Communications BiologyNeurosciencePeer-reviewed journal article
Cell environment shapes TDP-43 function: implications in neuronal and muscle disease
Urša Šušnjar, Neva Škrabar, Anna‐Leigh BrowniD, Yasmine AbbassiiD, Hemali P. PhatnaniiD, Pietro FrattaiD, Justin Y. KwaniD, Dhruv SareeniD, James Riley BroachiD, Zachary SimmonsiD, Ximena Arcila-LondonoiD, E. B. Lee, Vivianna M. Van DeerliniD, Neil A. ShneideriD, Ernest FraenkeliD, Lyle W. OstrowiD, Frank BaasiD, James D. BerryiD, Oleg ButovskyiD, Robert H. BalohiD, Ophir ShalemiD, Terry D Heiman-PattersoniD, Leonidas StefanisiD, Siddharthan ChandraniD, Suvankar PaliD, , Emanuele BurattiiD
Abstract
TDP-43 aggregation and redistribution have been recognised as a hallmark of amyotrophic lateral sclerosis, frontotemporal dementia and other neurological disorders. While TDP-43 has been studied extensively in neuronal tissues, TDP-43 inclusions have also been described in the muscle of inclusion body myositis patients, highlighting the need to understand the role of TDP-43 beyond the central nervous system. Using RNA-seq we performed the first direct comparison of TDP-43-mediated transcription and alternative splicing in muscle (C2C12) and neuronal (NSC34) mouse cells. Our results clearly show that TDP-43 displays a tissue-characteristic behaviour targeting unique transcripts in each cell type. This is not due to variable transcript abundance but rather due to cell-specific expression of RNA-binding proteins, which influences TDP-43 performance. Among splicing events commonly dysregulated in both cell lines, we identified some that are TDP-43-dependent also in human cells and show that inclusion levels of these alternative exons appear to be differentially altered in affected tissues of FTLD and IBM patients. We therefore propose that TDP-43 dysfunction, reflected in aberrant splicing, contributes to disease development but it does so in a tissue- and disease-specific manner.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 58
- FWCI
- 6.18
- Field percentile
- top 2%
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