High-resolution kinetics and modeling of hydrogen peroxide degradation in live cells

Free Radical Biology and Medicine 101 (2016) 143–153

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High-resolution kinetics and modeling of hydrogen peroxide degradation in live cells Ali Altıntaşa, Kristian Davidsena, Christian Gardea, Uffe H. Mortensena, J. Christian Brasenb, ⁎ ⁎ Thomas Samsb, , Christopher T. Workmana, a b

Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby DK-2800, Denmark Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby DK-2800, Denmark

A R T I C L E I N F O

A BS T RAC T

Keywords: Yeast Oxidative stress response Kinetic modeling

Although the role of oxidative stress factors and their regulation is well studied, the temporal dynamics of stress recovery is still poorly understood. In particular, measuring the kinetics of stress recovery in the first minutes after acute exposure provides a powerful technique for assessing the role of regulatory proteins or enzymes through the use of mutant backgrounds. This project endeavors to screen the temporal dynamics of intracellular oxidant levels in live cells as a function of gene deletion in the budding yeast, Saccharomyces cerevisiae. Using the detailed time dynamics of extra- and intra-cellular peroxide we have developed a mathematical model that describes two distinct kinetic processes, an initial rapid degradation in the first 10–20 min followed by a slower process. Using this model, a qualitative comparison allowed us to assign the dependence of temporal events to genetic factors. Surprisingly, we found that the deletion of transcription factors Yap1p or Skn7p was sufficient to disrupt the establishment of the second degradation phase but not the initial phase. A better fundamental understanding of the role protective factors play in the recovery from oxidative stress may lead to strategies for protecting or sensitizing cell to this stress.

1. Introduction The mitigation of oxidative stress due to environmental or metabolically generated reactive oxygen species (ROS) is a critical process that protects cells from numerous types of damage to proteins [1,2], DNA [3] and lipids [3–5]. While coordination of the complex and rapid stress response after acute environmental exposure is central to a cell's viability, intracellular metabolic sources of ROS require constant detoxification by many of the same mechanisms [6–9]. Cellular stress and damage due to ROS has been implicated in diseases such as cancer [10], cardiomyopathy [11], Parkinson's [12,13] and Alzheimer's [14,15] and is an important factor in the aging process in general [16]. Formation of reactive oxygen species (ROS) can be caused by normal aerobic metabolism or from environmental exposure to oxidants [17]. To mimic oxidative stress in laboratory conditions, a variety of ROS generating chemical agents are used, such as hydrogen peroxide (H2O2), menadione, diamide and paraquat [7]. Although there are differences in the oxidative stress responses for different oxidants [3,18], there is a core oxidative stress response which includes the glutathione/glutaredoxin system, thioredoxin system, catalases and superoxide dismutases [4,5,7,8,19], and H2O2 is an ideal oxidant for

⁎

triggering this response. H2O2 is a relatively stable oxidizing species that can be generated by cells as a secondary messenger molecule or as a cytotoxin [6–8,20] and has been used extensively in phenotypic studies by addition to media of submerged cells in liquid cultivations [10,21]. When H2O2 is introduced, it either freely diffuses across the plasma membrane or it passes through aquaporins in a variety of organisms (e.g. human, plants), although Saccharomyces cerevisiae aquaporins have not been shown to facilitate H2O2 transport [11,22]. Upon the exposure to H2O2, the intracellular antioxidants degrade H2O2 rapidly, thereby establishing a H2O2 gradient across the membrane [12,13,23]. Although the H2O2 induced oxidative stress response of S. cerevisiae has been heavily studied, the degradation kinetics of H2O2 is not well understood. Studies have primarily focused on the extracellular degradation kinetics [14,15,21]. Also, the lack of noninvasive and reversible detection methods has made real-time observation of intracellular H2O2 kinetics difficult [16,24,25]. To address this, we have expressed a genetically engineered ratiometric fluorescent probe, HyPer, in different genetic backgrounds of S. cerevisiae. HyPer is derived from Escherichia coli OxyR transcription factor, which is specifically sensitive to hydrogen peroxide [17,26,27]. Briefly, HyPer

Corresponding authors. E-mail addresses: [email protected] (T. Sams), [email protected] (C.T. Workman).

http://dx.doi.org/10.1016/j.freeradbiomed.2016.10.006 Received 27 April 2016; Received in revised form 25 September 2016; Accepted 10 October 2016 Available online 11 October 2016 0891-5849/ © 2016 Elsevier Inc. All rights reserved.

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utilizes a yellow fluorescent protein (YFP), which has been integrated between two cysteine amino acid residues of OxyR that form an disulfide bridge upon activation. This bond causes a ratiometric change in HyPer fluorescence intensity when excited at 500 and 420 nm and is rapidly reversed upon its reduction [7,25]. These features allow HyPer to monitor the H2O2 triggered redox changes in real-time unlike other H2O2 detection methods such as Amplex Red [28]. The flux of H2O2 across the membrane and subsequent intracellular degradation, as detected by HyPer fluorescence, can be modeled mathematically. Membrane permeability of H2O2 and kinetics of intracellular degradation, based on catalase and cytochrome C peroxidase activity, has been studied using extracellular H2O2 estimates [29]. More recent work proposed a detailed model of stress response based on 120 biochemical reactions that modeled membrane transport, extra- and intracellular H2O2 levels in C. albicans [30], although intracellular H2O2 levels were not measured directly. In this work, we propose a simple kinetic model for H2O2 degradation based on observations of both extra- and intracellular H2O2 levels. To gain a better understanding of the cellular factors roles in H2O2 degradation kinetics, we have selected a representative set of single gene deletions and transformed them with a 2-micron plasmid containing the HyPer gene. The knockout strains could be grouped according to their biological process and relevance in oxidative stress and include: direct enzymatic responses (superoxide dismutase, catalase); cell signaling (protein kinase, protein phosphatase); glutathione/glutaredoxin and thioredoxin systems; membrane stability and permeability (ergosterol content and aquaporins); formation of relevant organelles (autophagosome, peroxisome); and transcription factors. Based on our findings, we hypothesize that the cell's ability to degrade H2O2 can be separated into at least two components: a fast and finite chemical capacity from 0 to 20 min, and a slower enzymatic or metabolic process form 20–40 min that precede many of the induced transcripts and proteins in the oxidative stress response. These response modes could be shown to depend on the genetic background, and in particular, on specific candidate genes using gene deletion backgrounds. This project is, to our knowledge, the first to combine novel mathematical models of hydrogen peroxide degradation with real-time measurements in growing cell populations to assess a relative contribution of genes to the kinetics of intracellular H2O2 levels.

2.2. Intracellular marker for H2O2 The H2O2 sensitive genetically engineered fluorescent probe, HyPer, was tested in BY4741 strain expressing HyPer (HyP01) for the expected fluorescence properties. In particular, the fluorescence excitation spectrum of HyPer-containing background strain (HyP01) was scanned from 350 nm to 500 nm every 5 nm and the emission was read at 530 nm. Cells harvested in mid-exponential growth before treatment had the same excitation spectra with 2 emission peaks around 420 nm and 500 nm (Fig. 2A). After treatment with hydrogen peroxide, the 420 nm peak decreased while the 500 nm peak increased with H2O2 concentration as expected. As a result, the ratio between 500 and 420 nm excitation fluorescence increases with H2O2 concentration. By four hours after treatment, no significant difference was observed between the curves representing low to moderate H2O2 concentration exposure. However, elevated ratios could be observed 4 h after treatment with 1 mM H2O2 (see Fig. S2 for detailed spectral changes over time). 2.3. Intracellular dynamics revealed biphasic cellular response Cytosolic levels of H2O2 were profiled over time in un-adapted (naïve) HyP01 cells for up to 90 min after the media was treated to 0.2 and 0.5 mM H2O2 (n=6 for each condition). Although the cytosolic degradation profiles for 0.2 mM appeared to follow a single exponential decay, similar to the extracellular profiles (Figs. 1 and 3A), the 0.5 mM treatment generated a profile that was not adequately described by a single exponential function (Fig. 3B). In the higher treatment, a secondary peak was observed between 20 and 30 min indicating a biphasic response inside cells. We hypothesized that the secondary peak was due to a transition between processes, or sets of processes, with different (aggregate) rates in the biological response, thus allowing for a transient accumulation of intracellular levels. We therefore developed a descriptive kinetic model using ordinary differential equations (ODE) in the hope it would describe a biphasic H2O2 degradation profile as observed. 2.3.1. Kinetic model and simulations The extracellular H2O2 diffuses into the cells where it is degraded or diffuses back out should extracellular levels fall below intracellular levels. Once inside the cell, H2O2 appears to be initially degraded by a process composed of fast reactions that depend on a finite capacity component (antioxidant capacity). Once exhausted, only a slower background component remains (rate-limited response) (Fig. 3E). The ODE model accounted for intra- and extracellular peroxide levels, and the consumption of a finite antioxidant capacity using three rates: 1) the membrane permeability (k1), 2) a fast intracellular degradation rate (k3) dependent upon a finite capacity (g), e.g. NADPH levels in naïve cells, and 3) a slower intracellular degradation rate (k2). The rate of extracellular H2O2 depletion (Eq. (1)) depends on the volume fraction of cells, v, and the difference in intra- and extracellular H2O2. The k2-based process was not dependent upon expendable resources in the cell, but was intended to model a number of enzymatic processes, e.g. catalase, superoxide dismutase, etc. Using parameter values based on observed rates for catalase (k2=15 min−1) and observed membrane permeability (k1=19 min−1), a qualitatively similar set of extracellular profiles was obtained using our ODE model (Fig. 3C). Based on the same simulation, a biphasic profile was observed for the intracellular levels after 0.5 mM treatment and a single phase was predicted for 0.2 mM (Fig. 3D). The variables used in the model are given in Table 1. ODE Model

2. Results 2.1. Hydrogen peroxide degradation is dependent upon metabolically active cells In an effort to characterize the dynamics of peroxide decay, we measured extracellular H2O2 levels in liquid cultures of budding yeast using a solid-state probe (see Methods), recording at half-second intervals. When our background yeast strain, BY4741 expressing HyPer (HyP01), was treated with 0.2 and 0.5 mM H2O2, an exponential decay was observed in both cases. Half-lives of 13 and 11 min were estimated using a simple exponential fit for 0.2 and 0.5 mM, respectively (Fig. 1) and agree with previous half-life estimate of 11.5 min (0.06 min−1) after 0.15 mM H2O2 treatment in the same BY4741 background [29]. The extracellular decay rate was observed to be significantly lower when the cells were treated after suspension in PBS buffer that did not contain glucose or other nutrients. At both low and high treatment levels, the half-lives were 140 and 165 min, respectively. This finding strongly indicated that active cellular metabolism was important for the rapid removal of H2O2 from the extracellular environment. No appreciable degradation was detected in media without cells indicating that cells were responsible for the observed degradation rates and that these rates depended upon either metabolic activity or cell growth. Based on these findings, all subsequent experiments were performed in growth media (SC-Ura).

dpe =k1 ( pi −pe ) v dt 144

(1)

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Fig. 1. Extracellular hydrogen peroxide degradation profiles. A) Degradation profiles of cells (HyP01) treated with 0.2 mM H2O2 in PBS (green) or in SC-Ura (blue), and 0.5 mM H2O2 in PBS (orange) or in SC-Ura (red). B) Semi-log plots of H2O2 profiles shown in (A) highlighting the exponential fits (dashed lines).

Fig. 2. Excitation Spectrum of HyPer containing cells. The excitation spectrum was measured by monochromatic filter every 5 nm (x-axes) and the emission levels were measured at 530 nm (y-axes). (A) The excitation spectrum before treatment. The change of fluorescence emission was observed after treatment with 0, 0.1, 0.2, 0.3, 0.4 and 0.5 mM H2O2 for (B) 1 min, (C) 5 min and (D) 10 min.

Fig. 3. Extracellular vs. intracellular H2O2 levels and the kinetic model simulations. Extracellular (A) and intracellular (B) H2O2 decay profiles are shown for 0.2 mM in blue and 0.5 mM in red. Y-axis values represent the fold change of intracellular H2O2 (measured by HyPer at 500/420 nm) compared to untreated control. Example model simulations are displayed for extracellular (C) and intracellular (D) for model parameters k1=19 min−1, k2=15 min−1, k3=60 min−1, g=50 mM, Kg=15 mM. (E) Overview of the kinetic model for H2O2 degradation depicting the three rate equations (dependent on k1, k2, k3) proposed in this study. The transport process depends on k1 and the gradient across the membrane. A slower intracellular degradation process is dependent on rate k2, and a faster process, dependent upon k3, consumes finite resources, g.

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Table 1 Description of variables used in the kinetic model.

Table 2 The gene deletion strains used to study intracellular H2O2 with HyPer.

Variable

Explanation

Details

Strain

Gene

Description

Function

k1 k2 k3 g Kg pi pe v

Membrane permeability Slow degradation rate Fast degradation rate Finite capacity substance Finite capacity cutoff Intracellular/Cytosolic [H2O2] Extracellular [H2O2] Ratio of cell volume to media

– Dependent on enzymatic activity Dependent on antioxidant capacity – – – – (vol./vol.)

aqy1Hy aqy2Hy atg1Hy atg8Hy ccp1Hy cta1Hy ctt1Hy cup2Hy dun1Hy erg3Hy erg6Hy fus3Hy glr1Hy

AQY1 AQY2 ATG1 ATG8 CCP1 CTA1 CTT1 CUP2 DUN1 ERG3 ERG6 FUS3 GLR1

Flux Flux ORG ORG ENZ ENZ ENZ TF PK Flux Flux PK GSH

gpx1Hy

GPX1

gpx2Hy

GPX2

grx1Hy grx2Hy grx3Hy grx4Hy grx5Hy gsh1Hy gsh2Hy hog1Hy hyr1Hy mek1Hy msn2Hy msn4Hy pbs2Hy

GRX1 GRX2 GRX3 GRX4 GRX5 GSH1 GSH2 HOG1 HYR1 MEK1 MSN2 MSN4 PBS2

pex3Hy pho85Hy prx1Hy ptc1Hy rck2Hy

PEX3 PHO85 PRX1 PTC1 RCK2

sdp1Hy skn7Hy

SDP1 SKN7

sod1Hy sod2Hy srx1Hy ssk22Hy ste11Hy tel1Hy

SOD1 SOD2 SRX1 SSK22 STE11 TEL1

trx1Hy trx2Hy tsa1Hy vhs1Hy vps15Hy vps34Hy yap1Hy

TRX1 TRX2 TSA1 VHS1 VPS15 VPS34 YAP1

Spore-specific water channel Water channel, transport across cell membranes Protein serine/threonine kinase Component of autophagosomes and Cvt vesicles Mitochondrial cytochrome-c peroxidase Catalase A Cytosolic catalase T Copper-binding transcription factor Cell-cycle checkpoint serine-threonine kinase C-5 sterol desaturase Delta(24)-sterol C-methyltransferase MAP kinase involved in mating Cytosolic and mitochondrial glutathione oxidoreductase Phospholipid hydroperoxide glutathione peroxidase Phospholipid hydroperoxide glutathione peroxidase Glutathione-dependent disulfide oxidoreductase Cytoplasmic glutaredoxin Glutathione-dependent oxidoreductase Glutathione-dependent oxidoreductase Glutathione-dependent oxidoreductase Gamma glutamylcysteine synthetase Glutathione synthetase MAP kinase involved in osmoregulation Thiol peroxidase Meiosis-specific serine/threonine protein kinase Stress-responsive transcriptional activator Stress-responsive transcriptional activator MAP kinase kinase of the HOG signaling pathway Peroxisomal membrane protein (PMP) Cyclin-dependent kinase Mitochondrial peroxiredoxin Type 2C protein phosphatase (PP2C) Protein kinase, response to oxidative and osmotic stress MAP kinase phosphatase Nuclear response regulator and transcription factor Cytosolic copper-zinc superoxide dismutase Mitochondrial manganese superoxide dismutase Sulfiredoxin MAPKKK of HOG1 signaling pathway Signal transducing MEK kinase Protein kinase, involved in telomere length regulation Cytoplasmic thioredoxin isoenzyme Cytoplasmic thioredoxin isoenzyme Thioredoxin peroxidase Cytoplasmic serine/threonine protein kinase S/T kinase involved in vacuolar protein sorting Phosphatidylinositol 3-kinase Basic leucine zipper (bZIP) transcription factor

⎛ dpi g ⎞ ⎟ pi =k1 ( pe −pi )−⎜k2+k3 dt Kg + g ⎠ ⎝

⎛ g ⎞ dg ⎟ pi =−k3 ⎜ dt ⎝ Kg + g ⎠

(2)

(3)

2.4. Impact of single gene knockouts on peroxide degradation A panel of 48 single gene deletion strains was transformed with the HyPer plasmid in order to examine their intracellular decay profiles over the 90 min after exposed to 0.2 or 0.5 mM H2O2 (See Table 2). Each HyPer strain was tested in at least 3 trials. This series of experiments generated many different intracellular decay profiles due to the various ways the oxidative response was perturbed in each deletion strain. The level of HyPer expression per cell (HPC) was assessed for all strains by analyzing the log2 ratio of 420 nm fluorescence signal to OD600 (HPC420), and 500 nm fluorescence signal to OD600 (HPC500) under control conditions (no H2O2 treatment). The mean and standard deviation of HPC420 and HPC500 over the 49 strains was 12.9 ± 0.36 and 10.9 ± 0.35 respectively. 48 of the 49 strains had an HPC420 and HPC500 within one fold-change (one log2 unit) of the global mean HPC420 and HPC500 (See Fig. S6). Vps34Hy was the only exception. Curve fitting and a phase identification method (based on the second derivative) were applied to the average 500/420 nm ratios to identify two phases of intracellular degradation where possible. The ability to segment the response into two phases depended upon the strain and the treatment level, where the biphasic response was almost exclusively observed after treatment with 0.5 mM H2O2. The segmentation method depended upon either a secondary peak, i.e. a change in sign of the first derivative, or a rate inflection, i.e. a change in sign of second derivative (see Methods). In 31 of the 49 cases, a secondary peak was observed after 0.5 mM treatment while only a rate inflection could be identified for the remaining 18 cases. Intracellular profiles after 0.2 mM treatment were primarily single phase although peaks or rate inflections were observed in 11 strains at the lower treatment level. Secondary peaks were identified in both high and low treatment concentrations for four interesting cases, hyr1, skn7, tsa1, and yap1. After identifying the phases, simple model parameters were extracted from the curve fits to identify the duration of the initial phase (t1), the peak of the 500/420 nm ratio just after treatment (ymax1) and at the transition between phases (ymax2) as well as transition time (t2) (see Table S4). Where possible, exponential fits were applied to each phase in order to estimate decay rates. Based on these exponential fits, the initial decay rates were observed to be significantly faster than phase 2 rates, in agreement with the model. Initial cytosolic levels of H2O2, i.e. ymax1, typically achieved maximal levels at or before the first observation after treatment (within the first minute after treatment). Initial levels varied significantly between the knockout strains suggesting different membrane permeabilities, k1.

GSH GSH GSH GSH GSH GSH GSH GSH GSH PK GSH PK TF TF PK ORG PK TRX PP PK PP TF ENZ ENZ TRX PK PK PK TRX TRX TRX PK PK PK TF

2.5. k-means clusters of oxidative stress related knockout strains Strains were clustered based on combined intracellular profiles for 0.2 and 0.5 mM H2O2 treatments (Fig. 4). As exact measurement times and time intervals differed across experiments, curves fit to the average 500/420 nm ratios from experimental replicates (cubic splines) were sampled at minute intervals between 0 and 90 min. Samples from the two treatment levels were concatenated to create multi-treatment profiles for each strain and then clustered by the k-means method using an uncentered correlation (cosine angle) based distance. Clustering revealed six different profile clusters as shown in Fig. 4. Profiles that deviated most from the background strain were grouped in Cluster 1 through 3. The cluster with the largest number of strains, Cluster 5, displayed kinetic profiles similar to the background strain and included the background strain HyP01. When treated with 146

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Fig. 4. k-means clusters of HyPer expressing strains. Six clusters were identified by k-means clustering (see relevant text for details). x-axis: time in minutes after treatment, y-axis: fold change of ratiometric fluorescence (500/420 nm) compared to the untreated control. The legends indicate the strains of each cluster. The degradation profiles were shown in response to 0.5 mM H2O2 treatment. See Fig. S4 for detailed kinetics including 0.2 mM H2O2 treatment.

been shown to function as a peroxide sensor in a number of cell types including HeLa [25], insulin-producing RINm5F cells [31] and zebrafish embryos [32]. To our knowledge, this is the first study using HyPer to profile intracellular H2O2 dynamics in budding yeast. Yeast cells expressing HyPer showed the expected spectral properties of fluorescence [25] as shown in Fig. 2. Doses of H2O2 below 1 mM were selected to avoid decreasing the viability as more than 40% cell death has been observed with doses of 1 mM or higher [33].

0.5 mM H2O2, Cluster 4 strains showed a slight delay on the secondary peak compared to Cluster 5. Cluster 6 had similar degradation profiles to Cluster 5 although the primary and secondary peaks were lower than those in Cluster 5. The two strains in Cluster 1 displayed high initial and secondary peaks after treatment with 0.5 mM H2O2 compared to other strains. This pattern appears to be related to dysregulation of the thioredoxin and glutathione pathways through the loss of TRX2 and GSH1, respectively. Notably, trx2Hy had the highest secondary peak among all strains. The gene deletions in Cluster 2 resulted in significantly higher intracellular H2O2 concentration although the cells still managed to degrade H2O2 with a fast rate in the first 30 min. Profiles in Cluster 3 due to the deletion of HYR1, SKN7 and YAP1 were possibly the most altered and surprising compared to the background. Although 500/420 nm ratios were not initially high, these cells were not able to decrease intracellular levels in the first 20 min as in most other cases. More importantly, these three strains began to accumulate H2O2 after 20 min and continued to do so up to 60 min, as in the case of hyr1Hy, or were still increasing at 90 min, as in the cases of skn7Hy and yap1Hy (see Fig. 4 and S4). Using our ODE model with modified parameters, simulated decay profiles were shown to be qualitatively similar to genetically perturbed profiles for TRX2, CTA1 and HYR1 deletions representative of Clusters 1, 2, and 3 (Fig. 5). Moreover, when the enzymatic rate (k2) was decreased, simulation results achieved high ymax2 comparable to the initial ymax1 levels of H2O2 as seen in experimental decay profiles of HyPer containing TRX2 deletion strains (Fig. 5A). Reducing k1 by 6fold and g by 2-fold was required to obtain model profiles similar to the hyr1Hy strain profile (See Methods, Table 3).

3.1. Active metabolism is required for fast removal of intracellular H2O2 Extracellular H2O2 was degraded more rapidly when cells were tested in growth medium compared to cells in PBS (Fig. 1). Although cells were sampled from exponentially growing populations in growth media, a complete growth arrest was rapidly achieved for PBS washed and suspended cells. Presumably, this was due to the fact that the starved cells had low levels of metabolites and proteins required for the innate oxidative stress responses. The expression of stress response genes in the adaptive response have been shown to be correlated with the growth rate [34]. In addition, shaking cells in growth medium resulted in faster removal of extracellular H2O2 when compared to a non shaken condition presumably due to an increase in metabolic rates associated with higher observed growth rates. All of these observations agree with the expectations that active metabolism provides a more rapid oxidant removal. 3.2. The two-phase degradation of intracellular H2O2 is controlled by fast and slow degradation processes Intracellular degradation profiles of H2O2 often displayed two distinct phases, a fast first phase followed by a typically slower second phase (Fig. 3). We hypothesize that naïve cells have a finite antioxidant capacity, that is rapidly consumed, followed by a slower set of processes that work together to degrade excess intracellular H2O2 caused by

3. Discussion The noninvasive real-time quantitation of intracellular H2O2 levels has only recently been possible due to the development of genetically engineered fluorescent probes like HyPer. HyPer, in particular, has 147

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Fig. 5. Comparison of observed intracellular H2O2 profiles for gene deletion backgrounds to model simulations. The observed intracellular profiles for A) trx2Hy, B) cta1Hy, C) hyr1Hy were shown as representative strains from Cluster 1, 2 and 3, respectively. The model simulations of D) trx2Hy, E) ctaHy and F) hyr1Hy represents similar profiles with their observed intracellular H2O2 degradation kinetics. x-axis: time in minutes after treatment, y-axis: fold change of ratiometric fluorescence (500/420 nm) compared to the untreated control Profiles for treatments of 0.2 mM H2O2 are shown in blue, and 0.5 mM H2O2 in red. Transparent regions show the standard deviation confidence intervals of observed kinetics. Model parameters used in D-F can be found in Table 2.

tion of Hog1p and the transcription of STL1 take place within the first minutes, while Stl1p translation is followed by a 5–10 min delay after treatment with 0.4 M NaCl [37]. We suggest that the two-phases result from enzyme activity that is initially not rate-limited by the availability of metabolites during the first phase, while the second phase depends solely on enzymes that do not require other substrates than H2O2, e.g. catalase, or is rate-limited by the processes that regenerates the consumed substrates. Consequently, different aggregate kinetic rates are necessary to explain the intracellular degradation kinetics of H2O2. The fast component in our kinetic model is defined to depend on a finite capacity of the cells. The overall concentration of the antioxidant reservoir defines how long the initial rapid degradation can be maintained and, as such, defines a finite capacity. It is likely that this capacity is determined by NADPH levels and the use of NADPH by thioredoxin peroxidase (Tsa1p) and glutathione peroxidase (Gpx1p, Gpx2p) to hydrolyze H2O2 in the thioredoxin and glutathione systems [38]. When the finite capacity is large (g≫Kg), the degradation of H2O2 is determined by the sum of fast and slow rates (k3+k2), which means that k3 will initially dominate the overall rate. However, the intracellular H2O2 degradation rate becomes constant when the finite capacity is consumed (g≪Kg), after which point the degradation depends on k2 alone. The rate of the second phase likely depends on enzymes like catalase or the regeneration of NADPH by the pentose phosphate pathways (PPP) and the activity of glucose-6-phosphate dehydrogenase and 6-phosphogluconate dehydrogenase in particular. Depending on the model parameters, simulation results have shown that in the transition between phases the internal peroxide concentration can reach levels as high or higher than the initial maxima achieved within seconds after treatment (Fig. 5). Profiles observed for the trx2Δ mutant had similar intracellular H2O2 concentration at the beginning of phase 2 (Fig. 5A) compared to the initial H2O2 concen-

Table 3 Values assigned for variables in the simulations. Variable

Units

Sim. 1 HyP01

Sim. 2 trx2Hy

Sim. 3 cta1Hy

Sim. 4 hyr1Hy

k1 k2 k3 g Kg

1/min 1/min 1/min mM mM

19 15 60 50 10

11 6 70 70 10

10 15 55 55 15

3 12 50 25 20

Sim. 1 used in Fig. 3D, Sim. 2 used in Fig. 5D, Sim. 3 used in Fig. 5E, Sim. 4 used in Fig. 5F

acute exposure. The well-studied adaptive response, which requires transcriptional and translational responses, likely depends on processes in both naïve response phases. As free-living organisms must be ready for a range of environmental stresses, single-celled organisms have evolved important fast acting stress response mechanisms. The de novo synthesis of proteins that are responsible for the mitigation of stress often take tens of minutes implying that cells would be vulnerable if levels of those proteins and their activities were not sufficient in the naïve state. Therefore, a sufficient response capacity is crucial in order to buy time for synthesizing additional stress response metabolites and proteins, such as NADPH, glutathione, thioredoxin and catalase. For example, the oxidized glutathione (GSSG) concentration increases immediately after H2O2 treatment, reaches its maximum levels after 5 min, and then diminishes to minimum levels after 20 min [35]. Moreover, the synthesis of a number of oxidative stress proteins have been shown to be rapidly initiated upon 0.2 mM H2O2 treatment. The concentration of these proteins (e.g. Trr1p, Sod1p, Trx1/2p, Tsa1p) is maximal around 20 min [36]. Such fast and slow responses to stress are also observed in the osmotic stress response where the rapid phosphoryla148

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Fig. 6. The activity of antioxidant enzymes controlling Yap1p activation. (A) In the activation of Yap1p, Hyr1p and Tsa1p act as H2O2 receptor as both are oxidized by H2O2. Oxidized Hyr1p and Tsa1p can participate in redox reactions with Yap1p. Oxidized Yap1p goes into the nucleus and induces transcription of genes related to oxidative stress response. The activity of oxidized Yap1p is decreased by a reduction reaction carried out by thioredoxin system. Border color indicates the redox state of the proteins as reduced (blue) or oxidized (red). Oxidized Yap1p is transported to the nucleus (purple) where it initiates the transcription of oxidative stress response genes. (B) Protein-protein and protein-DNA interactions of cluster 3–4 genes retrieved from STRING [44] and YEASTRACT [45] databases, respectively. The grey lines show the interaction of transcription factors (Yap1p, Skn7p and Msn2p) with other proteins (peroxidases: Hyr1p, Tsa1p, Trx2p). Red lines display gene expression regulation mediated by transcription factors as they bind to the promoters of the specified genes (YAP1, GSH1, TSA1, TRX2). The colors inside each box represent the cluster they belong to as indicated in Fig. 4.

cantly lower growth rates in these three mutants compared to the control and 0.2 mM treatments (Fig. S5). Although this diminished growth rate and clearance may be due to cell death at the high treatment level in these mutant, less severe but similarly shaped profiles were observed for the 0.2 mM treatment (Fig. S4) where no appreciable effects on growth rate were observed in all three cases. This suggests that these profiles were likely not just the result of arrested growth or high levels of cell death. The disrupted second phase is likely caused by a lack of enzyme synthesis since the cytosolic H2O2 levels were still increasing after 30 min in the 0.2 mM treatment. This agrees with the well-known functions of Yap1p and Skn7p in the transcriptional regulation of oxidative stress responses by inducing various oxidative stress response genes [40]. It should be noted that other differences between strains were generally not attributed to inhibited growth as mutant strains (other than hyr1, skn7 and yap1) were observed to be growing at similar rates under the conditions tested. Hyr1p is a thiol peroxidase that acts as an H2O2 sensor and transmits the redox signal to Yap1p for transcriptional activation [41,42]. However, Hyr1p is not the only protein required for Yap1p activation. Alternatively, it has been shown that Tsa1p is required for the activation of Yap1p in a S. cerevisiae strain with a truncated allele of ybp1-1 (W303-1b). Normally, Ybp1 (YAP1 binding protein 1) is essential for Hyr1-catalyzed activity of Yap1p. In the absence of Ybp1 activity, Tsa1p activates Yap1p [42]. Moreover, Yap1p activated by Hyr1 is more resistant to reduction compared to Tsa1p activated Yap1p [43]. Our findings based on intracellular degradation kinetics of hyr1Hy and tsa1Hy are in agreement with this observation since hyr1Hy has a more similar degradation profile to yap1Hy than to tsa1Hy (Figs. 4, 6). Although changes in modeling parameters suggested a slightly lower phase two rate (k2) for intracellular H2O2 profiles like hyr1Hy, yap1Hy and skn7Hy, decreases in membrane permeability and finite capacity may play even larger roles in these altered response profiles (Table 2). Importantly, tsa1Hy was the only strain observed to have increasing intracellular H2O2 levels in phase one (Fig. 4D and Fig. S4). This suggests that the loss of thoredoxin peroxidase's ability to utilize NADPH to hydrolyze H2O2 is sufficient to diminish the rate in phase one to values similar to the flux of H2O2 into cells, and supports the

tration. Changes in the model parameters suggested that Trx2p is involved in the regulation of the slow response, as k2 was 60% lower than the background value. This is in agreement with the observation that trx2Δ mutants are extremely sensitive to H2O2 in phenotypic studies [39]. The decreased ability of the trx2Δ mutant (trx2Hy) to maintain its intracellular redox balance likely means these cells die at higher rates due to an impaired stress response compared to the background strain. Lastly, we believe that the cell volume fraction of v=0.005, corresponding to a cell density of approximately OD600=1 used in experiments, was important for observing the two phases. Increasing the starting cell volume fraction to v > 0.01 was predicted to generate a single phase at the 0.5 mM treatment level through simulations (data not shown). 3.3. Knockout screen revealed important regulators for H2O2 degradation response In this study we have examined various single gene deletions transformed to express HyPer to create a better understanding of the molecular basis of H2O2 degradation kinetics. Here we specifically focused on the cellular components that are known to be responsible for the H2O2 transport, formation of important organelles (autophagosomes and peroxisomes), molecular signaling (protein kinases and phosphatases) and H2O2 degradation (catalases, thioredoxin and glutathione/glutaredoxin) as well as H2O2 generation (superoxide dismutase). We performed a detailed analysis of H2O2 degradation in order to identify which genetic factors were more important for the fast or slow degradation process. Highly altered H2O2 decay profiles were observed in clusters 2 and 3 (Fig. 4). Strains in cluster 3 also displayed a dysregulation of the second-phase response, as the IFC values were highest after 60 min (ymax2) in contrast to the other clusters. In particular, yap1Hy, skn7Hy, and hyr1Hy were not able to degrade peroxide back to the basal levels observed in the reference strain within the 90 min after exposure to 0.5 mM (Fig. 4C). In fact, the 500/420 nm ratio appeared to still be increasing at 90 min in the case of yap1Hy and skn7Hy. These severe delays in clearance after 0.5 mM treatment corresponded to signifi149

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4. Materials and methods

importance of NADPH in the establishment of fast rates in phase one. A global proteomic analysis revealed that Yap1p controls the expression of 32 proteins, of which 15 are expressed with the coactivation of Skn7p, including cytoplasmic catalase T (CTT1), superoxide dismutases (SOD1, SOD2), thioredoxin 2 (TRX2), thioredoxin peroxidase (TSA1) and thioredoxin reductase (TRR1) [40]. The genes that are induced by co-regulation of Yap1p and Skn7p are primarily responsible for the enzymatic degradation of H2O2 (Fig. 6). Both yap1Δ or skn7Δ deletions, therefore, have a large impact on the global translation of these proteins. Evidence for the importance of the induction of co-regulated genes has also been reported to generate significantly higher protein carbonylation in either deletion background [1]. In order to simulate intracellular profiles of this type, model parameter estimates required lower rates for all processes k1, k2 and k3 (Table 2). We observed that the intracellular H2O2 degradation of ctt1Hy and cta1Hy was not strongly affected, and these profiles were grouped in clusters 2 and 6, respectively (Fig. 4). Although catalase T (Ctt1p) and catalase A (Cta1p) are important enzymes for the removal of intracellular H2O2, a lack of H2O2 sensitivity in null mutants (cta1Δ and ctt1Δ) has been explained to be due to compensatory mechanisms of the glutathione/glutaredoxin system [46]. Indeed, in the absence of glutaredoxin or thioredoxin enzymes, the inviability of the quadruple knockout trx1Δ trx2Δ grx1Δ grx2Δ could be rescued by any single gene from this set. This supports both the redundancy and importance of these pathways, and suggests that glutathione/glutaredoxin and thioredoxins systems are more important for intracellular H2O2 decomposition [19,47]. Knocking out important regulators of glutathione/glutaredoxin and thioredoxin systems were found to be very different as shown in cluster 4 and 5 (Fig. 5). For instance, the two-step synthesis of glutathione is blocked when either GSH1 or GSH2 is knocked out [48]. However, we found that only gsh1Δ was strongly affected when exposed to H2O2 as the initial intracellular H2O2 decreased more than that of gsh2Δ. The primary reason for the sensitivity of gsh1Δ to H2O2 is due to the twostep synthesis of glutathione (γ-L-Glutamyl-L-cysteinylglycine). The dipeptide γ-glutamylcysteine is formed by Gsh1p activity, then glycine molecules are ligated to γ-glutamylcysteine by Gsh2p. Importantly, γglutamylcysteine can still compensate the activity of glutathione [49]. Therefore, the lack of Gsh1p disrupts this compensatory mechanism and causes hypersensitivity to H2O2 while the role of Gsh2p is not essential for eliminating H2O2. The trx2Hy strain displayed high initial H2O2 concentrations with high secondary peaks of H2O2 around 20 min after the treatment. According to our simulation results, this can arise from a decrease in membrane permeability and the slower enzymatic degradation processes. As mentioned above, thioredoxin system is essential for Yap1pregulated oxidative stress response as well as direct degradation of H2O2. Unlike trx2Hy, we did not observe a sensitive decay profile for trx1Hy. Importantly, both Yap1p and Skn7p regulate the expression of TRX2, but not TRX1 [50]. Also, trx2Δ mutants have been shown to be hypersensitive to H2O2 [39], probably due to its central role on Yap1p regulation. Understanding the temporal kinetics of H2O2 degradation may help to implicate potential therapeutic targets and approaches. The combination of our modeling approach and intracellular H2O2 profiling resulted in a powerful functional assay for the role of genes in adapting to intracellular H2O2 levels. We hope that this approach will be extended to provide improved models of oxidative stress tolerance and can be adapted to wider applications in cancer, neurodegenerative and aging cell models.

4.1. Strain construction S. cerevisiae codon usage optimized HyPer gene was delivered in a Gateway® cloning vector with pUC origin of replication (ori) and Kanamycin resistance (Kanr) (Evrogen, FP943). Invitrogen TOP10 chemically competent E. coli cells were heat shock transformed with this vector following manufacturer’s description and the transformed cells were grown on LB agar medium with 40 µg/ml Kanamycin (LBKan agar). Isolated transformant colonies were grown overnight in liquid LB-Kan and plasmid isolation was done according to manufacturer guidelines (QIAprep Spin Miniprep Kit, 27104). To create a yeast expression plasmid, we used a backbone plasmid with 2-micron ori (pBOSAL1) and URA auxotrophic selection marker. TEF1 promoter and HyPer gene fragments were assembled with the backbone plasmid by uracil excision cloning (USER) [51]. Primers were acquired from IDT, restriction enzymes and all other cloning reagents were acquired from NEB with the exception of the polymerase. The DNA polymerase used was a uracil compatible Pfu mutant isolated following a published method [52,53]. Single uracil nucleotides were added by PCR primers to the HyPer fragment (HyPer_fw and HyPer_rv) and the TEF1 promoter fragment (TEF1_fw and TEF1_rv). The pBOSAL1 backbone was opened by cutting with AsiSI (NEB, R0630S) and nicked with Nb. BsmI (NEB, R0706S). Both enzyme digested backbone and PCR fragments were purified from agarose gel after size validation gel electrophoresis (QIAquick Gel Extraction Kit, 28704). Fragments and backbone were then mixed in a USER reaction to create the final 2-micron expression vector, named pBO1Hy (Fig. S1). The USER reaction product, pBO1Hy, transformed TOP10 chemically competent E. coli cells for cloning purpose. Several transformed E. coli colonies were verified for the existence of the pBO1Hy by colony PCR with primer TEF1_col_fw and HyPer_col_rv. One colony was chosen for plasmid purification and the plasmid was verified by sequencing the entire insert and flanking regions. As the final step, pBO1Hy was used to transform the whole yeast mutant library (see Table S1) using the LiAc/SS Carrier DNA/PEG method [51,54]. 4.2. Growth conditions and experimental setup Uracil was the auxotrophic marker in our 2-micron expression plasmid, pBO1Hy, so we used SC-Ura medium (synthetic complete medium lacking uracil) in our experiments. The strains were grown on SC-Ura agar plates (SC-Ura +20 g/L agar) for at least 2 days after transformation. Single colonies were then picked to inoculate overnight precultures in 5 ml SC-Ura at 160 RPM and 30 °C. Cell density of overnight precultures were measured by spectrophotometery (OD600) (BioRAD SmartSpec-3000) and diluted to 0.2 OD600 as starting density for the microfermentations (m2p-Labs, BioLector). The samples were distributed in 48-well BioLector flower plates (m2p-Labs, MTP-48-B), 1.5 ml culture in each well. The plates were sealed with gas permeable adhesive seals (Thermo, AB-0718). Cell growth and intracellular H2O2 in each culture well were measured by light scattering at 620 nm and GFP signal (Ex/Em: 488/520 nm) respectively. 4.3. Kinetics measurements The cells were treated with 0.2 and 0.5 mM H2O2 (Sigma, H1009) at mid-log phase (~6–9 h) during batch microfermentation. Mid-log phase cells were harvested for ratiometric fluorescence measurements in a 96 well microtiter plate at this stage. OD600 was measured again and adjusted to 1.00 (~3×107 cells/ml) in 200 µl per well. Then the ratiometric measurements were taken as technical triplicates in three different channels as 500/530 nm (Ex/Em), 420/530 nm (Ex/Em) and OD600 (absorbance) by using a 96-well microtiter plate reader (Synergy 150

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H1, BioTek). The samples were run 20 min at 30 °C, 4 s-orbital shaking in every cycle without treatment to let the cells adjust to the new environment. Then the cells were treated with 0.2 or 0.5 mM H2O2 including a no-treatment control and measurements were taken for the following 90 min. HyPer expression was maintained due to constant selection in –Ura medium and was expected to be highly expressed due to multi-copy 2 µ plasmid with HyPer expression driven by the TEF promoter. The ratio of 420 nm to OD600 and the ratio of 500 nm to OD600 both indicate the amount of HyPer per cell. Both ratios were calculated over all time points between 0 and 90 min in the control trials (0 mM H2O2) for each strain. The mean log2(420/OD) and mean log2(500/OD) over all trials and time points was used as a strain's measure of HyPer per cell (HPC420, HPC500).

∆I420=I420Straini | OD −I420Background | OD

(4)

∆I500=I500Straini | OD −I500Background | OD

(5)

After the background subtractions, the ratiometric signals were computed based on the ratio between 420/530 and 500/530 (Ex/Em) channels.

Iratio=

∆I420 ∆I500

(6)

To make the data comparable with the untreated conditions, the fold changes were calculated by dividing treatment time series to notreatment time series (cubic spline interpolated).

IFC =

4.4. Spectrum measurements

Iratio | hp ≠0 Iratio | hp =0

(7)

To determine the approximate time range for the first phase responses, we took the first and the second derivatives of a smoothing spline (cubic spline) fit to the mean IFC over replicates. The segmentation into phases was determined by the time when the first derivative changed signs from positive to negative indicating a peak, or when the second derivative changed signs in the absence of a peak, indicating the inflection point between deceleration and acceleration of decay (Fig. 4A).

When the HyP01 strain reached the mid-log phase (~6–9 h) during the microfermentation, the cells were harvested and washed twice with either SC-Ura media or PBS. Afterwards, the concentrations of the cells were adjusted to OD600=1.00 (~3×107 cells) and 200 µl of cell suspensions were transferred to 96-well microtiter plate. The cells were allowed to run 20 min in multiplate reader before treatment to adjust the temperature to 30 °C. The excitation spectrum was measured every 5 nm from 350 to 500 nm and the emission signals were recorded at 530 nm without shaking. Then the cells were treated with 0, 0.1, 0.2, 0.3, 0.4 and 0.5 mM H2O2 with a multi-pipette and the same procedure was applied to record the excitation spectrum at 530 nm emission for 90 min. Spectra of HyPer in PBS or SC-Ura cell suspensions were then obtained by subtracting the spectra of cell-free PBS or SC-Ura.

4.6.2. Extracellular H2O2 concentrations The changes in H2O2 concentration in the medium were recorded with the H2O2 sensor in millivolts over time. Since the background signal was unique for each measurement, a cubic spline interpolation was fit to the data (voltage vs. time) where the H2O2 concentrations were negligible, e.g. before treatment times and 90 min after treatment. The calculated background signal was subtracted from the raw data. The initial step increase in voltage after H2O2 addition typically maximized within 5 s after treatment with an estimated response time of 0.8 s as estimated from Hill function fit to the transition. Both the probe response and the mixing of treatment and media are likely responsible for the observed response time. The increase in voltage was converted to H2O2 concentrations considering the linearity of the H2O2 sensor in this concentration range (R2 > 0.999, data not shown).

4.5. Extracellular H2O2 measurements The extracellular H2O2 content was measured by a H2O2 sensor (ISO-H2O2 O-2 and ISO-H2O2 O-100, World Precision Instruments). The cells were washed twice with either SC-Ura medium or PBS depending on the experimental condition and the cell concentration was adjusted to OD600=1 (~3×107 cells/ml). The cells were stirred by a magnetic stirrer at 200 RPM at 25 °C and the baseline signal from the probe was monitored using measurements every 500 ms. The cells were treated with H2O2 and the kinetics of extracellular H2O2 was recorded. The calibration of the probe was done by creating a standard curve by adding defined amounts of H2O2 into either SC-Ura medium or PBS.

4.7. Numerical simulation The simulations in this study (see Fig. 3C,D and Fig. 5D-F) were performed both in COPASI [55] and in R using lsoda function form the deSolve package. The k2 parameter for H2O2 degradation was based on catalase catalysis [29]. The permeability constant was retrieved from various sources [29,56,57]. The starting cell volume fraction, v=0.005, was based on the estimated cell density of 3×107 cells/ml at the experimental conditions OD600=1, and the estimated cell volume of 8.6×10–11 to 1.3×10−10 ml/cell [58]. Models were simulated with and without growth, i.e. variable v, based on the observed change in OD. The differences between simulation with and without cell growth were qualitative and for this reason all simulations were performed assuming fixed cell density. The degradation driven by the consumption of a finite capacity (endogenous limited source) was modeled using reasonable assumptions where g set to a range of 25–50 mM in cells. These levels would allow for the exhaustion of g with a treatment of 0.5 mM and cell volume ratio v ~0.005 (OD600=1) as used experimentally (g=50 mM can hydrolyze 50% of 0.5 mM treatment). The parameters used for the simulations are given in Table 2.

4.6. Data analysis 4.6.1. Kinetics measurements OD600 absorbance, 420/530 and 500/530 from monochromatic excitation/emission were measured for three independent replicates (four for gsh1Hy and gsh2Hy) as described in “Kinetic measurement” methods. The OD600 measurements in the plate reader were calibrated to 1 cm path length OD600 measurements, which were measured by a spectrophotometer (BioRAD SmartSpec-3000), using a standard calibration curve (data not shown). The background fluorescence for measurements were calculated by using strains (BY4741-pBO1, pho85-pBO1, sod2-pBO1) transformed with the backbone 2-micron plasmid (pBOSAL1). The background signal versus OD600 was measured for each 420/530 and 500/530 (Ex/ Em) channels (Fig. S3) and exponential functions were fit to the each fluorescence channel versus OD600 values. Based on the OD600 of each HyPer containing strain, the background fluorescence was estimated from the exponential fits and subtracted individually from measured 420/530 and 500/530 channels:

Acknowledgements AA and CTW acknowledge funding from the FP7 PhenOxiGEn project (ref. no. 223539). The authors would also like to thank Christian Munck for his kind help with kinetics measurements and 151

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Morten Sommer for providing access to the Synergy H1 (BioTek) plate reader. We would also like to thank Sebastien Muller for his constructive comments on the manuscript.

[27] [28]

Appendix A. Supporting information [29]

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.freeradbiomed.2016.10.006.

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