Two Component Systems: Physiological Effect of a Third Component

Signal transduction systems mediate the response and adaptation of organisms to environmental changes. In prokaryotes, this signal transduction is often done through Two Component Systems (TCS). These TCS are phosphotransfer protein cascades, and in their prototypical form they are composed by a kinase that senses the environmental signals (SK) and by a response regulator (RR) that regulates the cellular response. This basic motif can be modified by the addition of a third protein that interacts either with the SK or the RR in a way that could change the dynamic response of the TCS module. In this work we aim at understanding the effect of such an additional protein (which we call “third component”) on the functional properties of a prototypical TCS. To do so we build mathematical models of TCS with alternative designs for their interaction with that third component. These mathematical models are analyzed in order to identify the differences in dynamic behavior inherent to each design, with respect to functionally relevant properties such as sensitivity to changes in either the parameter values or the molecular concentrations, temporal responsiveness, possibility of multiple steady states, or stochastic fluctuations in the system. The differences are then correlated to the physiological requirements that impinge on the functioning of the TCS. This analysis sheds light on both, the dynamic behavior of synthetically designed TCS, and the conditions under which natural selection might favor each of the designs. We find that a third component that modulates SK activity increases the parameter space where a bistable response of the TCS module to signals is possible, if SK is monofunctional, but decreases it when the SK is bifunctional. The presence of a third component that modulates RR activity decreases the parameter space where a bistable response of the TCS module to signals is possible

Regulation of the Na+/K+-ATPase Ena1 Expression by Calcineurin/Crz1 under High pH Stress: A Quantitative Study

[EN] Regulated expression of the Ena1 Na+-ATPase is a crucial event for adaptation to high salt and/or alkaline pH stress in the budding yeast Saccharomyces cerevisiae. ENA1 expression is under the control of diverse signaling pathways, including that mediated by the calcium-regulatable protein phosphatase calcineurin and its downstream transcription factor Crz1. We present here a quantitative study of the expression of Ena1 in response to alkalinization of the environment and we analyze the contribution of Crz1 to this response. Experimental data and mathematical models substantiate the existence of two stress-responsive Crz1-binding sites in the ENA1 promoter and estimate that the contribution of Crz1 to the early response of the ENA1 promoter is about 60%. The models suggest the existence of a second input with similar kinetics, which would be likely mediated by high pH-induced activation of the Snf1 kinase.This work was supported by grants BFU2011-30197-C3-01, BFU2014-54591-C2-1-P and EUI2009-04147 (SysMo2) to JA. (Ministry of Industry and Competitivity, Spain, and Fondo Europeo de Desarrollo Regional [FEDER]). JA is the recipient of an Ajut 2014SGR-4 award (Generalitat de Catalunya). DC was recipient of a predoctoral fellowship from the Spanish Ministry of Education. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Petrezsélyová, S.; López-Malo, M.; Canadell, D.; Roque, A.; Serra-Cardona, A.; Marques Romero, MC.; Vilaprinyó, E.... (2016). Regulation of the Na+/K+-ATPase Ena1 Expression by Calcineurin/Crz1 under High pH Stress: A Quantitative Study. PLoS ONE. 11(6):e0158424-e0158424. https://doi.org/10.1371/journal.pone.0158424Se0158424e015842411

A survey of HK, HPt, and RR domains and their organization in two-component systems and phosphorelay proteins of organisms with fully sequenced genomes

Two Component Systems and Phosphorelays (TCS/PR) are environmental signal transduction cascades in prokaryotes and, less frequently, in eukaryotes. The internal domain organization of proteins and the topology of TCS/PR cascades play an important role in shaping the responses of the circuits. It is thus important to maintain updated censuses of TCS/PR proteins in order to identify the various topologies used by nature and enable a systematic study of the dynamics associated with those topologies. To create such a census, we analyzed the proteomes of 7,609 organisms from all domains of life with fully sequenced and annotated genomes. To begin, we survey each proteome searching for proteins containing domains that are associated with internal signal transmission within TCS/PR: Histidine Kinase (HK), Response Regulator (RR) and Histidine Phosphotranfer (HPt) domains, and analyze how these domains are arranged in the individual proteins. Then, we find all types of operon organization and calculate how much more likely are proteins that contain TCS/PR domains to be coded by neighboring genes than one would expect from the genome background of each organism. Finally, we analyze if the fusion of domains into single TCS/PR proteins is more frequently observed than one might expect from the background of each proteome. We find 50 alternative ways in which the HK, HPt, and RR domains are observed to organize into single proteins. In prokaryotes, TCS/PR coding genes tend to be clustered in operons. 90% of all proteins identified in this study contain just one of the three domains, while 8% of the remaining proteins combine one copy of an HK, a RR, and/or an HPt domain. In eukaryotes, 25% of all TCS/PR proteins have more than one domain. These results might have implications for how signals are internally transmitted within TCS/PR cascades. These implications could explain the selection of the various designs in alternative circumstances

Determination of non-steroidal anti-inflammatory drugs in sewage sludge by direct hollow fiber supported liquid membrane extraction and liquid chromatography-mass spectrometry

In this study, a three-phase hollow fiber liquid-phase microextraction (HF-LPME) method combined with liquid chromatography-mass spectrometry was developed for direct determination of four non-steroidal anti-inflammatory drugs (ketoprofen, naproxen, diclofenac and ibuprofen) in sewage sludge. The drugs were extracted from non-spiked and spiked slurry samples with different amounts of sludge into an organic phase and then back-extracted into an aqueous phase held in the lumen of the hollow fiber. High enrichment factors ranging from 2761 to 3254 in pure water were achieved. In sludge samples, repeatability and inter-clay precision were tested with relative standard deviation values between 10-18% and 7-15%, respectively. Average concentrations of 29 +/- 9, 138 +/- 2, 39 +/- 5 and 122 +/- 7 ng/g were determined in dried sludge from Kallby sewage treatment plant (Sweden) for ketoprofen, naproxen, diclofenac and ibuprofen, respectively. (C) 2010 Elsevier B.V. All rights reserved

Comparison of two extraction methods for the determination of selective serotonin reuptake inhibitors in sewage sludge by hollow fiber liquid-phase microextraction

This paper presents two procedures for the determination of four selective serotonin reuptake inhibitors (citalopram, paroxetine, fluoxetine, and sertraline) and one metabolite (norfluoxetine) in sewage sludge utilizing three-phase hollow fiber liquid-phase microextraction (HF-LPME). First, direct HF-LPME was used for extraction, clean-up, and preconcentration. The pharmaceuticals were extracted from slurry samples into an organic phase and then back-extracted into an aqueous phase in the lumen of the hollow fiber. Second, a procedure combining pressurized hot water extraction and HF-LPME for clean-up and preconcentration was developed for the same analytes and matrix. The extracts were subsequently analyzed by liquid chromatographymass spectrometry. For direct HF-LPME, limits of detection were between 1 and 12 ng g-1 (dry weight) and the relative standard deviation (RSD) values were 312%. For the second method, limits of detection were approximately 6 ng g-1 for all the compounds and RSD values were 812%. The methods were validated by comparison of results for the same samples. Sewage sludge from a Swedish wastewater treatment plant was analyzed by both methods; average concentrations were similar for citalopram, paroxetine, and fluoxetine with values of approximately 530, 40, and 200 ng g-1, respectively

Quantitative Operating Principles of Yeast Metabolism during Adaptation to Heat Stress

Summary: Microorganisms evolved adaptive responses to survive stressful challenges in ever-changing environments. Understanding the relationships between the physiological/metabolic adjustments allowing cellular stress adaptation and gene expression changes being used by organisms to achieve such adjustments may significantly impact our ability to understand and/or guide evolution. Here, we studied those relationships during adaptation to various stress challenges in Saccharomyces cerevisiae, focusing on heat stress responses. We combined dozens of independent experiments measuring whole-genome gene expression changes during stress responses with a simplified kinetic model of central metabolism. We identified alternative quantitative ranges for a set of physiological variables in the model (production of ATP, trehalose, NADH, etc.) that are specific for adaptation to either heat stress or desiccation/rehydration. Our approach is scalable to other adaptive responses and could assist in developing biotechnological applications to manipulate cells for medical, biotechnological, or synthetic biology purposes. : Evolution selects coordinated adaptive changes in gene expression and metabolism that ensure survival to stress challenges. Pereira et al. identify quantitative ranges for those changes in a set of genes and physiological variables (production of ATP, trehalose, NADH, etc.) that are specific for adaptation to heat stress, desiccation/rehydration, or pH. Keywords: biological design principles, systems biology, computational biology, multilevel modeling, integrative biology, metabolism, optimizatio

Steady state signal-response curves for the various TCS modules.

<p>Each plot shows the steady state levels of the phosphorylated RR in the y axis at different values of the signal k₁ (SK autophosphorylation rate constant) or k₂ (SKP dephosphorylation rate constant) in the x axis. When the signal modulates SK dephosphorylation (changes in k₂), the system behaves symmetrically to when SK phosphorylation (changes in k₁) is modulated. In the first case, increases in signal intensity cause the fraction of RRP to decrease, while in the latter, increases in signal intensity cause the fraction of RRP to increase. A, C, E: Response curves of TCS modules with monofunctional sensor. B, D, F: Response curves of TCS modules with bifunctional sensor. A, B, Response curves of Model A. C, D: Mathematically controlled comparison between the response curves of Model B and those of Model A. E, F: Mathematically controlled comparison between the response curves of Model C and those of Model A. Mathematical controls are implemented to make sure that the differences in response between the alternative modules are due to the presence of third component and not to other spurious differences.</p

Summary of the comparison of physiologically relevant criteria between the alternative designs for monofunctional TCS^a.

a<p>The model with the largest number of “+” signs for a given criterion is the one with the best performance with respect to that criterion.</p><p>A|B stands for Model A controlled for Model B. A|C stands for Model A controlled for Model C.</p

Experiments to analyze the effect of changes in different parameter values and protein concentrations on the range of bistability for the alternative TCS modules^a.

a<p>The steady state(s) for the three models by scanning a)k₁ (SK autophosphorylation reaction rate constant) and b)k₂ (SKP autodephosphorylation reaction rate constant) between 10⁻⁶ and 10 at different values of the parameters named in the table (see text for details).</p

Controlled comparison of the overall response times between Models A and B, and between Models A and C^a.

a<p>The reported values represent the area below each curve in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031095#pone-0031095-g003" target="_blank">Figure 3</a>, that is, the sum of the transient times for each response. A|B stands for Model A controlled for Model B. A|C stands for Model A controlled for Model C.</p