A thematic review series: systems biology approaches to metabolic and cardiovascular disorders

Predicting mechanisms of action at genetic loci associated with discordant effects on type 2 diabetes and abdominal fat accumulation

Obesity is a major risk factor for cardiovascular disease, stroke, and type 2 diabetes (T2D). Excessive accumulation of fat in the abdomen further increases T2D risk. Abdominal obesity is measured by calculating the ratio of waist-to-hip circumference adjusted for the body-mass index (WHRadjBMI), a trait with a significant genetic inheritance. Genetic loci associated with WHRadjBMI identified in genome-wide association studies are predicted to act through adipose tissues, but many of the exact molecular mechanisms underlying fat distribution and its consequences for T2D risk are poorly understood. Further, mechanisms that uncouple the genetic inheritance of abdominal obesity from T2D risk have not yet been described. Here we utilize multi-omic data to predict mechanisms of action at loci associated with discordant effects on abdominal obesity and T2D risk. We find six genetic signals in five loci associated with protection from T2D but also with increased abdominal obesity. We predict the tissues of action at these discordant loci and the likely effector Genes (eGenes) at three discordant loci, from which we predict significant involvement of adipose biology. We then evaluate the relationship between adipose gene expression of eGenes with adipogenesis, obesity, and diabetic physiological phenotypes. By integrating these analyses with prior literature, we propose models that resolve the discordant associations at two of the five loci. While experimental validation is required to validate predictions, these hypotheses provide potential mechanisms underlying T2D risk stratification within abdominal obesity.

Relevance of Multi-Omics Studies in Cardiovascular Diseases

Cardiovascular diseases are the leading cause of death around the world. Despite the larger number of genes and loci identified, the precise mechanisms by which these genes influence risk of cardiovascular disease is not well understood. Recent advances in the development and optimization of high-throughput technologies for the generation of “omics data” have provided a deeper understanding of the processes and dynamic interactions involved in human diseases. However, the integrative analysis of “omics” data is not straightforward and represents several logistic and computational challenges. In spite of these difficulties, several studies have successfully applied integrative genomics approaches for the investigation of novel mechanisms and plasma biomarkers involved in cardiovascular diseases. In this review, we summarized recent studies aimed to understand the molecular framework of these diseases using multi-omics data from mice and humans. We discuss examples of omics studies for cardiovascular diseases focused on the integration of genomics, epigenomics, transcriptomics, and proteomics. This review also describes current gaps in the study of complex diseases using systems genetics approaches as well as potential limitations and future directions of this emerging field.

The Role of Systems Biologic Approach in Cell Signaling and Drug Development Responses-A Mini Review

The immune system is an integral aspect of the human defense system and is primarily responsible for and involved in the communication between the immune cells. It also plays an important role in the protection of the organism from foreign invaders. Recent studies in the literature have described its role in the process of hematopoiesis, lymphocyte recruitment, T cell subset differentiation and inflammation. However, the specific molecular mechanisms underlying these observations remain elusive, impeding the elaborate manipulation of cytokine sequential delivery in tissue repair. Previously, the discovery of new drugs and systems biology went hand in hand; although Systems biology as a term has only originated in the last century. Various new chemicals were tested on the human body, and studied through observation. Animal models replaced humans for initial trials, but the interactions, response, dose and effect between animals and humans could not be directly correlated. Therefore, there is a need to form disease models outside of human subjects to check the effectiveness and response of the newer natural or synthetic chemicals. These emulate human disease conditions wherein the behavior of the chemicals would be similar in the disease model and humans.

Linear low-dose extrapolation for noncancer heath effects is the exception, not the rule

The nature of the exposure-response relationship has a profound influence on risk analyses. Several arguments have been proffered as to why all exposure-response relationships for both cancer and noncarcinogenic endpoints should be assumed to be linear at low doses. We focused on three arguments that have been put forth for noncarcinogens. First, the general "additivity-to-background" argument proposes that if an agent enhances an already existing disease-causing process, then even small exposures increase disease incidence in a linear manner. This only holds if it is related to a specific mode of action that has nonuniversal properties-properties that would not be expected for most noncancer effects. Second, the "heterogeneity in the population" argument states that variations in sensitivity among members of the target population tend to "flatten out and linearize" the exposure-response curve, but this actually only tends to broaden, not linearize, the dose-response relationship. Third, it has been argued that a review of epidemiological evidence shows linear or no-threshold effects at low exposures in humans, despite nonlinear exposure-response in the experimental dose range in animal testing for similar endpoints. It is more likely that this is attributable to exposure measurement error rather than a true nonthreshold association. Assuming that every chemical is toxic at high exposures and linear at low exposures does not comport to modern-day scientific knowledge of biology. There is no compelling evidence-based justification for a general low-exposure linearity; rather, case-specific mechanistic arguments are needed.

[Why do we look at asthma through the keyhole?]

As happens with the rest of pathology, the study of asthma has been traditionally conducted from postulates set by reductionist science. That model still provides answers to theoretical and practical questions that establish diseases, but does not offer us a complete view of their complexity and multidimensionality. To overcome this limitation has emerged medicine directed towards systems based on the application of biological systems concepts and tools. Biological systems is a cross-disciplinary strategy which, from the data generated by the "-omic" sciences, helps to relate the elements of an organism or biological system, to understand the properties arising from the same and to generate mathematical models capable of predicting their dynamic behaviour. The application of biological systems to asthma starts is starting to make ground. The main challenge today is to understand the need to change focus. The starting point is to abandon the idea that asthma is exclusively an airways disease and considering that the whole lung is involved and, even more, the possibility that it is, at least in part, a systemic process. In view of our current limitations, to understand asthma and design personalised treatment strategies for each patient, requires thinking of systems medicine.

Genetic causes of high and low serum HDL-cholesterol

Plasma levels of HDL cholesterol (HDL-C) have a strong inherited basis with heritability estimates of 40-60%. The well-established inverse relationship between plasma HDL-C levels and the risk of coronary artery disease (CAD) has led to an extensive search for genetic factors influencing HDL-C concentrations. Over the past 30 years, candidate gene, genome-wide linkage, and most recently genome-wide association (GWA) studies have identified several genetic variations for plasma HDL-C levels. However, the functional role of several of these variants remains unknown, and they do not always correlate with CAD. In this review, we will first summarize what is known about HDL metabolism, monogenic disorders associated with both low and high HDL-C levels, and candidate gene studies. Then we will focus this review on recent genetic findings from the GWA studies and future strategies to elucidate the remaining substantial proportion of HDL-C heritability. Comprehensive investigation of the genetic factors conferring to low and high HDL-C levels using integrative approaches is important to unravel novel pathways and their relations to CAD, so that more effective means of diagnosis, treatment, and prevention will be identified.

[Complexity in asthma: inflammation and scale-free networks]

Our understanding of asthma has traditionally been based on linear deterministic relationships of the type stimulus-bronchial hyperresponsiveness-obstruction-symptoms. This notion however neglects the fact that nonlinear relationships may be present. To better define the disease, some authors therefore suggest that we should think in terms of complex systems with a scale-free topology. The idea of multiple inflammatory hits proposed by the group of Pavord is in its broadest sense a further contribution to this line of thought. According to this theory, the coexistence of additional inflammatory stimuli, which may or may not be localized to the lungs, are responsible for deteriorating lung function. The effects of these stimuli may be additive or act in synergy with the underlying inflammation of asthma itself. In addition to the practical implications, this hypothesis serves as a reminder that the body is made up of interconnected parts and that the pathogenesis of asthma includes distinct elements linked together. If this hypothesis proves valid, future approaches should start to look for the hubs in this network that constitutes asthma, and attempt to integrate information from genomics, proteomics, and metabolomics.

Systems biology approaches and pathway tools for investigating cardiovascular disease

Systems biology aims to understand the nonlinear interactions of multiple biomolecular components that characterize a living organism. One important aspect of systems biology approaches is to identify the biological pathways or networks that connect the differing elements of a system, and examine how they evolve with temporal and environmental changes. The utility of this method becomes clear when applied to multifactorial diseases with complex etiologies, such as inflammatory-related diseases, herein exemplified by atherosclerosis. In this paper, the initial studies in this discipline are reviewed and examined within the context of the development of the field. In addition, several different software tools are briefly described and a novel application for the KEGG database suite called KegArray is presented. This tool is designed for mapping the results of high-throughput omics studies, including transcriptomics, proteomics and metabolomics data, onto interactive KEGG metabolic pathways. The utility of KegArray is demonstrated using a combined transcriptomics and lipidomics dataset from a published study designed to examine the potential of cholesterol in the diet to influence the inflammatory component in the development of atherosclerosis. These data were mapped onto the KEGG PATHWAY database, with a low cholesterol diet affecting 60 distinct biochemical pathways and a high cholesterol exposure affecting 76 biochemical pathways. A total of 77 pathways were differentially affected between low and high cholesterol diets. The KEGG pathways "Biosynthesis of unsaturated fatty acids" and "Sphingolipid metabolism" evidenced multiple changes in gene/lipid levels between low and high cholesterol treatment, and are discussed in detail. Taken together, this paper provides a brief introduction to systems biology and the applications of pathway mapping to the study of cardiovascular disease, as well as a summary of available tools. Current limitations and future visions of this emerging field are discussed, with the conclusion that combining knowledge from biological pathways and high-throughput omics data will move clinical medicine one step further to individualize medical diagnosis and treatment.

Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks

We previously used high-density expression arrays to interrogate a genetic cross between strains C3H/HeJ and C57BL/6J and observed thousands of differences in gene expression between sexes. We now report analyses of the molecular basis of these sex differences and of the effects of sex on gene expression networks. We analyzed liver gene expression of hormone-treated gonadectomized mice as well as XX male and XY female mice. Differences in gene expression resulted in large part from acute effects of gonadal hormones acting in adulthood, and the effects of sex chromosomes, apart from hormones, were modest. We also determined whether there are sex differences in the organization of gene expression networks in adipose, liver, skeletal muscle, and brain tissue. Although coexpression networks of highly correlated genes were largely conserved between sexes, some exhibited striking sex dependence. We observed strong body fat and lipid correlations with sex-specific modules in adipose and liver as well as a sexually dimorphic network enriched for genes affected by gonadal hormones. Finally, our analyses identified chromosomal loci regulating sexually dimorphic networks. This study indicates that gonadal hormones play a strong role in sex differences in gene expression. In addition, it results in the identification of sex-specific gene coexpression networks related to genetic and metabolic traits.

Plasma lipoproteins: genetic influences and clinical implications

Susceptibility to the growing global public health problem of cardiovascular disease is associated with levels of plasma lipids and lipoproteins. Several experimental strategies have helped us to clarify the genetic architecture of these complex traits, including classical studies of monogenic dyslipidaemias, resequencing, phenomic analysis and, more recently, genome-wide association studies and analysis of metabolic networks. The genetic basis of plasma lipoprotein levels can now be modelled as a mosaic of contributions from multiple DNA sequence variants, both rare and common, with varying effect sizes. In addition to filling gaps in our understanding of plasma lipoprotein metabolism, the recent genetic advances will improve our ability to classify, diagnose and treat dyslipidaemias.

Using genetic markers to orient the edges in quantitative trait networks: the NEO software

Background

Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers.

Results

We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue.

Conclusion

The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait networks if the edges can be anchored to genetic marker data. R software tutorials, data, and supplementary material can be downloaded from: .

An application of the patient rule-induction method for evaluating the contribution of the Apolipoprotein E and Lipoprotein Lipase genes to predicting ischemic heart disease

Different combinations of genetic and environmental risk factors are known to contribute to the complex etiology of ischemic heart disease (IHD) in different subsets of individuals. We employed the Patient Rule-Induction Method (PRIM) to select the combination of risk factors and risk factor values that identified each of 16 mutually exclusive partitions of individuals having significantly different levels of risk of IHD. PRIM balances two competing objectives: (1) finding partitions where the risk of IHD is high and (2) maximizing the number of IHD cases explained by the partitions. A sequential PRIM analysis was applied to data on the incidence of IHD collected over 8 years for a sample of 5,455 unrelated individuals from the Copenhagen City Heart Study (CCHS) to assess the added value of variation in two candidate susceptibility genes beyond the traditional, lipid and body mass index risk factors for IHD. An independent sample of 362 unrelated individuals also from the city of Copenhagen was used to test the model obtained for each of the hypothesized partitions.

Exploring the perioptome: the role of genomics in thoracic surgery and anaesthesia

PURPOSE OF REVIEW

Genetic variation almost certainly affects every aspect of a patient's perioperative experience, yet almost nothing is known of the details involved. This review introduces the concept of static and dynamic genetic variation as a determinant of outcome after thoracic surgery and discusses some of the methodological issues involved in its study. Using a systems biology approach, it explores the ways in which data from many sources may be integrated into a common model of disease risk and outcome prediction.

RECENT FINDINGS

The incidence of mortality and morbidity after lung resection is unacceptably high, and has changed little in the last 20 years. New approaches to this problem are required if we are to improve outcomes after thoracic surgery.

SUMMARY

Patients seem to be predisposed to respond to a surgical stimulus in heterogeneous fashion, and this may be partly explained by variability in the genetic background with which they present for surgery. Although recent pilot data suggest that this is indeed the case, much more work remains to be done before this can be confirmed.