Chemometric models for toxicity classification based on NMR spectra of biofluids

Uncovering nasopharyngeal carcinoma from chronic rhinosinusitis and healthy subjects using routine medical tests via machine learning

Nasopharyngeal carcinoma (NPC) is one of the most common types of cancers in South China and Southeast Asia. Clinical data has shown that early detection is essential for improving treatment effectiveness and survival rate. Unfortunately, because the early symptoms of NPC are rather minor and similar to that of diseases such as Chronic Rhinosinusitis (CRS), early detection is a challenge. This paper proposes using machine learning methods to detect NPC using routine medical test data, namely Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), k-Nearest-Neighbor (KNN) and Logistic Regression (LR). We collected a dataset containing 523 newly diagnosed NPC patients before treatment, 501 newly diagnosed CRS patients before treatment as well as 600 healthy controls. The routine medical test data including age, gender, blood test features, liver function test features, and urine sediment test features. For comparison, we also used data from Epstein-Barr Virus (EBV) antibody tests, which is a specialized test not included among routine medical tests. In our first test, all four methods were tested on classifying NPC vs CRS vs controls; RF gives the best overall performance. Using only routine medical test data, it gives an accuracy of 83.1%, outperforming LR by 12%. In our second test, using only routine medical test data, when classifying NPC vs non-NPC (i.e. CRS or controls), RF achieves an accuracy of 88.2%. In our third test, when classifying NPC vs. controls, RF using only routine test data achieves an accuracy significantly better than RF using only EBV antibody data. Finally, in our last test, RF trained with NPC vs controls, using routine test data only, continued to perform well on an entirely separate dataset. This is a promising result because preliminary NPC detection using routine medical data is easy and inexpensive to implement. We believe this approach will play an important role in the detection and treatment of NPC in the future.

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

Quality control of Zingiberis Rhizoma and its processed products by UHPLC-Q-TOF/MS-based non-targeted metabonomics combining with SIBDV method

Zingiberis Rhizoma (ZR) is a homologous plant with pungent tastes and aromas, which has unique nutritional value and tremendous application potentiality. Zingiberis Rhizoma Praeparatum (ZRP) and Carbonised Ginger (CG) are processed products of ZR through different processing methods, and they are commonly used ingredients in food supplements. This study used ZR, ZRP and CG from different batches to further understand composition differences after processing. Additionally, we performed non-targeted metabolomics-based profiling of gingerols by ultra-high-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) in combination with multivariate analysis and compounds identification. In which, we developed a comprehensive SWATH-IDA bi-directionally verified (SIBDV) method integrating the advantages of Sequential Windowed Acquisition of all Theoretical fragment ions (SWATHTM) and traditional information-dependent acquisition (IDA) mode for characterization of gingerols. Potential chemical markers were selected by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of chemometrics methods. After that, the threshold variable importance in projection (VIP) value and P value were employed to screen the valuable MS features for discriminating ZR, ZRP and CG. In total, 59 gingerols in the different samples were structurally identified. Results allowed the selection of 33 gingerols, which are nominated as novel markers for materials authentication in ZR, ZRP and CG. The analysis of the study showed that the content of gingerols showed a downward trend after processing, but shogaols and gingerone compounds had an upward trend, resulting in differences in application and pharmacodynamic efficacy. These findings provide promising perspectives in the quality control of ZR, ZRP and CG, as well as for laying the foundation in food design and development.

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

Detection of Metabolite-Protein Interactions in Complex Biological Samples by High-Resolution Relaxometry: Toward Interactomics by NMR

Metabolomics, the systematic investigation of metabolites in biological fluids, cells, or tissues, reveals essential information about metabolism and diseases. Metabolites have functional roles in a myriad of biological processes, as substrates and products of enzymatic reactions but also as cofactors and regulators of large numbers of biochemical mechanisms. These functions involve interactions of metabolites with macromolecules. Yet, methods to systematically investigate these interactions are still scarce to date. In particular, there is a need for techniques suited to identify and characterize weak metabolite-macromolecule interactions directly in complex media such as biological fluids. Here, we introduce a method to investigate weak interactions between metabolites and macromolecules in biological fluids. Our approach is based on high-resolution NMR relaxometry and does not require any invasive procedure or separation step. We show that we can detect interactions between small and large molecules in human blood serum and quantify the size of the complex. Our work opens the way for investigations of metabolite (or other small molecules)-protein interactions in biological fluids for interactomics or pharmaceutical applications.

Comparative assessment for rat strain differences in metabolic profiles of 14 drugs in Wistar Han and Sprague Dawley hepatocytes

Knowledge of inter-strain and inter-gender differences in drug metabolism studies is important for animal selection in pharmacokinetic and toxicological studies. The effects of rat strain and gender in in vitro metabolism were investigated in Sprague Dawley (SD) and Wister Han (WH) rats based on the hepatocyte metabolic profiles of 14 small molecule drugs. Similarities were found between the hepatocyte metabolic clearances of SD and WH strains, suggesting that only one strain can be confidently used for the evaluation of hepatic clearance. Neither strain of rat was preferable over the other to cover human metabolites. Higher similarities in metabolic pathways were found between the same gender than the same strain. Differences in metabolite identities, metabolite formation rates and potential biotransformation pathways were observed between SD and WH rat strains. Eleven metabolites from six drugs were "disproportionally" formed between SD and WH rats. The use of a specific rat strain model and gender for ADME and toxicity testing should, therefore, be carefully considered as metabolic profiles may differ, even though metabolic clearance was similar between SD and WH rats.

Metabolomics analysis of Camellia sinensis with respect to harvesting time

The metabolites of green tea influence its quality and physiological characteristics. Therefore, to further increase the utilization of green tea leaves, it is imperative to understand the distribution and variation of their secondary metabolites with respect to different harvesting times. This study compared the metabolomes of young leaves of 'Anji Baicha' between early spring tea and late spring tea in positive and negative ESI modes using UPLC-ESI-Q-TOF/MS. Potential biomarkers were selected by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) of chemometrics methods. Results showed that the metabolic profiles of young leaves in early and late spring tea were significantly different. The metabolite-related pathways associated with these differences included those involved in biosynthesis of flavonoids, phenylpropanoids, flavone and flavonol, phenylalanine, tyrosine, and tryptophan. In early spring tea leaves, concentrations of amino acids (l-glutamine and l-tryptophan), (S)-(-)-limonene, most of the catechins, and flavonol/flavone glycosides were found to be significantly increased, while proanthocyanidins (proanthocyanidin A1, prodelphinidin A1, and prodelphinidin A₂ 3'-gallate) concentrations were significantly decreased. As a result of the metabolomics analysis of young leaves of green tea plants with respect to different harvesting time, information regarding physiological characteristics and optimal harvesting time was obtained.

Multi‑organ assessment via a 9.4‑Tesla MRS evaluation of metabolites during the embryonic development of cleft palate induced by dexamethasone

The aim of the present study was to determine the association between maternal metabolism and development of the fetal palate, and to suggest a potential non‑invasive prenatal diagnostic method for fetal cleft palate (CP). Dexamethasone (DXM) was used to create a CP mouse model. A 9.4‑Tesla (T) magnetic resonance spectroscopy (MRS) imager was used to measure an array of metabolites in the maternal serum, placental tissue, amniotic fluid and fetal palates. Multivariate statistical analysis was performed using SIMCA‑P 14.1 software. Following DXM treatment, variations were detected in multiple metabolites in the female mice and their fetuses based on 9.4T MRS. It was indicated that in the experimental group during CP formation, leucine, valine, creatine, acetate and citrate levels in the palatal tissue were lower, whereas lactate, alanine, proline/inositol and glutamate‑containing metabolite levels were higher, compared with the levels in the control group. In placental tissue and amniotic fluid, succinate and choline levels were lower in the experimental group. The relative concentrations of cholesterol and lipids in palatal tissues from mice treated with DXM were higher compared with the concentrations in tissues from mice in the control group, with the exception of (CH2)n lipids. In the placental tissue, the alteration in cholesterol level exhibited the opposite trend. Lipid levels for the different lipid forms varied and most of them were unsaturated lipids.

NMR Spectroscopy for Metabolomics Research

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

Biomarker Discovery in Animal Health and Disease: The Application of Post-Genomic Technologies

The causes of many important diseases in animals are complex and multifactorial, which present unique challenges. Biomarkers indicate the presence or extent of a biological process, which is directly linked to the clinical manifestations and outcome of a particular disease. Identifying biomarkers or biomarker profiles will be an important step towards disease characterization and management of disease in animals. The emergence of post-genomic technologies has led to the development of strategies aimed at identifying specific and sensitive biomarkers from the thousands of molecules present in a tissue or biological fluid. This review will summarize the current developments in biomarker discovery and will focus on the role of transcriptomics, proteomics and metabolomics in biomarker discovery for animal health and disease.

Metabotyping and its application in targeted nutrition: an overview

Metabolic diversity leads to differences in nutrient requirements and responses to diet and medication between individuals. Using the concept of metabotyping – that is, grouping metabolically similar individuals – tailored and more efficient recommendations may be achieved. The aim of this study was to review the current literature on metabotyping and to explore its potential for better targeted dietary intervention in subjects with and without metabolic diseases. A comprehensive literature search was performed in PubMed, Google and Google Scholar to find relevant articles on metabotyping in humans including healthy individuals, population-based samples and patients with chronic metabolic diseases. A total of thirty-four research articles on human studies were identified, which established more homogeneous subgroups of individuals using statistical methods for analysing metabolic data. Differences between studies were found with respect to the samples/populations studied, the clustering variables used, the statistical methods applied and the metabotypes defined. According to the number and type of the selected clustering variables, the definitions of metabotypes differed substantially; they ranged between general fasting metabotypes, more specific fasting parameter subgroups like plasma lipoprotein or fatty acid clusters and response groups to defined meal challenges or dietary interventions. This demonstrates that the term ‘metabotype’ has a subjective usage, calling for a formalised definition. In conclusion, this literature review shows that metabotyping can help identify subgroups of individuals responding differently to defined nutritional interventions. Targeted recommendations may be given at such metabotype group levels. Future studies should develop and validate definitions of generally valid metabotypes by exploiting the increasingly available metabolomics data sets.

Metabolomic approaches in the discovery of potential urinary biomarkers of drug-induced liver injury (DILI)

Drug-induced liver injury (DILI) is a major safety issue during drug development, as well as the most common cause for the withdrawal of drugs from the pharmaceutical market. The identification of DILI biomarkers is a labor-intensive area. Conventional biomarkers are not specific and often only appear at significant levels when liver damage is substantial. Therefore, new biomarkers for early identification of hepatotoxicity during the drug discovery process are needed, thus resulting in lower development costs and safer drugs. In this sense, metabolomics has been increasingly playing an important role in the discovery of biomarkers of liver damage, although the characterization of the mechanisms of toxicity induced by xenobiotics remains a huge challenge. These new-generation biomarkers will offer obvious benefits for the pharmaceutical industry, regulatory agencies, as well as a personalized clinical follow-up of patients, upon validation and translation into clinical practice or approval for routine use. This review describes the current status of the metabolomics applied to the early diagnosis and prognosis of DILI and in the discovery of new potential urinary biomarkers of liver injury.

NMR-based metabonomics study on the effect of Gancao in the attenuation of toxicity in rats induced by Fuzi

ETHNOPHARMACOLOGICAL RELEVANCE

Fuzi, the processed lateral root of Aconitum carmichaelii Debeaux, is a traditional Chinese medicine used for its analgesic, antipyretic, anti-rheumatoid arthritis and anti-inflammation effects; however, it is also well known for its toxicity. Gancao, the root of Glycyrrhiza uralensis Fisch., is often used concurrently with Fuzi to alleviate its toxicity. However, the mechanism of detoxication is still not well clear.

AIM OF THE STUDY

In this study, the effect of Gancao on the metabolic changes induced by Fuzi was investigated by NMR-based metabonomic approaches.

MATERIALS AND METHODS

Fifty male Wistar rats were randomly divided into five groups (group A: control, group B: Fuzi decoction alone, group C: Gancao decoction alone, group D: Fuzi decoction and Gancao decoction simultaneously, group E: Fuzi decoction 5h after Gancao decoction) and urine samples were collected for NMR-based metabolic profiling analysis. Statistical analyses such as unsupervised PCA, t-test, hierarchical cluster, and pathway analysis were used to detect the effects of Gancao on the metabolic changes induced by Fuzi.

RESULTS

The behavioral and biochemical characteristics showed that Fuzi exhibited toxic effects on treated rats (group B) and statistical analyses showed that their metabolic profiles were in contrast to those in groups A and C. However, when Fuzi was administered with Gancao, the metabolic profiles became similar to controls, whereby Gancao reduced the levels of trimethylamine N-oxide, betaine, dimethylglycine, valine, acetoacetate, citrate, fumarate, 2-ketoglutarate and hippurate, and regulated the concentrations of taurine and 3-hydroxybutyrate, resulting in a decrease in toxicity. Furthermore, important pathways that are known to be involved in the effect of Gancao on Fuzi, including phenylalanine, tyrosine and tryptophan biosynthesis, the synthesis and degradation of ketone bodies, and the TCA cycle, were altered in co-treated rats.

CONCLUSIONS

Gancao treatment mitigated the metabolic changes altered by Fuzi administration in rats, demonstrating that dosing with Gancao could reduce the toxicity of Fuzi at the metabolic level. Fuzi and Gancao administered simultaneously resulted in improved toxicity reduction than when Gancao was administrated 5h prior to Fuzi. In summary, co-administration of Gancao with Fuzi reduces toxicity at the metabolic level.

Metabonomic profiles reveal dose-dependent effects of Bu-Shen-Gu-Chi-Wan on the serum in experimental periodontitis of rat model

ETHNO-PHARMACOLOGICAL RELEVANCE

Bu-Shen-Gu-Chi-Wan is a Chinese patent medicine for the treatment of chronic periodontitis. It has an anti-inflammatory function in experimental periodontitis and can improve alveolar bone remodeling.

AIM OF THE STUDY

This study aims to explore the effects of Bu-Shen-Gu-Chi-Wan on serum metabolism in a rat model of periodontitis using ¹H nuclear magnetic resonance (¹H NMR) based metabonomics.

MATERIALS AND METHODS

The model of experimental periodontitis in a rat was established by steel wire ligation, plus a high glucose diet and Porphyromonas gingivalis inoculation. When the models had been established, 6-week-old Sprague-Dawley female rats (n=31) were divided into 5 groups: high dose group (Group H), medium dose group (Group M), low dose group (Group L), periodontitis group (Group P) and healthy control group (Group N). Rats in Group H, M and L were given the Bu-Shen-Gu-Chi-Wan solution (0.8, 2 and 4g/kg of body weight) daily for 60 days. Rats in Group P and N were administered normal saline (10ml/kg of body weight) in the same period. All rats were sacrificed at the end of the study and serum samples were collected. The metabolites in the serum were analyzed using ¹H NMR in conjunction with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

RESULTS

The metabonomic profiles of five groups showed the clustering of individual dataset in every group, and the 1st principal component and the 2nd principal component could significantly differentiate the datasets of medium dose or high dose group from other groups. The chemical shift regions at δ 1.22ppm, 1.86ppm, 2.26ppm, 2.34ppm and 2.42ppm showed the most obvious differences among the five groups. The correspondent metabolites were high density lipoprotein, pyruvate/oxaloacetate, arginine and glutamine.

CONCLUSION

The effects of Bu-Shen-Gu-Chi-Wan on the rat serum metabolites were dose dependent. High density lipoprotein, pyruvate/oxaloacetate, arginine and glutamine may be the serum biomarkers of Bu-Shen-Gu-Chi-Wan.

Urinary NMR-based metabolomic analysis of rats possessing variable sperm count following orally administered Eurycoma longifolia extracts of different quassinoid levels

ETHNOPHARMACOLOGICAL RELEVANCE

Eurycoma longifolia (Tongkat Ali, TA) roots have been ethnically used as a remedy to boost male sexual desire, libido, energy and fertility.

AIM OF THE STUDY

The study evaluated the effect of TA extracts with different quassinoid levels on rats sperm count and examined corresponding post-treatment urinary metabolic changes.

MATERIALS AND METHODS

Twenty-four male Sprague-Dawley rats, categorized into 4 groups of 6 rats each, were orally administered for 48 days with water for the control (group 1), 125mg/kg of TA water extract (TAW, group 2), 125mg/kg of TA quassinoid-poor extract (TAQP, group 3) and 21mg/kg of TA quassinoid-rich extract (TAQR, group 4). Upon completion of the 48-day treatment, the urine samples were analyzed by NMR and the animals were subsequently sacrificed for sperm count analysis. The urine profiles were categorized according to sperm count level.

RESULTS

The results showed that the sperm count in TAW- and TAQR-treated groups was significantly higher compared to the TAQP-administered and control groups. The orthogonal partial least squares discriminant analysis (OPLS-DA) model indicated a clear separation among the urine profiles with respect to sperm count level. Urine (1)H-NMR profiles of the high-sperm count group contained higher concentrations of trigonelline, alanine, benzoic acid and higher intensity of a signal at 3.42ppm, while ethanol was at higher concentration in the normal-sperm count group.

CONCLUSIONS

The results proved the efficacy of quassinoids on sperm count increase in rats and provided quantitative markers in urine suitable for analysis of sperm profile and male fertility status.

A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments

Metabonomics/metabolomics is an important science for the understanding of biological systems and the prediction of their behaviour, through the profiling of metabolites. Two technologies are routinely used in order to analyse metabolite profiles in biological fluids: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), the latter typically with hyphenation to a chromatography system such as liquid chromatography (LC), in a configuration known as LC-MS. With both NMR and MS-based detection technologies, the identification of the metabolites in the biological sample remains a significant obstacle and bottleneck. This article provides guidance on methods for metabolite identification in biological fluids using NMR spectroscopy, and is illustrated with examples from recent studies on mice.

A novel approach for testing the teratogenic potential of chemicals on the platform of metabolomics: studies employing HR-MAS nuclear magnetic resonance spectroscopy

The objective is to develop a quick, reliable method for testing the teratogenic potential of a new chemical entity (NCE) on the platform of metabonomics, as an alternative to conventional procedures.

Screening and verification of linearly dependent biomarkers with acute toxicity induced by Aconiti Radix based on liquid chromatography-mass spectrometry-based metabolite profiling

We built a method that has three parts: first is to screen the biomarkers with metabolic profiling analysis; second is to determine the linear dependence with acute toxicity biomarkers; third is to validate the biomarkers with different Aconiti Radix involved medicine.

Metabolomics study on model rats of chronic obstructive pulmonary disease treated with Bu‑Fei Jian‑Pi

The therapeutic effect of Traditional Chinese Medicine (TCM) on chronic obstructive pulmonary disease (COPD) has been know for numerous years; however, the mechanism of action of the beneficial effects of TCM remains to be elucidated. The present study aimed to investigate the molecular mechanisms of COPD through metabolomic analysis as well as explore the targets and intervention mechanisms of TCM therapy using the common TCM granules Bu‑Fei Jian‑Pi. COPD rat models were established using smoke inhalations and recurrent bacterial infections. Rats were then divided into three groups as follows: A1, control healthy rats; B1, COPD model; and D1, Bu‑Fei Jian‑Pi‑treated COPD rats. Following administration of the medicine, the metabolomic profile of the lung tissue of rats in each group was assessed using high‑performance liquid chromatography/quadrupole‑time‑of‑flight mass spectrometry. The results demonstrated that there was a significanlty different spectrum of metabolites in the lung tissue of the model group compared to that of the control group as well as the Bu‑Fei Jian‑Pi‑treated COPD group; in addition, following treatment with Bu‑Fei Jian‑Pi, the metabolites of COPD rats were comparable with those of the control. Notable changes were observed in 31 metabolites between the Bu‑Fei Jian‑Pi‑treated group and the model group; however, there were 13 comparable metabolites between the Bu‑Fei Jian‑Pi and control groups as well as the model and control groups. Eleven metabolites showed a negative fold change in the Bu‑Fei Jian‑Pi‑treated groups compared to concentrations in the model group; however, minimal changes were observed in phenylpyruvic acid and α‑D‑fucose expression. In conclusion, the results of the present study demonstrated that Bu‑Fei Jian‑Pi granules had beneficial effects on measured outcomes in a rat model of stable COPD, indicated by a significantly different spectrum of metabolites. This therefore indicated that the metabolites which had significantly altered expression in the model group compared with that of the control and Bu‑Fei Jian‑Pi‑treated groups may be potential biomarkers of COPD.

Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping

Proton nuclear magnetic resonance (NMR)-based metabolic phenotyping of urine and blood plasma/serum samples provides important prognostic and diagnostic information and permits monitoring of disease progression in an objective manner. Much effort has been made in recent years to develop NMR instrumentation and technology to allow the acquisition of data in an effective, reproducible, and high-throughput approach that allows the study of general population samples from epidemiological collections for biomarkers of disease risk. The challenge remains to develop highly reproducible methods and standardized protocols that minimize technical or experimental bias, allowing realistic interlaboratory comparisons of subtle biomarker information. Here we present a detailed set of updated protocols that carefully consider major experimental conditions, including sample preparation, spectrometer parameters, NMR pulse sequences, throughput, reproducibility, quality control, and resolution. These results provide an experimental platform that facilitates NMR spectroscopy usage across different large cohorts of biofluid samples, enabling integration of global metabolic profiling that is a prerequisite for personalized healthcare.

Metabonomics study of the effects of pretreatment with glycyrrhetinic acid on mesaconitine-induced toxicity in rats

ETHNOPHARMACOLOGICAL RELEVANCE

Aconitum carmichaelii Debx. (Fuzi), a commonly use traditional Chinese medicine (TCM), has often been used in combination with Rhizoma Glycyrrhizae (Gancao) to reduce its toxicity due to diester diterpenoid alkaloids aconitine, mesaconitine, and hypaconitine. However, the mechanism of detoxication is still unclear. Glycyrrhetinic acid (GA) is the metabolite of glycyrrhizinic acid (GL), the major component of Gancao. In present study, the effect of GA on the changes of metabolic profiles induced by mesaconitine was investigated using NMR-based metabolomic approaches.

MATERIALS AND METHODS

Fifteen male Wistar rats were divided into a control group, a group administered mesaconitine alone, and a group administered mesaconitine with one pretreatment with GA. Their urine samples were used for NMR spectroscopic metabolic profiling. Statistical analyses such as orthogonal projections to latent structures-discriminant analysis (OPLS-DA), t-test, hierarchical cluster, and pathway analysis were used to detect the effects of pretreatment with GA on mesaconitine-induced toxicity.

RESULTS

The OPLS-DA score plots showed the metabolic profiles of GA-pretreated rats apparently approach to those of normal rats compared to mesaconitine-induced rats. From the t-test and boxplot results, the concentrations of leucine/isoleucine, lactate, acetate, succinate, trimethylamine (TMA), dimethylglycine (DMG), 2-oxo-glutarate, creatinine/creatine, glycine, hippurate, tyrosine and benzoate were significantly changed in metabolic profiles of mesaconitine-induced rats. The disturbed metabolic pathways include amino acid biosynthesis and metabolism.

CONCLUSIONS

GA-pretreatment can mitigate the metabolic changes caused by mesaconitine-treatment on rats, indicating that prophylaxis with GA could reduce the toxicity of mesaconitine at the metabolic level.

Evidence for altered metabolic pathways during environmental stress: (1)H-NMR spectroscopy based metabolomics and clinical studies on subjects of sea-voyage and Antarctic-stay

The Antarctic context is an analogue of space travel, with close similarity in ambience of extreme climate, isolation, constrained living spaces, disrupted sleep cycles, and environmental stress. The present study examined the impact of the harsh habitat of Antarctica on human physiology and its metabolic pathways, by analyzing human serum samples, using (1)H-NMR spectroscopy for identification of metabolites; and quantifying other physiological and clinical parameters for correlation between expression data and metabolite data. Sera from seven adult males (of median age 36years) who participated in this study, from the 28th Indian Expeditionary group to the Antarctica station Maitri, were collected in chronological sequence. These included: i) baseline control; ii) during ship journey; iii) at Antarctica, in the months of March, May, August and November; to enable study of temporal evolution of monitored physiological states. 29 metabolites in serum were identified from the 400MHz (1)H-NMR spectra. Out of these, 19 metabolites showed significant variations in levels, during the ship journey and the stay at Maitri, compared to the base-line levels. Further biochemical analysis also supported these results, indicating that the ship journey, and the long-term Antarctic exposure, affected kidney and liver functioning. Our metabolite data highlights for the first time the effect of environmental stress on the patho-physiology of the human system. Multivariate analysis tools were employed for this metabonomics study, using (1)H-NMR spectroscopy.

Pattern recognition and biomarker validation using quantitative1H-NMR-based metabolomics

The collection of global metabolic data and their interpretation (both spectral and biochemical) using modern spectroscopic techniques and appropriate statistical approaches, are known as 'metabolic profiling', 'metabonomics' or 'metabolomics'. This review addresses 1H-nuclear magnetic resonance (NMR)-based metabolomic principles and their application in biomedical science, with special emphasis on their potential in translational research in transplantation, oncology, and drug toxicity or discovery. Various steps in metabolomics analysis are described in order to illustrate the types of biological samples, their respective handling and preparation for 1H-NMR analysis; provide a rationale for using pattern-recognition techniques (spectral database concept) versus quantitative 1H-NMR-based metabolomics (metabolite database concept); and identify necessary technological and logistical future developments that will allow 1H-NMR-based metabolomics to become an established tool in biomedical research and patient care.

Estrogenic endocrine-disrupting chemicals: molecular mechanisms of actions on putative human diseases

Endocrine-disrupting chemicals (EDC), including phthalates, bisphenol A (BPA), phytoestrogens such as genistein and daidzein, dichlorodiphenyltrichloroethane (DDT), and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), are associated with a variety of adverse health effects in organisms or progeny by altering the endocrine system. Environmental estrogens, including BPA, phthalates, and phytoestrogens, are the most extensively studied and are considered to mimic the actions of endogenous estrogen, 17β-estradiol (E2). Diverse modes of action of estrogen and estrogen receptors (ERα and ERβ) have been described, but the mode of action of estrogenic EDC is postulated to be more complex and needs to be more clearly elucidated. This review examines the adverse effects of estrogenic EDC on male or female reproductive systems and molecular mechanisms underlying EDC effects that modulate ER-mediated signaling. Mechanisms of action for estrogenic EDC may involve both ER-dependent and ER-independent pathways. Recent findings from systems toxicology of examining estrogenic EDC are also discussed.

Evaluation of cadmium-induced nephrotoxicity using urinary metabolomic profiles in sprague-dawley male rats

The aim of this study was to investigate urinary metabolomic profiles associated with cadmium (Cd)-induced nephrotoxicity and their potential mechanisms. Metabolomic profiles were measured by high-resolution (1)H-nuclear magnetic resonance (NMR) spectroscopy in the urine of rats after oral exposure to CdCl2 (1, 5, or 25 mg/kg) for 6 wk. The spectral data were further analyzed by a multivariate analysis to identify specific urinary metabolites. Urinary excretion levels of protein biomarkers were also measured and CdCl2 accumulated dose-dependently in the kidney. High-dose (25 mg/kg) CdCl2 exposure significantly increased serum blood urea nitrogen (BUN), but serum creatinine (sCr) levels were unchanged. High-dose CdCl2 (25 mg/kg) exposure also significantly elevated protein-based urinary biomarkers including osteopontin, monocyte chemoattractant protein-1 (MCP-1), kidney injury molecules-1 (Kim-1), and selenium-binding protein 1 (SBP1) in rat urine. Under these conditions, six urinary metabolites (citrate, serine, 3-hydroxyisovalerate, 4-hydroxyphenyllactate, dimethylamine, and betaine) were involved in mitochondrial energy metabolism. In addition, a few number of amino acids such as glycine, glutamate, tyrosine, proline, or phenylalanine and carbohydrate (glucose) were altered in urine after CdCl2 exposure. In particular, the metabolites involved in the glutathione biosynthesis pathway, including cysteine, serine, methionine, and glutamate, were markedly decreased compared to the control. Thus, these metabolites are potential biomarkers for detection of Cd-induced nephrotoxicity. Our results further indicate that redox metabolomics pathways may be associated with Cd-mediated chronic kidney injury. These findings provide a biochemical pathway for better understanding of cellular mechanism underlying Cd-induced renal injury in humans.

High-resolution magic angle spinning (1)H magnetic resonance spectroscopy detects choline as a biomarker in a swine obstructive chronic pancreatitis model at an early stage

Chronic pancreatitis (CP) is a progressive inflammatory and fibrotic disease of the pancreas which encompasses a variety of clinical syndromes ranging from mild to life-threatening complications. Metabolomics has increasingly been applied to identify biomarkers for disease diagnosis with particular interest in diseases at an early stage. In this study, we tested a swine obstructive CP model by subtotal ligation of the main pancreatic duct, and the metabolic profiles of the Bama miniature swine pancreas were investigated using high-resolution magic angle spinning proton magnetic resonance spectroscopy (HR MAS (1)H MRS) combined with principal components analysis (PCA). Increases in lactate and choline for mild CP and decreases in glycerophosphocholine, phosphocholine, betaine and glycine were observed from normal pancreas to mild, moderate and severe CP. PCA results showed visual separations among the groups. The increase of choline at an early stage of CP and the decrease of glycerophosphocholine, phosphocholine, betaine and glycine reveal the pathogenesis of CP at a molecular level. The MRS results presented here demonstrate the potential of metabolic profiles in discriminating a normal pancreas from different stages of CP, which may be used to achieve CP early diagnosis and timely intervention to prevent irreversible destruction of the pancreas.

Metabonomic studies of pancreatic cancer response to radiotherapy in a mouse xenograft model using magnetic resonance spectroscopy and principal components analysis

AIM: To investigate the metabolic profiles of xenograft pancreatic cancer before and after radiotherapy by high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS ¹H NMR) combined with principal components analysis (PCA) and evaluate the radiotherapeutic effect.

METHODS: The nude mouse xenograft model of human pancreatic cancer was established by injecting human pancreatic cancer cell SW1990 subcutaneously into the nude mice. When the tumors volume reached 800 mm³, the mice received various radiation doses. Two weeks later, tumor tissue sections were prepared for running the NMR measurements. ¹H NMR and PCA were used to determine the changes in the metabolic profiles of tumor tissues after radiotherapy. Metabolic profiles of normal pancreas, pancreatic tumor tissues, and radiation- treated pancreatic tumor tissues were compared.

RESULTS: Compared with ¹H NMR spectra of the normal nude mouse pancreas, the levels of choline, taurine, alanine, isoleucine, leucine, valine, lactate, and glutamic acid of the pancreatic cancer group were increased, whereas an opposite trend for phosphocholine, glycerophosphocholine, and betaine was observed. The ratio of phosphocholine to creatine, and glycerophosphocholine to creatine showed noticeable decrease in the pancreatic cancer group. After further evaluation of the tissue metabolic profile after treatment with three different radiation doses, no significant change in metabolites was observed in the ¹H NMR spectra, while the inhibition of tumor growth was in proportion to the radiation doses. However, PCA results showed that the levels of choline and betaine were decreased with the increased radiation dose, and conversely, the level of acetic acid was dramatically increased.

CONCLUSION: The combined methods were demonstrated to have the potential for allowing early diagnosis and assessment of pancreatic cancer response to radiotherapy.

Metabolomic changes in follicular fluid induced by soy isoflavones administered to rats from weaning until sexual maturity

Female Wistar rats at 21 days of age were treated with one of three concentrations of soy isoflavones (SIF) (50, 100 or 200mg/kg body weight, orally, once per day) from weaning until sexual maturity (3 months) in order to evaluate the influence of SIF on ovarian follicle development. After treatment, the serum sex hormone levels and enumeration of ovarian follicles of the ovary were measured. The metabolic profile of follicular fluid was determined using HPLC-MS. Principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) was used to identify differences in metabolites and reveal useful toxic biomarkers. The results indicated that modest doses of SIF affect ovarian follicle development, as demonstrated by decreased serum estradiol levels and increases in both ovarian follicle atresia and corpora lutea number in the ovary. SIF treatment-related metabolic alterations in follicular fluid were also found in the PCA and PLS-DA models. The 24 most significantly altered metabolites were identified, including primary sex hormones, amino acids, fatty acids and metabolites involved in energy metabolism. These findings may indicate that soy isoflavones affect ovarian follicle development by inducing metabolomic variations in the follicular fluid.

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.

Taurine detected using high-resolution magic angle spinning (1)H nuclear magnetic resonance: A potential indicator of early myocardial infarction

Magnetic resonance spectroscopy (MRS) is a unique non-invasive method for detecting cardiac metabolic changes. However, MRS in cardiac diagnosis is limited due to insensitivity and low efficiency. Taurine (Tau) is the most abundant free amino acid in the myocardium. We hypothesized that Tau levels may indicate myocardial ischemia and early infarction. Sprague-Dawley rats were divided into seven groups according to different time points during the course of myocardial ischemia, which was induced by left anterior descending coronary artery ligation. Infarcted myocardial tissue was obtained for high-resolution magic angle spinning (1)H nuclear magnetic resonance (NMR) analysis. Results were validated via high-performance liquid chromatography. The Tau levels in the ischemic myocardial tissue were reduced significantly within 5 min compared with those in the control group (relative ratio from 20.27±6.48 to 8.81±0.04, P<0.05) and were maintained for 6 h post-ischemia. Tau levels declined more markedly (56.5%) than creatine levels (48.5%) at 5 min after ligation. This suggests that Tau may have potential as an indicator in the early detection of myocardial ischemia by (1)H MRS.

Investigation of basic mobile phases with positive ESI LC–MS for metabonomics studies

Background: Accurate mass based LC–MS combined with statistical analysis is established as a core analytical technology for metabonomic studies. This is primarily due to the specificity, sensitivity and structural elucidation capabilities of the technology. The vast majority of these studies are performed using acidic-based mobile phases in combination with positive ESI mode LC–MS. Recent studies have investigated the use of highly basic pH mobile phases (>10 pH units) in bioanalytical studies that utilize positive ESI mode LC–MS. This non-traditional combination has been shown to improve analyte retention, chromatographic peak shape, and S/N for a variety of probe pharmaceutical compounds in biofluid samples. Results: The incorporation of basic pH mobile phases resulted in increased retention for analytes that where comparatively weakly retained by a traditional acidic-modified mobile phase. Increased resolution of isomers, which otherwise co-eluted under acidic conditions, was observed. Moreover, the implementation of basic pH mobile phases further allowed for the detection of complementary marker ions. Conclusion: Basic pH mobile phases utilized with positive ESI mode LC–MS have the potential for producing increased information from metabonomic studies and could lead to the detection of analytes that may prove to be valid biomarkers.

Integrating constitutive gene expression and chemoactivity: mining the NCI60 anticancer screen

Studies into the genetic origins of tumor cell chemoactivity pose significant challenges to bioinformatic mining efforts. Connections between measures of gene expression and chemoactivity have the potential to identify clinical biomarkers of compound response, cellular pathways important to efficacy and potential toxicities; all vital to anticancer drug development. An investigation has been conducted that jointly explores tumor-cell constitutive NCI60 gene expression profiles and small-molecule NCI60 growth inhibition chemoactivity profiles, viewed from novel applications of self-organizing maps (SOMs) and pathway-centric analyses of gene expressions, to identify subsets of over- and under-expressed pathway genes that discriminate chemo-sensitive and chemo-insensitive tumor cell types. Linear Discriminant Analysis (LDA) is used to quantify the accuracy of discriminating genes to predict tumor cell chemoactivity. LDA results find 15% higher prediction accuracies, using ∼30% fewer genes, for pathway-derived discriminating genes when compared to genes derived using conventional gene expression-chemoactivity correlations. The proposed pathway-centric data mining procedure was used to derive discriminating genes for ten well-known compounds. Discriminating genes were further evaluated using gene set enrichment analysis (GSEA) to reveal a cellular genetic landscape, comprised of small numbers of key over and under expressed on- and off-target pathway genes, as important for a compound’s tumor cell chemoactivity. Literature-based validations are provided as support for chemo-important pathways derived from this procedure. Qualitatively similar results are found when using gene expression measurements derived from different microarray platforms. The data used in this analysis is available at http://pubchem.ncbi.nlm.nih.gov/andhttp://www.ncbi.nlm.nih.gov/projects/geo (GPL96, GSE32474).

Metabolomic analysis of the toxic effect of chronic low-dose exposure to acephate on rats using ultra-performance liquid chromatography/mass spectrometry

To study the toxic effect of chronic exposure to acephate at low-dose levels, a metabolomics approach based on ultra-performance liquid chromatography/mass spectrometry (UPLC-MS) was applied. Three different doses of 0.5 mg/kg/day, 1.5 mg/kg/day, and 4.5 mg/kg/day acephate were administered to Wistar rats for 24 weeks. Endogenous metabolite profiles were obtained with UPLC-MS for all rats at six time points after treatment. Some metabolites like dimethylthiophosphate and uric acid in urine were detected at week 4. Dimethylthiophosphate, which had the most significant elevations compared with other biomarkers, was considered as an early, sensitive biomarker of exposure to acephate. Moreover, there were some endogenous metabolite changes, which demonstrated that the doses of 1.5 mg/kg/day and 4.5 mg/kg/day of acephate led to renal injury and perturbed the normal metabolic processes of rats, including glucose, nucleic acid, and protein metabolism. A connection between exposure to acephate and the metabolic disturbance has been found and interpreted. Our study indicates that the metabolomics approach based on UPLC-MS of urine provides more information on toxicity than the conventional toxicological evaluation methods in measuring changes and can be considered as a promising technique for the study of the toxic effect of acephate.

Metabolomics and its potential in drug development

Metabolomics is the global and unbiased survey of the complement of small molecules (say, <1 kDa) in a biofluid, tissue, organ or organism and measures the end-products of the cellular metabolism of both endogenous and exogenous substrates. Many drug candidates fail during Phase II and III clinical trials at an enormous cost to the pharmaceutical industry in terms of both time lost and of financial resources. The constantly evolving model of drug development now dictates that biomarkers should be employed in preclinical development for the early detection of likely-to-fail candidates. Biomarkers may also be useful in the preselection of patients and through the subclassification of diseases in clinical drug development. Here we show with examples how metabolomics can assist in the preclinical development phases of discovery, pharmacology, toxicology, and ADME. Although not yet established as a clinical trial patient prescreening procedure, metabolomics shows considerable promise in this regard. We can be certain that metabolomics will join genomics and transcriptomics in lubricating the wheels of clinical drug development in the near future.

Targeting the human genome-microbiome axis for drug discovery: inspirations from global systems biology and traditional Chinese medicine

Most chronic diseases impairing current human public health involve not only the human genome but also gene-environment interactions, and in the latter case the gut microbiome is an important factor. This makes the classical single drug-receptor target drug discovery paradigm much less applicable. There is widespread and increasing international interest in understanding the properties of traditional Chinese medicines (TCMs) for their potential utilization as a source of new drugs for Western markets as emerging evidence indicates that most TCM drugs are actually targeting both the host and its symbiotic microbes. In this review, we explore the challenges of and opportunities for harmonizing Eastern-Western drug discovery paradigms by focusing on emergent functions at the whole body level of humans as superorganisms. This could lead to new drug candidate compounds for chronic diseases targeting receptors outside the currently accepted "druggable genome" and shed light on current high interest issues in Western medicine such as drug-drug and drug-diet-gut microbial interactions that will be crucial in the development and delivery of future therapeutic regimes optimized for the individual patient.

Between Metabolite Relationships: an essential aspect of metabolic change

Not only the levels of individual metabolites, but also the relations between the levels of different metabolites may indicate (experimentally induced) changes in a biological system. Component analysis methods in current 'standard' use for metabolomics, such as Principal Component Analysis (PCA), do not focus on changes in these relations. We therefore propose the concept of 'Between Metabolite Relationships' (BMRs): common changes in the covariance (or correlation) between all metabolites in an organism. Such structural changes may indicate metabolic change brought about by experimental manipulation but which are lost with standard data analysis methods. These BMRs can be analysed by the INdividual Differences SCALing (INDSCAL) method. First the BMR quantification is described and subsequently the INDSCAL method. Finally, two studies illustrate the power and the applicability of BMRs in metabolomics. The first study is about the induced plant response of cabbage to herbivory, of which BMRs are a considerable part. In the second study-a human nutritional intervention study of green tea extract-standard data analysis tools did not reveal any metabolic change, although the BMRs were considerably affected. The presented results show that BMRs can be easily implemented in a wide variety of metabolomic studies. They provide a new source of information to describe biological systems in a way that fits flawlessly into the next generation of systems biology questions, dealing with personalized responses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0316-1) contains supplementary material, which is available to authorized users.

Metabonomics reveals plasma metabolic changes and inflammatory marker in polycystic ovary syndrome patients

Polycystic ovary syndrome (PCOS) is a common, clinically heterogeneous endocrine disorder affecting women of reproductive age, associated with endocrinopathy and metabolic abnormalities. Although some metabolic parameters have been investigated, very little information has been reported on the changes of small metabolites in biofluids. The aim of this study was to establish the metabolic profile of PCOS and compare it with that of controls. In this cross-sectional study of 34 women with PCOS and 36 controls, contents of small metabolites and lipids in plasma samples were measured using nuclear magnetic resonance (NMR)-based techniques and analyzed using multivariate statistical methods. Significant decrease (P < 0.05) in the levels of amino acids (leucine, isoleucine, methionine, glutamine, and arginine), citrate, choline, and glycerophosphocholine/phosphocholine (GPC/PC), and increase (P < 0.05) in the levels of lactate, dimethylamine (DMA), creatine, and N-acetyl glycoproteins were observed in PCOS patients compared with the controls. Subgroups of patients with obesity, metabolic syndrome, or hyperandrogenism exhibited greater metabolic deviations than their corresponding subgroups without these factors. PCOS patients have perturbations in amino acid metabolism, the tricarboxylic acid (TCA) cycle, and gut microflora, as well as mild disturbances in glucose and lipid metabolism. The elevated level of N-acetyl glycoproteins demonstrates the existence of low-grade chronic inflammation in PCOS patients.

Alternations of phospholipid profile in placenta of streptozotocin-induced diabetic rat

Phospholipids have important roles in many biological processes, but their role in fetal malformation in pregnancy in diabetes is unclear. Metabolic fingerprinting of placental phospholipids in pregnant streptozotocin-induced type 1 diabetic rats was performed using Fourier transform-ion cyclotron resonance mass spectrometry. Some of the fetuses from the pregnant diabetic rats exhibited ventricular septal defects. Alterations of phospholipid compositions in the diabetic rat placenta were detected. We suggest that these changes in phospholipids in the diabetic placenta might be involved in development of fetal malformation in a type 1 diabetic pregnancy.

Metabolomics study of alcohol-induced liver injury and hepatocellular carcinoma xenografts in mice

Alcohol abuse is one of the major causes of liver injury and a promoter for hepatocellular carcinoma (HCC). To understand the disease-associated metabolic changes, we investigated and compared the profiles of metabolites in nude mice with alcohol-induced liver injury or bearing a HCC xenograft (HCCX). Alcohol-induced liver injury was achieved by daily administration of grain liquor, and HCC xenografts were generated by subcutaneous inoculation of HepG2 cells in nude mice. Metabolites in serum samples were profiled by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS). The acquired data was analyzed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to identify potential disease-specific biomarkers. Results showed that the phosphatidylcholine (PC) levels were significantly higher in both liver injury and HCCX mice compared with the control. Interestingly, lysophosphatidylcholines (LPCs) that contain saturated or monounsaturated fatty acids were reduced in both liver injury and HCCX mice, but polyunsaturated fatty acids LPCs were elevated in liver injury mice only. These data delineated the disease-related metabolic alterations of LPCs in liver injury and HCC, suggesting that the LPC profile in serum may be biomarkers for these two common liver diseases.

Non-linear modeling of 1H NMR metabonomic data using kernel-based orthogonal projections to latent structures optimized by simulated annealing

Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and a study of Trypanosoma brucei brucei infection. Automated and user-friendly procedures for the kernel-optimization have been incorporated into version 1.1.1 of the freely available K-OPLS software package for both R and Matlab to enable easy application of K-OPLS for non-linear prediction modeling.