Phenotype-Dependent Coexpression Gene Clusters: Application to Normal and Premature Ageing

Hutchinson Gilford progeria syndrome (HGPS) is a rare genetic disease with symptoms of aging at a very early age. Its molecular basis is not entirely clear, although profound gene expression changes have been reported, and there are some known and other presumed overlaps with normal aging process. I...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2015-01, Vol.12 (1), p.30-39
Main Authors: Kun Wang, Das, Avinash, Zheng-Mei Xiong, Kan Cao, Hannenhalli, Sridhar
Format: Article
Language:English
Subjects:
Aging
Aging - genetics
Aging - physiology
Algorithms
Bioinformatics
Biology and genetics
Cells, Cultured
Computational biology
Computational Biology - methods
Educational institutions
Fibroblasts
Gene expression
Gene Expression Profiling
Humans
Male
Multigene Family - genetics
Parameter learning
Phenotype
Progeria - genetics
Progeria - physiopathology
Proteins
Vectors
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Summary:Hutchinson Gilford progeria syndrome (HGPS) is a rare genetic disease with symptoms of aging at a very early age. Its molecular basis is not entirely clear, although profound gene expression changes have been reported, and there are some known and other presumed overlaps with normal aging process. Identification of genes with agingor HGPS-associated expression changes is thus an important problem. However, standard regression approaches are currently unsuitable for this task due to limited sample sizes, thus motivating development of alternative approaches. Here, we report a novel iterative multiple regression approach that leverages co-expressed gene clusters to identify gene clusters whose expression co-varies with age and/or HGPS. We have applied our approach to novel RNA-seq profiles in fibroblast cell cultures at three different cellular ages, both from HGPS patients and normal samples. After establishing the robustness of our approach, we perform a comparative investigation of biological processes underlying normal aging and HGPS. Our results recapitulate previously known processes underlying aging as well as suggest numerous unique processes underlying aging and HGPS. The approach could also be useful in detecting phenotype-dependent co-expression gene clusters in other contexts with limited sample sizes.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2014.2359446