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. 2025 Sep 19;15(10):356. doi: 10.1007/s13205-025-04523-y

Untargeted metabolomics reveals Syzygium cumini (L.) alleviates ER stress and redox damage via activation of ATF-6/CHOP/NF-κB signaling axis in diabetic rats

Karan Singh Yadav 1,5, Gurvinder Singh 2,5, Anjali Mishra 3,5, Amol Chhatrapati Bisen 3,5, Akash Ranjan 1, Rabi Sankar Bhatta 3,5, Vineeta Tripathi 4,5, Dinesh Kumar 2,5, Madhav Nilakanth Mugale 1,5,✉
PMCID: PMC12449296  PMID: 40978323

Abstract

This study reports the therapeutic potential of aqueous seed extract Syzygium cumini (AESC) in alleviating type 1 diabetes mellitus (T1DM) associated endoplasmic reticulum (ER) and oxidative stress in a streptozotocin (STZ)-induced rat model. The LC–MS/MS analysis of AESC revealed gallic acid, ellagic acid, quercetin, malic acid, and citric acid as major antidiabetic phytoconstituents. Administration of AESC (250 mg/kg) normalized fasting blood glucose levels to those of healthy control rats. Moreover, AESC increases antioxidant levels and downregulates ER and inflammatory markers. Histopathological evaluation showed improved pancreatic tissue architecture, and immunohistochemistry revealed enhanced insulin expression within the islets. Mechanistically, AESC alleviated ER stress and oxidative damage through the ATF-6/CHOP/NF-κB signaling axis. Furthermore, serum metabolomics indicated aberrant accumulation of branched-chain amino acids and reduced 3-hydroxybutyrate levels (increased ketolysis) in diabetic rats that were reversed by AESC. This study is limited by using a single dosage of AESC and short-term evaluation, emphasizing acute rather than long-term effects. Furthermore, the lack of in vitro validation and genetic knockdown approaches restricts confirmation of the specific mechanisms underlying the antidiabetic action of the extract. Future studies employing multiple dosage regimens, chronic treatment models, and molecular validation strategies are warranted to establish the long-term efficacy, safety, and mechanistic specificity of AESC as a potential therapeutic for T1DM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-025-04523-y.

Keywords: Antioxidants, ATF-6/CHOP/NF-κB signaling axis, ER stress, Insulin, T1DM

Introduction

Type 1 Diabetes Mellitus (T1DM) is a metabolic disorder of the endocrine system characterized by a decline in insulin levels from impaired β-cell function. DM currently affects over 390 million individuals worldwide, which is projected to increase to approximately 500 million by 2030 (Dubey et al. 2023).

Oxidative stress is caused by an imbalance between excessive reactive oxygen species (ROS) generation and insufficient antioxidant defense systems (Liu et al. 2025). Reduced activities of antioxidant enzymes, such as superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH), increase lipid peroxidation, which causes DNA damage and genomic instability (Singh et al. 2022c). In addition, chronic hyperglycemia exacerbates these disturbances by promoting protein misfolding, inflammation, disruptions in calcium homeostasis and redox balance, which collectively trigger endoplasmic reticulum (ER) stress (Bellanti et al. 2025). These processes ultimately impair pancreatic β-cell function and insulin signaling, which contribute to the pathogenesis of T1DM.

Maintenance of ER homeostasis is essential for the survival and function of pancreatic β-cells. Moreover, its disruption is associated with the activation of cytokine-induced pro-inflammatory mediators and unfolded protein responses (UPR). This is achieved by three ER-localized, ubiquitously expressed proximal sensors: inositol-requiring 1α (IRE1α), PKR-like eukaryotic initiation factor 2 kinase (PERK), and activating Transcription factor 6 (ATF-6). These stress pathways trigger the expression of the transcription factor C/EBP homologous protein (CHOP), which stimulates the expression of molecular chaperones to assist in the correct folding of proteins and ER-associated degradation of aberrant proteins (Yi et al. 2020; Sharma et al. 2021, 2022).

Persistent hyperglycemia increases insulin production, which impairs the ER folding capacity in β cells and leads to ER stress. This contributes to the production of inflammatory cytokines like IL-6 and activates the UPR, potentially leading to β-cell destruction and impaired glucose homeostasis (Yong et al. 2021). Therefore, mitigation of ER stress might be a potential therapeutic approach in the management of T1DM.

Many plant-based products have been identified to protect against chemical and drug toxicity. The use of traditional medicinal plants is still continuously being explored due to their reduced adverse effects, easy availability, health benefits, and serve as hotspots for valuable bioactive secondary metabolites having diverse pharmacological properties (Ogale et al. 2018; Tran et al. 2020). Thus, there is a need to develop natural antidiabetic agents that are both safe and cost effective, which ameliorate hyperglycemia-mediated ER stress for the management of diabetes with minimal toxic effects.

Syzygium cumini (L.) Skeels has long been used in traditional medicine for the management of diabetes (Maji et al. 2024). Experimental studies have demonstrated that extracts of S. cumini enhance cAMP-dependent protein kinase expression in STZ-induced diabetic rats (Chagas et al. 2015) and exert insulinotropic effects through activation of peroxisome proliferator-activated receptors (PPAR-α and PPAR-γ) in 3T3-L1 preadipocytes (Lee et al. 2021). The extract has also been reported to increase glucose transporter-4 (GLUT-4) and phosphatidylinositol-3-kinase (PI3K) mRNA expression, which are critical mediators of insulin signaling in L6 myotubes (Malik et al. 2017). Mechanistic studies indicate that PPAR-α activation promotes mitochondrial fatty acid oxidation and mitigates lipid-induced oxidative damage, while PPAR-γ activation improves redox homeostasis by upregulating antioxidant enzymes (Lin et al. 2022; Dovinova et al. 2020). Moreover, both receptors contribute to the regulation of inflammatory pathways and the unfolded protein response, thereby alleviating ER stress and preserving pancreatic β-cell function (Kersten 2014; Gupta et al. 2010). Accordingly, the present study aimed to elucidate the mechanism underlying the therapeutic effects of AESC and to perform metabolic profiling to assess its role in alleviating ER stress and restoring redox homeostasis in a T1DM rat model.

Material and methods

Chemicals and reagents

Glucometer Accucheck strips, Streptozotocin (Sigma-Aldrich U.S.A), Metformin (Glyciphage plane), biochemistry reagents (ERBA diagnostics Mannheim GmbH, Germany), Masson’s trichrome staining kit (Polysciences), Bicinchoninic acid (BCA) protein assay kit (Thermo Fisher scientific, USA), β-actin antibody (Thermo Fisher scientific, USA), Rabbit ATF-6 polyclonal antibody (Affinity bioscience, USA), Rabbit CHOP polyclonal antibody (Affinity bioscience, USA), Rabbit GRP78 polyclonal antibody (Affinity bioscience, USA), Rabbit calreticulin polyclonal antibody (Affinity bioscience, USA), p-NF-κB p65 (Ser536) (Cell signaling technology), NF-κB p65 (Affinity bioscience, USA), Rabbit Nrf-2 polyclonal antibody (Affinity bioscience, USA), and Rabbit insulin polyclonal antibody (Affinity bioscience, USA).

Plant material

The fresh fruits of Syzygium cumini (L.) Skeels were procured from the local market. The fruits were harvested in 2019 and cultivated in the tropical regions of the western plains of the Mainpuri district of Uttar Pradesh, India. It was identified and authenticated by Dr. Vineeta Tripathi, Division of Botany, CSIR-CDRI. A voucher specimen with voucher number (6583) has been deposited in the medicinal plant herbarium of CSIR-CDRI, Lucknow, India. Following complete drying at room temperature for 2 weeks, the seeds were mechanically ground into a coarse powder using an electric blender, passed through a sieve with a 40 μm mesh size, and subsequently stored in an airtight container under dry conditions.

Preparation of the aqueous seed extract of Syzygium cumini (AESC)

The sample was mixed in distilled water to give the desired dose of 250 mg/kg body weight (Gupta and Saxena 2011). The extract was then filtered using a Whatman No.1 filter paper and was stored at 4 °C till further use.

Phytochemical analysis of Syzygium cumini seed extract by LC–MS/MS

LC–MS/MS analysis of bioactive compounds in aqueous extract of Syzygium cumini was quantitatively analyzed using QTRAP 5500 mass spectrometry (Applied Biosystem, MDS Sciex, Toronto, Canada). Electrospray ionization (ESI–MS) techniques were used to scan the compounds in the aqueous extract in both positive and negative modes. A continuous flow of 20 µl/min was injected through the infusion method to obtain mass spectra of the AESC in the range of 100 to 1000 m/z. Ellagic acid, gallic acid, quercetin, and citric acid were identified as the major antidiabetic components in the extract via LC–MS analysis, and the data were also supported by literature. Furthermore, an LC–MS/MS method was developed for quantitative analysis of the AESC using standards.

Major compounds were separated on a Hypersil Gold column (150 mm × 4.6 mm, 5 µm), using acetonitrile (B) and 0.1% formic acid in deionized water (DW) (A) as a mobile phase, with a linear gradient of 45 min was set for the separation of compounds present in the extract, as shown in Supplementary Table 3. The flow rate was set at 0.4 mL/min, and the column chamber temperature was kept at 40 °C. For MS scanning, the ESI ionization source was used in both the positive and negative ionization modes, with ion spray voltage and temperature of 4500 V and 350 °C, respectively. The LC–MS/MS method was developed in Multiple Reaction Monitoring (MRM) modes for quantitative estimation of the compound. Instrument-dependent parameters such as curtain gas, nebulizer gas, collision gas, auxiliary gas, and compound parameters like decluttering potential, collision energy, and entrance potential were defined. The raw data were processed using Analyst Software (Version 1.4.2) from AB Sciex.

Experimental animals and ethical approval

SD rats (n = 20, 8–10 weeks old, weight between 240 and 250 g ± 40 g) were obtained from the National Laboratory Animal Facility, CSIR-CDRI, India. Rats were maintained in a controlled environment with a 12 h light/dark cycle at 25 ± 2 °C and 50 ± 15% relative humidity. They were provided with a standard chow diet and water ad libitum. All the experiments were conducted as per the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA) guidelines. They were approved by the Institutional Animal Ethical Committee (ethical date of approval no. IAEC/2018/107).

Induction of diabetes

A single dose of streptozotocin (STZ) in sodium citrate buffer (pH = 4.5; 0.1 M) at 35 mg/kg was administered intraperitoneally after fasting for 6 h (except the control group (I)). Blood samples from the tail vein were withdrawn 72 h post-diabetes induction to estimate fasting blood glucose levels using a glucometer (ACCU-CHEK®). Rats displaying fasting blood glucose levels above 250 mg/dL were considered diabetic.

Experimental design

After 7 days of acclimatization, rats were divided into either a normal control group or an STZ-induced group. The hyperglycemic rats were randomly divided into a diabetic control group, an AESC treatment group, and a metformin group, with 5 rats in each group. The groupings are as follows:

Normal control group (I) (n = 5): Normal rats treated with saline for 4 weeks.

Diabetic control group (II) (n = 5): STZ-induced diabetic rats.

AESC treatment group (III) (n = 5): STZ-induced rats treated with AESC (250 mg/kg) solution (in replacement of the ordinary drinking water) daily for 4 weeks (Kumari et al. 2023).

Metformin group (IV) (n = 5): STZ-induced treated with metformin (300 mg/kg) dissolved in drinking water daily for 4 weeks (Inalegwu et al. 2021).

Measurement of oxidative damage markers in the pancreas

Estimation of superoxide dismutase (SOD)

The activity of SOD was executed as previously described (Yadav et al. 2024). Briefly, the reaction mixture containing 50 mM carbonate buffer and 20 mM hydroxylamine hydrochloride with 0.6% Triton X-100 was incubated for 2 min at 37 °C, followed by the addition of tissue lysate and 96 μM nitroblue tetrazolium (NBT). The absorbance was measured at 560 nm.

Estimation of catalase (CAT)

To the pancreatic tissue homogenate, a mixture of 0.8 M H2O2 was added. The reaction was stopped using a potassium dichromate solution and boiled at 100 °C for 10 min. The absorbance was then read at 570 nm against a blank (Ishteyaque et al. 2024).

Estimation of glutathione (GSH)

A sample mixture containing 0.2% Dithiobis (2-nitrobenzoic acid) (DTNB) was added to the tissue lysate and incubated for 10 min at 37 °C. Absorbance was then measured at 412 nm against a blank (Yadav et al. 2024).

Estimation of lipid peroxidation (MDA)

Briefly, the reaction mixture was mixed with 30% TCA and 5 N HCl, followed by incubation at 37 °C for 5 min with the addition of 60 μL TBA (2%). This was boiled at 90 °C for 15 min, and the supernatant was read at 532 nm (Melekh et al. 2017).

Histopathological analysis

A detailed necropsy was carried out, and any gross pathological lesions were noted. The Pancreas was collected and immediately fixed in freshly prepared 10% buffered formalin. The tissue samples were processed and embedded in paraffin and cut into 4 µm thin sections and stained with hematoxylin and eosin (H&E), and visualized under a light microscope (Nikon, Eclipse TS2). A pathologist, unaware of the different groups, evaluated all slides for histopathological alterations.

Immunohistochemical staining of pancreatic sections

The paraffin sections (40 µm) of the pancreas were deparaffinized, hydrated, and processed for IHC using a kit (Thermo Fisher Scientific, TL-015-QHD). The antigen retrieval was carried out in 10 mM citrate buffer (pH 6.0). Subsequently, the paraffin sections were probed with the primary anti-insulin antibody [Affinity Bioscience, USA (1:200)] overnight at 4 °C. Substrate 3,3’-Diaminobenzidine (DAB) was used to detect color development and visualized under 20X magnification using a bright-field microscope (Nikon Eclipse, TS2). Insulin-positive cells and total islet area were quantified using ImageJ software.

Western blot analysis

Western blot analysis was performed as previously described (Yadav et al. 2024; Ishteyaque et al. 2025). The desired amount of pancreatic tissues were lysed using RIPA lysis buffer. The protein concentration of each sample was quantified using a BCA protein assay kit (Thermo Fisher Scientific, U.S.A.). Protein samples (40 µg) were separated using sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to a polyvinylidene difluoride (PVDF) membrane. Following blocking with 5% bovine serum albumin, the membranes were incubated overnight with following primary antibodies: ATF-6 (1:1000), CHOP (1:1000), GRP78 (1:1000), calreticulin (1:1000), p-NF-κB p65 (Ser536) (1:1000), NF-κB p65 (Ser536) (1:1000) and Nrf-2 (1:1000). After washing, the blots were incubated at room temperature for 2 h with horseradish peroxidase (HRP)-conjugated anti-rabbit secondary antibody (Thermo Fisher scientific, U.S.A.). The blots were developed using ECL substrate (Bio-Rad), and bands were visualized using an Amersham GE imaging system. The densitometric analysis of the immune blots was performed using ImageJ software. β-actin (1:10,000) was employed as the internal control.

NMR-based serum metabolic profiling

Serum sample preparation

Serum samples for NMR measurements were prepared as described previously (Ishteyaque et al. 2024). Each sample consisted of 300 μL of pure serum extracted from rat and mixed with an equal volume of sodium phosphate buffer prepared in 100% deuterium oxide (D₂O) solvent (buffer strength 50 mM, pH 7.4, 0.9% saline and containing 0.2 mM sodium salt of 3-trimethylsilyl-(2,2,3,3)-propionic acid-d₄ (TSP)). The mixture was centrifuged at 10,000 rpm for 5 min, and after centrifugation, 550 μL of the prepared sample was transferred into a 5 mm NMR tube (Wilmad Glass, USA). The reagents for NMR buffer, including NMR solvent (D₂O) and the sodium salt of TSP used in the experiments, were procured from Sigma-Aldrich (Rhode Island, USA).

NMR data collection and data pre-processing

The 1H NMR spectra were recorded at a temperature of 300 K using a high-field NMR spectrometer (Bruker Biospin Avance-III; Bruker, Germany) which operated at a proton frequency of 800.21 MHz. The spectrometer was equipped with a CryoProbe and an actively shielded gradient unit capable of delivering a maximum gradient strength of 53 G/cm. Before data acquisition, the magnetic field homogeneity was optimized to within < 0.5 Hz using the TOPSHIM module of the Topspin software (Bruker, Germany). For quantitative metabolic profiling, the 1D 1H- NMR spectra were recorded using standard Bruker pulse Program library (cpmgpr1d) consisting of Carr–Purcell–Meiboom–Gill (CPMG) spin-echo pulse sequence block [RD-90°-(τ−180°-τ) n acquire]. In the CPMG sequence, RD refers to the relaxation delay, 5 s, 90°, and 180° refer to excitation and inversion radio frequency (RF) pulses, and n represents the number of loops. During the CPMG NMR experiment, water peak suppression was achieved through continuous irradiation during the recycle delay (RD) of 5 s. The broad signals from proteins and lipoproteins were attenuated using a total spin-echo time (2nτ) of ~ 66 ms, where n = 300, and 2τ = 220 μs (Kumar et al. 2020). For serum samples, NMR spectral data were acquired with 128 scans, respectively, over a spectral width of 12,820 Hz. The free induction decay (FID) data file contained 64 k data points. Each CPMG spectrum was acquired in approximately 15 min for serum samples.

The raw NMR data were processed using the standard Fourier Transformation (FT) method employing Bruker NMR Data Processing Software named Topspin (version 3.6). Prior to FT, each Free Induction Decay (FID) was zero-filled to 4096 data points, and a sine-bell apodization function was applied. After FT, the resulting NMR spectra were further manually corrected for phase and baseline distortions. Finally, the chemical shifts were calibrated with respect to the methyl peak of L-lactate (at δ = 1.33 ppm).

Concentration profiling and multivariate data analysis

Prior to concentration profiling, each one-dimensional 1H CPMG NMR spectrum was opened in the PROCESSOR module of the NMR Suite of commercial software CHENOMX (Chenomx Inc., Edmonton, Canada) and recalibrated against an internal reference metabolite (formate, concentration set to 0.03 mM). The baseline of each spectrum was further corrected using the WHITTAKER method, and baseline-corrected CPMG NMR spectra were then opened into the PROFILER module of CHENOMX NMR Suite for concentration profiling of circulatory metabolites. A total of 39 circulatory metabolites were annotated from the 1D 1H CPMG NMR spectra based on their chemical shift values and comparison with spectral databases, including HMDB and Chenomx NMR Suite, with spectral assignments further supported by reference literature (Kumar et al. 2021). The data matrix containing concentrations of all samples was grouped according to their respective class information and saved in CSV format for subsequent multivariate and univariate statistical analysis using the open-access web-based metabolomics tool, MetaboAnalyst v6.0 (Chong et al. 2020; Pang et al. 2021).

The profiled NMR spectra exported as a CSV data matrix were further normalized, and for initial evaluation, unsupervised Principal Component Analysis (PCA) was performed to evaluate inherent clustering trends and group separation. The resulting PCA score plots demonstrated clear group-wise clustering and separation among all 4 study groups. To enhance group discrimination and identify key variables contributing to intergroup differences, supervised Partial Least Squares Discriminant Analysis (PLS-DA) was subsequently applied. Three-dimensional 3D PLS-DA score plots were generated to visualize group-specific metabolic shifts. The model quality was assessed using R2 (explained variance) and Q2 (predictive accuracy), with permutation testing (n = 1000) indicative of a robust and predictive model, and further confirmed model validity and minimized overfitting risk. Variable Importance in Projection (VIP) scores derived from the PLS-DA model were used to identify metabolites contributing most significantly to group separation. Metabolites with VIP scores > 1 and statistically significant differences (p < 0.05, one-way ANOVA followed by post hoc correction) were selected as key discriminatory features. These metabolites were subsequently subjected to pathway analysis using the MetaboAnalyst v6.0. The pathway enrichment was conducted using the Global Test method, and pathway topology analysis was based on the Rattus norvegicus.

Statistical analysis

The results are expressed as mean ± SD. All the data were analyzed using GraphPad Prism, Version 7.0 (California, U.S.A) by one-way ANOVA followed by Bonferroni post-hoc test wherever possible. A value of *p < 0.05, **p < 0.01, and ***p < 0.001 was considered to be statistically significant.

Results

Phytochemical profiling of AESC extract by LC–MS/MS

The aqueous fraction was analyzed using UPLC-ESI–MS to acquire tentative identification of Syzygium cumini components, confirmed through LC–MS/MS chromatograms (Fig. 1) and reference mass spectra (Supplementary Fig. 1). The results revealed that gallic acid (GA), ellagic acid (EA), quercetin, and malic acid were major anti-diabetic compounds present in the extract. LC–MS/MS was developed for quantitative analysis, showing the composition of gallic acid, quercetin, ellagic acid, and citric acid as 85.72 ± 0.41 mg/g, 12.46 ± 0.14 mg/g, 62.10 ± 0.45 mg/g, and 68.32 ± 2.04 mg/g, respectively (Supplementary Table 1). The name of identified molecules was also listed (Supplementary Tables 2 and 3).

Fig. 1.

Fig. 1

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Representative LC–MS/MS chromatographic analysis of the aqueous seed extract of Syzygium cumini (AESC). A MS/MS spectra of five key phytoconstituents—gallic acid, quercetin, citric acid, ellagic acid, and maleic acid are shown by their characteristic m/z transitions used for identification and structural confirmation. B Chromatogram of the blank samples to confirm the absence of interfering peaks or background noise for validating the analytical specificity of the method. C Chromatogram of the AESC demonstrates the detection and separation of the target compounds. This analysis confirms the presence of bioactive polyphenols and organic acids that may contribute to the therapeutic potential of S. cumini

Effect of AESC treatment on feed consumption and body weight of diabetic rats

Body weight and food consumption were monitored and recorded weekly for all groups throughout the study period. All the animals were randomized based on body weight, and initially, no difference in the body weights was observed between all the experimental groups (260.44 ± 8.03) (Fig. 2A). Diabetic rats (II) showed polyphagia and a significant (p < 0.001) reduction in body weight as compared to the control rats (I) (Supplementary Table 4). Moreover, AESC-treated rats (III) depict feed intake and body weight close to the control rats (I) (Fig. 2B), with significantly (p < 0.001) increased body weight relative to diabetic rats. Compared to the diabetic control group, the feed consumption was significantly reduced (p < 0.001), while body weight was evidently increased (p < 0.001) in the metformin-treated rats (IV).

Fig. 2.

Fig. 2

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AESC treatment improves metabolic parameters in diabetic rats. A AESC administration mitigated diabetes-associated body weight loss. B Feed intake was normalized in AESC-treated rats compared with diabetic control, which suggests improved metabolic homeostasis. C Fasting blood glucose levels were markedly reduced, which highlights the glucose-lowering effect of AESC. D Oral glucose tolerance test (OGTT) showed enhanced glucose clearance in AESC-treated rats, which reflects improved insulin sensitivity E Correspondingly, the area under the curve (AUC) for OGTT was significantly reduced which further supports improved glucose tolerance. The data are expressed as mean ± SD (n = 5). **p < 0.01 and ***p < 0.001 vs. diabetic control group; ##p < 0.01 and ###p < 0.001 vs. control group

Antihyperglycemic effect of AESC in diabetic rats

The diabetic rats (II, III, and IV) exhibited a steady rise (p < 0.001) in blood glucose levels compared to the control rats (I). Administration of AESC at 250 mg/kg for 4 weeks statistically (p < 0.001) reduced the blood glucose levels in the treated groups of rats (III) and (IV) when compared to the diabetic control rats (II) (Fig. 2C).

AESC administration improved alterations in serum biochemical parameters

Compared to the control group (I) rats, mild alterations in the hematological parameters were observed in AESC-treated rats (III). However, all the values lie within the biological significance level (Supplementary Table 5). When compared with the control group of rats (I), a significant increase in the levels of serum biomarkers including ALT (56.42%) (p < 0.001), AST (26.37%) (p < 0.01), ALP (0.22%) (p < 0.05), and urea (40.69%) (p < 0.01) was observed in the diabetic group rats (II). In addition, the AESC treated (III) group rats exhibit significant decrease in ALT (37.78%) (p < 0.001), AST (19.42%) (p < 0.05), ALP (18.12%) (p < 0.05), and urea (34.83%) (p < 0.01), respectively, in comparison to those in the diabetic control group (II), while non-significant changes in the level of CRE, PHOS, and BUN were observed in all the groups (Supplementary Table 6A).

AESC maintains glucose homeostasis in diabetic rats

A prominent increase in the blood glucose levels was observed among all the groups, after the oral administration of glucose (2 g/kg) at 0, 15, 30, 60, and 120 min. The rats in the diabetic control group (II) showed higher blood glucose levels throughout the total measurement period (120 min) as compared to the normal control rats (Fig. 2D, E). Moreover, AESC (III) and metformin group (IV) exhibit a reduction in AUC relative to the diabetic control group (II) by 9.42% and 9.03%, respectively (Supplementary Table 6B).

AESC alleviates pancreatic damage against oxidative stress

The oxidative MDA levels were found to be evidently (p < 0.001) increased in the diabetic control group (II) as compared to those in the control group (I). The rats in AESC-treated (III) and metformin group rats (IV) exhibited a noticeable (p < 0.001) decrease in the MDA content compared to the diabetic control group (II). The levels of SOD, CAT, and GSH were significantly (p < 0.001) decreased in the diabetic control group (II) compared with the control group (I). In addition, the AESC and metformin-treated groups (III and IV, respectively) considerably (p < 0.001) restored the levels of these endogenous antioxidants compared to those in the diabetic control group (II) (Fig. 3).

Fig. 3.

Fig. 3

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AESC treatment restores antioxidant levels and reduces lipid peroxidation in the pancreas of diabetic rats. Quantitative analysis of A Superoxide dismutase (SOD), B Catalase, C Reduced glutathione (GSH), and D Malondialdehyde (MDA) levels. Diabetic rats exhibited significant reduction in SOD, catalase, and GSH, which indicates impaired antioxidant defense mechanisms, along with a concomitant elevation of MDA, an indicator of enhanced lipid peroxidation and oxidative stress. Treatment with AESC markedly restored antioxidant enzyme activities (SOD, catalase, and GSH) and significantly reduced MDA levels compared to the diabetic group, which suggests a protective efficacy of AESC against STZ-induced oxidative damage. The data are expressed as mean ± SD (n = 5). ***p < 0.001 vs. diabetic control group; ###p < 0.001 vs. control group

AESC restored morphological and histopathological alterations

In the pancreas, gross morphological changes such as inflammation, pale coloration, and swollen surface were seen in the diabetic control group (II) compared to the control group (I). However, minimal changes were observed in the pancreas of the AESC group (III) as well as in the metformin group (IV). The microscopic examination of pancreatic sections stained with H&E revealed normal histological architecture among the control group (I). The parenchyma of diabetic rats showed fibrinous degeneration, an increase in the space of pancreatic lobules, and a loss of islets/diminution (Fig. 4 Panel A). The pancreatic parenchyma of AESC group (III) and metformin-treated rats (IV) exhibited minimal cellular vacuolation and mild infiltration of mononuclear cells, increased number and compact distribution of pancreatic cells, as well as islets of Langerhans were normally distributed as compared to the diabetic control group (II) (Fig. 4 Panel A). In addition, Masson’s trichrome staining showed higher build-up of collagen fibers in the perivascular regions of diabetic control groups (II), which indicates progressive fibrosis and extracellular matrix remodeling from control group (I) rats, while no collagen deposition was observed in AESC group (III) (Fig. 4 Panel B).

Fig. 4.

Fig. 4

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To understand the effect of AESC on histological alterations in the pancreas of diabetic rats, H & E and Masson’s trichrome staining were performed. (A) Panel A H & E-staining demonstrates normal islet morphology and compact architecture in the control group. In contrast, the diabetic group shows islet degeneration (black arrow), widened interlobular spaces (red arrow), and mononuclear cell infiltration (yellow arrow), all indicative of pancreatic injury. Treatment with AESC (250 mg/kg) ameliorated these pathological changes, as evidenced by preserved islet integrity and reduced infiltration compared to the diabetic group. Panel B Masson’s trichrome staining reveals marked collagen deposition (brown arrow) in the perivascular and interstitial regions of the pancreas in diabetic rats, which indicates progressive fibrosis and extracellular matrix remodeling. AESC treatment significantly reduced collagen deposition. Panel C Immunohistochemistry (IHC) staining illustrates a substantial decline in β-cell expression in the diabetic control group (blue arrow). Notably, AESC restored insulin-positive β cells compared to the diabetic control group. Representative quantification of (B) insulin-positive cells and (C) islet area with 5 random areas per section was determined by ImageJ software. The data are expressed as mean ± SD (n = 5). **p < 0.01 and ***p < 0.001 vs. diabetic control group; ###p < 0.001 vs. control group

Protective effects of AESC against oxidative stress-induced pancreatic β-cell damage

The STZ-induced diabetic control rats (II) showed a smaller number of β-cells when stained with anti-insulin antibody. The AESC and metformin treatment markedly balanced the expression of insulin-producing β-cells when matched to the diabetic control group (II) (Fig. 4 Panel C). Furthermore, the AESC-treated group demonstrated a significant increase in the number of insulin-positive cells (p < 0.01) and total islet area (p < 0.001) compared to the diabetic control group (II) (Fig. 4B and C).

Effect of AESC on expressions of ER stress and inflammatory markers in pancreatic tissue

The expression for indicative markers of ER stress, inflammation, oxidative stress, and calcium-sensing proteins in the pancreatic tissues was assessed using western blotting. The band intensity of ATF-6 (p < 0.001), CHOP (p < 0.01), GRP78 (p < 0.001), calreticulin (p < 0.001), and p-NF-κB p65 (Ser536) (p < 0.01) was markedly augmented, while Nrf-2 (p < 0.01) expression was prominently reduced in the diabetic group (II), respectively, in contrary to the control group (I). However, these changes were evidently reversed by administration of AESC at 250 mg/kg (Fig. 5).

Fig. 5.

Fig. 5

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AESC alleviates ER stress and inflammation by modulating ATF-6/CHOP/NF-κB signaling axis. A Representative images illustrate relative protein expressions involved in ER stress and inflammation analyzed by immunoblotting. β-actin was used as a loading control. The graph depicts quantitative densitometric analysis of B ATF-6, a transcription factor activated during ER stress C CHOP, a pro-apoptotic marker induced by prolonged ER stress D GRP78, a molecular chaperone and ER stress marker E Calreticulin, another ER chaperone involved in protein folding and calcium homeostasis F NRF2, a key regulator of antioxidant responses G p-NF-κB p65, a key regulator of inflammation. The data are shown as mean ± SD (n = 3). **p < 0.01 vs. diabetic control group; ***p < 0.001 vs. diabetic control group; ##p < 0.01 vs. control group; ###p < 0.001 vs. control group

NMR-based serum metabolomics analysis

The metabolite-specific chemical shifts were identified by comparing them with those of the 800 MHz spectral library of metabolites available in the commercial software CHENOMX (https://www.chenomx.com/libraries). The remaining unassigned peaks in the CPMG 1H NMR spectra were determined using published literature reports and NMR assignment resources such as The Human Metabolome Database (HMDB). Figure 6 depicts the stack plot of cumulative NMR spectra of serum samples obtained from normal control, diabetic control, AESC, and metformin-treated groups. The concentrations of 39 circulating metabolites in the serum were quantified using formate as an internal reference, utilizing the profiler module of the commercial software CHENOMX. These metabolites were as follows: 2’-deoxyuridine, tyrosine, lactate, creatine, myo-inositol, trimethylamine N-oxide (TMAO), choline, creatinine, citrate, pyruvate, acetone, acetate, alanine, leucine, histidine, threonine, glycerol, betaine, ornithine, methionine, glutamine, arginine, valine, phenylalanine, fumarate, serine, glycine, Dimethyl sulfone (DMS), glutamate, lysine, isoleucine, urea, glucose, malonate, aspartate, succinate, 3-hydroxybutyrate (3-HB), proline, and isobutyrate. These multiple serum metabolic profiles were compared using PLS-DA, and the results are summarized in Fig. 7.

Fig. 6.

Fig. 6

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A cumulative stack plot of 1D 1H CPMG NMR spectra of serum samples administered with AESC for 28 days from four experimental groups: normal control (blue), diabetic control (red), metformin treatment (green), and AESC treatment (purple). The spectra are presented in the aliphatic and carbohydrate regions (0.8–4.2 ppm, left) and the aromatic region (5.7–8.5 ppm, right). The assignment of NMR peaks corresponding to specific serum metabolites is annotated. Peaks marked with asterisks (*) represent glucose signals typically elevated under diabetic conditions. The abbreviations used are as follows: 3HB: 3-hydroxybutyrate, TMAO: trimethylamine N-oxide, Phe: Phenylalanine and DMS: Dimethyl sulfone

Fig. 7.

Fig. 7

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Therapeutic potential of AESC in the modulation of diabetes-induced metabolic alterations. Multivariate statistical analysis of serum metabolic profile administered with AESC for 28 days of four groups: normal control (NC), diabetic control (DC), metformin-treated (MT), and AESC treated (AT) groups: A 3D PLS-DA scores plot derived from 1D 1H CPMG NMR spectra illustrate the metabolic disparity between all groups based on their serum metabolite profiles. B Variable Importance in Projection (VIP) scores plot highlights the most influential metabolites contributing to group separation in the PLS-DA model. C One-way ANOVA of quantified 39 metabolites across the four study groups, with statistically significant metabolites altered among the groups. The y-axis represents statistical significance (− log10(raw p-value), and the x-axis represents the total number of metabolites. Significant metabolites are distinguished by color intensity (yellow to red) and point size. D Violin plots illustrate the distribution and variability of key metabolites that reveal distinct metabolic shifts in the diabetic control group compared to the treatment groups (MT, AT) and normal control group

The PCA plot reveals clear separation among the four groups, indicating distinct metabolic profiles. The first two principal coordinates together explain 63.8% of the total variation in the dataset, with PC1 accounting for 33.2% and PC2 for 30.6%. (Supplementary Fig. 2A). The Permutational Multivariate Analysis of Variance (PERMANOVA) confirms that these group differences are statistically significant, with an F-value of 14.116, an R2 of 0.60197 (indicating that ~ 60% of the variance is explained by group identity), and a highly significant p-value of 0.001 based on 999 permutations. Collectively, these results demonstrate that the metabolomic composition varies substantially and meaningfully between the groups, suggesting that the classification represented by each group strongly influences metabolic profiles. Further, to get enhanced discrimination among study groups, the supervised PLS-DA was performed. The model quality was assessed using R2 and Q2, values for both close to 0.8, with permutation testing (n = 1000) indicative of a robust and predictive model (Supplementary Fig. 2B). The 3D score plot based on PLS-DA analysis (Fig. 7A) demonstrated a distinct separation that highlights exquisite metabolic reprogramming in treated experimental groups with respect to control (Fig. 7A). The VIP score plot was used to identify serum metabolites with significant discriminatory relevance between the study groups (Fig. 7B), and these metabolites were further evaluated for statistical significance using ANOVA (Fig. 7C). Multivariate statistical analysis distinctively illustrated the distribution and variability of key metabolites, highlighting significant metabolic shifts in the diabetic control group compared to the AESC and metformin-treated groups, as well as the normal control group (Fig. 7C). Pathway enrichment analysis of serum metabolites identified key metabolic pathways altered across the study groups. A comparison of the serum metabolomic profiles between the AESC-treated group and the diabetic control group revealed significant alterations in various metabolic pathways such as tyrosine metabolism (highly significant and impactful), arginine and proline metabolism, phenylalanine, tyrosine, tryptophan biosynthesis, and glycolysis/gluconeogenesis (Fig. 8 and Supplementary Table 7).

Fig. 8.

Fig. 8

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Metabolic effects of AESC on pathway enrichment analysis of serum metabolites, showing key metabolic pathways significantly altered across different study groups. The x-axis represents the pathway impact, which indicates the relative importance of the pathway, while the y-axis shows the statistical significance (− log10 (p-value). Significant pathways are highlighted by larger circles (impact score) based on pathway topology analysis, and the color scale represents increasing statistical significance, with red indicating the most significant changes and yellow representing less significant metabolic alterations

Discussion

This study investigated the protective efficacy of AESC against T1DM-associated ER and oxidative stress in a STZ-induced rat model. The LC–MS/MS analysis revealed that AESC contains major phytoconstituents such as gallic acid, ellagic acid, quercetin, and malic acid. Previous studies have consistently reported that these compounds have potent antihyperglycemic, antilipid peroxidative, antioxidant, and β-cell protective properties, which contribute to improved insulin function and reduced oxidative stress, and mitigate advanced glycation end products (Kumar et al. 2025). Collectively, these multifaceted activities highlight the anti-diabetic potential of AESC.

STZ-induced diabetic rats are a broadly accepted and validated model of T1DM. The diabetogenic agent selectively destroys pancreatic β-cells via GLUT2-mediated uptake and leads to an insulin-deficient diabetic-like phenotype (Dubey et al. 2023; Singh et al. 2022b; Ali et al. 2021). This model successfully mimics hallmark features such as hyperglycemia, hypoinsulinemia, muscle loss, and enhanced protein catabolism. As diabetes progresses, impaired glucose uptake in the satiety center reduces the arteriovenous glucose difference and results in persistent activation of the feeding center, leading to polyphagia (Singh et al. 2022b). However, it lacks the autoimmune mechanisms that limit its direct translational relevance to human T1DM. As a result, key immunological features of the disease, including autoantibody production, chronic insulitis, and genetic susceptibility factors such as HLA haplotypes, are not represented in the STZ model (Eleazu et al. 2013). Despite these limitations, the STZ model remains a practical and reproducible tool for evaluating pharmacological agents that modulate glycemic control and oxidative stress, as well as particularly valuable in early-stage drug discovery due to its reliability and ease of induction (Wu and Yan 2015). Consistent with these reports, histopathological and immunohistochemical analysis revealed that STZ-induced diabetic rats (II) showed distorted pancreatic architecture.

In the present study, administration of STZ (35 mg/kg) successfully induced hyperglycemia and polyphagia in diabetic rats. Treatment with AESC (250 mg/kg) markedly lowered fasting glucose levels toward normoglycemia, restored pancreatic morphology, preserved islet integrity, and alleviated associated metabolic disturbances. The protective action of AESC may be attributed to its bioactive constituents, particularly gallic acid and ellagic acid, which are known to enhance insulin sensitivity and β-cell function. These findings align with earlier reports demonstrating that S. cumini fractions enriched with such polyphenols promote glucose homeostasis and improve metabolic regulation in HFD-induced obese rats (Arman et al. 2025). Moreover, polysaccharide-based interventions demonstrate antidiabetic potential by modulating key signaling pathways and suppresses the overproduction of reactive oxygen and nitrogen species. Interestingly, mushroom-derived polysaccharides exhibit strong antioxidative capacity, which may protect and enhance β-cell function to promote glucose homeostasis in T1DM (Rai et al. 2022).

Previous studies have shown that aqueous and hydroalcoholic extracts of S. cumini (200–500 mg/kg) exhibit antioxidant, antidiabetic, and hepatoprotective effects without toxicity in rodent models (Sharma et al. 2017; Kumari et al. 2023; Kavitha and Sendhilvadivu 2024). Effective glycemic control in diabetic rats has been reported at 200 and 400 mg/kg/day of AESC with no detectable adverse effects (Sharma et al. 2017; Rafiullah et al. 2006). Collectively, these findings establish both the pharmacological rationale and safety profile that support the use of a 250 mg/kg as an optimal, biologically active, and non-toxic in vivo dose to maximize therapeutic efficacy while minimizing the risk of the ceiling effect at higher concentrations.

Transaminases are sensitive biomarkers of cellular injury, as they are released into circulation during ROS-mediated tissue damage (Tamber et al. 2023). In the present study, diabetic control rats exhibited elevated serum transaminases and urea, which indicates hepatic and renal impairment associated with STZ-induced oxidative stress. Administration of AESC (250 mg/kg) significantly reduced these biomarkers due to its free radical-scavenging properties. These findings align with previous reports on the hepatoprotective role of S. cumini against CCl4-induced hepatotoxicity in rats (Kumari et al. 2023).

Oxidative stress plays a pivotal role in the pathogenesis of TIDM through impairment of the structural and functional integrity of pancreatic islets (Singh and Rai 2019). Under diabetic conditions, excess ROS triggers protein and lipid damage through glycoxidation and peroxidation, which leads to impaired antioxidant defenses (Singh et al. 2022c; Shiming et al. 2021). In the present study, diabetic rats exhibited significantly (p < 0.001) elevated malondialdehyde (MDA) levels, concurrent with a marked (p < 0.001) reduction in the activities of SOD, CAT, and GSH compared to the control group rats. Furthermore, β-cells are highly susceptible to redox imbalance and ER stress due to a high secretory burden, which promotes protein misfolding and triggers unfolded protein responses, inflammation, and apoptosis (Zaher and Stephens 2025; Eguchi et al. 2021; Keane et al. 2015; Lytrivi et al. 2025). Together, the immunohistological and biochemical findings provide strong evidence that oxidative and ER stress contribute to the progression of pancreatic β-cell dysfunction. AESC markedly reduced MDA levels and restored antioxidant enzyme levels (p < 0.001) against hyperglycemia-induced oxidative damage. In addition, AESC shows protective effects against β-cell loss, as demonstrated through improved immunostaining and preserved islet morphology in treated groups.

At the molecular level, Nrf-2 and NF-κB signaling pathways are central regulators of oxidative stress and inflammation (Singh et al. 2025).  In agreement with previous findings that show protective effects of phloretamide against diabetic complications through Nrf-2 activation that suppresses NF-κB-induced cytokine production (Al-Hussan et al. 2024), our results indicate diabetes-induced Nrf-2 dysregulation enhances NF-κB activity and promotes inflammation. AESC demonstrates antioxidant and anti-inflammatory properties through upregulation of Nrf-2 expression and attenuates NF-κB-mediated inflammatory responses in treated rats (III). Moreover, under hyperglycemic conditions, GRP78 dissociates from ATF-6, which enables its activation through proteolytic cleavage in the Golgi apparatus. The cleaved ATF-6 then translocates to the nucleus and induces CHOP transcription that contributes to ER stress and inflammation (Hu et al. 2019; O'Neill et al. 2013). Aligned with these mechanisms, the present study demonstrated increased expression of ER stress markers such as ATF-6, CHOP, GRP78, and calreticulin in diabetic control rats. Notably, AESC treatment significantly reduced the expression of these proteins compared to the STZ-induced diabetic control group. Collectively, these findings highlight the antidiabetic and antioxidative potential of AESC in alleviating ER and oxidative stress through modulation of the ATF-6/CHOP/NF-κB signaling axis.

Phenylalanine is an essential aromatic amino acid involved in sugar and lipid metabolism (Pan et al. 2020). Previous studies have reported that elevated phenylalanine has been linked to impaired insulin receptor β (IRβ) function, reduced glucose uptake, and disrupted insulin signaling (Zhou et al. 2022). In line with previous reports, serum phenylalanine levels were increased in diabetic rats and restored to near-normal levels upon treatment with AESC.

3-Hydroxybutyrate (3-HB) is a major ketone body synthesized in the liver from acetyl-CoA that serves as an alternative energy source during glucose-deficient conditions (Li et al. 2025). In type 1 diabetes (T1D), insulin deficiency accelerates lipolysis and increases fatty acid flux to the liver and heart, resulting in increased ketogenesis. Concurrently, impaired ketolysis reduces ketone utilization, which causes mitochondrial dysfunction and excessive ketone accumulation, leading to diabetic ketoacidosis (DKA) (Kanikarla-Marie and Jain 2016). Moreover, preclinical studies have demonstrated an inverse relationship between fasting blood glucose levels and 3-HB concentrations, which may suggest impaired ketone body production or reduced clearance capacity associated with diabetes-induced metabolic alterations (Hu et al. 2022). In accordance with these reports, a prominent decrease in serum 3-HB levels in diabetic rats suggests a disruption in ketone body metabolism, as confirmed by one-way ANOVA (p < 0.05). A clinical study demonstrated that elevated 3-HB levels have been associated with augmented therapeutic responses to antidiabetic agents and improved glucose regulation. (Lee et al. 2023). In the present work, administration of AESC resulted in a reduction of 3-HB, which suggests enhanced peripheral uptake and utilization of ketone bodies or a restoration of glucose homeostasis. Additionally, integration of metabolomics with gut microbiota assessment provides a comprehensive framework to understand host–microbe interactions, as microbial metabolites play a pivotal role in the regulation of systemic metabolic pathways (Singh et al. 2022a). In this context, altered phenylalanine and 3-hydroxybutyrate profiles, along with the ability of AESC extract to alleviate oxidative and ER stress, highlight a gut microbiota–metabolite–stress axis as a potential therapeutic intervention for T1DM.

Furthermore, serum concentrations of branched-chain amino acids (BCAAs), which comprise isoleucine, leucine, and valine, were found to be upregulated in diabetic control rats. Consistently, evidence from randomized controlled trials and prospective cohort studies has demonstrated a positive association between increased BCAA levels and the risk of developing diabetes (Ramzan et al. 2022; Wan et al. 2023). BCAAs are primarily metabolized within mitochondria through the action of BCAA aminotransferase and the branched-chain α-ketoacid dehydrogenase. Under diabetic conditions, increased BCAA degradation results in the accumulation of intermediate metabolites that disrupt mitochondrial glucose oxidation, leading to mitochondrial stress and impaired insulin secretion. Thus, elevated BCAAs are a well-established biomarker of insulin resistance and mitochondrial dysfunction, which indicates impaired catabolic pathways in muscle and liver under diabetic conditions (Newgard et al. 2009). BCAAs are considered glucogenic amino acids, as their catabolism generates intermediates such as pyruvate, oxaloacetate, and α-ketoglutarate, which serve as critical substrates for gluconeogenesis. Under insulin-resistant conditions, valine metabolism can be diverted into the tricarboxylic acid (TCA) cycle to support adenosine triphosphate (ATP) production as an alternative energy source for skeletal muscle (Li et al. 2020). Therefore, BCAA catabolism plays a pivotal role in the regulation of energy homeostasis and maintenance of glucose balance. In agreement with previous reports, AESC-based intervention restored these dysregulated metabolic pathways, indicating improved amino acid metabolism and mitochondrial function, which supports its therapeutic potential in the management of diabetes.

This study has limitations, such as the utilization of a single dosage level for the preparation of AESC. Also, the study focused on short-term outcomes to characterize the acute effects and mechanism of action, with the absence of in vitro validation and genetic knockdown approaches are warranted to confirm the specificity of the observed antidiabetic effects.

Conclusion

In conclusion, the present study demonstrates that the AESC exerts significant antihyperglycemic and antioxidant effects in a STZ-induced diabetic rat model, which supports its traditional use in the management of metabolic disorders. AESC has shown a significant therapeutic efficacy in improving islet morphology, alleviating pancreatic oxidative stress, and increasing insulin secretion. Moreover, it significantly downregulated the expression of ER stress and inflammatory proteins, potentially via modulation of the ATF-6/CHOP/NF-κB signaling axis. Metabolomic profiling further suggests that AESC may influence key metabolic pathways related to amino acids like phenylalanine, BCAAs and 3-HB metabolism. However, these results are preliminary and derived from a single preclinical model. To fully establish the therapeutic relevance of AESC, further investigations are needed, including dose–response studies, mechanistic validation, long-term safety assessments, and well-controlled human clinical trials.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 2959 KB) (2.9MB, docx)

Acknowledgements

The authors are grateful to the director, CSIR-Central Drug Research Institute (CSIR-CDRI), Lucknow, India, for providing the necessary facilities and institutional support to conduct this research work. Karan Singh Yadav is thankful to the UGC, New Delhi, for providing doctoral fellowship. The CSIR-CDRI communication number allotted to this paper is 11041.

Abbreviations

TIDM

Type 1 Diabetes Mellitus

ROS

Reactive oxygen species

UPR

Unfolded protein responses

CHOP

C/EBP homologous protein

IRE1α

Inositol-requiring 1α

PERK

PKR-like eukaryotic initiation factor 2 kinase

ATF-6

Activating transcription factor 6

Nrf-2

Nuclear factor erythroid 2-related factor 2

SOD

Superoxide dismutase

CAT

Catalase

GSH

Glutathione

MDA

Malondialdehyde

GLUT-4

Glucose transporter-4

Funding

No funding was received for conducting this study.

Data availability

All relevant data are within the article.

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

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