An inflammatory arthritis-associated metabolite biomarker pattern revealed by 1H NMR spectroscopy

The metabolic fingerprint of chronic hepatitis C progression: Metabolome shifts and cutting-edge diagnostic options

Hepatitis C virus infection causes chronic diseases such as cirrhosis and hepatocellular carcinoma. Metabolomics research has been shown to be linked to pathophysiologic pathways in liver illnesses. The aim of this study was to investigate the serum metabolic profile of patients with chronic hepatitis C (CHC) infection and to identify underlying mechanisms as well as potential biomarkers associated with the disease. Nuclear magnetic resonance (NMR) was used to evaluate the sera of 83 patients with CHC virus and 52 healthy control volunteers (NMR). Then, multivariate statistical analysis was used to find distinguishing metabolites between the two groups. Sixteen out of 40 metabolites including include 3-HB, betaine, carnitine, creatinine, fucose, glutamine, glycerol, isopropanol, lysine, mannose, methanol, methionine, ornithine, proline, serine, and valine-were shown to be significantly different between the CHC and normal control (NC) groups (variable importance in projection >1 and p < 0.05). All the metabolic perturbations in this disease are associated with pathways of Glycine, serine, and threonine metabolism, glycerolipid metabolism, arginine and proline metabolism, aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, alanine, aspartate, and glutamate metabolism. Multivariate statistical analysis constructed using these expressed metabolites showed CHC patients can be discriminated from NCs with high sensitivity (90%) and specificity (99%). The metabolomics approach may expand the diagnostic armamentarium for patients with CHC while contributing to a comprehensive understanding of disease mechanisms.

Metabolomics Analysis of Mesenchymal Stem Cell (MSC) Therapy in a Phase I Clinical Trial of Septic Shock: An Exploratory Study

Sepsis is the result of an uncontrolled host inflammatory response to infection that may lead to septic shock with multiorgan failure and a high mortality rate. There is an urgent need to improve early diagnosis and to find markers identifying those who will develop septic shock and certainly a need to develop targeted treatments to prevent septic shock and its high mortality. Herein, we explore metabolic alterations due to mesenchymal stromal cell (MSC) treatment of septic shock. The clinical findings for this study were already reported; MSC therapy was well-tolerated and safe in patients in this phase I clinical trial. In this exploratory metabolomics study, 9 out of 30 patients received an escalating dose of MSC treatment, while 21 patients were without MSC treatment. Serum metabolomics profiling was performed to detect and characterize metabolite changes due to MSC treatment and to help determine the sample size needed for a phase II clinical trial and to define a metabolomic response to MSC treatment. Serum metabolites were measured using 1H-NMR and HILIC-MS at times 0, 24 and 72 h after MSC infusion. The results demonstrated the significant impact of MSC treatment on serum metabolic changes in a dose- and time-dependent manner compared to non-MSC-treated septic shock patients. This study suggests that plasma metabolomics can be used to assess the response to MSC therapy and that treatment-related metabolomics effects can be used to help determine the sample size needed in a phase II trial. As this study was not powered to detect outcome, how the treatment-induced metabolomic changes described in this study of MSC-treated septic shock patients are related to outcomes of septic shock in the short and long term will need to be explored in a larger adequately powered phase II clinical trial.

Metabolomics approach of Symphyotrichum squamatum ethanol extract and its nano-Ag formulation protective effect on gastric ulcer via bio-chemical and pathological analyses

ContextGastric ulcer (GU) a widely distributed ailment is associated with many causes, including alcohol consumption.Materials and MethodsChemical profiling of Symphyotrichum squamatum ethanol extract (SSEE) was established via ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-qTOF-MS) and employed in a silver nano-formulation (SSEE-N-Ag). SSEE and SSEE-N-Ag antiulcer activities were estimated against ethanol-induced rats by biochemical, histological, and metabolomics assessments. Reduced glutathione, total antioxidant capacity and prostaglandin E2 levels and gastric mucosa histopathological examination were analysed. The rats' metabolome changing alongside action pathways were elucidated via metabolite profile coupled to multivariate data analysis.ResultsUPLC-MS profiling of SSEE identified 75 components belonging to various classes. Compared with control, EtOH-treated rats showed decreased of tissue GSH, TAC and PGE2 by 62.32%, 51.85% and 47.03% respectively. SSEE and SSEE-N-Ag administration mitigated biochemical and histopathological alterations. Serum metabolomics analysis revealed for changes in several low molecular weight metabolites with ulcer development. These metabolites levels were restored to normal post-administration of SSEE-N-Ag. SSEE-N-Ag as mediated via modulating numerous metabolic pathways such as lipids, pyrimidine, energy metabolism and phosphatidylinositol signalling. This study provides novel insight for metabolic mechanisms underlying gastric ulcer relieving effect.ConclusionPresent results revealed potential antiulcer effect of SSEE and SSEE-N-Ag by decreasing ulcer-associated syndromes, supporting their anti-ulcerogenic action.

Metabolic Profiling in Rheumatoid Arthritis, Psoriatic Arthritis, and Psoriasis: Elucidating Pathogenesis, Improving Diagnosis, and Monitoring Disease Activity

Immune-mediated inflammatory diseases (IMIDs), such as rheumatoid arthritis (RA), psoriatic arthritis (PsA), and psoriasis (Ps), represent autoinflammatory and autoimmune disorders, as well as conditions that have an overlap of both categories. Understanding the underlying pathogeneses, making diagnoses, and choosing individualized treatments remain challenging due to heterogeneous disease phenotypes and the lack of reliable biomarkers that drive the treatment choice. In this review, we provide an overview of the low-molecular-weight metabolites that might be employed as biomarkers for various applications, e.g., early diagnosis, disease activity monitoring, and treatment-response prediction, in RA, PsA, and Ps. The literature was evaluated, and putative biomarkers in different matrices were identified, categorized, and summarized. While some of these candidate biomarkers appeared to be disease-specific, others were shared across multiple IMIDs, indicating common underlying disease mechanisms. However, there is still a long way to go for their application in a routine clinical setting. We propose that studies integrating omics analyses of large patient cohorts from different IMIDs should be performed to further elucidate their pathomechanisms and treatment options. This could lead to the identification and validation of biomarkers that might be applied in the context of precision medicine to improve the clinical outcomes of these IMID patients.

Gastro-protective effect of Artemisia sieberi essential oil against ethanol-induced ulcer in rats as revealed via biochemical, histopathological and metabolomics analysis

CONTEXT

Gastric ulcer is regarded as one of the main clinical ailments with high morbidity and mortality rates.

MATERIALS AND METHODS

Gastro-protective effect of Artemisia sieberi essential oil (AS-EO) in ethanol-induced rats was evaluated via biochemical, histopathological and large-scale metabolomics analyses. Glutathione (GSH), total antioxidant capacity (TAC), prostaglandin (PGE2) and tumor necrosis factor α (TNF-α) alongside with histopathological examination of gastric mucosa were analyzed. Metabolites profiling coupled to Global Natural Products Social molecular networking platform (GNPS) and multivariate data analyses to reveal for changes in rats metabolome with treatments and involved action mechanisms.

RESULTS

Pretreatment with 100 and 200 mg/kg of AS-EO in EtOH-treated rats restored all parameters towards normal status compared to disease model. AS-EO alleviated the histological and pathological damage of gastric tissue caused by ethanol. Metabolites profiling revealed an increase in uracil, cholesterol and fatty acids/fatty acyl amides levels in ulcer rats and restored to normal levels post AS-EO intervention. These results indicated the efficacy of AS-EO in a dose-dependent manner, and to exert protective effects in ulcer rat model by targeting several metabolic pathways viz. lipid, energy, and nucleotide metabolisms.

CONCLUSION

AS-EO adds to the known uses of genus Artemisia as anti-ulcerogenic agent by attenuating oxidative stress and inflammatory responses associated with an ulcer. Several novel biomarkers for ulcer progression in rats were identified and have yet to be confirmed in human models.

Metabolomics in Autoimmune Diseases: Focus on Rheumatoid Arthritis, Systemic Lupus Erythematous, and Multiple Sclerosis

The metabolomics approach represents the last downstream phenotype and is widely used in clinical studies and drug discovery. In this paper, we outline recent advances in the metabolomics research of autoimmune diseases (ADs) such as rheumatoid arthritis (RA), multiple sclerosis (MuS), and systemic lupus erythematosus (SLE). The newly discovered biomarkers and the metabolic mechanism studies for these ADs are described here. In addition, studies elucidating the metabolic mechanisms underlying these ADs are presented. Metabolomics has the potential to contribute to pharmacotherapy personalization; thus, we summarize the biomarker studies performed to predict the personalization of medicine and drug response.

NMR-Based Metabolomics Revealed the Underlying Inflammatory Pathology in Reactive Arthritis Synovial Joints

Reactive arthritis (ReA) is an aseptic synovitis condition that often develops 2-4 weeks after a distant (extra-articular) infection with Chlamydia, Salmonella, Shigella, Campylobacter, and Yersinia species. The metabolic changes in the synovial fluid (SF) may serve as indicative markers to both improve the diagnostic accuracy and understand the underlying inflammatory pathology of ReA. With this aim, the metabolic profiles of SF collected from ReA (n = 58) and non-ReA, i.e., rheumatoid arthritis (RA, n = 21) and osteoarthritis (OA, n = 20) patients, respectively, were measured using NMR spectroscopy and compared using orthogonal partial least-squares discriminant analysis (OPLS-DA). The discriminatory metabolic features were further evaluated for their diagnostic potential using the receiver operating characteristic (ROC) curve analysis. Compared to RA, two (alanine and carnitine), and compared to OA, six (NAG, glutamate, glycerol, isoleucine, alanine, and glucose) metabolic features were identified as diagnostic biomarkers. We further demonstrated the impact of ReA synovitis condition on the serum metabolic profiles through performing a correlation analysis. The Pearson rank coefficient (r) was estimated for 38 metabolites (profiled in both SF and serum samples obtained in pair from ReA patients) and was found significantly positive for 71% of the metabolites (r ranging from 0.17 to 0.87).

Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor

Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (¹H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel ¹H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the ¹H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).

Harnessing the Benefits of Neuroinflammation: Generation of Macrophages/Microglia with Prominent Remyelinating Properties

Excessive inflammation within the CNS is injurious, but an immune response is also required for regeneration. Macrophages and microglia adopt different properties depending on their microenvironment, and exposure to IL4 and IL13 has been used to elicit repair. Unexpectedly, while LPS-exposed macrophages and microglia killed neural cells in culture, the addition of LPS to IL4/IL13-treated macrophages and microglia profoundly elevated IL10, repair metabolites, heparin binding epidermal growth factor trophic factor, antioxidants, and matrix-remodeling proteases. In C57BL/6 female mice, the generation of M(LPS/IL4/IL13) macrophages required TLR4 and MyD88 signaling, downstream activation of phosphatidylinositol-3 kinase/mTOR and MAP kinases, and convergence on phospho-CREB, STAT6, and NFE2. Following mouse spinal cord demyelination, local LPS/IL4/IL13 deposition markedly increased lesional phagocytic macrophages/microglia, lactate and heparin binding epidermal growth factor, matrix remodeling, oligodendrogenesis, and remyelination. Our data show that a prominent reparative state of macrophages/microglia is generated by the unexpected integration of pro- and anti-inflammatory activation cues. The results have translational potential, as the LPS/IL4/IL13 mixture could be locally applied to a focal CNS injury to enhance neural regeneration and recovery.SIGNIFICANCE STATEMENT The combination of LPS and regulatory IL4 and IL13 signaling in macrophages and microglia produces a previously unknown and particularly reparative phenotype devoid of pro-inflammatory neurotoxic features. The local administration of LPS/IL4/IL13 into spinal cord lesion elicits profound oligodendrogenesis and remyelination. The careful use of LPS and IL4/IL13 mixture could harness the known benefits of neuroinflammation to enable repair in neurologic insults.

Involvement of Gut Microbiota, Microbial Metabolites and Interaction with Polyphenol in Host Immunometabolism

Immunological and metabolic processes are inextricably linked and important for maintaining tissue and organismal health. Manipulation of cellular metabolism could be beneficial to immunity and prevent metabolic and degenerative diseases including obesity, diabetes, and cancer. Maintenance of a normal metabolism depends on symbiotic consortium of gut microbes. Gut microbiota contributes to certain xenobiotic metabolisms and bioactive metabolites production. Gut microbiota-derived metabolites have been shown to be involved in inflammatory activation of macrophages and contribute to metabolic diseases. Recent studies have focused on how nutrients affect immunometabolism. Polyphenols, the secondary metabolites of plants, are presented in many foods and beverages. Several studies have demonstrated the antioxidant and anti-inflammatory properties of polyphenols. Many clinical trials and epidemiological studies have also shown that long-term consumption of polyphenol-rich diet protects against chronic metabolic diseases. It is known that polyphenols can modulate the composition of core gut microbiota and interact with the immunometabolism. In the present article, we review the mechanisms of gut microbiota and its metabolites on immunometabolism, summarize recent findings on how the interaction between microbiota and polyphenol modulates host immunometabolism, and discuss future research directions.

Metabolomics in the development and progression of rheumatoid arthritis: A systematic review

OBJECTIVE

A systematic review and analysis of data from several rheumatoid arthritis metabolomics studies attempts to determine which metabolites can be used as potential biomarkers for the diagnosis of rheumatoid arthritis and to explore the pathogenesis of rheumatoid arthritis.

METHODS

We searched all the subject-related documents published by EMBASE, PubMed, Web of Science, and Cochrane Library from the database to the September 2019 publication. Two researchers independently screened the literature and extracted the data. QUADOMICS tool was used to assess the quality of studies included in this systematic review.

RESULTS

A total of 10 studies met the inclusion criteria of systematic review, including 502 patients with rheumatoid arthritis and 373 healthy people. Among them, the biological samples utilised for metabolomic analysis include: serum (n=8), urine (n=1) and synovial fluid (n=1). Some metabolites play an important role in rheumatoid arthritis: glucose, lactic acid, citric acid, leucine, methionine, isoleucine, valine, phenylalanine, threonine, serine, proline, glutamate, histidine, alanine, cholesterol, glycerol, and ribose.

CONCLUSIONS

Metabolomics provides important new opportunities for further research in rheumatoid arthritis and is expected to elucidate the pathogenesis of rheumatoid arthritis that has not been fully understood before.

Circulating Pro- and Anti-Inflammatory Metabolites and Its Potential Role in Rheumatoid Arthritis Pathogenesis

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that affects synovial joints, leading to inflammation, joint destruction, loss of function, and disability. Although recent pharmaceutical advances have improved the treatment of RA, patients often inquire about dietary interventions to improve RA symptoms, as they perceive pain and/or swelling after the consumption or avoidance of certain foods. There is evidence that some foods have pro- or anti-inflammatory effects mediated by diet-related metabolites. In addition, recent literature has shown a link between diet-related metabolites and microbiome changes, since the gut microbiome is involved in the metabolism of some dietary ingredients. But diet and the gut microbiome are not the only factors linked to circulating pro- and anti-inflammatory metabolites. Other factors including smoking, associated comorbidities, and therapeutic drugs might also modify the circulating metabolomic profile and play a role in RA pathogenesis. This article summarizes what is known about circulating pro- and anti-inflammatory metabolites in RA. It also emphasizes factors that might be involved in their circulating concentrations and diet-related metabolites with a beneficial effect in RA.

Metabolomic and metallomic profile differences between Veterans and Civilians with Pulmonary Sarcoidosis

Sarcoidosis is a disorder characterized by granulomatous inflammation of unclear etiology. In this study we evaluated whether veterans with sarcoidosis exhibited different plasma metabolomic and metallomic profiles compared with civilians with sarcoidosis. A case control study was performed on veteran and civilian patients with confirmed sarcoidosis. Proton nuclear magnetic resonance spectroscopy (1H NMR), hydrophilic interaction liquid chromatography mass spectrometry (HILIC-MS) and inductively coupled plasma mass spectrometry (ICP-MS) were applied to quantify metabolites and metal elements in plasma samples. Our results revealed that the veterans with sarcoidosis significantly differed from civilians, according to metabolic and metallomics profiles. Moreover, the results showed that veterans with sarcoidosis and veterans with COPD were similar to each other in metabolomics and metallomics profiles. This study suggests the important role of environmental risk factors in the development of different molecular phenotypic responses of sarcoidosis. In addition, this study suggests that sarcoidosis in veterans may be an occupational disease.

NMR Spectroscopy-Based Metabolic Profiling of Biospecimens

Metabolomics refers to study of metabolites in biospecimens such as blood serum, tissues, and urine. Nuclear magnetic resonance (NMR) spectroscopy and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS; mass spectrometry coupled with liquid chromatography) are most frequently employed to analyze complex biological/clinical samples. NMR is a relatively insensitive tool compared to UPLC-MS/MS but offers straightforward quantification and identification and easy sample processing. One-dimensional ¹ H NMR spectroscopy is inherently quantitative and can be readily used for metabolite quantification without individual metabolite standards. Two-dimensional spectroscopy is most commonly used for identification of metabolites but can also be used quantitatively. Although NMR experiments are unbiased regarding the chemical nature of the analyte, it is crucial to adhere to the proper metabolite extraction protocol for optimum results. Selection and implementation of appropriate NMR pulse programs are also important. Finally, employment of the correct metabolite quantification strategy is crucial as well. In this unit, step-by-step guidance for running an NMR metabolomics experiment from typical biospecimens is presented. The unit describes an optimized metabolite extraction protocol, followed by implementation of NMR experiments and quantification strategies using the so-called "targeted profiling" technique. This approach relies on an underlying basis set of metabolite spectra acquired under similar conditions. Some strategies for statistical analysis of the data are also presented. Overall, this set of protocols should serve as a guide for anyone who wishes to enter the world of NMR-based metabolomics analysis. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Metabolite extraction from different biospecimens Basic Protocol 2: Preparation of dried upper fraction for NMR analysis Alternate Protocol: Preparation of urine samples for NMR analysis Basic Protocol 3: NMR experiments Basic Protocol 4: Spectral processing and quantification of metabolites Basic Protocol 5: Statistical analysis of the data.

1H NMR-based metabolomic study of metabolic profiling for pollinosis

Background

Allergic rhinitis is the main symptom of pollinosis, relieved by non-specific treatment universally. This study aimed to find the changes of serum metabolites between the seizure and remission periods of pollinosis and provide assistance in the diagnosis and/or therapy.

Methods

Metabonomics based on ¹H nuclear magnetic resonance (NMR) was used to study the 37 serum samples of pollinosis patients.

Results

We believed that the decreased levels of isoleutine, leutine, valine, 3-hydroxybutyric acid, allo-threonine, alanine, methionine, glutamine, lysine, glycine, l-tyrosine, histidine, phenylalanine, lactate, acetate, O-acetylcholine, creatine and creatinine and the increased level of N-acetylglutamine at the seizure stage were statistically significant.

Conclusions

Pollinosis could change the metabolic profiles of energy, amino acid and lipid in patients, which might be the diagnosis and/or prognosis markers for hay fever patients.

Metabolite assignment of ultrafiltered synovial fluid extracted from knee joints of reactive arthritis patients using high resolution NMR spectroscopy

Currently, there are no reliable biomarkers available that can aid early differential diagnosis of reactive arthritis (ReA) from other inflammatory joint diseases. Metabolic profiling of synovial fluid (SF)-obtained from joints affected in ReA-holds great promise in this regard and will further aid monitoring treatment and improving our understanding about disease mechanism. As a first step in this direction, we report here the metabolite specific assignment of ¹ H and ¹³ C resonances detected in the NMR spectra of SF samples extracted from human patients with established ReA. The metabolite characterization has been carried out on both normal and ultrafiltered (deproteinized) SF samples of eight ReA patients (n = 8) using high-resolution (800 MHz) ¹ H and ¹ H─¹³ C NMR spectroscopy methods such as one-dimensional ¹ H CPMG and two-dimensional J-resolved¹ H NMR and homonuclear ¹ H─¹ H TOCSY and heteronuclear¹ H─¹³ C HSQC correlation spectra. Compared with normal SF samples, several distinctive ¹ H NMR signals were identified and assigned to metabolites in the ¹ H NMR spectra of ultrafiltered SF samples. Overall, we assigned 53 metabolites in normal filtered SF and 64 metabolites in filtered pooled SF sample compared with nonfiltered SF samples for which only 48 metabolites (including lipid/membrane metabolites as well) have been identified. The established NMR characterization of SF metabolites will serve to guide future metabolomics studies aiming to identify/evaluate the SF-based metabolic biomarkers of diagnostic/prognostic potential or seeking biochemical insights into disease mechanisms in a clinical perspective.

Comprehensive urinary metabolomic characterization of a genetically induced mouse model of prostatic inflammation

Dysfunction of the lower urinary tract commonly afflicts the middle-aged and aging male population. The etiology of lower urinary tract symptoms (LUTS) is multifactorial. Benign prostate hyperplasia, fibrosis, smooth muscle contractility, and inflammation likely contribute. Here we aim to characterize the urinary metabolomic profile associated with prostatic inflammation, which could inform future personalized diagnosis or treatment, as well as mechanistic research. Quantitative urinary metabolomics was conducted to examine molecular changes following induction of inflammation via conditional Interleukin-1β expression in prostate epithelia using a novel transgenic mouse strain. To advance method development for urinary metabolomics, we also compared different urine normalization methods and found that normalizing urine samples based on osmolality prior to LC-MS most completely separated urinary metabolite profiles of mice with and without prostate inflammation via principal component analysis. Global metabolomics was combined with advanced machine learning feature selection and classification for data analysis. Key dysregulated metabolites and pathways were identified and were relevant to prostatic inflammation, some of which overlapped with our previous study of human LUTS patients. A binary classification model was established via the support vector machine algorithm to accurately differentiate control and inflammation groups, with an area-under-the-curve value of the receiver operating characteristic of 0.81, sensitivity of 0.974 and specificity of 0.995, respectively. This study generated molecular profiles of non-bacterial prostatic inflammation, which could assist future efforts to stratify LUTS patients and develop new therapies.

NMR-based metabolomics of biofluids in cancer

This review describes the current status of NMR-based metabolomics of biofluids with respect to cancer risk assessment, detection, disease characterization, prognosis, and treatment monitoring. While the metabolism of cancer cells is altered compared with that of non-proliferating cells, the metabolome of blood and urine reflects the entire organism. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, but translation to clinical practice is currently hindered by lack of validation, difficulties in biological interpretation, and non-standardized analytical procedures.

Metabolic fingerprinting of joint tissue of collagen-induced arthritis (CIA) rat: In vitro, high resolution NMR (nuclear magnetic resonance) spectroscopy based analysis

Rheumatoid arthritis (RA) is a systemic autoimmune disease whose major characteristics persistent joint inflammation that results in joint destruction and failure of the function. Collagen-induced arthritis (CIA) rat is an autoimmune disease model and in many ways shares features with RA. The CIA is associated with systemic manifestations, including alterations in the metabolism. Nuclear magnetic resonance (NMR) spectroscopy-based metabolomics has been successfully applied to the perchloric acid extract of the joint tissue of CIA rat and control rat for the analysis of aqueous metabolites. GPC (Glycerophosphocholine), carnitine, acetate, and creatinine were important discriminators of CIA rats as compared to control rats. Level of lactate (significance; p = 0.004), alanine (p = 0.025), BCA (Branched-chain amino acids) (p = 0.006) and creatinine (p = 0.023) was significantly higher in CIA rats as compared to control rats. Choline (p = 0.038) and GPC (p = 0.009) were significantly reduced in CIA rats as compared to control rats. Choline to GPC correlation was good and negative (Pearson correlation = -0.63) for CIA rats as well as for control rats (Pearson correlation = -0.79). All these analyses collectively considered as metabolic fingerprinting of the joint tissue of CIA rat as compared to control rat. The metabolic fingerprinting of joint tissue of CIA rats was different as compared to control rats. The metabolic fingerprinting reflects inflammatory disease activity in CIA rats with synovitis, demonstrating that underlying inflammatory process drives significant changes in metabolism that can be measured in the joint tissue. Therefore, the outcome of this study may be helpful for understanding the mechanism of metabolic processes in RA. This may be also helpful for the development of advanced diagnostic methods and therapy for RA.

Abnormal lipid metabolism in a rat model of arthritis: one possible pathway

Collagen-induced arthritis (CIA) animal model is associated with systemic manifestations, including alteration of lipid metabolism. In the present study, one possible pathway of altered lipid metabolism is proposed. Specimens of joint tissue and plasma were collected from the CIA and control rats, and quantitative analysis of lipid components was performed by nuclear magnetic resonance (NMR) spectroscopy technique. Correlation analysis was performed between the level of lipid components and antioxidant enzymes, lactate dehydrogenase (LDH), lipid peroxidation (LP), and cytokines in joint tissue and plasma. Differentiation between the CIA and control rats was established on the basis of the quantity of lipid components in the joint tissue and plasma. Positive correlation was observed for all the enzymes vs. lipid components as well as LP vs. lipid components in plasma and joint tissue. Positive correlation was observed for enzymes in plasma and joint tissue. A negative correlation was observed in between the plasma and joint tissue with the level of lipid components. Cytokine levels were also correlated with the level of lipid components and ratios of saturated fatty acids/unsaturated fatty acids in plasma and joint tissue. Inflammatory disease activity in CIA rats with synovitis brought about a significant change in lipid metabolism. Taken together, the results of our study are delineating a possible pathway of altered lipid metabolism in the CIA rat model, thereby contributing further to an understanding of the pathophysiology of rheumatoid arthritis (RA).

Serum Metabolomics of Activity Energy Expenditure and its Relation to Metabolic Syndrome and Obesity

Modifiable lifestyle factors, including exercise and activity energy expenditure (AEE), may attenuate the unfavorable health effects of obesity, such as risk factors of metabolic syndrome (MetS). However, the underlying mechanisms are not clear. In this study we sought to investigate whether the metabolite profiles of MetS and adiposity assessed by body mass index (BMI) and central obesity are inversely correlated with AEE and physical activity. We studied 35 men and 47 women, aged 30-60 years, using doubly labeled water to derive AEE and the Sedentary Time and Activity Reporting Questionnaire (STAR-Q) to determine the time spent in moderate and vigorous physical activity. Proton nuclear magnetic resonance spectroscopy was used for serum metabolomics analysis. Serine and glycine were found in lower concentrations in participants with more MetS risk factors and greater adiposity. However, serine and glycine concentrations were higher with increasing activity measures. Metabolic pathway analysis and recent literature suggests that the lower serine and glycine concentrations in the overweight/obese state could be a consequence of serine entering de novo sphingolipid synthesis. Taken together, higher levels of AEE and physical activity may play a crucial part in improving metabolic health in men and women with and without MetS risk factors.

A quantitative multimodal metabolomic assay for colorectal cancer

Background

Early diagnosis of colorectal cancer (CRC) simplifies treatment and improves treatment outcomes. We previously described a diagnostic metabolomic biomarker derived from semi-quantitative gas chromatography-mass spectrometry. Our objective was to determine whether a quantitative assay of additional metabolomic features, including parts of the lipidome could enhance diagnostic power; and whether there was an advantage to deriving a combined diagnostic signature with a broader metabolomic representation.

Methods

The well-characterized Biocrates P150 kit was used to quantify 163 metabolites in patients with CRC (N = 62), adenoma (N = 31), and age- and gender-matched disease-free controls (N = 81). Metabolites included in the analysis included phosphatidylcholines, sphingomyelins, acylcarnitines, and amino acids. Using a training set of 32 CRC and 21 disease-free controls, a multivariate metabolomic orthogonal partial least squares (OPLS) classifier was developed. An independent set of 28 CRC and 20 matched healthy controls was used for validation. Features characterizing 31 colorectal adenomas from their healthy matched controls were also explored, and a multivariate OPLS classifier for colorectal adenoma could be proposed.

Results

The metabolomic profile that distinguished CRC from controls consisted of 48 metabolites (R²Y = 0.83, Q²Y = 0.75, CV-ANOVA p-value < 0.00001). In this quantitative assay, the coefficient of variance for each metabolite was <10%, and this dramatically enhanced the separation of these groups. Independent validation resulted in AUROC of 0.98 (95% CI, 0.93–1.00) and sensitivity and specificity of 93% and 95%. Similarly, we were able to distinguish adenoma from controls (R²Y = 0.30, Q²Y = 0.20, CV-ANOVA p-value = 0.01; internal AUROC = 0.82 (95% CI, 0.72–0.93)). When combined with the previously generated GC-MS signatures for CRC and adenoma, the candidate biomarker performance improved slightly.

Conclusion

The diagnostic power for metabolomic tests for colorectal neoplasia can be improved by utilizing a multimodal approach and combining metabolites from diverse chemical classes. In addition, quantification of metabolites enhances separation of disease-specific metabolomic profiles. Our future efforts will be focused on developing a quantitative assay for the metabolites comprising the optimal diagnostic biomarker.

Electronic supplementary material

The online version of this article (10.1186/s12885-017-3923-z) contains supplementary material, which is available to authorized users.

1H NMR metabolic profiling of gastric cancer patients with lymph node metastasis

INTRODUCTION

Gastric cancer (GC) is a malignant tumor worldwide. As primary pathway for metastasis, the lymphatic system is an important prognostic factor for GC patients. Although the metabolic changes of gastric cancer have been investigated in extensive studies, little effort focused on the metabolic profiling of lymph node metastasis (LNM)-positive or negative GC patients.

OBJECTIVES

We performed ¹H NMR spectrum of GC tissue samples with and without LNM to identify novel potential metabolic biomarkers in the process of LNM of GC.

METHODS

¹H NMR-based untargeted metabolomics approach combined with multivariate statistical analyses were used to study the metabolic profiling of tissue samples from LNM-positive GC patients (n = 40), LNM-negative GC patients (n = 40) and normal controls (n = 40).

RESULTS

There was a clear separation between GC patients and normal controls, and 33 differential metabolites were identified in the study. Moreover, GC patients were also well-classified according to LNM-positive or negative. Totally eight distinguishing metabolites were selected in the metabolic profiling of GC patients with LNM-positive or negative, suggesting the metabolic dysfunction in the process of LNM. According to further validation and analysis, especially BCAAs metabolism (leucine, isoleucine, valine), GSH and betaine may be as potential factors of diagnose and prognosis of GC patients with or without LNM.

CONCLUSION

To our knowledge, this is the first metabolomics study focusing on LNM of GC. The identified distinguishing metabolites showed a promising application on clinical diagnose and therapy prediction, and understanding the mechanism underlying the carcinogenesis, invasion and metastasis of GC.

Metabolomics as a promising tool for early osteoarthritis diagnosis

Osteoarthritis (OA) is the main cause of disability worldwide, due to progressive articular cartilage loss and degeneration. According to recent research, OA is more than just a degenerative disease due to some metabolic components associated to its pathogenesis. However, no biomarker has been identified to detect this disease at early stages or to track its development. Metabolomics is an emerging field and has the potential to detect many metabolites in a single spectrum using high resolution nuclear magnetic resonance (NMR) techniques or mass spectrometry (MS). NMR is a reproducible and reliable non-destructive analytical method. On the other hand, MS has a lower detection limit and is more destructive, but it is more sensitive. NMR and MS are useful for biological fluids, such as urine, blood plasma, serum, or synovial fluid, and have been used for metabolic profiling in dogs, mice, sheep, and humans. Thus, many metabolites have been listed as possibly associated to OA pathogenesis. The goal of this review is to provide an overview of the studies in animal models and humans, regarding the use of metabolomics as a tool for early osteoarthritis diagnosis. The concept of osteoarthritis as a metabolic disease and the importance of detecting a biomarker for its early diagnosis are highlighted. Then, some studies in plasma and synovial tissues are shown, and finally the application of metabolomics in the evaluation of synovial fluid is described.

Targeted Metabolic Profiling of the Tg197 Mouse Model Reveals Itaconic Acid as a Marker of Rheumatoid Arthritis

Rheumatoid arthritis is a progressive, highly debilitating disease where early diagnosis, enabling rapid clinical intervention, would provide obvious benefits to patients, healthcare systems, and society. Novel biomarkers that enable noninvasive early diagnosis of the onset and progression of the disease provide one route to achieving this goal. Here a metabolic profiling method has been applied to investigate disease development in the Tg197 arthritis mouse model. Hind limb extract profiling demonstrated clear differences in metabolic phenotypes between control (wild type) and Tg197 transgenic mice and highlighted raised concentrations of itaconic acid as a potential marker of the disease. These changes in itaconic acid concentrations were moderated or indeed reversed when the Tg197 mice were treated with the anti-hTNF biologic infliximab (10 mg/kg twice weekly for 6 weeks). Further in vitro studies on synovial fibroblasts obtained from healthy wild-type, arthritic Tg197, and infliximab-treated Tg197 transgenic mice confirmed the association of itaconic acid with rheumatoid arthritis and disease-moderating drug effects. Preliminary indications of the potential value of itaconic acid as a translational biomarker were obtained when studies on K4IM human fibroblasts treated with hTNF showed an increase in the concentrations of this metabolite.

NMR based serum metabolomics reveals a distinctive signature in patients with Lupus Nephritis

Management of patient with Lupus Nephritis (LN) continues to remain a challenge for the treating physicians because of considerable morbidity and even mortality. The search of biomarkers in serum and urine is a focus of researchers to unravel new targets for therapy. In the present study, the utility of NMR-based serum metabolomics has been evaluated for the first time in discriminating LN patients from non-nephritis lupus patients (SLE) and further to get new insights into the underlying disease processes for better clinical management. Metabolic profiling of sera obtained from 22 SLE patients, 40 LN patients and 30 healthy controls (HC) were performed using high resolution 1D ¹H-CPMG and diffusion edited NMR spectra to identify the potential molecular biomarkers. Using multivariate analysis, we could distinguish SLE and LN patients from HC and LN from SLE patients. Compared to SLE patients, the LN patients had increased serum levels of lipid metabolites (including LDL/VLDL lipoproteins), creatinine and decreased levels of acetate. Our results revealed that metabolic markers especially lipids and acetate derived from NMR spectroscopy has high sensitivity and specificity to distinguish LN among SLE patients and has the potential to be a useful adjunctive tool in diagnosis and clinical management of LN.

Discovery, screening and evaluation of a plasma biomarker panel for subjects with psychological suboptimal health state using (1)H-NMR-based metabolomics profiles

Individuals in the state of psychological suboptimal health keep increasing, only scales and questionnaires were used to diagnose in clinic under current conditions, and symptoms of high reliability and accuracy are destitute. Therefore, the noninvasive and precise laboratory diagnostic methods are needed. This study aimed to develop an objective method through screen potential biomarkers or a biomarker panel to facilitate the diagnosis in clinic using plasma metabolomics. Profiles were based on H-nuclear magnetic resonance ((1)H-NMR) metabolomics techniques combing with multivariate statistical analysis. Furthermore, methods of correlation analysis with Metaboanalyst 3.0 for selecting a biomarker panel, traditional Chinese medicine (TCM) drug intervention for validating the close relations between the biomarker panel and the state and the receiver operating characteristic curves (ROC curves) analysis for evaluation of clinical diagnosis ability were carried out. 9 endogenous metabolites containing trimethylamine oxide (TMAO), glutamine, N-acetyl-glycoproteins, citrate, tyrosine, phenylalanine, isoleucine, valine and glucose were identified and considered as potential biomarkers. Then a biomarker panel consisting of phenylalanine, glutamine, tyrosine, citrate, N-acetyl-glycoproteins and TMAO was selected, which exhibited the highest area under the curve (AUC = 0.971). This study provided critical insight into the pathological mechanism of psychological suboptimal health and would supply a novel and valuable diagnostic method.

A validated metabolomic signature for colorectal cancer: exploration of the clinical value of metabolomics

Background:

Timely diagnosis and classification of colorectal cancer (CRC) are hindered by unsatisfactory clinical assays. Our aim was to construct a blood-based biomarker series using a single assay, suitable for CRC detection, prognostication and staging.

Methods:

Serum metabolomic profiles of adenoma (N=31), various stages of CRC (N=320) and healthy matched controls (N=254) were analysed by gas chromatography-mass spectrometry (GC-MS). A diagnostic model for CRC was derived by orthogonal partial least squares-discriminant analysis (OPLS-DA) on a training set, and then validated on an independent data set. Metabolomic models suitable for identifying adenoma, poor prognosis stage II CRC and discriminating various stages were generated.

Results:

A diagnostic signature for CRC with remarkable multivariate performance (R²Y=0.46, Q²Y=0.39) was constructed, and then validated (sensitivity 85% specificity 86%). Area under the receiver-operating characteristic curve was 0.91 (95% CI, 0.87–0.96). Adenomas were also detectable (R²Y=0.35, Q²Y=0.26, internal AUROC=0.81, 95% CI, 0.70–0.92). Also of particular interest, we identified models that stratified stage II by prognosis, and classified cases by stage.

Conclusions:

Using a single assay system, a suite of CRC biomarkers based on circulating metabolites enables early detection, prognostication and preliminary staging information. External population-based studies are required to evaluate the repeatability of our findings and to assess the clinical benefits of these biomarkers.

Variations in the metabolome in response to disease activity of rheumatoid arthritis

BACKGROUND

Anti-Tumor Necrosis Factor (TNF) therapies are able to control rheumatoid arthritis (RA) disease activity and limit structural damage. Yet no predictive factor of response to anti-TNF has been identified. Metabolomic profile is known to vary in response to different inflammatory rheumatisms so determining it could substantially improve diagnosis and, consequently, prognosis. The aim of this study was to use mass spectrometry to determine whether there is variation in the metabolome in patients treated with anti-TNF and whether any particular metabolomic profile can serve as a predictor of therapeutic response.

METHODS

Blood samples were analyzed in 140 patients with active RA before initiation of anti-TNF treatment and after 6 months of Anti-TNF treatment (100 good responders and 40 non-responders). Plasma was deproteinized, extracted and analyzed by reverse-phase chromatography-QToF mass spectrometry. Extracted and normalized ions were tested by univariate and ANOVA analysis followed by partial least-squares regression-discriminant analysis (PLS-DA). Orthogonal Signal Correction (OSC) was also used to filter data from unwanted non-related effects. Disease activity scores (DAS 28) obtained at 6 months were correlated with metabolome variation findings to identify a metabolite that is predictive of therapeutic response to anti-TNF.

RESULTS

After 6 months of anti-TNF therapy, 100 patients rated as good responders and 40 patients as non-responders according to EULAR criteria. Metabolomic investigations suggested two different metabolic fingerprints splitting the good-responders group and the non-responders group, without differences in anti-TNF therapies. Univariate analysis revealed 24 significant ions in positive mode (p < 0.05) and 31 significant ions in negative mode (p < 0.05). Once intersected with PLS results, only 35 ions remained. Carbohydrate derivates emerged as strong candidate determinants of therapeutic response.

CONCLUSIONS

This is the first study describing metabolic profiling in response to anti-TNF treatments using plasma samples. The study highlighted two different metabolic profiles splitting good responders from non-responders.

A quantitative metabolomics profiling approach for the noninvasive assessment of liver histology in patients with chronic hepatitis C

Background

High-throughput technologies have the potential to identify non-invasive biomarkers of liver pathology and improve our understanding of basic mechanisms of liver injury and repair. A metabolite profiling approach was employed to determine associations between alterations in serum metabolites and liver histology in patients with chronic hepatitis C virus (HCV) infection.

Methods

Sera from 45 non-diabetic patients with chronic HCV were quantitatively analyzed using ¹H-NMR spectroscopy. A metabolite profile of advanced fibrosis (METAVIR F3-4) was established using orthogonal partial least squares discriminant analysis modeling and validated using seven-fold cross-validation and permutation testing. Bioprofiles of moderate to severe steatosis (≥33 %) and necroinflammation (METAVIR A2-3) were also derived. The classification accuracy of these profiles was determined using areas under the receiver operator curves (AUROCSs) measuring against liver biopsy as the gold standard.

Results

In total 63 spectral features were profiled, of which a highly significant subset of 21 metabolites were associated with advanced fibrosis (variable importance score >1 in multivariate modeling; R² = 0.673 and Q² = 0.285). For the identification of F3–4 fibrosis, the metabolite bioprofile had an AUROC of 0.86 (95 % CI 0.74–0.97). The AUROCs for the bioprofiles for moderate to severe steatosis were 0.87 (95 % CI 0.76–0.97) and for grade A2–3 inflammation were 0.73 (0.57–0.89).

Conclusion

This proof-of-principle study demonstrates the utility of a metabolomics profiling approach to non-invasively identify biomarkers of liver fibrosis, steatosis and inflammation in patients with chronic HCV. Future cohorts are necessary to validate these findings.

Electronic supplementary material

The online version of this article (doi:10.1186/s40169-016-0109-2) contains supplementary material, which is available to authorized users.

Multicomponent, multitarget integrated adjustment - Metabolomics study of Qizhiweitong particles curing gastrointestinal motility disorders in mice induced by atropine

ETHNOPHARMACOLOGICAL RELEVANCE

Qizhiweitong particles (QZWT) which is derived from the Sinisan decoction in Shang Han Za Bing Lun, composed of Bupleurum chinenis, Paeonia obovata, Citrus aurantium L., Glycyrrhiza uralensis Fisch., Cyperus rotundus and Rhizoma Corydalis is a traditional Chinese medicine (TCM) treating gastrointestinal diseases. It have been used in clinical for years. It have been used in clinical for years. According to previous research, Bupleurum chinenis, Citrus aurantium, Cyperus rotundus in QZWT play the role of promoting gastric peristalsis, which consist of complex chemical constituents. The aim of this study is to probe the multiple effective components with gastrointestinal prokinetic efficacy in QZWT and investigate the multitarget integrated adjustment mechanism of QZWT curing atropine-induced gastrointestinal motility dysfunction mice.

MATERIALS AND METHODS

One hundred and thirty two male mice were randomly divided into 11 groups, including control group, model group, Domperidone group, Mosapride group, QZWT group and six components groups. With gastric retention rate, rate of small intestine propulsion, serum content of GAS and MTL as indexes to evaluate the curing effect on gastrointestinal movement disorders caused by atropine in mice. A serum metabonomics method based on the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) had been established to investigate the mechanism of QZWT and these components, and PCA and PLS-DA have been used to distinguish different groups and found potential biomarkers.

RESULTS

Four components from six present good prokinetic effects, including Bupleurum Polysaccharide, Citrus aurantium flavonoid, Citrus aurantium essential oil and Cyperus rotundus flavonoids. These components and QZWT regulate 5 potential biomarkers in the body, and primarily involved in 5 metabolic pathways. These potential biomarkers possess direct or indirect connections, each biomarker regulated by multiple components, each component adjusting multiple targets, and QZWT is nearly the sum of its components.

CONCLUSIONS

This experiment deepened our understanding of insufficient gastrointestinal dynamics, confirmed that QZWT treating gastrointestinal disorders was through multicomponent, multitarget ways. These results fully reflect the multiple targets synergy characteristics of TCM.

Host-targeted approaches to managing animal health: old problems and new tools

Our fellow medical and regulatory scientists question the animal producer's dependence on antibiotics and antimicrobial chemicals in the production of animal products. Retail distributors and consumers are putting even more pressure on the animal industry to find new ways to produce meat without antibiotics and chemicals. In addition, federal funding agencies are increasingly pressuring researchers to conduct science that has application. In the review that follows, we outline our approach to finding novel ways to improve animal performance and health. We use a strict set of guidelines in our applied research as follows: (1) Does the work have value to society? (2) Does our team have the skills to innovate in the field? (3) Is the product we produce commercially cost-effective? (4) Are there any reasons why the general consumer will reject the technology? (5) Is it safe for the animal, consumer, and the environment? Within this framework, we describe 4 areas of research that have produced useful products, areas that we hope other scientists will likewise explore and innovate such as (1) methods to detect infection in herds and flocks, (2) methods to control systemic and mucosal inflammation, (3) improvements to intestinal barrier function, and (4) methods to strategically potentiate immune defense. We recognize that others are working in these areas, using different strategies, but believe our examples will illustrate the vast opportunity for research and innovation in a world without antibiotics. Animal scientists have been given a new challenge that may help shape the future of both animal and human medicine.

Identification of key metabolic changes in renal interstitial fibrosis rats using metabonomics and pharmacology

Renal fibrosis is one of the important pathways involved in end-stage renal failure. Investigating the metabolic changes in the progression of disease may enhance the understanding of its pathogenesis and therapeutic information. In this study, (1)H-nuclear magnetic resonance (NMR)-based metabonomics was firstly used to screen the metabolic changes in urine and kidney tissues of renal interstitial fibrotic rats induced by unilateral ureteral obstruction (UUO), at 7, 14, 21, and 28 days after operation, respectively. The results revealed that reduced levels of bioenergy synthesis and branched chain amino acids (BCAAs), as well as elevated levels of indoxyl sulfate (IS) are involved in metabolic alterations of renal fibrosis rats. Next, by pharmacological treatment we found that reduction of IS levels could prevent the renal fibrotic symptoms. Therefore, we suggested that urinary IS may be used as a potential biomarker for the diagnosis of renal fibrosis, and a therapeutic target for drugs. Novel attempt combining metabonomics and pharmacology was established that have ability to provide more systematic diagnostic and therapeutic information of diseases.

Metabonomic profiles delineate potential role of glutamate-glutamine cycle in db/db mice with diabetes-associated cognitive decline

Background

Diabetes-associated cognition decline is one of central nervous system complications in diabetic mellitus, while its pathogenic mechanism remains unclear. In this study, ¹H nuclear magnetic resonance-based metabonomics and immunohistochemistry was used to explore key metabolic alterations in hippocampus of type 2 diabetic db/db mice with cognition decline in order to advance understanding of mechanisms underlying the pathogenesis of the disease.

Results

Metabonomics reveals that lactate level was significantly increased in hippocampus of db/db mice with cognition decline compared with age-matched wild-type mice. Several tricarboxylic acid cycle intermediates including succinate and citrate were reduced in hippocampus of db/db mice with cognition decline. Moreover, an increase in glutamine level and a decrease in glutamate and γ-aminobutyric acid levels were observed in db/db mice. Results from immunohistochemistry analysis show that glutamine synthetase was increased and glutaminase and glutamate decarboxylase were decreased in db/db mice.

Conclusions

Our results suggest that the development of diabetes-associated cognition decline in db/db mice is most likely implicated in a reduction in energy metabolism and a disturbance of glutamate-glutamine shuttling between neurons and astrocytes in hippocampus.

Electronic supplementary material

The online version of this article (doi:10.1186/s13041-016-0223-5) contains supplementary material, which is available to authorized users.

Gas chromatography-mass spectrometric method-based urine metabolomic profile of rats with pelvic inflammatory disease

Pelvic inflammatory disease (PID) can lead to a poor outcome of severe sequelae, and the current methods of clinical diagnosis are not satisfactory. Metabolomics is an effective method for the identification of disease-related metabolite biomarkers to facilitate disease diagnosis. However, to the best of our knowledge, no PID-associated metabolomic study has yet been carried out. The metabolomic changes of rats with PID were investigated in the present study. A PID model was constructed by the multi-pathogenic infection of the upper genital tract in rats. Infiltration of inflammatory cells and elevated expression levels of the cytokines interleukin (IL)-1β and IL-6 in the uterus and fallopian tubes validated the disease model. Gas chromatography-mass spectrometry coupled with derivatization was used to determine the urine metabolomic profile. Principal component analysis and partial least squares-discriminant analysis of the data sets showed a clear separation of metabolic profiles between rats with PID and control rats. Eighteen differentiating metabolites were found, including four amino acids, three fatty acids, nine organic acids, and two sugars, which indicated alterations in sugar metabolism, the citric acid cycle, amino acid metabolism and fatty acid metabolism. These metabolites could be potential biomarkers of PID, and this research may offer a new approach to evaluate the effect of anti-PID drugs in pre-clinical or clinical trials.

A Pilot Study on the Utility of Serum Metabolomics in Neuroblastoma Patients and Xenograft Models

BACKGROUND

Improved prediction of neuroblastoma (NB) behavior is needed to detect treatment-refractory disease and may allow further reduction in therapy for some patients. In this regard, serum metabolomic analysis has proven utility in several cancer types. We hypothesize that serum metabolomic analysis will correlate with risk-group classification for patients with NB, and sensitively detect NB in murine xenograft models.

PROCEDURE

A pilot study was done on Children's Oncology Group (COG) tumor bank sera from 10 patients (five high-, five low-risk). An institutional pilot study was carried out on five patients comparing sera obtained during active versus minimal disease (complete response/very good partial response; CR/VGPR).

XENOGRAFT

Flank tumors were established in Nu/Nu mice by injection of NB cell lines (IMR-32, SH-EP, SK-N-AS). Serum for comparison was drawn pre-injection, at 1 week after injection when there was no visible tumor, and again once tumors were grossly visible. Comparisons were also made between tumor bearing mouse serum and supernatants from NB cell lines.

METABOLOMIC ANALYSIS

Samples were analyzed by nuclear magnetic resonance and/or gas chromatography-mass spectrometry. Multivariate data analysis was conducted using SIMCA-P (Umetrics).

RESULTS

Serum metabolomic analysis differentiated high- and low-risk patients as well as active disease from CR/VGPR. Differences were in nitrogen, amino acid, and carbohydrate metabolism, as well as ketosis. The serum metabolomic signature in murine xenograft models sensitively detected NB cells and correlated with disease burden. Similar metabolic changes attributable to NB were noted in both human and murine serum.

CONCLUSIONS

Serum metabolomic analysis can distinguish several characteristics of NB. A larger analysis of COG banked sera is warranted.

Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment

Lung cancer causes more deaths in men and women than any other cancer related disease. Currently, few effective strategies exist to predict how patients will respond to treatment. We evaluated the serum metabolomic profiles of 25 lung cancer patients undergoing chemotherapy ± radiation to evaluate the feasibility of metabolites as temporal biomarkers of clinical outcomes. Serial serum specimens collected prospectively from lung cancer patients were analyzed using both nuclear magnetic resonance (¹H-NMR) spectroscopy and gas chromatography mass spectrometry (GC-MS). Multivariate statistical analysis consisted of unsupervised principal component analysis or orthogonal partial least squares discriminant analysis with significance assessed using a cross-validated ANOVA. The metabolite profiles were reflective of the temporal distinction between patient samples before during and after receiving therapy (¹H-NMR, p < 0.001: and GC-MS p < 0.01). Disease progression and survival were strongly correlative with the GC-MS metabolite data whereas stage and cancer type were associated with ¹H-NMR data. Metabolites such as hydroxylamine, tridecan-1-ol, octadecan-1-ol, were indicative of survival (GC-MS p < 0.05) and metabolites such as tagatose, hydroxylamine, glucopyranose, and threonine that were reflective of progression (GC-MS p < 0.05). Metabolite profiles have the potential to act as prognostic markers of clinical outcomes for lung cancer patients. Serial ¹H-NMR measurements appear to detect metabolites diagnostic of tumor pathology, while GC-MS provided data better related to prognostic clinical outcomes, possibility due to physiochemical bias related to specific biochemical pathways. These results warrant further study in a larger cohort and with various treatment options.

A GC-MS Based Metabonomics Study of Rheumatoid Arthritis and the Interventional Effects of the Simiaowan in Rats

Simiaowan (SMW) is a famous Chinese prescription widely used in clinical treatment of rheumatoid arthritis (RA). The aim of the present study is to determine novel biomarkers to increase the current understanding of RA mechanisms, as well as the underlying therapeutic mechanism of SMW, in RA-model rats. Plasma extracts from control, RA model, and SMW-treated rats were analyzed by gas chromatography coupled with mass spectrometry (GC-MS). An orthogonal partial least-square discriminant analysis (OPLS-DA) model was created to detect metabolites that were expressed in significantly different amounts between the RA model and the control rats and investigate the therapeutic effect of SMW. Metabonomics may prove to be a valuable tool for determining the efficacy of complex traditional prescriptions.

¹H-NMR-Based Metabolomic Study for Identifying Serum Profiles Associated with the Response to Etanercept in Patients with Rheumatoid Arthritis

OBJECTIVE

A considerable proportion of patients with rheumatoid arthritis (RA) do not have a satisfactory response to biological therapies. We investigated the use of metabolomics approach to identify biomarkers able to anticipate the response to biologics in RA patients.

METHODS

Due to gender differences in metabolomic profiling, the analysis was restricted to female patients starting etanercept as the first biological treatment and having a minimum of six months' follow-up. Each patient was evaluated by the same rheumatologist before and after six months of treatment. At this time, the clinical response (good, moderate, none) was determined according to the EUropean League Against Rheumatism (EULAR) criteria, based on both erythrocyte sedimentation rate (EULAR-ESR) and C-reactive protein (EULAR-CRP). Sera collected prior and after six months of etanercept were analyzed by 1H-nuclear magnetic resonance (NMR) spectroscopy in combination with multivariate data analysis.

RESULTS

Twenty-seven patients were enrolled: 18 had a good/moderate response and 9 were non responders according to both EULAR-ESR and EULAR-CRP after six months of etanercept. Metabolomic analysis at baseline was able to discriminate good, moderate, and non-responders with a very good predictivity (Q2 = 0.68) and an excellent sensitivity, specificity, and accuracy (100%). In good responders, we found an increase in isoleucine, leucine, valine, alanine, glutamine, tyrosine, and glucose levels and a decrease in 3-hydroxybutyrate levels after six months of treatment with etanercept with respect to baseline.

CONCLUSION

Our study confirms the potential of metabolomic analysis to predict the response to biological agents. Changes in metabolic profiles during treatment may help elucidate their mechanism of action.

Metabonomic analysis of potential biomarkers and drug targets involved in diabetic nephropathy mice

Diabetic nephropathy (DN) is one of the lethal manifestations of diabetic systemic microvascular disease. Elucidation of characteristic metabolic alterations during diabetic progression is critical to understand its pathogenesis and identify potential biomarkers and drug targets involved in the disease. In this study, (1)H nuclear magnetic resonance ((1)H NMR)-based metabonomics with correlative analysis was performed to study the characteristic metabolites, as well as the related pathways in urine and kidney samples of db/db diabetic mice, compared with age-matched wildtype mice. The time trajectory plot of db/db mice revealed alterations, in an age-dependent manner, in urinary metabolic profiles along with progression of renal damage and dysfunction. Age-dependent and correlated metabolite analysis identified that cis-aconitate and allantoin could serve as biomarkers for the diagnosis of DN. Further correlative analysis revealed that the enzymes dimethylarginine dimethylaminohydrolase (DDAH), guanosine triphosphate cyclohydrolase I (GTPCH I), and 3-hydroxy-3-methylglutaryl-CoA lyase (HMG-CoA lyase) were involved in dimethylamine metabolism, ketogenesis and GTP metabolism pathways, respectively, and could be potential therapeutic targets for DN. Our results highlight that metabonomic analysis can be used as a tool to identify potential biomarkers and novel therapeutic targets to gain a better understanding of the mechanisms underlying the initiation and progression of diseases.

Biomarkers of Nutrition for Development-Folate Review

The Biomarkers of Nutrition for Development (BOND) project is designed to provide evidence-based advice to anyone with an interest in the role of nutrition in health. Specifically, the BOND program provides state-of-the-art information and service with regard to selection, use, and interpretation of biomarkers of nutrient exposure, status, function, and effect. To accomplish this objective, expert panels are recruited to evaluate the literature and to draft comprehensive reports on the current state of the art with regard to specific nutrient biology and available biomarkers for assessing nutrients in body tissues at the individual and population level. Phase I of the BOND project includes the evaluation of biomarkers for 6 nutrients: iodine, iron, zinc, folate, vitamin A, and vitamin B-12. This review represents the second in the series of reviews and covers all relevant aspects of folate biology and biomarkers. The article is organized to provide the reader with a full appreciation of folate's history as a public health issue, its biology, and an overview of available biomarkers (serum folate, RBC folate, and plasma homocysteine concentrations) and their interpretation across a range of clinical and population-based uses. The article also includes a list of priority research needs for advancing the area of folate biomarkers related to nutritional health status and development.

Metabolomic Study on the Preventive Effect of Patrinia scabiosaefolia Fisch on Multipathogen Induced Pelvic Inflammatory Disease in Rats

Patrinia scabiosaefolia Fisch (PSF), a well-known traditional Chinese medicine (TCM), has been used as a “heat-clearing and detoxifying” agent. The present study was to illustrate the preventive effect of PSF on pelvic inflammatory disease (PID) in rats. The PID model was constructed by multipathogen infection of the upper genital tract with reference to the method previously reported. Urine metabolomic analysis was conducted with a GC-MS coupled with derivatization method. In this study, PID rats showed obvious infiltration of inflammatory cells and elevated expression of cytokines (IL-1β and IL-6) in upper genital tract, compared with control rats. Sixteen differentiating metabolites contributed to the alteration of metabolic profile in PID rats, including two amino acids, three fat acids, nine organic acids, and two types of sugars. The rats, infected by multipathogen and administered with PSF, showed decreased infiltration of inflammatory cells and lowered expression of cytokines in upper genital tract, compared with PID rats. Meanwhile, PSF intervened in the PID-associated alterations in TCA cycle, sugar metabolism, amino acid metabolism, and other uncertain metabolic pathways. These results indicate that PSF has preventive effect on multipathogen induced PID and holistic interventional effect on disease-associated metabolomic change.

(1)H NMR based metabolomics approach to study the toxic effects of herbicide butachlor on goldfish (Carassius auratus)

Butachlor, one of the most widely used herbicides in agriculture, has been reported with high ecotoxicity to aquatic plants and animals. In this study, a (1)H NMR based metabolomics approach combined with histopathological examination and biochemical assays was applied to comprehensively investigate the toxic effects of butachlor on four important organs (gill, brain, liver and kidney) of goldfish (Carassius auratus) for the first time. After 10 days' butachlor exposure at two dosages of 3.2 and 0.64 μmol/L, fish tissues (gill, brain, liver and kidney) and serum were collected. Histopathological inspection revealed severe impairment of gill filaments and obvious cellular edema in livers and kidneys. The increase of glutathione peroxidase (GSH-Px) activity in gill and methane dicarboxylic aldehyde (MDA) level in four tissues reflected the disturbance of antioxidative system in the intoxicated goldfish. Serum lactate dehydrogenase (LDH) activity and creatinine (CRE) level were increased in butachlor exposure groups, suggesting liver and kidney injuries induced by butachlor. Orthogonal signal correction partial least-squares discriminant analysis (OSC-PLS-DA) of NMR profiles disclosed metabolic changes that were related to the toxic effects of butachlor including oxidative stress, disorder of energy metabolism and amino acids metabolism, and disturbance of neurotransmitter balance in butachlor exposed goldfish. This integrated metabolomics approach provided a molecular basis underlying the toxicity of butachlor and demonstrated that metabolomics was a powerful and highly effective approach to elucidate the toxicity and underlying mechanisms of herbicides and pesticides, applicable for their risk assessment.

1H NMR Metabolomics Study of Metastatic Melanoma in C57BL/6J Mouse Spleen

Melanoma is a malignant tumor of melanocytes. Although extensive investigations have been done to study metabolic changes in primary melanoma in vivo and in vitro, little effort has been devoted to metabolic profiling of metastatic tumors in organs other than lymph nodes. In this work, NMR-based metabolomics combined with multivariate data analysis is used to study metastatic B16-F10 melanoma in C57BL/6J mouse spleen. Principal Component Analysis (PCA), an unsupervised multivariate data analysis method, is used to detect possible outliers, while Orthogonal Projection to Latent Structure (OPLS), a supervised multivariate data analysis method, is employed to find important metabolites responsible for discriminating the control and the melanoma groups. Two different strategies, i.e. spectral binning and spectral deconvolution, are used to reduce the original spectral data before statistical analysis. Spectral deconvolution is found to be superior for identifying a set of discriminatory metabolites between the control and the melanoma groups, especially when the sample size is small. OPLS results show that the melanoma group can be well separated from its control group. It is found that taurine, glutamate, aspartate, O-Phosphoethanolamine, niacinamide,ATP, lipids and glycerol derivatives are decreased statistically and significantly while alanine, malate, xanthine, histamine, dCTP, GTP, thymidine, 2'-Deoxyguanosine are statistically and significantly elevated. These significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in spleen.

Metabolomic profiling in cattle experimentally infected with Mycobacterium avium subsp. paratuberculosis

The sensitivity of current diagnostics for Johne's disease, a slow, progressing enteritis in ruminants caused by Mycobacterium avium subsp. paratuberculosis (MAP), is too low to reliably detect all infected animals in the subclinical stage. The objective was to identify individual metabolites or metabolite profiles that could be used as biomarkers of early MAP infection in ruminants. In a monthly follow-up for 17 months, calves infected at 2 weeks of age were compared with aged-matched controls. Sera from all animals were analyzed by 1H nuclear magnetic resonance spectrometry. Spectra were acquired, processed, and quantified for analysis. The concentration of many metabolites changed over time in all calves, but some metabolites only changed over time in either infected or non-infected groups and the change in others was impacted by the infection. Hierarchical multivariate statistical analysis achieved best separation between groups between 300 and 400 days after infection. Therefore, a cross-sectional comparison between 1-year-old calves experimentally infected at various ages with either a high- or a low-dose and age-matched non-infected controls was performed. Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS DA) yielded distinct separation of non-infected from infected cattle, regardless of dose and time (3, 6, 9 or 12 months) after infection. Receiver Operating Curves demonstrated that constructed models were high quality. Increased isobutyrate in the infected cattle was the most important agreement between the longitudinal and cross-sectional analysis. In general, high- and low-dose cattle responded similarly to infection. Differences in acetone, citrate, glycerol and iso-butyrate concentrations indicated energy shortages and increased fat metabolism in infected cattle, whereas changes in urea and several amino acids (AA), including the branched chain AA, indicated increased protein turnover. In conclusion, metabolomics was a sensitive method for detecting MAP infection much sooner than with current diagnostic methods, with individual metabolites significantly distinguishing infected from non-infected cattle.

Metabolomics analysis and modeling suggest a lysophosphocholines-PAF receptor interaction in fibromyalgia

Fibromyalgia Syndrome (FMS) is a chronic disease characterized by widespread pain, and difficult to diagnose and treat. We analyzed the plasma metabolic profile of patients with FMS by using a metabolomics approach combining Liquid Chromatography-Quadrupole-Time Of Flight/Mass Spectrometry (LC-Q-TOF/MS) with multivariate statistical analysis, aiming to discriminate patients and controls. LC-Q-TOF/MS analysis of plasma (FMS patients: n = 22 and controls: n = 21) identified many lipid compounds, mainly lysophosphocholines (lysoPCs), phosphocholines and ceramides. Multivariate statistical analysis was performed to identify the discriminating metabolites. A protein docking and molecular dynamic (MD) study was then performed, using the most discriminating lysoPCs, to validate the binding to Platelet Activating Factor (1-alkyl-2-acetyl-sn-glycero-3-phosphocholine, PAF) Receptor (PAFr). Discriminating metabolites between FMS patients and controls were identified as 1-tetradecanoyl-sn-glycero-3-phosphocholine [PC(14∶0/0∶0)] and 1-hexadecanoyl-sn-glycero-3-phosphocholine [PC(16∶0/0∶0)]. MD and docking indicate that the ligands investigated have similar potentialities to activate the PAFr receptor. The application of a metabolomic approach discriminated FMS patients from controls, with an over-representation of PC(14∶0/0∶0) and PC(16∶0/0∶0) compounds in the metabolic profiles. These results and the modeling of metabolite-PAFr interaction, allowed us to hypothesize that lipids oxidative fragmentation might generate lysoPCs in abundance, that in turn will act as PAF-like bioactivators. Overall results suggest disease biomarkers and potential therapeutical targets for FMS.

Metabolomics approach in allergic and rheumatic diseases

Metabolomics is the analysis of the concentration profiles of low molecular weight compounds present in biological fluids. Metabolites are nonpeptide molecules representing the end products of cellular activity. Therefore, changes in metabolite concentrations reveal the range of biochemical effects induced by a disease or its therapeutic intervention. Metabolomics has recently become feasible with the accessibility of new technologies, including mass spectrometry and high-resolution proton nuclear magnetic resonance, and has already been applied to several disorders. Indeed, it has the advantage of being a nontargeted approach for identifying potential biomarkers, which means that it does not require a preliminary knowledge of the substances to be studied. In this review, we summarize the main studies in which metabolomic approach was used in some allergic (asthma, atopic dermatitis) and rheumatic diseases (rheumatoid arthritis, systemic lupus erythematosus) to explore the feasibility of this technique as a novel diagnostic tool in these complex disorders.