Linking Gene Fusions to Bone Marrow Failure and Malignant Transformation in Dyskeratosis Congenita.
Ömer GüllülüBenjamin E MayerFran Bačić ToplekPublished in: International journal of molecular sciences (2024)
Dyskeratosis Congenita (DC) is a multisystem disorder intrinsically associated with telomere dysfunction, leading to bone marrow failure (BMF). Although the pathology of DC is largely driven by mutations in telomere-associated genes, the implications of gene fusions, which emerge due to telomere-induced genomic instability, remain unexplored. We meticulously analyzed gene fusions in RNA-Seq data from DC patients to provide deeper insights into DC's progression. The most significant DC-specific gene fusions were subsequently put through in silico assessments to ascertain biophysical and structural attributes, including charge patterning, inherent disorder, and propensity for self-association. Selected candidates were then analyzed using deep learning-powered structural predictions and molecular dynamics simulations to gauge their potential for forming higher-order oligomers. Our exploration revealed that genes participating in fusion events play crucial roles in upholding genomic stability, facilitating hematopoiesis, and suppressing tumors. Notably, our analysis spotlighted a particularly disordered polyampholyte fusion protein that exhibits robust higher-order oligomerization dynamics. To conclude, this research underscores the potential significance of several high-confidence gene fusions in the progression of BMF in DC, particularly through the dysregulation of genomic stability, hematopoiesis, and tumor suppression. Additionally, we propose that these fusion proteins might hold a detrimental role, specifically in inducing proteotoxicity-driven hematopoietic disruptions.
Keyphrases
- copy number
- genome wide
- bone marrow
- genome wide identification
- dendritic cells
- molecular dynamics simulations
- rna seq
- single cell
- deep learning
- end stage renal disease
- dna methylation
- mesenchymal stem cells
- genome wide analysis
- molecular docking
- chronic kidney disease
- newly diagnosed
- gene expression
- machine learning
- immune response
- ejection fraction
- electronic health record
- data analysis
- patient reported outcomes