We study the biological functions of several RBPs, especially those that are implicated in amyotrophic lateral sclerosis (ALS). This includes TDP-43, FUS and MATR3; mutations in genes encoding these RBPs are a common cause of familial ALS.
We collaborate with the groups of Rickie Patani and Nick Luscombe to study how mutations cause ALS or other age-related neurodegenerative diseases by changing the assembly of RNPs. We use human and mouse pluripotent stem cells as our cell culture model system, and we monitor the assembly of RNPs during the differentiation of these cells into motor neurons.
We have shown that the RBPs implicated in ALS regulate alternative splicing and 3’ end processing of important transcripts, many of which encode proteins implicated in neurodegenerative disorders. We also examined how RNA binding properties of TDP-43 change in the brain tissue from patients with FTLD, which contains aggregates of TDP-43. This showed that the greatest changes in binding of TDP-43 occur on the long non-coding RNAs NEAT1 and MALAT1.
To complement our the studies of specific RBPs, we use comprehensive methods to study changes in RNA processing that are associated with brain aging and age-related neurodegenerative diseases. We have examined human postmortem brain samples to compare the effects of healthy aging with two neurodegenerative diseases: Alzheimer's disease and frontotemporal lobar degeneration (FTLD). We found that most changes were specific to diseased samples, as expected, but some changes were common to aging and disease. Interestingly, we found that the changes in glial-specific genes are particularly common in aging, and we showed that oligodendrocyte-specific genes tend to decrease their expression. Stratifying the gene expression by cell type, we further found that increased expression of genes specific for endothelial cells and microglia is the best predictor of brain aging, while astrocytes and oligodendrocytes diminish their regional identity upon aging.
To complement the insights from gene expression, we developed a machine learning method to analyse high-resolution images of brain sections. This indicates that the number of oligodendrocytes decreases in aging, while the number of neurons remains largely unchanged, except of the neurons with largest cell bodies, which also appear to decline in number. We hope that these findings will be of use for further studies of the cellular phase of aging and the Alzheimer's Disease.