Mutagenesis and its biomedical consequences
Lead Research Organisation:
University of Edinburgh
Department Name: UNLISTED
Abstract
We are working to understand what causes changes to the DNA sequence in our cells. New DNA changes are called mutations. Some of these mutations drive the development of cancer, some cause inherited disease and they may contribute to the ageing of our bodies. But many, perhaps most, have no effect.
Mutations can come about through natural processes in our cells, or they can be caused by exposure to damaging environments such as ultraviolet radiation in sunlight, dangerous chemicals in tobacco smoke, and as a side-effect of some medical treatments. Our work reveals the mechanisms that lead to these mutations.
Learning about the causes of mutations tells us a great deal about the processes in our cells that copy and repair DNA. This can suggest new ways to treat cancers. The patterns of mutations we see in a cancer can reveal defects that may be allowing the cancer to grow, but might also be used to target treatments specifically to that cancer.
Working out the patterns of new mutations helps us find those rare ones that have an important effect. This can help understand disease or target therapies. We specialise in making the most of data that already exists, which extracts the maximum insight from past investments and minimises the use of animals. Where the data we need doesn’t exist, we work with other groups in the MRC Human Genetics Unit, and international collaborators to efficiently generate it.
Mutations can come about through natural processes in our cells, or they can be caused by exposure to damaging environments such as ultraviolet radiation in sunlight, dangerous chemicals in tobacco smoke, and as a side-effect of some medical treatments. Our work reveals the mechanisms that lead to these mutations.
Learning about the causes of mutations tells us a great deal about the processes in our cells that copy and repair DNA. This can suggest new ways to treat cancers. The patterns of mutations we see in a cancer can reveal defects that may be allowing the cancer to grow, but might also be used to target treatments specifically to that cancer.
Working out the patterns of new mutations helps us find those rare ones that have an important effect. This can help understand disease or target therapies. We specialise in making the most of data that already exists, which extracts the maximum insight from past investments and minimises the use of animals. Where the data we need doesn’t exist, we work with other groups in the MRC Human Genetics Unit, and international collaborators to efficiently generate it.
Technical Summary
Genetic mutations provide the raw material for evolution, they are responsible for heritable disease, drive the development of cancer and are implicated in somatic ageing. This programme strives to understand the molecular mechanisms that give rise to new mutations and to be able to interpret their consequences.
We previously discovered the process of lesion segregation, in which mutagenic DNA lesions can persist for multiple cellular generations resulting in strand asymmetric mutation patterns and generating genetic diversity within a clonally expanding cell population. In this project we will capitalise on these insights to explore the mechanisms of mutagenesis and DNA repair – with strand-specificity, at genome-wide scale, and single base resolution.
Understanding the patterns of mutation occurrence also helps us establish the functional and selective consequences of mutations. We will generate reliable null expectations of mutation occurrence against which we will compare observed mutations, allowing the inference of selection. This strategy will be applied across scales from between species evolution, to human population variation, and down to the cellular scale studying the clonal expansions of cells both in vitro and in vivo (zebrafish and mouse models). Through innovative high-throughput analyses of existing and collaboratively generated data, we will identify mutations that perturb gene expression and demonstrate how clonally accumulated genetic heterogeneity influences cellular phenotype and clonal selection. This is directly relevant to the accrual of somatic mosaicism through development, and the initiation and progression of cancer.
Leading with computational analysis, our research extracts maximum insight from pre-existing datasets, and then benefits from collaboration and infrastructure within the MRC Human Genetics Unit to generate key missing data. The results of this research will (1.) reveal why mutations occur where they do, (2.) give mechanistic insights that could both guide cancer treatment and identify therapeutic targets, and will (3.) advance understanding of clonal selection, particularly in the context of combinatorial genetic diversity.
We previously discovered the process of lesion segregation, in which mutagenic DNA lesions can persist for multiple cellular generations resulting in strand asymmetric mutation patterns and generating genetic diversity within a clonally expanding cell population. In this project we will capitalise on these insights to explore the mechanisms of mutagenesis and DNA repair – with strand-specificity, at genome-wide scale, and single base resolution.
Understanding the patterns of mutation occurrence also helps us establish the functional and selective consequences of mutations. We will generate reliable null expectations of mutation occurrence against which we will compare observed mutations, allowing the inference of selection. This strategy will be applied across scales from between species evolution, to human population variation, and down to the cellular scale studying the clonal expansions of cells both in vitro and in vivo (zebrafish and mouse models). Through innovative high-throughput analyses of existing and collaboratively generated data, we will identify mutations that perturb gene expression and demonstrate how clonally accumulated genetic heterogeneity influences cellular phenotype and clonal selection. This is directly relevant to the accrual of somatic mosaicism through development, and the initiation and progression of cancer.
Leading with computational analysis, our research extracts maximum insight from pre-existing datasets, and then benefits from collaboration and infrastructure within the MRC Human Genetics Unit to generate key missing data. The results of this research will (1.) reveal why mutations occur where they do, (2.) give mechanistic insights that could both guide cancer treatment and identify therapeutic targets, and will (3.) advance understanding of clonal selection, particularly in the context of combinatorial genetic diversity.
Related Projects
Project Reference | Relationship | Related To | Start | End | Award Value |
---|---|---|---|---|---|
MC_UU_00035/1 | 31/03/2023 | 30/03/2028 | £2,414,000 | ||
MC_UU_00035/2 | Transfer | MC_UU_00035/1 | 31/03/2023 | 30/03/2028 | £2,790,000 |
MC_UU_00035/3 | Transfer | MC_UU_00035/2 | 31/03/2023 | 30/03/2028 | £2,915,000 |
MC_UU_00035/4 | Transfer | MC_UU_00035/3 | 31/03/2023 | 30/03/2028 | £2,041,000 |
MC_UU_00035/5 | Transfer | MC_UU_00035/4 | 31/03/2023 | 30/03/2028 | £3,928,000 |
MC_UU_00035/6 | Transfer | MC_UU_00035/5 | 31/03/2023 | 30/03/2028 | £1,900,000 |
MC_UU_00035/7 | Transfer | MC_UU_00035/6 | 31/03/2023 | 30/03/2028 | £4,734,000 |
MC_UU_00035/8 | Transfer | MC_UU_00035/7 | 31/03/2023 | 30/03/2028 | £2,193,000 |
MC_UU_00035/9 | Transfer | MC_UU_00035/8 | 31/03/2023 | 30/03/2028 | £1,473,000 |
MC_UU_00035/10 | Transfer | MC_UU_00035/9 | 31/03/2023 | 30/03/2028 | £4,326,000 |
MC_UU_00035/11 | Transfer | MC_UU_00035/10 | 31/03/2023 | 30/03/2028 | £4,567,000 |
MC_UU_00035/12 | Transfer | MC_UU_00035/11 | 31/03/2023 | 30/03/2028 | £2,373,000 |
MC_UU_00035/13 | Transfer | MC_UU_00035/12 | 31/03/2023 | 30/03/2028 | £3,287,000 |
MC_UU_00035/14 | Transfer | MC_UU_00035/13 | 31/03/2023 | 31/03/2024 | £112,000 |
MC_UU_00035/15 | Transfer | MC_UU_00035/14 | 31/03/2023 | 31/03/2024 | £280,000 |
MC_UU_00035/16 | Transfer | MC_UU_00035/15 | 31/03/2023 | 30/03/2028 | £2,784,000 |
Description | Duncan Odom |
Organisation | German Cancer Research Center |
Country | Germany |
Sector | Academic/University |
PI Contribution | Co lead of collaboration along with Duncan Odom, DKFZ. Experimental design. Genomic data analysis following burst mutagenesis. |
Collaborator Contribution | Experimental design. Primary data generation. |
Impact | Outputs are currently in review. |
Start Year | 2022 |
Description | EdDASH |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | Out reach training using software carpentries format. Multiple group members working as tutors and course organisers. |
Year(s) Of Engagement Activity | 2022,2023 |
URL | https://edcarp.github.io/Ed-DaSH/ |