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Published December 24, 2019 | Supplemental Material + Submitted
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Physiological Adaptability and Parametric Versatility in a Simple Genetic Circuit

Abstract

The intimate relationship between the environment and cellular growth rate has remained a major topic of inquiry in bacterial physiology for over a century. Now, as it becomes possible to understand how the growth rate dictates the wholesale reorganization of the intracellular molecular composition, we can interrogate the biophysical principles underlying this adaptive response. Regulation of gene expression drives this adaptation, with changes in growth rate tied to the activation or repression of genes covering enormous swaths of the genome. Here, we dissect how physiological perturbations alter the expression of a circuit which has been extensively characterized in a single physiological state. Given a complete thermodynamic model, we map changes in physiology directly to the biophysical parameters which define the expression. Controlling the growth rate via modulating the available carbon source or growth temperature, we measure the level of gene expression from a LacI-regulated promoter where the LacI copy number is directly measured in each condition, permitting parameter-free prediction of the expression level. The transcriptional output of this circuit is remarkably robust, with expression of the repressor being largely insensitive to the growth rate. The predicted gene expression quantitatively captures the observations under different carbon conditions, indicating that the bio-physical parameters are indifferent to the physiology. Interestingly, temperature controls the expression level in ways that are inconsistent with the prediction, revealing temperature-dependent effects that challenge current models. This work exposes the strengths and weaknesses of thermodynamic models in fluctuating environments, posing novel challenges and utility in studying physiological adaptation. Significance. Cells adapt to changing environmental conditions by repressing or activating gene expression from enormous fractions of their genome, drastically changing the molecular composition of the cell. This requires the concerted adaptation of transcription factors to the environmental signals, leading to binding or releasing of their cognate sequences. Here, we dissect a well characterized genetic circuit in a number of physiological states, make predictions of the response, and measure how the copy number of a regulator and its gene target are affected. We find the parameters defining the regulators behavior are remarkably robust to changes in the nutrient availability, but are susceptible to temperature changes. We quantitatively explore these two effects and discuss how they challenge current models of transcriptional regulation.

Additional Information

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. bioRxiv preprint first posted online Dec. 19, 2019. We thank Prof. David Van Valen, Ed Pao, Geneva Miller, and Andy Halleran for access and training for the use of the Biotek plate reader for growth rate measurements. The experimental efforts of this work first took place during the Physiology course at the Marine Biological Laboratory operated by the University of Chicago in Woods Hole, MA, USA. We thank Celine Akelmade, George Bell, Cayla Jewett, Emily Meltzer, and Elizabeth Mueller for their work on the project during the course. We also thank Suzannah Beeler, Nathan Belliveau, Justin Bois, Rob Brewster, Soichi Hirokawa, Heun Jin Lee, Muir Morrison, Manuel Razo-Mejia, and Gabe Salmon for extensive advice and discussion. This work was supported by NIH funding under 1R35 GM118043 Maximizing Investigators' Research Award (MIRA) and through the John Templeton Foundation as part of the Boundaries of Life Initiative via grants 51250 and 60973. Code and Data Availability. All code used in this work is available on the paper website and associated GitHub repository(DOI:10.5281/zenodo.3560369). This work also used the open-source software tools SuperSegger v.1.1(45, 46) for lineage tracking and FitDeriv v.1.03 (44) for the nonparametric estimation of growth rates. Raw image files are large (~ 1.8 Tb) and are therefore available upon request. The clist.mat files used to calculate fold-change and to assign sibling cells can be accessed via the associated GitHub repository via (DOI: 0.5281/zenodo.3560369) or through Caltech DATA under the DOI:10.22002/D1.1315. The authors declare no conflict of interest.

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Submitted - 2019.12.19.878462v1.full.pdf

Supplemental Material - media-1.pdf

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Additional details

Created:
August 19, 2023
Modified:
October 23, 2023