Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease

Systems genetics analysis of human body fat distribution genes identifies adipocyte processes

Excess abdominal fat is a sexually dimorphic risk factor for cardio-metabolic disease and is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Whereas this trait is highly heritable, few causal genes are known. We aimed to identify novel drivers of WHRadjBMI using systems genetics. We used two independent cohorts of adipose tissue gene expression and constructed sex- and depot-specific Bayesian networks to model gene-gene interactions from 8,492 genes. Using key driver analysis, we identified genes that, in silico and putatively in vitro, regulate many others. 51-119 key drivers in each network were replicated in both cohorts. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We overexpressed or down-regulated seven key driver genes in human subcutaneous pre-adipocytes. Key driver genes ANAPC2 and RSPO1 inhibited adipogenesis, whereas PSME3 increased adipogenesis. RSPO1 increased Wnt signaling activity. In differentiated adipocytes, MIGA1 and UBR1 down-regulation led to mitochondrial dysfunction. These five genes regulate adipocyte function, and we hypothesize that they regulate fat distribution.

Neutrophil peptidylarginine deiminase 4 plays a systemic role in obesity-induced chronic inflammation in mice

BACKGROUND

Obesity is an increasing problem in our current society and is expected to keep rising in incidence. With its multiorigin, complex pathophysiology, it is difficult to treat and easy to acquire unnoticeably. During obesity, it has been established that the body is in a constant state of low-grade inflammation, thereby causing changes in immune cell physiology.

OBJECTIVES

Here, we investigated the influence of neutrophils, more specifically as a result of peptidylarginine deiminase 4 (PAD4) activity and the release of neutrophil extracellular traps (NETs), during obesity-induced chronic inflammation.

METHODS

Wild-type mice were placed on a high-fat diet (HFD) and investigated over a period of 10 weeks for NET formation and its impact on the heart. Neutrophil-selective PAD4 knockout (Ne-PAD4-/-) mice were studied in parallel.

RESULTS

As a result of high fat intake, we observed clear alteration in the priming status of isolated neutrophils toward NET release, including early stages of speck formation and histone citrullination of apoptosis-associated speck-like protein containing a CARD. Ne-PAD4-/- mice deficient in NET formation did not increase bodyweight to the same extent as their littermate controls, with Ne-PAD4-/- mice being leaner after 10 weeks of HFD feeding. Interestingly, obesity progression led to cardiac remodeling and diastolic dysfunction in wild-type mice after 10 weeks, while this remodeling and subsequent decrease in function were absent in Ne-PAD4-/- mice. Surprisingly, HFD did not alter NET content or thrombus formation in the inferior vena cava stenosis model.

CONCLUSION

Detrimental physiological effects, the result of obesity progression, can in part be attributed to neutrophil PAD4 and NETs in response to chronic inflammation.

Protein interaction networks in the vasculature prioritize genes and pathways underlying coronary artery disease

Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we perform interaction proteomics for 11 CAD-risk genes to map their protein-protein interactions (PPIs) in human vascular cells and elucidate their roles in CAD. The resulting PPI networks contain interactions that are outside of known biology in the vasculature and are enriched for genes involved in immunity-related and arterial-wall-specific mechanisms. Several PPI networks derived from smooth muscle cells are significantly enriched for genetic variants associated with CAD and related vascular phenotypes. Furthermore, the networks identify 61 genes that are found in genetic loci associated with risk of CAD, prioritizing them as the causal candidates within these loci. These findings indicate that the PPI networks we have generated are a rich resource for guiding future research into the molecular pathogenesis of CAD.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Gene Regulatory Networks in Coronary Artery Disease

PURPOSE OF REVIEW

Coronary artery disease is a complex disorder and the leading cause of mortality worldwide. As technologies for the generation of high-throughput multiomics data have advanced, gene regulatory network modeling has become an increasingly powerful tool in understanding coronary artery disease. This review summarizes recent and novel gene regulatory network tools for bulk tissue and single cell data, existing databases for network construction, and applications of gene regulatory networks in coronary artery disease.

RECENT FINDINGS

New gene regulatory network tools can integrate multiomics data to elucidate complex disease mechanisms at unprecedented cellular and spatial resolutions. At the same time, updates to coronary artery disease expression data in existing databases have enabled researchers to build gene regulatory networks to study novel disease mechanisms. Gene regulatory networks have proven extremely useful in understanding CAD heritability beyond what is explained by GWAS loci and in identifying mechanisms and key driver genes underlying disease onset and progression. Gene regulatory networks can holistically and comprehensively address the complex nature of coronary artery disease. In this review, we discuss key algorithmic approaches to construct gene regulatory networks and highlight state-of-the-art methods that model specific modes of gene regulation. We also explore recent applications of these tools in coronary artery disease patient data repositories to understand disease heritability and shared and distinct disease mechanisms and key driver genes across tissues, between sexes, and between species.

Methylglyoxal in Cardiometabolic Disorders: Routes Leading to Pathology Counterbalanced by Treatment Strategies

Methylglyoxal (MGO) is the major compound belonging to reactive carbonyl species (RCS) responsible for the generation of advanced glycation end products (AGEs). Its upregulation, followed by deleterious effects at the cellular and systemic levels, is associated with metabolic disturbances (hyperglycemia/hyperinsulinemia/insulin resistance/hyperlipidemia/inflammatory processes/carbonyl stress/oxidative stress/hypoxia). Therefore, it is implicated in a variety of disorders, including metabolic syndrome, diabetes mellitus, and cardiovascular diseases. In this review, an interplay between pathways leading to MGO generation and scavenging is addressed in regard to this system's impairment in pathology. The issues associated with mechanistic MGO involvement in pathological processes, as well as the discussion on its possible causative role in cardiometabolic diseases, are enclosed. Finally, the main strategies aimed at MGO and its AGEs downregulation with respect to cardiometabolic disorders treatment are addressed. Potential glycation inhibitors and MGO scavengers are discussed, as well as the mechanisms of their action.

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

Mouse models have been used extensively to study human coronary artery disease (CAD) or atherosclerosis and to test therapeutic targets. However, whether mouse and human share similar genetic factors and pathogenic mechanisms of atherosclerosis has not been thoroughly investigated in a data-driven manner. We conducted a cross-species comparison study to better understand atherosclerosis pathogenesis between species by leveraging multiomics data. Specifically, we compared genetically driven and thus CAD-causal gene networks and pathways, by using human GWAS of CAD from the CARDIoGRAMplusC4D consortium and mouse GWAS of atherosclerosis from the Hybrid Mouse Diversity Panel (HMDP) followed by integration with functional multiomics human (STARNET and GTEx) and mouse (HMDP) databases. We found that mouse and human shared >75% of CAD causal pathways. Based on network topology, we then predicted key regulatory genes for both the shared pathways and species-specific pathways, which were further validated through the use of single cell data and the latest CAD GWAS. In sum, our results should serve as a much-needed guidance for which human CAD-causal pathways can or cannot be further evaluated for novel CAD therapies using mouse models.

Systems genetics analysis of human body fat distribution genes identifies Wnt signaling and mitochondrial activity in adipocytes

BACKGROUND

Excess fat in the abdomen is a sexually dimorphic risk factor for cardio-metabolic disease. The relative storage between abdominal and lower-body subcutaneous adipose tissue depots is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Genome-wide association studies (GWAS) identified 346 loci near 495 genes associated with WHRadjBMI. Most of these genes have unknown roles in fat distribution, but many are expressed and putatively act in adipose tissue. We aimed to identify novel sex- and depot-specific drivers of WHRadjBMI using a systems genetics approach.

METHODS

We used two independent cohorts of adipose tissue gene expression with 362 - 444 males and 147 - 219 females, primarily of European ancestry. We constructed sex- and depot- specific Bayesian networks to model the gene-gene interactions from 8,492 adipose tissue genes. Key driver analysis identified genes that, in silico and putatively in vitro, regulate many others, including the 495 WHRadjBMI GWAS genes. Key driver gene function was determined by perturbing their expression in human subcutaneous pre-adipocytes using lenti-virus or siRNA.

RESULTS

51 - 119 key drivers in each network were replicated in both cohorts. We used single-cell expression data to select replicated key drivers expressed in adipocyte precursors and mature adipocytes, prioritized genes which have not been previously studied in adipose tissue, and used public human and mouse data to nominate 53 novel key driver genes (10 - 21 from each network) that may regulate fat distribution by altering adipocyte function. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We selected seven genes whose expression is highly correlated with WHRadjBMI to further study their effects on adipogenesis/Wnt signaling (ANAPC2, PSME3, RSPO1, TYRO3) or mitochondrial function (C1QTNF3, MIGA1, PSME3, UBR1).Adipogenesis was inhibited in cells overexpressing ANAPC2 and RSPO1 compared to controls. RSPO1 results are consistent with a positive correlation between gene expression in the subcutaneous depot and WHRadjBMI, therefore lower relative storage in the subcutaneous depot. RSPO1 inhibited adipogenesis by increasing β-catenin activation and Wnt-related transcription, thus repressing PPARG and CEBPA. PSME3 overexpression led to more adipogenesis than controls. In differentiated adipocytes, MIGA1 and UBR1 downregulation led to mitochondrial dysfunction, with lower oxygen consumption than controls; MIGA1 knockdown also lowered UCP1 expression.

SUMMARY

ANAPC2, MIGA1, PSME3, RSPO1, and UBR1 affect adipocyte function and may drive body fat distribution.

Advances and Challenges for GWAS Analysis in Cardiac Diseases: A Focus on Coronary Artery Disease (CAD)

The achievement of genome-wide association studies (GWAS) has rapidly progressed our understanding of the etiology of coronary artery disease (CAD). It unlocks new strategies to strengthen the stalling of CAD drug development. In this review, we highlighted the recent drawbacks, mainly pointing out those involved in identifying causal genes and interpreting the connections between disease pathology and risk variants. We also benchmark the novel insights into the biological mechanism behind the disease primarily based on outcomes of GWAS. Furthermore, we also shed light on the successful discovery of novel treatment targets by introducing various layers of "omics" data and applying systems genetics strategies. Lastly, we discuss in-depth the significance of precision medicine that is helpful to improve through GWAS analysis in cardiovascular research.

Pyridoxamine prevents increased atherosclerosis by intermittent methylglyoxal spikes in the aortic arches of ApoE-/- mice

Methylglyoxal (MGO) is a reactive glucose metabolite linked to diabetic cardiovascular disease (CVD). MGO levels surge during intermittent hyperglycemia. We hypothesize that these MGO spikes contribute to atherosclerosis, and that pyridoxamine as a MGO quencher prevents this injury. To study this, we intravenously injected normoglycemic 8-week old male C57Bl6 ApoE^-/- mice with normal saline (NS, n = 10) or 25 µg MGO for 10 consecutive weeks (MGO_iv, n = 11) with or without 1 g/L pyridoxamine (MGO_iv+PD, n = 11) in the drinking water. We measured circulating immune cells by flow cytometry. We quantified aortic arch lesion area in aortic roots after Sudan-black staining. We quantified the expression of inflammatory genes in the aorta by qPCR. Intermittent MGO spikes weekly increased atherosclerotic burden in the arch 1.8-fold (NS: 0.9 ± 0.1 vs 1.6 ± 0.2 %), and this was prevented by pyridoxamine (0.8 ± 0.1 %). MGO_iv spikes increased circulating neutrophils and monocytes (2-fold relative to NS) and the expression of ICAM (3-fold), RAGE (5-fold), S100A9 (2-fold) and MCP1 (2-fold). All these changes were attenuated by pyridoxamine. This study suggests that MGO spikes damages the vasculature independently of plasma glucose levels. Pyridoxamine and potentially other approaches to reduce MGO may prevent excess cardiovascular risk in diabetes.

Richardson R. New advances in CRISPR/Cas-mediated precise gene-editing techniques

Over the past decade, CRISPR/Cas-based gene editing has become a powerful tool for generating mutations in a variety of model organisms, from Escherichia coli to zebrafish, rodents and large mammals. CRISPR/Cas-based gene editing effectively generates insertions or deletions (indels), which allow for rapid gene disruption. However, a large proportion of human genetic diseases are caused by single-base-pair substitutions, which result in more subtle alterations to protein function, and which require more complex and precise editing to recreate in model systems. Precise genome editing (PGE) methods, however, typically have efficiencies of less than a tenth of those that generate less-specific indels, and so there has been a great deal of effort to improve PGE efficiency. Such optimisations include optimal guide RNA and mutation-bearing donor DNA template design, modulation of DNA repair pathways that underpin how edits result from Cas-induced cuts, and the development of Cas9 fusion proteins that introduce edits via alternative mechanisms. In this Review, we provide an overview of the recent progress in optimising PGE methods and their potential for generating models of human genetic disease.

Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits

Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.

Advanced glycation endproducts in diabetes-related macrovascular complications: focus on methylglyoxal

Diabetes is associated with vascular injury and the onset of macrovascular complications. Advanced glycation endproducts (AGEs) and the AGE precursor methylglyoxal (MGO) have been identified as key players in establishing the relationship between diabetes and vascular injury. While most research has focused on the link between AGEs and vascular injury, less is known about the effects of MGO on vasculature. In this review, we focus on the mechanisms linking AGEs and MGO to the development of atherosclerosis. AGEs and MGO are involved in many stages of atherosclerosis progression. However, more research is needed to determine the exact mechanisms underlying these effects. Nevertheless, AGEs and MGO could represent valid therapeutic targets for the macrovascular complications of diabetes.

Spatial proteomics in three-dimensional intact specimens

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT.

A review of causal discovery methods for molecular network analysis

BACKGROUND

With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions.

METHODS

We have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade.

RESULTS

In this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area.

CONCLUSION

Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.

A role for artificial intelligence in molecular imaging of infection and inflammation

The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers’ expertise. Although molecular imaging, like [¹⁸F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.

Dicarbonyl Stress in Diabetic Vascular Disease

Late vascular complications play a prominent role in the diabetes-induced increase in morbidity and mortality. Diabetes mellitus is recognised as a risk factor driving atherosclerosis and cardiovascular mortality; even after the normalisation of blood glucose concentration, the event risk is amplified-an effect called "glycolytic memory". The hallmark of this glycolytic memory and diabetic pathology are advanced glycation end products (AGEs) and reactive glucose metabolites such as methylglyoxal (MGO), a highly reactive dicarbonyl compound derived mainly from glycolysis. MGO and AGEs have an impact on vascular and organ structure and function, contributing to organ damage. As MGO is not only associated with hyperglycaemia in diabetes but also with other risk factors for diabetic vascular complications such as obesity, dyslipidaemia and hypertension, MGO is identified as a major player in the development of vascular complications in diabetes both on micro- as well as macrovascular level. In diabetes mellitus, the detoxifying system for MGO, the glyoxalase system, is diminished, accounting for the increased MGO concentration and glycotoxic load. This overview will summarise current knowledge on the effect of MGO and AGEs on vascular function.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

Multi-Cohort Transcriptomic Subtyping of B-Cell Acute Lymphoblastic Leukemia

RNA sequencing provides a snapshot of the functional consequences of genomic lesions that drive acute lymphoblastic leukemia (ALL). The aims of this study were to elucidate diagnostic associations (via machine learning) between mRNA-seq profiles, independently verify ALL lesions and develop easy-to-interpret transcriptome-wide biomarkers for ALL subtyping in the clinical setting. A training dataset of 1279 ALL patients from six North American cohorts was used for developing machine learning models. Results were validated in 767 patients from Australia with a quality control dataset across 31 tissues from 1160 non-ALL donors. A novel batch correction method was introduced and applied to adjust for cohort differences. Out of 18,503 genes with usable expression, 11,830 (64%) were confounded by cohort effects and excluded. Six ALL subtypes (ETV6::RUNX1, KMT2A, DUX4, PAX5 P80R, TCF3::PBX1, ZNF384) that covered 32% of patients were robustly detected by mRNA-seq (positive predictive value ≥ 87%). Five other frequent subtypes (CRLF2, hypodiploid, hyperdiploid, PAX5 alterations and Ph-positive) were distinguishable in 40% of patients at lower accuracy (52% ≤ positive predictive value ≤ 73%). Based on these findings, we introduce the Allspice R package to predict ALL subtypes and driver genes from unadjusted mRNA-seq read counts as encountered in real-world settings. Two examples of Allspice applied to previously unseen ALL patient samples with atypical lesions are included.

False Utopia of One Unifying Description of the Vulnerable Atherosclerotic Plaque: A Call for Recalibration That Appreciates the Diversity of Mechanisms Leading to Atherosclerotic Disease

Atherosclerosis is a complex disease characterized by the formation of arterial plaques with a broad diversity of morphological phenotypic presentations. Researchers often apply one description of the vulnerable plaque as a gold standard in preclinical and clinical research that could be applied as a surrogate measure of a successful therapeutic intervention, despite the variability in lesion characteristics that may underly a thrombotic occlusion. The complex mechanistic interplay underlying progression of atherosclerotic disease is a consequence of the broad range of determinants such as sex, risk factors, hemodynamics, medications, and the genetic landscape. Currently, we are facing an overwhelming amount of data based on genetic, transcriptomic, proteomic, and metabolomic studies that all point to heterogeneous molecular profiles of atherosclerotic lesions that lead to a myocardial infarction or stroke. The observed molecular diversity implies that one unifying model cannot fully recapitulate the natural history of atherosclerosis. Despite emerging data obtained from -omics studies, a description of a natural history of atherosclerotic disease in which cell-specific expression of proteins or genes are included is still lacking. This also applies to the insights provided by genome-wide association studies. This review will critically discuss the dogma that the progression of atherosclerotic disease can be captured in one unifying natural history model of atherosclerosis.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Emerging Glycation-Based Therapeutics-Glyoxalase 1 Inducers and Glyoxalase 1 Inhibitors

The abnormal accumulation of methylglyoxal (MG) leading to increased glycation of protein and DNA has emerged as an important metabolic stress, dicarbonyl stress, linked to aging, and disease. Increased MG glycation produces inactivation and misfolding of proteins, cell dysfunction, activation of the unfolded protein response, and related low-grade inflammation. Glycation of DNA and the spliceosome contribute to an antiproliferative and apoptotic response of high, cytotoxic levels of MG. Glyoxalase 1 (Glo1) of the glyoxalase system has a major role in the metabolism of MG. Small molecule inducers of Glo1, Glo1 inducers, have been developed to alleviate dicarbonyl stress as a prospective treatment for the prevention and early-stage reversal of type 2 diabetes and prevention of vascular complications of diabetes. The first clinical trial with the Glo1 inducer, trans-resveratrol and hesperetin combination (tRES-HESP)-a randomized, double-blind, placebo-controlled crossover phase 2A study for correction of insulin resistance in overweight and obese subjects, was completed successfully. tRES-HESP corrected insulin resistance, improved dysglycemia, and low-grade inflammation. Cell permeable Glo1 inhibitor prodrugs have been developed to induce severe dicarbonyl stress as a prospective treatment for cancer-particularly for high Glo1 expressing-related multidrug-resistant tumors. The prototype Glo1 inhibitor is prodrug S-p-bromobenzylglutathione cyclopentyl diester (BBGD). It has antitumor activity in vitro and in tumor-bearing mice in vivo. In the National Cancer Institute human tumor cell line screen, BBGD was most active against the glioblastoma SNB-19 cell line. Recently, potent antitumor activity was found in glioblastoma multiforme tumor-bearing mice. High Glo1 expression is a negative survival factor in chemotherapy of breast cancer where adjunct therapy with a Glo1 inhibitor may improve treatment outcomes. BBGD has not yet been evaluated clinically. Glycation by MG now appears to be a pathogenic process that may be pharmacologically manipulated for therapeutic outcomes of potentially important clinical impact.

Molecular Biology Networks and Key Gene Regulators for Inflammatory Biomarkers Shared by Breast Cancer Development: Multi-Omics Systems Analysis

As key inflammatory biomarkers C-reactive protein (CRP) and interleukin-6 (IL6) play an important role in the pathogenesis of non-inflammatory diseases, including specific cancers, such as breast cancer (BC). Previous genome-wide association studies (GWASs) have neither explained the large proportion of genetic heritability nor provided comprehensive understanding of the underlying regulatory mechanisms. We adopted an integrative genomic network approach by incorporating our previous GWAS data for CRP and IL6 with multi-omics datasets, such as whole-blood expression quantitative loci, molecular biologic pathways, and gene regulatory networks to capture the full range of genetic functionalities associated with CRP/IL6 and tissue-specific key drivers (KDs) in gene subnetworks. We applied another systematic genomics approach for BC development to detect shared gene sets in enriched subnetworks across BC and CRP/IL6. We detected the topmost significant common pathways across CRP/IL6 (e.g., immune regulatory; chemokines and their receptors; interferon γ, JAK-STAT, and ERBB4 signaling), several of which overlapped with BC pathways. Further, in gene–gene interaction networks enriched by those topmost pathways, we identified KDs—both well-established (e.g., JAK1/2/3, STAT3) and novel (e.g., CXCR3, CD3D, CD3G, STAT6)—in a tissue-specific manner, for mechanisms shared in regulating CRP/IL6 and BC risk. Our study may provide robust, comprehensive insights into the mechanisms of CRP/IL6 regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for associated disorders, such as BC.

The Glyoxalase System in Age-Related Diseases: Nutritional Intervention as Anti-Ageing Strategy

The glyoxalase system is critical for the detoxification of advanced glycation end-products (AGEs). AGEs are toxic compounds resulting from the non-enzymatic modification of biomolecules by sugars or their metabolites through a process called glycation. AGEs have adverse effects on many tissues, playing a pathogenic role in the progression of molecular and cellular aging. Due to the age-related decline in different anti-AGE mechanisms, including detoxifying mechanisms and proteolytic capacities, glycated biomolecules are accumulated during normal aging in our body in a tissue-dependent manner. Viewed in this way, anti-AGE detoxifying systems are proposed as therapeutic targets to fight pathological dysfunction associated with AGE accumulation and cytotoxicity. Here, we summarize the current state of knowledge related to the protective mechanisms against glycative stress, with a special emphasis on the glyoxalase system as the primary mechanism for detoxifying the reactive intermediates of glycation. This review focuses on glyoxalase 1 (GLO1), the first enzyme of the glyoxalase system, and the rate-limiting enzyme of this catalytic process. Although GLO1 is ubiquitously expressed, protein levels and activities are regulated in a tissue-dependent manner. We provide a comparative analysis of GLO1 protein in different tissues. Our findings indicate a role for the glyoxalase system in homeostasis in the eye retina, a highly oxygenated tissue with rapid protein turnover. We also describe modulation of the glyoxalase system as a therapeutic target to delay the development of age-related diseases and summarize the literature that describes the current knowledge about nutritional compounds with properties to modulate the glyoxalase system.

Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics

The Mergeomics web server is a flexible online tool for multi-omics data integration to derive biological pathways, networks, and key drivers important to disease pathogenesis and is based on the open source Mergeomics R package. The web server takes summary statistics of multi-omics disease association studies (GWAS, EWAS, TWAS, PWAS, etc.) as input and features four functions: Marker Dependency Filtering (MDF) to correct for known dependency between omics markers, Marker Set Enrichment Analysis (MSEA) to detect disease relevant biological processes, Meta-MSEA to examine the consistency of biological processes informed by various omics datasets, and Key Driver Analysis (KDA) to identify essential regulators of disease-associated pathways and networks. The web server has been extensively updated and streamlined in version 2.0 including an overhauled user interface, improved tutorials and results interpretation for each analytical step, inclusion of numerous disease GWAS, functional genomics datasets, and molecular networks to allow for comprehensive omics integrations, increased functionality to decrease user workload, and increased flexibility to cater to user-specific needs. Finally, we have incorporated our newly developed drug repositioning pipeline PharmOmics for prediction of potential drugs targeting disease processes that were identified by Mergeomics. Mergeomics is freely accessible at http://mergeomics.research.idre.ucla.edu and does not require login.

Systems genetics in diversity outbred mice inform BMD GWAS and identify determinants of bone strength

Genome-wide association studies (GWASs) for osteoporotic traits have identified over 1000 associations; however, their impact has been limited by the difficulties of causal gene identification and a strict focus on bone mineral density (BMD). Here, we use Diversity Outbred (DO) mice to directly address these limitations by performing a systems genetics analysis of 55 complex skeletal phenotypes. We apply a network approach to cortical bone RNA-seq data to discover 66 genes likely to be causal for human BMD GWAS associations, including the genes SERTAD4 and GLT8D2. We also perform GWAS in the DO for a wide-range of bone traits and identify Qsox1 as a gene influencing cortical bone accrual and bone strength. In this work, we advance our understanding of the genetics of osteoporosis and highlight the ability of the mouse to inform human genetics.

Osteoporosis GWAS faces two challenges, causal gene discovery and a lack of phenotypic diversity. Here, the authors use the Diversity Outbred mouse population to inform human GWAS using networks and map genetic loci for 55 bone traits, identifying new potential bone strength genes.

Carbonyl stress in diabetics with acute coronary syndrome

The prevalence and incidence of diabetes mellitus (DM) are increasing worldwide bringing with it a significantly higher rate of complications. Various mechanisms such as carbonyl stress, polyol pathway, oxidative stress, hexosamine pathways, diacylglycerol/protein kinase-C activation, etc., are responsible for the pathogenesis of DM and its complications. Persistent hyperglycaemia and inhibition of metabolising and detoxifying enzymes lead to the excessive synthesis of carbonyl compounds such as methylglyoxal, glyoxal, and 3-deoxyglucosone, resulting in carbonyl stress. The substrates, metabolizing and detoxifying enzymes of carbonyl compounds are discussed. The mechanistic roles of carbonyl compounds and advanced glycation end products (AGEs) in atherosclerosis, insulin resistance, thrombogenicity, and endothelial dysfunction in animal and cell culture model of DM and patients with DM are summarised. Because of the essential role of carbonyl stress, therapeutics are aimed at scavenging, metabolizing, detoxifying, and inhibiting carbonyl compounds or AGEs so that their harmful effects are minimized. Clinically used drugs, plants extracts and miscellaneous chemical with antiglycation properties are used in an animal model of DM to alleviates the impact of carbonyl compounds. Extensive clinical trials with derivatisation of available antiglycation agents to increase the bioavailability and decrease side effects are warranted further.

Protein glycation - biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine

Protein glycation provides a biomarker in widespread clinical use, glycated hemoglobin HbA1c (A1C). It is a biomarker for diagnosis of diabetes and prediabetes and of medium-term glycemic control in patients with established diabetes. A1C is an early-stage glycation adduct of hemoglobin with glucose; a fructosamine derivative. Glucose is an amino group-directed glycating agent, modifying N-terminal and lysine sidechain amino groups. A similar fructosamine derivative of serum albumin, glycated albumin (GA), finds use as a biomarker of glycemic control, particularly where there is interference in use of A1C. Later stage adducts, advanced glycation endproducts (AGEs), are formed by the degradation of fructosamines and by the reaction of reactive dicarbonyl metabolites, such as methylglyoxal. Dicarbonyls are arginine-directed glycating agents forming mainly hydroimidazolone AGEs. Glucosepane and pentosidine, an intense fluorophore, are AGE covalent crosslinks. Cellular proteolysis of glycated proteins forms glycated amino acids, which are released into plasma and excreted in urine. Development of diagnostic algorithms by artificial intelligence machine learning is enhancing the applications of glycation biomarkers. Investigational glycation biomarkers are in development for: (i) healthy aging; (ii) risk prediction of vascular complications of diabetes; (iii) diagnosis of autism; and (iv) diagnosis and classification of early-stage arthritis. Protein glycation biomarkers are influenced by heritability, aging, decline in metabolic, vascular, renal and skeletal health, and other factors. They are applicable to populations of differing ethnicities, bridging the gap between genotype and phenotype. They are thereby likely to find continued and expanding clinical use, including in the current era of developing precision medicine, reporting on multiple pathogenic processes and supporting a precision medicine approach.

Identification of a Core Module for Bone Mineral Density through the Integration of a Co-expression Network and GWAS Data

The "omnigenic" model of the genetic architecture of complex traits proposed two categories of causal genes: core and peripheral. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. Using a cell-type- and time-point-specific gene co-expression network for mineralizing osteoblasts, we identify a co-expression module enriched for genes implicated by bone mineral density (BMD) genome-wide association studies (GWASs), correlated with in vitro osteoblast mineralization and associated with skeletal phenotypes in human monogenic disease and mouse knockouts. Four genes from this module (B4GALNT3, CADM1, DOCK9, and GPR133) are located within the BMD GWAS loci with colocalizing expression quantitative trait loci (eQTL) and exhibit altered BMD in mouse knockouts, suggesting that they are causal genetic drivers of BMD in humans. Our network-based approach identifies a "core" module for BMD and provides a resource for expanding our understanding of the genetics of bone mass.

Precision Medicine Approaches to Vascular Disease: JACC Focus Seminar 2/5

In this second of a 5-part Focus Seminar series, we focus on precision medicine in the context of vascular disease. The most common vascular disease worldwide is atherosclerosis, which is the primary cause of coronary artery disease, peripheral vascular disease, and a large proportion of strokes and other disorders. Atherosclerosis is a complex genetic disease that likely involves many hundreds to thousands of single nucleotide polymorphisms, each with a relatively modest effect for causing disease. Conversely, although less prevalent, there are many vascular disorders that typically involve only a single genetic change, but these changes can often have a profound effect that is sufficient to cause disease. These are termed "Mendelian vascular diseases," which include Marfan and Loeys-Dietz syndromes. Given the very different genetic basis of atherosclerosis versus Mendelian vascular diseases, this article was divided into 2 parts to cover the most promising precision medicine approaches for these disease types.

Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease

This investigation seeks to dissect coronary artery disease molecular target candidates along with its underlying molecular mechanisms. Data on patients with CAD across three separate array data sets, GSE66360, GSE19339 and GSE97320 were extracted. The gene expression profiles were obtained by normalizing and removing the differences between the three data sets, and important modules linked to coronary heart disease were identified using weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and genomes (KEGG) pathway enrichment analyses were applied in order to identify statistically significant genetic modules with the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov ). The online STRING tool was used to construct a protein-protein interaction (PPI) network, followed by the use of Molecular Complex Detection (MCODE) plug-ins in Cytoscape software to identify hub genes. Two significant modules (green-yellow and magenta) were identified in the CAD samples. Genes in the magenta module were noted to be involved in inflammatory and immune-related pathways, based on GO and KEGG enrichment analyses. After the MCODE analysis, two different MCODE complexes were identified in the magenta module, and four hub genes (ITGAM, degree = 39; CAMP, degree = 37; TYROBP, degree = 28; ICAM1, degree = 18) were uncovered to be critical players in mediating CAD. Independent verification data as well as our RT-qPCR results were highly consistent with the above finding. ITGAM, CAMP, TYROBP and ICAM1 are potential targets in CAD. The underlying mechanism may be related to the transendothelial migration of leukocytes and the immune response.

Multi-Omics Data Analysis Uncovers Molecular Networks and Gene Regulators for Metabolic Biomarkers

The insulin-like growth factors (IGFs)/insulin resistance (IR) axis is the major metabolic hormonal pathway mediating the biologic mechanism of several complex human diseases, including type 2 diabetes (T2DM) and cancers. The genomewide association study (GWAS)-based approach has neither fully characterized the phenotype variation nor provided a comprehensive understanding of the regulatory biologic mechanisms. We applied systematic genomics to integrate our previous GWAS data for IGF-I and IR with multi-omics datasets, e.g., whole-blood expression quantitative loci, molecular pathways, and gene network, to capture the full range of genetic functionalities associated with IGF-I/IR and key drivers (KDs) in gene-regulatory networks. We identified both shared (e.g., T2DM, lipid metabolism, and estimated glomerular filtration signaling) and IR-specific (e.g., mechanistic target of rapamycin, phosphoinositide 3-kinases, and erb-b2 receptor tyrosine kinase 4 signaling) molecular biologic processes of IGF-I/IR axis regulation. Next, by using tissue-specific gene–gene interaction networks, we identified both well-established (e.g., IRS1 and IGF1R) and novel (e.g., AKT1, HRAS, and JAK1) KDs in the IGF-I/IR-associated subnetworks. Our results, if validated in additional genomic studies, may provide robust, comprehensive insights into the mechanisms of IGF-I/IR regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for the associated diseases, e.g., T2DM and cancers.

LncRNA TONSL-AS1 participates in coronary artery disease by interacting with miR-197

BACKGROUND

It has been reported that high expression levels of miR-197 can predict coronary artery disease (CAD). Our bioinformatics analysis showed that miR-197 may bind to long non-coding RNA (lncRNA) TONSL-AS1. This study aimed to investigate the role of TONSL-AS1 in CAD.

METHODS

This study included 60 CAD patients and 60 healthy controls. Coronary angiography was performed to diagnose CAD. The interaction between TONSL-AS1 and miR-197 was predicted by IntaRNA2.0. Western-blot analysis was performed to illustrate the effect of MTONSL-AS1, miR-197 and BCL2 on human primary coronary artery endothelial cells (HCAECs). Cell migration assay was performed to explore the roles of MTONSL-AS1, miR-197 and BCL2 in regulating cell migration. Cell apoptosis assay was performed to investigate the role of MTONSL-AS1, miR-197 and BCL2 in regulating the apoptosis of HCAECs.

RESULT

Significant differences in high-density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), and gensini score were observed in patients with CAD. In addition, TONSL-AS1 was downregulated in CAD. Follow-up study revealed that low expression levels of TONSL-AS1 and high expression levels of miR-197 predicted poor survival of CAD patients. Overexpression experiments showed that TONSL-AS1 and miR-197 had no significant effect on the expression of each other. We speculated that MAFG-AS1 may sponge miR-145. Moreover, overexpression of TONSL-AS1 increased, while overexpression of miR-197 decreased the expression levels of BCL2. Furthermore, overexpression of TONSL-AS1 attenuated the effects of overexpression of miR-197 on migration and apoptosis of HCAECs.

CONCLUSIONS

Therefore, the expression of TONSL-AS1 predicted the survival of CAD patients and it sponged miR-197 to inhibit the apoptosis of HCAECs.

Transcription Factor MAFF (MAF Basic Leucine Zipper Transcription Factor F) Regulates an Atherosclerosis Relevant Network Connecting Inflammation and Cholesterol Metabolism

BACKGROUND

Coronary artery disease (CAD) is a multifactorial condition with both genetic and exogenous causes. The contribution of tissue-specific functional networks to the development of atherosclerosis remains largely unclear. The aim of this study was to identify and characterize central regulators and networks leading to atherosclerosis.

METHODS

Based on several hundred genes known to affect atherosclerosis risk in mouse (as demonstrated in knockout models) and human (as shown by genome-wide association studies), liver gene regulatory networks were modeled. The hierarchical order and regulatory directions of genes within the network were based on Bayesian prediction models, as well as experimental studies including chromatin immunoprecipitation DNA-sequencing, chromatin immunoprecipitation mass spectrometry, overexpression, small interfering RNA knockdown in mouse and human liver cells, and knockout mouse experiments. Bioinformatics and correlation analyses were used to clarify associations between central genes and CAD phenotypes in both human and mouse.

RESULTS

The transcription factor MAFF (MAF basic leucine zipper transcription factor F) interacted as a key driver of a liver network with 3 human genes at CAD genome-wide association studies loci and 11 atherosclerotic murine genes. Most importantly, expression levels of the low-density lipoprotein receptor (LDLR) gene correlated with MAFF in 600 CAD patients undergoing bypass surgery (STARNET [Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task]) and a hybrid mouse diversity panel involving 105 different inbred mouse strains. Molecular mechanisms of MAFF were tested in noninflammatory conditions and showed positive correlation between MAFF and LDLR in vitro and in vivo. Interestingly, after lipopolysaccharide stimulation (inflammatory conditions), an inverse correlation between MAFF and LDLR in vitro and in vivo was observed. Chromatin immunoprecipitation mass spectrometry revealed that the human CAD genome-wide association studies candidate BACH1 (BTB domain and CNC homolog 1) assists MAFF in the presence of lipopolysaccharide stimulation with respective heterodimers binding at the MAF recognition element of the LDLR promoter to transcriptionally downregulate LDLR expression.

CONCLUSIONS

The transcription factor MAFF was identified as a novel central regulator of an atherosclerosis/CAD-relevant liver network. MAFF triggered context-specific expression of LDLR and other genes known to affect CAD risk. Our results suggest that MAFF is a missing link between inflammation, lipid and lipoprotein metabolism, and a possible treatment target.

Polymorphisms in Glyoxalase I Gene Are Not Associated with Glyoxalase I Expression in Whole Blood or Markers of Methylglyoxal Stress: The CODAM Study

Glyoxalase 1 (Glo1) is the rate-limiting enzyme in the detoxification of methylglyoxal (MGO) into D-lactate. MGO is a major precursor of advanced glycation endproducts (AGEs), and both are associated with development of age-related diseases. Since genetic variation in GLO1 may alter the expression and/or the activity of Glo1, we examined the association of nine SNPs in GLO1 with Glo1 expression and markers of MGO stress (MGO in fasting plasma and after an oral glucose tolerance test, D-lactate in fasting plasma and urine, and MGO-derived AGEs CEL and MG-H1 in fasting plasma and urine). We used data of the Cohort on Diabetes and Atherosclerosis Maastricht (CODAM, n = 546, 60 ± 7 y, 25% type 2 diabetes). Outcomes were compared across genotypes using linear regression, adjusted for age, sex, and glucose metabolism status. We found that SNP4 (rs13199033) was associated with Glo1 expression (AA as reference, standardized beta AT = −0.29, p = 0.02 and TT = −0.39, p = 0.3). Similarly, SNP13 (rs3799703) was associated with Glo1 expression (GG as reference, standardized beta AG = 0.17, p = 0.14 and AA = 0.36, p = 0.005). After correction for multiple testing these associations were not significant. For the other SNPs, we observed no consistent associations over the different genotypes. Thus, polymorphisms of GLO1 were not associated with Glo1 expression or markers of MGO stress, suggesting that these SNPs are not functional, although activity/expression might be altered in other tissues.

Gene networks and pathways for plasma lipid traits via multitissue multiomics systems analysis

Genome-wide association studies (GWASs) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance, and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely, total cholesterol, high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides, from GWASs were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in "interferon signaling," "autoimmune/immune activation," "visual transduction," and "protein catabolism" were significantly associated with all lipid traits. In addition, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL; glutathione metabolism for HDL; valine, leucine, and isoleucine biosynthesis for total cholesterol; and insulin signaling and complement pathways for triglyceride. Finally, by using gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g., APOH, APOA4, and ABCA1) and novel (e.g., F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (coagulation factor II, thrombin) in 3T3-L1 and C3H10T1/2 adipocytes altered gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36; reduced intracellular adipocyte lipid content; and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

Integrated Network and Gene Ontology Analysis Identifies Key Genes and Pathways for Coronary Artery Diseases

BACKGROUND

The prevalence of Coronary Artery Disease (CAD) in developing countries is on the rise, owing to rapidly changing lifestyle. Therefore, it is imperative that the underlying genetic and molecular mechanisms be understood to develop specific treatment strategies. Comprehensive disease network and Gene Ontology (GO) studies aid in prioritizing potential candidate genes for CAD and also give insights into gene function by establishing gene and disease pathway relationships.

METHODS

In the present study, CAD-associated genes were collated from different data sources and protein-protein interaction network was constructed using STRING. Highly interconnected network clusters were inferred and GO analysis was performed.

RESULTS

Interrelation between genes and pathways were analyzed on ClueGO and 38 candidates were identified from 1475 CAD-associated genes, which were significantly enriched in CAD-related pathways such as metabolism and regulation of lipid molecules, platelet activation, macrophage derived foam cell differentiation, and blood coagulation and fibrin clot formation.

DISCUSSION

Integrated network and ontology analysis enables biomarker prioritization for common complex diseases such as CAD. Experimental validation and future studies on the prioritized genes may reveal valuable insights into CAD development mechanism and targeted treatment strategies.

Impact of the Polymorphism rs5751876 of the Purinergic Receptor ADORA2A on Periprocedural Myocardial Infarction in Patients Undergoing Percutaneous Coronary Intervention

Aim: Periprocedural myocardial infarction (PMI), a severe complication of Percutaneous Coronary Intervention (PCI) procedures, has a negative prognostic effect, both at short and long-term follow-up. So far, adenosine's role in preventing PMI has shown contrasting results. A genetic variant of ADORA2A receptor, 1976 C > T, has been suggested as a potential determinant of the interindividual response to adenosine, thus conditioning its potential benefits on PMI. In our study, we investigated whether the ADORA2A 1976 C > T polymorphism is associated with PMI occurrence in patients undergoing coronary stenting.

Methods: The study included consecutive patients undergoing PCI at the Azienda Ospedaliera-Universitaria “Maggiore della Carità,” Novara, Italy, between January 2010 and January 2016. Their genetic status was assessed using polymerase chain reaction (PCR) and restriction-fragment-length-polymorphism technique. Myonecrosis biomarkers were measured at intervals from 6 to 48 hours. PMI was defined as CKMB increased 3 times over the Upper Limit of Normal (ULN), or 50% of pre-PCI value; periprocedural myonecrosis was defined as troponin I increased 3 times over the ULN or by 50% of the baseline value.

Results: We included 1,104 patients undergoing PCI, 863 (78.2%) of whom carried the ADORA2A T-allele. No difference was found for the main demographic, clinical features, or biochemistry parameters. However, C-carriers had lower statin therapy use (p = 0.008) and lower HDL-cholesterol levels (p = 0.01). Homozygous C/C patients had more frequent multivessel disease (p = 0.03), longer lesions (p = 0.01) and Type C lesions (p = 0.01), thus requiring more complex procedures. After correction for baseline confounding factors at multivariate analysis, there was no difference in myocardial necrosis according to the ADORA2A genotype (p = 0.40). In contrast, PMI tended to increase in the homozygous C/C population (p = 0.06), but this trend was attenuated at multivariate analysis after correction for baseline confounding factors (C/C: OR[95%CI]= 1.52 [0.88–2.6], p = 0.14).

Conclusions: Our study showed that the polymorphism rs5751876 of the ADORA2A receptor is associated with a higher prevalence of complex coronary lesions and multivessel disease. However, it does not significantly influence the occurrence of periprocedural MI or myonecrosis.

An integrative multi-omics network-based approach identifies key regulators for breast cancer

Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.

Immunogenetics of Atherosclerosis-Link between Lipids, Immunity, and Genes

PURPOSE OF REVIEW

Atherosclerosis is a complex disease process with lipid as a traditional modifiable risk factor and therapeutic target in treating atherosclerotic cardiovascular disease (ACVD). Recent evidence indicates that genetic influence and host immune response also are vital in this process. How these elements interact and modify each other and if immune response may emerge as a novel modifiable target remain poorly understood.

RECENT FINDINGS

Numerous preclinical studies have clearly demonstrated that hypercholesterolemia is essential for atherogenesis, but genetic variations and host immune-inflammatory responses can modulate the pro-atherogenic effect of elevated LDL-C. Clinical studies also suggest that a similar paradigm may also be operational in atherogenesis in humans. More importantly each element modifies the biological behavior of the other two elements, forming a triangular relationship among the three. Modulating any one of them will have downstream impact on atherosclerosis. This brief review summarizes the relationship among lipids, genes, and immunity in atherogenesis and presents evidence to show how these elements affect each other. Modulation of immune response, though in its infancy, has a potential to emerge as a novel clinical strategy in treating ACVD.

Key driver genes as potential therapeutic targets in renal allograft rejection

Acute rejection (AR) in renal transplantation is an established risk factor for reduced allograft survival. Molecules with regulatory control among immune pathways of AR that are inadequately suppressed, despite standard-of-care immunosuppression, could serve as important targets for therapeutic manipulation to prevent rejection. Here, an integrative, network-based computational strategy incorporating gene expression and genotype data of human renal allograft biopsy tissue was applied, to identify the master regulators - the key driver genes (KDGs) - within dysregulated AR pathways. A 982-meta-gene signature with differential expression in AR versus non-AR was identified from a meta-analysis of microarray data from 735 human kidney allograft biopsy samples across 7 data sets. Fourteen KDGs were derived from this signature. Interrogation of 2 publicly available databases identified compounds with predicted efficacy against individual KDGs or a key driver-based gene set, respectively, which could be repurposed for AR prevention. Minocycline, a tetracycline antibiotic, was chosen for experimental validation in a murine cardiac allograft model of AR. Minocycline attenuated the inflammatory profile of AR compared with controls and when coadministered with immunosuppression prolonged graft survival. This study demonstrates that a network-based strategy, using expression and genotype data to predict KDGs, assists target prioritization for therapeutics in renal allograft rejection.

Network-based Transcriptome-wide Expression Study for Postmenopausal Osteoporosis

PURPOSE

Menopause is a crucial physiological transition during a woman's life, and it occurs with growing risks of health issues like osteoporosis. To identify postmenopausal osteoporosis-related genes, we performed transcriptome-wide expression analyses for human peripheral blood monocytes (PBMs) using Affymetrix 1.0 ST arrays in 40 Caucasian postmenopausal women with discordant bone mineral density (BMD) levels.

METHODS

We performed multiscale embedded gene coexpression network analysis (MEGENA) to study functionally orchestrating clusters of differentially expressed genes in the form of functional networks. Gene sets net correlations analysis (GSNCA) was applied to assess how the coexpression structure of a predefined gene set differs in high and low BMD groups. Bayesian network (BN) analysis was used to identify important regulation patterns between potential risk genes for osteoporosis. A small interfering ribonucleic acid (siRNA)-based gene silencing in vitro experiment was performed to validate the findings from BN analysis.

RESULT

MEGENA showed that the "T cell receptor signaling pathway" and the "osteoclast differentiation pathway" were significantly enriched in the identified compact network, which is significantly correlated with BMD variation. GSNCA revealed that the coexpression structure of the "Signaling by TGF-beta receptor complex pathway" is significantly different between the 2 BMD discordant groups; the hub genes in the postmenopausal low and high BMD group are FURIN and SMAD3 respectively. With siRNA in vitro experiments, we confirmed the regulation relationship of TGFBR2-SMAD7 and TGFBR1-SMURF2.

MAIN CONCLUSION

The present study suggests that biological signals involved in monocyte recruitment, monocyte/macrophage lineage development, osteoclast formation, and osteoclast differentiation might function together in PBMs that contribute to the pathogenesis of postmenopausal osteoporosis.

Multitissue Multiomics Systems Biology to Dissect Complex Diseases

Most complex diseases involve genetic and environmental risk factors, engage multiple cells and tissues, and follow a polygenic or omnigenic model depicting numerous genes contributing to pathophysiology. These multidimensional complexities pose challenges to traditional approaches that examine individual factors. In turn, multitissue multiomics systems biology has emerged to comprehensively elucidate within- and cross-tissue molecular networks underlying gene-by-environment interactions and contributing to complex diseases. The power of systems biology in retrieving novel insights and formulating new hypotheses has been well documented. However, the field faces various challenges that call for debate and action. In this opinion article, I discuss the concepts, benefits, current state, and challenges of the field and point to the next steps toward network-based systems medicine.

Weighed Gene Coexpression Network Analysis Screens the Potential Long Noncoding RNAs and Genes Associated with Progression of Coronary Artery Disease

Background

Coronary artery disease (CAD) is a type of heart disease with a high morbidity rate. This study is aimed at identifying potential biomarkers closely related to the progression of CAD.

Materials and Methods

A microarray dataset of GSE59867 was downloaded from a public database, Gene Expression Omnibus, which included 46 cases of stable CAD without a history of myocardial infarction (MI), 30 cases of MI without heart failure (HF), and 34 cases of MI with HF. Differentially expressed long noncoding RNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified by the limma package, and functions of DEmRNAs were annotated by Gene Ontology and KEGG pathways. In addition, weighed gene coexpression network analysis (WGCNA) was used to construct a coexpression network of DEmRNAs, and a disease-related lncRNAs-mRNAs-pathway network was constructed. Finally, the datasets of GSE61145 and GSE57338 were used to verify the expression levels of the above highly correlated candidates.

Results

A total of 2362 upregulated mRNAs and 2816 downregulated mRNAs, as well as 235 upregulated lncRNAs and 113 downregulated lncRNAs were screened. These genes were significantly enriched in “cytokine-cytokine receptor interaction,” “RIG-I-like receptor signaling pathway,” and “natural killer cell-mediated cytotoxicity.” Five modules including 1201 DEmRNAs were enriched in WGCNA. A coexpression network including 19 DElncRNAs and 413 DEmRNAs was constructed. These genes were significantly enriched in “phosphatidylinositol signaling system,” “insulin signaling pathway,” and “MAPK signaling pathway”. Disease-related gene-pathway network suggested FASN in “insulin signaling pathway,” DGKZ in “phosphatidylinositol signaling system,” and TNFRSF1A in “MAPK signaling pathway” were involved in MI.

Conclusion

FASN, DGKZ, and TNFRSF1A were revealed to be CAD progression-associated genes by WGCNA coexpression network analysis.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

Disease Module Identification Based on Representation Learning of Complex Networks Integrated From GWAS, eQTL Summaries, and Human Interactome

The study of disease-relevant gene modules is one of the main methods to discover disease pathway and potential drug targets. Recent studies have found that most disease proteins tend to form many separate connected components and scatter across the protein-protein interaction network. However, most of the research on discovering disease modules are biased toward well-studied seed genes, which tend to extend seed genes into a single connected subnetwork. In this paper, we propose N2V-HC, an algorithm framework aiming to unbiasedly discover the scattered disease modules based on deep representation learning of integrated multi-layer biological networks. Our method first predicts disease associated genes based on summary data of Genome-wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL) studies, and generates an integrated network on the basis of human interactome. The features of nodes in the network are then extracted by deep representation learning. Hierarchical clustering with dynamic tree cut methods are applied to discover the modules that are enriched with disease associated genes. The evaluation on real networks and simulated networks show that N2V-HC performs better than existing methods in network module discovery. Case studies on Parkinson's disease and Alzheimer's disease, show that N2V-HC can be used to discover biological meaningful modules related to the pathways underlying complex diseases.

Cardio-Metabolic Effects of High-Fat Diets and Their Underlying Mechanisms-A Narrative Review

The majority of the epidemiological evidence over the past few decades has linked high intake of fats, especially saturated fats, to increased risk of diabetes and cardiovascular disease. However, findings of some recent studies (e.g., the PURE study) have contested this association. High saturated fat diets (HFD) have been widely used in rodent research to study the mechanism of insulin resistance and metabolic syndrome. Two separate but somewhat overlapping models-the diacylglycerol (DAG) model and the ceramide model-have emerged to explain the development of insulin resistance. Studies have shown that lipid deposition in tissues such as muscle and liver inhibit insulin signaling via the toxic molecules DAG and ceramide. DAGs activate protein kinase C that inhibit insulin-PI3K-Akt signaling by phosphorylating serine residues on insulin receptor substrate (IRS). Ceramides are sphingolipids with variable acyl group chain length and activate protein phosphatase 2A that dephosphorylates Akt to block insulin signaling. In adipose tissue, obesity leads to infiltration of macrophages that secrete pro-inflammatory cytokines that inhibit insulin signaling by phosphorylating serine residues of IRS proteins. For cardiovascular disease, studies in humans in the 1950s and 1960s linked high saturated fat intake with atherosclerosis and coronary artery disease. More recently, trials involving Mediterranean diet (e.g., PREDIMED study) have indicated that healthy monounsaturated fats are more effective in preventing cardiovascular mortality and coronary artery disease than are low-fat, low-cholesterol diets. Antioxidant and anti-inflammatory effects of Mediterranean diets are potential mediators of these benefits.

Interaction between Coffee Drinking and TRIB1 rs17321515 Single Nucleotide Polymorphism on Coronary Heart Disease in a Taiwanese Population

A complex interplay of several genetic and lifestyle factors influence coronary heart disease (CHD). We determined the interaction between coffee consumption and the tribbles pseudokinase 1 (TRIB1) rs17321515 variant on coronary heart disease (CHD). Data on CHD were obtained from the National Health Insurance Research Database (NHIRD) while genotype data were collected from the Taiwan Biobank (TWB) Database. From the linked electronic health record data, 1116 individuals were identified with CHD while 7853 were control individuals. Coffee consumption was associated with a lower risk of CHD. The multivariate-adjusted odds ratio (OR) and 95% confidence interval (CI) was 0.84 (0.72-0.99). Association of CHD with the TRIB1 rs17321515 variant was not significant. The OR (95% CI) was 1.01 (0.72-0.99). There was an interaction between TRIB1 rs17321515 and coffee consumption on CHD risk (p for interaction = 0.0330). After stratification by rs17321515 genotypes, coffee drinking remained significantly associated with a lower risk of CHD only among participants with GG genotype (OR, 0.62; 95% CI, 0.45-0.85). In conclusion, consumption of coffee was significantly associated with a decreased risk of CHD among Taiwanese adults with the TRIB1 GG genotype.

Exome-Wide Association Study Reveals Several Susceptibility Genes and Pathways Associated With Acute Coronary Syndromes in Han Chinese

Genome-wide association studies have identified more than 150 susceptibility loci for coronary artery disease (CAD); however, there is still a large proportion of missing heritability remaining to be investigated. This study sought to identify population-based genetic variation associated with acute coronary syndromes (ACS) in individuals of Chinese Han descent. We proposed a novel strategy integrating a well-developed risk prediction model into control selection in order to lower the potential misclassification bias and increase the statistical power. An exome-wide association analysis was performed for 1,669 ACS patients and 1,935 healthy controls. Promising variants were further replicated using the existing in silico dataset. Additionally, we performed gene- and pathway-based analyses to investigate the aggregate effect of multiple variants within the same genes or pathways. Although none of the association signals were consistent across studies after Bonferroni correction, one promising variant, rs10409124 at STRN4, showed potential impact on ACS in both European and East Asian populations. Gene-based analysis explored four genes (ANXA7, ZNF655, ZNF347, and ZNF750) that showed evidence for association with ACS after multiple test correction, and identification of ZNF655 was successfully replicated by another dataset. Pathway-based analysis revealed that 32 potential pathways might be involved in the pathogenesis of ACS. Our study identified several candidate genes and pathways associated with ACS. Future studies are needed to further validate these findings and explore these genes and pathways as potential therapeutic targets in ACS.