Sex prevalence of major congenital anomalies in the United Kingdom: a national population-based study and international comparison meta-analysis.

BACKGROUND: The aim of this study was to assess sex differences in major congenital anomaly (CA) diagnoses within a national population sample; to examine the influence of sociodemographic and maternal factors on these risks; and to conduct a meta-analysis using estimates from other population-based studies. METHODS: We conducted a population-based study in a United Kingdom research database of prospectively collected primary care data (The Health Improvement Network) including children born 1990 to 2009 (n = 794,169) and identified major CA diagnoses using EUROCAT (European Surveillance of Congenital Anomalies) classification. Prevalence ratios (PR) were used to estimate the risk of CA in males compared with females for any CA, system-specific subgroups and specific CA diagnoses. In a subpopulation of children whose medical records were linked to their mothers', we assessed the effect of adjusting for sociodemographic and maternal factors on sex odds ratios. PRs were pooled with measures from previously published studies. RESULTS: The prevalence of any CA was 307/10,000 in males (95% CI, 302-313) and 243/10,000 in females (95% CI, 238-248). Overall the risk of any CA was 26% greater in males (PR (male: female) 1.26, 95% CI, 1.23-1.30) however there was considerable variation across specific diagnoses. The magnitude and direction of risk did not change for any specific CA upon adjustment for sociodemographic and maternal factors. Our PRs were highly consistent with those from previous studies. CONCLUSION: The overall risk of CA is greater in males than females, although this masked substantial variation by specific diagnoses. Sociodemographic and maternal factors do not appear to affect these risks

A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests

<p>Abstract</p> <p>Background</p> <p>The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test.</p> <p>Results</p> <p>It is shown that SGoF behaves especially well with small sample sizes when 1) the alternative hypothesis is weakly to moderately deviated from the null model, 2) there are widespread effects through the family of tests, and 3) the number of tests is large.</p> <p>Conclusion</p> <p>Therefore, SGoF should become an important tool for multitest adjustment when working with high-dimensional biological data.</p

Interactome and Gene Ontology provide congruent yet subtly different views of a eukaryotic cell

15 pages, 6 figures.-- 19604360 [PubMed]BACKGROUND: The characterization of the global functional structure of a cell is a major goal in bioinformatics and systems biology. Gene Ontology (GO) and the protein-protein interaction network offer alternative views of that structure. RESULTS: This study presents a comparison of the global structures of the Gene Ontology and the interactome of Saccharomyces cerevisiae. Sensitive, unsupervised methods of clustering applied to a large fraction of the proteome led to establish a GO-interactome correlation value of +0.47 for a general dataset that contains both high and low-confidence interactions and +0.58 for a smaller, high-confidence dataset. CONCLUSION: The structures of the yeast cell deduced from GO and interactome are substantially congruent. However, some significant differences were also detected, which may contribute to a better understanding of cell function and also to a refinement of the current ontologiesResearch supported by grant BIO2008-05067 (Programa Nacional de Biotecnología; Ministerio de Ciencia e Innovación. Spain), awarded to IM. AM was a FPI fellow from Ministerio de Educación y Ciencia (Spain).Peer reviewe

Bacillus subtilis MreB Orthologs Self-Organize into Filamentous Structures underneath the Cell Membrane in a Heterologous Cell System

Actin-like bacterial cytoskeletal element MreB has been shown to be essential for the maintenance of rod cell shape in many bacteria. MreB forms rapidly remodelling helical filaments underneath the cell membrane in Bacillus subtilis and in other bacterial cells, and co-localizes with its two paralogs, Mbl and MreBH. We show that MreB localizes as dynamic bundles of filaments underneath the cell membrane in Drosophila S2 Schneider cells, which become highly stable when the ATPase motif in MreB is modified. In agreement with ATP-dependent filament formation, the depletion of ATP in the cells lead to rapid dissociation of MreB filaments. Extended induction of MreB resulted in the formation of membrane protrusions, showing that like actin, MreB can exert force against the cell membrane. Mbl also formed membrane associated filaments, while MreBH formed filaments within the cytosol. When co-expressed, MreB, Mbl and MreBH built up mixed filaments underneath the cell membrane. Membrane protein RodZ localized to endosomes in S2 cells, but localized to the cell membrane when co-expressed with Mbl, showing that bacterial MreB/Mbl structures can recruit a protein to the cell membrane. Thus, MreB paralogs form a self-organizing and dynamic filamentous scaffold underneath the membrane that is able to recruit other proteins to the cell surface

An algorithm for classifying tumors based on genomic aberrations and selecting representative tumor models

<p>Abstract</p> <p>Background</p> <p>Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.</p> <p>Method</p> <p>To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.</p> <p>Result</p> <p>We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.</p> <p>Conclusions</p> <p>We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.</p

Epidemiological Characteristics of Classical Scrapie Outbreaks in 30 Sheep Flocks in the United Kingdom

Most previous analyses of scrapie outbreaks have focused on flocks run by research institutes, which may not reflect the field situation. Within this study, we attempt to rectify this deficit by describing the epidemiological characteristics of 30 sheep flocks naturally-infected with classical scrapie, and by exploring possible underlying causes of variation in the characteristics between flocks, including flock-level prion protein (PrP) genotype profile. In total, the study involved PrP genotype data for nearly 8600 animals and over 400 scrapie cases.We found that most scrapie cases were restricted to just two PrP genotypes (ARQ/VRQ and VRQ/VRQ), though two flocks had markedly different affected genotypes, despite having similar underlying genotype profiles to other flocks of the same breed; we identified differences amongst flocks in the age of cases of certain PrP genotypes; we found that the age-at-onset of clinical signs depended on peak incidence and flock type; we found evidence that purchasing infected animals is an important means of introducing scrapie to a flock; we found some evidence that flock-level PrP genotype profile and flock size account for variation in outbreak characteristics; identified seasonality in cases associated with lambing time in certain flocks; and we identified one case that was homozygous for phenylalanine at codon 141, a polymorphism associated with a very high risk of atypical scrapie, and 28 cases that were heterozygous at this codon.This paper presents the largest study to date on commercially-run sheep flocks naturally-infected with classical scrapie, involving 30 study flocks, more than 400 scrapie cases and over 8500 PrP genotypes. We show that some of the observed variation in epidemiological characteristics between farms is related to differences in their PrP genotype profile; although much remains unexplained and may instead be attributed to the stochastic nature of scrapie dynamics

Spectrum of HLA associations: the case of medically refractory pediatric acute lymphoblastic leukemia

Although studies of HLA and disease now date back some 50 years, a principled understanding of that relationship has been slow to emerge. Here, we examine the associations of three HLA loci with medically refractory pediatric acute lymphoblastic leukemia (pALL) patients in a case–control study involving 2,438 cases and 41,750 controls. An analysis of alleles from the class I loci, HLA-A and HLA-B, and the class II locus DRB1 illuminates a spectrum of extremely significant allelic associations conferring both predisposition and protection. Genotypes constructed from predisposing, protective, and neutral allelic categories point to an additive mode of disease causation. For all three loci, genotypes homozygous for predisposing alleles are at highest disease risk while the favorable effect of homozygous protective genotypes is less striking. Analysis of A–B and B–DRB1 haplotypes reveals locus-specific differences in disease effects, while that all three loci influence pALL; the influence of HLA-B is greater than that of HLA-A, and the predisposing effect of DRB1 exceeds that of HLA-B. We propose that the continuum in disease susceptibility suggests a system in which many alleles take part in disease predisposition based on differences in binding affinity to one or a few peptides of exogenous origin. This work provides evidence that an immune response mediated by alleles from several HLA loci plays a critical role in the pathogenesis of pALL, adding to the numerous studies pointing to a role for an infectious origin in pALL

Regulatory and Activated T Cells in Human Schistosoma haematobium Infections

Acquired immunity against helminths is characterised by a complex interplay between the effector Th1 and Th2 immune responses and it slowly manifests with age as a result of cumulative exposure to parasite antigens. Data from experimental models suggest that immunity is also influenced by regulatory T cells (Treg), but as yet studies on Treg in human schistosome infections are limited