Metabolomic analysis of endometrial cancer by high-resolution magic angle spinning NMR spectroscopy

PURPOSE

To analyze endometrial metabolite profiles between patients with endometrial cancer and controls.

METHODS

Seventeen (17) women with endometrium cancer and 18 controls were enrolled in this study. ¹H HR-MAS (High Resolution-Magic Angle Spinning) NMR (Nuclear Magnetic Resonance) spectroscopy data obtained from endometrial tissue samples of patients with endometrial cancer and control group were analyzed with bioinformatics methods.

RESULTS

Principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA) score plots obtained with the multivariate statistical analysis of pre-processed spectral data shows a separation between the samples from patients with endometrial cancer and controls. Analysis results suggest that the levels of lactate, glucose, o-phosphoethanolamine, choline, glycerophosphocholine, phosphocholine, leucine, isoleucine, valine, glutamate, glutamine, n-acetyltyrosine, methionine, taurine, alanine, aspartate and phenylalanine are increased in patients with endometrial cancer compared to the controls.

CONCLUSION

The metabolomics signature of patients with endometrial cancer is different from that of benign endometrial tissue.

Estimation of the state of the microbiome in the elderly with impairments of carbohydrate and lipid exchange by the method of microbiome-associated exposomics

A biochemical analysis of blood and a study of the chemical composition of the blood samples of 163 people 45–90 years old with type 2 diabetes mellitus and dyslipidemia was conducted. The concentrations of the following compounds in the blood were determined: fatty acids, aldehydes, and styrenes of microbial origin as well as the levels of glucose triglycerides, cholesterol, and lipids of low and high density. The chemical composition of blood was determined by gas chromatography-mass spectrometry. The concentrations of fatty acids, aldehydes, and styrenes were used to calculate the total molar concentration of chemical compounds, the concentration of octadecene aldehyde (18a), the total concentration of hydroxy acids of derivatives of the hydroxyl residue of lipid A of bacterial endotoxin (3OH-FA), and the grouped total concentrations of chemical compounds of microbial origin, which determine the representation of the main four phylotypes of the human microbiome: Actinobacteria, Bacteroidetes, Proteobacteria, and Firmicutes.

As a result of the study, data were obtained on an increase in the total concentration of chemical compounds, the concentration of octadecene aldehyde, and the concentration of 3OH-FA in violation of carbohydrate metabolism by the type of diabetes mellitus type 2. There was a decrease in the representation of Bacteroidetes in violation of carbohydrate metabolism and a decrease in the representation of Proteobacteria, Firmicutes in violation of carbohydrate and lipid metabolism, and an increase in the representation of Actinobacteria in violation of lipid metabolism, including those combined with a violation of carbohydrate metabolism.

In patients with type 2 diabetes mellitus and in the control group, there was an inverse correlation between the presence of Firmicutes and blood glucose levels. In the type 2 diabetes mellitus group, there was a direct correlation between the representation of Bacteroidetes and the level of triglycerides in the blood.

In patients with lipid metabolism disorders, we did not reveal statistically significant changes in the concentrations of microbial markers in the blood of patients nor statistically significant correlations between the biochemical blood parameters and the representation of microbiome phylotypes.

High-resolution 1H-NMR spectroscopy indicates variations in metabolomics profile of follicular fluid from women with advanced maternal age

AIM

To reveal whether there are differences in follicular fluid metabolomics profile of women with advanced maternal age (AMA).

METHOD

The group with advanced maternal age includes 23 patients above the age of 40, and the control group includes 31 patients aged between 25 and 35 years and AMH values above 1.1 ng/mL with no low ovarian response history. A single follicular fluid sample from a MII oocyte obtained during the oocyte pick-up procedure was analyzed with high-resolution ¹H-NMR (nuclear magnetic resonance) spectroscopy. The results were evaluated using advanced bioinformatics analysis methods.

RESULTS

Statistical analysis of the NMR spectroscopy data from two groups showed that α-glucose and β-glucose levels of follicular fluid were decreased in the patients with AMA, while in contrast, lactate and trimethylamine N-oxide (TMAO) levels were increased in these patients compared with the controls. In addition to these, there was an increase in alanine levels and a decrease in acetoacetate levels in patients with AMA. However, these changes were not statistically significant.

CONCLUSION

Obtained results suggest that the follicular cell metabolism of patients with AMA is different from controls. These environmental changes could be associated with the low success rates of IVF treatment seen in these patients.

Estimation of psychological stress in humans: a combination of theory and practice

Stress has long been known to increase susceptibility to health disorders. In 2009, American Psychological Association further established association of stress to serious health problems. However, a quantitative and accurate way to evaluate and estimate stress status of individuals is still a big challenge. It has been shown, in large animal models using cattle, that psychological stress can be quantified as well as disease susceptibility could be predicted through biomarker discovery. Taking cue from those studies, we have evaluated and estimated psychological stress level of individuals theoretically and validated experimentally. Various biomarkers have also been identified which can be associated to psychological stress to predict stress status of unknown individuals.

Quantitative 1H NMR. Development and potential of an analytical method: an update

Covering the literature from mid-2004 until the end of 2011, this review continues a previous literature overview on quantitative (1)H NMR (qHNMR) methodology and its applications in the analysis of natural products. Among the foremost advantages of qHNMR is its accurate function with external calibration, the lack of any requirement for identical reference materials, a high precision and accuracy when properly validated, and an ability to quantitate multiple analytes simultaneously. As a result of the inclusion of over 170 new references, this updated review summarizes a wealth of detailed experiential evidence and newly developed methodology that supports qHNMR as a valuable and unbiased analytical tool for natural product and other areas of research.

Recent and potential developments in the analysis of urine: a review

Analysis of urine is a widely used diagnostic tool that traditionally measured one or, at most, a few metabolites. However, the recognition of the need for a holistic approach to metabolism led to the application of metabolomics to urine for disease diagnostics. This review looks at various aspects of urinalysis including sampling and traditional approaches before reviewing recent developments using metabolomics. Spectrometric approaches are covered briefly since there are already a number of very good reviews on NMR spectroscopy and mass spectrometry and other spectrometries are not as highly developed in their applications to metabolomics. On the other hand, there has been a recent surge in chromatographic applications dedicated to characterising the human urinary metabolome. While developments in the analysis of urine encompassing both classical approaches of urinalysis and metabolomics are covered, it must be emphasized that these approaches are not orthogonal - they both have their uses and are complementary. Regardless, the need to normalise analytical data remains an important impediment.

Quantitative two-dimensional HSQC experiment for high magnetic field NMR spectrometers

The finite RF power available on carbon channel in proton-carbon correlation experiments leads to non-uniform cross peak intensity response across carbon chemical shift range. Several classes of broadband pulses are available that alleviate this problem. Adiabatic pulses provide an excellent magnetization inversion over a large bandwidth, and very recently, novel phase-modulated pulses have been proposed that perform 90 degrees and 180 degrees magnetization rotations with good offset tolerance. Here, we present a study how these broadband pulses (adiabatic and phase-modulated) can improve quantitative application of the heteronuclear single quantum coherence (HSQC) experiment on high magnetic field strength NMR spectrometers. Theoretical and experimental examinations of the quantitative, offset-compensated, CPMG-adjusted HSQC (Q-OCCAHSQC) experiment are presented. The proposed experiment offers a formidable improvement to the offset performance; (13)C offset-dependent standard deviation of the peak intensity was below 6% in range of+/-20 kHz. This covers the carbon chemical shift range of 150 ppm, which contains the protonated carbons excluding the aldehydes, for 22.3 T NMR magnets. A demonstration of the quantitative analysis of a fasting blood plasma sample obtained from a healthy volunteer is given.

Analytical and statistical approaches to metabolomics research

Metabolomics, the global profiling of metabolites in different living systems, has experienced a rekindling of interest partially due to the improved detection capabilities of the instrumental techniques currently being used in this area of biomedical research. The analytical methods of choice for the analysis of metabolites in search of disease biomarkers in biological specimens, and for the study of various low molecular weight metabolic pathways include NMR spectroscopy, GC/MS, CE/MS, and HPLC/MS. Global metabolite analysis and profiling of two different sets of data results in a plethora of data that is difficult to manage or interpret manually because of their subtle differences. Multivariate statistical methods and pattern-recognition programs were developed to handle the acquired data and to search for the discriminating features between data acquired from two sample sets, healthy and diseased. Metabolomics have been used in toxicology, plant physiology, and biomedical research. In this paper, we discuss various aspects of metabolomic research including sample collection, handling, storage, requirements for sample analysis, peak alignment, data interpretation using statistical approaches, metabolite identification, and finally recommendations for successful analysis.