Integration of microarray profiles associated with cardiomyopathy and the potential role of Ube3a in apoptosis

  • Authors:
    • Jie Zhang
    • Rui Song
    • Yanfei Li
    • Jian Feng
    • Luying Peng
    • Jue Li
  • View Affiliations

  • Published online on: December 9, 2013     https://doi.org/10.3892/mmr.2013.1848
  • Pages: 621-625
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Abstract

Cardiomyopathy is the one of the primary causes of mortality. High‑throughput genome datasets provide novel information that aids the understanding of the complex mechanisms involved in cardiomyopathy. However, the causative mechanisms underlying cardiomyopathy are yet to be elucidated. In order to improve the use of the high‑throughput genome datasets, the present study employed 9 microarray datasets to mine for differentially expressed cardiomyopathy‑associated genes using bioinformatic methods. Following validation using quantitative polymerase chain reaction, ubiquitin‑protein ligase E3a (Ube3a) was selected as a candidate gene for the disease. Substantial evidence suggests that apoptosis may be involved in the pathophysiology of cardiomyopathies. Therefore, in the present study, H2O2 was utilized to induce apoptosis in H9C2 cells in order to understand the interrelation between Ube3a and the apoptosis-related protein p53. Ube3a and p53 were observed to be significantly increased at the transcriptional and translational levels in response to H2O2 treatment. The results of this study indicate the efficiency of the data integration and the significant interrelation between Ube3a and p53 in myocardial cells during apoptosis.

Introduction

Cardiomyopathy has been one of the primary causes of mortality over the past decade; however, the mechanism underlying the development of cardiomyopathy remains unclear. In vivo and in vitro studies have demonstrated that multiple gene networks, as well as factors other than blood pressure, may be involved during the initiation of cardiac hypertrophy (1,2). Apoptosis has been suggested to be a major contributor to heart failure, with myocyte apoptosis observed during acute cardiac dysfunction (3). Furthermore, the apoptotic marker, p53, is significantly increased during cardiac hypertrophy (4,5).

In normal cells, protein metabolism is a dynamic process of continuous degradation and re-synthesis. The ubiquitin-proteasome system is responsible for between 80 and 90% of this degradation. Alterations in the ubiquitin pathway have been reported to lead to protein metabolism disorders, which may lead to cardiomyopathy (4,6). Weekes et al (7) proposed that abnormalities in the ubiquitin system may cause myocardial hypertrophy and dilated cardiomyopathy.

Ubiquitin-protein ligase E3a (Ube3a) is an ubiquitin ligase that is responsible for recognizing target proteins in the ubiquitin-proteasome pathway. Ube3a is expressed in a number of tissues, including, the heart, liver and brain (GeneCards®; www.genecards.org). Since 1997, research has predominantly focused on the role of Ube3a in Angelman Syndrome (8,9). However, the role of Ube3a has also been investigated in Prader-Willi syndrome (10), autism (11) and Huntington's disease (12). In neural cells, Ube3a is capable of initiating the degradation of p53 in the ubiquitin-mediated pathway (13,14). Furthermore, in hypertrophic myocardial tissue, mouse double minute 2 homolog (Mdm2), a member of the E3 family, is significantly increased and has been proposed to regulate the expression of p53 (14).

In the present study, H2O2 was used to induce apoptosis in H9C2 cardiomyocytes. The pattern of Ube3a and p53 expression was analyzed to assess their roles in cardiomyocyte apoptosis. To the best of our knowledge, this is the first report to demonstrate an association between Ube3a and p53 upon H2O2 treatment in H9C2 cardiomyocytes. Ube3a and p53 may have a significant role in ubiquitin degradation in cardiomyocyte apoptosis.

Materials and methods

Data pre-processing and normalization

A total of 9 cardiomyopathy datasets were analyzed, including six oligonucleotide and three cDNA microarray datasets. The first oligonucleotide microarray dataset (GDS411) (15) consisted of 53 samples, including 12 normal, 12 heart failure, 12 rescue heart failure and 17 other types of samples. The second dataset (GDS651) comprised 37 samples, including 11 normal, 15 idiopathic dilated and 11 ischemic cardiomyopathy (ICM) samples. The third dataset (GDS1264) (16) consisted of 23 samples, including 11 normal and 12 cardiomyopathy samples. The fourth dataset (GDS1362) (17) contained 30 samples, including 15 normal and 15 ICM samples. The fifth dataset (GDS3386) (18,19) contained 32 samples, including 16 normal and 16 myocardial hypertrophy samples. The sixth dataset (GDS2145) (20) consisted of 28 samples, including 15 normal and 13 dilated cardiomyopathy samples. Regarding the cDNA microarray datasets, the first dataset (GDS2206) (21) comprised 20 samples, including normal samples and those from patients with myocardial infarction (MI). The second dataset (GSE1616) (22) consisted of 37 samples, from six normal and 21 nonischemic patients, as well as 10 patients with ICM. The third dataset (GSE18224) (23) contained 24 samples, including 12 normal and 12 MI samples.

The cDNA data was log2-transformed and each array was subsequently normalized as median zero and standard deviation (SD) one per array, as adopted from the Oncomine database (24). CloneIDs with a missing rate >20% were deleted. The remaining missing values were replaced using the k nearest neighbor imputation algorithm (k=15) (25). The Affymetrix GeneChip data were pre-processed using the robust multi-array analysis algorithm and then the between-array median was normalized (26).

Selection of differentially expressed genes (DEGs)

The significance analysis of microarrays (SAM) method was performed using the samr R package (27) to select DEGs. Multiple statistical tests were controlled by false discovery rate (FDR), defined as the expected percentage of false positives among the claimed DEGs (28). The FDR estimation of SAM may be overly conservative (29,30); therefore, the FDR estimation method suggested by Zhang (30) and influenced by Xie et al (29), was also employed and referred to as the modified SAM method.

Cell cultures

H9C2 cells (Chinese Academy of Sciences, Shanghai, China) were cultured on 96-well plates in Dulbecco's modified Eagle's Medium (DMEM) containing 1 g/l glucose supplemented with 10% heat-inactivated fetal bovine serum (Wisent Bioproducts, St. Bruno, QC, Canada), 100 U/ml penicillin and 100 μg/ml streptomycin (Invitrogen Life Technologies, Carlsbad, CA, USA) in a humidified atmosphere at 37°C in 5% CO2. To prevent cell loss, cells were subcultured prior to confluence, at a ratio of ~1:2, every two days. Cells in the logarithmic growth phase were used in the experiments.

H2O2 treatment

Following inoculation for 24 h, H9C2 cells were treated with the indicated concentrations of H2O2 (Sangon Biotech, Shanghai, China), which was added to the culture medium. Cells were then incubated further for the indicated times.

Cell viability

Following exposure to H2O2 for 4 h, H9C2 cells were treated with MTT (Nanjing KeyGen Biotech Co. Ltd., Nanjing, China) and then incubated for 4 h at 37°C in the dark. The supernatant was subsequently removed and 150 μl dimethylsulfoxide was added to each well. The optical density (OD) was measured at a wavelength of 550 nm once the crystals had dissolved. Cell viability was calculated as the percentage of the control OD.

Apoptosis assay

The percentage of apoptotic cells was measured using the Annexin V-fluorescein isothiocyanate (FITC) Apoptosis Detection kit (Nanjing KeyGen Biotech Co. Ltd.) according to the manufacturer's instructions. Following exposure to H2O2 for 48 h, cells were harvested using 0.25% Trypsin without ethylenediaminetetraacetic acid, and then washed twice with phosphate-buffered saline (PBS), prior to resuspension in 500 μl binding buffer with 5 μl Annexin V-FITC and 5 μl propidium iodide (PI). Subsequent to incubation for 15 min in the dark, the samples were analyzed using flow cytometry (BD Biosciences, Franklin Lakes, NJ, USA). The number of cells in different quadrants represented different cell populations, with the lower left quadrant representing normal cells, the lower right representing viable apoptotic cells, the upper right representing non-viable apoptotic cells and the upper left representing necrotic cells.

Western blot analysis

Subsequent to the relevant treatment, cells were washed twice with ice-cold PBS and then lysed in cell lysis buffer containing 1% phenylmethylsulfonyl fluoride (PMSF) for 30 min on ice. The lysate was centrifuged at 15,249 × g at 4°C for 20 min to remove the insoluble materials, followed by collection of the supernatants. All samples were mixed with 5X loading buffer and boiled for 5 min. Samples were separated using 8% SDS-PAGE and then transferred to polyvinylidene fluoride (PVDF) membranes (Billerica, Millipore, MA, USA). Membranes were blocked for 1 h using 5% non-fat milk in Tris-buffered saline containing 1% Tween-20 (TBST) at room temperature and then incubated with anti-UBE3A (Cell Signaling Technology, Danvers, MA, USA), anti-p53 (BioWorld Products Inc., Visalia, CA, USA) and anti-GAPDH (Epitomics Inc., Burlingame, CA, USA) antibodies overnight at 4°C. Membranes were washed with TBST three times every 10 min, then incubated with a horseradish peroxidase-conjugated secondary antibody (Beyotime Institute of Technology, Haimen, China) for 1 h at room temperature. Following the secondary antibody incubation, membranes were further washed with TBST and the immunoreactive bands were visualized using the enhanced chemiluminescence method. The images were analyzed using the Quantity One® software (Bio-Rad, Hercules, CA, USA).

Quantitiative polyermase chain reaction (qPCR) analysis

RNA was prepared using TRIzol® Reagent (Invitrogen Life Technologies) and RNA concentration and purity were then determined using a spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Total RNA was subsequently converted into cDNA by reverse transcription using the High-Capacity cDNA Reverse Transcription kit (Applied Biosystems, Carlsbad, CA, USA). qPCR was performed in triplicate using Power SYBR® Green PCR Master mix and a 7500 Fast Real-Time PCR system (Applied Biosystems) according to the manufacturer's instructions. Analysis was performed using the software supplied with the instrument. Primer sequences were as follows: Forward: 5′-GAGGACTCG GAAAATTGAAGATG-3′ and reverse: 5′-CCGGAAGTA AAAGGACATTAAAGC-3′ for Ube3a; and forward: 5′-CCA TCAACGACCCCTTCATT-3′ and reverse: 5′-GACCAG CTTCCCATTCTCAG-3′ for GAPDH.

Statistical analysis

Statistical analyses were performed using the SPSS 14.0 statistical software (SPSS, Inc., Chicago, IL, USA). All data are expressed as the mean ± SEM from at least three independent experiments. Results were analyzed using one-way analysis of variance. P<0.05 was considered to indicate a statistically significant difference.

Experimental procedures

Each experiment was repeated at least three times with comparable results, unless indicated otherwise.

Results

DEG selection

Current FDR control procedures, including that adopted in SAM (27), may be unstable in small samples particularly in the presence of correlated expression changes. Therefore, in the present study, the actual FDR of DEGs detected in simulated small samples was evaluated, according to predefined DEGs. Based on the simulated results, using SAM with 0.05% FDR control, the DEGs obtained from the full samples were defined as a nominal gold standard set (31). As shown in Table I, this procedure identified various DEGs. Although false positives were detected in the selected DEGs, this was a preliminary procedure to prepare for the subsequent analysis of various datasets.

Table I

Number of differentially expressed genes selected from different datasets (FDR control values).

Table I

Number of differentially expressed genes selected from different datasets (FDR control values).

ID<0.001<0.01<0.05<0.1
GDS41166718
GDS6511551656640710497
GDS1264932598691721
GDS13626640520675136
GDS338655519
GDS214530481816372130
GDS2206941264854917480
GSE1616782037511259
GSE18224892909041411
Generation of cardiomyopathy-associated E3 ubiquitin ligase genes

Different datasets supply different information. The DEGs selected using the aforementioned method were associated with numerous cardiomyopathy-associated genes. A total of five DEGs were selected, all of which belong to the E3 ubiquitin ligase family, and were termed cardiomyopathy-associated E3 ubiquitin ligase genes. These DEGs were: Ube3a; WW domain containing E3 ubiquitin protein ligase 1 (Wwp1); itchy E3 ubiquitin protein ligase (Itch); HECT, C2 and WW domain containing E3 ubiquitin protein ligase 1 (Kiaa0322) and SMAD specific E3 ubiquitin protein ligase 2 (Smurf2). The mRNA levels of these five candidate genes were detected using qPCR analysis. Ube3a mRNA levels were observed to be significantly higher in the H2O2-treated group than in the control group; therefore, Ube3a was selected as a candidate myocyte apoptosis-associated gene for further investigation.

H2O2 exposure decreases cell viability in H9C2 cells

H9C2 cells were exposed to increasing concentrations of H2O2 ranging between 150 and 300 μM for 4 h. Cell viability was then assessed by MTT uptake. The results showed that in the H2O2-treated group, cell viability decreased in a dose-dependent manner compared with the PBS-treated control group (Fig. 1).

H2O2 exposure induces apoptosis in H9C2 cells

Reports have shown that H2O2 is capable of inducing myocardial hypertrophy at low concentrations and apoptosis at higher concentrations (32). Annexin V/PI staining and flow cytometry were used to determine the apoptotic response of H9C2 cells to high concentrations of H2O2. Cells were harvested following exposure to H2O2 (150–250 μM) for 4 h. As shown in Fig. 2A–D, the Annexin V/PI point diagram exhibited a significant dose-dependent increase in apoptotic cells in response to H2O2. Following treatment with 250 μM H2O2 for 4 h, the percentage of apoptotic H9C2 cells increased approximately five-fold compared with the PBS-treated control cells (Fig. 2E). These results suggest that H2O2 exposure is capable of inducing apoptosis in a dose-dependent manner in H9C2 cells.

Ube3a transcription and translation increase in response to apoptosis in H9C2 cells

To examine the effect of H2O2-induced apoptosis on Ube3a levels in H9C2 cells, cells were treated with H2O2 in accordance with the aforementioned method. Ube3a protein levels were observed to increase in the same manner as that of p53 (Fig. 3). An increase in Ube3a transcription was also observed following H2O2 treatment (Fig. 4).

Discussion

The mechanism underlying cardiomyopathy is complex and its initiating mechanisms have yet to be elucidated. Different datasets supply different information; therefore, in the present study, several microarray datasets were integrated and Ube3a was selected as a cardiomyopathy-associated gene. Substantial evidence has suggested that apoptosis may be involved in the pathophysiology of cardiomyopathies; therefore, in the present study, H2O2 was used to induce apoptosis in H9C2 cells to assess whether Ube3a is responsible for degrading the apoptotic protein, p53.

In this study, H9C2 cell viability was found to decrease in a dose-dependent manner upon 4 h of exposure to H2O2 at concentrations ranging between 150 and 300 μM, detected using MTT assay (Fig. 1). In order to detect whether apoptosis occurred under these conditions, flow cytometry was used. H2O2 treatment at concentrations ranging between 150 and 250 μM for 4 h was observed to induce apoptosis in a dose-dependent manner in H9C2 cells (Fig. 2). p53 protein levels were also observed to increase upon induction of apoptosis. These results demonstrated that apoptosis occurred under H2O2 stimulation in H9C2 cells. Furthermore, the transcriptional and translational levels of Ube3a were increased in a similar manner to that of p53 (Figs. 3 and 4), suggesting that an association may exist between Ube3a and apoptosis.

Based on the results in the present study, it was hypothesized that Ube3a may have an important role in the process of cardiomyopathy. Ube3a is a member of the E3 family, which is responsible for recognizing target proteins in the ubiquitin-proteasome pathway. The ubiquitin proteasome system has an important role in the process of protein metabolism in normal cells. Therefore, it was hypothesized in the present study that when Ube3a protein levels are altered, homeostasis of the ubiquitin-proteasome system may be lost. This may induce abnormalities in protein degradation and ultimately lead to apoptosis or cardiomyopathy. Many researches are focusing on the potential role of Ube3a at the brain, particulary Angelman syndrome. The present study is the first time that novel function of Ube3a has been reported in the heart, which is likely to reveal its new features in cardiomyopathy.

Acknowledgements

The authors would like to thank the staff involved in the study for their valued contributions. This study was supported by the Young Talents Scheme of Tongji University (no. 1500219046) and the Shanghai Municipal Health Bureau Project (no. z0124y166).

References

1 

Sarkar S, Leaman DW, Gupta S, et al: Cardiac overexpression of myotrophin triggers myocardial hypertrophy and heart failure in transgenic mice. J Biol Chem. 279:20422–20434. 2004. View Article : Google Scholar : PubMed/NCBI

2 

Schaub MC, Hefti MA and Zaugg M: Integration of calcium with the signaling network in cardiac myocytes. J Mol Cell Cardiol. 41:183–214. 2006. View Article : Google Scholar : PubMed/NCBI

3 

Sarkar S, Chawla-Sarkar M, Young D, et al: Myocardial cell death and regeneration during progression of cardiac hypertrophy to heart failure. J Biol Chem. 279:52630–52642. 2004. View Article : Google Scholar : PubMed/NCBI

4 

Shi PP, Cao XR, Sweezer EM, et al: Salt-sensitive hypertension and cardiac hypertrophy in mice deficient in the ubiquitin ligase Nedd4-2. Am J Physiol Renal Physiol. 295:F462–F470. 2008. View Article : Google Scholar : PubMed/NCBI

5 

Haupt S, Berger M, Goldberg Z and Haupt Y: Apoptosis - the p53 network. J Cell Sci. 116:4077–4085. 2003. View Article : Google Scholar : PubMed/NCBI

6 

Li HH, Willis MS, Lockyer P, et al: Atrogin-1 inhibits Akt-dependent cardiac hypertrophy in mice via ubiquitin-dependent coactivation of Forkhead proteins. J Clin Invest. 117:3211–3223. 2007. View Article : Google Scholar : PubMed/NCBI

7 

Weekes J, Morrison K, Mullen A, Wait R, Barton P and Dunn MJ: Hyperubiquitination of proteins in dilated cardiomyopathy. Proteomics. 3:208–216. 2003. View Article : Google Scholar : PubMed/NCBI

8 

Matsuura T, Sutcliffe JS, Fang P, et al: De novo truncating mutations in E6-AP ubiquitin-protein ligase gene (UBE3A) in Angelman syndrome. Nat Genet. 15:74–77. 1997. View Article : Google Scholar : PubMed/NCBI

9 

Huang HS, Allen JA, Mabb AM, et al: Topoisomerase inhibitors unsilence the dormant allele of Ube3a in neurons. Nature. 481:185–189. 2011. View Article : Google Scholar : PubMed/NCBI

10 

Bürger J, Horn D, Tönnies H, Neitzel H and Reis A: Familial interstitial 570 kbp deletion of the UBE3A gene region causing Angelman syndrome but not Prader-Willi syndrome. Am J Med Genet. 111:233–237. 2002.PubMed/NCBI

11 

Veenstra-VanderWeele J, Gonen D, Leventhal BL and Cook EH Jr: Mutation screening of the UBE3A/E6-AP gene in autistic disorder. Mol Psychiatry. 4:64–67. 1999. View Article : Google Scholar : PubMed/NCBI

12 

Maheshwari M, Samanta A, Godavarthi SK, Mukherjee R and Jana NR: Dysfunction of the ubiquitin ligase Ube3a may be associated with synaptic pathophysiology in a mouse model of Huntington disease. J Biol Chem. 287:29949–29957. 2012. View Article : Google Scholar : PubMed/NCBI

13 

Long X, Boluyt MO, Hipolito ML, et al: p53 and the hypoxia-induced apoptosis of cultured neonatal rat cardiac myocytes. J Clin Invest. 99:2635–2643. 1997. View Article : Google Scholar : PubMed/NCBI

14 

Shi L, Nikolic D, Liu S, Lu H and Wang S: Activation of renal renin-angiotensin system in upstream stimulatory factor 2 transgenic mice. Am J Physiol Renal Physiol. 296:F257–F265. 2009. View Article : Google Scholar : PubMed/NCBI

15 

Blaxall BC, Spang R, Rockman HA and Koch WJ: Differential myocardial gene expression in the development and rescue of murine heart failure. Physiol Genomics. 15:105–114. 2003.PubMed/NCBI

16 

Rysä J, Leskinen H, Ilves M and Ruskoaho H: Distinct upregulation of extracellular matrix genes in transition from hypertrophy to hypertensive heart failure. Hypertension. 45:927–933. 2005.PubMed/NCBI

17 

da Silva R, Lucchinetti E, Pasch T, Schaub MC and Zaugg M: Ischemic but not pharmacological preconditioning elicits a gene expression profile similar to unprotected myocardium. Physiol Genomics. 20:117–130. 2004.

18 

Fliegner D, Schubert C, Penkalla A, et al: Female sex and estrogen receptor-beta attenuate cardiac remodeling and apoptosis in pressure overload. Am J Physiol Regul Integr Comp Physiol. 298:R1597–R1606. 2010. View Article : Google Scholar : PubMed/NCBI

19 

Kararigas G, Fliegner D, Gustafsson JÅ and Regitz-Zagrosek V: Role of the estrogen/estrogen-receptor-beta axis in the genomic response to pressure overload-induced hypertrophy. Physiol Genomics. 43:438–446. 2011. View Article : Google Scholar : PubMed/NCBI

20 

Barth AS, Kuner R, Buness A, et al: Identification of a common gene expression signature in dilated cardiomyopathy across independent microarray studies. J Am Coll Cardiol. 48:1610–1617. 2006. View Article : Google Scholar : PubMed/NCBI

21 

Husberg C, Nygård S, Finsen AV, et al: Cytokine expression profiling of the myocardium reveals a role for CX3CL1 (fractalkine) in heart failure. J Mol Cell Cardiol. 45:261–269. 2008. View Article : Google Scholar : PubMed/NCBI

22 

Kittleson MM, Minhas KM, Irizarry RA, et al: Gene expression analysis of ischemic and nonischemic cardiomyopathy: shared and distinct genes in the development of heart failure. Physiol Genomics. 21:299–307. 2005. View Article : Google Scholar : PubMed/NCBI

23 

Andersson KB, Florholmen G, Winer LH, Tønnessen T and Christensen G: Regulation of neuronal type genes in congestive heart failure rats. Acta Physiol (Oxf). 186:17–27. 2006. View Article : Google Scholar : PubMed/NCBI

24 

Rhodes DR, Kalyana-Sundaram S, Mahavisno V, et al: Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia. 9:166–180. 2007. View Article : Google Scholar : PubMed/NCBI

25 

Troyanskaya O, Cantor M, Sherlock G, et al: Missing value estimation methods for DNA microarrays. Bioinformatics. 17:520–525. 2001. View Article : Google Scholar : PubMed/NCBI

26 

Irizarry RA, Warren D, Spencer F, et al: Multiple-laboratory comparison of microarray platforms. Nat Methods. 2:345–350. 2005. View Article : Google Scholar : PubMed/NCBI

27 

Tusher VG, Tibshirani R and Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 98:5116–5121. 2001. View Article : Google Scholar : PubMed/NCBI

28 

Klipper-Aurbach Y, Wasserman M, Braunspiegel-Weintrob N, et al: Mathematical formulae for the prediction of the residual beta cell function during the first two years of disease in children and adolescents with insulin-dependent diabetes mellitus. Med Hypotheses. 45:486–490. 1995.PubMed/NCBI

29 

Xie Y, Pan W and Khodursky AB: A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data. Bioinformatics. 21:4280–4288. 2005. View Article : Google Scholar : PubMed/NCBI

30 

Zhang S: A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance. BMC Bioinformatics. 8:2302007. View Article : Google Scholar : PubMed/NCBI

31 

Pavlidis P, Li Q and Noble WS: The effect of replication on gene expression microarray experiments. Bioinformatics. 19:1620–1627. 2003. View Article : Google Scholar : PubMed/NCBI

32 

Clerk A, Kemp TJ, Zoumpoulidou G and Sugden PH: Cardiac myocyte gene expression profiling during H2O2-induced apoptosis. Physiol Genomics. 29:118–127. 2007. View Article : Google Scholar : PubMed/NCBI

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Spandidos Publications style
Zhang J, Song R, Li Y, Feng J, Peng L and Li J: Integration of microarray profiles associated with cardiomyopathy and the potential role of Ube3a in apoptosis. Mol Med Rep 9: 621-625, 2014
APA
Zhang, J., Song, R., Li, Y., Feng, J., Peng, L., & Li, J. (2014). Integration of microarray profiles associated with cardiomyopathy and the potential role of Ube3a in apoptosis. Molecular Medicine Reports, 9, 621-625. https://doi.org/10.3892/mmr.2013.1848
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Zhang, J., Song, R., Li, Y., Feng, J., Peng, L., Li, J."Integration of microarray profiles associated with cardiomyopathy and the potential role of Ube3a in apoptosis". Molecular Medicine Reports 9.2 (2014): 621-625.
Chicago
Zhang, J., Song, R., Li, Y., Feng, J., Peng, L., Li, J."Integration of microarray profiles associated with cardiomyopathy and the potential role of Ube3a in apoptosis". Molecular Medicine Reports 9, no. 2 (2014): 621-625. https://doi.org/10.3892/mmr.2013.1848