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Class switch recombination (CSR) is a programmed gene rearrangement in which a B cell which is producing IgM and IgD antibody develops into an IgG-, IgA- or IgE-expressing cell. This is achieved by recombination between switch regions located 5′ of each of the immunoglobulin heavy chain constant regions, except Cδ. The mechanism of CSR has not been resolved but it is thought to involve a double-strand break followed by end joining. It has previously been suggested that the nucleotide excision repair protein ERCC1 may be involved in CSR due to its known roles in removal of 3′ single-stranded tails in various types of recombination. In this study, we examined class switching in cultured splenocytes from ERCC1-deficient mice and found no evidence of any deficiency.

The Biological General Repository for Interaction Datasets (BioGRID: http://thebiogrid.org) is an open access database that houses genetic and protein interactions curated from the primary biomedical literature for all major model organism species and humans. As of September 2014, the BioGRID contains 749 912 interactions as drawn from 43 149 publications that represent 30 model organisms. This interaction count represents a 50% increase compared to our previous 2013 BioGRID update. BioGRID data are freely distributed through partner model organism databases and meta-databases and are directly downloadable in a variety of formats. In addition to general curation of the published literature for the major model species, BioGRID undertakes themed curation projects in areas of particular relevance for biomedical sciences, such as the ubiquitin-proteasome system and various human disease-associated interaction networks. BioGRID curation is coordinated through an Interaction Management System (IMS) that facilitates the compilation interaction records through structured evidence codes, phenotype ontologies, and gene annotation. The BioGRID architecture has been improved in order to support a broader range of interaction and post-translational modification types, to allow the representation of more complex multi-gene/protein interactions, to account for cellular phenotypes through structured ontologies, to expedite curation through semi-automated text-mining approaches, and to enhance curation quality control.

Increased rates of RNA polymerase (pol) III transcription constitute a central feature of the mitogenic response, but little is known about the mechanism(s) responsible. We demonstrate that the retinoblastoma protein RB plays a major role in suppressing pol III transcription in growth-arrested fibroblasts. RB knockout cells are compromised in their ability to down-regulate pol III following serum withdrawal. RB binds and represses the pol III-specific transcription factor TFIIIB during G(0) and early G(1), but this interaction decreases as cells approach S phase. Full induction of pol III coincides with mid- to late G(1) phase, when RB becomes phosphorylated by cyclin D- and E-dependent kinases. TFIIIB only associates with the underphosphorylated form of RB, and overexpression of cyclins D and E stimulates pol III transcription in vivo. The RB-related protein p130 also contributes to the repression of TFIIIB in growth-arrested fibroblasts. These observations provide insight into the mechanisms responsible for controlling pol III transcription during the switch between growth and quiescence.

The level of RNA polymerase (pol) III transcription is tightly linked to the rate of growth; it is low in resting cells and increases following mitogenic stimulation. When mammalian cells begin to proliferate, maximal pol III activity is reached shortly before the G1/S transition; it then remains high throughout S and G2 phases. Recent data suggest that the retinoblastoma protein RB and its relatives p107 and p130 may be largely responsible for this pattern of expression. During G0 and early G1 phase, RB and p130 bind and repress the pol III-specific factor TFIIIB; shortly before S phase they dissociate from TFIIIB, allowing transcription to increase. At the end of interphase, when cells enter mitosis, pol III transcription is again suppressed; this mitotic repression is achieved through direct phosphorylation of TFIIIB. Thus, pol III transcription levels fluctuate as mammalian cells cycle, being high in S and G2 phases and low during mitosis and early G1. In addition to this cyclic regulation, TFIIIB can be bound and repressed by the tumor suppressor p53. Conversely, it is a target for activation by several viruses, including SV40, HBV, and HTLV-1. Some viruses also increase the activity of a second pol III-specific factor called TFIIIC. A large proportion of transformed and tumor cell types express abnormally high levels of pol III products. This may be explained, at least in part, by the very high frequency with which RB and p53 become inactivated during neoplastic transformation; loss of function of these cardinal tumor suppressors may release TFIIIB from key restraints that operate in normal cells.

RNA polymerase (Pol) III transcription is abnormally active in fibroblasts that have been transformed by simian virus 40 (SV40). This report presents evidence that two separate components of the general Pol III transcription apparatus, TFIIIB and TFIIIC2, are deregulated following SV40 transformation. TFIIIC2 subunits are expressed at abnormally high levels in SV40-transformed cells, an effect which is observed at both protein and mRNA levels. In untransformed fibroblasts, TFIIIB is subject to repression through association with the retinoblastoma protein RB. The interaction between RB and TFIIIB is compromised following SV40 transformation. Furthermore, the large T antigen of SV40 is shown to relieve repression by RB. The E7 oncoprotein of human papillomavirus can also activate Pol III transcription, an effect that is dependent on its ability to bind to RB. The data provide evidence that both TFIIIB and TFIIIC2 are targets for activation by DNA tumor viruses.

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community., National Science Foundation (grant IIS-0844419 to L.H.); US National Institutes of Health National Library of Medicine (grant 1G08LM10720-01 to C.N.A. and C. H. W.); Work related to BioCreative III was supported by the US National Science Foundation (grant DBI-0850319 to C.N.A., L.H., C.H.W.); the US National Institute of General Medical Sciences (grant R01-GM083871 to G.A.P.C.B.); the National Science Foundation (DBI-0849977 to G.A.P.G.B); the European Union Seventh Framework MICROME project (Grant Agreement Number 222886-2 to M.K. and A.V.); the US National Science Foundation IGERT (Grant 0221625 to K.G.D) and a PhRMA Foundation predoctoral fellowship in informatics; US National Science Foundation (grant DBI-0850219 to E.H.); US National Human Genome Research Institute (grant HG001315 to R.N.); National Institutes of Health (NIH) (grant 2U01HG02712-04 to A.L.V.) and European Commission contract FELICS (grant 021902RII3); National Institute of Environmental Health Sciences (NIEHS) and the National Library of Medicine (NLM) (R01ES014065 to T.W.); NIEHS (R01ES014065-04S1 to T.W.); National Institutes of Health National Center for Research Resources(P20RR016463 to T.W.); Biotechnology and Biological Sciences Research Council of the UK (grant BB/F010486/1 to A.G.W); the National Institutes of Health National Center for Research Resources (1R01RR024031 to A.G.W); the European Commission FP7 Program (2007223411 to A.G.W). Funding for open access charge: The MITRE Corporation.

Background The vast amount of data published in the primary biomedical literature represents a challenge for the automated extraction and codification of individual data elements. Biological databases that rely solely on manual extraction by expert curators are unable to comprehensively annotate the information dispersed across the entire biomedical literature. The development of efficient tools based on natural language processing (NLP) systems is essential for the selection of relevant publications, identification of data attributes and partially automated annotation. One of the tasks of the Biocreative 2010 Challenge III was devoted to the evaluation of NLP systems developed to identify articles for curation and extraction of protein-protein interaction (PPI) data. Results The Biocreative 2010 competition addressed three tasks: gene normalization, article classification and interaction method identification. The BioGRID and MINT protein interaction databases both participated in the generation of the test publication set for gene normalization, annotated the development and test sets for article classification, and curated the test set for interaction method classification. These test datasets served as a gold standard for the evaluation of data extraction algorithms. Conclusion The development of efficient tools for extraction of PPI data is a necessary step to achieve full curation of the biomedical literature. NLP systems can in the first instance facilitate expert curation by refining the list of candidate publications that contain PPI data; more ambitiously, NLP approaches may be able to directly extract relevant information from full-text articles for rapid inspection by expert curators. Close collaboration between biological databases and NLP systems developers will continue to facilitate the long-term objectives of both disciplines.

The goal of the Biological General Repository for Interaction Datasets (BioGRID) (http://www.thebiogrid.org) is to archive and freely disseminate collections of genetic and protein interactions from major model organisms. BioGRID currently houses over 335,000 interactions curated from high-throughput datasets and individual focused studies found in the primary literature, as derived from some 23,000 publications. Complete coverage of the entire literature for both the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe has been achieved, resulting in the curation of over 246,000 interactions, and efforts to expand curation across multiple species are underway. Through collaborations with the Gene Ontology (GO) Consortium and the Linking Animal Models to Human Disease Initiative (LAMHDI), we are focusing our curation efforts across model organisms on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health.The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. A dedicated Interaction Management System (IMS) is used to track all curation and to prioritize publications across multiple curation projects. BioGRID data are incorporated in several model organism databases and other biological databases. The entire BioGRID interaction collection may be downloaded in multiple file formats, including PSI MI XML, and source code for BioGRID is freely available without any restrictions. This work is supported by NIH NCRR grant R01 RR024031 to MT and KD, and by grants from the CIHR and BBSRC to MT.

Background Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them. Results A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53%, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35%) the macro-averaged precision ranged between 50% and 80%, with a maximum F-Score of 55%. Conclusions The results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows.

To the Editor.-- Author Manuscript.-- et al., This study was supported by the European Commission under the Serving Life-science Information for the Next Generation contract 226073; Proteomics Standards Initiative and International Molecular Exchange contract FP7-HEALTH-2007-223411; Apoptosis Systems Biology Applied to Cancer and AIDS contract FP7-HEALTH-2007-200767; Experimental Network for Functional Integration contract LSHG-CT-2005-518254; German National Genome Research Network; German Research Foundation contract KFO 129/1-2; US National Institutes of Health grant R01GM071909; the Italian Association for Cancer Research; a Wellcome Trust Strategic Award to the European Molecular Biology Laboratory–European Bioinformatics Institute for Chemogenomics Databases; Grand Challenges in Global Health Research, the Canadian Institutes of Health Research, Foundation for the National Institutes of Health and Genome British Columbia; and a German Research Foundation–funded Cluster of Excellence for Multimodal Computing and Interaction.

The BioGRID database is an extensive repository of curated genetic and protein interactions for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe, and the yeast Candida albicans SC5314, as well as for several other model organisms and humans. This protocol describes how to use the BioGRID website to query genetic or protein interactions for any gene of interest, how to visualize the associated interactions using an embedded interactive network viewer, and how to download data files for either selected interactions or the entire BioGRID interaction data set.

The Biological General Repository for Interaction Datasets (BioGRID) is a freely available public database that provides the biological and biomedical research communities with curated protein and genetic interaction data. Structured experimental evidence codes, an intuitive search interface, and visualization tools enable the discovery of individual gene, protein, or biological network function. BioGRID houses interaction data for the major model organism species—including yeast, nematode, fly, zebrafish, mouse, and human—with particular emphasis on the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe as pioneer eukaryotic models for network biology. BioGRID has achieved comprehensive curation coverage of the entire literature for these two major yeast models, which is actively maintained through monthly curation updates. As of September 2015, BioGRID houses approximately 335,400 biological interactions for budding yeast and approximately 67,800 interactions for fission yeast. BioGRID also supports an integrated posttranslational modification (PTM) viewer that incorporates more than 20,100 yeast phosphorylation sites curated through its sister database, the PhosphoGRID.

Cisplatin is the most commonly used chemotherapeutic agent in the treatment of ovarian cancer. One of the mechanisms of resistance of ovarian tumours to cisplatin is increased nucleotide excision repair activity, in particular increased levels of the endonuclease ERCC1. Since 30-40% of ovarian cancers develop resistance to cisplatin after treatment and these tumours are usually incurable, ERCC1 expression is potentially useful as a predictive marker for the effectiveness of cisplatin-based chemotherapy. Using RT-PCR and Northern blotting, we have examined the expression of a 42 bp differentially spliced sequence in exon 1 of the human ERCC1 gene, loss of which has previously been reported to be correlated with higher levels of ERCC1 mRNA in ovarian cancer cell lines. We report here that this alternate transcript is ubiquitous in human tissues and cancer cell lines, is absent in mouse and thus does not appear to be cancer related.

Most transformed cells display abnormally high levels of RNA polymerase (pol) III transcripts. Although the full significance of this is unclear, it may be fundamental because healthy cells use two key tumor suppressors to restrain pol III activity. We present the first evidence that a pol III transcription factor is overexpressed in tumors. This factor, TFIIIC2, is a histone acetyltransferase that is required for synthesis of most pol III products, including tRNA and 5S rRNA. TFIIIC2 is a complex of five polypeptides, and mRNAs encoding each of these subunits are overexpressed in human ovarian carcinomas; this may explain the elevated TFIIIC2 activity that is found consistently in the tumors. Deregulation in these cancers is unlikely to be a secondary response to rapid proliferation, because there is little or no change in TFIIIC2 mRNA levels when actively cycling cells are compared with growth-arrested cells in culture. Using purified factors, we show that raising the level of TFIIIC2 is sufficient to stimulate pol III transcription in ovarian cell extracts. The data suggest that overexpression of TFIIIC2 contributes to the abnormal abundance of pol III transcripts in ovarian tumors.