Nina Ripin

Stress granules are RNA–protein condensates that form in response to an increase in untranslating mRNPs (messenger ribonucleoproteins). Stress granules form by the condensation of mRNPs through a combination of protein–protein, protein–RNA, and RNA–RNA interactions. Several reports have suggested that G-rich RNA sequences capable of forming G-quadruplexes (rG4s) promote stress granule formation. Here, we provide three observations arguing that G-tracts do not promote messenger RNA (mRNA) accumulation in stress granules in human osteosarcoma cells. First, we observed no difference in the accumulation in stress granules of reporter mRNAs with and without G-tracts in their 3′ UTRs. Second, in U-2 OS cell lines with reduced expression of DHX36, which is thought to unwind G-quadruplexes, the accumulation of endogenous mRNAs was independent of their predicted rG4-forming potential. Third, while mRNAs in stress granules initially appeared to have more rG4 motifs than bulk mRNAs, this effect disappeared when rG4 motif abundance was normalized to mRNA length. However, we observed that in a G3BP1/2 double knockout cell line, which strongly inhibits stress granule formation, reducing DHX36 expression rescued stress granule-like foci formation. This indicates that DHX36 can limit stress granule formation, potentially by unwinding trans-rG4s or limiting other intermolecular RNA–RNA interactions that promote stress granule formation.

Stress granules are RNA-protein condensates that form in response to an increase in untranslating mRNPs. Stress granules form by the condensation of mRNPs through a combination of protein-protein, protein-RNA, and RNA-RNA interactions. Several reports have suggested that G-rich RNA sequences capable of forming G-quadruplexes promote stress granule formation. Here, we provide three observations arguing that G-tracts capable of forming rG4s do not promote mRNAs partitioning into stress granules in human osteosarcoma cells. First, we observed no difference in the accumulation in stress granules of reporter mRNAs with and without G-tracts in their 3' UTRs. Second, in U-2 OS cell lines with reduced DHX36 expression, which is thought to unwind G-quadruplexes, the partitioning of endogenous mRNAs was independent of their predicted rG4-forming potential. Third, while mRNAs in stress granules initially appeared to have a higher probability of forming rG4s than bulk mRNAs, this effect disappeared when rG4 motif abundance was standardized by mRNA length. However, we observe that in a G3BP1/2 double knockout cell line, reducing DHX36 expression rescued stress granule-like foci formation. This indicates that DHX36 can limit stress granule formation, potentially by unwinding trans rG4s, or limiting other intermolecular RNA-RNA interactions that promote stress granule formation.

Stress granules and P-bodies are ribonucleoprotein (RNP) granules that accumulate during the stress response due to the condensation of untranslating mRNPs. Stress granules form in part by intermolecular RNA–RNA interactions and can be limited by components of the RNA chaperone network, which inhibits RNA-driven aggregation. Herein, we demonstrate that the DEAD-box helicase DDX6, a P-body component, can also limit the formation of stress granules, independent of the formation of P-bodies. In an ATPase, RNA-binding dependent manner, DDX6 limits the partitioning of itself and other RNPs into stress granules. When P-bodies are limited, proteins that normally partition between stress granules and P-bodies show increased accumulation within stress granules. Moreover, we show that loss of DDX6, 4E-T, and DCP1A increases P-body docking with stress granules, which depends on CNOT1 and PAT1B. Taken together, these observations identify a new role for DDX6 in limiting stress granules and demonstrate that P-body components can influence stress granule composition and docking with P-bodies.

SUMMARY: Cells respond to cellular stress by forming stress granules, molecular condensates containing non-translating messenger ribonucleoproteins. Stress granules form during chemotherapy and promote cell survival and chemoresistance, although the mechanism of this effect is not understood. We provide several lines of evidence that stress granules enhance cell survival by promoting cellular quiescence. First, we see a correlation between spontaneous stress granule formation and cell-cycle exit under non-stress conditions. Second, cells deficient in proteins required for stress granule formation (G3BP1/2) are less likely to exit the cell cycle under non-stress, stress, and chemotherapeutic conditions. Third, rescuing stress granule formation in G3BP1/2 knockout cells restores the fraction of cycling cells to wild-type levels. Finally, cells with enhanced stress granule formation (ddx6 knockout cells) show an increased propensity to exit the cell cycle. These results suggest that stress granules are important regulators of cellular quiescence, which could enable the identification of new anti-chemoresistance therapies that target stress granules.

Ribonucleoprotein granules are ubiquitous features of eukaryotic cells. Several observations argue that the formation of at least some RNP granules can be considered analogous to the formation of unfolded protein aggregates. First, unfolded protein aggregates form from the exposure of promiscuous protein interaction surfaces, while some mRNP granules form, at least in part, by promiscuous intermolecular RNA–RNA interactions due to exposed RNA surfaces when mRNAs are not engaged with ribosomes. Second, analogous to the role of protein chaperones in preventing misfolded protein aggregation, cells contain abundant “RNA chaperones” to limit inappropriate RNA–RNA interactions and prevent mRNP granule formation. Third, analogous to the role of protein aggregates in diseases, situations where RNA aggregation exceeds the capacity of RNA chaperones to disaggregate RNAs may contribute to human disease. Understanding that RNP granules can be considered as promiscuous, reversible RNA aggregation events allow insight into their composition and how cells have evolved functions for RNP granules.

Antiproliferative BTG/Tob proteins interact directly with the CAF1 deadenylase subunit of the CCR4-NOT complex. This binding requires the presence of two conserved motifs, boxA and boxB, characteristic of the BTG/Tob APRO domain. Consistently, these proteins were shown to stimulate mRNA deadenylation and decay in several instances. Two members of the family, BTG1 and BTG2, were reported further to associate with the protein arginine methyltransferase PRMT1 through a motif, boxC, conserved only in this subset of proteins. We recently demonstrated that BTG1 and BTG2 also contact the first RRM domain of the cytoplasmic poly(A) binding protein PABPC1. To decipher the mode of interaction of BTG1 and BTG2 with partners, we performed nuclear magnetic resonance experiments as well as mutational and biochemical analyses. Our data demonstrate that, in the context of an APRO domain, the boxC motif is necessary and sufficient to allow interaction with PABPC1 but, unexpectedly, that it is not required for BTG2 association with PRMT1. We show further that the presence of a boxC motif in an APRO domain endows it with the ability to stimulate deadenylation in cellulo and in vitro. Overall, our results identify the molecular interface allowing BTG1 and BTG2 to activate deadenylation, a process recently shown to be necessary for maintaining T-cell quiescence. GRAPHICAL ABSTRACT

The protocol describes how to setup and analyse observation of a co-transcriptional RNA folding network by Systems NMR approach. While most experimental approaches can monitor only a single molecule class or reaction type at a time, Systems NMR permits single-sample dynamic quantification of entire “heterotypic” networks – involving different reaction and molecule types. It thus provides a deeper systems-level understanding of biological network dynamics by combining the dynamic resolution of biochemical assays and the multiplexing ability of “omics”. This particular protocol describes the reconstruction of an 8-reaction co-transcriptional network - with simultaneous monitoring of RNA, metabolite, and proteins in a single sample at the same time. From reactions side, the protocol simultaneously quantifies RNA transcription, RNA folding and RNA-protein interactions (observed both from RNA and from protein side) and few other auxiliary reactions. In addition to fundamental analyses of reaction constants under different conditions, the current applications of this particular reconstruction are: (1) map RNA-binding interfaces on proteins without having to purify/order the RNA; (2) monitor co-transcriptional RNA folding perturbations by proteins and small molecules; (3) monitor RNA-transcription-driven protein phase-separation with the possibility to observe multiple proteins at once, each with residue-level resolution. Not counting the protein and RNA template preparation times, the NMR measurement and data analysis parts take about 1 day each. This protocol accompanies Nikolaev et al, Nature Methods, 2019 (doi:XXX). The most up-to-date version of the protocol (including example code and data) is available at: github.com/systemsnmr/ivtnmr

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cellular behavior is controlled by the interplay of diverse biomolecules. Most experimental methods, however, can only monitor a single molecule class or reaction type at a time. We developed an in vitro nuclear magnetic resonance spectroscopy (NMR) approach, which permitted dynamic quantification of an entire 'heterotypic' network-simultaneously monitoring three distinct molecule classes (metabolites, proteins and RNA) and all elementary reaction types (bimolecular interactions, catalysis, unimolecular changes). Focusing on an eight-reaction co-transcriptional RNA folding network, in a single sample we recorded over 35 time points with over 170 observables each, and accurately determined five core reaction constants in multiplex. This reconstruction revealed unexpected cross-talk between the different reactions. We further observed dynamic phase-separation in a system of five distinct RNA-binding domains in the course of the RNA transcription reaction. Our Systems NMR approach provides a deeper understanding of biological network dynamics by combining the dynamic resolution of biochemical assays and the multiplexing ability of 'omics'.