Adaptability of metabolic networks in evolution and disease

Abstract

There are 114.101 small molecule metabolites currently annotated in the Human Metabolome Database, which are highly connected amongst each other, with a few metabolites exhibiting an estimated number of more than 103 connections. Redundancy and plasticity are essential features of metabolic networks enabling cells to respond to fluctuating environments, presence of toxic molecules, or genetic perturbations like mutations. These system-level properties are inevitably linked to all aspects of biological systems ensuring cell viability by enabling processes like adaption and differentiation. To this end, the ability to interrogate molecular changes at omics level has opened new opportunities to study the cell at its different layers from the epigenome and transcriptome to its proteome and metabolome. In this thesis, I tackled the question how redundancy and plasticity shape adaptation in metabolic networks in evolutionary and disease contexts. I utilize a multi-omics approach to study comprehensively the metabolic state of a cell and its regulation at the transcriptional and proteomic level. One of the challenges with multi-omics approaches is the integration and interpretation of multi-layered data sets. To approach this challenge, I use genome scale metabolic models as a knowledge-based scaffold to overlay omics data and thereby to enable biological interpretation beyond statistical correlation. This integrative methodology has been applied to two different projects, namely the evolutionary adaptation towards a nutrient source in yeast and the metabolic adaptations following disease progression. For the latter, I also curated a current human genome-scale metabolic model and made it more suitable for flux predictions. In the yeast case study, I investigate the metabolic network adaptations enabling yeast to grow on an alternative carbon source – glycerol. I could show that network redundancy is one of the key features of fast adaptation of the yeast metabolic network to the new nutrient environment. Genomics, transcriptomics, proteomics, metabolomics and metabolic modeling together revealed a shift of the organism’s redox-balance under glycerol consumption as a driving force of adaption, which can be linked to the causal mutation in the enzyme Kgd1. On the other hand, the limitations of metabolic network adaptation also became apparent since all evolved and adapted strains exhibited metabolic trade-offs in other environmental conditions than the adaptation niche. Either an impaired diauxic shift (as in the case of the glycerol mutant) or an increased sensitivity towards osmotic stress (caused by mutations in the HOG pathway) was coupled with efficient use of glycerol. In the second project, the molecular phenotype of regressed breast cancer cells was studied to identify what differentiates these cells from healthy breast tissue and to characterize the potential source of tumor recurrence. Using a breast cancer mouse model with inducible oncogenes, transcriptomics together with an extensive set of different types of metabolomics (targeted and untargeted metabolomics, lipidomics and fluxomics) could show that regressed cancer cells, albeit their apparently normal morphology, possess a highly altered molecular phenotype with an oncogenic memory. While in cancer redundancy and plasticity enable the adaptation towards a proliferative state, in regressed cells, on the contrary, prolonged oncogenic signaling leads to a loss of metabolic network regulation and the entering of an irreversible metabolic state. This state appears to be insensitive to adaptation mechanisms as transcripts and metabolites reciprocally enhance each other to maintain the tumor-like metabolic phenotype. In conclusion, this work demonstrates how genome scale metabolic models can help identifying functional mechanisms from complex and multi-layered omics data. Appropriate genome scale metabolic models combined with metabolite measurements have proven particularly useful in this context. The comprehensive understanding of all integrated aspects of a cell’s physiology is a challenging endeavor and the results of this thesis might stimulate further research towards this goal.