Evidence map›Paper›PMID 39605391›Full record

ArticlebioRxiv : the preprint server for biology2024

Nanopore sequencing of intact aminoacylated tRNAs.

Laura K White, Aleksandar Radakovic, Marcin P Sajek, Kezia Dobson, Kent A Riemondy, Samantha Del Pozo, Jack W Szostak, Jay R Hesselberth

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Laura K WhiteUniversity of Colorado School of Medicine, Department of Biochemistry and Molecular Genetics, RNA Bioscience Initiative, Aurora, Colorado.
Aleksandar RadakovicHarvard Medical School, Department of Genetics, Boston, Massachusetts.
Marcin P SajekUniversity of Colorado School of Medicine, Department of Biochemistry and Molecular Genetics, RNA Bioscience Initiative, Aurora, Colorado.
Kezia DobsonUniversity of Colorado School of Medicine, Department of Biochemistry and Molecular Genetics, RNA Bioscience Initiative, Aurora, Colorado.
Kent A RiemondyUniversity of Colorado School of Medicine, Department of Biochemistry and Molecular Genetics, RNA Bioscience Initiative, Aurora, Colorado.
Samantha Del PozoUniversity of Colorado School of Medicine, Department of Biochemistry and Molecular Genetics, RNA Bioscience Initiative, Aurora, Colorado.
Jack W SzostakHoward Hughes Medical Institute, The University of Chicago, Department of Chemistry, Chicago, Illinois.
Jay R HesselberthUniversity of Colorado School of Medicine, Department of Biochemistry and Molecular Genetics, RNA Bioscience Initiative, Aurora, Colorado.ORCID 0000-0002-6299-179X

Funding

Nanopore analysis of transfer RNA forms and functionsR35GM119550 · NIGMS · UNIVERSITY OF COLORADO DENVER · PI Jay R Hesselberth · 2016 to 2026
$4.5M
Predoctoral Training Program in Molecular and Cellular Biology (Supplement: Mentoring in the Research Environment)T32GM136444 · NIGMS · UNIVERSITY OF COLORADO DENVER · PI MICHAEL A MCMURRAY, Rytis Prekeris · 2020 to 2026
$3.7M
NIGMS NIH HHS R35 GM119550NIGMS NIH HHS T32 GM136444
6 · The paper itself

Abstract

Transfer RNAs (tRNA) are decorated during biogenesis with a variety of modifications that modulate their stability, aminoacylation, and decoding potential during translation. The complex landscape of tRNA modification presents significant analysis challenges and to date no single approach enables the simultaneous measurement of important but disparate chemical properties of individual, mature tRNA molecules. We developed a new, integrated approach to analyze the sequence, modification, and aminoacylation state of tRNA molecules in a high throughput nanopore sequencing experiment, leveraging a chemical ligation that embeds the charged amino acid in an adapted tRNA molecule. During nanopore sequencing, the embedded amino acid generates unique distortions in ionic current and translocation speed, enabling application of machine learning approaches to classify charging status and amino acid identity. Specific applications of the method indicate it will be broadly useful for examining relationships and dependencies between tRNA sequence, modification, and aminoacylation.

Identifiers

PMID39605391
PMCPMC11601438

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.