Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes

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.

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.

Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study

We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.

Systems biology in cardiovascular disease: a multiomics approach

Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.

Met-Controlled Allosteric Module of Neural Generation as A New Therapeutic Target in Rodent Brain Ischemia

OBJECTIVE

To investigate a Met-controlled allosteric module (AM) of neural generation as a potential therapeutic target for brain ischemia.

METHODS

We selected Markov clustering algorithm (MCL) to mine functional modules in the related target networks. According to the topological similarity, one functional module was predicted in the modules of baicalin (BA), jasminoidin (JA), cholic acid (CA), compared with I/R model modules. This functional module included three genes: Inppl1, Met and Dapk3 (IMD). By gene ontology enrichment analysis, biological process related to this functional module was obtained. This functional module participated in generation of neurons. Western blotting was applied to present the compound-dependent regulation of IMD. Co-immunoprecipitation was used to reveal the relationship among the three members. We used IF to determine the number of newborn neurons between compound treatment group and ischemia/reperfusion group. The expressions of vascular endothelial growth factor (VEGF) and matrix metalloproteinase 9 (MMP-9) were supposed to show the changing circumstances for neural generation under cerebral ischemia.

RESULTS

Significant reduction in infarction volume and pathological changes were shown in the compound treatment groups compared with the I/R model group (P<0.05). Three nodes in one novel module of IMD were found to exert diverse compound-dependent ischemic-specific excitatory regulatory activities. An anti-ischemic excitatory allosteric module (AM_E) of generation of neurons (AM_E-GN) was validated successfully in vivo. Newborn neurons increased in BJC treatment group (P<0.05). The expression of VEGF and MMP-9 decreased in the compound treatment groups compared with the I/R model group (P<0.05).

CONCLUSIONS

AM_E demonstrates effectiveness of our pioneering approach to the discovery of therapeutic target. The novel approach for AM discovery in an effort to identify therapeutic targets holds the promise of accelerating elucidation of underlying pharmacological mechanisms in cerebral ischemia.

Next-Generation Machine Learning for Biological Networks

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.

Applying Systems Biology Methodology To Identify Genetic Factors Possibly Associated with Recovery after Traumatic Brain Injury

Traumatic brain injury (TBI) is one of the leading causes of morbidity and mortality worldwide. It is linked with a number of medical, neurological, cognitive, and behavioral sequelae. The influence of genetic factors on the biology and related recovery after TBI is poorly understood. Studies that seek to elucidate the impact of genetic influences on neurorecovery after TBI will lead to better individualization of prognosis and inform development of novel treatments, which are considerably lacking. Current genetic studies related to TBI have focused on specific candidate genes. The objectives of this study were to use a system biology-based approach to identify biologic processes over-represented with genetic variants previously implicated in clinical outcomes after TBI and identify unique genes potentially related to recovery after TBI. After performing a systematic review to identify genes in the literature associated with clinical outcomes, we used the genes identified to perform a systems biology-based integrative computational analysis to ascertain the interactions between molecular components and to develop models for regulation and function of genes involved in TBI recovery. The analysis identified over-representation of genetic variants primarily in two biologic processes: response to injury (cell proliferation, cell death, inflammatory response, and cellular metabolism) and neurocognitive and behavioral reserve (brain development, cognition, and behavior). Overall, this study demonstrates the use of a systems biology-based approach to identify unique/novel genes or sets of genes important to the recovery process. Findings from this systems biology-based approach provide additional insight into the potential impact of genetic variants on the underlying complex biological processes important to TBI recovery and may inform the development of empirical genetic-related studies for TBI. Future studies that combine systems biology methodology and genomic, proteomic, and epigenetic approaches are needed in TBI.

The Wright stuff: reimagining path analysis reveals novel components of the sex determination hierarchy in Drosophila melanogaster

BACKGROUND

The Drosophila sex determination hierarchy is a classic example of a transcriptional regulatory hierarchy, with sex-specific isoforms regulating morphology and behavior. We use a structural equation modeling approach, leveraging natural genetic variation from two studies on Drosophila female head tissues--DSPR collection (596 F1-hybrids from crosses between DSPR sub-populations) and CEGS population (75 F1-hybrids from crosses between DGRP/Winters lines to a reference strain w1118)--to expand understanding of the sex hierarchy gene regulatory network (GRN). This approach is completely generalizable to any natural population, including humans.

RESULTS

We expanded the sex hierarchy GRN adding novel links among genes, including a link from fruitless (fru) to Sex-lethal (Sxl) identified in both populations. This link is further supported by the presence of fru binding sites in the Sxl locus. 754 candidate genes were added to the pathway, including the splicing factors male-specific lethal 2 and Rm62 as downstream targets of Sxl which are well-supported links in males. Independent studies of doublesex and transformer mutants support many additions, including evidence for a link between the sex hierarchy and metabolism, via Insulin-like receptor.

CONCLUSIONS

The genes added in the CEGS population were enriched for genes with sex-biased splicing and components of the spliceosome. A common goal of molecular biologists is to expand understanding about regulatory interactions among genes. Using natural alleles we can not only identify novel relationships, but using supervised approaches can order genes into a regulatory hierarchy. Combining these results with independent large effect mutation studies, allows clear candidates for detailed molecular follow-up to emerge.

Integrating -Omics: Systems Biology as Explored Through C. elegans Research

-Omics data have become indispensable to systems biology, which aims to describe the full complexity of functional cells, tissues, organs and organisms. Generating vast amounts of data via such methods, researchers have invested in ways of handling and interpreting these. From the large volumes of -omics data that have been gathered over the years, it is clear that the information derived from one -ome is usually far from complete. Now, individual techniques and methods for integration are maturing to the point that researchers can focus on network-based integration rather than simply interpreting single -ome studies. This review evaluates the application of integrated -omics approaches with a focus on Caenorhabditis elegans studies, intending to direct researchers in this field to useful databases and inspiring examples.

Genetic and genomic analyses as a basis for new diagnostic nosologies

For schizophrenia, bipolar disorder, and autism, clinical descriptions are precise and reliable, but there is great overlap among diagnoses in associated genetic polymorphisms and rare variants, treatment response, and other phenomenological findings such as brain imaging. It is widely hoped that new diagnostic categories can be developed which are more precise and predictive of important features of illness, particularly response to pharmacological agents. It is the intent of this paper to describe the diagnostic implications of some current genetic findings, and to describe how the genetic associations with diagnosis may be teased apart into new associations with biologically coherent diagnostic entities and scales, based on the various functional aspects of the associated genes and functional genomic data.

Analysis of differentially expressed genes between rheumatoid arthritis and osteoarthritis based on the gene co-expression network

The aim of the current study was to investigate disease-associated genes and related molecular mechanisms of osteoarthritis (OA) and rheumatoid arthritis (RA). Using GSE7669 datasets downloaded from Gene Expression Omnibus databases, the differentially expressed genes (DEGs) between RA and OA synovial fibroblasts (SFBs) (n=6 each) were screened. DEG-associated co-expression and topological properties were analyzed to determine the rank of disease-associated genes. Specifically, the fold change of differentially expressed genes, the clustering coefficient and the degree of differential gene co-expression were integrated to determine the disease-associated gene ranking. The underlying molecular mechanisms of these crucial disease-associated genes were investigated by gene ontology (GO) enrichment analysis. A total of 1313 DEGs, including 1068 upregulated genes and 245 downregulated genes were observed. The top 20 disease-associated genes were identified, including proteoglycan 4, inhibin β B, carboxypeptidase M, alcohol dehydrogenase 1C and integrin β2. The major GO biological processes of these top 20 disease-associated genes were highly involved in the immune system, such as responses to stimuli, immune responses and inflammatory responses. This large-scale gene expression study observed disease-associated genes and their associated GO function in RA and OA, which may provide opportunities for biomarker development and novel insights into the molecular mechanisms of these two diseases.

Concise review: new paradigms for Down syndrome research using induced pluripotent stem cells: tackling complex human genetic disease

Down syndrome (DS) is a complex developmental disorder with diverse pathologies that affect multiple tissues and organ systems. Clear mechanistic description of how trisomy of chromosome 21 gives rise to most DS pathologies is currently lacking and is limited to a few examples of dosage-sensitive trisomic genes with large phenotypic effects. The recent advent of cellular reprogramming technology offers a promising way forward, by allowing derivation of patient-derived human cell types in vitro. We present general strategies that integrate genomics technologies and induced pluripotent stem cells to identify molecular networks driving different aspects of DS pathogenesis and describe experimental approaches to validate the causal requirement of candidate network defects for particular cellular phenotypes. This overall approach should be applicable to many poorly understood complex human genetic diseases, whose pathogenic mechanisms might involve the combined effects of many genes.

Transient treatment of pregnant Merino ewes with modulators of cortisol biosynthesis coinciding with primary wool follicle initiation alters lifetime wool growth

The economically important characteristics of the adult fleece of Merino sheep, such as increases in clean fleece weight, fibre length, fibre diameter and crimp characteristics are determined during critical phases of fetal development of the skin and its appendages. Genetics plays a major role in the development of traits, but the maternal uterine environment could also influence development. Treatment of pregnant ewes with cortisol and its analogues has previously been shown to produce changes in wool follicle morphology. The aim of this study was to determine the effect of transient manipulation of maternal cortisol status during critical phases of wool follicle initiation and development in utero. From Days 55–65 post-conception, singleton-bearing Merino ewes were treated with metyrapone (cortisol inhibitor) or betamethasone (cortisol analogue). Lambs exposed to metyrapone in utero were born with hairier birthcoats than the control or betamethasone treatment groups (P < 0.05), displayed a 10% increase in staple length and a reduction in crimp frequency for the first three shearings (P < 0.05). Co-expression network analysis of microarray data revealed up-regulation of members of the transforming growth factor-β and chemokine receptor superfamilies, gene families known to influence hair and skin development. These experiments demonstrate that presumptive transient manipulation of maternal cortisol status coinciding with the initiation of fetal wool follicle development results in long-term alteration in fleece characteristics, namely fibre length and fibre crimp frequency. These results indicate it is possible to alter the lifetime wool production of Merino sheep with therapeutics targeted to gene expression during key windows of development in utero.

Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets

Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptin^ob/ob mutation (Lep^ob). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein–protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

Alzheimer's disease and type 2 diabetes are two common aging-related diseases. Numerous studies have shown that the two diseases are associated. However, the mechanisms of such connection are not clear. Both diseases are complex diseases that are induced by multiple genetic factors and the environment. To understand the molecular network regulated by complex genetic factors causing type 2 diabetes, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant and diabetic mouse strains. We measured genotypes, clinical traits, and expression profiling in five tissues for each mouse. We then performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait, and developed a novel method for inferring key regulators for regulating plasma insulin. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

A systematic approach to multifactorial cardiovascular disease: causal analysis

The combination of systems biology and large data sets offers new approaches to the study of cardiovascular diseases. These new approaches are especially important for the common cardiovascular diseases that have long been described as multifactorial. This promise is undermined by biologists' skepticism of the spider web-like network diagrams required to analyze these large data sets. Although these spider webs resemble composites of the familiar biochemical pathway diagrams, the complexity of the webs is overwhelming. As a result, biologists collaborate with data analysts whose mathematical methods seem much like those of experts using Ouija boards. To make matters worse, it is not evident how to design experiments when the network implies that many molecules must be part of the disease process. Our goal is to remove some of this mystery and suggest a simple experimental approach to the design of experiments appropriate for such analysis. We will attempt to explain how combinations of data sets that include all possible variables, graphical diagrams, complementation of different data sets, and Bayesian analyses now make it possible to determine the causes of multifactorial cardiovascular disease. We will describe this approach using the term causal analysis. Finally, we will describe how causal analysis is already being used to decipher the interactions among cytokines as causes of cardiovascular disease.

Integrating the Alzheimer's disease proteome and transcriptome: a comprehensive network model of a complex disease

Network models combined with gene expression studies have become useful tools for studying complex diseases like Alzheimer's disease. We constructed a "Core" Alzheimer's disease protein interaction network by human curation of the primary literature. The Core network consisted of 775 nodes and 2,204 interactions. To our knowledge, this is the most comprehensive and accurate protein interaction network yet constructed for Alzheimer's disease. An "Expanded" network was computationally constructed by adding additional proteins that interacted with Core network proteins, and consisted of 4,945 nodes and 26,064 interactions. We then mapped existing gene expression studies to the Core network. This combined data model identified the MAPK/ERK pathway and clathrin-mediated receptor endocytosis as key pathways in Alzheimer's disease. Important proteins in the MAPK/ERK pathway that interacted in the Core network formed a downregulated cluster of nodes, whereas clathrin and several clathrin accessory proteins that interacted in the Core network formed an upregulated cluster of nodes. The MAPK/ERK pathway is a key component in synaptic plasticity and learning, processes disrupted in Alzheimer's. Clathrin and clathrin adaptor proteins are involved in the endocytosis of the APP protein that can lead to increased intracellular levels of amyloid beta peptide, contributing to the progression of Alzheimer's.

Systems-based approaches to cardiovascular disease

Common cardiovascular diseases, such as atherosclerosis and congestive heart failure, are exceptionally complex, involving a multitude of environmental and genetic factors that often show nonlinear interactions as well as being highly dependent on sex, age, and even the maternal environment. Although focused, reductionistic approaches have led to progress in elucidating the pathophysiology of cardiovascular diseases, such approaches are poorly powered to address complex interactions. Over the past decade, technological advances have made it possible to interrogate biological systems on a global level, raising hopes that, in combination with computational approaches, it may be possible to more fully address the complexities of cardiovascular diseases. In this Review, we provide an overview of such systems-based approaches to cardiovascular disease and discuss their translational implications.

GOrevenge: a novel generic reverse engineering method for the identification of critical molecular players, through the use of ontologies

The ever-increasing use of ontologies in modern biological analysis and interpretation facilitates the understanding of the cellular procedures, their hierarchical organization, and their potential interactions at a system's level. Currently, the gene ontology serves as a paradigm, where through the annotation of whole genomes of certain organisms, genes subsets selected, either from high-throughput experiments or with an established pivotal role regarding the probed disease, can act as a starting point for the exploration of their underlying functional interconnections. This may also aid the elucidation of hidden regulatory mechanisms among genes. Reverse engineering the functional relevance of genes to specific cellular pathways and vice versa, through the exploitation of the inner structure of the ontological vocabularies, may help impart insight regarding the identification and prioritization of the critical role of specific genes. The proposed graph-theoretical method is showcased in a pancreatic cancer and a T-cell acute lymphoblastic leukemia gene set, incorporating edge and Resnik semantic similarity metrics, and systematically evaluated regarding its performance.

What do 'omic technologies have to offer periodontal clinical practice in the future?

BACKGROUND AND OBJECTIVE

Periodontal diseases are the most common chronic inflammatory diseases of humans and a major cause of tooth loss. Inflammatory periodontitis is also a complex multifactorial disease involving many cell types, cell products and interactions. It is associated with a dysregulated inflammatory response, which fails to resolve, and which also fails to re-establish a beneficial periodontal microbiota. There is a rich history of biomarker research within the field of periodontology, but exemplary improvements in analytical platform technologies offer exciting opportunities for discovery. These include the 'omic technologies, such as genomics, transcriptomics, proteomics and metabolomics, which provide information on global scales that can match the complexity of the disease. This narrative review focuses on the recent advances made in in vivo human periodontal research by use of 'omic technologies.

MATERIAL AND METHODS

The Medline database was searched to identify articles currently available on 'omic technologies with regard to periodontal research.

RESULTS

One hundred and sixty-one articles focusing on biomarkers of and 'omic advances in periodontal research were analysed for their contributions to the understanding of periodontal diseases.

CONCLUSION

The data generated by the use of 'omic technologies have huge potential to inform paradigm shifts in our understanding of periodontal diseases, but data management, analysis and interpretation require a thoughtful and systematic bioinformatics approach, to ensure meaningful conclusions can be made.

Systems biology and heart failure: concepts, methods, and potential research applications

Dramatic advances in molecular biology dominated twentieth century biomedical science and delineated the function of individual genes and molecules in exquisite detail. However, biological processes cannot be fully understood based on the properties of individual genes and molecules alone, since these elements act in concert to enable the specific functions that make for living cells and organisms. The discipline of systems biology provides a novel conceptual framework for understanding biological phenomenon. Systems biology synthesizes information concerning the interactions of genes and molecules and allows characterization of the supramolecular networks and functional modules that represent the most essential aspects of cell organization and physiology.

Delineation and interpretation of gene networks towards their effect in cellular physiology- a reverse engineering approach for the identification of critical molecular players, through the use of ontologies

Exploiting ontologies, provides clues regarding the involvement of certain molecular processes in the cellular phenotypic manifestation. However, identifying individual molecular actors (genes, proteins, etc.) for targeted biological validation in a generic, prioritized, fashion, based in objective measures of their effects in the cellular physiology, remains a challenge. In this work, a new meta-analysis algorithm is proposed for the holistic interpretation of the information captured in -omic experiments, that is showcased in a transcriptomic, dynamic, DNA microarray dataset, which examines the effect of mastic oil treatment in Lewis lung carcinoma cells. Through the use of the Gene Ontology this algorithm relates genes to specific cellular pathways and vice versa in order to further reverse engineer the critical role of specific genes, starting from the results of various statistical enrichment analyses. The algorithm is able to discriminate candidate hub-genes, implying critical biochemical cross-talk. Moreover, performance measures of the algorithm are derived, when evaluated with respect to the differential expression gene list of the dataset.

Novel therapies in childhood heart failure: today and tomorrow

Heart failure is an important cause of morbidity and mortality in individuals of all ages. The many-faceted nature of the clinical heart failure syndrome has historically frustrated attempts to develop an overarching explanative theory. However, much useful information has been gained by basic and clinical investigation, even though a comprehensive understanding of heart failure has been elusive. Heart failure is a growing problem, in both adult and pediatric populations, for which standard medical therapy, as of 2010, can have positive effects, but these are usually limited and progressively diminish with time in most patients. If we want curative or near-curative therapy that will return patients to a normal state of health at a feasible cost, much better diagnostic and therapeutic technologies need to be developed. This review addresses the vexing group of heart failure etiologies that include cardiomyopathies and other ventricular dysfunctions of various types, for which current therapy is only modestly effective. Although there are many unique aspects to heart failure in patients with pediatric and congenital heart disease, many of the innovative approaches that are being developed for the care of adults with heart failure will be applicable to heart failure in childhood.

Intervention, integration and translation in obesity research: Genetic, developmental and metaorganismal approaches

Obesity is the focus of multiple lines of inquiry that have -- together and separately -- produced many deep insights into the physiology of weight gain and maintenance. We examine three such streams of research and show how they are oriented to obesity intervention through multilevel integrated approaches. The first research programme is concerned with the genetics and biochemistry of fat production, and it links metabolism, physiology, endocrinology and neurochemistry. The second account of obesity is developmental and draws together epigenetic and environmental explanations that can be embedded in an evolutionary framework. The third line of research focuses on the role of gut microbes in the production of obesity, and how microbial activities interact with host genetics, development and metabolism. These interwoven explanatory strategies are driven by an orientation to intervention, both for experimental and therapeutic outcomes. We connect the integrative and intervention-oriented aspects of obesity research through a discussion of translation, broadening the concept to capture the dynamic, iterative processes of scientific practice and therapy development. This system-oriented analysis of obesity research expands the philosophical scrutiny of contemporary developments in the biosciences and biomedicine, and has the potential to enrich philosophy of science and medicine.

Data-driven assessment of eQTL mapping methods

Background

The analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis.

Results

Here we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods.

Conclusions

Benchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.

Transcriptome profiling and network analysis of genetically hypertensive mice identifies potential pharmacological targets of hypertension

Hypertension is a condition with major cardiovascular and renal complications, affecting nearly a billion patients worldwide. Few validated gene targets are available for pharmacological intervention, so there is a need to identify new biological pathways regulating blood pressure and containing novel targets for treatment. The genetically hypertensive “blood pressure high” (BPH), normotensive “blood pressure normal” (BPN), and hypotensive “blood pressure low” (BPL) inbred mouse strains are an ideal system to study differences in gene expression patterns that may represent such biological pathways. We profiled gene expression in liver, heart, kidney, and aorta from BPH, BPN, and BPL mice and determined which biological processes are enriched in observed organ-specific signatures. As a result, we identified multiple biological pathways linked to blood pressure phenotype that could serve as a source of candidate genes causal for hypertension. To distinguish in the kidney signature genes whose differential expression pattern may cause changes in blood pressure from those genes whose differential expression pattern results from changes in blood pressure, we integrated phenotype-associated genes into Genetic Bayesian networks. The integration of data from gene expression profiling and genetics networks is a valuable approach to identify novel potential targets for the pharmacological treatment of hypertension.

Genome-wide gene and pathway analysis

Current GWAS have primarily focused on testing association of single SNPs. To only test for association of single SNPs has limited utility and is insufficient to dissect the complex genetic structure of many common diseases. To meet conceptual and technical challenges raised by GWAS, we suggest gene and pathway-based GWAS as complementary to the current single SNP-based GWAS. This publication develops three statistics for testing association of genes and pathways with disease: linear combination test, quadratic test and decorrelation test, which take correlations among SNPs within a gene or genes within a pathway into account. The null distribution of the suggested statistics is examined and the statistics are applied to GWAS of rheumatoid arthritis in the Wellcome Trust Case-Control Consortium and the North American Rheumatoid Arthritis Consortium studies. The preliminary results show that the suggested gene and pathway-based GWAS offer several remarkable features. First, not only can they identify the genes that have large genetic effects, but also they can detect new genes in which each single SNP conferred a small amount of disease risk, and their joint actions can be implicated in the development of diseases. Second, gene and pathway-based analysis can allow the formation of the core of pathway definition of complex diseases and unravel the functional bases of an association finding. Third, replication of association findings at the gene or pathway level is much easier than replication at the individual SNP level.

MicroRNAs--regulators of signaling networks in dilated cardiomyopathy

MicroRNAs (miRNAs) are endogenous small non-coding ribonucleotides that regulate expression of target genes governing diverse biological functions. Mechanistically, miRNA binding to the target complimentary sequences on the mRNA results in degradation or inhibition of protein translation. The short guiding and binding sequence of miRNA allows them to target a large repertoire of transcripts altering expression of many proteins. These miRNA targets are not restricted to specific signaling pathways but to a diverse group of transcripts, which harbor the target complimentary sequence. miRNA targeting of these diverse transcripts result in regulation of multiple signaling pathways establishing miRNAs as regulators of systems biomolecular networks. Accumulating evidence shows that miRNAs play an important role in cardiac development, hypertrophy, and failure, thereby are integral to regulating adaptive and maladaptive remodeling. Since cardiac remodeling and failure is a complex phenotype, it is apparent that global biomolecular networks and miRNAs profiles would be altered. Indeed, the miRNA profiles are varied with different etiologies of heart failure indicating that miRNAs could be the global regulators. Although the idea of miRNA being global regulators is not new, we believe that the time is ripe to discuss the role of miRNAs in regulating biomolecular networks. We discuss in the review, the use of Ingenuity Pathways Analysis algorithms with predicted targets of altered miRNA in dilated cardiomyopathy to computationally determine the alterations in canonical functional pathways and to generate biomolecular networks.

Exposure science and the U.S. EPA National Center for Computational Toxicology

The emerging field of computational toxicology applies mathematical and computer models and molecular biological and chemical approaches to explore both qualitative and quantitative relationships between sources of environmental pollutant exposure and adverse health outcomes. The integration of modern computing with molecular biology and chemistry will allow scientists to better prioritize data, inform decision makers on chemical risk assessments and understand a chemical's progression from the environment to the target tissue within an organism and ultimately to the key steps that trigger an adverse health effect. In this paper, several of the major research activities being sponsored by Environmental Protection Agency's National Center for Computational Toxicology are highlighted. Potential links between research in computational toxicology and human exposure science are identified. As with the traditional approaches for toxicity testing and hazard assessment, exposure science is required to inform design and interpretation of high-throughput assays. In addition, common themes inherent throughout National Center for Computational Toxicology research activities are highlighted for emphasis as exposure science advances into the 21st century.

A macrophage sterol-responsive network linked to atherogenesis

Cholesteryl ester accumulation by macrophages is a critical early event in atherogenesis. To test the hypothesis that sterol loading promotes foam cell formation and vascular disease by perturbing a network of interacting proteins, we used a global approach to identify proteins that are differentially expressed when macrophages are loaded with cholesterol in vivo. Our analysis revealed a sterol-responsive network that is highly enriched in proteins with known physical interactions, established roles in vesicular transport, and demonstrated atherosclerotic phenotypes in mice. Pharmacologic intervention with a statin or rosiglitazone and use of mice deficient in LDL receptor or apolipoprotein E implicated the network in atherosclerosis. Biochemical fractionation revealed that most of the sterol-responsive proteins resided in microvesicles, providing a physical basis for the network's functional and biochemical properties. These observations identify a highly integrated network of proteins whose expression is influenced by environmental, genetic, and pharmacological factors implicated in atherogenesis.

Pathogenic mechanisms of allergic inflammation: atopic asthma as a paradigm

Prospective studies tracking birth cohorts over periods of years indicate that the seeds for atopic asthma in adulthood are sewn during early life. The key events involve programming of functional phenotypes within the immune and respiratory systems which determine long-term responsiveness to ubiquitous environmental stimuli, particularly respiratory viruses and aeroallergens. A crucial component of asthma pathogenesis is early sensitization to aeroallergens stemming from a failure of mucosal tolerance mechanisms during the preschool years, which is associated with delayed postnatal maturation of a range of adaptive and innate immune functions. These maturational defects also increase risk for severe respiratory infections, and the combination of sensitization and infections maximizes risk for early development of the persistent asthma phenotype. Interactions between immunoinflammatory pathways stimulated by these agents also sustain the disease in later life as major triggers of asthma exacerbations. Recent studies on the nature of these interactions suggest the operation of an infection-associated lung:bone marrow axis involving upregulation of FcERlalpha on myeloid precursor populations prior to their migration to the airways, thus amplifying local inflammation via IgE-mediated recruitment of bystander atopic effector mechanisms. The key participants in the disease process are airway mucosal dendritic cells and adjacent epithelial cells, and transiting CD4(+) effector and regulatory T-cell populations, and increasingly detailed characterization of their roles at different stages of pathogenesis is opening up novel possibilities for therapeutic control of asthma. Of particular interest is the application of genomics-based approaches to drug target identification in cell populations of interest, exemplified by recent findings discussed below relating to the gene network(s) triggered by activation of Th2-memory cells from atopics.

Genome-wide association data reveal a global map of genetic interactions among protein complexes

This work demonstrates how gene association studies can be analyzed to map a global landscape of genetic interactions among protein complexes and pathways. Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. This work has the potential to transform gene association studies, by elevating the analysis from the level of individual markers to global maps of genetic interactions. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex.

Arabidopsis gene co-expression network and its functional modules

BACKGROUND

Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated.

RESULTS

We constructed a genome-wide Arabidopsis gene co-expression network (AGCN) by using 1094 microarrays. We then analyzed the topological properties of AGCN and partitioned the network into modules by using an efficient graph clustering algorithm. In the AGCN, 382 hub genes formed a clique, and they were densely connected only to a small subset of the network. At the module level, the network clustering results provide a systems-level understanding of the gene modules that coordinate multiple biological processes to carry out specific biological functions. For instance, the photosynthesis module in AGCN involves a very large number (> 1000) of genes which participate in various biological processes including photosynthesis, electron transport, pigment metabolism, chloroplast organization and biogenesis, cofactor metabolism, protein biosynthesis, and vitamin metabolism. The cell cycle module orchestrated the coordinated expression of hundreds of genes involved in cell cycle, DNA metabolism, and cytoskeleton organization and biogenesis. We also compared the AGCN constructed in this study with a graphical Gaussian model (GGM) based Arabidopsis gene network. The photosynthesis, protein biosynthesis, and cell cycle modules identified from the GGM network had much smaller module sizes compared with the modules found in the AGCN, respectively.

CONCLUSION

This study reveals new insight into the topological properties of biological networks. The preferential hub-hub connections might be necessary for the formation of modular structure in gene co-expression networks. The study also reveals new insight into the organization of gene functional modules.

The use of network analyses for elucidating mechanisms in cardiovascular disease

Systems biology offers the potential to provide new insights into our understanding of the pathogenesis of complex diseases such as atherosclerosis. It seeks to comprehend the system properties of the non-linear interactions of the multiple biomolecular components that characterize a living organism. An important component of this research approach is identifying the biological networks that connect the differing elements of a system and in the process describe the characteristics that define a shift in equilibrium from a healthy to a diseased state. The utility of this method becomes clear when applied to multifactorial diseases with complex etiologies such as inflammatory-related diseases, herein exemplified by cardiovascular disease. In this study, the application of network theory to systems biology is described in detail and an example is provided using data from a clinical biobank database of carotid endarterectomies from the Karolinska University Hospital (Biobank of Karolinska Endarterectomies, BiKE). Data from 47 microarrays were examined using a combination of Bioconductor modules and the Cytoscape resource with several associated plugins to analyze the transcriptomics data and create a combined gene association and correlation network of atherosclerosis. The methodology and workflow are described in detail, with a total of 43 genes found to be differentially expressed on a gender-specific basis, of which 15 were not directly linked to the sex chromosomes. In particular, the APOC1 gene was 2.1-fold down-regulated in plaques in women relative to men and was selected for further analysis based upon a purported role in cardiovascular disease. The resulting network was identified as a scale-free network that contained specific sub-networks related to immune function and lipid biosynthesis. These sub-networks link atherosclerotic-related genes to other genes that may not have previously known roles in disease etiology and only evidence small alterations, which are challenging to find by statistical and comparison-based methods. A number of Gene Ontology (GO), BioCarta and KEGG pathways involved in the atherosclerotic process were identified in the constructed sub-network, with 19 GO pathways related to APOC1 of which 'phospholipid efflux' evidenced the strongest association. The utility and functionality of network analysis and the different Cytoscape plugins employed are discussed. Lastly, the applications of these methods to cardiovascular disease are discussed with focus on the current limitations and future visions of this emerging field.

Dynamical approaches to modeling developmental gene regulatory networks

The network of interacting regulatory signals within a cell comprises one of the most complex and powerful computational systems in biology. Gene regulatory networks (GRNs) play a key role in transforming the information encoded in a genome into morphological form. To achieve this feat, GRNs must respond to and integrate environmental signals with their internal dynamics in a robust and coordinated fashion. The highly dynamic nature of this process lends itself to interpretation and analysis in the language of dynamical models. Modeling provides a means of systematically untangling the complicated structure of GRNs, a framework within which to simulate the behavior of reconstructed systems and, in some cases, suites of analytic tools for exploring that behavior and its implications. This review provides a general background to the idea of treating a regulatory network as a dynamical system, and describes a variety of different approaches that have been taken to the dynamical modeling of GRNs.

Systems genetics analysis of cancer susceptibility: from mouse models to humans

Genetic studies of cancer susceptibility have shown that most heritable risk cannot be explained by the main effects of common alleles. This may be due to unknown gene-gene or gene-environment interactions and the complex roles of many genes at different stages of cancer. Studies using mouse models of cancer suggest that methods that integrate genetic analysis and genomic networks with knowledge of cancer biology can help to extend our understanding of heritable cancer susceptibility.

Methods for the inference of biological pathways and networks

In this chapter, we discuss a number of approaches to network inference from large-scale functional genomics data. Our goal is to describe current methods that can be used to infer predictive networks. At present, one of the most effective methods to produce networks with predictive value is the Bayesian network approach. This approach was initially instantiated by Friedman et al. and further refined by Eric Schadt and his research group. The Bayesian network approach has the virtue of identifying predictive relationships between genes from a combination of expression and eQTL data. However, the approach does not provide a mechanistic bases for predictive relationships and is ultimately hampered by an inability to model feedback. A challenge for the future is to produce networks that are both predictive and provide mechanistic understanding. To do so, the methods described in several chapters of this book will need to be integrated. Other chapters of this book describe a number of methods to identify or predict network components such as physical interactions. At the end of this chapter, we speculate that some of the approaches from other chapters could be integrated and used to "annotate" the edges of the Bayesian networks. This would take the Bayesian networks one step closer to providing mechanistic "explanations" for the relationships between the network nodes.

A network modeling approach to analysis of the Th2 memory responses underlying human atopic disease

Complex cellular functions within immunoinflammatory cascades are conducted by networks of interacting genes. In this study, we employed a network modeling approach to dissect and interpret global gene expression patterns in allergen-induced Th cell responses that underpin human atopic disease. We demonstrate that a subnet of interconnected genes enriched for Th2 and regulatory T cell-associated signatures plus many novel genes is hardwired into the atopic response and is a hallmark of atopy at the systems level. We show that activation of this subnet is stabilized via hyperconnected "hub" genes, the selective disruption of which can collapse the entire network in a comprehensive fashion. Finally, we investigated gene expression in different Th cell subsets and show that regulatory T cell- and Th2-associated signatures partition at different stages of Th memory cell differentiation. Moreover, we demonstrate the parallel presence of a core element of the Th2-associated gene signature in bystander naive cells, which can be reproduced by rIL-4. These findings indicate that network analysis provides significant additional insight into atopic mechanisms beyond that achievable with conventional microarray analyses, predicting functional interactions between novel genes and previously recognized members of the allergic cascade. This approach provides novel opportunities for design of therapeutic strategies that target entire networks of genes rather than individual effector molecules.

Advances in systems biology are enhancing our understanding of disease and moving us closer to novel disease treatments

With tens of billions of dollars spent each year on the development of drugs to treat human diseases, and with fewer and fewer applications for investigational new drugs filed each year despite this massive spending, questions now abound on what changes to the drug discovery paradigm can be made to achieve greater success. The high rate of failure of drug candidates in clinical development, where the great majority of these drugs fail due to lack of efficacy, speak directly to the need for more innovative approaches to study the mechanisms of disease and drug discovery. Here we review systems biology approaches that have been devised over the last several years to understand the biology of disease at a more holistic level. By integrating a diversity of data like DNA variation, gene expression, protein-protein interaction, DNA-protein binding, and other types of molecular phenotype data, more comprehensive networks of genes both within and between tissues can be constructed to paint a more complete picture of the molecular processes underlying physiological states associated with disease. These more integrative, systems-level methods lead to networks that are demonstrably predictive, which in turn provides a deeper context within which single genes operate such as those identified from genome-wide association studies or those targeted for therapeutic intervention. The more comprehensive views of disease that result from these methods have the potential to dramatically enhance the way in which novel drug targets are identified and developed, ultimately increasing the probability of success for taking new drugs through clinical development. We highlight a number of the integrative approaches via examples that have resulted not only in the identification of novel genes for diabetes and cardiovascular disease, but in more comprehensive networks as well that describe the context in which the disease genes operate.

Natural genetic variation in transcriptome reflects network structure inferred with major effect mutations: insulin/TOR and associated phenotypes in Drosophila melanogaster

Background

A molecular process based genotype-to-phenotype map will ultimately enable us to predict how genetic variation among individuals results in phenotypic alterations. Building such a map is, however, far from straightforward. It requires understanding how molecular variation re-shapes developmental and metabolic networks, and how the functional state of these networks modifies phenotypes in genotype specific way. We focus on the latter problem by describing genetic variation in transcript levels of genes in the InR/TOR pathway among 72 Drosophila melanogaster genotypes.

Results

We observe tight co-variance in transcript levels of genes not known to influence each other through direct transcriptional control. We summarize transcriptome variation with factor analyses, and observe strong co-variance of gene expression within the dFOXO-branch and within the TOR-branch of the pathway. Finally, we investigate whether major axes of transcriptome variation shape phenotypes expected to be influenced through the InR/TOR pathway. We find limited evidence that transcript levels of individual upstream genes in the InR/TOR pathway predict fly phenotypes in expected ways. However, there is no evidence that these effects are mediated through the major axes of downstream transcriptome variation.

Conclusion

In summary, our results question the assertion of the 'sparse' nature of genetic networks, while validating and extending candidate gene approaches in the analyses of complex traits.