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Metabolomics-on-a-Chip and Predictive Systems Toxicology in Microfluidic Bioartificial Organs Laetitia Shintu,†,‡ Régis Baudoin,§ Vincent Navratil,† Jean-Matthieu Prot,§ Clément Pontoizeau,† Marianne Defernez,∥ Benjamin J. Blaise,† Céline Domange,† Alexandre R. Péry,⊥ Pierre Toulhoat,†,⊥ Cécile Legallais,§ Céline Brochot,⊥ Eric Leclerc,*,§ and Marc-Emmanuel Dumas*,†,# †

Université de Lyon, UMR 5280 CNRS/ENS-Lyon/UCBL1 Centre de RMN à Très Hauts Champs, 5 rue de la Doua, 69100 Villeurbanne, France ‡ Institut des Sciences Moléculaires de Marseille, Aix-Marseille Université, iSm2-UMR CNRS 7313, Campus scientifique de Saint Jérôme, case 512, 13397 Marseille Cedex 20, France § CNRS UMR 6600 Laboratoire de Biomécanique et Bioingénierie, Université de Technologie de Compiègne, Rue Personne de Roberval, Compiègne, 60205 France ∥ Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, United Kingdom ⊥ Institut National de l’Environnement Industriel et des Risques (INERIS), Parc Technologique Alata, BP2, 60550 Verneuil en Halatte, France # Department of Surgery and Cancer, Imperial College London, Biomolecular Medicine, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom S Supporting Information *

ABSTRACT: The world faces complex challenges for chemical hazard assessment. Microfluidic bioartificial organs enable the spatial and temporal control of cell growth and biochemistry, critical for organ-specific metabolic functions and particularly relevant to testing the metabolic dose− response signatures associated with both pharmaceutical and environmental toxicity. Here we present an approach combining a microfluidic system with 1H NMR-based metabolomic footprinting, as a high-throughput small-molecule screening approach. We characterized the toxicity of several molecules: ammonia (NH3), an environmental pollutant leading to metabolic acidosis and liver and kidney toxicity; dimethylsulfoxide (DMSO), a free radical-scavenging solvent; and N-acetyl-para-aminophenol (APAP, or paracetamol), a hepatotoxic analgesic drug. We report organ-specific NH3 dose-dependent metabolic responses in several microfluidic bioartificial organs (liver, kidney, and cocultures), as well as predictive (99% accuracy for NH3 and 94% for APAP) compound-specific signatures. Our integration of microtechnology, cell culture in microfluidic biochips, and metabolic profiling opens the development of so-called “metabolomics-on-a-chip” assays in pharmaceutical and environmental toxicology.

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Modern functional genomic approaches such as mRNA expression profiling can be used to identify gene responses to toxic exposure.2−6 Two recently published studies use hepatic gene signatures7,8 to assess toxicity of 150 compounds on three test set sites. Although the accuracy was typically 55−69%, poor overall predictivity related to study designs and microarray platforms remained a weakness. The comprehensive profiling of endogenous small-molecular-weight metabolites in biofluids or organs (metabonomics)9−11 helps in characterizing the end point response to drug treatments,12−14 but also to pathophysiology15 or genetic polymorphisms.16 In particular, the Consortium for Metabonomic Toxicology (COMET), which investigated the toxicity

or the past 25 years, the safety of new chemicals has been systematically evaluated in Europe and the United States. However, chemicals produced before 1981 (97% of chemicals in use, representing 99% of production volume) still present missing data.1 The European regulation “Registration, Evaluation, Authorization and Restriction of Chemical Substances” (REACH) aims at assessing the toxicity of chemicals sold in Europe.2 Pharmaceutical and chemical industries need to demonstrate that existing and new molecules are innocuous (i.e., harmless), an estimated 30,000 compounds preregistered in 2008. Since toxicity testing in animals has a tremendous economic cost,3 both REACH and the U.S. National Toxicology Program (NTP) roadmap for the 21st century toxicology4 favor the development of in vitro and in silico diagnostics, through high-throughput screening (HTS) and automated testing,5 aimed at reducing the use of animals in research (3Rs: replacement, refinement, reduction). © 2012 American Chemical Society

Received: April 29, 2011 Accepted: January 13, 2012 Published: January 13, 2012 1840

dx.doi.org/10.1021/ac2011075 | Anal. Chem. 2012, 84, 1840−1848

Analytical Chemistry

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the manufacturer). Batch cultures were performed in T75 flasks (Falcon, Merk Eurolab, Strasbourg, France) using 15 mL of medium. Cells were used between the 10th and 30th cycles. Biochips were coated with fibronectin for 40 min (10 μg/mL) prior to starting the cultures. Cells were cultivated in the biochips under static conditions for 24 h for adhesion, prior to 72 h perfusion. For tissue culture in 12 well-plates (Becton Dickinson), plates were covered initially by 0.5 mL of PDMS and then coated with fibronectin as in the biochip cultures. All dynamic and static experiments were performed for 96 h of culture, with exposures for 72 h after the 24 h adhesion period. Ammonium chloride (5 mM and 10 mM) was loaded on the HepG2/C3A cells, MDCK cells, and MDCK-C3A cell cocultures for 72 h after the 24 h adhesion period. DMSO (128, 256, and 512 mM) and APAP (0 and 1 mM) toxicities were tested only with HepG2/C3A cells. 1 H NMR Spectroscopy of Cell Media. Bioartifical organ medium samples were prepared according to widely accepted protocols39 using 350 μL of cell medium mixed with 200 μL of 0.9 g/L saline solution (10% D2O/H2O (v/v)) for NH3 studies, and a pH = 7.4 phosphate buffer (10% D2O/H2O (v/v)) for DMSO and APAP studies, respectively.32 The presence of 10% D2O in the buffer provides an NMR lock signal, while keeping the disruption of the aqueous solution to a minimum. NMR experiments were carried out on a Bruker Avance spectrometer operating at 700 (800) MHz 1H frequency using a standard 5mm TXI probe at 300 K for NH3 studies (DMSO and APAP studies). Conventional 1H NMR spectra were measured using presaturation pulse sequence for water signal suppression TrdP90-t1-P90-tm-P90-Taq with a recycle delay (Trd) of 2 s during which the water signal is selectively irradiated, a 90° radiofrequency pulse (P90), a switching time of 3 μs (t1), and second water irradiation (tm = 100 ms). A set of 128 free induction decays (FID) was collected using a spectral width of 10,504 Hz and an acquisition time of 1.95 s. The NMR spectra (line broadening of 0.3 Hz and zero-filled before Fourier transformation) were phased and baseline corrected manually and referenced to the glucose α-anomeric signal (δ5.23). Preparation of 1H NMR Spectroscopic Data. 1H NMR spectra were exported as full digital traces (40,960 data points) in Matlab (Mathwork, Inc.), and signals were realigned using an in-house algorithm based on the partial linear fitting method40 to compensate for signal shifts caused essentially by pH variation in the NH3 studies. This algorithm uses an iterative procedure, which aligns NMR peaks by minimizing the squared difference between each spectrum and a reference spectrum. The use of phosphate buffer prevented pH-induced chemical shifts in DMSO and APAP studies so that the alignment of signals was not necessary. All spectra were bucketed with a width of 0.001 ppm (9,600 variables). To remove the effects of variation in the efficiency of the suppression of the water resonance, the region δ4.68−5.00 around the water peak was discarded from the data set for further statistical analysis. Signals corresponding to ethanol resonances from biochip system preparation (δ3.64−3.67 and δ1.17−1.19), DMSO (δ2.60−2.82), APAP (δ7.22−7.26, δ6.88−6.92 and δ2.13− 2.15), and HEPES (N−CH2−CH2−N, δ2.5−2.8) were removed. The data set (X matrix) was then normalized to the total spectrum intensity and unit variance scaled. Multivariate Pattern Recognition. Orthogonal partial least-squares regression (O-PLS) was used to discriminate the samples according to different experimental factors (Y matrix: dose, cell types, etc.) from the NMR data (X matrix).41 Score

of 147 compounds in the rat using standardized studies (30 animals, 3 doses, 10 urinary collections), generated a comprehensive biological resource17 and produced an expert system predicting liver and kidney toxicity (n = 12,935).18 The replacement of animal models by cellular assays and computer modeling provides an incentive to develop similar “metabolomics-on-a-chip” assays for HTS toxicity testing. Microfluidic bioartificial organs19,20 enable the spatial and temporal control of cell growth and biochemistry21,22 as well as the combination of organ-specific metabolic functions in endogenous and xenobiotic metabolism.23−25 These properties are particularly relevant to testing the metabolic dose−response and building extrapolation models in both pharmaceutical and environmental toxicity screening. Thus, various microfluidic bioartificial organs have been proposed on a very small scale to reproduce liver tissue24,26−33 and kidney tissue.24,34 The cellular reorganization enabled by the microtopography of these systems together with the dynamic microfluidic culture conditions appeared to be a key feature for reproducing in vivo situations. These systems could equally well function in closed or open circuit mode, and thus simulate the chronic or acute exposure of tissues. In this frame, the first examples of a cell microchip that integrates the interactions between several micro-organs (liver, lung, and adipose tissue) have been proposed in order to model the systemic interaction.35,36 Here we present a microfluidic bioartificial organ system combined with 1H NMR-based metabolomic footprinting of organ culture media for high-throughput small-molecule screening, to characterize ammonia (NH3) toxicity (a common environmental pollutant leading to metabolic acidosis and associated toxicity in liver, kidney, and brain) as well as other compounds such as dimethylsulfoxide (DMSO), a solvent for biological research having radical-scavenging properties, and Nacetyl-para-aminophenol (APAP), an analgesic drug. We show different NH3 dose-dependent responses involving nitrogen metabolism in bioartificial liver, kidney, and liver−kidney cocultures, exemplifying the synergistic metabolic cooperation between different bioartifical organs. Finally, we present specific biomarker patterns and toxicity pathways for each small molecule tested. Such integration of microtechnology, tissue engineering, and metabolomics opens the development of “metabolomics-on-a-chip” assays for toxicity testing.37

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EXPERIMENTAL SECTION Biochip and Microfluidic Bioartifical Organs. To fabricate the biochip, we used the polydimethylsiloxane (PDMS) polymer (Dow Corning, Sylgard 184), a transparent material with high gas permeability, allowing oxygenation of cells in culture and real time analysis of the morphological views of the cells. Fabrication details have been reported previously.23,24 Cell lines were preferred to primary cell cultures because of genotype stability. In addition, for a proof-ofprinciple article on “metabolomics-on-a-chip”, cell lines avoid numerous animal sacrifices. Hepatocellular carcinoma-derived cells HepG2/C3A were used as liver model, a reference model in the European cytotoxicity tests.38 Madin−Darby canine kidney (MDCK) tubular epithelial cells were used as kidney model. HepG2/C3A and MDCK cell lines were maintained, as recommended by ATCC, in culture medium that contained minimal essential medium (MEM, Gibco), 2 mM L-glutamine, 0.1 mM nonessential amino acids, 1.0 mM sodium pyruvate, 10% of fetal bovine serum, and penicillin-streptomycin (100 units/mL) to prevent bacterial infections (as recommended by 1841



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Figure 1. Metabolomics-on-a-chip and toxicological response in microfluidic bioartificial livers. (a) HepG2/C3A after adhesion. (b) HepG2/C3A after 96 h culture. (c) HepG2/C3A incubated with 5 mM NH3 after 96 h culture. (d) HepG2/C3A incubated with 10 mM NH3 after 96 h culture. (e) 1H NMR spectrum of the bioartificial liver culture medium (abbreviations: Form = formate, Glc = glucose, Succ = succinate). (f) Observed vs predicted O-PLS score plots showing the discrimination of the HepG2/C3A samples according to the NH3 dose (Y-observed = 0, 5, and 10 mM) with 100% correct prediction. (g) O-PLS loadings plot showing the model coefficients for each NMR variable (HepG2/C3A samples). Horizontal axis corresponds to the NMR chemical shift scale; vertical axis corresponds to the O-PLS W.C′ model coefficients (also referred to as “loadings”). The line variation corresponds to model covariance derived from the mean-centered model, whereas the color map corresponds to model correlation derived from the unit-variance model. Metabolites significantly associated with the dose response were annotated on the model coefficient plot. (Signals marked with an asterisk represent HEPES signals, α1 = α-keto-methylvalerate, α2 = α-ketoisovalerate, Succ = succinate, U = unknown.) (h) Result of random permutation tests (999 random permutations of the observed dose) for the HepG2/C3A models showing a decline in model goodness-of-fit between the target R2 and Q2Yhat values on the top right corner and the swarm of random model R2 and Q2Yhat on the bottom left of the plot.

from O-PLS modeling of the 1H NMR spectra, we used the ROCR package from the R distribution software.50

and loading plots visualize, respectively, the samples and the NMR frequency signals (variables) in the predictive and orthogonal components. Variance components and model validations were performed by resampling the model 999 times under the null hypothesis, as described previously.42 Structural Assignment. Metabolite identification was performed using 1H−1H TOCSY NMR spectroscopy,43 statistical total correlation spectroscopy,44 in-house databases, as well as HMDB,45 Colmar,46 or MMCD.47 Metabolite-Set Enrichment Analysis (MSEA). MSEA48 tests metabolic pathway enrichment from metabolic profiling data. Discriminant metabolites were mapped onto KEGG database. Fisher’s exact test was used to assess the overrepresentation of discriminant metabolites. To control for false discovery rate in multiple testing, the exact Fisher test p-value was finally adjusted using the Benjamini and Hochberg procedure as described previously.49 Functional Integrative Analysis. Protein−protein interaction networks were compiled from different online resources and integrated to KEGG pathways in Ingenuity Pathway Analysis version 8.6 (Ingenuity Systems Inc., Redwood City, CA). The resulting knowledge network was then submitted to an overrepresentation analysis in order to identify the main components of the subnetwork. Receiver Operating Characteristic (ROC) Curves. To assess the accuracy of the dose−response predictions derived

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RESULTS AND DISCUSSION Metabolomic Toxicology via Biochip-Based Microfluidic Bioartifical Organs. To investigate the potential of metabolic footprinting to characterize toxicological responses of bioartificial organs to small molecule stimulation, we analyzed cell media (n = 259, 101, and 33 for HepG2/C3A liver cells, Madin−Darby canine kidney (MDCK) tubular epithelial cells, and HepG2/C3A-MDCK cocultures, respectively) obtained directly from microfluidic systems populated with the above cell types and derived the metabolic signature associated with NH3 exposure. Cells were grown at different densities (between 2 × 105 and 1 × 106 cells) in culture microchambers fitted with microfluidic microchannels and a pumping system (Supporting Information Figure 1a−d). Following cell adhesion to the biochip, performed for 24 h under resting conditions (Figure 1a for HepG2/C3A), cells were allowed to proliferate inside the biochip over a 96 h culture period including 72 h of perfusion (Figure 1b for HepG2/C3A). The cells were observed to first create a confluent monolayer at the bottom of the culture microchambers, eventually growing over the microstructures of the biochip to form a 3D dense tissue. The morphologies of the HepG2/C3A cells exposed to ammonium chloride at 5 and 10 mM are shown in Figure 1c,d, respectively. Cell media were then collected from the pumping system and analyzed by 1H 1842



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Figure 2. Effect of cell culture apparatus on the metabolic footprint. (a) Comparison of the HepG2/C3A proliferation in the Petri and biochip. (b) Daily glucose consumption. (c) Daily glutamine consumption (n = 6; mean ± SD). * p < 0.01. Cell media from Petri dishes and from 10 μL/min flow rate microfluidic station. Score (d) and loading (e) plots associated with the O-PLS-DA model segregating the nutrient consumption according to the cell culture apparatus (* = HEPES).

show that a dose-dependent predictive “metabolomics-on-achip” signature of toxicological insult can reliably be detected in microfluidic bioartificial organ cell media by 1H NMR spectroscopy. Effect of the Microfluidic Biochip Microenvironment. During a toxicological insult, metabolic variation in biofluids can be formalized using metabolic entropy: the stronger the insult, the stronger the variation and the longer the recovery.53 The theory of biological robustness (or structural stability of a biological system) explains biological variability via essential systems robustness, i.e., pathway redundancy, adaptation, or parameter insensitivity.54 To characterize the robustness of this 1 H NMR-based metabolomic approach, we then assessed several biological factors, which could potentially affect the dose-dependent signature. We first tested the effect of the microenvironment on cellular metabolism (Figure 2) by profiling HepG2/C3A cells grown in Petri dishes or in microfluidic biochips with a pumping system (n = 18). During the first 24 h of culture in biochips, the medium is not perfused to allow cell adherence. As a consequence, cells grow slowly in a constrained and rapidly exhausted microenvironment, as opposed to cells grown in Petri dishes. Dosing specific metabolites such as glucose and glutamine, which are used

NMR spectroscopy. 1H NMR spectra of each sample were recorded at 700 MHz (Figure 1e, see Supporting Information Table 1 for assignments) and imported at full resolution generating a 40K variable data set. Supervised multivariate statistics using orthogonal partial least-squares regression (OPLS)51,52 was able to significantly model the dose response (0, 5, and 10 mM NH3) in HepG2/C3A cells (Figure 1f) as well as in MDCK and coculture systems (Supporting Information Figure 1e,h, respectively). These statistical models also reveal cell-specific, dose-dependent metabolic signatures in the HepG2/C3A, MDCK, and HepG2/C3A-MDCK biochips (Figure 1g, Supporting Information Figure 1 g,j, with predictive goodness-of-fit parameters Q2Yhat equal to 0.86, 0.77, and 0.80, respectively). In order to validate the dose−response models in each of the cell types, we resampled our models under the null hypothesis (i.e., no metabolic response to NH3 dose) by generating 999 random permutations of the dose vector. For each bioartificial organ, the predictive goodness-of-fit parameter (i.e., Q2Yhat values) decreases with the correlation between the original regression vector and the random regression vectors. Such a relationship shows that none of these random models outperforms the initial model in terms of prediction (Figure 1h, Supporting Information Figure 1f,g, respectively). These results 1843



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Figure 3. Metabolic pathways of NH3 response in microfluidic bioartificial organs. (a) O-PLS-DA scores plot showing the dose−response shared by the three cell types and the component. (b) SMART-scaled56 analysis of the three cell types for each dose. Metabolic pathways involved in the dose insult for (c) HepG2/C3A, (d) MDCK, and (e) coculture systems.

Information Figure 2c). In this system, cells consume more glucose and amino acids than in the MDCK or HepG2/C3A models, since hepatic gluconeogenesis is counterbalanced by MDCK cell metabolism using glucose as fuel nutrient. We then further tested the effect of several technological factors on the cell biology of artificial organs, namely the effects of dose (0, 5, and 10 mM), cell medium flow rate (0, 10, and 25 μL/min), cell density (350,000, 450,000, and 650,000 cells), and duration of culture (2 and 4 days). An O-PLS variance component model42,55 (n = 177) was built on HepG2/C3a cells (Supporting Information Figure 3a). The strongest effect on NMR profiles was the culture duration (Q2Yhat = 0.72) and explained 63% of glucose variation, 58% of lactate and formate variation, and 47% of glutamine variation. Glucose and amino acid consumptions result from fuel metabolism and protein production, as denoted by the baseline variation in the aromatic region, characteristic of protein excretion in the cell media. The second strongest effect relates to dose (Q2Yhat = 0.64), and explains 37% of alanine variation. Interestingly, cell density and medium flow are the weakest effects tested in this experimental design (Q2Yhat = 0.13 and 0.025, respectively). However, we noted that high cell density leads to higher consumption of nutrients (amino acids mainly) as shown in Supporting Information Figure 3c. For subsequent analyses, we used optimal “metabolomics-on-a-chip” parameters: 400,000 cells and 10 μL/min flow. Metabolic Dose−Response Modeling. To test whether our approach can identify markers related to the mechanism of toxicity, we modeled the common dose−response trajectories of the three cell types in a single model (Figure 3a). The O-PLS model summarizes the differences between individual and combined cell types, as well as the common component of NH3 dosing on different bioartificial organs. A data scaling method, scaled to maximum aligned and reduced trajectories (SMART) analysis,56 was used to remove the differences between the metabolic starting positions of the different cell bioartificial

for fueling TCA cycle, showed a higher consumption of glucose and glutamine in biochips when compared to Petri dishes during the first 24 h of perfusion (the first 48 h of culture including 24 h of adhesion and 24 h of perfusion) (Figure 2b,c). When biochips are perfused, nutrient supply is restored, cells proliferate, and consequently, a higher glucose and glutamine consumption is observed. The O-PLS discriminant analysis (O-PLS-DA) performed on the consumption amounts of nutrients (per million cells) for each culture system after 48 h in culture confirmed these results. The O-PLS-DA score plot presents a clear discrimination between the nutrient consumption of cells grown on Petri dishes and those grown on biochips with a 10 μL/min flow rate (Figure 2d, Q2Yhat = 0.96). The O-PLS-DA loadings illustrate that cells from microfluidic biochips present higher glucose and glutamine consumption, as confirmed by dosing, as well as ethanol whereas lactate consumption is higher in Petri dishes (Figure 2e). These results are suggestive of a better nutrient bioavailability and oxygen supply in the microenvironment of microfluidic biochips as reported previously.16,17 Robustness and Metabolic Synergies in Microfluidic Biochips. Subsequently, we characterized the variation in basal metabolism of HepG2/C3A and MDCK and their HepG2/ C3A-MDCK coculture (n = 166, Supporting Information Figure 2). The O-PLS-DA model score plot (Supporting Information Figure 2a) demonstrates that HepG2/C3A liver cells and MDCK kidney cells cluster separately on the first predictive component. The HepG2/C3A bioartificial liver presents higher glucose concentrations and lower lactate and alanine concentrations compared to the MDCK bioartificial kidney, gluconeogenesis being a liver-dominated pathway (Supporting Information Figure 2b). However, the second component illustrates the synergistic metabolic effect of the HepG2/C3A-MDCK coculture system, with an increase in production of a new metabolite, ornithine, which was not observed with either of the individual cell-types (Supporting 1844



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Dosing the microfluidic bioartificial liver with NH3 leads to a sharp increase in alanine and succinate, associated with depletion in α-keto-methylvalerate and α-ketoisovalerate, corresponding to a significant enrichment of the following KEGG pathways: “valine, leucine and isoleucine biosynthesis and degradation”, “pantothenate and CoA biosynthesis”, and “propanoate metabolism” (Supporting Information Table 2). Valine, isoleucine, and leucine are branched-chain amino acids, which can be produced from the conversion of glutamate and α-keto-3-methylvalerate or α-isovalerate, respectively. These reactions produce 2-oxoglutarate, leading to alanine production (Figure 3c). In the case of the bioartificial kidneys, alanine was not a marker of the dose−response, and we observed a marked decrease in glutamine concentration and an increase in pyroglutamate (after formation of the ring structure of Nterminal glutamate), which may reflect a homeostatic incorporation of NH3 in the metabolic network in the form of other nitrogen-containing chemical species. MSEA on KEGG terms reveal a significant enrichment in the following pathways: “glycolysis and gluconeogenesis”, “pyruvate metabolism”, “TCA cycle”, “glyoxylate and dicarboxylate metabolism”, “biotin metabolism” and “glutamine and glutamate metabolism”, denoting a marked disruption of energy metabolism in kidney (Figure 3d). More importantly, we observe a specific dose-dependent metabotype in the bioartificial coculture system, involving glutamate, ornithine, and α-hydroxyisobutyrate, and enrichment in “arginine and proline metabolism”, “nitrogen metabolism”, as well as the degradation of valine. The presence of such metabolites highlights the emerging properties of the HepG2/C3A liver and MDCK kidney cell coculture system and indicates that the cometabolic network handles the NH3 challenge differently (Figure 3e). To visualize how the metabolic response of the microfluidic bioartificial organs to NH3 toxicity is connected to other neighboring biological processes, we mapped the metabolic markers onto an integrated knowledge network60 compiled from publicly available protein−protein interactions and metabolic pathways (Figure 4). Over-representation analysis of the proteins present in the toxicity perturbation network suggests that the liver response is mainly metabolic, the kidney response involves transport mechanisms, but the coculture response involves higher order functions such as cell cycle and cell signaling (Supporting Information Table 3). The “free radical scavenging pathway” denotes increased oxidative stress, which is compliant with pathological states such as cirrhosis or hepatic encephalopathy triggered by NH3 toxicity.61 Oxidative stress is generally associated with an inflammatory response62,63 (pathway “inflammatory disease” in Supporting Information Table 3, NF-KappaB, TNF, Figure 4). Branched-chain amino acid metabolism is also affected in liver failures.64 From this interaction network, disruption of carbohydrate metabolism can be expected due to insulin (INS1) or the glucose transporter GLUT4 (SLC2A4), and bioenergetics (ATP, AMP, NAD+, NADH, succinate and their associated enzymes Supporting Information Table 3). Toxicity Prediction and Model Extrapolation. To demonstrate the specificity of metabolic signatures, we then compared the metabolic responses of several compounds in microfluidic bioartificial livers at a flow rate of 10 μL/min [Table 2, for NH3, 0, 5, and 10 mM (n = 155); for DMSO, 128, 256, and 512 mM (0%, 1%, 2%, 4%, n = 61); for APAP, 0, 1

organs and facilitate the comparison of their response to NH3 insult (Figure 3b). This model shows that divergent trajectories can be identified for different bioartificial organs, confirming the specificity of their response. We also investigated organ-specific markers of dose− response (Figure 1, Supporting Information Figure 1 and Table 1). The HepG2/C3A liver response to NH3 dose leads to Table 1. Organ Specific Dose−Response Metabolic Biomarkers for Each Microfluidic System metabolic markers

sense of variation with NH3 dose

Bioartificial Liver (HepG2/C3A cells) alanine ↑ valine ↑ isoleucine ↑ leucine ↑ α-keto-methylvalerate ↓ α-ketoisovalerate ↓ succinate ↑ lysine and/or arginine ↑ Bioartificial Kidney (MDCK Cells) glutamine ↓ oxaloacetate and/or pyruvate ↓ pyroglutamate ↑ lysine ↓ Bioartificial HepG2/C3A-MDCK Coculture System glutamine ↑ alanine ↑ succinate ↑ glutamate ↑ α-hydroxyisobutyrate ↑ valine ↑ lysine ↑ ornithine ↓

an increase in alanine, valine, leucine, isoleucine, lysine and/or arginine, and succinate and a decrease of α-keto-β-methylvalerate and α-keto-isovalerate. Interestingly, HepG2/C3A hepatocytes were shown to produce ammonia in the absence of urea detoxification;57 our results show that several pathways are involved when HepG2/C3A bioartifical livers are exposed to NH3. The kidney response to NH3 dose involves a decrease of glutamine, pyroglutamate, pyruvate and/or oxaloacetate, lysine, and a few unknown compounds and is suggestive of a variation in excretion of TCA intermediates as observed in vivo.58 Finally, the coculture NH3 response leads to a marked increase in alanine, succinate, glutamine, glutamate, αhydroxyisobutyrate, and valine and a decrease in ornithine. These different organ-specific signatures illustrate how different bioartificial organs respond to the same small molecule dose (Figure 3c−e). From Toxicity Metabolic Markers to Toxicity Pathways. A single metabolite is rarely a biomarker, but instead several markers are needed to define a pattern, the relevance of which is sometimes difficult to assess. Metabolic pathway databases59 can be used for an interpretation of the biomarker patterns at the pathway level. We performed a metabolite-set enrichment analysis (MSEA),48 which identifies over-represented metabolic pathways, by mapping our “metabolomics-ona-chip” signatures onto the KEGG database59 (Supporting Information Table 2). 1845



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Figure 4. Systems toxicology of NH3 response in microfluidic bioartificial organs. This molecular interaction network generated in IPA depicts how the metabolic response of HepG2/C3A-based microfluidic bioartificial liver to NH3 insult relates to other biological networks. The subnetwork was obtained by merging the top 4 scoring networks in functional analysis (see Experimental Section and Supporting Information Table 3). All connections related to H2O, ATP, ADP, inorganic P, NAD+, NADH, NADP+, NADPH were removed as these are involved in the vast majority of the biochemical reactions.66

(samples taken after 96 h, Q2Yhat = 0.91). The different endogenous hepatic metabolic signatures (Table 2) and metabolic pathways (Supporting Information Table 4) for NH3, DMSO, and APAP are indicative of specific toxicity mechanisms. In addition, we iteratively trained our O-PLS models and predicted a test set in a full 7-fold cross-validation. The administered dose and the dose predicted by our O-PLS model were used to construct receiver operating characteristic (ROC) curves (Supporting Information Figure 4). For bioartificial livers treated with 10 mM NH3, the area under the curve (AUC) was 0.9994, showing that 99.94% of samples were accurately predicted (Supporting Information Figure 4a). In the case of microfluidic bioartificial livers treated with 1 mM APAP, AUC confidence reached 94.37% (Supporting Information Figure 4b). Altogether, our results demonstrate the specificity of metabolomic markers and pathways obtained from microfluidic biochip-based bioartifical organs and the accuracy of the toxicity predictions based on these patterns. Potential Applications. Here, we present a unique metabolomics-on-a-chip strategy using microfluidic bioartificial organs, 1H NMR spectroscopy, chemometrics, and bioinformatics. We prove that the “metabolomics-on-a-chip” strategy is robust and identifies metabolic markers of small molecule exposure and toxicological insult in mammalian cells.37 Altogether, our results show that the metabolomics-on-a-chip approach captures essential metabolic end points for toxicity screening of various small molecules. The current implementation presents the sufficient robustness and reproducibility to accurately predict deviation from normality and recognize the metabolic signature associated with various biologically active chemicals.

Table 2. Treatment-Specific Dose−Response Metabolic Biomarkers for HepG2/C3a Microfluidic Bioartificial Livers metabolic markers

sense of variation

DMSO on Bioartificial Liver (HepG2/C3A Biochip) glucose ↑ α-keto-methylvalerate ↓ α-ketoisovalerate ↓ phenylalanine ↑ tyrosine ↑ lactate ↓ APAP on Bioartificial Liver (HepG2/C3A Biochip) isoleucine ↓ acetate ↑ tyrosine ↑ pyruvate ↓ oxaloacetate ↓ succinate ↓ APAP on Liver HepG2/C3A Cells (Petri Dish) isoleucine ↓ leucine ↓ phenylalanine ↓ ethanol ↓ glutamate ↓ lysine ↓ arginine ↓

mM (n = 32)]. Additional controls were added for APAP (n = 13), in the form of HepG2/C3A cells in Petri dishes. For each target small molecule, an O-PLS model was fitted to identify a quantitative drug dose−response metabolic signature: NH3, DMSO, APAP in HepG2/C3A bioartificial livers (Q2Yhat equal to 0.88, 0.81, 0.67 respectively), as well as APAP in Petri dishes 1846



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The development of the “metabolomics-on-a-chip” approach is open-ended. Several avenues are already explored in terms of optimizing the hyphenation of microfluidic biochips with the analytics or increasing the sensitivity of the analytical technologies. Our approach is by no means limited to 1H NMR-based metabolic profiling, and could be easily adapted to mass spectrometry. Metabolic profiles and their associated concentrations or fluxes represent key molecular phenotypes allowing a precise monitoring of the intracellular and extracellular events leading to toxicological insult. The approach is fully compliant with the 3R requirements, especially replacement of animals by cell cultures, human volunteers, and computational approaches.1,2 The use of microfluidic bioarticial organs provides an efficient assay for small molecule toxicity testing in isolated organs, and more importantly in cocultures to identify potential secondary toxicity. In fact, the metabolic cooperation, or metabolic synergy observed between different organs in cocultures, may provide useful markers when assessing secondary kidney toxicity of phase I and II metabolites typically synthesized by the liver.35,36 Such “metabolomics-on-a-chip” assays should facilitate the characterization of cellular effects of individual phase I and II drug metabolites, as well as individual compounds from substitution series to derive useful quantitative structure−activity relationships. It is now possible to develop small-molecule toxicity-oriented “metabolomic-on-achip” databases and their data mining through pattern recognition expert systems.18 With the population of dedicated reference databases and the development of expert systems, the “metabolomics-on-a-chip” approach should generate a single spectrum within seconds or minutes, and this spectrum could be compared to reference responses within milliseconds, therefore providing an individual risk assessment for individual compounds. We also anticipate the coupling of spectroscopic devices with the microfluidic system, including the miniaturization and the internalization of the spectroscopic devices, or the biofunctionalization of microfluidic station surfaces in order to develop “intelligent” materials, which would routinely detect previously characterized toxicity biomarkers without the need for spectrometers in industrial, clinical, or regulatory settings in both the developed and the developing world. Profiling of metabolic markers and mapping them onto functional genomic databases represents a powerful compilation of the current knowledge and enhances visualization of the metabolic and signaling pathways49,65 to enhance the comprehension of toxicity pathways. Our “metabolomics-on-a-chip” approach using microfluidic bioartificial organs has the potential for serendipitous discovery of specific dose−response markers and toxicity pathways in target organs, while reducing the use of animals in toxicity screening. By submitting our microfluidic bioartificial organ system to a wide range of toxicological and technological challenges, including the microfluidic environment, variations in the toxicological insult, the duration of the exposure, the type of organ, or the strength of the microfluidic flow, we conclude that microfluidic bioartifical organs present high metabolic robustness,54 and, in the case of cocultures, metabolic cooperation between bioartificial organs. Our proof-of-concept study is currently limited to immortalized cell lines, which are likely to behave differently from normal cells, and our approach should be easily amenable to the study of not only primary cell lines, but also differentiated cells and stem cells. Furthermore, “metabolomics-on-a-chip” assays will bring a universal

perspective upon small molecule screening with numerous applications in environmental toxicity monitoring, pharmaceutical and neutraceutical compound testing, disease screening, and personalized healthcare.12

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ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] (E.L.); [email protected] (M.-E.D.).

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ACKNOWLEDGMENTS The authors would like to thank Prof. John Lindon and Dr. Muireann Coen for their helpful comments in the preparation of the manuscript. This research was funded by grants from the Agence Nationale de la Recherche (ANR, French National Research Agency: SysBioX ANR-07-CP2D-SYSBIOX-18, mQTL ANR-08-GENO-030-02). M.-E.D. holds a Young Investigator Award from ANR (ANR-07-JCJC-0042-01).

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REFERENCES

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