Dokument: Pleiotropy and Epistasis in constraint-based models of microbial metabolism
|Titel:||Pleiotropy and Epistasis in constraint-based models of microbial metabolism|
|URL für Lesezeichen:||https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=49720|
|Dokumententyp:||Wissenschaftliche Abschlussarbeiten » Dissertation|
|Autor:|| Alzoubi, Deya [Autor]|
|Beitragende:||Prof. Dr. Martin Lercher [Gutachter]|
Prof. Dr. Ebenhöh, Oliver [Gutachter]
|Dewey Dezimal-Klassifikation:||000 Informatik, Informationswissenschaft, allgemeine Werke » 004 Datenverarbeitung; Informatik|
|Beschreibung:||Understanding the relationships between genotypes and phenotypes remains a major challenge for biological research. Uncovering these relationships is hampered by the interconnectedness of biological systems, leading to non-independence of genes and of phenotypes. The most prominent emergent systems-level effects are summarized under the terms pleiotropy (one allele affecting multiple phenotypes) and epistasis (effects of one allele depend on the alleles of other genes). Metabolism is an ideal system to study pleiotropy and epistasis, as metabolic reactions can be studied in isolation. Constraint-based methods, in particular Flux Balance Analysis (FBA) represent the current state-of-the-art in genome-scale metabolic modelling. FBA has been successfully used to predict phenotypes such as growth rate, nutrient uptake rates, and gene essentiality (Edwards, Ibarra et al. 2001, Edwards and Palsson 2000, Famili, Förster et al. 2003, Forster, Famili et al. 2003, Ibarra, Edwards et al. 2002).
In Manuscript 1, we used constraint-based simulations of the metabolic models for the bacterium Escherichia coli and the Baker’s yeast Sacchormyces cerevisiae to predict the pleiotropy of metabolic genes, allowing for mutations of variable severity. This work also represents the first analysis of how pleiotropy is associated with the generation of currency metabolites such as ATP and NADPH. We found that the knockout of a majority of genes that contribute to fitness has pleiotropic effects. For most of these genes, pleiotropy increases strongly with increasingly debilitating effects of mutations; in many cases, this was associated with increasing effects on currency metabolite production. While standard FBA ignores the concentrations of enzymes catalyzing metabolic reactions, FBA with molecular crowding (ccFBA) accounts for the need to solve them in cellular volumes of limited capacity. In Manuscript 2, we tested if ccFBA can significantly improve the prediction of epistasis in yeast. The results indeed show that FBA with molecular crowding can predict some positive epistatic interactions not detectable with other constraint-based methods. However, the most important conclusion was that at least 70% of experimentally observed epistatic interactions are not detectable by any of the popular constraint-based methods. This result hinted at some fundamental problems of these methods.
These problems were addressed in Manuscript 3, where we found that all tested constraint-based methods are essentially useless when predicting the fitness effects of non-essential gene knockouts. If these models cannot quantify single gene knockout fitness reliably, it is no surprise that they fail to predict higher order effects (genetic interactions, i.e., epistasis). More generally, these results show that one has to be careful when interpreting computational predictions of gene knockouts, such as done in our analysis of pleiotropy in Manuscript 1.
Edwards, J. S., R. U. Ibarra and B. O. Palsson (2001). In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19(2): 125-130.
Edwards, J. S. and B. O. Palsson (2000). Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformatics 1: 1.
Famili, I., J. Förster, J. Nielsen and B. O. Palsson (2003). Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proceedings of the National Academy of Sciences 100(23): 13134.
Forster, J., I. Famili, B. O. Palsson and J. Nielsen (2003). Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae. Omics 7(2): 193-202.
Ibarra, R. U., J. S. Edwards and B. O. Palsson (2002). Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420(6912): 186-189.
|Fachbereich / Einrichtung:||Mathematisch- Naturwissenschaftliche Fakultät|
|Dokument erstellt am:||31.05.2019|
|Dateien geändert am:||31.05.2019|
|Datum der Promotion:||29.04.2019|