Biomarker discovery in biological specimens (plasma, hair, liver and kidney) of diabetic mice based upon metabolite profiling using ultra-performance liquid chromatography with electrospray ionization time-of-flight mass spectrometry

Development of a novel method for analysing N-acetyl-DL-leucine enantiomers in human fingernail by UPLC-ESI-MS/MS and the evaluation in diabetes mellitus

BACKGROUND

Recent research has been reported that N-acetyl-leucine content is significantly reduced in the saliva of diabetic patients, but no reports of detection in human nails have been found. This study aims to develop a novel method for the chiral separation of N-acetyl-DL-leucine (Ac-DL-Leu) in human fingernails to investigate the differences between healthy volunteers (HVs), prediabetes (PDs) and diabetic patients (DPs), and to verify its effectiveness in early warning of diabetes.

METHOD

Chiral resolution was performed using DBD-Apy pre-column derivatization on a C18 column (2.1 × 150 mm, 1.9 μm) at 40 ^oC, and detected by UPLC-ESI-MS/MS.

RESULTS

The resolution and the limit of detection (LOD) of Ac-DL-Leu were 1.75 and 1.50 fmol, respectively. The linear range of Ac-DL-Leu was 10-2000 fmol and the determination coefficient (R²) was above 0.9997. The recovery of Ac-DL-Leu in human nails was 96.92-105.69%. The contents of Ac-D-Leu and Ac-L-Leu were analyzed in 18 HVs, 13 PDs and 16 DPs fingernails. The results showed that their contents were significantly lower in DPs than in PDs and HVs (p < 0.0001).

CONCLUSIONS

A method for evaluating the effectiveness of Ac-DL-Leu enantiomers in human fingernails as a biomarker for diabetes was firstly developed.

Metabolite interactions between host and microbiota during health and disease: Which feeds the other?

Metabolites produced by the host and microbiota play a crucial role in how human bodies develop and remain healthy. Most of these metabolites are produced by microbiota and hosts in the digestive tract. Metabolites in the gut have important roles in energy metabolism, cellular communication, and host immunity, among other physiological activities. Although numerous host metabolites, such as free fatty acids, amino acids, and vitamins, are found in the intestine, metabolites generated by gut microbiota are equally vital for intestinal homeostasis. Furthermore, microbiota in the gut is the sole source of some metabolites, including short-chain fatty acids (SCFAs). Metabolites produced by microbiota, such as neurotransmitters and hormones, may modulate and significantly affect host metabolism. The gut microbiota is becoming recognized as a second endocrine system. A variety of chronic inflammatory disorders have been linked to aberrant host-microbiota interplays, but the precise mechanisms underpinning these disturbances and how they might lead to diseases remain to be fully elucidated. Microbiome-modulated metabolites are promising targets for new drug discovery due to their endocrine function in various complex disorders. In humans, metabolotherapy for the prevention or treatment of various disorders will be possible if we better understand the metabolic preferences of bacteria and the host in specific tissues and organs. Better disease treatments may be possible with the help of novel complementary therapies that target host or bacterial metabolism. The metabolites, their physiological consequences, and functional mechanisms of the host-microbiota interplays will be highlighted, summarized, and discussed in this overview.

Comparison of Hepatic Metabolite Profiles between Infant and Adult Male Mice Using 1H-NMR-Based Untargeted Metabolomics

Although age-related characteristics of hepatic metabolism are reported, those in infants are not fully understood. In the present study, we performed untargeted metabolomic profiling of the livers of infant (3-week-old) and adult (9-week-old) male ICR mice using ¹H-NMR spectroscopy and compared 35 abundant hepatic metabolite concentrations between the two groups. The liver/body weight ratio did not differ between the two groups; however, serum glucose, blood urea nitrogen, total cholesterol, and triglyceride concentrations were lower in infants than in adults. Hepatic carbohydrate metabolites (glucose, maltose, and mannose) were higher, whereas amino acids (glutamine, leucine, methionine, phenylalanine, tyrosine, and valine) were lower in infant mice than in adult mice. The concentrations of ascorbate, betaine, sarcosine, and ethanolamine were higher, whereas those of taurine, inosine, and O-phosphocholine were lower in infant mice than in adult mice. The differences in liver metabolites between the two groups could be due to differences in their developmental stages and dietary sources (breast milk for infants and laboratory chow for adults). The above results provide insights into the hepatic metabolism in infants; however, the exact implications of the findings require further investigation.

Small intestinal metabolomics analysis reveals differentially regulated metabolite profiles in obese rats and with prebiotic supplementation

INTRODUCTION

Obesity occurs partly due to consumption of a high-fat, high-sugar and low fiber diet and is associated with an altered gut microbiome. Prebiotic supplementation can reverse obesity and beneficially alter the gut microbiome, evidenced by previous studies in rodents. However, the role of the small intestinal metabolome in obese and prebiotic supplemented rodents has never been investigated.

OBJECTIVES

To investigate and compare the small intestinal metabolome of healthy and obese rats, as well as obese rats supplemented with the prebiotic oligofructose (OFS).

METHODS

Untargeted metabolomics was performed on small intestinal contents of healthy chow-fed, high fat diet-induced obese, and obese rats supplemented with oligofructose using UPLC-MS/MS. Quantification of enterohepatic bile acids was performed with UPLC-MS to determine specific effects of obesity and fiber supplementation on the bile acid pool composition.

RESULTS

The small intestinal metabolome of obese rats was distinct from healthy rats. OFS supplementation did not significantly alter the small intestinal metabolome but did alter levels of several metabolites compared to obese rats, including bile acid metabolites, amino acid metabolites, and metabolites related to the gut microbiota. Further, obese rats had lower total bile acids and increased taurine-conjugated bile acid species in enterohepatic circulation; this effect was reversed with OFS supplementation in high fat-feeding.

CONCLUSION

Obesity is associated with a distinct small intestinal metabolome, and OFS supplementation reverses some metabolite levels that were altered in obese rats. Future research into the effects of specific metabolites identified in this study will provide deeper insight into the mechanism of fiber supplementation on improved body weight.

Mass Spectrometry as a Crucial Analytical Basis for Omics Sciences

This review is devoted to the consideration of mass spectrometric platforms as applied to omics sciences. The most significant attention is paid to omics related to life sciences (genomics, proteomics, meta-bolomics, lipidomics, glycomics, plantomics, etc.). Mass spectrometric approaches to solving the problems of petroleomics, polymeromics, foodomics, humeomics, and exosomics, related to inorganic sciences, are also discussed. The review comparatively presents the advantages of various principles of separation and mass spectral techniques, complementary derivatization, used to obtain large arrays of various structural and quantitative information in the mentioned omics sciences.

The Effect of Exogenous Free Nε-(Carboxymethyl)Lysine on Diabetic-Model Goto-Kakizaki Rats: Metabolomics Analysis in Serum and Urine

The current study investigated the effects of exogenous free N^ε-(carboxymethyl) lysine (CML) from daily diet on diabetic-model Goto-Kakizaki rats. Rats were fed with free CML (2 mg/kg body weight) for 8 weeks, then metabolomics evaluation was performed on serum and urine, and biochemical and histopathologic examinations were conducted to verify metabolic results. Diabetic rats fed with free CML showed significantly increased (P < 0.05) fasting blood glucose (11.1 ± 1.07 mmol/L) and homeostasis model assessment values (homeostatic model assessment of insulin resistance: 16.0 ± 4.24; homeostatic model assessment of beta cell function: 6.66 ± 2.01; and modified beta cell function index: 11.5 ± 2.66) and a significantly altered (P < 0.05) oxidative stress level when compared to the control group. Serum and urine metabolomics showed a significantly altered (P < 0.05) level of aminomalonic acid, 2-oxoadipic acid, l-malic acid, β-alanine, 2-oxoglutaric acid, d-threitol, N-acetyl-leucine, methylmalonic acid, l-cysteine, thymine, glycine, l-alanine, 4-hydroxyproline, hexadecane, succinic acid, l-ornithine, gluconolactone, maleic acid, l-lactate, tryptophan, 5-methoxyindoleacetate, γ-aminobutyric acid, homoserine, maltose, and quinolinic acid. Our results indicated that these metabolites altered by exposure to exogenous free CML were mapped to the citric acid cycle and amino acid and carbohydrate metabolism, which might be related to increased progression of diabetes and some other diabetic complications, including diabetic brain and neurological diseases, retinopathy, nephropathy, and impaired wound healing.

Spatial patterns of the exposure-response relationship between mercury and cortisol in the fur of river otter (Lontra canadensis)

Fur has been validated as a useful biomarker medium for chemical exposures and biological responses in wildlife. Mercury (Hg) is known to act as an endocrine disruptor by altering brain neurochemistry. In this study, we investigated the spatial patterns of relationships between total Hg (THg) and cortisol in the fur of river otter (Lontra canadensis). Geotagged fur samples were obtained from a wildlife biomonitoring program (n = 72) and the North American Fur Auction (n = 37) between 2014 and 2017. Fur THg was measured using direct thermal decomposition and fur cortisol was measured using an enzyme-linked immunosorbent assay (ELISA). The average fur THg concentration was 11.50 ± 12.40 μg/g fur weight (f.w.), and the fur cortisol concentration was 5.71 ± 8.24 pg/mg. Results from the global ordinary least squares regression show no relationship between THg and fur cortisol concentrations. However, both Hg and cortisol were heterogeneously distributed across the landscape. When a localized geographically weighted regression (GWR) was used, a geographically distinct bi-phasic relationship was observed. We suggest this bi-phasic relationship is associated with a threshold THg concentration, beyond which, there was a negative association with measured fur cortisol. Results of a break-point analysis, with one break, indicate that the threshold is 16 ± 1.27 μg/g f. w of THg in fur. This research highlights the need to use appropriate spatial methods when assessing exposure-response relationships in wildlife across large geographical areas. The identified threshold can be used for regulatory purposes.

Revealing Metabolic Perturbation Following Heavy Methamphetamine Abuse by Human Hair Metabolomics and Network Analysis

Methamphetamine (MA) is a highly addictive central nervous system stimulant. Drug addiction is not a static condition but rather a chronically relapsing disorder. Hair is a valuable and stable specimen for chronic toxicological monitoring as it retains toxicants and metabolites. The primary focus of this study was to discover the metabolic effects encompassing diverse pathological symptoms of MA addiction. Therefore, metabolic alterations were investigated in human hair following heavy MA abuse using both targeted and untargeted mass spectrometry and through integrated network analysis. The statistical analyses (t-test, variable importance on projection score, and receiver-operator characteristic curve) demonstrated that 32 metabolites (in targeted metabolomics) as well as 417 and 224 ion features (in positive and negative ionization modes of untargeted metabolomics, respectively) were critically dysregulated. The network analysis showed that the biosynthesis or metabolism of lipids, such as glycosphingolipids, sphingolipids, glycerophospholipids, and ether lipids, as well as the metabolism of amino acids (glycine, serine and threonine; cysteine and methionine) is affected by heavy MA abuse. These findings reveal crucial metabolic effects caused by MA addiction, with emphasis on the value of human hair as a diagnostic specimen for determining drug addiction, and will aid in identifying robust diagnostic markers and therapeutic targets.

Microbiome-derived carnitine mimics as previously unknown mediators of gut-brain axis communication

Alterations to the gut microbiome are associated with various neurological diseases, yet evidence of causality and identity of microbiome-derived compounds that mediate gut-brain axis interaction remain elusive. Here, we identify two previously unknown bacterial metabolites 3-methyl-4-(trimethylammonio)butanoate and 4-(trimethylammonio)pentanoate, structural analogs of carnitine that are present in both gut and brain of specific pathogen-free mice but absent in germ-free mice. We demonstrate that these compounds are produced by anaerobic commensal bacteria from the family Lachnospiraceae (Clostridiales) family, colocalize with carnitine in brain white matter, and inhibit carnitine-mediated fatty acid oxidation in a murine cell culture model of central nervous system white matter. This is the first description of direct molecular inter-kingdom exchange between gut prokaryotes and mammalian brain cells, leading to inhibition of brain cell function.

A Data Mining Metabolomics Exploration of Glaucoma

Glaucoma is an age related disease characterized by the progressive loss of retinal ganglion cells, which are the neurons that transduce the visual information from the retina to the brain. It is the leading cause of irreversible blindness worldwide. To gain further insights into primary open-angle glaucoma (POAG) pathophysiology, we performed a non-targeted metabolomics analysis on the plasma from POAG patients (n = 34) and age- and sex-matched controls (n = 30). We investigated the differential signature of POAG plasma compared to controls, using liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS). A data mining strategy, combining a filtering method with threshold criterion, a wrapper method with iterative selection, and an embedded method with penalization constraint, was used. These strategies are most often used separately in metabolomics studies, with each of them having their own limitations. We opted for a synergistic approach as a mean to unravel the most relevant metabolomics signature. We identified a set of nine metabolites, namely: nicotinamide, hypoxanthine, xanthine, and 1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline with decreased concentrations and N-acetyl-L-Leucine, arginine, RAC-glycerol 1-myristate, 1-oleoyl-RAC-glycerol, cystathionine with increased concentrations in POAG; the modification of nicotinamide, N-acetyl-L-Leucine, and arginine concentrations being the most discriminant. Our findings open up therapeutic perspectives for the diagnosis and treatment of POAG.

Hair Metabolomics in Animal Studies and Clinical Settings

Metabolomics is a powerful tool used to understand comprehensive changes in the metabolic response and to study the phenotype of an organism by instrumental analysis. It most commonly involves mass spectrometry followed by data mining and metabolite assignment. For the last few decades, hair has been used as a valuable analytical sample to investigate retrospective xenobiotic exposure as it provides a wider window of detection than other biological samples such as saliva, plasma, and urine. Hair contains functional metabolomes such as amino acids and lipids. Moreover, segmental analysis of hair based on its growth rate can provide information on metabolic changes over time. Therefore, it has great potential as a metabolomics sample to monitor chronic diseases, including drug addiction or abnormal conditions. In the current review, the latest applications of hair metabolomics in animal studies and clinical settings are highlighted. For this purpose, we review and discuss the characteristics of hair as a metabolomics sample, the analytical techniques employed in hair metabolomics and the consequence of hair metabolome alterations in recent studies. Through this, the value of hair as an alternative biological sample in metabolomics is highlighted.

Metabolomics toward personalized medicine

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

1H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI)

BACKGROUND

Urinary tract infection (UTI) is one of the most common diagnoses in girls and women, and to a lesser extent in boys and men younger than 50 years. Escherichia coli, followed by Klebsiella spp. and Proteus spp., cause 75-90% of all infections. Infection of the urinary tract is identified by growth of a significant number of a single species in the urine, in the presence of symptoms. Urinary culture is an accurate diagnostic method but takes several hours or days to be carried out. Metabolomics analysis aims to identify biomarkers that are capable of speeding up diagnosis.

METHODS

Urine samples from 51 patients with a prior diagnosis of Escherichia coli-associated UTI, from 21 patients with UTI caused by other pathogens (bacteria and fungi), and from 61 healthy controls were analyzed. The ¹H-NMR spectra were acquired and processed. Multivariate statistical models were applied and their performance was validated using permutation test and ROC curve.

RESULTS

Orthogonal Partial Least Squares-discriminant Analysis (OPLS-DA) showed good separation (R²Y = 0.76, Q2=0.45, p < 0.001) between UTI caused by Escherichia coli and healthy controls. Acetate and trimethylamine were identified as discriminant metabolites. The concentrations of both metabolites were calculated and used to build the ROC curves. The discriminant metabolites identified were also evaluated in urine samples from patients with other pathogens infections to test their specificity.

CONCLUSIONS

Acetate and trimethylamine were identified as optimal candidates for biomarkers for UTI diagnosis. The conclusions support the possibility of a fast diagnostic test for Escherichia coli-associated UTI using acetate and trimethylamine concentrations.

Metabolomic and Lipidomic Profiling Identifies The Role of the RNA Editing Pathway in Endometrial Carcinogenesis

Endometrial cancer (EC) remains the most common malignancy of the genital tract among women in developed countries. Although much research has been performed at genomic, transcriptomic and proteomic level, there is still a significant gap in the metabolomic studies of EC. In order to gain insights into altered metabolic pathways in the onset and progression of EC carcinogenesis, we used high resolution mass spectrometry to characterize the metabolomic and lipidomic profile of 39 human EC and 17 healthy endometrial tissue samples. Several pathways including lipids, Kynurenine pathway, endocannabinoids signaling pathway and the RNA editing pathway were found to be dysregulated in EC. The dysregulation of the RNA editing pathway was further investigated in an independent set of 183 human EC tissues and matched controls, using orthogonal approaches. We found that ADAR2 is overexpressed in EC and that the increase in expression positively correlates with the aggressiveness of the tumor. Furthermore, silencing of ADAR2 in three EC cell lines resulted in a decreased proliferation rate, increased apoptosis, and reduced migration capabilities in vitro. Taken together, our results suggest that ADAR2 functions as an oncogene in endometrial carcinogenesis and could be a potential target for improving EC treatment strategies.

Metabolomics for clinical use and research in chronic kidney disease

Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.

Downstream process development strategies for effective bioprocesses: Trends, progress, and combinatorial approaches

The biopharmaceutical industry is at a turning point moving toward a more customized and patient-oriented medicine (precision medicine). Straightforward routines such as the antibody platform process are extended to production processes for a new portfolio of molecules. As a consequence, individual and tailored productions require generic approaches for a fast and dedicated purification process development. In this article, different effective strategies in biopharmaceutical purification process development are reviewed that can analogously be used for the new generation of antibodies. Conventional approaches based on heuristics and high-throughput process development are discussed and compared to modern technologies such as multivariate calibration and mechanistic modeling tools. Such approaches constitute a good foundation for fast and effective process development for new products and processes, but their full potential becomes obvious in a correlated combination. Thus, different combinatorial approaches are presented, which might become future directions in the biopharmaceutical industry.

The Potential Biomarkers to Identify the Development of Steatosis in Hyperuricemia

Hyperuricemia (HU) often progresses to combine with non-alcoholic fatty liver disease (NAFLD) in the clinical scenario, which further exacerbates metabolic disorders; early detection of biomarkers, if obtained during the HU progression, may be beneficial for preventing its combination with NAFLD. This study aimed to decipher the biomarkers and mechanisms of the development of steatosis in HU. Four groups of subjects undergoing health screening, including healthy subjects, subjects with HU, subjects with HU combined with NAFLD (HU+NAFLD) and subjects with HU initially and then with HU+NAFLD one year later (HU→HU+NAFLD), were recruited in this study. The metabolic profiles of all subjects' serum were analyzed by liquid chromatography quadruple time-of-flight mass spectrometry. The metabolomic data from subjects with HU and HU+NAFLD were compared, and the biomarkers for the progression from HU to HU+NAFLD were predicted. The metabolomic data from HU→HU+NAFLD subjects were collected for further verification. The results showed that the progression was associated with disturbances of phospholipase metabolism, purine nucleotide degradation and Liver X receptor/retinoic X receptor activation as characterized by up-regulated phosphatidic acid, cholesterol ester (18:0) and down-regulated inosine. These metabolic alterations may be at least partially responsible for the development of steatosis in HU. This study provides a new paradigm for better understanding and further prevention of disease progression.

Development of quantitative structure activity relationship (QSAR) model for disinfection byproduct (DBP) research: A review of methods and resources

Quantitative structure-activity relationship (QSAR) models are tools for linking chemical activities with molecular structures and compositions. Due to the concern about the proliferating number of disinfection byproducts (DBPs) in water and the associated financial and technical burden, researchers have recently begun to develop QSAR models to investigate the toxicity, formation, property, and removal of DBPs. However, there are no standard procedures or best practices regarding how to develop QSAR models, which potentially limit their wide acceptance. In order to facilitate more frequent use of QSAR models in future DBP research, this article reviews the processes required for QSAR model development, summarizes recent trends in QSAR-DBP studies, and shares some important resources for QSAR development (e.g., free databases and QSAR programs). The paper follows the four steps of QSAR model development, i.e., data collection, descriptor filtration, algorithm selection, and model validation; and finishes by highlighting several research needs. Because QSAR models may have an important role in progressing our understanding of DBP issues, it is hoped that this paper will encourage their future use for this application.

Change in N-Glycosylation of Plasma Proteins in Japanese Semisupercentenarians

An N-glycomic analysis of plasma proteins was performed in Japanese semisupercentenarians (SSCs) (mean 106.7 years), aged controls (mean 71.6 years), and young controls (mean 30.2 years) by liquid chromatography/mass spectrometry (LC/MS) using a graphitized carbon column. Characteristic N-glycans in SSCs were discriminated using a multivariate analysis; orthogonal projections to latent structures (O-PLS). The results obtained showed that multi-branched and highly sialylated N-glycans as well as agalacto- and/or bisecting N-glycans were increased in SSCs, while biantennary N-glycans were decreased. Since multi-branched and highly sialylated N-glycans have been implicated in anti-inflammatory activities, these changes may play a role in the enhanced chronic inflammation observed in SSCs. The levels of inflammatory proteins, such as CRP, adiponectin, IL-6, and TNF-α, were elevated in SSCs. These results suggested that responses to inflammation may play an important role in extreme longevity and healthy aging in humans. This is the first study to show that the N-glycans of plasma proteins were associated with extreme longevity and healthy aging in humans.

Metabonomic Study of Biochemical Changes in Human Hair of Heroin Abusers by Liquid Chromatography Coupled with Ion Trap-Time of Flight Mass Spectrometry

Hair analysis is with the advantage of non-invasive collection and long surveillance window. The present study employed a sensitive and reliable liquid chromatography coupled with ion trap-time of flight mass spectrometry method to study the metabonomic characters in the hair of 58 heroin abusers and 72 non-heroin abusers. Results indicated that certain endogenous metabolites, such as sorbitol and cortisol, were accelerated, and the level of arachidonic acid, glutathione, linoleic acid, and myristic acid was decreased in hair of heroin abusers. The metabonomic study is helpful for further understanding of heroin addiction and clinical diagnosis.

Metabolomics for Biomarker Discovery: Moving to the Clinic

To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases.

Characterization of N-glycan heterogeneities of erythropoietin products by liquid chromatography/mass spectrometry and multivariate analysis

RATIONALE

Glycan heterogeneity on recombinant human erythropoietin (rEPO) product is considered to be one of the critical quality attributes, and similarity tests of glycan heterogeneities are required in the manufacturing process changes and developments of biosimilars. A method for differentiating highly complex and diverse glycosylations is needed to evaluate comparability and biosimilarity among rEPO batches and products manufactured by different processes.

METHODS

The glycan heterogeneities of nine rEPO products (four innovator products and five biosimilar products) were distinguished by multivariate analysis (MVA) using the peak area ratios of each glycan to the total peak area of glycans in mass spectra obtained by liquid chromatography/mass spectrometry (LC/MS) of N-glycans from rEPOs.

RESULTS

Principal component analysis (PCA) using glycan profiles obtained by LC/MS proved to be a useful method for differentiating glycan heterogeneities among nine rEPOs. Using PC values as indices, we were able to visualize and digitalize the glycan heterogeneities of each rEPO. The characteristic glycans of each rEPO were also successfully identified by orthogonal partial least-squares discrimination analysis (OPLS-DA), an MVA method, using the glycan profile data.

CONCLUSIONS

PCA values were useful for evaluating the relative differences among the glycan heterogeneities of rEPOs. The characteristic glycans that contributed to the differentiation were also successfully identified by OPLS-DA. PCA and OPLS-DA based on mass spectrometric data are applicable for distinguishing glycan heterogeneities, which are virtually indistinguishable on rEPO products.

Metabolic profiling reveals therapeutic biomarkers of processed Aconitum carmichaeli Debx in treating hydrocortisone induced kidney-yang deficiency syndrome rats

ETHNOPHARMACOLOGICAL RELEVANCE

Kidney-yang deficiency syndrome (KYDS) is a diagnostic pattern in traditional Chinese medicine (TCM) and clinical data showed that the unbalance in adrenal cortical hormone is the key issue in KYDS patients. The processed Ranunculaceae aconitum carmichaeli debx (bai-fu-pian in Chinese, BFP) is one of the most commonly used Chinese herbs for treating KYDS. The present study was conducted to explore the therapeutic biomarkers of the BFP in treating hydrocortisone administration induced KYDS rats.

MATERIALS AND METHODS

Thirty male Sprague-Dawley rats were randomly divided into five groups with six in each group. KYDS in rats was induced by i.p. injection of hydrocortisone at the dose of 10mg/kg per day for 15 days as described previously. The rats with KYDS were administered orally, starting from the day of hydrocortisone administration stopped, with BFP extract at the dose of 0.32g/kg, 0.64g/kg and 1.28g/kg per day respectively for 15 days. The blood samples were collected for the liquid chromatography quadruple time-of-flight mass spectrometry (LC-Q-TOF-MS) test, as well as radioimmunoassay to determine the concentrations of cyclic adenosine monophosphate (cAMP), cyclic guanosine monophosphate (cGMP) and adrenocorticotrophic hormone (ACTH). The metabolic responses to BFP administration were investigated by using the principal components analysis (PCA) and orthogonal partial least squares analysis (OPLS). Bioinformatics analyses were performed by using the Ingenuity Pathway Analysis (IPA). Variance analysis and linear regression analysis were used in this study.

RESULTS

The signs and concentrations of cAMP, cGMP and ACTH in the model rats were similar to those previously described about KYDS rats and BFP treatment can reverse the changes. Seventeen significantly changed metabolites among different groups were identified. Thirteen metabolites were identified in the KYDS rats comparing to healthy rats with nine up-regulated and four down-regulated. After BFP treatment at three dosages, five up-regulated metabolites including phosphate, betaine, (4-hydroxyphenyl) acetaldehyde, 5-hydroxyindol-3-acetic acid and 5'-phosphoribosyl-N-formylglycinamide were dose-dependently reversed. The network analysis with IPA showed that four canonical pathways including superpathway of methionine degradation, purine nucleotides de novo biosynthesis II, tyrosine synthesis and serotonin receptor signaling involved the therapeutic mechanism of BFP in treating the KYDS rats.

CONCLUSIONS

Five therapeutic biomarkers (phosphate, betaine, (4-hydroxyphenyl) acetaldehyde, 5-hydroxyindol-3-acetic acid and 5'-phosphoribosyl-N-formylglycinamide) and two corresponding canonical pathways (amino acid metabolism and purine nucleotide metabolism) were identified to be involved in the therapeutic mechanism of BFP treating the KYDS.

Metabolic profiling of Alzheimer's disease brains

Alzheimer's disease (AD) is an irreversible, progressive brain disease and can be definitively diagnosed after death through an examination of senile plaques and neurofibrillary tangles in several brain regions. It is to be expected that changes in the concentration and/or localization of low-molecular-weight molecules are linked to the pathological changes that occur in AD, and determining their identity would provide valuable information regarding AD processes. Here, we propose definitive brain metabolic profiling using ultra-performance liquid chromatography coupled with electrospray time-of-flight mass spectrometry analysis. The acquired data were subjected to principal components analysis to differentiate the frontal and parietal lobes of the AD/Control groups. Significant differences in the levels of spermine and spermidine were identified using S-plot, mass spectra, databases and standards. Based on the investigation of the polyamine metabolite pathway, these data establish that the downstream metabolites of ornithine are increased, potentially implicating ornithine decarboxylase activity in AD pathology.

Changes in metabolite profiles caused by genetically determined obesity in mice

The Berlin Fat Mouse Inbred (BFMI) line harbors a major recessive gene defect on chromosome 3 (jobes1) leading to juvenile obesity and metabolic syndrome. The present study aimed at the identification of metabolites that might be linked to recessively acting genes in the obesity locus. Firstly, serum metabolites were analyzed between obese BFMI and lean B6 and BFMI × B6 F₁ mice to identify metabolites that are different. In a second step, a metabolite-protein network analysis was performed linking metabolites typical for BFMI mice with genes of the jobes1 region. The levels of 22 diacyl-phosphatidylcholines (PC aa), two lyso-PC and three carnitines were found to be significantly lower in obese mice compared with lean mice, while serine, glycine, arginine and hydroxysphingomyelin were higher for the same comparison. The network analysis identified PC aa C42:1 as functionally linked with the genes Ccna2 and Trpc3 via the enzymes choline kinase alpha and phospholipase A2 group 1B (PLA2G1B), respectively. Gene expression analysis revealed elevated Ccna2 expression in adipose tissue of BFMI mice. Furthermore, unique mutations were found in the Ccna2 promoter of BFMI mice which are located in binding sites for transcription factors or micro RNAs and could cause differential Ccna2 mRNA levels between BFMI and B6 mice. Increased expression of Ccna2 was consistent with higher mitotic activity of adipose tissue in BFMI mice. Therefore, we suggest a higher demand for PC necessary for adipose tissue growth and remodeling. This study highlights the relationship between metabolite profiles and the underlying genetics of obesity in the BFMI line.

Sampling and analysis of metabolomes in biological fluids

Metabolome analysis involves the study of small molecules that are involved in the metabolic responses that occur through patho-physiological changes caused by genetic stimuli or chemical agents.

Metabolomics in diabetes

Characterization of metabolic changes is key to early detection, treatment, and understanding molecular mechanisms of diabetes. Diabetes represents one of the most important global health problems. Approximately 90% of diabetics have type 2 diabetes. Identification of effective screening markers is critical for early treatment and intervention that can delay and/or prevent complications associated with this chronic disease. Fortunately, metabolomics has introduced new insights into the pathology of diabetes as well as to predict disease onset and revealed new biomarkers to improve diagnostics in a range of diseases. Small-molecule metabolites have an important role in biological systems and represent attractive candidates to understand T2D phenotypes. Characteristic patterns of metabolites can be revealed that broaden our understanding of T2D disorder. This technique-driven review aims to demystify the mechanisms of T2D, to provide updates on the applications of metabolomics in addressing T2D with a focus on metabolites based biomarker discovery.

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.

Power of metabolomics in biomarker discovery and mining mechanisms of obesity

Obesity, the prevalence of which is increasing rapidly worldwide, is recognized as a risk factor for diabetes, cardiovascular disease, liver disease and renal disease. Unfortunately, the mechanisms underlying it have not been well characterized. Fortunately, metabolomics - the systematic study of metabolites, which are small molecules generated by the process of metabolism - has been important in elucidating the pathways underlying obesity. Small-molecule metabolites have an important role in biological system and represent attractive candidates to understand obesity phenotypes. Metabolomic analysis is a valid and powerful tool with which to further define the mechanisms. Recent attention has focused on identifying biomarkers that would propose a better non-invasive way to detect or visualize obesity and prevent its events. The discovery of the biomarkers has become a key breakthrough towards a better molecular understanding of obesity. Thus, this review covers how recent metabolomic studies have advanced biomarker discovery and the elucidation of mechanisms underlying obesity and its comorbidities. The importance of identifying metabolic markers and pathways of disease-associated intermediate phenotypes is also emphasized. These biomarkers would be applicable as diagnostic tools in a personalized healthcare setting and may also open door to biomarker discovery, disease diagnosis and novel therapeutic avenues.

Foodomics platform for the assay of thiols in wines with fluorescence derivatization and ultra performance liquid chromatography mass spectrometry using multivariate statistical analysis

The presence of specific volatile and aminothiols in wine is associated with quality, worth, price, and taste. The identification of specific thiol-containing compounds in various wines has been reported in many valuable and interesting works. In this study, a novel foodomics assay of thiol-containing compounds, such as free aminothiols and related conjugates, was developed using ultra performance liquid chromatography (UPLC) with fluorescence (FL) and electrospray (ESI) time-of-flight mass spectrometric (TOF/MS) detections. FL specific derivatization was applied along with multivariate statistical analysis. First, the optimal experimental conditions were studied using representative thiols, such as l-cysteine, N-acetyl-l-cysteine, cysteamine, and l-glutathione, and then the UPLC-FL derivatization and separation steps were fixed for the subsequent screening of unknown thiol-containing compounds. The screening assay consisted of monitoring the UPLC-TOF/MS peaks of unknown thiols, which decreased due to the derivatization as compared to the nonderivatized thiols. The principal component analysis of the UPLC-TOF/MS data could be well-differentiated and categorized into two groups. The orthogonal signal correction partial least-squares discriminant analysis, the so-called S-plot, showed that the quality differentiation is directly related to the decrease of native thiols and increase of derivatized thiols. With this strategy, the mass difference from the derivatization reagent (+m/z 198) could be utilized for the identification of these thiols using the FL peaks retention time and metabolomics-databases. The presence of l-glutathione in rice wine was for the first time reported on the basis of the available metabolomics-databases and standard matching. This novel concept based on foodomics could be applied in food analysis for the ready screening of specific functional compounds by exploiting the various derivatization modes available.

Investigation by imaging mass spectrometry of biomarker candidates for aging in the hair cortex

BACKGROUND

Human hair is one of the essential components that define appearance and is a useful source of samples for non-invasive biomonitoring. We describe a novel application of imaging mass spectrometry (IMS) of hair biomolecules for advanced molecular characterization and a better understanding of hair aging. As a cosmetic and biomedical application, molecules whose levels in hair altered with aging were comprehensively investigated.

METHODS

Human hair was collected from 15 young (20±5 years old) and 15 older (50±5 years old) volunteers. Matrix-free laser desorption/ionization IMS was used to visualize molecular distribution in the hair sections. Hair-specific ions displaying a significant difference in the intensities between the 2 age groups were extracted as candidate markers for aging. Tissue localization of the molecules and alterations in their levels in the cortex and medulla in the young and old groups were determined.

RESULTS

Among the 31 molecules detected specifically in hair sections, 2--one at m/z 153.00, tentatively assigned to be dihydrouracil, and the other at m/z 207.04, identified to be 3,4-dihydroxymandelic acid (DHMA)--exhibited a higher signal intensity in the young group than in the old, and 1 molecule at m/z 164.00, presumed to be O-phosphoethanolamine, displayed a higher intensity in the old group. Among the 3, putative O-phosphoethanolamine showed a cortex-specific distribution. The 3 molecules in cortex presented the same pattern of alteration in signal intensity with aging, whereas those in medulla did not exhibit significant alteration.

CONCLUSION

Three molecules whose levels in hair altered with age were extracted. While they are all possible markers for aging, putative dihydrouracil and DHMA, are also suspected to play a role in maintaining hair properties and could be targets for cosmetic supplementation. Mapping of ion localization in hair by IMS is a powerful method to extract biomolecules in specified regions and determine their tissue distribution.