The Transcriptomic Toolbox: Resources for Interpreting Large Gene Expression Data within a Precision Medicine Context for Metabolic Disease Atherosclerosis

Abstract

As one of the most widespread metabolic diseases, atherosclerosis affects nearly everyone as they age; arteries gradually narrow from plaque accumulation over time reducing oxygenated blood flow to central and periphery causing heart disease, stroke, kidney problems, and even pulmonary disease. Personalized medicine promises to bring treatments based on individual genome sequencing that precisely target the molecular pathways underlying atherosclerosis and its symptoms, but to date only a few genotypes have been identified. A promising alternative to this genetic approach is the identification of pathways altered in atherosclerosis by transcriptome analysis of atherosclerotic tissues to target specific aspects of disease. Transcriptomics is a potentially useful tool for both diagnostics and discovery science, exposing novel cellular and molecular mechanisms in clinical and translational models, and depending on experimental design to identify and test novel therapeutics. The cost and time required for transcriptome analysis has been greatly reduced by the development of next generation sequencing. The goal of this resource article is to provide background and a guide to appropriate technologies and downstream analyses in transcriptomics experiments generating ever-increasing amounts of gene expression data.

The role of gut mycobiome in health and diseases

“…Atherosclerosis is associated with metabolic diseases. 142 Gut mycobiota dysbiosis has also been shown to be related with the development of carotid atherosclerosis. 132 In a study of 33 subjects who had fecal samples collected for ITS amplicon sequencing, the subjects were divided into two groups of 12 nonobese subjects versus 21 obese subjects.…”

Wholistic approach: Transcriptomic analysis and beyond using archival material for molecular diagnosis

“…Despite these disadvantages and when analytical and preanalytical conditions were optimized, many studies have reported a high correlation of gene expression between FFPE and FF samples, 54,57,59,[72][73][74][75][76][77][78][79][80][81][82] although the RNA extracted from the FFPE samples was most often degraded and continued to degrade with age. Some studies have also investigated different extraction techniques or sequencing methods to optimize the workflow for archival materials.…”

Inflammation and atherosclerosis: what is on the horizon?

“…Into the future, the diagnosis of atherosclerosis will move beyond simple anatomical information to more qualitative characterisation. This will likely consist of complex panels including circulating protein biomarkers, cellular transcriptome analysis34 and extracellular vesicle examination in combination with molecular non-invasive and invasive imaging tools. The ultimate aims of this approach would be in the first instance to identifying patients at high risk (‘upstream’ of an event) by identifying the presence and activity of specific pathogenic biological processes that may benefit from specific targeted intervention; and second, to provide specific patient-level insights into prognosis (‘downstream’ of the event) and duration and response of directed therapy.…”