Cx30 exhibits unique characteristics including a long half-life when assembled into gap junctions

In the present study we investigated the life cycle, trafficking, assembly and cell surface dynamics of a poorly characterized connexin family member, connexin 30 (Cx30; also known as GJB6), which plays a critical role in skin health and hearing. Unexpectedly, Cx30 localization at the cell surface and gap junctional intercellular communication was not affected by prolonged treatments with the endoplasmic reticulum (ER)-Golgi transport inhibitor brefeldin A or the protein synthesis inhibitor cycloheximide, whereas Cx43 (also known as GJA1) was rapidly cleared. Fluorescent recovery after photobleaching revealed that Cx30 plaques were rebuilt from the outer edges in keeping with older channels residing in the inner core of the plaque. Expression of a dominant-negative form of Sar1 GTPase led to the accumulation of Cx30 within the ER, in contrast to a report that Cx30 traffics via a Golgi-independent pathway. Co-expression of Cx30 with Cx43 revealed that these connexins segregate into distinct domains within common gap junction plaques, suggesting that their assembly is governed by different mechanisms. In summary, Cx30 was found to be an unusually stable, long-lived connexin (half-life >12 h), which may underlie its specific role in the epidermis and cochlea

Quantitative trait association in parent offspring trios: Extension of case/pseudocontrol method and comparison of prospective and retrospective approaches

The case/pseudocontrol method provides a convenient framework for family-based association analysis of case-parent trios, incorporating several previously proposed methods such as the transmission/disequilibrium test and log-linear modelling of parent-of-origin effects. The method allows genotype and haplotype analysis at an arbitrary number of linked and unlinked multiallelic loci, as well as modelling of more complex effects such as epistasis, parent-of-origin effects, maternal genotype and mother-child interaction effects, and gene-environment interactions. Here we extend the method for analysis of quantitative as opposed to dichotomous (e.g. disease) traits. The resulting method can be thought of as a retrospective approach, modelling genotype given trait value, in contrast to prospective approaches that model trait given genotype. Through simulations and analytical derivations, we examine the power and properties of our proposed approach, and compare it to several previously proposed single-locus methods for quantitative trait association analysis. We investigate the performance of the different methods when extended to allow analysis of haplotype, maternal genotype and parent-of-origin effects. With randomly ascertained families, with or without population stratification, the prospective approach (modeling trait value given genotype) is found to be generally most effective, although the retrospective approach has some advantages with regard to estimation and interpretability of parameter estimates when applied to selected samples. Genet. Epidemiol. 31:833, 2007. © 2007 Wiley-Liss, Inc

Semantics for first-order affine inductive data types via slice categories

Affine type systems are substructural type systems where copying of information is restricted, but discarding of information is permissible at all types. Such type systems are well-suited for describing quantum programming languages, because copying of quantum information violates the laws of quantum mechanics. In this paper, we consider a first-order affine type system with inductive data types and present a novel categorical semantics for it. The most challenging aspect of this interpretation comes from the requirement to construct appropriate discarding maps for our data types which might be defined by mutual/nested recursion. We show how to achieve this for all types by taking models of a first-order linear type system whose atomic types are discardable and then presenting an additional affine interpretation of types within the slice category of the model with the tensor unit. We present some concrete categorical models for the language ranging from classical to quantum. Finally, we discuss potential ways of dualising and extending our methods and using them for interpreting coalgebraic and lazy data types

A novel approach to simulate gene-environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study

Identifying rare variants using a Bayesian regression approach

Recent advances in next-generation sequencing technologies have made it possible to generate large amounts of sequence data with rare variants in a cost-effective way. Statistical methods that test variants individually are underpowered to detect rare variants, so it is desirable to perform association analysis of rare variants by combining the information from all variants. In this study, we use a Bayesian regression method to model all variants simultaneously to identify rare variants in a data set from Genetic Analysis Workshop 17. We studied the association between the quantitative risk traits Q1, Q2, and Q4 and the single-nucleotide polymorphisms and identified several positive single-nucleotide polymorphisms for traits Q1 and Q2. However, the model also generated several apparent false positives and missed many true positives, suggesting that there is room for improvement in this model

Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions

Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g. mammals and microbes) using diverse types of data

Overcoming intratumoural heterogeneity for reproducible molecular risk stratification: a case study in advanced kidney cancer.

BACKGROUND: Metastatic clear cell renal cell cancer (mccRCC) portends a poor prognosis and urgently requires better clinical tools for prognostication as well as for prediction of response to treatment. Considerable investment in molecular risk stratification has sought to overcome the performance ceiling encountered by methods restricted to traditional clinical parameters. However, replication of results has proven challenging, and intratumoural heterogeneity (ITH) may confound attempts at tissue-based stratification. METHODS: We investigated the influence of confounding ITH on the performance of a novel molecular prognostic model, enabled by pathologist-guided multiregion sampling (n = 183) of geographically separated mccRCC cohorts from the SuMR trial (development, n = 22) and the SCOTRRCC study (validation, n = 22). Tumour protein levels quantified by reverse phase protein array (RPPA) were investigated alongside clinical variables. Regularised wrapper selection identified features for Cox multivariate analysis with overall survival as the primary endpoint. RESULTS: The optimal subset of variables in the final stratification model consisted of N-cadherin, EPCAM, Age, mTOR (NEAT). Risk groups from NEAT had a markedly different prognosis in the validation cohort (log-rank p = 7.62 × 10(-7); hazard ratio (HR) 37.9, 95% confidence interval 4.1-353.8) and 2-year survival rates (accuracy = 82%, Matthews correlation coefficient = 0.62). Comparisons with established clinico-pathological scores suggest favourable performance for NEAT (Net reclassification improvement 7.1% vs International Metastatic Database Consortium score, 25.4% vs Memorial Sloan Kettering Cancer Center score). Limitations include the relatively small cohorts and associated wide confidence intervals on predictive performance. Our multiregion sampling approach enabled investigation of NEAT validation when limiting the number of samples analysed per tumour, which significantly degraded performance. Indeed, sample selection could change risk group assignment for 64% of patients, and prognostication with one sample per patient performed only slightly better than random expectation (median logHR = 0.109). Low grade tissue was associated with 3.5-fold greater variation in predicted risk than high grade (p = 0.044). CONCLUSIONS: This case study in mccRCC quantitatively demonstrates the critical importance of tumour sampling for the success of molecular biomarker studies research where ITH is a factor. The NEAT model shows promise for mccRCC prognostication and warrants follow-up in larger cohorts. Our work evidences actionable parameters to guide sample collection (tumour coverage, size, grade) to inform the development of reproducible molecular risk stratification methods.We acknowledge financial support from the Royal Society of Edinburgh Scottish Government Fellowship co-funded by Marie Curie Actions (IMO), Carnegie Trust (50115; IMO, DJH, GDS), IGMM DTF (IMO, GDS), Medical Research Council (MC_UU_12018/25; IMO), Chief Scientist Office Scotland (ETM37; GDS, DJH), Cancer Research UK (Experimental Medicine Centre; TP, DJH), Renal Cancer Research Fund (GDS), Kidney Cancer Scotland (GDS), MRC Clinical Training Fellowship (AL), RCSEd Robertson Trust (AL) and Melville Trust (AL)

Use of principal components to aggregate rare variants in case-control and family-based association studies in the presence of multiple covariates

Rare variants may help to explain some of the missing heritability of complex diseases. Technological advances in next-generation sequencing give us the opportunity to test this hypothesis. We propose two new methods (one for case-control studies and one for family-based studies) that combine aggregated rare variants and common variants located within a region through principal components analysis and allow for covariate adjustment. We analyzed 200 replicates consisting of 209 case subjects and 488 control subjects and compared the results to weight-based and step-up aggregation methods. The principal components and collapsing method showed an association between the gene FLT1 and the quantitative trait Q1 (P<10−30) in a fraction of the computation time of the other methods. The proposed family-based test has inconclusive results. The two methods provide a fast way to analyze simultaneously rare and common variants at the gene level while adjusting for covariates. However, further evaluation of the statistical efficiency of this approach is warranted

Imparting carrier status results detected by universal newborn screening for sickle cell and cystic fibrosis in England: a qualitative study of current practice and policy challenges

<p>Abstract</p> <p>Background</p> <p>Universal newborn screening for early detection of children affected by sickle cell disorders and cystic fibrosis is currently being implemented across England. Parents of infants identified as carriers of these disorders must also be informed of their baby's result. However there is a lack of evidence for most effective practice internationally when doing so. This study describes current or proposed models for imparting this information in practice and explores associated challenges for policy.</p> <p>Methods</p> <p>Thematic analysis of semi-structured interviews with Child Health Coordinators from all English Health Regions.</p> <p>Results</p> <p>Diverse methods for imparting carrier results, both within and between regions, and within and between conditions, were being implemented or planned. Models ranged from result by letter to in-person communication during a home visit. Non-specialists were considered the best placed professionals to give results and a similar approach for both conditions was emphasised. While national guidance has influenced choice of models, other factors contributed such as existing service structures and lack of funding. Challenges included uncertainty about guidance specifying face to face notification; how best to balance allaying parental anxiety by using familiar non-specialist health professionals with concerns about practitioner competence; and extent of information parents should be given. Inadequate consideration of resource and service workload was seen as the main policy obstacle. Clarification of existing guidance; more specific protocols to ensure consistent countrywide practice; integration of the two programmes; and 'normalising' carrier status were suggested as improvements.</p> <p>Conclusion</p> <p>Differing models for communicating carrier results raise concerns about equity and clinical governance. However, this variation provides opportunity for evaluation. Timely and more detailed guidance on protocols with clarification of existing recommendations is needed.</p

Genome-scale screen for DNA methylation-based detection markers for ovarian cancer.

The identification of sensitive biomarkers for the detection of ovarian cancer is of high clinical relevance for early detection and/or monitoring of disease recurrence. We developed a systematic multi-step biomarker discovery and verification strategy to identify candidate DNA methylation markers for the blood-based detection of ovarian cancer