Post-transcriptional regulation across human tissues
Alexander Franks, Edoardo Airoldi, Nikolai Slavov
Published: May 8, 2017 https://doi.org/10.1371/journal.pcbi.1005535
Source/Fonte: Nicolle Rager, National Science Foundation
Abstract
Transcriptional and post-transcriptional regulation shape tissue-type-specific proteomes , but their relative contributions remain contested. Estimates of the factors determining protein levels in human tissues do not distinguish between (i) the factors determining the variability between the abundances of different proteins, i.e., mean-level-variability and, (ii) the factors determining the physiological variability of the same protein across different tissue types, i.e., across -tissues variability. We sought to estimate the contribution of transcript levels to these two orthogonal sources of variability, and found that scaled mRNA levels can account for most of the mean-level-variability but not necessarily for across -tissues variability. The reliable quantification of the latter estimate is limited by substantial measurement noise. However, protein-to-mRNA ratios exhibit substantial across-tissues variability that is functionally concerted and reproducible across different datasets , suggesting extensive post-transcriptional regulation. These results caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation to shaping tissue-type-specific proteomes .
Author Summary
The identity of human tissues depends on their protein levels. Are tissue protein levels set largely by corresponding mRNA levels or by other (post-transcriptional) regulatory mechanisms? We revisit this question based on statistical analysis of mRNA and protein levels measured across human tissues. We find that for any one gene, its protein levels across tissues are poorly predicted by its mRNA levels, suggesting tissue-specific post-transcriptional regulation. In contrast, the overall protein levels are well predicted by scaled mRNA levels. We show how these speciously contradictory findings are consistent with each other and represent the two sides of Simpson’s paradox.
Citation: Franks A, Airoldi E, Slavov N (2017) Post-transcriptional regulation across human tissues. PLoS Comput Biol 13( 5): e1005535. https://doi.org/10.1371/journal.pcbi.1005535
Editor: Christine Vogel, NYU, UNITED STATES
Received: December 19, 2016; Accepted: April 26, 2017; Published: May 8, 2017
Copyright: © 2017 Franks et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data and code are available from https://github.com/afranks86/tissue-ptr and from https://web.northeastern.edu/slavovlab/2016_PTR/.
Funding: This work was partially funded by a SPARC grant from the Broad Institute to NS and EA (https://www.broadinstitute.org/), the Washington Research Foundation Fund for Innovation in Data-Intensive Discovery (wrf.washington.edu/) and the Moore/Sloan Data Science Environments Project at the University of Washington (msdse.org/), and NIGMS of the NIH under Award Number DP2GM123497 (https://www.nigms.nih.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
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