Establishing a Link Between Endothelial Cell Metabolism and Vascular Behaviour in a Type 1 Diabetes Mouse Model
Carolina Silvaa,b Vasco Sampaio-Pintob,c,d Sara Andradea,b,e Ilda Rodriguesa
Raquel Costaa,b Susana Guerreiroa,b,f Eugenia Carvalhog,h,i
Perpétua Pinto-do-Ób,c,d Diana S. Nascimentob,c,d Raquel Soaresa,b
aDepartment of Biomedicine, Unit of Biochemistry, Faculty of Medicine of the University of Porto, Porto, Portugal, bi3S, Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal, cInstituto Nacional de Engenharia Biomédica, Universidade de Porto, Porto, Portugal, dInstituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal, eInstituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal, fFaculdade de Ciências da Nutrição e Alimentação, Universidade do Porto, Porto, Portugal, gCenter of Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal, hThe Portuguese Diabetes Association, Lisbon, Portugal, iDepartment of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
Carbohydrate and lipid metabolism • Cell sorting • Endothelium metabolism • Genomics • Micro and macrovascular complications
complications contribute significantly to the extensive morbidity and mortality
rates observed in people with diabetes. Despite well known that the diabetic
kidney and heart exhibit imbalanced angiogenesis, the mechanisms implicated in
this angiogenic paradox
remain unknown. In this study, we examined the angiogenic and metabolic gene
expression profile (GEP) of endothelial cells (ECs) isolated from a mouse model
with type1 diabetes mellitus (T1DM). Methods: ECs were
isolated from kidneys and hearts of healthy and streptozocin (STZ)-treated mice.
RNA was then extracted for molecular studies. GEP of 84 angiogenic and 84 AMP-activated
Protein Kinase (AMPK)-dependent genes were examined by microarrays. Real time
PCR confirmed the changes observed in significantly altered genes. Microvessel
density (MVD) was analysed by immunohistochemistry, fibrosis was assessed by the
Sirius red histological staining and connective tissue growth factor (CTGF) was
quantified by ELISA. Results: The relative percentage of ECs and MVD
were increased in the kidneys of T1DM animals
whereas the opposite trend was observed in the hearts of diabetic mice. Accordingly,
the majority of AMPK-associated genes were upregulated in kidneys and
downregulated in hearts of these animals. Angiogenic GEP revealed significant
differences in Tgfβ, Notch signaling
and Timp2 in both diabetic organs. These findings were in agreement with
the angiogenesis histological assays. Fibrosis was augmented in both organs in
s as compared to healthy animals. Conclusion:
Altogether, our findings indicate, for the first time, that T1DM heart and
kidney ECs present opposite metabolic cues, which are accompanied by distinct
angiogenic patterns. These findings enable the development of innovative organ-specific
therapeutic strategies targeting diabetic-associated vascular disorders.
Type 1 diabetes mellitus (T1DM), is an autoimmune disorder characterized by beta cell failure, consequently resulting in decreased insulin release. Generally, β-cell failure is mediated by immune mechanisms, but its etiopathogenesis is not fully understood.
Glycemic variability is nowadays considered a key factor for the development of T1DM complications. Moreover, the chronic hyperglycemic state characteristic of DM, among other processes, can lead to macro and microvascular complications, namely diabetic retinopathy, nephropathy and peripheral neuropathy. These processes can evolve, resulting in blindness, renal failure, and foot ulcers development respectively.
In addition to the extensively morbid conditions, including limb amputations, neuropathic problems like Charcot joints and autonomic neuropathy, prolonged diabetes can also cause gastrointestinal, genitourinary problems, as well as sexual dysfunction . Moreover, T1DM patients have a 10-fold increased risk of developing cardiovascular disease when compared to normoglycemic subjects [2, 3], with coronary artery disease being the main cause of morbimortality in these patients .
Angiogenesis, the process by which new blood vessels are formed from pre-existing ones, when persistent and uncontrolled, is the main characteristic for vascular abnormalities and it is often impaired in diabetic patients . A key regulator of angiogenesis is the tight equilibrium between angiogenic inhibitors and stimulators . An important feature of T1DM is the existence of the so-called “angiogenic paradox”, a phenomenon in which the patient simultaneously presents accelerated angiogenesis in certain organs, such as the eye (retinopathy), kidney (nephropathy), in addition to atherosclerotic plaque progression, and decreased angiogenesis in others, preventing wound healing and impairing artery collateral formation [7, 8].
Vascular metabolism is altered not only in T1DM, but also in other types of diabetes (type 2 DM and gestational DM).
AMPK is an energy sensor that targets carbohydrate and lipid metabolism by phosphorylating enzymes and modulating gene expression . AMPK is an insulin sensitizing molecule, rendering it an ideal therapeutic target. As a result, several pharmacological agents, including metformin and thiazolidinediones, activate AMPK signaling pathways, one of the reasons they are currently used for diabetes treatment. Interestingly, in endothelial cells (ECs), AMPK activation by these vasculoprotective agents triggers several benefic physiological effects in diabetic vascular complications [10, 11]. Therefore, given the high morbidity and mortality rates associated with micro and macrovascular complications in diabetic patients, it is of paramount importance to highlight the AMPK-associated metabolic changes present in ECs of the distinct organs and their association with angiogenic imbalance.
The aim of the current study was to analyse the expression pattern of genes associated with metabolism and angiogenesis in kidney and heart ECs from T1DM mice. To address this, we isolated ECs by flourescence-activated cell sorting (FACS) and performed gene microarray assays to assess the AMPK-dependent and angiogenic gene expression profile in these specific organs.
Materials and Methods
Type 1 Diabetes animal model
Twenty-five twelve-week-old male C57Bl/6 mice (Charles River, Saint-Germain-Nuelles, France) were used in this study. Mice were kept under controlled conditions of humidity (35 ± 5%) and temperature (23 ± 5 °C), on a 12-hour light/dark cycle and had access to water and rodent chow ad libitum. Animals were randomly assigned into two experimental groups: 12 control (CTR) and 13 STZ-treated mice. As recommended by the NIH Animal Models of Diabetes Complication Consortium (Low dose protocol), T1DM was induced by intraperitoneal administration of STZ (Sigma-Aldrich, Portugal), for five consecutive days, at a dose of 50mg/kg body weight dissolved in 0.1M citrate buffer (pH 4.5). Fourteen days after the last STZ injection, mice were considered diabetic when they presented blood glucose levels above 250mg/dL. No adverse events were observed in the experimental group. Control animals were administered with the same volume of saline solution. Body weight and glycemia were monitored three times per week. After 10 weeks following diabetes onset, all animals were euthanized and organs were removed. Kidneys and hearts of 4 CTR and 6 STZ-treated animals were used for isolation of ECs by FACS. The kidneys and the left ventricle of the remaining animals were fixed in formalin and paraffin-embedded for histological analyses. All animal studies were performed by certified technicians. All animal care and procedures were in accordance with the Portuguese Act 1005/92 and European Community guidelines for the use of Experimental Animal studies. The guide for the care and use of laboratory animals, 8th edition (2011) was followed.
Fluorescence-activated cell sorting (FACS)
FACS was performed to isolate ECs from the heart and kidney. Both organs were digested using tissue-specific protocols of GentleMACS Dissociator in Hank’s balanced salt solution (HBSS) (H9269, Sigma-Aldrich®, Portugal) containing collagenase II (CLS-2, Worthington, USA) at 6, 5 mg/ml concentration for heart and collagenase IV (CLS-4, Worthington, USA) at 1mg/ml concentration for kidney. Both solutions had DNase (A3778, VWR, Portugal) at 60U/ml.
The suspension was filtered and the cellular portion was collected. The cellular suspension was mixed with HBSS with 10% FBS to neutralize enzymatic activity and centrifuged at 300xg for 10 minutes. Erythrocyte depletion was accomplished by osmotic shock as deyonized water was added to the pellet and kept under stirring for 11 seconds. FACS medium (0.01% sodium azide and 3% FBS in PBS) was immediately added to interrupt osmolysis and cells were centrifuged again. Cells were evenly distributed for each staining in a round bottom multi-well plate and incubated during 30 min with the respective antibody mix: CD31-PeCy7 (Rat IgG2a; eBiosciences - 25-0311), CD31-APC (Rat IgG2a; Biolegend – 102516); CD45-PeCy7 (Rat igG2b, k; eBiosciences - 25-0451), CD45-PE (Rat IgG2b, k; eBioscience - 12-0451-82); CD54-APC (Rat IgG2b, κ; Biolegend – 116120), CD90-FITC (Rat IgG2b, k; Biolegend – 105305); , TER119-PeCy7 (Rat IgG2b, k; Biolegend – 116221), TER119-PE (Rat IgG2b, k; Biolegend – 116208); CD105-PE (Rat IgG2a, k; Biolegend – 120407 and CD106-PE (Rat IgG2a, k; Biolegend - 105713) at 1:100 dilution, on ice and in the dark. Cells were washed twice in FACS media and transferred to FACS tubes.
In order to exclude nonviable cells from the analysis, 0.5% of propidium iodide (PI) (P4170, Sigma-Aldrich) was added to the cell suspension 1-2 min prior to analysis. Fifty thousand events (of appropriate size and complexity) per staining were acquired in the cytometer FACS ARIA (BD Biosciences). Subsequent analysis and graphing were performed in the FlowJo VX software.
RT2 PROFILER PCR arrays
Total RNA was extracted from isolated ECs of the kidney and heart by Quiagen RNeasy Micro kit (Werfen, Castellbisbal, Spain). After extraction and in accordance with the manufacturer, an identical amount of RNA for each sample (27 ng) was converted into cDNA and pre-amplified using primers specifically designed for the subsequent arrays by RT2 PreAMP Pathway Primer Mix (Werfen, Taracon-Cuenca, Spain). Three samples from each group were selected for analysis by Angiogenesis and AMPK RT2 Profiler PCR Arrays (Werfen, Castellbisbal, Spain). Each array evaluated the expression of 84 key genes involved in the angiogenic process or in the AMPK signaling pathway, as well as 5 housekeeping genes. Plates were prepared and ran on Light Cycler 96 thermal cycler (Roche, Mannheim, Germany), following manufacturer's instructions. Relative gene expression was determined using the ΔΔCT method and data was analyzed using the PCR Array Data Analysis Web Portal (Quiagen). Differentially-expressed genes relative to healhy controls were defined by the p-values obtained in the microarray’s analysis software.
Quantitative Real Time- PCR (qRT-PCR)
The remaining total RNA was used to validate the arrays results. cDNA was synthesized using PrimeScriptTM RT reagent Kit and was pre-amplified according to the protocol provided in SsoAdvanced™ PreAmp Supermix (Bio-Rad, Hercules, CA). Primers for mouse Adra1a, Adra2c, Cpt1a, Jag1, Pfkfb2, Pnpla2, Smad5, Tgfb2 and Timp2 were used and Gapdh as the housekeeping gene (sequences provided in Table 1). This reaction was performed in the LightCycler® 96 System (Roche, Mannheim, Germany) and each cDNA sample was used in duplicate as template for qRT-PCR. Relative gene expression was analyzed using the Livak method (2^-ΔΔCT).
Immunohistochemistry assay for CD31
Paraffin-embedded kidney and left ventricle tissue were cut into 3-μm sections. The Rat Detection Kit for Anti-Mouse CD31 (Biocare Medical, Pacheco, CA, USA) was used according to the supplied protocol in order to evaluate the microvascular density. The antibody against CD31 (Biocare Medical, Pacheco, CA, USA) was diluted 1:50 in Da Vinci Green diluent, provided in the kit.
Vessels were counted in approximately twenty non-overlapping fields (200x magnification) of each organ and normalized to the total tissue area. Any positively-stained endothelial cell or cluster of cells that was separated from adjacent vessels was considered as an individual vessel.
Sirius Red histological staining. Fibrosis was evaluated in kidney and left ventricle tissues by Sirius Red histological staining in parafin-embedded sections. Ten representative images (100x magnification) of each animal were obtained and fibrosis was calculated as the percentage of the red stained area and normalized to the total tissue area by the FIJI Software .
Connective tissue growth factor (CTGF) quantification by ELISA. Quantification of CTGF was performed in heart and kidney homogenates of diabetic and control animals using a commercial Mouse CTGF ELISA kit (LSBio-LifeSpan BioSciences, WA, USA), according to the manufacturer instructions. Absorbance was read at 450mm and the results analysed in GraphPad Prism 6.0 Software (GraphPad Software Inc., CA, USA).
All results were expressed as the mean ± SEM and were analysed in the GraphPad Prism 6.0 Software (GraphPad Software Inc., CA, USA), with a confidence interval of 95% and p value < 0,05 by t test.
Body weight and glycemia monitoring
When compared to controls, STZ-treated mice presented a sustained decrease in body weight during the whole experiment, although without reaching statistical significance (Fig. 1A). As expected, diabetic mice exhibited significantly increased blood glucose levels throughout the 10-week experimental period (Fig. 1B). Both findings confirmed the diabetic status of STZ-treated mice.
ECs quantification and microvessel density in kidney and heart
Heart and kidney from diabetic and control animals were homogeneized and analysed by flow cytometry. ECs were identified by the expression of CD31, following exclusion of hematopoietic cells (CD45+) and erythrocytes (TER-119+). Interestingly, when compared to controls, the kidney of STZ-treated mice were significantly enriched in ECs (3.857±0.5845 vs. 2.078±0.3644; p=0.053) (STZvsCTR), whereas in the heart an opposite trend was observed (Fig. 2A and 2B).
further confirm these findings, MVD was assessed by immunohistochemistry
analysis in paraffin-embedded sections. MVD, obtained by the ratio between the
number of vessels and the total tissue area (mm2), showed an
opposite angiogenic profile in both organs of STZ-treated mice, when compared
to healthy animals. While the number of blood vessels was increased in the
kidney cortex of STZ-treated animals relative to the controls, a decrease in MVD
was observed in the hearts of this group, (Fig. 2C).
Angiogenic gene expression profile in kidney and heart ECs
We next performed PCR microarrays for angiogenic markers in FACS-sorted ECs from both organs (Table 2 and 3). From the 84 genes examined, 10 were upregulated and 74 were downregulated in ECs from the STZ-treated heart, compared to controls (Table 2). Among these, the Notch1-ligand Jagged1 gene was significantly downregulated whereas Smad5, and the Transforming growth factor (Tgf) b signaling effector protein were significantly upregulated (Table 2). In ECs from T1DM kidney, 36 genes were upregulated, whereas 48 were downregulated in comparison to controls (Table 3). However, only the Tgfb2, Kinase insert domain receptor (Kdr) and the tissue inhibitor of metalloproteinases (Timp) 2 were significantly downregulated in renal ECs from STZ-treated animals relatively to healthy controls (Table 3).
Interestingly, most of the genes encoding for antiangiogenic factors were upregulated in heart and downregulated in the kidney (Fig. 3). These findings are in agreement with the angiogenesis imbalance previously observed in the heart and kidney, respectively.
Fibrosis evaluation in hearts and kidneys
Our angiogenic microarray assays revealed that the TGFb signalling pathway was altered in both heart and kidney of T1DM animals. The fact that TGFb is involved in fibrosis prompted us to examine whether this process was affected in the hearts and kidneys of diabetic animals. Fibrosis was analysed by Sirius Red histological staining in tissue sections of both organs collected from STZ-treated and control animals. Interestingly, diabetic animals showed a tendency for an increase in the fibrotic area in both heart and kidney, although not reaching statistical significance (Fig. 4A). Quantification of Connective tissue growth factor (CTGF), an important marker for fibrosis, exhibited the same trend in both diabetic organs (Fig. 4B).
AMPK-dependent gene expression profile in heart and kidney of T1D mice
Knowing that cellular behaviour depends on energy metabolism, we next investigated the expression pattern of 84 genes associated with the AMP-depending kinase, an energy-sensing enzyme. Remarkably, kidneys and hearts presented opposite expression profiles as illustrated in Fig. 5. The AMPK-associated gene expression pattern was mostly upregulated in kidney ECs from diabetic animals in comparison to controls, while in hearts it was primarily dowregulated (Table 4 and 5). Significantly upregulated genes in diabetic kidney ECs are involved in carbohydrate (Pfkfb2, Gusb) and lipid (Cpt1a) catabolic pathways and adrenoceptor signaling (Adra1a), as well as cell growth, migration and autophagy (Rb1cc1), and AMPK signaling (Strada, Prkaa1). On the other hand, significantly downregulated genes in ECs from diabetic hearts are implicated in signaling pathway cascades (Cab39, Akt2, Rps6kb2, Adra2c, Prkacb), as well as in triglyceride hydrolysis (Pnpla2). Genes that were significantly different in the microarrays were validated also by qRT-PCR.
The concept of the angiogenic paradox was first described by Waltenberg et al . who reported that chronic diabetes mellitus is associated with both impaired (collateral growth and wound healing) or enhanced (retinopathy and nephropathy) angiogenesis . Substantiating findings were then reported by others in the literature. Using a type 2 diabetes animal model, our group has also observed increased number of microvessels in kidneys and decreased in left ventricle of diabetic mice when compared to controls . By isolating ECs from mouse hearts and kidneys, the current study provides molecular evidence for this angiogenic paradox in a T1DM animal model. We were able to concurrently address the expression pattern of genes related with angiogenesis and cell energy metabolism.
Diabetic conditions of the STZ-treated mice were first established by the observed sustained reduced weight and hyperglycemia during the experimental protocol. We then found that mice presented increased number of blood vessels in kidneys and reduced in hearts, as confirmed by FACS and microvessel density assessed by immunohistochemistry. T1DM heart ECs exhibited a significant upregulation of BMP/TGFb signaling effector Smad5. BMP and TGFb bind type II receptor (TbRII), which in turn activates type 1 receptor (TbRI). In ECs, there are two TbRI (activin receptor-like kinase (ALK)1 and ALK5), resulting in activation of two distinct SMADs signaling pathways. By binding to ALK1, TGFβ phosphorylates SMAD1, 5 and 8, resulting in transcription of genes implicated in cell proliferation, diferentiation and migration. Conversely, TGFβ signaling through ALK5 results in SMAD2 and 3 recruitment, inhibiting these genes . Interestingly, when ALK1 is activated by BMP9, EC proliferation, migration and sprouting is inhibited [17, 18]. Therefore, ECs will behave differently depending on the ligand, as well as on the ALK receptor being activated. According to our results, Tgfβ did not significantly change in diabetic heart when compared to healthy animals, implying that Smad5 upregulation in heart ECs might promote ALK1 activation through BMP, which ends in angiogenesis inhibition.
Concomitantly, diabetic hearts also presented reduction in the Notch ligand Jagged1. The Notch signaling pathway consists of a highly conserved family of four membrane receptors (Notch1-4) and two transmembrane ligands: Jagged (1-2) and Delta-like (Dll) (1, 3 and 4) which are involved in cell growth, differentiation and tissue remodelling . By binding to its ligands in a neighboring cell surface, the intracellular Notch domain (NICD) translocates to the nucleus, ultimately leading to target gene activation [20, 21]. In blood vessels, Notch has been implicated in angiogenesis [22, 23]. But in contrast to DLL4, Jagged1 expression in ECs has proangiogenic functions, thus antagonizing DLL4/Notch signaling and inducing vascular maturation and tumor growth [23-25]. Therefore, our findings that Jagged1 is actually downregulated in diabetic hearts is in agreement with the reduced number of microvessels observed. Remarkably, Jagged1 was reported to be a TGFb target gene, through ALK5-induced SMAD3 . Taking into account that SMAD3 and SMAD5 are activated by opposite receptors, the overexpression of Smad5 observed in diabetic heart may explain the Jagged1 downregulation, and consequently the observed reduction in angiogenesis. These findings were corroborated by the metabolic gene expression profile found in T1DM hearts. Accordingly, Akt, Erk and p38 MAPK are downstream effectors of several membrane receptors involved in processes such as cell division and migration. These cytoplasmic kinases were downregulated in heart ECs, implying impairment of angiogenesis.
Conversely, diabetic kidneys presented exacerbated angiogenesis as found by the increased number of ECs (FACS analysis), as well as mcirovessel density. This behavior was documented by the downregulation of Timp2, an inhibitor of matrix metalloproteinases, Kdr and Tgfβ2. Besides participating in matrix remodeling and angiogenic process, TIMP2 is also associated with inflammation and tumor growth . The reduced expression of Timp2 enables extracellular matrix (ECM) degradation, explaining the proangiogenic phenotype observed in this organ in diabetic mice. Moreover, TIMP expression accompanies TGFb. According to Ruiz-Ortega et al . TGFb stimulates TIMP expression. TGFb plays activating and inhibiting multifunctional roles in a wide variety of cells [28-30]. Interestingly, low concentrations of TGFb induce cell growth, whereas, at higher concentrations, this growth factor is able to prevent proliferation of smooth muscle cells . In agreement, the observed TGFb downregulation may result in EC proliferation, and consequently in a proangiogenic phenotype. According to these findings, the TGFβ pathway is a putative therapeutic target to address diabetic vascular complications. We also found that Kdr mRNA was downregulated in T1DM kidneys. KDR is activated by VEGF binding, resulting in vessel sprouting. Nevertheless, Kdr downregulation was obtained in ECs isolated from the whole diabetic kidney and not from the kidney cortex where increased angiogenesis is well established in diabetes . In addition, our findings were obtained in animals already presenting diabetes for 10 weeks, where sprouting had already occurred and resulted in mature stabilized vessels, which no longer expressed Kdr. Supporting our findings, Cooper et al. compared the expression of Kdr at 3 weeks and 32 weeks upon STZ treatment in a T1DM rat model, and observed that although Kdr was upregulated at 3 weeks, the receptor expression was diminished at the later stages .
Interestingly, renal ECs overexpressed AMPK-associated genes implicated in catabolism. PFK-2/FBPase-2 (6-phosphofructo-2-kinase/fructose-2, 6-bis-phosphatase) is a bi-functional homodimeric enzyme that presents both kinase and phosphatase activity . PFKFB2, one of the four isoforms identified (1, 2, 3 and 4), is expressed in heart and kidney [33, 34]. In this study, Pfkfb2 was significantly upregulated in kidney ECs of T1DM mice. The Pfkfb3 isoform expression was also upregulated in these cells (10-fold increase), although not in a significant manner (data not shown). Glucose is a primary energy source for ECs and recent studies revealed that PFKFB3 plays a critical role as a regulator of glycolysis. During vessel sprouting, PFKFB3 drives glycolysis in tip cells and inhibits the stalk cell behavior by Notch signaling [35-37]. Using a T2DM mouse model, our group recently showed that this enzyme was upregulated in kidney and downregulated in left ventricle  Concomitantly, the number of microvessels as well as the expression of the phosphorylated (active) form of VEGFR2 accompanied this PFKFB3 expression. Thus, the current findings in T1DM corroborated our previous study in a type 2 DM mouse model , reinforcing the association between glycolysis activity and angiogenesis.
Although glycolysis is the favourite metabolic pathway for energy supply, fatty acid oxidation has also been shown to play an important role in stalk cell metabolism and synthesis of deoxynucleotide triphosphates (dNTPs) . The carnitine palmitoyl transferase 1A (CPT1A) enzyme favours the translocation of long chain fatty acids into the mitochondria where they can be oxidized. The observed upregulation of this gene in diabetic kidney enables fatty acids entry into the mitochondria, hence resulting in energy production and redox homeostasis, providing fuel for EC proliferation, migration and vessel assembly. In agreement, Rb1cc1, implicated in cell proliferation, migration and autophagy, was also upregulated.
A remarkable finding of the current study was the complete distinct metabolic gene profile found in ECs from diabetic kidneys and hearts. Whereas most AMPK-associated genes were overexpressed in kidney ECs, while in heart ECs the majority of AMPK pathway genes were downregulated (Fig 5A and 5B). AMPK is activated in several types of cells by different stimuli, controling metabolic processes through phosphorylation of key essential markers . In ECs, AMPK activity can be stimulated by changes in ATP levels, regulating fatty acid oxidation, glycolysis, nitrite oxide synthesis, inflammation and angiogenesis . Overall, AMPK signaling pathways are reduced in DM, therefore a potential therapeutic target. AMPK activation induces insulin sensitivity and glucose utilization .
Another important feature of this study is the fact that despite the opposite molecular landscape observed in heart and kidney ECs, both organs presented increased fibrosis as evaluated by histological staining. Tissue fibrosis is a common feature in diabetes, associated with hyperglycemia, advanced glycation end products and mechanical stress. TGFb, through the SMAD2 and SMAD3 cascade, plays a crucial role in this process, by accumulating ECM components. Although the three TGFb splice forms are present in the fibrotic tissue, TGFb1 is the key molecule responsible for the development of fibrotic tissue mainly through the activation of ALK5/SMAD2-3 signaling. Several stimuli can ativate TGFβ1, hence, increasing fibrosis, for instance, the MMPs and CTGF. MMPs stimulate TGFβ by cleaving the latent form and releasing it, whereas CTGF can bind directly to TGFβ, which stimulates the binding to the receptor and consequently its activity. However, we did not observe a significant increase in TGFb expression, neither in these SMAD members in isolated ECs of heart and kidney, nor in the CTGF quantification. Noteworthy, like Tgfβ2, the Ctgf gene expression was slightly reduced in kidney and increased in diabetic heart in the angiogenic microarray. These findings indicate that TGFβ2 in ECs is not involved in fibrosis in diabetic organs, a process likely attributed to fibroblasts or smooth muscle cells.
Altogether our findings show for the first time that the gene expression pattern implicated in energy metabolism in ECs isolated from T1DM kidneys and hearts is distinct, and accompanies different profiles of angiogenic gene expression. These findings suggest a link between metabolism and angiogenesis, and pave the way for the development of organ-specific innovative therapeutic approaches.
ADRA1A (Adrenoceptor alpha 1A); ADRA2C (Adrenoceptor alpha 2C); ALK (Activin receptor-like kinase); AMPK (AMP-activated protein kinase); CABP39 (Calcium binding protein 39); CPT1A (CarnitinePalmitoyl transferase 1 A); CTGF (Connective tissue growth factor); CTR (Control); DII (Delta-like protein); DM (Diabetes mellitus); dNTPs (Deoxynucleotide triphosphates); ELISA (Enzyme-linked immunosorbent assay); ECs (Endothelial cells); FACS (Flourescence-activated cell sorting); GEP (Gene expression profile); GUSB (Beta-glucoranidase); HBSS (Hank’s balanced salt solution); KDR (Kinase insert domin receptor); MMPs (Metalloproteinases); MVD (Microvessel density); NICD (Intracellular NOTCH domain); PFKFGB2 (6-phosphofructo-2-kinase/frutctose-2, 6.biphosphatase); PNPLA2 (Patatin like phospholipase domain containing 2); PRKAA1 (Protein kinase AMP-activated atalytic subunit alpha 1); PRKACB (cAMP-dependent protein kinase catalytic subunit beta); RB1CC1 (RB1-inducible coiled-coil protein 1); RPS6KB2 (Ribosomal protein S6 kinase B2); STRADA (STE20-related kinase adaptor-alpha); TGF (Transforming growth factor); TIMP (Tissue inhibitor of metalloproteinases); VEGFR2 (Vascular endothelial growth factor receptor-2); T1DM (Type 1 diabetes mellitus); STZ (Streptozotocin).
work was supported by CAPES (Sciences without Border - Full Doctorate
Fellowship – Process 10010-13-0); FEDER funds by COMPETE:
[POCI-01-0145-FEDER-007440, POCI-01-0145-FEDER-016385]; NORTE2020
[NORTE-01-0145-FEDER-000012]; HealthyAging2020 [CENTRO-01-0145-FEDER-000012-N2323]; FCT -
Fundação para a Ciência e a Tecnologia [UID/BIM/04293/2013,
EXPL/BIM-MED/0492/2012, SFRH/BPD/88745/2012, SFRH/BD/111799/2015]; Claude
Pepper Older Americans Independence Center; grant: P30 AG028718,
P20GM109096; European Structural and Investment Funds (ESIF)AUTHOR CONTRIBUTION: CS and RS
participated in the design and conception of the study; CS performed the whole
laboratory and statistical analyses and drafted the manuscript; VSP, PPO, DSN
carried out the FACS assay
design and data acquisition, as well as the interpretation of FACS data; SA
advised and performed microarray and RT-PCR assays; IR headed the parafin
embedded tissue and histologial staining; SG, EC were responsible for the
animal studies and immunohistochemistry analyses; RC advised the methodological
laboratorial analysis and animal studies; RS and EC critically revised the
manuscript for important intellectual content. All authors were involved in
drafting and revising the article. All authors read and approved the final
version of the manuscript.
No conflict of interest.
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