Whittemore G. Tingley

Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis.

Background—The molecular pathophysiology of coronary artery disease (CAD) includes cytokine release and a localized inflammatory response within the vessel wall. The extent to which CAD and its severity is reflected by gene expression in circulating cells is unknown. Methods and Results—From an initial coronary catheterization cohort we identified 41 patients, comprising 27 cases with angiographically significant CAD and 14 controls without coronary stenosis. Whole-genome microarray analysis performed on peripheral-blood mononuclear cells yielded 526 genes with >1.3-fold differential expression (P<0.05) between cases and controls. Real-time polymerase chain reaction on 106 genes (the 50 most significant microarray genes and 56 additional literature genes) in an independent subset of 95 patients (63 cases, 32 controls) from the same cohort yielded 14 genes (P<0.05) that independently discriminated CAD state in a multivariable analysis that included clinical and demographic factors. From an independent second catheterization cohort, 215 patients were selected for real-time polymerase chain reaction–based replication. A case:control subset of 107 patients (86 cases, 21 controls) replicated 11 of the 14 multivariably significant genes from the first cohort. An analysis of the 14 genes in the entire set of 215 patients demonstrated that gene expression was proportional to maximal coronary artery stenosis (P<0.001 by ANOVA). Conclusions—Gene expression in peripheral-blood cells reflects the presence and extent of CAD in patients undergoing angiography.

Multicenter Validation of the Diagnostic Accuracy of a Blood-Based Gene Expression Test for Assessing Obstructive Coronary Artery Disease in Nondiabetic Patients

BACKGROUND Diagnosing obstructive coronary artery disease (CAD) in at-risk patients can be challenging and typically requires both noninvasive imaging methods and coronary angiography, the gold standard. Previous studies have suggested that peripheral blood gene expression can indicate the presence of CAD. OBJECTIVE To validate a previously developed 23-gene, expression-based classification test for diagnosis of obstructive CAD in nondiabetic patients. DESIGN Multicenter prospective trial with blood samples obtained before coronary angiography. (ClinicalTrials.gov registration number: NCT00500617) SETTING: 39 centers in the United States. PATIENTS An independent validation cohort of 526 nondiabetic patients with a clinical indication for coronary angiography. MEASUREMENTS Receiver-operating characteristic (ROC) analysis of classifier score measured by real-time polymerase chain reaction, additivity to clinical factors, and reclassification of patient disease likelihood versus disease status defined by quantitative coronary angiography. Obstructive CAD was defined as 50% or greater stenosis in 1 or more major coronary arteries by quantitative coronary angiography. RESULTS The area under the ROC curve (AUC) was 0.70 ± 0.02 (P < 0.001); the test added to clinical variables (Diamond-Forrester method) (AUC, 0.72 with the test vs. 0.66 without; P = 0.003) and added somewhat to an expanded clinical model (AUC, 0.745 with the test vs. 0.732 without; P = 0.089). The test improved net reclassification over both the Diamond-Forrester method and the expanded clinical model (P < 0.001). At a score threshold that corresponded to a 20% likelihood of obstructive CAD (14.75), the sensitivity and specificity were 85% and 43% (yielding a negative predictive value of 83% and a positive predictive value of 46%), with 33% of patient scores below this threshold. LIMITATION Patients with chronic inflammatory disorders, elevated levels of leukocytes or cardiac protein markers, or diabetes were excluded. CONCLUSION A noninvasive whole-blood test based on gene expression and demographic characteristics may be useful for assessing obstructive CAD in nondiabetic patients without known CAD. PRIMARY FUNDING SOURCE CardioDx.

Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients.

BackgroundAlterations in gene expression in peripheral blood cells have been shown to be sensitive to the presence and extent of coronary artery disease (CAD). A non-invasive blood test that could reliably assess obstructive CAD likelihood would have diagnostic utility.ResultsMicroarray analysis of RNA samples from a 195 patient Duke CATHGEN registry case:control cohort yielded 2,438 genes with significant CAD association (p < 0.05), and identified the clinical/demographic factors with the largest effects on gene expression as age, sex, and diabetic status. RT-PCR analysis of 88 CAD classifier genes confirmed that diabetic status was the largest clinical factor affecting CAD associated gene expression changes. A second microarray cohort analysis limited to non-diabetics from the multi-center PREDICT study (198 patients; 99 case: control pairs matched for age and sex) evaluated gene expression, clinical, and cell population predictors of CAD and yielded 5,935 CAD genes (p < 0.05) with an intersection of 655 genes with the CATHGEN results. Biological pathway (gene ontology and literature) and statistical analyses (hierarchical clustering and logistic regression) were used in combination to select 113 genes for RT-PCR analysis including CAD classifiers, cell-type specific markers, and normalization genes.RT-PCR analysis of these 113 genes in a PREDICT cohort of 640 non-diabetic subject samples was used for algorithm development. Gene expression correlations identified clusters of CAD classifier genes which were reduced to meta-genes using LASSO. The final classifier for assessment of obstructive CAD was derived by Ridge Regression and contained sex-specific age functions and 6 meta-gene terms, comprising 23 genes. This algorithm showed a cross-validated estimated AUC = 0.77 (95% CI 0.73-0.81) in ROC analysis.ConclusionsWe have developed a whole blood classifier based on gene expression, age and sex for the assessment of obstructive CAD in non-diabetic patients from a combination of microarray and RT-PCR data derived from studies of patients clinically indicated for invasive angiography.Clinical trial registration informationPREDICT, Personalized Risk Evaluation and Diagnosis in the Coronary Tree, http://www.clinicaltrials.gov, NCT00500617

Gene-trapped mouse embryonic stem cell-derived cardiac myocytes and human genetics implicate AKAP10 in heart rhythm regulation.

Sudden cardiac death due to abnormal heart rhythm kills 400,000–460,000 Americans each year. To identify genes that regulate heart rhythm, we are developing a screen that uses mouse embryonic stem cells (mESCs) with gene disruptions that can be differentiated into cardiac cells for phenotyping. Here, we show that the heterozygous disruption of the Akap10 (D-AKAP2) gene that disrupts the final 51 aa increases the contractile response of cultured cardiac cells to cholinergic signals. In both heterozygous and homozygous mutant mice derived from these mESCs, the same Akap10 disruption increases the cardiac response to cholinergic signals, suggesting a dominant interfering effect of the Akap10 mutant allele. The mutant mice have cardiac arrhythmias and die prematurely. We also found that a common variant of AKAP10 in humans (646V, 40% of alleles) was associated with increased basal heart rate and decreased heart rate variability (markers of low cholinergic/vagus nerve sensitivity). These markers predict an increased risk of sudden cardiac death. Although the molecular mechanism remains unknown, our findings in mutant mESCs, mice, and a common human AKAP10 SNP all suggest a role for AKAP10 in heart rhythm control. Our stem cell-based screen may provide a means of identifying other genes that control heart rhythm.

Modeling Insertional Mutagenesis Using Gene Length and Expression in Murine Embryonic Stem Cells

Background High-throughput mutagenesis of the mammalian genome is a powerful means to facilitate analysis of gene function. Gene trapping in embryonic stem cells (ESCs) is the most widely used form of insertional mutagenesis in mammals. However, the rules governing its efficiency are not fully understood, and the effects of vector design on the likelihood of gene-trapping events have not been tested on a genome-wide scale. Methodology/Principal Findings In this study, we used public gene-trap data to model gene-trap likelihood. Using the association of gene length and gene expression with gene-trap likelihood, we constructed spline-based regression models that characterize which genes are susceptible and which genes are resistant to gene-trapping techniques. We report results for three classes of gene-trap vectors, showing that both length and expression are significant determinants of trap likelihood for all vectors. Using our models, we also quantitatively identified hotspots of gene-trap activity, which represent loci where the high likelihood of vector insertion is controlled by factors other than length and expression. These formalized statistical models describe a high proportion of the variance in the likelihood of a gene being trapped by expression-dependent vectors and a lower, but still significant, proportion of the variance for vectors that are predicted to be independent of endogenous gene expression. Conclusions/Significance The findings of significant expression and length effects reported here further the understanding of the determinants of vector insertion. Results from this analysis can be applied to help identify other important determinants of this important biological phenomenon and could assist planning of large-scale mutagenesis efforts.