Integrated Pathway Analysis of Rat Urine Metabolic Profiles and

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Integrated Pathway Analysis of Rat Urine Metabolic Profiles and Kidney Transcriptomic Profiles To Elucidate the Systems Toxicology of Model Nephrotoxicants Ethan Yixun Xu,*,† Ally Perlina,‡ Heather Vu,† Sean P. Troth,† Richard J. Brennan,‡ Amy G. Aslamkhan,† and Qiuwei Xu*,† Department of Safety Assessment, Merck Research Laboratories, West Point, PennsylVania 19486, and GeneGo Inc., St. Joseph, Michigan 49085 ReceiVed February 15, 2008

In this study, approximately 40 endogenous metabolites were identified and quantified by 1H NMR in urine samples from male rats dosed with two proximal tubule toxicants, cisplatin and gentamicin. The excreted amount of a majority of those metabolites in urine was found to be dose-dependent and exhibited a strong correlation with histopathology scores of overall proximal tubule damage. MetaCore pathway analysis software (GeneGo Inc.) was employed to identify nephrotoxicant-associated biochemical changes via an integrated quantitative analysis of both urine metabolomic and kidney transcriptomic profiles. Correlation analysis was applied to establish quantitative linkages between pairs of individual metabolite and gene transcript profiles in both cisplatin and gentamicin studies. This analysis revealed that cisplatin and gentamicin treatments were strongly linked to declines in mRNA transcripts for several luminal membrane transporters that handle each of the respective elevated urinary metabolites, such as glucose, amino acids, and monocarboxylic acids. The integrated pathway analysis performed on these studies indicates that cisplatin- or gentamicin-induced renal Fanconi-like syndromes manifested by glucosuria, hyperaminoaciduria, lactic aciduria, and ketonuria might be better explained by the reduction of functional proximal tubule transporters rather than by the perturbation of metabolic pathways inside kidney cells. Furthermore, this analysis suggests that renal transcription factors HNF1R, HNF1β, and HIF-1 might be the central mediators of drug-induced kidney injury and adaptive response pathways. Introduction The mammalian kidney plays a prominent role in the excretion of metabolic wastes and the reabsorption of water and nutrients to maintain physiological homeostasis. Nephrotoxic damage manifested as acute renal failure (ARF)1 or chronic renal failure (CRF) is a common side effect of many clinical and investigational drugs (1). Antibiotics, angiotensin converting enzyme inhibitors (ACEIs), and nonsteroidal anti-inflammatory drugs (NSAIDs) are three major classes of drugs that are prone to induce ARF, which is a major cause of morbidity and mortality in hospitalized patients (2). ARF is physiologically characterized by an abrupt decrease in the glomerular filtration rate (GFR) with consequent azotemia * To whom correspondence should be addressed. (E.Y.X.) E-mail: [email protected]. (Q.X.) E-mail:[email protected]. † Merck Research Laboratories. ‡ GeneGo Inc. 1 Abbreviations: ARF, acute renal failure; CRF, chronic renal failure; PCT, proximal convoluted tubule; PST, proximal straight tubule; GFR, glomerular filtration rate; BUN, blood urea nitrogen; ACE, angiotensin converting enzyme; ACEIs, angiotensin converting enzyme inhibitors; NSAIDs, nonsteroidal anti-inflammatory drugs; NMR, nuclear magnetic resonance; MRI, magnetic resonance imaging; DSS-d6, 2,2-dimethyl-2silapentane-5-sulfonate sodium; ANOVA, analysis of variance; IQR, interquartile range; PCC, Pearson correlation coefficient; HNF1R, hepatocyte nuclear factor 1R; HNF1β, hepatocyte nuclear factor 1β; HIF1, hypoxiainducible factor 1; IBABP, ileal bile acid binding protein; VEGF, vascular endothelial growth factor; IGFBP-1, insulin growth factor binding protein 1; HO-1, heme oxygenase 1; MDR1, multidrug resistance 1; NAA, neutral amino acid; TMA, trimethylamine; DMA, dimethylamine; KIM-1, kidney injury molecule 1; NAG, N-acetyl-D-glucosaminidase; IACUC, Institutional Animal Care and Use Committees; NIH, National Institutes of Health; PMI, Project Management Institute.

(1). The conventional diagnostic biomarkers for ARF, serum creatinine and blood urea nitrogen (BUN), have been found to be insensitive and nonspecific in clinical investigations. In recent years, urinary protein biomarkers such as N-acetyl-D-glucosaminidase (NAG), kidney injury molecule-1 (KIM-1), and Tamm-Horsfall glycoprotein have achieved higher sensitivity as noninvasive indicators of ARF (3, 4). Although gene and protein expression could be affected by toxicants, mechanistically relevant events such as the inhibition of enzyme and/or transporter activity might be completely unrelated to transcriptional, translational, or post-translational regulations. Under these circumstances, transcriptomic and proteomic profiles of target organs and biofluids are unlikely to reveal the relevant mechanisms of toxicity. Biofluid metabolomics may provide a complementary approach that allows researchers to eliminate potentially toxic candidates early in the drug development process based on diagnostic and/or mechanistic biomarkers (5). One-dimensional 1H nuclear magnetic resonance (NMR) of biofluids has been used by analytical toxicologists as a targeted biochemical tool since the advent of modern high-field NMR instrumentations (6). This type of biofluid NMR analysis requires some prior knowledge of the chemicals to be quantified, with the expectation that measurements of a combination of endogenous metabolites would reveal specific biochemical targets. For example, early targeted NMR analyses have suggested urinary trimethylamine (TMA), dimethylamine (DMA), and β-hydroxybutyrate (in the absence of other ketone bodies) as potential region-specific indicators of nephrotoxicity (7, 8). NMR-based metabolomics, on the other hand, is a global profiling of endogenous metabolites. It assumes no prior

10.1021/tx800061w CCC: $40.75  2008 American Chemical Society Published on Web 07/26/2008

Integrated Pathway Analysis of Nephrotoxicity

knowledge about the chemicals being analyzed and provides comprehensive data sets that enable a global evaluation of systemic responses to toxicants (9–11). Two major data analysis strategies are frequently applied to NMR metabolomics: multivariate pattern recognition (12) and metabolite quantification (13, 14). Only the second strategy is relevant to this study, and it aims at identifying and quantifying as many endogenous metabolites as possible by searching a 1H NMR reference spectra database. In spectral regions where peaks are wellresolved, metabolites corresponding to the assigned protons can be readily quantified by comparing the integrated peak area values with that of the internal standard [e.g., 2,2-dimethyl-2silapentane-5-sulfonate sodium (DSS-d6)]. In spectral regions with many overlapping peaks, deconvolution methods such as singular value decomposition (14) can be employed to assist the quantification process. Given that the molecular mechanisms of toxicity for classic nephrotoxicants such as cisplatin (15), gentamicin (16), and D-serine (17–19) remain elusive, microarray-based kidney transcriptomic profiling has been applied to animal toxicity studies of these toxicants (20–23). These toxicogenomics studies revealed several sets of gene-based transcriptional biomarkers of proximal tubule injury. More recently, metabolomic profiling of biofluids (mainly urine and blood samples) has been deployed to identify clinically accessible and minimally invasive metabolic biomarkers of drug-induced nephrotoxicities. 1H NMR profiling of urine samples from gentamicin-treated rats identified elevated levels of glucose and reduced levels of trimethylamine N-oxide (TMAO), while HPLC-TOF-MS/MS profiling of the same samples showed reduced xanthurenic acid and kynurenic acid in association with gentamicin nephrotoxicity (24). Portilla and co-workers applied 1H NMR to the analysis of urine samples from cisplatin-treated mice and showed that the appearance of glucose, amino acids, and Krebs cycle metabolites preceded the rise in serum levels of traditional biomarkers creatinine and BUN. Their biochemical studies, using separate colorimetric enzyme assays, found that the administration of cisplatin led to a time-dependent accumulation of nonesterified fatty acids and triglycerides in serum, urine, and kidney samples (25). The inherent complexity in the interpretation of metabolomic profiles necessitates a knowledge-based systematic approach of computational data integration (26). In the field of microarray functional genomics, software tools such as gene set enrichment analysis (GSEA), parametric analysis of gene set enrichment (PAGE), and ErmineJ have gained widespread popularity for interpreting genome-wide expression profiles (27–29). Commercially available pathway and network analysis tools such as GeneGo’s MetaCore (30) and Ingenuity Pathway Analysis (www.ingenuity.com) have enabled the visualization of cellular components as networks of biochemical interactions. Various one-step direct interactions between genes, proteins, and metabolites can be combined to form multistep modules and pathways, enabling the construction of intracellular, intercellular, intraorgan, and interorgan interaction networks (31). To apply these tools to the realm of metabolomics, small-molecule metabolites must be represented as nodes in biological networks, in the same way as for genes and proteins. Pathway enrichment analysis of differentially expressed gene lists from transcriptomic profiling alone will not detect post-transcriptional or nontranscriptional regulatory events. Because metabolites are substrates of enzymes and transporters, which are protein products of posttranscriptional processes, integration of metabolomic data into pathway enrichment analysis is expected to overcome this limitation to some extent.

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Previous works applying integrated analysis of transcriptomics, metabolomics, and proteomics to toxicity studies focused on different aspects of hepatotoxicity (32–36). Knowledge-based pathway analyses in previous reports were qualitative and presented in a schematic way. Only one report examined transcript-metabolite correlation coefficients between several genes and key regions of NMR spectra representing one or more metabolites (34). No integrated analysis of transcriptomic, proteomic, and metabolomic data has been reported in the study of drug-induced nephrotoxicity. In this study, quantitative pathway enrichment analysis was applied to the knowledge-based interpretation of 1H NMR urine metabolite profiles and kidney gene expression profiles from rat toxicity studies with two model nephrotoxicants: cisplatin and gentamicin. Transcript-metabolite correlation patterns were further analyzed to identify perturbed biochemical pathways by enrichment analysis. Our integrated pathway analysis provides potentially new explanations for the biochemical phenotypes and exploratory metabolic biomarkers observed in studies of model nephrotoxicants.

Materials and Methods Animal Studies of Cisplatin- and Gentamicin-Induced Nephrotoxicity. Male Sprague-Dawley rats were purchased from Charles River Laboratories (Raleigh, NC). Animals were 50-75 days old at study initiation. Each animal was identified by an implanted microchip. All animal husbandry procedures were in accordance with the Guide for the Care and Use of Laboratory Animals [National Institutes of Health (NIH) Publication, Vol. 25, No. 28, August 16, 1996], and all experimental procedures were approved by Institutional Animal Care and Use Committees (IACUC) of the facilities in which the studies were conducted. All animals were housed in standard laboratory animal facilities and were provided free access to water and measured amounts of food [Project Management Institute (PMI) certified rodent diet, 22 g/day/ animal]. Cisplatin was administered by a single intraperitoneal injection at 0 (n ) 10), 0.5 (n ) 10), 3.5 (n ) 10), and 7 (n ) 20) mg/kg (mpk). Gentamicin was administered by daily intraperitoneal injections at 0 (n ) 15), 20 (n ) 15), 80 (n ) 15), and 240 (n ) 20) mg/kg/day (mkd). The vehicle used in both studies was 0.9% sodium chloride. The animals were placed in metabolic cages, and food was removed during urine collection. Water was available ad libitum at all times. Urine samples from all surviving animals were collected overnight (approximately 16 h) in containers placed on dry ice for NMR analysis and biochemical measurements prior to their necropsy on day 3 or 8 after cisplatin administration or on day 3, 9, or 15 after gentamicin administration (Tables S1 and S2). Here, “day 3” refers to the time point of 48 h after the single cisplatin dose or that of 24 h after the second daily gentamicin dose. The same referencing convention was applied to other time points specified in this report. The 12 h light/dark cycles were enforced in the animal room; lights usually went off at 6 p.m. and came back on at 6 a.m. every day. Urine collections typically started around 3 p.m. until about 7 a.m. the next morning, when the urine samples on dry ice were thawed and volumes were measured by weight. A 2 mL aliquot of each urine sample was submitted for measurement of urinalysis parameters, and the remaining sample was frozen for subsequent NMR metabolic profiling. Blood samples were also collected for biochemical measurements at the time of necropsy when kidney samples were harvested for mRNA profiling with rat genome microarrays. Necropsy and Histopathology. Rats were fasted overnight prior to scheduled necropsies. They were anesthetized under isoflurane, bled via the vena cava, exsanguinated, and necropsied. Livers and kidneys from all animals were examined and sampled at necropsy. Terminal body weights and weights of liver and kidneys were recorded from all animals at scheduled necropsies. The left kidney

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was cut into two cross-sections, and each half was sectioned into approximately 2 mm slices and stored on dry ice for subsequent microarray gene expression analysis. The right kidney was cut into three cross-sections with the center section (about 5 mm) and anterior lobe fixed in 10% neutrally buffered formalin for routine histopathology. A severity scale of 0-5 was employed to grade histomorphologic changes: 0 (no observable pathology), 1 (very slight), 2 (slight), 3 (moderate), 4 (marked), and 5 (severe). In addition to grading specific kidney changes, a histopathology grade (0-5) was assigned by the pathologist reflecting overall proximal tubular injury: 0 ) no observable pathology, 1 ) less than 10%, 2 ) 10-35%, 3 ) 35-60%, 4 ) 60-85%, and 5 ) more than 85% of the proximal tubules affected. RNA Extraction and Transcriptomic Profiling. Merck standard protocols of RNA extraction, transcriptomic profiling, data processing, and quality control have previously been described (37, 38). Briefly, total RNA was isolated from homogenized rat kidney tissues using a combination of TRIzol RNA extraction (Invitrogen, Carlsbad, CA) with the RNeasy RNA extraction kit (Qiagen, Valencia, CA). Transcriptomic profiling was conducted using custom rat genome microarrays consisting of about 22.5K 60-mer oligonucleotide probes (Agilent, Palo Alto, CA). Cy3- and Cy5labeled cRNA samples prepared from compound-treated animals and from individual vehicle controls were hybridized on the 22.5K microarrays against an RNA mass-balanced pool made from vehicle-dosed control animals. All cRNA hybridizations were performed in duplicate, with fluor reversal (Cy3 or Cy5) in the second hybridization. The expression ratios of each kidney RNA sample to the control pool from the fluor-reversed hybridization pairs were averaged to give a single log-ratio measurement of each expressed gene in that sample. Sample Preparation for NMR Analysis. The urine samples were stored in a -70 °C freezer until sample preparation. After they were thawed on Eppendorf shakers at 10 °C, they were loaded onto a Tecan robotic system. The NMR sample preparation in deep 96 well plates on the Tecan included the following steps. A 500 µL aliquot of each of the urine samples was mixed with 300 µL of 0.2 M potassium phosphate (99.9% D2O) buffer at pH 7.0. Solutions were mixed on an Eppendorf shaker at 1200 rpm for 20 min at 10 °C and centrifuged at 4000 rpm (∼2700g) for 10 min at 10 °C. A 640 µL aliquot of supernatant was then mixed with 160 µL of 0.2 M potassium phosphate (99.9% D2O) buffer containing 5 mM DSSd6 [2,2-dimethyl-2-silapentane-5-sulfonate sodium or sodium 3-(trimethylsilyl)-1-propanesulfonate]. Solutions were mixed again on an Eppendorf shaker at 1200 rpm for 10 min at 10 °C. A 700 µL aliquot of the mixed solution was then transferred to 5 mm NMR tubes. Deuterium oxide was used as the magnetic field lock, and DSS-d6 was used as an internal chemical shift reference (0 ppm) and quantification reference (1 mM). NMR Conditions and Metabolite Quantification. NMR analyses were performed on a Varian UNITYINOVA 700 MHz NMR. The probe temperature was set to 25 °C (calibrated). The 1D proton NMR spectral width was 10000 Hz, covering a range of -2.37 to 11.91 ppm. The acquisition time was 3 s, corresponding to a digital resolution at acquisition of 0.33 Hz. Repetitive 128 scans were accumulated for each sample analysis. The delay between scans was 15 s, which allowed a full re-equilibration of all protons, especially DSS-d6, in the samples between 90° pulses. The water peak was suppressed using the wet1d pulse sequence (39), which was based on a selective excitation of the water peak and gradient dephasing. The selective excitation profile was based on the sinc waveform, which covered a region of 50 Hz around the water peak. Time domain data were multiplied with an exponential decay function of 0.2 Hz. The data were zero filled to 64k data points before Fourier transformation. The chemical shift was referenced to the internal DSS-d6 peak at 0 ppm. The sample analysis was automated under the control of a Varian 768AS automatic robot system and the VNMRJ 1.1D software (Varian Inc., Palo Alto, CA). The samples were stored in Peltier sample racks (Gilson, Middleton, WI) at 8 °C before and after analyses in the magnet. Each sample was preconditioned at 25 °C

Xu et al. for 15 min before gradient shimming. After the convergence of the gradient shimming (i.e., less than 1.0 in VNMRJ software), the water (i.e., HDO) peak was located for the setting of the transmitter frequency. The proton 90° pulse was calibrated at a given pulse power, and the power for water suppression in the wet1d pulse sequence was optimized to produce a minimum HDO signal. The metabolites were identified according to an in-house 1D proton NMR reference spectral library. Their concentrations in urine were calculated by peak integration of both endogenous metabolites and internal reference DSS-d6. The total excretion amounts of endogenous metabolites were expressed in micromoles over 16 h of urine collection (sample volumes provided in Tables S3 and S4). Statistical Analysis and Visualization. The calculations of analysis of variance (ANOVA) p values and Pearson correlation coefficients (PCCs) and the generation of box plots of metabolic and gene expression profiles were conducted in the R programming environment. Because of the longitudinal nature of the study designs, a three-way mixed-model ANOVA was applied to both urinary metabolite data sets with dose and time as fixed-effect factors and animal identification as a random-effect factor. The urinary metabolites were then ranked by their dose ANOVA p values. Heat maps of metabolite profiles along with the histopathology scores were generated with MATLAB. Prior to running ANOVA or drawing heat maps, we applied a logarithmic data transformation with an offset value of 1 (so that zero values remained zero after the transformation). To improve the visualization quality of metabolite heat maps, a data point was treated as an outlier if it was beyond the inner fence, which is defined as [Q1 1.5 × interquartile range (IQR), Q1 + 1.5 × IQR], where Q1 and Q3 are the first and third quartiles and IQR is the interquartile range (Q3 - Q1). Outlier values were replaced with (Q1 - 1.5 × IQR) if they were on the low end or with (Q1 + 1.5 × IQR) if they were on the high end. Then, the values in each metabolite profile across urine samples were normalized by a scaling factor of the maximum histopathology score divided by the range of metabolite amounts. Integrated Pathway Enrichment Analysis with GeneGo MetaCore. The cisplatin and gentamicin kidney transcriptomic data sets were extracted from the Merck internal Resolver gene expression database. A list of NCBI LocusLink IDs corresponding to the genes on the rat genome microarray (8760 out of 10626 genes on the microarray have LocusLink IDs) was provided by the Merck Molecular Profiling group. After the cisplatin and gentamicin microarray data were loaded into the Merck in-house version of MetaCore (Version 4.3, Build 9311), 7095 of the 8760 gene IDs were recognized by the software. Because the Merck cisplatin and gentamicin kidney transcriptomic data sets were generated from the two-color Agilent microarray platform (where the data are represented by log ratios of base 10), the NMR urine metabolite data set was converted to log ratios over the vehicle group at the earliest time point of each study to facilitate the integrated pathway analysis and the calculation of PCCs between the expression profile of a gene and the concentration profile of a metabolite. Metabolite quantities below the detection limit were set to a very small value (0.00001) before being used to calculate adjusted log ratios of base 10. Metabolite names were converted into GeneGo ChemID by querying the GeneGo MetaBase relational database before loading the NMR-adjusted log ratio data into MetaCore. Integrated pathway enrichment analysis was performed by using the knowledge-based canonical pathways and endogenous metabolic pathways in MetaCore. Ranking of relevant integrated pathways was based on hypergeometric p values (40).

Results and Discussion Mixed-Model ANOVA Ranking of Urinary Metabolites. The statistical significance of urinary metabolites was ranked according to the dose p values from mixed-model ANOVA analyses. We implemented a novel heat map visualization


method in MATLAB to display the patterns of correlation between the urine metabolite profiles and the corresponding histopathology scores of overall proximal tubule damage for the same set of dosed rats (Figure 1A,B). Thirty-eight of the 43 metabolites in the cisplatin study and 33 of the 38 metabolites in the gentamicin study were found to have significant dose p values (p < 0.05). Thirty-one significant metabolites were common between the two studies, suggesting that the biochemical manifestations of toxicity for cisplatin and gentamicin should be very similar. The most noteworthy dose-dependent increasing urine metabolites in both studies were glucose, lactate, acetoacetate, creatine, 3-hydroxybutyrate, alanine, valine, and isoleucine (Figure 1A,B). Two of the seven significant metabolites specific to the cisplatin study, succinate and fumarate, showed similar dose-dependent decreases that correlated strongly with the increase of histopathology scores (Figure 1A). Taurine showed a significant dose-dependent decrease only in the gentamicin study (Figure 1B). Histomorphological Findings. Kidney weights were increased in cispatin-treated rats on day 8 at 3.5 (by 31% of body weight) and 7 mpk (by 92%) and in gentamicin-treated rats on day 9 at doses g80 mkd (by 20-51%) and at all dose levels on day 15 (by 17-118%). The increase in kidney weights correlated with pallor and/or enlargement of the kidneys observed grossly and with histomorphological changes as described below. No gross or organ weight changes were observed in the liver. Histomorphological changes (slight to severe) were observed only in kidney tissues from those animals exposed to 3.5 and 7 mpk of cisplatin or to 80 and 240 mkd of gentamicin (Figure 1 and Tables S5-S8). The general progression of histomorphological changes in the kidney consisted of degeneration (including hyaline droplet formation) and necrosis of renal tubular epithelium at early time points with subsequent inflammation, tubular dilation, casts, and tubular regeneration occurring at later time points. On day 3, cisplatin-treated rats (3.5 and 7 mpk) had very slight degeneration and necrosis of the tubular epithelium primarily involving the outer stripe of the medulla. Day 8 histomorphological findings at 3.5 and 7 mpk include marked to severe tubular degeneration and necrosis involving the cortex and outer stripe of the medulla as well as tubular dilation, regeneration, proteinaceous casts, and inflammation. Very slight degeneration and necrosis of cortical tubules were observed in only one out of five gentamicin-treated rats on day 3 at 80 and 240 mkd, respectively. On day 9, degeneration and necrosis involving the cortex and outer stripe of the medulla were observed in four out of five rats at 80 mkd (very slight to slight) and five out of five rats at 240 mkd (marked to severe). Physical signs including decreased activity, decreased food consumption, and body weight loss (by 3-16%) prompted early termination of the remaining 240 mkd gentamicin group on day 12 rather than on day 15. The terminated animals had very slight to moderate tubular degeneration and necrosis. At scheduled necropsy (day 15), very slight tubular degeneration was observed at 20 mkd, and very slight to slight degeneration and necrosis were observed at 80 mkd. Additional histomorphological findings include tubular dilation, regeneration, proteinaceous casts, and inflammation observed at 240 and 80 mkd on days 12 and 15, respectively. Serum Biochemical Changes. Significant increases in serum creatinine were only observed in animals dosed with 7 mpk of cisplatin on day 8 and in animals dosed with 240 mkd of gentamicin after day 9, suggesting that serum creatinine is a


relatively insensitive biomarker for both nephrotoxicants (data not shown). On the other hand, a 2-fold increase of BUN was observed in animals dosed with 3.5 and 7 mpk on day 3 of cisplatin treatment and a more than 20-fold increase observed at 7 mpk on day 8. No change of BUN levels was observed on day 3 at any doses of gentamicin treatment; only the treatment at the highest dose (240 mkd) led to a 6-fold increase of BUN after day 9 (data not shown). These results suggest that urine NMR metabolic profiling could potentially overcome the limitations of traditional serum biochemistry biomarkers in some toxicity studies. In agreement with a previous study of cisplatin toxicity in mice (25), rat serum glucose levels were elevated in our cisplatin study. Moreover, there was a 2.5-fold increase on day 3 and a 1.5-fold increase on day 8 after injection of cisplatin at 7mpk when compared to vehicle-treated rats (Table S9). However, this drug-induced hyperglycemic effect was absent in the gentamicin study (Table S10), suggesting that nephrotoxicantinduced glucosuria was unlikely due to increased plasma glucose, as in the case of diabetes mellitus. Nevertheless, it has been reported that cisplatin-induced hyperglycemia in rats is secondary to glucose intolerance, impaired insulin response, and abnormal glucagon response to a glucose stimulus (41–43). Portilla et al. showed that the appearance of hyperglycemia and glucosuria preceded the rise of classic ARF biomarkers such as BUN and serum creatinine after intraperitoneal injection of mice with cisplatin at the dose of 20 mg/kg (25). Therefore, serum and urine glucose measurements alone may not be used as a specific biomarker for cisplatin-induced nephrotoxicity, but they are an indispensable component of the endogenous metabolic profiles, indicative of potential renal toxicity with this agent. Pathway Enrichment Analysis of Kidney Transcript and Urine Metabolite Profiles. Urine metabolic profiles from animals dosed with proximal tubule toxicants are functional readouts of the kidney dysfunction. Therefore, causal inferences on the potential mechanisms of toxicity should benefit from the integration of kidney transcriptomic profiling data into the pathway analysis. To make this comparison, enrichment patterns of canonical pathways and biological processes were analyzed by building networks connecting the measured kidney proteinencoding transcripts and urine metabolites after loading the metabolomic and transcriptomic data sets into MetaCore. The top 10 enriched canonical metabolic pathways in both cisplatin and gentamicin studies were related to the metabolism and transport of sugars and amino acids (Tables 1 and 2). The use of both hypergeometric p values from transcriptomic and metabolomic analyses for the ranking of enriched canonical pathways appears to provide a more objective measure of their mechanistic relevance. Cisplatin- and Gentamicin-Induced Glucosuria Correlates with the mRNA Decrease of Sodium-Dependent Glucose Transporters SLC5A1 and SLC5A2. One of the most significantly enriched canonical pathways in the context of cisplatin or gentamicin nephrotoxicity was glycolysis and glucose transport (Figure 2). In both studies, strong negative correlations were apparent between the decrease of kidney transcript levels of sodium-dependent luminal glucose transporter SLC5A1 or SLC5A2 and the increase of urine glucose with the corresponding PCCs ranging from -0.804 to -0.959 (Figure 3A,B and Table 3). As a rule of thumb in statistics, absolute values of PCC larger than 0.6 are generally viewed as significant. The magnitudes of those negative correlations were

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Figure 1. Heat map of rat urine metabolite amount in micromoles (volume times concentration, as determined by NMR, normalized to the scale between 0 and 5) shows positive or negative correlations with kidney histopathology scores of overall proximal tubular damage (represented by the vertical bar labeled “Histo” on the left, with 0 indicating no damage and 5 indicating maximal damage) after treatment with (A) cisplatin or (B) gentamicin. The rows in the heatmaps are samples (named by dose, time, and replicate animal number) sorted by their corresponding histopathology scores in ascending order. The columns from left to right are urine metabolites sorted by their corresponding ANOVA dose p values in ascending order with a black vertical line separating the significant metabolites (p < 0.05, indicated by a red horizontal underline) from the insignificant metabolites (indicated by a green horizontal underline).

mainly driven by the effects at the highest doses of cisplatin (7 mpk) and gentamicin (240 mkd).

SLC5A1 and SLC5A2 genes encode two sodium-dependent glucose transporters on the luminal membrane of renal tubular



Table 1. Top 10 Enriched Endogenous Metabolic Pathways Ranked by Hypergeometric p Values in the Cisplatin Studya pathway name

metabolomic max (-Log P)

TCA cycle and tricarboxylic 6.577 acids transport glycine pathway 2.465 Ala, Gly, and Cys metabolism 21.302 and transport branched-chain amino acid 20.017 metabolism pyruvate metabolism and 18.622 transport glucosylceramide pathways and 2.421 transport D-glucuronic acid pathway 18.050 Ala, Ser, Cys, Met, His, Pro, 17.947 Gly, Glu, and Gln metabolism and transport glycolysis, gluconeogenesis, and 8.527 glucose transport 1-palmitoyl-sn-glycero-3-phosphocholine 2.610 pathway

transcriptomic max (-Log P) 4.356 21.322 5.777 8.124 6.795 18.119 11.236 4.858 16.538 16.538

a Prior to running integrated pathway enrichment analysis, minimal absolute-value log ratio of 0.01 was applied to filter the 7095 MetaCore-recognizable kidney genes in the data set. Only statistically significant urine metabolites with ANOVA dose p values less than 0.05 entered into the analysis.

Table 2. Top 10 Enriched Endogenous Metabolic Pathways Ranked by Hypergeometric p Values in the Gentamicin Studya pathway name

metabolomic max (-Log P)

TCA cycle and tricarboxylic 7.540 acids transport Ala, Gly, and Cys metabolism 21.695 and transport branched-chain amino acid 20.756 metabolism glycine pathway 2.597 Ala, Ser, Cys, Met, His, Pro, 18.603 Gly, Glu, and Gln metabolism and transport 1-palmitoyl-sn-glycero-3-phosphocholine 2.744 pathway glucosylceramide pathways and 2.553 transport 2.568 O-hexanoyl-(L)-carnitine pathway glucose pathway 18.050 glycolysis, gluconeogenesis, and 8.946 glucose transport

transcriptomic max (-Log P) 4.202 4.934 8.780 19.271 4.429 17.996 17.457 17.283 2.511 15.175

a Prior to running integrated pathway enrichment analysis, similar statistical filters were applied to the data set as described in the footnote of Table 1. Because animal-to-animal response variation is too large on day 15, only day 3 and day 9 data were used in this analysis.

epithelial cells (44). Energetically unfavorable uptake of glucose from glomerular filtrate into tubular cells is provided by the concerted action of SLC5A1 and SLC5A2 in different sections along renal proximal tubules (45). After glucose is taken up into the proximal tubule cells, the process of reabsorption is not complete until glucose is transferred across the basolateral membrane into the capillary space in an energetically favorable manner by the GLUT family of facilitative glucose transporters (46). In stark contrast to the dose-dependent decrease of SLC5A1 and SLC5A2 transcripts, the mRNA levels of GLUT2 and GLUT9 increased after cisplatin or gentamicin treatments (Figure S1). The strong negative correlations between SLC5A1 or SLC5A2 mRNA and urinary glucose shown in both studies (Figure 3A,B) were consistent with a previous report showing

that gentamicin significantly reduced SLC5A1-dependent glucose transport and down-regulated mRNA and protein levels of SLC5A1 in cultured LLCPK1 cells as well as in mouse kidney tissues (47). Interestingly, the urine glucose to kidney SLC5A1 transcript correlation analysis displayed patterns that corresponded to the dosing scheme difference (Figure 3A,B). In the current studies, cisplatin was administered to the animals as a single dose, whereas gentamicin was dosed on a daily regimen. The elevation of urinary glucose at the highest dose was similar between day 3 and day 8 in the cisplatin study, whereas the decrease of SLC5A1 transcript was more significant on day 3 than on day 8 (Figure 3A). It is conceivable that some animals might be able to recover or offset the cisplatin toxicity under a singledose regimen; therefore, changes of some endogenous metabolites were more obvious on day 3 than on day 8. On the other hand, the daily dosing scheme used in the gentamicin study most likely produced cumulative effects on the phenotypic readouts, as the elevation of urine glucose and the decrease of kidney SLC5A1 were both more dramatic on day 9 than on day 3 (Figure 3B). This analysis is further supported by welldocumented evidence suggesting that aminoglycosides, including gentamicin, specifically accumulate in epithelial cells of renal proximal tubule (48, 49). Cisplatin- and Gentamicin-Induced Glucosuria Is Unlikely Associated with Flux Alterations in the Glycolysis Pathway. In biochemical pathway analyses of metabolomics data, drug-induced synchronized perturbation of endogenous metabolites that are either in different metabolic pathways or produced by different enzymatic reactions usually supports transporter-related mechanisms rather than direct modulations of metabolic fluxes. For example, urinary profiles of lactate and alanine were strikingly similar to each other, showing PCCs of 0.99 in the cisplatin study and 0.836 in the gentamicin study (data not shown). However, pyruvate-to-alanine and pyruvateto-lactate conversions are two disparate biochemical reactions. The former is a transamination that requires only pyridoxyl phosphate (PLP), while the latter depends on the oxidation of NADH generated from glycolysis. Steady-state lactate rather than alanine concentration will be affected more significantly by any inhibition of the glycolysis pathway. Therefore, similar fluxes through the two reactions strongly support the working hypothesis that the down-regulation of proximal tubule SLC5A1 and SLC5A2 transporters, rather than the inhibition of the glycolysis pathway, is a relevant biochemical explanation of the glucosuria phenotype. Cisplatin- and Gentamicin-Induced Reduction of Hepatocyte Nuclear Factor 1r (HNF1r) Transcript Correlates with the Transcriptional Down-Regulation of Both SLC5A1 and SLC5A2. Pontoglio et al. have shown that targeted gene deletion of the transcription factor HNF1R results in hepatic dysfunction and renal Fanconi-like syndromes manifested by glucosuria and hyperaminoaciduria (50). In a follow-up study, they further demonstrated that HNF1R directly controls the expression of mouse SLC5A2 gene in renal proximal tubule cells (51). On the other hand, Takamoto et al. reported that gentamicin reduced glucose reabsorption in mouse kidney through the down-regulation of SLC5A1 expression and function (47). To test the hypothesis that the nephrotoxicant-induced mRNA decrease of SLC5A1 and SLC5A2 is a downstream event of the reduced expression of HNF1R, the correlation patterns of the three genes were examined in our toxicity studies. Significant positive correlations were detected between the dosedependent decrease of kidney HNF1R transcript and that of the

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Figure 2. Dynamic metabolite and transcript changes in the canonical pathways of glycolysis and glucose transport after treatment with cisplatin or gentamicin. Urine metabolites and kidney transcripts measured in the studies are indicated by a filled circle on the upper right-hand corner of each network object; a red dot in the circle indicates up-regulation, whereas a blue dot indicates down-regulation from the vehicle control group. If the dot has mixed colors, it indicates differential regulation patterns across dose and time groups. Small-molecule metabolites are represented by purple hexagons, and reactions are represented by gray rectangles. The gray rectangle highlighted by a red oval indicates the luminal sodiumdependent glucose transport reactions captured in the MetaCore knowledgebase that led to the discovery of negative correlations between urinary glucose and kidney transcripts SLC5A1 and SLC5A2. Gene/protein objects are represented by other various shapes and colors depending on their functional annotations. Note that objects with “extracellular region” in their names are simply duplicates of their intracellular counterparts used in the knowledge-based canonical pathway representation by the MetaCore software; a urine NMR profile by itself is not able to distinguish intracellular vs extracellular metabolites. Green arrows represent activation, red arrows represent inhibition, and gray arrows represent other types of unspecified interactions (e.g., molecular transport or complex component binding).

two luminal glucose transporters after cisplatin or gentamicin treatment (Figure 3A,B and Table 4), suggesting that both SLC5A1 and SLC5A2 may be target genes under the control of HNF1R. These results are not in complete agreement with a published study by Pontoglio et al., where they showed that HNF1R directly controls the transcription of SLC5A2 but not that of SLC5A1 in mice (51). Whether the discrepancy is due to the species difference between mouse and rat remains unclear, and further investigation is warranted. Cisplatin- and Gentamicin-Induced Increase of HypoxiaInducible Factor HIF1r Transcript Showed Different Correlation Patterns with the Transcriptional Up-Regulation of Two Renal GLUT Isoforms. It has been well-documented that oxygen tensions in kidney tissues are comparatively low despite high blood flow (52) and that many GLUT genes are hypoxia responsive (53, 54). The relevance of hypoxia to druginduced ARF was highlighted by a report showing that transgenes of the hypoxia-inducible transcription factor HIF1R were significantly activated in rat kidneys during cisplatin treatment (55). Therefore, the observation of the dose-dependent renal mRNA increase of GLUT2 and GLUT9 after cisplatin or gentamicin administration (Figure S1) led to an interesting hypothesis that HIF1R might be responsible for those transcrip-

tional up-regulation events. Indeed, dose- and time-dependent elevation of renal HIF1R mRNA levels was observed in our cisplatin and gentamicin studies (Figure S1). Positive correlations between HIF1R and GLUT2 mRNA levels were detected in both studies (Table 4), whereas HIF1R and GLUT9 mRNA profiles were found to be correlated only in the gentamicin study (Table 4). Hypoxia-inducible factor 1 (HIF1) is a heterodimeric basic helix-loop-helix (bHLH) protein consisting of two subunits: HIF1R, which contains an oxygen-dependent degradation (ODD) domain that is modified at two key prolines by hydroxylation, and HIF1β, which is constitutively present in cells (56). When cells experience hypoxia, HIF1R is stabilized by the lack of proline hydroxylation and dimerizes with HIF1β in the nucleus to form transcriptionally active HIF1 to activate the expression of many target genes such as GLUT1, GLUT3, and insulin growth factor binding protein 1 (IGFBP-1) (57). Intriguingly, our observation that cisplatin or gentamicin treatment up-regulated renal HIF1R at the mRNA level (Figure S1) revealed an adaptive response mechanism in addition to the hypoxia-induced HIF1R protein stabilization reported in a cisplatin toxicity study by Tanaka et al. (55).



Figure 3. (A and B) The correlation patterns of urine glucose profiles (represented by adjusted log ratios of base 10 as described in the Materials and Methods) and kidney mRNA profiles of the transcription factor HNF1R with the mRNA profiles of two luminal sodium-dependent glucose transporters SLC5A1 and SLC5A2 (all renal mRNA profiles are represented by log ratios of base 10) in (A) the cisplatin study and (B) the gentamicin study. (C and D) The negative correlation of urinary profiles of lactate, acetoacetate, and 3-hydroxybutyrate with the kidney mRNA profiles of the monocarboxylate transporter SLC16A7 in (C) the cisplatin study and (D) the gentamicin study. In all box plots, a red color indicates positive median log ratios, and a blue color indicates negative median log ratios.

Because proximal tubular GLUTs are facilitative transporters mediating either efflux or influx of glucose depending on its concentration gradient across the basolateral membrane, upregulation of GLUT2 gene expression by increasing amount of functional HIF1 (Figure S1) might serve as a compensatory mechanism to sustain nutrient delivery to those tubular cells injured by nephrotoxicants. When the expression of SLC5A1 and SLC5A2 is reduced and the sodium gradient functionally collapses across the luminal membrane, increased expression of GLUTs on the basolateral membrane could provide an alternative means for the nephrotoxicant-injured tubular cells to obtain glucose from the blood side. In further support of an adaptive role for HIF1 in renal injury, it has been reported that HIF1 directly activates the expression of many genes involved in tissue remodeling and/or protective responses to renal toxicity, such as vascular endothelial growth factor (VEGF), heme oxygenase 1 (HO-1), IGFBP-1, vimentin, multidrug resistance 1 (MDR1), and MDR2, all of which were found to be

up-regulated in previous toxicogenomics studies of nephrotoxicants (20, 21, 23, 58). Cisplatin- and Gentamicin-Induced Hyperaminoaciduria Correlates with the mRNA Decrease of the Sodium-Dependent Renal Amino Acid Transporter SLC6A18. The elevation of urinary amino acids has also been well-documented in the studies of classic nephrotoxicants such as cisplatin, gentamicin, and D-serine (8, 17, 59). The integrated pathway analysis may also provide insights into the underlying biochemical processes that led to this phenotypic readout. The significant urinary loss of neutral amino acids (NAAs) such as leucine, isoleucine, and valine (Figure 1) is reminiscent of Hartnup disorder, where lossof-function mutations were identified for a majority of cases in the sodium-dependent system B amino acid transporter SLC6A19 (60, 61). Although the rat SLC6A19 gene was not included on the rat genome microarray (August 2004 version) used in this study, kidney expression profiles of a related system B transporter

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Table 3. Correlation Coefficients between Urinary Metabolite Profiles and Kidney Gene Expression Profiles in the Cisplatin and Gentamicin Studiesa metabolite/gene glucose leucine valine lactate acetoacetate 3-hydroxybutyrate glucose leucine valine lactate acetoacetate 3-hydroxybutyrate

SLC5A1

SLC5A2

SLC6A18

SLC16A7

cisplatin study -0.814 -0.804 -0.970 -0.983 -0.866 -0.837 -0.779 gentamicin study -0.848 -0.959 -0.976 -0.735 -0.979 -0.902 -0.821

a Those metabolite-transcript PCCs without known substrate-transporter relationships were not calculated and were left blank in this table.

Table 4. Correlation Coefficients between the Kidney Expression Profiles of Several Transcription Factors (“TF”) and Their Putative Target Genes (“TG”) in the Cisplatin and the Gentamicin Studiesa TF/TG HNF1A HNF1B HIF1A HNF1A HNF1B HIF1A

SLC5A1

SLC5A2

GLUT2

0.813

cisplatin study 0.739

0.900

gentamicin study 0.937

0.769

0.411

GLUT9

collectrin 0.780 0.434

0.005 0.883 0.895 0.813

a

PCCs between those gene pairs without a putative TF–TG relationship were not calculated and were left blank in this table.

SLC6A18 (62) were measured in our transcriptomic experiments. Very strong negative correlations between the mRNA decrease of SLC6A18 and the increase of urinary leucine and valine were observed in both studies (Figure 4A,B and Table 3). These results suggest that the transcriptional down-regulation of system B NAA transporters might play a major role in mediating cisplatin- or gentamicin-induced hyperaminoaciduria. SLC6A18 is an orphan amino acid transporter mainly localized on the luminal membrane of the S2 and S3 segments of renal proximal tubule (62, 63), whereas SLC6A19 is mainly localized in the S1 segment (60). Urinalysis of SLC6A18 knockout mice in comparison to wild-type mice showed significant loss of glycine (64) and other NAAs (65). The striking negative correlations found in our cisplatin and gentamicin studies (Figure 4A,B and Table 3) indicate that SLC6A18 might function as a sodium-dependent reabsorption transporter of NAAs such as leucine, valine, and isoleucine. Portilla et al. speculated that cisplatin-induced neutral aminoaciduria might be explained by the reduction of reabsorption by several system A or system L amino acid transporters (25). This model appears to be inconsistent with the energetics of renal NAA reabsorption, which is known to be driven by the sodium gradient across the luminal membrane (66). Most of the system A and system L NAA transporters are localized on the basolateral membrane of proximal tubule where they mediate the energetically favorable step of NAA reabsorption (62). Importantly, we provided convincing evidence that a luminal sodium-dependent NAA transporter SLC6A18 may in fact be linked to the hyperaminoaciduria that occurred after cisplatin

or gentamicin treatment. The mechanistic relevance of the sodium-dependent glucose and amino acid transporters in cisplatin nephrotoxicity is further supported by the dosedependent increase of fractional sodium excretions in urinalyses from both cisplatin and gentamicin studies (data not shown) and a 23Na live-animal magnetic resonance imaging (MRI) study showing that cisplatin treatment significantly reduced the renal sodium concentration gradient from cortex to medulla (Haiying Liu, Merck Imaging Group, personal communication). Functional Roles of HNF1r and HNF1β in Mediating Nephrotoxicant-Induced Hyperaminoaciduria through the Transcriptional Regulation of Collectrin. It has been reported that SLC6A18, SLC6A19, and SLC6A20 proteins all coimmunoprecipitate with collectrin, a homologue of angiotensinconverting enzyme 2 (ACE2) (67). Targeted deletion of the collectrin gene in mice was reported to cause a massive urinary loss of NAAs leading to the formation of urinary amino acid crystals (67, 68). Other reports showed that collectrin is a target gene downstream of the transcription factors HNF1R and HNF1β (69–71). In light of the dose-dependent decrease of collectrin mRNA in both cisplatin and gentamicin studies (Figure 4C,D), the transcript correlation patterns of collectrin with HNF1R and HNF1β were further examined to assess the reported transcriptional hierarchy in renal proximal tubule cells. Significant positive correlations were found between collectrin and HNF1R in both studies, whereas the collectrin-HNF1β correlation was only significant in the gentamicin study (Table 4A,B). Like the similarity of glucosuria found in these nephrotoxicant-treated rats (Figure 1) and in HNF1R knockout mice (50), the hyperaminoaciduria profiles found in our cisplatin and gentamicin studies also share some similarity with those found in collectrin knockout mice. The positive HNF1R-collectrin and HNF1β-collectrin correlations revealed in our nephrotoxicant studies are consistent with earlier studies reporting that the collectrin gene is under the direct transcriptional control of both HNF1R (70) and HNF1β (71). Cisplatin- and Gentamicin-Induced Lactic Aciduria and Ketonuria Correlate with the mRNA Decrease of Renal Monocarboxylate Transporter SLC16A7. The correlation analysis performed on the cisplatin and gentamicin studies also revealed a very strong negative correlation between the decrease of the renal mRNA level of monocarboxylate transporter SLC16A7 and the elevation of urinary lactate or ketone bodies such as acetoacetate and 3-hydroxybutyrate (Figure 3C,D). SLC16A7 is a proton-coupled transporter that catalyzes the proton-coupled transport of many monocarboxylates such as lactate, pyruvate, and ketone bodies across the plasma membrane (72). The absolute values of PCCs between lactate and SLC16A7 were found to be higher than PCCs between ketone bodies and SLC16A7 (Table 3), supporting the literature reports that lactate is one of the major substrates transported by SLC16A7. These findings suggest that the scope of nephrotoxicantinduced renal Fanconi-like syndromes could be expanded to include lactic aciduria and ketonuria. Nephrotoxicant-induced lactic aciduria has been previously reported in 1H NMR studies of mercuric chloride (73), p-aminophenol (74), sodium chromate (8), hexachloro-1,3-butadiene (8), thioacetamide (75), and ethionine (76). Furthermore, ketonuria (urinary elevation of acetoacetate and 3-hydroxybutyrate) was reported to occur in rats following mercuric chloride treatment (73). While the urinary increase of these monocarboxylates after cisplatin or gentamicin treatment could be attributed to causes other than renal cell injury (77, 78), the strong negative correlations



Figure 4. (A and B) Negative correlation of urine leucine and valine profiles with the kidney mRNA profiles of the sodium-dependent amino acid transporter SLC6A18 in (A) the cisplatin study and (B) the gentamicin study. (C and D) The positive kidney mRNA correlations of the transcription factors HNF1R and HNF1β with collectrin in (C) the cisplatin study and (D) the gentamicin study.

between the renal monocarboxylate transporter SLC16A7 and the urinary rise of lactate, acetoacetate, and 3-hydroxybutyrate (Figures 3C,D) suggest that the decrease of proximal tubule transporter reabsorption may be more relevant than metabolic flux perturbations. This mechanism is further supported by a previous kidney microarray study of six nephrotoxicants that identified SLC16A7 as one of the significantly down-regulated genes (58). Therefore, our integrated pathway analysis seems to bridge the gap between toxicogenomic and metabolomic findings for the elucidation of the connections between accessible biofluid phenotypic readouts and their underlying biochemical processes. Transcriptional Down-Regulation of Luminal SodiumDependent Transporters Plays an Important Role in Mediating Nephrotoxicant-Induced Renal Fanconi-Like Syndromes. In the kidney transcriptomic profiling of several other proximal tubule toxicants, Thukral et al. observed pronounced

transcriptional down-regulation of many families of renal transporter genes in association with high severity of tubular damage (58). Because of the presence of tubular cell necrosis (21, 79) and apoptosis (55, 80) under those treatment conditions, they could not rule out the possibility that the observed reduction in the expression of the membrane transporters might result from the loss of tubular epithelial cells. However, they still observed reduced expression of a subset of transporters in association with mild severity of proximal tubule toxicity without histopathological indication of tubular cell death (58). In our cisplatin and gentamicin studies, two lines of evidence were presented in favor of the transcriptional down-regulation of luminal sodium-dependent transporters over the loss of epithelial cells to explain the observed renal Fanconi-like syndromes despite the potentially confounding contribution of mRNA degradation resulting from tubular cell death.

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Figure 5. Schematic representation of the proximal tubule reabsorption processes perturbed in cisplatin- or gentamcin-induced renal Fanconi-like syndromes that are manifested by the urinary elevation of glucose, NAAs, and monocarboxylates. The uptake of glucose, NAA, and monocarboxylate into the epithelial cells is mediated by luminal sodium-dependent transporters such as SLC5A1, SLC5A2, SLC6A18, and SLC16A7. The sodium electrochemical gradient across the luminal membrane is provided by the activity of the basolateral sodium/potassium ATPase. The entry of cisplatin (denoted as “C”) or gentamicin (denoted as “G”) into the tubular epithelial cells results in the transcriptional down-regulation of HNF1R and HNF1β, which in turn leads to the reduction of mRNA levels of SLC5A1, SLC5A2, and collectrin. Cisplatin or gentamicin treatment also leads to the reduction of SLC6A18 and SLC16A7 mRNA through unknown transcription factors. Both nephrotoxicants also induce hypoxia in renal tubular cells, which leads to the transcriptional up-regulation and post-translational stabilization of hypoxia-inducible factor HIF1R. An increased amount of HIF1 up-regulates the transcription of basolateral GLUT transporters as one of the adaptive responses to proximal tubule injury. Up-regulated gene names are shown in a red color, while down-regulated ones are shown in a blue color. For schematic convenience, these regulated genes are shown next to each other, although they are located on different chromosomes inside the nucleus.

First, the basolateral GLUT2 and GLUT9 transporters were found to be transcriptionally up-regulated across all dose/time conditions of cisplatin or gentamicin treatment (Figure S1). GLUT1 and GLUT2 are two major isoforms of facilitative bidirectional glucose transporters on the basolateral membrane of renal tubular epithelial cells (45, 46, 81). There were large animal-to-animal variations of renal GLUT1 mRNA levels in both studies presumably due to its relatively low expression in kidney (82, 83), but its transcriptional up-regulation was still observed under a subset of dose/time conditions (data not shown). GLUT9 is highly expressed in kidney and might also contribute to renal glucose reabsorption, but its substrate specificity is yet to be determined (81, 83). A combination of in situ hybridization with immunohistochemistry has demonstrated that GLUT2 is expressed along with SLC5A2 in the S1 cells [the proximal convoluted tubule (PCT) segment] of rat kidney but not in S2 and S3 cells (84). On the other hand, renal GLUT2 was found exclusively in the PCT segment (82, 85). By contrast, GLUT1 was shown to be expressed at low levels in S3 cells [the proximal straight tubule (PST) segment] of rat kidney (82), where the predominant luminal glucose transporter is SLC5A1 (84). Cytosolic mRNA degradation during necrosis or apoptosis, if significantly affecting whole-kidney mRNA quantification, is expected to be nondiscriminatory on genes expressed in the same cells. Therefore, the mRNA decrease of SLC5A1 and SLC5A2 (Figure 3A,B), concomitant with the mRNA increase of GLUT1 (data not shown) and GLUT2 (Figure S1) in the same tubular epithelial cells clearly supports

the essential role of transcriptional regulations in mediating the nephrotoxic effect of cisplatin and gentamicin in spite of the potential complications caused by drug-induced tubular cell death. Second, luminal sodium-dependent bile acid transporter SLC10A2 was also found to be transcriptionally up-regulated after cisplatin or gentamicin treatment (Figure S2). In contrast to the results reported by Huang et al. (21), renal SLC10A1 mRNA levels actually showed a dose-dependent decrease in both studies (Figure S2). This discrepancy could be explained by a potential gene annotation error from their 250-gene cDNA microarray because only SLC10A2 is known to be expressed on the luminal membrane of renal proximal tubule, whereas SLC10A1 is mainly found in liver and pancreas (86). A similar dose-dependent mRNA increase of SLC34A2 was also observed in the current cisplatin and gentamicin studies (data not shown). The dose-dependent mRNA increase of at least two functionally unrelated luminal transporters in renal epithelial cells from independent studies of disjoint sets of nephrotoxicants reaffirms the importance of transcriptional regulation during the onset of drug-induced nephrotoxicity.

Concluding Remarks Both cisplatin and gentamicin induce renal Fanconi-like syndromes manifested by glucosuria and hyperaminoaciduria (8, 25, 87), but the causes of those biochemical phenotypes and the mechanisms by which both drugs cause renal cell injury have not been fully


elucidated (1). In this study, we applied integrated pathway analysis and metabolite-transcript correlation analysis to define perturbed biochemical pathways and molecular functions that may be relevant to the mechanisms of nephrotoxicity. Our absolute quantification of about 40 endogenous metabolites by 1H NMR in urine samples from both studies identified unique biochemical patterns or signatures that are more comprehensive than those documented in previously published studies (24, 25). This analysis also demonstrated the use of a set of indicative endogenous metabolites to deepen our understanding of the biochemical alterations associated with drug-induced nephrotoxicity. Furthermore, our metabolitetranscript correlation analysis, in the context of relevant literature knowledge in renal physiology, highlights the essential role of the transcriptional down-regulation of luminal sodium-dependent transporters SLC5A1, SLC5A2, SLC6A18, and SLC16A7 in causing renal Fanconi-like syndromes observed in drug-induced proximal tubule injury (Figure 5). The application of in silico pathway and network visualization tools has allowed us to integrate knowledge-based analysis and statistical analysis for the generation of experimentally testable hypotheses in investigative toxicology. As more types of biofluid and tissue samples become amenable to metabolite profiling with ever-increasing accuracy of quantification, it is imperative for us to raise pathway analysis software tools to a new level with more powerful automated text mining and robust statistical measures of relevance. Acknowledgment. We thank Janet Kerr, Beatrice SacreSalem, David Gerhold, Alema Galijatpovic, Holly Clouse, and Carolann Beare for coordinating the toxicity studies of cisplatin and gentamicin. We are grateful to Julie Bryant and Yuri Nikolsky for coordinating and supporting this collaboration between Merck and GeneGo. We appreciate efforts from Merck colleagues in generating the clinical chemistry and histopathology data for both studies. We also thank Huanying Ge and Lifeng Tian for help with the box plots and the NMR metabolite heatmaps. We express our gratitude to our colleagues at the Gene Expression Laboratory (GEL) of Merck Molecular Profiling for conducting the kidney transcriptomics experiments. We also thank John Metz, Yudong He, Olivia Fong, and Xiaodan Zhang for helping with the metabolite and gene ID translations. Assistance in preparing Figure 5 from Jill Williams and insightful discussions with Haiying Liu, Jun Zhu, Zhidong Tu, and Yan Yu are gratefully acknowledged. We are also grateful to Frank Sistare, Joe Sina, and Bill Schaefer for their scientific guidance and critical reading of the manuscript. Supporting Information Available: Tables showing the study designs, volumes of urine samples collected, and histopathological and serum biochemical findings for the cisplatin (Tables S1, S3, S5, S6, and S9) and gentamicin (Tables S2, S4, S7, S8, and S10) studies. Box plots showing different correlation patterns of GLUT2 and GLUT9 with HIF1R (Figure S1) and different transcript profiles of SLC10A1, SLC10A2, and ileal bile acid binding protein (IBABP) (Figure S2) in rat kidneys after cisplatin or gentamicin treatments. This material is available free of charge via the Internet at http://pubs.acs.org.

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