The NKR-stimulated NK cells, however, were more active in ATP-linked respiration than the unstimulated cells. The maximal respiration can be assessed by the addition of an uncoupler FCCP, which drives the respiratory chain to operate at maximum capacity.

NK cells stimulated by anti-CD16 antibody and NKG2D ligand significantly enhanced their maximal respiration, indicating that these NK cells have more spare respiratory capacity to meet higher energy demand upon NKR engagement Figures 1D,F. Because the expansion of NK cells using feeder cells would probably change the metabolic state of NK cells, thus the expanded NK cells may exhibit different metabolic responses from resting primary human NK cells upon NKR simulation.

Therefore, we also performed ECAR and OCR analyses on primary NK cells that were freshly isolated from human PBMCs. Because primary NK cells are less active than the expanded NK cells, we stimulated primary NK cells with anti-CD16 antibody alone, or co-engaged three NK cell receptors using MICA, anti-2B4 antibody and ICAM-1 to induce stronger activating signals for optimal cell activation.

The NK cells were successfully activated by these treatments, as indicated by their upregulation of CDa and IFN-γ mRNA expression Figures 2A,B. Unlike expanded NK cells, the glycolytic rate of freshly isolated primary NK cells was further enhanced when the ATP production in OXPHOS was inhibited by oligomycin, indicating that the primary NK cells depend on both glycolysis and OXPHOS to provide ATP and had relatively lower glycolytic machinery compared with the expanded NK cells Figure 2C.

Similar to the ex vivo expanded NK cells, we found freshly isolated NK cells to also exhibit significantly increased glycolysis, glycolytic capacity, and OXPHOS upon NKR stimulation Figures 2C—F. Figure 2. Freshly isolated primary NK cells upregulate glycolysis and OXPHOS in response to activating NKR stimulation.

NK cells were isolated from human PBMCs and stimulated by either anti-CD16 mAb or MICA plus ICAM-1 and anti-2B4 mAb for 4 h. NK cell activation was examined by A surface CDa expression as determined by flow cytometry and B IFN-γ mRNA level as quantified by RT-PCR. C,D ECAR and OCR analyses performed using Seahorse Extracellular Flux Analyzer as described in Figure 1.

Real-time changes of ECAR C and OCR D in NK cells without blue and with red stimulation by anti-CD16 mAb upper panel or MICA plus ICAM-1 and anti-2B4 lower panel were measured.

E Quantification of glycolysis and glycolytic capacity of unstimulated and stimulated NK cells. F Quantification of basal respiration, ATP-linked respiration and maximal respiration in NK cells with and without NKR stimulation.

Samples were compared using One-way ANOVA with Dunnett's multiple comparisons test B and independent Student's t -test E,F. Taken together, these data indicate that glycolysis and OXPHOS are both elevated in ex vivo expanded and freshly isolated NK cells upon NKR activation.

We next explored how NKR-induced metabolic changes affect NK cell functions. One of the major effector functions of NK cells is the secretion of cytokines, e.

Hence, we examined how cell metabolism affects NK cell production of IFN-γ upon NKR stimulation. We treated NK cells with 2-deoxyglucose 2-DG , which is an analog of glucose that blocks glycolysis. Anti-CD16 antibody and NKG2D ligand engagement successfully induced IFN-γ production in expanded NK cells as examined by flow cytometry analysis Figures 3A—C.

However, NKR-induced IFN-γ production was significantly reduced by the inhibition of glycolysis and OXPHOS. ELISA measurements of IFN-γ also showed that the inhibition of either OXPHOS or glycolysis decreased IFN-γ secretion of both expanded Figures 3D,E and freshly isolated NK cells Figures 3F,G upon NKR engagement.

These data suggest that both glycolysis and OXPHOS are required for IFN-γ production upon NKR activation of NK cells.

Figure 3. Glycolysis and OXPHOS are required for NKR-induced IFN-γ production of both ex vivo expanded and freshly isolated NK cells. A Flow cytometry analysis of IFN-γ production of NK cells. B,C Quantification of the percentage of IFN-γ-expressing NK cells after various stimulations and treatments as indicated.

D,E IFN-γ secretion of expanded NK cells after various stimulations and treatments as indicated. Supernatant were collected after stimulation and IFN-γ was detected by ELISA. F,G IFN-γ secretion of freshly isolated primary human NK cells. Culture supernatant were collected and IFN-γ was measured by ELISA.

Samples were compared using One-way ANOVA with Dunnett's test. As cytotoxicity is another important effector function of NK cell, we proceeded to explore how cell metabolism affects NK cell cytotoxicity. The chronic myeloid leukemia cell line K, which lacks MHC class I and expresses abundant activating NKR ligands, is an ideal target for the assessment of NK cell cytotoxicity Due to limiting access to freshly isolated primary human NK cells, we mainly focused on ex vivo expanded NK cells in assessing NK cell cytotoxicity.

This approach is also consistent with the protocol adapted for cell therapy, i. We first analyzed the effect of glycolysis inhibition on NK cell cytotoxicity. We started with the short-term inhibition of glycolysis by treating NK cells with 2-DG for 4 h. These NK cells were then co-cultured with K cells in the presence of 2-DG at effector to target E:T ratio of 0.

We next introduced a longer-term inhibition of glycolysis by incubating NK cells with glucose-free medium overnight. Inhibiting glycolysis by glucose starvation significantly reduced the killing of K cells by NK cells at all E:T ratios examined, with the most drastic decrease observed at E:T ratio of Figures 4A,D.

These data indicate that long-term inhibition of glycolysis impaired NK cell cytotoxicity. Figure 4. Glycolysis supports NK cell cytotoxicity against target K cells.

A,B Killing of K cells by glycolysis-inhibited NK cells. Ex vivo expanded NK cells were pretreated for 4 h with 2-DG or incubated overnight with glucose-free GF medium to inhibit glycolysis.

The pretreated NK cells were then A co-cultured with cell tracer violet-labeled K cells in the presence of glycolysis inhibitors i. Untreated NK cells co-cultured with K cells served as control.

Dead K cells were labeled with Fixable Viability Dyes FVD. C Killing of glycolysis-inhibited K cells by NK cells. K cells were left untreated or pretreated with 2-DG or glucose-free medium for 1 h to inhibit glycolysis.

These K cells were then washed twice with PBS before co-culturing with NK cells at various E:T ratios for 1 h. Dead K cells were labeled with FVD. D—F Quantification of the percentage of killing of K cells by NK cells as shown in A—C , respectively.

Samples were compared using two-way ANOVA with Dunnett's multiple comparisons test. In the above studies, the metabolic inhibitors were present in the co-culture, thereby affecting not only effector but also target cells. To examine if applying metabolic inhibitors to NK cells alone affects NK cell cytotoxicity, we washed the pretreated NK cells twice with PBS and co-cultured them with untreated K cells in culture medium free of glycolysis inhibitors.

Similarly, glucose starvation impaired NK cell cytotoxicity at all E:T ratios examined, with the most drastic decrease at E:T ratio of Figures 4B,E. To investigate if inhibition of glycolysis also affects K cells' susceptibility to NK cytotoxicity, we pretreated K cells with 2-DG or glucose-free medium for 1 h before co-culturing them with NK cells.

Inhibiting glycolysis in K cells had no impact on their susceptibility to NK cell cytotoxicity Figures 4C,F. Taken together, these data indicate that glycolysis is critical in supporting the cytotoxicity of ex vivo expanded NK cells.

Next, we explored the impact of OXPHOS inhibition on NK cell cytotoxicity. Although untreated NK cells were already potent in killing K cells, we were surprised to observe a drastic increase in the killing of K cells at all E:T ratios examined when OXPHOS was inhibited Figures 5A,D.

OXPHOS-inhibited NK cells exhibited comparable killing of K cells at E:T ratio of 0. However, when we washed off the metabolic inhibitors before incubating the pretreated NK cells with K cells, OXPHOS inhibition showed little effect on NK cell cytotoxicity, as they exhibited similar percentage of killing as untreated NK cells Figures 5B,E.

Figure 5. OXPHOS is not required for NK cell cytotoxicity but inhibition of OXPHOS renders K cells more susceptible to NK cell cytotoxicity. A,B Killing of K cells by OXPHOS-inhibited NK cells. C Killing of OXPHOS-inhibited K cells by NK cells. These K cells were washed twice with PBS before co-culturing with NK cells at various E:T ratios for 1 h.

Samples were compared using independent Student's t -test. Our data above indicated that inhibition of OXPHOS had little impact on NK cell cytotoxicity when applied to NK cells alone but increased the killing of K target cells when given in co-culture.

Hence, we next examined if the metabolic inhibitors could change target cells' susceptibility to NK cell cytotoxicity. The inhibitors were washed away before co-culturing the pretreated K cells with untreated NK cells.

Interestingly, inhibiting OXPHOS rendered K cells more susceptible to NK cell cytotoxicity Figures 5C,F. Therefore, inhibiting OXPHOS enhanced the susceptibility of K cells to NK cell killing without compromising NK cell cytotoxicity, suggesting that the inhibition of OXPHOS in tumor environment might be advantageous for NK cell-based immunotherapy.

Granule exocytosis pathway is one of the major pathways mediating NK cell cytotoxicity Activated NK cells and their target cells form immunological synapses, which facilitate the entry of lytic granules that contain perforin and granzymes into the target cells to induce cell apoptosis via caspase-dependent or -independent pathways.

This process is called degranulation and is associated with the expression of CDa on NK cell surface Thus, we explored how cell metabolism affects the granule exocytosis pathway. Subsequently, we stimulated these cells with anti-CD16 antibody or NKG2D ligand.

In contrast, OXPHOS-inhibited NK cells did not show a significant decrease in CDa expression Figures 6B,E,F. Similarly, the K cell-induced expression of CDa on NK cells was also dramatically reduced by glycolysis inhibition but was not affected by OXPHOS inhibition Figures 6G,H.

Figure 6. NK cell degranulation induced by NKR activation is dependent on glycolysis. A—F The effect of glycolysis or OXPHOS inhibition on CDa expression of NK cells induced by anti-CD16 mAb or MICA plus ICAM These NK cells were stimulated with anti-CD16 mAb or MICA plus ICAM-1 for 4 h.

The CDa expression of NK cells were examined by flow cytometry A,B. C—F Quantification of the percentage of CDa-expressing NK cells. I Granzyme B secretion of NK cells subjected to various stimulations and treatments as stated above.

Granzyme B secreted by NK cells was quantified using ELISA, and results were normalized to untreated NK cells. G,H The effect of inhibition of glycolysis or OXPHOS on CDa expression of NK cells induced by K cells.

Untreated and the pretreated NK cells were washed twice with PBS and then co-cultured with cell tracer violet-labeled K cells for 30 min. CDa expression of NK cells were examined by flow cytometry G. H Quantification of the percentage of CDa-expressing NK cells induced by K cells.

Samples were compared using one-way ANOVA with Dunnett's test. We also investigated the impact of cell metabolism on granzyme B secretion of NK cells. Our ELISA data indicated that anti-CD16 antibody and NKG2D ligand increased the granzyme B secretion of NK cells Figure 6I.

Similar to the results of CDa expression, granzyme B secretion induced by NKR-activation was abrogated by the inhibition of glycolysis but not OXPHOS.

Taken together, our data suggest that glycolysis is essential in supporting NK cell degranulation induced by NKR activation, whereas OXPHOS has little impact on this process.

Death receptor pathways also contribute to NK cell cytotoxicity. NK cells express death receptor ligands—FasL, TNF, and TNF-related apoptosis-inducing ligand TRAIL , which recognize the corresponding receptors Fas, TNFR, and TRAILR, respectively, on target cells. Upon receptor-ligand interaction, death-inducing signaling complexes are formed to activate caspase-dependent and -independent pathways that eventually induce cell apoptosis When stimulated with K cells, expanded NK cells showed a more drastic increase in FasL expression than the plate-bound anti-CD16 antibody and NKG2D ligand stimulated cells Figures 7G,H.

Similarly, the induction of FasL expression by K cells was significantly inhibited by glucose starvation. In contrast, the inhibition of OXPHOS exerted little effect on the NKR-induced FasL expression of NK cells Figures 7B,E—H.

Unstimulated ex vivo expanded NK cells constitutively express a substantial amount of TRAIL, and its expression could not be further increased by anti-CD16 and NKG2D ligand stimulation Figure S2. Inhibition of glycolysis and OXPHOS had no impact on TRAIL expression.

Figure 7. Glycolysis supports FasL expression on NK cells activated by NKR. A—F The effect of glycolysis or OXPHOS inhibition on FasL expression of NK cells induced by anti-CD16 mAb or MICA plus ICAM Untreated and the pretreated NK cells were stimulated with anti-CD16 mAb or MICA plus ICAM-1 for 4 h.

FasL expression of NK cells were examined by flow cytometry A,B. C—F Quantification of the percentage of FasL-expressing NK cells.

G,H The effect of inhibition of glycolysis or OXPHOS on K cell-induced FasL expression of NK cells. NK cells were pretreated to inhibit glycolysis or OXPHOS as stated above. Untreated and the pretreated NK cells were washed twice with PBS and then co-cultured with cell tracer violet-labeled K for 30 min.

FasL expression of NK cells was examined by flow cytometry G. H Quantification of the percentage of FasL-expressing NK cells induced by K cells. Together, these results indicate that the cytotoxicity of expanded NK cells induced by NKR stimulation is mainly through CDa expression, granzyme B secretion, and FasL expression, and are highly dependent on glycolysis.

Cellular metabolism plays a critical role in shaping immune responses by controlling immune cell differentiation and function. Resting immune cells e. However, pro-inflammatory and effector immune cells e.

Recent studies found that resting NK cells have a low basal metabolic rate. However, under long-term cytokine stimulation, mouse or human NK cells upregulate glycolysis and OXPHOS, which are required for their effector functions 12 , In this current study, we investigated the role of metabolism in NKR-induced NK cell activation, which reflects the situation when NK cells are activated by target cells.

We found that NKR stimulation elevates glycolysis and OXPHOS in human NK cells. We further demonstrated that IFN-γ production of NK cells induced by NKR stimulation requires both glycolysis and OXPHOS.

More importantly, glycolysis is critical for supporting NK cell degranulation and FasL expression and their killing of target cells. Interestingly, inhibition of OXPHOS in the tumor microenvironment might be advantageous for the elimination of tumor cells by NK cells, because it does not affect NK cell cytotoxicity but renders tumor cells more susceptible to killing.

Cytokines and NKRs are two major pathways that regulate NK cell functions. NK cells distinguish self and non-self targets via their NKR expression on cell surface. CD16 acts as an Fc receptor that induces antibody-dependent cellular cytotoxicity. Upon CD16 and NKG2D engagement, expanded and primary NK cells secrete cytokine IFN-γ Figure 3 and induce cell cytotoxicity by boosting cell degranulation and FasL expression Figures 2 , 6 , 7.

Because ex vivo expanded NK cells, in our experiments, were cultured with IL-2 and feeder cells that express mbIL and mbIL for 14 days, they could be pre-activated and might have different metabolic state from the resting primary NK cells.

Thus, we examined the metabolic changes in both expanded and primary NK cells activated by CD16 and NKG2D engagement to explore if they had distinct metabolic responses to NKR stimulation.

Indeed, the expanded NK cells had low oxygen consumption rate for ATP production and showed limited increase in glycolytic rate when ATP synthesis was blocked in mitochondria, indicating that they probably depended more on glycolysis than OXPHOS in supplying ATP, while both glycolysis and OXPHOS were found to be important for ATP production in primary NK cells Figures 1 , 2.

Interestingly, NKR stimulation induced similar metabolic reprogramming in both expanded and primary NK cells, i. However, ULBP1 apparently induced stronger glycolysis than MICA in expanded NK cells, although both ULBP1 and MICA are the ligands for NKG2D Figures 1C,E.

One possible reason is that the molar concentration of ULBP1 used in our experiments is higher than that of MICA, because we coated MICA and ULBP1 on plates with the same mass concentration, but the molecular weight of ULBP1 is smaller than MICA.

Besides, the binding of MICA and ULBP1 to the plastic plate might be different, which could also affect the responses of NK cells to these ligands.

Previous research reported that mouse NK cells activated by anti-NK1. Therefore, our results indicate that human and mouse NK cells might have different metabolic requirements upon NKR activation. Similar to our findings in NKR-activated NK cells, long-term stimulation of NK cells by cytokines IL-2, IL, and IL showed increased effector functions, which were accompanied by the upregulation of glycolysis and OXPHOS and the increase of nutrient transporters and mitochondrial mass 12 , Collectively, these results indicate both NKR and cytokine activation of human NK cells induce metabolic reprogramming in these cells.

We also investigated how metabolic reprogramming contributed to human NK cell effector functions, namely cytokine secretion and cytotoxicity. Previous studies showed that OXPHOS was required for cytokine-induced IFN-γ and granzyme B production by human primary NK cells, and glycolysis was required for the IFN-γ production by primary CD56 bright NK cells in response to IL and IL stimulation Similarly, our data suggested that both expanded and primary NK cells require glycolysis and OXPHOS to support their production and secretion of IFN-γ upon their activation by NKR.

Thus, it is possible that, when glycolysis increases, more GAPDH enzymes are released from IFN-γ mRNA and resulting in the increase in IFN-γ translation. Another possible mechanism by which glycolysis supports IFN-γ production might be through affecting histone acetylation of Ifng , as increased glycolysis produces more acetyl-CoA, which elevates histone acetylation of lysine 9 residue H3K9Ac at Ifng loci, thus increasing IFN-γ production Our data showed that glycolysis but not OXPHOS is critical for expanded NK cells to eliminate the target K cells.

NK cells induced rapid clearance of K cells as they eliminated most of the target cells within 1. This rapid cytotoxicity is mainly through the granule exocytosis and FasL pathways.

Recent studies emphasize the prerequisite of glycolysis in cytotoxicity and CDa expression of educated NK cells 22 , Similarly, our data indicated that the NKR-activated NK cells also require glycolysis for NK cell cytotoxicity, mainly through supporting NK cell degranulation, granzyme B secretion, and FasL expression.

It is possible that the rapid killing of target cells by NK cells requires fast metabolic mobilization, so NK cells are more reliant on glycolysis, which could be more rapidly activated than OXPHOS, for their cytotoxicity.

Interestingly, inhibiting OXPHOS in K cells made them more susceptible to NK cell killing but did not affect NK cell cytotoxicity. This is in line with a recent study showing that tyrosine kinase inhibitor-resistant chronic myeloid leukemia cells upregulate OXPHOS for their survival, and the combinational use of a tyrosine kinase inhibitor, imatinib with an OXPHOS inhibiting antibiotics, tigecycline can selectively kill chronic myeloid leukemia stem cells Thus, the differentiated responses of NK and K cells toward OXPHOS inhibition make it a promising NK cell-based tumor treatment strategy in the clinic.

Another study also reveals the ability of oligomycin to sensitize leukemia cells to tyrosine kinase inhibitor treatment. They demonstrated that the effect is partially through increasing the production of superoxide to induce cell apoptosis Besides, the DNA damage caused by reactive oxygen species was shown to promote the expression of stress-induced activating ligands on multiple myeloma cells Thus, it is possible that the inhibition of OXPHOS in K cells enhances their expression of activating ligands and elevates the superoxide production to increase K cells' susceptibility to NK cell killing.

In conclusion, our study reveals important metabolic requirements for NKR-activated human NK cells. Our data suggest that glycolysis and OXPHOS are both increased upon NKR activation of human NK cells.

However, glycolysis is more important than OXPHOS for the effector functions of expanded NK cells. In addition, inhibition of OXPHOS in the tumor environment might be advantageous for NK cell-based immunotherapy.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.

The studies involving human participants were reviewed and approved by the Institutional Review Board of National University of Singapore ZW, SX, and K-PL designed the work and wrote the manuscript. ZW and DG conducted the experiments.

SW and LC provided critical reagents. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

We thank Yuhan Huang for technical support and the members of our lab for insightful discussions. This work was funded by Singapore Agency for Science Technology and Research.

Smyth MJ, Hayakawa Y, Takeda K, Yagita H. New aspects of natural-killer-cell surveillance and therapy of cancer. Nat Rev Cancer. doi: PubMed Abstract CrossRef Full Text Google Scholar.

Trinchieri G. Biology of natural killer cells. Adv Immunol. Fogler WE, Volker K, McCormick KL, Watanabe M, Ortaldo JR, Wiltrout RH. J Immunol. PubMed Abstract Google Scholar. Henkart PA. Integrations of the ordinary differential equations were performed with the CVODE from SUNDIALS [71].

The algorithm SSm [72] was used for stochastic global optimization of the parameters and experiment settings using a least squares objective function, which considers the constraints given in the supporting information 2.

A bootstrap method [73] , [74] was used for assessment of the parameter confidence intervals with a total of runs. All simulations were carried out on a Linux-based system. As already described by Rehberg et al. Subsequent analytics were applied to at least three individual wells per time point.

For the perturbation experiments, cells were grown to the late exponential growth phase at which a sample was taken as time point zero.

After 2 h of limitation, a sample was taken as time point zero of the glucose pulse experiment where PBS was removed and 4 mL of fresh GMEM-Z added. The applied analytics are in detail described in [75].

In short, after removal of the supernatant, cells were washed three times with PBS and treated 30 min with trypsin 2. Cells were harvested using a cell scraper. A Vi-Cell TM XR Cell Viability Analyzer Beckman Coulter was used for cell counting and measurement of the diameter distribution.

Cell number and cell diameter distribution were used to determine the cell volume. GLC x concentrations in the supernatant were quantified as described by Genzel and Reichl [76] using a Bioprofile plus analyzer Nova Biomedical, relative standard deviation of the method 1.

For the measurement of intracellular metabolites, sample preparation was performed as described in detail by Ritter et al. The medium of the wells was discarded and the cell layer was washed with a 0.

Quantification of the intracellular metabolites was performed by anion exchange chromatography BioLC system, Dionex in combination with mass spectrometry LC-MS, relative standard deviation of the method 0.

The absolute amount of metabolites per well were related to the measured cell volume at respective times of cultivation. To reduce the error, regression analysis was used to interpolate the measured cell volume.

The limit of quantification was related to the simulated cell volume. Sensitivity analysis of initial conditions and model parameters. Adjusting the segregated growth model established previously [14] to MDCK cell proliferation in 6-well plates using DMEM medium.

Cell number A , mean cell diameter B and extracellular glucose concentration C during MDCK cell cultivations in 6-well plates and DMEM medium with 2. Lines represent the respective simulation result based on the modifications described in supporting information 4.

Prediction of ribose 5-phosphate and uridyl diphosphate glucose during cultivation of MDCK cells in DMEM with limited extracellular glucose. Lines represent the respective simulation result based on the parameters of Table 1 and experiment-specific parameters of Table 2. Flow of information and link of experimental data.

Green background: Coupling of segregated cell growth model and structured model of glycolysis; red background: coupling of adjusted segregated cell growth model, which renders cell growth under limited GLC x concentrations, to the structured model of glycolysis.

Adenosine-based nucleotide pools during perturbation experiments. ATP A — C , ADP D — F and AMP G — I concentrations in three independent perturbation experiments with MDCK cells in 6-well plates.

Cells, originating from a cultivation experiment, are limited in extracellular nutrients by removal of medium and addition of phosphate buffered saline PBS , shown in the first column Lim1, A , D , G and second column Lim2, B , E , H.

After two hours of incubation, PBS was exchanged by fresh medium Pulse, C , F , I. SBML model for yeast glycolysis adapted to simulate a glucose limitation scenario.

Segregated cell growth model coupled to the structured model of glycolysis for simulation of Cult1. The model is provided as. Structured model of glycolysis for simulation of Lim1. The model is provided in the SBML format level 2 version 4. Predicting the glycolytic activity during cell growth in DMEM medium.

Flow of information and initial conditions for parameter fitting. Nomenclature for parameter of the segregated cell growth model. The authors are grateful to Frank Stefan Heldt for critical remarks on the manuscript and to Yvonne Genzel for helpful discussions.

Conceived and designed the experiments: JBR UR. Performed the experiments: JBR. Analyzed the data: MR. Wrote the paper: MR UR.

Developed and employed the model: MR. Article Authors Metrics Comments Media Coverage Reader Comments Figures. Abstract Due to its vital importance in the supply of cellular pathways with energy and precursors, glycolysis has been studied for several decades regarding its capacity and regulation.

Author Summary Glycolysis generates biomass precursors and energy from sugars and is therefore a key element in the metabolism of mammalian cells. Introduction The primary metabolism of cells is essential for cell growth and maintenance.

Results Glycolytic activity under changing growth regimes In three independent experiments, adherent MDCK cells were grown in 6-well plates with the serum-containing medium GMEM-Z, which provides sufficient amounts of extracellular substrates over the chosen cultivation time.

Download: PPT. Figure 1. Scheme of glycolysis model B and its link to the segregated cell growth model A established previously [23]. Upper glycolysis. Figure 2. Metabolites pools of glycolysis during adherent MDCK cell cultivation. Table 1. Initial conditions for the structured model comprising metabolic status, growth status and culture conditions for the simulated experiment.

Figure 3. Estimated fluxes for energy and precursors production during adherent MDCK cell cultivation. Lower glycolysis. Exchange of glycolytic metabolites through other reactions. Response of glycolysis to perturbation experiments Limitation experiments.

Figure 4. The response of intracellular metabolite pools to perturbation experiments. Pulse experiments. Link to pentose phosphate pathway and glycogenesis The implemented G6PDH and UT mediated conversion of G6P are entry points into the PPP and the glycogenesis, respectively. Figure 5. Intracellular metabolite pools of pentose phosphate pathway and glycogenesis during adherent MDCK cell cultivation.

Figure 6. The response of metabolite pools of pentose phosphate pathway and glycogenesis to perturbation experiment. Estimations and predictions for the metabolic activity ATP and biomass precursors generation under modulation of the glucose transport activity.

Figure 7. Impact of in silico GLUT modulation on A ATP and B pentose phosphate pathway PPP production rates.

Prediction of metabolic activity during growth in different medium. Figure 8. Prediction of glycolytic metabolite pools during cultivation of adherent MDCK cells in DMEM with 2.

Discussion Model structure We developed a kinetic description of glycolysis that, coupled to a segregated cell growth model, enabled describing and analyzing the experimental data of this study comprising roughly data points by using a single set of parameters for the enzyme kinetics.

Model coupling and simulation The derived kinetic description of glycolysis simultaneously integrates data of three independent cell cultivation experiments, two limitation experiments and one pulse experiment and therefore required coupling to a model that takes explicitly into account the progress of the cell through different growth phases during the cultivation experiments Cult1—3 [23].

Table 2. Measured and estimated parameters of adherent MDCK cell glycolysis used to simultaneously capture all experiments of this study with confidence intervals between 0. Glycolytic activity during cell cultivation Over the full course of cultivation cells pass through several growth-phases with varying cell-specific volumes and with glucose uptake rates that both strongly influence the metabolite pool dynamics Fig.

Materials and Methods Model and simulation The differential algebraic equations of the glycolytic model were composed of first order rate laws, Michaelis-Menten and Hill kinetics which describe enzyme activity in dependence of metabolite concentrations and allosteric influences.

Coupling of glycolysis to a segregated cell growth model. The concentration of decreases over cultivation time with 1 For the simulation of the perturbation experiments we assumed that the glucose uptake depends on a variable capacity for glucose trans-membrane transport as well as on the affinity of GLUT for such that Eq.

Kinetics of glycolytic enzyme reactions. Pentose phosphate pathway and glycogenesis. The model of glycolysis was extended at the expense of two additional parameters by R5P and UGLC: 11 12 The ribose-phosphate diphosphokinase and glycogen synthase with activity Eq.

Simulation procedure, initial values and parameter settings. Experiments and analytics Cell cultivation. Supporting Information.

Figure S1. s TIF. Figure S2. Figure S3. Figure S4. Figure S5. File S1. s XML. Model S1. s TXT. Model S2. Model S3. Supporting Information S1. s DOCX. Supporting Information S2. Constraints for metabolite exchange with the PPP. Supporting Information S3.

Detailed description of enzyme kinetics. Supporting Information S4. Supporting Information S5. Supporting Information S6. Acknowledgments The authors are grateful to Frank Stefan Heldt for critical remarks on the manuscript and to Yvonne Genzel for helpful discussions.

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Sometimes called the citric acid cycle because it deals with citric acid. The same thing that's in orange juice or lemons. And then from there we proceed to the electron transport chain. And we learned in the first overview video of cellular respiration that this is where the bulk of the ATP is actually produced.

Although it uses raw materials that came out of these phases up here. Now what I want to do in this video is just focus on glycolysis. And this is kind of-- it's sometimes a challenging task because you can really get stuck in the weeds. And I'll show you the weeds in a little bit, and the actual mechanism.

And it can be very daunting. But what I want to do is simplify it for you so you can have the big take-aways.

And then we can appreciate, and then maybe when we look at the weeds of glycolysis we can make a little bit more sense of it. So glycolysis, or really cellular respiration, it starts off with glucose.

And glucose, we know its formula. It's C6H12O6. And I could draw its whole structure; it would take a little time. But I'm just going to focus on the carbon backbone. So it is a ring, or can be a ring. But I'm just going to draw it as six carbons in a row.

Now there's two kind of important phases of glycolysis that are good to know. One, I call the investment phase. And the investment phase actually uses two ATPs. So you know, the whole purpose of cellular respiration is to generate ATPs, but right from the get-go I actually have to use two ATPs.

But I use two ATPs and then I'm essentially going to break up the glucose into two 3-carbon compounds right here that actually also have a phosphate group on them.

The phosphate groups are coming from those ATPs. They also have a phosphate group on them and this is often called-- well, there's a lot of names for it. Sometimes it's called PGAL. You really don't have to know this.

Or phosphoglyceraldehyde, really challenging my spelling skills right here. That's not that important to know. All you have to know is in this first phase you use two ATPs. That's why I call it the investment phase.

If we use a business analogy, investment phase. And then each of these two PGAL molecules can then go into the payoff phase. So in the payoff phase, each of these PGALs turn into pyruvate. Which is another 3-carbon, but it's reconfigured. But the process of it going to pyruvate-- and let me write pyruvate in blue, because this is something that, at least it's good to know the word.

And I'll show you the structure in a second. Sometimes it's called pyruvic acid. Same thing. And that's essentially the end product of glycolysis.

So you start off with glucose in the investment phase. You end up in this phosphoglyceraldehyde, which essentially you broke up your glucose and you put a phosphate on either end of it. And then those each independently go through the payoff phase.

So you end up with two molecules of pyruvate for every molecule of glucose you started off with. Now you're saying, hey, Sal, there was a payoff phase, what was our payoff? Well our payoff, we got, for each-- let me write this down as a payoff phase.

This is our payoff phase. And I apologize for the white background. I did it because, the mechanism I'm showing you, I copy-and-pasted it from Wikipedia, and they had a white background so I just ran with the white background for this video. But I, personally at least, like the black background a lot better.

But this is the payoff phase right here. And so when we go from the phosphoglyceraldehyde to the pyruvate or the pyruvic acid, we produce two things. Or I guess we could say we produce three things. We produce, each of these PGALs to pyruvates produce two ATPs. So I'm going to produce two ATPs there, I'm going to produce two ATPs there.

And then they each produce an NADH. And I'll do it in a darker color. And of course they're not producing the whole molecule in a vacuum. Essentially what they're doing is they're starting with the raw material of an NAD plus-- so they start off with an NAD plus-- and they essentially reduce it by adding a hydrogen.

Remember, we learned a couple of videos ago that you could view reduction as a gain in hydrogen. So the NAD gets reduced to NADH. And then later on, these NADHs are used in electron transport chain to actually produce ATPs. So the big take-away here, if I were to write the reaction that we get for glycolysis, is that you start off with a glucose.

And you need some NAD plus. And actually, for every mole of glucose, you're going to need two NAD plusses. You're going to need two ATPs. So I'm just writing all the ingredients that we need to start off with.

And then you're going to need-- well, let me say, these guys are going to be ADPs before we turn them to ATPs. So I'll write plus four ADPs.

And then, after performing glycolysis-- and let me write it here. Let me write also-- sorry that was ADPs. Let me just rewrite that part right there. Four ADPs. And then you maybe need two phosphate groups. Because we're going to need four phosphate groups.

Plus four-- I'll just call them, sometimes they're written like that. But maybe I'll write it like this. Four phosphate groups. And then once you perform glycolysis, you have two pyruvates, you have two NADHs.

The NAD has been reduced. It gained a hydrogen. OIL RIG. Reduction is gain an electron. But in the biological sense, we think of it gaining the hydrogen. Because hydrogen is very non-electronegative, so you're hogging its electrons. You've gained its electrons.

So two NADHs and then plus these two ATPs get used in the investment phase. That's why I kind of wrote them a little separately. So these two get used. So then you're left with two ADPs. And then these guys, essentially, get turned into ATPs. So plus four ATPs. I guess we didn't need four.

We only needed a net of two phosphate groups. Because two jump off of here. And then we need a total of two more to get four jumping on there. But the big picture is, you start with a glucose, you end up with two pyruvates.

You use up two ATPs. You get four ATPs. So you have a net of two ATPs formed. Let me write that very big. Net, what you get out of glycolysis, is two ATPs. You get two NADHs that can each later be used in the electron transport chain to produce three ATPs.

You get two NADHs and you get two pyruvates, which are going to be re-engineered into acetyl-CoAs that are going to be the raw materials for the Krebs cycle. But these are the outputs of glycolysis. So now that we have that big picture, let's actually look at the mechanism.

Because this is a little bit more daunting when you see it here. But we'll see the same themes that I just talked about. We're starting with a glucose right there.

It is a six chain. It's in a circle, in a ring. One, two, three, four, five, six carbons. I could write it like that, just to make a huge oversimplification. It goes through a few steps. I use an ATP here. So let me do that in a color.

Let me do it in orange whenever I use an ATP. I use one ATP there. And just like I told you, they have a slightly different name for it. But this is the phosphoglyceraldehyde right here.

They call it glyceraldehyde 3-phosphate. It's the exact same molecule. Overall, glycolysis converts one six-carbon molecule of glucose into two three-carbon molecules of pyruvate.

Detailed steps: Energy-requiring phase. Each step is catalyzed by its own specific enzyme, whose name is indicated below the reaction arrow in the diagram below.

The diagram shows the steps of glycolysis. The diagram begins with an image of the chemical structure and name of glucose and an arrow pointing towards the chemical structure and name glucosephosphate.

Above the arrow there is 1 inside a circle to indicate the first step in the reaction. The arrow has the enzyme hexokinase written below the arrow, and there is a looped arrow showing ATP going in and ADP being released.

The next reaction is indicated by a 2 inside a circle to show glucosephosphate with an arrow pointing at the chemical structure and label fructose 6-phosphate.

Below the arrow the enzyme phosphoglucoisomerase is written. Step 3 of the reaction is indicated by a 3 inside a circle and has an arrow pointing from Fructosephosphate towards the chemical structure and label fructose 1,6-bisphosphate.

Above the arrow there is a looped arrow showing ATP going in and ADP being released, and the enzyme phosphofructokinase is written below the arrow. Step 4 of the reaction is indicated by a 4 inside a circle, and shows 2 arrows pointing from fructose 1,6-bisphosphate, and the enzyme fructose bisphosphate aldolase is written between the 2 arrows.

Next to the top arrow is the chemical structure and name Dihydroxyacetone phosphate, and next to the bottom arrow is the chemical structure and name Glyceraldehyde 3 phosphate.

Step 5 of the reaction is indicated by a 5 inside a circle and shows 2 arrows; one arrow is pointing from Dihydroxyacetone phosphate towards Glyceraldehyde 3-phosphate, and the other arrow is pointing from Glyceraldehyde 3-phosphate towards Dihydroxyacetone phosphate.

The enzyme Triose phosphate isomerase is written next to the 2 arrows. Step 1. Step 2. Glucosephosphate is converted into its isomer, fructosephosphate.

Step 3. This step is catalyzed by the enzyme phosphofructokinase, which can be regulated to speed up or slow down the glycolysis pathway. Step 4. They are isomers of each other, but only one—glyceraldehydephosphate—can directly continue through the next steps of glycolysis.

Step 5. Detailed steps: Energy-releasing phase. In the second half of glycolysis, the three-carbon sugars formed in the first half of the process go through a series of additional transformations, ultimately turning into pyruvate.

The reactions shown below happen twice for each glucose molecule since a glucose splits into two three-carbon molecules, both of which will eventually proceed through the pathway.

Detailed steps of the second half of glycolysis. All of these reactions will happen twice for one molecule of glucose. Glyceraldehydephosphate is converted into 1,3-bisphosphoglycerate. An inorganic phosphate is also a reactant for this reaction, which is catalyzed by glyceraldehydephosphate dehydrogenase.

This step converts an ADP to an ATP. This reaction releases a water molecule. Phosphoenolpyruvate PEP is converted to pyruvate by pyruvate kinase. An ADP is converted to an ATP in this reaction. Image modified from " Glycolysis: Figure 2 ," by OpenStax College, Biology CC BY 3.

Step 6. The overall reaction is exergonic, releasing energy that is then used to phosphorylate the molecule, forming 1,3-bisphosphoglycerate. Step 7. Step 8. Step 9. Step You can learn how this works in the videos and articles on pyruvate oxidation , the citric acid cycle , and oxidative phosphorylation.

It can't just sit around in the cell, piling up. There are two basic ways of accomplishing this. This process is called fermentation , and you can learn more about it in the fermentation videos.

Want to join the conversation? Log in. Sort by: Top Voted. Ahmad Nawaz. Posted 8 years ago. Why the 1st phase are same in aerobic and anaerobic respiration. Downvote Button navigates to signup page.

Flag Button navigates to signup page. Show preview Show formatting options Post answer. Yeongseo Lee. This is because oxidation in glycolysis doesn't involve oxygen atoms. It's just movement of hydrogen. So it's behaving in the same way with or without oxygen.

when NAD is turned into NADH, G3P 5H loses two electrons and "two" protons. but in the next step, 3-bis not sure how this happens. The other H comes from HPO4 with a 2- charge which eventually turns itself into inorganic phosphate.

Comment Button navigates to signup page. Stephen Hutchinson. In the Investment phase, where did the 2 atps come from that were used up? was it taken from somewhere else? if the goal is to produce atp in glycilysis, where do we get atp to begin the process?

Nicholas Cook-Rostie. The ATPs originally came from your mother through parental nutrition, while you where developing in the womb. When you are born you will have a stock pile of ATP in your body, which must be replenished to stay alive.

The body has many ways to make ATP, which can be seen by looking at the vast amount of metabolic reactions that occur with the body. This is also why we can survive for a long time without any additional consumption of food as the many catabolism pathways in the body that breakdown larger molecules and transfer the energy from the breakdown to ATP.