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Proceedings of the National Academy of Sciences of the United States of America logoLink to Proceedings of the National Academy of Sciences of the United States of America
. 2018 Feb 26;115(11):2734–2739. doi: 10.1073/pnas.1800038115

RNA self-assembly contributes to stress granule formation and defining the stress granule transcriptome

Briana Van Treeck a, David S W Protter a, Tyler Matheny a, Anthony Khong a,b, Christopher D Link c, Roy Parker a,b,1
PMCID: PMC5856561  PMID: 29483269

Significance

Stress granules, which are ubiquitous, non–membrane-bound assemblies of protein and RNA, form when translation initiation is inhibited, contribute to the regulation of gene expression, and are implicated in the pathologies of cancer and neurodegenerative disease. Understanding the mechanisms of stress granule assembly is crucial to gaining greater insight into their biological function and pathological misregulation. We provide evidence that RNA–RNA interactions contribute to the assembly of stress granules. Furthermore, we show that pathogenic dipeptides increase the propensity of RNA to assemble. Together, this argues that RNAs are assembly prone and must be carefully regulated. A summative model of stress granule assembly, which includes trans-RNA–RNA interactions, can be extended to other ribonucleoprotein granules in the cell.

Keywords: RNP granules, dipeptides, RNA self-assembly, stress granules

Abstract

Stress granules are higher order assemblies of nontranslating mRNAs and proteins that form when translation initiation is inhibited. Stress granules are thought to form by protein–protein interactions of RNA-binding proteins. We demonstrate RNA homopolymers or purified cellular RNA forms assemblies in vitro analogous to stress granules. Remarkably, under conditions representative of an intracellular stress response, the mRNAs enriched in assemblies from total yeast RNA largely recapitulate the stress granule transcriptome. We suggest stress granules are formed by a summation of protein–protein and RNA–RNA interactions, with RNA self-assembly likely to contribute to other RNP assemblies wherever there is a high local concentration of RNA. RNA assembly in vitro is also increased by GR and PR dipeptide repeats, which are known to increase stress granule formation in cells. Since GR and PR dipeptides are involved in neurodegenerative diseases, this suggests that perturbations increasing RNA–RNA assembly in cells could lead to disease.

Ribonucleoprotein (RNP) granules are eukaryotic non–membrane-bound organelles composed of RNA and protein. RNP granules are located in the cytoplasm or the nucleus and include P-bodies, germ granules, neuronal granules, paraspeckles, Cajal bodies, and stress granules (1–6). Stress granules are cytoplasmic assemblies of nontranslating mRNAs and RNA-binding proteins that form during stress responses where bulk translation initiation is inhibited.

Stress granules are of interest for four reasons. First, stress granules are thought to play a role in the stress response and gene regulation (7, 8). Second, related RNP granules exist in neurons and can affect synaptic plasticity (9, 10). Third, mutations in RNA-binding proteins, or stress granule-remodeling complexes, which lead to constitutive or increased stress granule formation, are causative in amyotrophic lateral sclerosis, frontotemporal lobar degeneration, inclusion body myopathy, and other degenerative disorders (11–13). Fourth, stress granule formation can influence both tumor progression and viral infection (14–17).

Stress granules, as well as other RNP granules, are thought to form by RNA providing a scaffold for RNA-binding proteins that form protein–protein interactions in trans to drive assembly. For example, mammalian stress granule assembly is promoted by interactions of the TIA1, G3BP1, and ATXN2 proteins (18–20). Similarly, assembly of yeast P-bodies is promoted by the dimerization of the RNA-binding protein Edc3, as well as an intrinsically disordered region (IDR) on Lsm4p (21). Consistent with interactions between RNA-binding proteins promoting RNP granule assembly, several purified RNA-binding proteins, in isolation or with RNA, can form self-assemblies in vitro as coacervates, fibers, or hydrogels, although the specific relationship of these in vitro assemblies to RNP granules remains unclear (22–27). The role of trans-RNA–RNA interactions in stress granule formation remains relatively unexplored.

Herein, we illustrate that RNA–RNA interactions contribute to the assembly of stress granules and the targeting of RNAs to stress granules. We show that trans-RNA–RNA interactions play a role in the recruitment of given RNAs to stress granules. We find that various RNAs are capable of self-interactions, even in the absence of Watson–Crick base pairing. More importantly, we show that mixtures of cellular RNAs partition into self-assemblies in vitro under physiologically relevant conditions and that the RNAs enriched in these assemblies reflect the RNAs enriched in stress granules in vivo. This argues that RNA–RNA interactions contribute to both stress granule assembly and to the determination of which RNAs are enriched in stress granules. Strikingly, we also observed that RNA self-assembly is promoted by the pathogenic dipeptide repeats GR and PR. This work highlights that RNPs exchange between monomeric and multimeric states within the cell and that the cell utilizes a variety of mechanisms to regulate this equilibrium.

Results

Four points led us to hypothesize that RNA–RNA interactions in trans might contribute to stress granule formation. First, stress granules form when there is ribosome run-off, exposing the previously ribosome-occupied coding regions that would be expected to form RNA–RNA interactions both in cis and in trans. Second, long mRNAs partition highly into stress granules but only show a modest increase in the binding sites of stress granule proteins, suggesting length might contribute to the partitioning of mRNAs into stress granules through trans-RNA–RNA interactions (28). Third, stress granule cores in lysates are resistant to high salt or aliphatic alcohols, which are known to disrupt many protein–protein, but not RNA–RNA, interactions (29). Stress granule cores have been suggested to be independent of RNA based on their resistance to RNase treatment (29), but a reexamination of this observation showed that extensive RNase treatment fails to degrade the RNA within stress granule cores (Fig. S1). Fourth, trans-RNA–RNA interactions contribute to the assembly of multimeric RNPs in Drosophila embryos, as well as RNA foci containing transcripts with repeat expansions (30, 31).

Additional evidence that RNA–RNA interactions could contribute to stress granule assembly came from the partitioning of noncoding RNAs (ncRNAs) into stress granules in mammalian cells (28). By data-mining the mammalian stress granule transcriptome (28), we observed that ncRNAs shorter than 3,000 bases were generally depleted from stress granules, as predicted by length (Fig. 1A). However, antisense ncRNAs shorter than 3,000 bases showed a bimodal distribution, with one population enriched in stress granules and one depleted (Fig. 1B). Of the 14 antisense ncRNAs significantly enriched greater than twofold in stress granules and with fragments per kilobase of transcript per million mapped reads (FPKM) values above 1 in total RNA samples, 10 were antisense to long mRNAs that were enriched in stress granules (Fig. 1D), five of which are illustrated (Fig. 1F). This is striking, since only 14.5% of mRNAs in the cell are enriched in stress granules (Fig. 1C). In contrast, of the 23 antisense ncRNAs significantly depleted greater than twofold from stress granules and with FPKM values above 1 in total RNA samples, only one had an antisense partner that was enriched in stress granules (Fig. 1E). Thus, there is a correlation between the localization of antisense ncRNAs and their ability to base-pair to longer mRNAs, implying that RNA–RNA interactions may partition specific RNAs into stress granules.

Fig. 1.

Fig. 1.

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RNA–RNA base-pairing influences RNA localization. All ncRNAs (excluding antisense) (A) and antisense ncRNAs (B) with lengths <3,000 nt and significant sequencing reads were plotted in a histogram showing their log2(fold change) enrichment in stress granule/total RNA. A red bracket highlights the second peak for RNAs enriched in stress granules. (C) Pie chart illustrating the proportion of all mammalian mRNAs enriched (red), depleted (blue), or neither (gray) in stress granules. Stress granule localization of binding partners to enriched (D) and depleted (E) antisense ncRNAs is shown. *One enriched antisense ncRNA transcript has a secondary binding partner to a depleted mRNA. (F) Examples of enriched antisense ncRNAs and their binding partners. SG, stress granule.

To determine if RNA–RNA interactions could contribute to stress granule formation, we calculated the approximate concentration of the coding region of mRNAs exposed during polysome collapse. For yeast, we estimated the concentration of exposed coding regions to be between 170 and 800 μg/mL (Materials and Methods), while in the mammalian U-2 OS cell line, the exposed ORFs were estimated at ∼180 μg/mL (Materials and Methods). If RNA–RNA interactions contribute to stress granule formation, then we predicted that RNA at these concentrations would spontaneously assemble under conditions mimicking the intracellular milieu.

To test this prediction, we assessed whether purified protein-free total RNA from Saccharomyces cerevisiae at 150 μg/mL would form assemblies under conditions where we varied the salt and used PEG to mimic molecular crowding. We observed that total yeast RNA readily self-assembled and formed two types of assemblies (Fig. 2A). At higher PEG and lower salt, the RNA was observed to form small droplets (Fig. 2 A, a). With increasing salt, we observed the formation of more amorphous assemblies that contained a larger percentage of the RNA, which, due to their morphology, we refer to as RNA tangles (Fig. 2 A, b and c and Fig. S2 A and B). Based on fluorescence recovery after photobleaching (FRAP) of spiked-in fluorescent RNAs or dilution into lower ionic strength, droplets were more dynamic and less stable than RNA tangles (Fig. S2 C and D). Specifically, 97% of the RNA in tangles formed at high salt is immobile (Fig. S2C), and dilution takes nearly an hour to disrupt tangles, whereas droplets disperse on the order of seconds (Fig. S2D). Importantly, both droplets and tangles were enriched for the RNA-specific dye SYTO RNASelect (Fig. S2E) and depleted for PEG (Fig. S2F). This is consistent with PEG functioning primarily as a crowding agent, which is further supported by Ficoll-promoting RNA self-assembly (Fig. S2G). Moreover, in the absence of crowding agents, RNA also self-assembled under physiological concentrations of salt (150 mM) and the polyamines spermine (223 μM) and spermidine (1,339 μM), which are known to stabilize RNA–RNA interactions (32) (Fig. 2B). Thus, under in vitro conditions analogous to the cytosol during a stress response, purified RNA undergoes self-assembly.

Fig. 2.

Fig. 2.

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Various RNAs self-assemble in vitro. (A) Phase diagram of RNA assembly morphology under varying PEG and NaCl concentrations. Images correspond to labeled positions in the phase diagram: Droplets formed at 0 mM NaCl, 10% PEG (a); droplet/tangles formed at 300 mM NaCl, 7.5% PEG (b); tangles formed at 750 mM NaCl, 10% PEG (c); and no assemblies at 300 mM NaCl, 2.5% PEG (d). All conditions contain 1 mM MgCl2 and 150 μg/mL yeast total RNA. (B) Total yeast RNA in 150 mM NaCl and 1 mM MgCl2 with and without physiologically relevant conditions of spermine (223 μM) and spermidine (1,339 μM). (C) polyU, polyC, polyA, and polyG self-partition in vitro at 500 μg/mL (respective) homopolymers, 10% PEG, and 750 mM NaCl.

Stress granules recruit a diversity of RNA-binding proteins in addition to RNA. Similarly, we observed that RNA self-assemblies recruited RNA-binding proteins (Fig. S3). Specifically, when fused to GFP, the IDRs of yeast Lsm4 and eIF4GII, both of which bind RNA (24), are recruited to RNA droplets more than the IDR of FUS or GFP alone (Fig. S3 A and B). Similarly, RNA droplets recruit the RNA-binding protein hnRNPA1 (Fig. S3C). Thus, assemblies formed by RNA interactions in cells would be expected to recruit RNA-binding proteins.

RNA–RNA interactions contributing to RNA self-assemblies could be helical stacking (33), specific base-pairing (31), or promiscuous interactions between mRNA involving both traditional Watson–Crick interactions and additional interactions (34). Evidence that non-Watson–Crick interactions can promote RNA self-assembly is that all four homopolymers self-assemble, with polyU forming rapidly relaxing droplets [as previously observed (32)], polyC forming slower relaxing droplets, polyA forming asymmetrical assemblies with very slow relaxation rates, and polyG forming an aggregate that is presumably based on G-quadruplexes (Fig. 2C and Fig. S4). Thus, RNA sequences may impart biophysical characteristics to their assemblies, but, more importantly, RNA self-assembly is a general property of diverse RNAs and is not restricted to Watson–Crick interactions.

If the self-assembly of RNA in vitro is relevant to stress granule assembly, we predicted that similar mRNAs would assemble into RNA droplets in vitro and stress granules in vivo. To test this prediction, we purified RNA droplets formed under physiological salt (150 mM NaCl) and PEG from total yeast RNA in triplicate, sequenced the assembled RNA, and compared that RNA population with the mRNAs that partition into stress granules (28). This experiment revealed several important observations.

First, triplicates of in vitro assemblies and total RNA agreed within themselves but showed clear differences (Fig. S5A), allowing the identification of both enriched and depleted RNAs in the in vitro assemblies (Fig. 3A). RNAs significantly (P < 0.01) and twofold enriched and depleted under these conditions will be referred to throughout the remainder of this paper as in vitro-enriched and -depleted, respectively. On average, the in vitro-enriched RNAs were significantly longer, while the in vitro-depleted RNAs were shorter (Fig. 3B). This closely mirrored the length effect seen in the analysis of the yeast stress granule transcriptome (28) (Fig. 3C).

Fig. 3.

Fig. 3.

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RNAs in self-assemblies in vitro largely recapitulate the stress granule transcriptome. (A) RNAs from in vitro assemblies formed at 150 mM NaCl identified as significantly (P < 0.01) and twofold enriched (1,488 RNAs, red dots) and depleted (1,456 RNAs, blue dots) compared to total yeast RNA. The box plots show the correlation between transcript length and RNA localization to in vitro assemblies (B) and yeast stress granules (C) (28). ***P < 0.001 between any three box plots. (D) In vitro-enriched mRNAs significantly overlap with mRNAs identified in the yeast stress granule transcriptome and exhibit significant lack of overlap with RNAs depleted from stress granules. n.s., not significant. (E) In vitro-depleted RNAs significantly overlap with RNAs depleted from stress granules and exhibit a significant lack of overlap with RNAs identified in the yeast stress granule transcriptome. (F) Degree of enrichment and depletion between in vitro assemblies and stress granules correlates (Pearson’s correlation, R = 0.53). RNAs that are more enriched in vitro than in vivo (Upper Left) tend to have greater proportions of optimal codons (orange).

A second key observation was a strong overlap between in vitro- and stress granule-enriched mRNAs (Fig. 3D and Fig. S5B). Specifically, of the 916 mRNAs enriched in yeast stress granules (28), 634 are enriched in vitro. Conversely, of the 1,111 mRNAs depleted from yeast stress granules (28), only 56 are enriched in vitro. This correlation extends to RNAs depleted from in vitro assemblies and stress granules in vivo (Fig. 3E and Fig. S5C). Of the 1,456 mRNAs depleted from in vitro assemblies, only 10 are enriched in stress granules, and of the 1,111 mRNAs depleted from stress granules, 573 are also depleted from the RNA self-assembly in vitro. This indicates that the biophysical properties of RNA that drive RNA self-assembly in vitro correlate with a critical mRNA feature for partitioning mRNAs into stress granules in vivo.

Plotting the relative enrichment of RNAs in in vitro assemblies vs. in stress granules shows a correlation in the degree of enrichment in both cases, with a Pearson’s correlation of 0.53 (Fig. 3F). In general, RNAs more enriched in stress granules are also more enriched in assemblies in vitro, and vice versa. The RNAs that show less correlation between in vivo and in vitro recruitment can be explained by the effects of translation efficiency on mRNA recruitment into stress granules, where efficient translation has been shown to correlate with depletion from stress granules (28). Removal of transcripts with fractions of optimal codons below 0.4 or above 0.6 increases the Pearson’s correlation coefficient to 0.61 (Fig. S5E). This makes sense, as translation would be expected to affect mRNAs partitioning into stress granules in vivo but not to affect RNA self-assembly in vitro, where there is no translational apparatus. Taken together, these observations suggest that the partitioning of mRNAs into stress granules is modulated, in part, by the self-assembly capabilities of RNA.

We suggest a working model where stress granules form when the summation of protein–protein, protein–RNA, and RNA–RNA interactions increase over a threshold for assembly. This model can be illustrated in a phase diagram, with transitions between “phases” explaining stress granule formation (Fig. 4). For example, increasing the concentration of exposed RNA, either by inhibiting translation initiation leading to ribosome run-off or by transfection or injection of RNA into cells (35–37), could cause a transition in the cell that leads toward stress granule assembly via increased RNA–RNA interactions (Fig. 4, yellow arrow). Similarly, deletion of G3BP1 and G3BP2, which are abundant proteins that contribute to stress granule assembly (19), prevents assembly under most stresses and moves components back into the nonassembled phase in our model (Fig. 4, red arrow). Conversely, overexpression of TIA1, G3BP1, or FMR1, all of which can contribute to stress granule assembly (18, 38–40), may drive the cell into a regime of assembled stress granules by increasing the protein–protein interactions (Fig. 4, black arrow).

Fig. 4.

Fig. 4.

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Four-phase model of stress granule assembly. Model illustrating how assemblies may be RNA-dominated (Bottom Right) or protein-dominated (Top Left); however, a combination of interactions is often responsible for assembly within cells (Top Right). Arrows denote examples from the literature that lead to stress granule formation or dissolution. The yellow arrow shows the formation of stress granules through a large influx of nontranslating RNAs. The red arrow signifies a lack of stress granules when key proteins are deleted. The black arrow denotes the formation of stress granules through overexpression of certain RNA-binding proteins.

Since RNA–RNA interactions contribute to stress granule assembly, we hypothesized that any small molecule or peptide that stabilizes RNA–RNA interactions would promote stress granule assembly. Strikingly, prior work has shown that arginine-containing dipeptides GR and PR produced by RAN translation of hexanucleotide (G4C2) repeat expansions of C9orf72 (41, 42) are both toxic to cells and trigger stress granule formation (43, 44). Although these dipeptides perturb many cellular processes (45–47), we predicted they may directly induce stress granules by stabilizing RNA–RNA interactions. Indeed, we observed that (PR)10 and (GR)10 robustly stimulated RNA self-assembly in vitro, while (GP)10 had little effect (Fig. 5 and Fig. S6). Assemblies with (PR)10 or (GR)10 were enriched for both RNA and dipeptides (Fig. 5A), and neither RNA nor protein alone was sufficient for robust assembly under these conditions (Fig. 5A and Fig. S6A). Since the most toxic dipeptides are the same dipeptides that stimulate the assembly of RNA in vitro, we suggest that dipeptides may exert some of their toxic effects by promoting RNA–RNA assemblies in the cell, such as through the formation of stress granules.

Fig. 5.

Fig. 5.

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Pathogenic dipeptides increase RNA assembly. (A) Dipeptides (GR)10 and (PR)10 promote assembly, while (GP)10 does not. Fluorescent dipeptides (green) and RNA (SYTO 17, red) are both enriched in assemblies. (B) Phase diagram illustrating the assembly of (GR)10 and RNA. The squares signify lack of assembly. +, sparse and small assembly; ++, moderate assemblies (constituting either frequent but smaller assemblies or larger assemblies that were more sparse); +++, robust assembly. Green indicates protein-only assembly.

Discussion

We present several lines of evidence that stress granules form, in part, by RNA–RNA interactions and that those interactions can influence the partitioning of mRNA. First, we show that a portion of total yeast RNA effectively self-assembles in vitro under conditions mimicking intracellular stress conditions (Fig. 2). Importantly, this self-assembly of yeast RNA largely reproduces the stress granule transcriptome (Fig. 3). Moreover, the enrichment of short antisense ncRNAs in mammalian stress granules correlated with their ability to base-pair to a longer mRNA enriched in stress granules (Fig. 1). Finally, dipeptide repeats, which are known to induce stress granules in cells, strongly increase RNA self-assembly in vitro (Fig. 5). In combination with genetic experiments showing proteins can enhance stress granule assembly, we conclude that stress granules assemble by a summation of protein–RNA, protein–protein, and RNA–RNA interactions (Fig. 4). The precise set of interactions that drive stress granule formation can vary under different conditions as long as the total summation is above the critical threshold for assembly.

There is growing evidence that RNA–RNA interactions can help drive the assembly of stress granules. A role for RNA–RNA interactions could explain why purified stress granule cores are stable against many insults (29). In addition, a role for RNA–RNA interactions could suggest why the ATPase activity of Ded1p is required to disassemble stress granules (48). Our results also highlight the importance of charge shielding in promoting RNA self-assembly, as increasing salt concentrations or adding positively charged molecules, such as polyamines or arginine-containing dipeptides, greatly increases assembly. This is consistent with observations in the literature suggesting that ionic strength, which has a strong impact on RNA self-assembly in vitro (Fig. 2 and Fig. S2), could be an important regulator of stress granule formation. Stress granule assembly can be triggered by hypertonic shock and, conversely, can fail to form in conditions of low intracellular osmolality (49). Moreover, sorbitol, another osmotic stressor, can partially rescue formation of stress granules in ΔΔG3BP1/2 cells (19), perhaps because the sorbitol increases the intracellular osmotic strength, thereby stabilizing RNA–RNA interactions. In another line of evidence, the addition of G4C2 RNA to U-2 OS cell lysates condenses an assembly containing many stress granule components and cellular RNAs, perhaps by nucleating interactions between various mRNAs in the lysates (37). Finally, the prevalence of RNA–RNA interactions helps explain why injection or transfection of concentrated RNA into cells leads to the formation of large, higher order assemblies (35–37). This is analogous to the huge influx of exposed RNAs during a stress response, which may allow for the emergence of interactions that are normally outcompeted by more specific, high-affinity interactions.

The role of RNA–RNA interactions in stress granule assembly has two broader implications. First, one anticipates that RNA–RNA interactions will contribute to other RNP granules. Indeed, given the thermodynamic strength of RNA–RNA interactions, they should be expected to be in a stable state in cells and will form in trans whenever there is a sufficiently high local concentration of RNA. For example, we suggest that very efficient transcription of long RNAs would be expected to drive the formation of RNA–RNA interactions between nascent transcripts. One possible example of this process would be the assembly of paraspeckles, which form at sites of transcription of NEAT1 RNA and contain multiple NEAT1 copies (50).

A second implication is that RNA–RNA interactions can be promiscuous and form between any two RNAs with single-stranded regions. For example, by chance, the average mammalian mRNA in stress granules (7.5 kb) (28) should have over 300 possible sites of six consecutive base pairs with another 7.5 kb of mRNA. Even if the vast majority of these sites are lost to intramolecular RNA folding or RNA-binding proteins, numerous possible sites for RNA–RNA interaction will remain. Thus, one anticipates that RNA–RNA interactions can arise between many different RNAs. In some cases, evolution will have created definitive interaction sites to give specificity to assemblies, as has been seen with Oscar and Bicoid mRNAs in Drosophila embryos (30).

Although promiscuous RNA–RNA interactions are capable of forming between any two RNAs, it is important to note that there will be a gradient of any given RNA’s propensity to assemble. We show that longer RNAs are more enriched in assemblies in vitro and in stress granules (Fig. 3). In addition, one anticipates that the ability of RNAs to form base-pairing interactions will increase their self-assembly properties. Given these two inputs, short-structured RNAs, such as tRNAs (originally “soluble” RNAs), will be excluded from RNA assemblies. Alternatively, longer RNAs, particularly those with repeat sequences capable of self–base-pairing, will be highly efficient at self-assembly. Recent reports have illustrated that longer repeat RNAs more effectively form intracellular and in vitro RNA assemblies (31) and that transfection of G-quadruplex–capable G4C2 RNA elicits robust stress granule assembly, whereas its antisense counterpart C4G2 does not (37). In this light, it is notable that many repeat-containing RNAs, including both pathogenic toxic RNAs and satellite RNAs, form specific nuclear foci (43, 51–53). This suggests that formation of hyperstable RNA assemblies in cells is toxic, and this provides a possible explanation for why repeat RNAs with a strong tendency to base-pair cause disease once they are expanded beyond a certain length.

This work suggests that RNPs exist within cells at an equilibrium between monomeric RNPs and multimeric RNP granules that is influenced by many parameters. For example, RNA helicases, ribosome association, and monovalent RNA-binding proteins are expected to play a role in maintaining RNPs in the monomeric state. Consistent with this view, it is known that depletion of the abundant Tdp-43 ortholog in Caenorhabditis elegans leads to the accumulation of dsRNA foci in the nucleus (54). In contrast, longer RNA lengths, high local or transient RNA concentrations, and the propensity of RNA to interact with itself will promote RNA–RNA association. Taken together, we suggest that RNA–RNA assemblies in cells may be a default state and that cells prevent this RNA aggregation by active means, ribosomes, and RNA-binding proteins.

Materials and Methods

Materials.

Homopolymer RNAs were purchased from Amersham Pharmacia Biotech, Inc. (polyA, polyC, polyU, and polyG) and Sigma (polyU). Fluorescently labeled RNA containing consensus sequences for polypyrimidine tract-binding protein (PTB RNA) (24) and short homopolymer oligos were purchased from IDT (Dataset S1). Dipeptides were ordered through New England Peptide (Dataset S1).

Antisense RNA Analysis.

Briefly, ncRNAs from the mammalian stress granule transcriptome (28) were split into those defined as antisense and all other ncRNAs (55). Binding partners were found on the UCSC Genome Browser, and their enrichment in stress granule was graphed.

Preparation of Stress Granule-Enriched Fraction and RNase Treatment.

Stress granules were enriched from BY4741 cells transformed with Ded1Δ141–150 as described previously (29). RNase mixture (AM2286; Ambion) or RNase III (M0245L; New England Biolabs) was added to the granule-enriched fraction as per the relevant manufacturer’s directions and placed at 37 °C for 2 h before imaging on a microscope. Additional details are provided in Supporting Information.

In Vitro Assembly and Dynamics.

Specific information regarding in vitro assembly formation (total RNA, homopolymer, RNA-binding protein recruitment, and dipeptide/RNA assemblies) as well as dynamics (relaxation times, FRAP, and dilution) can be found in Supporting Information.

Microscopy of RNA Assemblies.

Mixtures were placed in 96-well glass-bottomed plates with high-performance no. 1.5 cover glass (Fisher Scientific). Images were acquired on a DeltaVision epifluorescence microscope with a 100× objective (Applied Biosystems) equipped with a sCMOS camera. Images for FRAP analysis were acquired using a Nikon A1R laser scanning confocal microscope.

Total RNA Pelleting for Tape Station and Sequencing Analysis.

Total yeast RNA was mixed with 150 mM NaCl and 10% PEG, and then pelleted for sequencing. RNA pellets and total RNA were treated with a DNA-free DNA removal kit (Thermo Fisher Scientific) and sent to the University of Colorado BioFrontiers Institute Next-Gen Sequencing Core Facility for Ribo-Zero treatment, library construction, and NextSeq run. Read quality was assessed using fastqc. Illumina adaptors were trimmed with Trimmomatic 0.32 (56). An index genome was built with Bowtie 0.12.7 using the S288C reference genome (57). Reads were aligned using Bowtie 0.12.7, and mapped reads were counted using HTSeq (58). Normalization and differential expression was performed with DESeq 1.22.1 (59). Sequences can be viewed in the National Center for Biotechnology Information Gene Expression Omnibus database (accession no. GSE99170). More details are provided in Supporting Information.

Length and Optimal Codon Analysis.

Sequencing data for in vitro RNA assemblies were analyzed for length and codon optimality based on previous reports (60, 61). Specific details are provided in Supporting Information.

Numerical Calculations.

All numerical manipulations, including estimation of exposed RNA during stress, and statistical tests can be found in Supporting Information.

Supplementary Material

Supplementary File
pnas.201800038SI.pdf (2.4MB, pdf)
Supplementary File
pnas.1800038115.sd01.xlsx (854.4KB, xlsx)

Acknowledgments

We thank all members of the R.P. laboratory and Olke Uhlenbeck for valuable conversations. We thank Yuan Lin for purifying and sending all MBP-GFP-IDR-HIS protein constructs. We thank the University of Colorado BioFrontiers Institute Next-Gen Sequencing Core Facility, which performed the Illumina sequencing and library construction. Some of the imaging in this work was performed at the BioFrontiers Institute Advanced Light Microscopy Core. Laser scanning confocal microscopy was performed on a Nikon A1R microscope supported by National Institute of Standards and Technology-University of Colorado Cooperative Agreement Award 70NANB15H226. National Science Foundation SCR Training Grant T32GM08759 (to B.V.T.) and the Howard Hughes Medical Institute (R.P.) funded this work.

Footnotes

The authors declare no conflict of interest.

Data deposition: The data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, https://www.ncbi.nlm.nih.gov/geo (accession no. GSE99170).

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1800038115/-/DCSupplemental.

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