Tools enabling the elucidation of molecular pathways active in human disease: application to Hepatitis C virus infection

Systems level analysis and identification of pathways and networks associated with liver fibrosis

Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.

Transcription factor complex AP-1 mediates inflammation initiated by Chlamydia pneumoniae infection

Chlamydia pneumoniae is responsible for a high prevalence of respiratory infections worldwide and has been implicated in atherosclerosis. Inflammation is regulated by transcription factor (TF) networks. Yet, the core TF network triggered by chlamydiae remains largely unknown. Primary human coronary artery endothelial cells were mock-infected or infected with C. pneumoniae to generate human transcriptome data throughout the chlamydial developmental cycle. Using systems network analysis, the predominant TF network involved receptor, binding and adhesion and immune response complexes. Cells transfected with interfering RNA against activator protein-1 (AP-1) members FOS, FOSB, JUN and JUNB had significantly decreased expression and protein levels of inflammatory mediators interleukin (IL)6, IL8, CD38 and tumour necrosis factor compared with controls. These mediators have been shown to be associated with C. pneumoniae disease. Expression of AP-1 components was regulated by MAPK3K8, a MAPK pathway component. Additionally, knock-down of JUN and FOS showed significantly decreased expression of Toll-like receptor (TLR)3 during infection, implicating JUN and FOS in TLR3 regulation. TLR3 stimulation led to elevated IL8. These findings suggest that C. pneumoniae initiates signalling via TLR3 and MAPK that activate AP-1, a known immune activator in other bacteria not previously shown for chlamydiae, triggering inflammation linked to C. pneumoniae disease.

A systemic network triggered by human cytomegalovirus entry

Virus entry is a multistep process that triggers various cellular pathways that interconnect into a complex network; yet the molecular complexity of this network remains largely elusive. Here, by employing systems biology approaches, we reveal a systemic virus-entry network initiated by human cytomegalovirus (HCMV), a widespread opportunistic pathogen. This network contains ten functional modules (i.e., groups of proteins) that coordinately respond to HCMV entry. Functional modules activated (up- and downregulated) in this network dramatically decline shortly within 25 minutes post infection. While modules annotated as receptor system, ion transport, and immune response are continuously activated during the entire process of HCMV entry, those annotated for cell adhesion and skeletal movement are specifically activated during viral early attachment. The up-regulated network contains various functional modules, such as cell surface receptors, skeletal development, endocytosis, ion transport, and chromatin remodeling. Interestingly, macromolecule metabolism and chromatin remodeling module predominates this over-expressed system, suggesting that the fundamental nuclear process modulation is one of the most important events in HCMV entry. The entire up-regulated network is primarily controlled by multiple elements like SLC10A1. Thus, virus entry triggers multiple cellular processes especially nuclear processes to facilitate its entry.

A systemic network for Chlamydia pneumoniae entry into human cells

Bacterial entry is a multistep process triggering a complex network, yet the molecular complexity of this network remains largely unsolved. By employing a systems biology approach, we reveal a systemic bacterial-entry network initiated by Chlamydia pneumoniae, a widespread opportunistic pathogen. The network consists of nine functional modules (i.e., groups of proteins) associated with various cellular functions, including receptor systems, cell adhesion, transcription, and endocytosis. The peak levels of gene expression for these modules change rapidly during C. pneumoniae entry, with cell adhesion occurring at 5 min postinfection, receptor and actin activity at 25 min, and endocytosis at 2 h. A total of six membrane proteins (chemokine C-X-C motif receptor 7 [CXCR7], integrin beta 2 [ITGB2], platelet-derived growth factor beta polypeptide [PDGFB], vascular endothelial growth factor [VEGF], vascular cell adhesion molecule 1 [VCAM1], and GTP binding protein overexpressed in skeletal muscle [GEM]) play a key role during C. pneumoniae entry, but none alone is essential to prevent entry. The combination knockdown of three genes (coding for CXCR7, ITGB2, and PDGFB) significantly inhibits C. pneumoniae entry, but the entire network is resistant to the six-gene depletion, indicating a resilient network. Our results reveal a complex network for C. pneumoniae entry involving at least six key proteins.

Interpreting metabolomic profiles using unbiased pathway models

Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for "active modules"--regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.

Biomedical discovery acceleration, with applications to craniofacial development

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.

System biology of gene regulation

A famous joke story that exhibits the traditionally awkward alliance between theory and experiment and showing the differences between experimental biologists and theoretical modelers is when a University sends a biologist, a mathematician, a physicist, and a computer scientist to a walking trip in an attempt to stimulate interdisciplinary research. During a break, they watch a cow in a field nearby and the leader of the group asks, "I wonder how one could decide on the size of a cow?" Since a cow is a biological object, the biologist responded first: "I have seen many cows in this area and know it is a big cow." The mathematician argued, "The true volume is determined by integrating the mathematical function that describes the outer surface of the cow's body." The physicist suggested: "Let's assume the cow is a sphere...." Finally the computer scientist became nervous and said that he didn't bring his computer because there is no Internet connection up there on the hill. In this humorous but explanatory story suggestions proposed by theorists can be taken to reflect the view of many experimental biologists that computer scientists and theorists are too far removed from biological reality and therefore their theories and approaches are not of much immediate usefulness. Conversely, the statement of the biologist mirrors the view of many traditional theoretical and computational scientists that biological experiments are for the most part simply descriptive, lack rigor, and that much of the resulting biological data are of questionable functional relevance. One of the goals of current biology as a multidisciplinary science is to bring people from different scientific areas together on the same "hill" and teach them to speak the same "language." In fact, of course, when presenting their data, most experimentalist biologists do provide an interpretation and explanation for the results, and many theorists/computer scientists aim to answer (or at least to fully describe) questions of biological relevance. Thus systems biology could be treated as such a socioscientific phenomenon and a new approach to both experiments and theory that is defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel.

Lost in the world of functional genomics, systems biology, and translational research: is there life after the Milstein award?

We have always wanted to save the world from the scourges of virus infection by developing better drugs and vaccines. But fully understanding the intricacies of virus-host interactions, the first step in achieving this goal, requires the ability to view the process on a grand scale. The advent of high-throughput technologies, such as DNA microarrays and mass spectrometry, provided the first opportunities to obtain such a view. Here, we describe our efforts to use these tools to focus on the changes in cellular gene expression and protein abundance that occur in response to virus infection. By examining these changes in a comprehensive manner, we have been able to discover exciting new insights into innate immunity, interferon and cytokine signaling, and the strategies used by viruses to overcome these cellular defenses. Functional genomics may yet save the world from killer viruses.

BioNetBuilder: automatic integration of biological networks

BioNetBuilder is an open-source client-server Cytoscape plugin that offers a user-friendly interface to create biological networks integrated from several databases. Users can create networks for approximately 1500 organisms, including common model organisms and human. Currently supported databases include: DIP, BIND, Prolinks, KEGG, HPRD, The BioGrid and GO, among others. The BioNetBuilder plugin client is available as a Java Webstart, providing a platform-independent network interface to these public databases.

AVAILABILITY

http://err.bio.nyu.edu/cytoscape/bionetbuilder/

SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology

Background

The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types.

Results

SBEAMS-Microarray, a module of the open-source Systems Biology Experiment Analysis Management System (SBEAMS), enables MIAME-compliant storage, management, analysis, and integration of high-throughput genomic expression data. It is interoperable with the Cytoscape network integration, visualization, analysis, and modeling software platform.

Conclusion

SBEAMS-Microarray provides end-to-end support for genomic expression analyses for network-based systems biology research.

Disentangling information flow in the Ras-cAMP signaling network

The perturbation of signal-transduction molecules elicits genomic-expression effects that are typically neither restricted to a small set of genes nor uniform. Instead there are broad, varied, and complex changes in expression across the genome. These observations suggest that signal transduction is not mediated by isolated pathways of information flow to distinct groups of genes in the genome. Rather, multiple entangled paths of information flow influence overlapping sets of genes. Using the Ras-cAMP pathway in Saccharomyces cerevisiae as a model system, we perturbed key pathway elements and collected genomic-expression data. Singular value decomposition was applied to separate the genome-wide transcriptional response into weighted expression components exhibited by overlapping groups of genes. Molecular interaction data were integrated to connect gene groups to perturbed signaling elements. The resulting series of linked subnetworks maps multiple putative pathways of information flow through a dense signaling network, and provides a set of testable hypotheses for complex gene-expression effects across the genome.