Explore the vast range of titles available.

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.

Williams–Beuren syndrome (WBS) is a developmental disorder with characteristic physical, cognitive and behavioural traits caused by a microdeletion of ∼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.

Matthew Nelson and colleagues investigate how well genetic evidence for disease susceptibility predicts drug mechanisms. They find a correlation between gene products that are successful drug targets and genetic loci associated with the disease treated by the drug and predict that selecting genetically supported targets could increase the success rate of drugs in clinical development. 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.

ObjectivesAn interferon (IFN) gene signature (IGS) is present in approximately 50% of early, treatment naive rheumatoid arthritis (eRA) patients where it has been shown to negatively impact initial response to treatment. We wished to validate this effect and explore potential mechanisms of action.MethodsIn a multicentre inception cohort of eRA patients (n=191), we examined the whole blood IGS (MxA, IFI44L, OAS1, IFI6, ISG15) with reference to circulating IFN proteins, clinical outcomes and epigenetic influences on circulating CD19+ B and CD4+ T lymphocytes.ResultsWe reproduced our previous findings demonstrating a raised baseline IGS. We additionally showed, for the first time, that the IGS in eRA reflects circulating IFN-α protein. Paired longitudinal analysis demonstrated a significant reduction between baseline and 6-month IGS and IFN-α levels (p<0.0001 for both). Despite this fall, a raised baseline IGS predicted worse 6-month clinical outcomes such as increased disease activity score (DAS-28, p=0.025) and lower likelihood of a good EULAR clinical response (p=0.034), which was independent of other conventional predictors of disease activity and clinical response. Molecular analysis of CD4+ T cells and CD19+ B cells demonstrated differentially methylated CPG sites and dysregulated expression of disease relevant genes, including PARP9, STAT1, and EPSTI1, associated with baseline IGS/IFNα levels. Differentially methylated CPG sites implicated altered transcription factor binding in B cells (GATA3, ETSI, NFATC2, EZH2) and T cells (p300, HIF1α).ConclusionsOur data suggest that, in eRA, IFN-α can cause a sustained, epigenetically mediated, pathogenic increase in lymphocyte activation and proliferation, and that the IGS is, therefore, a robust prognostic biomarker. Its persistent harmful effects provide a rationale for the initial therapeutic targeting of IFN-α in selected patients with eRA.

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.

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.