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Article

Chemical Composition and Expression Analysis of Oil-Related Genes in Upland Cottonseeds

by
Pengfei Liu
1,2,
Zhong Wang
1,
Xiaoshuang Lu
1,3,
Yujie Chang
1,4,
Kai Zheng
1,5,
Qianli Zu
1,5 and
Xiaojuan Deng
1,5,*
1
College of Agriculture, Xinjiang Agricultural University, Urumqi 830052, China
2
People’s Government of Yangmao Town, Midong District, Urumqi 831400, China
3
Institute of Agricultural Science and Technology of Bayingol Mongolian Autonomous Prefecture, Urumqi 841000, China
4
Institute of Advanced Agricultural Science, Peking University, Weifang 261325, China
5
Engineering Research Centre of Cotton of Ministry of Education, Xinjiang Agricultural University, Urumqi 830052, China
*
Author to whom correspondence should be addressed.
Agronomy 2026, 16(1), 56; https://doi.org/10.3390/agronomy16010056
Submission received: 23 October 2025 / Revised: 18 December 2025 / Accepted: 20 December 2025 / Published: 24 December 2025
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

Cottonseed is an important resource for edible oil and protein. Here, we evaluated cottonseed oil, protein, and gossypol contents using traditional chemical methods and near-infrared reflectance spectroscopy (NIRS) in diverse upland cotton (n = 456) and sea island cotton (n = 205) germplasm. In upland cotton, oil content averaged 21.23 ± 3.98% (12.74–43.56%), protein averaged 23.63 ± 4.63% (15.53–49.79%), and gossypol averaged 1.47 ± 0.21 mg/g (0.06–2.07). Correlation analysis showed a significant negative association between oil and protein (r = −0.125, p < 0.01; confirmed by NIRS: r = −0.171, p < 0.01), a significant negative association between protein and gossypol (r = −0.375, p < 0.01), and a significant positive association between oil and gossypol (r = 0.409, p < 0.01). In sea island cotton, oil, protein, and gossypol contents averaged 24.82 ± 6.15% (14.64–41.43%), 25.75 ± 2.04% (18.84–39.00%), and 1.60 ± 0.15 mg/g (1.22–2.08), respectively. The oil–protein association was strongly negative by NIRS (r = −0.744, p < 0.01), whereas correlations with gossypol were weak and not significant by the traditional method. After screening and evaluation, high oil and protein varieties were identified in upland cotton (n = 15) and sea island cotton (n = 6). Fourteen extreme-oil upland materials were further used to examine flowering-date effects on oil accumulation and physiological indicators, showing rapid oil accumulation and a flowering-date-dependent maximum. Finally, qRT-PCR analysis of lipid-metabolism-related candidate genes showed that seven genes were expressed significantly higher in high-oil than in low-oil materials (p < 0.05), peaking at the late stage of oil accumulation. GhDGAT1 and GhDGAT2 showed positive regulatory effects on oil accumulation, whereas GhFAD3 and GhKCR2 showed negative regulatory effects. Collectively, these findings provide compositional benchmarks, clarify trait relationships, and identify candidate genes useful for breeding cotton cultivars with improved seed oil/protein traits.

1. Introduction

Cotton (Gossypium spp.) is a major global crop supporting the agricultural, food, and textile industries [1]. It is cultivated in approximately 80 countries and occupies about 2.5% of the world’s arable land, serving as a key economic commodity in both developed and developing regions [2]. More than 55 Gossypium species have been described, but commercial production is dominated by G. hirsutum (>90% of global output) and G. barbadense (3–4%), while G. arboreum and G. herbaceum contribute ≤2% [3]. In China, sustained cultivar improvement since 1949 has greatly increased cotton production and established China as a major producer and consumer [4]. The Xinjiang Uygur Autonomous Region is the largest cotton-growing region in China and contributes roughly 90% of national output and over one-fifth of global production in 2024 [5]. Although yield has increased historically, progress has slowed in recent years, maintaining strong demand for improved productivity and raw cotton quality [3]. Consequently, breeding programs have traditionally prioritized lint yield and fiber quality because fiber traits largely determine cotton’s economic value [6,7].
While cotton fiber remains the primary product, cotton is also valued for its by-products, particularly cottonseed, which provides substantial nutritional and industrial potential [8]. Cotton fiber cells undergo well-characterized developmental stages from initiation through maturation [9,10,11], and many studies have therefore emphasized fiber biology and improvement [2,8]. However, compared with lint-focused research, cottonseed improvement and utilization have received comparatively less attention despite cottonseed being the second major output of cotton production.
Cottonseed represents an important resource for both edible oil and protein. Global cottonseed production has reached 43.91 million tons, with China, India, and the USA among the largest producers [12]. In China alone, annual cottonseed production is nearly 10 million tons and has increased steadily over time [13]. At a global scale, cottonseed is among the major oilseed crops and a substantial potential protein resource, producing about 5 million metric tons (MMT) of oil and 15 MMT of cottonseed meal (CSM), which supports growing demand for oil and protein in animal feed [12]. These figures highlight cottonseed’s value beyond lint and support renewed interest in developing cultivars with improved seed quality traits.
Cottonseed oil is rich in polyunsaturated fatty acids and is reported to be free of trans fatty acids, supporting its use as a vegetable oil in food systems [14,15]. In seeds, oil accumulates mainly as triacylglycerols stored in oil bodies, and this biosynthetic process is controlled by extensive gene networks regulating lipid formation and deposition [16,17]. Cottonseed oil has long been used in frying, cooking, and baking and is also relevant in industrial applications such as pharmaceuticals, cosmetics, lubricants, and biofuels [14,18]. The oil contains fatty acids and other beneficial constituents that contribute to functionality in food and non-food uses [19,20]. In addition, cottonseed has been considered a source of high-value bioactive compounds, including polyphenolic extracts with potential health-related applications [21]. After oil extraction and processing (including removal of lint and hulls), nearly 45% of the remaining product is CSM [22]. Cottonseed meal can serve as an economical alternative to soybean meal because it contains relatively high protein (30–50%) and favorable amino acid profiles [23,24,25].
Despite these advantages, broader utilization of cottonseed meals, particularly for human food and non-ruminant feed, is constrained by gossypol. Cottonseed meal is generally unsuitable for human consumption due to its gossypol content [26]. The allowable inclusion of CSM in diets depends strongly on gossypol concentration [24], and the presence of free gossypol, a toxic polyphenolic compound concentrated in cottonseed glands, has historically restricted CSM use in food and feed industries [27,28,29]. Emerging technological and breeding advances, such as glandless cottonseed, reduce or eliminate this constraint and have renewed interest in expanding cottonseed applications [30].
At the same time, there is increasing demand for cottonseed with enhanced protein, oil, fatty acid profiles, and amino acid quality for food, feed, and biofuel applications [31,32]. Although conventional breeding and hybridization approaches have produced productive cotton varieties [8,33], progress in seed-quality breeding is limited by an incomplete understanding of the genetic basis of cottonseed oil and protein accumulation and fatty acid composition [31,32,33]. Moreover, the relationship between seed nutritional traits and agronomic and fiber traits adds further complexity to breeding programs [34]. Another practical limitation is that traditional methods used for cottonseed oil and protein determination are often destructive, reducing the ability to conserve seed material for further selection and research.
To support seed-quality breeding, there is a need to integrate large-scale compositional screening with developmental profiling and targeted expression analysis of lipid-related genes. Specifically, we aimed to (i) quantify cottonseed oil, protein, and gossypol contents in a diverse set of upland cotton and sea island cotton germplasm using traditional methods and near-infrared reflectance spectroscopy (NIRS); (ii) evaluate correlations among these key quality traits and identify high-value materials; (iii) characterize temporal patterns of oil accumulation and associated physiological indicators across flowering-date-marked materials; and (iv) analyze the expression profiles of selected candidate genes related to lipid metabolism by qRT-PCR to support cottonseed quality breeding and marker-assisted selection.

2. Materials and Methods

2.1. Experimental Materials

Seeds of upland cotton (Gossypium hirsutum L.; n = 492; Supplementary Table S1) and sea island cotton (Gossypium barbadense L.; n = 205; Supplementary Table S2) were obtained from the 2612 Cotton Research Team of Xinjiang Agricultural University. Field trials were conducted at the cotton breeding base of Xinjiang Agricultural University (Shawan, Xinjiang, China) in 2019 and 2020 using a randomized complete block design with three field replicates per accession, followed by standard local agronomic management.
For compositional analyses, accessions with complete measurements and adequate seed quantity were retained for statistical evaluation (upland cotton, n = 456). To examine developmental oil accumulation and candidate-gene expression, 14 extreme upland cotton accessions were selected based on seed oil content (seven high-oil and seven low-oil; Supplementary Table S3). Flowers were tagged at 5-day intervals at eight time points (1, 6, 11, 16, 21, 26, and 31 July 2019; 5 August 2019). Bolls from tagged flowers were harvested at maturity. For each accession and time point, 20 bolls were collected per replicate plant/plot, and full seeds were used for compositional measurements; developing ovules/immature seeds were used to characterize oil accumulation during seed development.

2.2. Determination of Cottonseed Oil Content, Protein Content, and Gossypol Content

Traditional wet-chemistry (“classical”) methods and near-infrared reflectance spectroscopy (NIRS; FOSS DS2500, Denmark) were used to determine cottonseed composition. For NIRS screening, freshly harvested seeds from the upland cotton panel (n = 492) were analyzed. Briefly, seeds were evenly spread on the bottom of the sample cup/glass cell and scanned; oil and protein contents were obtained automatically based on the instrument’s spectral model/standard calibration curve [35]. To confirm the reliability of NIRS-predicted values, compositional estimates were verified by classical reference methods on the corresponding seed samples. Oil content was quantified by the Soxhlet extraction (residual) method following the national standard procedure for seed fat determination [36]. Crude protein and gossypol contents were determined using the modified biuret method and the phenolphthalein-based method, respectively [37,38]. Three biological replicates were performed for each sample.

2.3. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) Analysis

Total RNA was extracted from developing cottonseed tissues of 14 extreme upland materials (7 high-oil and 7 low-oil) sampled at 10, 20, 30, and 40 days after pollination (DAP). These specific time points were selected to capture critical phases of cottonseed development, spanning from the initial stage (10 DAP) and the onset of oil accumulation (20 DAP) to the peak period of lipid synthesis (30 DAP) and the maturation phase (40 DAP). RNA extraction and purification were performed using an RNAprep Pure Plant Kit (Tiangen Biotech (Beijing) Co., Ltd., Beijing, China), following the manufacturer’s protocol and a previously described procedure [39]. RNA purity and concentration were assessed using an ultra-micro spectrophotometer (Colibri, Titertek-Berthold, Bad Wildbad, Germany), and only RNA preparations with A260/280 ratios between 1.8 and 2.0 were used for subsequent cDNA synthesis. First-strand cDNA was synthesized from total RNA using a reverse transcription kit (ABM Biotechnology Co., Ltd.) according to the manufacturer’s instructions.
Gene-specific primers for qRT-PCR were synthesized by Beijing Huada Gene Company. Quantitative PCR was carried out using SYBR qPCR Master Mix (ABM Biotechnology Co., Ltd.) on an ABI 7500 Fast Real-Time PCR System. GhUBQ7 (GenBank: DQ116441) was used as the internal reference gene for normalization. Each assay included three biological replicates per variety, and reactions were repeated across three independent experimental runs. Relative expression levels were calculated based on Ct values after normalization to GhUBQ7. Primer sequences are provided in Supplementary Table S4.

2.4. Data Analysis

All statistical analyses were performed using SPSS 26.0 (IBM, Armonk, NY, USA). Data are presented as mean ± standard deviation (mean ± SD). For comparisons between two groups, Student’s t-test was used. For comparisons among more than two groups, one-way analysis of variance (ANOVA) was performed, followed by Tukey’s multiple-comparison test. In tables and figures, different lowercase letters indicate statistically significant differences among groups at p < 0.05, whereas the same letter indicates no significant difference (letter-based multiple-comparison notation). Associations among cottonseed oil, protein, and gossypol contents were assessed using two-tailed Pearson correlation analysis, and correlation coefficients (r) with corresponding significance levels were reported. Hierarchical cluster analysis was performed to classify cotton germplasm into oil-content groups for visualization of clustering patterns. Statistical significance was set at p < 0.05.

3. Results and Discussion

3.1. Parameter Statistics and Coefficient of Variation (CV) Analysis of the Main Traits of Cotton Seed

Upland cotton (Gossypium hirsutum) is the major cultivated species, whereas sea island cotton (G. barbadense) is valued for fiber quality and stress resistance; cottonseed is an important by-product contributing oil and protein for food/feed uses [40,41]. To quantify the extent of variation in cottonseed quality traits across germplasm panels, oil, protein, and gossypol contents were determined by classical reference methods, and oil/protein were additionally estimated by NIRS (Table 1).
In the upland cotton panel (n = 456), classical measurements showed mean oil and protein contents of 21.23 ± 3.98% and 23.63 ± 4.63%, respectively, with wide ranges (12.74–43.56% for oil; 15.53–49.79% for protein). Gossypol content averaged 1.47 ± 0.21 mg g−1 (range 0.06–2.07 mg g−1). The combined trait (protein + oil) averaged 44.86 ± 6.07% (range 32.81–71.82%) (Table 1). Collectively, these ranges and CV values indicate substantial phenotypic dispersion among accessions, providing scope for the selection of contrasting seed-quality profiles within the upland panel. NIRS screening of the upland germplasm produced mean estimates of 19.78 ± 2.04% for oil and 24.37 ± 2.10% for protein, with ranges of 12.56–31.47% and 11.90–38.49%, respectively; protein + oil averaged 44.15 ± 2.67% (range 30.56–59.57%) (Table 1). Across traits, NIRS and classical methods produced broadly comparable central tendencies, although NIRS tended to yield a narrower dispersion (lower SD/CV) relative to the classical assays, consistent with its role as a rapid screening approach rather than a direct chemical quantification (Table 1). This dual-measurement framework supports efficient compositional screening while retaining classical methods as the reference baseline.
In sea island cotton (n = 205), classical assays indicated mean oil and protein contents of 24.82 ± 6.15% and 25.75 ± 2.09%, respectively, and mean gossypol content of 1.60 ± 0.15 mg g−1; the combined protein + oil content averaged 50.56 ± 6.44% (Table 1). NIRS estimates for sea island cotton were 22.27 ± 2.04% for oil and 22.97 ± 1.90% for protein, with protein + oil averaging 45.24 ± 1.42% (Table 1). Notably, the largest dispersion in sea island cotton was observed for oil content measured by classical assays (CV 24.78%), whereas the corresponding NIRS-based CV for oil was lower (CV 9.18%) (Table 1). These patterns again suggest that classical assays capture broader quantitative variability, while NIRS provides a standardized high-throughput screening estimate.
To visualize oil-content structure across germplasm, hierarchical clustering grouped accessions into discrete oil-content classes in both upland and sea island panels (Figure 1). This clustering highlights the presence of multiple phenotypic oil categories within each species panel and provides an objective basis for selecting contrasting materials for downstream analyses and interpretation.

3.2. Correlation Analysis Between Nutritional Qualities of Cottonseeds

Understanding how major cottonseed quality traits co-vary can inform selection strategies aimed at improving seed value while managing trade-offs among oil, protein, and gossypol contents [41]. In the upland cotton panel, correlation analysis based on classical measurements indicated a significant negative association between protein content and oil content (r = −0.125, p < 0.01) and between protein content and gossypol content (r = −0.375, p < 0.01), whereas oil content was positively associated with gossypol content (r = 0.409, p < 0.01) [32,42] (Table 2). NIRS-based estimates showed the same direction for the oil–protein relationship (r = −0.171, p < 0.01) (Table 2). These results suggest that, within upland germplasm, higher oil content tends to coincide with lower protein content, and higher oil tends to coincide with higher gossypol content; importantly, these are associations and do not by themselves establish physiological causation or regulatory mechanisms [43]. A potential genetic explanation for the oil–protein trade-off reported in cotton is that loci affecting these traits may co-localize, with opposite additive effects on oil and protein accumulation [44].
In sea island cotton, classical measurements showed weak and non-significant correlations among protein, oil, and gossypol (Table 2). By contrast, NIRS estimates indicated a strong negative association between protein and oil (r = −0.744, p < 0.01) (Table 2). This discrepancy highlights that correlation patterns can differ depending on both the species panel and the measurement approach, and it underscores the need to interpret correlation structure in the context of trait distributions, panel composition, and the quantification method used. From a practical perspective, these results indicate that seed-quality improvement should consider oil and protein jointly and evaluate gossypol concurrently, particularly when selecting high-oil materials intended for broader utilization [45].

3.3. Cluster Analysis of Cottonseed Oil Content

To facilitate germplasm screening and the identification of contrasting materials for downstream interpretation, hierarchical cluster analysis was used to classify cotton accessions into discrete oil-content grades based on measured seed oil content (Figure 1). This trait-based grouping provides an objective framework for comparing compositional profiles and for selecting extreme phenotypes for further evaluation [45].
In the upland panel, accessions were separated into four oil-grade clusters (Figure 1A). Cluster I (LPF001–LPF050; 50 accessions) represented the lowest-oil group (approximately 12.74–16.95%). Cluster II (LPF051–LPF342; 292 accessions) comprised the major intermediate group (approximately 16.96–22.86%). Cluster IV (LPF343–LPF484; 142 accessions) corresponded to a higher intermediate group (approximately 22.94–29.63%). Cluster III (LPF485–LPF492; 8 accessions) contained the highest oil materials (approximately 33.55–40.56%). The accessions representing the highest- and lowest-oil groups identified by clustering are summarized in Supplementary Table S5.
Similarly, sea island cotton accessions were grouped into five oil-grade clusters (Figure 1B). Cluster I (LPF693–LPF697; 5 accessions) represented the highest-oil group, Cluster II (LPF664–LPF692; 29 accessions) represented a higher-oil group, Cluster III (LPF548–LPF663; 116 accessions) comprised the main intermediate group, Cluster V (LPF500–LPF547; 48 accessions) represented a lower intermediate group, and Cluster IV (LPF493–LPF499; 7 accessions) contained the lowest-oil materials. The corresponding extreme accessions in sea island cotton are summarized in Supplementary Table S6.
Because clustering was performed on oil content, it does not by itself demonstrate causal relationships with other traits. However, these oil-based clusters provide a useful structure to interpret the trait associations identified in Section 3.2 (e.g., oil–protein and oil–gossypol relationships) and to guide selection decisions that consider both nutritional value and utilization constraints. In particular, high-oil clusters can be prioritized for oil-oriented breeding and downstream applications (e.g., edible oil or bio-based industrial uses) [46], whereas materials with favorable protein content and manageable gossypol levels are more relevant for food/feed utilization pathways [47]. Where applicable, high-gossypol materials may also have specialized non-food value in industrial or biomedical contexts [48]. Finally, the clustering-derived extreme groups can serve as a transparent, phenotype-based rationale for selecting contrasting materials for subsequent compositional and expression comparisons, while acknowledging that genetic relatedness among accessions was not evaluated in this study.
To further characterize the stability of the oil phenotype used for downstream analyses, we examined oil content in ovules/seeds sampled from flowers tagged at multiple dates during the flowering period. As shown in Figure 2, high-oil materials consistently exhibited higher oil content than low-oil materials across all tagging dates, indicating that the high/low oil grouping remained distinguishable across the sampling window. This temporal characterization supports the use of these extreme materials as contrasting phenotypes for subsequent developmental and molecular comparisons, while noting that the observed differences represent phenotypic trends rather than direct evidence of causal regulation.

3.4. Analysis of Cottonseed Oil Content, Phenotypic Statistics, and Variation

Across the 14 selected upland cotton materials, substantial phenotypic variation was observed for several agronomic and seed-related traits (Table 3) [49]. Total boll number (TBN) showed the greatest dispersion (range 6.00–15.20 bolls per plant; CV 27.22%). Seed oil content also varied widely (range 18.15–34.29%; CV 23.02%), whereas fruit branch number showed the lowest dispersion (range 7.40–9.60 per plant; CV 7.61%). Overall, these CV values indicate marked phenotypic heterogeneity within the extreme-material set, supporting their use as contrasting materials for subsequent comparative analyses (Table 3). In this panel, the lowest and highest oil contents were observed in Zao 20 and Zhong R2016, respectively, and the minimum and maximum seed index values were observed in AST104 Gao Fen and Zhong R2016, respectively (Table 3).
Pearson correlation analysis of the 14 materials indicated that oil content measured in mid-position bolls (third and fourth fruit branches) was positively associated with overall seed oil content and with seed index-related traits (Table 4) [50,51]. In addition, several mid-position boll phenotypes were positively correlated with corresponding phenotypes measured in other fruit branches (first, second, fifth, sixth, and seventh branches), suggesting that trait rankings across plants tend to be consistent across boll positions (Table 4). These associations imply coordinated variation in seed traits across fruiting positions within plants; however, they do not establish causal relationships or physiological mechanisms. Accordingly, future work that explicitly tests positional effects and underlying developmental drivers would be needed to confirm causality.

3.5. Dynamic Changes of Oil Content at Different Flowering Dates

To evaluate whether seed oil content varied with flowering (tagging/pollination) date within the same genotype, oil content was measured in bolls derived from flowers tagged at eight dates (07-01 to 08-05) for each of the 14 extreme upland cotton materials [50]. Within each variety, a one-way ANOVA followed by multiple-comparison testing was used to compare oil content across the eight tagging dates; different letters within a row indicate significant differences among dates (p < 0.05) (Table 5). Differences among tagging dates may reflect changes in environmental conditions experienced during boll development and seed filling [52].
Among the seven high-oil materials, the timing of the maximum oil content was variety dependent. For example, the highest values occurred on 07-16 in Shi he zi 913 and De mian 6, on 07-06 in Zhong R2069, on 07-21 in Ji 589, on 07-31 in 10615-1, and on 08-05 in Shi kang 278 (Table 5). In contrast, Zhong R2016 showed consistently high oil content across the eight dates, with no significant differences among time points (Table 5), indicating comparatively stable oil expression across the flowering window in this genotype.
For the seven low-oil materials, peak values also differed by genotype, with maximum oil content observed on 07-06 (Shi da 6201), 07-16 (Wan 217), 07-21 (Zhong 105160 and Zao 20), 07-26 (Qiu xian 0905), and 07-31 (509 H and AsT104 gao fen) (Table 5). When viewed across the group, higher values were often observed around mid-July in several materials; however, the date effect was not uniform, and some varieties showed relatively modest fluctuations across the eight tagging dates. Overall, these results indicate that oil content in mature seed can exhibit within-variety variation associated with flowering/tagging date, and that the magnitude and direction of this variation are genotype specific (Table 5) [53]. Importantly, these comparisons describe phenotypic trends across tagging dates within the same genotype and do not by themselves establish causal developmental mechanisms underlying oil accumulation.

3.6. Dynamics of Oil Accumulation and Physiological Indexes at Different Flowering Dates

Developing embryo sacs/immature seeds were collected from the extreme upland cotton materials at multiple stages after pollination (10, 20, 25, 30, 35, and 40 days after pollination (DAP)) to characterize oil accumulation dynamics and associated physiological status. Oil content and two oxidative-status indicators, peroxidase (POD) activity and malondialdehyde (MDA) content, were measured to describe how these indices change during seed development and to explore whether their temporal patterns coincide with the oil accumulation trajectory.
Across the sampled stages, oil content increased with development and generally reached its highest level at 40 DAP (August 10) in the high-oil materials, with relatively high values also observed at 35 DAP (August 5) (Table 6) [54]. In contrast, POD activity and MDA content tended to peak around 35 DAP (August 5) in multiple materials and then declined toward 40 DAP [55]. When summarized across stages, oil accumulation was most rapid during 30–35 DAP, after which the increase slowed and approached a plateau between 35 and 40 DAP (Figure 3). POD and MDA showed a similar “rise-to-peak” pattern around 30–35 DAP, followed by a decline after 35 DAP (Figure 3). Thus, the peak of POD/MDA occurred earlier than the peak oil content, suggesting that oxidative-status dynamics are temporally coupled with, but not necessarily determinative of, later oil accumulation.
Importantly, these data describe stage-dependent trends in oil content and oxidative indices. While the temporal coincidence of rapid oil accumulation with elevated POD and MDA is consistent with a relationship between redox status and lipid metabolism during seed filling, the present measurements do not establish causality or mechanistic regulation. Therefore, conclusions are framed as associative developmental patterns, and mechanistic links between POD/MDA and fatty-acid metabolism would require targeted biochemical or genetic validation.

3.7. Screening Genes Related to Oil Content Accumulation

Genes involved in fatty-acid synthesis and triacylglycerol (TAG) assembly constitute major determinants of seed oil traits and are widely used as candidate targets for oil-quality improvement in crops [56]. Based on prior evidence from oilseed crops and cotton lipid metabolism studies, we selected a focused set of genes representing key steps in TAG biosynthesis and fatty-acid metabolic pathways, including GhDGAT1, GhDGAT2, GhFAD3, GhKCR2, GhKAS2 homologs (e.g., Gh_A13G1675.1 and Gh_A01G103200.1), and GhMcat [57,58,59,60]. Consistent with established models of TAG formation, the Kennedy pathway (DGAT-catalyzed conversion of diacylglycerol to TAG) is generally considered a principal route for storage oil accumulation in developing seeds [61]. Accordingly, the developmental expression patterns of these genes were profiled by qRT-PCR across seed developmental stages in the 14 extreme upland cotton materials (7 high-oil and 7 low-oil) to identify expression trends that coincide with the oil accumulation window.
Type I DGAT (DGAT1) catalyzes the final, committed step of TAG biosynthesis and is widely implicated in seed oil accumulation across plant species [61,62]. In the present study, GhDGAT1 generally showed higher transcript abundance at later developmental stages in several high-oil materials, with elevated expression frequently observed during the period when oil accumulation increased toward its maximum (Figure 4). In contrast, several low-oil materials tended to show earlier peaks and/or weaker late-stage expression. This pattern is consistent with, but does not by itself prove, a contribution of DGAT1-mediated TAG assembly to high-oil phenotypes. Functional evidence from other systems supports DGAT1’s importance for seed oil formation, including reduced oil in DGAT1-deficient mutants and increased oil in DGAT1-overexpression lines [63,64,65].
Similarly, GhDGAT2 showed stage- and genotype-dependent expression patterns (Figure 4). In multiple high-oil materials, relatively higher expression at late stages (often 30–40 DAP) coincided with the period of rapid oil accumulation identified in Section 3.6, whereas low-oil materials showed comparatively earlier or lower expression peaks (Figure 4). These findings support GhDGAT1 and GhDGAT2 as candidate genes whose developmental expression profiles are associated with oil accumulation differences among extreme materials, in line with prior reports on DGAT isozyme function and substrate utilization in oilseed contexts [66].
FAD3 encodes an ω-3 fatty-acid desaturase involved in converting linoleic acid to linolenic acid and is primarily linked to fatty-acid composition rather than total TAG quantity [67]. In this study, GhFAD3 transcript abundance varied across materials and developmental stages, with high expression observed in some high-oil and some low-oil materials at particular stages (Figure 4). Because expression patterns were not uniquely enriched in either group across all stages, and because FAD3 is more directly connected to desaturation chemistry, our data support a role for GhFAD3 in developmental lipid metabolism and potential oil quality differences, while avoiding claims that it increases or decreases total oil accumulation.
Ketoacyl-ACP synthase II (KAS2) participates in fatty-acid chain elongation within plastids and has been associated with shifts in saturated fatty-acid levels in developing cotton embryos [68]. In the present study, the two GhKAS2 homologs displayed developmental increases and genotype-specific expression peaks, with several high-oil materials showing elevated transcript abundance at later stages (30–40 DAP), while some low-oil materials peaked earlier (Figure 4). These patterns suggest that plastidial fatty-acid synthesis capacity and timing may contribute to observed phenotypic differences, although causal inference would require functional validation.
Malonyl-CoA:ACP transacylase (GhMcat) participates in early steps of fatty-acid biosynthesis. Here, GhMcat exhibited stage-dependent transcriptional changes, often increasing during early-to-mid developmental stages and declining toward later stages in several materials (Figure 4). Notably, peak GhMcat expression frequently occurred around mid-development (e.g., near 30 DAP) in multiple high-oil materials, which temporally overlaps with the period of rapid oil accumulation described in Section 3.6. These results support GhMcat as a developmentally regulated candidate linked to seed lipid biosynthesis capacity.
GhKCR2 encodes a 3-ketoacyl-CoA reductase implicated in the fatty-acid elongation pathway and has been reported to function in endoplasmic reticulum-associated very-long-chain fatty-acid metabolism in cotton [69,70]. In this study, GhKCR2 showed variable expression across materials, with higher transcription at later stages in some high-oil materials and comparatively lower expression in most low-oil materials (Figure 4). Given the known pathway context, these data suggest that GhKCR2 may participate in developmental lipid metabolism differences among materials; however, the present expression profiles alone do not demonstrate whether GhKCR2 promotes or suppresses total oil accumulation. Overall, the targeted qRT-PCR profiles indicate that several lipid-pathway genes, including GhDGAT1, GhDGAT2, GhKAS2 homologs, GhMcat, GhFAD3, and GhKCR2, exhibit developmentally regulated and genotype-dependent expression patterns, and that late-stage enrichment of some TAG-assembly transcripts is more frequently observed in high-oil materials. Importantly, these results identify candidate genes and associative expression trends that align with the oil accumulation window; mechanistic claims regarding positive/negative regulation would require broader transcriptomic coverage and/or functional validation, which are beyond the scope of this study.

4. Conclusions

Traditional chemical assays and near-infrared reflectance spectroscopy (NIRS) provided comparable estimates of cottonseed oil and protein contents and revealed substantial phenotypic variation across both species (e.g., CV ≥ 8.13% for key traits), supporting NIRS as a rapid, non-destructive approach for screening large germplasm sets. Correlation and clustering analyses indicated an overall oil–protein trade-off and clarified species-specific relationships with gossypol. In upland cotton, oil content was positively associated with gossypol (r = 0.409, p < 0.01) while protein content was negatively associated with gossypol (r = −0.375, p < 0.01). In sea island cotton, associations with gossypol were weak (r = 0.038 for oil–gossypol; r = 0.003 for protein–gossypol), whereas NIRS indicated a strong negative oil–protein relationship (r = −0.744, p < 0.01). At the molecular level, expression profiling in extreme upland materials supported the involvement of lipid-metabolism genes in seed oil accumulation: GhDGAT1 and GhDGAT2 showed patterns consistent with positive regulation of oil accumulation, whereas GhFAD3 and GhKCR2 showed opposite trends; additional candidate genes (e.g., GhKAS2, GhMcat) were also implicated. Overall, integrating high-throughput phenotyping with targeted candidate-gene expression analysis provides a practical framework to identify and utilize germplasm with improved cottonseed oil and/or protein traits while accounting for gossypol-related utilization constraints, thereby supporting cottonseed-oriented breeding and downstream food/feed/biofuel applications.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy16010056/s1, Table S1: Code and variety names of 492 upland cotton germplasm; Table S2: Code and variety names of 203 island cotton germplasm; Table S3: 14 extreme upland cotton materials with high or low oil content; Table S4: Primers for qPCR; Table S5: Screening extreme materials of upland cotton and sea island cotton; Table S6: Screening extreme materials of sea island cotton.

Author Contributions

Conceptualization, X.D.; methodology, X.D. and P.L.; software, P.L.; validation, X.D., K.Z., and Q.Z.; formal analysis, P.L., K.Z., and Q.Z.; investigation, P.L., K.Z., and Q.Z.; data curation, P.L., X.L., Y.C., and Z.W.; writing—original draft preparation, P.L.; writing—review and editing, X.D., K.Z., and Q.Z.; visualization, P.L., X.L., Y.C., and Z.W.; funding acquisition, X.D. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Xinjiang Key Research and Development Program (2024B02001-1/-2), the Xinjiang University Basic Research Project (XJEDU2024J037), and the Xinjiang Major Science and Technology Project (2024A02003-3).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

All authors have no conflicts of interest to declare.

Abbreviations

The following abbreviations are used in this manuscript:
ANOVAAnalysis of variance
CSMCottonseed meal
CVCoefficient of variation
DPADays post anthesis
FBNFruit branch number
LPLint percentage
MDAMalondialdehyde
MLPMedian lint percentage
MMTMillion metric tons
MOCMedian oil content
NFFBNode of first fruiting branch
NIRSNear-infrared reflectance spectroscopy
OCOil content
PODPeroxidase
PHPlant height
qRT-PCRQuantitative real-time polymerase chain reaction
SDStandard deviation
SISeed index
TBNTotal boll numbers

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Figure 1. Clustering results of oil content in upland cottonseed (A) and sea island cottonseed (B). (A) Cluster I, LPF001-LPF050; Cluster II, LPF01-LPF342; Cluster III, LPF485-LPF492; Cluster IV, LPF343-F485. (B) Cluster I, LPF693-LPF697; Cluster II, LPF664-LPF692; Cluster III, LPF548-LPF663; Cluster IV, LPF493-LPF499; Cluster V, LPF500-LPF547.
Figure 1. Clustering results of oil content in upland cottonseed (A) and sea island cottonseed (B). (A) Cluster I, LPF001-LPF050; Cluster II, LPF01-LPF342; Cluster III, LPF485-LPF492; Cluster IV, LPF343-F485. (B) Cluster I, LPF693-LPF697; Cluster II, LPF664-LPF692; Cluster III, LPF548-LPF663; Cluster IV, LPF493-LPF499; Cluster V, LPF500-LPF547.
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Figure 2. Dynamic changes in oil content at different flowering dates.
Figure 2. Dynamic changes in oil content at different flowering dates.
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Figure 3. Oil accumulation and dynamic changes in physiological indices in ovules at different flowering dates.
Figure 3. Oil accumulation and dynamic changes in physiological indices in ovules at different flowering dates.
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Figure 4. Transcriptome analysis of cottonseed oil accumulation in different cotton materials. (A) Gh_S007G0119.1 (GhDGAT1) gene; (B) Gh_A11G0516.1 (GhDGAT2) gene; (C) Gh_D09G0870.1 (GhFAD3) gene; (D) Gh_A13G1675.1 (GhKAS2) gene; (E) Gh_A01G103200.1 (GhKAS2) gene; (F) Gh-A03G153700.1 (GhKCR2) gene; (G) Gh-D03G160400.1 (GhMcat) gene.
Figure 4. Transcriptome analysis of cottonseed oil accumulation in different cotton materials. (A) Gh_S007G0119.1 (GhDGAT1) gene; (B) Gh_A11G0516.1 (GhDGAT2) gene; (C) Gh_D09G0870.1 (GhFAD3) gene; (D) Gh_A13G1675.1 (GhKAS2) gene; (E) Gh_A01G103200.1 (GhKAS2) gene; (F) Gh-A03G153700.1 (GhKCR2) gene; (G) Gh-D03G160400.1 (GhMcat) gene.
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Table 1. Variation analysis of seed quality traits.
Table 1. Variation analysis of seed quality traits.
ParameterTraditional Classical MethodNear-Infrared Reflectance Spectroscopy
Oil Content/%Protein Content/%Gossypol Content/mg/gProtein + Oil Content/%Oil Content/%Protein Content/%Protein + Oil Content/%
Upland cotton
Maximum43.5649.792.0771.8231.4738.4959.57
Minimum12.7415.530.0632.8112.5611.9030.56
Average21.2323.631.4744.8619.7824.3744.15
SD3.984.630.216.072.042.102.67
CV/%18.7619.5914.0213.5410.308.646.04
Sea island cotton
Maximum41.4339.002.0871.9528.7027.6852.50
Minimum14.6418.841.2240.4717.6619.6941.10
Average24.8225.751.6050.5622.2722.9745.24
SD6.152.090.156.442.041.901.42
CV/%24.788.139.4812.749.188.253.13
Table 2. Correlation analysis of cottonseed quality traits.
Table 2. Correlation analysis of cottonseed quality traits.
Main ContentTraditional Classical MethodNear-Infrared Reflectance Spectroscopy
Protein Content/%Oil Content/%Gossypol Content/mg/gProtein Content/%Oil Content/%
Upland cotton
Protein content/%1−0.125 **−0.375 **1−0.171 **
Oil content/%−0.125 **10.409 **−0.171 **1
Gossypol content/mg/g−0.375 **0.409 **1--
Sea island cotton
Protein content/%1−0.0280.0031−0.744 **
Oil content/%−0.02810.038−0.744 **1
** indicate significant difference at p < 0.01, respectively.
Table 3. Analysis of cottonseed oil content, phenotype statistics, and variation.
Table 3. Analysis of cottonseed oil content, phenotype statistics, and variation.
IndexOil Content/%SI/gLP/%PH/cmBSH/cmFBNTBN
Minimum18.158.0029.5849.0012.207.406.00
Median26.049.2936.6861.0016.008.207.90
Maximum34.2913.0040.0776.0019.409.6015.20
Average25.649.7236.1660.4415.918.218.24
SD6.611.303.067.862.120.622.24
CV/%25.7713.418.4613.0013.327.6127.22
SD = standard deviation, CV = coefficient of variation, SI = seed index, LP = lint percentage, PH = plant height, BSH =boll-setting height, FBN = fruit branch number, TBN = total boll numbers.
Table 4. Correlation analysis between median boll cottonseed oil content and phenotype.
Table 4. Correlation analysis between median boll cottonseed oil content and phenotype.
IndexMOC/%OC/%MSISIMLPLPPHNFFBFBNTBN
MOC/%10.986 **0.861 **0.826 **−0.163−0.249−0.281−0.1960.274−0.058
OC/%0.986 **10.851 **0.831 **−0.128−0.217−0.267−0.1620.273−0.039
MSI0.861 **0.851 **10.950 **0.035−0.02−0.0370.0670.261−0.071
SI0.826 **0.831 **0.950 **10.0870.006−0.0450.0450.3280.014
MLP−0.163−0.1280.0350.08710.938 **0.4320.4930.2570.17
LP−0.249−0.217−0.020.0060.938 **10.564 *0.5220.2580.217
PH−0.281−0.267−0.037−0.0450.4320.564 *10.761 **0.585 *0.646 *
NFFB−0.196−0.1620.0670.0450.4930.5220.761 **10.2540.413
FBN0.2740.2730.2610.3280.2570.2580.585 *0.25410.836 **
TBN−0.058−0.039−0.0710.0140.170.2170.646 *0.4130.836 **1
* p < 0.05 and ** p < 0.01, indicate a significant difference. MOC = median oil content, OC = oil content, MSI = median seed index, SI = seed index, MLP = median lint percentage, LP = lint percentage, PH = plant height, NFFB = node of first fruiting branch, FBN = fruit branch number, TBN = total boll number.
Table 5. Dynamic changes in oil content in the extreme materials among 8 sampling dates [%].
Table 5. Dynamic changes in oil content in the extreme materials among 8 sampling dates [%].
VarietiesPollination Date (Month-Day)
07-0107-0607-1107-1607-2107-2607-3108-05
High oil cotton materials
Shi he zi 91331.15 ab30.82 ab32.56 ab33.78 a32.20 ab31.00 ab29.51 b31.95 ab
Zhong R201633.58 a34.38 a34.54 a34.00 a34.72 a35.24 a34.45 a32.71 a
Zhong R206928.19 ab30.29 a24.71 cd25.36 cd24.36 d23.38 d26.83 bc28.56 ab
10615-127.49 c28.69 c28.22 c28.85 bc30.17 abc28.74 c32.03 a31.46 ab
Shi kang 27825.28 cd26.38 bc25.30 cd27.77 ab27.74 ab24.36 d28.08 ab29.49 a
De mian 632.48 b29.63 c34.52 a34.90 a33.03 b32.25 b34.74 a32.87 b
Ji 58935.41 ab35.44 ab34.51 ab33.10 b37.56 a33.51 b36.21 ab34.28 ab
Low oil cotton materials
Zhong 10516018.93 ab19.42 ab18.99 ab19.20 ab17.77 b19.05 ab20.71 ab22.82 a
Shi da 620117.55 ab18.42 a17.51 ab17.09 ab16.62 bc15.85 c18.24 ab18.32 ab
Zao 2016.31 d18.25 bc18.32 bc18.22 bc20.66 a18.09 bc19.10 b17.16 cd
509 H19.27 c19.11 c21.82 ab18.93 c20.56 bc19.13 c22.74 a20.56 bc
Qiu xian 090518.81 abc19.64 ab19.34 ab19.12 abc17.05 b20.02 a19.24 abc17.46 bc
Wan 21721.03 a20.74 a21.55 a21.81 a20.66 a21.21 a20.40 a20.06 a
AsT104 gao fen16.44 c19.21 b19.15 b18.45 b18.99 b17.22 c20.43 a18.32 b
During flowering, the detected oil content was labeled in alphabetical order for each cotton material. A single alphabetic labeled group was not significantly different from the same letter group, and the polyalphabetic labeled group was not significantly different from any group that shares at least one alphabetic labeled group (e.g., the “ab” labeled group is not significantly different from both “a” labeled group and “b” labeled group). Conversely, there was a significant difference between adjacent letters (e.g., the “a” labeled group is significantly different from “b” labeled group).
Table 6. Oil accumulation and dynamic changes in physiological indices in ovules at different flowering dates.
Table 6. Oil accumulation and dynamic changes in physiological indices in ovules at different flowering dates.
VarietiesContentPollination Date (Month-Day)
07-1107-2107-2607-3108-0508-10
Shi he zi 913Oil/%2.92 d0.37 e1.17 e4.84 c13.97 b18.70 a
MDA nmol/g × Fw4.48 c4.09 d5.73 a5.91 a5.26 b2.60 e
POD U/g × Fw × min1093.33 b1440.00 b1440.00 b960.00 b7186.67 a1306.67 b
Zhong R2016Oil/%2.53 d e3.91 d1.16 e7.25 c9.54 b17.39 a
MDA nmol/g × Fw8.30 ab4.07 e7.23 bc8.73 a5.29 d6.29 cd
POD U/g × Fw × min480.00 d1013.33 c1066.67 c2720.00 a1493.33 b2493.33 a
Zhong R2069Oil/%1.86 d1.65 d0.92 d8.23 c18.37 b22.74 a
MDA nmol/g × Fw5.74 a4.57 b4.06 b5.96 a6.40 a3.95 b
POD U/g × Fw × min1226.67 c1066.67 c906.67 c3333.33 b1386.67 c5680 a
10615-1Oil/%1.04 c0.52 b1.03 b9.00 b9.84 b13.00 a
MDA nmol/g × Fw7.67 c5.60 d7.22 c8.32 b9.17 a7.25 c
POD U/g × Fw × min800.00 c906.67 c1093.33 c1506.67 c4480.00 a2346.67 b
Shi kang 278
De mian 6		Oil/%3.80 d1.96 e2.61 e9.18 c16.06 b21.81 a
MDA nmol/g × Fw1.69 a4.82 c5.29 c5.21 c8.55 a6.47 b
POD U/g × Fw × min1440.00 b1253.33 b1520.00 b1786.67 b6933.33 a7226.67 a
Oil/%1.71 d0.95 d e0.71 e5.06 c8.16 b19.36 a
MDA nmol/g × Fw5.58 e7.34 c6.41 d7.67 b8.16 a4.38 f
POD U/g × Fw × min1253.33 c1466.67 c1093.33 c5666.67 b8506.67 a5466.67 b
Ji 589Oil/%3.21 d1.10 e2.39 d, e5.38 c15.48 b17.24 a
MDA nmol/g × Fw6.74 d8.06 c6.63 d8.62 b9.39 a5.58 e
POD U/g × Fw × min986.67 d1733.33 c826.67 d2266.67 bc4613.33 a2480.00 b
Zhong 105160Oil/%2.97 c0.49 d0.87 d3.87 c10.04 b12.64 a
MDA nmol/g × Fw4.73 c4.75 c5.09 bc5.82 b9.15 a5.33 bc
POD U/g × Fw × min1813.33 b720.00 c1440.00 b1493.33 b5226.67 a1413.33 b
Shi da 6201Oil/%1.82 c2.28 c2.74 c6.24 b13.10 a14.33 a
MDA nmol/g × Fw8.46 c5.29 de4.88 e9.07 b9.79 a5.47 d
POD U/g × Fw × min640.00 d906.67 d1013.33 d1573.33 c6413.33 a2386.67 b
Zao 20Oil/%3.72 d0.88 e0.63 e6.67 c15.09 b17.20 a
MDA nmol/g × Fw5.66 b4.70 c5.03 bc5.70 b6.77 a5.81 b
POD U/g × Fw × min1173.33 d1146.67 d2133.33 c1786.67 cd4960.00 a3680.00 b
509HOil/%1.66 d0.69 d0.97 d6.07 c11.14 b14.91 a
MDA nmol/g × Fw4.37 e3.37 f6.04 c6.44 b7.45 a5.23 d
POD U/g × Fw × min1280.00 c1253.33 c773.33 c5920.00 b8133.33 a1493.33 e
Qiu xian 0905Oil/%1.87 d1.47 d4.42 c8.15 b9.40 b12.73 a
MDA nmol/g × Fw6.19 d5.25 e8.36 c8.84 b9.64 a5.18 e
POD U/g × Fw × min1040.00 c960.00 c1626.67 c1413.33 c7826.67 b11626.67 a
Wan 217Oil/%0.70 e0.50 e1.93 d7.95 c9.10 b14.24 a
MDA nmol/g × Fw4.60 e7.34 ab6.06 d6.80 bc7.67 a6.59 cd
POD U/g × Fw × min1013.33 c1333.33 bc1013.33 c1573.33 b1680.00 b4160.00 a
AsT104 Gao fenOil/%0.54 e2.22 d1.68 d5.63 c9.86 b15.48 a
MDA nmol/g × Fw4.73 c5.32 b3.30 d7.05 a7.21 a4.78 c
POD U/g × Fw × min906.67 c1360.00 c960.00 c1440.00 c4986.67 a3013.33 b
During flowering, the detected oil content was labeled in alphabetical order for each cotton material. A single alphabetic labeled group was not significantly different from the same letter group, and the polyalphabetic labeled group was not significantly different from any group that shares at least one alphabetic labeled group (e.g., the “ab” labeled group is not significantly different from both “a” labeled group and “b” labeled group). Conversely, there was a significant difference between adjacent letters (e.g., the “a” labeled group is significantly different from “b” labeled group).
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Liu, P.; Wang, Z.; Lu, X.; Chang, Y.; Zheng, K.; Zu, Q.; Deng, X. Chemical Composition and Expression Analysis of Oil-Related Genes in Upland Cottonseeds. Agronomy 2026, 16, 56. https://doi.org/10.3390/agronomy16010056

AMA Style

Liu P, Wang Z, Lu X, Chang Y, Zheng K, Zu Q, Deng X. Chemical Composition and Expression Analysis of Oil-Related Genes in Upland Cottonseeds. Agronomy. 2026; 16(1):56. https://doi.org/10.3390/agronomy16010056

Chicago/Turabian Style

Liu, Pengfei, Zhong Wang, Xiaoshuang Lu, Yujie Chang, Kai Zheng, Qianli Zu, and Xiaojuan Deng. 2026. "Chemical Composition and Expression Analysis of Oil-Related Genes in Upland Cottonseeds" Agronomy 16, no. 1: 56. https://doi.org/10.3390/agronomy16010056

APA Style

Liu, P., Wang, Z., Lu, X., Chang, Y., Zheng, K., Zu, Q., & Deng, X. (2026). Chemical Composition and Expression Analysis of Oil-Related Genes in Upland Cottonseeds. Agronomy, 16(1), 56. https://doi.org/10.3390/agronomy16010056

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