The phytochemical epigallocatechin gallate prolongs the lifespan by improving lipid metabolism, reducing inflammation and oxidative stress in high‐fat diet‐fed obese rats

2.2. EGCG reduced serum glucose, serum lipids, inflammation and oxidative stress and improved the function of the liver and kidney

Serum biomarkers were measured six times at 10, 28, 46, 64, 82 and 100 weeks of age during the whole life of rats. As age increased, serum glucose (GLU), insulin, total cholesterol (TC), triglycerides (TG) and low‐density lipoprotein cholesterol (LDL‐C) generally increased up to 82 weeks old, followed by a slight decline. The levels of GLU, insulin, TC, TG and LDL‐C were significantly increased in the HF group compared with the NC group. Additionally, compared with the HF group, EGCG obviously decreased the levels of serum GLU, insulin, TC and TG from 64 weeks and LDL‐C from 46 weeks. High‐density lipoprotein cholesterol (HDL‐C) showed no difference in the whole study among the three groups (Figure 3a–f).

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Figure 3

Epigallocatechin gallate reduced serum glucose, serum lipids, inflammation and oxidative stress and improved the function of the liver and kidney. (a) Serum glucose (GLU). (b) Serum insulin. (c) Serum total cholesterol (TC). (d) Serum triglycerides (TG). (e) Serum high‐density lipoprotein cholesterol (HDL‐C). (f) Serum low‐density lipoprotein cholesterol (LDL‐C). All values are the means ± SD, n = 7 for 100 weeks and n = 10 for other weeks.^* p < 0.05 or **p < 0.01 compared with NC; ^# p < 0.05 or ^## p < 0.01 compared with HF

The indicators including alanine aminotransferase (ALT), aspartate aminotransferase (AST), interleukin‐6 (IL‐6), tumour necrosis factor‐α (TNF‐α), superoxide dismutase (SOD), reactive oxygen species (ROS) and malondialdehyde (MDA) showed an overall upward trend, but glutathione peroxidase (GSH) had a downward trend with the gradual increase in age. Compared with the NC group, serum ALT (from 64 weeks) and AST (from 82 weeks) were significantly increased in the HF group (P < 0.05). In the EGCG group, ALT and AST levels were significantly decreased from 82 weeks compared with the HF group (P < 0.05). The levels of IL‐6, TNF‐α and ROS associated with inflammation and oxidative stress were significantly increased from 82 weeks in the HF group compared with the NC group (P < 0.05). Compared with the HF group, IL‐6 (from 82 weeks), TNF‐α (from 64 weeks) and ROS (from 82 weeks) were significantly decreased in the EGCG group (P < 0.05). From 28 weeks old to the end of the experiment, SOD levels in the HF and EGCG groups were significantly lower than those in the NC group, and the SOD level in the EGCG group was significantly higher than that in the HF group (P < 0.05). Although MDA and GSH showed a slight trend of improvement, there was no significant difference in the EGCG intervention group (Table ​(Table1).1). These results indicated that EGCG extended the median lifespan possibly by improving glycolipid metabolism, alleviating liver and kidney function and reducing inflammation and oxidative stress associated with the high‐fat diet.

2.3. EGCG decreases serum‐free fatty acid (FFA) levels but increases N‐3 FFA levels

To further characterize the metabolic changes in the three groups, we analysed the fatty acid profiles in the chow diet and serum at 46 and 100 weeks of age. The composition of saturated fatty acid (SFA) and monounsaturated fatty acid (MUFA) levels was significantly higher, and polyunsaturated fatty acids (PUFAs) were significantly lower in normal chow than in high‐fat chow (Figure S1A). All the levels of serum FFAs except n‐3 FFAs were significantly increased in the HF group compared with the NC group, and EGCG significantly decreased these FFA levels at both 46 and 100 weeks of age (Figure S1B and Table S3). In addition, almost all kinds of total serum‐free fatty acids in the EGCG group were significantly decreased compared with those in the HF group. Of interest, only the proportion of serum N‐3 FFAs, especially α‐linolenic acid, in the EGCG group was obviously increased compared with the HF group, which was lower than that in the normal group with increasing age (Table S3). Taken together, these results suggested that EGCG improved the physiological and metabolic FFA metabolism associated with longevity and especially increased serum n‐3 levels in obese rats.

2.4. Impacts of lifelong EGCG treatment on the liver transcriptome and proteome

To clarify how the EGCG group extended the lifespan of the obese rats, mRNA sequencing and iTraq proteomics were performed on liver samples of aging rats at 100 weeks. PCA found that the three groups were significantly separated, which indicated that their transcriptomes were dramatically different (Figure ​(Figure4a).4a). A further Venn diagram analysis revealed 13,733 transcripts as co‐expressed transcripts in the three groups (Figure ​(Figure4b).4b). We identified the differentially expressed genes according to the parameters of a significant difference (p < 0.05) and the transformed fold change (log₂ FC) of transcripts >1 or < −1 between the two groups. A total of 125 genes were upregulated, and 92 genes were downregulated in the HF group versus the NC group, and 86 genes were upregulated and 96 genes were downregulated in the EGCG group versus the HF group (Figure 4c,d, red represents upregulated, blue represents downregulated, and grey represents non‐regulated, the greater the fold change is, the farther away from the horizontal centreline). Gene Ontology (GO) enrichment analyses for significantly altered genes in the EGCG group identified that the gene pathways were markedly downregulated, such as suppression of hydrogen peroxide and oxygen species metabolism, suppression of lipid metabolism and suppression of oxidative stress (Table ​(Table2,2, Figure S2).

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Figure 4

Effects of EGCG on the liver transcriptome and proteome using mRNA sequencing and iTraq proteomic methods. (a) PCA for gene quantification data derived from RNA sequencing. (b) Venn diagram analysis for co‐expressed genes. (c) MA plot analysis for differentially expressed genes; we marked the points with p < 0.05 and log₂ FC of transcripts >1 or <−1 in the HF group vs NC. (d) MA plot analysis for differentially expressed genes in the EGCG group vs HF. (e) The protein expression changes based on a P < 0.05. (f) Volcano plot analysis for differentially expressed proteins depicting the points with p < 0.05, and the greater the fold change is, the farther away from the vertical centreline in the HF group vs NC. (g) Volcano plot analysis for differentially expressed proteins in the EGCG group vs HF. (h) Heap map for classification of the differentially expressed proteins. n = 3 for each group and aged 100 weeks, and all red dots represent upregulated, blue dots represent downregulated, and grey dots represent no obvious changes in the figures

Likewise, a global proteomic analysis found that the total number of differentially expressed proteins in the EGCG group compared with the HF group was 491, with 249 upregulated and 242 downregulated (Figure ​(Figure4E),4E), and the others detailed differentially expressed proteins between two groups are shown in Figure S3A. Volcano plot analysis for the differentially expressed proteins was performed between the two groups. Red represents upregulated, blue represents downregulated, and grey represents non‐regulated based on a p < 0.05, and the greater the fold change is, the farther away from the vertical centreline (Figure 4f,g and Figure S3b). Visually, compared with the NC group, the upregulated proteins in the HF group were downregulated by EGCG treatment. However, the downregulated proteins in the HF group were upregulated by EGCG (Figure ​(Figure4h).4h). In addition, nearly half of the proteins were downregulated in both the EGCG and HF groups, but surprisingly, most proteins were uniquely upregulated in the EGCG group (Figure S3c). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses identified that PPAR signalling and fatty acid degradation were upregulated by comparing the EGCG group with the HF group. GO analyses identified that the protein pathways were markedly upregulated, including lipid metabolism and catabolism, fatty acid oxidation, long‐chain fatty acid transport and cholesterol metabolism (Table ​(Table2,2, Figure S3d,e).

To determine the key role of gene and protein pathways induced by EGCG treatment, we integrated gene‐level mRNA expression data with quantitative proteomics. The results of the peptide mapping indicated that 4762 genes and proteins were co‐expressed between the HF group and the NC group, and 4776 genes and proteins were co‐expressed between the EGCG group and the HF group (Figure ​(Figure5a).5a). The distribution of differential genes and proteins is shown in Figure ​Figure5b.5b. For further analyses of co‐expressed genes and proteins, 25.5% showed larger (fold change ≥1.5) region‐specific expression differences in the same direction, and they were considered ‘agree’ and ‘partially agree’ (same direction of change, a different magnitude of fold change). In addition, 61.2% of co‐expressed genes and proteins were found with fold changes ≤1.5 for mRNA and protein, 9.2% with fold change ≥1.5 for mRNA but ≤1.5 for proteins, and 1.4% with fold change ≤1.5 for mRNA but ≥1.5 for proteins in the correlation analysis. A small fraction (1.4%) of genes disagreed in the direction of change between mRNA and protein (Figure 5c,d). All results suggested that there was good consistency in the transcriptome and proteomics changes in rat liver induced by EGCG, especially for the regulation of lipid metabolism.

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Figure 5

Correlation analysis of the transcriptome and proteome. (a) Counts of co‐expressed genes and proteins. The blue represents only genes, the pink represents only the proteome, and the overlap represents the co‐expressed genes and proteins. (b) Analysis of co‐expressed genes and proteins. Different colours represent different meanings, such as grey (fold change ≤1.5 for both mRNA and protein), yellow (fold change ≥1.5 for both mRNA and protein), green (fold change ≥1.5 for mRNA but ≤1.5 for protein) and blue (fold change ≤1.5 for mRNA but ≥1.5 for protein). Red genes represents those with a consistent direction but variable magnitude of change (≥1.5‐fold) between the regions at the protein and RNA level, while purple genes disagree in the direction of change between the mRNA and protein. (c) Different genes or proteins are coloured based on their fold changes of mRNA and protein in the HF group vs NC. (d) Different genes or proteins are coloured based on their fold changes of mRNA and protein in the EGCG group vs HF. n = 3 for each group and aged 100 weeks

4.2. Liver and kidney pathology

Paraformaldehyde‐fixed paraffin sections of the liver and kidney were stained with haematoxylin and eosin (H&E) after washing with distilled water, dehydration, wax penetration, embedding, sectioning, spreading, dewaxing and rehydration, dyeing and sealing. Histological examinations were performed by pathologists blinded to the conditions. At least 5–10 fields were reviewed for each slide. The histological changes in kidney were scored by counting the percentage of tubules that displayed cell necrosis, loss of brush border, cast formation and tubule dilatation as follows: 0, none; 1, <10%; 2, 11%25%; 3, 26%–45%; 4, 46%–75%; and 5, >76% (Oh et al., 2008). The histological diagnosis of the liver was based on accepted criteria and the histological activity index (HAI) (Knodell et al., 1981). Briefly, HAI represents the sum of the scores attributed to the necroinflammatory lesions, that is, periportal and bridging necrosis (0–10), intralobular degeneration and focal necrosis (0–4), portal inflammation (0–4) and fibrosis (0–4).

4.3. Blood chemistry

Rats were deprived of food for 8 h prior to serum collection, which was used for the measurement of all blood chemistry. GLU, TC, TG, HDL‐C, LDL‐C, AST and ALT (Co‐Health Laboratories Co. Ltd) were measured using a ROCHE Modular P800 Automatic Biochemical Analyzer (Roche Diagnostics). SOD, GSH, MDA and ROS were measured with commercial kits using enzymatic methods (Beyotime Biotechnology). Insulin, TNF‐α and IL‐6 (R&D Systems Europe) were assayed using an enzyme‐linked immunosorbent assay (ELISA) with commercial kits, according to the manufacturers’ instructions.

4.4. Free fatty acid measurement

Serum FFA profile was measured using gas chromatography–mass spectrometry method (GC‐MS) described previously in article (Liu, Li, & Guan, 2010). Firstly, we purchased 16 fatty acid standards from Sigma (≥ 99% purity), including myristic acid (14:0), palmitic acid (16:0), palmitoleic acid (16:1n‐7), stearic acid (18:0), oleic acid (18:1n‐9), linoleic acid (18:2n‐6), linolenic acid (18:3n‐3), γ‐linolenic acid (γ‐18:3n‐6), cris‐6,9,12,15‐octadecatetraenoic acid (18:4n‐3), eicosadienoic acid (20:2n‐6), cris‐8,11,14 (20:3n‐6), arachidonic acid (20:4n‐6), cis‐5,8,11,14,17‐eicosapentaenoic acid (20:5n‐3), cris‐7,10,13,16‐adrenic acid (22:4n‐6), cis‐7,10,13,16,19‐docosapentaenoic acid (22:5n‐6) and cis‐4,7,10,13,16,19‐docosahexaenoic acid (22:6n‐3). Stock solutions of all the above fatty acids, internal standards (heptadecanoic acid) and calibration samples (spiking with 16 different concentrations of fatty acids standards) were prepared.

Briefly, aliquots (200 μl) of serum were spiked with an internal standard working solution (200 μl heptadecanoic acid C17:0 200 μg/ml), and 1 ml 0.05% H₂SO₄ was added to deposit protein. FFA was extracted using 3 ml ethyl acetate, mixed for 60 s (using a vortex mixer) and centrifuged at 4000 × g for 10 min at room temperature. The ethyl acetate phase was evaporated to dryness under N2. Following the addition of 2 ml and 10% H2SO4‐CH3OH, and incubation in 62°C water bath for 2 h, 2 ml saturated sodium chloride and 2 ml hexane were sequentially added and mixed for 60 s to obtain the fatty acid methyl esters. Samples were evaporated to dryness under N2 gas, and 100 μl hexane was added to each tube prior to analysis.

Free fatty acid concentrations of all standard and serum samples were determined using TRACE gas chromatograph with a Polaris Q mass spectrometer (Thermo Finnigan; GC–MS). A split injector (the split ratio being 1:10) at 230°C was used to add the sample (1.0 μl) onto a J&W DB‐WAX (30 m × 0.25 mm I.D., 0.25 μm film thickness) capillary column and fatty acid methyl esters were separated at constant flow article (Liu et al., 2010). The ion trap mass spectrometer was operated under electron bomb ionization (EI) mode. Mass spectra of m/z 30–450 were collected using full‐scan mode with 0.58 s/scan velocity. The solvent delay time was 5 min, source temperature was 230°C with the electron energy at 70 eV, limit of detection (LOD) was defined as lowest concentrations with signal‐to‐noise (S/N) ratios of 10, and all calibration samples showed high repeatability.

4.5. Transcriptome process and bioinformatics analysis

In our project, we sequenced 9 liver samples (n = 3 in each group) on Illumina HiSeq Platform in total. Agilent 2100 Bioanalyzer (Agilent RNA 6000 Nano Kit) was used to do the total RNA sample QC. mRNAs are isolated from total RNA with oligo (dT) method. Then, the mRNAs are fragmented under certain conditions. Then first‐strand cDNA and second‐strand cDNA are synthesized. cDNA fragments are purified and resolved with EB buffer for end reparation and single nucleotide A (adenine) addition. After that, the cDNA fragments are linked with adapters. These cDNA fragments with suitable sizes are selected for the PCR amplification. Agilent 2100 Bioanalyzer and ABI StepOnePlus Real‐Time PCR System are used in the quantification and qualification of those libraries. For the bioinformatics part, firstly, we filter the low‐quality reads (more than 20% of the qualities of the base are lower than 10), reads with adaptors and reads with unknown bases (N bases more than 5%) to get the clean reads. Then, we map those clean reads onto the reference genome. Finally, we identify DEGs (differentially expressed genes) between samples and do functional annotations (BGI).

4.6. Proteomics and bioinformatics analysis

We used iTRAQ (Isobaric tags for relative and absolute quantitation) technology to measure eight samples at one experiment process (protein extraction, enzymolysis, iTRAQ labelling, sample mixing, HPLC separation and LC‐MS/MS analysis) (BGI). After getting raw data, bioinformatics analysis was used in this study. All the proteins with a false discovery rate (FDR) <1% will proceed with downstream analysis including GO and pathway. Furthermore, we also can perform deep analysis based on differentially expressed proteins, including Gene Ontology (GO) and KEGG pathway enrichment analysis.

4.7. Association analysis

To investigate the concordance between transcriptome and proteome results in this study, we calculated the Pearson's correlation for these data and created scatter plots. Values were considered significantly positively correlated when R > 0.80, while moderate positive correlation was determined when 0.50 < R < 0.80.

4.8. RNA isolation and real‐time PCR

Total mRNAs from rat liver were extracted using TRIzol (Invitrogen) and treated with RNase‐free DNase to remove any residual genomic DNA. Single‐stranded cDNAs were synthesized from 1 μg of total RNA using High‐Capacity cDNA Reverse Transcription Kits (Invitrogen), according to the manufacturer's instructions. The real‐time PCR amplification reactions were conducted in a volume of 20 μl containing 10 μl of SYBR PCR Green Master Mix (Applied Biosystems), and 2 μl of forward and reverse primers each at a final concentration of 10 pmol/μl and 1 μl of cDNA. The primer sequences used in real‐time PCR were as follows: FAS, forward 5′‐CTG CAG ATA TGC TGT GGA TCA‐3′, reverse 5′‐TTT GGT GTT GCT GGT TGG T‐3′; ACC1, forward 5′‐TCT ATT CGG GGT GAC TTT C‐3′, reverse 5′‐CTA TCA GTC TGT CCA GCC C‐3′; ACSL1, forward 5′‐ATG CCA GAG CTG ATT GAC ATT CGG‐3′, reverse 5′‐CAA GGA CTG CTG ATC TTC GGA CAC‐3′; FABP1, forward 5′‐CTG TGG AAA GGA AAC CTC ATT G‐3′, reverse 5′‐GGT GAT GGT GAG TTT GAC TTT C‐3′; Cyp1a1, forward 5′‐GAT GCT GAG GAC CAG AAG ACC GC‐3′, reverse 5′‐CAG GAG GCT GGA CGA GAA TGC‐3′; Alox15, forward 5′‐CGT TGC TCC TCC TCC AGT CTC C‐3′, reverse 5′‐GTT GCA GAA CCA GGC GTC ATC C −3′; Ehhadh, forward 5′‐TCA GTT GGC GTT CTT GGC TTG G‐3′, reverse 5′‐CCG TTC TGA TGC GCT CTG GAT G‐3′; Eci1, forward 5′‐GCC GAG CGT GCC CTT CAA C‐3′, reverse 5′‐GCC ATC ACT GAG CGA GCC TTG‐3′; Hadh, forward 5′‐CAC CGA TGA CCA GCC AGA AGA C‐3′, reverse 5′‐GGC ACC AAG AGA CGG TTC ACG‐3′; CAT, forward 5′‐CGT CAC TCA GGT GCG GAC ATT C −3′, reverse 5′‐TCA GGT GGT TGG CAA TGT TCT CAC‐3′; CPT2, forward 5′‐CAA CAT CCT GTC CAC CAG CAC TC‐3′, reverse 5′‐GCA GCC TAT CCA GTC ATC GTG AAC‐3′; GSTA2, forward 5′‐TGC TGG AAC TTC TCC TCT ATG TTG‐3′, reverse 5′‐AGG CTG CTG ATT CTG CTC TTG‐3′; β‐actin, forward 5′‐CCT GTG GCA TCC ATG AAA CTA C‐3′, reverse 5′‐CCA GGG CAG TAA TCT CCT TCT G‐3′. Thermal cycling and fluorescence detection were performed on an ABI Prism 7500HT Sequence Detection System (Applied Biosystems). Thermal cycling was carried out at 95°C for 10 min, followed by 40 cycles of denaturing at 95°C (15 s), annealing at 60°C (30 s) and extension at 72°C (30 s). Data were analysed using the comparative 2^{−ΔΔ C t} method, taking β‐actin as the normalizer.

4.9. Protein measurement and Western blotting

The protein expressions of CAT, GSTA2, FAS, ACC1, FABP1, CPT2, ACSL1, FOXO1, NF‐kB and SIRT1 were determined by Western blot analysis. Antibodies against CAT, FAS, ACC1 and FABP1 were purchased from Cell Signaling Technology, antibodies against SIRT1, NF‐kB and β‐actin were purchased from Affinity Biosciences, antibodies against CPT2 and ACSL1 were purchased from Abcam, antibodies against GSTA2 were purchased from Biorbyt, and antibodies against FOXO1 were purchased from Wanleibio. The liver was placed in RIPA lysis buffer (Beyotime Biotechnology, China) and centrifuged (20627 g for 15 min) to remove impurities. Protein was quantitated using the Bradford method (Kruger, 1994). Equal amounts of protein were separated by SDS‐PAGE and electrotransferred onto polyvinylidene difluoride (PVDF) membranes. Non‐specific binding was blocked with 5% BSA with 0.05% Tween in triethanolamine‐buffered saline solution (TBS) and incubated with primary antibodies at 4°C overnight, followed by appropriate secondary antibodies for 1 h at 30°C. The membranes were washed three times with TBST (TBS containing 0.05% Tween). The signal was amplified by colour development using the ProtoBlot II AP System with a stabilized substrate (Promega Corporation). For each study, Western blot analysis was conducted three times and representative blots were shown.

This work was supported by the National Natural Science Foundation of China (No. 81973035, 81673152) and the Applied Technology Research and Development Plan of Heilongjiang Province (GA18C005).