Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning

May, Gemma E.; Akirtava, Christina; Agar-Johnson, Matthew; Micic, Jelena; Woolford, John L.; McManus, C. Joel

doi:10.7554/elife.69611

Cited by 13 publications

(8 citation statements)

References 96 publications

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“…Previous studies reached different conclusions concerning the impact of IF uAUGs on translation [15, 20, 27, 28]. To clarify this, we determined the relationship between the location of OOF and IF uAUGs in the 5’UTR and the translation output of the mRNAs, in both yeast and human sequences, synthetic or endogenous.…”

Section: Resultsmentioning

confidence: 97%

Current limitations in predicting mRNA translation with deep learning models

Schlusser,

González,

Pandey

et al. 2024

Preprint

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Background: The design of nucleotide sequences with defined properties is long-standing problem in bioengineering. An important application is protein expression, be it in the context of research or the production of mRNA vaccines. The rate of protein synthesis depends on the 5,untranslated region (5,UTR) of the mRNAs, and recently, deep learning models were proposed to predict the translation output of mRNAs from the 5,UTR sequence. At the same time, large data sets of endogenous and reporter mRNA translation have become available. Results: In this study we use complementary data obtained in two different cell types to assess the accuracy and generality of currently available models of translation. We find that while performing well on the data sets on which they were trained, deep learning models do not generalize well to other data sets, in particular of endogenous mRNAs, which differ in many properties from reporter constructs. Conclusions: These differences limit the ability of deep learning models to uncover mechanisms of translation control and to predict the impact of genetic variation. We suggest directions that combine high-throughput measurements and machine learning to unravel mechanisms of translation control and improve construct design.

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Section: Resultsmentioning

confidence: 97%

Current limitations in predicting mRNA translation with deep learning models

Schlusser,

González,

Pandey

et al. 2024

Preprint

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“…The observed stimulation of translation by Nsp1 was dependent on both EIF1 and 1A, because depletion of EIF1 lowered and 1A nearly abrogated the Nsp1-mediated increase in translation ( Fig 6G , middle panel). Finally, replacing CUG(59) with AUG to enforce higher translation [ 43 ] of uORF1 strongly inhibited translation of the main ORF. Under these conditions, Nsp1 failed to stimulate translation at any of the concentrations tested ( Fig 6G , right).…”

Section: Resultsmentioning

confidence: 99%

SARS-CoV-2 Nsp1 cooperates with initiation factors EIF1 and 1A to selectively enhance translation of viral RNA

Aviner,

Lidsky,

Xiao

et al. 2024

PLoS Pathog

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A better mechanistic understanding of virus-host dependencies can help reveal vulnerabilities and identify opportunities for therapeutic intervention. Of particular interest are essential interactions that enable production of viral proteins, as those could target an early step in the virus lifecycle. Here, we use subcellular proteomics, ribosome profiling analyses and reporter assays to detect changes in protein synthesis dynamics during SARS-CoV-2 (CoV2) infection. We identify specific translation factors and molecular chaperones that are used by CoV2 to promote the synthesis and maturation of its own proteins. These can be targeted to inhibit infection, without major toxicity to the host. We also find that CoV2 non-structural protein 1 (Nsp1) cooperates with initiation factors EIF1 and 1A to selectively enhance translation of viral RNA. When EIF1/1A are depleted, more ribosomes initiate translation from a conserved upstream CUG start codon found in all genomic and subgenomic viral RNAs. This results in higher translation of an upstream open reading frame (uORF1) and lower translation of the main ORF, altering the stoichiometry of viral proteins and attenuating infection. Replacing the upstream CUG with AUG strongly inhibits translation of the main ORF independently of Nsp1, EIF1, or EIF1A. Taken together, our work describes multiple dependencies of CoV2 on host biosynthetic networks and proposes a model for dosage control of viral proteins through Nsp1-mediated control of translation start site selection.

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“…Previously published research has identified potential uORFs upstream of several genes that are amplified by CNVs in one or more strains, including GAP1 , DAL80 , and UTH1 (Zhang and Dietrich 2005; Cvijović et al 2007; Spealman et al 2018; May et al 2023). Using ribosome profiling data from each strain we manually identified potential uORFs in 49 of the 109 CNV amplified genes.…”

Section: Resultsmentioning

confidence: 99%

“…Because uORFs have been known to play a role in translational regulation we sought to determine if previously identified uORF may be present in our data. We calculated the mean TL length of genes containing either ‘predicted’ uORFs from (Spealman et al 2018) or ‘validated’ uORFs from (May et al 2023). Because of the validation methodology used in (May et al 2023), uORFs greater than 180bp distance from the main ORF TIS were not tested.…”

Section: Methodsmentioning

confidence: 99%

“…We calculated the mean TL length of genes containing either ‘predicted’ uORFs from (Spealman et al 2018) or ‘validated’ uORFs from (May et al 2023). Because of the validation methodology used in (May et al 2023), uORFs greater than 180bp distance from the main ORF TIS were not tested. We calculated the mean length of SGD annotated TLs for each set ( Table S17 ).…”

Section: Methodsmentioning

confidence: 99%

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Post-transcriptional mechanisms modulate the consequences of adaptive copy number variation

Spealman,

de Santana,

et al. 2023

Preprint

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Copy-number variants (CNVs) are large-scale amplifications or deletions of DNA that can drive rapid adaptive evolution and result in large-scale changes in gene expression. Whereas alterations in the copy number of one or more genes within a CNV can confer a selective advantage, other genes within a CNV can decrease fitness when their dosage is changed. Dosage compensation - in which the gene expression output from multiple gene copies is less than expected - is one means by which an organism can mitigate the fitness costs of deleterious gene amplification. Previous research has shown evidence for dosage compensation at both the transcriptional level and at the level of protein expression; however, the extent of compensation differs substantially between genes, strains, and studies. Here, we investigated sources of dosage compensation at multiple levels of gene expression regulation by defining the transcriptome, translatome and proteome of experimentally evolved yeast (Saccharomyces cerevisiae) strains containing adaptive CNVs. We quantified the gene expression output at each step and found evidence of widespread dosage compensation at the protein abundance (~47%) level. By contrast we find only limited evidence for dosage compensation at the transcriptional (~8%) and translational (~3%) level. We also find substantial divergence in the expression of unamplified genes in evolved strains that could be due to either the presence of a CNV or adaptation to the environment. Detailed analysis of 82 amplified and 411 unamplified genes with significantly discrepant relationships between RNA and protein abundances identified enrichment for upstream open reading frames (uORFs). These uORFs are enriched for binding site motifs for SSD1, a RNA binding protein that has previously been associated with tolerance of aneuploidy. Our findings suggest that, in the presence of CNVs, SSD1 may act to alter the expression of specific genes by potentiating uORF mediated translational regulation.

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Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning

Cited by 13 publications

References 96 publications

Current limitations in predicting mRNA translation with deep learning models

Current limitations in predicting mRNA translation with deep learning models

SARS-CoV-2 Nsp1 cooperates with initiation factors EIF1 and 1A to selectively enhance translation of viral RNA

Post-transcriptional mechanisms modulate the consequences of adaptive copy number variation

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