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Liebig's law of the minimum in the TGF-β/SMAD pathway.

Authors :
Li Y
Deng D
Höfer CT
Kim J
Do Heo W
Xu Q
Liu X
Zi Z
Source :
PLoS computational biology [PLoS Comput Biol] 2024 May 16; Vol. 20 (5), pp. e1012072. Date of Electronic Publication: 2024 May 16 (Print Publication: 2024).
Publication Year :
2024

Abstract

Cells use signaling pathways to sense and respond to their environments. The transforming growth factor-β (TGF-β) pathway produces context-specific responses. Here, we combined modeling and experimental analysis to study the dependence of the output of the TGF-β pathway on the abundance of signaling molecules in the pathway. We showed that the TGF-β pathway processes the variation of TGF-β receptor abundance using Liebig's law of the minimum, meaning that the output-modifying factor is the signaling protein that is most limited, to determine signaling responses across cell types and in single cells. We found that the abundance of either the type I (TGFBR1) or type II (TGFBR2) TGF-β receptor determined the responses of cancer cell lines, such that the receptor with relatively low abundance dictates the response. Furthermore, nuclear SMAD2 signaling correlated with the abundance of TGF-β receptor in single cells depending on the relative expression levels of TGFBR1 and TGFBR2. A similar control principle could govern the heterogeneity of signaling responses in other signaling pathways.<br />Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: X.L. is a cofounder of OnKure Therapeutics, Inc and Vesicle Therapeutics, Inc and owns equity in both companies. None of these companies has relationships or competing interests to this study. All other authors declare that they have no conflict of interests.<br /> (Copyright: © 2024 Li 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.)

Details

Language :
English
ISSN :
1553-7358
Volume :
20
Issue :
5
Database :
MEDLINE
Journal :
PLoS computational biology
Publication Type :
Academic Journal
Accession number :
38753874
Full Text :
https://doi.org/10.1371/journal.pcbi.1012072