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Domain I of ribosomal protein L1 is sufficient for specific RNA binding.

ABSTRACT: Ribosomal protein L1 has a dual function as a ribosomal protein binding 23S rRNA and as a translational repressor binding its mRNA. L1 is a two-domain protein with N- and C-termini located in domain I. Earlier it was shown that L1 interacts with the same targets on both rRNA and mRNA mainly through domain I. We have suggested that domain I is necessary and sufficient for specific RNA-binding by L1. To test this hypothesis, a truncation mutant of L1 from Thermus thermophilus, representing domain I, was constructed by deletion of the central part of the L1 sequence, which corresponds to domain II. It was shown that the isolated domain I forms stable complexes with specific fragments of both rRNA and mRNA. The crystal structure of the isolated domain I was determined and compared with the structure of this domain within the intact protein L1. This comparison revealed a close similarity of both structures. Our results confirm our suggestion that in protein L1 its domain I alone is sufficient for specific RNA binding, whereas domain II stabilizes the L1-rRNA complex.

SUBMITTER: Tishchenko S

PROVIDER: S-EPMC2175363 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Domain I of ribosomal protein L1 is sufficient for specific RNA binding.

Tishchenko Svetlana S Nikonova Ekaterina E Kljashtorny Vladislav V Kostareva Olga O Nevskaya Natalia N Piendl Wolfgang W Davydova Natalia N Streltsov Victor V Garber Maria M Nikonov Stanislav S

Nucleic acids research 20071025 21

Ribosomal protein L1 has a dual function as a ribosomal protein binding 23S rRNA and as a translational repressor binding its mRNA. L1 is a two-domain protein with N- and C-termini located in domain I. Earlier it was shown that L1 interacts with the same targets on both rRNA and mRNA mainly through domain I. We have suggested that domain I is necessary and sufficient for specific RNA-binding by L1. To test this hypothesis, a truncation mutant of L1 from Thermus thermophilus, representing domain ...[more]

PMID: 17962298

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Project description:BackgroundRibosomal L1 domain-containing protein 1 (RSL1D1) is a nucleolar protein that is essential in cell proliferation. In the current opinion, RSL1D1 translocates to the nucleoplasm under nucleolar stress and inhibits the E3 ligase activity of HDM2 via direct interaction, thereby leading to stabilization of p53.MethodsGene knockdown was achieved in HCT116p53+/+, HCT116p53-/-, and HCT-8 human colorectal cancer (CRC) cells by siRNA transfection. A lentiviral expression system was used to establish cell strains overexpressing genes of interest. The mRNA and protein levels in cells were evaluated by qRT-PCR and western blot analyses. Cell proliferation, cell cycle, and cell apoptosis were determined by MTT, PI staining, and Annexin V-FITC/PI double staining assays, respectively. The level of ubiquitinated p53 protein was assessed by IP. The protein-RNA interaction was investigated by RIP. The subcellular localization of proteins of interest was determined by IFA. Protein-protein interaction was investigated by GST-pulldown, BiFC, and co-IP assays. The therapeutic efficacy of RSL1D1 silencing on tumor growth was evaluated in HCT116 tumor-bearing nude mice.ResultsRSL1D1 distributed throughout the nucleus in human CRC cells. Silencing of RSL1D1 gene induced cell cycle arrest at G1/S and cell apoptosis in a p53-dependent manner. RSL1D1 directly interacted with and recruited p53 to HDM2 to form a ternary RSL1D1/HDM2/p53 protein complex and thereby enhanced p53 ubiquitination and degradation, leading to a decrease in the protein level of p53. Destruction of the ternary complex increased the level of p53 protein. RSL1D1 also indirectly decreased the protein level of p53 by stabilizing HDM2 mRNA. Consequently, the negative regulation of p53 by RSL1D1 facilitated cell proliferation and survival and downregulation of RSL1D1 remarkably inhibited the growth of HCT116p53+/+ tumors in a nude mouse model.ConclusionWe report, for the first time, that RSL1D1 is a novel negative regulator of p53 in human CRC cells and more importantly, a potential molecular target for anticancer drug development.

Project description:Precise identification of target sites of RNA-binding proteins (RBP) is important to understand their biochemical and cellular functions. A large amount of experimental data is generated by in vivo and in vitro approaches. The binding preferences determined from these platforms share similar patterns but there are discernable differences between these datasets. Computational methods trained on one dataset do not always work well on another dataset. To address this problem which resembles the classic "domain shift" in deep learning, we adopted the adversarial domain adaptation (ADDA) technique and developed a framework (RBP-ADDA) that can extract RBP binding preferences from an integration of in vivo and vitro datasets. Compared with conventional methods, ADDA has the advantage of working with two input datasets, as it trains the initial neural network for each dataset individually, projects the two datasets onto a feature space, and uses an adversarial framework to derive an optimal network that achieves an optimal discriminative predictive power. In the first step, for each RBP, we include only the in vitro data to pre-train a source network and a task predictor. Next, for the same RBP, we initiate the target network by using the source network and use adversarial domain adaptation to update the target network using both in vitro and in vivo data. These two steps help leverage the in vitro data to improve the prediction on in vivo data, which is typically challenging with a lower signal-to-noise ratio. Finally, to further take the advantage of the fused source and target data, we fine-tune the task predictor using both data. We showed that RBP-ADDA achieved better performance in modeling in vivo RBP binding data than other existing methods as judged by Pearson correlations. It also improved predictive performance on in vitro datasets. We further applied augmentation operations on RBPs with less in vivo data to expand the input data and showed that it can improve prediction performances. Lastly, we explored the predictive interpretability of RBP-ADDA, where we quantified the contribution of the input features by Integrated Gradients and identified nucleotide positions that are important for RBP recognition.

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Domain I of ribosomal protein L1 is sufficient for specific RNA binding.

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Domain I of ribosomal protein L1 is sufficient for specific RNA binding.

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