functions for the human body. It is also a carrier of fat-soluble vitamins[5]. The composition of fatty acids in vegetable oils is the key to determining their nutritional value and use[6]. Sunflower seed oil is a high-quality vegetable oil with more healthy unsaturated fatty acids (oleic acid, linoleic acid), ranging from 87–94%[7, 8]. There is a negative correlation between the content of oleic acid and linoleic acid in sunflower. Currently, the main raw material for sunflower oil on the market is traditional sunflower, whose unsaturated fatty acid composition is: linoleic acid, 55% − 65%; Oleic acid, 20% − 30%[9].

Studies have shown that monounsaturated oleic acid and polyunsaturated linoleic acid are beneficial fatty acids. However, oleic acid is more stable because its molecular structure has fewer unsaturated olefinic bonds than linoleic acid, and foods cooked with high oleic sunflower oil have a longer shelf life and are healthier [10, 11]. Therefore, the market is increasingly interested in high oleic sunflower oil. As early as 1999, the Food and Agriculture Organization of the United Nations divided the genotypes of sunflowers into three categories based on their oleic acid content: ① low oleic acid sunflowers (traditional sunflowers), with an oleic acid content of 14% − 39%; ② Medium oleic sunflower, oleic acid content: 42% − 72%; ③ High oleic acid sunflower, oleic acid content: 75% − 91%.

It has been confirmed that the proportion of oleic acid and linoleic acid in sunflower seed oil is the key to determining its quality. Its fatty acid content and composition are determined by genotype, and are also affected by some environmental factors such as temperature, light, and humidity [12, 13]. Among these environmental factors, temperature has the greatest impact on it [14, 15]. Further research shows that with the increase of temperature, the content of oleic acid in low oleic sunflower seeds significantly increases, while the content of linoleic acid decreases, while the lower the temperature, the opposite is true. However, the oleic acid content in high-oleic sunflowers is less sensitive to environmental changes, and their fatty acid composition changes less [16].

In summary, sunflower seed oil is an irreplaceable vegetable oil in people's daily life, and there have been many achievements that can be used for reference in the study of its fatty acid composition and content. However, there are few studies on the molecular mechanism of fatty acid metabolism in sunflowers under different growth conditions at the transcriptome level. With the high-quality development of agriculture in China, the demand for sunflowers with different fatty acid component phenotypes will continue to increase, and molecular breeding of sunflowers with different fatty acid components will be put on the agenda. In order to make up for the shortcomings of relevant research and meet the needs of social development, this study adopts a sampling method that combines sampling at the same period of sowing at different periods (S-test) and sampling at different periods of sowing at the same time (A-test). Through transcriptome analysis, it attempts to clarify the molecular regulatory mechanism of fatty acid content and component changes in sunflowers, in order to promote the steady development of molecular breeding for high oleic acid sunflowers, Provide theoretical basis for high-quality development of sunflower industry.

Analysis of the Fatty acid composition and contents

The results showed that there are four main components in sunflower, including Oleic acid, Linoleic acid, Stearic acid, Linolenic acid, Palmitic acid, and the sum of oleic acid and linoleic acid content accounted for more than 85% of sunflower fatty acids. Furthermore, The relative content of oleic acid and linoleic acid in sunflower seeds was negatively correlated with the growth and development of sunflower seeds (Fig. 1), which was confirmed by previous research results[17]. The value of oleic acid in the seeds first increased and then decreased, reaching a maximum of 30% at 20 DAFs. The value of linoleic acid showed an S-shaped trend, with the minimum value appearing at 20 DAFs. Therefore, the key turning point in the synthesis of sunflower main fatty acids (oleic acid and linoleic acid) is 20 DAFs.

Under the S-test, the relative content of oleic acid was measured in inbred lines J9 and P50 at 20 DAFs, and the average temperature during the sampling period (from pollination to 20 DAF) was calculated too. The result of J9 was not significantly affected by daily average temperature changes, and it was basically stable at about 85%; The result of P50 decreased significantly with the decrease of daily average temperature, with the highest relative content of oleic acid reaching 42% during the S1 sowing period, while it was only 20.49% during the S6 sowing period (Fig. 2). It can be seen that the fatty acid composition and content of high oleic acid sunflower were less affected by temperature, while low oleic acid sunflower lines were just the opposite.

Sequencing read filtering and de novo assembly

The RNA sample testing of 66 sunflower samples to be sequenced showed that their quality met the sequencing requirements (Table S1).In the present study, we performed transcriptome analysis of 36 different S-test samples to exclude affection of environmental factors. A total of 265.79G data was measured form the two strains (J9 and P50), with an average size of about 7.38G for each sample. The average Q20 index of the sample was 96.59%, and the average Q30 index was 92.93%. The distribution of base error rate conformed to the general rule. The average GC content was 46.50%, and there was no obvious separation phenomenon. The quality control results are shown in Table S2.We performed transcriptome analysis of 30 A-test samples to explore the main genes for sunflower oleic acid synthesis. And, a total of 221.87G data was measured, with an average of about 7.40G per sample. The average Q20 index of the sample was 95.62%, and the average Q30 index was 91.45%. The distribution of base error rate conformed to the general rule. The average GC content was 45.93%, and there was no obvious separation phenomenon (Table S3).A total of 66 samples were sequenced, and 119895 predicted genes were identified, including 81676 known protein coding genes and 38219 new protein coding genes.

According to the value of gene expression in each sample, the square of Pearson correlation coefficient (R2) was calculated to obtain the correlation between duplicate samples. After analysis, the correlation of each biological duplicate in this study was better (Figure S1).

Gene expression analysis and functional annotation

The expression level for each gene was calculated using the RPKM [18] method (Reads Per kb per Million reads). There were more genes in low oleic acid strain P50 than in high oleic acid strain J9 (Figure S2). In the S-test, J9 had the highest number of genes enriched in the S1 period, with 3910 genes, while P50 had more genes enriched in S1 and S4 periods, both with more than 40,000 genes (Figure S2a). In the A-test, the two strains enriched the most genes at the CK stage, and P50 had 1757 more genes than J9 (Figure S2b). To gain insight into the functions of the genes related to sunflower fatty acid synthesis, all unigenes were annotated using WEGO [19] and Blast2GO [20] software and were classified into functional categories. A total of 119,895 genes were functionally annotated, and a total of 51408 Unigenes were annotated in the GO library. The top ten GO terms in the three GO ontologies were listed in Figure S3a. A total of 3286 Unigenes were annotated in the KEGG library, among which the Cutin, suberin, and wax biosynthesis in the top 20 belonged to lipid metabolism(Figure S3b).

Principal Component Analysis

J9 and P50 were clustered in two large populations, respectively, and there was a big difference between the two test materials. The clusters of samples of J9 with high oleic acid were more concentrated, while P50 with low oleic acid was more loose, indicating that the dominant factor affecting the lipid metabolism of sunflower was its own genotype rather than the environment, and the critical period of its fatty acid metabolism was between 0 DAFs and 20 DAFs (Figure S4).

Screening and analysis of differentially expressed genes

Comparative analysis of the sequencing results of each sample between the two varieties was done, and the sum of the differentially expressed genes obtained from the comparison was defined as the S dataset and the A dataset (Table 1).

Comparing the S-CK and A-CK datasets for within-species variation analysis showed that, the high oleic acid variety J9 exhibited the most differentially expressed genes in S5-vs-S1, with a total of 1022 genes, according to an examination of intra-varietal differences in the S-CK dataset and A-CK dataset. With a total of 1712 genes, the low oleic acid variety P50 exhibited the most differentially expressed genes between S3 and S1. With a total of 16133 genes, the high oleic acid variety J9 exhibited the most differentially expressed genes in the A4-vs-CK dataset. With a total of 11476 genes, the low oleic acid variety P50 exhibited the most differentially expressed genes in the A3-vs-CK comparison (Fig. 3a). Since there were more important DEGs for fatty acid metabolism in the A-CK dataset than in the S-CK dataset, it is likely that genetics, rather than environmental factors, are the major determinants of the fatty acid components and content of sunflower. Among the five comparisons in the S-CK dataset, the high oleic acid line J9 contained 372, 279, 115, 322, and 147 specifically up-regulated DEGs; there were 664, 386, 718, 660, and 1,249 specifically down-regulated DEGs (Fig. 3b). In a comparison of the A-CK dataset, 2,252, 4,882, 2,398, and 3,857 DEGs were found to be specifically up-regulated in J9; 1,919, 1,565, 2,735, and 4,909 DEGs were found to be specifically down-regulated (Fig. 3c). The A-CK data and the S-CK dataset were subjected to inter-trial specific expression analysis, the J9 had 354 DEGs co-expressed in the up-regulated DEGs in the S- and A-tests and 730 DEGs co-expressed in the down-regulated DEGs. The P50 varieties had DEGs 500 DEGs co-expressed in up-regulated DEGs and 200 co-expressed in down-regulated DEGs(Fig. 3d). After excluding the genes showing specific expression in the S-experiment, the analysis of specific expression in the experimental chambers showed that there were a total of 30,564 differentially expressed genes (DEGs) related to sunflower genotypes and their connection to fatty acid metabolism.

The S and A datasets' inter-varietal specific expression analyses produced 15,885 and 18,220 DEGs related to sunflower fatty acid metabolism, respectively(Fig. 4).

Table 1

The resulted data sets of pairwise comparisons from J9 and P50
Comparision	Data set	Comparision	Data set	Comparision	Data set	Comparision	Data set
S2vsS1(CK)	S2-CK	A1vsCK	A1-CK	S1(J9)vsS1(P50)	S1	A1(J9)vsA1(P50)	A1
S3vsS1(CK)	S3-CK	A2vsCK	A2-CK	S2(J9)vsS2(P50)	S2	A2(J9)vsA2(P50)	A2
S4vsS1(CK)	S4-CK	A3vsCK	A3-CK	S3(J9)vsS3(P50)	S3	A3(J9)vsA3(P50)	A3
S5vsS1(CK)	S5-CK	A4vsCK	A4-CK	S4(J9)vsS4(P50)	S4	A4(J9)vsA4(P50)	A4
S6vs(CK)	S6-CK	---	---	S5(J9)vsS5(P50)	S5	A5(J9)vsA5(P50)	A5
---	---	---	---	S6(J9)vsS6(P50)	S6	---	---

Functional analysis of specific expressed genes

In the GO annotation specifically expressing DEGs in the S-test, J9 accumulated 96 genes related to lipid metabolism (Figure S5a). 43 were obtained in the S-test of P50 (Figure S5b), 330 were obtained in the A-test of J9 (Figure S5c), 299 were obtained in the A-test of P50 (Figure S5d), and 278 were obtained in the co expression GO terms mapping results of J9 and P50 in the A-test (Figure S5e). In summary, in the specific expression analysis within the variety, a total of 403 DEGs were annotated under GO terms related to lipid metabolism (Figure S5f); Using the same research method, 81 genes related to lipid metabolism were found in the inter variety specific expression analysis results.

In the analysis of specific expression within the variety, 403 DEGs directly related to lipid metabolism obtained through GO functional enrichment were mapped to KEGG metabolic pathways related to lipid metabolism, with a total of 29 DEGs directly participating in 19 related KEGG pathways (Figure S6a). 14 of these genes were down-regulated in comparison to the control, whereas 15 of these genes showed elevated expression in the A-test (Fig. 5a).

A total of 16 DEGs were annotated and 81 DEGs were mapped to the KEGG metabolic pathway linked to lipid metabolism in the findings of the interspecies specific expression analysis(Figure S6b). One of the genes (LOC110868833) was over-expressed in the high oleic acid line J9, whereas the gene LOC110925594 was under-expressed in J9 (Fig. 5b).

Analysis of DEGs related to sunflower fatty acid metabolism

Most fatty acids in plants are stored in the form of TAG[21]. We analyzed the 42 differentially expressed genes (DEGs) found in the pathways metabolizing sunflower fatty acids and looked at how they varied in connection to 22 distinct enzymes (Fig. 6).

The genes 110889119 and 110925594 control the activity of acetyl-CoA carboxylase (ACCase), a crucial enzyme in the production of fatty acids. When fatty acid buildup is in its early stages in both J9 and P50, gene 110889119 shows comparatively greater expression levels, with higher expression in J9. However, as seen in both the S and A tests, gene 110925594 exhibits greater relative expression levels in P50 compared to J9, showing its vulnerability to environmental influences.

The A-experiment made clear the expression characteristics of two genes controlling ketoacyl-ACP reductase (KAR). While gene 110893771 revealed the opposite trend, gene 110864403 showed low expression in the early stages of accumulation followed by high expression in the latter stages. In the S-experiment, four genes were implicated in the control of several enzymes, including NADPH. The expression patterns of the two genes, 1109382561 and 110868833, were different. While gene 110868833 had greater expression during the whole planting time in J9, gene 1109382561 displayed the reverse trend. Similar traits were also shown by these genes in the A-experiment. It is interesting that, although having substantially greater expression levels throughout the J9 fatty acid accumulation phase, gene 110868833's expression levels peaked during the crucial oleic acid time period.

Gene 110893751 participates in the activation of MFP throughout this process. The fatty acid production at DAFs 28 and 35 is when this gene is most highly expressed in J9. Genes 110941185 and 110893751 are involved in the production and transport of fatty acyl-CoA, whereas genes 110890752 and 110871748 contribute to the synthesis of saturated fatty acids C16:0 and C18:1. The rate-limiting enzyme KCS is regulated by four genes, however in the S experiment, these genes did not exhibit any notable expression characteristics. Although genes 110889575 and 110904708 were strongly expressed in the early stages of fatty acid buildup in both types in the A experiment, gene 110889575's expression levels were greater in J9. Gene 110929271, on the other hand, had strong expression in J9 throughout the early stages of fatty acid buildup but low expression in P50. In the later stage of fatty acid buildup in sunflower, gene 110942343 showed strong expression, which was especially noticeable in P50.

Factors ACX and MFP2 are controlled by the genes 110893751 and 110941185. These two genes are more strongly increased in the high oleic acid cultivar J9 in the latter stages of sunflower fatty acid accumulation. Gene 110890752 is involved in the control of the PC-Pool's ability to directly catalyze the production of oleic acid from certain substrates. It is important to note that this gene has a negative link with oleic acid production since it exhibits low expression in J9 and P50 during the early stage of fatty acid accumulation and extremely low expression in J9 at 35 DAFs during the latter stage of accumulation. Glycerol-3-phosphate is synthesized by three genes, and in the A experiment, gene 110884358 has high expression in J9 but low expression in P50. Lysophosphatidic acid (LPA) is produced by two different factors, one of which is the long non-coding RNA regulatory factor 110890321. In the regulation of LPAT and gene novel, seven genes are active.A novel gene called 9052 controls the metabolism of sunflower fatty acids. Gene 110930343 is involved in the synthesis of diacylglycerol (DAG), while 110886458 is involved in TAG synthesis.

Most plants contain accumulated oil (mainly TAG) in their seeds to provide energy for seed germination[22]. The majority of plant oils are utilized as edible oil in the processing and preparation of food, and they play a significant role in agricultural production[23]. During the formation process of sunflower seeds, the composition and content of fatty acids change over time. In the formation process of low oleic acid sunflower seeds, the relative content of oleic acid increases first and then decreases, showing a parabolic shape. The relative content of oleic acid gradually increases after 5 DAF, reaching its peak at 20 DAF, and then gradually decreases, reaching a stable level at 40 DAF. According to research findings, it has been demonstrated that the content of oleic acid in sunflowers reaches 29.9% on the 10th day after flowering, and decreases to 14.7% by the 40th day. Furthermore, the accumulation of linoleic acid significantly intensifies during the later stages of fruit development, while stearic acid and palmitic acid tend to stabilize during the same period[24]. During the formation process of sunflower seeds, there is a significant decrease in the content of oleic acid, while the content of linoleic acid shows the opposite trend. There is a significant negative correlation between the content of oleic acid and linoleic acid[25]. Although the research findings of individuals may vary slightly, the overall trend of sunflower oleic acid accumulation remains consistent. Specifically, the relative content of oleic acid is higher during the early stages of fruit development, and gradually decreases in the later stages. The results of this study show that the relative amount of oleic acid increases with an increase in daily average temperature, indicating that the fatty acid composition and content of low oleic sunflower materials are sensitive to temperature impacts. The average temperature during sampling periods of the S1-S6 planting stages reduced from 25℃ to 18℃ when the seeds of the low-oleic sunflower inbred line P50 were matured to 20 DAF, and the oleic acid content also varied proportionally, falling from 40–20%. This is comparable to the outcomes that Demurin found.

In the study of genes related to plant fatty acid and oil biosynthesis, numerous gene cloning methods have been widely adopted, including gene library technology, gene chips, functional proteomics, PCR cloning, insertion mutations, mRNA differential display, map based cloning, yeast two hybrid, and bioinformatics and sequencing techniques. Among them, transcriptome technology has played an important role in the research of isolating oil synthesis genes. Through transcriptome technology, gene sequences can be obtained efficiently and on a large scale. Combined with the rapid development of bioinformatics data processing technology, a large number of new genes can be discovered and gene functions can be predicted and analyzed. At present, many oil crops, such as soybean [26, 27], rape [28], sesame [29], castor [30], cotton [31], have conducted transcriptome sequencing for multiple organs and research purposes. Sunflowers have the highest oil content in their seeds among oil crops. However, the molecular mechanisms of sunflower seed oil synthesis and regulation have not yet been revealed. On the one hand, the complexity of oil synthesis and regulation pathways has increased research difficulty, and on the other hand, the large and complex genome of sunflowers is also a major obstacle to the study of oil synthesis mechanisms. Transcriptome sequencing technology provides an economic, efficient and fast tool for mining sunflower oil synthesis related genes and gene function research. In recent years, major sunflower research units have used different sunflower materials to carry out a lot of research focusing on the expression regulation and gene function research of fatty acid and oil metabolism genes during seed development. Many key genes or gene family on fatty acid synthesis and TAG assembly pathway have been cloned successively [32, 33], laying a foundation for clarifying the mechanism of sunflower seed oil synthesis, And has accumulated relatively rich genetic resources for molecular breeding to improve the oleic acid content of sunflowers. Through transcriptome sequencing of S-test and A-test samples, gene function annotation, and differential gene screening, our laboratory obtained the coding genes of catalytic enzymes in key steps of multiple oil synthesis pathways, including KAS, ACX, DGAT, MGD and OLE.

Plant materials and sample collection

The self-crossing lines J9 (with a relative oleic acid content greater than 85.0%) and P50 (with an oleic acid content less than 35.0%) of high oleic acid sunflower were used as experimental materials in the current investigation. The relevant research was carried out using two experimental approaches, namely the simultaneous sowing with staggered sampling (S-test) and the simultaneous sowing with simultaneous sampling (A-test).

S-experiment: After sowing J9 and P50, set a sowing date every 10 days, set three replicates, and set a total of 6 different sowing date treatments. Samples were taken 20 days after flowering and pollination (DAF), with the number J9_ S1、J9_ S2、J9_ S3、J9_ S4、J9_ S5、J9_ S6 and P50_ S1、P50_ S2、P50_ S3、P50_ S4、P50_ S5、P50_ S6。

Simultaneous sowing and staggered sampling experiment (A-experiment): J9 and P50 were planted simultaneously, with three replicates, and samples were taken every 5 or 7 days after flowering and pollination. P50 samples were collected every 5 days, with a total of 8 different accumulation periods for the determination of fatty acid composition and content; J9 and P50 collect samples every 7 days, set the first accumulation period as the control (CK), and set a total of 5 different accumulation periods for transcriptome assay analysis, and the number is J9_ CK、J9_ A1、J9_ A2、J9_ A3、J9_ A4 and P50_ CK、P50_ A1、P50_ A2、P50_ A3、P50_ A4.

Meteorological data acquisition

The meteorological data during the sampling period is sourced from the China Meteorological Data Network (http://data.cma.cn/).

Fatty acid composition and content determination

Use liquid chromatography to determine the main fatty acids in each sample (totaling 144), such as oleic acid, linoleic acid, stearic acid, linolenic acid, palmitic acid, and other components. Refer to the third method in GB5009.168-2016 for content determination.

RNA extraction and cDNA preparation for Illumina sequencing

RNAs were isolated from the sunflower seed embryos to RNA using Trizol reagent (Invitrogen, US) according to the manufacturer’s instructions. The total RNA samples were first treated with DNase I to remove any potential DNA contamination. Subsequently, the products were purified using magnetic beads and the mRNAs were enriched using oligo (dT) magnetic beads. These beads were mixed with fragmentation buffer, and the mRNAs were fragmented into short fragments (approximately 200 bp). Subsequently, first-strand cDNA was synthesized using random hexamer-primed reverse transcription. Buffer, dNTPs, RNase H and DNA polymerase I were added to synthesize second-strand cDNA. The double-standed cDNA was purified using magnetic beads. End reparation was subsequently performed. After the previous step, adaptors were ligated to the ends of these fragments. Next, ligation products were selected according to size and purified on TAE-agarose gels. Finally, the fragments were enriched through PCR amplification, purified using magnetic beads and dissolved in the appropriate amount of Epstein-Barr solution. During the QC step, an Agilent 2100 Bioanalyzer was used to qualify and quantify of the sample library. The libraries were sequenced using an Ion Proton sequencer when necessary.

Principal Component Analysis (PCA)

To further elucidate the heterogeneity and applicability of the various sample data, a principal component analysis was conducted on the sequencing data of 36 S-experimental samples and 30 A-experimental samples sown during the same period. By analyzing the results, a comprehensive exploration of the relationships and differences between the samples was carried out. By utilizing gene expression levels from 66 samples as indicator values and calculating correlation coefficients between various indicators, a principal component analysis was conducted to obtain the contribution rates of each characteristic. Additionally, a distance relationship plot was generated among the 66 samples.

Screening of DEGs

By comparing the relative expression levels of genes among different samples, the DEGs can be selected to serve as research subjects for subsequent analyses such as differential gene expression pattern clustering analysis, Gene Ontology functional enrichment analysis, and pathway enrichment analysis. In this experiment, the DEseq software package was employed for the standardization of gene expression and differential analysis. DEseq is based on a negative binomial distribution model and employs a shrinkage estimation method to detect differential expression. It complies with the criteria of |FC value|≥2 (P-value < = 0.01) and FDR ≤ 0.001.

GO and KEGG Enrichment Analysis of DEGs

Using the Gene Ontology database (http://www.geneontology.org/), we mapped all differentially expressed genes obtained from the screening process to the various terms within the GO database. We then calculated the number of genes associated with each term, along with other relevant information. Subsequently, we applied a hypergeometric test to identify significantly enriched GO terms among the differentially expressed genes compared to the background transcriptome. Additionally, we utilized the NR database for gene annotation information and employed the Blast2GO software to obtain GO annotation information for all differentially expressed genes. Furthermore, we performed GO functional classification analysis on the differentially expressed genes using the WEGO software to determine the distribution characteristics of gene functional categories. For pathway enrichment analysis, we initially used the KEGG public database and applied a hypergeometric test on a per-KEGG pathway basis to identify significantly enriched pathways among the differentially expressed genes obtained from the screening process. Subsequently, we calculated the number of genes within each pathway, as well as the gene information associated with each node within the pathway.

Analysis of DEGs related to fatty acid synthesis

The analysis of DEGs related to fatty acid synthesis was conducted, and a metabolic pathway diagram for fatty acid metabolism was constructed. By analyzing the genes in the subpathways, the key genes were identified.

Statistical analysis

All data were based on three independent samples. Data and graphs were processed using Excel 2019. The statistical analysis was performed using the SPSS 21.0 software package. Differences among treatments were identified by data analysis using t-tests and one-way ANOVA, and Duncan's multiple comparisons were used for sample comparisons at significance levels of P < 0.05.

DEGs: Differentially expressed genes; PCA: Principal Component Analysis; DAF: Days after flowering; GO: Gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; TAG: Triacylglycerol; FPKM: Fragments per kilobase of exon model per million mapped fragment; ACCase: acetyl-CoA carboxylase; KAR: ketoacyl-ACP reductase; LPA: Lysophosphatidic acid; DAG: diacylglycerol

Data Availability

The raw RNA-seq datasets supporting the conclusions of this article are avail able in the National Center for Biotechnology Information Bioproject repository, accession number: PRJNA1147912.

Acknowledgements

We sincerely appreciate all the authors who contributed to this post!The authors would also like to thank the anonymous commenters for their constructive comments.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Funding

This work was supported by Funded by the National Modern Agricultural Industrial Technology System of the Ministry of Finance and the Ministry of Agriculture and Rural Development (CARS-14-1-27); Science and Technology Program of Inner Mongolia Autonomous Region (2021GG0014); 2022 Inner Mongolia Reveals List of Marshal Programs (2022JBGS0034); National Natural Science Foundation of China (32260514); Inner Mongolia Autonomous Region Sunflower Industry Technology Innovation and Promotion System Funding (CARS-IMAR-4-1-04); National Natural Science Foundation of China (31860558).

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

Hongmei Miao conceived the study, Shuchun Guo, Yongchun Zuo, Suping Li and Yanfang Zhang performed the experiments, Shuchun Guo and Ying Shao analyzed the data, Haiyang Zhang, Congzi Zhang, Yingxue Che and Hui Nie participated in some statistical analyses, Haifeng Yu and Yingnan Mou are helpful for materials collection, Shuchun Guo and Lingmin Zhao prepared the manuscript. All authors read and approved the manuscript.

Authors' information

1 Institute of Crop Science, Inner Mongolia Academy of Agricultural &Animal Husbandry Sciences, Hohhot, 010031, China

2 Henan Sesame Research Center, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China

3 State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China;

4 Department of horticulture and plant protection college, Inner Mongolia Agricultural University, Hohhot, 010011, China.

Guo S, Zuo Y, Zhang Y, Wu C, Su W, Jin W, Yu H, An Y, Li Q. Large-scale transcriptome comparison of sunflower genes responsive to Verticillium dahliae. BMC Genomics. 2017;18(1):42.
Seiler GJ, Qi LL, Marek LF. Utilization of sunflower crop wild relatives for cultivated sunflower improvement. Crop Sci. 2017;57(3):1083–101.
Buti M, Giordani T, Cattonaro F, Cossu R, Pistelli L, Vukich M, Morgante M, Cavallini A, Natali L. Temporal dynamics in the evolution of the sunflower genome as revealed by sequencing and annotation of three large genomic regions. Theor Appl Genet. 2011;123:779–91.
Huang Y, Lu M, Wu H, Zhao T, Wu P, Cao D. High Drying Temperature accelerates sunflower seed deterioration by regulating the fatty acid metabolism, glycometabolism, and abscisic acid/gibberellin balance. Front Plant Sci. 2021;12:628251.
Janssen M, Zuidema J, Wanhill R. Fracture Mechanics: Fundamentals and Applications. Fracture Mechanics: Fundamentals and Applications; 2004.
Jiang WZ, Henry IM, Lynagh PG, Comai L, Cahoon EB, Weeks DP. Significant enhancement of fatty acid composition in seeds of the allohexaploid, Camelina sativa, using CRISPR/Cas9 gene editing. Plant Biotechnol J 2017, 15.
Onemli F. Changes in Oil Fatty Acid Composition During Seed Development of Sunflower. Asian J Plant Sci. 2012;11(5):241–5.
Zhou F, Liu Y, Wang W, Wu L, Ma J, Zhang S, Wang J, Feng F, Yuan H, Huang X. Identification and functional prediction of CircRNAs of developing seeds in high oleic acid sunflower (Helianthus annuus L). Acta Physiol Plant. 2023;45(1):1–13.
Miller JF, Zimmerman DC, Vick BA. Genetic Control of High Oleic Acid Content in Sunflower Oil. Crop Sci. 1987;27(5):923–6.
Abdulkarim SM, Long K, Lai OM, Muhammad SKS, Ghazali HM. Frying quality and stability of high-oleic Moringa oleifera seed oil in comparison with other vegetable oils. Food Chem. 2007;105(4):1382–9.
Uematsu T, Parkányiová L, Endo T, Matsuyama C, Yano T, Miyahara M, Sakurai H, Pokorný J. Effect of the unsaturation degree on browning reactions of peanut oil and other edible oils with proteins under storage and frying conditions. Int Congr Ser 2002.
Schulte LR, Ballard T, Samarakoon T, Yao L, Vadlani P, Staggenborg S, Rezac M. Increased growing temperature reduces content of polyunsaturated fatty acids in four oilseed crops. Industrial Crops Prod. 2013;51(6):212–9.
Ahmad S. Environmental Effects on Seed Characteristics of Sunflower (Helianthus annuus L). J Agron Crop Sci. 2010;187(3):213–6.
McMaster G, Moragues M, Meyers R. Crop development related to temperature and photoperiod. Encyclopedia of sustainability science and technology 2018.
Fernandez-Martinez J, Jimenez A, Dominguez J, Garcia J, Garces R, Mancha M. Genetic analysis of the high oleic acid content in cultivated sunflower (Helianthus annuus L). Euphytica. 1989;41:39–51.
Ahmad S. Environmental effects on seed characteristics of sunflower (Helianthus annuus L). J Agron Crop Sci. 2001;187(3):213–6.
Premnath A, Narayana M, Ramakrishnan C, Kuppusamy S, Chockalingam V. Mapping quantitative trait loci controlling oil content, oleic acid and linoleic acid content in sunflower (Helianthus annuus L). Mol Breeding. 2016;36:1–7.
Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–8.
Ye J, Fang L, Zheng H, Zhang Y, Chen J, Zhang Z, Wang J, Li S, Li R, Bolund L. WEGO: a web tool for plotting GO annotations. Nucleic Acids Res. 2006;34(suppl2):W293–7.
Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21(18):3674–6.
Meng J-S, Tang Y-H, Sun J, Zhao D-Q, Zhang K-L, Tao J. Identification of genes associated with the biosynthesis of unsaturated fatty acid and oil accumulation in herbaceous peony ‘Hangshao’(Paeonia lactiflora ‘Hangshao’) seeds based on transcriptome analysis. BMC Genomics. 2021;22(1):1–21.
Izquierdo N, Benech-Arnold R, Batlla D, Belo RG, Tognetti J. Seed composition in oil crops: its impact on seed germination performance. Oilseed crops: yield adaptations under Environ stress 2017:34–51.
Spector AA, Kim H-Y. Discovery of essential fatty acids. J Lipid Res. 2015;56(1):11–21.
Turhan H, Citak N, Pehlivanoglu H, Mengul Z. Effects of ecological and topographic conditions on oil content and fatty acid composition in sunflower. Bulgarian J Agricultural Sci. 2010;16(5):553–8.
Baydar H, ERBAŞ S. Influence of seed development and seed position on oil, fatty acids and total tocopherol contents in sunflower (Helianthus annuus L). Turkish J Agric Forestry. 2005;29(3):179–86.
Xuan H, Huang Y, Zhou L, Deng S, Wang C, Xu J, Wang H, Zhao J, Guo N, Xing H. Key soybean seedlings drought-responsive genes and pathways revealed by comparative transcriptome analyses of two cultivars. Int J Mol Sci. 2022;23(5):2893.
Chen H, Liu C, Li Y, Wang X, Pan X, Wang F, Zhang Q. Developmental dynamic transcriptome and systematic analysis reveal the major genes underlying isoflavone accumulation in soybean. Front Plant Sci. 2023;14:1014349.
Hong B, Zhou B, Peng Z, Yao M, Wu J, Wu X, Guan C, Guan M. Tissue-specific transcriptome and metabolome analysis reveals the response mechanism of Brassica napus to waterlogging stress. Int J Mol Sci. 2023;24(7):6015.
Zhang Y, Li D, Zhou R, Wang X, Dossa K, Wang L, Zhang Y, Yu J, Gong H, Zhang X. Transcriptome and metabolome analyses of two contrasting sesame genotypes reveal the crucial biological pathways involved in rapid adaptive response to salt stress. BMC Plant Biol. 2019;19:1–14.
Wang X, Wu Y, Sun M, Wei X, Huo H, Yu L, Zhang J. Dynamic transcriptome profiling revealed key genes and pathways associated with cold stress in castor (Ricinus communis L). Ind Crops Prod. 2022;178:114610.
Yang H, Yang Q, Zhang D, Wang J, Cao T, Bozorov TA, Cheng L, Zhang D. Transcriptome reveals the molecular mechanism of the ScALDH21 gene from the desert moss syntrichia caninervis conferring resistance to salt stress in cotton. Int J Mol Sci. 2023;24(6):5822.
Celik Altunoglu Y, Unel NM, Baloglu MC, Ulu F, Can TH, Cetinkaya R. Comparative identification and evolutionary relationship of fatty acid desaturase (FAD) genes in some oil crops: the sunflower model for evaluation of gene expression pattern under drought stress. Biotechnol Biotechnol Equip. 2018;32(4):846–57.
Salas JJ, Bootello MA, Martínez-Force E, Calerón MV, Garcés R. High stearic sunflower oil: Latest advances and applications. OCL. 2021;28:35.

No competing interests reported.

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Transcriptome analysis reveals the potential molecular mechanism involved in fatty acids biosynthesis of Sunflower

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Abstract

Background

Results

Conclusions

Figures

Background

Results

Analysis of the Fatty acid composition and contents

Sequencing read filtering and de novo assembly

Gene expression analysis and functional annotation

Principal Component Analysis

Screening and analysis of differentially expressed genes

Functional analysis of specific expressed genes

Analysis of DEGs related to sunflower fatty acid metabolism

Discussion and conclusions

Methods

Plant materials and sample collection

Meteorological data acquisition

Fatty acid composition and content determination

RNA extraction and cDNA preparation for Illumina sequencing

Principal Component Analysis (PCA)

Screening of DEGs

GO and KEGG Enrichment Analysis of DEGs

Analysis of DEGs related to fatty acid synthesis

Statistical analysis

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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