Long non‐coding RNAs (lncRNAs) play a crucial role in tumour initiation and progression. However, little is known about their contributions to p53‐related bladder cancer (BC) inhibition.By using high‐throughput sequencing, we screened the expression profiles of lncRNAs in BC and adjacent non‐tumour tissues. The roles of a novel lncRNA, named LNPPS [a lncRNA for programmed cell death 5 (PDCD5) and p53 stability], were determined by gain‐ and loss‐of‐function assays. RNA pull‐down followed by mass spectrometry analysis, RNA immunoprecipitation assays and other immunoprecipitation assays were performed to reveal the interactions among LNPPS, PDCD5 and p53, and the regulatory effect of LNPPS on the complex ubiquitination network comprising PDCD5, p53 and mouse double minute 2 homologue (MDM2).6A) modification.LNPPS was downregulated in BC and markedly inhibited the viability of BC cells by inducing PDCD5/p53‐related apoptosis in vivo and in vitro. Mechanistically, LNPPS, serving as a scaffold, connected PDCD5 and p53 with nucleotides (nt) located at 121‒251 nt and 251‒306 nt of LNPPS, respectively. This process allowed LNPPS to protect PDCD5 from proteasomal degradation by blocking its K20 site ubiquitination. On the other hand, the increased interaction between PDCD5 and p53 displaced p53 from the MDM2‒p53 ubiquitination complex, resulting in an increase in p53 expression and related apoptosis levels. Moreover, LNPPS could induce the accumulation of PDCD5 and p53 in the nucleus and exert a synergistic effect on the prevention of protein degradation. In addition, we confirmed that the downregulation of LNPPS in BC was mediated by the decreased N6‐methyladenosine (mOur findings highlight a novel cross‐talk between LNPPS and the PDCD5/p53/MDM2 ubiquitination axis in BC development, indicating its potential as a therapeutic target for BC patients. 6A‐regulated lncRNA LNPPS played a suppressive role in BC development.1. A novel m2. LNPPS, a novel PDCD5‐interacting partner, protected PDCD5 from proteasome degradation by masking its K20 site ubiquitination.3. LNPPS acted as a scaffold to connect PDCD5 with p53, impairing MDM2‐mediated p53 ubiquitinationb. Mechanistically, LNPPS acted as a scaffold for PDCD5 and p53, blocking the K20 site ubiquitination of PDCD5 and disrupting MDM2‐mediated p53 ubiquitination, which promoted p53‐related cell apoptosis. Our findings provide novel insights into lncRNA‐related protein ubiquitination in BC and contribute to the identification of potential therapeutic targets.In this study, based on RNA‐seq data from BC tissues and the gain‐ and loss‐of function assays, we identified a novel tumour‐suppressive lncRNA, named LNPPS . Further investigations showed that the downregulation of LNPPS was regulated by N6‐methyladenosine  buffer, frozen in liquid nitrogen for 30 min, and subsequently stored at –80°C. This work was approved by the Ethics Committee of The Second Hospital of Shandong University.2.22. 5637, T24, NCI‐H1299, T‐47D and A549 were cultured in Roswell Park Memorial Institute 1640 medium (Gibco). J82, UM‐UC‐3 and HEK293T were cultured in Dulbecco's modified Eagle's medium (Gibco). SW116, SW620, MDA‐MB‐468 and MDA‐MB‐231 were cultured in Leibovitz's L‐15 medium (Gibco). RT4, SV‐HUC‐1 and MCF‐7 were cultured in McCoy's 5A (Gibco), Ham's F‐12K medium  and Minimum Essential Medium (Macgene), respectively. Medium was added with 10% foetal bovine serum (FBS)  and 1% penicillin/streptomycin .5637, J82, UM‐UC‐3, T24, SV‐HUC‐1, RT4, NCI‐H1299, A549, MDA‐MB‐468, MDA‐MB‐231, MCF‐7, T‐47D, SW116, SW620 and HEK293T were acquired from the Cell Bank of the Chinese Academy of Sciences , tested negative for mycoplasma and incubated at 37°C with 5% COThe transfection of plasmids and siRNAs was performed with Lipofectamine 2000  based on the manufacturer's protocols. Lentivirus overexpressing LNPPS or the CRISPR‐dcas9‐KRAB/sgRNAs for silencing LNPPS were transfected into BC cells with 5 mg/ml polybrene for 18 h. Cells were then selected by puromycin (2 μg/ml) or blasticidin S (1 μg/ml). Detailed descriptions of siRNAs and sgRNAs are shown in Table 2.3−ΔΔCT method [Glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH) was used as the endogenous control]. All primer sequences are shown in Table Total RNA was extracted from tissues or cells by using TRIzol Reagent  or RNA Fast2000 Reagent , respectively. PrimeScript RT Reagent Kit and TB Green Premix Ex Taq  were used for cDNA synthesis and quantitative application, respectively. The relative levels of genes were analysed by the 22.4Proteins were extracted from indicated cells by radio‐immunoprecipitation assay (RIPA) lysis buffer containing protease inhibitors . The samples were separated by sodium dodecyl sulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE) gels and transferred to poly(vinylidene fluoride) membranes . After blocking with 5% bovine serum albumin (BSA) buffer, membranes were incubated with primary antibodies overnight at 4°C and subsequently incubated with horseradish peroxidase‐labelled secondary antibodies . Finally, signals from blots were determined by the enhanced chemiluminescence system (Bio‐Rad). The primary antibodies are listed in Table 2.5Following the manufacturer's protocols, subcellular fractions of RNA and protein were separated and purified with the PARISTM Kit Protein and RNA Isolation System . RNAs extracted from different fractions were performed with quantitative reverse transcription‐polymerase chain reaction (qRT‐PCR) using GAPDH and U6 as markers of the cytoplasm and nucleus, respectively. Similarly, the expression of proteins in subcellular fractionations was measured by Western blotting (WB) analysis. The anti‐β‐actin antibody and anti‐LaminB1 antibody specifically marked the cytoplasm and nucleus, respectively.2.6The indicated cells were placed on the culture slides before assays. After three washes with PBS, the slides were fixed using 4% paraformaldehyde, permeabilised with 0.5% Triton X‐100 at 4°C, blocked in pre‐made hybridisation buffer at 37°C, and incubated in hybridisation buffer containing RNA probes  overnight at 37°C. Next, the slides were rinsed with citric acid–sodium citrate buffer, stained with 4′,6‐diamidino‐2‐phenylindole (DAPI) and rinsed another three times. Finally, images of the slides were taken by the confocal imaging system . For immunofluorescence (IF) assays, slides were briefly washed, fixed and permeabilised as described above. After blocking in 3% BSA, slides were first incubated with specific primary antibodies overnight at 4°C and then incubated with Alexa488‐ or Alexa555‐labelled secondary antibodies in the dark. After being stained with DAPI and another wash, slides were observed under a confocal imaging system (LSM 780). The species of primary antibodies should be different in double IF assays.2.72O2 to remove endogenous catalase. After being blocked and incubated with specific primary antibodies, slides were treated with the MaxVision TMHRP‐Polymer anti‐rabbit immunohistochemistry (IHC) kit , stained with 3,3‐diaminobenzidine tetra‐hydrochloride and counter‐stained with haematoxylin. The slides were captured by an Olympus BX51 microscope and quantified by the histological score (H‐score). H‐score = ∑(pi × i) = (percentage of weak intensity × 1) + (percentage of moderate intensity × 2) + (percentage of strong intensity × 3).After being fixed with 4% paraformaldehyde, tumour tissues from mice were made into paraffin‐embedded slides. Slides were washed in xylene, rehydrated via serial dilutions of alcohol, and incubated in H2.84 cells/well) in advance and measured every 24 h. Before detection, cells were incubated with CCK8 reagent  following the manufacturer's protocols. Cell viability was evaluated by OD values (450 nm) with a microplate reader . For colony formation assays, 1 × 103 cells were plated into the six‐well plates. Colonies were fixed in methanol and stained with 1% crystal violet (Solarbio) after 2 weeks of culture.After transfected with lentivirus or indicated sets of plasmids/siRNA, cell dynamic growth was obtained by an xCELLigence RTCA DP instrument . For CCK8 assays, indicated cells were plated into 96‐well plates (2 × 102.94 or 8 × 104 cells in 200 μl medium (serum‐free) were added to the upper chambers of inserts  for migration or invasion assays, respectively. Then, 800 μl medium (20% FBS) was added to the lower chambers. After the 24 or 48 h of culture, inserts were washed, fixed in methanol and stained with Giemsa (Solarbio), and the cells in the upper chambers of the inserts were removed with cotton swabs. The images were taken under a microscope . For invasion assays, the inserts (upper chamber) should be pre‐coated with Matrigel .A total of 5 × 102.10The indicated cells were re‐suspended to obtain single‐cell suspensions in advance. For cell cycle assays, cell suspensions were fixed with appropriate amount of ethanol at –20°C, stained with propidium iodide containing RNase A (BestBio) and finally subjected to the flow cytometry (BD Biosciences). For apoptosis assays, cells were treated with the Annexin V‐FITC/PI or Annexin V‐APC/PI apoptosis detection kits (BestBio). ModFit LT software  and FlowJo software  were used for the further analysis of cell cycle and cell apoptosis, respectively.2.11The sense, antisense and truncated sequences were obtained by in vitro transcription using the Riboprobe Combination System‐T3/T7 Kit  and labelled with desthiobiotinylate by the Pierce RNA 3′ End Desthiobiotinylation Kit (Thermo Fisher Scientific). Protein lysates were obtained from indicated cells using IP lysis buffer containing protease inhibitors. The RNA pull‐down assays were then carried out with the Pierce Magnetic RNA‐Protein Pull‐Down Kit (Thermo Fisher Scientific). The precipitated proteins were subjected to mass spectrometry (MS), which was further visualised by silver staining and WB. In advance of MS, proteins were digested with trypsin , desalted, and concentrated using C18‐based solid phase extraction. Peptides were further analysed by using high resolution/high mass accuracy reversed phase (18) nano‐liquid chromatography–mass spectrometry (LC–MS)/MS .2.12The assays were performed by the EZ‐Magna RNA immunoprecipitation (RIP) RNA Binding Protein Immunoprecipitation Kit . In short, magnetic beads coated with indicated primary antibodies were incubated with the indicated cell lysates overnight. The immunoprecipitated RNA samples were purified and subjected to qRT‐PCR. The levels of relative enrichment were normalised to 10% RNA input.2.136A methylated RNA immunoprecipitation (MeRIP) assays were performed with the Manga MeRIP m6A Kit . In short, total RNA from cells was fragmented into 100 nt or smaller fragments using fragmentation buffer followed by magnetic immunoprecipitation (IP) with indicated primary antibody‐conjugated protein A/G beads. After being washed and purified by the RNeasy Mini Elute Cleanup Kit , the isolated RNA fragments were subjected to qRT‐PCR. Ten percent of RNA fragments before IP were served as input to normalise m6A enrichment.m2.14After being transfected, protein lysates were obtained from the indicated cells by RIPA buffer (weak) with protease inhibitors and were immunoprecipitated with indicated primary antibodies overnight at 4°C. Immune complexes were captured with magnetic Protein A/G agarose beads  for 2–4 h. The immunoprecipitated proteins were analysed by immunoblotting (IB) blots after washing. For ubiquitination assays, cells were treated with 10 μM MG132  for 6 h before IP assays.2.15The RNeasy mini kit was used to extract total RNA from the indicated tissues and cells . TruSeq RNA Sample Preparation Kit  was used to create paired‐end libraries that were then sequenced on the Illumina NovaSeq 6000 (Illumina). Gene abundance was presented as fragments per kilobase of exon per million reads mapped (FPKM). R package ‘edgeR’ was used to screen differentially expressed genes. For functional enrichment analysis, such as Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, was performed via the ‘enrich’ R package. Gene set enrichment analysis (GSEA) was performed by using the GSEA v2.0 tool.2.16Cells overexpressing LNPPS plasmid or control empty vector were transfected by mixing either PG13‐luc , MG15‐luc  or an empty vector without both WT and mutated p53‐binding sequence with pRL‐TK plasmid (Promega) in a 100:1 ratio using the Lipofectamine 2000 (Invitrogen). After the 48 h of culture, the luciferase activities of cells were evaluated in a Dual‐Glo Luciferase reporter assay system (Promega) in a Glomax 96 (Promega). The relative luciferase activities were normalised to Renilla luciferase activities.2.17Protein A and G Dynabeads  were mixed and incubated with primary antibodies  for 3 h. The indicated cells were immediately cross‐linked with 1% formaldehyde for 10 min and 125 mM glycine was quickly added to terminate the cross‐link. According to the study by Guo et al.,2.186 LNPPS stably overexpressed or knockdown 5637 cells and corresponding control cells were injected into the right flank of mice, respectively. After 2 weeks post‐injection, the volume of tumours was measured and calculated (volume = 0.5 × width2 × length). Mice were euthanised by carbon dioxide inhalation after 7 weeks, and the subcutaneous tumours were dissected and weighed. The in vivo assays were approved by the Ethics Committee of the Second Hospital of Shandong University.BALB/c nude mice  were obtained from Beijing Vital River Laboratory Animal Technology (China) and were randomly placed in different groups (5 mice/group). A total of 5 × 102.19t‐test. The Mann–Whitney U‐test was used when the population did not have a normal distribution. One‐way analysis of variance was performed for the analysis of variance in multiple groups. The correlation between the expression levels of LNPPS and m6A regulators [methyltransferase‐like 3 (METTL3)/methyltransferase‐like 14 (METTL14)/AlkB homologue 5 (ALKBH5)/fat mass and obesity‐associated protein (FTO)], LNPPS and PDCD5 or p53, and PDCD5 and p53 was analysed using Spearman correlation tests. All experiments were performed in at least three replicates. Quantitative data are shown as mean ± standard error of mean. Statistical significance was defined as a p‐value < .05.Statistical analyses were assessed and viewed by SPSS 18.0  or GraphPad Prism5 . The data between the two groups were compared by the Student's 33.1p < .05, log2|fold change| > 2.0)  was significantly downregulated in BC tissues compared with paired non‐tumour tissues, consistent with the RNA‐seq data . The heatmap depicted the 25 differently expressed lncRNAs  Figure . Accordi Figures .LNPPS is located on human chromosome 5 at 149 425 771‒149 428 289 and is composed of two exons with a transcript length of 422 nt. We verified the full‐length sequence of LNPPS by PCR amplification with a series of gene‐specific primers in BC cells Figure . The Pyh3.2To explore the roles of LNPPS in BC progression, we transfected lentiviral‐LNPPS and its control vector to construct BC cells that stably overexpressed LNPPS Figure . As indiFurthermore, cell apoptosis analysis indicated that the percentage of apoptotic cells increased upon LNPPS overexpression, which was further confirmed by WB analysis of apoptosis‐related proteins Figure . In cont3.3Interaction with specific protein(s) is one of the most important mechanisms by which lncRNAs exert their functions.To further determine the regions responsible for the interaction between LNPPS and PDCD5, we constructed three truncated fragments of LNPPS  and used them in RNA pull‐down assays. The results showed that F1 and F2, but not F3, could bind to PDCD5, suggesting that the region of 121–251 nt in LNPPS was required for the interaction with PDCD5 Figure , which w3.4Next, we sought to elucidate the relative contribution of the specific interaction between LNPPS and PDCD5. qRT‐PCR showed that the RNA levels of PDCD5 remained steady after either overexpression or knockdown of LNPPS in BC cells Figure . ImportaK20) that mutated the 1–30 a.a. region of PDCD5 but preserved its K20 site. RIP assays indicated that the mutant PDCD5K20 lacked the interaction with LNPPS  (a de novo protein synthesis inhibitor). The half‐life of PDCD5 protein was remarkably prolonged in the LNPPS‐overexpressing cells than in the control group Figure . NotablyS Figure . Ubiquit0 Figure . These f3.5To provide insights into the mechanisms by which LNPPS regulated BC development, RNA‐seq was used to examine the gene expression profiles in LNPPS‐overexpressing cells (raw data accessible via GSE190917). Eighty‐two genes were upregulated and 102 genes were negatively related to LNPPS overexpression Figure . AccordiNext, we assessed whether the repressor role of LNPPS was dependent on PDCD5. Silencing of PDCD5 partly reversed the suppressed effect of LNPPS overexpression on BC cell viability Figure . PDCD5 s3.6As mentioned above, LNPPS not only activated p53 signalling but also participated in regulation of p53 itself. We found that overexpression and silencing of LNPPS had negligible effects on the RNA level of p53, suggesting that the promoting effect of LNPPS on p53 might not be involved in transcriptional regulation Figure . IHC assIt is known that p53 protein is mainly degraded via the ubiquitin–proteasome pathway.Strikingly, we observed that LNPPS was similarly the RNA‐binding partner of p53, as it physically interacted with p53 in BC cells Figure . RNA FIS3.7MDM2, an E3 ubiquitin–protein ligase, binds to p53 and facilitates its polyubiquitination with subsequent 26S proteasome degradation.3.8To study the mechanisms by which LNPPS is downregulated in BC, we first treated BC cells with 5‐aza‐dC. qRT‐PCR showed that the DNA methyltransferase inhibitor had no significant effect on LNPPS expression Figures  and S8A.6A modification plays a crucial role in RNA metabolism, including its transcription splicing, subcellular localisation, translation and decay.6A was responsible for the downregulation of LNPPS in BC. According to the results from a public prediction server SRAMP database, we found two RRACU m6A sequence motifs in LNPPS . MeRIP assays verified that the enrichment of m6A by LNPPS at site 1 was lower in 5637 cells than in SV‐HUC‐1 cells  and demethylases (‘erasers’). METTL3 and METTL14 have been identified as the key ‘writers’, while ALKBH5 and FTO are the main ‘erasers’.6A regulators and LNPPS in BC tissues. qRT‐PCR indicated that the level of METTL3 was positively correlated with the expression of LNPPS in BC tissues and that ALKBH5 was negatively correlated with LNPPS, whereas METTL14 and FTO had no significant correlation with LNPPS  might be the potential LNPPS‐binding protein , located on the reverse strand of chromosome 5q32, was significantly downregulated in BC specimens. The 5q32 locus encodes multiple non‐coding RNAs. Among them, lncRNA CARMN, miR‐143 and miR‐145 are closest to LNPPS, which are localised in the forward strand of 5q32. Several studies have reported that all three non‐coding transcripts can accelerate the development of atherosclerosis.PDCD5, a highly conserved protein, was first identified as an apoptosis‐promoting signal in many diseases, including cancers.As a known master tumour suppressor, p53 regulates a variety of cellular processes, notably cell cycle arrest and apoptosis.It is well known that PDCD5 functions as a p53‐positive regulator by blocking the interaction of p53 with E3 ubiquitin ligase MDM2.6A modification, emerging as a key post‐transcription regulator of gene expression patterns, participates in various eukaryotic cellular processes, including circadian rhythm, external stimulus response and tumourigenesis. As a dynamic and reversible modification, the correct deposition of m6A is mediated by a multi‐protein machinery consisting of ‘writers’, ‘erasers’ and ‘readers’. Several studies have recently demonstrated the increase or decrease of m6A modification in BC.6A level of LNPPS in BC cells was lower than that of the human urothelial cell line. Moreover, the enriched m6A modification of LNPPS induced by METTL3 resulted in a prolonged half‐life of LNPPS. Consistent with this finding, overexpression of ALKBH5 decreased m6A modification on LNPPS and facilitated its decay, suggesting a clear role of m6A methylation in LNPPS suppression of BC. Considering the biological importance of m6A dependent on specific m6A readers, we checked out the potential LNPPS‐binding proteins and found hnRNPA2B1 among them. As expected, the m6A site 1 of LNPPS was identified as the binding target of hnRNPA2B1. A recent study has shown that hnRNPA2B1 recognises the m6A sites of ILF3 and maintains its mRNA transcript stability.6A‐bearing LNPPS and may serve as an intermediary for LNPPS stability mediated by METTL3 and ALKBH5.mAs an emerging star in cancer therapy, more and more lncRNAs have shown the potential to improve therapeutic efficacy and development of combination therapy in various cancers. Most studies focus on targeted silencing of oncogenic lncRNAs to hinder cancer progression, such as using antisense oligonucleotides or small synthetic molecules/peptides that block the association with lncRNAs and their specifically functional binding partners.56A‐regulated LNPPS functions as a scaffold for PDCD5 and p53, blocking PDCD5 ubiquitination and competitively inhibiting MDM2‐mediated p53 ubiquitination, which promotes PDCD5/p53‐related cell apoptosis. This work provides insights into the pathophysiology and treatment of BC from a lncRNA perspective.Our study reveals that mThe authors declare they have no conflicts of interest.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures of long RNA sequences with high precision and reasonable computational cost remains challenging. Here, we propose a deep learning model, RNA-par, which could partition an RNA sequence into several independent fragments (i-fragments) based on its exterior loops. Each i-fragment secondary structure predicted individually could be further assembled to acquire the complete RNA secondary structure. In the examination of our independent test set, the average length of the predicted i-fragments was 453 nt, which was considerably shorter than that of complete RNA sequences (848 nt). The accuracy of the assembled structures was higher than that of the structures predicted directly using the state-of-the-art RNA secondary structure prediction methods. This proposed model could serve as a preprocessing step for RNA secondary structure prediction for enhancing the predictive performance  and reducing the computational cost. In the future, predicting the secondary structure of long-sequence RNA with high accuracy can be enabled by developing a framework combining RNA-par with various existing RNA secondary structure prediction algorithms. Our models, test codes and test data are provided at  The ncRNAs are involved in many important biological processes such as protein synthesis, gene regulation, and immunoregulation4. They also play an important role in many diseases such as cancer, diabetes, and atherosclerosis5. The RNA function is associated with its spatial structure, which exhibits a hierarchical folding process. Hence, initially, the secondary structure is formed, followed by the tertiary structure (3D structure) formation with further folding4. The secondary structure rapidly forms with large energy change and rich conformations while being stable and independent of its tertiary structure6. These features lead to the feasibility of its application in the functional inference of non-coding RNA, drug target discovery, and anti-RNA virus drug design7.In a genome, most of the genes are transcribed into non-coding RNAs (ncRNAs)9 based on the 3D structures determined by wet lab experiments , the highly unstable RNA molecules and their crystallization challenges render this method difficult to practice. In addition, the experimental approach is often expensive and time- and labor-consuming. Hence, the generalization of this approach is difficult. Secondary structure probing by chemical reagents  is another class of methods to obtain RNA secondary structures10. However, these methods are generally used for detecting RNA structures in vitro, and the RNAs in vitro are not always consistent with those in living cells11. To date, the structures of less than one out of a total of ten thousand known RNAs have been determined by this approach12. Thus, this task largely relies on the computational approach.An RNA secondary structure is composed of the base pairs connected via hydrogen bonds within this RNA sequence, which include canonical base pairs  and special base pairs . At present, two main approaches are utilized to obtain the RNA secondary structure: experimental approach and computational approach. Even though the experimental approach provides highly accurate RNA secondary structure14 and de novo folding algorithm16. The comparative sequence analysis is based on the covariant alignment of complementary bases in the RNA sequences, and the structure is determined via homologous sequences13. Although this method exhibits high accuracy, it requires a set of homologous sequences. Thus, the identification of a limited number of RNA families has led to the unavailability of homologous sequences of most RNAs, resulting in limited usage of comparative sequence analysis.In general, the computational approach can be classified into two categories, i.e., comparative sequence analysis21. Therefore, the secondary structure can be predicted from a single RNA sequence25. The base-pairing accuracy of the known de novo folding algorithms with the datasets composed of multiple short-sequence RNAs (< 300 nt) is 71%26; however, for long-sequence RNA  or messenger RNA (mRNA)), most of the known de novo folding algorithms are not applicable due to their low precision and cost-extensive nature26.The de novo folding algorithms are designed assuming the RNA folding mechanism, based on which the structure partition functions and optimization goals are proposed. The secondary structure of RNA sequences can be obtained using optimization algorithms, such as dynamic programming, which deduce the global optimal or expectation maximized structure in the structure space27. Singh et al.28 proposed the first hybrid deep neural network, SPOT-RNA model, which combines residual network and long-short term memory (LSTM) for high accuracy in predicting secondary structures for RNA sequences. Subsequently, Sato et. al29 proposed a weighted-based approach based on deep learning by combining a fairly deep model with a dynamic programming algorithm. However, the aforementioned two models are inadequate for predicting long-sequence RNA secondary structures. Lu et al.30 proposed an LSTM network that can predict the secondary structure of RNA of any length. Yet, its prediction accuracy needs further improvement. In short, it is a significant challenge to predict secondary structure for long RNAs.Recently, rapid advances have been made in RNA secondary structure prediction owing to the application of deep neural networks31 ;it is not located between two paired bases.An RNA exterior loopThe independent fragments (i-fragments) are the subsequences divided by the exterior loops. Hence, the secondary structure prediction of an i-fragment is independent of other fragments, and the simple assembly of i-fragment structures can form the secondary structure of the complete RNA. In this study, we propose a deep learning model, RNA-par, which can predict the bases in exterior loops of a given RNA sequence. The RNA sequence can be partitioned into several short i-fragments, whose secondary structures can be predicted separately followed by their simple assembly into the secondary structure of the complete RNA. With the proposed RNA-par model, the task of long-sequence RNA structure prediction can be addressed via the prediction of several short RNA fragments, which can be easily obtained by using common RNA prediction methods.32, RNA Strand33, PDB34, Archive II35, RNAStralign36, and RMDB37 by January 2021. All these data can be downloaded easily from the corresponding databases. Structural data in RNA Strand could be classified into two categories: data obtained by experiments (RNA Strand-experiment) and those obtained by prediction (RNA Strand-prediction). We collected RNA data from PDB with less than 3.5 Å resolution. As PDB offers only tertiary structures, they were converted to secondary structures using RNApdbee38.In this study, all the base pairs  were regarded as secondary structure. The RNA sequence and structure data used in this research were collected from the bpRNA-1 mData obtained by experiments ;Data obtained by comparative sequence analysis and is widely used as benchmark in other studies. ;Data obtained by comparative sequence analysis or other methods with uncertain accuracy (bpRNA-1 m and RNA Strand-prediction).All structural data in these datasets could be classified into three groups Fig.  as follo39 was used, i.e., at first, the RNA-par model was pre-trained with the data in group three, then trans-trained with the data in group two. Furthermore, the data in group one were used to evaluate the performance of RNA-par while avoiding possible bias across the databases.The structural data in the first group are the most accurate but comprise a low number of entities Fig. . Hence, 40 . The unduplicated sequences were partitioned into subsequences of equal length (200 nt) by a sliding window . If the length of the last subsequence was shorter than 200 nt, ‘-’ was used to pad out the gaps. Subsequently, CD-HIT-EST was used again with the same parameters to remove duplicated or similar sequences within and across each subsequence dataset. During this process, we retained the cross-dataset duplications in group two while eliminating their counterparts in other groups  either to an exterior loop  or not .We built the labels for each RNA subsequence involved in training, validation, and test sets, in accordance with the RNA secondary structure data. Each RNA subsequence label was composed of the labels of each base in its sequence. A base was labeled ‘1’ if it belongs to an exterior loop  with Our RNA-par model was composed of 4 blocks Fig. . The 4-l39 for training our RNA-par model. The training process comprised two stages: pre-training and trans-training. In the pre-training stage, the data with a large number of entities but lower accuracy in group three were used, while the limited data with higher precision in group two was used in the trans-training stage. The model with the same training scheme was used in both stages, which was different from the general approach in which the learning rate is lower or shallow layers are frozen during trans-training. To avoid overfitting, an early stopping42 was employed with patience of 10. We also employed Bayesian Optimization (BO)43 to find the best combinations of the undecided hyper-parameters  for validation set V2 , RNA-par model outputs a prediction  for each base in this sequence . For analyzing the performance of RNA-par, commonly used metrics, i.e., sensitivity (SEN), precision (PRE) accuracy (ACC) and MCC were calculated . The components of four species of base showed a significant difference between these two patterns of sequences . The C and G contents in external-loop group are significantly lower than those in nonexternal-loop group. This could result in the formation of a stable secondary structure due to the lower energy of G-C pairs. To better distinguish the bases in exterior loops and non-exterior loops, and for an accurate prediction of the potential i-fragments, a more complex binary-classification model RNA-par was built.The RNA-par model proposed in this study was composed of 4 blocks Fig. , i.e., aWe used the transfer learning approach for training the RNA-par model. The training process consisted of two steps: pre-training and trans-training. In pre-training, coarse datasets T1 and V1 from group three were used as the training set and validation set, respectively. In trans-training, the pre-trained model was directly trained and validated again with accurate datasets T2 and V2 derived from group two, respectively , the model was trained with a dataset containing both T1 and T2, and validated with a dataset containing both V1 and V2; and in the second approach (Only accurate), the model was directly trained and validated with T2 and V2, respectively. We compared the model-performance with different training modes on TS dataset in terms of PRE, SEN, ACC and MCC Table . In all To further evaluate the performance of the RNA-par model, we used another independent test set TS’ (see “Methods”) from group one. All 128 subsequences in TS’ were obtained by experiments and different from those already used in training, validation, or test sets. Their labels were predicted by the best three models obtained through BO. The results are shown in Table Furthermore, the lengths of i-fragments analyzed were determined via the predicted labels of RNA-par. When one or successive bases were predicted, the structure of these bases are regarded as an exterior loop (not needs further structure prediction), thereby leading to the production of several short i-fragments. To illustrate the ability to partition long sequences effectively, RNAs longer than 200 nt were analyzed. For these RNAs, the i-fragments were significantly shorter than the length of the RNAs themselves (average 453 nt compared to 848 nt), as illustrated in Fig. 18, CONTRAfold, Linearfold20, SPOT-RNA28, and mfold45), and validated them with the independent test set. The performance of these methods with or without RNA-par as preprocessing is shown in Table To evaluate the benefits of employing RNA-par as a preprocessing step of RNA secondary structure prediction, we combined RNA-par with five state-of-the-art RNA secondary structure prediction methods  with and without RNA-par is shown in Fig. In bioinformatics, the prediction of RNA secondary structures with long sequences remains a significant challenge. Further complexities were observed when it was discovered that the structures of lncRNA, mRNA, and virus RNA with long sequences are critical for their functions. Here, we proposed a deep learning model, RNA-par, to predict the bases in exterior loops of an RNA sequence, further partitioning it into i-fragments. Thus, the secondary structure prediction of long-sequence RNAs can be turned into several sub-tasks which can be easily addressed using existing algorithms, i.e., predicting the secondary structure of i-fragments. RNA-par renders many existing prediction algorithms applicable for long-sequence RNAs, which are otherwise inadequate due to their cost-extensive nature or inferior performance.In the early days, the length of most discovered functional RNAs is very short, such as tRNAs. With the in-depth research, especially the discovery of the lncRNA, more and more functional RNAs with long chain have been found. Their length ranges from 200 to serval thousand nucleic acids. However, the number of long-chain RNAs with decided secondary structures is still very limited due to the difficulty in the structure determination experiment. In our study, we collected as much data  as possible to train and test our model, but the number of long-chain RNA  is limited , and trained and validated four models in the transfer learning mode. Then, two comprehensive indexes ACC and MCC of four models were measured with TS dataset  and only accounts for a small part of the entire structure prediction time (a few seconds to a few hours). Hence, RNA-par scarcely increases the time complexity of structure prediction. For most RNA secondary structure prediction methods, the time complex is at least The structures may not be reliable at the single base-pair level, especially the long-range base pairs in long RNAs.The structures may be incomplete.In our study, the amount of training data in group three was large enough for training our deep learning model RNA-par; however, these structure data were predicted using comparative sequence analysis, which showed the following two problems:These two problems are hard to overcome completely, and may limit the performance of the RNA-par model. In order to make full use of the large amount of data in group three, while reducing the impact of the inaccurate or incomplete structures, we employed the transfer learning method. Specifically, a pre-training step was performed before the trans-training step. All data in group three were only used in the pre-training step Table  to buildUnlike proteins, high-precision RNA structure data is scarce. Many RNA secondary structure prediction models based on machine learning use the structure data obtained by comparative sequence analysis as a training set, especially the deep learning-based model with a large number of parameters. RNA-par model does not predict the complete RNA secondary structure, while only predict bases in exterior loops, hence, it does not need a large training set. We also believe our RNA-par model and the machine learning based RNA structure prediction method will be further improved along with the accumulation of reliable experiment-based RNA structure data.47. The method of building new models through the integration of mature blocks according to their characteristics could aid in the search for a suitable architecture for a specific task. In addition, addressing a problem using a mature block is also of significant value. In addition to RNA secondary structure prediction, deep learning was also widely used in many other RNA-related fields. For example, the LSTM model48 and dilated convolutional neural network49 were used to predict the RNA solvent accessibility, and multi-layer stacked autoencoder50 was used to predict the subcellular localization for lncRNAs.The RNA-par is composed of several blocks  which have been successfully applied in other fields, such as image recognition and translation51. Thus, dividing the dataset into several subsets based on the data quality followed by training of the model in order from coarse subset to precise subset is an effective approach to obtain a well-performing model.In the transfer learning process of RNA-par, neither the weights of the blocks in shallow layers of the model were frozen nor the learning rate was lowered. The model that was already trained with a coarse dataset (T1) was retained using a more precise dataset (T2). Results showed the effectiveness of this transfer learning strategy. This approach has also shown to be more effective than the traditional transfer learning approaches in previous studies42 strategy was employed and the patience was set to 10 by experience. Specifically, when no improvement was observed with the validation set within the last p (patience parameter) epochs, the model training was stopped, rather than going with all the epochs.To overcome overfitting when training RNA-par, early stoppingLoC) and the number of layers of Bi-LSTM (LoL), and the results  with different hyper-parameters. These models predicted the label of a base from different aspects, and the prediction results were improved upon averaging three models. We also tested the performance of RNA-par under different combinations of the number of layers of 1D-CNN  were labeled as bases do not belong to exterior loop. However, an FP predicted by RNA-par may falsely partition a single i-fragment into two and interactions may exist between these two falsely partitioned parts. It is true that our solution is not able to predict such interaction and will give false negative prediction. The RNA-par achieved 0.9998 S-PRE in TS dataset and 0.9474 S-PRE in TS’ dataset with trans-training strategy, indicating that such cases are rare. Existing results also showed that our solution could improve the performance of structure prediction of long-sequence RNA Table . In addiWe believe that developing a framework by combining RNA-par and existing RNA secondary structure prediction algorithms will advance the structure prediction of long-sequence RNAs. In addition, if an algorithm is developed to predict the outmost base pairs of each i-fragment, then RNA-par could be used iteratively to partition RNA into shorter subsequences.Here, we propose a deep learning model, RNA-par, to predict the bases in exterior loops of an RNA sequence. Using these bases, the RNA sequence could be partitioned into i-fragments. This turned the long-sequence secondary structure prediction into the prediction of much shorter i-fragments. Each i-fragment secondary structure predicted individually can be further assembled to obtain the complete RNA secondary structure with high accuracy. We believe that developing a framework by combining RNA-par and existing RNA secondary structure prediction algorithms can improve the structure prediction of long-sequence RNAs.Supplementary Information.


Tumor budding is associated with lymph node (LN) metastasis in submucosal colorectal cancer (CRC). However, the rate of LN metastasis associated with the number of tumor buds is unknown. Here, we determined the optimal tumor budding cut-off number and developed a composite scoring system (CSS) for estimating LN metastasis of submucosal CRC.In total, 395 patients with histologically confirmed T1N0–2M0 CRC were evaluated. The clinicopathological characteristics were subjected to univariate and multivariate analyses. The Akaike information criterion (AIC) values of the multivariate models were evaluated to identify the optimal cut-off number. A CSS for LN metastasis was developed using independent risk factors.The prevalence of LN metastasis was 13.2%. Histological differentiation, lymphatic or venous invasion, and tumor budding were associated with LN metastasis in univariate analyses. In multivariate models adjusted for histological differentiation and lymphatic or venous invasion, the AIC value was lowest for five tumor buds. Unfavorable differentiation , lymphatic or venous invasion , and five or more tumor buds  were independent risk factors. In a CSS using these three risk factors, the rates of LN metastasis were 5.6%, 15.5%, 31.0%, and 52.4% for total composite scores of 0, 1, 2, and ≥ 3, respectively.For the estimation of LN metastasis in submucosal CRC, the optimal tumor budding cut-off number was five. Our CSS can be utilized to estimate LN metastasis.The online version contains supplementary material available at 10.1186/s12885-022-09957-8. Colorectal cancer (CRC) is a major cause of cancer-associated mortality and is the most common cancer worldwide . CRC canThe prevalence of LN metastasis in submucosal CRC is 0–17.3% –7, poorlThis was retrospective study determined the optimal tumor budding cut-off number and developed a scoring system to estimate LN metastasis of submucosal CRC. This study was reviewed and approved by the Institutional Review Board of the Seoul National University Hospital Biomedical Research Institute . All patients provided written informed consent and approval were obtained from all patients. All procedures were carried out in accordance with the relevant guidelines and regulations.In total, 12,749 patients underwent surgery for CRC at Seoul National University Hospital from January 1, 2002, to December 31, 2019. Among them, patients who underwent a radical operation for submucosal CRC and had available histopathological reports were eligible for this study. Submucosal CRC was defined as an adenocarcinoma that invaded the submucosal layer and conformed to the classification guidelines of the American Joint Committee on Cancer Staging. Patients who received neoadjuvant therapy or underwent local resection were excluded. Finally, 395 patients were enrolled.Data concerning patients’ clinicopathological parameters were collected from the electronic medical records. Sex, age, body mass index, American Society of Anesthesiologists score, carcinoembryonic antigen level, and tumor location were analyzed as clinical characteristics. Right colon cancer was defined as tumors in the cecum, ascending colon, and transverse colon; left colon cancer was defined as cancers in the descending and sigmoid colon; and rectal cancer was defined as tumors in the recto-sigmoid junction and the rectum .Pathological features assessed were tumor histological type, lymphatic or venous invasion, perineural invasion, number of tumor buds, distance from the proximal to the distal margin, number of harvested LNs, and number of metastasized LNs. Tumors were histologically classified as favorable differentiation (well or moderately differentiated carcinoma) or unfavorable differentiation , in accordance with the World Health Organization guidelines. Pathological features were evaluated via hematoxylin and eosin staining alone. Lymphatic or venous invasion was considered present when tumor cells invaded non-muscle-walled small vessels or large vessels with a smooth muscle layer and/or an elastic lamina layer Fig.  24]. Pe. Pe24]. 2 test or Fisher’s exact test was used to compare categorical variables; Student’s t-test was used to compare continuous variables. Multivariate logistic regression analysis was performed to identify independent risk factors that were predictive of LN metastasis. We used the Akaike information criterion (AIC) of the adjusted multivariate models to identify the optimal tumor budding cut-off number. The tumor budding cut-off values, from 0 to 12 at intervals of 1, were assessed as an indicator of LN metastasis based on the AIC. The best model exhibited the lowest AIC value. The cut-off value was determined when the model had the lowest AIC value. A composite scoring system (CSS) was developed to estimate LN metastasis by adding rounded values of the coefficients of independent risk factors. All statistical analyses were performed using SPSS 22 software . A p-value < 0.05 was considered statistically significant.The clinical characteristics and pathological features were compared according to LN metastasis status to identify risk factors for LN metastasis. Pearson’s χp = 0.022) and lymphatic or venous invasion (p < 0.001). The number of buds was higher in tumors with LN metastasis than in tumors without LN metastasis (p = 0.001).The prevalence of LN metastasis was 13.2% (52/395). No clinical characteristics significantly differed between patients with and without LN metastasis , positive lymphatic or venous invasion , and tumor budding  were significant predictive parameters for LN metastasis  of the invasive front [2 field was defined as follows: grade 1, 0–4; grade 2, 5–9; and grade 3, ≥ 10 [For the estimation of LN metastasis in submucosal CRC, the optimal tumor budding cut-off number was five. Imai reported that tumor budding or sprouting reflects faster tumor growth . In 1989ve front . In the  3, ≥ 10 . A multi 3, ≥ 10 . When LN 3, ≥ 10 . In the p = 0.075), for patients with submucosal CRC undergoing a radical operation [The histopathological predictors of LN metastasis in submucosal CRC were the depth of invasion , unfavorable differentiation , and lymphatic or venous invasion. We confirmed that unfavorable differentiation and lymphatic or venous invasion were independent predictors in a multivariate analysis. Ryu et al. reported that lymphatic invasion and histopathological differentiation were significant risk factors in 179 patients with early CRC . In a meperation .The prevalence of LN metastasis is 10–15% in patients who undergo additional operations after endoscopic resection –35. In oTo our knowledge, this is one of few studies to investigate the optimal tumor budding cut-off number. Our work had some limitations. First, the study was retrospective in nature; selection bias may have been present. Second, the study was performed in a single institution and enrolled a small number of patients. Moreover, we did not evaluate the invasion depth when deriving risk factors. A prospective, multi-center study is needed to obtain more accurate and detailed results.For the estimation of LN metastasis in submucosal CRC, the optimal tumor budding cut-off number was five. Our CSS can be utilized to estimate LN metastasis.Additional file 1: Table S1. Lymph node (LN) metastasis status according to the three-tier system of tumor budding. Table S2. Multivariate analysis of lymph node (LN) metastasis status according to the three-tier system of tumor budding.


In the originally published article, there were errors each of the author affiliations.Affiliation 1 should have been presented as “Shanghai Center of Thyroid Diseases, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China”, and not as “Shanghai Center of Thyroid Diseases, Shanghai, China”.Affiliation 2 should have been presented as “Department of Endocrinology and Metabolism, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China”, and not as “Department of Endocrinology and Metabolism, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China”Affiliation 3 should have been presented as “Department of Pathology, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China”, and not as Department of Pathology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China”.The authors apologize for these errors and state that they do not change the scientific conclusions of the article in any way. The original article has been updated.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In this study, we show that the coronavirus (CoV) genome may encode many functional hydrophobic alpha-helical peptides (HAHPs) in overlapping reading frames of major coronaviral proteins throughout the entire viral genome. These HAHPs can theoretically be expressed from non-canonical sub-genomic (sg)RNAs that are synthesized in substantial amounts in infected cells. We selected and analyzed five and six HAHPs encoded in the S gene regions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV), respectively. Two and three HAHPs derived from SARS-CoV-2 and MERS-CoV, respectively, specifically interacted with both the SARS-CoV-2 and MERS-CoV S proteins and inhibited their membrane fusion activity. Furthermore, one of the SARS-CoV-2 HAHPs specifically inhibited viral RNA synthesis by accumulating at the site of viral RNA synthesis. Our data show that a group of HAHPs in the coronaviral genome potentially has a regulatory role in viral propagation. Coronaviridae in the order Nidovirales includes significant human pathogens and animal viruses with diverse pathogenicity [The family genicity ,2. A novgenicity ,4, is cugenicity . Infectigenicity . It has genicity ,10,11,12genicity . Each coThe major coronaviral proteins are translated from sub-genomic (sg)RNAs synthesized by template switching using transcription-regulating sequences (TRSs). In addition, many non-canonical sgRNAs of unknown importance are also synthesized by TRS-independent mechanisms, and template switching occurs throughout the genome ,16,17,18In this section, the frame encoding a major coronaviral protein is referred to as the +1 frame, and frames shifted back one base or two bases are referred to as +2 or +3 frames, respectively. The deduced amino acids in these frames revealed the existence of a highly biased proportion of hydrophobic amino acids in the +2 frame of coronaviruses. This feature was observed throughout the entire coronaviral genome, but it was somewhat challenging to assess unambiguously in the 3’-terminal region after the S gene because of the various accessory genes encoded there. An example of this is shown in This highly biased amino acid composition resulted in many short open reading frames (ORFs) with large amounts of hydrophobic amino acids in the +2 frame. Examples of this are shown in This study first focused on the five HAHPs within the S gene region of SARS-CoV-2 (GenBank accession: MN908947.3) because non-canonical sgRNAs that can likely express these HAHPs have been detected in infected cells, albeit in minimal amounts . In thisWe next analyzed HAHPs within the S gene region of MERS-CoV (GenBank accession: NC_019843). In the +2 frame of the MERS-CoV S gene, there were nine ORFs, which may encode peptides longer than 20 amino acids E. AlthouThe S proteins of coronaviruses share a high degree of structural and functional similarity. Therefore, we analyzed whether HAHPs of SARS-CoV-2 could inhibit the syncytium formation of MERS-CoV S protein and vice versa. As observed with the SARS-CoV-2 S protein, MERS-CoV S-mediated syncytium formation was suppressed by SARS2-2c and -2g A. FurtheCoronavirus RNA synthesis occurs in specialized double-membrane vesicles (DMVs) induced by coronavirus infection ,30. The In this study, we showed a unique property of the coronavirus genome. The presence of hydrophobic alpha-helical short peptides (referred to as HAHPs) was deduced in the coronavirus genomes in a number and frequency that cannot be considered coincidental. Previous studies ,33,34,35The most important question is whether such HAHPs are expressed in coronavirus-infected cells. Because the initiation codon of HAHP ORFs is located downstream of the initiation codon of the major coronavirus proteins in canonical sgRNAs, HAHPs are unlikely to be translated from these canonical sgRNAs. However, the internal entry of ribosomes or a ribosome leaky scanning may be used to translate HAHPs, as has been observed for specific coronavirus accessory proteins ,37,38,39Although we have not been able to confirm the expression of HAHPs in infected cells or infected individuals, we consider the finding that these HAHPs specifically affected critical steps in coronavirus propagation to be significant. The membrane fusion inhibitory activity of several SARS-CoV-2 or MERS-CoV HAHPs was shown to be specific to coronaviruses but may act broadly against coronaviruses. Considering the short length and the nature of the transmembrane hydrophobic alpha-helix region of these HAHPs, which inhibited S protein membrane fusion activity, the HAHPs may form a specific interaction with the transmembrane region or somewhere in the vicinity of the transmembrane region of the coronavirus S protein. The coronavirus S proteins have a highly conserved tryptophan-rich region in and near the transmembrane region that contacts the outer membrane of the lipid bilayer ,43. ThisAlthough it is unknown whether all HAHPs are expressed and functional, coronaviruses encode many HAHPs throughout their ~30-kb long genomes. Many HAHPs are predicted to have a hydrophobic alpha-helix region, but there is a great deal of variation in HAHP amino acid sequences, HAHP length, the position of alpha-helices within HAHPs, and HAHP coding position within the genome. Coronaviruses may have diverse effects on host cells by combining many different HAHPs. However, conducting a functional analysis of HAHPs is expected to be more difficult compared with simply assessing single proteins. We speculate that involvement in viral RNA synthesis is one of the essential functions of HAHPs. Indeed, we observed that one HAHP (SARS2-2c) accumulated explicitly at the site of coronavirus RNA synthesis (possibly by incorporation into the DMV membranes) and inhibited RNA synthesis, although the details of this remain to be studied further. Because the assay conditions differ greatly from natural infection conditions under which each HAHP may be expressed in small amounts, we consider the assay results as evidence that HAHPs are a factor involved in RNA synthesis rather than that this HAHP has an RNA synthesis inhibitory activity.Regarding the inhibition of S-protein-mediated membrane fusion by SARS2-2c, this study did not yet show how the fusion-inhibitory activity of SARS2c benefits SARS-CoV-2 replication in infected cells. Our preliminary experiments showed that the cell surface expression of S proteins was reduced in cells co-expressed with SARS2-2c. Coronavirus buds into the ER-Golgi intermediate compartment (ERGIC) to form viral particles, which are released extracellularly . To achiIn conclusion, this study found that coronaviruses potentially encode many hydrophobic short peptides with alpha-helix structures throughout the entire virus genome. We also demonstrated that some of these peptides have functions that specifically affect coronaviral membrane fusion and RNA synthesis. This study will spark a new discussion about our understanding of coronaviruses and will stimulate further research in this area.Nasopharyngeal swab specimens were collected from pediatric inpatients in Japan between 2018 and 2021, and those that were found to be HCoV positive by multiplex real-time PCR assays were used for virus isolation using an air–liquid interface culture of human bronchial tracheal epithelial cells as described previously . The whoVeroE6/TMPRSS2, Vero/hSLAM, HeLa/TMPRSS2 cells, and stable SARS-CoV-2 replicon VeroE6/Rep3 cells were reported previously ,49,50,51The codon-optimized S protein expression plasmid (VG40589-UT) of the Wuhan strain of SARS-CoV-2 was purchased from Sino Biological, and the open reading frame of the S protein was cloned into a pCAGGS vector . The cDNVeroE6/TMPRSS2 cells, Vero/SLAM cells, HeLa/TMPRSS2 cells, and stable SARS-CoV-2 replicon VeroE6/Rep3 cells were seeded into six-well plates (1 × 106 cells/well), and the transfection of these cells with protein expression plasmids was carried out using Lipofectamine LTX  or TransIT-LT1 reagent . At 48 h post-transfection (hpt), the cells were fixed with 10% buffered formalin solution and then permeabilized with 0.5% Triton-X 100. After blocking, the cells were incubated with primary antibodies (Abs) and subsequently with secondary Abs conjugated with Alexa Fluor 488, 594, or 647 . The following Abs were used as primary Abs: rabbit anti-SARS-CoV-2 S protein polyclonal Ab (pAb) , rabbit anti-MERS-CoV S protein pAb , mouse anti-HA tag monoclonal Ab (mAb) (Proteintech), rabbit anti-HA tag pAb (MBL), rat anti-HA tag mAb (Proteintech), mouse anti-HA mAb12-1G6 (for the detection of PR8 HA) , rabbit https://harrier.nagahama-i-bio.ac.jp/sosui/ (accessed on 23 June 2022)” [https://services.healthtech.dtu.dk/service.php?TMHMM-2.0 (accessed on 23 June 2022)”, and by Alphafold2 [The transmembrane helices in peptides and peptide structures were predicted by SOSUI ver. 1.11 “Available online: e 2022)” ,26, by Tphafold2 . These s


IFIH1 (interferon induced with helicase C domain 1), which encodes the viral pattern-recognition receptor melanoma differentiation-associated protein 5 (MDA5), supports a potential link between enterovirus infection and type 1 diabetes.Enteroviral infection has been implicated consistently as a key environmental factor correlating with the appearance of autoimmunity and/or the presence of overt type 1 diabetes, in which pancreatic insulin-producing beta cells are destroyed by an autoimmune response. Genetic predisposition through variation in the type 1 diabetes risk gene n=43) with type 1 diabetes.We used molecular techniques to detect enterovirus RNA in peripheral blood samples (in separated cellular compartments or plasma) from two cohorts comprising 79 children or 72 adults that include individuals with and without type 1 diabetes who had multiple autoantibodies. We also used immunohistochemistry to detect the enteroviral protein VP1 in the pancreatic islets of post-mortem donors (p<0.0001). In addition, we show that children with autoimmunity are more likely to test positive for enterovirus RNA than those without autoimmunity . Furthermore, we found that individuals carrying the predisposing allele (946Thr) of the common variant in IFIH1  are more likely to test positive for enterovirus in peripheral blood . In contrast, using immunohistochemistry, there was no correlation between the common variant in IFIH1 and detection of enteroviral VP1 protein in the pancreatic islets of donors with type 1 diabetes.We observed enhanced detection sensitivity when sampling the cellular compartment compared with the non-cellular compartment of peripheral blood (OR 21.69; 95% CI 3.64, 229.20; Our data indicate that, in peripheral blood, antigen-presenting cells are the predominant source of enterovirus infection, and that infection is correlated with disease stage and genetic predisposition, thereby supporting a role for enterovirus infection prior to disease onset. Type 1 diabetes is caused by progressive loss of the insulin-producing beta cells in pancreatic islets. Genetic factors are important in the predisposition to disease development  and the Enterovirus (EV) (Picornaviridae family) that has been extensively studied and linked to type 1 diabetes  dendritic cell isolation kit), plasmacytoid dendritic cells (pDCs) (using a CD304 [BDCA-4/neuropilin-1] MicroBead kit). All reagents for cell separation were obtained from Miltenyi Biotec, and the post-separation enrichment was >90%, according to the manufacturer. Samples were pelleted and stored at -80°C until RNA extraction.For both cohorts, individuals who reported or showed symptoms of systemic ‘virus-like’ illness were not recruited to the study or did not undergo blood sampling. In the children cohort, none of the individuals were excluded from blood sampling due to ‘virus-like’ illness.RNA was extracted using a QIAamp viral RNA kit (Qiagen) and TRIzol reagent , in the adult and children cohorts, respectively, according to the manufacturer’s instructions. Detection of EV-RNA was performed by RT-PCR and liquid-phase hybridisation using a primer pair  from the highly conserved 5′ non-coding region as previously described . PCR ampIFIH1 genotypes for 43 of the previously reported 72 post-mortem donors with type 1 diabetes [In the adult cohort, DNA was extracted from whole blood collected using the QIamp blood mini kit (Qiagen) according to the manufacturer’s instructions, and genotyping for the SNP rs1990760 was performed by TaqMan assay . In the children cohort, DNA was extracted from EDTA-treated blood samples by a salting-out protocol , and gendiabetes .Formalin-fixed, paraffin-embedded pancreatic tissue from 43 individuals  with recent-onset type 1 diabetes, whose pancreatic histology has been described previously , was usep values were calculated using two-sided Fisher’s exact test, and 95% confidence intervals were computed using the Baptista–Pike method [p value <0.05 was considered statistically significant. Power analysis (post hoc and a priori) was performed using G*Power (version 3.1.9.7) [Sample size calculation with a power of 0.8 predicted that a sample size of 69 was required to detect a threefold increase in EV detection sensitivity from a proportion in population 1 (p1)=0.1 to p2=0.3. Statistical analysis was performed using GraphPad Prism . Odds ratios and e method . A p val3.1.9.7) .p<0.0001) , in which 18 of 101 samples (17.8%) tested positive for EV-RNA, compared with the non-cellular compartment (i.e. plasma) in which 1 of 101 samples (1.0%) tested positive for EV-RNA  Fig. . In the We detected EV-RNA in a higher proportion of individuals when analysing APC subsets combined  than when analysing whole PBMCs from the same individuals   Fig. . IndividNext we investigated whether positivity for EV-RNA in PBMCs correlates with defined stages of type 1 diabetes, i.e. adults with recently diagnosed type 1 diabetes (<3 months) and children positive for mAAb with an ongoing autoimmune reaction.p=0.79)   compared with 10 of 35 individuals without type 1 diabetes (28.6%) (79) Fig. . In the 65) Fig. .Thr) of the common variant in IFIH1 . The distribution of the common variant in IFIH1 in cohorts, and detection of EV-RNA according to subgroup and genotype, is summarised in Table Ala) significantly reduced the OR to detect EV-RNA in both the recessive model  and the additive protective model , when analysing the children and adult cohorts in combination (Table IFIH1 (Table We next investigated whether detection of EV infection (by detecting EV-RNA or VP1) in individuals correlates with the predisposing allele (946Ala) and reduced detection of EV infection in the cellular compartment of peripheral blood also extends to pancreatic islets studied in situ. To this end, we assessed the presence of the EV capsid subunit viral protein 1 (VP1) in pancreatic tissue sections recovered from 43 donors with type 1 diabetes and held within the Exeter Archival Diabetes Biobank  of the common variant  in IFIH1. VP1 was detected in the pancreatic islets of 70.0% (7/10), 76.2% (16/21) and 66.7% (8/12) of donors with the homozygous risk variant, those who were heterozygous, and those with the homozygous protective common variant  in IFIH1, respectively . VP1 waOur data from the children cohort show a significantly increased sensitivity for detection of EV-RNA within the cellular compartment of peripheral blood compared with plasma. Additionally, using the adult cohort, we found that EV infection was detected in more individuals when APC subsets  were analysed for EV-RNA, compared with whole PBMCs. These observations had statistical power (post hoc) of >0.9. Hence, our data indicate that APCs are ‘carriers’ of EV-RNA in peripheral blood as every individual that tested positive for EV-RNA in the PBMC sample also tested positive for EV-RNA in at least one subset of APCs. Similar observations, that EV-RNA is found more frequently in PBMCs than serum, have been made previously, albeit in a smaller cohort . We postp=0.79) between positivity for EV-RNA and type 1 diabetes, potentially due to the increased sensitivity of detection of EV-RNA in PBMC subsets. Overall, our findings are in line with the results of previous studies summarised in meta-analyses by Yeung et al and Wang et al [Our analysis shows that positivity for EV-RNA is associated with islet autoimmunity. Children positive for mAAb were more likely to test positive for EV-RNA than those without mAAb . In children positive for mAAb, we found a similar frequency of EV-RNA positivity among children who later progressed to type 1 diabetes and those who have not yet progressed. In the adult cohort, we did not detect a correlation  of the common variant in IFIH1  were more likely to test positive for EV-RNA than those without the predisposing allele (in both the additive and protective recessive models). However, our results are based on a limited sample size and low statistical power (post hoc) . In the few studies reported so far, no correlation was found between IFIH1  homozygous genotypes and EV-RNA detection in peripheral blood [n=68 per homozygous group, based on our reported proportions of EV-RNA detection per group) should allow definition of the relationship between the IFIH1 Thr946Ala genotype and EV infection detected in peripheral blood.To obtain a larger cohort, we combined our two cohorts, and found that individuals carrying the predisposing allele  may overlap with those of a virus infection. This overlap in symptoms may introduce a sampling bias between study groups  if symptoms observed in individuals with recent-onset type 1 diabetes are misinterpreted as the exclusion criterion, or vice versa. However, we did not observe a sampling bias with regard to the exclusion criterion ‘virus-like’ illness in the children cohort. In the adult cohort, none of the sampled participants exhibited symptoms of ‘virus-like’ illness at the time of recruitment and sampling. However, we do not have data on individuals that were not recruited to the study due to meeting the exclusion criterion. Therefore, we cannot state whether such a sampling bias occurred in the adult cohort. Thus, while we think it unlikely that a sampling bias occurred between the study groups of individuals without type 1 diabetes and with recent-onset type 1 diabetes in the adult cohort, we cannot exclude this.Thr) of the common variant in IFIH1  and the presence of EV infection (i.e. positivity for VP1) in pancreatic islets. This probably reflects the fact that most individuals with type 1 diabetes display signs of EV infection in pancreatic islets, and that the effect of the variant in IFIH1 may be more nuanced than simply the presence or absence of VP1 positivity in islets.We then further tested whether our finding of an association between the predisposing allele of the common variant and increased EV-RNA detection in peripheral blood extends to pancreatic tissue of post-mortem donors with type 1 diabetes. As previously reported, we detected the EV capsid protein VP1 in pancreatic islets in the majority (>70%) of donors with type 1 diabetes , 12. HerIFIH1 genotype, suggests that a dysregulated immune response and ongoing autoimmunity may interfere with the control and/or clearance of EV infection. We cannot exclude the possibility that infection of pancreatic islet cells by enterovirus is influenced by genetic predisposition. Our analysis focused solely on detection of immunopositivity for the capsid protein VP1. Future analysis of the level of expression of VP1 within islet cells and/or the frequency of VP1-positive cells within pancreatic islets may provide further insights into the effects of genetic predisposition to type 1 diabetes by the common variant in IFIH1 .Our finding of a significantly increased prevalence of EV-RNA in children positive for mAAb, regardless of their Thr) of the common variant  in the type 1 diabetes risk gene IFIH1. We also show that enterovirus is detected more often in children with islet autoimmunity compared to those without. Our data further support the view that analysis of APCs increases the sensitivity for detection of EV infection in peripheral blood, and that EV infection is part of the aetiology of type 1 diabetes. Ongoing studies for development of vaccines against Coxsackievirus strains to prevent type 1 diabetes will also be informative [In summary, our results indicate a correlation between the ability to detect EV-RNA in the cellular compartment of peripheral blood and the presence of the predisposing allele (946ormative , and may


Starting from computational works  and long non-coding RNA (lncRNA) interactions with RNA-binding proteins (RBPs). The authors emphasize the need for developing larger interaction datasets to increase performance and expand on approaches amenable to machine learning. In their Perspective, Vandelli et al. provide an example on how the integration of RNA-protein interaction data with computational analysis tools can lead to new hypotheses. The authors used data from four experimental and one computational study of interactors of SARS-CoV-2 genomic RNA. Although hundreds of proteins have been identified across the different studies, only twenty-one appear in all the experiments to interact with SARS-CoV-2 RNA. The identified proteins refer to known or predicted stress granule forming proteins, which leads to the intriguing hypothesis that a mechanism of action for SARS-CoV 2 could be to target proteins that attract other partners through phase separation.Several theoretical methods for studying physical interactions between RNA and proteins are currently being developed. The review by Yang et al. performed a systematic analysis of mRNA, microRNA (miRNA) and circRNA expression in type II alveolar epithelial cells (ATII) in Tibetan pigs. These pigs live at high altitude and developed an efficient metabolism for living at lower oxygen levels than “standard” landrace pigs. The authors simulated a hypoxic environment for cultured ATII cells of tibetan pigs and landrace pigs grown under normoxic and hypoxic conditions. The differential expression of circRNA, miRNAs and mRNAs were combined to construct competing endogenous RNA (ceRNA) networks for these cells, thereby identifying critical regulatory modes for metabolic control. A similar approach was used by Zhang et al. that explored the transcriptomic network of Rotator cuff tears (RCT), the most common cause of shoulder dysfunction. Specifically, the authors performed RNA-seq analysis of five patient samples with RTC in supraspinatus muscles and matched unharmed subscapularis muscles from the same individual to dissect the dysregulated transcriptome including mRNAs, miRNAs, lncRNAs, and circRNAs. Based on network construction considering the ceRNA hypothesis, they suggested dysregulation of the ceRNA network in RTC through several ncRNAs. Hence, the authors propose that these ncRNAs could play a role in the development of RCT, possibly adding a new angle for therapeutic approaches.Two articles focus on transcriptome analysis for construction of RNA-RNA networks. Giambruno et al. describes recently developed complementary proximity labeling techniques to map RNA-protein interactions in mammalian cells. This technology permits the identification of relatively transient interactions as well as poorly expressed molecules by relying on the inducible activity of enzymes (biotin ligases or peroxidases) that are expressed in living cells and biotinylate amino acids and nucleic acids of binding factors in proximity–within 20 nM range. Besides the interaction with their target RNAs, RBPs often interact with other proteins which further influence the fate of ribonucleoprotein (RNP) complexes. Kieffer et al. combined affinity purification and mass spectrometry (MS) to define the network of nuclear proteins interacting with the N-terminal region of FMRP. FMRP is encoded by the FMR1 gene (Fragile X messenger ribonucleoprotein 1) whose absence of expression results in Fragile X-syndrome (FXS), the most common inherited form of intellectual disability. While it was well known that FMRP regulates translation of subsets of mRNA in the cytoplasm, the protein shuttles between the nucleus and cytoplasm but little was known about its nuclear functions. Hence, this study adds a base to further mechanistic studies on its nuclear functions in neuronal physiology.Cross-linking is often used to preserve functional protein-RNA interactions present in the cell. The review by Fan et al. discuss the role of lncRNAs and aerobic glycolysis in tumorigenesis and tumor progression and explore the interaction networks to provide insights into therapeutic targets for treatment. The authors suggest that lncRNAs could regulate key enzymes related to glycolysis to promote aerobic glycolysis . An important highlight of the work is that lncRNAs can act as sponges for miRNAs to regulate expression of enzymes or modulate signaling pathways. The association of lncRNAs with enzymes could also promote formation of protein complexes  thus conferring additional functions. The article by Li et al. elaborates on the relationship between lncRNAs and diseases. In the case of tumor development, the authors report that a lncRNA can act either as proto-oncogene or tumor suppressor. As for neurodegenerative and psychiatric diseases, examples are provided for DISC1 (schizophrenia); PINK1 (endocrine diseases), MALAT-1 and NEAT1/2 and others. One important case is BACE1 antisense (BACE1AS) that forms complex with BACE1 messenger RNA to increase the stability of the latter thus promoting aggregation of β-amyloid protein in Alzheimer’s disease.Two review articles highlight the specific and broader implications of lncRNAs in disease. Buchanan et al. discuss the potential role of ribosome heterogeneity in brain plasticity. After briefly reviewing the importance of translation regulation in neuroplasticity, the authors highlight recent results showing that complex brain functions such as sleep and learning have a significant impact on ribosomal protein (RP) expression. As sleep is critical in promoting brain plasticity, the authors then propose that changes in RP stoichiometry during sleep could establish a mechanism for regulation of translation of subsets of mRNAs that code for proteins mediating brain plasticity. Zhang et al.investigate the regulation of Hsp70 chaperones Ophraella communa. In concert with many cofactors, Hsp mediates essential activities such as protein folding and assembly. Normally Hsp70 is located in the cytoplasm, however, when cells are stimulated by heat stress, the protein is transferred to the nucleus. In the case of O. communa, an important biological control agent of the ragweed Ambrosia artemisiifolia worldwide, Hsp70 was found to be highly expressed in the female ovaries and male testes and induced by mating. The regulatory mechanisms of Hsp70 and the investigation of its RNA stability and translation should be further investigated.Besides the many ncRNA and viral RNAs concerned in this Research Topic, two articles focus on mRNAs for cell fate determination and in translation. In their perspective,


In comparison with exome-capture and 3′ kits, the Smarter appeared more comparable to Truseq (p < 0.001). Overall, our results suggest that the Smarter is the most robust RNA-Seq technique to study small FFPE samples and 3′ Lexogen presents an interesting quality–price ratio for samples with less limiting quantities. RNA sequencing (RNA-Seq) appears as a great tool with huge clinical potential, particularly in oncology. However, sufficient sample size is often a limiting factor and the vast majority of samples from patients with cancer are formalin-fixed paraffin-embedded (FFPE). To date, several sequencing kits are proposed for FFPE samples yet no comparison on low quantities were performed. To select the most reliable, cost-effective, and relevant RNA-Seq approach, we applied five FFPE-compatible kits  using 8 ng to 400 ng of FFPE-derived RNA and compared them to Nanostring on FFPE samples and to a reference PolyA (Truseq) approach on flash-frozen samples of the same tumors. We compared gene expression correlations and reproducibility. The Smarter Pico V3 ribodepletion approach appeared systematically the most comparable to Nanostring and Truseq ( High throughput sequencing is a keystone of precision and predictive medicine with most approach relying on analysis of DNA alterations ,2. RNA sStandard RNA sequencing approaches require large amounts of high-quality RNA. The best quality RNAs are often obtained when samples are flash-frozen, which is not compatible with routine clinical practice in most centers. Clinical applicability of RNA-Seq is thus conditioned on its applicability to formalin-fixed paraffin-embedded (FFPE) samples. Moreover, sample quantity is highly variable. For biopsies performed on some organs , the tissue obtained from fine-needle biopsies can be highly limiting in quantity. This commonly used approach leads to only thousands of cells in samples that are often only available in FFPE, therefore with low RNA quality (RNA integrity number < 3) . This reTo date, several sequencing kits are proposed to be compatible with FFPE samples with little data comparing them, especially in a low quantity setting. It has been previously reported that transcriptome analyses depend on the sequencing kits used ,9,10. OnIn order to select the most reliable, cost-effective, and relevant RNA-Seq approaches, we applied several sequencing kits marketed as FFPE-compatible and compared their expression profiles to Nanostring quantification of a set of selected genes and to poly-A enriched RNAseq profiles of the flash-frozen samples of the same tumor.The RNA was extracted from 20 breast cancer tumors for which a flash-frozen and FFPE sample was available. Six different RNA-sequencing kits were used. Illumina’s TruSeq polyA enrichment kit  was applied to RNA derived from the frozen samples and used as a reference. Illumina’s TruSeq RNA exome (referred to as RNAaccess) was applied to 12 of the 20 FFPE samples. Lexogen’s QuantSeq 3′ approach (hereby referred to as Lexogen) was applied to FFPE samples using a range of input quantities, 50 ng, 150 ng, and 400 ng. Three ribodepletion approaches were applied each to FFPE-derived RNA: SMARTer Stranded Total RNA-Seq Kit v3-Pico, Ovation SoLo and SEQuoia Complete Stranded RNA . For the three ribodepletion kits, four samples were replicated. Finally, a Nanostring nCounter panel of the molecular subtypes of breast cancer (91 genes) was applied to FFPE-derived RNA.In total, 20 breast tumors, for which both frozen and a formalin-fixed paraffine embedded tissue sample, were available were obtained from the Institut regional du cancer Montpellier. RNA was extracted using trizol and Guanidine Isothiocyanate and isolated using RNeasy Mini kit (Qiagen) and the ALLPrep FFPE tissue kit (Qiagen) for frozen and FFPE samples, respectively, following the manufacturer’s instruction. The quality of FFPE-derived RNA was measured by the proportion of fragments above 200b (DV200) and ranged from 13% to 69% with an average of 42%. The FFPE-derived RNA of all 20 samples were used on 4 FFPE-compatible RNAseq library preparation: Lexogene’s QuantSeq FWD 3′ kit, BIO-RAD’s SEQuoia complete stranded RNA library kit, Takara’s SMARTer Stranded total RNA-seq kit v3-pico (Smarter), and TECAN’s Ovation SoLo (Solovation) RNA-Seq library preparation kit.The following total quantities of FFPE-derived RNA were used : 400 ng A nCounter Nanostring breast cancer gene panel was applied to 150 ng of FFPE-derived RNA.Sequencing reads were mapped to the human genome GRCh38 (Ensembl v101) and Ensembl’s reference transcriptome (v101) using a single-pass of STAR with the following arguments changed from their default: seedSearchSartLmax 12, outFilterType BySJout and alignEndsType Local. FeatureCount was used to count RNAseq reads per any overlapping exon (-O argument) and then summarized per gene (as meta-features) using appropriate manufacturer defined stranding. Raw gene-level RNA counts were then normalized using the upper-quartile normalization and log2 + 1 transformed.p value of <0.05.Gene-wise and sample-wise correlations of gene expressions was performed using a Spearman correlation test. Comparison of genes expressions and sample coefficients correlation between RNAAccess and other sequencing kits  was performed using a Mann–Whitney test. For each test, statistical significance was set at a two-sided The number of genes with at least one count per sample by each technique is shown in Truseq and Smarter quantified the largest number of genes  whereas Lexogen using 50 ng of RNA and Sequoia identified about 2 times less .p = 0.006), Lexogen 400 ng (p = 0.004), Sequoia (p = 0.02), and a significantly higher correlation with Nanostring using Truseq (p = 0.05) and Smarter (p < 0.0001).In order to evaluate the accuracy of gene expression quantification, the transcriptomic profiles were first correlated to those obtained by Nanostring using a gene-wise correlation A. In thiSecondly, we correlated each profile with its Nanostring reference for each technique (sample-wise correlation), focusing only on the genes composing the panel B. By comIn order to perform a broader analysis, we used the frozen-based Truseq profiles as reference. In order to take into account the potential difficulty to quantify lowly expressed genes in FFPE, genes were split in three groups (tercile) based on their median expression levels in Truseq. The upper and lower terciles were 3.7 and 9.0 log2 counts, respectively.p < 0.0001). In the intermediate and highest terciles, the correlation with Truseq was higher with Lexogen 150 ng, Lexogen 400 ng, Solovation and Smarter (p < 0.0001), and less robust with Lexogen 50 ng and Sequoia (p < 0.0001).First, we correlated gene levels expression between Truseq and each technique  and Smarter showed higher correlation with Truseq (p < 0.05).We then performed sample-wise correlation between Truseq and each technique B. We anaFinally, in all terciles and by comparing gene or samples, Smarter on small quantities of FFPE-derived RNA had the best correlation with Truseq applied to standard quantities of Frozen-derived RNA.p = 0.029). Distribution of correlation between each replicate for each sequencing kit is presented in The kit reproducibility was assessed for Sequoia, Solovation, and Smarter by correlating gene expression profiles with duplicates of lower input quantity (2 ng) A. CorrelThis study aims to compare recent RNA-sequencing library kits suggested by vendors as compatible with FFPE, and for ribodepletion kits, with small quantities of FFPE-derived RNA. A 3′ capture for standard RNA quantities and several ribodepletions techniques for quantities below 10 ng of total RNA were used. Although ribodepletion techniques were compatible with low quantities out of the box, we tested the lower limits of 3′ captures to test their applicability to small samples. The previous FFPE RNAseq approach RNAaccess was also applied as a historical reference. By analyzing gene-wise and sample-wise correlations of gene expression against a reference frozen polyA RNAseq or an FFPE-derived Nanostring panel, Smarter’s Pico V3 appeared systematically the most reliable sequencing kit and one of the most reproducible technique. These results are particularly relevant since Smarter performed well on small amounts of FFPE-derived total RNA. Previous studies assessing performance of different library prep also showed that Smarter’s kit was the most comparable with Truseq among other kits ,11,12,13The main limit of our study is that samples are obtained from breast cancers only. RNA-seq performance may also be dependent on the type of tissue and we did not have other tissue to validate our results. However, we can assume that low-quality RNA related to specific tissues  affect similarly all sequencing kits.Overall, the Pico V3 Smarter approach should be preferred to study the transcriptome of FFPE samples whereas Lexogen presents an interesting quality price ratio for large quantities of RNA.


Subviral agents are nucleic acids which lack the features for classification as a virus. Tombusvirus-like associated RNAs (tlaRNAs) are subviral positive-sense, single-stranded RNAs that replicate autonomously, yet depend on a coinfecting virus for encapsidation and transmission. TlaRNAs produce abundant subgenomic RNA (sgRNA) upon infection. Here, we investigate how the well-studied tlaRNA, ST9, produces sgRNA and its function. We found ST9 is a noncoding RNA, due to its lack of protein coding capacity. We used resistance assays with eukaryotic Exoribonuclease-1 (XRN1) to investigate sgRNA production via incomplete degradation of genomic RNA. The ST9 3’ untranslated region stalled XRN1 very near the 5’ sgRNA end. Thus, the XRN family of enzymes drives sgRNA accumulation in ST9-infected tissue by incomplete degradation of ST9 RNA. This work suggests tlaRNAs are not just parasites of viruses with compatible capsids, but also mutually beneficial partners that influence host cell RNA biology. Encodinsviridae . In coinsviridae . TlaRNAssviridae . The funSubgenomic RNAs have been shown to play various critical roles in viral infections, from encoding proteins as messenger sgRNAs, fulfilling regulatory functions in replication and transcription, and aiding in suppression of host immune responses . DiscoveAccumulation of a large amount of the sgRNA produced by ST9 and other tlaRNAs during infections underscores the potential importance of this sgRNA and led to investigations into their potential as a messenger sgRNA or its function as a noncoding RNA. Synthesis of sgRNAs has been extensively studied in plant systems, and different mechanisms for their generation have been proposed . Two conDuring internal initiation of transcription, the viral RdRp binds to a non-terminal nucleotide sequence on the negative (non-genome) RNA strand and begins transcription at this internal locus, thus generating subgenomic RNAs. A key feature underlying this mechanism for sgRNA production is a subgenomic promoter, the nucleotide sequence that is recognized and bound by the RdRp for initiation . The proFlavivirus, and the crystal structure of the RNA elements responsible for stalling XRN1 was determined for Murray Valley encephalitis virus and Zika virus (Opiumpoppy mosaic virus (OPMV) was found to generate a translated sgRNA via XRN’s exonucleolytic activity  tails which shield RNAs from endogenous decay, thus these viruses must find ways to evade the host RNA decay machinery . The prika virus . RNA secka virus . Multiplka virus , suggestactivity . In addiactivity .Tombusviridae, tlaRNAs fall into the group of +ssRNAs especially susceptible to host RNA decay since they possess neither 5’ caps nor poly(A) tails  tails . Recent A) tails . In the 2.2.1.in planta, the Agrobacterium tumefaciens  strain GV3101 was used to infect plants with an infectious clone of ST9 designated JL89:ST9 and JL89:ST9-derived mutants (600 of 0.8 (Nicotiana benthamiana (N. benthamiana) plants constitutively expressing the turnip mosaic virus silencing suppressor protein HC-Pro to increase viral RNA and protein accumulation, as done previously  or have been previously described  beginning just upstream of the 3’ UTR and spanning to downstream of the sgRNA 5’ end was directly subjected to challenge by the enzyme eviously , 2015. Tescribed . Samplesescribed . The seq3.3.1.Tombusviridae, which comprises only ~ 7% of the genome)  . Viral 3in vitro. A previously described infectious clone of ST9, termed JL89:ST9 was agroinfiltrated into Nicotiana benthamiana (N. benthamiana) plants and infiltrated tissue was harvested four days later and used in hybridizations alongside the in vitro transcribed RNA NaugGC where R is a purine and N is any nucleotide . The mosN. benthamiana plants and in agreement with previous observations that tlaRNAs in the absence of a coinfecting virus don’t induce symptoms, infected plants were indistinguishable from wild type. RNA was extracted from infected plants, used in northern hybridizations, and sequencing results confirmed all mutations were maintained throughout the duration of the experiment. The mutant yielded genomic and subgenomic RNA accumulations indistinguishable from wild type ST9. The results of the mutational analysis indicate that a hypothetical protein derived from the P4 ORF has no essential function related to the replication, infection, or sgRNA production of ST9  3’ UTR that forms a three helix junction (THJ) structure that has been shown previously to strongly inhibit and stall XRN1  Moon et, 2015. Tin vitro into RNA and subjected to XRN1 degradation  in the genomic context resulted in the ablation of positive sense sgRNA, though the sgRNA accumulated in the negative sense  which acts as a potent post transcriptional gene silencing suppressor . A mutuasmission . Interessmission . Anothersmission . Similarsmission . Notablysmission . An unspsmission . CollectIntriguingly, coinfection of ST9 with the polerovirus beet western yellows virus (BWYV) has been shown to produce more severe symptoms and lead to higher accumulation of BWYV RNA than in single infections (11).While the molecular mechanisms underlying the increased symptom severity observed in coinfections of BWYV and tlaRNA ST9 have yet to be elucidated , it is wCampbell et al, Supplementary Material