Non-linear interactions between candidate genes of myocardial infarction revealed in mRNA expression profiles

Coherent pathway enrichment estimation by modeling inter-pathway dependencies using regularized regression

MOTIVATION

Gene set enrichment methods are a common tool to improve the interpretability of gene lists as obtained, for example, from differential gene expression analyses. They are based on computing whether dysregulated genes are located in certain biological pathways more often than expected by chance. Gene set enrichment tools rely on pre-existing pathway databases such as KEGG, Reactome, or the Gene Ontology. These databases are increasing in size and in the number of redundancies between pathways, which complicates the statistical enrichment computation.

RESULTS

We address this problem and develop a novel gene set enrichment method, called pareg, which is based on a regularized generalized linear model and directly incorporates dependencies between gene sets related to certain biological functions, for example, due to shared genes, in the enrichment computation. We show that pareg is more robust to noise than competing methods. Additionally, we demonstrate the ability of our method to recover known pathways as well as to suggest novel treatment targets in an exploratory analysis using breast cancer samples from TCGA.

AVAILABILITY AND IMPLEMENTATION

pareg is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/pareg.html) as well as on https://github.com/cbg-ethz/pareg. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here.

Genome-wide association study of early ischaemic stroke risk in Brazilian individuals with sickle cell disease implicates ADAMTS2 and CDK18 and uncovers novel loci

Ischaemic stroke is a common complication of sickle cell disease (SCD) and without intervention can affect 11% of children with SCD before the age of 20. Within the Trans-Omics for Precision Medicine (TOPMed), a genome-wide association study (GWAS) of ischaemic stroke was performed on 1333 individuals with SCD from Brazil (178 cases, 1155 controls). Via a novel Cox proportional-hazards analysis, we searched for variants associated with ischaemic stroke occurring at younger ages. Variants at genome-wide significance (p < 5 × 10-8 ) include two near genes previously linked to non-SCD early-onset stroke (<65 years): ADAMTS2 (rs147625068, p = 3.70 × 10-9 ) and CDK18 (rs12144136, p = 2.38 × 10-9 ). Meta-analysis, which included the independent SCD cohorts Walk-PHaSST and PUSH, exhibited consistent association for variants rs1209987 near gene TBC1D32 (p = 3.36 × 10-10 ), rs188599171 near CUX1 (p = 5.89 × 10-11 ), rs77900855 near BTG1 (p = 4.66 × 10-8 ), and rs141674494 near VPS13C (1.68 × 10-9 ). Findings from this study support a multivariant model of early ischaemic stroke risk and possibly a shared genetic architecture between SCD individuals and non-SCD individuals younger than 65 years.

Interpreting coronary artery disease GWAS results: A functional genomics approach assessing biological significance

Genome-wide association studies (GWAS) have implicated 58 loci in coronary artery disease (CAD). However, the biological basis for these associations, the relevant genes, and causative variants often remain uncertain. Since the vast majority of GWAS loci reside outside coding regions, most exert regulatory functions. Here we explore the complexity of each of these loci, using tissue specific RNA sequencing data from GTEx to identify genes that exhibit altered expression patterns in the context of GWAS-significant loci, expanding the list of candidate genes from the 75 currently annotated by GWAS to 245, with almost half of these transcripts being non-coding. Tissue specific allelic expression imbalance data, also from GTEx, allows us to uncover GWAS variants that mark functional variation in a locus, e.g., rs7528419 residing in the SORT1 locus, in liver specifically, and rs72689147 in the GUYC1A1 locus, across a variety of tissues. We consider the GWAS variant rs1412444 in the LIPA locus in more detail as an example, probing tissue and transcript specific effects of genetic variation in the region. By evaluating linkage disequilibrium (LD) between tissue specific eQTLs, we reveal evidence for multiple functional variants within loci. We identify 3 variants (rs1412444, rs1051338, rs2250781) that when considered together, each improve the ability to account for LIPA gene expression, suggesting multiple interacting factors. These results refine the assignment of 58 GWAS loci to likely causative variants in a handful of cases and for the remainder help to re-prioritize associated genes and RNA isoforms, suggesting that ncRNAs maybe a relevant transcript in almost half of CAD GWAS results. Our findings support a multi-factorial system where a single variant can influence multiple genes and each genes is regulated by multiple variants.

Crosstalk between Circulatory Microenvironment and Vascular Endothelial Cells in Acute Myocardial Infarction

Background

The reason of high mortality of acute myocardial infarction (AMI) was the lack of exploring the cellular and molecular mechanism of AMI. Therefore, we explored the crosstalk among cells, as well as its potential molecular mechanism of mediating AMI.

Methods

The gene expression profile of peripheral blood, endothelial, platelets and mononuclear cells were applied to differentially expressed genes (DEGs) analysis. ClusterProfiler and the package of gene set enrichment analysis (GSEA) were applied to explore the potential functional pathways of DEGs in 3 types of intravascular cells (endothelial, platelets and mononuclear cells) and peripheral blood. Subsequently, we extracted the surface receptors, secreted proteins and extracellular matrix from the up-regulated DEGs to explore their potential interactions mechanism of AMI by crosstalk and pivot analysis.

Findings

A total 11 common regulated DEGs (CDEGs) were identified, which might be potential biomarkers for AMI diagnosis. The abnormal pathways involved in DEGs of 3 types of intravascular cells and peripheral blood were shown, which also verified by GSEA. Afterwards, it was found that there was crosstalk in 3 types of intravascular cells and peripheral blood. Furthermore, we constructed a cell–cell interaction map among cells in AMI regulated by exosome lncRNA, which was involved in the development of AMI. Finally, we identified 8 hub genes, which might be potential biomarkers of AMI.

Interpretation

The result of this study can not only be used as a reference for subsequent experiments and further exploration, but also contribute to the development of novel cell and molecular therapies.

Association of PHACTR1 intronic variants with the first myocardial infarction and their effect on PHACTR1 mRNA expression in PBMCs

BACKGROUND

Myocardial infarction (MI) and underlining atherosclerosis are the main causes of death worldwide. Phosphatase and actin regulator 1 (PHACTR1) variants have been associated with early onset MI, coronary artery disease and carotid dissection. PHACTR1 mRNA expression has been detected in tissues and cells related to atherosclerosis. Nonetheless, the true effect of PHACTR1 on vascular diseases is still unknown. Our aim was to examine the association of PHACTR1 intronic variants, rs9349379, rs2026458 and rs2876300, with MI and multi-vessel disease (MVD) and to assess their effect on PHACTR1 and EDN1 mRNA expression in PBMCs of patients six months after MI.

METHODS

The study enrolled 537 patients with the first MI and 310 controls. Gene expression was assessed in 74 patients six months after MI and 37 healthy controls. Rs9349379, rs2026458, rs2876300 and relative mRNA expressions were detected by TaqMan® technology.

RESULTS

The significant association between PHACTR1 variants and MI was not found, either individually or in haplotype. A higher frequency of rs2876300G-allele in MVD was rendered not significant after Bonferroni correction. PHACTR1 mRNA was significantly increased in PBMCs of patients six months after MI compared to controls (p = 0.02). Patients that carry ACG haplotype have increased PHACTR1 mRNA expression in PBMCs (p = 0.04). There was no effect of PHACTR1 variants on EDN1 mRNA expression.

CONCLUSION

Our findings suggest that PHACTR1 intronic variants may have a role in severity and progression of coronary atherosclerosis. Future research is needed to clarify the mechanism underlying the role of PHACTR1 in coronary atherosclerosis and MI.

IL-18/IL-37/IP-10 signalling complex as a potential biomarker for discriminating active and latent TB

Background

Currently, there are serious limitations in the direct diagnosis of active tuberculosis (ATB). We evaluated the levels of the IL-18/IL-37/IP-10 signalling complex proteins in Mycobacterium tuberculosis (M.tb)-specific antigen-stimulated QuantiFERON® Gold In-Tube (QFT) cultures and in serum samples from ATB patients, healthy individuals with latent M.tb infection (LTBI) and healthy controls (HC) to examine whether combined analyses of these proteins were useful in the differentiation of M.tb states.

Methods

The concentrations of IL-18, IL-18BP, IFN-γ, IL-37 and IP-10 in the serum and QFT supernatants were measured using specific enzyme-linked immunosorbent assay (ELISA) kits. Free IL-18 levels were calculated using the law of mass action.

Results

Increased concentrations of total and free IL-18, IL-18BP, IFN-γ and IP-10 in the sera of ATB patients were detected. These increases were not counterbalanced by the overproduction of IL-37. Complex co-expression of serum IL-18BP and IL-37, IP-10 and IFN-γ was identified as the highest discriminative biomarker set for the diagnosis of ATB.

Conclusions

Our results suggest that the IL-18 signalling complex might be exploited by M. tuberculosis to expand the clinical manifestations of pulmonary TB. Therefore, direct analysis of the serum components of the IL-18/IL-37 signalling complex and IP-10 may be applicable in designing novel diagnostic tests for ATB.

Fluid-based assays and precision medicine of cardiovascular diseases: the ‘hope’ for Pandora’s box?

Progresses in liquid-based assays may provide novel useful non-invasive indicators of cardiovascular (CV) diseases. By analysing circulating cells or their products in blood, saliva and urine samples, we can investigate molecular changes present at specific time points in each patient allowing sequential monitoring of disease evolution. For example, an increased number of circulating endothelial cells may be a diagnostic biomarker for diabetic nephropathy and heart failure with preserved ejection fraction. The assessment of circulating cell-free DNA (cfDNA) levels may be useful to predict severity of acute myocardial infarction, as well as diagnose heart graft rejection. Remarkably, circulating epigenetic biomarkers, including DNA methylation, histone modifications and non-coding RNAs are key pathogenic determinants of CV diseases representing putative useful biomarkers and drug targets. For example, the unmethylated FAM101A gene may specifically trace cfDNA derived from cardiomyocyte death providing a powerful diagnostic biomarker of apoptosis during ischaemia. Moreover, changes in plasma levels of circulating miR-92 may predict acute coronary syndrome onset in patients with diabetes. Now, network medicine provides a framework to analyse a huge amount of big data by describing a CV disease as a result of a chain of molecular perturbations rather than a single defect (reductionism). We outline advantages and challenges of liquid biopsy with respect to traditional tissue biopsy and summarise the main completed and ongoing clinical trials in CV diseases. Furthermore, we discuss the importance of combining fluid-based assays, big data and network medicine to improve precision medicine and personalised therapy in this field.

Identification of a blood-based 12-gene signature that predicts the severity of coronary artery stenosis: An integrative approach based on gene network construction, Support Vector Machine algorithm, and multi-cohort validation

BACKGROUND AND AIMS

We aimed to identify a blood-based gene expression score (GES) to predict the severity of coronary artery stenosis in patients with known or suspected coronary artery disease (CAD) by integrative use of gene network construction, Support Vector Machine (SVM) algorithm, and multi-cohort validation.

METHODS

In the discovery phase, a public blood-based microarray dataset of 110 patients with known CAD was analyzed by weighted gene coexpression network analysis and protein-protein interaction network analysis to identify candidate hub genes. In the training set with 151 CAD patients, bioinformatically identified hub genes were experimentally verified by real-time polymerase chain reaction, and statistically filtered with the SVM algorithm to develop a GES. Internal and external validation of GES was performed in patients with suspected CAD from two validation cohorts (n = 209 and 206).

RESULTS

The discovery phase screened 15 network-centric hub genes significantly correlated with the Duke CAD Severity Index. In the training cohort, 12 of 15 hub genes were filtered to construct a blood-based GES12, which showed good discrimination for higher modified Gensini scores (AUC: 0.798 and 0.812), higher Sullivan Extent scores (AUC: 0.776 and 0.778), and the presence of obstructive CAD (AUC: 0.834 and 0.792) in two validation cohorts. A nomogram comprising GES12, smoking status, hypertension status, low density lipoprotein cholesterol level, and body mass index further improved performance, with respect to discrimination, risk classification, and clinical utility, for prediction of coronary stenosis severity.

CONCLUSIONS

GES12 is useful in predicting the severity of coronary artery stenosis in patients with known or suspected CAD.

UBA6 and Its Bispecific Pathways for Ubiquitin and FAT10

Questions have been raised since the discovery of UBA6 and its significant coexistence with UBE1 in the ubiquitin–proteasome system (UPS). The facts that UBA6 has the dedicated E2 enzyme USE1 and the E1–E2 cascade can activate and transfer both ubiquitin and ubiquitin-like protein FAT10 have attracted a great deal of attention to the regulational mechanisms of the UBA6–USE1 cascade and to how FAT10 and ubiquitin differentiate with each other. This review recapitulates the latest advances in UBA6 and its bispecific UBA6–USE1 pathways for both ubiquitin and FAT10. The intricate networks of UBA6 and its interplays with ubiquitin and FAT10 are briefly reviewed, as are their individual and collective functions in diverse physiological conditions.

A transatlantic perspective on the integration of immuno-oncology prognostic and predictive biomarkers in innovative clinical trial design

Immuno-therapeutics aim to activate the body's own immune system against cancer and are one of the most promising cancer treatment strategies, but currently limited by a variable response rate. Biomarkers may help to distinguish those patients most likely to respond to therapy; they may also help guide clinical decision making for combination therapies, dosing schedules, and determining progression versus relapse. However, there is a need to confirm such biomarkers in preferably prospective clinical trials before they can be used in practice. Accordingly, it is essential that clinical trials for immuno-therapeutics incorporate biomarkers. Here, focusing on the specific setting of immune therapies, we discuss both the scientific and logistical hurdles to identifying potential biomarkers and testing them in clinical trials.

Whole Transcriptome Sequencing Analyses Reveal Molecular Markers of Blood Pressure Response to Thiazide Diuretics

Thiazide diuretics (TD) are commonly prescribed anti-hypertensives worldwide. However, <40% of patients treated with thiazide monotherapy achieve BP control. This study uses whole transcriptome sequencing to identify novel molecular markers associated with BP response to TD. We assessed global RNA expression levels in whole blood samples from 150 participants, representing patients in the upper and lower quartile of BP response to TD from the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) (50 whites) and from PEAR-2 (50 whites and 50 blacks). In each study cohort, we performed poly-A RNA-sequencing in baseline samples from 25 responders and 25 non-responders to hydrochlorothiazide (HCTZ) or chlorthalidone. At FDR adjusted p-value < 0.05, 29 genes were differentially expressed in relation to HCTZ or chlorthalidone BP response in whites. For each differentially expressed gene, replication was attempted in the alternate white group and PEAR-2 blacks. CEBPD (meta-analysis p = 1.8 × 10⁻¹¹) and TSC22D3 (p = 1.9 × 10⁻⁹) were differentially expressed in all 3 cohorts, and explain, in aggregate, 21.9% of response variability to TD. This is the first report of the use of transcriptome-wide sequencing data to identify molecular markers of antihypertensive drug response. These findings support CEBPD and TSC22D3 as potential biomarkers of BP response to TD.

Estimation of Sobol's Sensitivity Indices under Generalized Linear Models

We derive explicit formulas for Sobol's sensitivity indices (SSIs) under the generalized linear models (GLMs) with independent or multivariate normal inputs. We argue that the main-effect SSIs provide a powerful tool for variable selection under GLMs with identity links under polynomial regressions. We also show via examples that the SSI-based variable selection results are similar to the ones obtained by the random forest algorithm but without the computational burden of data permutation. Finally, applying our results to the problem of gene network discovery, we identify though the SSI analysis of a public microarray dataset several novel higher-order gene-gene interactions missed out by the more standard inference methods. The relevant functions for SSI analysis derived here under GLMs with identity, log, and logit links are implemented and made available in the R package SobolSensitivity.