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dc.contributor.author
Dubuis, Sébastien
dc.contributor.supervisor
Zamboni, Nicola
dc.contributor.supervisor
Sauer, Uwe
dc.contributor.supervisor
Claassen, Manfred
dc.date.accessioned
2017-11-08T06:47:15Z
dc.date.available
2017-11-07T19:51:50Z
dc.date.available
2017-11-08T06:47:15Z
dc.date.issued
2017
dc.identifier.uri
http://hdl.handle.net/20.500.11850/205761
dc.identifier.doi
10.3929/ethz-b-000205761
dc.description.abstract
During tumorigenesis, metabolism of cancer cells is altered and fluxes in central metabolism are rerouted. Under the influence of oncogenes and tumor suppressors that modulate the expression of multiple enzymes, cancer cells increase fermentative metabolism of glucose to lactate independent of oxygenation, i.e. the so-called Warburg effect. Simultaneously, they activate glutaminolysis, upregulate serine synthesis or deregulate fatty acid synthesis. All these alterations allow cancer cells to grow by providing the essential building blocks necessary for proliferation. Yet, aberrant metabolic activity renders cancer cells more susceptible than normal tissue to drugs that target metabolic enzymes and hence provide potential therapeutic targets. However, the intertwined influences of oncogenes and the tumor’s microenvironment result in large heterogeneity in the metabolic alterations. For this reason, targeting cancer cell metabolism requires methods that allow investigation of intracellular metabolic fluxes. Intracellular metabolic fluxes cannot be measured directly and are inferred indirectly from metabolite levels and in silico models. However, approaches such as 13C metabolic flux analysis (MFA) or flux balance analysis (FBA) are poorly suited to calculate fluxes in mammalian cells or animal models. Alternatively, enzyme centric measurements, such as proteomics or transcriptomics, can be used to monitor the regulatory responses of cells, but fail to reveal the functional outcome, i.e. the impact on the metabolic fluxes. In this thesis, we set out to explore the pertinence of metabolomics as a tool to discover metabolic alterations in cancer cells. By comprehensively measuring the metabolic content of cells, we aim at discovering functional change, i.e. change in the metabolic fluxes. By using both top down (chapter 2) and data-driven (chapter 3 and 4) approaches we provide substantial evidences that changes in the metabolic pools revealed by untargeted metabolomics mirror changes in intracellular fluxes. For thorough investigation of the variability found in the metabolome content of cancer cells, we analyzed the intracellular metabolome of 18 breast cell lines using non-targeted mass spectrometry-based metabolomics in chapter 2. The cell lines were clinically well-characterized in terms of subtypes, which were relevant to breast cancer treatments, but no prior knowledge existed at a metabolic level. The expectations were that the intracellular metabolome of these cell lines is governed by clinical subtypes that are known to broadly affect cellular transcription. Contrary to the expectations, the metabolome patterns were not associated with any of the known clinical classes and we discovered that the cell lines have cell-specific metabolome patterns. To assess whether these patterns can reveal changes in metabolic fluxes, we measured extracellular fluxes and subsequently showed that some metabolic pools indeed correlate with the extracellular fluxes. This indicated that, despite not correlating with clinical classifications, metabolic pools are pertinent to investigate metabolic flux alterations. In chapter 3, we aimed to further deepen our understanding of the relation between a change in metabolic pools and a change in fluxes. Because a top-down approach failed to reveal systematic changes in the metabolome content of our cell lines, we set out to use a data-driven approach to uncover potential changes in flux. Hence, we systematically investigated some of the principal markers presented by the cell-specific metabolome patterns found in the previous chapter. By targeting singularities, i.e. a significant change in the metabolic pool found in only one cell lines out of our panel, we aimed at uncovering radical flux changes. Subsequently, we compared the found singularities to functionally relevant data, i.e. gene expression and gene essentiality (obtained from an RNA interference screen targeting enzyme). Moreover, for singularities of two cell lines in central carbon metabolism, we used 13C-based metabolic flux analysis and enzyme assays to verify that the changes in the metabolic pool are associated with changes in metabolic fluxes. Despite the interesting results, this approach is limited as singularities are rare, albeit present in most cell lines. To build further on the previous results, in chapter 4, we devised two approaches to computationally stratify cell lines based on their metabolic content. With those approaches we aimed to uncover functionally relevant changes, i.e. changes in the metabolic fluxes. We first designed an approach based uniquely on metabolite levels and showed that stratification can reveal change in fluxes. However, this approach suffered from the very local aspect of the approach. Indeed, association between ion clusters and fluxes were limited to fluxes in the very close vicinity of the ions used for clustering. To obtain a stratification of cell lines that better accounts for pathway activity and organization, we developed a pathway-centric approach, i.e. metabotyping, which for each pathway recognized types from the metabolome data. This procedure provided a granular yet global view of metabolism. In follow-up experiments, we demonstrated the validity of this approach for defined changes in metabolic fluxes in different pathways. Metabotyping provides a powerful data-driven approach to investigate the heterogeneous metabolome content of cancer cell lines as well as the change in metabolic fluxes associated. In the concluding remarks, we summarize the key findings of this thesis and assessed the significance of the heterogeneity in metabolic content found in our breast cancer panel for therapies that target metabolism. With our ion-centric approach we propose an improvement based on pathway enrichment and subnetwork extraction. Furthermore, we provide possible solutions to render metabotyping and ion-centric clustering predictive of flux directionality, notably via flux balance analysis. Finally, we examine the possible source of the heterogeneous metabolic content and propose further ways to investigate them. The data-driven approaches presented here are directly applicable to larger screens of cancer cell lines. Overall, this thesis presents good examples of the pertinence of an untargeted metabolomics approach in the discovery of metabolic alterations in cancer cells.
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Cancer
en_US
dc.subject
Metabolism
en_US
dc.subject
Metabolomics
en_US
dc.subject
Breast Cancer
en_US
dc.title
Analysis of metabolic activity in breast cancer cell lines via untargeted metabolomics
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2017-11-08
ethz.size
201 p.
en_US
ethz.identifier.diss
24253
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02538 - Institut für Molekulare Systembiologie / Institute for Molecular Systems Biology::03713 - Sauer, Uwe / Sauer, Uwe
en_US
ethz.relation.cites
10.1016/j.ymben.2016.12.009
ethz.relation.cites
handle/20.500.11850/126088
ethz.date.deposited
2017-11-07T19:52:01Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-11-08T06:47:46Z
ethz.rosetta.lastUpdated
2020-02-15T08:56:01Z
ethz.rosetta.versionExported
true
ethz.COinS
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