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Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer’s Disease Progression

BACKGROUND: One primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe perturbations of mult...

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Autores principales: Arefin, Ahmed Shamsul, Mathieson, Luke, Johnstone, Daniel, Berretta, Regina, Moscato, Pablo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3448659/
https://www.ncbi.nlm.nih.gov/pubmed/23029078
http://dx.doi.org/10.1371/journal.pone.0045535
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author Arefin, Ahmed Shamsul
Mathieson, Luke
Johnstone, Daniel
Berretta, Regina
Moscato, Pablo
author_facet Arefin, Ahmed Shamsul
Mathieson, Luke
Johnstone, Daniel
Berretta, Regina
Moscato, Pablo
author_sort Arefin, Ahmed Shamsul
collection PubMed
description BACKGROUND: One primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe perturbations of multiple molecular systems, such as Alzheimer’s disease (AD), this univariate approach often results in a large list of seemingly unrelated transcripts. We utilised a powerful multivariate clustering approach to identify clusters of RNA biomarkers strongly associated with markers of AD progression. We discuss the value of considering pairs of transcripts which, in contrast to individual transcripts, helps avoid natural human transcriptome variation that can overshadow disease-related changes. METHODOLOGY/PRINCIPAL FINDINGS: We re-analysed a dataset of hippocampal transcript levels in nine controls and 22 patients with varying degrees of AD. A large-scale clustering approach determined groups of transcript probe sets that correlate strongly with measures of AD progression, including both clinical and neuropathological measures and quantifiers of the characteristic transcriptome shift from control to severe AD. This enabled identification of restricted groups of highly correlated probe sets from an initial list of 1,372 previously published by our group. We repeated this analysis on an expanded dataset that included all pair-wise combinations of the 1,372 probe sets. As clustering of this massive dataset is unfeasible using standard computational tools, we adapted and re-implemented a clustering algorithm that uses external memory algorithmic approach. This identified various pairs that strongly correlated with markers of AD progression and highlighted important biological pathways potentially involved in AD pathogenesis. CONCLUSIONS/SIGNIFICANCE: Our analyses demonstrate that, although there exists a relatively large molecular signature of AD progression, only a small number of transcripts recurrently cluster with different markers of AD progression. Furthermore, considering the relationship between two transcripts can highlight important biological relationships that are missed when considering either transcript in isolation.
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spelling pubmed-34486592012-10-01 Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer’s Disease Progression Arefin, Ahmed Shamsul Mathieson, Luke Johnstone, Daniel Berretta, Regina Moscato, Pablo PLoS One Research Article BACKGROUND: One primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe perturbations of multiple molecular systems, such as Alzheimer’s disease (AD), this univariate approach often results in a large list of seemingly unrelated transcripts. We utilised a powerful multivariate clustering approach to identify clusters of RNA biomarkers strongly associated with markers of AD progression. We discuss the value of considering pairs of transcripts which, in contrast to individual transcripts, helps avoid natural human transcriptome variation that can overshadow disease-related changes. METHODOLOGY/PRINCIPAL FINDINGS: We re-analysed a dataset of hippocampal transcript levels in nine controls and 22 patients with varying degrees of AD. A large-scale clustering approach determined groups of transcript probe sets that correlate strongly with measures of AD progression, including both clinical and neuropathological measures and quantifiers of the characteristic transcriptome shift from control to severe AD. This enabled identification of restricted groups of highly correlated probe sets from an initial list of 1,372 previously published by our group. We repeated this analysis on an expanded dataset that included all pair-wise combinations of the 1,372 probe sets. As clustering of this massive dataset is unfeasible using standard computational tools, we adapted and re-implemented a clustering algorithm that uses external memory algorithmic approach. This identified various pairs that strongly correlated with markers of AD progression and highlighted important biological pathways potentially involved in AD pathogenesis. CONCLUSIONS/SIGNIFICANCE: Our analyses demonstrate that, although there exists a relatively large molecular signature of AD progression, only a small number of transcripts recurrently cluster with different markers of AD progression. Furthermore, considering the relationship between two transcripts can highlight important biological relationships that are missed when considering either transcript in isolation. Public Library of Science 2012-09-21 /pmc/articles/PMC3448659/ /pubmed/23029078 http://dx.doi.org/10.1371/journal.pone.0045535 Text en © 2012 Arefin et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Arefin, Ahmed Shamsul
Mathieson, Luke
Johnstone, Daniel
Berretta, Regina
Moscato, Pablo
Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer’s Disease Progression
title Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer’s Disease Progression
title_full Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer’s Disease Progression
title_fullStr Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer’s Disease Progression
title_full_unstemmed Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer’s Disease Progression
title_short Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer’s Disease Progression
title_sort unveiling clusters of rna transcript pairs associated with markers of alzheimer’s disease progression
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3448659/
https://www.ncbi.nlm.nih.gov/pubmed/23029078
http://dx.doi.org/10.1371/journal.pone.0045535
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