Utilising an in silico model to predict outcomes in senescence-driven acute liver injury
Currently liver transplantation is the only treatment option for liver disease, but organ availability cannot meet patient demand. Alternative regenerative therapies, including cell transplantation, aim to modulate the injured microenvironment from inflammation and scarring towards regeneration. The...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Nature Research
2024
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| _version_ | 1826314607664300032 |
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| author | Ashmore-Harris, C Antonopoulou, E Aird, RE Man, TY Finney, SM Speel, AM Lu, W Forbes, SJ Gadd, VL Waters, SL |
| author_facet | Ashmore-Harris, C Antonopoulou, E Aird, RE Man, TY Finney, SM Speel, AM Lu, W Forbes, SJ Gadd, VL Waters, SL |
| author_sort | Ashmore-Harris, C |
| collection | OXFORD |
| description | Currently liver transplantation is the only treatment option for liver disease, but organ availability cannot meet patient demand. Alternative regenerative therapies, including cell transplantation, aim to modulate the injured microenvironment from inflammation and scarring towards regeneration. The complexity of the liver injury response makes it challenging to identify suitable therapeutic targets when relying on experimental approaches alone. Therefore, we adopted a combined in vivo-in silico approach and developed an ordinary differential equation model of acute liver disease able to predict the host response to injury and potential interventions. The Mdm2fl/fl mouse model of senescence-driven liver injury was used to generate a quantitative dynamic characterisation of the key cellular players (macrophages, endothelial cells, myofibroblasts) and extra cellular matrix involved in liver injury. This was qualitatively captured by the mathematical model. The mathematical model was then used to predict injury outcomes in response to milder and more severe levels of senescence-induced liver injury and validated with experimental in vivo data. In silico experiments using the validated model were then performed to interrogate potential approaches to enhance regeneration. These predicted that increasing the rate of macrophage phenotypic switch or increasing the number of pro-regenerative macrophages in the system will accelerate the rate of senescent cell clearance and resolution. These results showcase the potential benefits of mechanistic mathematical modelling for capturing the dynamics of complex biological systems and identifying therapeutic interventions that may enhance our understanding of injury-repair mechanisms and reduce translational bottlenecks. |
| first_indexed | 2024-12-09T03:09:42Z |
| format | Journal article |
| id | oxford-uuid:724bd4a3-7225-4028-a483-f362526ac20a |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:09:42Z |
| publishDate | 2024 |
| publisher | Nature Research |
| record_format | dspace |
| spelling | oxford-uuid:724bd4a3-7225-4028-a483-f362526ac20a2024-09-30T20:07:22ZUtilising an in silico model to predict outcomes in senescence-driven acute liver injuryJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:724bd4a3-7225-4028-a483-f362526ac20aEnglishJisc Publications RouterNature Research2024Ashmore-Harris, CAntonopoulou, EAird, REMan, TYFinney, SMSpeel, AMLu, WForbes, SJGadd, VLWaters, SLCurrently liver transplantation is the only treatment option for liver disease, but organ availability cannot meet patient demand. Alternative regenerative therapies, including cell transplantation, aim to modulate the injured microenvironment from inflammation and scarring towards regeneration. The complexity of the liver injury response makes it challenging to identify suitable therapeutic targets when relying on experimental approaches alone. Therefore, we adopted a combined in vivo-in silico approach and developed an ordinary differential equation model of acute liver disease able to predict the host response to injury and potential interventions. The Mdm2fl/fl mouse model of senescence-driven liver injury was used to generate a quantitative dynamic characterisation of the key cellular players (macrophages, endothelial cells, myofibroblasts) and extra cellular matrix involved in liver injury. This was qualitatively captured by the mathematical model. The mathematical model was then used to predict injury outcomes in response to milder and more severe levels of senescence-induced liver injury and validated with experimental in vivo data. In silico experiments using the validated model were then performed to interrogate potential approaches to enhance regeneration. These predicted that increasing the rate of macrophage phenotypic switch or increasing the number of pro-regenerative macrophages in the system will accelerate the rate of senescent cell clearance and resolution. These results showcase the potential benefits of mechanistic mathematical modelling for capturing the dynamics of complex biological systems and identifying therapeutic interventions that may enhance our understanding of injury-repair mechanisms and reduce translational bottlenecks. |
| spellingShingle | Ashmore-Harris, C Antonopoulou, E Aird, RE Man, TY Finney, SM Speel, AM Lu, W Forbes, SJ Gadd, VL Waters, SL Utilising an in silico model to predict outcomes in senescence-driven acute liver injury |
| title | Utilising an in silico model to predict outcomes in senescence-driven acute liver injury |
| title_full | Utilising an in silico model to predict outcomes in senescence-driven acute liver injury |
| title_fullStr | Utilising an in silico model to predict outcomes in senescence-driven acute liver injury |
| title_full_unstemmed | Utilising an in silico model to predict outcomes in senescence-driven acute liver injury |
| title_short | Utilising an in silico model to predict outcomes in senescence-driven acute liver injury |
| title_sort | utilising an in silico model to predict outcomes in senescence driven acute liver injury |
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