Transcript specific regulation of expression influences susceptibility to multiple sclerosis.

Genome-wide association studies (GWAS) have identified over 100 loci containing single nucleotide variants (SNVs) that influence the risk of developing multiple sclerosis (MS). Most of these loci lie in non-coding regulatory regions of the genome that are active in immune cells and are therefore thought to modify risk by altering the expression of key immune genes. To explore this hypothesis we screened genes flanking MS-associated variants for evidence of allele specific expression (ASE) by quantifying the transcription of coding variants in linkage disequilibrium with MS-associated SNVs. In total, we were able to identify and successfully analyse 200 such coding variants (from 112 genes) in both CD4+ and CD8+ T cells from 106 MS patients and 105 controls. Fifty-six of these coding variants (from 43 genes) showed statistically significant evidence of ASE in one or both cell types. In the Lck interacting transmembrane adaptor 1 gene (LIME1), for example, we were able to show that in both cell types, the MS-associated variant rs2256814 increased the expression of some transcripts while simultaneously reducing the expression of other transcripts. In CD4+ cells from an additional independent set of 96 cases and 93 controls we were able to replicate the effect of this SNV on the balance of alternate LIME1 transcripts using qPCR (p = 5 × 10-24). Our data thus indicate that some of the MS-associated SNVs identified by GWAS likely exert their effects on risk by distorting the balance of alternate transcripts rather than by changing the overall level of gene expression.

Best practice data life cycle approaches for the life sciences.

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

VRK1 interacts with p53 forming a basal complex that is activated by UV-induced DNA damage.

DNA damage immediate cellular response requires the activation of p53 by kinases. We found that p53 forms a basal stable complex with VRK1, a Ser-Thr kinase that responds to UV-induced DNA damage by specifically phosphorylating p53. This interaction takes place through the p53 DNA binding domain, and frequent DNA-contact mutants of p53, such as R273H, R248H or R280K, do not disrupt the complex. UV-induced DNA damage activates VRK1, and is accompanied by phosphorylation of p53 at Thr-18 before it accumulates. We propose that the VRK1-p53 basal complex is an early-warning system for immediate cellular responses to DNA damage.

The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases.

IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple additional curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their separate efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org).

Regulatory elements associated with paternally-expressed genes in the imprinted murine Angelman/Prader-Willi syndrome domain.

The Angelman/Prader-Willi syndrome (AS/PWS) domain contains at least 8 imprinted genes regulated by a bipartite imprinting center (IC) associated with the SNRPN gene. One component of the IC, the PWS-IC, governs the paternal epigenotype and expression of paternal genes. The mechanisms by which imprinting and expression of paternal genes within the AS/PWS domain - such as MKRN3 and NDN - are regulated by the PWS-IC are unclear. The syntenic region in the mouse is organized and imprinted similarly to the human domain with the murine PWS-IC defined by a 6 kb interval within the Snrpn locus that includes the promoter. To identify regulatory elements that may mediate PWS-IC function, we mapped the location and allele-specificity of DNase I hypersensitive (DH) sites within the PWS-IC in brain cells, then identified transcription factor binding sites within a subset of these DH sites. Six major paternal-specific DH sites were detected in the Snrpn gene, five of which map within the 6 kb PWS-IC. We postulate these five DH sites represent functional components of the murine PWS-IC. Analysis of transcription factor binding within multiple DH sites detected nuclear respiratory factors (NRF's) and YY1 specifically on the paternal allele. NRF's and YY1 were also detected in the paternal promoter region of the murine Mrkn3 and Ndn genes. These results suggest that NRF's and YY1 may facilitate PWS-IC function and coordinately regulate expression of paternal genes. The presence of NRF's also suggests a link between transcriptional regulation within the AS/PWS domain and regulation of respiration. 3C analyses indicated Mkrn3 lies in close proximity to the PWS-IC on the paternal chromosome, evidence that the PWS-IC functions by allele-specific interaction with its distal target genes. This could occur by allele-specific co-localization of the PWS-IC and its target genes to transcription factories containing NRF's and YY1.

Protein interaction data curation: the International Molecular Exchange (IMEx) consortium.

The International Molecular Exchange (IMEx) consortium is an international collaboration between major public interaction data providers to share literature-curation efforts and make a nonredundant set of protein interactions available in a single search interface on a common website (http://www.imexconsortium.org/). Common curation rules have been developed, and a central registry is used to manage the selection of articles to enter into the dataset. We discuss the advantages of such a service to the user, our quality-control measures and our data-distribution practices.

The IntAct molecular interaction database in 2012.

IntAct is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. Two levels of curation are now available within the database, with both IMEx-level annotation and less detailed MIMIx-compatible entries currently supported. As from September 2011, IntAct contains approximately 275,000 curated binary interaction evidences from over 5000 publications. The IntAct website has been improved to enhance the search process and in particular the graphical display of the results. New data download formats are also available, which will facilitate the inclusion of IntAct's data in the Semantic Web. IntAct is an active contributor to the IMEx consortium (http://www.imexconsortium.org). IntAct source code and data are freely available at http://www.ebi.ac.uk/intact.

MINT and IntAct contribute to the Second BioCreative challenge: serving the text-mining community with high quality molecular interaction data.

BACKGROUND: In the absence of consolidated pipelines to archive biological data electronically, information dispersed in the literature must be captured by manual annotation. Unfortunately, manual annotation is time consuming and the coverage of published interaction data is therefore far from complete. The use of text-mining tools to identify relevant publications and to assist in the initial information extraction could help to improve the efficiency of the curation process and, as a consequence, the database coverage of data available in the literature. The 2006 BioCreative competition was aimed at evaluating text-mining procedures in comparison with manual annotation of protein-protein interactions. RESULTS: To aid the BioCreative protein-protein interaction task, IntAct and MINT (Molecular INTeraction) provided both the training and the test datasets. Data from both databases are comparable because they were curated according to the same standards. During the manual curation process, the major cause of data loss in mining the articles for information was ambiguity in the mapping of the gene names to stable UniProtKB database identifiers. It was also observed that most of the information about interactions was contained only within the full-text of the publication; hence, text mining of protein-protein interaction data will require the analysis of the full-text of the articles and cannot be restricted to the abstract. CONCLUSION: The development of text-mining tools to extract protein-protein interaction information may increase the literature coverage achieved by manual curation. To support the text-mining community, databases will highlight those sentences within the articles that describe the interactions. These will supply data-miners with a high quality dataset for algorithm development. Furthermore, the dictionary of terms created by the BioCreative competitors could enrich the synonym list of the PSI-MI (Proteomics Standards Initiative-Molecular Interactions) controlled vocabulary, which is used by both databases to annotate their data content.