title: Normalisation against Circadian and Age-Related Disturbances Enables Robust Detection of Gene Expression Changes in Liver of Aged Mice.
abstract: The expression of some genes is affected by age. To detect such age-related changes, their expression levels are related to constant marker genes. However, transcriptional noise increasing with advancing age renders difficult the identification of real age-related changes because it may affect the marker genes as well. Here, we report a selection procedure for genes appropriate to normalise the mouse liver transcriptome under various conditions including age. These genes were chosen from an initial set of 16 candidate genes defined based on a RNA-sequencing experiment and published literature. A subset of genes was selected based on rigorous statistical assessment of their variability using both RNA-sequencing and Nanostring hybridization experiments. The robustness of these marker genes was then verified by the analysis of 130 publicly available data sets using the mouse liver transcriptome. Altogether, a set of three genes, Atp5h, Gsk3β, and Sirt2 fulfilled our strict selection criteria in all assessments, while four more genes, Nono, Tprkb, Tspo, and Ttr passed all but one assessment and were included into the final set of marker genes to enhance robustness of normalisation against outliers. Using the geometric mean of expression of the genes to normalise Nanostring hybridization experiments we reliably identified age-related increases in the expression of Casein kinase 1δ and 1ϵ, and Sfpq, while the expression of the glucose transporter Glut2 decreased. The age-related changes were verified by real-time PCR and Western blot analysis. As conclusion, proper normalisation enhances the robustness of quantitative methods addressing age-related changes of a transcriptome.
authors.affiliation: Department of Biology, University of Fribourg, Fribourg, Switzerland.
authors.forename: Sara S
authors.initials: SS
authors.lastname: Fonseca Costa
authors.affiliation: Department of Biology, University of Fribourg, Fribourg, Switzerland.|Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland.
authors.forename: Daniel
authors.initials: D
authors.lastname: Wegmann
authors.affiliation: Department of Biology, University of Fribourg, Fribourg, Switzerland.
authors.forename: Jürgen A
authors.initials: JA
authors.lastname: Ripperger
mesh_terms: D000375:Aging / Q000235:genetics*; D000818:Animals; D002940:Circadian Rhythm / Q000235:genetics*; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D005819:Genetic Markers; D008099:Liver / Q000378:metabolism*; D051379:Mice; D059467:Transcriptome
chemical_list: D005819:Genetic Markers