Decoding the Noncoding Transcripts in Human Heart Failure Xinshu (Grace) Xiao, Marlin Touma and Yibin Wang Circulation. 2014;129:958-960; originally published online January 15, 2014; doi: 10.1161/CIRCULATIONAHA.114.007548 Circulation is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Copyright © 2014 American Heart Association, Inc. All rights reserved. Print ISSN: 0009-7322. Online ISSN: 1524-4539
The online version of this article, along with updated information and services, is located on the World Wide Web at: http://circ.ahajournals.org/content/129/9/958
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published in Circulation can be obtained via RightsLink, a service of the Copyright Clearance Center, not the Editorial Office. Once the online version of the published article for which permission is being requested is located, click Request Permissions in the middle column of the Web page under Services. Further information about this process is available in the Permissions and Rights Question and Answer document. Reprints: Information about reprints can be found online at: http://www.lww.com/reprints Subscriptions: Information about subscribing to Circulation is online at: http://circ.ahajournals.org//subscriptions/
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Editorial Decoding the Noncoding Transcripts in Human Heart Failure Xinshu (Grace) Xiao, PhD; Marlin Touma, MD; Yibin Wang, PhD
H
eart failure is a complex disease with a broad spectrum of pathological features. Despite significant advancement in clinical diagnosis through improved imaging modalities and hemodynamic approaches, reliable molecular signatures for better differential diagnosis and better monitoring of heart failure progression remain elusive. The few known clinical biomarkers for heart failure, such as plasma brain natriuretic peptide and troponin, have been shown to have limited use in defining the cause or prognosis of the disease.1,2 Consequently, current clinical identification and classification of heart failure remain descriptive, mostly based on functional and morphological parameters. Therefore, defining the pathogenic mechanisms for hypertrophic versus dilated or ischemic versus nonischemic cardiomyopathies in the failing heart remain a major challenge to both basic science and clinic researchers. In recent years, mechanical circulatory support using left ventricular assist devices (LVADs) has assumed a growing role in the care of patients with end-stage heart failure.3 During the earlier years of LVAD application as a bridge to transplant, it became evident that some patients exhibit substantial recovery of ventricular function, structure, and electric properties.4 This led to the recognition that reverse remodeling is potentially an achievable therapeutic goal using LVADs. However, the underlying mechanism for the reverse remodeling in the LVAD-treated hearts is unclear, and its discovery would likely hold great promise to halt or even reverse the progression of heart failure.
Article see p 1009 During cardiac development, gene expression dictates the cellular differentiation of cardiac cells, including cardiomyocytes, endothelial cells, smooth muscle cells, and fibroblasts, each driven by cell-type–specific regulatory circuits of transcription.5 Significant changes in cardiac gene expression have also been studied extensively in diseased hearts, leading to the identification of the so-called “fetal-like” gene expression profile associated with the onset of cardiac pathology.6 These insights allow us to hypothesize that specific changes in gene expression profiles may reveal the underlying cause The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association. From the Molecular Biology Institute (X.X., Y.W.), Department of Integrative Biology and Physiology, College of Life Sciences (X.X.), and Departments of Pediatrics (M.T.) and Anesthesiology, Medicine, and Physiology (Y.W.), David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA. Correspondence to Yibin Wang, PhD, Division of Molecular Medicine, Department of Anesthesiology, David Geffen School of Medicine, University of California at Los Angeles, 650 Charles E. Young Dr, Room BH 569, CHS, Los Angeles, CA 90095. E-mail [email protected] (Circulation. 2014;129:958-960.) © 2014 American Heart Association, Inc. Circulation is available at http://circ.ahajournals.org DOI: 10.1161/CIRCULATIONAHA.114.007548
and pathological features of specific forms of cardiomyopathy and suggest that normalization of gene expression is both the signal of and a molecular mechanism for the reverse remodeling in LVAD-treated failing hearts. Using GeneChip (a cDNA microarray platform), Margulies et al7 performed a comprehensive transcriptome analysis of 199 human myocardial samples from nonfailing, failing, and LVAD-supported human hearts. The study revealed significant heart failure-related changes in >3088 transcripts, of which 11% exhibited partial recovery after LVAD and only 5% showed normalization. Most notably, the normalization of gene expression, like the functional recovery after LVAD implantation, is highly heterogeneous among patients. In a similar microarray study by Hall et al,8 6 paired human heart specimens were harvested at the time of LVAD implant, as well as from explants after a significant recovery of the ventricular function. In this cohort, significant changes in integrin signaling and several new target genes, including EPAC2, were found to be associated with post-LVAD reverse remodeling. By comparing the gene expression profiles between the LVAD responsive hearts and the nonresponsive hearts, Birks et al9 also found distinct differences in the expression of sarcomeric and cytoskeletal proteins between the 2 patient groups. Finally, Kittleson et al10 used a similar GeneChip approach to profile differentially expressed genes in nonischemic cardiomyopathy versus ischemic cardiomyopathy hearts and identified a pattern of gene expression that differentiated these 2 forms of cardiomyopathy. These previous studies demonstrate that changes in mRNA expression profile are very complex in the failing hearts. Despite some tantalizing new insights, the original hypothesis that mRNA profiles could be used to define the pathological nature of a patient’s heart failure and recovery after mechanical unloading appears to be overly simplistic. It is increasingly clear that the cellular transcriptome is populated not only by protein coding mRNAs but also noncoding (nc) RNAs beyond the familiar rRNAs and tRNAs species. These noncoding transcripts are categorized into short/ small noncoding RNAs, including micro (mi) RNAs, small nucleolar RNAs, Piwi-interacting (pi) RNAs, etc, as well as long noncoding (lnc) RNAs. Many of them, such as miRNA and Piwi-interacting RNAs, are generated from dedicated biosynthesis machinery. The landscape of the eukaryotic transcriptome has expanded significantly thanks to the application of RNA sequencing (RNA-seq) integrated with bioinformatic approaches.11,12 ncRNA species are now recognized as a significant component in the total complexity of the transcriptome. The emerging evidence suggest that these abundant ncRNAs have a major role in the regulation of almost all important gene expression processes, from epigenetic modification to transcription, mRNA splicing, and processing and translation.13,14 A number of RNA-seq–based transcriptome profiling studies have been performed in the experimental models
Downloaded from http://circ.ahajournals.org/ at Virginia 958Polytechnic Institute & State Univ. on August 18, 2014
Xiao et al RNA-seq Profiling in Human Failing Heart 959 of heart failure, revealing dramatic changes of transcriptome for both mRNA and ncRNAs during pathogenesis.15–17 In this issue, Yang et al,18 led by Jeanne M. Nerbonne and her team, report a comprehensive RNA-seq–based transcriptome analysis in failing human hearts that expands our current knowledge of transcriptome complexity and dynamics in human heart failure. The study used the standard Illumina TruSeq method and the well-established TopHat and Cufflinks suite to map RNA-seq reads and to reconstruct transcript isoforms. In addition to mRNA and miRNA, a more comprehensive profiling of lncRNAs was performed by taking advantage of the recently updated lncRNA databases and improved de novo Cufflinks prediction. As expected from other RNA-seq studies, the increased detection sensitivity afforded by RNA-seq uncovered a large number of lncRNA transcripts (nearly 18 000) in the human myocardium.18 Somewhat unexpectedly, however, the clustering analyses showed that differences in expression signatures of the lncRNAs were able to distinguish between cardiomyopathy of ischemic and nonischemic origin and, furthermore, discern between before and after LVAD treatment for either type of cardiomyopathy. In contrast, the profiles of miRNA and mRNA expression did not have similar discriminatory power. It should be noted that clustering results are highly sensitive to the specific set of genes selected for the analysis. Better clustering can be achieved when more genes are differentially expressed across samples. Even in their own analysis, the discrimination between ischemic cardiomyopathy and nonischemic cardiomyopathy samples was improved when only the 100 most differentially expressed mRNAs were used to cluster the samples. Nonetheless, the finding that clustering was able to achieve highly discriminative results using all expressed lncRNAs without stringent selection criteria suggests that there is a larger fraction of lncRNAs that are dynamically regulated during heart failure or in response to LVAD treatment. However, the overall sample sizes in this study (8 pairs of pre-LVAD and post-LVAD samples) are low, and this conclusion would need to be validated in a larger independent cohort. The majority of the reported lncRNAs from this study have relatively low abundance in the human hearts (reads perkilobase of transcript per million mapped reads 0.8). Finally, as the authors alluded to, lncRNAs can also function beyond the mode of cis-regulation and extend their influence on posttranscriptional regulation to mRNA splicing, decay, and translation. In summary, RNA-seq–based transcriptome profiling in the failing human heart revealed additional transcriptional complexity associated with the disease and recovery after mechanical unloading. The newly uncovered importance of lncRNAs in disease pathogenesis highlights their potential value as potential diagnostic and therapeutic targets. Decoding the function of lncRNAs in cardiac physiology and diseases is both a major challenge and an exciting new opportunity for future studies.
Acknowledgments We thank Dr Christoph Raul for discussion and proofreading.
Sources of Funding Dr Xiao is supported by National Institutes of Health grants R01HG006264 and U01HG007013, Dr Touma is supported by a K12 Child Health Research Center Development Award, and Dr Wang is supported in part by National Heart, Lung, and Blood Institute grants HL103205, HL098954, HL108186, and HL114437.
Disclosures None.
References 1. Burke MA, Cotts WG. Interpretation of B-type natriuretic peptide in cardiac disease and other comorbid conditions. Heart Fail Rev. 2007;12:23–36. 2. Sato Y, Fujiwara H, Takatsu Y. Cardiac troponin and heart failure in the era of high-sensitivity assays. J Cardiol. 2012;60:160–167. 3. Deng MC, Edwards LB, Hertz MI, Rowe AW, Keck BM, Kormos R, Naftel DC, Kirklin JK, Taylor DO; International Society for Heart and Lung Transplantation. Mechanical circulatory support device database of the International Society for Heart and Lung Transplantation: third annual report–2005. J Heart Lung Transplant. 2005;24:1182–1187. 4. Oz MC, Argenziano M, Catanese KA, Gardocki MT, Goldstein DJ, Ashton RC, Gelijns AC, Rose EA, Levin HR. Bridge experience with long-term implantable left ventricular assist devices. Are they an alternative to transplantation? Circulation. 1997;95:1844–1852. 5. Latif S, Masino A, Garry DJ. Transcriptional pathways direct cardiac development and regeneration. Trends Cardiovasc Med. 2006;16:234–240. 6. Nanni L, Romualdi C, Maseri A, Lanfranchi G. Differential gene expression profiling in genetic and multifactorial cardiovascular diseases. J Mol Cell Cardiol. 2006;41:934–948. 7. 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. 8. 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. 9. Birks EJ, Latif N, Owen V, Bowles C, Felkin LE, Mullen AJ, Khaghani A, Barton PJ, Polak JM, Pepper JR, Banner NR, Yacoub MH. Quantitative myocardial cytokine expression and activation of the apoptotic pathway in patients who require left ventricular assist devices. Circulation. 2001;104(12 Suppl 1):I233–I240. 10. 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.
Downloaded from http://circ.ahajournals.org/ at Virginia Polytechnic Institute & State Univ. on August 18, 2014
960 Circulation March 4, 2014 11. Shi X, Sun M, Liu H, Yao Y, Song Y. Long non-coding RNAs: a new frontier in the study of human diseases. Cancer Lett. 2013;339:159–166. 12. Martens-Uzunova ES, Olvedy M, Jenster G. Beyond microRNA–novel RNAs derived from small non-coding RNA and their implication in cancer. Cancer Lett. 2013;340:201–211. 13. Scheuermann JC, Boyer LA. Getting to the heart of the matter: long non-coding RNAs in cardiac development and disease. EMBO J. 2013;32:1805–1816. 14. Ounzain S, Crippa S, Pedrazzini T. Small and long non-coding RNAs in cardiac homeostasis and regeneration. Biochim Biophys Acta. 2013;1833: 923–933. 15. Churko JM, Mantalas GL, Snyder MP, Wu JC. Overview of high throughput sequencing technologies to elucidate molecular pathways in cardiovascular diseases. Circ Res. 2013;112:1613–1623.
16. Lee JH, Gao C, Peng G, Greer C, Ren S, Wang Y, Xiao X. Analysis of transcriptome complexity through RNA sequencing in normal and failing murine hearts. Circ Res. 2011;109:1332–1341. 17. Matkovich SJ, Van Booven DJ, Eschenbacher WH, Dorn GW 2nd. RISC RNA sequencing for context-specific identification of in vivo microRNA targets. Circ Res. 2011;108:18–26. 18. Yang KC, Yamada KA, Patel AY, Topkara VK, George I, Cheema FH, Ewald GA, Mann DL, Nerbonne JM. Deep RNA sequencing reveals dynamic regulation of myocardial noncoding RNA in failing human heart and remodeling with mechanical circulatory support. Circulation. 2014;129:1009–1021. Key Words: Editorials ◼ heart failure ◼ humans ◼ RNA, long noncoding ◼ transcriptome
Downloaded from http://circ.ahajournals.org/ at Virginia Polytechnic Institute & State Univ. on August 18, 2014
The online version of this article, along with updated information and services, is located on the World Wide Web at: http://circ.ahajournals.org/content/129/9/958
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published in Circulation can be obtained via RightsLink, a service of the Copyright Clearance Center, not the Editorial Office. Once the online version of the published article for which permission is being requested is located, click Request Permissions in the middle column of the Web page under Services. Further information about this process is available in the Permissions and Rights Question and Answer document. Reprints: Information about reprints can be found online at: http://www.lww.com/reprints Subscriptions: Information about subscribing to Circulation is online at: http://circ.ahajournals.org//subscriptions/
Downloaded from http://circ.ahajournals.org/ at Virginia Polytechnic Institute & State Univ. on August 18, 2014
Editorial Decoding the Noncoding Transcripts in Human Heart Failure Xinshu (Grace) Xiao, PhD; Marlin Touma, MD; Yibin Wang, PhD
H
eart failure is a complex disease with a broad spectrum of pathological features. Despite significant advancement in clinical diagnosis through improved imaging modalities and hemodynamic approaches, reliable molecular signatures for better differential diagnosis and better monitoring of heart failure progression remain elusive. The few known clinical biomarkers for heart failure, such as plasma brain natriuretic peptide and troponin, have been shown to have limited use in defining the cause or prognosis of the disease.1,2 Consequently, current clinical identification and classification of heart failure remain descriptive, mostly based on functional and morphological parameters. Therefore, defining the pathogenic mechanisms for hypertrophic versus dilated or ischemic versus nonischemic cardiomyopathies in the failing heart remain a major challenge to both basic science and clinic researchers. In recent years, mechanical circulatory support using left ventricular assist devices (LVADs) has assumed a growing role in the care of patients with end-stage heart failure.3 During the earlier years of LVAD application as a bridge to transplant, it became evident that some patients exhibit substantial recovery of ventricular function, structure, and electric properties.4 This led to the recognition that reverse remodeling is potentially an achievable therapeutic goal using LVADs. However, the underlying mechanism for the reverse remodeling in the LVAD-treated hearts is unclear, and its discovery would likely hold great promise to halt or even reverse the progression of heart failure.
Article see p 1009 During cardiac development, gene expression dictates the cellular differentiation of cardiac cells, including cardiomyocytes, endothelial cells, smooth muscle cells, and fibroblasts, each driven by cell-type–specific regulatory circuits of transcription.5 Significant changes in cardiac gene expression have also been studied extensively in diseased hearts, leading to the identification of the so-called “fetal-like” gene expression profile associated with the onset of cardiac pathology.6 These insights allow us to hypothesize that specific changes in gene expression profiles may reveal the underlying cause The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association. From the Molecular Biology Institute (X.X., Y.W.), Department of Integrative Biology and Physiology, College of Life Sciences (X.X.), and Departments of Pediatrics (M.T.) and Anesthesiology, Medicine, and Physiology (Y.W.), David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA. Correspondence to Yibin Wang, PhD, Division of Molecular Medicine, Department of Anesthesiology, David Geffen School of Medicine, University of California at Los Angeles, 650 Charles E. Young Dr, Room BH 569, CHS, Los Angeles, CA 90095. E-mail [email protected] (Circulation. 2014;129:958-960.) © 2014 American Heart Association, Inc. Circulation is available at http://circ.ahajournals.org DOI: 10.1161/CIRCULATIONAHA.114.007548
and pathological features of specific forms of cardiomyopathy and suggest that normalization of gene expression is both the signal of and a molecular mechanism for the reverse remodeling in LVAD-treated failing hearts. Using GeneChip (a cDNA microarray platform), Margulies et al7 performed a comprehensive transcriptome analysis of 199 human myocardial samples from nonfailing, failing, and LVAD-supported human hearts. The study revealed significant heart failure-related changes in >3088 transcripts, of which 11% exhibited partial recovery after LVAD and only 5% showed normalization. Most notably, the normalization of gene expression, like the functional recovery after LVAD implantation, is highly heterogeneous among patients. In a similar microarray study by Hall et al,8 6 paired human heart specimens were harvested at the time of LVAD implant, as well as from explants after a significant recovery of the ventricular function. In this cohort, significant changes in integrin signaling and several new target genes, including EPAC2, were found to be associated with post-LVAD reverse remodeling. By comparing the gene expression profiles between the LVAD responsive hearts and the nonresponsive hearts, Birks et al9 also found distinct differences in the expression of sarcomeric and cytoskeletal proteins between the 2 patient groups. Finally, Kittleson et al10 used a similar GeneChip approach to profile differentially expressed genes in nonischemic cardiomyopathy versus ischemic cardiomyopathy hearts and identified a pattern of gene expression that differentiated these 2 forms of cardiomyopathy. These previous studies demonstrate that changes in mRNA expression profile are very complex in the failing hearts. Despite some tantalizing new insights, the original hypothesis that mRNA profiles could be used to define the pathological nature of a patient’s heart failure and recovery after mechanical unloading appears to be overly simplistic. It is increasingly clear that the cellular transcriptome is populated not only by protein coding mRNAs but also noncoding (nc) RNAs beyond the familiar rRNAs and tRNAs species. These noncoding transcripts are categorized into short/ small noncoding RNAs, including micro (mi) RNAs, small nucleolar RNAs, Piwi-interacting (pi) RNAs, etc, as well as long noncoding (lnc) RNAs. Many of them, such as miRNA and Piwi-interacting RNAs, are generated from dedicated biosynthesis machinery. The landscape of the eukaryotic transcriptome has expanded significantly thanks to the application of RNA sequencing (RNA-seq) integrated with bioinformatic approaches.11,12 ncRNA species are now recognized as a significant component in the total complexity of the transcriptome. The emerging evidence suggest that these abundant ncRNAs have a major role in the regulation of almost all important gene expression processes, from epigenetic modification to transcription, mRNA splicing, and processing and translation.13,14 A number of RNA-seq–based transcriptome profiling studies have been performed in the experimental models
Downloaded from http://circ.ahajournals.org/ at Virginia 958Polytechnic Institute & State Univ. on August 18, 2014
Xiao et al RNA-seq Profiling in Human Failing Heart 959 of heart failure, revealing dramatic changes of transcriptome for both mRNA and ncRNAs during pathogenesis.15–17 In this issue, Yang et al,18 led by Jeanne M. Nerbonne and her team, report a comprehensive RNA-seq–based transcriptome analysis in failing human hearts that expands our current knowledge of transcriptome complexity and dynamics in human heart failure. The study used the standard Illumina TruSeq method and the well-established TopHat and Cufflinks suite to map RNA-seq reads and to reconstruct transcript isoforms. In addition to mRNA and miRNA, a more comprehensive profiling of lncRNAs was performed by taking advantage of the recently updated lncRNA databases and improved de novo Cufflinks prediction. As expected from other RNA-seq studies, the increased detection sensitivity afforded by RNA-seq uncovered a large number of lncRNA transcripts (nearly 18 000) in the human myocardium.18 Somewhat unexpectedly, however, the clustering analyses showed that differences in expression signatures of the lncRNAs were able to distinguish between cardiomyopathy of ischemic and nonischemic origin and, furthermore, discern between before and after LVAD treatment for either type of cardiomyopathy. In contrast, the profiles of miRNA and mRNA expression did not have similar discriminatory power. It should be noted that clustering results are highly sensitive to the specific set of genes selected for the analysis. Better clustering can be achieved when more genes are differentially expressed across samples. Even in their own analysis, the discrimination between ischemic cardiomyopathy and nonischemic cardiomyopathy samples was improved when only the 100 most differentially expressed mRNAs were used to cluster the samples. Nonetheless, the finding that clustering was able to achieve highly discriminative results using all expressed lncRNAs without stringent selection criteria suggests that there is a larger fraction of lncRNAs that are dynamically regulated during heart failure or in response to LVAD treatment. However, the overall sample sizes in this study (8 pairs of pre-LVAD and post-LVAD samples) are low, and this conclusion would need to be validated in a larger independent cohort. The majority of the reported lncRNAs from this study have relatively low abundance in the human hearts (reads perkilobase of transcript per million mapped reads 0.8). Finally, as the authors alluded to, lncRNAs can also function beyond the mode of cis-regulation and extend their influence on posttranscriptional regulation to mRNA splicing, decay, and translation. In summary, RNA-seq–based transcriptome profiling in the failing human heart revealed additional transcriptional complexity associated with the disease and recovery after mechanical unloading. The newly uncovered importance of lncRNAs in disease pathogenesis highlights their potential value as potential diagnostic and therapeutic targets. Decoding the function of lncRNAs in cardiac physiology and diseases is both a major challenge and an exciting new opportunity for future studies.
Acknowledgments We thank Dr Christoph Raul for discussion and proofreading.
Sources of Funding Dr Xiao is supported by National Institutes of Health grants R01HG006264 and U01HG007013, Dr Touma is supported by a K12 Child Health Research Center Development Award, and Dr Wang is supported in part by National Heart, Lung, and Blood Institute grants HL103205, HL098954, HL108186, and HL114437.
Disclosures None.
References 1. Burke MA, Cotts WG. Interpretation of B-type natriuretic peptide in cardiac disease and other comorbid conditions. Heart Fail Rev. 2007;12:23–36. 2. Sato Y, Fujiwara H, Takatsu Y. Cardiac troponin and heart failure in the era of high-sensitivity assays. J Cardiol. 2012;60:160–167. 3. Deng MC, Edwards LB, Hertz MI, Rowe AW, Keck BM, Kormos R, Naftel DC, Kirklin JK, Taylor DO; International Society for Heart and Lung Transplantation. Mechanical circulatory support device database of the International Society for Heart and Lung Transplantation: third annual report–2005. J Heart Lung Transplant. 2005;24:1182–1187. 4. Oz MC, Argenziano M, Catanese KA, Gardocki MT, Goldstein DJ, Ashton RC, Gelijns AC, Rose EA, Levin HR. Bridge experience with long-term implantable left ventricular assist devices. Are they an alternative to transplantation? Circulation. 1997;95:1844–1852. 5. Latif S, Masino A, Garry DJ. Transcriptional pathways direct cardiac development and regeneration. Trends Cardiovasc Med. 2006;16:234–240. 6. Nanni L, Romualdi C, Maseri A, Lanfranchi G. Differential gene expression profiling in genetic and multifactorial cardiovascular diseases. J Mol Cell Cardiol. 2006;41:934–948. 7. 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. 8. 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. 9. Birks EJ, Latif N, Owen V, Bowles C, Felkin LE, Mullen AJ, Khaghani A, Barton PJ, Polak JM, Pepper JR, Banner NR, Yacoub MH. Quantitative myocardial cytokine expression and activation of the apoptotic pathway in patients who require left ventricular assist devices. Circulation. 2001;104(12 Suppl 1):I233–I240. 10. 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.
Downloaded from http://circ.ahajournals.org/ at Virginia Polytechnic Institute & State Univ. on August 18, 2014
960 Circulation March 4, 2014 11. Shi X, Sun M, Liu H, Yao Y, Song Y. Long non-coding RNAs: a new frontier in the study of human diseases. Cancer Lett. 2013;339:159–166. 12. Martens-Uzunova ES, Olvedy M, Jenster G. Beyond microRNA–novel RNAs derived from small non-coding RNA and their implication in cancer. Cancer Lett. 2013;340:201–211. 13. Scheuermann JC, Boyer LA. Getting to the heart of the matter: long non-coding RNAs in cardiac development and disease. EMBO J. 2013;32:1805–1816. 14. Ounzain S, Crippa S, Pedrazzini T. Small and long non-coding RNAs in cardiac homeostasis and regeneration. Biochim Biophys Acta. 2013;1833: 923–933. 15. Churko JM, Mantalas GL, Snyder MP, Wu JC. Overview of high throughput sequencing technologies to elucidate molecular pathways in cardiovascular diseases. Circ Res. 2013;112:1613–1623.
16. Lee JH, Gao C, Peng G, Greer C, Ren S, Wang Y, Xiao X. Analysis of transcriptome complexity through RNA sequencing in normal and failing murine hearts. Circ Res. 2011;109:1332–1341. 17. Matkovich SJ, Van Booven DJ, Eschenbacher WH, Dorn GW 2nd. RISC RNA sequencing for context-specific identification of in vivo microRNA targets. Circ Res. 2011;108:18–26. 18. Yang KC, Yamada KA, Patel AY, Topkara VK, George I, Cheema FH, Ewald GA, Mann DL, Nerbonne JM. Deep RNA sequencing reveals dynamic regulation of myocardial noncoding RNA in failing human heart and remodeling with mechanical circulatory support. Circulation. 2014;129:1009–1021. Key Words: Editorials ◼ heart failure ◼ humans ◼ RNA, long noncoding ◼ transcriptome
Downloaded from http://circ.ahajournals.org/ at Virginia Polytechnic Institute & State Univ. on August 18, 2014
