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

A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation

BACKGROUND

Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathological mechanisms underlying depression and candidate clinical biomarkers.

METHODS

Depression-associated metabolomics was studied in 2 datasets from the UK Biobank database: participants with lifetime depression (N = 123,459) and participants with current depression (N = 94,921). The Whitehall II cohort (N = 4744) was used for external validation. CatBoost machine learning was used for modeling, and Shapley additive explanations were used to interpret the model. Fivefold cross-validation was used to validate model performance, training the model on 3 of the 5 sets with the remaining 2 sets for validation and testing, respectively. Diagnostic performance was assessed using the area under the receiver operating characteristic curve.

RESULTS

In the lifetime depression and current depression datasets and sex-specific analyses, 24 significantly associated metabolic biomarkers were identified, 12 of which overlapped in the 2 datasets. The addition of metabolic features slightly improved the performance of a diagnostic model using traditional (nonmetabolomics) risk factors alone (lifetime depression: area under the curve 0.655 vs. 0.658 with metabolomics; current depression: area under the curve 0.711 vs. 0.716 with metabolomics).

CONCLUSIONS

The machine learning model identified 24 metabolic biomarkers associated with depression. If validated, metabolic biomarkers may have future clinical applications as supplementary information to guide early and population-based depression detection.

Mastitis: What It Is, Current Diagnostics, and the Potential of Metabolomics to Identify New Predictive Biomarkers

Periparturient diseases continue to be the greatest challenge to both farmers and dairy cows. They are associated with a decrease in productivity, lower profitability, and a negative impact on cows’ health as well as public health. This review article discusses the pathophysiology and diagnostic opportunities of mastitis, the most common disease of dairy cows. To better understand the disease, we dive deep into the causative agents, traditional paradigms, and the use of new technologies for diagnosis, treatment, and prevention of mastitis. This paper takes a systems biology approach by highlighting the relationship of mastitis with other diseases and introduces the use of omics sciences, specifically metabolomics and its analytical techniques. Concluding, this review is backed up by multiple studies that show how earlier identification of mastitis through predictive biomarkers can benefit the dairy industry and improve the overall animal health.

Bioactive Compounds from Marine Sponges and Algae: Effects on Cancer Cell Metabolome and Chemical Structures

Metabolomics represent the set of small organic molecules generally called metabolites, which are located within cells, tissues or organisms. This new “omic” technology, together with other similar technologies (genomics, transcriptomics and proteomics) is becoming a widely used tool in cancer research, aiming at the understanding of global biology systems in their physiologic or altered conditions. Cancer is among the most alarming human diseases and it causes a considerable number of deaths each year. Cancer research is one of the most important fields in life sciences. In fact, several scientific advances have been made in recent years, aiming to illuminate the metabolism of cancer cells, which is different from that of healthy cells, as suggested by Otto Warburg in the 1950s. Studies on sponges and algae revealed that these organisms are the main sources of the marine bioactive compounds involved in drug discovery for cancer treatment and prevention. In this review, we analyzed these two promising groups of marine organisms to focus on new metabolomics approaches for the study of metabolic changes in cancer cell lines treated with chemical extracts from sponges and algae, and for the classification of the chemical structures of bioactive compounds that may potentially prove useful for specific biotechnological applications.

Serum Metabolic Disturbances in Lung Cancer Investigated through an Elaborative NMR-Based Serum Metabolomics Approach

Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using high-field 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89-1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with p-value < 0.05) and diagnostic potential using the ROC curve analysis. The analysis revealed relevant metabolic disturbances associated with LC. Among various circulatory metabolites, six metabolites, including histidine, glutamine, glycine, threonine, alanine, and valine, were found to be of apposite diagnostic potential for clinical implications. These metabolic alterations indicated altered glucose metabolism, aberrant fatty acid synthesis, and augmented utilization of various amino acids including active glutaminolysis in LC.

Metabolomics and metabolites in ischemic stroke

Stroke is a major reason for disability and the second highest cause of death in the world. When a patient is admitted to a hospital, it is necessary to identify the type of stroke, and the likelihood for development of a recurrent stroke, vascular dementia, and depression. These factors could be determined using different biomarkers. Metabolomics is a very promising strategy for identification of biomarkers. The advantage of metabolomics, in contrast to other analytical techniques, resides in providing low molecular weight metabolite profiles, rather than individual molecule profiles. Technically, this approach is based on mass spectrometry and nuclear magnetic resonance. Furthermore, variations in metabolite concentrations during brain ischemia could alter the principal neuronal functions. Different markers associated with ischemic stroke in the brain have been identified including those contributing to risk, acute onset, and severity of this pathology. In the brain, experimental studies using the ischemia/reperfusion model (IRI) have shown an impaired energy and amino acid metabolism and confirmed their principal roles. Literature data provide a good basis for identifying markers of ischemic stroke and hemorrhagic stroke and understanding metabolic mechanisms of these diseases. This opens an avenue for the successful use of identified markers along with metabolomics technologies to develop fast and reliable diagnostic tools for ischemic and hemorrhagic stroke.

An elaborative NMR based plasma metabolomics study revealed metabolic derangements in patients with mild cognitive impairment: a study on north Indian population

Mild cognitive impairment (MCI) is transition phase between cognitive decline and dementia. The current study aims to investigate altered metabolic pattern in plasma of MCI for potential biomarkers. MCI (N = 50) and healthy controls (HC, N = 50) age group 55-75 years were screened based on Mini Mental State Examination Test (MMSE) and diffusion tensor imaging (DTI imaging). The MMSE score of MCI was significantly lower (25.74 ± 1.83) compared to healthy control subjects (29 ± 1). The MCI patients exhibit significant changes in white matter integrity in the right frontal lobe, right temporal lobe, left frontal lobe, forcep major, fornix, corpus callosum. Further, the plasma samples of twenty seven MCI patients (N = 27) and twenty HC subjects (N = 20; having no significant differences in any demographics) were analyzed using ¹H NMR based metabolomics approach. Consistent with many previous reports, the levels of several plasma metabolites were found to be elevated in MCI patients compared to healthy controls. Further univariate and multivariate ROC curve analyses provided three plasma metabolites as a diagnostic panel of biomarker for MCI; which are lysine, glycine, and glutamine. Overall, the results of this study will help to improve the diagnostic and prognostic strategies of MCI in addition to improving our understanding about disease pathogenesis. We believe that the over-nutritional metabolic phenotype of MCI needs to be targeted for developing future dietary interventions so that the progression of MCI can be limited. Metabolic derangements associated with Mild Cognitive Impairment.

Metabolomics: An emerging potential approach to decipher critical illnesses

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.

Metabolic Profile of Patients with Severe Endometriosis: a Prospective Experimental Study

Endometriosis is a common disease affecting women in reproductive age. There are several hypotheses on the pathogenesis of this disease. Often, its lesions and symptoms overlap with those of many other medical and surgical conditions, causing a delay in diagnosis. Metabolomics represents a useful diagnostic tool for the study of metabolic changes during a different physiological or pathological status. We used ¹H-NMR to explore metabolic alteration in a cohort of patients with endometriosis in order to contribute to a better understanding of the pathophysiology of the disease and to suggest new useful biomarkers. Thirty-seven patients were recruited for the metabolomic analysis: 22 patients affected by symptomatic endometriosis and 15 not affected by it. Their serum samples were collected and analyzed with ¹H-NMR. Multivariate statistical analysis was conducted, followed by univariate and pathway analyses. Partial Least Square Discriminant Analysis (PLS-DA) was performed to determine the presence of any differences between the non-endometriosis and endometriosis samples (R²X = 0.596, R²Y = 0.713, Q² = 0.635, and p < 0.0001). β-hydroxybutyric acid and glutamine were significantly increased, whereas tryptophan was significantly decreased in the endometriosis patients. ROC curves were built to test the diagnostic power of the metabolites (β-hydroxybutyric acid: AUC = 0.85 CI = 0.71-0.99; glutamine: AUC = 0.83 CI = 0.68-0.98; tryptophan: AUC = 0.75 CI = 0.54-0.95; β-hydroxybutyric acid + glutamine + tryptophan AUC = 0.92 CI = 0.81-1). The metabolomic approach enabled the identification of several metabolic alterations occurring in women with endometriosis. These findings may provide new bases for a better understanding of the pathophysiological mechanisms of the disease and for the discovery of new biomarkers. Trial registration number NCT02337816.

1H-NMR based metabolomics reveals the nutrient differences of two kinds of freshwater fish soups before and after simulated gastrointestinal digestion

Soups show diverse health functions, which could be linked to their original nutrient profiles and metabolites derived from digestion. NMR spectroscopy is a robust and rapid method that unveils or identifies the chemical composition of food or food-derived metabolites. In the current study, the 1H-NMR spectroscopy approach was applied to identify the differences in metabolic profiling of two kinds of home-cooked freshwater fish soups (crucian carp and snakehead fish) before and after in vitro gastrointestinal digestion. The nutritional profiles of these soups were studied using the 1H-NMR method for the first time. Two metabolomics methods, PCA (Principal Component Analysis) and OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis), were used to analyze the data. On the whole, levels of amino acid metabolites such as valine (Val), tyrosine, choline, taurine (Tau) and glycine were higher in the crucian carp soup, whereas higher levels of fatty acids and unsaturated fatty acids were found in the snakehead soup. Furthermore, the high content of seven metabolites valine, leucine, EPA C20:5 (PUFA eicosapentaenoic acid), acetic acid, taurine, GPCho (phosphatidylcholine) and creatine showed an upward trend after simulated gastrointestinal digestion. The results demonstrate that the 1H-NMR metabolic profile of different fish soups can shed some light on our understanding of food functional properties and dietary therapy. Furthermore, changes of metabolites in digested fish soups could reveal information about chemical compounds which play important roles in the body.

[Metabolomic profiling in patients with metabolic syndrome]

OBJECTIVE

To identify biomarkers, which are most specific for patients with metabolic syndrome (MS) using metabolomic profiling.

MATERIALS AND METHODS

Metabolomic profiling of patients with MS and comparison of their profile with the profile of volunteers was performed using high-performance liquid chromatography-mass-spectrometry.

RESULTS

The metabolomic profile of MS patients differed in several amino acids, including choline, cysteine, and serine and in the acylcarnitine group (р&lt;0.05 for all comparisons).

CONCLUSION

The metabolites most specific for MS patients were identified. Increased concentrations of a combination of amino acids and carnitines can be considered as possible additional risk factors for cardiovascular diseases.

An NMR based panorama of the heterogeneous biology of acute respiratory distress syndrome (ARDS) from the standpoint of metabolic biomarkers

Acute respiratory distress syndrome (ARDS), manifested by intricate etiology and pathophysiology, demands careful clinical surveillance due to its high mortality and imminent life support measures. NMR based metabolomics provides an approach for ARDS which culminates from a wide spectrum of illness thereby confounding early manifestation and prognosis predictors. ¹ H NMR with its manifold applications in critical disease settings can unravel the biomarker of ARDS thus holding potent implications by providing surrogate endpoints of clinical utility. NMR metabolomics which is the current apogee platform of omics trilogy is contributing towards the possible panacea of ARDS by subsequent validation of biomarker credential on larger datasets. In the present review, the physiological derangements that jeopardize the whole metabolic functioning in ARDS are exploited and the biomarkers involved in progression are addressed and substantiated. The following sections of the review also outline the clinical spectrum of ARDS from the standpoint of NMR based metabolomics which is an emerging element of systems biology. ARDS is the main premise of intensivists textbook, which has been thoroughly reviewed along with its incidence, progressive stages of severity, new proposed diagnostic definition, and the preventive measures and the current pitfalls of clinical management. The advent of new therapies, the need for biomarkers, the methodology and the contemporary promising approaches needed to improve survival and address heterogeneity have also been evaluated. The review has been stepwise illustrated with potent biometrics employed to selectively pool out differential metabolites as diagnostic markers and outcome predictors. The following sections have been drafted with an objective to better understand ARDS mechanisms with predictive and precise biomarkers detected so far on the basis of underlying physiological parameters having close proximity to diseased phenotype. The aim of this review is to stimulate interest in conducting more studies to help resolve the complex heterogeneity of ARDS with biomarkers of clinical utility and relevance.

Nuclear magnetic resonance spectroscopy of human body fluids and in vivo magnetic resonance spectroscopy: Potential role in the diagnosis and management of prostate cancer

Prostate cancer is the most common solid organ cancer in men, and the second most common cause of male cancer-related mortality. It has few effective therapies, and is difficult to diagnose accurately. Prostate-specific antigen (PSA), which is currently the most effective diagnostic tool available, cannot reliably discriminate between different pathologies, and in fact only around 30% of patients found to have elevated levels of PSA are subsequently confirmed to actually have prostate cancer. As such, there is a desperate need for more reliable diagnostic tools that will allow the early detection of prostate cancer so that the appropriate interventions can be applied. Nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance spectroscopy (MRS) are 2 high throughput, noninvasive analytical procedures that have the potential to enable differentiation of prostate cancer from other pathologies using metabolomics, by focusing specifically on certain metabolites which are associated with the development of prostate cancer cells and its progression. The value that this type of approach has for the early detection, diagnosis, prognosis, and personalized treatment of prostate cancer is becoming increasingly apparent. Recent years have seen many promising developments in the fields of NMR spectroscopy and MRS, with improvements having been made to hardware as well as to techniques associated with the acquisition, processing, and analysis of related data. This review focuses firstly on proton NMR spectroscopy of blood serum, urine, and expressed prostatic secretions in vitro, and then on 1- and 2-dimensional proton MRS of the prostate in vivo. Major advances in these fields and methodological principles of data collection, acquisition, processing, and analysis are described along with some discussion of related challenges, before prospects that proton MRS has for future improvements to the clinical management of prostate cancer are considered.

Analysis of metabolites in chardonnay dry white wine with various inactive yeasts by 1H NMR spectroscopy combined with pattern recognition analysis

The study aimed to investigate the effect of five inactive yeasts on the metabolites of Chardonnay dry white wines vinified in 2016 in Shacheng Manor Wine Co. Ltd., Hebei province, China. In this research, proton nuclear magnetic resonance (NMR) spectroscopy coupled multivariate analysis (¹H NMR-PCA/PLS-DA) were applied to identify and discriminate the different wine products. The results of principle component analysis (PCA) showed that there was significant difference between the metabolites of sample wines with different inactive yeasts, among them, the content of polyols, organic acids, amino acids and choline was notably influenced. The results of partial least squares discrimination analysis (PLS-DA) confirmed that the metabolites contributed to the discrimination of the wines were 2,3-butanediol, ethyl acetate, malic acid, valine, succinic acid, lactic acid, tartaric acid, glycerol, gallic acid, choline, proline, and alanine.

Metabolic fingerprinting of chorionic villous samples in normal pregnancy and chromosomal disorders

OBJECTIVE

Placenta-related biological samples are used in biomedical research to investigate placental development. Metabolomics represents a promising approach for studying placental metabolism in an effort to explain physiological and pathological mechanisms. The aim of this study was to investigate metabolic changes in chorionic villi during the first trimester of pregnancy in euploid and aneuploid cases.

METHODS

Samples from 21 women (13 euploid and eight aneuploid) were analyzed with ¹ H-nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC). Multivariate statistical analysis was performed, and differences in metabolites were used to identify the altered metabolic pathways.

RESULTS

A regression model to test the correlation between fetal crown-rump length (CRL) and metabolic profile of chorionic villi was performed in euploid pregnancies (R² was 0.69 for the NMR analysis and 0.94 for the GC-MS analysis). Supervised analysis was used to compare chorionic villi of euploid and aneuploid fetuses (NMR: R² X = 0.70, R² Y = 0.65, Q²  = 0.30, R² X = 0.62; GC-MS: R² Y = 0.704, Q²  = 0.444). Polyol pathways, myo-inositol, and oxidative stress seem to have a fundamental role in euploid and aneuploid pregnancies.

CONCLUSION

Polyol pathways may have a crucial role in energy production in early pregnancy. Excessive activation in aneuploid pregnancies may lead to increased oxidative stress. Metabolomics represents a promising approach to investigate placental metabolic changes.

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.

A biplot correlation range for group-wise metabolite selection in mass spectrometry

Background

Analytic methods are available to acquire extensive metabolic information in a cost-effective manner for personalized medicine, yet disease risk and diagnosis mostly rely upon individual biomarkers based on statistical principles of false discovery rate and correlation. Due to functional redundancies and multiple layers of regulation in complex biologic systems, individual biomarkers, while useful, are inherently limited in disease characterization. Data reduction and discriminant analysis tools such as principal component analysis (PCA), partial least squares (PLS), or orthogonal PLS (O-PLS) provide approaches to separate the metabolic phenotypes, but do not offer a statistical basis for selection of group-wise metabolites as contributors to metabolic phenotypes.

Methods

We present a dimensionality-reduction based approach termed ‘biplot correlation range (BCR)’ that uses biplot correlation analysis with direct orthogonal signal correction and PLS to provide the group-wise selection of metabolic markers contributing to metabolic phenotypes.

Results

Using a simulated multiple-layer system that often arises in complex biologic systems, we show the feasibility and superiority of the proposed approach in comparison of existing approaches based on false discovery rate and correlation. To demonstrate the proposed method in a real-life dataset, we used LC-MS based metabolomics to determine spectrum of metabolites present in liver mitochondria from wild-type (WT) mice and thioredoxin-2 transgenic (TG) mice. We select discriminatory variables in terms of increased score in the direction of class identity using BCR. The results show that BCR provides means to identify metabolites contributing to class separation in a manner that a statistical method by false discovery rate or statistical total correlation spectroscopy can hardly find in complex data analysis for predictive health and personalized medicine.

Electronic supplementary material

The online version of this article (10.1186/s13040-019-0191-2) contains supplementary material, which is available to authorized users.

NMR-Based Metabolomic Approach To Elucidate the Differential Cellular Responses during Mitigation of Arsenic(III, V) in a Green Microalga

Nuclear magnetic resonance (NMR)-based metabolomic approach is a high-throughput fingerprinting technique that allows a rapid snapshot of metabolites without any prior knowledge of the organism. To demonstrate the applicability of NMR-based metabolomics in the field of microalgal-based bioremediation, novel freshwater microalga Scenedesmus sp. IITRIND2 that showed hypertolerance to As(III, V) was chosen for evaluating the metabolic perturbations during arsenic stress in both its oxidation states As(III) and As(V). Using NMR spectroscopy, we were able to identify and quantify an array of ∼45 metabolites, including amino acids, sugars, organic acids, phosphagens, osmolytes, nucleotides, etc. The NMR metabolomic experiments were complemented with various biophysical techniques to establish that the microalga tolerated the arsenic stress using a complex interplay of metabolites. The two different arsenic states distinctly influenced the microalgal cellular mechanisms due to their altered physicochemical properties. Eighteen differentially identified metabolites related to bioremediation of arsenic were then correlated to the major metabolic pathways to delineate the variable stress responses of microalga in the presence of As(III, V).

Metabolomic profile of systemic sclerosis patients

Systemic sclerosis (SSc) is an autoimmune disease of unknown aetiology characterized by vascular lesions, immunological alterations and diffuse fibrosis of the skin and internal organs. Since recent evidence suggests that there is a link between metabolomics and immune mediated disease, serum metabolic profile of SSc patients and healthy controls was investigated by ¹H-NMR and GC-MS techniques. The results indicated a lower level of aspartate, alanine, choline, glutamate, and glutarate in SSc patients compared with healthy controls. Moreover, comparing patients affected by limited SSc (lcSSc) and diffuse SSc (dcSSc), 6 discriminant metabolites were identified. The multivariate analysis performed using all the metabolites significantly different revealed glycolysis, gluconeogenesis, energetic pathways, glutamate metabolism, degradation of ketone bodies and pyruvate metabolism as the most important networks. Aspartate, alanine and citrate yielded a high area under receiver-operating characteristic (ROC) curves (AUC of 0.81; CI 0.726–0.93) for discriminating SSc patients from controls, whereas ROC curve generated with acetate, fructose, glutamate, glutamine, glycerol and glutarate (AUC of 0.84; CI 0.7–0.98) discriminated between lcSSc and dcSSc. These results indicated that serum NMR-based metabolomics profiling method is sensitive and specific enough to distinguish SSc from healthy controls and provided a feasible diagnostic tool for the diagnosis and classification of the disease.

Serum and plasma amino acids as markers of prediabetes, insulin resistance, and incident diabetes

Presently, routine screening misses many cases of prediabetes and early type 2 diabetes (T2D). Therefore, better biomarkers are needed for a simple and early detection of abnormalities of glucose metabolism and prediction of future T2D. Possible candidates for this include plasma or serum amino acids because glucose and amino acid metabolism are closely connected. This review presents the available evidence of this connectivity and discusses its clinical implications. First, we examine the underlying physiological, pre-analytical, and analytical issues. Then, we summarize results of human studies that evaluate amino acid levels as markers for insulin resistance, prediabetes, and future incident T2D. Finally, we illustrate the interconnection of amino acid levels and metabolic syndrome with our own data from a deeply phenotyped human cohort. We also discuss how amino acids may contribute to the pathophysiology of T2D. We conclude that elevated branched-chain amino acids and reduced glycine are currently the most robust and consistent amino acid markers for prediabetes, insulin resistance, and future T2D. Yet, we are cautious regarding the clinical potential even of these parameters because their discriminatory power is insufficient and their levels depend not only on glycemia, but also on other components of the metabolic syndrome. The identification of more precise intermediates of amino acid metabolism or combinations with other biomarkers will, therefore, be necessary to obtain in order to develop laboratory tests that can improve T2D screening.

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.

Nuclear magnetic resonance- and mass spectrometry-based metabolomics to study maleic acid toxicity from repeated dose exposure in rats

Maleic acid (MA), a chemical intermediate used in many consumer and industrial products, was intentionally adulterated in a variety of starch-based foods and instigated food safety incidents in Asia. We aim to elucidate possible mechanisms of MA toxicity after repeated exposure by (1) determining the changes of metabolic profile using ¹ H nuclear magnetic resonance spectroscopy and multivariate analysis, and (2) investigating the occurrence of oxidative stress using liquid chromatography tandem mass spectrometry by using Sprague-Dawley rat urine samples. Adult male rats were subjected to a 28 day subchronic study (0, 6, 20 and 60 mg kg^-1 ) via oral gavage. Urine was collected twice a day on days 0, 7, 14, 21 and 28; organs underwent histopathological examination. Changes in body weight and relative kidney weights in medium- and high-dose groups were significantly different compared to controls. Morphological alterations were evident in the kidneys and liver. Metabolomic results demonstrated that MA exposure increases the urinary concentrations of 8-hydroxy-2'-deoxyguanosine, 8-nitroguanine and 8-iso-prostaglandin F_2α ; levels of acetoacetate, hippurate, alanine and acetate demonstrated time- and dose-dependent variations in the treatment groups. Findings suggest that MA consumption escalates oxidative damage, membrane lipid destruction and disrupt energy metabolism. These aforementioned changes in biomarkers and endogenous metabolites elucidate and assist in characterizing the possible mechanisms by which MA induces nephro- and hepatotoxicity.

Investigation of discriminant metabolites in tamoxifen-resistant and choline kinase-alpha-downregulated breast cancer cells using 1H-nuclear magnetic resonance spectroscopy

Metabolites linked to changes in choline kinase-α (CK-α) expression and drug resistance, which contribute to survival and autophagy mechanisms, are attractive targets for breast cancer therapies. We previously reported that autophagy played a causative role in driving tamoxifen (TAM) resistance of breast cancer cells (BCCs) and was also promoted by CK-α knockdown, resulting in the survival of TAM-resistant BCCs. There is no comparative study yet about the metabolites resulting from BCCs with TAM-resistance and CK-α knockdown. Therefore, the aim of this study was to explore the discriminant metabolic biomarkers responsible for TAM resistance as well as CK-α expression, which might be linked with autophagy through a protective role. A total of 33 intracellular metabolites, including a range of amino acids, energy metabolism-related molecules and others from cell extracts of the parental cells (MCF-7), TAM-resistant cells (MCF-7/TAM) and CK-α knockdown cells (MCF-7/shCK-α, MCF-7/TAM/shCK-α) were analyzed by proton nuclear magnetic resonance spectroscopy (¹H-NMRS). Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) revealed the existence of differences in the intracellular metabolites to separate the 4 groups: MCF-7 cells, MCF-7/TAM cells, MCF-7-shCK-α cells, and MCF-7/TAM/shCK-α cells. The metabolites with VIP>1 contributed most to the differentiation of the cell groups, and they included fumarate, UA (unknown A), lactate, myo-inositol, glycine, phosphocholine, UE (unknown E), glutamine, formate, and AXP (AMP/ADP/ATP). Our results suggest that these altered metabolites would be promising metabolic biomarkers for a targeted therapeutic strategy in BCCs that exhibit TAM-resistance and aberrant CK-α expression, which triggers a survival and drug resistance mechanism.

Diagnostic Applications of Nuclear Magnetic Resonance-Based Urinary Metabolomics

Metabolomics is a rapidly growing field with potential applications in various disciplines. In particular, metabolomics has received special attention in the discovery of biomarkers and diagnostics. This is largely due to the fact that metabolomics provides critical information related to the downstream products of many cellular and metabolic processes which could provide a snapshot of the health/disease status of a particular tissue or organ. Many of these cellular products eventually find their way to urine; hence, analysis of urine via metabolomics has the potential to yield useful diagnostic and prognostic information. Although there are a number of analytical platforms that can be used for this purpose, this review article will focus on nuclear magnetic resonance-based metabolomics. Furthermore, although there have been many studies addressing different diseases and metabolic disorders, the focus of this review article will be in the following specific applications: urinary tract infection, kidney transplant rejection, diabetes, some types of cancer, and inborn errors of metabolism. A number of methodological considerations that need to be taken into account for the development of a clinically useful optimal test are discussed briefly.

NMR Techniques in Metabolomic Studies: A Quick Overview on Examples of Utilization

Metabolomics is a rapidly developing branch of science that concentrates on identifying biologically active molecules with potential biomarker properties. To define the best biomarkers for diseases, metabolomics uses both models (in vitro, animals) and human, as well as, various techniques such as mass spectroscopy, gas chromatography, liquid chromatography, infrared and UV-VIS spectroscopy and nuclear magnetic resonance. The last one takes advantage of the magnetic properties of certain nuclei, such as ¹H, ¹³C, ³¹P, ¹⁹F, especially their ability to absorb and emit energy, what is crucial for analyzing samples. Among many spectroscopic NMR techniques not only one-dimensional (1D) techniques are known, but for many years two-dimensional (2D, for example, COSY, DOSY, JRES, HETCORE, HMQS), three-dimensional (3D, DART-MS, HRMAS, HSQC, HMBC) and solid-state NMR have been used. In this paper, authors taking apart fundamental division of nuclear magnetic resonance techniques intend to shown their wide application in metabolomic studies, especially in identifying biomarkers.

Mass Spectrometry Based Metabolomics Comparison of Liver Grafts from Donors after Circulatory Death (DCD) and Donors after Brain Death (DBD) Used in Human Orthotopic Liver Transplantation

Use of marginal liver grafts, especially those from donors after circulatory death (DCD), has been considered as a solution to organ shortage. Inferior outcomes have been attributed to donor warm ischaemic damage in these DCD organs. Here we sought to profile the metabolic mechanisms underpinning donor warm ischaemia. Non-targeted Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry metabolomics was applied to biopsies of liver grafts from donors after brain death (DBD; n = 27) and DCD (n = 10), both during static cold storage (T₁) as well as post-reperfusion (T₂). Furthermore 6 biopsies from DBD donors prior to the organ donation (T₀) were also profiled. Considering DBD and DCD together, significant metabolic differences were discovered between T₁ and T₂ (688 peaks) that were primarily related to amino acid metabolism, meanwhile T₀ biopsies grouped together with T₂, denoting the distinctively different metabolic activity of the perfused state. Major metabolic differences were discovered between DCD and DBD during cold-phase (T₁) primarily related to glucose, tryptophan and kynurenine metabolism, and in the post-reperfusion phase (T₂) related to amino acid and glutathione metabolism. We propose tryptophan/kynurenine and S-adenosylmethionine as possible biomarkers for the previously established higher graft failure of DCD livers, and conclude that the associated pathways should be targeted in more exhaustive and quantitative investigations.

Quantitative Metabolomic Profiling of Plasma, Urine, and Liver Extracts by 1H NMR Spectroscopy Characterizes Different Stages of Atherosclerosis in Hamsters

Atherosclerosis (AS) is a progressive disease that contributes to cardiovascular disease and shows a complex etiology, including genetic and environmental factors. To understand systemic metabolic changes and to identify potential biomarkers correlated with the occurrence and perpetuation of diet-induced AS, we applied ¹H NMR-based metabolomics to detect the time-related metabolic profiles of plasma, urine, and liver extracts from male hamsters fed a high fat and high cholesterol (HFHC) diet. Conventional biochemical assays and histopathological examinations as well as protein expression analyses were performed to provide complementary information. We found that diet treatment caused obvious aortic lesions, lipid accumulation, and inflammatory infiltration in hamsters. Downregulation of proteins related to cholesterol metabolism, including hepatic SREBP2, LDL-R, CYP7A1, SR-BI, HMGCR, LCAT, and SOAT1 was detected, which elucidated the perturbation of cholesterol homeostasis during the HFHC diet challenge. Using "targeted analysis", we quantified 40 plasma, 80 urine, and 60 liver hydrophilic extract metabolites. Multivariate analyses of the identified metabolites elucidated sophisticated metabolic disturbances in multiple matrices, including energy homeostasis, intestinal microbiota functions, inflammation, and oxidative stress coupled with the metabolisms of cholesterol, fatty acids, saccharides, choline, amino acids, and nucleotides. For the first time, our results demonstrate a time-dependent metabolic progression of multiple biological matrices in hamsters from physiological status to early AS and further to late-stage AS, demonstrating that ¹H NMR-based metabolomics is a reliable tool for early diagnosis and monitoring of the process of AS.

Global Isotope Metabolomics Reveals Adaptive Strategies for Nitrogen Assimilation

Nitrogen cycling is a microbial metabolic process essential for global ecological/agricultural balance. To investigate the link between the well-established ammonium and the alternative nitrate assimilation metabolic pathways, global isotope metabolomics was employed to examine three nitrate reducing bacteria using (15)NO3 as a nitrogen source. In contrast to a control (Pseudomonas stutzeri RCH2), the results show that two of the isolates from Oak Ridge, Tennessee (Pseudomonas N2A2 and N2E2) utilize nitrate and ammonia for assimilation concurrently with differential labeling observed across multiple classes of metabolites including amino acids and nucleotides. The data reveal that the N2A2 and N2E2 strains conserve nitrogen-containing metabolites, indicating that the nitrate assimilation pathway is a conservation mechanism for the assimilation of nitrogen. Co-utilization of nitrate and ammonia is likely an adaption to manage higher levels of nitrite since the denitrification pathways utilized by the N2A2 and N2E2 strains from the Oak Ridge site are predisposed to the accumulation of the toxic nitrite. The use of global isotope metabolomics allowed for this adaptive strategy to be investigated, which would otherwise not have been possible to decipher.

1H NMR-based serum metabolomics reveals erythromycin-induced liver toxicity in albino Wistar rats

Introduction:

Erythromycin (ERY) is known to induce hepatic toxicity which mimics other liver diseases. Thus, ERY is often used to produce experimental models of drug-induced liver-toxicity. The serum metabolic profiles can be used to evaluate the liver-toxicity and to further improve the understanding of underlying mechanism.

Objective:

To establish the serum metabolic patterns of Erythromycin induced hepatotoxicity in albino wistar rats using ¹H NMR based serum metabolomics.

Experimental:

Fourteen male rats were randomly divided into two groups (n = 7 in each group): control and ERY treated. After 28 days of intervention, the metabolic profiles of sera obtained from ERY and control groups were analyzed using high-resolution 1D ¹H CPMG and diffusion-edited nuclear magnetic resonance (NMR) spectra. The histopathological and SEM examinations were employed to evaluate the liver toxicity in ERY treated group.

Results:

The serum metabolic profiles of control and ERY treated rats were compared using multivariate statistical analysis and the metabolic patterns specific to ERY-induced liver toxicity were established. The toxic response of ERY was characterized with: (a) increased serum levels of Glucose, glutamine, dimethylamine, malonate, choline, phosphocholine and phospholipids and (b) decreased levels of isoleucine, leucine, valine, alanine, glutamate, citrate, glycerol, lactate, threonine, circulating lipoproteins, N-acetyl glycoproteins, and poly-unsaturated lipids. These metabolic alterations were found to be associated with (a) decreased TCA cycle activity and enhanced fatty acid oxidation, (b) dysfunction of lipid and amino acid metabolism and (c) oxidative stress.

Conclusion and Recommendations:

Erythromycin is often used to produce experimental models of liver toxicity; therefore, the established NMR-based metabolic patterns will form the basis for future studies aiming to evaluate the efficacy of anti-hepatotoxic agents or the hepatotoxicity of new drug-formulations.

Metabolomic fingerprint of severe obesity is dynamically affected by bariatric surgery in a procedure-dependent manner

BACKGROUND

Obesity is associated with multiple diseases. Bariatric surgery is the most effective therapy for severe obesity that can reduce body weight and obesity-associated morbidity. The metabolic alterations associated with obesity and respective changes after bariatric surgery are incompletely understood.

OBJECTIVE

We comprehensively assessed metabolic alterations associated with severe obesity and distinct bariatric procedures.

DESIGN

In our longitudinal observational study, we applied a (1)H-nuclear magnetic resonance-based global, untargeted metabolomics strategy on human serum samples that were collected before and repeatedly ≤1 y after distinct bariatric procedures [i.e., a sleeve gastrectomy, proximal Roux-en Y gastric bypass (RYGB), and distal RYGB]. For comparison, we also analyzed serum samples from normal-weight and less-obese subjects who were matched for 1-y postoperative body mass index (BMI) values of the surgical groups.

RESULTS

We identified a metabolomic fingerprint in obese subjects that was clearly discriminated from that of normal-weight subjects. Furthermore, we showed that bariatric surgery (sleeve gastrectomy and proximal and distal RYGB) dynamically affected this fingerprint in a procedure-dependent manner, thereby establishing new fingerprints that could be discriminated from those of BMI-matched and normal-weight control subjects. Metabolites that largely contributed to the metabolomic fingerprints of severe obesity were aromatic and branched-chain amino acids (elevated), metabolites related to energy metabolism (pyruvate and citrate; elevated), and metabolites suggested to be derived from gut microbiota (formate, methanol, and isopropanol; all elevated).

CONCLUSION

Our data indicate that bariatric surgery, irrespective of the specific kind of procedure used, reverses most of the metabolic alterations associated with obesity and suggest profound changes in gut microbiome-host interactions after the surgery. This trial was registered at clinicaltrials.gov as NCT02480322.

Biomarker differences between cadaveric grafts used in human orthotopic liver transplantation as identified by coulometric electrochemical array detection (CEAD) metabolomics

Metabolomics in systems biology research unravels intracellular metabolic changes by high throughput methods, but such studies focusing on liver transplantation (LT) are limited. Microdialysate samples of liver grafts from donors after circulatory death (DCD; n=13) and brain death (DBD; n=27) during cold storage and post-reperfusion phase were analyzed through coulometric electrochemical array detection (CEAD) for identification of key metabolomics changes. Metabolite peak differences between the graft types at cold phase, post-reperfusion trends, and in failed allografts, were identified against reference chromatograms. In the cold phase, xanthine, uric acid, and kynurenine were overexpressed in DCD by 3-fold, and 3-nitrotyrosine (3-NT) and 4-hydroxy-3-methoxymandelic acid (HMMA) in DBD by 2-fold (p<0.05). In both grafts, homovanillic acid and methionine increased by 20%-30% with each 100 min increase in cold ischemia time (p<0.05). Uric acid expression was significantly different in DCD post-reperfusion. Failed allografts had overexpression of reduced glutathione and kynurenine (cold phase) and xanthine (post-reperfusion) (p<0.05). This differential expression of metabolites between graft types is a novel finding, meanwhile identification of overexpression of kynurenine in DCD grafts and in failed allografts is unique. Further studies should examine kynurenine as a potential biomarker predicting graft function, its causation, and actions on subsequent clinical outcomes.

Serum metabolomic profiles evaluated after surgery may identify patients with oestrogen receptor negative early breast cancer at increased risk of disease recurrence. Results from a retrospective study

PURPOSE

Metabolomics is a global study of metabolites in biological samples. In this study we explored whether serum metabolomic spectra could distinguish between early and metastatic breast cancer patients and predict disease relapse.

METHODS

Serum samples were analysed from women with metastatic (n = 95) and predominantly oestrogen receptor (ER) negative early stage (n = 80) breast cancer using high resolution nuclear magnetic resonance spectroscopy. Multivariate statistics and a Random Forest classifier were used to create a prognostic model for disease relapse in early patients.

RESULTS

In the early breast cancer training set (n = 40), metabolomics correctly distinguished between early and metastatic disease in 83.7% of cases. A prognostic risk model predicted relapse with 90% sensitivity (95% CI 74.9-94.8%), 67% specificity (95% CI 63.0-73.4%) and 73% predictive accuracy (95% CI 70.6-74.8%). These results were reproduced in an independent early breast cancer set (n = 40), with 82% sensitivity, 72% specificity and 75% predictive accuracy. Disease relapse was associated with significantly lower levels of histidine (p = 0.0003) and higher levels of glucose (p = 0.01), and lipids (p = 0.0003), compared with patients with no relapse.

CONCLUSIONS

The performance of a serum metabolomic prognostic model for disease relapse in individuals with ER-negative early stage breast cancer is promising. A confirmation study is ongoing to better define the potential of metabolomics as a host and tumour-derived prognostic tool.

Mid-ATR-FTIR spectroscopic profiling of HIV/AIDS sera for novel systems diagnostics in global health

Global health, whether in developed or developing countries, is in need of robust systems diagnostics for major diseases, such as HIV/AIDS, impacting the world populations. Fourier transform Infrared (FTIR) spectroscopy of serum is a quick and reagent-free methodology with which to analyze metabolic alterations such as those caused by disease or treatment. In this study, Attenuated Total Reflectance Fourier-Transform (ATR-FTIR) Spectroscopy was investigated as a means of distinguishing HIV-infected treatment-experienced (HIV(pos) ART(pos), n=39) and HIV-infected-treatment-naïve (HIV(pos) ART(neg), n=16) subjects from uninfected control subjects (n=30). Multivariate pattern recognition techniques, including partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA), successfully distinguished sample classes, while univariate approaches identified significant differences (p<0.05) after Benjamini-Hochberg corrections. OPLS-DA discriminated between all groups with sensitivity, specificity, and accuracy of >90%. Compared to uninfected controls, HIV(pos) ART(pos) and HIV(pos) ART(neg) subjects displayed significant differences in spectral regions linked to lipids/fatty acids (3010 cm(-1)), carbohydrates (1299 cm(-1); 1498 cm(-1)), glucose (1035 cm(-1)), and proteins (1600 cm(-1); 1652 cm(-1)). These are all molecules shown by conventional biochemical analysis to be affected by HIV/ART interference. The biofluid metabolomics approach applied here successfully differentiated global metabolic profiles of HIV-infected patients and uninfected controls and detected potential biomarkers for development into indicators of host response to treatment and/or disease progression. Our findings therefore contribute to ongoing efforts for capacity-building in global health for robust omics science and systems diagnostics towards major diseases impacting population health.

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.

The current state of drug discovery and a potential role for NMR metabolomics

The pharmaceutical industry has significantly contributed to improving human health. Drugs have been attributed to both increasing life expectancy and decreasing health care costs. Unfortunately, there has been a recent decline in the creativity and productivity of the pharmaceutical industry. This is a complex issue with many contributing factors resulting from the numerous mergers, increase in out-sourcing, and the heavy dependency on high-throughput screening (HTS). While a simple solution to such a complex problem is unrealistic and highly unlikely, the inclusion of metabolomics as a routine component of the drug discovery process may provide some solutions to these problems. Specifically, as the binding affinity of a chemical lead is evolved during the iterative structure-based drug design process, metabolomics can provide feedback on the selectivity and the in vivo mechanism of action. Similarly, metabolomics can be used to evaluate and validate HTS leads. In effect, metabolomics can be used to eliminate compounds with potential efficacy and side effect problems while prioritizing well-behaved leads with druglike characteristics.

Detection of hepatocellular carcinoma in hepatitis C patients: biomarker discovery by LC-MS

Hepatocellular carcinoma (HCC) accounts for most cases of liver cancer worldwide; contraction of hepatitis C (HCV) is considered a major risk factor for liver cancer even when individuals have not developed formal cirrhosis. Global, untargeted metabolic profiling methods were applied to serum samples from patients with either HCV alone or HCC (with underlying HCV). The main objective of the study was to identify metabolite based biomarkers associated with cancer risk, with the long term goal of ultimately improving early detection and prognosis. Serum global metabolite profiles from patients with HCC (n=37) and HCV (n=21) were obtained using high performance liquid chromatography-mass spectrometry (HPLC-MS) methods. The selection of statistically significant metabolites for partial least-squares discriminant analysis (PLS-DA) model creation based on biological and statistical significance was contrasted to that of a traditional approach utilizing p-values alone. A PLS-DA model created using the former approach resulted in a model with 92% sensitivity, 95% specificity, and an AUROC of 0.93. A series of PLS-DA models iteratively utilizing three to seven metabolites that were altered significantly (p<0.05) and sufficiently (FC≤0.7 or FC≥1.3) showed good performance using p-values alone; the best of these PLS-DA models was capable of generating 73% sensitivity, 95% specificity, and an AUROC of 0.92. Metabolic profiles derived from LC-MS readily distinguish patients with HCC and HCV from those with HCV only. Differences in the metabolic profiles between high-risk individuals and HCC indicate the possibility of identifying the early development of liver cancer in at risk patients. The use of biological significance as a selection process prior to PLS-DA modeling may offer improved probabilities for translation of newly discovered biomarkers to clinical application.

Hands-on workshops as an effective means of learning advanced technologies including genomics, proteomics and bioinformatics

Genomics and proteomics have emerged as key technologies in biomedical research, resulting in a surge of interest in training by investigators keen to incorporate these technologies into their research. At least two types of training can be envisioned in order to produce meaningful results, quality publications and successful grant applications: (1) immediate short-term training workshops and (2) long-term graduate education or visiting scientist programs. We aimed to fill the former need by providing a comprehensive hands-on training course in genomics, proteomics and informatics in a coherent, experimentally-based framework. This was accomplished through a National Heart, Lung, and Blood Institute (NHLBI)-sponsored 10-day Genomics and Proteomics Hands-on Workshop held at National Jewish Health (NJH) and the University of Colorado School of Medicine (UCD). The course content included comprehensive lectures and laboratories in mass spectrometry and genomics technologies, extensive hands-on experience with instrumentation and software, video demonstrations, optional workshops, online sessions, invited keynote speakers, and local and national guest faculty. Here we describe the detailed curriculum and present the results of short- and long-term evaluations from course attendees. Our educational program consistently received positive reviews from participants and had a substantial impact on grant writing and review, manuscript submissions and publications.

Coefficient of Variation, Signal-to-Noise Ratio, and Effects of Normalization in Validation of Biomarkers from NMR-based Metabonomics Studies

A primary goal of metabonomics research is biomarker discovery for human diseases based on differences in metabolic profiles between healthy and diseased patient populations. One of the most significant challenges in biomarker discovery is validation, which implicitly depends on the coefficient of variation (CV) associated with the measurement technique. This paper investigates how the CV of metabolite resonances measured by nuclear magnetic resonance spectroscopy (NMR) depends on signal-to-noise ratio (SNR) and normalization method. CVs were calculated for NMR resonance peaks in a series of NMR spectra of five synthetic urine samples collected over an eight-month period. An inverse correlation was detected between SNR and CV for all normalization methods. Small peaks with SNR<15 tended to have larger CVs (15-30%) compared to peaks with the highest SNR>150, which typically had smaller CVs (5-10%). The inverse relationship between CV and SNR roughly obeyed a log₁₀ dependence. Quotient normalization (QN) tended to produce smaller CVs for smaller peaks, but larger CVs for the strongest peaks in the data, compared to no normalization, normalization to total intensity (NTI) or normalization to an internal standard (NIS). Consequently, quotient normalization appears optimal for validating low concentration metabolites. NTI or NIS appear superior to QN for samples that have very small variation in total signal intensity. While the inverse relationship between CV and log₁₀(SNR) did not strictly hold for all metabolites, weaker concentration metabolites will likely require more rigorous validation as potential biomarkers since they tend to have poorer reproducibility.

Recent advances in asthma biomarker research

Asthma is characterized by recurrent and reversible airflow obstruction, which is routinely monitored by history and physical examination, spirometry and home peak flow diaries. As airway inflammation is central to asthma pathogenesis, its monitoring should be part of patient management plans. Fractional exhaled nitric oxide level (FeNO) is the most extensively studied biomarker of airway inflammation, and FeNO references were higher in Chinese (Asians) than Whites. Published evidence was inconclusive as to whether FeNO is a useful management strategy for asthma. Other biomarkers include direct (histamine, methacholine) and indirect (adenosine, hypertonic saline) challenges of bronchial hyperresponsiveness (BHR), induced sputum and exhaled breath condensate (EBC). A management strategy that normalized sputum eosinophils among adult patients resulted in reductions of BHR and asthma exacerbations. However, subsequent adult and pediatric studies failed to replicate these benefits. Asthma phenotypes as defined by inflammatory cell populations in sputum were also not stable over a 12-month period. A recent meta-analysis concluded that induced sputum is not accurate enough to be applied in routine monitoring of childhood asthma. There is poor correlation between biomarkers that reflect different asthma dimensions: spirometry (airway caliber), BHR (airway reactivity) and FeNO or induced sputum (airway inflammation). Lastly, EBC is easily obtained noninvasively by cooling expired air. Many biomarkers ranging from acidity (pH), leukotrienes, aldehydes, cytokines to growth factors have been described. However, significant overlap between groups and technical difficulty in measuring low levels of inflammatory molecules are the major obstacles for EBC research. Metabolomics is an emerging analytical method for EBC biomarkers. In conclusion, both FeNO and induced sputum are useful asthma biomarkers. However, they will only form part of the clinical picture. Longitudinal studies with focused hypotheses and well-designed protocols are needed to establish the roles of these biomarkers in asthma management. The measurement of biomarkers in EBC remains a research tool.

Combining Hydrophilic Interaction Chromatography (HILIC) and Isotope Tagging for Off-Line LC-NMR Applications in Metabolite Analysis

The complementary use of liquid chromatography (LC) and nuclear magnetic resonance (NMR) has shown high utility in a variety of fields. While the significant benefit of spectral simplification can be achieved for the analysis of complex samples, other limitations remain. For example, (1)H LC-NMR suffers from pH dependent chemical shift variations, especially during urine analysis, owing to the high physiological variation of urine pH. Additionally, large solvent signals from the mobile phase in LC can obscure lower intensity signals and severely limit the number of metabolites detected. These limitations, along with sample dilution, hinder the ability to make reliable chemical shift assignments. Recently, stable isotopic labeling has been used to detect quantitatively specific classes of metabolites of interest in biofluids. Here we present a strategy that explores the combined use of two-dimensional hydrophilic interaction chromatography (HILIC) and isotope tagged NMR for the unambiguous identification of carboxyl containing metabolites present in human urine. The ability to separate structurally related compounds chromatographically, in off-line mode, followed by detection using (1)H-(15)N 2D HSQC (two-dimensional heteronuclear single quantum coherence) spectroscopy, resulted in the assignment of low concentration carboxyl-containing metabolites from a library of isotope labeled compounds. The quantitative nature of this strategy is also demonstrated.

Metabolite analysis distinguishes between mice with epidermolysis bullosa acquisita and healthy mice

Background

Epidermolysis bullosa acquisita (EBA) is a rare skin blistering disease with a prevalence of 0.2/ million people. EBA is characterized by autoantibodies against type VII collagen. Type VII collagen builds anchoring fibrils that are essential for the dermal-epidermal junction. The pathogenic relevance of antibodies against type VII collagen subdomains has been demonstrated both in vitro and in vivo. Despite the multitude of clinical and immunological data, no information on metabolic changes exists.

Methods

We used an animal model of EBA to obtain insights into metabolomic changes during EBA. Sera from mice with immunization-induced EBA and control mice were obtained and metabolites were isolated by filtration. Proton nuclear magnetic resonance (NMR) spectra were recorded and analyzed by principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA) and random forest.

Results

The metabolic pattern of immunized mice and control mice could be clearly distinguished with PCA and PLS-DA. Metabolites that contribute to the discrimination could be identified via random forest. The observed changes in the metabolic pattern of EBA sera, i.e. increased levels of amino acid, point toward an increased energy demand in EBA.

Conclusions

Knowledge about metabolic changes due to EBA could help in future to assess the disease status during treatment. Confirming the metabolic changes in patients needs probably large cohorts.

Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer

Breast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long-term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n = 8), partial (n = 14) and no response (n = 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography-mass spectrometry (LC-MS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LC-MS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients.

Tissue-based approaches to study pharmacodynamic endpoints in early phase oncology clinical trials

Anti-cancer clinical drug development is currently costly and slow with a high attrition rate. There is thus an urgent and unmet need to integrate pharmacodynamic biomarkers into early phase clinical trials in the framework provided by the “pharmacologic audit trail” in order to overcome this challenge. This review discusses the rationale, advantages and disadvantages, as well as the practical considerations of various tissue-based approaches to perform pharmacodynamic studies in early phase oncology clinical trials using case histories of molecular targeting agents such as PI3K, m-TOR, HSP90, HDAC and PARP inhibitors. These approaches include the use of normal “surrogate” tissues such as peripheral blood mononuclear cells, platelet-rich plasma, plucked hair follicles, skin biopsies, plasma-based endocrine assays, proteomics, metabolomics and circulating endothelial cells. In addition, the review discusses the use of neoplastic tissues including tumor biopsies, circulating tumor DNA and tumor cells and metabolomic approaches. The utilization of these tissues and technology platforms to study biomarkers will help accelerate the development of molecularly targeted agents for the treatment of cancer.

Integration of ¹H NMR and UPLC-Q-TOF/MS for a comprehensive urinary metabonomics study on a rat model of depression induced by chronic unpredictable mild stress

Depression is a type of complex psychiatric disorder with long-term, recurrent bouts, and its etiology remains largely unknown. Here, an integrated approach utilizing ¹H NMR and UPLC-Q-TOF/MS together was firstly used for a comprehensive urinary metabonomics study on chronic unpredictable mild stress (CUMS) treated rats. More than twenty-nine metabolic pathways were disturbed after CUMS treatment and thirty-six potential biomarkers were identified by using two complementary analytical technologies. Among the identified biomarkers, nineteen (10, 11,16, 17, 21–25, and 27–36) were firstly reported as potential biomarkers of CUMS-induced depression. Obviously, this paper presented a comprehensive map of the metabolic pathways perturbed by CUMS and expanded on the multitude of potential biomarkers that have been previously reported in the CUMS model. Four metabolic pathways, including valine, leucine and isoleucine biosynthesis; phenylalanine, tyrosine and tryptophan biosynthesis; tryptophan metabolism; synthesis and degradation of ketone bodies had the deepest influence in the pathophysiologic process of depression. Fifteen potential biomarkers (1–2, 4–6, 15, 18, 20–23, 27, 32, 35–36) involved in the above four metabolic pathways might become the screening criteria in clinical diagnosis and predict the development of depression. Moreover, the results of Western blot analysis of aromatic L-amino acid decarboxylase (DDC) and indoleamine 2, 3-dioxygenase (IDO) in the hippocampus of CUMS-treated rats indicated that depletion of 5-HT and tryptophan, production of 5-MT and altered expression of DDC and IDO together played a key role in the initiation and progression of depression. In addition, none of the potential biomarkers were detected by NMR and LC-MS simultaneously which indicated the complementary of the two kinds of detection technologies. Therefore, the integration of ¹H NMR and UPLC-Q-TOF/MS in metabonomics study provided an approach to identify the comprehensive potential depression-related biomarkers and helpful in further understanding the underlying molecular mechanisms of depression through the disturbance of metabolic pathways.

Metabolomics in chronic kidney disease

Chronic kidney disease (CKD) represents a major challenge to public healthcare. Traditional clinical biomarkers of renal function (blood urea nitrogen and serum creatinine) are not sensitive or specific enough and only increase significantly after the presence of substantial CKD. Therefore, more sensitive biomarkers of CKD are needed. CKD-specific biomarkers at an early disease stage and early diagnosis of specific renal diseases would enable improved therapeutic treatment and reduced the personal and financial burdens. The goal of metabolomics is to identify non-targeted, global small-molecule metabolite profiles of complex samples, such as biofluids and tissues. This method offers the potential for a holistic approach to clinical medicine, as well as improvements in disease diagnoses and the understanding of pathological mechanisms. This review article presents an overview of the recent developments in the field of metabolomics, followed by an in-depth discussion of its application to the study of CKD (primary, chronic glomerulonephritis such as IgA nephropathy; secondary, chronic renal injury such as diabetic nephropathy; chronic renal failure including end-stage kidney disease with and without undergoing replacement therapies, etc), including metabolomic analytical technologies, chemometrics, and metabolomics in experimental and clinical research. We describe the current status of the identification of metabolic biomarkers in CKD. Several markers have been confirmed across multiple studies to detect CKD earlier than traditional clinical chemical and histopathological methods. The application of metabolomics in CKD studies provides researchers the opportunity to gain new insights into metabolic profiling and pathophysiological mechanisms. Particular challenges in the field are presented and placed within the context of future applications of metabolomic approaches to the studies of CKD.

Integrated analysis of transcriptomics and metabolomics profiles

BACKGROUND

Integrated analysis of transcriptomics and metabolomics data has the potential greatly to increase our understanding of metabolic networks and biological systems leading to various potential clinical applications.

OBJECTIVE

The aim is to present different applications as well as analysis tools utilized for the parallel study of gene and metabolite expressions.

METHODS

Publications dealing with integrated analysis of gene and metabolite expression data as well as publications describing tools that can be used for integrated analysis are reviewed.

RESULTS/CONCLUSION

The full benefit of integrated analysis can be achieved only if data from all utilized methods are treated equally by multidisciplinary teams. This approach can lead to advances in functional genomics with possible clinical developments in diagnostics and improved drug target selection.

Utilization of quantitative in vivo pharmacology approaches to assess combination effects of everolimus and irinotecan in mouse xenograft models of colorectal cancer

Purpose

The PI3K/AKT/mTOR pathway is frequently dysregulated in cancers and inhibition of mTOR has demonstrated the ability to modulate pro-survival pathways. As such, we sought to determine the ability of the mTOR inhibitor everolimus to potentiate the antitumor effects of irinotecan in colorectal cancer (CRC).

Experimental Design

The combinatorial effects of everolimus and irinotecan were evaluated in vitro and in vivo in CRC cell lines harboring commonly found mutations in PIK3CA, KRAS and/or BRAF. Pharmacokinetically-directed dosing protocols of everolimus and irinotecan were established and used to assess the in vivo antitumor effects of the agents. At the end of treatment, 3–6 tumors per treatment arm were harvested for biomarker analysis by NMR metabolomics.

Results

Everolimus and irinotecan/SN38 demonstrated synergistic anti-proliferative effects in multiple CRC cell lines in vitro. Combination effects of everolimus and irinotecan were determined in CRC xenograft models using clinically-relevant dosing protocols. Everolimus demonstrated significant tumor growth inhibition alone and when combined with irinotecan in HT29 and HCT116 tumor xenografts. Metabolomic analysis showed that HT29 tumors were more metabolically responsive than HCT116 tumors. Everolimus caused a decrease in glycolysis in both tumor types whilst irinotecan treatment resulted in a profound accumulation of lipids in HT29 tumors indicating a cytotoxic effect.

Conclusions

Quantitative analysis of tumor growth and metabolomic data showed that the combination of everolimus and irinotecan was more beneficial in the BRAF/PIK3CA mutant HT29 tumor xenografts, which had an additive effect, than the KRAS/PIK3CA mutant HCT116 tumor xenografts, which had a less than additive effect.