Study of cytokine induced neuropathology by high resolution proton NMR spectroscopy of rat urine

Dose-response characteristics of Clematis triterpenoid saponins and clematichinenoside AR in rheumatoid arthritis rats by liquid chromatography/mass spectrometry-based serum and urine metabolomics

Clematidis Radix et Rhizoma is a traditional Chinese medicine widely used for treating arthritic disease. Clematis triterpenoid saponins (TS) and clematichinenoside AR (C-AR) have been considered to be responsible for its antiarthritic effects. However, the underling mechanism is still unclear because of their low bioavailability. To address of this issue, metabolomics tools were performed to determine metabolic variations associated with rheumatoid arthritis (RA) and responses to Clematis TS, C-AR and positive drug (Triptolide, TP) treatments. This metabolomics investigation of RA was conducted in collagen-induced arthritis (CIA) rats. Liquid chromatography/mass spectrometry and multivariate statistical tools were used to identify the alteration of serum and urine metabolites associated with RA and responses to drug treatment. As a result, 45 potential metabolites associated with RA were identified. After treatment, a total of 24 biomarkers were regulated to normal like levels. Among these, PC(18:0/20:4), 9,11-octadecadienoic acid, arachidonic acid, 1-methyladenosine, valine, hippuric acid and pantothenic acid etc, were reversed in Clematis TS and C-AR groups. Tetrahydrocortisol was regulated to normal levels in Clematis TS and TP groups, while 3,7,12-trihydroxycholan-24-oic acid was regulated in C-AR and TP groups. Biomarkers like citric acid, p-cresol glucuronide, creatinine, cortolone were reversed in TP group.

Early Diagnosis of Brain Metastases Using a Biofluids-Metabolomics Approach in Mice

Over 20% of cancer patients will develop brain metastases. Prognosis is currently extremely poor, largely owing to late-stage diagnosis. We hypothesized that biofluid metabolomics could detect tumours at the micrometastatic stage, prior to the current clinical gold-standard of blood-brain barrier breakdown. Metastatic mammary carcinoma cells (4T1-GFP) were injected into BALB/c mice via intracerebral, intracardiac or intravenous routes to induce differing cerebral and systemic tumour burdens. B16F10 melanoma and MDA231BR-GFP human breast carcinoma cells were used for additional modelling. Urine metabolite composition was analysed by 1H NMR spectroscopy. Statistical pattern recognition and modelling was applied to identify differences or commonalities indicative of brain metastasis burden. Significant metabolic profile separations were found between control cohorts and animals with tumour burdens at all time-points for the intracerebral 4T1-GFP time-course. Models became stronger, with higher sensitivity and specificity, as the time-course progressed indicating a more severe tumour burden. Sensitivity and specificity for predicting a blinded testing set were 0.89 and 0.82, respectively, at day 5, both rising to 1.00 at day 35. Significant separations were also found between control and all 4T1-GFP injected mice irrespective of route. Likewise, significant separations were observed in B16F10 and MDA231BR-GFP cell line models. Metabolites underpinning each separation were identified. These findings demonstrate that brain metastases can be diagnosed in an animal model based on urinary metabolomics from micrometastatic stages. Furthermore, it is possible to separate differing systemic and CNS tumour burdens, suggesting a metabolite fingerprint specific to brain metastasis. This method has strong potential for clinical translation.

Metabolomics to identify biomarkers and as a predictive tool in inflammatory diseases

There is an overwhelming need for a simple, reliable tool that aids clinicians in diagnosing, assessing disease activity and treating rheumatic conditions. Identification of biomarkers in partially understood inflammatory disorders has long been sought after as the Holy Grail of Rheumatology. Given the complex nature of inflammatory conditions, it has been difficult to earmark the potential biomarkers. Metabolomics, however, is promising in providing new insights into inflammatory conditions and also identifying such biomarkers. Metabolomic studies have generally revealed increased energy requirements for by-products of a hypoxic environment, leading to a characteristic metabolic fingerprint. Here, we discuss the significance of such studies and their potential as a biomarker.

NMR-Based Metabolomics Separates the Distinct Stages of Disease in a Chronic Relapsing Model of Multiple Sclerosis

Relapsing experimental allergic encephalomyelitis (Cr-EAE) is commonly used to explore the pathogenesis and efficacy of new therapies for MS, but it is unclear whether the metabolome of Cr-EAE is comparable to human multiple sclerosis (MS). For MS, the diagnosis and staging can be achieved by metabolomics on blood using a combination of magnetic resonance spectroscopy and partial least squares discriminant analysis (PLS-DA). Here, we sought to discover whether this approach could be used to differentiate between sequential disease states in Cr-EAE and whether the same metabolites would be discriminatory. Urine and plasma samples were obtained at different time-points from a clinically relevant model of MS. Using PLS-DA modelling for the urine samples furnished some predictive models, but could not discriminate between all disease states. However, PLS-DA modelling of the plasma samples was able to distinguish between animals with clinically silent disease (day 10, 28) and animals with active disease (day 14, 38). We were also able to distinguish Cr-EAE mice from naive mice at all-time points and control mice, treated with complete Freund's adjuvant alone, at day 14 and 38. Key metabolites that underpin these models included fatty acids, glucose and taurine. Two of these metabolites, fatty acids and glucose, were also key metabolites in separating relapsing-remitting MS from secondary-progressive MS in the human study. These results demonstrate the sensitivity of this metabolomics approach for distinguishing between different disease states. Furthermore, some, but not all, of the changes in metabolites were conserved in humans and the mouse model, which could be useful for future drug development.

A type 2 biomarker separates relapsing-remitting from secondary progressive multiple sclerosis

OBJECTIVE

We tested whether it is possible to differentiate relapsing-remitting (RR) from secondary progressive (SP) disease stages in patients with multiple sclerosis (MS) using a combination of nuclear magnetic resonance (NMR) metabolomics and partial least squares discriminant analysis (PLS-DA) of biofluids, which makes no assumptions on the underlying mechanisms of disease.

METHODS

Serum samples were obtained from patients with primary progressive MS (PPMS), SPMS, and RRMS; patients with other neurodegenerative conditions; and age-matched controls. Samples were analyzed by NMR and PLS-DA models were derived to separate disease groups.

RESULTS

The PLS-DA models for serum samples from patients with MS enabled reliable differentiation between RRMS and SPMS. This approach also identified significant differences between the metabolite profiles of each of the MS groups (PP, SP, and RR) and the healthy controls, as well as predicting disease group membership with high specificity and sensitivity.

CONCLUSIONS

NMR metabolomics analysis of serum is a sensitive and robust method for differentiating between different stages of MS, yielding diagnostic markers without a priori knowledge of disease pathogenesis. Critically, this study identified and validated a type II biomarker for the RR to SP transition in patients with MS. This approach may be of considerable benefit in categorizing patients for treatment and as an outcome measure in future clinical trials.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that serum metabolite profiles accurately distinguish patients with different subtypes and stages of MS.

Cerebral biochemical pathways in experimental autoimmune encephalomyelitis and adjuvant arthritis: a comparative metabolomic study

Many diseases, including brain disorders, are associated with perturbations of tissue metabolism. However, an often overlooked issue is the impact that inflammations outside the brain may have on brain metabolism. Our main goal was to study similarities and differences between brain metabolite profiles of animals suffering from experimental autoimmune encephalomyelitis (EAE) and adjuvant arthritis (AA) in Lewis rat models. Our principal objective was the determination of molecular protagonists involved in the metabolism underlying these diseases. EAE was induced by intraplantar injection of complete Freund’s adjuvant (CFA) and spinal-cord homogenate (SC-H), whereas AA was induced by CFA only. Naive rats served as controls (n = 9 for each group). Two weeks after inoculation, animals were sacrificed, and brains were removed and processed for metabolomic analysis by NMR spectroscopy or for immunohistochemistry. Interestingly, both inflammatory diseases caused similar, though not identical, changes in metabolites involved in regulation of brain cell size and membrane production: among the osmolytes, taurine and the neuronal marker, N-acetylaspartate, were decreased, and the astrocyte marker, myo-inositol, slightly increased in both inoculated groups compared with controls. Also ethanolamine-containing phospholipids, sources of inflammatory agents, and several glycolytic metabolites were increased in both inoculated groups. By contrast, the amino acids, aspartate and isoleucine, were less concentrated in CFA/SC-H and control vs. CFA rats. Our results suggest that inflammatory brain metabolite profiles may indicate the existence of either cerebral (EAE) or extra-cerebral (AA) inflammation. These inflammatory processes may act through distinct pathways that converge toward similar brain metabolic profiles. Our findings open new avenues for future studies aimed at demonstrating whether brain metabolic effects provoked by AA are pain/stress-mediated and/or due to the presence of systemic proinflammatory molecules. Regardless of the nature of these mechanisms, our findings may be of interest for future clinical studies, e.g. by in-vivo magnetic resonance spectroscopy.

Metabolomics--a novel window into inflammatory disease

Inflammation is an important component of normal responses to infection and injury. However, chronic activation of the immune system, due to aberrant responses to normal stimuli, can lead to the establishment of a persistent inflammatory state. Such inflammatory conditions are often debilitating, and are associated with a number of important co-morbidities including cardiovascular disease. Resting non-proliferative tissues have distinctive metabolic activities and requirements, which differ considerably from those in infiltrating immune cells, which are undergoing proliferation and differentiation. Immune responses in tissues may therefore be modulated by the relative abundance of substrates in the inflamed site. In turn immune cell activity can feed back and affect metabolic behaviour of the tissues, as most clearly demonstrated in cachexia - the loss of cellular mass driven by tumour necrosis factor-alpha (TNF-α) a key mediator of the inflammatory response. Here we discuss the potential for metabolomic analysis to clarify the interactions between inflammation and metabolic changes underlying many diseases. We suggest that an increased understanding of the interaction between inflammation and cellular metabolism, energy substrate use, tissue breakdown markers, the microbiome and drug metabolites, may provide novel insight into the regulation of inflammatory diseases.

Neuroprotective and neurodegenerative effects of the chronic expression of tumor necrosis factor α in the nigrostriatal dopaminergic circuit of adult mice

Tumor necrosis factor (TNF)-α, a pro-inflammatory cytokine, has been implicated in both neuronal death and survival in Parkinson's disease (PD). The substantia nigra (SN), a CNS region affected in PD, is particularly susceptible to inflammatory insults and possesses the highest density of microglial cells, but the effects of inflammation and in particular TNF-α on neuronal survival in this region remains controversial. Using adenoviral vectors, the CRE/loxP system and hypomorphic mice, we achieved chronic expression of two levels of TNF-α in the SN of adult mice. Chronic low expression of TNF-α levels reduced the nigrostriatal neurodegeneration mediated by intrastriatal 6-hydroxydopamine administration. Protective effects of low TNF-α level could be mediated by TNF-R1, GDNF, and IGF-1 in the SN and SOD activity in the striatum (ST). On the contrary, chronic expression of high levels of TNF-α induced progressive neuronal loss (63% at 20 days and 75% at 100 days). This effect was accompanied by gliosis and an inflammatory infiltrate composed almost exclusively by monocytes/macrophages. The finding that chronic high TNF-α had a slow and progressive neurodegenerative effect in the SN provides an animal model of PD mediated by the chronic expression of a single cytokine. In addition, it supports the view that cytokines are not detrimental or beneficial by themselves, i.e., their level and time of expression among other factors can determine its final effect on CNS damage or protection. These data support the view that new anti-parkinsonian treatments based on anti-inflammatory therapies should consider these dual effects of cytokines on their design.

Metabonomic study on the toxicity of Hei-Shun-Pian, the processed lateral root of Aconitum carmichaelii Debx. (Ranunculaceae)

The purpose of this paper was to study the effects of Hei-Shun-Pian, the processed lateral root of Aconitum carmichaelii Debx. (Ranunculaceae), on the metabolic profile of rats, to discuss the mechanism of toxicology and to find out the potential biomarkers of the toxic effects. Twenty male Wistar rats were divided into four groups (n=5) and each group were administered orally with the decoction of Hei-Shun-Pian (88.1g/kg per day, 35.6g/kg per day, 17.6g/kg per day) or equal volume of drinking water respectively for 14 days. Urine of every 24-h and the plasma of the last day were collected for NMR experiments, and then analyzed by multivariate analysis methods. Decreases in urinary excretion of taurine and trimethylamine-N-oxide (TMAO) and increases in urinary levels of citrate, 2-oxoglutarate (2-OG), succinate and hippurate were observed in the high and medium dosed groups at the early stage of the dosing period. Taurine level increased at the later stage of the dosing period to the normal value, and then even to a value higher than that of the control group at the end of the experiment. No metabolic differences were observed between low dosed and control groups until the later stage of the dosing period when a slight increase in urinary taurine level was observed, suggesting a cumulative effect. These results suggest the toxic effect of Hei-Shun-Pian on rat heart in a dose dependent manner.

Liquid chromatography/mass spectrometry for metabonomics investigation of the biochemical effects induced by aristolochic acid in rats: the use of information-dependent acquisition for biomarker identification

The toxic effects of oral administrations of nephrotoxic and carcinogenic aristolochic acid (AA) to male Sprague-Dawley rats were investigated by using high-performance liquid chromatography coupled with a quadrupole time-of-flight mass spectrometer. Analysis of the urine and plasma samples revealed distinct changes in the biochemical patterns in the AA-dosed rats. After peak finding and alignment, principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were used for multivariate data analysis. Potential biomarkers were studied by high-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) analyses. The MS/MS spectra of all endogenous metabolites satisfying the pre-defined criteria were acquired in a single information-dependent acquisition (IDA) analysis, demonstrating that IDA was an efficient approach for structural elucidation in metabonomic studies. Citric acid and a glucuronide-containing metabolite were observed as potential biomarkers in rat urine. A significant increase in plasma creatinine concentration was also observed in the AA-dosed rats, which indicated that AA induced an adverse effect on the renal clearance function.

NMR-based metabonomic studies reveal changes in the biochemical profile of plasma and urine from pigs fed high-fibre rye bread

This study presents an NMR-based metabonomic approach to elucidate the overall endogenous biochemical effects of a wholegrain diet. Two diets with similar levels of dietary fibre and macronutrients, but with contrasting levels of wholegrain ingredients, were prepared from wholegrain rye (wholegrain diet (WGD)) and non-wholegrain wheat (non-wholegrain diet (NWD)) and fed to four pigs in a crossover design. Plasma samples were collected after 7 d on each diet, and 1H NMR spectra were acquired on these. Partial least squares regression discriminant analysis (PLSDA) on spectra obtained for plasma samples revealed that the spectral region at 3·25 parts per million dominates the differentiation between the two diets, as the WGD is associated with higher spectral intensity in this region. Spiking experiments and LC–MS analyses of the plasma verified that this spectral difference could be ascribed to a significantly higher content of betaine in WGD plasma samples compared with NWD samples. In an identical study with the same diets, urine samples were collected, and1H NMR spectra were acquired on these. PLS-DA on spectra obtained for urine samples revealed changes in the intensities of spectral regions, which could be ascribed to differences in the content of betaine and creatine/creatinine between the two diets, and LC–MS analyses verified a significantly lower content of creatinine in WGD urine samples compared with NWD urine samples. In conclusion, using an explorative approach, the present studies disclosed biochemical effects of a wholegrain diet on plasma betaine content and excretion of betaine and creatinine.

Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GC/MS and PLS-LDA

Metabolic profiling has increasingly been used as a probe in disease diagnosis and pharmacological analysis. Herein, plasma fatty acid metabolic profiling including non-esterified fatty acid (NEFA) and esterified fatty acid (EFA) was investigated using gas chromatography/mass spectrometry (GC/MS) followed by multivariate statistical analysis. Partial least squares-linear discrimination analysis (PLS-LDA) model was established and validated to pattern discrimination between type 2 diabetic mellitus (DM-2) patients and health controls, and to extract novel biomarker information. Furthermore, the PLS-LDA model visually represented the alterations of NEFA metabolic profiles of diabetic patients with abdominal obesity in the treated process with rosiglitazone. The GC/MS-PLS-LDA analysis allowed comprehensive detection of plasma fatty acid, enabling fatty acid metabolic characterization of DM-2 patients, which included biomarkers different from health controls and dynamic change of NEFA profiles of patients after treated with medicine. This method might be a complement or an alternative to pathogenesis and pharmacodynamics research.

Genomics, proteomics, metabolomics: what is in a word for multiple sclerosis?

PURPOSE OF REVIEW

Multiple sclerosis (MS) is the most common chronic inflammatory neurological disease. Despite major advances the aetiology of this disease it is still not completely understood. In the post-genome era, advances in global screening technologies offer an opportunity to accelerate the search of new pathological pathways and to identify new therapeutic targets. Some recent publications using novel global screening methods at the genome, transcriptome, proteome and metabolome levels are discussed.

RECENT FINDINGS

The genetic association of susceptibility to MS with loci outside the MHC has been reconfirmed. Evidence of parent-of-origin and seasonal effects on disease susceptibility add further complexity to the genetics of MS. The search for MS susceptibility genes continues using the candidate-gene approach as well as large-scale single-nucleotide-polymorphism association studies and novel cross-species synteny analysis. Genome-wide expression profiling using microarrays produced numerous therapeutic targets and is progressing towards profiling of rare cells. Advances in classical proteomics methods paved the way to new initiatives aiming at determining the proteome of the nervous system in normal and diseased states. Although progress is still slow, array-based methods are making an impact on the MS field.

SUMMARY

The complexity of MS is clearly reflected in the latest findings using global profiling methods. Nevertheless, these new technologies are confirming some of the basic aspects of the disease pathophysiology, i.e. its polygenicity, the central role of neuroinflammation and the emerging neurodegenerative processes. These data are primarily the results of genomic approaches, yet promising attempts are also made using proteomics and metabolomics.