Description
After induction of ischemic chronic heart failure (CHF), mice exhibited depression-like behavior, in terms of increased anhedonia, and decreased both exploratory activity and interest in novelty. On histology, ischemic CHF mice showed no alterations in overall cerebral morphology. To further evaluate relevant behavioral changes found in CHF mice, RNA-sequencing analysis of prefrontal cortex and hippocampus - the brain regions, whose structural and functional alterations are associated with an increased risk for developing major depressive disorder - and of left myocardial tissue was performed in CHF vs. sham-operated animals. RNA-sequencing revealed relevant changes in hippocampal or prefrontal cortical expression of genes responsible for axonal vesicle transport (Kif5b), signal transduction (Arc, Gabrb2), limitation of inflammation (RORA; Nr4a1) and of hypoxic brain damage (Hif3a). Besides, the actual literature describes some of the genes (RORA, Gabrb2, Npas4, and Junb) being associated with depression-like behavior. Nr4a1 significantly regulated in both brain and heart tissue after induction of ischemic CHF could be a potential link and reveals the central role of inflammation in the interrelation of the brain and the failing heart. Overall design: Heart failure vs. sham-operation were performed in C57BL/6 male mice. After development of chronic heart failure (CHF) 8 weeks after the operation RNA was extracted out of prefrontal cortex, hippocampus and left ventricular myocardium in both groups. RNA of 3 ischemic CHF mice versus 6 sham operated mice was pooled and further subjected to RNA sequencing. To fabricate singular pools each probe of the group equally contributed with the final amount of 2 µg RNA per pool with the result that we had 6 different pools to be further evaluated. The mRNA profile was generated by IGA Technology, Italy (http://www.igatechnology.com/) by deep sequencing, using Illumina HiSeq 2000 platform (HiSeq). CLC-Bio Genomics Workbench software (CLC Bio, Denmark) was used to calculate gene expression levels based on Mortazavi et al. (Nat Methods. 2008;5:621-628) approach.