Multistability maintains redox homeostasis in human cells
2021; Springer Nature; Volume: 17; Issue: 10 Linguagem: Inglês
10.15252/msb.202110480
ISSN1744-4292
AutoresJo‐Hsi Huang, Hannah K. C. Co, Yi‐Chen Lee, Chia‐Chou Wu, Sheng‐hong Chen,
Tópico(s)Biochemical effects in animals
ResumoArticle6 October 2021Open Access Transparent process Multistability maintains redox homeostasis in human cells Jo-Hsi Huang orcid.org/0000-0003-1221-7285 Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA These authors contributed equally to this work Search for more papers by this author Hannah KC Co orcid.org/0000-0002-1445-1967 Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan These authors contributed equally to this work Search for more papers by this author Yi-Chen Lee orcid.org/0000-0002-8354-6310 Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Chia-Chou Wu orcid.org/0000-0002-4009-6858 Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Sheng-hong Chen Corresponding Author [email protected] orcid.org/0000-0002-7722-2221 Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei, Taiwan Search for more papers by this author Jo-Hsi Huang orcid.org/0000-0003-1221-7285 Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA These authors contributed equally to this work Search for more papers by this author Hannah KC Co orcid.org/0000-0002-1445-1967 Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan These authors contributed equally to this work Search for more papers by this author Yi-Chen Lee orcid.org/0000-0002-8354-6310 Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Chia-Chou Wu orcid.org/0000-0002-4009-6858 Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Search for more papers by this author Sheng-hong Chen Corresponding Author [email protected] orcid.org/0000-0002-7722-2221 Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei, Taiwan Search for more papers by this author Author Information Jo-Hsi Huang1, Hannah KC Co2,3, Yi-Chen Lee2, Chia-Chou Wu2 and Sheng-hong Chen *,2,3,4 1Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA 2Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan 3Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica and Graduate Institute of Life Science, National Defense Medical Center, Taipei, Taiwan 4Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei, Taiwan *Corresponding author. Tel: +886 2 2789 9318; E-mail: [email protected] Mol Syst Biol (2021)17:e10480https://doi.org/10.15252/msb.202110480 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Cells metabolize nutrients through a complex metabolic and signaling network that governs redox homeostasis. At the core of this, redox regulatory network is a mutually inhibitory relationship between reduced glutathione and reactive oxygen species (ROS)—two opposing metabolites that are linked to upstream nutrient metabolic pathways (glucose, cysteine, and glutamine) and downstream feedback loops of signaling pathways (calcium and NADPH oxidase). We developed a nutrient-redox model of human cells to understand system-level properties of this network. Combining in silico modeling and ROS measurements in individual cells, we show that ROS dynamics follow a switch-like, all-or-none response upon glucose deprivation at a threshold that is approximately two orders of magnitude lower than its physiological concentration. We also confirm that this ROS switch can be irreversible and exhibits hysteresis, a hallmark of bistability. Our findings evidence that bistability modulates redox homeostasis in human cells and provide a general framework for quantitative investigations of redox regulation in humans. SYNOPSIS In-silico modeling of a nutrient-redox network and single-cell measurements of ROS demonstrate that ROS dynamics follow a bistable response to glucose deprivation in human cells. A mathematical model of a human nutrient-redox network recapitulates redox dynamics and steady states upon nutrient perturbations in human cells. ROS levels in single cells exhibit switch-like and all-or-none responses to glucose deprivation. The human redox system displays an ultrasensitive and hysteric response to glucose perturbations. Introduction Cells gain energy, mass, and reducing potential by metabolizing environmental nutrients. During these metabolic processes, reactive oxygen species (ROS) can be generated as a signaling molecule. At physiological concentrations, ROS regulate various cellular and physiological functions such as mitogen signaling and the inflammatory response (D'Autreaux & Toledano, 2007; Finkel, 2011; Ayala et al, 2014; Zhang et al, 2016; Katikaneni et al, 2020; Sies & Jones, 2020). However, if they escape regulatory control, ROS can also cause oxidative stress, cell death, and pathological abnormalities such as neurodegeneration and abnormal aging (Sastre et al, 2003; Ayala et al, 2014; Yang & Stockwell, 2016; Angelova & Abramov, 2018; Katikaneni et al, 2020; Sies & Jones, 2020). Therefore, ROS must be tightly controlled by maintaining proper levels of reducing molecules to ensure cell survival and physiological homeostasis. NADPH and reduced glutathione (GSH) are two major reducing molecules that dampen ROS in cells. NADPH is the cofactor for GSH regeneration, peroxiredoxin recycling (Hanschmann et al, 2013), and cystine reduction into bioactive cysteine (a precursor for de novo GSH synthesis) (Pader et al, 2014). Likewise, GSH buffers protein oxidation (Dalle-Donne et al, 2009), recycles glutaredoxins (Fernandes & Holmgren, 2004), and powers glutathione peroxidases such as GPX4 (an enzyme that prevents ferroptosis by removing lipid hydroperoxides) (Yang et al, 2014). NADPH and GSH production require extracellular nutrients, including glucose, glutamine, and cystine. Glucose fluxes through the oxidative branch of the pentose phosphate pathway (oxPPP) and regenerates NADPH from NADP+. Downstream metabolites of both glucose and glutamine can regenerate NADPH through enzymes (ME1 and IDH1) associated with the TCA cycle. Together, the TCA cycle and oxPPP account for nearly all cytosolic NADPH production (Chen et al, 2019). For GSH synthesis, glutamine and cystine are metabolized into glutamate and cysteine (Cys), respectively, the two substrates necessary for the rate-limiting step of de novo GSH synthesis, namely glutamate-cysteine ligation that is catalyzed by glutamate-cysteine ligase (GCL) (Grant et al, 1997; Huang et al, 2000). Accordingly, depriving cells of glucose (Lee et al, 1998; Jelluma et al, 2006; Graham et al, 2012; Goji et al, 2017; Joly et al, 2020; Liu et al, 2020), glutamine (Cetinbas et al, 2016; Schulte et al, 2018; Jin et al, 2020), or cystine (Murphy et al, 1989; Gao et al, 2015; Tang et al, 2016; Yu & Long, 2016; Poursaitidis et al, 2017) greatly diminishes NADPH and GSH levels, leading to oxidative stress and triggering cell death. Although glucose, glutamine, and cystine all contribute to cellular reducing potential, their metabolism also creates an oxidative burden. Downstream metabolites of glucose and glutamine feed into the TCA cycle and drive oxidative phosphorylation (OXPHOS), leading to ROS production through electron leakage from the electron transport chain (ETC). Reduction of intracellular cystine consumes NADPH (Pader et al, 2014). When NADPH is limited due to starvation (Goji et al, 2017; Joly et al, 2020; Liu et al, 2020), cystine switches from being a precursor of the GSH antioxidant into an oxidative toxin by further depleting NADPH. Moreover, cystine uptake is coupled in a 1:1 ratio to glutamate export through the cystine-glutamate antiporter system xCT (or Xc−) that is encoded by the SLC7A11 gene (Bannai, 1986; Koppula et al, 2018). Excessive extracellular cystine drives cystine-glutamate exchange, which competes for intracellular glutamate with the TCA cycle for energy (Muir et al, 2017; Sayin et al, 2017) and NADPH (Goji et al, 2017) production. This highly complex cost–benefit relationship between redox activity and nutrients is exacerbated by signaling pathways, including calcium and NADPH oxidase (NOX)-mediated tyrosine kinase (TK) signaling (Graham et al, 2012), which feed back to ROS production. Therefore, it is a daunting challenge to understand how extracellular nutrients determine cellular redox state and how redox homeostasis can be maintained in the presence of nutrient fluctuations. We reasoned that elucidating systems and quantitative features contributing to this metabolism and signaling network is fundamental to understanding how cells respond to nutrient perturbations. In recent years, several mathematical models have been developed to help understand various aspects of redox metabolism and ROS-mediated signaling. These include nutrient transport and its links to intracellular glutathione metabolism (Reed et al, 2008; Geenen et al, 2012, 2013), ROS-mediated signaling at the plasma membrane and across mitochondrial networks (Zhou et al, 2010; Grecco et al, 2011; Nivala et al, 2011; Travasso et al, 2017), as well as the ferroptosis signaling cascade (Konstorum et al, 2020). However, none of these models integrate nutrient metabolism and ROS-mediated signaling as a redox system, nor did the respective studies investigate how deprivation of redox-modulating nutrients—glucose, cystine, and glutamine, particularly via the cystine-glutamate antiporter SLC7A11 (Goji et al, 2017; Joly et al, 2020; Liu et al, 2020)—may lead to elevated ROS and, consequently, cell death. High glucose-consuming organ systems, such as heart and brain (Hawkins et al, 1992), as well as cancer cells, can be predisposed to oxidative stress under glucose restriction. Although this redox imbalance causes pathological conditions in humans (e.g., hypoglycemia-induced brain failure), glucose deprivation can be harnessed to target metabolic vulnerabilities in cancer cells. Acute and stringent glucose deprivation was shown to trigger oxidative cell death of multidrug-resistant cancers (Lee et al, 1997, 1998). Subsequently, the molecular mechanisms underlying glucose deprivation-induced oxidative cell death were further delineated in terms of contributory metabolic and signaling pathways, including nutrient dependency (Lee et al, 1998; Goji et al, 2017; Koppula et al, 2017; Shin et al, 2017), metabolic adaptation via AMPK signaling (Jeon et al, 2012), metabolic flexibility mediated by the antiporter SLC7A11 (Koppula et al, 2017; Shin et al, 2017; Liu et al, 2020), and signaling feedback to ROS (Graham et al, 2012; Lee et al, 2018), together highlighting potential therapeutic innovations (Joly et al, 2020; Liu et al, 2020). To integrate these mechanistic insights for a predictive model, we synthesize an inter-connected metabolic and signaling network to build the first nutrient-redox model, linking environmental nutrient availability to cellular redox state, as well as the feedback signaling, between glutathione and ROS (Fig 1A). Through this model, we recapitulate the redox dynamics of human cells, highlighting key mechanisms regulating redox imbalance during nutrient deprivation, either individually or in concert. At the systems level of the nutrient-redox network, we investigate whether interlinked feedback loops give rise to bistability. Our simulations and experimental results show that ROS dynamics follow a switch-like and all-or-none response upon glucose starvation at a threshold that is approximately two orders of magnitude lower than its physiological concentration. This feature of ROS bistability is further evidenced by its irreversibility and hysteresis. Our study provides a mathematical framework for investigating the human redox system and implicates bistability as a key mechanism responsible for redox homeostasis. Figure 1. A nutrient-redox model for human cells The network of nutrient metabolism and ROS signaling for building the nutrient-redox model. Computing redox dynamics and steady state (OUTPUT) with the nutrient-redox model by perturbing extracellular nutrient concentrations (INPUT) and metabolic parameters (MODEL). The hypotheses generated from the model were then tested experimentally using ROS imaging and cell death quantification (PHENOTYPES) that, in turn, feed back to all of the in silico-modeled elements. Download figure Download PowerPoint Results A nutrient-redox model of human cells To investigate how nutrient availability determines the redox state of human cells, we built a nutrient-redox model describing a redox regulatory network of human cells (Fig 1A and Materials and Methods). This redox regulatory network centers on the mutual inhibitory relationship between ROS and GSH, and it extends to the upstream nutrient metabolism and downstream signaling pathways that regulate the production and consumption of ROS and GSH (Fig 1A). The cellular redox state is modeled as a steady-state balance between ROS and GSH. In striving for a more realistic recapitulation of cellular redox state, we constrained our model with experimentally measured metabolite and protein concentrations (first panel, Fig 1B and Appendix Table S1), as well as metabolic fluxes (second panel, Fig 1B and Appendix Table S4). Our model parameterizes extracellular nutrient concentrations (glucose, cystine, and glutamine) and expression of their transporters (e.g., SLC7A11 for cystine import) in order to simulate dynamic transitions and steady-state changes in redox state under differential uptake of extracellular nutrients (third panel, Fig 1B). Moreover, our approach allowed us to pinpoint key regulatory steps for redox balance under various conditions of nutrient transport including the coupled import/export of cystine/glutamate by SLC7A11. Models such as ours can recapitulate systems-level behaviors, inspiring hypotheses for experimental testing (fourth panel, Fig 1B). The behavior of our model, i.e., ROS and GSH steady states, exhibits differential dependency on glucose and is contingent on the parameter of choice. Parameter settings in this study correspond to those of glucose-addicted human cells (see Materials and Methods and Discussion). Metabolite dynamics and redox catastrophe after glucose deprivation To determine how cells respond to low-glucose conditions, we simulated dynamic changes of redox-regulating metabolites (NADPH and GSH) and ROS after switching to low-glucose concentrations (0–12 µM) (Fig 2A). In our simulations, NADPH was instantly depleted in response to low levels of glucose (slope ˜ 90% within 10 min; left panel, Fig 2B). Such instantaneous depletion could reflect our assumptions of rapid glucose breakdown and lack of glycogen storage in the model. In contrast, GSH exhibited delayed biphasic depletion, whereby an initially slow hyperbolic decline was followed by a second phase of rapid collapse (right panel, Fig 2B). Moreover, the duration of the first phase of GSH decline is modulated by glucose concentration in the low µM range (right panel, Fig 2B). Thus, depending on the concentration of glucose, the time difference between the 90% declines of NADPH and GSH can range from 20 min (0 µM) to 80 min (12 µM). In a glucose-addicted cell line (T98), the experimentally measured NADPH time series shows a sharp 80% decrease in NADPH concentration within 10 min of glucose deprivation and a 90% decrease within 30 min (left panel, Fig 2C) (Joly et al, 2020). In contrast, the measured GSH time series in the T98 cell line reveals a 30-min time lag before GSH concentration gradually declines thereafter over the course of 90 min (right panel, Fig 2C). Figure 2. Redox dynamics and redox catastrophe upon glucose deprivation A. Illustration of our in silico simulation strategy to establish the temporal dynamics of metabolites. Glucose perturbations (changed from 10 mM to 0–12 µM) were applied to the nutrient-redox model, and then, the temporal dynamics of NADPH, cystine, GSH, and ROS were recorded to investigate changes in cellular redox state. B, C. Simulated (in silico, (B)) and experimentally measured (in cells, (C)) NADPH and GSH dynamics upon glucose deprivation. Solid blue lines: simulated dynamics; solid red circles: mean of measured metabolite levels; red error bars: standard deviation of measured metabolite levels; dashed red circles: not detected in experiments. Experimental measurements were adopted from (Joly et al, 2020). D. Upper panel: simulated ROS dynamics; lower panel: illustration of the occurrence of redox catastrophe, as defined by the time of maximum ROS rate of increase after glucose deprivation. Download figure Download PowerPoint Consistent with our simulation results, we observed differing timescales for NADPH and GSH decline upon glucose deprivation, operating at fast and slow rates, respectively. Nevertheless, both NADPH and GSH in T98 cells exhibited delayed decline after glucose deprivation compared to simulations. In particular, GSH showed no sign of decline during the first 30 min of glucose deprivation (right panel, Fig 2C). These delays may be caused by potential compensatory mechanisms (e.g., glycogen storage) in T98 cells that sustain NADPH and GSH production upon glucose withdrawal. Alternatively, since our model uses metabolic rate constants approximated from multiple cell lines, it is possible that selection of parameters tailored to the T98 cell line would capture more precisely its redox dynamics upon glucose starvation. Interestingly, in our simulations this rapid decline in GSH is concomitant with a swift increase in ROS (upper panel, Fig 2D). We refer to this rapid transition in redox state (collapse in GSH and rise in ROS) as a cellular redox catastrophe (lower panel, Fig 2D). It has been shown previously that NADPH depletion and cystine accumulation build up oxidative stress and are early determinants of cell death following glucose deprivation (Goji et al, 2017; Joly et al, 2020; Liu et al, 2020). In agreement with that notion, the initial rates of NADPH depletion and cystine accumulation in our simulations dictate the timing of redox catastrophe (Fig EV1). Thus, our model recapitulates redox dynamics in human cells undergoing glucose deprivation, providing a mathematical formulation for nutrient-redox mapping in human cells. Click here to expand this figure. Figure EV1. Rates of NADPH depletion and cystine accumulation determine the timing of redox catastrophe Time to redox catastrophe as functions of the NADPH depletion rate (left panel) or the cystine accumulation rate (right) after changing from maximal glucose (10,000 μM) to low glucose (0–12 μM) in silico. Download figure Download PowerPoint NADPH as a critical metabolite-determining redox catastrophe NADPH is an essential metabolite for both de novo synthesis and regeneration of GSH, so we explored if lowering the NADPH level alone (i.e., without glucose deprivation) could lead to similar redox catastrophe. We simulated redox dynamics upon decreasing total NADPH from 1 to 0.05–0.2 µM. We found that GSH was depleted when total NADPH ≤ 0.1 µM (upper panel, Fig EV2A), and this GSH depletion coincided with a swift increase in ROS (lower panel, Fig EV2A), which is reminiscent of the redox catastrophe observed after glucose deprivation. Given that NADPH can be a limiting factor for GSH production, we wondered if a larger pool of total NADPH could help prevent glucose deprivation-induced redox catastrophe. To answer that question, we simulated glucose deprivation at varying concentrations of total NADPH and found that increasing total NADPH concentration from 1 to 1.3 µM was sufficient to prevent redox catastrophe after glucose deprivation (Fig EV2B), further underscoring the importance of NADPH for redox homeostasis. Together, these simulation results indicate that there is a threshold-like NADPH level crucial for GSH maintenance. Glucose deprivation renders NADPH to fall below that threshold, thereby triggering GSH collapse and redox catastrophe. Click here to expand this figure. Figure EV2. Total NADPH concentration determines redox dynamics and steady state Simulation of redox dynamics after a sudden decrease in total NADPH (NADPH + NADP+) concentration. The steady-state metabolite concentrations (with 10,000 μM glucose and 1 μM total NADPH) were used as initial conditions for the simulation. Simulation of redox dynamics after glucose deprivation (from 10,000 to 0 μM) with varying total NADPH concentrations. Steady-state metabolite concentrations with varying total NADPH concentrations before glucose deprivation were used as initial conditions for the simulation. Download figure Download PowerPoint Feedback loop-mediated redox bistability Systems containing multiple feedback loops, such as the human redox system, can exhibit threshold-like responses to input signals and multistability in steady states (Fig 3A). Therefore, we examined the steady-state behavior of our modeled system with respect to varying concentrations of glucose and identified a bistable regime for redox state in the micromolar glucose range (12–160 µM), whereby a stable high-GSH/low-ROS state and a stable low GSH/high ROS state coexist (left panels for broader glucose range of 0–200 µM and right panels for focused glucose range of 0–40 µM, Fig 3B). This outcome indicates the existence of a glucose threshold (≈ 12 µM based on our current parameter settings) below which the redox state switches from being high-GSH/low-ROS to low GSH/high ROS, with this latter reflecting redox catastrophe. Next, we examined whether redox bistability depends on the strength of feedback loops in our nutrient-redox model. Perturbations in all feedback loops, i.e., double-negative feedback (cystine-NADPH and ROS-GSH) and positive feedback (ROS-calcium and ROS-PPTase-TK) loops, abolished redox bistability (Fig 3C), consistent with previous reports on the involvement of these pathways for ROS regulation (Graham et al, 2012; Lee et al, 2018; Joly et al, 2020; Liu et al, 2020). Figure 3. Bistability of the cellular redox state upon glucose deprivation Illustration of our in silico simulation strategy for revealing feedback effects on the redox steady state after glucose deprivation. Bifurcation diagrams of the redox state as a function of extracellular glucose concentration. Steady-state concentrations of GSH (upper panel) and ROS (lower panel) were solved across a range of extracellular glucose concentrations. The high-GSH/low-ROS state (blue dots) disappears, with an unstable middle state (gray open circles) at a bifurcation point equivalent to ≈ 12 µM glucose. The panels at right are expanded views of the panels at left. Changes in redox bistability (light blue: bistable; mid-blue: monostable; and dark blue: no steady state available) under perturbations of four different feedback loops. Download figure Download PowerPoint Redox catastrophe as a ROS bistable switch at a low-glucose threshold We sought to experimentally explore the existence of redox bistability during glucose deprivation in human cells. To assess ROS dynamics, we utilized a genetically encoded hydrogen peroxide (H2O2) probe, HyPer7 (Pak et al, 2020), for single-cell H2O2 quantification. Unlike conventional chemical-based ROS reporters (e.g., DCF-DA), HyPer7 is reversible and responds quantitatively to H2O2 in the low nanomolar range, thereby permitting real-time measurements of H2O2 dynamics and its steady state in the relevant concentration range. We starved HyPer7-expressing LN18 cells, a glucose-addicted human glioblastoma cell line, with a glucose concentration (6.3 µM) below the predicted glucose threshold to examine the occurrence of a ROS bistable switch (Fig 4A). As illustrated in Fig 4B and C, we observed sharp increases in ROS level (≥ 2-fold elevation from basal levels) in individual cells within a timeframe of 0.5–1.5 h. Approximately 80% of cells displayed this rapid increase in ROS in response to low-glucose levels, accordingly termed responsive cells (see Fig 4C for single-cell measurements and Fig 4D for a population-level assessment of 100 cells). Non-responsive cells exhibited a less than twofold increase in ROS within 1.5 h (as an example, see the asterisk-labeled Cell 2 in Fig 4B). It is worth noting that due to heterogeneity of ROS dynamics within a cell population, such as arising from responsive versus non-responsive cells (Fig 4B) and ROS increase time (Fig 4C), single-cell quantification is necessary to reveal these sharp increases in cellular ROS levels, which can be easily masked by population-level measurements. Figure 4. Redox catastrophe as a ROS bistable switch at the lethal glucose threshold A–D. ROS bistable switch upon glucose deprivation at 6.3 µM. (A) Illustration of an experimental strategy to induce the ROS bistable switch by lowering glucose to 6.3 µM. Time-lapse images (B) and quantification (C) of ROS in individual cells. The asterisk-labeled cell in (B) represents a non-responsive cell with no increase in ROS after glucose starvation. (D) The median (solid line), as well as Q1 (lower bound) and Q3 (upper bound), of ROS levels for responsive (red) and non-responsive (blue) cells after glucose starvation at 6.3 µM (N > 100). E. A dose–response curve of ROS 1.5 h after glucose deprivation (from 200 to 6.3 µM). For each glucose concentration, the ROS levels of 80 cells were quantified. To deconvolute the effects of non-responsive cells, they were not considered in the analyses of the lower three glucose concentrations (6.3 µM, 12.5 µM, and 25 µM). A Hill exponent (n) was fitted to be 6.2, with a 95% confidence interval of 4–8. F. Temporal changes of single-cell ROS after glucose deprivation at 0 µM (left panel, ctrl) or with additional inhibition of NOX (GKT137831, 10 µM, middle-left panel) or calcium (BAPTA-AM, 12.5 µM, middle-right panel) signaling or inhibition of both (right panel). G. Percentages of non-responsive and responsive cells after glucose deprivation at 0 µM with or without inhibitors of calcium (BAPTA-AM, 12.5 µM) and NOX (GKT137831, 10 µM) signaling. Three experimental repeats generated qualitatively identical results. H. Left panel: cell death was quantified with YoYo-1 dye 6 h after glucose treatments ranging from 1.6 mM to 0 µM. Error bars represent standard deviations of six technical repeats. Three experimental repeats generated quantitatively similar results. Right panel: representative bright-field cell images 6 h after glucose deprivation. I. Death times for responsive and non-responsive cells are significantly different (N > 100, Mann–Whitney U-test ***P-value = 3.28 × 10−11). On each box, the central band indicates the median, and the bottom and top edges of the box indicate the 25th (q1) and 75th (q3) percentiles, respectively. The whiskers (upper one = q3 + 1.5(q3 − q1), lower one = q1–1.5(q3 − q1)) extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the dot symbol. Download figure Download PowerPoint If the redox system in LN18 cells is indeed bistable, they would display a threshold-like, ultrasensitive ROS response to different levels of glucose starvation. Indeed, the majority of LN18 cells only exhibit ROS increases when the glucose concentration is ≤ 25 µM, which is ≈ 2 orders of magnitude lower than the physiological glucose concentration (Fig 4E). For the dose-response curve, a Hill exponent (n) was fitted to be 6.2, with a 95% confidence interval of 4–8 (Fig 4E) suggesting ROS is ultrasensitive to glucose concentration. Similar to LN18, another glucose-addicted cell line (U87-MG) also presented the same pattern of elevated ROS when the glucose concentration was ≤ 25 µM (Appendix Fig S1A) and exhibited an ultrasensitive dose-response curve with a Hill exponent (n) of 6.5 and a 95% confidence interval of 2–11 (Appendix Fig S1B). Previous studies have demonstrated the contribution of NOX and calcium signaling to elevated ROS during glucose starvation (Graham et al, 2012). ROS elevation mediated by these signaling pathways activates tyrosine kinases, which then feed back to further amplify ROS. To test the involvement of these two major ROS feedback signaling loops in the switch-like ROS response, we deployed chemical inhibitors to inhibit NOX (GKT137831: NOX1/4 inhibitor, 10 µM) and calcium (BAPTA-AM, 12.5 µM, Appendix Fig S2A) signaling during glucose starvation (0 µM). Consistent with previous findings, glucose starvation induced tyrosine kinase phosphorylation, as measured by immunoblott
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