Exploring the Effects of Delphinidin Treatment on Browning Processes in 3T3-L1 Preadipocyte Cells: A Foodomics Approach

doi:10.21203/rs.3.rs-4940682/v1

Purpose:

Foodomics uses advanced omics technologies to evaluate the molecular effects of food components in organisms. Delphinidin, a valuable polyphenol anthocyanidin, has demonstrated protective effects against obesity-related conditions, making it a promising candidate for dietary supplements. The purpose of this study is to investigate the effects of delphinidin treatment (5, 10, and 20 µM) on 3T3-L1 preadipocyte cell culture on thermogenesis and browning processes.

Methods:

We treated cells in two different stages of adipocyte formation: in the differentiation and the maturation process. To achieve this, we evaluated the expressions of main protein markers (UCP-1, PGC1-α, and PPARγ) and genes correlated with browning (UCP-1, PPARγ, C/EBPβ, PGC1-α, CIDEA, FABP4, and PRDM16) and lipid metabolism in delphinidin treated 3T3-L1 preadipocytes cells. The GC/MS-based Metabolomics method was used to understand the effect of delphinidin at the lipid level.

Results:

The results suggest that delphinidin might contribute to the browning of tissue in relation to important regulators of browning genes like UCP1 and PRDM16. However, it did not impact PPARγ, C/EBPβ, and FABP4 – which are regulators of adipogenesis. The foodomics approach combining multi-omics data suggest that delphinidin interacts in different pathways of metabolism. Delphinidin exhibited effects on metabolites such as glyceraldehyde, alanine, and porphine – indicating its involvement in metabolic pathways.

Conclusion:

These findings provide a valuable foundation for developing obesity therapeutics through dietary supplements, though further studies are needed to confirm delphinidin’s browning effects.

Delphinidin

Obesity

Foodomics

Adipocyte browning

UCP1

“Foodomics” integrates advanced omics technologies, like transcriptomics, metabolomics, and proteomics, with biostatistics, bioinformatics, and chemometrics to assess complex biological systems and their interaction with bioactive food compounds [1]. Recent omics technologies are used to explore how phytochemicals, secondary metabolites from plants, can positively affect human health by altering protein levels, functions, and gene expressions, and reducing disease risk [2]. Delphinidin (2-(3, 4, 5-trihydroxyphenyl) chromenylium-3, 5, 7-triol) is a polyphenolic ring structure based on diphenyl propane [3]. It belongs to polyphenol group anthocyanidins, a subfamily of flavonoids, that includes different varieties as follows: cyanidin, malvidin, pelargonidin, delphinidin, peonidin, and petunidin [4]. Recent studies have reported that delphinidin may be protective against obesity-related conditions such as oxidative stress, inflammation, insulin resistance, and steatosis by altering redox signalling in mice fed a high-fat diet [5, 6]. In an animal study, Maqui berry supplementation with high amounts of delphinidin as an anthocyanin was shown to improve insulin response and reduce weight gain [7]. Delphinidin shows promise in reducing intracellular lipid accumulation, promoting lipolysis, and regulating adipogenic genes, which could help prevent metabolic diseases like obesity [8]. Similar studies suggest that black soybean anthocyanins, primarily containing delphinidin, also benefit obesity by inhibiting adipocyte differentiation and stimulating lipolysis while reducing adipogenesis [9-11]. Delphinidin inhibits PPARγ expression, leading to lower 3T3-L1 cell viability and differentiation [9]. Moreover, supplementation of C57BL/6 mice with blueberry and mulberry juice containing delphinidin as the major anthocyanin for 12 weeks, inhibited weight gain, reduced serum cholesterol levels, insulin resistance, leptin levels, and lipid accumulation, and thus may prevent obesity [12]. However, only a limited number of in vitro, in vivo, and human studies have been conducted thus far, and it is premature to generalize that the consumption of delphinidin consistently leads to favourable effects. Further study is necessary since their molecular metabolic responses and safety issues are not fully understood. The aim of this study is to evaluate the effects of delphinidin on thermogenesis and browning processes in 3T3-L1 preadipocytes cells with a foodomics approach.

2.1. Materials

Delphinidin (≥97% purity) was purchased from Cayman Chemical Co. Cholesterol, 3-isobutyl-L-methylxanthine (IBMX), insulin, dimethyl Sulfoxide (DMSO), Dulbecco’s modified Eagle’s medium (DMEM), bovine calf serum (BCS), trypsin-EDTA, and fetal bovine serum (FBS) were purchased from Merck-Sigma Aldrich. Dexamethasone (DEX) was purchased from Cayman Chemical Co. 3T3-L1 preadipocyte cells were purchased from ATCC company.

2.2. Cell Culture

3T3- L1 preadipocyte cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) containing 10% FBS, penicillin (100 units/ml) and streptomycin (100mg/ml). The cells were incubated at 37°C in 95% humidity, and 5% CO2. The differentiation of 3T3-L1 preadipocyte cells was induced by adding 0.5M IBMX, 1µM DEX, and 10µg/ml insulin to the medium. To evaluate the effects of delphinidin on preadipocyte differentiation, 3T3-L1 was treated with delphinidin (20, 10, and 5 µM) during the differentiation process with each medium change. Furthermore, treatment was administered to another set of cells for 48-hours only after the cell maturation. Control group was treated with only PBS.

2.3. Measuring Gene Expression Levels

Total RNA was isolated using RNA extraction kit. RNA was converted into cDNA with OneScript® Plus cDNA Synthesis Kit and Piko® Thermal Cyclers. Quantification of gene expression was performed using Lightcycler 480. Amplification was performed for each target gene using 4 µl cDNA, 1 µl primer (forward and reverse), 10 µl BlasTaq™ 2X qPCR MasterMix, and 5 µl deionized water. The expression levels of the genes (PPARγ, C/EBPβ, PGC1-α, CIDEA, FABP4, UCP-1, PRDM16) were normalized to β-actin and 36B4. The sequences for the primers are listed in Table 1.

Table 1. Primer sequences of target genes.

Gene name	Primer sequences
PPARγ	Forward	GTACTGTCGGTTTCAGAAGTGCC
PPARγ	Reverse	ATCTCCGCCAACAGCTTCTCCT
C/EBPβ	Forward	GGTTTCGGGACTTGATGCA
C/EBPβ	Reverse	CAACAACCCCGCAGGAAC
PGC1-α	Forward	GAATCAAGCCACTACAGACACCG
PGC1-α	Reverse	CATCCCTCTTGAGCCTTTCGTG
CIDEA	Forward	AGAAGGTCCTACTGACCCCC
CIDEA	Reverse	ACCCGGTGTCCATTTCTGTC
FABP4	Forward	TGAAATCACCGCAGACGACAGG
FABP4	Reverse	GCTTGTCACCATCTCGTTTTCTC
UCP-1	Forward	CCTGCCTCTCTCGGAAACAA
UCP-1	Reverse	GTAGCGGGGTTTGATCCCAT
PRDM16	Forward	GATGGGAGATGCTGACGGAT
PRDM16	Reverse	TGATCTGACACATGGCGAGG

2.4. Measuring Protein Levels

Protein was isolated by three steps: precipitation of the protein, washing of the proteins, and solubilization of the protein, respectively. BCA protein assay kit, purchased by Thermo Fisher Scientific, was used to measure the quantification of total protein. UCP1, PPARγ, and PGC1-α protein concentrations of protein extracts were measured using Sandwich enzyme immunoassay [13] kit per the manufacturer’s instructions.

2.5. Measuring Triglyceride Levels

The Triglyceride Colorimetric Assay kit was purchased by Cayman Chemical Company and was used for triglyceride quantification. The resulting absorbance was detected at 530-550 nm using a BioTek Synergy Htx plate reader. The concentration of triglycerides contained in each sample was calculated with the help of the “triglyceride standard curve” created by using the absorbance values of standard solutions with known triglyceride concentrations.

2.6. Metabolics

The cell culture samples were washed with PBS solution and centrifuged. The pellet was resuspended with homogenization buffer (10 mM Tris–HCl, 5 mM EDTA, 120 mM NaCl, at pH 7.4). Cells were disrupted with a homogenizer and centrifuged (14 min at 14,000× g and 4°C). Pellet (mainly containing nuclear and mitochondrial fractions) was discarded and supernatant was centrifuged for 1 h at 100,000× g and 4°C. Supernatant (cytosolic fraction) was stored at −80 °C until analysis. Homogenised cell culture samples were diluted to contain 10,000 µg/mL protein for Metabolomics analysis. Three technical replicates were prepared for each sample. First, the diluted samples were extracted using a methanol-water mixture (8:1, v/v) and they were centrifuged. Then supernatant was evaporated to dryness and the residues were methoxyamined and derivatized with MSTFA (N-Methyl-N-(trimethylsilyl) trifluoroacetamide) containing 1% TMCS (trimethylchlorosilane). After derivatization, the samples were transferred to GC-MS vials and analysed using a GC-MS instrument (Shimadzu GCMS-QP2010 Ultra, JP) with a DB-5MS stationary phase column (30 m + 10 m DuraGuard × 0.25 mm i.d. and 0.25-um film thickness). The mass range was from 50 to 650 Daltons. Data deconvolution, peak alignment, normalization (MSTUS) and data matrix generation were performed with MS-DIAL software (ver. 4.0). Metabolites were identified to a threshold of >70% using a commercial library (Fiehn Retention Index Library). The data matrix obtained from MS-DIAL software was exported to an Excel file (See Table 1S in the Supplementary Material). Metabolites with missing values of more than 50% of the samples were removed from the data matrix. Other missing values were filled with the half value of the lowest concentration in the metabolite group. The final version of the data matrix was uploaded to Metaboanalyst software to perform multivariate statistical analyses. Multivariate analyses included partial least squares differencing analysis (PLS-DA), variable importance plots (VIP) analysis and heat map analysis (See Figure 1S and Figure 2S in the Supplementary Material).

2.7. Statistical analyses

Statistical analysis was performed using the computer program SPSS 22.0. (IBM Corp., USA). One-way ANOVA was performed followed by multiple comparison tests with Student- Mann-Whitney U to compare with controls. Furthermore, LSD or Games-Howell was used to compare the difference between the groups. All data were presented as numbers and percentages or mean ± standard deviation (SD). A value of p <0.05 was considered to be significant.

3.1. Gene Expression Levels

PPARγ, C/EBPβ, and FABP4, which are key regulators of adipogenesis, exhibited similar gene expression levels (p > 0.05) during both the differentiation and maturation processes (Figure 1). However, when it comes to the key regulators of browning, including PGC1α, CIDEA, UCP1, and PRDM16, their gene expression levels varied among different groups (Figure 2). During the differentiation process, UCP-1 gene expression levels were significantly higher in the 5 µM delphinidin group compared to other groups (p = 0.38). In the maturation process, PRDM16 gene expression levels for delphinidin were significantly higher in the 20 µM group (p= 0.37).

3.2. Protein Levels

UCP-1, PGC1-α, and PPARγ concentrations of delphinidin were compared in the differentiation and maturation processes (Table 2). There was not a significant difference between delphinidin groups for UCP-1 protein levels concentration in both process (p>0.05). In the differentiation process PGC1-α protein levels concentration of 5 µM of delphinidin was significantly lower than 20 µM and the control group (p<0.05). PGC1-α protein levels concentration of 5 µM of delphinidin was significantly lower than 10 µM (p=0.011) during maturation. PPARγ protein levels of delphinidin 20 µM was significantly lower than other groups in differentiation (p<0.05).

Table 2. UCP-1, PGC1-α, and PPARγ concentration of delphinidin ¹^,²^,³.

	Protein Levels Concentration (mg/dl)
	UCP-1	PGC1-α	PPARγ
Differentiation (Concentration, µM)
20	46.1 ± 4.67 ^a	40.1 ± 4.20^a	75.7 ± 1.73^a
10	37.5 ± 3.42 ^a	36.8 ± 0.54^{a, b}	80.3 ± 2.52^a
5	37.5 ± 8.81^a	30.9 ± 0.65^b	80.0 ± 2.91^a
Control	40.6 ± 4.14^a	28.6 ± 0.86 ^b	88.6 ± 2.72^b
	p>0.05	p<0.05	p<0.05
Maturation (Concentration, µM)
20	33.5 ± 0.91 ^a	31.4 ± 1.82 ^{a, b}	86.3 ± 1.36 ^a
10	46.8 ± 17.64 ^a	34.0 ± 0.33 ^a	87.0 ± 1.53 ^a
5	41.0 ± 4.31 ^a	29.5 ± 0.33 ^b	85.2 ± 1.75 ^a
Control	35.1 ± 3.24 ^a	31.4 ± 0.75 ^{a, b}	72.9 ± 3.28 ^b
	p>0.05	p<0.05	p<0.05

¹Data were analyzed by one-way ANOVA

²Data are presented as mean ± SD

³Means within a column without a common letter differ, p<0.05

3.3. Triglyceride Levels

Triglyceride concentration of delphinidin in the differentiation and maturation process was analysed (Table 3). The dose of 20 µM delphinidin was significantly higher than 10, 5 µM, and the control group in the differentiation. There was a significant difference between all groups (p<0.001). In the maturation, 20 µM of delphinidin was significantly higher than 10 and 5 µM (p<0.05). A dose of 10 µM delphinidin had significantly the lowest triglyceride concentration (p<0.05).

Table 3. Triglyceride concentration of delphinidin ¹^,²^,³.

	Triglyceride Concentration (mg/dl)
	Differentiation	Maturation
Delphinidin Concentration (µM)
20	67.7 ± 1.10 ^a	29.4 ± 2.21 ^a
10	28.8 ± 1.33 ^b	17.7 ± 1.55 ^c
5	43.2 ± 3.54 ^c	21.5 ± 1.99 ^c
Control	52.9 ± 0.88 ^d	52.9 ± 0.88 ^b
	p<0.001	p<0.05

¹Data were analyzed by one-way ANOVA

²Data are presented as mean ± SD

³Means within a column without a common letter differ, p<0.05

3.4. Metabolics

The GC-MS-based Metabolomics analysis identified 205 metabolites in cell samples. Only 37 of these metabolites were identified and the changes between the groups were statistically analysed. During the differentiation process, fifteen of these metabolites exhibited significant alterations between the control groups (p<0.05). To examine the differences in metabolomics profiles between the various groups in detail, a PLS-DA score plot was created to illustrate the distinctions in metabolite profiles between the control group (DFDK) and the lowest concentration of delphinidin treatment (5 µM) in our study (DFD5) (Figure 3a). A VIP graph was also utilized in order to identify the metabolites affecting differentiation in the PLS-DA score plot (Figure 3b). From GC-MS-based metabolomics profile analysis results, levels of glyceraldehyde, alanine, porphine, linoleic acid, and glycine were found to be different in terms of importance. During the maturation process two of identified metabolites were significantly altered between the control groups (p<0.05). The PLS-DA score plot presented a partially similar metabolomic profiles between groups (Figure 4a). A VIP graph was also utilized in order to identify the metabolites affecting differentiation in the PLS-DA score plot (Figure 4b). From GC-MS-based metabolomics profile analysis results, levels of mannose, p-cresol, threitol, talose, and glycerol were found to be different in order of importance.

4.1. Gene Expression Levels and Protein Levels

The differentiation of preadipocytes into adipocytes is tightly regulated by transcription factors, including PPARγ and C/EBP, required for growth arrest [12, 14]. Two studies examined delphinidin's impact on PPARγ. In the first, 3T3-L1 cells treated with delphinidin (25-100 µM) reduced PPARγ mRNA and protein levels dose-dependently [10]. In the second, delphinidin suppressed intermediate PPARγ markers [8]. However, our study showed no significant change in PPARγ protein or mRNA levels, suggesting no anti-adipogenic effect, possibly due to lower dosage compared to other studies. C/EBPβ, a key transcription factor in early adipocyte differentiation [14], was examined in one study, which found that delphinidin significantly downregulated C/EBPβ and C/EBPδ expression in 3T3-L1 cells [8]. PPARγ and C/EBPα expression are followed by the expression of adiponectin and fatty acid-binding protein 4 (FABP4), which are responsible for terminal differentiation and formation of the fat cell phenotype [14]. While two studies suggest delphinidin as a potential anti-obesity agent due to its suppression of adipogenesis and mesenchymal stem cell adipogenesis [15], our data did not show a difference in FABP4 expression. Further studies are needed to confirm its effect.

CIDEA regulates beige adipocytes and it is associated with genes as PGC-1α and UCP1 involved in thermogenesis, during the transformation of WAT into BAT [16]. Although no studies explore delphinidin's effect on CIDEA expression, anthocyanin oligomers significantly up-regulated CIDEA [15]. UCP1, another key player in thermogenesis [17], lacks delphinidin-specific research, however anthocyanidins have shown potential in boosting energy expenditure by reducing mitochondrial respiration and dissipation of the mitochondrial proton gradient [18]. In a recent study, after the treatment of beige-like adipocytes with anthocyanin-rich black soybean, the level of UCP1 in mitochondria was fivefold higher than that in WAT [19]. On the contrary, current research demonstrated that there was not a significant difference after treatment for UCP1 protein concentration. However, we observed the highest UCP1 expression levels were in 5µM delphinidin-exposed cells during the differentiation stage. This result suggests delphinidin induced browning in 3T3-L1 adipocytes, especially in the early phase of adipogenesis, but not in a dose-dependent manner. However, the current study is the first that uses only delphinidin and it is limited to demonstrating the preventive role of delphinidin as a thermogenic compound in adipocytes.

PGC1-α, PPARγ, and PRDM16 are known crucial regulators of browning and UCP1 mRNA expression [17, 20]. PGC1-α increased oxygen consumption via mitochondrial activation and its expression increased in BAT [21]. There are no studies on the effect of delphinidin on PGC1-α, however, there are limited studies evaluating the anthocyanidin effect on this protein. In a recent study, the expression of PGC1-α was dose-dependently increased by anthocyanin treatment (500 mg/kg/day) cell culture of BAT from rats fed anthocyanins compared to that in BAT from non-fed rats [19]. Similarly, 3T3-L1 cells increased PGC-1α protein levels after treatment with anthocyanin oligomers [15]. In the present research, the protein concentration of PGC1-α was dose-dependently increased during maturation. However, delphinidin did not induce significant changes in PGC1α gene expression at any stage. PRDM16 is one of the key transcription factors that regulate the thermogenic gene program in brown and beige adipocytes [22]. PPARγ deacetylation is strictly connected with the up-regulation of PRDM16, which promotes browning [23]. To our knowledge, this is the first study to show the effect of delphinidin on PRDM16. There is only one study on the treatment of anthocyanin oligomers in 3T3-L1 that showed a significantly increased expression of the PRDM16 gene and proteins [15]. According to this study, this research showed an increase level of PRDM16 during the maturation process, especially at high dose of delphinidin. Our results suggest that delphinidin has browning effect on 3T3-L1 cells. It is necessary to conduct more studies to demonstrate the preventive role of delphinidin as a thermogenic compound in adipocytes.

4.2. Triglyceride Levels

In vitro and in vivo studies have shown that treatment with delphinidin had a great inhibitory effect on intracellular triglyceride accumulation in a dose-dependent manner. The triglyceride accumulation in HepG2 cells was reduced by 50% with 100 µM and 59% with 180 µM of delphinidin [24]. Another study showed that 25, 50, and 100 µM of delphinidin reduced intracellular triglyceride accumulation in 3T3-L1 cells by 30%, 38%, and 58%, respectively [8]. Black soybean seed coat extract reduced triglyceride levels significantly within a high-fat diet and streptozotocin (STZ)-induced diabetic mice [25]. However, some studies did not show an effect of delphinidin on triglyceride accumulation [24, 26, 27]. In the present study, intracellular triglyceride accumulation was significantly reduced by delphinidin. However, triglyceride did not reduce in a dose-dependent manner. Interestingly, in both process 10 µM of delphinidin reduced intracellular triglyceride accumulation more than 20 µM. Moreover, 10 µM reduced triglyceride levels in the maturation process (67%) instead of the early differentiation process (46%). A possible reason for this result could be the delayed effect of delphinidin treatment on cell cycle progression. The lipid-lowering effect of delphinidin is largely attributed to the early stage of adipogenesis, but along with this study, the positive effect of delphinidin appears in the intermediate and late adipogenesis phases.

4.3. Metabolomics

This study is the first to show the effect of delphinidin treatment on metabolic pathways in 3T3-L1 cells. During the differentiation process, the metabolite significantly increased with delphinidin was glyceraldehyde, a triose monosaccharide intermediate in the metabolism of fructose [28]. Glyceraldehyde is highly reactive and can modify and cross-link proteins, which are cytotoxic and inhibit intracellular glutathione levels [29]. The study suggests that delphinidin might have an effect on fructose metabolism only in lower concentrations.

During the maturation, the mannose metabolite significantly changed. It belongs to the class of organic compounds known as hexose phosphates [30]. Mannose is involved in a number of enzymatic reactions, especially it can be converted into fructose 6-phosphate through its interaction with the enzyme mannose-6-phosphate isomerase. Mannose inhibits adipogenic differentiation and regulates function for glucose metabolism [31]. Our results suggest that 5 µM delphinidin treated cells could be more effective. Surprisingly, the higher levels of delphinidin show lower levels of mannose. Therefore, delphinidin could be positively reduce adipogenic differentiation at a lower dose. To further explore the role of delphinidin with metabolites, it is necessary more studies, where metabolomic analyses were carried out using both GC-MS and liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-qTOF-MS) methods.

Plant-based bioactive compounds show potential in anti-obesity effects, however research on delphinidin is limited. Our study explored delphinidin's effects on 3T3-L1 preadipocytes, indicating its potential to contribute to browning by increasing UCP1 and PRDM16 expression and reducing triglyceride levels during maturation. However, we did not observe any effects of delphinidin on the key expressions of beige specific genes (PPARγ, PGC1 α and CIDEA). Additionally, the impact on metabolites such as glyceraldehyde, alanine, porphine, mannose and p cresol remain uncertain. While our foodomics approach offers valuable insights into the potential of delphinidin as a dietary supplement for obesity management, further in-depth in vitro and in vivo studies are essential to fully elucidate its browning effects and therapeutic viability.

Uncoupled Protein-1, UCP-1; Peroxisome Proliferators-Activated Receptor Γ, PPARγ; CCAAT/Enhancer Binding Protein β, C/EBP β ;Peroxisome Proliferator-Activated Receptor-Gamma Coactivator-1alpha, PGC1-α; Cell Death Inducing DFFA Like Effector A, CIDEA; Fatty Acid Binding Protein 4, FABP4; PR Domain Containing 16, PRDM16; isomethyl-butylxanthine, IBMX; Dimethyl sulfoxide, DMSO; Dulbecco’s modified Eagle’s medium, DMEM; Fetal bovine serum, FBS; Brown Adipose Tissue, BAT; White Adipose Tissue, WAT.

Acknowledgement

Authors thanks TUBITAK for their supply to this project with project number 220S742.

Competing interests

The authors declare no competing interests.

Funding Statement

This work was supported by TUBITAK 1001-The Scientific and Technological Research Projects Funding Program [project no. 220S742].

CRediT authorship contribution statement

Elif Didem Örs Demet: Investigation, Data curation, Formal analysis, Visualization, Writing- Original draft preparation; Kübra Ucar Baş, Aslıhan Agacdiken, Dilem Tugal Aslan: Investigation, Resources, Formal analysis; Tuba Recber: Software, Methodology, Validation, Formal analysis, Data curation; Tugba Gulsun: Investigation, Writing- Reviewing and Editing. Mustafa Çelebier; Software, Methodology, Validation, Supervision, Data curation, Writing-Review & Editing; Zeynep Göktaş: Conceptualization, Methodology, Project administration, Super-vision, Funding acquisition, Writing - Review & Editing. All authors approved the final manuscript

Data Availability

The data supporting the present study will be made available on request from the corresponding author.

Valdés, A., et al., Foodomics: Analytical Opportunities and Challenges. Analytical Chemistry, 2022. 94(1): p. 366-381.
Ortea, I., Foodomics in health: Advanced techniques for studying the bioactive role of foods. TrAC Trends in Analytical Chemistry, 2022. 150: p. 116589.
Chen, Z., et al., The Multifunctional Benefits of Naturally Occurring Delphinidin and Its Glycosides. J Agric Food Chem, 2019. 67(41): p. 11288-11306.
Khoo, H.E., et al., Anthocyanidins and anthocyanins: colored pigments as food, pharmaceutical ingredients, and the potential health benefits. Food Nutr Res, 2017. 61(1): p. 1361779.
Cremonini, E., et al., Anthocyanins protect the gastrointestinal tract from high fat diet-induced alterations in redox signaling, barrier integrity and dysbiosis. Redox Biol, 2019. 26: p. 101269.
Daveri, E., et al., Cyanidin and delphinidin modulate inflammation and altered redox signaling improving insulin resistance in high fat-fed mice. Redox Biol, 2018. 18: p. 16-24.
Sandoval, V., et al., Lyophilized Maqui (Aristotelia chilensis) Berry Induces Browning in the Subcutaneous White Adipose Tissue and Ameliorates the Insulin Resistance in High Fat Diet-Induced Obese Mice. 2019. 8(9).
Rahman, N., M. Jeon, and Y.S. Kim, Delphinidin, a major anthocyanin, inhibits 3T3-L1 pre-adipocyte differentiation through activation of Wnt/β-catenin signaling. Biofactors, 2016. 42(1): p. 49-59.
Kim, H.K., et al., Black soybean anthocyanins inhibit adipocyte differentiation in 3T3-L1 cells. Nutr Res, 2012. 32(10): p. 770-7.
Long, Q., et al., Delphinidin-3-sambubioside from Hibiscus sabdariffa. L attenuates hyperlipidemia in high fat diet-induced obese rats and oleic acid-induced steatosis in HepG2 cells. Bioengineered, 2021. 12(1): p. 3837-3849.
Park, M., A. Sharma, and H.J. Lee, Anti-Adipogenic Effects of Delphinidin-3-O-β-Glucoside in 3T3-L1 Preadipocytes and Primary White Adipocytes. Molecules, 2019. 24(10).
Wu, T., et al., Blueberry and mulberry juice prevent obesity development in C57BL/6 mice. PLoS One, 2013. 8(10): p. e77585.
Aglago, E.K., et al., Consumption of Fish and Long-chain n-3 Polyunsaturated Fatty Acids Is Associated With Reduced Risk of Colorectal Cancer in a Large European Cohort. Clinical Gastroenterology and Hepatology, 2020. 18(3): p. 654-666.e6.
Farmer, S.R., Transcriptional control of adipocyte formation. Cell Metab, 2006. 4(4): p. 263-73.
Choi, M., S. Mukherjee, and J.W. Yun, Anthocyanin oligomers stimulate browning in 3T3-L1 white adipocytes via activation of the β3-adrenergic receptor and ERK signaling pathway. Phytother Res, 2021. 35(11): p. 6281-6294.
Jash, S., et al., CIDEA Transcriptionally Regulates UCP1 for Britening and Thermogenesis in Human Fat Cells. iScience, 2019. 20: p. 73-89.
Bonet, M.L., P. Oliver, and A. Palou, Pharmacological and nutritional agents promoting browning of white adipose tissue. Biochim Biophys Acta, 2013. 1831(5): p. 969-85.
Skates, E., et al., Berries containing anthocyanins with enhanced methylation profiles are more effective at ameliorating high fat diet-induced metabolic damage. Food Chem Toxicol, 2018. 111: p. 445-453.
Lee, M. and M. Lee, The Effects of C3G and D3G Anthocyanin-Rich Black Soybean on Energy Metabolism in Beige-like Adipocytes. J Agric Food Chem, 2020. 68(43): p. 12011-12018.
Richard, D. and F. Picard, Brown fat biology and thermogenesis. Front Biosci (Landmark Ed), 2011. 16(4): p. 1233-60.
Gill, J.A. and M.A. La Merrill, An emerging role for epigenetic regulation of Pgc-1α expression in environmentally stimulated brown adipose thermogenesis. Environ Epigenet, 2017. 3(2): p. dvx009.
Saulite, L., et al., Effects of malvidin, cyanidin and delphinidin on human adipose mesenchymal stem cell differentiation into adipocytes, chondrocytes and osteocytes. Phytomedicine, 2019. 53: p. 86-95.
Bargut, T.C.L., et al., Browning of white adipose tissue: lessons from experimental models. Horm Mol Biol Clin Investig, 2017. 31(1).
Parra-Vargas, M., et al., Delphinidin Ameliorates Hepatic Triglyceride Accumulation in Human HepG2 Cells, but Not in Diet-Induced Obese Mice. Nutrients, 2018. 10(8).
Chen, Z., et al., Hypoglycemic and hypolipidemic effects of anthocyanins extract from black soybean seed coat in high fat diet and streptozotocin-induced diabetic mice. Food Funct, 2018. 9(1): p. 426-439.
van der Heijden, R.A., et al., Effects of Anthocyanin and Flavanol Compounds on Lipid Metabolism and Adipose Tissue Associated Systemic Inflammation in Diet-Induced Obesity. Mediators of Inflammation, 2016. 2016: p. 2042107.
Aboufarrag, H., et al., No Effect of Isolated Anthocyanins from Bilberry Fruit and Black Rice on LDL Cholesterol or other Biomarkers of Cardiovascular Disease in Adults with Elevated Cholesterol: A Randomized, Placebo-Controlled, Cross-Over Trial. Mol Nutr Food Res, 2022. 66(21): p. e2101157.
Mayes, P.A., Intermediary metabolism of fructose. Am J Clin Nutr, 1993. 58(5 Suppl): p. 754s-765s.
Usui, T., et al., Cytotoxicity and oxidative stress induced by the glyceraldehyde-related Maillard reaction products for HL-60 cells. Biosci Biotechnol Biochem, 2004. 68(2): p. 333-40.
Wood, F.C., Jr., et al., Metabolism of mannose and glucose by adipose tissue and liver slices from normal and alloxan-diabetic rats. J Biol Chem, 1961. 236: p. 18-21.
Sharma, V., M. Ichikawa, and H.H. Freeze, Mannose metabolism: more than meets the eye. Biochem Biophys Res Commun, 2014. 453(2): p. 220-8.

No competing interests reported.

Supplementalmaterial19.08.2024.docx
Supplemental Online Material Table S1. The metabolites and relative concentrations identified by GC-MS analysis in the differentiation process and in the maturation process Figure S1. Heat colour graphics of selected metabolites that are effective in the differentiation of metabolic profiles in the differentiation process Figure S2. Heat color graphics of selected metabolites that are effective in the differentiation of metabolic profiles in the maturation process.

Exploring the Effects of Delphinidin Treatment on Browning Processes in 3T3-L1 Preadipocyte Cells: A Foodomics Approach

Status:

Version 1

Abstract

Figures

1. Introduction

2. Material and Methods

3. Results

4. Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1