Spatial transcriptomics identifies spatially dysregulated expression of <i>GRM3</i> and <i>USP47</i> in amyotrophic lateral sclerosis

Research Funding Medical Research Council. Grant Number: MR/L016400/1 Biogen Academy of Medical Sciences. Grant Number: 210JMG 3102 R45620 MND Scotland Engineering and Physical Sciences Research CouncilPeer reviewedPublisher PD

Identification of Ceruloplasmin as a Gene that Affects Susceptibility to Glomerulonephritis Through Macrophage Function

Crescentic glomerulonephritis (Crgn) is a complex disorder where macrophage activity and infiltration are significant effector causes. In previous linkage studies using the uniquely susceptible Wistar Kyoto (WKY) rat strain, we have identified multiple crescentic glomerulonephritis QTL (Crgn) and positionally cloned genes underlying Crgn1 and Crgn2, which accounted for 40% of total variance in glomerular inflammation. Here, we have generated a backcross (BC) population (n = 166) where Crgn1 and Crgn2 were genetically fixed and found significant linkage to glomerular crescents on chromosome 2 (Crgn8, LOD = 3.8). Fine mapping analysis by integration with genome-wide expression QTLs (eQTLs) from the same BC population identified ceruloplasmin (Cp) as a positional eQTL in macrophages but not in serum. Liquid chromatography-tandem mass spectrometry confirmed Cp as a protein QTL in rat macrophages. WKY macrophages overexpress Cp and its downregulation by RNA interference decreases markers of glomerular proinflammatory macrophage activation. Similarly, short incubation with Cp results in a strain-dependent macrophage polarization in the rat. These results suggest that genetically determined Cp levels can alter susceptibility to Crgn through macrophage function and propose a new role for Cp in early macrophage activation

EMAAS: An extensible grid-based Rich Internet Application for microarray data analysis and management

<p>Abstract</p> <p>Background</p> <p>Microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large data sets. This, together with the proliferation of tools and techniques for microarray data analysis, makes it very challenging for a laboratory scientist to keep up-to-date with the latest developments in this field. Our aim was to develop a distributed e-support system for microarray data analysis and management.</p> <p>Results</p> <p>EMAAS (Extensible MicroArray Analysis System) is a multi-user rich internet application (RIA) providing simple, robust access to up-to-date resources for microarray data storage and analysis, combined with integrated tools to optimise real time user support and training. The system leverages the power of distributed computing to perform microarray analyses, and provides seamless access to resources located at various remote facilities. The EMAAS framework allows users to import microarray data from several sources to an underlying database, to pre-process, quality assess and analyse the data, to perform functional analyses, and to track data analysis steps, all through a single easy to use web portal. This interface offers distance support to users both in the form of video tutorials and via live screen feeds using the web conferencing tool EVO. A number of analysis packages, including R-Bioconductor and Affymetrix Power Tools have been integrated on the server side and are available programmatically through the Postgres-PLR library or on grid compute clusters. Integrated distributed resources include the functional annotation tool DAVID, GeneCards and the microarray data repositories GEO, CELSIUS and MiMiR. EMAAS currently supports analysis of Affymetrix 3' and Exon expression arrays, and the system is extensible to cater for other microarray and transcriptomic platforms.</p> <p>Conclusion</p> <p>EMAAS enables users to track and perform microarray data management and analysis tasks through a single easy-to-use web application. The system architecture is flexible and scalable to allow new array types, analysis algorithms and tools to be added with relative ease and to cope with large increases in data volume.</p

Accounting for uncertainty when assessing association between copy number and disease: a latent class model

<p>Abstract</p> <p>Background</p> <p>Copy number variations (CNVs) may play an important role in disease risk by altering dosage of genes and other regulatory elements, which may have functional and, ultimately, phenotypic consequences. Therefore, determining whether a CNV is associated or not with a given disease might be relevant in understanding the genesis and progression of human diseases. Current stage technology give CNV probe signal from which copy number status is inferred. Incorporating uncertainty of CNV calling in the statistical analysis is therefore a highly important aspect. In this paper, we present a framework for assessing association between CNVs and disease in case-control studies where uncertainty is taken into account. We also indicate how to use the model to analyze continuous traits and adjust for confounding covariates.</p> <p>Results</p> <p>Through simulation studies, we show that our method outperforms other simple methods based on inferring the underlying CNV and assessing association using regular tests that do not propagate call uncertainty. We apply the method to a real data set in a controlled MLPA experiment showing good results. The methodology is also extended to illustrate how to analyze aCGH data.</p> <p>Conclusion</p> <p>We demonstrate that our method is robust and achieves maximal theoretical power since it accommodates uncertainty when copy number status are inferred. We have made <monospace>R</monospace> functions freely available.</p

Next-generation sequencing of vertebrate experimental organisms

Next-generation sequencing technologies are revolutionizing biology by allowing for genome-wide transcription factor binding-site profiling, transcriptome sequencing, and more recently, whole-genome resequencing. While it is currently not possible to generate complete de novo assemblies of higher-vertebrate genomes using next-generation sequencing, improvements in sequence read lengths and throughput, coupled with new assembly algorithms for large data sets, will soon make this a reality. These developments will in turn spawn a revolution in how genomic data are used to understand genetics and how model organisms are used for disease gene discovery. This review provides an overview of the current next-generation sequencing platforms and the newest computational tools for the analysis of next-generation sequencing data. We also describe how next-generation sequencing may be applied in the context of vertebrate model organism genetics

Copy number, linkage disequilibrium and disease association in the FCGR locus.

The response of a leukocyte to immune complexes (ICs) is modulated by receptors for the Fc region of IgG (FcgammaRs), and alterations in their affinity or function have been associated with risk of autoimmune diseases, including systemic lupus erythematosus (SLE). The low-affinity FcgammaR genomic locus is complex, containing regions of copy number variation (CNV) which can alter receptor expression and leukocyte responses to IgG. Combined paralogue ratio tests (PRTs) were used to distinguish three intervals within the FCGR locus which undergo CNV, and to determine FCGR gene copy number (CN). There were significant differences in FCGR3B and FCGR3A CNV profiles between Caucasian, East Asian and Kenyan populations. A previously noted association of low FCGR3B CN with SLE in Caucasians was supported [OR = 1.57 (1.08-2.27), P = 0.018], and replicated in Chinese [OR = 1.65 (1.25-2.18), P = 4 x 10(-4)]. There was no association of FCGR3B CNV with vasculitis, nor with malarial or bacterial infection. Linkage disequilibrium (LD) between multi-allelic FCGR3B CNV and SLE-associated SNPs in the FCGR locus was defined for the first time. Despite LD between FCGR3B CNV and a variant in FcgammaRIIB (I232T) which abolishes inhibitory function, both reduced CN of FCGR3B and homozygosity of the FcgammaRIIB-232T allele were individually strongly associated with SLE risk. Thus CN of FCGR3B, which controls IC responses and uptake by neutrophils, and variations in FCGR2B, which controls factors such as antibody production and macrophage activation, are important in SLE pathogenesis. Further interpretations of contributions to pathogenesis by FcgammaRs must be made in the context of LD involving CNV regions

Characterization of the macrophage transcriptome in glomerulonephritis-susceptible and -resistant rat strains

Crescentic glomerulonephritis (CRGN) is a major cause of rapidly progressive renal failure for which the underlying genetic basis is unknown. WKY rats show marked susceptibility to CRGN, while Lewis rats are resistant. Glomerular injury and crescent formation are macrophage-dependent and mainly explained by seven quantitative trait loci (Crgn1-7). Here, we used microarray analysis in basal and lipopolysaccharide (LPS)-stimulated macrophages to identify genes that reside on pathways predisposing WKY rats to CRGN. We detected 97 novel positional candidates for the uncharacterised Crgn3-7. We identified 10 additional secondary effector genes with profound differences in expression between the two strains (>5-fold change, <1% False Discovery Rate) for basal and LPS-stimulated macrophages. Moreover, we identified 8 genes with differentially expressed alternatively spliced isoforms, by using an in depth analysis at probe-level that allowed us to discard false positives due to polymorphisms between the two rat strains. Pathway analysis identified several common linked pathways, enriched for differentially expressed genes, which affect macrophage activation. In summary, our results identify distinct macrophage transcriptome profiles between two rat strains that differ in susceptibility to glomerulonephritis, provide novel positional candidates for Crgn3-7, and define groups of genes that play a significant role in differential regulation of macrophage activity

Allele-Specific Gene Expression Is Widespread Across the Genome and Biological Processes

Allelic specific gene expression (ASGE) appears to be an important factor in human phenotypic variability and as a consequence, for the development of complex traits and diseases. In order to study ASGE across the human genome, we have performed a study in which genotyping was coupled with an analysis of ASGE by screening 11,500 SNPs using the Mapping 10 K Array to identify differential allelic expression. We found that from the 5,133 SNPs that were suitable for analysis (heterozygous in our sample and expressed in peripheral blood mononuclear cells), 2,934 (57%) SNPs had differential allelic expression. Such SNPs were equally distributed along human chromosomes and biological processes. We validated the presence or absence of ASGE in 18 out 20 SNPs (90%) randomly selected by real time PCR in 48 human subjects. In addition, we observed that SNPs close to -but not included in- segmental duplications had increased levels of ASGE. Finally, we found that transcripts of unknown function or non-coding RNAs, also display ASGE: from a total of 2,308 intronic SNPs, 1510 (65%) SNPs underwent differential allelic expression. In summary, ASGE is a widespread mechanism in the human genome whose regulation seems to be far more complex than expected

Consequences of perinatal treatment with l-arginine and antioxidants for the renal transcriptome in spontaneously hypertensive rats

Treating spontaneously hypertensive rats (SHR) with l-arginine, taurine, and vitamins C and E (ATCE) during nephrogenesis (2 weeks before to 4 weeks after birth) persistently lowers blood pressure. Hypothetically, differential gene expression in kidney of SHR vs. normotensive Wistar–Kyoto rats (WKY) is partially corrected by maternal ATCE in SHR. Differential gene expression in 2-days, 2-weeks, and 48-week-old rats was studied using oligonucleotide chips. Transcription factor binding sites (TFBS) of differentially expressed genes were analyzed in silico. Differential gene expression varied between SHR+ATCE and SHR, suggesting both direct and indirect effects; but, few genes were modulated toward WKY level and there was little overlap between ages. TFBS analysis suggests less Elk-1-driven gene transcription in both WKY and SHR+ATCE vs. SHR at 2 days and 2 weeks. Concluding, in SHR, persistent antihypertensive effects of maternal ATCE are not primarily due to persistent corrective transcription. Less Elk-1-driven transcription at 2 days and 2 weeks may be involved

Defining the Sister Rat Mammary Tumor Cell Lines HH-16 cl.2/1 and HH-16.cl.4 as an In Vitro Cell Model for Erbb2

Cancer cell lines have been shown to be reliable tools in genetic studies of breast cancer, and the characterization of these lines indicates that they are good models for studying the biological mechanisms underlying this disease. Here, we describe the molecular cytogenetic/genetic characterization of two sister rat mammary tumor cell lines, HH-16 cl.2/1 and HH-16.cl.4, for the first time. Molecular cytogenetic analysis using rat and mouse chromosome paint probes and BAC/PAC clones allowed the characterization of clonal chromosome rearrangements; moreover, this strategy assisted in revealing detected breakpoint regions and complex chromosome rearrangements. This comprehensive cytogenetic analysis revealed an increase in the number of copies of the Mycn and Erbb2 genes in the investigated cell lines. To analyze its possible correlation with expression changes, relative RNA expression was assessed by real-time reverse transcription quantitative PCR and RNA FISH. Erbb2 was found to be overexpressed in HH-16.cl.4, but not in the sister cell line HH-16 cl.2/1, even though these lines share the same initial genetic environment. Moreover, the relative expression of Erbb2 decreased after global genome demethylation in the HH-16.cl.4 cell line. As these cell lines are commercially available and have been used in previous studies, the present detailed characterization improves their value as an in vitro cell model. We believe that the development of appropriate in vitro cell models for breast cancer is of crucial importance for revealing the genetic and cellular pathways underlying this neoplasy and for employing them as experimental tools to assist in the generation of new biotherapies