Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Circulation. Author manuscript; available in PMC 2010 March 10.
Published in final edited form as:
PMCID: PMC2749457

Reciprocal regulation of myocardial miR and mRNA in human cardiomyopathy and reversal of the miR signature by biomechanical support



Much has been learned about transcriptional control of cardiac gene expression in clinical and experimental congestive heart failure (CHF), but less is known about dynamic regulation of microRNAs (miRs) in CHF and during CHF treatment. We performed comprehensive microarray profiling of both miRs and mRNAs in myocardial specimens from human CHF with (n=10) or without (n=17) biomechanical support with left ventricular assist devices (LVAD), in comparison to non-failing hearts (n=11).

Methods and Results

Twenty-eight miRs were upregulated >2.0-fold (P<0.001) in CHF, with nearly complete normalization of the heart failure miR signature by LVAD treatment. In contrast, of 444 mRNAs that were altered by >1.3-fold in failing hearts, only 29 mRNAs normalized by as much as 25% in post-LVAD hearts. Unsupervised hierarchical clustering of upregulated miRs and mRNAs with nearest centroid analysis and leave-one-out cross validation revealed that combining the miR and mRNA signatures increased the ability of RNA profiling to serve as a clinical biomarker of diagnostic group and functional class.


These results show that miRs are more sensitive than mRNAs to the acute functional status of end-stage heart failure, consistent with important functions for regulated miRs in the myocardial response to stress. Combined miR and mRNA profiling may have superior potential as a diagnostic and prognostic test in end-stage cardiomyopathy.

Keywords: cardiomyopathy, heart-assist device, genes, diagnosis, microRNA (suggested term)


One of the promises of transcriptional profiling is that RNA patterns from diseased tissues will enhance the accuracy of clinical diagnosis and prognostication. To date, this approach has been most successful for cancer, in which characteristic transcriptional signatures of numerous malignancies have provided insight into etiology 1, 2 and outcome 35. There is a similarly pressing need for novel molecular diagnostics to better assess heart failure, so as to direct optimal management.

Previous mRNA profiling studies in heart failure revealed that distinct messenger RNA (mRNA) signatures detected early in the course of the disease can differentiate between cardiomyopathies of ischemic and non-ischemic etiology and provide prognostic information 6, 7. In contrast, mRNA signatures of end-stage cardiomyopathy vary little between different etiologies and do not reflect striking improvements in functional performance provided by biomechanical support 813. These findings suggest that additional potent factors are regulating the interaction between transcript abundance and tissue phenotype.

A hallmark of heart failure mRNA signatures is that more transcripts are downregulated than are upregulated 14, 15, suggesting importance of molecular mechanisms that suppress mRNA steady-state levels. MicroRNAs (miRs) are small, non-coding RNAs that bind mRNAs at their 3’ untranslated regions, stimulating mRNA degradation or inhibiting protein translation 16. Many miRs are upregulated in response to cellular stress 17 and can modify essential cellular functions of proliferation, differentiation, and programmed death 1820. miR signatures are being used as markers of cancer etiology 21, 22 and to predict cancer course 2325. A recent explosion of experimental data indicates that miRs are also regulated in cardiac disease 2628 and have the capacity to create cardiac pathology 2931. Given that miRs respond to acute changes in cell stress, we hypothesized that combining information from myocardial miR profiles and mRNA signatures could reconcile discrepancies between mRNA levels and cardiac phenotype during heart failure and reverse remodeling. We examined this notion through comprehensive analyses of miR and mRNA expression levels in myocardial samples from patients with end-stage heart failure, off and on biomechanical support with left ventricular assist devices (LVAD), in comparison with normal heart samples.

Clinical Perspective

Prognosis in heart failure is notoriously difficult to assess. Inter-individual variability in disease susceptibility, course, and response to therapy is a major problem for physicians, their patients, and the health care system in general. Improved metrics for categorizing patients based on relative risk and benefit for particular treatment strategies could greatly facilitate clinical decision making. Toward this end, transcriptional profiling of myocardial mRNA has been used in attempts to identify unique genomic “signatures” for heart failure of different etiologies, and more importantly, different prognoses. However, these efforts have met with limited success, in part because the mRNA signature for end-stage heart failure does not adequately discriminate between ischemic and non-ischemic cardiomyopathy, or between hearts with poor function versus those with better function. The latter functional analyses are based on mRNA profiling of hearts with and without mechanical unloading from left ventricular assist devices (LVADs), which markedly improve ventricular ejection performance and, in rare instances, facilitate long-term myocardial recovery after device removal. We identified a likely molecular signature for this myocardial “recovery” phenotype using microarray technology to comprehensively assay both microRNA and mRNA levels from 38 normal, failing, or LVAD-treated hearts. Although neither mRNA profile nor microRNA profile alone provided acceptable specificity and sensitivity to differentiate the three categories of hearts, combining the two molecular signatures correctly classified 22 of 23 samples. These studies reveal a promising approach for individual genomic profiling of failing myocardium in order to improve clinical prognostication.


Tissue cohort

Nonfailing and failing tissues were obtained from the University of Pennsylvania Cardiovascular Research Institute, Philadelphia, and LVAD-treated tissues (average time, 1.7 months) were obtained from the Methodist Hospital, Houston, in accordance with IRB-approved protocols. RNA integrity and quality were assessed prior to microarray analysis by gel electrophoresis and on an Agilent Bioanalyzer, and showed no differences in pass/fail rate of samples obtained from different centers. Clinical parameters for all subjects are described in Supplemental Table 1. All samples were subjected to miRNA profiling. RNA amounts limited mRNA array analysis to 6 nonfailing, 13 failing (4 ischemic, 9 non-ischemic) and 4 post-LVAD (3 ischemic, 1 non-ischemic) samples.


Total RNA was isolated using Trizol reagent (Invitrogen). MicroRNA hybridization was performed by Invitrogen Custom Services using the NCode Multi-Species miRNA Microarray v2 which recognizes 467 separate human miRs in accordance with the Sanger Institute miRBase v9.0 32. Data were analyzed using Invitrogen NCode algorithms. miR nomenclature in this manuscript reflects miRBase v11.0, April 2008. Total RNA from the same specimens was processed and hybridized to Affymetrix HuEx v1.0 arrays by the Multiplexed Gene Analysis core at Washington University. A total of 232,448 probesets corresponding to well-annotated and confirmed exons are represented, grouped into 21,980 transcript clusters (whole genes) by Partek Genomics Suite v6.3 software (Partek, St Louis, MO) using Affymetrix metafiles. Expression data were analyzed with Partek. All miR and mRNA array data, regardless of tissue origin, were imported and normalized together. Significance (P values) of miR expression changes was determined with a bootstrapping algorithm from the distribution of residuals used in the initial data-fitting model (see Supplemental Methods). Partek software was used to compute significance of mRNA expression changes using 1-way ANOVA at a false discovery rate of 0.03. Pairwise comparisons were significant at P=0.001. Detailed analytical methods are in Supplemental Methods.

The authors had full access to the data and take responsibility for its integrity. All authors have read and agree to the manuscript as written.


miR expression profile of end-stage and LVAD-supported hearts

Forty-eight myocardial samples were obtained. Unamplified RNA was prepared from each, and was suitable in quantity and quality for multi-platform analysis in 38 (11 non-failing, 17 end-stage failing, and 10 LVAD-supported). Characteristics of the study subjects are in Supplemental Table 1.

Myocardial miR profiles were examined using Invitrogen NCode Multi-species miRNA v2 microarrays, on which triplicate probes for 467 separate human miRs are represented. After filtering signals below the threshold level for detection, 81 miRs, including 25 of the 34 (74%) listed in miRBase 8.2 as being specific for human cardiovascular tissue 33, were confidently detected (see Supplemental Methods for threshold determination) in one or more of the three clinical groups (Supplemental Table 2).

Twenty-eight confidently detected miRs were significantly upregulated in cardiomyopathic hearts (n=17), defined as a 2.0 fold or greater increase over nonfailing hearts (n=11), P-value of <0.001 (Figure 1a, Supplemental Table 3), and designated as “heart failure” miRs. Three miRs previously reported to be regulated in experimental or human cardiac disease, miR-133a, miR-21, and miR-23a 26, 31, showed strong trends for being increased at P values between 0.001 and 0.01 (Figure 1a, Supplemental Table 3). No miRs met the same level of stringency (P<0.001, 50% or greater decrease) for downregulation in heart failure.

Figure 1
miR expression in human heart failure

Biomechanical support using LVADs can produce striking improvements in contractile performance and reverse ventricular remodeling in end-stage cardiomyopathy 911, 13. However, global myocardial mRNA expression signatures do not reflect clinical improvements in post-LVAD hearts 8. We examined miR expression levels in myocardium from LVAD-supported hearts (n=10). The overall trend was for reversal or normalization of the heart failure miR signature (Figure 1a, Supplemental Table 3). Twenty of the 28 heart failure miRs showed full normalization or significant decreases in expression level between the cardiomyopathy and LVAD-treated groups (Supplemental Tables 3a and 3b). The remaining eight heart failure miRs showed intermediate values that were not significantly different from cardiomyopathy (Supplemental Table 3c). In no instance did a miR that was not primarily regulated in heart failure show altered expression in LVAD-supported myocardium.

Unsupervised clustering of the expression profiles for cardiac miRs in cardiomyopathy and non-failing hearts revealed a distinct heart failure miR profile (Figure 1b and Supplemental Figure 1). Eight of the ten LVAD samples grouped between the non-failing and failing clusters, but were not recognized as a distinct class (Figure 1b). Within this group, sub-clustering based on the branching points of the dendrogram identified three samples with a less normal profile than the others (P4, P7, P10), and a fourth sample that was clearly abnormal (P3) (Figure 1b), but there was no clinical feature that clearly distinguished these four samples from the other LVAD-treated hearts.

Nearest centroid analysis with leave-one-out cross-validation, using the signature of 28 heart failure miRs, correctly classified 64% of non-failing samples, 94% of failing samples and 50% of post-LVAD samples. A single failing sample was incorrectly assigned to the “non-failing” group, and two of the four misclassified non-failing hearts (NF3 and NF4) had cardiac masses greater than 500 grams, consistent with substantial overlap between miR profiles for heart failure and cardiac hypertrophy 26. Three of the ten post-LVAD samples were designated as “non-failing”, and two as “failing”, raising the possibility that myocardial miR levels are particularly sensitive to functional status.

mRNA expression profiles of heart failure and heart failure recovery

We used Affymetrix HuEx v1.0 arrays to perform a comprehensive analysis of mRNA expression from a subset of the hearts on which we had miR expression profiles, excluding the hypertrophied, non-failing hearts (6 normal, 13 heart failure, 4 post-LVAD). Of 21,980 core genes represented on the array, 13,382 genes were confidently detected in the pooled set of samples (see Supplemental Methods for threshold determination). Comparative analysis of non-failing and failing mRNA profiles showed significant regulation (defined as >1.3-fold change, P<0.001, false discovery rate 0.03) of 444 genes in heart failure, of which 155 (35%) were upregulated and 289 (65%) were downregulated (Supplemental Table 4). The microarray gave similar results for the signature regulated “fetal genes” as have prior studies of mRNA expression in human heart failure (Supplemental Table 5). Functional classification of mRNA transcripts by Gene Ontology ( terms showed a preponderance of dysregulated genes associated with receptor signaling, cytoskeletal, transcriptional, and metabolic processes (not shown).

Prior studies using older analytical platforms to compare cardiac mRNA expression pre- and post-LVAD implantation identified only a limited number of dysregulated transcripts that corrected with LVAD treatment 8, 3436. In our samples only 12 of the 155 upregulated mRNAs (7.7%) and 17 of the 290 downregulated mRNAs (5.9%) normalized by at least 25% (Figure 2a, Table 1). Four of the 17 downregulated genes (ANKRD2, C18orf1, GADD45B, MT1H) and two of the 13 upregulated genes (CRYM, SLC14A1) were previously shown to recover with LVAD treatment 8, 34. Hierarchical clustering of the expression profiles for the fifty most upregulated cardiac mRNAs across the cardiomyopathy and non-failing samples revealed distinct transcriptional profiles for normal and failing hearts (Supplemental Figure 2). However, when the four LVAD-treated mRNA profiles were added to the analysis, they scattered within the cardiomyopathy grouping (Figure 2b). Nearest centroid analysis using the fifty most upregulated mRNAs as variables correctly classified 100% of nonfailing samples and 77% of failing samples, but only 50% of post-LVAD samples. Each of the misclassified failing samples were assigned to the “post-LVAD” group, and half of post-LVAD samples were incorrectly classified as “failing”, consistent with the established tendency of most dysregulated mRNAs not to normalize in LVAD-supported hearts 8.

Figure 2
mRNA expression in human heart failure
Table 1
Recovery of mRNA dysregulation in LVAD-supported hearts

Combined miR and mRNA profiles as a molecular signature for cardiac failure and recovery

In the above studies the mRNA signature was better at differentiated between non-failing and cardiomyopathic hearts, but failed to distinguish between failing and recovering (LVAD-supported) myocardium. In contrast, the miR signature was exquisitely sensitive to the mechanical unloading status of end-stage hearts, but failed to differentiate some hearts subjected to biomechanical unloading from those in the non-failing group. We tested whether an analysis combining upregulated RNAs from both signatures increased the utility of RNA profiling as a biomarker. Combining the miR and mRNA profiles resolved each of the misidentified samples, and clustered the LVAD-supported population as a near-normal subset of cardiomyopathic hearts (Figure 3). Nearest centroid analysis of the combined miR and mRNA signatures correctly classified 100% of nonfailing and 100% of post-LVAD samples, and 92% of failing samples (one failing sample was incorrectly assigned to the post-LVAD class).

Figure 3
Hierarchical clustering of miR and mRNA expression profile between nonfailing, heart failure and LVAD-supported subjects


In a comprehensive examination of miR and mRNA expression from failing, non-failing, and LVAD-supported human myocardium, we show that the cardiac miR signature is an exquisitely discriminating biomarker of the severely failing heart and its response to biomechanical unloading, and greatly enhances the predictive ability of mRNA profiles to categorize clinical status of heart failure.

The miRNA signature of human cardiomyopathy

There have been only a handful of previous studies examining miR levels in failing human hearts, and the results are not consistent 26, 28, 31, 37, 38. Here, using stringent criteria, we identified approximately 30 miRs that are upregulated in failing human myocardium. These findings confirm four of five miRs initially reported in the seminal myocardial miR study 26 to be upregulated in failing human myocardium by Northern blot analysis (miR-24, miR-125b and miR-195). In addition, our results suggest that miR-21, miR-23a and miR-199a-3p, which in that study and a subsequent report were increased in murine heart disease 26, 39, are also regulated in the human condition. Notwithstanding some variance attributable to diverse study cohorts and different analytical platforms, the three larger miR studies of human heart failure to date (i.e. studies of 6 or more diseased hearts; current study plus references 37 and 38), demonstrate consistent upregulation of miR-23a, miR-125b, miR-195, and mir-199a-3p, and a plurality agree on upregulation of miR-24, miR-27 (current study and reference 37) and miR-26b (current study and reference 38) (Table 2). We believe that the available data are sufficient to confidently assert that these eight miRs are part of a “heart failure” miR program. miR-1, which is reported to have pro-arrhythmic consequences 29, 40, is upregulated in subjects with coronary artery disease 40 and in the current and one other heart failure study 28, but in other heart failure surveys has been either invariant or downregulated 37, 38. It is possible that assays of this muscle-specific miR are especially sensitive to fibrous content of the myocardial sample.

Table 2
Comparison of miR expression data between failing human heart studies

The most striking finding of the end-stage heart failure miR signature is the extent to which it is normalized by LVAD support. This distinguishes it from the mRNA signature of end-stage cardiomyopathy, which is largely invariant. Although specific mRNAs appear to reflect functional changes in the heart after β-blockade 41 or left ventricular synchronization therapy 42, the vast majority of regulated cardiac genes do not mirror functional improvements 8. Our results suggest that miRs are more reactive than mRNAs to acute alterations in pathophysiological status, which may make them better molecular markers of stress and the stress response. It will be of interest to determine if the broad normalization of miR profile we observed with LVAD therapy is reproduced by pharmacological treatment with β-blockers and ACE inhibitors, or with ventricular re-synchronization.

Several of the heart failure-regulated miRs in this study have been implicated in relevant cellular signaling pathways. miRs-24, -26, -27 and -103 are induced in response to a hypoxic cellular microenvironment 43. Expression of let-7f and miR-27b favors angiogenesis 44, while increased levels of miR-29b promotes apoptosis via downregulation of Mcl-1 45. Increased expression of miR-199a-3p may be pro-apoptotic by virtue of its ability to downregulate the survival kinase ERK2 46. Overexpression of miR-195 and -199a in cardiomyocytes provoked myocyte enlargement, while overexpression of miR-195 in the mouse heart caused myocyte hypertrophy that progressed to dilated cardiomyopathy 26. As more genetic models of miR upregulation are developed, a more complete understanding will be developed of the role of other regulated miRs, alone and in combination, on interdiction of mRNA translation and destabilization of mRNA targets in heart disease.

Clinical utility of miR and mRNA profiling

Transcriptional profiling performed at multiple centers has revealed a characteristic pattern of cardiac gene regulation in cardiomyopathy. Whereas the myocardium of early heart failure retains sufficient molecular plasticity for mRNA signatures to vary with etiology and prognosis 6, 7, 47, the mRNA signature of end-stage cardiomyopathy appears to be relatively invariant, and has not provided insight that adds to standard clinical metrics 8.

Several miRs have been identified that are regulated in cardiac hypertrophy and heart failure and can participate in cardiac remodeling 26, but whether these miRs were dynamically regulated in failing versus recovering end-stage cardiomyopathy was unknown. As an initial step toward deriving a useful clinical biomarker for heart failure recovery, the current results clearly demonstrate synergism between miR and mRNA profiling in assessing the functional status of end-stage cardiomyopathic hearts and show that miRs are more sensitive than mRNAs to the functional status of end-stage heart failure. These findings support important functions for regulated miRs in the acute myocardial response to stress. Given this increased sensitivity, it is tempting to speculate that miRs may also prove useful as diagnostic and prognostic markers in cardiomyopathies of less severity and shorter duration. Such application is consistent with, and complementary to, recent consensus statements 48 concerning the clinical use of endomyocardial biopsy in cardiomyopathy and heart failure.

Study limitations

We applied a very high level of statistical rigor in the studies, accepting only results that were both statistically significant (P<0.001) and unambiguously regulated (fold-expression >2.0 for miR and >1.3 for mRNA). It is certain that we fail to report miR and mRNA changes, including some of substantial biological significance, which occur in heart failure. Indeed, the fact that we did not identify significantly downregulated miRs in the current study should not be interpreted as suggesting that downregulation does not occur, but rather that for the purposes of developing a clinical tool we opted for specificity, rather than sensitivity, of the raw findings. Furthermore, we did not have access to pre- and post-LVAD cardiac samples from the same individuals. The concordance of our miR and mRNA results with previous studies suggests that the dynamic miR regulation we observe is not a result of sampling error, but reflects true differences between cardiac miR and mRNA regulation. Finally, it is possible that normalization of both miR and mRNA profiles occurs after LVAD therapy, but over different periods of time. Based on absence of mRNA normalization in a previous, larger study 8, we believe that this is unlikely. However, serial studies before and after biomechanical support in the same individual will be an important next step to establishing the utility of combined miR and mRNA profiling in clinical assessment of end-stage heart failure.

Supplementary Material


Sources of Funding

This publication was made possible by NIH HL59888, 77101, 80008, and 87871 to GW Dorn and R01 AG017022 to KB Margulies and support from UL1 RR024992 from the National Center for Research Resources (NCRR).



There are no conflicts of interest to disclose.


1. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–537. [PubMed]
2. Potti A, Dressman HK, Bild A, Riedel RF, Chan G, Sayer R, Cragun J, Cottrill H, Kelley MJ, Petersen R, Harpole D, Marks J, Berchuck A, Ginsburg GS, Febbo P, Lancaster J, Nevins JR. Genomic signatures to guide the use of chemotherapeutics. Nat Med. 2006;2:1294–1300. [PubMed]
3. Bild AH, Potti A, Nevins JR. Linking oncogenic pathways with therapeutic opportunities. Nat Rev Cancer. 2006;6:735–741. [PubMed]
4. Potti A, Mukherjee S, Petersen R, Dressman HK, Bild A, Koontz J, Kratzke R, Watson MA, Kelley M, Ginsburg GS, West M, Harpole DH, Jr, Nevins JR. A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. N Engl J Med. 2006;355:570–580. [PubMed]
5. Liu R, Wang X, Chen GY, Dalerba P, Gurney A, Hoey T, Sherlock G, Lewicki J, Shedden K, Clarke MF. The prognostic role of a gene signature from tumorigenic breast-cancer cells. N Engl J Med. 2007;356:217–226. [PubMed]
6. Kittleson MM, Ye SQ, Irizarry RA, Minhas KM, Edness G, Conte JV, Parmigiani G, Miller LW, Chen Y, Hall JL, Garcia JG, Hare JM. Identification of a gene expression profile that differentiates between ischemic and nonischemic cardiomyopathy. Circulation. 2004;110:3444–3451. [PubMed]
7. Heidecker B, Kasper EK, Wittstein IS, Champion HC, Breton E, Russell SD, Kittleson MM, Baughman KL, Hare JM. Transcriptomic biomarkers for individual risk assessment in new onset heart failure. Circulation. 2008;118:238–246. [PMC free article] [PubMed]
8. Margulies KB, Matiwala S, Cornejo C, Olsen H, Craven WA, Bednarik D. Mixed messages: transcription patterns in failing and recovering human myocardium. Circ Res. 2005;96:592–599. [PubMed]
9. Levin HR, Oz MC, Chen JM, Packer M, Rose EA, Burkhoff D. Reversal of chronic ventricular dilation in patients with end-stage cardiomyopathy by prolonged mechanical unloading. Circulation. 1995;91:2717–2720. [PubMed]
10. Dipla K, Mattiello JA, Jeevanandam V, Houser SR, Margulies KB. Myocyte recovery after mechanical circulatory support in humans with end-stage heart failure. Circulation. 1998;97:2316–2322. [PubMed]
11. Zafeiridis A, Jeevanandam V, Houser SR, Margulies KB. Regression of cellular hypertrophy after left ventricular assist device support. Circulation. 1998;98:656–662. [PubMed]
12. Torre-Amione G, Stetson SJ, Youker KA, Durand JB, Radovancevic B, Delgado RM, Frazier OH, Entman ML, Noon GP. Decreased expression of tumor necrosis factor-alpha in failing human myocardium after mechanical circulatory support : A potential mechanism for cardiac recovery. Circulation. 1999;100:1189–1193. [PubMed]
13. Heerdt PM, Holmes JW, Cai B, Barbone A, Madigan JD, Reiken S, Lee DL, Oz MC, Marks AR, Burkhoff D. Chronic unloading by left ventricular assist device reverses contractile dysfunction and alters gene expression in end-stage heart failure. Circulation. 2000;102:2713–2719. [PubMed]
14. Kaab S, Barth AS, Margerie D, Dugas M, Gebauer M, Zwermann L, Merk S, Pfeufer A, Steinmeyer K, Bleich M, Kreuzer E, Steinbeck G, Nabauer M. Global gene expression in human myocardium-oligonucleotide microarray analysis of regional diversity and transcriptional regulation in heart failure. J Mol Med. 2004;82:308–316. [PubMed]
15. NHLBI Program for Genomic Applications , Harvard Medical School. Genomics of Cardiovascular Development, Adaptation, and Remodeling. [Date accessed: June 12, 2008]. Available at
16. Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–297. [PubMed]
17. Marsit CJ, Eddy K, Kelsey KT. MicroRNA responses to cellular stress. Cancer Res. 2006;66:10843–10848. [PubMed]
18. Brennecke J, Hipfner DR, Stark A, Russell RB, Cohen SM. bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell. 2003;113:25–36. [PubMed]
19. Chen CZ, Li L, Lodish HF, Bartel DP. MicroRNAs modulate hematopoietic lineage differentiation. Science. 2004;303:83–86. [PubMed]
20. He L, Thomson JM, Hemann MT, Hernando-Monge E, Mu D, Goodson S, Powers S, Cordon-Cardo C, Lowe SW, Hannon GJ, Hammond SM. A microRNA polycistron as a potential human oncogene. Nature. 2005;435:828–833. [PubMed]
21. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR, Jacks T, Horvitz HR, Golub TR. MicroRNA expression profiles classify human cancers. Nature. 2005;435:834–838. [PubMed]
22. Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M, Prueitt RL, Yanaihara N, Lanza G, Scarpa A, Vecchione A, Negrini M, Harris CC, Croce CM. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA. 2006;103:2257–2261. [PubMed]
23. Calin GA, Ferracin M, Cimmino A, Di Leva G, Shimizu M, Wojcik SE, Iorio MV, Visone R, Sever NI, Fabbri M, Iuliano R, Palumbo T, Pichiorri F, Roldo C, Garzon R, Sevignani C, Rassenti L, Alder H, Volinia S, Liu CG, Kipps TJ, Negrini M, Croce CM. A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. N Engl J Med. 2005;353:1793–1801. [PubMed]
24. Bloomston M, Frankel WL, Petrocca F, Volinia S, Alder H, Hagan JP, Liu CG, Bhatt D, Taccioli C, Croce CM. MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis. JAMA. 2007;297:1901–1908. [PubMed]
25. Schetter AJ, Leung SY, Sohn JJ, Zanetti KA, Bowman ED, Yanaihara N, Yuen ST, Chan TL, Kwong DL, Au GK, Liu CG, Calin GA, Croce CM, Harris CC. MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. JAMA. 2008;299:425–436. [PMC free article] [PubMed]
26. van Rooij E, Sutherland LB, Liu N, Williams AH, McAnally J, Gerard RD, Richardson JA, Olson EN. A signature pattern of stress-responsive microRNAs that can evoke cardiac hypertrophy and heart failure. Proc Natl Acad Sci USA. 2006;103:18255–18260. [PubMed]
27. Sayed D, Hong C, Chen IY, Lypowy J, Abdellatif M. MicroRNAs play an essential role in the development of cardiac hypertrophy. Circ Res. 2007;100:416–424. [PubMed]
28. Thum T, Galuppo P, Wolf C, Fiedler J, Kneitz S, van Laake LW, Doevendans PA, Mummery CL, Borlak J, Haveric A, Gross C, Engelhardt S, Ertl G, Bauersachs J. MicroRNAs in the human heart: a clue to fetal gene reprogramming in heart failure. Circulation. 2007;116:258–267. [PubMed]
29. Zhao Y, Ransom JF, Li A, Vedantham V, von Drehle M, Muth AN, Tsuchihashi T, McManus MT, Schwartz RJ, Srivastava D. Dysregulation of cardiogenesis, cardiac conduction, and cell cycle in mice lacking miRNA-1–2. Cell. 2007;129:303–317. [PubMed]
30. van Rooij E, Sutherland LB, Qi X, Richardson JA, Hill J, Olson EN. Control of stress-dependent cardiac growth and gene expression by a microRNA. Science. 2007;316:575–579. [PubMed]
31. Care A, Catalucci D, Felicetti F, Bonci D, Addario A, Gallo P, Bang ML, Segnalini P, Gu Y, Dalton ND, Elia L, Latronico MV, Hoydal M, Autore C, Russo MA, Dorn GW, 2nd, Ellingsen O, Ruiz-Lozano P, Peterson KL, Croce CM, Peschle C, Condorelli G. MicroRNA-133 controls cardiac hypertrophy. Nat Med. 2007;13:613–618. [PubMed]
32. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006;34:D140–D144. [PMC free article] [PubMed]
33. Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO, Landthaler M, Lin C, Socci ND, Hermida L, Fulci V, Chiaretti S, Foa R, Schliwka J, Fuchs U, Novosel A, Muller RU, Schermer B, Bissels U, Inman J, Phan Q, Chien M, Weir DB, Choksi R, De Vita G, Frezzetti D, Trompeter HI, Hornung V, Teng G, Hartmann G, Palkovits M, Di Lauro R, Wernet P, Macino G, Rogler CE, Nagle JW, Ju J, Papavasiliou FN, Benzing T, Lichter P, Tam W, Brownstein MJ, Bosio A, Borkhardt A, Russo JJ, Sander C, Zavolan M, Tuschl T. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell. 2007;129:1401–1414. [PMC free article] [PubMed]
34. Chen Y, Park S, Li Y, Missov E, Hou M, Han X, Hall JL, Miller LW, Bache RJ. Alterations of gene expression in failing myocardium following left ventricular assist device support. Physiol Genomics. 2003;14:251–260. [PubMed]
35. Blaxall BC, Tschannen-Moran BM, Milano CA, Koch WJ. Differential gene expression and genomic patient stratification following left ventricular assist device support. J Am Coll Cardiol. 2003;41:1096–1106. [PubMed]
36. Hall JL, Grindle S, Han X, Fermin D, Park S, Chen Y, Bache RJ, Mariash A, Guan Z, Ormaza S, Thompson J, Graziano J, de Sam Lazaro SE, Pan S, Simari RD, Miller LW. Genomic profiling of the human heart before and after mechanical support with a ventricular assist device reveals alterations in vascular signaling networks. Physiol Genomics. 2004;17:283–291. [PubMed]
37. Ikeda S, Kong SW, Lu J, Bisping E, Zhang H, Allen PD, Golub TR, Pieske B, Pu WT. Altered microRNA expression in human heart disease. Physiol Genomics. 2007;31:367–373. [PubMed]
38. Sucharov C, Bristow MR, Port JD. miRNA expression in the failing human heart: functional correlates. J Mol Cell Cardiol. 2008;45:185–192. [PMC free article] [PubMed]
39. Tatsuguchi M, Seok HY, Callis TE, Thomson JM, Chen JF, Newman M, Rojas M, Hammond SM, Wang DZ. Expression of microRNAs is dynamically regulated during cardiomyocyte hypertrophy. J Mol Cell Cardiol. 2007;42:1137–1141. [PMC free article] [PubMed]
40. Yang B, Lin H, Xiao J, Lu Y, Luo X, Li B, Zhang Y, Xu C, Bai Y, Wang H, Chen G, Wang Z. The muscle-specific microRNA miR-1 regulates cardiac arrhythmogenic potential by targeting GJA1 and KCNJ2. Nat Med. 2007;13:486–491. [PubMed]
41. Lowes BD, Gilbert EM, Abraham WT, Minobe WA, Larrabee P, Ferguson D, Wolfel EE, Lindenfeld J, Tsvetkova T, Robertson AD, Quaife RA, Bristow MR. Myocardial gene expression in dilated cardiomyopathy treated with beta-blocking agents. N Engl J Med. 2002;346:1357–1365. [PubMed]
42. Iyengar S, Haas G, Lamba S, Orsinelli DA, Babu GJ, Ferketich AK, Yamokoski L, Periasamy M, Abraham WT. Effect of cardiac resynchronization therapy on myocardial gene expression in patients with nonischemic dilated cardiomyopathy. J Card Fail. 2007;13:304–311. [PubMed]
43. Kulshreshtha R, Ferracin M, Wojcik SE, Garzon R, Alder H, Agosto-Perez FJ, Davuluri R, Liu CG, Croce CM, Negrini M, Calin GA, Ivan M. A microRNA signature of hypoxia. Mol Cell Biol. 2007;27:1859–1867. [PMC free article] [PubMed]
44. Kuehbacher A, Urbich C, Zeiher AM, Dimmeler S. Role of Dicer and Drosha for endothelial microRNA expression and angiogenesis. Circ Res. 2007;101:59–68. [PubMed]
45. Mott JL, Kobayashi S, Bronk SF, Gores GJ. mir-29 regulates Mcl-1 protein expression and apoptosis. Oncogene. 2007;26:6133–6140. [PMC free article] [PubMed]
46. Kim S, Lee UJ, Kim MN, Lee EJ, Kim JY, Lee MY, Choung S, Kim YJ, Choi YC. MicroRNA miR-199a* regulates the MET proto-oncogene and the downstream extracellular signal-regulated kinase 2 (ERK2) J Biol Chem. 2008;283:18158–18166. [PubMed]
47. Dorn GW, 2nd, Matkovich SJ. Put your chips on transcriptomics. Circulation. 2008;118:216–218. [PubMed]
48. Cooper LT, Baughman KL, Feldman AM, Frustaci A, Jessup M, Kuhl U, Levine GN, Narula J, Starling RC, Towbin J, Virmani R. The role of endomyocardial biopsy in the management of cardiovascular disease: a scientific statement from the American Heart Association, the American College of Cardiology, and the European Society of Cardiology. Circulation. 2007;116:2216–2233. [PubMed]