Mining integrated semantic networks for drug repositioning opportunities.

Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions.

The support of human genetic evidence for approved drug indications.

Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.

The application of the open pharmacological concepts triple store (open PHACTS) to support drug discovery research.

Integration of open access, curated, high-quality information from multiple disciplines in the Life and Biomedical Sciences provides a holistic understanding of the domain. Additionally, the effective linking of diverse data sources can unearth hidden relationships and guide potential research strategies. However, given the lack of consistency between descriptors and identifiers used in different resources and the absence of a simple mechanism to link them, gathering and combining relevant, comprehensive information from diverse databases remains a challenge. The Open Pharmacological Concepts Triple Store (Open PHACTS) is an Innovative Medicines Initiative project that uses semantic web technology approaches to enable scientists to easily access and process data from multiple sources to solve real-world drug discovery problems. The project draws together sources of publicly-available pharmacological, physicochemical and biomolecular data, represents it in a stable infrastructure and provides well-defined information exploration and retrieval methods. Here, we highlight the utility of this platform in conjunction with workflow tools to solve pharmacological research questions that require interoperability between target, compound, and pathway data. Use cases presented herein cover 1) the comprehensive identification of chemical matter for a dopamine receptor drug discovery program 2) the identification of compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease and 3) the evaluation of established targets in the Vitamin D metabolism pathway to aid novel Vitamin D analogue design. The example workflows presented illustrate how the Open PHACTS Discovery Platform can be used to exploit existing knowledge and generate new hypotheses in the process of drug discovery.

A probabilistic model of biological ageing of the lungs for analysing the effects of smoking, asthma and COPD.

BACKGROUND: Although a large body of literature is available that describes the effects of smoking, asthma and COPD on lung function, most studies are restricted to a small age range and to one factor. As a consequence, available results are incomplete and often difficult to compare, also due to the ways the effects are expressed. Furthermore, current approaches consider one type of measurement only or several types separately. METHODS: We propose a probabilistic model that expresses the effects as number of years added to chronological age or, in other words, that estimates the biological age of the lungs. Using biological age as a measure of the effects has the advantage of facilitating the understanding of their severity and comparison of results. In our model, chronological age and other factors affecting the health status of the lungs generate biological age, which in turn generates lung function measurements. This structure enables the use of multiple types of measurement to obtain a more precise estimate of the effects and parameter sharing for characterization over large age ranges and of co-occurrence of factors with little data. We treat the parameters that model smoking habits and lung diseases as random variables to obtain uncertainty in the estimated effects. RESULTS: We use the model to investigate the effects of smoking, asthma and COPD on the TwinsUK Registry. Our results suggest that the combination of smoking with lung disease(s) has higher effect than smoking or lung disease(s) alone, and that in smokers, co-occurrence of asthma and COPD is more detrimental than asthma or COPD alone. CONCLUSIONS: The proposed model or other models based on a similar approach could be of help in improving the understanding of factors affecting lung function by enabling characterizations over large age ranges and of co-occurrence of factors with little data and the use of multiple types of measurement. The software implementing the model can be downloaded at the first author's webpage.

Synergizing genomic analysis with biological knowledge to identify and validate novel genes in pancreatic development.

OBJECTIVE: This study investigated the utility of advanced computational techniques to large-scale genome-based data to identify novel genes that govern murine pancreatic development. METHODS: An expression data set for mouse pancreatic development was complemented with high-throughput data analyzer to identify and prioritize novel genes. Quantitative real-time polymerase chain reaction, in situ hybridization, and immunohistochemistry were used to validate selected genes. RESULTS: Four new genes whose roles in the development of murine pancreas have not previously been established were identified: cystathionine ß-synthase (Cbs), Meis homeobox 1, growth factor independent 1, and aldehyde dehydrogenase 18 family, member A1. Their temporal expression during development was documented. Cbs was localized in the cytoplasm of the tip cells of the epithelial chords of the undifferentiated progenitor cells at E12.5 and was coexpressed with the pancreatic and duodenal homeobox 1 and pancreas-specific transcription factor, 1a-positive cells. In the adult pancreas, Cbs was localized primarily within the acinar compartment. CONCLUSIONS: In silico analysis of high-throughput microarray data in combination with background knowledge about genes provides an additional reliable method of identifying novel genes. To our knowledge, the expression and localization of Cbs have not been previously documented during mouse pancreatic development.

Profiling impaired hepatic endoplasmic reticulum glycosylation as a consequence of ethanol ingestion.

Alcoholic liver disease (ALD) is a prominent cause of morbidity and mortality in the United States. Alterations in protein folding occur in numerous disease states, including ALD. The endoplasmic reticulum (ER) is the primary site of post-translational modifications (PTM) within the cell. Glycosylation, the most abundant PTM, affects protein stability, structure, localization, and activity. Decreases in hepatic glycosylation machinery have been observed in rodent models of ALD, but specific protein targets have not been identified. Utilizing two-dimensional gel electrophoresis and liquid chromatography-tandem mass spectrometry, glycoproteins were identified in hepatic microsomal fractions from control and ethanol-fed mice. This study reports for the first time a global decrease in ER glycosylation. Additionally, the identification of 30 glycoproteins within this fraction elucidates pathway-specific alterations in ALD impaired glycosylation. Among the identified proteins, triacylglycerol hydrolase (TGH) is positively affected by glycosylation, showing increased activity following the addition of sugar moieties. Impaired TGH activity is associated with increased cellular storage of lipids and provides a potential mechanism for the observed pathologies associated with ALD.

HIGH-PRECISION BIOLOGICAL EVENT EXTRACTION: EFFECTS OF SYSTEM AND OF DATA.

We approached the problems of event detection, argument identification, and negation and speculation detection in the BioNLP'09 information extraction challenge through concept recognition and analysis. Our methodology involved using the OpenDMAP semantic parser with manually written rules. The original OpenDMAP system was updated for this challenge with a broad ontology defined for the events of interest, new linguistic patterns for those events, and specialized coordination handling. We achieved state-of-the-art precision for two of the three tasks, scoring the highest of 24 teams at precision of 71.81 on Task 1 and the highest of 6 teams at precision of 70.97 on Task 2. We provide a detailed analysis of the training data and show that a number of trigger words were ambiguous as to event type, even when their arguments are constrained by semantic class. The data is also shown to have a number of missing annotations. Analysis of a sampling of the comparatively small number of false positives returned by our system shows that major causes of this type of error were failing to recognize second themes in two-theme events, failing to recognize events when they were the arguments to other events, failure to recognize nontheme arguments, and sentence segmentation errors. We show that specifically handling coordination had a small but important impact on the overall performance of the system. The OpenDMAP system and the rule set are available at http://bionlp.sourceforge.net.

Claudin 13, a member of the claudin family regulated in mouse stress induced erythropoiesis.

Mammals are able to rapidly produce red blood cells in response to stress. The molecular pathways used in this process are important in understanding responses to anaemia in multiple biological settings. Here we characterise the novel gene Claudin 13 (Cldn13), a member of the Claudin family of tight junction proteins using RNA expression, microarray and phylogenetic analysis. We present evidence that Cldn13 appears to be co-ordinately regulated as part of a stress induced erythropoiesis pathway and is a mouse-specific gene mainly expressed in tissues associated with haematopoietic function. CLDN13 phylogenetically groups with its genomic neighbour CLDN4, a conserved tight junction protein with a putative role in epithelial to mesenchymal transition, suggesting a recent duplication event. Mechanisms of mammalian stress erythropoiesis are of importance in anaemic responses and expression microarray analyses demonstrate that Cldn13 is the most abundant Claudin in spleen from mice infected with Trypanosoma congolense. In mice prone to anaemia (C57BL/6), its expression is reduced compared to strains which display a less severe anaemic response (A/J and BALB/c) and is differentially regulated in spleen during disease progression. Genes clustering with Cldn13 on microarrays are key regulators of erythropoiesis (Tal1, Trim10, E2f2), erythrocyte membrane proteins (Rhd and Gypa), associated with red cell volume (Tmcc2) and indirectly associated with erythropoietic pathways (Cdca8, Cdkn2d, Cenpk). Relationships between genes appearing co-ordinately regulated with Cldn13 post-infection suggest new insights into the molecular regulation and pathways involved in stress induced erythropoiesis and suggest a novel, previously unreported role for claudins in correct cell polarisation and protein partitioning prior to erythroblast enucleation.

An introduction to effective use of enrichment analysis software.

In recent years, there has been an explosion in the range of software available for annotation enrichment analysis. Three classes of enrichment algorithms and their associated software implementations are introduced here. Their limitations and caveats are discussed, and direction for tool selection is given.

Spatial and temporal analysis of gene expression during growth and fusion of the mouse facial prominences.

Orofacial malformations resulting from genetic and/or environmental causes are frequent human birth defects yet their etiology is often unclear because of insufficient information concerning the molecular, cellular and morphogenetic processes responsible for normal facial development. We have, therefore, derived a comprehensive expression dataset for mouse orofacial development, interrogating three distinct regions - the mandibular, maxillary and frontonasal prominences. To capture the dynamic changes in the transcriptome during face formation, we sampled five time points between E10.5-E12.5, spanning the developmental period from establishment of the prominences to their fusion to form the mature facial platform. Seven independent biological replicates were used for each sample ensuring robustness and quality of the dataset. Here, we provide a general overview of the dataset, characterizing aspects of gene expression changes at both the spatial and temporal level. Considerable coordinate regulation occurs across the three prominences during this period of facial growth and morphogenesis, with a switch from expression of genes involved in cell proliferation to those associated with differentiation. An accompanying shift in the expression of polycomb and trithorax genes presumably maintains appropriate patterns of gene expression in precursor or differentiated cells, respectively. Superimposed on the many coordinated changes are prominence-specific differences in the expression of genes encoding transcription factors, extracellular matrix components, and signaling molecules. Thus, the elaboration of each prominence will be driven by particular combinations of transcription factors coupled with specific cell:cell and cell:matrix interactions. The dataset also reveals several prominence-specific genes not previously associated with orofacial development, a subset of which we externally validate. Several of these latter genes are components of bidirectional transcription units that likely share cis-acting sequences with well-characterized genes. Overall, our studies provide a valuable resource for probing orofacial development and a robust dataset for bioinformatic analysis of spatial and temporal gene expression changes during embryogenesis.

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.

Leveraging existing biological knowledge in the identification of candidate genes for facial dysmorphology.

BACKGROUND: In response to the frequently overwhelming output of high-throughput microarray experiments, we propose a methodology to facilitate interpretation of biological data in the context of existing knowledge. Through the probabilistic integration of explicit and implicit data sources a functional interaction network can be constructed. Each edge connecting two proteins is weighted by a confidence value capturing the strength and reliability of support for that interaction given the combined data sources. The resulting network is examined in conjunction with expression data to identify groups of genes with significant temporal or tissue specific patterns. In contrast to unstructured gene lists, these networks often represent coherent functional groupings. RESULTS: By linking from shared functional categorizations to primary biological resources we apply this method to craniofacial microarray data, generating biologically testable hypotheses and identifying candidate genes for craniofacial development. CONCLUSION: The novel methodology presented here illustrates how the effective integration of pre-existing biological knowledge and high-throughput experimental data drives biological discovery and hypothesis generation.

Consistent visualizations of changing knowledge.

Networks are increasingly used in biology to represent complex data in uncomplicated symbolic form. However, as biological knowledge is continually evolving, so must those networks representing this knowledge. Capturing and presenting this type of knowledge change over time is particularly challenging due to the intimate manner in which researchers customize those networks they come into contact with. The effective visualization of this knowledge is important as it creates insight into complex systems and stimulates hypothesis generation and biological discovery. Here we highlight how the retention of user customizations, and the collection and visualization of knowledge associated provenance supports effective and productive network exploration. We also present an extension of the Hanalyzer system, ReOrient, which supports network exploration and analysis in the presence of knowledge change.

Comparison of TFII-I gene family members deleted in Williams-Beuren syndrome.

Williams-Beuren syndrome (WBS) is a neurological disorder resulting from a microdeletion, typically 1.5 megabases in size, at 7q11.23. Atypical patients implicate genes at the telomeric end of this multigene deletion as the main candidates for the pathology of WBS in particular the unequal cognitive profile associated with the condition. We recently identified a gene (GTF2IRD2) that shares homology with other members of a unique family of transcription factors (TFII-I family), which reside in the critical telomeric region. Using bioinformatics tools this study focuses on the detailed assessment of this gene family, concentrating on their characteristic structural components such as the leucine zipper (LZ) and I-repeat elements, in an attempt to identify features that could aid functional predictions. Phylogenetic analysis identified distinct I-repeat clades shared between family members. Linking functional data to one such clade has implicated them in DNA binding. The identification of PEST, synergy control motifs, and sumoylation sites common to all family members suggest a shared mechanism regulating the stability and transcriptional activity of these factors. In addition, the identification/isolation of short truncated isoforms for each TFII-I family member implies a mode of self-regulation. The exceptionally high identity shared between GTF2I and GTF2IRD2, suggests that heterodimers as well as homodimers are possible, and indicates overlapping functions between their respective short isoforms. Such cross-reactivity between GTF2I and GTF2IRD2 short isoforms might have been the evolutionary driving force for the 7q11.23 chromosomal rearrangement not present in the syntenic region in mice.

Isolation and characterisation of GTF2IRD2, a novel fusion gene and member of the TFII-I family of transcription factors, deleted in Williams-Beuren syndrome.

Williams-Beuren syndrome (WBS) is a developmental disorder with characteristic physical, cognitive and behavioural traits caused by a microdeletion of approximately 1.5 Mb on chromosome 7q11.23. In total, 24 genes have been described within the deleted region to date. We have isolated and characterised a novel human gene, GTF2IRD2, mapping to the WBS critical region thought to harbour genes important for the cognitive aspects of the disorder. GTF2IRD2 is the third member of the novel TFII-I family of genes clustered on 7q11.23. The GTF2IRD2 protein contains two putative helix-loop-helix regions (I-repeats) and an unusual C-terminal CHARLIE8 transposon-like domain, thought to have arisen as a consequence of the random insertion of a transposable element generating a functional fusion gene. The retention of a number of conserved transposase-associated motifs within the protein suggests that the CHARLIE8-like region may still have some degree of transposase functionality that could influence the stability of the region in a mechanism similar to that proposed for Charcot-Marie-Tooth neuropathy type 1A. GTF2IRD2 is highly conserved in mammals and the mouse ortholgue (Gtf2ird2) has also been isolated and maps to the syntenic WBS region on mouse chromosome 5G. Deletion mapping studies using somatic cell hybrids show that some WBS patients are hemizygous for this gene, suggesting that it could play a role in the pathogenesis of the disorder.