Gene co-expression network topology provides a framework for molecular characterization of cellular state

Carter, Scott L.; Brechbühler, Christian M.; Griffin, Michael; Bond, Andrew T.

doi:10.1093/bioinformatics/bth234

Cited by 317 publications

(268 citation statements)

References 21 publications

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“…A distance measure commonly used for coexpression analysis is based on the Pearson's coefficient of correlation; in this approach, gene pairs with a coefficient of correlation below a given cutoff value (e.g., 0.8) are considered as not correlated. However, this kind of "hard thresholding" may be insensitive to subtle and yet important expression patterns (85). We therefore used the WGCNA method (33,34), which uses a "soft thresholding" and the concept of topological overlap or shared neighbors to identify clusters of coexpressed genes.…”

Section: Methodsmentioning

confidence: 99%

Protein homeostasis of a metastable subproteome associated with Alzheimer’s disease

Kundra

Ciryam

Morimoto

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Alzheimer's disease is the most common cause of dementia. A hallmark of this disease is the presence of aberrant deposits containing by the Aβ peptide (amyloid plaques) and the tau protein (neurofibrillary tangles) in the brains of affected individuals. Increasing evidence suggests that the formation of these deposits is closely associated with the age-related dysregulation of a large set of highly expressed and aggregation-prone proteins, which make up a metastable subproteome. To understand in more detail the origins of such dysregulation, we identify specific components of the protein homeostasis system associated with these metastable proteins by using a gene coexpression analysis. Our results reveal the particular importance of the protein trafficking and clearance mechanisms, including specific branches of the endosomal-lysosomal and ubiquitin-proteasome systems, in maintaining the homeostasis of the metastable subproteome associated with Alzheimer's disease.Alzheimer's disease | protein aggregation | protein homeostasis | supersaturation

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Section: Methodsmentioning

confidence: 99%

Protein homeostasis of a metastable subproteome associated with Alzheimer’s disease

Kundra

Ciryam

Morimoto

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…We tested whether the commonly used coexpression method (Carter et al, 2004) generates a network similar to that of Figure 4 (see Supplemental Methods 1 online). We found that 75% of Arabidopsis GSN edges are different from those found in the corresponding coexpression network, supporting our conclusion (Figure 2) that GSN and coexpression networks capture different properties of the expression data (see Discussion).…”

Section: Arabidopsis Gsnmentioning

confidence: 99%

Gene-Sharing Networks Reveal Organizing Principles of Transcriptomes in Arabidopsis and Other Multicellular Organisms

Pandey

Gookin

et al. 2012

Plant Cell

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“…This basic molecular mechanism leads naturally toward a network view of transcription in which individual genes serve as nodes connected by edges to other genes (Lee et al, 2002). Transcriptional networks can be constructed from microarray data based upon co-expression analysis (Carter et al, 2004;Rice et al, 2005;Wolfe et al, 2005;Wang et al, 2006;Freeman et al, 2007;Huttenhower et al, 2007). Co-expression analysis connects genes within a network when the correlation between their expression profiles exceeds a certain threshold.…”

Section: Introductionmentioning

confidence: 99%

Genes regulated by caloric restriction have unique roles within transcriptional networks

Swindell

2008

Mechanisms of Ageing and Development

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Caloric restriction (CR) has received much interest as an intervention that delays age-related disease and increases lifespan. Whole-genome microarrays have been used to identify specific genes underlying these effects, and in mice, this has led to the identification of genes with expression responses to CR that are shared across multiple tissue types. Such CR-regulated genes represent strong candidates for future investigation, but have been understood only as a list, without regard to their broader role within transcriptional networks. In this study, co-expression and network properties of CR-regulated genes were investigated using data generated by more than 600 Affymetrix microarrays. This analysis identified groups of co-expressed genes and regulatory factors associated with the mammalian CR response, and uncovered surprising network properties of CR-regulated genes. Genes downregulated by CR were highly connected and located in dense network regions. In contrast, CR-upregulated genes were weakly connected and positioned in sparse network regions. Some network properties were mirrored by CR-regulated genes from invertebrate models, suggesting an evolutionary basis for the observed patterns. These findings contribute to a systems-level picture of how CR influences transcription within mammalian cells, and point towards a comprehensive understanding of CR in terms of its influence on biological networks.

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Gene co-expression network topology provides a framework for molecular characterization of cellular state

Abstract: Software implementing the methods for network generation presented in this paper is available for academic use by request from the authors in the form of compiled linux binary executables.

Cited by 317 publications

References 21 publications

Protein homeostasis of a metastable subproteome associated with Alzheimer’s disease

Protein homeostasis of a metastable subproteome associated with Alzheimer’s disease

Gene-Sharing Networks Reveal Organizing Principles of Transcriptomes in Arabidopsis and Other Multicellular Organisms

Genes regulated by caloric restriction have unique roles within transcriptional networks

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