Introduction

Obesity mainly occurs when energy intake is not equal to energy consumption, over a specific period. It is a major health problem that restricts all aspects of an individual’s life quality1. The surplus of energy obtained from food is stored, mainly in adipose tissue as fat, in case energy consumption is more than required for metabolism and daily activities. Energy balance disturbances caused by overconsumption of food can generate the obesity phenotype and influence the metabolism and properties of the cells located in adipose tissue2.

The adipose tissue is considered a paracrine organ, and damage caused by obesity-related stress may cause adverse negative effects rest of the body3. Numerous studies in the literature indicate that the regeneration and maintenance potential of tissue is directly dependent on stem cells located inside4,5. It is a well-known fact that mesenchymal stem cells (MSCs) are the most crucial residents of adipose tissue and can be easily affected by obesity-related alterations in tissue6,7. Stemness properties of MSCs enhance the regeneration potential of adipose tissue. In addition to the alteration of physiological and molecular properties of these cells, MSCs that are affected by obesity-associated oxidative stress have the potential to spread this negative effect to other tissues8.

Obesity-associated changes lead to metabolic, mitochondrial alterations, and DNA damage in adipose tissue-resident MSCs9. Oxidative stress and inflammation in fat tissue may cause early cellular senescence in many types of mammalian cells10,11,12. Therefore, MSCs of adipose tissue may lose their stemness and hence regenerative capacity. It has been recently shown, that obese mice MSCs have the potential to trigger inflammation and senescence while normal mice MSCs have immunomodulatory and anti-inflammatory properties13. The reason for alteration in MSCs properties may be associated with loss of stemness via obesity-caused stress conditions. These cells are known as multipotent, heterogenous cell populations which are capable of differentiating into mesenchymal lineage cells14. Recent studies showed the presence of pluripotent stem cells in this heterogeneous population15. Due to the presence of pluripotent cells, expressions of pluripotent cell markers were observed in mesenchymal stem cell populations such as SSEA3, SSEA4, and TRA-1-6016. The question has begun to be asked whether the stemness feature of MSCs may arise from pluripotent cells within the cell population. Furthermore, it is important to answer, how the number of pluripotent stem cells is affected by obesity-associated alterations.

Here we investigate the link between obesity and stemness, by examining the expression of some key molecules associated with stemness. Investigating the stemness-associated molecules and the number of pluripotent cells of the population in the obesity model may give clues about the connection between cellular senescence, apoptosis, proliferation potential, and obesity. In this study, we analyzed the expression of the SCD1, LEPTIN STAT3, C-MYC, CCND1, KLF4, SOX2, BCL-21L genes in mesenchymal stem cells of obese and non-obese mice models and evaluated the biological properties such as cell cycle and senescence of these cells. Additionally, we followed a shotgun proteomic approach to gain further insight into stemness-associated networks and pathways. Therefore, we aimed to test whether the potency of mesenchymal stem cells may be impaired by obesity-associated stressors.

Materials and methods

Grouping and feeding of experimental animals

All experiments of this study were carried out in accordance with the approval of the Animal Experiments Ethical Committee of Erciyes University (19/183). In this study, healthy male mice of the C57BL/6 race were mated, and after they had pups, we waited for 21 days of breastfeeding, then we took the healthy male pups and separated them from the mother. Young male mice had been fed for 4 weeks with standard pellets (Optima—23% protein, 5, 70% fat, 3,4% cellulose) then, they were separated according to their weight considering their initial weight average. The experimental group was fed ad libitum on a high-fat diet (Diet Western 1635, Safe, Augy, France—17% casein, 0.3% DL- methionine, 34% sucrose, 14.5% corn starch, 0.2% cholesterol, 21% fat, 5% cellulose, %7 CM 205 Bve 1% vit200) while the control group was fed with standard pellets for about 16 more weeks.

Cell harvest and culture

After 20 weeks mice were anesthetized via CO2 inhalation and were sacrificed by cervical dislocation. All methods of this study were performed in accordance with American veterinary medical association (AVMA) Guidelines for the Euthanasia of Animals (2020). Visceral adipose tissues of animals were collected, minced, and incubated with type II collagenase for 10–15 min at 37° C to liberalize the cells. DPBS (without Mg, Ca) was added to reduce collagenase activity. After centrifugations, the pellet of adipose originated cells was dissolved in 8 mL 10% FBS containing DMEM Low glucose (1 g/L) medium and transplanted into the flask. During expansion process cells other cell types of adipose niche such as adipocytes, macrophages etc. were eliminated and spindle shape mesenchymal-like cells were enriched. Cells were expanded and 3 × 106 cells were stored by freezing at the end of passage 2 from each animal.

Adipose tissue-derived stem cells of HFD and ND were thawed and pooled in equal numbers from each animal. Cells were cultured a day in 10% FBS containing DMEM low glucose media. We conducted characterization and the other experiments before end of the passage 3 to prevent replicative senescence associated phenotype alterations.

Mesenchymal stem cell characterization

Following the pooling step, cells were characterized by using flow cytometry. CD29 and CD90 were regarded as positive, and CD45 was used as a negative marker. Analyses were done by using FACS Aria III and evaluations were completed by using FACS Diva 8.0.1 software.

RNA isolation and cDNA synthesis

Total RNA was extracted from obese AD-MSCs and corresponding Non-obese AD-MSCs samples by using TRIzol reagent (Roche). The amount and purity of the obtained RNA were measured by ND-1000 NanoDrop spectrophotometer. 300 ng of total RNA was reverse transcribed into cDNA by using Roche EvoScript cDNA Kit (Thermo Scientific, Lithuania) (Roche Diagnostics, Mannheim, Germany).

Real-time polymerase chain reaction (RT-PCR)

Expression levels of STAT3, KLF4, SOX2, C-MYC, CCND1, LEPTIN, SCD1, and BCL-21L (Roche, Catalog Assay 05 532 957 001) genes in high-fat diet (HFD) and normal diet (ND) samples were analyzed by Quantitative Real-Time PCR (qRT-PCR) using LightCycler 480 System (Roche Diagnostics, Mannheim, Germany). The GAPDH and β-ACTIN genes were used as the reference to normalize the mRNA levels of each sample for quantification. Expression level of STAT3, KLF4, SOX2, C-MYC, CCND1, LEPTIN, SCD1 and BCL-21L compared with reference genes. For Real-time analysis, primers of each gene were taken from Roche RealTime ready Designer-Catalog Assays. (Roche Diagnostics, Mannheim, Germany). qRT-PCR was performed using probe master mix (LightCycler 480 Probes Master) for STAT3, KLF4, SOX2, C-MYC, CCND1, LEPTIN, SCD1, BCL-21L, β-ACTIN and GAPDH expression analysis.

Ct values of target genes STAT3, KLF4, SOX2, C-MYC, CCND1, LEPTIN, SCD1, BCL-21L, and reference genes β-ACTIN, GAPDH were analyzed by LightCycler Software in HFD AD-MSCs and ND AD-MSCs. Expression changes were determined using relative mRNA levels using the 2−∆∆CT method. 2−∆∆CT values were calculated by using the formula shown below.

$$ {2}^{{ - \Delta \Delta {\text{CT}}}} \, = \,{2}^{{ - [{\text{HFD AD}} - {\text{MSCs }}\left( {{\text{Target gene Ct}} - {\text{Reference gene Ct}}} \right) - {\text{ND AD}} - {\text{MSCs }}\left( {{\text{Target gene Ct}} - {\text{Reference gene Ct}}} \right)]}} . $$

Pluripotency marker analysis

In order to determine the number of pluripotent cells in the MSCs population, flow cytometry analyses were performed. The TRA-1-60 antibodies conjugated with PE (Miltenyi Biotec) were used to analyze the cells by diluting 1 to 50. Analyses were done by using FACS Aria III and evaluations were completed by using FACS Diva 9.0.1 software.

ALP (alkaline phosphatase) assay

The culture media were removed from the wells After washing with DPBS three times, 4% paraformaldehyde (Sigma) was added to the wells and incubated for 20 min at room temperature. After incubation, the cells were washed 3 times with DPBS to remove paraformaldehyde. Following this step 9 ml of NTMT and 180 of NBT/BCIP (stock solution, ROCHE) were added to the cells, then we incubated them for 45 min at room temperature, and purple cells were counted by using an inverted microscope (Leica, Germany).

Senescence assay

Cells were cultured at 6 well plates for senescence-associated beta-galactosidase assay. After the population reached 70%, confluency cells were washed twice with DPBS and 0.2% glutaraldehyde (Sigma) was added to each well and incubated for 15 min for fixation. After the incubation step, glutaraldehyde was removed, and the cells were washed 3 times with DPBS. The complete staining solution containing 40 mg/mL X-gal was added to the wells and was incubated at 37 °C for 18 h. In the end, 100 cells were counted in each well of the two fields for each experimental group under an inverted microscope (Leica, Germany).

Cell cycle analysis

Cell groups of HFD and ND were collected and fixed in ice-cold 70% ethanol, washed with PBS, and dissolved in PI-containing cell cycle reagent. Cell groups of HFD and ND were analyzed using a flow cytometer (Muse EasyCyte, Merck Millipore, Germany).

Sample preparation for proteome analysis

For sample preparation for whole-cell proteome analysis, we followed a protocol previously described by Kulak and colleagues (2014). 2 × 105 MSCs from each group were obtained and lysis buffer (6 M Guanidinium chloride (Sigma), 40 mM CAA (Sigma), 10 mM TCEP (Sigma), 25 mM Tris–HCl pH: 8,5) was added to the cell pellet. Samples were boiled at 95 OC for 5 min and sonicated 3 times (each 5 min with 5 min intervals). We centrifuged the samples at 20000 × g for 20 min and supernatant was collected for downstream processing. Protein lysate diluted by dilution buffer (25 mM Tris-HCl pH 8.5, 10% ACN) and 280 ng Lys-C (Promega) were added for proteolytic digestion. Following the overnight incubation at 37 °C 600 ng trypsin gold (Promega) was added to the samples and an additional 4 h incubation was performed. Digested peptides were acidified via loading buffer (1% TFA) and loaded to the SDB-RPS disks. Samples were washed 3 times and eluted with 40% ACN, 60% ACN, and 80% ACN containing elution buffers respectively. Liquid samples were evaporated using SpeedVac (Eppendorf) and stored at −20 °C.

LC-MS/MS analysis

We dissolved the peptides by adding 5% ACN and 0.1% formic acid containing dH2O and performed the LC-MS/MS analysis via AB Sciex Triple ToF 5600 + (AB SCIEX, CA, USA) integrated with LC-MS/MS Eksigent ekspert™ nanoLC 400 System (AB SCIEX, CA, USA). nanoACQUITY UPLC 1,8 μM HSS T3 C18 column (250 mm) (Waters) was used in the separation of peptides. 4–40% ACN gradient was used for 180 min following the trap elution. Data-dependent acquisition (DDA) MS/MS analysis of peptides was performed after electrospray ionization. Raw data analysis for each sample was performed with Analyst® TF v.1.6 (AB SCIEX, CA, USA). The peptides and the ion product of the MS and MS/MS data were evaluated with PeakView (AB SCIEX, CA, USA). Mass spectrometry peak lists were evaluated in consideration of the UniProtKB-based reference sequence of the Homo sapiens species on our server with ProteinPilot 4.5 Beta (AB SCIEX, CA, USA).

Bioinformatic evaluations

Identified unique proteins were listed for each group and Gene Ontology Biological Process analysis were performed. Analysis was done and visualized by using HemI 2.0. Enrichment analysis was performed considering p < 0.05 and bubble chart visualization was used. With the aid of Gene Ontology Cellular Component analysis nuclear proteins were determined. Protein-protein interaction (PPI) networks of nuclear proteins and stemness-associated upstream regulators were completed by using StringDB. We used CytoScape 3.9.1 for the visualization of PPI networks.

Biostatistical analysis

Statistical significance was determined with one way and two way ANOVA analysis followed by Sidak’s multiple comparisons tests and unpaired t tests. That data were analyzed with a GraphPad Prism version 9.0.0 statistical software package (GraphPad, CA, USA).

Ethical statement

All experiments in this study were carried out in compliance with the approval of Erciyes University’s Animal Experiments Ethical Committee (19/183). All methods were performed in accordance with American Veterinary Medical Association (AVMA) Guidelines for the Euthanasia of Animals (2020). We also declare that experiments of this study involving live animals has been reported as described by the ARRIVE the guidelines17.

Results

According to experimental plan presented in (Fig. 1A), weight of animals was measured from 4 to 20th week and animals were sacrificed after observation of significant difference between average weight of ND and HFD group (Fig. 1B). Following the primary culture, we characterize the MSCs via flow cytometry analysis. In both groups over 99% of cells were positive for CD29. We observed that approximately %92 of cells was positive in the ND group and 94% positive in the HFD group for CD90 antigen (Fig. 1C).

Figure 1
figure 1

(A) Summary of experimental design. (B) Weekly weight changes of mice from ND and HFD groups. Created with BioRender.com. (C) Characterization of MSCs isolated from normal and obese mice adipose tissue with flow cytometry analysis. The yellow population represents CD29 positive cells, the orange population represents CD90 positive cells, and the blue population represents CD45 positive cells. Isolated MSCs were positive for CD29, CD90 markers and negative for CD45 haemopoietic markers.

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High fat diet increases senescence and reduces the number of pluripotent markers expressing cells in population

In gene expression analysis on an obese and non-obese animal model, we observed an increase in the expression of SCD1, STAT3, MYC, and CCND1 genes in the HFD group when compared to ND group cells. Additionally, the expression of the LEPTIN gene was decreased in high-fat mesenchymal stromal cells compared to ND-MSCs. As the stemness gene expression or core transcription factors SOX2 and KLF4 expressions were decreased in HFD-MSCs compared to ND-MSCs. BCL-2LL anti-apoptosis gene expression did not show significant changes in both high-fat diet and normal fat mesenchymal stromal cell populations (Fig. 2).

Figure 2
figure 2

Expression analysis of STAT3, SOX2, KLF4, MYC, CCND1, LEPTIN, SCD1 and BCL21L genes. Expression levels of STAT3, MYC, CCND1, and SCD1 were increased. SOX2 and LEPTIN expression were decreased with obesity treatment. Data are expressed with SD, *p < 0.05, **p < 0.01, *** p < 0.001.

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Senescence-associated β-galactosidase assay outputs show that the HFD group animals’ cells were more senescent when compared to ND animals’ cells. The percentage of senescence cells was around 20% in cells obtained from HFD animals, while the senescent cells obtained from ND animals were approximately 10% of the total population (Fig. 3A,B).

Figure 3
figure 3

(A) Senescence associated β-Galactosidase Assay staining (Magnification: 100X and 200X). (B) Comparison Senescence Associated β-Galactosidase Assay results of ND and HFD groups. Data are expressed with SD, with SD (n = 3 for each experimental condition), *p < 0.05, **p < 0.01. (C) Histograms represents cell cycle distributions of ND and HFD groups. (D) Comparison of cell cycle distribution of normal and obese mice groups. Data are expressed with SD, with SD (n = 3 for each experimental condition), *p < 0.05, **p < 0.01, ***p < 0.001. (E) Alkaline phosphatase assay results. (ns: no significant difference) (F) Flow cytometry analysis TRA-1-60 pluripotent state surface marker expression of MSCs.

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Following the β-Gal assay, we analyzed the cell cycle phases of the HFD and ND group in order to determine how obesity-related stemness loss affects the cell cycle distribution. In accordance with β-Gal assay, in samples obtained from the high-fat diet group, cell cycle analysis showed that cells were triggered at the G0/G1 phase and a significant increase at this phase when compared with the normal-fat diet group. In the HFD group, the percentage of G0/G1 cells was approximately 60%, while ND group cells were around 50% (Fig. 3C,D).

After observing the decrease in the expression of the stemness gene, we decided to analyze ALP expression and pluripotent state surface marker TRA-1-60. ALP expression analysis of the total cell population did not show significant differences (Fig. 3E). Flow cytometry analysis showed that the number of TRA-1-60 positive cells in the population was decreased with diet-induced obesity. The percentage of positive cells in the ND group was 1,8% and it was decreased to 1% with a high-fat diet (Fig. 3F).

Proteomics analysis of ND and HFD mice MSCs

To obtain more information about pathways and networks of stemness-associated molecules, we performed shotgun proteomic analysis for whole cells of ND and HFD groups. The number of identified unique proteins for the ND group was 314 while the HFD group is represented by 385 unique proteins. We observed that 2180 proteins were commonly expressed (Fig. 4A). GO enrichment analysis revealed that, ND group cells proteome annotated various stemness and cell proliferation associated ontologies, such as Wnt signaling, cell proliferation, and tissue regeneration. Cell cycle regulation, telomere maintenance, and mRNA splicing. Wnt signaling and cell proliferation which are stemness-associated ontologies, were not enriched in the HFD group while negative regulation of cell proliferation and white fat cell differentiation-related terms were observed. Furthermore, NF- κB signaling and Granzyme mediated pathway, known as crucial in an inflammatory response, were observed in the HFD group (Fig. 4B).

Figure 4
figure 4

(A) Venn diagram showing the number of identified proteins in each group. Proteins identified with ⩾2 unique peptides from all entry. (B) Gene ontology biological process analysis of identified proteins from each group. Enrichment analysis was performed considering p < 0.05 and bubble chart visualization was used. The x axis shows the enrichment significance presented with –log2 (p-value). Generated by using HemI 2.0. (hemi.biocuckoo.org). (C) Protein-protein interaction networks of identified nucleus annotated proteins and known stemness-associated upstream regulators. Orange-labeled proteins represent the interactors that exist in our dataset. Generated by using Cytoscape 3.9.0 (cytoscape.org) (D) Word clouds display the gene ontology biological process analysis of identified proteins that interact with known upstream regulators generated by using HemI 2.0 (hemi.biocuckoo.org).

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To observe the interaction between the known stemness-associated transcription factors and nucleus-annotated proteins in our data set, we applied protein-protein interaction network analysis. Protein-protein interaction networks revealed that normal diet mice have nuclear proteins associated with stemness, differentiation, and self-renewal. In the gene ontology analysis of upstream regulator interacting molecules, Wnt signaling and embryonic development terms were enriched. HFD mice PPI analysis revealed the loss of stemness-associated ontologies which was observed in ND mice. Instead of stemness-associated ontologies, PPI analysis of HFD animals revealed the molecules that annotated stress-related ontologies and pathways such as inflammatory response signaling, NF-KB signaling, and senescence signaling. We also observed white fat cell differentiation and cholesterol response-associated molecules in the HFD dataset (Fig. 4C,D).

Discussion

Obesity emerges with a disturbance of the balance between food intake and energy consumption. This undesired physiological condition can lead to other comorbidities in the organism, such as diabetes, cardiovascular disease, and cancer18. Studies have consistently shown a higher prevalence of senescent cells in obese individuals, which may contribute to decreased regenerative capacity and increased disease risk due to impaired cellular mechanisms19,20. This study explores the hypothesis that obesity can also result in the loss of stemness in mesenchymal stem cells (MSCs), thereby diminishing their regenerative potential. In order to get some clues about stemness features of obese individuals’ MSCs, we performed phenotypic and molecular analysis. In our results senescence phenotypes, cell cycle arrest and a decrease in TRA-1-60 pluripotent state marker were observed in mesenchymal stem cells (MSCs) from obese mice. Additionally, SOX2 and LEPTIN gene, was decreased in obese mice while the expression of MYC, STAT3, CCND1, and SCD1 was upregulated. Shotgun proteomic analysis revealed that ontologies related to Wnt signaling, telomere maintenance, and wound healing were diminished in HFD group. Conversely, ontologies associated with inflammation, insulin stimulus, and white fat cell differentiation were enriched in the MSCs of obese mice (Fig. 5).

Figure 5
figure 5

The study’s findings are summarized and depicted in the figure. Senescence phenotypes and cell cycle arrest were observed in mesenchymal stem cells (MSCs) from obese mice. The expression of pluripotency markers TRA-1-60, SOX2 and LEPTIN gene, was decreased in obese mice. Conversely, the expression of MYC, STAT3, CCND1, and SCD1 was upregulated. A shotgun proteomic comparison between MSCs from normal diet (ND) and high-fat diet (HFD) groups revealed significant differences. In the HFD group, ontologies related to Wnt signaling, telomere maintenance, and wound healing were diminished. In contrast, ontologies associated with inflammation, insulin stimulus, and white fat cell differentiation were enriched in the MSCs of obese mice. Created with BioRender.com.

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Our results indicated a decrease in expression level of leptin in HFD MSCs, consistent with existing literature on leptin's role in fat homeostasis and energy balance21,22. A deficiency of leptin may induce obesity as well and obesity can lead decrease in leptin expression levels23,24,25. Previous proteomic study also identified leptin signaling pathway-associated proteins in normal diet mice MSCs and these molecules were lost in high-fat diet-induced obese mice13.

Gene expression analysis showed that SCD1 was significantly upregulated in HFD adipose-derived MSCs compared to those from a normal diet (ND). This overexpression is likely due to leptin deficiency, as leptin is known to inhibit SCD1 at the transcriptional level26. SCD1 plays roles in ion binding, fat oxidation, oxidoreductase activity, and stearoyl-CoA 9-desaturase activity, linking it to metabolic disorders such as obesity and insulin resistance27,28,29.

We also observed upregulated in HFD MSCs, with increased expression of MYC, CCND1, and STAT3 genes which are associated with the cell proliferation, cell cycle and differentiation. MYC, a well-known oncogene, is associated with cell growth and proliferation30. Studies have shown that diet-induced obesity can promote cancer through the upregulation of MYC, linking obesity with increased cancer risk31,32,33,34,35. Our findings highlighted that obesity exacerbates cancer risk by enhancing MYC expression, making it a potential therapeutic target. The expression level of STAT3 is increased in obese mice. Since STAT3 expression is regulated by IL-6, elevated IL-6 level produced by senescent mesenchymal stem cells could be underlying cause36.

The negative impact of obesity on insulin-like growth factor (IGF) signaling could influence cellular senescence mechanisms. IGFs and IGF-binding proteins (IGFBPs) are key molecules in the senescence-associated secretory phenotype (SASP), helping to propagate senescence signals across tissues37,38. Obesity-related dysfunction and loss of stemness may result from cellular senescence. Our senescence-associated beta-galactosidase assay indicated that obesity induces cellular senescence, as reported in previously6. Our results revealed that a higher proportion of cells in the HFD group were arrested in the G0/G1 phase compared to the ND group, as evidenced by increased CCND1 expression. This loss of proliferation and cell cycle arrest may reduce tissue regeneration capacity.

To understand the impact of a high-fat diet on the stemness of MSCs, we also analyzed the expression of SOX2 and KLF4 genes. The expression analysis revealed a substantial decrease in the expression of these genes. Both SOX2 and KLF4 works as transcription factors and involves in development and differentiation39,40. The decreased expression of these factors could impair the differentiation capacity and stemness of MSCs in obese animals, as shown in the literature41. Consistent with gene expression results, the number of TRA-1-60 positive cells, which is a pluripotency marker, decreased in the HFD group. Previous studies estimated that there are around 1.5–2% TRA-1-60 positive cells in the population, and our study showed that this number decreased in the HFD group42.

Proteomic analysis demonstrated significant differences between ND and HFD mice MSCs. While the ND group exhibited proteins associated with stemness and cell proliferation, these were not enriched in the HFD group, which instead showed annotations related to inflammation and senescence. Our proteomics data highlight the loss of Wnt/β-catenin signaling in MSCs from obese mice. The Wnt/β-catenin signaling pathway is essential for stem cell regeneration, expansion, differentiation, and homeostasis43. Wnt signals also inhibit production of cytokines that induce senescence, thereby reducing cellular aging and promoting MSC expansion44. Additionally, Wnt/β-catenin activation suppresses adipocyte differentiation of MSCs by downregulating the expression of genes such as PPARγ, C/EBPα45. Our study indicates that a high-fat diet downregulates Wnt, which could increase the senescence of MSCs and may also explain the increase in adipose tissue due to adipose differentiation. Wnt signaling can be stimulated by Wnt ligands, small molecules, natural products, and specific microRNAs46. Investigating the therapeutic potential of these factors could offer new strategies for preventing obesity by reduction of senescence and enhancing the stemness capacity of MSCs. NF- κB signaling and inflammatory response-associated annotations were remarkable among observed ontologies in obese mice. Production of SASP and inflammatory cytokines depends on the functioning of the mentioned pathways47,48,49. Also, we observed white fat cell differentiation, mesenchymal cell migration, and angiogenesis ontologies as indicators of adipose tissue expansion. Considering the adipose tissue as a paracrine organ3 its expansion means an increase in residency for senescent MSCs. Accumulation of senescent cells in adipose tissue decreases the stemness by impairing stemness-associated networks and may cause adverse effects on the organism such as wound healing difficulties and bone fragility41,50. Protein-protein interaction networks further confirmed the loss of stemness-associated proteins in the HFD group, replaced by stress-related and inflammatory response proteins, cellular senescence, and altered insulin-like growth factor signaling. Obesity-related oxidative stress may induce senescence and cause loss of stemness properties in the MSCs population and senescence-associated factors and inflammatory cytokines secreted by these cells impair the insulin metabolism in the body51,52,53,54. The aforementioned changes in cellular proteome may indicate that MSCs of adipose tissue lost their stem cell features because obesity caused cellular senescence.

As a consequence, obesity and related comorbidities have the potential to induce cellular pathologies such as senescence. Senescence and apoptosis, which are caused by obesity-associated oxidative stress, may lead to decreased expression of stemness associated markers in adipose tissue resident MSCs. Stress associated decline in stemness of MSCs may results with dysfunction of cellular mechanisms and emerge diseases by decreasing the regeneration rate and have potential consequences at organismal level such as wound healing difficulties, bone fractures etc. Further research into the mechanistic pathways linking obesity with stem cell dysfunction and senescence could offer new therapeutic targets to mitigate the adverse effects of obesity on cellular health and overall organismal physiology.