Changes in the concentrations of NTPs or NDPs modified NS5B RNA polymerase activity in vitro, in particular de novo RNA synthesis and template switching. Furthermore, the Gua-mediated changes were associated with a significant increase in the number of indels in viral RNA, which may account for the reduction of the specific infectivity of the viral progeny, suggesting the presence of defective genomes. Thus, a proper NTP:NDP balance appears to be critical to ensure HCV polymerase fidelity and minimal production of defective genomes.In the course of experiments aimed at deciphering the inhibition mechanism of mycophenolic acid and ribavirin in hepatitis C virus (HCV) infection, we observed an inhibitory effect of the nucleoside guanosine (Gua). Here, we report that Gua, and not the other standard nucleosides, inhibits HCV replication in human hepatoma cells. Gua did not directly inhibit the  Ribonucleoside metabolism is essential for replication of RNA viruses. In this article we describe the antiviral activity of the natural ribonucleoside guanosine (Gua). We demonstrate that hepatitis C virus (HCV) replication is inhibited in the presence of increasing concentrations of this ribonucleoside and that this inhibition does not occur as a consequence of a direct inhibition of HCV polymerase. Cells exposed to increasing concentrations of Gua show imbalances in the intracellular concentrations of nucleoside-diphosphates and triphosphates and as the virus is passaged in these cells, it accumulates mutations that reduce its infectivity and decimate its normal spreading capacity. Flaviviridae family of (+)ssRNA viruses. The HCV genome encodes information for the synthesis of ten proteins: core (C), envelope glycoproteins (E1 and E2), an ion channel (p7), NS2 protease, protease/helicase NS3 (and its cofactor NS4A), membrane-associated protein NS4B, regulator of viral replication NS5A, and RNA-dependent RNA-polymerase NS5B [Positive-sense single-stranded RNA viruses [(+)ssRNA viruses] are the most abundant pathogens for humans. The hepatitis C virus (HCV) is a hepacivirus that belongs to the roteins E and E2, There are several ways to approach the control of RNA viral diseases. Inhibition of HCV functions by direct-acting antiviral agents (DAAs) has yielded sustained virological responses of about 98% ,3. Thus,de novo biosynthesis of GTP  (genotype 2a) and GNN [GNNFLAG2(p7-nsGluc2A)] (a replication-defective virus with a mutation in the NS5B RNA-dependent RNA polymerase)  was added at 500 μg/ml; after 4 h crystals were dissolved with 100 μl of DMSO and the O.D. measured at 550 nm; 50% cytotoxicity was calculated from quadruplicate determinations CTP  and nucleoside-triphosphate as indicated in each experiment. When appropriate, reactions were performed in the presence of increasing concentrations of nucleotide diphosphates. Reactions were carried out in a final volume of 10 μl, at room temperature for 30 minutes, and stopped using EDTA/formamide loading buffer. E1/E2 products were resolved using 1% agarose gel electrophoresis. Agarose gels were dried in an electrophoresis gel dryer (BioRad). LE19 products were resolved using denaturing polyacrylamide  gel electrophoresis. Gels were exposed to phosphorimager screens and scanned with a Typhoon 9600 phosphorimager (Molecular Dynamics). Sample quantification was performed from parallel experiments. Band volume values were obtained by using the ImageQuant software provided with the apparatus .RNA polymerase assays were carried out using two different RNA substrates, the symmetric substrate LE-19 (sequence 5’ UGUUAUAAUAAUUGUAUAC 3’), which is capable of primer-extension (PE), ing (TS) ,57, and ing (TS) . Except 2 test, two-way ANOVA, and ANCOVA. Unless indicated otherwise, the statistical significance is indicated by asterisks: * p<0.05; ** p<0.005; *** p<0.0005.The statistical significance of differences between mutation frequencies was evaluated by the chi-square test. Statistical comparisons among groups were performed with Student’s T-tests, χS1 Fig(TIF)Click here for additional data file.S2 FigA) Effect of the addition of 800 μM of guanosine on VSV infectious progeny production. The differences between the slope of the treated and untreated has been calculated using an ANCOVA test in GraphPad .  Same as A but the virus used are FMDV (B) and LCMV (C). Statistical differences between slopes were not found for FMDV:  and for LCMV: . Titrations were carried out in triplicate.BHK21c2 cells were seeded and 16 hours prior to infection cells were untreated or treated with 800 μM of guanosine. Cells were infected with two different m.o.i. (0.05 or 0.0005), supernatants were collected at different times after infection  and viral progeny production was analyzed. The m.o.i. and the treatment used are indicated in the upper box. The viral titer expressed as log10 pfu/ml is indicated in the ordinate. The time post-infection is indicated in the abscissa expressed in hours. ((TIF)Click here for additional data file.


Finally, BLLF2 was proven to transport into the nucleus via a Ran-dependent and importin β1-dependent pathway. This mechanism may contribute to a more extensive insight into the assembly and synthesis of EBV virions in the nucleus, thus affording a new direction for the treatment of viruses.Epstein–Barr virus (EBV), the pathogen of several human malignancies, encodes many proteins required to be transported into the nucleus for viral DNA reproduction and nucleocapsids assembly in the lytic replication cycle. Here, fluorescence microscope, mutation analysis, interspecies heterokaryon assays, co-immunoprecipitation assay, RNA interference, and Western blot were performed to explore the nuclear import mechanism of EBV encoded BLLF2 protein. BLLF2 was shown to be a nucleocytoplasmic shuttling protein neither by a chromosomal region maintenance 1 (CRM1)- nor by a transporter associated with antigen processing (TAP)-dependent pathway. Yet, BLLF2’s two functional nuclear localization signals (NLSs), NLS1 ( Epstein–Barr virus (EBV), a member of the gamma herpes virus subfamily, is the most universal and persistent pathogen that prevails in humanity, with almost 90% of the world’s population maintaining a lifelong subclinical infection . As the via various cellular transporter exportins and importins by distinguishing favorable motifs on the target proteins named nuclear localization signal (NLS) and nuclear export signal (NES) (The trafficking of target proteins between the nucleus and cytoplasm of a eukaryote is achieved through the nuclear pore complex (NPC) embedded in the nuclear membrane, which bears an extremely preserved architecture with an eightfold rotational symmetry that carries a central aqueous cylindrical tunnel embraced with a huge number of specialized appendages . Dissimial (NES) .via binding to RanGTP and release them from the nucleus into the cytoplasm after GTP hydrolysis  . Transporter associated with antigen processing (TAP/NXF1) is also a major export receptor; both of them export mRNA from the nucleus to cytoplasm in metazoan cells and have been profoundly investigated. Although TAP is not a strong RNA-binding protein, it mainly binds to the Aly/REF mRNA adaptor protein, an element of the messenger ribonucleoprotein particles (mRNPs), which is carried out of the nucleus ear pore . TAP/NXFear pore , whereasear pore . The CRMear pore . In addiear pore . Exportiear pore . Importaear pore . Besidesear pore .via its NLS to accelerate its nuclear import , EBNA-LP interplays with importin α1 through functional NLS to facilitate its efficient nuclear localization for its interaction with EBNA2 . The N-tr import , which mr import . Furtherr import . HoweverBLLF2 is an EBV-encoded protein with an unknown function. Our previous study manifested that BLLF2 localizes in the nucleus ; nonetheAll cloning enzymes were supplied by Thermo Scientific, with the exclusion of KOD-Plus-Neo DNA polymerase and T4 DNA Ligase, which were afforded by TOYOBO (Japan) and Takara , respectively. Mouse anti-Flag monoclonal antibody (mAb) and rabbit anti-YFP polyclonal antibody (pAb) were offered by Abmart  and RayBiotech, respectively. Nonspecific IgG was purchased from Proteintech . Alkaline phosphatase (AP)-linked anti-rabbit IgG and anti-mouse IgG were provided by Cell Signaling Technology , and protein A/G PLUS-Agarose was bought from Santa Cruz .EcoRI- and BamHI-digested vectors pMyc-N1 (regenerated from pEYFP-N1) and pEYFP-N1 , respeciversity . All conBesides, pseudorabies virus (PRV) UL31-EYFP, pEYFP-BFLF2, pUL4-EYFP, pTAP-mCherry, pCRM1-mCherry, pDN kα1-mCherry, pDN kβ1-mCherry, pRan-Q69L-mCherry, and pFlag-importin β2 expression plasmids were also constructed in our lab previously . PlasmidHEK393T cells, COS-7 cells, and NIH3T3 cells were cultured and transfected by employing polyethylenimine , as previously described . 24 h poCOS-7 cells, cultured on cover-slips in the 24-well plate , were co-transfected with the indicated expression plasmids for 24 h. Transfected cells were then fixed with 4% paraformaldehyde  for 30 min. Subsequently, the fixed cells were washed three times with PBS , followed by staining with DAPI for 5 min at 37°C. Then, the stained cells were placed on the microscope slides  for confocal microscope analysis. Samples were assayed through a Leica confocal laser scanning microscope (Leica SP8) using 63 × 1.4 NA immersion oil lenses, with excitation wavelength at 512 nm for YFP/EGFP, 405 nm for DAPI, and 555–580 nm for mCherry/RFP.The interspecies heterokaryon assays were performed following previous studies . In shorg at 4°C for 15 min. For each Co-IP, supernatants were immunoprecipitated with anti-YFP pAb, anti-Flag mAb, or nonspecific IgG at 4°C for 2 to 4 h, and then incubated overnight with protein A/G beads. The beads were then washed at least four times with PBS buffer at 2500 g at 4°C for 5 min. Subsequently, the complex with SDS-PAGE loading buffer was boiled for 10 min and subjected to SDS-PAGE/WB analysis after centrifuging at 12,000 g for 5 min. For WB analysis, the immunoprecipitated proteins were stained with the indicated Abs. Cell lysates were also directly subjected to WB analysis to verify the expression of specific proteins.Co-immunoprecipitation (Co-IP) and Western blot (WB) assays were performed as described previously . Briefly44RRPR47 and 52KRRR55, and pat7 48PPVAKRR54 and 49PVAKRRR55) and two supposed NES, namely, NES1 (83VQSPPQITAVIQL95) and NES2 (102MRPPIYL108)  . SubsequPIYL108) . To exclPIYL108) . BesidesPIYL108) .A nuclear localization protein equipped with functional NES and NLS can be theoretically transported from a donor to a recipient nucleus . ConcernBased on the predicted motifs of NESs and NLSs, full-length BLLF2 was firstly cut into two fragments [amino acids (aa)1–58 and aa59–148] and then fused to the C-terminus of EYFP  to detecTo further find out the functional NES of BLLF2, pEYFP-BLLF2(83–148) was constructed  and tran44RRPR47) is a functional NLS, pEYFP-BLLF2(1–47) and pEYFP-BLLF2(1–43) were constructed  and pat7 (48PPVAKRR54 and 49PVAKRRR55) in aa48–58 of BLLF2 are functional, pEYFP-BLLF2(48–82), pEYFP-BLLF2(48–58), and pEYFP-BLLF2(59–82) were constructed  of aa44–58 has a role in nucleolus localization, pEYFP-BLLF2(44–58) was constructed (44RRPR47) may be a functional nucleolus localization signal and 44RRPRPPVAKRRRFPR58 is an authentic NLS. To certify the nucleolus localization role of 44RRPR47, 44RRPR47 was mutated to 44AAAA47 in the full length of BLLF2 and then fused to the C-terminus of EYFP to yield pEYFP-BLLF2(44–47)m. As a result, the nuclear and nucleolus localization pattern of BLLF2 was alternated into pan-nuclear localization without nucleolus  and pEYFP-BLLF2(21–43) were constructed  and assaTo eventually determine the minimum NLS region of aa1–43, pEYFP-BLLF2(6–31) and pEYFP-BLLF2(16–31) were constructed  and tranRan (Ras-associated nuclear protein), a small GTPase belonging to the RAS superfamily, is specialized and crucial for the nuclear accumulation of proteins with a canonical NLS . Here, dvia Ran- and importin β1-dependent pathway.It is universally established that the importin α/β heterodimer can discern canonical NLS and facilitate the nucleocytoplasmic transport of particular target proteins . To resoChromosomal region maintenance 1 (Exportin1/XPO1), a member of the importin β family, mediates the nuclear export of proteins by combining them to their classical NESs . TherefoIt is reported that CRM1 overexpression can advance the nuclear export of CRM1-dependent proteins . As a reBesides CRM1, TAP (NXF1), the critical mRNA export receptor, is also related to the nuclear export of distinct proteins , which cTo further test the assumption mentioned above, the interactions of BLLF2 with human importin α/β molecules, importin α1 (kα2), importin α3 (kα4), importin α5 (kα1), importin α7 (kα6), importin β1, and importin β2, were inspected. Plasmid expressing EYFP-BLLF2, EYFP-BFLF2, PRV UL31-EYFP, or EYFP vector was co-transfected with Flag-tagged importins or Flag vector into HEK293T cells for 24 h and then cell lysates were harvested for Co-IP assays. In comparison to the negative IgG , EYFP-BLvia importin β1.To finally verify the nuclear translocation mechanism of BLLF2, an shRNA expression plasmid was created to knock down the expression of importin β1. Compared to the pSuper vector and shRNA control vector (shRandom), shImportin-β1 could efficiently downregulate the expression of importin β1 , suggestAmoeba proteus  to prepare its antibody by using the mature antibody preparation technology in our lab , simulating the function(s) and/or characteristics of BLLF2 to a certain extent during EBV infection. As we have known, a specific antibody is a key tool to investigate the function of the target protein. In our previous experiment design, we had considered we could use a specific BLLF2 antibody to detect the time-course expression and subcellular localization of BLLF2 during EBV lytic infection-induced from EBV latent cells, which can be used to analyze the correlation between the function(s) and characteristics of BLLF2 during EBV lytic infection and BLLF2 transient expression. We had tried to induce the expression of BLLF2 in prokaryotes  and two potential NES 83VQSPPQITAVIQL95 (NES1) and 102MRPPIYL108 (NES2). In the present study, aa1–82 was divided into two regions aa1–47 and aa48–82. In aa1–47, aa16–31 was identified as a genuine functional NLS (NLS1). Moreover, aa48–58 was also established as a functional NLS in aa48–82, and aa44–47 exerted a substantial role in the nucleolus localization of BLLF2, aa44–48 was therefore another bona fide functional NLS (NLS2). These two functional NLSs are not adjacent to each other, and the functional NLS is generally not long. Thus, the functional NLSs of BLLF2 are aa16–31 and aa44–58. However, no functional NES was identified yet.For accomplishing nucleocytoplasmic shuttling, nuclear localization of a specific protein demands functional NES and NLS engagement. Bioinformatics analysis revealed that BLLF2 carries four potential NLS motifs  are accomplished by associating with TAP/NXF1 . HSV-1 I pathway . Influen pathway . Additio pathway , which iical NES . Besides pathway . In thisvia a Ran-, importin α1-, and α7-mediated pathway  and 44RRPRPPVAKRRRFPR58 (NLS2), and the nucleolus localization signal 44RRPR47. Additionally, BLLF2 was demonstrated to traffic from the cytoplasm to the nucleus via a Ran- and importin β1-dependent mechanism, without importin α. However, the nuclear expert of BLLF2 was mediated neither by a CRM1- nor a TAP-dependent pathway.In conclusion, we identified that EBV BLLF2 is located in the nucleus and nucleolus, which were achieved by two functional NLSs, The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author/s.MSC and MLL conceived and designed the experiments. JJL, YJG, YXD, LH, BLL, SYD, JYZ, LX, SXS, XJH, and XLZ performed the experiments and analyzed the data. JJL, MSC, and MLL wrote the manuscript. All authors contributed to and have approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Here, we establish a convenient and reliable RNA fluorescent three-hybrid (rF3H) method to detect/interrogate the interactions between RNAs and proteins in cells. A GFP tagged highly specific RNA trap is constructed to anchor the RNA of interest to an artificial or natural subcellular structure, and RNA–protein interactions can be detected and visualized by the enrichment of RNA binding proteins (RBPs) at these structures. Different RNA trapping systems are developed and detection of RNA–protein complexes at multiple subcellular structures are assayed. With this new toolset, interactions between proteins and mRNA or noncoding RNAs are characterized, including the interaction between a long noncoding RNA and an epigenetic modulator. Our approach provides a flexible and reliable method for the characterization of RNA–protein interactions in living cells.RNA–protein interactions are the structural and functional basis of significant numbers of RNA molecules. RNA–protein interaction assays though, still mainly depend on biochemical tests   The RNA in cells is commonly associated with RNA binding proteins (RBPs), which is required for the proper function of both RNAs and proteins. On the one hand, the processing, transport, function and stability of RNAs are modulated by the RBPs, e.g. mRNA processing and ribosome assembly require different groups of RBPs to accomplish these biological processes . On the in vitro, which is in praxis complicated and with limited throughput. Other biochemical methods like immunoprecipitation, cross-linking and proximity-labeling, in combination with high throughput sequencing or mass spectrometry  require purification of RNAs as well as proteins to identify their physical interaction by electrophoresis trometry , have betrometry ,13 or RNtrometry , allow ftrometry .To overcome these limitations, here we introduced a RNA fluorescence three-hybrid (rF3H) method for RNA–protein interaction analyses in cells. In this method, RNA molecules are recruited and anchored at specific subcellular structures by a designed RNA trap, and the interaction between the trapped RNAs and fluorescently labeled RBPs is visualized and identified via fluorescence co-localization at these subcellular structures. With this new method, we measured the interactions between proteins and different types of RNAs, and in particular studied the interaction between an epigenetic factor EZH2 protein and the HOTAIR non-coding RNA (ncRNA). Different RNA trapping systems and multiple cellular anchoring structures were also explored for broad applications of this tool. Our study established a fluorescence hybrid assay in mammalian cells, providing a flexible and reliable approach for the characterization of RNA–protein interactions.The information of plasmids constructed in this study was shown in The CMV cassette test RNA plasmids were developed on pEGFP-N1 (Clontech). The whole EGFP ORF was firstly replaced with 6 times of ms2 stem–loop sequences to construct the ms2 RNA plasmid. After that, 4 of 6 times ms2 stem–loops were replaced by 4 times of wildtype or mutant  pp7 stem–loops, whole-length NORAD, HOTAIR 1–300 and its shortened fragments, as well as NORAD/HOTAIR 201–300 hybrid fragment, to get the respective ms2 tagged RNA plasmids. The PUF recognition sequence-tagged RNAs were constructed by inserting a 9-nt sequence (TGTTGTATA) to the 3′ end of ms2, pp7 and HOTAIR 1–300 sequences of their plasmids. And the U6 cassette ms2-pp7 RNA plasmid was constructed by cloning the RNA sequence from its CMV cassette plasmid into the U6 cassette of the pEX-A-u6 plasmid , EZH2C (371–751 amino acids of whole-length EZH2 protein) and the mutant proteins  with a N-terminal nuclear localization sequence (NLS) to the upstream of mCherry. The mScarlet tagged PCP protein was constructed by replacing the mCherry ORF of the PCP-mCherry protein with a mScarlet-I ORF.lacO sites . All the cells were incubated at 37 °C in a humidified environment with 5% CO2.BHK cells containing a genomic integration of multiple cO sites  and HeLaTransient transfection was performed with Lipofectamine 3000 (Thermo Scientific) following the manufacturer's instruction. For one well of a six-well plate, in total 2.4 μg plasmid DNA (with the mass ratio of RNA trap: ROI: POI = 0.8 μg: 0.8 μg: 0.8 μg) was used for transfection. For transfection, 4 μl of Lipofectamine 3000 was diluted in 120 μl Opti-MEM (Thermo Scientific) in one tube and incubated for 5 min at room temperature. The three plasmids for rF3H (2.4 μg) as well as 4 μl of P3000 were diluted and mixed in another tube with 120 μl Opti-MEM. The contents of both tubes were then mixed gently and incubated for 10 min at room temperature. The mixtures were then added to cells drop by drop and the cells were put back into the incubator overnight.For fixed cell imaging, cells were seeded on 18 mm × 18 mm coverslips. About 24 h after transfection, cells were firstly fixed with 3.7% formaldehyde in PBS for 10 min. The fixed cells were then stained with 1 μg/ml DAPI in PBS directly and then mounted onto slides with Vectashield mounting medium (Vector Laboratories).For immunofluorescence, cells were fixed as described before and permeabilized with 0.25% Triton X-100 in PBS for 5 min, blocked with 1% BSA for 30 min, incubated with BSA diluted mouse anti-HA primary antibody  and Alexa Fluor 594 labeled donkey anti-mouse IgG secondary antibody , then counterstained with DAPI, and the samples were mounted as described above.in situ hybridization (FISH), a set of Cy5 labeled single stranded oligonucleotide probes  that target pp7 sequence was used. After fixation and permeabilization of the cells, FISH was processed according to Vidisha's protocol  1 day before transfection. Nuclear staining was performed by adding 1μM SiR-DNA (Cytoskeleton) 30 min before imaging.Both fixed and live-cell imaging were carried out with an SP8 confocal microscope (Leica). A 405 nm diode laser was used for DAPI excitation, while the 488, 561, 594 and 647 nm beams from a 470–670 nm white laser were used for the excitations of EGFP, mCherry, Alexa Fluor 594, Cy5 and SiR-DNA. The emission of GFP was detected by a PMT sensor while the other three emissions were all received by HyD sensors. A 63x oil objective was chosen for imaging, and a sequential imaging method, which detects the fluorescence of DAPI, EGFP, mCherry, Alexa Fluor 594, Cy5 or SiR-DNA individually, was set as the default scanning method. Image analyses were performed with LAS X and ImageJ software.lacO spots, the signal from DAPI channel was applied to partition the area of nucleus at first, the mean fluorescence intensities  of the lacO spot and the whole nucleus from EGFP channel were calculated as lacOGreen and nucleusGreen, respectively, and then the same process was utilized to get lacORed and nucleusRed from mCherry channel. Both green and red signals of the whole nucleus were applied to normalize variation in expression levels in cells. The relative fluorescence at the lacO spots was calculated as follows.As shown in For each experiment, the relative fluorescence was normalized by the control group without RNA.2) by pipetting up and down several times and incubated on ice for 15 min. 20 μl detergent (10% NP40) was added and vortexed for 10 s. The homogenate was centrifuged for 10 min at 3000 rpm and at 4°C. The pellet corresponded to the nuclear fraction. The total and nuclear RNA were extracted with the NucleoSpin RNA kit (MACHEREY-NAGEL) following the manufacturer's instructions, and the synthesis of cDNA was performed with High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems) as instructed by the manufacturer. The expression level of the ms2-pp7 RNA under the control of CMV and U6 cassettes were quantified by real-time PCR with the primers .Nuclear extraction was performed as follows. Cells were detached by trypsin and harvested by centrifugation Cells were washed twice with cold PBS and the pellet was gently resuspended in 500 μl 1x hypotonic buffer  array that is integrated into the genome of a mammalian cell line. The MCP binds the ms2 stem–loop tagged RNA of interest (ROI) and anchors the RNAs at the genomic lacO loci via the fused LacI protein. This RNA trap, together with the trapped RNA of interest, is visualized as a fluorescent spot in cell nuclei via EGFP under a fluorescence microscope. The potential RNA binding protein  tagged with a red fluorescent protein (e.g. RFP or mCherry) is recruited to the lacO array by interacting with the trapped ROIs, and the RNA–protein interaction thereby can be identified by co-localization of the green and red fluorescence at the nuclear lacO spot.To visualize RNA–protein interactions, we designed an RNA fluorescence three-hybrid (rF3H) assay Figure . In thislacO array, the RNA trap together with the trapped pp7 RNA was clearly imaged as a nuclear spot marked by EGFP fluorescence. The enrichment of the mCherry-PCP at the lacO loci was also exclusively detected in the presence of pp7 RNAs, but not with the RNA trap itself, irrelevant RNAs and the mutant pp7 RNA . We tested the interaction between pp7 and PCP protein. When triply expressed in cells containing A Figure , which iA Figure , demonstA Figure , which flacO foci both in the presence and absence of the ROI, the enrichment ratio between the experimental and background binding control groups was basically constant . Under the conditions tested, although a higher RNA trap amount increased the relative signal at the constant , suggestconstant . This opMoreover, we tried to enhance the trapping efficiency of the MCP RNA trap by doubling the ms2 RNA binding unit, and the RNA trap with two tandem ms2 binding units (2MCP RNA trap) showed a slight improvement of the POI enrichment over the original MCP RNA trap . We alsolacO foci than RNAs produced by the CMV cassette  together with the SV40 polyadenylation (poly(A)) signal was applied for target RNA transcription; and for the U6 cassette, the RNA was transcribed via a U6 promoter (transcribed with RNA polymerase III), which does not cause additional poly(A) modification on the transcribed RNAs . We obsecassette . After ocassette , the U6 cassette , which mlac operator array by the MCP RNA trap, and recruitment of the mCherry tagged PABPC1 to the lacO array was observed in the presence of ms2 mRNA mimic  are non-coding RNAs with the length exceeding 200 nucleotides. Although thousands of lncRNAs are transcribed in the human genome, for most of them the functions are not known. The non-coding RNA activated by DNA damage (NORAD) is one conserved lncRNA transcribed in multiple species and is critical for the maintenance of genome stability ,28. WhilA Figure , confirmA Figure ,30.lacO spot by the H300 RNA fragment  is an ncRNA transcribed from the Homeobox C (HoxC) cluster . This nct Figure  albeit Et Figure , which it Figure .lacO site ..lacO arra Figure , C, suppNuclear bodies are membrane-less organelles in the cell nucleus with multiple functions, including RNA processing . Next, wCatalytically deactivated Cas9 (dCas9) protein has been used for targeting and visualization of genomic loci ,50,51. TAll together, RNA traps anchoring to these multiple subcellular structures expanded the applicability of the rF3H assay, suitable for characterization of the interaction between RNAs and proteins with different properties.lacO spot. The ms2 RNAs tagged with the 9-nt PUF binding sequence were recruited and visualized by the GFP fused PUF RNA trap, and enrichment of the mCherry tagged MCP at the lacO array was detected by confocal imaging  bind to RNAs containing the PUM binding sequences via the Pumilio and FBF homology (PUF) domain. The PUF domain typically consists of multiple tri-helix motifs, and the crystal structure showed that each of the tri-helix motifs recognizes and binds one nucleotide, which makes the PUF domain a suitable candidate for RNA binding engineering Figure  52–54)..52–54). g Figure . Althougg Figure , which dLeptotrichia buccalis-sourced dCas13a (Lbu-dCas13a) derived RNA trap, together with the corresponding gRNA that recognizes a specific RNA sequence (GAU UCU AGA ACU AGU GGA UCC UAA GGU A) in the 5′ of ms2 RNAs. The dCas13a RNA trap showed successful trapping of target RNAs and detected RNA–protein interactions as shown by ms2-MCP interaction pair , precise measurements of RNA–protein binding kinetics may offer quantitative data on these dynamic processes.As RNA–protein interactions are usually dynamic under physiological conditions, detection of the interaction in living cells may offer insights on the dynamic regulation of RNPs. To extend the rF3H assay from fixed cells to living cells, we tested the well characterized pp7-PCP interaction under live conditions. As shown in All data could be found in paper or supplementary data. Additional data could be requested from the corresponding authors.gkab614_Supplemental_FileClick here for additional data file.


Bunyavirales. In this work, even higher virus variability was found in a single isolate of Phytophthora condilina, a recently described member of Phytophthora phylogenetic Clade 6a, which was isolated from brackish estuarine waters in southern Portugal. Using total and small RNA-seq the full RdRp of 13 different potential novel bunya-like viruses and two complete toti-like viruses were detected. All these viruses were successfully confirmed by reverse transcription polymerase chain reaction (RT-PCR) using total RNA as template, but complementarily one of the toti-like and five of the bunya-like viruses were confirmed when dsRNA was purified for RT-PCR. In our study, total RNA-seq was by far more efficient for de novo assembling of the virus sequencing but small RNA-seq showed higher read numbers for most viruses. Two main populations of small RNAs (21 nts and 25 nts-long) were identified, which were in accordance with other Phytophthora species. To the best of our knowledge, this is the first study using small RNA sequencing to identify viruses in Phytophthora spp.Marine oomycetes have recently been shown to be concurrently infected by (−)ssRNA viruses of the order  Phytophthora condilina T.I. Burgess, sp. nov. belongs to Phytophthora phylogenetic Clade 6a, recently described in Western Australia from rhizosphere soil of dying Casuarina obesa, commonly known as swamp She-oak [P. condilina is a sister species to Phytopthora inundata and Phytophthora humicola sharing a common ancestor with Phytophthora balyanboodja, Phytophthora “personii” and Phytophthora gemini [P. inundata and P. gemini have a particular dominant aquatic lifestyle and are commonly found in brackish or even marine water. Although many Clade 6 species have been reported as pathogens, there are often other contributing environmental factors associated with the disease reports [ She-oak . This tr She-oak . P. conda gemini . Many spa gemini , but P.  reports .Phytophthora was an alphaendornavirus found in an isolate of the undescribed Phytophthora taxon “douglas fir” in the USA [Phytophthora ramorum isolates from several hosts in Europe [Phytophthora infestans, has been more extensively studied and four RNA viruses were characterised. Phytophthora infestans RNA virus 1 and 2,  seem to represent novel virus families [Fusagraviridae” [Narnaviridae [P. infestans, thereby boosting the virulence of its host [Phytophthora cactorum RNA virus 1 (PcRV1), a toti-like virus, was recently reported in an isolate of the pathogen Phytophthora cactorum from silver birch [Phytophthora species causing asparagus rot in Japan [Plasmopara halstedii and Plasmopara viticola [Pythium irregular [Pythium nuun [Globisporangium splendens (formerly Pythium splendes) and three virus-like sequences, Pythium polare RNA virus 1 (PpRV1), Pythium polare RNA virus 2 (PpRV2) and Pythium polare bunya-like RNA virus 1 (PpBRV1) were identified in Pythium polare infecting mosses in the Arctic [Halophytophthora [The first virus described from the genus  the USA . Subsequn Europe . The caufamilies ,7, Phytoviridae”  and Phytaviridae . PiRV2, its host . Phytopher birch . Furtherin Japan . In otheviticola ,15,16,17rregular , an unclium nuun , a toti-e Arctic . Finallyophthora .Cryphonectria parasitica [Rosellinia necatrix [Aspergillus fumigatus [Gigaspora margarita [Heterobasidion annosum [Botrytis cinerea [Phytophthora, and Dicer-like (DCL), Argonaute (AGO) and RNA-dependent RNA polymerase (RDR) proteins have been identified in P. infestans [P. infestans, P. sojae, P. ramorum and P. parasitica and Haloperonospora arabidopsidis [Nowadays, a variety of next-generation sequencing (NGS) techniques are often applied to detect and describe fungal and oomycete viruses (mycoviruses). Stranded RNA sequencing appears to be the most predominant and successful technique but others like deep-sequencing small RNA (sRNA) libraries have also been proven to be efficient . Since trasitica ,24, the necatrix , the molumigatus , the arbargarita . Moreove annosum , the pla cinerea  and a co cinerea  but nevenfestans ,32. Furtdopsidis ,34.Megaviridae in Tara Oceans [Marine ecosystems appear to have an extraordinary virus abundance and diversity  with a ra Oceans .Halophytophthora isolated from brackish and marine water in saltmarshes in the South of Portugal [Phytophthora species have also been obtained including P. condilina, an oomycete with high tolerance to salinity and unclear phytopathogenic behavior recently described from aquatic and swampy ecosystems in Australia [P. condilina from Portugal.Recently, multiple co-occurring bunya-like viruses have been described in marine oomycetes from the genus Portugal . In the ustralia ,2. The aPhytophthora condilina isolate BD661 was collected in December 2015 from brackish water in a salt marsh in Parque Natural da Ria Formosa,  using an in situ baiting technique [http://www.ncbi.nlm.nih.gov/BLAST/, last accessed on 31 March 2021) For the different purposes of the present study the isolate was grown in darkness on V8-juice Agar (V8A) [echnique . The isoechnique . The idear (V8A)  covered Double-stranded RNA (dsRNA) a was ext® RT Column Kit Brochure, 2017, and treated with TURBO DNA-free™ Kit . RNA quantity was measured in Qubit® 2.0 Fluorometer . The RNA quality was checked by Tape Station 4200  resulting in a RNA integrity number (RIN) of at least 7.Total RNA was purified from approximately 100 mg of fresh mycelium using RNAzol® Small RNA Sample Preparation Kit . The library was sequenced in single reads (SE) (1 × 50 nt) run on an Illumina NextSeq 500 instrument.Approximately 1 μg of total RNA eluted in RNase-free water was sent to Fasteris SA . A polyacrylamide gel was prepared for visualization and selection of RNAs between 18 and 30 nucleotides. RNA library construction with Illumina TruSeq® Stranded RNA LT Sample Preparation Kit . The library underwent paired-end (PE) (2 × 101 nt) sequencing on a NovaSeq6000 .Approximately 1 μg of total RNA eluted in RNase-free water was sent to MACROGEN  for RNA library construction and deep sequencing. The library rRNA was depleted with Ribo-Zero rRNA removal kit (Human/Mouse/Rat) and built using the Illumina TruSeqThe corresponding data are available in Sequence Read Archive (SRA) as BioProject PRJNA700086.Phytophthora infestans with BWA 0.7.5a. Consequently, the unmapped reads were extracted. BAM post-processing was performed with toolbox for manipulation of SAM/BAM files V. 1.13. De novo assembly was performed with VELVET 1.2.10.Low-quality reads were cleaned with TRIMMOMATIC 0.32. For the analyses, reads with sizes ranging 10 to 50 nt were selected and mapped to the genome sequence of Phytophthora parasitica) with default settings. The count of mapped reads was extracted using SAMTOOLS 1.7. De novo assembly was performed using Trinity v2.5.1 program [Low quality reads were removed from the raw data  as well as adapters and very short sequences (<25 bp) using Trim Galore (0.6.4_dev). The BWA mem program (v0.7.17-r1188) was applied for mapping samples against the viral and a host reference genome sequence .® 2020.0.4. For the calculation of the coverage depth we used the following formula: ) * (average read length)/virus genome or contig length. Average sRNA and total RNA read lengths were 30 and 101-long, respectively.The numbers of reads of the final identified viral sequences were calculated with Bowtie2 v2.4.2 under the platform Geneious Primehttps://dotknot.csse.uwa.edu.au/, last accessed on 26 February 2021) was used to search the H-type pseudoknots with maximum free energy (MFE). RNAfold web server  was used to search the potential secondary structures with MFE in the 5′- and 3′-UTR. Predicted RNA secondary structures were visualized by Pseudoviewer . In order to search for conserved domains within the putative viral proteins the NCBI CD-search tool was used . IRESpy was applied in order to look for IRES in the UTRs of all the viruses .DotKnot .Maximum likelihood (ML) phylogenic trees were constructed using a rapid bootstrapping algorithm (Stamatakis 2008) in RAxML-HPC v.8 on XSEDE conducted in CIPRES Science Gateway . Tree seIn order to confirm the length of the putative totivirus-like genomes we used the SMARTer RACE 5′/3′ KIT  as described in Botella et al.  and follTaq 2× Master Mix  including 25 µL Master Mix, 1 µL of each primer (10 mM) and 4 µL of cDNA in a total volume of 50 µL. All the primers used for the partial amplification of the RNA dependent RNA polymerase (RdRp) of each virus (® 2020.0.4. Cycling conditions were set according to the manufacturer’s recommendations.The occurrence of each identified virus was confirmed by direct reverse transcription polymerase chain reaction (RT-PCR) with specific primers using both dsRNA (via cutting the bands) and total RNA as templates. A High-Capacity cDNA Reverse Transcription Kit  was used for the cDNA synthesis. PCRs were performed with Hot Start ch virus  were desPCR products were visualised using gel electrophoresis . Analysed fragments were separated on 1.5% agarose gel prepared with a TBE buffer  and stained by Ethidium bromide . PCR products showing the amplicons of expected length were purified and sequenced by GATC BioTech  by both directions with the primers used for PCR amplification. Primer sequences can be consulted in P. condilina from Western Australia [cox1 (MW836948) and nadh1 (MW836949) genes of the isolate BD661.Isolate BD661, baited from marine water in a saltmarsh in the south of Portugal, was homothallic with golden-brown oogonia, slightly wavy oogonial wall, thick-walled aplerotic oospores and both paragynous and amphigynous antheridia. Sporangia were non-papillate and proliferated internally in both a nested and extended way. This combination of morphological characters agreed with the description of P. condilina isolate BD661 revealed the presence of one clear band usually around 6–7 kb (The dsRNA extraction from d 6–7 kb a. Subseqd 6–7 kb b.P. infestans) 16.60 × 106 SE reads were unmapped and, selected for the following steps. De novo assembling obtained very short contigs, with an average length of 82 bases and the mapping in algae, plant and fungal virus databases failed to achieve decent computing coverage scores. Therefore, we decided to carry out total stranded RNA sequencing. The number of reads produced in the total RNA library  was almost three times higher than the small RNA reads with 94.2% of reads with a phred quality score of over 30 (Q30). When they were assembled against a related host reference sequence 1.12 × 106 PE reads were unmapped and selected for the following steps. The final contig file had 6670 contigs with an average length of 395 nt, the maximum contig length was 9384 nt. Fifteen contigs were identified as possible viruses due to their significant E-values and identity percentage of their predicted amino acid sequences , oomycetes, such as Halophytophthora sp. 04  and the Arctic and Antarctic moss pathogen Pythium polare , and by arthropods like freshwater isopteran . The pairwise (pw) sequence comparison (PASC) of the 13 bunyavirus-like nucleotide sequences showed an overall pw identity of 32.2% and always differed >10% between one another. As according to the ICTV there are not primary classification and delimitation criteria for genus and species in the order Bunyavirales, PASC and phylogenetic analyses seem to be the main point of reference to name new bunyaviruses. Therefore, we propose that these 13 bunyavirus-like sequences can be considered 13 putative different bunya-like viruses designated as Phytophthora condilina negative stranded RNA virus (PcoNSRV) 1–13 (Thirteen contigs were similar to (-)ssRNA viruses, representing members of the order database , they weRV) 1–13 , and we RV) 1–13 , and betRV) 1–13 . ConversPythium polare  and in a diatom colony from rock pools in Tokyo Bay  (The BLASTX search of both totivirus-like sequences showed ~32% identity to unclassified viruses described in 9551504) . The PASP. polare [Plasmopara viticola-infected grapevine samples [Bunya-like and toti-like viruses have been also identified in the same host isolate of . polare  and the  samples . This ma samples ,50,51,52 samples . Initial samples ,54.Bunyaviruses are enveloped viruses with a genome consisting of three ssRNA segments . The S RNA encodes the nucleocapsid protein whereas the M glycoproteins and the L segments encode the RNA polymerase. Each genome segment is coated by the viral nucleoproteins (NPs) and the polymerase (L protein) to form a functional ribonucleoprotein (RNP) complex, necessary for the RNA replication and gene transcription . However′ and 3′ termini seems to constitute untranslated regions (UTRs), whose complete lengths remains unsure as RACE was not carried out in this case. Nevertheless, the size of the RdRp segment of PcoNSRV1-13 corresponds to the typical size of bunyavirus L segment.The L segment of the putative PcoNSRV1-13 encloses the complete coding region within the sequence using the standard genetic code, a single large open reading frame (ORF) coding for the RdRp . The RdR−26); PcoNSRV3 from aa 928 to 1204 ; PcoNSRV4 from aa 1232 to 1637 ; PcoNSRV5 from aa 930 to 1446 ; PcoNSRV6 from aa 1363 to 1697 ; PcoNSRV7 from aa 1153 to 1788 ; PcoNSRV8 from aa 989 to 1467 ; PcoNSRV9 from aa 1063 to 687  and Bunya L protein N-terminus, an endonuclease domain found at the N-terminus of many bunyavirus L proteins (pfam15518) from aa 277 to 357 ; PcoNSRV10 from aa 731 to 1211 ; PcoNSRV11 from aa 1253 to 1661 ; PcoNSRV13 from aa 1331 to 1636 . It was only in PcoNRV12 that we could not identify traditional conserved regions belonging to pfam04196. However, the amino acid alignment of the CDD regions , another conserved region was identified from aa 2563–2844 (9.98 × 10−4). This seems to belong to cas_TM1794_Cmr2 super family, a CRISPR –Cas (CRISPR-associated) protein Cas10/Cmr2, subtype III-B, typified by TM1794 from Thermotoga maritima, a marine hyperthermophilic anaerobic organism. The functional features of the proteins suggest that they comprise a specific repair system [Interestingly, in PcoNSRV2, which contained a bunyavirus RdRp conserved domain from aa 1080 to 1603  and RdRp (ORF2). In PcoRV1 both ORFs overlap  by sevenIn PcoRV2, ORF 1 and 2 overlap by 292 nts (from 3440 to 3731). The stop codon for ORF 2 is UAA and the putative start codon for ORF1 might be AUC (isoleucine), an alternative start codon shown to allow less efficient but still appreciable levels of initiation (2–30%)  AUC-initHypoviridae, Megabinaviridae, Fusagraviridae and Totiviridae. This mechanism allows the virus to produce a demarcated ratio of Gag, Gag-Pol that is likely to be optimized for virion assembly and facile targeting of the replicative enzymes to the virion core. It also avoids the need to produce a separate mRNA for expression of the viral polymerase [Both PcoRV1 and 2 follow a non-canonical translation as seen in many other RNA viruses . Alternalymerase .9) and in aa 270–655  of PcoRV2. Conserved aa sequence motifs (I-VIII) of RdRp of PcoRV1 and 2 were aligned together with other totiviruses showing high similarity (Totiviridae [Based on the amino acid (aa) sequence analysis, conserved regions of the RT_like super family cl02808 and, in particular pfam02123, were identified in the putative RdRp sequence of PcoRV1 in the interval 310–539 ssRNA viruses deposited in the GenBank shows that they cluster with unclassified viruses with genomic affinities of the order Bunyavirales , identified in three isolates of Halophytophthora sp. 04 in Santa Luzia, Portugal [Bunyavirales does not have a clear criterion to define species, genus and family we have decided not to use these unofficial family names in the tree. Finally, PcoNSRV2 clusters with two unclassified fungal orthobunyaviruses, Botrytis cinerea orthobunya-like virus 1 (QKW91261) and Tulasnella bunyavirales-like 1 (QPB44676). The latter has an unprecedented 12 kb genome organization containing three ORFs, the largest one encoding the RdRp, the second largest ORF encoding an unknown protein with Tom22 motifs  and the smallest ORF encoding a nucleocapsid protein [Separated clusters contain PcoNSRV1 and PcoNSRV2. PcoNSRV1 groups with Wuhan insect virus 3 (AJG39263) detected in Portugal , previouPortugal . However protein . This unDaphnia magna, D. carinata and Moina macrocopa) from freshwater ecosystems in China [Chlorarachnion reptans, a macroalgae collected from marine sand in Victoria, Australia [Likewise, PcoNSRV3 appears to belong to another novel taxon which also includes Shahe bunya-like virus 1 (APG79317), associated with small planktonic crustaceans , and a number of toti-like viruses found in arthropods, many of them riparian and coastal species [The reconstruction of the phylogenic relationships of P. condilina toti-like viruses with other related viruses from the GenBank clearly separated PcoRV1 and 2 in two different clades . PcoRV2 omycetes . Clade I species .P. condilina viruses with viruses found in diverse organisms from similar brackish water habitats (Both phylogenetic trees revealed close relatedness between viruses hosted by distinct oomycetes and fungi dwelling unalike ecosystems. Also, the relation of habitats . Some of


When an object casts a shadow on a background surface, both the offset of the shadow and the blur of its penumbra are potential cues to the distance between the object and the background. However, the shadow offset and blur are also affected by the direction and angular extent of the light source and these are often unknown. This means that the observer must make some assumptions about the illumination, the expected distribution of depth, or the relation between offset and depth in order to use shadows to make distance judgments. Here, we measure human judgments of perceived depth over a range of shadow offsets, blurs, and lighting directions to gain insight into this internal model. We find that distance judgments are relatively unaffected by blur or light direction, whereas the shadow offset has a strong and linear effect. The data are consistent with two models, a generic shadow-to-depth model and a Bayesian model. The position of a cast shadow depends on the direction of illumination and the separation of the object from the surface on which its shadow falls. If any two of these three parameters are known , the third can be recovered. However, it is often the case that the direction of illumination is unknown, making the estimation of separation between the object and the shadowed surface underdetermined. This does not stop human observers in such situations – we experience a clear separation that is relatively easy to quantify see  and we wOne approach is that the visual system assumes a generic, single light source direction, the same for every scene or image, and then uses this together with the shadow offset to recover the separation of the object from its background — a generic shadow-depth (GSD) model. In this case, the recovered depth would always scale linearly with the shadow's offset. The proposal of a single light source is controversial  because A second approach is to find the separation between the object and its background that is most consistent with the shadow offset across the range of possible illumination directions, weighting the likelihood of different separations by their expected distribution in natural scenes. We will evaluate the predictions of this Bayesian model along with those of the GSD model in the discussion.Whether the brain infers a specific shadow-to-depth relation or estimates depth across a range of possible light directions, it seems to produce reasonable depth estimates in scenes where the light source and the object distributions are within the “normal” range. However, in a scene that has a light source at an unusual position, the underlying assumptions lead to misjudgments of depth. Flash photography, for example, typically produces “tight shadows” because the light source is near the camera lens . These tShadow offset is only one of the shadow properties that might influence the perception of depth. Another is the sharpness of the shadow boundary. Natural shadows have boundaries with a gradient from dark to light . The visFinally, the effect of the shadow on perceived depth may depend not only on the offset and blur of the shadow but also on its direction relative to the object, reflecting the well-established biases that favor lighting from above  and perhTwenty-five healthy adults took part in the experiment . All participants were undergraduates at Glendon College and naïve to the purpose of the experiment. All participants reported normal or corrected-to-normal vision and all gave informed consent in writing prior to participation. The protocols for the study were approved by the York University Review Board in accordance with the principles of the Declaration of Helsinki (2003).2 and that of the blue (cyan) square was 36 cd/m2.The experiment took place in a darkened room. Stimuli were presented on a gamma-corrected CRT monitor  controlled by a Macintosh G4 laptop running Vision Shell Graphics libraries . ParticiParticipants performed 300 method-of-adjustment trials, four trials in each of the 72 conditions  and 12 control trials with no shadow see . During F = 280.2, p < 0.001, p2η = 0.92, rP = 1.0) increase with the offset of the shadow (2η = 0.57). Only three other effects reached significance — left versus right shadow offset, the interaction of offset with upper versus lower shadow position, and the interaction of offset with blur — and these explained far less of the variance . Specifically, there was a small but significant increase in depth (1.6%) for shadows on the right  versus on the left  = 10.6, p = 0.003, p2η = 0.31, rP = 0.88). Second, there was a small but significant interaction between offset size and upper versus lower shadows  = 5.1, p < 0.001, p2η = 0.18, rP = 0.98) with the lower shadows  producing a slightly steeper slope  = 4.6, p < 0.001, p2η = 0.16, rP = 0.99) with the no-blur results having a slightly lower slope than the two blurred cases . The depth estimates for the 25 participants showed a strong  = 2.34, p = 0.014) although the validity of the test is questionable as the distribution of settings is far from normal .The control condition with no shadow was frequently judged as having no depth — 54% of all responses were zero. The rest of the settings indicated that some depth was seen by some observers on some trials. The average depth setting across all participants and trials was 0.07 ± 0.03 dva significantly greater than zero  shown in t(24) = 4.71, p = 8.6 × 10−5). This relation between depth and offset is equivalent to a lighting direction of about 35.5 degrees from the head , with a range of 26 degrees to 49 degrees across the participants.The results showed that perceived depth scaled quite linearly with shadow offset and was largely unaffected by the degree of blur or by the direction of the offset. Overall, the perceived depth was 30 to 40% larger than the shadow offset. This ratio of depth from offset varied considerably across participants, from 0.88 to 2.01, so it would be an overgeneralization to say that there is a bias for specifically 40% more depth than the offset. Nevertheless, the depth from offset relation is roughly linear not just in the aggregate, but for each individual participant see , and theInterestingly, there was little effect of the direction of the shadow offset. Although this seems at odds with previous reports of a bias of light from above  and perhIn addition, we found only a small effect of shadow blur on perceived depth. These modest effects of shadow direction and blur are in contrast to the results of Our stimulus was a flat, vertical textured surface with a square lying on or in front of it, viewed directly so that the line of sight to the shadow was approximately aligned with the normal to the display surface. We characterize possible light directions in terms of the slant of the light source vector away from the surface normal and its tilt  conditioned on the observed variable (shadow offset). The posterior probability of a given depth estimate is proportional to the product of the likelihood of the shadow offset given the depth, and the prior probability of that depth. The uncertainty encoded by the likelihood function derives from two sources: noise in the estimated shadow offset, and uncertainty about the illumination slant. In the We find that both the empirical prior over depth and the non-uniform illumination prior are required to fit the data as well as the GSD model . The estAlthough both models provide a good quantitative account of mean human judgments, we note that there was considerable variation among participants, with slopes of perceived depth versus shadow offset ranging from 0.88 to 2.01. Future work could examine whether these large individual differences can be related to differences in lived experiences or related to other perceptual measures.Our results suggest that the perceived depth derived from a shadow is roughly a fixed proportion of its offset, at least for the small shadow offsets tested here. We also found little or no effect of different levels of blur or directions of the shadows. Both the GSD and Bayesian models fit the data closely and indicated an equivalent light direction of 35.5 degrees slant or a broad light prior with a mean of 37.4 degrees slant, respectively. There was no statistical difference between the predictions of the two models. Our findings make it clear why flash photographs see  create s


SARS-CoV-2 is a betacoronavirus with a single-stranded, positive-sense, 30-kilobase RNA genome responsible for the ongoing COVID-19 pandemic. Although population average structure models of the genome were recently reported, there is little experimental data on native structural ensembles, and most structures lack functional characterization. Here we report secondary structure heterogeneity of the entire SARS-CoV-2 genome in two lines of infected cells at single nucleotide resolution. Our results reveal alternative RNA conformations across the genome and at the critical frameshifting stimulation element (FSE) that are drastically different from prevailing population average models. Importantly, we find that this structural ensemble promotes frameshifting rates much higher than the canonical minimal FSE and similar to ribosome profiling studies. Our results highlight the value of studying RNA in its full length and cellular context. The genomic structures detailed here lay groundwork for coronavirus RNA biology and will guide the design of SARS-CoV-2 RNA-based therapeutics. Lan et al. report RNA structure ensembles across the entire SARSCoV-2 genome in infected human cells at single nucleotide resolution. They find alternative RNA conformations critical for promoting near-native frameshifting rates in ORF1ab. Betacoronavirus, which also includes SARS-CoV-1, the virus responsible for the 2003 SARS outbreak, and Middle East respiratory syndrome coronavirus (MERS-CoV), the virus responsible for the 2012 MERS outbreak. Despite the devastating effects these viruses have had on public health and the economy, distributing vaccines globally has been challenging1, and the first two therapeutics that substantially reduce mortality of COVID-19 were identified only in late 2021, nearly two years after the disease was discovered4. There is therefore an urgent need to understand the unique RNA biology of and develop new therapeutics against viruses in this genus.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), was declared a global pandemic by the World Health Organization (WHO). SARS-CoV-2 is an enveloped virus belonging to the genus 5. Prior to the emergence of SARS-CoV-2, most studies on secondary structures within coronavirus RNA genomes focused on several conserved regions that are essential for viral replication: the 5′ UTR, the 3′ UTR, and the frameshifting stimulation element (FSE)7. Functional studies have revealed the importance of their secondary structures for viral transcription and replication10.Coronavirus genomes consist of single-stranded, positive-sense RNA and are the largest among all known RNA viruses (27–32 kb)11. A stop codon near the middle of ORF1 partitions it into an upstream ORF1a and downstream ORF1b. While some ribosomes stop after translating a polyprotein from only ORF1a (nsps 1–11), the FSE causes a fraction of ribosomes to “slip” backward by 1 nt, bypass the stop codon, and translate a polyprotein from the entire ORF1ab 6. Several proteins unique to ORF1ab are central to RNA replication and transcription, including the viral RNA-dependent RNA polymerase (nsp12) and helicase (nsp13)12. Studies on multiple coronaviruses have shown that an optimal ribosomal frameshifting rate is critical, and small differences in the percentage of frameshifting lead to dramatic differences in genomic RNA production and infectivity. Therefore, the FSE has emerged as a major drug target for small molecules that can influence the rate of ribosome slippage and is under active investigation to be used as a treatment against SARS-CoV-216.The first roughly two-thirds of every coronavirus genome consists of one open reading frame (ORF1) encoding 16 non-structural proteins (nsps)7 and SARS-CoV-214 fold into a complex structure with a three-stemmed pseudoknot. Small molecules, locked nucleic acids (LNAs), and mutations that are intended to disrupt this structure have been shown to impair viral replication16. However, despite the importance of the FSE structure, there is to date no direct validation of the relationship between the RNA folding conformation and frameshifting rate in infected cells.The structures of coronavirus FSEs have been studied extensively. Short segments of the core FSE from both SARS-CoV-117 and reagents in the SHAPE18 and icSHAPE19 families. DMS reacts with the Watson–Crick face of adenine (A) and cytosine (C) bases and probes base-pairing directly, while SHAPE and icSHAPE reagents react with the 2′-OH group of all four nucleotides and measure nucleotide flexibility as a proxy for base pairing20. Predictions of RNA structure that use DMS reactivities as folding constraints are of similar or marginally higher accuracies than predictions using SHAPE reactivities, as the specificity of DMS for Watson–Crick base-pairing compensates for the ability of SHAPE to probe all four nucleotides20.Over the last decade, major advances in methods for RNA chemical probing have enabled genome-wide characterization of RNA structures in living cells. The most commonly used chemical probes are dimethyl sulfate (DMS)23 recently proposed models of the secondary structure of the entire genome of SARS-CoV-2 in human or monkey cells using SHAPE-MaP18 or icSHAPE19. These models are based on the average (ic)SHAPE reactivity at each nucleotide, and cannot provide direct experimental evidence for alternative structures. However, the genomes of RNA viruses form not one structure but an ensemble of many structures whose dynamics regulate critical viral processes, such as splicing in HIV-124. Thus, more work is needed to determine the dynamics of RNA structures within the SARS-CoV-2 genome and their functional roles in the viral life cycle.Three studies25 and DREEM clustering24 on infected Vero and Huh7 cells to generate experimentally determined, single-nucleotide resolution genome-wide secondary structure ensembles of SARS-CoV-2. Our results reveal major differences with in silico and population-average structure predictions. Importantly, we highlight the physiological structure dynamics of known functional elements, such as the alternative structures at the FSE that determine frameshifting rates in cells. Our work provides experimental data on the structural biology of RNA viruses and will inform efforts on the development of RNA-based diagnostics and therapeutics for SARS-CoV-2.In this study, we perform DMS mutational profiling with sequencing (DMS-MaPseq)25 25 Fig. . We chosage Fig. , and theage Fig. , with ad25. We determined that AUROC values of roughly 0.95 or higher indicate high-quality probing data by benchmarking two RNAs with known, robust structures for which we had previously collected DMS-MaPseq data:24 the U4/U6 snRNA (AUROC = 0.98) and the Rev response element (RRE) from HIV-1 (AUROC = 0.95) . Compared to this literature consensus structure, our Huh7 and Vero in-cell DMS-MaPseq datasets yielded AUROC values of 0.99 and 0.98, respectively,  to evaluate an RNA in the dataset whose structure had been solved previously95) Fig. . By cont95) Fig. . To valiSL5 Fig.  and ORF1SL5 Fig. .Fig. 2Qu30. In agreement with previous studies, we found five stem–loops (SL1–5) within the 5′ UTR (nucleotides 1–265). These structures perform essential functions in viral replication (SL18 and SL29), subgenomic RNA production (SL36 and SL431), and escape of nsp1-mediated translational suppression (SL132). SL5 contains the start codon of ORF1 and branches into three additional stems , which our model recapitulates perfectly with respect to previous studies28. In agreement with another in-cell structure model22, we did not find evidence for a short stem–loop (SL4.5) proposed in several other studies28. Additional structures exist immediately downstream of the 5′ UTR. We found three stem–loops (SL6–8) in this region, in nearly perfect agreement with two previous in-cell studies22. In further support of the accuracy of our model, the AUROC was 0.93 across SL1–5, indicating that our model of this region agrees very well with our chemical probing data  Figs.  and 3′ U33, whose mean is the Fowlkes-Mallows index (FMI)34. We introduced a modified FMI (mFMI) that also accounts for bases that are unpaired in both structures (see Methods). We determined regions of local similarity by computing the mFMI across the genome and found multiple structures that are supported across published studies 11 and is predicted to lie in a short stem–loop (SL3), consistent with the previous results27. Of the nine body TRSs, we find that seven  lie within a stem–loop  are necessary for the synthesis of sgRNAs, we analyzed our structural models of the leader TRS (TRS-L) and the nine body TRSs (TRS-B). The leader TRS (TRS-L) is the central component of the 5′ UTR involved in discontinuous transcription37. Most methods for analyzing covariation in RNA were developed for non-coding RNAs, although covariation is also present at the amino acid level38, which could confound the analysis of RNA structure. With this caveat in mind, we divided our genome-wide model into 353 structural elements encompassing 75% of the genome, built a covariance model for each element using Infernal37, and identified base pairs supported by covariation using R-scape36 with a database of 301,535 non-redundant, full-length coronavirus genomes. We detected 95 base pairs supported by covariation at an E-value threshold of 0.05 , albeit weakly, indicating that large differences between alternative structures are associated with lower agreement between the population average structure and the DMS reactivities  would tend to be low, and vice versa. Consistent with our hypothesis, AUROC and ∆DMS correlated negatively  would be those with high ∆DMS  and low AUROC . From our Vero model, we identified all coordinates at which the AUROC and ∆DMS were, respectively, below and above their genome-wide medians. Denoising these coordinates with a low-pass filter (see Methods) yielded 69 regions best represented as structural ensembles, covering 24% of the genome Fig. . For Huh21 or measured them using short segments of the FSE and/or in vitro, outside of the context of genomic RNA and cellular factors39. We find that the FSE indeed forms at least two distinct structures, consistent in Vero and Huh7 cells, and characterize them in detail below.Interestingly, we found that the FSE, which is critical for regulating the translation of ORF1b, meets the above criteria for alternative structures in both Vero and Huh7 cells  including nsp12, an RNA-dependent RNA polymerase (RdRP)43. We also in vitro-transcribed, refolded, and DMS-probed an 88 nt segment of the SARS-CoV-2 FSE similar to those used in the previous studies14. Our in vitro data-driven model for the predominant structure of this RNA , and cryo-electron microscopy (cryo-EM)RNA Fig. , top agr25. We designed primers targeting 283 nt surrounding the FSE and amplified this region from SARS-CoV-2 infected Vero and Huh7 cells treated with DMS. Our analysis revealed a strikingly different structure than the prevailing model27 . These results suggest that AS1 is unique to SARS-related coronaviruses.To determine if other coronaviruses may have a similar alternative structure of the FSE, we searched for the sequence that pairs with canonical stem 1 in a set of curated coronavirusesats Fig. , purple.The major differences we observed in the structure of the FSE in cells vs. in vitro could either be due to 1) length of the in vitro refolded viral RNA or 2) factors in the cellular environment that is absent in vitro. To distinguish between these two possibilities, we re-folded the FSE in vitro in the context of longer native sequences.r = 0.95). These results show that the FSE folds correctly in the absence of protein factors. Importantly, the upstream and downstream sequence is necessary for folding the FSE, suggesting the presence of long-range RNA:RNA interactions , the DMS reactivity patterns became progressively more similar to the pattern we observed in cells Fig. . Indeed,ons Fig. .47.We further analyzed the intracellular folding of the FSE using DREEM. We found two distinct patterns of DMS reactivities Fig. , showing48. We used either a “short” FSE of only a 92 nt region that folds into the canonical three-stemmed pseudoknot or a “long” 2924 nt sequence containing the FSE, which folds nearly identically as the full-length genome in infected cells  may result either from each species of genomic and subgenomic RNA forming a distinct structure, or all species forming similar ensembles of alternative structures. Although our data cannot distinguish between these possibilities, one study suggests that genomic and subgenomic RNAs form similar structures, except for two regions 50. Within both of these regions  frameshifts at a much higher rate than the minimal FSE . These results underscore a functional role for long-range RNA interactionsOur in-cell data-derived model of SARS-CoV-2 presents major RNA structures and sites of RNA structure heterogeneity across the entire genome and provides the foundation for further studies. Importantly, our work reveals that drugs such as small molecules or antisense oligos intended to abolish SARS-CoV-2 frameshifting should be designed and tested against the correct structure ensemble that forms in cells. Further work to better understand the functional significance of other structured elements across the SARS-CoV-2 genome will enable the design of more targeted therapeutics.2 for 48 h before DMS treatment.Monkey Vero cells (ATCC CCL-81) cultured in DMEM (Gibco) supplemented with 10% fetal bovine serum (FBS) (Gibco) and 1% 260 Penicillin/Streptomycin were plated onto 100 mm dishes and infected at an MOI of 0.01 with 2019-nCoV/USA-WA1/2020 (Passage 6). Infected cells were incubated at 37 °C, 5% CO2. Cells were infected with 2019-nCoV/USA-WA1/2020 (Passage 2) at a MOI of 0.05 for 24 h then treated with DMS.Human Huh7 cells were cultured in Dulbecco’s Modified Eagle 259 Medium (DMEM) supplemented with 10% FBS and 1% 260 penicillin/streptomycin. Cells were maintained at 37 °C in 5% COThe 2019n-CoV/USA_WA1/2020 isolate of SARS-CoV-2 was obtained from the US Centers for Disease Control and Prevention. Infectious stocks were produced by inoculating Vero E6 cells and collecting the cell culture media upon observation of cytopathic effect; debris was removed by centrifugation and passaged through a 0.22 µm filter.g for 5 min at 4 °C and then washed twice with 15 ml 30% β-mercaptoethanol in PBS and once with just PBS. Cell pellet was resuspended in 1 ml TRIzol (ThermoFisher Scientific).For Vero cells, 200 μl DMS (or 2% v/v) was added dropwise to the cells and incubated for 4 min at 37 °C. For Huh7 cells, 250 μl DMS (or 2.5% v/v) was added dropwise to the cells and incubated for 3 min at 37 °C. DMS treated cells were then neutralized with 15 ml 30% β-mercaptoethanol in PBS (ThermoFisher Scientific). The cells were centrifuged at 1000RNA was extracted following the manufacturer’s specifications with the aqueous phase transferred to a new tube and equal volume 100% EtOH added. Total RNA was then purified using RNA Clean and Concentrator −25 kit (Zymo) following the manufacturer’s specifications.Fifteen micrograms of total RNA per reaction were used as the input for rRNA subtraction. First, 1 μl rRNA subtraction mix (15 μg/μl) and 2 μl 5× hybridization buffer  were added to each reaction, and the final volume was then adjusted with water to 10 μl. The samples were denatured at 95 °C for 2 min and then the temperature was reduced by 0.1 °C/s until the reaction was at 45 °C. Next, 10 μl RNase H buffer and 2 μl hybridase thermostable RNase H (Lucigen) preheated to 45 ° was added. The samples were incubated at 45 °C for 30 min. The RNA was cleaned with RNA Clean and Concentrator −5, following the manufacturer’s instructions, and eluted in 45 μl water. Then, 5 μl Turbo DNase buffer and 3 μl Turbo DNase (ThermoFisher Scientific) were added to each reaction and incubated for 30 min at 37 °C. The RNA was purified with RNA Clean and Concentrator −5 (Zymo) following the manufacturer’s instructions.After rRNA subtraction (described above), extracted DMS-modified RNA was fragmented using the RNA Fragmentation kit (ThermoFisher Scientific). Totally, 1.5 μg of rRNA subtracted total RNA was fragmented at 70 °C for 2.5 min. The fragmented RNA was mixed with an equal volume 2× Novex TBE–urea sample buffer (ThermoFisher Scientific) and run on a 10% TBE–urea gel (ThermoFisher Scientific) at 200 V for 1 h 15 min for size selection of RNA that is ~150 nt. To dephosphorylate and repair the ends of randomly fragmented RNA, 2 μl 10× CutSmart buffer (New England Biolabs), 10 μl shrimp alkaline phosphatase (New England Biolabs), 2 μl RNaseOUT (ThermoFisher Scientific), and water were added to a final volume of 20 μl and 37 °C for 1 h. Next, 4 μl 50% PEG-800 (New England Biolabs), 4 μl 10× T4 RNA ligase buffer (New England Biolabs), 4 μl T4 RNA ligase, truncated KQ (England Biolabs), and 2 μl linker were added to the reaction and incubated for 18 h at 22 °C. The RNA was purified with RNA Clean and Concentrator −5 following the manufacturer’s instructions for recovery of all fragments and eluted in 10 μl water. The excess linker was degraded by adding 2 μl 10× RecJ buffer (Lucigen), 1 μl RecJ exonuclease (Lucigen), 1 μl 5′ deadenylase (New England Biolabs) and 1 μl RNaseOUT, then incubating for 1 h at 30 °C. The RNA was purified with RNA Clean and Concentrator −5, following the manufacturer’s instructions, and eluted in 11 μl water.For reverse transcription, 1.5 μg of rRNA subtracted total RNA or 10 μg of in vitro-transcribed RNA was added to 4 μl 5× first strand buffer (ThermoFisher Scientific), 1 μl 10 μM reverse primer, 1 μl dNTP, 1 μl 0.1 M DTT, 1 μl RNaseOUT and 1 μl TGIRT-III (Ingex). The reverse-transcription reaction was incubated at 60 °C for 1.5 h. 1 μl 4 M NaOH was then added and incubated at 95 °C for 3 min to degrade the RNA. The reverse-transcription product was mixed with an equal volume 2× Novex TBE-urea sample buffer (ThermoFisher Scientific) and run on a 10% TBE–urea gel (ThermoFisher Scientific) at 200 V for 1 h 15 min for size selection of cDNA that is ~250 nt. The size-selected and purified cDNA was circularized using CircLigase ssDNA ligase kit (Lucigen) following manufacturer’s instructions. 2 μl of the circularized product was used for amplification and addition of custom Illumina adaptors using Phusion High-Fidelity DNA Polymerase (NEB) for a maximum of 16 cycles (Supplementary Data ™ II DNA Library Prep Kit) and sequenced on a Nextseq system .After rRNA subtraction and purification (described above), primers with Tm higher than 60 °C were used directly to reverse transcribe the extracted DMS-modified RNA as described above without fragmentation and linker ligation. For the whole genome rt-PCR library, primers were designed to cover the entire 30 kb with ~50 nt overlap  was reverse-transcribed from SARS-CoV-2 and cloned into pmirGLO. The regions of interest were amplified by PCR with a forward primer that contained the T7 promoter sequence  with 6 mM MgCl2+ (refolding buffer) was added so that the final volume was 100 μl.  Then, 2.5 μl was added and incubated at 37 °C for 5 min while shaking at 500 r.p.m. on a thermomixer. The DMS was neutralized by adding 60 μl β-mercaptoethanol (Millipore-Sigma). The RNA was purified using RNA Clean and Concentrator −5 kit.To DMS modify, 10 μg of RNA in 10 μl HBoth linker and target-based DMS-MaPseq library generation protocol as described above can be used after this step.Total SARS-CoV-2 RNA was extracted from the supernatant of infected Vero cells (as described above), resuspended in 1 ml TRIzol (ThermoFisher Scientific) and RNA was extracted following the manufacturer’s specifications. The RNA was purified using RNA Clean and Concentrator −5 kit (Zymo) and DMS modified as described in “In vitro transcribed FSE and DMS modification”, For the FSE regions a target-based DMS-MaPseq library generation protocol was used using primers P4 and P5  were inserted into dual-luciferase reporter plasmid pmirGLO (Promega) between firefly luciferase (Fluc) coding sequence in the 0 frame and Renilla luciferase (Rluc) coding sequence in the −1 frame. Insertion of 0-frame stop codon between Fluc and the FSE is used as negative control construct while a construct of matching length in which Fluc and Rluc were translated continuously without frameshifting is used as a positive control.E. coli Poly(A) Polymerase (New England Biolabs) according to the manufacturers’ instructions. Purified mRNAs were transfected in HEK293T cells in 24-well plates using Lipofectamine MessengerMAX (ThermoFisher). Twenty-four hours after transfection, cells were washed once with phosphate-buffered saline (PBS), and lysed in Glo Lysis Buffer (Promega) at room temperature for 5 min. Ten microlitres of lysate was diluted with 30 µL PBS before being mixed with 40 µL Dual-Glo Fluc substrate (Promega). After 10 min, Fluc activity was measured in a GloMax 20/20 luminometer (Promega). Subsequently, 40 µL Dual-Glo Stop & Glo reagent was added to the mixture, incubated for 10 min, and measured for Rluc luminescence. The ratio between Rluc and Fluc activities minus the negative control background luminescence and normalized to positive control luminescence was calculated as frameshift efficiency.Frameshifting reporter as well as positive and negative control mRNAs were in vitro transcribed and polyadenylated using HiScribe T7 mRNA kit (New England Biolabs) and capped using the Vaccinia Capping System (New England Biolabs), and a poly(A) tail was added using 52 using Bowtie253 with the following parameters: --local --no-unal --no-discordant --no-mixed -L 12 -X 1000. Reads aligning equally well to more than one location were discarded. SAM files from Bowtie2 were converted into BAM files using Picard Tools SamFormatConverter (broadinstitute.github.io/picard).FASTQ files were trimmed using TrimGalore  to remove Illumina adapters. Trimmed paired reads were mapped to the genome of SARS-CoV-2 isolate SARS-CoV-2/human/USA/USA-WA1/2020 (GenBank: MN985325.1)24. Bit vectors contained a 0 at every position in the reference sequence where the reference sequence matched the read, a 1 at every base at which there was a mismatch or deletion in the read, and no information for every base that was either not in the read or had a Phred score <20. We refer to positions in a bit vector with a 0 or 1 as “informative bits” and all other positions as “uninformative bits.”For each pair of aligned reads, a bit vector of the length of the reference sequence was generated using DREEMFor each position in the reference sequence, the number of bit-vectors covering the position and the number of reads with mismatches and deletions at the position was counted using DREEM. The ratio of mismatches plus deletions to total coverage at each position was calculated to obtain the population average DMS reactivity for each position.In cases indicated below, bit vectors were discarded if they had two mutations closer than 4 nucleotides apart, had a mutation next to an uninformative bit, or had more than an allowed total number of mutations . The DMS reactivity for each position was computed from the filtered bit vectors in the same way as described above.Genome-wide coverage Fig.  was comp54.The Pearson correlation of DMS reactivities between biological replicates genome-wide Fig.  was comp23. Only datasets from the same family of chemical probes (DMS or SHAPE/icSHAPE) were compared to each other. The top 0.05% most reactive bases in each dataset were considered outliers and excluded from calculations. Only bases for which both datasets had reactivity data were included in the calculations. Pearson’s and Spearman’s correlations were computed with SciPy54.For Pearson and Spearman correlations among other genome-wide chemical probing datasets Fig. , the che55 by setting them to 1.0.For purposes of folding RNA structures using DMS reactivity constraints, DMS reactivities were normalized to a scale of 0–1 as follows. The median was computed among the top 5% of DMS reactivities (except where a different percentage is specified). All DMS reactivities were divided by this median to compute the normalized reactivities. Normalized reactivities greater than 1.0 were winsorized27. For each segment, the population average DMS reactivities were normalized according to “Normalizing the DMS reactivities” using the 1500 most reactive positions for normalization. All values on bases without data  were set to −999 (unavailable constraints). The segment was then folded using the Fold algorithm from RNAstructure26 with parameters -m 3 to generate the top three structures, -md 350 to specify a maximum distance of 350 nt between paired bases, and -dms to use the normalized mutation rates as constraints in folding. Connectivity Table files output from Fold were converted to dot-bracket format using ct2dot from RNAstructure26. The ten dot-bracket structures were concatenated into a single genome-wide structure.The population average DMS reactivities from Vero cells were obtained from the in-cell library reads as described in “Mapping and quantification of mutations” without “Filtering bit vectors”. The 29,882 nt genomes of SARS-CoV-2 was divided into 10 segments, each roughly 3 kb, whose boundaries were predicted to be open and accessible by RNAzK = 3 clusters. The number of clusters (K) resulting in the minimum Bayesian Information Criterion (BIC) was chosen as the optimal K, as done previously24.Bit vectors for each PCR amplicon were generated separately as described in “Mapping and quantification of mutations”. For each amplicon, bit vectors were filtered out if they did not pass the criteria in “Filtering bit vectors” or if <95% of their positions were informative. Positions with raw DMS reactivities less than 0.005 were set to zero to remove noise. The bit-vectors were clustered with DREEM using a maximum of K = 2: (1) at least 100,000-bit vectors passed the filtering step, (2) the maximum DMS reactivity in either cluster was no more than 0.30, (3) the coefficient of determination (R2) between the DMS reactivities  in the two clusters was no more than 0.5, and (4) the ratio of the maximum DMS reactivity in cluster 1 and cluster 2 was between 1/3 and 3. The locations of valid clusters are shown in Fig. Clusters were deemed valid if they met four criteria based on the clusters at 54. Here, “true” and “false” positives represent, respectively paired and unpaired nucleotides with DMS reactivities less than the sliding threshold.The AUROC quantifies how well DMS/SHAPE reactivities support the predicted RNA structure, under the assumption that paired bases should be less reactive than unpaired bases. Based on the secondary structure, each position was labeled as paired or unpaired, and the DMS reactivities were partitioned into paired and unpaired groups based on these labels. The ROC curves and AUROC values were computed using SciPy33 to compare two RNA structures by quantifying their shared and unique base pairs using true positive rate  and PPV. The FMI—the geometric mean of TPR and PPV—compresses these values into one similarity score. However, FMI does not account for shared regions of open bases, such that two mostly unstructured RNAs differing by a few base pairs could have a low FMI despite having a similar lack of structure. This modified FMI is a weighted average of the FMI (weighted by the fraction of positions at which at least one RNA has a paired base) and 1 (weighted by the fraction of positions at which both RNAs have an unpaired base). As the amount of structure in both RNAs increases, mFMI approaches FMI; as the number of structures decreases in both RNAs, mFMI approaches 1.The mFMI computes the similarity between two RNA structures from 0 (entirely dissimilar) to 1 . It is common practice33. In the case that Given two RNA structures of the same length  from the Protein Data Bank57 with RNApdbee 2.058. We obtained ground truth secondary structures for the 5-stem and 4-stem structures of HIV-1 RRE from a previous study using SHAPE59. For each RNA structure, we generated decoy structures in two ways: (1) refolded the RNA sequence using RNAstructure’s Fold algorithm60 without chemical probing constraints at a temperature of 273.15 K  and (2) eliminated base pairs randomly from each ground truth structure to generate a set of 1000 additional decoys. For each decoy structure, we computed the AUROC with respect to our previously collected DMS-MaPseq data24 and computed the mFMI with respect to the ground truth structure.We obtained a ground truth secondary structure of the U4/U6 snRNA by processing an NMR structure . Connectivity Table files output from Fold were converted to dot-bracket format using ct2dot from RNAstructure26.Reads from RT-PCR of a 283 nt segment of in-cell RNA spanning the FSE  were used to generate bit vectors. The bit-vectors were filtered as described above, and the filtered average mutation rates were normalized. The RNA was folded using the ShapeKnots algorithm from RNAstructureUsing the same bit vectors as described in “Folding and clustering … Huh7 DMS reactivities”, we repeated clustering and folding for the amplicon spanning the FSE region . In order to check for long-distance interactions, we folded a sequence containing an additional 1500 nt upstream and downstream of the FSE  using the Fold algorithm from RNA structure with the DMS reactivity constraints only on As and Cs in the region 13,434 – 13,601, no maximum base pair distance, and -m 3 to generate three structures.45 and downloaded from NCBI. The sequences were aligned using the MUSCLE62 web service with default parameters. The region of the multiple sequence alignment spanning the two sides of Alternative Stem 1 was located and the sequence conservation was computed using custom Python scripts.Accession numbers of curated SARS- and MERS-related coronavirus genomes were obtained from46, all reference genomes of betacoronaviruses were downloaded from RefSeq using the query “betacoronavirus[organism] AND complete genome” with the RefSeq source database as a filter. The sequences were aligned using the MUSCLE62 web service with default parameters. The subgenus of betacoronavirus to which each virus belonged was obtained from the NCBI taxonomy database63.For the alignment of all betacoronaviruses with genomes in NCBI RefSeq24 using a maximum of two clusters per region, ignoring G and U residues, and setting all mutation rates less than 0.005 to 0.0.The reference genome  was partitioned into 373 regions of 80 nt each and one final region of 42 nt. For each region, reads were filtered out if they did not pass the criteria in “Filtering Bit Vectors” or if they did not overlap with at least 20% (16 nt) of the region. The reads were then clustered using the EM algorithm implemented previouslyn = 42) or if either cluster contained a nucleotide with a mutation rate exceeding 30% (n = 16). For each remaining region with two clusters (n = 316), each cluster’s DMS reactivities  in the Vero model of the genome .To identify regions better explained by structural ensembles than by a single population average structure, we first smoothed the ∆DMS values by taking the median over a sliding window of 80 nt in increments of 1 nt, leaving blank any windows containing fewer than 10 informative ∆DMS values on As and Cs. We first identified all positions at which smoothed ∆DMS was greater than its global median (0.162) and local AUROC (see “Genome-wide computation of AUROC and mFMI”) was less than its global median (0.897). This process yielded a bit vector corresponding to meeting the aforementioned criteria or not . Intuitively, a structural element is anything that protrudes from the main horizontal line of unpaired bases in the secondary structure diagram  using cmsearch, and keep only unique homologs with no more than 5% ambiguous bases . Align the unique homologs using cmalign, then return to the build and calibrate step and repeat the whole process a total of three times to refine the alignment iteratively. Finally, identify covarying base pairs using R-scape36 with the -s option to specify the structure of the covariance model.For each structural element, we used the following procedure, based on Infernal, to identify covarying bases. First, build and calibrate a covariance model from a Stockholm alignment file containing only the sequence and structure of the element from our genome-wide model, using cmbuild and cmcalibrate from Infernal65: PAIRED and PROPER_PAIR and ({READ1 and MREVERSE and not REVERSE} or {READ2 and REVERSE and not MREVERSE}) and not (UNMAP or MUNMAP or SECONDARY or QCFAIL or DUP or SUPPLEMENTARY).Mapped reads from the in-cell library were classified as minus-strand using a custom Python script if they had the following SAM flags66. The bases were colored using the normalized DMS signals.RNA structures were drawn using VARNAFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 8Supplementary Data 6Supplementary Data 7Reporting Summary