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Model-driven design of synthetic N-terminal coding sequences for fine-tuning gene expression in yeast and bacteria

ABSTRACT: N-terminal coding sequences (NCS) are key regulatory elements for fine-tuning gene expression during translation initiation, the rate-limiting step of translation. However, due to complex combinatory effects of NCS biophysical factors and endogenous regulation, designing NCS remains challenging. Herein, we implemented multi-view learning strategy for model-driven generation of synthetic NCS for Saccharomyces cerevisiae and Bacillus subtilis, which are model microorganisms widely used in the laboratory and industry.

ORGANISM(S): Bacillus subtilis Saccharomyces cerevisiae

PROVIDER: GSE186378 | GEO | 2023/10/31

REPOSITORIES: GEO

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Project description:Dendritic cells (DCs) are the sentinels of the mammalian immune system and they undergo a complex maturation process mediated by activation upon pathogen detection. Recent studies described the analysis of activated DCs by transcriptional profiling, but translation regulation was never taken in account. Therefore, the nature of the mRNAs being translated at various stages of DC activation was determined with the help of translational profiling, which is the sucrose gradient fractionation of polysomal-bound mRNAs combined to microarrays analysis. Total and polysomal-bound mRNA populations were compared in immature (0h) and LPS-stimulated (4h and 16h) human monocyte-derived DCs with the help of Affymetrix microarrays. Biostatistical analysis indicated that 296 mRNA molecules are translationally regulated during DC-activation. The most abundant biological process among the regulated mRNAs was protein biosynthesis, indicating the existence of a negative feedback loop regulating translation. Interestingly, a cluster of 17 ribosomal proteins were part of the regulated mRNAs, indicating that translation may be fine-tuned by particular components of the translational machinery. Our observations highlight the importance of translation regulation during the immune response, and may favour the identification of novel gene clusters or protein networks relevant for immunity. Our study also provides information on the possible absence of correlation between gene expression and real protein production in DCs. To identify translationally regulated mRNA molecules, gene expression derived from the polysome-bound mRNAs was compared by Affymetrix microarrays analysis to the gene expression derived from unfractionated total mRNAs derived from whole-cell lysates, as recently described on several reports (Johannes 1999, Rajasekhar 2003, Bushell 2006, Lü 2006, Parent 2008). Polysomal RNA (P) and total RNA (T) were isolated from MoDCs generated from four different blood donors. Since three timepoints (0h, 4h and 16h) were chosen for each blood donor and RNA type, twenty-four RNA samples were totally analyzed by microarrays.

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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Model-driven design of synthetic N-terminal coding sequences for fine-tuning gene expression in yeast and bacteria

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