Evidence map›Paper›PMID 39636683›Full record

ArticleeLife2024

Minimal twister sister-like self-cleaving ribozymes in the human genome revealed by deep mutational scanning.

Zhe Zhang, Xu Hong, Peng Xiong, Junfeng Wang, Yaoqi Zhou, Jian Zhan

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zhe ZhangInstitute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China.ORCID https://orcid.org/0000-0001-7783-7683
Xu HongInstitute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China.
Peng XiongUniversity of Science and Technology of China, Hefei, China.
Junfeng WangHigh Magnetic Field Laboratory, Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Yaoqi ZhouInstitute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China.ORCID https://orcid.org/0000-0002-9958-5699
Jian ZhanInstitute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China.

Funding

Australian Research Council DP180102060Australian Research Council DP210101875China Postdoctoral Science Foundation 2021M702295National Health and Medical Research Council 1121629National Natural Science Foundation of China 22350710182
6 · The paper itself

Abstract

Despite their importance in a wide range of living organisms, self-cleaving ribozymes in the human genome are few and poorly studied. Here, we performed deep mutational scanning and covariance analysis of two previously proposed self-cleaving ribozymes (LINE-1 and OR4K15). We found that the regions essential for ribozyme activities are made of two short segments, with a total of 35 and 31 nucleotides only. The discovery makes them the simplest known self-cleaving ribozymes. Moreover, the essential regions are circular permutated with two nearly identical catalytic internal loops, supported by two stems of different lengths. These two self-cleaving ribozymes, which are shaped like lanterns, are similar to the catalytic regions of the twister sister ribozymes in terms of sequence and secondary structure. However, the nucleotides at the cleavage site have shown that mutational effects on two twister sister-like (TS-like) ribozymes are different from the twister sister ribozyme. The discovery of TS-like ribozymes reveals a ribozyme class with the simplest and, perhaps, the most primitive structure needed for self-cleavage.

Indexed as

Genome, HumanMutationNucleic Acid ConformationRNA, CatalyticHumansRNA, Catalyticbiochemistrycatalytic RNAchemical biologycovariationdeep mutational scanninghumanRNA structureself-cleaving ribozyme

Identifiers

PMID39636683
PMCPMC11620745

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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.