Fluctuations In Protein Concentration Research Articles

Longitudinal Fluctuations in Protein Concentrations and Higher-Order Structures in the Plasma Proteome of Kidney Failure Patients Subjected to a Kidney Transplant.

Using proteomics and complexome profiling, we evaluated in a year-long study longitudinal variations in the plasma proteome of kidney failure patients, prior to and after a kidney transplantation. The post-transplant period was complicated by bacterial infections, resulting in dramatic changes in the proteome, attributed to an acute phase response (APR). As positive acute phase proteins (APPs), being elevated upon inflammation, we observed the well-described C-reactive protein and Serum Amyloid A (SAA), but also Fibrinogen, Haptoglobin, Leucine-rich alpha-2-glycoprotein, Lipopolysaccharide-binding protein, Alpha-1-antitrypsin, Alpha-1-antichymotrypsin, S100, and CD14. As negative APPs, being downregulated upon inflammation, we identified the well-documented Serotransferrin and Transthyretin, but added Kallistatin, Heparin cofactor 2, and interalpha-trypsin inhibitor heavy chain H1 and H2 (ITIH1, ITIH2). For the patient with the most severe APR, we performed plasma complexome profiling by SEC-LC-MS on all longitudinal samples. We observed that several plasma proteins displaying alike concentration patterns coelute and form macromolecular complexes. By complexome profiling, we expose how SAA1 and SAA2 become incorporated into high-density lipid particles, replacing largely Apolipoprotein (APO)A1 and APOA4. Overall, our data highlight that the combination of in-depth longitudinal plasma proteome and complexome profiling can shed further light on correlated variations in the abundance of several plasma proteins upon inflammatory events.

Protein concentration fluctuations in the high expression regime: Taylor's law and its mechanistic origin.

Protein concentration in a living cell fluctuates over time due to noise in growth and division processes. In the high expression regime, variance of the protein concentration in a cell was found to scale with the square of the mean, which belongs to a general phenomenon called Taylor's law (TL). To understand the origin for these fluctuations, we measured protein concentration dynamics in single E. coli cells from a set of strains with a variable expression of fluorescent proteins. The protein expression is controlled by a set of constitutive promoters with different strength, which allows to change the expression level over 2 orders of magnitude without introducing noise from fluctuations in transcription regulators. Our data confirms the square TL, but the prefactor A has a cell-to-cell variation independent of the promoter strength. Distributions of the normalized protein concentration for different promoters are found to collapse onto the same curve. To explain these observations, we used a minimal mechanistic model to describe the stochastic growth and division processes in a single cell with a feedback mechanism for regulating cell division. In the high expression regime where extrinsic noise dominates, the model reproduces our experimental results quantitatively. By using a mean-field approximation in the minimal model, we showed that the stochastic dynamics of protein concentration is described by a Langevin equation with multiplicative noise. The Langevin equation has a scale invariance which is responsible for the square TL. By solving the Langevin equation, we obtained an analytical solution for the protein concentration distribution function that agrees with experiments. The solution shows explicitly how the prefactor A depends on strength of different noise sources, which explains its cell-to-cell variability. By using this approach to analyze our single-cell data, we found that the noise in production rate dominates the noise from cell division. The deviation from the square TL in the low expression regime can also be captured in our model by including intrinsic noise in the production rate.

Dynamical analysis of the fission yeast cell cycle via Markov chain.

The cell cycle is a complex network involved in the regulation of cell growth and proliferation. Intrinsic molecular noise in gene expression in the cell cycle network can generate fluctuations in protein concentration. How the cell cycle network maintains its robust transitions between cell cycle phases in the presence of these fluctuations remains unclear. To understand the complex and robust behavior of the cell cycle system in the presence of intrinsic noise, we developed a Markov model for the fission yeast cell cycle system. We quantified the effect of noise on gene and protein activity and on the probability of transition between different phases of the cell cycle. Our analysis shows how network perturbations decide the fate of the cell. Our model predicts that the cell cycle pathway (subsequent transitions from [Formula: see text]) is the most robust and probable pathway among all possible trajectories in the cell cycle network. We performed a sensitivity analysis to find correlations between protein interaction weights and transition probabilities between cell cycle phases. The sensitivity analysis predicts how network perturbations affect the transition probability between different cell cycle phases and, consequently, affect different cell fates, thus, forming testable in vitro/in vivo hypotheses. Our simulation results agree with published experimental findings and reveal how noise in the cell cycle regulatory network can affect cell cycle progression.

Intracellular droplets suppress fluctuations in protein concentrations

Intracellular droplets suppress fluctuations in protein concentrations

Trade-offs between effectiveness and cost in bifunctional enzyme circuit with concentration robustness

A fundamental trade-off in biological systems is whether they consume resources to perform biological functions or save resources. Bacteria need to reliably and rapidly respond to input signals by using limited cellular resources. However, excessive resource consumption will become a burden for bacteria growth. To investigate the relationship between functional effectiveness and resource cost, we study the ubiquitous bifunctional enzyme circuit, which is robust to fluctuations in protein concentration and responds quickly to signal changes. We show that trade-off relationships exist between functional effectiveness and protein cost. Expressing more proteins of the circuit increases concentration robustness and response speed but affects bacterial growth. In particular, our study reveals a general relationship between free-energy dissipation rate, response speed, and concentration robustness. The dissipation of free energy plays an important role in the concentration robustness and response speed. High robustness can only be achieved with a large amount of free-energy consumption and protein cost. In addition, the noise of the output increases with increasing protein cost, while the noise of the response time decreases with increasing protein cost. We also calculate the trade-off relationships in the EnvZ-OmpR system and the nitrogen assimilation system, which both have the bifunctional enzyme. Similar results indicate that these relationships are mainly derived from the specific feature of the bifunctional enzyme circuits and are not relevant to the details of the models. According to the trade-off relationships, bacteria take a compromise solution that reliably performs biological functions at a reasonable cost.

Receptor Tyrosine Kinase Signalling in the Absence of Kinase Activity and Cancer of Non‐Genetic Origin

FGFR2‐expressing cancer cells with low concentrations of the adaptor protein Grb2 show high prevalence for metastatic outcome. In non‐stimulated cells the SH3 domain (and not the SH2 domain(s)) of Plcγ1 directly competes for a proline‐rich binding site at the very C‐terminus of FGFR2 with the C‐terminal SH3 domain of Grb2. Reduction of Grb2 concentration permits access of Plcγ1 to the receptor. Recruitment of Plcγ1 in this way is sufficient to up‐regulate phospholipase activity. This results in increased cell proliferation, motility and promotion of cell invasive behaviour in the absence of extracellular receptor stimulation. Therefore tumour growth and metastatic outcome can be dictated by the constitutive competition between Grb2 and Plcγ1 for the phosphorylation‐independent binding site on FGFR2. Since the majority of receptor tyrosine kinases have proline‐rich sequences in their C‐termini, the possibility of a second tier of signal transduction in the absence of growth factor stimulation, or kinase‐activating mutations emerges – leading to cancer of non‐genetic origin. Further examples of this second tier of signalling have been investigated and mechanistic detail of how protein concentration fluctuations in response to cellular stress can lead to pathogenesis has been established.Support or Funding InformationThis work is funded by CRUKThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Fluctuation sensitivity of a transcriptional signaling cascade.

The internal biochemical state of a cell is regulated by a vast transcriptional network that kinetically correlates the concentrations of numerous proteins. Fluctuations in protein concentration that encode crucial information about this changing state must compete with fluctuations caused by the noisy cellular environment in order to successfully transmit information across the network. Oftentimes, one protein must regulate another through a sequence of intermediaries, and conventional wisdom, derived from the data processing inequality of information theory, leads us to expect that longer sequences should lose more information to noise. Using the metric of mutual information to characterize the fluctuation sensitivity of transcriptional signaling cascades, we find, counter to this expectation, that longer chains of regulatory interactions can instead lead to enhanced informational efficiency. We derive an analytic expression for the mutual information from a generalized chemical kinetics model that we reduce to simple, mass-action kinetics by linearizing for small fluctuations about the basal biological steady state, and we find that at long times this expression depends only on a simple ratio of protein production to destruction rates and the length of the cascade. We place bounds on the values of these parameters by requiring that the mutual information be at least one bit-otherwise, any received signal would be indistinguishable from noise-and we find not only that nature has devised a way to circumvent the data processing inequality, but that it must be circumvented to attain this one-bit threshold. We demonstrate how this result places informational and biochemical efficiency at odds with one another by correlating high transcription factor binding affinities with low informational output, and we conclude with an analysis of the validity of our assumptions and propose how they might be tested experimentally.

A physical mechanism of cancer heterogeneity.

We studied a core cancer gene regulatory network motif to uncover possible source of cancer heterogeneity from epigenetic sources. When the time scale of the protein regulation to the gene is faster compared to the protein synthesis and degradation (adiabatic regime), normal state, cancer state and an intermediate premalignant state emerge. Due to the epigenetics such as DNA methylation and histone remodification, the time scale of the protein regulation to the gene can be slower or comparable to the protein synthesis and degradation (non-adiabatic regime). In this case, many more states emerge as possible phenotype alternations. This gives the origin of the heterogeneity. The cancer heterogeneity is reflected from the emergence of more phenotypic states, larger protein concentration fluctuations, wider kinetic distributions and multiplicity of kinetic paths from normal to cancer state, higher energy cost per gene switching, and weaker stability.

Determinants of Cell-to-Cell Variability in Protein Kinase Signaling

Cells reliably sense environmental changes despite internal and external fluctuations, but the mechanisms underlying robustness remain unclear. We analyzed how fluctuations in signaling protein concentrations give rise to cell-to-cell variability in protein kinase signaling using analytical theory and numerical simulations. We characterized the dose-response behavior of signaling cascades by calculating the stimulus level at which a pathway responds (‘pathway sensitivity’) and the maximal activation level upon strong stimulation. Minimal kinase cascades with gradual dose-response behavior show strong variability, because the pathway sensitivity and the maximal activation level cannot be simultaneously invariant. Negative feedback regulation resolves this trade-off and coordinately reduces fluctuations in the pathway sensitivity and maximal activation. Feedbacks acting at different levels in the cascade control different aspects of the dose-response curve, thereby synergistically reducing the variability. We also investigated more complex, ultrasensitive signaling cascades capable of switch-like decision making, and found that these can be inherently robust to protein concentration fluctuations. We describe how the cell-to-cell variability of ultrasensitive signaling systems can be actively regulated, e.g., by altering the expression of phosphatase(s) or by feedback/feedforward loops. Our calculations reveal that slow transcriptional negative feedback loops allow for variability suppression while maintaining switch-like decision making. Taken together, we describe design principles of signaling cascades that promote robustness. Our results may explain why certain signaling cascades like the yeast pheromone pathway show switch-like decision making with little cell-to-cell variability.

Nonlinear features in protein circuitry

The noise involved in protein circuit can result in fluctuations in protein concentrations. Then we have explored the effect of such noise on the feedback loop between p53 and its repressor Mdm2, the negative feedback dynamics and oscillatory activities are presented. Recent experimental results show that under certain conditions, the activity of the average protein level of p53 behaves with dampened oscillation in response to DNA damage, and it has non-decaying oscillatory behavior in individual cells, and we show that the dampening is induced by intrinsic noise, namely the uncertainty associated with chemical kinetics in dealing with when and in what order reactions take place in the p53 system. Furthermore, the experimental results are reproduced in this paper.

Quantitative analysis of confocal laser scanning microscopy images of heat-set globular protein gels

Gels of the globular protein β-lactoglobulin were made by heating solutions at pH 7 and different NaCl concentrations ( C s). The influence of the ionic strength on the gel structure was studied by confocal laser scanning microscopy (CLSM). For C s < 0.2 M the images were homogeneous, but at higher NaCl concentrations micro-phase separation was observed. The protein concentration in the two phases was determined from the images. It is shown how CLSM images can be quantitatively analysed in terms of the pair correlation function of the protein concentration fluctuations, yielding results that can be directly compared to those obtained from light scattering. The transition between so-called finely stranded and particulate gels is explained by a switch from net repulsive to net attractive interaction between growing protein aggregates.

Regulatory Control and the Costs and Benefits of Biochemical Noise

Experiments in recent years have vividly demonstrated that gene expression can be highly stochastic. How protein concentration fluctuations affect the growth rate of a population of cells is, however, a wide-open question. We present a mathematical model that makes it possible to quantify the effect of protein concentration fluctuations on the growth rate of a population of genetically identical cells. The model predicts that the population's growth rate depends on how the growth rate of a single cell varies with protein concentration, the variance of the protein concentration fluctuations, and the correlation time of these fluctuations. The model also predicts that when the average concentration of a protein is close to the value that maximizes the growth rate, fluctuations in its concentration always reduce the growth rate. However, when the average protein concentration deviates sufficiently from the optimal level, fluctuations can enhance the growth rate of the population, even when the growth rate of a cell depends linearly on the protein concentration. The model also shows that the ensemble or population average of a quantity, such as the average protein expression level or its variance, is in general not equal to its time average as obtained from tracing a single cell and its descendants. We apply our model to perform a cost-benefit analysis of gene regulatory control. Our analysis predicts that the optimal expression level of a gene regulatory protein is determined by the trade-off between the cost of synthesizing the regulatory protein and the benefit of minimizing the fluctuations in the expression of its target gene. We discuss possible experiments that could test our predictions.

The Nucleation of Lysozyme from a Fluctuation Point of View

The enzyme lysozyme is a useful model for the study of protein nucleation. The nucleation of lysozyme can be very well discussed in the framework of classical nucleation theory. However, the crystallization process develops differently below and above the critical temperature, Tc, of liquid−liquid phase separation. Above Tc, nucleation follows the line of the classical theory, but below Tc, nucleation is preceded by the growth of protein concentration fluctuations. This results in a relationship between the induction time vs. temperature curve and the left branch of the liquid−liquid phase separation curve. In fact, these two curves are equal. The transition in the nature of the nucleation process near Tc is in accordance with a principle proposed several years ago by Prigogine and colleagues.

Predictive Model of Pheromone Signaling

To fully understand complex signaling networks and to be able to predict their behavior, quantitative models are required to keep track of dynamic changes in signal propagation. Hao et al. provide a clear example of the power of combined experimental and computational analysis in a study of G protein signaling in response to mating pheromone in yeast. They used quantitative immunoblotting to determine the amounts per cell of G protein α subunit (about 8000 copies) and of an RGS protein (about 2000 copies). RGS, regulator of G protein signaling, is an inhibitory factor that enhances guanosine triphosphatase activity of the Gα protein. They also measured changes in the abundance of these proteins in response to pheromone and how twofold increases in the abundance of these proteins or of Gβγ subunits affected signaling. A model based on ordinary differential equations was consistent with a feedback loop in which increased synthesis of the RGS protein in response to pheromone would prevent prolonged signaling. However, inconsistencies in predictions of the model with the experimental data and observation of binary (all or none) signaling in cells overexpressing the RGS protein led the authors to consider a positive feedback loop that would cause pheromone-dependent degradation of the RGS protein. A revised model, which included a stochastic term simulating random fluctuations in protein concentrations, was consistent with the experimental data. To determine whether the unanticipated positive-feedback loop predicted from the modeling does in fact exist, the authors monitored pheromone-dependent degradation of the RGS protein and discovered a previously unknown pheromone-induced ubiquitination of the RGS protein. The authors note that the relatively simple and highly studied yeast pheromone signaling pathway may be amenable to further computer simulation of the entire pathway. N. Hao, N. Yildirim, Y. Wang, T. C. Elston, H. G. Dohlman, Regulators of G protein signaling and transient activation of signaling: Experimental and computational analysis reveals negative and positive feedback controls on G protein activity. J. Biol. Chem. 278 , 46506-46515 (2003). [Abstract] [Full Text]

Equilibrium kinetics of lysozyme crystallization under an external electric field

The influence of an external electric field on a lysozyme solution has been analyzed with respect to protein concentration fluctuations and crystallization kinetics. The migration of lysozyme under an external electric field in a different media was investigated with a new device developed in the laboratory. The resulting diagrams show that an external electric field influences the protein concentration leading to a concentration gradient between the electrodes. The variation of the residual protein concentration during the crystallization process in the presence of an external electric field was studied at different salt and overall protein concentrations (in the range 0.4–1.5 M NaCl and 5–100 mg/ml lysozyme). At low ionic strength, the apparent residual protein concentration decreases more rapidly when the electric field is present.

Concentration dependence of protein diffusion

The concentration dependence of protein self-diffusion constants is described by a free volume diffusion theory which accounts for the effects of local protein concentration fluctuations.

Fluctuations in Protein Levels of Tobacco Leaves and Consequences for Extractability

Abstract Greenhouse and field experiments were conducted to evaluate the effect of time, cultural conditions and genetic factors on protein extractability from tobacco leaves. Leaf protein was periodically extracted from green tobacco from time of transplanting through early senescence. Intensive sampling revealed substantial fluctuations in protein concentrations superimposed upon an age-related decline in protein levels with approaching plant maturity. Predictably, these oscillations affected the degree of protein extractability. Combined totals of the three major soluble protein fractions, viz. the green chlorophyll/protein complex, the white Fraction I protein (cryoprecipitate), and the white Fraction II protein (thermoprecipitate), deviated occasionally from protein values in unfractionated homogenates, but the fluctuation trends were similar. Investigations of variations in protein extractability among greenhouse-grown tobacco and selected genotypes grown under field conditions revealed that protein fluctuations during plant development are a common phenomenon. It is concluded that the occurrence of periodic fluctuations in total protein concentration and in the amount of extractable protein can severely distort estimates of leaf protein yields that are based upon insufficient numbers of samples and prolonged intervals between sampling.