bioRxivMolecular BiologyPreprint
Cytoplasmic recapping commences the translation of a micropeptide, MARS from MALAT1 after the removal of an inhibitory RNA G-quadruplex
Avik MukherjeeiD, Ritesh Sonar, Padmaja Prasad MishraiD, Shubhra MajumderiD, Chandrama MukherjeeiD
Abstract
Long non-coding RNAs (lncRNAs), have emerged as key regulators of diverse biological processes, including metabolism, development, cellular differentiation, and stress responses. Most lncRNAs are transcribed by RNA polymerase II and undergo canonical RNA processing, including splicing, polyadenylation, and 5' N7-methylguanosine (mG) capping and cytoplasmic recapping. However, whether cytoplasmic recapping of lncRNAs can promote the production of functional micropeptides remains poorly understood. In this study, we investigated the functional consequences of cytoplasmic recapping of MALAT1, a highly abundant and broadly expressed lncRNA. We demonstrate that cytoplasmic processing and recapping of a MALAT1 decay intermediate support the production of a previously uncharacterized micropeptide, designated MARS (MALAT1 Responsive to Stress). We further identify RNA G-quadruplex structures in the 5' region of MALAT1 that inhibit MARS translation, revealing an additional layer of structural regulation. MARS expression increases in response to arsenite-induced oxidative stress and is associated with nuclear factor erythroid 2-related factor 2 (Nrf2)-containing complexes. Furthermore, MARS is implicated in the regulation of antioxidant response element (ARE) associated genes suggesting a potential role in cellular antioxidant defence. This study highlights a mechanism through which cytoplasmic RNA processing may contribute to micropeptide production and suggests a role for MALAT1-derived MARS in oxidative stress-responsive gene regulation.
- organism: human
- assay: bulk RNA-seq
- assay: proteomics
- research focus: cell signaling & trafficking
- research focus: metabolism & bioenergetics
- research focus: development & morphogenesis
- approach: experimental assay
- approach: statistical model
- Citations
- 0
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