Dokument: Bacterial resource allocation to metabolism and protein translation: optimality and relationship to genome organization

Titel:	Bacterial resource allocation to metabolism and protein translation: optimality and relationship to genome organization
URL für Lesezeichen:	https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=59526
URN (NBN):	urn:nbn:de:hbz:061-20220506-110758-4
Kollektion:	Dissertationen
Sprache:	Englisch
Dokumententyp:	Wissenschaftliche Abschlussarbeiten » Dissertation
Medientyp:	Text
Autor:	Hu, Xiao-Pan [Autor]
Dateien:	[Dateien anzeigen] Adobe PDF [Details] 5,79 MB in einer Datei [ZIP-Datei erzeugen] Dateien vom 04.05.2022 / geändert 04.05.2022
Beitragende:	Prof. Dr. Lercher, Martin [Gutachter] Prof. Dr. Ebenhöh, Oliver [Gutachter]
Stichwörter:	Ribosome, growth law, translation efficiency, metabolic pathway, proteome efficiency, gene position, RNA growth law, genome organization
Dewey Dezimal-Klassifikation:	000 Informatik, Informationswissenschaft, allgemeine Werke » 004 Datenverarbeitung; Informatik
Beschreibung:	Phenomenologically, the macromolecular composition of exponentially growing microorganisms largely dependents on their exponential growth rate, not on environmental details. For example, the cellular RNA content increases almost linearly with growth rate in Escherichia coli. What are the mechanisms behind these growth rate dependencies? This cumulative thesis examines if these growth rate dependencies can be explained as consequences of the optimal allocation of cellular resources. Manuscript 1 studies the theoretically optimal expression of components of the E. coli protein translation machinery from first principles, and compares the predictions to published experimental data. Translation is the most expensive cellular process at high growth rates in bacteria, both in terms of the proteome fraction of the translation machinery and in terms of ATP usage. It has been suggested that translation components are expressed at optimal efficiency. But what does optimal efficiency mean in an evolutionary context? The cytosol density is near constant across growth conditions in E. coli. Thus, if more cytosolic dry mass is allocated to one cellular process, less is available for other processes. We thus hypothesized that the translation machinery has been optimized by natural selection such that its components together amount to the smallest possible mass concentration of all its components at the given growth rate. To test this hypothesis, I built a detailed mechanistic translation model and fully parametrized the model with kinetics constants reported in the literature. The model is constrained only by the physicochemical properties of the molecules and has no adjustable parameters. The growth rate-dependent concentrations of all modeled translational components, including ribosome, tRNAs, mRNA, elongation factor Tu, and elongation factor Ts are accurately predicted by minimizing the combined cost of the whole translation machinery at the given protein synthesis rate. Further, the resulting optimal configurations explain experimental data for the RNA/protein ratio and ribosome activity in both normal growth and antibiotics stress conditions. Minimizing alternative cost measures, such as carbon content, energy cost, and biosynthesis cost, leads to similar results. Thus, the translation machinery works close to optimal efficiency in E. coli. Manuscript 2 examines the growth rate-dependent proteome efficiency of metabolic pathways. In manuscript 1, we found that the protein translation machinery is expressed for maximal efficiency in E. coli. However, other recent studies indicate that the overall proteome is not allocated in a way that achieves maximal efficiency. Especially at low growth rates, a substantial fraction of the proteome is unneeded for balanced cell growth. More than half of the total proteome is allocated to metabolism in E. coli growing on minimal media. Prior to our work, it was unclear if proteome allocation to different metabolic pathways is similarly efficient, or if some pathways are systematically closer to maximal proteome efficiency than others. In manuscript 2, the minimal proteome demand of individual pathways was predicted by minimizing the proteome cost with a modified version of flux balance analysis with molecular crowding. By comparing the predicted optimal proteome demand of individual pathways with the experimental data, I found that proteome efficiency can qualitatively explain the growth rate-dependent expression of biosynthesis pathways, glycolysis, and the pentose phosphate pathway, but is unable to explain the expression of other pathways. Unexpectedly, by mass, more than half of the metabolic pathways show a growth rate dependence opposite of that expected from optimal demand. Overall, growth rate-dependent proteome efficiency increases along the carbon flow through the metabolic network. While this work provides a bird’s-eye view of proteome efficiency at the pathway level, future work will have to elucidate why proteome allocation evolved this way, and how it gives rise to the widely used bacterial growth laws when averaging over sets of pathways. Manuscript 3 builds on manuscript 1 by exploring an RNA composition growth law and its partial implementation through the genes’ genomic positions in fast-growing bacteria. In contrast to the proteome composition, RNA composition is usually assumed to be independent of the growth rate, despite experimental evidence to the contrary. By minimizing the combined costs of the ribosome and ternary complex, I analytically derived an RNA growth law. This law describes how the optimal tRNA/rRNA ratio decreases monotonically with growth rate, consistent with experimental data from E. coli and other fast-growing microbes. In most of these species, rRNA genes are located closer to origin of replication than tRNA genes. Accordingly, the number of rRNA gene copies increases faster than the number of tRNA gene copies with increasing growth rate, a consequence of replication-associated gene dosage effects. The tRNA/rRNA gene copy ratio thus decreases with increasing growth rate, consistent with the RNA growth law. I conclude that the RNA growth law is partially implemented through the relative positions of tRNA and rRNA genes, indicating that natural selection on growth rate-dependent resource allocation patterns can influence the genome organization of bacteria. In sum, the three manuscripts of this thesis quantify the optimality of growth rate-dependent allocation of bacterial resources into macromolecules involved in different biochemical pathways, linking optimal resource allocation to genome organization.
Lizenz:	Urheberrechtsschutz
Fachbereich / Einrichtung:	Mathematisch- Naturwissenschaftliche Fakultät » WE Informatik » Bioinformatik
Dokument erstellt am:	06.05.2022
Dateien geändert am:	06.05.2022
Promotionsantrag am:	04.01.2022
Datum der Promotion:	29.04.2022