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Detection of Genome-Wide IGF-1R Recruitment to Enhancer and Promoter Regions of Chromatin in Clinical Prostate Cancers.
IntroductionNuclear insulin-like growth factor-1 receptor (IGF-1R) undergoes IGF-induced recruitment to cancer cell chromatin in vitro and associates with advanced prostate cancer (PCa) stage in clinical tissue, prompting this investigation of IGF-1R chromatin recruitment in vivo.MethodsHuman tissues surplus to diagnostic need were obtained from consenting patients undergoing transurethral resection of the prostate (TURP) or radical prostatectomy (RP). Initial tissue samples were processed for H3K4me1-positive control ChIP to optimise homogenisation, fixation and ChIP conditions. Following successful method optimization, IGF-1R and H3K4me1 ChIP-seq was performed on six treatment-naïve localized PCa samples, along with parallel IGF-1R immunohistochemistry analysis. MACS2 and LanceOtron peak callers were used to identify binding sites from ChIP-seq data and MEME Suite was used to identify an IGF-1R binding motif. In vitro chromatin immunoprecipitation qPCR (ChIP-qPCR) was used for ChIP-seq data validation.ResultsWe identified 5743 unique IGF-1R binding sites, with 37% within 3 kb of gene transcription start sites (TSSs). Of these sites, 72.3% coincided with enhancer mark H3K4me1, suggesting regulatory function. Motif analysis identified an IGF-1R consensus binding motif for the first time, with a sequence resembling that of the insulin receptor and PITX2 transcription factor binding motifs, supporting functional similarities. In vitro ChIP-qPCR confirmed IGF-1R recruitment to a site identified in vivo in the RRM2 TSS, a gene involved in DNA replication and repair and regulated by the IGF-axis, highlighting potential regulatory function of nuclear IGF-1R.ConclusionOverall, these data represent the first characterization of genome-wide IGF-1R recruitment in PCa tissue and are consistent with a transcriptional regulatory role, further elucidating the contribution of nuclear IGF-1R to advanced clinical stage.
GeneFEAST: the pivotal, gene-centric step in functional enrichment analysis interpretation
Abstract Summary GeneFEAST, implemented in Python, is a gene-centric functional enrichment analysis summarization and visualization tool that can be applied to large functional enrichment analysis (FEA) results arising from upstream FEA pipelines. It produces a systematic, navigable HTML report, making it easy to identify sets of genes putatively driving multiple enrichments and to explore gene-level quantitative data first used to identify input genes. Further, GeneFEAST can juxtapose FEA results from multiple studies, making it possible to highlight patterns of gene expression amongst genes that are differentially expressed in at least one of multiple conditions, and which give rise to shared enrichments under those conditions. Thus, GeneFEAST offers a novel, effective way to address the complexities of linking up many overlapping FEA results to their underlying genes and data, advancing gene-centric hypotheses, and providing pivotal information for downstream validation experiments. Availability and implementation GeneFEAST GitHub repository: https://github.com/avigailtaylor/GeneFEAST; Zenodo record: 10.5281/zenodo.14753734; Python Package Index: https://pypi.org/project/genefeast; Docker container: ghcr.io/avigailtaylor/genefeast.
The genomic landscape shaped by selection on transposable elements across 18 mouse strains
Abstract Background Transposable element (TE)-derived sequence dominates the landscape of mammalian genomes and can modulate gene function by dysregulating transcription and translation. Our current knowledge of TEs in laboratory mouse strains is limited primarily to those present in the C57BL/6J reference genome, with most mouse TEs being drawn from three distinct classes, namely short interspersed nuclear elements (SINEs), long interspersed nuclear elements (LINEs) and the endogenous retrovirus (ERV) superfamily. Despite their high prevalence, the different genomic and gene properties controlling whether TEs are preferentially purged from, or are retained by, genetic drift or positive selection in mammalian genomes remain poorly defined. Results Using whole genome sequencing data from 13 classical laboratory and 4 wild-derived mouse inbred strains, we developed a comprehensive catalogue of 103,798 polymorphic TE variants. We employ this extensive data set to characterize TE variants across the Mus lineage, and to infer neutral and selective processes that have acted over 2 million years. Our results indicate that the majority of TE variants are introduced though the male germline and that only a minority of TE variants exert detectable changes in gene expression. However, among genes with differential expression across the strains there are twice as many TE variants identified as being putative causal variants as expected. Conclusions Most TE variants that cause gene expression changes appear to be purged rapidly by purifying selection. Our findings demonstrate that past TE insertions have often been highly deleterious, and help to prioritize TE variants according to their likely contribution to gene expression or phenotype variation.
Duplications in ADHD patients harbour neurobehavioural genes that are co‐expressed with genes associated with hyperactivity in the mouse
Attention deficit/hyperactivity disorder (ADHD) is a childhood onset disorder, prevalent in 5.3% of children and 1–4% of adults. ADHD is highly heritable, with a burden of large (>500 Kb) copy number variants (CNVs) identified among individuals with ADHD. However, how such CNVs exert their effects is poorly understood. We examined the genes affected by 71 large, rare, and predominantly inherited CNVs identified among 902 individuals with ADHD. We applied both mouse‐knockout functional enrichment analyses, exploiting behavioral phenotypes arising from the determined disruption of 1:1 mouse orthologues, and human brain‐specific spatio‐temporal expression data to uncover molecular pathways common among genes contributing to enriched phenotypes. Twenty‐two percent of genes duplicated in individuals with ADHD that had mouse phenotypic information were associated with abnormal learning/memory/conditioning (“l/m/c”) phenotypes. Although not observed in a second ADHD‐cohort, we identified a similar enrichment among genes duplicated by eight de novo CNVs present in eight individuals with Hyperactivity and/or Short attention span (“Hyperactivity/SAS”, the ontologically‐derived phenotypic components of ADHD). In the brain, genes duplicated in patients with ADHD and Hyperactivity/SAS and whose orthologues’ disruption yields l/m/c phenotypes in mouse (“candidate‐genes”), were co‐expressed with one another and with genes whose orthologues’ mouse models exhibit hyperactivity. Moreover, genes associated with hyperactivity in the mouse were significantly more co‐expressed with ADHD candidate‐genes than with similarly identified genes from individuals with intellectual disability. Our findings support an etiology for ADHD distinct from intellectual disability, and mechanistically related to genes associated with hyperactivity phenotypes in other mammalian species. © 2015 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.
GeneNet Toolbox for MATLAB: a flexible platform for the analysis of gene connectivity in biological networks
Abstract Summary : We present GeneNet Toolbox for MATLAB (also available as a set of standalone applications for Linux). The toolbox, available as command-line or with a graphical user interface, enables biologists to assess connectivity among a set of genes of interest (‘seed-genes’) within a biological network of their choosing. Two methods are implemented for calculating the significance of connectivity among seed-genes: ‘seed randomization’ and ‘network permutation’. Options include restricting analyses to a specified subnetwork of the primary biological network, and calculating connectivity from the seed-genes to a second set of interesting genes. Pre-analysis tools help the user choose the best connectivity-analysis algorithm for their network. The toolbox also enables visualization of the connections among seed-genes. GeneNet Toolbox functions execute in reasonable time for very large networks (∼10 million edges) on a desktop computer. Availability and implementation : GeneNet Toolbox is open source and freely available from http://avigailtaylor.github.io/gntat14 . Supplementary information : Supplementary data are available at Bioinformatics online. Contact: avigail.taylor@dpag.ox.ac.uk
Concepts and Misconceptions about the Polygenic Additive Model Applied to Disease
It is nearly one hundred years, since R.A. Fisher published his now famous paper that started the field of quantitative genetics. That paper reconciled Mendelian genetics (as exemplified by Mendel's peas) and the biometrical approach to quantitative traits (as exemplified by the correlation and regression approaches from Galton and Pearson), by showing that a simple model of many genes of small effects, each following Mendel's laws of segregation and inheritance, plus environmental variation could account for the observed resemblance between relatives. In this review, we discuss a number of concepts and misconceptions about the assumptions and limitations of polygenic models of common diseases in human populations.
Hypermethylation in the ZBTB20 gene is associated with major depressive disorder
Abstract Background Although genetic variation is believed to contribute to an individual’s susceptibility to major depressive disorder, genome-wide association studies have not yet identified associations that could explain the full etiology of the disease. Epigenetics is increasingly believed to play a major role in the development of common clinical phenotypes, including major depressive disorder. Results Genome-wide MeDIP-Sequencing was carried out on a total of 50 monozygotic twin pairs from the UK and Australia that are discordant for depression. We show that major depressive disorder is associated with significant hypermethylation within the coding region of ZBTB20, and is replicated in an independent cohort of 356 unrelated case-control individuals. The twins with major depressive disorder also show increased global variation in methylation in comparison with their unaffected co-twins. ZBTB20 plays an essential role in the specification of the Cornu Ammonis-1 field identity in the developing hippocampus, a region previously implicated in the development of major depressive disorder. Conclusions Our results suggest that aberrant methylation profiles affecting the hippocampus are associated with major depressive disorder and show the potential of the epigenetic twin model in neuro-psychiatric disease.