Higher chylomicron remnants and LDL particle numbers associate with CD36 SNPs and DNA methylation sites that reduce CD36

Cluster of differentiation 36 (CD36) variants influence fasting lipids and risk of metabolic syndrome, but their impact on postprandial lipids, an independent risk factor for cardiovascular disease, is unclear. We determined the effects of SNPs within a ~410 kb region encompassing CD36 and its proximal and distal promoters on chylomicron (CM) remnants and LDL particles at fasting and at 3.5 and 6 h following a high-fat meal (Genetics of Lipid Lowering Drugs and Diet Network study, n = 1,117). Five promoter variants associated with CMs, four with delayed TG clearance and five with LDL particle number. To assess mechanisms underlying the associations, we queried expression quantitative trait loci, DNA methylation, and ChIP-seq datasets for adipose and heart tissues that function in postprandial lipid clearance. Several SNPs that associated with higher serum lipids correlated with lower adipose and heart CD36 mRNA and aligned to active motifs for PPARγ, a major CD36 regulator. The SNPs also associated with DNA methylation sites that related to reduced CD36 mRNA and higher serum lipids, but mixed-model analyses indicated that the SNPs and methylation independently influence CD36 mRNA. The findings support contributions of CD36 SNPs that reduce adipose and heart CD36 RNA expression to inter-individual variability of postprandial lipid metabolism and document changes in CD36 DNA methylation that influence both CD36 expression and lipids

Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome

<p>Abstract</p> <p>Background</p> <p>The chicken genome was sequenced because of its phylogenetic position as a non-mammalian vertebrate, its use as a biomedical model especially to study embryology and development, its role as a source of human disease organisms and its importance as the major source of animal derived food protein. However, genomic sequence data is, in itself, of limited value; generally it is not equivalent to understanding biological function. The benefit of having a genome sequence is that it provides a basis for functional genomics. However, the sequence data currently available is poorly structurally and functionally annotated and many genes do not have standard nomenclature assigned.</p> <p>Results</p> <p>We analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the <it>in vivo </it>expression of 7,809 computationally predicted proteins, including 30 chicken proteins that were only electronically predicted or hypothetical translations in human. To improve functional annotation (based on Gene Ontology), we mapped these identified proteins to their human and mouse orthologs and used this orthology to transfer Gene Ontology (GO) functional annotations to the chicken proteins. The 8,213 orthology-based GO annotations that we produced represent an 8% increase in currently available chicken GO annotations. Orthologous chicken products were also assigned standardized nomenclature based on current chicken nomenclature guidelines.</p> <p>Conclusion</p> <p>We demonstrate the utility of high-throughput expression proteomics for rapid experimental structural annotation of a newly sequenced eukaryote genome. These experimentally-supported predicted proteins were further annotated by assigning the proteins with standardized nomenclature and functional annotation. This method is widely applicable to a diverse range of species. Moreover, information from one genome can be used to improve the annotation of other genomes and inform gene prediction algorithms.</p

In patients with severe uncontrolled asthma, does knowledge of adherence and inhaler technique using electronic monitoring improve clinical decision making? A protocol for a randomised controlled trial

Introduction: Many patients with asthma remain poorly controlled despite the use of inhaled corticosteroids and long-acting beta agonists. Poor control may arise from inadequate adherence, incorrect inhaler technique or because the condition is refractory. Without having an objective assessment of adherence, clinicians may inadvertently add extra medication instead of addressing adherence. This study aims to assess if incorporating objectively recorded adherence from the Inhaler Compliance Assessment (INCA) device and lung function into clinical decision making provides more cost-effective prescribing and improves outcomes. Methods and analysis: This prospective, randomised, multicentre study will compare the impact of using information on adherence to influence asthma treatment. Patients with severe uncontrolled asthma will be included. Data on adherence, inhaler technique and electronically recorded peak expiratory flow rate will be used to promote adherence and guide a clinical decision protocol to guide management in the active group. The control group will receive standard inhaler and adherence education. Medications will be adjusted using a protocol based on Global Initiativefor Asthma (GINA) recommendations. The primary outcome is the between-group difference in the proportion of patients who have refractory disease and are prescribed appropriate medications at the end of 32 weeks. A co-primary outcome is the difference between groups in the rate of adherence to salmeterol/fluticasone inhaler over the last 12 weeks. Secondary outcomes include changes in symptoms, lung function, type-2 cytokine biomarkers and clinical outcomes between both groups. Cost-effectiveness and cost-utility analyses of the INCA device intervention will be performed. The economic impact of a national implementation of the INCA-SUN programme will be evaluated. Ethics and dissemination:The results of the study will be published as a manuscript in peer-reviewed journals. The study has been approved by the ethics committees in the five participating hospitals. Trial registration NCT02307669; Pre-results

ArrayIDer: automated structural re-annotation pipeline for DNA microarrays

<p>Abstract</p> <p>Background</p> <p>Systems biology modeling from microarray data requires the most contemporary structural and functional array annotation. However, microarray annotations, especially for non-commercial, non-traditional biomedical model organisms, are often dated. In addition, most microarray analysis tools do not readily accept EST clone names, which are abundantly represented on arrays. Manual re-annotation of microarrays is impracticable and so we developed a computational re-annotation tool (<it>ArrayIDer</it>) to retrieve the most recent accession mapping files from public databases based on EST clone names or accessions and rapidly generate database accessions for entire microarrays.</p> <p>Results</p> <p>We utilized the Fred Hutchinson Cancer Research Centre 13K chicken cDNA array – a widely-used non-commercial chicken microarray – to demonstrate the principle that <it>ArrayIDer </it>could markedly improve annotation. We structurally re-annotated 55% of the entire array. Moreover, we decreased non-chicken functional annotations by 2 fold. One beneficial consequence of our re-annotation was to identify 290 pseudogenes, of which 66 were previously incorrectly annotated.</p> <p>Conclusion</p> <p><it>ArrayIDer </it>allows rapid automated structural re-annotation of entire arrays and provides multiple accession types for use in subsequent functional analysis. This information is especially valuable for systems biology modeling in the non-traditional biomedical model organisms.</p

Structural and functional-annotation of an equine whole genome oligoarray

<p>Abstract</p> <p>Background</p> <p>The horse genome is sequenced, allowing equine researchers to use high-throughput functional genomics platforms such as microarrays; next-generation sequencing for gene expression and proteomics. However, for researchers to derive value from these functional genomics datasets, they must be able to model this data in biologically relevant ways; to do so requires that the equine genome be more fully annotated. There are two interrelated types of genomic annotation: structural and functional. Structural annotation is delineating and demarcating the genomic elements (such as genes, promoters, and regulatory elements). Functional annotation is assigning function to structural elements. The Gene Ontology (GO) is the <it>de facto </it>standard for functional annotation, and is routinely used as a basis for modelling and hypothesis testing, large functional genomics datasets.</p> <p>Results</p> <p>An Equine Whole Genome Oligonucleotide (EWGO) array with 21,351 elements was developed at Texas A&M University. This 70-mer oligoarray was designed using the approximately 7× assembled and annotated sequence of the equine genome to be one of the most comprehensive arrays available for expressed equine sequences. To assist researchers in determining the biological meaning of data derived from this array, we have structurally annotated it by mapping the elements to multiple database accessions, including UniProtKB, Entrez Gene, NRPD (Non-Redundant Protein Database) and UniGene. We next provided GO functional annotations for the gene transcripts represented on this array. Overall, we GO annotated 14,531 gene products (68.1% of the gene products represented on the EWGO array) with 57,912 annotations. GAQ (GO Annotation Quality) scores were calculated for this array both before and after we added GO annotation. The additional annotations improved the <it>meanGAQ </it>score 16-fold. This data is publicly available at <it>AgBase </it><url>http://www.agbase.msstate.edu/</url>.</p> <p>Conclusion</p> <p>Providing additional information about the public databases which link to the gene products represented on the array allows users more flexibility when using gene expression modelling and hypothesis-testing computational tools. Moreover, since different databases provide different types of information, users have access to multiple data sources. In addition, our GO annotation underpins functional modelling for most gene expression analysis tools and enables equine researchers to model large lists of differentially expressed transcripts in biologically relevant ways.</p

Evaluating rehabilitation following lumbar fusion surgery (REFS): study protocol for a randomised controlled trial

BACKGROUND: The rate of lumbar fusion surgery (LFS) is increasing. Clinical recovery often lags technical outcome. Approximately 40% of patients undergoing LFS rate themselves as symptomatically unchanged or worse following surgery. There is little research describing rehabilitation following LFS with no clear consensus as to what constitutes the optimum strategy. It is important to develop appropriate rehabilitation strategies to help patients manage pain and recover lost function following LFS. METHODS/DESIGN: The study design is a randomised controlled feasibility trial exploring the feasibility of providing a complex multi-method rehabilitation intervention 3 months following LFS. The rehabilitation protocol that we have developed involves small participant groups of therapist led structured education utilising principles of cognitive behavioral therapy (CBT), progressive, individualised exercise and peer support. Participants will be randomly allocated to either usual care (UC) or the rehabilitation group (RG). We will recruit 50 subjects, planning to undergo LFS, over 30 months. Following LFS all participants will experience normal care for the first 3 months. Subsequent to a satisfactory 3 month surgical review they will commence their allocated post-operative treatment (RG or UC). Data collection will occur at baseline (pre-operatively), 3, 6 and 12 months post-operatively. Primary outcomes will include an assessment of feasibility factors (including recruitment and compliance). Secondary outcomes will evaluate the acceptability and characteristics of a limited cluster of quantitative measures including the Oswestry Disability Index (ODI) and an aggregated assessment of physical function (walking 50 yards, ascend/descend a flight of stairs). A nested qualitative study will evaluate participants' experiences. DISCUSSION: This study will evaluate the feasibility of providing complex, structured rehabilitation in small groups 3 months following technically successful LFS. We will identify strengths and weakness of the proposed protocol and the usefulness and characteristics of the planned outcome measures. This will help shape the development of rehabilitation strategies and inform future work aimed at evaluating clinical efficacy. TRIAL REGISTRATION: ISRCTN60891364, 10/07/2014

AgBase: a unified resource for functional analysis in agriculture

Analysis of functional genomics (transcriptomics and proteomics) datasets is hindered in agricultural species because agricultural genome sequences have relatively poor structural and functional annotation. To facilitate systems biology in these species we have established the curated, web-accessible, public resource ‘AgBase’ (). We have improved the structural annotation of agriculturally important genomes by experimentally confirming the in vivo expression of electronically predicted proteins and by proteogenomic mapping. Proteogenomic data are available from the AgBase proteogenomics link. We contribute Gene Ontology (GO) annotations and we provide a two tier system of GO annotations for users. The ‘GO Consortium’ gene association file contains the most rigorous GO annotations based solely on experimental data. The ‘Community’ gene association file contains GO annotations based on expert community knowledge (annotations based directly from author statements and submitted annotations from the community) and annotations for predicted proteins. We have developed two tools for proteomics analysis and these are freely available on request. A suite of tools for analyzing functional genomics datasets using the GO is available online at the AgBase site. We encourage and publicly acknowledge GO annotations from researchers and provide an online mechanism for agricultural researchers to submit requests for GO annotations

Gene Ontology annotation quality analysis in model eukaryotes

Functional analysis using the Gene Ontology (GO) is crucial for array analysis, but it is often difficult for researchers to assess the amount and quality of GO annotations associated with different sets of gene products. In many cases the source of the GO annotations and the date the GO annotations were last updated is not apparent, further complicating a researchers’ ability to assess the quality of the GO data provided. Moreover, GO biocurators need to ensure that the GO quality is maintained and optimal for the functional processes that are most relevant for their research community. We report the GO Annotation Quality (GAQ) score, a quantitative measure of GO quality that includes breadth of GO annotation, the level of detail of annotation and the type of evidence used to make the annotation. As a case study, we apply the GAQ scoring method to a set of diverse eukaryotes and demonstrate how the GAQ score can be used to track changes in GO annotations over time and to assess the quality of GO annotations available for specific biological processes. The GAQ score also allows researchers to quantitatively assess the functional data available for their experimental systems (arrays or databases)