Post-transcriptional regulation across human tissues
Open accessPreprint — published as 10.1371/journal.pcbi.1005535 · Paper page
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · see citing works
SEQC/MAQC-III Consortium (2014) · Nature Biotechnology · doi:10.1038/nbt.2957 · Cited by 983 · see citing works
Dynamic profiling of the protein life cycle in response to pathogens
Jovanovic, M., Rooney, M. S., Mertins, P., Przybylski, D., Chevrier, N., Satija, R., Rodriguez, E. H., Fields, A. P., Schwartz, S., Raychowdhury, R., et al. (2015) · Science · doi:10.1126/science.1259038 · Cited by 460 · see citing works
Li, J. J., Bickel, P. J., & Biggin, M. D. (2014) · PeerJ · doi:10.7717/peerj.270 · Cited by 279 · see citing works
Csárdi, G., Franks, A., Choi, D. S., Airoldi, E. M., & Drummond, D. A. (2015) · PLOS Genetics · doi:10.1371/journal.pgen.1005206 · Cited by 181 · see citing works
Self-citationCorrelation signature of the macroscopic states of the gene regulatory network in cancer
Slavov, N., & Dawson, K. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810803106 · Cited by 49 · see citing works
Self-citation
Flagged first, then by citations
- Authors
- Franks, A., Airoldi, E., & Slavov, N.
- Affiliations
- University of Washington (US)
- Harvard University (US)
- Northeastern University (US)
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2015-05-31
- OpenAlex
- W2613755444
- Cited by
- 7
- References
- 6
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 1 · 2018: 3 · 2017: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion)
- TUScholarShare (Temple University) (submittedVersion, cc-by)
- Funders
- Washington Research Foundation
- University of Washington
- Broad Institute
- Scheme for Promotion of Academic and Research Collaboration
- National Institutes of Health
- National Institute of General Medical Sciences (DP2GM123497)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- post-transcriptional regulation, transcriptome, human tissue, measurement noise
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review