Inference of Gene-Phenotype Associations via Protein-Protein Interaction and Orthology

One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.     

RIDDLE: reflective diffusion and local extension reveal functional associations for unannotated gene sets via proximity in a gene network

The growing availability of large-scale functional networks has promoted the development of many successful techniques for predicting functions of genes. Here we extend these network-based principles and techniques to functionally characterize whole sets of genes. We present RIDDLE (Reflective Diffusion and Local Extension), which uses well developed guilt-by-association principles upon a human gene network to identify associations of gene sets. RIDDLE is particularly adept at characterizing sets with no annotations, a major challenge where most traditional set analyses fail. Notably, RIDDLE found microRNA-450a to be strongly implicated in ocular diseases and development. A web application is available at http://www.functionalnet.org/RIDDLE.     

Ensemble-Based Network Aggregation Improves the Accuracy of Gene Network Reconstruction

Reverse engineering approaches to constructing gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings, such as the discovery of novel drug targets. However, the reliability of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improving the accuracy of network topologies constructed from mRNA expression data. To evaluate the performances of different approaches, we created dozens of simulated networks from combinations of gene-set sizes and sample sizes and also tested our methods on three Escherichia coli datasets. We demonstrate that the ensemble-based network aggregation approach can be used to effectively integrate GRNs constructed from different studies – producing more accurate networks. We also apply this approach to building a network from epithelial mesenchymal transition (EMT) signature microarray data and identify hub genes that might be potential drug targets. The R code used to perform all of the analyses is available in an R package entitled “ENA”, accessible on CRAN (http://cran.r-project.org/web/packages/ENA/).     

Integrating Constitutive Gene Expression and Chemoactivity: Mining the NCI60 Anticancer Screen

Studies into the genetic origins of tumor cell chemoactivity pose significant challenges to bioinformatic mining efforts. Connections between measures of gene expression and chemoactivity have the potential to identify clinical biomarkers of compound response, cellular pathways important to efficacy and potential toxicities; all vital to anticancer drug development. An investigation has been conducted that jointly explores tumor-cell constitutive NCI60 gene expression profiles and small-molecule NCI60 growth inhibition chemoactivity profiles, viewed from novel applications of self-organizing maps (SOMs) and pathway-centric analyses of gene expressions, to identify subsets of over- and under-expressed pathway genes that discriminate chemo-sensitive and chemo-insensitive tumor cell types. Linear Discriminant Analysis (LDA) is used to quantify the accuracy of discriminating genes to predict tumor cell chemoactivity. LDA results find 15% higher prediction accuracies, using ∼30% fewer genes, for pathway-derived discriminating genes when compared to genes derived using conventional gene expression-chemoactivity correlations. The proposed pathway-centric data mining procedure was used to derive discriminating genes for ten well-known compounds. Discriminating genes were further evaluated using gene set enrichment analysis (GSEA) to reveal a cellular genetic landscape, comprised of small numbers of key over and under expressed on- and off-target pathway genes, as important for a compound’s tumor cell chemoactivity. Literature-based validations are provided as support for chemo-important pathways derived from this procedure. Qualitatively similar results are found when using gene expression measurements derived from different microarray platforms. The data used in this analysis is available at http://pubchem.ncbi.nlm.nih.gov/and http://www.ncbi.nlm.nih.gov/projects/geo ({"type":"entrez-geo","attrs":{"text":"GPL96","term_id":"96"}}GPL96, {"type":"entrez-geo","attrs":{"text":"GSE32474","term_id":"32474"}}GSE32474).     

Expression of lynx1 in developing lung and its modulation by prenatal nicotine exposure

The expression of nicotinic acetylcholine receptors (nAChR) in fetal lung suggests maternal smoking during pregnancy effects newborn lung structure and function by the direct interaction of nicotine with nAChR in the developing lung. The recent identification of the lynx1 nAChR modulator protein in nicotinic neurons in the brain suggests that lynx1 may be similarly expressed in the lung. To study this, cDNAs encoding lynx1 were cloned from rhesus monkey lung. The temporal expression of lynx1 was studied in pre- and postnatal monkey lungs by in situ hybridization, immunohistochemistry, and realtime polymerase chain reaction (PCR). Lynx1 mRNA signal and lynx1 immunohistochemical staining were localized predominantly in airway epithelial cells, submucous glands, and smooth muscle cells, in endothelial and smooth muscle cells in vessel walls, and in alveolar type II cells. The distribution of lynx1 was similar to that of 4, 2, and 4 nAChR expression as determined by immunohistochemistry. Immunohistochemical staining also co-localized choline acetyltransferase, the enzyme that synthesizes acetylcholine, with lynx1 expression. Lynx1 expression was first observed in 71-day fetal lungs and increased with age. Immunohistochemistry, Western analysis, and realtime PCR analysis showed increased lynx1 expression in lungs following prenatal nicotine exposure. Thus, lynx1 is co-expressed with nAChR in the lung. Alteration of lynx1 levels is a potential new mechanism by which nicotine affects lung development. This research was supported by National Institute of Health grants RR-00163 and HD/HL-37131.     

Multi-Edge Gene Set Networks Reveal Novel Insights into Global Relationships between Biological Themes

Curated gene sets from databases such as KEGG Pathway and Gene Ontology are often used to systematically organize lists of genes or proteins derived from high-throughput data. However, the information content inherent to some relationships between the interrogated gene sets, such as pathway crosstalk, is often underutilized. A gene set network, where nodes representing individual gene sets such as KEGG pathways are connected to indicate a functional dependency, is well suited to visualize and analyze global gene set relationships. Here we introduce a novel gene set network construction algorithm that integrates gene lists derived from high-throughput experiments with curated gene sets to construct co-enrichment gene set networks. Along with previously described co-membership and linkage algorithms, we apply the co-enrichment algorithm to eight gene set collections to construct integrated multi-evidence gene set networks with multiple edge types connecting gene sets. We demonstrate the utility of approach through examples of novel gene set networks such as the chromosome map co-differential expression gene set network. A total of twenty-four gene set networks are exposed via a web tool called MetaNet, where context-specific multi-edge gene set networks are constructed from enriched gene sets within user-defined gene lists. MetaNet is freely available at http://blaispathways.dfci.harvard.edu/metanet/.     

Gene Network Rewiring to Study Melanoma Stage Progression and Elements Essential for Driving Melanoma

Metastatic melanoma patients have a poor prognosis, mainly attributable to the underlying heterogeneity in melanoma driver genes and altered gene expression profiles. These characteristics of melanoma also make the development of drugs and identification of novel drug targets for metastatic melanoma a daunting task. Systems biology offers an alternative approach to re-explore the genes or gene sets that display dysregulated behaviour without being differentially expressed. In this study, we have performed systems biology studies to enhance our knowledge about the conserved property of disease genes or gene sets among mutually exclusive datasets representing melanoma progression. We meta-analysed 642 microarray samples to generate melanoma reconstructed networks representing four different stages of melanoma progression to extract genes with altered molecular circuitry wiring as compared to a normal cellular state. Intriguingly, a majority of the melanoma network-rewired genes are not differentially expressed and the disease genes involved in melanoma progression consistently modulate its activity by rewiring network connections. We found that the shortlisted disease genes in the study show strong and abnormal network connectivity, which enhances with the disease progression. Moreover, the deviated network properties of the disease gene sets allow ranking/prioritization of different enriched, dysregulated and conserved pathway terms in metastatic melanoma, in agreement with previous findings. Our analysis also reveals presence of distinct network hubs in different stages of metastasizing tumor for the same set of pathways in the statistically conserved gene sets. The study results are also presented as a freely available database at http://bioinfo.icgeb.res.in/m3db/. The web-based database resource consists of results from the analysis presented here, integrated with cytoscape web and user-friendly tools for visualization, retrieval and further analysis.     

Phosphodiesterase 6 subunits are expressed and altered in idiopathic pulmonary fibrosis

Background

Idiopathic Pulmonary Fibrosis (IPF) is an unresolved clinical issue. Phosphodiesterases (PDEs) are known therapeutic targets for various proliferative lung diseases. Lung PDE6 expression and function has received little or no attention. The present study aimed to characterize (i) PDE6 subunits expression in human lung, (ii) PDE6 subunits expression and alteration in IPF and (iii) functionality of the specific PDE6D subunit in alveolar epithelial cells (AECs).

Methodology/Principal Findings

PDE6 subunits expression in transplant donor (n = 6) and IPF (n = 6) lungs was demonstrated by real-time quantitative (q)RT-PCR and immunoblotting analysis. PDE6D mRNA and protein levels and PDE6G/H protein levels were significantly down-regulated in the IPF lungs. Immunohistochemical analysis showed alveolar epithelial localization of the PDE6 subunits. This was confirmed by qRT-PCR from human primary alveolar type (AT)II cells, demonstrating the down-regulation pattern of PDE6D in IPF-derived ATII cells. In vitro, PDE6D protein depletion was provoked by transforming growth factor (TGF)-β1 in A549 AECs. PDE6D siRNA-mediated knockdown and an ectopic expression of PDE6D modified the proliferation rate of A549 AECs. These effects were mediated by increased intracellular cGMP levels and decreased ERK phosphorylation.

Conclusions/Significance

Collectively, we report previously unrecognized PDE6 expression in human lungs, significant alterations of the PDE6D and PDE6G/H subunits in IPF lungs and characterize the functional role of PDE6D in AEC proliferation.     

TBI Server: A Web Server for Predicting Ion Effects in RNA Folding

Background

Metal ions play a critical role in the stabilization of RNA structures. Therefore, accurate prediction of the ion effects in RNA folding can have a far-reaching impact on our understanding of RNA structure and function. Multivalent ions, especially Mg²⁺, are essential for RNA tertiary structure formation. These ions can possibly become strongly correlated in the close vicinity of RNA surface. Most of the currently available software packages, which have widespread success in predicting ion effects in biomolecular systems, however, do not explicitly account for the ion correlation effect. Therefore, it is important to develop a software package/web server for the prediction of ion electrostatics in RNA folding by including ion correlation effects.

Results

The TBI web server http://rna.physics.missouri.edu/tbi_index.html provides predictions for the total electrostatic free energy, the different free energy components, and the mean number and the most probable distributions of the bound ions. A novel feature of the TBI server is its ability to account for ion correlation and ion distribution fluctuation effects.

Conclusions

By accounting for the ion correlation and fluctuation effects, the TBI server is a unique online tool for computing ion-mediated electrostatic properties for given RNA structures. The results can provide important data for in-depth analysis for ion effects in RNA folding including the ion-dependence of folding stability, ion uptake in the folding process, and the interplay between the different energetic components.     

Localization of gamma-glutamylcysteine synthetase messenger rna expression in lungs of smokers and patients with chronic obstructive pulmonary disease

Cigarette smoking results in an oxidant/antioxidant imbalance in the lungs and inflammation, which are considered to be key factors in the pathogenesis of chronic obstructive pulmonary disease (COPD). Glutathione (GSH) is an important protective antioxidant in lung epithelial cells and epithelial lining fluid. De novo GSH synthesis in cells occurs by a two-enzyme process. The rate-limiting enzyme is gamma-glutamylcysteine synthetase (gamma-GCS), in which the heavy subunit (HS) constitutes most of its catalytic activity. The localization and expression of gamma-GCS-HS in specific lung cells as well as possible differences in its expression between smokers with and without COPD have not yet been studied. The purpose of this study was to investigate gamma-GCS-HS expression using messenger RNA in situ hybridization in peripheral lung tissue. We studied 23 current or ex-smokers with similar smoking histories with (n = 11; forced expiratory volume in 1 s [FEV(1)] < 75% predicted) or without COPD (n = 12; FEV(1) < 84% predicted). We assessed the relations between pulmonary gamma-GCS-HS expression, FEV(1) and transforming growth factor-beta1 (TGFbeta(1)), because TGFbeta(1) can modulate gamma-GCS-HS expression in lung epithelial cells. Gamma-GCS-HS is predominantly expressed by airway and alveolar epithelial cells, alveolar CD68+ cells (macrophages), and endothelial cells of both arteries and veins. In subjects with COPD, semiquantitative analysis revealed higher levels of gamma-GCS-HS messenger RNA in alveolar epithelium (1.5 times, p <.04) and a trend for a higher expression in bronchiolar epithelium (1.3 times, p =.075) compared with subjects without COPD. We did not observe a significant correlation between airway and alveolar epithelial gamma-GCS-HS expression and TGFbeta(1) expression (r =.20), FEV(1) percentage predicted (r =.18), or FEV(1)/forced vital capacity ratio (r =.14; p.05). Our results show that gamma-GCS-HS is localized, particularly in lung epithelium, and shows higher expression in smokers with COPD. This suggests a specific role for enhanced GSH synthesis as a mechanism to provide an adaptive response against oxidative stress in patients with COPD.     

Quantifying the Displacement of Mismatches in Multiple Sequence Alignment Benchmarks

Multiple Sequence Alignment (MSA) methods are typically benchmarked on sets of reference alignments. The quality of the alignment can then be represented by the sum-of-pairs (SP) or column (CS) scores, which measure the agreement between a reference and corresponding query alignment. Both the SP and CS scores treat mismatches between a query and reference alignment as equally bad, and do not take the separation into account between two amino acids in the query alignment, that should have been matched according to the reference alignment. This is significant since the magnitude of alignment shifts is often of relevance in biological analyses, including homology modeling and MSA refinement/manual alignment editing. In this study we develop a new alignment benchmark scoring scheme, SPdist, that takes the degree of discordance of mismatches into account by measuring the sequence distance between mismatched residue pairs in the query alignment. Using this new score along with the standard SP score, we investigate the discriminatory behavior of the new score by assessing how well six different MSA methods perform with respect to BAliBASE reference alignments. The SP score and the SPdist score yield very similar outcomes when the reference and query alignments are close. However, for more divergent reference alignments the SPdist score is able to distinguish between methods that keep alignments approximately close to the reference and those exhibiting larger shifts. We observed that by using SPdist together with SP scoring we were able to better delineate the alignment quality difference between alternative MSA methods. With a case study we exemplify why it is important, from a biological perspective, to consider the separation of mismatches. The SPdist scoring scheme has been implemented in the VerAlign web server (http://www.ibi.vu.nl/programs/veralignwww/). The code for calculating SPdist score is also available upon request.     

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

This is a PLOS Computational Biology Software paper.