Study of the Lipolysis Effect of Nanoliposome-Encapsulated Ganoderma lucidum Protein Hydrolysates on Adipocyte Cells Using Proteomics Approach

Excessive lipid accumulation is a serious condition. Therefore, we aimed at developing safe strategies using natural hypolipidemic products. Lingzhi is an edible fungus and potential lipid suppression stimulant. To use Lingzhi as a functional hyperlipidemic ingredient, response surface methodology (RSM) was conducted to optimize the time (X1) and enzyme usage (X2) for the hydrolysate preparation with the highest degree of hydrolysis (DH) and % yield. We encapsulated the hydrolysates using nanoscale liposomes and used proteomics to study how these nano-liposomal hydrolysates could affect lipid accumulation in adipocyte cells. RSM analysis revealed X1 at 8.63 h and X2 at 0.93% provided the highest values of DH and % yields were 33.99% and 5.70%. The hydrolysates were loaded into liposome particles that were monodispersed. The loaded nano-liposomal particles did not significantly affect cell survival rates. The triglyceride (TG) breakdown in adipocytes showed a higher TG increase compared to the control. Lipid staining level upon the liposome treatment was lower than that of the control. Proteomics revealed 3425 proteins affected by the liposome treatment, the main proteins being TSSK5, SMU1, GRM7, and KLC4, associated with various biological functions besides lipolysis. The nano-liposomal Linzghi hydrolysate might serve as novel functional ingredients in the treatment and prevention of obesity


Introduction
Modern functional food products are available on the market, ranging from isolated nutrients, dietary supplements, and specific products to processed or engineered foods. Peptides from foodstuff are candidates for functional food ingredients due to their beneficial health aspects such as immune-boosting, anti-oxidative stress, hypolipidemic and tumor suppressing activity [1,2]. One of the above-mentioned beneficial aspects is the hypolipidemic activity on adipocytes, affecting lipid storage, directly associated with obesity, a contemporary health problem. Obesity is caused by excessive triacylglycerol (TAG)

Lingzhi Protein Hydrolysate Optimization by Response Surface Methodology (RSM)
The two independent variable factors used in this study were the digestion time (X 1 ) and the enzyme concentration (X 2 ). The experimental outputs were the degree of hydrolysis (DH) (Y 1 ) and the product yield (Y 2 ). The DH determination was performed according to the method of Nielsen et al. [19] and the product yield was calculated as a percentage of the proteins found in the hydrolysates divided by the raw protein content. While calculating the optimal condition of an independent factor, the values of the other independent factors were fixed. An experimental design was set with 11 conditions, including 9 experimental conditions and 2 central points. The correlation of the independent factors and experimental outputs was used to generate RSM by the following equation: where y is the experimental output; β 0 is constant intercept value; β i , β ii , and β ij are the linear, quadratic, and interaction coefficients, respectively; and x i and x j are the independent variable factors. Three-dimensional response surface plots were drawn to illustrate the correlation between the levels of the process variable factors and the outcome results.

Nano-Liposome Carrier Preparation and Characterization
Soybean lecithin (Sigma Aldrich Co., St. Louis, MO, USA) and cholesterol (Sigma Aldrich Co., St. Louis, MO, USA) (8:1, w/w) were dissolved in 10 mL of diethyl ether in a 50-mL round bottom flask for 5 min. Once the lipids were thoroughly mixed in diethyl ether, the solvent was removed to yield a lecithin-cholesterol film layer by rotary evaporation (Buchi Co., Flawil, Switzerland) at 100 rpm under reduced pressure. The hydration of the lecithin-cholesterol film layer was accomplished by adding 10 mL of Lingzhi extract and agitating on an orbital shaker at 220 rpm for 6 h at 28 • C to obtain a vesicular white suspension. The vesicular suspension was forced through a membrane filter with a defined pore size of 200 nm by an extruder (GE Healthcare Co., Amersham, UK). After day 7, the loading efficiency of the loaded nanoliposome was determined by a protein-based spectrophotometric analysis. We mixed 100 µL samples of loaded liposomes with 1% Triton X-100 (Sigma Aldrich Co.) and sonicated for 10 min (10 s-interval) to disassemble the liposomes and release the extract. Afterward, the protein content of the clearance solution was assessed by Lowry protein assay using Bovine Serum albumin (Sigma Aldrich Co.) as a reference. The loading efficiency was calculated using the following equation: Extracted loading Efficiency (w/w) (%) = (protein extracted of which encapsulated in liposomes (mg) ÷ protein content of extracted Lingzhi (mg)) × 100 (2) Foods 2021, 10, 2157 4 of 20 The hydrodynamic diameter of the liposomal formulations in deionized water was measured by dynamic light scattering (DLS) using ZetaSizer Nano-ZS (Malvern Instruments, Worcestershire WR, UK), in which the zeta potential was also examined (n = 3).

Effect of Loaded Nanoliposomes on 3T3-L1 Adipocyte Cells
Cell cytotoxicity of the loaded liposome and unloaded liposome control was evaluated through an MTT assay. Human fibroblasts (American Type Culture Collection., Manassas, VA, USA)) and 3T3-L1 mouse differentiated adipocyte cells (induced by an adipogenic cocktail containing 2.5 mM dexamethasone, 0.5 mM 3-isobutyl-1-methylxanthine, and 10 g/mL insulin for 8 days) were tested for cytotoxicity at various concentrations (104.68, 52. 34, 26.17, 13.09, 6.54, 3.27, 1.64, 0.82, 0.41, and 0.20 µg/mL) of loaded liposomes and unloaded liposome as control for 24 h. Next, we measured the optical absorbance at 570 nm using a microplate reader and transformed the results into cell survival rate percentage [20].
The lipolytic effect of the loaded nanoliposome was used to quantify glycerol, a byproduct of lipolysis (EnzyChrom™ Glycerol Assay Kit, BioAssay Systems, Hayward, CA., USA) in cell culture supernatant after 24 h of treatment with the loaded nanoliposome.
To determine the intracellular TG content, the differentiated 3T3-L1 cells were treated with the loaded nanoliposomes, as described previously, for 24 h. The cells were washed twice with PBS and fixed with 4% paraformaldehyde for 1 hour at room temperature. Next, the cells were washed once with PBS and isopropanol 60% (v/v), then they were allowed to dry. Next, the cells were stained with 0.5% (v/v) Oil Red O (ORO) (Sigma Aldrich Co.) in an isopropanol solution of 60% for 1 hour. After staining, the unstained dye was removed by rinsing with distilled water. The stained lipid droplets were observed under a stereomicroscope. The stained oil droplets indicating lipid accumulation were solubilized by absolute isopropanol for 15 min and their absorbance was measured at 510 nm using a microplate reader (Multiskan Go, Thermo Scientific, Waltham, MA, USA).

Proteomic Analysis and Data Processing
To investigate the adipocyte protein expression profiles after the exposure to the loaded liposomes, the cells were lysed by a lysis buffer solution (10 mM HEPES-NaOH pH 8.0 and 0.5% Triton X-100) supplemented with a protease inhibitor cocktail (Thermo Scientific Co.). The supernatant was collected by centrifugation, followed by ice-cold acetone precipitation (1:5 v/v). After precipitation, the protein pellet was reconstituted in 0.2% RapidGest SF (Waters Co.) in 10 mM of Ammonium bicarbonate (Sigma Aldrich Co.). The total protein (50 µg) was subjected to gel-free based digestion. Next, sulfhydryl bond reduction was performed using 5 mM DTT (Sigma Aldrich Co.) in 10 mM ammonium bicarbonate at 72 • C for 1 h and sulfhydryl alkylation using IAA (Sigma Aldrich Co.) at room temperature for 30 min in the dark. The solution was cleaned up using a Desalting Zebra-spin column (Thermo Scientific Co.). The flow-through solution was enzymatically digested by Trypsin (Promega Co., Madison, WI, USA) at a ratio of 1:50 (enzyme: protein) and incubated at 37 • C for 3 h. The digested solution was dried and reconstituted in 0.1% formic acid before being subjected to tandem-mass spectroscopy using a nanoLC-system coupled with high resolution 6600 TripleTOF TM (AB-Sciex, Concord, ON, Canada). The LC conditions were as follows: mobile phase A and B were used, with mobile phase A being composed of 0.1% formic acid in water and mobile phase B comprising 95% acetonitrile with 0.1% formic acid. The LC-method parameters comprised a 135-min long process for a single injection. The analytical column was maintained at 55 • C. Using the datadependent acquisition mode of mass spectroscopy, the MS scans over a mass range of 400-1600 m/z, selecting the top 20 most abundant peptide ions with charge state in the range of 2-5 (positive mode) for fragmentation. The dynamic exclusion duration was set at 15 s. The raw MS-spectra resulting (.wiff) file was extracted and annotated with protein sequences using the Paragon™ Algorithm by ProteinPilot™ Software [21]. The Mus musculus protein database, retrieved from UniProtKB (16,477 sequences) and used in Paragon™, was assembled in FASTA format and downloaded in May 2021. We set a detected protein threshold of (Unused ProtScore (Conf)) ≥ 0.05 with 1% false discovery rate (FDR) with ≥10 peptides/protein. The protein and peptide comparisons exhibiting >20% coefficient of variation (C.V.) between the replicates were rejected. Both library and SWATH-MS data were imported into SWATH TM processing microapp in PeakView ® software. The normalization of the relative protein abundances was performed using the R package, NormalyzerDE [22], in which Quantile-normalization was applied to expression data analysis, after adding 1 to all expression values to avoid errors upon log transformation.

Statistical Analysis
All experiments were carried out in at least three independent replicates (n = 3), and all data were expressed as the means ± standard deviation. The statistical significance was determined by Duncan's multiple range test (p-values < 0.05). For the RSM analysis, the generated 3D surface was determined from the fitted polynomial equation, and significant coefficients (p < 0.01) were used in the model. The variance table was generated from both independent variables and experimental outputs using the Design Expert statistical software (version 11.0; State-Ease Inc., Minneapolis, MN, USA). For the pairwise comparisons during the proteomic analysis, we performed One-Way analysis of variance (One-Way ANOVA) at the protein-level analysis with two multiple testing correction methods including the Bonferroni and the Benjamini-Hochberg FDR corrections using the ProteinPilot™ Software.

Lingzhi-Derived Protein Hydrolysate Optimization
The biological activity of the hydrolysates depends on the processing conditions. The activities of various foodstuff hydrolysates were reportedly directly dependent on the degree of hydrolysis, protease activity, and amino acid arrangement [23]. The optimum conditions for the Lingzhi hydrolysate regarding DH and product yield for functional food product manufacturing have not yet been established. Therefore, the present study was aimed at Lingzhi hydrolyzing proteins using RSM to study the effect of the processing conditions including time, enzyme usage on DH, and product yield of the resulting hydrolysates. We applied quadratic analysis statistics to fit an RSM model for independent variable factors. The experimental design using two independent variable factors with two center points (experiment no. 10 and 11) in RSM generation resulted in the observed DH and yield as displayed in Table 1. The RSM generation-related statistical value is shown in Appendix A. As outputs from the overall experimental design, the DH and product yield ranged from 28.11% ± 1.03% to 34.18% ± 1.12% and 4.16% ± 0.13% to 5.70% ± 0.20%, respectively. The difference in the DH and yield could be due to the difference in the digestion time and enzyme concentration. The equation for multiple regression analysis during the RMS was performed to resolve the coefficients of the independent factors of the linear (x 1 , x 2 ), quadratic (x 1 2 , x 2 2 ), and two-factor relation (x 1×2 ) to fit the RSM. According to the multiple regression analysis, the explanatory model equation of the DH (y 1 ) and percentage of product yield (y 2 ) is given as follows in Table 2. Table 2. The experimental design and experimental outputs of the independent factors for the degree of hydrolysate and yield produced from Lingzhi proteins.

Responding
Quadratic Model R 2 p-Value The total coefficient value (R 2 ) was used to imply the model suitability. The R 2 of the DH and the product % yield were 0.958 and 0.968, respectively. This result indicated that the variation in the experimental data was lower than 5% (within 95% level of confidence). The 3-dimensional response model surfaces (3D-RMS) for each variable are illustrated in Figure 1. As outputs from the overall experimental design, the DH and product yield ranged from 28.11% ± 1.03% to 34.18% ± 1.12% and 4.16% ± 0.13% to 5.70% ± 0.20%, respectively. The difference in the DH and yield could be due to the difference in the digestion time and enzyme concentration. The equation for multiple regression analysis during the RMS was performed to resolve the coefficients of the independent factors of the linear (x1, x2), quadratic (x1 2 , x2 2 ), and two-factor relation (x1×2) to fit the RSM. According to the multiple regression analysis, the explanatory model equation of the DH (y1) and percentage of product yield (y2) is given as follows in Table 2. Table 2. The experimental design and experimental outputs of the independent factors for the degree of hydrolysate and yield produced from Lingzhi proteins.

Responding
Quadratic Model R 2 p-Value y1 y 1 = 33.14 + 2.08x1−0.497x2−0.283x1x2−1.53x1 2 −0.635x2 2 0.96 0.0019 y2 The total coefficient value (R 2 ) was used to imply the model suitability. The R 2 of the DH and the product % yield were 0.958 and 0.968, respectively. This result indicated that the variation in the experimental data was lower than 5% (within 95% level of confidence). The 3-dimensional response model surfaces (3D-RMS) for each variable are illustrated in Figure 1. The experimental outputs of the processing related to both independent factors, DH ( Figure 1A) and % yield ( Figure 1B) indicating that the hydrolysate processing depended on the digestion time and enzyme usage. The 3D-RMS for the DH of hydrolysate as a function of digestion time, at fixed enzyme usage, revealed that DH was dependent on the digestion time. Also, DH increased with enzyme usage at the fixed digestion time, suggesting that DH was also dependent on the enzyme usage. Yield also had correlative results, dependent on the digestion time and enzyme usage. In order to obtain the highest The experimental outputs of the processing related to both independent factors, DH ( Figure 1A) and % yield ( Figure 1B) indicating that the hydrolysate processing depended on the digestion time and enzyme usage. The 3D-RMS for the DH of hydrolysate as a function of digestion time, at fixed enzyme usage, revealed that DH was dependent on the digestion time. Also, DH increased with enzyme usage at the fixed digestion time, suggesting that DH was also dependent on the enzyme usage. Yield also had correlative results, dependent on the digestion time and enzyme usage. In order to obtain the highest DH and product yield, the RSM model was optimized by setting the highest value of response variable factors. As a result, X 1 was 8.63 h and X 2 was 0.93%, and the highest values of y 1 and y 2 were 33.99% and 5.70%, respectively. These characteristics of DH and yield curves were associated with feedback inhibition during the hydrolysis, where products may act as an inhibitor to protease [24]. The curves strongly suggested that the processing at different conditions and factors were involved. The independent factors, both time and enzyme concentration, had the optimum range for hydrolysate production to gain the maximum DH and yield. To endorse the reliability and validity of the model for processing, the assays were performed under those optimal conditions. The actual experimental values for DH and product yield were 32.71 ± 0.17% and 5.44 ± 0.14%, respectively; the experimental values fitted with the values that were predicted by the model within a 95% confidence interval. These results confirmed that the model was suitable for Lingzhi protein hydrolysate processing for use as functional ingredients regarding cost-and time-efficiency.

Encapsulation Efficiency and Loaded Liposome Size, Polydispersity Index, and Zeta Potential
The encapsulation efficiency of the liposomal formulation was estimated. The liposomes would passively entrap the protein hydrolysate in their hydrophilic region. However, many factors influence the entrapping efficiency such as lipid molar ratios, molecular size, charge, and molecule stability. To evaluate the entrapping efficiency, we used a non-ionic detergent, Triton X-100, as a neutral detergent to disrupt the liposome shell structure, thereby allowing the leakage of the encapsulated Lingzhi protein hydrolysate [25]. Based on the encapsulation condition, 61.24 ± 3.18% of the encapsulation efficiency was achieved. The encapsulation efficiency showed that the liposomal preparation for protein hydrolysate moderates the encapsulated level. The protein hydrolysate has a mixture of peptides with a variety of molecular weights, sizes, charges, and structures. Middle-sized peptides might interact with the lipid layer and form an oligomerization structure like a beta-barrel. This could disrupt the entrapped protein hydrolysate inside the core structure of the liposome [26]. Another reason was the fluctuation in electrostatic interaction between the charges of various peptides and the liposome surface, which might negatively affect the encapsulation efficiency.
The diameter of the nanoliposome in the closest realistic physiological condition was determined. Dynamic light scattering (DLS) analysis showed loaded liposome diameters in the PBS solution were at 149.84 ± 0.58 nm ( Figure S1). Low polydispersity index (PdI) of =0.048 ± 0.014 supported that particles were monodispersed. In addition, the low PdI value also reflected that the particle exhibits a narrow size distribution, providing a very high surface area that would be ideal for the correct order. This evidence suggested the homogeneity of the loaded liposome. The overall charge of loaded liposomes was neutral. Zeta (ζ)-potential of the loaded liposome was −3.75 ± 0.25 mV ( Figure S2). This could suggest that the overall structure of the liposome exhibited neutral charge particle, due to the value of ζ-potential ranging from −10 to +10 mV, is considered neutral [27]. The hydrodynamic size of the loaded liposome was roughly 140 nm, indicating that the liposome was characterized in the nanoscale. As the efficiency of cellular uptake relates to the particle size, a small particle size of around 100-160 nm would have great potential for cellular uptake into the blood steam via clathrin-dependent mechanisms [28]. Beneficial properties of the negative value of ζ-potential were particle stability under physiological conditions and the prevention of cellular fusion and aggression of phagocytosis, responding less than the positive value of ζ-potential [29]. Therefore, the hydrodynamics of loaded liposome size and negative ζ-potential are the two key criteria that have been considered for various applications.

Effect of Loaded Nanoliposome on 3T3-L1 Adipocyte Cells
The safety of using the loaded liposomes is a crucial factor for establishing commercialized products. Therefore, we investigated cell cytotoxicity to evaluate the safety of loaded liposomes using human fibroblasts as normal cell controls and the differentiated 3T3-L1 adipocyte cell line as a lipid storage cell model. Cell viability was measured through an MTT assay and illustrated in Figure 2. The safety of using the loaded liposomes is a crucial factor for establishing commercialized products. Therefore, we investigated cell cytotoxicity to evaluate the safety of loaded liposomes using human fibroblasts as normal cell controls and the differentiated 3T3-L1 adipocyte cell line as a lipid storage cell model. Cell viability was measured through an MTT assay and illustrated in Figure 2. As a result, the loaded liposomes did not significantly affect the viability of either cell lines at concentrations up to 52.34 µg/mL. However, a further increment (104.68 µg/mL) resulted in slight cytotoxic effects on the fibroblast cells. Therefore, we considered the cytotoxicity-related no-observed-adverse-effect level of the loaded liposomes was 52.34 µg/mL for further experiments. Oral delivery of liposomal protein and peptide is the easy and convenient route. The liposome particles made by cholesterol and lecithin were moderately stable (~80% stability measured by particle leakage) in gastric environment (pH 2) at 37 °C at 1 h and stable (~95% stability measured by particle leakage) in pancreatin [30]. These results indicate that our liposome formulations may be suitable as oral delivery particles due to their stable behavior through the oral route. As the potential application of the loaded liposome would be in functional food ingredients, this concentration was used in the determination of lipolysis activity and proteomics.
The lipolysis process is a metabolic process that breaks down TGs to free fatty acid (FA) and glycerol. It controls the energy homeostasis by regulating the breakdown of TGs [31]. Therefore, the effect of 52.34 µg/mL loaded liposome on the TG breakdown in adipocyte cells was investigated through the measurement of glycerol released into the medium culture. In the present study, the loaded liposome significantly increased glycerol release and reduced lipid accumulation. The loaded nanoliposome affected the adipocytes by inducing the TG breakdown, as we observed the release of glycerol at 1.63 ± 0.25-fold greater than that in the control (p < 0.01). The intracellular lipid exposed by the loaded As a result, the loaded liposomes did not significantly affect the viability of either cell lines at concentrations up to 52.34 µg/mL. However, a further increment (104.68 µg/mL) resulted in slight cytotoxic effects on the fibroblast cells. Therefore, we considered the cytotoxicity-related no-observed-adverse-effect level of the loaded liposomes was 52.34 µg/mL for further experiments. Oral delivery of liposomal protein and peptide is the easy and convenient route. The liposome particles made by cholesterol and lecithin were moderately stable (~80% stability measured by particle leakage) in gastric environment (pH 2) at 37 • C at 1 h and stable (~95% stability measured by particle leakage) in pancreatin [30]. These results indicate that our liposome formulations may be suitable as oral delivery particles due to their stable behavior through the oral route. As the potential application of the loaded liposome would be in functional food ingredients, this concentration was used in the determination of lipolysis activity and proteomics.
The lipolysis process is a metabolic process that breaks down TGs to free fatty acid (FA) and glycerol. It controls the energy homeostasis by regulating the breakdown of TGs [31]. Therefore, the effect of 52.34 µg/mL loaded liposome on the TG breakdown in adipocyte cells was investigated through the measurement of glycerol released into the medium culture. In the present study, the loaded liposome significantly increased glycerol release and reduced lipid accumulation. The loaded nanoliposome affected the adipocytes by inducing the TG breakdown, as we observed the release of glycerol at 1.63 ± 0.25-fold greater than that in the control (p < 0.01). The intracellular lipid exposed by the loaded nanoliposome was visualized by ORO staining where the lower staining intensity represented the lower lipid accumulation (Figure 3).  The ORO staining demonstrated lower intracellular lipid accumulation in cells exposed to loaded liposomes compared to the control. The loaded liposome increased glycerol release corresponding to 50% release at 13.085 µg/mL. ORO staining revealed the most pronounced TG clearance at a peak concentration (52.34 µg/mL), with lower staining severity representing lower lipid aggregation ( Figure 3). This evidence implied that the loaded nanoliposomes were able to reduce the lipid accumulation as determined by the reduced ORO staining level and the free glycerol level increase. Therefore, we next applied a label-free proteomics approach to study the molecular mechanisms of lipid breakdown activity that could potentially lead to the reduced lipid accumulation in the adipocytes for a better understanding of the loaded liposome-induced lipolytic pathways.

Quantitative Proteomic Analysis
We used a proteomics approach to investigate the signaling pathways that could be potentially affected by the loaded liposomes in the adipocyte cells. The LC-MS/MS analysis revealed a total number of 3425 proteins among the loaded liposome and the control groups. The interpretation of the quantitative proteomics and bioinformatics data showed that 439 proteins were affected by the loaded liposomes as shown in Figure 4. Although we used differentiated adipocytes from mice, this was a widely accepted cell-based model [32]. The raw data from the LC-MS/MS analysis showed a small difference in the total ion count between each LC-MS injection. Therefore, data normalization of the raw dataset was strongly required prior to further analysis. After the log transformation and VSN normalization, pooled intragroup median absolute deviation (PMAD) of the identified proteins among replicates was lower than 0.22 (Control and loaded liposomes n = 3 and 3, respectively; Figure S3). In general, a PMAD value of ≤0.3 was accepted as the superior precision dataset [33]. According to the normalized proteomic analysis, the volcano plot of the differential protein expression identifying the most significant protein expression changes is depicted in Figure 4. Each spot represents the protein expression ratio (loaded liposome: control) according to their log10 p-values. The differentially expressed proteins associated with these spots are listed in the proteomics table (Appendix B). The ORO staining demonstrated lower intracellular lipid accumulation in cells exposed to loaded liposomes compared to the control. The loaded liposome increased glycerol release corresponding to 50% release at 13.085 µg/mL. ORO staining revealed the most pronounced TG clearance at a peak concentration (52.34 µg/mL), with lower staining severity representing lower lipid aggregation ( Figure 3). This evidence implied that the loaded nanoliposomes were able to reduce the lipid accumulation as determined by the reduced ORO staining level and the free glycerol level increase. Therefore, we next applied a label-free proteomics approach to study the molecular mechanisms of lipid breakdown activity that could potentially lead to the reduced lipid accumulation in the adipocytes for a better understanding of the loaded liposome-induced lipolytic pathways.

Quantitative Proteomic Analysis
We used a proteomics approach to investigate the signaling pathways that could be potentially affected by the loaded liposomes in the adipocyte cells. The LC-MS/MS analysis revealed a total number of 3425 proteins among the loaded liposome and the control groups. The interpretation of the quantitative proteomics and bioinformatics data showed that 439 proteins were affected by the loaded liposomes as shown in Figure 4. Although we used differentiated adipocytes from mice, this was a widely accepted cell-based model [32]. The raw data from the LC-MS/MS analysis showed a small difference in the total ion count between each LC-MS injection. Therefore, data normalization of the raw dataset was strongly required prior to further analysis. After the log transformation and VSN normalization, pooled intragroup median absolute deviation (PMAD) of the identified proteins among replicates was lower than 0.22 (Control and loaded liposomes n = 3 and 3, respectively; Figure S3). In general, a PMAD value of ≤0.3 was accepted as the superior precision dataset [33]. According to the normalized proteomic analysis, the volcano plot of the differential protein expression identifying the most significant protein expression changes is depicted in Figure 4. Each spot represents the protein expression ratio (loaded liposome: control) according to their log 10 p-values. The differentially expressed proteins associated with these spots are listed in the proteomics table (Appendix B). We identified four significantly different proteins, compared between the loaded liposome and control groups. The global protein expression changes were mostly down-regulated (79.37%; for 350 of 441 proteins). Specifically, three significantly different proteins (p < 0.05 and −4 > log2 (fold change) > 4) were down-regulated (green region, Figure 4) whereas one was up-regulated (red region, Figure 3). Considering the biological functions of the significantly different proteins, the down-regulated ones were Testis-specific serine/threonine-protein kinase 5 (TSSK5_MOUSE), WD40 repeat-containing protein SMU1 (SMU1_MOUSE), and metabotropic glutamate receptor 7 (GRM7_MOUSE), whereas the up-regulated one was Kinesin light chain 4 (KLC4_MOUSE). The detailed description and function of these proteins are presented in Table 3.  We identified four significantly different proteins, compared between the loaded liposome and control groups. The global protein expression changes were mostly downregulated (79.37%; for 350 of 441 proteins). Specifically, three significantly different proteins (p < 0.05 and −4 > log 2 (fold change) > 4) were down-regulated (green region, Figure 4) whereas one was up-regulated (red region, Figure 3). Considering the biological functions of the significantly different proteins, the down-regulated ones were Testis-specific serine/threonine-protein kinase 5 (TSSK5_MOUSE), WD40 repeat-containing protein SMU1 (SMU1_MOUSE), and metabotropic glutamate receptor 7 (GRM7_MOUSE), whereas the up-regulated one was Kinesin light chain 4 (KLC4_MOUSE). The detailed description and function of these proteins are presented in Table 3. The biological functions of these proteins were variable, including cell differentiation, intracellular signal transduction, organism development, protein phosphorylation, spermatogenesis, mRNA splicing, cAMP-related G protein inhibition, chemical synapsis-related activities, and the regulation of neuronal death. Notably, the liposome-encapsulated protein hydrolysates affected the 3T3-L1 cells in various biological functions beyond lipolysis.
Although these significant proteins were not directly associated with lipolysis, differentially expressed proteins in lipid biosynthesis and lipolysis could also be identified. Our investigation detected that fatty acid synthase (FAS; FAS_MOUSE), the major actor of lipogenesis, was suppressed more than 5-fold (log 2 fold change as 2.35) in the loaded liposome group (supplementary data 2). The lipogenesis works via FAS to synthesize the long-chain FA from acetyl-CoA, malonyl-CoA, and NADPH. Hence, FAS downregulation could imply that cellular lipogenesis might be reduced due to the decrease in its abundance and activity. FAS-down regulation, an increased rate of lipolysis, and TG release could lead to a net TG loss on the cellular level. Moreover, another protein that elongates the longchain fatty acids, protein 5 (ELOV5_MOUSE), was also down-regulated. Elov5, known as PUFA elongase, is a major PPARα-regulated enzyme functioning in monounsaturated and polyunsaturated fatty acid synthesis [34].

Conclusions
The concordance between the proteomics results and the cellular lipidemic activity could imply that the Lingzhi protein hydrolysate-loaded nano-liposomes induced cellular lipolysis without affecting cell viability. The proteomic study also indicated that loaded liposomes exhibited lipid accumulation with the suppression of FAS and ELOV5. Finally, other proteins including TSSK5, SMU1, GRM7_MOUSE, and KLC4, were identified in the loaded liposome treatment group, associated with various biological mechanisms beyond lipid metabolism. Therefore, the nano-liposomal hydrolysates might serve as novel functional ingredients in the treatment and prevention of obesity.

Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.