Untargeted serum metabolites profiling in high-fat diet mice supplemented with enhanced palm tocotrienol-rich fraction using UHPLC-MS

Excessive high fat dietary intake promotes risk of developing non-alcoholic fatty liver disease (NAFLD) and predisposed with oxidative stress. Palm based tocotrienol-rich fraction (TRF) has been reported able to ameliorate oxidative stress but exhibited poor bioavailability. Thus, we investigated whether an enhanced formulation of TRF in combination with palm kernel oil (medium-chain triglycerides) (ETRF) could ameliorate the effect of high-fat diet (HFD) on leptin-deficient male mice. All the animals were divided into HFD only (HFD group), HFD supplemented with ETRF (ETRF group) and HFD supplemented with TRF (TRF group) and HFD supplemented with PKO (PKO group). After 6 weeks, sera were collected for untargeted metabolite profiling using UHPLC-Orbitrap MS. Univariate analysis unveiled alternation in metabolites for bile acids, amino acids, fatty acids, sphingolipids, and alkaloids. Bile acids, lysine, arachidonic acid, and sphingolipids were downregulated while xanthine and hypoxanthine were upregulated in TRF and ETRF group. The regulation of these metabolites suggests that ETRF may promote better fatty acid oxidation, reduce oxidative stress and pro-inflammatory metabolites and acts as anti-inflammatory in fatty liver compared to TRF. Metabolites regulated by ETRF also provide insight of its role in fatty liver. However, further investigation is warranted to identify the mechanisms involved.


Results
Differential metabolomic analysis between groups against HFD respectively. Principal Component Analysis (PCA) of PKO against HFD generated a total of 20,832 peaks in positive mode with average of 2604 peaks per sample. The generated peaks per groups were 2060. The PCA score plot generated showed 43.4% variation of PKO when compared to HFD in the positive mode whereby the first principal component (PC1) score was 25.5% and second principal component (PC2) score was 17.9% (Fig. 1a). The PCA of PKO against HFD in negative mode generated a total peak of 10,136 peaks with 1267 peaks average peaks per sample and total peak groups generated were 1001. The PCA score plot for negative mode described variation of 42.9% with 26.9% of PC1 and 16% of PC2 (Fig. 1b). Univariate analysis of PKO against HFD showed 101 metabolic features were detected in positive mode ( Supplementary Fig. 1a) and 38 in negative mode ( Supplementary Fig. 1b). The total of metabolic features from both positive and negative mode was 139.
Principal Component Analysis (PCA) of TRF against HFD generated a total peak of 31,248 peaks in positive mode with average of 2604 total peaks per sample and 2060 peak groups formed. The PCA score showed total variation of 40.9% with PC1 at 24.6% and PC2 at 16.3% on positive mode when compared to HFD (Fig. 1c). The PCA in negative mode of TRF against HFD generated a total peak 15,204 in which the average peaks per sample were at 1267 peaks and total peak groups formed were 1001. The PCA score of generated a total variation of 43% where PC1 score was 26.4% and PC2 score was 16.6% (Fig. 1d). Univariate analysis of TRF group against HFD detected 141 metabolic features in positive mode ( Supplementary Fig. 1c) and 55 metabolic features in negative mode ( Supplementary Fig. 1d). This gave rise to the total of 196 metabolic features.
The total peaks generated from PCA of ETRF against HFD in positive mode were 31,248 peaks where each sample achieved average of 2604 peaks and peak groups formed were 2060. The PCA showed total variation of 33% (Fig. 1e) where PC1 score was 18.8% and PC2 score was 14.2%. The PCA in negative mode of ETRF against HFD generated a total peak of 15,204 peaks where average peaks per sample were 1267 and peak groups formed were 1001. The PCA plot showed complete separation in PCA with total variation of 32.5% (Fig. 1f) where PC1 described 19.8% variation and 12.7% for PC2. Univariate analysis of ETRF against HFD resulted in total of 239 metabolic features identified using volcano plot. This include 156 metabolic features in positive mode and 83 metabolic features were identified in negative mode. The plot is shown in Supplementary Fig. 1e and Supplementary Fig. 1f, respectively.
In addition, PCA generated a total of 36,456 peaks in positive mode when ETRF was compared against TRF where the average peaks per sample were 2604 and the total peak groups were 2060. The PCA score showed total of 39.3% variation in positive mode (Fig. 1g) where PC1 score was 22.4% and PC2 at 16.9%. The PCA of ETRF against TRF in negative mode generated 17,738 total peaks which average peaks per sample were 1267 and the total peak groups generated were 1001. The PCA score in negative mode showed total variation of 39.9% with PC1 score of 24.4% and PC2 of 15.5% (Fig. 1h). Univariate analysis of ETRF group against TRF showed 42 metabolic features were detected in positive mode and 23 metabolic features in negative mode. Total metabolic features Figure 1. PCA score plots between PKO (green filled circle) and HFD (red filled circle) in positive mode (a) and negative mode (b), PCA score plot between TRF (green filled circle) and HFD (red filled circle) in positive mode (c) and in negative mode (d), PCA score plot between ETRF (red filled circle) and HFD (green filled circle) in positive mode (e) and negative mode (f), PCA score plot between ETRF (red filled circle) and TRF (green filled circle) in positive mode (g) and negative mode (h). www.nature.com/scientificreports/ identified were 65. The volcano plot for positive mode and negative mode are illustrated in Supplementary Fig. 1g and Supplementary Fig. 1h, respectively.
Metabolites profiling and regulation in treatment against control group. Treatment group consisting of ETRF, TRF and PKO groups were compared against HFD group, the control group respectively. In PKO group, a total of 33 metabolites were successfully identified from 139 metabolic features (Table 1). These metabolites belong to group of alkaloids, fatty acids, bile acids, lipids, and amines. Xanthine was found to be the most up-regulated and deoxyadenosine monophosphate was the most down-regulated metabolites.
In TRF supplemented group, 42 metabolites were identified from a total of 196 metabolic features (Table 2). Here, the profiled metabolites consisted of lipids, alkaloids, and bile acids. Isovaltrate was found to be the most up-regulated while N-Undecanoylglycine was the most down-regulated when compared against HFD group. www.nature.com/scientificreports/ Out of the 239 metabolic features identified in the ETRF supplemented group, 40 metabolites were successfully identified (Table 3). Similar as to profiled metabolites in PKO group, metabolites profiled for ETRF comprised of alkaloids, fatty acids, bile acids, lipids, amines, and amino acids. Xanthine was found to be the most up-regulated in this group followed by (+)-abscisic acid beta-D-glucopyranosyl ester. The most down-regulated profiled metabolite was phytosphingosine.
Apart from comparison between treatment groups with control group, ETRF group was also compared against TRF group. The total metabolites identified when ETRF was compared against TRF were 15 from the total of 65 metabolic features (Table 4). From 15 of successfully profiled metabolites, four belong to bile acids metabolism. Other metabolites profiled fall under fatty acids and amino acids. Similar to comparison of ETRF against HFD, the comparison of the former against TRF also showed that (+)-abscisic acid beta-D-glucopyranosyl ester was the most upregulated. The most downregulated metabolite profiled was 2,2-dichloro-1,1-ethanediol.
Profiled metabolites that were similar across groups were identified and were compared based on fold-change. The comparison was made between PKO (Table 1), TRF (Table 2) and ETRF (Table 3) groups against HFD, respectively. Based on these comparisons, the major metabolite group involved is bile acid. Cholic acid was found to be downregulated in ETRF and PKO group. Taurochenodeoxycholic acid was found to be upregulated in TRF and PKO. However, bile acids such as Taurocholic acid, Taurochenodeoxycholic acid, Taurohyocholic acid and Sulfoglycolithocholic acid were all downregulated in ETRF when compared to TRF.
Another metabolite group detected across the groups are amino acids. DL-Lysine was found downregulated in ETRF and TRF when compared to HFD group. The amino acid 2-amino-hexadecanoic acid was also found to be downregulated in ETRF, TRF and PKO. Apart from that, the amino acid N-Undecanoylglycine was downregulated in TRF and PKO. However, indospicine was found to be upregulated in TRF and PKO. When comparing to TRF, the amino acid N6, N6, N6-Trimethyl-L-lysine was found to be upregulated in ETRF. The derivative of aspartic acid, Succinyladenosine was found to be upregulated in ETRF as well.
Fatty acids were also found to be regulated by the groups. Arachidonic acid was downregulated in ETRF and TRF group. Oleamide was also found to be downregulated in TRF and PKO. The fatty acid (15Z)-9,12,13-Trihydroxy-15-octadecenoic acid was found to upregulated in TRF and PKO. Isovaltrate was found to be upregulated in TRF and PKO as well. However, comparison between ETRF and TRF, Tuliposide B, a fatty acid derivative was found to be downregulated in the former.
Other significant different metabolites across the groups were identified. Organic acid such as 3-Phenyllactic acid was found to be downregulated in both ETRF and TRF. Alkaloids were also found to be regulated. Xanthine was found upregulated in ETRF and PKO. Hypoxanthine was found to be upregulated in ETRF only. The alkaloid trigonelline was found upregulated in ETRF and TRF.
Biochemical pathways analysis of metabolites profiled in ETRF against TRF. Pathway analysis using MetaboAnalyst on the metabolites profiled in TRF and ETRF against HFD generated total of 15 biochemical pathways (Table 5). Significant biochemical pathways detected were Sphingolipid and Vitamin B6 metabolism ( Fig. 2a) (p < 0.05). Analysis of the metabolites profiled in ETRF against TRF showed three biochemical pathways that were altered. The pathways involved were primary bile acid synthesis, taurine and hypotaurine metabolism and lysine degradation (Fig. 2b). From these pathways, primary bile acid synthesis was found to be significant (p < 0.05) ( Table 6).
The effect of TRF and ETRF on liver histology and Farnesoid-X receptor (fxr) expression. The effect of TRF and ETRF were assessed on each liver histologically and stratified using NAFLD Activity Score (NAS) 32 . The scoring was assessed based on the presence of macrovesicular and microvesicular steatosis, lobular inflammation and fibrosis. Scoring for each group is outlined in Table 7. Results from NAS showed that more than half of TRF group (57%) had mid-grade steatosis with 52% of livers sampled presented with Zone 3 steatosis. On the other hand, ETRF presented with about 90% had high grade steatosis and 81% with Zone 3 steatosis. Fibrosis was not found in TRF while only 5% presented with perisinusoidal fibrosis. In terms of lobular inflammation, about 67% in TRF group presented with < 2 foci of lobular inflammation and 24% with > 2 foci of lobular inflammation. In contrast, 57% from ETRF presented with < 2 foci of lobular inflammation while 38% presented with no inflammation.
Liver sampled from each mouse from HFD, PKO, TRF and ETRF were assessed on their fxr expression using immunohistochemistry (IHC) technique. The expression was measured using IHC scoring method [33][34][35] . The representative of IHC staining from each group is shown in Fig. 3 where the expression of fxr is demarcated by red circle. The scoring of fxr expression is shown in Fig. 4. The highest fxr score was found in ETRF (5.1 ± 0.55) followed by TRF (4.62 ± 0.37), PKO (1.67 ± 0.11) and HFD (1.57 ± 0.11). Statistical analysis showed TRF and ETRF were significantly higher compared to PKO and HFD respectively (p < 0.05).
The effect of TRF and ETRF on liver Farnesoid-X receptor (fxr) and its target genes, small heterodimer partner (shp) and signal transducer and activator of transcription 3 (stat3). The

Discussion
This study reported untargeted metabolomic analysis of enhanced formulation of TRF (ETRF) supplementation in mice fed with HFD (60% kcal from fat) using the genetically modified B6.Cg-LepOb/J strain. This mice model was used as it has been reported as an appropriate model to study fatty liver since the leptin-deficiency was able to withheld from involvement of inflammation without the "second-hit" 36 . Gogiashvili et al. 37 has also reported that the development of fatty liver in similar strain being fed with HFD (60% kcal from fat). In this study, ETRF group was compared to HFD group and TRF group respectively to provide insight of the different metabolites being regulated by each supplementation. The ETRF carrier, PKO was also compared to HFD group in terms of the difference in metabolite regulations. The source of TRF utilised in this study was derived from Malaysian palm tree Elaeis guineensis. The palm-based TRF from this tree has been investigated of their therapeutic properties primarily in Malaysia as it is being readily accessible while Western countries have inclination towards annatto oil or rice bran oil-based TRF. The difference of these oils are the percentages of Vitamin E homologs contained in these oils. Annatto and rice bran oil-based TRF contain highest concentration of δ-tocotrienol and minimal amount of tocopherol 23,38,39 . On the other hand, palm oil, both its seed and kernel possessed tocotrienol and tocopherol in which each homolog is relatively of the same concentration 40 . Therefore, investigation utilises www.nature.com/scientificreports/ palm-based TRF may provide robust information in metabolomic analysis as the comprehensive content of tocotrienol and tocopherol would affect multiple genes and metabolic pathways 41 . Our study has detected both primary and secondary bile acids (BAs), in conjugated or unconjugated form, in all treatment groups when compared to HFD group. In ETRF group, cholic acid (CA), a primary BA, was found to be downregulated. In contrast, TRF group showed upregulation of primary BA, taurochenodeoxycholic acid (TCDA). Meanwhile, the PKO groups demonstrated both profiles seen in TRF and ETRF, whereby CA was downregulated and TCDA was upregulated. In addition, comparing ETRF to TRF, both primary and secondary BAs which are taurocholic acid (TCA), TCDA, taurohyocholic acid (THCA) and sulfoglycolithocholic acid were downregulated in the former. In NAFLD, it was reported that the synthesis and reuptake of BAs were disrupted 42,43 . Studies have reported that both primary and secondary BAs synthesis were increased but hepatic  www.nature.com/scientificreports/ bile signaling was suppressed 44,45 . Apart from that, study by Tang et al. 46 and Kodama et al. 47 reported that rats with hepatic steatosis showed upregulation of TCA and downregulation of hyodeoxycholic acid and taurohyodeoxycholic acid. Our findings suggested that the ETRF supplementation, as opposed to TRF, most likely to regulate and improve bile acids synthesis in fatty liver. The downregulation of these primary and secondary BAs detected in ETRF was perhaps explained by the ability of its gamma tocotrienol in suppressing the expression of cytochrome P450 7A1 (CYP7A1), a key enzyme for synthesis of primary bile acids 48 . Apart from that, Farnesoid-X Receptor (fxr), a key receptor in bile acid regulation, was reported repressed in NAFLD 49 . In our result, liver fxr protein and gene expressions were shown to be highest in ETRF suggesting that CA downregulation may occur as result of a negative feedback mechanism. The negative feedback mechanism occurred by the increase in fxr expression leading to increase in shp gene expression. The increase in shp promotes repression of BAs reuptake transporter such as Na + taurocholate cotransporting polypeptide (NTCP) and suppression of CYP7A1 50 . On the other hand, TRF findings were similar but the fold changes are lower than ETRF. This could be the reason why TRF unable to improve BAs regulations which resulted in spilling of the BAs into the circulation as profiled in the metabolomic data. The outcome observed with ETRF supplementation was most likely contributed by the www.nature.com/scientificreports/ use of MCT carrier. MCT would promote direct absorption into the liver by bypassing the vascular circulation, allowing high bioavailability of ETRF promoting higher activation of fxr and repressing BAs synthesis while LCT require catabolism and packaging, in process, lower fxr activation and BAs synthesis repression. The metabolites profiled in ETRF and TRF have detected metabolites that are responsible in promoting inflammation or act as anti-inflammatory. Pro-inflammatory metabolites such as arachidonic acid and sphingolipids were found to be downregulated in both ETRF and TRF. Anti-inflammatory metabolite such as trigonelline was found to be upregulated in both ETRF and TRF when compared against HFD.
Arachidonic acid was found downregulated in both ETRF and TRF group. Arachidonic acid is metabolised by the cyclooxygenase pathway and lipoxygenase pathway. Study by Hall et al. 51 demonstrated that remodeling of phospholipid membrane occurred in NAFLD whereby release of arachidonic acid from the membrane promote cell injury and inflammation. In another study, individuals with NAFLD were reported to express high level of 5-hydroxyeicosatetraenoic acid (5-HETE) and 9-hydroxyoctadecadienoic acid (9-HODE), product of arachidonic acid metabolism 52 . It was also reported that NAFLD has higher level of leukotrienes and lower level of eicosanoid 53 . HFD animals supplemented with ETRF or TRF exhibited reduction of arachidonic acid would most likely result from the supplement's antioxidant activities and preventing remodeling of phospholipid membrane in hepatocytes. Apart from that, it was also reported that Vitamin E has capability in modulating arachidonic acid oxidation by inhibiting 5-lipoxygenase activity 54 . Inhibition of the enzyme would result in inhibition of leukotrienes synthesis, preventing inflammatory response in the liver. In addition, the gene expression of Signal Transducer and Activator of Transcription 3 (stat3) was detected to increase in ETRF and TRF despite not being  www.nature.com/scientificreports/ significant. Although increase in stat3 has shown to accelerate fibrosis 55 and γ-tocotrienol has been demonstrated to prevent fibrosis by suppressing stat3 by inhibiting its phosphorylation 56 . However, paradoxical effect from independent increase of stat3 was observed to promotes anti-inflammatory properties in NAFLD 57,58 . Therefore, it is most likely that the increase in stat3 in NAFLD as observed in this study promoted by ETRF and TRF. Further investigation should be performed in order to elucidate the paradoxical mechanism. In our study, sphingolipids such as sphinganine and phytosphinganine were found to be downregulated in all groups when compared to HFD group. Sphingolipid signaling mediator, sphingosine-1-phosphate (S1P), was found to be downregulated in ETRF and TRF. Sphingolipids were reported to play role in pathogenesis of NAFLD. Gorden, et al. 59 reported that sphingolipids such as ceramides, dihydroceramides and 1-deoxy-dihydroceramides were increased in NAFLD and NASH. Studies have reported that caloric restriction and vitamin E supplementation were able to reduce sphingolipids level and prevent progression of NAFLD by suppression of mRNA expression of ceramide 60,61 . Similar to our findings, downregulation of sphingolipids, primarily sphinganine and phytosphinganine most likely occurred in the same mechanism. S1P is a signaling molecule and its formation occurs by enzymatic reaction of sphingosine kinase on sphingosine 62 and has role in progression of NASH 63 . S1P activates sphingosine-1-phosphate receptor (S1PR) especially S1PR2 which highly expressed in the liver, responsible in mass cell degranulation and inflammatory response 64,65 . Hence, ETRF and TRF showed downregulation of S1P which may promote lower inflammation in the liver. Apart from that, S1P signaling was reported to be closely related to bile acids signaling. It was reported that bile acid, along with S1P, activates S1PR2 to regulate hepatic glucose and lipid metabolism through ERK1/2 and AKT signaling pathways [66][67][68] . Therefore, although ETRF and TRF exhibited similar capacity to normalise sphingolipids in NAFLD, ETRF most likely to have robust properties by further decreasing the level of S1P, which would prevent the progression of NAFLD to NASH.
In this study, the antioxidant trigonelline was found to be upregulated in both ETRF and TRF when compared to HFD. The plant alkaloid trigonelline is a nicotamide that is commonly found in coffee and plants 69 . Trigonelline supplementation was reported to promote protective effect against NAFLD 70 . Similarly, study has reported that

Figure 2. Biochemical pathways identified in TRF and ETRF against HFD (a) and ETRF against TRF (b) using
MetaboAnalyst. Analysis of the metabolites profiled in TRF and ETRF against HFD generated 15 biochemical pathways while the metabolites profiled in ETRF against TRF derived three biochemical pathways that were altered. c: taurine and hypotaurine metabolism; d: biotin metabolism; e: aminoacyl-tRNA metabolism; f: lysine degradation; g: biosynthesis of unsaturated fatty acids. Table 6. List of biochemical pathways (MetaboAnalyst) identified in ETRF against TRF. *p-value of less than 0.05 is regard as significant (p < 0.05). www.nature.com/scientificreports/ supplementation of trigonelline in NAFLD model was able to normalise liver function readings and increase the expression of Bcl-2 protein and decrease Bax protein expression 71 . However, in this study, the source of trigonelline would be most likely derived within tocotrienol-rich fraction but not within the carrier. This is because the alkaloid was undetected in PKO. However, further investigation is warranted to identify the presence of this alkaloid in palm oil. The upregulation of trigonelline in both ETRF and TRF group when compared to HFD suggest that the presence of this alkaloid may prevent steatosis in these group. Histologic assessment of the liver Table 7. Assessment of NAFLD activity score (NAS). www.nature.com/scientificreports/ would be able to support this finding. Comparison based on fold-change between ETRF and TRF showed that the alkaloid is upregulated higher in the former group providing insight that ETRF may have higher. Our study found that N-undecanoylglycine, a N-fatty acylglycine was downregulated in TRF and PKO. However, the metabolite was not observed in ETRF. N-Undecanoylglycine, an acylglycine is a derivative of glycine and undecanoic acid have shown to be involved in fatty acid oxidation 72 . Study by Liu et al. 73 and Du et al. 74 have demonstrated that N-Undecanoylglycine was upregulated in hepatocytes subjected to oxidative stress or alteration in lipid metabolism occurred. Fatty acids that are unable to be further oxidised will lead to accumulation due to mitochondrial damage in NAFLD. The accumulation would involve microsomal ω-oxidation commonly occur in animal or peroxisome β-oxidation in human by conjugating fatty acid with glycine 75 . Fatty acid oxidation that takes place in peroxisome would increase the expression of L-carnitine in order to increase shuttling of fatty acid into peroxisome to overcome the burden of excessive fatty acid deposition 76 . However, the fatty acid oxidation is not as effective as mitochondrial β-oxidation. In TRF and PKO, the downregulation of N-Undecanoylglycine suggested that the fatty acid oxidation in these groups have been improved compared to HFD. This was perhaps due to inhibition of de novo lipogenesis (DNL) protein expression 77 activation of PPARα receptor and gene expression of Carnitine Palmitoyltransferase 1A (CPT1A) and CYP3A4 38,78,79 . On the other hand, ETRF showed undetected N-Undecanoylglycine which may suggest that fatty acid oxidation in this group has improved due to its MCT carrier to increase its bioavailability. This is further supported by the downregulation of L-acetylcarnitine and upregulation of N6, N6, N6-Trimethyl-L-lysine. L-acetylcarnitine has shown to inversely reflect the capacity of mitochondrial β-oxidation 80,81 . Apart from that, the increase in carnitine synthesis was reported to be increased with N6, N6, N6-Trimethyl-L-lysine 82 . Based on the regulations of these metabolites, it is suggested that β-oxidation has been increased in ETRF compared to TRF and HFD group.

Steatosis
Amino acids that are involved in fatty acid oxidation showed alteration in regulation of metabolites in ETRF and TRF group when compared to HFD. The amino acid lysine was detected downregulated in both ETRF and TRF group. These amino acids are known as ketogenic amino acids that form acetoacetate and acetyl-CoA 83 . Previous studies have reported that lysine was found high in serum of NAFLD patients 84,85 and in animal model 86 . It was also reported that increase in urine lysine and leucine concentration are associated with NASH 87,88 which similarly observed in NAFLD animal model 89,90 . These ketogenic amino acids were demonstrated to be regulated by silent mating type information regulation 2 homolog 5 (SIRT5) 91 where previous study has shown that SIRT5 was downregulated in NAFLD leading to increase in serum lysine level 92 . Apart from that, ketogenesis is shown to be favored as source of energy in NAFLD due to suppression of β-oxidation 93 . Therefore, the downregulation of these ketogenic amino acids in both ETRF and TRF suggested that the source of energy in these groups favored towards improved β-oxidation. However, compared to TRF, additional downregulation of lysine detected in ETRF suggested that better β-oxidation occurred in this group and reduce the reliance of these amino acids as source of energy. However, further investigation is required to further support these findings in order to elucidate the roles of ETRF and TRF on ketogenic pathway in NAFLD.
Xanthine was detected to be upregulated in ETRF and PKO. The precursor of xanthine, hypoxanthine was identified to be upregulated in ETRF. Product of xanthine metabolism such as allantoic acid was detected to be downregulated in both ETRF and TRF group when compared to HFD. In human, the catabolism of hypoxanthine and xanthine ended with uric acid. However, in animal, the end product of xanthine metabolism is allantoic acid 94 . Study has reported that the level of serum uric acid was higher in NAFLD patients 95 . In animal model, similar findings have been reported 96,97 . The metabolites are known to be involved in purine salvage pathway. In Expression of fxr is significant with TRF when compared to HFD and PKO, respectively. Expression of fxr was also significant with ETRF when compared to HFD and PKO, respectively (*p < 0.05). Comparison between TRF and ETRF was not significant (p > 0.05). Data is expressed as mean of scoring ± standard deviation (SD). (n = 7). www.nature.com/scientificreports/ our study, the precursor of uric acid, xanthine and hypoxanthine were upregulated in ETRF and PKO but uric acid was not detected in the serum metabolomics analysis. This positive outcome is perhaps contributed by ETRF and PKO respectively. However, ETRF group may had additional benefits from the combination with MCT. Studies have demonstrated that palm oil-based vitamin E was able to reduce plasma xanthine oxidase activities which may prevent hyperuricemia 98,99 . Studies have also reported that dietary MCT was able to prevent hyperuricemia by reducing xanthine oxidase activity but the underlying mechanism require further investigation 100,101 . Nevertheless, the presence of high amount of uric acid in serum inferred from the increased level of xanthine may not always be pathological. Uric acid may be regarded as antioxidant in serum, not in cytoplasm 102 .
Although both ETRF and TRF possessed all the Vitamin E homologs, the higher percentage of tocotrienol in ETRF and the use of MCT to increase its bioavailability will be able to provide the findings discovered in this study. However, further investigations are required to fully deduce these findings. Investigation at genomic and proteomic levels would provide the answer in confirming the expression of metabolites involved. Information regarding the effect of ETRF and TRF on the gene and protein expression could be inferred as well. Apart from that, targeted metabolomic analyses based on findings in this study would validate these findings. Based on the PCA score plot, the comparison between PKO and ETRF with HFD respectively provides distinct differences between the groups, especially in ETRF group negative mode, which presented with clear separation between groups. Therefore, lipidomic analysis in this model will provide valuable and important information on lipids metabolism that plays a significant role in fatty liver and information on lipids regulated by ETRF and TRF. Among the limitation faced in this study was from technical perspective. The B6.Cg-LepOb/J mice were obese and had cause a challenge in cardiac blood aspiration upon euthanising. This occurred due to the heart displacement from the excessive intraperitoneal fat deposition. Therefore, it is recommended to perform the aspiration by utilising ultrasound probe or handled by a highly trained personnel in blood collection.
This study has investigated the metabolites regulated in fatty liver induced by HFD in B6.Cg-LepOb/J mice subjected to ETRF supplementation. Metabolomic analysis showed supplementation of ETRF in mice with fatty  www.nature.com/scientificreports/ liver suggested promotion of β-oxidation, reduction in oxidative stress and inflammation through downregulation of primary and secondary bile acids, lysine, arachidonic acid and sphingolipids. Supplementation of ETRF has shown its capabilities in mediating the activity of signaling molecule in several pathways. This includes bile acids synthesis, fatty acid β-oxidation, glycine, serine and lysine metabolism, sphingolipids metabolism and arachidonic acid metabolism. Apart from that, the supplementation also exhibited antioxidant activities though xanthine and trigonelline upregulation. TRF supplementation may exhibit similar capacity to ETRF when compared against HFD but lacking additional properties such as inability to exert as signaling molecule in carnitine pathway and sphingosine-1-phosphate signaling.

Methods
Tocotrienol-rich fraction. Tocotrienol-rich Fraction (TRF; Tri.E™), Enhanced TRF (ETRF; Gold Tri.E™), stripped palm oil (PO) and stripped palm kernel oil (PKO) were gifts from Sime Darby Oils (Selangor, Malaysia). The composition of Tri.E™ was 72% tocotrienol and 28% tocopherol. In Gold Tri.E™, the percentage of tocotrienol was 81% and the remaining was tocopherol. The TRF was prepared by mixing with PO (long-chain triglycerides; LCT) at the ratio of 1:1. ETRF was prepared by mixing with PKO (medium-chain triglycerides; MCT) with the same ratio.  103 . In this study, fatty liver was induced using genetically modified mice through high-fat diet (60% kcal from fat). Twenty-eight B6.Cg-LepOb/J male mice at 8 weeks old were purchased from Jackson Laboratory (Maine, USA). The sample size was calculated using G*Power 104 calculator with the determined by the statistical test to be ANOVA with the parameters of effect size (E) of 0.9 and alpha (α) of 0.05, giving rise to the total sample of 28 (n = 28). The age of mice represented adult human age as reported by Dutta and Sengupta 105 . These mice were caged individually with unlimited supply of reverse-osmosis water in room with regulated temperature of 24 ± 2° C and 12-h light/dark cycle in the Laboratory Animal Care Unit (LACU), UiTM, Sungai Buloh Campus. Mice were randomly selected and distributed evenly into four groups. No inclusion or exclusion criteria were applied on mice during grouping. Each group consisted of seven mice (n = 7). The groupings were based on the type of treatment they received during the study. The first group was control group whereby they received highfat diet (HFD; 60% kcal from fat; Altromin, Lage, Germany) ad libitum. The second group was the carrier group in which the mice were provided HFD ad libitum and PKO, the carrier for ETRF at dose of 200 mg/kg daily. The third group was TRF group where the mice were provided with HFD ad libitum and 200 mg/kg daily supplementation of TRF. Lastly, the fourth group comprised of ETRF group. The group was provided ad libitum supply of HFD and daily supplementation of ETRF at dose of 200 mg/kg daily. The study was conducted for 6-weeks. At the end of 6-weeks, the mice were euthanised by rapid cervical dislocation. This method was selected as the use of chemicals to euthanise would affect the outcome of serum metabolomics analysis. Blood was collected in each mouse by cardiac aspiration using 26 G needle and 1 mL syringe. Collected blood was transferred into redtop blood tube (Becton Dickinson, USA). No mice were excluded at the end of study. However, due to technical challenge, the total number of blood able to be collected from the mice were seven for ETRF (n = 7), seven for TRF (n = 7), three for PKO (n = 3) and five for HFD (n = 5). Blood tubes were allowed to sit in room temperature for 20 min. Minimal of three biological replicates are adequate in metabolomics study 106 . Sera were acquired by centrifuging at 1500 × g, 4° C for 10 min. The serum appeared as supernatant in each tube was transferred into 2 mL microtubes and was stored in − 80 °C until required for analysis. Liver from each mouse was collected for histology assessment by fixing in 10% neutral buffered formalin (NBF) (R&M Chemicals, Malaysia) prior to processing.
Serum preparation for metabolite extraction. Collected  Statistical analysis. Statistical analysis was conducted by comparing PKO, TRF and ETRF group against HFD group respectively. ETRF group was also compared against TRF. The total number of samples analyzed for serum metabolomics were seven for ETRF (n = 7), seven for TRF (n = 7), three for PKO (n = 3) and five for HFD (n = 5). Data normalisation was performed using MetaboAnalyst 107 . Chemometric and univariate analyses were also performed using MetaboAnalyst 107 . Principal Component Analysis (PCA) was selected as the tool of chemometric analysis to visualise the difference and outliers of the groupings in the form of score plot. Univariate analysis using volcano-plot was performed following PCA scoring by using parameter of fold-change of 1.5 and p-value less than 0.05 (p < 0.05). List of metabolic features generated from volcano plot was further identified using m/zcloud and chemspider included in Xcalibur™ version 3.1 (Thermo Fisher Scientific, USA), CEU Mass Mediator (CEUMM) 108 , Human Metabolome Database (HMDB) 72 and METLIN.
Histology assessment. The fixed livers collected were sampled from three main lobes giving total of 21 samples per group. After fixation in NBF for 72-h, all liver tissues were processed by the tissue processor (Sakura, Europe). Then, tissues were embedded into histology cassette using a paraffin-embedding machine (Thermo Scientific, Germany). The embedded tissues were allowed to cool for 24 h at room temperature and sectioned at 5 µm thick. The tissues were stained with Hematoxylin and Eosin (H&E; Merck, Germany) and Masson Trichrome (MT; Bio-Optical, Italy). Immunohistochemistry staining using fxr antibody was performed using the formalin fixed paraffin embedded liver tissues prepared prior. Tissue section of 5 μm thick was fixed on polylysine histology glass slide (Thermo Scientific, USA). Antigen retrieval was performed using Antigen Retrieval Solution (R&D System, USA) and IHC was carried out using HRP-DAB tissue staining kit (R&D System, USA) and fxr antibody (R&D System, USA). All tissue sections were assessed by resident histopathologists via single blinded assessment. The assessment of fxr by histopathologist was carried out using IHC Scoring Method [33][34][35] comprising of assessing the positive cell and intensity of staining.
RNA extraction. RNA was extracted from mice liver tissue using GeneJET RNA Purification Kit (Thermo Scientific, USA) following the protocol provided. Liver at approximately 30 mg was homogenised and supernatant collected were subjected to RNA collection and purification. Purified RNA was analysed using Quickdrop spectrophotometer to check for concentration and purity.
First strand cDNA synthesis. CDNA was synthesized from RNA using Maxima First Strand cDNA Synthesis Kit for RT-qPCR (Thermo Scientific, USA). Master mix was prepared accordingly for each sample and control comprised of blank sample were incorporated in each session to assess signs of contamination. The microcentrifuge tubes are then placed into a thermal cycler with the following protocol:10 min at 25 °C, 15 min at 50 °C, 5 min at 85 °C. The acquired cDNA was aliquot to final concentration of 10 ng/µL.
Step www.nature.com/scientificreports/ used are listed in Table 8. Target gene comprised of fxr, shp and stat3. The house-keeping gene comprised of gapdh, itih4 and ambh. Prepared samples were subjected to RT-qPCR using Bio-Rad CFX 96 and were analysed using Bio-Rad CFX Maestro software. The protocols used are listed in Table 9. The annealing temperature varies according to the optimal melting temperature for each primer. Data were tabulated into excel and the expression of target genes are being quantified relative to the housekeeping genes.

Statistical analysis of group comparison.
Non-parametric analysis using Kruskall-Wallis was selected to compare means between groups for IHC score and gene fold-change. P-value of less than 0.05 (p < 0.05) was regarded as statistically significant. www.nature.com/scientificreports/