Browsing by Subject "metabolomics"
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(2011)Metabolomics is a rapidly growing research field that studies the response of biological systems to environmental factors, disease states and genetic modifications. It aims at measuring the complete set of endogenous metabolites, i.e. the metabolome, in a biological sample such as plasma or cells. Because metabolites are the intermediates and end products of biochemical reactions, metabolite compositions and metabolite levels in biological samples can provide a wealth of information on on-going processes in a living system. Due to the complexity of the metabolome, metabolomic analysis poses a challenge to analytical chemistry. Adequate sample preparation is critical to accurate and reproducible analysis, and the analytical techniques must have high resolution and sensitivity to allow detection of as many metabolites as possible. Furthermore, as the information contained in the metabolome is immense, the data set collected from metabolomic studies is very large. In order to extract the relevant information from such large data sets, efficient data processing and multivariate data analysis methods are needed. In the research presented in this thesis, metabolomics was used to study mechanisms of polymeric gene delivery to retinal pigment epithelial (RPE) cells. The aim of the study was to detect differences in metabolomic fingerprints between transfected cells and non-transfected controls, and thereafter to identify metabolites responsible for the discrimination. The plasmid pCMV-β was introduced into RPE cells using the vector polyethyleneimine (PEI). The samples were analyzed using high performance liquid chromatography (HPLC) and ultra performance liquid chromatography (UPLC) coupled to a triple quadrupole (QqQ) mass spectrometer (MS). The software MZmine was used for raw data processing and principal component analysis (PCA) was used in statistical data analysis. The results revealed differences in metabolomic fingerprints between transfected cells and non-transfected controls. However, reliable fingerprinting data could not be obtained because of low analysis repeatability. Therefore, no attempts were made to identify metabolites responsible for discrimination between sample groups. Repeatability and accuracy of analyses can be influenced by protocol optimization. However, in this study, optimization of analytical methods was hindered by the very small number of samples available for analysis. In conclusion, this study demonstrates that obtaining reliable fingerprinting data is technically demanding, and the protocols need to be thoroughly optimized in order to approach the goals of gaining information on mechanisms of gene delivery.
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(2021)Personalized medicine tailors therapies for the patient based on predicted risk factors. Some tools used for making predictions on the safety and efficacy of drugs are genetics and metabolomics. This thesis focuses on identifying biomarkers for the activity level of the drug transporter organic anion transporting polypep-tide 1B1 (OATP1B1) from data acquired from untargeted metabolite profiling. OATP1B1 transports various drugs, such as statins, from portal blood into the hepatocytes. OATP1B1 is a genetically polymorphic influx transporter, which is expressed in human hepatocytes. Statins are low-density lipoprotein cholesterol-lowering drugs, and decreased or poor OATP1B1 function has been shown to be associated with statin-induced myopathy. Based on genetic variability, individuals can be classified to those with normal, decreased or poor OATP1B1 function. These activity classes were employed to identify metabolomic biomarkers for OATP1B1. To find the most efficient way to predict the activity level and find the biomarkers that associate with the activity level, 5 different machine learning models were tested with a dataset that consisted of 356 fasting blood samples with 9152 metabolite features. The models included both a Random Forest regressor and a classifier, Gradient Boosted Decision Tree regressor and classifier, and a Deep Neural Network regressor. Hindrances specific for this type of data was the collinearity between the features and the large amount of features compared to the number of samples, which lead to issues in determining the important features of the neural network model. To adjust to this, the data was clustered according to their Spearman’s rank-order correlation ranks. Feature importances were calculated using two methods. In the case of neural network, the feature importances were calculated with permutation feature importance using mean squared error, and random forest and gradient boosted decision trees used gini impurity. The performance of each model was measured, and all classifiers had a poor ability to predict decreasead and poor function classes. All regressors performed very similarly to each other. Gradient boosted decision tree regressor performed the best by a slight margin, but random forest regressor and neural network regressor performed nearly as well. The best features from all three models were cross-referenced with the features found from y-aware PCA analysis. The y-aware PCA analysis indicated that 14 best features cover 95% of the explained variance, so 14 features were picked from each model and cross-referenced with each other. Cross-referencing highest scoring features reported by the best models found multiple features that showed up as important in many models.Taken together, machine learning methods provide powerful tools to identify potential biomarkers from untargeted metabolomics data.
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(2023)Infants´ and children´s diet influences normal development and overall health. A balanced diet providing essential nutrients is crucial. Recent research has examined the dietary patterns of children and infants, exploring potential associations between food components and the emergence of illnesses. Notably, investigations into relationships between dietary factors and metabolite have gained prominence. Metabolomics offers a means to investigate individual´s nutrition, health status, illnesses, and the interaction of medications and contaminants. This study aimed to elucidate the connections between diet and the serum metabolic profiles of 1-year-old Finnish children. This master´s thesis used data from Finnish infants (n=439) collected by 3-day food record and questionnaires, in conjunction with metabolite assessments from blood samples collected at the age of 12 months. The investigation particularly focused on cow´s milk products and breast milk. Spearman correlation coefficient served as the primary statistical tool utilising data derived from the DIPP Nutrition study. Infant diets´ primarily comprised various cow´s milk products, milks and infant formulas. Noteworthy findings revealed that distinct lipids and free fatty acids, significantly associated with cow´s milk product consumption and breastfeeding. In the future, this study holds potential for enhancing comprehension of diet-related disease development by employing metabolites as markers to dissect dietary impacts.
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(2023)Infants´ and children´s diet influences normal development and overall health. A balanced diet providing essential nutrients is crucial. Recent research has examined the dietary patterns of children and infants, exploring potential associations between food components and the emergence of illnesses. Notably, investigations into relationships between dietary factors and metabolite have gained prominence. Metabolomics offers a means to investigate individual´s nutrition, health status, illnesses, and the interaction of medications and contaminants. This study aimed to elucidate the connections between diet and the serum metabolic profiles of 1-year-old Finnish children. This master´s thesis used data from Finnish infants (n=439) collected by 3-day food record and questionnaires, in conjunction with metabolite assessments from blood samples collected at the age of 12 months. The investigation particularly focused on cow´s milk products and breast milk. Spearman correlation coefficient served as the primary statistical tool utilising data derived from the DIPP Nutrition study. Infant diets´ primarily comprised various cow´s milk products, milks and infant formulas. Noteworthy findings revealed that distinct lipids and free fatty acids, significantly associated with cow´s milk product consumption and breastfeeding. In the future, this study holds potential for enhancing comprehension of diet-related disease development by employing metabolites as markers to dissect dietary impacts.
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(2023)Sorghum (Sorghum bicolor L. Moench) has high contents of phenolic compounds which have both beneficial and antinutritional health effects, including forming insoluble complexes with proteins. This is significant because sorghum has low protein digestibility. Lactic acid bacteria (LAB) fermentation has been found to decrease and modify sorghum phenolic compounds and condensed tannins. The aim of this thesis was to evaluate the effects of LAB fermentation on the phenolic compounds in white and red sorghum using a metabolomics approach. The hypothesis was that fermentation would degrade phenolic compounds into smaller metabolites. Free phenolic compounds were extracted from sorghum using 80% ethanol. The samples were analysed using ultra-performance liquid chromatography coupled with a photodiode-array detector and quadrupole time-of-flight mass spectrometry (UPLC-PDA-Q-TOF). In order to identify phenolic compounds, both targeted and untargeted metabolomics approaches were used. Multivariate analysis was employed to determine compounds with different abundances between sample groups. The study confirmed the identification of 40 compounds, 37 of which were phenolic compounds, and 23 were distinct between sample groups. Red sorghum contained more flavonoids and condensed tannins compared to white sorghum. Native samples were statistically different from fermented samples, with most changes involving the release of phenolic acids from their conjugated forms and an increase in phenolamines. The metabolomics approach effectively covered the wide range of phenolic compound analysis in sorghum.
Now showing items 1-5 of 5