11 research outputs found
Physiological Adaptability and Parametric Versatility in a Simple Genetic Circuit
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
Physiological Adaptability and Parametric Versatility in a Simple Genetic Circuit
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
The Energetics of Molecular Adaptation in Transcriptional Regulation
Mutation is a critical mechanism by which evolution explores the functional
landscape of proteins. Despite our ability to experimentally inflict mutations
at will, it remains difficult to link sequence-level perturbations to
systems-level responses. Here, we present a framework centered on measuring
changes in the free energy of the system to link individual mutations in an
allosteric transcriptional repressor to the parameters which govern its
response. We find the energetic effects of the mutations can be categorized
into several classes which have characteristic curves as a function of the
inducer concentration. We experimentally test these diagnostic predictions
using the well-characterized LacI repressor of Escherichia coli, probing
several mutations in the DNA binding and inducer binding domains. We find that
the change in gene expression due to a point mutation can be captured by
modifying only a subset of the model parameters that describe the respective
domain of the wild-type protein. These parameters appear to be insulated, with
mutations in the DNA binding domain altering only the DNA affinity and those in
the inducer binding domain altering only the allosteric parameters. Changing
these subsets of parameters tunes the free energy of the system in a way that
is concordant with theoretical expectations. Finally, we show that the
induction profiles and resulting free energies associated with pairwise double
mutants can be predicted with quantitative accuracy given knowledge of the
single mutants, providing an avenue for identifying and quantifying epistatic
interactions.Comment: 11 pages, 6 figures, supplemental info. available via
http://rpgroup.caltech.edu/mwc_mutant
Predictive shifts in free energy couple mutations to their phenotypic consequences
Mutation is a critical mechanism by which evolution explores the functional landscape of proteins. Despite our ability to experimentally inflict mutations at will, it remains difficult to link sequence-level perturbations to systems-level responses. Here, we present a framework centered on measuring changes in the free energy of the system to link individual mutations in an allosteric transcriptional repressor to the parameters which govern its response. We find that the energetic effects of the mutations can be categorized into several classes which have characteristic curves as a function of the inducer concentration. We experimentally test these diagnostic predictions using the well-characterized LacI repressor of Escherichia coli, probing several mutations in the DNA binding and inducer binding domains. We find that the change in gene expression due to a point mutation can be captured by modifying only the model parameters that describe the respective domain of the wild-type protein. These parameters appear to be insulated, with mutations in the DNA binding domain altering only the DNA affinity and those in the inducer binding domain altering only the allosteric parameters. Changing these subsets of parameters tunes the free energy of the system in a way that is concordant with theoretical expectations. Finally, we show that the induction profiles and resulting free energies associated with pairwise double mutants can be predicted with quantitative accuracy given knowledge of the single mutants, providing an avenue for identifying and quantifying epistatic interactions
The Energetics of Molecular Adaptation in Transcriptional Regulation
Mutation is a critical mechanism by which evolution explores the functional landscape of proteins. Despite our ability to experimentally inflict mutations at will, it remains difficult to link sequence-level perturbations to systems-level responses. Here, we present a framework centered on measuring changes in the free energy of the system to link individual mutations in an allosteric transcriptional repressor to the parameters which govern its response. We find the energetic effects of the mutations can be categorized into several classes which have characteristic curves as a function of the inducer concentration. We experimentally test these diagnostic predictions using the well-characterized LacI repressor of Escherichia coli, probing several mutations in the DNA binding and inducer binding domains. We find that the change in gene expression due to a point mutation can be captured by modifying only a subset of the model parameters that describe the respective domain of the wild-type protein. These parameters appear to be insulated, with mutations in the DNA binding domain altering only the DNA affinity and those in the inducer binding domain altering only the allosteric parameters. Changing these subsets of parameters tunes the free energy of the system in a way that is concordant with theoretical expectations. Finally, we show that the induction profiles and resulting free energies associated with pairwise double mutants can be predicted with quantitative accuracy given knowledge of the single mutants, providing an avenue for identifying and quantifying epistatic interactions
Processed Images and Summary Files for "Phsyiological Adaptability and Parametric Versatility in a Simple Genetic Circuit"
This data set contains Matlab `clist` files that are derived from segmentation and lineage tracking of bacterial cells growing under a microscope. This data set can be used to extract information about segmented cells, but contains no images
Raw flow cytometry data for "Predictive shifts in free energy couple mutations to their phenotypic consequences""
This data set contains raw flow cytometry files for the publication "The Energetics of Molecular Adaptation in Transcriptional Regulation". Please see the included README for details of the files and missing data sets