Tim Beckmann

AIM: Heart disease is recognized as a consequence of dysregulation of cardiac gene regulatory networks. Previously, unappreciated components of such networks are the long non-coding RNAs (lncRNAs). Their roles in the heart remain to be elucidated. Thus, this study aimed to systematically characterize the cardiac long non-coding transcriptome post-myocardial infarction and to elucidate their potential roles in cardiac homoeostasis. METHODS AND RESULTS: We annotated the mouse transcriptome after myocardial infarction via RNA sequencing and ab initio transcript reconstruction, and integrated genome-wide approaches to associate specific lncRNAs with developmental processes and physiological parameters. Expression of specific lncRNAs strongly correlated with defined parameters of cardiac dimensions and function. Using chromatin maps to infer lncRNA function, we identified many with potential roles in cardiogenesis and pathological remodelling. The vast majority was associated with active cardiac-specific enhancers. Importantly, oligonucleotide-mediated knockdown implicated novel lncRNAs in controlling expression of key regulatory proteins involved in cardiogenesis. Finally, we identified hundreds of human orthologues and demonstrate that particular candidates were differentially modulated in human heart disease. CONCLUSION: These findings reveal hundreds of novel heart-specific lncRNAs with unique regulatory and functional characteristics relevant to maladaptive remodelling, cardiac function and possibly cardiac regeneration. This new class of molecules represents potential therapeutic targets for cardiac disease. Furthermore, their exquisite correlation with cardiac physiology renders them attractive candidate biomarkers to be used in the clinic.

Recently released sequence information on Chinese hamster ovary (CHO) cells promises to not only facilitate our understanding of these industrially important cell factories through direct analysis of the sequence, but also to enhance existing methodologies and allow new tools to be developed. In this article we demonstrate the utilization of CHO specific sequence information to improve mass spectrometry (MS) based proteomic identification. The use of various CHO specific databases enabled the identification of 282 additional proteins, thus increasing the total number of identified proteins by 40-50%, depending on the sample source and methods used. In addition, a considerable portion of those proteins that were identified previously based on inter-species sequence homology were now identified by a larger number of peptides matched, thus increasing the confidence of identification. The new sequence information offers improved interpretation of proteomic analyses and will, in the years to come, prove vital to unraveling the CHO proteome.

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