Transcriptomics as Precision Medicine to Classify <em>In Vivo</em> Models of Dietary-Induced Atherosclerosis at Cellular and Molecular Levels

Abstract

The central promise of personalized medicine is individualized treatments that target molecular mechanisms underlying the physiological changes and symptoms arising from disease. We demonstrate a bioinformatics analysis pipeline as a proof-of-principle to test the feasibility and practicality of comparative transcriptomics to classify two of the most popular in vivo diet-induced models of coronary atherosclerosis, apolipoprotein E null mice and New Zealand White rabbits. Transcriptomics analyses indicate the two models extensively share dysregulated genes albeit with some unique pathways. For instance, while both models have alterations in the mitochondrion, the biochemical pathway analysis revealed, Complex IV in the electron transfer chain is higher in mice, whereas the rest of the electron transfer chain components are higher in the rabbits. Several fatty acids anabolic pathways are expressed higher in mice, whereas fatty acids and lipids degradation pathways are higher in rabbits. This reflects the differences between two translational models of atherosclerosis. This study validates transcriptome analysis as a potential method to precisely identify altered cellular and molecular pathways in atherosclerotic disease, which can be used to individualize treatment even in the absence of genetic data.

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

“…Unlike DNA sequencing focusing on genome, RNA sequencing produces the snapshot of the full transcriptome supporting its potential capability to fulfill precision medicine to classify patients at both molecular and cellular levels when used in conjunction with programs for ontologies and pathway analysis. Development of RNA sequencing pipelines is important for implementation of transcriptomics as precision medicine [94], which can be used successfully to classify patient or model attributes and predict therapeutic response and ultimate outcomes [48].…”

“…Multiple tools exist for determining pathway enrichment; among preferred tools in our laboratory is the VisuaL Annotation Display (VLAD) [85], which allows to define the “universe set” (i.e., the background list of genes), rather than just GO. An example of VLAD analysis (Figure 7D) compares the overrepresented GO terms among the 100 highest-expressed genes in aortas of Apoe -/- mice fed Western diet, and the 100 highest-expressed genes in aortas of high-fat, high-cholesterol fed New Zealand White (NZW) rabbits (data from [94]), and illustrates similarities and differences between these two translational models of atherosclerosis. In this example, the lists of the highest-expressed genes (and thus presumably most important “housekeeping” genes involved in the disease) are independently analyzed to identify the common overrepresented GO categories for each list (i.e., for both rabbit and mouse aortas).…”

Proteomics and Precision Medicine

“…In addition, another challenge then arised as to whether these increased findings should be acted upon, keeping in mind that genomics driven precision medicine is not targeted or precise. Third, in the field of transcriptome, Alexei Evsikov and Marín de Evsikova used transcriptome analysis as a robust alternative method to identify specific cellular and molecular facts of atherosclerotic disease, which could be used to individualize treatment and developed novel avenues of therapeutic intervention . But current diagnostic and medical technologies used in clinical research provided only a small snapshot into the state of disease.…”