Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitry
- Columbia University, United States
Abstract
Cells adapt to familiar changes in their environment by activating predefined regulatory programs that establish adaptive gene expression states. These hard-wired pathways, however, may be inadequate for adaptation to environments never encountered before. Here, we reveal evidence for an alternative mode of gene regulation that enables adaptation to adverse conditions without relying on external sensory information or genetically predetermined cis-regulation. Instead, individual genes achieve optimal expression levels through a stochastic search for improved fitness. By focusing on improving the overall health of the cell, the proposed stochastic tuning mechanism discovers global gene expression states that are fundamentally new and yet optimized for novel environments. We provide experimental evidence for stochastic tuning in the adaptation of Saccharomyces cerevisiae to laboratory-engineered environments that are foreign to its native gene-regulatory network. Stochastic tuning operates locally at individual gene promoters, and its efficacy is modulated by perturbations to chromatin modification machinery.
Data availability
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NGS data from Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitryPublicly available at the NCBI Sequence Read Archive (accession no: SAMN07652631).
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NGS data from Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitryPublicly available at the NCBI Sequence Read Archive (accession no: SAMN07652632).
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NGS data from Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitryPublicly available at the NCBI Sequence Read Archive (accession no: SAMN07652633).
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NGS data from Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitryPublicly available at the NCBI Sequence Read Archive (accession no: SAMN07652634).
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NGS data from Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitryPublicly available at the NCBI Sequence Read Archive (accession no: SAMN07652635).
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NGS data from Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitryPublicly available at the NCBI Sequence Read Archive (accession no: SAMN07652636).
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NGS data from Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitryPublicly available at the NCBI Sequence Read Archive (accession no: SAMN07652637).
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NGS data from Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitryPublicly available at the NCBI Sequence Read Archive (accession no: SAMN07652638).
Article and author information
Author details
Peter L Freddolino
Funding
NIH Office of the Director (8DP1ES022578)
- Saeed Tavazoie
National Institutes of Health (K99 (GM097033-01A1))
- Peter L Freddolino
National Institutes of Health (MSTP)
- Jamie Yang
National Institutes of Health (R01-AI077562)
- Saeed Tavazoie
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Naama Barkai, Weizmann Institute of Science, Israel
Version history
- Received: September 9, 2017
- Accepted: April 4, 2018
- Accepted Manuscript published: April 5, 2018 (version 1)
- Version of Record published: April 26, 2018 (version 2)
Copyright
© 2018, Freddolino et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitry
eLife 7:e31867.
https://doi.org/10.7554/eLife.31867
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