Pharmacogenomics of heart failure

Novel Biomarkers of Heart Failure in Pediatrics

Novel biomarkers of heart failure are the subject of numerous studies. Biomarkers of heart failure can be determined in the blood and in the urine. Seven groups of biomarkers of heart failure based on pathophysiological mechanisms are presented in this review, namely biomarkers of myocardial stretch, myocyte injury, myocardial remodeling, biomarkers of inflammation, renal dysfunction, neurohumoral activation, and oxidative stress. Studies of biomarkers in the pediatric population are scarce, therefore, further investigation is needed for reliable prognostic and therapeutic implications. The future of biomarker use is in multimarker panels that include a combination of biomarkers with different pathophysiological mechanisms in order to improve their diagnostic and prognostic predictive value.

GTPγS Assay for Measuring Agonist-Induced Desensitization of Two Human Polymorphic Alpha2B-Adrenoceptor Variants

α2-Adrenergic receptors (ARs) mediate many cellular actions of epinephrine and norepinephrine, including inhibition of their secretion (sympathetic inhibition) from adrenal chromaffin cells. Like many other G protein-coupled receptors (GPCRs), they undergo agonist-dependent phosphorylation and desensitization by GPCR kinases (GRKs), a phenomenon recently shown to play a major role in the sympathetic overdrive that accompanies and aggravates chronic heart failure. A three-glutamic acid deletion polymorphism in the human α2B-AR subtype gene (Glu301-303) causes impaired agonist-promoted receptor phosphorylation and desensitization, resulting in enhanced signaling to inhibition of cholinergic-induced catecholamine secretion in adrenal chromaffin cells. One of the various pharmacological assays that can be used to quantify and quantitatively compare the degrees of agonist-dependent desensitization, i.e., G protein decoupling, of these two polymorphic α2B-AR variants (or of any two GPCRs for that matter) is the guanosine-5'-O-3-thiotriphosphate (GTPγS) assay that can directly quantify heterotrimeric G protein activation.

Pharmacogenetic factors affecting β-blocker metabolism and response

INTRODUCTION

β-blockers are among the most widely prescribed of all drugs, used for treatment of a large number of cardiovascular diseases. Herein we evaluate literature pertaining to pharmacogenetics of β-blocker therapy, provide insight into the robustness of the genetic associations, and determine the appropriateness for translating these genetic associations into clinical practice.

AREAS COVERED

A literature search was conducted using PubMed to collate evidence on associations between CYP2D6, ADRB1, ADRB2, and GRK5 genetic variation and drug-response outcomes in the presence of β-blocker exposure. Pharmacokinetic, pharmacodynamic, and clinical outcomes studies were included if genotype data and β-blocker exposure were documented.

EXPERT OPINION

Substantial data suggest that specific ADRB1 and GRK5 genotypes are associated with improved β-blocker efficacy and have potential for use to guide therapy decisions in the clinical setting. While the data do not justify ordering a CYP2D6 pharmacogenetic test, if CYP2D6 genotype is available in the electronic health record, there may be clinical utility for understanding dosing of β-blockers.

Biased Agonism/Antagonism of Cardiovascular GPCRs for Heart Failure Therapy

G protein-coupled receptors (GPCRs) are among the most important drug targets currently used in clinic, including drugs for cardiovascular indications. We now know that, in addition to activating heterotrimeric G protein-dependent signaling pathways, GPCRs can also activate G protein-independent signaling, mainly via the βarrestins. The major role of βarrestin1 and -2, also known as arrestin2 or -3, respectively, is to desensitize GPCRs, i.e., uncoupled them from G proteins, and to subsequently internalize the receptor. As the βarrestin-bound GPCR recycles inside the cell, it serves as a signalosome transducing signals in the cytoplasm. Since both G proteins and βarrestins can transduce signals from the same receptor independently of each other, any given GPCR agonist might selectively activate either pathway, which would make it a biased agonist for that receptor. Although this selectivity is always relative (never absolute), in cases where the G protein- and βarrestin-dependent signals emanating from the same GPCR result in different cellular effects, pharmacological exploitation of GPCR-biased agonism might have therapeutic potential. In this chapter, we summarize the GPCR signaling pathways and their biased agonism/antagonism examples discovered so far that can be exploited for heart failure treatment. We also highlight important issues that need to be clarified along the journey of these ligands from bench to the clinic.

Use of semantic workflows to enhance transparency and reproducibility in clinical omics

BACKGROUND

Recent highly publicized cases of premature patient assignment into clinical trials, resulting from non-reproducible omics analyses, have prompted many to call for a more thorough examination of translational omics and highlighted the critical need for transparency and reproducibility to ensure patient safety. The use of workflow platforms such as Galaxy and Taverna have greatly enhanced the use, transparency and reproducibility of omics analysis pipelines in the research domain and would be an invaluable tool in a clinical setting. However, the use of these workflow platforms requires deep domain expertise that, particularly within the multi-disciplinary fields of translational and clinical omics, may not always be present in a clinical setting. This lack of domain expertise may put patient safety at risk and make these workflow platforms difficult to operationalize in a clinical setting. In contrast, semantic workflows are a different class of workflow platform where resultant workflow runs are transparent, reproducible, and semantically validated. Through semantic enforcement of all datasets, analyses and user-defined rules/constraints, users are guided through each workflow run, enhancing analytical validity and patient safety.

METHODS

To evaluate the effectiveness of semantic workflows within translational and clinical omics, we have implemented a clinical omics pipeline for annotating DNA sequence variants identified through next generation sequencing using the Workflow Instance Generation and Specialization (WINGS) semantic workflow platform.

RESULTS

We found that the implementation and execution of our clinical omics pipeline in a semantic workflow helped us to meet the requirements for enhanced transparency, reproducibility and analytical validity recommended for clinical omics. We further found that many features of the WINGS platform were particularly primed to help support the critical needs of clinical omics analyses.

CONCLUSIONS

This is the first implementation and execution of a clinical omics pipeline using semantic workflows. Evaluation of this implementation provides guidance for their use in both translational and clinical settings.