Dynamic reorganization of the AC16 cardiomyocyte transcriptome in response to TNFα signaling revealed by integrated genomic analyses

Defining cell type-specific transcriptomes in mammals can be challenging, especially for unannotated regions of the genome. We have developed an analytical pipeline called groHMM for annotating primary transcripts using global nuclear run-on sequencing (GRO-seq) data. Herein, we use this pipeline to characterize the transcriptome of an immortalized adult human ventricular cardiomyocyte cell line (AC16) in response to signaling by tumor necrosis factor alpha (TNFα), which is controlled in part by NF-κB, a key transcriptional regulator of inflammation. A unique aspect of this work is the use of the RNA polymerase II (Pol II) inhibitor α-amanitin, which we used to define a set of RNA polymerase I and III (Pol I and Pol III) transcripts.

Our studies illustrate how computational genomics can be used to characterize the signal-regulated transcriptome in biologically relevant cell types, providing new information about how the human genome is organized, transcribed and regulated. In addition, they show how α-amanitin can be used to reveal the Pol I and Pol III transcriptome. Furthermore, they shed new light on the regulation of the cardiomyocyte transcriptome in response to a proinflammatory signal and help to clarify the link between inflammation and cardiomyocyte function at the transcriptional level.

Using groHMM, we identified ~30,000 coding and non-coding transcribed regions in AC16 cells, which includes a set of unique Pol I and Pol III primary transcripts. Many of these transcripts have not been annotated previously, including enhancer RNAs originating from NF-κB binding sites. In addition, we observed that AC16 cells rapidly and dynamically reorganize their transcriptomes in response to TNFα stimulation in an NF-κB-dependent manner, switching from a basal state to a proinflammatory state affecting a spectrum of cardiac-associated protein-coding and non-coding genes. Moreover, we observed distinct Pol II dynamics for up- and downregulated genes, with a rapid release of Pol II into productive elongation for TNFα-stimulated genes. As expected, the TNFα-induced changes in the AC16 transcriptome resulted in corresponding changes in cognate mRNA and protein levels in a similar manner, but with delayed kinetics. Conclusions: Our studies illustrate how computational genomics can be used to characterize the signal-regulated transcriptome in biologically relevant cell types, providing new information about how the human genome is organized, transcribed and regulated. In addition, they show how α-amanitin can be used to reveal the Pol I and Pol III transcriptome. Furthermore, they shed new light on the regulation of the cardiomyocyte transcriptome in response to a proinflammatory signal and help to clarify the link between inflammation and cardiomyocyte function at the transcriptional level.