Total RNA-seq to identify pharmacological effects on specific stages of mRNA synthesis

Abstract

Pharmacological perturbation is a powerful tool for understanding mRNA synthesis, but identification of the specific steps of this multi-step process that are targeted by small molecules remains challenging. Here we applied strand-specific total RNA sequencing (RNA-seq) to identify and distinguish specific pharmacological effects on transcription and pre-mRNA processing in human cells. We found unexpectedly that the natural product isoginkgetin, previously described as a splicing inhibitor, inhibits transcription elongation. Compared to well-characterized elongation inhibitors that target CDK9, isoginkgetin caused RNA polymerase accumulation within a broader promoter-proximal band, indicating that elongation inhibition by isoginkgetin occurs after release from promoter-proximal pause. RNA-seq distinguished isoginkgetin and CDK9 inhibitors from topoisomerase I inhibition, which alters elongation across gene bodies. We were able to detect these and other specific defects in mRNA synthesis at low sequencing depth using simple metagene-based metrics. These metrics now enable total-RNA-seq-based screening for high-throughput identification of pharmacological effects on individual stages of mRNA synthesis.

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Figure 1: Strand-specific total RNA-seq reveals global accumulation of nonpolyadenylated promoter-proximal RNA transcripts upon isoginkgetin (IsoG) treatment.
Figure 2: RNA-seq metagene-based metrics classify IsoG as a transcription elongation inhibitor.
Figure 3: Clustering by metrics outperforms clustering by gene expression.
Figure 4: IsoG's effect on promoter-proximal RNA polymerase accumulation is distinct from that of CDK9 inhibition.
Figure 5: The topoisomerase inhibitor camptothecin (CPT) decreases pre-mRNA expression along the bodies of long genes.

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Acknowledgements

We thank M. Hemberg and D. Harmin for advice on bioinformatic analyses. This work was funded by R01 MH101528-01. M.S. is also supported by National Science Foundation grant 1349248. S.A.B. is supported by the Harvard Medical School Center of Excellence in Systems Pharmacology NIH grant P50 GM107618 and the Giovanni Armenise-Harvard Foundation. H.M.L. and L.S.C's contributions were funded by NHGRI: R01 HG007173. We thank K. Koide (University of Pittsburgh) for sharing meayamycin.

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M.S. and J.M.G. conceived the study. S.A.B. performed all of the experiments except those mentioned below. A.S. performed the western blot in Figure 4a and data analysis of meayamycin experiments. H.M.L. performed the NET-seq protocol. H.M.L. and L.S.C. helped analyze the NET-Seq data. J.M.G., S.A.B., and M.S. performed bioinformatics analysis. J.M.G., S.A.B., A.S., and M.S. interpreted the results. J.M.G., S.A.B., and M.S. wrote the manuscript.

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Correspondence to Jesse M Gray or Michael Springer.

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The authors declare no competing financial interests.

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Supplementary Results, Supplementary Table 1 and Supplementary Figures 1–5 (PDF 9252 kb)

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Boswell, S., Snavely, A., Landry, H. et al. Total RNA-seq to identify pharmacological effects on specific stages of mRNA synthesis. Nat Chem Biol 13, 501–507 (2017). https://doi.org/10.1038/nchembio.2317

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