Dynamics and functional roles of splicing factor autoregulation
Non-core spliceosome components are essential, conserved regulators of alternative splicing. They provide concentration-dependent control of diverse pre-mRNAs. Many splicing factors direct unproductive splicing of their own pre-mRNAs through negative autoregulation. However, the impact of such feedback loops on splicing dynamics at the single-cell level remains unclear. Here, we developed a system to quantitatively analyze negative autoregulatory splicing dynamics by splicing factor SRSF1 in response to perturbations in single HEK293 cells. We show that negative autoregulatory splicing provides critical functions for gene regulation, establishing a ceiling of SRSF1 protein concentration, reducing cell-cell heterogeneity in SRSF1 levels, and buffering variation in transcription. Most important, it adapts SRSF1 splicing activity to variations in demand from other pre-mRNA substrates. A minimal mathematical model of autoregulatory splicing explains these experimentally observed features and provides values for effective biochemical parameters. These results reveal the unique functional roles that splicing negative autoregulation plays in homeostatically regulating transcriptional programs.
Additional Information© 2022 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Received 19 July 2021, Revised 1 February 2022, Accepted 31 May 2022, Available online 21 June 2022, Version of Record 21 June 2022. We thank F. Tan for providing the western blot protocol, N. Nandagopal and Y. Antebi for technical assistance, and Life Sciences Editors (Sabbi Lall) for critical feedback on the manuscript. We also thank S. Sun, A.R. Krainer, D. Sprinzak, D. Baltimore, J.G. Ojalvo, and N. Wingreen for discussion and feedback on the project. F.D. was supported by a fellowship from the Schlumberger Foundation. C.J.S. is supported by NIH National Institute of General Medical Sciences training grant no. GM008042, and by a David Geffen Medical Scholarship. The work was funded by the Gordon and Betty Moore Foundation through grant no. GBMF2809 to the Caltech Programmable Molecular Technology Initiative and the Institute for Collaborative Biotechnologies through grant no. W911NF-09-0001 from the US Army Research Office, AWS Machine Learning Research Awards, and the Intel Corporation. The content of the information does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred. M.B.E. is a Howard Hughes Medical Institute investigator. This article is subject to HHMI's Open Access to Publications policy. HHMI lab heads have previously granted a nonexclusive CC BY 4.0 license to the public and a sublicensable license to HHMI in their research articles. Pursuant to those licenses, the author-accepted manuscript of this article can be made freely available under a CC BY 4.0 license immediately upon publication. Author contributions: F.D. and M.B.E. designed the experiments. F.D., K.E., and G.L. performed experiments. F.D. analyzed data. C.S. and F.D. performed the mathematical modeling. F.D., C.J.S., and M.B.E. wrote the manuscript. The authors declare no competing interests. Data and code availability: Data generated in this study are available from the lead contact upon request. All data reported in this paper will be shared by the lead contact upon request. Original codes are deposited and publicly accessible. DOI is listed in the Key resources table. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
Published - 1-s2.0-S2211124722007719-main.pdf
Submitted - 2020.07.22.216887v1.full.pdf
Supplemental Material - 1-s2.0-S2211124722007719-mmc1.pdf