Deciphering impact of single nucleotide polymorphisms on cotranscriptional modification in CCM gene mRNAs.
Concetta ScimoneLuigi DonatoSimona AlibrandiConcetta AlafaciAngela D'AscolaSergio VinciRosalia D'AngeloAntonina SidotiPublished in: American journal of physiology. Cell physiology (2022)
Novel insights on regulation of gene expression mechanisms highlight the pivotal role of epitranscriptomic modifications on decision about transcript fate. These modifications include methylation of adenosine and cytosine in RNA molecules. Impairment of the normal epitranscriptome profile was observed in several pathological conditions, such as cancer and neurodegeneration. However, it is still unknown if alteration of this regulatory mechanism can be involved in cerebral cavernous malformation (CCM) development. CCM is a rare genetic condition affecting brain microvasculature, resulting from mutations in the three genes <i>KRIT1</i>, <i>CCM2</i>, and <i>PDCD10</i>. By data integration of association study, in silico prediction, and gene expression analysis, we evaluated role of single nucleotide polymorphisms (SNPs) highly recurrent in patients with CCM, on CCM gene expression regulation. Results showed that several of these SNPs lead to a drastic downexpression, in <i>KRIT1</i> and <i>CCM2</i> genes and this downregulation can be due to alteration of epitranscriptome profile, occurring these SNPs in gene regions that are subject to epitranscriptome modifications. These data suggest that this novel mechanism of gene expression regulation can be consider to further investigation on CCM pathogenesis.
Keyphrases
- genome wide
- dna methylation
- gene expression
- genome wide identification
- copy number
- transcription factor
- genome wide analysis
- cell proliferation
- machine learning
- squamous cell carcinoma
- papillary thyroid
- brain injury
- molecular docking
- young adults
- white matter
- resting state
- single cell
- functional connectivity
- genome wide association
- lymph node metastasis
- squamous cell
- molecular dynamics simulations
- data analysis