Direct RNA sequencing dataset of SMG1 KO mutant Physcomitrella (Physcomitrium patens)

Andrey Knyazev1, Anna Glushkevich1, Igor Fesenko1

  • 1Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 16/10, Ulitsa Miklukho-Maklaya, Moscow, 117997, Russian Federation.

Data in Brief
|December 14, 2020
PubMed

Insights

Nonsense-mediated mRNA decay (NMD) quality control identifies aberrant transcripts. This study used long-read sequencing in a Physcomitrella patens mutant to characterize NMD targets, aiding transcriptome regulation studies.

Area of Science:

  • Molecular Biology
  • Genetics
  • Plant Science

Background:

  • Nonsense-mediated mRNA decay (NMD) is a crucial eukaryotic post-transcriptional quality control pathway.
  • NMD regulates gene expression by degrading aberrant mRNAs, including those with premature termination codons (PTCs).
  • In plants, NMD is vital for development and stress responses, but identifying its direct targets remains challenging.

Purpose of the Study:

  • To characterize the native transcriptome of a Physcomitrella patens mutant lacking SMG1 kinase, a key NMD component.
  • To identify direct targets of the Nonsense-mediated mRNA decay pathway in plants using long-read sequencing.
  • To advance the understanding of NMD-mediated transcriptome regulation in plants.

Main Methods:

  • Long-read sequencing of the native transcriptome from a *smg1* knockout mutant of *Physcomitrella patens* (line 2) using Oxford Nanopore Technology (ONT).
  • RNA isolation using Trizol from 5-day-old protonemata, followed by sequencing with kit SQK-RNA002 on a MinION device.
  • Data processing included basecalling with Guppy v.4.0.15 for three biological replicates.

Main Results:

  • Generation of high-quality native transcriptomes for the *smg1*Δ mutant of *Physcomitrella patens*.
  • Provides a dataset advantageous for identifying and functionally characterizing direct NMD targets.
  • Establishes a foundation for studying NMD pathway regulation in plants.

Conclusions:

  • Long-read sequencing is effective for characterizing transcriptomes and identifying NMD targets.
  • The generated data facilitates the study of mRNA quality control mechanisms in plants.
  • This research contributes to understanding how NMD influences plant development and stress responses.

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