A directed network analysis of the cardiome identifies molecular pathways contributing to the development of HFpEF.

AIMS: The metabolic syndrome and associated comorbidities, like diabetes, hypertension and obesity, have been implicated in the development of heart failure with preserved ejection fraction (HFpEF). The molecular mechanisms underlying the development of HFpEF remain to be elucidated. We developed a cardiome-directed network analysis and applied this to high throughput cardiac RNA-sequencing data from a well-established rat model of HFpEF, the obese and hypertensive ZSF1 rat. With this novel system biology approach, we explored the mechanisms underlying HFpEF. METHODS AND RESULTS: Unlike ZSF1-Lean, ZSF1-Obese and ZSF1-Obese rats fed with a high-fat diet (HFD) developed diastolic dysfunction and reduced exercise capacity. The number of differentially expressed genes amounted to 1591 and 1961 for the ZSF1-Obese vs. Lean and ZSF1-Obese+HFD vs. Lean comparison, respectively. For the cardiome-directed network analysis (CDNA) eleven biological processes related to cardiac disease were selected and used as input for the STRING protein-protein interaction database. The resulting STRING network comprised 3.460 genes and 186.653 edges. Subsequently differentially expressed genes were projected onto this network. The connectivity between the core processes within the network was assessed and important bottleneck and hub genes were identified based on their network topology. Classical gene enrichment analysis highlighted many processes related to mitochondrial oxidative metabolism. The CDNA indicated high interconnectivity between five core processes: endothelial function, inflammation, apoptosis/autophagy, sarcomere/cytoskeleton and extracellular matrix. The transcription factors Myc and Peroxisome Proliferator-Activated Receptor-α (Ppara) were identified as important bottlenecks in the overall network topology, with Ppara acting as important link between cardiac metabolism, inflammation and endothelial function. CONCLUSIONS: This study presents a novel systems biology approach, directly applicable to other cardiac disease-related transcriptome data sets. The CDNA approach enabled the identification of critical processes and genes, including Myc and Ppara, that are putatively involved in the development of HFpEF.

NF-κB-mediated metabolic remodelling in the inflamed heart in acute viral myocarditis.

Acute viral myocarditis (VM), characterised by leukocyte infiltration and dysfunction of the heart, is an important cause of sudden cardiac death in young adults. Unfortunately, to date, the pathological mechanisms underlying cardiac failure in VM remain incompletely understood. In the current study, we investigated if acute VM leads to cardiac metabolic rewiring and if this process is driven by local inflammation. Transcriptomic analysis of cardiac biopsies from myocarditis patients and a mouse model of VM revealed prominent reductions in the expression of a multitude of genes involved in mitochondrial oxidative energy metabolism. In mice, this coincided with reductions in high-energy phosphate and NAD levels, as determined by Imaging Mass Spectrometry, as well as marked decreases in the activity, protein abundance and mRNA levels of various enzymes and key regulators of cardiac oxidative metabolism. Indicative of fulminant cardiac inflammation, NF-κB signalling and inflammatory cytokine expression were potently induced in the heart during human and mouse VM. In cultured cardiomyocytes, cytokine-mediated NF-κB activation impaired cardiomyocyte oxidative gene expression, likely by interfering with the PGC-1 (peroxisome proliferator-activated receptor (PPAR)-γ co-activator) signalling network, the key regulatory pathway controlling cardiomyocyte oxidative metabolism. In conclusion, we provide evidence that acute VM is associated with extensive cardiac metabolic remodelling and our data support a mechanism whereby cytokines secreted primarily from infiltrating leukocytes activate NF-κB signalling in cardiomyocytes thereby inhibiting the transcriptional activity of the PGC-1 network and consequently modulating myocardial energy metabolism.

A novel 72-kDa leukocyte-derived osteoglycin enhances the activation of toll-like receptor 4 and exacerbates cardiac inflammation during viral myocarditis.

BACKGROUND: Viral myocarditis can severely damage the myocardium through excessive infiltration of immune cells. Osteoglycin (OGN) is part of the small leucine-rich repeat proteoglycan (SLRP) family. SLRP's may affect inflammatory and fibrotic processes, but the implication of OGN in cardiac inflammation and the resulting injury upon viral myocarditis is unknown. METHODS AND RESULTS: This study uncovered a previously unidentified 72-kDa variant of OGN that is predominant in cardiac human and mouse samples of viral myocarditis. Its absence in mice significantly decreased cardiac inflammation and injury in Coxsackievirus-B3-induced myocarditis. It also delayed mortality in lipopolysaccharide-induced endotoxemia going along with a reduced systemic production of pro-inflammatory cytokines. This 72-kDa OGN is expressed in the cell membrane of circulating and resident cardiac macrophages and neutrophils. Co-immunoprecipitation and OGN siRNA experiments revealed that this 72-kDa variant activates the toll-like receptor-4 (TLR4) with a concomitant increase in IL-6, TNF-α, IL-1ß, and IL-12 expression. This immune cell activation by OGN occurred via MyD88 and increased phosphorylation of c-jun. Finally, the 72-kDa chondroitin sulfate is the result of O-linked glycosylation of the 32-kDa protein core of OGN. In contrast, the 34-kDa dermatan sulfate-OGN, involved in collagen cross linking, was also the result of O-linked glycosylation. CONCLUSION: The current study discovered a novel 72-kDa chondroitin sulfate-OGN that is specific for innate immune cells. This variant is able to bind and activate TLR4. The absence of OGN decreases cytokine production by both circulating and cardiac leukocytes upon (systemic) LPS exposure, and reduces cardiac inflammation and injury in viral myocarditis.

The Network Library: a framework to rapidly integrate network biology resources.

MOTIVATION: Much of the biological knowledge accumulated over the last decades is stored in different databases governed by various organizations and institutes. Integrating and connecting these vast knowledge repositories is an extremely useful method to support life sciences research and help formulate novel hypotheses. RESULTS: We developed the Network Library (NL), a framework and toolset to rapidly integrate different knowledge sources to build a network biology resource that matches a specific research question. As a use-case we explore the interactions of genes related to heart failure with miRNAs and diseases through the integration of 6 databases. AVAILABILITY AND IMPLEMENTATION: The NL is open-source, developed in Java and available on Github (https://github.com/gsummer). CONTACT: georg.summer@gmail.com.

cyNeo4j: connecting Neo4j and Cytoscape.

UNLABELLED: We developed cyNeo4j, a Cytoscape App to link Cytoscape and Neo4j databases to utilize the performance and storage capacities Neo4j offers. We implemented a Neo4j NetworkAnalyzer, ForceAtlas2 layout and Cypher component to demonstrate the possibilities a distributed setup of Cytoscape and Neo4j have. AVAILABILITY AND IMPLEMENTATION: The app is available from the Cytoscape App Store at http://apps.cytoscape.org/apps/cyneo4j, the Neo4j plugins at www.github.com/gsummer/cyneo4j-parent and the community and commercial editions of Neo4j can be found at http://www.neo4j.com. CONTACT: georg.summer@gmail.com.

The microRNA-221/-222 cluster balances the antiviral and inflammatory response in viral myocarditis.

AIMS: Viral myocarditis (VM) is an important cause of heart failure and sudden cardiac death in young healthy adults; it is also an aetiological precursor of dilated cardiomyopathy. We explored the role of the miR-221/-222 family that is up-regulated in VM. METHODS AND RESULTS: Here, we show that microRNA-221 (miR-221) and miR-222 levels are significantly elevated during acute VM caused by Coxsackievirus B3 (CVB3). Both miRs are expressed by different cardiac cells and by infiltrating inflammatory cells, but their up-regulation upon myocarditis is mostly exclusive for the cardiomyocyte. Systemic inhibition of miR-221/-222 in mice increased cardiac viral load, prolonged the viraemic state, and strongly aggravated cardiac injury and inflammation. Similarly, in vitro, overexpression of miR-221 and miR-222 inhibited enteroviral replication, whereas knockdown of this miR-cluster augmented viral replication. We identified and confirmed a number of miR-221/-222 targets that co-orchestrate the increased viral replication and inflammation, including ETS1/2, IRF2, BCL2L11, TOX, BMF, and CXCL12. In vitro inhibition of IRF2, TOX, or CXCL12 in cardiomyocytes significantly dampened their inflammatory response to CVB3 infection, confirming the functionality of these targets in VM and highlighting the importance of miR-221/-222 as regulators of the cardiac response to VM. CONCLUSIONS: The miR-221/-222 cluster orchestrates the antiviral and inflammatory immune response to viral infection of the heart. Its inhibition increases viral load, inflammation, and overall cardiac injury upon VM.

MicroRNA profiling identifies microRNA-155 as an adverse mediator of cardiac injury and dysfunction during acute viral myocarditis.

RATIONALE: Viral myocarditis results from an adverse immune response to cardiotropic viruses, which causes irreversible myocyte destruction and heart failure in previously healthy people. The involvement of microRNAs and their usefulness as therapeutic targets in this process are unknown. OBJECTIVE: To identify microRNAs involved in viral myocarditis pathogenesis and susceptibility. METHODS AND RESULTS: Cardiac microRNAs were profiled in both human myocarditis and in Coxsackievirus B3-injected mice, comparing myocarditis-susceptible with nonsusceptible mouse strains longitudinally. MicroRNA responses diverged depending on the susceptibility to myocarditis after viral infection in mice. MicroRNA-155, -146b, and -21 were consistently and strongly upregulated during acute myocarditis in both humans and susceptible mice. We found that microRNA-155 expression during myocarditis was localized primarily in infiltrating macrophages and T lymphocytes. Inhibition of microRNA-155 by a systemically delivered LNA-anti-miR attenuated cardiac infiltration by monocyte-macrophages, decreased T lymphocyte activation, and reduced myocardial damage during acute myocarditis in mice. These changes were accompanied by the derepression of the direct microRNA-155 target PU.1 in cardiac inflammatory cells. Beyond the acute phase, microRNA-155 inhibition reduced mortality and improved cardiac function during 7 weeks of follow-up. CONCLUSIONS: Our data show that cardiac microRNA dysregulation is a characteristic of both human and mouse viral myocarditis. The inflammatory microRNA-155 is upregulated during acute myocarditis, contributes to the adverse inflammatory response to viral infection of the heart, and is a potential therapeutic target for viral myocarditis.

Functional data analysis for identifying nonlinear models of gene regulatory networks.

BACKGROUND: A key problem in systems biology is estimating dynamical models of gene regulatory networks. Traditionally, this has been done using regression or other ad-hoc methods when the model is linear. More detailed, realistic modeling studies usually employ nonlinear dynamical models, which lead to computationally difficult parameter estimation problems. Functional data analysis methods, however, offer a means to simplify fitting by transforming the problem from one of matching modeled and observed dynamics to one of matching modeled and observed time derivatives-a regression problem, albeit a nonlinear one. RESULTS: We formulate a functional data analysis approach for estimating the parameters of nonlinear dynamical models and evaluate this approach on data from two real systems, the gap gene system of Drosophila melanogaster and the synthetic IRMA network, which was created expressly as a test case for genetic network inference. We also evaluate the approach on simulated data sets generated by the GeneNetWeaver program, the basis for the annual DREAM reverse engineering challenge. We assess the accuracy with which the correct regulatory relationships within the networks are extracted, and consider alternative methods of regularization for the purpose of overfitting avoidance. We also show that the computational efficiency of the functional data analysis approach, and the decomposability of the resulting regression problem, allow us to explicitly enumerate and evaluate all possible regulator combinations for every gene. This gives deeper insight into the the relevance of different regulators or regulator combinations, and lets one check for alternative regulatory explanations. CONCLUSIONS: Functional data analysis is a powerful approach for estimating detailed nonlinear models of gene expression dynamics, allowing efficient and accurate estimation of regulatory architecture.

White adipose tissue reference network: a knowledge resource for exploring health-relevant relations.

Optimal health is maintained by interaction of multiple intrinsic and environmental factors at different levels of complexity-from molecular, to physiological, to social. Understanding and quantification of these interactions will aid design of successful health interventions. We introduce the reference network concept as a platform for multi-level exploration of biological relations relevant for metabolic health, by integration and mining of biological interactions derived from public resources and context-specific experimental data. A White Adipose Tissue Health Reference Network (WATRefNet) was constructed as a resource for discovery and prioritization of mechanism-based biomarkers for white adipose tissue (WAT) health status and the effect of food and drug compounds on WAT health status. The WATRefNet (6,797 nodes and 32,171 edges) is based on (1) experimental data obtained from 10 studies addressing different adiposity states, (2) seven public knowledge bases of molecular interactions, (3) expert's definitions of five physiologically relevant processes key to WAT health, namely WAT expandability, Oxidative capacity, Metabolic state, Oxidative stress and Tissue inflammation, and (4) a collection of relevant biomarkers of these processes identified by BIOCLAIMS ( http://bioclaims.uib.es ). The WATRefNet comprehends multiple layers of biological complexity as it contains various types of nodes and edges that represent different biological levels and interactions. We have validated the reference network by showing overrepresentation with anti-obesity drug targets, pathology-associated genes and differentially expressed genes from an external disease model dataset. The resulting network has been used to extract subnetworks specific to the above-mentioned expert-defined physiological processes. Each of these process-specific signatures represents a mechanistically supported composite biomarker for assessing and quantifying the effect of interventions on a physiological aspect that determines WAT health status. Following this principle, five anti-diabetic drug interventions and one diet intervention were scored for the match of their expression signature to the five biomarker signatures derived from the WATRefNet. This confirmed previous observations of successful intervention by dietary lifestyle and revealed WAT-specific effects of drug interventions. The WATRefNet represents a sustainable knowledge resource for extraction of relevant relationships such as mechanisms of action, nutrient intervention targets and biomarkers and for assessment of health effects for support of health claims made on food products.

Concept and application of a computational vaccinology workflow.

BACKGROUND: The last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders. RESULTS: We introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage. CONCLUSION: Based on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges.