Involvement of miR-190b in Xbp1 mRNA Splicing upon Tocotrienol Treatment.
Roberto AmbraSonia MancaGuido LeoniBarbara GuantarioRaffaella CanaliRaffaella ComitatoPublished in: Molecules (Basel, Switzerland) (2020)
We previously demonstrated that apoptosis induced by tocotrienols (γ and δT3) in HeLa cells is preceded by Ca2+ release from the endoplasmic reticulum. This event is eventually followed by the induction of specific calcium-dependent signals, leading to the expression and activation of the gene encoding for the IRE1α protein and, in turn, to the alternative splicing of the pro-apoptotic protein sXbp1 and other molecules involved in the unfolded protein response, the core pathway coping with EndoR stress. Here, we showed that treatment with T3s induces the expression of a specific set of miRNAs in HeLa cells. Data interrogation based on the intersection of this set of miRNAs with a set of genes previously differentially expressed after γT3 treatment provided a few miRNA candidates to be the effectors of EndoR-stress-induced apoptosis. To identify the best candidate to act as the effector of the Xbp1-mediated apoptotic response to γT3, we performed in silico analysis based on the evaluation of the highest ∆ in Gibbs energy of different mRNA-miRNA-Argonaute (AGO) protein complexes. The involvement of the best candidate identified in silico, miR-190b, in Xbp1 splicing was confirmed in vitro using T3-treated cells pre-incubated with the specific miRNA inhibitor, providing a preliminary indication of its role as an effector of EndoR-stress-induced apoptosis.
Keyphrases
- induced apoptosis
- endoplasmic reticulum stress
- oxidative stress
- cell cycle arrest
- signaling pathway
- cell death
- binding protein
- endoplasmic reticulum
- poor prognosis
- dendritic cells
- regulatory t cells
- amino acid
- anti inflammatory
- machine learning
- genome wide
- gene expression
- quantum dots
- long non coding rna
- copy number
- combination therapy
- molecular dynamics simulations
- replacement therapy
- deep learning
- big data