Automated metabolite identification from biological fluid <Superscript>1</Superscript>H NMR spectra

Introduction

Metabolite identification in biological samples using Nuclear Magnetic Resonance (NMR) spectra is a challenging task due to the complexity of the biological matrices.

Objectives

This paper introduces a new, automated computational scheme for the identification of metabolites in 1D ¹H NMR spectra based on the Human Metabolome Database.

Methods

The methodological scheme comprises of the sequential application of preprocessing, data reduction, metabolite screening and combination selection.

Results

The proposed scheme has been tested on the 1D ¹H NMR spectra of: (a) an amino acid mixture, (b) a serum sample spiked with the amino acid mixture, (c) 20 blood serum, (d) 20 human amniotic fluid samples, (e) 160 serum samples from publicly available database. The methodological scheme was compared against widely used software tools, exhibiting good performance in terms of correct assignment of the metabolites.

Conclusions

This new robust scheme accomplishes to automatically identify peak resonances in ¹H-NMR spectra with high accuracy and less human intervention with a wide range of applications in metabolic profiling.

     

Characterization of ankylosing spondylitis and rheumatoid arthritis using <Superscript>1</Superscript>H NMR-based metabolomics of human fecal extracts

Introduction

The differences in fecal metabolome between ankylosing spondylitis (AS)/rheumatoid arthritis (RA) patients and healthy individuals could be the reason for an autoimmune disorder.

Objectives

The study explored the fecal metabolome difference between AS/RA patients and healthy controls to clarify human immune disturbance.

Methods

Fecal samples from 109 individuals (healthy controls 34, AS 40, and RA 35) were analyzed by ¹H NMR spectroscopy. Data were analyzed with principal component analysis (PCA) and orthogonal projection to latent structure discriminant (OPLS-DA) analysis.

Results

Significant differences in the fecal metabolic profiles could distinguish AS/RA patients from healthy controls but could not distinguish between AS and RA patients. The significantly decreased metabolites in AS/RA patients were butyrate, propionate, methionine, and hypoxanthine. Significantly increased metabolites in AS/RA patients were taurine, methanol, fumarate, and tryptophan.

Conclusion

The metabolome variations in feces indicated AS and RA were two homologous diseases that could not be distinguished by ¹H NMR metabolomics.

     

rDolphin: a GUI R package for proficient automatic profiling of 1D <Superscript>1</Superscript>H-NMR spectra of study datasets

Introduction

Adoption of automatic profiling tools for ¹H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices.

Objectives

To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process.

Methods

rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality.

Results

The information and quality achieved in two public datasets of complex matrices are maximized.

Conclusion

rDolphin is an open-source R package (http://github.com/danielcanueto/rDolphin) able to provide the best balance between accuracy, reproducibility and ease of use.

     

Modelling the acid/base <Superscript>1</Superscript>H NMR chemical shift limits of metabolites in human urine

Introduction

Despite the use of buffering agents the ¹H NMR spectra of biofluid samples in metabolic profiling investigations typically suffer from extensive peak frequency shifting between spectra. These chemical shift changes are mainly due to differences in pH and divalent metal ion concentrations between the samples. This frequency shifting results in a correspondence problem: it can be hard to register the same peak as belonging to the same molecule across multiple samples. The problem is especially acute for urine, which can have a wide range of ionic concentrations between different samples.

Objectives

To investigate the acid, base and metal ion dependent ¹H NMR chemical shift variations and limits of the main metabolites in a complex biological mixture.

Methods

Urine samples from five different individuals were collected and pooled, and pre-treated with Chelex-100 ion exchange resin. Urine samples were either treated with either HCl or NaOH, or were supplemented with various concentrations of CaCl₂, MgCl₂, NaCl or KCl, and their ¹H NMR spectra were acquired.

Results

Nonlinear fitting was used to derive acid dissociation constants and acid and base chemical shift limits for peaks from 33 identified metabolites. Peak pH titration curves for a further 65 unidentified peaks were also obtained for future reference. Furthermore, the peak variations induced by the main metal ions present in urine, Na⁺, K⁺, Ca²⁺ and Mg²⁺, were also measured.

Conclusion

These data will be a valuable resource for ¹H NMR metabolite profiling experiments and for the development of automated metabolite alignment and identification algorithms for ¹H NMR spectra.

     

Measurement of the <Superscript>15</Superscript>N/<Superscript>14</Superscript>N ratio of phenylalanine in fermentation matrix by isotope ratio mass spectrometry

Objectives

To determine the origin of ¹⁵N-labeled phenylalanine in microbial metabolic flux analysis using ¹⁵N as a tracer, a method for measuring phenylalanine δ¹⁵N using HPLC coupled with elemental analysis-isotope ratio mass spectrometry (EA-IRMS) was developed.

Results

The original source of the ¹⁵N-labeled phenylalanine was determined using this new method that consists of three steps: optimization of the HPLC conditions, evaluation of the isotope fractionation effects, and evaluation of the effect of pre-processing on the phenylalanine nitrogen stable isotope. In addition, the use of a ¹⁵N-labeled inorganic nitrogen source, rather than ¹⁵N-labeled amino acids, was explored using this method.

Conclusions

The method described here can also be applied to the analysis of metabolic flux.

     

Accumulation of Fascin<Superscript>+</Superscript> cells during experimental autoimmune neuritis

Background

Experimental autoimmune neuritis (EAN) is a well-known animal model of human demyelinating polyneuropathies and is characterized by inflammation and demyelination in the peripheral nervous system. Fascin is an evolutionarily highly conserved cytoskeletal protein of 55 kDa containing two actin binding domains that cross-link filamentous actin to hexagonal bundles.

Methods

Here we have studied by immunohistochemistry the spatiotemporal accumulation of Fascin?+?cells in sciatic nerves of EAN rats.

Results

A robust accumulation of Fascin?+?cell was observed in the peripheral nervous system of EAN which was correlated with the severity of neurological signs in EAN.

Conclusion

Our results suggest a pathological role of Fascin in EAN.

Virtual slides

The virtual slides for this article can be found here: http://www.diagnosticphatology.diagnomx.eu/vs/6734593451114811

     

Lipid levels,insulin resistance and cardiovascular risk over 96 weeks of antiretroviral therapy: a randomised controlled trial comparing low-dose stavudine and tenofovir

Background

HIV infection and antiretroviral treatment are associated with changes in lipid levels, insulin resistance and risk of cardiovascular disease (CVD). We investigated these changes in the first 96 weeks of treatment with low-dose stavudine or tenofovir regimens.

Methods

This is a secondary analysis of a double blind, randomised controlled trial performed in South-Africa, Uganda and India comparing low-dose stavudine (20 mg twice daily) with tenofovir in combination with efavirenz and lamivudine in antiretroviral-naïve adults (n?=?1067) (Clinicaltrials.gov, NCT02670772). Over 96 weeks, data were collected on fasting lipids, glucose and insulin. Insulin resistance was assessed with the HOMA-IR index and 10-year CVD risk with the Framingham risk score (FRS). A generalized linear mixed model was used to estimate trends over time.

Results

Participants were on average 35.3 years old, 57.6% female and 91.8% Black African. All lipid levels increased following treatment initiation, with the sharpest increase in the first 24 weeks of treatment. The increase in all lipid subcomponents over 96 weeks was higher among those in the stavudine than the tenofovir group. Insulin resistance increased steadily with no difference detected between study groups. FRS rose from 1.90% (1.84–1.98%) at baseline to 2.06 (1.98–2.15%) at week 96 for the total group, with no difference between treatment arms (p?=?0.144). Lipid changes were more marked in Indian than African participants.

Conclusion

Lipid levels increased in both groups, with low-dose stavudine resulting in a worse lipid profile compared to tenofovir. Insulin resistance increased, with no difference between regimens. CVD risk increased over time and tended to increase more in the group on stavudine. The low CVD risk across both arms argues against routine lipid and glucose monitoring in the absence of other CVD risk factors. In high risk patients, monitoring may only be appropriate at least a year after treatment initiation.

     

<Superscript>1</Superscript>H NMR metabolic profiling of gastric cancer patients with lymph node metastasis

Introduction

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

Objectives

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

Methods

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

Results

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

Conclusion

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

     

<Superscript>123</Superscript>I-ADAM SPET imaging of serotonin transporter in patients with epilepsy and comorbid depression

Background

Purpose of the study was to investigate alterations in midbrain serotonin transporter (SERT) binding in patients with epilepsy and symptoms of depression compared to patients with epilepsy with no symptoms of depression.

Methods

We studied 12 patients with epilepsy (7 patients had focal and 5 had generalized epilepsy syndromes). The presence of self-reported symptoms of depression was assessed using Beck Depression Inventory (BDI) and the Emotional State Questionnaire (EST-Q). The binding potential of the SERT was assessed by performing brain single photon emission tomography (SPET) using the SERT radioligand 2-((2-((dimethylamino)methyl)phenyl)thio)-5-(123)iodophenylamine (¹²³I-ADAM).

Results

Seven patients had BDI and EST-Q subscale scores greater than 11 points, which was interpreted as the presence of symptoms of depression. We found that ¹²³I-ADAM binding was not significantly different between patients with epilepsy with and without symptoms of depression. In addition, ¹²³I-ADAM binding did not show a significant correlation to either BDI or EST-Q depression subscale scores and did not differ between patients with focal vs. generalized epilepsy.

Conclusion

The results of our study failed to demonstrate alterations of SERT binding properties in patients with epilepsy with or without symptoms of depression.

     

<Superscript>13</Superscript>C metabolomics reveals widespread change in carbon fate during coral bleaching

Introduction

Rising seawater temperatures are threatening the persistence of coral reefs; where above critical thresholds, thermal stress results in a breakdown of the coral-dinoflagellate symbiosis and the loss of algal symbionts (coral bleaching). As symbiont-derived organic products typically form a major portion of host energy budgets, this has major implications for the fitness and persistence of symbiotic corals.

Objectives

We aimed to determine change in autotrophic carbon fate within individual compounds and downstream metabolic pathways in a coral symbiosis exposed to varying degrees of thermal stress and bleaching.

Methods

We applied gas chromatography–mass spectrometry coupled to a stable isotope tracer (¹³C), to track change in autotrophic carbon fate, in symbiont and host individually, following exposure to elevated water temperature.

Results

Thermal stress resulted in partner-specific changes in carbon fate, which progressed with heat stress duration. We detected modifications to carbohydrate and fatty acid metabolism, lipogenesis, and homeostatic responses to thermal, oxidative and osmotic stress. Despite pronounced photodamage, remaining in hospite symbionts continued to produce organic products de novo and translocate to the coral host. However as bleaching progressed, we observed minimal ¹³C enrichment of symbiont long-chain fatty acids, also reflected in ¹³C enrichment of host fatty acid pools.

Conclusion

These data have major implications for our understanding of coral symbiosis function during bleaching. Our findings suggest that during early stage bleaching, remaining symbionts continue to effectively translocate a variety of organic products to the host, however under prolonged thermal stress there is likely a reduction in the quality of these products.

     

G-CSF/anti-G-CSF antibody complexes drive the potent recovery and expansion of CD11b<Superscript>+</Superscript>Gr-1<Superscript>+</Superscript> myeloid cells without compromising CD8<Superscript>+</Superscript> T cell immune responses

Background

Administration of recombinant G-CSF following cytoreductive therapy enhances the recovery of myeloid cells, minimizing the risk of opportunistic infection. Free G-CSF, however, is expensive, exhibits a short half-life, and has poor biological activity in vivo.

Methods

We evaluated whether the biological activity of G-CSF could be improved by pre-association with anti-G-CSF mAb prior to injection into mice.

Results

We find that the efficacy of G-CSF therapy can be enhanced more than 100-fold by pre-association of G-CSF with an anti-G-CSF monoclonal antibody (mAb). Compared with G-CSF alone, administration of G-CSF/anti-G-CSF mAb complexes induced the potent expansion of CD11b⁺Gr-1⁺ myeloid cells in mice with or without concomitant cytoreductive treatment including radiation or chemotherapy. Despite driving the dramatic expansion of myeloid cells, in vivo antigen-specific CD8⁺ T cell immune responses were not compromised. Furthermore, injection of G-CSF/anti-G-CSF mAb complexes heightened protective immunity to bacterial infection. As a measure of clinical value, we also found that antibody complexes improved G-CSF biological activity much more significantly than pegylation.

Conclusions

Our findings provide the first evidence that antibody cytokine complexes can effectively expand myeloid cells, and furthermore, that G-CSF/anti-G-CSF mAb complexes may provide an improved method for the administration of recombinant G-CSF.

     

Comprehensive <Superscript>1</Superscript>H NMR metabolic profiling of body fluids for differentiation of meningitis in adults

Introduction

Meningitis, a morbidly infectious central nervous system pathology is accompanied by acute inflammation of the meninges, causing raised intracranial pressure linked with serious neurological sequelae.

Objective

To observe the variation in the metabolic profile, that may occur in serum and urine along with CSF in adults using ¹H NMR spectroscopy, with an attempt of appropriate and timely treatment regimen.

Methods

The ¹H NMR-based metabolomics has been performed in 115 adult subjects for differentiating bacterial meningitis (BM) and tubercular meningitis (TBM).

Results

The discriminant function analysis (DFA) of the three bio-fluids collectively identified 3-hydroxyisovalerate, lactate, glucose, formate, valine, alanine, ketonic bodies, malonate and choline containing compounds (choline and GPC) as significant metabolites among cases versus control group. The differentiation of bacterial meningitis and tuberculous meningitis (BM vs. TBM) can be done on the basis of identification of 3-hydroxyisovalerate, isobutyrate and formate in case of CSF (with a correct classification of 78 %), alanine in serum (correct classification 60 %), valine and acetone in case of urine (correct classification 89.1 %). The NMR spectral bins based orthogonal signal correction principal component analysis score plots of significant metabolites obtained from DFA also provided group classification among cases versus control group in CSF, serum and urine samples. The variable importance in projection scores also identified similar significant metabolites as obtained from DFA, collectively in CSF, serum and urine samples, responsible for differentiation of meningitis.

Conclusion

The CSF contained metabolites which are formed during infection and inflammation, and these were also found in significant quantity in serum and urine samples.

     

Metabolic profiling by <Superscript>1</Superscript>H NMR spectroscopy of saliva shows clear distinction between control and diseased case of periodontitis

Introduction

Periodontitis is a chronic, non-reversible inflammatory disease of the oral cavity leading to destruction of periodontal tissues. Thus, the estimation of bacterial metabolite, tissue damage and secretory metabolites of the triggered inflammatory cells likely to yield results. It may be of value for understanding the pathophysiology of the disease by metabolic profiling of saliva samples using high-resolution NMR spectroscopy.

Objective

The study will evaluate the difference in salivary metabolites in healthy and periodontal condition along with fetching of possible biomarkers in case of chronic periodontitis.

Methods

¹H- NMR spectroscopy has been employed in 114 saliva samples in search of distinctive differences and spectral data were further subjected to multivariate analysis.

Result

One-hundred metabolites were characterised and assigned in the ¹H NMR spectra of saliva. The statistical analysis of control (Healthy subjects) and diseased (Periodontal subjects) using PLS-DA model resulted in R² of 0.84 and Q² of 0.79. There was an elevation in the concentration of statistically discriminant metabolites. The twenty newly identified metabolites in saliva indicates bacterial population shift along with change in homeostasis. These disturbs the biofilm, a real protector against any possible bio-damage on tooth surface. These newly identified metabolites could define better geographically diversified periodontal condition.

Conclusion

Analysis clearly differentiates healthy subjects from the diseased ones. Few newly identified metabolites along with the pool of metabolites may serve as biomarkers for distinguishing the severity and complexity of periodontitis.

     

ASICS: an automatic method for identification and quantification of metabolites in complex 1D <Superscript>1</Superscript>H NMR spectra

Introduction

Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.

Objectives

In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.

Methods

A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.

Results

The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.

Conclusion

ASICS is a completely automated procedure to identify and quantify metabolites in ¹H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

     

A multidimensional <Superscript>1</Superscript>H NMR lipidomics workflow to address chemical food safety issues

Introduction

Although it is still at a very early stage compared to its mass spectrometry (MS) counterpart, proton nuclear magnetic resonance (NMR) lipidomics is worth being investigated as an original and complementary solution for lipidomics. Dedicated sample preparation protocols and adapted data acquisition methods have to be developed to set up an NMR lipidomics workflow; in particular, the considerable overlap observed for lipid signals on 1D spectra may hamper its applicability.

Objectives

The study describes the development of a complete proton NMR lipidomics workflow for application to serum fingerprinting. It includes the assessment of fast 2D NMR strategies, which, besides reducing signal overlap by spreading the signals along a second dimension, offer compatibility with the high-throughput requirements of food quality characterization.

Method

The robustness of the developed sample preparation protocol is assessed in terms of repeatability and ability to provide informative fingerprints; further, different NMR acquisition schemes—including classical 1D, fast 2D based on non-uniform sampling or ultrafast schemes—are evaluated and compared. Finally, as a proof of concept, the developed workflow is applied to characterize lipid profiles disruption in serum from β-agonists diet fed pigs.

Results

Our results show the ability of the workflow to discriminate efficiently sample groups based on their lipidic profile, while using fast 2D NMR methods in an automated acquisition framework.

Conclusion

This work demonstrates the potential of fast multidimensional ¹H NMR—suited with an appropriate sample preparation—for lipidomics fingerprinting as well as its applicability to address chemical food safety issues.

     

HspX protein as a candidate vaccine against <Emphasis Type="Italic">Mycobacterium tuberculosis</Emphasis>: an overview

Background

Tuberculosis (TB) is a contagious infectious disease caused by Mycobacterium tuberculosis (Mtb). This disease with two million deaths per year has the highest mortality rate among bacterial infections. The only available vaccine against TB is BCG vaccine. BCG is an effective vaccine against TB in childhood, however, due to some limitations, has not proper efficiency in adults. Also, BCG cannot produce an adequately protective response against reactivation of latent infections.

Objective

In the present study we will review the most recent findings about contribution of HspX protein in the vaccines against tuberculosis.

Methods

Therefore, many attempts have been made to improve BCG or to find its replacement. Most of the subunit vaccines for TB in various phases of clinical trials were constructed as prophylactic vaccines using Mtb proteins expressed in the replicating stage. These vaccines might prevent active TB but not reactivation of latent tuberculosis infection (LTBI). A literature search was performed on various online databases (PubMed, Scopus, and Google Scholar) regarding the roles of HspX protein in tuberculosis vaccines.

Results

Ideal subunit post-exposure vaccines should target all forms of TB infection, including active symptomatic and dormant (latent) asymptomatic forms. Among these subunit vaccines, HspX is the most important latent phase antigen of M. tuberculosis with a strong immunological response. There are many studies that have evaluated the immunogenicity of this protein to improve TB vaccine.

Conclusion

According to the studies, HspX protein is a good candidate for development of subunit vaccines against TB infection.

     

Characterizing alternative feeds for rainbow trout (<Emphasis Type="Italic">O. mykiss</Emphasis>) by <Superscript>1</Superscript>H NMR metabolomics

Introduction

Fish feed formulations are constantly evolving to improve the quality of diets for farmed fish and to ensure the sustainability of the aquaculture sector. Nowadays, insect, microalgae and yeast are feedstuff candidates for new feeds. However, the characterization of aquafeed is still based on proximate and targeted analyses which may not be sufficient to assess feed quality.

Objectives

Our aim was to highlight the soluble compounds that specifically differ between selected plant-based feeds complemented with alternative feedstuffs and discuss their origin and potential for fish nutrition.

Methods

A growth trial was carried out to evaluate growth performances and feed conversion ratios of fish fed plant-based, commercial, insect, spirulina and yeast feeds. ¹H NMR metabolomics profiling of each feed was performed using a CPMG sequence on polar extracts. Spectra were processed, and data were analyzed using multivariate and univariate analyses to compare alternative feeds to a plant-based feed.

Results

Fish fed insect or yeast feed showed the best growth performances associated with the lowest feed conversion ratios compared to plant-based feed. Soluble compound ¹H NMR profiles of insect and spirulina alternative feeds differed significantly from the plant-based one that clustered with yeast feed. In insect and spirulina feeds, specific differences compared to plant-based feed concerned glycerol and 3-hydroxybutyrate, respectively.

Conclusion

This strategy based on compositional differences between plant-based and alternative feeds can be useful for detecting compounds unsuspected until now that could impact fish metabolism.

     

Metabolic heterogeneity of the normal human brain: multivariate analysis of <Superscript>1</Superscript>H MRS in vivo spectra acquired at 3T

Introduction

In recent years multivariate projection techniques of data analysis (PCA, PLS-DA) have been increasingly used for detection of complex ¹H MRS derived metabolic signatures in pathologic conditions. However, these techniques have not been applied in the studies of metabolic heterogeneity of the normal human brain.

Objective

In this work we extended current knowledge about regional distribution of metabolites by multivariate analysis of metabolite levels obtained from various cortical and subcortical regions.

Methods

The studied group consisted of 71 volunteers with no neurological disorders. The metabolite levels obtained from short echo time ¹H MRS in vivo spectra were subjected to univariate and multivariate analysis.

Results

The major variance direction in the dataset was dominated by glutamine?+?glutamate, creatine, myo-inositol and was successful in differentiation of the cortical grey matter and cerebellar vermis from the cortical white matter, pons, basal ganglia, hippocampus and thalamus. The projection plane formed by the second and third variance directions was dominated by N-acetylaspartate?+?N-acetylaspartylglutamate, choline and glutamine?+?glutamate variation not explained by the first direction. This plane revealed a huge metabolic contrast between the pons and basal ganglia, differentiation between the cortical grey matter regions and cerebellar vermis as well as biochemical heterogeneity between the regions such as: thalamus, basal ganglia and hippocampus.

Conclusion

Multivariate approach to ¹H MRS data analysis provides an insight into the normal brain biochemistry and is helpful in understanding the regional heterogeneity of the normal brain. Such knowledge is crucial for a proper interpretation of altered metabolic pathways in diseases.

     

Alteration in lipid composition differentiates breast cancer tissues: a <Superscript>1</Superscript>H HRMAS NMR metabolomic study

Introduction

Breast cancer is the most frequent diagnosed cancer among women with a mortality rate of 15% of all cancer related deaths in women. Breast cancer is heterogeneous in nature and produces plethora of metabolites allowing its early detection using molecular diagnostic techniques like magnetic resonance spectroscopy.

Objectives

To evaluate the variation in metabolic profile of breast cancer focusing on lipids as triglycerides (TG) and free fatty acids (FFA) that may alter in malignant breast tissues and lymph nodes from adjacent benign breast tissues by HRMAS ¹H NMR spectroscopy.

Methods

The ¹H NMR spectra recorded on 173 tissue specimens comprising of breast tumor tissues, adjacent tissues, few lymph nodes and overlying skin tissues obtained from 67 patients suffering from breast cancer. Multivariate statistical analysis was employed to identify metabolites acting as major confounders for differentiation of malignancy.

Result

Reduction in lipid content were observed in malignant breast tissues along with a higher fraction of FFA. Four small molecule metabolites e.g., choline containing compounds (Chocc), taurine, glycine, and glutamate were also identified as major confounders. The test set for prediction provided sensitivity and specificity of more than 90% excluding the lymph nodes and skin tissues.

Conclusion

Fatty acids composition in breast cancer using in vivo magnetic resonance spectroscopy (MRS) is gaining its importance in clinical settings (Coum et al. in Magn Reson Mater Phys Biol Med 29:1–4, 2016). The present study may help in future for precise evaluation of lipid classification including small molecules as a source of early diagnosis of invasive ductal carcinoma by employing in vivo magnetic resonance spectroscopic methods.

     

Investigation of species and environmental effects on rhubarb roots metabolome using <Superscript>1</Superscript>H NMR combined with high performance thin layer chromatography

Introduction

The pharmacological activities of medicinal plants are reported to be due to a wide range of metabolites, therein, the concentrations of which are greatly affected by many genetic and/or environmental factors. In this context, a metabolomics approach has been applied to reveal these relationships. The investigation of such complex networks that involve the correlation between multiple biotic and abiotic factors and the metabolome, requires the input of information acquired by more than one analytical platform. Thus, development of new metabolomics techniques or hyphenations is continuously needed.

Objectives

Feasibility of high performance thin-layer chromatography (HPTLC) were investigated as a supplementary tool for medicinal plants metabolomics supporting ¹H nuclear magnetic resonance (¹H NMR) spectroscopy.

Method

The overall metabolic difference of plant material collected from two species (Rheum palmatum and Rheum tanguticum) in different geographical locations and altitudes were analyzed by ¹H NMR- and HPTLC-based metabolic profiling. Both NMR and HPTLC data were submitted to multivariate data analysis including principal component analysis and orthogonal partial least square analysis.

Results

The NMR and HPTLC profiles showed that while chemical variations of rhubarb are in some degree affected by all the factors tested in this study, the most influential factor was altitude of growth. The metabolites responsible for altitude differentiation were chrysophanol, emodin and sennoside A, whereas aloe emodin, catechin, and rhein were the key species-specific markers.

Conclusion

These results demonstrated the potential of HTPLC as a supporting tool for metabolomics due to its high profiling capacity of targeted metabolic groups and preparative capability.