Finally, susceptibility of G4 HEV was demonstrated in intrahepatically inoculated gerbils with infectious HEV RNA transcripts, whereas no evidence for G1 HEV susceptibility was found. The availability of the convenient gerbil model will greatly facilitate HEV-specific antiviral development and assess the mechanism of host immune response during HEV infection.Hepatitis E virus (HEV) is a major cause of acute viral hepatitis in humans. A convenient small mammalian model for basic research and antiviral testing is still greatly needed. Although a small rodent, the Mongolian gerbil, was reported to be susceptible to swine genotype-4 HEV infection, whether the previous results were reliable and consistent needs to be validated by using biologically pure HEV stocks or infectious RNA. In this study, we revisited this gerbil infection model for human HEV of genotype 1, 3, or 4  by HEV reverse genetics. Gerbils inoculated intrahepatically with capped G3 HEV RNA transcripts or intraperitoneally with infectious G3 cloned HEV produced robust infection, as evidenced by presence of HEV in livers, spleens, and feces for up to 7 weeks post inoculation, seroconversion, and pathological liver lesions. Furthermore, the value of the gerbil model in antiviral testing and type I IFN in host defense was assessed. We demonstrated the effectiveness of peg-IFNα-2a and ribavirin in inhibiting HEV replication in gerbils. By treatment with two molecule inhibitors of TBK1, we also revealed a role of RIG-I like receptor-interferon regulatory factor 3 in host anti-HEV innate immune sensing in this IMPORTANCE HEV infects >20 million people annually, causing acute viral hepatitis as well as chronic hepatitis, neurological diseases, and pregnancy-associated high mortality, which require therapeutic intervention. The HEV antiviral research is largely limited by the lack of a convenient small animal model. Here we revisit the Mongolian gerbil model for three genotypes of human HEV by infectious HEV clones and recognized standards of experimental procedures. Fecal virus shedding, seroconversion, and pathological liver lesions could be detected in HEV-inoculated gerbils. We demonstrate the effectiveness and usefulness of this model in testing antiviral drugs, and in assessing the mechanism of host innate immune response upon HEV infection. This conventional rodent model will aid in future antiviral development and delineating mechanism of host immune response. Orthohepevirus A of the family Hepeviridae (in vitro (Hepatitis E virus (HEV) is the main pathogen of acute viral hepatitis. It causes large-scale hepatitis E (HE) outbreaks involving tens of thousands of individuals in developing countries with inadequate sanitation , 2. The eviridae , 2. HEV eviridae , 6. The eviridae . The ORFin vitro , 9, and in vitro , 11.Thus far, four major genotypes (G1 to G4) of human HEV have been identified. G1 and G2 HEV are restricted to humans, and are commonly transmitted by the fecal-oral route via virus-contaminated water , 12. G3 15–However, studies on HEV pathogenesis, HEV-host interactions, and development of HEV-specific antiviral remain very challenging, partly due to the lack of a convenient small mammalian model. Laboratory mice or rats are not susceptible to human HEV infection. Animal models for studying HEV infection include pigs , 20, chiMeriones unguiculatus) is a small rodent that belongs to a species different from the more commonly used laboratory mice such as BALB/c (Mus musculus) or C57BL/6 inbred mice  inoculation of infectious HEV generated by reverse genetics. We further illustrated the unique value of this model by demonstrating the usefulness of this model in testing antiviral drugs, and in assessing the mechanism of host innate immune response against HEV infection. Finally, we showed that, in addition to G3 HEV, gerbils are susceptible to G4 HEV, but not G1 HEV infection.in vitro-transcribed capped and uncapped (as negative control) RNA transcripts from G3 human HEV Kernow-C1 P6 strain  route of inoculation in rodents has been used to establish infection models for enteric viruses such as norovirus and coronavirus since it bypasses the interference of the intestinal acidic environment and mucosal immune barriers –34. To f–opies/mg . Fecal vopies/mg . HEV RNAopies/mg  to D. Suopies/mg . Seroconopies/mg , concomiin vitro . The inain vitro , or was in vitro  to D. HEin vitro .Gross lesions were observed primarily in the liver, with increased vascular congestion in HEV-infected gerbils . The hisHEV is naturally transmitted via the fecal-oral route. In order to test whether gerbils could be infected orally, the animals were inoculated per orally (p.o.) at the same infectious dose as the i.p. route with pure HEV stock generated by the HEV p6 infectious clones . In three out of five inoculated gerbils, we detected HEV RNA in fecal samples starting at 11, 13, or 21 dpi and lasting for 1 to 2 weeks , which aTo further corroborate the utility of the HEV gerbil model for antiviral research, two antivirals ribavirin and peg-IFNα-2a, which have been validated by using the HEV p6-EZ replicon model in our previous study , were utTo further confirm the effectiveness, the gerbils were postponed being treated with IFNα-2a and ribavirin at 7 dpi when HEV replication was reaching to the peak at this time point  and B. Ain vitro when using two small molecule inhibitors targeting TANK-binding kinase 1 (TBK1) to block the RIG-I-like receptor (RLR)-IRF3 (interferon regulatory factor 3) pathway  and genotype 4 (TW6196E strain) were inoculated into the liver of gerbils, respectively . Fecal HEV RNA was only detected continuously in gerbils that received the capped G4 RNA transcripts, starting at 3 dpi and peaked at 19 dpi (n = 4) seroconverted to IgG anti-HEV antibodies at 27 dpi  is a small rodent that had been used to study infection of coxsackievirus A16 and enterovirus 71   and G3 . The Huh6 genome equivalents (GE) of viral RNA/100 μL medium, giving an infectivity of 1 fluorescent focus-forming unit (FFU)/5,618 GE, as determined previously , male Mongolian gerbils of approximately 8–10 weeks of age were purchased from the Experimental Animal Center at the Zhejiang Academy of Medical Sciences . Prior to inoculation, the animals were confirmed to be seronegative for HEV by a commercial ELISA kit . All animal experiments were performed in strict accordance with the Experimental Animal Ethics Committee of Zhejiang University .In vitro transcription reactions were performed in a 60 μL reaction mixture containing 3 μg of linearized HEV plasmid DNA, and the purity and quality of the full-length RNA transcripts were verified by agarose gel electrophoresis as described previously (Full-length capped or uncapped RNA transcripts were prepared from the p6 (Kernow-C1) HEV infectious cDNA clone using the mMESSAGE mMACHINE T7 ULTRA transcription kit (Ambion). eviously . To dete7 GE/mL. As negative controls, 30 gerbils were similarly inoculated with supernatants of Huh-7-S10-3 cells transfected with uncapped HEV RNA transcripts that were produced by repeated freeze/thaw cycles and centrifugation to clarify the cellular debris. Fecal samples were continuously collected from three given gerbils in each group at different time points and were tested for HEV RNA by real-time RT-PCR as shown in Briefly, 30 SPF gerbils were each inoculated intraperitoneally with 1 mL of the infectious virus stock containing approximately 6.83 × 107 GE/mL. The remaining 5 gerbils in the control group each received 1 mL of DMEM. Fecal samples were collected and tested for HEV RNA at 1, 3, 5, 7, 9, 11, 13, 21, 27, and 34 dpi by real-time RT-PCR  or treated via intraperitoneally with PBS (12 gerbils), peg-IFNα-2a , BX795 , or MRT67307 . The gerbils were treated with antivirals or TBK1 inhibitors at 1 dpi based on the results from animal experiment 2, in which the viral replication was rapid as HEV viral RNA could be detected as early as 3 dpi  as well as TBK1 inhibitors . Briefly, 54 SPF gerbils were inoculated intraperitoneally with 1 mL of the infectious virus stock containing approximately 6.83 × 10as 3 dpi  and B, aas 3 dpi . HEV-infAlternatively, in the second drug trial, the gerbils were treated with IFNα-2a and ribavirin at 7 dpi (instead of 1 dpi) based on the results from animal experiment 2, in which the viral replication had reached to a high level at this time point  and B. BFull-length capped or uncapped HEV RNA transcripts from the Sar-55 or the TW6196E infectious cDNA clone were generated as described above. Twenty SPF gerbils were randomly assigned into 5 groups with four gerbils each. Gerbils in each group were intrahepatically injected with capped or uncapped G1 HEV RNA transcripts, capped or uncapped G4 HEV RNA transcripts (10 μL transcription reaction mixture diluted in 200 μl PBS for each gerbil), or equal amounts of PBS as controls, respectively. Fecal samples were collected and tested for HEV RNA at different time points shown in For histological examinations, liver samples from infected and control animals were dissected, fixed in 4% paraformaldehyde for 12 h at 4°C, dehydrated in a graded series of ethanol, embedded in paraffin, and sliced into 6-μm-thick sections. The sections were subjected to histological examinations by hematoxylin and eosin (HE) staining. The anti-HEV-ORF2 antibody was used for IHC staining .4 ELISA titer of anti-HEV IgG from an HEV-infected patient in Jiashan, Zhejiang province, China , and viral RNA in tissue samples and feces was detected by real-time RT-qPCR . Selectee, China .t test. Statistical analyses were carried out using GraphPad Prism 6.0. Differences were considered significant at P < 0.05. All samples, if preserved and properly processed, were included in the analyses, and no samples or animals were excluded. No statistical method was used to predetermine sample sizes, and gerbils in animal experiments were randomized to different groups.All the quantitative data are presented with dot and bar. Analyses of independent data were performed by Student’s unpaired two-tailed


Finally, we demonstrated that both Ago1 and Ago2 are involved in this transcriptional silencing. Altogether, our data demonstrate that siRNAs targeting a chromatin-associated RNA at a distance from its promoter induce its transcriptional silencing. Our results thus extend the possible repertoire of endogenous or exogenous interfering RNAs.Transcriptional gene silencing by small interfering RNAs (siRNAs) has been widely described in various species, including plants and yeast. In mammals, its extent remains somewhat debated. Previous studies showed that siRNAs targeting gene promoters could induce the silencing of the targeted promoter, although the involvement of off-target mechanisms was also suggested. Here, by using nascent RNA capture and RNA polymerase II chromatin immunoprecipitation, we show that siRNAs targeting a chromatin-associated noncoding RNA induced its transcriptional silencing. Deletion of the sequence targeted by one of these siRNAs on the two alleles by genome editing further showed that this silencing was due to base-pairing of the siRNA to the target. Moreover, by using cells with heterozygous deletion of the target sequence, we showed that only the wild-type allele, but not the deleted allele, was silenced by the siRNA, indicating that transcriptional silencing occurred only in  RNA interference (RNAi), first discovered in plants during the 1990s and observed later in other systems, like worms, yeast, and mammals, is a rapid and efficient tool to induce the knockdown of specific transcripts. This main mechanism of posttranscriptional gene silencing has been largely conserved throughout evolution. In mammals and nonmammalian organisms, exogenous or endogenously synthetized small double-stranded RNA molecules of 19 to 24 nucleotides, called small interfering RNA (siRNA), are loaded on the RNA-induced silencing complex (RISC) containing argonaute proteins (Ago) .Given that the above-described processes usually take place in the cytoplasm, RNAi is in this respect a cytoplasmic process functioning at a posttranscriptional level and leading to RNA degradation. Nevertheless, in some organisms, such as yeast or plants, RNAi has been described to work also in the nucleus to silence repeated elements and to nucleate heterochromatin . However7–15–In addition, transcriptional gene silencing by siRNAs has also been reported in mammalian cells. siRNAs directed against the promoter of the elongation factor 1α gene repress the expression of a reporter gene driven by this promoter . Further19–33–cis and was not due to off-target mechanisms, thus demonstrating RNA interference mechanisms acting at distance in mammals.Despite these studies, to our knowledge, siRNAs repressing promoters while targeting RNAs far downstream of their promoter region have not been reported so far. Here, we show that siRNAs directed against a chromatin-associated RNA from the very long intergenic noncoding RNA (vlincRNA) family can silence its promoter even when targeted sequences are located thousands of bases downstream from the promoter. Moreover, through the use of genome editing, we found that this effect occurred in In previous works, we successfully used siRNAs to deplete the expression of various RNAs associated with chromatin –17. This–From these RNA-Seq data sets, analysis of reads containing at least one spliced junction showed that VINK is indeed poorly spliced, with less than 0.4% of spliced reads relative to all reads aligned in the VINK region. For comparison, we previously showed that the median percentage of spliced reads in annotated genes was around 20% . The maiData showing that siRNAs can efficiently decrease VINK expression, as well as the expression of other nuclear RNAs, which we have published previously –17, were–Thus, altogether these data indicate that siRNAs can efficiently induce the knockdown of the chromatin-associated VINK RNA.Given that the use of siRNAs to target nuclear RNAs is under debate, we intended to investigate the mechanism of this effect. To that goal, we first analyzed whether siRNAs directed against VINK induced its degradation. We transfected senescent WI38 cells with the VINK-1 siRNA (which is the most efficient ) or contWe next assessed whether VINK siRNAs affected VINK transcription. To that goal, we analyzed ongoing transcription by nascent RNA capture, which relies on metabolic labeling of RNAs being transcribed with a nucleotide analog, allowing their biotinylation and purification on streptavidin beads. Analysis of recovered nascent RNAs thus provides an accurate measure of ongoing transcription. We transfected senescent WI38 cells with each of the two siRNAs against VINK and measured nascent transcripts at different locations along VINK . We founTo test whether this effect is restricted to VINK or to RNAs from the vlincRNA family, we transfected senescent WI38 cells with an siRNA directed against the ARHGAP18 readthrough RNA belonging to the family of START RNAs that we characterized in a previous study . ReadthrThus, we concluded from these experiments that siRNAs can repress the transcription of the chromatin-associated RNA they target, even when the targeted site is far from the promoter.Drosophila melanogaster . We next transfected WI38-Δ/Δ #1 cells with the siRNAs directed against  compare  and 1C,  compare . Importa compare . The abs compare .Thus, removal of its target sequence abolished the effect of the VINK-1 siRNA on VINK expression, indicating that it represses the expression of its target RNA through base-pairing to its target sequence. Of note, under the conditions we used to achieve VINK knockdown, VINK-1 siRNA induced prominent off-target effects with significant changes in the expression of thousands of genes upon siRNA transfection in the cell line in which the siRNA target site was deleted .cis that would lead to the repression of transcriptional initiation. However, we cannot formally rule out the possibility that the VINK-1 siRNA targets and induces the degradation by conventional RNAi mechanisms of a subpopulation of VINK, an effect that would not be observed in our previous experiments because it only represents a minor proportion of VINK. If this population was able to indirectly regulate VINK expression, its depletion could then be signalized to the VINK promoter to repress VINK transcription. Of note, the VINK-1 siRNA targets a region located 300 bases upstream from a donor splice site . If the effects of siRNAs were due to base-pairing at chromatin-inducing local chromatin modifications, only the siRNA-targeted allele would be repressed. On the contrary, if the repression of VINK transcription was indirect, then the two alleles would be similarly regulated. We thus transfected WI38-Δ/+ cells with VINK-1 and VINK-2 siRNAs and performed RT-qPCR to monitor allele-specific VINK expression , which mediates amplification and propagation of double-strand RNAs toward the 5′ end of RNAs . HoweverContrary to the studies showing transcriptional gene silencing by miRNA targeting downstream regions of genes , 28, we Despite the fact that intronic sequences are also found associated with chromatin, siRNAs directed against intronic sequences of pre-mRNAs are usually not effective in decreasing the expression of pre-mRNAs , 6, 42. cis effects of nuclear noncoding RNAs or of their transcription, provided that an existing polymorphism or heterozygous genome editing, as we have done here, allows targeting a single allele.The use of siRNAs to silence nuclear RNAs has been debated for a long time, because of the low levels of some RNAi proteins in the nucleus. As a consequence, siRNA-mediated degradation of RNA is mostly efficient in the cytoplasm. Nonetheless, as discussed above, siRNAs targeting promoters can in some instances repress transcription from the promoter they target. Here, we showed that the promoter of chromatin-associated RNAs can be silenced using siRNAs targeting these RNAs in regions located far downstream of the promoter. Thus, siRNAs can represent interesting tools to investigate the function of chromatin-associated RNA by allowing their efficient and specific knockdown, even if the promoter is not well-characterized or if the RNA is produced through multiple promoters. In addition, and coupled to a method allowing the degradation of RNA without interfering with any transcription step, it may help to distinguish between the role of the RNA itself and the role of its transcription, an often-raised question in the field. Most importantly, since we found that siRNAs inhibit the transcription of the allele they target but not of the allele they do not target, they could allow studying Note, however, that under the conditions we used to achieve the knockdown of VINK, VINK-1 siRNA induced prominent off-target effects with significant changes in the expression of thousands of genes upon siRNA transfection in the cell line in which the siRNA target site was deleted. Moreover, the use of two independent siRNAs was not sufficient to cope with off-target effects. Indeed, most of the genes showing expression changes with two independent siRNAs directed against VINK in wild-type cells had a tendency to be changed in a similar way in the cell line deleted for the siRNA-1 target site (data not shown). These off-target effects were probably due to the fact that an siRNA can bind to and degrade target RNAs without perfect sequence complementarity, as the seed region of only 6 to 7 nucleotides in the siRNA is involved in targeting numerous RNAs . It is pFinally, our findings suggest that siRNAs targeting chromatin-associated RNAs may have therapeutic potential. Indeed, the therapeutic value of siRNAs is currently being widely investigated . Moreovel-glutamine, nonessential amino acids, sodium pyruvate, penicillin-streptomycin, and 10% fetal bovine serum under 5% CO2 and 5% O2. Senescence was induced by treating cells with 20 nM 4-hydroxy-tamoxifen for 3 days. U2OS cells (ATCC) were grown in Dulbecco’s modified Eagle’s medium containing Glutamax supplemented with sodium pyruvate, penicillin-streptomycin, and 10% fetal bovine serum under 5% CO2. WI38 cells and derivatives were transfected using the siRNA transfection reagent Dharmafect 4 (Dharmacon) following the manufacturer’s instructions, with 100 nM siRNA (unless indicated in a figure legend), and harvested 72 h later. U2OS cells were transfected with the INTERFERin transfection reagent (Polyplus) or with Dharmafect 4 according to the manufacturers’ instructions with 50 nM siRNA and harvested 48 h later. For RNA stability analysis, actinomycin D (Sigma) was added at 10 μg/mL. The siRNAs used are listed in WI38-hTERT/ER-RAF1 immortalized fibroblastic cell line was kindly provided by C. Mann . They we2). After 5 min on ice, 0.5% NP-40 was added. After another 10 min on ice, one-third of the volume was kept as whole-cell extract, and the remaining lysate was centrifuged for 10 min at 5,000 rpm. The supernatant corresponded to the cytosolic fraction. The pellet (nuclei) was resuspended in 1 volume of buffer 3  and then incubated for 30 min on ice, with shaking every 5 min. After 10 min of centrifugation at maximum speed, the supernatant was kept as the nuclear soluble fraction. The pellet was the chromatin fraction. All the fractions  were equilibrated to the same final volume and same buffer compositions, adding 1% SDS in each. The whole-cell extract was also adjusted to the same composition. The whole-cell extract and chromatin were sonicated until viscosity disappeared. Laemmli sample buffer was added to the samples, and the same volume of each fraction was loaded on an SDS-PAGE, except for the whole-cell extract, for which twice the volume of fractions was loaded.Phosphatases and protease inhibitors  were added in all buffers. Fresh trypsinized cells were lysed in 5 cell volumes of lysis buffer  antibody from Invitrogen  and the monoclonal anti-AGO2 (R.386.2) antibody from Invitrogen ; GAPDH was detected using the monoclonal anti-GAPDH (MAB374) antibody from Millipore ; PARP was detected with anti-PARP from Cell Signaling  at 1/1,000; HDAC1, HDAC2, and HDAC3 were detected with anti-HDAC-3 from BD Transduction Lab  at 1/500; H3 was detected with anti-H3 from Abcam (Ab1791) at 1/1,000; and α-tubulin was detected with anti-tubulin from Sigma (T6199) at 1/1,000.For the ChIP experiment, anti-RNA Pol II was from Bethyl Laboratories (A304-405A), anti-H3K27ac was from Abcam (Ab4729), anti-H3K4me3 was from Diagenode (pAb-003), anti-H3K9me3 was from Abcam (Ab8898), anti-H3 was from Abcam (Ab1791), and anti-H3K36me3 was from Abcam (Ab9050). Purified rabbit IgG control was from Millipore (PP64B).TAGGAGGCCAGTGCTCCAGG; sgRNA-2, TTCCTGACTCTTGAAAACCA.Genome editing of WI38 was performed with CRISPR-Cas9 tools in ribonucleic particle (RNP) format. Briefly, the Alt-R S.p. HiFi Cas9 nuclease V3 (Integrated DNA Technologies [IDT]) was assembled with an equimolar amount of hybridized single guide RNA (tracrRNA and crRNA from IDT) to generate RNPs. According to IDT recommendations, RNPs were transfected at 5 nM each with Lipofectamine CRISPRMAX (Thermofisher). Cells were harvested 1 week later to analyze the efficiency of genome editing. Genomic DNA was extracted with a Master Pure purification kit (Epicentre) according to the manufacturer’s instructions, except that 250 μg of proteinase K was used for cell lysis. Genome editing efficiency was checked by PCR on genomic DNA from the transfected population with the GoTaq G2 polymerase (Promega) using primers surrounding the 210-bp deleted region. PCR products were visualized on an agarose gel. Clones were then isolated and screened by PCR as described above. Two homozygous clones and one heterozygous clone were selected. The sequences of sgRNAs were the following: sgRNA-1, 2 TRIzol during 10 min, and the lysate was harvested before adding 0.1 mL of chloroform/mL of TRIzol. After centrifugation, the upper phase was collected and the Epicentre kit was used for the DNase digestion and subsequent precipitation. After total nucleic acids recovery, DNA was removed with a cocktail of DNase I and Baseline Zero DNase supplemented with Riboguard RNase inhibitor for 45 min at 37°C.Except when indicated, total RNA was prepared using the MasterPure RNA purification kit (Epicentre) according to the manufacturer’s instructions, except that 250 μg of proteinase K was used for cell lysis. For the experiment shown in Ex Taq from TaKaRa and the Bio-Rad CFX thermocycler. GADPH mRNA expression was used to normalize the results of RT-qPCR. Primers are listed in A 500-ng amount of total RNA was reverse transcribed with SuperScript III reverse transcriptase (Invitrogen) according to the manufacturer’s instructions. A condition without SuperScript III was included for each sample and analyzed by qPCR using GAPDH exon 9 primers to check for DNA contamination. qPCR was performed in triplicate with TB green Premix Cells were incubated with 0.2 mM ethynyl-uridine, a uridine analog, for 1 h. Cells were then harvested, total RNA was prepared, and nascent RNA was purified from 2 μg of total RNA with the Click IT nascent RNA capture kit from Life Technologies according to the manufacturer’s recommendations.6 transfected senescent WI38 cells as previously described  (pH 8), 85 mM KCl, 0.5% NP-40] and homogenized 40 times with a dounce tool . After centrifugation, nuclear pellets were resuspended in 1.5 mL of nuclear lysis buffer  and sonicated 10 times for 5 s  to obtain DNA fragments of about 500 bp. DNA concentration was determined using a Nanodrop system, and samples were adjusted to the same concentration of chromatin. Samples were diluted five times in dilution buffer  and precleared for 2 h with 250 μL of previously blocked 50% protein A and protein G beads . Blocking was achieved by incubating the beads with 0.5 mg/mL of Ultrapure bovine serum albumin and 0.2 mg/mL of salmon sperm DNA for 4 h at 4°C. A 100-μL volume of chromatin was kept for inputs. A 100-μg amount of precleared samples per ChIP was incubated overnight for RNA Pol II ChIP or a 40-μg amount for histones ChIP with 4 μg of antibody at 4°C. A mock sample without antibody was processed similarly. Then, 40 μL of blocked 50% protein A/G beads was added for 2 h at 4°C to recover immune complexes. Beads were washed once in dialysis buffer , five times in wash buffer  and twice in TE buffer . The bead-chromatin complexes were resuspended in 200 μL of TE buffer and incubated 30 min at 37°C with 10 μg of RNase A (Abcam), as well as input DNA. Formaldehyde cross-linking was reversed in the presence of 0.2% SDS at 70°C overnight with shaking. After 2 h of proteinase K (0.2 mg/mL) treatment at 45°C, immunoprecipitated and input DNA were purified on columns using an Illustra GFX kit . All buffers for ChIP experiments were supplemented with EDTA-free protease inhibitor cocktail (Roche) and filtered at 0.2 μm. Results were analyzed by qPCR. Primers are listed in ChIP was performed on 10 × 10escribed , except 2 fold change compared to this control was calculated in order to obtain data with a normal distribution. All experiments were then pooled together to calculate the variance of these experiments with a sufficient number of points. We then determined using Student's t test with this fixed variance the probability that the mean of each data set was equal to 0 , which represented the P value of the difference from the control. In some cases, we using the paired Student's t test to determine the probability that the mean of two populations was equal, which represented the P value of the difference between the two populations. Asterisks are included in the figures when the P value was <0.05. The P values are indicated in the figure when they were between 0.05 and 0.1. When above 0.1, the difference was considered nonsignificant.In all figures with statistical analysis results, data were normalized to 1 relative to a control included in each experiment . For significance analysis, the logWe performed strand-specific RNA-Seq, relying on UTP incorporation in the second cDNA strand. For each sample, 5 to 10 μg of total RNA was submitted to EMBL-GeneCore, Heidelberg, Germany. Paired-end sequencing was performed by using Illumina’s NextSeq 500 technology. Two replicates of each sample were sequenced.The quality of each raw sequencing file (fastq) was verified with FastQC . Files wGSE197308.RNA-Seq data are available in the Geo database under accession number


Benchmarked with synthetic m1A RNAs, our workflow identified specific groups of m1A-containing small RNAs, which are otherwise disproportionally under-represented. In particular, 22-nucleotides long 3′ tRNA-fragments are highly enriched for TRMT6/61A-dependent m1A located within the seed region. TRMT6/61A-dependent m1A negatively affects gene silencing by tRF-3s. In urothelial carcinoma of the bladder, where TRMT6/61A is over-expressed, higher m1A modification on tRFs is detected, correlated with a dysregulation of tRF targetome. Lastly, TRMT6/61A regulates tRF-3 targets involved in unfolded protein response. Together, our results reveal a mechanism of regulating gene expression via base modification of small RNA.RNA modifications are important regulatory elements of RNA functions. However, most genome-wide mapping of RNA modifications has focused on messenger RNAs and transfer RNAs, but such datasets have been lacking for small RNAs. Here we mapped N 1-methyladenosine (m1A) enrichment on 22-nucleotide tRNA fragments and its effect on gene-silencing. Higher level of m1A in bladder cancer is accompanied by gene dysregulation in unfolded protein response.RNA modifications are important regulators of RNA biology. Here we report N These modifications regulate base pairing and/or protein-binding and thus play vital roles in regulating mRNA functions such as protein translation, mRNA stability, splicing, phase separation, virus infection and immune response. Transfer RNAs (tRNAs) also harbor a wide array of modifications, which often poses a challenge to sequencing the full-length tRNAs. Recently developed modification-friendly sequencing workflows have enabled better quantification of tRNA modification levels and tRNA abundance14, lending more evidence that RNA modifications are not static and could be associated with diseases. Analogous to modifications on DNAs and histones, RNA modifications were found to be specific and subjected to dynamic regulation and may be reversible. One great example is the use of pseudouridine modification to improve mRNA stability and translational efficiency for COVID-19 vaccine development17. The field of RNA modification is poised to expand greatly due to the introduction of new techniques for mapping modifications, especially when coupled with high-throughput sequencing (HTS).RNA modifications are important regulators of RNA biology. In particular, recent advances in epitranscriptome mapping uncovers messenger RNA (mRNA) modifications including but not limited to N18. microRNAs can be regulated by 3′ end modifications and A-to-I editing19, with more recent but limited evidence of base modifications such as 7-methylguanosine and 8-oxoguanosine21. As another emerging group of small RNAs in this size range, tRNA-derived fragments (tRFs) were first identified via HTS methods about 12 years ago and represent abundant small RNAs with diverse biological functions23. For example, specific tRFs could regulate target gene expression via Argonaute-dependent mechanism27. In one such case, the host plant Argonaute was hijacked by the rhizobial tRFs to promote symbiosis28. In support of this notion, microRNA-sized tRFs were detected in complexes with Argonaute and GW182/TNRC6 proteins along with complementary mRNAs29. tRFs have also been implicated in mediating transposable element activity30, transgenerational inheritance32, ribosome biogenesis33, cell proliferation and differentiation35, and more. Nevertheless, to what extent tRFs inherit modifications from the parental tRNAs and how RNA modifications regulate tRF functions is still largely unknown.Besides modifications on mRNAs and tRNAs, modifications on small non-coding RNAs of around 15–30 nucleotides length could have important functions. By far most small RNA research has been focused on microRNAs, as they are well-known regulators to fine tune gene expression via base pairing with target RNAs in Argonaute RNA-induced silencing complex (RISC)1-methyladenosine (m1A) is one of the modifications that has received more attention in mRNAs and tRNAs but have not been comprehensively profiled in the small RNAs. m1A disrupts regular Watson-Crick base pairing, increases local positive charge, and thus is highly likely to play regulatory functions. Indeed, m1A is enriched on specific regions and motifs on mRNAs and is found to decrease protein translation from a mitochondria mRNA38. Most recently, through directed evolution approach, HIV-1 reverse transcriptase was engineered to facilitate the mapping of hundreds of m1A mRNA sites39. Furthermore, m1A on both mRNAs and tRNAs are dynamically regulated in response to various stresses or conditions41, suggesting this modification may be regulated. Indeed, ALKBH1, ALKBH3, and FTO have been found to have m1A demethylation activity43.N1A base modification of the mammalian small RNAs by combining antibody enrichment and reverse-transcriptase-induced mismatch signature. We found specific groups of small RNAs that harbor high degree of m1A modification, which are otherwise disproportionally under-represented in regular small RNA cloning protocol. In particular, the 22-nucleotide long 3′ fragments from tRNAs (tRF-3b) are enriched in m1A modification at their fourth position, a modification that is dependent on the TRMT6/TRMT61A methyltransferase complex. Interestingly, m1A modification within the seed region of tRFs negatively affects gene-silencing activity of the tRNA fragments, and regulates global gene expression via small RNA activity. In urothelial carcinoma of the bladder, higher TRMT6/61A expression is accompanied by higher m1A modification on tRF-3b, accompanied by dysregulation of tRF targeted mRNAs. TRMT6/61A-dependent m1A regulates the unfolded protein response via tRF-3 targets. Our results reveal a mechanism of regulating gene expression via changes in tRF base modification.Here we focus on profiling m1A is known to interrupt regular Watson-Crick A:U base pairing  at m1A positions. To systematically map potential m1A sites in small RNAs (<50 nucleotides long), we combine two independent approaches  and m1A-induced mismatch signature by sequencing. To first identify the m1A-compatible RT for standard small RNA-sequencing, three candidate RTs  were tested with a synthetic small RNA with single m1A site . The experimental workflow  at the synthetic m1A site shows great sensitivity and dynamic range over different m1A stoichiometry for both TGIRT and RT-1306 , both TGIRT and ProtoScriptII are able to detect enrichment of synthetic m1A RNAs spiked into cellular small RNAs  revealed that tRNA-derived fragments  are relatively enriched among short RNAs  also confirms that the tRFs are most enriched by m1A RIP  tRF-3s that map to the extreme 3′ CCA end of mature tRNAs, (2) tRF-5s that map to the extreme 5′ end of mature tRNAs, and (3) tRF-1s that contain the trailer sequence (often ends with poly-U) from the precursor tRNAs. In particular, 3′ tRFs have been shown to have two major species: tRF-3a (18 nt) and tRF-3b (22 nt), which are recapitulated by TGIRT-seq  of all 22-nt tRF-3b . All of these indicate tRF-3b are m1A-containing small RNAs.Based on the start and end positions, tRNA fragments can be separated into three major groups Fig. 22,30,34:NAs Fig. . Further-3a Fig. , suggest-3b Fig.  is to thNAs Fig. . The TGI-3b Fig. . This hi-3b Fig. , or on t1A RIP, particularly 5′ fragments from most mitochondria tRNAs . To test whether the m1A on tRF-3b was a modification that was introduced by TRMT6/61A , the misincorporation signature was assessed after TRMT6/61A knockdown  and the target RNAs .To directly test the effect of mnal Fig. . Only A4ing Fig. . This efTo ensure that repression by tRF was not an artefact from over-expression, we also knocked down endogenous tRF-3 by LNA (locked nucleic acid). tRF-3021 reporter activity was de-repressed specifically by anti-tRF-3021 Fig. , but not1A attenuates gene-silencing by tRF-3b due to problems with the seed annealing to the target mRNA.Overall this suggests that m1A on tRF-3b prevents silencing by tRF-3b  decreased in the Ago2-associated fraction but (b) not enriched or depleted in the Ago2-associated fraction relative to the input  increase of several tRF-3s in the Ago2-associated fraction upon knockdown of TRMT6/61A, but the increase does not cross the FDR threshold of <0.05  in both input and Ago-bound fractions is regulated by TRMT6/61A   Fig. . To examTRMT6 expression level and the overall m1A mismatch level of tRF-3b, and a significant increase of the m1A mismatch in the BLCA tumor samples that is consistent with the higher TRMT6/61A expression  and CREB3L2, have previously been implicated in UPR and protein secretion49, suggesting TRMT6/61A might help to sustain UPR in growing cells by de-repressing these tRF-3 targets. MBTPS1 (S1P) is well known for its role to cleave and activate different protein substrates on the Golgi, including the transcription factors ATF6 and CREB3L252. To directly test whether TRMT6/61A regulates UPR in response to ER stress, we utilized a luciferase reporter driven by minimal promoter with ER stress response element 2 (ERSE2) that can be activated by ATF6 transcription factor53. The reporter showed increased luciferase activity dependent on both the ER response element and the ER stress  as expected. Such UPR induced stress response is significantly dampened by TRMT6/61A knockdown in both HEK293T and T24 bladder cancer cell lines  Not all short RNAs showing mismatch in the TGIRT-seq were enriched by m1A RIP. This will help identify additional modifications in the short RNAs that cause mismatch and not just m1A. (b) Some other short RNAs, including several microRNAs and RNY4 Y-RNA fragment, were also consistently enriched by both m1A antibodies (Supplementary Table 1A-containing RNAs or due to cross-reactivity with the antibody. (c) While m1A RIP confirms TGIRT mismatch results, m1A RIP did not help us find newer short RNAs with modifications at low stoichiometry. Although high confidence m1A sites were focused on tRFs in this study, other m1A-containing small RNAs are very likely to emerge by more sensitive measurement or under specific biological conditions, especially various stress conditions that regulate m1A on mRNAs or tRNAs41.To map mNAs Fig. . Consist1A-containing RNAs are normally under-represented, as commonly used M-MuLV reverse transcriptases  are prone to stall at the m1A sites due to interrupted base pairing. Applying TGIRT for small RNA-seq allows a more accurate representation of m1A-containing small RNAs  have more tRF-3b than tRF-3a by TGIRT-seq or ProtoScriptII after siTRMT6/61A. Although the synthetic m1A RNA tested in this study shows a linear relationship between m1A stoichiometry and TGIRT mismatch rate  also shows a good linear relationship between mismatch rate and m1A% for synthetic m1A-containing RNA  and is uniquely destabilized and degraded in loss of TRMT6/61A62. It has been noted in human cells, tRNAiMet remains highly m1A modified, even after the loss of TRMT6/61A, probably due to the degradation of the hypomodified tRNAs63. Furthermore, it has been noted that tRNAGlu and tRNAAsp have lower m1A modification at A58 position compared to other tRNAs63, and we observed very low levels of tRF-3s from tRNAGlu and tRNAAsp, suggesting tRF-3 biogenesis may have some link to the T-loop modification. While biogenesis factors like endoribonuclease or exoribonuclease involved in generating tRF-3s remain to be identified, this report suggests tRF-3 modification levels could be altered under physiological conditions. It is known that TRMT6 and TRMT61A work together as heterodimers, with TRMT61A containing the catalytic activity and TRMT6 facilitating tRNA binding64. TRMT6/61A has been shown to methylate specific mRNAs and protein translation is attenuated for m1A-modified mRNAs36. To further examine whether the observed target RNA level changes . Bladder tissue specimens were obtained from patients undergoing transurethral resection of bladder tumors (TURBT) at Vestre Viken Hospital Trust following written informed consent without participant compensation. Only patients with non-muscle invasive papillary urothelial carcinomas were included. Anonymized collective patient information of samples used is listed in Supplementary Table 2 at 37 °C.HEK293T , HeLa , and U251  were maintained in HyClone Dulbecco’s High Glucose Modified Eagles medium with L-glutamine (GE #SH30081.01) plus 10% fetal bovine serum (Gibco #10437028) and 1% penicillin/streptomycin (Gibco #15140122). HEK293T was obtained from ATCC (ATCC #CRL-3216) and similarly for HeLa (ATCC #CCL-2). U251 was a kind gift from Roger Abounader. T24 bladder cancer cell line  was obtained from ATCC (ATCC #HTB-4) and maintained in MyCoy’s 5A medium with L-glutamine (Corning #10-050-CV) plus 10% FBS and 1% penicillin/streptomycin. Mycoplasma contamination was routinely checked by PCR kit (SouthernBiotech #13100-01). Cells were grown in humidified incubators with 5% CO70. pMD2.G (Addgene plasmid #12259) and psPAX2 (Addgene plasmid #12260) were gifts from Didier Trono. The expression of tagged Ago2 was confirmed by western blotting against pan-Ago   was a gift from Joshua MendellA8) Fig.  and agaiA8) Fig. .For cell lines: Total RNAs was extracted from cell lines by TRIzol Reagent (Invitrogen #15596018) and Direct-zol RNA Miniprep Plus Kit with on-column DNase treatment (ZYMO #R2071).For patient samples: Total RNA was extracted from patient samples using RNAzol RT  according to the manufacturer’s instructions. RNA quality was assessed using RNA ScreenTape on TapeStation (Agilent Technologies #5067-5576) or Agilent RNA 6000 Pico Kit on Bioanalyzer (Agilent Technologies #5067-1513).1A-containing RNA oligos were used to benchmark the m1A RIP or mismatch analysis by small RNA-seq , 2 pmole of total oligos were mixed with 1 μl QIAseq miRNA Library QC Spike-ins (Qiagen #331535) before small RNA-seq . Four pairs of m1A-modified and unmodified RNA oligos with tRF-3b sequences were used for dual luciferase assay  was used with 107 starting cells or 50–100 μg total RNAs. Briefly, 1–5 μg purified small RNAs were immunoprecipitated with 5 μg anti-m1A antibody  or corresponding IgG controls  conjugated on Protein G Plus Agarose (Pierce #22851) in the presence of SUPERase•In RNase Inhibitor (Invitrogen #AM2694) at 4 °C for 3 h. Post binding, resin was washed with 1X IPP buffer  twice, low salt buffer  once, high salt buffer  once and TEN buffer  twice. To elute bound RNAs, 3 mg/ml free 1-methyladenosine  in 1X IPP buffer was used to compete and displace m1A-containing RNAs at room temperature under rotation for 1 h with a total number of two elutions. The eluted RNAs were further purified by acidic phenol-chloroform method and dissolved in water. The efficiency of m1A RIP was monitored by relative fold enrichment (RIP over Input) of spiked in synthetic m1A oligos detected by small RNA-seq . 77 of small RNAs  are significantly enriched by both m1A antibodies.To identify m73 using NEBNext Small RNA Library Prep Set for Illumina (NEB #7330) with the following modifications. Briefly, 0.1–1 μg total RNAs were ligated with 3′ adaptor and 5′ adaptor provided with NEBNext kit. In the case of purified small RNAs, RIP RNAs or synthetic m1A-containing RNAs, diluted adaptor amount (5% of the recommended amount by the kit) was used instead. Adaptor-ligated RNAs were converted to cDNAs by different reverse transcriptases as follows.Small RNA-seq library preparation was performed similarly as previously described(1) For TGIRT: 1 μL TGIRT-III enzyme (InGex #TGIRT50) was used per cDNA synthesis reaction at 60 °C for 15 min. The reaction is then incubated with 250 mM  NaOH at 95 °C for 3 min and 65 °C for 15 min to degrade RNAs. After reaction cools down, same amount of HCl was used to neutralize the reaction and the whole reaction is further purified by Dynabeads MyOne Silane (Thermo Fisher #37002D) or QIAquick Nucleotide Removal Kit (Qiagen #28304).(2) For ProtoScriptII (in NEBNext Kit): 1 μL enzyme was used per reaction at 50 °C for 1 h and heat inactivated at 70 °C for 15 min; no purification was performed before PCR amplification.(3) For RT-1306 (a kind gift from Bryan Dickinson): 1 μL of 10 μM recombinantly expressed and purified enzyme39 was used per reaction. Other conditions are the same as ProtoScriptII.All cDNA synthesis was carried out in buffer  plus 1 μL RNase Inhibitor and followed by 12–16 cycles of PCR amplification with indexed primers (NEB #E6609). PCR cycle number is optimized per batch of reactions by testing different cycle numbers with small amount of cDNA and checked on gel. The resulting amplified libraries were individually purified by ZYMO DNA Clean & Concentrator Kit (ZYMO #D4033) and size selected on 8% TBE polyacrylamide Novex gel (Thermo Fisher #EC6215) to enrich for insert of 15–50 nucleotides long (longer than primer dimer and shorter than full-length tRNA). Final libraries were pooled and sequenced on Illumina NextSeq500, according to validated standard operating procedures established by the University of Virginia Genome And Technology Core, RRID: SCR_018883.73. Briefly, cutadapt v1.1574 was used to quality trim with cut-off 20 (with nextseq trim option), trim 3′ adaptor sequence, and discard trimmed read length shorter than 15 nt. Due to higher frequency of cloning 5′ adaptor for TGIRT-seq libraries, reads containing 5′ adaptor sequence were discarded with cutadapt; this is to avoid mis-annotation of 5′ adaptor sequence to hsa-miR-3168. In general, each library has 2–10 million mapped reads. To quantify small RNAs, unitas v1.7.375 with SeqMap v1.0.1376 was used to map the reads first to human sequence of miRBase Release 2277, then to other small RNA sequences including genomic tRNA database78, Ensembl Release 97 and SILVA rRNA database Release 13279. Unless otherwise specified  at the given site divided by total read count for the given sequence  is calculated for each position: mismatch index = reads that have mismatch at that position/total reads. Only tRF/tRNA with more than 50 reads is considered for mismatch index calculation.To knock down TRMT6/61A in cell lines, 10 nM final total concentration of siRNA  was transfected twice with Lipofectamine RNAiMax (Thermo Fisher #13778150) and collected 96 h post first transfection (48 h post second transfection). Non-targeting control siRNA (Dharmacon #D-001810-10-20) was used as negative control. Knock-down efficiency was confirmed by both RT-qPCR Fig.  by TRMT673. Briefly, 1–5 μg purified total RNA or 5 pmole synthetic RNA oligos were resolved on 15% Novex TBE-Urea gel (Invitrogen #EC6885BOX) and transferred to Amersham Hybond-N + nylon membrane (Cytiva #RPN203B) by Trans-Blot SD semi-dry transfer apparatus (Bio-Rad). The membrane was cross-linked with 254 nm wavelength by Stratalinker (Strategene).Northern blot was performed similarly as beforeFor Northern blot: After UV crosslinking, the membrane was blocked by ExpressHyb Hybridization Solution (Takara Bio #636833) and probed with biotinylated DNA probe  followed by anti-mouse HRP-linked secondary antibody . Chemiluminescence detection was performed with Immobilon HRP substrate (Millipore #WBKLS0500). Oligo size was compared to microRNA marker (NEB #N2102S) on the gel.Ala, tRF-3030bTyr and tRF-3004bGln reporters were constructed with single tRF-3b target sequence (reverse complement to tRF-3b sequence); tRF-3009bLeu was previously constructed similarly25. Endogenous 3′ UTR sequences of MBTPS1  and CREB3L2  were PCR amplified by MyTaq HS Red Mix (Bioline #BIO-25047) from HEK293T genomic DNA extracted by QuickExtract DNA Extraction Solution (Epicenter #QE09050) and cloned into psiCHECK-2. 3′ UTR reporter. HEK293T was co-transfected 2 ng of luciferase plasmid and 100 nM tRF-3b mimic by Lipofectamine 2000 (Invitrogen #11668019) in 24-well plates; 24–48 h post transfection, cells were lysed and luciferase activity was measured by Dual-Luciferase Reporter 1000 Assay System (Promega #E1980). For each condition, Renilla luciferase value is normalized to Firefly luciferase value to account for well-to-well variation in cell density or transfection efficiency. In addition, empty psiCHECK-2 was included as a control for 3′ UTR reporter and used for normalization.Dual luciferase reporter was constructed in psiCHECK-2 vector (Promega #C8021) to insert target sequence into the 3′ UTR of Renilla luciferase gene. tRF-3021bLNA/DNA (synthesized by Qiagen or IDT) was used to knock-down endogenous tRF-3s  by GoScript Reverse Transcriptase (Promega #A5004) with random primers (Invitrogen #48190011). RT-qPCR reactions were monitored on StepOnePlus Real-Time PCR System (Applied Biosystems) with PowerUp SYBR Green Master Mix (Applied Biosystems #A25741) and gene-specific primer sets to detect TRMT6/61A or tRF-3 targets .For patient samples: To examine mRNA expression level, 0.25–1 μg of total RNA (refer to RNA isolation) was used to synthesize complementary DNA (cDNA) using SuperScript IV VILO Master Mix with ezDNase Enzyme kit (Invitrogen #11766050). RT-qPCR was performed using PowerUp SYBR Green Master Mix and specific primers against targets. Relative mRNA levels were normalized to ACTB (chosen based on RNA-seq). All sequences of qPCR primers are listed in Supplementary Table 7 cells were collected by scraping and washed with DPBS, before lysis in RIP buffer . Cleared lysates were incubated at 4 °C for 1 h with M2 FLAG affinity gel (Sigma #A2220) or anti-MYC mouse antibody 9E10 conjugated on beads as a negative control. After washes, bound RNAs are eluted by Trizol extraction and ethanol precipitation. IP efficiency was confirmed by western blot .Ago2 RIP was performed in stable FH-Ago2 HEK293T cells. Briefly, 10lot Fig. . Both in5js146. The corresponding nucleic acid base at 4th position of guide RNA was substituted manually using Coot v0.8.9.182 “replacing residue” function. The replaced m1A (code 1MA) backbone was fitted into the map. The final modeled structure was visualized using MacPyMOL v1.7.0.3.Co-crystal structure of human Ago2 and single-stranded guide RNA was obtained from PDB ID: 83: briefly, kallisto v0.46.184 was used with paired-end and stranded mode to quantify reads to human GRCh38 Refseq Transcripts (from NCBI Human Genome Resources). Differential gene expression analysis was carried out by DESeq281 after tximport to convert estimated counts from kallisto to gene-level analysis. DESeq2 results were visualized by EnhancedVolcano85 with lowest adjusted p value set to 10−10. Gene Set Enrichment Analysis (GSEA) was performed on DESeq2-ranked genes by fgsea86 with 1000 permutations on pathway annotation from Molecular Signatures Databases87. Adjusted p value is corrected for multiple hypothesis testing on all Reactome pathways.Total RNAs (0.1–1 μg) were poly-A selected by NEBNext poly(A) mRNA Magnetic Isolation Module (NEB #E7490) and followed by NEBNext UltraII Directional RNA Kit (NEB #7765). The resulting libraries were pooled and sequenced as paired-end reads on Illumina HiSeq2000 (Novogene) or NextSeq  with >20 million reads per sample. Standard RNA-seq analysis workflow was used88 Release 7.0 custom perl program was used to predict tRF targets based on seed sequence matches in 3′ UTR . Four types of target sites were predicted according to common definition for microRNA targets88: 8mer-A1 sites ; 7mer-m8 sites (2–8 nt base pairing), 7mer-A1 sites ; 6mer sites (2–7 nt base pairing). In this order, target genes are counted only by the highest rank of target type . For CDF plots, only genes with 3′ UTR sequences and expression level higher than 100 normalized read count (by DESeq2) were considered (conclusions remained the same regardless of different expression cut-off tested). CDF plots were centered by the non-targets median log2 Fold Change value. P value is calculated by Kolmogorov–Smirnov test to compare overall distribution between each type of tRF-3 targets and the non-targets.To predict tRF-3b targets, Ago2-bound tRF-3b sequences were first clustered by their seed sequences (1–8 nt) and 6 seeds were identified to cover >90% of Ago2-bound tRF-3b sequences Fig. . Among tFor protein extraction, ~5 mg of tissue was placed in Metal Bead Lysing Matrix S tubes  to which 5 mL of RIPA Lysis and Extraction Buffer (Thermo Fisher #8990) and 50 μL Protease Inhibitor Cocktail (Merck Life Sciences #O8340-5ML) was added. The tubes were processed using a FastPrep-24™ Classic Instrument  and left to rotate for 2 h at 4 °C. Samples were transferred to Eppendorf tubes and sonicated at 50% amplification and 0.8 cycles . Following centrifugation at 16,000 rcf for 20 min at 4 °C, supernatant was recovered, and protein concentration was estimated by Bradford Assay (BioRad #500-0006). For Western blot analysis, 40 μg lysate was mixed with 4X Bolt™ LDS Sample Buffer (Invitrogen #B0007), heated for 10 min at 70 °C, and separated on Bolt 12% Bis-Tris Plus gel (Invitrogen #NW00120BOX) for 1.5 h at 150 V. Proteins were transferred onto a PVDF membrane (Biorad #1704156) with a Trans-blot turbo system (Biorad). Following blocking of the membranes for 1 h in 5% milk in PBS with 0.05% Tween-20 (PBST), the primary antibodies and corresponding dilutions were used in 5% milk in 0.05% PBST: anti-β-actin , anti-TRMT6 , and anti-TRMT61A . Anti-mouse  and anti-rabbit  HRP-linked secondary antibodies were used for detection of the respective targets. Blots were developed using the SuperSignal West Dura Extended Duration Substrate (Thermo Fisher #34095) for β-actin and SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher #34095) for TRMT6 and TRMT61A on Biorad ChemiDoc XRS + System. Band signal intensities were obtained with ImageLab Software (v5.2.1) and used to determine relative target protein levels normalized to β-actin which were visualized with GraphPad Prism (v9.1.0).89 was used to compare TRMT6/61A mRNA expression between tumor (TCGA) and normal (TCGA plus GTEX) samples across cancers. TRMT6 mRNA expression across BLCA patients from TCGA pan-cancer study90 was downloaded using cBioPortal92. Patients were stratified by the highest and lowest quartile of TRMT6 expression, and patient IDs extracted. High and low TRMT6 expressing patient IDs were then used as input for differential expression analysis using TCGAbiolinks v2.20.193 with DESeq281. DESeq2 output was used for CDF plot and GSEA analysis. Correlational analysis between TRMT6/61A expression and tRF-3 targets was performed by GEPIA289.GEPIA2http://n2t.net/addgene:101790; RRID:Addgene_101790), a gift from Seiichi Oyadomari. pRL-TK Renilla Luciferase Control plasmid was co-transfected as an internal control. Cells were transfected with reporter plasmids for 24 h before the readout; to trigger UPR, cells were treated with 5 μg/ml tunicamycin (Sigma #T7765) for 4 h before the readout. Luciferase readout is performed similarly as above dual-luciferase reporter assays. UPR response is measured by Firefly luciferase signals divided by Renilla luciferase signals (on co-transfected plasmid) and normalized to basal level.UPR reporter assay in HEK293T or T24 cells was performed with ATF6-dependent promoter reporter . Differential analysis for small RNA-seq and RNA-seq was performed by DESeq2 (v1.30.1) Wald test with Benjamini–Hochberg adjustment for multiple hypothesis testing.Further information on research design is available in the Supplementary InformationReporting Summary


The current monocular 3D object detection technology mainly uses RGB images and pseudo radar point clouds as input. The methods of taking RGB images as input need to learn with geometric constraints and ignore the depth information in the picture, leading to the method being too complicated and inefficient. Although some image-based methods use depth map information for post-calibration and correction, such methods usually require a high-precision depth estimation network. The methods of using the pseudo radar point cloud as input easily introduce noise in the conversion process of depth information to the pseudo radar point cloud, which cause a large deviation in the detection process and ignores semantic information simultaneously. We introduce dynamic convolution guided by the depth map into the feature extraction network, the convolution kernel of dynamic convolution automatically learns from the depth map of the image. It solves the problem that depth information and semantic information cannot be used simultaneously and improves the accuracy of monocular 3D object detection. MonoDCN is able to significantly improve the performance of both monocular 3D object detection and Bird’s Eye View tasks within the KITTI urban autonomous driving dataset. One is to use LIDAR sensors –4 to obtThe existing monocular 3D object detection methods are mainly divided into two categories, namely image-based methods , 9–19 anMost image-based monocular 3D object detection methods need to learn from geometric constraints and ignore the picture’s depth information, resulting in the method’s complexity and low efficiency. The pseudo-LiDAR-based methods use the estimated depth map to project the 2D image information into the 3D space to complete the conversion of the pseudo point cloud. There may be deviations during the conversion process. At the same time, this method ignores the semantic information in the image, causing the final result to be biased.Most prior methods cannot use the semantic information in the image and the depth information in the depth map simultaneously. Some image-based methods use depth map information for post-calibration and correction, but this method usually requires a high-precision depth estimation network. To solve this problem, we introduce dynamic convolution on the image-based method and do not convert the predicted depth map into a pseudo point cloud. In the case of 3D object detection, a smoother depth map and dynamic convolution network are used. We use the depth map generated by the densedepth network  as a guiTo summarize, our contributions are the following:We formulate a monocular 3D object detection based on the dynamic convolution network (MonoDCN). The convolution kernel of dynamic convolution is generated by depth map information, and the dynamic convolution is used to replace the 2D convolution in the feature extraction network.Our method directly predicts 3D bounding boxes from RGB images with a compact architecture, making the training and inference simple and efficient.We evaluate the proposed module and design methods on the monocular 3D Object Detection and Bird’s Eye View tasks of the KITTI dataset, and we achieve competitive results.Monocular image is naturally of limited 3D information compared with LiDAR or stereo vision. Therefore, most of the current image-based methods , 9–19 imWeng and Kitani firstly proposed the concept of the pseudo point cloud in , but thePeople pay more and more attention to the development of monocular depth estimation. In recent years, although many pixel-level depth estimation networks , 25 haveOur overall framework consists of three important components: a depth map generation network, a feature extraction network, and a dynamic convolution layer guided by the depth map. We refer to our method as monocular 3D object detection based on the dynamic convolution network (MonoDCN), as illustrated in The backbone of the feature extraction network for RGB images is ResNet-50 . ResNet-The depth map generation network is based on densedepth network , which ihn×wn input map, and then input the convolution kernel into the dynamic convolution layer, where hn and wn are the height and width of the feature maps at layer n, cn is the number of channels of the feature map is generally RGB three channels, k represents the size of the convolution kernel of the dynamic convolution layer. The convolution kernel in the dynamic convolution layer is generated by the filter generation network. For the filter generation network, the backbone also uses the first three blocks of ResNet-50 for calculation, which is the same as the first three layers of the feature extraction network. Structure reuse to reduce computing costs. The number of channels in each block in the first three layers of the feature extraction network and the filter generation network is also the same. The main task of this network is to learn the depth information in the depth map to obtain a dynamic filter (convolution kernel in the dynamic convolution), and apply it to the dynamic convolution layer of RGB image feature extraction. The feature maps obtained by the first three layers of the feature extraction network and the feature maps obtained by the dynamic convolution layer are subjected to the element-wise product operation and used as the input of the last layer of the feature extraction network.Dynamic convolution is the core of our network. The convolution kernel of dynamic convolution is trained and generated by the depth map, using the feature information in the depth map as a guide. We firstly introduce the concept of dynamic convolution. Generally speaking, the convolution kernel in dynamic convolution is a form of dynamic filter, in which the convolution kernel changes with the input picture. Since the convolution kernel generated based on the depth map is actually a feature body, a simple way to do this is to convert the feature body to a specific convolution kernel of the The traditional 2D full convolution uses a fixed convolution kernel for feature extraction. Different images can only use the same convolution kernel when performing feature extraction, resulting in poor training effects. It cannot effectively reflect the depth-related scale changes of the object, nor can it effectively reflect the spatial relationship between foreground and background pixels. On the other hand, the pseudo-LiDAR-based methods rely too much on depth accuracy and ignore semantic information in RGB images. To solve these problems at the same time, we introduce dynamic convolution . The conIn this work, we adopt a single-stage detector based on a priori-based 2D-3D anchor box . FirstlyInputs. The output feature map δ ∈ R3×4 are available both at the train and test time. The 3D-to-2D projection can be written as x, y, z]D3 denotes the horizontal position, height and depth of the 3D point in camera coordinates, and p is the projection of the 3D point in 2D image coordinates, zD3 represents the depth of its true label.Ground truth. We define a ground truth (GT) box using the following parameters:D2 represents the 2D bounding box, where  is the center of the 2D box, w, h are the width and height of the 2D box; D3 represents the position of the 3D center in camera coordinates; D3 of the 3D object), and the different-center posture in the 3D space θD3 ) . We use Outputs. We choose nα to represent the number of anchor boxes, and nc to represent the number of classes. For each input position , the corresponding output anchor box contains 35+nc parameters: tx, ty, tw, th]D2 represents the predicted 2D box, and p is the predicted 3D center in 2D projection position, D3 represents the predicted depth, 3D shape; and rotation; m ∈ {1, 2, …, 8}), which represents the 8 corner points of the 3D box, s is the confidence score of each class, the input size is h4×w4×nα×(35+ nc), where  is the size of the input image, and the down-sampling factor is 16. The output is actually based on the anchor transformation of the 2D-3D box.Anchor. As in D2, D3, where D3 represents a 3D anchor, and D2 represents a 2D anchor. For each 2D anchor, we use the statistical data of all 3D boxes that match the real label as its corresponding 3D anchor D3. Note that we use the same anchor parameter D2 for regressions of D2 and p. The anchor-based method enables our network to learn the relative value  of the true label, which significantly reduces the difficulty of learning.r. As in  and 30]Anchor. Ax′, y′]p represents the projection coordinates of the predicted 3D center on 2D, and p is the eight vertices of the 3D box, and [z]D3 is the corresponding depth.The SmoothL1 regression loss is selected for 2D and 3D regression, and each specific loss function is defined as Eqs We evaluate our proposed framework on the KITTI  3D objecThe KITTI dataset of 3D object detection consists of 7481 training images and 7518 test images, as well as corresponding point clouds and calibration parameters. It includes 80,256 2D and 3D labeled three types of objects, namely cars, pedestrians, and bicycles. According to the occlusion and truncation level of the object, each 3D ground truth frame is divided into three difficulty categories: easy, moderate, and difficult. The KITTI dataset has two kinds of the training set and validation set segmentation, one is split1 , which cWe evaluate our model on the KITTI  dataset.In order to facilitate subsequent experiments, the input images are firstly scaled to 512 × 1760, Which are used for feature extraction and depth map generation respectively. According to the generated depth map training, dynamic convolution is obtained. Then, the feature map generated by the first three layers of the feature extraction network and the feature map generated by the dynamic convolution layer are multiplied by the corresponding elements, and the obtained result is used as the input of the last layer of the feature extraction network, and the 2D and 3D frame coordinates of the target are finally output. After non-maximum suppression, the final visualization result is output. The experiment is evaluated under the IoU threshold of 0.5 and 0.7, the learning rate is selected as 0.01, which decays with training, the optimizer selects the SAD algorithm, the momentum is selected as 0.8, and the batch size is 8, and the training is performed for 500 rounds.APD3 represents the average accuracy of the 3D detection frame, and APBEV represents the average accuracy of the detection frame in the BEV view, which is calculated using the The visualized 3D object detection result is shown in In order to verify the degree of influence of each processing part on the network of this article, we conduct an ablation study on our model, only a comparative experiment was carried out under 3D detection, and the results are shown in We use dynamic convolution to improve the model’s perception of depth information to improve the accuracy of 3D object detection. The network can learn specific kernels applied to different pictures. To better understand the effect of dynamic convolution, we conduct experiments and use the same experimental configuration. The network model uses dynamic convolution. The traditional 2D convolution is replaced, and the rest is unchanged. The results obtained are shown in We use a dynamic convolution network based on depth map features for monocular 3D object detection. Our method has an excellent performance in three-dimensional detection, positioning, and pose estimation among the existing monocular methods. This paper proposes to use the depth information in the depth map to generate the convolution kernel of the dynamic convolution network, use dynamic convolution to replace the traditional 2D full convolution, and combine the RGB image information with the depth information, which effectively improves the monocular 3D object detection Accuracy.


Therefore, disruption of the interaction between the viral replication proteins and viral genomic RNA is expected to inhibit the replication of TMV. In this study, we demonstrate that the addition of small RNA fragments (18–33 nts in length) derived from different regions within the NR region inhibit the binding of TMV replication proteins to viral RNA and TMV RNA replication in a cell-free system. Intriguingly, some of the small RNA fragments also inhibited the translation of mRNA in a sequence-nonspecific manner. These results highlight the pleiotropic roles of the 5′-proximal region of the TMV genome.Replication proteins of tobacco mosaic virus (TMV), a positive-sense RNA virus, co-translationally bind to a 5′-proximal ~70-nucleotide (nt) region of the genomic RNA, referred to as the  Tobacco mosaic virus (TMV) belongs to the genus Tobamovirus of the family Virgaviridae CTP. When TMV18-35, TMV18-44, TMV45-72, or TMV60-92 RNA was added at a concentration of 4,000 nM, the synthesis of TMV-related RNA was inhibited, while TMV5830-5859 or TMV18-26 had a much smaller effect (if any) than other RNA fragments  was translated in mdBYL in the presence of TMV18-44, TMV45-72, TMV60-92, or TMV5830-5859, and luciferase activity was measured. In the presence of TMV45-72 or TMV60-92 at higher concentrations, luciferase activity was markedly decreased , but also TMV RNA replication when added exogenously to a cell-free translation–replication system. This result suggests that template selection by the replication proteins, despite its cis-preferential [cis-preferential PMTC formation. The small inhibitory RNAs may be able to access the nascent Met-IR polypeptide before PMTC formation when supplied at high concentrations (400–4000 nM).TMV18-35, TMV18-44, TMV45-72, and TMV60-92, but not TMV18-26, inhibited the binding of TMV Met-IR to TMV1-110-st RNA . It woulGiven that TMV18-44 and TMV45-72 do not overlap, the NR region would have multiple Met-IR binding sites. TMV18-44 inhibited the binding of Met-IR to the NR region at lower concentrations than other fragments. Therefore, TMV18-44 is likely to have the highest affinity to TMV replication proteins among the small RNA fragments examined in this study. From these results, we propose a model of PMTC formation in which one or a few molecules of TMV replication protein co-translationally bind to the TMV18-44-containing region first, and then several molecules of replication proteins are assembled to cover the whole NR region. Further studies of the NR region will yield more information about the PMTC formation process.TMV18-26 RNA is only 9 nts in length and showed a much weaker inhibitory effect on the binding of TMV Met-IR to the NR region-containing RNA and RNA replication than TMV18-35 (18 nts) and TMV18-44 (27 nts). Considering the repetitive nature of the sequences of these RNAs , TMV reptrans. Although TMV18-44 consists of the CAA-rich sequence, this RNA fragment showed a much weaker inhibitory effect on translation than TMV45-72 and TMV60-92 (TMV45-72 and TMV60-92 inhibited RNA translation . The traTMV60-92 . Thus, t


RNA-mediated drugs are a rapidly growing class of therapeutics. Over the last five years, the list of FDA-approved RNA therapeutics has expanded owing to their unique targets and prolonged pharmacological effects. Their absorption, distribution, metabolism, and excretion (ADME) have important clinical im-plications, but their pharmacokinetic properties have not been fully understood. Most RNA therapeutics have structural modifications to prevent rapid elimination from the plasma and are administered intravenously or subcutaneously, with some exceptions, for effective distribution to target organs. Distribution of drugs into tissues depends on the addition of a moiety that can be transported to the target and RNA therapeutics show a low volume of distribution because of their molecular size and negatively-charged backbone. Nucleases metabolize RNA therapeutics to a shortened chain, but their metabolic ratio is relatively low. Therefore, most RNA therapeutics are excreted in their intact form. This review covers not only ADME features but also clinical pharmacology data of the RNA therapeutics such as drug–drug interaction or population pharmacokinetic analyses. As the market of RNA therapeutics is expected to rapidly expand, comprehensive knowledge will contribute to interpreting and evaluating the pharmacological properties. In recent decades, RNA therapeutics have pioneered a broad range of medical uses in rare diseases related to gene expression as well as vaccines for combatting SARS-CoV-2 virus COVID-19). The exclusive target specificity of RNA therapeutics can offer great advantages over traditional drugs by modulating the translation of disease-causing proteins via the downregulation of a gene target or by injecting synthetic mRNA to translate the encoded target protein 9. The ex.Over the last five years, numerous RNA therapeutics have been approved by the US Food and Drug Administration (FDA) for various applications . This reRNA therapeutics differ from conventional drugs in terms of their pharmacological mechanisms of action and pharmacokinetic properties ,5. UnderRNA therapeutics are oligonucleotides consisting of RNA bases . Each base is linked to a phosphate backbone, which provides structural support to the strand. RNA therapeutics exhibit their therapeutic potency by binding to specific sites of pre- or mature RNA through Watson–Crick base pairing . Based oDefibrotide, a polydisperse mixture of single- and double-stranded oligonucleotides with variable length and sequence composition, was the first approved RNA drug in 2016 and is derived from the porcine intestinal mucosa . It was Taken together, the majority of RNA therapeutics approved within the past five years have been ASOs or siRNA-based drugs. ASOs are short, single-stranded nucleic acids that are typically 15–30 bases in length . siRNAs 2000-C-DMG. Owing to its limited chemical modifications, patisiran uses the LNP system for stability in circulation and effective liver uptake [2000-C-DMG (0.76 mg) per 1 mg dose of ALN-18328 is considerable [2000-C-DMG in the amount of ALN-18328 [Patisiran was the first approved siRNA drug in 2018 and formulated as a lipid-nanoparticle (LNP) for effective delivery to target. It consists of a 21-mer double-stranded siRNA (ALN-18328) encapsulated in four lipid excipients, including two novel lipid complex formulations: DLin-MC3-DMA and PEGr uptake . For sufiderable . TherefoLN-18328 .Native oligonucleotides are rapidly degraded by nucleases and have a low affinity to proteins present in the plasma, resulting in their rapid elimination ,16. To oRNA therapeutics are administered parenterally, commonly via the subcutaneous or intravenous route . BioavaiFomivirsen and pegaptanib are used to treat ocular diseases, which necessitates a local approach of administration to the drug target and optimization, rather than a systematic approach. Therefore, both drugs are administered via direct intravitreal injections. Based on a nonclinical study, the fomivirsen concentration of the retinal compartment was measured as 3.5 µmol/L and 0.88 µmol/L in the eyes of rabbits and monkeys, respectively, after single intravitreal administration ,29. FurtSystemically administered RNA therapeutics cannot cross the brain-blood barrier because of their bulky molecular size and negatively charged atoms in the backbone. To facilitate to effect on the central nervous system, nusinersen, used in the treatment of spinal muscular atrophy (SMA), is administered via intrathecal injection ,32. AdmiRNA therapeutics interact with their RNA targets in the cytosol or nucleus of cells . However2000-C-DMG making up patisiran was much lower than that of other components [For patisiran formulated as an LNP, plasma protein binding was assessed in two different ways. In the first method, the binding of patisiran to serum albumin and alpha-1-acid glycoprotein was measured to be 0.46% and 2.07%, respectively. In another method, human plasma protein binding of PEG2000-C-DMG alone, as an LNP PEG excipients, was determined to be ~97%. However, protein binding of patisiran followed the serum albumin and alpha-1-acid glycoprotein results because the molar ratio of PEGThe majority of RNA therapeutics are systemically distributed to the kidney and liver, which are primary excretion and metabolic organs ,42,43. ADegradation of RNA therapeutics is typically mediated by exo- and endonucleases. Generally, unmodified oligonucleotides are rapidly degraded by 3-exonucleases ,50,51. AOf all the organs, drugs are largely distributed to the liver as this is the location of most drug metabolism. For this reason, some RNA therapeutics were tested using the s9 fraction of the liver, liver microsomes, or recombinant cytochrome P450 (CYP) systems to understand the metabolism of each drug. Most GalNAc siRNA drugs are not substrates of CYP enzymes; therefore, clinical interactions involving CYP enzymes are unlikely . AdditioIn general, human radiolabeled ADME studies is a key study for new drug applications ,58. HoweAlthough renal clearance is the major excretion pathway for most RNA therapeutics, each individual RNA therapeutic has specific pharmacokinetic parameters related to their excretion . For exaGalNAc-conjugated siRNA drugs, such as givosiran, inclisiran, lumasiran, and vutrisiran, have been approved by the ASO and Drug Administration. Based on non-clinical data, the initial blood clearance of siRNA drugs appears to occur through high distribution to the liver. Givosiran is cleared from the plasma by conversion to AS(N-1)3′ givosiran (36%), uptake in the liver (52%), and urinary excretion (12%) . It has Most RNA therapeutics have not been reported to be inhibitors or inducers of the CYP system yet, which causes classical drug–drug interactions. Therefore, they are not predicted to interact with small-molecule drugs primarily cleared through oxidative metabolic pathways. However, a clinical study on an indirect drug–drug interaction of givosiran has been reported . In thisTransporters can also have clinically relevant effects on the pharmacokinetics and pharmacodynamics of a drug by modulating its absorption, distribution, and elimination . In contSome RNA therapeutics are highly bound to plasma proteins, and thus, protein-binding displacement can be considered another potential cause of pharmacokinetic interactions . HoweverPK/PD analyses link dose-concentration relationships  and concentration-effect relationships , thereby predicting the effect of drug dosing over time . Biomarkmax was significantly different from the corresponding PD Tmax. For example, the plasma PK Tmax of vutrisiran was 0.2–12 h in healthy volunteers, whereas nadir TTR levels, considered as PD Tmax, were achieved by 50–90 days [Although the PD biomarkers for RNA therapeutics are well described, the PK/PD relationship is more complex than that of conventional small molecules The complex PK/PD relationship is also shown in biologics and target-mediated drug disposition model was presented to explain this relationship . Followi–90 days . Second,–90 days . This meIt is necessary to determine other PK profiles to resolve this discordance in the plasma profile with PD activity. It was found that PD activity is better correlated with drug concentration in organs than plasma concentration if the RNA therapeutics had target organs . For siRPopulation pharmacokinetic or pharmacokinetic-pharmacodynamic (PK/PD) modeling and simulation has been applied in the clinical evaluation of some RNA therapeutics approved over the last decade. DMD treatment therapeutics had been approved using limited Pop PK or without PK/PD model because DMD is an ultra-rare disease, and most patients are too young to get sample sufficient for effective analysis ,65,66. TThe two published population PK/PD studies were for inotersen and patisiran, which were approved for the same disease. A population PK/PD model for inotersen was developed by on data from phase I, II, and III clinical trials . The PK/The first population PK/PD model of an siRNA therapeutic was developed for patisiran . The PK/The drawbacks of RNA therapeutics were related to their stability, rapid degradation, and delivery to target cells . As mentThe development of RNA therapeutics has advanced significantly over the last decade, resulting in the recent approval of several new drugs by the FDA and/or European Medicines Agency (EMA). Furthermore, numerous new RNA therapeutics are in clinical trials and are expected to be approved in the next decade, owing to being able to address unmet clinical needs about rare disease. Hence, a comprehensive understanding of the clinical pharmacokinetic properties of these drugs is needed to evaluate their therapeutic efficacy and safety. In particular, RNA therapeutics have distinctive ADME properties that can be distinguished from those of small-molecule drugs. Parenteral administration and a long terminal half-life are the most pronounced of these pharmacokinetic distinctions, in addition to their pharmacological specificity. Prolonged half-life caused by base modification can contribute to convenient dosing with regimens as infrequent as once a week to twice a year. Drug-drug interactions are rare because the metabolism of RNA therapeutics is not related to traditional drug-metabolizing enzymes or transporters. Instead, interactions caused by the manipulation of gene expression may continue to be relevant after treatment with RNA therapeutics. Several challenges regarding the interpretation of the PK/PD relationship and its application to various models remain, but some models have been successfully developed during drug development. Although, diverse drawbacks of RNA therapeutics can be solved by chemical modification and development delivery system, several advances are still needed. Further knowledge of the pharmacokinetic mechanism would support dose selection, dosing interval and a suitable formulation selection. Ultimately, new drug development for the clinical use of this class of drugs can be improved.


Exitianus capicola) via transcriptomic sequencing and rapid amplification of cDNA ends. Results of the sequence alignment suggested that the new virus is a member of the genus Iflavirus, family Iflaviridae. Thus, it was proposed to be named Exitianus capicola iflavirus 2 (ECIV2). ECIV2 has a single positive-strand RNA genome of 7,821 nucleotides [excluding the poly(A) tail] containing a single open reading frame (ORF) with typical iflavirus conserved domains, including a picornavirus capsid protein-like domain, a cricket paralysis virus capsid-like domain, a helicase, a peptidase, and an RNA-dependent RNA polymerase domain.The draft genome sequence of a novel virus was determined from a leafhopper insect ( Exitianus capicola is one of numerous species in the Exitianus genus and is considered among the most common tropical and temperate grassland leafhoppers , with a cytochrome c oxidase I (COI) sequence identical to the COI sequence deposited in GenBank with accession number MK188553.1. In addition, to identify the iflavirus-like viral contig, the assembled contigs were compared to nonredundant limited species viruses with –taxid 10239. The result indicated the existence of iflavirus-like contigs, which was verified by reverse transcription (RT)-PCR. Moreover, the draft genome sequence of this putative iflavirus was successfully obtained using rapid amplification of cDNA ends (RACE) with a SMARTer RACE amplification kit . The primers used for RT-PCR and RACE are listed in During the growth season of 2020, adult leafhopper samples were collected from several rice fields located in Al Diwaniyah Province , Iraq. For RNA sequencing, total RNA was extracted from all samples using TRIzol reagent . Sequencing libraries were generated using the NEBNext Ultra RNA library preparation kit for Illumina  following the manufacturer’s recommendations. Paired-end sequencing using an Illumina HiSeq 2000 platform was performed based on standard procedures followed by the BGI Company, Hongkong Tech Solution, China. Subsequently, a total of 30,698,435 paired-end raw reads (100 nucleotides [nt] long) generated were subjected to quality trimming and adaptor removal by Trimmomatic version 0.39 with default parameters. The clean data obtained were Exitianus capicola iflavirus 2 [ECIV2]) was from nucleotide position 10 to nucleotide position 7471, encoding 2,487 amino acids, as predicted using the Expasy online server (https://web.expasy.org/translate), a 9-nt untranslated region (UTR) at the 5′ end, and a 349-nt UTR at the 3′ end. The prediction of conserved domains using InterProScan (https://www.ebi.ac.uk/interpro) showed that the ORF of ECIV2 includes a picornavirus capsid protein (Rhv)-like domain, a cricket paralysis virus (CrPV) capsid-like domain, a helicase domain, a peptidase domain, and an RNA-dependent RNA polymerase domain  tail], with a GC content of 34.4%. The predicted long open reading frame (ORF) of the discovered virus (e domain . BLASTP OL778829. The corresponding transcriptome data were deposited under the accession number SRR18046885.The draft genome sequence of ECIV2 was deposited in the GenBank database under the accession number


After incubation for 0, 4 and 24 h, RNA was extracted from the incubations and separated by density gradient centrifugation. However, RNA quantification of density-resolved fractions revealed no incorporation of the 13C isotope into the extracted RNA, suggesting that α-synuclein was not assimilated by the murine gut bacteria. Potential reasons and consequences for follow-up-studies are discussed.In Parkinson’s disease (PD), α-synuclein is a key protein in the process of neurodegeneration. Besides motor symptoms, most PD patients additionally suffer from gastrointestinal tract (GIT) dysfunctions, even several years before the onset of motor disabilities. Studies have reported a dysbiosis of gut bacteria in PD patients compared to healthy controls and have suggested that the enteric nervous system (ENS) can be involved in the development of the disease. As α-synuclein was found to be secreted by neurons of the ENS, we used RNA-based stable isotope probing (RNA-SIP) to identify gut bacteria that might be able to assimilate this protein. The gut contents of 24 mice were pooled and incubated with isotopically labelled ( The authors confirm supporting data, code and protocols have been provided within the article or through supplementary data files or are available from the corresponding author on reasonable request.Parkinson’s disease (PD) is one of the most common neurodegenerative diseases and even today there is still no treatment available that can completely cure affected patients . PD patiet al. reported that enteric neurons are physiologically secreting α-synuclein [The α-synuclein protein plays an essential role in the development of PD, although the detailed mechanisms are not yet fully understood . As a tyynuclein , which cynuclein . Howeverynuclein .In this study, we aimed to use RNA-based stable isotope probing (RNA-SIP) to identify potentially synuclein-assimilating bacteria in the gut contents of mice being used as model organisms. RNA-SIP is an elegant technique to unravel which groups of microorganisms assimilate a certain substrate within a complex community . We prevad libitum. Animals were killed by cervical dislocation. The large intestines of the animals were dissected and brought to an anaerobic work bench. From that point, the intestines and contents were handled under anaerobic conditions.Twenty-five adult Balb/c mice of both sexes were reared in the animal house of experimental surgery at the Saarland University Medical Centre  under standard laboratory conditions with a 12 h light/dark rhythm and were fed on a standard maintenance diet for rats and mice . Both food and drinking water were available to the mice 13C-labelled α-synuclein (AlexoTech). All samples were incubated at 37 °C under anaerobic conditions for 0, 4 or 24 h in triplicate, respectively, and then immediately frozen and stored at −80 °C.Murine caecal contents were collected and pooled in M9 minimal medium  as a 15 2), 0.125 µl DreamTaq DNA polymerase (5 U µl−1), 20.375 µl nuclease-free water and 1 µl of template. The thermal profile was set as described previously [Total RNA from each sample was extracted using the RNeasy Power Microbiome kit containing an on-column DNase I treatment  following the manufacturer’s instructions. Follow-up in-solution DNase treatment was performed with the Monarch Total RNA Miniprep kit  according to the manufacturer’s instructions. The three replicate incubations per time point were finally combined during the DNase treatment and absence of DNA was verified by PCR of the 16S rRNA gene using 0.25 µl of each conventional 27F and 519R primer (50 µM) , 0.5 µl eviously , using 3−1, GE Healthcare, Freiburg, Germany), 1.498 ml gradient buffer [−1 [g and 20 °C for 68 h using the Optima MAX-XP Ultracentrifuge with an MLN-80 near vertical Rotor (Beckman Coulter).For density gradient centrifugation, centrifugation tubes  were filled with 8.5 ml of a centrifugation mix containing 6.739 ml cesium trifluoroacetate , according to manufacturer’s instructions. The cDNA was amplified in accordance with the same protocol as mentioned above, using 25 PCR-cycles.PCR products were analysed by standard gel electrophoresis, using a 0.8 % agarose gel and Midori Green Advance  as DNA dye.Lactobacillus, Akkermansia and Bifidobacterium were relatively enriched, bacteria belonging to the family Lachnospiraceae and the genus Faecalibacterium, both short-chain fatty acid producers, were relatively depleted. Such dysbiosis might lead to a proinflammatory status, which could be linked to the recurrent gastrointestinal symptoms affecting PD patients. In addition, differences in microbiota composition might not only be of therapeutic but also of diagnostic value, preparing a new basis for an early diagnosis of PD using faecal microbiota analyses. We hypothesized that specifically α-synuclein-degrading bacteria might be of particular diagnostic interest and set up a pilot RNA-SIP study using murine faecal samples and fully 13C-labelled α-synuclein as substrate.A synopsis of recent studies suggests that there are small but robust differences in the bacterial community composition of PD patients in comparison to healthy controls . While g−1 (fraction 16)  to 1.7143 g mltion 16) . The den−1 for all incubation samples. From previous publications it is known that unlabelled RNA usually peaks around this density [13C assimilation into RNA, we would have expected to see shifts of the distribution curves towards higher densities, particularly for the 4 and 24 h incubations with 13C synuclein. However, there was no difference in RNA distribution between both control incubations (0 h) and 12C and the 13C incubations. In fact, all distribution curves were nearly identical.After RNA extraction from the gradient fractions, the quantification showed one peak in the RNA distribution over the gradient fractions . The hig density . In case13C incubations in comparison to the respective 12C incubations. For all inspected gradients, the largest amount of cDNA was obviously present in fractions 8 to 11, corroborating the RNA distribution determined by means of RiboGreen  substrates stemming from the intestinal tract samples might also have hampered the production of isotopically labelled RNA. Moreover, incorporation just might have been too low to be measurable with the methods applied here. However, in previous RNA-SIP studies with complex carbohydrates (resistant potato starch), isotopically labelled RNA from murine intestinal bacteria was detectable within 2–4 h of incubation, albeit using relatively high substrate concentrations  [Our failure to determine any formation of isotopically labelled RNA suggests that α-synuclein had not been incorporated into the RNA of the murine gut bacteria. As to the best of our knowledge, RNA-SIP has never been used to analyse the degradation and assimilation of valents) . Clearlyvalents) , suggestvalents) , may be Finally, it might also be speculated that human faecal bacteria, in particular those from PD patients, might be more adapted to α-synuclein degradation than bacteria from mice, which usually thrive on a carbohydrate-rich diet. Therefore, SIP follow-up experiments using human  bacteria appear particularly reasonable to elucidate whether α-synuclein is specifically metabolized by human intestinal microorganisms.Using RNA-SIP, no assimilation of α-synuclein (50 µM) by murine gut bacteria within 24 h could be shown. We nevertheless believe that the identification of specifically α-synuclein-assimilating intestinal bacteria might help to improve an early diagnosis of PD. Follow-up studies should consider our results regarding substrate concentration, incubation time, microbial inoculum and type of SIP technology.