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Differential gene expression in thrombus-derived white blood cells of patients with acute coronary syndrome

ABSTRACT: Inflammatory mechanisms and immune cells are involved in acute coronary syndromes (ACS) and may lead to acute plaque rupture. However, the local expression of the different genes potentially involved is largely unknown. We therefore performed an Affymetrix analysis of genes expressed in white blood cells obtained from an occluding coronary thrombus or peripheral blood of patients with ST-elevation myocardial infarction.

ORGANISM(S): Homo sapiens

PROVIDER: GSE19339 | GEO | 2011/12/04

SECONDARY ACCESSION(S): PRJNA121711

REPOSITORIES: GEO

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Differential gene expression in thrombus-derived white blood cells of patients with acute coronary syndrome

Project description:Inflammatory mechanisms and immune cells are involved in acute coronary syndromes (ACS) and may lead to acute plaque rupture. However, the local expression of the different genes potentially involved is largely unknown. We therefore performed an Affymetrix analysis of genes expressed in white blood cells obtained from an occluding coronary thrombus or peripheral blood of patients with ST-elevation myocardial infarction. Thrombi of ACS patients were harvested from the site of coronary occlusion. Leukocytes were isolated by Ficoll centrifugation. Peripheral blood leukocytes (PBL) were treated in a similar fashion and mRNA was extracted from both cells.

2011-12-04 | E-GEOD-19339 | biostudies-arrayexpress

Characterisation of Myocardial Infarction and Unstable Angina with mRNA Profiles from Whole Blood of individual patients

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Coronary Plaque Sampling Reveals Molecular Insights into Coronary Artery Disease

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Project description:Acute coronary syndrome (ACS) is presented as a group of symptoms resulting from acute ischemia of the myocardium due to rupture of a coronary arterial plaque. It comprises different classes; unstable angina, STEMI and NSTEMI. The current cornerstones of ACS diagnosis are ECG, CK-MB and cardiac troponins however there are several limitations leading to evading early diagnosis and poor prognosis. This highlights the need for more sensitive and specific biomarkers for a better outcome. In this study,an initial screening set is RNA sequenced for retrieval of differentially expressed genes and miRNAs. Then, a microRNA-long non-coding RNA multi biomarker panel is retrieved via in silico data analysis related to PINK1/Parkin-induced mitophagy. This is followed by clinical validation of their expression in serum of ACS patients in comparison to non-cardiac chest pain patients and healthy volunteers via qRT-PCR.

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Different gene expression profile in acute coronary sindrome (ACS) patients compared to stable angina(SA) patients.

Project description:We have employed whole genome microarray expression profiling as a discovery platform to identify genes involved in plaque rupture. Human coronary artery endothelial cells (ECs) were stimulated in vitro for 12 hours with plasma obtained from the coronary sinus (CS) and the aorta (Ao) of patients with ACS (n=8), or patients with stable angina (SA, n=4). For each patient, gene expression profile was evaluated by microarray technology in ECs exposed to plasma obtained from CS and compared to that of ECs exposed to plasma sampled from Ao. In patient with ACS we found 684 genes up-regulated and 283 down-regulated was observed as compared to patients with SA. Functional and network analyses of statistically significant gene showed that the up regulated genes were associated to pathways IL17 Signaling. To validate the microarray data the RNAs were used for Real Time PCR experiment. Human coronary artery endothelial cells (ECs) were stimulated in vitro for 12 hours with plasma obtained from the coronary sinus (CS) and the aorta (Ao) of patients with ACS (n=8), or patients with stable angina (SA, n=4). For each patient, gene expression profile was evaluated by microarray technology in ECs exposed to plasma obtained from CS and compared to that of ECs exposed to plasma sampled from Ao.

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Different gene expression profile in acute coronary sindrome (ACS) patients compared to stable angina(SA) patients.

2014-02-14 | GSE54977 | GEO

Characterisation of Myocardial Infarction and Unstable Angina with mRNA Profiles from Whole Blood of individual patients

Project description:Coronary artery disease (CAD) remains a leading cause of death worldwide. Acute coronary syndromes (ACS) are the spectrum of diseases arising from coronary atherosclerotic plaque rupture, ranging from unstable angina (UA; clinical symptoms of cardiac ischemia without myocardial necrosis) to myocardial infarction (MI; clinical symptoms of cardiac ischemia with myocardial necrosis). We use microrray to identify changes in pathways following MI.This study examines mRNA expression levels in human whole blood at 7 and 30 days post ACS. Patients with MI are compared to those with UA (not healthy controls), thus focusing on differences in mRNA expression due to the acute clinical events rather than underlying atherosclerosis and its treatment. We recruited 26 patients presenting with acute coronary syndromes (ACS); 8 with unstable angina (UA) and 18 with MI. Supplementary files: The files contain the combined values (for each group) of the single patients' expression levels, the fold changes and the significance levels associated. Gene expression levels were estimated using probabilistic models implemented in puma (Propagating Uncertainty in Microarray Analysis, bioconductor.org), which provide estimates for the variance and credibility interval for probe level errors of each transcript. FCs were calculated after combining gene expression values within groups using Bayesian hierarchical model, incorporating probe level errors into the variance estimate. Significance levels for differentially expressed genes were detected by calculating the probability of positive log ratio (PPLR). The higher is the probability the more confident is the estimate of that positive FC, conversely the lower is the probability the more confident is the estimate of that FC to be negative. This model was implemented in the pumaComb and pumaDE modules within puma. file1 = mRNA_MI_combday30_exprs file2 = mRNA_MI_combDay7_exprs

2011-05-06 | E-GEOD-29111 | biostudies-arrayexpress

Transcriptome analysis of circulating circRNA expression profiles from patients with coronary artery disease and healthy controls

Project description:Transcriptome analysis of Peripheral Blood Mononuclear Cells (PBMC) samples from nine participants, including three in the Ctrl group, three in the (stable angina) SA group, and three in the acute coronary syndrome (ACS) group, was performed.

2024-06-01 | GSE252710 | GEO

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2020-06-05 | PXD009356 | Pride

Whole-blood transcriptional profiles early predict the presence of coronary atherosclerosis and high-risk plaque features at coronary CT angiography

Project description:This study aims at identifying gene expression patterns in the whole blood that could differentiate patients with severe coronary atherosclerosis from subjects without detectable coronary artery disease (CAD), and assess associations of gene expression patterns with plaque features at coronary CT angiography (CCTA). Patients undergoing CCTA for suspected CAD, with no cardiovascular history, were enrolled. Coronary stenosis was quantified and CCTA plaque features were assessed. The whole-blood transcriptome was analyzed by RNA-Sequencing. We detected highly significant differences in the circulating transcriptome between patients with high-degree coronary stenosis (> 70%) at CCTA and subjects with the absence of coronary plaques. Noteworthy, regression analysis revealed expression signatures associated with Leaman score, segment involved score, segment-stenosis score, and plaque volume with density <150 HU at CCTA. This pilot study shows that patients with significant coronary stenosis are characterized by whole blood transcriptome profiles that may discriminate them from patients without CAD. Furthermore, our results suggest that whole blood transcriptional profiles may predict plaque characteristics.

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