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Tauopathies: Patterns of regional vulnerability and the impact of genetic risk factor

A recent study has used network modelling to show that diffusion through the connectome is the best predictor of tau pathology patterns, providing insight as to how neurodegenerative conditions, such as Alzheimer’s disease, progress.

Tauopathies are a collection of heterogeneous neurodegenerative diseases that are characterised by the accumulation of the tau protein into aggregates. Alzheimer’s disease (AD) is the most common tauopathy. Currently, there are over 50 million people worldwide living with the condition.

It is now understood that patients with more clinically severe AD show elevated levels of pathological tau in many regions of the brain. Nevertheless, a debate still exists in terms of how tau pathology ‘spreads’. This is mainly due to uncertainty about how neuroanatomical connectivity, spatial proximity of regions, and intrinsic neuronal vulnerability all contribute to the progression.

Network modelling of tau pathology progression

A recent study used computational analysis of spatiotemporal tau pathology patterns to investigate 134 regions of the mouse brain. Following an intracranial injection of pathogenic tau, more regions of the mouse brain became affected over time, supporting the notion of pathology ‘spread’.

To identify the causal mechanisms behind the spread, the brain was considered as a network of interconnected regions. Using an interdisciplinary approach, linking quantitative pathology and network analysis, it was found that tau pathology spread was best explained by diffusion along neuroanatomical connections in a bidirectional manner.

Furthermore, comparisons of regional vulnerability and regional gene expression identified several previously unknown candidate genes that may play a role in controlling susceptibility to tau pathology. Notably, one such gene was the Leucine Rich Repeat Kinase 2 (LRRK2), which has been linked to having a potential role in AD for a long time. Results showed that there were clear differences in the regional distribution of tau pathology in mice expressing a G2019S mutation in LRRK2 (LRRK2G2019S). LRRK2G2019S is reported to be the most prevalent mutation in LRRK2 and, historically, it has been strongly correlated to several forms of dementia and Parkinson’s disease. Now, the results from the recent study suggest that LRRK2G2019S alters some aspect of tau pathology spread. The mice with the mutation also exhibited a bias toward the retrograde spread of tau pathology.

Why was this research important?

This study agreed with a cohort of previous work using computational modelling to understand the distribution of tau pathology. However, this study extended the previous efforts in several ways:

  1. Tau seeding was used, providing a precise spatiotemporal starting point.
  2. Quantitative measures of mouse tau pathology were used in 134 regions of the mouse brain, providing a depth of data.
  3. Mice were euthanised at four time points following the injection of pathogenic tau, providing pseudo-longitudinal data for model fitting.

Overall, the study has provided a framework for understanding the progression of pathological tau throughout the brain. It has also allowed investigation into the impact of genetic risk factors on neurodegenerative disease progression.

Future work should focus on the alterations related to LRRK2 kinase activity and whether deleting connections could impact the pathological spread. If successful, perhaps LRRK2 inhibitors could be a viable therapeutic treatment for tauopathies in the future.

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