Metabolic activities of bioactive compounds on cellular models of hepatosteatosis and adipose tissue: the role of peroxisome proliferator-activated receptors (PPARs)

doi:10.21203/rs.3.rs-4700469/v1

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Metabolic activities of bioactive compounds on cellular models of hepatosteatosis and adipose tissue: the role of peroxisome proliferator-activated receptors (PPARs)

https://doi.org/10.21203/rs.3.rs-4700469/v1

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Obesity is associated with fatty liver disease. Available therapies show modest efficacy, and nutraceuticals with good effectiveness and safety are largely investigated. We focused on five natural compounds, three plant phenolic compounds (carvacrol, rosmarinic acid, silybin), and two thyroid hormones to compare their beneficial effects on two cellular models of hepatosteatosis and adipogenesis. All compounds ameliorated the lipid accumulation and oxidative stress in both models, but with different potencies. The peroxisome proliferator-activated receptors (PPARs) are pivotal controllers of adipogenesis and lipid metabolism. For the main isoforms, PPARγ and PPARa, we assessed their possible binding to the compounds by molecular docking calculations, and their expression pattern by real-time PCR. All compounds bind both PPARs with different affinity, while not all compounds affect their expression. The results may clarify the distinctive molecular mechanisms underlying the action of the five compounds in the different cell models with possible applications to treat obesity.

Biological sciences/Molecular biology/Transcription

Biological sciences/Chemical biology/Natural products

Biological sciences/Biological techniques/Biological models

Natural products

Hepatosteatosis

Adipogenesis

metabolic activities

Nuclear receptors

Molecular Docking

Metabolic syndrome (MS) is a clinical condition characterized by a cluster of abnormalities, including visceral obesity, fatty liver, dyslipidemia, hyperinsulinemia, insulin resistance, and type 2 diabetes mellitus (T2DM) ¹. Obesity is a complex disorder defined by visceral fat depot due to excess caloric intake and physical inactivity in genetically susceptible individuals; in Caucasian population obesity is diagnosed by a body mass index (BMI) ≥ 30kg/m2. In parallel with the escalating prevalence of obesity worldwide, the nonalcoholic fatty liver disease (NAFLD) is also increasing ². In fact, a mechanistic interplay connects NAFLD with adipose tissue hypertrophy and obesity ³. Although many drugs have been proposed to treat obesity and NAFLD, their side effects make them poorly attractive, and the current recommended alternative relies on lifestyle modifications, including diet and physical activity ⁴.

In this context, natural compounds represent an attractive possibility, and the use of nutraceuticals for weight loss is increasing ⁵. Natural products encompass molecules with enormous structural and chemical diversity. Many studies have shown the potential of phenolic compounds (PC) in modulating gene expression and metabolic pathways, remodeling the epigenetic profile, and potentially contributing to weight loss ⁵. PCs are plant-derived compounds characterized by the presence of at least one phenol ring in their molecular structure, and they are classified into 4 main groups: lignans, phenolic acids, flavonoids, and stilbenes. Silybin (2,3-dihydro-3-(4-hydroxy-3-methoxyphenyl)-2-(hydroxymethyl)-1,4-benzodioxin-6-yl]-2,3-dihydro-3,5,7-trihydroxy-4H-1-benzopyran-4-one) is a flavonolignan representing the main active ingredient of silymarin, a PC mixture extracted from the milk thistle Silybum marianum. Milk thistle has been used for thousands of years as a remedy for a variety of conditions due to its anti-inflammatory and hepatoprotective properties (Skottová & Krecman, 1998; Flora et al., 1998; Abenavoli et al., 2010). Silybin is a strong antioxidant able to reduce inflammation and mitochondrial dysfunction, and a potent lipid lowering agent in NAFLD ^8–10. Carvacrol (2-methyl-5-(1-methylethyl)-phenol) is a phenolic monoterpenoid found in essential oils of many aromatic plants of the Lamiaceae family ¹¹. Several in vitro and in vivo studies demonstrated that carvacrol possesses a wide range of bioactivities such antimicrobial, antioxidant, and anticancer activities ^12–15. Rosmarinic acid (α-o-caffeoyl-3,4-dihydroxyphenyllactic acid), the ester of caffeic acid, is a phenolic acid abundant in aromatic plants of the Lamiaceae family, known for its antimicrobial, anti-inflammatory, antioxidant effects ¹⁶, and for its anti-diabetic potency ¹⁷.

Among the endogenous molecules, thyroid hormones are the main controller of the body metabolism ¹⁸. The main bioactive hormone is the 3,5,3’-triiodo-L-thyronine (T3) which can be deiodinated to the bioactive derivative 3,5-diiodo-l-thyronine (T2) (de Lange et al., 2011; Vergani, 2014). Thyroid hormones (THs) stimulate lipolysis from fat stores in white adipose tissue and from dietary fat sources to generate circulating free fatty acids (FAs), which are the major source of lipids for the liver. In the liver, THs play catabolic actions by mobilizing lipids ¹⁸.

The present study investigated and compared the metabolic effects of five natural compounds, both exogenous (carvacrol, rosmarinic acid, and silybin), and endogenous (T2 and T3 hormones), using the cellular models of hepatic steatosis and mature adipocytes, in the attempt to clarify the distinctive molecular mechanisms underlying their beneficial action on different tissues. The research focused on the peroxisome proliferator-activated receptors (PPARs) as they are key regulators of metabolic homeostasis, thus being attractive therapeutic targets for metabolic disorders. PPARs belong to the nuclear hormone receptor superfamily; they bind FAs and FA-derivatives to regulate lipid and carbohydrate metabolism ²², but they act also in inflammation, cell proliferation, and differentiation. In mammals, three subtypes (PPARα, γ, β/δ) are encoded by different genes, and show tissue-specific expression patterns ²³. PPARα is found mainly in tissues with high catabolic rate, such as the liver, where it controls expression of FA transporters and enzymes of FA oxidation ^24,25. PPARγ is the main isoform in white adipose tissue, but is also expressed in the healthy liver; in obese patients, PPARγ over-expression seems to correlate positively with liver steatosis ²⁶. PPARβ/δ is expressed almost ubiquitously, but its role is less clear. The activation of the different PPAR isoforms is coordinated by both natural and synthetic ligands acting as agonists or antagonists to regulate distinct homeostatic pathways. Molecular docking is a computational technique that predicts the binding affinity of ligands to proteins, which have potential applications in nutraceutical research and drug development. Therefore, the present study compared the five bioactive compounds in terms of their binding affinity to PPARγ and PPARa, as well as their effect on modulating the expression of the two PPARs. The findings of the present study provide new insights into the molecular mechanism of their action in the different tissues.

2.1. Chemicals

All chemicals, unless otherwise indicated, were supplied by Sigma- Aldrich Corp. (Milan, Italy).

2.2. Cell culture and treatments

FaO cells are a rat hepatoma cell line supplied by European Collection of Authenticated Cell Cultures (ECACC, Sigma–Aldrich Corp.). These cells represent a well-differentiated liver cell line expressing a variety of liver-specific functions ^27,28 and showing a very stable phenotype ²⁹. Cells are cultured at 37°C in a humidified atmosphere of 5% CO2. The medium was the Coon’s modified Ham’s F12 supplemented with 10% heat-inactivated Foetal Bovine Serum (FBS, Euroclone, Milan, Italy). For the in vitro model of hepatic steatosis, FaO cells were seeded on Petri-dishes, and when they reached 80% confluence, they were treated with a mixture of oleate and palmitate (OP) in a 2:1 molar ratio, at a final concentration of 0.75mM in starvation medium (0.25% BSA without FBS) ⁸. After 3hrs, the medium was removed and fresh medium was added containing the following lipid-lowering agents: SIL (50µM), CVL (10µM), RA (10µM), T3 (1µM), and T2 (1µM), alternatively. The incubation with the lipid-lowering agents was maintained for 24 hrs. The chemical structures of these compounds are reported in Fig. 1. At the end, the cells were harvested and kept at -80°C for further experimental measurements.

The mouse fibroblasts 3T3-L1 are a pre-adipocyte cell line supplied by the American Type Culture Collection (ATCC, Manassas, VA, USA). 3T3-L1 cells were cultured in Dulbecco's modified Eagle medium (DMEM) supplemented with 10% FBS and 25 mmol/L glucose. The cells were cultured for 2 days to get 70–80% confluence (day 0), and then adipogenic differentiation was induced by adding the adipogenic mix containing 1.7µM insulin, 1µM dexamethasone (DEX), and 500µM 3-isobuthyl-1-methylxanthine (IBMX) to the complete medium. Incubation was maintained for 2 days. Then, the medium was supplemented with complete medium containing insulin alone (1.7µM) and the medium was replaced every 2 days. Simultaneously with the third insulin addition, the cells were treated with one of the lipid-lowering agents, alternatively: SIL (50µM), CVL (10µM), RA (10µM), T3 (1µM), and T2 (1µM). The treatment was maintained for 10 days. At the end of the treatments, the cells were harvested. Figure 2 illustrates both models of hepatosteatosis and adipogenesis over a time-scale manner.

For each experiment, the treatments were performed in quadruplicates. MTT assay was performed on both FaO and 3T3-L1 cells to exclude any cytotoxicity of the different treatments. No significant changes were observed (data not shown).

2.3. Protein quantification

The protein content was determined by the Bradford assay using BSA as a standard ³⁰.

2.4. Intracellular lipid quantification

At the end of each treatment, both FaO and 3T3 cells were scraped, centrifuged, and lysed to extract lipids using a chloroform-methanol (2:1) mixture, as previously described ³¹. The triglyceride content of the cellular samples was quantified using the ‘Triglycerides liquid’ kit (Sentinel, Milan, Italy). The absorbance was read at 546nm using a Varian Cary 50 spectrophotometer (Agilent, Milan, Italy). Values were normalized for the protein content. Data are expressed as percent TG content relative to controls.

Lipid peroxidation

Lipid peroxidation was determined spectrophotometrically through the thiobarbituric acid reactive substances (TBARS) assay which is based on the reaction of malondialdehyde (MDA;1,1,3,3-tetramethoxypropane) with thiobarbituric acid (TBA) ³². Briefly, 1 vol. of cell suspension was incubated for 45 min at 95℃ with 2 vol. of TBA solution (0.375% TBA, 15% trichloroacetic acid, 0.25N HCl). Then, 1 vol. of N-butanol was added, and the organic phase was read using a Varian Cary50 spectrophotometer at 532 nm. Results were expressed as pmol MDA/mL per mg protein.

2.5. RNA extraction and quantitative real-time PCR

Total RNA was isolated from cultured cells by the acid phenol: chloroform procedure using Trizol reagent according to the manufacturers’ instructions and then treated with RNase ³³. First strand cDNA was synthesized from total RNA using M-MuLV Reverse Transcriptase (Fermentas, Dasit, Milan, Italy). Quantitative real-time PCR (qPCR) was carried out in quadruplicate using 1 × IQTM SybrGreen SuperMix and Chromo4TM System apparatus (Bio-Rad, Milan, Italy). The relative quantity of target mRNA was calculated using the comparative Cq (represents the cycle number at which the amount of amplified target reaches the fixed threshold) method and was normalized for the expression of glyceraldehyde 3-phosphate dehydrogenase (GAPDH). The expression of the target genes was then calculated as relative quantity of mRNA (fold induction) with respect to controls. Primer pairs were designed ad hoc starting from the coding sequences of Rattus norvegicus and Mus musculus (http://www.ncbi.nlm.nih.gov/Genbank/GenbankSearch.html) and are listed in Table 1.

Table 1

Primer sequences used for quantitative real-time PCR (qRT-PCR).
Gene	Primer Name	Primer Sequence 5’-> 3’
PPARα	Fwd	AAGCCATCTTCACGATGCTG
PPARα	Rev	GAGGTCCCTGAACAGTGGCA
PPARγ	Fwd	CGGAGTCCTCCCAGCTGTTCGCC
PPARγ	Rev	GGCTCATATCTGTCTCCGTCTTC
GAPDH	Fwd	GACCCCTTCATTGACCTCAAC
GAPDH	Rev	CGCTCCTGGGAAGATGGTGATGGG

2.6. In silico docking

Molecular docking is a computational technique used to predict the binding affinity of ligands to receptor proteins. In silico methodology is employed to elucidate the binding mode of compounds at the target site. For docking studies, the structures of bioactive compounds were retrieved from the PubChem website (https://pubchem.ncbi.nlm.nih.gov/) and converted from .mol files to .pdb files using UCSF Chimera software ³⁴.

The structures of the PPARa and of the PPARg rat transcription factors were obtained with the help of the Swiss-Model server ³⁵ by using as models the human PPARs crystal structures found in Protein Data Bank (pdb-id: 3fur for PPARg and pdb-id: 3vi8 for PPARa).

We carried out docking calculations using Autodock 4.2 suite of programs ³⁶. The Gasteiger charge calculation method was used, and partial charges were added to the ligand atoms prior to docking. The Lamarckian genetic algorithm (LGA), which is available in Autodock, was employed. Finally, Autodock was used to calculate the binding free energy of each bioactive compound in the PPARa and PPARg molecular structures. Before docking the bioactive compounds to the PPAR proteins, we utilized the CavityPlus web server ³⁷ to compute the primary docking cavities of the PPAR proteins, which represent larger regions than the specific docking sites.

The binding energy (ΔG in kcal/mol) of each compound with the two PPAR isoforms was evaluated by using Autodock 4.2 software. The higher negative value for ΔG defines the stronger interaction of the ligand with the target site. The Ki (inhibition constant in µM), representing the dissociation constant (Kd) of the protein-inhibitor complex, was also calculated from the binding energy using the formula: Ki = exp(ΔG/RT), where R is the universal gas constant (1.985 × 10 − 3 kcal mol − 1 K − 1) and T is the temperature (298.15 K).

2.7. Statistical analysis

Statistical analysis was performed using GraphPad Prism 8 (GraphPad Software Inc., La Jolla, CA, USA). Differences between groups were compared using one-way ANOVA with post hoc testing. A two-sided p value < 0.05 was considered statistically significant. Continuous variables are presented as mean ± SD.

3.1. Metabolic activity of the compounds on steatotic hepatocytes

Upon exposure to oleate/palmitate, FaO hepatocytes increase their TG depots (+ 152% vs Ctrl; p ≤ 0.0001) thus representing a reliable model of mild hepatic steatosis on which we tested the lipid lowering potential of the five bioactive compounds (Fig. 3). All compounds significantly reduced the TG accumulation of about − 149% (CVL), -139% (T2), -125% (T3), -123% (SIL) and − 61% (RA) compared to steatotic hepatocytes (p ≤ 0.0001). Therefore, a similar lipid lowering potential was observed for all the compounds, with rosmarinic acid being the least effective one (p ≤ 0.01).

The ectopic fat accumulation in hepatocytes typically stimulates ROS over-production due to the stimulation of FA oxidation. In turn, the ROS excess triggers the lipid peroxidation of cellular membranes, a marker for oxidative stress that we quantified in terms of MDA production. In steatotic hepatocytes, we observed an increase in the MDA level compared to control cells (+ 87%; p ≤ 0.001) that was reduced after exposure to the single compounds (Fig. 3B). All the bioactive compounds exhibit a similar antioxidant efficacy by reducing the MDA level of -116% (RA), -105% (CVL), -101% (T3), -88% (SIL) (p ≤ 0.0001), and − 85% (T2) (p ≤ 0.001) respect to steatotic hepatocytes.

3.2. Metabolic activity of the compounds on mature adipocytes

The adipogenic differentiation of 3T3 pre-adipocytes to mature adipocytes is accompanied by the accumulation of TGs in cytosolic lipid droplets (+ 153% with respect to pre-adipocytes; p ≤ 0.0001). When during adipogenesis the cells were treated with the single compounds, the lipid accumulation was markedly reduced of about − 159% (SIL), -147% (CVL), -122% (RA), -123% (T3) and − 93% (T2) respect to mature adipocytes (p ≤ 0.0001) (Fig. 4A). Therefore, we observed a similar lipid-lowering ability for all the compounds with T2 being the least effective one.

Along with adipocyte maturation, we observed a stimulation of ROS generation leading to a significant increase in the MDA level (+ 136% with respect to pre-adipocytes; p ≤ 0.0001) (Fig. 4B). A reduction in ROS generation was detected when mature adipocytes were treated with the five compounds: -155% (T3), 134% (CVL), -122% (T2), -113% (SIL), and − 102% (RA) respect to mature adipocytes (p ≤ 0.001). We can highlight that also in adipocytes the five bioactive compounds exhibit a similar antioxidant efficacy, and that the thyroid hormone T3 was the most effective antioxidant agent.

3.3. PPAR expression is modulated by the compounds

We investigated the possible effects of the five compounds on the mRNA expression of two PPAR isoforms: PPARγ and PPARα in both hepatocytes and adipocytes (Fig. 5). In steatotic hepatocytes, we observed a significant increase in the PPARγ mRNA expression (2.8-fold induction vs Ctrl, p ≤ 0.0001), and all the compounds significantly reduced this up-regulation (Fig. 5A). The thyroid hormones T2 and T3 were the most effective in repressing the PPARγ up-regulation (0.6 and 0.5-fold induction vs steatotic hepatocytes, respectively; p ≤ 0.0001) On the other hand, also the PPARα mRNA expression was up-regulated in steatotic hepatocytes (2.89-fold induction vs Ctrl, p ≤ 0.0001), but in this case a further up-regulation was observed when steatotic hepatocytes were treated with all the compounds except for silybin and rosmarinic acid (Fig. 5B).

In mature adipocytes, the PPARγ mRNA expression was up-regulated by 4.2-fold induction vs pre-adipocytes (p ≤ 0.0001) to sustain the in vitro adipogenic differentiation (Fig. 5C). Interestingly, the 5 compounds impacted differently on PPARγ transcription. The PPARγ expression was further up-regulated by RA (7.18-fold induction vs mature adipocytes; p ≤ 0.0001), T2 (3.14-fold induction vs mature adipocytes; p ≤ 0.0001), and T3 (1.3-fold induction vs mature adipocytes; p ≤ 0.01), whereas carvacrol and silybin did not modify it. Conversely, mature adipocytes showed a marked decrease in the PPARα mRNA level compared to pre-adipocytes (0.6-fold induction vs pre-adipocytes; p ≤ 0.05), also in this case the five compounds impacted differently on this PPAR isoform. While carvacrol and rosmarinic acid did not modify significantly the PPARα expression, silybin and T3 further down-regulated the PPARα expression with respect to mature adipocytes (0.41-fold induction and 0.35-fold induction vs mature adipocytes; p ≤ 0.01 and p ≤ 0.05, respectively) (Fig. 5D).

3.4. Docking results

The binding energy (ΔG) and the inhibition constant (Ki) of each compound with the two PPAR isoforms was evaluated by using Autodock 4.2 software, as described in Materials and Methods. A higher negative value for ΔG defines a stronger interaction of the ligand with the target site. PPARs function as sensors for a variety of molecules that act as agonists or antagonists. As positive controls, we tested two selective agonists of each PPAR. Rosiglitazone and Pioglitazone are standard agonists of PPARγ, belonging to the thiazolidinedione type ³⁸. Bezafibrate and Clofibrate are PPARα agonists belonging to the fibrate group. Of note, bezafibrate operates as a pan-agonist for all three PPAR isoforms. The docking scores of the five ligands with both PPARγ and PPARα proteins, alongside those of the standard agonists, were listed in Table 2. For all the 5 compounds, we observed the highest docking score with either PPARγ or PPARα within the same cavity bound by the selective agonists, despite some slight differences in the binding sites (Fig. 6 & Fig. 7).

Table 2

Amino acids in close contact, binding affinities and inhibition constant (T = 298.15 K) of the five bioactive compounds and the standard agonists with respect to PPARg (first part of the Table) and PPARa (second part of the Table).
Protein / Receptor	Ligand	Close contacts	Binding energy [kcal/mol]	Ki predicted [mM]
PPARg	Silybin	PHE310; CYS313; GLN314; ARG316; SER317; HIS351; TYR355; LEU358; LEU361; ILE369; SER370; GLU371; PHE391; MET392; HIS477; LEU481	-9.19	0.19
	T2	ASP288; GLY312; CYS313; ARG316; TYR355; LEU358; VAL367; ILE369; SER370; MET376; PHE391; MET392	-9.09	0.22
	T3	ASP288; GLY312; CYS313; TYR355; LEU358; VAL367; ILE369; SER370; MET376; LEU381; PHE391; MET392	-7.33	4.25
	Carvacrol	PHE310; CYS313; GLN314; TYR355; PHE388; PHE391; HIS477	-5.94	46.7
	Rosmarinic acid	PRO255; LEU256; ASP288; LYS289; PHE315; ARG316; GLU319; GLU323; ILE369; SER370; GLU371	-5.33	124.7
	Pioglitazone	ALA306; ILE309; PHE310; CYS313; ARG316; TYR355; LEU358;, LEU361; ILE369; SER370; LEU381; PHE388; PHE391; HIS477	-9.05	0.23
	Rosiglitazone	ILE309; PHE310; CYS313; ARG316; TYR355; LEU358; LEU361; LEU368; ILE369; PHE388; PHE391	-8.17	1.03
PPARa	Silybin	ASN219; CYS276; MET279; GLU286; MET320; LEU321; LEU324; MET330; ILE332; ILE354; MET355; LYS358	-10.13	0.04
	T2	PHE218; ASN219; MET220; CYS276; MET279; SER280; THR283; GLU286; ILE317; MET320; LEU321; LEU324	-9.05	0.23
	T3	PHE272; CYS275; CYS276; GLN277; MET279; SER280; TYR314; ILE317; LEU321; MET330; ILE332; ALA333; MET355; HIS440; TYR464	-8.05	1.25
	Carvacrol	TYR214; ASN219; MET220 ASN221; THR283; GLU286; MET320; SER323; LEU324	-6.27	25.6
	Rosmarinic acid	PHE218; ASN219; MET220; CYS276; MET279; SER280; THR283; GLU286; ILE317; MET320; LEU321	-6.85	9.50
	Bezafibrate	CYS276; GLN277; MET279; SER280; THR283; TYR314; ILE317; MET320; LEU321; HIS440; VAL444; TYR464	-9.20	0.18
	Clofibrate	CYS276; GLN277; SER280; TYR314; PHE318; LEU321; ILE354; LYS358; HIS440; LEU456; LEU460; TYR464	-7.07	6.53

Regarding PPARg, the binding domain for the standard agonists Pioglitazone and Rosiglitazone have the following residues in common: ILE309; PHE310; CYS313; ARG316; TYR355; LEU358; LEU361; ILE369; PHE388; PHE391 (Table 2). A ΔG of − 9.05 and − 8.17 kcal/mol were calculated for Pioglitazone and Rosiglitazone, respectively, thus indicating that they effectively dock at the PPARg binding site. Also, the five compounds can be recognized as active agonists based on the negative energy of their docking scores. The docking scores reveal that silybin and T2 have the highest affinity for PPARγ (with Ki values of 0.19 and 0.22 µM, respectively), similar to that predicted for the standard agonist Pioglitazone (Ki 0.23 µM). It is noteworthy that the binding sites of silybin and T2 are slightly different from that of Pioglitazone, sharing 8 and 6 identical amino acids, respectively, with the binding site of Pioglitazone.

Regarding PPARα, the binding domain for the standard agonists Bezafibrate and Clofibrate shares the following amino acid sequence: CYS276, GLN277, SER280, TYR314, LEU321, HIS440, LEU456, and TYR464 (Table 2). A ΔG of -9.20 and − 7.07 kcal/mol were calculated for Bezafibrate and Clofibrate, respectively thus indicating that they effectively dock at the PPARα binding site. However, for Clofibrate our calculations indicate a predicted Ki higher than that of Bezafibrate, that suggest a higher affinity of Clofibrate for PPARα. Interestingly, the docking score suggests that silybin exhibits an affinity for PPARα (Ki 0.04 µM) higher than that of the standard agonist Bezafibrate (Ki 0.18 µM), even if there is a slight difference in the residues of the binding site, with silybin sharing only 4 amino acids with Bezafibrate (CYS276, MET279, MET320, LEU321) and Clofibrate (CYS276, LEU321, ILE354, LYS358) binding sites.

Due to the escalating epidemic of overweight and obesity, the identification of nutraceuticals with better therapeutic activity and minimal side-effects is of increasing interest for human health. It is of primary importance not only to identify new lipid-lowering candidates, but also to shed the light on the molecular pathways sustaining their beneficial effects. In the present study, we compared the lipid-lowering and antioxidant effect of a pool of natural bioactive compounds, three phenolic compounds and two hormones, using two different cellular models of fatty liver and adipose tissue. The main findings of this study indicate that, nevertheless a similar metabolic efficacy as lipid-lowering and antioxidant agents, the analyzed compounds impacted differently on the expression of the two main PPAR isoforms and bind them with different affinity, thus suggesting that different mechanisms might sustain the biological activity of these natural compounds.

Regarding the metabolic effects, our results clearly indicate that all the analyzed compounds are effective lipid lowering and antioxidant agents in steatotic hepatocytes, except the rosmarinic acid which showed poor efficacy against hepatosteatosis. Also in mature adipocytes, all the compounds were able to reduce the fat accumulation and the oxidative stress, and in these cells T2 was the least effective compound. Of note, regulating the maturation of adipocytes by influencing the lipid metabolism may be of interest for obesity and metabolic disorders, as well as the anti-steatotic effects for fatty liver.

To shed the light on the mechanisms sustaining the action of the five compounds, we focused on the PPARs, as these nuclear receptors act as lipid sensors to connect the nutritional inputs with the reprogram of lipid and glucose homeostasis ³⁹. Three are the PPAR isoforms that show different tissue distributions and physiological role, and PPARγ and PPARα are the main isoforms in liver and adipose tissue, respectively. Agonists /antagonists of PPARs are attractive therapeutic approaches in both obesity and NAFLD conditions. PPARα agonists (i.e. fibrates) normalize dyslipidaemia, lipid metabolism, and energy homeostasis, whereas PPARγ agonists (e.g., thiazolidinediones) improve insulin resistance and diabetes. ⁴⁰. ³⁸.

When we quantified the mRNA expression of PPARγ and PPARα we could appreciate some interesting differences depending on the compounds and the cell type. In steatotic hepatocytes, PPARγ expression was markedly up-regulated, and all the compounds significantly counteracted this up-regulation, mostly the thyroid hormones. Also, the mRNA expression of PPARα was up-regulated in steatotic hepatocytes, but in this case only the thyroid hormones and carvacrol further increased the mRNA expression while silybin and rosmarinic acid had no effects. Different patterns were identified in mature adipocytes. During adipogenesis, as expected, the mRNA expression of PPARγ was up-regulated, and the thyroid hormones and rosmarinic acid further up-regulated it. By contrast, the mRNA level of PPARα was down-regulated during adipocyte maturation, and all the compounds are very weak modulators of the PPARα transcription. Silybin and T3 further reduced it, while T2, carvacrol, and rosmarinic acid did not affect it.

It is well known that PPARγ is the predominant isoform in adipose tissue, and in the liver of both humans and animal models, increased expression of PPARγ associates with hepatic steatosis. Pioglitazone and rosiglitazone are synthetic agonists of PPARγ which find application as antidiabetic agents to induce insulin sensitization and improve glycemic control in T2DM patients ^41–43. Conversely, PPARα is the main isoform in the liver ⁴⁴, where it controls genes encoding for FA uptake and β-oxidation. Bezafibrate and clofibrate are synthetic agonists of PPARα which are employed for the treatment of dyslipidemia and obesity (Staels & Fruchart, 2005; Rakhshandehroo et al., 2010, Corrales et al., 2018). To this regard, our efforts focused on testing the possible binding and affinity of the five compounds with these two PPAR isoforms.

PPARγ protein consists of 5 domains ⁴⁷, where the E region is the largest domain representing the ligand-binding domain (LBD), and the C region is the DNA-binding domain (DBD) ⁴⁸. FAs are the endogenous agonists of PPARγ but they are weak agonists compared to the synthetic thiazolidinediones (Wang et al., 2014). PPARa protein consists of a N-terminal activating function-1 (AF-1) domain, a central DBD, and a C-terminal LBD. Natural ligands of PPARa include FAs and FA derivatives, as well as molecules with structural resemblance to FAs.

Our molecular docking analysis identified silybin as the phenolic compound with the strongest affinity for both PPARγ and PPARα, when compared with both the standard PPARγ agonist pioglitazone and the standard PPARα agonist bezafibrate. Also, the thyroid hormones, T2 in particular, are effective ligands for both PPARγ and PPARα. Therefore, we can assume that the strong interaction with the two PPAR isoforms may be, at least in part, the main event sustaining the highest metabolic functionality of both silybin and T2. Moreover, our findings are in line with previous reports showing that silybin binds and activates efficiently PPARα ^49,50. Moreover, many studies addressed the effects of natural dietary flavonoids such as quercetin and resveratrol on modulating transcription of PPARγ through agonist binding mode. Quercetin could decrease the level of cholesterol in macrophages via increased PPARγ expression ⁵¹, whereas resveratrol has shown to elicit PPARγ stability in 3T3-L1 adipocytes and decrease mRNA and protein levels of PPARγ ⁵².

It is interesting to note that the compounds under analysis act in a double key on PPARγ and PPARα: they are both modulators of their mRNA expression and agonists by binding them similarly with the standard agonists. Of note, also thiazolidinediones bind and activate PPARγ, and at the same time decrease the PPARγ protein levels.

In conclusion, natural products have proven historically to be a promising pool of structures for drug discovery. A big effort has recently been undertaken to explore the PPARα– and PPARγ-activating potential of a wide range of natural products originating from traditionally used medicinal plants or dietary sources. Many natural PPARγ ligands have been identified showing different binding modes to the receptor in comparison to the full thiazolidinedione agonists, and on some occasions, they were able to activate also PPARα (e.g. genistein). Therefore, our insights demonstrating that silybin and T2 are strong agonists of PPARα, showing an affinity similar to that of the synthetic agonists, and also of PPARγ can suggest their future applications due to the possibility to modulate PPARs activation by dietary interventions or food supplements. In fact, the severe adverse effects of thiazolidinediones led to their restricted clinical applications.

3,5-diiodo-l-thyronine	T2
3,5,3’-triiodo-L-thyronine	T3
Carvacrol	CVL
DNA-binding domain	DBD
Fatty acids	FAs
Ligand-binding domain	LBD
Malondialdehyde	MDA
Metabolic syndrome	MS
Mouse pre-adipocyte cell-line	3T3-L1
Nonalcoholic fatty liver disease	NAFLD
Peroxisome proliferator-activated receptors	PPARs
Peroxisome proliferator-activated receptors alpha	PPARα
Peroxisome proliferator-activated receptors gamma	PPARγ
Phenolic compounds	PC
Rat hepatoma cell-line	FaO
Reactive oxygen species	ROS
Rosmarinic acid	RA
Silybin	SIL
Triglyceride	TG
Binding energy	ΔG
Inhibition constant	Ki

conflict of interest

The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author Contribution

All authors contributed to this work significantly. F.D. carried out spectrophotometric experiments, performed real-time PCR and statistical analysis, and wrote the manuscript. H.Z. carried out the FaO cell growth and treatments. L.Z. carried out the adipocyte culture and adipogenesis. F.B. contributed to real-time PCR. A.P. participated in the study design and data interpretation. V.M. conducted the molecular docking analyses and contributed to writing the manuscript. L.V. conceived, designed and supervised the study, wrote, and reviewed the manuscript.

ACKNOWLEDGMENT

This work was supported by grants from the University of Genova (FRA 2020). Part of this work benefits the PRIMA 2022-B4HT_Prima 2022_00124 “Box for Health by Tradition & Innovation: promoting sustainable mediterranean diet by Healthy Foods Progetto” (CUP D33C22002150007).

Data Availability

Data is provided within the manuscript, but the data may be provided upon request. Contact the corresponding author: Laura Vergani ([email protected]).

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No competing interests reported.

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Metabolic activities of bioactive compounds on cellular models of hepatosteatosis and adipose tissue: the role of peroxisome proliferator-activated receptors (PPARs)

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Version 1

Abstract

Figures

1. Introduction

2. Materials & Methods

2.1. Chemicals

2.2. Cell culture and treatments

2.3. Protein quantification

2.4. Intracellular lipid quantification

2.5. RNA extraction and quantitative real-time PCR

2.6. In silico docking

2.7. Statistical analysis

3. Results

3.1. Metabolic activity of the compounds on steatotic hepatocytes

3.2. Metabolic activity of the compounds on mature adipocytes

3.3. PPAR expression is modulated by the compounds

3.4. Docking results

4. DISCUSSION

Abbreviations

Declarations

conflict of interest

Author Contribution

ACKNOWLEDGMENT

Data Availability

References

Additional Declarations

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Version 1