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Ivanov DK, Escott-Price V, Ziehm M, Magwire MM, Mackay TFC, Partridge L, Thornton JM. Longevity GWAS Using the Drosophila Genetic Reference Panel. J Gerontol A Biol Sci Med Sci 2015; 70:1470-8. [PMID: 25922346 PMCID: PMC4631106 DOI: 10.1093/gerona/glv047] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2014] [Accepted: 03/26/2015] [Indexed: 01/09/2023] Open
Abstract
We used 197 Drosophila melanogaster Genetic Reference Panel (DGRP) lines to perform a genome-wide association analysis for virgin female lifespan, using ~2M common single nucleotide polymorphisms (SNPs). We found considerable genetic variation in lifespan in the DGRP, with a broad-sense heritability of 0.413. There was little power to detect signals at a genome-wide level in single-SNP and gene-based analyses. Polygenic score analysis revealed that a small proportion of the variation in lifespan (~4.7%) was explicable in terms of additive effects of common SNPs (≥2% minor allele frequency). However, several of the top associated genes are involved in the processes previously shown to impact ageing (eg, carbohydrate-related metabolism, regulation of cell death, proteolysis). Other top-ranked genes are of unknown function and provide promising candidates for experimental examination. Genes in the target of rapamycin pathway (TOR; Chrb, slif, mipp2, dredd, RpS9, dm) contributed to the significant enrichment of this pathway among the top-ranked 100 genes (p = 4.79×10(-06)). Gene Ontology analysis suggested that genes involved in carbohydrate metabolism are important for lifespan; including the InterPro term DUF227, which has been previously associated with lifespan determination. This analysis suggests that our understanding of the genetic basis of natural variation in lifespan from induced mutations is incomplete.
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Affiliation(s)
- Dobril K Ivanov
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
| | - Valentina Escott-Price
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK
| | - Matthias Ziehm
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. Department of Genetics Evolution and Environment, The Institute of Healthy Ageing, University College London, UK
| | - Michael M Magwire
- Department of Biological Sciences, Program in Genetics and W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh. Syngenta, Research Triangle Park, North Carolina
| | - Trudy F C Mackay
- Department of Biological Sciences, Program in Genetics and W. M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh
| | - Linda Partridge
- Department of Genetics Evolution and Environment, The Institute of Healthy Ageing, University College London, UK. Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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Papatheodorou I, Oellrich A, Smedley D. Linking gene expression to phenotypes via pathway information. J Biomed Semantics 2015; 6:17. [PMID: 25901272 PMCID: PMC4404592 DOI: 10.1186/s13326-015-0013-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Accepted: 03/19/2015] [Indexed: 11/10/2022] Open
Abstract
Establishing robust links among gene expression, pathways and phenotypes is critical for understanding diseases and developing treatments. In recent years there have been many efforts to develop the computational means to traverse from genes to gene expression, model pathways and classify phenotypes. Numerous ontologies and other controlled vocabularies have been developed, as well as computational methods to combine and mine these data sets and establish connections. Here we discuss these efforts and identify areas of future work that could lead to a better integration of genes, pathways and phenotypes to provide insights into the mechanisms under which gene mutations affect expression and pathways and how these effects are manifested onto the phenotype.
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Affiliation(s)
- Irene Papatheodorou
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
| | - Anika Oellrich
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
| | - Damian Smedley
- Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK
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Papatheodorou I, Petrovs R, Thornton JM. Comparison of the mammalian insulin signalling pathway to invertebrates in the context of FOXO-mediated ageing. ACTA ACUST UNITED AC 2014; 30:2999-3003. [PMID: 25064569 PMCID: PMC4201157 DOI: 10.1093/bioinformatics/btu493] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION A large number of experimental studies on ageing focus on the effects of genetic perturbations of the insulin/insulin-like growth factor signalling pathway (IIS) on lifespan. Short-lived invertebrate laboratory model organisms are extensively used to quickly identify ageing-related genes and pathways. It is important to extrapolate this knowledge to longer lived mammalian organisms, such as mouse and eventually human, where such analyses are difficult or impossible to perform. Computational tools are needed to integrate and manipulate pathway knowledge in different species. RESULTS We performed a literature review and curation of the IIS and target of rapamycin signalling pathways in Mus Musculus. We compare this pathway model to the equivalent models in Drosophila melanogaster and Caenorhabtitis elegans. Although generally well-conserved, they exhibit important differences. In general, the worm and mouse pathways include a larger number of feedback loops and interactions than the fly. We identify 'functional orthologues' that share similar molecular interactions, but have moderate sequence similarity. Finally, we incorporate the mouse model into the web-service NetEffects and perform in silico gene perturbations of IIS components and analyses of experimental results. We identify sub-paths that, given a mutation in an IIS component, could potentially antagonize the primary effects on ageing via FOXO in mouse and via SKN-1 in worm. Finally, we explore the effects of FOXO knockouts in three different mouse tissues. AVAILABILITY AND IMPLEMENTATION http://www.ebi.ac.uk/thornton-srv/software/NetEffects.
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Affiliation(s)
- Irene Papatheodorou
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Institute of Healthy Ageing and Department of Genetics Evolution and Environment, University College London, London WC1E 6BT, UK EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Institute of Healthy Ageing and Department of Genetics Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Rudolfs Petrovs
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Institute of Healthy Ageing and Department of Genetics Evolution and Environment, University College London, London WC1E 6BT, UK
| | - Janet M Thornton
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Institute of Healthy Ageing and Department of Genetics Evolution and Environment, University College London, London WC1E 6BT, UK
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Oellrich A, Smedley D. Linking tissues to phenotypes using gene expression profiles. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau017. [PMID: 24634472 PMCID: PMC3982582 DOI: 10.1093/database/bau017] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Despite great biological and computational efforts to determine the genetic causes
underlying human heritable diseases, approximately half (3500) of these diseases are still
without an identified genetic cause. Model organism studies allow the targeted
modification of the genome and can help with the identification of genetic causes for
human diseases. Targeted modifications have led to a vast amount of model organism data.
However, these data are scattered across different databases, preventing an integrated
view and missing out on contextual information. Once we are able to combine all the
existing resources, will we be able to fully understand the causes underlying a disease
and how species differ. Here, we present an integrated data resource combining tissue
expression with phenotypes in mouse lines and bringing us one step closer to consequence
chains from a molecular level to a resulting phenotype. Mutations in genes often manifest
in phenotypes in the same tissue that the gene is expressed in. However, in other cases, a
systems level approach is required to understand how perturbations to gene-networks
connecting multiple tissues lead to a phenotype. Automated evaluation of the predicted
tissue–phenotype associations reveals that 72–76% of the phenotypes are
associated with disruption of genes expressed in the affected tissue. However,
55–64% of the individual phenotype-tissue associations show spatially
separated gene expression and phenotype manifestation. For example, we see a correlation
between ‘total body fat’ abnormalities and genes expressed in the
‘brain’, which fits recent discoveries linking genes expressed in the
hypothalamus to obesity. Finally, we demonstrate that the use of our predicted
tissue–phenotype associations can improve the detection of a known
disease–gene association when combined with a disease gene candidate prediction
tool. For example, JAK2, the known gene associated with Familial
Erythrocytosis 1, rises from the seventh best candidate to the top hit
when the associated tissues are taken into consideration. Database URL:http://www.sanger.ac.uk/resources/databases/phenodigm/phenotype/list
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Affiliation(s)
- Anika Oellrich
- Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
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Guziolowski C, Videla S, Eduati F, Thiele S, Cokelaer T, Siegel A, Saez-Rodriguez J. Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming. Bioinformatics 2013; 29:2320-6. [PMID: 23853063 PMCID: PMC3753570 DOI: 10.1093/bioinformatics/btt393] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2013] [Revised: 06/17/2013] [Accepted: 07/04/2013] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions. RESULTS We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. AVAILABILITY caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. SUPPLEMENTARY INFORMATION Supplementary materials are available at Bioinformatics online. CONTACT santiago.videla@irisa.fr.
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Ivanov DK, Papatheodorou I, Ziehm M, Thornton JM. Transcriptional feedback in the insulin signalling pathway modulates ageing in both Caenorhabditis elegans and Drosophila melanogaster. MOLECULAR BIOSYSTEMS 2013; 9:1756-64. [PMID: 23624434 PMCID: PMC3693544 DOI: 10.1039/c3mb25485b] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Several components have been previously identified, that modulate longevity in several species, including the target of rapamycin (TOR) and the Insulin/IGF-1 (IIS) signalling pathways. In order to infer paths and transcriptional feedback loops that are likely to modulate ageing, we manually built a comprehensive and computationally efficient signalling network model of the IIS and TOR pathways in worms. The core insulin transduction is signalling from the sole insulin receptor daf-2 to ultimately inhibit the translocation of the transcription factor daf-16 into the nucleus. Reduction in this core signalling is thought to increase longevity in several species. In addition to this core insulin signalling, we have also recorded in our worm model the transcription factors skn-1 and hif-1, those are also thought to modulate ageing in a daf-16 independent manner. Several paths that are likely to modulate ageing were inferred via a web-based service NetEffects, by utilising perturbed components (rheb-1, let-363, aak-2, daf-2;daf-16 and InR;foxo in worms and flies respectively) from freely available gene expression microarrays. These included "routes" from TOR pathway to transcription factors daf-16, skn-1, hif-1 and daf-16 independent paths via skn-1/hif-1. Paths that could be tested by experimental hypotheses, with respect to relative contribution to longevity, are also discussed. Direct comparison of the IIS and TOR pathways in both worm and fly suggest a remarkable similarity. While similarities in the paths that could modulate ageing in both organisms were noted, differences are also discussed. This approach can also be extended to other pathways and processes.
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Affiliation(s)
- Dobril K Ivanov
- EMBL-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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