关键词: Dyskeratosis congenita RNA-Seq bone marrow failure gene fusions genomic instability polyampholytes telomere disorders

Mesh : Humans Dyskeratosis Congenita / genetics pathology Telomerase / metabolism Pancytopenia Bone Marrow Failure Disorders Mutation Telomere / metabolism Genomic Instability

来  源:   DOI:10.3390/ijms25031606   PDF(Pubmed)

Abstract:
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.
摘要:
先天性角化病(DC)是一种与端粒功能障碍内在相关的多系统疾病,导致骨髓衰竭(BMF)。尽管DC的病理主要是由端粒相关基因突变驱动的,基因融合的含义,由于端粒诱导的基因组不稳定性而出现,仍未探索。我们精心分析了来自DC患者的RNA-Seq数据中的基因融合,以提供对DC进展的更深入了解。随后将最重要的DC特异性基因融合体进行计算机评估,以确定生物物理和结构属性。包括电荷图案化,固有的紊乱,和自我联想的倾向。然后使用深度学习驱动的结构预测和分子动力学模拟来分析选定的候选物,以评估它们形成高阶低聚物的潜力。我们的探索表明,参与融合事件的基因在维护基因组稳定性中起着至关重要的作用。促进造血,抑制肿瘤。值得注意的是,我们的分析突出了一个特别无序的聚两性电解质融合蛋白,表现出强大的高阶寡聚动力学。最后,这项研究强调了几种高置信度基因融合在DC中BMF进展中的潜在意义,特别是通过基因组稳定性的失调,造血,和肿瘤抑制。此外,我们认为这些融合蛋白可能具有有害作用,特别是在诱导蛋白毒性驱动的造血破坏中。
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