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. 2026 Sep 25;39:104511. doi: 10.1016/j.fochx.2026.104511

Origin-specific metabolomic profiling and network pharmacology propose potential multi-target antioxidant mechanisms of black sesame seeds

Lei Wang a, Huihui Gong b, Chenxi Sun a, Junsheng Zhao b, Shancang Zhao a, Yanjie Dong a,⁎
PMCID: PMC13635498  PMID: 42835574

Abstract

Black Sesame Seeds (BSS) are consumed as an edible herbal food with medicinal food homology and possess potent antioxidant properties; however, their metabolic compositions and corresponding antioxidant efficacies substantially vary across different geographical origins. This study investigates the underlying multi-component and multi-target antioxidant mechanisms of BSS from various geographical origins. An integrated computational and experimental approach combining UHPLC-Q-Exactive Orbitrap MS/MS-based metabolomics, network pharmacology, and molecular docking was employed. Metabolomic profiling identified a diverse array of active phytochemicals enriched in BSS, including lipophilic lignans (e.g., (+)-eudesmin), vitamin E derivatives (e.g., γ-tocotrienol), and hydrophilic flavonoids (e.g., quercetin). Subsequent network and structural biology analyses revealed that these diverse compounds exert complementary multi-target effects to mitigate oxidative stress by targeting core hub proteins, particularly AKT1 and TP53. Specifically, molecular docking demonstrated that lipophilic components exhibit strong binding affinities to the hydrophobic cavities of AKT1, whereas hydrophilic polyphenols establish robust hydrogen-bonding networks with TP53. Pathway enrichment analyses further suggested that BSS exerts its profound cytoprotective effects primarily by modulating the PI3K-Akt and apoptosis signaling cascades, thereby restoring intracellular redox homeostasis. In conclusion, this study systematically predicts the complex structural and pharmacological basis of BSS against oxidative damage, laying a solid theoretical hypothesis for the future development of BSS-derived natural antioxidants and functional therapeutics.

Keywords: Black sesame seeds, UPLC-Orbitrap MS, Metabolite biomarkers, Geographical variations, Oxidative damage

1. Introduction

Sesame (Sesamum Indicum L.) is the ninth most-produced oilseed globally and is widely cultivated in Asia and Africa because of its rich nutritional value and economic potential (Kim et al., 2020). The global sesame seed market, valued at approximately USD 7.7 billion in 2024, is expected to increase steadily in the coming decade, and China is a leading producer (Langyan et al., 2025). Sesame seeds are classified according to the color of their hulls: white, yellow, brown, or black (Wang et al., 2025). Black sesame seeds (BSS) are considered a traditional Chinese food with medicinal properties. In the Chinese Pharmacopoeia, BSS are considered to have the effect of nourishing the liver and kidneys (Li et al., 2020). According to modern medical and biological composition analysis, BSS have antioxidative, anti-inflammatory, anticancer, and antidiabetic properties (Jafar et al., 2025). The nutritional and medicinal properties of BSS are closely related to their metabolites such as proteins, carbohydrates, lipids, minerals, lignans, and pigments (Hou et al., 2022; Zhang et al., 2022). These bioactive compounds exhibit potent biological activities, particularly in scavenging free radicals and modulating oxidative stress, making BSS a promising dietary intervention for oxidative damage-related conditions.

Oxidative stress, characterized by an imbalance between the production of reactive oxygen species (ROS) and the body's antioxidant defense systems, is a primary driver of various chronic diseases, including neurodegeneration, inflammation, and cellular aging (Hu et al., 2024). While the general antioxidant capacity of BSS extracts has been reported, the accumulation of specific functional metabolites in BSS is heavily influenced by environmental factors and geographical origins. Currently, BSS from major producing countries such as China, India, and Myanmar dominate the global market (Xu et al., 2024). However, the significant variations in their metabolic profiles and how these geographical differences translate to specific pharmacological pathways against oxidative stress remain largely unexplored (Guo et al., 2025; Liang et al., 2025; Pang et al., 2023). Unraveling the underlying mechanisms of BSS serves as a critical foundation for its translation into functional foods.

In recent years, network pharmacology has emerged as a multidisciplinary approach tailored to the multi-component, multi-target characteristics of traditional Chinese herbal medicines. Integrating pharmacology, molecular biology, medicine, bioinformatics, and statistics, it employs network analysis to examine drug constituents and disease targets (Shi et al., 2023). However, network pharmacology depends on predictive analyses derived from public databases, which limits its capacity to fully elucidate the mechanistic basis underlying the differential antioxidant properties exhibited by various BSS. Furthermore, geographical differences in soil, climate, and water conditions contribute to considerable chemical variation among plants, adding complexity to accurate prediction. Metabolomics, which focuses on profiling metabolite composition and abundance, overcomes these constraints by enabling a systems-level exploration of the relationships between bioactive compounds and disease pathways (Guo et al., 2025; Xiao et al., 2024). Guo et al. (2025) integrated UHPLC-Q-Exactive Orbitrap MS-based metabolomics, network pharmacology, and molecular docking to compare the antioxidant activities of four Fritillaria species, identifying key active compounds and their putative mechanisms. By integrating multivariate metabolomic profiling with network pharmacology, Xiao et al. (2024) elucidated the phytochemical disparities and core antioxidant signaling mechanisms across four distinct tissues of Citrus reticulata (Chachi). Similarly, Guo et al. (2024) combined metabolomic and network pharmacology analyses to identify major metabolic components across different segments of Angelica sinensis root (ASR) and preliminarily revealed how these segments modulate hemostasis, blood nourishment, and circulation through multi-component, multi-target, and multi-pathway interactions. Thus, metabolomics not only compensates for the predictive limitations of network pharmacology but also offers a more comprehensive and functionally relevant perspective for elucidating the compositional basis of herbal bioactivity.

This study employed an integrated approach of untargeted metabolomics network pharmacology, molecular docking approach to analyze how the distinct chemical matrices of black sesame samples from three major geographical origins contribute to potential protection against oxidative damage. These findings advance our understanding of the active compounds in black sesame, providing theoretical evidence for food origin traceability, quality standardization, and the targeted formulation of health products tailored for mitigating oxidative stress.

2. Materials and methods

2.1. Chemicals

LC-MS grade methanol, acetonitrile, formic acid and isopropyl alcohol were purchased from ANPEL (Shanghai, China). Ultra-pure water was prepared in-house using a Milli-Q water purification system (Millipore, Bedford, MA, USA). 12 BSS samples originating from China (CBS) was bought from local market in Shandong and were of the Zhongzhi 9 variety. 12 Myanmar-origin (MBS) and 12 Indian-origin black sesame (IBS) samples (all imported within the past year) obtained from Shandong Luochen Food Co., Ltd. (Jinan, China), an authorized trading company. According to the import information, IBS were collected from Gujarat and were of the GT10 variety, and MBS were collected from Magway and were of the ZZM2541 variety. All the BBS samples were stored at −20 °C until use.

2.2. Preparation of extracts and evaluation of in vitro antioxidant activities

2.2.1. Preparation of BSS extracts

The sample preparation method was adopted from (Dossou et al., 2022). Hence, 0.5 g of ground powder from BSS sample was extracted with 5 mL of 80% ethanol in a shaking incubator for four hours at room temperature. The supernatants were filtered and carefully collected individually after centrifugation of 5000 rpm for 15 min.

2.2.2. In vitro antioxidant activity evaluation

The antioxidant activity of the samples was evaluated through several assays with different mechanisms. The DPPH radical scavenging activity (DPPH), and ABTS cation radical scavenging activity (ABTS) were measured following (Ahmed et al., 2021; Kim et al., 2014; Song et al., 2025). To ensure quantitative comparability, a parallel gradient series of positive controls was established. Calibration curves were plotted using different concentrations of vitamin C (ranging from 10 to 60 μg/mL for DPPH, and 5 to 30 μg/mL for ABTS). The resulting linear regression equations were y = 1.5991× − 1.1533 (R2 = 0.9991) for DPPH, and y = 3.556× − 5.48 (R2 = 0.9990) for ABTS (Fig. S1A, B). Results were expressed as milligrams of vitamin C equivalents per gram of dry weight of water extract powder (mg VCE/g d.w.). A ferric reducing antioxidant power (FRAP) assay based on Fe3 + −TPTZ reduction was assessed using a similarly established FeSO4 gradient (ranging from 0.1 to 1.0 mM, y = 1.5655× + 0.0138, R2 = 0.9994, Fig. S1C), with results expressed as milligrams of FeSO4 equivalents per gram of dry weight (mg FeSO4 eq/g d.w.).

2.3. Metabolomics analysis

2.3.1. Extraction of metabolites

The BSS samples were slowly thawed at 4 °C. The ground seed powder (50 mg) were vortexed with 1 mL of a solvent mixture comprising water, acetonitrile, and isopropanol (1:1:1 v/v/v) (1 mL) for 60 s. The mixture was then ultrasonicated in an ice-water bath (−10 °C) for 30 min, followed by centrifugation at 12,000 rpm and 4 °C for 10 min. To facilitate protein precipitation, the extract was incubated at −20 °C for 1 h. The supernatant was subsequently recovered and diluted with 200 μL of 30% (v/v) aqueous acetonitrile. After another centrifugation step (12,000 rpm, 4 °C, 10 min), the collected supernatant was concentrated to dryness under a gentle stream of nitrogen gas. The dried residue was reconstituted in 200 μL of 30% aqueous acetonitrile. The final solution was vortexed thoroughly and centrifuged at 14,000 rpm and 4 °C for 15 min. The resulting clear supernatant was transferred to an autosampler vial for UHPLC-MS/MS analysis (Wang et al., 2023).

2.3.2. UHPLC-MS/MS analysis

Untargeted metabolomics analysis was carried out on a Vanquish ultra-performance liquid chromatography system (UHPLC, Thermo Fisher Scientific, Waltham, MA, USA) connected to a Q Exactive HF-X quadrupole-Orbitrap mass spectrometry system (Thermo Fisher Scientific). The UHPLC system was equipped with an ACQUITY HSS T3 column (100 Å, 1.8 μm, 2.1 mm × 100 mm; Waters, Milford, MA, USA). The injection volume was 2 μL, the flow rate was 0.3 mL/min, and the column temperature was 40 °C. The binary elution used ultrapure water as solvent A and acetonitrile as solvent B, both supplemented with 0.1% formic acid, and the elution gradient was as follows: 0–12 min, from 100% to 5% A; 12–13 min, held at 5% A; 13–17 min, held at 100% A to re-equilibrate (total run time 17 min). Biological samples were organized in random order and analyzed sequentially, and pooled QC samples were tested after every five analyses. The autosampler was kept at 4 °C.

The mass spectrometer was equipped with an electrospray ionization (ESI) source operating under the following conditions: sheath gas, 40 arb N2; auxiliary gas, 10 arb N2 (350 °C); ion spray voltage, +3000 V/−2800 V; ion source temperature, 350 °C; ion transfer tube temperature, 320 °C. The scanning mode was positive/negative polarity switching Full MS/dd-MS2. The primary scan range was m/z = 70–1050. The primary resolution was 70,000 (AGC target 3e6; max IT 100 ms) and the secondary resolution was 17,500 (AGC target 1e5; max IT 50 ms).

2.4. Network pharmacology

2.4.1. Prediction of the potential active ingredient

The raw MS datasets were processed for peak alignment, noise filtering, and compound annotation. The identified chemical features were categorized based on the Metabolomics Standards Initiative (MSI) confidence levels (Sumner et al., 2007). To identify candidate active ingredients for network pharmacology, a comprehensive dual-screening strategy was implemented. First, 17 key origin-specific metabolic drivers were prioritized from the 23 core differential metabolites identified via our untargeted metabolomics profiling, using strict statistical thresholds (VIP ≥ 1.0 and P < 0.05 after Benjamini–Hochberg FDR correction). Second, to ensure a systematic evaluation of the pharmacological potential, this metabolomic panel was supplemented with 7 classical high-activity compounds retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (https://www.tcmsp-e.com/tcmsp.php). These additional ingredients strictly met our joint screening criteria, which incorporated database pharmacokinetic thresholds (OB ≥ 20, DL ≥ 0.1) alongside their antioxidant efficacy in black sesame (Table S2) (Liu et al., 2024). Consequently, a robust dataset of 24 core active compounds was finalized for subsequent target prediction and network analysis. Finally, potential macromolecular target receptors for these assessed chemical entries were retrieved using the SwissTargetPrediction database (http://www.swisstargetprediction.ch/, restricting candidates to Homo sapiens with probability scores > 0.1) and the PubChem chemical database (https://pubchem.ncbi.nlm.nih.gov/) (Wang et al., 2023).

2.4.2. Identification of Core targets for oxidative stress

Genes associated with “oxidative damage”, “oxidative stress”, and “antioxidant” were retrieved from the GeneCards database (https://www.genecards.org/). A total of over 3000 disease-related genes were retrieved from the GeneCards database, and the top 2000 genes were subsequently selected based on their relevance scores. After normalizing target names and removing duplicates, Venn analysis was performed to identify intersection targets between BSS-derived active ingredients and oxidative damage-related diseases. Intersection targets were analyzed using the STRING 11.5 database to construct a protein-protein interaction (PPI) network, mandating a high-confidence interaction score threshold ≥0.700. The resulting network topology was imported into Cytoscape software (version 3.9.1) for advanced network parameters evaluation. Crucial hub target nodes were systematically extracted based on the simultaneous topological filtering criteria where individual degree, closeness centrality, and betweenness centrality parameters all exceeded their respective network-wide mathematical means.

2.5. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis

To characterize the biological functions and involved pathways of the core targets, GO and KEGG analyses were performed using the DAVID database (https://david.ncifcrf.gov/). Statistical significance was validated using a hypergeometric test against the background reference set, applying a Benjamini-Hochberg adjusted P-value (P-adj < 0.05). The resulting data clusters were visualized utilizing the Bioinformatics online graphing platform (https://www.bioinformatics.com.cn/).

2.6. Molecular docking verification

To evaluate the binding affinity and explore the interaction modes between the key geographical differential metabolites (ligands) and the core oxidative stress targets (receptors), molecular docking simulations were performed using AutoDock v4.2.6. Prior to the docking simulations, the 3D structures of both the target proteins (PDB IDs detailed in Table S3) were retrieved from the RCSB Protein Data Bank (Kim et al., 2020). Both the target proteins and candidate metabolites were meticulously prepared to mimic physiological conditions. Specifically, non-interacting water molecules and co-crystallized heteroatoms were removed. The protonation states of the amino acid residues and the ligands were assigned at a physiological pH of 7.4. Finally, polar hydrogens alongside Gasteiger partial charges were systematically added, and the 3D structures of the candidate metabolites (ligands) were energy-minimized.

During the docking protocol, the grid box was specifically centered on the active site of each respective receptor. The exact grid box dimensions (size x, y, z), and algorithmic parameters (including the number of runs and exhaustiveness settings) for each target are comprehensively documented in Supplementary Table S3 to ensure full computational reproducibility. The optimal docking conformations were selected based on the lowest binding energy (kcal/mol). Visualization of the 3D binding conformations, alongside the identification of key interacting amino acid residues, was rendered utilizing PyMOL.

2.7. Data analysis

Multivariate analyses including principal component analysis (PCA) for unsupervised pattern screening and orthogonal partial least-squares discriminant analysis (OPLS-DA) for supervised classification were conducted using Metware Cloud (Metware Biotechnology, Wuhan, China). Prior to modeling, the feature intensity matrix was preprocessed by probabilistic quotient normalization (PQN) to correct sample dilution variations, log10 transformation to reduce heteroscedasticity, and unit variance (UV) scaling to equalize metabolite contributions. OPLS-DA model validity was evaluated via 7-fold cross-validation, giving R2Y > 0.5 and Q2 > 0.5, with permutation testing (n = 200, p < 0.001) indicating the absence of overfitting. The variable importance in projection (VIP) metric was computed for all features in the validated OPLS-DA model to quantify classification contributions. Metabolites with VIP >1.0 were subjected to univariate analysis using Student's t-test. The resulting p values underwent Benjamini–Hochberg FDR correction, and metabolites with p < 0.05 were considered statistically significant (Xia et al., 2023).

Metabolites identified with Level 1 and 2 identification confidence (Sumner et al., 2007) were annotated using the KEGG Compound database (http://www.kegg.jp/kegg/compound/), and the annotated compounds were mapped to the KEGG Pathway database (http://www.kegg.jp/kegg/pathway.html). Pathway enrichment analysis was performed using a hypergeometric test against the all KEGG-annotated metabolites detected in this study as the reference set. Pathways with adj. p value <0.05 and containing at least 3 matched metabolites were considered significantly enriched.

3. Results and discussion

3.1. Antioxidant capacity of BSS

Based on the experimental results, the antioxidant activities of BSS from different geographical origins exhibited significant variations (p < 0.05), highlighting the profound influence of cultivation environments on the accumulation of bioactive metabolites (Table 1). In terms of radical scavenging activities, IBS displayed the highest DPPH scavenging activity (2.80 ± 0.36 mg VCE/g d.w.), which was significantly superior to MBS, 1.81 ± 0.12 mg VCE/g d.w.), while no statistically significant difference was observed between CBS (2.58 ± 0.61 mg VCE/g d.w.) and IBS. A similar trend was observed in the ABTS• + assay, where CBS (2.15 ± 0.30 mg VCE/g d.w.) and IBS (2.18 ± 0.10 mg VCE/g d.w.) both exhibited robust and comparable scavenging capacities, both of which were significantly higher than the 1.72 ± 0.16 mg VCE/g d.w. recorded for MBS. In the evaluation of total reducing capacity, CBS demonstrated the most potent Ferric Reducing Antioxidant Power (FRAP) (2.20 ± 0.22 mg FeSO4 eq/g d.w.), closely followed by IBS (2.0 ± 0.41 mg FeSO4 eq/g d.w.), whereas MBS showed the lowest reducing capacity (1.18 ± 0.24 mg FeSO4 eq/g d.w.), representing a nearly two-fold difference compared to CBS. Previous studies utilizing ABTS, DPPH, and FRAP assays consistently emphasize the positive antioxidant activity of BSS (Dossou et al., 2024; Kim et al., 2023; Magdamit et al., 2025).

Table 1.

Antioxidant activity of BSS extracts from different origins.

DPPH
(mg VCE/g d.w)		 ABTS•+
(mg VCE/g d.w)		 FRAP
(mg FeSO4 equivalent/g d.w.)
MBS 1.81 ± 0.12b 1.72 ± 0.16b 1.18 ± 0.24b
IBS 2.80 ± 0.36a 2.18 ± 0.10a 2.0 ± 0.41a
CBS 2.58 ± 0.61a 2.15 ± 0.30a 2.20 ± 0.22a
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Mean ± standard deviation of samples (n = 12). Different letters in the same column indicated that the difference was statistically significant, P < 0.05.

3.2. Global metabolomic profiling and multivariate statistical analysis of BSS from distinct origins

The repeatability and reliability of the UPLC-Q-Exactive-MS method was evaluated using pooled QC replicates. Visual inspection of PCA score plot confirmed a dense clustering of all QC samples (Fig. S2A). Additionally, exceptional analytical stability was substantiated by the fact that the vast majority of detected peaks maintained a relative standard deviation (RSD) of less than 30%, specifically accounting for 97.06% of the total ion features (Fig. S2B). From the BSS, 629 metabolites belonging to 10 categories were identified (Fig. 1A), and compound classification revealed that lipids and lipid-like molecules (28.93%), alongside phenylpropanoids and polyketides (20.35%), dominated the annotated metabolome, which chemically aligns with the high-oil trait of BSS (Fig. 1A). Subsequently, a correlation heatmap (Fig. 1B) revealed the similarity of the three BSSs. The PCA plot showed that CBS, IBS, and MBS fell into three distinct clusters (Fig. 1C), suggesting that substantial differences existed among the BSS groups. The clustering of the twelve replicates for each BSS species demonstrated good reproducibility within each group. The cumulative variance explained by the two principal components (PC1 and PC2) was 70.48%, with contributions of 49.89% (PC1) and 20.59% (PC2). Furthermore, CBS and MBS were separated along PC2, and they were both separated from IBS along PC1. To identify the core metabolic drivers responsible for this origin-specific discrimination observed in the score plot, a PCA loading plot analysis was systematically performed (Fig. 1D). As illustrated in Fig. 1D, each green scatter point represents an individual molecular feature mapped against PC1 and PC2. Metabolites positioning at the extreme peripheries of the loading ellipse exert the most profound leverage on sample clustering.

Fig. 1.

Fig. 1

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Global metabolomic landscape and multivariate statistical discrimination of black sesame seeds (BSS) from three distinct geographical origins. (A) Chemical classification and relative proportions of the 629 annotated metabolites in BSS. (B) Pearson correlation heatmap with hierarchical clustering, demonstrating the robust intra-group reproducibility and significant inter-group variations among the Chinese (CBS), Indian (IBS), and Myanmar (MBS) cultivars. (C) Principal Component Analysis (PCA) score plot showing the distinct spatial separation and clustering of BSS samples based on their geographical origins. (D) PCA loading plot illustrating the distribution of individual molecular features (green points), highlighting their leverage and contribution to the principal components (PC1 and PC2). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Utilizing these compounds as dependent variables and BSS species as independent variables, an OPLS-DA model was constructed (Fig. S3A, B, and C), which effectively distinguished among the three BSS species. A random permutation test was subsequently conducted 200 times (Fig. S3D, E, and F). We observed a good predictive power (Q2) and well interpretability of these OPLS-DA models. For instance, the Q2 values for CBS vs IBS, MBS vs CBS, and MBS vs IBS were 0.997, 0.998, and 0.996, respectively, R2X values for CBS vs IBS, MBS vs CBS, and MBS vs IBS were 0.896, 0.941,0.902, and the R2Y values for all four comparisons were greater than 0.990. These robust parameters demonstrate the model's reliability and effectiveness in distinguishing BSS from different geographical origins with significant statistical relevance.

3.3. Identification of origin-specific differential biomarkers and conserved baseline defense mechanisms

According to the volcano plots, the number of differential metabolites was 68 between MBS and CBS (Fig. S4A), 255 between IBS and CBS (Fig. S4B), and 270 between IBS and MBS (Fig. S4C), indicated significant differences in the composition and regulation of secondary metabolites among these different groups. Notably, most of the differential metabolites belong to categories such as prenol lipids, organooxygen compounds, flavonoids, and carboxylic acids and derivatives, which are known to possess diverse biological activities and are believed to contribute to the health benefits associated with BSS.

By implementing a stringent screening threshold of Variable Importance in Projection (VIP) ≥ 1, 23 critical differential metabolites were identified (Fig. 2; Table S4). Specifically, (+)-eudesmin, γ-tocotrienol, and adenosine 3′-monophosphate were significantly up-regulated in CBS, 3-methylquercetin, 5-amino-6-(5-phospho-D-ribitylamino)uracil, epicatechin and quercetin dominated in MBS, whereas IBS was characterized by specific lipid derivatives like L-phenylalanine, PG 30:0, and phytosphingosine-1-P. Flavonolic compounds like quercetin are well-documented to mitigate cellular degradation by independently neutralizing ROS and fine-tuning redox enzyme activities (Shen et al., 2022). Meanwhile, diverse in vitro and in vivo models have consistently demonstrated that adenosine 3′-monophosphate extensive cytoprotection against oxidative insults (Fragoso-Morales et al., 2021; Zhang et al., 2024). Despite these profound geographical variations, metabolomic profiling also revealed a substantial subset of compounds that exhibited no significant differences among the three origins. These consistently expressed metabolites predominantly comprise core structural lipids, such as phosphatidylcholines (e.g., GPCho (18:2/18:1)) rich in characteristic oleic and linoleic acids, alongside an array of potent polyphenolic antioxidants, including epicatechin gallate, kaempferol glycosides, and various phenolic acids (e.g., gallate and rosmarinate) (Table S5). The stable accumulation of these shared components suggests that while environmental factors dynamically modulate specific metabolic fluxes, BSS preserves a genetically conserved baseline defense mechanism. These metabolites guarantee the intrinsic nutritional value and basal free-radical scavenging capacity of BSS, seamlessly complementing the highly variable, origin-specific active compounds.

Fig. 2.

Fig. 2

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Heatmaps of 23 key differential compounds between black sesame seeds from China (CBS), India (IBS), and Myanmar (MBS).

From a botanical perspective, this hierarchical accumulation reflects a highly coordinated plant adaptation strategy to distinct ecological niches. The genetically conserved constitutive metabolites, primarily structural glycerophospholipids, are regulated by foundational lipid biosynthesis pathways to maintain essential cell membrane integrity across all cultivars. In contrast, the origin-specific differential metabolites are dynamically modulated by geographical environmental pressures. Specifically, pathway analysis indicates that the preferential accumulation of flavonoids and lignans in Myanmar (MBS) and Chinese (CBS) cultivars is fundamentally driven by the differential activation of the phenylpropanoid biosynthetic pathway. In MBS, the intense tropical solar radiation likely upregulates the downstream branches of flavonoid biosynthesis to serve as a robust photoprotective screen against UV-induced oxidative damage (Dias et al., 2021). Meanwhile, specific soil conditions and temperate climates in the CBS region favor the redirection of phenylpropanoid fluxes toward lignan accumulation, which serves as a core geographical and antioxidant signature of sesame matrices (Wei et al., 2022). Conversely, the distinct enrichment of specific sphingolipids and lipid derivatives in Indian (IBS) cultivars points toward the targeted activation of sphingolipid metabolism. In plants, the modulation of sphingolipid biosynthesis is a well-documented adaptive response to severe drought and heat stress, functioning to reinforce cellular barrier rigidity and initiate stress-responsive lipid signaling (Xu et al., 2024; Liu et al., 2021). Ultimately, these environment-driven shifts in biosynthetic fluxes elucidate the molecular basis for the distinct chemotypes observed among BSS from different geographical origins.

3.4. Network pharmacology analysis

TCMSP is a well-recognized platform for systemic pharmacology of herbal medicines to retrieve relationships between drug ingredients, targets and diseases (Ru. et al., 2014). Combined with widely targeted metabolomics, the TCMSP database and published literature, we have successfully identified 24 core active compounds or subsequent target prediction and network analysis (Table 2).

Table 2.

The relative quantified abundance (mean peak area) and selection criteria of the 24 core active ingredients across three geographical origins (CBS, IBS, and MBS).

No. Metabolite Source/Selection criterion Mean peak area (CBS) Mean peak area (IBS) Mean peak area (MBS)
1 3-methylquercetin Untargeted metabolomics (VIP > 1) 186,035.9 10,232.87 0
2 Quercetin Untargeted metabolomics (VIP > 1) 544,199.6 673,773.8 707,008.8
3 γ-Tocotrienol Untargeted metabolomics (VIP > 1) 125,027.5 44,369.75 395,466.8
4 α-Tocotrienol Untargeted metabolomics (VIP > 1) 124,899.5 44,463.19 395,509.2
5 (+)-Eudesmin Untargeted metabolomics (VIP > 1) 0 47,532.8 127,716.8
6 Lariciresinol-sesquilignan Untargeted metabolomics (VIP > 1) 4,398,696 8,028,772 4,255,231
7 Isorhamnetin-3-glucoside Untargeted metabolomics (VIP > 1) 49,022 414,988.7 20,738.5
8 Kaempferol 3-(2″-acetylrhamnoside) Untargeted metabolomics (VIP > 1) 36,197.56 97,068.19 47,109.55
9 Chlorogenic acid Untargeted metabolomics (VIP > 1) 3.07E+06 2.47E+06 3.30E+06
10 Abscisic acid Untargeted metabolomics (VIP > 1) 5100.169 572.3184 1905.38
11 Phytosphingosine-1-P Untargeted metabolomics (VIP > 1) 12,839.23 175,721.8 26,199.96
12 Adenosine 3′-monophosphate Untargeted metabolomics (VIP > 1) 3,010,456 526,335.7 1,128,767
13 L-phenylalanine Untargeted metabolomics (VIP > 1) 132,433.9 821,921.5 62,224.17
14 trans-2-Octenal Untargeted metabolomics (VIP > 1) 1899.837 712.6259 0
15 Isoleucylglutamate Untargeted metabolomics (VIP > 1) 14,074.94 26,117.94 0
16 5-amino-6-(5-phospho-D-ribitylamino)uracil Untargeted metabolomics (VIP > 1) 967,183.1 128,614.7 374,794.8
17 PG 30:0 Untargeted metabolomics (VIP > 1) 480,713.6 2,708,127 1,155,689
18 Sesamol TCMSP & Abundance Joint Screening 827,417.4736 1,921,478.889 746,437.1158
19 (+)-catechin TCMSP & Abundance Joint Screening 98,378.89274 6920.990346 30,079.03208
20 3,4,5-trihydroxybenzoic acid TCMSP & Abundance Joint Screening 44,245.42 35,991.14 54,320.1
21 Oleic acid TCMSP & Abundance Joint Screening 228,859.1 228,576.9 202,280.5
22 (−)-alpha-Pinene TCMSP & Abundance Joint Screening 373,772.7 438,038.7 409,673.1
23 Ferulic acid TCMSP & Abundance Joint Screening 1,040,527.665 807,471.2019 848,890.8891
24 l-Limonene TCMSP & Abundance Joint Screening 22,482.01141 25,678.55512 9079.64255
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Note: CBS, IBS, and MBS represent black sesame seeds from China, India, Myanmar; VIP, Variable Importance in Projection; TCMSP, Traditional Chinese Medicine Systems Pharmacology. The relative abundance is expressed as the mean peak area acquired via UHPLC-MS/MS.

Furthermore, a comprehensive search across the OMIM, DisGeNET, and GeneCards databases yielded 9919 targets associated with oxidative damage. As illustrated in the Venn diagram (Fig. 3A), 38 core hub targets were systematically extracted by calculating the intersection of three key topological parameters, maximal clique centrality (MCC), degree, and betweenness. The complex interaction landscape among these highly interconnected hub proteins is visualized in the circular PPI network (Fig. 3B). These targets play a crucial role in a series of interrelated reactions closely associated with oxidative stress. AKT1, also known as protein kinase Bα, serves as a critical component of the PI3K/AKT signaling pathway (Sun et al., 2019). This cascade of events ultimately leads to a decrease in oxidative stress levels (Lee et al., 2024). HSP90AA1 is a member of the heat shock protein 90 family, which plays a pivotal role in various biological processes within the cell, including protein folding, post-translational modification, and the maintenance of protein stability. As a crucial molecular chaperone, HSP90AA1 facilitates the transduction of cell survival signals by binding to and stabilizing key proteins such as AKT1 and EGFR (Verzella et al., 2020). ESR1, is a member of the nuclear receptor superfamily, which promotes receptor dimerization and nuclear translocation. ESR1 may indirectly regulate the transcription of antioxidant enzyme-related genes through estrogen, thereby enhancing cellular antioxidant capacity and reducing ROS accumulation (Schultz-Norton et al., 2011). The compound-target-pathway network (Fig. 3C) visually elucidates the complex in silico pharmacological landscape of BSS. The dense topological connections illustrate a multi-component, multi-target paradigm. Notably, AKT1 and EGFR emerged as the most prominent hub genes within the target network (indicated by node size), receiving extensive modulation from diverse BSS active ingredients such as quercetin and sesamol. Furthermore, the integrated KEGG enrichment bubble plot highlights hsa04151 (PI3K-Akt pathway) as a major highly significant node (large circle, high GeneRatio), robustly supporting our hypothesis that BSS extracts putatively protect against oxidative damage by engaging survival and apoptotic signaling networks.

Fig. 3.

Fig. 3

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Systematic network pharmacology analysis of the core active ingredients in black sesame seeds against oxidative stress. (A) A Venn diagram illustrating the topological screening of 38 core hub targets based on the intersection of Maximal Clique Centrality (MCC), Degree, and Betweenness parameters. (B) The Protein-Protein Interaction (PPI) circular network of the identified core targets. Nodes represent target proteins (e.g., AKT1, TP53), and edges denote the complex interaction networks among them. (C) A Sankey-bubble hybrid plot demonstrating the comprehensive “Compound-Target-Pathway” mechanisms. The left column lists the 24 core active ingredients (Mol_ID), the middle column displays the highly targeted hub genes (Gene_symbol), and the right column maps to the significantly enriched KEGG pathways (KEGG ID), with the bubble size and color gradient reflecting the GeneRatio and statistical significance (−log10 p-value), respectively.

3.5. GO functional enrichment and KEGG pathway analysis

To systematically elucidate the biological significance and underlying molecular mechanisms of the intersection targets between BSS active metabolites and oxidative stress-related diseases, GO and KEGG pathway enrichment analyses were performed (Fig. 4A and B). The top 10 significantly enriched terms (p-adjt < 0.05) demonstrate that BSS exerts its protective effects through a highly coordinated, multi-target network. Within the Biological Process (BP) category, targets exhibited significant enrichment in defense mechanisms, particularly in response to oxidative stress and cellular reactions to chemical stress. This statistical enrichment supports the hypothesis that bioactive compounds from BSS may play a role in systemic redox regulation. Notably, cellular component (CC) analysis indicated a substantial concentration of targets within membrane rafts and membrane microdomains. This topographical enrichment likely correlates with the high lipophilicity of core metabolites from BSS, such as lignans and tocotrienols, suggesting that these compounds are structurally optimized for interaction with cellular membranes, potentially aiding in the prevention of localized lipid peroxidation. Additionally, molecular function (MF) analysis identified protein serine/threonine kinase activity as a prominently enriched function, thereby providing a rational molecular basis for the computationally predicted modulation of kinase-driven networks, including the PI3K-Akt signaling cascade.

Fig. 4.

Fig. 4

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GO function and KEGG pathway enrichment analysis diagrams: (A) Enrichment analysis results for GO terms related to BP, CC, and MF; (B) Bubble map of KEGG-pathway enrichment results.

To further elucidate the biological significance of the predicted targets, a KEGG pathway enrichment analysis was illustrated through a bubble plot (Fig. 4B). This analysis indicated that the putative targets of BSS were predominantly associated with pathways linked to lipid metabolism, oxidative stress, and cellular survival. Notably, the lipid and atherosclerosis pathways emerged as the most statistically significant, as evidenced by the highest -log10 p-value, which corresponds with the high lipophilic antioxidant content characteristic of BSS. In terms of target density, the PI3K-Akt signaling pathway and the MAPK signaling pathway exhibited the largest gene counts (indicated by the largest node sizes) and elevated GeneRatios, underscoring their potential role as critical signaling networks involved in BSS-mediated cellular defense, as suggested by computational modeling. Furthermore, significant enrichment in apoptosis, AGE-RAGE signaling, and reactive oxygen species-related pathways supports the theoretical premise that constituents of BSS are likely to interact with downstream metabolic and apoptotic cascades, thereby necessitating future empirical validation to verify their actual biological regulatory roles.

Topological annotation of the PI3K-Akt cascade revealed that upstream redox sensors, such as EGFR and SRC, are frequently hyperactivated by oxidative stress (Wu et al., 2024). These signals are transduced to midstream mediators like MAPK1, HSP90AA1, and AKT1. While MAPK1 dictates the balance between cell survival and ROS-induced apoptosis (Kuttikrishnan et al., 2025), HSP90AA1 ensures the structural stability of these kinases (Fig. S4A). As a central regulator of this cascade, AKT1 is computationally identified as a primary candidate target for BSS. In parallel, the apoptosis pathway was enriched with key regulatory and executioner genes, including CASP3, TNF, and TP53 (Fig. S4B). This highlights the theoretical involvement of tumor suppressors like TP53 in clearing irreparably damaged cells. In silico analysis suggested that BSS compounds might interact with these apoptotic factors to limit cellular injury. Additionally, the enrichment of the AGE-RAGE and lipid/atherosclerosis pathways aligns well with the BSS phytochemical profile (Li et al., 2024). Rich in lipophilic antioxidants (e.g., specific lignans and tocopherols), BSS active ingredients are hypothesized to integrate into membrane lipid rafts, potentially preventing metabolic and vascular damage (Zhu et al., 2025).

3.6. Molecular docking

A total of 9 key functional targets strictly associated with oxidative stress, which are pivotal in regulating cellular redox homeostasis, survival signaling, and ROS-induced apoptosis, were included in this docking analysis: TP53, AKT1, MAPK, BCL-2, ESR1, HSP90AA1, SRC, PTGS2, and GSK3B (Fig. 5). The ligand dataset encompassed various classes of natural active constituents, such as catechin, quercetin, (+)-eudesmin, and γ-tocotrienol. The generated 3D docking visualizations indicated that all examined small molecules exhibited precise conformational fits within the active binding sites of their target proteins, stabilized by multiple hydrogen bonds with key internal residues. Additionally, the simultaneous binding of different small molecules to distinct targets along the same signaling axis highlights a strong potential for additive multi-target interactions. These phytochemicals demonstrated excellent affinity for most core antioxidant targets, supported by docking scores (binding energies) lower than −5.0 kcal/mol (Liu et al., 2024; Wang et al., 2023). The above results computationally support the binding ability of these natural bioactive small molecules to core disease targets at the molecular conformation level, suggesting that these components possess theoretical antioxidant and cytoprotective properties. This provides a reliable structural biological hypothesis for subsequent in vitro activity verification and development of natural-derived lead compounds. To systematically evaluate the interaction strength, a quantitative comparison of the binding affinities between different marker metabolites and the same core targets was conducted. For instance, when targeting AKT1, the lipophilic γ-tocotrienol exhibited a superior binding affinity (−7.2 kcal/mol). Detailed structural analysis revealed that this robust affinity is primarily stabilized by key interactions with residues Lys14 and Thr21 in the active site. This quantitative hierarchy suggests that while multiple origin-specific metabolites can interact with the same macromolecular receptor, their competitive binding potentials vary significantly, likely driven by differences in their hydrogen-bond donor/acceptor networks and spatial lipophilic features.

Fig. 5.

Fig. 5

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Molecular docking results of different BSSs with core anti-oxidative damage targets.

KEGG suggested that the PI3K-Akt cascade and apoptosis-related networks, both exhibiting high statistical significance (p < 0.01), are the primary signaling routes mediating the antioxidant properties of BSS. The initiation of PI3K-Akt signaling is intrinsically linked to the plasma membrane interface, where ligand-activated growth factor receptors (e.g., EGFR) stimulate PI3K-Akt (Guo et al., 2025).

Our molecular docking simulations further substantiate this systemic view by identifying AKT1 and TP53 as central molecular hubs that interact favorably with diverse BSS-derived compounds. Specifically, lipophilic phytochemicals, notably (+)-eudesmin and γ-tocotrienol, exhibit strong binding affinities for AKT1. Modulating AKT phosphorylation is widely recognized as a critical determinant of cellular resilience against oxidative stress, as its appropriate activation sustains survival pathways and suppresses ROS-induced apoptosis (Arciuch et al., 2009; Cao et al., 2025). By stabilizing the PI3K-Akt cascade, these lipophilic constituents likely curtail intracellular ROS accumulation and shield cellular architecture from oxidative insults. This is consistent with previous reports indicating that the PI3K-Akt axis is instrumental in γ-tocotrienol-mediated protection of lipid membranes against peroxidation, thereby preserving metabolic homeostasis and preventing oxidative cell death (Wang et al., 2024).

Complementing these lipophilic actions, structural simulations demonstrate that hydrophilic flavonoids, such as quercetin, effectively target the core tumor suppressor TP53 (Osawe & Farombi, 2025). As a master nuclear transcription factor, TP53 governs essential biological responses including DNA repair and apoptotic programming, making it an invaluable target for managing oxidative stress (Güllülü et al., 2021). Thus, quercetin-mediated modulation of TP53 may fine-tune the transcription of downstream antioxidant enzymes, fundamentally boosting the endogenous free-radical scavenging capacity. Given that dietary flavonoids are known to attenuate inflammatory markers and suppress ROS generation via TP53 regulation, we postulate that the phytochemical diversity of BSS enables a highly comprehensive additive antioxidant defense. Ultimately, both lipophilic and hydrophilic components cooperate to modulate core hubs like AKT1 and TP53, providing comprehensive cytoprotection through the interconnected PI3K-Akt and apoptotic signaling networks.

3.7. Limitations and future perspectives

In contrast to prior metabolomic studies on sesame seeds that primarily concentrated on general lipid dynamics or single-cultivar profiles (Weldemichael & Gebremedhn, 2023), our findings provide a more comprehensive perspective across multiple regions. Although earlier research emphasized the prevalence of core phenylpropanoids and tocopherols in oilseed matrices (Wei et al., 2022), the significant geographical variation identified in this study illustrates how environmental factors influence specific metabolic networks, resulting in distinct hierarchies of markers, such as variations in tocotrienol and flavonoid derivatives.

Several limitations should be considered. The reliance solely on cell-free assays without individual metabolite testing and cellular validation confines the inferences regarding the PI3K-Akt and apoptosis pathways to computational working hypotheses. Consequently, the modeled multi-target interactions indicate complementary additive effects rather than confirmed pharmacological synergy. Additionally, our in silico models employed empirical database thresholds and semi-flexible docking, lacking comprehensive ADME/T predictions and explicit solvent interactions. Importantly, highly lipophilic dietary antioxidants undergo significant hepatic first-pass metabolism, raising doubts about achieving effective intracellular concentrations at standard dietary doses. Therefore, these findings propose theoretical pharmacological hypotheses rather than definitive clinical mechanisms.

Future research should prioritize rigorous in vivo validations using animal models of oxidative stress to evaluate the actual protective effectiveness of BSS extracts and isolated compounds. Additionally, molecular dynamics (MD) simulations should be employed to investigate complex conformational stability. Identified origin-specific metabolites can serve as dependable biomarkers for ensuring BSS geographical traceability and standardizing quality in the industry. Moreover, as a product with both medicinal and food applications, the translation of these discoveries necessitates the optimization of processing parameters. This includes the establishment of technical standards for black sesame oil extraction to enhance the preservation of lipophilic antioxidants like γ-tocotrienol. Ultimately, these efforts will inform the targeted creation of standardized, high-value functional food products.

4. Conclusion

In summary, this study employed UHPLC-Q-Exactive Orbitrap MS/MS metabolomics, network pharmacology, and molecular docking to decode the origin-specific metabolic signatures and potential antioxidant mechanisms of BSS. Our metabolomic profiling identified 23 core differential metabolites that drive the metabolic divergence among Chinese, Myanmar, and Indian BSS cultivars. By integrating 17 key origin-specific drivers derived from these metabolites with 7 classical high-activity compounds, a comprehensive panel of 24 core active ingredients was established. Subsequent computational analyses predicted that these 24 compounds (such as γ-tocotrienol and quercetin) may exert cytoprotective effects by targeting key signaling hubs, notably the PI3K-Akt and apoptosis pathways, through interactions with central kinases like AKT1 and TP53. While these computational models effectively prioritize bioactive components, their therapeutic implications against oxidative damage remain preliminary. Future research will require targeted pharmacokinetic tracking and in vivo animal intervention trials to investigate the metabolic fate and actual protective efficacy of these BSS-derived biomarkers, ultimately establishing a rigorous scientific foundation for functional food standardization.

CRediT authorship contribution statement

Lei Wang: Writing – original draft, Methodology. Huihui Gong: Project administration, Data curation. Chenxi Sun: Project administration, Formal analysis. Junsheng Zhao: Resources, Funding acquisition. Shancang Zhao: Supervision, Software. Yanjie Dong: Writing – review & editing, Visualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We thank Shandong Luochen Food Co. for kindly providing the BSS samples. This work was funded by the Shandong Province Technology Innovation Guidance Plan (Central Guidance for Local Science and Technology Development Funds) (2023TZXD002) and Shandong Academy of Agricultural Sciences under the Agricultural Science and Technology Innovation Engineering Industry Major Technological Innovation Program (CXGC2025B14, CXGC2026B19).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.fochx.2026.104511.

Appendix A. Supplementary data

Supplementary material
mmc1.docx (1.2MB, docx)

Data availability

Data will be made available on request.

References

  1. Ahmed I.A.M., Uslu N., Ozcan M.M., Juhaimi F.A.L., Ghafoor K., Babiker E.E., Alqah H.A.S. Effect of conventional oven roasting treatment on the physicochemical quality attributes of sesame seeds obtained from different locations. Food Chemistry. 2021;338 doi: 10.1016/j.foodchem.2020.128109. [DOI] [PubMed] [Google Scholar]
  2. Arciuch V.G.A., et al. Akt1 intramitochondrial cycling is a crucial step in the redox modulation of cell cycle progression. PLoS One. 2009;4 doi: 10.1371/journal.pone.0007523. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Cao C., et al. Lingguizhugan decoction alleviates gestational diabetes mellitus by modulating the PI3K–AKT pathway and oxidative stress: Network pharmacology and experimental evidence. Biomedical Chromatography. 2025;39 doi: 10.1002/bmc.6042. [DOI] [PubMed] [Google Scholar]
  4. Dias M.C., Pinto D.C.G., Silva A.M.S. Plant flavonoids: Chemical characteristics and biological activity. Molecules. 2021;26(17) doi: 10.3390/molecules26175377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Dossou S.S.K., Luo Z., Deng Q., Zhou R., Zhang Y., Li D.…Wang L. Biochemical and molecular insights into variation in sesame seed antioxidant capability as revealed by metabolomics and transcriptomics analysis. Antioxidants. 2024;13(5) doi: 10.3390/antiox13050514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Dossou S.S.K., Xu F., You J., Zhou R., Li D., Wang L. Widely targeted metabolome profiling of different colored sesame (Sesamum indicum L.) seeds provides new insight into their antioxidant activities. Food Research International. 2022;151 doi: 10.1016/j.foodres.2021.110850. [DOI] [PubMed] [Google Scholar]
  7. Fragoso-Morales L.G., Correa-Basurto J., Rosales-Hernandez ´.M.C. Implication of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase and its inhibitors in Alzheimer’s disease murine models. Antioxidants. 2021;10(2) doi: 10.3390/antiox10020218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Güllülü Ö., Hehlgans S., Rödel C., Fokas E., Rödel F. Tumor suppressor protein p53 and inhibitor of apoptosis proteins in colo-rectal cancer—A promising signaling network for therapeutic inter-ventions. Cancers. 2021;13(4) doi: 10.3390/cancers13040624. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Guo X., Chen X., Zeng M., Xuan T., Xue J., Chen K.…Shen Q. Revealing key antioxidant compounds and mechanisms in Fritillaria Bulbus by metabolomics and network pharmacology. npj Science of Food. 2025;9 doi: 10.1038/s41538-025-00481-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Guo X., Li X., Zhang S., Zhu S., Guo R., Gao Y.…Shi H. A joint analysis of metabolomics, network pharmacology, and molecular docking reveals the efficacy patterns in various medicinal segments of Angelica sinensis (Oliv.) Diels root. Journal of Food Biochemistry. 2024;2024 [Google Scholar]
  11. Hou T., Netala V.R., Zhang H., Zhang Z., Xing Y., Li H., 1 Perilla frutescens: A rich source of pharmacological active compounds. Molecles. 2022;27 doi: 10.3390/molecules27113578. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Hu B., et al. Antioxidant hydrogels: Antioxidant mechanisms, design strategies, and applications in the treatment of oxidative stressrelated diseases. Advanced Healthcare Materials. 2024;13 doi: 10.1002/adhm.202303817. [DOI] [PubMed] [Google Scholar]
  13. Jafar H.M., Akhtar I., Saleem M.Z., Malik T., Ayub A. A comprehensive review of the health benefits, nutritional composition, and agricultural aspects of sesame seeds. Journal of Food Biochemistry. 2025;2025 doi: 10.1155/2025/8713698. [DOI] [Google Scholar]
  14. Kim J.H., Seo W.D., Lee S.K., Lee Y.B., Park C.H., Ryu H.W., Lee J.H. Comparative assessment of compositional components, antioxidant effects, and lignan extractions from Korean white and black sesame (Sesamum indicum L.) seeds for different crop years. Journal of Functional Foods. 2014;7:495–505. doi: 10.1016/j.jff.2014.01.006. [DOI] [Google Scholar]
  15. Kim M.-Y., Kim S., Lee J., Kim J.-I., Oh E., Kim S.-W.…Lee M.-H. Lignan-rich sesame (Sesamum indicum L.) cultivar exhibits in vitro anti-cholinesterase activity, anti-neurotoxicity in amyloid-β induced SH-SY5Y cells, and produces an in vivo nootropic effect in scopolamine-induced memory impaired mice. Antioxidants. 2023;12 doi: 10.3390/antiox12051110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kim S.-Y., Kim E., Shin B.K., Seo J.-A., Kim Y.-S., Lee D.Y., Choi H.-K. NMR-based metabolic profiling discriminates the geographical origin of raw sesame seeds. Food Control. 2020;112 doi: 10.1016/j.foodcont.2020.107113. [DOI] [Google Scholar]
  17. Kuttikrishnan S., Mariyam Z., Ahmad F., Suleman M., Habeeba U., Panicker A.J., et al. Selective induction of apoptosis in T-cell acute lymphoblastic leukemia by pristimerin through dual PI3K/AKT pathway inhibition and ROS generation. European Journal of Pharmacology. 2025;1008(c) doi: 10.1016/j.ejphar.2025.178329. [DOI] [PubMed] [Google Scholar]
  18. Langyan S., Yadava P., Yadav R., Bhardwaj R. Springer Singapore; 2025. Sesame: Sustainable production and applications. [DOI] [Google Scholar]
  19. Lee H., Wang Z., Deng Z., Wang Y. Assessment of six blackberry cultivars using a combination of metabolomics, biological activity, and network pharmacology approaches. Antioxidants. 2024;13(3) doi: 10.3390/antiox13030319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Li C.X., Li Y.H., Ma Y.X., Wang D.Y., Zheng Y.Z., Wang X.D. Effect of black and white sesame on lowering blood lipids of rats with hyperlipidemia induced by high-fat diet. Grain & Oil Science and Technology. 2020;3(2):57–63. doi: 10.1016/j.gaost.2020.06.001. [DOI] [Google Scholar]
  21. Li Y., Chang Y., Zhang Y., Tu W., Xu F., Zhang L., et al. The potential health benefits and mechanisms of sesame lignans in age-related diseases. Food Bioscience. 2024;62 [Google Scholar]
  22. Liang H.Z., Wang M.Y., Yang G., Li G., Zhang J., Yao L., et al. Untargeted qualitative and targeted quantitative analysis of saponins reveal differential chemotypes of Gynostemma pentaphyllum and G. Longipes from different geographical origins. Food Chemistry. 2025;468 doi: 10.1016/j.foodchem.2024.142412. [DOI] [PubMed] [Google Scholar]
  23. Liu N., Hou L., Bao J., Wang L., Chen X. Sphingolipid metabolism, transport, and functions in plants: Recent progress and future perspectives. Plant Communications. 2021;2(5) doi: 10.1016/j.xplc.2021.100214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Liu Y., Xiao X., Wang Z., Shan X., Liu G., Wei B. Metabolomic analysis of black sesame seeds: Effects of processing and active compounds in antioxidant and anti-inflammatory properties. Food Research International. 2024;176 doi: 10.1016/j.foodres.2023.113789. [DOI] [PubMed] [Google Scholar]
  25. Magdamit G.D., Hsieh C.Y., Lee M.J., De Castro-Cruz K.A., Austria S.F.B., Sipat B.D., et al. In vitro and molecular docking evaluation of black sesame seeds’ anti-prostate cancer and antioxidant activity processed by nine steaming nine drying. Applied Biological Chemistry. 2025;68(1) [Google Scholar]
  26. Osawe S.O., Farombi E.O. Quercetin mitigates sulfasalazine-induced toxicity by inhibiting p53-dependent apoptosis and DNA damage in rat epididymis, semen and bone marrow. Journal of Biochemical and Molecular Toxicology. 2025;39(5) doi: 10.1002/jbt.70277. [DOI] [PubMed] [Google Scholar]
  27. Pang S., Piao X., Zhang X., Chen X., Zhang H., Jin Y.…Wang Y. Discrimination for geographical origin of Panax quinquefolius L. using UPLC Q-Orbitrap MS-based metabolomics approach. Food Science & Nutrition. 2023;11(8):4843–4852. doi: 10.1002/fsn3.3461. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Ru. J., Li P., Wang J., Zhou W., Li B., Huang C.…Yang L. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. Journal of Cheminformatics. 2014;6(1):13. doi: 10.1186/1758-2946-6-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Schultz-Norton J.R., Ziegler Y.S., Nardulli A.M. ERα-associated protein networks. Trends in Endocrinology and Metabolism. 2011;22:124–129. doi: 10.1016/j.tem.2010.11.005. [DOI] [PubMed] [Google Scholar]
  30. Shen N., Wang T., Gan Q., Liu S., Wang L., Jin B. Plant flavonoids: Classification, distribution, biosynthesis, and antioxidant activity. Food Chemistry. 2022;383:132531. doi: 10.1016/j.foodchem.2022.132531. [DOI] [PubMed] [Google Scholar]
  31. Shi P., Chen J., Ge W., Liu Z., Han N., Yin J. Antichilblain components in eggplant based on network pharmacology and biological evaluation. Journal of Agricultural and Food Chemistry. 2023;71(30):11442–11453. doi: 10.1021/acs.jafc.3c01108. [DOI] [PubMed] [Google Scholar]
  32. Song G., et al. Exploring the noncovalent interaction between β-lactoglobulin and flavonoids under nonthermal process: Characterization, physicochemical properties, and potential for lycopene delivering. Food Chemistry: X. 2025;25 doi: 10.1016/j.fochx.2025.102160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Sumner L.W., Amberg A., Barrett D., Beale M.H., Beger R., Daykin C.A.…Viant M.R. Proposed minimum reporting standards for chemical analysis. Metabolomics. 2007;3(3):211–221. doi: 10.1007/s11306-007-0082-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Sun X., Chen L., He Z. PI3K/Akt-Nrf2 and anti-inflammation effect of macrolides in chronic obstructive pulmonary disease. Current Drug Metabolism. 2019;20:301–304. doi: 10.2174/1389200220666190227224748. [DOI] [PubMed] [Google Scholar]
  35. Verzella D., Pescatore A., Capece D., Vecchiotti D., Ursini M.V., Franzoso G., et al. Life, death, and autophagy in cancer: NF-κB turns up everywhere. Cell Death & Disease. 2020;11 doi: 10.1038/s41419-020-2399-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Wang H., Cui C., Wang J., Du Z., Wu K., Jiang X.…Li C. Fine mapping and functional characterization of SiLAC3 in regulating brown seed coat pigmentation, integrated transcriptomic and metabolomic analyses in sesame (Sesamum indicum L.) Plant Physiology and Biochemistry. 2025;221 doi: 10.1016/j.plaphy.2024.108920. [DOI] [PubMed] [Google Scholar]
  37. Wang X., et al. Protective effect of the total alkaloid extract from Bulbus Fritillariae Pallidiflorae on cigarette smoke-induced Beas-2B cell injury model and transcriptomic analysis. Food & Nutrition Research. 2024;11 doi: 10.29219/fnr.v68.10689. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Wang Z., Chen X., Liu Q., Zhang L., Liu S., Su Y., et al. Untargeted metabolomics analysis based on lc-im-qtof-ms for discriminating geographical origin and vintage of Chinese red wine. Food Research International. 2023;165 doi: 10.1016/j.foodres.2023.112547. [DOI] [PubMed] [Google Scholar]
  39. Wei P., Zhao F., Wang Z., Wang Q., Chai X., Hou G., Meng Q. Sesame (Sesamum indicum L.): A comprehensive review of nutritional value, phytochemical composition, health benefits, development of food, and industrial applications. Nutrients. 2022;14(19) doi: 10.3390/nu14194079. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Weldemichael M.Y., Gebremedhn H.M. Omics technologies towards sesame improvement: a review. Molecular Biology Reports. 2023;50(8) doi: 10.1007/s11033-023-08551-w. [DOI] [PubMed] [Google Scholar]
  41. Wu L., Li L., Wang X., Wu H., Li M., Wang Y., et al. The inhibition of rutin on src kinase blocks high glucose-induced egfr/erk transactivation in diabetic nephropathy by integrative approach of network pharmacology and experimental verification. Phytomedicine. 2024;135 doi: 10.1016/j.phymed.2024.156220. [DOI] [PubMed] [Google Scholar]
  42. Xia T., Xiong Z., Sun X., Chen J., Wang C., Chen Y., Zheng D. Metabolomic profiles and health-promoting functions of Camellia drupifera mature-seeds were revealed relate to their geographical origins using comparative metabolomic analysis and network pharmacology approach. Food Chemistry. 2023;426 doi: 10.1016/j.foodchem.2023.136619. [DOI] [PubMed] [Google Scholar]
  43. Xiao J., Sun T., Jiang S., Xiao Z., Shan Y., Li T.…Fu F. Antioxidant effects and potential mechanisms of Citrus reticulata ‘chachi’ components: An integrated approach of network pharmacology and metabolomics. Foods. 2024;13 doi: 10.3390/foods13244018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Xu Z., Li M., Jiang N., Gui C., Wang Y., An Y., et al. Black sesame seeds: Nutritional value, health benefits, and food industrial applications. Trends in Food Science & Technology. 2024;153(000) [Google Scholar]
  45. Zhang L., Zhang L., Liang H., Huang D., Ren M. Effects of taurine and vitamin C on the improvement of antioxidant capacity, immunity and hypoxia tolerance in gibel carp (Carrassius auratus gibeilo) Antioxidants. 2024;13(10) doi: 10.3390/antiox13101169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Zhang Y., Xiao H., Lv X., Wang D., Chen H., Wei F. Comprehensive review of composition distribution and advances in profiling of phenolic compounds in oilseeds. Frontiers in Nutrition. 2022;9 doi: 10.3389/fnut.2022.1044871. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Zhu Y.-F., Wu A.-G., Chen M.-Y., Zhou X.-Y., Huang F.-H., Wang L.…Zhou X.-G. Plant-based strategies against aging: Focus on bioactive compounds from medicine-food homology plants. Phytomedicine. 2025;145 doi: 10.1016/j.phymed.2025.157052. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material
mmc1.docx (1.2MB, docx)

Data Availability Statement

Data will be made available on request.

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