Interplay between m6A modification and overall transcripts quantity: Impacts on mRNA composition in plant stress granules
Kubiak, D. J.; Szczesniak, M. W.; Ostrowska, K.; Bielewicz, D.; Bhat, S. S.; Niedojadlo, K.; Szweykowska-Kulinska, Z.; Jarmolowski, A.; Fray, R. G.; Niedojadlo, J.
Show abstract
Stress granules (SGs) are cytoplasmic structures that emerge in response to unfavorable environmental conditions. They contain a rich pool of RNA, including non-translated mRNA. The mechanisms governing transcripts accumulation in SGs is only partially understood. Despite the recognized role of m6A in plant transcriptome regulation, its impact on SGs composition and assembly remains elusive. We examined the formation of SGs, the presence of m6A, and the transcription-level-dependent localization of selected mRNAs within these granules during hypoxia in the roots of Lupinus angustifolius and Arabidopsis thaliana. In lupine, SGs exhibit a distinctive bi-zonal structure, comprising of a ring and a central area with differences in ultrastructure and composition. Following the transcriptome analysis, mRNAs were selected for examination of their localization in SGs and m6A levels. Transcripts from genes responsive to hypoxia (ADH1 and HUP7) exhibited significant lower levels of m6A compared to housekeeping genes but only ADH1 was not present in SGs. HUP7 mRNA with low quantity of m6A, is present both in the SGs and cytoplasm probably due to extremely high expression level. It was also shown that the amount of m6A in SGs was higher than in the cytoplasm only in the first hours of hypoxia and then decreased. In mutants of A. thaliana with reduced level of m6A, formation and quantity of SGs were studied. In this line, ECT2 was not observed and poly(A) RNA levels were slightly reduced in SGs. Additionally the number of SGs was lower than that of the wild type. In summary, our findings demonstrate the limited impact of m6A modification on SGs assembly. However the interplay between m6A modification and the overall transcript quantity in the cytoplasm plays a regulatory role in mRNA partitioning into SGs.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- FRIENDLY (FMT) is an RNA binding protein associated with cytosolic ribosomes at the mitochondrial surface 95%
- Phellem translational landscape throughout secondary development in Arabidopsis roots 95%
- WHIRLY1 regulates aliphatic glucosinolate biosynthesis in early seedling development of Arabidopsis 95%
Similar papers in this journal
- Elucidating the unknown transcriptional responses and PHR1 mediated biotic and abiotic stress tolerance during phosphorus-limitation 95%
- Differential degradation of RNA species by autophagy related pathways in plants 95%
- Arabidopsis thaliana Iron Superoxide Dismutase FeSOD1 Protects ARGONAUTE 1 in a Copper-Dependent Manner 94%
Similar papers in this journal
- Epitranscriptome changes triggered by ammonium nutrition regulate the proteome response of maritime pine roots 96%
- Exploring the miRNA-mediated response to combined stresses in melon plants 95%
- Red light controls adventitious root regeneration by modulating hormone homeostasis in Picea abies seedlings 94%
Similar papers in this journal
- Auxin-responsive (phospho)proteome analysis reveals regulation of cell cycle and ethylene signaling during rice crown root development 94%
- Nucleotide limitation results in impaired photosynthesis, reduced growth and seed yield together with massively altered gene expression 94%
- Multifaceted roles of rice ABA/stress-induced intrinsically disordered proteins in augmenting drought resistance 93%
Similar papers in this journal
- 2,3-cAMP treatment mimics stress molecular response in Arabidopsis thaliana 96%
- Alternative 3' UTRs contributes to post-transcriptional gene expression regulation under high salt stress 95%
- Regulation of CYP94B1 by WRKY33 controls apoplastic barrier formation in the roots leading to salt tolerance 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.