Systems Biology Approaches and Applications in Obesity, Diabetes, and Cardiovascular Diseases

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.

Childhood Obesity and the Cryptic Language of the Microbiota: Metabolomics’ Upgrading

The growing obesity epidemic in childhood is increasingly concerning for the related physical and psychological consequences, with a significant impact on health care costs in both the short and the long term. Nonetheless, the scientific community has not yet completely clarified the complex metabolic mechanisms underlying body weight alterations. In only a small percentage of cases, obesity is the result of endocrine, monogenic, or syndromic causes, while in much more cases, lifestyle plays a crucial role in obesity development. In this context, the pediatric age appears to be of considerable importance as prevention strategies together with early intervention can represent important therapeutic tools not only to counteract the comorbidities that increasingly affect children but also to hinder the persistence of obesity in adulthood. Although evidence in the literature supporting the alteration of the microbiota as a critical factor in the etiology of obesity is abundant, it is not yet fully defined and understood. However, increasingly clear evidence is emerging regarding the existence of differentiated metabolic profiles in obese children, with characteristic metabolites. The identification of specific pathology-related biomarkers and the elucidation of the altered metabolic pathways would therefore be desirable in order to clarify aspects that are still poorly understood, such as the consequences of the interaction between the host, the diet, and the microbiota. In fact, metabolomics can characterize the biological behavior of a specific individual in response to external stimuli, offering not only an eventual effective screening and prevention strategy but also the possibility of evaluating adherence and response to dietary intervention.

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.

Porcine models for studying complications and organ crosstalk in diabetes mellitus

The worldwide prevalence of diabetes mellitus and obesity is rapidly increasing not only in adults but also in children and adolescents. Diabetes is associated with macrovascular complications increasing the risk for cardiovascular disease and stroke, as well as microvascular complications leading to diabetic nephropathy, retinopathy and neuropathy. Animal models are essential for studying disease mechanisms and for developing and testing diagnostic procedures and therapeutic strategies. Rodent models are most widely used but have limitations in translational research. Porcine models have the potential to bridge the gap between basic studies and clinical trials in human patients. This article provides an overview of concepts for the development of porcine models for diabetes and obesity research, with a focus on genetically engineered models. Diabetes-associated ocular, cardiovascular and renal alterations observed in diabetic pig models are summarized and their similarities with complications in diabetic patients are discussed. Systematic multi-organ biobanking of porcine models of diabetes and obesity and molecular profiling of representative tissue samples on different levels, e.g., on the transcriptome, proteome, or metabolome level, is proposed as a strategy for discovering tissue-specific pathomechanisms and their molecular key drivers using systems biology tools. This is exemplified by a recent study providing multi-omics insights into functional changes of the liver in a transgenic pig model for insulin-deficient diabetes mellitus. Collectively, these approaches will provide a better understanding of organ crosstalk in diabetes mellitus and eventually reveal new molecular targets for the prevention, early diagnosis and treatment of diabetes mellitus and its associated complications.

Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a progressive condition of the liver encompassing a range of pathologies including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Research into this disease is imperative due to its rapid growth in prevalence, economic burden, and current lack of FDA approved therapies. NAFLD involves a highly complex etiology that calls for multi-tissue multi-omics network approaches to uncover the pathogenic genes and processes, diagnostic biomarkers, and potential therapeutic strategies. In this review, we first present a basic overview of disease pathogenesis, risk factors, and remaining knowledge gaps, followed by discussions of the need and concepts of multi-tissue multi-omics approaches, various network methodologies and application examples in NAFLD research. We highlight the findings that have been uncovered thus far including novel biomarkers, genes, and biological pathways involved in different stages of NAFLD, molecular connections between NAFLD and its comorbidities, mechanisms underpinning sex differences, and druggable targets. Lastly, we outline the future directions of implementing network approaches to further improve our understanding of NAFLD in order to guide diagnosis and therapeutics.

Systems biology in inflammatory bowel diseases: on the way to precision medicine

Inflammatory bowel diseases (IBD) are chronic and recurrent inflammatory disorders of the gastrointestinal tract. The elucidation of their etiopathology requires complex and multiple approaches. Systems biology has come to fulfill this need in approaching the pathogenetic mechanisms of IBD and its etiopathology, in a comprehensive way, by combining data from different scientific sources. In combination with bioinformatics and network medicine, it uses principles from computer science, mathematics, physics, chemistry, biology, medicine and computational tools to achieve its purposes. Systems biology utilizes scientific sources that provide data from omics studies (e.g., genomics, transcriptomics, etc.) and clinical observations, whose combined analysis leads to network formation and ultimately to a more integrative image of disease etiopathogenesis. In this review, we analyze the current literature on the methods and the tools utilized by systems biology in order to cover an innovative and exciting field: IBD-omics.

SyNDI: synchronous network data integration framework

BACKGROUND

Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights.

RESULTS

In order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified.

CONCLUSIONS

Our SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.

Leptin, An Adipokine With Central Importance in the Global Obesity Problem

Leptin has central importance in the global obesity and cardiovascular disease problem. Leptin is principally secreted by adipocytes and acts in the hypothalamus to suppress appetite and food intake, increase energy expenditure, and regulate body weight. Based on clinical translation of specific and networked actions, leptin affects the cardiovascular system and may be a marker and driver of cardiometabolic risk factors with interventions that are actionable by cardiologists. Leptin subnetwork analysis demonstrates a statistically significant role for ethnoculturally and socioeconomically appropriate lifestyle intervention in cardiovascular disease. Emergent mechanistic components and potential diagnostic or therapeutic targets include hexokinase 3, urocortins, clusterin, sialic acid-binding immunoglobulin-like lectin 6, C-reactive protein, platelet glycoprotein VI, albumin, pentraxin 3, ghrelin, obestatin prepropeptide, leptin receptor, neuropeptide Y, and corticotropin-releasing factor receptor 1. Emergent associated symptoms include weight change, eating disorders, vascular necrosis, chronic fatigue, and chest pain. Leptin-targeted therapies are reported for lipodystrophy and leptin deficiency, but they are investigational for leptin resistance, obesity, and other chronic diseases.

Weighted Gene Co-Expression Network Analysis Identifies Gender Specific Modules and Hub Genes Related to Metabolism and Inflammation in Response to an Acute Lipid Challenge

SCOPE

Inflammation is characteristic of diet-related diseases including obesity and type 2 diabetes (T2D). However, biomarkers of inflammation that reflect the early stage metabolic derangements are not optimally sensitive. Lipid challenges elicit postprandial inflammatory and metabolic responses. Gender-specific transcriptomic networks of the peripheral blood mononuclear cell (PBMC) were constructed in response to a lipid challenge.

METHODS AND RESULTS

Eighty-six adult males and females of comparable age, anthropometric, and biochemical profiles completed an oral lipid tolerance test (OLTT). PBMC transcriptome was profiled following OLTT. Weighted gene coexpression networks were constructed separately for males and females. Functional ontology analysis of network modules was performed and hub genes identified. Two modules of interest were identified in females-an "inflammatory" module and an "energy metabolism" module. NLRP3, which plays a central role in inflammation and STARD3 that is involved in cholesterol metabolism, were identified as hub genes for the respective modules.

CONCLUSION

The OLTT induced some gender-specific correlations of gene coexpression network modules. In females, biological processes relating to energy metabolism and inflammation pathways were evident. This suggests a gender specific link between inflammation and energy metabolism in response to lipids. In contrast, G-protein coupled receptor protein signaling pathway was common to both genders.

Co-expression analysis reveals key gene modules and pathway of human coronary heart disease

Coronary heart disease is a kind of disease which causes great injury to people world-widely. Although gene expression analyses had been performed previously, to our best knowledge, systemic co-expression analysis for this disease is still lacking to date. Microarray data of coronary heart disease was downloaded from NCBI with the accession number of GSE20681. Co-expression modules were constructed by WGCNA. Besides, the connectivity degree of eigengenes was analyzed. Furthermore, GO and KEGG enrichment analysis was performed on these eigengenes in these constructed modules. A total of 11 co-expression modules were constructed by the 3000 up-regulated genes from the 99 samples with coronary heart disease. The average number of genes in these modules was 270. The interaction analysis indicated the relative independence of gene expression in these modules. The functional enrichment analysis showed that there was a significant difference in the enriched terms and degree among these 11 modules. The results showed that modules 9 and 10 played critical roles in the occurrence of coronary disease. Pathways of hsa00190 (oxidative phosphorylation) and (hsa01130: biosynthesis of antibiotics) were thought to be closely related to the occurrence and development of coronary heart disease. Our result demonstrated that modules 9 and 10 were the most critical modules in the occurrence of coronary heart disease. Pathways as hsa00190 (oxidative phosphorylation) and (hsa01130: biosynthesis of antibiotics) had the potential to serve as the prognostic and predictive marker of coronary heart disease.

Mini-Review: The Contribution of Intermediate Phenotypes to GxE Effects on Disorders of Body Composition in the New OMICS Era

Studies of gene-environment (GxE) interactions describe how genetic and environmental factors influence the risk of developing disease. Intermediate (molecular or clinical) phenotypes (IPs) are traits or metabolic biomarkers that mediate the effects of gene-environment influences on risk behaviors. Functional systems genomics discovery offers mechanistic insights into how DNA variations affect IPs in order to detect genetic causality for a given disease. Disorders of body composition include obesity (OB), Type 2 diabetes (T2D), and osteoporosis (OSTP). These pathologies are examples of how a GxE interaction contributes to their development. IPs as surrogates for inherited genotypes play a key role in models of genetic and environmental interactions in health outcomes. Such predictive models may unravel relevant genomic and molecular pathways for preventive and therapeutic interventions for OB, T2D, and OSTP. Annotation strategies for genomes, in contrast to phenomes, are well advanced. They generally do not measure specific aspects of the environment. Therefore, the concepts of deep phenotyping and the exposome generate new avenues to exploit with high-resolution technologies for analyzing this sophisticated phenome. With the successful characterization of phenomes, exposomes, and genomes, environmental and genetic determinants of chronic diseases can be united with multi-OMICS studies that better examine GxE interactions.

Vascular Endothelial Regulation of Obesity-Associated Insulin Resistance

Obesity is a worldwide epidemic that predisposes individuals to metabolic complications, such as type 2 diabetes mellitus and non-alcoholic fatty liver disease, all of which are related to an imbalance between food intake and energy expenditure. Identification of the pathogenic molecular mechanisms and effective therapeutic approaches are urgently needed. A well-accepted paradigm is that crosstalk between organs/tissues contributes to diseases. Endothelial dysfunction characterizes metabolic disorders and the related vascular complications. Over the past two decades, overwhelming studies have focused on mechanisms that lead to endothelial dysfunction. New investigations, however, have begun to appreciate the opposite direction of the crosstalk: endothelial regulation of metabolism, although the underlying mechanisms remain to be elucidated. This review summarizes the evidence that supports the concept of endothelial regulation of obesity and the associated insulin resistance in fat, liver, and skeletal muscles, the classic targets of insulin. Outstanding questions and future research directions are highlighted. Identification of the mechanisms of vascular endothelial regulation of metabolism may offer strategies for prevention and treatment of obesity and the related metabolic complications.

The Munich MIDY Pig Biobank - A unique resource for studying organ crosstalk in diabetes

OBJECTIVE

The prevalence of diabetes mellitus and associated complications is steadily increasing. As a resource for studying systemic consequences of chronic insulin insufficiency and hyperglycemia, we established a comprehensive biobank of long-term diabetic INSC94Y transgenic pigs, a model of mutant INS gene-induced diabetes of youth (MIDY), and of wild-type (WT) littermates.

METHODS

Female MIDY pigs (n = 4) were maintained with suboptimal insulin treatment for 2 years, together with female WT littermates (n = 5). Plasma insulin, C-peptide and glucagon levels were regularly determined using specific immunoassays. In addition, clinical chemical, targeted metabolomics, and lipidomics analyses were performed. At age 2 years, all pigs were euthanized, necropsied, and a broad spectrum of tissues was taken by systematic uniform random sampling procedures. Total beta cell volume was determined by stereological methods. A pilot proteome analysis of pancreas, liver, and kidney cortex was performed by label free proteomics.

RESULTS

MIDY pigs had elevated fasting plasma glucose and fructosamine concentrations, C-peptide levels that decreased with age and were undetectable at 2 years, and an 82% reduced total beta cell volume compared to WT. Plasma glucagon and beta hydroxybutyrate levels of MIDY pigs were chronically elevated, reflecting hallmarks of poorly controlled diabetes in humans. In total, ∼1900 samples of different body fluids (blood, serum, plasma, urine, cerebrospinal fluid, and synovial fluid) as well as ∼17,000 samples from ∼50 different tissues and organs were preserved to facilitate a plethora of morphological and molecular analyses. Principal component analyses of plasma targeted metabolomics and lipidomics data and of proteome profiles from pancreas, liver, and kidney cortex clearly separated MIDY and WT samples.

CONCLUSIONS

The broad spectrum of well-defined biosamples in the Munich MIDY Pig Biobank that will be available to the scientific community provides a unique resource for systematic studies of organ crosstalk in diabetes in a multi-organ, multi-omics dimension.

Molecular and genetic inflammation networks in major human diseases

It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervention.

Multidimensional Integrative Genomics Approaches to Dissecting Cardiovascular Disease

Elucidating the mechanisms of complex diseases such as cardiovascular disease (CVD) remains a significant challenge due to multidimensional alterations at molecular, cellular, tissue, and organ levels. To better understand CVD and offer insights into the underlying mechanisms and potential therapeutic strategies, data from multiple omics types (genomics, epigenomics, transcriptomics, metabolomics, proteomics, microbiomics) from both humans and model organisms have become available. However, individual omics data types capture only a fraction of the molecular mechanisms. To address this challenge, there have been numerous efforts to develop integrative genomics methods that can leverage multidimensional information from diverse data types to derive comprehensive molecular insights. In this review, we summarize recent methodological advances in multidimensional omics integration, exemplify their applications in cardiovascular research, and pinpoint challenges and future directions in this incipient field.

Metabolomics-guided insights on bariatric surgery versus behavioral interventions for weight loss

OBJECTIVE

To review the metabolomic studies carried out so far to identify metabolic markers associated with surgical and dietary treatments for weight loss in subjects with obesity.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed.

RESULTS

Thirty-two studies successfully met the eligibility criteria. The metabolic adaptations shared by surgical and dietary interventions mirrored a state of starvation ketoacidosis (increase of circulating ketone bodies), an increase of acylcarnitines and fatty acid β-oxidation, a decrease of specific amino acids including branched-chain amino acids (BCAA) and (lyso)glycerophospholipids previously associated with obesity, and adipose tissue expansion. The metabolic footprint of bariatric procedures was specifically characterized by an increase of bile acid circulating pools and a decrease of ceramide levels, a greater perioperative decline in BCAA, and the rise of circulating serine and glycine, mirroring glycemic control and inflammation improvement. In one study, 3-hydroxybutyrate was particularly identified as an early metabolic marker of long-term prognosis after surgery and proposed to increase current prognostic modalities and contribute to personalized treatment.

CONCLUSIONS

Metabolomics helped in deciphering the metabolic response to weight loss treatments. Moving from association to causation is the next challenge to move to a further level of clinical application.

Diabetes and cardiovascular disease: let's push forward with translational research

Albeit advances in therapy have reduced morbidity and mortality in patients with diabetes, cardiovascular (CV) risk is far to be eradicated. This is partially due to the fact that breakthrough therapies have yet to be approved to counteract the atherosclerotic burden in this setting. Therefore, it is very important to understand the molecular mechanisms underpinning diabetes-related CV complications. Growing evidence is supporting the concept that translational research is perhaps the best approach to unveil novel insights into disease etiology and its link with CV phenotypes. The recent employment of high throughput "omics" (i.e., metabolomics, transcriptomics, proteomics) is a clinically relevant approach which may provide insightful interpretations of diabetes-related biological signals. The possibility to analyse thousands or more molecules simultaneously has given "omics" the ability to generate enormous quantities of data which may somehow offer a precious "window on the disease". In the present article, we critically discuss the importance of translational research in diabetes, including potential difficulties which may arise in the implementation and development of promising technologies from the laboratory to the marketplace.

Effects of Sample Size and Dimensionality on the Performance of Four Algorithms for Inference of Association Networks in Metabonomics

We investigated the effect of sample size and dimensionality on the performance of four algorithms (ARACNE, CLR, CORR, and PCLRC) when they are used for the inference of metabolite association networks. We report that as many as 100-400 samples may be necessary to obtain stable network estimations, depending on the algorithm and the number of measured metabolites. The CLR and PCLRC methods produce similar results, whereas network inference based on correlations provides sparse networks; we found ARACNE to be unsuitable for this application, being unable to recover the underlying metabolite association network. We recommend the PCLRC algorithm for the inference on metabolite association networks.

Nutritional systems biology of type 2 diabetes

Type 2 diabetes (T2D) has become an increasingly challenging health burden due to its high morbidity, mortality, and heightened prevalence worldwide. Although dietary and nutritional imbalances have long been recognized as key risk factors for T2D, the underlying mechanisms remain unclear. The advent of nutritional systems biology, a field that aims to elucidate the interactions between dietary nutrients and endogenous molecular entities in disease-related tissues, offers unique opportunities to unravel the complex mechanisms underlying the health-modifying capacities of nutritional molecules. The recent revolutionary advances in omics technologies have particularly empowered this incipient field. In this review, we discuss the applications of multi-omics approaches toward a systems-level understanding of how dietary patterns and particular nutrients modulate the risk of T2D. We focus on nutritional studies utilizing transcriptomics, epigenomomics, proteomics, metabolomics, and microbiomics, and integration of diverse omics technologies. We also summarize the potential molecular mechanisms through which nutritional imbalances contribute to T2D pathogenesis based on these studies. Finally, we discuss the remaining challenges of nutritional systems biology and how the field can be optimized to further our understanding of T2D and guide disease management via nutritional interventions.

[Bardet-Biedl syndrome: cilia and obesity - from genes to integrative approaches]

The primary cilium is a specialized organelle, present at the surface of most eukaryotic cells, whose main function is to detect, integrate and transmit intra- and extra-cellular signals. Its dysfunction usually results in a group of severe clinical manifestations nowadays termed ciliopathies. The latter can be of syndromic nature with multi-organ dysfunctions and can also be associated with a morbid obese phenotype, like it is the case in the iconic ciliopathy, the Bardet Biedl syndrome (BBS). This review will discuss the contribution of the unique context offered by the emblematic BBS for understanding the mechanisms leading to obesity via the involvement of the primary cilium together with identification of novel molecular players and signaling pathways it has helped to highlight. In the current context of translational medicine and system biology, this article will also discuss the potential benefits and challenges posed by these techniques via multi-level approaches to better dissect the underlying mechanisms leading to the complex condition of obesity.

Network-based association of hypoxia-responsive genes with cardiovascular diseases

Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiology of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct an hypoxia-CVD bipartite network and find several interesting hypoxia-CVD modules with significant Gene Ontology (GO) similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology. Based on these observations, we can predict novel genes that may be associated with CVD. This network-based association study gives us a broad view of the relationships between hypoxia and cardiovascular diseases and provides new insights into the role of hypoxia in cardiovascular biology.

Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal.

The pathogenesis of obesity from a genomic and systems biology perspective

The recent obesity epidemic has imposed significant health, economical, and societal concerns. However, effective preventive and therapeutic strategies are currently lacking, primarily due to a lack of comprehensive understanding of the underlying molecular mechanisms. Recent genome-wide scans of genetic variants, transcriptome, and epigenome have uncovered >50 genetic loci that predispose individuals to obesity and revealed hundreds of genes with altered transcriptional activity and/or epigenetic variations in obesity-related tissues upon various environmental challenges such as high caloric diets, lack of physical activity, and environmental chemicals. These discoveries highlight the importance of genes involved in the control of energy homeostasis and food intake by the central nervous system, as well as genes contributing to lipid metabolism, adipogenesis, fat cell differentiation, and immune response in peripheral tissues, in obesity development. Future studies that are directed to obtain a more comprehensive, systems-level understanding of disease mechanisms and that test novel therapeutic strategies aiming at systems-level normalization of the obesity-related molecular alterations are warranted.

Genetic associations with micronutrient levels identified in immune and gastrointestinal networks

The discovery of vitamins and clarification of their role in preventing frank essential nutrient deficiencies occurred in the early 1900s. Much vitamin research has understandably focused on public health and the effects of single nutrients to alleviate acute conditions. The physiological processes for maintaining health, however, are complex systems that depend upon interactions between multiple nutrients, environmental factors, and genetic makeup. To analyze the relationship between these factors and nutritional health, data were obtained from an observational, community-based participatory research program of children and teens (age 6–14) enrolled in a summer day camp in the Delta region of Arkansas. Assessments of erythrocyte S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), plasma homocysteine (Hcy) and 6 organic micronutrients (retinol, 25-hydroxy vitamin D3, pyridoxal, thiamin, riboflavin, and vitamin E), and 1,129 plasma proteins were performed at 3 time points in each of 2 years. Genetic makeup was analyzed with 1 M SNP genotyping arrays, and nutrient status was assessed with 24-h dietary intake questionnaires. A pattern of metabolites (met_PC1) that included the ratio of erythrocyte SAM/SAH, Hcy, and 5 vitamins were identified by principal component analysis. Met_PC1 levels were significantly associated with (1) single-nucleotide polymorphisms, (2) levels of plasma proteins, and (3) multilocus genotypes coding for gastrointestinal and immune functions, as identified in a global network of metabolic/protein–protein interactions. Subsequent mining of data from curated pathway, network, and genome-wide association studies identified genetic and functional relationships that may be explained by gene–nutrient interactions. The systems nutrition strategy described here has thus associated a multivariate metabolite pattern in blood with genes involved in immune and gastrointestinal functions.

Perspective: a systems approach to diabetes research

We review here the status of human type 2 diabetes studies from a genetic, epidemiological, and clinical (intervention) perspective. Most studies limit analyses to one or a few omic technologies providing data of components of physiological processes. Since all chronic diseases are multifactorial and arise from complex interactions between genetic makeup and environment, type 2 diabetes mellitus (T2DM) is a collection of sub-phenotypes resulting in high fasting glucose. The underlying gene–environment interactions that produce these classes of T2DM are imperfectly characterized. Based on assessments of the complexity of T2DM, we propose a systems biology approach to advance the understanding of origin, onset, development, prevention, and treatment of this complex disease. This systems-based strategy is based on new study design principles and the integrated application of omics technologies: we pursue longitudinal studies in which each subject is analyzed at both homeostasis and after (healthy and safe) challenges. Each enrolled subject functions thereby as their own case and control and this design avoids assigning the subjects a priori to case and control groups based on limited phenotyping. Analyses at different time points along this longitudinal investigation are performed with a comprehensive set of omics platforms. These data sets are generated in a biological context, rather than biochemical compound class-driven manner, which we term “systems omics.”

Safety biomarkers in preclinical development: translational potential

The identification, application, and qualification of safety biomarkers are becoming increasingly critical to successful drug discovery and development as companies are striving to develop drugs for difficult targets and for novel disease indications in a risk-adverse environment. Translational safety biomarkers that are minimally invasive and monitor drug-induced toxicity during human clinical trials are urgently needed to assess whether toxicities observed in preclinical toxicology studies are relevant to humans at therapeutic doses. The interpretation of data during the biomarker qualification phase should include careful consideration of the analytic method used, the biology, pharmacokinetic and pharmacodynamic properties of the biomarker, and the pathophysiology of the process studied. The purpose of this review is to summarize commonly employed technologies in the development of fluid- and tissue-based safety biomarkers in drug discovery and development and to highlight areas of ongoing novel assay development.

Obesity-related dysfunction of the aorta and prevention by melatonin treatment in ob/ob mice

In this study, we hypothesized that melatonin administration can minimize alterations in aorta morphology in an animal model of obesity (ob/ob mice). The animals were divided into four groups: (i) control lean mice, (ii) control lean mice treated with melatonin, (iii) ob/ob mice and (iv) ob/ob mice treated with melatonin. The synthetic melatonin was dissolved in 1% ethanol and added to the drinking water from postnatal week 5-13 at a final dose of 100 mg/kg body weight/day. Compared with the obese mice, melatonin intake was associated with a significant decrease in body weight and water consumption. Histological analysis showed that the aortic wall of ob/ob mice had a high Tunica media/lumen ratio and that the elastic fibers in the media layer appeared disrupted and degraded. Moreover, the aorta of ob/ob mice displayed a higher degree of collagen accumulation in the Tunica media compared to the normal aorta. The aorta of ob/ob mice treated with melatonin had a lower Tunica media/lumen ratio and collagen accumulation in comparison with untreated ob/ob mice. Our results showed that whereas melatonin had no apparent histological effects on the aorta in lean mice with normal weight, its administration in ob/ob mice can lead to a reduction in body weight and can ameliorate aorta histopathological dysfunction. This experimental study indicates an apparent protective role for melatonin on the aorta in obesity and melatonin could possibly be an effective tool in the management of obesity-related vascular complications.

Metabolomics and fetal-neonatal nutrition: between "not enough" and "too much"

Metabolomics is a new analytical technique defined as the study of the complex system of metabolites that is capable of describing the biochemical phenotype of a biological system. In recent years the literature has shown an increasing interest in paediatric obesity and the onset of diabetes and the metabolic syndrome in adulthood. Some studies show that fetal malnutrition, both excessive and insufficient, may permanently alter the metabolic processes of the fetus and increase the risk of future chronic pathologies. At present then, attention is being focused mainly on the formulation of new hypotheses, by means of metabolomics, concerning the biological mechanisms to departure from fetal-neonatal life that may predispose to the development of these diseases.

Systems Epidemiology: A New Direction in Nutrition and Metabolic Disease Research

Systems epidemiology applied to the field of nutrition has potential to provide new insight into underlying mechanisms and ways to study the health effects of specific foods more comprehensively. Human intervention and population-based studies have identified i) common genetic factors associated with several nutrition-related traits and ii) dietary factors altering the expression of genes and levels of proteins and metabolites related to inflammation, lipid metabolism and/or gut microbial metabolism, results of high relevance to metabolic disease. System-level tools applied type 2 diabetes and related conditions have revealed new pathways that are potentially modified by diet and thus offer additional opportunities for nutritional investigations. Moving forward, harnessing the resources of existing large prospective studies within which biological samples have been archived and diet and lifestyle have been measured repeatedly within individual will enable systems-level data to be integrated, the outcome of which will be improved personalized optimal nutrition for prevention and treatment of disease.

The systems genetics resource: a web application to mine global data for complex disease traits

The Systems Genetics Resource (SGR) (http://systems.genetics.ucla.edu) is a new open-access web application and database that contains genotypes and clinical and intermediate phenotypes from both human and mouse studies. The mouse data include studies using crosses between specific inbred strains and studies using the Hybrid Mouse Diversity Panel. SGR is designed to assist researchers studying genes and pathways contributing to complex disease traits, including obesity, diabetes, atherosclerosis, heart failure, osteoporosis, and lipoprotein metabolism. Over the next few years, we hope to add data relevant to deafness, addiction, hepatic steatosis, toxin responses, and vascular injury. The intermediate phenotypes include expression array data for a variety of tissues and cultured cells, metabolite levels, and protein levels. Pre-computed tables of genetic loci controlling intermediate and clinical phenotypes, as well as phenotype correlations, are accessed via a user-friendly web interface. The web site includes detailed protocols for all of the studies. Data from published studies are freely available; unpublished studies have restricted access during their embargo period.