Cardiac protein kinases: the cardiomyocyte kinome and differential kinase expression in human failing hearts

Aims. Protein kinases are potential therapeutic targets for heart failure, but most studies of cardiac protein kinases derive from other systems, an approach that fails to account for specific kinases expressed in the heart and the contractile cardiomyocytes. We aimed to define the cardiomyocyte kinome (i.e. the protein kinases expressed in cardiomyocytes) and identify kinases with altered expression in human failing hearts. Methods and Results. Expression profiling (Affymetrix microarrays) detected >400 protein kinase mRNAs in rat neonatal ventricular myocytes (NVMs) and/or adult ventricular myocytes (AVMs), 32 and 93 of which were significantly upregulated or downregulated (>2-fold), respectively, in AVMs. Data for AGC family members were validated by qPCR. Proteomics analysis identified >180 cardiomyocyte protein kinases, with high relative expression of mitogen-activated protein kinase cascades and other known cardiomyocyte kinases (e.g. CAMKs, cAMP-dependent protein kinase). Other kinases are poorly-investigated (e.g. Slk, Stk24, Oxsr1). Expression of Akt1/2/3, BRaf, ERK1/2, Map2k1, Map3k8, Map4k4, MST1/3, p38-MAPK, PKCδ, Pkn2, Ripk1/2, Tnni3k and Zak was confirmed by immunoblotting. Relative to total protein, Map3k8 and Tnni3k were upregulated in AVMs vs NVMs. Microarray data for human hearts demonstrated variation in kinome expression that may influence responses to kinase inhibitor therapies. Furthermore, some kinases were upregulated (e.g. NRK, JAK2, STK38L) or downregulated (e.g. MAP2K1, IRAK1, STK40) in human failing hearts. Conclusions. This characterization of the spectrum of kinases expressed in cardiomyocytes and the heart (cardiomyocyte and cardiac kinomes) identified novel kinases, some of which are differentially expressed in failing human hearts and could serve as potential therapeutic targets.

Signaling pathways mediating cardiac myocyte gene expression in physiological and stress responses

The contractile cells in the heart (the cardiac myocytes) are terminally differentiated. In response to pathophysiological stresses, cardiac myocytes undergo hypertrophic growth or apoptosis, responses associated with the development of cardiac pathologies. There has been much effort expended in gaining an understanding of the stimuli which promote these responses, and in identifying the intracellular signaling pathways which are activated and potentially involved. These signaling pathways presumably modulate gene and protein expression to elicit the end-stage response. For the regulation of gene expression, the signal may traverse the cytoplasm to modulate nuclear-localized transcription factors as occurs with the mitogen-activated protein kinase or protein kinase B/Akt cascades. Alternatively, the signal may promote translocation of transcription factors from the cytoplasm to the nucleus as is seen with the calcineurin/NFAT and JAK/STAT systems. We present an overview of the principal signaling pathways implicated in the regulation of gene expression in cardiac myocyte pathophysiology, and summarize the current understanding of these pathways, the transcription factors they regulate and the changes in gene expression associated with the development of cardiac pathologies. Finally, we discuss how intracellular signaling and gene expression may be integrated to elicit the overall change in cellular phenotype.

Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort

Background Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers (“biomarkers”) of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and methods of a discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). The CAN-BIND research program investigates and identifies biomarkers that help to predict outcomes in patients with MDD treated with antidepressant medication. The primary objective of this initial study (known as CAN-BIND-1) is to identify individual and integrated neuroimaging, electrophysiological, molecular, and clinical predictors of response to sequential antidepressant monotherapy and adjunctive therapy in MDD. Methods CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10–20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2–10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response. Discussion From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants) has been evaluated at baseline. The clinical characteristics of this cohort are similar to other studies of MDD. Recruitment at all sites is ongoing to a target sample of 290 participants. CAN-BIND will identify biomarkers of treatment response in MDD through extensive clinical, molecular, and imaging assessments, in order to improve treatment practice and clinical outcomes. It will also create an innovative, robust platform and database for future research.