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Simple questions about neurodegenerative disease: Where? When? What?

£12.24M GBP

Funder UK Research and Innovation Future Leaders Fellowship
Recipient Organization Imperial College London
Country United Kingdom
Start Date Feb 01, 2021
End Date Jan 31, 2026
Duration 1,825 days
Number of Grantees 2
Roles Fellow; Award Holder
Data Source UKRI Gateway to Research
Grant ID MR/T04327X/1
Grant Description

Alzheimer's disease is largely caused by genetics, rather than lifestyle factors. The probability of an identical twin developing the disease, if their co-twin already has is ~79%. By comparing the DNA of people who develop a disease against healthy people, we can identify sites in their DNA which increase the likelihood of getting the disease.

In recent years it has been established that thousands of changes to the human genome can contribute to disease risk. Each DNA variant explains only a tiny part of the disease risk, but thousands of these variants can explain much of an individual's disease risk. Biological processes implicated by these loci can be understood as being causally involved in the disease.

Understanding the mechanisms of neurodegenerative diseases has thus become a statistical problem: we just need to find the patterns that link the variants together.

There are many open questions about neurodegenerative diseases. Indeed, for most brain diseases, we do not even have answers to seemingly basic questions: which part of the brain has gone wrong, and at which age did this occur? For neurodegenerative diseases, which do not show symptoms until late life, it would be easy to assume that the disease causing changes occur in late life: several lines of evidence suggest that this may not be entirely the case though, and that changes which occur early in brain development may be involved.

In recent years I used genetics to identify the cell types which cause schizophrenia and to explain why it's age of onset occurs in early adulthood. This was done by showing that the variants which cause the disease preferentially affect genes which act in particular cells. Using a similar approach, I published the first paper showing that Alzheimer's genes have enriched expression in a type of cell called 'microglia' which are thought of as the immune system of the brain.

This finding was a surprise to the field as Alzheimer's had traditionally been considered a neuronal disease. Contrary to this expectation, no enrichment of Alzheimer's risk genes has been found in neurons. Recently I extended the approach to Parkinson's disease: for this disease, the results confirmed the dominant theory of disease mechanism (showing an enrichment in dopaminergic neurons) but also implicated a type of cell known as oligodendrocytes, which had never previously been associated with the disease.

The first part of this project will involve further investigating these two discrepancies: are neurons not involved in Alzheimer's, and are oligodendrocytes involved in Parkinson's?

Once we have identified the cell types which cause neurodegeneration, we can address the issue of the age at which the disease acts. We know that the behaviour of cells changes across the lifespan, but we don't know how or why this occurs molecularly. First we will develop a 'map' of the changes which occur in the causal cell types, then we'll test whether the disease associated genetic variants are more associated with development or old age.

What we really need to know is: what happens within a cell to cause the disease? Once we understand this, we can develop drugs to reverse this process. We can investigate this using statistics again (although we will again need plenty of data describing biological regulatory processes within the relevant cell types).

Genetic variants which cause disease are known to mostly act by disrupting molecular interactions between DNA and proteins/RNA. Sites on DNA where other molecules bind tend to have distinct sequences: we can learn these sequences from publicly available datasets, then predict the effect of genetic variants on molecular interactions. We can then test statistically, whether a particular type of molecular interaction (for instance, the binding of a particular transcription factor) has been disrupted. Once identified, we can begin attempts to reverse disease effects on these binding sites.

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Imperial College London

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