Owen J. L. Rackham

Bio: Owen J. L. Rackham is an academic researcher from National University of Singapore. The author has contributed to research in topics: Reprogramming & Regulation of gene expression. The author has an hindex of 26, co-authored 66 publications receiving 6160 citations. Previous affiliations of Owen J. L. Rackham include Hammersmith Hospital & Medical Research Council.

An atlas of active enhancers across human cell types and tissues

Abstract: Enhancers control the correct temporal and cell-type-specific activation of gene expression in multicellular eukaryotes. Knowing their properties, regulatory activity and targets is crucial to understand the regulation of differentiation and homeostasis. Here we use the FANTOM5 panel of samples, covering the majority of human tissues and cell types, to produce an atlas of active, in vivo-transcribed enhancers. We show that enhancers share properties with CpG-poor messenger RNA promoters but produce bidirectional, exosome-sensitive, relatively short unspliced RNAs, the generation of which is strongly related to enhancer activity. The atlas is used to compare regulatory programs between different cells at unprecedented depth, to identify disease-associated regulatory single nucleotide polymorphisms, and to classify cell-type-specific and ubiquitous enhancers. We further explore the utility of enhancer redundancy, which explains gene expression strength rather than expression patterns. The online FANTOM5 enhancer atlas represents a unique resource for studies on cell-type-specific enhancers and gene regulation.

A promoter-level mammalian expression atlas

Abstract: Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body We find that few genes are truly 'housekeeping', whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research

An atlas of human long non-coding RNAs with accurate 5′ ends

Abstract: Long non-coding RNAs (lncRNAs) are largely heterogeneous and functionally uncharacterized. Here, using FANTOM5 cap analysis of gene expression (CAGE) data, we integrate multiple transcript collections to generate a comprehensive atlas of 27,919 human lncRNA genes with high-confidence 5' ends and expression profiles across 1,829 samples from the major human primary cell types and tissues. Genomic and epigenomic classification of these lncRNAs reveals that most intergenic lncRNAs originate from enhancers rather than from promoters. Incorporating genetic and expression data, we show that lncRNAs overlapping trait-associated single nucleotide polymorphisms are specifically expressed in cell types relevant to the traits, implicating these lncRNAs in multiple diseases. We further demonstrate that lncRNAs overlapping expression quantitative trait loci (eQTL)-associated single nucleotide polymorphisms of messenger RNAs are co-expressed with the corresponding messenger RNAs, suggesting their potential roles in transcriptional regulation. Combining these findings with conservation data, we identify 19,175 potentially functional lncRNAs in the human genome.

A single-cell atlas of entorhinal cortex from individuals with Alzheimer's disease reveals cell-type-specific gene expression regulation.

Abstract: There is currently little information available about how individual cell types contribute to Alzheimer’s disease. Here we applied single-nucleus RNA sequencing to entorhinal cortex samples from control and Alzheimer’s disease brains (n = 6 per group), yielding a total of 13,214 high-quality nuclei. We detail cell-type-specific gene expression patterns, unveiling how transcriptional changes in specific cell subpopulations are associated with Alzheimer’s disease. We report that the Alzheimer’s disease risk gene APOE is specifically repressed in Alzheimer’s disease oligodendrocyte progenitor cells and astrocyte subpopulations and upregulated in an Alzheimer’s disease-specific microglial subopulation. Integrating transcription factor regulatory modules with Alzheimer’s disease risk loci revealed drivers of cell-type-specific state transitions towards Alzheimer’s disease. For example, transcription factor EB, a master regulator of lysosomal function, regulates multiple disease genes in a specific Alzheimer’s disease astrocyte subpopulation. These results provide insights into the coordinated control of Alzheimer’s disease risk genes and their cell-type-specific contribution to disease susceptibility. These results are available at http://adsn.ddnetbio.com. Grubman et al. generated a single-cell transcriptomic atlas of the entorhinal cortex from patients with Alzheimer’s disease and identified transcription factor networks predicted to control disease progression in a cell-subtype-specific way.

IL-11 is a crucial determinant of cardiovascular fibrosis

Abstract: Fibrosis is a common pathology in cardiovascular disease. In the heart, fibrosis causes mechanical and electrical dysfunction and in the kidney, it predicts the onset of renal failure. Transforming growth factor β1 (TGFβ1) is the principal pro-fibrotic factor, but its inhibition is associated with side effects due to its pleiotropic roles. We hypothesized that downstream effectors of TGFβ1 in fibroblasts could be attractive therapeutic targets and lack upstream toxicity. Here we show, using integrated imaging-genomics analyses of primary human fibroblasts, that upregulation of interleukin-11 (IL-11) is the dominant transcriptional response to TGFβ1 exposure and required for its pro-fibrotic effect. IL-11 and its receptor (IL11RA) are expressed specifically in fibroblasts, in which they drive non-canonical, ERK-dependent autocrine signalling that is required for fibrogenic protein synthesis. In mice, fibroblast-specific Il11 transgene expression or Il-11 injection causes heart and kidney fibrosis and organ failure, whereas genetic deletion of Il11ra1 protects against disease. Therefore, inhibition of IL-11 prevents fibroblast activation across organs and species in response to a range of important pro-fibrotic stimuli. These results reveal a central role of IL-11 in fibrosis and we propose that inhibition of IL-11 is a potential therapeutic strategy to treat fibrotic diseases.

The Phyre2 web portal for protein modeling, prediction and analysis

Abstract: Phyre2 is a web-based tool for predicting and analyzing protein structure and function. Phyre2 uses advanced remote homology detection methods to build 3D models, predict ligand binding sites, and analyze amino acid variants in a protein sequence. Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2 . A typical structure prediction will be returned between 30 min and 2 h after submission.

InterProScan 5: genome-scale protein function classification

Abstract: Motivation: Robust, large-scale sequence analysis is a major challenge in modern genomic science, where biologists are frequently trying to characterise many millions of sequences. Here we describe a new Java-based architecture for the widely-used protein function prediction software package InterProScan. Developments include improvements and additions to the outputs of the software and the complete re-implementation of the software framework, resulting in a flexible and stable system that is able to utilise both multiprocessor machines and/or conventional clusters to achieve scalable distributed data analysis. InterProScan is freely available for download from the EMBl-EBI FTP site and the (open) source code is hosted at Google Code. Availability: InterProScan is distributed via FTP at ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/ and the source code is available from http://code.google.com/p/interproscan/. Contact: http://www.ebi.ac.uk/support or interhelp@ebi.ac.uk

Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association.

Abstract: Each chapter listed in the Table of Contents (see next page) is a hyperlink to that chapter. The reader clicks the chapter name to access that chapter. Each chapter listed here is a hyperlink. Click on the chapter name to be taken to that chapter. Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together in a single document the most up-to-date statistics related to heart disease, stroke, and the cardiovascular risk factors listed in the AHA’s My Life Check - Life’s Simple 7 (Figure1), which include core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents …

The mutational constraint spectrum quantified from variation in 141,456 humans

Abstract: Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases. A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.

The Pfam protein families database: towards a more sustainable future

Abstract: In the last two years the Pfam database (http://pfam.xfam.org) has undergone a substantial reorganisation to reduce the effort involved in making a release, thereby permitting more frequent releases. Arguably the most significant of these changes is that Pfam is now primarily based on the UniProtKB reference proteomes, with the counts of matched sequences and species reported on the website restricted to this smaller set. Building families on reference proteomes sequences brings greater stability, which decreases the amount of manual curation required to maintain them. It also reduces the number of sequences displayed on the website, whilst still providing access to many important model organisms. Matches to the full UniProtKB database are, however, still available and Pfam annotations for individual UniProtKB sequences can still be retrieved. Some Pfam entries (1.6%) which have no matches to reference proteomes remain; we are working with UniProt to see if sequences from them can be incorporated into reference proteomes. Pfam-B, the automatically-generated supplement to Pfam, has been removed. The current release (Pfam 29.0) includes 16 295 entries and 559 clans. The facility to view the relationship between families within a clan has been improved by the introduction of a new tool.