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

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

10.1101/014787 ◽

2015 ◽

Cited By ~ 3

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.

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AMP-activated protein kinase regulates gene expression by direct phosphorylation of nuclear proteins

Biochemical Society Transactions ◽

10.1042/bst0310224 ◽

2003 ◽

Vol 31 (1) ◽

pp. 224-227 ◽

Cited By ~ 109

Author(s):

T. Leff

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Protein Kinase ◽

Gene Transcription ◽

Metabolic Pathways ◽

Energy Charge ◽

Nuclear Proteins ◽

Cellular Energy ◽

Ampk Activity ◽

Amp Activated Protein Kinase

One of the primary functions of AMP-activated protein kinase (AMPK) is to regulate the metabolic pathways in response to reduced cellular energy charge. Most of the known targets of the kinase are cytoplasmic enzymes involved in both catabolic and anabolic metabolism. In addition, activation of AMPK in many cells results in changes in the pattern of gene expression. Although some of these effects are undoubtedly secondary responses to modified cellular metabolism, it is possible that in addition to its well-characterized function in the cytoplasm, AMPK also directly phosphorylates and regulates proteins involved in gene transcription. There are now several examples of transcription factors, cofactors and components of the transcriptional core machinery that are directly phosphorylated and regulated by AMPK. Here I review these examples and discuss the significance of AMPK activity in the nucleus.

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

10.1101/2021.04.02.438188 ◽

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.

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

10.1101/246165 ◽

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.

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Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield

10.1101/111161 ◽

2017 ◽

Author(s):

Meike T. Wortel ◽

Elad Noor ◽

Michael Ferris ◽

Frank J. Bruggeman ◽

Wolfram Liebermeister

Keyword(s):

Growth Rate ◽

Growth Rates ◽

Metabolic Pathways ◽

Biomass Yield ◽

Cost Minimization ◽

Fast Growth ◽

Basic Question ◽

Model Parameters ◽

Trade Off ◽

Trade Offs

AbstractMicrobes may maximize the number of daughter cells per time or per amount of nutrients consumed. These two strategies correspond, respectively, to the use of enzyme-efficient or substrate-efficient metabolic pathways. In reality, fast growth is often associated with wasteful, yield-inefficient metabolism, and a general thermodynamic trade-off between growth rate and biomass yield has been proposed to explain this. We studied growth rate/yield trade-offs by using a novel modeling framework, Enzyme-Flux Cost Minimization (EFCM) and by assuming that the growth rate depends directly on the enzyme investment per rate of biomass production. In a comprehensive mathematical model of core metabolism inE. coli, we screened all elementary flux modes leading to cell synthesis, characterized them by the growth rates and yields they provide, and studied the shape of the resulting rate/yield Pareto front. By varying the model parameters, we found that the rate/yield trade-off is not universal, but depends on metabolic kinetics and environmental conditions. A prominent trade-off emerges under oxygen-limited growth, where yield-inefficient pathways support a 2-to-3 times higher growth rate than yield-efficient pathways. EFCM can be widely used to predict optimal metabolic states and growth rates under varying nutrient levels, perturbations of enzyme parameters, and single or multiple gene knockouts.Author SummaryWhen cells compete for nutrients, those that grow faster and produce more offspring per time are favored by natural selection. In contrast, when cells need to maximize the cell number at a limited nutrient supply, fast growth does not matter and an efficient use of nutrients (i.e. high biomass yield) is essential. This raises a basic question about metabolism: can cells achieve high growth rates and yields simultaneously, or is there a conflict between the two goals? Using a new modeling method called Enzymatic Flux Cost Minimization (EFCM), we predict cellular growth rates and find that growth rate/yield trade-offs and the ensuing preference for enzyme-efficient or substrate-efficient metabolic pathways are not universal, but depend on growth conditions such as external glucose and oxygen concentrations.

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

10.1101/2020.10.15.340554 ◽

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.

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Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity

Applied Sciences ◽

10.3390/app11135859 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5859

Author(s):

Fernando N. Santos-Navarro ◽

Yadira Boada ◽

Alejandro Vignoni ◽

Jesús Picó

Keyword(s):

Gene Expression ◽

Gene Transcription ◽

Performance Metrics ◽

Cell Mass ◽

Gene Copy ◽

Substrate Uptake ◽

Promoter Strength ◽

Expression Levels ◽

Trade Offs ◽

Wide Range

Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, productivity rate, and yield (TRY). Here we use a multiscale model incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and expression of the protein of interest. Our model predicts cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the following main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. We evaluated the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature.

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Submarine lava deltas of the 2018 eruption of Kīlauea volcano

Bulletin of Volcanology ◽

10.1007/s00445-020-01424-1 ◽

2021 ◽

Vol 83 (4) ◽

Author(s):

S. Adam Soule ◽

Michael Zoeller ◽

Carolyn Parcheta

Keyword(s):

Grain Size ◽

Pore Pressure ◽

Mechanistic Model ◽

Large Fraction ◽

Volcanic Hazards ◽

Lava Flows ◽

Coarse Grained ◽

Fine Grained ◽

Fine Grain ◽

Delta Formation

AbstractHawaiian and other ocean island lava flows that reach the coastline can deposit significant volumes of lava in submarine deltas. The catastrophic collapse of these deltas represents one of the most significant, but least predictable, volcanic hazards at ocean islands. The volume of lava deposited below sea level in delta-forming eruptions and the mechanisms of delta construction and destruction are rarely documented. Here, we report on bathymetric surveys and ROV observations following the Kīlauea 2018 eruption that, along with a comparison to the deltas formed at Pu‘u ‘Ō‘ō over the past decade, provide new insight into delta formation. Bathymetric differencing reveals that the 2018 deltas contain more than half of the total volume of lava erupted. In addition, we find that the 2018 deltas are comprised largely of coarse-grained volcanic breccias and intact lava flows, which contrast with those at Pu‘u ‘Ō‘ō that contain a large fraction of fine-grained hyaloclastite. We attribute this difference to less efficient fragmentation of the 2018 ‘a‘ā flows leading to fragmentation by collapse rather than hydrovolcanic explosion. We suggest a mechanistic model where the characteristic grain size influences the form and stability of the delta with fine grain size deltas (Pu‘u ‘Ō‘ō) experiencing larger landslides with greater run-out supported by increased pore pressure and with coarse grain size deltas (Kīlauea 2018) experiencing smaller landslides that quickly stop as the pore pressure rapidly dissipates. This difference, if validated for other lava deltas, would provide a means to assess potential delta stability in future eruptions.

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Coactivator PC4 Mediates AP-2 Transcriptional Activity and Suppresses ras-Induced Transformation Dependent on AP-2 Transcriptional Interference

Molecular and Cellular Biology ◽

10.1128/mcb.19.1.899 ◽

1999 ◽

Vol 19 (1) ◽

pp. 899-908 ◽

Cited By ~ 44

Author(s):

Perry Kannan ◽

Michael A. Tainsky

Keyword(s):

Gene Expression ◽

Growth Rate ◽

Transcription Factors ◽

Cell Lines ◽

Transcriptional Activity ◽

Suppressive Effect ◽

Transcriptional Coactivator ◽

Transcriptional Interference ◽

Mechanism Of Inhibition ◽

Teratocarcinoma Cells

ABSTRACT ras oncogene-transformed PA-1 human teratocarcinoma cells have abundant AP-2 mRNA but, paradoxically, little AP-2 transcriptional activity. We have previously shown that overexpression of AP-2 in nontumorigenic variants of PA-1 cells results in inhibition of AP-2 activity and induction of tumorigenicity similar to that caused by ras transformation of PA-1 cells. Evidence indicated the existence of a novel mechanism of inhibition of AP-2 activity involving sequestering of transcriptional coactivators. In this study, we found that PC4 is a positive coactivator of AP-2 and can restore AP-2 activity in ras-transformed PA-1 cells. Relative to vector-transfected ras cell lines,ras cell lines stably transfected with and expressing the PC4 cDNA have a diminished growth rate and exhibit a loss of anchorage-independent growth, and they are unable to induce the formation of tumors in nude mice. These data suggest that a transcriptional coactivator, like a tumor suppressor, can have a growth-suppressive effect on cells. Our experiments are the first to show that ras oncogenes and oncogenic transcription factors can induce transformation through effects on the transcription machinery rather than through specific programs of gene expression.

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