Affiliations: Siena Biotech SpA, strada del Petriccio e Belriguardo, Siena, Italy | Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London
Note:  These authors equally contributed to the work. Correspondence to: Daniela Diamanti, Siena Biotech SpA, Department of Pharmacology, strada del Petriccio e Belriguardo, 35, 53100, Siena, Italy. Tel.: +39 0577381386; Fax: +39 0577381208; E-mail: email@example.com
Note:  These authors equally contributed to the work.
Abstract: Background: Huntington's disease is a neurodegenerative disorder characterized by transcriptional alterations both in central and peripheral tissues. Therefore, the identification of a transcriptional signature in an accessible tissue can meaningfully complement current efforts in clinical biomarker development. Gene expression normalization represents an essential step in transcriptional signatures identification, and since many reference genes show altered expressions in several pathologies, the definition of stable genes in the desired tissue is required to allow correct result interpretations. Objective: The present work aimed at identifying a set of suitable reference genes for expression normalization in blood of HD patients and R6/2 mice. Methods: By crossing literature investigation and analysis of microarrays performed on blood of HD patients and healthy subjects, a set of genes was selected and tested by RT-qPCR. Employment of statistical algorithms allowed the identification of the most stable genes in human samples that were than confirmed in R6/2. Results: PPIB, PGK1, ACTB and YWHAZ represent the best possible genes combination, useful to normalize blood transcriptional analysis. To link clinical and preclinical studies, the identified genes were investigated also in blood of R6/2 and wild type mice, confirming that Ppib, Actb and Ywhaz were appropriate for expression normalization. Selected references were subsequently applied to evaluate expression of genes known to be involved in Huntington's pathological progression. Conclusions: This work highlights the importance for correct data normalization to avoid misinterpretation of results, while providing a suitable method to support quantitative gene expression analysis in preclinical and clinical investigations.