Chemometric analysis of biofluids following toxicant induced hepatotoxicity: A metabonomic approach to distinguish the effects of 1-naphthylisothiocyanate from its products

Metabolic product response profiles of Cherax quadricarinatus towards white spot syndrome virus infection

White spot syndrome virus (WSSV) is one of the most devastating viral pathogens in both shrimp and crayfish farms, which often causes disease outbreak and leads to massive moralities with significant economic losses of aquaculture. However, limited research has been carried out on the intrinsic mechanisms toward WSSV challenge at the metabolic level. To gain comprehensive insight into metabolic responses induced by WSSV, we applied an NMR approach to investigate metabolic changes of crayfish gill and hepatopancreas infected by WSSV for 1, 6 and 12 h. In gill, an enhanced energy metabolism was observed in WSSV-challenged crayfish samples at 1 h, as marked by increased glucose, alanine, methionine, glutamate and uracil. Afterwards, energy metabolism, lipid metabolism as well as osmoregulation were markedly increased at 6 hpi, as shown by elevated glucose, alanine, methionine, fumarate, tyrosine, tryptophan, histidine, phosphorylcholine, betaine and uracil, whereas no obvious metabolites change was detected at 12 hpi. As for hepatopancreas, disturbed lipid metabolism and induced osmotic regulation was found at 6 hpi based on the metabolic biomarkers such as branched chain amino acids, threonine, alanine, methionine, glutamate, glutamine, tyrosine, phenylalanine, lactate and lipid. However, no obvious metabolic change was shown in hepatopancreas at both 1 hpi and 12 hpi. Taken together, our present results provided essential metabolic information about host-pathogen interactions in crayfish, which shed new light on our understanding of WSSV infection at metabolic level.

Dynamic metabolomic responses of Escherichia coli to nicotine stress

Previously, we reported the metabolic responses of Pseudomonas sp. strain HF-1, a nicotine-degrading bacterium, to nicotine stress. However, the metabolic effects of nicotine on non-nicotine-degrading bacteria that dominate the environment are still unclear. Here, we have used nuclear magnetic resonance based metabolomics in combination with multivariate data analysis methods to comprehensively analyze the metabolic changes in nicotine-treated Escherichia coli. Our results showed that nicotine caused the changes of energy-related metabolism that we believe are due to enhanced glycolysis and mixed acid fermentation as well as inhibited tricarboxylic acid cycle activity. Furthermore, nicotine resulted in the alteration of choline metabolism with a decreased synthesis of betaine but an increased production of dimethylamine. Moreover, nicotine caused a decrease in amino acid concentration and an alteration of nucleotide synthesis. We hypothesize that these changes caused the decrease in bacterial cell density observed in the experiment. These findings provide a comprehensive insight into the metabolic response of E. coli to nicotine stress. Our study highlights the value of metabolomics in elucidating the metabolic mechanisms of nicotine action.

Identification of metabolites, clinical chemistry markers and transcripts associated with hepatotoxicity

Early and accurate pre-clinical and clinical biomarkers of hepatotoxicity facilitate the drug development process and the safety monitoring in clinical studies. We selected eight known model compounds to be administered to male Wistar rats to identify biomarkers of drug induced liver injury (DILI) using transcriptomics, metabolite profiling (metabolomics) and conventional endpoints. We specifically explored early biomarkers in serum and liver tissue associated with histopathologically evident acute hepatotoxicity. A tailored data analysis strategy was implemented to better differentiate animals with no treatment-related findings in the liver from animals showing evident hepatotoxicity as assessed by histopathological analysis. From the large number of assessed parameters, our data analysis strategy allowed us to identify five metabolites in serum and five in liver tissue, 58 transcripts in liver tissue and seven clinical chemistry markers in serum that were significantly associated with acute hepatotoxicity. The identified markers comprised metabolites such as taurocholic acid and putrescine (measured as sum parameter together with agmatine), classical clinical chemistry markers like AST (aspartate aminotransferase), ALT (alanine aminotransferase), and bilirubin, as well as gene transcripts like Igfbp1 (insulin-like growth factor-binding protein 1) and Egr1 (early growth response protein 1). The response pattern of the identified biomarkers was concordant across all types of parameters and sample matrices. Our results suggest that a combination of several of these biomarkers could significantly improve the robustness and accuracy of an early diagnosis of hepatotoxicity.

Studies of single-walled carbon nanotubes-induced hepatotoxicity by NMR-based metabonomics of rat blood plasma and liver extracts

The toxicological effects of single-walled carbon nanotubes (SWCNTs) were investigated after intratracheal instillation in male Wistar rats over a 15-day period using metabonomic analysis of 1H (nuclear magnetic resonance) NMR spectra of blood plasma and liver tissue extracts. Concurrent liver histopathology examinations and plasma clinical chemistry analyses were also performed. Significant changes were observed in clinical chemistry features, including alkaline phosphatase, total protein, and total cholesterol, and in liver pathology, suggesting that SWCNTs clearly have hepatotoxicity in the rat. 1H NMR spectra and pattern recognition analyses from nanomaterial-treated rats showed remarkable differences in the excretion of lactate, trimethylamine oxide, bilineurin, phosphocholine, amylaceum, and glycogen. Indications of amino acid metabolism impairment were supported by increased lactate concentrations and decreased alanine concentrations in plasma. The rise in plasma and liver tissue extract concentrations of choline and phosphocholine, together with decreased lipids and lipoproteins, after SWCNTs treatment indicated a disruption of membrane fluidity caused by lipid peroxidation. Energy, amino acid, and fat metabolism appeared to be affected by SWCNTs exposure. Clinical chemistry and metabonomic approaches clearly indicated liver injury, which might have been associated with an indirect mechanism involving nanomaterial-induced oxidative stress.

The role of metabolic biomarkers in drug toxicity studies

ABSTRACT Metabolic profiling is a technique that can potentially provide more sensitive and specific biomarkers of toxicity than the current clinical measures benefiting preclinical and clinical drug studies. Both nuclear magnetic resonance (NMR) and mass spectrometry (MS) platforms have been used for metabolic profiling studies of drug toxicity. Not only can both techniques provide novel biomarker(s) of toxicity but the combination of both techniques gives a broader range of metabolites evaluated. Changes in metabolic patterns can provide insight into mechanism(s) of toxicity and help to eliminate a potentially toxic new chemical entity earlier in the developmental process. Metabolic profiling offers numerous advantages in toxicological research and screening as sample collection and preparation are relatively simple. Further, sample throughput, reproducibility, and accuracy are high. The area of drug toxicity of therapeutic compounds has already been impacted by metabolic profiling studies and will continue to be impacted as new, more specific biomarker(s) are found. In order for a biomarker or pattern of biomarkers to be accepted, it must be shown that they originate from the target tissue of interest. Metabolic profiling studies are amenable to any biofluid or tissue sample making it possible to link the changes noted in urine for instance as originating from renal injury. Additionally, the ease of sample collection makes it possible to follow a single animal or subject over time in order to determine whether and when the toxicity resolves itself. This review focuses on the advantages of metabolic profiling for drug toxicity studies.

Quantitative analysis in magnetic resonance spectroscopy: from metabolic profiling to in vivo biomarkers

Nuclear magnetic resonance spectroscopy (called NMR for ex vivo techniques and MRS for in vivo techniques) has become a useful analytical and diagnostic tool in biomedicine. In the past two decades, an MR-based spectroscopic approach for translational and clinical research has emerged that allows for biochemical characterization of the tissue of interest either ex vivo (NMR-based metabolomics) or in vivo (localized MRS-single voxel or multivoxel-spectroscopic imaging). The greatest advantages of MRS techniques are their ability to detect multiple tissue-specific metabolites in a single experiment, their quantitative nature and translational component (in vitro/ex vivo-discovered metabolic biomarkers can be translated into noninvasive spectroscopic imaging protocols). Disadvantages of MRS include low sensitivity and spectral resolution and, in case of NMR-metabolomics, metabolite degradation and incomplete recovery in processed samples. In vivo MRS has worse spectral resolution than ex vivo high-resolution NMR due to the inherently wider lines of metabolites in vivo and the difficulty of using traditional line-narrowing methods (e.g., sample spinning). It also suffers from poor time-resolution, therefore offering fewer metabolic biomarkers to be followed in vivo. In the present review article, we provide considerations for establishing reliable protocols (both in vivo and ex vivo) for metabolite detection, recovery and quantification from in vivo and ex vivo MR spectra.

UPLC-MS metabolic profiling of second trimester amniotic fluid and maternal urine and comparison with NMR spectral profiling for the identification of pregnancy disorder biomarkers

We report on the first untargeted UPLC-MS study of 2nd trimester maternal urine and amniotic fluid (AF), to investigate the possible metabolic effects of fetal malformations (FM), gestational diabetes mellitus (GDM) and preterm delivery (PTD). For fetal malformations, considerable metabolite variations were identified in AF and, to a lesser extent, in urine. Using validated PLS-DA models and statistical correlations between UPLC-MS data and previously acquired NMR data, a metabolic picture of fetal hypoxia, enhanced gluconeogenesis, TCA activity and hindered kidney development affecting FM pregnancies was reinforced. Moreover, changes in carnitine, pyroglutamate and polyols were newly noted, respectively, reflecting lipid oxidation, altered placental amino acid transfer and alterations in polyol pathways. Higher excretion of conjugated products in maternal urine was seen suggesting alterations in conjugation reactions. For the pre-diagnostic GDM group, no significant changes were observed, either considering amniotic fluid or maternal urine, whereas, for the pre-PTD group, some newly observed changes were noted, namely, the decrease of particular amino acids and the increase of an hexose (possibly glucose), suggesting alteration in placental amino acid fluxes and a possible tendency for hyperglycemia. This work shows the potential of UPLC-MS for the study of fetal and maternal biofluids, particularly when used in tandem with comparable NMR data. The important roles played by sampling characteristics (e.g. group dimensions) and the specific experimental conditions chosen for MS methods are discussed.

Systems responses of rats to aflatoxin B1 exposure revealed with metabonomic changes in multiple biological matrices

Exposure to aflatoxins causes liver fibrosis and hepatocellular carcinoma posing a significant health risk for human populations and livestock. To understand the mammalian systems responses to aflatoxin-B1 (AFB1) exposure, we analyzed the AFB1-induced metabonomic changes in multiple biological matrices (plasma, urine, and liver) of rats using (1)H NMR spectroscopy together with clinical biochemistry and histopathologic assessments. We found that AFB1 exposure caused significant elevation of glucose, amino acids, and choline metabolites (choline, phosphocholine, and glycerophosphocholine) in plasma but reduction of plasma lipids. AFB1 also induced elevation of liver lipids, amino acids (tyrosine, histidine, phenylalanine, leucine, isoleucine, and valine), choline, and nucleic acid metabolites (inosine, adenosine, and uridine) together with reduction of hepatic glycogen and glucose. AFB1 further caused decreases in urinary TCA cycle intermediates (2-oxoglutarate and citrate) and elevation of gut microbiota cometabolites (phenylacetylglycine and hippurate). These indicated that AFB1 exposure caused hepatic steatosis accompanied with widespread metabolic changes including lipid and cell membrane metabolisms, protein biosynthesis, glycolysis, TCA cycle, and gut microbiota functions. This implied that AFB1 exposure probably caused oxidative-stress-mediated impairments of mitochondria functions. These findings provide an overview of biochemical consequences of AFB1 exposure and comprehensive insights into the metabolic aspects of AFB1-induced hepatotoxicity in rats.

NMR-based metabonomic analysis of the hepatotoxicity induced by combined exposure to PCBs and TCDD in rats

A metabonomic approach using (1)H NMR spectroscopy was adopted to investigate the metabonomic pattern of rat urine after oral administration of environmental endocrine disruptors (EDs) polychlorinated biphenyls (PCBs) and 2,3,7,8-tetrachlorodibenzo- p-dioxin (TCDD) alone or in combination and to explore the possible hepatotoxic mechanisms of combined exposure to PCBs and TCDD. (1)H NMR spectra of urines collected 24h before and after exposure were analyzed via pattern recognition by using principal component analysis (PCA). Serum biochemistry and liver histopathology indicated significant hepatotoxicity in the rats of the combined group. The PCA scores plots of urinary (1)H NMR data showed that all the treatment groups could be easily distinguished from the control group, so could the PCBs or TCDD group and the combined group. The loadings plots of the PCA revealed remarkable increases in the levels of lactate, glucose, taurine, creatine, and 2-hydroxy-isovaleric acid and reductions in the levels of 2-oxoglutarate, citrate, succinate, hippurate, and trimethylamine-N-oxide in rat urine after exposure. These changes were more striking in the combined group. The changed metabolites may be considered possible biomarker for the hepatotoxicity. The present study demonstrates that combined exposure to PCBs and TCDD induced significant hepatotoxicity in rats, and mitochondrial dysfunction and fatty acid metabolism perturbations might contribute to the hepatotoxicity. There was good conformity between changes in the urine metabonomic pattern and those in serum biochemistry and liver histopathology. These results showed that the NMR-based metabonomic approach may provide a promising technique for the evaluation of the combined toxicity of EDs.

Metabolic Fingerprinting in Toxicological Assessment Using FT-ICR MS

Detection of the toxicity of a candidate compound at an early stage of drug development is an emerging area of interest. It is difficult to determine all of the effects of metabolism of a compound using traditional approaches such as histopathology and serum biochemistry. The goal of a metabolomics approach is to determine all metabolites in a living system, with the potential to detect and identify biomarkers involved in toxicity onset. Here, we summarize the metabolic fingerprints for detection and identification of metabolic changes and biomarkers related to drug-induced toxicity using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS).

Characterization of 1H NMR spectroscopic data and the generation of synthetic validation sets

MOTIVATION

Common contemporary practice within the nuclear magnetic resonance (NMR) metabolomics community is to evaluate and validate novel algorithms on empirical data or simplified simulated data. Empirical data captures the complex characteristics of experimental data, but the optimal or most correct analysis is unknown a priori; therefore, researchers are forced to rely on indirect performance metrics, which are of limited value. In order to achieve fair and complete analysis of competing techniques more exacting metrics are required. Thus, metabolomics researchers often evaluate their algorithms on simplified simulated data with a known answer. Unfortunately, the conclusions obtained on simulated data are only of value if the data sets are complex enough for results to generalize to true experimental data. Ideally, synthetic data should be indistinguishable from empirical data, yet retain a known best analysis.

RESULTS

We have developed a technique for creating realistic synthetic metabolomics validation sets based on NMR spectroscopic data. The validation sets are developed by characterizing the salient distributions in sets of empirical spectroscopic data. Using this technique, several validation sets are constructed with a variety of characteristics present in 'real' data. A case study is then presented to compare the relative accuracy of several alignment algorithms using the increased precision afforded by these synthetic data sets.

AVAILABILITY

These data sets are available for download at http://birg.cs.wright.edu/nmr_synthetic_data_sets.

Urinary Metabolic Fingerprinting for α-naphthylisothiocyanate-induced Intrahepatic Cholestasis in Rats Using Fourier Transform-ion Cyclotron Resonance Mass Spectrometry

Urinary metabolic fingerprinting with Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) was performed to monitor metabolic changes in an α-naphthylisothiocyanate (ANIT)-induced rat model of intrahepatic cholestasis and to investigate the relationships among metabolic changes, histopathology, and blood chemistry. ANIT was administered orally as a single dose of 100 mg/kg. Urine samples were collected predose (–31 to –24 hours) and postdose at 0–7, 7–24, 24–31, 31–48, 48–55, 55–72, and 72–96 hours, and serum samples were collected on days 1, 2, and 4 postdose. Increased levels of alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, and total bilirubin were seen on day 2. The negative ion profiles for urine samples collected after 7–24, 24–31, 31–48, and 48–55 hours differed from the predose profile based on principal component analysis. Onset of recovery was observed after 24–31 hours, when the urinary composition reverted toward the predose position. In conclusion, it is possible to monitor the progression of and recovery from drug-induced hepatotoxicity by urinary metabolic fingerprinting with FT-ICR MS and to search for potential biomarkers involved in intrahepatic cholestasis.

NMR-based urine analysis in rats: prediction of proximal tubule kidney toxicity and phospholipidosis

INTRODUCTION

The aim of safety pharmacology is early detection of compound-induced side-effects. NMR-based urine analysis followed by multivariate data analysis (metabonomics) identifies efficiently differences between toxic and non-toxic compounds; but in most cases multiple administrations of the test compound are necessary. We tested the feasibility of detecting proximal tubule kidney toxicity and phospholipidosis with metabonomics techniques after single compound administration as an early safety pharmacology approach.

METHODS

Rats were treated orally, intravenously, inhalatively or intraperitoneally with different test compounds. Urine was collected at 0-8 h and 8-24 h after compound administration, and (1)H NMR-patterns were recorded from the samples. Variation of post-processing and feature extraction methods led to different views on the data. Support Vector Machines were trained on these different data sets and then aggregated as experts in an Ensemble. Finally, validity was monitored with a cross-validation study using a training, validation, and test data set.

RESULTS

Proximal tubule kidney toxicity could be predicted with reasonable total classification accuracy (85%), specificity (88%) and sensitivity (78%). In comparison to alternative histological studies, results were obtained quicker, compound need was reduced, and very importantly fewer animals were needed. In contrast, the induction of phospholipidosis by the test compounds could not be predicted using NMR-based urine analysis or the previously published biomarker PAG.

DISCUSSION

NMR-based urine analysis was shown to effectively predict proximal tubule kidney toxicity after single compound administration in rats. Thus, this experimental design allows early detection of toxicity risks with relatively low amounts of compound in a reasonably short period of time.

Piecewise multivariate modelling of sequential metabolic profiling data

Background

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints.

Results

A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted.

Conclusion

The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

NMR-based metabolic profiling and metabonomic approaches to problems in molecular toxicology

We have reviewed the main contributions to the development of NMR-based metabonomic and metabolic profiling approaches for toxicological assessment, biomarker discovery, and studies on toxic mechanisms. The metabonomic approach, (defined as the quantitative measurement of the multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification) was originally developed to assist interpretation in NMR-based toxicological studies. However, in recent years there has been extensive fusion with metabolomic and other metabolic profiling approaches developed in plant biology, and there is much wider coverage of the biomedical and environmental fields. Specifically, metabonomics involves the use of spectroscopic techniques with statistical and mathematical tools to elucidate dominant patterns and trends directly correlated with time-related metabolic fluctuations within spectral data sets usually derived from biofluids or tissue samples. Temporal multivariate metabolic signatures can be used to discover biomarkers of toxic effect, as general toxicity screening aids, or to provide novel mechanistic information. This approach is complementary to proteomics and genomics and is applicable to a wide range of problems, including disease diagnosis, evaluation of xenobiotic toxicity, functional genomics, and nutritional studies. The use of biological fluids as a source of whole organism metabolic information enhances the use of this approach in minimally invasive longitudinal studies.

Uniform Procedure of 1H NMR Analysis of Rat Urine and Toxicometabonomics Part II: Comparison of NMR Profiles for Classification of Hepatotoxicity

A procedure of nuclear magnetic resonance (NMR) urinalysis using pattern recognition is proposed for early detection of toxicity of investigational compounds in rats. The method is applied to detect toxicity upon administration of 13 toxic reference compounds and one nontoxic control compound (mianserine) in rats. The toxic compounds are expected to induce necrosis (bromobenzene, paracetamol, carbon tetrachloride, iproniazid, isoniazid, thioacetamide), cholestasis (alpha-naphthylisothiocyanate (ANIT), chlorpromazine, ethinylestradiol, methyltestosterone, ibuprofen), or steatosis (phenobarbital, tetracycline). Animals were treated daily for 2 or 4 days except for paracetamol and bromobenzene (1 and 2 days) and carbon tetrachloride (1 day only). Urine was collected 24 h after the first and second treatment. The animals were sacrificed 24 h after the last treatment, and NMR data were compared with liver histopathology as well as blood and urine biochemistry. Pathology and biochemistry showed marked toxicity in the liver at high doses of bromobenzene, paracetamol, carbon tetrachloride, ANIT, and ibuprofen. Thioacetamide and chlorpromazine showed less extensive changes, while the influences of iproniazid, isoniazid, phenobarbital, ethinylestradiol, and tetracycline on the toxic parameters were marginal or for methyltestosterone and mianserine negligible. NMR spectroscopy revealed significant changes upon dosing in 88 NMR biomarker signals preselected with the Procrustus Rotation method on principal component discriminant analysis (PCDA) plots. Further evaluation of the specific changes led to the identification of biomarker patterns for the specific types of liver toxicity. Comparison of our rat NMR PCDA data with histopathological changes reported in humans and/or rats suggests that rat NMR urinalysis can be used to predict hepatotoxicity.

Mild hypothermia reduces the inflammatory response and hepatic ischemia/reperfusion injury in rats

BACKGROUND/AIMS

Hypothermia is known to protect against ischemia/reperfusion (I/R) injury. The mechanisms of protection are incompletely understood and a temperature threshold for protection has not been established.

METHODS

In anesthetized Wistar rats, partial (70%) hepatic ischemia was applied for 45 min. Three study groups were used. Group T31 (n = 6) spontaneously cooled to 31.3 +/- 0.8 degrees C, while group T34 (n = 6) spontaneously cooled to 34 degrees C and was then maintained at 34.0 +/- 0.1 degrees C using a heat lamp. The normothermic group (T37, n = 6) was maintained at 37.1 +/- 0.3 degrees C. Hepatic injury, inflammation, lipid peroxidation and metabolic function (using quantitative 1H-NMR) were assessed 24 h after reperfusion.

RESULTS

At 24 h following reperfusion, alanine aminotransferase and aspartate aminotransferase increased to 5101 +/- 2378 and 6409 +/- 4202 U/l in the normothermic T37 group (P < 0.05 vs. T34 and T31), whereas transaminases in hypothermic groups (T31 and T34) were significantly lower. Severe liver necrosis was only noted with T37. Myeloperoxidase activity was increased in the T37 group when compared with hypothermic groups (223 +/- 161 (T37) vs. 16 +/- 10 (T31) and 8 +/- 5 (T34) mU/min/mg of tissue, P<0.05 vs. T31 and T34). 1H-NMR analysis of the blood of normothermic animals revealed metabolic changes consistent with increased ischemic injury, which was almost completely ameliorated in T34 and T31 groups.

CONCLUSIONS

Mild hypothermia of 34 degrees C is sufficient to reduce I/R injury by inhibiting the inflammatory response. Further spontaneous cooling to 31 degrees C did not demonstrate any additional protective effect.