Abstract 581: Determining the Nuclear Transcription Factor Network Controlling Expression of the Cardiac Inotropic Factor S100A1

Abstract

Down-regulation of cardiac inotropic EF-hand calcium sensor protein S100A1 expression is a hallmark of human heart failure and drives progression and mortality. An AAV-S100A1 gene replacement therapy, which has been shown in preclinical models to rescue chronic heart failure, is in preparation for first clinical trials. However, the nuclear mechanisms that convey S100A1 gene locus silencing in the failing heart have not been determined yet. Given the steady increase of S100A1 expression in the developing heart until young adulthood and its decrease in the aged and failing heart, we developed a novel approach to decipher the mechanisms of S100a1 transcriptional regulation. We performed serial RNAseq analyses in an in vitro cardiac differentiation model of H9c2 cells that displays robust concurrent S100A1 and e.g. sarcoplasmic reticulum calcium ATPase 2 (SERCA2a) mRNA and protein during differentiation. Potential transcription factors (TF) were derived by computational motif analysis from S100A1’s gene locus regulatory elements that delivered more than 200 candidates. Subsequent computational TF footprint binding motive analysis from all differentially expressed transcripts of the serial RNAseq data provided a smaller group of less than 30 putatively TF families in the cH9C2 model that is actively during the in vitro biological process. Additional comparative TF expression analyses with developing rat hearts then enabled subtraction of a final TF group of 10 members for RNA interference screens in the cH9C2 model using S100A1 RNA expression as a read-out. Using a siRNA-mediated TF knock-down screen, we identified a small group of TFs such as zink finger and coiled-coil TFs (e.g. KLFs) and signal transducer and activator of transcription (STATs) TFs with an hitherto unknown ability to independently regulate S100A1 expression. Of note, other inotropic factors such as SERCA2a were found to be positively co-regulated by some of the TFs, such as KLFs, that enhance S100A1 expression. Further analyses were performed to confirm these results. In summary, we present a feasible computational filter and experimental strategy to identify relevant TF networks which we have successfully applied to identify cardiac regulatory networks.