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Genomic prediction and genome-wide association studies of metabolic networks
Schwarzerová, Jana ; Weckwerth, Wolfram (oponent) ; Ramberger,, Benjamin (vedoucí práce)
Current research on the interface of bioinformatics and ecology engineering offers potential due to the combination of laboratory analysis and advanced bioinformatics algorithms. The thesis investigates the combination of GC-MS -based metabolomic analysis for identification and quantitation of metabolites of environmental perturbations with the advanced bioinformatics approach of genome-wide association studies (GWAS). The analysis is performed using genomic prediction based on two different temperature-related growth conditions of the collected dataset from 241 Arabidopsis thaliana natural accessions (genotypes). Current challenges that arise from climate change and industrial pollution encourage fundamental research on the adaption of organisms due to environmental effects. The findings in this field may play a key role in solving associated problems for the environment. Particularly plants, which serve as net primary producers of our most vital resources, food, health and energy, represent most important research subjects in this regard. The findings presented in this thesis reveal the individual adaptation strategies to cold stress of the plant depending on its original habitate. Furthermore, the association of metabolites with GWAS revealed potential genomic regions involved in the adapation of the plant to cold temperature. While simple observations reveal only phenotype changes, changes in genotypes of organisms can be captured using this metabolic GWAS technology presented in this thesis and further utilized in industrial ecology and biotechnology. The final part of the thesis is extended by using the inverse stochastic Lyapunov Matrix equation for the obtained results using to investigate the regulation of metabolism during adaptation to cold temperature.
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Dynamic metabolomic prediction from genetic variation
Nemčeková, Petra ; Weckwerth, Wolfram (oponent) ; Schwarzerová, Jana (vedoucí práce)
Hordeum vulgare, like many other crops, suffers from the reduction of genetic diversity caused by climate changes. Therefore, it is necessary to improve the performance of its breeding. Nowadays, the area of interest in current research focuses on indirect selection methods based on computational prediction modeling. This thesis deals with dynamic metabolomic prediction based on genomic data consisting of 33,005 single nucleotide polymorphisms. Metabolomic data include 128 metabolites belonging to 25 Halle exotic barley families. The main goal of this thesis is to create dynamic metabolomic predictions using different approaches chosen from relevant publications. The created models can be helpful for the prediction of phenotype or for revealing important traits of Hordeum vulgare.
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Comprehensive analysis of putrescine metabolism in A thaliana using GWAS, genetic risk score, metabolic modelling and data mining
Schwarzerová, Jana ; Bartoň, Vojtěch ; Walther, Dirk ; Weckwerth, Wolfram
Polyamines are known to be functionally involvedin plant responses to stress conditions. Despite a large amount ofphysiological and genetic data, the mode of action of polyamines,especially putrescine, at the molecular level still remains unclear.An increasing number of studies point to a role of putrescine instress defense of plants due to influencing the formation of reactiveoxygen species (ROS) in cellular stress conditions. Moreover,putrescine has been found to modulate abscisic acid (ABA)biosynthesis at the transcriptional level in response to lowtemperature, revealing a novel mode of action of polyamines asregulators of hormone biosynthesis. This study presents a newholistic approach towards the analysis of putrescine metabolismusing genome-wide association studies and the calculation ofgenetic risk scores extended by Boolean analysis in metabolitesnetwork as well as data mining from available databases andliterature. It can lead to a better understanding of biologicalprocesses involved in adaptation of plants to environmentalchanges.
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Dynamic metabolomic prediction from genetic variation
Nemčeková, Petra ; Weckwerth, Wolfram (oponent) ; Schwarzerová, Jana (vedoucí práce)
Hordeum vulgare, like many other crops, suffers from the reduction of genetic diversity caused by climate changes. Therefore, it is necessary to improve the performance of its breeding. Nowadays, the area of interest in current research focuses on indirect selection methods based on computational prediction modeling. This thesis deals with dynamic metabolomic prediction based on genomic data consisting of 33,005 single nucleotide polymorphisms. Metabolomic data include 128 metabolites belonging to 25 Halle exotic barley families. The main goal of this thesis is to create dynamic metabolomic predictions using different approaches chosen from relevant publications. The created models can be helpful for the prediction of phenotype or for revealing important traits of Hordeum vulgare.
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