Insights into Network of Hot Spots of Aggregation in Nucleophosmin 1.
Daniele FlorioSara La MannaConcetta Di NataleMarilisa LeoneFlavia Anna MercurioFabiana NapolitanoAnna Maria MalfitanoDaniela MarascoPublished in: International journal of molecular sciences (2022)
In a protein, point mutations associated with diseases can alter the native structure and provide loss or alteration of functional levels, and an internal structural network defines the connectivity among domains, as well as aggregate/soluble states' equilibria. Nucleophosmin (NPM)1 is an abundant nucleolar protein, which becomes mutated in acute myeloid leukemia (AML) patients. NPM1-dependent leukemogenesis, which leads to its aggregation in the cytoplasm (NPMc+), is still obscure, but the investigations have outlined a direct link between AML mutations and amyloid aggregation. Protein aggregation can be due to the cooperation among several hot spots located within the aggregation-prone regions (APR), often predictable with bioinformatic tools. In the present study, we investigated potential APRs in the entire NPM1 not yet investigated. On the basis of bioinformatic predictions and experimental structures, we designed several protein fragments and analyzed them through typical aggrsegation experiments, such as Thioflavin T (ThT), fluorescence and scanning electron microscopy (SEM) experiments, carried out at different times; in addition, their biocompatibility in SHSY5 cells was also evaluated. The presented data clearly demonstrate the existence of hot spots of aggregation located in different regions, mostly in the N-terminal domain (NTD) of the entire NPM1 protein, and provide a more comprehensive view of the molecular details potentially at the basis of NPMc+-dependent AML.
Keyphrases
- acute myeloid leukemia
- electron microscopy
- protein protein
- amino acid
- binding protein
- allogeneic hematopoietic stem cell transplantation
- high resolution
- induced apoptosis
- newly diagnosed
- cell proliferation
- risk assessment
- oxidative stress
- functional connectivity
- white matter
- endoplasmic reticulum stress
- cell death
- deep learning