Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model.
It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture model...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1011374 |
| _version_ | 1827807237296357376 |
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| author | Elias Vera-Siguenza Cristina Escribano-Gonzalez Irene Serrano-Gonzalo Kattri-Liis Eskla Fabian Spill Daniel Tennant |
| author_facet | Elias Vera-Siguenza Cristina Escribano-Gonzalez Irene Serrano-Gonzalo Kattri-Liis Eskla Fabian Spill Daniel Tennant |
| author_sort | Elias Vera-Siguenza |
| collection | DOAJ |
| description | It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. Although this is increasingly recognised, and work towards addressing the issue is becoming routinary much remains poorly understood. For instance, knowledge regarding the biochemical mechanisms through which cancer cells manipulate non-cancerous cell metabolism, and the subsequent impact on their survival and proliferation remains limited. Additionally, the variations in these processes across different cancer types and progression stages, and their implications for therapy, also remain largely unexplored. This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells. |
| first_indexed | 2024-03-11T21:54:31Z |
| format | Article |
| id | doaj.art-3120aec55e704f36a79cae22cabc2538 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-11T21:54:31Z |
| publishDate | 2023-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-3120aec55e704f36a79cae22cabc25382023-09-26T05:30:46ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-09-01199e101137410.1371/journal.pcbi.1011374Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model.Elias Vera-SiguenzaCristina Escribano-GonzalezIrene Serrano-GonzaloKattri-Liis EsklaFabian SpillDaniel TennantIt is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. Although this is increasingly recognised, and work towards addressing the issue is becoming routinary much remains poorly understood. For instance, knowledge regarding the biochemical mechanisms through which cancer cells manipulate non-cancerous cell metabolism, and the subsequent impact on their survival and proliferation remains limited. Additionally, the variations in these processes across different cancer types and progression stages, and their implications for therapy, also remain largely unexplored. This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells.https://doi.org/10.1371/journal.pcbi.1011374 |
| spellingShingle | Elias Vera-Siguenza Cristina Escribano-Gonzalez Irene Serrano-Gonzalo Kattri-Liis Eskla Fabian Spill Daniel Tennant Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. PLoS Computational Biology |
| title | Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. |
| title_full | Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. |
| title_fullStr | Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. |
| title_full_unstemmed | Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. |
| title_short | Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. |
| title_sort | mathematical reconstruction of the metabolic network in an in vitro multiple myeloma model |
| url | https://doi.org/10.1371/journal.pcbi.1011374 |
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