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LC/MS/MS method revealed that lung cancer cells exhibited distinct metabolite profiles upon the treatment with different pyruvate dehydrogenase kinases (PDKs) inhibitors Wen Zhang, Xiaohui Hu, Wei Zhou, and Kin Yip Tam J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00184 • Publication Date (Web): 20 Jul 2018 Downloaded from http://pubs.acs.org on July 26, 2018

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Journal of Proteome Research

LC/MS/MS method revealed that lung cancer cells exhibited distinct metabolite profiles upon the treatment with different pyruvate dehydrogenase kinases (PDKs) inhibitors

Wen Zhang, Xiaohui Hu, Wei Zhou, Kin Yip Tam* Faculty of Health Sciences, University of Macau, Macau, China

*

Corresponding author

Phone: +853 88224988, Fax: +853 88222314 E-mail: [email protected]

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ACS Paragon Plus Environment

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Abstract Pyruvate dehydrogenase kinases (PDKs) dominates the critical switch between mitochondria-based respiration and cytoplasm-based glycolysis by controlling pyruvate dehydrogenase (PDH) activity. Upregulated PDKs plays a great role in the Warburg effect in cancer cells and accordingly presents a therapeutic target. Dichloroacetate (DCA) and AZD7545 are the two most well-known PDKs inhibitors exhibiting distinct pharmacological profiles. DCA showed anticancer effects in various preclinical models and clinical studies, while the primary preclinical indication of AZD7545 was on the improvement of glucose control in type II diabetes. Little, if any, study has been undertaken to elucidate the effects of PDKs inhibition on the metabolites in the tricarboxylic acid (TCA) cycle. Herein the metabolite alterations of lung cancer cells (A549) upon the treatment with PDKs inhibitors were studied using a reliable liquid chromatography-based tandem mass spectrometry (LC-MS/MS) method. The developed method was validated for quantification of all common glycolysis and TCA cycle catabolites with good sensitivity and reproducibility, including glucose, pyruvate, lactate, acetyl coenzyme A (acetyl-CoA), citrate, α-ketoglutarate, fumarate, succinate, malate and oxaloacetate. Our results suggested that A549 cells exhibited distinct metabolite profiles following the treatment with DCA or AZD7545 which may reflect the different pharmacological indications of these two drugs. Key words: aerobic glycolysis; AZD7545; dichloroacetate; LC-MS/MS; pyruvate dehydrogenase kinases; cancer metabolism.

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1. Introduction Altered glucose homeostasis is a major factor that contributes to several metabolic diseases, including cancer and type II diabetes. Pyruvate dehydrogenase kinases (PDKs) and pyruvate dehydrogenase (PDH) are key enzymes in the glucose metabolic pathway, which provides most of the energy used by the human body.1 PDH links glycolysis and mitochondrial respiration by catalyzing the oxidative decarboxylation of pyruvate to acetyl-CoA, which is the main substrate for tricarboxylic acid (TCA) cycle.2 The activity of PDH is largely controlled by PDKs via the reversible phosphorylation of three serine residue sites.3 Overexpression of PDKs has been found in various human tumors, such as head and neck squamous cancer,4 gastric cancer,5 myeloma,6 and renal cell carcinoma.7 Suppression of PDH by PDKs upregulation decreases the production of acetyl-CoA from pyruvate, and thus attenuates oxidative phosphorylation, augments lactate fermentation and initiates the oncogenic switch to glycolysis in cancer cells, known as the Warburg effect.8 In this way, inhibitors targeting PDKs, by reactivating PDH and rectifying the dysregulated metabolic switch, could reduce tumorigenicity.9 On the other hand, hyperglycemia and the impaired balance between glucose and lipid metabolism are key features of type II diabetes. A number of factors regulate the glucose metabolic pathway; among these, PDH plays a critical role.10 Activation of PDH by inhibiting PDKs would improve pyruvate oxidation and decrease substrates for gluconeogenesis, thus ultimately reducing blood glucose levels in peripheral tissues.11 Therefore, inhibition of PDKs could also be an effective strategy to treat type II diabetes.12 Dichloroacetate (DCA) (Figure 1), a structural analogue of the PDH substrate 3



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pyruvate, is the most commonly used PDKs inhibitor.13 DCA has been reported to inhibit PDKs activity by binding to the pyruvate-binding pocket and inducing conformational changes.14 Owing to its significant therapeutic effects, simple chemical structure and more than 30 years of clinical use, DCA entered Phase II clinical trials for cancer treatment as a PDKs inhibitor soon after its first anticancer report.15

AZD7545,

(2R)-N-{4-[4-(dimethylcarbamoyl)phenylsulfonyl]-2-chlorophenyl}-3,3,3-trifluoro-2hydroxy-2-methylpropanamide (Figure 1), is a dihydrolipoamide mimetics developed for treating type II diabetes.16 AZD7545 indirectly inhibited the kinase activity with an IC50 of about 70 nM by projecting its trifluoromethylpropanamide end into PDKs and hindering PDKs binding to PDH.17 However, the anticancer effects of AZD7545 were weak. According to our own data, AZD7545 at a high concentration of 400 µM inhibited cancer cell growth by only about 10% (in three different cancer cell lines).18 To the best of our knowledge, the use of AZD7545 as a PDKs inhibitor for anticancer treatment has never been reported in the scientific literature. Although these two PDKs inhibitors were known to reactivate PDH by inhibiting PDKs, their pharmacological indications were vastly different.19 We hypothesize that this could be due to differences in the ways these inhibitors modulate the TCA cycle. As the levels of intermediates in TCA cycle are potential metabolic indicators of physiological changes,20 the catabolite assessment might shed light into how cancer cells respond to PDKs inhibitors treatment and could also be used to predict the anticancer effect of PDKs inhibitors.

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Figure 1 Chemical structures of DCA and AZD7545.

Thus, the development of an analytical approach for detecting metabolic intermediates in cancer cells is crucial to deepening our understanding of PDKs inhibitors and their roles in cancer metabolism. In this study, we aimed to assess alterations in catabolites, including glucose, pyruvate, lactate, acetyl-CoA, citrate, α-ketoglutarate, fumarate, succinate, malate and oxaloacetate (Figure 2), in cancer cells after treatment with PDKs inhibitors. As most of these analytes have a small molecular weight, high polarity and low concentration in a complex matrix, the selection of the analytical approach remained an open question. Various analytical approaches have been applied to profile cellular metabolism, such as enzymatic spectrophotometric

assays,21

nuclear

magnetic

resonance

(NMR),22-23

high-performance liquid chromatography (HPLC),24-25 and chromatography coupled to mass spectrometry (MS). Among these techniques, chromatography coupled to MS stands out for its potential for high sensitivity and specificity. Chromatography-MS approaches could be classified according to the type of chromatography and MS performed. While gas chromatography-mass spectrometry (GC-MS) is widely used,26-29 its derivatization reactions often require a change of solvent and could lead to poor analyte recovery. For instance, Jun used 3-nitrophenylhydrazine as a derivatizing reagent for the LC-MS/MS determination of ten carboxylic compounds in central carbon metabolism.30 Kloos described selective derivitization with N-methyl-2-phenylethanamine for measurement of carboxylic acids in TCA cycle.31 However, these derivatization steps are often time-consuming and not suitable for all 5



organic acids. Therefore, liquid chromatography-based tandem mass spectrometry (LC-MS/MS) without derivatization is preferred in the field. The measurement of a some of the TCA cycle intermediates has been achieved in Escherichia coli (E. Coli.)32 and mouse model33 using hydrophilic interaction chromatography (HILIC). Reversed-phase chromatography (RP) was employed to analyse some TCA cycle acids in different matrices, such as microdialysis samples,34 fruits and vegetables,35 mammalian cells,36-38 human plasma,37 urine and prostate tissue,37 as well as mitochondrial extracts.38 Moreover, ion-exchange chromatography was applied to determine certain carboxylic acids in cultured cells39 and ion exclusion chromatography was used to assess some organic acids in natural waters.40 However, to the best of our knowledge, there has been no report on the application of HILIC chromatography for the simultaneous quantification of glycolysis related substances (glucose, pyruvate, lactate, acetyl-CoA) and TCA cycle acids in cultured cancer cells.

Figure 2 Compound structures of the analyzed intermediates in cancer cells. Glucose, pyruvate, lactate, acetyl-CoA, citrate, α-ketoglutarate, fumarate, succinate, malate and oxaloacetate were included in the analytes. The concentrations of lactate in the culture medium from cancer cells was also determined, and all the other molecules were assessed for their cytosol concentrations as well. 6


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In the present study, we reported on the development of a reproducible LC-MS/MS method for analysing the key catabolites involved in the bioenergetic pathways of glycolysis and the TCA cycle. Then, the optimized LC-MS/MS method was applied to monitor changes in the concentration of intermediates and to quantitatively profile these metabolic alterations after treating lung cancer cells (A549) with PDKs inhibitors. Moreover, the Agilent Seahorse XF24 instrument was employed to investigate the alterations in mitochondrial respiration and glycolysis in A549 cells after treatment with DCA and AZD7545. The influences of these two PDKs inhibitors on the metabolic switch in cancer cells were then elucidated.

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2. Material and methods 2.1 Chemicals and cell culture Acetonitrile and ammonium hydroxide with MS grade were bought from Fisher Scientific (PA, USA) and water was deionized and filtered by a Millipore water purification system (Millipore, MA, USA) throughout the experiment. All the compounds other than sodium pyruvate (Sigma-aldrich, USA). Sodium pyruvate

13

C3 were obtained from Sigma

13

C3 was bought from Cambridge Isotope

Labs (Cambridge, MA, USA). A549 cell line (non-small-lung cancer cell line) was purchased from American Type Culture Collection (Manassas, VA, USA) and cultured in F-12K (Kaighn's) medium (Gibco) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin/streptomycin (Gibco). 2.2 Preparation of standard solutions for LC-MS/MS analysis Stock solutions of sodium succinate dibasic and sodium fumarate dibasic were prepared in methanol/water mixture (1:1, v/v) individually while all the other analytes were prepared in methanol. Primary stock solutions were prepared at 10 mM and stored at -80 °C. The mixed stock solution was provided by combination of all the stock

solutions,

including

glucose,

pyruvate,

lactate,

acetyl-CoA,

citrate,

α-ketoglutarate, fumarate, succinate, malate and oxaloacetate. The working standard solutions with indicated concentrations were obtained by serial dilutions of the mixed stock solution with solvent of methanol/acetonitrile/H2O (5:3:2 v/v/v). Based on the preliminary experiment, calibration curves with various concentrations were prepared in the range listed in Table 2. Mixed calibration curves were freshly prepared for each batch of samples. 2.3 Preparation of cell samples for LC-MS/MS analysis 8


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A549 cells were cultured on 6-cm dishes overnight, treated with 0.1% DMSO or indicated concentrations of PDKs inhibitors for 12 hours. The cells and medium were then collected for further measurement. In every experiment, three biological replicates were prepared and one additional plate was grown for each group to normalize cell viability. Sample preparation procedures were modified based on the method previously reported.41 In brief, for extracellular intermediates analyzation, 200 µl of culture medium was added to 600 µl acetonitrile, vortexed vigorously for 10 min for complete deproteinization and spun down at 16,000 g for 10 min at 4 °C. The supernatants were collected for LC-MS/MS analysis. For intracellular intermediates determination, cells were quickly washed twice with ice-cold water to remove medium components, after which they were immediately lysed with 200 µl of methanol/acetonitrile/H2O (5:3:2 v/v/v) on ice and quickly scraped from the dishes within 30 s. All the solutions, tubes and scrapers used were pre-chilled. The extracts were centrifuged at 16,000 g for 10 min to pellet the insoluble material at 4 °C. The supernatants were transferred to new vials and stored in -80 °C for LC-MS/MS analysis. The experiment was repeated for three times. 2.4 LC-MS/MS equipment and conditions Chromatographic analysis was performed on the Waters Acquity ultra-performance liquid chromatography (UPLC) H-Class system (Waters Co., Milford MA, USA) consisting of a quaternary pump, an online solvent degasser and an autosampler. A 100 mm ⅹ 2.1 mm Acquity UPLC BEH HILIC 1.7 µm column was used. The oven temperature was maintained at 40 °C. The samples were injected with an autosampler set at 4 °C and the injection volume of each sample was 1 µl. The mobile phase consisted of 0.03% (v/v) ammonium hydroxide in water (eluent A), water (eluent C) 9



and acetonitrile (eluent D) with a gradient elution (SI Section 1). MS/MS detection was carried out using the Waters Xevo TQD equipped with an electrospray ionization (ESI) source in the negative-ion mode. The default settings of the instrument for ion transfer and quadruple scanning were used. The capillary voltage was 4.0 kV. Nitrogen gas was employed as the cone gas and desolvation gas at flow rates of 50 L/h and 1000 L/h, respectively. The source temperature and desolvation temperature were set as 150 °C and 500 °C, respectively. The standard solutions of every target compound were infused directly into the mass spectrometer to optimize the transition and spectrometric parameters individually. Waters MassLynx V4.1 workstation software was used to generate peak areas of each analyte. 2.5 LC-MS/MS method validation The method was validated according to “Guidance for Industry: Bio-analytical Method Validation” protocol of the US Food and Drug Administration (FDA). The peak region and mass spectra of cell samples and mixed standard solutions were compared to determine the level of coeluting interfering components and moreover to assess the selectivity of the method. The calibration curves were obtained by plotting analyte concentrations against the corresponding peak areas. A 1/R2 weighted linear least-squares regression model was used to obtain the best fit to a straight line. The matrix effect was evaluated by post-column infusion of combined standard solutions in this experiment. Standard mixtures were injected first and then cell samples were infused. Ion influence (suppression or enhancement) was checked in the MRM channel of every target compound.

13

C3 sodium pyruvate was used as internal

standard to assess the recovery during sample preparation and variations occurred in the replications. Six replicate dishes of A549 cells were scraped with 10


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methanol/acetonitrile/H2O (5:3:2 v/v/v) containing 50 µM 13C3 sodium pyruvate. The remaining procedures for sample preparation were performed according to Section 2.3. Lastly, the amount of 13C3 sodium pyruvate in the supernatants were analyzed by the developed LC-MS/MS method. The percent recoveries of the internal standard were calculated as: Recovery (%) = (mean observed concentration/spiked concentration) ⅹ 100%. The accuracy the developed method was assessed by measuring quality control samples. Accuracy was evaluated by calculating the relative error (RE%) with following

equation:

RE

(%)

=

(measured

concentration

–

nominal

concentration)/nominal concentration ⅹ 100%. The precisions were assessed by measuring cell samples for six times on the same day and three consecutive days, the relative standard deviation (CV% = standard deviation/mean ⅹ 100%) was calculated. Intra-day and inter-day stability tests of all the analytes were performed in the relevant conditions since the samples were usually not assessed directly after collection but after processing or storing procedures. To evaluate the stability of the target compounds during preparation and storage, the stability experiments were performed by assessing six replicates of cell samples. The concentrations of analytes after storing at room temperature for 12 h (intra-day) and storing in -80 °C fridge for 72 h (inter-day) were measured by the developed method. Stabilities in different conditions were assessed by calculation of concentration deviations with the equation: deviation = measured concentration/endogenous concentration

100%. The target

compounds were considered stable in samples when the concentration deviations were not larger than 15%. 2.6 Mitochondrial bioenergetics assay 11



Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), indicators of mitochondrial respiration and lactate excretion or glycolysis rate respectively, were investigated by Agilent Seahorse XF technology according to the instructions provided by the manufacturer. In brief, the sensor cartridge was hydrated one night prior to the assay. 1 mL of Agilent Seahorse XF Calibrant was added to each well of the utility plate and the sensor cartridge was placed on top of the utility plate and kept in a CO2-free incubator at 37 °C overnight. A549 cells were seeded at a density of 2 × 104 cells per well in F-12K medium in the Agilent Seahorse XF24 cell culture microplate other than the background correction wells (A1, B4, C3 and D6). After overnight, the cells were treated with serial concentrations of DCA and AZD7545 for 12 h. For basal measurement of OCR or ECAR, cells were changed from growth media to the prewarmed Seahorse XF Assay/Base medium and the plate was equilibrated in a CO2-free incubator at 37 °C for 1 h prior to the assay. The experiment was repeated for three times. For each experiment, three replicates were prepared and cell viabilities were normalized at the end of the experiment. 2.7 Statistical analysis Data were shown as Mean ± SD (n = 3) and representative of three independent experiments. Statistical analysis was carried out using GraphPad Prism. P values less than 0.05, 0.01 and 0.001 were considered statistically significant and indicated by one, two and three asterisks, respectively.

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3. Results and discussion 3.1 LC-MS/MS method development and optimization GC-MS is widely employed in metabolite analysis and in many targeted applications. However, the use of GC in this approach was limited for several reasons, the most important of which is that the targeted molecules need to be volatile. As most of the intermediates in TCA cycle are of high polarity and are relatively involatile, complex and lengthy derivatization procedures are needed to prepare volatile samples suitable for a GC study. Compared with GC, LC is highly suitable for assessing polar and hydrophobic analytes. To this end, a sensitive LC-MS/MS method was developed for the quantification of 10 intermediates in cells in the present study. All the intermediates, other than pyruvate and actyl-CoA, were detected in MRM mode. MRM was performed throughout the gradient. The m/z of the precursor and the main product ion, the cone voltage and the collision energy of all analytes were listed in

Table 1. Table 1 LC-MS/MS parameters of the target compounds. Compound

Retention

Precursor

Product ion

Cone

Collision

time (min)

(m/z)

(m/z)

voltage (V)

energy (V)

Glucose

1.45

179.0

88.9

26

8

Pyruvate

0.74

87.0

SIR

22

-

Actyl-CoA

0.72

808.2

SIR

54

-

Lactate

0.77

88.9

88.8

30

10

Citrate

0.73

191.0

111.0

28

13

α-ketoglutarate

0.74

145.0

101.0

20

10

Succinate

0.74

117.0

73.0

22

10

Fumarate

0.74

115.0

71.1

20

6

Malate

0.74

133.0

115.0

24

14

Oxaloacetate

0.78

131.0

87.0

25

10

As all the analytes were endogenous intermediates in cells, a real blank matrix to construct standard curves was not available. The calibration curves were obtained in 13



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the organic solvent methanol/acetonitrile/H2O (5:3:2 v/v/v, the same as that used in the cell sample preparation procedure). Moreover, we did not observe unexpected high background signals in the cell samples as compared with the standards, suggesting the preparation of the calibration curves in organic solvent mixture was acceptable. The correlation coefficients for all calibration curves were greater than 0.99, and every targeted compound showed good linearity (Table 2). The resulting linear range was considered satisfactory as it was wide enough to detect intermediates in cell samples. As concentrations of several analytes were too high to be covered by the range of the standard curves, the sample solutions were firstly diluted before applying the LC-MS/MS method. The reliability and reproducibility of the developed approach as assessed by measuring the accuracy (shown as the relative recovery, RE) and precision (shown as the relative standard deviation, RSD) with quality control samples were found to be satisfactory (Table S-1). Table 2 Linearity of calibration curves determined by the established LC-MS/MS method. Analyte

Range (µM)

Linear regression equation

Correlation coefficient

Glucose

3.91 - 2000.00

y = 7.5479x + 202.09

0.996

Pyruvate

6.25 - 200.00

y = 33.682x + 216.72

0.991

Actyl-CoA

0.78 - 12.50

y = 64.081x + 3868.5

0.993

Lactate

1.96 - 2000.00

y = 11.195x + 616.59

0.996

Citrate

0.39 - 25.00

y = 3402.6x + 2668.2

0.992

α-Ketoglutarate

0.098 - 6.25

y = 855.09x + 229.79

0.996

Succinate

0.039 - 50.00

y = 441.17x + 1095.9

0.993

Fumarate

0.098 - 50.00

y = 208.8x + 601.01

0.991

Malate

0.39 - 12.50

y = 544.27x + 853.93

0.994

Oxaloacetate

1.25 - 20.00

y = 5.9172x + 101.92

0.990

3.2 Analysis of metabolic profile in cancer cells after treatment with DCA and AZD7545 with the developed LC-MS/MS method Glucose is metabolized by several steps to pyruvate after it entering into the cytosol. A certain proportion of pyruvate is decarboxylated by PDH to acetyl-CoA which is 14


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the substrate for the TCA cycle and that gives rise to citrate, while the remaining portion is reduced to lactate by lactate dehydrogenase. PDKs inhibitors divert pyruvate from cytoplasm-based glycolysis to mitochondria-based respiration via upregulating PDH. Herein, in order to evaluate the effect of PDKs inhibitors, we applied the developed LC-MS/MS method to quantify glucose, pyruvate, acetyl-CoA, lactate and TCA cycle intermediates in A549 cells after 12 h of treatment with PDKs inhibitors. The A549 cancer cell line employed in this study is frequently used as a model to investigate the functions of PDKs inhibitors in cellular metabolism.42-43 In comparison with many other cancer cell lines, A549 cells show a relatively high extracellular acidification rate/oxygen consumption rate ratio, which indicates that the cells are highly dependent on glycolysis for energy and may be responsive to PDKs inhibitors.44 3.2.1 Determination of metabolic intermediates in A549 cells after DCA treatment DCA is the most well-known PDKs inhibitor and the only one that has entered Phase II clinical trials for cancer treatment. According to our previous data, DCA inhibited A549 cell proliferation with an IC50 around 20 mM.42 Accordingly, serial concentrations of 5, 10 and 20 mM DCA were selected for the following experiment. As shown in Table 3 and Figure 3, DCA at 5, 10 and 20 mM did not significantly influence intracellular glucose and pyruvate concentrations, indicating that DCA did not affect glucose uptake or the glycolytic process from glucose to pyruvate. It was seen that 10 and 20 mM DCA notably increased the amount of acetyl-CoA (P < 0.01 and 0.001, respectively), which is the main substrate for the TCA cycle. Of note, similar increases were found in the downstream metabolites, i.e. citrate, succinate, fumarate, malate and oxaloacetate, in A549 cells after treatment with DCA for 12 h. 15



Compared with the control group, as the DCA concentration was increased to 10 and 20 mM, the production of citrate was enhanced by about two- to three-fold, respectively (P < 0.01 and 0.001, respectively). Similarly, a significant increase was identified in succinate formation in a dose-dependent manner in the DCA treated cells (P < 0.05, 0.01 and 0.001, respectively). Moreover, incubation of A549 cells with 10 and 20 mM DCA for 12 h led to an obvious increase in fumarate and malate production (P < 0.01). The concentrations of oxaloacetate in A549 cells after treatment with 5, 10 and 20 mM DCA were 158, 438 and 838 µM, respectively, which were significantly higher than the concentration observed in control cells (c.a. 3 µM). It is interesting to note that the presence of DCA increased the concentrations of every intermediate in the TCA cycle other than α-ketoglutarate (Table 3 and Figure 3). In particular, 5, 10 and 20 mM DCA significantly decreased α-ketoglutarate production in a dose-dependent manner (P < 0.01). We speculated that high concentrations of DCA (in the millimolar range) might influence the generation α-ketoglutarate, resulting in a dose-dependent reduction in α-ketoglutarate formation. This phenomenon deserves further investigation. Importantly, lactate production in cells treated with 10 and 20 mM DCA dropped by about 28% and 55% compared to the control group (P < 0.01 and 0.001, respectively). Similarly, a dramatic reduction was also observed in lactate accumulation in the culture medium from A549 cells after incubation with serial concentrations of DCA (P < 0.001). Compared with the control group, 5 mM, 10 mM and 20 mM DCA decreased the extracellular lactate concentration from 73 to 47, 28 and 20 µM, respectively. These results suggested that DCA inhibited lactate generation in cancer 16


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cells (Table 3 and Figure 3), which was consistent with the fact that DCA exhibited beneficial effects in lactic acidosis.45 Table 3 Analysis of catabolites in A549 cells after DCA treatment. The developed LC-MS/MS method was applied to assess intermediate concentrations in A549 cells after treatment with the indicated concentrations of DCA (5, 10 and 20 mM) for 12 h. Analyte

Control (µM)

5 mM

10 mM

20 mM

Glucose

47.95 ± 5.63

47.34 ± 1.71

46.28 ± 2.60

52.71 ± 3.71

Pyruvate

36.89 ± 5.28

37.36 ± 2.62

37.00 ± 3.61

39.05 ± 2.45

Acetyl-CoA

2.30 ± 0.020

2.29 ± 0.020

2.50 ± 0.050

2.88 ± 0.0036 ***

Lactate (in a)

36.83 ± 2.94

33.09 ± 2.01

26.01 ± 2.10 **

15.76 ± 1.11 ***

***

19.71 ± 0.36 ***

b

***

Lactate (ex )

73.03 ± 3.21

Citrate

4.58 ± 1.15

6.51 ± 1.14

α-ketoglutarate

0.84 ± 0.036

Succinate

0.19 ± 0.019

0.28 ± 0.022

Fumarate

0.76 ± 0.063

0.85 ± 0.12

Malate

1.67 ± 0.21

1.96 ± 0.16

Oxaloacetate a

DCA

3.56 ± 0.24

47.21 ± 3.17

28.02 ± 3.88

9.24 ± 1.18 *

11.18 ± 2.10 **

0.66 ± 0.047 **

0.50 ± 0.034 **

0.43 ± 0.039 **

*

**

1.00 ± 0.11 **

1.05 ± 0.068 *

1.41 ± 0.10 **

2.36 ± 0.35 *

3.38 ± 0.47 **

157.89 ± 22.76

indicated lactate concentrations in cell samples.

b

0.46 ± 0.026

***

438.28 ± 65.69

***

808.38 ±86.45 ***

indicated extracellular lactate concentrations in

media samples.

Figure 3 Analysis of catabolites in A549 cells after DCA treatment. The developed LC-MS/MS method was utilized to measure intracellular glucose, pyruvate, acetyl-CoA, lactate, citrate, 17



α-ketoglutarate, succinate, fumarate, malate, oxaloacetate concentrations as well as extracellular lactate concentration of A549 cells after treatment with the serial doses of DCA (5, 10 and 20 mM) for 12 h.

Taken together, DCA dose-dependently elevated levels of most TCA cycle intermediates, which correlated with increased acetyl-CoA and decreased shunting of pyruvate-derived carbons toward lactate. The results provided an evidence from a metabolic point of view that DCA-treated cancer cells responded to this PDKs inhibitor by refueling mitochondrial respiration and suppressing aerobic glycolysis. 3.2.2 Determination of intermediates in A549 cells after treatment with AZD7545 AZD7545 has been reported to inhibit PDKs activity in an in vitro kinase assay with an IC50 of 87 to 600 nM.17 Moreover, AZD7545 was found to strongly bind to the lipoyl-binding pocket in the N-terminal domain of PDKs with a Kd value of 24 to 48 nM.46-48 Based on these data, 100 nM, 1 µM and 10 µM AZD7545 were employed as representative doses in the present study. As shown in Figure 4 and Table 4, AZD7545 significantly decreased intracellular glucose concentrations and pyruvate production in a dose-dependent manner, which is consistent with the fact that AZD7545 was developed to lower glucose levels for treating type II diabetes.49 Although the quantity of pyruvate decreased significantly (ranging from 25.57 to 9.48 µM) as the concentration of AZD7545 increased, the acetyl-CoA concentrations remained at comparable levels. Our data suggested that, in the presence of AZD7545, the decrease in cytosol glucose and cytosol pyruvate did not translate to a similar decrease in mitochondrial acetyl-CoA, suggesting that a decrease in the latter could be compensated for by endogenous sources via the catabolism of some amino acids or the β-oxidation of fatty acids.

18


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Unlike DCA, although AZD7545 improved PDH activity by suppressing PDKs,16 AZD7545 did not increase TCA cycle intermediate production downstream of acetyl-CoA. On the contrary, AZD7545 decreased some of the intermediates. Compared with the citrate concentration of 2.58 µM in control cells, 1 µM and 10 µM AZD7545 decreased citrate production to 1.47 µM (P < 0.05) and 0.48 µM (P < 0.01), respectively (Table 4). Similar to citrate, AZD7545 at 1 µM and 10 µM remarkably reduced succinate yields to 44% and 19% of the control cells, respectively (Table 4). In agreement with the results for citrate and succinate, AZD7545 dose-dependently inhibited oxaloacetate production to 83% (P < 0.05), 64% (P < 0.01) and 52% (P < 0.01) of control cells, respectively (Table 4). In contrast to citrate, succinate and oxaloacetate, AZD7545 treatment did not substantially alter α-ketoglutarate concentrations, but maintained them all around 1.0 µM, which resembled levels in control cells (Table 4). This may suggest that not only glucose metabolism but also glutamine metabolism supported α-ketoglutarate production, resulting in a relatively stable level. On the other hand, it is interesting to point out the change in the concentration of malate in the presence of AZD7545, which at 100 nM and 1 µM elevated malate concentrations to about 3.0 and 3.4 µM respectively, yet 10 µM AZD7545 increased the malate concentration to only 2.9 µM (Table 4). As seen in Figure 4, upstream of malate, the trends for fumarate formation in cells treated with AZD7545 100 nM and 1 µM were different from that of the 10 µM AZD7545 group. Fumarate production decreased significantly (P < 0.01) after treatment with 10 µM AZD7545 but did not change significantly with 100 nM or 1 µM AZD7545 (Figure 4). These findings were in line with prior work reporting that 1 µM AZD7545 (and lower doses) inhibited PDKs activity, whereas AZD7545 at 19



Page 20 of 35

saturating concentrations (10 µM) enhanced PDKs activity.17 Therefore, the effects of AZD7545 on certain TCA cycle intermediates were different when the doses were higher than 10 µM or lower than 1 µM. Although AZD7545 activated PDH at the nanomolar level by inhibiting PDKs,50 it did not decrease lactate production in cancer cells as DCA did (Section 3.2.1). As shown in Table 4 and Figure 4, in comparison with control cells, 100 nM, 1 µM and 10 µM AZD7545 induced a significant concentration dependent increase in extracellular lactate accumulation (P < 0.01). Observed differences of the compound concentrations in experiments of DCA and AZD7545 was probably due to the fact the studies were carried out independently at different dates (Table 3 and 4). The passage numbers and the growth state of cells in DCA and AZD7545 experiments were not the same and contributed to the divergence of compound concentrations in the control group. It should be pointed out that it is more informative to compare the trend of the alternations of the metabolites in DCA and AZD7545 groups respectively. Table 4 Analysis of catabolites in A549 cells after AZD7545 treatment. The developed LC-MS/MS method was applied to assess intermediate concentrations in A549 cells after treatment with the the indicated concentrations of AZD7545 (100 nM, 1 µM and 10 µM) for 12 h. AZD7545 (µM)

Analyte

Control (µM)

100 nM

1 µM

10 µM

Glucose

67.73 ± 3.64

60.46 ± 5.17

57.94 ± 2.51 *

49.10 ± 2.38 **

Pyruvate

25.57 ± 1.02

17.62 ± 1.28 **

13.44 ± 0.34 ***

9.48 ± 0.97 ***

Acetyl-CoA

1.52 ± 0.042

1.53 ± 0.034

1.55 ± 0.021

1.53 ± 0.023

Lactate (ina)

37.45 ± 2.34

42.69 ± 0.22

49.61 ± 0.032 **

59.70 ± 3.52 **

Lactate (exb)

73.61 ± 1.75

88.19 ± 2.25 **

90.63 ± 2.26 **

95.42 ± 4.84 **

Citrate

2.58 ± 0.050

2.04 ± 0.37

1.47 ± 0.21 *

0.84 ± 0.21 **

*

0.90 ± 0.10 *

α-ketoglutarate

1.15 ± 0.023

1.04 ± 0.12

0.94 ± 0.086

Succinate

0.21 ± 0.046

0.16 ± 0.022

0.093 ± 0.0024

0.039 ± 0.0092 **

Fumarate

0.72 ± 0.039

0.74 ± 0.046

0.78 ± 0.085

0.53 ± 0.038 **

*

Malate

1.79 ± 0.18

3.01 ± 0.34

Oxaloacetate

2.54 ± 0.11

2.10 ± 0.10 *

***

2.94 ± 0.92 **

1.63 ± 0.058 **

1.31 ± 0.14 ***

3.35 ± 0.24

20


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a

indicated lactate concentrations in cell samples.

b

indicated extracellular lactate concentrations in

media samples.

Figure 4 Analysis of catabolites in A549 cells after AZD7545 treatment. The developed LC-MS/MS method was utilized to measure intracellular glucose, pyruvate, acetyl-CoA, lactate, citrate, α-ketoglutarate, succinate, fumarate, malate and oxaloacetate concentrations as well as extracellular lactate production of A549 cells after treatment with the serial doses of AZD7545 (100 nM, 1 µM and 10 µM) for 12 h.

Collectively, unlike DCA, AZD7545 neither increased TCA cycle intermediate generation nor decreased lactate production. Although AZD7545 activated PDH, it did not promote mitochondrial respiration or inhibit aerobic glycolysis. This might explain why AZD7545 could not inhibit cancer cell proliferation from a metabolic perspective. Further work was required to improve our understanding as to why AZD7545 reactivated PDH by inhibiting PDKs but failed to fuel the TCA cycle or reduce glycolysis. 3.3 Assessment of cellular bioenergetics in cancer cells after treatment with DCA and AZD7545 21



To verify the results obtained by LC-MS/MS, indicators of mitochondrial respiration and lactate excretion/glycolysis, namely the oxygen consumption rate (OCR) and the extracellular acidification rate (ECAR), were assessed in cancer cells after treatment with different concentrations of DCA and AZD7545. The analysis was performed on the Agilent Seahorse XF24 system.

Figure 5 Analysis of OCR, ECAR and OCR/ECAR in A549 cells after DCA or AZD7545 treatment. Real-time measurement of OCR (Figure 5A and 5D) and ECAR (Figure 5B and 5E) in A549 cells after treatment with DCA or AZD7545. The OCR/ECAR ratio (Figure 5C and 5F) was calculated and cell viability was normalized at the end of the experiment.

Figure 5A showed that, relative to control cells, DCA significantly elevated the mitochondrial respiration rate (P < 0.001). At the same time, DCA dose-dependently reduced glycolysis (Figure 5B), suggesting that DCA boosted the use of pyruvate by mitochondrial respiration and suppressed lactate formation by inhibiting PDKs. Together, these results were consistent with the findings presented in Section 3.2.1, in which DCA increased TCA cycle intermediate concentrations and decreased lactate production. Furthermore, compared with the control cells, the OCR/ECAR ratios in cells treated with 10 and 20 mM DCA were remarkably increased (P < 0.05, Figure 22


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5C), indicating that DCA treatment shifted the metabolism of A549 cells from glycolysis to oxidative phosphorylation through rectification of the Warburg effect. Conversely, as shown in Figure 5D and 5E, AZD7545 (100 nM, 1 µM and 10 µM) decreased the mitochondrial respiration rate and increased glycolytic activity in a dose dependent manner (P < 0.05). This observation is in line with the result discussed in Section 3.2.2 in which AZD7545 decreased TCA cycle catabolites while increasing lactate production in cancer cells. Furthermore, cancer cells exhibited a decreased OCR/ECAR ratio after treatment with AZD7545 (Figure 5F). It was concluded that AZD7545 did not redivert the Warburg effect in A549 cells, which provided an explanation as to why AZD7545 did not inhibit cancer cell proliferation in the context of metabolism.

23



4

Conclusion

We validated a LC-MC/MS method specifically developed for the quantification of all common glycolysis and TCA cycle intermediates including glucose, pyruvate, lactate, acetyl-CoA, citrate, α-ketoglutarate, fumarate, succinate, malate and oxaloacetate in cancer cells. The developed method was rapid (18-min run time), accurate and reproducible. The calibration curves showed good linearity for all analytes and all the correlation coefficients (R) were greater than 0.99. The validated method was applied to examine metabolic intermediates in A549 cells following treatment with DCA or AZD7545. Our results suggested that DCA dramatically increased TCA cycle intermediates and mitochondrial function, and decreased lactate production and glycolytic activity in A549 cells. However, AZD7545 neither elevated mitochondrial metabolites nor suppressed lactate generation. These detailed analyses supported the notion that DCA rediverted the metabolic switch from glycolysis to mitochondrial respiration, exhibiting an anticancer effect, while offering a mechanistic interpretation as to why AZD7545 did not inhibit cancer cell proliferation in the context of metabolism. To the best of our knowledge, this is the first systematic investigation into the effects of PDKs inhibitors on metabolic intermediates in cancer cells. It is envisaged that the developed LC-MS/MS method could also be used as a generic useful tool to study metabolite alterations and understand complex metabolic events in cancers.

24


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Author information Corresponding author: * Phone: +853 88224988. Fax: +853 88222314. E-mail: [email protected]. Notes The authors declare no conflict of interest. Acknowledgement We thank the financial support from the Science and Technology Development Fund, Macao S.A.R (FDCT) (Project reference no. 086/ 2014/A2). Thanks are due to Dr. Li Wang from Metabolomics Core in Faculty of Health Sciences for operation of BioProfile Flex Analyzer and Seahorse XFe24. Supporting Information Section 1 LC method optimization Section 2 LC-MS/MS method validation Figure S-1. Representative chromatograms of analyte standards assessed with optimized method. Table S-1 Gradient profile used in the developed LC-MS/MS method. Table S-2 Accuracy, precision and stability of analytes in samples determined by the established LC-MS/MS method.

25



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Abstract graphic:

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