scholarly journals Noise propagation in an integrated model of bacterial gene expression and growth

2018 ◽  
Author(s):  
Istvan T. Kleijn ◽  
Laurens H. J. Krah ◽  
Rutger Hermsen
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Integrated Approach ◽  
Control Analysis ◽  
Instantaneous Growth Rate ◽  
Bacterial Cells ◽  
Noise Propagation ◽  
Bacterial Gene ◽  
Trade Offs ◽  
Cross Correlations

AbstractIn bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple proteins that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections between noise propagation and the field of metabolic control analysis. We then present a many-protein model containing > 1000 proteins parameterized by previously measured abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements.

scholarly journals The interplay between growth rate and nutrient quality defines gene expression capacity

2021 ◽  
Author(s):  
Juhyun Kim ◽  
Alexander P.S. Darlington ◽  
Declan G Bates ◽  
Jose Ignacio Jimenez
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Physiological Parameter ◽  
Bacterial Cells ◽  
Heterologous Proteins ◽  
Efficient Design ◽  
Translational Machinery ◽  
Trade Offs ◽  
Individual Contributions ◽  
The Individual

The gene expression capacity of bacterial cells depends on the interplay between growth and the availability of the transcriptional and translational machinery. Growth rate is widely accepted as the global physiological parameter controlling the allocation of cell resources. This allocation has an impact on the ability of the cell to produce both host and heterologous proteins required for synthetic circuits and pathways. Understanding the relationship between growth and resources is key for the efficient design of artificial genetic constructs, however, it is obscured by the mutual dependence of growth and gene expression on each other. In this work, we investigate the individual contributions of molecular factors, growth rate and metabolism to gene expression by investigating the behaviour of bacterial cells growing in chemostats in growth-limited conditions. We develop a model of the whole cell that captures trade-offs in gene expression arising from the individual contributions of different factors, and validate it by analysing gene couplings which emerge from competition for the gene expression machinery. Our results show that while growth rate and molecular factors, such as the number of rRNA operons, set the abundance of transcriptional and translational machinery available, it is metabolism that governs the usage of those resources by tuning elongation rates. We show that synthetic gene expression capacity can be maximised by using low growth in a high-quality medium. These findings provide valuable insights into fundamental trade-offs in microbial physiology that will inform future strain and bioprocesses optimisation.

scholarly journals A mechanistic link between cellular trade-offs, gene expression and growth

2015 ◽  
Author(s):  
Andrea Y. Weisse ◽  
Diego A. Oyarzun ◽  
Vincent Danos ◽  
Peter S. Swain
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Metabolic Pathways ◽  
Mechanistic Model ◽  
Living Cells ◽  
Coarse Grained ◽  
Empirical Relationships ◽  
Cellular Energy ◽  
Modelling Framework ◽  
Trade Offs

Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that because of limitations in levels of cellular energy, free ribosomes, and proteins are faced by all living cells and construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod's law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth related effects in dosage compensation by paralogs and predict host-circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modelling framework has potentially wide application, including in both biotechnology and medicine.

scholarly journals Noise propagation in an integrated model of bacterial gene expression and growth

2018 ◽  
Vol 14 (10) ◽  
pp. e1006386 ◽  
Author(s):  
Istvan T. Kleijn ◽  
Laurens H. J. Krah ◽  
Rutger Hermsen
Keyword(s):  
Gene Expression ◽  
Integrated Model ◽  
Noise Propagation ◽  
Bacterial Gene ◽  
Bacterial Gene Expression

scholarly journals Coordination of Growth Rate, Cell Cycle, Stress Response, and Metabolic Activity in Yeast

2008 ◽  
Vol 19 (1) ◽  
pp. 352-367 ◽  
Author(s):  
Matthew J. Brauer ◽  
Curtis Huttenhower ◽  
Edoardo M. Airoldi ◽  
Rachel Rosenstein ◽  
John C. Matese ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Growth Rate ◽  
Stress Response ◽  
System Level ◽  
Cycle Phase ◽  
Instantaneous Growth Rate ◽  
Batch Cultures ◽  
Limiting Nutrient ◽  
Yeast Genes

We studied the relationship between growth rate and genome-wide gene expression, cell cycle progression, and glucose metabolism in 36 steady-state continuous cultures limited by one of six different nutrients (glucose, ammonium, sulfate, phosphate, uracil, or leucine). The expression of more than one quarter of all yeast genes is linearly correlated with growth rate, independent of the limiting nutrient. The subset of negatively growth-correlated genes is most enriched for peroxisomal functions, whereas positively correlated genes mainly encode ribosomal functions. Many (not all) genes associated with stress response are strongly correlated with growth rate, as are genes that are periodically expressed under conditions of metabolic cycling. We confirmed a linear relationship between growth rate and the fraction of the cell population in the G0/G1 cell cycle phase, independent of limiting nutrient. Cultures limited by auxotrophic requirements wasted excess glucose, whereas those limited on phosphate, sulfate, or ammonia did not; this phenomenon (reminiscent of the “Warburg effect” in cancer cells) was confirmed in batch cultures. Using an aggregate of gene expression values, we predict (in both continuous and batch cultures) an “instantaneous growth rate.” This concept is useful in interpreting the system-level connections among growth rate, metabolism, stress, and the cell cycle.

scholarly journals Robust, linear correlations between growth rates and β-lactam–mediated lysis rates

2018 ◽  
Vol 115 (16) ◽  
pp. 4069-4074 ◽  
Author(s):  
Anna J. Lee ◽  
Shangying Wang ◽  
Hannah R. Meredith ◽  
Bihan Zhuang ◽  
Zhuojun Dai ◽  
...  
Keyword(s):  
Growth Rate ◽  
Single Cells ◽  
Bacterial Species ◽  
Population Level ◽  
Instantaneous Growth Rate ◽  
Bacterial Cells ◽  
Bacterial Growth Rate ◽  
Lysis Rate ◽  
Dynamic Signature ◽  
Linear Correlations

It is widely acknowledged that faster-growing bacteria are killed faster by β-lactam antibiotics. This notion serves as the foundation for the concept of bacterial persistence: dormant bacterial cells that do not grow are phenotypically tolerant against β-lactam treatment. Such correlation has often been invoked in the mathematical modeling of bacterial responses to antibiotics. Due to the lack of thorough quantification, however, it is unclear whether and to what extent the bacterial growth rate can predict the lysis rate upon β-lactam treatment under diverse conditions. Enabled by experimental automation, here we measured >1,000 growth/killing curves for eight combinations of antibiotics and bacterial species and strains, including clinical isolates of bacterial pathogens. We found that the lysis rate of a bacterial population linearly depends on the instantaneous growth rate of the population, regardless of how the latter is modulated. We further demonstrate that this predictive power at the population level can be explained by accounting for bacterial responses to the antibiotic treatment by single cells. This linear dependence of the lysis rate on the growth rate represents a dynamic signature associated with each bacterium–antibiotic pair and serves as the quantitative foundation for designing combination antibiotic therapy and predicting the population-structure change in a population with mixed phenotypes.

Dynamics of Bacterial Gene Regulatory Networks

2018 ◽  
Vol 47 (1) ◽  
pp. 447-467 ◽  
Author(s):  
David L. Shis ◽  
Matthew R. Bennett, ◽  
Oleg A. Igoshin
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Network Architecture ◽  
Regulatory Networks ◽  
Single Cells ◽  
Cell Physiology ◽  
Bacterial Cells ◽  
Bacterial Gene ◽  
Gene Expression Dynamics ◽  
Gene Regulatory

The ability of bacterial cells to adjust their gene expression program in response to environmental perturbation is often critical for their survival. Recent experimental advances allowing us to quantitatively record gene expression dynamics in single cells and in populations coupled with mathematical modeling enable mechanistic understanding on how these responses are shaped by the underlying regulatory networks. Here, we review how the combination of local and global factors affect dynamical responses of gene regulatory networks. Our goal is to discuss the general principles that allow extrapolation from a few model bacteria to less understood microbes. We emphasize that, in addition to well-studied effects of network architecture, network dynamics are shaped by global pleiotropic effects and cell physiology.

scholarly journals A single-component light sensor system allows highly tunable and direct activation of gene expression in bacterial cells

2020 ◽  
Vol 48 (6) ◽  
pp. e33-e33 ◽  
Author(s):  
Xie Li ◽  
Changcheng Zhang ◽  
Xiaopei Xu ◽  
Jun Miao ◽  
Jing Yao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression System ◽  
Logic Gates ◽  
Single Component ◽  
Boolean Logic ◽  
Bacterial Cells ◽  
Expression Systems ◽  
Activation Kinetics ◽  
Bacterial Gene ◽  
Bacterial Gene Expression

Abstract Light-regulated modules offer unprecedented new ways to control cellular behaviour with precise spatial and temporal resolution. Among a variety of bacterial light-switchable gene expression systems, single-component systems consisting of single transcription factors would be more useful due to the advantages of speed, simplicity, and versatility. In the present study, we developed a single-component light-activated bacterial gene expression system (eLightOn) based on a novel LOV domain from Rhodobacter sphaeroides (RsLOV). The eLightOn system showed significant improvements over the existing single-component bacterial light-activated expression systems, with benefits including a high ON/OFF ratio of >500-fold, a high activation level, fast activation kinetics, and/or good adaptability. Additionally, the induction characteristics, including regulatory windows, activation kinetics and light sensitivities, were highly tunable by altering the expression level of LexRO. We demonstrated the usefulness of the eLightOn system in regulating cell division and swimming by controlling the expression of the FtsZ and CheZ genes, respectively, as well as constructing synthetic Boolean logic gates using light and arabinose as the two inputs. Taken together, our data indicate that the eLightOn system is a robust and highly tunable tool for quantitative and spatiotemporal control of bacterial gene expression.

scholarly journals Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments

2020 ◽  
Author(s):  
Yu Chen ◽  
Eunice van Pelt-KleinJan ◽  
Berdien van Olst ◽  
Sieze Douwenga ◽  
Sjef Boeren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Proteomics Data ◽  
Available Nitrogen ◽  
Regulatory Strategies ◽  
Trade Offs ◽  
Evolutionary Changes ◽  
Genome Scale

Cells adapt to different conditions via gene expression that tunes metabolism and stress resistance for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade-offs1; Resource allocation under proteome constraints has emerged as a powerful paradigm to explain regulatory strategies in bacteria2. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient-rich conditions, i.e., multiple available nitrogen sources, such as the lactic acid bacterium Lactococcus lactis. Here we present an approach to identify preferred nutrients from integration of experimental data with a proteome-constrained genome-scale metabolic model of L. lactis (pcLactis), which explicitly accounts for gene expression processes and associated constraints. Using glucose-limited chemostat data3, we identified the uptake of glucose and arginine as dominant constraints, whose pathway proteins were indeed upregulated in evolved mutants. However, above a growth rate of 0.5 h-1, pcLactis suggests that available enzymes function at their maximum capacity, which allows an increase in growth rate only by altering gene expression to change metabolic fluxes, as was mainly observed for arginine metabolism. Thus, our integrative analysis of flux and proteomics data with a proteome-constrained model is able to identify and explain the constraints that form targets of regulation and fitness improvement in nutrient-rich growth environments.

scholarly journals Mechanistic links between cellular trade-offs, gene expression, and growth

2015 ◽  
Vol 112 (9) ◽  
pp. E1038-E1047 ◽  
Author(s):  
Andrea Y. Weiße ◽  
Diego A. Oyarzún ◽  
Vincent Danos ◽  
Peter S. Swain
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Metabolic Pathways ◽  
Mechanistic Model ◽  
Living Cells ◽  
Coarse Grained ◽  
Modeling Framework ◽  
Empirical Relationships ◽  
Cellular Energy ◽  
Trade Offs

Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that, because of limitations in levels of cellular energy, free ribosomes, and proteins, are faced by all living cells and we construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod’s law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth-related effects in dosage compensation by paralogs and predict host–circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modeling framework has potentially wide application, including in both biotechnology and medicine.

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