Disrupting upstream translation in mRNAs leads to loss-of-function associated with human disease

Lee, David S. M.; Park, Joseph; Kromer, Andrew; Rader, Daniel J.; Ritchie, Marylyn D.; Ghanem, Louis; Barash, Yoseph

doi:10.1101/2020.09.09.287912

Cited by 3 publications

(2 citation statements)

References 67 publications

(70 reference statements)

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“…Moreover, conservation analysis of the amino acid sequence, Kozak sequence and stop codon suggests that the amino acid sequence of the uORF1 does not have an evolutionarily ancient function, but its translation process itself may be important. This hypothesis is consistent with D. S. M. Lee et al (2021) analysis of widespread genetic variation affecting translated human uORFs. Authors found that human uORFs were not under selection to maintain amino acid identity, but uORF start codons were conserved and under strong selective pressure.…”

Section: Discussionsupporting

confidence: 91%

Upstream ORF frameshift variants in thePAX65ʹUTR cause congenital aniridia

et al. 2021

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Congenital aniridia (AN) is a severe autosomal dominant panocular disorder associated with pathogenic variants in the PAX6 gene. Previously, we performed a molecular genetic study of a large cohort of Russian patients with AN and revealed four noncoding nucleotide variants in the PAX6 5ʹUTR. 14 additional PAX6-5ʹUTR variants were also reported in the literature, but the mechanism of their pathogenicity remained unclear. In the present study, we experimentally analyze five patient-derived PAX6 5ʹUTR-variants: four variants that we identified in Russian patients (c.-128-2delA, c.-125dupG, c.-122dupG, c.-118_-117del) and one previously reported (c.-52+5G>C). We show that the variants lead to a decrease in the protein translation efficiency, while mRNA expression level is not significantly reduced. Two of these variants also affect splicing. Furthermore, we predict and experimentally validate the presence of an evolutionarily conserved small uORF in the PAX6 5ʹUTR.All studied variants lead to the frameshift of the uORF, resulting in its extension.This extended out-of-frame uORF overlaps with the downstream CDS and thereby reduces its translation efficiency. We conclude that the uORF frameshift may be the main mechanism of pathogenicity for at least 15 out of 18 known PAX6 5ʹUTR variants. Moreover, we predict additional uORFs in the PAX6 5ʹUTR.

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Section: Discussionsupporting

confidence: 91%

Upstream ORF frameshift variants in thePAX65ʹUTR cause congenital aniridia

et al. 2021

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“…In providing the first, to our knowledge, high-throughput functional analysis of natural uORF regulatory activities, our work defines the range of uORF activity and reveals location is as important as Kozak strength in determining yeast uORF functions. Recent work has also highlighted the large number of human uORFs ( Barbosa et al, 2013 ; Calvo et al, 2009 ; Lee et al, 2020 ; McGillivray et al, 2018 ; Wethmar et al, 2010 ). While many aspects of translation initiation are deeply conserved, the mechanisms involved in transcript leader scanning may differ substantially in human cells and tissues.…”

Section: Discussionmentioning

confidence: 99%

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

May

Akirtava

Agar-Johnson

et al. 2023

eLife

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Upstream open reading frames (uORFs) are potent cis-acting regulators of mRNA translation and nonsense-mediated decay (NMD). While both AUG- and non-AUG initiated uORFs are ubiquitous in ribosome profiling studies, few uORFs have been experimentally tested. Consequently, the relative influences of sequence, structural, and positional features on uORF activity have not been determined. We quantified thousands of yeast uORFs using massively parallel reporter assays in wildtype and ∆upf1 yeast. While nearly all AUG uORFs were robust repressors, most non-AUG uORFs had relatively weak impacts on expression. Machine learning regression modeling revealed that both uORF sequences and locations within transcript leaders predict their effect on gene expression. Indeed, alternative transcription start sites highly influenced uORF activity. These results define the scope of natural uORF activity, identify features associated with translational repression and NMD, and suggest that the locations of uORFs in transcript leaders are nearly as predictive as uORF sequences.

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

May¹,

Akirtava²,

Agar-Johnson³

et al. 2021

Preprint

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Upstream open reading frames (uORFs) are potent cis-acting regulators of mRNA translation and nonsense-mediated decay (NMD). While both AUG- and non-AUG initiated uORFs are ubiquitous in ribosome profiling studies, few uORFs have been experimentally tested. Consequently, the relative influences of sequence, structural, and positional features on uORF activity have not been determined. We quantified thousands of yeast uORFs using massively parallel reporter assays in wildtype and ∆upf1 yeast. While nearly all AUG uORFs were robust repressors, most non-AUG uORFs had relatively weak impacts on expression. Machine learning regression modeling revealed that uORF functions are strongly impacted by both their sequences and locations within transcript leaders. Indeed, alternative transcription start sites highly influenced uORF activity. These results define the scope of natural uORF activity, identify features associated with translational repression and NMD, and suggest that the locations of uORFs in transcript leaders are nearly as important as uORF sequences.

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Disrupting upstream translation in mRNAs leads to loss-of-function associated with human disease

Cited by 3 publications

References 67 publications

Upstream ORF frameshift variants in thePAX65ʹUTR cause congenital aniridia

Upstream ORF frameshift variants in thePAX65ʹUTR cause congenital aniridia

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

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

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