22 research outputs found
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
First-principles prediction of the information processing capacity of a simple genetic circuit
Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has been hypothesized to have consequences for how cells are able to survive in changing environments. Recent work has explored the use of information theory as a framework to understand the accuracy with which cells can ascertain the state of their surroundings. Yet the predictive power of these approaches is limited and has not been rigorously tested using precision measurements. To that end, we generate a minimal model for a simple genetic circuit in which all parameter values for the model come from independently published data sets. We then predict the information processing capacity of the genetic circuit for a suite of biophysical parameters such as protein copy number and protein-DNA affinity. We compare these parameter-free predictions with an experimental determination of protein expression distributions and the resulting information processing capacity of E. coli cells. We find that our minimal model captures the scaling of the cell-to-cell variability in the data and the inferred information processing capacity of our simple genetic circuit up to a systematic deviation
Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems
A crucial step towards engineering biological systems is the ability to precisely tune the genetic response to environmental stimuli. In the case of Escherichia coli inducible promoters, our incomplete understanding of the relationship between sequence composition and gene expression hinders our ability to predictably control transcriptional responses. Here, we profile the expression dynamics of 8269 rationally designed, IPTG-inducible promoters that collectively explore the individual and combinatorial effects of RNA polymerase and LacI repressor binding site strengths. We then fit a statistical mechanics model to measured expression that accurately models gene expression and reveals properties of theoretically optimal inducible promoters. Furthermore, we characterize three alternative promoter architectures and show that repositioning binding sites within promoters influences the types of combinatorial effects observed between promoter elements. In total, this approach enables us to deconstruct relationships between inducible promoter elements and discover practical insights for engineering inducible promoters with desirable characteristics
Deciphering a gene regulation network in normal mouse pancreas through a multiomic integrative approach
Pancreatic acinar cells compose around 85% of the exocrine component of the
pancreas, which constitutes the vast majority of the tissue. Genetically Engineered
Mouse Models (GEMMs) provide evidence that pancreatic ductal adenocarcinoma
(PDAC) can efficiently arise from acinar cells through a transdifferentiation process
called acinar-to-ductal-metaplasia (ADM), proposing the loss of acinar cell identity as
the predominant origin for PDAC. Here, we present a comprehensive multi-omic
integrative approach to generate a network-based resource to interrogate the
transcriptional regulation underlying acinar cell identity in wild type (WT) mouse
pancreas. As a proof-of-concept, we examine the regulatory activity of several acinarexpressed transcription factors (TFs) involved in pancreas regulation and validate it by
comparison with experimental ChIP-seq analysis, obtaining consistent results. We
consider that this approach represents a valuable resource to perform a priori analyses
that can be experimentally validated providing new knowledge to the field. Moreover,
the presented methodology will be further explored to determine the optimal
parameters for improving the potential in the detection of different regulatory events,
and will be applied to GEMMs displaying different conditions, as well as to other
organisms like human to cross-validate the results and the usefulness of our resource
First-principles prediction of the information processing capacity of a simple genetic circuit
Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has been hypothesized to have consequences for how cells are able to survive in changing environments. Recent work has explored the use of information theory as a framework to understand the accuracy with which cells can ascertain the state of their surroundings. Yet the predictive power of these approaches is limited and has not been rigorously tested using precision measurements. To that end, we generate a minimal model for a simple genetic circuit in which all parameter values for the model come from independently published data sets. We then predict the information processing capacity of the genetic circuit for a suite of biophysical parameters such as protein copy number and protein-DNA affinity. We compare these parameter-free predictions with an experimental determination of protein expression distributions and the resulting information processing capacity of E. coli cells. We find that our minimal model captures the scaling of the cell-to-cell variability in the data and the inferred information processing capacity of our simple genetic circuit up to a systematic deviation
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