Open Access

Combining patient proteomics and in vitro cardiomyocyte phenotype testing to identify potential mediators of heart failure with preserved ejection fraction

  • Roseanne Raphael1,
  • Diana Purushotham2,
  • Courtney Gastonguay1, 2, 3,
  • Marla A. Chesnik5,
  • Wai-Meng Kwok6,
  • Hsiang-En Wu6,
  • Sanjiv J. Shah7,
  • Shama P. Mirza5 and
  • Jennifer L. Strande1, 2, 3, 4Email author
Contributed equally
Journal of Translational Medicine201614:18

https://doi.org/10.1186/s12967-016-0774-3

Received: 2 September 2015

Accepted: 6 January 2016

Published: 20 January 2016

Abstract

Background

Heart failure with ejection fraction (HFpEF) is a syndrome resulting from several co-morbidities in which specific mediators are unknown. The platelet proteome responds to disease processes. We hypothesize that the platelet proteome will change composition in patients with HFpEF and may uncover mediators of the syndrome.

Methods and results

Proteomic changes were assessed in platelets from hospitalized subjects with symptoms of HFpEF (n = 9), the same subjects several weeks later without symptoms (n = 7) and control subjects (n = 8). Mass spectrometry identified 6102 proteins with five scans with peptide probabilities of ≥0.85. Of the 6102 proteins, 165 were present only in symptomatic subjects, 78 were only found in outpatient subjects and 157 proteins were unique to the control group. The S100A8 protein was identified consistently in HFpEF samples when compared with controls. We validated the fining that plasma S100A8 levels are increased in subjects with HFpEF (654 ± 391) compared to controls (352 ± 204) in an external cohort (p = 0.002). Recombinant S100A8 had direct effects on the electrophysiological and calcium handling profile in human induced pluripotent stem cell-derived cardiomyocytes.

Conclusions

Platelets may harbor proteins associated with HFpEF. S100A8 is present in the platelets of subjects with HFpEF and increased in the plasma of the same subjects. We further established a bedside-to-bench translational system that can be utilized as a secondary screen to ascertain whether the biomarkers may be an associated finding or causal to the disease process. S100A8 has been linked with other cardiovascular disease such as atherosclerosis and risk for myocardial infarction, stroke, or death. This is the first report on association of S100A8 with HFpEF.

Keywords

Platelet proteomeHeart failure with preserved ejection fractionInflammationS100A8Induced pluripotent stem cell-derived cardiomyocytes

Background

The platelet proteome is an untapped resource for identifying proteins that may reflect a disease process. Platelets are easily accessible and free from major highly abundant proteins making them an attractive model for proteomic studies. Platelets change the composition of their proteins in diseases such as Alzheimer’s, cancer, diabetes, coronary artery disease and acute coronary syndrome [14]. Platelets are largely under-studied in heart failure, yet evidence indicates that both platelet function [5, 6] and platelet-derived proteins such as adhesion molecules and the natriuretic peptide receptor-C [710] are altered in heart failure. Therefore, changes in the platelet proteome may allow for the identification of proteins that influence the disease process in heart failure.

Heart failure with preserved ejection fraction (HFpEF) affects almost 50 % of patients with heart failure and is increasing in prevalence [11], yet the pathophysiological mechanisms are poorly understood. HFpEF is associated with diabetes, hypertension, renal dysfunction, atrial fibrillation and obesity. The systemic inflammatory state induced by these co-morbidities is predictive of HFpEF [12, 13]. Platelets are both contributors and responders of inflammatory processes [14]. Considering there are no targeted therapies for HFpEF and morbidity and mortality are high, it is paramount to identify biomarkers associated with HFpEF and clarify their mechanistic role in clinical heart failure in order to develop targeted treatments. Consequently, by examining the platelet proteome of subjects with HFpEF, there is the potential to identify proteins that may provide insight into the disease mechanisms.

We established a novel bed-to-bench translational system to identify potential mediators of HFpEF using both platelet proteome analysis and mechanistic studies in induced pluripotent stem cell-derived cardiomyocytes. The broad utility of this strategy is to incorporate bioactivity studies into guiding the selection of proteins from proteomic studies for further investigation. We sought to compare the platelet proteome among subjects with HFpEF in the uncompensated (hospitalized) state, compensated (outpatient) state, and controls combined with validation in plasma samples from an external cohort and bioactivity studies using human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. We hypothesized that [1] platelet proteomic analysis would successfully identify a protein associated with HFpEF, and [2] human iPSC-derived cardiomyocytes treated with recombinant proteins could serve as further validation by demonstrating phenotypic changes in cardiomyocyte calcium handling, which is altered in HFpEF.

Methods

Study population

For the discovery phase, subjects ≥50 years old presenting with New York Heart Association class II–III heart failure symptoms, a left ventricular ejection fraction (LVEF) >50 %, echocardiographic evidence of diastolic dysfunction and increased LV filling pressure were evaluated at the Medical College of Wisconsin between June 2012 to December 2013 for participation in this study. Increased LV filling pressures were defined as E/e′ ≥ 15, or E/e′ ≥ 8 and ≤ 15 with either a BNP ≥ 200 pg/ml or a left atrial (LA) volume index > 40 ml/m2. Subjects were excluded if they had a clinical condition that potentially changed the platelet or plasma proteomic profile independent of HFpEF such as uncontrolled diabetes, an active infection or inflammatory disorder, chronic renal failure requiring dialysis, severe liver disease, malignancy, acute myocardial infarction, chronic obstructive pulmonary disease requiring steroids, or recent surgical or invasive cardiac procedures. Subjects were excluded if they had other cardiac causes for their symptoms such as severe valvular disease, amyloidosis, or hypertrophic cardiomyopathy. Blood was drawn from the nine subjects enrolled in the study (HFpEF hospitalized group). Five of these subjects (HFpEF outpatient group) returned ≥2 weeks after discharge for second blood draw. Subjects with an LVEF ≥50 % and without evidence of increased LV filling pressures served as the control group.

For further biomarker validation, an additional set of 25 HFpEF subjects and 18 age and co-morbidity matched control subjects were recruited from Northwestern University. All subjects gave written informed consent to participate in the study. The Institutional Review Board at the Medical College of Wisconsin and Northwestern University approved the respective study protocols, which conformed to the principles of the Declaration of Helsinki.

Reagents

Supplies and other reagents were purchased from Sigma-Aldrich (St. Louis, MO) unless specified. Recombinant S100A8 was purchased from Creative BioMart (Shirley, NY).

Platelet preparation

Blood was separated into serum and platelet fractions. Platelets were extensively washed in buffer (45 mM sodium citrate, 25 mM citric acid, 80 mM d-glucose). During all steps, care was taken to avoid activation of platelets. Flow cytometry with anti-CD41 (Life Technologies, Grand Island, NY) and anti-P-selectin (BioLegend, San Diego, CA) was performed to assess for platelet activation (Additional file: 1. Figure S1). Microscopy confirmation verified that the purified platelets had leukocyte and red blood cell contamination that was less than 0.02 and 1 %, respectively (Additional file: 2. Figure S2).

Global proteomic studies

Platelets from individual samples were resuspended in lysis buffer (125 mM Tris pH 6.8, 4 % SDS, 10 % glycerol, 5 % β-mercaptoethanol, Roche Complete Protease Inhibitor, Thermo HALT Phosphatase Inhibitor Cocktail). After determining protein concentration, the protein sample was separated by 1-dimensional SDS-PAGE gel (Bis-Tris 4–12 %) with internal DNA markers as described in our earlier publication [15]. The gel was stained with indoine blue and divided into three pieces. The proteins were reduced with 100 mM dithiotreitol (DTT) in 25 mM NH4HCO3 for 30 min at 56 °C and alkylated with 55 mM iodoacetamide (IAA) in 25 mM NH4HCO3 for 30 min at room temperature followed by trypsin digestion overnight. Peptides were extracted with 0.1 % trifluoroacetic acid (TFA) and 70 % acetonitrile/5 % TFA in water, respectively. Extracts were dried in a Speedvac and subsequently acidified to 0.1 % TFA. The samples were desalted using a ZipTip (C18).

For biomarker discovery, all samples were subject to tandem mass spectrometry. Three injection replicates of each fraction (three fractions per sample) were run on an LTQ-Orbitrap Velos mass spectrometer (Thermo Scientific). For each injection replicate, 1.5 µl sample was separated via C18 column over the course of a 150 min gradient from buffer A (2 % acetonitrile, 98 % H2O, 0.1 % formic acid) to buffer B (98 % acetonitrile, 2 % H2O, 0.1 % formic acid). The gradient program began with 2 min at 98 % A, followed by a 3 min ramp to 95 % A, a 115 min ramp to 60 % A, a 15 min ramp to 2 % A, 3 min at 2 % A, 2 min ramp to 98 % A, then a 10 min equilibration in 98 % A. MS1 scans were detected in the FTMS section of the Orbitrap Velos in profile mode at a resolution of 30,000 (full width of peak at half-maximum at 400 m/z). The ten most abundant parent ions from each MS1 scan were selected for fragmentation via collision induced dissociation. Results of SEQUEST searches against UniProt human database (version April 2013) and all nine runs of each sample were combined using Visualize software. Visualize software was also used to generate comparison data [16]. The protein lists include proteins identified with at least five scans that were observed with peptide probability >0.85.

S100A8 expression

S100A8 levels were determined using a S100A8 enzyme-linked immunoassay kit from MBL International (Des Plaines, IL).

Induced pluripotent stem cell induced-cardiomyocyte differentiation

The induced pluripotent stem cell (iPSC) line used in this study was a generous gift from Dr. Stephan Duncan. This iPSC line was generated from human foreskin fibroblasts and previously characterized [17]. The iPSC line was maintained on Matrigel (BD Biosciences, San Jose, CA) in mTeSR-1 media (Stem Cell Technologies, BC, Canada) and differentiated into cardiomyocytes according to published protocols [18, 19]. Differentiated cells were maintained in cardiomyocyte maintenance media (RPMI/B27; Life Technologies, Grand Island, NY). For all experiments, 35 ± 5 day old contracting cardiomyocytes were used.

Electrophysiology

Action potentials were recorded from the human iPSC-derived cardiomyocytes using the current clamp configuration of the patch clamp technique, as previously described [20, 21]. Briefly, patch pipettes were pulled from borosilicate glass capillaries (King Precision Glass, Claremont, CA) with a micropipette puller (PC-10; Harishige, Tokyo, Japan) and heat polished using a microforge (MF-830; Narishige). The pipette resistances ranged from 3–5 MΩ when filled with the intracellular recording solution. This pipette solution contained 60 mM K-glutamate, 50 mM KCL, 10 mM HEPES, 1 mM MgCl2, 11 mM EGTA, 1 mM CaCl2, and 5 mM K2-ATP (pH adjusted to 7.4 with KOH). The extracellular bath solution contained 132 mM NaCl, 4.8 mM KCl, 1.2 mM MgCl2, 1.0 mM CaCl2, 5 mM dextrose, and 10 mM HEPES (pH adjusted 7.4 with NaOH). Action potentials were recorded using a Multiclamp 700B amplifier and Digidata 1440A interface (Molecular Devices, Sunnyvale, CA). pClamp 10 software (Molecular Devices) was used for data acquisition and analysis. Spontaneously beating nodal-, atrial-, and ventricular-like cells were characterized based on the maximum rate of depolarization (dV/dt), action potential duration (APD) at 50 and 90 % repolarization, and maximum diastolic potential. Recordings were conducted at physiological temperature (37 °C). The temperature of the recording chamber was controlled via a temperature control unit (TC 344B; Warner Instruments, Hamden, CT).

Ratiometric Ca2+ microfluorometry

Briefly, human iPSC-derived cardiomyocytes plated on coverslips were exposed to Fura-2-AM (5 µM) for 30 min at room temperature, washed three times with extracellular bath solution, and given 30 min for de-esterification. For Ca2+ microfluorometry, the fluorophore was excited alternately with 340 and 380 nm wavelength illumination and images were acquired at 510 nm through a 20× objective. Recordings from each cell were obtained at a rate of 3 Hz. After background subtraction, the fluorescence ratio R for individual cell was determined as the intensity of emission during 340 nm excitation (I340) divided by I380, on a pixel-by-pixel basis. Activation-induced transients were generated by depolarization produced by microperfusion application of 50 mM KCl [22].

Statistical analysis

Data is presented as either mean ± SD or as total percentage. Continuous variables were compared using the Student t test, assuming equal variance and dichotomous variables were compared using the Fisher exact test. Mass spectrometry measurements between groups were compared for either the presence (assigned a number value of 1) or absence (assigned a number of value of 0) of the protein identified in the sample using non-parametric Wilcoxon rank-sum tests without adjusting for multiple testing. Mass spectrometry data analysis was performed by the biostatical consulting service at the Medical College of Wisconsin.

Results

Clinical and echocardiographic characteristics of the discovery cohort

As described in Table 1 the median age of the HFpEF subjects is slightly greater than the control subjects (p = 0.04). The HFpEF group had a higher incidence of atrial fibrillation and cerebral vascular accident/transient ischemia in comparison to control subjects. Although not statistically significant, HFpEF subjects were more likely to have diabetes, coronary heart disease, hyperlipidemia and a distant smoking history. A significant number of HFpEF subjects were taking beta blockers compared to the control group. Echocardiogram studies confirmed the presence of diastolic dysfunction and increased LV pressure in the HFpEF group (Table 2). Left atrial volume indices were significantly elevated along with an increase in LV wall thickness in the HFpEF group compared to control.
Table 1

Clinical characteristics of subjects

Characteristic

HFpEF (n = 9)

Control (n = 7)

p value <0.05

Age, years

75 ± 10

62 ± 13

0.03

Women (%)

75

71

n.s.

Body mass index

33 ± 9

33 ± 10

n.s.

Hypertensive (%)

67

75

n.s.

Hyperlipidemia (%)

67

63

n.s.

Diabetes (%)

56

25

n.s.

Coronary artery disease (%)

56

29

n.s.

h/o CVA/TIA (%)

50

0

0.02

h/o Afib (%)

78

0

<0.001

Smoking history (%)

100

29

<0.001

Current smoker (%)

11

14

n.s.

Former smoker (%)

89

14

n.s.

Medications

ACEI/ARB (%)

50

57

n.s

Beta-blocker (%)

100

50

0.009

Aldosterone antagonist (%)

0

0

n.s.

Statin (%)

75

57

n.s.

Diuretic (%)

44

43

n.s.

h/o history of; CVA/TIA cerebral vascular accident/transient ischemic attack, Afib atrial fibrillation, ACEI/ARB angiotensin converting enzyme inhibitor/angiotensin receptor blocker

The p value was calculated using two tailed student t-tests for numerical variables and using Chi squared and Fisher’s exact tests for categorical values

Table 2

Echocardiographic characteristics of subjects

Characteristic

HFpEF (n = 9)

Control (n = 7)

p value <0.05

2D Echocardiography

LA volume index, ml/m2

49 ± 15

32 ± 7.7

0.018

LV internal diameter, cm

4.64 ± 0.37

4.73 ± 0.08

NS

Interventricular septum, cm

1.25 ± 0.12

0.92 ± 0.01

0.001

Posterior wall, cm

1.20 ± 0.18

0.88 ± 0.09

0.004

LV mass index, g/m2

112 ± 20

90 ± 45

NS

Ejection fraction,  %

55 ± 6

60 ± 3

NS

Doppler data

E peak, cm/s

86.6 ± 26

68.0 ± 5.8

NS

e′ peak

6.9 ± 1.66

7.7 ± 1.03

NS

E/e′ ratio

14.4 ± 5.13

9.28 ± 0.37

NS

Diastolic dysfunction, %

100

14

<0.001

LA left atrium, LV left ventricle

The p value was calculated using two-tailed student t-tests

Overall description of proteomic findings

Global proteomic experiments were performed using 21 separate platelet preparations. Combining these experiments, a total of 6102 proteins were identified with at least five scans with a protein probability of >0.85. The HFpEF hospitalized group had a total of 5546 proteins, the HFpEF outpatient group had a total of 4854 proteins and the control group had a total of 5498 proteins identified. A total of 4172 proteins were found to be shared among all three groups. When comparing two groups, 321 proteins were identified as being shared amongst the outpatient and control group. A total of 361 proteins were found in both the hospitalized and outpatient groups and a total of 848 proteins were found in both the control and hospitalized groups. The number of unique proteins in each group consisted of 165 proteins in the HFpEF hospitalized group, 78 proteins in the HFpEF outpatient group, and 157 unique proteins in the control group (Fig. 1). To assess for possible contamination from other blood cells, the data set was scanned for the presence of CD45 and MHC II chains; proteins that are expressed in leukocytes. These proteins were not found in the data set; therefore, the contamination from leukocytes was likely to be minimal. However, complement C5 and β-2-glycoprotein were identified in the data sets denoting some serum contamination was present.
Fig. 1

Global proteomic analysis of platelets identifies 6102 proteins. The Venn diagram displays he results of the analysis of platelet proteins from the individual subjects by in-depth LC–MS/MS. In total, 6102 proteins were identified with 4172 common among all data sets. There were 165, 78, and 157 proteins identified that were unique to the HFpEF Symptomatic, HFpEF outpatient and Control groups

Unique proteins in each study group

The platelet proteome from nine subjects were analyzed in the HFpEF hospitalized group, five subjects in the HFpEF outpatient and seven subjects in the control group. The unique proteins identified with a scan count of >9 are listed in Table 3. In addition after applying the non-parametric Wilcoxon rank-sum test, 37 proteins were found to be more prevalent amongst the combined HFpEF groups than with the control and 77 proteins were identified that were found to be more prevalent amongst the control with a p value <0.05. These proteins are listed Table 4.
Table 3

List of unique proteins identified in each group with >9 scans total

Protein

Accession

Description

Present only in HFpEF symptomatic group

 NALP2

Q9NX02

NACHT, LRR and PYD domains-containing protein

 ZEP3

Q5T1R4

Transcription factor HIVEP3

 MET25

Q8N6Q8

Methyltransferase-like protein 25

 SCAF8

Q9UPN6

Protein SCAF8

 CC105

Q8IYK2

Coiled-coil domain-containing protein 105

 FILA

P20930

Filaggrin

 MEG11

A6BM72

Multiple epidermal growth factor-like domains protein 11

 F19A2

Q8N3H0

Protein FAM19A2

 GRM1

Q13255

Metabotropic glutamate receptor

 YP010

Q96M66

Putative uncharacterized protein FLJ32790

 PSMD4

P55036

26S proteasome non-ATPase regulatory subunit 4

 PCCA

P05165

Propionyl-CoA carboxylase alpha chain, mitochondrial

 TCPR2

O15040

Tectonin beta-propeller repeat-containing protein

 KPRP

Q5T749

Keratinocyte proline-rich protein

 GTPB5

Q9H4K7

GTP-binding protein 5

 CV031

O95567

Uncharacterized protein C22orf31

 TFB2 M

Q9H5Q4

Dimethyladenosine transferase 2, mitochondrial

 SPXN4

Q5MJ08

Sperm protein associated with the nucleus on the X chromosome N4

 PF21A

Q96BD5

PHD finger protein 21A

Present only in HFpEF asymptomatic group

 H2A1H

Q96KK5

Histone H2A type

 H2A3

Q7L7L0

Histone H2A type 3

 POK7

Q9QC07

HERV-K_1q23.3 provirus ancestral Pol protein

 CC127

Q96BQ5

Coiled-coil domain-containing protein 127

 CC85C

A6NKD9

Coiled-coil domain-containing protein 85C

 WDR75

Q8IWA0

WD repeat-containing protein 75

 CXCL3

P19876

C-X-C motif chemokine 3

 RGPS1

Q5JS13

Ras-specific guanine nucleotide-releasing factor RalGPS1

 CXCL2

P19875

C-X-C motif chemokine 2

 CHMP7

Q8WUX9

Charged multivesicular body protein 7

 CK2N2

Q96S95

Calcium/calmodulin-dependent protein kinase II inhibitor 2

 CHIT1

Q13231

Chitotriosidase-1

 NOX1

Q9Y5S8

NADPH oxidase 1

 RBY1C

P0DJD4

RNA-binding motif protein, Y chromosome, family 1 member C

 WFDC3

Q8IUB2

WAP four-disulfide core domain protein 3

 ABCBB

O95342

Bile salt export pump

 HHAT

Q5VTY9

Protein-cysteine N-palmitoyltransferase HHAT

 MID51

Q9NQG6

Mitochondrial dynamic protein MID51

 LMNB1

P20700

Lamin-B1

Present only in control group

 MY15B

Q96JP2

Putative unconventional myosin-XVB

 CC020

Q8ND61

Uncharacterized protein C3orf20

 MCTS1

Q9ULC4

Malignant T cell-amplified sequence 1

 KSR1

Q8IVT5

Kinase suppressor of Ras 1

 PRP6

O94906

Pre-mRNA-processing factor 6

 DDX59

Q5T1V6

Probable ATP-dependent RNA helicase DDX59

 AL1A3

P47895

Aldehyde dehydrogenase family 1 member A3

 PCCB

P05166

Propionyl-CoA carboxylase beta chain, mitochondrial

 HNRCL

O60812

Heterogeneous nuclear ribonucleoprotein C-like 1

 BIRC3

Q13489

Baculoviral IAP repeat-containing protein 3

 NDUF4

Q9P032

NADH dehydrogenase 1 alpha subcomplex assembly factor 4

 MIRO2

Q8IXI1

Mitochondrial Rho GTPase 2

Present in HFpEF symptomatic and HFpEF asymptomatic groups but not control group

 MBD5

Q9P267

Methyl-CpG-binding domain protein 5

 RRBP1

Q9P2E9

Ribosome-binding protein 1

 ZNF79

Q15937

Zinc finger protein 79

 DCNL5

Q9BTE7

DCN1-like protein 5

 RGS3

P49796

Regulator of G-protein signaling 3

 TMOD2

Q9NZR1

Tropomodulin-2

 MYO5B

Q9ULV0

Unconventional myosin-Vb

 SC24D

O94855

Protein transport protein Sec24D

 SHIP1

Q92835

Phosphatidylinositol 3,4,5-trisphosphate 5-phosphatase 1

 ASIC1

P78348

Acid-sensing ion channel 1

 DMXL1

Q9Y485

DmX-like protein 1

 RECQ1

P46063

ATP-dependent DNA helicase Q1

 LY10L

Q9H930

Nuclear body protein SP140-like protein

 MBNL1

Q9NR56

Muscleblind-like protein 1

 KCC2B

Q13554

Calcium/calmodulin-dependent protein kinase type II subunit beta

 LIPA3

O75145

Liprin-alpha-3

 CD109

Q6YHK3

CD109 antigen

 ZN141

Q15928

Zinc finger protein 141

 YTHD2

Q9Y5A9

YTH domain family protein 2

 PLCD

Q9NRZ5

1-acyl-sn-glycerol-3-phosphate acyltransferase delta

 KIFA3

Q92845

Kinesin-associated protein 3

 TRI25

Q14258

E3 ubiquitin/ISG15 ligase TRIM25

 ETUD1

Q7Z2Z2

Elongation factor Tu GTP-binding domain-containing protein 1

 CDN1B

P46527

Cyclin-dependent kinase inhibitor 1B

 CO4A4

P53420

Collagen alpha-4(IV) chain

 TEX35

Q5T0J7

Testis-expressed sequence 35 protein

 MUC16

Q8WXI7

Mucin-16

 NPIL2

A6NJ64

NPIP-like protein LOC729978

 IRF2

P14316

Interferon regulatory factor 2

 MK07

Q13164

Mitogen-activated protein kinase 7

 APOA

P08519

Apolipoprotein(a)

 HIBCH

Q6NVY1

3-hydroxyisobutyryl-CoA hydrolase, mitochondrial

 USH1C

Q9Y6N9

Harmonin

 GOG8O

A6NCC3

Golgin subfamily A member 8O

 NADE

Q6IA69

Glutamine-dependent NAD(+) synthetase

 MET17

Q9H7H0

Methyltransferase-like protein 17, mitochondrial

 PITH1

Q9GZP4

PITH domain-containing protein 1

 IL1R1

P14778

Interleukin-1 receptor type 1

 C1GLT

Q9NS00

Glycoprotein-N-acetylgalactosamine 3-beta-galactosyltransferase 1

 OR2L3

Q8NG85

Olfactory receptor 2L3

 KV122

P04430

Ig kappa chain V-I region BAN

 GG8L2

A6NP81

Golgin subfamily A member 8-like protein 2

 ZFYV1

Q9HBF4

Zinc finger FYVE domain-containing protein 1

 CJ076

Q5T2E6

UPF0668 protein C10orf76

 STAB 1

Q9NY15

Stabilin-1

 EHBP1

Q8NDI1

EH domain-binding protein 1

 ANR24

Q8TF21

Ankyrin repeat domain-containing protein 24

 FAHD1

Q6P587

Acylpyruvase FAHD1, mitochondrial

 IWS1

Q96ST2

Protein IWS1 homolog

 THAP2

Q9H0W7

THAP domain-containing protein 2

 FNIP1

Q8TF40

Folliculin-interacting protein 1

 STK16

O75716

Serine/threonine-protein kinase 16

 CXX1

O15255

CAAX box protein 1

 GOG8R

I6L899

Golgin subfamily A member 8R

 SRRT

Q9BXP5

Serrate RNA effector molecule homolog

 ZN611

Q8N823

Zinc finger protein 611

 MRE11

P49959

Double-strand break repair protein MRE11A

 LONM

P36776

Lon protease homolog, mitochondrial

 GOG8 N

F8WBI6

Golgin subfamily A member 8 N

 ALPK2

Q86TB3

Alpha-protein kinase 2

 EI2BG

Q9NR50

Translation initiation factor eIF-2B subunit gamma

 NBPFL

A6NDD8

Neuroblastoma breakpoint family member 21

 ETV7

Q9Y603

Transcription factor ETV7

Table 4

Proteins preferential to either HFpEF or control groups

Protein

Accession

Description

p value

Proteins preferentially found in HFpEF group

SAA2

P0DJI9

Serum amyloid A-2 protein

0.0019

SAA1

P0DJI8

Serum amyloid A-1 protein

0.0019

PHF3

Q92576

PHD finger protein 3

0.0090

RGPD5

Q99666

RANBP2-like and GRIP domain-containing protein 5/6

0.0123

RGPD8

O14715

RANBP2-like and GRIP domain-containing protein 8

0.0124

YMEL1

Q96TA2

ATP-dependent zinc metalloprotease YME1L1

0.0256

FHR2

P36980

Complement factor H-related protein 2

0.0269

RGPD3

A6NKT7

RanBP2-like and GRIP domain-containing protein 3

0.0278

CG010

Q9HAC7

CaiB/baiF CoA-transferase family protein C7orf10

0.0279

RRBP1

Q9P2E9

Ribosome-binding protein 1

0.0279

ZNF79

Q15937

Zinc finger protein 79

0.0279

DCNL5

Q9BTE7

DCN1-like protein 5

0.0279

RECQ1

P46063

ATP-dependent DNA helicase Q1

0.0283

PERQ2

Q6Y7W6

PERQ amino acid-rich with GYF domain-containing protein 2

0.0285

MBD5

Q9P267

Methyl-CpG-binding domain protein 5

0.0286

GPCP1

Q9NPB8

Glycerophosphocholine phosphodiesterase GPCPD1

0.0286

NOL10

Q9BSC4

Nucleolar protein 10

0.0351

LBP

P18428

Lipopolysaccharide-binding protein

0.0432

AFF1

P51825

AF4/FMR2 family member 1

0.0442

SOX30

O94993

Transcription factor SOX-30

0.0458

DCP1A

Q9NPI6

mRNA-decapping enzyme 1A

0.0465

AN20B

Q5CZ79

Ankyrin repeat domain-containing protein 20B

0.0468

TCOF

Q13428

Treacle protein

0.0479

MEN1

O00255

Menin

0.0486

S10A8

P05109

S100A8

0.0808

Proteins preferentially found in control group

MY15B

Q96JP2

Putative unconventional myosin-XVB

0.0012

ASXL3

Q9C0F0

Putative Polycomb group protein ASXL3

0.0045

CC020

Q9NX02

NACHT, LRR and PYD domains-containing protein 2

0.0045

TEKT1

Q969V4

Tektin-1

0.0070

SEP10

Q9P0V9

Septin-10 OS = Homo sapiens

0.0103

LMNB2

Q03252

Lamin-B2 OS = Homo sapiens

0.0103

ZN469

Q96JG9

Zinc finger protein 469

0.0146

PARI

Q9NWS1

PCNA-interacting partner

0.0148

NOP2

P46087

Putative ribosomal RNA methyltransferase NOP2

0.0148

FIGL2

A6NMB9

Putative fidgetin-like protein 2

0.0148

MCTS1

Q9ULC4

Malignant T-cell-amplified sequence 1

0.0148

TANC2

Q9HCD6

Protein TANC2

0.0148

HEM0

P22557

5-aminolevulinate synthase, erythroid-specific, mitochondrial

0.0148

PRP6

O94906

Pre-mRNA-processing factor 6

0.0148

TACC2

O95359

Transforming acidic coiled-coil-containing protein 2

0.0200

SMC3

Q9UQE7

Structural maintenance of chromosomes protein 3

0.0261

GTF2I

P78347

General transcription factor II-I

0.0262

CI084

Q5VXU9

Uncharacterized protein

0.0268

CCS

O14618

Copper chaperone for superoxide dismutase

0.0294

COX6C

P09669

Cytochrome c oxidase subunit 6C

0.0324

INT11

Q5TA45

Integrator complex subunit 11

0.0352

DCLK1

O15075

Serine/threonine-protein kinase DCLK1

0.0363

SSH1

Q8WYL5

Protein phosphatase Slingshot homolog 1

0.0380

PJA1

Q8NG27

E3 ubiquitin-protein ligase Praja-1

0.0390

BRK1

Q8WUW1

Protein BRICK1

0.0422

UBP44

Q9H0E7

Ubiquitin carboxyl-terminal hydrolase 44

0.0422

PLCG2

P16885

1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma-2

0.0428

IGS22

Q8N9C0

Immunoglobulin superfamily member 22

0.0431

RPGFL

Q9UHV5

Rap guanine nucleotide exchange factor-like 1

0.0431

CN070

Q86TU6

Putative uncharacterized protein encoded by LINC00523

0.0431

TRI35

Q9UPQ4

Tripartite motif-containing protein 35

0.0431

TOPB1

Q92547

DNA topoisomerase 2-binding protein 1

0.0431

R3HD4

Q96D70

R3H domain-containing protein 4

0.0431

ABR

Q12979

Active breakpoint cluster region-related protein

0.0431

ZN441

Q8N8Z8

Zinc finger protein 441

0.0431

ZN451

Q9Y4E5

Zinc finger protein 451

0.0431

DCE2

Q05329

Glutamate decarboxylase 2

0.0431

RAB31

Q13636

Ras-related protein Rab-31

0.0431

PDE3A

Q14432

cGMP-inhibited 3′, 5′-cyclic phosphodiesterase A

0.0431

TRPM2

O94759

Transient receptor potential channel subfamily M member 2

0.0431

C163B

Q9NR16

Scavenger receptor cysteine-rich type 1 protein M160

0.0431

CA094

Q6P1W5

Uncharacterized protein C1orf94

0.0431

RSBN1

Q5VWQ0

Round spermatid basic protein 1

0.0431

GRM8

O00222

Metabotropic glutamate receptor 8

0.0431

KLHL7

Q8IXQ5

Kelch-like protein 7

0.0431

SHAN3

Q9BYB0

SH3 and multiple ankyrin repeat domains protein 3

0.0431

TTI1

O43156

TELO2-interacting protein 1 homolog

0.0431

FMO4

P31512

Dimethylaniline monooxygenase [N-oxide-forming] 4

0.0431

RARB

P10826

Retinoic acid receptor beta

0.0431

UTY

O14607

Histone demethylase UTY

0.0431

SLK

Q9H2G2

STE20-like serine/threonine-protein kinase

0.0431

RB39B

Q96DA2

Ras-related protein Rab-39B

0.0435

RB43L

A6NDJ8

Putative Rab-43-like protein

0.0435

RAB4B

P61018

Ras-related protein Rab-4B

0.0435

RAB12

Q6IQ22

Ras-related protein Rab-12

0.0435

RAB43

Q86YS6

Ras-related protein Rab-43

0.0435

RAB30

Q15771

Ras-related protein Rab-30

0.0435

GRM7

Q14831

Metabotropic glutamate receptor 7

0.0435

ZNF67

Q15940

Putative zinc finger protein 726P1

0.0435

FAKD5

Q7L8L6

FAST kinase domain-containing protein 5

0.0435

ZNF98

A6NK75

Zinc finger protein 98

0.0435

MFSD9

Q8NBP5

Major facilitator superfamily domain-containing protein 9

0.0435

RECK

O95980

Reversion-inducing cysteine-rich protein with Kazal motifs

0.0435

AL1A3

P47895

Aldehyde dehydrogenase family 1 member A3

0.0435

VP37C

A5D8V6

Vacuolar protein sorting-associated protein 37C

0.0435

ZN492

Q9P255

Zinc finger protein 492

0.0435

VPS29

Q9UBQ0

Vacuolar protein sorting-associated protein 29

0.0435

HNRCL

O60812

Heterogeneous nuclear ribonucleoprotein C-like 1

0.0435

DHRS7

Q9Y394

Dehydrogenase/reductase SDR family member 7

0.0452

BRD8

Q9H0E9

Bromodomain-containing protein 8

0.0455

IF2P

O60841

Eukaryotic translation initiation factor 5B

0.0455

GDPD3

Q7L5L3

Glycerophosphodiesterase domain-containing protein 3

0.0456

SYSC

P49591

Serine–tRNA ligase, cytoplasmic

0.0465

NEK9

Q8TD19

Serine/threonine-protein kinase Nek9

0.0473

p values are calculated based on the non-parametric Wilcoxon rank-sum tests

Discovery and validation cohort ELISA confirmation

One particularly interesting finding was the identification of S100A8. The m/z ratio graph representing S100A8 is shown in Fig. 2. Even though the p value was 0.08, it was identified in six out of the nine HFpEF subjects. S100A8 has not been previously associated with HFpEF but has been linked to advanced heart failure [23]. Additionally, S100A8 has been found to correlate with traditional cardiovascular risk factors and the manifestation of cardiovascular disease [24, 25]. For these reasons, we decided to look more closely at S100A8 to verify its association with HFpEF. S100A8 is found in platelets [26, 27] and the plasma [25, 28]; because we used the platelet lysates for the mass spect analysis, we used the plasma samples for quantitative ELISA analysis. Figure 3 shows that plasma S100A8 levels are increased symptomatic HFpEF when compared to control (MCW cohort). We then validated these findings by studying a larger cohort of subjects recruited from the Northwestern University HFpEF Program. In this larger cohort, we saw a similar increase in plasma S100A8 levels in the HFpEF group (Fig. 3; Northwestern cohort).
Fig. 2

Representative MS/MS scan for S100A8 peptide sequence ALNSIIDVYHK. Raw m/z spectral images with peak assignments and b and y ion lists along with a representation of peptide sequencing by tandem mass spectrometry

Fig. 3

Plasma levels of S100A8 in control vs. HFpEF groups. a S100A8 is found in increased levels in the plasma of subjects with HFpEF vs. control subjects as detected by ELISA. The MCW columns include the control (n = 7) and HFpEF (n = 9) from the discovery cohort and the NWU colums include the control (n = 18) and HFpEF (n = 25) samples from the validation cohort. *p < 0.006 vs MCW Control. # p < 0. 002 vs NWU Control

Exogenously applied rS100A8 affects cardiomyocyte function in vitro

To ascertain whether S100A8 may play a causal role in the HFpEF disease process; we developed a bedside-to-bench translational system (Fig. 4) to screen for biological effects of identified proteins on cardiomyocyte function in vitro. We added recombinant S100A8 (800 ng/ml) to iPSC-derived cardiomyocytes in vitro and measured action potentials and intracellular Ca2+ concentrations separately. This specific concentration of rS100A8 was selected as it was the average plasma concentration observed in the HFpEF group (Fig. 3).
Fig. 4

Overview of primary and secondary screening methods to identify potential mediators of HFpEF. a Platelet proteomes were subject to mass spectral analysis and novel proteins were identified. b Human cardiomyocytes derived from induced pluripotent stem cells were used to determine whether proteins that were identified in a had direct effects on cardiomyocytes function in vitro. Purified recombinant protein S100A8 was tested in this assay

Action potentials (APs) were recorded in the current clamp mode using the patch clamp technique. The recordings were acquired from spontaneously beating cells. External application of rS100A8 slowed the spontaneous pacing within 25 s which suggests the interaction with a membrane receptor. In the example shown in Fig. 5a, the spontaneous generation of APs with atrial-like properties was slowed in the presence of rS100A8. The peak-to-peak AP interval increased from 1.5 to 2.4 s. This effect was reversible upon washout of rS100A8 (results not shown). In a different beating cell cluster, the recorded atrial-like APs showed arrhythmogenic tendencies characterized by infrequent incidents of failed triggering of APs, as shown in Fig. 5b. The rS100A8 exacerbated this trend by increasing the frequency of these failed events. Thus, the electrophysiological profile of these iPSC-derived cardiomyocytes is profoundly impacted by rS100A8.
Fig. 5

S100A8-mediated effects on human iPSC-derived cardiomyocytes. a Shows example action potentials recorded from rS100A8 treated iPSC derived human cardiomyocytes. The addition of rS100A8 to the buffer extended the period between action potentials. This period is phase 4; the diastolic membrane potential between action potentials. b rS100A8 exacerbates the arrhythmic tendencies of human cardiomyocytes. c Spontaneous Ca2+ transients recorded from human cardiomyocytes treated with rS100A8 as indicated by the blue line. rS100A8 significantly delayed the recovery of depolarization. Wash out of rS100A8 reversed these effects

Intracellular Ca2+ concentrations ([Ca2+]i) were measured using the ratiometric Ca2+ microfluorometry technique with Fura-2-AM fluorescent dye. The [Ca2+]i were monitored in spontaneously beating cells. The sample trace (Fig. 5c) shows a spontaneous Ca2+ transient recording that was interrupted by activity-induced depolarization (50 mM K+; duration of application as noted) at certain time points (indicated by the red arrows) using a microperfusion system. Of particular note is the recovery of the spontaneous Ca2+ transient following each depolarizing pulse. In the absence of rS100A8, the recovery was relatively fast. In contrast, the recovery was considerably slower in the presence of rS100A8. Following a third depolarizing pulse, recovery was not evident until the washout of rS100A8; this observation also suggests that rS100A8 effects are mediated through a membrane receptor. In summary, rS100A8 adversely affected the calcium handling of iPSC-derived cardiomyocytes.

Conclusions

The key finding of this study was that it was possible to derive platelet protein data sets specific for HFpEF patients. These proof-of-concept findings suggest that the platelet proteome might provide a useful tool for screening for HFpEF-associated biomarkers. Although several platelet proteins were identified in HFpEF subjects; their exact connection to HFpEF has yet to be determined. Though our data is limited by the small size, our discovery cohort has similar characteristics of larger HFpEF cohorts reported in the literature [2931]. By combining proteomics with bioactivity assays, we have demonstrated that the platelet proteome is an untapped resource for determining disease mediators in HFpEF.

The platelet proteome in healthy individuals is remarkably stable with only minor differences in protein expression patterns [32]. Veitinger et al. suggests the difference in platelet proteins between individuals is a results of the uptake of plasma proteins by the platelet [33]. Inflammation is closely linked with HFpEF [34] and considering that platelets are involved in the inflammatory process, it is not surprising that our proteomics screen led to the identification of several proteins also involved in inflammation. These include serum amyloid A (SAA), Lipopolysaccharide binding protein, apolipoprotein A1 and S100A8. Two proteins, serum amyloid-A (SAA) protein 1 and apolipoprotein A1 were increased in the sera of non-human primates after drug-induced cardiac injury [35]. In addition, increased levels of SAA in serum have been associated with coronary heart disease [36], as well as systolic heart failure [37] and has been shown to be a predictor of cardiovascular outcomes in women [38].

S100A8 is a member of the S100 calcium-binding family of proteins, which exhibit increased levels in a number of inflammatory states. S100A8 is commonly mentioned with its binding partner, S100A9. Even though S100A8 is found in the plasma [23], it is known that platelets and megakaryocytes might serve as an additional source of S100A8 and might contribute to the plasma pool of S100A8/A9 in inflammatory diseases and cardiovascular events [26, 27, 39].

S100A8 and S100A9 are not normally expressed in cardiomyocytes [40] although its cardiac expression can be induced by endotoxins or angiotensin II [40, 41]. Release of S100A8/A9 from cells allows it to act in a paracrine or autocrine fashion. These extracellular functions are mediated by the toll-like receptor 4 (TLR4) [42, 43] or the receptor for advanced glycation end products (RAGE) [40, 44, 45]. More recently, CD36 has been identified as a receptor [26]. In the mouse, S100A8/A9 signals through RAGE to promote inflammation and fibrosis after angiotensin II or hypoxic-induced cardiac injury [41, 45].

Increased platelet S100A8 mRNA and plasma protein levels were present in patients with acute myocardial infarction [39]. Plasma levels of S100A8/A9 predicted risk of future myocardial infarction, stroke or death in post-menopausal healthy women [25]. Elevated S100A8 levels have also been found in other inflammatory disorders which are associated with abnormalities of vascular and cardiac function, particularly diastolic dysfunction, such as diabetes [4648], end-stage renal disease [49, 50], and inflammatory bowel disease [51, 52]. This is the first association of S100A8 with HFpEF, yet its role in the disease process still needs be elucidated. S100A8 has immediate effects on the electrophysiological and Ca2+ handling profiles of human induced cardiomyocytes suggesting that S100A8 is acting through a membrane receptor. S100A8 interaction with RAGE affects calcium flux in neonatal rat ventricular cardiomyocytes and HL-1 cardiomyocytes [40, 53]. The adverse effects on the electrophysiological and Ca2+ handling profiles resulting from S100A8 treatment of human induced cardiomyocytes; validates our bedside-to-bench translational screen as an approach to identify bioactive proteins that may contribute to the disease mechanisms in HFpEF.

We also considered the possibility that subjects progress to HFpEF through loss of cardioprotective proteins. Therefore, we searched amongst our control group and were able to identify four proteins that could potentially have protective qualities against the development of heart failure. Cyclic nucleotide phosphodiesterase 3A1 (PDE3A) regulates β-adrenergic signaling to effect physiological cardiac performance. Furthermore, PDE3A protects the heart against angiotensin II-induced cardiac remodeling in mice [54]. Copper Chaperone for Superoxide Dismutase (CCS) plays a role in copper delivery to tissues; disturbances in copper homeostasis mediates cardiomyopathy [55]. Zinc finger protein 451 a negative regulator of TGF-beta signaling [56]. The transient receptor potential cation channel subfamily M member 2 (TRPM2) protein limits oxidative stress injury and dampens the inflammatory response [57].

The present study must be interpreted within the context of its limitations. First of all, this was a discovery effort and not designed as a quantitative proteomic analysis. Therefore, we cannot determine if specific proteins are up- or down-regulated. In addition, it is unlikely that one protein is responsible for a complex disease as HFpEF, but our findings offer new perspectives regarding HFpEF and further confirmation of the platelet proteins identified in this study will need to be validated in a larger cohort. In addition, combining proteomics with functional bioactivity assessments may be a strategy to complement and strengthen the search for biomarkers by combining protein identified with biological activity in a relevant in vitro model system.

In conclusion, from the discovery set in HFpEF patients, we derived a panel of platelet proteins that may be specific for HFpEF. Furthermore, this set distinguished a set of platelet proteins which are consistent in HFpEF subjects whether they are decompensated and hospitalized or compensated after discharge. We further established a bedside-to-bench translational system that can be utilized as a secondary screen to ascertain whether the biomarkers may be an associated finding or causal to the disease process.

Notes

Declarations

Authors’ contributions

JLS conceived and designed the research. RR, DP and SJS contributed clinical samples. SPM designed the proteomics experiments and MAC assisted in performing mass spectral analysis. RR, DP, JLS, WMK and HEW performed research and analyzed the data. RR DP and JLS drafted the paper and all coauthors edited the paper. All authors read and approved final manuscript.

Acknowledgements

This work was supported by funds awarded to J.L.S. from the National Institutions of Health K08 Grant Number HL111148, Steve Cullen Healthy Heart Walk/Run Event and also by grant 1UL1RR031973 from the Clinical and Translational Science Award (CTSI) program of the National Center for Research Resources, National Institutes of Health. We appreciate study subject referrals from Dr. Joshua Meskin and the biostatistics consulting services provided by Drs. Tao Wang and Shi Zhao from the Division of Biostatistics at the Medical College of Wisconsin.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Cardiovascular Center, Medical College of Wisconsin
(2)
Department of Medicine, Medical College of Wisconsin
(3)
Division of Cardiovascular Medicine, Medical College of Wisconsin
(4)
(5)
Biotechnology and Bioengineering, Medical College of Wisconsin
(6)
Department of Anesthesiology, Medical College of Wisconsin
(7)
Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine

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© Raphael et al. 2016

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