Full text

Turn on search term navigation

ARTICLE

Received 23 Jun 2011 | Accepted 21 Oct 2011 | Published 22 Nov 2011 DOI: 10.1038/ncomms1562

Metabolism is altered in many highly prevalent diseases and is controlled by a complex network of intracellular regulators. Monitoring cell metabolism during treatment is extremely valuable to investigate cellular response and treatment efcacy. Here we describe a nuclear magnetic resonance-based method for screening of the metabolomic response of drug-treated mammalian cells in a 96-well format. We validate the method using drugs having well-characterized targets and report the results of a screen of a kinase inhibitor library. Four hits are validated from their action on an important clinical parameter, the lactate to pyruvate ratio. An eEF-2 kinase inhibitor and an NF-kB activation inhibitor increased lactate/pyruvate ratio, whereas an MK2 inhibitor and an inhibitor of PKA, PKC and PKG induced a decrease. The method is validated in cell lines and in primary cancer cells, and may have potential applications in both drug development and personalized therapy.

Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library

Stefano Tiziani1, Yunyi Kang1, Janet S. Choi1, William Roberts2 & Giovanni Paternostro1

1 Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, California 92037, USA. 2 Rady Childrens Hospital, Department of Pediatrics, UCSD, 3020 Childrens Way MC 5101, San Diego, California 92123, USA. Correspondence and requests for materials should be addressed to G.P. (email: [email protected]).

NATURE COMMUNICATIONS | 2:545 | DOI: 10.1038/ncomms1562 | www.nature.com/naturecommunications

ARTICLE

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms1562

Metabolism has a central role in many diseases and recent genome-wide reconstructions have dened the number of metabolic enzymes in the human genome and their

relationships1. The large size and the connectivity of the metabolic network suggest that multiple controllers are needed for a robust control of its function and indeed metabolism is regulated within cells by large combinations of regulators, including transcription factors, microRNAs (miRs), allosteric eects of metabolites and signal transduction pathways. Therefore, there is a clear need for a well-characterized set of drugs and research tools that function on metabolism.

To accelerate drug discovery, over the last decade, high-throughput screening (HTS) has gained widespread popularity in pharmaceutical companies and increasingly in academia to conduct a large number of biochemical, genetic or pharmacological tests24. Most

screens monitor a single variable, oen related to the action on a single target. Screening with a multivariate readout, also called high content screening (HCS), has recently become more popular, and might facilitate the identication of interventions for more complex phenotypes. Until now, HCS has been mainly associated with automated digital microscopy35. Using omic measurements for HCS would have the advantage of providing multivariate readouts more clearly linked to the drug targets and more easily amenable to network-based modelling and therefore to mechanistic insight. For example, a model could include the kinases targeted by kinase inhibitors (KIs), metabolic enzymes regulated by these kinases and the metabolites aected by these enzymes. We are not aware of any report of a drug library screen on mammalian cells that uses metabolomics.

High-resolution NMR spectroscopy and mass spectrometry (MS) are the most common analytical platforms for the identication and quantication of intracellular and extracellular metabolites611. Regardless of sample volume and analytical techniques used, several crucial steps are required for separating the culture media from the cells, and extracting the intracellular metabolites using organic solvents7,10. Depending on the characteristics of the cells being extracted, the overall extraction process typically includes centrifugation steps, organic phase separation and lengthy drying procedures. The dried intracellular extract is then re-dissolved in solvents suitable for the analytical technique. A recent high-throughput metabolomic study has been applied to a 96-well plate to study the intracellular yeast metabolome12.

To maximize the information contained in a multi-well plate, the authors have optimized cultivation, quenching and extraction of yeast pellets before chemical derivatization and subsequent gas chromatography/time of ight MS analysis. Although all the abovementioned approaches are extremely useful for obtaining clear and detailed information from both intra- and extra-cellular metabolism, they have not been optimized and used for the rapid preparation and metabolomic screening of hundreds of drug-treated mammalian cell samples.

In this paper, we describe a high-resolution NMR-based method for screening the global metabolic changes induced by drug interventions in primary cells and cell lines performed in a 96-well plate format with a simple and rapid sample preparation. We rst validated the screening method using both suspension and adherent carcinoma cell lines, and primary cells treated with a small number of drugs having well characterized targets. To validate this approach, we applied unsupervised multivariate statistical modelling and calculated the Z-factor value, a commonly used parameter for monitoring the quality of HTS assays1315. Then, as a large screening

application, we proled the metabolomic response of cancer cells to a library of KIs. The development of a robust high-content metabolomic platform would be extremely valuable to accelerate the understanding of the in vitro and in vivo actions of drugs and aid their incorporation into therapeutic settings.

ResultsMetabolomic NMR-based drug screening validation. A crucial step in the development of a metabolomics screening method exploiting a NMR platform is to design a quick, simple, robust and reproducible sample preparation protocol. Here, we have developed a procedure for screening the response of a sub-selection of metabolites from cells seeded in a 96-well plate without a lengthy sample preparation.

The cells of interest were seeded in a 96-well plate and treated with several drugs (Fig. 1a). To lyse the cells and immediately quench their metabolism we used the procedure detailed in the Methods section and in Figure 1. Briey, sodium dodecyl sulphate (SDS; nal concentration 0.5% w/v) was added to each well of the 96-well plate and the cells (in medium and SDS) were immediately ultrasonicated by immersing the plate in an ice-cold sonicating water bath. The entire content of the well (endo- and exo-metabolome) was then transferred into a 3-mm NMR tube containing phosphate buer and trimethylsilylpropionate-2,2,3,3-d4 (TMSP). This simple and rapid sample preparation was then followed by NMR spectra acquisition. The overall time for lysing the cells and quenching their enzymatic activity was approximately 5 min.

To evaluate the robustness and reproducibility of this method in stopping the cellular enzymatic activity, we compared the eect of adding dierent concentrations of SDS (nal concentration 0.012% w/v) on cell viability and ATP levels (Supplementary Fig. S1). No enzymatic activity was detected upon addition of 0.1% SDS; however, we decided to exceed the critical micelle concentration of this detergent and use a nal concentration 0.5% w/v to guarantee the complete quenching of cellular metabolism.

To further validate these results, we monitored the metabolic variations in CCRF-CEM (non-adherent) samples by acquiring a series of NMR spectra every 30 min over a period of 8 h. We compared live cells (Fig. 1b), cells lysed using only ultrasonication (Supplementary Fig. S2) and cells lysed and inactivated using combined 0.5% SDS-d25 and ultrasonication (Fig. 1c). All the samples prepared using the combined SDS/ultrasonication method displayed no detectable metabolic alteration for the entire period of acquisition (8 h). On the contrary, metabolic changes (for example, glucose consumption and lactate production; Fig. 1a; Supplementary Fig. S2) were observed for samples containing live or ultrasonicated cells (incomplete quenching process). Multivariate statistical modelling using principal component analysis (PCA) was also performed on the spectra acquired on live, lysed and on simultaneously lysed and inactivated cells (Supplementary Fig. S3). The PCA plot shows important metabolic perturbation for live as well as lysed cells; the extremely tight grouping of the time-series NMR spectra acquired on samples of cells previously lysed and SDS-inactivated clearly demonstrates the absence of residual metabolic activity.

Notably, the use of labelled SDS (98% SDS-d25) minimizes the signal interference of the resonances arising from the detergent in the NMR analysis. The small signals deriving from residual unlabelled SDS were constant through all the samples, as the same amount of SDS was added to each well. Therefore, SDS does not interfere with NMR processing and multivariate analysis.

A legitimate question might arise regarding the relative contribution of the intracellular metabolome to the NMR spectrum acquired on the well content, including both medium and the lysed cell metabolomes. To address this question we acquired NMR spectra on the entire content of the well (endo- and exo-metabolomes), the exometabolome, the endometabolome and medium (Supplementary Fig. S4). The samples containing only the endometabolome were prepared by extracting PBS-washed cells from one well using the combined SDS/ultrasonication method. As expected, the major NMR signals arose from the extracellular metabolites; however, several signals arising from the intracellular metabolites were detected (for example, glutamate, choline, phosphocholine

NATURE COMMUNICATIONS | 2:545 | DOI: 10.1038/ncomms1562 | www.nature.com/naturecommunications

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms1562

ARTICLE

Lac

2.5

1.5

0.5

Glucose

Gln

Glucose

Lac

Intensity

Lac

Intensity

2.5

1.5

0.5

BAA

Gln

BAA

Time (h)

0 3.5 2.5 1.5 1.5

2.5

3.5

3 p.p.m.

Live cells Lysed and inactivated cells

4.5

2.5

3.5

0.5

Validation

Untreated cells Treated cells

Glucose

Control

DCA

Lac

Gln

Pyr Glu

Ala

PCho

Cho

Pro

Orn Asn

Asp

1 2 6

4 5 9

7 8 12

10 11

High-throughput

1H NMR

21.5

Metabolomics screening

3.5

2.5

1.5

3 p.p.m.

Media without cells

60 Wells containing media with untreated and treated cells (randomized drug delivery)

Each content of the well transferred into a 3-mm NMR tube

PCA analysis

and multivariate Z-factor calculation

Asp 1U ml1

Asp 0.1U ml1

Lac

0.9

2.5

0.9

Loadings on PC1

(94.60%)

Scores on PC3 (7.18%)

0.8

Z bin-factor

Multivariate Z-factor

Control

0.7

1.5

0.6

0.7

0.6

0.5

PCA loading plot

Control

0.5

Glu

0.4

0.3

andZbin factor calculation

0.4

0.5

Cho

Pyr

Ala

0.2

Vin

Dex

0.3

Scores on PC2

(9.12%)

DCA

0.1

DCA Rap

Asp 0.01U ml1

Glucose

0.2

3.5

2.5

1.5

0.1

2 6 4 2 0 2 4 6 2

p.p.m.

Scores on PC1 (69.52%)

Figure 1 | Validation and workow for NMR-based metabolomic drug screening. (a) The cells are seeded and treated in a 96-well plate. At the end of the 24 h treatment period, cells are lysed and their metabolism quenched by combined ultrasonication and by addition of SDS-d25. The content of the well is then transferred into a 3-mm NMR tube. To validate the quenching of any residual enzymatic activity, metabolic changes were monitored by NMR in live cells (b) and cells lysed and inactivated by addition of SDS-d25 and ultrasonication (c). 1-D NMR spectra were acquired every 30 min for a total of 8 h.

(d) 1H-NMR spectra showing the metabolomic perturbations induced in CCRF-CEM cells after 24 h of drug treatment ((Dex, Rap, DCA, Vin) and three different doses of asparaginase (A1, 1 U ml 1; A01, 0.1 U ml 1; A001, 0.01 U ml 1)). Overlay of six replicate spectra (1.34 p.p.m. section) per treatment condition (with and without DCA treatment) highlights the degree of reproducibility. The metabolic changes detected determine a good separation between the different treatment groups, as shown in the 3-D scores plots (e), obtained from the PCA of the CPMG NMR spectra. Each treatment group is colour-coded according to the multivariate Z-factor value (colour bar) obtained by the pairwise PCA comparison of each drug treatment versus solvent control (Dex (Z = 0.15), DCA (Z = 0.91), Rap (Z = 0.74), Vin (Z = 1.97), A1 (Z = 0.91), A01 (Z = 0.86), A001 (Z = 0.84)). (f) PCA loadings plot and the superimposed Zbin-values identify both the contribution and the Z-factor values of every bin of NMR spectra from the comparison of control and DCA intervention. (Lac, lactate; Ala, alanine; Pro, proline; Glu, glutamate; Pyr, pyruvate; Gln, glutamine; Asp, aspartate; Asn, asparagine; Orn, ornithine;

Cho, choline; PCho, phosphocholine).

and so on). Similarly, in spectra acquired on samples containing both endo- and extracellular metabolomes, signals arising exclusively from the endometabolome and not overlapping with other extracellular resonances were clearly identied (for example, phosphocholine and glycerophosphocholine; Supplementary Fig. S4).

To validate the robustness of the method across multiple 96-well plates, we applied PCA to 1H-NMR spectra acquired on samples seeded with either solvent control or l-asparaginase in randomized positions within one plate as well as in dierent plates. No plate-to-plate variability was observed and further details are reported in Supplementary Fig. S5.

Metabolic changes induced by drug treatment in cell lines. To evaluate the sensitivity of this method for monitoring metabolic perturbations induced by a 24-h drug treatment, we compared the metabolic changes of both suspension (human leukaemia cells, CCRF-CEM) and adherent mammalian carcinoma cell lines (human ovarian cancer cells, SKOV-3) in response to dierent drug

treatments (dexamethasone (Dex), rapamycin (Rap), dichloroacetate (DCA), vincristine (Vin) and three dierent doses of l-asparaginase (A1, 1 U ml 1; A01, 0.1 U ml 1; A001, 0.01 U ml 1)). For each cell line, we compared spectra acquired using three dierent 1H NMR pulse sequences: One-dimensional (1-D) spectra (Fig. 2a), Carr-Purcell-Meiboom-Gill (CPMG) spin echo (Fig. 2b) and two-dimensional (2-D) 1H J-resolved (JRES; included in Figure 2c as 1-D projections of 2-D JRES spectra, pJRES) sequences. The enlarged sections (0.71.1 and 6.67.5 p.p.m.) of 1H NMR spectra acquired on CCRFCEM cells using the aforementioned techniques, highlight the efficacy of relaxation-edited CPMG and pJRES pulse sequences in removing the residual broad signals from cell debris including macromolecules (for example, proteins and lipoproteins). This enables identication and relative quantication of small metabolites in both the aliphatic and in the aromatic regions of the spectra (for example, valine, leucine, tyrosine) without the need of centrifugation steps. In addition, pJRES spectra provide proton-decoupled NMR spectra useful for reducing congestion and increasing metabolite specicity.

NATURE COMMUNICATIONS | 2:545 | DOI: 10.1038/ncomms1562 | www.nature.com/naturecommunications

ARTICLE

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms1562

Leu

Ile

CPMG

pJRES

3.5

Ile Val

Val Val

Asp 1U ml1

PCA 1D

Control

Asp 0.1U ml1

Asp 0.01U ml1

Dex

Rap Vin

DCA

Scores on PC 3 (5.12%)

Val

2.5

Tyr Phe

Phe

His

1.5

Tyr

1.1

0.9

0.8

0.7

0.5

7.5 7.4 7.3 7.2 7.1 6.9 6.8 6.7 6.6

1.5 6 4 2 0 2 4 6

0 p.p.m.

Scores on PC 1 (71.07%)

Leu

Ile

Val Val

Ile

1 Asp 1U ml1

Asp 0.1U ml1

Asp 0.01U ml1

PCA CPMG

Control

Dex

Vin

DCA

Rap

Tyr

1 0.7

0.8

0.9

Scores on PC 3 (7.18%)

Tyr

1.1

His

2 8 6 4 2 0 2 4 6 8

0 p.p.m.

Scores on PC 1 (69.52%)

Asp 1U ml1

Asp 0.1U ml1

Asp 0.01U ml1

DCA

PCA pJRES

Control

Vin

Rap

Dex

Ile Ile

Scores on PC 2 (12.47%)

Leu

Tyr

Phe

1.1

1 0.7

0.8

0.9

His

6 10 5 0 5 10

0 p.p.m.

Scores on PC 1 (52.40%)

Figure 2 | Comparison of different NMR pulse sequences. Full spectra and expanded sections (071.1 and 6.67.5 p.p.m.) of 1H NMR spectra acquired using (a) 1-D 1H NMR, (b) CPMG spin-echo pulse sequence and (c) by projecting the acquired 2-D JRES (pJRES). Spectra were acquired on CCRF-CEM leukaemia cells treated with solvent control (Control), Dex (50 nM), DCA (20 mM), RAP (100 nM) Vin (1 nM) and asparaginase at three different doses (A1, 1 U ml 1; A01, 0.1 U ml 1; A001, 0.01 U ml 1) following cell lysis and metabolism inactivation via addition of SDS and ultrasonication. The spectra acquired using the aforementioned pulse sequences show important alterations on the resonance intensities of selected metabolites (Ile, isoleucine; Leu, leucine; Val, valine; Tyr, tyrosine, His histidine; Phe, phenylalanine) as highlighted by the PCA scores plots obtained from analysis of spectra acquired using (d) 1-D (PC1 versus PC3), (e) CPMG (PC1 versus PC3) and (f) JRES pulse sequences (PC1 versus PC2) on CCRF-CEM cells with and without drug treatments.

Similar groupings and separation among all the eight treatments (six replicates per treatment) were observed comparing the PCA scores plots (PC1 and PC3) of CCRF-CEM leukaemia cells of 1D (Fig. 2d) and CPMG spectra (Fig. 2e). The PCA scores plots (PC1 and PC2), obtained using JRES pulse sequence (Fig. 2f), show an excellent separation and superior groupings for all the treatments compared with the scores plots using the other two techniques.

We also performed pairwise PCAs comparing each of the drug treatments to the control samples. The percent variability obtained from these analyses (PC1 and PC2) and the multivariate Z-factor (Supplementary Methods) values (Supplementary Table S2) show again the benets of JRES in particular for Dex and Vin drug treatments. These dierences of JRES can be in part attributed to a smaller number of variables, due not only to simplication of the spectra by decoupling of the multiplets but also to the reduced sensitivity, due to smaller number of scans in direct dimension, compared with 1-D and CPMG techniques.

The loadings plot (Fig. 1e) indicates the weighting to the rst component of the PCA score plot (Fig. 1d) of dierent spectral bins; in a similar manner, we have introduced Z-factor values at each bin of the NMR spectra (Supplementary Methods). The Zbin-

values, superimposed to the loadings plots for each comparison of control and individual drug intervention (Fig. 1e), indicate that treatment with DCA-induced relevant metabolic changes (for example, pyruvate, glutamate, lactate) characterized by Zbin-

factor values greater than 0.2 satisfying the requirements for a high-throughput assay15.

To test the general applicability of high-content NMR-based metabolomic screening, we delivered the same drug treatments administered to non-adherent CCRF-CEM cells to the adherent ovarian SKOV-3 cells, a more drug-resistant mammalian cell line. As expected, the responses to drugs treatments in SKOV-3 cells were less pronounced compared with CCRF-CEM cells, as indicated by the smaller percent variation of the rst and second principal

NATURE COMMUNICATIONS | 2:545 | DOI: 10.1038/ncomms1562 | www.nature.com/naturecommunications

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms1562

ARTICLE

2.5

Control

0.8

ControlRapAsp 1U ml1

Pyr Glu

1.5

0.6

Scores on PC2 (16.81%)

Multivariate Z-factor

0.4

1 Rap

0.5

0.2

Gln

Asp 1U ml1

Lac

Gly

Orn+Lys

0.5

Asp 0.1U ml1

0.2

0.4

Asn

Asp Ala

Lac

1.5

0.6

Cho

0.8

Glucose Ile

3.5

2.5

1.5

2.5 3 2 1 0 1 2 3 4 5 6 7

Asp 0.01U ml1

Scores on PC1 (69.78 %)

p.p.m.

Scores on PC 1

(43.44%)

0.9

0.2

Loadings in PC1 (43.44%)

Control

Rap

Loadings in PC1 (71.28%)

Scores on PC 1

(71.28%)

Control

Asp 1U ml1

0.9

Glucose

Control

Rap

Control

0.8

0.15

0.8

0.7

Gln

0.1

0.7

0.6

Glucose

Z bin-factor

Sample

0.6

0.5

0.05

Asn

Asp Ala

Lac

Met

0.5

0.4

0.3

0.2

0.05

0.2

Pyr

Glu

0.1

Glu

0.1

3.5

2.5

1.5

3.5

2.5

1.5

3 p.p.m.

Figure 3 | NMR-based metabolomic screening of drug treatment in AML primary cells. (a) Difference spectra obtained by subtracting the average NMR spectrum of the medium, incubated without the cells in the 96-well plate subtracted from the average NMR spectra of AML primary cells with and without 24 h of 100 nM Rap or 1 U ml 1 Asp drug treatment. (b) PCA scores plot (PC1 versus PC2) colour-coded according to the multivariate Z-factor values for each treatment group (Rap (Z = 0.90), A1 (Z = 0.83), A01 (Z = 0.77) and A001 (Z = 0.42)). (c,d) PCA loadings plots and the superimposed Zbin-values identify both the contribution and the Z-factor values of each bin of NMR spectra from a pairwise comparison of control and drug intervention (c) RAP and (d) Asparaginase treatments. Ile, isoleucine; Lac, lactate; Ala, alanine; Glu, glutamate; Pyr, pyruvate; Gln, glutamine; Asp, aspartate; Asn, asparagine; Orn, ornithine; Lys, lysine; Cho, choline; Gly, glycine.

1515

components (Fig. 3a). However, the observed metabolic variations (Fig. 3a) support the applicability of this approach also to drug resistant and adherent cell lines.

We believe that the applicability of high-content NMR-based metabolomic screening might be extended also to other types of intervention such as miRs treatments. Indeed, the extent of the metabolic responses in human cell lines following transfection of miRs is not known16. Therefore, we tested whether the sensitivity of our HCS method was sufficient to detect metabolic changes in HeLa cells transfected with mir-121 and mir-16. We used premiRs as negative controls, which are chemically modied double-stranded RNA molecules designed to mimic endogenous miR molecules (Supplementary Methods). These miRs were selected because they were computationally predicted to target multiple metabolic enzymes. We found that the developed method was capable of detecting metabolic perturbation induced by miRs treatments as observed by the PCA scores plots (PC1 and PC2) and the multivariate Z-factor values (Fig. 3b). More specically, mir-16 induced a more pronounced metabolomic alteration on HeLa cells compared with mir-121.

Metabolomic NMR-based drug screening in acute myeloid leukaemia (AML) primary cells. Owing to the small amount of cells required for our high-throughput metabolomic screening method, we believe that this procedure could be valuable to study the response to treatment in primary cells, while avoiding phenotypic changes induced by growth in culture. Cells isolated from bone marrow specimens of an untreated AML patient were treated for 24 h with Rap and l-asparaginase at dierent doses. Following treatment, the samples were prepared for the NMR analysis as detailed above.

To emphasize the metabolic changes in the primary cell samples aer the 24 h of treatment, we subtracted the NMR spectra acquired on unconditioned medium (incubated without cells in the same 96-well plate; Fig. 1a) from those acquired on AML primary cells with and without drug intervention (for example, l-asparaginase 1 U ml 1, Fig. 4a). The dierence spectra clearly indicated distinct changes in the metabolome of primary cells, including changes in isoleucine, alanine and ornithine. An unsupervised multivariate analysis was then performed on the acquired 1H NMR spectra

(Fig. 4b). PCA scores plots (PC1 and PC2) and related multivariate Z-factor values for Rap (Z = 0.90), A1 (Z = 0.83), A01 (Z = 0.77) and A001 (Z = 0.42) conrm that dierent responses of primary cells to the drug treatments are detected (Fig. 4b). The overall response of AML primary cells to treatment was limited for Rap treatment (as observed from the negative multivariate Z-factor values), and increasingly more pronounced following treatment with increasing doses of l-asparaginase. In addition, for Rap treatment few metabolic changes (for example, pyruvate and glucose) had Zbin-factor values greater than 0.5 (Fig. 4c). This point-by-point analysis of Z-values for the l-asparaginase treatment indicated that besides the metabolites directly aected by the enzymatic activity of l-asparaginase (that is, asparagine and glutamine17,18), other compounds,

including glucose, lactate, alanine and methionine, were aected by this drug intervention (Fig. 4d).

SKOV-3 cell line HeLa cell line

Control

Dex

DCA

Vin

0.8

Scores on PC 2 (11.23%)

0.5

1.5

Multivariate Z-factor

Scores on PC 2 (18.88%)

0.6

Multivariate Z-factor

0.4

miR-121

miR-16

0.4

0.2

Control

0.2

Rap

0 0.2

0.4

0.6

0.8

0.6

0.8

1.5

2 1

0.5

1.5

0 1 2.5

2 3 3.5

10 5 0 5 10

Scores on PC 1 (25.87%)

Scores on PC 1 (60.73%)

Figure 4 | High-content NMR-based metabolomic screening in adherent cell lines. PCA scores plots (PC1 versus PC2) obtained from the analysis of the 1H-NMR spectra of (a) human ovarian carcinoma cell line (SKOV-3) after 24 h of treatment with different types of drugs and (b) human epithelial carcinoma cell lines (HeLa) after 24 h of miRs (miR-16 and miR-121) treatment. Each treatment-group on the PCA scores plots is colour-coded according to the multivariate Z-factor value (colour bar) obtained from the pairwise PCA comparison of treatment versus control for SKOV-3 (Dex (Z = 5.83), DCA (Z = 0.45), Rap (Z = 3.07), Vin (Z = 1.84 ), A1 (Z = 0.70), A01 (Z = 0.01), A001 (Z = 0.24)) and HeLa (mir-16 (Z = 0.34) and mir-121 (Z = 0.55)) cell lines.

NATURE COMMUNICATIONS | 2:545 | DOI: 10.1038/ncomms1562 | www.nature.com/naturecommunications

ARTICLE

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms1562

ATP

0.5

Multivariate

Z-factor

0.5

Alanine Arginine Asparagine

Aspartate

Choline Glucose Glutamate Glutamine

Glycine

Histidine

Isoleucine

Lactate Leucine Phenylalanine

Proline Pyruvate

Serine

Tyrosine

Valine 3Methyl-oxovalerate

0.9

0.8

Selected metabolites

0.7

0.6

0.5

Z bin-values

0.4

0.3

0.2

0.1

150

140

130

Selected metabolites

Alanine Arginine Asparagine

Aspartate

Choline Glucose Glutamate Glutamine

Glycine

Histidine

Isoleucine

Lactate Leucine Phenylalanine

Proline Pyruvate

Serine

Tyrosine

Valine 3Methyl-oxovalerate

% Relative quantification

(% of control)

120

110

100

1 BIM0207152

2 BIM0207163

3 BIM0086775

4 BIM0207133

5 BIM0207143

6 BIM0207183

7 BIM0207192

8 BIM0086749

9 BIM0086759

10 BIM0207134

11 BIM0050229

12 BIM0207144

13 BIM0207165

14 BIM0207193

15 BIM0086768

16 BIM0053458

17 BIM0207135

18 BIM0086675

19 BIM0086735

20 BIM0086751

21 BIM0050990

22 BIM0207156

23 BIM0086693

24 BIM0086716

25 BIM0207195

26 BIM0086736

27 BIM0086762

28 BIM0207137

29 BIM0207157

30 BIM0086686

31 BIM0207186

32 BIM0086753

33 BIM0086780

34 BIM0086695

35 BIM0207138

36 BIM0086696

37 BIM0207178

38 BIM0086708

39 BIM0086723

40 BIM0086737

41 BIM0086758

42 BIM0086710

43 BIM0086659

44 BIM0086668

45 BIM0207170

46 BIM0051107

47 BIM0207207

48 BIM0207140

49 BIM0086681

50 BIM0207160

51 BIM0207189

52 BIM0207208

53 BIM0086789

54 BIM0206950

55 BIM0207209

56 BIM0086776

Kinase inhibitors

Figure 5 | NMR-based metabolomic screening of a library of KIs. (a) ATP assay was used to measure the cell viability of CCRF-CEM cells following 24 h of treatment with 56 KIs (156 BIM). The nal concentration of each drug treatment was 1 M. The mean luminescence values of cells treated with KI were normalized to the average ATP value for untreated cells (solvent control). (b) Principal component analysis was performed on the 1H NMR spectra acquired in triplicates and a multivariate Z-factor value was calculated for each KI. For a subset of all the observed metabolites we determined the Zbin-values (c) and their relative concentration (d, as percent of control).

High-content NMR-based metabolomic screening of KI library. Protein kinases control a wide range of cellular processes including cell metabolism19,20. Therefore, monitoring cell metabolism during treatment is extremely valuable to investigate the cellular response and treatment efficacy, in particular, as an evaluation criterion complimentary to standard screening methods. As a rst application, here we applied the high-content NMR-based metabolomic screening combined with the ATP assay to monitor the cellular metabolomic response for a library of KIs (Supplementary Table S4).

We rst used the ATP bioluminescence measurements to investigate changes in CCRF-CEM cell viability following treatment with approximately 250 KIs at 24 and 72 h. We then selected 56 KIs based on ATP values showing very low to moderate changes. Compounds causing very pronounced changes in viability aer 24 h were excluded. Cell viability measurements (Fig. 5a) and the NMR-based metabolomic screening were performed aer 24 h of treatment with 56 KIs in CCRF-CEM cells (in triplicates). We calculated the multivariate Z-factor for each KI treatment (Fig. 5b), and for a subset of metabolites we determined the Zbin-values

(Fig. 5c) and their relative concentration (as a percent of control; Fig. 5d). Several metabolic changes induced by the drugs were clearly correlated (Supplementary Figure S6).

To conrm some of the hits of the rst metabolomic screen (Fig. 5), nine KIs were chosen for a secondary screen (Supplementary

Figs S7S9). Four of the repeated compounds were selected because of the action on a well-known clinical phenotype, the lactate/ pyruvate ratio (Fig. 6ad). Five more compounds were chosen to provide additional data on measurement reproducibility. The results of the second screen (Supplementary Fig. S7a,b) were consistent with the rst assay and the average coefficient of variation in metabolite changes between the two screens was 0.09 0.01 (mean s.e.m.). Most of the metabolites showed comparable changes in the two screenings (Supplementary Fig. S7c,d). The second screen was also performed on CCRF-CEM cells treated using a 10-fold lower dose of KIs (0.1 M; Supplementary Fig. S8). Most low-dose treatments did not aect the ATP cell viability; however signicant metabolic alterations were observed for some treatments (for example, BIM-00866768 and BIM-0086776; Supplementary Fig. S8). To conrm the observed results, hit follow-up doseresponse experiments were performed using six dierent concentrations21 of KI in the range of 0.052 M (Supplementary Fig. S9). These concentrations were chosen with the intent of investigating the metabolomic response of CCRF-CEM cells to KI treatment without aecting their cell bio-mass. IC50 values were calculated based on the 50% reduction of ATP levels induced by treatment (Supplementary Table S5).

Four of the nine KIs considered above were then chosen, selecting the compounds inducing moderate ATP level drops to prevent relevant changes in cell biomass. These four KIs reproduc-

NATURE COMMUNICATIONS | 2:545 | DOI: 10.1038/ncomms1562 | www.nature.com/naturecommunications

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms1562

ARTICLE

100

Luminescence

(% of control)

Control

BIM0207152 (HD)

BIM0086768 (HD)

BIM0086776 (LD)

BIM0086775 (HD)

Gln

Pyr

Lac

Glu

2.48

2.5 2.32

2.34

1.5

2.46

2.44

2.42

2.4

2.38

2.36

1.4

1.39

1.38

1.37

1.36

1.35

1.34

1.33

1.32

1.31

1.3

Gluc

Gly

Myo-in

Cho

3.7 3.6 3.5 3.4 3.3 3.2

p.p.m.

180

Scores on PC 2 (7.54%)

160

***

Lactate Pyruvate

Relative quantification

(% of control)

Control

140

120

***

0.5

BIM-0086768 (HD)

BIM -0207152 (HD)

100

***

0.5

BIM-0086775 (HD)

BIM0086776 (LD)

1 4 3 2 1 0 1 2 3

Scores on PC 1 (84.69%)

BIM-0086768

(HD)

BIM-0086775

(HD)

BIM-0207152

(HD)

BIM-0086776

(LD)

Figure 6 | Metabolic modulation induced in CCRF-CEM cells by fourKI hits. Four KIs found to cause similar drop (by ~20%) of ATP valuesin CCRF-CEM cells selected. The drugs eEF-2 KI (BIM-0207152), MK2 inhibitor (BIM-0086775) and an inhibitor of PKA, PKC and PKG (BIM-0086768) were administered at the high dose (1 M), and NF-KB activation inhibitor (BIM-0086776) at low dose (0.1 M). (a) ATP assay (as % of control) following KI treatments is reported as the mean values (bars) s.e.m. (error bars; N = 3). (b) Representative sections of average 1H

NMR spectra acquired on CCRF-CEM cells with and without KI treatment are expanded (Gln, glutamine; Pyr, pyruvate; Glu, glutamate; Cho, choline; Gluc, glucose; Gly, glycine; Myo-in, myo-inositol). (c) Principal component analysis (PC1 versus PC2) was performed on the 1H NMR spectra of treated and untreated CCRF-CEM cells acquired in triplicates. (d) Relative concentrations of lactate and pyruvate calculated as percent of control are reported as the mean values (bars) s.e.m. (error bars; N = 3). Statistical comparison between data obtained from untreated and KIs treatment was performed using an unpaired Students t-test (statistical signicance: **P < 0.01 and ***P < 0.001).

ibly aected the relative concentrations of lactate and pyruvate in opposite directions (Figs 5 and 6; Supplementary Figs S7S9). An eEF-2 KI (BIM-0207152), and a NF-B activation inhibitor (BIM-0086776) increased lactate/pyruvate ratio (Fig. 6d), whereas an MK2 inhibitor (BIM-0086775), and an inhibitor of PKA, PKC and PKG (BIM-0086768) induced a decrease. Concurrently, mild and comparable eects on ATP levels were observed, consistent with reduced

cell proliferation (Fig. 6a). Moreover, these inhibitors were not identical in their action in fact engendered distinct metabolic changes, including glucose, alanine, serine, choline and glutamine (Fig. 6b). Multivariate statistical analysis (Fig. 6c) indicates that BIM-0207152 and BIM-0086776 induced similar alterations in cellular metabolic proles, dierent from those induced by BIM-0086775 and BIM-0086768, and distinct from untreated controls. The two inhibitors (BIM-0207152 and BIM-0085776) that induce similar metabolic modulations in CCRF-CEM cells (Fig. 6) also show similar responses in cell survival of primary cells from acute leukaemia patients. Preliminary results from four leukaemia patients indeed showed a high correlation in the individual response between these two drugs aer 24 h in culture (Supplementary Fig. S10).

Discussion

Cultured cell lines are well-established models for the study of cancer2224. Despite the numerous in vitro and ex vivo studies, a high-content metabolomic assay for screening drug libraries using mammalian cells has not been reported. The methodology developed in this work describes an innovative approach using NMR-based metabolomics for drug screening. The rationale for using NMR as the platform of choice was dictated by the ability of this analytical technique to handle samples containing complex and relatively unrened mixtures of compounds. More specically, the samples considered in this study required only minimal sample preparation without the need of additional separation or ltration step (for example, proteins removal). Furthermore advantageous are the low experimental variability and reasonable cost per sample.

The main drawback associated with the use of NMR is the relatively limited number of compounds that can be detected. Owing to this limitation, our metabolomic HCS method do not provide metabolic proles as those described by us and others using more in-depth metabolomic techniques8,11,21,22,2528. Nevertheless, the assay

we describe is suitable for a high-throughput primary screen, and should be followed, as it is commonly the case, by secondary assays analysing the exo- and endo-metabolomes of selected hits likely using a combination of dierent analytical platforms8,11,21,22,2528.

Other techniques including turbulent ow chromatography interfaced with MS might represent a valid tool for a metabolomic screening capable of increasing the number of identied metabolites compared with NMR techniques29, with additional sample preparation steps.

Although not comprehensive of all metabolites, the wealth of information obtained from the multivariate metabolic readout is of great advantage for drug screening purposes. In fact, although most primary drug screens on mammalian cells are based on single readout, high-content metabolomic screening allows the parallel measurements of an array of metabolites and is designed to provide additional valuable information and criteria for the selection of the most eective individual or combinatorial drug interventions.

To allow the rapid preparation and metabolomic screening of hundreds of samples, we validated a novel protocol for lysing the cells and immediately quenching cellular metabolism, using SDS, a powerful denaturating detergent widely used in protein biochemistry30, and in micelle-assisted diusion-ordered spectroscopy for stereoisomer resolution31. The content of a single well from a 96-well plate was used for the assay without the need of pooling the content of several wells, enabling maximized yield per plate. Moreover, the sensitivity of our high-content metabolomic screening method was proven adequate to monitor metabolic perturbations induced by drug intervention in both cultured cell lines and in AML primary cells as well as following treatment with miRs. The metabolomic study of miRs could potentially be extended to the entire set all known human miRs (around 500), and therefore contribute to the understanding of this complex intracellular regulatory system.

NATURE COMMUNICATIONS | 2:545 | DOI: 10.1038/ncomms1562 | www.nature.com/naturecommunications

ARTICLE

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms1562

Altogether, using our approach, hundreds of samples can metabolically be inactivated in ~5 min and a metabolomic screening of around 100 samples can be performed in 24 h. Progress in NMR spectroscopy, for example, in probe hardware design, will increase the sensitivity of this method and allow the applicability to volumes obtained from higher density plates (for example, 384-well plates). In particular, the relatively new area of microprobe and multiple coil probeheads shows great promises for high-throughput NMR experiments32. In the area of NMR data processing, continuous eorts are devoted to the development of new algorithms for reducing multiple readout parameters and automating identication and quantication of metabolites33. Here we capitalized on NMR pulse sequences (CPMG and JRES), which warrant highly reproducible analyses of small molecules even in the presence of residual proteins or cell debris in the sample. Multivariate Z-factor values derived from PCA were used to evaluate the quality of the NMR-based metabolomic screening assay. Furthermore, because NMR spectra produce arrays of complex data (as opposed to a single outcome), we also introduced the Zbin-value as a localized parameter assessing the eect of drug treatment on each metabolite.

Since the development of imatinib (known as CGP 57148 (ref. 34) and subsequent application for treatment of chronic myeloid leukaemia, KIs have received increasing attention as anti-cancer drugs. Activation of oncogenic pathways including PI3K/ AKT/mTOR, involved with altered bioenergetic pathways, such as glycolysis, fatty acid and glutamine metabolisms, has suggested novel targets for cancer treatment23,24,27,35,36. Therefore, monitoring

cell metabolism during treatment is extremely valuable to investigate cellular response and treatment efficacy. Here we screened a library of KIs and shown dierent metabolic adaptations following 24 h of treatment. Metabolomic data showed that inhibitors could modulate metabolic substrates uptake and product excretion in the presence of similar ATP levels. In our rst application, we focused on four KIs that can change the lactate/pyruvate ratio, an important and clinically validated measure of the intracellular redox potential and of cellular respiration37,38. The lactate/pyruvate ratio is a potential therapeutic target relevant not only to hypoxic and ischaemic disease but also to many other pathologies, including genetic mitochondrial diseases37,38. The focus on compounds functioning on this important parameters emerged aer the rst screen, demonstrating one advantage (hypothesis generation) of the metabolomic method we describe as compared with drug screens based on the measurement of one or two metabolites.

There are many other possible applications of this method, for example lactate production and substrate utilization in cancer versus non-cancer cells24,36 or gluconeogenesis from dierent substrates in hepatocytes, relevant to diabetes39,40. Importantly, because the measurements are performed within a global metabolic prole, they can also provide a series of compounds with partially dierent mechanisms of actions, which can be explored for potential synergies4144.

Because the inhibitors we used have known targets, the screen can also be used to generate hypotheses about metabolic relevant signal transduction mechanisms. For example, the two most closely related KIs (Fig. 4c) target eEF-2 kinase45 and NF-kB46. In addition, eEF-2 kinase has been linked to other metabolically relevant pathways and to the autophagic process47. These relationships can be followed up experimentally, with the caveat that we might not know all the targets of the inhibitors we have used.

We believe that this NMR-based assay might nd an immediate relevant application for screening a large number of individual or combinatorial drug interventions reducing the number of possible drugs to be studied more in detail. In addition, it might nd an immediate relevant application into clinical studies. The possibility of using cells directly taken from patients might open new routes for the development of personalized drug treatment48,49.

Methods

Primary AML cells and cell lines. Bone marrow specimens from untreated AML and lymphoblastic leukaemia patients were obtained from the Rady Childrens hospital. Informed consent was obtained from all patients involved and study was performed in accordance with the Institutional Review Board guidelines. Within 6 h aer sampling, mononuclear cells were isolated by Ficoll-paque (GE Healthcare) density gradient centrifugation (1.077 g cm 3) at 400 g for 30 min followed by three washes in PBS. Isolated mononuclear cells were allowed to adhere to the 96-well plate overnight in RPMI 1640 medium (HyClone). Non-adherent mononuclear cells were collected to enrich lymphocyte population.

CCRF-CEM and SKOV-3 cell lines were maintained in exponential proliferation in RPMI 1640 medium (HyClone), whereas HeLa cells were grown in DMEM (HyClone). Both media were supplemented with 10% FBS (HyClone), 1% 200 mM l-glutamine (Omega) and 1% penicillin/streptomycin (Omega). The cells were cultured in a humidied chamber at 37 C with 5% CO2.

Isolated non-adherent mononuclear, CCRF-CEM, SKOV-3 and HeLa cells were seeded in 96-well plates (Greiner Bio One) at the density of 3105, 1105, 3104, 1.5104 cells per well, respectively, and allowed to adapt/attach for 6 h before treatment. Doubling time was 24, 48 and 24 h for CCRF-CEM, SKOV-3 and HeLa cells, respectively.

For each cell type, the number of cells seeded per well was determined usinga luminescent ATP-detection assay (Perkin Elmer; Supplementary Methods) such that the cells remained in their linear range of growth and the maximum number of cells per well was obtained 30 h aer seeding (accounting for 6 h of adaptation and 24 h of treatment).

Drug treatments. Both CCRF-CEM and SKOV-3 cells were treated for 24 h with the following drugs: DCA (20 M; Sigma-Aldrich), Dex (50 nM, Calbiochem), Rap (100 nM; Sigma-Aldrich), Vin (1 nM; Sigma-Aldrich) and three dierent concentrations of l-asparaginase (0.01, 0.1, 1 U ml 1; BioVendor Inc.). An untreated control group (solvent control) was also included in all the assays. All the drug treatments were performed in six replicates per condition. No alterations in cell numbers were observed following 24 h of drug treatment. To reduce bias, the treatments were administered based on a randomized list of well positions.

Sample preparation for HCS experiments. The cells of interest were seeded in a 96-well plate with 200 l of appropriate medium. A total volume of 10 l of SDS was added to the culture medium via a multichannel electronic pipette.

The nal concentration of SDS in each well was 0.5% w/v, unless otherwise noted (all experiments with the exception of the optimization procedure detailed below). Immediately aer addition of SDS, the 96-well plates were sealed and sonicatedin an ice-cold ultrasonicating water bath for 5 min. The water bath temperatureat the beginning of sonication was 4 C and during sonication never exceeded6 C. At the end of sonication 160 l was transferred from each well into a 3-mm NMR tube (Norell) containing TMSP-d4 (Cambridge Isotope Laboratories; nal concentration 0.5 mM), phosphate buer (nal concentration 100 mM) and 10% of D2O (Cambridge Isotope Laboratories) to obtain a total volume of 200 l. The overall time between dispensing SDS to the 96-well plate and sonicating the entire content of the well for lysing the cells and quenching their enzymatic activity was approximately 5 min.

NMR experiments. One-dimensional standard 1H NMR and CPMG and 2-D

1H JRES spectra were acquired on 500-MHz Bruker and 600-MHz Bruker Avance equipped with a TCI cryoprobe spectrometers (Bruker BioSpin Corp.), both equipped with an autosampler at 30 C5052. All the pulse sequences were implemented with excitation sculpting to suppress the water signal53. One-dimensional and CPMG spectra were acquired using a 90 ip angle, 6 kHz spectral width, relaxation delay of 1 s, 16k data points, 8 dummy scans and 256 or 512 transients (approximately 15 or 30 min of acquisition per sample). Spin-spin relaxation delay of CPMG was set to 100 ms. Double spin-echo sequence suppressing strong coupling artifacts was used to acquire 2-D JRES spectra52. Thirty-two increments were recorded in the indirect dimension using 16 transients per increment, 16k data points with 8 dummy scans and a recycle delay of 2 s; spectral widths of 6 kHz and 50 Hz were set in direct and indirect dimension, respectively, (~45 min of acquisition per sample).

Before Fourier transformation, 1-D and CPMG spectra were zero-lledand processed by multiplying the free induction decay by an exponential line broadening function of 0.5 Hz. Before Fourier transformation, 2-D JRES spectra were zero-lled in both dimensions, and apodization using combined sine-bell/exponential window function in the direct dimension and by a sine-bell function in the incremented dimension has been applied54. Following Fourier transformation, the magnitude mode spectra were tilted by 45, symmetrized and skyline projected (pJRES). All the NMR data sets were processed using NMRLab55

in the MATLAB programming environment (MathWorks, Inc.). Post processing of NMR spectra included scaling according to the probabilistic quotient method56,

alignment, exclusion of selected signals arising from solvents, TMSP and DCA, binning at 0.005 p.p.m., and application of a generalized log transformation57. NMR resonances of metabolites were assigned using the Chenomx NMR Suite (version 6.0; Chenomx Inc.) and other available libraries58,59.

NATURE COMMUNICATIONS | 2:545 | DOI: 10.1038/ncomms1562 | www.nature.com/naturecommunications

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms1562

ARTICLE

Optimization and validation of metabolomics HCS method. To optimize the amount of SDS required to completely quench cell metabolism we monitored residual cell viability and metabolic activity in samples prepared without or with various amounts of SDS. Ten dierent concentration of SDS were considered ranging between 0.01 and 2% w/v (nal concentration per well). Cellular viability and residual metabolism were monitored using both ATPlite assay (Supplementary Methods) and cell count (Countess automated cell counter, Invitrogen) measurements in samples prepared using ultrasonication and with and without the addition of SDS. All the measurements were performed with six replicates per condition.

Furthermore, to check for residual metabolism, a series of 1-D NMR spectra were collected every 30 min over a period of 8 h for live CCRF-CEM cells, cell lysed using only ultrasonication and cells simultaneously lysed and SDS inactivated. Subsequent multivariate analysis (PCA) was performed on 16 spectra per condition. For these experiments, the NMR spectra were acquired at 37 C to maintain more suitable conditions for live cells.

To further validate the sample preparation method developed and assess a possible metabolomic bias arising from plate-to-plate variability, we acquired 1-D 1H

NMR spectra of six wells per plate and per treatment condition over ve dierent plates (in total 60 1H-NMR spectra, 30 replicates per condition) for CCRF-CEM cells with and without 1 U ml 1 l-asparaginase treatment.

Screening of KIs library. Fiy-six KIs from the 384-well protein KI library I (InhibitorSelect, EMD Chemicals Inc.; Supplementary Table S4) were dissolved in dimethylsulphoxide, administered to CCRF-CEM cells at 1 M (nal concentration) for 24 h and luminescence and NMR experiments were performed at least in triplicates. NMR spectra of the medium, containing solvent control and incubated with and without the cells in the same 96-well plate, were acquired in triplicates. To conrm initial hits, nine KIs were chosen, and ATP assay and NMR experiments were acquired for three or more independent repeats on CCRF-CEM cells following 24 h of treatment of KI at two dierent doses. For every plate, NMR spectra of two dierent controls, cells receiving solvent control and media incubated in the same 96-well plate without the cells but containing solvent control were acquired as well. The drugs were eEF-2 KI (BIM-0207152), 5-iodo-indirubin-3-monoxime (BIM-0207163), MK2a inhibitor (BIM-0086775), ERK inhibitor II (BIM-0086749), PKC inhibitor (BIM-0050229), K-252a, Nocardiopsis sp. (BIM-0086768), Fascaplysin, Synthetic (BIM-0086751), Wee1 inhibitor II (BIM-0207209) and NF-KB activation inhibitor (BIM-0086776) all administered at 1 M (high dose) and 0.1 M (low dose). Hit follow-up studies were performed using doseresponse experiments for the abovementioned nine KIs. Doseresponse experiments were performed treating CCRF-CEM cells using six dierent clinically relevant concentrations of KI in the range of 0.052 M. The intent of our study was to investigate the metabolomic response to KI treatment without aecting the cell biomass. IC50 values were calculated (Supplementary Table S4) based on the 50% reduction of ATP levels induced by treatment. No alterations in cell numbers were observed aer 24 h induced by KI treatments.

Statistical and chemometric analysis. Aer processing, NMR spectra were mean-centered and then subjected to PCA60 using MATLAB. Multivariate Z-factor values were calculated as indicated in Supplementary Equations S1-S2 (refs 13,15; Supplementary Methods).

The relative concentrations of selected metabolites (as percent of control) are reported as mean values s.e.m Statistical comparison between data obtained from untreated and individual KIs treatment was performed using an unpaired Students t-test (statistical signicance: *P < 0.05, **P < 0.01 and ***P < 0.001).

References

1. Duarte, I. F. et al. Analytical approaches toward successful human cell metabolome studies by NMR spectroscopy. Anal. Chem. 81, 50235032 (2009).

2. Abraham, V. C., Taylor, D. L. & Haskins, J. R. High content screening applied to large-scale cell biology. Trends Biotechnol. 22, 1522 (2004).

3. Baker, M. Academic screening goes high-throughput. Nat. Methods 7, 787792 (2010).

4. Neumann, B. et al. High-throughput RNAi screening by time-lapse imaging of live human cells. Nat. Methods 3, 385390 (2006).

5. Zanella, F., Lorens, J. B. & Link, W. High content screening: seeing is believing. Trends Biotechnol. 28, 237245 (2010).

6. Bradley, S. A. et al. Fermentanomics: monitoring mammalian cell cultures with NMR spectroscopy. J. Am. Chem. Soc. 132, 95319533 (2010).

7. Dietmair, S., Timmins, N. E., Gray, P. P., Nielsen, L. K. & Kromer, J. O. Towards quantitative metabolomics of mammalian cells: development of a metabolite extraction protocol. Anal. Biochem. 404, 155164 (2010).

8. Kell, D. B. et al. Metabolic footprinting and systems biology: the medium is the message. Nat. Rev. Microbiol. 3, 557565 (2005).

9. Sellick, C. A. et al. Eective quenching processes for physiologically valid metabolite proling of suspension cultured mammalian cells. Anal. Chem. 81, 174183 (2009).

10. Wu, H. F., Southam, A. D., Hines, A. & Viant, M. R. High-throughput tissue extraction protocol for NMR and MS-based metabolomics. Anal. Biochem. 372, 204212 (2008).

11. Fischer, E. & Sauer, U. Metabolic ux proling of Escherichia coli mutants in central carbon metabolism using GC-MS. Eur. J. Biochem. 270, 880891 (2003).

12. Ewald, J. C., Heux, S. & Zamboni, N. High-throughput quantitative metabolomics: workow for cultivation, quenching, and analysis of yeast in a multiwell format 81, 36233629 (2009).

13. Kummel, A. et al. Integration of multiple readouts into the Z factor for assay quality assessment. J. Biomol. Screen 15, 95101 (2010).

14. Malo, N., Hanley, J. A., Cerquozzi, S., Pelletier, J. & Nadon, R. Statistical practice in high-throughput screening data analysis. Nat. Biotechnol. 24, 167175 (2006).

15. Zhang, J. H., Chung, T. D. Y. & Oldenburg, K. R. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J. Biomol. Screen 4, 6773 (1999).16. Blower, P. E. et al. MicroRNA expression proles for the NCI-60 cancer cell panel. Mol. Cancer Ther. 6, 14831491 (2007).

17. Avramis, V. I. & Tiwari, P. N. Asparaginase (native ASNase or pegylated ASNase) in the treatment of acute lymphoblastic leukemia. Int. J. Nanomed. 1, 241254 (2006).

18. Pui, C. H., Campana, D. & Evans, W. E. Childhood acute lymphoblastic leukaemiacurrent status and future perspectives. Lancet Oncol. 2, 597607 (2001).

19. Cohen, P. Protein kinasesthe major drug targets of the twenty-rst century? Nat. Rev. Drug Discov. 1, 309315 (2002).

20. Manning, G., Whyte, D. B., Martinez, R., Hunter, T. & Sudarsanam, S. The protein kinase complement of the human genome. Science 298, 19121934 (2002).

21. Gottschalk, S., Anderson, N., Hainz, C., Eckhardt, S. G. & Serkova, N. J. Imatinib (STI571)-mediated changes in glucose metabolism in human leukemia BCR-ABL-positive cells. Clin. Cancer Res. 10, 66616668 (2004).

22. Griffin, J. L. & Shockcor, J. P. Metabolic proles of cancer cells. Nat. Rev. Cancer 4, 551561 (2004).

23. Hsu, P. P. & Sabatini, D. M. Cancer cell metabolism: Warburg and beyond. Cell 134, 703707 (2008).

24. DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G. & Thompson, C. B. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 7, 1120 (2008).

25. Beloueche-Babari, M. et al. Magnetic resonance spectroscopy monitoring of mitogen-activated protein kinase signaling inhibition. Cancer Res. 65, 33563363 (2005).

26. Lodi, A. et al. Hypoxia triggers major metabolic changes in AML cells without altering indomethacin-induced TCA cycle deregulation. ACS Chem. Biol. 6, 169175 (2011).

27. Romanska, H. M. et al. Nuclear magnetic resonance detects phosphoinositide 3-kinase/Akt-independent traits common to pluripotent murine embryonic stem cells and their malignant counterparts. Neoplasia 11, 1301 (2009).28. Tiziani, S. et al. Metabolomic proling of drug responses in acute myeloid leukaemia cell lines 4, e4251 (2009).

29. Shushan, B. A review of clinical diagnostic applications of liquid chromatography-tandem mass spectrometry. Mass Spectrom. Rev. 29, 930944 (2010).

30. Howell, S. C., Mesleh, M. F. & Opella, S. J. NMR structure determination of a membrane protein with two transmembrane helices in micelles: MerF of the bacterial mercury detoxication system. Biochemistry 44, 51965206 (2005).

31. Evans, R., Haiber, S., Nilsson, M. & Morris, G. A. Isomer resolution by micelle-assisted diusion-ordered spectroscopy. Anal. Chem. 81, 45484550 (2009).

32. Raery, D. High-throughput NMR spectroscopy. Anal. Bioanal. Chem. 378, 14031404 (2004).

33. Mercier, P., Lewis, M. J., Chang, D., Baker, D. & Wishart, D. S. Towards automatic metabolomic proling of high-resolution one-dimensional proton NMR spectra. J. Biomol. NMR 49, 307323 (2011).

34. Druker, B. J. et al. Eects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl positive cells. Nat. Med. 2, 561566 (1996).

35. Harrington, L. S., Findlay, G. M. & Lamb, R. F. Restraining PI3K: mTOR signalling goes back to the membrane. Trends Biochem Sci 30, 3542 (2005).

36. Heiden, M. G. V. et al. Evidence for an alternative glycolytic pathway in rapidly proliferating cells. Science 329, 14921499 (2010).

37. Larach, D. B., Kofke, W. A. & Le Roux, P. Potential non-hypoxic/ischemic causes of increased cerebral interstitial uid lactate/pyruvate ratio: a review of available literature. Neurocrit Care 114 (2011).

38. DePinieux, G. et al. Lipid-lowering drugs and mitochondrial function: eects of HMG-CoA reductase inhibitors on serum ubiquinone and blood lactate/ pyruvate ratio 42, 333337 (1996).

39. Yang, L. L. et al. Metabolomic and mass isotopomer analysis of liver gluconeogenesis and citric acid cycleII. Heterogeneity of metabolite labeling pattern. J. Biol. Chem. 283, 2198821996 (2008).

40. Bain, J. R. et al. Metabolomics applied to diabetes research moving from information to knowledge. Diabetes 58, 24292443 (2009).

41. Feala, J. D. et al. Systems approaches and algorithms for discovery of combinatorial therapies. Rev. Syst. Biol. Med. 2, 181193 (2010).

NATURE COMMUNICATIONS | 2:545 | DOI: 10.1038/ncomms1562 | www.nature.com/naturecommunications

ARTICLE

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms1562

42. Bollenbach, T. & Kishony, R. Resolution of gene regulatory conicts caused by combinations of antibiotics. Mol. Cell. 42, 413425 (2011).

43. Geva-Zatorsky, N. et al. Protein dynamics in drug combinations: a linear superposition of individual-drug responses. Cell 140, 643651 (2010).

44. Calzolari, D. et al. Search algorithms as a framework for the optimization of drug combinations. PLoS Comput. Biol. 4 (2008).

45. Arora, S. et al. Identication and characterization of an inhibitor of eukaryotic elongation factor 2 kinase against human cancer cell lines. Cancer Res. 63, 68946899 (2003).

46. Kane, L. P., Shapiro, V. S., Stokoe, D. & Weiss, A. Induction of NF-kappa B by the Akt PKB kinase. Curr. Biol. 9, 601604 (1999).

47. Cheng, Y. et al. Cytoprotective eect of the elongation factor-2 kinase-mediated autophagy in breast cancer cells subjected to growth factor inhibition. PLoS One 5 (2010).

48. Clayton, T. A. et al. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 440, 10731077 (2006).

49. Serkova, N. J., Spratlin, J. L. & Eckhardt, S. G. NMR-based metabolomics: translational application and treatment of cancer. Curr. Opin. Mol. Ther. 9, 572585 (2007).

50. Beckonert, O. et al. Metabolic proling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 26922703 (2007).

51. Aue, W., Karhan, J. & Ernst, R. Homonuclear broad-band decoupling and 2-dimensional J-resolved NMR spectroscopy. J. Chem. Phys. 64, 42264227 (1976).

52. Thrippleton, M. J., Edden, R. A. E. & Keeler, J. Suppression of strong coupling artefacts in J-spectra. J. Magn. Reson. 174, 97109 (2005).

53. Hwang, T. L. & Shaka, A. J. Water suppression that worksexcitation sculpting using arbitrary wave-forms and pulsed-eld gradients. J. Magn. Reson. Ser. A 112, 275279 (1995).

54. Tiziani, S., Lodi, A., Ludwig, C., Parsons, H. M. & Viant, M. R. Eects of the application of dierent window functions and projection methods on processing of H-1 J-resolved nuclear magnetic resonance spectra for metabolomics. Anal. Chim. Acta 610, 8088 (2008).

55. Gunther, U. L., Ludwig, C. & Ruterjans, H. NMRLABadvanced NMR data processing in MATLAB. J. Magn. Reson. 145, 201208 (2000).

56. Dieterle, F., Ross, A., Schlotterbeck, G. & Senn, H. Probabilistic quotient normalization as robust method to account for dilution of complex biological

mixtures. Application in H-1 NMR metabonomics. Anal. Chem. 78, 42814290 (2006).57. Parsons, H. M., Ludwig, C., Gunther, U. L. & Viant, M. R. Improved classication accuracy in 1-and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation. BMC Bioinformatics 8 (2007).

58. Cui, Q. et al. Metabolite identication via the madison metabolomics consortium database. Nat. Biotechnol. 26, 162164 (2008).

59. Wishart, D. S. et al. HMDB: the human metabolome database. Nucleic Acids Res. 35, D521D526 (2007).

60. Wold, S., Esbensen, K. & Geladi, P. Principal component analysis. Chemom. Intell. Lab. Syst. 2, 37 (1987).

Acknowledgements

We thank Ziming Zhang and Jinghua Yu for their technical support for NMR experiments, Professor Maurizio Pellecchia (Sanford-Burnham Medical Research Institute) for access to his instrumentation. This work was supported by grants NIH R21AG030685, NSF CCF-0829891 and NIH P01-HL098053.

Author contributions

S.T. was involved in planning, experimentation, data analysis and manuscript writing; Y.K. performed the experimentation cell biology (primary cells and miR treatments); J.C. performed the experimentation cell biology (cell lines); R.W. was involved in clinical leadership; G.P. was involved in scientic leadership and manuscript writing.

Additional information

Supplementary Information accompanies this paper at http://www.nature.com/ naturecommunications

Competing nancial interests: The authors declare no competing nancial interests.

Reprints and permission information is available online at http://npg.nature.com/ reprintsandpermissions/

How to cite this article: Tiziani, S. et al. Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library. Nat. Commun. 2:545 doi: 10.1038/ncomms1562 (2011).

NATURE COMMUNICATIONS | 2:545 | DOI: 10.1038/ncomms1562 | www.nature.com/naturecommunications

Word count: 9625

Show less

Abstract

Translate

Metabolism is altered in many highly prevalent diseases and is controlled by a complex network of intracellular regulators. Monitoring cell metabolism during treatment is extremely valuable to investigate cellular response and treatment efficacy. Here we describe a nuclear magnetic resonance-based method for screening of the metabolomic response of drug-treated mammalian cells in a 96-well format. We validate the method using drugs having well-characterized targets and report the results of a screen of a kinase inhibitor library. Four hits are validated from their action on an important clinical parameter, the lactate to pyruvate ratio. An eEF-2 kinase inhibitor and an NF-kB activation inhibitor increased lactate/pyruvate ratio, whereas an MK2 inhibitor and an inhibitor of PKA, PKC and PKG induced a decrease. The method is validated in cell lines and in primary cancer cells, and may have potential applications in both drug development and personalized therapy.

Details

Title

Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library

Author

Tiziani, Stefano; Kang, Yunyi; Choi, Janet S; Roberts, William; Paternostro, Giovanni

Pages

545

Publication year

2011

Publication date

Nov 2011

Publisher

Nature Publishing Group

e-ISSN

20411723

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.1038/ncomms1562

ProQuest document ID

906644619

Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library

Jump to:

Full text

Abstract

Details

Suggested sources