Next Article in Journal
Ciliary Proteins: Filling the Gaps. Recent Advances in Deciphering the Protein Composition of Motile Ciliary Complexes
Next Article in Special Issue
Mesenchymal Stem Cell Migration and Tissue Repair
Previous Article in Journal
Role of Cardiolipin in Mitochondrial Function and Dynamics in Health and Disease: Molecular and Pharmacological Aspects
Previous Article in Special Issue
Advances in Regenerative Stem Cell Therapy in Androgenic Alopecia and Hair Loss: Wnt Pathway, Growth-Factor, and Mesenchymal Stem Cell Signaling Impact Analysis on Cell Growth and Hair Follicle Development
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Characterization of Dermal Stem Cells of Diabetic Patients

1
Maria Cecilia Hospital, GVM Care & Research, 48033 Cotignola (RA), Italy
2
Department of Medical Sciences, University of Ferrara, via Fossato di Mortara 70, 44121 Ferrara, Italy
3
Department of Morphology, Surgery and Experimental Medicine, Section of Pathology, Oncology and Experimental Biology and Laboratory for Technologies of Advanced Therapies (LTTA), University of Ferrara, 44121 Ferrara, Italy
*
Author to whom correspondence should be addressed.
Cells 2019, 8(7), 729; https://doi.org/10.3390/cells8070729
Submission received: 26 June 2019 / Revised: 12 July 2019 / Accepted: 15 July 2019 / Published: 16 July 2019
(This article belongs to the Special Issue Advances in Stem Cells and Regenerative Medicine)

Abstract

:
Diabetic foot ulcers (DFUs) are lesions that involve loss of epithelium and dermis, sometimes involving deep structures, compartments, and bones. The aim of this work is to investigate the innate regenerative properties of dermal tissue around ulcers by the identification and analysis of resident dermal stem cells (DSCs). Dermal samples were taken at the edge of DFUs, and genes related to the wound healing process were analyzed by the real-time PCR array. The DSCs were isolated and analyzed by immunofluorescence, flow cytometry, and real-time PCR array to define their stemness properties. The gene expression profile of dermal tissue showed a dysregulation in growth factors, metalloproteinases, collagens, and integrins involved in the wound healing process. In the basal condition, diabetic DSCs adhered on the culture plate with spindle-shaped fibroblast-like morphology. They were positive to the mesenchymal stem cells markers CD44, CD73, CD90, and CD105, but negative for the hematopoietic markers CD14, CD34, CD45, and HLA-DR. In diabetic DSCs, the transcription of genes related to self-renewal and cell division were equivalent to that in normal DSCs. However, the expression of CCNA2, CCND2, CDK1, ALDH1A1, and ABCG2 was downregulated compared with that of normal DSCs. These genes are also related to cell cycle progression and stem cell maintenance. Further investigation will improve the understanding of the molecular mechanisms by which these genes together govern cell proliferation, revealing new strategies useful for future treatment of DFUs.

1. Introduction

Diabetes mellitus is a universal health problem. People suffering from diabetes endure long-term complications such as cardiovascular diseases, nephropathy, retinopathy, neuropathy, and ulcers in the lower limbs. Indeed, diabetic neuropathy and peripheral arterial disease are common factors that cause diabetic foot ulcers (DFUs). In particular, motor neuropathy causes muscle weakness, atrophy, and paresis, sensory neuropathy leads to loss of the protective sensation of pain, pressure, and heat, and autonomic dysfunction causes vasodilation and decreased sweating, resulting in a loss of skin integrity, providing a site vulnerable to microbial infection [1]. It is estimated that between 15% and 25% of patients suffering from diabetes mellitus develop during their lifetime skin ulcers below the ankle, which seriously affect their quality of life, can be limb- and life-threatening, and are responsible for the majority of hospital admissions among diabetics. DFUs constitute a major public health burden in both the developed and developing countries [2]. Current treatment of DFUs encompasses glycaemia control, appropriate wound dressing management, offloading, revascularization if critical limb ischemia is assessed, and when required, prompt surgical debridement. DFUs predispose patients to local infection, osteomyelitis, gangrene, systemic sepsis, and amputation [3]. The 5 year mortality rate associated with DFUs requiring amputation ranges from 39 to 80%, extremely close to the most aggressive forms of cancer [4]. Therefore, the research continuously investigates novel strategies to promote healing in the diabetic foot and reduce the morbidity and mortality.
DFUs can be defined as lesions that involve loss of epithelium and dermis, sometimes involving deep structures and bones. Several studies have focused on the investigation of granulation tissue in diabetic ulcers. A series of molecular alterations were observed in the healing process, including high concentration of metalloproteinases, nonphysiological inflammatory response, oxidative stress, deficient neoangiogenesis, insufficient concentrations of growth factors, and high probability of infection [5,6,7,8].
Adult mammalian dermis contains tissue-derived stem cells with a high proliferation potential and an expression profile similar to adult mesenchymal stem cells isolated from many other adult tissues [9]. The dermal stem cells (DSCs) are responsible for the high regenerative potential of the skin, and may represent a means for the purposes of regenerative medicine. The clinical utility of stem cells in caring for injuries is primarily based on repairing and replacing cellular substrates, attenuation of inflammation, increasing angiogenesis, and enhancing migration of reparative cells. Stem cells are sought after due to their unique ability to initiate different wound healing programs, depending on the environmental milieu [10,11,12].
The aim of this work is to investigate the innate regenerative properties of dermal tissue around ulcers by the identification and analysis of resident DSCs. Dermal samples were taken at the edge of DFUs, and the expression profile of genes associated to the wound healing process was analyzed by the real-time PCR array technique. In an effort to characterize resident stem cells, we isolated DSCs from the same samples and analyzed their morphology by immunofluorescence as well as flow cytometry. Furthermore, a real-time PCR array of stem cell markers was carried out to define stemness properties of diabetic DSCs.

2. Materials and Methods

2.1. Patients Recruitment and Samples Collection

The study was conducted at Maria Cecilia Hospital (Cotignola, Ravenna, Italy) following the ethical principles for medical research involving human subjects of the World Medical Association Declaration of Helsinki. The patients participated in the study after signing the consent form. Patients met the following inclusion criteria: older than age 18 years; known diagnosis of diabetes mellitus; assessment of critical limb ischemia and infection was carried out; signature of the consent form. The exclusion criteria were known or suspected cancer diagnosis, chronic renal failure in dialytic treatment, and life expectancy of less than 1 year.
The study involved 20 diabetic patients with critical ischemia of the lower limbs and ulcer. The management of the infected ulcer involved antibiotic therapy targeted to microbiological isolations and surgical debridement to remove the necrotic and infected area. During surgery, the discarded tissue was stored and destined for the laboratory investigations. The dermis at the edge of the ulcer was used for gene expression analysis by real-time PCR array and for the isolation and characterization of resident stem cells.

2.2. RNA Extraction and Real-Time PCR Array

Total RNA was extracted from dermis or cultured cells with the RNeasy Mini Kit (Qiagen, Hilden, Germany), including an on-column DNase digestion step by the RNase-Free DNase Set (Qiagen), according to the manufacture procedure. The concentration and purity of the RNA were checked by spectrophotometric measurement on the NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Of each sample, 500 ng of total RNA was reverse-transcribed with RT2 First Strand Kit (Qiagen) in a SimpliAmp Thermal Cycler (Applied Biosystems, Thermo Fisher Scientific) following the manufacture procedures. The resultant first-strand cDNAs were stored at −20 °C until the next step. Human wound healing RT2 Profiler PCR Array and human stem cell RT2 Profiler PCR Array (Qiagen) were performed, in accord with the manufacture protocol. Briefly, the cDNA samples were mixed with RT2 SYBR Green Mastermix (Qiagen), and then aliquoted into the wells of the RT2 Profiler PCR Array. A StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) was set up with the following thermal cycling conditions: denaturation at 95 °C for 10 min; followed by 40 cycles of denaturation at 95 °C for 15 s; annealing and elongation at 60 °C for 1 min. A dissociation curve for each well was performed by running the following program: 95 °C for 1 min; 65 °C for 2 min; 65 °C to 95 °C at 2 °C/min. Relative expression was determined using the 2ΔΔCT method. Ct values of target genes were normalized to the geometric mean Ct values of five housekeeping genes (ACTB, B2M, GAPDH, HPRT1, and RPLP0). Results were reported as fold regulation of target genes in the test group (pathological) compared with the control group (healthy). Fold regulation values greater than 2 indicate increased gene expression, fold regulation values less than −2 indicate decreased gene expression, and fold regulation values between −2 and 2 indicate indifferently expressed genes. Data analysis was performed by using the GeneGlobe Data Analysis Center (Qiagen). The data analysis software does not perform any statistical analysis beyond the calculation of p values using Student’s t-test based on 2ΔCT values for each gene of the test group compared with the those of control group. Statistical significance was set at p < 0.05.

2.3. Cells Isolation and Characterization

Dermis was removed from DFU biopsy and washed in phosphate-buffered saline (PBS, EuroClone, Milano, Italy) added with 1% antibiotic–antimycotic (AA, Thermo Fisher Scientific, Waltham, MA, USA). Dermal tissue was minced and digested with 200 U/mL collagenase type II (Gibco, Thermo Fisher Scientific) in Hanks’ balanced salts solution (HBSS, Euroclone) at 37 °C for 16 h. The resulting cells were pelleted, rinsed with PBS, and counted using the trypan blue exclusion assay. They were seeded at a density of 5 × 104 cells/cm2 in Basal Medium (BM) consisting of Dulbecco’s modified Eagle’s medium (DMEM, Euroclone) supplemented with 10% Fetal Bovine Serum (FBS, EuroClone), and 1% AA. Cell cultures were maintained at 37 °C and 5% CO2, and medium was changed twice a week.
Cells within 3–5 passages were harvested by trypsin treatment (trypsin/EDTA, EuroClone), then counted under Bürker Chamber (Paul Marienfeld GmbH & Co. KG, Lauda-Königshofen, Germany).
For immunofluorescence staining, 2 × 104 cells/cm2 were seeded on glass coverslips put into 24 well plates and cultured in BM. The following day, cells were fixed in 4% paraformaldehyde (Sigma-Aldrich, St. Louis, MO, USA) for 10 min. After three washes, cells were incubated in 3% bovine serum albumin (BSA; Sigma-Aldrich) solution in PBS at room temperature (RT) for 1 h. Then, cells were incubated overnight at 4 °C with the primary antibodies: mouse anti-human CD44 (Thermo Fisher Scientific), rabbit anti-human CD73 (Abcam, Cambridge, UK), rabbit anti-human CD90 (Abcam, Cambridge, UK), and mouse anti-human CD105 (Thermo Fisher Scientific). Then, cells were incubated with the fluorescent secondary antibodies goat anti-rabbit Alexa Fluor 555 (Thermo Fisher Scientific), or goat anti-mouse Alexa Fluor 488 (Thermo Fisher Scientific) at RT for 1 h. Actin staining was performed with Phalloidin Alexa Fluor 555 (Thermo Fisher Scientific) in PBS for 20 min at RT, and nuclear staining with NucBlue Fixed Cell Stain (4′,6-diamidin-2-fenilindolo, DAPI; Thermo Fisher Scientific) in PBS for 5 min at RT. Immunofluorescence images were acquire on an Upright ECLIPSE Ni Microscope (Nikon, Minato, Tokyo, Japan).
For flow cytometry, as previously described [13], cells were dissociated and resuspended in flow cytometry staining buffer (R&D Systems, Minneapolis, MN, USA) at a final cell concentration of 1 × 106 cells/mL. Cells were incubated with the following fluorescent monoclonal mouse anti-human antibodies (eBioscienceTM, Thermo Fisher Scientific): CD14 R-PE; CD34 FITC; CD44 FITC; CD45 APC; CD73 APC; CD90 R-PE; CD105 PE-Cy 7; HLA-DR FITC. Cells were washed twice with 2 mL of flow cytometry staining buffer and resuspended in 500 µL of flow cytometry staining buffer. Flow cytometry analyses were performed on an Attune NxT flow cytometer (Thermo Fisher Scientific) with the Attune NxT software (Thermo Fisher Scientific). Each experiment was performed independently three times. Results were expressed as mean ± standard deviation (SD).
Cell growth has been investigated by the cumulative population doubling (CPD) assay. Briefly, 1.2 × 105 cells at passage 2 (p2) were seeded into 6 well plates. Every two days, cells were detached, counted, and seeded again at the same density in a new 6 well plate. This was repeated until the cells reached p6. The population doubling (PD) of the cells was calculated according to the formula:
PD = (logNt − logN0)/log2
where PD represents the number of cell divisions that occur in each passage; Nt corresponds to cell number on the second day, and N0 is the initial seeding number of cells. To determine the CPD, the PD level for each passage was calculated and added to the levels of the previous passages. The experiment was performed independently three times. Results were expressed as mean ± standard deviation (SD). Student’s t-test was performed to determine the statistical significance. Statistical significance was set at p < 0.05.
Cell migration ability was investigated by in vitro wound healing assay. Briefly, 2 × 104 cells were seeded in 24 well plates and cultured until they reached confluence. Cell monolayers were scratched by a 100 µL sterile pipette tip. After a wash with PBS, fresh BM was added. Images of the whole wound were taken immediately after the scratch and after 24 h with a Nikon Inverted Microscope Eclipse Ti-E equipped with a Digital Sight camera DS-03. Scratch areas were measured at 0 and 24 h, and migration rate was calculated according to the formula:
Migration (%) = 100 × (A0 − At)/A0
where A0 corresponds to whole wound taken immediately after the scratch, and At corresponds to whole wound taken after 24 h. At least three independent experiments were performed. Results were expressed as mean ± standard deviation (SD). Student’s t-test was performed to determine the statistical significance. Statistical significance was set at p < 0.05.

3. Results

3.1. Gene Expression Profile of Diabetic Dermis

In order to define the molecular alterations underlying the development of chronic ulcers in diabetic patients, the expression of 84 key wound healing associated genes was probed by real-time PCR array. The array comprises genes expressed during the four phases of the inflammatory response: coagulation, inflammatory, proliferative, and maturation phase. The expression profile of wound healing genes of diabetic dermis taken from the edge of 20 ulcers was compared with that of normal dermis. Plotting of the 84 detected transcripts on a volcano plot (Figure 1) indicated that 31 genes were differentially expressed in diabetic dermis and healthy dermis by 2-fold or greater. A total amount of 11 genes was upregulated, whereas 20 genes were downregulated. The complete list of genes investigated and the fold regulation values are reported in Table 1.
Among the genes involved in the coagulation phase, coagulation factor III (F3), fibrinogen alpha chain (FGA), and plasminogen (PLG) were downregulated, whereas plasminogen activator urokinase receptor (PLAUR) was upregulated. Transcripts of inflammatory chemokines and cytokines, such as chemokine (C-C motif) ligand 2 (CCL2), chemokine (C-X-C motif) ligand 1 (CXCL1), chemokine (C-X-C motif) ligand 2 (CXCL2), and interleukin 1 beta (IL1B) were upregulated, and colony stimulating factor 2 (CSF2) and interleukin 2 (IL2) were downregulated. The proteolytic enzymes cathepsin G (CTSG), matrix metallopeptidase 1 (MMP1), and matrix metallopeptidase 9 (MMP9) were upregulated, whereas matrix metallopeptidase 2 (MMP2) downregulated. Overall, the proteins constituting the extracellular matrix showed a reduction in the gene expression profile. In particular, collagen type IV alpha 3 (COL4A3), collagen type V alpha 3 (COL5A3), and vitronectin (VNT) were downregulated. Also cell adhesion molecules integrin alpha 1 (ITGA1), integrin alpha 2 (ITGA2), integrin beta 3 (ITGB3) showed a downregulation. Moreover, the expression of growth factors registered a broad downregulation: fibroblast growth factor 2 (FGF2), platelet-derived growth factor (PDGF), connective tissue growth factor (CTGF), and colony stimulating factor 2 (CSF2) were particularly downregulated.

3.2. Stem Cell Isolation and Morphological Characterization

Cells were isolated from diabetic and normal dermis by enzymatic digestion and plated under basal conditions in cell culture flasks. Like the normal cells, the diabetic cells have adhered to flask’s plastic forming a monolayer. The immunostaining of actin filaments with phalloidin revealed a spindle-shaped fibroblast-like morphology both in diabetic and in normal cells (Figure 2).
The cell surface antigens were characterized by immunofluorescence and flow cytometry analyses (Figure 3). Diabetic cells were positive to immunofluorescent staining with CD44 (in green, Figure 3a), CD73 (in red, Figure 3b), CD90 (in red, Figure 3c), and CD105 (in green, Figure 3d). Flow cytometry confirmed that diabetic cells were positive to the mesenchymal stem cells markers CD44, CD73, CD90, and CD105, but negative for the hematopoietic markers CD14, CD34, CD45, and HLA-DR (Figure 3e). The percentages of isolated cells expressing the cell surface markers are reported in Table 2.
The rate of growth and migration of diabetic cells was compared with that of normal cells (Figure 4). A CPD assay was performed to establish growth potential of diabetic and normal dermal cells during five consecutive passaging (from p2 to p6). The CPD corresponds to the total number of estimated divisions during the considered interval. The CPD tended to be lower for diabetic cells with respect to normal cells at all passages examined (Figure 4a). The migration rate was investigated by in vitro wound healing assay (Figure 4b–f). Even the migration rate of diabetic cells was lower than that of normal cells (Figure 4f).

3.3. Gene Expression Profile of Diabetic Dermal Cells

The expression profile of genes characterizing stem cells was investigated in diabetic dermal cells by real-time PCR array. The array comprises genes that control self-renewal, cell division, cell cycle, cell communication, cell adhesion, metabolism, and differentiation. The transcripts of diabetic cells were compared with those of healthy DSCs. The volcano plot in Figure 5 shows that 29 genes were differentially expressed in diabetic DSCs and healthy DSCs by 2-fold or greater. In particular, 19 genes were upregulated and 10 were downregulated. The fold regulations of the 84 investigated genes are reported in Table 3.
In diabetic DSCs, the genes related to self-renewal, symmetric, and asymmetric cell division showed a comparable expression profile to those of healthy DSCs. The transcription of NOTCH1, NOTCH2, WNT1, SOX2, and desert hedgehog (DHH) were equivalent to that in normal DSCs. In addition, the genes involved in the WNT signaling including APC, AXIN1, cyclin D1 (CCND1), frizzled family receptor 1 (FZD1), FRAT1, and MYC were equally expressed. The genes connected to the Notch signal were investigated: the transcription of delta-like 1 (DDL1) gene was equivalent to that in normal DSCs, whereas jagged1 (JAG1) and deltex 2 (DXT2) transcription was upregulated. The transcription of genes regulating cell cycle progression, such as cyclin A2 (CCNA2), cyclin D2 (CCND2), and cyclin-dependent kinase 1 (CDK1) were downregulated. The gene expression of growth factors, including fibroblast growth factor 1 (FGF1), fibroblast growth factor 2 (FGF2), and fibroblast growth factor 3 (FGF3), was equal in diabetic DSCs compared to normal DSCs. Instead the expression of bone morphogenetic protein 1 (BMP1), bone morphogenetic protein 3 (BMP3), and growth differentiation factor 2 (GDF2, alias BMP9) was upregulated in diabetic DSCs. Moreover, the gene expression of the metabolism genes such as ATP-binding cassette sub-family G member 2 (ABCG2) and aldehyde dehydrogenase 1 family member A1 (ALDH1A1) were downregulated.

4. Discussion

Skin wound healing is a dynamic process tightly regulated. The regular order in which tissue healing takes place is divided into four stages, corresponding roughly to the four waves of predominant cell type appearing in the wound bed. These are the hemostasis, inflammatory, proliferative, and maturation phases [14]. In the hemostasis phase, platelets and circulating coagulant factors accumulate at the site of tissue injury. The blood clotting is achieved by a cascade of enzymatic reactions, which involves prothrombin and a series of factors, which are converted to active proteases by hydrolysis. F3 is the primary initiator of the extrinsic blood coagulation. Upon injury of the vessel and surrounding tissue, F3 is exposed to blood coagulation factors. The complex with coagulation factor VII catalyzes the proteolytic activation of the coagulation factors X and IX, leading to thrombin generation [15]. Thrombin converts the soluble protein FGA to an insoluble fibrin gel. Thrombin also activates Factor XIII (F13A1), which crosslinks the fibrin polymers and forms a fibrin mesh that traps circulating platelets, leucocytes, and red blood cells [16]. The actions of thrombin and several other activated coagulation factors are inhibited by circulating antithrombin, e.g., serpin peptidase inhibitor clade E member 1 (SERPINE1). Once sufficient thrombin is produced to overcome the effect of circulating antithrombin, the coagulation is able to initiate. The clot dissolution is carried out by plasmin, which catalyzes the fibrin hydrolysis. Plasmin is generated from PLG by extracellular protease plasminogen activator urokinase (PLAU) and tissue-type PLAU (PLAT) that are directly activated and released in extracellular matrix (ECM) by a number of growth factors, e.g. hepatocyte growth factor (HGF) [17]. PLAU also plays a pivotal role in cell adhesion and migration by binding PLAUR. Even a modest increase in PLAUR expression is correlated with the ability of monocytes to penetrate stromal tissue in a PLAU-dependent process. In addition, PLAUR mediates cellular adhesion to VTN, promotes integrin-dependent migration, and initiates intracellular signaling events [18]. In diabetic dermis, the enzymes involved in the creation (F3 and FGA) and the dissolution (PLG) of fibrin clot were downregulated. On the contrary, PLAUR was upregulated to favor the migration of monocyte in the site of injury. The inflammatory phase is characterized by immune cells infiltration. Neutrophils are initially captured from the fast-flowing blood stream in the postcapillary microvasculature to the endothelial vessel wall, followed by rolling along the endothelium, integrin-dependent arrest and firm adhesion, intravascular crawling, and trans-endothelial migration into the inflamed tissue. Inflammatory stimuli, such as tumor necrosis factor alpha (TNFα) and lipopolysaccharide (LPS), induce the upregulation of adhesion molecules on the surface of platelets and vascular endothelial cells that enables these cells to stick to each other and to neutrophils [19]. The migration, adhesion, and activation of leucocytes also depends on the expression of specific integrins. Each integrin consists of one α (ITGA) subunit and one β (ITGB) subunit. Leucocytes by integrin α4β1 (alias CD49d/CD29) bind vascular cell adhesion protein 1 (VCAM-1) and FGA, whereas integrin αVβ3 (alias CD51/CD61) binds intercellular adhesion molecule 1 (ICAM-1), platelet endothelial cell adhesion molecule (PECAM-1), VCAM-1, VTN, and FGA [20]. Compared with the control condition, in the diabetic dermis the expression of ITGB3 was downregulated while ITGA4, ITGAV, and ITGB1 genes were indifferently expressed. This could be related to a lesser lymphocyte activation and a lesser adhesion of them to platelets and endothelial cells. In addition to physical contacts, platelets secrete proinflammatory chemokines such as, CXCL1 with a chemotactic activity for neutrophils, chemokine (C-X-C motif) ligand 5 (CXCL5) a neutrophils activator, CCL2 a potent chemoattractant for monocytes and basophils, and chemokine (C-C motif) ligand 7 (CCL7) a chemotactic factor for monocytes and eosinophils. Platelets also release soluble CD40 ligand (CD40L), which induces in endothelial cells the expression of inflammatory adhesion receptors (e.g., E-selectin, VCAM-1, ICAM-1), the production of chemokines and interleukins (ILs) (e.g., CCL2, IL6, and IL8), and the production of MMP9 [21]. Moreover, immune cells, including monocytes, macrophages, and lymphocytes secrete many proinflammatory cytokines and growth factor to amplify the inflammatory response. Proinflammatory ILs (e.g., IL1B, IL6, and IL8) and chemokines (e.g., CXCL2, CXCL11), TNFα, CSF2, colony-stimulating factor 3 (CSF3) are released to stimulate cytokine production, cell proliferation, macrophage activation, and increase neutrophil and monocyte function [22,23]. Compared with in the control group, the dermis of diabetic patients showed an upregulation of the pro-inflammatory chemokines and cytokine (e.g. CCL2, CXCL1, CXCL2, and IL1B), but a downregulation of CSF2 and IL2. CSF2 can enhance macrophage proliferation as well as modulate their differentiation and function, while IL2 modulates T-cell proliferation and activation. Despite an increase in chemokine expression, the downregulation of CSF2 and IL2 in diabetic dermis could underline an impairment in cell-mediated immune response. However, in diabetic dermis, the serine protease CTSG was upregulated. CTSG is released by activated neutrophils to clear pathogens and regulate inflammation by modifying chemokines, cytokines, and cell surface receptors. CTSG changes CXCL5 and chemokine (C-C motif) ligand 15 (CCL15) into more potent chemotactic factors by a proteolytic processing. In addition, CTSG causes cell shape modification and intercellular gap formation in endothelial cell that increase endothelium permeability, supporting the migration of neutrophils, monocytes and antigen-presenting cells in the injured site [24]. CTSG affects also tissue remodelling directly by degrading components of the matrix and indirectly by cleaving and activating matrix metalloproteinases (MMPs), such as MMP1 and MMP2 [24]. Activated neutrophils also release many angiogenic molecules, such as IL8, CXCL1, and MMP9. In particular, MMP9 and IL8, by degrading the basement membrane and promoting the endothelial cells migration, respectively, are responsible for the proangiogenic effects of neutrophils [25]. MMPs and their tissue inhibitors (TIMPs) are involved in various stages of the wound healing process, and their expression is regulated by several growth factors, including TNFα, transforming growth factor beta 1 (TGFB1), insulin-like growth factor 1 (IGF1), PDGF, epidermal growth factor (EGF), FGF2, vascular endothelial growth factor A (VEGFA), IL6, and IL10 [26]. During the inflammatory phase, MMPs activate or inhibit several cytokines and improve leukocyte invasion, creating a chemotactic gradient. Several chemokines, including CCL7 and chemokine (C-X-C motif) ligand 12 (CXCL12) are substrates for MMP2. MMP9 cleaves and activates chemokine (C-X-C motif) ligand 6 (CXCL6) and IL8, whereas it inactivates CXCL1 and chemokine (C-X-C motif) ligand 4 (CXCL4) [27]. The gelatinase MMP9 and MMP2 are also responsible for the breaking down of type IV collagen in the basement membrane, thus are involved in keratinocyte migration and re-epithelialization [26]. In diabetic dermis, the expression of MMP9 gene was upregulated, while their inhibitor TIMP1 was indifferently expressed. Previous studies on DFUs have reported that a high concentration of MMP9 in wound liquid and a high ratio of MMP9 to TIMP1 are related to poor wound healing [28,29]. A 5-fold increase in the expression of MMP1 was also detected in diabetic dermis. MMP1 is responsible for proteolytic degradation of type-1 and type-3 collagens as well as elastic fibres. It is crucial for wound re-epithelialization, and their dysregulation is associated to several pathological conditions, including cutaneous ulcer [30]. In diabetic dermis, the upregulation of CTSG, MMP9, and MMP1 may depend on bacterial infection at the site of DFU and on a dysregulation of growth factors. Compared with the healthy control, diabetic dermis showed a downregulation of various growth factors, particularly PDGF, FGF2, and CTGF. PDGF works as a chemoattractant for inflammatory cells and fibroblasts. It induces fibroblast migration and proliferation, matrix production, production of granulation tissue proteins and provisional ECM, and angiogenesis during the healing process. Various clinical studies have shown that a downregulation of PDGF and its receptor are associated to an enhanced healing time [26]. FGF2 stimulates the growth and differentiation of fibroblasts, vascular smooth muscle cells, and endothelial cells. It increases the rate and degree of granulation tissue formation, stimulating the healing process [31]. The formation of new blood vessels in new-formed tissue is initiated by PDGF, FGF2, as well as VEGFA [32]. Instead, the production of ECM and the inhibition of proteases are influenced predominantly by the fibrogenic growth factors, including PDGF, IGF1, and TGFB1 [33]. CTGF modulates the interaction of cells with the ECM, promoting collagen deposition, mesenchymal cell activation and differentiation, and tissue remodelling [34]. The balance between matrix degradation and matrix formation is crucial in the wound healing process. In the diabetic dermis, different types of collagen were downregulated, including COL4A3, COL5A3, and VTN. Type IV collagen is the major structural component of basement membranes. Type V collagen regulates the assembly of heterotypic fibers composed of both type I and type V collagen. Therefore, the collagen fibers involved in the organization of the ECM, in angiogenesis and re-epithelialization appear to be compromised in diabetic dermis. VTN interacts with glycosaminoglycans and proteoglycans and is recognized by certain integrins and serves as a cell-to-substrate adhesion molecule. In diabetic dermis, even the ITGA1, ITGA2, and ITGB1 integrin subunits able to bind collagens are downregulated. Smooth muscle cells, fibroblasts, and microvascular endothelium express integrin α1β1 to bind type IV collagen, whereas integrin α2β1 has predominantly epithelial distribution, showing a preference for type I collagen [35]. Among investigated integrins, only ITGB6 gene showed upregulation in diabetic dermis. However, integrin αvβ6 is an epithelium-restricted molecule expressed at low levels in healthy skin that is rapidly upregulated in response to inflammation and injury [36].
Overall, the gene expression profile of proximal dermis to DFUs showed a dysregulation in growth factors, MMPs, collagens, and integrins involved in the proliferation phase, angiogenesis, re-epithelialization, and remodeling. In this scenario, the resident stem cells of dermal tissue around DFUs were isolated and analyzed. The cells of diabetic dermis comply with the parameters of dermis stem cells (DSCs) defined by Vapniarsky and colleagues [37], although they showed a lower proliferation and migration rates compared with the cells of nondiabetic DSCs. Diabetic DSCs have adhered to culture flasks with a spindle-shaped fibroblast-like morphology under the basal condition. Moreover, in basal medium diabetic DSCs were positive to the mesenchymal stem cells markers CD44, CD73, CD90, and CD105, but negative for the hematopoietic markers CD14, CD34, CD45, and HLA-DR.
Despite the morphology and phenotype of diabetic DSCs reflecting that of mesenchymal stem cells, the expression profile of stemness genes in diabetic DSCs showed some differences compared with that in DSCs of nondiabetic donors. Stem cells are defined as cells able to continuously divide to produce more undifferentiated stem cells, the so-called self-renewing property, and to differentiate into specialized cells, namely multilineage differentiation potency [38]. These properties are closely related to the ability of stem cells to proliferate by symmetric and asymmetric division. Asymmetric cell division gives rise to two daughter cells with distinct cell fates: one daughter maintains stem cell properties and functions, while the other daughter loses these characteristics. Symmetric cell division enables stem cells to generate two daughter cells having less potency than the parental stem cell. This mode of division is critical for expanding stem cell reservoirs, leading to rapid production of cells but potential depletion of the stem cell pool. Both symmetric and asymmetric cell divisions are employed in vivo to maintain the fine balance between self-renewal and differentiation of stem cells [39]. Notch, WNT, FGF, BMP, and DHH signaling play a key role in maintaining tissue homeostasis by regulating self-renewal of stem cells as well as proliferation or differentiation of progenitor cells [40]. In diabetic DSCs, the transcription of several genes related to cell division and self-renewal, including NOTCH1, NOTCH2, WNT1, SOX2, and DHH was equivalent to that in normal DSCs. The gene expression of growth factors, including FGF1, FGF2, and FGF3, involved in cell proliferation and differentiation was equal in diabetic DSCs as compared with normal DSCs. Instead, the expression of BMP1, BMP3, BMP9 (alias GDF2) was upregulated in diabetic DSCs.
Notch1 and Notch2 coordinately maintain the stem cell pool in the quiescent state by preventing activation [41], whereas WNT proteins promote the proliferation of stem cells and regulate stem cell fate [42]. The WNT proteins, by promoting the ß-catenin translocation in nucleus, activate the transcription of target genes involving cell proliferation control, including FGF20, DKK1, WISP1, MYC, and CCND1 [43]. The expression of MYC and CCND1 genes in diabetic DSCs was like to that in normal DSCs. Moreover, in diabetic DSCs the transcription of FZD1 and FRAT1 genes (positive regulators of the canonical pathway of Wnt signaling), and of APC and AXIN1 genes (negative regulators) was equivalent to that in normal DSCs.
Asymmetric cell divisions can be controlled by Notch signaling through the activating ligands Jagged1 (JAG1) and Delta-like 1 (DDL1), and the negative regulator Deltex (DXT2) [40,44]. Compared to normal DSCs, the transcription of DDL1 gene was equal in diabetic DSCs, whereas JAG1 and DXT2 transcription was upregulated.
Cell division and proliferation are also dependent by cyclins (CNNs) and cyclin-dependent protein kinases (CDKs), which together control cell cycle progression. In diabetic DSCs, the transcription of CCNA2, CCND2, and CDK1 was downregulated compared with in normal DSCs. The cell cycle is a tightly regulated process that orchestrates genome duplication and accurate distribution of DNA and other factors into daughter cells after mitosis. CCNA2 binds to and activates its catalytic partners, CDK2 and CDK1. These complexes phosphorylate proteins like pocket proteins (RB, p107, p130) and proteins involved in DNA synthesis, thereby driving S-phase progression. In line with its role in regulating S phase, CCNA2 expression is induced upon entry into S phase, persists through the S and G2 phases, and is degraded upon entry into mitosis. During early mitosis, CCNA2 associates with CDK1 and drives chromosome condensation and nuclear envelope breakdown [45]. Instead, CCND2 regulating CDK4 and CDK6 drives the G1-to-S phase transition of the cell cycle [46]. Several works report that cyclins and CDKs are upregulated in different tumors such as breast, liver, and lung cancers, and the downregulation of them represents a good strategy to counteract the proliferation of the cancer stem cells [47,48]. Instead, the expression of cyclins and CDKs in stem cells had not yet been investigated.
The proliferation and differentiation of stem cells are also influenced by the activity of enzymes such us ALDH1A1 and ABCG2, which are downregulated in diabetic DSCs. Aldehyde dehydrogenase (ALDH) is considered a biomarker for stem cells, and its expression is also thought to closely correlate with the stemness of cancer stem cells [49]. ALDHs are family members of NAD-dependent enzymes that catalyze the oxidation of aldehydes to acids. They are localized in the cytoplasm, mitochondria, or nucleus and have been implicated in a wide variety of biological processes, including the detoxification of exogenously and endogenously generated aldehydes and the metabolism of vitamin A, alcohol, and reactive oxygen species. Of these, ALDH1A1 mainly catalyzes the conversion of retinaldehyde to retinoic acid (RA) in vitamin A metabolism. RA enters the nucleus, binds to and activates the RA receptors (RARs) or the retinoid X receptors (RXRs), which are nuclear transcription factors that promote target gene expression. The genes downstream of RA are involved in many important biological processes, including cell differentiation, proliferation, and lipid metabolism [50]. RXR functions as an obligate heterodimeric partner for multiple nuclear receptors including the peroxisome proliferator-activated receptor-gamma (PPARG). The nuclear receptor PPARG is a master regulator of adipogenesis that controls the expression of multiple genes within complex transcriptional networks [51]. In the absence of ALDH1A1, both adipogenesis in vitro and diet-induced fat formation in vivo are markedly impaired. ALDH1A1 deficiency increases retinaldehyde levels that inhibits RXR and PPARG activation, inhibiting PPARG-induced adipogenesis [52]. In diabetic DSCs, adipogenic differentiation is also prevented by the downregulation of the PPARG gene. Moreover, ALDH1A1 controls the cell proliferation by regulating notch signaling. Notch genes have been shown to directly activate cyclins and CDK2, which control the cell cycle progression. Li et al. demonstrated that the deletion of ALDH1A1 inhibits cell cycle progression and cell proliferation by the suppression of the Notch/CDK2/Cyclin pathway [53]. Moreover, ALDH1A1 isozyme has been shown to play an important functional role in maintaining cancer stem cells. In cancer stem cells, the overexpression of ALDH1A1 is associated with enhanced invasion, colony formation, and chemoresistance. It was demonstrated that the stable downregulation of ALDH1A1 isozyme alone dramatically decreased their ability to form colonies and to proliferate [54]. Therefore, the downregulation of ALDH1A1 observed in diabetic DSCs could be the cause of a reduced proliferative capacity due to a downregulation of cyclin and CDKs. In addition, the ABCG2 gene was downregulated in diabetic DSCs. The ABCG2 transporters belong to the ATP Binding Cassette (ABC) superfamily of transporters and use ATP hydrolysis to catalyze the transport of a wide range of substrates from the intracellular to the extracellular milieu. The overexpression of ABCG2 has been shown to promote stemness, while the loss of ABCG2 expression has been shown to promote lineage commitment in several adult stem cells. While the importance of ABCG2 in stem cell maintenance has been shown, the precise mechanism by which ABCG2 prevents stem cells from undergoing differentiation is unknown [55]. It seems that the high ABCG2 activity increases the levels of transcripts involved in stemness such as MEF, SOX2, OCT4, ID1, and HES1, and that the activity of ABCG2 is required for maintaining these stem markers and self-renewal property via Notch-independent manner [56]. Both enzymes have been broadly studied in cancer stem cells, however, data concerning their expression profile in diabetic stem cells are not previously reported in literature.
Overall, the resident stem cells of dermal tissue around DFUs showed morphology and phenotype equal to normal DSCs. However, diabetic DSCs showed reduced proliferative and migration capability. This could be related to the downregulation of the ALDH1A1 enzyme that inhibits cell cycle progression and cell proliferation. Only further investigations could explain the mechanism by which the inflammatory and ischemic condition, typical of DFU, may cause a downregulation of this enzyme.

5. Conclusions

In conclusion, the present study has demonstrated that in diabetic dermal tissue around DFUs there is an impairment in the gene expression profile of growth factors, metalloproteinases, collagens, and integrins involved in the wound healing process. Moreover, we have isolated and characterized the resident DSCs. Although diabetic DSCs have the same morphology and phenotype as normal DSCs, the gene expression has revealed a downregulation of cyclins, CDK1, ALD1H1A, and ABCG2. These proteins are related to cell cycle progression, cell proliferation, and stem cell maintenance. Further investigation will improve the understanding of the molecular mechanisms by which these proteins together govern cell proliferation and will reveal new strategies aimed at enhancing the expression or activity of these proteins that will be useful for future treatment of DFUs.

Author Contributions

Analysis, L.F.; investigation, L.F., C.G., G.B., P.C. and B.Z.; resources, B.Z.; writing—original draft preparation, L.F.; writing—review and editing, L.F. and B.Z.; critical revision, L.D.P.; visualization, L.F., C.G., G.B., and B.Z.; supervision, B.Z., P.P.; project administration, B.Z., G.C. and L.D.P.; funding acquisition, B.Z. All authors read and approved the final manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Alexiadou, K.; Doupis, J. Management of Diabetic Foot Ulcers. Diabetes Ther. 2012, 3, 4. [Google Scholar] [CrossRef] [PubMed]
  2. Zhang, P.; Lu, J.; Jing, Y.; Tang, S.; Zhu, D.; Bi, Y. Global epidemiology of diabetic foot ulceration: A systematic review and meta-analysis. Ann. Med. 2017, 49, 106–116. [Google Scholar] [CrossRef] [PubMed]
  3. Hitchman, L.H.; Totty, J.P.; Raza, A.; Cai, P.; Smith, G.E.; Carradice, D.; Wallace, T.; Harwood, A.E.; Chetter, I.C. Extracorporeal Shockwave Therapy for Diabetic Foot Ulcers: A Systematic Review and Meta-Analysis. Ann. Vasc. Surg. 2019, 56, 330–339. [Google Scholar] [CrossRef] [PubMed]
  4. Shi, R.; Jin, Y.; Cao, C.; Han, S.; Shao, X.; Meng, L.; Cheng, J.; Zhang, M.; Zheng, J.; Xu, J.; et al. Localization of human adipose-derived stem cells and their effect in repair of diabetic foot ulcers in rats. Stem Cell Res. Ther. 2016, 7, 1047. [Google Scholar] [CrossRef] [PubMed]
  5. GadElkarim, M.; Abushouk, A.I.; Ghanem, E.; Hamaad, A.M.; Saad, A.M.; Abdel-Daim, M.M. Adipose-derived stem cells: Effectiveness and advances in delivery in diabetic wound healing. Biomed. Pharmacother. 2018, 107, 625–633. [Google Scholar] [CrossRef] [PubMed]
  6. López-Delis, A.; Rosa, S.D.S.R.F.; De Souza, P.E.N.; Carneiro, M.L.B.; Rosa, M.F.F.; Macedo, Y.C.L.; Veiga-Souza, F.H.; Da Rocha, A.F. Characterization of the Cicatrization Process in Diabetic Foot Ulcers Based on the Production of Reactive Oxygen Species. J. Diabetes Res. 2018, 2018, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Karam, R.A.; Rezk, N.A.; Rahman, T.M.A.; Al Saeed, M. Effect of negative pressure wound therapy on molecular markers in diabetic foot ulcers. Gene 2018, 667, 56–61. [Google Scholar] [CrossRef]
  8. Ferroni, L.; Gardin, C.; De Pieri, A.; Sambataro, M.; Seganfreddo, E.; Iacopi, E.; Goretti, C.; Zavan, B.; Piaggesi, A. Treatment of diabetic foot ulcers with Therapeutic Magnetic Resonance (TMR®) improves the quality of granulation tissue. Eur. J. Histochem. 2017, 61, 2800. [Google Scholar] [CrossRef]
  9. Lorenz, K.; Sicker, M.; Schmelzer, E.; Rupf, T.; Salvetter, J.; Schulz-Siegmund, M.; Bader, A. Multilineage differentiation potential of human dermal skin-derived fibroblasts. Exp. Dermatol. 2008, 17, 925–932. [Google Scholar] [CrossRef]
  10. Gardin, C.; Vindigni, V.; Bressan, E.; Ferroni, L.; Nalesso, E.; Della Puppa, A.; D’Avella, D.; Lops, D.; Pinton, P.; Zavan, B. Hyaluronan and Fibrin Biomaterial as Scaffolds for Neuronal Differentiation of Adult Stem Cells Derived from Adipose Tissue and Skin. Int. J. Mol. Sci. 2011, 12, 6749–6764. [Google Scholar] [CrossRef] [Green Version]
  11. Salerno, S.; Messina, A.; Giordano, F.; Bader, A.; Drioli, E.; De Bartolo, L. Dermal-epidermal membrane systems by using human keratinocytes and mesenchymal stem cells isolated from dermis. Mater. Sci. Eng. C 2017, 71, 943–953. [Google Scholar] [CrossRef] [PubMed]
  12. Agabalyan, N.A.; Rosin, N.L.; Rahmani, W.; Biernaskie, J. Hair follicle dermal stem cells and skin-derived precursor cells: Exciting tools for endogenous and exogenous therapies. Exp. Dermatol. 2017, 26, 505–509. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Ferroni, L.; Gardin, C.; Bellin, G.; Vindigni, V.; Pavan, C.; Zavan, B. Effects of novel antidepressant drugs on mesenchymal stem cell physiology. Biomed. Pharmacother. 2019, 114, 108853. [Google Scholar] [CrossRef] [PubMed]
  14. Boniakowski, A.E.; Kimball, A.S.; Jacobs, B.N.; Kunkel, S.L.; Gallagher, K.A. Macrophage-Mediated Inflammation in Normal and Diabetic Wound Healing. J. Immunol. 2017, 199, 17–24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Witkowski, M.; Landmesser, U.; Rauch, U.; Information, P.E.K.F.C. Tissue factor as a link between inflammation and coagulation. Trends Cardiovasc. Med. 2016, 26, 297–303. [Google Scholar] [CrossRef] [PubMed]
  16. Kattula, S.; Byrnes, J.R.; Wolberg, A.S. Fibrinogen and fibrin in hemostasis and thrombosis. Arter. Thromb. Vasc. Boil. 2017, 37, e13–e21. [Google Scholar] [CrossRef]
  17. Dally, J.; Khan, J.S.; Voisey, A.; Charalambous, C.; John, H.L.; Woods, E.L.; Steadman, R.; Moseley, R.; Midgley, A.C. Hepatocyte Growth Factor Mediates Enhanced Wound Healing Responses and Resistance to Transforming Growth Factor-β₁-Driven Myofibroblast Differentiation in Oral Mucosal Fibroblasts. Int. J. Mol. Sci. 2017, 18, 1843. [Google Scholar] [CrossRef]
  18. Cancello, R.; Rouault, C.; Guilhem, G.; Bedel, J.-F.; Poitou, C.; Di Blasio, A.M.; Basdevant, A.; Tordjman, J.; Clément, K. Urokinase Plasminogen Activator Receptor in Adipose Tissue Macrophages of Morbidly Obese Subjects. Obes. Facts 2011, 4, 17–25. [Google Scholar] [CrossRef]
  19. Pitchford, S.; Pan, D.; Welch, H.C.E. Platelets in neutrophil recruitment to sites of inflammation. Curr. Opin. Hematol. 2017, 24, 23–31. [Google Scholar] [CrossRef] [Green Version]
  20. Zhang, Y.; Wang, H. Integrin signalling and function in immune cells. Immunology 2012, 135, 268–275. [Google Scholar] [CrossRef] [Green Version]
  21. Lievens, D.; Eijgelaar, W.J.; Biessen, E.A.L.; Daemen, M.J.A.P.; Lutgens, T. The multi-functionality of CD40L and its receptor CD40 in atherosclerosis. Thromb. Haemost. 2009, 102, 206–214. [Google Scholar] [CrossRef] [PubMed]
  22. Chen, L.; Deng, H.; Cui, H.; Fang, J.; Zuo, Z.; Deng, J.; Li, Y.; Wang, X.; Zhao, L. Inflammatory responses and inflammation-associated diseases in organs. Oncotarget 2017, 9, 7204–7218. [Google Scholar] [CrossRef] [PubMed]
  23. López-Cotarelo, P.; Gómez-Moreira, C.; Criado-García, O.; Sánchez, L.; Rodríguez-Fernández, J.L. Beyond Chemoattraction: Multifunctionality of Chemokine Receptors in Leukocytes. Trends Immunol. 2017, 38, 927–941. [Google Scholar] [CrossRef] [PubMed]
  24. Gao, S.; Zhu, H.; Zuo, X.; Luo, H. Cathepsin G and Its Role in Inflammation and Autoimmune Diseases. Arch. Rheumatol. 2018, 33, 498–504. [Google Scholar] [CrossRef]
  25. Rouault, C.; Pellegrinelli, V.; Schilch, R.; Cotillard, A.; Poitou, C.; Tordjman, J.; Sell, H.; Clément, K.; Lacasa, D. Roles of Chemokine Ligand-2 (CXCL2) and Neutrophils in Influencing Endothelial Cell Function and Inflammation of Human Adipose Tissue. Endocrinology 2013, 154, 1069–1079. [Google Scholar] [CrossRef]
  26. Patel, S.; Srivastava, S.; Singh, M.R.; Singh, D. Mechanistic insight into diabetic wounds: Pathogenesis, molecular targets and treatment strategies to pace wound healing. Biomed. Pharmacother. 2019, 112, 108615. [Google Scholar] [CrossRef] [PubMed]
  27. Page-McCaw, A.; Ewald, A.J.; Werb, Z. Matrix metalloproteinases and the regulation of tissue remodelling. Nat. Rev. Mol. Cell Boil. 2007, 8, 221–233. [Google Scholar] [CrossRef]
  28. Jindatanmanusan, P.; Luanraksa, S.; Boonsiri, T.; Nimmanon, T.; Arnutti, P. Wound Fluid Matrix Metalloproteinase-9 as a Potential Predictive Marker for the Poor Healing Outcome in Diabetic Foot Ulcers. Pathol. Res. Int. 2018, 2018, 1–5. [Google Scholar] [CrossRef]
  29. Liu, Y.; Min, D.; Bolton, T.; Nubé, V.; Twigg, S.M.; Yue, D.K.; McLennan, S.V. Increased matrix metalloproteinase-9 predicts poor wound healing in diabetic foot ulcers. Diabetes Care 2009, 32, 117–119. [Google Scholar] [CrossRef]
  30. Kim, K.-H.; Jung, J.-Y.; Son, E.D.; Shin, D.W.; Noh, M.; Lee, T.R. miR-526b targets 3′ UTR of MMP1 mRNA. Exp. Mol. Med. 2015, 47, e178. [Google Scholar] [CrossRef]
  31. Komi, D.E.A.; Khomtchouk, K.; Maria, P.L.S. A Review of the Contribution of Mast Cells in Wound Healing: Involved Molecular and Cellular Mechanisms. Clin. Rev. Allergy Immunol. 2019, 1–15. [Google Scholar] [CrossRef] [PubMed]
  32. Serra, M.B.; Barroso, W.A.; Da Silva, N.N.; Silva, S.D.N.; Borges, A.C.R.; Abreu, I.C.; Borges, M.O.D.R. From Inflammation to Current and Alternative Therapies Involved in Wound Healing. Int. J. Inflamm. 2017, 2017, 1–17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Xiao, K.; Luo, X.; Wang, X.; Gao, Z. MicroRNA-185 regulates transforming growth factor-β1 and collagen-1 in hypertrophic scar fibroblasts. Mol. Med. Rep. 2017, 15, 1489–1496. [Google Scholar] [CrossRef] [PubMed]
  34. Derrick, T.; Luthert, P.J.; Jama, H.; Hu, V.H.; Massae, P.; Essex, D.; Holland, M.J.; Burton, M.J. Increased Epithelial Expression of CTGF and S100A7 with Elevated Subepithelial Expression of IL-1β in Trachomatous Trichiasis. PLOS Neglected Trop. Dis. 2016, 10, e0004752. [Google Scholar] [CrossRef] [PubMed]
  35. De Fougerolles, A.R.; Sprague, A.G.; Nickerson-Nutter, C.L.; Chi-Rosso, G.; Rennert, P.D.; Gardner, H.; Gotwals, P.J.; Lobb, R.R.; Koteliansky, V.E. Regulation of inflammation by collagen-binding integrins alpha1beta1 and alpha2beta1 in models of hypersensitivity and arthritis. J. Clin. Investig. 2000, 105, 721–729. [Google Scholar] [CrossRef] [PubMed]
  36. Ghannad, F.; Nica, D.; Fulle, M.I.G.; Grenier, D.; Putnins, E.E.; Johnston, S.; Eslami, A.; Koivisto, L.; Jiang, G.; McKee, M.D.; et al. Absence of αvβ6 Integrin Is Linked to Initiation and Progression of Periodontal Disease. Am. J. Pathol. 2008, 172, 1271–1286. [Google Scholar] [CrossRef] [PubMed]
  37. Nolta, J.A.; Vapniarsky, N.; Arzi, B.; Hu, J.C.; Nolta, J.A.; Athanasiou, K.A.; Vapniarsky, N.; Arzi, B.; Hu, J.C.; Nolta, J.A.; et al. Concise Review: Human Dermis as an Autologous Source of Stem Cells for Tissue Engineering and Regenerative Medicine. Stem Cells Transl. Med. 2015, 4, 1187–1198. [Google Scholar] [Green Version]
  38. Ferroni, L.; Gardin, C.; Tocco, I.; Epis, R.; Casadei, A.; Vindigni, V.; Mucci, G.; Zavan, B. Potential for neural differentiation of mesenchymal stem cells. Adv. Biochem. Eng. Biotechnol. 2013, 129, 89–115. [Google Scholar]
  39. Molofsky, A.V.; Pardal, R.; Morrison, S.J. Diverse mechanisms regulate stem cell self-renewal. Curr. Opin. Cell Boil. 2004, 16, 700–707. [Google Scholar] [CrossRef]
  40. Katoh, M. Networking of WNT, FGF, Notch, BMP, and Hedgehog signaling pathways during carcinogenesis. Stem Cell Rev. 2007, 3, 30–38. [Google Scholar] [CrossRef]
  41. Fujimaki, S.; Seko, D.; Kitajima, Y.; Yoshioka, K.; Tsuchiya, Y.; Masuda, S.; Ono, Y. Notch1 and Notch2 Coordinately Regulate Stem Cell Function in the Quiescent and Activated States of Muscle Satellite Cells. Stem Cells. 2018, 36, 278–285. [Google Scholar] [CrossRef] [PubMed]
  42. Schrader, S.; O’Callaghan, A.R.; Tuft, S.J.; Beaconsfield, M.; Geerling, G.; Daniels, J.T. Wnt signalling in an in vitro niche model for conjunctival progenitor cells. J. Tissue Eng. Regen. Med. 2014, 8, 969–977. [Google Scholar] [CrossRef] [PubMed]
  43. Katoh, M. WNT Signaling Pathway and Stem Cell Signaling Network. Clin. Cancer Res. 2007, 13, 4042–4045. [Google Scholar] [CrossRef] [PubMed]
  44. Lehar, S.M.; Bevan, M.J. T Cells Develop Normally in the Absence of both Deltex1 and Deltex2. Mol. Cell. Boil. 2006, 26, 7358–7371. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Gopinathan, L.; Tan, S.L.W.; Padmakumar, V.C.; Coppola, V.; Tessarollo, L.; Kaldis, P.; Tan, S.L.W. Loss of CDK2 and cyclin A2 impairs cell proliferation and tumorigenesis. Cancer Res. 2014, 74, 3870–3879. [Google Scholar] [CrossRef] [PubMed]
  46. Li, Y.-L.; Wang, J.; Zhang, C.-Y.; Shen, Y.-Q.; Wang, H.-M.; Ding, L.; Gu, Y.-C.; Lou, J.-T.; Zhao, X.-T.; Ma, Z.-L.; et al. MiR-146a-5p inhibits cell proliferation and cell cycle progression in NSCLC cell lines by targeting CCND1 and CCND2. Oncotarget 2016, 7, 59287–59298. [Google Scholar] [CrossRef] [PubMed]
  47. Dachineni, R.; Ai, G.; D, R.K.; Sadhu, S.; Tummala, H.; Gunaje, J.B. Abstract A02: Cyclin A2 and CDK2 as Novel Targets of Aspirin and Salicylic acid: a Potential Role in Cancer Prevention. Mol. Cancer Res. 2016, 14, 241–252. [Google Scholar] [CrossRef] [PubMed]
  48. Shi, Q.; Zhou, Z.; Ye, N.; Chen, Q.; Zheng, X.; Fang, M. MiR-181a inhibits non-small cell lung cancer cell proliferation by targeting CDK1. Cancer Biomark. 2017, 20, 539–546. [Google Scholar] [CrossRef] [PubMed]
  49. Moreb, J.S. Aldehyde dehydrogenase as a marker for stem cells. Curr. Stem Cell Res. Ther. 2008, 3, 237–246. [Google Scholar] [CrossRef]
  50. Zhao, D.; Mo, Y.; Li, M.-T.; Zou, S.-W.; Cheng, Z.-L.; Sun, Y.-P.; Xiong, Y.; Guan, K.-L.; Lei, Q.-Y. NOTCH-induced aldehyde dehydrogenase 1A1 deacetylation promotes breast cancer stem cells. J. Clin. Investig. 2014, 124, 5453–5465. [Google Scholar] [CrossRef] [Green Version]
  51. Rosen, E.D.; MacDougald, O.A. Adipocyte differentiation from the inside out. Nat. Rev. Mol. Cell Boil. 2006, 7, 885–896. [Google Scholar] [CrossRef] [PubMed]
  52. Ziouzenkova, O.; Plutzky, J. Retinoid metabolism and nuclear receptor responses: New insights into coordinated regulation of the PPAR-RXR complex. FEBS Lett. 2008, 582, 32–38. [Google Scholar] [CrossRef] [PubMed]
  53. Li, Z.; Xiang, Y.; Xiang, L.; Xiao, Y.; Li, F.; Hao, P. ALDH Maintains the Stemness of Lung Adenoma Stem Cells by Suppressing the Notch/CDK2/CCNE Pathway. PLOS ONE 2014, 9, e92669. [Google Scholar] [CrossRef] [PubMed]
  54. Meng, E.; Mitra, A.; Tripathi, K.; Scalici, J.; MCCLellan, S.; Da Silva, L.M.; Reed, E.; Palle, K.; Rocconi, R.P.; Finan, M.A.; et al. ALDH1A1 maintains ovarian cancer stem cell-like properties by altered regulation of cell cycle checkpoint and DNA repair network signaling. PLOS ONE 2014, 9, e107142. [Google Scholar] [CrossRef] [PubMed]
  55. Sabnis, N.G.; Miller, A.; Titus, M.A.; Huss, W.J. The Efflux Transporter ABCG2 Maintains Prostate Stem Cells. Mol. Cancer Res. 2017, 15, 128–140. [Google Scholar] [CrossRef] [PubMed]
  56. Wee, B.; Pietras, A.; Ozawa, T.; Bazzoli, E.; Podlaha, O.; Antczak, C.; Westermark, B.; Nelander, S.; Uhrbom, L.; Forsberg-Nilsson, K.; et al. ABCG2 regulates self-renewal and stem cell marker expression but not tumorigenicity or radiation resistance of glioma cells. Sci. Rep. 2016, 6, 25956. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Volcano plot of the wound healing real-time PCR array results. The vertical black line indicates a 1.0-fold change in gene expression. The vertical dashed lines indicate the desired threshold of a 2.0-fold change in gene expression. The horizontal black line indicates the desired 0.05 threshold for the p-value of the t-test.
Figure 1. Volcano plot of the wound healing real-time PCR array results. The vertical black line indicates a 1.0-fold change in gene expression. The vertical dashed lines indicate the desired threshold of a 2.0-fold change in gene expression. The horizontal black line indicates the desired 0.05 threshold for the p-value of the t-test.
Cells 08 00729 g001
Figure 2. Morphology of cells isolated from (a) diabetic dermis and (b) normal dermis. The immunostaining of actin filaments with phalloidin (red) shows a spindle-shaped fibroblast-like morphology. Cell nuclei are counterstained in blue with DAPI (magnification 40×).
Figure 2. Morphology of cells isolated from (a) diabetic dermis and (b) normal dermis. The immunostaining of actin filaments with phalloidin (red) shows a spindle-shaped fibroblast-like morphology. Cell nuclei are counterstained in blue with DAPI (magnification 40×).
Cells 08 00729 g002
Figure 3. Characterization of cells isolated from diabetic dermis. Immunofluorescent staining of: (a) CD44 (in green), (b) CD73 (in red), (c) CD90 (in red), (d) CD105 (in green). Nuclei are stained with DAPI in blue (magnification 40×). (e) Detection of cell surface markers by flow cytometry: cells are positive to CD44, CD73, CD90, and CD105, and negative to CD14, CD34, CD45, and HLA-DR.
Figure 3. Characterization of cells isolated from diabetic dermis. Immunofluorescent staining of: (a) CD44 (in green), (b) CD73 (in red), (c) CD90 (in red), (d) CD105 (in green). Nuclei are stained with DAPI in blue (magnification 40×). (e) Detection of cell surface markers by flow cytometry: cells are positive to CD44, CD73, CD90, and CD105, and negative to CD14, CD34, CD45, and HLA-DR.
Cells 08 00729 g003aCells 08 00729 g003b
Figure 4. Growth and migration of diabetic dermal cells compared with those of normal dermal cells. (a) Cumulative population doubling (CPD) of diabetic cells (square indicator) and normal cells (round indicator). (be) In vitro wound healing assay: representative images (10x magnification) of the whole wound area taken in the scratch assay for (b) diabetic cells at 0, (c) diabetic cells at 24 h, (d) normal cells at 0, (e) normal cells at 24 h. (f) The migration percentages (%) are expressed as mean ± standard deviation (SD). * p < 0.05, ** p < 0.01.
Figure 4. Growth and migration of diabetic dermal cells compared with those of normal dermal cells. (a) Cumulative population doubling (CPD) of diabetic cells (square indicator) and normal cells (round indicator). (be) In vitro wound healing assay: representative images (10x magnification) of the whole wound area taken in the scratch assay for (b) diabetic cells at 0, (c) diabetic cells at 24 h, (d) normal cells at 0, (e) normal cells at 24 h. (f) The migration percentages (%) are expressed as mean ± standard deviation (SD). * p < 0.05, ** p < 0.01.
Cells 08 00729 g004
Figure 5. Volcano plot of the stem cell real-time PCR array results. The vertical black line indicates a 1.0-fold change in gene expression. The vertical dashed lines indicate the desired threshold of a 2.0-fold change in gene expression. The horizontal black line indicates the desired 0.05 threshold for the p-value of the t-test.
Figure 5. Volcano plot of the stem cell real-time PCR array results. The vertical black line indicates a 1.0-fold change in gene expression. The vertical dashed lines indicate the desired threshold of a 2.0-fold change in gene expression. The horizontal black line indicates the desired 0.05 threshold for the p-value of the t-test.
Cells 08 00729 g005
Table 1. Wound-healing-related genes probed by real-time PCR arrays.
Table 1. Wound-healing-related genes probed by real-time PCR arrays.
SymbolGeneFold Regulationp-Value
ACTA2Actin, alpha 2,−3.890.050
ACTC1Actin, alpha 11.190.702
ANGPT1Angiopoietin 1−1.230.767
CCL2Chemokine (C-C motif) ligand 25.010.192
CCL7Chemokine (C-C motif) ligand 7−1.140.717
CD40LGCD40 ligand−1.380.721
CDH1Cadherin 1, type 1, E-cadherin1.470.519
COL14A1Collagen, type XIV, alpha 1−1.670.666
COL1A1Collagen, type I, alpha 1−1.990.825
COL1A2Collagen, type I, alpha 2−1.830.982
COL3A1Collagen, type III, alpha 11.030.568
COL4A1Collagen, type IV, alpha 1−1.080.562
COL4A3Collagen, type IV, alpha 3−2.540.765
COL5A1Collagen, type V, alpha 1−1.970.786
COL5A2Collagen, type V, alpha 21.480.491
COL5A3Collagen, type V, alpha 3−2.140.962
CSF2Colony stimulating factor 2−14.710.009
CSF3Colony stimulating factor 3−1.290.727
CTGFConnective tissue growth factor−3.220.031
CTNNB1Catenin1.400.557
CTSGCathepsin G3.220.427
CTSKCathepsin K−1.330.741
CTSVCathepsin L21.100.959
CXCL1Chemokine (C-X-C motif) ligand 15.230.570
CXCL11Chemokine (C-X-C motif) ligand 111.090.590
CXCL2Chemokine (C-X-C motif) ligand 22.600.626
CXCL5Chemokine (C-X-C motif) ligand 5−1.860.771
EGFEpidermal growth factor−1.030.702
EGFREpidermal growth factor receptor−3.150.000
F13A1Coagulation factor XIII, A1 polypeptide−1.670.724
F3Coagulation factor III (thromboplastin)−2.370.485
FGAFibrinogen alpha chain−3.250.751
FGF10Fibroblast growth factor 10−1.370.723
FGF2Fibroblast growth factor 2−2.870.121
FGF7Fibroblast growth factor 7−1.160.984
HBEGFHeparin-binding EGF-like growth factor1.970.442
HGFHepatocyte growth factor−1.940.731
IFNGInterferon, gamma1.080.696
IGF1Insulin-like growth factor 13.030.294
IL10Interleukin 101.030.704
IL1BInterleukin 1, beta3.570.472
IL2Interleukin 2−2.730.765
IL4Interleukin 4−1.480.718
IL6Interleukin 61.590.415
IL6STInterleukin 6 signal transducer−2.530.047
ITGA1Integrin, alpha 1−2.460.625
ITGA2Integrin, alpha 2 (CD49B)−2.080.439
ITGA3Integrin, alpha 3 (CD49C)−1.110.902
ITGA4Integrin, alpha 4 (CD49D)1.380.644
ITGA5Integrin, alpha 5−1.660.990
ITGA6Integrin, alpha 6−1.030.740
ITGAVIntegrin, alpha V (CD51)−1.090.962
ITGB1Integrin, beta 1 (CD29)−1.170.873
ITGB3Integrin, beta 3 (CD61)−2.140.872
ITGB5Integrin, beta 5−1.410.755
ITGB6Integrin, beta 62.130.668
MAPK1Mitogen-activated protein kinase 1−1.660.002
MAPK3Mitogen-activated protein kinase 3−1.450.967
MIFMacrophage migration inhibitory factor1.000.943
MMP1Matrix metallopeptidase 15.450.285
MMP2Matrix metallopeptidase 2−2.620.648
MMP7Matrix metallopeptidase 7−1.840.711
MMP9Matrix metallopeptidase 92.190.562
PDGFPlatelet-derived growth factor−2.210.833
PLATPlasminogen activator, tissue−1.360.724
PLAUPlasminogen activator, urokinase1.630.482
PLAURPlasminogen activator, urokinase receptor2.230.563
PLGPlasminogen−2.200.719
PTENPhosphatase and tensin homolog1.470.300
PTGS2Prostaglandin-endoperoxide synthase 2−1.180.699
RAC1Ras-related C3 botulinum toxin substrate 11.120.480
RHOARas homolog gene family, member A1.020.693
SERPINE1Serpin peptidase inhibitor, clade E, member 11.390.592
STAT3Signal transducer and activator of transcription 31.190.385
TAGLNTransgelin−3.700.064
TGFATransforming growth factor, alpha1.240.812
TGFB1Transforming growth factor, beta 1−1.840.551
TGFBR3Transforming growth factor, beta receptor III−2.800.048
TIMP1TIMP metallopeptidase inhibitor 11.040.715
TNFTumor necrosis factor1.220.548
VEGFAVascular endothelial growth factor A1.350.562
VTNVitronectin−2.750.794
WISP1WNT1 inducible signaling pathway protein 11.660.635
WNT5AWingless-type MMTV integration site family, member 5A2.390.664
p-values show the comparison results between the diabetic dermis and the healthy controls.
Table 2. Cell surface marker expression of cells isolated from diabetic dermis.
Table 2. Cell surface marker expression of cells isolated from diabetic dermis.
Surface Marker% Expression
CD4499,765 ± 0.340
CD7399,805 ± 0.316
CD9099,765 ± 1.613
CD10598,468 ± 1.992
CD140.231 ± 0.248
CD340.098 ± 0.093
CD450.084 ± 0.092
HLA-DR0.059 ± 0.041
Data are displayed as percentages expressed as mean ± standard deviation (SD).
Table 3. Stem cell-related genes probed by real-time PCR arrays.
Table 3. Stem cell-related genes probed by real-time PCR arrays.
SymbolGeneFold Regulationp-Value
ABCG2ATP-binding cassette, sub-family G, member 2−4.040.007
ACANAggrecan2.890.299
ACTC1Actin, alpha, cardiac muscle 12.350.164
ADARAdenosine deaminase, RNA-specific1.150.302
ALDH1A1Aldehyde dehydrogenase 1 family, member A1−41.080.000
ALDH2Aldehyde dehydrogenase 2 family1.860.065
ALPIAlkaline phosphatase, intestinal1.280.456
APCAdenomatous polyposis coli−1.500.031
ASCL2Achaete-scute complex homolog 2−1.850.505
AXIN1Axin 11.630.090
BGLAPBone gamma-carboxyglutamate (gla) protein1.480.148
BMP1Bone morphogenetic protein 12.380.007
BMP2Bone morphogenetic protein 2−1.290.060
BMP3Bone morphogenetic protein 32.070.165
BTRCBeta-transducin repeat containing1.200.271
CCNA2Cyclin A2−11.080.000
CCND1Cyclin D11.140.604
CCND2Cyclin D2−2.700.930
CCNE1Cyclin E1−1.620.003
CD3DCD3d molecule−1.110.886
CD4CD4 molecule2.360.508
CD44CD44 molecule1.550.092
CD8ACD8a molecule1.690.320
CD8BCD8b molecule1.070.769
CDC42Cell division cycle 42−1.440.019
CDH1Cadherin 1, type 1, E-cadherin2.770.161
CDH2Cadherin 2, type 1, N-cadherin−1.110.903
CDK1Cyclin-dependent kinase 1−10.700.000
COL1A1Collagen, type I, alpha 14.240.006
COL2A1Collagen, type II, alpha 11.240.300
COL9A1Collagen, type IX, alpha 11.520.215
CTNNA1Catenin (cadherin-associated protein), alpha 11.000.808
CXCL12Chemokine (C-X-C motif) ligand 12−1.070.641
DHHDesert hedgehog1.440.456
DLL1Delta-like 1−1.520.506
DLL3Delta-like 31.730.233
DTX1Deltex homolog 11.140.903
DTX2Deltex homolog 22.520.022
DVL1Dishevelled, dsh homolog 11.680.059
EP300E1A binding protein p3001.070.618
FGF1Fibroblast growth factor 1−1.510.142
FGF2Fibroblast growth factor 2−1.500.096
FGF3Fibroblast growth factor 31.670.290
FGF4Fibroblast growth factor 42.130.217
FGFR1Fibroblast growth factor receptor 11.380.103
FOXA2Forkhead box A2−3.430.007
FRAT1Frequently rearranged in advanced T-cell lymphomas1.170.317
FZD1Frizzled family receptor 12.000.127
GDF2Growth differentiation factor 22.380.235
GDF3Growth differentiation factor 31.610.382
GJA1Gap junction protein, alpha 16.800.030
GJB1Gap junction protein, beta 1a−19.000.008
GJB2Gap junction protein, beta 21.700.214
HDAC2Histone deacetylase 2−1.120.470
HSPA9Heat shock 70kDa protein 9−1.030.991
IGF1Insulin-like growth factor 1−3.420.859
ISL1ISL LIM homeobox 1−12.770.000
JAG1Jagged 14.550.128
KAT2AK acetyltransferase 2A2.910.003
KAT7K acetyltransferase 71.070.546
KAT8K acetyltransferase 81.920.019
KRT15Keratin 151.310.588
MMEMembrane metallo-endopeptidase−1.050.923
MSX1Msh homeobox 13.160.099
MYCV-myc myelocytomatosis viral oncogene homolog−1.150.941
MYOD1Myogenic differentiation 11.830.252
NCAM1Neural cell adhesion molecule 12.650.154
NEUROG2Neurogenin 2−1.530.256
NOTCH1Notch 11.910.037
NOTCH2Notch 21.050.626
NUMBNumb homolog−1.040.973
PARD6APar-6 partitioning defective 6 homolog alpha2.350.027
PDX1Pancreatic and duodenal homeobox 12.420.225
PPARDPeroxisome proliferator-activated receptor delta1.370.148
PPARGPeroxisome proliferator-activated receptor gamma−8.280.022
RB1Retinoblastoma 1−1.250.113
S100BS100 calcium binding protein B1.050.110
SIGMAR1Sigma non-opioid intracellular receptor 1−1.090.407
SOX1SRY (sex determining region Y)-box 11.650.304
SOX2SRY (sex determining region Y)-box 2−1.000.046
TT, brachyury homolog2.610.059
TERTTelomerase reverse transcriptase1.560.376
TUBB3Tubulin, beta 3−1.440.183
WNT1Wingless-type MMTV integration site family, member 11.530.331
p-values show the comparison results between the diabetic dermal stem cells (DSCs) and the normal DSCs.

Share and Cite

MDPI and ACS Style

Ferroni, L.; Gardin, C.; Dalla Paola, L.; Campo, G.; Cimaglia, P.; Bellin, G.; Pinton, P.; Zavan, B. Characterization of Dermal Stem Cells of Diabetic Patients. Cells 2019, 8, 729. https://doi.org/10.3390/cells8070729

AMA Style

Ferroni L, Gardin C, Dalla Paola L, Campo G, Cimaglia P, Bellin G, Pinton P, Zavan B. Characterization of Dermal Stem Cells of Diabetic Patients. Cells. 2019; 8(7):729. https://doi.org/10.3390/cells8070729

Chicago/Turabian Style

Ferroni, Letizia, Chiara Gardin, Luca Dalla Paola, Gianluca Campo, Paolo Cimaglia, Gloria Bellin, Paolo Pinton, and Barbara Zavan. 2019. "Characterization of Dermal Stem Cells of Diabetic Patients" Cells 8, no. 7: 729. https://doi.org/10.3390/cells8070729

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop