Development and Implementation of an Integrated Preclinical Atherosclerosis Database.

BACKGROUND: Basic scientists have used preclinical animal models to explore mechanisms driving human diseases for decades, resulting in thousands of publications, each supporting causative inferences. Despite substantial advances in the mechanistic construct of disease, there has been limited translation from individual studies to advances in clinical care. An integrated approach to these individual studies has the potential to improve translational success. METHODS: Using atherosclerosis as a test case, we extracted data from the 2 most common mouse models of atherosclerosis (ApoE [apolipoprotein E]-knockout and LDLR [low-density lipoprotein receptor]-knockout). We restricted analyses to manuscripts published in 2 well-established journals, Arteriosclerosis, Thrombosis, and Vascular Biology and Circulation, as of query in 2021. Predefined variables including experimental conditions, intervention, and outcomes were extracted from each publication to produce a preclinical atherosclerosis database. RESULTS: Extracted data include animal sex, diet, intervention type, and distinct plaque pathologies (size, inflammation, and lipid content). Procedures are provided to standardize data extraction, attribute interventions to specific genes, and transform the database for use with available transcriptomics software. The database integrates hundreds of genes, each directly tested in vivo for causation in a murine atherosclerosis model. The database is provided to allow the research community to perform integrated analyses that reflect the global impact of decades of atherosclerosis investigation. CONCLUSIONS: This database is provided as a resource for future interrogation of sub-data sets associated with distinct plaque pathologies, cell type, or sex. We also provide the methods and software needed to expand this data set and apply this approach to the extensive repository of peer-reviewed data utilizing preclinical models to interrogate mechanisms of diverse human diseases.

Inhibition of the histone methyltransferase EZH2 induces vascular stiffness.

Vascular stiffness increases with aging, obesity and hypertension and predicts cardiovascular risk. The levels of histone H3-lysine-27 methylation (H3K27me) and the histone methyltransferase EZH2 both decrease in aging vessels, driving vascular stiffness. The impact of EZH2 inhibitors on vascular stiffness is unknown. We tested the hypothesis that the EZH2 inhibitor GSK126, currently in development for cancer treatment, increases vascular stiffness and explored underlying molecular mechanisms. Young (3 month) and middle-aged (12 month) male mice were treated with GSK126 for 1-2 months and primary human aortic smooth muscle cells (HASMCs) from young male and female donors were treated with GSK126 for 24-48 h. Stiffness was measured in vivo by pulse wave velocity and in vitro by atomic force microscopy (AFM) and vascular structure was quantified histologically. Extracellular matrix proteins were studied by qRT-PCR, immunoblotting, zymography and chromatin immunoprecipitation. GSK126 treatment decreased H3K27 methylation (H3K27me) and increased acetylation (H3K27ac) in mouse vessels and in HASMCs. In GSK126-treated mice, aortic stiffness increased without changes in vascular fibrosis. EZH2 inhibition enhanced elastin fiber degradation and matrix metalloprotease-2 (MMP2) expression. In HASMCs, GSK126 treatment increased synthetic phenotype markers and intrinsic HASMCs stiffness by AFM with altered cytoskeletal structure and increased nuclear actin staining. GSK126 also increased MMP2 protein expression, activity and enrichment of H3K27ac at the MMP2 promoter in HASMCs. GSK126 causes vascular stiffening, inducing MMP2 activity, elastin degradation, and modulation of SMC phenotype and cytoskeletal stiffness. These findings suggest that EZH2 inhibitors used to treat cancer could negatively impact the vasculature by enhancing stiffness and merits examination in human trials.

Systems Approach to Integrating Preclinical Apolipoprotein E-Knockout Investigations Reveals Novel Etiologic Pathways and Master Atherosclerosis Network in Humans.

OBJECTIVE: Animal models of atherosclerosis are used extensively to interrogate molecular mechanisms in serial fashion. We tested whether a novel systems biology approach to integration of preclinical data identifies novel pathways and regulators in human disease. Approach and Results: Of 716 articles published in ATVB from 1995 to 2019 using the apolipoprotein E knockout mouse to study atherosclerosis, data were extracted from 360 unique studies in which a gene was experimentally perturbed to impact plaque size or composition and analyzed using Ingenuity Pathway Analysis software. TREM1 (triggering receptor expressed on myeloid cells) signaling and LXR/RXR (liver X receptor/retinoid X receptor) activation were identified as the top atherosclerosis-associated pathways in mice (both P<1.93×10-4, TREM1 implicated early and LXR/RXR in late atherogenesis). The top upstream regulatory network in mice (sc-58125, a COX2 inhibitor) linked 64.0% of the genes into a single network. The pathways and networks identified in mice were interrogated by testing for associations between the genetically predicted gene expression of each mouse pathway-identified human homolog with clinical atherosclerosis in a cohort of 88 660 human subjects. Homologous human pathways and networks were significantly enriched for gene-atherosclerosis associations (empirical P<0.01 for TREM1 and LXR/RXR pathways and COX2 network). This included 12(60.0%) TREM1 pathway genes, 15(53.6%) LXR/RXR pathway genes, and 67(49.3%) COX2 network genes. Mouse analyses predicted, and human study validated, the strong association of COX2 expression (PTGS2) with increased likelihood of atherosclerosis (odds ratio, 1.68 per SD of genetically predicted gene expression; P=1.07×10-6). CONCLUSIONS: PRESCIANT (Preclinical Science Integration and Translation) leverages published preclinical investigations to identify high-confidence pathways, networks, and regulators of human disease.