Dataset Information

Identification of a signature motif for the eIF4a3-SECIS interaction.

ABSTRACT: eIF4a3, a DEAD-box protein family member, is a component of the exon junction complex which assembles on spliced mRNAs. The protein also acts as a transcript-selective translational repressor of selenoprotein synthesis during selenium deficiency. Selenocysteine (Sec) incorporation into selenoproteins requires a Sec Insertion Sequence (SECIS) element in the 3' untranslated region. During selenium deficiency, eIF4a3 binds SECIS elements from non-essential selenoproteins, preventing Sec insertion. We identified a molecular signature for the eIF4a3-SECIS interaction using RNA gel shifts, surface plasmon resonance and enzymatic foot printing. Our results support a two-site interaction model, where eIF4a3 binds the internal and apical loops of the SECIS. Additionally, the stability of the complex requires uridine in the SECIS core. In terms of protein requirements, the two globular domains of eIF4a3, which are connected by a linker, are both critical for SECIS binding. Compared to full-length eIF4a3, the two domains in trans bind with a lower association rate but notably, the uridine is no longer important for complex stability. These results provide insight into how eIF4a3 discriminates among SECIS elements and represses translation.

SUBMITTER: Budiman ME

PROVIDER: S-EPMC3177196 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Identification of a signature motif for the eIF4a3-SECIS interaction.

Budiman Michael E ME Bubenik Jodi L JL Driscoll Donna M DM

Nucleic acids research 20110617 17

eIF4a3, a DEAD-box protein family member, is a component of the exon junction complex which assembles on spliced mRNAs. The protein also acts as a transcript-selective translational repressor of selenoprotein synthesis during selenium deficiency. Selenocysteine (Sec) incorporation into selenoproteins requires a Sec Insertion Sequence (SECIS) element in the 3' untranslated region. During selenium deficiency, eIF4a3 binds SECIS elements from non-essential selenoproteins, preventing Sec insertion. ...[more]

PMID: 21685449

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Project description:BackgroundRibosomal protein L30 belongs to the L7Ae family of RNA-binding proteins, which recognize diverse targets. L30 binds to kink-turn motifs in the 28S ribosomal RNA, L30 pre-mRNA, and mature L30 mRNA. L30 has a noncanonical function as a component of the UGA recoding machinery that incorporates selenocysteine (Sec) into selenoproteins during translation. L30 binds to a putative kink-turn motif in the Sec Insertion Sequence (SECIS) element in the 3' UTR of mammalian selenoprotein mRNAs. The SECIS also interacts with SECIS-binding protein 2 (SBP2), an essential factor for Sec incorporation. Previous studies showed that L30 and SBP2 compete for binding to the SECIS in vitro. The SBP2:SECIS interaction has been characterized but much less is known about how L30 recognizes the SECIS.ResultsHere we use enzymatic RNA footprinting to define the L30 binding site on the SECIS. Like SBP2, L30 protects nucleotides in the 5' side of the internal loop, the 5' side of the lower helix, and the SECIS core, including the GA tandem base pairs that are predicted to form a kink-turn. However, L30 has additional determinants for binding as it also protects nucleotides in the 3' side of the internal loop, which are not protected by SBP2. In support of the competitive binding model, we found that purified L30 repressed UGA recoding in an in vitro translation system, and that this inhibition was rescued by SBP2. To define the amino acid requirements for SECIS-binding, site-specific mutations in L30 were generated based on published structural studies of this protein in a complex with its canonical target, the L30 pre-mRNA. We identified point mutations that selectively inhibited binding of L30 to the SECIS, to the L30 pre-mRNA, or both RNAs, suggesting that there are subtle differences in how L30 interacts with the two targets.ConclusionsThis study establishes that L30 and SBP2 bind to overlapping but non-identical sites on the SECIS. The amino acid requirements for the interaction of L30 with the SECIS differ from those that mediate binding to the L30 pre-mRNA. Our results provide insight into how L7Ae family members recognize their cognate RNAs.

Project description:BACKGROUND:The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites. METHODS:In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes. RESULTS:We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme). In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures. CONCLUSIONS:A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.

Dataset Information

Identification of a signature motif for the eIF4a3-SECIS interaction.

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Identification of a signature motif for the eIF4a3-SECIS interaction.

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