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Computational and mathematical approaches to understand glioma cells metabolism: characterization from transcriptomic data and focus on HIF pathways / KEVIN SPINICCI

Swansea University Author: KEVIN SPINICCI

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DOI (Published version): 10.23889/SUthesis.66117

Abstract

This Thesis questions the effect of hypoxia on cell metabolism, how different oxygen levels within the tumour lead to heterogeneity, and what is the impact on tumour growth. The study focuses on the main protein orchestrating the cellular adaptation to hypoxia: the Hypoxia Inducible Factor (HIF). De...

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Published: Swansea, Wales, UK 2024
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Powathil, Gibin
URI: https://cronfa.swan.ac.uk/Record/cronfa66117
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Despite being the topic of many studies, HIF seems to be only characterized through its effect on genes observed during biological experiments. Mathematical modelling of HIF does not integrate the protein in models simulating large population of cells. This work will try to unravel some of the mechanisms by which HIF impact the tumour growth with a focus on the metabolim and the cell invasion. In this regard, an agent-based model (ABM) has been developed and the investigation paths for the model have been determined from a statistical analysis.The mathematical model developed describes a network of genes regulated by HIF which includes the two important metabolic genes lactate dehydrogenase (LDH) and pyruvate dehydrogenase (PDH) with a focus on the appearance of the Warburg Effect, a mechanism often considered as a tumour cell characteristic. An individual-based approach has been chosen to better represent cellular heterogeneity as each cell has its own set of parameters and experiences a different local environment. With that model, we could test different environmental conditions and genetic regulation. We saw that both rapid variations of extracellular oxygen and increased levels of HIF are enough to induce a Warburg phenotype, which therefore appears as influenced by both a contextual effect and a genetic effect.A statistical analysis of glioblastoma transcriptomic data has been performed to unravel the deregulated mechanisms into two datasets: one available on the The Cancer Genome Atlas (TCGA) platform and a Patient Derived Cell Lines (PDCL) dataset provided by the ICM (Paris). The workflow of analysis begins with a Differential Expression (DE) analysis of the transcriptomic data to find the deregulated genes, which are submitted to pathway enrichment tools to determine what are the corresponding pathways. Among the results, collagen biosynthesis was often deregulated in both datasets and cholesterol metabolism was often deregulated in PDCL data. Owing to its link to the current thematic, collagen biosynthesis has been selected as the new candidate for mathematical description in the current model.Thus, following the results of the statistical analysis, the collagen biosynthesis have been implemeted in the model of metabolism. Collagen and matrix remodelling have a noticeable impact on cellular migration and thus the invasion of distant tissues by cancer cells. The three genes P4HA1, MT1-MMP and LOX have been documented to be upregulated by HIF in the literature and to impact the secretion, degradation and cross-linking of collagen. Genetic parameters were fitted to transcriptomic data to define the genetic parameters of these new genes as well as for the ones already included. The results obtained with the new version of the model indicated that collagen density has a substantial effect on the proliferation of the tumour and its shape. Like collagen, oxygen alone could impact the tumour growth. Moreover, results showed that the oxygen and collagen seems to have a dual effect on tumour proliferation. Increased P4HA1 and MT1-MMP quantities, respectively decreased and increased cellular migration speed while LOX seemed to have little effect. Here, no cells adopted a Warburg phenotype in any of the simulations. The similarity of the production of H+ in this model observed when PDH sensitivity to HIF was reduced in the previous model indicates that this may be due to the new parameters values. Consequently, even if the microenvironment favours a Warburg phenotype, genetic regulation may dictate the cell ability to induce it.This model made it possible to show how HIF can impact the tumour growth and cellular invasion through few regulations. 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spelling v2 66117 2024-04-22 Computational and mathematical approaches to understand glioma cells metabolism: characterization from transcriptomic data and focus on HIF pathways a1c53ecd60a1e5c342e2bdaa93461c12 KEVIN SPINICCI KEVIN SPINICCI true false 2024-04-22 This Thesis questions the effect of hypoxia on cell metabolism, how different oxygen levels within the tumour lead to heterogeneity, and what is the impact on tumour growth. The study focuses on the main protein orchestrating the cellular adaptation to hypoxia: the Hypoxia Inducible Factor (HIF). Despite being the topic of many studies, HIF seems to be only characterized through its effect on genes observed during biological experiments. Mathematical modelling of HIF does not integrate the protein in models simulating large population of cells. This work will try to unravel some of the mechanisms by which HIF impact the tumour growth with a focus on the metabolim and the cell invasion. In this regard, an agent-based model (ABM) has been developed and the investigation paths for the model have been determined from a statistical analysis.The mathematical model developed describes a network of genes regulated by HIF which includes the two important metabolic genes lactate dehydrogenase (LDH) and pyruvate dehydrogenase (PDH) with a focus on the appearance of the Warburg Effect, a mechanism often considered as a tumour cell characteristic. An individual-based approach has been chosen to better represent cellular heterogeneity as each cell has its own set of parameters and experiences a different local environment. With that model, we could test different environmental conditions and genetic regulation. We saw that both rapid variations of extracellular oxygen and increased levels of HIF are enough to induce a Warburg phenotype, which therefore appears as influenced by both a contextual effect and a genetic effect.A statistical analysis of glioblastoma transcriptomic data has been performed to unravel the deregulated mechanisms into two datasets: one available on the The Cancer Genome Atlas (TCGA) platform and a Patient Derived Cell Lines (PDCL) dataset provided by the ICM (Paris). The workflow of analysis begins with a Differential Expression (DE) analysis of the transcriptomic data to find the deregulated genes, which are submitted to pathway enrichment tools to determine what are the corresponding pathways. Among the results, collagen biosynthesis was often deregulated in both datasets and cholesterol metabolism was often deregulated in PDCL data. Owing to its link to the current thematic, collagen biosynthesis has been selected as the new candidate for mathematical description in the current model.Thus, following the results of the statistical analysis, the collagen biosynthesis have been implemeted in the model of metabolism. Collagen and matrix remodelling have a noticeable impact on cellular migration and thus the invasion of distant tissues by cancer cells. The three genes P4HA1, MT1-MMP and LOX have been documented to be upregulated by HIF in the literature and to impact the secretion, degradation and cross-linking of collagen. Genetic parameters were fitted to transcriptomic data to define the genetic parameters of these new genes as well as for the ones already included. The results obtained with the new version of the model indicated that collagen density has a substantial effect on the proliferation of the tumour and its shape. Like collagen, oxygen alone could impact the tumour growth. Moreover, results showed that the oxygen and collagen seems to have a dual effect on tumour proliferation. Increased P4HA1 and MT1-MMP quantities, respectively decreased and increased cellular migration speed while LOX seemed to have little effect. Here, no cells adopted a Warburg phenotype in any of the simulations. The similarity of the production of H+ in this model observed when PDH sensitivity to HIF was reduced in the previous model indicates that this may be due to the new parameters values. Consequently, even if the microenvironment favours a Warburg phenotype, genetic regulation may dictate the cell ability to induce it.This model made it possible to show how HIF can impact the tumour growth and cellular invasion through few regulations. It suggests that both fast changes of oxygen levels and increased HIF stabilization in normoxia can induce a Warburg Effect. However, the ability of the cell to reduce the oxygen consumption through adaptation to hypoxia seemed to be a limiting factor preventing the adoption of a Warburg phenotype. E-Thesis Swansea, Wales, UK Computational Oncology, Metabolism, Hypoxia Inducible Factor, Pathway Enrichment, Cell migration 29 1 2024 2024-01-29 10.23889/SUthesis.66117 COLLEGE NANME COLLEGE CODE Swansea University Powathil, Gibin Doctoral Ph.D SUSPRS SUSPRS 2024-04-22T11:58:11.3962998 2024-04-22T11:32:31.5597011 Faculty of Science and Engineering School of Mathematics and Computer Science - Mathematics KEVIN SPINICCI 1 66117__30103__f8b39e73936143bd9db1a42e05531bfa.pdf Spinicci_Kevin_PhD_Thesis_Final_Redacted_Signature.pdf 2024-04-22T11:45:18.5402010 Output 35548320 application/pdf E-Thesis – open access true Copyright: The author, Kévin Spinicci, 2024. This thesis is released under the terms of a CC-BY license. Third party content is excluded for use under the license terms. true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Computational and mathematical approaches to understand glioma cells metabolism: characterization from transcriptomic data and focus on HIF pathways
spellingShingle Computational and mathematical approaches to understand glioma cells metabolism: characterization from transcriptomic data and focus on HIF pathways
KEVIN SPINICCI
title_short Computational and mathematical approaches to understand glioma cells metabolism: characterization from transcriptomic data and focus on HIF pathways
title_full Computational and mathematical approaches to understand glioma cells metabolism: characterization from transcriptomic data and focus on HIF pathways
title_fullStr Computational and mathematical approaches to understand glioma cells metabolism: characterization from transcriptomic data and focus on HIF pathways
title_full_unstemmed Computational and mathematical approaches to understand glioma cells metabolism: characterization from transcriptomic data and focus on HIF pathways
title_sort Computational and mathematical approaches to understand glioma cells metabolism: characterization from transcriptomic data and focus on HIF pathways
author_id_str_mv a1c53ecd60a1e5c342e2bdaa93461c12
author_id_fullname_str_mv a1c53ecd60a1e5c342e2bdaa93461c12_***_KEVIN SPINICCI
author KEVIN SPINICCI
author2 KEVIN SPINICCI
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publishDate 2024
institution Swansea University
doi_str_mv 10.23889/SUthesis.66117
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Mathematics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Mathematics
document_store_str 1
active_str 0
description This Thesis questions the effect of hypoxia on cell metabolism, how different oxygen levels within the tumour lead to heterogeneity, and what is the impact on tumour growth. The study focuses on the main protein orchestrating the cellular adaptation to hypoxia: the Hypoxia Inducible Factor (HIF). Despite being the topic of many studies, HIF seems to be only characterized through its effect on genes observed during biological experiments. Mathematical modelling of HIF does not integrate the protein in models simulating large population of cells. This work will try to unravel some of the mechanisms by which HIF impact the tumour growth with a focus on the metabolim and the cell invasion. In this regard, an agent-based model (ABM) has been developed and the investigation paths for the model have been determined from a statistical analysis.The mathematical model developed describes a network of genes regulated by HIF which includes the two important metabolic genes lactate dehydrogenase (LDH) and pyruvate dehydrogenase (PDH) with a focus on the appearance of the Warburg Effect, a mechanism often considered as a tumour cell characteristic. An individual-based approach has been chosen to better represent cellular heterogeneity as each cell has its own set of parameters and experiences a different local environment. With that model, we could test different environmental conditions and genetic regulation. We saw that both rapid variations of extracellular oxygen and increased levels of HIF are enough to induce a Warburg phenotype, which therefore appears as influenced by both a contextual effect and a genetic effect.A statistical analysis of glioblastoma transcriptomic data has been performed to unravel the deregulated mechanisms into two datasets: one available on the The Cancer Genome Atlas (TCGA) platform and a Patient Derived Cell Lines (PDCL) dataset provided by the ICM (Paris). The workflow of analysis begins with a Differential Expression (DE) analysis of the transcriptomic data to find the deregulated genes, which are submitted to pathway enrichment tools to determine what are the corresponding pathways. Among the results, collagen biosynthesis was often deregulated in both datasets and cholesterol metabolism was often deregulated in PDCL data. Owing to its link to the current thematic, collagen biosynthesis has been selected as the new candidate for mathematical description in the current model.Thus, following the results of the statistical analysis, the collagen biosynthesis have been implemeted in the model of metabolism. Collagen and matrix remodelling have a noticeable impact on cellular migration and thus the invasion of distant tissues by cancer cells. The three genes P4HA1, MT1-MMP and LOX have been documented to be upregulated by HIF in the literature and to impact the secretion, degradation and cross-linking of collagen. Genetic parameters were fitted to transcriptomic data to define the genetic parameters of these new genes as well as for the ones already included. The results obtained with the new version of the model indicated that collagen density has a substantial effect on the proliferation of the tumour and its shape. Like collagen, oxygen alone could impact the tumour growth. Moreover, results showed that the oxygen and collagen seems to have a dual effect on tumour proliferation. Increased P4HA1 and MT1-MMP quantities, respectively decreased and increased cellular migration speed while LOX seemed to have little effect. Here, no cells adopted a Warburg phenotype in any of the simulations. The similarity of the production of H+ in this model observed when PDH sensitivity to HIF was reduced in the previous model indicates that this may be due to the new parameters values. Consequently, even if the microenvironment favours a Warburg phenotype, genetic regulation may dictate the cell ability to induce it.This model made it possible to show how HIF can impact the tumour growth and cellular invasion through few regulations. It suggests that both fast changes of oxygen levels and increased HIF stabilization in normoxia can induce a Warburg Effect. However, the ability of the cell to reduce the oxygen consumption through adaptation to hypoxia seemed to be a limiting factor preventing the adoption of a Warburg phenotype.
published_date 2024-01-29T11:58:08Z
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