Peripheral Blood RNA Levels of QSOX1 and PLBD1 Are New Independent Predictors of Left Ventricular Dysfunction After Acute Myocardial Infarction.

BACKGROUND: The identification of patients with acute myocardial infarction (MI) at risk of subsequent left ventricular (LV) dysfunction remains challenging, but it is important to optimize therapies. The aim of this study was to determine the unbiased RNA profile in peripheral blood of patients with acute MI and to identify and validate new prognostic markers of LV dysfunction. METHODS: We prospectively enrolled a discovery cohort with acute MI (n=143) and performed whole-blood RNA profiling at different time points. We then selected transcripts on admission that related to LV dysfunction at follow-up and validated them by quantitative polymerase chain reaction in the discovery cohort, in an external validation cohort (n=449), and in a representative porcine MI model with cardiac magnetic resonance-based measurements of infarct size and postmortem myocardial pathology (n=33). RESULTS: RNA profiling in the discovery cohort showed upregulation of genes involved in chemotaxis, IL (interleukin)-6, and NF-κB (nuclear factor-κB) signaling in the acute phase of MI. Expression levels of the majority of these transcripts paralleled the rise in cardiac troponin T and decayed at 30 days. RNA levels of QSOX1, PLBD1, and S100A8 on admission with MI correlated with LV dysfunction at follow-up. Using quantitative polymerase chain reaction, we confirmed that QSOX1 and PLBD1 predicted LV dysfunction (odds ratio, 2.6 [95% CI, 1.1-6.1] and 3.2 [95% CI, 1.4-7.4]), whereas S100A8 did not. In the external validation cohort, we confirmed QSOX1 and PLBD1 as new independent markers of LV dysfunction (odds ratio, 1.41 [95% CI, 1.06-1.88] and 1.43 [95% CI, 1.08-1.89]). QSOX1 had an incremental predictive value in a model consisting of clinical variables and cardiac biomarkers (including NT-proBNP [N-terminal pro-B-type natriuretic peptide]). In the porcine MI model, whole-blood levels of QSOX1 and PLBD1 related to neutrophil infiltration in the ischemic myocardium in an infarct size-independent manner. CONCLUSIONS: Peripheral blood QSOX1 and PLBD1 in acute MI are new independent markers of LV dysfunction post-MI.

Analyzing magnetic bead QuantiGene® Plex 2.0 gene expression data in high throughput mode using QGprofiler.

BACKGROUND: The QuantiGene® Plex 2.0 platform (ThermoFisher Scientific) combines bDNA with the Luminex/xMAP magnetic bead capturing technology to assess differential gene expression in a compound exposure setting. This technology allows multiplexing in a single well of a 96 or 384 multi-well plate and can thus be used in high throughput drug discovery mode. Data interpretation follows a three-step normalization/transformation flow in which raw median fluorescent gene signals are transformed to fold change values with the use of proper housekeeping genes and negative controls. Clear instructions on how to assess the data quality and tools to perform this analysis in high throughput mode are, however, currently lacking. RESULTS: In this paper we introduce QGprofiler, an open source R based shiny application. QGprofiler allows for proper QuantiGene® Plex 2.0 assay optimization, choice of housekeeping genes and data pre-processing up to fold change, including appropriate QC metrics. In addition, QGprofiler allows for an Akaike information criterion based dose response fold change model selection and has a built-in tool to detect the cytotoxic potential of compounds evaluated in a high throughput screening campaign. CONCLUSION: QGprofiler is a user friendly, open source available R based shiny application, which is developed to support drug discovery campaigns. In this context, entire compound libraries/series can be tested in dose response against a gene signature of choice in search for new disease relevant chemical entities. QGprofiler is available at: https://qgprofiler.openanalytics.eu/app/QGprofiler.

ACE-inhibition induces a cardioprotective transcriptional response in the metabolic syndrome heart.

Cardiovascular disease associated with metabolic syndrome has a high prevalence, but the mechanistic basis of metabolic cardiomyopathy remains poorly understood. We characterised the cardiac transcriptome in a murine metabolic syndrome (MetS) model (LDLR-/-; ob/ob, DKO) relative to the healthy, control heart (C57BL/6, WT) and the transcriptional changes induced by ACE-inhibition in those hearts. RNA-Seq, differential gene expression and transcription factor analysis identified 288 genes differentially expressed between DKO and WT hearts implicating 72 pathways. Hallmarks of metabolic cardiomyopathy were increased activity in integrin-linked kinase signalling, Rho signalling, dendritic cell maturation, production of nitric oxide and reactive oxygen species in macrophages, atherosclerosis, LXR-RXR signalling, cardiac hypertrophy, and acute phase response pathways. ACE-inhibition had a limited effect on gene expression in WT (55 genes, 23 pathways), and a prominent effect in DKO hearts (1143 genes, 104 pathways). In DKO hearts, ACE-I appears to counteract some of the MetS-specific pathways, while also activating cardioprotective mechanisms. We conclude that MetS and control murine hearts have unique transcriptional profiles and exhibit a partially specific transcriptional response to ACE-inhibition.

Methylome analysis for spina bifida shows SOX18 hypomethylation as a risk factor with evidence for a complex (epi)genetic interplay to affect neural tube development.

BACKGROUND: Neural tube defects (NTDs) are severe congenital malformations that arise from failure of neurulation during early embryonic development. The molecular basis underlying most human NTDs still remains largely unknown. Based on the hypothesis that folic acid prevents NTDs by stimulating methylation reactions, DNA methylation changes could play a role in NTDs. We performed a methylome analysis for patients with myelomeningocele (MMC). Using a candidate CpG analysis for HOX genes, a significant association between HOXB7 hypomethylation and MMC was found. METHODS: In the current study, we analyzed leukocyte methylome data of ten patients with MMC and six controls using Illumina Methylation Analyzer and WateRmelon R-packages and performed validation studies using larger MMC and control cohorts with Sequenom EpiTYPER. RESULTS: The methylome analysis showed 75 CpGs in 45 genes that are significantly differentially methylated in MMC patients. CpG-specific methylation differences were next replicated for the top six candidate genes ABAT, CNTNAP1, SLC1A6, SNED1, SOX18, and TEPP but only for the SOX18 locus a significant overall hypomethylation was observed (P value = 0.0003). Chemically induced DNA demethylation in HEK cells resulted in SOX18 hypomethylation and increased expression. Injection of sox18 mRNA in zebrafish resulted in abnormal neural tube formation. Quantification of DNA methylation for the SOX18 locus was also determined for five families where parents had normal methylation values compared to significant lower values for both the MMC as their non-affected child. SOX18 methylation studies were performed for a MMC patient with a paternally inherited chromosomal deletion that includes BMP4. The patient showed extreme SOX18 hypomethylation similar to his healthy mother while his father had normal methylation values. CONCLUSIONS: This is the first genome-wide methylation study in leukocytes for patients with NTDs. We report SOX18 as a novel MMC risk gene but our findings also suggest that SOX18 hypomethylation must interplay with environmental and (epi)genetic factors to cause NTDs. Further studies are needed that combine methylome data with next-generation sequencing approaches to unravel NTD etiology.

Galahad: a web server for drug effect analysis from gene expression.

Galahad (https://galahad.esat.kuleuven.be) is a web-based application for analysis of drug effects. It provides an intuitive interface to be used by anybody interested in leveraging microarray data to gain insights into the pharmacological effects of a drug, mainly identification of candidate targets, elucidation of mode of action and understanding of off-target effects. The core of Galahad is a network-based analysis method of gene expression. As an input, Galahad takes raw Affymetrix human microarray data from treatment versus control experiments and provides quality control and data exploration tools, as well as computation of differential expression. Alternatively, differential expression values can be uploaded directly. Using these differential expression values, drug target prioritization and both pathway and disease enrichment can be calculated and visualized. Drug target prioritization is based on the integration of the gene expression data with a functional protein association network. The web site is free and open to all and there is no login requirement.

Finding the targets of a drug by integration of gene expression data with a protein interaction network.

Polypharmacology, which focuses on designing drugs that bind efficiently to multiple targets, has emerged as a new strategic trend in today's drug discovery research. Many successful drugs achieve their effects via multi-target interactions. However, these targets are largely unknown for both marketed drugs and drugs in development. A better knowledge of a drug's mode of action could be of substantial value to future drug development, in particular for side effect prediction and drug repositioning. We propose a network-based computational method for drug target prediction, applicable on a genome-wide scale. Our approach relies on the analysis of gene expression following drug treatment in the context of a functional protein association network. By diffusing differential expression signals to neighboring or correlated nodes in the network, genes are prioritized as potential targets based on the transcriptional response of functionally related genes. Different diffusion strategies were evaluated on 235 publicly available gene expression datasets for treatment with bioactive molecules having a known target. AUC values of up to more than 90% demonstrate the effectiveness of our approach and indicate the predictive power of integrating experimental gene expression data with prior knowledge from protein association networks.