Systems biology approaches and pathway tools for investigating cardiovascular disease

Exploring pathway interactions to detect molecular mechanisms of disease: 22q11.2 deletion syndrome

BACKGROUND

22q11.2 Deletion Syndrome (22q11DS) is a genetic disorder characterized by the deletion of adjacent genes at a location specified as q11.2 of chromosome 22, resulting in an array of clinical phenotypes including autistic spectrum disorder, schizophrenia, congenital heart defects, and immune deficiency. Many characteristics of the disorder are known, such as the phenotypic variability of the disease and the biological processes associated with it; however, the exact and systemic molecular mechanisms between the deleted area and its resulting clinical phenotypic expression, for example that of neuropsychiatric diseases, are not yet fully understood.

RESULTS

Using previously published transcriptomics data (GEO:GSE59216), we constructed two datasets: one set compares 22q11DS patients experiencing neuropsychiatric diseases versus healthy controls, and the other set 22q11DS patients without neuropsychiatric diseases versus healthy controls. We modified and applied the pathway interaction method, originally proposed by Kelder et al. (2011), on a network created using the WikiPathways pathway repository and the STRING protein-protein interaction database. We identified genes and biological processes that were exclusively associated with the development of neuropsychiatric diseases among the 22q11DS patients. Compared with the 22q11DS patients without neuropsychiatric diseases, patients experiencing neuropsychiatric diseases showed significant overrepresentation of regulated genes involving the natural killer cell function and the PI3K/Akt signalling pathway, with affected genes being closely associated with downregulation of CRK like proto-oncogene adaptor protein. Both the pathway interaction and the pathway overrepresentation analysis observed the disruption of the same biological processes, even though the exact lists of genes collected by the two methods were different.

CONCLUSIONS

Using the pathway interaction method, we were able to detect a molecular network that could possibly explain the development of neuropsychiatric diseases among the 22q11DS patients. This way, our method was able to complement the pathway overrepresentation analysis, by filling the knowledge gaps on how the affected pathways are linked to the original deletion on chromosome 22. We expect our pathway interaction method could be used for problems with similar contexts, where complex genetic mechanisms need to be identified to explain the resulting phenotypic plasticity.

lncRNA-miRNA-mRNA ceRNA Network Involved in Sheep Prolificacy: An Integrated Approach

Understanding the molecular pattern of fertility is considered as an important step in breeding of different species, and despite the high importance of the fertility, little success has been achieved in dissecting the interactome basis of sheep fertility. However, the complex mechanisms associated with prolificacy in sheep have not been fully understood. Therefore, this study aimed to use competitive endogenous RNA (ceRNA) networks to evaluate this trait to better understand the molecular mechanisms responsible for fertility. A competitive endogenous RNA (ceRNA) network of the corpus luteum was constructed between Romanov and Baluchi sheep breeds with either good or poor genetic merit for prolificacy using whole-transcriptome analysis. First, the main list of lncRNAs, miRNAs, and mRNA related to the corpus luteum that alter with the breed were extracted, then miRNA−mRNA and lncRNA−mRNA interactions were predicted, and the ceRNA network was constructed by integrating these interactions with the other gene regulatory networks and the protein−protein interaction (PPI). A total of 264 mRNAs, 14 lncRNAs, and 34 miRNAs were identified by combining the GO and KEGG enrichment analyses. In total, 44, 7, 7, and 6 mRNAs, lncRNAs, miRNAs, and crucial modules, respectively, were disclosed through clustering for the corpus luteum ceRNA network. All these RNAs involved in biological processes, namely proteolysis, actin cytoskeleton organization, immune system process, cell adhesion, cell differentiation, and lipid metabolic process, have an overexpression pattern (Padj < 0.01). This study increases our understanding of the contribution of different breed transcriptomes to phenotypic fertility differences and constructed a ceRNA network in sheep (Ovis aries) to provide insights into further research on the molecular mechanism and identify new biomarkers for genetic improvement.

Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.

System Biology-Guided Chemical Proteomics to Discover Protein Targets of Monoethylhexyl Phthalate in Regulating Cell Cycle

Chemical proteomics methods have been used as effective tools to identify novel protein targets for small molecules. These methods have great potential to be applied as environmental toxicants to figure out their mode of action. However, these assays usually generate dozens of possible targets, making it challenging to validate the most important one. In this study, we have integrated the cellular thermal shift assay (CETSA), quantitative proteomics, metabolomics, computer-assisted docking, and target validation methods to uncover the protein targets of monoethylhexyl phthalate (MEHP). Using the mass spectrometry implementation of CETSA (MS-CETSA), we have identified 74 possible protein targets of MEHP. The Gene Ontology (GO) enrichment integration was further conducted for the target proteins, the cellular dysregulated proteins, and the metabolites, showing that cell cycle dysregulation could be one primary change due to the MEHP-induced toxicity. Flow cytometry analysis confirmed that hepatocytes were arrested at the G1 stage due to the treatment with MEHP. Subsequently, the potential protein targets were ranked by their binding energy calculated from the computer-assisted docking with MEHP. In summary, we have demonstrated the development of interactomics workflow to simplify the redundant information from multiomics data and identified novel cell cycle regulatory protein targets (CPEB4, ANAPC5, and SPOUT1) for MEHP.

Analysis of biomarkers and metabolic pathways in patients with unstable angina based on ultra‑high‑performance liquid chromatography‑quadrupole time‑of‑flight mass spectrometry

Unstable angina (UA) is a coronary disease with a high mortality and morbidity worldwide. The present study aimed to use non-invasive techniques to identify urine biomarkers in patients with UA, so as to provide more information for the early diagnosis and treatment of the disease. Based on metabolomics, urine samples from 28 patients with UA and 28 healthy controls (HCs) were analyzed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). A total of 16 significant biomarkers that could distinguish between patients with UA and HCs, including D-glucuronic acid, creatinine, succinic acid and N-acetylneuraminic acid, were identified. The major metabolic pathways associated with UA were subsequently analyzed by non-targeted metabolomics. The results demonstrated that amino acid and energy metabolism, fatty acid metabolism, purine metabolism and steroid hormone biosynthetic metabolism may serve important roles in UA. The results of the current study may provide a theoretical basis for the early diagnosis of UA and novel treatment strategies for clinicians. The trial was registered with the Chinese Clinical Trial Registration Center (registration no. ChiCTR-ROC-17013957) at Tianjin University of Traditional Chinese Medicine.

(R)-Salbutamol Improves Imiquimod-Induced Psoriasis-Like Skin Dermatitis by Regulating the Th17/Tregs Balance and Glycerophospholipid Metabolism

Psoriasis is a skin disease that is characterized by a high degree of inflammation caused by immune dysfunction. (R)-salbutamol is a bronchodilator for asthma and was reported to alleviate immune system reactions in several diseases. In this study, using imiquimod (IMQ)-induced mouse psoriasis-like dermatitis model, we evaluated the therapeutic effects of (R)-salbutamol in psoriasis in vivo, and explored the metabolic pathway involved. The results showed that, compared with IMQ group, (R)-salbutamol treatment significantly ameliorated psoriasis, reversed the suppressive effects of IMQ on differentiation, extreme keratinocyte proliferation, and infiltration of inflammatory cells. Enzyme-linked immunosorbent assays (ELISA) showed that (R)-salbutamol markedly reduced the plasma levels of IL-17. Cell analysis using flow cytometry showed that (R)-salbutamol decreased the proportion of CD4+ Th17+ T cells (Th17), whereas it increased the percentage of CD25+ Foxp3+ regulatory T cells (Tregs) in the spleens. (R)-salbutamol also decreased the weight ratio of spleen to body. Furthermore, untargeted metabolomics showed that (R)-salbutamol affected three metabolic pathways, including (i) arachidonic acid metabolism, (ii) sphingolipid metabolism, and (iii) glycerophospholipid metabolism. These results demonstrated that (R)-salbutamol can alleviate IMQ-induced psoriasis through regulating Th17/Tregs cell response and glycerophospholipid metabolism. It may provide a new use of (R)-salbutamol in the management of psoriasis.

Lipidomics from sample preparation to data analysis: a primer

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid–liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.

Systematic analysis of lncRNA expression profiles and atherosclerosis-associated lncRNA-mRNA network revealing functional lncRNAs in carotid atherosclerotic rabbit models

Atherosclerosis, a multifactorial and chronic immune inflammatory disorder, is the main cause of multiple cardiovascular diseases. Researchers recently reported that lncRNAs may exert important functions in the progression of atherosclerosis (AS). Some studies found that lncRNAs can act as ceRNAs to communicate with each other by the competition of common miRNA response elements. However, lncRNA-associated ceRNA network in terms of atherosclerosis is limited. In present study, we pioneered to construct and systematically analyze the lncRNA-mRNA network and reveal its potential roles in carotid atherosclerotic rabbit models. Atherosclerosis was induced in rabbits (n = 3) carotid arteries via a high-fat diet and balloon injury, while age-matched rabbits (n = 3) were treated with normal chow as controls. RNA-seq analysis was conducted on rabbits carotid arteries (n = 6) with or without plaque formation. Based on the ceRNA mechanism, a ternary interaction network including lncRNA, mRNA, and miRNA was generated and an AS-related lncRNA-mRNA network (ASLMN) was extracted. Furthermore, we analyzed the properties of ASLMN and discovered that six lncRNAs (MSTRG.10603.16, 5258.4, 12799.3, 5352.1, 12022.1, and 12250.4) were highly related to AS through topological analysis. GO and KEGG enrichment analysis indicated that lncRNA MSTRG.5258.4 may downregulate inducible co-stimulator to perform a downregulated role in AS through T cell receptor signaling pathway and downregulate THBS1 to conduct a upregulated function in AS through ECM-receptor interaction pathway. Finally, our results elucidated the important function of lncRNAs in the origination and progression of AS. We provided an ASLMN of atherosclerosis development in carotid arteries of rabbits and probable targets which may lay the foundation for future research of clinical applications.

Overexpression of the Rybp Gene Inhibits Differentiation of Bovine Myoblasts into Myotubes

RING1 and YY1 binding protein (Rybp) genes inhibit myogenesis in mice, but there are no reports on the effects of these genes in cattle. The aim of this study is to investigate the roles of the Rybp gene on bovine skeletal muscle development and myoblast differentiation. In the present study, the Rybp gene was overexpressed in bovine myoblasts via adenovirus. RNA-seq was performed to screen differentially expressed genes (DEGs). The results showed that overexpressing the Rybp gene inhibits the formation of myotubes. The morphological differences in myoblasts began on the second day and were very significant 6 days after adenovirus induction. A total of 1311 (707 upregulated and 604 downregulated) DEGs were screened using RNA-seq between myoblasts with added negative control adenoviruses (AD-NC) and Rybp adenoviruses (AD-Rybp) after 6 days of induction. Gene ontology (GO) and KEGG analysis revealed that the downregulated DEGs were mainly involved in biological functions related to muscle, and, of the 32 pathways, those associated with muscle development were significantly enriched for the identified DEGs. This study can not only provide a theoretical basis for the regulation of skeletal muscle development in cattle by exploring the roles of the Rybp gene in myoblast differentiation, but it can also lay a theoretical foundation for molecular breeding of beef cattle.

Identification of novel drug targets for diamond-blackfan anemia based on RPS19 gene mutation using protein-protein interaction network

BACKGROUND

Diamond-Blackfan anemia (DBA) is a congenital erythroid aplasia that usually presents in infancy. In order to explore the molecular mechanisms of wild and mutated samples from DBA patients were exposed to bioinformatics investigation. Biological network of differentially expressed genes was constructed. This study aimed to identify novel therapeutic signatures in DBA and uncovered their mechanisms. The gene expression dataset of GSE14335 was used, which consists of 6 normal and 4 diseased cases. The gene ontology (GO), as well as Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and then protein-protein interaction (PPI) network of the identified differentially expressed genes (DEGs) was constructed by Cytoscape software.

RESULTS

A total of 607 DEGs were identified in DBA, including 433 upregulated genes and 174 downregulated genes. GO analysis results showed that upregulated DEGs were significantly enriched in biological processes, negative regulation of transcription from RNA polymerase II promoter, chemotaxis, inflammatory response, immune response, positive regulation of cell proliferation, negative regulation of cell proliferation, response to mechanical stimulus, positive regulation of cell migration, response to lipopolysaccharide, and defence response. KEGG pathway analysis revealed the TNF signalling pathway, Osteoclast differentiation, Chemokine signalling pathway, Cytokine -cytokine receptor interaction, Rheumatoid arthritis, Biosynthesis of amino acids, Biosynthesis of antibiotics and Glycine, serine and threonine metabolism. The top 10 hub genes, AKT1, IL6, NFKB1, STAT3, STAT1, RAC1, EGR1, IL8, RELA, RAC3, mTOR and CCR2 were identified from the PPI network and sub-networks.

CONCLUSION

The present study flagged that the identified DEGs and hub genes enrich our understanding of the molecular mechanisms underlying the development of DBA, and might shine some lights on identifying molecular targets and diagnostic biomarkers for DBA.

Comparative transcriptomic analysis of mice liver treated with different AMPK activators in a mice model of atherosclerosis

Atherosclerosis is known to be the primary underlying factor responsible for the development of cardiovascular diseases. Suppression of AMP-activated protein kinase stimulates arterial deposition of excess lipids, resulting in the development of atherosclerotic lesions. In this study we successfully developed the disease model of mice and mimicked the therapeutic effect, for that we chose three different AMP-activated protein kinase activators (IMM-H007, A-769662 and Metformin) to identify which one has a superior effect in the atherosclerosis model. We combined the transcriptomes of four groups of mice liver including high-fat diet group and the experimental groups treated with different AMP-activated protein kinase activators. We analyzed the increased genes to candidate metabolic and disease pathways. Compared to the high-fat diet group, a total of 799 differentially expressed genes were identified in treatment groups. There were 291, 473, and 323 differentially expressed genes in H007, Metformin, and A-769662 group respectively. And seven statistically significant pathways were observed in both H007 and Metformin groups. We expect that gene expression profiling in the mice model would extend our understanding of atherosclerosis in the molecular level. This study provides a fundamental framework for future clinical research on human atherosclerosis and new clues for developing novel drugs for the treatment of atherosclerosis.

Serum Metabolomics Profiling to Identify Biomarkers for Unstable Angina

Although statistical evidence is clear regarding the dangerousness of unstable angina (UA), a form of coronary heart disease (CHD) characterised by high mortality and morbidity globally, it is important to recognise that diagnostic precision for the condition is unfavourable. In the present research, to gain insight into candidate biomarkers, the author draws on ¹H NMR-based serum metabolic profiling to analyze the unstable angina pectoris (UAP) metabolic signatures; this constitutes an effective way to produce medical diagnosis. 101 unstable angina pectoris patients and 132 healthy controls were enrolled and 22 serum samples from each group were analyzed. Effective separation was noted regarding the UAP and control groups, and, for the former group considered in relation to their counterpart, the serum concentrations of Lac, m-I, lipid, VLDL, 3-HB, and LDL were higher whereas the concentrations of Thr, Cr, Cho, PC/GPC, Glu, Gln, Lys, HDL, Ile, Leu, and Val were lower. The conclusion drawn in view of the results is that the plasma metabolomics examined by ¹H NMR displayed promise for biomarker identification for UA. In addition to this, the analysis illuminated the metabolic processes of UA.

Biomarkers of unstable angina pectoris and yangxin decoction intervention: An exploratory metabonomics study of blood plasma

BACKGROUND

This study aimed to explore the related metabolic biomarkers and to observe the effects of Yangxin Decoction (YXD) on plasma metabolism of patients with unstable angina (UA).

METHODS

In total, 10 patients with UA (intervention group) and 10 healthy participants (control group) were recruited for this study from January 2009 to December 2010. Plasma samples from both groups were analyzed using liquid chromatography mass spectrometry (LC-MS). Principle component analysis (PCA) and partial least squares (PLS) were used to explore the correlations between metabolic markers in patients with UA.

RESULTS

The LC-MS results indicated that the serum levels of 5 potential metabolic markers, namely, ceramide, glycocholic acid, allocholic acid, lithocholic acid, and leukotriene (LT) B4, were significantly higher in the intervention group than those in the control group.

CONCLUSION

The results of this study demonstrated potential metabolic markers that can be used to distinguish and diagnose patients with UA.

Chemogenomic analysis of neuronal differentiation with pathway changes in PC12 cells

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database creates networks from interrelations between molecular biology and underlying chemical elements. This allows for analysis of biologic networks, genomic information, and higher-order functional information at a system level. Through high throughput experiments and system biology analysis, we investigated the genes and pathways associated with NGF induced neuronal differentiation. We performed microarray experiments and used the KEGG database, system biology analysis, and annotation of pathway functions to study NGF-induced differentiation in PC12 cells. We identified 2020 NGF-induced genes with altered expressions over time. Cross-matching with the KEGG database revealed 830 genes; among which, 395 altered genes were found to have a 2-fold increase in gene expression over a two-hour period. We then identified 191 associated biologic pathways in the KEGG database; the top 15 pathways showed correlation with neural differentiation. These included the neurotrophin pathways, mitogen-activated protein kinase (MAPK) pathways, genes associated with axonal guidance and the Wnt pathways. The activation of these pathways synchronized with nerve growth factor (NGF)-induced differentiation in PC12 cells. In summary, we have established a model system that allows one to systematically characterize the functional pathway changes in a group of neuronal population after an external stimulus.

Systems Medicine as an Emerging Tool for Cardiovascular Genetics

Cardiovascular disease (CVD) is a major contributor to morbidity and mortality worldwide. However, the pathogenesis of CVD is complex and remains elusive. Within the last years, systems medicine has emerged as a novel tool to study the complex genetic, molecular, and physiological interactions leading to diseases. In this review, we provide an overview about the current approaches for systems medicine in CVD. They include bioinformatical and experimental tools such as cell and animal models, omics technologies, network, and pathway analyses. Additionally, we discuss challenges and current literature examples where systems medicine has been successfully applied for the study of CVD.

Scoring the correlation of genes by their shared properties using OScal, an improved overlap quantification model

Scoring the correlation between two genes by their shared properties is a common and basic work in biological study. A prospective way to score this correlation is to quantify the overlap between the two sets of homogeneous properties of the two genes. However the proper model has not been decided, here we focused on studying the quantification of overlap and proposed a more effective model after theoretically compared 7 existing models. We defined three characteristic parameters (d, R, r) of an overlap, which highlight essential differences among the 7 models and grouped them into two classes. Then the pros and cons of the two groups of model were fully examined by their solution space in the (d, R, r) coordinate system. Finally we proposed a new model called OScal (Overlap Score calculator), which was modified on Poisson distribution (one of 7 models) to avoid its disadvantages. Tested in assessing gene relation using different data, OScal performs better than existing models. In addition, OScal is a basic mathematic model, with very low computation cost and few restrictive conditions, so it can be used in a wide-range of research areas to measure the overlap or similarity of two entities.

Disease networks. Uncovering disease-disease relationships through the incomplete interactome

According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data have sufficient coverage to map out modules associated with each disease. Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases. For example, diseases with overlapping network modules show significant coexpression patterns, symptom similarity, and comorbidity, whereas diseases residing in separated network neighborhoods are phenotypically distinct. These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases, even if they do not share primary disease genes.

Transcriptomic analysis of hepatic responses to testosterone deficiency in miniature pigs fed a high-cholesterol diet

Background

Recent studies have indicated that low serum testosterone levels are associated with increased risk of developing hepatic steatosis; however, the mechanisms mediating this phenomenon have not been fully elucidated. To gain insight into the role of testosterone in modulating hepatic steatosis, we investigated the effects of testosterone on the development of hepatic steatosis in pigs fed a high-fat and high-cholesterol (HFC) diet and profiled hepatic gene expression by RNA-Seq in HFC-fed intact male pigs (IM), castrated male pigs (CM), and castrated male pigs with testosterone replacement (CMT).

Results

Serum testosterone levels were significantly decreased in CM pigs, and testosterone replacement attenuated castration-induced testosterone deficiency. CM pigs showed increased liver injury accompanied by increased hepatocellular steatosis, inflammation, and elevated serum alanine aminotransferase levels compared with IM pigs. Moreover, serum levels of total cholesterol, low-density lipoprotein cholesterol, and triglycerides were markedly increased in CM pigs. Testosterone replacement decreased serum and hepatic lipid levels and improved liver injury in CM pigs. Compared to IM and CMT pigs, CM pigs had lower serum levels of superoxide dismutase but higher levels of malondialdehyde. Gene expression analysis revealed that upregulated genes in the livers of CM pigs were mainly enriched for genes mediating immune and inflammatory responses, oxidative stress, and apoptosis. Surprisingly, the downregulated genes mainly included those that regulate metabolism-related processes, including fatty acid oxidation, steroid biosynthesis, cholesterol and bile acid metabolism, and glucose metabolism. KEGG analysis showed that metabolic pathways, fatty acid degradation, pyruvate metabolism, the tricarboxylic acid cycle, and the nuclear factor-kappaB signaling pathway were the major pathways altered in CM pigs.

Conclusions

This study demonstrated that testosterone deficiency aggravated hypercholesterolemia and hepatic steatosis in pigs fed an HFC diet and that these effects could be reversed by testosterone replacement therapy. Impaired metabolic processes, enhanced immune and inflammatory responses, oxidative stress, and apoptosis may contribute to the increased hepatic steatosis induced by testosterone deficiency and an HFC diet. These results deepened our understanding of the molecular mechanisms of testosterone deficiency-induced hepatic steatosis and provided a foundation for future investigations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1283-0) contains supplementary material, which is available to authorized users.

Analysis of urinary metabolomic profiling for unstable angina pectoris disease based on nuclear magnetic resonance spectroscopy

The NMR-based metabolomics approach showed good performance in identifying diagnostic urinary biomarkers, providing new insights into the metabolic process related to UAP.

Pathway reconstruction of airway remodeling in chronic lung diseases: a systems biology approach

Airway remodeling is a pathophysiologic process at the clinical, cellular, and molecular level relating to chronic obstructive airway diseases such as chronic obstructive pulmonary disease (COPD), asthma and mustard lung. These diseases are associated with the dysregulation of multiple molecular pathways in the airway cells. Little progress has so far been made in discovering the molecular causes of complex disease in a holistic systems manner. Therefore, pathway and network reconstruction is an essential part of a systems biology approach to solve this challenging problem. In this paper, multiple data sources were used to construct the molecular process of airway remodeling pathway in mustard lung as a model of airway disease. We first compiled a master list of genes that change with airway remodeling in the mustard lung disease and then reconstructed the pathway by generating and merging the protein-protein interaction and the gene regulatory networks. Experimental observations and literature mining were used to identify and validate the master list. The outcome of this paper can provide valuable information about closely related chronic obstructive airway diseases which are of great importance for biologists and their future research. Reconstructing the airway remodeling interactome provides a starting point and reference for the future experimental study of mustard lung, and further analysis and development of these maps will be critical to understanding airway diseases in patients.

Survey of network-based approaches to research of cardiovascular diseases

Cardiovascular diseases (CVDs) are the leading health problem worldwide. Investigating causes and mechanisms of CVDs calls for an integrative approach that would take into account its complex etiology. Biological networks generated from available data on biomolecular interactions are an excellent platform for understanding interconnectedness of all processes within a living cell, including processes that underlie diseases. Consequently, topology of biological networks has successfully been used for identifying genes, pathways, and modules that govern molecular actions underlying various complex diseases. Here, we review approaches that explore and use relationships between topological properties of biological networks and mechanisms underlying CVDs.

Network representations and methods for the analysis of chemical and biochemical pathways

Systems biologists increasingly use network representations to investigate biochemical pathways and their dynamic behaviours. In this critical review, we discuss four commonly used network representations of chemical and biochemical pathways. We illustrate how some of these representations reduce network complexity but result in the ambiguous representation of biochemical pathways. We also examine the current theoretical approaches available to investigate the dynamic behaviour of chemical and biochemical networks. Finally, we describe how the critical chemical and biochemical pathways responsible for emergent dynamic behaviour can be identified using network mining and functional mapping approaches.

Nutritional lipidomics: molecular metabolism, analytics, and diagnostics

The field of lipidomics is providing nutritional science a more comprehensive view of lipid intermediates. Lipidomics research takes advantage of the increase in accuracy and sensitivity of mass detection of MS with new bioinformatics toolsets to characterize the structures and abundances of complex lipids. Yet, translating lipidomics to practice via nutritional interventions is still in its infancy. No single instrumentation platform is able to solve the varying analytical challenges of the different molecular lipid species. Biochemical pathways of lipid metabolism remain incomplete and the tools to map lipid compositional data to pathways are still being assembled. Biology itself is dauntingly complex and simply separating biological structures remains a key challenge to lipidomics. Nonetheless, the strategy of combining tandem analytical methods to perform the sensitive, high-throughput, quantitative, and comprehensive analysis of lipid metabolites of very large numbers of molecules is poised to drive the field forward rapidly. Among the next steps for nutrition to understand the changes in structures, compositions, and function of lipid biomolecules in response to diet is to describe their distribution within discrete functional compartments lipoproteins. Additionally, lipidomics must tackle the task of assigning the functions of lipids as signaling molecules, nutrient sensors, and intermediates of metabolic pathways.

Complex disease interventions from a network model for type 2 diabetes

There is accumulating evidence that the proteins encoded by the genes associated with a common disorder interact with each other, participate in similar pathways and share GO terms. It has been anticipated that the functional modules in a disease related functional linkage network are informative to reveal significant metabolic processes and disease's associations with other complex disorders. In the current study, Type 2 diabetes associated functional linkage network (T2DFN) containing 2770 proteins and 15041 linkages was constructed. The functional modules in this network were scored and evaluated in terms of shared pathways, co-localization, co-expression and associations with similar diseases. The assembly of top scoring overlapping members in the functional modules revealed that, along with the well known biological pathways, circadian rhythm, diverse actions of nuclear receptors in steroid and retinoic acid metabolisms have significant occurrence in the pathophysiology of the disease. The disease's association with other metabolic and neuromuscular disorders was established through shared proteins. Nuclear receptor NRIP1 has a pivotal role in lipid and carbohydrate metabolism, indicating the need to investigate subsequent effects of NRIP1 on Type 2 diabetes. Our study also revealed that CREB binding protein (CREBBP) and cardiotrophin-1 (CTF1) have suggestive roles in linking Type 2 diabetes and neuromuscular diseases.

Systems biology as a comparative approach to understand complex gene expression in neurological diseases

Systems biology interdisciplinary approaches have become an essential analytical tool that may yield novel and powerful insights about the nature of human health and disease. Complex disorders are known to be caused by the combination of genetic, environmental, immunological or neurological factors. Thus, to understand such disorders, it becomes necessary to address the study of this complexity from a novel perspective. Here, we present a review of integrative approaches that help to understand the underlying biological processes involved in the etiopathogenesis of neurological diseases, for example, those related to autism and autism spectrum disorders (ASD) endophenotypes. Furthermore, we highlight the role of systems biology in the discovery of new biomarkers or therapeutic targets in complex disorders, a key step in the development of personalized medicine, and we demonstrate the role of systems approaches in the design of classifiers that can shorten the time for behavioral diagnosis of autism.

Questing for Circadian Dependence in ST-Segment–Elevation Acute Myocardial Infarction

Rationale:

Four monocentric studies reported that circadian rhythms can affect left ventricular infarct size after ST-segment–elevation acute myocardial infarction (STEMI).

Objective:

To further validate the circadian dependence of infarct size after STEMI in a multicentric and multiethnic population.

Methods and Results:

We analyzed a prospective cohort of subjects with first STEMI from the First Acute Myocardial Infarction study that enrolled 1099 patients (ischemic time <6 hours) in Italy, Scotland, and China. We confirmed a circadian variation of STEMI incidence with an increased morning incidence (from 6:00 am till noon). We investigated the presence of circadian dependence of infarct size plotting the peak creatine kinase against time onset of ischemia. In addition, we studied the patients from the 3 countries separately, including 624 Italians; all patients were treated with percutaneous coronary intervention. We adopted several levels of analysis with different inclusion criteria consistent with previous studies. In all the analyses, we did not find a clear-cut circadian dependence of infarct size after STEMI.

Conclusions:

Although the circadian dependence of infarct size supported by previous studies poses an intriguing hypothesis, we were unable to converge toward their conclusions in a multicentric and multiethnic setting. Parameters that vary as a function of latitude could potentially obscure the circadian variations observed in monocentric studies. We believe that, to assess whether circadian rhythms can affect the infarct size, future study design should not only include larger samples but also aim to untangle the molecular time–dynamic mechanisms underlying such a relation.

Proteomics and metabolomics in renal transplantation-quo vadis?

The improvement of long-term transplant organ and patient survival remains a critical challenge following kidney transplantation. Proteomics and biochemical profiling (metabolomics) may allow for the detection of early changes in cell signal transduction regulation and biochemistry with high sensitivity and specificity. Hence, these analytical strategies hold the promise to detect and monitor disease processes and drug effects before histopathological and pathophysiological changes occur. In addition, they will identify enriched populations and enable individualized drug therapy. However, proteomics and metabolomics have not yet lived up to such high expectations. Renal transplant patients are highly complex, making it difficult to establish cause-effect relationships between surrogate markers and disease processes. Appropriate study design, adequate sample handling, storage and processing, quality and reproducibility of bioanalytical multi-analyte assays, data analysis and interpretation, mechanistic verification, and clinical qualification (=establishment of sensitivity and specificity in adequately powered prospective clinical trials) are important factors for the success of molecular marker discovery and development in renal transplantation. However, a newly developed and appropriately qualified molecular marker can only be successful if it is realistic that it can be implemented in a clinical setting. The development of combinatorial markers with supporting software tools is an attractive goal.

Global analysis of the human pathophenotypic similarity gene network merges disease module components

The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, "the human diseases networks" (HDN) and "the orphan disease networks" (ODN). However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in the "Human Phenotype Ontology". The resulting network contains 1075 genes (nodes) and 26197 significant pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a coherent pathological module.Our results indicate that pathophenotypes contribute to identify underlying co-dependencies among disease-causing genes that are useful to describe disease modularity.

Integrative pathway dissection of molecular mechanisms of moxLDL-induced vascular smooth muscle phenotype transformation

Background

Atherosclerosis (AT) is a chronic inflammatory disease characterized by the accumulation of inflammatory cells, lipoproteins and fibrous tissue in the walls of arteries. AT is the primary cause of heart attacks and stroke and is the leading cause of death in Western countries. To date, the pathogenesis of AT is not well-defined. Studies have shown that the dedifferentiation of contractile and quiescent vascular smooth muscle cells (SMC) to the proliferative, migratory and synthetic phenotype in the intima is pivotal for the onset and progression of AT. To further delineate the mechanisms underlying the pathogenesis of AT, we analyzed the early molecular pathways and networks involved in the SMC phenotype transformation.

Methods

Quiescent human coronary artery SMCs were treated with minimally-oxidized LDL (moxLDL), for 3 hours and 21 hours, respectively. Transcriptomic data was generated for both time-points using microarrays and was subjected to pathway analysis using Gene Set Enrichment Analysis, GeneMANIA and Ingenuity software tools. Gene expression heat maps and pathways enriched in differentially expressed genes were compared to identify functional biological themes to elucidate early and late molecular mechanisms of moxLDL-induced SMC dedifferentiation.

Results

Differentially expressed genes were found to be enriched in cholesterol biosynthesis, inflammatory cytokines, chemokines, growth factors, cell cycle control and myogenic contraction themes. These pathways are consistent with inflammatory responses, cell proliferation, migration and ECM production, which are characteristic of SMC dedifferentiation. Furthermore, up-regulation of cholesterol synthesis and dysregulation of cholesterol metabolism was observed in moxLDL-induced SMC. These observations are consistent with the accumulation of cholesterol and oxidized cholesterol esters, which induce proinflammatory reactions during atherogenesis. Our data implicate for the first time IL12, IFN-α, HGF, CSF3, and VEGF signaling in SMC phenotype transformation. GPCR signaling, HBP1 (repressor of cyclin D1 and CDKN1B), and ID2 and ZEB1 transcriptional regulators were also found to have important roles in SMC dedifferentiation. Several microRNAs were observed to regulate the SMC phenotype transformation via an interaction with IFN-γ pathway. Also, several “nexus” genes in complex networks, including components of the multi-subunit enzyme complex involved in the terminal stages of cholesterol synthesis, microRNAs (miR-203, miR-511, miR-590-3p, miR-346*/miR- 1207-5p/miR-4763-3p), GPCR proteins (GPR1, GPR64, GPRC5A, GPR171, GPR176, GPR32, GPR25, GPR124) and signal transduction pathways, were found to be regulated.

Conclusions

The systems biology analysis of the in vitro model of moxLDL-induced VSMC phenotype transformation was associated with the regulation of several genes not previously implicated in SMC phenotype transformation. The identification of these potential candidate genes enable hypothesis generation and in vivo functional experimentation (such as gain and loss-of-function studies) to establish causality with the process of SMC phenotype transformation and atherogenesis.

Application of metabolomics approaches to the study of respiratory diseases

Metabolomics is the global unbiased analysis of all the small-molecule metabolites within a biological system, under a given set of conditions. These methods offer the potential for a holistic approach to clinical medicine, as well as improving disease diagnosis and understanding of pathological mechanisms. Respiratory diseases including asthma and chronic obstructive pulmonary disorder are increasing globally, with the latter predicted to become the third leading cause of global mortality by 2020. The root causes for disease onset remain poorly understood and no cures are available. This review presents an overview of metabolomics followed by in-depth discussion of its application to the study of respiratory diseases, including the design of metabolomics experiments, choice of clinical material collected and potentially confounding experimental factors. Particular challenges in the field are presented and placed within the context of the future of the applications of metabolomics approaches to the study of respiratory diseases.

Vascular proteomics

Cardiovascular diseases constitute the largest of death in developed countries, being atherosclerosis the major contributor. Atherosclerosis is a process of chronic inflammation, characterized by the accumulation of lipids, cells, and fibrous elements in medium and large arteries. There is a continuum in atherosclerotic cardiovascular pathology that extends from the initial endothelial damage to diseases such as angina, myocardial infarction, and stroke. The extent of inflammation, proteolysis, calcification, and neovascularization influences the development of advanced lesions (atheroma plaques) on the arteries. Plaque rupture and the ensuing thrombosis cause the acute complications of atherosclerosis, i.e., myocardial infarction and cerebral ischemia. Thus, identification of early biomarkers of plaque unstability and susceptibility to rupture is of capital importance in preventing acute events. In recent years proteomics has been successfully applied to study proteins involved in these pathological processes. Thus, proteomic studies have been carried out focusing on different elements such as vascular tissues (arteries), artery layers, cells looking at proteomes and secretomes, plasma/serum, exosomes, lipoproteins, and metabolites. This chapter will provide an overview of latest advances in proteomic studies of atherosclerosis and related vascular diseases.

Processing of Mass Spectrometry Data in Clinical Applications

Mass spectrometry-based proteomics has become the leading approach for analyzing complex biological samples at a large-scale level. Its importance for clinical applications is more and more increasing, thanks to the development of high-performing instruments which allow the discovery of disease-specific biomarkers and an automated and rapid protein profiling of the analyzed samples. In this scenario, the large-scale production of proteomic data has driven the development of specific bioinformatic tools to assist researchers during the discovery processes. Here, we discuss the main methods, algorithms, and procedures to identify and use biomarkers for clinical and research purposes. In particular, we have been focused on quantitative approaches, the identification of proteotypic peptides, and the classification of samples, using proteomic data. Finally, this chapter is concluded by reporting the integration of experimental data with network datasets, as valuable instrument for identifying alterations that underline the emergence of specific phenotypes. Based on our experience, we show some examples taking into consideration experimental data obtained by multidimensional protein identification technology (MudPIT) approach.

Network-based characterization of drug-regulated genes, drug targets, and toxicity

Proteins do not exert their effects in isolation of one another, but interact together in complex networks. In recent years, sophisticated methods have been developed to leverage protein-protein interaction (PPI) network structure to improve several stages of the drug discovery process. Network-based methods have been applied to predict drug targets, drug side effects, and new therapeutic indications. In this paper we have two aims. First, we review the past contributions of network approaches and methods to drug discovery, and discuss their limitations and possible future directions. Second, we show how past work can be generalized to gain a more complete understanding of how drugs perturb networks. Previous network-based characterizations of drug effects focused on the small number of known drug targets, i.e., direct binding partners of drugs. However, drugs affect many more genes than their targets - they can profoundly affect the cell's transcriptome. For the first time, we use networks to characterize genes that are differentially regulated by drugs. We found that drug-regulated genes differed from drug targets in terms of functional annotations, cellular localizations, and topological properties. Drug targets mainly included receptors on the plasma membrane, down-regulated genes were largely in the nucleus and were enriched for DNA binding, and genes lacking drug relationships were enriched in the extracellular region. Network topology analysis indicated several significant graph properties, including high degree and betweenness for the drug targets and drug-regulated genes, though possibly due to network biases. Topological analysis also showed that proteins of down-regulated genes appear to be frequently involved in complexes. Analyzing network distances between regulated genes, we found that genes regulated by structurally similar drugs were significantly closer than genes regulated by dissimilar drugs. Finally, network centrality of a drug's differentially regulated genes correlated significantly with drug toxicity.

Cardiovascular genomics: a biomarker identification pipeline

Genomic biomarkers are essential for understanding the underlying molecular basis of human diseases such as cardiovascular disease. In this review, we describe a biomarker identification pipeline for cardiovascular disease, which includes 1) high-throughput genomic data acquisition, 2) preprocessing and normalization of data, 3) exploratory analysis, 4) feature selection, 5) classification, and 6) interpretation and validation of candidate biomarkers. We review each step in the pipeline, presenting current and widely used bioinformatics methods. Furthermore, we analyze several publicly available cardiovascular genomics datasets to illustrate the pipeline. Finally, we summarize the current challenges and opportunities for further research.

The Use of High-Throughput Technologies to Investigate Vascular Inflammation and Atherosclerosis

The greatest challenge of scientific research is to understand the causes and consequences of disease. In recent years, great efforts have been devoted to unraveling the basic mechanisms of atherosclerosis (the underlying pathology of cardiovascular disease), which remains a major cause of morbidity and mortality worldwide. Because of the complex and multifactorial pathophysiology of cardiovascular disease, different research techniques have increasingly been combined to unravel genetic aspects, molecular pathways, and cellular functions involved in atherogenesis, vascular inflammation, and dyslipidemia to gain a multifaceted picture addressing this complexity. Thanks to the rapid evolution of high-throughput technologies, we are now able to generate large-scale data on the DNA, RNA, and protein levels. With the help of sophisticated computational tools, these data sets are integrated to enhance information extraction and are being increasingly used in a systems biology approach to model biological processes as interconnected and regulated networks. This review exemplifies the use of high-throughput technologies—such as genomics, transcriptomics, proteomics, and epigenomics—and systems biology to explore pathomechanisms of vascular inflammation and atherosclerosis.

The KEGG databases and tools facilitating omics analysis: latest developments involving human diseases and pharmaceuticals

In this chapter, we demonstrate the usability of the KEGG (Kyoto encyclopedia of genes and genomes) databases and tools, especially focusing on the visualization of the omics data. The desktop application KegArray and many Web-based tools are tightly integrated with the KEGG knowledgebase, which helps visualize and interpret large amount of data derived from high-throughput measurement techniques including microarray, metagenome, and metabolome analyses. Recently developed resources for human disease, drug, and plant research are also mentioned.

A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue

Background

Humans and other organisms are equipped with a set of responses that can prevent damage from exposure to a multitude of endogenous and environmental stressors. If these stress responses are overwhelmed, this can result in pathogenesis of diseases, which is reflected by an increased development of, e.g., pulmonary and cardiac diseases in humans exposed to chronic levels of environmental stress, including inhaled cigarette smoke (CS). Systems biology data sets (e.g., transcriptomics, phosphoproteomics, metabolomics) could enable comprehensive investigation of the biological impact of these stressors. However, detailed mechanistic networks are needed to determine which specific pathways are activated in response to different stressors and to drive the qualitative and eventually quantitative assessment of these data. A current limiting step in this process is the availability of detailed mechanistic networks that can be used as an analytical substrate.

Results

We have built a detailed network model that captures the biology underlying the physiological cellular response to endogenous and exogenous stressors in non-diseased mammalian pulmonary and cardiovascular cells. The contents of the network model reflect several diverse areas of signaling, including oxidative stress, hypoxia, shear stress, endoplasmic reticulum stress, and xenobiotic stress, that are elicited in response to common pulmonary and cardiovascular stressors. We then tested the ability of the network model to identify the mechanisms that are activated in response to CS, a broad inducer of cellular stress. Using transcriptomic data from the lungs of mice exposed to CS, the network model identified a robust increase in the oxidative stress response, largely mediated by the anti-oxidant NRF2 pathways, consistent with previous reports on the impact of CS exposure in the mammalian lung.

Conclusions

The results presented here describe the construction of a cellular stress network model and its application towards the analysis of environmental stress using transcriptomic data. The proof-of-principle analysis described here, coupled with the future development of additional network models covering distinct areas of biology, will help to further clarify the integrated biological responses elicited by complex environmental stressors such as CS, in pulmonary and cardiovascular cells.

Metabolomics as a tool for cardiac research

Metabolomics represents a paradigm shift in metabolic research, away from approaches that focus on a limited number of enzymatic reactions or single pathways, to approaches that attempt to capture the complexity of metabolic networks. Additionally, the high-throughput nature of metabolomics makes it ideal to perform biomarker screens for diseases or follow drug efficacy. In this Review, we explore the role of metabolomics in gaining mechanistic insight into cardiac disease processes, and in the search for novel biomarkers. High-resolution NMR spectroscopy and mass spectrometry are both highly discriminatory for a range of pathological processes affecting the heart, including cardiac ischemia, myocardial infarction, and heart failure. We also discuss the position of metabolomics in the range of functional-genomic approaches, being complementary to proteomic and transcriptomic studies, and having subdivisions such as lipidomics (the study of intact lipid species). In addition to techniques that monitor changes in the total sizes of pools of metabolites in the heart and biofluids, the role of stable-isotope methods for monitoring fluxes through pathways is examined. The use of these novel functional-genomic tools to study metabolism provides a unique insight into cardiac disease progression.

Human metabolic profiles are stably controlled by genetic and environmental variation

A comprehensive variation map of the human metabolome identifies genetic and stable-environmental sources as major drivers of metabolite concentrations. The data suggest that sample sizes of a few thousand are sufficient to detect metabolite biomarkers predictive of disease.

We designed a longitudinal twin study to characterize the genetic, stable-environmental, and longitudinally fluctuating influences on metabolite concentrations in two human biofluids—urine and plasma—focusing specifically on the representative subset of metabolites detectable by ¹H nuclear magnetic resonance (¹H NMR) spectroscopy.

We identified widespread genetic and stable-environmental influences on the (urine and plasma) metabolomes, with (30 and 42%) attributable on average to familial sources, and (47 and 60%) attributable to longitudinally stable sources.

Ten of the metabolites annotated in the study are estimated to have >60% familial contribution to their variation in concentration.

Our findings have implications for the design and interpretation of ¹H NMR-based molecular epidemiology studies. On the basis of the stable component of variation quantified in the current paper, we specified a model of disease association under which we inferred that sample sizes of a few thousand should be sufficient to detect disease-predictive metabolite biomarkers.

Metabolites are small molecules involved in biochemical processes in living systems. Their concentration in biofluids, such as urine and plasma, can offer insights into the functional status of biological pathways within an organism, and reflect input from multiple levels of biological organization—genetic, epigenetic, transcriptomic, and proteomic—as well as from environmental and lifestyle factors. Metabolite levels have the potential to indicate a broad variety of deviations from the ‘normal' physiological state, such as those that accompany a disease, or an increased susceptibility to disease. A number of recent studies have demonstrated that metabolite concentrations can be used to diagnose disease states accurately. A more ambitious goal is to identify metabolite biomarkers that are predictive of future disease onset, providing the possibility of intervention in susceptible individuals.

If an extreme concentration of a metabolite is to serve as an indicator of disease status, it is usually important to know the distribution of metabolite levels among healthy individuals. It is also useful to characterize the sources of that observed variation in the healthy population. A proportion of that variation—the heritable component—is attributable to genetic differences between individuals, potentially at many genetic loci. An effective, molecular indicator of a heritable, complex disease is likely to have a substantive heritable component. Non-heritable biological variation in metabolite concentrations can arise from a variety of environmental influences, such as dietary intake, lifestyle choices, general physical condition, composition of gut microflora, and use of medication. Variation across a population in stable-environmental influences leads to long-term differences between individuals in their baseline metabolite levels. Dynamic environmental pressures lead to short-term fluctuations within an individual about their baseline level. A metabolite whose concentration changes substantially in response to short-term pressures is relatively unlikely to offer long-term prediction of disease. In summary, the potential suitability of a metabolite to predict disease is reflected by the relative contributions of heritable and stable/unstable-environmental factors to its variation in concentration across the healthy population.

Studies involving twins are an established technique for quantifying the heritable component of phenotypes in human populations. Monozygotic (MZ) twins share the same DNA genome-wide, while dizygotic (DZ) twins share approximately half their inherited DNA, as do ordinary siblings. By comparing the average extent of phenotypic concordance within MZ pairs to that within DZ pairs, it is possible to quantify the heritability of a trait, and also to quantify the familiality, which refers to the combination of heritable and common-environmental effects (i.e., environmental influences shared by twins in a pair). In addition to incorporating twins into the study design, it is useful to quantify the phenotype in some individuals at multiple time points. The longitudinal aspect of such a study allows environmental effects to be decomposed into those that affect the phenotype over the short term and those that exert stable influence.

For the current study, urine and blood samples were collected from a cohort of MZ and DZ twins, with some twins donating samples on two occasions several months apart. Samples were analysed by ¹H nuclear magnetic resonance (¹H NMR) spectroscopy—an untargeted, discovery-driven technique for quantifying metabolite concentrations in biological samples. The application of ¹H NMR to a biological sample creates a spectrum, made up of multiple peaks, with each peak's size quantitatively representing the concentration of its corresponding hydrogen-containing metabolite.

In each biological sample in our study, we extracted a full set of peaks, and thereby quantified the concentrations of all common plasma and urine metabolites detectable by ¹H NMR. We developed bespoke statistical methods to decompose the observed concentration variation at each metabolite peak into that originating from familial, individual-environmental, and unstable-environmental sources.

We quantified the variability landscape across all common metabolite peaks in the urine and plasma ¹H NMR metabolomes. We annotated a subset of peaks with a total of 65 metabolites; the variance decompositions for these are shown in Figure 1. Ten metabolites' concentrations were estimated to have familial contributions in excess of 60%. The average proportion of stable variation across all extracted metabolite peaks was estimated to be 47% in the urine samples and 60% in the plasma samples; the average estimated familiality was 30% for urine and 42% for plasma. These results comprise the first quantitative variation map of the ¹H NMR metabolome. The identification and quantification of substantive widespread stability provides support for the use of these biofluids in molecular epidemiology studies. On the basis of our findings, we performed power calculations for a hypothetical study searching for predictive disease biomarkers among ¹H NMR-detectable urine and plasma metabolites. Our calculations suggest that sample sizes of 2000–5000 should allow reliable identification of disease-predictive metabolite concentrations explaining 5–10% of disease risk, while greater sample sizes of 5000–20 000 would be required to identify metabolite concentrations explaining 1–2% of disease risk.

¹H Nuclear Magnetic Resonance spectroscopy (¹H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top-down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non-identical twin pairs donated plasma and urine samples longitudinally. We acquired ¹H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common-environmental), individual-environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual-environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in ¹H NMR-detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker-discovery studies. We provide a power-calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect ¹H NMR-based biomarkers quantifying predisposition to disease.

Asthmatics exhibit altered oxylipin profiles compared to healthy individuals after subway air exposure

BACKGROUND

Asthma is a chronic inflammatory lung disease that causes significant morbidity and mortality worldwide. Air pollutants such as particulate matter (PM) and oxidants are important factors in causing exacerbations in asthmatics, and the source and composition of pollutants greatly affects pathological implications.

OBJECTIVES

This randomized crossover study investigated responses of the respiratory system to Stockholm subway air in asthmatics and healthy individuals. Eicosanoids and other oxylipins were quantified in the distal lung to provide a measure of shifts in lipid mediators in association with exposure to subway air relative to ambient air.

METHODS

Sixty-four oxylipins representing the cyclooxygenase (COX), lipoxygenase (LOX) and cytochrome P450 (CYP) metabolic pathways were screened using liquid chromatography-tandem mass spectrometry (LC-MS/MS) of bronchoalveolar lavage (BAL)-fluid. Validations through immunocytochemistry staining of BAL-cells were performed for 15-LOX-1, COX-1, COX-2 and peroxisome proliferator-activated receptor gamma (PPARγ). Multivariate statistics were employed to interrogate acquired oxylipin and immunocytochemistry data in combination with patient clinical information.

RESULTS

Asthmatics and healthy individuals exhibited divergent oxylipin profiles following exposure to ambient and subway air. Significant changes were observed in 8 metabolites of linoleic- and α-linolenic acid synthesized via the 15-LOX pathway, and of the COX product prostaglandin E(2) (PGE(2)). Oxylipin levels were increased in healthy individuals following exposure to subway air, whereas asthmatics evidenced decreases or no change.

CONCLUSIONS

Several of the altered oxylipins have known or suspected bronchoprotective or anti-inflammatory effects, suggesting a possible reduced anti-inflammatory response in asthmatics following exposure to subway air. These observations may have ramifications for sensitive subpopulations in urban areas.

Systems biology of infectious diseases: a focus on fungal infections

The study of infectious disease concerns the interaction between the host species and a pathogen organism. The analysis of such complex systems is improving with the evolution of high-throughput technologies and advanced computational resources. This article reviews integrative, systems-oriented approaches to understanding mechanisms underlying infection, immune response and inflammation to find biomarkers of disease and design new drugs. We focus on the systems biology process, especially the data gathering and analysis techniques rather than the experimental technologies or latest computational resources.

A systems biology strategy for predicting similarities and differences of drug effects: evidence for drug-specific modulation of inflammation in atherosclerosis

Background

Successful drug development has been hampered by a limited understanding of how to translate laboratory-based biological discoveries into safe and effective medicines. We have developed a generic method for predicting the effects of drugs on biological processes. Information derived from the chemical structure and experimental omics data from short-term efficacy studies are combined to predict the possible protein targets and cellular pathways affected by drugs.

Results

Validation of the method with anti-atherosclerotic compounds (fenofibrate, rosuvastatin, LXR activator T0901317) demonstrated a great conformity between the computationally predicted effects and the wet-lab biochemical effects. Comparative genome-wide pathway mapping revealed that the biological drug effects were realized largely via different pathways and mechanisms. In line with the predictions, the drugs showed differential effects on inflammatory pathways (downstream of PDGF, VEGF, IFNγ, TGFβ, IL1β, TNFα, LPS), transcriptional regulators (NFκB, C/EBP, STAT3, AP-1) and enzymes (PKCδ, AKT, PLA2), and they quenched different aspects of the inflammatory signaling cascade. Fenofibrate, the compound predicted to be most efficacious in inhibiting early processes of atherosclerosis, had the strongest effect on early lesion development.

Conclusion

Our approach provides mechanistic rationales for the differential and common effects of drugs and may help to better understand the origins of drug actions and the design of combination therapies.

The complexity of the cardio-renal link: taxonomy, syndromes, and diseases

Bidirectional mechanisms exist that link diseases affecting the heart and kidney. This link is complex and remains poorly understood; therefore, charting the shared territory of cardiovascular (CV) and renal medicine poses major problems. Until now, no convincing rationale for delineating new syndromes existed. The multiple connections of the arterial system and the heart and kidney with other systems, from energy and protein balance to the musculoskeletal, clearly require special focus and rigorous framing. Nephrologists have yet to fully understand why the application of dialysis has had only limited success in halting the parallel burdens of CV and non-CV death in patients with end-stage renal disease. Cardiologists, intensivists, and nephrologists alike should settle whether and when extracorporeal ultrafiltration benefits patients with decompensated heart failure. These sparse but interconnected themes spanning from the basic science-clinical transition phase to clinical science, epidemiology, and medical technology already form the basis for the young discipline of 'CV and renal medicine'.