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Fine-Tuning of mTOR mRNA and Nucleolin Complexes by SMN.

ABSTRACT: Increasing evidence points to the Survival Motor Neuron (SMN) protein as a key determinant of translation pathway. Besides its role in RNA processing and sorting, several works support a critical implication of SMN in ribosome biogenesis. We previously showed that SMN binds ribosomal proteins (RPs) as well as their encoding transcripts, ensuring an appropriate level of locally synthesized RPs. SMN impacts the translation machinery in both neural and non-neural cells, in agreement with the concept that SMN is an essential protein in all cell types. Here, we further assessed the relationship between SMN and translation-related factors in immortalized human fibroblasts. We focused on SMN-nucleolin interaction, keeping in mind that nucleolin is an RNA-binding protein, highly abundant within the nucleolus, that exhibits a central role in ribosomes production. Nucleolin may also affects translation network by binding the mammalian target of rapamycin (mTOR) mRNA and promoting its local synthesis. In this regard, for the first time we provided evidence that SMN protein itself associates with mTOR transcript. Collectively, we found that: (1) SMN coexists with nucleolin-mTOR mRNA complexes at subcellular level; (2) SMN deficiency impairs nucleolar compartmentalization of nucleolin, and (3) this event correlates with the nuclear retention of mTOR mRNA. These findings suggest that SMN may regulate not only structural components of translation machinery, but also their upstream regulating factors.

SUBMITTER: Gabanella F

PROVIDER: S-EPMC8616268 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:To create a dataset of PSMs that does not exhibit tryptic bias, we selected PSMs with a uniform distribution of amino acids at the C-terminal peptide positions from two datasets: MassIVE-KB [Wang2018] and PROSPECT [Shouman2022]. The MassIVE-KB dataset contains 30 million PSMs and consists entirely of data generated using trypsin; hence, only a small proportion of the MassIVE-KB peptides do not end in K or R, corresponding to those that appear at the C-terminus of the corresponding protein. The PROSPECT dataset is a collection of 61 million PSMs generated from synthetic peptides. To avoid overrepresentation of some peptides in this dataset, we randomly selected at most 100 PSMs per unique peptide, similar to the processing that was done during the creation of the MassIVE-KB dataset. This pre-selection step reduced the size of the PROSPECT dataset to 12.6 million PSMs. Finally, to create a non-enzymatic dataset containing 1 million peptides, we selected 50,000 PSMs for each canonical amino acid. These PSMs were selected at random from MassIVE-KB, then supplemented as necessary from PROSPECT to obtain the desired count (see Yilmaz et al. [Yilmaz2023] Supplementary Table S1). This dataset contained PSMs from 247,859 unique peptides, which were randomly split into training, validation and test sets in the ratio 80/10/10. FTP directory contains the mgf files corresponding to train, validation and test splits. [Wang2018] M. Wang, J. Wang, J. Carver, B. Pullman, S. Won Cha, N. Bandeira, "Assembling the Community-Scale Discoverable Human Proteome", Cell Systems, Volume 7, Issue 4, 2018. [Shouman2022] O. Shouman, W. Gabriel, V. Giurcoiu, V. Sternlicht, M. Wilhelm, "PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics", NeurIPS Datasets and Benchmarks, 2022 [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

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Fine-Tuning of mTOR mRNA and Nucleolin Complexes by SMN.

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