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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Proteome Res. Author manuscript; available in PMC Aug 6, 2011.
Published in final edited form as:
PMCID: PMC2917755
NIHMSID: NIHMS215878

Identification of Noninvasive Biomarkers for Alcohol-Induced Liver Disease Using Urinary Metabolomics and the Ppara-null Mouse

Abstract

Alcohol-induced liver disease (ALD) is a leading cause of non-accident-related deaths in the United States. Although liver damage caused by ALD is reversible when discovered at the earlier stages, current risk assessment tools are relatively non-specific. Identification of an early specific signature of ALD would aid in therapeutic intervention and recovery. In this study the metabolic changes associated with alcohol-induced liver disease were examined using alcohol-fed male Ppara-null mouse as a model of ALD. Principal components analysis of the mass spectrometry-based urinary metabolic profile showed that alcohol-treated wild-type and Ppara-null mice could be distinguished from control animals without information on history of alcohol consumption. The urinary excretion of ethyl-sulfate, ethyl-β-D-glucuronide, 4-hydroxyphenylacetic acid, and 4-hydroxyphenylacetic acid sulfate was elevated and that of the 2-hydroxyphenylacetic acid, adipic acid, and pimelic acid was depleted during alcohol treatment in both wild-type and the Ppara-null mice albeit to different extents. However, indole-3-lactic acid was exclusively elevated by alcohol exposure in Ppara-null mice. The elevation of indole-3-lactic acid is mechanistically related to the molecular events associated with development of ALD in alcohol-treated Ppara-null mice. This study demonstrated the ability of metabolomics approach to identify early, noninvasive biomarkers of ALD pathogenesis in Ppara-null mouse model.

Keywords: Alcohol-induced liver disease, PPARα, Ppara-null mouse, steatosis, metabolomics, UPLC-ESI-QTOF-MS, multivariate data analysis, biomarker, indole-3-lactic acid

1. Introduction

Excessive alcohol consumption is the third most common cause of lifestyle-associated mortality in the United States and in 2003 more than half if these deaths were attributed to alcohol-induced liver disease (ALD) 1. Chronic alcohol consumption can lead to steatosis (fatty liver) due in part to alterations in lipid metabolism 2 and, without intervention, may progress to advanced, irreversible stages of ALD including fibrosis and cirrhosis 3. Since the initial stages of ALD are reversible 4, an early and reliable tool to assess ALD risk would be helpful for intervention. Current diagnosis includes biochemical assays for enzymes such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), along with patient history and other clinical symptoms 4, 5. However, the elevated activity of these enzymes is not strictly limited to ALD and other liver disorders may present similarly 6, 7. Additionally, the initial stages of ALD can be largely asymptomatic 4 and cases are frequently reported only at an advanced, irreversible stage. Presently, an early, reliable, and noninvasive ALD-specific risk assessment tool remains elusive.

Since the earliest observable change in ALD pathogenesis is the deposition of free fatty acids in the liver, substantial effort to understand ALD pathogenesis has been devoted to identify pathways involved in fatty acid metabolism. The nuclear receptor peroxisome proliferator-activated receptor alpha (PPARα) 8 plays a crucial role in the catabolism of fatty acids in the liver. Recently, studies involving knockout mice fed an alcohol-containing liquid diet, reported that PPARα activity protects against ALD in the mouse 9. The marked similarities of the liver pathology of the alcohol-treated Ppara-null mice to hallmarks of the human disease makes it an excellent model for studying system level (epigenomic, transcriptomic, proteomic, and metabolomic) changes associated with ALD.

Metabolomics is a rapidly evolving field that aims to identify and quantify the concentration changes of all the metabolites due to endogenous or exogenous perturbations. Since the production of a particular metabolite is the end result of a cascade of interactions involving numerous biological molecules (including DNA, RNA, and proteins), they together, i.e., the metabolome, represent the closest molecular level description of the physiological state. Thus, in principle, any physiological perturbation is expected to be associated with characteristic changes in the metabolome. This approach to understand systems biology has yielded promising results in a number of recent studies including radiation biodosimetry, pharmacometabolomics, and cancer 1013. Hence, the application of metabolomics to understand the effects of ALD represents a powerful means not only to identify the earliest biomarkers, but also to unravel the molecular mechanism of its pathogenesis 14, 15.

This study combines the power of metabolomics along with the well-characterized Ppara-null mouse model to search for biomarkers for ALD. Wild-type control mice were studied in parallel with the Ppara-null mice in order to differentiate pathways specifically associated with ALD development from those related to the general effects of alcohol consumption. A combination of ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) and chemometrics were used to identify urinary biomarkers associated with ALD. A discussion of the newly identified biomarkers and implicated biochemical pathways is presented.

2. Materials and Methods

2.1. Chemicals

All compounds were obtained from Sigma-Aldrich (St. Louis, MO) and were of the highest grade available. HPLC grade solvents were purchased from Fisher Scientific (Hampton, NH).

2.2. Animals and Treatments

Male 6-to-8-week-old wild-type and Ppara-null (129Sv background, four animals per group) mice were fed ad libitum a 4% alcohol-containing liquid diet (Lieber-DeCarli Diet, Dyets, Inc., Bethlehem, PA). Control animals were fed ad libitum an isocaloric diet supplemented with maltose dextran (Dyets, Inc.). All animal studies were approved by the Georgetown University Animal Care and Use Committee.

2. 3. Histology

After one month on the alcohol diet, mice were euthanized, serum collected and portions of liver harvested for histology. Livers were formalin-fixed, paraffin-embedded, sectioned, and stained with hematoxylin and eosin.

2.4. Biochemistry

The serum AST and ALT activities were measured using VetSpec kits (Catachem Inc, Bridgeport, CT) following the manufacturer’s instruction. Liver and serum triglycerides were estimated using ELISA kit from Wako (Richmond, VA).

2.5. Urine Collection

After biochemical and histological evaluation of the model and ensuring that all animals were tolerating the liquid diet, mice were transferred to urinary metabolomics protocol at two months. Urine samples were collected monthly from mice placed in Nalgene metabolic cages (Tecniplast USA, Inc., Exton, PA) over 24 hours and stored at −80°C in glass vials until analyzed. All the mice were acclimated to the metabolic cages by placing them in the metabolic cages before the actual sample collection.

2.6. Preparation of Urine Samples and UPLC-ESI-QTOFMS Analysis

One volume of urine was added to one volume of 50% aqueous acetonitrile containing internal standards (50 μM 4-nitrobenzoic acid and 1 μM debrisoquine) in a Sirroco protein precipitation plate (Waters Corp., Milford, MA) and vortexed briefly. The deproteinated extracts were collected into 96-well collection plates, under vacuum, according to the manufacturer’s instructions. A 5 μL aliquot of supernatant was injected into a Waters UPLC-ESI-QTOFMS system (Milford, MA). An Acquity UPLC BEH C18 column (Waters Corp.) was used to separate urinary constituents. The mobile phase was comprised of 0.1% aqueous formic acid (A) and acetonitrile containing 0.1% formic acid (B). A 0.5 ml/min flow rate was maintained during a 10-min run. The QTOF Premier mass spectrometer was operated in electrospray ionization positive (ESI+) and negative (ESI−) mode. Capillary voltage and cone voltage were maintained at 3 kV and 20 V, respectively. Source temperature and desolvation temperature were set at 120 °C and 350 °C, respectively. Nitrogen was used as both cone gas (50 L/h) and desolvation gas (600 L/h), and argon was used as collision gas. Sulfadimethoxine was used as the lock mass (m/z 311.0814+) for accurate mass calibration in real time. As for MS/MS fragmentation of target ions, collision energy ranging from 10 to 40 eV was applied. All urine samples were analyzed in a randomized fashion to avoid complications due to artifacts related to injection order and changes in instrument efficiency. Mass chromatograms and mass spectral data were acquired using MassLynx software (Waters Corp.) in centroid format.

2.7. Multivariate Data Analysis

Centroided and integrated raw mass spectrometric data were processed using MarkerLynx software (Waters Corp., Milford, MA). The intensity of each ion was normalized with respect to the total ion count (TIC) to generate a data matrix that consisted of the retention time, m/z value, and the normalized peak area. The multivariate data matrix was analyzed by SIMCA-P+12 software (Umetrics, Kinnelon, NJ). The unsupervised segregation of control and alcohol-treated animals was checked by principal components analysis (PCA) using Pareto-scaled data 16. The supervised orthogonal projection to latent structures (OPLS) model was used to concentrate group discrimination into the first component with remaining unrelated variation contained in subsequent components. The magnitude of the parameter p(corr)[1] obtained from the OPLS analysis correlates with the group discriminating power of a variable. Since it was observed that ions with p(corr)[1] > 0.8 or p(corr)[1] < −0.8 showed statistically significant (P < 0.05) difference in abundance between control and alcohol-treated animals a list of ions showing considerable group discriminating power (−0.8 > p(corr)[1] or p(corr)[1] > 0.8) was generated from the loading S-plot for metabolic pathway analysis. However, only the ions that were consistently attenuated on alcohol treatment throughout the study, at least in case of one genotype, were selected for further identification and quantitation.

2.8. Metabolic Pathway Analysis

MassTRIX (http://metabolomics.helmholtz-muenchen.de/masstrix/), a web-based tool designed to assign ions of interest from a metabolomics experiment to annotated pathways 17 without any systematic identification 18, was used to identify the affected metabolic pathways. The masses of the ions that are significantly elevated (p(corr)[1] > 0.8) or depleted (p(corr)[1] < −0.8) on alcohol treatment were used to identify affected pathways using the KEGG (http://www.genome.jp/kegg/) database (including HMDB, Lipidmaps, and updated KEGG). A mass error of 5 ppm in the respective ionization modes and the possibility of formation of Na+-adducts in the electrosprayer (ESI+ mode) was also taken into account.

2.9. Identification of Urinary Biomarkers

Elemental compositions were derived considering a mass error less than 5 ppm following the Seven Golden Rules 19. Metabolomics databases were also searched to find out possible candidates for these ions 20, 21. Finally, identities of the ions were confirmed by comparison of retention time and fragmentation pattern with authentic standards. Sulfate conjugates were confirmed by the disappearance of the peak corresponding to the metabolite following treatment of the urine samples with sulfatase enzyme (Sigma-Aldrich, St. Louis, MO). Briefly, urine samples and the standards were incubated with 40 U/ml of the enzyme solution in 200 mM sodium acetate buffer (pH 5.0) overnight at 37°C. The enzyme and other particulates were precipitated with 50% aqueous acetonitrile, and the supernatant was analyzed by UPLC-ESI-QTOFMS. 4-Nitrocatechol sulfate was used as a positive control for the sulfatase activity. Acid hydrolysis was carried out by heating the urine samples with 6M HCl at 100°C for 1 hour under refluxing condition.

2.10. Quantitation of Urinary Metabolites

Quantitation of urinary metabolites was carried out using an Acquity® UPLC system coupled with a XEVO triple-quadrupole tandem mass spectrometer (Waters Corp.) by multiple reaction monitoring (MRM). The following MRM transitions were monitored for the respective compounds: indole-3-lactic acid (206→118; ESI+), indole-3-pyruvic acid (204→130; ESI+), tryptophan (205→118; ESI+), 2-hydroxyphenylacetic acid (151→107; ESI−), 4-hydroxyphenylacetic acid (151→107; ESI−), adipic acid (147→101; ESI+), pimelic acid (159→97; ESI−) and creatinine (114→86). Standard calibration plots for quantitation were generated using authentic standards. Deproteinated urine samples containing 0.5 μM debrisoquine were analyzed in the same fashion as that of authentic compounds. The quantitative abundances were calculated from the normalized (with respect to internal standard) peak area with the help of the calibration plot.

2.11. Statistics

All values are presented as mean ± standard error of the mean (SEM). One-way ANOVA with Bonferroni’s correction for multiple comparisons were performed using GraphPad Prism 4 software and P < 0.05 was considered statistically significant.

3. Results

3.1. Animal Monitoring and Liver Histology

There was no significant difference in the body weight of wild-type and Ppara-null mice either in control or alcohol-treated groups (data not shown). Although not statistically significant, liver histology (Figure 1A) and triglyceride measurements (Figure 1B) showed a clear trend indicating increase in hepatic fat deposition in the Ppara-null mice after one month of alcohol treatment. The alcohol-fed wild-type animals showed no such increase. In addition, there were no significant changes in the serum ALT, AST, and triglyceride levels after one month of alcohol treatment (see supplementary Figure S1).

Figure 1
Histology, biochemistry and multivariate data analysis. (A) Liver histology (HE staining) of wild-type (WT, left panel) and peroxisome proliferator-activated receptor alpha knockout (Ppara-null, right panel) mice after one month of control (upper panel) ...

3.2. Metabolomic Analysis

PCA analysis of metabolomic data showed distinct segregation of control and alcohol-treated mice (Figure 1C and supplementary Figure S2A) at 2 months. PCA analysis including samples from all the time points (2 to 6 months) resulted in separate clustering of the control and alcohol-treated wild-type animals indicating consistent underlying differences in the metabolic pattern arising from chronic alcohol consumption (supplementary Figure S2B). However, similar analysis for the Ppara-null animals (supplementary Figure S2C) showed that samples after 4 months of alcohol treatment occupied completely different metabolomic space. Moreover, the distance between the wild-type and Ppara-null genotype clusters was found to increase on chronic alcohol treatment (Figure 1D) further indicating the underlying difference in their metabolic response. The supervised OPLS model was used to enhance biomarker discovery efforts. The ions that showed significant difference in abundance between the control and treated animals (−0.8 > p(corr)[1] or p(corr)[1] > 0.8) and contributed to the observed separation were selected from the respective S-plots for wild-type ( Figure 1E, ESI+ and Figure S2B, ESI−) and Ppara-null animals (Figure 1F, ESI+ and Figure S2D, ESI−) as potential markers. The lists of discriminating ions at different time points were further screened to identify the ions that consistently contributed to the separation of control and alcohol-treated mice. Table 1 and and22 contain lists of such ions that were found to be significantly depleted or elevated in the urine of the wild-type and Ppara-null mice, respectively, during the course of alcohol treatment. It is interesting to note that almost all the markers were elevated or depleted from the beginning of sample collection (after 2 months of alcohol treatment) in case of the wild-type animals. However, many markers were found to either arrive late (3–4 months) or be minimally attenuated during the early course of alcohol exposure in case of the Ppara-null animals. This parallels the observation that the Ppara-null mice clustered separately after 4 months of alcohol treatment in the PCA analysis. Table 1 and and22 show that ions such as N1, N4, N4a, N18, N18a, N19, N21, P5, P5a, P6 and P7 were more abundant in the wild-type urine compared to their Ppara-null counterparts. Ions such as N5, N5a, N6, N6a, N6b, N7, N7a, N15, N16, N16a, N17, N20, P4 and P9 were prevalent in the urine of the Ppara-null animals. Moreover, many of the ions were found to be exclusively present only in wild-type (N8, N10, N12, N13, N24, N25, N26, N27, P3, P6, P7; Table 1) or Ppara-null animals (N28, N29, P2, P10, P11; Table 2). Hence, these ions presumably represent the metabolic pathways that are differentially affected in the wild-type and Ppara-null animals on alcohol treatment.

Table 1
List of marker ions for chronic alcohol exposure in the wild-type mice.
Table 2
List of marker ions for chronic alcohol exposure in the Ppara-null mice.

3.3. Metabolic Pathway Analysis

Although mass measurement of ions with high accuracy (<5 ppm) is helpful to arrive at possible elemental compositions, mass accuracy alone is not always sufficient for unequivocal identification 19, 22. In order to identify possible pathways that were affected by the alcohol treatment, ions contributing to the separation of control and alcohol-treated animals were analyzed using MassTRIX. Metabolites related to the tryptophan metabolism were significantly upregulated following chronic alcohol exposure (Figure 2A). The number of upregulated metabolites potentially belonging to this pathway gradually decreased with time in wild-type animals while the corresponding number of metabolites increased in Ppara-null animals. In addition, tyrosine, phenylalanine, lysine, arginine and proline metabolic pathways were also indicated to be differentially affected in wild-type and Ppara-null animals due to alcohol treatment (data not shown). However, since assignment of the identity of the metabolites through MassTRIX is only tentative, detailed characterization of important marker ions were carried out by comparing with authentic standards.

Figure 2
Effect of alcohol treatment on tryptophan and phase II alcohol metabolism. (A) Variation in the number of putative metabolites elevated during alcohol treatment in tryptophan metabolism detected in ESI+ mode predicted by MassTRIX analysis. The solid and ...

3.4. Identification and Quantitation of Metabolites

Ion N1 was identified as ethyl sulfate (C2H6O3S) by MSMS fragmentation. The presence of the sulfate group was further confirmed by the disappearance of this peak after treatment with sulfatase (supplementary Figure S5A and B). The increase in the urinary abundance of ethyl sulfate was observed at 2 months in alcohol-treated wild-type (P < 0.001) but not in Ppara-null animals (Figure 2B and 2C, respectively). Moreover, the increase in abundance of this metabolite in the urine of alcohol-treated Ppara-null mice was significantly lower than wild-type counterparts at 2 (P < 0.001) and 3 (P < 0.001) months of alcohol treatment. A significant (P < 0.001) elevation in urinary concentration of this metabolite was observed after 4 months of alcohol treatment in the Ppara-null mice (Figure 2C). However, the elevation in the excretion of ethyl sulfate in the Ppara-null mice (4-fold) was significantly (P < 0.01) lower than that observed in wild-type mice (7-fold).

Ions N2, P1, and P1a, all of which eluted at the retention time of 0.6 min, disappeared after acid hydrolysis of the urine sample (supplementary Figure S6). Subsequently, these were identified as the deprotonated form, Na+-adduct, and in-source fragment of Na+-adduct (with a neutral loss of HCOOH) of the glucuronide metabolite, respectively, of ethyl-β-D-glucuronide (C8H14O7). A number of other co-eluting ions (N2a, N2b, N2c, N2d, P1b, P1c, P1d and P1e) were identified as either fragments or adducts of ethyl-β-D-glucuronide. Figures 2D and 2E show that the increase in the urinary concentration of ethyl-β-D-glucuronide in the Ppara-null mice was 25% of that found in the wild-type mice after 3 months of alcohol consumption (P < 0.001). As observed in case of ethyl sulfate, the excretion of ethyl glucuronide increased markedly after 4 months in the Ppara-null mice (P < 0.01 at 6 months), albeit only to a lower level compared to wild-type mice (P < 0.001 at 6 months).

The analysis of the metabolic signatures in the ESI- mode (Table 1 and and2)2) showed that the abundance of three ions (N3, N4a and N5) with m/z = 151.04 and empirical formula C8H8O6S were attenuated by alcohol consumption. N5 was identified as 2-hydroxyphenylacetic acid. MRM-based quantitation showed that the basal level of this metabolite in the control Ppara-null animals (Figure 3B) was 4-fold higher than that in the control wild-type (Figure 3A) animals (P < 0.001). It was found to decrease by 50% in the wild-type mice on alcohol treatment (Figure 3A). Alcohol-treated Ppara-null mice also showed a continuous depletion in the urinary excretion of this metabolite leading to a 3-fold decrease (P < 0.01) after 6 months of alcohol treatment (Figure 3B). N3 was identified as 4-hydroxyphenylacetic acid. Chronic alcohol treatment was found to result in elevation of this metabolite in urine from both wild-type (Figure 3C) and Ppara-null (Figure 3D) mice. However, Ppara-null animals showed insignificant elevation of this metabolite in the early period (up to 3 months) of the alcohol treatment. The urinary excretion of 4-hydroxyphenyl acetic acid was found to increase by 25-fold and 20-fold in the wild type (P < 0.001) and Ppara-null (P < 0.01) mice after 6 months of alcohol treatment. Fragmentation pattern and sulfatase assay (see supplementary Figure S5C and D) established the identity of N4a as the in-source fragmentation product of 4-hydroxyphenylacetic acid sulfate (C8H8O6S; N4). Alcohol treatment resulted in early elevation in the excretion of this metabolite by 4-fold (P < 0.001) in the wild-type animals (Figure 3E). The Ppara-null animals showed only 1.5-fold (P < 0.05) elevation of this metabolite after 2 months of alcohol treatment. Although 3-fold (P < 0.001) elevation was observed after 4 months, the overall excretion of this metabolite was significantly (P < 0.05) lower in the Ppara-null mice compared to wild-type animals.

Figure 3
Effect of alcohol treatment on phenylalanine and tyrosine metabolism. The dotted and solid lines represent the variation in the concentration of these metabolites in the urine of control and alcohol-treated mice, respectively. (A) Decrease in urinary ...

N6 and N7 were identified as pimelic acid (C7H12O4) and adipic acid (C6H10O4), respectively. The abundance of adipic acid and pimelic acid was found to be 4-fold (P < 0.001) and 3-fold (P < 0.01) higher in the urine of the control Ppara-null animals (Figure 4B and D) compared to wild-type (Figure 4A and C) counterparts. Alcohol treatment of wild-type mice resulted in a 2-fold and 3-fold decrease in the urinary concentration of adipic acid (Figure 4A) and pimelic acid (Figure 4C), respectively, throughout the duration of treatment. However, the urinary excretion of these dicarboxylic acids was found to decrease minimally or insignificantly up to 3 months of alcohol treatment in Ppara-null animals (Figure 4B and 4D). Nevertheless, on continued alcohol consumption, urinary excretion of adipic acid and pimelic acid was found to decrease, respectively, by 2-fold (P < 0.01, Figure 4B) and 5-fold (P < 0.05, Figure 4D) on average after 4 months in the Ppara-null animals, which is more than the decrease observed in the wild-type animals.

Figure 4
Effect of alcohol treatment on urinary excretion dicarboxylic acids. The dotted and solid lines represent the variation in the concentration of these metabolites in the urine of control and alcohol-treated mice, respectively. (A) Decrease in the urinary ...

Markers P2 and N29 were elevated exclusively in the Ppara-null mice and identified as the protonated and deprotonated forms of indole-3-lactic acid (C11H11NO3), respectively. This tryptophan metabolite (Figure 5B) showed a 1.5-fold increase in the urinary excretion in the Ppara-null animals after two months of alcohol exposure. This was increased further to 3-fold (P < 0.001) by 3 months with continued alcohol consumption. The urinary concentration of this metabolite in the alcohol-treated Ppara-null animals remained 2.5-fold higher (P < 0.01) than that in the control animals during the rest of the study. Moreover, the urinary excretion of this marker in the alcohol-treated Ppara-null mice was significantly higher (P < 0.05) than their wild-type counterparts throughout the experiment. However, the urinary concentration of its precursors (supplementary Figure S4), i.e, indole-3-pyruvic acid and tryptophan were found to be unaffected by alcohol consumption in wild-type as well as in Ppara-null mice.

Figure 5
Identification and assessment discriminatory power of non-invasive urinary ALD biomarkers. The dotted and solid lines represent the variation in the concentration of these metabolites in the urine of control and alcohol-treated mice, respectively. (A) ...

3.5. Discriminatory Power of the Identified Metabolites

PCA analysis of the variation in the urinary excretion (creatinine normalized concentration) of the identified markers (Figure 5C) showed segregation of the animals according to alcohol exposure and genotype at 2 months. The four group of animals distinctly populated four quadrants of the scores scatter plot (Figure 5D) according to the alcohol treatment and genotype after 3 months of alcohol treatment. The separation along the vertical component reflected alcohol exposure and that along the horizontal component reflected the inherent ALD susceptibility due to difference in PPARα expression. Interestingly, exclusion of ethyl sulfate and ethyl-β-D-glucuronide did not change the clustering pattern (Figure 5E and 5F).

4. Discussion

Alcohol-treated Ppara-null mice in this study had considerable deposition of lipid after one month indicating the onset of steatosis, and confirming that Ppara-null animals served as a model for early stages of ALD development. Mass spectrometry-based metabolomic analysis revealed that alcohol exposure generates a distinct metabolic phenotype such that alcohol-treated mice are distinguishable from control mice. However, the metabolomic response to the alcohol exposure was found to be significantly different in the Ppara-null mice compared to the wild-type. This was evident from the differences in the onset time as well as the abundances of biomarker metabolites, and was also reflected in the difference in the PCA segregation pattern of the wild-type and Ppara-null animals. These general observations demonstrate that the metabolic traits closely represent the observed phenotype.

PPARα is a key transcriptional regulator of many genes involved in fatty acid β-oxidation. It activates the tryptophan-quinolinic acid-NAD+ pathway by down-regulating α-amino-β-carboxymuconate-ε-semialdehyde decarboxylase 23. Therefore, in the Ppara-null mice, NAD+ production is reduced thus decreasing fatty acid catabolism and predisposing these mice towards fat deposition in the liver. Chronic alcohol consumption further complicates this situation. Alcohol is initially converted into acetaldehyde (a reactive electrophile) in the liver by the action of alcohol dehydrogenase 24. Aldehyde dehydrogenase further oxidizes acetaldehyde to acetic acid which can take part either in the Krebs cycle or in fatty acid synthesis 24. Both of these reactions consume NAD+ and produce NADH. Thus, chronic alcohol consumption leads to a marked decrease in the redox ratio (NAD+/NADH) 25. Such a shift in the redox balance significantly impairs fatty acid catabolism and results in fat deposition in the liver (Figure 6). However, a minor amount of alcohol is also shunted through urine as sulfate and glucuronide conjugates by the action of the respective phase II enzymes in the liver 26. This not only reduces the concentration of free alcohol and its conversion to acetaldehyde and acetic acid, but also prevents the drop in NAD+ levels. The present results showed that in contrast to the wild-type mice, Ppara-null animals had very low glucuronide and sulfate conjugates of alcohol in urine during the initial period of alcohol treatment. Although there was an increase in activities of these pathways after 4 months, they were still lower than that in the wild-type. The inefficiency of the Ppara-null animals to excrete alcohol would result in the increase in acetaldehyde production and a decrease in NAD+ levels. Thus, the lower activity of these phase II metabolic pathways in the Ppara-null animals may contribute to the initiation of ALD pathogenesis in Ppara-null animals. Such difference in the in the phase II metabolic fluxes was also evident from the difference in 4-hydroxyphenyl acetic acid sulfate excretion of wild-type and Ppara-null mice. In case of humans, some of these enzymes were shown to be regulated by PPARα 27, 28. However, a recent study suggested that altered host-gut microbiome interaction may also modulate these enzymatic activities 29. The differential excretion of 2-hydroxyphenyl acetic acid and 4-hydroxyphenyl acetic acid, which are potential microbial metabolites, are also indicative of a change in the host-microbiome interactions.

Figure 6
Proposed mechanism behind the origin of indole-3-lactic as a biomarker of alcohol-induced liver disease in the alcohol-treated Ppara-null. The deletion of PPARα gene impairs conversion of tryptophan to NAD+ through quinolinic acid and fatty acid ...

Although β–oxidation is the dominant route for fatty acid catabolism, a minor fraction is also metabolized through the ω–oxidation pathway to small chain dicarboxylic acids like adipic acid and pimelic acid. The down-regulation of the genes related to fatty acid β–oxidation, can elevate fatty acid catabolism through this route. Thus, the basal levels of these dicarboxylic acid were found to be much higher in the control Ppara-null animals due to the activation of the ω–oxidation pathway compared to their wild-type counterparts. However, this pathway involves consumption of the NAD+ during the conversion of the hydroxyacid to the dicarboxylic acid. Therefore, chronic alcohol consumption reduces the ω–oxidation activity in all alcohol-treated animals due to the decrease in the NAD+ level. This results in the decrease in the concentration of adipic acid and pimelic acid. An earlier human study showed that, in contrast to alcoholic men, alcoholic women showed no elevation in the urinary dicarboxylic acids and this was implicated in their increased susceptibility to alcohol-induced liver damage 30. Thus, the fact that the decrease in the urinary excretion of these acids was more prominent in the Ppara-null animals may be associated with the higher susceptibility of these mice towards ALD development.

The MassTRIX analysis indicated that chronic alcohol consumption affected the tryptophan metabolism more in the Ppara-null mice compared to the wild-type. Subsequently, indole-3-lactic acid was found to be exclusively elevated in the Ppara-null animals that developed ALD. It was notable that this elevation was statistically significant not only compared to the control Ppara-null mice but also compared to the alcohol-treated wild-type animals throughout the study. Indole-3-lactic acid is a terminal tryptophan metabolite that is produced by reduction of indole-3-pyruvic acid. Tryptophan is an essential amino acid that is metabolized in the liver. Typically, NAD+, tryptamine, and serotonin are the major metabolic products of tryptophan 31. A minor pathway converts tryptophan to the indole-3-pyruvic acid through the action of tryptophan aminotransferase 31, 32. Tryptophan-2,3-dioxygenase oxidizes tryptophan to N-formyl kynurenine which is either catabolized to acetyl-CoA or converted to NAD+. Loss of PPARα results in decreased conversion of tryptophan into NAD+ 23. In addition, chronic alcohol consumption was shown to decrease the activity of tryptophan-2,3-dioxygenase 33. This unused tryptophan gets shunted through the aminotransferase pathway to indole-3-pyruvic acid. The huge build up of NADH due to oxidation of alcohol helps readily to reduce the indole-3-pyruvic acid to indole-3-lactic acid in order to restore the redox balance. This drives the equilibrium forward to convert the unused tryptophan finally into indole-3-lactic acid. Chronic alcohol treatment of the Ppara-null animals results in severe impairment of fatty acid catabolism leading to the deposition of free fatty acids. Therefore, both the development of alcoholic fatty liver (steatosis) and the rise of indole-3-lactic acid in the Ppara-null animals likely represent a shift in the redox balance due to chronic alcohol consumption (Figure 6).

In spite of considerable differences in their genomes, the mouse is an excellent model for elucidating the molecular events leading to the development of human diseases. The observed similarities between the histopathology of the human and mouse ALD as well as the attenuation of the PPARα expression levels during chronic alcohol consumption 9, 34, 35 suggest that this metabolite may serve as a potential biomarker for the early stages of the ALD pathogenesis in humans subject to further experiments. Moreover, it was interesting to note that the identified endogenous biomarkers (indole-3-lactic acid, 4-hydroxyphenylacetic acid, 4-hydroxyphenylacetic acid sulfate, 2-hydroxyphenylacetic acid, adipic acid, and pimelic acid) could collectively distinguish the phenotypes without any information about the status of alcohol consumption as well as irrespective of the inclusion of the phase II alcohol metabolites (ethyl-sulfate, ethyl-β-D-glucuronide). These alcohol metabolites are currently used to detect recent alcohol consumption but have a detection limit of less than 3 days 26, 36 and unavailability of information on the drinking history of the patients often cause problem in attributing the observed liver damage to ALD. Thus a combined targeted profiling of the endogenous biomarkers may offer an opportunity not only to detect ALD onset but also to identify ALD susceptibility without the requirement of knowledge of drinking history of the subjects and, thus, invites further studies in animal as well as human subjects. These biomarkers may be particularly useful for understanding the physiological effects of PPARα and potential impact of allelic variants 3739 in the receptor in humans on ALD.

Future metabolomic studies in human populations with ALD will be needed to validate the biomarkers found in the mouse model. Indeed, the human urinary metabolome can be a rich, noninvasive source of disease biomarkers. However, it should be noted that factors such as the presence of other diseases, nutritional status, lifestyle, and xenobiotic, and environmental exposures, should be taken into account when attempting to establish a urinary metabolite as an ALD biomarker. This is especially important in humans where many of these factors cannot be strictly controlled.

5. Conclusions

The power of mass spectrometry-based metabolomics to capture and elucidate metabolic changes during chronic alcohol consumption has been demonstrated in this study. The identified metabolites were found to encompass a variety of pathways related to alcohol, amino acid, and fatty acid metabolism. The simultaneous use of wild-type and Ppara-null animals helped to distinguish the metabolic processes associated with alcohol-induced liver damage from the non-specific effects of chronic alcohol consumption. Finally, a tryptophan metabolite, indole-3-lactatic acid, was found to be exclusively elevated in the alcohol-treated Ppara-null mice and mechanistically associated with the molecular events leading to ALD. The collective ability of these identified biomarkers to predict not only the ongoing liver damage but also the ALD susceptibility may contribute to the simplification and cost reduction of screening and diagnostic procedure subject to further investigation and validation.

Supplementary Material

1_si_001

Acknowledgments

This work was supported by the National Cancer Institute Intramural Research Program, the National Institute of Environmental Health Sciences (grant number U01ES016013) and a grant for collaborative research from United States Smokeless Tobacco Company (J.R.I.).

Abbreviations

ALD
alcohol-induced liver disease
PPARα
peroxisome proliferator-activated receptor alpha
Ppara-null
peroxisome proliferator-activated receptor alpha knockout mouse model
UPLC-ESI-QTOF-MS
ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry
ESI+
electrospray ionization in positive mode
ESI−
electrospray ionization in negative mode
MRM
multiple reaction monitoring
PCA
principal components analysis
OPLS
orthogonal projection to latent structures
NAD+
oxidized nicotinamide adenine dinucleotide
NADH
reduced nicotinamide adenine dinucleotide

Footnotes

Supporting Information Available: This material is available free of charge via the Internet at http://pubs.acs.org.

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