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Gene signature-based mapping of immunological systems and diseases

BACKGROUND: The immune system is multifaceted, structured by diverse components that interconnect using multilayered dynamic cellular processes. Genomic technologies provide a means for investigating, at the molecular level, the adaptations of the immune system in host defense and its dysregulation...

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Detalles Bibliográficos
Autores principales: Liu, Hong, Liu, Jessica, Toups, Michelle, Soos, Timothy, Arendt, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4836068/
https://www.ncbi.nlm.nih.gov/pubmed/27089880
http://dx.doi.org/10.1186/s12859-016-1012-y
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author Liu, Hong
Liu, Jessica
Toups, Michelle
Soos, Timothy
Arendt, Christopher
author_facet Liu, Hong
Liu, Jessica
Toups, Michelle
Soos, Timothy
Arendt, Christopher
author_sort Liu, Hong
collection PubMed
description BACKGROUND: The immune system is multifaceted, structured by diverse components that interconnect using multilayered dynamic cellular processes. Genomic technologies provide a means for investigating, at the molecular level, the adaptations of the immune system in host defense and its dysregulation in pathological conditions. A critical aspect of intersecting and investigating complex datasets is determining how to best integrate genomic data from diverse platforms and heterogeneous sample populations to capture immunological signatures in health and disease. RESULT: We focus on gene signatures, representing highly enriched genes of immune cell subsets from both diseased and healthy tissues. From these, we construct a series of biomaps that illustrate the molecular linkages between cell subsets from different lineages, the connectivity between different immunological diseases, and the enrichment of cell subset signatures in diseased tissues. Finally, we overlay the downstream genes of drug targets with disease gene signatures to display the potential therapeutic applications for these approaches. CONCLUSION: An in silico approach has been developed to characterize immune cell subsets and diseases based on the gene signatures that most differentiate them from other biological states. This modular ‘biomap’ reveals the linkages between different diseases and immune subtypes, and provides evidence for the presence of specific immunocyte subsets in mixed tissues. The over-represented genes in disease signatures of interest can be further investigated for their functions in both host defense and disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1012-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-48360682016-04-20 Gene signature-based mapping of immunological systems and diseases Liu, Hong Liu, Jessica Toups, Michelle Soos, Timothy Arendt, Christopher BMC Bioinformatics Research Article BACKGROUND: The immune system is multifaceted, structured by diverse components that interconnect using multilayered dynamic cellular processes. Genomic technologies provide a means for investigating, at the molecular level, the adaptations of the immune system in host defense and its dysregulation in pathological conditions. A critical aspect of intersecting and investigating complex datasets is determining how to best integrate genomic data from diverse platforms and heterogeneous sample populations to capture immunological signatures in health and disease. RESULT: We focus on gene signatures, representing highly enriched genes of immune cell subsets from both diseased and healthy tissues. From these, we construct a series of biomaps that illustrate the molecular linkages between cell subsets from different lineages, the connectivity between different immunological diseases, and the enrichment of cell subset signatures in diseased tissues. Finally, we overlay the downstream genes of drug targets with disease gene signatures to display the potential therapeutic applications for these approaches. CONCLUSION: An in silico approach has been developed to characterize immune cell subsets and diseases based on the gene signatures that most differentiate them from other biological states. This modular ‘biomap’ reveals the linkages between different diseases and immune subtypes, and provides evidence for the presence of specific immunocyte subsets in mixed tissues. The over-represented genes in disease signatures of interest can be further investigated for their functions in both host defense and disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1012-y) contains supplementary material, which is available to authorized users. BioMed Central 2016-04-18 /pmc/articles/PMC4836068/ /pubmed/27089880 http://dx.doi.org/10.1186/s12859-016-1012-y Text en © Liu et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Liu, Hong
Liu, Jessica
Toups, Michelle
Soos, Timothy
Arendt, Christopher
Gene signature-based mapping of immunological systems and diseases
title Gene signature-based mapping of immunological systems and diseases
title_full Gene signature-based mapping of immunological systems and diseases
title_fullStr Gene signature-based mapping of immunological systems and diseases
title_full_unstemmed Gene signature-based mapping of immunological systems and diseases
title_short Gene signature-based mapping of immunological systems and diseases
title_sort gene signature-based mapping of immunological systems and diseases
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4836068/
https://www.ncbi.nlm.nih.gov/pubmed/27089880
http://dx.doi.org/10.1186/s12859-016-1012-y
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