M207. REVEALING HYPOTHALAMIC PATHWAYS CONTRIBUTION TO OLANZAPINE- INDUCED METABOLIC SYNDROME: FROM MURINE MODEL TO HUMAN TRANSLATION
Authors: Veneziani Federica , Rampino Antonio , Marakhovskaia Aleksandra , Quintana Clementine , Blasi Giuseppe , Zezza Marco , Bertolino Alessandro , Martin Beaulieu Jean 
Published: May 2020

Abstract

Background
Olanzapine (OL) represents one of the main choices for the treatment of psychotic symptoms. However, OL increase the risk of metabolic syndrome (MS). The mechanism of Olanzapine induced MS remains still unclear but hypothalamic pathways seem to be involved. The purpose of our study is to validate an innovative approach for translational studies to investigate the hypothalamic pathways contribution to OL induced MS.

Methods
To establish a murine model of Olanzapine induced MS, OL compounded in chow (54mg/Kg of HFD food) has been administered for 30 days to C57BL/6J female mice of 10 weeks old (20 mice/group). Food intake and weight gain are tested.
After the 4 weeks of treatment, mice are sacrificed by rapid cervical dislocation. Blood is collected for Glucose, Insulin and Leptin evaluation. Hypothalamus and Liver are rapidly dissected and analyzed with qPCR. Fatty liver is histologically tested with Red Oil-O- staining.
The identification of mice hypothalamic coexpression network with a Genome-wide Weighted Genes Co-expression Network Analysis (WGCNA) is performed using a publicly available mice hypothalamic RNASeq a dataset.
From the RNASeq data obtained from Perez-Gomez et al. study (PMID: 30532051) a differential gene expression (DGE) analysis is performed to identify the gene impacted by Olanzapine and verified with qPCR on our sample. The segregation of differentially expressed genes in specific modules of the mice hypothalamic network is tested.
Human hypothalamic network identification is performed using the publicly available GTEx dataset of Hypothalamic RNASeq data for a WGCNA. The segregation of differentially expressed genes of mice model in human network has been studied. An eigengene network approach is used to study the relationship between the human affected modules.

Results
From the 2nd week of treatment, the weight gain shows a significant increase (p= 0.02) in OL group compared to Control. The difference in weight gain remains unchanged until the 30th day. Likewise Blood glucose, Insuline and Leptine levels appear increased in Olanzapine group compared to control (p= 0,0089, p= 0,01, p= 0,0012 respectively). The percentage of liver parenchyma occupied by lipid droplets shows a statistically significant increase in OL treated group (p=0,0001).
14 of the 29 identified hypothalamic differentially expressed genes between OL- treated mice compared to control clusters in a single module of the WGCNA. The pathway analysis of this module reveals that Wnt signaling pathway reaches the statistical significance (FDR= 0,02 p value = 0,00006).
The co-occurrence of OL-induced hypothalamic differentially expressed genes, previously identified in mice, is analyzed on human WGCNA on hypothalamic RNASeq data. The impacted module in humans seems to be three with no identifiable pathways involved. From the eigengene analysis results that two of the three impacted modules cluster in a single hierarchical module. The pathway analysis performed on the whole eigengine module reveals that Wnt signaling pathway reaches the statistical significance (FDR= 0,01 p value = 0,00003).

Discussion
Our study firstly demonstrates the full MS-phenotype induced by Olanzapine avoiding the use of weight gain as a proxy of OL-MS as shown in previous literature. The high comparability shows by hypothalamic network analysis in mice and humans underlines the highly interspecies conservation of hypothalamic functional pathways. So the present study represents an innovative approach for translational studies on hypothalamic pathway contribution to MS induced by OL. Combining a murine model, network analysis and human translation it proposes a reliable method for translation of pre-clinical studies.