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.

Epigenetics is the study of phenotypic variation arising from developmental and environmental factors regulating gene transcription at molecular, cellular, and physiological levels. A naturally occurring biological process driven by epigenetics is the egg-to-embryo developmental transition when two fully differentiated adult cells – egg and sperm – revert to an early stem cell type with totipotency but subsequently differentiates into pluripotent embryonic stem cells that give rise to any cell type. Transposable elements (TEs) are active in mammalian oocytes and early embryos, and this activity, albeit counterintuitive because TEs can lead to genomic instability in somatic cells, correlates to successful development. TEs bridge genetic and epigenetic landscapes because TEs are genetic elements whose silencing and de-repression are regulated by epigenetic mechanisms that are sensitive to environmental factors. Ultimately, transposition events can change size, content, and function of mammalian genomes. Thus, TEs act beyond mutagenic agents reshuffling the genomes, and epigenetic regulation of TEs may act as a proximate mechanism by which evolutionary forces increase a species’ hidden reserve of epigenetic and phenotypic variability facilitating the adaptation of genomes to their environment.

In mammalian ovarian follicles, follicle stimulating hormone (FSH) and luteinizing hormone (LH) signal primarily through the G-protein G _s to elevate cAMP, but both of these hormones can also elevate Ca ²⁺ under some conditions. Here we investigate FSH- and LH-induced Ca ²⁺ signaling in intact follicles of mice expressing genetically encoded Ca ²⁺ sensors, Twitch-2B and GCaMP6s. At a physiological concentration (1 nM), FSH elevates Ca ²⁺ within the granulosa cells of preantral and antral follicles. The Ca ²⁺ rise begins several minutes after FSH application, peaks at ~10 minutes, remains above baseline for another ~10 minutes, and depends on extracellular Ca ²⁺ . However, suppression of the FSH-induced Ca ²⁺ increase by reducing extracellular Ca ²⁺ does not inhibit FSH-induced phosphorylation of MAP kinase, estradiol production, or the acquisition of LH responsiveness. Like FSH, LH also increases Ca ²⁺ , when applied to preovulatory follicles. At a physiological concentration (10 nM), LH elicits Ca ²⁺ oscillations in a subset of cells in the outer mural granulosa layer. These oscillations continue for at least 6 hours and depend on the activity of G _q family G-proteins. Suppression of the oscillations by G _q inhibition does not inhibit meiotic resumption, but does delay the time to 50% ovulation by about 3 hours. In summary, both FSH and LH increase Ca ²⁺ in the granulosa cells of intact follicles, but the functions of these Ca ²⁺ rises are only starting to be identified. Summary sentence: Both FSH and LH increase Ca ²⁺ in the granulosa cells of intact ovarian follicles from mice expressing genetically encoded sensors.

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.