In this study, we examined transporter genes in metagenomic and metatranscriptomic data from a time-series survey in the temperate marine environment of the Baltic Sea. We analyzed the abundance and taxonomic distribution of transporters in the 3μm–0.2μm size fraction comprising prokaryotes and some picoeukaryotes. The presence of specific transporter traits was shown to be guiding the succession of these microorganisms. A limited number of taxa were associated with the dominant transporter proteins that were identified for the nine key substrate categories for microbial growth. Throughout the year, the microbial taxa at the level of order showed highly similar patterns in terms of transporter traits. The distribution of transporters stayed the same, irrespective of the abundance of each taxon. This would suggest that the distribution pattern of transporters depends on the bacterial groups being dominant at a given time of the year. Also, we find notable numbers of secretion proteins that may allow marine bacteria to infect and kill prey organisms thus releasing nutrients. Finally, we demonstrate that transporter proteins may provide clues to the relative importance of biogeochemical processes, and we suggest that virtual transporter functionalities may become important components in future population dynamics models. Using ta (Chl a) and nutrients were processed in the laboratory and data depicted in Surface water samples were collected from 2m depth at the Linnaeus Microbial Observatory (LMO), 10km east off the island Öland, Sweden, in the central Baltic Sea (56°55′0.51.24′N 17°3′38.52′E) using a Ruttner sampler. Water was collected in 10L polycarbonate containers and transported within 1h to the laboratory at Linnaeus University in Kalmar. Samples for abiotic and biotic variables for 2012–2014, other than DNA and RNA (described below), were taken as published previously in A total of 37 DNA samples for metagenome sequencing were collected between March and December 2012, by filtering seawater (around 3L) first through a 3.0μm polycarbonate filter and then collecting microbial biomass onto a 0.22μm filter  Samples for metatranscriptomics sequencing were collected on 25 occasions during the period 2012 to 2014, although only four samples from 2012 coincided in time with the metagenomic samples. For RNA, approximately 8L of seawater were prefiltered through a 3.0μm, 142mm diameter polycarbonate filter , followed by a 0.2μm, 142mm diameter polycarbonate membrane disk  in less than 20min. The 0.2μm filters were then added to 15ml RNase/DNase-free tubes with 2ml RLT buffer (QIAGEN) containing 0.01% Beta-mercaptoethanol, and flash frozen in liquid nitrogen and stored at -80°C until further RNA extraction processing.DNA extraction from the sterivex filters was done by a phenol-chloroform protocol, including a 30min lysozyme digestion step at 37°C and a proteinase K digestion overnight at 55°C, as detailed in g), the supernatant was collected without carryover of beads and transferred to a new tube. One volume of 70% ethanol was added and tubes were gently mixed five times. The extraction was then continued using RNeasy mini columns  according to manufacturer’s instructions. The samples were then cleaned from DNA using Turbo DNA-free kit (AMBION) and mRNA was linearly amplified using Message Amplification II bacteria kit (AMBION) according to the manufacturer’s protocol. Sequencing was completed at SciLifeLab Stockholm, Sweden, on a HiSeq2500  with a 2×126bp paired-end reads setup in RapidHighOutput mode.Extraction of RNA was performed according to a protocol adopted from The current analysis of metagenomes and metatranscriptomes was made extensive use of the Baltic Sea Reference Metagenome BARM; . BARM isAs a first step in the current analysis of transporter genes, the entire set of metagenome sequencing reads was preprocessed as in Next, to quantify the distribution of sequencing reads among the total set of ORFs in BARM, the preprocessed metagenomic and metatranscriptomic reads were mapped to the BARM reference assembly using bowtie2 v2.2.6;  with defAlthough taxonomic assignments were made for individual ORFs, these assignments were then used in a voting strategy to assign a lowest common ancestor to the corresponding contigs. This is similar to the method used by, e.g., CAT, but is based on alignment fraction and percent identity of hits and not directly on bit scores, as described in https://github.com/johnne/transporters/wiki/1.-Identifying-protein-families). This search identified 528, 464, and 443 transporter protein families in the PFAM, TIGRFAM and COG database, respectively. These protein families were merged into transport clusters using cross-reference information for 25,552 reviewed entries in the Uniprot databasehttps://github.com/ChristoferLNU/transporters/.To obtain a curated identification of membrane transporters among the ORFs in BARM, beyond the direct annotations in BARM obtained by individual PFAMs, TIGRFAMs, or COGs, we screened the PFAM, TIGRFAM, and COG databases (as of March 2018) for protein families related to transporter proteins using text searches  carbohydrates, (3) nucleosides, (4) anions, (5) cations, (6) nitrate, (7) urea, (8) phosphate, and (9) phosphonate. With respect to transporter function, the anion group represents mechanisms for the uptake of negatively charged ions, such as sulfate and molybdate, but also importantly small charged organic molecules. The cation group is directed toward metal-ions, such as iron, cobalt, magnesium, nickel, and zinc, but also includes transporters for the uptake of organic molecules, in particular large and complex molecules. Analyses conducted for this study are available online at https://github.com/ChristoferLNU/transporters/tree/master/article as a set of python notebooks.Based on the protein family description, the transporter clusters were divided into two main groups importers and exporters to facilitate the analysis, although a clear distinction regarding the direction of transport was sometimes difficult to establish, e.g., for metal ion transporters. The importer proteins could be further subdivided, based on the putative substrate recognition of the suggested transporter clusters, into one of nine substrate categories: (1) amino acids (AA) and peptides + NHhttps://doi.org/10.6084/m9.figshare.c.4508144.v2.Data generated in this study can be found through the following DOI link A compilation of 3years  of biological and environmental data from the LMO station is presented as background information in Throughout 2012, the MG dataset was comprised of roughly 75% bacterial, 18% pico-eukaryotic, and 6% viral protein coding sequences . ArchaeaBacteroidetes, Actinobacteria, and Alphaproteobacteria showed the highest relative abundances, and most taxa showed a two- to three-fold range of variation in abundance during the year. On average, 54% of the transcripts were bacterial, 44% picoeukaryotic, and 1% viral whereas archaeal transcripts made up 0.2%.The seven most abundant major bacterial taxa (Phylum/Classes) together accounted for between 40 and 60% of the MG dataset on an annual basis . OverallIn the BARM co-assembly, a total of 66,029 ORFs  were identified as membrane transporters or subunits thereof. The number of transporter ORFs detected in the MG and MT datasets by read mapping was 39,634  and 20,166 , respectively.The assembled transporter ORFs were classified into 1,149 membrane transporter clusters , based oAs defined in the Transporter Classification Database TCDB; , the majSynechococcus and picoeukaryotes, contributed only minimally to the abundance of transporter proteins in the MG and MT dataset.The abundance of importer proteins of bacteria, picoeukaryotes, and cyanobacteria in relation to nine substrate categories are shown in amin B12 . More inThe average number of taxa (orders) for the three groups bacteria, picoeucaryota, and cyanobacteria associated with each substrate category is shown in There was no reason to expect that gene abundance would be similar to the number of transcripts but it was seen that the relative abundances in the MG, and the transcripts in the MT datasets of transporter proteins within the different substrate categories were similar, with the exception being cations that showed higher relative transcription .Flavobacteriales, Cellvibrionales, Sphingobacteriales, and Mamiellales that had more than six substrate categories that could be tested . Potential correlations between transporter abundance and transcription were further examined in the MG and MT datasets for the four sampling dates when both MG and MT data were available, focusing on the four orders e tested . This comporters .Cellvibrionales, Flavobacteriales, Rhodobacterales, and Burkholderiales, although with high variability around the averages  and the green algae order Mamiellales. In the MT dataset, the highest number of transporter proteins was found in the bacterial orders Flavobacteriales and Cellvibrionales with between 15 and 20 importers per sample (data not shown). Collectively, these findings show that there are pronounced differences in the number of importers between orders.In the MG dataset, 59 abundant importers have been identified in 185 bacterial orders, while in the MT dataset, 131 orders were represented in the same 59 abundant importers, indicating that the importer genes were expressed by ~70% of the taxa comprising the 59 abundant importers. We next analyzed the average number of different importer types associated with different taxa (orders) during the 2012 growth period, in the MG and MT datasets, where >500 reads mapped to the set of 59 abundant transporters. On average, around 20–25 importers were detected across samples in the orders averages . In cont genomes . SimilarFlavobacteriales and Sphingobacteriales showed dramatic peaks in abundance of importers in the metagenome during the year . Interestingly, the peaks in importer abundance for the different taxa varied in a successional manner, starting at the time of the spring bloom with the orders Flavobacteriales and Rhodobacterales which peaked in mid-April and that had declined by mid-May. Cellvibrionales and Burkholderiales peaked later in June after which a pronounced peak in Sphingobacteriales could be seen ending the summer succession in early August . This demonstrated that, most notably, the the year . In conty August . From reFlavobacteriales were cations and amino acids/peptides, showing mean values over the entire sampling period of 50 and 27%, respectively  but rich in transporters for carbohydrates (36%), amino acids/peptides (19%), and phosphonate (19%). Also, the next two taxa in the succession of transporters, Cellvibrionales and Burkholderiales that followed in May and June, exhibited notable differences in transporter composition; primarily due to sizable portions of anion importers (26%) and amino acids/peptides importers (27%) in Cellvibrionales compared to high proportion of carbohydrate transporters (39%), but transporters for few amino acids/peptides (8%), cations (18%), and phosphonate (9%) in the Burkholderiales. Curiously, this distribution of transporters in Burkholderiales was rather similar to that of the Rhodobacterales that were dominant in early spring. Compared to the importer composition of the Flavobacteriales, the late summer-peaking Bacteroidetes order Sphingobacteriales demonstrated an even more pronounced dominance of cation transporters (70%) and a lower proportion of amino acids/peptides transporters. Compared to the other taxa, the Pelagibacterales were primarily characterized by a high proportion of transporters for phosphate (on average 15%) and nitrate . The Mamiellales stood out as having a particularly high proportion of transporters for anions (≥25%).In spite of the large variation in the abundance of transporters between orders in the MG dataset , the subectively . Even thate 23%; . InteresphnC, phnD, and phnE found in the phn operon ; and the second of ammonium, salinity, phosphate, and nitrate.Pair-wise spearman rank correlations between the abundance of importer proteins in the MG data set and environmental parameters measured at LMO are shown in the heat map in For several transporters, the relative gene abundance was positively correlated to temperature and at the same time negatively correlated to phosphate. Three examples of proteins showing this pattern were (a) members of the solute:Na+ in symport (SSS) family of importers that have been suggested to transport sugars, amino acids, nucleosides, inositols, vitamins, and urea or anions depending on the system , (b), thThe abundant exporter proteins found in the MG dataset (>0.5%) could be placed into two main groups . The fira spring peak and again during the autumn bloom (data not shown). This protein family shows high similarity to the pathogenicity trait expressed by Bacillus cereus, where the responsible gene has been shown to function as a channel-forming cytolysin . The substrate group “cations” notably includes several metal ions, but in relation to the spring bloom, most importantly, transport a broad variety of charged organic carbon compounds via the TonB dependent transporters (TBDTs).The spring bloom period is an obvious starting point for interpreting patterns in the annual succession in the Baltic Sea as in most temperate waters. The no-growth winter period is followed by conditions with increasing light and temperatures, resulting in a diatom and dinoflagellate spring bloom . These s mid-May . Typicaln blooms , and theTo underscore this notion, TonB-linked outer membrane proteins, SusC/RagA/OmpW , showed Taken together, our results show differences in transporter cluster abundances related to phylogenetic affiliation across growth seasons. This would suggest that most major types of transporters are present at any given time, but that the distribution pattern stems from different taxa, mostly bacterial groups being dominant at different times of the year. The succession of transporter proteins with clear taxonomic links discussed above is difficult to explain by any other mechanism than a bacterial species succession during the growth period. We therefore find it reasonable to conclude that the substrate affinity traits that are revealed in this study represent a major mechanism explaining bacterial succession in nature.Bacillus cereus. In Bacillus cereus, the responsible gene has been shown to function as a channel-forming cytolysin , showed the highest abundance in the LMO MG data set as well as the highest activity (transcription) in the metatranscriptome see . The “pangitidis , the adhhia coli  and pertertussis  among thPseudomonas aeruginosa that encodes a protein secretion apparatus. Upon infection in humans, the substances secreted through this mechanism play a role in the pulmonary disease cystic fibrosis. Interestingly, HSI-I–related loci are widely distributed among bacterial pathogens and may play a general role in mediating host interactions  is one of the dominant bloom-forming cyanobacteria in the Baltic Sea and has recently been found to carry phosphonate degrading genes . This suggests that also at least some picocyanobacteria carry the genes for phosphonate uptake. The abundance and transcription of transporters linked to phosphonate uptake in both bacteria and cyanobacteria in the MG and MT data thus indicates that phosphonate uptake and transformations could be an important but largely overlooked component of P cycling in the Baltic Sea.For phosphorus, these analyses were uncovered a surprising abundance of phosphonate transporters, in addition to the traditionally recognized phosphate transporters. Phosphonate has not received much attention as a potential P-source in the Baltic Sea but see nes phn; . Still, 3 importers was consistently low, in accordance with more energy demanding uptake of NO3 compared to With regard to nitrogen, the abundance and expression of transporters at the LMO study site indicated that bacteria, picoeukaryotes, and picocyanobacteria all relied on uptake of both amino acids and ammonium and for cyanobacteria also urea. Abundance and expression of NOelevated . During  <0.5μmN , the upt <0.5μmN . Active  <0.5μmN  and high <0.5μmN . This caFor carbon compounds, an important group of importers observed in the LMO data set was the TRAP-type transporter family. The TRAP substrates are diverse but share the feature that they are all organic acids . These iTonB-dependent transporters are important for active uptake across the outer membrane in gram-negative bacteria  and the For future management of the Baltic Sea, the specific transporter proteins discussed in this section represent possible indicators for the assessment of good environmental status according to the EU Marine Strategy Framework Directive . MeasurernfABCDGE that encodes an NADH oxidoreductase responsible for electron transport to the nitrogenase was particularly interesting since it is necessary for nitrogen fixation , ÅH, UZ, JSu, and BM contributed to conception and design of the study. JSu, UZ, ÅH, and CO performed the bioinformatic analysis and organized the database. CO, CB, and JSj performed field work and laboratory analysis at the LMO sampling site. ÅH, JSu, and UZ wrote the first draft of the manuscript. JP, ÅH, and CO did the final editing. ÅH and JP were responsible for funding acquisition. All authors contributed to manuscript revision, read, and approved the submitted version.Via the BONUS BLUEPRINT project, ÅH, JP, and BM received funding from BONUS, the joint Baltic Sea research and development program (Art 185), and the Swedish research council FORMAS. Funding for field sampling and sequencing was also provided through the Swedish governmental strong research programme EcoChange to JP. JSu received funding from the Knut and Alice Wallenberg Foundation. CB was financially supported by HIFMB, a collaboration between the Alfred-Wegener-Institute, Helmholtz-Center for Polar and Marine Research, and the Carl-von-Ossietzky University Oldenburg, initially funded by the Ministry for Science and Culture of Lower Saxony and the Volkswagen Foundation through the “Niedersächsisches Vorab” grant program (grant number ZN3285).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Packaging of segmented, double-stranded RNA viral genomes requires coordination of viral proteins and RNA segments. For mammalian orthoreovirus (reovirus), evidence suggests either all ten or zero viral RNA segments are simultaneously packaged in a highly coordinated process hypothesized to exclude host RNA. Accordingly, reovirus generates genome-containing virions and “genomeless” top component particles. Whether reovirus virions or top component particles package host RNA is unknown. To gain insight into reovirus packaging potential and mechanisms, we employed next-generation RNA-sequencing to define the RNA content of enriched reovirus particles. Reovirus virions exclusively packaged viral double-stranded RNA. In contrast, reovirus top component particles contained similar proportions but reduced amounts of viral double-stranded RNA and were selectively enriched for numerous host RNA species, especially short, non-polyadenylated transcripts. Host RNA selection was not dependent on RNA abundance in the cell, and specifically enriched host RNAs varied for two reovirus strains and were not selected solely by the viral RNA polymerase. Collectively, these findings indicate that genome packaging into reovirus virions is exquisitely selective, while incorporation of host RNAs into top component particles is differentially selective and may contribute to or result from inefficient viral RNA packaging. S. S54]. SIT1L1 virions and TC particles exhibited notable differences. While rsT1L virions and TC particles packaged reads mapping to most viral segments in proportion to length, in several instances, rsT3DIT1L1 TC particles deviated from expected proportions (IT1L1 TC particles, but typically not rsT1L TC particles, often were higher than expected for dsRNA (IT1L1 TC particles consistently packaged higher percentages of viral reads, relative to host reads, than rsT1L, though the level of virion contamination in rsT3DIT1L1 TC particle preparations was not quantified (I may clarify whether differences in rsT1L and rsT3DIT1L1 viral RNA packaging are strain specific or result from mismatch with the RdRp. Of the 34 host transcripts significantly enriched in rsT1L TC particles, 5 were enriched in rsT3DIT1L1 TC particles, and rsT3DIT1L1 TC particles were enriched for another 27 distinct host transcripts (Viral and host RNA packaging by rsT1L and rsT3Dportions . Proportor dsRNA . Finallyantified . Future nscripts A,B. ThusIT1L1 TC particles mapped to cellular RNA (IT1L1 TC particles [IT1L1 TC particles (Our NGS analyses indicated that an average of 50% of reads for rsT1L TC particles and 71% of reads for rsT3Dular RNA . Of thesarticles . Since rarticles ,20,63,64articles . For rsTarticles . These fWhether TC packaging of host RNA has significant functional consequences for reovirus replication is an open question. Reovirus packages viral +RNA and uses it as a template for minus-strand synthesis to make the dsRNA genome . Since TReoviridae family is mediated by cis- and trans-segment interactions reliant upon specific nucleotide sequence and structural motifs. Here, we demonstrate that reovirus TC particles can package diverse cellular RNA transcripts, while virions fail to do so, supporting a highly selective genome packaging model for virions. Packaging of host transcripts within TC particles is not based solely on transcript abundance and may differ based on virus strain, suggesting some selectivity, but is not determined solely by the viral RdRp. We speculate that encapsidation of host transcripts is unlikely to significantly affect the biology of cells into which TC particles enter, as packaged cellular transcript abundance is low, and there is no apparent mechanism for host transcript exit or amplification. Rather, host transcript packaging may interfere with viral +RNA packaging or simply be permitted when a full complement of viral +RNAs fails to be packaged. Future studies are required to reveal the mechanism and outcome of host transcript packaging by reovirus TC particles.RNA packaging by viruses belonging to the


Our results show that VSV needs to replicate for a robust interferon response. Analysis of RIG-I-associated RNA identified a copy-back defective-interfering (DI) genome and full-length viral genomes as main trigger of RIG-I. VSV stocks depleted of DI genomes lost most of their interferon-stimulating activity. The remaining full-length genome and leader-N-read-through sequences, however, still triggered RIG-I. Awareness for DI genomes as trigger of innate immune responses will help to standardize DI genome content and to purposefully deplete or use DI genomes as natural adjuvants in VSV-based therapeutics.Replication competent vesicular stomatitis virus (VSV) is the basis of a vaccine against Ebola and VSV strains are developed as oncolytic viruses. Both functions depend on the ability of VSV to induce adequate amounts of interferon-α/β. It is therefore important to understand how VSV triggers interferon responses. VSV activates innate immunity  Vesicular stomatitis virus (VSV) is a non-segmented negative-stranded RNA virus that belongs to the family rhabdoviridae . It is cIn addition, due to an inherent tumor specificity and potential to break immune tolerance in the tumor microenvironment VSV is developed as an oncolytic virus for cancer therapy , 6. OncoRetinoic acid-inducible gene I (RIG-I) is a key cytoplasmic pattern-recognition receptor. Once activated it triggers a signaling cascade that culminates in an antiviral gene program characterized by a type I interferon signature. RIG-I functions as a major sensor for RNA viruses of negative polarity such as influenza A, paramyxoviruses or rhabdoviruses , 8 and wAs defined by us and others the minimal structural requirements of synthetically produced RIG-I ligands are RNA molecules carrying a 5’-tri- or diphosphate moiety with a blunt-ended double-stranded base pairing of > 20 bp in direct vicinity –15.A wide range of candidate RNAs has since been published to activate RIG-I in infections with Sendai, influenza and measles viruses , 16–18. In this study we combine different methods, including deep sequencing, enzyme digestion and length fractionation to investigate the RNA ligands co-immunoprecipitating with RIG-I in VSV-infected cells. This allowed us to identify the RNA-species of VSV that are relevant to activate RIG-I and trigger the interferon responses necessary for its function as live vaccine and oncolytic agent.via PCR or MycoAlert Mycoplasma Detection Kit (Lonza). Authentication of human cell lines by STR DNA profiling analysis was conducted in house.1205Lu cells were provided by Dr. R. Besch (LMU Munich) and were cultured in DMEM medium supplemented with FCS (10% v/v), L-glutamine (1.5 mM) and ciprofloxacin (10 µg/ml). Flp-In™-T-REx™-293 cells were purchased from Invitrogen and cultured in DMEM medium supplemented as above with additional added zeocin (100 µg/ml) and blasticidin (15 µg/ml). For the generation of HEK 293 FLAG-RIG-I cells, N-terminally FLAG-tagged RIG-I was cloned into the pcDNA™5/FRT/TO vector and Flp-In™-T-REx™-293 cells were cotransfected with this vector along with the pOG44 plasmid. FLAG-RIG-I cells were then cultured in DMEM containing hygromycin (100 µg/ml) and blasticidin. Expression of FLAG-RIG-I was started by the addition of 1 µg/ml tetracycline to the culture medium 24 hours prior to infection. All cell lines were regularly tested for mycoplasma contamination via western blot. The following sgRNAs were used: DDX58 (RIG-I) GGGTCTTCCGGATATAATCC; IFIH1 (MDA5) GAGGGCTGCCGGTTCTCCGG; MAVS GTACTTCATTGCGGCACTGA; EIF2AK2 (PKR) GTACTACTCCCTGCTTCTGA. Genotypes of the respective knockout cell lines are available upon request.1205Lu RIG-I, MDA5 and MAVS knockout cells were generated as described previously  by trans-1 – 10-4 PFU/cell. Adsorption of viral particles was allowed to occur for one hour at 37°C temperature before the medium was removed, cells were washed in PBS and GMEM medium was added to the cells. 48-72 hours post infection when infected cells had fully detached virus containing supernatant was collected, centrifuged and stored at -80 C. Virus concentration in stock solutions was determined by standard plaque assay. Both virus strains used as our starting stocks for this study had been propagated for an unknown number of passages with the described protocol unaware of their DI-content without purposefully enriching or depleting DIs.Vesicular stomatitis Indiana virus (VSV wt) was a gift from A. Krug (LMU Munich), who had received it from D. Kolakofsky`s lab, University of Geneva, Geneva, Switzerland and is originally derived from the Mudd-Summers isolate obtained from cattle in Indiana in 1925. The VSV M51R virus containing a methionine to arginine substitution at positions 51 was a kind gift of Oliver Ebert . It is derived from the recombinant Indiana strain rVSV generated by Jack Rose  and cont-6 PFU/cell. The virus containing supernatant was collected 24 hours post infection. The titer of the resulting viral stock was determined by plaque assay and used for another round of virus production on BHK cells with an MOI of 10-6 PFU/cell. This procedure was repeated five times for the wt stock depicted in For the generation of viral stocks with decreased amounts of defective-interfering particles, BHK cells were infected with VSV at an MOI of 10For infection assays the previous medium was removed and the indicated cell lines were infected with VSV wt or the VSV M51R mutant at an MOI of 1 in Opti-Mem. At the indicated time points, cell supernatant was harvested and cells were lysed to isolate RNA and/or proteins. For immunoprecipitation experiments cells were lysed either 9 or 24 hpi. Where indicated, cycloheximide (100 µg/ml) was added to the medium 30 min prior to infection.RNA was isolated from cell lysates or eluates from immunoprecipitations using the miRNeasy Mini Kit (QIAGEN) according to the manufacturer’s instructions.4 cells plated in a 96-well were either transfected with 100 ng of total cellular RNA or 10 μl of the fragmented RNA (see below). The RNA was diluted in Opti-MEM in a total volume of 20 μl and mixed with 0.3 μl Lipofectamine RNAimax diluted in 20 μl Opti-MEM. After 10 min at room temperature the transfection mix was added to the cells. IP-10 was measured in the supernatant after 24 hours using the OPtEIA IP-10 ELISA kit (BD Biosciences) according to the manufacturer’s instructions.To determine the immunostimulatory capacity of VSV-derived RNAs, purified RNA from infected cells was retransfected into 1205Lu cells. For that 1x10in vitro transcription (IVT) of a double-stranded DNA template  as previously described (100 ng/ml high molecular weight polyinosine-polycytidylic acid (poly I:C) from InvivoGen  and 500 ng/ml triphosphate RNA (pppRNA) transfected with lipofectamin RNAimax were used as known ligands for MDA5 and RIG-I respectively. pppRNA was synthesized by escribed .For some experiments purified RNA from VSV-infected cells was treated with RNase prior to retransfection into 1205Lu cells. For that 4 µl of RNA in 20 µl buffer was treated for 30 minutes at 37°C with 20 U of RNase R , 1 U of RNase III  or 1 U of 5’-polyphosphatase  using their respective buffers (Epicentre). RNase III buffer contained 330 mM Tris acetate (pH 7.5), 660 mM potassium acetate, 200 mM magnesium acetate, 5 mM DTT. Buffers for RNase R and 5’-polyphosphatase were provided by the manufacturer along with the enzymes in a 10x stock solution.6 cells grown in a 10 cm² dish) or RNA co-immunoprecipitating with RIG-I (see below) was isolated 24 hpi. 8 µg of total RNA diluted in 1.5 x RNA loading dye  was incubated at 70°C for 10 min and immediately put on ice. In the case of RNA co-immunoprecipitating with RIG-I all the RNA recovered after immunoprecipitation was used. After 10 min the RNA was separated on a 0.8% agarose gel at 60 V for 2.5 h. Guided by a 10 kbp RNA ladder loaded on the same gel, the RNA was cut size-dependently into ten fractions. RNA was recovered using the Zymoclean™ gel RNA recovery kit (Zymo Research). Equal volumes of recovered RNA of each slice were retransfected into 1205Lu cells and the immunostimulatory effect was measured as described above.RNA of infected HEK 293 cells , 1:100 Protease Inhibitor Cocktail (SIGMA)). Lysates were precleared with sepharose beads (SIGMA) and incubated at 4°C for two hours with either anti-FLAG M2 Affinity Gel (SIGMA) or IgG control beads (SIGMA). Beads were washed five times in lysis buffer and FLAG-RIG-I was eluted using 3x FLAG peptide .Immunoprecipitation of RIG-I/RNA complexes was performed as described in detail previously . BrieflyCellular extracts or eluates from immunoprecipitations were diluted in 2x Laemmli sample buffer (BioRad), separated by SDS-PAGE in 10% Tris-glycine-gels and blotted onto a PVDF-membrane . After blocking in 5% milk in TBS with 0.1%Tween 20 or 5% BSA (when anti-phospho-IRF3 antibody was used consecutively) for 1 hour at RT. Membranes were then incubated over night at 4°C with the indicated antibodies. After incubation with the corresponding secondary antibody for 1 hour, blots were washed and chemiluminescence was detected with the LAS imager (Fujifilm). Antibodies used in this study were anti-phospho-IRF3 antibody , anti-VSV G antibody , anti-FLAG M2 antibody (1:1000 SIGMA), anti-actin antibody, HRP-linked , anti-mouse immunoglobulin, HRP-linked (CST), anti-rabbit immunoglobulin, HRP-linked (CST).GCCTCTCA TGCTGACGAA TTT TGAGAGGC AAAGTTTC; self-complementary regions are underlined). Quantitative PCR assays were designed by the use of the universal probe library design center . Primer sequences are detailed in RNA isolated from cell lysates or eluates from immunoprecipitations was reverse-transcribed into cDNA using random hexamer primers (ThermoScientific). As the 51 nucleotides of the leader sequence were too short for the design of a conventional qPCR assay, an additional, partially self-complementary primer, that elongates the cDNA of the leader-RNA by 29 nucleotides, was added to the cDNA synthesis reaction  that binds RNA-sequences containing the trailer sequence on their 5’-end, i.e. trailer-terminated defective interfering particles and the full length antigenome. In a second step the produced cDNA was PCR-amplified with the same primer leading to the amplification of cDNAs containing the trailer sequence on the 5’-end and its reverse complementary sequence on the 3’-end. PCR products were gel purified and subjected to Sanger sequencing using primers binding at different sites within the L gene matching the region with the expected DI breakpoint based on the size of the observed PCR product . The identified breakpoint at genomic position 8103 in VSV M51R stocks was used to design an RT-qPCR assay that allowed for specific quantification of the DI 8103. DI 6494, identified in next generation sequencing experiments, served as a positive control for the assay.RNA isolated from cell lysates or from FLAG-RIG-I-IPs was processed by removing rRNA with the Ribo-Zero rRNA Removal Kit (Illumina). cDNA libraries were then generated using either the TruSeq Stranded Total RNA Sample Prep Kit (Illumina) or ScriptSeq™ v2 RNA-Seq Library Preparation Kit (Epicentre). Quality of cDNA libraries was assessed on a DNA1500 chip . Sequencing was performed by the Laboratory for Functional Genome Analysis (LAFUGA) at the Gene Center Munich on the Illumina Genome Analyzer.http://www.ncbi.nlm.nih.gov/nuccore/NC_001560.1Sequences recovered from the Illumina Genome Analyzer were aligned in two steps with BWA (version 0.7.4) using default parameters. First, a single-end alignment against the human genome (GRCh37.59) identified high-quality nonviral reads. The remaining, unmapped sequences were aligned with BWA-MEM against the VSV reference genome. Coverage was plotted against the genome for every nucleotide or with 50 nucleotid windows or with windows representing the different genes of the VSV genome and the non-coding 3’-leader and 5’-trailer region. Relative enrichment was calculated by dividing coverage from the experimental condition to the control condition for every nucleotide of the VSV genome. Annotations and VSV genome reference were taken from via the accession number PRJEB42788.RNA-Seq data was deposited in the European Nucleotide Archive and can be accessed For a systematic analysis of breakpoint reads, reads previously unmapped to the human genome were realigned to the VSV genome with the BWA-SW algorithm. It allows the detection of reads, which split up into two sub-sequences that map to two distant locations on the reference genome (split-reads). The distance between the sub-sequences gives information about deleted segments and a different orientation reveals potential inversions. Split-reads supporting the same aberration and break-points were clustered and reported if their BWA-SW mapping quality was greater than 10  and the cluster size exceeded 2 reads. The post processing of the split-reads following BWA-SW alignment was done with R.It has been shown before that RIG-I is triggered after infection with VSV , 10. UsiIn order to identify which RNA species trigger RIG-I in the course of VSV infection we first searched for the best time point to immunoprecipitate immunostimulatory RNA bound to RIG-I. We therefore performed a time course experiment infecting HEK 293 cells with our stocks of the VSV Indiana strain. Using the production of IFN-β mRNA and phosphorylation of IRF-3 in infected cells as markers for an activated RIG-I signal we detected a first increase in IFN-β transcription 5 hours post infection (hpi) - visible only on an x-axis with logarithmic scale (data not shown). RIG-I-signaling was robustly turned on 9 hpi and increased further till 31 hpi  , 34. In In order to characterize the RNA species triggering RIG-I during VSV infection, we co-immunoprecipitated FLAG-tagged RIG-I and the RNAs bound to it 9 hpi. When the co-immunoprecipitated RNA was isolated and retransfected into 1205Lu melanoma cells, RNA from infected, FLAG-RIG-I over-expressing cells was capable of inducing IP-10. Neither RNA co-immunoprecipitating with RIG-I in uninfected cells nor RNA from infected cells co-immunoprecipitating with control beads, nor RNA co-immunoprecipitating with FLAG-beads from infected cells lacking overexpressed FLAG-RIG-I showed immunostimulatory capacity  after depleting ribosomal RNA, about 33% of the reads could be aligned to the VSV genome and 51% of RNAs could be aligned to the human genome with 16% unmapped reads. Similar numbers were found for RNAs co-immunoprecipitating with RIG-I (FLAG-RIG-I IP). However, also in the condition controlling for unspecific binding of RNA to FLAG-beads (w/o FLAG-RIG-I IP) a significant amount of RNA was recovered showing a pattern with 24% (only 6% less than in the RIG-I condition) of reads matching the VSV genome and 52% aligning to the human genome  genome.DI RNAs are truncated forms of the viral genomes generated by many viruses including VSV during viral replication . AsThe copy-back defective interfering particle identified here consists of approximately the last 4000 nucleotides of the genome with the trailer region being attached to its 3’-end in reversed orientation leading to a panhandle with a double-stranded trailer region.To prove that this enrichment of trailer and L-gene sequences is due to a defective interfering genome bound to RIG-I, we searched for reads that would be specific for the proposed DI genome. These reads should cover the breakpoint between the position in the L gene at approximately position 6500 and the reverse trailer attached to it. Applying an algorithm looking for hybrid reads that partly aligned to the reference in forward and reverse orientation, we indeed found breakpoint reads covering both the trailer region starting at position 11 107 in positive orientation and the L gene from position 6496 in negative orientation. We could find 18 of these reads in the FLAG-RIG-I pulldown but none in the control condition. We could not find any other breakpoint reads in our libraries showing a coverage higher than 3 reads or enrichment between the pulldown-conditions. From this breakpoint-analysis we could deduce the sequence of the 4719 nucleotides long DI genome that is schematically depicted in via the production of an anti-DI genome that is then used as a template to produce new DI genome copies. As the sequence of the double-stranded stretch of the panhandle is identical for the identified DI genome and its DI-anti-genome it is not surprising that we found the sequence of the DI genome enriched not only in negatively oriented reads (DI genome) but also in positively oriented reads (anti-DI genome). The latter however in reduced amounts .-6 and after 24 h the supernatant was collected and reused for a repetitive infection each time with an MOI of 10-6. This procedure was repeated for 5 passages until in the resulting stock called P5 the DI genome was nearly completely diluted out as confirmed by RT-qPCR using a DI-specific qPCR assay, that is based on the unique sequence of the breakpoint between position 6496 and position 11 107  and DI 611  but also of the full-length genome (11 kb). When cell extracts from cells infected with virus stocks with reduced DI content were analyzed, only the RNA migrating at the size of the full-length genome retained immunostimulatory properties. Together these data suggest, that the full-length genome can activate RIG-I in the absence of DI genomes.Interestingly, we also detected an enrichment of leader-N read-through sequences in our RIG-I pulldown experiments which were detectable both in qPCR and NGS. According to the NGS data, the enriched sequence spanning the leader/N junction is of positive orientation, while the qPCR assay cannot distinguish positive from negative oriented RNA. However, while one would expect leader-N-read-through products to migrate at sizes below 0.5 kb in the size fractionation experiment, RNA isolated from this fraction was not immunostimulatory. Therefore, we consider it possible that the leader-N-read-through transcript is only immunostimulatory when attached to the full-length genome, thereby forming a double-strand that can be detected by RIG-I.in vitro using the recombinant VSV L protein  protein . It is wThe fact that a VSV-based vaccine against EBOLA is already in clinical use, that the VSV-vaccine platform is currently used to develop a vaccine against COVID-19 and that VSV-strains are tested in clinical studies as oncolytic viruses for the treatment of tumors confers clinical relevance to our findings. The question how these therapeutic applications are being influenced by the content of defective interfering RNA in the viral preparations has not been addressed to date. In light of our findings this warrants further investigations. We assume that the DI content of a VSV-based vaccine strain or oncolytic virus will influence immunization success, anti-tumor immunity and side-effects. Depending on the specific context DI content will thereby enhance or reduce the wanted effect. We hope that awareness for the potential role of DI genomes as trigger of innate immune responses in VSV-based therapeutics will help to standardize DI genome content in virus stocks and might help to purposefully deplete or use DI genomes as natural adjuvants in VSV-based therapeutics.The data presented in the study are deposited in the European Nucleotide Archive (ENA) repository, accession number PRJEB42788.AL and VB designed and performed experiments, analyzed results, and co-wrote the manuscript. SR initiated the project, designed the research, supervised and analyzed experiments and co-wrote the manuscript. AS and NL initiated the project, performed preliminary experiments, and gave conceptual advice. CB and MD performed bioinformatic analyses of the NGS data. GS, PS, FD, JR and DFRB performed experiments, contributed reagents and analyzed results. DP, JTS, LMK, HK, MS, SK, and SE supervised experiments and gave conceptual advice. All authors contributed to the article and approved the submitted version.Einheit für Klinische Pharmakologie (EKLIP), Helmholtz Zentrum München, Neuherberg, Germany , the international doctoral program “iTarget: Immunotargeting of cancer” funded by the Elite Network of Bavaria , the Marie-Sklodowska-Curie Training Network for the Immunotherapy of Cancer  funded by the H2020 program of the European Union , the Else Kröner-Fresenius-Stiftung 2017_A50 to LMK, the Friedrich-Baur-Stiftung to LMK, JTS and HK and the Deutsche Forschungsgemeinschaft KO50552-1 to SK, DFG - project number 442265435 to LMK, SCHN 664/6-1 and SFB 1321/P16 grant number 329628492 to MS, Ro 25257/-1 grant number 391217598 and SFB/TR-237-B14 Grant No. 369799452 –Projektnumber 404450088 to SR and funding through endowments to the medical faculty of the LMU to SR and HK.This work was supported by 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.


During the early stages of the COVID-19 pandemic, developing safe and effective coronavirus vaccines was considered critical to arresting the spread of the disease. News and social media discussions have extensively covered the issue of coronavirus vaccines, with a mixture of vaccine advocacies, concerns, and oppositions.This study aimed to uncover the emerging themes in Twitter users’ perceptions and attitudes toward vaccines during the early stages of the COVID-19 outbreak.This study employed topic modeling to analyze tweets related to coronavirus vaccines at the start of the COVID-19 outbreak in the United States . We created a predefined query  to extract the tweet text and metadata  from the Meltwater database. After preprocessing the data, we tested Latent Dirichlet Allocation models to identify topics associated with these tweets. The model specifying 20 topics provided the best overall coherence, and each topic was interpreted based on its top associated terms.News Related to Coronavirus and Vaccine Development, 25.4%  as General Discussion and Seeking of Information on Coronavirus, 12.9%  as Financial Concerns, 12.7%  as Venting Negative Emotions, 9.9%  as Prayers and Calls for Positivity, 8.1%  as Efficacy of Vaccine and Treatment, and 4.9%  as Conspiracies about Coronavirus and Its Vaccines. Different themes demonstrated some changes over time, mostly in close association with news or events related to vaccine developments. Twitter users who discussed conspiracy theories, the efficacy of vaccines and treatments, and financial concerns had more followers than those focused on other vaccine themes. The engagement level—the extent to which a tweet being retweeted, quoted, liked, or replied by other users—was similar among different themes, but tweets venting negative emotions yielded the lowest engagement.In total, we analyzed 100,209 tweets containing keywords related to coronavirus and vaccines. The 20 topics were further collapsed based on shared similarities, thereby generating 7 major themes. Our analysis characterized 26.3%  of the tweets as This study enriches our understanding of public concerns over new vaccines or vaccine development at early stages of the outbreak, bearing implications for influencing vaccine attitudes and guiding public health efforts to cope with infectious disease outbreaks in the future. This study concluded that public concerns centered on general policy issues related to coronavirus vaccines and that the discussions were considerably mixed with political views when vaccines were not made available. Only a small proportion of tweets focused on conspiracy theories, but these tweets demonstrated high engagement levels and were often contributed by Twitter users with more influence. The COVID-19 pandemic has affected more than 200 countries and territories, killed more than 1.2 million people, devastated the global economy, and disrupted the daily life of billions of people . It is cFrequent infectious outbreaks are an ongoing reality for globalized societies, and the early stage of an outbreak is always challenging. The beginning of an outbreak is typically characterized by a lack of accuracy, widespread misinformation, as well as heightened uncertainty and fear among the general public ,14. In tSocial media platforms shape public experience and opinions, while also serving as platforms for public health. During the initial phase of the pandemic, social media became the hotspot of all sorts of issues for the pandemic. Previous studies have shown that social media content about COVID-19 is mixed with a deluge of stigmas, rumors, and misinformation -18 and iDespite the scientific consensus that vaccination is a safe and effective approach to prevent infectious diseases, there is more controversy over the use of vaccines than over other preventive measures . These concerns include fear of side effects, uncertainty about vaccine efficacy, and general mistrust of the sciences and the government. These contentions have resulted in vaccine hesitancy, declines in immunization, and even small outbreaks of vaccine-preventable diseases -29. ContScholars propose several strategies for tackling the vaccine controversy and addressing antivaccination information on social media ,24, suchThis study aims to investigate the discussion related to coronavirus vaccines on Twitter during the early stage of the COVID-19 outbreak in the United States . This study will contribute to our understanding of coronavirus vaccines and connections to attitudes related to vaccines by tracking back to the initial public concerns. The findings of this study will elucidate the public discussions on new vaccines or vaccines under development, and the concerns and issues revealed in this study can show the implications on public health efforts in coping with infectious disease outbreaks in the future. Provided that the coronavirus vaccines show plenty of uncertainty in efficacy and effectiveness, using unsupervised learning methods, we aim to explore the main themes that emerged from the tweets related to coronavirus vaccines during the initial stage of the pandemic in the United States (RQ1). We also seek to examine how these themes evolved over time (RQ2).Out of the different types of misinformation, conspiracy theories have merged as a significant concern in the “social media infodemic.” Since the COVID-19 pandemic, several studies have analyzed certain types of conspiracy theories such as the coronavirus as a bioweapon , the 5G Previous studies also indicate that the spread of conspiracy theories and antivaccination messages follow a different pattern compared to that of provaccination messages. On social media, antivaccination content, in general, attracts more likes and engages more discussion because content producers are inclined to use a variety of persuasive strategies  and present antivaccination in the form of public criticism aggressively ,53. AntiThe study period was set from February 20 to March 31, 2020. We marked this period as the early stage because it corresponded to a sharp increase in the coronavirus case count and death toll in the United States . At the end of March 2020, the United States became the country with the most number of confirmed cases in the world. Moreover, in March 2020, most state and local governments declared COVID-19 as a public health emergency, issued stay-at-home orders, and mandated closures of schools and public meeting places . We purpUsing the social media monitoring and data collection platform provided by Meltwater, we collected tweets originating from the United States and written in English that were related to the coronavirus vaccine by using the following Boolean query: (covid OR coronavirus) AND (vaccine OR vaccines OR vaccination OR vaccinations OR vaccinate OR vax OR vaxine OR vaxx OR vaccinated). Using this strategy, we identified 117,718 tweets . The text of the tweet and relevant metadata, including username, date of the post, and follower count, were stored. We also stored the engagement metric provided by Meltwater, which was a composite score representing how many times a tweet was retweeted, quoted, liked, or prompted a reply by other users. A higher engagement value indicated that the tweet received more attention by other Twitter users.To analyze the obtained data set, we applied topic modeling—an unsupervised machine learning algorithm that allows researchers to uncover hidden thematic structures in a sizable collection of documents . A topicTo prepare the corpus for LDA topic modeling, we first removed the quoted content within the quote tweets and the “QT” (meaning a quote tweet) to retain only the original content of the tweet. As the length of document plays a significant role in the topic modeling method , tweets To determine the optimal number of topics for this tweet set, we performed 10 sets of topic models with topic numbers ranging from 5 to 50  by implementing the LDA model from the Python package MALLET. The topic coherence —a metric focusing on the interpretability—of the 10 topic models were then calculated and evaluated for selecting the appropriate number of topics ,65. We dThe output of the LDA topic model based on 20 topics was reviewed. Although the LDA model presumed that each document contained a mixture of topics and the model produced a probability topic distribution for each topic, we considered only the dominant topic, that is, the topic with the highest probability in that document, and categorized each tweet subject to its dominant topic ,67. We tNews of Vaccine Development. Next, the remaining 17 topics were organized into 7 themes. The themes, topic labels, and associated words for each topic are presented in News Related to the Coronavirus and Vaccine Development  and General Discussion and Seeking of Information on the Coronavirus , followed by Financial Concerns , Venting Negative Emotions , Prayers and Calls for Positivity , Efficacy of Vaccines and Treatments , and Conspiracies about Coronavirus and Its Vaccines .We analyzed 100,209 tweets in this study. The average number of followers of each tweet was 19,300.62 , and the average engagement value of the tweets was 29.41 (SD 624.91). To examine themes that have emerged in coronavirus vaccine discourses on social media (RQ1), LDA modeling with 20 topics was performed. During the labeling process, it was noticed that 4 of the topics were related to news concerning human trials and testing of coronavirus vaccines. As the 4 topics were similar and closely related, the 3 authors agreed to merge these discussions into 1 overall topic, that is, News of Vaccine Development, News of US government Research Funding/Plans for the Pandemic, and News of Research Plans for Vaccines. Tweets categorized in this theme included general news on the progress of human trials and vaccine development across different countries , announcements of US government funding for scientists and companies conducting research, and upcoming prevention plans for the pandemic released by official bodies as well as coronavirus vaccine research across the globe .During the COVID-19 pandemic, social media users frequently shared news related to coronavirus as well as the development of coronavirus vaccines. There were 3 topics under this theme: Seeking of Information on Vaccines, Discussion of the Coronavirus and Its Vaccines, Discussion of Coronavirus Spread and Infection Trends, Comparisons with Influenza, and Preventive Measures. The coronavirus was often compared with the influenza virus in terms of death rate, speed of transmission, and so on . The importance of preventive measures, including handwashing and social distancing, was also stressed because there is currently no vaccine nor effective treatment for the coronavirus infection .A total of 5 topics were grouped under this theme: Disparity over Income and Price of the Vaccines. In the topic Disparity over Income, conversations were related to the gap between the rich and the poor during the pandemic as well as the differences in access to future coronavirus vaccines . Worries of inequality brought about by capitalism in obtaining vaccination were also expressed . As for the price of vaccination, “free” instead of “affordable” coronavirus vaccines for all Americans were urged .There were 2 topics under this theme: Negative Emotions  and Trump-related frustrations. Negative emotions, including anger and disappointment, toward Donald Trump or big pharmaceutical companies, were presented, as those Twitter users believed that Trump and Big Pharma were trying to profit from the pandemic. Additionally, negative emotions were expressed toward Trump explicitly owing to claims he made that are believed to have been mistaken, such as the claim that receiving the influenza vaccine would prevent COVID-19 .This theme had 2 topics: Emotional Expressions/Prayers and Calls for Positivity. Tweets allocated within these 2 topics included messages that aimed to encourage others during the pandemic, expressed hopes for and needs for effective coronavirus vaccines, and hopes for an end to the pandemic .The 2 topics under this theme were Efficacy of Vaccine and Efficacy of Treatment/Prevention. These topics stressed the uncertainties of how well the vaccines for coronavirus work as well as the effectiveness of the current treatment and prevention strategies .The 2 topics under this theme were There were different conspiracies about coronavirus and its vaccines on social media (RQ3). Many of these were related to the companies developing coronavirus vaccines, stock markets, as well as the government. For example, some tweets were claiming that the coronavirus vaccine would contain a microchip that would allow the government or company to track the vaccine receivers . There were also claims that the US government spread the coronavirus deliberately and withheld the coronavirus vaccines News Related to Coronavirus and Vaccine Development and General Discussion and Seeking of Information on Coronavirus were the most frequently presented overall. Coronavirus vaccine–related discourses on Twitter were promoted by breaking news or announcements and speeches made by the governments and political elites. As shown in Financial Concerns reached the peak and exceeded other themes on February 28, 2020 and March 9, 2020. The rising discussions about the prices and affordability of the coronavirus vaccines were related to Health and Human Services Secretary Alex Azar’s refusal of promising affordable coronavirus vaccines for all US citizens on February 27, 2020 and Bernie Sanders’ promises of free coronavirus vaccine for all Americans on March 9, 2020, respectively. We observed some co-occurring patterns across the themes during the same peaks and time periods. General discussions and information on coronavirus highly mirrored the themes of news related to coronavirus and vaccine developments. The expression of negative emotions also increased when the discussions of these 2 themes reached a spike. There were also some observed differences in the themes across time. The efficacy of the vaccine and treatment, conspiracies about the coronavirus and vaccines, and prayers and calls for positivity appeared more periodically, while other themes  were more episodic, featured with several peaks instigated by breaking news or events related to vaccine developments.H test. Post-hoc analysis was also performed using the Bonferroni-corrected Dunn test. As the hypotheses focus on the difference between conspiracy tweets and the other tweets with themes that presented attitudes and concerns toward the coronavirus vaccines, tweets labeled as news or discussion/information seeking were excluded from the analyses. H1a and H1b hypothesized that conspiracy tweets’ contributors had more followers, and conspiracy tweets received higher levels of engagement than tweets with other themes. To examine the differences between themes in the number of followers and levels of engagement, further analyses were performed. First, the results of classification of LDA topic modeling were attached in the original data . The data were then entered into the SPSS software (IBM Corp). Next, we created a categorical variable according to the themes and used it as the independent variable to examine the differences in the number of followers and levels of engagement across themes by using the Kruskal-Wallis 24χ=77.8, P<.001). The post-hoc test further suggested that conspiracy tweets were more likely to be posted by users with a large number of followers than the tweets classified as Venting Negative Emotions (P<.001), Prayers and Calls for Positivity (P<.001), and Financial Concerns (P=.04). Tweets that discussed the efficacy of vaccines and treatments were also more likely to be posted by users with a large number of followers than the tweets classified as Venting Negative Emotions (P<.001) and Prayers and Call for Positivity (P<.001). Similarly, tweets expressing financial concerns were more likely to be posted by users with a large number of followers than the tweets classified as Venting Negative Emotions (P<.001) and Prayers and Calls for Positivity (P=.007). As conspiracy tweets were more likely to be posted by users with more followers than the tweets identified as Financial Concerns, Venting Negative Emotions, and Prayers and Calls for Positivity, H1a was partially supported.The results of our study suggested that there were significant differences in the number of followers between different themes . The post-hoc test further suggested that tweets classified as Venting Negative Emotions significantly received lower levels of engagement than tweets classified as Conspiracies about Coronavirus and Its Vaccines (P<.001), Efficacy of Vaccines and Treatments (P<.001), Prayers and Calls for Positivity (P<.001), and Financial Concerns (P<.001). As conspiracy tweets only received higher levels of engagement than the tweets classified as Venting Negative Emotions, H1b was partially supported.The results of our study suggested that there were significant differences in the levels of engagement between different themes  were more periodic and some themes  were more episodic. We speculate that conspiracies and efficacy concerns were largely about unconfirmed but expected issues ; thus, such discussions were likely to merge periodically. However, unexpected events  will stimulate heated discussions, leading to a peak in the data. When these events were later addressed by the authority’s responses, the discussions gradually vanished.Consistent with other studies that examined coronavirus vaccine sentiments and attitudes on social media over different periods of the pandemic ,74,75, oThis study offers several practical implications for addressing the infodemic at the early stage of outbreaks or health crises. First, public health professionals should timely and appropriately address the public needs for vaccine-related information. Our analysis revealed that many Twitter discussions were by people seeking more information or expressing concerns on coronavirus and vaccines. Such surges in information demand should be addressed by supplying with appropriate information that is easy to follow.Second, health communication may differentiate communication strategies for episodic and periodic themes. As indicated by the results, episodic themes  tended to emerge when breaking news or unexpected events occurred. Quick and appropriate responses to these events would effectively reassure the public and eliminate “epidemics of fear” . For perHealth organizations and health professionals should make more systematic and organized efforts to address antivaccination content and other vaccine-related misinformation. Together with other studies -49, thisLast but not the least, social media influencers  play an important role in the spread of vaccine-related opinions. Fact-checking the content published by social media influencers may effectively limit the spread of conspiracy theories, which requires efforts from both social media platforms and the influencers themselves ,80. TwitThis study has the following limitations. First, the study findings are limited to the Twitter discussions during the first phase of the COVID-19 pandemic in the United States. The public’s concerns might have changed over time as the development of vaccines progressed. The recent suspension of a coronavirus vaccine owing to adverse effects has brought a lot of discussions on vaccine safety [There are also some limitations in this analysis. For example, we relied on keyword inquiry to extract vaccine tweets from a database, but we cannot guarantee that all posts were related to coronavirus vaccine conversations. Some outliers might have been included in the data. When interpreting the topic themes, although the 3 authors independently coded the 20 topics, the intercoder reliability was not calculated owing to the small number of topics revealed from the LDA results. The analysis also did not distinguish the nature of Twitter accounts, which may be a mixture of personal, organizational, and bot accounts. Bot accounts may have contributed to a certain portion of the Twitter discussions, but we did not estimate the potential bot traffic. Because Twitter data did not account for users’ demographics, while we had a limited understanding of the types of users engaged in the discussion , we do not know more details about the users who were contributing to the discourse. Provided the difficulty of manual classification, future studies should seek to apply more sophisticated machine learning techniques to identify the types of Twitter accounts—ideally, the characteristics of personal accounts . Such knowledge will allow us to go beyond the aggregated data to look at individual users.Overall, the spread of information and opinions on social media platforms during the early stage of the outbreak has profoundly affected individuals’ beliefs and attitudes toward vaccines and, ultimately, their vaccination decisions. During the early stage of the COVID-19 pandemic in the United States, Twitter discussions related to coronavirus vaccines were centered on general policy issues and were largely mixed with political discussions. The public discussions demonstrated mixed concerns for coronavirus vaccines even before the vaccines were available, and some concerns appeared periodically. These issues call for more preparatory work to cope with the infodemic challenge and to handle infectious breaks in the future.


To date, several different types of synthetic genetic switches, including riboregulators, riboswitches, and toehold switches, have been developed to construct AND, OR, NOT, NAND, NOR, and NOT IMPLICATION (NIMP) gates. The logic gate can integrate multiple input signals following a set of algorithms and generate a response only if strictly defined conditions are met. However, there are still some logic gates that have not been implemented but are necessary to build complex genetic circuits. Here, based on the toehold switches and three-way-junction (3WJ) repressors, we designed two novel biological Boolean logic gates of IMPLICATION (IMP) and XOR. Subsequently, the outputs of these two logic gates were characterized by fluorescence analysis, indicating that they can achieve the truth tables of logical gates. Furthermore, the fluorescence intensity under the logical TRUE condition was significantly higher than under the logical FALSE condition, suggesting the high dynamic range of the ON/OFF ratios. Because of the programmability of synthetic RNA switches, the constructed RNA logic gates could serve as elementary units to build a versatile and powerful platform for translational regulation and RNA-based biological computation. Biological circuits can be implemented in living organisms, thereby regulating the movement of life and performing different tasks. DNA, RNA, and proteins all have the potential to be assembled into circuits for the design of programmable modules . In addiin silico [Among the regulatory elements, synthetic RNA switches are quite favorable in many respects due to their advantages and unique features. Since RNA is an intermediate between DNA and protein, RNA switches can respond to different regulatory elements. Furthermore, a wide assortment of RNA-based parts operating at the transcriptional and post-transcriptional levels are known to exist in nature . Becausen silico . In addin silico ,10.So far, researchers have developed several different types of synthetic genetic switches, including riboregulators, riboswitches, and toehold switches ,11,12. GIt has been reported that the logic function can be implemented by toehold switches or 3WJ repressors ,17. So, 2.2.1Kpn I (5ʹ) and site Aat II (3ʹ). Segments of the switch were inserted into pACYC184 by the site SnaB I (5ʹ) and the site Hind III (3ʹ). Plasmids were extracted using TIAN prep Mini Plasmid Kit II (TIANGEN Code No. DP106-02).All DNA oligonucleotides and plasmids construction were from GENEWIZ. pUC19 plasmid was used as the vector for cloning the trigger RNA, and a pACYC184 plasmid was used for the switch RNA. Segments of the trigger were inserted into pUC19 by the site E. coli strain to verify the two-plasmid system. Upstream and downstream primers  gal dcm rne131 (DE3); Shanghai Yuanye Bio-Technology Co., Ltd) and DH5α (F- φ80 lac ZΔM15 Δ(lacZYA-arg F) U169 endA1 recA1 hsdR17 supE44λ- thi −1 gyrA96 relA1 phoA; GENEWIZ) were used in this study. DH5α was for amplification, and BL21 Star DE3 was for verification. The E. coli competent cells were prepared and transformed using standard molecular biology techniques. After E. coli were cultured in Luria-Bertani (LB) agar plates for 24 h, the robust colonies were picked into 250 mL conical flasks containing 50 mL LB broth. The cells were shake-flask cultured at 37°C and 200 rpm for 10 h with 50 μg·mL−1 ampicillin or/and 34 μg·mL−1 chloramphenicol. All the strains used in this study are listed in 2.3g, and then were washed and resuspended in buffer PBS. Subsequently, the biomass and fluorescence intensity were measured by the plate reader . Briefly, the fluorescence intensity of green fluorescent protein (GFP) was measured at excitation/emission wavelengths of 485 nm/528 nm, and the biomass was measured at 600 nm. Finally, the output (F) was calculated as a follow formula,The constructed strains  were cul600 of experimental groups strains and F_neg and Abs_neg represent fluorescence intensity and OD600 of negative control strains. The negative control is the competent BL21 Star DE3 strain without any plasmid. The outputs are identified as ‘0’ and ‘1’ according to the threshold (relative fluorescence intensity: 50%) [where F_exp and Abs_exp represent fluorescence intensity and ODty: 50%) ,5.Cross-talk was determined by dividing the arithmetic mean of the GFP fluorescence intensity from a given trigger switch pair by the arithmetic mean of the GFP fluorescence intensity for the cognate trigger switch interaction.2.4in silico screening and selection.This section describes the initial  stages of the toehold switch generation process, including design specification and 2.4.1n + 2 in length, where n is a non-negative integer, were used to connect different toehold switches and 3WJ repressors. Spacers of this length enabled successive repressor modules to remain in-frame through the full length of the gate RNA. A previous study proved that 17-nt spacers worked well in the NAND gates [The first stage in the design process involves the definition of the logic gate secondary structure and interaction domain sizes. The IMP gate RNAs based on toehold switches and 3WJ repressors were generated by taking the core regulatory sequence of the toehold and the 3WJ repressors. There is always a 21-nt linker region between the hairpin module and coding sequence of the regulated gene , and theND gates . So, in The function of the XOR gate is based on its triggers. The trigger RNAs were generated by taking the core sequence of the trigger of the toehold. The core of a trigger is the sequence that can be combined with the switch. The blue-colored regions in 2.4.2+, and 0.0 mol/L Mg2+ were used for the design algorithm. Monomeric repetitive sequence patterns, such as AAAA, CCCC, GGGG, UUUU, KKKKKK, MMMMMM, RRRRRR, SSSSSS, WWWWWW, YYYYYY, were prevented in the design specification to preclude runs of monomeric nucleotides.NUPACK design algorithm computes candidate RNA sequences and progressively refines them until their deviation from the specified design constraints falls below a specified stop condition. In designing IMP gate, stop conditions were imposed on switch RNA. In designing the XOR gate, stop conditions were imposed on trigger RNAs. Free energies specified in Serra et al. , a tempeIMP design is shown below (using dot-bracket notation):trials = 10structure switch = … …. …. ….(((((((((… ((((((…. … ….)))))) …))))))))) …. …. …. …. …. …. … ….((((((… ((((((…. … ….)))))) …)))))) …. …. …. …. ….((….)) … ((((….)))) ….domain a = GGGAUAAGUAGAUAAGAUUGUUAGAUGGCUUCGAACAGAGGAGACGAAGCAUGCUAACAAUCdomain b = N17domain c = CUCCUAUCACUUUACUUGUUAUAGUUAUGAACAGAGGAGACAUAACAUGAACAAGCACACUAACUACAAAUUCAACCUGGCGGCAGCGCAAAAGAUGCGUAAAprevent = aaaa, cccc, gggg, uuuu, kkkkkk, mmmmmm, rrrrrr, ssssss, wwwwww, yyyyyyswitch.seq = a b c‘domain a’ is a sequence of the toehold, ‘domain b’ is a sequence of spacer and ‘domain c’ is a sequence of the 3WJ repressor.XOR design is shown below (using dot-bracket notation):trials = 10structure trigger1 = … ((((((((… …)))))))) …. …. …. …. …. …. …. …. …. …. …. …. …. …. …. …. …. …. … ….structure trigger2 = … ((((((((… …)))))))) …. …. …. …. …. …. …. …. …. …. …. …. …. …. …. …. …. …. … ….domain a = GGGdomain b = N27domain c = N22domain d = N22domain t = core sequenceprevent = aaaa, cccc, gggg, uuuu, kkkkkk, mmmmmm, rrrrrr, ssssss, wwwwww, yyyyyytrigger1.seq = a b c t dtrigger2.seq = a b d* t c*‘domain a’ is a GGG leader sequence, ‘domain b’ is a hairpin sequence that minimizes the degradation of triggers, ‘domain c’ and ‘domain d’ are sequences of complementary domains, and ‘domain t’ is the core sequence.2.4.3+, and 0.0 mol/L Mg2+ using Serra et al. [The resulting designs were then screened to ensure they had no duplicate sequences or in-frame stop codons that would prematurely terminate translation of the output gene. The screened designs were then analyzed for their behaviors. We began this process by first computing the pairwise interactions between the trigger RNA and switch RNA sequences. Simulations were performed with NUPACK. The NUPACK functions were run with a specified temperature of 37°C, 1.0 mol/L Naa et al.  free ene3.The logic gate can integrate multiple input signals following a set of algorithms and generate a response only if strictly defined conditions are met. Therefore, logic gates are the key components for building complex circuits or a complete synthetic biological system. However, the logic gates of IMP and XOR are still not be constructed. To address this, we exploited two types of novel RNA-only biological Boolean logic gates, IMP and XOR, which were built with toehold switch and 3WJ repressor. This section demonstrates that they can achieve the truth tables of logic gates by adding different trigger RNAs. Subsequently, the results of fluorescence analyzing and characterization of their outputs suggested that these logic gates are feasible and prospect for practical use.3.1In a previous study, it was reported that a toehold switch had the characteristics of low leakage and a high ON/OFF ratio , so it w3WJ repressor switch RNA employs an unstable hairpin secondary structure, which has been demonstrated to be translationally active in the 3WJ repressor . On eithOne of the prerequisites for higher-order logic processing is the orthogonality of regulatory components . Thus, t3.2Previously, Kim, et al. constructed a NAND gate consisting of two 3WJ repressors . They deThe scalability of the ribocomputing devices is also a key indicator of the performance of logic gates . Previou3.3u, u*, v and v* in u, v) and input B  can pair specifically with each other, forming a ring in the middle. The sequence was de-novo designed using NUPACK [Previously, Green et al. reported an A AND (NOT B) logic gate, consisting of a gate RNA and two regulatory RNAs (trigger RNA and deactivating RNA) . The deag NUPACK  , are capable of carrying out simple binary mathematical operations necessary for digital computing. These arithmetic units are often constructed using different types of logic gates. XOR gate is an important part of the half adder , thus ou3.4IMP gate, assembled from toehold switch and 3WJ repressor, was supposed to operate in the following way. Trigger RNAs work like the input and GFP as the output. When no trigger RNA was transcribed, IMP gate acted like a 3WJ. Because the RBS and the start codon (AUG) in the unstable hairpin structure were easily exposed, the translation process could occur normally without any trigger RNAs. Input A would bind to the toehold switch, which allowed a branch migration process and exposed RBS and AUG for translation initiation. Input B would bind to the 3WJ switch, and the resulting trigger-switch complex had a stable 3WJ structure that respectively sequestered the RBS and the start codon within the loops of the switch RNA, which strongly repressed the translation. When both input trigger RNAs were added, the RBS of the toehold switch would be exposed and allow ribosome binding, which would break to open the stable 3WJ hairpin, allowing downstream genes to translate . The out3.5For the XOR gate, the key to ensuring that the triggers work as expected is that the 5ʹ end and the 3ʹ end complementary domains bind to each other through desired base pairing. If they could combine successfully without affecting the functionality of the intermediate sequence, the logic gate works. The toehold switch had a stable hairpin structure and concealed RBS and AUG in the loop region, so when there was no input trigger, the ribosome would not find the binding site, and the translation process would not start. Since both triggers contained sequences that can open the hairpin, the toehold switch could be turned ON when either of them is input. However, when both triggers were input simultaneously, they pair with each other and form a ring in the middle, so the switch remains in the OFF state . The out3.6In recent years, molecular platforms based on logic gates are maturing, and all kinds of mainstream logic gates and cascade logic gates have been constructed one by one . In prinBoolean logic gates have a wide range of applications, for example, in the construction of arithmetic units, including the half adder and the subtractor ,24–27. TMoreover, these logic gates may have many practical applications, such as virus detection and disease treatment. The programmability of RNA-based regulatory elements is based on the complementary sequence pairing, which guarantees a sufficient specificity for biosensors. By designing specific regulatory elements, multiple viruses could be detected simultaneously in an orthogonal test. By combining different types of logic gates, the performance of biosensors can be improved by avoiding false negatives and false positives. For example, an OR gate can reduce false negatives, an AND gate can reduce false positives, and an IMP gate can exclude non-homologous viruses using a highly conserved viral sequence as the input (4.In this study, the IMP gate based on the toehold switch and the 3WJ repressor and the XOR gate based on trigger pairs with complementary ends, were constructed successfully. Furthermore, the IMP gate provided the first structural assembly of the toehold switch and 3WJ repressor, suggesting that multiple types of RNA switches can be used in combination. For the XOR gate, the 5ʹ end and the 3ʹ end complementary domains of trigger RNAs could combine successfully without affecting the functionality of the intermediate sequence. The novel RNA-only IMP and XOR logic gates have wide dynamic ranges, less cross-talk and logical computing power. These advantages confer their possibility of designing novel biological circuits and broadening their applications. It is still worth exploring how to give the normalized parameters of fluorescence intensity to make the logic gate universal.Click here for additional data file.


G-quadruplexes are the non-canonical nucleic acid structures that are preferentially formed in G-rich regions. This structure has been shown to be associated with many biological functions. Regardless of the broad efforts on DNA G-quadruplexes, we still have limited knowledge on RNA G-quadruplexes, especially in a transcriptome-wide manner. Herein, by integrating the DMS-seq and the bioinformatics pipeline, we profiled and depicted the RNA G-quadruplexes in the human transcriptome. The genes that contain RNA G-quadruplexes in their specific regions are significantly related to immune pathways and the COVID-19-related gene sets. Bioinformatics analysis reveals the potential regulatory functions of G-quadruplexes on miRNA targeting at the scale of the whole transcriptome. In addition, the G-quadruplexes are depleted in the putative, not the real, PAS-strong poly(A) sites, which may weaken the possibility of such sites being the real cleaved sites. In brief, our study provides insight into the potential function of RNA G-quadruplexes in post-transcription. G-quadruplexes are the non-canonical nucleic acid secondary structures that are formed by stacking G-quartets on top of each other. Each G-quartet planar comprises four guanines, where adjacent guanines are bonded to each other via Hoogsteen hydrogen . The staxN1-7GxN1-7GxN1-7Gx, where x ≥ 3 and N can be any base) in UTRs were under selective constraints and were tightly associated with several biological functions [DNA G-quadruplexes have been extensively studied in the past few decades. In recent years, researchers started to focus on RNA G-quadruplexes. In 2007, Sunita Kumari et al. demonstrated the RNA G-quadruplex in the 5′-UTR (untranslated region) of NRAS  proto-oncogene repress translation . After tunctions . Individunctions  and the unctions . NeverthMoreover, the SARS-unique domain (SUD) was confirmed to preferentially bind to the RNA G-quadruplex on the host mRNAs , and a sDMS-seq is one of the most robust methods that can be used to detect the RNA secondary structures . DimethyxN1−7GxN1−7GxN1−7Gx, where x ≥ 3 and N can be any base). The length of OG4s exhibits a left-skewed distribution with an interval between 15 and 34 nucleotides (nt), and most of the OG4 lengths are shorter than 24 nt .The former findings provide a hint that only when the RNA G-quadruplexes are close enough to the 5′ end can they act as translational repressors . The relp = 5.321 × 10−5), which is conducive to the regulation of translation when considering the distance effect [Then, we collected the genes with one or more OG4s present in the first 100 nt of their 5′-UTRs. The KEGG (Kyoto Encyclopedia of Genes and Genomes) over-representation test was performed to see which biological pathways are possibly affected by the gene set. As a result, several pathways were significantly enriched (FDR < 0.05), such as the pathways that are related to the differentiation of Th17 (T-helper 17), Th1 (T-helper 1), and Th2 (T-helper 2) cells and the pathway involving in the regulation of PD1/PD-L1 (Programmed cell Death protein 1/Programmed Death-Ligand 1), which were closely related to the immune balance and events . We nexte effect . The abop < 0.05, p < 0.0001). Those COVID-19-related gene sets were closely relevant to the genes possessing OG4s in its 3′-UTR, which leads us to speculate that SARS-CoV-2 may interfere with the gene expression of some important genes through binding to the G-quadruplex structures in host transcripts; however, direct evidence is needed to clarify the interference mechanism of SARS-CoV-2.A previous study indicated that the SARS-Unique Domain (SUD) in SARS-CoV preferentially binds to the G-quadruplex structures in the 3′-UTR of the host transcripts and may be responsible for its high pathogenicity by obstructing the stability of the host transcripts . In addiPrevious studies have suggested that the mRNA secondary structures and their stability may affect the targeting of miRNAs . Therefop = 9.063 × 10−6). Considering that the transcripts without OG4s in their 3′-UTRs were heavily outnumbered by the 3′-UTR-OG4-containing transcripts, we conducted a simple random sampling of the mRNAs 10,000 times (Please see Methods). We visualized the distribution of all p-values obtained from sampling . Another simple random sampling was performed to eliminate the order-of-magnitude bias, and a considerable number of rounds . This probability increased with the increase in the distance to the putative PAS-strong poly(A) sites, which allowed us to presume that the depletion of RNA G-quadruplexes in the vicinity of the putative PAS-strong poly(A) sites, especially the low probability of forming G4s, could potentially reduce the possibility of these putative sites being the real cleavage sites. In the absence of OG4s that are located directly downstream of the putative PAS-strong poly(A) sites, the effect of stalling the transcription complex may be weakened, eventually causing the transcriptional complex to pass over the putative PAS-strong poly(A) sites instead of being the real poly(A) sites.Accumulating evidence indicates that RNA G-quadruplexes play an important role in mRNA cleavage ,20. TherDespite the extensive research on DNA G-quadruplexes, the properties and functions of RNA G-quadruplexes still need to be elucidated. Recent progress in uncovering the role of RNA G-quadruplexes presents evidence for their relevance in post-transcriptional regulation. However, profiling the RNA G-quadruplexes at the scale of the whole transcriptome, particularly in human cells, is vital but insufficient. In this study, we applied the DMS-seq followed by the modified StructureFold software to probe the RNA G-quadruplexes in the YanHuang cell line. DMS-seq can provide nucleotide status, which can be used as experimental constraints to improve the prediction of RNA secondary structures. By adopting this strategy, we determined the OG4s on each transcript at single-nucleotide resolutions.The OG4s discovered in the YanHuang cell line displayed shorter loops, which is in accordance with the physical chemistry experiment results showing that RNA G-quadruplexes with shorter loops usually possess higher thermal stability ,17. CompWe observed a notable enrichment of OG4s in the vicinity of the 5′ cap, and a previous study demonstrated that the RNA G-quadruplex, which is close enough to the 5′ cap, exhibits a repression effect in the NRAS proto-oncogene transcript, leading us to propose that these OG4s could act as modulation elements by impeding the assembly of translation initiation complexes. In addition, the relative density of OG4s decreased sharply when approaching the start codon ; howeverG-quadruplexes have been linked to numerous diseases due to their flexible and effective functions in regulating biological processes. We here show the suggestive indication that the RNA G-quadruplexes are tightly associated with immune events, such as T-cell differentiation. The immune-related cluster of genes, the 5′-UTRs which contain OG4s, may be dynamically controlled through the folding conformation of OG4s; this ultimately influences the immune response. Predictably, targeting the OG4s in immune-related genes might be a promising strategy for cancer treatment.The mechanism of SARS-CoV-2 to disrupt the expression of host genes has attracted widespread attention, and several recent studies have discovered some potential mechanisms ,24. HoweOur findings also yield suggestive evidence for the protective effect of RNA G-quadruplexes on transcripts, as they prevent the targeting of miRNAs. However, massive biological experiments should be undertaken to carefully confirm the impact of RNA G-quadruplexes on miRNA targeting in vivo. Particularly, it is necessary to investigate whether the RNA G-quadruplexes have dual effects on miRNA targeting. As Samuel Rouleau et al. described in their study , we hereIncreasing evidence has emphasized the important biological role of RNA G-quadruplexes and their close relationship with certain diseases ,28, and We cultured the YanHuang cells in Dulbecco’s Modified Eagle’s Medium  for 72 h at a temperature of 38.5 °C in which the 15% FBS, 50 U/mL penicillin, 2 mM L-glutamine and 50 μg/mL streptomycin were contained.Cells were suspended in 2 mL of 1 × DMS reaction buffer in the tube and treated with 15 µL pure DMS. We also prepared control samples by adding 15 µL of nuclease-free water. The experimental and control samples were performed in parallel. The reaction was carried out with periodic swirling at room temperature for 15 min. Then, 0.15 g of DDL was added to the reaction, which was subsequently dissolved through swirling the tube for 2 min. Finally, all of the solution was poured out, and the remaining cells were washed twice with ~50 mL of nuclease-free water.We obtained 3 µg of total RNA for the construction of the library, and quality control was performed using Agilent RNA 6000 nano kit running on Agilent 2100 Bioanalyzer. The RNA passed the quality control with an RNA integrity number >8. We used random hexamers containing Illumina adapters for the reverse transcription, and then the complementary sequence for each fragment was generated. The amplification of fragments was carried out on Illumina, followed by deep sequencing on HiSeq 2000.https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/, accessed on 25 June 2021), and the refMrna.fa was chosen as the standard RNA reference sequence. To annotate the reference transcriptome sequence data, we also downloaded the 5′-UTR, 3′-UTR, and CDS (coding sequence) data from the table Browser page in the UCSC Genome Browser website. The genome assembly was set as hg38, while the annotation source was set as GENCODE. To annotate the exact position of 5′-UTR, CDS, and 3′-UTR in each reference transcript, we applied a customized python script, which can map and record the position of the above regions for each transcript based on the download file above.The human reference transcriptome sequence data in FASTA format were obtained from the UCSC Genome Browser Downloads page . The modified StructureFold [The workflow of the probing strategy is mainly composed of two parts: DMS-seq and OG4s detection algorithm. We set up the experimental group and the control group, in which the YanHuang cells in the experimental group were treated with DMS, while the control group was not treated with DMS. The computational part was basically derived from the StructureFold  softwaretureFold  softwareWe applied and modified the StructureFold  softwareConsidering that our sequencing data are paired-end data with a length of 150 nt per read, we then re-customized the mapping module. This module consists of three steps: first, assessing the quality of sequence data using FastQC software; second, cutting the reads through Trimmomatic  software(1)Aligning all the transcripts to their 5′ end;(2)For each absolute position Since the median length of the 5′-UTR is about 220 nt, we computed the relative density of RNA G-quadruplexes downstream of the 5′ end using the following two steps:The average RNA G-quadruplex density in all of the 5′-UTRs was calculated as follows:The relative density of RNA G-quadruplexes upstream of the start codons was calculated in a similar way to the method described above, except that all the transcripts were aligned to their start codons.http://www.mirbase.org/, accessed on 25 June 2021). Only the identical prediction targeting sites that appeared in both result datasets generated by the two independent software were utilized as putative miRNA target sites for further analysis.The identification of putative miRNA target sites in each transcript was conducted using miRmap  and miRaThe POLYAR  softwarexN1−7GxN1−7GxN1−7Gx, where x ≥ 3 and N can be any base). In order to obtain the unfolded RNA G-quadruplexes, we excluded the observed RNA G-quadruplexes from putative RNA G-quadruplexes, and the remaining putative RNA G-quadruplexes were regarded as UG4s.We developed an R script program to search for all the putative RNA G-quadruplexes in 3′-UTRs, which corresponds to the motif of canonical RNA G-quadruplexes  sites, and the probability is defined as follows:https://cytoscape.org, accessed on 25 June 2021) software.The KEGG over-representation testing was performed using the Bioconductor’s clusterProfiler package . The enrp-value < 0.05) COVID-19-related gene sets in the Disease/Drugs module of Enrichr.The COVID-19 gene set enrichment analysis was conducted in the Enrichr  online sThe simple random sampling procedure was described as follows,Input: dataset S1 and S2; follows S1 ∩ S2 = Ø, |S1| > |S2|Output: dataset Pwhile i ≤ 10,000 dotmp ← sampleiP ← p value in wilcox.test endreturn P