Structural Integrity of Nucleolin Is Required to Suppress TDP-43-Mediated Cytotoxicity in Yeast and Human Cell Models.
Caterina PeggionMaria Lina MassiminoDaniel PereiraSara GranuzzoFrancesca RighettoRaissa BortolottoJessica AgostiniGeppo SartoriAlessandro BertoliRaffaele LopreiatoPublished in: International journal of molecular sciences (2023)
The Transactivating response (TAR) element DNA-binding of 43 kDa (TDP-43) is mainly implicated in the regulation of gene expression, playing multiple roles in RNA metabolism. Pathologically, it is implicated in amyotrophic lateral sclerosis and in a class of neurodegenerative diseases broadly going under the name of frontotemporal lobar degeneration (FTLD). A common hallmark of most forms of such diseases is the presence of TDP-43 insoluble inclusions in the cell cytosol. The molecular mechanisms of TDP-43-related cell toxicity are still unclear, and the contribution to cell damage from either loss of normal TDP-43 function or acquired toxic properties of protein aggregates is yet to be established. Here, we investigate the effects on cell viability of FTLD-related TDP-43 mutations in both yeast and mammalian cell models. Moreover, we focus on nucleolin ( NCL ) gene, recently identified as a genetic suppressor of TDP-43 toxicity, through a thorough structure/function characterization aimed at understanding the role of NCL domains in rescuing TDP-43-induced cytotoxicity. Using functional and biochemical assays, our data demonstrate that the N-terminus of NCL is necessary, but not sufficient, to exert its antagonizing effects on TDP-43, and further support the relevance of the DNA/RNA binding central region of the protein. Concurrently, data suggest the importance of the NCL nuclear localization for TDP-43 trafficking, possibly related to both TDP-43 physiology and toxicity.
Keyphrases
- amyotrophic lateral sclerosis
- single cell
- gene expression
- cell therapy
- dna binding
- endothelial cells
- electronic health record
- stem cells
- transcription factor
- mesenchymal stem cells
- high throughput
- binding protein
- single molecule
- deep learning
- machine learning
- artificial intelligence
- bone marrow
- saccharomyces cerevisiae