Role of Adenylate Cyclase 9 in the Pharmacogenomic Response to Dalcetrapib: Clinical Paradigm and Molecular Mechanisms in Precision Cardiovascular Medicine

Genetic Variants Associated with the Risk of Stroke in Sickle Cell Anemia: Systematic Review and Meta-Analysis

Sickle cell anemia (SCA) is the most common cause of stroke in children. As it is a rare disease, studies investigating the association with complications like stroke in SCD have small sample sizes. Here, we performed a systematic review and meta-analysis of the studies exploring an association of genetic variants with stroke to get a better indication of their association with stroke. PubMed and Google Scholar were searched to identify studies that had performed an association analysis of genetic variants for the risk of stroke in SCA patients. After screening of eligible studies, summary statistics of association analysis with stroke and other general information were extracted. Meta-analysis was performed using the fixed effect method on the tool METAL and forest plots were plotted using the R program. The random effect model was performed as a sensitivity analysis for loci where significant heterogeneity was observed. 407 studies were identified using the search term and after screening 37 studies that cumulatively analyzed 11,373 SCA patients were included. These 37 studies included a total of 2,222 SCA patients with stroke, predominantly included individuals of African ancestry (N = 16). Three of these studies performed whole exome sequencing while 35 performed single nucleotide-based genotyping. Though the studies reported association with 132 loci, meta-analyses could be performed only for 12 loci that had data from two or more studies. After meta-analysis we observed that four loci were significantly associated with risk for stroke: -α3.7 kb Alpha-thalassemia deletion (P = 0.00000027), rs489347-TEK (P = 0.00081), rs2238432-ADCY9 (P = 0.00085), rs11853426-ANXA2 (P = 0.0034), and rs1800629-TNF (P = 0.0003396). Ethnic representation of regions with a high prevalence of SCD like the Mediterranean basin and India needs to be improved for genetic studies on associated complications like stroke. Larger genome-wide collaborative studies on SCD and associated complications including stroke need to be performed.

Adcy9 Gene Inactivation Improves Cardiac Function After Myocardial Infarction in Mice

BACKGROUND

Polymorphisms in the adenylate cyclase 9 (ADCY9) gene influence the benefits of the cholesteryl ester transfer protein (CETP) modulator dalcetrapib on cardiovascular events after acute coronary syndrome. We hypothesized that Adcy9 inactivation could improve cardiac function and remodelling following myocardial infarction (MI) in absence of CETP activity.

METHODS

Wild-type (WT) and Adcy9-inactivated (Adcy9Gt/Gt) male mice, transgenic or not for human CETP (tgCETP+/-), were subjected to MI by permanent left anterior descending coronary artery ligation and studied for 4 weeks. Left ventricular (LV) function was assessed by echocardiography at baseline, 1, and 4 weeks after MI. At sacrifice, blood, spleen and bone marrow cells were collected for flow cytometry analysis, and hearts were harvested for histologic analyses.

RESULTS

All mice developed LV hypertrophy, dilation, and systolic dysfunction, but Adcy9Gt/Gt mice exhibited reduced pathologic LV remodelling and better LV function compared with WT mice. There were no differences between tgCETP+/- and Adcy9Gt/Gt tgCETP+/- mice, which both exhibited intermediate responses. Histologic analyses showed smaller cardiomyocyte size, reduced infarct size, and preserved myocardial capillary density in the infarct border zone in Adcy9Gt/Gt vs WT mice. Count of bone marrow T cells and B cells were significantly increased in Adcy9Gt/Gt mice compared with the other genotypes.

CONCLUSIONS

Adcy9 inactivation reduced infarct size, pathologic remodelling, and cardiac dysfunction. These changes were accompanied by preserved myocardial capillary density and increased adaptive immune response. Most of the benefits of Adcy9 inactivation were only observed in the absence of CETP.

Predictive genomic tools in disease stratification and targeted prevention: a recent update in personalized therapy advancements

In the current era of medical revolution, genomic testing has guided the healthcare fraternity to develop predictive, preventive, and personalized medicine. Predictive screening involves sequencing a whole genome to comprehensively deliver patient care via enhanced diagnostic sensitivity and specific therapeutic targeting. The best example is the application of whole-exome sequencing when identifying aberrant fetuses with healthy karyotypes and chromosomal microarray analysis in complicated pregnancies. To fit into today's clinical practice needs, experimental system biology like genomic technologies, and system biology viz., the use of artificial intelligence and machine learning is required to be attuned to the development of preventive and personalized medicine. As diagnostic techniques are advancing, the selection of medical intervention can gradually be influenced by a person's genetic composition or the cellular profiling of the affected tissue. Clinical genetic practitioners can learn a lot about several conditions from their distinct facial traits. Current research indicates that in terms of diagnosing syndromes, facial analysis techniques are on par with those of qualified therapists. Employing deep learning and computer vision techniques, the face image assessment software DeepGestalt measures resemblances to numerous of disorders. Biomarkers are essential for diagnostic, prognostic, and selection systems for developing personalized medicine viz. DNA from chromosome 21 is counted in prenatal blood as part of the Down's syndrome biomarker screening. This review is based on a detailed analysis of the scientific literature via a vigilant approach to highlight the applicability of predictive diagnostics for the development of preventive, targeted, personalized medicine for clinical application in the framework of predictive, preventive, and personalized medicine (PPPM/3 PM). Additionally, targeted prevention has also been elaborated in terms of gene-environment interactions and next-generation DNA sequencing. The application of 3 PM has been highlighted by an in-depth analysis of cancer and cardiovascular diseases. The real-time challenges of genome sequencing and personalized medicine have also been discussed.