The authors have declared that no competing interests exist.
Conceived and designed the experiments: HN GS OB AM PN TF SM NG. Performed the experiments: HN GS OB AM PN TF SM NG EV. Analyzed the data: HN GS OB AM PN TF SM NG AV K. Nair SD SN K. Nanthakumar EV. Contributed reagents/materials/analysis tools: PB SN AV SD K. Nanthakumar EV. Wrote the paper: HN GS AM K. Nanthakumar.
¶ These authors also contributed equally to this work.
Structural differences between ventricular regions may not be the sole determinant of local ventricular fibrillation (VF) dynamics and molecular remodeling may play a role.
To define regional ion channel expression in myopathic hearts compared to normal hearts, and correlate expression to regional VF dynamics.
High throughput real-time RT-PCR was used to quantify the expression patterns of 84 ion-channel, calcium cycling, connexin and related gene transcripts from sites in the LV, septum, and RV in 8 patients undergoing transplantation. An additional eight non-diseased donor human hearts served as controls. To relate local ion channel expression change to VF dynamics localized VF mapping was performed on the explanted myopathic hearts right adjacent to sampled regions. Compared to non-diseased ventricles, significant differences (p<0.05) were identified in the expression of 23 genes in the myopathic LV and 32 genes in the myopathic RV. Within the myopathic hearts significant regional (LV
Ion channel expression profile in myopathic human hearts is significantly altered compared to normal hearts. Multi-channel ion changes influence VF dynamic in a complex manner not predicted by known single channel linear relationships.
The grave hemodynamic consequence of human ventricular fibrillation (VF) limits its study in vivo. Cell cultures
VF can be studied in explanted human hearts using a modified Langendorff perfusion system
Therefore, we investigated comprehensive regional transcriptional differences in cardiac ion channel subunits between myopathic and non-diseased human hearts, and protein expression in a subset of important genes. We then tested the hypothesis that regional heterogeneity of ion channel transcripts will correlate with heterogeneity in local fibrillation dynamics.
The experimental protocol was approved by the University Health Network ethics committee. Informed written consent was obtained from each patient and appropriate forms and documentation outlining the use of these myopathic human hearts and the purpose of this research study was provided to each patient. The University Health Network ethics committee approved the consent procedure.
The experimental protocol was approved by the Ethical Review Board of the Medical Center of the University of Szeged. Informed written consent was obtained for the use of these non-diseased human hearts in this research study.
All procedures conformed to the Helsinki Declaration of the World Medical Association.
This experimental protocol was approved by the University Health Network ethics committee, and informed consent was obtained from each patient. Human cardiac tissue was dissected from eight cardiomyopathic patients (2 women, 6 men) who underwent cardiac transplantation. The mean age was 53±9 years, all patients had ejection fractions <20%. Immediately after explantation, hearts were immersed in cold Tyrode solution, and flushed thoroughly to remove blood particles. Left and right ventricular (LV and RV) and septal samples (1 mm thick) were dissected from the endocardial surfaces of the mid portion of the heart and snap-frozen with liquid nitrogen. As the study sought to determine the effects of myopathy induced ion channel remodeling on VF dynamics, as opposed to structural architecture effects, samples were taken from areas of healthy looking myocardium distant to areas of visual and palpable scar. We have previously demonstrated that ion channel activity is significantly different within hearts between areas of abnormal and relatively normal histology
# | Sex (M/F) | Age (Yrs) | Diagnosis | EF (%) | LV Dimensions Diastolic/Systolic (mm) | Medication prior to explant |
1 | F | 48 | ICM | <20 | 60/48 | Digoxin, Spironolactone, Aspirin |
2 | M | 60 | ICM | <20 | 59/48 | Furosemide, Metolazone, K-Dur, Ramipril |
3 | M | 57 | ICM | 21 | 73/64 | Carvedilol, Candesartan, Hydralazine |
4 | M | 63 | IDCM | <20 | 57/39 | Metoprolol, Omeprazole, Sildenafil, Coumadin |
5 | F | 47 | IDCM | <20 | 78/64 | Citalopram, Furosemide |
6 | M | 61 | ICM | 13 | 78/73 | Pravastatin, Furosemide, Glyburide, Aspirin |
7 | M | 50 | VCM | 20 | 69/51 | Digoxin, Aldactone, Ranitidine |
8 | M | 36 | ARVC | 19 | 71/60 | Sotalol, Amiodarone, Ramipril |
IDCM - Idiopathic dilated Cardiomyopathy; ICM - Ischemic Cardiomyopathy; VCM – Valvular Cardiomyopathy; ARVC – Arrhtyhmogenic Right Ventricular Cardiomyopathy.
This experimental protocol was approved by the Ethical Review Board of the Medical Center of the University of Szeged. Eight (2 women, 6 men) non-diseased human hearts (age 45±8 years) were explanted from organ donors to collect pulmonary and aortic valves for transplant surgery. Tissue was collected and stored as described for the myopathic hearts.
Total RNA from each cardiac tissue was isolated and DNase-treated with the RNeasy Fibrous Tissue Mini Kit (Qiagen). The quality of total RNA was assessed by polyacrylamide-gel microelectrophoresis (Agilent 2100 Bioanalyser). Lack of genomic DNA contamination was verified by PCR.
The TaqMan Low-Density Array (TLDA, Applied Biosystems) technology was used in a two-step reverse-transcriptase-polymerase chain reaction process, as previously reported
For data normalization, we selected the Mean Ct
Freshly-isolated left ventricular endocardial samples from normal and myopathic hearts were fast-frozen in liquid nitrogen, pulverized and homogenized in TNE-buffer containing: Tris 25-mmol/L, EDTA 5-mmol/L, EGTA 5-mmol/L, NaCl 150-mmol/L, NaF 20-mmol/L, Na3VO4 0.2-mmol/L, ß-glycerophosphate 20-mmol/L, AEBSF 0.1-mmol/L, leupeptin 25-µg/mL, aprotinin 10-µg/mL, pepstatin 1-µg/mL, microcystin-LR 1-µmol/L; pH 7.34, HCl. Homogenized samples were centrifuged at 1,000 g for 10 minutes, supernatant collected and ultracentrifuged at 100,000 g for 1 hour. The supernatant was resuspended and incubated in TNE-buffer containing 1% Triton-X100. Protein concentration was determined by Bradford assay (Biorad). All steps were carried out on ice at 4–5°C. Protein samples (20 µg) were separated on 8% poly-acrylamide SDS-PAGE and transferred electrophoretically onto PVDF membranes. The PVDF membranes were blocked in a PBS-solution containing 0.05% (v/v) Tween-20 and 5% (w/v) nonfat dried milk (NDM) and incubated overnight at 4°C with primary antibodies diluted in PBS containing 0.05% Tween-20 and 1%-NDM. After washing with PBS-Tween solution/1%-NDM, membranes were hybridized with HRP-conjugated secondary antibody. Immunoreactive bands were detected by enhanced chemiluminescence using BioMax MS/MR films. Protein quantification was performed with the Quantity One® software (Biorad). All of the expression data are provided relative to GAPDH staining for the same samples on the same gels.
Primary antibodies (1/2000) included monoclonal mouse anti-SERCA2 ATPase (2A7-A1; MA3-919), monoclonal mouse anti-Phospholamban (2D12; MA3-922), polyclonal rabbit anti-Calsequestrin (PA1-913) and monoclonal mouse anti-Na+,K+-ATPase alpha-3 (XVIF9-G10; MA3-915) from Thermo scientific, monoclonal mouse anti-Kir2.2 (S24-1; ab84821) from Abcam, monoclonal mouse anti-Kir2.3 (N25/35; 75-069) from Neuromab, and monoclonal anti-GAPDH (10R-G109a) from Fitzgerald. Peroxidase-conjugated AffiniPure goat anti-rabbit IgG (111-035-144) and Affinipure donkey anti-mouse IgG (715-035-151) from Jackson ImmunoResearch were used as secondary antibodies (1/10,000).
After the samples were removed from the myopathic hearts the coronary arteries were selectively cannulated and flushed thoroughly with Tyrode solution (composition: 118.1 mM NaCl, 4.7 mM KCl, 2.5 mM CaCl2, 1.2 mM MgSO4, 24.9 mM NaHCO3, 1.2 mM KH2PO4, 6.1 mM glucose). The Tyrode was oxygenated with a pediatric oxygenator connected to a carbogen (95% O2, 5% Co2) cylinder. The flow was subsequently maintained at 0.9 to 1.1 ml/g/min at 38°C, and the hearts were placed in a heat-jacketed reservoir at 38°C. Electrical mapping was performed on the epicardium using a custom sock array and on the LV endocardium using a second balloon array as described previously
To determine fibrillation dynamics right adjacent to the region sampled we used the most robust of the near field VF dynamic. Two local parameters of local fibrillation dynamics were assessed; cycle length (activation rate) and conduction block. Local activation rates were calculated using both unipolar and bipolar electrograms at the local recording sites. Peak dV/dt in the unipolar signal was used as a marker of local activation, which was confirmed to coincide with the activation in the bipolar signal. Local cycle length (interval between activations) was measured over 5 seconds and the values averaged to estimate the cycle length for that region. To estimate spatial organization we used double peak incidence (DPI) as described by Evans
Analysis of variance was used to compare mean values between myopathic and normal heart ventricular transcript expression, both LV and RV comparisons. Comparisons of gene expression between ventricular regions were conducted within sets of samples from individual patients, thus controlling for inter-individual variability. Comparisons of gene expression within myopathic heart chambers (LV vs RV vs Septum) was performed with a general linear model, each gene entered as a dependent variable, region entered as the fixed factor, and individual heart identification included in the model. Corrections using LSD were used for paired comparisons. Significance was evaluated for P<0.05.
Local transcriptomal profiling was correlated to VF parameters by regression analysis. To limit the number of variables entered in regression analysis, we entered only genes that are functionally important in the heart (34/84). The independent predictors of VF cycle length and conduction block (DPI) were determined by multiple regression including stepwise selection. The independent variables were entered one at a time in the order in which they most improved the model R2. The alpha level for retention of a variable in the model was 0.15.
Computer simulations were performed using the ten Tusscher human epicardial ventricular ionic model [PMID:16565318] as a basis. Myopathic versions for the left and right ventricles were created by modifying the simulation parameters of the model according to protein expressions levels (
Parameter | LV myopathic | RV myopathic |
Cx43 | 0.8 | 1.15 |
IKr | 0.55 | 0.6 |
NaK | 0.66 | 0.8 |
IK1 | 1.64 | 1.6 |
NCX | 1 | 1.2 |
SERCA | 0.5 | 0.5 |
CASQ | 1.25 | 1.4 |
Expression levels for the myopathic hearts and normal hearts were compared by ventricular chamber. The genes showing significant differences (p<0.05) in expression are presented for the LV (
Subunit | Normal | Myopathic | P-value |
Nav1.3 | 3.51±0.78 | 6.80±1.02 | 0.022 |
Nav2 | 220.88±22.41 | 380.50±53.26 | 0.015 |
Navβ2 | 33.40±8.42 | 74.81±3.48 | <0.001 |
Cav3.2 | 3.49±0.26 | 8.08±1.51 | 0.010 |
Cavα2δ1 | 159.86±15.38 | 236.21±15.84 | 0.004 |
CFTR | 2.27±0.64 | 0.15±0.04 | 0.015 |
Na/K ATPase α3 | 1599.52±146.13 | 1128.90±70.86 | 0.012 |
Na/K ATPase β1 | 1119.32±74.20 | 836.58±30.03 | 0.003 |
SERCA2 | 4635.35±333.73 | 2214.78±160.50 | <0.001 |
PLN | 16292.37±1380.77 | 8658.84±460.77 | <0.001 |
CASQ2 | 2108.07±90.71 | 2671.54±121.65 | 0.002 |
Units for all values are 2−ΔCt versus reference gene (×100), expressed as mean ± SEM. Only expression comparisons achieving p<0.05 are presented.
Subunit | Normal | Myopathic | P-Value |
Kv1.4 | 11.64±1.82 | 21.19±1.20 | 0.001 |
Kv1.5 | 12.55±1.18 | 17.99±1.63 | 0.017 |
Kv1.7 | 5.40±1.27 | 0.81±0.22 | 0.033 |
Kv3.4 | 10.66±1.19 | 17.23±1.36 | 0.003 |
Kv4.3 | 26.72±1.60 | 16.30±0.91 | <0.001 |
Kv11.1/HERG | 191.23±17.96 | 105.68±5.08 | <0.001 |
Kir2.2 | 74.62±8.69 | 46.42±2.16 | 0.007 |
Kir2.3 | 80.89±10.26 | 132.44±12.81 | 0.007 |
Kir3.4 | 37.04±5.87 | 10.17±2.01 | 0.001 |
Kvβ1 | 9.53±1.53 | 19.19±1.87 | 0.001 |
KCHIP2 | 222.26±33.29 | 47.73±11.61 | <0.001 |
SUR1 | 4.11±0.49 | 7.92±1.66 | 0.045 |
Units for all values are 2−ΔCt versus reference gene (×100), expressed as mean ± SEM. Only expression comparisons achieving p<0.05 are presented.
Subunit | Normal | Myopathic | P-Value |
Nav1.3 | 3.84±0.89 | 6.73±0.67 | 0.013 |
Nav1.7 | 6.56±0.67 | 4.04±0.42 | 0.007 |
Navβ2 | 27.00±6.45 | 61.77±4.40 | 0.001 |
Cavα2δ1 | 131.48±8.85 | 216.61±14.74 | <0.001 |
CIC-7 | 93.32±13.89 | 53.16±3.85 | 0.015 |
CFTR | 2.48±0.53 | 0.32±0.14 | 0.005 |
HCN3 | 4.43±0.67 | 2.75±0.21 | 0.031 |
HCN4 | 73.05±9.05 | 43.08±7.48 | 0.023 |
Na/K ATPase α3 | 1769.95±97.85 | 1277.98±91.14 | 0.002 |
NCX1 | 625.55±49.09 | 762.10±25.93 | 0.028 |
SERCA2 | 4827.01±306.21 | 2863.62±253.89 | <0.001 |
SERCA3 | 15.92±1.48 | 11.32±1.42 | 0.042 |
PLN | 14969.11±2155.61 | 9954.70±593.50 | 0.042 |
CASQ2 | 2032.56±84.64 | 2809.94±170.95 | 0.001 |
ITPR1 | 76.11±5.92 | 55.36±4.40 | 0.014 |
Units for all values are 2−ΔCt versus reference gene (×100), expressed as mean ± SEM. Only expression comparisons achieving p<0.05 are presented.
Subunit | Normal | Myopathic | P-Value |
Kv1.4 | 8.43±0.87 | 20.35±2.32 | <0.001 |
Kv1.7 | 5.77±1.25 | 0.69±0.33 | 0.002 |
Kv3.3 | 2.18±0.24 | 1.52±0.16 | 0.038 |
Kv4.3 | 26.07±1.73 | 18.17±1.62 | 0.005 |
Kv11.1/HERG | 195.47±14.86 | 115.55±3.47 | <0.001 |
KvLQT1 | 91.47±3.23 | 63.01±3.23 | 0.001 |
TWIK-1 | 80.93±8.82 | 52.64±7.18 | 0.026 |
Kir2.1 | 95.99±12.98 | 169.16±29.44 | 0.004 |
Kir2.2 | 90.23±9.83 | 53.12±2.47 | 0.039 |
Kir2.3 | 46.48±6.30 | 84.15±8.27 | 0.003 |
Kir3.4 | 20.90±13.92 | 12.32±2.22 | 0.036 |
Kir6.2 | 41.44±6.10 | 27.26±2.26 | 0.047 |
Kvβ1 | 10.66±1.04 | 16.91±1.50 | 0.004 |
KCHIP2 | 320.57±22.74 | 119.68±25.22 | <0.001 |
TASK2 | 2.27±0.47 | 0.97±0.18 | 0.021 |
MinK | 11.80±0.99 | 16.82±1.83 | 0.003 |
SUR1 | 3.84±0.84 | 8.26±1.57 | 0.026 |
Units for all values are 2−ΔCt versus reference gene (×100), expressed as mean ± SEM. Only expression comparisons achieving p<0.05 are presented.
In the myopathic ventricles the expression of SERCA2 and its regulatory protein PLN were significantly reduced compared to the normal hearts. Expression of the sarcoplasmic reticulum (SR) Ca binding protein calsequestrin was significantly increased in both myopathic ventricles.
In the myopathic LV, expression levels of the α1 subunit Cav3.2 and the α2δ subunit Cavα2δ1 were higher than control LVs. In the myopathic RVs expression of Cavα2δ1 was higher than controls.
In the myopathic LV and RV the expression of Na+/K+ ATPase α3 was significantly reduced compared to the normal cohort.
Expression for Nav1.3, Nav2, and Navβ2 was significantly higher in the LV of the myopathic hearts compared to normal hearts, while, in the myopathic RV, expression was higher for Nav1.3 and Navβ2, and lower for Nav1.7 than controls. No significant difference was observed for Cx40, Cx43, or Cx45 transcript expression for either ventricle between the myopathic and normal hearts.
Transcripts responsible for transient inward current Ito were altered in an inconsistent pattern in the myopathic hearts. In the LV, Kv1.7 and Kv4.3 expression was reduced, while Kv3.4 expression was increased. In the RV Kv1.4 expression was increased, while expression of Kv1.7 and Kv4.3 was decreased. Transcripts responsible for the inward rectifying current IK1, Kir2.1 and Kir2.2, showed differing expression profiles between LV and RV when compared to the normal ventricles. Kir2.2 was significantly decreased in both LV and RV, while Kir2.1was significantly upregulated in the RV. The expression of Kir3.4 transcripts, which contribute towards the production of the inward rectifying current IkACh, was significantly weaker in the myopathic ventricles than the normal ventricles. The α- (Kir6.1 and Kir6.2) and ß-subunits (SUR1 and SUR2) responsible for the production of IkATP were also analyzed. Of these subunits SUR1 showed increased expression in both myopathic ventricles, while the Kir6.2 showed decreased expression in myopathic RV when compared to normal ventricles.
We compared expression between the LV, RV and septum within the 8 myopathic hearts.
Differentially expressed genes in LV versus RV. Units for all expression values are 2−ΔCt versus reference gene (×100). Only expressions comparisons achieving p<0.05 are depicted.
Differentially expressed genes in LV versus septum. Units for all expression values are 2−ΔCt versus reference gene (×100). Only expressions comparisons achieving p<0.05 are depicted.
Differentially expressed genes in RV versus septum. Units for all expression values are 2−ΔCt versus reference gene (×100). Only expressions comparisons achieving p<0.05 are depicted.
Expression of Cav3.1 was significantly higher in the RV compared to the septum and LV. RyR expression was much higher in the RV than the LV. Calsequestrin expression was significantly higher in the septum. The expression of the Cavß2 showed higher levels of expression within the RV compared to the LV, while the Cavα2δ2 was higher in the septum compared to both the LV and RV.
The expression of Cx43 was greater in the RV than the LV or the septum. Expression of Na+/K+ ATPase α3 was greater in the RV than the LV and septum.
Kir2.3 (Ik1) expression was significantly greater in the LV and septum compared to the RV. Kir3.4 (IkACh) expression was significantly stronger in the septum compared to the LV. Expression for the pacemaker channel HCN2 was much stronger in the LV than the RV. SUR2 expression was significantly higher in the RV compared to the septum.
Mean VF cycle length was 313 ms (LV = 239 ms; RV = 384 ms; Septum = 308 ms, p = NS). Fraction of time DPI was present was in average 25.7% (LV = 31.4%; RV = 20.2%; Septum = 25.4%. p = NS).
Local unipolar recordings of VF are displayed from the LV and RV. For the LV recording unipolar activation is marked (maximal negative dV/dt) demonstrating local activation rate and cycle length. The FFT of the RV recording shows 2 peaks indicating local conduction block. The bottom panels show the determinants of conduction block (left) and cycle length (right) in the order they entered the regression models.
Stepwise Multiple Regression (for predicting Cycle Length and incidence of Conduction Block) | |||
Model R2 | Regression Coefficient | p-Value | |
Predictor Variables |
|||
Cx43 | 0.57 | 0.415 | <0.001 |
Na+/K+ ATPase β1 | 0.79 | −0.428 | 0.090 |
Kir2.1 | 0.86 | 8.82 | 0.044 |
hERG | 0.95 | −7.95 | 0.004 |
Predictor Variables |
|||
Cx45 | 0.41 | −0.59 | 0.001 |
Kir3.1 | 0.63 | 2.24 | 0.068 |
Cx43 | 0.72 | −0.03 | 0.006 |
SUR2 | 0.79 | 0.0197 | 0.014 |
Kir2.3 | 0.88 | 0.19 | 0.049 |
Stepwise multiple regression analysis was performed to evaluate the contribution of ion channels to Cycle length or incidence of conduction block. Expression level of each ion channel was added to the regression model and those with significant contribution to dependent variables were retained in the model. Data presented in the table demonstrates the most appropriate fit, capable of predicting the incidence of CL and conduction block.
Among all ion channels assessed, expression of Cx43, Na/K ATPase β1, Kir2.1 and hERG were the most significant predictors of Cycle length.
Among all ion channels assessed, expression of Cx45, Kir3.1, Cx43, SUR2 and Kir2.3 significantly correlated with incidence of conduction block.
We determined the protein expression levels for a subset of genes with Western blot analysis of freshly isolated LV endocardial tissue from control and myopathic hearts (
A: Top; representative SERCA2 and respective GAPDH stainings obtained in normal and cardiomyopathic LV samples. Bottom; Mean±SEM, *** P<0.001. B: Top; representative phospholamban (PLB) and respective GAPDH stainings obtained in normal and cardiomyopathic LV samples. Bottom; Mean±SEM, P = 0.051. C: Top; representative calsequestrin 2 (CSQ) and respective GAPDH stainings obtained in normal and cardiomyopathic LV samples. Bottom; Mean±SEM, * P<0.05. D: Top; representative Na/K-ATPase-α3 and respective GAPDH stainings obtained in normal and cardiomyopathic LV samples. Bottom; Mean±SEM, *** P<0.001. E: Top; representative Kir2.2 and respective GAPDH stainings obtained in normal and cardiomyopathic LV samples. Bottom; Mean±SEM, * P<0.05. F: Top; representative Kir2.3 and respective GAPDH stainings obtained in normal and cardiomyopathic LV samples. Bottom; Mean±SEM. n = 8 control LV samples, n = 6 cardiomyopathic LV samples.
Using the ventricular myocyte model described in the methods section, we manipulated only the parameters of the model which were associated with frequency changes to see their effect in isolation and in combination with each other, using protein expression levels derived from our study (
Effect of protein level changes on average frequency. Starting with the left (A) or right (B) ventricular myopathic ionic model, parameters were changed to match expression levels in the other ventricle as specified in
The circular plots represent the changes in rotor frequency after manipulation of four different molecular components: Cx43, hERG, Na+/K+ ATPase ß1 and Kir2.1. Within the data plots, red indicates an increase and blue a decrease in cycle frequency, with color intensity corresponding to the degree of change. Examining the plots radially from the inner to outer circles illustrates the contribution of each molecular component to cycle frequency as the number of components changed was increased. The only component, which showed a consistent response across all changes, was IK1, which was associated with an increase in frequency when it was decreased in the LV, regardless of the other parameters changed. However, increasing IK1 in the myopathic RV was not always associated with a decrease in frequency. In the RV, decreasing Cx43 and IKr individually increased frequency, but combined, decreased frequency. These results indicate that the Cx43 and IKr act as molecular components that most likely alter frequency by two different mechanisms, and together the two channels have antagonistic effects on frequency. Furthermore, if the system was simply linear, the two plots would be complements of each other, i.e., increasing a parameter leading to an increase of frequency in one ventricle would lead to a decrease in frequency when the parameter is decreased in the other ventricle, resulting in blue and red being exchanged between the two plots. However, this is not the case since additional parameters were different in the RV and LV, which were not changed. Specifically, NCX and CASQ were slightly different. In the LV, the largest decrease was seen with changes in Cx43 and NaK, which corresponds to the largest frequency increase seen in the RV. However, the largest frequency increase in the LV, changing IK1 and IKr, did not correspond to the largest frequency decrease in the RV. The results of our computer simulations illustrate the complexity of determinants regulating cycle frequency. Since frequency was averaged over an area, factors, which affected it, included the absolute refractory period, the propensity for block, tissue dimensions, and the size and movement of the rotor core. The core region was important since the core itself did not demonstrate action potentials, serving to decrease average frequency. In the RV, the maximum frequency observed over the tissue was decreased for only one case (NaK−,IK1+) yet average frequencies either increased and decreased. An increase in both the minimum and maximum frequencies was observed when Cx43, NaK and IK1 were changed in the RV but an overall decrease in frequency was observed. Thus, global behavior could not be predicted by changes in one single characteristic.
In this study the comprehensive regional ion channel transcript profile in failing human hearts was compared to normal hearts and demonstrated significant differences in multiple transcripts. As a practical strategy, in a subset of genes we studied the protein expression levels with Western blot analysis of LV endocardial tissue from control and myopathic hearts. This analysis demonstrated differences in protein expression between control and myopathic hearts, which were concordant with the mRNA expression profiles for the genes studied. The regional differences in transcript expression were correlated to functional electrophysiological parameters during ventricular fibrillation and through use of statistical model we suggest that a limited number ion channels correlate with the heterogeneity in regional fibrillation dynamics, and may serve as targets for future studies for modulation of human VF in myopathic human hearts.
Previous studies using microarrays for transcriptomal profiling have examined gene expression differences across cardiac regions in various models
Reductions in the transient outward current Ito during phase 1 of the cardiac action potential prolongs action potential duration and may be proarrhythmic
Heart failure causes defective Ca2+ sequestration into the sarcoplasmic reticulum due to a reduction in the expression of SERCA2, which leads to reduced amplitude and slowed decay of the intracellular Ca transient
As it would be impractical to perform protein analysis of all 84 genes studied in the transcriptomal analysis, we performed selective Western blot analysis of important genes to verify that the comparison of normal to myopathic was indeed valid. Our protein analysis indicated that the transcriptome analysis was effective at assessing which gene products were up- or downregulated between normal and myopathic hearts. As such, provides additional support to the correlative relationships between gene expression and VF dynamics we set forth in our study.
Animal studies have linked alteration of specific ionic currents, particularly Ik1, with VF dynamics
While our study primarily focused on characterizing the transcriptomal profile of myopathic and normal hearts, we attempted to make further correlations between specific genes identified in our analysis with VF generation. The correlative model we constructed suggests ion channel transcript expressional variability correlates to regional differences in electrophysiological parameters observed during VF. The role of Ik1 in local VF dynamics is complex and has a varying role depending how far it is from the core of the rotor
As expected, increasing expression of Na+/K+ ATPase ß1 resulted in faster conduction velocity and shorter VF cycle lengths. Increased hERG (Ikr) resulted in decreased cycle length, possibly by increasing conduction velocity as a result of shortening repolarization time. In our model, the most significant predictor of VF cycle length at each of the three sites was the level of Cx43 expression. Surprisingly increased expression of Cx43 resulted in an increase in cycle length. Though this study cannot establish the mechanistic rationale, this may suggest the regions studied have provided pivot points for reentry and thus have registered higher activation in regions with decreased Cx43 expression, as a result of spatial heterogeneity
In this study, we examined alterations in mRNA expression levels of various ion channel transcripts in the septum, RV and LV. There is no mRNA-based assay that provides information on mRNA stability, posttranslational processes, or subunit assembly. Therefore, mRNA transcript levels may not correspond directly to channel activity. The goal of this study was to utilize a shot-gun gene analysis approach to identify as many potentially novel molecular targets involved in VF dynamics, such that future ion channel specific studies can be formulated with the channels identified. While we acknowledge that definitive correlations on VF dynamics cannot be made unless channel activity can be isolated for all 84 ion channels studies, it nevertheless provides a compilation of potential targets, which will be the focus of future studies. These studies will be focused on individual molecular targets identified within this study and will include cell isolation and patch clamp experiments. Local VF cycle length and conduction block is also determined by local myocardial architecture, particularly the amount of scar/fibrosis. Our previous study of VF dynamics evaluating the role of anatomic structure suggested that regional VF dynamics are only partially explained by such differences
Samples were taken from areas of visually and palpably healthy myocardium in order to isolate ion channel remodeling effects, from local fibrosis, on VF dynamics. Additionally, we were not able to fully eliminate the possibility of contamination by purkinje fibers in our isolated endocardial biopsies. Finally, we acknowledge the heterogeneity of the myopathic hearts used in this study. The different disease states among the myopathic hearts may lead to undetermined differences in ion channel expression and VF dynamics which were not examined separately in this study.
To study fibrillation dynamics, we used VFCL as a surrogate for refractoriness during VF as previously published (28). In this study we did not specifically evaluate regional electrical remodeling as measured by APD and CV and its relationship to VF dynamics. This was the basis of detailed studies conducted by Nanthakumar et al
Ion channel expression profile in myopathic human hearts is significantly altered compared to normal hearts and reveals regional differences. The correlative relationships between several specific genes with VF dynamics and the modeling studies indicate a complex interactive influence on cycle length and conduction block. Our study provides an expression profile of molecular targets, which may contribute to VF within myopathic hearts. Using regression analysis and computer simulations we have uncovered complex interactions of key ion channel determinants of VF dynamics that could provide novel substrates for safe therapeutic strategies.
List of genes analyzed using the TaqMan low-density gene arrays. The Gene ID numbers are provided along with the identification for each reference probe used in the analysis.
(PDF)
Expression profile data for all genes analyzed. Gene name and corresponding protein expressed are listed. The genes are grouped according to different families of ion channels. Raw expression values for normal and myopathic LV and RV are listed, along with N and SEM.
(PDF)
We wish to thank Dr. Joan Ivanov for performing statistical analysis.