nephrontoxicityprofilingviauntargetedmetabolome ... · we performed a large scale profiling study...

Nephron Toxicity Profiling via Untargeted MetabolomeAnalysis Employing a High Performance LiquidChromatography-Mass Spectrometry-based Experimentaland Computational Pipeline*

Received for publication, February 10, 2015, and in revised form, May 11, 2015 Published, JBC Papers in Press, June 8, 2015, DOI 10.1074/jbc.M115.644146

Christina Ranninger‡1, Marc Rurik§1, Alice Limonciel¶1, Silke Ruzek‡, Roland Reischl‡, Anja Wilmes¶, Paul Jennings¶,Philip Hewitt�, Wolfgang Dekant**, Oliver Kohlbacher§, and Christian G. Huber‡2

From the ‡Department of Molecular Biology, Division of Chemistry and Bioanalytics, University of Salzburg, 5020 Salzburg, Austria,the §Applied Bioinformatics Group, Center for Bioinformatics, Quantitative Biology Center and Department of Computer Science,University of Tubingen, Tubingen 72076, Germany, the ¶Division of Physiology, Department of Physiology and Medical Physics,Medical University of Innsbruck, Innsbruck 6020, Austria, �Merck KGaA, Merck Serono, Nonclinical Safety, Darmstadt 64293,Germany, and the **Department of Toxicology, University of Wurzburg, Wurzburg 97078, Germany

Background: Drug toxicity testing calls for in vitro assays as alternatives to animal models.Results: OpenMS and KNIME are applicable for processing of HPLC-MS data sets to reveal metabolic changes upon chloro-acetaldehyde treatment of kidney cells.Conclusion: Most significant changes are related to oxidative stress.Significance: Comprehensive multiomics studies support the risk assessment at an early stage of drug development.

Untargeted metabolomics has the potential to improve thepredictivity of in vitro toxicity models and therefore may aid thereplacement of expensive and laborious animal models. Here wedescribe a long term repeat dose nephrotoxicity study con-ducted on the human renal proximal tubular epithelial cell line,RPTEC/TERT1, treated with 10 and 35 �mol�liter�1 of chloro-acetaldehyde, a metabolite of the anti-cancer drug ifosfamide.Our study outlines the establishment of an automated and easyto use untargeted metabolomics workflow for HPLC-high reso-lution mass spectrometry data. Automated data analysis work-flows based on open source software (OpenMS, KNIME)enabled a comprehensive and reproducible analysis of the com-plex and voluminous metabolomics data produced by the pro-filing approach. Time- and concentration-dependent responseswere clearly evident in the metabolomic profiles. To obtain amore comprehensive picture of the mode of action, transcrip-tomics and proteomics data were also integrated. For toxicityprofiling of chloroacetaldehyde, 428 and 317 metabolite fea-tures were detectable in positive and negative modes, respec-tively, after stringent removal of chemical noise and unstablesignals. Changes upon treatment were explored using principalcomponent analysis, and statistically significant differenceswere identified using linear models for microarray assays. Theanalysis revealed toxic effects only for the treatment with 35

�mol�liter�1 for 3 and 14 days. The most regulated metaboliteswere glutathione and metabolites related to the oxidative stressresponse of the cells. These findings are corroborated by pro-teomics and transcriptomics data, which show, among otherthings, an activation of the Nrf2 and ATF4 pathways.

Metabolites represent the final products of all cellular andregulatory processes, providing a snap shot of the sum of allcellular process occurring at that moment (1). Consequently, tostudy the risks and/or consequences of exposure of humans tochemicals or drugs, untargeted metabolome analysis offers aviable approach to study the biological effects upon exposure tosuch compounds. However, because ethical reasons precludethe study of biological consequences of chemicals in humansubjects (2), suitable models, including cultured human cells,can be employed to derive characteristic biomarkers and/orbiochemical pathways that can be used for extrapolation tohumans.

Untargeted metabolomics aims at analyzing the broadestpossible range of metabolites present in a biological sample toobtain an unbiased view of the organization and function ofbiological systems and to characterize their responses tochanges in their environment such as drug or chemical treat-ment (3– 6). Cultured cells can be utilized as very discriminativesensors for external stimulation and are very well suited forstudying the action of drugs and their toxicity in vitro (7, 8). Invitro cell culture systems have numerous advantages over ani-mal models, because they are easier to control as well as tohandle, and biochemical changes are easier to interpret (8, 9).

Different analytical methods such as NMR spectroscopy orMS in combination with separation by HPLC or GC are the keytechnologies applied to metabolomics (7, 10, 11). An NMR-based metabolic profiling method has previously been

* This work was supported by the European Union’s 7th Framework Pro-gramme FP7/2007–2013 under Grant Agreement 202222, Predict-IV. Partof this work was presented by C. R. at Metabolomics 2013 in Glasgow, forwhich she received a travel grant awarded by the Austrian Society for Ana-lytical Chemistry. The authors declare no other competing financialinterest.

1 These authors contributed equally.2 To whom correspondence should be addressed: Dept. of Molecular Biology,

Div. of Chemistry and Bioanalytics, University of Salzburg, HellbrunnerStrasse 34, 5020 Salzburg, Austria. Tel.: 43-662-8044-5704; Fax: 43-662-8044-5751; E-mail: [email protected].

THE JOURNAL OF BIOLOGICAL CHEMISTRY VOL. 290, NO. 31, pp. 19121–19132, July 31, 2015© 2015 by The American Society for Biochemistry and Molecular Biology, Inc. Published in the U.S.A.

JULY 31, 2015 • VOLUME 290 • NUMBER 31 JOURNAL OF BIOLOGICAL CHEMISTRY 19121

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employed to screen the acute effects of six chemicals in thehuman renal proximal tubule epithelial cell line, RPTEC/TERT1 (12). The study showed the usefulness of metabolomics(MTX)3 for discrimination of toxins. A more complex experi-mental design was utilized for toxicity profiling of long termcyclosporine A exposures (2, 13). Cells were cultured on micro-porous filters and treated with two concentrations of com-pounds for 14 days. At set intervals (1, 3, and 14 days),transcriptomics (TCX), proteomics (PTX), and MTX were per-formed. Cellular metabolome analysis was conducted via directinfusion electrospray ionization mass spectrometry lacking thepotential of compound separation and identification via HPLCor GC, which makes qualitative and quantitative analysis quitedifficult.

A comprehensive quantitative metabolic profiling thereforerequires the combination of HPLC with high resolution massspectrometry (HRMS) (14). The high information content ofdata generated by HRMS implies a strong need for automatablecomputational processing of the otherwise unmanageableamount of data (15). In addition to vendor software packages,numerous open source software tools are available for data pro-cessing like XCMS (16), MetAlign (17), MzMine (18), PeakML/mzMatch (19), and OpenMS/TOPP (20 –22).

Here we present a fully automated data analysis pipelinebased on OpenMS, a comprehensive open source softwarelibrary for the analysis of MS data, KNIME (23), an open sourceworkflow engine that enables visual and interactive data analy-sis, and R, a programming platform for statistical analysis (24).Metabolite identification is based on matching the accuratemasses against the theoretical mass of databases such asHuman Metabolome Database (HMDB) or METLIN, followedby confirmation of hits by fragmentation using MS/MS andretention time matching using a reference standard (26).

We performed a large scale profiling study of the long termnephrotoxicity of chloroacetaldehyde. Chloroacetaldehyde isof particular interest as a metabolite of ifosfamide, which is apharmaceutical used for the treatment of cancer (25). Finally,proteomics and transcriptomics data were also integrated toincrease the depth of understanding of the metabolomic alter-ations brought about by chloroacetaldehyde exposure.

Experimental Procedures

Chemicals and Materials—Acetonitrile for LC-MS was pur-chased from VWR (Radnor, PA). Reagent grade formic acid,methanol, and chloroacetaldehyde were obtained from Sigma-Aldrich. High purity water was produced using a Milli-Q Inte-gral 3 purification system from Merck Millipore. Standard sub-stances used for identification were obtained from Merck(amino acids) or from Sigma-Aldrich (all other compounds).

Cell Culture—The human renal proximal tubule cell lineRPTEC/TERT1 was obtained from Evercyte GmbH (Vienna,Austria). The RPTEC/TERT1 cells (26) were cultured on 1-�m

PET 24-mm tissue culture inserts for MTX and PTX, and on0.2-�m aluminum oxide 25 mm inserts for TCX and differen-tiated in a serum-free hormonally defined medium for a mini-mum of 10 days before exposure commencement (2). Duringthis time, the cells cover the surface of the filter, become con-tact-inhibited and quiescent, and develop a stable barrier func-tion of approximately 150 Ohm�cm2 (27). Because no furtherproliferation takes place after differentiation, each well has thesame number of cells (28). The mature monolayers wereexposed to either a low (10 �mol�liter�1) or high (35�mol�liter�1) concentration of chloroacetaldehyde or controlmedium, on both the apical and the basolateral sides. The cellswere treated every 24 h for 14 days and lysed on days 1, 3, and 14in ice-cold methanol for MTX/PTX or RLT buffer (RNeasymini kit; Qiagen) for TCX. Epithelial monolayer integrity wasmonitored every day by transepithelial electrical resistancemeasurement and showed no breach in monolayer integrityand thus no significant alteration in cell numbers throughoutthe experiments at all time points and all treatments (transepi-thelial electrical resistance results for these experiments havebeen previously published in Ref. 29). All experiments wereconducted on three biological replicates.

Metabolite Extraction and HPLC-MS Sample Preparation—The cell culture medium was removed, and the cells werewashed twice with ice cold PBS (Sigma-Aldrich), followed by anadditional very fast washing step with ammonium bicarbonate(185 mmol�liter�1, 289 mOsm, pH 7.8; Sigma-Aldrich). To pre-serve metabolites, 750 �l of ice-cold methanol containing deu-tero-alanine (50 �mol�liter�1) as an internal standard, wasadded. Two wells were pooled in an Eppendorf tube, vortexed,and sonicated with an in-probe sonicator from Branson Ultra-sonics Corporation (Danbury, CT) for 20 s to fully homogenizethe sample. Finally, the cell lysates were centrifuged at 4.0 °Cand 14,000 rpm for 10 min. The supernatant was used for MTXand was stored at �80 °C until analysis.

For HPLC-MS analysis, the samples were thawed and 1:5diluted with MilliQ water. Additionally, a pool sample was pre-pared by blending 20 �l of each available sample followed by thesame dilution step. This pool sample was used as quality control(QC) in the sample queues and was injected after every ninthrun. The sample injection order was randomized.

High Performance Liquid Chromatography-Mass Spectrome-try (HPLC-MS)—An HPLC system (Model Accela II; ThermoFisher Scientific, Bremen, Germany) was coupled to anOrbitrap mass spectrometer (Model Exactive; Thermo FisherScientific) equipped with a heated electrospray ion source oper-ating in the positive or negative ion mode. Reversed phaseHPLC separations were performed with a 100 � 2.1-mm innerdiameter Hypersil Gold aQ column (Thermo Fisher Scientific)packed with 1.9-�m particles. Water (A) and acetonitrile (B)each containing 0.10% (v/v) formic acid were used as eluents.The flow rate was 0.30 ml�min�1, and gradient elution was car-ried out as follows: 100% A for 1.5 min followed by a lineargradient to 100% B in 6.5 min and holding for 2 min. In the end,a re-equilibration step was performed for 3.0 min at 0.0% B. Thetotal run time was 13 min. The column temperature was main-tained at 30 °C, and 2.7 �l of sample was injected per run.

3 The abbreviations used are: MTX, metabolomics; Nrf2, nuclear factor E2-re-lated factor 2; TCX, transcriptomics; PTX, proteomics; QC, quality control;PCA, principal component analysis; LIMMA, linear models for microarrayassays; HMDB, Human Metabolome Database; ATF4, activation transcrip-tion factor 4; ASNS, asparagine synthetase (glutamine-hydrolyzing); HRMS,high resolution mass spectrometry.

Metabolic Drug Toxicity Profiling by HPLC-MS

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An electrospray ionization heater temperature of 350 °C waschosen and sheath gas and auxiliary gas flow rates were set to 20and 5.0 arbitrary units in positive mode and to 35 and 10 in thenegative mode, respectively. A sprayer voltage of 3.0 kV wasapplied, and the resolution was set to 50,000. The mass rangewas split into a low mass range m/z 50 –200 and a high massrange m/z 200 –1000 and measured in two separate runs. TheMS parameters were tuned for each method and polarity sepa-rately with selected metabolites. For the low mass range thebest intensities were achieved with a capillary voltage of 25 V,tube lens voltage of 70 V, and skimmer voltage of 14 V forpositive and negative mode with changed polarity. The highmass range measurements were conducted with a capillaryvoltage of 55 and �45 V, tube lens voltage of 115 and �105 V,and skimmer voltage of 22 and �24 V for positive and negativemode, respectively.

The MS/MS analysis was performed with a quadrupoleOrbitrap mass spectrometer (Model Q Exactive; ThermoFisher Scientific). For fragmentation experiments an S-lens RFlevel of 50, a sprayer voltage of 3.5 kV, and capillary and heatertemperature of 350 °C were chosen. A sheath gas flow of 50 andauxiliary gas of 15 for positive electrospray ionization mode and40 and 10 for negative mode, respectively, were employed. InSelected ion monitoring mode (m/z 100 –1000), the resolutionwas set to 70,000, and for the data-dependent fragmentation, aresolution of 17,500 was chosen. In both cases, the automaticgain control target was 1 � 106, and the maximum injectiontime was set to 25 ms. The quadrupole isolation window was 1.0m/z, and a normalized collision energy of 25 arbitrary units wasused. The data-dependent fragmentation settings were anunderfill ratio of 0.1%, resulting in an intensity threshold forfragmentation of 4 � 104, the apex trigger was set to 2–5 s, andmolecules charged higher than 3 were excluded.

Data Evaluation—Both chromatograms and mass spectrawere recorded with Xcalibur 2.2 (Thermo Fisher Scientific).The generated HPLC-MS data were further processed withProteoWizard 3.0.4243 (30), OpenMS 1.11 (22), KNIME 2.9.2(23), R 2.15.1 (24), and SIMCA 13.0.3 (Umetrics, Umea,Sweden).

Transcriptome Analysis—Transcriptomic analysis was per-formed on Illumina� HT 12 v4 BeadChip arrays (�47,000 tran-scripts). Deregulated probes were identified using a moderatedone-way analysis of variance with a Benjamini-Hochberg cor-rected p value with a cutoff value of 0.05. A moderated two-sided t test with a Benjamini-Hochberg correction for multipletesting was calculated for the remaining probes. Both stepswere carried out in R with the LIMMA package. The number ofdifferentially expressed probes was calculated for each treat-ment condition based on a fold cutoff of 1.5 (0.58 log2 fold) anda p value cutoff of 0.001.

Proteome Analysis—A detailed description of transcriptom-ics and proteomics analysis can be found in Refs. 2 and 13.Briefly, peptides for PTX were labeled after protein extractionand digestion with isobaric tags for relative and absolute quan-titation (iTRAQ) and measured by HPLC-MS. The HPLC sys-tem was directly coupled to a linear ion-trap Orbitrap massspectrometer (Model LTQ Orbitrap XL; ThermoFisher Scien-tific) with a nano-electrospray ionization source operated in

positive ionization mode. A total of nine 4plex iTRAQ sets wereanalyzed, each comparing a low and a high chloroacetaldehydedose to a control sample at a given time point (1, 3, or 14 days)with three biological replicates. Protein identification andquantification workflows were implemented in OpenMS (ver-sion 1.9) (21) and TOPPAS (31). Statistical analysis was per-formed using the isobar package (32) and custom R code. Sig-nificantly increased or decreased proteins were reported if twop values were below 0.05: the first was based on a Cauchy dis-tribution fitted to the global protein ratio distribution, and thesecond was based on the spread of peptide ratios contributingto the protein ratio.

Results

Experimental Design—To map both concentration, as wellas time-dependent effects of drug exposure, our experimen-tal design involved treatment of the RPTEC/TERT1 cellswith three different conditions, namely null control (0.0�mol�liter�1 chloroacetaldehyde, labeled C), low concentra-tion (10 �mol�liter�1 chloroacetaldehyde, labeled L), andhigh concentration (35 �mol�liter�1 chloroacetaldehyde, la-beled H). The cells were treated for 14 days with mediumexchange every 24 h and were harvested at three different timepoints, specifically after 1, 3, and 14 days of exposure. Eachexperiment was performed in triplicate, which led to 3 � 3 �3 � 27 samples to characterize the biological effects of drugtreatment. Analysis of the samples was preceded with one blankinjection (20% methanol in water) followed by two analyses ofthe QC sample (pool sample diluted 1:5 with water). The injec-tion order of the 27 metabolite extracts was randomized; blanksamples were analyzed after three metabolite extracts, whereasQC samples were injected after six metabolite extracts. Thisresulted in a total set 44 raw data files (27 samples, 11 blanks,and 6 QC) to be sequentially analyzed.

Data Handling, Quality Control, and Statistical Analysis—Computational workflows established in OpenMS and KNIMEwere utilized for the automated processing of HPLC-MS data.These open source tools allowed the detection and linking offeatures by means of tailor-made downstream analysis includ-ing restrictive filtering criteria to leave only those features thatwere detected with high confidence for further statisticalevaluation.

All steps of data handling and analysis are graphically illus-trated in Fig. 1. Data evaluation was performed essentially intwo stages. First, all features related to potential metaboliteswere detected, aligned, and statistically analyzed in the full scanhigh resolution Orbitrap MS data to reveal features differen-tially regulated upon chloroacetaldehyde treatment (steps 1–14in Fig. 1). Second, differentially regulated features were identi-fied based on the criteria outlined in Ref. 33, also incorporatingadditional targeted metabolite identification experiments usingMS/MS (steps 15 and 16 in Fig. 1).

The 44 raw files were converted into mzML format using theProteoWizard tool msconvert (step 1 in Fig. 1). The individualdata files were then centroided by using OpenMS PeakPicker-HiRes (step 2 in Fig. 1), followed by feature detection by meansof FeatureFinderMetabo (step 3 in Fig. 1), which detects andquantifies metabolic features within an HPLC-MS map (20).




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We used a noise threshold of 250 for the negative mode and 500for the positive mode and a signal to noise ratio of 3 for featuredetection. Detected features may represent metabolites in dif-ferent charge states and/or as various adduct species. Featuredetection was performed for each HPLC-HRMS map individu-ally. After identifying features in all 44 HPLC-HRMS runs, cor-responding features were matched using FeatureLinker-UnlabeledQT (steps 4 and 5 in Fig. 1). Features were mergedinto a cluster across runs if the differences in retention time andm/z were smaller than chosen thresholds (�10 s and �5 ppmhere).

This resulted in a so-called consensus map representing alldetected metabolite features across runs. The consensus mapwas exported as a consensus XML (step 8 in Fig. 1). This file waspreprocessed with the tool FileFilter (step 6 in Fig. 1), which wasused to remove features with a retention time smaller than thecolumn flow-through time. The TextExporter (step 7 in Fig. 1)converted the file format into comma separated values for fur-ther data processing.

Differential features in the consensus XML file were thenidentified in a computational workflow implemented inKNIME. The consensus file contained several thousand fea-tures, most of which could be assigned to background signalsand chemical noise. Therefore, all features for which themedian blank intensity was more than 20% of the sample inten-sity (average intensity of the corresponding feature in the 11blank analyses) were removed (step 9 in Fig. 1). To normalizethe data, we performed a global normalization based on thenotion that for a comparison between any two maps only asmall number of features will be differentially regulated,whereas most of the features will have ratios of approximately

1:1. Utilizing this, we performed pairwise comparisons betweeneach map and the map with the highest number of features. Foreach pairwise comparison, we computed all ratios, excludingthe ones below 0.67 and above 1.5 (step 10 in Fig. 1). Subse-quently, we computed the average intensity ratio and multi-plied all feature intensities in the respective map by the inverse(robust regression normalization).

In addition, all features were removed that were not presentin at least six study samples with a secondary criterion that theyhad to be present in at least two of the three biological replicates(step 11 in Fig. 1). The QC samples were used in the automateddata filtering workflow to account for signal (in)stability. Therespective feature had to be present in five of six pool samples,and the relative standard deviation of the signal had to be lessthan 25% to be considered as a significant feature. Steps 9 –11were carried out by R scripts organized as separate nodes inKNIME, so that the filtering and normalization could be per-formed automatically. Missing features in the filtered data set(�4% for positive and negative mode) were assigned an inten-sity value of 1 (to avoid division by 0 in the ratios), if they areabsent in all three biological replicates.

Aiming for differential features, the filtered data sets (step 12in Fig. 1) were evaluated using multivariate and univariate sta-tistical approaches (step 13 in Fig. 1). Principal componentanalysis (PCA) was performed to identify grouping patterns andto see outliers using the statistics software package SIMCA(step 14 in Fig. 1). All data sets were scaled to unit variance withan inverse square root block weight prior to principal compo-nent analysis. Differentially regulated features were determinedusing linear models for microarray data (LIMMA) (34) usingthe R integration in KNIME. The p values were corrected for

FIGURE 1. Data processing workflow for untargeted metabolome analysis. a, feature detection and linking performed in OpenMS. b, the resultingconsensusXML file is processed in KNIME. Individual steps of data processing are labeled with arabic numbers and are explained in the text.




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multiple testing using the Benjamini-Hochberg method (35)(step 14 in Fig. 1).

For feature identification, all features of the filtered data setwere searched by their exact mass against the HMDB (36) toobtain candidate lists of matching metabolite structures (step15 in Fig. 1). Subsequently, different levels of experimental ver-ification, as outlined in detail in Ref. 33, were used for the iden-tification of differential features. The m/z values of differen-tially regulated features as determined above were put on aninclusion list for targeted fragmentation analysis. If an MS/MSspectrum was triggered, it was searched in a fragment databasesuch as METLIN (37) or MassBank (38) to confirm structuressuggested by the HMDB (step 16 in Fig. 1). The first level com-prised the comparison of fragment spectra and retention timeswith an authentic reference standard. In some cases, the inten-sity of the signal was too low to trigger fragmentation, but therewas a hit in the HMDB (search within 5 ppm), and a comparisonof the retention time and the MS spectra with a reference sub-stance was possible (level 2). If no reference substance wasavailable, we assigned level 3. Some m/z values could not befound in the databases and were hence referred to as unknowns(level 4). The final output of the computational workflow was afiltered feature list displaying the differentially expressedmetabolites (if positively identified) or features (for unknowns)for the respective time point and treatment (step 17 in Fig. 1).

Results of Differential Metabolome Analysis—HPLC-MS dataobtained from the chloroacetaldehyde nephrotoxicity studywere evaluated according to the data processing workflow pre-sented in Fig. 1, which resulted in 428 and 317 features forpositive and negative mode, respectively, after strict filteringmeeting the quality criteria described above. The filtered fea-tures lists were used for PCA to visually explore the data sets.No outliers were detected in the distance to model plot(DModX plot); therefore the model was built on all 27 samples.As depicted in Fig. 2, the PCA scores plot revealed a clear dif-ference between the cell extracts treated with the high concen-tration of chloroacetaldehyde for 3 and 14 days (H03 and H14)compared with the other samples of the study (C01, C03, C14,L01, L03, L14, and H01). Moreover, positive and negative modeshowed comparable trends as are shown in Fig. 2 (a and b).

Significantly regulated metabolites were evaluated by pair-wise comparison of control to low or high concentration foreach time point using LIMMA, which has a higher discrimina-tory power than t tests without increasing the false positive rate,especially when dealing with small sample sizes (39). Thereturned p values were corrected for multiple testing accordingto Hochberg and Benjamini (35). LIMMA clearly confirmed thequalitative findings of the initial PCA analysis. It also revealedthat the majority of significant changes occurred for the highconcentration treatment for 3 and 14 days (Fig. 3).

A considerable overlap could be observed between the differ-entially regulated features at days 3 and 14 for both positive andnegative ionization mode. In positive mode, 42 of the 72 fea-tures that were differentially expressed on day 3 were also dif-ferentially expressed on day 14. In the negative mode, 45 fea-tures were differentially regulated both on days 3 and 14.Additionally, in nearly all of these cases, the log2 fold changesshowed the same trend for days 3 and 14, which means that the

respective feature was consistently up- or down-regulated onboth time points. In most of these cases, day 14 showed morepronounced effects than day 3 (37 of 52 features for positivemode and 34 of 45 features for negative mode).

Feature Identification—All features of the filtered data setwere first annotated by accurate mass search against the HMDBwithin a 5-ppm mass window using the OpenMS tool Accurate-MassSearch. Approximately 20 –30% of all features were anno-tated searching for [M�H]�/[M-H]�, exclusively. Allowingcommon electrospray ionization adducts (40), the number ofannotations rose to �50 – 60% of the filtered data set. Unam-biguous identification was sought only for significantly regu-lated features, and therefore, targeted fragmentation experi-ments were carried out to confirm metabolite structures basedon tandem mass spectra. In most cases, substance confirmationwas established by comparison of retention time, mass spec-trum, and tandem mass spectrum of the sample and an authen-tic reference substance, which led to the identification of 13significantly regulated metabolites (level 1). The correspondingchromatographic and mass spectrometric data are collected inTables 1 and 2. Additionally, two features could be identifiedbased on the comparison of retention time and accurate mass

FIGURE 2. PCA score plots of differential metabolome analysis involvingthree conditions and three time points of three biological replicates (K1,K2, and K3). The data were scaled to unit variance. The conditions were nullcontrol (C), low concentration (L), and high concentration (H). The three timepoints were 1 (01), 3 (03), and 14 days (14). a, positive mode scores plotR2X(cum) 0.386; Q2(cum) 0.206. b, negative mode scores plot R2X(cum) 0.436;Q2(cum) 0.276.




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with that of a reference substance (level 2). Finally, the tandemmass spectra of three features, for which no standard substancewas available, were matched to the reference spectra containedin METLIN (37, 41) (level 3).

Features identified as described above are referred to asmetabolites. Altogether, 18 differentially regulated metaboliteswere found, and each of them showed a significant p value at

least for H03 or H14 after performing LIMMA followed by Ben-jamini-Hochberg correction (Table 3).

The differentially regulated metabolites belong to differentmetabolite classes, such as amino acids, and glutathione pathway-related metabolites showing up-regulation, whereas purine nucle-otides showed strong down-regulation. The trends of signal inten-sities for all 18 differentially regulated metabolites are graphicallyillustrated in Fig. 4. The p values and log2 ratios are collected inTable 3 along with the significantly regulated transcripts and pro-teins. The observed changes from all three omics disciplines wereintegrated for biological interpretation.

Discussion

Biological Effects of Chloroacetaldehyde Treatment Revealedby Combined TCX, PTX, and MTX—Oxidative stress is a majorcomponent of proximal tubule injury induced by chemical oxi-dants or mitochondrial disrupters. Here we show that themetabolomic profiles upon chloroacetaldehyde treatment ofRPTEC/TERT1 cells indicated major changes in the metabo-lites related to the defense against oxidative stress, in particularintermediates of the glutathione pathway, and in abundance ofamino acids.

At the transcriptome level, there was a major impact on theNrf2 and ATF4 pathways, which are involved in the oxidativestress response and the unfolded protein response, respectively.A major feature of Nrf2 stress response is to promote glutathi-one synthesis and recycling (2). The sharp increase in glutathi-one species (both reduced GSH and oxidized GSSG) was inaccordance with the increase in both the catalytic and modifiersubunits of the rate-limiting enzyme in the synthesis of GSH:�-glutamyl cysteine synthetase (42). After 14 days of exposure

FIGURE 3. Number of significantly regulated features according toLIMMA for positive and negative mode.

TABLE 1Identification data for compounds detected in the positive ionization modetR, retention time.

Compound Molecular formula m/z �M�H�� tR Fragments Error

s ppmPyroglutamic acid C5H7NO3 130.0499 85.47 84.0450 0.00Isoleucine C6H13NO2 132.1019 106.64 86.0969 0.00

69.0706Leucine C6H13NO2 132.1019 114.85 86.0969 0.00Aspartic acid C4H7NO4 134.0447 47.43 116.0346 �0.75

88.039974.0244

L-Carnitine C7H15NO3 162.1125 50.76 103.0394 0.0060.0816

Tyrosine C9H11NO3 182.0812 127.82 165.0542 0.19147.0440136.0756123.0442119.0493

Pantothenic acid C9H17NO5 220.1178 263.21 202.1071 �0.4590.0554

Glycerophosphocholine C8H20NO6P 258.1099 48.85 104.1073 �0.94Glutathione C10H17N3O6S 308.0908 75.35 233.0588 �0.93

179.0483162.0217144.0111130.0497116.016784.045076.0222

Oxidized glutathione C20H32N6O12S2 613.1589 128.18 355.0732 �0.51231.0429177.0325130.050192.1811




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to chloroacetaldehyde, both transcript and protein levels of theglutamate-cysteine ligase catalytic and modifier subunits wereincreased (Table 3). In addition, these two proteins were amongthe most impacted in the whole proteomic data set upon chlo-roacetaldehyde exposure.

�-Glutamyltransferases (GGTs) contribute to the degrada-tion of GSH, either by cleaving glutamate off the cysteinyl-gly-cine moiety or by transferring the resulting glutamyl group to asubstrate protein. The transcripts of �-glutamyltransferase 1,an extracellular GGT of particular importance in the proximaltubule, and GGTLC2 (�-glutamyltransferase light chain 2), asmaller gene encoding only a light GGT chain, were down-regulated at all time points in 35 �mol�liter�1 chloroacetalde-hyde treatment. A decrease in GGTLC2 protein was alsodetected on day 14. Although it is unlikely that GGTLC2 pos-sesses GGT catalytic activity because this is carried out by theheavy GGT chain, it has been shown to bind glutamate, whichcould constitute a level of interference with other processes,including de novo GSH synthesis.

Several metabolic routes lead to the production of glutamate,the first building block in GSH synthesis. One source is theconversion of pyroglutamic acid (aka 5-oxoproline) toglutamate by OPLAH (5-oxoprolinase (ATP-hydrolyzing)).Although glutamate levels remained very stable over timeexcept for a mild decrease on day 14 and pyroglutamic acidlevels were strongly increased on days 3 and 14, the mRNAlevels of OPLAH were drastically decreased on days 3 (�2.5log2 folds) and 14 (�1.1 log2 folds), suggesting a role of OPLAHin pyroglutamic acid accumulation. However, it is difficult toconclude on the impact of these transcriptional changes onOPLAH protein levels, because OPLAH was not detected in theproteomic analysis. Pyroglutamic acid itself can be formednonenzymatically from glutamine, glutamate, and �-glu-tamyl proteins (GGT products) but can also be the result ofthe degradation of �-glutamyl cysteine (GSH synthesis inter-mediary) into cysteine and pyroglutamic acid by the enzyme�-glutamylcyclotransferase (43), which was up-regulated atthe transcript level on day 1 only (0.63 log2 fold over control).An increased production of this metabolite would withdraw�-glutamyl-cystein from glutathione synthesis but providefree cysteine, possibly for de novo protein synthesis. Fig. 5

shows the glutathione pathway containing the informationof all three omics levels.

In addition to the glutathione pathway, Nrf2 also up-regu-lates a set of target enzymes involved in the enzymatic elimina-tion of reactive oxygen species. In the transcriptomic data set,the genes for heme oxygenase 1 and NAD(P)H dehydrogenasequinone 1, encoding reactive oxygen species detoxifyingenzymes, glutathione reductase, and members of the thiore-doxin system that reduce oxidized thiols on proteins (thiore-doxin and thioredoxin reductase 1) were all up-regulated. Atthe protein level, heme oxygenase 1 was strongly increased at alltime points, whereas NAD(P)H dehydrogenase quinone 1 wasincreased on days 3 and 14.

Another pathway affected by chloroacetaldehyde is theunfolded protein response, in particular the branch governedby the transcription factor ATF4. We have previously shownthat under certain cellular stresses, Nrf2 and ATF4 cooperate inGSH recycling. ATF4 drives the transcription of genes involvedin the entry and biosynthesis of amino acids, the production oftRNA synthetases that present the amino acids to the ribosomeduring translation. ATF4 primes the cell to reactivate transla-tion via up-regulation of eukaryotic initiation factor genes.Genes related to the response to endoplasmic reticulum stress(or unfolded protein response), notably driven by ATF4, werealso strongly affected by chloroacetaldehyde, particularly onday 14. Among the up-regulated ATF4 targets were severalamino acid transporters (SLC7A11 (solute carrier family 7; ani-onic amino acid transporter light chain, xc system; member 11);SLC7A5 (solute carrier family 7; amino acid transporter lightchain, L system; member 5); SLC3A2 (solute carrier family 3;amino acid transporter heavy chain; member 2); and SLC1A5(solute carrier family 1; amino acid transporter light chain, Lsystem; member 5)), asparagine synthetase, and the glycyl-tRNA synthetase (GARS). On day 14, the tRNA synthetasesfor asparagine, leucine, and threonine were down-regulated(asparaginyl-tRNA synthetase, leucyl-tRNA synthetase, andthreonyl-tRNA synthetase, respectively). Substrates of the up-regulated transporters include isoleucine, leucine, tyrosine, andglutamine, all of which were increased in the metabolomicsmeasurements, and alanine, which was decreased. Interest-ingly, although the levels of the substrates of asparagine synthe-

TABLE 2Identification data for compounds detected in the negative ionization modetR, retention time.

Compound Molecular formula m/z �M�H�� tR Fragments Error

s ppmAlanine C3H7NO2 88.0404 47.4 0.00O-Phosphoethanolamine C2H8NO4P 140.0119 46.6 122.907 0.71

78.9585Glutamine C5H10N2O3 145.0620 47.63 127.0514 0.47

102.0557Glutamate C5H9NO4 146.046 48.67 102.0559 0.68

128.035AMP C10H14N5O7P 346.0563 74.80 134.0471 1.37

96.969378.9585

ADP C10H15N5O10P2 426.0228 87.10 158.9254 1.73134.047278.9585

CDP-Ethanolamine C11H20N4O11P2 445.0538 50.20 201.9671 1.5378.9585




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tase were increased (glutamine) and decreased (aspartic acid) atboth time points, the levels of the two products of the reaction(glutamic acid and asparagine) were comparable with time-matched controls, suggesting a tight regulation of the levels of

these two amino acids and a probable consumption of gluta-mine and aspartic acid by other processes.

In addition to these impacts on glutathione and amino acids,the metabolomics data set revealed changes in more isolated

TABLE 3Differentially regulated metabolites, proteins, and mRNA transcripts upon chloroacetaldehyde treatment

a Compound ID was established by accurate mass, fragment spectra and retention time of a reference substance (level 1).b Compound ID was established by accurate mass and retention time of a reference substance (level 2).c Compound ID was established by accurate mass and fragment spectra (level 3) (according to standards proposed in Ref. (33).d Reported values are log2 ratios relative to controls.e p value calculated using R package LIMMA followed by Benjamini-Hochberg correction.f Average sample p value calculated using R package isobar.g p value calculated using moderated t test followed by Benjamini-Hochberg correction.




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metabolites of particular interest to proximal tubule cells,namely carnitine and pantothenic acid. Upon chloroacetalde-hyde exposure, we observed a strong down-regulation of L-car-nitine at day 14, as well as of �-butyrobetaine hydroxylase, theenzyme catalyzing the formation of L-carnitine from �-butyro-betaine, at genome and proteome levels. �-Butyrobetainehydroxylase is highly expressed in the proximal tubule in vivoand is also increased in RPTEC/TERT1 upon contact inhibitionand dedifferentiation (28). Additionally we have shown thatthis gene is one of the most strongly and frequently deregulatedcompounds in a study with 9 chronic nephrotoxins (27). Adecrease in �-butyrobetaine hydroxylase expression suggestsan impairment of fatty acid shuttling into the mitochondria,which would result in a reduction in fatty acid beta oxidationrates. For fatty acids to be transported by the carnitine shuttle,they must first be conjugated to CoA, which is primarily pro-duced via the degradation of pantothenic acid, initiated by thepantothenate kinase 1 enzymes. Pantothenic acid was increasedupon chloroacetaldehyde treatment and because it is not

thought to be produced by mammalian cells. Increased intra-cellular level of pantothenic acid is thus likely due to theimpaired fatty acid transport as less CoA, and therefore lesspantothenic acid is consumed. This alteration suggests adecrease in the capacity of the cells to perform beta oxidation,which could be caused by reactive oxygen species inducedmitochondrial injury or other types of mitochondrial injury.

Interestingly we have previously reported a dose- and time-dependent increase in supernatant lactate in RPTEC/TERT1cells exposed to chloroacetaldehyde, which may point to a shiftto glycolysis as an energy source (44). Supplementation in bothcarnitine and pantothenic acid has been shown to protect cellsagainst oxidative stress (45– 47). Wojtczak and Slyshenkov (47)also described for Ehrlich ascites tumor cells that pantothenicacid was only protective when de novo glutathione synthesiswas enabled, thus suggesting a direct role for pantothenic acidin glutathione metabolism regulation.

Taken together, the results demonstrate that deep mechanis-tic insights of chemically induced cellular injury can be

FIGURE 4. Bar plots of significantly regulated metabolites representing signal intensities in untreated (C, control) and treated (L, low dose; H, High dose) cellculture experiments. Numbers 1, 3, and 14 indicate the duration of treatment, 1, 3, and 14 d, respectively.




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achieved using differentiated cells together with standardizedexperimental and computational workflows. We describe thefirst application of the new computational tool FeatureFinder-Metabo in combination with automated filtering routinesimplemented in KNIME for large scale metabolomics profilingstudies employing HPLC-HRMS. The developed workflowsreturn a list of trustworthy and statistically relevant metabolitefeatures that could be mapped onto several pathways relevantfor the biological response to drug treatment. Integratingmetabolomics with proteomics and transcriptomics datahelped corroborate these findings and allowed a better under-standing of the influenced pathways.

The overall picture reveals that chloroacetaldehydeapplied at a high concentration of 35 �mol�liter�1 for anextended exposure period (more than 3 days) has consider-

able effects in the form of oxidative stress, endoplasmicreticulum stress (unfolded protein response) and a possibleimpairment of fatty acid shuttling into the mitochondria ofRPTEC/TERT1 cells.

Author Contributions—C. G. H., P. J., W. D., and O. K. conceivedand coordinated the study and wrote the paper. C. R., R. R., and M. R.designed, performed, and analyzed the metabolomics experimentsand wrote the paper. S. R. and P. H. designed, performed, and ana-lyzed the proteomics and transcriptomics experiments, respectively.A. L., A. W., and P. J. performed the cell culture experiments andworked on the biological interpretation of the data. C. R., M. R., R. R.,O. K., and C. G. H. designed and implemented the computationalworkflows in this study. All authors reviewed the results andapproved the final version of the manuscript.

FIGURE 5. Influence of chloroacetaldehyde treatment on glutathione metabolism. The bar plots show the mean metabolite signal intensities forcontrol, as well as high concentration treatment at days 3 and 14. The error bars represent 95% confidence intervals deduced from biological triplicates.The regulation of involved enzymes is indicated by E, which indicates unchanged (�arbitrary cutoff 0.5- and 1.5-fold change), or1, which indicatesincreased/up-regulated, or2, which indicates decreased/down-regulated) on the right side for TCX and on the left side for PTX (for all three time pointscomparing control to high concentration). If a corresponding gene or protein was not detected, it is marked with �. Fold change values for metabolites,proteins, and genes are given in Table 3.




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Acknowledgments—Dr. Gunther Bohm (ThermoFisher Scientific,Reinach, Switzerland) and Kornelia Weidemann (ThermoFisher Sci-entific, Dreieich, Germany) are acknowledged for supplying the ultraHPLC instrument used in this study.

References1. Fiehn, O. (2002) Metabolomics: the link between genotypes and pheno-

types. Plant Mol. Biol. 48, 155–1712. Wilmes, A., Limonciel, A., Aschauer, L., Moenks, K., Bielow, C., Leonard,

M. O., Hamon, J., Carpi, D., Ruzek, S., Handler, A., Schmal, O., Herrgen,K., Bellwon, P., Burek, C., Truisi, G. L., Hewitt, P., Di Consiglio, E., Testai,E., Blaauboer, B. J., Guillou, C., Huber, C. G., Lukas, A., Pfaller, W., Muel-ler, S. O., Bois, F. Y., Dekant, W., and Jennings, P. (2013) Application ofintegrated transcriptomic, proteomic and metabolomic profiling for thedelineation of mechanisms of drug induced cell stress. J. Proteomics 79,180 –194

3. Bijlsma, S., Bobeldijk, I., Verheij, E. R., Ramaker, R., Kochhar, S., Macdon-ald, I. A., van Ommen, B., and Smilde, A. K. (2006) Large-scale humanmetabolomics studies: a strategy for data (pre-) processing and validation.Anal. Chem. 78, 567–574

4. Brown, M., Dunn, W., Ellis, D., Goodacre, R., Handl, J., Knowles, J.,O’Hagan, S., Spasic, I., and Kell, D. (2005) A metabolome pipeline: fromconcept to data to knowledge. Metabolomics 1, 39 –51

5. Jenkins, S., Fischer, S. M., Chen, L., and Sana, T. R. (2013) Global LC/MSmetabolomics profiling of calcium stressed and immunosuppressant drugtreated Saccharomyces cerevisiae. Metabolites 3, 1102–1117

6. Ramautar, R., Berger, R., van der Greef, J., and Hankemeier, T. (2013)Human metabolomics: strategies to understand biology. Curr. Opin.Chem. Biol. 17, 841– 846

7. Kleinstreuer, N. C., Smith, A. M., West, P. R., Conard, K. R., Fontaine, B. R.,Weir-Hauptman, A. M., Palmer, J. A., Knudsen, T. B., Dix, D. J., Donley,E. L., and Cezar, G. G. (2011) Identifying developmental toxicity pathwaysfor a subset of ToxCast chemicals using human embryonic stem cells andmetabolomics. Toxicol. Appl. Pharmacol. 257, 111–121

8. Cuperlovic-Culf, M., Barnett, D. A., Culf, A. S., and Chute, I. (2010) Cellculture metabolomics: applications and future directions. Drug Discov.Today 15, 610 – 621

9. West, P. R., Weir, A. M., Smith, A. M., Donley, E. L., and Cezar, G. G.(2010) Predicting human developmental toxicity of pharmaceuticals usinghuman embryonic stem cells and metabolomics. Toxicol. Appl. Pharma-col. 247, 18 –27

10. Ruiz-Aracama, A., Peijnenburg, A., Kleinjans, J., Jennen, D., van Delft, J.,Hellfrisch, C., and Lommen, A. (2011) An untargeted multi-techniquemetabolomics approach to studying intracellular metabolites of HepG2cells exposed to 2,3,7,8-tetrachlorodibenzop-dioxin. BMC Genomics 12,251–269

11. Roux, A., Lison, D., Junot, C., and Heilier, J. F. (2011) Applications of liquidchromatography coupled to mass spectrometry-based metabolomics inclinical chemistry and toxicology: a review. Clin. Biochem. 44, 119 –135

12. Ellis, J. K., Athersuch, T. J., Cavill, R., Radford, R., Slattery, C., Jennings, P.,McMorrow, T., Ryan, M. P., Ebbels, T. M., and Keun, H. C. (2011) Meta-bolic response to low-level toxicant exposure in a novel renal tubule epi-thelial cell system. Mol. Biosyst. 7, 247–257

13. Wilmes, A., Bielow, C., Ranninger, C., Bellwon, P., Aschauer, L., Limon-ciel, A., Chassaigne, H., Kristl, T., Aiche, S., Huber, C. G., Guillou, C.,Hewitt, P., Leonard, M. O., Dekant, W., Bois, F., and Jennings, P. (2014)Mechanism of cisplatin proximal tubule toxicity revealed by integratingtranscriptomics, proteomics, metabolomics and biokinetics. Toxicol. InVitro 10.1016/j.tiv.2014.10.006

14. Patti, G. J. (2011) Separation strategies for untargeted metabolomics. J.Sep. Sci. 34, 3460 –3469

15. Patti, G. J., Yanes, O., and Siuzdak, G. (2012) Innovation: metabolomics:the apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 13, 263–269

16. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., and Siuzdak, G. (2006)XCMS: processing mass spectrometry data for metabolite profiling usingnonlinear peak alignment, matching, and identification. Anal. Chem. 78,

779 –78717. Lommen, A. (2009) MetAlign: interface-driven, versatile metabolomics

tool for hyphenated full-scan mass spectrometry data preprocessing.Anal. Chem. 81, 3079 –3086

18. Pluskal, T., Castillo, S., Villar-Briones, A., and Oresic, M. (2010) MZmine2: modular framework for processing, visualizing, and analyzing massspectrometry-based molecular profile data. BMC Bioinformatics 11, 395

19. Scheltema, R. A., Jankevics, A., Jansen, R. C., Swertz, M. A., and Breitling,R. (2011) PeakML/mzMatch: a file format, Java library, R library, andtool-chain for mass spectrometry data analysis. Anal. Chem. 83,2786 –2793

20. Kenar, E., Franken, H., Forcisi, S., Wormann, K., Haring, H. U., Lehmann,R., Schmitt-Kopplin, P., Zell, A., and Kohlbacher, O. (2014) Automatedlabel-free quantification of metabolites from liquid chromatography-massspectrometry data. Mol. Cell. Proteomics 13, 348 –359

21. Kohlbacher, O., Reinert, K., Gropl, C., Lange, E., Pfeifer, N., Schulz-Trieglaff, O., and Sturm, M. (2007) TOPP: the OpenMS proteomics pipe-line. Bioinformatics 23, e191– e197

22. Sturm, M., Bertsch, A., Gropl, C., Hildebrandt, A., Hussong, R., Lange, E.,Pfeifer, N., Schulz-Trieglaff, O., Zerck, A., Reinert, K., and Kohlbacher, O.(2008) OpenMS-An open-source software framework for mass spectrom-etry. BMC Bioinformatics 9, 163

23. Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kotter, T., Meinl, T.,Ohl, P., Sieb, C., Thiel, K., and Wiswedel, B. (2008) KNIME: The KonstanzInformation Miner, Springer-Verlag, New York Inc., New York

24. Bloomfield, V. A. (2014) Using R for Numerical Analysis in Science andEngineering, Chapman & Hall/CRC, Boca Raton, FL

25. Brassfield, H. A., Jacobson, R. A., and Verkade, J. G. (1975) Letter: molec-ular structure of the carcinostat isophosphamide. J. Am. Chem. Soc. 97,4143– 4144

26. Wieser, M., Stadler, G., Jennings, P., Streubel, B., Pfaller, W., Ambros, P.,Riedl, C., Katinger, H., Grillari, J., and Grillari-Voglauer, R. (2008) hTERTalone immortalizes epithelial cells of renal proximal tubules withoutchanging their functional characteristics. Am. J. Physiol. Renal Physiol.295, F1365–F1375

27. Aschauer, L., Limonciel, A., Wilmes, A., Stanzel, S., Kopp-Schneider, A.,Hewitt, P., Lukas, A., Leonard, M. O., Pfaller, W., and Jennings, P. (2014)Application of RPTEC/TERT1 cells for investigation of repeat dose neph-rotoxicity: a transcriptomic study. Toxicol. In Vitro, in press

28. Aschauer, L., Gruber, L. N., Pfaller, W., Limonciel, A., Athersuch, T. J.,Cavill, R., Khan, A., Gstraunthaler, G., Grillari, J., Grillari, R., Hewitt, P.,Leonard, M. O., Wilmes, A., and Jennings, P. (2013) Delineation of the keyaspects in the regulation of epithelial monolayer formation. Mol. Cell. Biol.33, 2535–2550

29. Aschauer, L., Limonciel, A., Wilmes, A., Stanzel, S., Kopp-Schneider, A.,Hewitt, P., Lukas, A., Leonard, M. O., Pfaller, W., and Jennings, P. (2014)Application of RPTEC/TERT1 cells for investigation of repeat dose neph-rotoxicity: a transcriptomic study. Toxicol. In Vitro, in press

30. Chambers, M. C., Maclean, B., Burke, R., Amodei, D., Ruderman, D. L.,Neumann, S., Gatto, L., Fischer, B., Pratt, B., Egertson, J., Hoff, K., Kessner,D., Tasman, N., Shulman, N., Frewen, B., Baker, T. A., Brusniak, M. Y.,Paulse, C., Creasy, D., Flashner, L., Kani, K., Moulding, C., Seymour, S. L.,Nuwaysir, L. M., Lefebvre, B., Kuhlmann, F., Roark, J., Rainer, P., Detlev, S.,Hemenway, T., Huhmer, A., Langridge, J., Connolly, B., Chadick, T., Holly,K., Eckels, J., Deutsch, E. W., Moritz, R. L., Katz, J. E., Agus, D. B., MacCoss,M., Tabb, D. L., and Mallick, P. (2012) A cross-platform toolkit for massspectrometry and proteomics. Nat. Biotechnol. 30, 918 –920

31. Junker, J., Bielow, C., Bertsch, A., Sturm, M., Reinert, K., and Kohlbacher,O. (2012) TOPPAS: a graphical workflow editor for the analysis of high-throughput proteomics data. J. Proteome Res. 11, 3914 –3920

32. Breitwieser, F. P., Muller, A., Dayon, L., Kocher, T., Hainard, A., Pichler, P.,Schmidt-Erfurth, U., Superti-Furga, G., Sanchez, J.-C., Mechtler, K., Ben-nett, K. L., and Colinge, J. (2011) General statistical modeling of data fromprotein relative expression isobaric tags. J. Proteome Res. 10, 2758 –2766

33. Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin,C. A., Fan, T. W., Fiehn, O., Goodacre, R., Griffin, J. L., Hankemeier, T.,Hardy, N., Harnly, J., Higashi, R., Kopka, J., Lane, A. N., Lindon, J. C.,Marriott, P., Nicholls, A. W., Reily, M. D., Thaden, J. J., and Viant, M. R.




ww

.jbc.org/D

ownloaded from

http://www.jbc.org/

(2007) Proposed minimum reporting standards for chemical analysis.Metabolomics 3, 211–221

34. Smyth, G. K. (2004) Linear models and empirical bayes methods for as-sessing differential expression in microarray experiments. Stat. Appl.Genet Mol. 3, Article3

35. Hochberg, Y., and Benjamini, Y. (1990) More powerful procedures formultiple significance testing. Stat. Med. 9, 811– 818

36. Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y.,Djoumbou, Y., Mandal, R., Aziat, F., Dong, E., Bouatra, S., Sinelnikov, I.,Arndt, D., Xia, J., Liu, P., Yallou, F., Bjorndahl, T., Perez-Pineiro, R., Eisner,R., Allen, F., Neveu, V., Greiner, R., and Scalbert, A. (2013) HMDB 3.0: thehuman metabolome database in 2013. Nucleic Acids Res. 41, D801–D807

37. Smith, C. A., O’Maille, G., Want, E. J., Qin, C., Trauger, S. A., Brandon,T. R., Custodio, D. E., Abagyan, R., and Siuzdak, G. (2005) METLIN: ametabolite mass spectral database. Ther. Drug Monit. 27, 747–751

38. Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., Ojima, Y.,Tanaka, K., Tanaka, S., Aoshima, K., Oda, Y., Kakazu, Y., Kusano, M.,Tohge, T., Matsuda, F., Sawada, Y., Hirai, M. Y., Nakanishi, H., Ikeda, K.,Akimoto, N., Maoka, T., Takahashi, H., Ara, T., Sakurai, N., Suzuki, H.,Shibata, D., Neumann, S., Iida, T., Tanaka, K., Funatsu, K., Matsuura, F.,Soga, T., Taguchi, R., Saito, K., and Nishioka, T. (2010) MassBank: a publicrepository for sharing mass spectral data for life sciences. J. Mass Spec-trom. 45, 703–714

39. Jeanmougin, M., de Reynies, A., Marisa, L., Paccard, C., Nuel, G., andGuedj, M. (2010) Should we abandon the t-test in the analysis of geneexpression microarray data?: a comparison of variance modeling strate-gies. PLoS One 5, e12336

40. Huang, N., Siegel, M. M., Kruppa, G. H., and Laukien, F. H. (1999) Auto-

mation of a Fourier transform ion cyclotron resonance mass spectrometerfor acquisition, analysis, and E-mailing of high-resolution exact-masselectrospray ionization mass spectral data. J. Am. Soc. Mass Spectr. 10,1166 –1173

41. Tautenhahn, R., Cho, K., Uritboonthai, W., Zhu, Z., Patti, G. J., and Si-uzdak, G. (2012) An accelerated workflow for untargeted metabolomicsusing the METLIN database. Nat. Biotechnol. 30, 826 – 828

42. Dickinson, D. A., Levonen, A.-L., Moellering, D. R., Arnold, E. K., Zhang,H., Darley-Usmar, V. M., and Forman, H. J. (2004) Human glutamatecysteine ligase gene regulation through the electrophile response element.Free Radic. Biol. Med. 37, 1152–1159

43. Kumar A., and Bachhawat, A. K. (2012) Pyroglutamic acid: throwing lighton a lightly studied metabolite. Curr. Sci. 102, 288 –297

44. Aschauer, L., Limonciel, A., Wilmes, A., Stanzel, S., Kopp-Schneider, A.,Hewitt, P., Lukas, A., Leonard, M. O., Pfaller, W., and Jennings, P. (2014)Application of RPTEC/TERT1 cells for investigation of repeat dose neph-rotoxicity: a transcriptomic study. Toxicol. in vitro, in press

45. Yapar, K., Kart, A., Karapehlivan, M., Atakisi, O., Tunca, R., Erginsoy, S.,and Citil, M. (2007) Hepatoprotective effect of l-carnitine against acuteacetaminophen toxicity in mice. Exp. Toxicol. Pathol. 59, 121–128

46. Savitha, S., Tamilselvan, J., Anusuyadevi, M., and Panneerselvam, C.(2005) Oxidative stress on mitochondrial antioxidant defense system inthe aging process: role of DL-�-lipoic acid and l-carnitine. Clin. Chim. Acta355, 173–180

47. Wojtczak, L., and Slyshenkov, V. S. (2003) Protection by pantothenic acidagainst apoptosis and cell damage by oxygen free radicals: the role ofglutathione. BioFactors 17, 61–73




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Christian G. HuberWilmes, Paul Jennings, Philip Hewitt, Wolfgang Dekant, Oliver Kohlbacher and

Christina Ranninger, Marc Rurik, Alice Limonciel, Silke Ruzek, Roland Reischl, AnjaExperimental and Computational Pipeline

High Performance Liquid Chromatography-Mass Spectrometry-based Nephron Toxicity Profiling via Untargeted Metabolome Analysis Employing a

doi: 10.1074/jbc.M115.644146 originally published online June 8, 20152015, 290:19121-19132.J. Biol. Chem.

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