Association of polymorphisms in the aldosterone-regulated sodium reabsorption pathway with blood pressure among Hispanics

Systematic Investigation of Ginkgo Biloba Leaves for Treating Cardio-cerebrovascular Diseases in an Animal Model

Globally, cardio-cerebrovascular diseases (CCVDs) are the leading cause of death, and thus the development of novel strategies for preventing and treating such diseases is in urgent need. Traditional Chinese medicine (TCM), used for thousands of years in Asia and other regions, has been proven effective in certain disorders. As a long-time medicinal herb in TCM, Ginkgo biloba leaves (GBLs), have been widely used to treat various diseases including CCVDs. However, the underlying molecular mechanisms of medicinal herbs in treating these diseases are still unclear. Presently, by incorporating pharmacokinetic prescreening, target fishing, and network analysis, an innovative systems-pharmacology platform was introduced to systematically decipher the pharmacological mechanism of action of GBLs for the treatment of CCVDs. The results show that GBLs exhibit a protective effect on CCVDs probably through regulating multiple pathways and hitting on multiple targets involved in several biological pathways. Our work successfully explains the mechanism of efficiency of GBLs for treating CCVDs and, meanwhile, demonstrates that GDJ, an injection generated from GBLs, could be used as a preventive or therapeutic agent in cerebral ischemia. The approach developed in this work offers a new paradigm for systematically understanding the action mechanisms of herb medicine, which will promote the development and application of TCM.

Pathway-based analyses

BACKGROUND

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation and penalties for multiple testing.

METHODS

The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge.

RESULTS

Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data.

CONCLUSIONS

The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.