Long-range DNA looping and gene expression analyses identify DEXI as an autoimmune disease candidate gene

The chromosome 16p13 region has been associated with several autoimmune diseases, including type 1 diabetes (T1D) and multiple sclerosis (MS). CLEC16A has been reported as the most likely candidate gene in the region, since it contains the most disease-associated single-nucleotide polymorphisms (SNPs), as well as an imunoreceptor tyrosine-based activation motif. However, here we report that intron 19 of CLEC16A, containing the most autoimmune disease-associated SNPs, appears to behave as a regulatory sequence, affecting the expression of a neighbouring gene, DEXI. The CLEC16A alleles that are protective from T1D and MS are associated with increased expression of DEXI, and no other genes in the region, in two independent monocyte gene expression data sets. Critically, using chromosome conformation capture (3C), we identified physical proximity between the DEXI promoter region and intron 19 of CLEC16A, separated by a loop of >150 kb. In reciprocal experiments, a 20 kb fragment of intron 19 of CLEC16A, containing SNPs associated with T1D and MS, as well as with DEXI expression, interacted with the promotor region of DEXI but not with candidate DNA fragments containing other potential causal genes in the region, including CLEC16A. Intron 19 of CLEC16A is highly enriched for transcription-factor-binding events and markers associated with enhancer activity. Taken together, these data indicate that although the causal variants in the 16p13 region lie within CLEC16A, DEXI is an unappreciated autoimmune disease candidate gene, and illustrate the power of the 3C approach in progressing from genome-wide association studies results to candidate causal genes

Genes, Cognition, and Communication: Insights from Neurodevelopmental Disorders

Twin and family studies have demonstrated that most cognitive traits are moderately to highly heritable. Neurodevelopmental disorders such as dyslexia, autism, and specific language impairment (SLI) also show strong genetic influence. Nevertheless, it has proved difficult for researchers to identify genes that would explain substantial amounts of variance in cognitive traits or disorders. Although this observation may seem paradoxical, it fits with a multifactorial model of how complex human traits are influenced by numerous genes that interact with one another, and with the environment, to produce a specific phenotype. Such a model can also explain why genetic influences on cognition have not vanished in the course of human evolution. Recent linkage and association studies of SLI and dyslexia are reviewed to illustrate these points. The role of nonheritable genetic mutations (sporadic copy number variants) in causing autism is also discussed. Finally, research on phenotypic correlates of allelic variation in the genes ASPM and microcephalin is considered; initial interest in these as genes for brain size or intelligence has been dampened by a failure to find phenotypic differences in people with different versions of these genes. There is a current vogue for investigators to include measures of allelic variants in studies of cognition and cognitive disorders. It is important to be aware that the effect sizes associated with these variants are typically small and hard to detect without extremely large sample sizes

Evaluating methods for ranking differentially expressed genes applied to microArray quality control data

<p>Abstract</p> <p>Background</p> <p>Statistical methods for ranking differentially expressed genes (DEGs) from gene expression data should be evaluated with regard to high sensitivity, specificity, and reproducibility. In our previous studies, we evaluated eight gene ranking methods applied to only Affymetrix GeneChip data. A more general evaluation that also includes other microarray platforms, such as the Agilent or Illumina systems, is desirable for determining which methods are suitable for each platform and which method has better inter-platform reproducibility.</p> <p>Results</p> <p>We compared the eight gene ranking methods using the MicroArray Quality Control (MAQC) datasets produced by five manufacturers: Affymetrix, Applied Biosystems, Agilent, GE Healthcare, and Illumina. The area under the curve (AUC) was used as a measure for both sensitivity and specificity. Although the highest AUC values can vary with the definition of "true" DEGs, the best methods were, in most cases, either the weighted average difference (WAD), rank products (RP), or intensity-based moderated <it>t </it>statistic (ibmT). The percentages of overlapping genes (POGs) across different test sites were mainly evaluated as a measure for both intra- and inter-platform reproducibility. The POG values for WAD were the highest overall, irrespective of the choice of microarray platform. The high intra- and inter-platform reproducibility of WAD was also observed at a higher biological function level.</p> <p>Conclusion</p> <p>These results for the five microarray platforms were consistent with our previous ones based on 36 real experimental datasets measured using the Affymetrix platform. Thus, recommendations made using the MAQC benchmark data might be universally applicable.</p

Dissection of genetic associations with language-related traits in population-based cohorts

Recent advances in the field of language-related disorders have led to the identification of candidate genes for specific language impairment (SLI) and dyslexia. Replication studies have been conducted in independent samples including population-based cohorts, which can be characterised for a large number of relevant cognitive measures. The availability of a wide range of phenotypes allows us to not only identify the most suitable traits for replication of genetic association but also to refine the associated cognitive trait. In addition, it is possible to test for pleiotropic effects across multiple phenotypes which could explain the extensive comorbidity observed across SLI, dyslexia and other neurodevelopmental disorders. The availability of genome-wide genotype data for such cohorts will facilitate this kind of analysis but important issues, such as multiple test corrections, have to be taken into account considering that small effect sizes are expected to underlie such associations

A Novel Framework for the Comparative Analysis of Biological Networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes

Mathematical modeling of the West Africa Ebola epidemic

As of November 2015, the Ebola virus disease (EVD) epidemic that began in West Africa in late 2013 is waning. The human toll includes more than 28,000 EVD cases and 11,000 deaths in Guinea, Liberia, and Sierra Leone, the most heavily-affected countries. We reviewed 66 mathematical modeling studies of the EVD epidemic published in the peer-reviewed literature to assess the key uncertainties models addressed, data used for modeling, public sharing of data and results, and model performance. Based on the review, we suggest steps to improve the use of modeling in future public health emergencies. DOI: http://dx.doi.org/10.7554/eLife.09186.00

Translating advances in the molecular basis of schizophrenia into novel cognitive treatment strategies

The presence and severity of cognitive symptoms, including working memory, executive dysfunction and attentional impairment, contributes materially to functional impairment in schizophrenia. Cognitive symptoms have proven resistant to both first- and second-generation antipsychotic drugs. Efforts to develop a consensus set of cognitive domains that are both disrupted in schizophrenia and are amenable to cross-species validation (e.g. the NIMH CNTRICS and RDoC initiatives) are an important step towards standardisation of outcome measures that can used in preclinical testing of new drugs. While causative genetic mutations have not been identified, new technologies have identified novel genes as well as hitherto candidate genes previously implicated in the pathophysiology of schizophrenia and/or mechanisms of antipsychotic efficacy. This review comprises a selective summary of these developments, particularly phenotypic data arising from preclinical genetic models for cognitive dysfunction in schizophrenia, with the aim of indicating potential new directions for pro-cognitive therapeutics

SARS-CoV-2 lineage B.1.1.7 is associated with greater disease severity among hospitalised women but not men: multicentre cohort study.

BACKGROUND: SARS-CoV-2 lineage B.1.1.7 has been associated with an increased rate of transmission and disease severity among subjects testing positive in the community. Its impact on hospitalised patients is less well documented. METHODS: We collected viral sequences and clinical data of patients admitted with SARS-CoV-2 and hospital-onset COVID-19 infections (HOCIs), sampled 16 November 2020 to 10 January 2021, from eight hospitals participating in the COG-UK-HOCI study. Associations between the variant and the outcomes of all-cause mortality and intensive therapy unit (ITU) admission were evaluated using mixed effects Cox models adjusted by age, sex, comorbidities, care home residence, pregnancy and ethnicity. FINDINGS: Sequences were obtained from 2341 inpatients (HOCI cases=786) and analysis of clinical outcomes was carried out in 2147 inpatients with all data available. The HR for mortality of B.1.1.7 compared with other lineages was 1.01 (95% CI 0.79 to 1.28, p=0.94) and for ITU admission was 1.01 (95% CI 0.75 to 1.37, p=0.96). Analysis of sex-specific effects of B.1.1.7 identified increased risk of mortality (HR 1.30, 95% CI 0.95 to 1.78, p=0.096) and ITU admission (HR 1.82, 95% CI 1.15 to 2.90, p=0.011) in females infected with the variant but not males (mortality HR 0.82, 95% CI 0.61 to 1.10, p=0.177; ITU HR 0.74, 95% CI 0.52 to 1.04, p=0.086). INTERPRETATION: In common with smaller studies of patients hospitalised with SARS-CoV-2, we did not find an overall increase in mortality or ITU admission associated with B.1.1.7 compared with other lineages. However, women with B.1.1.7 may be at an increased risk of admission to intensive care and at modestly increased risk of mortality.This report was produced by members of the COG-UK-HOCI Variant substudy consortium. COG-UK-HOCI is part of COG-UK. COG-UK is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute of Health Research (NIHR) and Genome Research Limited, operating as the Wellcome Sanger Institute

The fractal globule as a model of chromatin architecture in the cell

The fractal globule is a compact polymer state that emerges during polymer condensation as a result of topological constraints which prevent one region of the chain from passing across another one. This long-lived intermediate state was introduced in 1988 (Grosberg et al. 1988) and has not been observed in experiments or simulations until recently (Lieberman-Aiden et al. 2009). Recent characterization of human chromatin using a novel chromosome conformational capture technique brought the fractal globule into the spotlight as a structural model of human chromosome on the scale of up to 10 Mb (Lieberman-Aiden et al. 2009). Here, we present the concept of the fractal globule, comparing it to other states of a polymer and focusing on its properties relevant for the biophysics of chromatin. We then discuss properties of the fractal globule that make it an attractive model for chromatin organization inside a cell. Next, we connect the fractal globule to recent studies that emphasize topological constraints as a primary factor driving formation of chromosomal territories. We discuss how theoretical predictions, made on the basis of the fractal globule model, can be tested experimentally. Finally, we discuss whether fractal globule architecture can be relevant for chromatin packing in other organisms such as yeast and bacteria

The role of DNA insertions in phenotypic differentiation between humans and other primates

What makes us human is one of the most interesting and enduring questions in evolutionary biology. To assist in answering this question, we have identified insertions in the human genome which cannot be found in five comparison primate species: Chimpanzee, gorilla, orangutan, gibbon, and macaque. A total of 21,269 nonpolymorphic human-specific insertions were identified, of which only 372 were found in exons. Any function conferred by the remaining 20,897 is likely to be regulatory. Many of these insertions are likely to have been fitness neutral; however, a small number has been identified in genes showing signs of positive selection. Insertions found within positively selected genes show associations to neural phenotypes, which were also enriched in the whole data set. Other phenotypes that are found to be enriched in the data set include dental and sensory perception-related phenotypes, features which are known to differ between humans and other apes. The analysis provides several likely candidates, either genes or regulatory regions, which may be involved in the processes that differentiate humans from other apes