Co-Culture of Human Primary Hepatocytes and Nonparenchymal Liver Cells in the Emulate® Liver-Chip for the Study of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a significant public health issue, but standard animal tests and clinical trials sometimes fail to predict DILI due to species differences and the relatively low number of human subjects involved in preapproval studies of a new drug, respectively. In vitro models have long been used to aid DILI prediction, with primary human hepatocytes (PHHs) being generally considered the gold standard. However, despite many efforts and decades of work, traditional culture methods have been unsuccessful in either fully preserving essential liver functions after isolation of PHHs or in emulating interactions between PHHs and hepatic nonparenchymal cells (NPCs), both of which are essential for the development of DILI under in vivo conditions. Recently, various liver-on-a-chip (Liver-Chip) systems have been developed to co-culture hepatocytes and NPCs in a three-dimensional environment on microfluidic channels, enabling better maintenance of primary liver cells and thus improved DILI prediction. The Emulate® Liver-Chip is a commercially available system that can recapitulate some in vivo DILI responses associated with certain compounds whose liver safety profile cannot be accurately evaluated using conventional approaches involving PHHs or animal models due to a lack of innate immune responses or species-dependent toxicity, respectively. Here, we describe detailed procedures for the use of Emulate® Liver-Chips for co-culturing PHHs and NPCs for the purpose of DILI evaluation. First, we describe the procedures for preparing the Liver-Chip. We then outline the steps needed for sequential seeding of PHHs and NPCs in the prepared Liver-Chips. Lastly, we provide a protocol for utilizing cells maintained in perfusion culture in the Liver-Chips to evaluate DILI, using acetaminophen as an example. In all, use of this system and the procedures described here allow better preservation of the functions of human primary liver cells, resulting in an improved in vitro model for DILI assessment. © 2022 Wiley Periodicals LLC. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Basic Protocol 1: Liver-Chip preparation Basic Protocol 2: Seeding primary human hepatocytes and nonparenchymal cells on Liver-Chips Basic Protocol 3: Perfusion culture for the study of acetaminophen-induced liver injury.

MALDI imaging mass spectrometry: an emerging tool in neurology

Neurological disease and disorders remain a large public health threat. Thus, research to improve early detection and/or develop more effective treatment approaches are necessary. Although there are many common techniques and imaging modalities utilized to study these diseases, existing approaches often require a label which can be costly and time consuming. Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) is a label-free, innovative and emerging technique that produces 2D ion density maps representing the distribution of an analyte(s) across a tissue section in relation to tissue histopathology. One main advantage of MALDI IMS over other imaging modalities is its ability to determine the spatial distribution of hundreds of analytes within a single imaging run, without the need for a label or any a priori knowledge. Within the field of neurology and disease there have been several impactful studies in which MALDI IMS has been utilized to better understand the cellular pathology of the disease and or severity. Furthermore, MALDI IMS has made it possible to map specific classes of analytes to regions of the brain that otherwise may have been lost using more traditional methods. This review will highlight key studies that demonstrate the potential of this technology to elucidate previously unknown phenomenon in neurological disease.

Metabolomics Test Materials for Quality Control: A Study of a Urine Materials Suite

There is a lack of experimental reference materials and standards for metabolomics measurements, such as urine, plasma, and other human fluid samples. Reasons include difficulties with supply, distribution, and dissemination of information about the materials. Additionally, there is a long lead time because reference materials need their compositions to be fully characterized with uncertainty, a labor-intensive process for material containing thousands of relevant compounds. Furthermore, data analysis can be hampered by different methods using different software by different vendors. In this work, we propose an alternative implementation of reference materials. Instead of characterizing biological materials based on their composition, we propose using untargeted metabolomic data such as nuclear magnetic resonance (NMR) or gas and liquid chromatography-mass spectrometry (GC-MS and LC-MS) profiles. The profiles are then distributed with the material accompanying the certificate, so that researchers can compare their own metabolomic measurements with the reference profiles. To demonstrate this approach, we conducted an interlaboratory study (ILS) in which seven National Institute of Standards and Technology (NIST) urine Standard Reference Material^®s (SRM^®s) were distributed to participants, who then returned the metabolomic data to us. We then implemented chemometric methods to analyze the data together to estimate the uncertainties in the current measurement techniques. The participants identified similar patterns in the profiles that distinguished the seven samples. Even when the number of spectral features is substantially different between platforms, a collective analysis still shows significant overlap that allows reliable comparison between participants. Our results show that a urine suite such as that used in this ILS could be employed for testing and harmonization among different platforms. A limited quantity of test materials will be made available for researchers who are willing to repeat the protocols presented here and contribute their data.

Stability of the Human Plasma Proteome to Pre-analytical Variability as Assessed by an Aptamer-Based Approach

Variable processing and storage of whole blood and/or plasma are potential confounders in biomarker development and clinical assays. The goal of the study was to investigate how pre-analytical variables impact the human plasma proteome. Whole blood obtained from 16 apparently healthy individuals was collected in six EDTA tubes and processed randomly under six pre-analytical variable conditions including blood storage at 0 °C or RT for 6 h (B6h0C or B6hRT) before processing to plasma, plasma storage at 4 °C or RT for 24 h (P24h4C or P24hRT), low centrifugal force at 1300 × g, (Low×g), and immediate processing to plasma under 2500 × g (control) followed by plasma storage at -80 °C. An aptamer-based proteomic assay was performed to identify significantly changed proteins (fold change ≥1.2, P < 0.05, and false discovery rate < 0.05) relative to the control from a total of 1305 proteins assayed. Pre-analytical conditions Low×g and B6h0C resulted in the most plasma proteome changes with 200 and 148 proteins significantly changed, respectively. Only 36 proteins were changed under B6hRT. Conditions P24h4C and P24hRT yielded changes of 28 and 75 proteins, respectively. The complement system was activated in vitro under the conditions B6hRT, P24h4C, and P24hRT. The results suggest that particular pre-analytical variables should be controlled for clinical measurement of specific biomarkers.

An Integrated Analysis of Metabolites, Peptides, and Inflammation Biomarkers for Assessment of Preanalytical Variability of Human Plasma

Discrepancies in blood sample collection and processing could have a significant impact on levels of metabolites, peptides, and protein biomarkers of inflammation in the blood; thus, sample quality control is critical for successful biomarker identification and validation. In this study, we analyzed the effects of several preanalytical processing conditions, including different storage times and temperatures for blood or plasma samples and different centrifugation forces on the levels of metabolites, peptides, and inflammation biomarkers in human plasma samples using ethylenediaminetetraacetic acid (EDTA) as an anticoagulant. Temperature was found to be the major factor for metabolite variation, and both time and temperature were identified as major factors for peptide variation. For inflammation biomarkers, temperature played different roles depending on the sample type (blood or plasma). Low temperature affected inflammation biomarkers in blood, while room temperature impacted inflammation biomarkers in plasma.

Metabolomics Analysis of Urine Samples from Children after Acetaminophen Overdose

Acetaminophen (APAP), a commonly used over-the-counter analgesic, accounts for approximately fifty percent of the cases of acute liver failure (ALF) in the United States due to overdose, with over half of those unintentional. Current clinical approaches for assessing APAP overdose rely on identifying the precise time of overdose and quantitating acetaminophen alanine aminotransferase (ALT) levels in peripheral blood. Novel specific and sensitive biomarkers may provide additional information regarding patient status post overdose. Previous non-clinical metabolomics studies identified potential urinary biomarkers of APAP-induced hepatotoxicity and metabolites involved pathways of tricarboxylic acid cycle, ketone metabolism, and tryptophan metabolism. In this study, biomarkers identified in the previous non-clinical study were evaluated in urine samples collected from healthy subjects ( N = 6, median age 14.08 years) and overdose patients ( N = 13, median age 13.91 years) as part of an IRB-approved multicenter study of APAP toxicity in children. The clinical results identified metabolites from pathways previously noted, and pathway analysis indicated analogous pathways were significantly altered in both the rats and humans after APAP overdose. The results suggest a metabolomics approach may enable the discovery of specific, translational biomarkers of drug-induced hepatotoxicity that may aid in the assessment of patients.

Dose-response analysis of epigenetic, metabolic, and apical endpoints after short-term exposure to experimental hepatotoxicants

Identification of sensitive and novel biomarkers or endpoints associated with toxicity and carcinogenesis is of a high priority. There is increasing interest in the incorporation of epigenetic and metabolic biomarkers to complement apical data; however, a number of questions, including the tissue specificity, dose-response patterns, early detection of those endpoints, and the added value need to be addressed. In this study, we investigated the dose-response relationship between apical, epigenetic, and metabolomics endpoints following short-term exposure to experimental hepatotoxicants, clofibrate (CF) and phenobarbital (PB). Male F344 rats were exposed to PB (0, 5, 25, and 100 mg/kg/day) or CF (0, 10, 50, and 250 mg/kg/day) for seven days. Exposure to PB or CF resulted in dose-dependent increases in relative liver weights, hepatocellular hypertrophy and proliferation, and increases in Cyp2b1 and Cyp4a1 transcripts. These changes were associated with altered histone modifications within the regulatory units of cytochrome genes, LINE-1 DNA hypomethylation, and altered microRNA profiles. Metabolomics data indicated alterations in the metabolism of bile acids. This study provides the first comprehensive analysis of the apical, epigenetic and metabolic alterations, and suggests that the latter two occur within or near the dose response curve of apical endpoint alterations following exposure to experimental hepatotoxicants.

Early metabolomics changes in heart and plasma during chronic doxorubicin treatment in B6C3F1 mice

The present study aimed to identify molecular markers of early stages of cardiotoxicity induced by a potent chemotherapeutic agent, doxorubicin (DOX). Male B6C3F1 mice were dosed with 3 mg kg(-1) DOX or saline via tail vein weekly for 2, 3, 4, 6 or 8 weeks (cumulative DOX doses of 6, 9, 12, 18 or 24 mg kg(-1) , respectively) and euthanized a week after the last dose. Mass spectrometry-based and nuclear magnetic resonance spectrometry-based metabolic profiling were employed to identify initial biomarkers of cardiotoxicity before myocardial injury and cardiac pathology, which were not noted until after the 18 and 24 mg kg(-1) cumulative doses, respectively. After a cumulative dose of 6 mg kg(-1) , 18 amino acids and four biogenic amines (acetylornithine, kynurenine, putrescine and serotonin) were significantly increased in cardiac tissue; 16 amino acids and two biogenic amines (acetylornithine and hydroxyproline) were significantly altered in plasma. In addition, 16 acylcarnitines were significantly increased in plasma and five were significantly decreased in cardiac tissue compared to saline-treated controls. Plasma lactate and succinate, involved in the Krebs cycle, were significantly altered after a cumulative dose of 6 mg kg(-1) . A few metabolites remained altered at higher cumulative DOX doses, which could partly indicate a transition from injury processes at 2 weeks to repair processes with additional injury happening concurrently before myocardial injury at 8 weeks. These altered metabolic profiles in mouse heart and plasma during the initial stages of injury progression due to DOX treatment may suggest these metabolites as candidate early biomarkers of cardiotoxicity. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Translational biomarkers of acetaminophen-induced acute liver injury

Acetaminophen (APAP) is a commonly used analgesic drug that can cause liver injury, liver necrosis and liver failure. APAP-induced liver injury is associated with glutathione depletion, the formation of APAP protein adducts, the generation of reactive oxygen and nitrogen species and mitochondrial injury. The systems biology omics technologies (transcriptomics, proteomics and metabolomics) have been used to discover potential translational biomarkers of liver injury. The following review provides a summary of the systems biology discovery process, analytical validation of biomarkers and translation of omics biomarkers from the nonclinical to clinical setting in APAP-induced liver injury.

Metabolomics as a tool for personalizing medicine: 2012 update

Numerous factors in conjunction with an individual’s genetic make up will determine predisposition to disease, adverse or beneficial effects of drug treatment or therapy, and disease progression. A major limitation of current clinical measures is that the disease phenotype, which is comprised of the genotype and other environmental factors, is underestimated. Rather, each disease is treated similarly even though the disease process is highly complex. Methods that evaluate the interaction of genotype and environmental factors would likely be a better indicator of patients’ response to medical treatments. The omics technologies, specifically metabolomics, will play a major role in the movement towards personalized medicine. Metabolomics is phenotype driven and should provide better clinical biomarkers. Furthermore, recent studies have shown that associations between genetic variants and downstream metabolite changes can provide a unique description of an individual’s genotype and phenotype, which will further enhance the movement towards personalized medicine.

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.

The liver toxicity biomarker study phase I: markers for the effects of tolcapone or entacapone

The Liver Toxicity Biomarker Study is a systems toxicology approach to discover biomarkers that are indicative of a drug's potential to cause human idiosyncratic drug-induced liver injury. In phase I, the molecular effects in rat liver and blood plasma induced by tolcapone (a "toxic" drug) were compared with the molecular effects in the same tissues by dosing with entacapone (a "clean" drug, similar to tolcapone in chemical structure and primary pharmacological mechanism). Two durations of drug exposure, 3 and 28 days, were employed. Comprehensive molecular analysis of rat liver and plasma samples yielded marker analytes for various drug-vehicle or drug-drug comparisons. An important finding was that the marker analytes associated with tolcapone only partially overlapped with marker analytes associated with entacapone, despite the fact that both drugs have similar chemical structures and the same primary pharmacological mechanism of action. This result indicates that the molecular analyses employed in the study are detecting substantial "off-target" markers for the two drugs. An additional interesting finding was the modest overlap of the marker data sets for 3-day exposure and 28-day exposure, indicating that the molecular changes in liver and plasma caused by short- and long-term drug treatments do not share common characteristics.

Serum metabolomic profiles from patients with acute kidney injury: a pilot study

Low sensitivity of current clinical markers (serum creatinine and blood urea nitrogen (BUN)) in early stages of the development of acute kidney injury (AKI) limits their utility. Rapid LC/MS-based metabolic profiling of serum demonstrated in a pilot study that metabolomics could provide novel indicators of AKI. Metabolic profiles of serum samples from seventeen hospitalized patients with newly diagnosed AKI were compared with the profiles of serum from age-matched subjects with normal kidney function. Increases in acylcarnitines and amino acids (methionine, homocysteine, pyroglutamate, asymmetric dimethylarginine (ADMA), and phenylalanine) and a reduction in serum levels of arginine and several lysophosphatidyl cholines were observed in patients with AKI compared to healthy subjects. Increases in homocysteine, ADMA and pyroglutamate have been recognized as biomarkers of cardiovascular and renal disease, and acylcarnitines represent biomarkers of defective fatty acid oxidation. The results of this pilot study demonstrate the utility of metabolomics in the discovery of novel serum biomarkers that can facilitate the diagnosis and determine prognosis of AKI in hospitalized patients.

Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2–3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D ¹³C-NMR and 1D ¹⁵N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.

Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals—drugs, pesticides, and environmental pollutant—interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D ¹³C and 1D ¹⁵N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D ¹³C-NMR and ¹⁵N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold² descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

Metabolomics techniques in nanotoxicology studies

The rapid growth in the development of nanoparticles for uses in a variety of applications including targeted drug delivery, cancer therapy, imaging, and as biological sensors has led to questions about potential toxicity of such particles to humans. High-throughput methods are necessary to evaluate the potential toxicity of nanoparticles. The omics technologies are particularly well suited to evaluate toxicity in both in vitro and in vivo systems. Metabolomics, specifically, can rapidly screen for biomarkers related to predefined pathways or processes in biofluids and tissues. Specifically, oxidative stress has been implicated as a potential mechanism of toxicity in nanoparticles and is generally difficult to measure by conventional methods. Furthermore, metabolomics can provide mechanistic insight into nanotoxicity. This chapter focuses on the application of both LC/MS and NMR-based metabolomics approaches to study the potential toxicity of nanoparticles.

Single valproic acid treatment inhibits glycogen and RNA ribose turnover while disrupting glucose-derived cholesterol synthesis in liver as revealed by the [U-C(6)]-d-glucose tracer in mice

Previous genetic and proteomic studies identified altered activity of various enzymes such as those of fatty acid metabolism and glycogen synthesis after a single toxic dose of valproic acid (VPA) in rats. In this study, we demonstrate the effect of VPA on metabolite synthesis flux rates and the possible use of abnormal (13)C labeled glucose-derived metabolites in plasma or urine as early markers of toxicity. Female CD-1 mice were injected subcutaneously with saline or 600 mg/kg) VPA. Twelve hours later, the mice were injected with an intraperitoneal load of 1 g/kg [U-(13)C]-d-glucose. (13)C isotopomers of glycogen glucose and RNA ribose in liver, kidney and brain tissue, as well as glucose disposal via cholesterol and glucose in the plasma and urine were determined. The levels of all of the positional (13)C isotopomers of glucose were similar in plasma, suggesting that a single VPA dose does not disturb glucose absorption, uptake or hepatic glucose metabolism. Three-hour urine samples showed an increase in the injected tracer indicating a decreased glucose re-absorption via kidney tubules. (13)C labeled glucose deposited as liver glycogen or as ribose of RNA were decreased by VPA treatment; incorporation of (13)C via acetyl-CoA into plasma cholesterol was significantly lower at 60 min. The severe decreases in glucose-derived carbon flux into plasma and kidney-bound cholesterol, liver glycogen and RNA ribose synthesis, as well as decreased glucose re-absorption and an increased disposal via urine all serve as early flux markers of VPA-induced adverse metabolic effects in the host.

Studies of acetaminophen and metabolites in urine and their correlations with toxicity using metabolomics

A LC/MS-based metabolomic assay was utilized to investigate a drug's excretion kinetic profile in urine so that the drug toxicity information could be obtained. Groups of 10 male Sprague-Dawley rats per dose were orally gavaged with a single dose of 0.2% carboxymethylcellulose, 400 mg acetaminophen (APAP)/kg body weight or 1600 mg APAP/kg. UPLC/MS and NMR were used to evaluate the excretion kinetics of major drug metabolites. N-acetyl-L-cysteine acetaminophen (APAP-NAC) had statistically significant correlations with clinical chemistry data, endogenous metabolite concentrations and histopathology data. The potential toxicity of a drug can be assessed through the study of the drug's metabolite profiles.

Delineating liver events in trichloroethylene-induced autoimmune hepatitis

Exposure to the environmental pollutant trichloroethylene (TCE) has been linked to autoimmune disease development in humans. Chronic (32-week) low-level exposure to TCE has been shown to promote autoimmune hepatitis in association with CD4(+) T cell activation in autoimmune-prone MRL+/+ mice. MRL+/+ mice are usually thought of as a model of systemic lupus rather than an organ-specific disease such as autoimmune hepatitis. Consequently, the present study examined gene expression and metabolites to delineate the liver events that skewed the autoimmune response toward that organ in TCE-treated mice. Female MRL+/+ mice were treated with 0.5 mg/mL TCE in their drinking water. The results showed that TCE-induced autoimmune hepatitis could be detected in as little as 26 weeks. TCE exposure also generated a time-dependent increase in the number of antibodies specific for liver proteins. The gene expression correlated with the metabolite analysis to show that TCE upregulated the methionine/homocysteine pathway in the liver after 26 weeks of exposure. The results also showed that TCE exposure altered the expression of selective hepatic genes associated with immunity and inflammation. On the basis of these results, future mechanistic studies will focus on how alterations in genes associated with immunity and inflammation, in conjunction with protein alterations in the liver, promote liver immunogenicity in TCE-treated MRL+/+ mice.

Interaction of dietary resveratrol with animal-associated bacteria

Resveratrol (3,5,4'-trihydroxy-trans-stilbene), an antifungal phytoalexin produced by grapes, peanuts, and Japanese knotweeds, is thought to be a beneficial dietary phytochemical in red wine and grape juice. Information on its antibacterial properties and biotransformation, however, is limited. We surveyed the interactions of resveratrol with 43 strains of bacterial species that are often animal- or human-associated. Resveratrol at 50 mg L(-1) reduced the growth rates of most of the bacteria tested, but did not totally prevent growth even at much higher levels. Eleven of the 43 bacteria were capable of transforming at least 20% of the resveratrol. Three major metabolites were identified as resveratroloside, piceid, and dihydroresveratrol, and three other metabolites were partially characterized.

Global metabolic profiling and its role in systems biology to advance personalized medicine in the 21st century

Systems biology attempts to elucidate the complex interaction between genes, proteins and metabolites to provide a mechanistic understanding of cellular function and how this function is affected by disease processes, drug toxicity or drug efficacy effects. Global metabolic profiling is an important component of systems biology that can be applied in both preclinical and clinical settings for drug discovery and development, and to study disease mechanisms. The metabolic profile encodes the phenotype, which is composed of the genotype and environmental factors. The phenotypic profile can be used to make decisions about the best course of treatment for an individual patient. Understanding the combined effects of genetics and environment through a systems biology framework will enable the advancement of personalized medicine.

The Liver Toxicity Biomarker Study: Phase I Design and Preliminary Results

Drug-induced liver injury (DILI) is the primary adverse event that results in withdrawal of drugs from the market and a frequent reason for the failure of drug candidates in development. The Liver Toxicity Biomarker Study (LTBS) is an innovative approach to investigate DILI because it compares molecular events produced in vivo by compound pairs that (a) are similar in structure and mechanism of action, (b) are associated with few or no signs of liver toxicity in preclinical studies, and (c) show marked differences in hepatotoxic potential. The LTBS is a collaborative preclinical research effort in molecular systems toxicology between the National Center for Toxicological Research and BG Medicine, Inc., and is supported by seven pharmaceutical companies and three technology providers. In phase I of the LTBS, entacapone and tolcapone were studied in rats to provide results and information that will form the foundation for the design and implementation of phase II. Molecular analysis of the rat liver and plasma samples combined with statistical analyses of the resulting datasets yielded marker analytes, illustrating the value of the broad-spectrum, molecular systems analysis approach to studying pharmacological or toxicological effects.

Metabolomics: a tool for personalizing medicine?

The concept of personalized medicine is based upon using personal genetic information to predict individual responses to drug therapy. However, environmental factors such as diet, energy status, gut microbiota, health status and age will influence the expression of one’s genetic potential. Metabolomics data from biofluid and tissue sample analysis hold information regarding a patient’s genotype and phenotype. Metabolomics data can be rapidly collected from biofluid samples over time, providing temporal metabolic analyses of patient samples. In addition to metabolic markers of a patient’s phenotype, metabolomics can provide markers of drug efficacy, toxicity and clearance.

Metabonomics of acute kidney injury in children after cardiac surgery

Acute kidney injury (AKI) is a major complication in children who undergo cardiopulmonary bypass surgery. We performed metabonomic analyses of urine samples obtained from 40 children that underwent cardiac surgery for correction of congenital cardiac defects. Serial urine samples were obtained from each patient prior to surgery and at 4 h and 12 h after surgery. AKI, defined as a 50% or greater rise in baseline level of serum creatinine, was noted in 21 children at 48-72 h after cardiac surgery. The principal component analysis of liquid chromatography/mass spectrometry (LC/MS) negative ionization data of the urine samples obtained 4 h and 12 h after surgery from patients who develop AKI clustered away from patients who did not develop AKI. The LC/MS peak with mass-to-charge ratio (m/z) 261.01 and retention time (tR) 4.92 min was further analyzed by tandem mass spectrometry (MS/MS) and identified as homovanillic acid sulfate (HVA-SO4), a dopamine metabolite. By MS single-reaction monitoring, the sensitivity was 0.90 and specificity was 0.95 for a cut-off value of 24 ng/microl for HVA-SO4 at 12 h after surgery. We concluded that urinary HVA-SO4 represents a novel, sensitive, and predictive early biomarker of AKI after pediatric cardiac surgery.

Metabonomics evaluation of urine from rats given acute and chronic doses of acetaminophen using NMR and UPLC/MS

Urinary metabolic perturbations associated with acute and chronic acetaminophen-induced hepatotoxicity were investigated using nuclear magnetic resonance (NMR) spectroscopy and ultra performance liquid chromatography/mass spectrometry (UPLC/MS) metabonomics approaches to determine biomarkers of hepatotoxicity. Acute and chronic doses of acetaminophen (APAP) were administered to male Sprague-Dawley rats. NMR and UPLC/MS were able to detect both drug metabolites and endogenous metabolites simultaneously. The principal component analysis (PCA) of NMR or UPLC/MS spectra showed that metabolic changes observed in both acute and chronic dosing of acetaminophen were similar. Histopathology and clinical chemistry studies were performed and correlated well with the PCA analysis and magnitude of metabolite changes. Depletion of antioxidants (e.g. ferulic acid), trigonelline, S-adenosyl-L-methionine, and energy-related metabolites indicated that oxidative stress was caused by acute and chronic acetaminophen administration. Similar patterns of metabolic changes in response to acute or chronic dosing suggest similar detoxification and recovery mechanisms following APAP administration.

Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis

Background

Urine from male Sprague-Dawley rats 25, 40, and 80 days old was analyzed by NMR and UPLC/MS. The effects of data normalization procedures on principal component analysis (PCA) and quantitative analysis of NMR-based metabonomics data were investigated. Additionally, the effects of age on the metabolic profiles were examined by both NMR and UPLC/MS analyses.

Results

The data normalization factor was shown to have a great impact on the statistical and quantitative results indicating the need to carefully consider how to best normalize the data within a particular study and when comparing different studies. PCA applied to the data obtained from both NMR and UPLC/MS platforms reveals similar age-related differences. NMR indicated many metabolites associated with the Krebs cycle decrease while citrate and 2-oxoglutarate, also associated with the Krebs cycle, increase in older rats.

Conclusion

This study compared four different normalization methods for the NMR-based metabonomics spectra from an age-related study. It was shown that each method of normalization has a great effect on both the statistical and quantitative analyses. Each normalization method resulted in altered relative positions of significant PCA loadings for each sample spectra but it did not alter which chemical shifts had the highest loadings. The greater the normalization factor was related to age, the greater the separation between age groups was observed in subsequent PCA analyses. The normalization factor that showed the least age dependence was total NMR intensity, which was consistent with UPLC/MS data. Normalization by total intensity attempts to make corrections due to dietary and water intake of the individual animal, which is especially useful in metabonomics evaluations of urine. Additionally, metabonomics evaluations of age-related effects showed decreased concentrations of many Krebs cycle intermediates along with increased levels of oxidized antioxidants in urine of older rats, which is consistent with current theories on aging and its association with diminishing mitochondrial function and increasing levels of reactive oxygen species. Analysis of urine by both NMR and UPLC/MS provides a comprehensive and complementary means of examining metabolic events in aging rats.

Metabolomic biomarkers: their role in the critical path

Global metabolic profiling is being applied to identify biomarkers of health. Some small molecules are exquisitely sensitive indicators of health status. Metabolic profiling analyses are being used to determine biomarkers of drug safety and effectiveness as well as disease diagnosis and prognosis including organ transplant rejection. To understand the mechanism(s) of drug toxicity and disease, a systems biology approach that considers the information generated from metabolic profiling, genetics, transcriptomics and proteomics research paradigms is necessary. This will allow for a better understanding of the mechanism(s) of drug interactions and disease while possibly identifying susceptible populations, an important goal in the move toward personalized medicine.:

Monitoring the health to disease continuum with global metabolic profiling and systems biology

Global metabolic profiling, which includes both metabolomics and metabonomics studies, is the latest ‘omics’ research platform that is being applied to understand the health and disease continuum. Metabolic profiling analyses have been demonstrated for the investigation of inborn errors of metabolism, organ transplant rejection, drug toxicity, disease diagnosis and prognosis, drug efficacy and nutritional status. Combining information generated from a metabolic profiling platform with that obtained based on genetics, transcriptomics and proteomics research paradigms will pave the way for a better understanding of the mechanisms of disease and toxicity. Metabolomics and nutrition will lay the groundwork for the application of personalized medicine in the 21st century.

Whole-molecule calculation of log p based on molar volume, hydrogen bonds, and simulated 13C NMR spectra

The prediction of Log P is usually accomplished using either substructure or whole-molecule approaches. However, these methods are complicated, and previous whole-molecule approaches have not been successful for the prediction of Log P in very complex molecules. The observed chemical shifts in nuclear magnetic resonance (NMR) spectroscopy are related to the electrostatics at the nucleus, which are influenced by solute-solvent interactions. The different solvation effects on a molecule by either water or methanol have a strong effect on the NMR chemical shift value. Therefore, the chemical shift values observed in an aqueous and organic solvent should correlate to Log P. This paper develops a rapid, objective model of Log P based on molar volume, hydrogen bonds, and differences in calculated 13C NMR chemical shifts for a diverse set of compounds. A partial least squares (PLS) model of Log P built on the sum of carbon chemical shift differences in water and methanol, molar volume, number of hydrogen bond donors and acceptors in 162 diverse compounds gave an r2 value of 0.88. The average r2 for 10 training models of Log P made from 90% of the data was 0.87+/-0.01. The average q2 for 10 leave-10%-out cross-validation test sets was 0.87+/-0.05.

In vitro cytotoxicity of nonpolar constituents from different parts of kava plant (Piper methysticum)

Kava (Piper methysticum), a perennial shrub native to the South Pacific islands, has been used to relieve anxiety. Recently, several cases of severe hepatotoxicity have been reported from the consumption of dietary supplements containing kava. It is unclear whether the kava constituents, kavalactones, are responsible for the associated hepatotoxicity. To investigate the key components responsible for the liver toxicity, bioassay-guided fractionation was carried out in this study. Kava roots, leaves, and stem peelings were extracted with methanol, and the resulting residues were subjected to partition with a different polarity of solvents (hexane, ethyl acetate, n-butanol, and water) for evaluation of their cytotoxicity on HepG2 cells based on the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and lactate dehydrogenase and aspartate aminotransferase enzyme leakage assays. Organic solvent fractions displayed a much stronger cytotoxicity than water fractions for all parts of kava. The hexane fraction of the root exhibited stronger cytotoxic effects than fractions of root extracted with other solvents or extracts from the other parts of kava. Further investigations using bioassay-directed isolation and analysis of the hexane fraction indicated that the compound responsible for the cytotoxicity was flavokavain B. The identity of the compound was confirmed by (1)H and (13) C NMR and MS techniques.

An Integrated Study of Acute Effects of Valproic Acid in the Liver Using Metabonomics, Proteomics, and Transcriptomics Platforms

An integrated omics approach was undertaken in order to elucidate a systems biology level understanding of the acute hepatotoxcity of valproic acid (VPA). Metabonomics, proteomics and gene expression microarray platforms were employed in this systems biology study. CD-1 female pregnant mice were injected subcutaneously with 600 mg/kg VPA or vehicle control. Urine, serum, and liver tissue were collected at 6, 12, and 24 h after dosing. Principal component analysis (PCA) of the metabonomics data showed clustering of the dosed groups away from the controls for the urine samples. Looser clustering was seen in the other sample sets investigated. However, VPA administration resulted in altered glucose concentrations in urine samples at 12 and 24 h and in aqueous liver tissue extracts at 12 h after VPA administration. Proteomics studies identified two proteins, glycogen phosphorylase and amylo-1,6-glucosidase, which were increased in dosed animals relative to control. Both of these proteins are involved in converting glycogen to glucose. Examination of the expression of 20,000 liver genes did not reveal significantly altered expression at 6, 12, or 24 h after VPA exposure. The combined studies indicated a perturbation in the glycogenolysis pathway following administration of VPA.

High Resolution1H NMR Structural Studies of Sucrose Octaacetate in Supercritical Carbon Dioxide

High pressure (HP), high resolution (HR), proton nuclear magnetic resonance (1H NMR) spectroscopy has been utilized for the first time to investigate the solution structure of a carbohydrate based system, sucrose octaacetate (SOA), in supercritical CO2. The studies indicate that the average solution state conformation of the alpha-D-Glucopyranosyl ring of SOA in scCO2 medium is consistent with the 4C1 chair form, while the beta-D-fructofuranosyl ring adopts an envelope conformation. The investigations also suggest that scCO2 is a promising medium to study the solution structure and conformation of acetylated sugar systems. Spectral manifestations of a specific interaction between the acetate methyl protons and CO2 molecules are also presented.