Defective viral genome (DVG) is a truncated version of the full-length virus genome identified in most RNA viruses during infection. The synthesis of DVGs in coronavirus has been suggested; however, the fundamental characteristics of coronavirus DVGs in gene expression and pathogenesis have not been systematically analyzed.Nanopore direct RNA sequencing was used to investigate the characteristics of coronavirus DVGs in gene expression including reproducibility, abundance, species and genome structures for bovine coronavirus in cells, and for mouse hepatitis virus (MHV)-A59 (a mouse coronavirus) in cells and in mice. The MHV-A59 full-length genomic cDNAs (~ 31 kilobases) were in vitro constructed to experimentally validate the origin of coronavirus DVG. The synthesis of DVGs was also experimentally identified by RT-PCR followed by sequencing. In addition, the alterations of DVGs in amounts and species under different infection environments and selection pressures including the treatment of antiviral remdesivir and interferon were evaluated based on the banding patterns by RT-PCR.The results are as follows: (i) the structures of DVGs are with diversity, (ii) DVGs are overall synthesized with moderate (MHV-A59 in cells) to high (BCoV in cells and MHV-A59 in mice) reproducibility under regular infection with the same virus inoculum, (iii) DVGs can be synthesized from the full-length coronavirus genome, (iv) the sequences flanking the recombination point of DVGs are AU-rich and thus may contribute to the recombination events during gene expression, (v) the species and amounts of DVG are altered under different infection environments, and (vi) the biological nature of DVGs between in vitro and in vivo is similar.The identified biological characteristics of coronavirus DVGs in terms of abundance, reproducibility, and variety extend the current model for coronavirus gene expression. In addition, the biological features of alterations in amounts and species of coronavirus DVGs under different infection environments may assist the coronavirus to adapt to the altered environments for virus fitness and may contribute to the coronavirus pathogenesis. Consequently, the unveiled biological features may assist the community to study the gene expression mechanisms of DVGs and their roles in pathogenesis, contributing to the development of antiviral strategy and public health.The online version contains supplementary material available at 10.1186/s12985-023-02189-7. Coronaviridae, order Nidovirales [Coronaviruses (CoVs) are in the family ovirales , 2. CoVsovirales –6. CoVs ovirales .cis-acting elements required for gene expression in their 5’ and 3’ termini, they have been intensively employed as surrogates of the ~ 30 kb full-length genome for studies on coronavirus gene expression [Defective viral genomes (DVGs) is a truncated version of the virus genome and can be found in most RNA viruses during infection –10. Becapression –21. WithIt has been suggested that in Brome mosaic virus that the AU-rich sequence is a hot spot involved in the recombination and synthesis of a smaller size of viral RNA . Since tIn the current study, in addition to the well-known coronavirus genomes and sgmRNAs, coronavirus DVGs were comprehensively and experimentally analyzed both in vitro and in vivo by RT-PCR with the assistance of nanopore direct RNA sequencing. Furthermore, the biological features of coronavirus DVGs in terms of the structure, classification, abundance, origin, reproducibility and altered species and amounts under different infection environments were also determined. It is expected that the unveiled characteristic of coronavirus DVGs may provide a database for studies of coronavirus gene expression and pathogenesis and thus assist the coronavirus community to develop antiviral strategy.2. Mice were maintained according to the guidelines established in the “Guide for the Care and Use of Laboratory Animals” prepared by the Committee for the Care and Use of Laboratory Animals of the Institute of Laboratory Animal Resources Commission on Life Sciences, National Research Council, USA. The animal study was reviewed and approved (IACUC No.: 108–110) by the Institutional Animal Care and Use Committee of National Chung Hsing University, Taiwan.The plaque-purified Mebus strain of BCoV (GenBank: U00735.2) and MHV-A59 (GenBank: NC_048217.1) were used for the study. BCoV-p95 (GenBank: OP296992.1) is a BCoV variant with an altered genome structure of 106 nucleotide mutations obtained from supernatant of HRT-18 cells persistently infected with BCoV. Human rectal tumor (HRT)-18 cells, mouse L (ML) cells, adenocarcinomic human alveolar basal epithelial (A549) cells and baby hamster kidney (BHK) cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum  at 37 °C with 5% CO6 PFU of MHV-A59 in 500 µl of DMEM and total cellular RNA was harvested from the liver at 3 days postinfection. TRIzol  was used to extract total cellular RNA and 500 ng of poly(A)-containing RNA was used for library preparation according to the manufacturer’s instructions . Note that ENO2 mRNA, which was added during the library preparation for nanopore direct RNA sequencing supplied by SQK-RNA001 kit (Oxford Nanopore Technologies), was used as an RNA calibrant strand (RCS) to allow assess the RNA degradation during the library preparation based on the coverage of reads [For nanopore direct RNA sequencing, total cellular RNA was collected from BCoV-infected HRT-18 cells and MHV-A59-infected ML cells at a multiplicity of infection (MOI) of 0.1. Total cellular RNA was collected at 24 hours (for BCoV) or 20 hours (for MHV-A59) postinfection. In addition, 3-week-old male and specific pathogen-free BALB/c mice  were infected by intraperitoneal inoculation of 10of reads , 24. Twoof reads .To determine the synthesis of BCoV DVGs, HRT-18 cells were infected with 0.1 MOI of BCoV followed by total cellular RNA collection at 2, 8, 24 and 48 h postinfection. To determine the origin of DVGs, the reverse-genetics system of infectious clone MHV-A59-1000 (icMHV), which is divided into 7 cDNA fragments and developed by Dr. Ralph Baric and colleagues, was used . After a3, 104 or 105 U/mL. After 16 h of treatment, IFN β-treated ML cells were infected with 0.1 MOI of MHV-A59 followed by total cellular RNA collection at 16 h postinfection. To experimentally determine the synthesis of DVG in mice, 3-week-old male and specific pathogen-free BALB/c mice  were infected with 106 PFU of MHV-A59 in 500 µl of DMEM by intraperitoneal inoculation. The livers of MHV-A59-infected mice were collected at 3 days postinfection, and total cellular RNA was prepared.To evaluate whether the species and the amounts of DVGs were altered in different cells, HRT-18 cells, BHK cells, ML cells and A549 cells were infected with BCoV or BCoV-p95 at an MOI of 0.1, followed by total cellular RNA collection at 24 h postinfection. To determine whether the species and the amounts of DVGs were altered under antiviral selection pressure, HRT-18 cells were infected with 0.1 MOI of BCoV, and after 1 h of infection, HRT-18 cells were treated with the antiviral remdesivir (GS-5734) at final concentrations of 125, 250, 500 or 1000 nM. After 48 h of treatment with remdesivir, total cellular RNA was collected. To evaluate whether the species and the amounts of MHV-A59 DVGs were altered under IFN β treatment, ML cells in 2 ml of DMEM were treated with IFN β at final concentrations of 10The collected total cellular RNA from aforementioned procedures was used for cDNA synthesis. For this, 10 µg of collected total cellular RNA were used and reverse transcription (RT) was performed by SuperScript III reverse transcriptase . The resulting cDNA was then used for detection of DVGs by PCR and primers Table  and the https://osf.io/cm7z6/) for bovine coronavirus (BCoV) and mouse hepatitis virus (MHV)-A59, the abundances of total DVGs and each subgroup was analyzed. The results suggested that coronavirus DVGs were synthesized abundantly when compared with well-known coronavirus canonical sgmRNAs  are shown in Figure RNA transcripts other than those encoded by the coronavirus genome are also synthesized during infection . Based oWhether the DVGs in coronavirus are opportunistically synthesized or reproducible remains unknown. To this end, two independent infection experiments were performed with the same BCoV and MHV-A59 inoculum for nanopore direct RNA sequencing, and transcripts with a read count of ≥ 5 is used for Spearman’s correlation coefficient test to examine the reproducibility. As shown in Fig. Based on the data obtained from nanopore RNA direct sequencing, it was suggested that the 5’3’DVG species in BCoV-and MHV-A59-infected cells and MHV-A59 infected mice contained different lengths of 5’ and 3’ terminal sequences of the genome Fig. A. As expTo examine whether DVGs can be synthesized from the full-length coronavirus genome, the MHV-A59 full-length genomic cDNAs  were emphttps://osf.io/cm7z6/) from BCoV-and MHV-A59-infected cells  to another end of the nucleotide designated recombination acceptor point (RAP), as shown in Fig. As shown in Fig. To investigate whether the species and amounts of DVGs synthesized from different cells were altered, HRT-18, BHK, ML and A549 cells were infected with BCoV or BCoV-p95. Note that BCoV-p95 (GenBank: OP296992.1) is a BCoV variant with an altered genome structure of 106 nucleotide mutations obtained from supernatant of HRT-18 cells persistently infected with BCoV. Because a substantial amount of 5’3’DVG species  the synthesized DVG species may differ depending on the infection environments Fig.  and (ii)With the assistance of nanopore RNA direct sequencing, we in the current study experimentally revealed the fundamental characteristics of coronavirus DVGs both in vitro and in vivo. The biological features of coronavirus DVGs in terms of abundance, reproducibility, and variety extend the current model for coronavirus gene expression. The unveiled characteristics of coronavirus DVGs in terms of abundance, reproducibility, the variety of the DVG structures and their protein-coding potential may contribute to the pathogenesis. In addition, the findings that the amounts and DVG species are alterted under different infection environments and selection pressures may further contribute to virus fitness and thus the pathogenesis. Consequently, the current study may contribute to a variety of biomedical studies including the synthesis mechanism of DVGs and its role in pathogenesis, contributing to development of antiviral strategy.Below is the link to the electronic supplementary material.Supplementary Material 1


S-adenosyl-L-methionine (SAM) a central metabolic hub. Here we show that metabolic labeling with a clickable metabolic precursor of SAM, propargyl-selenohomocysteine (PSH), enables detection and identification of various methylation sites. Propargylated A, C, and G nucleosides form at detectable amounts via intracellular generation of the corresponding SAM analogue. Integration into next generation sequencing enables mapping of N6-methyladenosine (m6A) and 5-methylcytidine (m5C) sites in mRNA with single nucleotide precision (MePMe-seq). Analysis of the termination profiles can be used to distinguish m6A from 2′-O-methyladenosine (Am) and N1-methyladenosine (m1A) sites. MePMe-seq overcomes the problems of antibodies for enrichment and sequence-motifs for evaluation, which was limiting previous methodologies. Metabolic labeling via clickable SAM facilitates the joint evaluation of methylation sites in RNA and potentially DNA and proteins.Internal modifications of mRNA have emerged as widespread and versatile regulatory mechanism to control gene expression at the post-transcriptional level. Most of these modifications are methyl groups, making  S-adenosyl-L-methionine (SAM) can be produced in cells, allowing for identification and mapping of different methylated nucleosides in mRNA.Methylation is the dominant modification in mRNA and occurs at a variety of sites. Here, Hartstock et al. show that a clickable analogue of the key cosubstrate  N6-methylation of adenosine (m6A) is the most abundant and best-studied internal modification of mRNA. It has been linked to cellular differentiation, cancer progression, development and ageing7. Most of the more than 12,000 sites are introduced by the METTL3-14 complex, whereas METTL16 is responsible for six additional validated sites in mRNA12. Several reader proteins have been identified and mediate the effects of m6A in mRNA translation and degradation25. m5C is another internal modification in mammalian mRNA. The reported number of sites ranges from a few hundred to 40,000 sites, and various writer proteins  have been identified for mammalian cells29. Several reader proteins have been found, linking m5C to repair (via RAD52), export (via ALYREF), and proliferation (via YBX1 and ELAV) causing bladder cancer34. In total, ten different internal modifications of eukaryotic mRNA have been described and mapped35. In addition to m6A and m5C, these comprise the altered nucleobases inosine and pseudouridine, the acetylation ac4C, the oxidation 8-oxo-G and further methylations or derivatives thereof at either the nucleobase  or the ribose (Nm). The prevalence of methylation as mRNA modification mark is striking, suggesting that the responsible cofactor SAM plays a key role for their abundance and potential interconnectivity36.Eukaryotic mRNA is canonically modified by the addition of the 5ʹ cap and bears additional modifications at internal sites. The 6A, multiple approaches have been developed to assign its positions on a transcriptome-wide level. Most methods rely on antibodies in combination with next-generation sequencing (NGS). While early transcriptome-wide detection methods had limited resolution39, crosslinking and bioinformatic analyses including search for the DRACH motif, improved the accuracy of single nucleotides42. Concerns regarding bias of antibodies in recognizing the tiny nucleobase as epitope along with the inability to distinguish between m6A and m6Am have prompted the development of antibody-free methods. DART-seq uses a YTH reader protein for m6A recognition and introduces adjacent C-to-U mutations by a fused deaminase44. MAZTER seq relies on the methylation-sensitive ribonuclease and bioinformatic alignment of cleaved versus uncleaved sequences at its target ACA sites46. m6A-SEAL uses m6A-specific methyl oxidation by FTO for further derivatization and enrichment47. m6A-REF-seq combines m6A demethylation by FTO and cleavage of ACA-sites with a m6A-sensitive RNA endonuclease48. eTAM-seq uses TadA for specific deamination of A to induce an A-to-I conversion during reverse transcription (RT)49. m6A-SAC-seq relies on N6-allylation of m6A using the methyltransferase (MTase) MjDim1 followed by N1-N6-cyclization, which leads to a mismatch in RT50. These methods rely on exogenous enzymes for modification, which bring about their own biases, such as preference for or even limitation to certain sequences. Furthermore, some of these approaches require transfection of cells. GLORI is a different recently released methodology. This approach is ideal for transcriptome-wide localization of m6A with single-nucleotide precision and quantification and also relies on deamination of A utilizing glyoxal and nitrite treatment instead of enzymes51. In summary, there is a plethora of methods for mapping m6A sites, however, most of them cannot be used to assign other methylation sites.Owing to the importance and abundance of m6A detection53. After feeding cells with PSH, modified adenosines in rRNA could be detected via click chemistry and enrichment53. Label-Seq determined m6A-sites in mRNA by feeding allyl-selenohomocysteine followed by a highly specific cyclization reaction of the resulting N6-allyladenosine, causing mutations in RT identified by NGS. However, an antibody was required to enrich the allyl-modified mRNA and other modified nucleosides were not detected52.Metabolic labeling with methionine (Met) analogues presents an interesting alternative approach for m5C, another abundant internal modification of mRNA, chemical conversion of C to U in bisulfite sequencing is frequently used for mapping55. This treatment risks damaging RNA and causing artifacts, necessitating careful and repeated controls to obtain reliable data56. Antibody-based methods with and without photo-crosslinking have been developed in analogy to m6A mapping methods and underlie the same limitations58. The development of antibody-free methods includes progress towards nanopore sequencing and TAWO-seq, but the latter has not yet been implemented on a transcriptome-wide scale60.For m6A and m5C as well as other methylations rely on the cofactor SAM, suggesting that the methyl-based modifications could be interconnected via SAM levels, and it would be important to study them in context36. Yet, current methodology has mainly focused on specific binding and detection of the modified nucleoside instead of the underlying and unifying process. The enrichment via antibodies or binding proteins, or the specific modification by m6A-sensitive enzymes counteracts a more global look at possible links.Taken together, both m61. Above all, the propargyl group is bioorthogonal and specifically reacts with azides in a click reaction, making it possible to chemically enrich target RNA without the need for antibodies  can be efficiently made in genetically unaltered mammalian cells, (2) is accepted by promiscuous activity of several MTases, and (3) provides a universal handle for efficient antibody-free enrichment of different nucleosides to (4) make modified nucleosides amenable to detection in NGS. A perfect metabolite is the propargylic SAM analogue SeAdoYn, which is readily produced in cells with unaltered genetic makeup and is recognized by most MTasesies Fig. .Fig. 1Sc63. However, the potential to investigate more than one type of modification has not been exploited, as the level of most non-natural modifications remains low64. We therefore sought to maximize the level of metabolic RNA propargylation while maintaining cell viability and treated HeLa cells with different concentrations of PSH or Met  compared to total RNA, whereas the m6A level remained almost constant (0.3% vs. 0.12%) + RNA -enrichment, which is reported to reduce N2%) Fig. , suggest06% Fig. . This suprop level after poly(A)-enrichment (0.011% vs. 0.10%), which is in line with the observed Am depletion + RNA under conditions retaining cell viability.Analyzing the propagylation by LC-QqQ-MS, we observed a ~10-fold reduction of the Aion Fig. . In polylls Fig. . It repl 6A Fig.  and was  6A Fig. . Taken t+ RNA. Current work agrees that mRNA contains m6A and m5C in addition to various methylations at the 5′ cap and suggests that m1A, m7G and m3C as well as ribose methylation (Nm) may exist, albeit with controversial reports68. To test for the respective propargylated nucleosides, we assembled a panel of nine propargyl-containing nucleosides that—in addition to prop6A and Aprop—contained all sugar modified nucleosides (Nprop) as well as prop5C, prop1A, prop7G, and prop3C for which natural methylation had been reported. Synthesis of the synthetic standards prop5C, prop1A, prop3C and prop7G + RNA after metabolic labeling with PSH.As metabolic labeling with PSH via the respective SAM analogues would be expected to propargylate also other nucleosides at various positions, we investigated whether additional propargylated nucleosides could be detected in poly(A)p7G Fig.  is descr5C in poly(A)+ RNA from metabolically PSH-labeled HeLa cells  and ~100-fold lower than for Aprop, which means that 1.4% of m5C was substituted by prop5C + RNA + RNA after PSH feeding. These data show that metabolic labeling via the methylation pathway leads to various nucleoside modifications that can be analyzed from the same sample. As poly(A)+ RNA is known to contain residual rRNA, the LC-MS measurements of poly(A)+ RNA do not permit conclusions regarding their presence in mRNA56.Further LC-MS analysis revealed that in addition to propRNA Fig. . HoweverTo the best of our knowledge, this is the first dataset quantifying non-natural nucleosides from RNA comprehensively and provides insights into how well different MTases accept non-natural substrates. It also shows the scope of this methodology, by pointing out which modifications can be made available to NGS.53. Click chemistry relies on covalent bond formation and requires the terminal alkyne as a functional group. As a result, it is not affected by the interactions of nearby nucleotides, which is an advantage compared to methods relying on non-covalent interactions73. Propargylated nucleosides will therefore be universally enriched by metabolic PSH labeling and click chemistry.The bioorthogonal propargyl group can be reacted with azides in a copper-catalyzed azide-alkyne cycloaddition (CuAAC). In order to effectively enrich modified sites and increase steric bulk to prevent reverse transcription, we installed an affinity tag using biotin-azide Fig. 53. Click+ RNA from cells after metabolic PSH labeling and performed click chemistry and enrichment , mapped to the human genome (hg38) and duplicate reads removed based on the introduced unique molecule identifier (UMIs). We obtained 14.8 (rep1) and 15.5 (rep2) million reads for the PSH-treated samples ent Fig. . The reaent Fig. . We firs6A sites showed remarkably sharp edges one nucleotide downstream of m6A sites. This is exemplified by the six m6A positions in the hairpins of MAT2A, the m6A2515 and m6A2577 in MALAT1 as well as m6A1216 in β-actin  that metabolic labeling results in METTL3-14 as well as METTL16-mediated propargylation at the N6-position of adenosine in poly(A)+ RNA and (2) that reverse transcription in isolated mRNA terminates one nucleotide downstream of clicked sites, allowing assignment of m6A sites in mRNA with single nucleotide precision in NGS data.Visual inspection of the coverage profiles for known and validated m77. This improved version of JACUSA, a software for site-specific identification of RNA editing events from replicate sequencing data, is able to identify read termination events by calculating the arrest rate, i.e. the fraction of reads stopping at the position, from NGS data77. The difference between arrest rates from the sample and control  and 3714 (rep2) modified internal sites for all four nucleosides in mRNA from PSH labeled cells   and 2312 (rep2) internal As as MTase target sites in mRNA from HeLa cells using high stringency filtering Fig. .6A sites identified by JACUSA2 in mRNA from labeled cells that are not observed in mRNA from control cells. This is illustrated for the AHNAK and the YTHDF2 mRNAs . We calculated the distance between neighboring m6As on the same transcript and found that they tend to cluster in short distances  to each other.Next, we looked at the abundance of mtes Fig. . Some ofNAs Fig. , in lineces Fig. , emphasi6A sites by performing a metagene analysis of all modified As detected by MePMe-seq. The density plot shows enrichment at the 3′ end of the coding sequence (CDS) and around the stop codons , which are closely related to the DRACH motif and non-NRACN motifs   Fig. . The nonal) Fig. . Within ant Fig. . This se6A sites have been mapped previously using antibody-dependent and antibody-independent methods91. To compare MePMe-seq results with m6A sites found in previous studies, we assembled data from the databases REPIC82, ATLAS81 and publications comprising various methodologies91. However, in a pairwise comparison of published datasets from individual miCLIP experiments, the detected m6A sites differed significantly, even for the same cell line  of the MePMe-seq sites were found in other antibody-free single nucleotide resolution techniques, i.e. PA-m6A-seq, m6A-REF-seq, m6A-label-seq  to sites identified in DART-seq and the overlap between the techniques increases up to ~11% if an uncertainty range of ±150 nt is allowed + RNA, five of them with a DRACH motif and three with a non-DRACH motif  that escaped many methods and was only recently reported93 + RNA from untreated HeLa cells and propargylated it in vitro using recombinantly produced METTL16 and SeAdoYn  and MePMe-seq (1651 hits) and identified only four overlapping sites as hits + RNA samples  in mRNA to identify the m6A termination signature. We verified the strong and nearly exclusive termination at position –1 for m6A sites , but with the more accurate analysis, we can attribute the stepwise termination to Am. We conducted a cluster analysis of the termination signals to identify probable Am sites in mRNA in our MePMe-seq data. Two groups of termination signatures were found. Cluster 1 displayed the precise termination at –1 typical of m6A sites, while cluster 2 displayed a distinct pattern that did not correspond to the termination signature seen for Am sites  and were removed during filtering in our MePMe-seq-analysis. The remaining 5 sites (less than 0.2%) could be false positives brought on by alternative TSS. According to the additional pattern analysis MePMe-seq identifies m6A sites in mRNA and we do not have evidence for Am sites in mRNA.JACUSA2 would classify both, the A1A sites. However, position N1 is in the Watson-Crick side and its methylation impedes polymerases100. Inspecting known m1A sites in rRNA, such as the conserved m1A1322 in human 28S rRNA102, confirmed that the IGV coverage tracks for methionine-fed controls exhibit a strong termination at m1A sites p3C Fig. . The ter5C-containing FURIN- and PXDN-mRNAs shows sharp drop-offs not only for m6A sites but also for putative m5C sites  can be found in the results but is not prevalent, in line with their low abundance , thus independently confirming their existence.Similar to sequencing data for m5C and Cprop after click chemistry. Comparison with previous datasets indicates that we detect prop5C sites in mRNA representing natural m5C sites. Similar to Am, we have no evidence for Cm sites in MePMe-seq data from poly(A)+ RNA.Taken together, our data indicate that termination is induced at prop112. The bioorthogonal propargyl group replaces 1.5–3.5% of methyl groups in m6A and m5C, respectively and provides two key features, (i) it enables enrichment of the modified sites via click reaction with biotin and (ii) it induces termination when reverse transcription is performed on beads. MePMe-seq proved powerful for the precise assignment of m6A sites in mRNA even if multiple sites were in closer proximity. Importantly, it is completely independent of antibodies and sequence-searches, such as the DRACH- or NRACN-motif that is used in many cases. MePMe-seq is therefore able to find non-NRACN motifs, such as METTL16 sites and sites that escape many methods. The combined analysis of metabolic labeling and in vitro labeling proves suitable to assign m6A sites as target sites to a specific MTase in cells, as demonstrated for METTL16. The refined analysis of termination signatures allowed to distinguish m6A from Am and m1A sites. The m5C sites vary depending on the method used, however, our data confirm repeatedly reported sites and identify additional sites.We demonstrated that metabolic labeling with PSH produces a broad scope of propargylated nucleotides that are amenable to enrichment from cellular RNA. Different methylation sites can then be detected in mRNA with single nucleotide precision6A and m5C in poly(A)+ RNA. According to LC-QqQ-MS analysis, Nm is also present in these samples. We found termination at m6A, m5C and Nm sites, but while we observed one remarkably sharp edge in the coverage profile for m6A and m5C sites one nucleotide downstream of the modification site, for 2′-O-modified sites it resulted in a stepwise termination. Additionally, the promiscuity of different MTases regarding their acceptance of SeAdoYn as co-substrate seems to vary. Nm sites were only detected on rRNA and m6A and m5C sites in rRNA that are usually used to benchmark a method did not show signals in MePMe-seq. As a result, MePMe-seq provides no evidence for a detection of Am sites in mRNA, suggesting that detected Nm originates from residual rRNA. m1A sites are not called by JACUSA2 because the methylation itself already increases termination.MePMe-seq is, to our knowledge, the first method enabling simultaneous mapping of m6A and m5C but it is conceivable that further improvements might allow to get insights into methylated uridines and guanines.Cellular labeling via SAM analogues as central metabolic hub will be important for future studies about the interconnectivity of the different modifications. We showed this for m53. Synthesis and characterization of prop5C, prop1A, prop3C, prop7G and 5-propargylcytidine-5′-triphosphate are described in the Supplementary Methods.PSH and SeAdoYn were synthesized as previously describedThe LC-QqQ-MS system we used herein is an Agilent 1260 Infinity II LC-system coupled with and an Agilent Ultivo Triple Quadrupole mass spectrometer equipped with an advanced electrospray ionization (ESI) source (JetStream). Chromatographic separation was performed on an Agilent Poroshell 120 EC-C18 3 × 150 mm 2.7 μm column with complementary pre-column Agilent EC-C18 3 × 5 mm 1.9 µm.The system was controlled by the MassHunter Data Acquisition Software . For basic analysis the MassHunter Qualitative Analysis Navigator (version B.08.00) was used and for quantification the MassHunter Quantitative Analysis Software  was used. For optimization of MRM parameters, the manual methods developing and the MassHunter Optimizer (version C.01.00) were used complementary.MS data were conducted via ESI in positive ion mode and multiple reaction monitoring (MRM) was employed for the detection and quantification. The ion source parameters used herein include: Main gas temperature = 250 °C, Sheath gas temperature = 375 °C and Capillary Voltage = 2400 V. The mass spectrometr Ultivo has the Cell Acceleration Voltage (CAV) hard set at 9 V.113.In the method presented herein, the dynamic MRM (dMRM) mode was used. In dMRM mode, the number of concurrently measured transitions is minimized by using a defined time window for each analyte. Moreover, dMRM mode is able to prevent potential problems with partially overlapping analytes5C was more difficult than for other analytes. For this reason, details are provided here. The solution of the prop5C standard for preparing a calibration curve had concentration 4.9 nM (1.39 pg/µL). The calibration curve . T7 primer was prehybridized with T7 template (10 μM each) before transcription was performed in 1× CARO buffer  Triton-X, 4.5% (w/v) PEG6000, 15 mM DTT, pH 8.1), 15 mM Mg(OAc)2, 3.3 mM per NTP , 0.4 mM ATP (or modified ATP), 1 mg/mL T7 RNAP and 0.1 U PPase for 4 h at 37 °C. The template DNA was digested using 5 U/μL DNase I. The resulting test RNA was purified via PAGE , the desired band was cut out, RNA was eluted and precipitated. For biotinylated RNA, in vitro transcription with N6-propargyl-ATP and PAGE purification were followed by CuAAC  were pre-mixed) 100 mM sodium ascorbate) and an additional PAGE purification.32P]-ATP using 0.4 U/μL T4 PNK . Excess [γ-32P]-ATP was removed by MicroSpin G-25 columns. The propargylated test RNA was modified with biotin-N3 and purified as described before. The purified biotinylated test RNA was enriched via streptavidin coated magnetic beads. For this, 5 µL RNase free M-280 Streptavidine Dynabeads were saturated by adding oligo(dT) . Afterwards, the biotinylated test RNA  was added and incubated for 1 h at rt and constant rotation. The beads were washed with the binding & washing buffer recommended by the manufacturer 5 × 1 mL and 1 × 1 mL ddH2O. The bead-bound RNA was resuspended in ddH2O. The test RNA (0.25 µM) with or without propargylation was hybridized with 2 µM [32P]-labeled RT-primer  and incubated with 0.1 U/µL reverse transcriptase SSIV and 0.1 mM dNTP for 30 min at 50 °C. The reaction was stopped by addition of 0.1 M HClO4. The RNA was digested via alkaline hydrolysis by addition of 185 mM NaOH , neutralized with HCl and analyzed via PAGE. Termination bands were quantified using ImageJ and normalized to the total amount of primer extension products.RT primer 1/3 (20 μM) was radioactively labeled with , 72 °C for 3 min) to reduce loss in the first size amplification step. To remove adapter and primer contaminations from previous steps, reaction was size-selected using the ProNex size-selective purification system (Promega) following manufacturer instructions using a 3:1 v/v ratio of ProNex Chemistry to sample. Deviating from the protocol, the pre-library was eluted from the beads in 23 µL ddH2O instead of the provided elution buffer.2O instead of the provided elution buffer. Quality of the finished libraries was checked via bioanalyzer-High sensitivity DNA assay (Agilent) and concentration was determined by Quant it PicoGreen Assay (Thermo Fisher).Cycle number for the final amplification of the libraries was determined by performing test-PCR reactions using 1 µL of the pre-library for 10 µL of reaction split to 3 aliquotes  and 0.02 U/µL Phusion DNA polymerase in 1× HF buffer amplified with different cycle numbers . Checking the size profile of these libraries either on a nat. PAGE or via bioanalyzer, cycle numbers were chosen to ensure enough amplification without giving rise to overamplification (increasing background amplification) or adapter-dimers . Final PCR was performed with the determined cycle number (reduced by one cycle because the input is increased) in two separate reactions to ensure backup in case of unforeseen problems  and 0.02 U/µL Phusion DNA polymerase in 1× HF buffer). Final library was purified by size-selection using the ProNex size-selective purification system (Promega) following manufacturer instructions using a 2.4:1 v/v ratio of ProNex Chemistry to sample and eluted from the beads in 20 µL ddHThe libraries were sequenced on Illumina platform as paired-end with the read length 150 (PE150). The received amount of data per sample was 5G which corresponds to ~33 million reads.6A sites identified via MePMe-seq. Experiments were performed with poly(A)+ RNA (one time enriched via oligo(dT) magnetic beads) from HeLa cells. RNA was split and methylations were removed by treatment with FTO ( + FTO) from one half but not the other (– FTO) in 50 mM MES-buffer (pH 5.5), 283 µM (NH4)2Fe(SO4)2, 300 µM α-ketoglutarate, 2 mM ascorbic acid, 50 ng/µL poly(A)+ RNA, 0.2 U/µL RiboLock, 0.1 µg/µL FTO ( + FTO) or ddH2O (-FTO) for 30 min at 37 °C. Reaction was stopped by adding 20 mM EDTA 92. The poly(A)+ RNAs were purified immediately via RNA clean & concentrator kit by Zymo Research. For the SELECT assay poly(A)+ RNA  was mixed with 85 pmol dTTP, 1× CutSmart buffer , 20 U RiboLock, 80 fmol up primer and 80 fmol down primer in 15 µL ddH2O. Depending on the transcript expression level either 50 ng or 100 ng of + FTO and – FTO poly(A)+ RNA were used. The primers were annealed to the RNA  and the enzyme mixture  was added. The reaction was carried out at 40 °C for 20 min, denatured at 80 °C for 20 min and kept at 4 °C. For the qPCR 1 µL of the reaction mixture was added to 9 µL of the PowerUp™ SYBR® Green Master Mix. The data was analyzed with Bio-Rad CFX Maestro 1.0 software. The obtained results were normalized by N-site.SELECT was used to independently validate selected putative m115. E. coli BL21 (DE3) cells were transformed with pProEx plasmid coding for MTAN and directly used to inoculate an overnight pre-culture in 2YT/Amp. After inoculating the main cultures in 2YT/Amp with 1% of the pre-culture, the cultures were grown at 37 °C for 3 h to an OD600 of 0.6. The cultures were cooled by placing at room temperature for 30 minutes then induced using 0.2 mM isopropyl-β-d-thiogalactopyranoside . After sonification  in lysis buffer (50 mM sodium phosphate pH 7.5) on ice, the cell lysate was centrifuged and the supernatant purified via ÄKTApurifier with a HisTrap FF column .Recombinant MTAN was expressed as described previouslyE. coli BL21 (DE3) cells were transformed with a pET28a vector encoding FTO. The cells were grown in LB medium at 37 °C to an OD600 of 1, kept at room temperature for 30 min, induced by adding 1 mM IPTG followed by expression overnight (16 °C). The cells were lysed in binding buffer (50 mM sodium phosphate buffer (pH 8), 300 mM NaCl, 50 mM imidazole) and the enzyme was purified in two steps using the ÄKTApurifier system. In a first purification step, the lysate was loaded on a 1 mL HisTrap FF column. The column was washed with 4% elution buffer (50 mM sodium phosphate buffer (pH 8), 300 mM NaCl, 500 mM imidazole) in binding buffer at 1 mL/min in 6 column volumes (CV), followed by a linear gradient from 4–100% elution buffer in 10 CV. The gradient was held at 21% elution buffer when FTO started to elute and then continued the linear gradient to 100%. In a second step, the enzyme was purified using a Superdex 200 increase column with running buffer (50 mM sodium phosphate buffer (pH 8), 300 mM NaCl, 10% glycerol) at 0.45 mL/min.For the recombinant production of FTO, 116. For construct refer to Supplementary Fig. 2) . After centrifugation , the supernatant was sterile filtered  and the protein was purified via affinity chromatography  on ÄKTA Purifier System  (elution buffer: 50 mM Tris (pH 8), 200 mM NaCl, 10 mM reduced L-glutathione). For RNA-free preparation, an anion exchange purification step was added  (buffer: 25 mM HEPES (pH 7.50), 50 mM NaCl, 1 mM TCEP). GST-METTL16 was concentrated to approximately 100 µM , and the buffer was supplemented with glycerol (to 40%) for storage. The aliquots were flash-frozen in liquid N2 and stored at −80 °C.Recombinant GST-METTL16 was expressed as described previously+ RNA, 4 µg of RNA were incubated in 30 µL volume with 1 mM SeAdoYn, 10 µM METTL16 (RNA free), 1 U/µL Ribolock and 0.4 µM MTAN in 10× METTL16 activity buffer  in 30 µL for 1 h at 37 °C. RNA was purified with RNA Clean & ConcentratorTM-5 kit (Zymo Research Europe GmbH). 100 ng of reaction product were used for analysis on a denaturing agarose gel.To propargylate isolated poly(A)117. Since the first 15 bases of read 1 had the structure (UMI1)5 nt6 nt(UMI2)4 nt, the entire 15-mer was treated as UMI. The barcode-based demultiplexing was performed manually with standard command-line tools, in order to retain only the reads containing the correct experimental barcode. The alignment was performed with hisat2118, with the disabled soft-clipping, as recommended by Busch et al.119. The deduplication step was performed with UMI-tools120 with the default directional method to define duplicates, as recommended for highly over-amplified libraries119.The quality filtering, trimming and UMI preprocessing were performed in one step with fastp software77https://github.com/dieterich-lab/JACUSA2 using run mode “rt-arrest”. As one replicate of MePMe-seq CTR failed, MePMe-seq PSH rep1 was compared in further analysis with the libraries from untreated HeLa mRNA (METTL16 CTR). We used the following set of parameters: rt-arrest -m 0 -c 4 -p 10 -P1 FR-SECONDSTRAND -P2 FR-SECONDSTRAND, which disable quality clipping of read alignments (-m), require a minimal coverage of 4 reads across all samples (-c), run with 10 CPU threads (-p 10) and set library orientation to second strand in cDNA synthesis . JACUSA2 outputs genomic site coordinates, read coverage and termination rates per sample. The difference between arrest rates from sample and control (Δ(RT arrest)) was used to filter the terminations identified by the algorithm and to remove false positives. We retained high and low confident predictions by setting a sample read coverage threshold ( > 35 for high stringency (HS) filter, >20 for low stringency (LS) filter) and arrest score threshold 121 and the Bioconductor package ensembldb . We always selected the longest transcript per locus to which genomic coordinates mapped to. For every protein-coding transcript, we computed the respective cDNA coordinate of a given termination site and reported the length of this matching transcript (in nt), the length of the coding sequence, five and three prime untranslated regions . Subsequently, and using this information, we used the same strategy as in MetaPlotR (PMID: 28158328) to visualize the read termination site distribution along a metatranscript profile (5′UTR + CDS + 3′UTR). Graphical density plots were generated using the ggplot2 library (version 3.3.2)122.Genomic coordinates were converted into cDNA coordinates using R language  of positions –3,…, 0,…,2 by hierarchical clustering using Euclidean distance. We cut the corresponding dendrogram into two clusters and visualize the respective sites in a box plot.6A, Nm and m5C datasets were downloaded from ATLAS81 and REPIC82 database. Additional datasets were acquired from the respective publications91. Coordinates that were not in hg38 were converted using browser based LiftOver tool (available at https://genome.ucsc.edu/cgi-bin/hgLiftOver123).For comparison with MePMe-seq sites mP > 0.05; *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001) was used. No data was excluded except from outliers of technical replicates of SELECT assay that were determined via Grubbs’ test.For statistical analysis of metabolic labeling and SELECT, a one-sample one-tailed t-test . All spectra are included as an attachment in Supplement Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary data 1Supplementary data 2Supplementary data 3Supplementary data 4Supplementary data 5Supplementary data 6Supplementary data 7Reporting SummarySource Data


Sapovirus RNA was not detected in any product. No significant differences were found between berry type, fresh/frozen status, or supermarket source. This study suggests a risk that exists across all retail outlets however only low levels of nucleic acid ranging from 0 to 16 genome copies/g were present. Although these findings may reflect non-viable/non-infectious virus the continued provision of risk mitigation advice to consumers is warranted and further work is required to ensure control measures to reduce contamination are implemented and enforced.Soft fruits are at particular risk of contamination with enteric viruses such as Hepatitis A virus (HAV), Hepatitis E Virus (HEV), Norovirus (NoV), Human Adenovirus (HAdV) and Sapovirus (SaV). The aim of this study was to investigate, for the first time, the presence of these biological agents in ready to eat (RTE) berries at point of retail in Ireland. A sampling strategy was designed in which RTE fresh and frozen strawberries and raspberries were purchased from five retailers between May and October 2018. Reverse Transcriptase Polymerase Chain Reaction (RT-qPCR) assays for HEV RNA, Nov RNA, SaV RNA, and human Adenovirus species F DNA (HAdV-F) were performed on 239 samples (25g portions). Viral nucleic acid was present in 6.7% ( In Ireland, 33% of adults report consuming 5 portions or more of fruit and vegetables each day, the highest among EU member states , fresh raspberry punnets (n = 60), frozen strawberries (n = 57) and frozen raspberries (n = 60) were purchased. Data collected for each sample included a description of the product and the date of purchase. Details of the vendor and country of origin were not recorded. Samples were transported to the laboratory in a non-temperature monitored cool bag before freezing and stored at − 20 °C prior to testing.As this was the first study to investigate for the presence of viral RNA in our country a number of assumptions were made for the sampling strategy. Consumption of fresh fruit, including nationally produced berries is known to be highest during summer months so the survey was carried out between May and October 2018. Samples were purchased on a weekly basis from the top five Irish retailers, selected based on their grocery market share, three large supermarket chains and two discount supermarkets. Over the 25-week period, 240 samples comprising fresh strawberry punnets . The bag was rocked for 20 min before the supernatant was removed via the filter compartment of the bag and centrifuged at 10,000 × g for 30 min at 4 °C to pellet the food debris. The pH of the decanted supernatant was adjusted to 7.0 + / − 0.2 with the addition of 5 N HCl. 0.25 volumes of 5 × 500/1 poly-ethylene glycol (PEG) 8000 (Sigma-Aldrich), and 1.5 M NaCl, and was added to the supernatant and incubated 1 h at 4°C. Viruses were concentrated by centrifugation at 10,000 × g for 30 min at 4 °C. The supernatant was discarded, and an additional centrifugation was performed at 10,000 × g for 5min at 4 °C to compact the pellet. The pellet was suspended in 500 µl of PBS and vortexed with 500 µl of 1:1 chloroform: butanol (v/v). The suspension was then incubated for 5 min at room temperature, and centrifuged at 10,000 × g for 5min at 5 °C. The upper aqueous phase containing viruses was directly processed using the nucleic acid extraction procedure. A negative process control was also prepared along with each batch of samples.This protocol was as described for detection of NoV RNA and HAV RNA from soft fruit in the ISO technical specification ISO 15216-1:2017. 100 μl of nucleic acid was eluted. A further cleaning step to reduce PCR inhibition was carried out using OneStep-96 PCR Inhibitor Removal kit . In brief, 150 μl of the assay’s Prep- Solution was added to Silicon-A™-HRC Plate, incubated for 5 min, before centrifuging the plate at exactly 3,500 × g for 5 min. 100μl of eluted RNA was then added to the plate and centrifuged at 3,500 × g for 3 min. The filtered RNA was stored at – 80 °C for further analysis.One-step Real -time PCR (RT-qPCR) amplifications were performed in duplicate on Applied Biosystems 7500 Fast Real-Time PCR System. Reactions were performed in a 25 μL reaction mixture containing 12.5 μL of SuperScript III Platinum One Step Quantitative RT-qPCR reaction mix (Invitrogen), and 0.8 µl of Superscript III/Platinum Taq Mix. The following concentrations for primers and probes as outlined in the ISO technical specification (ISO 15216-1:2017), 500 nM  of forward primer, 900nM  of anti-sense primer, 250 nM of probe for all viral targets and 5 μL of RNA extract. The primers and probes for the detection of SaV RNA were made up to the following concentrations: 800 nm for forward and reverse primers and 50 nm for each of the probes , 0.5 µl of Superscript III/Platinum Taq Mix was added to the following concentrations for primers and probe: 200 nM for forward primer 200 nM for reverse primer (Metabion), and 200 nM for probe (Life Technologies) , 0.8 µl of Superscript III/Platinum Taq Mix and 1 µl of 25 × detection mix containing 600nM of forward primer, 600nM of anti-sense primer, 100 nM of probe  and GII (95 nucleotides), and HAV RNA inserts into pGEM-3Zf (+) vectors for the production of dsDNA linear plasmids. A SaV RNA (104 nucleotide) insert was cloned into a pUC57 vector, this allowed for the preparation of RNA transcripts of the same sequence, and were supplied by The Marine Institute, Ireland. HEV RNA standard curve was prepared using synthetic dsDNA . All standards were diluted 10CtLoD) of < 1 and detection > 95% was used to calculate the average CtLoD. The CtLOQ was calculated as the CtLOD minus 2(σCtLOD). The tLOD and tLOQ were calculated by subtracting the y-intercept and dividing by the slope.The theoretical limit of detection”(tLOD’) and theoretical limit of quantification (tLOQ) were determined for NoV GI RNA, NoV GII RNA, HAV RNA, SaV RNA, and HEV RNA using the modified method of Armbruster and Pry  and BustTo assess RT-qPCR inhibition an external amplification control RNA (EC RNA) was added to each sample. The Cq value of sample RNA+EC RNA should be < 2,00 greater than the Cq value of the water+EC RNA well, If the Cq value > 2,00 greater than the Cq value of the water+EC RNA well, results are invalid, and the sample should be retested..Extraction efficiency was calculated using the Cq values obtained from each test sample RNA well by reference to the mengo virus RNA standard curve. Mengo virus recovery = 10^(ΔCq/slope) × 100%where Δcq = Cq value (sample RNA)—Cq value (undiluted process control virus RNA) A sample producing the same Cq value as undiluted mengo virus RNA has an extraction efficiency of 100%. Where the extraction efficiency was < 1% sample results were considered invalid and are retested® , the trend in detection rate was plotted over time to illustrate any potential seasonal exposure effects for the public. Logistic regression was used to assess the influence of month on the proportion of positive samples, for viruses individually and for all viruses in aggregate.n = 239). All quality controls passed and no RT-PCR inhibition was observed. A summary of results is described in Table n = 16). The most frequently detected nucleic acids were HAV RNA (n = 5) and HAdV-F DNA (n = 5). In addition, HEV RNA (n = 3) and NoV GII RNA (n = 3) were detected, but SaV RNA and NoV GI RNA was not found in any sample. HAV RNA was found at levels below the limit of detection of the standard assays suggesting very low levels of contamination. The product type with the highest level of contamination was the frozen strawberries  samples. It is notable that HAV was the most frequently detected viral RNA in this study (n = 5) albeit at very low levels. Ireland is considered to be a country with a low incidence of HAV infection  of berries tested had detectable NoV GII RNA, two of which were above the limit of detection of the assay yielding 90 copies/25 g and 195 copies/25 g respectively. Though it is traditionally thought NoV GI is more commonly associated with food and water borne outbreaks no NoV GI strains were found in this study  of the products with detectable viral RNA were frozen berries. A review by Bozkurt et al., (n = 36) and HAV (n = 14) (2021). The increased risk of contamination from frozen berries may be involved on a larger scale due to poor processing practices in the factory and the introduction of many berries from multiple country of origin packed together , statistical significance could not be established for any predictor. Of the viral pathogens isolated from the soft fruit purchased at retail, 69% ( et al.,  reportedAside from the constraints in statistical analysis posed by the low number of positive results one of the key limitations of this study is that it was not possible to determine if detection of nucleic acid reflected viable virus which could potentially cause infection. The development of viral integrity assays and more robust cell culture systems may help to address this in future work (Leifels et al., In conclusion, this is the first comprehensive study of foodborne viruses in fresh and frozen berries in Ireland. While a burden of viral contamination was observed at the point of retail, further work is required to quantitate the risk to public health. Notwithstanding these limitations our findings have implications for Irish and international fruit producers highlighting the need for development and enforcement of control strategies to prevent contamination and cross contamination of soft fruit for human consumption.


L = 1000). The most important motifs for classification are the number of bulges, loops, and bonds. This finding may be useful in using SS to detect candidates for functional RNA within ‘junk’ DNA regions.An important question in evolutionary biology is whether (and in what ways) genotype–phenotype (GP) map biases can influence evolutionary trajectories. Untangling the relative roles of natural selection and biases (and other factors) in shaping phenotypes can be difficult. Because the RNA secondary structure (SS) can be analyzed in detail mathematically and computationally, is biologically relevant, and a wealth of bioinformatic data are available, it offers a good model system for studying the role of bias. For quite short RNA (length  Within evolutionary biology, a long-standing debate has centered on whether, and in what ways, development and biases in the genotype–phenotype (GP) map can be a directive force in evolution ,2. In prOne system that can be used to shed light on the question of bias in evolution is the RNA sequence-to-structure map, where the secondary structure (SS) is taken to be a phenotype describing the pattern of nucleotide-base bonding. This map is both computationally and mathematically tractable; for example, algorithms exist for predicting the SS directly from sequences ,14. At tIt has been known for many years that the RNA sequence-to-structure map is biased in the sense that some SSs disproportionately have many sequences underlying them ,25. In oneutral set sizes . They found that the distributions were surprisingly similar. The authors mathematically inferred the distribution of neutral set sizes, which would appear from uniform sampling over SS phenotypes , and compared it to the observed computationally generated distribution from genotype sampling , as well as the distribution from natural data. They also studied other properties of SSs, again finding similarities between natural and random SSs. Moreover, the properties of natural RNAs were very different from those of a typical SS of the same length within the full space of all possible SSs. Their main conclusion was that the GP structure appears to strongly constrain which SSs are found in nature. Later, Dingle et al. , [], and []. In the Sepia pharaonis 5S ribosomal RNA, uncultured bacterium partial 16S ribosomal RNA, Hevea brasiliensis miscellaneous RNA, and uncultured bacterium bacterial SSU rRNA, for L = 100, 200, 300, and 400, respectively.We now look at shapes with high and low frequencies in terms of what biological functions they perform. If an RNA has a high frequency, then it can be found by only modest sampling. This is interesting from an evolutionary perspective because it suggests that natural selection does not have to ‘work hard’ in order to shape the RNA. On the other hand, it would be interesting to see if some natural RNAs have very low frequencies, which would suggest that selection had to ‘work hard’ to form that shape. We extracted the highest frequency shape, which appeared in random sampling. We then searched for any molecules in the natural RNA samples from the RNAcentral database, which had the same shapes. The most frequent random abstract shapes for the lengths L = 100 appeared only once in random sampling; the molecule labeled as the unclassified sequence of pemK RNA was found to have the same shape. Among the lowest frequency shapes from the random samples for L = 200 is [[][]], no natural RNAs were found to have this shape. One of the lowest frequency shapes from the random sampling for L = 300 was [[]], and this had one occurrence in the natural data, namely uncultured bacterium partial 16S ribosomal RNA. For L = 400, one of the lowest frequency shapes was [[][]][], but this was not found among the natural RNA samples.There is more than one random shape that occurs only once for each length; hence, there are numerous ‘least frequent’ shapes in the random samples. Some examples are the random abstract shape [] with The preceding sections showed that natural and random RNAs are overall very similar, especially when compared to the full space of possible RNA SS. However, there are some differences in the motif counts between natural and random RNAs. Given that we saw some small differences in the motif frequencies, here, we will attempt to distinguish or classify natural and random RNA using RNA SS motif counts. One potential application of this would be in detecting functional RNA in supposed ‘junk’ non-coding regions of the genome, as discussed in the Introduction.Several studies attempted to identify functional RNA or classify different types of RNA. Noteworthy examples include the following. Rivas and Eddy  attemptek-nearest neighbor (kNN), which is a very common and versatile learning algorithm. We performed five-fold cross-validation; classification accuracy was quantified in terms of ROC AUC. Bootstrap sampling was used to obtain 95% confidence intervals for the ROC AUC values. Note that for binary classification, an ROC AUC value of ∼0.5 indicates very poor classification, no better than guessing classes. Higher values indicate better performances, with 1.0 denoting perfect classification ability.We attempted to classify natural and random RNA. The datasets have five dimensions, where the features (variables) are the frequency counts of bonds, helices, loops, bulges, and junctions on each SS. There are many algorithms in machine learning that can be used for classification. At first, we used To experiment, we used the following datasets: Due to the importance of checking that predictive accuracy is not merely due to a different GC content value , for L=1variable importance, i.e., a signed value indicating which features (variables) are the most important in distinguishing the groups (larger magnitudes indicate greater importances). The PLSDA method gave ROC AUC values that are similar to—but slightly lower than—the kNN method, as indicated in The kNN method has the benefit of being able to handle arbitrary patterns in data, provided that enough data are available. However, it does not provide an indication of which features (variables) are important in distinguishing the groups. As a different machine learning perspective, we also implemented the partial least squares discriminant analysis (PLSDA) method, which is a linear method that also yields We compared random and natural non-coding RNA secondary structures (SSs), arriving at two main conclusions: Firstly, agreeing with and extending earlier works, we showed that natural and random RNA abstract shapes are overall very similar for lengths a priori, one would not expect them to (merely) be similar to random shapes.A major motivation for our work was to study the impact of GP map bias on evolutionary trajectories. By “bias” we mean that certain shapes have exponentially more sequences underlying them. Hence random mutations are far more likely to find such preferentially biased shapes. This bias is a known common property of many GP maps ,56. In tOur work accords with experimental studies that have found that diverse structures with potential functionalities can be found in samples of random sequences ,58, and Our earlier findings that state that natural and random shapes are similar can be explained by the ‘arrival of the frequent’ theory proposed by Schaper and Louis , which sAnother possible explanatory factor for the similarity between natural and random SS is that some fitness-related properties of phenotype shapes are linked to bias. Recently, it has been mathematically argued that certain generic fitness requirements based on physics and engineering principles  may lead to highly optimal values for particular types of phenotype shapes, which may also have high probability or be favorably biased ,72. In aFrom a completely different angle, it may be countered that natural selection has adapted RNA SS over time so that the folding rules are tweaked in order to make the types of RNA that are needed by organisms ‘easy’ to generate . This would explain the similarity of random and natural RNA from a purely selection-based argument. This proposal seems quite unlikely to be valid because RNA-folding rules are primarily based on chemistry and physics, so it is hard to see how selection could have much impact on these rules. Additionally, it has been shown that bias (and probability) are closely related to the information content, the complexity of shapes, and a general mathematical property ,73,74. AIt is known that a single strand of RNA can fold into more than one possible structure, and some strands even form different structures in vivo and in vitro . FurtherL = 1000 natural structures to L = 1000 random sequences. In practice, given a long non-protein coding region of a genome, we would not know in advance the relevant length to study. Therefore this should be addressed in a future study before directly applying our classification result to detecting functional RNA. Other limitations of the current study are that the SS prediction methods employed cannot handle pseudoknots; we ignored the role of single-stranded regions in stabilizing tertiary interactions through noncanonical base pairing, and we ignored kinetic co-transcriptional structure effects, which may imply that the minimum free energy structure is different from the structure found in nature [A limitation of our proposed method to detect functional RNA using SS motif counts is that we implicitly assumed knowledge of the length of the relevant functional RNA sequences, such that we compared, for example, length n nature .q is Research on proteins has shown that while the natural protein sequence space is vast, the number of corresponding protein folds is much smaller, estimated to be a few thousand at most ,80. MoreWhile we focused on the sequence-to-shape map, the secondary structure shape is by no means the only important aspect of an RNA that is coded in the genome. Instead, there are many possible sequence-to-phenotype maps that may be studied in relation to RNA, such as the sequence-to-catalytic function, among others. Studying these other maps would be interesting for future work. Returning to the question of bias, in future work, it would be interesting to incorporate different structure prediction methods , especia


This editorial summarizes the 22 scientific papers published in the Special Issue “Overcoming Biological Barriers: Importance of Membrane Transporters in Homeostasis, Disease, and Disease Treatment” of the International Journal of Molecular Sciences. In this Special Issue, the readers will find integrative information in the field of transporters research. Transporters play an important role for signal molecules, nutrients, metabolites, xenobiotics, and drugs to overcome the barrier posed by biological membranes. Therefore, transporters help in maintaining homeostasis and in the handling of drugs. Changes in the function of transporters can impair homeostasis, cause disease, or modify the efficacy of disease treatment with drugs. This Special Issue aims to collect the newest information on transporters, with a special focus on their function and regulation, their pathological roles, and their importance for drug effects and unwanted side effects. In addition to 4 reviews, readers will find 18 original research works focusing on specific aspects of transporter physiology and pharmacology.SLC28 and SLC29)” by Pastor-Anglada et al. methylpurine, as described in the paper “Use of PET Imaging to Assess the Efficacy of Thiethylperazine to Stimulate Cerebral MRP1 Transport Activity in Wild-Type and APP/PS1-21 Mice” by Wölfl-Duchek et al. [It is known that liver injury can lead to hepatic retinopathy. To investigate the mechanisms of hepatic retinopathy, in the paper “Bile Duct Ligation Impairs Function and Expression of Mrp1 at Rat Blood–Retinal Barrier via Bilirubin-Induced P38 MAPK Pathway Activations“, Li et al.  used a bFocusing on renal transporters, the paper “In Vitro Characterization of Renal Drug Transporter Activity in Kidney Cancer” by Caetano-Pinto et al.  investig


Human pose estimation is one of the key technologies in action recognition, motion analysis, human–computer interaction, animation generation etc. How to improve its performance has become a current research hotspot. Lite-HRNet establishes long range connections between keypoints and exhibits good performance in human pose estimation tasks. However, the scale of this method to extract features is relatively single and lacks sufficient information interaction channels. To solve this problem, we propose an improved lightweight high-resolution network based on multi-dimensional weighting, named MDW-HRNet, which is implemented by the following aspects: first, we propose global context modeling, which can learn multi-channel and multi-scale resolution information weights. Second, a cross-channel dynamic convolution module is designed, it performs inter-channel attention aggregation between dynamic and parallel kernels, replacing the basic convolution module. These make the network capable of channel weighting, spatial weighting and convolution weighting. At the same time, we simplify the network structure to perform information exchange and information compensation between high-resolution modules while ensuring speed and accuracy. Experimental results show that our method achieves good performance on both COCO and MPII human pose estimation datasets, and its accuracy surpasses mainstream lightweight pose estimation networks without increasing computational complexity. An early solution for pose estimation is to regress the coordinate points, and more and more researchers are now using convolutional neural networks to deal with this task.As a basic task of motion recognition, human pose estimation is widely used in emerging fields such as action recognition and behavior analysis. The core of this task is to locate and classify the keypoints of human in the image4 can be seen as a representative method among them. However, this will bring relatively high computing requirements. When the model accuracy is sufficient for application, people begin to pursue more efficient pose estimation networks. Existing lightweight networks are mainly designed from two aspects, the first is to obtain modular depthwise separable convolutions from efficient networks such as MobileNet6 and ShuffleNet8, then combine them with high-resolution models to form the network backbone. The second is to weight the channel information to establish a long-distance connection, which is also the basic design idea of Lite-HRNet9. Lite-HRNet shows that an efficient convolution module can replace the basic 3 × 3 convolution and 1 × 1 convolution for feature extraction, but the static and fixed network structure limits the performance to a certain extent. From a dynamic point of view, Dite-HRNet10 proposes that using different design parameters at different depths and widths of the network will bring certain performance gains, and design a dynamic network structure accordingly. Our work borrows the idea of dynamic parameters from Dite-HRNet and focuses on further streamlining the network and improving performance.Recent studies have shown that maintaining high-resolution representations of heatmaps is an important means of ensuring accuracy in pose estimation tasks. HRNet14, parallel and multi-dimensional computation can lead to improvements in human pose estimation, which can be achieved by convolution and channel weighting. The existing weighting methods17 are mostly based on SE Attention18 and use global average pooling for preprocessing. However, this approach will bring some performance loss to the fine-grained task of pose estimation. Therefore, how to design an efficient and accurate information extraction method is a problem we must face.From recent studiesTo address this issue, we propose an improved multi-dimensional weighted high-resolution network named MDW-HRnet, we designed a convolution module with multi-dimensional weighting and a new information exchange module to further improve the performance. Specifically, the multi-dimensional weighted convolution module consists of contextual information modeling and cross-channel dynamic convolution, this makes the network capable of channel weighting, spatial weighting and convolution weighting. Context information modeling is divided into channel branch and space branch. High-resolution features are always maintained in channel and space dimensions to reduce information compression caused by dimensionality reduction operations. At the same time, cross-channel dynamic convolution adopts one-dimensional convolution as the basic operation of cross-channel interaction, and conducts sufficient information exchange, which is used to replace normal convolution. Our information exchange module can perform multi-scale fusion with reduced computational requirements, and the above components constitute the basic components of our MDW-HRNet.We propose contextual information modeling, perform pixel-level weighting on the corresponding dimensions of the input tensor, perform sufficient compression and restoration, and always maintain high-resolution features.We design cross-channel dynamic convolution, which uses one-dimensional convolution to form the exchange part of dynamic attention, assists in cross-channel information extraction, and is used to replace the basic convolution module.Perform information exchange between high-resolution modules while ensuring accuracy and speed, make full use of the channel and space representation capabilities, and perform information exchange and information compensation within multiple resolution branches without increasing additional computing power requirements.Our main contributions include:4 and its derived structures can maintain high-resolution representations throughout the feature extraction process, gradually generate low-resolution subnetworks from high-resolution subnetworks, and fully exchange information between different resolution subnetworks. Lite-HRNet inherits this structural property and replaces expensive bottleneck convolutional layers with conditional channel weighting composed of attention. Dite-HRNet proposes that using different network parameters at different depths and widths of the network will bring certain gains to performance, and adds a high-efficiency CNN module composed of Dynamic Kernel Aggregation to further connect the dependencies between the upper and lower environments. It is worth noting that the attention module adopted by Dynamic Kernel Aggregation in Dite-HRNet is highly coincident with its proposed Global Context Modeling, which will bring about repetitive information modeling to a certain extent. Our solution is to establish cross-channel dynamic convolution for replacement, and employ 1D convolution as a means of exchanging channel information.Early networks for pose estimation usually consist of a series of high-resolution to low-resolution subnetworks connected, and this structure causes a certain loss of accuracy for fine pixel-level tasks. The high-resolution network19 explored a collection of convolution kernels of different sizes, which became the basic idea of split convolution; Inspired by ResNet20, RSN1 performs feature fusion between multiple convolutional blocks stepwise and cumulatively; ConvNext21 proposed a large kernel convolution combined with an inverse bottleneck layer to reduce the computational complexity while increasing the accuracy.Depthwise separable convolution was proposed in MobileNet V1 and is widely used in lightweight network structures, which can compress computational costs while ensuring performance; MobileNet V2 introduces a reverse bottleneck layer, which increases the network width and improves accuracy and efficiency compared to traditional bottleneck blocks; ShuffleNetV2 proposes channel shuffling, which only performs convolution operations on half of the channels after separating channels, and enhances cross-channel information interaction through channel shuffling, which provides design ideas for lightweight networks; MixNet22 breaks the traditional static convolution characteristics by changing the input calculation convolution kernel parameters, avoiding the disadvantage that all samples share a convolution kernel in the traditional static convolution, and only increases a small amount of calculation, which is used to replace ordinary convolution. Dynamic convolution23 uses input-based attention weights for dynamic aggregation, it shares output channels through parallel convolution kernels, at the same time, the attention is used to weight the convolution kernels, that is, the convolution kernels of the same input and output are aggregated through the attention dimension. ODConv24 utilizes a multi-dimensional attention mechanism with a parallel strategy to learn complementary attention for convolutional kernels along all four dimensions of the kernel space at any convolutional layer. Based on the above work, we propose cross-channel dynamic convolution, which performs local channel interaction through one-dimensional convolution while avoiding excessive dimensionality reduction operations.CondConvolution25 considers that the response of a position is also the sum of the response weights of the feature maps inputted at all positions, thus linking the local to the global. GCNet26 establishes a unified long-range dependency for different locations of the network to strengthen the original features, which reduces a certain amount of computation compared to NLNet. DANet27 adopts a dual attention mechanism, adding parallel spatial pooling module and position pooling module to the network and fuses the output of the attention module to increase performance. PSA28 uses a polarization filtering mechanism to compress a certain dimension while maintaining high-resolution information in its orthogonal dimension, and uses nonlinearity to compensate for the loss of information, and achieves high performance in attitude estimation tasks. The implementation of GCM draws on the PSA method to a certain extent, which will be described in “NLNetThe MDW-HRNet network structure is shown in Fig. 30, and fusing features of different spatial sizes helps the network to extract more refined local representations1. In order to obtain accurate spatial information, we design a multi-dimensional weighted convolution module and implement it into three specific types, namely module A, module B and module C, which are composed of global information modeling and dynamic convolution. this makes it capable of space weighting, channel weighting and convolution weighting. It will run through all stages of the network, and the specific structure is shown in Fig. Recent studies have shown that: a large receptive field is suitable for extracting feature relationships between different key points31 and convnext21 networks, the settings of kernel_size = 2 and stride = 2 are used for downsampling. We follow this new setting, which is applied in the process of generating new branches in the high-resolution network and in the repeated multi-scale fusion process, and for the upsampling operation, we use bilinear interpolation for implementation. The number of channels will be aligned simultaneously during the downsampling operation. This will save some memory consumption.In previous high-resolution networks, the downsampling operation was performed using 3 × 3 convolutions, which seems to be the undisputed first choice for the downsampling operation of the convolutional neural network, in the ViTPoseFor Fig. For Fig. For Table The tensor dimensions of the input network can generally be expressed as C, H and W, which represent the number of channels of the feature tensor, and the height and width of the input tensor, denotes as Filtering: Compress the height and width features and channel features separately in a predetermined dimension, increase the nonlinear feature range through the softmax operation at the minimum channel feature, and fit the compressed features to the original dimensions to obtain long distance information.Information fusion: The information is further integrated by 1 × 1 convolution. Add normalization and new nonlinear information. This process can be expressed as (2)We can abstract the process of computing Among them, The design of the Channel weighting is based on the idea of PSA to a certain extent, it will be used throughout the whole network, it always maintains the highest resolution in the process of compressing dimensions, while discarding global average pooling. Its weight matrix is composed of two compression matrices. One of the compression matrix dimensions is N × c × hw, another compression matrix dimension is N × hw × 1, and use softmax operation to increase nonlinear information, then merge the two compression matrices and perform information fusion. Due to the design of information filtering, the channel branch abandons the use of global average pooling to collect spatial information, and reduces the operation of compressing dimensions, further maintaining accuracy. Its structure is shown in Fig. For Fig. MDW-HRNet has a total of 4 stages. In the nth stage, there are n parallel branches. The network width and resolution represented by each branch are different. The nth branch of any nth stage has the smallest heatmap resolution size and largest number of channels for that stage. We take advantage of this feature to properly compress the spatial information in the channel, perform dense modeling, and restore the resolution of the heatmap by upsampling after completing the information fusion, and perform a channel-by-channel weighting operation. In the nth stage, all heatmaps will be compressed to the minimum resolution size corresponding to this stage, as shown in (3)Among them, conv represents the operation of conv2d (1 × 1), and layernorm is the normalization operation, and bilinear interpolation is used to achieve upsampling.Dynamic convolution has two basic elements, convolution kernel and weighting function for computing attention. It can be seen from the experiments of ODConv that the attention mechanism in dynamic convolution plays a key role in performance gain. After removing the attention of CondConv or DyConv, their gain to the network is almost 0. Therefore, designing an efficient convolutional attention mechanism is a necessary means to improve performance.Traditional convolutional layers have a single, static kernel that is applied to all inputs. Dynamic convolution divides a single core into multiple cores and aggregates them linearly, and introduces an attention mechanism to associate convolution operations with input information. For the current mainstream dynamic convolutions such as CondConv, DyConv, and ODconv, they all use an attention structure similar to SE Attention. The difference is that CondConv uses Sigmoid and DyConv uses Softmax as the activation function to calculate the attention weight.32, we introduce channel information interaction in dynamic convolution while reducing dimensionality reduction operations to replace the traditional attention module, and achieve excellent performance. We will use one-dimensional convolution for cross-channel information interaction, as shown in Fig. The attention mechanisms embedded in the current mainstream dynamic convolution algorithms are mostly variants of SE Attention, or improve the attention module by combining more complex channel or spatial dependencies, which indirectly increases the complexity of the network. Inspired by ECANetAmong them, A(X) is the attention weight of the convolution kernel, conv1d represents one-dimensional convolution, k = 3, Gap is the global average pooling operation, and one-dimensional convolution is calculated in the channel dimension, which will not affect the network depth.* represents convolution operation.Dynamic convolution layer can be expressed as a linear combination of n convolution cores, each convolution kernel will obtain learnable cross-channel attention weight, this makes the convolution operation have the information interaction of the input dimension. The dynamic convolution operation can be represented by formula :6\docume* represents convolution operation. Global average pooling(GAP) is used to obtain the corresponding aggregation features, and the channel information is adjusted according to the one-dimensional convolution of size K to obtain the attention weight, and dynamic information aggregation is performed through multiple parallel convolution kernels.For Fig. 33 has more than 200 K images of human instances, each with 17 keypoint labels. We train our network on the train2017 dataset (containing 57 k images and 150 K human body instances) and evaluate the val2017 set according to the average precision (AP) and average recall (AR) scores based on object keypoint similarity (OKS) (contains 5 k images) and test-dev2017 set (contains 20 k images) for evaluation. To further validate our network, we also conduct experiments on the MPII Human Pose Dataset34, which contains about 25 k images with 40 k human instances and is scored by the head-normalized probability of correct keypoints (PCKh) to assess accuracy.The COCO datasetThe network is trained on a single RTX Titan with 32 samples. We use the Adam optimizer with an initial learning rate of 235. We estimate the heatmap through a Gaussian filter and average the predicted heatmaps for the original and flipped images. Apply a quarter shift in the direction from the highest response peak to the second highest response peak to obtain the position of each keypoint. For the MPII dataset, we adopted the standard testing strategy from the provided Person box.We will conduct experiments in a two-stage top-down paradigm: first human detection by a person detector, followed by keypoint detection, where the person detector is provided by Simple BaselineThe results of comparing our method with some mainstream methods are shown in Table For Table The results of comparing our network with other methods are shown in Table For the results of the above types of experiments, MDW-HRNet shows higher accuracy compared to Lite-HRNet and Dite-HRNet with almost the same computational power consumption, and even the performance of MDW-HRNet-18 It is close to the case of Lite-HRNet-30, which is enough to show the state-of-the-art of our network structure. We publish a comparison of these small networks in Fig. Table Our ablation experiments rely on the COCO dataset with a default input image size of 256 × 192.In order to verify the effectiveness of the context modeling module and cross-channel dynamic convolution proposed by us in MDW-HRNet, we conducted a series of ablation experiments in COCO val. First of all, we remove the CDC and GCM modules from the network structure, only retain the basic model structure, define this network as Simple MDW-HRNet-18, and on this basis, gradually add functional modules, and compare the results with the latest Lite-HRNet and Dite-HRNet. The results are shown in Table The channel interaction in cross-channel dynamic convolution relies on one-dimensional convolution, and the number of convolution kernels K in one-dimensional convolution will affect the final performance of the network to a certain extent, in order to obtain the best effect, we have carried out a series of experimental studies on the value of K. On this basis, the specific value of the one-dimensional convolution K is analyzed, and the experimental results are shown in Table In this section, we replace the cross-channel dynamic convolution in the network with other dynamic convolutions and carry out experiments, including condconv, dynamicconv, ODConv, to verify the performance gain of CDC in the attitude estimation task. The experimental results are shown in Table In Table In order to solve the problem that the scale of extracted feature weights in the lightweight pose estimation network is relatively single and lacks sufficient information interaction channels, we propose an improved multi-dimensional weighted high-resolution network named MDW-HRnet, which has achieved good experimental results in COCO and MPII data sets, surpassing The current mainstream small pose estimation network. The performance improvement depends on the following aspects: (i) The new structure retains the advantages of high-resolution networks while further optimizing the way of feature fusion; (ii) Cross-channel dynamic convolution is proposed, which will further facilitate accurate keypoint localization. (iii) Multi-dimensional weighted convolution module is proposed, including spatial weighting, channel weighting and convolution weighting, so as to further mine performance. It is worth mentioning that although our method has achieved good results in lightweight networks, the performance of the network still has a certain gap compared with large networks. In the next step, we will further verify the effect of the proposed performance module on other tasks, and focus on further performance improvement.


Small RNAs (sRNAs) are short non-coding regulatory RNA sequences that silence the complementary expressive transcripts through an endogenous RNA mediated interference mechanism (RNAi). These sRNAs typically move through plasmodesmata and phloem in plants to support disease resistance, and also through septal pores and vesicles in fungi to act as effector of pathogenicity. Notably, recent reports have shown the occurrence of a bidirectional trafficking of these sRNAs between the host plants and the attacking fungal phytopathogen which have significant implication in the nature of the infection. While the trans-species sRNAs from the pathogen can silence the host mRNAs and inhibit the host immunity genes, the sRNA modules from the host plants can silence the mRNA in the pathogen by impeding the expression of the pathogenicity-related genes. In the present review, we discuss the current state of sRNA trafficking between the plant and the pathogen with special emphasis on the mechanism of cross-kingdom communication which could contribute to the development of pathogen and pest control in future agriculture. Eukaryotic small RNAs (sRNAs) are 20–30 nucleotides short functional regulatory RNA molecules that are implicated in growth, development, metabolism, genome integrity, and plant–pathogen interaction . sRNAs aBotrytis cineria . HI. HI68]. pathogen . Suppresriticina . Transge kernels . In riceor MoAP1 . More re bananas . Transgen leaves . All then leaves . Also, Hn leaves . The siln leaves . In addin leaves . MoreoveB. cinerea affects the vegetables, fruits, flowers, and leaves of several plants. The SIGS-based application of dsRNA and sRNA suppresses the grey mould disease in all types of plant tissues by silencing the B. cinerea DCL1 and DCL2 genes [B. cinerea by silencing three prominent virulence-related genes-BcCYP51, Bcchs1, and BcEF2 [Fusarium head blight in barley and wheat by silencing the ergosterol biosynthetic genes [B. cinerea, F. graminearum, S. sclerotiorum, Rhizoctonia solani, Zymoseptoria tritici, Aspergillus niger and Verticillium dahlia [Similar to the environmental RNAi exhibited by some invertebrate systems, external long dsRNA as well as short sRNAs are taken up by fungal cells that induce silencing of fungal genes in a sequence specific manner . This haL2 genes . In anotnd BcEF2 . The dirnd BcEF2 . Similaric genes . What mom dahlia . All them dahlia . TherefoPisum sativum and Nicotiana benthamiana [Helicoverpa armigera virulence genes [Escherichia coli-derived anucleated minicells were used for dsRNA encapsulation in SIGS [Chs3a, Chs3b) and DICER-like proteins (DCL1 and DCL2) genes of Botryotinia fuckeliana leading to inhibition of fungal growth in strawberry [RNA, being highly unstable under extracellular conditions, is often encapsulated with artificial vesicles, lipidosomes, or nanocarriers to increase the efficiency of RNAi molecules . Micro- thamiana . In a sithamiana . The CNPce genes . In a re in SIGS . The minrawberry . What’s rawberry . Hence, The RNA-mediated cross-kingdom communication has been recently established in diverse plant systems, confirming that movable RNA is a key regulatory molecule governing host-fungal pathogen interactions. Fungal pathogens deliver sRNAs into plants to inhibit the host immunity, while the plant sRNAs are transported into the interacting pathogen to suppress their virulence. Evidence indicates that EVs are the carriers of sRNAs across the plant host to pathogens and vice versa. While a plethora of plant EVs have been employed in sRNA transport to fungi, the details mechanisms for fungal EV biogenesis, cellular secretion, loading of RNA into the EV, and biological functions are still unknown. Understanding the different classes of EVs and their ability for encapsulation of sRNA in fungi would be crucial for ck-RNAi. Contrary to this, the recent discovery that the majority of sRNAs in the apoplast reside outside the EVs has further contradicted the actual mechanism of sRNA translocation across the species. Further, the presence of RNA-binding proteins like AGO and GRP bound with the lncRNAs in the apoplast indicates their possible function in the secretion and/or stabilization of sRNAs. It will be worth investigating the role of these lncRNAs, especially the circRNAs, in cell-to-cell communication or immune responses through sRNA trafficking. Nevertheless, the current knowledge of ck-RNAi and fungal RNA has allowed for the development of disease control strategies like HIGS and SIGS in several agriculturally important pathosystems. However, HIGS and SIGS also suffer from technical difficulties in terms of RNA uptake, mechanisms of RNA translocation by pathogens, and off-target activities. The recent innovation in novel RNA delivery methods, including nanocarriers and artificial vesicles, has tremendously improved the stability, efficacy, and scalability of ck-RNAi under field conditions. It is possible that future agriculture will concentrate around the application of environmentally friendly RNA-based fungicides, and cross-kingdom communication of sRNA will form an important research direction for controlling eukaryotic pests and pathogens.