Article Cite This: J. Proteome Res. 2018, 17, 2521−2532
pubs.acs.org/jpr
Proteomics of the Rat Myocardium during Development of Type 2 Diabetes Mellitus Reveals Progressive Alterations in Major Metabolic Pathways Anders Valdemar Edhager,*,†,⊥ Jonas Agerlund Povlsen,‡,⊥ Bo Løfgren,‡,§ Hans Erik Bøtker,‡ and Johan Palmfeldt†
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†
Research Unit for Molecular Medicine, Department of Clinical Medicine, Aarhus University and Aarhus University Hospital, 8200, Aarhus N, Denmark ‡ Department of Cardiology, Aarhus University Hospital, 8200, Aarhus N, Denmark § Institute for Experimental Clinical Research, Aarhus University, 8000, Aarhus C, Denmark S Supporting Information *
ABSTRACT: Congestive heart failure and poor clinical outcome after myocardial infarction are known complications in patients with type-2 diabetes mellitus (T2DM). Protein alterations may be involved in the mechanisms underlying these disarrays in the diabetic heart. Here we map proteins involved in intracellular metabolic pathways in the Zucker diabetic fatty rat heart as T2DM develops using MS based proteomics. The prediabetic state only induced minor pathway changes, whereas onset and late T2DM caused pronounced perturbations. Two actin-associated proteins, ARPC2 and TPM3, were up-regulated at the prediabetic state indicating increased actin dynamics. All differentially regulated proteins involved in fatty acid metabolism, both peroxisomal and mitochondrial, were up-regulated at late T2DM, whereas enzymes of branched chain amino acid degradation were all downregulated. At both onset and late T2DM, two members of the serine protease inhibitor superfamily, SERPINA3K and SERPINA3L, were down-regulated. Furthermore, we found alterations in proteins involved in clearance of advanced glycation end-products and lipotoxicity, DCXR and CBR1, at both onset and late T2DM. These proteins deserve elucidation with regard to their role in T2DM pathogenesis and their respective role in the deterioration of the diabetic heart. Data are available via ProteomeXchange with identifiers PXD009538, PXD009554, and PXD009555. KEYWORDS: LC−MS/MS, proteomics, type 2 diabetes mellitus, rat, heart, mitochondrion, peroxisomes, BCAA metabolism
1. INTRODUCTION
responsible for the impaired prognosis of myocardial infarction and other cardiovascular diseases (CVD) in patients with T2DM. Additionally, studies in experimental models have shown that mitochondrial dysfunction, more specifically reduced oxidative capacity and cardiac efficiency, is an inherent part of the pathophysiology of T2DM.12,13 However, only few studies have examined what underlying changes in the heart proteome might be accountable for these findings14−18 and no study has to our knowledge, investigated the effect of disease progression. The present study aimed to analyze the rat myocardial proteome at different stages of T2DM to identify potential alterations in metabolic pathways, which may add to the current knowledge about detrimental effects in the heart accompanying the development of T2DM.
The number of patients suffering from diabetes, predominantly type 2 diabetes mellitus (T2DM), is increasing worldwide and was estimated to comprise 422 million people in 2014.1 T2DM and obesity are important risk factors for developing congestive heart failure2 and death after myocardial infarction (MI).3 Clinical studies have shown that T2DM is associated with increased mortality rate after acute myocardial infarction due to more extensive atherosclerotic lesions and a hypertrophied and dysfunctional left ventricle.4−6 Large-scale clinical trials on the effect of glycemic control in T2DM patients suffering myocardial infarction did not show a significant improvement on mortality following myocardial infarction.7−10 Furthermore, the rates of cardiovascular death and heart failure increased in T2DM patients following acute myocardial infarction despite of comparable left-ventricular systolic function and left-ventricular remodeling in the two groups.11 These results indicate that other factors than elevated blood glucose and systolic ventricular dysfunction may be © 2018 American Chemical Society
Received: April 24, 2018 Published: May 30, 2018 2521
DOI: 10.1021/acs.jproteome.8b00276 J. Proteome Res. 2018, 17, 2521−2532
Article
Journal of Proteome Research
2. MATERIALS AND METHODS
quality control with regards to number of proteins detected and mass accuracy. Furthermore, retention time coherence between runs was examined using Qual-browser (Thermo Scientific). Identification and quantification of proteins was then carried out using MaxQuant’s Andromeda algorithm version 1.4.0.222 with common contaminants included in the database search. Mass spectra from ZDFfa/fa and ZDFfa/+ heart tissue were searched against a Rattus norvegicus sequence database (.fasta file obtained from UniProt, 7964 entries, September 2016). Carbamidomethylation (CAM) of cysteines was set as static modification and oxidation of methionine as variable modification. Peptide and protein false discovery rate (FDR) of identification were both set at 0.01. Common contaminants were excluded from further analysis. For each analyzed proteome sample, protein intensities were normalized to the mean protein intensity of the respective data set to adjust for possible variation between analyses. Only proteins with at least two unique peptides were included in further analyses. Missing values were imputed by random insertion of a value between zero and the minimum intensity measured for a protein in each sample. Additionally, proteins were excluded if the fold change was less than two times the global standard error of the mean (GSEM), calculated from the variance of the entire data set, before applying an unpaired, two-tailed, student’s t test and correction for multiple testing (Benjamini-Hochberg, 5% FDR (BH-FDR)). The mass spectrometry proteomics data from Study 1 have been deposited to the ProteomeXchange Consortium via the PRIDE23 partner repository with the data set identifier PXD009555.
2.1. Ethical Statement
Animals were handled in accordance with national and institutional guidelines for animal research. The experimental work was approved by the Danish Animal Experiments Inspectorate (license no. 2011/561−2010-C2). 2.2. Animals and Collection of Rat Myocardial Tissue
Male Zucker diabetic fatty (ZDF) rats (homozygous (ZDFfa/fa)) and their age-matched lean controls (heterozygous (ZDFfa/+)) (Charles River Laboratories, Kisslegg, Germany) were studied at ages 6, 12, and 24 weeks corresponding to a prediabetic state, onset of, and late T2DM. Rats were fed a Purina 5008 diet as recommended by the supplier (Charles River) and housed under controlled conditions with 12:12h light-dark cycles. Animals were subjected to 12−16 h of fasting before experimental procedures. Rats were anesthesized with Dormicum (midazolam, 0.5 mg/kg bodyweight (bwt), Matrix Pharmaceuticals, Herlev, DK) and Hypnorm (fentanylcitrate, 0.158 mg/kg bwt and fluanisone, 0.5 mg/kg bwt, Vetapharma Ltd., Leeds, UK) by subcutaneous injection. Intubation was carried out through a tracheal incision and the tracheal tube was connected to a ventilator (Ugo Basile, Varese, Italy). A midline thoracotomy was performed followed by excision of a left ventricular biopsy with immediate snap-freezing in liquid nitrogen. The tissue biopsy was stored at −80 °C. Biochemical parameters were analyzed which included fasting Blood (B)-glucose, Serum (S)-total cholesterol, S-triglyceride, and S-free fatty acid as previously described.19,20 They confirmed the prediabetic-, onset-, and late diabetic phenotype, respectively. 2.3. Study 1 (Label-Free)
2.4. Study 2 (TMT-10plex Labeling, Onset, and Late T2DM (12 and 24 Weeks)
2.3.1. Preparation of Samples for Mass Spectrometry Analysis. Thirty-six excised heart tissues (60−75 mg each) were homogenized for 30 s at 5000 rpm (Ultra-Turrax T8 homogenizer; IKA Labortechnik, Staufen, Germany) in 2 mL of lysis buffer (20 mmol/L Tris-HCl, 100 mmol/L NaCl, 10 mmol/L EDTA, 0.5 mmol/L dithiotreitol (DTT; reducing agent), 1.5% Nonidet P-40 (nonionic surfactant), and protease inhibitor cocktail (Complete mini, Roche diagnostics, pH 7.4). Homogenized samples were centrifuged at 1000g for 15 min at 4 °C and the supernatant transferred to new Eppendorf tubes and kept at −20 °C until further analysis. Protein concentrations were determined using the Bradford assay using an iMARK microplate absorbance reader (Bio-Rad). After protein concentrations were determined, the 36 samples were pooled two and two to 18 samples (3 ZDFfa/fa and 3 ZDFfa/+ heart tissues at each stage in the development of T2DM). Label free proteomics analyses were carried out essentially as previously described.21 In short, proteins from homogenized heart tissue were subjected to separation by SDS-PAGE (Criterion TGX Any-kD gel, 220 V, 5 °C, Bio-Rad Laboratories). Gel lanes were cut into 10 equally sized pieces, disulfide bonds reduced with tris (2-carboxyethyl)phosphine (TCEP), and thiols alkylated with iodoacetamide (IAA) before in-gel trypsin digestion of proteins (Trypsin Gold, mass spectrometry grade, Promega). Peptides were purified by Mixed Cation Exchange (MCX, Oasis, Waters) and analyzed by liquid chromatography−tandem mass spectrometry (nanoLC−MS/MS) (LTQ Orbitrap, Thermo Scientific). 2.3.2. Database Searches and Statistical Analysis. Proteome Discoverer 1.4 (Thermo Scientific) equipped with the Mascot algorithm (www.matrixscience.com) was used for
Excised heart tissue samples from 20 of the same ZDF rats as in study 1 (5 ZDFfa/fa and 5 ZDFfa/+ at each stage) were analyzed separately by TMT-based proteomics. Tissue samples were homogenized by mixer milling (Precellys Evolution, Bertin Instruments) in lysis buffer as described in previous section with a minor modification (HEPES instead of Tris). Samples of 60 μg protein, measured by BCA-assay (Thermo Scientific), were in-solution trypsin digested and TMT-labeled according to manufacturer’s protocol (TMT-10plex, Thermo Scientific). Labeled samples were pooled and subjected to strong cation exchange (SCX) purification (Strata SCX, 55 μM, 70A, 100 mg/mL, Phenomenex) followed by vacuum centrifugation and isoelectric focusing (IEF) in a Immobiline DryStrip gel strip (GE Healthcare, Immobiline DryStrip pH 3−10, 18 cm) as previously described.24 The strip was then cut in 10 equally sized pieces and peptides were extracted for further C18-spin column (8 mg C18 resin, Pierce) purification according to manufacturer’s instructions. 2.4.1. NanoLC−MS/MS. Each of the 10 purified IEF fractions were injected twice on NanoLC (Easy-nLC 1000, Thermo Scientific)−MS/MS (Q-Exactive Plus, Thermo Scientific, Bremen). Precolumn (Acclaim PepMap 100, 75 μm x 2 cm, Nanoviper, Thermo Scientific) and analytical column (EASY-Spray column, PepMap RSLC C18, 2 μm, 100 Å, 75 μM × 25 cm) was used to trap and separate peptides using a 180 min gradient (4−40% Acetonitrile, 0.1% Formic acid) followed by wash and equilibration. All spectra were collected in positive mode using higher energy collisional dissociation (HCD) with a stepped normalized collision energy (NCE) ranging from 31 to 34%. In full scan (MS1) resolution was set at 70 000 (at 200 m/z) and AGC target set at 1 × 106 with scan range between 391 and 2522
DOI: 10.1021/acs.jproteome.8b00276 J. Proteome Res. 2018, 17, 2521−2532
Article
Journal of Proteome Research Table 1. Animal Characteristics and Biochemical Profile Measured in Fasting Blood (B) and Serum (S)a prediabetic (6 weeks) ZDFfa/fa (n = 6) body weight (g) B-Glucose (mmol/L) S-Total Cholesterol (mmol/L) S-Triglycerides (mmol/L) S-Free Fatty Acids (mmol/L)
193.0 12.11 2.45 2.80 5.58
± ± ± ± ±
10.7 3.90* 0.04* 0.74* 3.16*
onset T2DM (12 weeks)
ZDFfa/+ (n = 6) 193.5 6.60 1.99 0.54 1.66
± ± ± ± ±
6.8 0.63 0.16 0.07 0.61
ZDFfa/fa (n = 6) 397.5 19.77 3.58 7.38 6.26
± ± ± ± ±
17.1* 2.67* 0.18* 1.18* 3.34*
late T2DM (24 weeks)
ZDFfa/+ (n = 6) 320.2 10.39 1.79 0.59 1.13
± ± ± ± ±
8.5 1.71 0.10 0.13 0.48
ZDFfa/fa (n = 5) 405.8 31.21 8.92 13.95 8.43
± ± ± ± ±
21.3 6.91* 1.63* 3.90* 8.52
ZDFfa/+ (n = 6) 432.5 12.37 2.33 0.84 2.68
± ± ± ± ±
24.4 1.81 0.10 0.17 0.69
a Values display mean ± standard deviation (SD). Level of significance is ∗ = p < 0.01, comparing the difference between ZDFfa/fa and ZDFfa/+ with an unpaired studentś t-test within each stage.
3. RESULTS AND DISCUSSION
1500 m/z. Data dependent analysis (DDA) was applied to fragment up to 10 of the most intense peaks in MS1. Resolution for fragment scans (MS2) was set at 35 000 with first fixed mass at 121 m/z and AGC target at 2 × 105. Dynamic exclusion was set at 18 s and unassigned and +1 charge states were excluded. Furthermore, peptides with more than 8 peptide spectrum matches (PSMs) in the first analysis were excluded from the second analysis. Qual-browser and Proteome Discoverer 2.1 (Mascot) was used for quality control of all raw files for retention time coherence and mass accuracy before further data processing. 2.4.2. Database Search and Statistical Analysis. Final database search was conducted in Proteome Discover 2.1 (Thermo Scientific) with Mascot (Matrix Science) on all raw files merged for each stage. Swiss-Prot was used as database with maximum two missed cleavages using trypsin as enzyme and taxonomy was set for Rattus norvegicus (September 2016). Precursor and fragment mass tolerance was set at 10 ppm and 20 mmu, respectively. Oxidation of methionine was set as dynamic modification and static modifications were CAM of cysteines and TMT-labels on lysine and N-terminus. In the quantification node unique and razor peptides were included. Furthermore, correction factors from each TMT kit was added and coisolation threshold set at 45%. Within the percolator node, strict FDR of identification was set to 0.01. Output data files (.csv) from the Mascot database search was imported into R version 3.4.225 with R-studio interface. Proteins with a minimum of two unique peptides and four quantifiable PSMs were included in further statistical analysis. TMT ratios were normalized to the median ratio of each TMT channel. Proteins with a fold change >2 x GSEM were log-transformed before applying of an unpaired student’s t test between ZDFfa/fa and ZDFfa/+ mean protein intensities and correction for multiple testing (BH-FDR). The mass spectrometry proteomics data from Study 2 have been deposited to the ProteomeXchange Consortium via the PRIDE23 partner repository with the data set identifiers PXD009538 and PXD009554.
3.1. Animal Characteristics and Biochemical Profile
We measured animal characteristics and biochemical profiles with respect to variables relevant for T2DM pathogenesis (Table 1). ZDFfa/fa-rats had an increased body weight at 12 weeks, compared with controls, but not at 6 or 24 weeks of age. Bloodglucose, plasma-cholesterol, and plasma-triglyceride concentrations were significantly higher in ZDFfa/fa-rats than in their age matched ZDFfa/+ controls and the amount increased over time for all three variables. Serum-free fatty acid levels were significantly increased in ZDFfa/fa-rats at 6 and 12 weeks however not at 24 weeks. Furthermore, published data of the same ZDF rat model describe significantly elevated insulin levels at 6 and 12 weeks, but not at 24 weeks, for ZDFfa/fa rats compared to their age matched lean controls.19 3.2. Proteomics
To assess proteome alterations in myocardial tissue from ZDFrats during development of T2DM, we employed quantitative proteomics using label-free (Study 1) and TMT labeling (Study 2). Male ZDF rats with homozygous mutation in the leptin receptor gene (ZDFfa/fa) gradually develops insulin resistance, hyperglycemia, and diabetes.28 To identify molecular metabolic pathways affected by the progression of T2DM, we identified nominally differentially abundant proteins (nDAPs) (Figure 1A,B) and performed functional annotation cluster analyses (Figure 1C). 3.2.1. Proteome Alterations in Prediabetic Hearts (6 Weeks). Label-free proteomics analysis (Study 1) identified 99 nDAPs in the prediabetic state (6 weeks), and 57 of these were up-regulated while 16 were down-regulated. The only enriched cluster during DAVID functional clustering analysis was Keratin (Figure 1C). Complete lists of quantified proteins are in Supplementary Table S1A−C and functional clusters, obtained through DAVID in Supplementary Table S2A−C. The effects of prediabetes on the cardiac proteome have previously only been sparsely addressed. Cruz-Topete et al. used C57BL/6J mice to assess protein changes at the prediabetic state and showed dysregulation in proteins of energy metabolism, Krebs cycle, fatty acid oxidation (FAO), and cardiac contractile function.15 In the present study, we could not detect enrichment in mitochondrial metabolic pathways at the prediabetic state, although a few altered mitochondrial proteins were differentially regulated. Seven proteins were statistically significant after correction for multiple testing (BH-FDR) (Table 2). Propionyl-CoA carboxylase beta chain (PCCB) was down-regulated. PCCB is, together with PCCA, a part of the mitochondrial propionyl-CoA carboxylase complex (PCC), which is responsible for the conversion of propionyl-CoA to methylmalonyl-CoA from various substrates including branched chain amino acids (BCAA).29 The lower abundance of PCCB, and PCC, might lead to
2.5. Pathway Analysis
The database for annotation, visualization and integrated discovery (DAVID, version 6.8) was used for functional annotation cluster analysis.26,27 Proteins, from the prediabetic samples, with a p-value 2× GSEM were considered nominally differentially abundant proteins (nDAPs). At onset and late stages of T2DM, proteins were considered nDAPs when they passed fold change criterion of >2× GSEM together with a p-value
