Abstract

   Difficult-to-heal wounds management accounts for about 4% of healthcare
   costs, highlighting the need for innovative solutions. Extracellular
   signals drive cell proliferation during tissue regeneration, while
   epigenetic mechanisms regulate stem cell homeostasis, differentiation,
   and skin repair. Exploring epigenetic regulation in adipose-derived
   stem cells (ADSCs) holds promise for improving skin injury treatments.
   We investigated the effects of histone deacetylase inhibitor (SAHA) on
   ADSCs to better understand its cellular and molecular impacts. ADSCs
   were treated with SAHA for 72 h, showing no change in cell viability at
   the studied concentrations. However, the expression of histone
   deacetylase decreased at 1000 nM, while the cell proliferation marker
   Ki-67 increased after SAHA treatment, as confirmed by
   immunofluorescence. CCND1 gene expression increased, whereas protein
   expression of the proliferating cell nuclear antigen (PCNA) decreased.
   Cell cycle analysis showed an increase in G2 phase in SAHA-treated
   cells. Microarray analysis revealed 74 upregulated and 40 downregulated
   differentially expressed genes, including upregulation of P53 targets,
   CDKN1A and MDM2. Proteomic analysis identified 631 upregulated and 823
   downregulated proteins compared to the vehicle. Pathway enrichment
   analysis showed cell cycle, ATP-dependent chromatin remodeling and DNA
   processes were among the affected pathways. This study suggests SAHA
   modulates ADSCs’ biological processes, highlighting its potential for
   skin regeneration.

   Keywords: Histone deacetylase inhibitor, Suberoylanilide hydroxamic
   acid, Adipose derived stem cells, Differentiation, Vorinostat,
   Epigenetic, Epigenetic modifier

   Subject terms: Cell biology, Molecular biology, Stem cells

Introduction

   The consistent exposure of the skin to environmental, mechanical, and
   chemical stress necessitates continuous self-renewal, in order to
   retain its barrier function. Skin has an extremely fast cellular
   turnover rate, which allows for efficient adaption to environmental
   triggers^[42]1. Skin cells response for the latter encompasses several
   epigenetic mechanisms for controlling gene expression which, in turn,
   modifies cellular phenotype, proliferation, migration, and
   differentiation^[43]2. Histone acetylation is one of the well-explained
   post-translational modifications, which is based upon the opposing
   activities of histone acetyltransferase (HATs) and histone deacetylase
   (HDAC)^[44]3. The importance of histone acetylation has been
   demonstrated in skin development and homeostasis, paving the way for
   potential investigations into its role during the repair of skin
   injuries^[45]2. For example, the acetylation pattern of epidermal
   histone H4 varied over time, during different stages of wound healing
   in distant and adjacent skin, migrating epithelium and newly formed
   epidermis^[46]4. Furthermore, epigenetic mechanisms control the
   differentiation and commitment outcomes of epidermal stem cells in
   wound healing process as the restoration of functional skin relies on
   the plasticity of stem cells^[47]5. Epigenetic modification was
   identified as a fundamental contributor to stem cell-mediated tissue
   maintenance, by coordinating their fate decisions and safeguarding
   their homeostasis^[48]6. The epigenetic profiles are emerging for skin
   stem cells that are associated with their cell fate plasticity and
   proper activity in tissue regeneration, the role and the mechanism of
   which adipose derived stem cells use to contribute to restoration of
   skin disruption, is largely unclear.

   Adult stem cells stand out as a preferred choice for stem cells based
   therapy with increasing body of evidence confirming their safety, as
   well as their efficacy in regeneration and repair^[49]7. Most attention
   is drawn to adipose derived stem cells (ADSCs), as a special subset of
   adult stem cells with the advantage of their abundance and ease of
   access, in addition to their ability to self-renew, differentiate into
   multilineages, and dynamically interact with the cell niche^[50]8. On
   the other hand, ADSCs are a source of bioactive growth factors which
   participate in paracrine signaling during tissue repair process,
   including skin wound healing. The interplay between ADSCs and the
   surrounding environment can provide important aspects for improving
   their use in clinical practice. They may participate in wound repair
   and reconstruction as well as the inflammatory response^[51]9.

   Epigenetic pharmaceuticals are promising interventions as they support
   stem-cell differentiation^[52]10–[53]15. Histone deacetylase inhibitors
   (HDACi) are known to modulate the expression of genes by increasing
   histone acetylation and thereby regulating chromatin structure and
   transcription^[54]16. Current evidence indicates that HDAC inhibitors
   not only act to reduce the catalytic activity of the enzyme but may
   also disrupt the protein–protein interaction of specific HDACs with
   various critical protein partners^[55]17. These target proteins usually
   participate in a variety of biological pathways, including gene
   expression, cell proliferation, differentiation, cell migration, and
   cell death, as well as angiogenesis and immune response^[56]18.
   Therefore, we opted to use pharmacological manipulation to regulate
   histone acetylation by using HDACi, Suberoylanilide hydroxamic acid
   (SAHA), also known as Vorinostat. This pan HDACi inhibits both the
   enzymes from class I (HDAC1, HDAC2, HDAC3) and class II (HDAC4, HDAC5,
   HDAC6 and HDAC7). Although several HDACi are at various stages of
   preclinical and clinical development, SAHA, is a clinically approved
   histone deacetylase inhibitor, by the U.S. Food and Drug Administration
   (FDA) for the treatment of T-cell lymphomas^[57]19.

   In this study, we hypothesize that altering ADSCs through
   pharmacological intervention with SAHA may represent a potential
   therapeutic strategy to improve wound therapy and skin regeneration. To
   test this, we investigated the effect of SAHA on ADSCs proliferation
   and the regulatory pathways involved in various biological processes,
   aiming at enhancing the application of ADSCs in cell therapy for skin
   tissue regeneration. To the best of our knowledge, no studies have
   examined the effect of SAHA on ADSCs cellular and molecular processes.
   Therefore, our data could provide valuable insights for developing
   novel therapeutic approaches to treat sever skin injuries.

Results

The reduction of HDACs demonstrates no adverse impact on cell viability

   The viability of ADSCs were examined after being treated with SAHA at
   different concentrations (Fig. [58]1A). Our findings revealed no
   changes in viability with the studied concentrations, in comparison to
   untreated cells. In the following experiments, 1000 nM SAHA was used at
   72 h of exposure. To determine whether SAHA has efficiently inhibited
   HDACs at 1000 nM, the protein expression of certain HDACs was screened.
   HDACs expression level in SAHA group were significantly reduced for
   HDAC1, HDAC2, HDAC3, HDAC4 and HDAC6 in comparison with EtOH group
   (Fig. [59]1B). This can demonstrate that the method of application used
   in our study positively influenced the reduction of HDACs level.

Fig. 1.

   [60]Fig. 1
   [61]Open in a new tab

   The viability of ADSCs remains unaffected by SAHA despite the
   significant reduction in the protein expression levels of HDACs. (A)
   ADSCs viability percentages were assessed through the MTT assay after a
   72-h with different concentrations of SAHA (10, 100, 1000, and
   2000 nM). The control group for this experiment was untreated cells.
   (B) Western blotting was performed to analyze HDACs protein expression
   level and showed significant reduction of HDAC1,2,3,4 and 6. Data were
   presented as means with the corresponding standard error of the mean
   (SEM), the original blots/gels can be accessed in Supplemetary [62]1.
   MTT assay (n = 4) and for western blotting (n = 3). p value < 0.05 was
   indicating of a statistically significant difference, which presented
   as follows: * for p < 0.05, ** for p < 0.01, and *** for p < 0.001.

ADSCs displayed active division despite evidence of slow proliferation rate

   ASC52 cell cycle progression was performed by measuring DNA content
   which was stained with propidium iodide (PI) using flowcytometry. The
   stained DNA histogram, presented in Supplementary [63]2 Fig. 2), along
   with the quantification of DNA content percentages. Our results
   demonstrated an increase in G2/M cell cycle phases with no increase in
   Sub G1 population in SAHA treated cells. Following treatment with SAHA,
   we observed notable changes in the morphological features of the cells
   compared to untreated controls. The cells exhibited a polygonal shape
   with a larger cytoplasm, which was clearly visible under the light
   microscope (Supplementary [64]2 Fig. 1). These morphological
   alterations suggest changes at the cellular levels. As these changes
   could be related to cell proliferation, we investigated the expression
   of the Ki-67 protein. Our results revealed a significant increase in
   Ki-67 protein expression in SAHA group compared with EtOH group.
   Nevertheless, the expression level was similar to the control group
   (Fig. [65]2A). For a better understanding of SAHA influence on ADSCs
   cell cycle progression and proliferation, the gene expression of the
   cell cycle G1/S transition check point CCND1 was investigated. A
   significant increase in the expression of CCND1 gene expression was
   observed in SAHA group compared with EtOH group (Fig. [66]2B).
   Furthermore, the nuclear protein PCNA, a cellular proliferation marker,
   was studied and a significant decrease was observed compared to the
   EtOH group (Fig. [67]2C).

Fig. 2.

   [68]Fig. 2
   [69]Open in a new tab

   Proliferation capability of ADSCs under the effect of SAHA. (A)
   Quantification of Ki-67 fluorescence intensity in ADSCs was determined
   using immunofluorescence staining. (B) CCND1 gene expression using
   qPCR. (C) PCNA relative protein expression was evaluated by western
   blotting, the original blots/gels can be accessed in Full-length blots
   file. Data were presented as means with the corresponding standard
   error of the mean (SEM). qPCR (n = 3) and western blotting (n = 4). p
   value < 0.05 was indicating of a statistically significant difference,
   which presented as follows: * for p < 0.05, ** for p < 0.01, and ***
   for p < 0.001.

Microarray analysis identification of genes and pathways on ADSCs

   To characterize the effect of SAHA on ADSCs biological activity,
   microarray analyses were performed to identify genes and regulatory
   pathways which influence the cellular and molecular mechanisms in ADSCs
   after SAHA treatment. Our findings showed that 40 genes were
   downregulated, while 70 genes were upregulated (Fig. [70]3A, B),
   (Supplementary [71]3). For the downregulated genes, the top five
   enriched pathways from reactome pathway analysis were related to
   extracellular matrix organization and metabolism of nitic oxide, while
   the upregulated genes were related to the regulation of insulin-like
   growth factor (IGF) transport and uptake by insulin-like growth factor
   binding proteins (Fig. [72]3C), (Supplementary [73]2 Table 2–5). To
   confirm microarray results, candidate genes from the previously
   mentioned enriched pathways were selected. The qPCR results confirmed
   the microarray data (Supplementary [74]2 Fig. 2), (Supplementary [75]2
   Table 6–7). Moreover, the expression of p53/p21(CDKN1A) pathway related
   gene were among the influenced genes by SAHA. A significant increase in
   CDKN1A and MDM2 gene expression was observed in response to SAHA
   treatment, while p53 gene expression exhibited a notable decrease
   (Fig. [76]4A). Notably, we observed similar trends in the protein
   levels of p21, which was accompanied with an increase in p53 and a
   decrease in MDM2. This indicates that SAHA activates a p53 response in
   the context of ADSCs, subsequently leading to the activation of certain
   p53 target genes (Fig. [77]4B).

Fig. 3.

   [78]Fig. 3
   [79]Open in a new tab

   Microarray analysis for the differentially expressed genes and
   enrichment pathways analysis. (A) Heat map of the top110 genes and (B)
   Volcano plot for the differentially expressed genes. (C) Bubble plot
   shows the top enriched pathways of DEGs from Gene ontology and
   reactome. Data were presented as means with the corresponding standard
   error of the mean (SEM). n = 3. p value < 0.05 was indicating of a
   statistically significant difference, which presented as follows: * for
   p < 0.05, ** for p < 0.01, and *** for p < 0.001.

Fig. 4.

   [80]Fig. 4
   [81]Open in a new tab

   p21/p53 evaluation on ASC52 under the influence of SAHA treatment (A)
   Differentially expressed genes in relation to p53 pathway using qPCR.
   (B) Protein expression in relation to p53 target genes using western
   blotting, For MDM2 blot shows both full length and cleaved MDM2
   fragment called p60. The original blots/gels can be accessed in
   Full-length blots file. Data were presented as means with the
   corresponding standard error of the mean (SEM). qPCR (n = 3), western
   blotting (n = 2). p value < 0.05 was indicating of a statistically
   significant difference, which is presented as follows: * for p < 0.05,
   ** for p < 0.01, and *** for p < 0.001.

Proteomic profiling showed regulation of cell fate related pathways

   The proteomics profile of ADSCs lysates were screened using mass
   spectrometry coupled with independent acquisition data analysis (DIA).
   Principle component analysis (PCA) showed the overlapping clustering of
   EtOH and control group with distinct separation of the SAHA group
   (Fig. [82]5A). A pairwise analysis in SAHA vs EtOH group showed 1454
   differentially expressed proteins; 823 proteins were downregulated
   while 631 were upregulated proteins (Fig. [83]5B), (Supplementary
   [84]4, [85]5). Gene ontology reflected the biological function of RNA
   and its metabolic processing, while most of the molecular function were
   related to DNA binding (Fig. [86]5C, D), (Supplementary [87]2 Table 8,
   9). Pathway enrichment analysis showed that cell cycle, ATP-dependent
   chromatin remodeling and DNA replication are among the suppressed
   regulated pathways (Fig. [88]5E), (Supplementary [89]2 Table 10).

Fig. 5.

   [90]Fig. 5
   [91]Open in a new tab

   Differentially expressed proteins identification using mass
   spectrometry analysis identified; 823 proteins were downregulated while
   631 were upregulated. (A) Principal components analysis (PCA) showed
   the distribution of the identified proteins among different groups. (B)
   Volcano plot for the downregulated and upregulated differentially
   expressed proteins which are shown in red, while the proteins in gray
   are non-significant. (C, D) Gene ontology enrichment maps for
   biological process and molecular function. (E) Bubble plot showing
   pathways enrichment analysis for the regulated downstream pathways.
   Data were presented as means with the corresponding standard error of
   the mean (SEM). n = 5. p value < 0.05 was indicating of a statistically
   significant difference, which presented as follows: * for p < 0.05, **
   for p < 0.01, and *** for p < 0.001.

Discussion

   Epigenetics regulation is involved in several biological processes in
   skin repair, and dynamically regulates cell proliferation,
   differentiation, and migration. Epigenetic regulatory mechanisms can
   both stimulate and repress gene activation, which alter cellular
   phenotype and behavior^[92]20. Histone acetylation is a key epigenetic
   process that neutralizes the positive charge on the histone surface,
   leading to a less compact chromatin structure. In contrast, histone
   deacetylation generally results in a tighter chromatin structure,
   thereby repressing gene expression^[93]21. Epigenetic regulators,
   including histone deacetylases (HDACs), are well known to play a
   crucial role in various processes governing gene expression, including
   the regulation of epidermal stem cells^[94]22. Furthermore, modulation
   of epigenetic signals associated with alterations of lineage-specific
   differentiation, could aid in the creation of modified therapeutic
   approaches. ADSCs are among the types of stem cells sources which have
   received much attention due to lack of invasive characteristics and
   high self-renewal^[95]9. In our study, we focused on demonstrating the
   effect of HDACi, SAHA, as a pharmaco-epigenetic intervention on ADSCs.
   Drug concentration appears to be a critical factor in screening HDACi,
   as most HDACi exhibit cytotoxicity and their up-take ability is time
   and cell type dependent. Our results showed no difference in cell
   survival within the studied concentration of SAHA and 1000 nM was
   selected as this concentration has been used in similar
   studies^[96]12,[97]13. HDACi can decrease the expression of certain
   HDAC proteins in stem cells, but the effect in this context varies and
   depends on cell type and the specific HDAC inhibitors used. In our
   study model, HDACs protein expression was noticeably decreased in ADSCs
   treated with SAHA. On the other hand, Paino et al. investigated the
   impact of valproic acid (VPA), another HDACi, on the differentiation of
   human dental pulp stem cells (DPSCs) into osteoblasts. Their findings
   showed that VPA does not generate toxic effects on DPSCs and is
   accompanied by an increase in overall histone acetylation^[98]23. In
   another study, low concentrations of MS-275 showed no significant
   effects on DPSC proliferation compared to the control group. However,
   the proliferation of DPSC decreased at higher concentrations of
   MS-275^[99]24. With this respect, the proliferation of ADSCs treated
   with SAHA was investigated using Ki67. Ki67 is present during active
   phases of the cell cycle and is absent in resting cells (G0 phase),
   with maximal Ki-67 levels detected during mitosis^[100]25. Our findings
   demonstrated a considerable increase of Ki-67 protein expression in
   ADSCs treated with SAHA in comparison to the EtOH group. This was in
   line with measurement of Ki-67 protein levels in human immortalized
   foreskin (hTERT-BJ), during quiescence and different stages of cell
   cycle. Ki-67 protein expression was undetectable during the first 18 h
   after the cells were restimulated to enter the cell cycle and reached
   the maximal expression around 30 h, with the majority of cells in G2
   phase or mitosis^[101]26. Furthermore, D-type cyclins D1, D2, and D3
   (cyclin D) regulate the cell cycle, metabolism, fat cell
   differentiation, and migration^[102]27,[103]28. In highly proliferating
   cells, both D cyclins and Ki67 are present at high levels, unlike in
   quiescent cells^[104]29. Our findings demonstrated enhanced gene
   expression of D-type cyclins D1 (CCND1) in ADSCs treated with SAHA,
   which aligns with another study that quantified the mRNA and protein
   levels of CCND1 in placenta derived mesenchymal stem cells with high
   proliferation capacity^[105]30. In addition, immortalized canine ADSCs
   transduced to overexpress CCND1 and cyclin-dependent kinase 4
   (CDK4R24C) showed a dramatic increase in proliferation without cellular
   senescence compared to the primary ADSCs^[106]31.

   Histone modifications have been linked to gene expression, the cell
   cycle, DNA replication, damage, and repair processe. Some HDACi induce
   differentiation while others promote self-renewal^[107]32. In our cell
   model, ASC52 were treated with SAHA at 40–50% confluency to ensure they
   were actively dividing, thus avoiding any contact inhibition that might
   interfere with the drug potency. In this context, SAHA-treated cells
   showed an increase in the G2 phase of the cell cycle, with no
   significant changes observed in the sub-G1 population. This suggests
   that, at least in ADSCs, SAHA primarily induces growth arrest without
   notable apoptosis. Similar results were observed in human prostate
   cancer cell lines DU145 and PC-3, where SAHA treatment caused
   dose-dependent G2/M cell cycle arrest. However, in these cells,
   apoptosis was noted, potentially indicating differences between
   transformed cancer cells and non-transformed cells, such as
   ADSCs^[108]33. In another study, MI192, a selective inhibitor of HDAC2
   and HDAC3, accelerated the osteogenic differentiation of hDPSCs,
   leading to enhanced histone acetylation and cell cycle arrest at the
   G2/M phase^[109]34. Thus, it appears that HDACi induce growth arrest in
   the G2 phase; however, the outcome, whether apoptosis or
   differentiation is cell type dependent. This highlights the importance
   of understanding the cellular context when evaluating the effects of
   HDACi for therapeutic applications.

   ADSCs are known for their capacity for DNA repair, with several markers
   associated with DNA repair processes. The p53/p21(CDKN1A) pathway
   potentially allows cells to tolerate external triggers from treatment
   and activates the repair system to avoid apoptosis, enabling
   progression through the G1 phase and entry into the S phase of the cell
   cycle^[110]35,[111]36. Additionally, while histone modifications can
   activate DNA damage, DNA damage itself triggers cell cycle checkpoints,
   which are crucial for slowing down cell cycle progression and
   facilitating repair^[112]37. DNA repair is a complex process involving
   multiple proteins. PCNA is a key factor that transiently accumulates at
   sites of DNA damage and initiates recombination-associated DNA
   synthesis after injury. An increase in PCNA levels may be induced by
   growth factors or as a result of DNA damage in non-cycling
   cells^[113]38. To gain further insight into the regulation of SAHA on
   cell proliferation, we investigated PCNA protein expression. Our
   results revealed a significant reduction in PCNA levels in ADSCs
   treated with SAHA, suggesting that these cells may require more time to
   proliferate in order to recover from DNA damage. Similar findings
   regarding another HDACi, VPA, were reported by Najafipour et al.,
   indicating that the proliferation capacity of ADSCs was also affected
   by the treatment. While studying their potential to differentiate into
   cardiomyocytes, the cells gradually proliferated and reached 70–80%
   confluency over four weeks^[114]39. Another study has reported that
   goat ADSCs treated with 500 nM TSA and 16 μM SAHA showed an increase in
   H3K9 acetylation and decreased PCNA expression, which inhibits DNA
   replication and results in G0 & G1 cell cycle arrest^[115]40.

   Several in vivo and in vitro studies have revealed that human
   mesenchymal stem cells are particularly resistant to apoptosis caused
   by DNA damage^[116]41. Furthermore, hematopoietic stem cells and
   mammary stem cells exhibit a unique, CDKN1A-dependent DNA damage
   response that inhibits p53 activation and prevents cell growth
   arrest^[117]42. CDKN1A halts the progression of the cell cycle at the
   G2/M checkpoint, while MDM2, another well-known target gene of p53,
   delays the cell cycle progression through the G2/M phase is and
   inhibiting MDM2 protein expression synergistically increase p53
   activation in response to DNA damage. Many drugs have been discovered
   to reactivate the p53 gene by limiting MDM2 association with p53,
   inhibiting MDM2 can reactivate p53 and is a potential cancer
   therapeutic method^[118]43,[119]44. Our microarray gene expression
   results showed a significant upregulation of CDKN1A and MDM2,
   accompanied by a slight decrease in p53 expression in response to SAHA
   treatment. Consistent with these findings, our Western blotting
   analysis demonstrated increased of p53, p21, accompanied by a decrease
   in MDM2 protein level, suggesting that SAHA stabilizes p53 levels,
   leading to the activation of p21 and MDM2 gene expression. It appears
   that p53 has an antiproliferative function in differentiated cells, as
   it regulates the transcription of proliferative genes in embryonic stem
   cells. This may explain why high levels of p53 are associated with the
   efficient multiplication of stem cells^[120]45. Additionally, p53
   protein expression was reported to play a role in cadiogenic gene
   activation in HDAC1 depleted cardiac mesenchymal stromal cells (CMCs),
   establishing a direct biological relationship between p53 signaling and
   lineage transcriptional activation^[121]46. In another study
   P53-deficient cells were shown to differentiate to endoderm with high
   efficiency, indicating an unexpected role in regulating precursor or
   stem cell differentiation programs. P53 appears to enforce a
   “differentiation checkpoint” during early endoderm differentiation,
   which alters cell fate^[122]45. Moreover, the cellular process derived
   from our microarray data, as identified by gene ontology analysis,
   provided evidence for the activation of development process and
   repression of gene expression related to the cell cycle G2/M phase
   transition. Enhancing stem cell differentiation capacity is a
   challenging endeavor; however, it can be effectively improved through
   histone modification^[123]47.

   Interestingly, the proteomics profile of ADSCs treated with SAHA showed
   downregulation of pathways related to the cell cycle, DNA replication
   and ATP-dependent chromatin modeling. The latter involves complexes
   that disrupt or alter histone-DNA interactions, helping in the
   establishment of tissue identity, especially at the regulatory sites of
   specific transcription factors^[124]48. ATP-dependent chromatin
   remodeling is tightly regulated in stem cells to balance pluripotency
   maintenance and differentiation through regulation of chromatin
   structure and gene expression^[125]49. In the context of cell
   differentiation, ATP-dependent chromatin remodeling, and DNA
   replication are considered important mechanisms that regulate gene
   expression and establish cellular identity^[126]50. To this end
   Marcotte et al. provided supportive evidence of SAHA effects on
   cutaneous wound healing through affecting endogenous adipose derived
   stem cells to promote the regeneration process^[127]51.

   Although ADSCs showed great potential for future clinical use in
   regenerative medicine, they exhibit certain limitations, including
   restricted in vitro growth and a tendency to replicate aging patterns.
   Using SAHA as pharmacoepigenetic agent, at nanomolar concentrations
   could alter ADSCs fate determination through various biological
   processes in a coordinated manner. This approach could allow for the
   effective development of functional and safe direction for regenerative
   therapies. Nevertheless, in vivo studies are essential to validate our
   findings and to further explore the epigenetic mechanisms of resident
   stem cells in various tissues, including the integumentary system,
   which can support wound healing process.

Materials and Methods

Cell culture and histone deacetylase inhibitor treatment regimen

   ASC52telo cells (ASC52), adipose-derived mesenchymal stem cells
   immortalized with hTERT (SCRC-4000) were purchased from ATCC, USA and
   was maintained in a humidified chamber set at 37 °C with 5% CO[2],
   using Dulbecco’s Modified Eagle’s Medium (DMEM); (Sigma-Aldrich,
   Germany) enriched with 10% fetal bovine serum albumin (FBS);
   (Sigma-Aldrich, Germany). ADSCs were initially seeded at 50%
   confluency, subjected to 24 h of serum deprivation, followed by
   treatment with SAHA (Abcam, UK) at concentrations of 10, 100, 1000, and
   2000 nM. SAHA was dissolved in EtOH, and the latter was considered as a
   control vehicle. The treatment was applied sequentially for 72 h.
   Following the treatment, fresh DMEM media was added for 24 h. The
   treatment regimen is shown in Fig. [128]6.

Fig. 6.

   [129]Fig. 6
   [130]Open in a new tab

   A diagram of ADSCs treatment regimen with SAHA.

Cell viability assessment

   Cells were seeded in 96-well plates at a density of 5000 cells per 200
   µl per well. Cell viability was assessed after treating the cells with
   the previously mentioned concentrations or EtOH, while cells that
   received no treatment served as a control. 5 mg/ml MTT salt
   (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide);
   (Sigma-Aldrich, Germany) were added to the cells for 3 h in CO[2]
   incubator. MTT reduction reaction was stopped by using 100 µl Dimethyl
   sulfoxide (DMSO); (Sigma-Aldrich, Germany) as a solubilization
   solution. Absorbance readings were taken at 570 nm using a Molecular
   Devices SpectraMax Plus 384 microplate reader (VWR, USA).

Cell cycle progression analysis

   Cells were initially seeded at 50% confluency and then subjected to
   treatment with either 1 µM SAHA or EtOH as a control vehicle.
   Additionally, cells that received no treatment served as a control
   group. On the day of cell harvesting, media containing dead cells was
   collected, and the cells were detached using trypsin and added to
   collected media. The cell suspension was then centrifuged at 1200 g for
   5 min and subsequently fixed by incubating in 70% pre-chilled ethanol,
   followed by storage at − 20 °C. Fixed cells were washed with PBS,
   pelleted, and resuspended in a PI solution containing (50 µg/mL) and
   (100 µg/mL) of RNase A and incubated for 30 min at 37 °C. The
   PI-stained cells were analyzed using Beckman Coulter Gallios Flow
   Cytometer (Beckman, USA). Blue laser with an excitation wavelength of
   488 nm was employed, and PI fluorescence was detected using the red
   channel (560-615 nm). Low flow rate was used, and approximately
   1 × 10^5 nuclei were examined in each sample. Qualitative gating was
   applied to eliminate clumps and aggregates, using a dot blot with PI
   fluorescence area (PI-A) vs. PI fluorescence width (PI-W) displayed on
   a linear scale. The acquisition settings remained consistent throughout
   the experiment. A PI-A histogram was utilized to visualize the nuclear
   DNA content. Data analysis was performed using ModFit LT™ software
   (Verity Software House, USA).

SDS–polyacrylamide gel electrophoresis and immunoblotting

   Whole-cell lysates were isolated as previously reported with brief
   modifications^52. Radioimmunoprecipitation assay buffer (RIPA) was used
   accompanied with 1 × complete protease inhibitor (Roche, Switzerland).
   Total protein was quantified using Bio-Rad Protein Assay (Bio-Rad
   Laboratories, USA), and read at 750 nm in SpectraMax Plus 384
   Microplate Reader (Molecular Devices, USA). Twenty-five μg of protein
   was loaded onto 4–12% Bis–Tris precast polyacrylamide gels (Bio-Rad
   Laboratories, USA) and subjected to electrophoresis for 90 min at 150 V
   in the Bio-Rad PowerPac™ HC system (Bio-Rad, USA). Subsequently, they
   were transferred to nitrocellulose membranes using the Trans-Blot
   Transfer Turbo System (Bio-Rad Laboratories, USA). Afterwards, the
   membranes were blocked in a 5% skimmed milk solution for 1 h at room
   temperature and probed with primary antibodies for Histone deacetylase
   (HDAC) antibody sampler kit (cell signaling technology, USA), including
   HDAC1, HDAC2, HDAC3, HDAC4, HDAC6, and PCNA, P21 Waf1/Cip1,
   MDM2(SMP14), p53(DO-1), p27 Kip1 (D37H1), and β-Actin overnight at
   4 °C, following the manufacturer’s recommendations. The membranes were
   washed three times with 0.1% Tween-20 for 10 min each, then incubated
   with corresponding secondary antibodies (1:1000) in the darkness for
   1 h. Following washing, chemiluminescent signal was detected using ECL
   Western Blotting Detection Reagents (Bio-Rad Laboratories, USA) and
   scanned in ChemiDoc imaging system (Bio-Rad Laboratories, USA).
   Densitometric analysis was conducted using Image Lab, version 6.0.1
   (Bio-Rad Laboratories, USA).

Immunofluorescence staining and imaging

   Cells were fixed and permeabilized using 4% paraformaldehyde (Histolab
   Products AB, Sweden) for 15 min and 0.5% TritonX-100 (Sigma-Aldrich,
   Germany) for 10 min respectively. The cells were incubated with a
   blocking solution containing 3% BSA for 1 h, followed by overnight
   incubation with monoclonal rabbit anti-human Ki-67/MKI67 (Novus
   Biologicals Inc, USA); (1:250) at 4 °C, followed by 1 h incubation in
   the darkness with the secondary polyclonal goat anti-rabbit labeled
   with Alexa Fluor® 488 (Abcam, USA); (1:1000). The cells were
   counterstained with NucBlue™ Fixed Cell Stain (Life Technologies
   Corporation, USA) to visualize the nucleus, and mounted onto microscope
   slides using ProLong™ Glass Antifade Mountant (Thermo Fisher
   Scientific, USA) according to the manufacturers recommendations. The
   slides were then examined under a confocal microscope (Carl Zeiss LSM
   700, Germany) with a Plan-Apochromat 63x/1.4 oil immersion objective
   lens. Images were captured using the ZEN 2011 SP3 software and analyzed
   with imageJ software (NIH, USA).

Microarray gene expression analysis

   Total cellular RNAs were extracted using RNeasy Kit (Qiagen, Germany),
   following the manufacturer’s instructions. RNA concentration quantified
   and A260/A280 ratio were determined using the NanoDropTM 1000
   spectrophotometer (Thermo Fisher Scientific, USA). RNA quality was
   checked using Agilent Bioanalyzer 2100 RNA (Agilent, USA)^[131]28. The
   samples with RNA integrity number (RIN) equal to 9 were hybridized with
   Human Clariom™ S array (Thermo Fisher Scientific, USA) following the
   manufacturer’s protocols. The arrays were washed, stained using
   GeneChip Fluidics Station 450 (Thermo Fisher Scientific, USA), and
   scanned with GeneChip Scanner 3000 7G (Affymetrix, Santa Clara, CA,
   USA).

   Arrays were processed with the oligo package to summarize probe-set
   transcript clusters. One sample from EtOH group was removed because the
   intensity was low (mean = 36.49, sd = 56.19). Raw RNA expression data
   were normalized using the robust multi-array average (RMA) method
   applied through the oligo package^[132]53. Hierarchical clustering was
   carried out, while heatmaps of significantly regulated genes were
   plotted using the pheatmap v1.0.12 package^[133]54. Annotation of
   probes was performed using clariomshumantranscriptcluster.db package.
   The Limma R package^[134]56 was used to identify the differentially
   expressed genes (DEGs), adjusting the associated p value with
   Benjamini–Hochberg (BH) method to control the false discovery rate.
   Following the exclusion of one EtOH sample, the control groups (EtOH
   and untreated) were combined, and the gene expression was compared to
   the study group (SAHA). The log2 fold change values of probe sets
   mapped on the same gene were averaged and genes with fold change value
   |FC |≥ 1.5 and an adjusted FDR p value < 0.05 were identified as
   differentially expressed.

Reverse transcription and quantitative real-time polymerase reaction (qPCR)

   Reverse transcription of 1 µg of total RNA was done using the
   QuantiTect® Reverse Transcription Kit (Qiagen, Germany) following the
   manufacturer’s instructions. The gene expression levels were determined
   using the PowerUp™ SYBR™ Green Master Mix (Thermo Scientific, USA). The
   primer sequences for the specific genes of interest are detailed in
   (Table [135]1). Real-time quantitative PCR was performed using the
   Applied Biosystems™ 7500 Real-Time PCR System (Applied Biosystem, USA),
   The reaction temperature profile was as follows: 95 °C for 20 min, 40
   cycles of 95 °C for 10 s and 60 °C for 1 min. To calculate the relative
   fold change in mRNA expression, we utilized the delta delta Ct (ΔΔCt)
   method, and the results were normalized to GAPDH.

Table 1.

   Primers sequences used for the qualitative polymerase chain reaction
   (q-PCR).
   Accession number Gene name Sequence (5'—> 3') Product length (bp)
   [136]NM_053056 Cyclin D1 (CCND1) F 5′-GAGGAGCTGCTGCAAATGG-3′ 58
   R 5′-CGGCCAGGTTCCACTTGA-3′
   [137]NM_001374511.1 Cyclin-dependent kinase inhibitor 1A (p21, Cip1)
   (CDKN1A/P21) F 5′-ATGAAATTCACCCCCTTTCC-3′ 174
   R 5′-CCCTAGGCTGTGCTCACTTC-3′
   [138]NM_001367990.1 MDM2 proto-oncogene, E3 ubiquitin protein ligase
   (MDM2) F 5′-CAGTAGCAGTGAATCTACAGGGA-3′ 85
   R 5′-CTGATCCAACCAATCACCTGAAT-3′
   [139]NM_001276696.3 Homo sapiens tumor protein p53 (TP53) F
   5′-ACCTATGGAAACTACTTCCTGAAA-3′ 257
   R 5′-GCTGCCCTGGTAGGTTTTCT-3′
   [140]Open in a new tab

Mass spectrometry samples preparation

   Whole-cell lysates containing protein amounts ranging from 60 to 260 µg
   were treated with 200 mM dithiothreitol (Sigma, Germany) at 56 °C for
   30 min, alkylated with 400 mM iodoacetamide (Sigma, Germany) in the
   darkness for 60 min, then precipitated overnight at 4 °C with 1:9
   volume 95% EtOH with 50 mM sodium acetate (Sigma, Germany). The
   precipitated samples were centrifuged at 13,000 rpm for 1 h, the
   supernatants were removed and dried in SpeedVac, then suspended to
   0.1 M ammonium bicarbonate (Sigma, Germany) buffer followed by
   digestion with Trypsin Gold (Promega, UK) at a ratio of 1:50
   (protease/protein) at 37 °C overnight^[141]57. Samples were diluted in
   1 M sodium chloride with 1% formic acid (Sigma, Germany) and cleaned
   through Nanosep® Centrifugal Devices with Omega™ Membrane 30 K filter
   (Pall corporation, USA) to remove peptides with glycosaminoglycan (GAG)
   chains. The peptides concentrations were determined using the pierce
   quantitative colorimetric peptide assay (Thermo Scientific, USA).

Instrumentation and data-independent acquisition (DIA) analysis

   The samples were further spiked with iRT peptides before analyses with
   mass spectrometry. Digested peptides were separated with nanoflow
   reversed-phase chromatography with an Evosep One liquid chromatography
   (LC) system (Evosep, Denmark). Separation was performed with the 30 SPD
   method (gradient length 44 min) using a 15 cm × 150 µm Evosep column
   packed with 1.5 μm ReproSil-Pur C18-AQ particles at 40 °C. A captive
   source mounted on a timsTOF Pro mass spectrometer from Bruker Daltonics
   was used with timstof control (4.0) and Hystar (6.0) software. Samples
   were analyzed in DIA-PASEF mode with a m/z range of 400–1201 and an IM
   range of 0.6–1.43 1/K0 [V s/cm^2]. DIA-PASEF windows and collision
   energy had a base of 0.85 1/K0 [V s/cm^2] set at 20 eV and 1.30 1/K0 [V
   s/cm^2] set at 59 eV.

   Direct-DIA was conducted using Spectronaut ™ 17.0 software, subsequent
   protein search was conducted using spectral library. The human protein
   fasta files were downloaded from the uniprot database (2022-10-12).
   Human species were selected for all the identified proteins. The
   protein identification threshold was set at < 1% for both peptide and
   protein false discovery rates. Precursor quantitation was performed at
   MS2 level^[142]58. We excluded proteins with three or more missing
   values within each biological group to avoid any potential bias. The
   total count of identified proteins after this exclusion was 6272
   proteins. Prior to the analysis, logFC transformation to all intensity
   values was applied.

   The mass spectrometry proteomics data have been deposited to the
   ProteomeXchange Consortium via the PRIDE partner repository with the
   dataset identifier PXD053005.

Gene ontology and enrichment analyses

   To identify differentially expressed genes (DEGs), we employed a
   multivariate linear regression approach and principal components
   analysis (PCA) with the Limma R package. Heatmap was plotted by
   ComplexHeatmap (version 2.18.0) R package.Gene ontology (GO) was
   applied to annotate genes and proteins, which reflects biological
   processes (BP) and molecular function (MF) for microarray and mass
   spectrometry data. Gene Set Enrichment was analyzed with
   ClusterProfiler to evaluate overrepresentation of certain pathways and
   reactome database was used^[143]59–[144]61.

Statistical analysis

   All experiments were conducted at a minimum of three independent
   biological replicates. Data were presented as means with the
   corresponding standard error of the mean (SEM). Statistical analysis
   was carried out using GraphPad Prism software (version 9.4.0, USA)
   employing an unpaired t-test. p value < 0.05 was indicating of a
   statistically significant difference, which presented as follows: * for
   p < 0.05, ** for p < 0.01, and *** for p < 0.001.

Supplementary Information

   [145]Supplementary Information 1.^ (61.9MB, pptx)
   [146]Supplementary Information 2.^ (4.1MB, docx)
   [147]Supplementary Information 3.^ (18.7KB, xlsx)
   [148]Supplementary Information 4.^ (10.5KB, xlsx)
   [149]Supplementary Information 5.^ (316.1KB, xlsx)

Acknowledgements