Cargando…

Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification

BACKGROUND: Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of kno...

Descripción completa

Detalles Bibliográficos
Autores principales: Gormley, Michael, Tozeren, Aydin
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2620272/
https://www.ncbi.nlm.nih.gov/pubmed/19014681
http://dx.doi.org/10.1186/1471-2105-9-486
_version_ 1782163367495991296
author Gormley, Michael
Tozeren, Aydin
author_facet Gormley, Michael
Tozeren, Aydin
author_sort Gormley, Michael
collection PubMed
description BACKGROUND: Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets. RESULTS: Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression. Bimodal expression patterns were also highly effective in differentiating between infectious diseases in model-based clustering of microarray data. Supervised classification with feature selection restricted to switch-like genes also recognized tissue specific and infectious disease specific signatures in independent test datasets reserved for validation. Determination of "on" and "off" states of switch-like genes in various tissues and diseases allowed for the identification of activated/deactivated pathways. Activated switch-like genes in neural, skeletal muscle and cardiac muscle tissue tend to have tissue-specific roles. A majority of activated genes in infectious disease are involved in processes related to the immune response. CONCLUSION: Switch-like bimodal gene sets capture genome-wide signatures from microarray data in health and infectious disease. A subset of bimodal genes coding for extracellular and membrane proteins are associated with tissue specificity, indicating a potential role for them as biomarkers provided that expression is altered in the onset of disease. Furthermore, we provide evidence that bimodal genes are involved in temporally and spatially active mechanisms including tissue-specific functions and response of the immune system to invading pathogens.
format Text
id pubmed-2620272
institution National Center for Biotechnology Information
language English
publishDate 2008
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-26202722009-01-13 Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification Gormley, Michael Tozeren, Aydin BMC Bioinformatics Research Article BACKGROUND: Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets. RESULTS: Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression. Bimodal expression patterns were also highly effective in differentiating between infectious diseases in model-based clustering of microarray data. Supervised classification with feature selection restricted to switch-like genes also recognized tissue specific and infectious disease specific signatures in independent test datasets reserved for validation. Determination of "on" and "off" states of switch-like genes in various tissues and diseases allowed for the identification of activated/deactivated pathways. Activated switch-like genes in neural, skeletal muscle and cardiac muscle tissue tend to have tissue-specific roles. A majority of activated genes in infectious disease are involved in processes related to the immune response. CONCLUSION: Switch-like bimodal gene sets capture genome-wide signatures from microarray data in health and infectious disease. A subset of bimodal genes coding for extracellular and membrane proteins are associated with tissue specificity, indicating a potential role for them as biomarkers provided that expression is altered in the onset of disease. Furthermore, we provide evidence that bimodal genes are involved in temporally and spatially active mechanisms including tissue-specific functions and response of the immune system to invading pathogens. BioMed Central 2008-11-17 /pmc/articles/PMC2620272/ /pubmed/19014681 http://dx.doi.org/10.1186/1471-2105-9-486 Text en Copyright © 2008 Gormley and Tozeren; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Gormley, Michael
Tozeren, Aydin
Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification
title Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification
title_full Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification
title_fullStr Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification
title_full_unstemmed Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification
title_short Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification
title_sort expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2620272/
https://www.ncbi.nlm.nih.gov/pubmed/19014681
http://dx.doi.org/10.1186/1471-2105-9-486
work_keys_str_mv AT gormleymichael expressionprofilesofswitchlikegenesaccuratelyclassifytissueandinfectiousdiseasephenotypesinmodelbasedclassification
AT tozerenaydin expressionprofilesofswitchlikegenesaccuratelyclassifytissueandinfectiousdiseasephenotypesinmodelbasedclassification