Identification and Characterisation of a Novel RNA-Binding Partner for the US11 Protein of HSV-1

The US11 protein herpes simplex virus type-1 is a small, highly basic phosphoprotein, which localises to the nucleoli of infected cells. Although this non-essential protein has a number of activities attributed to it, its actual role during infection is unknown. The two major functions that have been associated with US 11 are the antagonism of host-mediated translational shut-off and the ability to bind RNA, the latter of which is the focus of this thesis. To date, US11 has been shown to bind five RNAs; the antisense transcript of the US11 5' UTR, a truncated transcript of the UL34 gene, termed A34, the Rev-response element of human immunodeficiency virus type-1, the Rex-response element of human T-cell lymphotrophic virus type-1 and rRNA derived from the 60S ribosome subunit. The function of the RNA-binding activity of US1 1 during infection is as yet unclear. The RNAs bound by USl 1 appear, at least superficially to possess little in common, both in terms of their sequence and the biological influence of US11 on them. US11 may potentially interact with other RNAs in infected cells and the aim of this work was to attempt to test this hypothesis. Firstly, methods in which US11-RNA complexes could be isolated from infected cell lysates were examined. Using a GST-US11 fusion protein to pull out interacting RNAs from lysates it proved possible to isolate the known binder, ?34 RNA. Secondly, a reverse transcription-polymerase chain reaction (RT-PCR) method was developed to allow the amplification of sequences pulled out with GST-US11. This lead to the identification of a 585nt sequence that was present in three HSV-13' co-terminal genes, UL12, UL13 and UL14, which encode alkahne nuclease, a protein kinase and a nuclear protein of unknown function, respectively. The interaction between this RNA, termed 12/14 RNA, and US 11 was examined in vitro. The binding was found to be sequence-specific and mediated by the C-terminal domain of US11. US11 bound 12/14 RNA in a multimeric fashion, for which the N-terminal domain was not required. The affinity of the C-terminal domain of US11 for 12/14 RNA is less that that for A34 RNA, indicating that these RNAs may be bound in a slightly different fashion. The binding site for US11 was mapped to a 232nt region which encompasses 108nt of the UL12 5' UTR, 225nt of the 3' ORP and 7nt of the 3' UTR of UL13 and lies within the 3' UTR of UL14. The interaction between this shorter RNA and US11 was examined and the binding found to be dependent on secondary structure. The influence of US 11 on the expression of UL12, UL13 and UL14 was examined in infected cells. In the absence of US11 the levels of UL12 and UL14 remain unchanged, but the expression of UL13 is elevated at early times post-infection. It therefore appears that US11 can down-regulate the expression of the UL13 protein kinase at early times during infection

The DNA polymerases of Drosophila melanogaster.

DNA synthesis during replication or repair is a fundamental cellular process that is catalyzed by a set of evolutionary conserved polymerases. Despite a large body of research, the DNA polymerases of Drosophila melanogaster have not yet been systematically reviewed, leading to inconsistencies in their nomenclature, shortcomings in their functional (Gene Ontology, GO) annotations and an under-appreciation of the extent of their characterization. Here, we describe the complete set of DNA polymerases in D. melanogaster, applying nomenclature already in widespread use in other species, and improving their functional annotation. A total of 19 genes encode the proteins comprising three replicative polymerases (alpha-primase, delta, epsilon), five translesion/repair polymerases (zeta, eta, iota, Rev1, theta) and the mitochondrial polymerase (gamma). We also provide an overview of the biochemical and genetic characterization of these factors in D. melanogaster. This work, together with the incorporation of the improved nomenclature and GO annotation into key biological databases, including FlyBase and UniProtKB, will greatly facilitate access to information about these important proteins

Gene Ontology curation of the blood-brain barrier to improve the analysis of Alzheimer's and other neurological diseases.

Funder: National Institute for Health Research University College London Hospitals Biomedical Research CentreThe role of the blood-brain barrier (BBB) in Alzheimer's and other neurodegenerative diseases is still the subject of many studies. However, those studies using high-throughput methods have been compromised by the lack of Gene Ontology (GO) annotations describing the role of proteins in the normal function of the BBB. The GO Consortium provides a gold-standard bioinformatics resource used for analysis and interpretation of large biomedical data sets. However, the GO is also used by other research communities and, therefore, must meet a variety of demands on the breadth and depth of information that is provided. To meet the needs of the Alzheimer's research community we have focused on the GO annotation of the BBB, with over 100 transport or junctional proteins prioritized for annotation. This project has led to a substantial increase in the number of human proteins associated with BBB-relevant GO terms as well as more comprehensive annotation of these proteins in many other processes. Furthermore, data describing the microRNAs that regulate the expression of these priority proteins have also been curated. Thus, this project has increased both the breadth and depth of annotation for these prioritized BBB proteins. Database URLhttps://www.ebi.ac.uk/QuickGO/

Exploring FlyBase Data Using QuickSearch.

FlyBase (flybase.org) is the primary online database of genetic, genomic, and functional information about Drosophila species, with a major focus on the model organism Drosophila melanogaster. The long and rich history of Drosophila research, combined with recent surges in genomic-scale and high-throughput technologies, mean that FlyBase now houses a huge quantity of data. Researchers need to be able to rapidly and intuitively query these data, and the QuickSearch tool has been designed to meet these needs. This tool is conveniently located on the FlyBase homepage and is organized into a series of simple tabbed interfaces that cover the major data and annotation classes within the database. This unit describes the functionality of all aspects of the QuickSearch tool. With this knowledge, FlyBase users will be equipped to take full advantage of all QuickSearch features and thereby gain improved access to data relevant to their research. © 2016 by John Wiley & Sons, Inc

FlyBase 2.0: the next generation.

FlyBase (flybase.org) is a knowledge base that supports the community of researchers that use the fruit fly, Drosophila melanogaster, as a model organism. The FlyBase team curates and organizes a diverse array of genetic, molecular, genomic, and developmental information about Drosophila. At the beginning of 2018, 'FlyBase 2.0' was released with a significantly improved user interface and new tools. Among these important changes are a new organization of search results into interactive lists or tables (hitlists), enhanced reference lists, and new protein domain graphics. An important new data class called 'experimental tools' consolidates information on useful fly strains and other resources related to a specific gene, which significantly enhances the ability of the Drosophila researcher to design and carry out experiments. With the release of FlyBase 2.0, there has also been a restructuring of backend architecture and a continued development of application programming interfaces (APIs) for programmatic access to FlyBase data. In this review, we describe these major new features and functionalities of the FlyBase 2.0 site and how they support the use of Drosophila as a model organism for biological discovery and translational research

Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns.

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa

Representing kidney development using the gene ontology.

Gene Ontology (GO) provides dynamic controlled vocabularies to aid in the description of the functional biological attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). Here we describe collaboration between the renal biomedical research community and the GO Consortium to improve the quality and quantity of GO terms describing renal development. In the associated annotation activity, the new and revised terms were associated with gene products involved in renal development and function. This project resulted in a total of 522 GO terms being added to the ontology and the creation of approximately 9,600 kidney-related GO term associations to 940 UniProt Knowledgebase (UniProtKB) entries, covering 66 taxonomic groups. We demonstrate the impact of these improvements on the interpretation of GO term analyses performed on genes differentially expressed in kidney glomeruli affected by diabetic nephropathy. In summary, we have produced a resource that can be utilized in the interpretation of data from small- and large-scale experiments investigating molecular mechanisms of kidney function and development and thereby help towards alleviating renal disease

Annotation of gene product function from high-throughput studies using the Gene Ontology.

High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community

Annotation of gene product function from high-throughput studies using the Gene Ontology

High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community