Quantitative proteomics study reveals differential proteomic signature in dilated, restrictive, and hypertrophic cardiomyopathies

Cardiomyopathy is a disease of the heart muscle with varying etiologies and leads to heart failure. The pathways altered in the three forms of cardiomyopathy are not very clearly understood and hence in this study we attempted to identify differentially expressed proteins and pathways that are altered in the plasma of dilated, hypertrophic, and restrictive cardiomyopathy patients. For relative quantitation of the proteins we used both serial window acquisition of all theoretical mass spectra (SWATH-MS) and isobaric tags for relative and absolute quantitation techniques (iTRAQ). A total of 20 samples of DCM, HCM, RCM, and controls (5 each) were analyzed using SWATH while 3 samples in each group were analyzed using iTRAQ technique. Using SWATH, we could identify approximately 300 proteins in each of the four groups of which 205 proteins were found to be common. Of these 205 common proteins, 52, 58, and 52 proteins were found to be significantly differentially expressed in DCM, HCM, and RCM groups, respectively. Using iTRAQ, we could identify only about 150–180 proteins in the three experiments of which 96 were common. Our results indicated that most of the pathways that were enriched with the differentially expressed proteins, such as complement activation, platelet degranulation, immune response, etc., were common for DCM, RCM, and HCM. However, some of the pathways were unique as well to these groups. This study suggested that label-free SWATH in conjunction with iTRAQ-based quantitative proteomics approach could identify larger number of proteins and also highlights the importance of integrating two methods to dissect the molecular pathways involved in the progression of cardiomyopathies.


Introduction
Cardiomyopathies are a heterogeneous group of muscle disorder with abnormalities in the left ventricular function (Burke et al. 2016). It is a leading cause of death worldwide irrespective of age, gender, and socioeconomic status (Priori et al. 2015). If left untreated, some conditions of cardiomyopathies can cause debilitating heart failure. According to WHO classification, cardiomyopathies are majorly of four types: dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), restrictive cardiomyopathy (RCM), and arrhythmogenic right ventricular cardiomyopathy (ARVCM) which can manifest in both genetic and non-genetic manner (Vikhorev and Vikhoreva 2018) (Anonymous 1980). Dilated cardiomyopathy (DCM) is the most common form of cardiomyopathy accounting for 55% of the cardiomyopathies and affecting mostly males of age group 20-50 years (Luk et al. 2009). Mutations in the cytoskeletal and sarcomeric proteins have been attributed to the causes of familial DCM. Mostly mutations in genes like TTN (titin), MYH7 (β-cardiac myosin heavy chain), TPM1 (tropomyosin alpha-1 chain), and cardiac troponin have been reported to be associated with DCM (Vikhorev and Vikhoreva 2018). However, there are cases of idiopathic DCM, where the primary cause is unknown (Hazebroek et al. 2012). Hypertrophic cardiomyopathy is characterized by asymmetric ventricular wall thickness and inter-ventricular septal thickening leading to left ventricular hypertrophy. HCM can be both obstructive and non-obstructive in nature where obstructive form (HOCM) is considered to be more commonly associated with left ventricular outflow obstruction (Prinz et al. 2011). The prevalence of HCM in the general adult population is about 0.2% (Maron 2002). Mutations in β myosin heavy chain (MYH7), myosin binding protein C (MYBPC3), cardiac troponin (TNNT2/TNNT3), Z disc, and calcium-handling genes have been reported to be associated with hypertrophic cardiomyopathies (Maron et al. 2012). Restrictive cardiomyopathy (RCM) is the least common form of all the cardiomyopathies which is defined as a myocardial disorder with stiffened ventricles leading to impaired diastolic filling but with preserved systolic function (Mogensen and Arbustini 2009). Several secondary causes such as connective tissue disorder, amyloidosis, sarcoidosis, and hemochromatosis can all contribute to RCM (Nihoyannopoulos and Dawson 2009). Although a majority of the patients (~ 57%) report genetic form of RCM, some idiopathic cases also exist, but with different phenotypic expression of the same genetic disorder (Albakri 2018). It has been reported that different mutations in the same genes might result in different forms of cardiomyopathies (Mcnally et al. 2015). With variable etiologies and different onset of cardiomyopathies, it has also become pertinent to understand the underlying pathways relevant to the pathophysiology of cardiomyopathies. Apart from various genomic and epigenomic studies, transcriptomic profiling highlighted pathways such as myocyte survival, fatty acid oxidation, mTOR signaling, mitochondrial function, calcium handling, angiogenesis, and apoptosis to be associated with cardiac hypertrophy (Raghow 2016). Notable overlap between genomic and transcriptomic profiles also provoked interest in understanding the role of altered protein expression in pathogenesis of cardiomyopathies.
Over the years, proteomics-based studies of the left ventricle of animal models of heart failure identified alterations in proteins involved with cardiac energy metabolism, mitochondrial stress response, and associated ventricular remodeling (Cieniewski-Bernard et al. 2008;Schott et al. 2008). In a canine model of heart failure, proteins such as SERCA2a related to calcium handling and myofibrils have also been reported by MALDI-TOF technique (Fu et al. 2008). Interestingly, a comparative analysis in human left ventricle samples of DCM patients identified proteins related to cellular stress response, respiratory chain, cardiac metabolism, cell death, and apoptotic pathways relevant to the pathophysiology of heart failure (Rosello-Lleti et al. 2012). In an independent study, iTRAQ-based proteomic analysis of plasma of HCM and DCM patients showed enrichment of pathways related to inflammatory cascade, coagulation, and complement cascade (Rehulkova et al. 2016) but in this study RCM was not included.
In our current study, we performed comparative differential proteome analysis in the plasma of dilated, hypertrophic, and restrictive cardiomyopathy patients in a single study using both iTRAQ and label-free SWATH-MS methodology to identify proteins and pathway that are altered in these groups. Since the estimated prevalence of ARVCM is considered to be very low (1:2000-1:5000) and leads to right ventricular failure (Pilichou et al. 2016), we did not consider this group for this study.

Materials and method
Materials DTT (dithiothreitol), IAA (iodoacetamide), ammonium formate, and formic acid were procured from Sigma (St. Louis, MO, USA). Modified trypsin (sequencing grade) was procured from Promega. Polysulfoethyl SCX cartridge, SCX cartridge (5 micron, 300 Å bead from Sciex, USA) with a cartridge holder (Sciex, USA), and nano-LC column were procured from Sciex (USA). The Nano spray pico tip was purchased from New Objective (USA). LC-MS grade water and acetonitrile were procured from J. T. Baker (USA). All other chemicals used were of analytical grade.

Study population
The study was done on 32 individuals of whom 8 were healthy individuals (controls) while 24 were patients of DCM, RCM, and HCM (8 in each group). The diagnosis of cardiomyopathy was done through echocardiography and electrocardiogram measurements which were performed at the All India Institute of Medical Sciences (AIIMS), New Delhi. This study was performed in accordance with the principles of the Helsinki Declaration and was approved by the ethical committees of AIIMS and Institute of Genomics and Integrative Biology (IGIB). Prior written consent was also taken from all the patients. ECG abnormalities were scored by Romhilt-Estes criteria for LV hypertrophy and echocardiography examination was used to record the thickness of wall of the heart chambers in 2D and M mode (Biswas et al. 2018). Information on age, gender, dietary habits, BMI, family history, NYHA classification, and lifestyle factors were also collected from the questionnaire. Patients with diseases such as pulmonary dysfunction, diabetes, thyroid, and other autoimmune complications were excluded from the study. Healthy controls were selected from the general population with no family history of cardiovascular diseases.

Plasma collection
Blood samples were collected from all the patients using EDTA vacutainers. For the cardiomyopathy patients, blood was collected following echocardiography measurements performed at Department of Cardiology, AIIMS, New Delhi. The blood samples were centrifuged at 1200 rpm for 20 min at 4 °C. 500 µl aliquots of plasma were stored for further biochemical and proteomic studies. Total cholesterol, HDL, triglycerides, and creatinine levels were measured in all the 32 samples using Cobas Integra 400 analyzer. Of the 32 samples, 20 were analyzed using SWATH (5 in each group) while 12 were analyzed using iTRAQ (3 in each group).

Reduction, alkylation, and trypsin digestion
40 μg of protein from samples of each group was reduced with 25 mM of DTT for 30 min at 56 °C, followed by alkylation using 55 mM IAA at room temperature for 15-20 min, and trypsin digestion in 1:10 ratio (trypsin: protein) for 18 h at 37 °C. Tryptic peptides were vacuum dried in vacuum concentrator.

SWATH-based proteomics
Peptides were reconstituted in 1 ml of 8 mM of ammonium formate buffer (pH 3) and applied to the cartridge using hand syringe setup. Peptides were then eluted in 250 mM ammonium formate buffer (pH 3) and were vacuum dried. For SWATH analysis, dried samples were reconstituted in 200 µl of 0.1% formic acid. Human serum library containing 465 proteins was obtained from Sciex for Swath analysis. 2 µl of samples was loaded onto a reverse phase peptide Chromo LC trap (200 mm-0.5 mm) column and desalted for 30 min. After desalting peptides were eluted from C18 column at a flow rate of 250 nl per min with buffer A as 2% acetonitrile + 0.1% formic acid and buffer B as acetonitrile + 0.1% formic acid, starting with 5% buffer B for 2 min and then increasing to 50% in 85 min and then was kept at 90% B for 6 min before equilibrating back to 5% buffer B. The samples were analyzed using 6600TTOF coupled to Eksigent Nano-LC system. Source parameters for 6600TTOF were optimized to curtain gas at 25, ion source gas (GS1) at 20, ion spray voltage at 2000, and interface heater temperature at 75. For TOF MS experiment, parameters were set to TOF MS scan range 350-1250 m/z with accumulation time of 50 ms, de-clustering potential at 60 V and collision energy at 10 V. In product ion experiment, parameters were set to mass range of 100-1400 m/z in high sensitivity mode and accumulation time of 90 ms. For SWATH-MS, mass range was set to 350-1250 m/z, with SWATH width of 54.4 Da (calculated from variable window swath calculator software, Sciex), accumulation time of 96 ms, cycle time of 3.3, and rolling collision energy was set for each SWATH cycle.

SWATH data analysis
For identification of the proteins using SWATH analysis, we used human plasma high pH library (obtained from Sciex) comprising of 465 proteins. SWATH peaks were extracted using this library in peak view software (version 2.1). Peak extraction parameters were set as following: no. of peptides per proteins-10, no. of transitions per peptide-8, peptide confidence threshold-95%, XIC window-100 min, FDR-1%, XIC width ppm-50. Retention time correction for each sample was done manually by selecting the peptides of high abundant proteins and RT fit was calculated, which was then applied to the chromatogram. After retention time calibration, the processed data files were exported in .mrkvw format and analyzed using marker view software.

iTRAQ-based proteomics
Three sets of four plex iTRAQ experiments were performed containing control, RCM, DCM, HCM samples. For iTRAQ labeling, 114,115,116,117 Da tags were ice thawed for 2 h prior to labeling and were reconstituted in 70 µl of ethanol. To the digested samples, iTRAQ tags were added and were incubated for 2 h at room temperature. Reaction was quenched by adding 50 µl LC-MS grade water. All four samples were then pooled and vacuum dried. The dried sample were then reconstituted in 8 mM ammonium formate buffer (pH 3) and was subjected to further cation exchange fractionation via ICAT Cartridge. The peptides were than eluted with 500 μl of increasing concentration of ammonium formate buffer pH 3 from 35 mM, 75 mM, 100 mM, 125 mM, 150 mM, 250 mM, 350 mM, 500 mM and vacuum dried. Peptide fractions were reconstituted in 0.1% formic acid and were then loaded onto reverse phase C18 column connected to desalting trap column in Eksigent nano-LC system coupled to 5600 TTOF (Sciex). 10 μl of sample was loaded on a trap column with a flow rate of 10 μl/min. The retained peptides were washed isocratically by loading buffer for 40 min to remove excess salt. The peptides were then resolved on an analytical column with a multistep linear gradient of loading buffer mobile phase A (95% water, 5% and 0.1% formic acid) and elution buffer mobile phase B (95% ACN, 5% water and 0.1% formic acid) at a flow rate of 250 nl/min. The gradient started at 5% buffer B and was held for 1 min, with linear increases up to 30% B at 80 min and 90% B in another 20 min. The gradient was held at 90% B for 5 min before being re-equilibrated to 5% B for 15 min. The triple TOF 5600 (Sciex) was operated in information-dependent acquisition (IDA) mode. The full MS spectra were acquired in positive ion mode in m/z 350-1200 Da with a 0.25 s TOF MS accumulation time, whereas the MS/MS product ion scan was performed in the mass range of 100-2000 Da with a 0.1 s accumulation time. The MS settings were as follows: ion spray voltage floating = 1950 V, curtain gas = 30, ion source gas 1 = 20, interface heater temperature = 130, and de-clustering potential = 80 V. For 24 s former target ions were excluded and 20 candidate ions were monitored per MS cycle. IDA advanced "rolling collision energy" was applied for subsequent MS and MS/MS scans.

Database search and analysis for iTRAQ
iTRAQ datasets comprising of MS and MS/MS spectra scan were obtained from Triple TOF 5600 in the form of .wiff files. These .wiff files from each iTRAQ experiment were submitted to Protein Pilot™ software (v. 4.5) for protein identification against the Homo sapiens SwissProt database using paragon search method. The search parameters were given as follows: trypsin as the digestion enzyme with two missed cleavages, IAA modification on cysteine residue, iTRAQ 4-plex modification of the N termini of peptides and of the side chains of lysine, bias correction was applied, and proteins were identified with global protein false discovery rate (FDR) of 1%.

Baseline characteristics of the study subjects
The age group of the study population was between 30 and 50 years. Demographic characteristics of the subjects have been provided in Table 1. Echocardiographic measurements suggest that ejection fraction (EF%) is significantly decreased in the DCM group, whereas in the HCM, RCM group ejection fraction is not affected but left ventricle (LV) appears to be hypertrophic in HCM patients. We measured left ventricular dimension and wall thickness for all the cardiomyopathy patients through echocardiography (Table 1). Echocardiographic measurements were not performed for the healthy controls.
We also measured biochemical parameters such as total cholesterol, creatinine, HDL cholesterol, triglyceride in all the 32 subjects. Interestingly, HDL cholesterol levels were found to be lower in all the groups compared to controls. Triglyceride levels were found to be elevated in all the three groups as compared to controls but only in the DCM group triglyceride levels were significantly higher (p < 0.01). We also found creatinine levels to be elevated in DCM, HCM, and RCM than controls (Table 1).

SWATH-MS to identify differential proteome in DCM, HCM, and RCM
Plasma proteomic profiles were compared between 15 cardiomyopathy patients (control, DCM, HCM, and RCM, 5 samples in each group) and 5 healthy controls using LC-MS/ MS SWATH (Fig. 1). The samples were run in triplicates (technical replicates) and the median normalized intensities in each group were considered. We checked for concordance between the normalized intensities in the three technical replicates and found that all the peptides identified had a very good correlation among technical replicates (r > 0.90, Fig. 2).

Identification of common proteins in all the groups in SWATH-MS
We identified a total of 309, 301, 311, and 308 proteins in control, DCM, HCM and RCM groups, respectively, through SWATH-MS mode. Of these, 205 proteins were common in all the groups (Fig. 3). 25 proteins were present in cardiomyopathy samples (in DCM, RCM, and HCM) but not in controls while 22 proteins were present in healthy individuals (control) only. There were 15, 15, 17, 22 proteins that were unique to DCM, RCM, HCM, and controls, respectively. Detailed list of commonly identified proteins among all the groups along with their average peak area has been provided in Supplementary Table 1. (* indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001).

Identification of differentially expressed proteins by SWATH
To identify significantly differentially expressed proteins, we applied a fold change cut off of 1.5 (for upregulation) and 0.66 (for downregulation). Using this criteria, we found a total of 52, 58, and 52 proteins to be significantly differentially expressed (p < 0.05) in DCM, HCM, and RCM groups, respectively, as compared to controls. Of the 52 proteins, 30 proteins were upregulated and 22 were downregulated in DCM patients. Likewise 35 proteins were upregulated and 23 were downregulated in HCM group, while 29 proteins were upregulated and 23 were downregulated in RCM patients. Interestingly, we found that 12 proteins were upregulated in all the three cardiomyopathy groups (Fig. 4a), while six proteins were commonly downregulated in DCM, RCM, and HCM (Fig. 4b). There were 7, 11, and 6 proteins that were upregulated and 13, 13, and 12 proteins that were downregulated exclusively in DCM, RCM, and HCM groups, respectively. This clearly indicates that although there are several pathways that may be commonly affected due to the three different forms of cardiomyopathy, there are proteins that are selectively affected in each of these three categories. A heat map of the 205 identified proteins in all the groups is shown in Fig. 5. A hierarchical clustering analysis of the differential proteomic signatures could discriminate between different groups of cardiomyopathies again highlighting the fact that there are pathways that are uniquely affected in each of these disease categories. To maximize the identification of the differences in the proteome profile amongst the groups, a two dimensional PCA plot was generated through Metabo-Analyst v4.0 software (Supplementary figure 1). It suggests that there was overlap between the differential proteome of DCM and HCM groups, whereas control and RCM groups could be separated based on their proteome profile.

Gene ontology (GO) enrichment of biological pathways
The gene ontology of differentially expressed proteins was performed using DAVID online tool (in the categories biological process, cellular component, and molecular function). Gene ontology analysis of 12 common upregulated proteins mostly included complement activation, platelet aggregation and degranulation, negative regulation of endopeptidase activity, etc. On the other hand, six common downregulated proteins were enriched in proteolysis, complement activation, etc. However, there were several pathways which were unique to DCM, HCM, and RCM groups, respectively ( Table 2). The significantly differentially expressed proteins in the DCM group were enriched in extracellular matrix organization, positive regulation of apoptotic processes, cholesterol transport, complement activation, etc. Proteins in the RCM group were enriched in HDL and VLDL remodeling, reverse cholesterol transport, phagocytosis, etc., while the HCM group showed pathways such as rennin angiotensin regulation, proteolytic processes, phagocytosis, immune response to be differentially altered and unique to HCM group.

Differential proteomics analysis in DCM, HCM, RCM, and controls through iTRAQ methodology
Apart from SWATH, we also used iTRAQ-based relative quantitation approach and analyzed three independent plasma samples of DCM, HCM, RCM, and control group. This also provided with an opportunity to compare between the two quantitative methods. In three sets, the total number of proteins identified was 170, 159, and 172, respectively (supplementary Table 2). A total of 96 proteins were common to all the 4 groups (considering 2 unique peptides) of which 46 proteins were also identified through SWATH at 1% FDR (supplementary Table 3). Proteins with iTRAQ ratios > 1.2 and < 0.8 were considered to be differentially expressed between the groups. Following comparative analysis between the individual groups, we identified that between control and DCM, 23 proteins were upregulated and 35 were downregulated while 29 were upregulated and 28 were downregulated between control and HCM group, and 31 were upregulated and 28 were downregulated in the control and RCM group.

Comparative analysis of differentially expressed proteins between iTRAQ and SWATH
We analyzed the differentially expressed proteins identified through iTRAQ and SWATH to identify both common and unique set of proteins that were found to be differentially expressed in the plasma of cardiomyopathy patients using the two techniques. We found five differentially expressed proteins in DCM, four in HCM and four in RCM to be common between both iTRAQ and SWATH with the same trend of fold change.
Proteins such as afamin, coagulation factor XII, leucine rich alpha 2 glycoprotein, and zinc alpha 2 glycoprotein were commonly differentially expressed between control and HCM group. Between control and DCM group, proteins such as complement C4, afamin, coagulation factor XII, plastin 2, and apolipoprotein AII were found to differentially expressed in both iTRAQ and SWATH. Similarly, phosphatidylinositol-glycan-specific phospholipase D, extracellular matrix proteins such as lumican, complement C1q subcomponent, and phosphatidylcholine-sterol

Discussion
In the current study, we have performed two different quantitative techniques (iTRAQ and SWATH) to identify differentially expressed proteins in cardiomyopathies. We had earlier shown that the strength of SWATH-MS is in quantitating low abundant proteins as compared to iTRAQ (Bhat et al. 2016). In this study, we could identify more number of proteins through SWATH as compared to iTRAQ in the plasma of human cardiomyopathy patients, even without depleting the abundant proteins in the SWATH method. Numerous plasma proteomic studies have been performed to identify novel biomarkers for different forms of cardiomyopathies using both animal models and human plasma samples (Kruska et al. 2017;Gopal and Sam 2013). Plasma being an easily available clinical specimen and a repertoire of largest proteome of the human body is extremely useful for quantitative proteomics studies (Geyer et al. 2016;Schoenhoff et al. 2009;Geyer et al. 2017). Some abundant proteins in the plasma include components of complement system, coagulation cascade, inflammatory processes, and also proteins involved in lipid transfer, alteration of which reflect cardiovascular pathologies (Anderson 2005). Low HDL cholesterol levels in the cardiomyopathy patients indicate that there is possibly an impairment of reverse cholesterol pathway which also corroborate with the previous reports where HDL and APOA1 levels were reported to be lower in heart failure patients (Martinelli et al. 2018). APOA1, APOA4 levels have earlier been reported to be lower in the plasma of CAD patients (Basak et al. 2016). Interestingly, gene ontology enrichment of differentially expressed proteins in all the groups revealed that pathways related to lipid transfer, reverse cholesterol, lipoprotein metabolism, HDL particle remodeling were mostly altered in all the groups. APOA2, major constituent of HDL particle was found to be downregulated in DCM group. In contrast, transcriptomic profiling of end-stage dilated cardiomyopathy patients identified APOA2 to be upregulated (Colak et al. 2016). Other members of the apolipoprotein family APOM was downregulated in HCM. We found apolipoprotein C1 to be significantly upregulated which corroborates with the earlier reports indicating Apo C1 to be upregulated in HCM (Rehulkova et al. 2016). On the other hand, we observed the protein LCAT (phosphatidylcholine-sterol acyltransferase) participating in cholesterol esterification in the plasma to be downregulated in RCM group implying that cholesterol homeostasis is impaired in several forms of cardiomyopathies.
Moreover, we observed pathways related to extracellular matrix organization, lipid transport, and apoptotic processes were altered in the DCM group which had also been reported in human end-stage dilated cardiomyopathy patients (Liu et al. 2017). EGF containing extracellular matrix protein fibulin, attractin (belonging to lectin family) was found to be downregulated in all the groups. Downregulation of fibulin 1 could potentiate myocardial structural changes (Garcia et al. 2011) and lead to atrial fibrillation and arrhythmia which are characteristic features of DCM patients. We also identified upregulation of fibronectin, an important component of ECM in all the groups. This protein is known to act in conjunction with syndecan to activate Wnt signaling pathway which leads to cardiac hypertrophy (Konstandin et al. 2013). Pathway analysis of differentially expressed proteins in the HCM group revealed processes such as regulation of blood vessel size by renin-angiotensin system which has been known to cause atrial fibrillation observed in HCM patients (Huang et al. 2018). Besides, proteins such as vitamin D-binding protein, anti-thrombin-III were upregulated in all the groups which is in agreement with literature reports where these proteins were found to be increased in DCM patients carrying LMNA mutation (Izquierdo et al. 2016). Increased levels of vitamin D-binding protein could favor coagulation and thrombus formation playing a major role in inflammatory response (Trujillo and Kew 2004). Besides, we also identified a protein lumican, belonging to the family of leucine-rich proteoglycans to be significantly (p < 0.01) upregulated in all the groups indicating that extracellular matrix remodeling was affected in heart failure which is linked to pro inflammatory cytokine production (Brown et al. 2005). A recent study suggests that cardiac lumican mRNA expression is accentuated during progression of heart failure in experimental models and clinical samples of heart failure and this also leads to increased expression of lysyl oxidases (Engebretsen et al. 2013). Oxidative stress is an integral component of heart failure. Antioxidant enzymes such as glutathione peroxidase was found to be downregulated in RCM and DCM groups, whereas mitochondrial stress-related proteins (HSP70) were upregulated. Interestingly, sulfhydryl oxidases which participate in forming disulfide bonds during oxidative protein folding were found to be upregulated in all the groups as identified through SWATH. Literature suggests that this enzyme is upregulated during acute decompensated heart failure (Caillard et al. 2018). Another protein clusterin, which takes part in alleviating cardiomyocyte stress via activation of anti-apoptotic pathways (Jun et al. 2011) was found to be upregulated in RCM but downregulated in DCM. This again implies that differential regulation of the same protein in different groups of cardiomyopathies could promote different pathways relevant to the disease.
Interestingly, we observed an overall upregulation of the components of the complement cascade majorly complement C1q subunit was found to be significantly upregulated in all the groups. Complement C1q, majorly composed of 18 polypeptides is known to activate canonical Wnt signaling pathway by binding to Frizzled receptors, found to be upregulated in the serum of mice with heart failure (Naito et al. 2012). Complement factors C9 and C4 were also upregulated. Activation of C4 is presumed to be a signature of activation of both classical and lectin-mediated alternative pathways which has already been reported in post-MI heart failure patients (Orrem et al. 2018). Activation of complement pathways was found to be common across all the groups.
Interestingly, we found gelsolin, an actin capping protein to be upregulated in the plasma of HCM patients from iTRAQ data, whereas this was consistently downregulated in both DCM and RCM groups. Gelsolin protein has been shown to downregulate proapoptotic factors and increase the expression of HIF-1α which leads to cardiac hypertrophy in experimental model of heart failure (Li et al. 2009). Moreover, expression of gelsolin has been reported to be increased in post-MI heart failure models of rat (Guo et al. 2017). Apart from that, serotransferrin, an iron binding and transporter protein was found to be upregulated in all the cardiomyopathy conditions in our study. Interestingly, in a rabbit model of heart failure, it had earlier been shown that carbonylation of serotransferrin is associated with contractility of myofibrillar proteins and led to heart failure conditions (Emdin et al. 2009). Moreover, increased levels of serotransferrin were also found in extracellular membrane vesicles usually found in DCM and thought to be a causative factor of anemia-induced heart failure (Roura et al. 2018).
Vitamin E-binding glycoprotein afamin was found to be upregulated in all the groups identified from SWATH and iTRAQ. Interestingly, vitamin E levels have been reported to be lower in plasma of animals and clinical samples of heart failure (Verk et al. 2017). However, the role of afamin in cardiomyopathies has not been reported earlier. Comparison of pathways across the groups identified processes such as blood vessel remodeling and blood vessel size regulation by renin-angiotensin system to be selectively enriched in the HCM group which is thought to be causal for hypertension but its role in developing cardiac hypertrophy needs to be investigated. So it is observed that using quantitative proteomics we could identify proteomic signatures relevant to cardiomyopathies which can help in unraveling pathophysiological basis of the disease and identifying putative biomarkers.
In summary, we conducted both labeled and label-free relative quantitative proteomic approaches to identify differential proteins in the plasma of different groups of cardiomyopathies as compared to healthy controls in independent samples and using different instruments. Using SWATH, we were able to identify a greater number of proteins in a comparatively large dynamic range because plasma samples were not depleted for high abundant proteins. We could identify the common pathways associated with all the groups of cardiomyopathies, however, there were proteomic signatures in individual groups which could potentially help in identifying therapeutic target. A handful of literature have identified pathways related to heart failure but our study is unique since it provides information about proteins differentially expressed in dilated, hypertrophic, and restrictive groups of cardiomyopathies. Our study has a few limitations which include small sample size and gender differences between different groups. This study would provide advantage in designing large scale follow-up studies where these proteins could be tested to identify the predisposition of these cardiomyopathy patients towards heart failure.