Escherichia coli and Mycoplasma pneumoniae. Formation of this supercomplex, called expressome, is mediated by physical interactions between the RNAP-bound transcription elongation factors NusG and/or NusA and the ribosomal proteins including uS10. Based on the structural conservation of the RNAP core enzyme, the ribosome, and the universally conserved elongation factors Spt5 (NusG) and NusA, we discuss requirements and functional implications of transcription-translation coupling in archaea. We furthermore consider additional RNA-mediated and co-transcriptional processes that potentially influence expressome formation in archaea.The lack of a nucleus is the defining cellular feature of bacteria and archaea. Consequently, transcription and translation are occurring in the same compartment, proceed simultaneously and likely in a coupled fashion. Recent cryo-electron microscopy (cryo-EM) and tomography data, also combined with crosslinking-mass spectrometry experiments, have uncovered detailed structural features of the coupling between a transcribing bacterial RNA polymerase (RNAP) and the trailing translating ribosome in  The controlled and coordinated expression of genes plays a fundamental role in all cellular life forms and occurs in two steps: transcription of DNA to RNA by RNA polymerase (RNAP) and translation of RNA to protein by the ribosome. Cellular RNAPs share a conserved core architecture . HoweverBacillus subtilis showed that coupling of transcription and translation is not conserved across all bacteria  cryo-EM of samples formed by direct reconstitution of purified ribosome , or (ii)eumoniae . With sueumoniae . In E. chelicase , proposehelicase . Additiohelicase ,E. In coentation . This isAll three studies concluded that short spacings between RNAP and the ribosome, either directed by the mRNA or by adding a drug to halt RNAP, form expressomes that resemble an earlier lower-resolution reconstruction formed by collision of a translating ribosome with a stalled RNAP . ImportaWhile it is tempting to suggest uncoupled, NusG coupled, and collided expressome structures represent a ribosome approaching RNAP (in agreement with a reduction in RNA separating the two machineries), there is no other experimental evidence to support this chronological order of events, and this remains subject for further research.Thermococcus kodakarensis, DNA-attached polysomes have been visualized by electron microscopy , which binds nascent RNA  and stimE. coli expressome, RNA-dependent TTC is further mediated by NusG, which is the only universally conserved TF  to and association of RNA chaperones and transcription termination factors with the RNA may influence expressome formation and will be shortly discussed in this section compare –I.Haloferax volcanii, Thermococcus onnurineus, Pyrococcus abyssi, Saccharolobus solfataricus, Methanocaldococcus jannaschii and Marinobacter psychrophilus  intrinsic termination at poly(U) stretches  tretches . Importaof genes . Furtherof genes . Even thmination . Alternamination . This womination . Whethermination .in vitro. In order to rationalize the underlying molecular mechanisms, we do need to understand the structural determinants of the RNAP-ribosome interactions and the potential role of general regulatory factors including NusG-Spt4/5 and NusA, as well as ribosomal proteins. A crucial question to be solved in the future is how co-transcriptional processes like SmAP binding, transcription termination, or RNA processing are coordinated with ribosome coupling in space and time. This also includes coordination of translation initiation and TTC. In bacteria, translation initiation was delineated in great detail showing that the 30S subunit is recruited to the mRNA with the help of the initiator tRNA and initiation factors before the 50S subunit joins to form the translation-competent ribosome (see for example, Are transcription and translation coupled in archaea similar to bacteria? We propose that this is likely, but definitive proof is still lacking. This problem can only be solved by a multidisciplinary effort that reaches beyond a molecular-structural analysis The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.AW prepared the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Transcription termination of protein-coding genes in eukaryotic cells usually relies on a tight coordination between the cleavage and polyadenylation of the pre-mRNA, and 5′-3′ degradation of the downstream nascent transcript. Here we investigated the contribution of the essential fission yeast endonuclease Pac1, a homolog of human Drosha that cleaves hairpin RNA structures, in triggering polyadenylation-independent transcription termination. Using ChIP-sequencing in Pac1-deficient cells, we found that Pac1 triggers transcription termination at snRNA and snoRNA genes as well as at specific protein-coding genes. Notably, we found that Pac1-dependent premature termination occurred at two genes encoding conserved transmembrane transporters whose expression were strongly repressed by Pac1. Analysis by genome editing indicated that a stem-loop structure in the nascent transcript directs Pac1-mediated cleavage and that the regions upstream and downstream of the Pac1 cleavage site in the targeted mRNAs were stabilized by mutation of nuclear 3′-5′ and 5′-3′ exonucleases, respectively. Our findings unveil a premature transcription termination pathway that uncouples co-transcriptional RNA cleavage from polyadenylation, triggering rapid nuclear RNA degradation. Escherichia coli (Ribonucleases (RNases) are found in all domains of life and play important roles in the post-transcriptional control of gene expression through RNA maturation and catabolism. RNases are classified into two main groups depending on whether they cleave within RNA molecules (endoribonucleases) or degrade RNA from its extremities (exoribonucleases). RNase III is an ancient family of endonucleases that specifically target double-stranded (ds) RNA substrates. The first RNase III enzyme was identified in hia coli , where ihia coli , a role hia coli .Saccharomyces cerevisiae, the RNase III homolog Rnt1 processes RNA polymerase (RNAP) II-transcribed polycistronic transcripts into individual entities  biogenesis. miRNAs are short (about 22-nt long) single-stranded RNAs derived from endogenous hairpin dsRNA precursors that are matured by Drosha cleavage activity . FunctioSchizosaccharomyces pombe also possesses two RNAse III family members: Pac1 and spDicer. While the functions of spDicer are well described, the essential Pac1 protein, homolog to both S. cerevisiae Rnt1 and human Drosha, remains poorly characterized. The pac1 gene was initially isolated as a high copy suppressor of the untimely meiosis phenotype that occurs in a S. pombe pat1 mutant, and may therefore contribute to fission yeast sexual differentiation, although the underlying mechanism remains unknown  supplemented with adenine, uracil, histidine, and leucine. Cells were collected at OD600nm of ∼0.5–0.7. A list of all escribed . Nuclearescribed  were relescribed  using liescribed  was usedPac1-FRB-GFP localization was detected by using fluorescence microscopy as previously described . BrieflyS. pombe strain and were measured with the ΔΔCT method using the gene nda2 as internal reference. The oligonucleotides used in the qPCR experiments are listed in Supplementary Table S4.Total RNA was extracted using the hot-acid phenol method, as previously described . RT-qPCRpac1-ts RNA-seq experiment) or Illumina Truseq Stranded Total RNA-sequencing after ribodeletion with the RiboZero Yeast ribodepletion (Epicentre). RNA-seq libraries were sequenced in paired-end (2 × 50nt) using Illumina technologies (MiSeq or NovaSeq) at Genome Québec.rRNA-depleted RNA-seq libraries were prepared following manufacturer's instructions using either the Zymo-Seq RiboFree Total RNA Library Kit (for the 32P] using T4 polynucleotide kinase phosphorylation (NEB), following manufacturer's instruction. Probes were hybridized overnight to the membrane at 42°C.For northern blot analysis, 8 μg of total RNA were separated on 7% acrylamide gels containing 8% urea. After transfer on a nitrocellulose membrane (Amersham Hybond™), mature and precursors snoRNA were detected using DNA probes radiolabeled with ATP according to the manufacturer's instructions. Following column purification (Zymoresearch RNA clean & concentrator™), the transcription product was heated at 85°C for 5 min then annealed at room temperature for 30 min. The annealed RNA was then added to the purified Pac1 and incubated at 30°C for the indicated times. The cleavage reactions were stopped by the addition of 2× formamide RNA loading, followed by migration on a 8% polyacrylamide gel containing 8M urea.Radiolabeled Pac1 substrates were transcribed by T7 RNA polymerase (NEB) from a DNA template generated by PCR in presence of UTP[α-ChIP-qPCR and ChIP-seq experiments were performed as described previously . BrieflyS. pombe genome (version ASM294v2.26) using HISAT2  using HISAT2  and ChIP-seq (bigwig) data generated in this study are available on GEO under accession numbers GSE167041 and GSE167040, respectively.To identify transcripts co-transcriptionally cleaved by Pac1—a process that presumably occurs in close proximity to the chromatin template—we performed Pac1 ChIP-sequencing using a strain expressing a functional TAP-tagged version of Pac1 from its endogenous chromosomal locus. Peak calling on the resulting reads revealed that Pac1 is significantly associated with almost 200 genomic loci covering rRNA, snRNA, snoRNA, mRNA and lncRNA genes Figure . Among tpac1 mutant, we questioned the fate of RNAPI and II occupancy after Pac1 nuclear exclusion using both ChIP-qPCR and ChIP-seq assays. In the case of RNAPI, Pac1 robustly bound the rDNA repeats in the 3′ETS region downstream of the 25s rDNA  was not , stable hairpin structures with stems of 23–32 nucleotides ending with small loops of 3–5 nucleotides were predicted directly downstream of the annotated mature snRNA units  , whereas a control H/ACA box snoRNA (sno12) was unaffected by Pac1 nuclear exclusion  . Althougransgene , our datransgene , with th8 Figure  and F. A snoRNAs . While b snoRNAs , a diffe snoRNAs . This acxclusion . Furtherxclusion . These oCollectively, our data reveal that Pac1 is recruited to an extensive set of coding and non-coding RNA genes with a key role in transcription termination at snRNA and selected snoRNA genes.mfs2 and SPBC530.02  of transporters , but st by cell .mfs2 and SPBC530.02 is caused by the loss of Pac1 activity in the nucleus and not as a consequence of Pac1 activity in the cytoplasm, we took advantage of a previously described thermosensitive pac1 allele called snm1-1 —controlled with the well-described oxidative stress response gene trr1—reproducibly induced mfs2 expression ∼10-fold   , while sd Figure . We alsosistance , providimfs2 induction in oxidative stress is dependent on Pac1, treating the mfs2 stemdead mutant with H2O2 should not further induce mfs2 expression. However, we observed an almost multiplicative effect between the stemdead mutation and the H2O2 treatment, with a 120-fold induction compared to the wild-type untreated condition  regulate mfs2 expression through independent mechanisms.Next, we reasoned that if n Figure . A similn Figure . Togethemfs2 expression independently of Pac1 repression through increased RNAPII initiation. Indeed, two CRE motifs (TGACGT), known to recruit the stress response transcription factors Atf1, Pap1 as well as the Pap1-Prr1 heterodimer  strongly reduced mfs2 induction upon H2O2 treatment . ChIP-qPCR analysis indeed revealed a clear increase in Pac1 association with mfs2 upon H2O2 treatment . Accordingly, we have analyzed the distance between the Pac1 cleavage site (stem-loop) and the next poly(A) signal consensus sequence (AAUAAA), and this correlates with the extent of the termination defects observed upon Pac1 inactivation , which elicits RNAPII disengagement from the DNA template. However, as Pac1 recruitment occurs independently from the CPF complex  could be the functionally relevant components of the transcriptional induction of mfs2 in stress conditions. This hypothesis, however, is currently not supported by our results as neither the full mfs2 gene nor the Pac1 cleavage site within mfs2 were required for survival upon oxidative stress  and S. octospora (SOCG_01852), which retained the hairpin structure and core sequence elements that we described in S. pombe mfs2  shows the potential to form a hairpin of similar RNA secondary structure, although the hairpin sequence itself is not conserved in that species (mfs2 ortholog (SPNS2) encodes a sphingolipid transporter  termination complex in budding yeast ,50, it ds Figure  and F an snoRNAs . Togethe snoRNAs , with th snoRNAs ,53. ThesS. cerevisiae Pac1 ortholog, Rnt1, in the co-transcriptional removal of the m7G cap, facilitating 5′ maturation of C/D box snoRNAs  the association of Pac1 with some of those genes could be spurious and not supported by any functional relationship, (ii) Pac1 could function in RNA maturation processes that occur post-transcriptionally, with no consequences on transcription termination and (iii) redundant mechanisms of transcription termination could ensure proper RNAP termination in the absence of Pac1 function. For instance, the association of Pac1 with C/D box snoRNA genes is likely to contribute to snoRNA processing. Specifically, Pac1 nuclear exclusion did not affect RNAPII density profiles, but lead to the accumulation of 5′-extended precursors at Pac1-bound C/D box snoRNAs, suggesting that Pac1 functions in snoRNA biogenesis . This ob snoRNAs .S. pombe. Besides the torpedo-dependent mechanism of RNAPI transcription termination, a second ‘roadblock mechanism’ has been described in budding yeast (S. cerevisiae) to a terminator sequence element downstream of the rRNA repeat, which promotes transcription termination by pausing incoming RNAPI, likely through direct interaction with its Rpa12 subunit  . As Pac1ng yeast ,55. This subunit ,57. The In summary, our study provides new insights into mechanisms of transcription termination at genes that encode structural noncoding RNAs, such as snRNAs and snoRNAs, as well as discloses further evidence for miRNA-independent roles of Drosha-related proteins in gene regulation.gkab654_Supplemental_FilesClick here for additional data file.


Patients with lymph node (LN) metastatic bladder cancer (BCa) present with extremely poor prognosis. BCa‐derived exosomes function as crucial bioactive cargo carriers to mediate the signal transduction in tumor microenvironment triggering tumor metastasis. However, the mechanisms underlying exosome‐mediated LN metastasis in BCa are unclear.BCYRN1 in a larger 210‐case cohort. The functional role of exosomal BCYRN1 was evaluated through the migration and tube formation assays in vitro and the footpad‐popliteal LN metastasis model in vivo. RNA pull‐down assays, luciferase assays, and actinomycin assays were conducted to detect the regulatory mechanism of exosomal BCYRN1.We conducted the high‐throughput sequencing to explore the expression profile of long noncoding RNA (lncRNA) in urinary exosomes (urinary‐EXO) from patients with BCa and further evaluated the clinical relevance of exosomal lncRNA BCYRN1 was substantially upregulated in urinary‐EXO from patients with BCa, and associated with the LN metastasis of BCa. We demonstrated that exosomal BCYRN1 markedly promoted tube formation and migration of human lymphatic endothelial cells (HLECs) in vitro and lymphangiogenesis and LN metastasis of BCa in vivo. Mechanistically, BCYRN1 epigenetically upregulated WNT5A expression by inducing hnRNPA1‐associated H3K4 trimethylation in WNT5A promoter, which activated Wnt/β‐catenin signaling to facilitate the secretion of VEGF‐C in BCa. Moreover, exosomal BCYRN1 was transmitted to HLECs to stabilize the VEGFR3 mRNA and thus formed an hnRNPA1/WNT5A/VEGFR3 feedforward regulatory loop, ultimately promoting the lymphatic metastasis of BCa. Importantly, blocking VEGFR3 with specific inhibitor, SAR131675 significantly impaired exosomal BCYRN1‐induced the LN metastasis in vivo. Clinically, exosomal BCYRN1 was positively associated with the shorter survival of BCa patients and identified as a poor prognostic factor of patients.LncRNA BCYRN1 synergistically enhances VEGF‐C/VEGFR3 signaling‐induced lymphatic metastasis of BCa, indicating that BCYRN1 may serve as an encouraging therapeutic target for patients with BCa.Our results uncover a novel mechanism by which exosomal  BCYRN1 is upregulated in urinary exosomes from bladder cancer (BCa) patients and associated with LN metastasis and poor prognosis of patients.lncRNA BCYRN1 upregulates WNT5A expression to activate WNT/β‐catenin pathway and promotes the secretion of VEGF‐C in BCa.BCYRN1 stabilizes VEGFR3 mRNA in HLECs, constituting a feedforward loop to promote lymphangiogenesis and LN metastasis of BCa.Exosomal  We demonstrated that exosomal BCYRN1 enhanced lymphangiogenesis and LN metastasis of BCa in vitro and in vivo. Mechanistically, exosomal BCYRN1 constituted a feedforward loop with hnRNPA1/WNT5A/VEGFR3 regulatory axis and synergistically enforced the VEGF‐C/VEGFR3 signaling to facilitate the lymphangiogenesis of BCa. Our results uncover the precise mechanism by which exosomal BCYRN1 promotes lymphatic metastasis of BCa, implying that targeting BCYRN1 may become an encouraging therapy for patients with LN metastatic BCa.In the present study, we conducted a high‐throughput sequencing to identify a long noncoding RNA (lncRNA) 22.1The tissues and urine specimens were obtained from patients in Sun Yat‐sen Memorial Hospital, Sun Yat‐sen University . Pathologically, tissues specimens were verified as BCa through two independent professional pathologists. The clinical characteristics of the participants were listed in Table 2.22.All cell lines involved in our study except human lymphatic endothelial cells (HLECs), human dermal lymphatic endothelial cells (HDLECs), and human umbilical vein endothelial cells (HUVECs) were purchased from American Type Culture Collection . T24 and 5637 cells were cultured in Roswell Park Memorial Institute 1640 , UM‐UC‐3 cells in Dulbecco's modified Eagle's medium , and SVHUC‐1 cells in F‐12K medium . All media were supplemented with 10% fetal bovine serum (FBS) . HLECs were purchased from ScienCell Research Laboratories  while HDLECs and HUVECs from Promocell company , both of which were kept in endothelial cell medium with 5% FBS and 1% matched growth factor . All cell lines mentioned above were cultured in a humidified incubator at 37°C with 5% CO2.3The extraction of total RNA in the urinary‐EXO isolated from five patients with muscle invasive BCa and five healthy controls was conducted with TRIZOL . The RNA was qualified by Agilent 2100 bioanalyzer , after which the qualified RNA was subjected to remove rRNA through a Ribo‐Zero Magnetic Kit (Epicentre) and sequenced on a Hiseq4000 platform.2.45 UM‐UC‐3 cells supplemented in 20 μl phosphate buffered saline (PBS) and 10 μg indicated exosomes supplemented in 20 μl PBS were slowly injected into the footpad of mice, respectively. Then, the footpad tumors and popliteal LNs were resected and embedded with paraffin for further analysis. The experiments were carried out under the approval of the Institutional Animal Care and Use Committee of Sun Yat‐sen University.The BALB/c nude mice (about 4‐ to 5‐week‐old) were purchased form the Experimental Animal Center, Sun Yat‐sen University  and used to construct the footpad tumor model as described in our previous study,2.5BCYRN1‐interating proteins, biotin RNA pull‐down assays were conducted. Transcript Aid T7 High Yield Transcription Kit and Pierce Magnetic RNA‐Protein Pull‐Down Kit (Thermo Fisher Scientific) were used to synthesize biotinylated BCYRN1 and captured the BCYRN1 binding proteins, respectively.To examine 2.6BCYRN1‐specific probes were designed with an online tool and labeled with biotin in its 3′ terminus  with instructions of the manufacturers. The us Table .2.7BCYRN1 and WNT5A promoter, the fluorescence resonance energy transfer (FRET) analysis was conducted, in which the triplex‐forming oligonucleotides (TFO) in BCYRN1 were labeled with 5‐carboxytetramethylrhodamine, and the triplex target site (TTS) in WNT5A promoter was labeled with 5‐carboxyfluorescein. Then, the binding buffer added with TFO and TTS was incubated at 55°C for 10 min and 37°C for 10 h, after which the fluorescence wavelengths were detected with a Molecular Device M5 Plate Reader.To verify the triplex structure forming between 2.8The chromatin immunoprecipitation (ChIP) assays were conducted to detect the interaction between hnRNPA1 or H3K4me3 and their target chromatins with EZ‐Magna ChIP A/G kit (Millipore). Briefly, BCa cells were harvested to fixe in 4% paraformaldehyde for 10 min after which the fixed cells were resuspended in cell lysis buffer to thoroughly lyse the cells. Then, the chromatins of cell lysate were sheared by sonicator and added into the mixtures of primary antibodies and magnetic beads for further incubation at 4°C overnight. Finally, the binding chromatins were eluted and further analyzed through quantitative real‐time reverse transcription polymerase chain reaction (qRT‐PCR) assays.2.9BCYRN1 were purchased from GenePharma  and further transfected into HLECs to construct the BCYRN1‐knockouting HLECs. qRT‐PCR analysis was performed to evaluate the knockout efficiency.The lentiCRISPR v2 vectors contain with single guide RNAs targeted the 2.10in situ hybridization (ISH) analysis, cytosolic and nuclear fraction, immunofluorescence, fluorescence in situ hybridization (FISH), isolation of exosomes, transmission electron microscopy (TEM) lentivirus infection and cell transfection, 5‐ethynyl‐20‐deoxyuridine (EdU) assays, Cell Counting Kit 8 (CCK‐8) assays, subcutaneous tumorigenicity model, tube formation and Transwell assays, western blotting analysis, RNA extraction, RNA immunoprecipitation (RIP) assays, serial deletion analysis, TOP‐flash/FOP‐flash assays, dual‐luciferase reporter assays, enzyme‐linked immunosorbent assay (ELISA), internalization analysis of exosomes, actinomycin assays and bioinformatic analysis were provided in Supplementary Methods.Further methods of immunohistochemistry (IHC) analysis, 2.11p < 0.05 was considered as statistically significant. All quantitative data performed in triplicate were expressed as mean ± SD. Kaplan–Meier analysis was carried out to evaluate the survival time of patients with BCa. Moreover, chi‐square tests (χ2 tests) and one‐way ANOVA or two‐tailed Student's t‐test were performed to assess the statistical significance between non‐parametric variables and parametric variables, respectively.Statistical analysis was carried out using SPSS v.13.0 , and 33.1BCYRN1, MAP4K3‐DT, and RP5‐857K21.7 were positively associated with the VEGF‐C/VEGFR3‐induced LN metastasis of BCa   in IHC analysis  Figure . As shows Figure , indicat Figures  and 1K. s Figure . A posit Figures  and 1N, 3.2BCYRN1 was remarkably overexpressed in the urinary‐EXO from patients with BCa, the level of BCYRN1 in exosomes secreted by BCa cells was further determined. First, we collected the culture medium (CM) of BCa cells to isolate and purify the exosomes. TEM and nanoparticle tracking analysis (NTA) were performed to show the cup‐shaped structure of isolated particles with a size distribution of 30–150 nm   Figure . Intrigul Figure , suggest Figures , indicat3.3BCYRN1 overexpression positively correlated with lymphangiogenesis in BCa tissues. Therefore, the tube formation and migration assays were performed to explore the regulatory function of BCYRN1 in lymphangiogenesis of BCa. As shown in Figures BCYRN1‐silenced UM‐UC‐3 and 5637 cells. Importantly, we found that silencing BCYRN1 attenuated the ability of exosomes secreted by UM‐UC‐3 and 5637 cells to enhance the tube formation and migration of HLECs, the results of which were confirmed by another lymphatic endothelial cell lines, HDLECs  or BCYRN1 plasmid‐transfected UM‐UC‐3 cell‐secreted exosomes (UM‐UC‐3‐EXOBCYRN1)   Figure . Strikin Figures . We foun Figures . IHC ana Figures  and 3I, 3.5BCYRN1 and antisense BCYRN1 as control to determine the BCYRN1 interacting proteins in BCa cells. The results showed that an apparent band in 35–40 kDa was enriched by biotinylated BCYRN1, which was validated as heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) using mass spectrometry (MS)  impaired the BCYRN1 induction of luciferase activity of the WNT5A promoter  dramatically increased the level of BCYRN1 in HLECs, while silencing BCYRN1 abolished the upregulation of BCYRN1 level in HLECs  of target genes via the recognition of complementary sequences is the pivotal manner of exosomal lncRNA in regulating target genes,3 Figure . Moreove Figures . Lucifer Figures  and 7K,  Figures , indicate Figure . Mutatin Figures . Collect3.10BCYRN1 in inducing lymphangiogenesis and LN metastasis of BCa. As shown in Figures BCYRN1‐overexpressing BCa cells promoted the tube formation and migration of HLECs, which was abolished by silencing VEGFR3, indicating that the upregulation of VEGFR3 expression in HLECs contributes to the exosomal BCYRN1‐mediated lymphangiogenesis of BCa in vitro. Moreover, we further determined the critical role of VEGFR3 in exosomal BCYRN1‐induced LN metastasis of BCa in vivo by blocking VEGFR3 activity with its specific inhibitor, SAR131675, in the constructed footpad‐popliteal LN metastasis model. Strikingly, the exosomal BCYRN1‐induced promotion of the fluorescence intensity in nude mice popliteal LN was significantly impaired by treating with SAR131675, as determined using IVIS  of patients with BCa, as confirmed by Kaplan–Meier analysis .Hanhao Zheng, Changhao Chen, and Tianxin Lin contributed to the design of study. Min Yu, Yao Kong, and Bowen Gao performed the clinical statistical analysis. Mingjie An, Yiyao Ya, and Jian Huang conducted the CHIRP and ChIP assays. Yuting Li, Wang He and Yan Lin carried out the ISH and IHC analysis. Keji Xie, Hanhao Zheng, and Changhao Chen conducted the in vitro experiments. YuMing Luo performed the in vivo assays. Hanhao Zheng, Changhao Chen, and Tianxin Lin wrote the manuscript. All the authors have read and approved the final manuscript.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi). All relevant data in the present study are obtainable from the authors.The data of next‐generation sequencing in our study (GSE156308) are obtainable in a public repository from NCBI (Supporting InformationClick here for additional data file.


Applied research in machine learning progresses faster when a clean dataset is available and ready to use. Several datasets have been proposed and released over the years for specific tasks such as image classification, speech-recognition and more recently for protein structure prediction. However, for the fundamental problem of RNA structure prediction, information is spread between several databases depending on the level we are interested in: sequence, secondary structure, 3D structure or interactions with other macromolecules. In order to speed-up advances in machine-learning based approaches for RNA secondary and/or 3D structure prediction, a dataset integrating all this information is required, to avoid spending time on data gathering and cleaning.Here, we propose the first attempt of a standardized and automatically generated dataset dedicated to RNA combining together: RNA sequences, homology information (under the form of position-specific scoring matrices) and information derived by annotation of available 3D structures . The data are retrieved from public databases PDB, Rfam and SILVA. The paper describes the procedure to build such dataset and the RNA structure descriptors we provide. Some statistical descriptions of the resulting dataset are also provided.https://evryrna.ibisc.univ-evry.fr/evryrna/rnanet). An efficient parallel pipeline to build the dataset is also provided for easy reproduction or modification.The dataset is updated every month and available online (in flat-text file format) on the EvryRNA software platform (Bioinformatics online. A major part of any data-science work consists in finding appropriate data that contains enough signal to tackle the problem we are interested in. Then, cleaning the data to ensure uniformity of the measures, compatibility of the various data sources and protocols, and a reasonable amount of noise is sometimes the most time-consuming step.https://rna.bgsu.edu/rna3dhub/pdb). It provides annotations of all RNA 3D structures in a browseable website, but their annotations cannot be downloaded as a whole and ready-to-use dataset for machine learning applications. Indeed, such dataset is needed to allow quick testing of new ideas, and to perform benchmarks and comparisons between future knowledge-based structure prediction algorithms.Data-based methods have been popular for a long time in the field of RNA structural bioinformatics, for structure prediction  rather than the physico-chemical properties that are used for amino-acids . Then, it is easier to encode the three-dimensional conformation of a nucleotide, because the 3D descriptors are defined in the same way for all residues. This is not the case with amino-acids, one example being the number of torsion angles to consider differs depending on the considered amino-acid. The interactions between residues also are easier to encode. Most models consider that each base can interact one time on each of its 3 sides. This makes the interaction space of a nucleotide finite, while we have to consider many faces and rotamers when working with amino acids.A common first problem in RNA computational biology is the prediction of structure from sequence. The basis for a dataset is then to gather sequences and their experimentally verified secondary structure and tertiary (3D) structure descriptors. A good start is to get the Watson–Crick basepairs, which permits to describe stems and various loops .Then, to capture the 3D structure, two kinds of descriptors can be used in complementarity: those which focus on the backbone configuration , and those which focus on all the base–base and base-backbone interactions . So, we decided to re-align these sequences differently. Instead of using Infernal and Rfam sequences, we consider the specialized database SILVA , which basically are a summary of the base counts per column in the alignments.As the RNA sequences of the chains have now been extracted using DSSR, we can now compute position-specific nucleotide frequencies. To do so, we use the previously retrieved PDB-Rfam mapping of every chain. We gather together all the sequence hits of a given Rfam family, plus the sequences of the 3D chains that are mapped to this family, in a single file. A multiple sequence alignment is then recomputed using Infernal’s cmalign produces two kinds of gaps: gaps to the family consensus whose symbols are ‘-’ and describe an important deletion with respect to the family pattern, and gaps whose symbols are ‘.’ that are just padding because another sequence contains an insertion with respect to the family consensus. Starting with the two different gapped sequences, our mapping strategy is the following:The final step is to merge the per-position data concerning the RNA 3D chain with the per-position nucleotide frequencies in the multiple sequence alignments. As each sequence extracted from a 3D chain has been realigned with its homologs, it now contains gaps in its aligned version. On the 3D chain side, gaps may only occur if some residues are not resolved. On the multiple sequence alignment side, If the two sequence symbols (nucleotide letter or gap) match, we merge the position descriptors, and move to the next position.If the 3D chain contains a gap (‘-’ symbol) but does not face a gap to the consensus in the multiple alignment, we first try to search for a real gap to the consensus in the following positions (ignoring insertion gaps ‘.’ until a resolved nucleotide letter comes). If one is found, we map the two positions together. If none is found, we search for an insertion gap ‘.’. If no gap is found at all, we map the 3D chain descriptors with unknown homology descriptors . This happens when the alignment is locally incorrect.If the aligned sequence contains a gap, but not the 3D chain, we just skip this position and move to the following.If the sequence symbols do not match but are not gaps, we throw an error. This never happens in practice.N2. We include them, but label them in a group named ‘other’.In order to maximize the amount of data, problematic cases were not discarded. We try to overcome particular situations as much as possible to include all the data. First, it is very common to observe missing residues in RNA chains, because of unresolved portions in the mmCIF file. Their positions are marked as gaps in the sequence. By default, they are later replaced by the most common nucleotide at this position among the homologous sequences in a dedicated database field . A gap character ‘-’ remains in the main field . It heavily relies on the BioPython package , with 48 GB of RAM. Later runs will, by default, only perform necessary computations to keep the database up-to-date more quickly.RNANet is built automatically using a Python script available on the EvryRNA website  and without further filtering based on structure resolution, we registered 10 173 RNA 3D chains available in the PDB. Rfam provided 5505 mappings to known families (54%). Using BGSU’s redundancy lists (v3.145), we extended this list to 6217 unique RNA chains, or 6602 chains if we accept to map copies of chain portions to different Rfam families. These figures are slightly lower than the ones given in Section 2.1, because they exclude chains that are too short, contain no nucleotides (backbone only), or are incorrectly parsed by DSSR. The chains were mapped to 92 Rfam families. The number of homologous sequences found by family ranges from 5 (RF00044) to 1 429 931 , or 2 225 272 (SSU sequences from SILVA). Detailed numbers of families as a function of the resolution threshold considered, and numbers of sequences mapped to each Rfam family are given in Concerning the nucleotide distribution over the RNA families, To quantify the redundancy among RNA families, we compute sequence identity matrices for every Rfam family. The sequence identity matrices can also be used to perform a hierarchical clustering over chains. The result is presented graphically on η and θ in non-helical nucleotides, like Ramachandran did with amino-acids in proteins. For a nucleotide i, iη is defined as the torsional angle between the four atoms iP, iθ is defined as the torsion angle between iP, η/θ pseudotorsions in our dataset is given on In backbone . They pret al. to focus on significant peaks. Nevertheless, the proportion of these small peaks relatively to the large central one has changed over the years. The second hypothesis could be a difference in the way we filter non-helical nucleotides, which fall in the central region. We indeed removed nucleotides annotated by DSSR as being part of an A-form, B-form or Z-form 3D stem. As many nucleotides may not fulfil DSSR’s detection criteria, but still be part of or near a helix, they are included and participate to the massive peak.The simplest hypothesis about the difference of clusters between et al. proposed to use another definition of the pseudotorsions, called A few years later, Keating d of C′4 . Indeed,Besides the fact that the inspiration for RNANet came from ProteinNet, the two are actually different, because of methodology variations and because of the gap in the available amount of data between proteins and RNA.The convenience of the Rfam/Infernal ecosystem, providing precomputed hits of non-coding RNA sequences on the families covariance models, comes at the price of heavy data reduction. Because a mapping is not (yet) available for every 3D chain, more than a third of the available chains are unused. This can be critical to deep-learning models. The development of a nucleic-acid equivalent of jackHMMER , could bAs RNANet will be continuously updated to integrate newer data as the PDB grows. The first thing to do, as soon as the data permits it, would be to further cluster all the RNA chains by similarity and define sets of points with enough distance between them to be considered independent. This would allow pre-computed training, testing and validation sets.Moreover, if an equivalent of the protein tools jackHMMER, HHBlits or MMSeqs2 is released with support for RNA sequences, improvements would become possible to closer follow the ProteinNet strategy. A sequence-alignment-based clustering step could then be added, and the search for homologous sequences upgraded.In addition, more descriptors would be easy to compute and could be added in future versions. For example, the BGSU RNA Structure Atlas which uses annotations by FR3D (https://EvryRNA.ibisc.univ-evry.fr/evryrna/rnanet).To speed up the development of machine-learning based RNA structure prediction tools, we developed a pipeline which builds a multi-scale dataset, called RNANet, from public data. Both the dataset and pipeline code are available on the EvryRNA software platform (The dataset gathers information about the sequences, the secondary structures and the tertiary structures of available RNA chains chosen with resolution better than a given threshold. We propose a list of various descriptors, including nucleotide frequencies computed on sequences from the Rfam and SILVA databases known to be homologous to the chains, realigned with the 3D chains sequences.The dataset can be easily sub-sampled or filtered on demand using custom SQL queries. For example, structures available before a certain date can be retrieved, to allow comparisons with previous RNA-Puzzles benchmarks . A strucThe amount of data, being directly dependant on the content of public databases like the PDB, is for now an obvious limit when it comes to deep models, which would be subject to over-fitting issues. In the meantime, the dataset is useful to compute descriptive statistics about RNA structures [e.g. reproducing (btaa944_Supplementary_DataClick here for additional data file.


Influenza A virus (IAV) is a respiratory pathogen that has caused significant mortality throughout history and remains a global threat to human health. Although much is known about IAV replication, the regulation of IAV replication dynamics is not completely understood. influenza virus enumerator of RNA transcripts (InVERT), that allows for the simultaneous quantification of all three RNA species produced by IAV. Using InVERT, we provide a full landscape of the IAV replication kinetics and found that different groups of viral genes follow different kinetics. The segments coding for RNA-dependent RNA polymerase (RdRP) produced more vRNA than mRNA, while some other segments  consistently made more mRNA than vRNA. vRNA expression levels did not correlate with cRNA expression, suggesting complex regulation of vRNA synthesis. Furthermore, by studying the kinetics of a virus lacking the capacity to generate new polymerase complexes, we found evidence that further supports a model in which cRNA synthesis requires newly synthesized RdRP and that incoming RdRP can only generate mRNA. Overall, InVERT is a powerful tool for quantifying IAV RNA species to elucidate key features of IAV replication.Influenza A virus (IAV) is a segmented negative-sense RNA virus and is the cause of major epidemics and pandemics. The replication of IAV is complex, involving the production of three distinct RNA species, namely mRNA, cRNA, and viral RNA (vRNA), for all eight genome segments. While understanding IAV replication kinetics is important for drug development and improving vaccine production, current methods for studying IAV kinetics have been limited by the ability to detect all three different RNA species in a scalable manner. Here, we report the development of a novel pipeline using total stranded RNA sequencing (RNA-Seq), which we named IMPORTANCE Influenza A virus (IAV) is a respiratory pathogen that has caused significant mortality throughout history and remains a global threat to human health. Although much is known about IAV replication, the regulation of IAV replication dynamics is not completely understood. This is due in part to both technical limitations and the complicated replication of the virus, which has a segmented genome and produces three distinct RNA species for each gene segment. We developed a new approach that allows the methodical study of IAV replication kinetics, shedding light on many interesting features of IAV replication biology. This study advances our understanding of the kinetics of IAV replication and will help to facilitate future research in the field. Each of the viral RNAs (vRNAs) are coated in nucleocapsid protein (NP) and bound by the tripartite viral RNA-dependent-RNA polymerase (RdRp), resulting in eight viral ribonucleoprotein (vRNP) complexes. After entry and uncoating of the virion, vRNP complexes traffic to the nucleus. The RdRp first transcribes vRNA into mRNA, termed primary transcription. Then, the RdRp generates new vRNA through a cRNA intermediate, which is also in complex with an RNP (cRNP) , 2. Recelication –5. We haication –, furtherThe ability to characterize the kinetics of IAV replication and transcription will help advance our understanding of the biology of IAV replication, which can impact the development of drugs that target viral transcription and replication , 7. Many8–influenza virus enumerator of RNA transcripts (InVERT). This pipeline takes advantage of the differences in the 3′ end of the positive-sense viral RNA species. Our strategy allows for quantification of all IAV genes and RNA species independent of primer bias. We validate our findings by comparing the level of mRNA through our new approach against traditional poly(A)-based sequencing and analysis. By distinguishing the kinetics of all three viral species, we found that different RNA species follow different kinetic profiles, as follows: vRNA levels increase during the entire course of infection, cRNA levels increase and then stabilize, and mRNAs reach a peak and then decline. We also quantified the kinetics of each virus segment and discovered segment-specific heterogeneity in replication kinetics. Specifically, the RdRP genes  produce significantly lower levels of mRNA than vRNA, whereas several other genes, such as NP, HA, and NS, consistently generate a higher level of mRNA than vRNA. Interestingly, the change in the level of vRNAs is not necessarily correlated with its temporary template cRNAs. By employing InVERT to study the kinetics of an engineered IAV lacking PB1, we also found that without newly synthesized PB1 proteins, only mRNAs were detected. This finding indicates that incoming RdRPs cannot trigger cRNA synthesis to initiate viral genome replication. Overall, using this novel InVERT pipeline, we have gained insight into how IAV regulates its replication kinetics between different viral segments and RNA species.To overcome these technical hurdles to rigorously evaluate the viral RNA species kinetics during influenza virus replication, we developed a new method using RNA sequencing and computational calculations we termed Currently, methods for evaluating IAV replication can be time consuming and laborious. Given the expanded use, reduced cost, and ease of deep sequencing technology, we developed a pipeline that allows for scalable analysis of IAV replication kinetics . Total RTo validate our pipeline, we compared the level of relative viral mRNAs quantified by InVERT to mRNA independently quantified by poly(A)-based RNA sequencing (mRNA-Seq). Importantly, the RNA used for each of these methods was generated from one experiment, eliminating any differences due to viral stocks, kits, or buffers, or to timing of infections or harvests, allowing us to directly compare the sequencing methods. At each time point, the transcripts per kilobase per million (TPM) of all viral mRNAs were normalized to that of NP for both InVERT and mRNA-Seq. The relative expression levels of all viral mRNAs, except for NA and PA, obtained by total stranded RNA-Seq (SMARTer-Seq) and InVERT were comparable with those measured by direct mRNA sequencing . The relR-squared = 0.817) , while TruSeq mRNA sequencing assays only mRNA  and C. H= 0.817) . Overall= 0.817) . Most seWe used InVERT to evaluate the replication kinetics of all three IAV RNA species for all IAV segments . From 0 We also noted kinetic patterns among RNA species of different segments . The abuThe segments NS and M each generate two mRNAs from a single vRNA template via splicing, namely NS1 and NEP from NS and M1 and M2 from M. Positive-sense RNAs from total RNA-Seq contain both cRNAs and mRNAs, so traditional alternative splicing isoform quantification tools would also count cRNA reads as unspliced transcripts. We further adapted the pipeline to be able to distinguish between the unspliced and spliced variants of M and NS. We developed an algorithm to quantify splice isoform transcripts based on the ratio of spliced to unspliced reads at the splice junctions. We then used the ratio of cRNA to mRNA to determine the total read counts of spliced and unspliced mRNA. Consistent with previous reports –21, the –trans . As RNA-Seq has become low cost and readily accessible, InVERT can serve as a strong alternative method for studying IAV replication kinetics.Here, we report a pipeline to quantify the three species of IAV RNAs  from all genome segments using stranded total RNA-Seq. Using this pipeline, we demonstrate distinct dynamics of vRNA, cRNA, and mRNA of different viral genes upon IAV infection. The disparate detection of IAV RNA species demonstrates highly dynamic transcription and replication kinetics that can vary across segments. By applying the pipeline to study the replication kinetics of delta-PB1 virus, we validated that without As InVERT relies on the sequence difference of reads mapped to the 3′ end of RNA to distinguish cRNAs and mRNAs, a sufficient coverage of the 3′ end of a transcript is critical for confident calculation of the cRNA/mRNA ratio. The number of reads mapped to the influenza genome changes according to the time point, the multiplicity of infection (MOI), and the quality of the RNA-Seq run. At earlier time points during infection (before 4 hpi), the low number of viral reads leads and lower sequencing coverage and could affect accuracy . This can be improved by increasing sequencing depth. For samples starting from 4 hpi, the coverage of reads is high enough to distinguish between cRNA and mRNA . Based oThe RNA profile assessed by our pipeline provides a reliable picture of the replication dynamics of all viral genes except for some uncertainty in mRNA levels of NA and PA because of the discrepancy with direct mRNA sequencing. Lower mRNA expression of NA with similar dynamics has also been reported using qRT-PCR , suggest27–trans-activating RdRP  and human lung adenocarcinoma epithelial A549 cells were maintained in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS). Recombinant influenza A/Puerto Rico/8/34 (PR8) was rescued by plasmid-based transfection, plaque purified, and propagated in embryonic chicken eggs. The single-cycle viruses (delta-HA and delta-PB1) in the PR8 backbone were created by replacing the coding sequence of HA and PB1 by mCherry and amplified in MDCK cells engineered to express HA and PB1, respectively, as previously described (MDCK cells were infected with IAV PR8 (MOI = 0.5) and incubated at 4°C for 30 min to allow the virus to adsorb to the cell surface but not to enter the cytosol. Synchronized IAV infection was initiated by shifting the temperature to 37°C. Cells were harvested at 0, 2, 4, 6, 8, and 12 h post temperature shift for RNA-Seq to quantify the cellular RNA and viral RNA levels. Uninfected MDCK cells were also collected for a negative control. Three independent infections at each time point were performed. Infections of single-cycle viruses (delta-HA and delta-PB1) in A549 cells were as previously described (GSE162281.RNA from three independent time series infections of IAV PR8 in MDCK cells were extracted using the RNeasy minikit (Qiagen). RNA quality was assessed by Agilent TapeStation or Agilent Bioanalyzer. All RNA samples had RNA integrity score of 9.0 or higher. To assay both positive- and negative-sense RNAs, total RNA was prepped using the Clontech Pico SMARTer stranded total RNA-Seq kit v2, in which strand orientation of the original RNA was preserved by the template-switching reactions. The cDNA library, with an insert size of ∼200 bp, was then subjected to sequencing, either on the HiSeq 2500 High Output using v4 chemistry (infection 1) or on the NovaSeq SPrime sequencing platform (infections 2 and 3) . For the(i) Reference genome and annotation files. Reference genomes, namely CanFam3.1 for MDCK cells or GRCh38.p13 for A549 cells, were downloaded from Ensembl. The IAV PR8 genome was assembled by combining complete sequences for all eight genome segments from the Influenza Research Database (https://www.fludb.org) with NCBI taxon identifier (ID) 211044. The IAV delta-HA and delta-PB1 virus genomes were assembled by concatenating 6 genome segments of the IAV PR8 (excluding HA and PB1). The host genome and IAV genome were concatenated into one combined reference genome that was used for mapping. The annotation file for IAV PR8, delta-HA, or delta-PB1 was curated such that each genome segment was annotated for negative-sense and positive-sense RNA. The stranded library prep preserved the orientation of all reads.All of the raw RNA-Seq reads were processed with adapter trimming and low-quality base removal by Trimmomatic  and checThe expression levels of all of the genes, including both host and viral genes, was quantified first by Cufflinks  to countViral cRNAs and mRNAs were distinguished using SAMtools . cRNA anFor alternatively spliced genes (M and NS), the expression levels of spliced (M2 and NEP) and unspliced (M1 and NS1) isoforms were calculated using the ratio of the spliced to unspliced isoforms and total TPM of mRNA. The ratio of the spliced to unspliced isoforms was determined from the difference in coverage at the splice junctions, including both 5′ and 3′ splice sites. Unspliced mRNA reads were calculated as the total mRNA reads minus the spliced reads. Total mRNA reads at the splice sites were calculated from the total positive-sense RNA-Seq reads using the ratio of mRNA to cRNA determined by InVERT. The spliced to unspliced ratio was then used to calculate the adjusted TPM of total mRNA to calibrate for the shorter length of the spliced transcripts. The spliced to unspliced isoform ratio and the adjusted TPM of total mRNA were used to determine the expression level of spliced and unspliced mRNAs .TPM values of viral mRNAs determined by InVERT and mRNA-Seq were plotted against each other using Spotfire , and linear regression was established by least-squares approximation. Pearson correlation was calculated using GraphPad Prism 8 to estimate the similarity between mRNA kinetics determined by TruSeq mRNA-Seq versus by those determined by InVERT. Error bars indicate standard error of the mean (SEM).GSE162281.Raw data, reported as fastq files, have been deposited at NCBI-GEO under series


The human sweat is a mixture of secretions from three types of glands: eccrine, apocrine, and sebaceous. Eccrine glands open directly on the skin surface and produce high amounts of water-based fluid in response to heat, emotion, and physical activity, whereas the other glands produce oily fluids and waxy sebum. While most body fluids have been shown to contain nucleic acids, both as ribonucleoprotein complexes and associated with extracellular vesicles (EVs), these have not been investigated in sweat. In this study we aimed to explore and characterize the nucleic acids associated with sweat particles.We used next generation sequencing (NGS) to characterize DNA and RNA in pooled and individual samples of EV-enriched sweat collected from volunteers performing rigorous exercise. In all sequenced samples, we identified DNA originating from all human chromosomes, but only the mitochondrial chromosome was highly represented with 100% coverage. Most of the DNA mapped to unannotated regions of the human genome with some regions highly represented in all samples. Approximately 5 % of the reads were found to map to other genomes: including bacteria (83%), archaea (3%), and virus (13%), identified bacteria species were consistent with those commonly colonizing the human upper body and arm skin. Small RNA-seq from EV-enriched pooled sweat RNA resulted in 74% of the trimmed reads mapped to the human genome, with 29% corresponding to unannotated regions. Over 70% of the RNA reads mapping to an annotated region were tRNA, while misc. RNA , protein coding RNA (5%) and miRNA  were much less represented. RNA-seq from individually processed EV-enriched sweat collection generally resulted in fewer percentage of reads mapping to the human genome (7–45%), with 50–60% of those reads mapping to unannotated region of the genome and 30–55% being tRNAs, and lower percentage of reads being rRNA, LincRNA, misc. RNA, and protein coding RNA.Our data demonstrates that sweat, as all other body fluids, contains a wealth of nucleic acids, including DNA and RNA of human and microbial origin, opening a possibility to investigate sweat as a source for biomarkers for specific health parameters.The online version contains supplementary material available at 10.1186/s12864-021-07733-9. Sweat is a biofluid continuously produced by skin glands for secretion to the body surface. Unlike urine, which accumulates in the bladder over time, and is flushed out only when the bladder is emptied, sweat is released continuously, from less than 1 pL/minute in resting conditions to several nL/minute per gland during exercise –4. The sIn addition to ions and macromolecules, biofluids carry insoluble particles containing nucleic acids, including lipid droplets , ribonucSweat secretion is qualitatively and quantitatively affected by stimuli such as heat, exercise, emotions, and health status. We recently reported differential sweat EV miRNA secretion in relation to specific exercise , supportBoth cell free DNA (cfDNA) and extracellular RNA (exRNA) have shown great promise as biomarkers (Reviewed in ), therefTo our knowledge, this is the first published study characterizing EV-associated nucleic acids in exercise induced human sweat.We collected sweat from people undergoing vigorous biking exercise. We first collected 1,4 l of sweat from 13 volunteers of both gender aged from 26 to 56 years at the time of collection, amounts of individual collections were not recorded, the sweat was stored at − 20 °C and mixed after thawing for processing to DNA and RNA for sequencing  showed small coverage with some clear hot spots where high number of reads from all samples were detected  sweat pellets were made from individually processed samples and analyzed by transmission electron microscopy (TEM) . We have submitted all relevant data of our experiments to the EV-TRACK knowledgebase (EV-TRACK ID: EV210083) [We chose ExoRNEasy kit to directly purify RNA from concentrated sweat to capture a more diverse selection of EVs . ConcentV210083) .We used the remaining ultracentrifugation pellets from pooled sweat to extract RNA from EV-enriched sweat fraction using ExoRNEasy kit Fig. . Profili66 miRNAs with read count 10 or higher were identified Table . miR26a-We then prepared RNA from individual sweat collections, from 6 females and 14 males  around 20%, SNORD63 and SNORD101 6%, SNORD100 4% Fig. . AnotherUnprocessed pseudogenes Fig. E are crePiwi-interacting RNA (piRNA) are small noncoding RNA first identified in the germline. They are short: 24-30 bp and their first identified function was to silence transposons. They have since been also identified in other cells and body fluids and may have potential as biomarkers. Only a very small percentage of the reads of each sample can be identified as piRNAs, but 5 piRNAs were identified in all 20 samples, and 6 in 19 samples  FTL mRNA, FTL gene has 4 exons, and using primers designed to amplify mRNA of the last 2 exons (3 and 4), we were able to amplify cDNA from several samples Fig. A. YWHAE DNA-seq had 5–9% of unmapped reads, and to determine their origin, the reads were assembled and aligned against the metagenome and the main taxonomical orders were identified. In addition to bacterial DNA, there was a small proportion of virus and archaeal DNA. Dominant bacterial orders were Proteobacteria, Actinobacteria followed by equal proportion of Bacteroidetes and Firmicutes Fig. , a distrRNA-seq produced much higher amounts of unmapped reads, and metagenomic analysis attributed the highest proportion of them to bacteria, but fungi and virus could also be identified. The distribution of the main bacterial orders was relatively similar to what we observe for DNA, except for a larger proportion of Firmicutes than Bacteroidetes Fig. , again aA fraction of the sequences identified corresponded to microbial protein coding genes. We retrieved the protein IDs with GO annotations from UniProt database, then counted the GO annotations. The cell component annotations showed mostly integral components of membrane and cytoplasm for both DNA and RNA sequencing. DNA sequencing included also at least 20 protein coding genes with annotations for cell, ribosome and integral component of plasma membrane, while RNA-seq included periplasmic space linked mRNA Fig. A. The moWe found 2 types of viral DNA, from human virus: papilloma, polyoma, herpes virus and from bacteriophages infecting the bacteria from the skin. Viral sequences represent 13% of the total identified, with papilloma sequences representing 24% of viral sequences, polyomavirus represent 5% and herpes 2%. Only a portion of the DNA identified encodes identified protein, but from papillomavirus 6 protein coding genes can be identified , capsid protein major L1 and Minor L2, replication protein E1, regulatory protein E2, and protein E4 and E6. From polyoma viruses, and Merkel cell polyoma virus, Small T antigen and capsid protein Vp1  were identified and from human Cytomegalovirus, only uncharacterized protein UL126 (more than 165 protein coding genes). The gene encoded are capsid protein, regulatory and replication protein from papilloma virus and capsid and small T antigen from polyomavirus  acnes and their associated bacteriophages.It is unclear how the characterized sweat DNA is associated with sweat EVs, but it is very likely that some of it is associated with apoptotic bodies resulting from sebum secretion collected by the flow of sweat during exercise, which is consistent with the presence of nucleic acids from bacteria typical of the sebaceous glands, such as Small RNA sequencing with small sample quantity is challenging, resulting in a large proportion of unmapped reads. EVs have been shown to have RNA both on their surfaces and inside, but most reports show that the larger RNA species (larger than 200 bp) are absent altogether. Best studied EV-associated RNAs are miRNAs. Even though we did not use RNAse, obtaining detectable amount of RNA proved challenging. Even with a small RNA protocol, our samples were mostly tRNAs and miscRNA with a small representation of miRNAs. We were nevertheless able to confirm the presence of even the lowest represented miRNA in most samples tested using qPCR. Based on our list of miRNA we were able to identify miR21-5p and miR26a-5p as regulated by exercise .Using an unbiased sequencing approach with individual samples confirmed the predominance of tRNA, rRNA and miscRNA observed in many other EV RNA studies . It was Of further interest is the presence of microbiome derived particles. The most abundant phyla identified by NGS analysis were Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes, which are usually found on human arms, hands, and axilla. Proteobacteria are dominant on face and torso . City scFurther studies are needed to determine if any of the RNAs found in our study are of clinical value, SNPs in some mRNA identified are associated with known diseases and could be further studied. For example, CALM2, which mRNA was identified in all the samples, has SNPs associated with cardiac arrhythmia and sudden death of young people after exercise . Larger Sweat as a biofluid presents many challenges, and the most important ones in the frame of this study are that the human skin is exposed to the environment, and it is an ecosystem where many organisms live. Skin secretions, including sweat, are metabolised by skin microbes, and the skin microbes secrete their own products, including outer membrane vesicles. The non-human nucleic acids we identified originated primarily from the skin microbiota, but also possibly from clothing and working surfaces, or from the collection material. Distinguishing contaminant nucleic acids in human sweat is especially challenging, since contaminants introduced in the process of sample handling are also mainly derived from human skin secretions. RNA extraction columns have been shown to contain contaminant RNA, and a small RNA sequencing data set available from data repository also show the presence of these contaminating RNAs . CapturiIn line with MISEV2018 recommendations  and becaOur data shows that that sweat particles are a good source of nucleic acid as has been reported for other biofluids. As the skin surface offers a site for non-invasive and real-time sample collection our study opens the path for future sweat EV biomarkers discovery.Table Adult volunteers were recruited among persons of different ethnic background residing in Northern Finland in Oulu area  with QIAamp blood DNA mini-kit by Qiagen [2O). Samples from 3 individual donors  according to manufacturer’s instructions.Total RNA concentration was measured with Qubits RNA HS and profiled on Bioanalyzer 6000 Pico chips (Agilent). Pooled EV-enriched sweat RNA-seq RNA library was made using Ion Total RNA-Seq Kit v2 (Thermo Scientific) following instructions for small RNA libraries. In this case, purification beads were included in kit and used to remove adapter dimers. Final libraries were checked on a Bioanalyzer with High Sensitivity DNA kit (Agilent). Sequencing of libraries was done with Ion PGM Hi-Q OT2 Template (200 bp protocol), Ion PGM Hi-Q Sequencing Kit and Ion PGM 318 chip kits (Thermo Scientific).Sweat was collected from the upper body, arms and torso using plastic raincoat  and disposable gloves VETbasic . If volunteers sweated heavily from their head, dripping sweat was collected in the head cover of the coat and pooled with the rest. Volunteers used exercise bike  indoors for 30 min , according to instructions by manufacturer. Concentrated sweat RNA was extracted using exoRNeasy kit (Qiagen).RNA-seq libraries were made using NEBNext Small RNA kit (New England Biolabs). After 16 cycles of PCR amplification, Libraries were checked with Bioanalyzer using DNA 1000 chips (Agilent). Before size selection on pippin blue (Thermo Fisher) libraries were mixed in 2 pools according to DNA yield. Size selection was set to collect fragment from 145 bp–200 bp. Size selected Pools were amplified an additional 5 cycles, purified wit PCR clean-up kit (Qiagen) and quantified by KAPA PCR kit (Roche). After dilution adjusting for library number in each pool, they were loaded on NextSeq550 (Illumina) and run 51 cycles.DNA fastQ files were checked with FastQC , merged https://chipster.csc.fi/ [Reads from different lanes were first merged into single fastq files and a QC was performed . Then, l.csc.fi/ .Reads that could not be mapped against the HG38 genome were then de-novo assembled to contig level using MEGAHIT . These cgeneontology.org for enrichment analysis .Following LNA primers were used for QPCR using SYBR Green III master mix (Agilent) miRCURY LNA miRNA QPCR Assay: miR24-3p (YP00204260), miR99a-5p (YP00204521), miR193 (YP00204226), miR-21-5p (MS00009079), miR-26a-5p (MS00029239), miR320b (MIMAT0005792), U6 snRNA (X59362).cDNA was made from 5 ng of RNA using VILO or Maxima H- first strand cDNA synthesis kit with DS DNAse (Thermo Fisher). After 1/2 dilution cDNA was amplified using AmpliTaq Gold and specific primers:PCR conditions: 95oC 5 min, 60oC 20s,72oC 20s, 95oC20s, 40 cycles. Products were analyzed on 2% agarose gel, stained with midori green and photographed. PCR products were purified using Qiagen minElute columns (Qiagen) and sequenced in Biocenter Oulu sequencing core facility using capillary sequencing with BigDyeTERminator v1.1 cycle sequencing (ABI) and ABI3500xL Genetic Analyzer.The immunoelectron microscopy was performed using biotinylated anti-CD9 antibody as a primary antibody at a 1:10 dilution. Vesicles were deposited on a Formvar carbonated grid (glow-discharged). The grids were incubated in blocking serum  in PBS). Afterwards, the grids were incubated for 20 mins with the primary anti-CD9 antibody (Miltenyi Biotec), followed by the secondary anti-biotin antibody for 20 min and finally the protein A-gold complex (PAG 10 nm) for 20 min. Samples after immunonegative staining as well as after negative staining with 2% uranyl acetate were examined using a Tecnai G2 Spirit transmission electron microscope  and images were captured with a charge-coupled device camera . Anti-CD63 antibody for immuno-TEM was used at dilution 1:50 (Abcam ab193349) and polyclonal anti-Glypican 1 antibody  at dilution 1:100.For preparing plastic sections concentrated sweat was filtered on 0.45 um Minisart filter (Sartorius), then sweat EVs were stained with CellVue Claret Far Fluorescent Cell Linker Midi Kit (MIDCLARET-1KT) according to manufacturer’s instructions. After staining, samples were centrifuged for 4–6 h at 120 K rpm k-factor = 16 at 4 °C Beckman TLA 120.2). Supernatants were removed, pellets fixed and plastic embedded in Biocenter Oulu electron microscopy core facility. Thin sections were observed with Tecnai G2 Spirit electron microscope.EV samples were diluted in 5 X Laemmli loading buffer and proteins were separated on 10% SDS PAGE gel, then transferred to nitrocellulose membrane. Anti-CD63 , GM130  and Ago2  antibodies were used for detection.2O was used to dilute the starting material.Nanoparticle tracking analysis (NTA) was performed using a NanoSight NS300  equipped with a 405 nm laser. At least three 40 s videos were recorded of each sample with camera level and detection threshold set up at 13. Temperature was monitored throughout the measurements. Videos recorded for each sample were analyzed with NTA software version 3.1. (build 3.1.46) to determine the concentration and size of measured particles with corresponding standard error. For analysis, auto settings were used for blur, minimum track length and minimum expected particle size. Double distilled HAdditional file 1: Supplementary Figure 1. Bioanalyzer profile of RNA from individual samples of EV-enriched sweat. RNA analysis profiles for all subjects 1 ul of RNA was run on Agilent pico600 chips. Supplementary Figure 2. piRNA in individual samples. piRNA percentages in 20 individual samples, below table with normalized value for each sample. Supplementary Figure 3. TEM images, negative staining of EV-enriched sweat. Negative control image (PBS wash of collection glove processed as sweat samples), images of ExoEasy processed sweat from 4 different volunteers. Supplementary Figure 4. Nanoparticle Tracking analysis from Exoeasy prepared sweat, summary of 5 different isolations. Supplementary Figure 5. Western blots with protein from negative control (collection glove washed in PBS and processed with exoEasy as sweat), unbound material from ExoEasy column (flowthrough), ExoEasy eluted fraction (EV-enriched), and concentrated sweat (cut-off 100 kDa), were stained with anti-CD63 antibody (EV marker) and antibodies against non-EV markers Ago2 and GM130. A: membrane B: the same membrane probed with anti-CD63 antibdy. Fluorescent images were inverted, contrast and brightness adjusted to make bands visible. C: the same membrane probed with anti-Ago2 antibody. D: membrane E: the same membrane probed with anti-GM130 antibody. Supplementary Figure 6. Western blot, whole membrane from Fig. Supplementary Figure 7. Whole 2% agarose gel images for Fig.  cut-off 0 kDa, weAdditional file 2: Supplementary Table 1. Transcripts identified in at least 18 samples.Additional file 3: Supplementary Table 2. Comparison of top 1% RNA identified in Illumina and Ion Torrent.


Systemic lupus erythematosus (SLE) is an autoimmune disease with complicated clinical manifestations. Although our understanding of the pathogenesis of SLE has greatly improved, the understanding of the pathogenic mechanisms of SLE is still limited by disease heterogeneity, and targeted therapy is still unavailable. Substantial evidence shows that RNA methylation plays a vital role in the mechanisms of the immune response, prompting speculation that it might also be related to the occurrence and development of SLE. RNA methylation has been a hot topic in the field of epigenetics in recent years. In addition to revealing the modification process, relevant studies have tried to explore the relationship between RNA methylation and the occurrence and development of various diseases. At present, some studies have provided evidence of a relationship between RNA methylation and SLE pathogenesis, but in-depth research and analysis are lacking. This review will start by describing the specific mechanism of RNA methylation and its relationship with the immune response to propose an association between RNA methylation and SLE pathogenesis based on existing studies and then discuss the future direction of this field. Systemic lupus erythematosus (SLE) is a common chronic systemic autoimmune disease that mainly affects young or middle-aged women, with a male-to-female incidence ratio of approximately 1:9 ; men areBoth environmental factors and genetic susceptibility play vital roles in SLE development. Collectively, evidence has shown that the sustained production of autoantibodies, complement activation, immune complex deposition, neutrophil extracellular traps, lymphocyte signaling and inflammation are all the key mechanisms underlying the loss of tolerance and tissue damage . PatientIn recent years, an increasing number of scholars have focused their attention on the field of epigenetics, such as DNA methylation, which has led to a better understanding of the pathogenesis of SLE . HoweverTherefore, this article will start by describing RNA methylation, summarize the application of RNA methylation in immunology, and speculate on the relationship between RNA methylation and the pathogenesis of SLE to provide more ideas for future research.Chemical modifications of cellular RNAs are natural and abundant. The dynamic nature and increasing number of RNA modifications provide new possibilities for rapidly altering gene expression to adapt to specific environments. Due to the development of genomic approaches, various modifications on RNA have been identified and investigated. As of 2017, more than 150 distinct chemical modifications on RNA have been detected .6-methyladenosine (m6A) is the most widespread and investigated modification of mammalian mRNA and has a broad range of critical functions in development  . It was The m6A methyltransferase complex transfers a methyl group from the donor substrate S-adenosyl methionine (SAM) to adenine nucleotides in the recipient RNA subunit . The com177, K211, K212, and K215, significantly repressing the methyltransferase activity of METTL3 , which was associated with white blood cell count and monocyte count  was the first RNA demethylase discovered, and its C-terminal structure demethylates mRNA, mainly in the nucleus . SeveralALKBH5, the second RNA eraser identified, was shown to affect mouse spermatogenesis . ALKBH5 The downstream function of the presence of m6A on mRNA molecules is closely related to the recognition and binding of m6A-methylated recognition proteins, termed m6A readers. Various m6A readers have been identified, but their mechanism is similar: m6A readers recognize and bind the RNA decorated by m6A. The regulatory function of m6A is achieved by enhancing or weakening the recruitment of different RNA-binding proteins (RBPs) to target mRNAs or directly inducing secondary structural changes in target mRNAs to influence the interaction between RNAs and RBPs .The most important m6A recognition protein in eukaryotes is the YTH domain-containing family proteins, which comprise the conserved C-terminal RNA recognition and binding domain YTH and the N-terminal variable region. This protein family is also considered the most primitive m6A reader, falling into three classes: YTHDC1, YTHDC2, and the YTHDF family . Among tThe m6A modification on RNA regulates the transcription of genes to achieve functional regulation at the cellular or tissue level, which is mainly achieved by the functions of various m6A methyltransferases, m6A demethylases and m6A recognition proteins. m6A is mainly modified on mRNA, and thus the main effects of the m6A modification include pre-mRNA shearing, the stability of mRNA, nuclear transfer of mRNA, and translation of mRNA, thus achieving the regulation of mRNA function . For exavia m6A methylation of rRNA   and the Drosophila  is also an important m5C methyltransferase, and it has been proven to be relevant to tRNA methylation and root growth in bidopsis , but thiin vitro, which plays a critical role in SLE. Because TET2 functions in both DNA demethylation and RNA demethylation, the specific mechanism of m5C in SLE deserves further study (The modification of m5C is reversible. Recent studies identified the m5C demethylase ten-eleven translocation 2 (TET2) . Silencier study .To date, three m5C-binding proteins have been identified as m5C readers: Aly/REF output factor  , cytoplaLeu CAA, the m5C modification of tRNA mainly occurred outside anticodon rings  with less m6A modification exhibit slower mRNA decay and increased protein expression levels  play important roles in the innate and adaptive immune responses, as well as in the progression of SLE . A studyl3KO DCs . As show T cells . Thus, Rin vitro and in vivo studies have shown that RNA methylation regulates the occurrence of inflammation through several signaling pathways.RNA methylation is also an essential mechanism in the process of inflammation, and it is a key component of the pathogenesis of SLE . The dynvia the interaction of YTHDF1  revealed significantly elevated METTL3 levels in patients with RA that played an important role in LPS-induced inflammation in macrophages via the NF-κB signaling pathway  (The regulatory effect on these inflammasome pathways in different autoimmune diseases has been reported.  pathway . In the ix (ECM) . These fix (ECM) .IFN I is a vital component of the antiviral innate immune response and is also the most important cytokine involved in SLE pathogenesis. We outline the studies that revealed the role of RNA methylation in IFN I production to determine the potential correlation between RNA methylation and SLE.via the DEAD helicase domain  recognize viral RNA, which is an important information transmission process in the innate immune response . Methyla+ T cells recognizes the m6A modification on HIV and limits its infection . In individuals with lupus nephritis, interstitial fibrosis is associated with CKD progression . A mouseK2 cells .Misu–/– mice, along with delayed exit from the bulge, increased self-renewal, and aberrant hair cycling (Damage to stem cells in follicles might be one process leading to permanent loss of follicles in patients with SLE or cutaneous lupus erythematosus (CLE) , which m cycling .Mesenchymal stem cells originally isolated from the bone marrow stoma are multipotent and possess strong immunomodulatory activities that interact with multiple immune cells, including DCs, neutrophils, NK cells, T cells, and B cells. Bone marrow-derived mesenchymal stem cells (BM-MSCs) from patients with SLE exhibit defective immune regulation, which might contribute to the imbalance between Treg and Th17 cells in patients with SLE . On the We discussed the role of RNA methylation in the pathogenesis of SLE, including innate immunity and adaptive immunity. Although the evidence has generally indicated a potential relationship between RNA methylation and SLE, few studies have interpreted a direct relationship between them. Therefore, in the next step, we must investigate the direct relationship by quantitatively measuring m6A levels and their association with these existing mechanisms to establish a stronger causal link. Moreover, we investigated the function of RNA methylation in a cohort with SLE to obtain more biomarkers for the diagnosis, treatment, and prognosis of SLE and related complications. Further in-depth research on RNA methylation may clarify the pathogenesis of SLE and provide additional insights into diagnostic and therapeutic strategies.XLV and XLI analyzed the literature and studies and wrote the manuscript. MZ, HW, and WZ assisted with constructing figures and polishing the language. QL and XC revised the manuscript. All authors listed have made substantial, direct and intellectual contributions to the work, and approved the article for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.