pubmed.ncbi.nlm.nih.gov

Determinants of expression variability - PubMed

Determinants of expression variability

Elfalem Y Alemu et al. Nucleic Acids Res. 2014 Apr.

Abstract

The amount of tissue-specific expression variability (EV) across individuals is an essential characteristic of a gene and believed to have evolved, in part, under functional constraints. However, the determinants and functional implications of EV are only beginning to be investigated. Our analyses based on multiple expression profiles in 41 primary human tissues show that a gene's EV is significantly correlated with a number of features pertaining to the genomic, epigenomic, regulatory, polymorphic, functional, structural and network characteristics of the gene. We found that (i) EV of a gene is encoded, in part, by its genomic context and is further influenced by the epigenome; (ii) strong promoters induce less variable expression; (iii) less variable gene loci evolve under purifying selection against copy number polymorphisms; (iv) genes that encode inherently disordered or highly interacting proteins exhibit lower variability; and (v) genes with less variable expression are enriched for house-keeping functions, while genes with highly variable expression tend to function in development and extra-cellular response and are associated with human diseases. Thus, our analysis reveals a number of potential mediators as well as functional and evolutionary correlates of EV, and provides new insights into the inherent variability in eukaryotic gene expression.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.

Overall analysis pipeline. Our goal is to exhaustively characterize genetic and epigenetic determinants of gene EV in normal human tissues. We obtained and processed a curated data set of gene expression experiments from multiple tissues and estimated tissue-specific measure of expression variation (EV) for each gene. We then created a large compendium of genetic and epigenetic features for each genic region, along with additional gene features, e.g. association with disease and interaction characteristics in the PPI network. Finally we analyzed the relationships between EV and various genic features using LR, Wilcoxon test and Fisher test, as appropriate.

Figure 2.
Figure 2.

Extreme expected variability is not correlated across tissue types. Across-sample variability as a function of average expression across samples in astrocytes samples (A). We model expected across-sample variability (y-axis) of a given probeset as a function of average expression across samples (x-axis) using a gamma local regression method (red line). We show a smoothed density estimate over 54 613 probesets, where darker color indicates more probesets fall into that region of the plot. We found that probesets tend to be hypo or hypervariable in few number of tissues (B and C, respectively), indicating that neither hyper nor hypovariability for a given probeset is consistent across tissues.

Similar articles

Cited by

References

    1. Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, Zhang J, Soden R, Hayakawa M, Kreiman G, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl Acad. Sci. USA. 2004;101:6062–6067. - PMC - PubMed
    1. Sul JY, Kim TK, Lee JH, Eberwine J. Perspectives on cell reprogramming with RNA. Trends Biotechnol. 2012;30:243–249. - PMC - PubMed
    1. Kim TK, Sul JY, Peternko NB, Lee JH, Lee M, Patel VV, Kim J, Eberwine JH. Transcriptome transfer provides a model for understanding the phenotype of cardiomyocytes. Proc. Natl Acad. Sci. USA. 2011;108:11918–11923. - PMC - PubMed
    1. Raj A, van Oudenaarden A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell. 2008;135:216–226. - PMC - PubMed
    1. Kaern M, Elston TC, Blake WJ, Collins JJ. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 2005;6:451–464. - PubMed

Publication types

MeSH terms

Substances