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Journal of Diabetes and Metabolic Disorders logoLink to Journal of Diabetes and Metabolic Disorders
. 2022 Feb 1;21(1):379–397. doi: 10.1007/s40200-022-00985-6

Integrated omics analysis revealed the Tinospora cordifolia intervention modulated multiple signaling pathways in hypertriglyceridemia patients-a pilot clinical trial

Amey Shirolkar 1,#, Aarti Yadav 2,#, Amit Nale 1, Jatin Phogat 2, Rajesh Dabur 1,2,✉
PMCID: PMC9167413  PMID: 35673460

Abstract

Purpose

Hypertriglyceridemia (HTG) is strongly associated with the various types of disease conditions and evolving as epidemics. Hence, it is important to identify molecules that lower the triglyceride and chylomicron levels. Tinospora cordifolia is an illustrious Ayurveda drug, has proved juvenile and immunomodulatory properties.

Methods

Twenty four (24) patients having >499 mg/dL TG and 130–230 mg/dL of cholesterol were randomized and given 100 mL/day (~3.0 g) water extract of T. cordifolia (TCE) for 14 days. Basal parameters were analyzed before and after TC intervention to analyzed primary outcomes. Further, unbiased metabolomics and proteomics profiling was explored to assess the efficacy of TCE in HTG patients.

Results

TCE intervention decreased the levels of triglycerides, and VLDL to 380.45 ± 17.44, and 31.85 ± 5.88, and increased the HDL levels to 47.50 ± 9.05 mg/dL significantly (p < 0.05). Metabolomics analysis identified the significant alteration in 69 metabolites and 72 proteins in plasma of HTG patients. TCE intervention reduced the level of isoprostanes, ROS, BCAA, and fatty acid derivatives, significantly. The annotation databases, Metboanalyst predicted Akt and Rap1 signaling, and ECM-receptor interaction is the most affected in HTG patients. TCE intervention normalized these events by increasing the peroxisome biogenesis and modulating Akt and Rap1 signaling pathway.

Conclusion

T. cordifolia intervention suppresses the baseline in HTG patients. Omics analysis showed that TCE intervention modulates the Akt and Rap signaling, and peroxisome biogenesis to control the cellular switches and signaling pathways. Hence, TCE can be used as a supplement or alternate of standard drugs being used in the management of HTG.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-022-00985-6.

Keywords: Hyperlipidemia, Hypertriglyceridemia, Tinospora cordifolia, HPLC-ESI-QTOF-MS, 2-dimensional gel electrophoresis

Introduction

Hypertriglyceridemia (HTG) is characterized by isolated elevation of triglycerides, VLDL (very low-density lipoprotein) and LDL (low-density lipoprotein) particles, and low count of HDL (high-density lipoproteins). It is asymptomatic by nature, hence often observed after the development of onsets of problems [1]. Its silent etiology is a milieu of various metabolic disorders, obesity, acute pancreatitis, atherosclerosis, non-alcoholic fatty liver, and cardiovascular diseases (CVDs). Moreover, it may be secondary in insulin resistance, obesity, alcohol abuse, chronic kidney disease, and several endocrinal diseases [2–4]. Obesity is now epidemic, hence the frequency of HTG will rise being concurrent metabolic syndrome of obesity [4]. It has been reported that hypertriglyceridemia contributes to 10% of acute pancreatitis and 30% to insulin resistance [5–7]. Despite all efforts, the exact mechanism behind the HTG is not fully understood.

Major drugs being used are fibrates and statins [7]. Fibrates modulate peroxisome proliferator-activated receptor-α (PPAR- α) that decreases the triglyceride concentration by 50%, however, it also increases the LDL-C up to ~20%, a major risk factor of CVDs [8]. The second class of drugs i.e. statins inhibit the activity of 3-hydroxy-3-methylglutaryl-coenzyme A reductase, hence these are not the first-line therapy for HTG [9]. Other HTG therapies involve niacin and bile acid-binding resins to increase lipid catabolism and reduce intestinal absorbance of lipid, respectively [10] . More focus is now on the therapies which decrease the ApoB levels rather than LDL-C. Hence, statins with cholesteryl ester transfer protein (CETP) inhibitors are found to be more useful to decrease apoB levels without any change in LDL-C and HDL-C [10, 11]. Hence, present drugs alone are not sufficient to control triglyceride levels in HT patients and need extra managements like diet and exercise control. Moreover, even after successful management of HTG, residual CVD risk is always there. Therefore need to identify a novel drug that suppresses the remnants of chylomicron and to counteract the progressive accumulation of triglycerides.

Tinospora cordifolia (TC) (Willd) Miers is a large, deciduous, climbing shrub belongs to Menispermaceae family and commonly known as sangrahi, guduchi, amrita, etc [10]. Ayurveda, an ancient Indian system of medicine prescribed the use of T. cordifolia in form of its water extract (TCE), decoction, and other formulations alone and with other herbal drugs. T. cordifolia accommodates numerous bioactive compounds such as alkaloid, flavonoids, glycosides, tannins, terpenoids, and coumarins [12, 13]. It was reported to attribute as anti-inflammatory, anti-oxidative, anti-diabetic, anti-arthritis, hypoglycemic, hypolipidemic, and anti-allergic [14, 15]. TC intervention was reported to attenuate the accumulation of thiobarbituric acid reactive substances (TBARS) and restored insulin sensitivity in T2DM rats [15]. Another study showed that TC suppressed the release of pro-inflammatory cytokine in arthritis individuals through inhibition of NF-kB [16]. Moreover, TC supplementation supported the β-oxidation and reduces the triglycerides level [17]. Berberine is well studied alkaloid has proved efficacy in hyperlipidemia [18] that could not enter in clinical trials due to high toxicity [19]. In TCE several protoberberines alkaloids including palmatine and magnoflorine are present in significant amounts have in vitro efficacy to treat metabolic disorders [12]. Therefore, based on its multi-regulatory efficacy, TCE might act as potent therapeutic agent to suppress the HTG and its associated complication. Hence, the purpose of the present randomized pilot clinical trial was to evaluate the efficacy of TCE in HTG and to delineate its mechanism.

Materials and methods

Chemicals & reagents

Routine biochemical assays were carried out using commercial kits obtained from Erba Mannheim kits (Lot) (TG-B111817; Total Cholesterol-S111927; ALT, BLT00052; AST-BLT00050; Creatinine-E111229; Glucose-S111909; Total protein-BLT00054; Albumin-BLT00001, Uric acid-BLT00062; Haemoglobin- BLT20020. HDL and VLDL/LDL(ab65390) assay kit procured from Abcam, USA. Internal and external calibrants and ESI tuning solution were purchased from Agilent Technologies. Standards of amino acids and metabolic markers were purchased from Sigma (St. Louis, MO). HPLC grade solvents and reagents used in experimentation like acetonitrile, methanol, formic acid, water, isopropanol, glacial acetic acid, and Bradford reagent were purchased from Sigma (St. Louis, MO). Immobiline dry strips (IPG, pH 3–10, 18 cm), DeStreak™ rehydration solution and pharmalyte (pH 3–10), Mineral oil were procured from GE Healthcare, acrylamide, bisacrylamide, SDS, urea, bromophenol blue, DTT, agarose, iodoacetamide, glycerol, tris base, ammonium persulfate, glycine, TEMED, silver nitrate, formaldehyde, sodium carbonate, sodium thiosulfate were of molecular biology/ analytical grade.

Ethical clearance

The present clinical trial was approved by the Human Ethics Committee of the PDDYP Ayurveda College, Pune, India wide letter no. RRI/2011/HEC/2023 dated 18-02-2011. This random trial was registered with the Clinical Trials Registry - India (CTRI) wide registration number CTRI/2016/08/007187. All the protocols approved by the ethical committee were as per guidelines of the Indian Council of Medical Research (ICMR), India and no deviation has been made in the protocol while doing experimentation.

Drug preparation

T. cordifolia (Willd) Miers stems were collected in September month from the garden of NRIBAS voucher specimens (No. 207), Kothrud, Pune, India. After washing in tap water the stems were crushed and extracted with distilled water overnight at room temperature (1:5 w/v). The extract was filtered and centrifuged at 5000 rpm. The fresh extracts were given to the volunteers to avoid the degradation of Phyto-constituents [20]. Random extracts were subjected to LC-MS analysis for quality control purposes.

Study design

The study design, sample collection, data processing, and data analysis opted for current study is listed in Fig. 1. Male volunteers were randomly assigned based on their TG levels. Normal individuals serve as the control groups (n = 10). The individual having TG levels more than 499 mg/dL and completed the criteria serve as the HTG group (n = 24). All individuals were consigned to follow the normal diet routine of breakfast, lunch, and dinner with 2 cups of tea in the day time during the trial. After the baseline assessment, HLT and HTG groups were treated with TCE and assigned as healthy treated with TCE (HLT_T) and HTG treated with TCE (HTG_T). Individuals who able to maintain the regime of TG level [21, 22] has been included in the study. While individuals having prior history of diabetes mellitus, alcoholic, autoimmune, reproductive disorder etc. were excluded from the study. The sample sizes were calculated using Gpower analysis 3.1. The randomized block container method has been used to equally allocate the individuals to decrease the inconsistency among the groups. An Ayurveda physician recruited the random allocation through numbered containers and masked the sequence until intervention was distributed.

Fig. 1.

Fig. 1

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A CONSORT flow diagram of study design, sample collection, data processing, and data analysis in different study groups

Both healthy individuals and HTG patients received 100 mL extract (~3.0 g. solid extract) in the morning before breakfast for 14 days with pre and post follow up phase. In contrast, 16 individuals were unable to maintain the regime of treatment and excluded from the study. Fasting blood has been collected on day 0 and day 14 in K+-EDTA vials, centrifuged and collected plasma samples were stored at −80 °C for further use. Aspirated plasma fraction was subjected to assess baseline parameters. The preliminary efficacy of TCE was evaluated through the change in TG, TC, VLDL, LDL, HDL, AST, ALT, etc. on day 0 and day 14 via auto-analyzer (ERBA, Germany) and blood cell counter using Erba Mannheim kits and GRA H360 Hematology Analyzer. While secondary outcomes were characterized through the identification of oxidative stress markers, altered metabolic pathways, proteomics study and to generate the interlinking bridge between the altered markers and signaling event.

HPLC

Qualitative and quantitative metabolomics experiments were performed on Agilent 1290 Infinity Series HPLC interfaced to an Agilent 6538 Accurate-Mass Q-TOF MS. Plasma samples were diluted with acetonitrile: water (80:20 v/v) in 1:4 ratio and centrifuged at 8000 rpm for 5 min at 15 °C [17]. The volume of 20 μL of each sample was injected into an assembly of the C18 ZORBAX (4.6 × 12.5 mm) guard column followed by ZORBAX 300SB-C18 (4.6 × 150 mm) column of particle size 5.0 μm. The column temperature was set at 40 °C and that of the autosampler was 5 °C. The solvent system (A) had aqueous, 0.1% formic acid - water, and (B) organic, 0.1% formic acid - acetonitrile in the proportion of 90:10. The gradient mode {concentration/time (%/min)} used for solvent B was 0%/0; 100%/30; 0%/42; 0%/50}, with 0.5 mL/min flow rate. The mass spectrometer was operated in positive ion polarity mode with parameters: electrospray ionization (ESI), gas temperature 350 °C, nebulizer 50 psi, gas flow 12 l/min, capillary voltage 3500 V, nozzle 750 V, and fragmentor voltage 195 V. The data acquisition was carried out in the range of 100–1200 m/z. The quadrupole was operated in the extended dynamic range (1700 m/z, 2GHz).

Quality control of the data

To ensure the mass accuracy of recorded data, continuous internal calibration was performed using standards of signals of standard compounds. Pooled samples from the different groups were also used for quality control purposes. Blank samples (a solvent mixture of acetonitrile and water) were also injected between the sample runs. The chromatograms of blank samples were used to study the carry-over effects in LC-MS runs.

Multivariate analysis

Mass Hunter Qualitative Analysis (B.04.00, Agilent Technologies) software was used for preliminary investigation of raw MS/MS data such as correction, noise reduction, background removal, contaminants, and extraction of molecular features. Molecular features having >5000 cycles per second (cps), minimum absolute counts 10, relative height (5%) of largest peak, peak space tolerance, i.e. 0.0025 m/z, and 5.0 ppm were extracted from mass spectra to avoid false molecular features.

Quantitative analysis of selected molecular markers

To support qualitative analysis, a set of 45 metabolic markers and nine isoprostanes (IsoPs) were analyzed quantitatively in targeted MS/MS mode (SRM-selective reaction monitoring). HPLC-QTOF-MS conditions for quantitative analysis were similar to qualitative metabolomics studies except for collision energies. Collision energy for each metabolite was kept as suggested by [23]. The standard curves of standards were prepared in a range of 0.09 to 50 μg/mL. Various metabolites in plasma were quantified with help of their standards using Quantitative Analysis software (version B.04.00) from Agilent Technologies from the area ratio of the peaks. Concentrations of metabolites in plasma samples were analyzed using a standard curve with the highest correlation coefficient (R2 > 0.95). A signal-to-noise ratio (SNR) threshold of >3.0 was used. The limit of detection (LOD/ qualifier) and limit of quantitation (LOQ/quantifier) values were calculated based on the reported formulae, LOD = 2 N/m, where N is the noise value in SNR and m is the slope in peak height v/s concentration plot; LOQ = 5 x LOD.

Depletion of abundant proteins and protein quantitation

Prior proteomic studies the albumin; globulin and other abundant proteins were removed from plasma samples by using a kit from Calbiochem and according to the manufacturer instructions. The total protein concentrations of whole and depleted plasma samples were determined using the Bradford assay kit (Sigma) and bovine serum albumin (BSA) as the standard (A595).

2-dimensional gel electrophoresis

For the isoelectric focusing (IEF, first dimension) sample containing 40 μg of protein were mixed in DeStreak ™ solution (340 μL) containing optimized concentrations of urea, thiourea, CHAPS, and bromophenol blue (BPB). The samples were applied on an 18 cm IPG strip covering a pH range of 4–7 and rehydrated for 20 h. Rehydrated IPG strips were subjected to Ettan IPGphor 3 system (GE Healthcare). For the separation appropriate time and voltage profile were used: 500 V for 1 h, 1000 V for 1 h gradient, 8000 V for 4 h gradient, and 8000 V for 2 h to attain 35,250 Vhs of potential difference in 8 h. Focussed IPG strip was subjected to equilibration with buffer I (GE Healthcare) containing SDS, urea, glycerol, and DTT for 15 min. IPG strips again equilibrated in the buffer II where DTT was replaced with iodoacetamide for another 15 min. After equilibration, focussed strips were placed on top of each gel (12.5%) of Ettan DALT six systems and sealed with 1% of agarose solution along with protein marker (3.5–434 kDa, Genei). A current of 100 mA was applied for the first hour and then 150 mA was applied till the bromophenol blue dye front reached the anodic end (+). Three replicate gels were run for each of the plasma samples to further cut-down the gel-to-gel variation and handling errors.

The electrophoresed protein gels were silver stained according to the protocol described by [24]. Computerized 2-D gel analysis was performed using Melanie powered Image Master 2D Platinum software (GE Healthcare). All gels in parallel compared in this software as HTG_T vs HLT and HTG_T vs HTG. Few most repeated spots were first labeled as landmarks and then class and match sets were made, all spots were labeled. The analysis included creating match sets of HTG vs HLT and HTG_T vs HTG for gel images, detecting spots, and creating classes. ANOVA (p < 0.05) test was applied over the sampling class to obtain the statistically significant differentially expressed protein spots. The 3D view was obtained for these spots to mark the effect of treatment on the level of proteins. Spot intensity parameter was used for discriminating proteins as up or downregulated in classes of comparison. Differentially expressed protein spots were excised from gels and transferred into fresh vials for trypsin digestion. Tryptic digestions of differential protein spots were performed using the in-gel trypsin digestion kit (Calbiochem) following the supplier’s instructions. In brief, excised protein spots were de-stained, dehydrated, reduced with DTT, alkylated with iodoacetamide, and digested overnight at 37 °C in the presence of trypsin. The reaction was quenched with formic acid and reaction volume was transferred to sampling vials into autosampler.

Identification of differential proteins

For protein identification, HPLC-QToF-MS experiments were performed on Agilent 1290 Infinity Series HPLC interfaced with an Agilent 6538 Accurate-Mass Q-TOF MS. A volume of 20 μL of each sample was injected into an assembly of C18 ZORBAX 300 extend (4.6 × 150 mm) column of particle size 3.5 μm. The column temperature was set at 40 °C and 5 °C of the autosampler. The solvent system had aqueous (A), 0.1% formic acid-water, and organic (B), 0.1% formic acid-acetonitrile in the proportion of 95:5. The gradient mode {concentration/time (%/ min)} used for solvent B was 3%/0; 60%/10; 97%/14; 97%/15.5; 30%/21; 5%/25; and 5% at 30 min with 0.3 mL/min flow rate. The mass spectrometer was operated in positive ion polarity mode with parameters: ionization type dual electrospray ionization (ESI), gas temperature 325 °C, nebulizer 45 psi, gas flow 9.0 l/min, capillary voltage 3500 V, nozzle 750 V, and fragmentor voltage 150 V. The data acquisition was carried out in the range of 100–3000 m/z. The quadrupole was operated in the extended dynamic range (1700 m/z, 2GHz). The data was stored in profile mode that corresponds to continuous data storage. Protein identification was performed with the spectrum mill database search tool (A.03.03, Agilent Technologies). Obtained MS/MS spectrums were searched against the Homo sapiens NCBI database. The searches were run using the fixed modification of carbamidomethylation labeled cysteine and methionine oxidation as a variable modification parameter. Other parameters were set as MS spectral features, M + H+ 600 to 60,000 m/z, extraction time range 0 to 30 min., a loud charge states 2–4, maximum ambiguous precursor charge 3, precursor mass tolerance ±1.0 Da, product mass tolerance ±0.5 Da, maximum missed cleavages 4, minimum detected peaks 4, sequence tag length > 3, protein pI 3.0–10.0 and maximum reported hits 5. After validation, a summary of peptides was obtained from a single protein spot. This data was further confirmed using the deconvolution tool (Bioconfirm) in the MassHunter (Agilent) to calculate the actual molecular mass of the proteins from the concerned mass data files.

Statistical analysis

Power and sample size was calculated using Gpower analysis 3.1. having sample size (n = 24), the size effect of 0.4677, significance level (α > 0.05), and 80% of statistical power were used to evaluate the potential of TCE. In the present randomized trial, basal parameters were represented as mean ± SD analyzed using GraphPad Prism 7 to evaluate the potential of TCE in healthy and HTG individuals.

For metabolomics data analysis mass profiles were imported in Mass Profiler Professional software (B.12.02, Agilent Technologies) and all the molecular features were aligned using mass error < 5.0 ppm and retention time (RT) variation <0.2 min. Compounds present in less than 75% of samples of a group and having p > 0.05, fold change <2.0, and coefficient of variance <15 were removed from data sets and subjected to principal component analysis (PCA). Box-Whisker plot was also generated to analyze the distribution of data segregated under different categories. Finally, partial least-squares-discriminant analysis (PLS-DA) was performed to distinguish different groups and explore unique features present across the groups. Data obtained were further subjected to Wilcoxon paired t-test, One way ANOVA with Bonferroni multiple testing corrections to minimize the false discovery rate, keeping up p value cut off <0.05 and fold change >2.0. Differential metabolites were identified by comparing the fragmentation pattern of the metabolites with the standards in Metlin, HMDB, NIST databases, and literature.

Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6.8, online software was used to explore biological processes and signaling pathways associated with proteome data. Cytoscape 3.8.0 and MetaboAnalyst (statistical, functional, and integrative analysis of metabolomics and proteomics data) were used to explore various pathways alerted in HTG and HTG_T groups.

Results

Characterization of TCE

The optimized solvent system for HPLC and QTOF-MS parameters to resolve and identify the TC constitutes in TCE were described earlier [20]. The chromatograms of blank samples contained only low-intensity peaks, hence it was concluded that the carry-over effect did not occur. The LC-MS analysis of TCE was also carried out in comparison to solvent blanks. The TCE (drug) showed great repeatability and had many prominent peaks that corresponded to Tinospora specific compounds such as magnoflorine, palmatine, etc. The composition of TCE and quality control for the extracts used in this study has already been reported [17] 12]. TCE was found to be enriched in alkaloids namely magnoflorine, palmatine, and some other compounds.

Clinical biochemistry

Basal parameters including TG, VLDL, LDL, HDL, AST, ALT, etc. revealed a significant difference in TCE treated group. In the HTG_T group, the TG level was depleted significantly (p < 0.05). Likewise, decreased levels of LDL and increased levels of HDL were observed in HTG_T group. Levels of uric acid were also reduced significantly after treatment with TCE. The ALT/AST ratio was observed to be higher in HTG as compared to HLT group. The decrease in the activity of AST along with ALT/AST ratio was also observed in the healthy group as well in TCE treated healthy group. TCE supplementation had also reduced the levels of urea, creatinine, albumin and glucose in HTG pateints. TCE treatment reduced glucose levels but enhanced the Hb%, number of RBCs, and platelets in both HTG_T as well in HLT_T groups (Table 1).

Table 1.

The blood biochemistry, lipid profiles and hematocrit of all the groups employed in the study are expressed as mean ± SD (p < 0.05). BMI: Body Mass Index; TG: Triglycerides; TC: Total Cholesterol; HDL: High Density Lipoprotein; LDL: Low Density Lipoprotein; VLDL: Very Low-Density Lipoprotein; AST: Aspartate transaminase; ALT: Alanine transaminase; RBCs: Red Blood Cells; WBCs: White Blood Cells

S No Parameters Healthy Individuals Hypertriglycedemic patients p value
HLT HLT_T HTG HTG_T
(Day 0) (Day 14) (Day 0) (Day 14)
1. Age (years) 39.2 ± 2.4 39.2 ± 2.4 39.4 ± 1.6 39.3 ± 1.4 ns
2. BMI 22.1 ± 2.2 22.9 ± 1.5 25.6 ± 2.1 24.08 ± 1.9 0.006426
3. TG (mg/dL) 98.36 ± 3.0 85.20 ± 4.30 550.00 ± 18.5 380.45 ± 17.4 < 0.0001
4. TC (mg/dL) 155.13 ± 4.4 140.20 ± 4.5 167.26 ± 6.0 160.55 ± 3.3 < 0.0001
5. HDL (mg/dL) 45.10 ± 2.5 48.66 ± 5.8 35.50 ± 6.5 47.50 ± 9.0 < 0.0001
6. LDL (mg/dL) 108.50 ± 6.1 99.73 ± 3.1 157.91 ± 11.1 133.25 ± 3.1 < 0.0001
7. VLDL 18.26 ± 3.6 14.65 ± 2.8 69.56 ± 5.6 31.85 ± 5.8 < 0.0001
8. Urea (mg/dL) 17.56 ± 8.9 16.10 ± 4.0 30.26 ± 1.7 27.60 ± 5.9 < 0.0001
9. Creatinine (mg/dL) 0.89 ± 0.6 0.83 ± 0.4 1.35 ± 0.4 0.95 ± 0.0 < 0.0001
10. Total Protein (g/dL) 5.6 ± 0.3 6.3 ± 0.3 5.35 ± 0.4 5.0 ± 0.5 0.00066
11. Albumin (g/dL) 4.06 ± 0.4 3.30 ± 2.0 5.20 ± 0.1 3.90 ± 1.2 0.00002
12. ALT (U/L) 21.46 ± 2.2 21.06 ± 6.5 36.05 ± 6.3 24.95 ± 4.0 < 0.0001
13. AST (IU/L) 27.10 ± 3.6 18.96 ± 7.5 23.45 ± 4.2 20.55 ± 6.3 < 0.0001
14. Uric acid (mg/dL) 4.93 ± 0.9 4.16 ± 0.6 7.10 ± 0.9 5.15 ± 0.9 0.000277
15. Glucose (mg/dL) 97.06 ± 7.0 85.16 ± 6.5 105.95 ± 8.5 102.05 ± 7.8 0.000203
16. Hemoglobin % (g/dL) 13.63 ± 1.6 14.90 ± 1.7 12.65 ± 1.3 14.25 ± 0.6 0.003895
17. RBCs (× 106) 4.79 ± 0.3 5.23 ± 0.1 4.66 ± 0.6 5.06 ± 0.0 0.081382
18. WBCs (× 103) 7.35 ± 1.0 6.48 ± 1.2 7.95 ± 0.3 7.35 ± 1.7 0.02267
19. Platelets (× 103) 296.40 ± 12.3 313.60 ± 8.2 276.50 ± 9.4 342.50 ± 4.7 < 0.0001
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*ns = not significant

Multivariate analysis of plasma metabolome

Analysis of total ion chromatograms showed an average of 73 peaks containing around 1050 compounds. The analysis method was optimized and the results were reproducible with minimum variations across the samples. Variability in the higher peaks was normalized successfully using internal standards and Z-transforms. The overall percent variance was represented as a box-whisker plot showing altered metabolites as well proteomics across all groups, i.e. HLT, HTG, and HTG_T (Figs. 2, 3, 4, and 5). Out of these, HTG_T showed the highest compound variability represented by elongated box shapes towards either side to the grand mean. Further to explore the clustering in different groups, the molecular features sorted out from different groups after treatments showed a shift of HTG_T group toward healthy controls. Supervised PCA of HLT, HTG, and HTG_T groups showed 47.55%, 20.54%, and 5.22% variability along the X, Y, and Z axes respectively (Fig. 3). However, variability between TCE treated and healthy decreased significantly. To refine data further and to identify major changes in molecular features, in partial least square discrimination analysis (PLS-DA) and support vector machines (SVMs) to support the satisfactory model for LC-MS (R2 = 0.97 and Q2 = 0.86). Hence, the results from PLS-DA and SVM are showing consistency with PCA observations and high accuracy of models. These results showed clear differences among groups and maximum metabolites perturbation in HTG patients.

Fig. 2.

Fig. 2

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The box-and-whisker plot reflecting the distribution of metabolites in healthy (HLT), hypertriglyceridemic (HTG) and T. cordifolia treated hypertriglyceridemic (HTG_T) patients

Fig. 3.

Fig. 3

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PCA plots of variable metabolites obtained in positive mode showed significant differences among groups. X axis component value was 36.2% while for Y and Z it was 21.3%, 9.5% respectively. Inline graphic indicates healthy (HLT) population, Inline graphic indicates HLT_T treated, Inline graphic indicates HTG individuals, Inline graphic indicates HTG_T individuals

Fig. 4.

Fig. 4

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Pearson heat map alignment of differential metabolites across healthy (HLT), hypertriglyceridemic (HTG), and TCE-treated HTG (HTG_T) groups. The columns correspond to different individual groups and rows correspond to the altered metabolites

Fig. 5.

Fig. 5

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Box-whisker plot also showed that HTG_T_UP proteins had highest intergroup variation as evidenced by extended whiskers across lower and upper quartiles

Qualitative analysis and identification of variable metabolites

Data from SVM was further processed performing Mann-Whitney U-test with 50 permutations and Bonferroni FWER multiple testing corrections to remove false molecular features. Moreover, the metabolites with any missing values were filtered out. Finally, 69 molecular features with variable importance on projection (VIP) values >1.5 and p < 0.05 were identified (Table 2). These metabolites were identified using their corresponding MS/MS spectra and matching them with different databases. These 69 metabolites broadly can be divided into lipids, haems, vitamins, bile acids, nucleotides, amino acids, and oxidative stress markers and are presented in Table 2. For example, most of the lipids were identified as phospholipids and observed to be increased in the HTG group as compared to HLT, and decreased after TCE intervention in the HTG group. Bile acid intermediates i.e. 4α-formyl-5α-cholesta-8, 24-dien-3β-ol, and 3α, 7α-dihydroxy-5β-cholestane, intermediates in bile acid synthesis and nutriacholic acid was observed to be upregulated in HTG group (FC > 2.0) and down-regulated after TCE intervention (Table 2). Similarly, vitamins i.e. folic acid, nicotinic acid, α-tocopherol acetate, 2-oxo-3-hydroxy-4-phosphobutanoic acid, and thiamine monophosphate showed a common decreased trend in the HTG group in comparison to HLT and increased levels in the HTG_T group (Table 2).

Table 2.

Differentially expressed metabolites in various groups without missing values after PLS-DA and Bonferroni multiple testing according to their significance and variability. All metabolites are identified using various databases in ESI + mode (p < 0.05)

Sr. No Tentative Compounds Mass RT KEGG ID p (Corr) Fold Change
HTG vs HLT HTG_T vs HLT HTG_T vs HTG
1 Hypotaurine 109.0019 5.01 C00519 2.40E-18 1.39 9.31 6.69
2 L-Valine 117.0795 5.54 C00183 2.40E-18 1.3 −9.16 −6.99
3 L-Cysteine 121.0907 38.2 C00097 3.24E-05 −2.07 −1.32 −1.56
4 Nicotinic acid 123.0796 38.0 C00253 6.87E-05 1.13 2.82 2.5
5 Acetylphosphate 139.9101 5.02 C00227 7.13E-15 −1.44 7.36 5.08
6 Mevalonic acid 148.0162 35.4 C00418 6.97E-12 2.1 3.3 −1.56
7 L-Carnitine 161.105 5.57 – 7.90E-16 1.39 7.09 5.10
8 3-Hydroxysuberic acid 190.0697 5.25 – 3.69E-14 −1.49 −7.32 −4.89
9 5-Dodecenoic acid 197.9326 5.01 C02679 1.52E-18 1.31 9.19 6.97
10 Capryloylglycine 201.1733 38.1 – 6.85E-06 2.35 1.45 1.61
11 Sebacic acid 202.0452 5.41 C08277 1.52E-18 −1.32 9.19 6.94
12 L-Acetylcarnitine 203.1164 7.83 C02571 1.89E-18 −1.2 −4.96 −6
13 2-Oxo-3-hydroxy-4- phosphobutanoic acid 213.9108 5.02 C06054 1.52E-18 1.29 9.12 7.06
14 12-Hydroxy dodecanoic acid 216.0994 35.8 C08317 1.31E-05 −1.02 2.97 3.05
15 2,5-Dichloro-4-oxohex-2-enedioate 225.9448 37.9 C12835 5.83E-04 1.71 1.41 −2.42
16 Deoxyuridine 227.9262 5.01 C00526 1.89E-18 1.36 9.25 −6.79
17 5-Hydroxyindoleacetyl glycine 248.1016 5.75 C05832 1.33E-17 −1.13 6.92 6.07
18 Deoxyinosine 252.2487 33.9 C05512 2.77E-11 1.01 3.96 3.89
19 Vaccenic acid 281.9015 5.16 C08367 1.03E-11 −1.47 −3.43 2.33
20 Unknown 287.9149 5.14 – 1.18E-17 −1.09 5.21 5.70
21 N-Acetyl neuraminic acid 309.197 19.6 C19910 4.93E-09 −1.46 5.79 8.46
22 Unknown 333.9084 5.11 – 1.18E-17 −1.09 5.21 5.70
23 Prostaglandin A1 336.2671 27.0 C04685 1.98E-09 1.48 5.67 8.44
24 Unknown 343.8683 5.02 – 1.89E-18 −1.17 4.78 5.61
25 Tricosanoic acid 354.0638 32.8 – 2.49E-04 2.95 −3.25 −1.1
26 Cortisone 360.2712 27.0 C00762 6.16E-09 −1.52 5.5 8.4
27 Nervonic acid 366.3537 34.0 C08323 2.00E-10 −1.53 5.49 8.4
28 Ferulic acid 4-O-glucuronide 370.0947 34.4 – 1.37E-04 −3.73 −3.23 −1.15
29 16-phenoxy tetranor Prostaglandin A2 370.0979 34.4 – 3.00E-04 −2.97 −3.23 1.08
30 5α-Cholestanol 388.3389 31.3 – 5.45E-05 −1.67 1.82 3.04
31 Nutriacholic acid 390.2816 35.4 – 0.011724 2.52 2.67 1.06
32 Ursodeoxycholic acid 393.2926 27.1 C07880 8.76E-06 1.48 5.65 8.42
33 1-[3-(3,7-dimethylocta-2,6-dien-1-yl)-2,4,6-trihydroxyphenyl]-3-phenylpropan-1-one 394.2349 28.5 – 1.89E-18 1.56 9.53 6.08
34 LPA(P-16:0e/0:0) 394.2381 34.1 – 1.89E-18 −1.56 −9.53 −6.08
35 3a,7a-Dihydroxy-5b-cholestane 404.3697 33.9 – 2.42E-11 −2.20 −3.30 −1.49
36 Unknown 412.2588 33.9 – 4.70E-04 −3.67 −3.58 1.02
37 LysoPC(10:0) 412.2598 33.9 – 4.70E-04 3.67 3.58 −1.02
38 Folic acid 441.3099 31.5 C00504 1.25E-11 −2.31 −3.35 1.44
39 Unknowm 441.871 5.0 – 1.89E-18 1.34 9.23 6.88
40 3a,7a,12a-Trihydroxy-5b-cholestanoic acid 450.3721 34.0 C04722 9.28E-12 1.59 3.49 2.19
41 10,11-Dihydro-12R-hydroxy-leukotriene E4 457.3778 31.1 – 5.66E-16 −1.48 −5.67 8.44
42 6-Dehydrotestosterone glucuronide 462.333 34.1 C03033 9.28E-12 −1.09 4.23 4.64
43 α-Tocopherol acetate 472.3564 29.7 C13202 4.61E-18 2.47 2.08 5.15
44 LysoPE(0:0/18:2(9Z,12Z) 477.2865 27.1 – 1.28E-04 1.41 5.95 −8.43
45 11-Oxo-androsterone glucuronide 480.3862 4.9 – 8.37E-06 −2.39 −1.47 1.62
46 PA(20:4(5Z,8Z,11Z,14Z)e/2:0) 486.3378 32.7 – 3.30E-04 3.78 3.79 1
47 Citicoline 488.3536 34.1 C00307 3.99E-04 3.71 3.75 1
48 LysoPE(0:0/20:4(8Z,11Z,14Z,17Z)) 501.2867 27.1 – 2.40E-18 1.23 −8.95 −7.26
49 LysoPE(20:4(8Z,11Z,14Z,17Z)/0:0) 501.3675 27.1 – 8.50E-05 1.43 5.86 8.43
50 Unknown 502.4023 33.9 – 1.87E-09 −4.22 −4.20 1.01
51 Unknown 514.3646 34.1 – 3.99E-04 −3.77 −3.17 1.01
52 LysoPE(0:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) 525.2868 26.9 – 3.47E-04 1.49 5.54 8.31
53 LysoPE(0:0/22:4(7Z,10Z,13Z,16Z)) 529.34 30.7 – 2.97E-18 1.45 3.83 −5.59
54 unknown 534.3972 33.9 – 4.30E-12 −2.86 −3.88 −1.35
55 Unknown 545.3973 30.1 – 7.72E-13 1.22 −5.07 6.23
56 LysoPC(22:4(7Z,10Z,13Z,16Z)) 571.8308 5.02 C04230 1.89E-18 −1.34 −9.22 −6.85
57 Bilirubin 584.265 35.8 C00486 0.048275 2.36 2.42 −1.02
58 Unidentified 587.8134 5.02 – 1.89E-18 1.33 −9.19 −6.9
59 DG(20:4(5Z,8Z,11Z,14Z)/15:0/0:0) 602.291 25.6 – 6.85E-06 1.47 5.7 8.43
60 Unknown 603.3998 29.6 – 2.97E-18 1.31 9.16 6.97
61 DG(18:2n6/0:0/22:6n3) 678.5129 36.5 – 0.003148 2.14 2.89 −1.34
62 PE(18:3(9Z,12Z,15Z)/14:0) 685.8087 5.1 C00350 5.32E-18 1.091 −5.16 −5.64
63 PC(14:0/15:0) 691.4524 29.5 C00157 2.16E-18 −1.31 −9.17 −6.96
64 CerP(d18:1/22:0) 701.7883 5.02 – 2.97E-18 1.09 −6.82 −6.21
65 PE(18:4(6Z,9Z,12Z,15Z)/P-18:1(9Z)) 721.4979 29.7 C00350 1.89E-18 −1.27 9.07 7.11
66 PE(DiMe(11,3)/DiMe(9,3) 795.5195 38.5 – 1.08E-05 −1.49 5.64 8.42
67 PE(20:5(5Z,8Z,11Z,14Z,17Z)/24:1(15Z)) 847.5421 37.9 C00350 1.03E-11 −1.43 5.91 8.48
68 Nicotinamide ribotide 336.2671 27.1 C00455 1.18E-17 1.09 −5.21 −5.7
69 Aminoadipic acid 117.0795 5.5 C00956 7.90E-16 1.39 −7.09 −5.1
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Note: LysoPC(10:0): 1-decanoyl-sn-glycero-3-phosphocholine; LysoPE(0:0/18:2(9Z,12Z): 2-linoleoyl-sn-glycero-3-phosphoethanolamine; PA(20:4(5Z,8Z,11Z,14Z)e/2:0): 2-Acetyl-1-(5Z,8Z,11Z,14Z-eicosatetraenyl)-sn-glycero-phosphate; DG: Diacylglycerol; PE(18:3(9Z,12Z,15Z)/14:0): phophatidylethanolamine (18:3(9Z,12Z,15Z)/14:0); CerP(d18:1/22:0): ceramide phosphate (N-docosanoylsphingosine-1-phosphate)

Oxidative burden measurement

Isoprostanes (IsoPs) are the products of arachidonic acid due to enzymatic or non-enzymatic actions. These are the best markers of oxidative stress as well as inflammation. Hence, IsoPs levels in the plasma samples from all groups were analyzed using selective ion monitoring method with predetermined LC-MS parameters including fixed collision energy (eV) in both positive and negative modes of ion polarities (Table 3). The average LOD and LOQ for IsoPs were in the range of 0.146 and 0.734 μg/mL respectively with >0.95 correlation coefficient (R2) in both ionic modes (Table S3). 8-iPGF2α and 8-iso-iPGE2 have a major role to stimulate vasoconstriction and proliferation of vascular smooth muscles. In the present study it was observed that the levels of most of the IsoPs were increased (about 1.5 to −3.0 folds, p < 0.001) in the HTG patients compared to control and it reverted to normal or below normal upon treatment with TCE in the HTG_T group (Table 3).

Table 3.

Table with quantity of each of IsoPs analysed under study categories in variety of samples in both ion polarity modes expressed in μg/mL with mean ± SD (p < 0.01). 8-iPGA1: 8-iso Prostaglandin A1; PGF2α: Prostaglandin F2α; PGF2α-IV: Prostaglandin F2α-IV; PGF1α: Prostaglandin F1α; PGB1: Prostaglandin B1

S. No. Name of IsoPs Plasma +ve mode Plasma -ve mode p value
HLT HTG HTG_T HLT HTG HTG _T
1. 8-iso PGA1 – – – – – – –
2. PGF2α 0.33 ± 0.018 0.45 ± 0.014 0.37 ± 0.011 < 0.0001
3. 8,12-isoPGF2α-VI 5.319 ± 1.718 6.078 ± 3.434 3.607 ± 0.210 – – – < 0.0001
4. 8-epi PGF1α 4.446 ± 1.58 5.935 ± 1.41 2.237 ± 1.41 – – – < 0.0001
5. PGB1 0.654 ± 6.33 1.344 ± 0.40 5.687 ± 1.41 < 0.0001
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Quantitation of other metabolites

The LOD and LOQ values, retention time, the precursor to product transition, correlation coefficient (R2), collision energy of 45 metabolites have been recorded. The average LOD and LOQ for markers were 2.6 and 12.9 μg/mL respectively with >0.95 correlation coefficient (R2) in both ionic modes (Table S2). Out of 45, arachidonic acid, bilirubin, cysteine glycine, DL-homocysteine, and linoleic acid were analyzed in positive as well as the negative mode of ion polarity. Overall, 4 types of trends were observed, i.e. higher concentration in HTG than HLT get reduced in HTG_T; higher concentration in HTG than HLT get increased further in HTG_T; lower in HTG than HLT get increased after TCE treatment; and lower in HTG than HLT get reduced further after TCE treatment (Table 4).

Table 4.

Table is showing the quantity of each metabolites analysed in both ion polarity modes and expressed in μg/mL with Mean ± SD form (p < 0.01)

S. No. Metabolites KEGG ID HTL HTG HTG_T Reference value (μg/mL)
1. β-Alanine C00099 25.08 ± 1.68 31.67 ± 1.87 24.189 ± 1.5 45.90
2. L-Serine C00065 22.03 ± 1.61 10.18 ± 1.27 15.44 ± 1.85 15.91
3. L-Proline C00148 53.21 ± 2.77 61.80 ± 0.96 54.81 ± 2.17 41.40
4. L-Valine C00183 23.28 ± 1.59 31.97 ± 1.97 24.72 ± 1.46 41.02
5. L-Threonine C00188 15.81 ± 1.36 18.66 ± 1.53 36.34 ± 1.38 28.58
6. Taurine C00245 41.84 ± 3.63 38.03 ± 2.73 26.09 ± 0.72 16.26
7. L-Leucine C00123 35.98 ± 2.49 53.65 ± 2.57 44.49 ± 3.68 22.30
8. L-Ornithine C00077 17.88 ± 1.85 43.59 ± 2.08 34.30 ± 2.26 11.89
9. L-Aspartic acid C00049 13.98 ± 0.92 19.03 ± 2.63 15.76 ± 2.023 3.46
10. L-Lysine C00047 12.43 ± 1.03 17.27 ± 0.94 24.55 ± 1.93 42.39
11. L-Glutamic acid C00025 5.03 ± 0.39 7.61 ± 0.58 9.79 ± 0.65 20.59
12. L-Methionine C00073 5.82 ± 1.09 5.39 ± 0.45 2.75 ± 0.37 4.4
13. L-Histidine C00135 30.95 ± 1.87 39.71 ± 0.29 44.69 ± 0.45 18.6
14. L-Phenylalanine C00079 12.95 ± 0.99 12.58 ± 1.05 7.72 ± 0.81 14.20
15. L-Arginine C00062 20.08 ± 1.93 12.96 ± 1.19 23.03 ± 1.79 21.00
16. L-Tyrosine C00082 20.75 ± 1.87 16.28 ± 0.93 – 20.00
17. L-Tryptophan C00078 13.64 ± 1.44 17.85 ± 1.03 4.09 ± 1.21 22.40
18. L-Cystine C00491 3.46 ± 1.5 3.49 ± 0.35 5.45 ± 0.32 15.60
19. L-Pyroglutamic acid C01879 3.05 ± 0.98 10.07 ± 2.32 6.09 ± 0.6 11.20
20. Nicotinamide (ng/mL) C00153 40.26 ± 1.134 32.01 ± 1.24 38.47 ± 0.67 53.00 (ng/mL)
21. DL-5-HydroxyLysine C16741 0.39 ± 0.09 0.46 ± 0.08 0.46 ± 0.06 0.405
22. L-Homocysteic acid C16511 1.25 ± 0.90 2.12 ± 0.73 0.93 ± 0.63 4.00
23. L-Glutathione oxidized C00127 6.58 ± 0.14 5.34 ± 0.17 5.54 ± 0.45 1.22
24. L-Glutathione reduced – 2.67 ± 0.28 2.06 ± 0.46 2.16 ± 0.55 1.22
25. Hexanal (ng/mL) C02373 36.94 ± 2.28 40.95 ± 1.87 23.76 ± 1.51 52 (ng/mL)
26. Testosterone C00535 3.76 ± 0.80 2.73 ± 0.98 2.89 ± 0.77 2.60
27. Malondialdehyde (ng/mL) C19440 397.1 ± 6.51 460.65 ± 6.23 356.49 ± 10.8 389 (ng/mL)
28. L-Citrulline C00327 10.21 ± 0.20 11.65 ± 0.44 9.28 ± 0.59 12.20
29 Linoleic acid C01595 60.84 ± 4.91 58.77 ± 6.45 10.56 ± 0.92 58.0
-ve 71.92 ± 10.82 55.54 ± 5.32 51.83 ± 4.09
30. Carnitine C00318 4.56 ± 0.71 9.87 ± 1.84 3.06 ± 0.28 7.25
31 DL-Homocysteine C05330 9.55 ± 1.09 7.09 ± 6.93 – 1.40
-ve 2.97 ± 0.44 1.29 ± 1.33 0.67 ± 0.95 –
32. 8-Hydroxy-2-deoxyguanosine – 1.43 ± 0.64 1.64 ± 0.73 0.97 ± 0.32 0.84 (ng/mL)
33. Cysteineglycine C01419 14.66 ± 0.90 19.01 ± 1.16 16.93 ± 1.26 12.40
34. Porphobilinogen C00931 0.95 ± 0.38 0.92 ± 0.39 1.50 ± 0.65 0.700
35. Bilirubin C00486 10.53 ± 1.96 17.27 ± 1.92 0 8.70
-ve 8.93 ± 0.79 14.93 ± 1.36 10.91 ± 0.80 –
36. Protoporphyrin IX C02191 0.31 ± 0.05 0.23 ± 0.06 0.35 ± 0.07 0.61
37. β-Carotene C02094 0.52 ± 0.19 0.35 ± 0.09 0.52 ± 0.08 0.63
38. α -tocopherol C02477 16.66 ± 2.81 12.64 ± 1.66 0 21.10
39. γ- tocopherol C02483 5.87 ± 0.76 3.45 ± 0.73 5.44 ± 2.57 3.00
40. Cholesterol C00187 1838.9 ± 92.4 2109.3 ± 76.9 1855.3 ± 63.6 4446.0
41. Lipoic acid C16241 6.37 ± 2.23 4.55 ± 1.28 4.8 ± 1.66 NR
42. Glycocholate C01921 0.36 ± 0.13 0.35 ± 0.14 0.24 ± 0.09 0.40
43. Glycitin – 5.35 ± 0.45 4.67 ± 0.33 0 NR
44. Arachidonic acid C00219 12.8 ± 0.29 13.57 ± 0.49 0 12.1
-ve 7.19 ± 0.48 8.02 ± 0.47 2.93 ± 0.67
45. Leukotriene B4 (pg/mL) C02165 -ve 99.44 ± 4.7 139.59 ± 8.05 110.10 ± 5.7 67.3 (pg/mL)
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Unbiased proteome analysis

As a result of thorough standardization attempts and controlled variables a great extent of repeatability and resolution was observed in 2D protein gels (Fig. S2). The variations in protein spot intensities would be the most basic criteria of comparison of changes in protein expression hence spot intensities of some major proteins within HTG vs HLT and HTG_T vs HTG were compared. A total of 757 match identities were allotted to the protein spots. The factor projection plot of HTG vs HLT comparison displayed the projection and contribution of each match and each gel (blue vector) on the two factorial axes. It measures if the projection of the match is well represented on the factorial subspace. In HTG vs HLT comparison, 65 proteins were analyzed, out of them, 46 were down-regulated and the rest were found upregulated (Table 5). HTG_T vs HTG comparison had 88 proteins, among 39 were down-regulated and 49 were upregulated (Table 6). Differentially expressed proteins were subjected to in-gel digestion and Q-TOF-MS. Data obtained were imported in the Spectrum mill, a database search tool provided probable identification of differentially expressed proteins.

Table 5.

The differentially expressed proteins in HTG group in comparison to healthy (HTL) group

S No MS/MS Score SPI (%) Spectrum Intensity % AA coverage MW (Da) Protein Name Uniprot ID FC in HT
1. 30.53 88.7 4.99e+004 3 107,905.0 ALS2CL Q60I27 −4.0
2. 102.61 90.4 4.62e+004 27 30,778.0 APOA1 P02647 −4.0
3. 22.16 90.6 2.95e+004 13 16,508.1 ARP-PCAR Q9UMX4 −1.8
4. 19.34 94.1 4.89e+005 3 58,024.5 CCT6A P40227 −2.8
5. 27.27 77.0 3.13e+004 11 316,416.8 CENPE Q02224 −6.3
6. 19.15 79.3 1.65e+004 3 207,433.7 CHD1 O14646 1.9
7. 29.22 93.2 4.73e+004 12 226,037.5 CKAP5 Q14008 −4.0
8. 33.79 85.0 1.90e+005 2 161,790.5 COL4A5 P29400 2.0
9. 34.59 88.1 1.72e+005 2 106,187.3 CTBP2 P56545 −4.0
10. 26.40 69.9 1.99e+004 4 532,841.8 DYNC1H1 Q14204 −4.0
11. 18.59 94.1 4.64e+004 5 33,818.4 FBL P22087 2.8
12. 29.84 68.0 3.77e+004 2 532,231.0 HERC1 Q15751 2.2
13. 19.50 90.9 4.86e+005 0 546,378.1 HMCN2 Q8NDA2 4.0
14. 18.08 83.8 1.01e+005 12 159,709.7 INSR P06213 −2.1
15. 18.51 94.4 1.28e+005 11 36,869.9 JMJD8 Q96S16 −2.4
16. 18.47 92.0 4.80e+004 13 17,572.5 LMBRD2 Q68DH5 −1.8
17. 18.32 83.7 4.64e+004 3 110,136.6 MIB1 Q86YT6 1.9
18. 18.91 78.7 1.21e+004 5 47,995.1 MTX1 Q13505 −1.9
19. 29.28 99.3 2.44e+006 8 49,433.2 NADK2 Q4G0N4 4.0
20. 44.92 86.9 2.94e+005 12 264,800.3 NBAS A2RRP1 −4.0
21. 20.19 90.1 5.95e+004 13 16,395.7 NPPA A0A0P6J1W3 −1.9
22. 26.54 84.0 2.52e+004 20 8872.8 NUPR1 O60356 −1.9
23. 37.51 99.8 9.99e+005 5 129,468.6 CO1A2 P08123 −4.0
24. 19.47 92.4 3.01e+004 16 12,202.9 PAOX Q6QHF9 −4.0
25. 19.47 92.4 3.01e+004 2 70,290.2 PAOX Q6QHF9 3.7
26. 20.45 77.2 3.05e+004 2 136,065.1 PCDH9 Q9HC56 −2.5
27. 27.29 89.1 5.26e+004 15 61,223.7 PDE9A O76083 −1.8
28. 20.26 72.9 2.29e+005 3 138,799.7 PLCB3 Q01970 −5.5
29. 21.24 87.5 2.85e+004 8 68,464.1 PPM2C Q6P1N1 −1.9
30. 33.44 91.9 7.29e+004 2 227,842.7 PRRC2A P48634 −4.0
31. 66.19 91.1 2.34e+004 5 190,718.9 RAPGEF2 Q9Y4G8 −4.0
32. 26.70 86.7 3.98e+004 2 114,803.8 RBM25 P49756 15.1
33. 57.49 90.3 4.27e+005 2 187,671.9 RIMS2 Q9UQ26 4.0
34. 19.65 88.3 1.72e+004 10 24,678.6 SLC25A43 Q8WUT9 −4.0
35. 33.88 91.1 2.95e+005 12 42,896.4 SKIL P12757 −1.9
36. 18.91 93.6 1.59e+004 7 43,366.0 SOHLH1 Q5JUK2 1.7
37. 19.67 82.0 1.61e+004 16 22,195.9 SYNGR1 O43759 −4.0
38. 35.19 94.6 3.37e+005 3 218,220.6 TCF20 Q9UGU0 4.0
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Table 6.

The differentially expressed proteins in HTG_T group in comparison to HTG group

S No MS/MS Score SPI (%) Spectrum Intensity % AA coverage Protein MW (Da) Gene Name Uniprot ID FC in HTT
1. 17.15 96.7 4.04e+004 3 65,628.8 ANO8 Q9HCE9 3.9
2. 101.27 90.2 3.08e+004 31 30,778.0 APOA1 P02647 1.9
3. 22.16 90.6 2.95e+004 13 16,508.1 Apoptotic-related protein PCAR Q9UMX4 −4.0
4. 18.43 97.8 1.14e+004 8 17,369.1 ATPSCKMT Q6P4H8 4.0
5. 11.17 70.3 8.40e+003 5 56,748.0 BTNL8 Q6UX41 4.1
6. 16.86 80.7 1.60e+004 5 35,880.7 C1QBP Q07021 4.0
7. 18.56 91.5 1.01e+005 3 57,793.7 CCT6B Q92526 4.2
8. 24.87 93.1 1.53e+004 4 37,237.1 CEACAM6 P40199 −3.8
9. 27.27 77.0 3.13e+004 1 316,416.8 CENPE Q02224 −4.1
10. 19.15 79.3 1.65e+004 1 207,433.7 CHD1 O14646 −4.2
11. 29.22 93.2 4.73e+004 1 226,037.5 CKAP5 Q14008 4.0
12. 19.74 95.9 2.10e+004 4 64,479.3 COL2A1 P02458 −3.9
13. 31.91 74.8 2.15e+005 2 172,121.6 COL5A3 P25940 4.0
14. 17.57 91.1 8.18e+004 6 15,349.6 CSDA1/YBX3 A0A024RAQ1 4.2
15. 17.51 78.4 4.05e+004 2 90,174.4 DACT1 Q9NYF0 4.0
16. 18.71 80.8 4.39e+004 1 89,251.1 DDX21 Q9NR30 4.3
17. 17.63 80.0 7.79e+004 1 106,866.0 DENND2C Q68D51 1.9
18. 33.35 95.3 1.04e+005 9 40,339.9 EDG-7 Q9NRB8 4.0
19. 20.07 79.7 1.44e+004 4 61,161.7 EHD2 Q9NZN4 −3.9
20. 19.59 93.2 2.53e+004 1 99,491.5 EPB41L4B Q9H709 4.1
21. 19.98 92.6 3.55e+004 3 56,987.6 EPHX2 P34913 4.1
23. 18.59 94.1 4.64e+004 5 33,818.4 FBL P22087 −3.8
24 18.09 90.4 3.22e+004 19 17,412.1 FGF2 P09038 −4.0
25 10.17 95.1 5.87e+003 1 124,762.3 FHDC1 Q9C0D6 3.8
26. 19.80 81.7 3.54e+004 4 69,422.7 FLJ00357 Q6ZMJ9 4.0
27. 17.23 87.5 2.33e+004 6 28,490.7 GALNT10 Q4G0E1 4.4
28 19.67 88.6 2.36e+004 1 111,998.6 GLB1L3 Q8NCI6 3.9
29. 17.17 84.4 1.72e+004 4 39,065.2 GPR57 Q9P1P4 −4.2
30. 19.56 77.3 4.29e+004 2 103,109.6 GRIA2 P42262 −4.5
31. 18.26 76.4 6.39e+004 32 5497.3 hCG1745092 P30050 3.9
32. 12.49 93.5 2.89e+004 8 33,408.3 hCG2044603 Q8NH61 3.6
33. 29.84 68.0 3.77e+004 0 532,231.0 HERC1 Q15751 −4.0
34. 18.51 83.9 1.81e+004 9 35,844.9 HMG20B Q9P0W2 −4.3
35. 12.93 78.4 3.56e+003 7 12,905.7 IGHV4–4 A0A075B6R2 4.1
36. 19.35 79.2 2.79e+004 4 36,869.9 JMJD8 Q96S16 4.0
37. 18.47 92.0 4.80e+004 13.0 17,572.5 LMBRD2 Q68DH5 −4.0
38. 16.9 82.6 1.46e+005 3 68,023.7 MEN1 O00255 5.1
39. 18.32 83.7 4.64e+004 2 110,136.6 MIB1 Q86YT6 −4.0
40. 16.45 89.4 4.26e+004 8 22,115.8 MPV17L Q2QL34 4.0
41. 18.91 78.7 1.21e+004 5 47,995.1 MTX1 Q13505 −3.9
42. 16.05 70.4 5.57e+004 1 149,367.9 MYBBP1A Q9BQG0 4.1
43. 17.46 84.1 4.32e+004 0 200,332.6 NALCN Q8IZF0 4.2
44. 20.19 90.1 5.95e+004 13 16,395.7 NPPA A0A0P6J1W3 −3.9
45. 20.16 84.5 1.75e+004 5 61,466.5 NTN3 O00634 −4.2
46. 19.24 86.0 5.86e+004 2 70,804.2 NUMB P49757 4.4
47. 26.54 84.0 2.52e+004 20 8872.8 NUPR1 O60356 −4.3
48. 19.47 92.4 3.01e+004 2 70,290.2 PAOX Q6QHF9 −3.5
49. 20.45 77.2 3.05e+004 2 136,065.1 PCDH9 Q9HC56 −4.0
50. 27.29 89.1 5.26e+004 5 61,223.7 PDE9A O76083 −4.2
51. 19.36 93.4 1.73e+004 4 31,700.4 PITPNB P48739 4.3
52. 20.26 72.9 2.29e+005 1 138,799.7 PLCB3 Q01970 −3.6
53. 17.19 78.7 2.73e+004 1 146,897.2 PLEKHA5 Q9HAU0 4.0
54. 39.04 91.4 1.40e+005 11 40,671.9 PODN Q7Z5L7 −4.0
55. 19.04 89.7 5.67e+004 4 41,970.3 POLB P06746 −3.5
56. 17.30 85.0 1.43e+004 9 15,131.1 POLR2M P0CAP2 3.6
57. 16.91 85.4 1.93e+004 7 22,949.1 PSMB3 P49720 4.0
58. 20.39 85.7 1.49e+004 17 16,022.6 PXT1 Q8NFP0 −4.1
59. 57.49 90.3 4.27e+005 2 187,671.9 RIMS2 Q9UQ26 −4.0
60. 16.01 79.5 2.05e+004 4 46,629.0 RNF149 Q8NC42 4.0
61. 17.19 81.1 3.51e+004 4 29,245.7 RNF187 Q5TA31 4.2
62. 20.65 94.9 6.47e+004 11 9357.8 RPL19 J3KTE4 −3.8
63. 21.72 97.5 6.20e+005 1 50,246.5 SBK1 Q52WX2 4.1
64. 17.82 91.6 9.22e+005 4 42,896.4 SNO1/SKIL P12757 3.8
65. 18.91 93.6 1.59e+004 7 43,366.0 SOLH O75808 −4.3
66. 7.38 90.0 1.25e+004 1 16,844.4 SSPOP A2VEC9 4.3
67. 18.30 94.2 4.52e+004 2 63,153.5 TMPRSS13 Q9BYE2 3.4
68. 31.60 84.8 3.56e+005 1 228,584.9 TRIP12 Q14669 4.0
69. 34.73 93.1 1.11e+006 1 3,994,674.4 TTN Q8WZ42 4.0
70. 36.16 84.4 3.12e+004 26.0 14,957.0 TTR P02766 −1.9
71. 18.34 89.1 8.99e+004 1 77,154.6 TWNK Q96RR1 4.1
72. 17.13 74.7 4.45e+004 1 119,171.4 ZBTB21 Q9ULJ3 4.0
73. 24.78 93.1 3.54e+004 6 54,714.9 ZNF517 Q6ZMY9 −4.0
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Pathway analysis

Cytoscape 3.8.0 plugin MetScape and MetaboAnalyst (statistical, functional, and integrative analysis of metabolomics data) analysis of metabolomics data showed the significant alteration in thiamine, glycerophospholipid, taurine, and nicotinamide metabolism (Table S4). Quantitative analysis confirmed the alterations in the metabolic pathways along with increased oxidative stress. Moreover, the quantitative analysis also showed alterations in arginine, BCCA, aromatic amino acids, and pantothenate biosynthesis and glutathione, linoleic acid, and thiamine metabolism. The Functional annotation chart obtained through DAVID suggested down-regulated PI3K-Akt and Rap1 signaling pathway, decreased ECM-receptor interaction, and increased protein digestion in hypertriglyceridemia patients. TCE supplementation modulated both signaling pathways through activation of peroxisome biogenesis and increasing the synthesis of ECM proteins including actin and tubulin (Table S5). Final pathways were analysed using Cluego plugin Cytoscape 3.8.0 plugin (Fig. 6).

Fig. 6.

Fig. 6

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Mechanism of action of TCE to regulate TG levels and secondary disorders in HTG patients explored from metabolomics and proteomics profiles. Inline graphic indicate protein up regulated in HLT; Inline graphic indicate protein downregulated in HLT; Inline graphic indicate protein up regulated in HTG_T; Inline graphic indicate protein down regulated in HTG_T; Inline graphic indicate co-activation; Inline graphic indicate activation; Inline graphic indicate binding

Integrated omics analysis

Integrated analysis of metabolomics and proteomics outcome through Cytoscape 3.8.2 plugin Metscape and Metaboanalyst revealed that hypertriglyceridemia altered the protein digestion, HIF-1 signaling pathway, AGE-RAGE signaling, Rap1, PI3K-Akt signaling, and ECM remodeling (Table S6).

Discussion

In the present clinical trial, a two-week treatment with TCE significantly (p < 0.05) reduced the basal albumin, TG and VLDL levels and increased the HDL, RBC, and platelet levels (p < 0.05) in HTG patients. Decresed in level of albumin is further supported by Sun et al., 2020 after fenofibrate treatment [25]. TCE intervention also reduced the levels of total proteins, creatinine, uric acid, and urea significantly (p < 0.05) (Table 1). The efficacy data are consistent with the reports from previous studies [26]. Moreover, the healthy individual didn’t show any kind of toxicity symptoms and maintained all the basal parameters at normal levels after the 14th day of treatment. Likewise, a slight increase in HDL, RBCs, and platelets indicated the beneficial effects of TCE in healthy individuals (Table 1). Hence, the primary outcomes of the pilot clinical trial illustrate the beneficial effects of TCE intervention in HTG patients.

Metabolomics profiles indicated that the protein, glutathione, taurine and hypotaurine metabolism, ferroptosis, and arginine biosynthesis are significantly affected in HTG patients (Table 2). All these events are directly or indirectly associated with increased oxidative stress and inflammation. It was confirmed by increased levels of PGF-2α and leukotriene B4 in HTG patients. In contrast, TCE supplementation significantly modulated the PGF-2α, leukotriene B4, BCAA, arginine, and hypotaurine levels (p < 0.05) in HTG patients (Table S2). Leukotriene B4 is reported to increase oxidative stress through the downregulation of thiamine [17] and up-regulation of ether lipids, lysophosphatidylethanolamines (LysoPE), and diacylglycerols (DG) as observed in the current study (Table 2). LysoPE and DG are reported to responsible for abnormal metabolic regulation and obesity [27]. Increased lipid peroxidation was further supported by increased levels of MDA, capryloylglycine and aminoadipic acid, the markers of disorders in mitochondrial fatty acid β-oxidation and insulin resistance (Table 2). High levels of lipid peroxides in HTG patients also induce ferroptosis, a less explored apoptosis phenomenon [28]. A previous study also showed that oxidative stress is a primary event in the HTG patients confirmed by [26].

Fatty acid metabolism is primarily regulated by PPARs present in peroxisomes [29]. PPARs also regulate lipolysis and bile acid secretion and synthesis [30]. In HTG patients all three events i.e. peroxisome biogenesis, lipolysis in adipocytes, and bile acid secretion and synthesis was found to be disturbed (Table S4). Moreover, metabolomics profiles indicated the disturbed function of ATP-binding cassette transporter (ABC) in HTG patients (Table S4). It was the well-known fact that increased TGs and reduced HDL levels are related to mutation or depressed ABC function, particularly ABCA1, membrane integral protein required for nascent HDL apolipoprotein A-I particle formation [31]. Depressed ABCA1 is reported to increase lipid rafts in the plasma membrane and provide access to inflammatory signals to increase inflammation [28, 29]. TCE intervention downregulated the levels of peroxisomal N-acetyl-spermine/spermidine and increased the levels of ApoA1 in HTG individuals and downregulate the FGF2 in HTG_T individuals (Table 6; Fig. 6).

In previous studies, it was observed that HTG reduces the retaining power of vitamins that increased their excretion in urine. Previous studies reported that a low level of vitamin B6 is linked to the increased risk of coronary artery disease (CAD) [32]. Decreased vitamin B6 levels are directly linked to the decreased response of the renin-angiotensin system [33]. The renin-angiotensin-aldosterone system (RAAS) plays an important role to regulate several pathophysiologic changes related to the progression of renal disease, blood volume, and systemic vascular resistance [33]. High calcium concentrations have been reported to inhibit renin release and stimulate aldosterone release [34]. Hence, these are events interrelated with the cGMP-PKG signaling pathway found to be affected in HTG patients (Table S4). Increased cGMP phosphorylates the protein substrate responsible for the cellular damages and abnormal calcium regulation [35]. Defective RAAS and cGMP-PKG signaling are also associated with the increased interstitial fibrosis, thickening of coronary arteries contribute towards the hypertrophic cardiomyopathy (HCM) [36, 37].

As observed in metabolomics studies, proteome profile also indicated the compromised peroxisome biogenesis in HTG patients. Peroxisomes specific protein MPV17-like protein and bifunctional epoxide hydrolase 2 and Polyamine oxidase splice variant 11 were found to be down-regulated in HTG_T patients (Table 6). However, TCE supplementation increased the levels of all three proteins important in trafficking proteins to the peroxisome and to maintain arachidonic acid signaling via binding with PAX2 and ADAM10. Also, ADAM10 controls the expression of MMP3, hence responsible for the regulation of cholesterol metabolism and ECM remodeling. Similarly, polyamine catabolism has been reported to improve the PGC-1α activity and decrease triglycerides level and normalize the bile acid metabolism [38, 39]. Hence, debilitated peroxisome function increased the TG levels that lead to oxidative stress and insulin resistance in HTG patients.

Insulin resistance impaired the PI3K dependent Akt phosphorylation [40]. Proteomics analysis showed the downregulation of insulin receptors in HTG patients (Table S4). It was further confirmed by the up-regulated expression of E3 ligases under transcription control of FoxO3, a downstream target of Akt [41]. Reduced Akt signaling resulted in the increase of RNA binding motif protein 25 in HTG to promote the upregulation of BCL-2 and suppress the C-terminal-binding protein 2 in HTG individuals essential for the PPAR signaling and differentiation of adipocytes [42]. Moreover, reduced INSR expression decline the expression of SKIL protein associated with LATS2 responsible for the Wnt and protein kinase signaling. Also, reduced expression of NUPR were found in HTG individual. Besides NUPR control the expression of MMP13 contributes towards the ECM remodeling (Fig. 6). Similarly, decreased FBL in HTG_T downregulate the expression of TP53, leads to increase expression of NUMB in HTG_T and reduces the EHD2 expression, hence increase the GLUT 4 sensitivity (Fig. 6).

PI3K is a downstream target of INSR, hence increased expression of INSR also activates PI3K [43]. Catalytic subunit p110δ of PI3K is the activator of Rap GTPase, important in the cytoskeletal arrangement [44]. INSR as well as RAP guanine nucleotide exchange factor 2 (Repac) another activator of Rap1 signaling were found to be down-regulated in HTG patients (Table 5). However, TCE intervention activated PI3K being the downstream target of INSR, and increased the expression levels of Repgef2, to activate the Rap1 signaling pathway. Furthermore, decreased levels of 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase beta-3 (PLC) in HTG patients confirmed the depressed signaling of Rap1 (Table 5). The present study showed that TCE treatment could not normalize the levels of PLC but increased its levels to 2 fold as compared to HTG patients, hence contributed to the activation of the Rap1 signaling pathway.

It has been reported that Rap1 is essential for Extracellular matrix (ECM) molding including cell adhesion and cell junctions [45]. Hence, disrupted Rap1 signaling led to down-regulation of ECM specific α-1 type 2 collagen, transthyretin,pre-pro-α-2 type I collagen, protocadherin-9, CENPE, CKAP5, and DYNC1H1 in HTG individual (Table 5) and led to ECM hardening. Moreover, the present study indicated that TCE restored the expression levels of collagen α-3(V) chain, T-complex protein 1, transmembrane protease serine 13, transmembrane protein 16H, etc. (Table 6, Fig. 6). TCE supplementation increases the expression of TTN through MYL1 to regulate skeletal muscle differentiation. Also, downregulated NTN3 and PODN in HTG_T individuals lead to reduction in expression of RBM25 as well BCL2L1, to eliminate the prevalence of apoptosis. Moreover, the present study also found that TCE supplementation increases the expression of MYBBP1A, to regulate the intrinsic pathway of apoptosis through p53 signaling. In association with MYBBP1A, DDX21 promote the expression of KLK2/3 to stimulate the androgen receptor (AR). Activated AR upregulates the DCNT2 expression also supported by MEN1 in HTG_T individuals to control the ECM remodeling (Fig. 6).

It has been found that alkaloidal constituents, (−)-epicatechin, secoisolariciresinol, and an arabinogalactan polysaccharide from T. cordifolia were reported to reduce oxidative stress and lipid peroxidation [12]. Surprisingly, the overall analysis also illustrated the positive effects of TCE on the hypoxia-inducible factor 1 (HIF-1) signaling pathway. Hence, HTG impaired HIF-1 activation or vice-versa like T2DM that may lead to cardiomyopathy in the individuals [46]. Similarly, TCE administration also decreases the TTR protein responsible for the upregulation of IL-6 in HTG_T individuals (Fig. 6).

Genetic Association Database (GAD) analysis of proteome data showed the down-regulated proteins in HTG groups are associated with hyperlipidemia, obesity, brain aneurysm, kidney failure, chronic, diabetes type 2 (T2DM), cardiovascular diseases due to disturbed regulation of APOA1 and INSR. However, ApoA1 was also reported to activate FoxO1 which increases insulin secretion and β-cell regeneration [47].

Combine analysis of proteomics and metabolomics data using Metascape plugin in Cytoscape and Metaboanalyst (online software) illustrated that high TG levels disturbed the PI3K-Akt signaling pathway independent of cholesterol levels (Table S5). Disrupted PI3K-Akt signaling in HTG patients affected glucose, lipid, and protein metabolism along with cell proliferation and survival. It leads to oxidative stress, inflammation, and insulin resistance which induce T2DM and lead to activation of the AGE-RAGE signaling pathway (Table S5). The present study found that TC supplementation increased the expression of ApoA1 which regulate the expression of INSR through the NR1H4 gene. Moreover, protein catabolism was found to be increased due to the activation of FoxO3, downstream target of Akt, evident by increased levels of E3 ligases. ApoA1 was also reported to activate FoxO1 which increases insulin secretion and β-cell regeneration [48]. Also, protoberberine alkaloid of TCE seems to act similarly to berberine to increase APOA1 expression [49]. Increased levels of APOA1 and decreased levels of VLDL, and TG conferred the risk of atherosclerosis [50]. The current study provides the first practical evidence that TCE increased INSR and APOA1 expression. Drugs that increase APOA1 expression or ApoA1/ApoB ratio are found to be useful to HTG.

The present study accommodates certain lacuna of having a limited number of subjects that may induce certain errors in statistical analysis and limits the efficacy of TCE. Therefore, there is a need to investigate the potential of T.cordifolia with a large number of the individual to eliminate the chances of statistical error. It will be helpful towards the development of herbal therapeutics aid against hypertriglyceridemia as well as other associated disease conditions like pancreatitis, CVDs, and hypoxia. Primary as well as secondary outcomes from the biochemistry analysis and omics data suggested that TCE supplementation efficiently controlled the hypertriglyceridemia and associated complications in the patients.

Supplementary Information.

ESM 1 (3.8MB, doc)

(DOC 3905 kb)

Acknowledgements

Authors wish to acknowledge the CCRAS, Ministry of AYUSH, Govt of India for supporting the work.

Abbreviations

HTG

Hypertriglyceridemia

TCE

Water extract of T. cordifolia

PPAR

Peroxisome proliferator-activated receptors

PLS-DA

Partial Least Square Discriminant Analysis

HPLC

High-Performance Liquid Chromatography

ESI-QTOF-MS

Electrospray Ionization Quadrupole Time of Flight Mass Spectrometry

AST

Aspartate transaminase

ALT

Alanine transaminase.

Author’s contributions

AS, AN and RD design the study and conducted the experiment. AY analysed the data and drafted the manuscript. JP and RD assisted in drafting the manuscript.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval

Human Ethics Committee of the PDDYP Ayurveda College, Pune, India approved the study wide letter no. RRI/2011/HEC/2023 dated 18-02-2011.

Conflict of interest

All authors declare that they have no conflict of interest.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Amey Shirolkar and Aarti Yadav contributed equally to the research work discussed in the research article, hence both should be treated as first authors.

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Associated Data

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Supplementary Materials

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Journal of Diabetes and Metabolic Disorders are provided here courtesy of Springer

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