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MicroRNAs (miRNAs) are members of the small non-coding RNA family regulating gene expression at the post-transcriptional level. MiRNAs have been found to have critical roles in various biological and pathological processes. Research in this field has significantly progressed, with increased recognition of the importance of miRNA regulation. As a result of the vast data and information available regarding miRNAs, numerous online tools have emerged to address various biological questions related to their function and influence across essential cellular processes. This review includes a brief introduction to available resources for an investigation covering aspects such as miRNA sequences, target prediction/validation, miRNAs associated with disease, pathway analysis and genetic variants within miRNAs.

Mutations in LRRK2 are the most common genetic cause of Parkinson’s disease. Despite substantial research efforts, the physiological and pathological role of this multidomain protein remains poorly defined. In this study, we used a systematic approach to construct the general protein-protein interactome around LRRK2, which was then evaluated taking into consideration the differential expression patterns and the co-expression behaviours of the LRRK2 interactors in 15 different healthy tissue types. The LRRK2 interactors exhibited distinct expression features in the brain as compared to the peripheral tissues analysed. Moreover, a high degree of similarity was found for the LRRK2 interactors in putamen, caudate and nucleus accumbens, thus defining a potential LRRK2 functional cluster within the striatum. The general LRRK2 interactome paired with the expression profiles of its members constitutes a powerful tool to generate tissue-specific LRRK2 interactomes. We exemplified the generation of the tissue-specific LRRK2 interactomes and explored the functions highlighted by the “core LRRK2 interactors” in the striatum in comparison with the cerebellum. Finally, we illustrated how the LRRK2 general interactome reported in this manuscript paired with the expression profiles can be used to trace the relationship between LRRK2 and specific interactors of interest, here focusing on the LRRK2 interactors belonging to the Rab protein family.

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills., Author summary The primary scientific literature catalogs the results from publicly funded scientific research about gene function in human-readable format. Information captured from those studies in a widely adopted, machine-readable standard format comes in the form of Gene Ontology (GO) annotations about gene functions from all domains of life. Manual annotations based on inferences directly from the scientific literature, including the evidence used to make such inferences, represent the best return on investment by improving data accessibility across the biological sciences and allowing novel insights between evolutionarily related organisms. To supplement professional curation, our Community Assessment of Community Annotation with Ontologies (CACAO) project enabled annotation of the scientific literature by community annotators, in this case undergraduates, which resulted in the contribution of thousands of unique, validated entries to public resources. Importantly, the annotations described here initiated by nonexperts often deal with topics not typically covered by the experts. These annotations are now being used by scientists worldwide in their research efforts.

Motivation Combining multiple layers of information underlying biological complexity into a structured framework represent a challenge in systems biology. A key task is the formalization of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signalling information, focus on capturing, organizing and displaying signalling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources. Results Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signalling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated PSI-MITAB 2.8. We define the new term ‘causal interaction’, and related child terms, which are children of the PSI-MI ‘molecular interaction’ term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signalling networks more precisely and with higher coverage than before. Availability and implementation PSI-MITAB 2.8 format and the new reference implementation of PSICQUIC are available online (https://psicquic.github.io/ and https://psicquic.github.io/MITAB28Format.html). Supplementary information Supplementary data are available at Bioinformatics online.

Stroke is a common disorder with significant morbidity and mortality, and complex aetiology involving both environmental and genetic risk factors. Although some of the major risk factors for stoke, such as smoking and hypertension, are well-documented, the underlying genetic and detailed molecular mechanisms remain elusive. Exploring the relevant biochemical pathways may contribute to the clinical diagnosis of stroke and shed light on its aetiology. A comparative proteomic analysis of blood serum of a pair of monozygotic (MZ) twins discordant for ischaemic stroke (IS) was performed using a label-free quantitative proteomics approach. To overcome the limit of reproducibility in the serum preparation, two separate runs were performed, each consisting of three technical replicates per sample. Biological processes associated with proteins differentially expressed between the twins were explored with gene ontology (GO) classification using the functional analysis tool g:Profiler. ANOVA test performed in Progenesis LC-MS identified 179 (run 1) and 209 (run 2) proteins as differentially expressed between the affected and unaffected twin (p

Gene regulation computational research requires handling and integrating large amounts of heterogeneous data. The Gene Ontology has demonstrated that ontologies play a fundamental role in biological data interoperability and integration. Ontologies help to express data and knowledge in a machine processable way, which enables complex querying and advanced exploitation of distributed data. Contributing to improve data interoperability in gene regulation is a major objective of the GREEKC Consortium, which aims to develop a standardized gene regulation knowledge commons. GREEKC proposes the use of ontologies and semantic tools for developing interoperable gene regulation knowledge models, which should support data annotation. In this work, we study how such knowledge models can be generated from cartoons of gene regulation scenarios. The proposed method consists of generating descriptions in natural language of the cartoons; extracting the entities from the texts; finding those entities in existing ontologies to reuse as much content as possible, especially from well known and maintained ontologies such as the Gene Ontology, the Sequence Ontology, the Relations Ontology and ChEBI; and implementation of the knowledge models. The models have been implemented using Protege, a general ontology editor, and Noctua, the tool developed by the Gene Ontology Consortium for the development of causal activity models to capture more comprehensive annotations of genes and link their activities in a causal framework for Gene Ontology Annotations. We applied the method to two gene regulation scenarios and illustrate how to apply the models generated to support the annotation of data from research articles.

The Sequence Ontology (SO) is a structured, controlled vocabulary that provides terms and definitions for genomic annotation. The Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) initiative has gathered input from many groups of researchers, including the SO, the Gene Ontology (GO), and gene regulation experts, with the goal of curating information about how gene expression is regulated at the molecular level. Here we discuss recent updates to the SO reflecting current knowledge. We have developed more accurate human-readable terms (also known as classes), including new definitions, and relationships related to the expression of genes. New findings continue to give us insight into the biology of gene regulation, including the order of events, and participants in those events. These updates to the SO support logical reasoning with the current understanding of gene expression regulation at the molecular level.

Transcription plays a central role in defining the identity and functionalities of cells, as well as in their responses to changes in the cellular environment. The Gene Ontology (GO) provides a rigorously defined set of concepts that describe the functions of gene products. A GO annotation is a statement about the function of a particular gene product, represented as an association between a gene product and the biological concept a GO term defines. Critically, each GO annotation is based on traceable scientific evidence. Here, we describe the different GO terms that are associated with proteins involved in transcription and its regulation, focusing on the standard of evidence required to support these associations. This article is intended to help users of GO annotations understand how to interpret the annotations and can contribute to the consistency of GO annotations. We distinguish between three classes of activities involved in transcription or directly regulating it - general transcription factors, DNA-binding transcription factors, and transcription co-regulators.

Motivation A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called ‘causal interaction’ takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g. gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources. Results Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources. Availability and implementation The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST Supplementary information Supplementary data are available at Bioinformatics online.

BackgroundThe past decade has seen the rise of omics data for the understanding of biological systems in health and disease. This wealth of information includes protein-protein interaction (PPI) data derived from both low- and high-throughput assays, which are curated into multiple databases that capture the extent of available information from the peer-reviewed literature. Although these curation efforts are extremely useful, reliably downloading and integrating PPI data from the variety of available repositories is challenging and time consuming.MethodsWe here present a novel user-friendly web-resource called PINOT (Protein Interaction Network Online Tool; available athttp://www.reading.ac.uk/bioinf/PINOT/PINOT_form.html) to optimise the collection and processing of PPI data from IMEx consortium associated repositories (members and observers) and WormBase, for constructing, respectively, human andCaenorhabditis elegansPPI networks.ResultsUsers submit a query containing a list of proteins of interest for which PINOT extracts data describing PPIs. At every query submission PPI data are downloaded, merged and quality assessed. Then each PPI is confidence scored based on the number of distinct methods used for interaction detection and the number of publications that report the specific interaction. Examples of how PINOT can be applied are provided to highlight the performance, ease of use and potential utility of this tool.ConclusionsPINOT is a tool that allows users to survey the curated literature, extracting PPI data in relation to a list of proteins of interest. PINOT extracts a similar numbers of PPIs as other, analogous, tools and incorporates a set of innovative features. PINOT is able to process large queries, it downloads human PPIs live through PSICQUIC and it applies quality control filters on the downloaded PPI data (i.e. removing the need for manual inspection by the user). PINOT provides the user with information on detection methods and publication history for each downloaded interaction data entry and outputs the results in a table format that can be straightforwardly further customised and/or directly uploaded into network visualization software.

Autophagy is a fundamental cellular process that is well conserved among eukaryotes. It is one of the strategies that cells use to catabolize substances in a controlled way. Autophagy is used for recycling cellular components, responding to cellular stresses and ridding cells of foreign material. Perturbations in autophagy have been implicated in a number of pathological conditions such as neurodegeneration, cardiac disease and cancer. The growing knowledge about autophagic mechanisms needs to be collected in a computable and shareable format to allow its use in data representation and interpretation. The Gene Ontology (GO) is a freely available resource that describes how and where gene products function in biological systems. It consists of 3 interrelated structured vocabularies that outline what gene products do at the biochemical level, where they act in a cell and the overall biological objectives to which their actions contribute. It also consists of ‘annotations’ that associate gene products with the terms. Here we describe how we represent autophagy in GO, how we create and define terms relevant to autophagy researchers and how we interrelate those terms to generate a coherent view of the process, therefore allowing an interoperable description of its biological aspects. We also describe how annotation of gene products with GO terms improves data analysis and interpretation, hence bringing a significant benefit to this field of study.

DNA-binding transcription factors recognise genomic addresses, specific sequence motifs in gene regulatory regions, to control gene transcription. A complete and reliable catalogue of all DNA-binding transcription factors is key to investigating the delicate balance of gene regulation in response to environmental and developmental stimuli. The need for such a catalogue of proteins is demonstrated by the many lists of DNA-binding transcription factors that have been produced over the past decade.The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) Consortium brought together experts in the field of transcription with the aim of providing high quality and interoperable gene regulatory data. The Gene Ontology (GO) Consortium provides strict definitions for gene product function, including factors that regulate transcription. The collaboration between the GREEKC and GO Consortia has enabled the application of those definitions to produce a new curated catalogue of human DNA-binding transcription factors, that can be accessed at https://www.ebi.ac.uk/QuickGO/targetset/dbTF.In addition, this curation effort has led to the GO annotation of almost sixty thousand DNA-binding transcription factors in over a hundred species. Thus, this work will aid researchers investigating the regulation of transcription in both biomedical and basic science.

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.

[Background]: Systems biologists study interaction data to understand the behaviour of whole cell systems, and their environment, at a molecular level. In order to effectively achieve this goal, it is critical that researchers have high quality interaction datasets available to them, in a standard data format, and also a suite of tools with which to analyse such data and form experimentally testable hypotheses from them. The PSI-MI XML standard interchange format was initially published in 2004, and expanded in 2007 to enable the download and interchange of molecular interaction data. PSI-XML2.5 was designed to describe experimental data and to date has fulfilled this basic requirement. However, new use cases have arisen that the format cannot properly accommodate. These include data abstracted from more than one publication such as allosteric/cooperative interactions and protein complexes, dynamic interactions and the need to link kinetic and affinity data to specific mutational changes. [Results]: The Molecular Interaction workgroup of the HUPO-PSI has extended the existing, well-used XML interchange format for molecular interaction data to meet new use cases and enable the capture of new data types, following extensive community consultation. PSI-MI XML3.0 expands the capabilities of the format beyond simple experimental data, with a concomitant update of the tool suite which serves this format. The format has been implemented by key data producers such as the International Molecular Exchange (IMEx) Consortium of protein interaction databases and the Complex Portal. [Conclusions]: PSI-MI XML3.0 has been developed by the data producers, data users, tool developers and database providers who constitute the PSI-MI workgroup. This group now actively supports PSI-MI XML2.5 as the main interchange format for experimental data, PSI-MI XML3.0 which additionally handles more complex data types, and the simpler, tab-delimited MITAB2.5, 2.6 and 2.7 for rapid parsing and download., MD, MK, AS, JS, JH and YY were funded by BBSRC MIDAS grant (BB/L024179/1), this grant provided the funds for the design of PSI-MI XML3.0 and its implementation by the IntAct database. KVR was funded by European Commission (FP7-HEALTH-2009-242129 SyBoSS), LL by ELIXIR-IIB, the Italian Node of the European ELIXIR infrastructure, IJ was funded by Ontario Research Fund (GL2–01-030, #34876) and Canada Research Chair Program (#225404), DJL by EMBL Australia and FP7-HEALTH2011-278568, SRB and NTM by Fondation pour la Recherche Médicale (grant n° DBI20141231336) and by the French Institute of Bioinformatics (2015 call), NHC and RCL by British Heart Foundation (RG/13/5/30112), GC by the European Research Council (Grant Agreement 32274), CC was funded by the Wellcome Trust (grant numbers 103139, 063412, 203149) and LS by National Institutes of Health.

The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the ‘GO ribbon’ widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page. This is an open access article distributed under the terms of the Creative Commons CC BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The major histocompatibility complex (MHC) is the most important region in the vertebrate genome with respect to infection and autoimmunity, and is crucial in adaptive and innate immunity. Decades of biomedical research have revealed many MHC genes that are duplicated, polymorphic and associated with more diseases than any other region of the human genome. The recent completion of several large-scale studies offers the opportunity to assimilate the latest data into an integrated gene map of the extended human MHC. Here, we present this map and review its content in relation to paralogy, polymorphism, immune function and disease.

BACKGROUND: The Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and clinical data. The core data structure is the annotation, an association between a gene product and a term from one of the three ontologies comprising the GO. Historically, it has not been possible to provide additional information about the context of a GO term, such as the target gene or the location of a molecular function. This has limited the specificity of knowledge that can be expressed by GO annotations. RESULTS: The GO Consortium has introduced annotation extensions that enable manually curated GO annotations to capture additional contextual details. Extensions represent effector-target relationships such as localization dependencies, substrates of protein modifiers and regulation targets of signaling pathways and transcription factors as well as spatial and temporal aspects of processes such as cell or tissue type or developmental stage. We describe the content and structure of annotation extensions, provide examples, and summarize the current usage of annotation extensions. CONCLUSIONS: The additional contextual information captured by annotation extensions improves the utility of functional annotation by representing dependencies between annotations to terms in the different ontologies of GO, external ontologies, or an organism's gene products. These enhanced annotations can also support sophisticated queries and reasoning, and will provide curated, directional links between many gene products to support pathway and network reconstruction.

X-linked agammaglobulinemia, a B cell immunodeficiency, is caused by mutations in the Bruton's tyrosine kinase (Btk) gene. The absence of a functional Btk protein leads to a failure of B cell differentiation and antibody production. B cell receptor stimulation leads to the phosphorylation of the Btk protein and it is, therefore, likely that Btk is involved in B cell receptor signaling. As a nonreceptor tyrosine kinase, Btk is likely to interact with several proteins within the context of a signal transduction pathway. To understand such interactions, we have generated glutathione S-transferase fusion proteins corresponding to different domains of the human Btk protein. We have identified a 120-kD protein present in human B cells as being bound by the SH3 domain of Btk and which, after B cell receptor stimulation, is one of the major substrates of tyrosine phosphorylation. We have shown that this 120-kD protein is the protein product of c-cbl, a protooncogene, which is known to be phosphorylated in response to T cell receptor stimulation and to interact with several other tyrosine kinases. Association of the SH3 domain of Btk with p120cbl provides evidence for an analogous role for p120cbl in B cell signaling pathways. The p120cbl protein is the first identified ligand of the Btk SH3 domain.

To date, the nomenclature of mammalian genes encoding the numerous subunits and their many isoforms that comprise the family of vacuolar H(+)-ATPases has not been systematic, resulting in confusion both in the literature and among investigators. We present the official new system for these genes, approved by both Human and Mouse Gene Nomenclature Committees.

We report the development of a relatively quick and simple method for the assessment of X inactivation status for carrier determination in families affected by X-linked agammaglobulinemia (XLA). This method utilises an immunomagnetic separation technique for B cell purification and a polymerase chain reaction (PCR) based assay for the determination of methylation status at the androgen receptor (AR) gene locus to assess whether X inactivation is random or non-random at this locus. We report the results we have obtained using this assay to investigate females known to be carriers of various X-linked immunodeficiency disorders. In addition, we investigated four females from different families affected by XLA, two of whom were of unknown carrier status, and we discuss the results obtained with this and other X-inactivation assays. A similar assay has recently been described by Allen et al. (1992) and applied to members of one family affected by XLA.

The Gene Ontology (GO) is a collaborative effort that provides structured vocabularies for annotating the molecular function, biological role, and cellular location of gene products in a highly systematic way and in a species-neutral manner with the aim of unifying the representation of gene function across different organisms. Each contributing member of the GO Consortium independently associates GO terms to gene products from the organism(s) they are annotating. Here we introduce the Reference Genome project, which brings together those independent efforts into a unified framework based on the evolutionary relationships between genes in these different organisms. The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project, and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms. In addition, the project has several important incidental benefits, such as increasing annotation consistency across genome databases, and providing important improvements to the GO's logical structure and biological content., Author Summary Biological research is increasingly dependent on the availability of well-structured representations of biological data with detailed, accurate descriptions provided by the curators of the data repositories. The Reference Genome project's goal is to provide comprehensive functional annotation for the genomes of human as well as eleven organisms that are important models in biomedical research. To achieve this, we have developed an approach that superposes experimentally-based annotations onto the leaves of phylogenetic trees and then we manually annotate the function of the common ancestors, predicated on the assumption that the ancestors possessed the experimentally determined functions that are held in common at these leaves, and that these functions are likely to be conserved in all other descendents of each family.

A cDNA was isolated from a T cell library using an oligonucleotide probe corresponding to a sequence conserved in proteins with multiple zinc fingers of the C2H2 type. The predicted protein structure of this cDNA (ZNF43) showed that it contained 22 of the Krüppel type of zinc finger motifs in tandem. The amino acid sequence was strongly conserved between each of the finger domains of this cDNA, except for variable residue positions within the putative DNA binding site. Within the zinc finger domain the amino acid sequence of the four zinc fingers 6 to 9 was very similar to the amino acid sequence of fingers 10 to 13, the DNA sequence bound by this group of eight fingers may include a short repeat. Southern blotting showed that ZNF43 was one of a closely related family of proteins with 10 to 20 members. The members of the ZNF43 family did not appear to be clustered at the chromosomal level. The transcription of many members of this gene family was increased in lymphoid cell lines. After in vitro induced terminal differentiation of the human HL60 cell line the expression of the ZNF43 family was reduced. The expression of the ZNF43 gene was mainly limited to T and B cell lines. The gene was differentially spliced and different cell lines expressed different combinations of transcripts.

Until recently, biologists have concentrated on studying specifi c pathways or individual molecules as an approach to unravelling the intricate details of cellular events. However recent advances in high-throughput proteomic methodologies have made it possible to profi le the global compositions of entire tissues, organelles or interactomes at specifi c time points or under particular developmental or disease states. While such methods provide researchers with a greater understanding of large-scale, complex biological changes, it has necessitated an increasing reliance on standardised annotation to link results to known biological activities. The Gene Ontology (GO) Consortium provides standardised functional annotations, which support high-throughput analyses and systems biology by enabling genes or proteins to be classifi ed and grouped according to their function, involvement in a particular process or subcellular location, providing researchers with an indication of underlying mechanisms behind a certain phenotype. In this article we provide an overview of the Gene Ontology, its uses and the software available for the analysis of proteomic data. Many of the examples presented in this paper relate to cardiovascular studies, the focus of a new GO annotation initiative. Abbreviations

Gene Ontology (GO) vocabularies are an established standard for linking functional information to genes and gene products (www.geneontology.org/). A recent collaboration between University College London and the European Bioinformatics Institute is providing GO annotation to human cardiovascular-associated genes (http://www.ucl.ac.uk/medicine/cardiovascular-genetics/geneontology.html). This report outlines the aims of this collaboration and summarizes how the cardiovascular community can help improve the quality and quantity of GO annotations. This new initiative is funded by the British Heart Foundation and fully supported by the GO Consortium.

The plethora of names given to S100 proteins resulted in considerable confusion. Here we present the official and updated nomenclature of this protein family, approved by the HGNC (HUGO gene nomenclature committee) and ECS (European Calcium Society).

X-linked agammaglobulinemia is a primary inherited immunodeficiency resulting in a lack of or dramatic reduction in the number of mature B lymphocytes and, thus, greatly reduced levels of serum immunoglobulin. The defect results from mutations in the gene for Bruton's tyrosine kinase (Btk). Using rabbit antisera generated against Btk, we have demonstrated an increase in the level of in vitro kinase activity present in anti-Btk immunoprecipitates from B cells following stimulation with anti-immunoglobulin antibody. This increase in immune complex kinase activity is detectable 1 to 2 min following stimulation and remains elevated for over 30 min. A similar increase was not seen with two late pre-B cell lines investigated in the same way. This stimulation of activity may suggest a role for Btk in signalling through the B cell receptor or associated proteins, in mature B cells.

We have produced physical maps of the proximal part of Xq22, containing the Bruton's tyrosine kinase (BTK) and alpha-galactosidase A (GLA) gene loci, using long range physical mapping techniques and yeast artificial chromosomes (YACs). These maps reveal five previously unidentified CpG islands which could indicate the presence of other genes in this region.

Defects in the gene encoding Bruton's tyrosine kinase (Btk), normally expressed in B cells, cause X-linked agammaglobulinemia (XLA). The phenotype of XLA is characterized by a lack of circulating B cells and immunoglobulin. It has been suggested that B cell maturation from the pre-B cell stage to more mature stages is dependent on the appropriate expression of this gene. The Btk mRNA is expressed in B cells and myeloid cells, but protein expression in relation to B cell maturation has not been determined. Moreover, expression of the Btk protein has so far only been investigated in human Epstein-Barr virus-transformed B cell lines, and in murine splenocytes and B cell lines. We have developed an antiserum which recognizes the human Btk protein and shown that normal human tonsillar B cells, peripheral blood monocytes and myeloid cells express the protein, whereas tonsil-derived T cells do not. We also show that the protein is present in early and mature human B cell lines, but is absent in terminally differentiated plasma cell lines. Furthermore, expression is reduced or absent in three B lineage cell lines derived from two patients with defined genetic mutations in Btk and suffering from XLA.

Mutations within the btk gene have recently been shown to cause X-linked agammaglobulinaemia (XLA). Altered patterns of DNA restriction fragments are seen by Southern blot analysis of DNA from affected patients with deletions in the btk gene. We have identified seven affected families in which altered restriction fragments can be used to diagnose and confirm the carrier status of female relatives of affected boys and in prenatal diagnosis.

The gene defective in X-linked agammaglobulinemia (XLA) has recently been isolated and identified as btk, a non-receptor protein tyrosine kinase. We have utilized the technique of single strand conformation polymorphism (SSCP) analysis for the btk gene to identify mutations in XLA patients. The btk gene in affected boys from 10 families was analysed and mutations were identified in eight cases; seven of these were point mutations and one was a small insertion. The mutations were found throughout the gene coding region. Six of the patients have classical XLA and two have less severe forms of the disease. We have also identified a polymorphism at nucleotide position 2031. This technique will allow us to provide more accurate diagnoses of the disease and to determine the nature of the functional defects in the btk gene in these families.

Genetic linkage analysis has been instrumental in mapping the gene for X-linked agammaglobulinemia (XLA) to the proximal long arm of the human X chromosome, to Xq22. Due to the relative rarity of this disease the localization of the gene within Xq22 has remained imprecise. We have investigated twenty-nine families affected by XLA and have found no recombinants with the DXS178 locus in over 30 informative meioses. DXS178 is now the most reliable and informative locus for use in pre-natal diagnosis and carrier detection of XLA. In addition, we have identified new closely linked proximal and distal flanking markers for XLA, DXS442 and DXS101, respectively. These loci are separated by 2cM, considerably reducing the extent of DNA within which the XLA locus can be contained. This will open up the way for more directed positional cloning efforts for the isolation of the XLA gene.