Principles of mRNA control by human PUM proteins elucidated from multimodal experiments and integrative data analysis
- Michael B. Wolfe1,10,
- Trista L. Schagat2,
- Michelle T. Paulsen3,
- Brian Magnuson4,5,
- Mats Ljungman3,5,6,9,
- Daeyoon Park7,
- Chi Zhang8,
- Zachary T. Campbell8,
- Aaron C. Goldstrohm7 and
- Peter L. Freddolino1
- 1Department of Biological Chemistry and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA
- 2Promega Corporation, Fitchburg, Wisconsin 53711, USA
- 3Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109, USA
- 4Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, USA
- 5Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan 48109, USA
- 6Department of Environmental Health Sciences, University of Michigan, Ann Arbor, Michigan 48109, USA
- 7Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota 55455, USA
- 8Department of Biological Sciences, University of Texas at Dallas, Richardson, Texas 75080, USA
- 9Center for RNA Biomedicine, University of Michigan, Ann Arbor, Michigan 48109, USA
- Corresponding authors: petefred{at}umich.edu, agoldstr{at}umn.edu
Abstract
The human PUF-family proteins, PUM1 and PUM2, posttranscriptionally regulate gene expression by binding to a PUM recognition element (PRE) in the 3′-UTR of target mRNAs. Hundreds of PUM1/2 targets have been identified from changes in steady-state RNA levels; however, prior studies could not differentiate between the contributions of changes in transcription and RNA decay rates. We applied metabolic labeling to measure changes in RNA turnover in response to depletion of PUM1/2, showing that human PUM proteins regulate expression almost exclusively by changing RNA stability. We also applied an in vitro selection workflow to precisely identify the binding preferences of PUM1 and PUM2. By integrating our results with prior knowledge, we developed a “rulebook” of key contextual features that differentiate functional versus nonfunctional PREs, allowing us to train machine learning models that accurately predict the functional regulation of RNA targets by the human PUM proteins.
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Article is online at http://www.rnajournal.org/cgi/doi/10.1261/rna.077362.120.
- Received July 20, 2020.
- Accepted July 30, 2020.
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