NMR-based metabolomics of urine in a mouse model of Alzheimer's disease: identification of oxidative stress biomarkers

Human wild-type and D76N β2-microglobulin variants are significant proteotoxic and metabolic stressors for transgenic C. elegans

β2-microglobulin (β2-m) is a plasma protein derived from physiological shedding of the class I major histocompatibility complex (MHCI), causing human systemic amyloidosis either due to persistently high concentrations of the wild-type (WT) protein in hemodialyzed patients, or in presence of mutations, such as D76N β2-m, which favor protein deposition in the adulthood, despite normal plasma levels. Here we describe a new transgenic Caenorhabditis elegans (C. elegans) strain expressing human WT β2-m at high concentrations, mimicking the condition that underlies dialysis-related amyloidosis (DRA) and we compare it to a previously established strain expressing the highly amyloidogenic D76N β2-m at lower concentrations. Both strains exhibit behavioral defects, the severity of which correlates with β2-m levels rather than with the presence of mutations, being more pronounced in WT β2-m worms. β2-m expression also has a deep impact on the nematodes' proteomic and metabolic profiles. Most significantly affected processes include protein degradation and stress response, amino acids metabolism, and bioenergetics. Molecular alterations are more pronounced in worms expressing WT β2-m at high concentration compared to D76N β2-m worms. Altogether, these data show that β2-m is a proteotoxic protein in vivo also in its wild-type form, and that concentration plays a key role in modulating pathogenicity. Our transgenic nematodes recapitulate the distinctive features subtending DRA compared to hereditary β2-m amyloidosis (high levels of non-mutated β2-m vs. normal levels of variant β2-m) and provide important clues on the molecular bases of these human diseases.

Urinary and faecal metabolic characteristics in APP/PS1 transgenic mouse model of Alzheimer's disease with and without cognitive decline

Alzheimer's disease (AD) has been considered to be a systematic metabolic disorder, but little information is available about metabolic changes in the urine and feces. In this study, we investigated urinary and faecal metabolic profiles in amyloid precursor protein/presenilin 1 (APP/PS1) mice at 3 and 9 months of age (3 M and 9 M) and age-matched wild-type (WT) mice by using ¹H NMR-based metabolomics, and aimed to explore changes in metabolic pathways during amyloid pathology progression and identify potential metabolite biomarkers at earlier stage of AD. The results show that learning and memory abilities were impaired in APP/PS1 mice relative to WT mice at 9 M, but not at 3 M. However, metabolomics analysis demonstrates that AD disrupted metabolic phenotypes in the urine and feces of APP/PS1 mice at both 3 M and 9 M, including amino acid metabolism, microbial metabolism and energy metabolism. In addition, several potential metabolite biomarkers were identified for discriminating AD and WT mice prior to cognitive decline with the AUC values from 0.755 to 0.971, such as taurine, hippurate, urea and methylamine in the urine as well as alanine, leucine and valine in the feces. Therefore, our results not only confirmed AD as a metabolic disorder, but also contributed to the identification of potential biomarkers at earlier stage of AD.

Alterations in Urine Metabolomics Following Sport-Related Concussion: A 1H NMR-Based Analysis

Objective: Millions of sport-related concussions (SRC) occur annually in North America, and current diagnosis of concussion is based largely on clinical evaluations. The objective of this study was to determine whether urinary metabolites are significantly altered post-SRC compared to pre-injury.

Setting: Outpatient sports medicine clinic.

Participants: Twenty-six male youth sport participants.

Methods: Urine was analyzed pre-injury and after SRC by ¹H NMR spectroscopy. Data were analyzed using multivariate statistics, pairwise t-test, and metabolic pathway analysis. Variable importance analysis based on random variable combination (VIAVC) was applied to the entire data set and resulted in a panel of 18 features. Partial least square discriminant analysis was performed exploring the separation between pre-injury and post-SRC groups. Pathway topography analysis was completed to identify biological pathway involvement. Spearman correlations provide support for the relationships between symptom burden and length of return to play and quantifiable metabolic changes in the human urinary metabolome.

Results: Phenylalanine and 3-indoxysulfate were upregulated, while citrate, propylene glycol, 1-methylhistidine, 3-methylhistidine, anserine, and carnosine were downregulated following SRC. A receiver operator curve (ROC) tool constructed using the 18-feature classifier had an area under the curve (AUC) of 0.887. A pairwise t-test found an additional 19 altered features, 7 of which overlapped with the VIAVC analysis. Pathway topology analysis indicated that aminoacyl-tRNA biosynthesis and beta-alanine metabolism were the two pathways most significantly changed. There was a significant positive correlation between post-SRC 2-hydroxybutyrate and the length of return to play (ρ = 0.482, p = 0.02) as well as the number of symptoms and post-SRC lactose (ρ = 0.422, p = 0.036).

Conclusion: We found that ¹H NMR metabolomic urinary analysis can identify a set of metabolites that can correctly classify SRC with an accuracy of 81.6%, suggesting potential for a more objective method of characterizing SRC. Correlations to both the number of symptoms and length of return to play indicated that 2-hydroxybutyrate and lactose may have potential applications as biomarkers for sport-related concussion.

A UPLC-Q-TOF/MS-Based Metabolomics Study on the Effect of Corallodiscus flabellatus (Craib) B. L. Burtt Extract on Alzheimer's Disease

A UPLC-Q-TOF/MS-based metabolomics study was carried out to explore the intervening mechanism of Corallodiscus flabellatus (Craib) B. L. Burtt (CF) extract on Alzheimer's disease (AD). The AD model group consisted of senescence-accelerated mouse prone 8 (SAMP8) mice, and the control group consisted of senescence-accelerated mouse resistant 1 (SAMR1) mice. UPLC-Q-TOF/MS detection, multivariate statistical analysis, and pathway enrichment were jointly performed to research the change in metabolite profiling in the urine of AD mice. The result suggested that the metabolite profiling of SAMP8 mice significantly changed at the sixth month compared with SAMR1 mice of the same age, and the principal component analysis (PCA) score scatter plots of the CF group closely resembled those of the control and positive drug (huperzine A, HA) group. A total of 28 metabolites were considered potential biomarkers associated with the metabolism of beta-alanine, glycine, serine, threonine, cysteine, methionine, arginine, proline, and purines in AD mice. Furthermore, the CF group was clustered with the control and positive group and was clearly separated from the model group in the heat map. In conclusion, significant anti-AD effects were firstly observed in mice after treatment with the CF extract, and the urinary metabolomics approach assisted with dissecting the underlying mechanism.

Urinary metabolic phenotyping for Alzheimer's disease

Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.

Targeted Metabolic Profiling of Urine Highlights a Potential Biomarker Panel for the Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment: A Pilot Study

The lack of sensitive and specific biomarkers for the early detection of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) is a major hurdle to improving patient management. A targeted, quantitative metabolomics approach using both ¹H NMR and mass spectrometry was employed to investigate the performance of urine metabolites as potential biomarkers for MCI and AD. Correlation-based feature selection (CFS) and least absolute shrinkage and selection operator (LASSO) methods were used to develop biomarker panels tested using support vector machine (SVM) and logistic regression models for diagnosis of each disease state. Metabolic changes were investigated to identify which biochemical pathways were perturbed as a direct result of MCI and AD in urine. Using SVM, we developed a model with 94% sensitivity, 78% specificity, and 78% AUC to distinguish healthy controls from AD sufferers. Using logistic regression, we developed a model with 85% sensitivity, 86% specificity, and an AUC of 82% for AD diagnosis as compared to cognitively healthy controls. Further, we identified 11 urinary metabolites that were significantly altered to include glucose, guanidinoacetate, urocanate, hippuric acid, cytosine, 2- and 3-hydroxyisovalerate, 2-ketoisovalerate, tryptophan, trimethylamine N oxide, and malonate in AD patients, which are also capable of diagnosing MCI, with a sensitivity value of 76%, specificity of 75%, and accuracy of 81% as compared to healthy controls. This pilot study suggests that urine metabolomics may be useful for developing a test capable of diagnosing and distinguishing MCI and AD from cognitively healthy controls.

Sensitive mass spectrometric analysis of carbonyl metabolites in human urine and fecal samples using chemoselective modification

Metabolites with ketone or aldehyde functionalities comprise a large proportion of the human metabolome, most notably in the form of sugars. However, these reactive molecules are also generated through oxidative stress or gut microbiota metabolism and have been linked to disease development. The discovery and structural validation of this class of metabolites over the large concentration range found in human samples is crucial to identify their links to pathogenesis. Herein, we have utilized an advanced chemoselective probe methodology alongside bioinformatic analysis to identify carbonyl-metabolites in urine and fecal samples. In total, 99 metabolites were identified in urine samples and the chemical structure for 40 metabolites were unambiguously validated using a co-injection procedure. We also describe the preparation of a metabolite-conjugate library of 94 compounds utilized to efficiently validate these ketones and aldehydes. This method was used to validate 33 metabolites in a pooled fecal sample extract to demonstrate the potential for rapid and efficient metabolite detection over a wide metabolite concentration range. This analysis revealed the presence of six metabolites that have not previously been detected in either sample type. The constructed library can be utilized for straightforward, large-scale, and expeditious analysis of carbonyls in any sample type.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

Next-generation biomarker discovery in Alzheimer's disease using metabolomics - from animal to human studies

Alzheimer's disease (AD) is a complex disease driven mainly by neuronal loss due to accumulation of intracellular neurofibrillary tangles and amyloid β aggregates in the brain. The diagnosis of AD currently relies on clinical symptoms while the disease can only be confirmed at autopsy. The few available biomarkers allowing for diagnosis are typically detected many years after the onset of the disease. New diagnostic approaches, particularly in easily-accessible biofluids, are essential. By providing an exhaustive information of the phenotype, metabolomics is an ideal approach for identification of new biomarkers. This review investigates the current position of metabolomics in the field of AD research, focusing on animal and human studies, and discusses the improvements carried out over the past decade.

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide and the fifth leading cause of death from cancer in men. Early detection and risk stratification is the most effective way to improve the survival of PCa patients. Current PCa biomarkers lack sufficient sensitivity and specificity to cancer. Metabolite biomarkers are evolving as a new diagnostic tool. This review is aimed to evaluate the potential of metabolite biomarkers for early detection, risk assessment, and monitoring of PCa. Of the 154 identified publications, 27 and 38 were original papers on urine and serum metabolomics, respectively. Nuclear magnetic resonance (NMR) is a promising method for measuring concentrations of metabolites in complex samples with good reproducibility, high sensitivity, and simple sample processing. Especially urine-based NMR metabolomics has the potential to be a cost-efficient method for the early detection of PCa, risk stratification, and monitoring treatment efficacy.

A prebiotic intervention study in children with autism spectrum disorders (ASDs)

BACKGROUND

Different dietary approaches, such as gluten and casein free diets, or the use of probiotics and prebiotics have been suggested in autistic spectrum disorders in order to reduce gastrointestinal (GI) disturbances. GI symptoms are of particular interest in this population due to prevalence and correlation with the severity of behavioural traits. Nowadays, there is lack of strong evidence about the effect of dietary interventions on these problems, particularly prebiotics. Therefore, we assessed the impact of exclusion diets and a 6-week Bimuno® galactooligosaccharide (B-GOS®) prebiotic intervention in 30 autistic children.

RESULTS

The results showed that children on exclusion diets reported significantly lower scores of abdominal pain and bowel movement, as well as lower abundance of Bifidobacterium spp. and Veillonellaceae family, but higher presence of Faecalibacterium prausnitzii and Bacteroides spp. In addition, significant correlations were found between bacterial populations and faecal amino acids in this group, compared to children following an unrestricted diet. Following B-GOS® intervention, we observed improvements in anti-social behaviour, significant increase of Lachnospiraceae family, and significant changes in faecal and urine metabolites.

CONCLUSIONS

To our knowledge, this is the first study where the effect of exclusion diets and prebiotics has been evaluated in autism, showing potential beneficial effects. A combined dietary approach resulted in significant changes in gut microbiota composition and metabolism suggesting that multiple interventions might be more relevant for the improvement of these aspects as well as psychological traits.

TRIAL REGISTRATION

NCT02720900 ; registered in November 2015.

Microvascular ultrastructural changes precede cognitive impairment in the murine APPswe/PS1dE9 model of Alzheimer's disease

Cerebral and systemic organ microvascular pathologies coexist with human Alzheimer's disease (AD) neuropathology. In this study, we hypothesised that both cerebral and systemic microvascular pathologies exist in 4- to 5-month-old male APPswe/PS1dE9 (APP/PS1) transgenic mice prior to the onset of cognitive impairment. To assess this we examined recognition memory in both wild-type and APP/PS1 mice using the object recognition task (ORT; n = 11 per group) and counted thioflavin-S-positive plaques in brain (n = 6 per group). Vascular casts of brain, liver, spleen and kidneys were examined using scanning electron microscopy (n = 6 per group), and the urinary albumin-to-creatinine ratio (uACR; n = 5 per group) was measured as an index of glomerular permeability. Murine recognition memory was intact, as demonstrated by a significant preference for the novel object in the ORT paradigm. Brain sections of wild-type mice were devoid of thioflavin-S positivity, whereas age-matched APP/PS1 mice had an average of 0.88 ± 0.22 thioflavin-S-positive plaques in the cortex, 0.42 ± 0.17 plaques in the dentate gyrus and 0.30 ± 0.07 plaques in the cornus ammonis 1 region. The profiles of casted cerebral capillaries of wild-type mice were smooth and regular in contrast to those of APP/PS1 mice which demonstrate characteristic (0.5-4.6 μm) 'tags'. APP/PS1 mice also had a significantly reduced hepatic vessel number (p = 0.0002) and an increase in the number of splenic microvascular pillars (p = 0.0231), in the absence of changes in either splenic microvascular density (p = 0.3746) or glomerular ultrastructure. The highly significant reduction in uACR in APP/PS1 mice compared to wild-type (p = 0.0079) is consistent with glomerular microvascular dysfunction. These findings highlight early microvascular pathologies in 4- to 5-month-old APP/PS1 transgenic mice and may indicate an amenable target for pharmacological intervention in AD.

Metabolic status of CSF distinguishes rats with tauopathy from controls

Background

Tauopathies represent heterogeneous groups of neurodegenerative diseases that are characterised by abnormal deposition of the microtubule-associated protein tau. Alzheimer’s disease is the most prevalent tauopathy, affecting more than 35 million people worldwide. In this study we investigated changes in metabolic pathways associated with tau-induced neurodegeneration.

Methods

Cerebrospinal fluid (CSF), plasma and brain tissue were collected from a transgenic rat model for tauopathies and from age-matched control animals. The samples were analysed by targeted and untargeted metabolomic methods using high-performance liquid chromatography coupled to mass spectrometry. Unsupervised and supervised statistical analysis revealed biochemical changes associated with the tauopathy process.

Results

Energy deprivation and potentially neural apoptosis were reflected in increased purine nucleotide catabolism and decreased levels of citric acid cycle intermediates and glucose. However, in CSF, increased levels of citrate and aconitate that can be attributed to glial activation were observed. Other significant changes were found in arginine and phosphatidylcholine metabolism.

Conclusions

Despite an enormous effort invested in development of biomarkers for tauopathies during the last 20 years, there is no clinically used biomarker or assay on the market. One of the most promising strategies is to create a panel of markers (e.g., small molecules, proteins) that will be continuously monitored and correlated with patients’ clinical outcome. In this study, we identified several metabolic changes that are affected during the tauopathy process and may be considered as potential markers of tauopathies in humans.

Electronic supplementary material

The online version of this article (doi:10.1186/s13195-017-0303-5) contains supplementary material, which is available to authorized users.

Early Effect of Amyloid β-Peptide on Hippocampal and Serum Metabolism in Rats Studied by an Integrated Method of NMR-Based Metabolomics and ANOVA-Simultaneous Component Analysis

Amyloid β (Aβ) deposition has been implicated in the pathogenesis of Alzheimer's disease. However, the early effect of Aβ deposition on metabolism remains unclear. In the present study, thus, we explored the metabolic changes in the hippocampus and serum during first 2 weeks of Aβ25-35 injection in rats by using an integrated method of NMR-based metabolomics and ANOVA-simultaneous component analysis (ASCA). Our results show that Aβ25-35 injection, time, and their interaction had statistically significant effects on the hippocampus and serum metabolome. Furthermore, we identified key metabolites that mainly contributed to these effects. After Aβ25-35 injection from 1 to 2 weeks, the levels of lactate, N-acetylaspartate, creatine, and taurine were decreased in rat hippocampus, while an increase in lactate and decreases in LDL/VLDL and glucose were observed in rat serum. Therefore, we suggest that the reduction in energy and lipid metabolism as well as an increase in anaerobic glycolysis may occur at the early stage of Aβ25-35 deposition.

Metabolomic investigation of systemic manifestations associated with Alzheimer's disease in the APP/PS1 transgenic mouse model

There is growing evidence that Alzheimer's disease may be a widespread systemic disorder, so peripheral organs could be affected by pathological mechanisms occurring in this neurodegenerative disease. For this reason, a double metabolomic platform based on the combination of gas chromatography-mass spectrometry and ultra-high performance liquid chromatography-mass spectrometry was used for the first time to investigate metabolic changes in liver and kidney from the transgenic mice APP/PS1 against wild-type controls. Multivariate statistics showed significant differences in levels of numerous metabolites including phospholipids, sphingolipids, acylcarnitines, steroids, amino acids and other compounds, which denotes that multiple pathways might be associated with systemic pathogenesis of Alzheimer's in this mouse model, such as bioenergetic failures, oxidative stress, altered metabolism of membrane lipids, hyperammonemia or impaired homeostasis of steroids. Furthermore, it is noteworthy that some novel pathological mechanisms were found, such as impaired gluconeogenesis, polyol pathway or metabolism of branched chain amino acids, not previously described for Alzheimer's disease. Therefore, these findings clearly support the hypothesis that Alzheimer's disease may be considered as a systemic disorder.

Detection of inflammatory biomarkers in saliva and urine: Potential in diagnosis, prevention, and treatment for chronic diseases

Inflammation is a part of the complex biological response of inflammatory cells to harmful stimuli, such as pathogens, irritants, or damaged cells. This inflammation has been linked to several chronic diseases including cancer, atherosclerosis, rheumatoid arthritis, and multiple sclerosis. Major biomarkers of inflammation include tumor necrosis factor, interleukins (IL)-1, IL-6, IL-8, chemokines, cyclooxygenase, 5-lipooxygenase, and C-reactive protein, all of which are regulated by the transcription factor nuclear factor-kappaB. Although examining inflammatory biomarkers in blood is a standard practice, its identification in saliva and/or urine is more convenient and non-invasive. In this review, we aim to (1) discuss the detection of these inflammatory biomarkers in urine and saliva; (2) advantages of using salivary and urinary inflammatory biomarkers over blood, while also weighing on the challenges and/or limitations of their use; (3) examine their role(s) in connection with diagnosis, prevention, treatment, and drug development for several chronic diseases with inflammatory consequences, including cancer; and (4) explore the use of innovative salivary and urine based biosensor strategies that may permit the testing of biomarkers quickly, reliably, and cost-effectively, in a decentralized setting.

Urinary Biomarkers of Brain Diseases

Biomarkers are the measurable changes associated with a physiological or pathophysiological process. Unlike blood, urine is not subject to homeostatic mechanisms. Therefore, greater fluctuations could occur in urine than in blood, better reflecting the changes in human body. The roadmap of urine biomarker era was proposed. Although urine analysis has been attempted for clinical diagnosis, and urine has been monitored during the progression of many diseases, particularly urinary system diseases, whether urine can reflect brain disease status remains uncertain. As some biomarkers of brain diseases can be detected in the body fluids such as cerebrospinal fluid and blood, there is a possibility that urine also contain biomarkers of brain diseases. This review summarizes the clues of brain diseases reflected in the urine proteome and metabolome.

Low-Temperature-Induced Expression of Rice Ureidoglycolate Amidohydrolase is Mediated by a C-Repeat/Dehydration-Responsive Element that Specifically Interacts with Rice C-Repeat-Binding Factor 3

Nitrogen recycling and redistribution are important for the environmental stress response of plants. In non-nitrogen-fixing plants, ureide metabolism is crucial to nitrogen recycling from organic sources. Various studies have suggested that the rate-limiting components of ureide metabolism respond to environmental stresses. However, the underlying regulation mechanism is not well understood. In this report, rice ureidoglycolate amidohydrolase (OsUAH), which is a recently identified enzyme catalyzing the final step of ureide degradation, was identified as low-temperature- (LT) but not abscisic acid- (ABA) regulated. To elucidate the LT regulatory mechanism at the transcriptional level, we isolated and characterized the promoter region of OsUAH (P OsUAH ). Series deletions revealed that a minimal region between -522 and -420 relative to the transcriptional start site was sufficient for the cold induction of P OsUAH . Detailed analyses of this 103-bp fragment indicated that a C-repeat/dehydration-responsive (CRT/DRE) element localized at position -434 was essential for LT-responsive expression. A rice C-repeat-binding factors/DRE-binding proteins 1 (CBFs/DREB1s) subfamily member, OsCBF3, was screened to specifically bind to the CRT/DRE element in the minimal region both in yeast one-hybrid assays and in in vitro gel-shift analysis. Moreover, the promoter could be exclusively trans-activated by the interaction between the CRT/DRE element and OsCBF3 in vivo. These findings may help to elucidate the regulation mechanism of stress-responsive ureide metabolism genes and provide an example of the member-specific manipulation of the CBF/DREB1 subfamily.

High throughput multiorgan metabolomics in the APP/PS1 mouse model of Alzheimer's disease

Metabolomics has demonstrated a great potential for the study of pathological mechanisms occurring in brain from Alzheimer's disease patients and transgenic models. However, its application to peripheral samples is not so common, although it can provide interesting information about systemic abnormalities underlying to disease. This work represents the first metabolomic investigation of multiple peripheral organs (liver, kidney, spleen, and thymus) from the APP/PS1 mice by using a high-throughput approach based on direct infusion MS. Our findings demonstrated that these organs suffer significant metabolic impairments related to energy metabolism (e.g. glycolysis, Krebs cycle, β-oxidation), lipid homeostasis (e.g. cellular membrane breakdown and fatty acid metabolism), degradation of nucleotides, oxidative stress, hyperammonemia, and metabolism of amino acids. It is noteworthy that many of these alterations have been previously described in brain, confirming the systemic character of this neurodegenerative disorder and the utility of peripheral samples to understand its pathogenesis.

Therapeutic effect of Xue Niao An on glyoxylate-induced calcium oxalate crystal deposition based on urinary metabonomics approach

The anti-nephrolithiasis effect of Xue Niao An (XNA) capsules is explored by analyzing urine metabolic profiles in mouse models, with ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). An animal model of calcium oxalate crystal renal deposition was established in mice by intra-abdominal injection of glyoxylate. Then, treatment with XNA by intra-gastric administration was performed. At the end of the study, calcium deposition in kidney was measured by Von Kossa staining under light microscopy, and the Von Kossa staining changes showed that XNA significantly alleviated the calcium oxalate crystal deposition. Meanwhile, urine samples for fifteen metabolites, including amino acids and fatty acids, with significant differences were detected in the calcium oxalate group, while XNA treatment attenuated metabolic imbalances. Our study indicated that the metabonomic strategy provided comprehensive insight on the metabolic response to XNA treatment of rodent renal calcium oxalate deposition.

Development of isotope labeling liquid chromatography mass spectrometry for mouse urine metabolomics: quantitative metabolomic study of transgenic mice related to Alzheimer's disease

Because of a limited volume of urine that can be collected from a mouse, it is very difficult to apply the common strategy of using multiple analytical techniques to analyze the metabolites to increase the metabolome coverage for mouse urine metabolomics. We report an enabling method based on differential isotope labeling liquid chromatography mass spectrometry (LC-MS) for relative quantification of over 950 putative metabolites using 20 μL of urine as the starting material. The workflow involves aliquoting 10 μL of an individual urine sample for ¹²C-dansylation labeling that target amines and phenols. Another 10 μL of aliquot was taken from each sample to generate a pooled sample that was subjected to ¹³C-dansylation labeling. The ¹²C-labeled individual sample was mixed with an equal volume of the ¹³C-labeled pooled sample. The mixture was then analyzed by LC-MS to generate information on metabolite concentration differences among different individual samples. The interday repeatability for the LC-MS runs was assessed, and the median relative standard deviation over 4 days was 5.0%. This workflow was then applied to a metabolomic biomarker discovery study using urine samples obtained from the TgCRND8 mouse model of early onset familial Alzheimer's disease (FAD) throughout the course of their pathological deposition of beta amyloid (Aβ). It was showed that there was a distinct metabolomic separation between the AD prone mice and the wild type (control) group. As early as 15-17 weeks of age (presymptomatic), metabolomic differences were observed between the two groups, and after the age of 25 weeks the metabolomic alterations became more pronounced. The metabolomic changes at different ages corroborated well with the phenotype changes in this transgenic mice model. Several useful candidate biomarkers including methionine, desaminotyrosine, taurine, N1-acetylspermidine, and 5-hydroxyindoleacetic acid were identified. Some of them were found in previous metabolomics studies in human cerebrospinal fluid or blood samples. This work illustrates the utility of this isotope labeling LC-MS method for biomarker discovery using mouse urine metabolomics.

Metabolic profiling of a transgenic Caenorhabditis elegans Alzheimer model

Despite decades of research, no early-onset biomarkers are currently available for Alzheimer's disease, a cureless neurodegenerative disease afflicting millions worldwide. In this study, transgenic Caenorhabditis elegans were used to investigate changes in the metabolome after induced expression of amyloid-β. GC- and LC-MS-based platforms determined a total of 157 differential features. Some of these were identified using in-house (GC-MS) or public libraries (LC-MS), revealing changes in allantoin, cystathionine and tyrosine levels. Since C. elegans is far better suited to metabolomics studies than most other model systems, the accordance of these findings with vertebrate literature is promising and argues for further use of C. elegans as a model of human pathology in the study of AD.

Biomarkers in Alzheimer's disease analysis by mass spectrometry-based proteomics

Alzheimer’s disease (AD) is a common chronic and destructive disease. The early diagnosis of AD is difficult, thus the need for clinically applicable biomarkers development is growing rapidly. There are many methods to biomarker discovery and identification. In this review, we aim to summarize Mass spectrometry (MS)-based proteomics studies on AD and discuss thoroughly the methods to identify candidate biomarkers in cerebrospinal fluid (CSF) and blood. This review will also discuss the potential research areas on biomarkers.

Profiling convoluted single-dimension proton NMR spectra: a Plackett-Burman approach for assessing quantification error of metabolites in complex mixtures with application to cell culture

Single-dimension hydrogen, or proton, nuclear magnetic resonance spectroscopy (1D-(1)H NMR) has become an attractive option for characterizing the full range of components in complex mixtures of small molecular weight compounds due to its relative simplicity, speed, spectral reproducibility, and noninvasive sample preparation protocols compared to alternative methods. One challenge associated with this method is the overlap of NMR resonances leading to "convoluted" spectra. While this can be mitigated through "targeted profiling", there is still the possibility of increased quantification error. This work presents the application of a Plackett-Burman experimental design for the robust estimation of precision and accuracy of 1D-(1)H NMR compound quantification in synthetic mixtures, with application to mammalian cell culture supernatant. A single, 20 sample experiment was able to provide a sufficient estimate of bias and variability at different metabolite concentrations. Two major sources of bias were identified: incorrect interpretation of singlet resonances and the quantification of resonances from protons in close proximity to labile protons. Furthermore, decreases in measurement accuracy and precision could be observed with decreasing concentration for a small fraction of the components as a result of their particular convolution patterns. Finally, the importance of a priori concentration estimates is demonstrated through the example of interpreting acetate metabolite trends from a bioreactor cultivation of Chinese hamster ovary cells expressing a recombinant antibody.

NMR-based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis

Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so-called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice. In this review, we will present several special examples of a successful application of NMR for biomarker discovery, implications for disease diagnosis, prognosis, and therapy evaluation, and discuss possible future improvements.