C-reactive protein binds to alphaIIbbeta3.

C-reactive protein binds to alphaIIbbeta3

Elucidating the role of Staphylococcus epidermidis serine-aspartate repeat protein G in platelet activation.

BACKGROUND: Staphylococcus epidermidis is a commensal of the human skin that has been implicated in infective endocarditis and infections involving implanted medical devices. S. epidermidis induces platelet aggregation by an unknown mechanism. The fibrinogen-binding protein serine-aspartate repeat protein G (SdrG) is present in 67-91% of clinical strains. OBJECTIVES: To determine whether SdrG plays a role in platelet activation, and if so to investigate the role of fibrinogen in this mechanism. METHODS: SdrG was expressed in a surrogate host, Lactococcus lactis, in order to investigate its role in the absence of other staphylococcal components. Platelet adhesion and platelet aggregation assays were employed. RESULTS: L. lactis expressing SdrG stimulated platelet aggregation (lag time: 2.9 +/- 0.5 min), whereas the L. lactis control did not. L. lactis SdrG-induced aggregation was inhibited by alpha(IIb)beta3 antagonists and aspirin. Aggregation was dependent on both fibrinogen and IgG, and the platelet IgG receptor FcgammaRIIa. Preincubation of the bacteria with Bbeta-chain fibrinopeptide inhibited aggregation (delaying the lag time six-fold), suggesting that fibrinogen acts as a bridging molecule. Platelets adhered to L. lactis SdrG in the absence of fibrinogen. Adhesion was inhibited by alpha(IIb)beta3 antagonists, suggesting that this direct interaction involves alpha(IIb)beta3. Investigation using purified fragments of SdrG revealed a direct interaction with the B-domains. Adhesion to the A-domain involved both a fibrinogen and an IgG bridge. CONCLUSION: SdrG alone is sufficient to support platelet adhesion and aggregation through both direct and indirect mechanisms

Genomes2Drugs: Identifies Target Proteins and Lead Drugs from Proteome Data

Background: Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials. Methodology/Principal Findings: To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins. Conclusions/Significance: Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines

Integrated computational prediction and experimental validation identifies promiscuous T cell epitopes in the proteome of Mycobacterium bovis

The discovery of novel antigens is an essential requirement in devising new diagnostics or vaccines for use in control programmes against human tuberculosis (TB) and bovine tuberculosis (bTB). Identification of potential epitopes recognised by CD4+ T cells requires prediction of peptide binding to MHC class-II, an obligatory prerequisite for T cell recognition. To comprehensively prioritise potential MHC-II-binding epitopes from Mycobacterium bovis, the agent of bTB and zoonotic TB in humans, we integrated three binding prediction methods with the M. bovisproteome using a subset of human HLA alleles to approximate the binding of epitope-containing peptides to the bovine MHC class II molecule BoLA-DRB3. Two parallel strategies were then applied to filter the resulting set of binders: identification of the top-scoring binders or clusters of binders. Our approach was tested experimentally by assessing the capacity of predicted promiscuous peptides to drive interferon-γ secretion from T cells of M. bovis infected cattle. Thus, 376 20-mer peptides, were synthesised (270 predicted epitopes, 94 random peptides with low predictive scores and 12 positive controls of known epitopes). The results of this validation demonstrated significant enrichment (>24 %) of promiscuously recognised peptides predicted in our selection strategies, compared with randomly selected peptides with low prediction scores. Our strategy offers a general approach to the identification of promiscuous epitopes tailored to target populations where there is limited knowledge of MHC allelic diversity

TIN-a combinatorial compound collection of synthetically feasible multicomponent synthesis products.

The synthetic feasibility of any compound library used for virtual screening is critical to the drug discovery process. TIN, a recursive acronym for \u27TIN Is Not commercial\u27, is a virtual combinatorial database enumeration of diversity-orientated multicomponent syntheses (MCR). Using a \u27one-pot\u27 synthetic technique, 12 unique small molecule scaffolds were developed, predominantly styrylisoxazoles and bis-acetylenic ketones, with extensive derivatization potential. Importantly, the scaffolds were accessible in a single operation from commercially available sources containing R-groups which were then linked combinatorially. This resulted in a combinatorial database of over 28 million product structures, each of which is synthetically feasible. These structures can be accessed through a free Web-based 2D structure search engine or downloaded in SMILES, MOL2, and SDF formats. Subsets include a 10% diversity subset, a drug-like subset, and a lead-like subset that are also freely available for download and virtual screening ( http://mmg.rcsi.ie:8080/tin )

Writing impact case studies: a comparative study of high-scoring and low-scoring case studies from REF2014

This paper reports on two studies that used qualitative thematic and quantitative linguistic analysis, respectively, to assess the content and language of the largest ever sample of graded research impact case studies, from the UK Research Excellence Framework 2014 (REF). The paper provides the first empirical evidence across disciplinary main panels of statistically significant linguistic differences between high- versus low-scoring case studies, suggesting that implicit rules linked to written style may have contributed to scores alongside the published criteria on the significance, reach and attribution of impact. High-scoring case studies were more likely to provide specific and high-magnitude articulations of significance and reach than low-scoring cases. High-scoring case studies contained attributional phrases which were more likely to attribute research and/or pathways to impact, and they were written more coherently (containing more explicit causal connections between ideas and more logical connectives) than low-scoring cases. High-scoring case studies appear to have conformed to a distinctive new genre of writing, which was clear and direct, and often simplified in its representation of causality between research and impact, and less likely to contain expressions of uncertainty than typically associated with academic writing. High-scoring case studies in two Main Panels were significantly easier to read than low-scoring cases on the Flesch Reading Ease measure, although both high-scoring and low-scoring cases tended to be of “graduate” reading difficulty. The findings of our work enable impact case study authors to better understand the genre and make content and language choices that communicate their impact as effectively as possible. While directly relevant to the assessment of impact in the UK’s Research Excellence Framework, the work also provides insights of relevance to institutions internationally who are designing evaluation frameworks for research impact

Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features

The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom's 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia

Sustaining remission of psychotic depression: Rationale, design and methodology of STOP-PD II

Background: Psychotic depression (PD) is a severe disabling disorder with considerable morbidity and mortality. Electroconvulsive therapy and pharmacotherapy are each efficacious in the treatment of PD. Expert guidelines recommend the combination of antidepressant and antipsychotic medications in the acute pharmacologic treatment of PD. However, little is known about the continuation treatment of PD. Of particular concern, it is not known whether antipsychotic medication needs to be continued once an episode of PD responds to pharmacotherapy. This issue has profound clinical importance. On the one hand, the unnecessary continuation of antipsychotic medication exposes a patient to adverse effects, such as weight gain and metabolic disturbance. On the other hand, premature discontinuation of antipsychotic medication has the potential risk of early relapse of a severe disorder.Methods/design: The primary goal of this multicenter randomized placebo-controlled trial is to assess the risks and benefits of continuing antipsychotic medication in persons with PD once the episode of depression has responded to treatment with an antidepressant and an antipsychotic. Secondary goals are to examine age and genetic polymorphisms as predictors or moderators of treatment variability, potentially leading to more personalized treatment of PD. Individuals aged 18-85 years with unipolar psychotic depression receive up to 12 weeks of open-label treatment with sertraline and olanzapine. Participants who achieve remission of psychosis and remission/near-remission of depressive symptoms continue with 8 weeks of open-label treatment to ensure stability of remission. Participants with stability of remission are then randomized to 36 weeks of double-blind treatment with either sertraline and olanzapine or sertraline and placebo. Relapse is the primary outcome. Metabolic changes are a secondary outcome.Discussion: This trial will provide clinicians with much-needed evidence to guide the continuation and maintenance treatment of one of the most disabling and lethal of psychiatric disorders.Trial registration and URL: NCT: NCT01427608. © 2013 Flint et al; licensee BioMed Central Ltd