Extending pathways based on gene lists using InterPro domain signatures

Transcriptional network analysis of peripheral blood leukocyte subsets in multiple sclerosis identifies a pathogenic role for a cytotoxicity-associated gene network in myeloid cells

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system affecting predominantly adults. It is a complex disease associated with both environmental and genetic risk factors. Although over 230 risk single-nucleotide polymorphisms have been associated with MS, all are common human variants. The mechanisms by which they increase the risk of MS, however, remain elusive. We hypothesized that a complex genetic phenotype such as MS could be driven by coordinated expression of genes controlled by transcriptional regulatory networks. We, therefore, constructed a gene coexpression network from microarray expression analyses of five purified peripheral blood leukocyte subsets of 76 patients with relapsing remitting MS and 104 healthy controls. These analyses identified a major network (or module) of expressed genes associated with MS that play key roles in cell-mediated cytotoxicity which was downregulated in monocytes of patients with MS. Manipulation of the module gene expression was achieved in vitro through small interfering RNA gene knockdown of identified drivers. In a mouse model, network gene knockdown modulated the autoimmune inflammatory MS model disease-experimental autoimmune encephalomyelitis. This research implicates a cytotoxicity-associated gene network in myeloid cells in the pathogenesis of MS.

Modulation of TCR signalling components occurs prior to positive selection and lineage commitment in iNKT cells

iNKT cells play a critical role in controlling the strength and character of adaptive and innate immune responses. Their unique functional characteristics are induced by a transcriptional program initiated by positive selection mediated by CD1d expressed by CD4⁺CD8⁺ (double positive, DP) thymocytes. Here, using a novel Vα14 TCR transgenic strain bearing greatly expanded numbers of CD24^hiCD44^loNKT cells, we examined transcriptional events in four immature thymic iNKT cell subsets. A transcriptional regulatory network approach identified transcriptional changes in proximal components of the TCR signalling cascade in DP NKT cells. Subsequently, positive and negative selection, and lineage commitment, occurred at the transition from DP NKT to CD4 NKT. Thus, this study introduces previously unrecognised steps in early NKT cell development, and separates the events associated with modulation of the T cell signalling cascade prior to changes associated with positive selection and lineage commitment.

REFINING CELLULAR PATHWAY MODELS USING AN ENSEMBLE OF HETEROGENEOUS DATA SOURCES

Improving current models and hypotheses of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of new high-throughput studies. Moreover, the available sources of data are heterogeneous, and the data need to be integrated in different ways depending on which part of the pathway they are most informative for. In this paper, we introduce a compartment specific strategy to integrate edge, node and path data for refining a given network hypothesis. To carry out inference, we use a local-move Gibbs sampler for updating the pathway hypothesis from a compendium of heterogeneous data sources, and a new network regression idea for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.

Validation of Transcriptomics-Based In Vitro Methods

The field of transcriptomics has expanded rapidly during the last decades. This methodology provides an exceptional framework to study not only molecular changes underlying the adverse effects of a given compound, but also to understand its Mode of Action (MoA). However, the implementation of transcriptomics-based tests within the regulatory arena is not a straightforward process. One of the major obstacles in their regulatory implementation is still the interpretation of this new class of data and the judgment of the level of confidence of these tests. A key element in the regulatory acceptance of transcriptomics-based tests is validation, which still represents a major challenge. Although important advances have been made in the development and standardisation of such tests, to date there is limited experience with their validation. Taking into account the experience acquired so far, this chapter describes those aspects that were identified as important in the validation process of transcriptomics-based tests, including the assessment of standardisation, reliability and relevance. It also critically discusses the challenges posed to validation in relation to the specific characteristics of these approaches and their application in the wider context of testing strategies.

Leveraging Comparative Genomics to Identify and Functionally Characterize Genes Associated with Sperm Phenotypes in Python bivittatus (Burmese Python)

Comparative genomics approaches provide a means of leveraging functional genomics information from a highly annotated model organism's genome (such as the mouse genome) in order to make physiological inferences about the role of genes and proteins in a less characterized organism's genome (such as the Burmese python). We employed a comparative genomics approach to produce the functional annotation of Python bivittatus genes encoding proteins associated with sperm phenotypes. We identify 129 gene-phenotype relationships in the python which are implicated in 10 specific sperm phenotypes. Results obtained through our systematic analysis identified subsets of python genes exhibiting associations with gene ontology annotation terms. Functional annotation data was represented in a semantic scatter plot. Together, these newly annotated Python bivittatus genome resources provide a high resolution framework from which the biology relating to reptile spermatogenesis, fertility, and reproduction can be further investigated. Applications of our research include (1) production of genetic diagnostics for assessing fertility in domestic and wild reptiles; (2) enhanced assisted reproduction technology for endangered and captive reptiles; and (3) novel molecular targets for biotechnology-based approaches aimed at reducing fertility and reproduction of invasive reptiles. Additional enhancements to reptile genomic resources will further enhance their value.

Smooth-muscle progenitor cells isolated from patients with moyamoya disease: novel experimental cell model

OBJECT

Moyamoya disease (MMD) is a cerebrovascular occlusive disease affecting bilateral internal carotid termini. Smooth-muscle cells are one of the major cell types involved in this disease process. The characteristics of circulating smooth-muscle progenitor cells (SPCs) in MMD are poorly understood. The authors purified SPCs from the peripheral blood of patients with MMD and sought to identify differentially expressed genes (DEGs) in SPCs from these patients.

METHODS

The authors cultured and isolated SPCs from the peripheral blood of patients with MMD (n = 25) and healthy control volunteers (n = 22). After confirmation of the cellular phenotype, RNA was extracted from the cells and DEGs were identified using a commercially available gene chip. Real-time quantitative reverse transcription polymerase chain reaction was performed to confirm the putative pathogenetic DEGs.

RESULTS

The SPC-type outgrowth cells in patients with MMD invariably showed a hill-and-valley appearance under microscopic examination, and demonstrated high α-smooth muscle actin, myosin heavy chain, and calponin expression (96.5% ± 2.1%, 42.8% ± 18.6%, and 87.1% ± 8.2%, respectively), and minimal CD31 expression (less than 1%) on fluorescence-activated cell sorter analysis. The SPCs in the MMD group tended to make more irregularly arranged and thickened tubules on the tube formation assay. In the SPCs from patients with MMD, 286 genes (124 upregulated and 162 downregulated) were differentially expressed; they were related to cell adhesion, cell migration, immune response, and vascular development.

CONCLUSIONS

With adequate culture conditions, SPCs could be established from the peripheral blood of patients with MMD. These cells showed specific DEGs compared with healthy control volunteers. This study provides a novel experimental cell model for further research of MMD.

A pathway analysis method for genome-wide association studies

For genome-wide association studies, we propose a new method for identifying significant biological pathways. In this approach, we aggregate data across single-nucleotide polymorphisms to obtain summary measures at the gene level. We then use a hierarchical Bayesian model, which takes the gene-level summary measures as data, in order to evaluate the relevance of each pathway to an outcome of interest (e.g., disease status). Although shifting the focus of analysis from individual genes to pathways has proven to improve the statistical power and provide more robust results, such methods tend to eliminate a large number of genes whose pathways are unknown. For these genes, we propose to use a Bayesian multinomial logit model to predict the associated pathways by using the genes with known pathways as the training data. Our hierarchical Bayesian model takes the uncertainty regarding the pathway predictions into account while assessing the significance of pathways. We apply our method to two independent studies on type 2 diabetes and show that the overlap between the results from the two studies is statistically significant. We also evaluate our approach on the basis of simulated data.

Opposing effects of monomeric and pentameric C-reactive protein on endothelial progenitor cells

C-reactive protein (CRP) has been linked to the pathogenesis of atherosclerosis. The dissociation of native, pentameric (p)CRP to monomeric (m)CRP on the cell membrane of activated platelets has recently been demonstrated. The dissociation of pCRP to mCRP may explain local pro-inflammatory reactions at the site of developing atherosclerotic plaques. As a biomarker, pCRP predicts cardiovascular adverse events and so do reduced levels and function of circulating endothelial progenitor cells (EPCs). We hypothesised that mCRP and pCRP exert a differential effect on EPC function and differentiation. EPCs were treated with mCRP or pCRP for 72 h, respectively. Phenotypical characterisation was done by flow cytometry and immunofluorescence microscopy, while the effect of mCRP and pCRP on gene expression was examined by whole-genome gene expression analysis. The functional capacity of EPCs was determined by colony forming unit (CFU) assay and endothelial tube formation assay. Double staining for acetylated LDL and ulex lectin significantly decreased in cells treated with pCRP. The length of tubuli in a matrigel assay with HUVECs decreased significantly in response to pCRP, but not to mCRP. The number of CFUs increased after pCRP treatment. RNA expression profiling demonstrated that mCRP and pCRP cause highly contradictory gene regulation. Interferon-responsive genes (IFI44L, IFI44, IFI27, IFI 6, MX1, OAS2) were among the highly up-regulated genes after mCRP, but not after pCRP treatment. In conclusion, EPC phenotype, genotype and function were differentially affected by mCRP and pCRP, strongly arguing for differential roles of these two CRP conformations. The up-regulation of interferon-inducible genes in response to mCRP may constitute a mechanism for the local regulation of EPC function.

Successful in vitro expansion and differentiation of cord blood derived CD34+ cells into early endothelial progenitor cells reveals highly differential gene expression

Endothelial progenitor cells (EPCs) can be purified from peripheral blood, bone marrow or cord blood and are typically defined by a limited number of cell surface markers and a few functional tests. A detailed in vitro characterization is often restricted by the low cell numbers of circulating EPCs. Therefore in vitro culturing and expansion methods are applied, which allow at least distinguishing two different types of EPCs, early and late EPCs. Herein, we describe an in vitro culture technique with the aim to generate high numbers of phenotypically, functionally and genetically defined early EPCs from human cord blood. Characterization of EPCs was done by flow cytometry, immunofluorescence microscopy, colony forming unit (CFU) assay and endothelial tube formation assay. There was an average 48-fold increase in EPC numbers. EPCs expressed VEGFR-2, CD144, CD18, and CD61, and were positive for acetylated LDL uptake and ulex lectin binding. The cells stimulated endothelial tube formation only in co-cultures with mature endothelial cells and formed CFUs. Microarray analysis revealed highly up-regulated genes, including LL-37 (CAMP), PDK4, and alpha-2-macroglobulin. In addition, genes known to be associated with cardioprotective (GDF15) or pro-angiogenic (galectin-3) properties were also significantly up-regulated after a 72 h differentiation period on fibronectin. We present a novel method that allows to generate high numbers of phenotypically, functionally and genetically characterized early EPCs. Furthermore, we identified several genes newly linked to EPC differentiation, among them LL-37 (CAMP) was the most up-regulated gene.

PhenoFam-gene set enrichment analysis through protein structural information

BACKGROUND

With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam.

RESULTS

PhenoFam performs gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Our tool is designed to analyse complete sets of results from quantitative high-throughput studies (gene expression microarrays, functional RNAi screens, etc.) without prior pre-filtering or hits-selection steps. PhenoFam utilizes Ensembl databases to link a list of user-provided identifiers with protein features from the InterPro database, and assesses whether results associated with individual domains differ significantly from the overall population. To demonstrate the utility of PhenoFam we analysed a genome-wide RNA interference screen and discovered a novel function of plexins containing the cytoplasmic RasGAP domain. Furthermore, a PhenoFam analysis of breast cancer gene expression profiles revealed a link between breast carcinoma and altered expression of PX domain containing proteins.

CONCLUSIONS

PhenoFam provides a user-friendly, easily accessible web interface to perform GSEA based on high-throughput data sets and structural-functional protein information, and therefore aids in functional annotation of genes.

Functional genomics and proteomics approaches to study the ERBB network in cancer

Substantial progress in functional genomic and proteomic technologies has opened new perspectives in biomedical research. The sequence of the human genome has been mostly determined and opened new visions on its complexity and regulation. New technologies, like RNAi and protein arrays, allow gathering knowledge beyond single gene analysis. Increasingly, biological processes are studied with systems biological approaches, where qualitative and quantitative data of the components are utilized to model the respective processes, to predict effects of perturbations, and to then refine these models after experimental testing. Here, we describe the potential of applying functional genomics and proteomics, taking the ERBB family of growth-factor receptors as an example to study the signaling network and its impact on cancer.

Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission

Despite known heritability, the complex genetic architecture of bipolar disorder (likely including trait, locus and allelic heterogeneity, as well as genetic interactions) has confounded genetic discovery for many years. Even modern day whole genome association studies (WGAS) using over half a million common SNPs have implicated only a handful of genes at the genomewide level. Temporally coincident with this series of WGAS, a host of pathways-based analyses (PBAs) have emerged as novel computational approaches in the examination of large-scale datasets, but thus far rarely have been applied to WGAS data in psychiatric disorders. Here, we report a series of PBAs conducted using exploratory visual analysis, an analytic and visualization software tool for examining genomic data, to examine results from the National Institutes of Mental Health and Wellcome-Trust Case Control Consortium WGAS in bipolar disorder. Consistent with a host of prior linkage findings, some candidate gene association studies, and recent WGAS, our strongest findings suggest involvement of ion channel structural and regulatory genes, including voltage-gated ion channels and the broader ion channel group that comprises both voltage- and ligand-gated channels. Moreover, we found only modest overlap in the particular genes driving the significance of these gene sets across the analyses. This observation strongly suggests that variation in ion channel genes, as a class of genes, may contribute to the susceptibility of bipolar disorder and that heterogeneity may figure prominently in the genetic architecture of this susceptibility.

Predicting pathway membership via domain signatures

MOTIVATION

Functional characterization of genes is of great importance for the understanding of complex cellular processes. Valuable information for this purpose can be obtained from pathway databases, like KEGG. However, only a small fraction of genes is annotated with pathway information up to now. In contrast, information on contained protein domains can be obtained for a significantly higher number of genes, e.g. from the InterPro database.

RESULTS

We present a classification model, which for a specific gene of interest can predict the mapping to a KEGG pathway, based on its domain signature. The classifier makes explicit use of the hierarchical organization of pathways in the KEGG database. Furthermore, we take into account that a specific gene can be mapped to different pathways at the same time. The classification method produces a scoring of all possible mapping positions of the gene in the KEGG hierarchy. Evaluations of our model, which is a combination of a SVM and ranking perceptron approach, show a high prediction performance. Moreover, for signaling pathways we reveal that it is even possible to forecast accurately the membership to individual pathway components.

AVAILABILITY

The R package gene2pathway is a supplement to this article.