Metabolic profiling technologies for biomarker discovery in biomedicine and drug development

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.

Patient Stratification in Sepsis: Using Metabolomics to Detect Clinical Phenotypes, Sub-Phenotypes and Therapeutic Response

Infections are common and need minimal treatment; however, occasionally, due to inappropriate immune response, they can develop into a life-threatening condition known as sepsis. Sepsis is a global concern with high morbidity and mortality. There has been little advancement in the treatment of sepsis, outside of antibiotics and supportive measures. Some of the difficulty in identifying novel therapies is the heterogeneity of the condition. Metabolic phenotyping has great potential for gaining understanding of this heterogeneity and how the metabolic fingerprints of patients with sepsis differ based on survival, organ dysfunction, disease severity, type of infection, treatment or causative organism. Moreover, metabolomics offers potential for patient stratification as metabolic profiles obtained from analytical platforms can reflect human individuality and phenotypic variation. This article reviews the most relevant metabolomic studies in sepsis and aims to provide an overview of the metabolic derangements in sepsis and how metabolic phenotyping has been used to identify sub-groups of patients with this condition. Finally, we consider the new avenues that metabolomics could open, exploring novel phenotypes and untangling the heterogeneity of sepsis, by looking at advances made in the field with other -omics technologies.

Exploring the Use of Gas Chromatography Coupled to Chemical Ionization Mass Spectrometry (GC-CI-MS) for Stable Isotope Labeling in Metabolomics

Isotopic-labeling experiments have been valuable to monitor the flux of metabolic reactions in biological systems, which is crucial to understand homeostatic alterations with disease. Experimental determination of metabolic fluxes can be inferred from a characteristic rearrangement of stable isotope tracers (e.g., 13C or 15N) that can be detected by mass spectrometry (MS). Metabolites measured are generally members of well-known metabolic pathways, and most of them can be detected using both gas chromatography (GC)-MS and liquid chromatography (LC)-MS. In here, we show that GC methods coupled to chemical ionization (CI) MS have a clear advantage over alternative methodologies due to GC's superior chromatography separation efficiency and the fact that CI is a soft ionization technique that yields identifiable protonated molecular ion peaks. We tested diverse GC-CI-MS setups, including methane and isobutane reagent gases, triple quadrupole (QqQ) MS in SIM mode, or selected ion clusters using optimized narrow windows (∼10 Da) in scan mode, and standard full scan methods using high resolution GC-(q)TOF and GC-Orbitrap systems. Isobutane as a reagent gas in combination with both low-resolution (LR) and high-resolution (HR) MS showed the best performance, enabling precise detection of isotopologues in most metabolic intermediates of central carbon metabolism. Finally, with the aim of overcoming manual operations, we developed an R-based tool called isoSCAN that automatically quantifies all isotopologues of intermediate metabolites of glycolysis, TCA cycle, amino acids, pentose phosphate pathway, and urea cycle, from LRMS and HRMS data.

Secondary Metabolites in the Treatment of Diabetes Mellitus: A Paradigm Shift

Diabetes mellitus (DM) is a chronic, polygenic and non-infectious group of diseases that occurs due to insulin resistance or its low production by the pancreas and is also associated with lifelong damage, dysfunction and collapse of various organs. Management of diabetes is quite complex having many bodily and emotional complications and warrants efficient measures for prevention and control of the same. As per the estimates of the current and future diabetes prevalence, around 425 million people were diabetic in 2017 which is anticipated to rise up to 629 million by 2045. Various studies have vaguely proven the fact that several vitamins, minerals, botanicals and secondary metabolites demonstrate hypoglycemic activity in vivo as well as in vitro. Flavonoids, anthocyanin, catechin, lipoic acid, coumarin metabolites, etc. derived from herbs were found to elicit a significant influence on diabetes. However, the prescription of herbal compounds depend on various factors, including the degree of diabetes progression, comorbidities, feasibility, economics as well as their ADR profile. For instance, cinnamon could be a more favorable choice for diabetic hypertensive patients. Diabecon®, Glyoherb® and Diabeta Plus® are some of the herbal products that had been launched in the market for the favorable or adjuvant therapy of diabetes. Moreover, Aloe vera leaf gel extract demonstrates significant activity in diabetes. The goal of this review was to inscribe various classes of secondary metabolites, in particular those obtained from plants, and their role in the treatment of DM. Recent advancements in recognizing the markers which can be employed for identifying altered metabolic pathways, biomarker discovery, limitations, metabolic markers of drug potency and off-label effects are also reviewed.

Metabolomic Strategies in Biomarker Research-New Approach for Indirect Identification of Drug Consumption and Sample Manipulation in Clinical and Forensic Toxicology?

Drug of abuse (DOA) consumption is a growing problem worldwide, particularly with increasing numbers of new psychoactive substances (NPS) entering the drug market. Generally, little information on their adverse effects and toxicity are available. The direct detection and identification of NPS is an analytical challenge due to their ephemerality on the drug scene. An approach that does not directly focus on the structural detection of an analyte or its metabolites, would be beneficial for this complex analytical scenario and the development of alternative screening methods could help to provide fast response on suspected NPS consumption. A metabolomics approach might represent such an alternative strategy for the identification of biomarkers for different questions in DOA testing. Metabolomics is the monitoring of changes in small (endogenous) molecules (<1,000 Da) in response to a certain stimulus, e.g., DOA consumption. For this review, a literature search targeting "metabolomics" and different DOAs or NPS was conducted. Thereby, different applications of metabolomic strategies in biomarker research for DOA identification were identified: (a) as an additional tool for metabolism studies bearing the major advantage that particularly a priori unknown or unexpected metabolites can be identified; and (b) for identification of endogenous biomarker or metabolite patterns, e.g., for synthetic cannabinoids or also to indirectly detect urine manipulation attempts by chemical adulteration or replacement with artificial urine samples. The majority of the currently available literature in that field, however, deals with metabolomic studies for DOAs to better assess their acute or chronic effects or to find biomarkers for drug addiction and tolerance. Certain changes in endogenous compounds are detected for all studied DOAs, but often similar compounds/pathways are influenced. When evaluating these studies with regard to possible biomarkers for drug consumption, the observed changes appear, albeit statistically significant, too small to reliably work as biomarker for drug consumption. Further, different drugs were shown to affect the same pathways. In conclusion, metabolomic approaches possess potential for detection of biomarkers indicating drug consumption. More studies, including more sensitive targeted analyses, multi-variant statistical models or deep-learning approaches are needed to fully explore the potential of omics science in DOA testing.

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 and MS Methods for Metabolomics

Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, metabolomics has become more and more popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, UPLC-MS, and GC-MS, have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabolomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.

The re-emergence of natural products for drug discovery in the genomics era

Natural products have been a rich source of compounds for drug discovery. However, their use has diminished in the past two decades, in part because of technical barriers to screening natural products in high-throughput assays against molecular targets. Here, we review strategies for natural product screening that harness the recent technical advances that have reduced these barriers. We also assess the use of genomic and metabolomic approaches to augment traditional methods of studying natural products, and highlight recent examples of natural products in antimicrobial drug discovery and as inhibitors of protein-protein interactions. The growing appreciation of functional assays and phenotypic screens may further contribute to a revival of interest in natural products for drug discovery.

Development of personalized functional foods needs metabolic profiling

PURPOSE OF REVIEW

There is growing interest in applying metabolic profiling technologies to food science as this approach is now embedded into the foodomics toolbox. This review aims at exploring how metabolic profiling can be applied to the development of functional foods.

RECENT FINDINGS

One of the biggest challenges of modern nutrition is to propose a healthy diet to populations worldwide that must suit high inter-individual variability driven by complex gene-nutrient-environment interactions. Although a number of functional foods are now proposed in support of a healthy diet, a one-size-fits-all approach to nutrition is inappropriate and new personalized functional foods are necessary. Metabolic profiling technologies can assist at various levels of the development of functional foods, from screening for food composition to identification of new biomarkers of food intake to support diet intervention and epidemiological studies.

SUMMARY

Modern 'omics' technologies, including metabolic profiling, will support the development of new personalized functional foods of high relevance to 21st century medical challenges, such as controlling the worldwide spread of metabolic disorders and ensuring healthy ageing.

Increased levels of urinary phenylacetylglycine associated with mitochondrial toxicity in a model of drug-induced phospholipidosis

BACKGROUND

Phospholipidosis (PLD) is a lysosomal storage disorder induced by a class of cationic amphiphilic drugs. However, drug-induced PLD is reversible. Evidence of PLD from animal studies with some compounds has led to discontinuation of development. Regulatory authorities are likely to request additional studies when PLD is linked to toxicity.

OBJECTIVE

We conducted a trial to investigate urinary phenylacetylglycine (uPAG) as a biomarker for PLD.

MATERIALS AND METHODS

Five groups of 12 male Wistar rats were dosed once with vehicle, 300 mg/kg or 1500 mg/kg of compound A (known to induce PLD), or 300 mg/kg or 1000 mg/kg of compound B (similar structure, but does not induce PLD) to achieve similar plasma exposures. Following dosing, urine and blood samples underwent nuclear magnetic resonance (NMR), proteomic, and biochemical analyses. Necropsies were performed at 48 and 168 h, organ histopathology evaluated, and gene expression in liver analyzed by microarray. Electron microscopic examination of peripheral lymphocytes was performed.

RESULTS

For compound A, uPAG increased with dose, correlating with lamellar inclusion bodies formation in peripheral lymphocytes. NMR analysis showed decreased tricarboxylic acid cycle intermediates, inferring mitochondrial toxicity. Mitochondrial dysfunction was suggested by uPAG increase, resulting from a switch to anaerobic metabolism or disruption of the urea cycle.

DISCUSSION AND CONCLUSION

uPAG shows utility as a noninvasive biomarker for mitochondrial toxicity associated with drug-induced PLD, providing a mechanistic hypothesis for toxicity associated with PLD likely resulting from combined direct and indirect mitochondrial toxicity via impairment of the proton motor force and alteration of fatty acid catabolism.

Urinary metabolite profiles in premature infants show early postnatal metabolic adaptation and maturation

Objectives: Early nutrition influences metabolic programming and long-term health. We explored the urinary metabolite profiles of 48 premature infants (birth weight < 1500 g) randomized to an enhanced or a standard diet during neonatal hospitalization. Methods: Metabolomics using nuclear magnetic resonance spectroscopy (NMR) was conducted on urine samples obtained during the first week of life and thereafter fortnightly. Results: The intervention group received significantly higher amounts of energy, protein, lipids, vitamin A, arachidonic acid and docosahexaenoic acid as compared to the control group. Enhanced nutrition did not appear to affect the urine profiles to an extent exceeding individual variation. However, in all infants the glucogenic amino acids glycine, threonine, hydroxyproline and tyrosine increased substantially during the early postnatal period, along with metabolites of the tricarboxylic acid cycle (succinate, oxoglutarate, fumarate and citrate). The metabolite changes correlated with postmenstrual age. Moreover, we observed elevated threonine and glycine levels in first-week urine samples of the small for gestational age (SGA; birth weight < 10th percentile for gestational age) as compared to the appropriate for gestational age infants. Conclusion: This first nutri-metabolomics study in premature infants demonstrates that the physiological adaptation during the fetal-postnatal transition as well as maturation influences metabolism during the breastfeeding period. Elevated glycine and threonine levels were found in the first week urine samples of the SGA infants and emerged as potential biomarkers of an altered metabolic phenotype.

Urine NMR metabolomics analysis of breastfeeding biomarkers during and after pregnancy in a large prospective cohort study

BACKGROUND

Modern metabolomic profiling has not yet been applied to human breastfeeding research. A common reason for breastfeeding cessation is perceived insufficient milk production. We investigated broad biochemical profiles in maternal urine collected during and after pregnancy to identify biomarkers related to reduced reported breastfeeding.

METHODS

Fasting urine was collected at three consultations (visit V1: gestational week 8-20; V2: week 28 ± 2; V3: 10-16 weeks postpartum) in the STORK Groruddalen program, a prospective, multiethnic cohort study of gestational diabetes involving healthy, pregnant women in Oslo, Norway, and analyzed using NMR spectroscopy. Breastfeeding at V3 was recorded in three categories: Exclusively breastfeeding (n = 326), partially breastfeeding (n = 156) and formula feeding (n = 67).

RESULTS

Five metabolites were relevant to breastfeeding. Lactose was detected at V1 and increased to 0.1 mM/mM creatinine at V2. Postpartum excretion at V3 was significantly higher in exclusively breastfeeding women than partially or non-breastfeeding (median = 0.29, 0.23 and 0.04 mM/mM creatine, respectively; ANOVA p-value = 2e-70). Glycine excretion at V3 (0.12, 0.10 and 0.06, respectively; p = 2e-5) and at V2 were associated with breastfeeding (0.34, 0.33 and 0.26, respectively; p = 4e-5). Creatine and two unidentified substances also correlated with breastfeeding. NMR metabolomics found no other metabolites differing between categories during pregnancy (V1, V2), and did not predict individual breastfeeding postpartum (V3).

CONCLUSION

Decreased glycine excretion at V2 may indicate difficulties meeting the metabolic demands of the growing fetus, but urine profiles contained otherwise little indication of early adaptations during pregnancy towards reduced biological potential to breastfeed.

Stable isotope-labeled tracers for metabolic pathway elucidation by GC-MS and FT-MS

Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), over the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly widespread metabolic diseases such as cancer, diabetes, and obesity. Emerging NMR and MS atom-tracking technologies and informatics are poised to revolutionize metabolomics-based research because they deliver the high information throughput (HIT) that is needed for deciphering systems biochemistry. In particular, stable isotope-resolved metabolomics (SIRM) enables unambiguous tracking of individual atoms through compartmentalized metabolic networks in a wide range of experimental systems, including human subjects. MS offers a wide range of instrumental capabilities involving different levels of initial capital outlay and operating costs, ranging from gas-chromatography (GC) MS that is affordable by many individual laboratories to the HIT-supporting Fourier-transform (FT) class of MS that rivals NMR in cost and infrastructure support. This chapter focuses on sample preparation, instrument, and data processing procedures for these two extremes of MS instrumentation used in SIRM.

Quality markers addressing preanalytical variations of blood and plasma processing identified by broad and targeted metabolite profiling

BACKGROUND

Metabolomics is a valuable tool with applications in almost all life science areas. There is an increasing awareness of the essential need for high-quality biospecimens in studies applying omics technologies and biomarker research. Tools to detect effects of both blood and plasma processing are a key for assuring reproducible and credible results. We report on the response of the human plasma metabolome to common preanalytical variations in a comprehensive metabolomics analysis to reveal such high-quality markers.

METHODS

Human EDTA blood was subjected to preanalytical variations while being processed to plasma: microclotting, prolonged processing times at different temperatures, hemolysis, and contamination with buffy layer. In a second experiment, EDTA plasma was incubated at different temperatures for up to 16 h. Samples were subjected to GC-MS and liquid chromatography-tandem mass spectrometry-based metabolite profiling (MxP™ Broad Profiling) complemented by targeted methods, i.e., sphingoids (as part of MxP™ Lipids), MxP™ Catecholamines, and MxP™ Eicosanoids.

RESULTS

Short-term storage of blood, hemolysis, and short-term storage of noncooled plasma resulted in statistically significant increases of 4% to 19% and decreases of 8% to 12% of the metabolites. Microclotting, contamination of plasma with buffy layer, and short-term storage of cooled plasma were of less impact on the metabolome (0% to 11% of metabolites increased, 0% to 8% decreased).

CONCLUSIONS

The response of the human plasma metabolome to preanalytical variation demands implementation of thorough quality assurance and QC measures to obtain reproducible and credible results from metabolomics studies. Metabolites identified as sensitive to preanalytics can be used to control for sample quality.

Reconstruction and analysis of human kidney-specific metabolic network based on omics data

With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases.

Looking back into the future: 30 years of metabolomics at TNO

Metabolites have played an essential role in our understanding of life, health, and disease for thousands of years. This domain became much more important after the concept of metabolism was discovered. In the 1950s, mass spectrometry was coupled to chromatography and made the technique more application-oriented and allowed the development of new profiling technologies. Since 1980, TNO has performed system-based metabolic profiling of body fluids, and combined with pattern recognition has led to many discoveries and contributed to the field known as metabolomics and systems biology. This review describes the development of related concepts and applications at TNO in the biomedical, pharmaceutical, nutritional, and microbiological fields, and provides an outlook for the future.

Recent advances in the application of metabolomics to Alzheimer's Disease

The pathophysiological changes associated with Alzheimer's Disease (AD) begin decades before the emergence of clinical symptoms. Understanding the early mechanisms associated with AD pathology is, therefore, especially important for identifying disease-modifying therapeutic targets. While the majority of AD clinical trials to date have focused on anti-amyloid-beta (Aβ) treatments, other therapeutic approaches may be necessary. The ability to monitor changes in cellular networks that include both Aβ and non-Aβ pathways is essential to advance our understanding of the etiopathogenesis of AD and subsequent development of cognitive symptoms and dementia. Metabolomics is a powerful tool that detects perturbations in the metabolome, a pool of metabolites that reflects changes downstream of genomic, transcriptomic and proteomic fluctuations, and represents an accurate biochemical profile of the organism in health and disease. The application of metabolomics could help to identify biomarkers for early AD diagnosis, to discover novel therapeutic targets, and to monitor therapeutic response and disease progression. Moreover, given the considerable parallel between mouse and human metabolism, the use of metabolomics provides ready translation of animal research into human studies for accelerated drug design. In this review, we will summarize current progress in the application of metabolomics in both animal models and in humans to further understanding of the mechanisms involved in AD pathogenesis.

Emerging applications of metabolomics in studying chemopreventive phytochemicals

Phytochemicals from diet and herbal medicines are under intensive investigation for their potential use as chemopreventive agents to block and suppress carcinogenesis. Chemical diversity of phytochemicals, together with complex metabolic interactions between phytochemicals and biological system, can overwhelm the capacity of traditional analytical platforms, and thus pose major challenges in studying chemopreventive phytochemicals. Recent progresses in metabolomics have transformed it to become a robust systems biology tool, suitable for examining both chemical and biochemical events that contribute to the cancer prevention activities of plant preparations or their bioactive components. This review aims to discuss the technical platform of metabolomics and its existing and potential applications in chemoprevention research, including identifying bioactive phytochemicals in plant extracts, monitoring phytochemical exposure in humans, elucidating biotransformation pathways of phytochemicals, and characterizing the effects of phytochemicals on endogenous metabolism and cancer metabolism.

An investigation of fecal volatile organic metabolites in irritable bowel syndrome

Diagnosing irritable bowel syndrome (IBS) can be a challenge; many clinicians resort to invasive investigations in order to rule out other diseases and reassure their patients. Volatile organic metabolites (VOMs) are emitted from feces; understanding changes in the patterns of these VOMs could aid our understanding of the etiology of the disease and the development of biomarkers, which can assist in the diagnosis of IBS. We report the first comprehensive study of the fecal VOMs patterns in patients with diarrhea-predominant IBS (IBS-D), active Crohn's disease (CD), ulcerative colitis (UC) and healthy controls. 30 patients with IBS-D, 62 with CD, 48 with UC and 109 healthy controls were studied. Diagnosis of IBS-D was made using the Manning criteria and all patients with CD and UC met endoscopic, histologic and/or radiologic criteria. Fecal VOMs were extracted by solid phase microextraction (SPME) and analyzed by gas chromatography-mass spectrometry (GC-MS). 240 VOMs were identified. Univariate analysis showed that esters of short chain fatty acids, cyclohexanecarboxylic acid and its ester derivatives were associated with IBS-D (p<0.05), while aldehydes were more abundant in IBD (p<0.05). A predictive model, developed by multivariate analysis, separated IBS-D from active CD, UC and healthy controls with a sensitivity of 94%, 96% and 90%; and a specificity of 82%, 80% and 80% respectively (p<0.05). The understanding of the derivation of these VOMs may cast light on the etiology of IBS-D and IBD. These data show that fecal VOMs analyses could contribute to the diagnosis of IBS-D, for which there is no laboratory test, as well as IBD.

LC-MS-based Metabolomics of Xenobiotic-induced Toxicities

Xenobiotic exposure, especially high-dose or repeated exposure of xenobiotics, can elicit detrimental effects on biological systems through diverse mechanisms. Changes in metabolic systems, including formation of reactive metabolites and disruption of endogenous metabolism, are not only the common consequences of toxic xenobiotic exposure, but in many cases are the major causes behind development of xenobiotic-induced toxicities (XIT). Therefore, examining the metabolic events associated with XIT generates mechanistic insights into the initiation and progression of XIT, and provides guidance for prevention and treatment. Traditional bioanalytical platforms that target only a few suspected metabolites are capable of validating the expected outcomes of xenobiotic exposure. However, these approaches lack the capacity to define global changes and to identify unexpected events in the metabolic system. Recent developments in high-throughput metabolomics have dramatically expanded the scope and potential of metabolite analysis. Among all analytical techniques adopted for metabolomics, liquid chromatography-mass spectrometry (LC-MS) has been most widely used for metabolomic investigations of XIT due to its versatility and sensitivity in metabolite analysis. In this review, technical platform of LC-MS-based metabolomics, including experimental model, sample preparation, instrumentation, and data analysis, are discussed. Applications of LC-MS-based metabolomics in exploratory and hypothesis-driven investigations of XIT are illustrated by case studies of xenobiotic metabolism and endogenous metabolism associated with xenobiotic exposure.

Metabolic changes in urine during and after pregnancy in a large, multiethnic population-based cohort study of gestational diabetes

This study aims to identify novel markers for gestational diabetes (GDM) in the biochemical profile of maternal urine using NMR metabolomics. It also catalogs the general effects of pregnancy and delivery on the urine profile. Urine samples were collected at three time points (visit V1: gestational week 8-20; V2: week 28±2; V3 10-16 weeks post partum) from participants in the STORK Groruddalen program, a prospective, multiethnic cohort study of 823 healthy, pregnant women in Oslo, Norway, and analyzed using (1)H-NMR spectroscopy. Metabolites were identified and quantified where possible. PCA, PLS-DA and univariate statistics were applied and found substantial differences between the time points, dominated by a steady increase of urinary lactose concentrations, and an increase during pregnancy and subsequent dramatic reduction of several unidentified NMR signals between 0.5 and 1.1 ppm. Multivariate methods could not reliably identify GDM cases based on the WHO or graded criteria based on IADPSG definitions, indicating that the pattern of urinary metabolites above micromolar concentrations is not influenced strongly and consistently enough by the disease. However, univariate analysis suggests elevated mean citrate concentrations with increasing hyperglycemia. Multivariate classification with respect to ethnic background produced weak but statistically significant models. These results suggest that although NMR-based metabolomics can monitor changes in the urinary excretion profile of pregnant women, it may not be a prudent choice for the study of GDM.

Surrogate end points: how well do they represent patient-relevant end points?

This review takes a critical look at the concept of replacing patient-relevant end points, such as morbidity or mortality, with surrogate end points in clinical trials. Surrogate end points can be measured earlier in the course of a clinical trial and so are thought to accelerate the drug development process. Furthermore, they might be beneficial to the patients themselves by allowing faster adjustment of therapeutic strategies. However, the fact that in the past several promising surrogate end points have not fulfilled their expectations emphasizes the importance of applying strict evaluation criteria. The evaluation of the candidate surrogate end point prostate-specific antigen using the Prentice criteria and a meta-analytic approach is discussed. Prostate-specific antigen is often used to replace overall or progression-free survival in prostate cancer trials testing the benefit of medical interventions.

Stable isotope-resolved metabolomics and applications for drug development

Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), during the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly cancer. The metabolome is the functional readout of the genome, functional genome, and proteome; it is also an integral partner in molecular regulations for homeostasis. The interrogation of the metabolome, or metabolomics, is now being applied to numerous diseases, largely by metabolite profiling for biomarker discovery, but also in pharmacology and therapeutics. Recent advances in stable isotope tracer-based metabolomic approaches enable unambiguous tracking of individual atoms through compartmentalized metabolic networks directly in human subjects, which promises to decipher the complexity of the human metabolome at an unprecedented pace. This knowledge will revolutionize our understanding of complex human diseases, clinical diagnostics, as well as individualized therapeutics and drug response. In this review, we focus on the use of stable isotope tracers with metabolomics technologies for understanding metabolic network dynamics in both model systems and in clinical applications. Atom-resolved isotope tracing via the two major analytical platforms, NMR and MS, has the power to determine novel metabolic reprogramming in diseases, discover new drug targets, and facilitates ADME studies. We also illustrate new metabolic tracer-based imaging technologies, which enable direct visualization of metabolic processes in vivo. We further outline current practices and future requirements for biochemoinformatics development, which is an integral part of translating stable isotope-resolved metabolomics into clinical reality.

Metabolite profiling of Alzheimer's disease cerebrospinal fluid

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive loss of cognitive functions. Today the diagnosis of AD relies on clinical evaluations and is only late in the disease. Biomarkers for early detection of the underlying neuropathological changes are still lacking and the biochemical pathways leading to the disease are still not completely understood. The aim of this study was to identify the metabolic changes resulting from the disease phenotype by a thorough and systematic metabolite profiling approach. For this purpose CSF samples from 79 AD patients and 51 healthy controls were analyzed by gas and liquid chromatography-tandem mass spectrometry (GC-MS and LC-MS/MS) in conjunction with univariate and multivariate statistical analyses. In total 343 different analytes have been identified. Significant changes in the metabolite profile of AD patients compared to healthy controls have been identified. Increased cortisol levels seemed to be related to the progression of AD and have been detected in more severe forms of AD. Increased cysteine associated with decreased uridine was the best paired combination to identify light AD (MMSE>22) with specificity and sensitivity above 75%. In this group of patients, sensitivity and specificity above 80% were obtained for several combinations of three to five metabolites, including cortisol and various amino acids, in addition to cysteine and uridine.

Applications of mass spectrometry to metabolomics and metabonomics: detection of biomarkers of aging and of age-related diseases

Every 5 years or so new technologies, or new combinations of old ones, seemingly burst onto the science scene and are then sought after until they reach the point of becoming commonplace. Advances in mass spectrometry instrumentation, coupled with the establishment of standardized chemical fragmentation libraries, increased computing power, novel data-analysis algorithms, new scientific applications, and commercial prospects have made mass spectrometry-based metabolomics the latest sought-after technology. This methodology affords the ability to dynamically catalogue and quantify, in parallel, femtomole quantities of cellular metabolites. The study of aging, and the diseases that accompany it, has accelerated significantly in the last decade. Mutant genes that alter the rate of aging have been found that increase lifespan by up to 10-fold in some model organisms, and substantial progress has been made in understanding fundamental alterations that occur at both the mRNA and protein level in tissues of aging organisms. The application of metabolomics to aging research is still relatively new, but has already added significant insight into the aging process. In this review we summarize these findings. We have targeted our manuscript to two audiences: mass spectrometrists interested in applying their technical knowledge to unanswered questions in the aging field, and gerontologists interested in expanding their knowledge of both mass spectrometry and the most recent advances in aging-related metabolomics.

Metabolic profiling reveals key metabolic features of renal cell carcinoma

Recent evidence suggests that metabolic changes play a pivotal role in the biology of cancer and in particular renal cell carcinoma (RCC). Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. α-tocopherol, hippuric acid, myoinositol, fructose-1-phosphate and glucose-1-phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC.

The application of micro-coil NMR probe technology to metabolomics of urine and serum

Increasing the sensitivity and throughput of NMR-based metabolomics is critical for the continued growth of this field. In this paper the application of micro-coil NMR probe technology was evaluated for this purpose. The most commonly used biofluids in metabolomics are urine and serum. In this study we examine different sample limited conditions and compare the detection sensitivity of the micro-coil with a standard 5 mm NMR probe. Sample concentration is evaluated as a means to leverage the greatly improved mass sensitivity of the micro-coil probes. With very small sample volumes, the sensitivity of the micro-coil probe does indeed provide a significant advantage over the standard probe. Concentrating the samples does improve the signal detection, but the benefits do not follow the expected linear increase and are both matrix and metabolite specific. Absolute quantitation will be affected by concentration, but an analysis of relative concentrations is still possible. The choice of the micro-coil probe over a standard tube based probe will depend upon a number of factors including number of samples and initial volume but this study demonstrates the feasibility of high-throughput metabolomics with the micro-probe platform.

NMR and MS methods for metabonomics

Metabonomics, also often referred to as "metabolomics" or "metabolic profiling," is the systematic profiling of metabolites in bio-fluids or tissues of organisms and their temporal changes. In the last decade, metabonomics has become increasingly popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabonomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabonomics, i.e., NMR, LC-MS, UPLC-MS, and GC-MS have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabonomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation, to determining the measurement details of all analytical platforms, and finally, to discussing the corresponding specific steps of data analysis.

Human cytochrome P450 4F11: heterologous expression in bacteria, purification, and characterization of catalytic function

Human cytochrome P450 (P450) 4F11 is still considered an "orphan" because its function is not well characterized. A bacterial expression system was developed for human P450 4F11, producing approximately 230nmol P450 from a 3-l culture of Escherichia coli. P450 4F11 was purified and utilized for untargeted substrate searches in human liver extract using a liquid chromatography/mass spectrometry-based metabolomic and isotopic labeling approach (Tang et al., 2009 [19]). Four fatty acids-palmitic, oleic, arachidonic, and docosahexaenoic-were identified in human liver and verified as substrates of P450 4F11. The products were characterized as omega-hydroxylated fatty acids by gas chromatography-mass spectrometry analysis of their trimethylsilyl derivatives. Kinetic analysis of the oxidation products confirmed that the fatty acids are substrates oxidized by P450 4F11. P450 4F11 also exhibited low activity for some drug N-demethylation reactions but none for activation of several pro-carcinogens.

Exploring disease through metabolomics

Metabolomics approaches provide an analysis of changing metabolite levels in biological samples. In the past decade, technical advances have spurred the application of metabolomics in a variety of diverse research areas spanning basic, biomedical, and clinical sciences. In particular, improvements in instrumentation, data analysis software, and the development of metabolite databases have accelerated the measurement and identification of metabolites. Metabolomics approaches have been applied to a number of important problems, which include the discovery of biomarkers as well as mechanistic studies aimed at discovering metabolites or metabolic pathways that regulate cellular and physiological processes. By providing access to a portion of biomolecular space not covered by other profiling approaches (e.g., proteomics and genomics), metabolomics offers unique insights into small molecule regulation and signaling in biology. In the following review, we look at the integration of metabolomics approaches in different areas of basic and biomedical research, and try to point out the areas in which these approaches have enriched our understanding of cellular and physiological biology, especially within the context of pathways linked to disease.

Elevated pro-inflammatory and lipotoxic mucosal lipids characterise irritable bowel syndrome

AIM: To investigate the pathophysiology of irritable bowel syndrome (IBS) by comparing the global mucosal metabolic profiles of IBS patients with those of healthy controls.

METHODS: Fifteen IBS patients fulfilling the Rome II criteria, and nine healthy volunteers were included in the study. A combined lipidomics (UPLC/MS) and metabolomics (GC × GC-TOF) approach was used to achieve global metabolic profiles of mucosal biopsies from the ascending colon.

RESULTS: Overall, lipid levels were elevated in patients with IBS. The most significant upregulation was seen for pro-inflammatory lysophosphatidylcholines. Other lipid groups that were significantly upregulated in IBS patients were lipotoxic ceramides, glycosphingolipids, and di- and triacylglycerols. Among the metabolites, the cyclic ester 2(3H)-furanone was almost 14-fold upregulated in IBS patients compared to healthy subjects (P = 0.03).

CONCLUSION: IBS mucosa is characterised by a distinct pro-inflammatory and lipotoxic metabolic profile. Especially, there was an increase in several lipid species such as lysophospholipids and ceramides.

Fiber evanescent wave spectroscopy using the mid-infrared provides useful fingerprints for metabolic profiling in humans

Fiber evanescent wave spectroscopy (FEWS) explores the mid-infrared domain, providing information on functional chemical groups represented in the sample. Our goal is to evaluate whether spectral fingerprints obtained by FEWS might orientate clinical diagnosis. Serum samples from normal volunteers and from four groups of patients with metabolic abnormalities are analyzed by FEWS. These groups consist of iron overloaded genetic hemochromatosis (GH), iron depleted GH, cirrhosis, and dysmetabolic hepatosiderosis (DYSH). A partial least squares (PLS) logistic method is used in a training group to create a classification algorithm, thereafter applied to a test group. Patients with cirrhosis or DYSH, two groups exhibiting important metabolic disturbances, are clearly discriminated from control groups with AUROC values of 0.94+/-0.05 and 0.90+/-0.06, and sensibility/specificity of 8684% and 8787%, respectively. When pooling all groups, the PLS method contributes to discriminate controls, cirrhotic, and dysmetabolic patients. Our data demonstrate that metabolic profiling using infrared FEWS is a possible way to investigate metabolic alterations in patients.

Elucidation of functions of human cytochrome P450 enzymes: identification of endogenous substrates in tissue extracts using metabolomic and isotopic labeling approaches

One of the central problems in biochemistry in the postgenomic era is the elucidation of functions of proteins, including "orphan" human cytochromes P450 (P450s), when the substrates are unknown. A general strategy for identification of endogenous substrates of P450s in tissue extracts using metabolomic and isotopic labeling approaches is described, involving four main steps: (1) In vitro incubation of a P450 enzyme system with cofactor and tissue extract is done under a mixture of (18)O(2)/(16)O(2) (1:1). (2) Liquid chromatography/mass spectrometry (LC/MS) assay of an organic extract of the reaction mixture is performed. (3) The isotopic labeling products appearing as M/M + 2 doublets can be directly identified using the program DoGEX (Sanchez-Ponce, R. and Guengerich, F. P. Anal. Chem. 2007, 79, 3355-3362). (4) Characterization of potential candidates is done. Validation of the strategy was established using human P450 7A1 as an initial model to identify its known product, 7alpha-hydroxycholesterol, in liver extracts. The strategy was then applied to human P450s 1A2, 2C8, and 2C9 in untargeted substrate searches with human liver extracts. A total of seven fatty acids were identified and verified as substrates of these three hepatic P450s. The products were subsequently characterized as hydroxylation and epoxidation derivatives of fatty acids, using gas chromatography/mass spectrometry (GC/MS) analysis. Finally, kinetic studies were performed to confirm that the fatty acids are oxidized by P450s 1A2, 2C8, and 2C9. Thus, this strategy has been demonstrated to be useful in identifying reactions in tissue extracts with orphan human P450s.

Advances in separation science applied to metabonomics

Metabonomics focuses on metabolite profile changes in diverse living systems caused by a biological perturbation. These metabolite signatures can be achieved with techniques such as gas chromatography, high-performance liquid chromatography (ultra-high-performance/pressure liquid chromatography and capHPLC), capillary electrophoresis, and capillary electrochromatography normally hyphenated with MS. In this review we present the latest developments of the abovementioned techniques applied in the field of metabonomics, with applications covering phytochemistry, toxicology and clinical research.

Perspectives for Metabolomics in Human Nutrition: An Overview

Metabolomics describes the measurement of the full complement of the products of metabolism in a single biological sample and correlating these metabolomic profiles with known physiological or pathological states. The metabolome offers the possibility of finding unique fingerprints responsible for different phenotypes. Analytical techniques such as nuclear magnetic resonance or mass spectrometry measure thousands of compounds within the metabolome simultaneously and appropriate data mining and database tools allow the finding of significant correlations between the measured metabolomes. The first direct outcome of nutritional metabolomics will be the discovery of biomarkers, which can reveal changes in health and disease but also indicate short term and long-term dietary intake. The concerted actions of nutrigenomics and metabolomics will play a crucial role in understanding how specific interactions of single nucleotide polymorphisms (SNP) influence a person's response to a diet. Finally, systems biology approaches to human nutrition combine transcriptomics, proteomics and metabolomics with the aim of understanding how diets interact within the human being.

Metabolic profiling of a Schistosoma mansoni infection in mouse tissues using magic angle spinning-nuclear magnetic resonance spectroscopy

In order to enhance our understanding of physiological and pathological consequences of a patent Schistosoma mansoni infection in the mouse, we examined the metabolic responses of different tissue samples recovered from the host animal using a metabolic profiling strategy. Ten female NMRI mice were infected with approximately 80 S. mansoni cercariae each, and 10 uninfected age- and sex-matched animals served as controls. At day 74 post infection (p.i.), mice were killed and jejunum, ileum, colon, liver, spleen and kidney samples were removed. We employed (1)H magic angle spinning-nuclear magnetic resonance spectroscopy to generate tissue-specific metabolic profiles. The spectral data were analyzed using multivariate modelling methods including an orthogonal signal corrected-projection to latent structure analysis and hierarchical principal component analysis to assess the differences and/or similarities in metabolic responses between infected and non-infected control mice. Most tissues obtained from S. mansoni-infected mice were characterized by high levels of amino acids, such as leucine, isoleucine, lysine, glutamine and asparagine. High levels of membrane phospholipid metabolites, including glycerophosphoryl choline and phosphoryl choline were found in the ileum, colon, liver and spleen of infected mice. Additionally, low levels of energy-related metabolites, including lipids, glucose and glycogen were observed in ileum, spleen and liver samples of infected mice. Energy-related metabolites in the jejunum, liver and renal medulla were found to be positively correlated with S. mansoni worm burden upon dissection. These findings show that a patent S. mansoni infection causes clear disruption of metabolism in a range of tissues at a molecular level, which can be interpreted in relation to the previously reported signature in a biofluid (i.e. urine), giving further evidence of the global effect of the infection.

An iterative block-shifting approach to retention time alignment that preserves the shape and area of gas chromatography-mass spectrometry peaks

Background

Metabolomics, petroleum and biodiesel chemistry, biomarker discovery, and other fields which rely on high-resolution profiling of complex chemical mixtures generate datasets which contain millions of detector intensity readings, each uniquely addressed along dimensions of time (e.g., retention time of chemicals on a chromatographic column), a spectral value (e.g., mass-to-charge ratio of ions derived from chemicals), and the analytical run number. They also must rely on data preprocessing techniques. In particular, inter-run variance in the retention time of chemical species poses a significant hurdle that must be cleared before feature extraction, data reduction, and knowledge discovery can ensue. Alignment methods, for calibrating retention reportedly (and in our experience) can misalign matching chemicals, falsely align distinct ones, be unduly sensitive to chosen values of input parameters, and result in distortions of peak shape and area.

Results

We present an iterative block-shifting approach for retention-time calibration that detects chromatographic features and qualifies them by retention time, spectrum, and the effect of their inclusion on the quality of alignment itself. Mass chromatograms are aligned pairwise to one selected as a reference. In tests using a 45-run GC-MS experiment, block-shifting reduced the absolute deviation of retention by greater than 30-fold. It compared favourably to COW and XCMS with respect to alignment, and was markedly superior in preservation of peak area.

Conclusion

Iterative block-shifting is an attractive method to align GC-MS mass chromatograms that is also generalizable to other two-dimensional techniques such as HPLC-MS.

Introduction to cheminformatics

Cheminformatics is a relatively new field of information technology that focuses on the collection, storage, analysis, and manipulation of chemical data. The chemical data of interest typically includes information on small molecule formulas, structures, properties, spectra, and activities (biological or industrial). Cheminformatics originally emerged as a vehicle to help the drug discovery and development process, however cheminformatics now plays an increasingly important role in many areas of biology, chemistry, and biochemistry. The intent of this unit is to give readers some introduction into the field of cheminformatics and to show how cheminformatics not only shares many similarities with the field of bioinformatics, but that it can also enhance much of what is currently done in bioinformatics.

Human metabolic network reconstruction and its impact on drug discovery and development

This paper describes the process for the reconstruction of a high quality human metabolic network from the genome information, the existing problems in the reconstruction and why a time-consuming literature based consolidation process is needed. The reconstructed metabolic network provides a unified platform to integrate all the biological and medical information on genes, proteins, metabolites, disease, drugs and drug targets for a system level study of the relationship between metabolism and disease. System analysis of metabolic networks will help us, not only in identifying new drug targets but also in developing a system-oriented drug design strategy.

Advances in biosecurity to 2010 and beyond: towards integrated detection, analysis and response to exotic pest invasions

In order to limit the number and impact of exotic pest invasions, leading-edge technologies must be embraced and embedded within integrated national and international biosecurity systems. Outlined here are recent advances in the detection of exotic pests, and prospects for the early recognition of disease. Applications of new tools are described, using our understanding of the genomes of pathogens and vectors. In addition, the role of mathematical and simulation models to aid both biosecurity planning, and decision making in the face of an epidemic, are discussed, and recent attempts to unify epidemiology and evolutionary dynamics are outlined. Given the importance of emerging diseases and zoonoses, the need to align human and veterinary surveillance within fully integrated systems is underlined.

New technologies around biomarkers and their interplay with drug development

What conductors are to their orchestras, biomarkers are to their associated technologies. Building fundamental science, supporting early diagnosis of diseases and following their progression, improving efficacy and safety of treatments, optimizing patient selection and adapting dosing of drugs, helping decide which therapy is most appropriate; these are examples of a few contexts in which biomarkers are key players. Technology development can definitely not escape being associated with these steps. In other words, today's biomarkers are the thermometers of tomorrow's therapies. This review provides an overview of recently established platforms as well as new and upcoming technologies for biomarker development in the context of drug development. The roles as well as the pros and cons of different disciplines such as genetics, genomics, proteomics, metabonomics, and assay development will be discussed.