Sex bias evaluation of classic and novel Housekeeping Genes in adipose tissue through the massive analysis of transcriptomics data

Abstract

ABSTRACTHousekeeping genes (HKG), those involved in the maintenance of basic cell functions, are considered to have constant expression levels in all cell types, and are therefore commonly used as internal controls in gene expression studies. Nevertheless, multiple studies have shown that not all of them have stable expression levels across different cells, tissues, and conditions, introducing a systematic error in the experimental results. The proper selection and validation of control housekeeping genes in the specific studied conditions is crucial for the validity of the obtained results, although, up to date, sex has never been taken into account as a biological variable.In this work, we evaluate the expression profiles of six classical housekeeping genes, (four metabolic: HPRT, GAPDH, PPIA and UBC, and two ribosomal: 18S and RPL19) used as controls in several tissues, to determine the stability of their expression in adipose tissue of Homo sapiens and Mus musculus and asses sex bias and control suitability. We also evaluated gene expression stability of the genes included in different whole transcriptome microarrays available at the Gene Expression Omnibus database (GEO), to identify new genes suitable to be used as sex-unbiased controls. We perform a sex-based analysis to test for/reveal sexual dimorphism of mRNA expression stability.We use a novel computational strategy based on meta-analysis techniques which evidence that some classical housekeeping genes do not fit to analyze human adipose tissue when sex variable is included. For instance, the extensively used 18S has shown to be variable in this tissue, while PPIA and RPL19 have shown to be good HKG targets. Further, we propose new sex-unbiased human and mouse housekeeping genes, derived from sex-specific expression profiles, including, RPS8 or UBB. All the results generated in this work are available in an open web resource (https://bioinfo.cipf.es/metafun-HKG), so that they can be consulted and used in further studies.