Multi-omics therapeutic perspective on ACVR1 gene: from genetic alterations to potential targeting.
Garima NagarPooja MittalShradheya R R GuptaMonika PahujaManisha SangerRuby MishraArchana SinghIndrakant Kumar SinghPublished in: Briefings in functional genomics (2022)
Activin A receptor type I (ACVR1), a transmembrane serine/threonine kinase, belongs to the transforming growth factor-β superfamily, which signals via phosphorylating the downstream effectors and SMAD transcription factors. Its central role in several biological processes and intracellular signaling is well known. Genetic variation in ACVR1 has been associated with a rare disease, fibrodysplasia ossificans progressive, and its somatic alteration is reported in rare cancer diffuse intrinsic pontine glioma. Furthermore, altered expression or variation of ACVR1 is associated with multiple pathologies such as polycystic ovary syndrome, congenital heart defects, diffuse idiopathic skeletal hyperostosis, posterior fossa ependymoma and other malignancies. Recent advancements have witnessed ACVR1 as a potential pharmacological target, and divergent promising approaches for its therapeutic targeting have been explored. This review highlights the structural and functional characteristics of receptor ACVR1, associated signaling pathways, genetic variants in several diseases and cancers, protein-protein interaction, gene expression, regulatory miRNA prediction and potential therapeutic targeting approaches. The comprehensive knowledge will offer new horizons and insights into future strategies harnessing its therapeutic potential.
Keyphrases
- transforming growth factor
- polycystic ovary syndrome
- gene expression
- epithelial mesenchymal transition
- protein protein
- transcription factor
- cancer therapy
- copy number
- protein kinase
- signaling pathway
- genome wide
- insulin resistance
- small molecule
- healthcare
- low grade
- dna methylation
- multiple sclerosis
- papillary thyroid
- binding protein
- drug delivery
- metabolic syndrome
- skeletal muscle
- oxidative stress
- machine learning
- young adults
- big data
- adipose tissue
- pi k akt
- climate change