Development and application of multiple isotopes-assisted untargeted metabolomics in human health and disease

Global metabolite profiling, also known as untargeted metabolomics, is constantly used in the qualitative and quantitative assessment of a wide range of metabolites in human metabolomics research, where scientists are able to monitor changes in metabolite concentrations in human biofluids and tissues affected by complex clinical diseases including rheumatoid arthritis, Alzheimer’s, Parkinson’s and variety of cancers, to name a few. These metabolite profiling studies are regularly applied in human clinical samples in order to detect potential biomarkers, assist in drug discovery, monitor disease onset and its progress and many other bioanalytical areas. The application of the LC-MS analytical tool has been most widely used in comprehensive metabolic studies, due to its high throughput, soft ionisation and good metabolite coverage, in comparison to other analytical instruments such as NMR, FT-IR or Raman. LC-MS based metabolomics studies are currently facing challenges due to non-linear responses derived from matrix effects and biological variations within samples, which can result in biased quantitative analysis affecting true measurement of metabolite levels and their biological relevance. To overcome these issues, stable multiple 13C isotopically labelled metabolites acting as internal standards can be employed in metabolomics studies to reduce metabolome data variation and improve its accuracy, also known as a normalisation technique. The main limitation of this normalisation technique in global metabolite profiling studies, however, is a lack of available 13C labelled standards to cover a wide range of metabolites, as well as their high cost to produce. To solve this problem, uniformly (U) 13C labelled bacterial organisms, such as E. coli or Spirulina, can be used instead, acting as a source of multiple labelled internal standards. Currently, in metabolomics research, there is a lack of established validated 13C normalisation techniques that can be applied to a wide range of metabolites in mammalian samples using a global metabolite profiling method. In this thesis the proposed LC-MS method, involving a (U)-13C labelled bacterial organism as a source of internal standards for normalisation, has been developed and applied to a range of untargeted clinical studies to demonstrate the effectiveness of normalisation and improvement in data accuracy to answer biological questions. E. coli and Spirulina extracts were analysed using LC-MS-based metabolite profiling to select the appropriate source of internal standards. In E. coli samples, around 780 putative metabolites were detected with high peak signal response, compared to approximately 600 putative metabolites in the Spirulina bacterium with poor peak signal response. E. coli appeared to have more metabolites in common with human biofluid or tissue metabolomes than Spirulina, fully confirming E. coli to be a suitable bacterial organism to use for internal standards. The chosen (U)-13C labelled E. coli showed a large proportion of metabolites labelled with 13C isotope (77%), with only 23% of the metabolome unlabelled. To validate the proposed normalisation method, (U)-13C labelled E. coli was applied in human urine, human brain tissue and mouse plasma samples. 13C-labelled E. coli extract was added to the extraction solvent (methanol) and mixed with the samples of interest in a 1:1 ratio. In all three studies, percentage RSD of peak height intensities was calculated for detected metabolites, along with constructed PCA and OPLS-DA plots, to assess the efficiency of normalisation. In human urine and brain studies, approximately 70% of identified metabolites in each group had their percentage RSD reduced, while in the mouse plasma study the result was observed to be even better with 90% of metabolites successfully normalised. When compared to other normalisation techniques such as MSTUS, TIC and creatinine, the 13C normalisation has shown better results with percentage RSD range being less variable. In all three validation studies, PCA and OPLS-DA score plots showed clearer separation between sample groups with their replicates clustered and high Q2 scores in normalised metabolomes, compared to non-normalised. Finally, the normalisation technique has helped to detect more statistically significant metabolites in all three studies, compared to non-normalised datasets. This method has shown to be fully validated, reducing metabolite variation significantly and improving the accuracy of data by detecting a high number of statistically significant metabolites. A fully validated LC-MS method with proposed normalisation technique has been applied in two clinical investigation studies, to obtain a highly accurate metabolome from clinical samples and answer the main biological questions set up by the studies: mainly searching for potential biomarkers and the effect of a disease on metabolic pathways. One clinical study investigated the effect of a fatty meal (diabetes condition) on the human urine and plasma metabolome of healthy volunteers, while the other study performed metabolomics analysis on human low-grade glioma affected brain tissue. In both studies, normalised metabolome data have helped to detect a number of statistically significant metabolites which were observed to affect certain metabolic pathways involved in the investigated diseases, showing potential for being biomarkers. Overall, the proposed normalisation technique using multiple 13C labelled internal standards, with the assistance of a bacterial organism as their source and a powerful analytical LC-MS method, has achieved great results in reducing metabolite data variation and improving data accuracy of a wide range of metabolites in mammalian biofluid and tissue samples, analysed by untargeted metabolomics, and has shown great promise in the search for potential biomarkers in future clinical untargeted studies

Анализ современных организационных и информационных технологий в управлении профессиональным здоровьем и профессиональным долголетием

At present, great importance is attached to the problem of maintaining the professional health of the working-age population and prolonging professional longevity in the world and in the Russian Federation. This problem has become especially urgent in recent years, in connection with the change in the country's retirement age

Electrochemical measurements of the levels of nitric oxide metabolites in the blood serum

Background: Sepsis is a serious clinical condition caused by a dysregulated immune response to infection resulting in multiple organ failure. In the pathogenesis of sepsis, especially that of septic shock, great importance is given to the endothelial marker of vascular regulation, nitric oxide (NO). In septic shock, dysregulation of the vascular tone plays a key role in the development of hypotension. Therefore, the control of the level of nitric oxide and its stable metabolites in critically ill patients is a very important task. Aim: the aim of this study was to evaluate the potential of the electrochemical nitrite detection in the patients blood serum. Methods: The levels of nitric oxide stable metabolites in the blood serum of healthy individuals (n=20) and septic patients (n=25) were studied by the electrochemical method using a composite electrode and by the spectrophotometric method using the Griess reagent. Results: The data in the groups of healthy people and patients with sepsis differ significantly (p 0.00001) both when measured using electrochemical and spectrophotometric methods. The median value of the current response in healthy people was 0.41 A (0.33; 0.55), and the total content of nitric oxide metabolites (NOx) was 26.8 mol/L (20.8; 31.0), while in patients with sepsis, these values were 0.79 A (0.61; 1.28) and 38.89 mol/L (29.64; 57.45), respectively. A negative correlation was found between the data obtained for practically healthy persons (r=-0.696, p=0.0007). Conclusion: The obtained results allow us to conclude that the nitrite measurement in the blood serum by amperometry using a composite electrode is promising as a diagnostic technique

Lipoprotein Deprivation Reveals a Cholesterol-Dependent Therapeutic Vulnerability in Diffuse Glioma Metabolism

Poor outcomes associated with diffuse high-grade gliomas occur in both adults and children, despite substantial progress made in the molecular characterisation of the disease. Targeting the metabolic requirements of cancer cells represents an alternative therapeutic strategy to overcome the redundancy associated with cell signalling. Cholesterol is an integral component of cell membranes and is required by cancer cells to maintain growth and may also drive transformation. Here, we show that removal of exogenous cholesterol in the form of lipoproteins from culture medium was detrimental to the growth of two paediatric diffuse glioma cell lines, KNS42 and SF188, in association with S-phase elongation and a transcriptomic program, indicating dysregulated cholesterol homeostasis. Interrogation of metabolic perturbations under lipoprotein-deficient conditions revealed a reduced abundance of taurine-related metabolites and cholesterol ester species. Pharmacological reduction in intracellular cholesterol via decreased uptake and increased export was simulated using the liver X receptor agonist LXR-623, which reduced cellular viability in both adult and paediatric models of diffuse glioma, although the mechanism appeared to be cholesterol-independent in the latter. These results provide proof-of-principle for further assessment of liver X receptor agonists in paediatric diffuse glioma to complement the currently approved therapeutic regimens and expand the options available to clinicians to treat this highly debilitating disease

Development and application of multiple isotopes-assisted untargeted metabolomics in human health and disease

Global metabolite profiling, also known as untargeted metabolomics, is constantly used in the qualitative and quantitative assessment of a wide range of metabolites in human metabolomics research, where scientists are able to monitor changes in metabolite concentrations in human biofluids and tissues affected by complex clinical diseases including rheumatoid arthritis, Alzheimer’s, Parkinson’s and variety of cancers, to name a few. These metabolite profiling studies are regularly applied in human clinical samples in order to detect potential biomarkers, assist in drug discovery, monitor disease onset and its progress and many other bioanalytical areas. The application of the LC-MS analytical tool has been most widely used in comprehensive metabolic studies, due to its high throughput, soft ionisation and good metabolite coverage, in comparison to other analytical instruments such as NMR, FT-IR or Raman. LC-MS based metabolomics studies are currently facing challenges due to non-linear responses derived from matrix effects and biological variations within samples, which can result in biased quantitative analysis affecting true measurement of metabolite levels and their biological relevance. To overcome these issues, stable multiple 13C isotopically labelled metabolites acting as internal standards can be employed in metabolomics studies to reduce metabolome data variation and improve its accuracy, also known as a normalisation technique. The main limitation of this normalisation technique in global metabolite profiling studies, however, is a lack of available 13C labelled standards to cover a wide range of metabolites, as well as their high cost to produce. To solve this problem, uniformly (U) 13C labelled bacterial organisms, such as E. coli or Spirulina, can be used instead, acting as a source of multiple labelled internal standards. Currently, in metabolomics research, there is a lack of established validated 13C normalisation techniques that can be applied to a wide range of metabolites in mammalian samples using a global metabolite profiling method. In this thesis the proposed LC-MS method, involving a (U)-13C labelled bacterial organism as a source of internal standards for normalisation, has been developed and applied to a range of untargeted clinical studies to demonstrate the effectiveness of normalisation and improvement in data accuracy to answer biological questions. E. coli and Spirulina extracts were analysed using LC-MS-based metabolite profiling to select the appropriate source of internal standards. In E. coli samples, around 780 putative metabolites were detected with high peak signal response, compared to approximately 600 putative metabolites in the Spirulina bacterium with poor peak signal response. E. coli appeared to have more metabolites in common with human biofluid or tissue metabolomes than Spirulina, fully confirming E. coli to be a suitable bacterial organism to use for internal standards. The chosen (U)-13C labelled E. coli showed a large proportion of metabolites labelled with 13C isotope (77%), with only 23% of the metabolome unlabelled. To validate the proposed normalisation method, (U)-13C labelled E. coli was applied in human urine, human brain tissue and mouse plasma samples. 13C-labelled E. coli extract was added to the extraction solvent (methanol) and mixed with the samples of interest in a 1:1 ratio. In all three studies, percentage RSD of peak height intensities was calculated for detected metabolites, along with constructed PCA and OPLS-DA plots, to assess the efficiency of normalisation. In human urine and brain studies, approximately 70% of identified metabolites in each group had their percentage RSD reduced, while in the mouse plasma study the result was observed to be even better with 90% of metabolites successfully normalised. When compared to other normalisation techniques such as MSTUS, TIC and creatinine, the 13C normalisation has shown better results with percentage RSD range being less variable. In all three validation studies, PCA and OPLS-DA score plots showed clearer separation between sample groups with their replicates clustered and high Q2 scores in normalised metabolomes, compared to non-normalised. Finally, the normalisation technique has helped to detect more statistically significant metabolites in all three studies, compared to non-normalised datasets. This method has shown to be fully validated, reducing metabolite variation significantly and improving the accuracy of data by detecting a high number of statistically significant metabolites. A fully validated LC-MS method with proposed normalisation technique has been applied in two clinical investigation studies, to obtain a highly accurate metabolome from clinical samples and answer the main biological questions set up by the studies: mainly searching for potential biomarkers and the effect of a disease on metabolic pathways. One clinical study investigated the effect of a fatty meal (diabetes condition) on the human urine and plasma metabolome of healthy volunteers, while the other study performed metabolomics analysis on human low-grade glioma affected brain tissue. In both studies, normalised metabolome data have helped to detect a number of statistically significant metabolites which were observed to affect certain metabolic pathways involved in the investigated diseases, showing potential for being biomarkers. Overall, the proposed normalisation technique using multiple 13C labelled internal standards, with the assistance of a bacterial organism as their source and a powerful analytical LC-MS method, has achieved great results in reducing metabolite data variation and improving data accuracy of a wide range of metabolites in mammalian biofluid and tissue samples, analysed by untargeted metabolomics, and has shown great promise in the search for potential biomarkers in future clinical untargeted studies

Construction of models of microwave transistors when changing the probing signal in the frequency and power range

It is shown that to model a transistor in the form of S-parameters in the large signal mode, it is necessary to present the model as two S-matrices that describe the transistor at two phase differences between the incident and reflected waves equal to 0 and 90 degrees. The problem of matching a transistor with a load is reduced to solving a nonlinear equation with respect to a previously unknown phase difference, after which the load impedance is selected from the complex-conjugate matching condition