Successional theory proposes that fast growing and well dispersed opportunistic species are the first to occupy available space. However, these pioneering species have relatively short life cycles and are eventually outcompeted by species that tend to be longer-lived and have lower dispersal capabilities. Using Autonomous Reef Monitoring Structures (ARMS) as standardized habitats, we examine the assembly and stages of ecological succession among sponge species with distinctive life history traits and physiologies found on cryptic coral reef habitats of Kāneʻohe Bay, Hawaiʻi. Sponge recruitment was monitored bimonthly over 2 years on ARMS deployed within a natural coral reef habitat resembling the surrounding climax community and on ARMS placed in unestablished mesocosms receiving unfiltered seawater directly from the natural reef deployment site. Fast growing haplosclerid and calcareous sponges initially recruited to and dominated the mesocosm ARMS. In contrast, only slow growing long-lived species initially recruited to the reef ARMS, suggesting that despite available space, the stage of ecological succession in the surrounding habitat influences sponge community development in uninhabited space. Sponge composition and diversity between early summer and winter months within mesocosm ARMS shifted significantly as the initially recruited short-lived calcareous and haplosclerid species initially recruit and then died off. The particulate organic carbon contribution of dead sponge tissue from this high degree of competition-free community turnover suggests a possible new component to the sponge loop hypothesis which remains to be tested among these pioneering species. This source of detritus could be significant in early community development of young coastal habitats but less so on established coral reefs where the community is dominated by long-lived colonial sponges. In terrestrial systems, when physical or biological disturbances open space for species to occupy, species composition varies according to the stage of ecological succession within the habitat which is influenced by life history traits among species4. During early succession, fast growing and well dispersed opportunistic species grow first5. However, these species have relatively short life cycles and are eventually displaced by more competitive ones that tend to have lower dispersal capabilities but live longer6. Similar patterns of succession are prevalent across temperate and neotropical forests. While the patterns of ecological succession in terrestrial systems have been observed over centuries7 marine systems such as coral reefs have received far less attention10.Understanding species compositional variation across temporal scales is a central goal in community ecology and fundamental for predicting the process of community development under environmental or anthropogenic change11. The considerable diversity within this ecosystem is primarily attributed to the species occupying the cryptobenthic spaces within the reef framework14 which are largely overlooked, and can harbor up to 90% of the reef volume and about 50% of reef surface area17. Sponges are a diverse group of sessile benthic organisms that can comprise up to 73% of the biomass in cryptic habitats18 in the Caribbean and 60% of the surface area19 within these cryptic habitats in the Red Sea. Sponges are pivotal in sustaining the diversity in these ecosystems through their essential role in nutrient uptake and release21. Unlike surface-dwelling macrosponges that assimilate dissolved organic matter (DOM) primarily for somatic growth22, encrusting and predominantly cryptic sponges use a large portion of their DOM uptake (up to 58%) to fuel rapid cell turnover23. Thus, somatic growth for cryptic sponges is assumed to be close to zero since newly formed cells largely replace old cells which are released as detritus24. This released detritus feeds detritivorous invertebrates thereby fueling the coral reef food web25 and making cryptic sponges functionally critical to coral reef ecosystems26. Yet despite their functional importance, we know very little about the ecological succession and temporal variability of this important and understudied group of organisms.Coral reefs are one of the most biologically rich ecosystems on the planet27. By increasing their persistence over time through lateral expansion, colonial organisms have an advantage over solitary organisms by completely overgrowing space-limited hard substrata29. While previous studies of cryptobenthic succession provided invaluable insight on colonization and competition of forms, they were limited by taxonomic resolution of Porifera as a phylum. Porifera are notoriously difficult to identify to species, but such identification is essential because groups have distinctive life history traits and physiologies that could influence the succession of these communities. Orders within the most abundant and specious sponge class (Demospongiae) have a variety of growth morphologies30, life history traits32, reproductive strategies33, and chemical defense mechanisms against predators34 – the latter being a tradeoff with rapid growth35. On the other hand, the Calcarea are a small, inconspicuous sponge class36 that tend to be more solitary than colonial and can reproduce throughout the year exhibiting high fecundity but are short lived39. In addition to the taxonomic challenges with identifying sponges, previous studies on cryptobenthic succession have primarily been limited to caves and ledges because these environments could easily be observed and monitored. However, the vast diversity of sponges are hidden deep within the interstices of the reef framework19, rendering observational and sampling difficulties. As a result, few studies have targeted the cryptobenthic succession of these functionally important sponges.Previous work on the succession of Caribbean cryptic reef fauna under ledges and within caves showed that sponges with colonial behavior dominate after pioneering stages42. In a recent study targeting Hawaiian sponge diversity, ~ 100 new records were documented from the interstitial spaces of the ARMS, despite field surveys covering more than two orders of magnitude more area (1750 m2 of reef habitat) than the ARMS (15 m2)44. The proven success of ARMS in capturing a rich diversity of cryptic sponges, combined with the ease by which the structure is disassembled and reassembled, provides an unprecedented opportunity to evaluate the stages of ecological succession among cryptobenthic sponges.Standardized artificial sampling devices, such as Autonomous Reef Monitoring Structures (ARMS), overcome cryptobenthic sampling constraints by providing suitable habitat for cryptobenthic marine invertebrates to colonize. ARMS were explicitly designed as a tiered structure to mimic the three-dimensional complexity of the reef and provide unobtrusive access to these elusive cryptobiotaHere we examine sponge community development over two years in ARMS deployed on an established coral reef habitat resembling the local climax community and within ARMS placed in mesocosms receiving unfiltered seawater from the same reef. We report temporal trends in sponge community composition and investigate whether adaptive strategies or physiological constraints among Porifera groups influence the order in which they occupy cryptic spaces throughout ecological succession.Modified two-tiered ARMS  that collectively account for greater than 95% of coral cover in the state of Hawai‘i47. These corals were placed on a plastic platform above 2 cm of carbonate sand and gravel, and a collection of 3 pieces of reef rubble (roughly 10–20 cm) were collected from the adjacent reef habitat48. Each mesocosm tank also contained herbivorous reef snails (Trochus spp.) a juvenile Threadfin butterflyfish (Chaetodon auriga), a juvenile Convict tang (Acanthurus triostegus), and the two-tiered ARMS placed beneath the coral platform to simulate the cryptobenthic habitat of the living reef matrix at biomass values similar to those reported for Hawaiian reef fishes49. In addition to ~ 1.2 L min−1 of inflowing unfiltered seawater (turnover), water circulation within each mesocosm tank was generated with a Maxi-Jet Pro propeller seawater pump (~ 4900 L hr−1 flow) to closely simulate adjacent reef conditions. Chemical and physical parameters of mesocosm ARMS simulating ambient open reef habitats are reported in Timmers et al. (2021) and Jury et al. (2021) as “control”. Temperature data from both mesocosm and reef habitats were monitored throughout  that were set up to mimic the Hawaiian reef community. Considering a mesocosm tank volume of 70 L and an inflow rate of ~ 1.2 L min51, and the limited number of species descriptions that currently exists for the majority of these highly understudied cryptic sponge taxa we used individual OTUs as our proxy for species. A total of 314 sponges were collected and vouchered during this survey. All sequences were deposited in GenBank. Accession numbers pertaining to either COI sequences, 28S rRNA sequences or sequences from both loci were provided for 87 vouchers and the remaining 219 vouchers were identified by matching morphological features to the 87 vouchers with DNA accession numbers  following Vicente et al. (2021). Due to the taxonomic challenges associated with this group-1.Vouchered and barcoded sponges were labeled on high-resolution images of individual modified ARMS plates. Metadata for each sponge included: date, ARMS number, plate number (1 through 3), and side of the plate (top or bottom). Each individual sponge was monitored through time using the initial position of recruits as a reference. Alpha diversity estimates were calculated as the total number of OTUs encountered per ARMS at a given time point. For each ARMS, the abundance of each OTU was based on the presence of that OTU on each side of the three plates resulting in values between 0 and 6. When considering the abundance of multiple or all OTUs on an ARMS, the abundance value could range between 1 and 438 for all 73 OTUs ARMSR v.3.6.352. Observed richness, Shannon and Simpsons indices were calculated using the estimate_richness function from the ‘vegan v.2.5-653 package  and nonparametric (wilcox.test function) paired t-tests. The ‘nlme v.3.1-151′54 package with repeated-measures ANOVA and multilevel modeling were used to compare mean differences in species richness and abundance of sponges across time points using the lme function. We tested three models (1) a base linear mixed effect model (lme function) with ‘time’ and ‘habitat’ as fixed effects and ‘ARMS number’ as a random effect; (2) the base model with compound symmetry; and (3) the base model with autoregressive lag 1 (corAR1) covariance structure. The autoregressive lag 1(corAR1) covariance structure produced the best model based on an ANOVA model comparison. Residuals were normally distributed  and randomly distributed . The homogeneity of variance (HOV) assumption for abundance was both visually inspected with box plots of residuals vs. fitted values showing similar variation across residuals55 and tested through a Levene’s test (p = 0.303). HOV for richness was confirmed visually with boxplots showing similar variation across residuals. Significant Tukey’s post hoc pairwise comparisons using the R package ‘lsmeans’ v.2.3056 was used to measure significant differences (p < 0.05) in sponge diversity and abundance between habitats at different time points.Data was analyzed and visualized using metaMDS function) was used to plot sponge community composition between time points showing peak (June 2017) and minimum (November 2017) recruitment periods. Bray–Curtis dissimilarity indices were computed (vegdist function) on square-root transformed sponge OTU abundance. Permutational analysis of multivariate dispersion (PERMDISP – betadisper function) was performed to examine community dispersion of OTUs between habitat types and at time points showing peak and minimum recruitment throughout the two years. Permutational analysis of variance (PERMANOVA – adonis function) was used to analyze sponge community composition among habitats and time points based on 1000 permutations. OTUs responsible for significant temporal shifts (p = 0.01) within the sponge community composition were identified as vectors and plotted within NMDS plots using the envfit function. Nonparametric Kruskal–Wallis tests identified sponge classes or orders responsible for significant shifts (p < 0.05) in community composition between the two time points. All figures were generated using the R package ‘ggplot 2’ v3.3.357.Nonmetric multidimensional scaling (NMDS) (−1 (t (7.70) = 5.84, pWelch =< 0.0001; pWilcoxon = 0.0042) and a mean abundance of 72.50 individuals ARMS−1 (t (5.94) = 8.36, pWelch =< 0.0001; pWilcoxon = 0.0040). Tetractinellid sponges in mesocosms exceeded those on the reef by a mean difference of 2.17 OTUs ARMS−1 (t(5.00) = 5.40, pWelch =< 0.0029; pWilcoxon = 0.0023) and 42.83 individuals ARMS−1 t(7.70) = 5.84, pWelch = 0.0043; pWilcoxon = 0.0028))   that recruited onto ARMS  , but not diversity (p > 0.05), of mesocosm ARMS between these two time points was significant   ; chi-square = 5.45, df = 1, p = 0.0196 (abundance) Fig. p = 0.0365 (diversity); chi-square = 5.53, df = 1, p = 0.0187 (abundance) Supplementary Figure Significant differences through time within the Calcarea were due to a decrease in abundance of Leucosolenida sp. 1 (OTU15), and Clathrinidae sp. 2 (OTU 8) from 11 months during summer to 16 months in early winter Fig. c. The nuClathrina aurea in south-eastern Brazil58. In contrast, recruitment to the reef ARMS was slower and more consistent, steadily increasing diversity throughout the two-year monitoring period, never reaching an asymptote. Unlike mesocosm ARMS, the reef ARMS were dominated by individuals belonging to the class Homoscleromorpha and orders Poecilosclerida, Tethyida, Keratosa, and Suberitida within the class Demospongiae.The process and stages of ecological succession for the Hawaiian cryptic sponge community differed between ARMS in mesocosms and those on the adjacent reef. Higher recruitment resulted in mesocosm ARMS reaching carrying capacity in one year, at more than double the abundance and richness of sponge OTUs observed on the reef ARMS, which continued to increase in diversity and abundance throughout the two years. Interestingly, the calcareous, haplosclerid and tetractinellid sponges which were responsible for the higher cumulative diversity and abundance observed in mesocosms were rarely observed if not absent on reef ARMS. However, this increased diversity in the mesocosm ARMS was ephemeral; five months after reaching maximum recruitment during the summer, overall richness and abundance in early winter significantly declined due to the reduction in calcareous and haplosclerid sponges. Similar seasonal trends in individual abundance have been reported for the calcareous sponge 27 which might explain why recruitment on reef ARMS did not climax after two years. However, community differences between time points on reef ARMS never transitioned between species as observed in mesocosm ARMS during the same period. Thus, we argue that our inability to observe maximum recruitment after a year on the reef was due to the absence of calcareous and haplosclerid sponges which were only present during pioneering stages of community development. Even though the mesocosm and reef ARMS were ~ 15 m apart, it is possible that the isolation of the mesocosm ARMS from the overall reef could have triggered a true pioneering stage of the cryptobenthic community with the settlement of fast growing but short-lived sponge species. Although mesocosms were established with corals, fish, snails, sand, and rubble to simulate the reef environment48, these components do not recreate a fully developed community within the reef matrix. In contrast, reef ARMS were deployed on a mature reef where corals have accounted for 40–60% of benthic cover in the past 20 years59. Such long stability in the ecosystems surpasses the time observed for other cryptic communities to reach a climax community. Therefore, we assume that the pioneering stage on this reef has long passed, and a climax cryptobenthic community is already established. Replacement of highly dispersive, solitary and short-lived species by more competitive slower growing and colonial species is characteristic of other systems5, including sponges on Caribbean reefs28. However, we can discount direct competition as the mechanism by which this replacement occurs because image analyses through time showed that calcareous and haplosclerid sponges were not overgrown or smothered by competitors, but simply disappeared from individual ARMS plates between sampling periods  and any sediment that was present on the ARMS plates in both habitats was cleared during each sampling period. Fourth, water flow has been shown to impact community composition47 recruitment63, and bleaching susceptibility45 of benthic marine organisms. Species richness of benthic invertebrates settling to experimental substrata is expected to increase proportionally to flow rate because of increased delivery of rare species64. However, flow rates ensured with water pumps in this study were designed to mimic that of the adjacent reef (~ 10 cm/s), and we see dominance of an entire group of sponges that are not observed on the reef rather than simply adding more rare species as has been observed in previous studies64. Fifth, the nutritional environment in the mesocosms might differ from that of the reef either because of mass transfer limiting nutrient uptake65 or differing particulate or dissolved organic matter (DOM) levels. We supplied unfiltered seawater at a rate sufficient to turn over the entire mesocosm tank volume roughly once per hour in an effort to minimize differences between the reef and mesocosm water chemistry and nutrient content, but we did not measure nutrients. However, periodic filtering of incoming mesocosm water did show that particulates and zooplankton were at the low end of natural reef densities and consistently lower than mean values on the reefs of Kāne‘ohe Bay48 suggesting that nutrients may have influenced compositional differences between mesocosm and reef ARMS. Although this list is not exhaustive, none of these factors appear to be obvious drivers of the difference in successional trajectories observed between mesocosms and reef environments.Without evidence for direct overgrowth of calcareous and haplosclerid sponges, some other differences in biotic or abiotic factors must drive the observed differences in cryptic sponge community succession in this system. Differences in succession between the reef and mesocosm ARMS could be related to a variety of factors that differ between them, such as 1. larval supply, 2. temperature, 3. sedimentation, 4. flow, or 5. nutrition as some of the obvious possibilities. Although our experiment was not set up to test these alternatives, we have some anecdotal evidence to address each. First, it is possible that the composition of larval supply to the mesocosms is different than on the reef, but we used unfiltered seawater pumped directly from the same reef which proved to supply hundreds of species to recruit to the mesocosms over the course of this experiment67; reviewed by Hixon68. Signs of predation within cryptic coral reef communities are rare28 as cryptic habitats are known to provide physical protection for palatable sponges from spongivorous fish, cowries, and turtles74. ARMS provide only 1 cm spacing between plates, similarly, limiting predation from these organisms. Unfortunately, little is known about the species composition or trophic guilds of the cryptic coral reef communities which may harbor a multitude of sponge predators75. However, some caridean shrimp, polychaetes, and nudibranchs are known to consume sponges but none were observed in our study77. It is possible that high local densities of invertebrate spongivores on the natural reef consumed the calcareous and haplosclerid sponges, but such predation pressure would need to be extreme to eradicate nearly every individual before they could be sampled from our experimental reef ARMS. Even if these invertebrate predators consume small calcareous or haplosclerid sponges, predation seems an unlikely explanation for the difference between the mesocosm and reef ARMS because we observe a seasonal die-off of these small solitary sponges in the mesocosms which must be free of these predators if that were to explain why we see these sponges in only the mesocosm ARMS. Thus, we see no direct evidence of competition or predation structuring these sponge communities, and do not expect that larval supply differs fundamentally between the mesocosms and the adjacent reef from which our seawater is drawn. This leaves open the important question of which processes drive the difference in primary succession observed for cryptic sponges between reef and mesocosm ARMS as a fertile topic for further research.The debate about whether recruitment limitation, predation or competition is the predominant force in structuring coral reef communities has raged on for decades  via the sponge-loop. Detritus produced through the sponge-loop was originally reported as a result of rapid turnover of sponge cells83. However, previous studies have been limited by their inability to capture cryptic diversity deep within the reef matrix, monitoring succession of the reef matrix commuinty in an entirely undeveloped community isolated from coral reef conditions, and by low taxonomic resolution of sponges as a group. Using ARMS in our study and species identification of sponges as a single group subsampled on a temporal scale through barcoding and microscopy overcomes these early limitations. Although it is difficult to estimate complete diversity of sponges in our study considering that cryptic diversity could exist within samples exhibiting the same morphospecies84, our data show that the sponge community is diverse, and that this diversity contributes to different stages of ecological succession with some species being early colonists, and others being more competitive throughout community development. ARMS on the reef show trends much more similar to previous work, with some species being early colonists in undeveloped communities, and others being more competitive throughout development in more mature communities; whereas mesocosm ARMS highlight pioneering sponge species  during early stages of succession in undeveloped communities. These early successional sponges might play a previously unappreciated role by contributing to the particulate organic matter budget during the formation of coral reefs. Such processes are critical for maintenance of reef biodiversity throughout early reef development and understanding the contribution of individual species in this rich community is essential to manage and conserve coral reef productivity and biodiversity through space and time.Historically, cryptobenthic sponges have been thought of as a competitive phylum that appears only after pioneering stages of ecological succession in coral reef habitatsSupplementary Figures.Supplementary Tables.


Rotaviruses transcribe 11 distinct RNAs that must be co-packaged prior to their replication to make an infectious virion. During infection, nontranslating rotavirus transcripts accumulate in cytoplasmic protein-RNA granules known as viroplasms that support segmented genome assembly and replication via a poorly understood mechanism. Here, we analysed the RV transcriptome by combining DNA-barcoded smFISH of rotavirus-infected cells. Rotavirus RNA stoichiometry in viroplasms appears to be distinct from the cytoplasmic transcript distribution, with the largest transcript being the most enriched in viroplasms, suggesting a selective RNA enrichment mechanism. While all 11 types of transcripts accumulate in viroplasms, their stoichiometry significantly varied between individual viroplasms. Accumulation of transcripts requires the presence of 3’ untranslated terminal regions and viroplasmic localisation of the viral polymerase VP1, consistent with the observed lack of polyadenylated transcripts in viroplasms. Our observations reveal similarities between viroplasms and other cytoplasmic RNP granules and identify viroplasmic proteins as drivers of viral RNA assembly during viroplasm formation. RNA genome segmentation poses challenges for assembly and genome packaging of viruses, including rotaviruses, a large group of human and animal pathogens. The rotavirus (RV) genome is enclosed in a protein shell, inside which 11 double-stranded (ds)RNAs, also known as genomic segments, iteratively undergo rounds of transcription . ConsequRecently, we discovered that early replication stage  viroplasms represent RNA-protein condensates that are formed via phase-separation of the non-structural phosphoprotein NSP5 To unravel the principles of RNA partitioning into viroplasms, we have visualised the RV transcriptome using a DNA barcode-based multiplexing approach To investigate RV transcript accumulation in viroplasms, we took advantage of the MA104 cell line stably expressing an EGFP-tagged NSP5 that readily distributes into viroplasms Given that each RV transcript is targeted by three transcript-specific probes, and the observed point sources could only be detected after 2–3 hpi, these signals are unlikely to originate from the hybridisation events to single transcripts Given that EGFP-tagged viroplasms accumulate large amounts of viral RNA-binding proteins We then carried out oligo(dT) FISH Methods), at 2 hpi, both Seg3 and Seg4 transcripts were readily detectable as high-intensity single point sources that were sufficiently far apart to be resolved  was comparable to the GAPDH RNA signal distribution visualised using a set of smFISH probes labelled with an identical fluorophore under the same imaging conditions (Methods), further confirming that these objects represented single viral transcripts. Both Seg3 and Seg4 transcripts were equally abundant and randomly distributed in the cytoplasm of infected cells without a discernible pattern. Such random point distribution further suggested a lack of directional transport of RNAs 2=0.93) between Seg4 and Seg3 RNA intensities was noted, with both RNA intensities linearly increase as the intensity of the NSP5-EGFP signal increases, i.e., proportionally to the size of an EGFP-NSP5-tagged viroplasm. Interestingly, a similar Seg3/Seg4 RNA ratio was maintained in viroplasms at a later infection stage at 6 hpi (R2=0.9), while the transcript to NSP5-EGFP ratio was significantly different from that seen at 4 hpi . These observations suggest that while both Seg3 and Seg4 mRNAs are present in viroplasms, individual mRNA ratios vary between viroplasms even within the same cell.To discern gene-specific viral transcripts, we designed two distinct sets of single-molecule (sm)FISH probes, each targeting the coding regions of the RV gene segments (Seg) Seg3 and Seg4 transcripts, respectively. Since each target-specific pool consisted of 48 probes –1 that corresponds to approximately ten smFISH probes per transcript We hypothesised that the observed RNA assembly depends on the production of RNA-binding proteins that concentrate in viroplasms such as NSP2 To visualise the entire RV transcriptome in single cells, we developed and employed Universal DNA Exchange approach Using UDEx-FISH multiple copies of each transcript Seg1-Seg11 were readily detected  at 2 hpi21,32,47of transcripts.Next, we examined the RV transcriptome during the formation of viroplasms. At 4 hpi, EGFP-tagged viroplasms contained all 11 types of RV transcripts . At 6 hp2=0.41), followed by Seg10 (R2=0.67), Seg8 and Seg 11 (R2=0.74), with the remaining transcripts having correlation coefficients in the range of R2=0.79–0.92.To support genome assembly, viroplasms are expected to contain all 11 RV transcripts; we therefore also analysed transcript stoichiometries in individual viroplasms at 6 hpi (N=21) by comparing their relative intensities (abundance) to those of Seg1 transcripts . As expeRotavirus transcript 3' untranslated regions (UTRs) are required for binding of the viral RNA polymerase VP1 2=0.78 ± 0.1). Collectively, these results strongly suggest that segment-specific UTRs are important for the RV transcript localisation and enrichment in viroplasms.We also tested whether another viral transcript, NSP5-EGFP, containing the coding region of Seg11 RNA but lacking the 3'UTR would undergo enrichment in viroplasms during RV infection . Again, 2=0.62 ± 0.17), while at the non-permissive temperature RV transcripts clustered outside viroplasms (R2=0.18 ± 0.07), indicating that RNA partitioning to viroplasms is linked to the ability of VP1 to localize to these granules. Together, these results suggest that RV RNA partitioning to viroplasms requires both 3' UTRs and viral polymerase VP1 localisation to these replication factories.Finally, since 3'UTRs are required for VP1 binding, we tested whether RNA accumulation in viroplasms is impaired when VP1 localisation to viroplasms is affected. We chose a well-characterised temperature-sensitive mutant C (tsC) with a single L138P substitution in VP1 that abrogates its ability to accumulate in viroplasms Ribonucleoprotein (RNP) granules formed via LLPS are ubiquitous in cells RNA-Seq and single-cell UDEx-FISH analyses have revealed that at 6 hpi, rotavirus infection resulted in non-stoichiometric accumulation of distinct viral transcripts, in agreement with previous bulk kinetic studies of RV replication carried out during late infection A plausible model for such enrichment is based on a high affinity, specific protein-RNA recognition, such as previously reported conserved interaction between the 3' terminal sequence of each RV transcript and its RNA-dependent RNA polymerase (RdRP) VP1 that exhibits high affinity for NSP5 Kd value of the promiscuous RNA-binding protein NSP2 for ssRNA is low nanomolar RNA-binding proteins NSP2 and NSP5 are the most abundant viral proteins produced during early stages of RV infection Cercopithecus aethiops kidney epithelial cells) stably expressing NSP5-EGFP Cercopithecus aethiops kidney epithelial cells) stably expressing NSP2-mCherry Mycoplasma sp. contamination .MA104  cells were cultivated as previously described 5 MA104 cells were co-transfected with the plasmid pCMV-HyPBase encoding the hyperactive variant of PiggyBac transposase MA104-shRNA-NSP2 cell line was generated using the PiggyBac system. Briefly, 10Proteins were treated with SDS and 2-mercaptoethanol at 95 °C, resolved by SDS-PAGE on Tris-glycine gels and transferred onto nitrocellulose membranes. Afterwards, the membranes were blocked with PBS containing 5% (w/v) skimmed milk and 0.1% Tween-20 and then incubated with guinea pig NSP2-specific antibody diluted 1:1,000 in PBS containing 1% milk and 0.1% Tween-20. Blots were washed with PBS and incubated with HRP-conjugated secondary antibodies in PBS  containing 1% milk and 0.1% Tween-20. Blots were developed with SuperSignal West Pico Chemiluminescent Substrate (Pierce) and exposed to BioMax MR film (Kodak). All scanned images were post-processed in Adobe Illustrator.5) cultured in 12-well plates were co-transfected using 2.5 μL of TransIT-LT1 transfection reagent (Mirus) per microgram of DNA plasmid. Each mixture comprised 0.8 μg of SA11 rescue plasmids: pT7-VP1, pT7-VP2, pT7-VP3, pT7-VP4, pT7-VP6, pT7-VP7, pT7-NSP1, pT7-NSP3-2A-EGFP, pT7-NSP4, and 2.4 μg of pT7-NSP2 and pT7-NSP5. 0.8 μg of pcDNA3-NSP2 and 0.8 μg of pcDNA3-NSP5, encoding NSP2 and NSP5 proteins, were also co-transfected to increase the efficiency of virus rescue. At 24 hr post-transfection, MA104 cells (5×104 cells) were added to transfected cells. The cells were co-cultured for 3 days in FBS-free medium supplemented with porcine trypsin (0.5 μg/mL) (Sigma Aldrich). After incubation, transfected cells were lysed by freeze-thawing and 0.2 ml of the lysate was used to infect fresh MA104 cells. After adsorption at 37 °C for 1 hr, cells were washed three times with PBS and further cultured at 37 °C for 4 days in FBS-free DMEM supplemented with 0.5 μg/mL trypsin  until a clear cytopathic effect was visible. Successful production of EGFP by the recombinant virus was confirmed microscopically.Rescue of the recombinant NSP3-2A-EGFP virus (strain SA11) was carried out as previously described C.aethiops GAPDH transcripts), 62.5 nM final concentration, see Rotavirus-infected and mock-infected MA104 cell controls, where appropriate, were fixed with 4% (v/v) methanol-free paraformaldehyde in nuclease-free phosphate saline buffer (PBS) for 10 min at room temperature. Samples were then washed twice with PBS, and fixed cells were permeabilised with 70% (v/v) ethanol (200 proof) in RNAse-free water, and stored in ethanol at +4 °C for at least 12 hr prior to hybridisation, and no longer than 24 hr. Permeabilized cells were then re-hydrated for 5 min in a pre-hybridisation buffer , supplemented with 2 mM vanadyl ribonucleoside complex). Re-hydrated samples were hybridized with an equimolar mixture of DNA probes specific to the mRNA targets (RVA RF or https://www.biosearchtech.com/stellaris-designer), using each gene-specific sequences , for approximately 5 min. Samples were rinsed with the labelling buffer, followed by addition of the imaging buffer (vide supra). After a round of image acquisition, hybridized labelling strands were dissociated by briefly incubating samples in 30% (v/v) formamide in PBS (‘Wash buffer’) for 2–3 min at RT, repeating this procedure twice, whilst monitoring for any residual fluorescence signal in four acquisition channels . Formamide-containing wash buffer was then aspirated, and samples were rinsed twice with fresh labelling buffer prior to the introduction of the next batch of ‘Imager’ DNA strands, thus concluding a single imaging cycle. To calibrate the recorded fluorescence intensities to quantify the signals originating from the individual transcripts in an unbiased manner due to differences in properties of spectrally distinct dyes (Cy3 vs Cy5), we swapped Cy5 and Cy3-dye-labelled DNA imagers for one of the RNA targets in each imaging cycle. A total number of six imaging cycles were required to image the RV transcriptome, and the last cycle always contained an Imager targeting the segment imaged during cycle 1 to control for a possibility of the signal loss due to the dissociation of FISH probes, and for signal calibration purposes.For UDEx-FISH, sequences of the oligonucleotide RNA FISH probes are listed in A pT7-NSP3-EGFP construct (see Key Resources Table) was used for amplification of EGFP ORF-containing transcription templates using a Q5 High-Fidelity DNA Polymerase with the forward primer (For_UTR) and either Rev_UTR (to make the 5’UTR-EGFP-3’UTR DNA template) or Rev_dUTR (to produce the EGFP-3’ΔUTR DNA template). DNA templates were purified using Monarch PCR purification kit New England Biolabs following the manufacturer’s protocol. Purified DNA templates were used for run-off transcription with T7 polymerase-based HiScribe kit (NEB) following the manufacturer’s recommendations to generate 5’UTR-EGFP-3’UTR and EGFP-3’ΔUTR transcripts, as described in 2 laser power. 5’-ATACATTGA-Cy3B-3’ was used as imager strand sequence. Further details of imaging conditions for each experiment are summarised below .Widefield imaging was carried out on a Leica  DMI6000B inverted microscope equipped with a LEICA HCX PL APO 63 x/NA1.4 oil immersion objective. Dye excitation was performed with a cooled pe-4000 LED source illumination system at the wavelengths 385 nm (DAPI), 470 nm (EGFP), 550 nm (Cy3 and Quasar 570), and 635 nm (Cy5 and Quasar 670). Fluorescent signals were detected with a Leica DFC9000 GT sCMOS camera with a pixel size of 6.5 µm. Images were acquired over a full field of view of the camera chip (2048×2048 pixels) resulting in a total imaging region of 211 µm × 211 µm. Exposure times were adjusted accordingly to the signal intensity to avoid pixel saturation. Typical exposure times were 250ms for DAPI, 500ms for EGFP, 750ms – 1 s for Cy3/Cy5 or Quasar 570/670 dyes. Image stacks were acquired using 250 nm Z-axis steps across a range of approximately 5 µm. Full stacks were recorded consecutively for each channel, from the lowest to the highest energy excitation wavelength. Deconvolution analysis was applied to all acquired widefield images using the Huygens Essential software . All channels and Z-planes were deconvolved using Huygens batch express tool (Standard profile).Z-stacks were loaded with ImageJ and out-of-focus planes were manually discarded. Prior to analysis a maximum intensity Z-projection was performed with the remaining Z-planes.http://icy.bioimageanalysis.org/). Regions of interest were drawn around individual cells prior to the analysis. Pearson’s correlation coefficient (PCC) was chosen as a statistic for quantifying colocalisation to measure the pixel-by-pixel covariance in the signal levels between two distinct channels. This allows subtraction of the mean intensity from each pixel’s intensity value independently of signal levels and the signal offset for each ROI. Pearson’s correlation coefficient values were calculated in Icy Colocalization Studio that employs pixel scrambling method 2D colocalisation analysis was performed with maximum intensity Z-projections using Icy (Version 1.9.10.0), an open bioimage informatics platform  from Ensemble release 103 --keepDuplicates) to quantify the pre-processed Illumina reads using salmon quant [6]  RV-infected MA104 cells were harvested at 6 hpi, and total RNA was extracted using RNEasy kit (QIAGEN). One ug of total RNA was depleted of rRNA using NEBNext rRNA Depletion Kit (Human/Mouse/Rat) prior to cDNA synthesis primed with random hexanucleotide oligonucleotides . Sequencing library construction was carried out using NEBNext Ultra II FS DNA Library Prep Kit for Illumina (NEB), and the resulting library was sequenced using Illumina MiSeq v2 (2x150 bp) platform. Paired-end run raw sequencing data were pre-processed with fastp (v0.20.1) using default command line parameters  Viral replication in the cell requires the assembly of multiple viral components into individual viral particles, while maintaining a relatively strict ratio between individual components. This manuscript uses an imaging approach to study proposed aggregates of viral protein and nucleic acid components referred to as 'viroplasms' and their role in achieving ordered viral assembly. Although this work provides a glimpse of viral assembly in the cell, further work is required to better understand this complex and important process. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and myself (Sara Sawyer – U Colorado Boulder – USA) as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Roy Parker (Reviewer #1).Thank you for submitting your article "Principles of RNA recruitment to viral ribonucleoprotein condensates in a segmented dsRNA virus" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:In this particular case, we are going to ask you to go through and address all reviewers' comments carefully. Both reviewers felt that the manuscript would be improved by careful quantification of all experiments and by putting the results into more rigorous analyses. We do feel that you will have a much stronger paper after addressing the comments of these reviewers, which will help you better interpret and explain your findings to readers.Reviewer #1 (Recommendations for the authors):1) In general, the manuscript would be improved by careful quantification of all experiments and putting the results into a more rigorous analysis.2) I think the work would be much more interesting if additional analyses were performed by the images where all 11 segments are examined. For example:a) Do all viroplasms have all 11 segments? If so, this implies specificity in the assembly mechanism that ensures this distribution.b) Is the stoichiometry the same in each viroplasm, or is there significant variation between assemblies? Again, this would have implications for the assembly mechanisms.ELife, the work should provide new significant biological or mechanistic insight.I raise this issue since for publication in 3) The manuscript would be improved by adding a co-localization quantification of the data presented in figure S5 (seg3 and seg4), which would communicate the importance of NSP2 more effectively than trying to gauge intensity from the images.4) The analysis of the requirement for a specific 3' UTR is limited since there are multiple differences between the RNAs being examined. To really make this conclusion, one would need to express the positive control RNA from a nuclear transgene with a ribozyme to generate the proper 3' end, and show that nuclear RNA is still efficiently recruited to viroplasms. in addition, the mRNA with the poly(A) does seem to partition into the viroplasms to some extent. How does that affect the conclusions presented?Reviewer #2 (Recommendations for the authors):eLife.Strauss et al. studied the mechanism of RNA recruitment to ribonucleoprotein condenstates using rotavirus. They used multiplexed DNA-barcorded smFISH and DNA-PAINT for direct visualization of the RNP condensates in cells. They observed the early onset of viral transcript oligomerization before the formation of viroplasms and the process of enrichment in RNP condensates. They imaged all eleven transcript in a RNP condensate and quantified the amount of the transcripts. Based on these findings, they suggested a selective RNA enrichment mechanism of rotavirus. The authors conducted well the experiments with good control measurement. The results looks significant enough for understanding the RNA recruitment to RNP condensates and provide a potential usefulness of their approach in the future work. I have a few concerns before fully supporting its publication in 1. It may be very helpful for a non-expert in rotavirus to understand the purpose of this work by putting a schematic illustration of their biological systems in Supplementary Figures. For example, Figure 4 is easy to understand the experimental schemes and the results with the help of the illustration.2. In the same manner, the conclusion of this work is rather difficult to understand. It may be very helpful to provide a schematic illustration of their results in a figure.3. The last paragraph of introduction part. The authors provided all their results, which seems to make it difficult to understand the major findings of this work. It will be helpful to be concise here.4. in Figure 1, the authors performed colocalization analysis. What is the error bar of their colocalization methods (how many pixels)? Please provide the dyes they used here for smFISH. Is there any cross-talk between EGFP and the dyes?5. In Supplementary Figure 1. How did the authors obtained the intensity distribution of GAPDH? It is an intensity based on a pixel?6. In Figure 3. The authors estimated 20-50 transcript for RNA clusters. When RNA formed clusters, the accessibility of DNA probes will be low compared with bare (or single) RNAs. The discussion on this point may be needed for the RNA quantification.7. in page 13, "Assuming similar rates of transcription for each individual genomic segment, transcription of longer segments is expected to yield fewer copies of longer Seg1-Seg4 transcripts (3.4-2.6 kb)." The copy number of transcript typically depends on the promoter strength, i.e., initiation, not the length of transcript. Degradation rate may be different.8. in Figure 5. It is difficult to compare the result in Figure 5c (RNA-seq) and 5d (FISH). Bar graphs (or other graphs) comparing the amount of transcripts for both methods will be helpful for understanding the resultseLife. Your revised article has been evaluated by a Senior Editor and a Reviewing Editor. We are sorry to say that we have decided that this submission will not be considered further for publication by eLife.Thank you for resubmitting the paper entitled "Principles of RNA recruitment to viral ribonucleoprotein condensates in a segmented dsRNA virus" for further consideration by eLife would need to address these points of Review #1 in a very solid and convincing way. The verbatim comments of Reviewer #1 are:We have determined that several important reviewer comments from round 1 of the review have not been adequately addressed. In particular, any further consideration of this manuscript to a) Do all viroplasms have all 11 segments? If so, this implies specificity in the assembly mechanism that ensures this distribution.b) Is the stoichiometry the same in each viroplasm, or is there significant variation between assemblies? Again, this would have implications for the assembly mechanisms.eLife, the work should provide new significant biological or mechanistic insight.I raise this issue since for publication in eLife. Your revised article has been evaluated by Miles Davenport (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Principles of RNA recruitment to viral ribonucleoprotein condensates in a segmented dsRNA virus" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed as outlined by reviewer 1 below.Reviewer #1 (Recommendations for the authors):This manuscript is significantly improved. Some comments to address before publication:1) I think the manuscript would be strengthened by calculating the fraction of each RV segment in viroplasms at different time points. From looking at the images, it will probably be a small fraction and this would be of interest to the community going forward.2) A formal caveat of the Nsp2 shRNA experiment is that the Nsp2 encoding RNA could be required for the viroplasm assembly. I think the authors should explicitly acknowledge this limitation.3) The manuscript would be greatly strengthened by quantification of Figure 6 and 7, which otherwise just show one image . This would be important to make this experiment robust. Reviewer #1 (Recommendations for the authors):1) In general, the manuscript would be improved by careful quantification of all experiments and putting the results into a more rigorous analysis.2) I think the work would be much more interesting if additional analyses were performed by the images where all 11 segments are examined. For example:a) Do all viroplasms have all 11 segments? If so, this implies specificity in the assembly mechanism that ensures this distribution.b) Is the stoichiometry the same in each viroplasm, or is there significant variation between assemblies? Again, this would have implications for the assembly mechanisms.I raise this issue since for publication in ELife, the work should provide new significant biological or mechanistic insight.2 = 0.93) between Seg4 and Seg3 RNA intensities , both of which linearly increase as the intensity of the NSP5-EGFP signal increases, i.e., proportionally to the size of an EGFP-NSP5-tagged viroplasm. Interestingly, a similar Seg3/Seg4 RNA ratio is also maintained in viroplasms at 6 HPI (R2=0.9), however, the RNA:EGFP-NSP5 ratio is distinct from that seen at 4 HPI (R2=0.8 for 4 HPI and R2 = 0.13 for 6 HPI). These observations suggest that while all 11 distinct transcripts are present in viroplasms, transcript : protein ratios are likely to change during infection, reflecting the dynamic composition of viroplasmic RNP granules.We thank the Reviewer for these comments. We carried out additional analyses of the imaging data, and we have provided further comments and interpretation throughout the text. More specifically, we have shown that all analysed viroplasms contained 11 distinct RNA transcripts, albeit the transcript stoichiometry appears to deviate from the equimolar ratio of dsRNA segments present in each rotavirus particle . However, we cannot rule out that this would universally apply to all viroplasms, as we have only analysed those detectable around 6 hours post infection (HPI), when all transcripts are highly abundant in RV-infected cells. Furthermore, since viroplasms represent RNP granules with poorly defined RNA and protein stoichiometries, we expect significant variation in the RNA content between individual viroplasms. To test this, we have carried out additional quantitative analyses of Seg3 and Seg4 RNAs in viroplasms detected at 4 HPI and 6 HPI . Figure 2c reveals a strong correlation (R3) The manuscript would be improved by adding a co-localization quantification of the data presented in figure S5 (seg3 and seg4), which would communicate the importance of NSP2 more effectively than trying to gauge intensity from the images.We thank the Reviewer for their suggestion, and we have significantly improved the layout of Figure 2 —figure supplement 4 by (i) adding schematic diagrams of the experimental system; (ii) adding signal intensity analysis for both Seg3 and Seg4 for RV-infected WT MA104 cells, and after shRNA-mediated depletion of NSP2, as suggested by the Reviewer. As you can see from the signal intensity analysis, the overall RNA signal intensities in shRNA-expressing cells were much lower than those observed in WT cells. Thus, we refrained from carrying out colocalization analysis due to significant differences in signal intensities to avoid biases introduced by significantly different thresholds required for estimating colocalizations between these conditions ). More importantly, high intensity foci containing colocalising Seg3 and Seg4 RNAs were only present in RV-infected WT cells, whereas no RNA clusters containing both Seg3 and Seg4 RNAs could be detected in NSP2-depleted cells after RV infection.4) The analysis of the requirement for a specific 3' UTR is limited since there are multiple differences between the RNAs being examined. To really make this conclusion, one would need to express the positive control RNA from a nuclear transgene with a ribozyme to generate the proper 3' end, and show that nuclear RNA is still efficiently recruited to viroplasms. in addition, the mRNA with the poly(A) does seem to partition into the viroplasms to some extent. How does that affect the conclusions presented?We thank the Reviewer for their comment above, we agree that the presented data do not rule out the possibility of other factors being involved in viral transcript partitioning into viroplasmic condensates. However, expressing viral transcript as a transgene would pose many additional challenges. Specifically, such a strategy could be confounded by (i) cryptic splice sites resulting in splicing of the exported viral transcript; (ii) processing of the 3’ end during nuclear export; (iii) processing of the 3’ end by the HDV ribozyme in the nucleus, resulting in the loss of its nuclear export. We expanded our discussion to mention that association of cellular RBPs with the viral transcripts exported from the nucleus may also impede their partitioning into viroplasms. We have also mentioned that given the high density of NSP5-EGFP in viroplasms, it is hard to rule out the possibility of residual cross-talk between high-density EGFP and the low-level RNA FISH signal :Strauss et al. studied the mechanism of RNA recruitment to ribonucleoprotein condenstates using rotavirus. They used multiplexed DNA-barcorded smFISH and DNA-PAINT for direct visualization of the RNP condensates in cells. They observed the early onset of viral transcript oligomerization before the formation of viroplasms and the process of enrichment in RNP condensates. They imaged all eleven transcript in a RNP condensate and quantified the amount of the transcripts. Based on these findings, they suggested a selective RNA enrichment mechanism of rotavirus. The authors conducted well the experiments with good control measurement. The results looks significant enough for understanding the RNA recruitment to RNP condensates and provide a potential usefulness of their approach in the future work. I have a few concerns before fully supporting its publication in eLife.We thank the Reviewer for their comments, and for appreciating the usefulness of our approach for investigating RNA composition of RNP granules.1. It may be very helpful for a non-expert in rotavirus to understand the purpose of this work by putting a schematic illustration of their biological systems in Supplementary Figures. For example, Figure 4 is easy to understand the experimental schemes and the results with the help of the illustration.We thank the Reviewer for this comment – we have added new schematic diagrams  to clarify the experimental designs, as well as a new Figure 7 to summarize our findings. We hope the readers will find these new schematics helpful and improve the overall narrative of the manuscript.2. In the same manner, the conclusion of this work is rather difficult to understand. It may be very helpful to provide a schematic illustration of their results in a figure.3. The last paragraph of introduction part. The authors provided all their results, which seems to make it difficult to understand the major findings of this work. It will be helpful to be concise here.We thank the Reviewer for their excellent suggestion – we have re-written the Introduction section to highlight the major findings in the context of viroplasm formation and RNP selectivity towards RNAs.4. in Figure 1, the authors performed colocalization analysis. What is the error bar of their colocalization methods (how many pixels)? Please provide the dyes they used here for smFISH. Is there any cross-talk between EGFP and the dyes?The errors are included in the violin plots shown in panel (e). Each data point represents the PCC value calculated for a single cell, i.e., the whole image was used for calculating PCCs. We have added the information about the fluorophores (Quasar570-labelled FISH probes) in the Figure legend. All images were acquired by sequential excitation with the lowest energy to the highest energy laser lines to minimise potential cross-talk between EGFP and Quasar570 dye, as mentioned in the Materials and methods section.5. In Supplementary Figure 1. How did the authors obtained the intensity distribution of GAPDH? It is an intensity based on a pixel?We have added the following information in the Materials and methods section:‘For Figure 1 —figure supplement 1, GAPDH and RV RNA transcripts peak intensities were detected using the ImageJ ‘Find Maxima’ function, with noise settings 100 (GAPDH) and 1000 (RV). Spot detections for intensity correlations  were performed using ‘Spot Detection’ tool in Icy (Version 1.9.10.0), an open bioimage informatics platform . The spot detection input parameter sensitivity was set to 20 and the object size to 7 pixels. The sum intensity of each detection was calculated prior to the intensity correlation analysis. For spot intensities measured in Figure 2 —figure supplement 4 sensitivity was set to 20 and the object size to 3 pixels.’6. In Figure 3. The authors estimated 20-50 transcript for RNA clusters. When RNA formed clusters, the accessibility of DNA probes will be low compared with bare (or single) RNAs. The discussion on this point may be needed for the RNA quantification.We thank the Reviewer for making this excellent point. Indeed, target accessibility inside the dense phase of RNP granules/other phase-separated systems remains one of many challenges for quantitative imaging analyses of their composition. We have added the following sentence in the Results section: ‘We note that within viroplasms, target accessibility for smFISH probes is expected to be lower than that for single transcripts. Thus, Seg3 transcripts present in these RNA clusters and larger viroplasms are likely to be undercounted’.7. in page 13, "Assuming similar rates of transcription for each individual genomic segment, transcription of longer segments is expected to yield fewer copies of longer Seg1-Seg4 transcripts (3.4-2.6 kb)." The copy number of transcript typically depends on the promoter strength, i.e., initiation, not the length of transcript. Degradation rate may be different.We have modified this statement to highlight that in rotaviruses, the RdRP-binding sites for each individual dsRNA segment are identical, and their segment-specific transcription rates deduced from previous in vitro studies suggest that the number of transcripts produced by a transcriptionally active particle is inversely proportional to their length . We have added a sentence ‘Indeed, purified transcriptionally active DLPs yield similar amounts of individual transcripts produced in vitro’.8. in Figure 5. It is difficult to compare the result in Figure 5c (RNA-seq) and 5d (FISH). Bar graphs (or other graphs) comparing the amount of transcripts for both methods will be helpful for understanding the resultsThank you for this comment, we have modified the figure, and we also added a Figure 5 —figure supplement 1.We have determined that several important reviewer comments from round 1 of the review have not been adequately addressed. In particular, any further consideration of this manuscript to eLife would need to address these points of Review #1 in a very solid and convincing way. The verbatim comments of Reviewer #1 are:a) Do all viroplasms have all 11 segments? If so, this implies specificity in the assembly mechanism that ensures this distribution.b) Is the stoichiometry the same in each viroplasm, or is there significant variation between assemblies? Again, this would have implications for the assembly mechanisms.I raise this issue since for publication in eLife, the work should provide new significant biological or mechanistic insight.We thank the Reviewer for these comments. We carried out additional analyses of the imaging data, and we have provided further comments and interpretation throughout the text. Specifically, we have shown that all viroplasms contained 11 distinct RNA transcripts, however, the transcript stoichiometry varies between individual viroplasms, particularly for shorter segments. These results are now included in Figure 5b and Figure 5—figure supplement 1.The manuscript has been improved but there are some remaining issues that need to be addressed as outlined by reviewer 1 below.Reviewer #1 (Recommendations for the authors):This manuscript is significantly improved. Some comments to address before publication:1) I think the manuscript would be strengthened by calculating the fraction of each RV segment in viroplasms at different time points. From looking at the images, it will probably be a small fraction and this would be of interest to the community going forward.As we have shown in the revised version of the manuscript, even at the same infection time point within the same cell, the number of transcripts greatly varies across viroplasms. This can be seen from Figure 2 (quantitation of Seg3 and Seg4 RNAs). Further quantitation of the RV transcriptome in viroplasm at different time points across multiple cells would require a significant investment in time and resources and would extend beyond the scope of the original manuscript.2) A formal caveat of the Nsp2 shRNA experiment is that the Nsp2 encoding RNA could be required for the viroplasm assembly. I think the authors should explicitly acknowledge this limitation.Excellent point – we have added the following statement in Results section ‘Although we observed a lack of transcript clustering in rotavirus-infected cells expressing shRNA targeting NSP2, it is important to note that the formation of viroplasms was also impaired under these conditions, supporting the essential role of NSP2 in viroplasm assembly’ (p.5). We have also added the following statement in Discussion: ‘While NSP2 has been demonstrated to promote RNA oligomerization in vitro, the shRNA-mediated knockout of NSP2 may also disrupt RNA distribution and localization due to the impaired viroplasm formation, suggesting a potential role for NSP2 in these processes.’ (p.8).3) The manuscript would be greatly strengthened by quantification of Figure 6 and 7, which otherwise just show one image . This would be important to make this experiment robust.We thank the reviewer for their comment regarding Figure 6. We inspected all datasets carefully, and we agree that the GFP background appears to be higher, also due to the lower intensity of the transfected RNA, for which we had to use longer exposure times. In addition, we had our concerns that covalent ATTO647N dye-labelling of the RNA could also impact the physicochemical properties of labelled transcripts . We therefore devised a modified strategy that avoids the use of dye labelled RNAs and relies on the following:(i) Production of unmodified, capped EGFP mRNA containing intact UTRs derived from Segment 1 RNA. We chose the EGFP ORF sequence as a target since we have already validated our EGFP-targeting FISH probes labelled with the 647 dye.(ii) NSP2-mCherry cell line was then electroporated with unmodified capped EGFP mRNAs containing either both 5’ and 3’ UTRs derived from Segment 1 RNA, or without the 3’UTR (denoted EGFP-D3’UTR), and then these cells were infected with rotaviruses, subsequently fixed and EGFP mRNA distribution was analysed by smFISH.(iii) Imaging viroplasms using the NSP2-mCherry MA104 cell line was carried out as both NSP5-EGFP and NSP2-mCherry are equally susceptible to RVs, both being useful for visualising viroplasms (doi.org/10.15252/embj.2021107711). mCherry has approximately two-fold lower molecular brightness compared to EGFP , while its absorption spectrum does not overlap with the 641 nm excitation line required for smFISH visualisation of transfected EGFP mRNAs. This allowed us to further reduce the fluorescence background, while EGFP-specific probes do not cross-react with mCherry transcripts (53.6% sequence similarity between mCherry and EGFP).The new images further support the notion that functional, UTR-containing non-viral EGFP mRNAs electroporated to MA104 cells would partially localise to viroplasms in RV-infected cells. We have included these new data in Figure 6, and we updated Methods section.2 = 0.78; Figure 7c, R2 = 0.62 vs R2 = 0.18; please see revised figure legends, and Results section, highlighted in red, pp. 8)Furthermore, for Figure 7, we have also included quantitation of colocalizing signals, as requested by the Reviewer. The Pearson’s correlation coefficients (PCC) and associated errors are now included in the revised manuscript (Figure 7b, R


Ψ) detection on Illumina and Nanopore sequencing data sets. Our software and its R helper package are available as open source solutions.Several high-throughput antibody-free methods for RNA modification detection from sequencing data have been developed. We present JACUSA2 as a versatile software solution and comprehensive analysis framework for RNA modification detection assays that are based on either the Illumina or Nanopore platform. Importantly, JACUSA2 can integrate information from multiple experiments, such as replicates and different conditions, and different library types, such as first- or second-strand cDNA libraries. We demonstrate its utility, showing analysis workflows for N6-methyladenosine (m6A) and pseudouridine . Lately, we have seen a whole wave of antibody-free methods to map RNA modifications transcriptome-wide based on high-throughput sequencing data , 2. Whathttps://github.com/dieterich-lab/JACUSA2), which features better running time performance and captures more complex read signatures involving base substitutions, insertions, deletions, and read truncations , and N1-methyladenosine (m1A) and beyond by sequencing (reviewed in [https://github.com/dieterich-lab/JACUSA2helper) Our manuscript is accompanied by Supplementary Material . In thiiewed in . An addiiewed in . In the METTL3 -/-) [The first use case focuses on a methylation-sensitive RNA restriction enzyme (MazF) assay to identify m6A modified residues in mRNA of HEK293 cells . The secTL3 -/-) .rt-arrest. Briefly, 3 replicates were treated with an Alpha-ketoglutarate-dependent dioxygenase (FTO), which converts m6A to A on mRNA [p−value from the beta-binomial test in JACUSA2 rt-arrest. Our R package JACUSA2helper provides a vignette for MazF assay data analysis. Finally, a comparison to the MAZTER-mine workflow [Lately, we have seen a whole wave of antibody-free methods to map m6A RNA modifications transcriptome-wide based on high-throughput sequencing data. A need for antibody-free complementary approaches has been clearly identified by a rigorous assessment of published MeRIP data sets. McIntyre et al. show that m6A peak overlap varies from ≈30 to 60% between studies, even in the same cell type . An anti on mRNA , and 3 r on mRNA –17 depenworkflow  can be fbona fide candidates for m6A RNA modification. Elevated C → U deamination on RNA level for the active domain over the mutated domain are used to predict m6A modifications on mRNA. Figure A complementary genetic approach is an extension of the TRIBE technique called DART-seq . Meyer aDirect RNA sequencing without involving any cDNA library preparation or PCR-based amplification steps is a unique feature of the ONT sequencing platform. Briefly, native poly-adenylated RNA is threaded through a nanopore in 3’ → 5’ orientation Fig. A. Some gcall-2 mode) to identify read error profile differences between the respective paired samples  are subsequently used in training. The respective feature set consists of deletion, insertion, and substitution scores for every considered 5mer. This amounts to a feature matrix of 3 event scores ×5 motif positions ×2 experiments = 30 features. We subjected this feature matrix to a non-negative matrix factorization (NMF) analysis Fig. : Fig. S5Ψ and 12 2’-O-methyl uridines have been mapped on human 18S rRNA along with 339 unmodified uridines. We compute single nucleotide base substitution, deletion and insertion scores with JACUSA2 for every U residue. The UMAP projection of these scores is shown in Fig. call-2 scores in green and the overlap with annotated uridine modifications in brown. A comparison to a very recent solution for pseudouridine prediction, NanoPsu [Complementarily, we show case the utility of JACUSA2 on another RNA species: human 18S rRNA. We have established custom adapters for rRNA sequencing  and sequenced both, HEK293 wild-type 18S rRNA molecules and unmodified in vitro transcribed 18S rRNA molecules. In this particular use case, we employ JACUSA2 to identify uridine modifications. Taoka et al.  define t NanoPsu , is summhttps://github.com/dieterich-lab/JACUSA2helper). We provide all relevant information to reproduce our results from the presented workflows as vignettes in the JACUSA2helper repository (https://dieterich-lab.github.io/JACUSA2helper/). We will continue to develop JACUSA2 to meet new requirements in terms of prediction performance for all aforementioned sequencing platforms.We present JACUSA2 as a versatile software solution and comprehensive analysis framework for RNA modification detection assays that are based on either the Illumina or Nanopore platform. We have explored different use cases for m6A and uridine modification detection. All of which use antibody-free methods and encompass complex read signatures. JACUSA2 supersedes our initial software in terms of features and speed  from JACUSA1. The goal was to model category count vectors x= for arbitrary K.In order to model counts beyond base calls observed in variant calling, we revised the original implementation of the Dirichlet-Multinomial distribution x consist of 4 elements and is defined as the observed base call counts xBC= at an arbitrary site within the genome. The Dirichlet Multinomial DirMult(α) is used to model base call counts xBC and the Newton iteration method presented by Minka (https://tminka.github.io/papers/minka-newton.pdf) is used to determine parameters α.In variant variant calling, the category count vector BetaBin can be defined as a special case of the more general DirMult when K=2. Given some arbitrary binary feature y the count vector is defined as y=:y=y1+y2. To account for zero counts and where appropriate, we add a pseudocount of one.We implemented a general framework based on the Beta-Binomial distribution to model counts of binary features derived from reads. The Beta-Binomial distribution BetaBin distributions are fitted with the aforementioned method and compared with a likelihood ratio test. The null hypothesis is that the counts of each condition  have the same underlying distribution: For each observed and pooled count vectors LL with a χ2 distribution and provide the p-value.We approximate c∈{I,II}). Given the general modelling framework, the category count vector is defined as the number of observed Arrest events and read Through events yAT=.An arrest event is identified by comparing stop and read through events counts between two conditions  and without (ytot) includes all reads that span the respective insertion, including intron regions and low quality base calls. For each insertion site the category vector pvalue for each insertion site are reported.The underlying statistical framework of INDEL calling is comparable to the previously presented modelling of read arrest events. The general modelling framework is used to model observed insertion and/or deletion counts. In insertion calling, the category vector Deletion calling works analogously to insertion calling with the category vector defined as: Additional file 1 Supplementary Text (PDF). Additional sures and results.Additional file 2 Table S1 (XLSX). m6A target site predictions from MazF-FTO differential digestion assay.Additional file 3 Table S2 (XLSX). m6A target site predictions from YTH-APOBEC mapping assay.Additional file 4 Table S3 (XLSX). m6A target site predictions from Nanopore direct RNA-seq (KO vs. WT).Additional file 5 Table S4 (XLSX). Ψ target site predictions from CMC rt-arrest assay [st assay .Additional file 6 Table S5 (XLSX). Ψ target site predictions from Nanopore direct human 18S rRNA sequencing.Additional file 7 Review history (DOCX). Peer review history.


Lymph node metastasis is usually detected based on the images obtained from clinical examinations. Detecting lymph node metastasis from clinical examinations is a direct way of diagnosing metastasis, but the diagnosis is done after lymph node metastasis occurs.We developed a new method for predicting lymph node metastasis based on differential correlations of miRNA-mediated RNA interactions in cancer. The types of RNAs considered in this study include mRNAs, lncRNAs, miRNAs, and pseudogenes. We constructed cancer patient-specific networks of miRNA mediated RNA interactions and identified key miRNA–RNA pairs from the network. A prediction model using differential correlations of the miRNA–RNA pairs of a patient as features showed a much higher performance than other methods which use gene expression data. The key miRNA–RNA pairs were also powerful in predicting prognosis of an individual patient in several types of cancer.Differential correlations of miRNA–RNA pairs identified from patient-specific networks of miRNA mediated RNA interactions are powerful in predicting lymph node metastasis in cancer patients. The key miRNA–RNA pairs were also powerful in predicting prognosis of an individual patient of solid cancer.The online version contains supplementary material available at 10.1186/s12920-022-01231-x. The spread of cancer cells from the original (primary) tumor to another part of the body is called metastasis. During metastasis, cancer cells travel to other areas through either the bloodstream or the lymph system. As one of the steps of tumor metastasis, lymph node metastasis is commonly observed in cancer patients. Lymph node metastasis itself does not directly endanger the life of patients, but malignant tumors can metastasize to other parts of the body through lymph node metastasis . Many stLymph node metastasis is usually detected based on the images obtained from clinical examinations. Recently deep learning methods such as convolutional neural networks (CNN) have been used to help clinicians detect lymph node metastasis in ultrasound images –5. DetecSeveral studies have reported abnormal gene expression in the process of lymph node metastasis . For exaExpression data of mRNAs and noncoding RNAs are valuable resources for studying and predicting lymph node metastasis. But, cancer is a complex and heterogeneous disease, so abnormal expression of individual genes cannot fully explain the development and metastasis of cancer. The development and metastasis of cancer is better explained by the dysregulation of gene interactions rather than by individual genes alone. For example, AKT1 is abnormally expressed in many types of cancer and the up-regulation of AKT1 has been known to be related to lymph node metastasis. But, recent studies found that miR-138 binding to AKT1 regulates the expression of AKT1 in tongue squamous cell carcinoma . miR-519Salmena et al.  proposedIn this study, we propose a new method for predicting lymph node metastasis based on differential correlations of miRNA-mediated RNA interactions in cancer. The types of RNAs considered in this study include mRNAs, lncRNAs, miRNAs, and pseudogenes. We constructed cancer patient-specific networks of miRNA mediated RNA interactions, and identified key miRNA–RNA interactions from the networks. We built a model using the correlations of the miRNA–RNA pairs as features for predicting lymph node metastasis. The model showed a much higher performance than other methods which use gene expressions alone. The key miRNA–RNA pairs were also powerful in predicting prognosis of individual patients in several types of cancer. The rest of this paper presents the method and the experimental results in several types of cancer.Using the mentclass2pt{minimp-value We compared the performance of stacking models using two different types of features: Tables We also compared the performance of our method with that of Zhang’s method  using thp-value from the log-rank test in each type of cancer. The remaining miRNA–RNA pairs with p-value less than 0.01 are available in Additional file We analyzed the overall survival of cancer patients by performing a log-rank test with respect to entclass1pt{minimap-values from the log-rank test with p-value  data portal  for primIn TCGA, there were 18 types of solid cancer which have at least 200 samples. Among the 18 types, 6 types were excluded due to insufficient data on lymph node metastasis in their tumor samples. In the remaining 12 types of solid cancer, we selected the types which have at least 30 normal samples and 50 tumor samples with lymph node metastasis. Only 7 types of solid cancer satisfied such criteria: breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD) and thyroid carcinoma (THCA).Samples with lymph node metastasis: tumor samples with T stage of 1–4, N stage of 1–3, and M stage of 0Samples without lymph node metastasis: tumor samples with T stage of 1–4, N stage of 0, and M stage of 0Table The clinical profiles of the TCGA data includes the Tumor, Node, Metastasis (TNM) stage of samples. Samples with an M stage of 1 were excluded because distant organ metastasis often coexists with lymph node metastasis and makes the evaluation of prediction difficult. Based on the TNM staging system, we clustered the tumor samples into those with lymph node metastasis and and those without lymph node metastasis.The gene names of the TCGA data are represented by Ensembl ID. Thus, we obtained the annotation files from the Ensembl project  and deteWe filtered out genes with an average count below 1. In RNA-seq data, counts are non-negative integer values. The count of unexpressed genes is 0, so the count of expressed genes is at least 1. Since the genes with the average count ) method .Given RNA-seq gene expression data of miRNAs and ceRNAs , compute MIC of miRNA–RNA pairs in tumor samples and normal samples.Select those miRNA–RNA pairs with MIC Compute the Pearson correlation coefficient (PCC) of each miRNA–RNA pair in normal samples.Recompute PCC in normal samples perturbed by a single tumor sample.Compute the difference in PCC  of Our approach to predicting lymph node metastasis is based on the differential correlations of miRNA–RNA interactions of a sample from normal samples. To obtain the differential correlations of miRNA–RNA interactions of a sample, we first selected miRNA–RNA interactions with the maximal information coefficient (MIC). Pearson correlation coefficient (PCC) is the most commonly used for gene association. However, we used MIC instead of PCC to select potential miRNA–RNA pairs for a few reasons: (1) PCC can measure linear association only, but MIC measures linear or non-linear association between two variables. (2) MIC is less susceptible to outliers than PCC.As mentioned earlier, any of lncRNAs, mRNAs, and pseudogenes with common miRNA response elements compete to bind to the same miRNA, so can act as competitive endogenous RNAs (ceRNAs). To obtain initial miRNA–RNA pairs we computed the maximal information coefficient (MIC)  of each I by Eq.  and logistic regression (LR) as base models and combined them using stacking ensemble learning in the scikit-learn library . We firsThe tumor samples obtained from TCGA were divided into training and test sets with the ratio of 7:3. The parameters of the prediction model were determined by the grid search in the training set. When training and validating the prediction model, tumor samples with lymph node metastasis were considered as positive instances, and tumor samples without lymph node metastasis were considered as negative instances.For each type of cancer, we constructed a ceRNA network with the miRNA–RNA pairs obtained by the Wilcox test. A node of the ceRNA network represents one of miRNA, mRNA, lncRNA or pseudogene, and an edge represents the interaction of miRNA with other RNAs.The patient-specific ceRNA network is a sub-network of the ceRNA network. For each miRNA–RNA pair, we computed the median of the absolute value of http://bclab.inha.ac.kr/LNM/.Additional file 1. Performance of two base models (logistic regression and SVM) and the ensemble model by stacking the base models in predicting lymph node metastasis.Additional file 2. Potential prognostic miRNA–RNA pairs in seven types of cancer.Additional file 3. TCGA barcodes of all normal samples and tumor samples studied in our work.All additional files are available at


Dihydrouridine is a modified nucleotide universally present in tRNAs, but the complete dihydrouridine landscape is unknown in any organism. We introduce dihydrouridine sequencing (D-seq) for transcriptome-wide mapping of D with single-nucleotide resolution and use it to uncover novel classes of dihydrouridine-containing RNA in yeast which include mRNA and small nucleolar RNA (snoRNA). The novel D sites are concentrated in conserved stem-loop regions consistent with a role for D in folding many functional RNA structures. We demonstrate dihydrouridine synthase (DUS)-dependent changes in splicing of a D-containing pre-mRNA in cells and show that D-modified mRNAs can be efficiently translated by eukaryotic ribosomes in vitro. This work establishes D as a new functional component of the mRNA epitranscriptome and paves the way for identifying the RNA targets of multiple DUS enzymes that are dysregulated in human disease. This study presents dihydrouridine sequencing (D-seq) for transcriptome-wide mapping of this RNA modification with single-nucleotide resolution, revealing dihydrouridine at new locations across the yeast transcriptome that suggest a broad role in folding functional RNA structures. Dihydrouridine (D) is a modified version of uridine that is installed by dihydrouridine synthase (DUS) enzymes in all domains of life. It is of great interest to determine the locations of D modifications because elevated expression of DUS and elevated D levels in tumors are associated with worse outcomes for patients in lung , liver , and kidThe D modification is a reduction of the C5-C6 double bond in uridine that has multiple effects on RNA structure. First, D subtly distorts the pyrimidine ring  causing Profound alteration of RNA conformation and structure by D would be expected to affect multiple steps in mRNA metabolism depending on the location of the D nucleotide. For example, D antagonizes formation of RNA duplexes ,14, whicIn this paper, we report the development of a novel method to map D residues in RNA in high-throughput. Our method takes advantage of known D-selective chemistry –22 to redus1Δ dus2Δ dus3Δ dus4Δ quadruple mutant strain lacking all DUS activity [In light of previous work showing that DUS1 and DUS3 cross-link to mRNA in both yeast and human cells ,10, we pactivity  and a ma- treatment conditions used previously to map D in tRNA by primer extension [To identify RT stopping conditions for D, we tested different chemistries for selective RT stopping at D compared to U. Strong OHxtension  proved txtension . We prepxtension . We combWe tested the D-seq approach in budding yeast where positive control D sites in cytoplasmic tRNAs have been extensively although not exhaustively characterized ,6. We ob2,2G) [2,2G as well as 1-methyladnosine (m1A) and 1-methylguanosine (m1G) [As implemented here, D-seq has specific “blind spots” in tRNAs. First, the cDNA size selection step precluded detection of DUS3-dependent Ds at position 47 because they are too close to the 3′ end of the transcript. In addition, several known target sites of DUS1, DUS2, and DUS4 were not detected because they are shadowed by another D 3′ of them . Other k2,2G) . Pretreane (m1G) –27 shoulWe then examined other classes of non-coding RNAs (ncRNAs) with sufficient coverage . We idenLike tRNAs, snoRNAs must fold to perform their cellular function ,30. GiveWe next analyzed yeast mRNA for D. We used a simple statistical metric, a modified Z-score, to distinguish robust DUS-dependent RT stops from noise in these less abundant RNAs. See . Applyin35S-Met incorporation into full-length Top7 protein by SDS-PAGE and autoradiography  was generated by run off transcription with T7 RNA polymerase and gel purified on an 8% urea-TBE polyacrylamide gel electrophoresis (PAGE) gel. To compare the harshness of the different D-modifying treatments, a synthetic RNA was incubated either under published D mapping conditions , under pls as in .dus1Δ dus2Δ) and FX-42(dus4Δ dus3Δ), followed by sporulation on SD media and subsequent tetrad dissection. Genotyping was confirmed by PCR using the following oligos: oAD56_DUS1kanR_F, GCAAGGTGATCGTCAAACTGCACT, oAD57_DUS1kanR_R, ATGGAGACGGAGTTGAACATTTTCT, oAD58_DUS2kanR_F, TAGAGACGTAGTTATCCATTCGTCC, oAD59_DUS2kanR_R, CTTTGGACGATAAACTAAAGGGTTT, oAD60_DUS3kanR_F, GGTAATAGTACACGGGATGAAGAGA, oAD61_DUS3kanR_R, TATTTTGATTTTCTTGGAACCCATA, oAD62_DUS4kanR_F, ACTGCATTCATTTTTGTTAGAAAGG, oAD63_DUS4kanR_R, CAAGCTATCTGGAAAAGAGGTGTTA.Quadruple DUS mutant strain was generated by mating double-knockout strains FX-34 , as described . Two seq4 treatment of RNA was as follows: Poly-A+ mRNA was resuspended in 18 μL ddH2O and treated 2 μL of 10 mg/mL NaBH4 in 500 mM Tris pH 7.5 at 0°C for 1 hour. The BH4 treatment was quenched with 4 μL of 6 N CH3COOH and precipitated. RNA fragments were dephosphorylated and end repaired with CIP (NEB) and PNK (NEB), followed by size selection of RNA fragments (70 to 80 nt) on an 8% urea-TBE PAGE gel. RNA fragments were eluted from gel slices overnight at 4°C with gentle rocking in 400 μL RNA elution buffer  followed by precipitation). Ligation of a preadenylated adaptor (IDT) was carried out with T4 RNA ligase (NEB) in buffer without ATP  at 22°C for 2.5 hours, followed by precipitation. Adapter ligated RNA fragments were reverse transcribed with SuperScript III RT . RNA and primer were denatured and annealed at 95°C for 5 minutes then placed on ice for 5 minutes. After annealing, 1 μL 10 mM dNTPs, 1 μL 0.1 M DTT, 1 μL RNAsin (Promega), 3 μL 5× First Strand Buffer (Thermo), 1 μL SSIII RT were added, and reverse transcription was carried out at 50°C for 1 hour. Truncated cDNAs were size selected (50 to 80 nt) and purified on an 8% urea-TBE PAGE gel, followed by precipitation. cDNAs were eluted from gel slices overnight at room temperature with gentle rocking in 400 μl DNA elution buffer . A 5′ adapter was ligated on to the cDNA using T4 RNA ligase. 0.8 μL 80 uM linker (IDT) was mixed with 1 μL DMSO, 5 μL of eluted cDNA, incubated at 75°C for 2 minutes and immediate placed on ice for 2 minutes. After cooling, 2 uL RNA Ligase buffer (NEB), 0.2 μL.1 M ATP (NEB), 6.5 μL PEG-8000 (NEB), 3.6 μL ddH2o, and 0.5 μL T4 RNA ligase 2 were added to the cDNA/linker mix. The ligation was incubated overnight at 22°C. The ligation was cleaned up with Dynabead MyOne Silane magnetic beads (Thermo) according to the manufacturer’s instructions. Sequencing libraries were amplified from 5′ and 3′ linker ligated cDNA using Phusion DNA polymerase (NEB). PCR products were gel-purified, precipitated, pooled, and sequenced on an Illumina HiSeq 2500.Yeast RNA was fragmented in 10-mM ZnCl2 at 94°C for 1 minute and precipitated. BH4 treatment and selecting for full length (90 to 100 nt) RT products.RNA seq libraries were generated in parallel with D-seq libraries by omitting the NaBHDemultiplexed reads were adapter trimmed using BBTools ,40 bbdukD-sites were identified as statistical outliers in position-specific accumulation of D-seq reads. We required at least 50 reads in the 100 nt window surrounding the test position and quantified and compared sites using a modified Z-score, in which the position of interest is excluded when calculating the mean and standard deviation. For all of the test positions that met the read cutoff, we calculated a Z-score of read ends based on the distribution of read ends in the 100 nt window centered on the test position.In the highly structured and heavily modified tRNAs and snRNAs, we identified D sites by analyzing the absolute deviation around the median (MAD), which scores sites relative to the median rather than the mean. See below regarding the choice of metric for different classes of RNA. We considered every position in the test transcriptome where there were more than 50 reads in the 100 nt window surrounding the test position and additionally required that the window median was greater than zero. For all test positions that met the read cutoff, we calculated a MAD score of read ends based on the distribution of read ends in the 100 nt window centered on the test position.And:Two different scoring approaches were necessary for unbiased identification of D sites in different classes of RNA due to large differences in the distributions of strong RT stops as well as RNA abundance. The presence of multiple RT stops in close proximity, which is common in tRNA due to pervasive RNA modification and strong secondary structure, precludes use of the intuitive and statistically principled Z-score which we use for less structured mRNAs. The Z-score cannot identify RT stop signals in windows with multiple strong signals because these background RT stops in ncRNA dampen the signal at the position of interest  S2E FigS2E Fig. wt > 40 and MADdus < 40. For snRNA, MAD score cutoffs were MADwt > 12 and MADdus < 8. For mRNA, the Z-score cutoffs were Zwt > 10 and Zdus < 7.For each set of peaks , we defined library cutoffs for peak score by plotting the distribution of peak scores in each library as an inverse CDF. We set cutoffs for D peaks by requiring D site scores be substantially greater than the score at which the distributions of WT and DUS KO diverge in 3 out of 3 WT replicates, and less than the score at which the distributions diverge in the quad KO. For tRNA, MAD score cutoffs were MADS. cerevisiae was downloaded from GEO (accession number GSE84537). Raw reads were preprocessed and aligned according to the original publication, and DMS reactivities were calculated as the ratio of mutations to coverage. Reactivities around called D sites in mRNAs or snRNAs were pooled for each nucleotide position relative to the respective D coordinate. Values were included in the analysis only if the coverage was larger than 350 and the nucleotide identity in the transcript was A or C. To visualize background reactivities and control for nucleotide bias, 70 samples matching the size of called D sites were randomly drawn from a population of background sites that fell below the Z-score cutoffs in WT and quad KO. Significance testing was done using 2-sided Mann–Whitney U tests between reactivities around the full set of background positions and the called D sites.A previously published DMS-MaPseq  data set2 in a final reaction volume of 50 μL for 6 hours at 37°C. An equal volume of water was then added to each sample before filtration through a 0.2 μm PVDF filter . A total of 10 μL of each sample was separated by reverse phase ultra-performance liquid chromatography on a Shim-pack GIST C18 2 μm, 2.1 × 50 mm column  on a Nexera LC-40D XS liquid chromatography system using a gradient of 5 mM ammonium acetate pH 5.3 and acetonitrile. After separation, samples were analyzed by mass spectrometry on a Shimadzu LCMS-8060 Triple Quadrupole Liquid Chromatograph Mass Spectrometer (Shimadzu). Nucleosides were quantified using the following nucleoside-to-base transitions: 267.966 > 136.000 (A), 284.004 > 152.100 (G), 245.30 > 113.10 (U), and 247.20 > 115.15 (D). Mixes of nucleoside standards were injected alongside the samples in the same run to generate standard curves, from which concentrations of each nucleoside in each sample were calculated. The percentages of modified to unmodified nucleoside in each sample were calculated based on calibrated concentrations. These conditions were adapted from reference [For each sample, duplicate digestions of 50 ng and 750 ng for each sample were digested with 5 U/uL Benzonase , 0.1 U/uL phosphodiesterase , 1 U/uL alkaline phosphatase (Sigma #P5521) in 500 mM Tris-HCl pH 8.0, 10 mM MgClwww.gelanalyzer.com).mRNAs were designed based on the coding sequence of Top7  by repla35S methionine, 0.24 μL RNasin, and ddH2O to 20 μL. Translation reactions were incubated at 30°C for 90 minutes and quenched with 20 μL 2X SDS sample buffer. Translation reactions were then incubated at 60°C for 20 minutes and resolved on a 14% to 20% SDS-PAGE gel. Gels were fixed in 30% methanol, 10% acetic acid, incubated in Amplify solution , and dried on a vacuum drier. Dried gels were exposed for a minimum of 12 hours on a storage phosphor screen , scanned (Bio-Rad), and quantified using GelAnalyzer 19.1 .Uncapped Top7 mRNAs were translated in nuclease-treated RRL (Promega). The 500 ng mRNA was incubated in 8.4 μL RRL, 0.24 μL 1 mM amino acid mixture minus methionine, 0.48 μL S1 Fig3CN, or NaBH4, precipitated and run on an 8% urea-PAGE gel. (B) Multiple RTs stall at reduced D. 1: 4D RNA treated with borohydride and benzyhydrazide, 1: 4D RNA treated with borohydride 3: 4U RNA 4: 4D RNA. (C) Bioanalyzer analysis of total RNA and PolyA+ mRNA for mass spectrometry. D-seq, dihydrouridine sequencing; RT, reverse transcriptase.(A) Unmodified 194 nt RNA was treated with strong base (OH‒), NaBH(EPS)Click here for additional data file.S2 Fig2,2G. m2,2G position is highlighted. (D) Plots of cDNA end positions in cytoplasmic 25S rRNA at the position orthologous to Escherichia coli position 2449. The data underlying this figure can be found in 2,2G, N2,N2-dimethylguanosine; snRNA, small nuclear RNA.(A) Inverse CDF plots of MAD score for possible D positions  in tRNAs, MAD score for all tRNA U positions, and all snRNA U positions. (B). Plots of cDNA end positions in tRNA GlnCTG and tRNA LysTTT containing multiple Ds. The 3′ most D peak is highlighted. (C) Plots of cDNA end positions in SerGCT where D signal is blocked by m(EPS)Click here for additional data file.S3 Figdus cutoff and red line is Zwt cutoff. (B) Plots of cDNA end positions for 5 false positive sites in mRNAs. Shadowing D peaks are highlighted in black. False positive sites are highlighted in red. (C) Uncropped Top7 35S SDS-PAGE and mRNA 1% BP-TE agarose gels from (A). Inverse CDF plots of Z-score for mRNA mapping reads. Blue line is Z(EPS)Click here for additional data file.S1 TabletRNA name, position, strand, MAD score, standard deviation, and reads in window for each replicate.(XLSX)Click here for additional data file.S2 TablesnRNA name, position, strand, MAD score, standard deviation, and reads in window for each replicate. snRNA, small nuclear RNA.(XLSX)Click here for additional data file.S3 TablemRNA name, position, strand, Z-score, standard deviation, and reads in window for each replicate.(XLSX)Click here for additional data file.S1 Raw images(PDF)Click here for additional data file.