Serendipita indica is an endophytic root symbiont fungus that enhances the growth of various plants under different stress conditions, including salinity. Here, the functional characterization of two fungal Na+/H+ antiporters, SiNHA1 and SiNHX1 has been carried out to study their putative role in saline tolerance. Although their gene expression does not respond specifically to saline conditions, they could contribute, together with the previously characterized Na+ efflux systems SiENA1 and SiENA5, to relieve Na+ from the S. indica cytosol under this stressed condition. In parallel, an in-silico study has been carried out to define its complete transportome. To further investigate the repertoire of transporters expressed in free-living cells of S. indica and during plant infection under saline conditions, a comprehensive RNA-seq approach was taken. Interestingly, SiENA5 was the only gene significantly induced under free-living conditions in response to moderate salinity at all the tested time points, revealing that it is one of the main salt-responsive genes of S. indica. In addition, the symbiosis with Arabidopsis thaliana also induced SiENA5 gene expression, but significant changes were only detected after long periods of infection, indicating that the association with the plant somehow buffers and protects the fungus against the external stress. Moreover, the significant and strongest induction of the homologous gene SiENA1 occurred during symbiosis, regardless the exposure to salinity. The obtained results suggest a novel and relevant role of these two proteins during the establishment and maintenance of fungus-plant interaction. Saccharomyces cerevisiae  out of the cells, alleviating the toxic and osmotic cellular stress. Hypothetically, the activity of Na+ pumps of endophytic fungi surrounding the roots could directly extrude and alleviate Na+ from plants. Other Na+ transporters that have been shown to play a relevant role in salinity tolerance in fungi by excluding Na+ from the cytosol are Na+-H+ antiporters. The so-called NHA, are located in the plasma membrane and extrude Na+ out of the cell; the so-called NHX show abundance in the early endosome and pre-vacuolar vesicles shuttling Na+ into these organelles (+ gradient generated by H+ ATPases to move the Na+ (and/or K+) across membranes . Incubation was performed by placing the plates vertically in a Conviron phyto-chamber with a day/night cycle of 16/8 h (light 21 intensity: 110 μmol m−2 s−1) at a temperature of 22/18°C. After 1 week, the plants were individually dissected into roots and shoots for further analysis, e.g., total RNA extraction or cation quantification.The experiments carried out in To check for root colonization, 5 root samples were selected randomly from the plants. The samples were softened in 10% KOH solution for 15 min, acidified with 1 N HCl for 10 min and finally stained with 0.02% Trypan blue overnight . The samSiNHA1 and SiNHX1 cDNAs were obtained by RT-PCR from total RNA extracted from S. indica using specific primers that amplified DNA fragments containing the respective predicted START and STOP codons . After cDNA integrity verification by sequencing, the obtained fragments were cloned into the XbaI and KpnI sites and EcoRI sites, respectively, of the yeast expression vector pYPGE15 (The full-length  pYPGE15 . Yeast tS. indica with the well-annotated and extensively studied transportome of the ascomycete S. cerevisiae (Sc); and with that of a more phylogenetically related basidiomycete Cryptococcus neoformans (Cn), both available in TransportDB 2.0. MMseqs2 software  representatives of the three fungi; (ii) representatives of Sc and S. indica; (iii) representatives of Cn and S. indica; (iv) representatives of Sc and Cn; (v) representatives only of Sc; and (vi) representatives only of Cn. Those transporters of S. indica included in any of the first three clusters were directly considered to be part of the transportome. Representative transporters from S. cerevisiae or C. neoformans included in any of the last three clusters in which no representative proteins of S. indica were identified, were used for a BLASTp search , retrieved only 7,984 hits. The screening of the keywords related to transporters and channels: “transport/er,” “efflux,” “uptake,” “exporter,” “importer,” “pump,” “permease,” “exchange/r,” “channel,” “carrier,” “influx,” “symporter,” “antiporter” and “translocator”; identified 218 hits that were not previously annotated in the JGI transportome. The analysis of COG description proposed 91 protein candidates to be considered as transporters. Following these two different strategies and after revising the protein length, the presence and number of transmembrane fragments (TM) and EuKaryotic Orthologous Groups (KOG) descriptions of the hits obtained, a total of 80 new transporters were identified.EggNOG S. indica was previously grown in liquid AP medium supplemented with 5 mM KCl and then transferred to fresh AP medium with or without 50 mM NaCl. Fungal samples were taken and immediately frozen in liquid nitrogen after the incubation at 25°C for 1.5 h, 7, and 14 days ] of <0.05 an absolute differential expression of log2 fold change (FC) ≥1 was chosen to select differentially expressed genes (DEGs).p < 0.05 . Along wS. indica cultures grown in AP minimal medium in the absence or presence of 50 mM NaCl (free-living conditions) and from total RNA of Arabidopsis roots previously infected with spores of S. indica (under symbiosis). Regarding the transcriptional analysis of fungus growing in free living conditions, 93% of on average of 50,000,000 of total clean reads mapped the fungal reference genome.S. indica genome, being 11,106 out of the known genes and only 92 of them are novel genes ; the other cluster was formed by the samples of established symbiosis (after 7 and 14 dpi). These results indicated that the largest source of variation was the interaction of the fungus to plant roots with respect to free living conditions and not the saline treatment revealing that the establishment and fungal growth in symbiosis caused the most important transcriptional response of is study . To reflis study . The res+-H+ antiporters are known Na+ transport systems that are involved in salt tolerance in fungi (S. indica genome database by a BLAST search using the amino acid sequence of S. cerevisiae NHX1 (GenBank accession number: KZV12696.1) and NHA1 (GenBank accession number: CAA97709.1). The corresponding genes (access number PIIN_0302 for SiNHX1 and PIIN_01646 for SiNHA1) were identified in the different contigs . The gene structure indicated that SiNHX1 and SiNHA1 consist of 6 and 14 exons that encode for two proteins of 675 and 809 amino acids, respectively. Interestingly, the cDNA amplification of SiNHX1 from total RNA of the fungus, either grown in the presence or absence of NaCl, rendered a fragment of 2025 base pairs which differed from the proposed automatically annotated gene included in the JGI . The complementation assay was carried out by a drop test, comparing the ability of these two proteins to restore the growth of the yeast defective mutant in the presence of NaCl and at different pH values, in comparison to that of the previously described SiENA1 and SiENA5 Na+, K+ ATPases . Thus, as a result of BLAST search, a complete full Transportome of S. indica was obtained, formed by 764 proteins, all these related to transport to which a TC number was ascribed  was performed (accessed in December 2022).ascribed . The setlanation . Comparichannels . Figure channels .S. indica transportome, a classification of the 764 transporters was carried out on the basis of the transported substrate  , NH4+ transporters (2), urea (1), and oligopeptides (12) among others. Related to carbohydrate transport a total of 25 transporters were identified, most of them for monosaccharides (14), including high and low affinity for glucose among other monosaccharides; but also, for disaccharides (11). In addition, 8, 13, 10, and 12 homologs to fungal phosphate, potassium, sulfur, and vitamin transporters, respectively, were also identified. Besides the transporters that guarantee the supply of the main macronutrients, S. indica is endowed with several transporters for Ca2+, which are key players for Ca2+ homeostasis and signal generation. There are 21 between Ca2+ channels, Ca2+/H+ antiporters, and Ca2+-ATPases homologous to those present in other fungi. Regarding Mg2+, 8 channels and 1 electrochemical potential-driven transporter were identified and, interestingly, two of these Mg2+ transporters  are among those not previously annotated in JGI that were included in the S. indica transportome in this study. The last pool of proteins grouped under the name of “metal cations” were those involved in the transport of various micronutrients such as Fe, Zn, Cu, Mn, and Ni, that together make up the largest group of nutrient transporters (66 transporters).Based on this roteins) . In addiOther large cluster of transporters identified in the analysis of the transportome deals with efflux transporters responsible for removing substances that can be toxic to the cell  encoding transporters were compared between the fungal samples grown under control conditions and after the salt treatment at the same time of incubation, either in free living conditions  or under symbiosis . Of the 752 genes encoding transporters for which some FPKM value was obtained, a total of 375 genes were differentially expressed in some conditions tested. As shown in +, K+, and Ca2+ homeostasis and among them, the SiENA5 gene (corresponding to Protein ID: PIRIN_72634), which encodes a Na+ efflux ATPase from S. indica (SiKHA1 (Protein ID: PIRIN_75414), a gene which encodes a Golgi localized K+-H+ antiporter  was significantly repressed , as well as a nicotinate transporter gene (Protein ID: PIRIN_77505) were also induced in response to NaCl  what indicates that a metabolic failure of the fungus can be occurred in response to salinity.S. indica may have a role during the establishing and maintenance of the symbiotic relationship with Arabidopsis roots, a differential expression analysis was performed between free living conditions and during co-cultivation with plants growing in the absence and presence of 50 mM NaCl. Interestingly, only 9 total DEGs were identified at any time of the experiment in comparison of DEGs NaSymb vs. Symb and none of them were a gene encoding transporter  that can be highlighted; (ii) group of few DEGs that responded by induction or repression to salinity in early infection phase at 1.5 hpi; (iii) groups of DEGs that were clearly distinguished according to the time of colonization but independently of the saline treatment: on the one hand there is a group of those that were regulated in the early stages of infection (1.5 hpi) and, on the other hand, those that were regulated in late stages of symbiosis (7 and 14 dpi), without observing notable differences between these two long times studied , being markedly induced Sec66 which reached a fold change of 11.8, the highest level on induction among all. Interestingly, their induction would support the involvement of the vesicle secretion system in the movement of NaCl in cells previously described in plants, which would exist as a parallel route for the transport of Na+ from the outside to be discharged into the vacuole by endocytosis and, in turn, could remove Na+ from the cell to the outside by exocytosis , and is activated by stretch to release vacuolar Ca2+ into the cytoplasm upon osmotic shock  and SiKHA2 (protein ID_75414), both related to Na+ and K+ homeostasis, the former being one of the DEGs that was the most induced during symbiosis  that are actively regulated under symbiosis . In addiutrition .4+ (Protein ID_72831 and Protein ID_75176) and one in urea uptake, the latter homologous to DUR3 (Protein ID_74652). In contrast, two DEGs encoding NH4+ exporters (Protein ID_73482 and Protein ID_75508) were highly induced only in the early stages of colonization.Among the transporters of macronutrients, those related to N nutrition, which form a numerous and diverse group  deserve especial attention. There are 28 DEGs that were regulated in some of the conditions of symbiosis . As show+ and K+ homeostasis during the growth of the fungus in symbiosis, the analysis of DEGs of this group of transporters  showed an induction at the beginning of infection (1.5 hpi) in the absence of Na+ , was also observed, in early stages of colonization  and presence of NaCl (7 and 14 dpi)  . These r+ uptake system SiHAK1 (Protein ID_79610) was induced in all the symbiosis conditions studied (in the presence and absence of NaCl) with respect to free living conditions. This suggest that it could be the main transporter in charge of the K+ nutrition of the fungus during the symbiosis. However, it should be noted that another S. indica K+ uptake system, SiTRK2, was also induced but exclusively at some point during the symbiosis under saline conditions.As mentioned above, the gene encoding the high-affinity KSiENA1 gene (Protein ID_72635) was induced in the presence of NaCl at beginning of infection (1.5 h dpi) and during the symbiosis independently of Na+, being surprisingly the gene that showed the highest induction of all the transporters , was also strongly induced at 7 and 14 dpi during symbiosis resembling the coordinated induction in expression described previously of the two homologs transporter genes from S. cerevisiae, ScENA1 and ScPHO89 in response to an alkaline environment , SiENA5 is expressed more than SiENA1 in both free-living and symbiosis conditions regardless of the presence or absence of salt  macromolecules, (ii) metabolic substrates and products, (iii) nutrients, and (iv) osmorregulators. It should be emphasized that this classification is only an lles see . RegardiDUR3  and another for a pectate-lyase (Protein ID_75472), were strongly induced  during symbiosis  can be found in the article/S. indica transportome and part of the transcriptional analysis. ES-G and ML performed the cloning and functional characterization of SiNHA1 and SiNHX1 in yeast. All authors reviewed the manuscript and approved it for publication.BB and RH conceived and designed the research. BB wrote the manuscript. RH designed and carried out most of the figures and tables. ML developed the fungal growth experiments under different culture conditions and total RNA extractions for RNA-seq analysis. MA developed the computational tasks that allowed the construction of the This work was supported by grant PID2020-119441RB-I00 and AGL2016-80593-R funded by MCIN/AEI/10.13039/501100011033. Additional financial support was provided by the DGUI-UPM/CAM Research Group Program.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Moreover, circNOLC1 is regulated by the transcription factor YY1 and specifically stabilized HuR induces its parental gene mRNA expression. The associations between circNOLC1 and these signaling molecules are validated in primary CRC and corresponding liver metastasis tissues. These findings reveal that circNOLC1 interacting with AZGP1 and circNOLC1/miR‐212‐5p/c‐Met axis plays a key role in oxidative pentose phosphate pathway‐mediated colorectal cancer liver metastasis, which may provide a novel target for precision medicine of colorectal cancer.Liver metastasis is a common cause of death in progressive colorectal cancer patients, but the molecular mechanisms remain unclear. Here, it is reported that a conserved and oxidative pentose phosphate pathway‐associated circular RNA, circNOLC1, plays a crucial role in colorectal cancer liver metastasis. It is found that circNOLC1 silencing reduces the oxidative pentose phosphate pathway‐related intermediate metabolites and elevates NADP Liver metastasis is a common cause of death in progressive colorectal cancer patients. This study identifies a novel pathway underlying colorectal cancer liver metastasis by circNOLC1‐mediated oxidative pentose phosphate pathway reprogramming. This effect is achieved by circNOLC1 interacting with AZGP1 and sponging miR‐212‐5p to induce G6PD activity, which can provide a novel target for precision medicine of colorectal cancer. Therefore, uncovering the underlying genetic alterations and regulatory targets in CRC liver metastases is urgently necessary for the development of novel anticancer therapies.Metastasis is the leading cause of death in patients with colorectal cancer (CRC), and the liver is the most common site for distant metastasis.1 Amon Growing evidence suggests that metabolic reprogramming is required during metastatic dissemination and colonization in CRC. Metabolic reprogramming‐involved multiple metabolic pathways including glucose metabolism, lipid homeostasis, and amino acid metabolism may be involved in CRC liver metastasis. The pentose phosphate pathway (PPP), a branch of glucose metabolism, has become the focus of metabolic reprogramming. The oxidative pentose phosphate pathway (oxPPP) can provide both nucleotide precursors for tumor growth and nicotinamide adenine dinucleotide phosphate (NADPH) for tumor metastasis. Glucose‐6‐phosphate dehydrogenase (G6PD) is the first rate‐limiting enzyme in the oxPPP, and its function is strictly regulated in normal cells but highly activated in cancer cells. Recent reports have shown that G6PD activity may be affected by numerous factors, such as tumor suppressor p53, p73, PTEN, transcription factor YY1, protein kinase mTOR, and Plk1. However, despite the crucial roles of G6PD and the oxPPP in CRC, their regulatory mechanisms in liver metastases still remain unclear.Metabolic reprogramming has emerged as a key hallmark of cancer.4 Grow6N‐methyladenosine modification of circNSUN2 modulates cytoplasmic export and promotes epithelial‐to‐mesenchymal transition (EMT) in CRC liver metastasis via forming a circNSUN2/IGF2BP2/HMGA2 RNA‐protein ternary complex. CircPPP1R12A encodes a functional protein, circPPP1R12A‐73aa, that facilities the occurrence and liver metastasis of colon cancer by activating the Hippo‐YAP (Yes‐associated protein) signaling pathway. In addition, circPPFIA1s functioned as sponges of oncogenic miR‐155‐5p and Hu antigen R (HuR) to inhibit the CRC liver metastasis through the miR‐155‐5p/CDX1 and HuR/RAB36 pathways. Recently, it has become evident that multiple metabolic pathways including glycolysis, fatty acid oxidation, and amino acid metabolism in CRC are tightly regulated by circRNAs. Notably, however, little is known about circRNA‐induced metabolic pathways in CRC liver metastasis.CircRNAs play an important role in CRC growth and metastasis. By sponging, recruitment, or scaffolding of microRNAs or proteins, and translation of specific peptides, circRNAs can promote or suppress various hallmarks of cancer. It has been reported that  It is reported that circSTX6 was confirmed to participate in the ubiquitin‐dependent degradation of hypoxia‐inducible factor 1‐alpha (HIF1A) by interacting with CUL2. circMYBL2 was found to enhance the translational efficiency of the FLT3 kinase by increasing the binding of polypyrimidine tract‐binding protein 1 (PTBP1) to FLT3 messenger RNA. In addition, circACTN4 promotes intrahepatic cholangiocarcinoma progression by recruiting YBX1 to initiate FZD7 transcription. However, to date, potent and sensitive circRNA binding proteins for CRC liver metastasis are mostly unknown.RNA binding proteins (RBPs) play key roles in circRNA‐mediated gene regulation, and obtaining the full interaction map of proteins bound to a circRNA of interest is critical to our understanding of its function.24 It  Sprouty‐2 was found to increase the metastatic potential by regulating the expression of c‐Met in colon cancer cells. Our previous study found that liver and lymph node sinus endothelial cell c‐type lectin (LSECtin) promoted liver metastasis by upregulating c‐Met expression in colon cancer cells.c‐Met is a hepatocyte growth factor receptor with tyrosine kinase activity that is associated with a variety of oncogene products and regulatory proteins and is involved in the regulation of cellular signal transduction and cytoskeletal rearrangement. It has been reported that c‐Met is a key factor in promoting liver metastasis of colorectal cancer. Upregulation of c‐Met expression in colon cancer cells with overexpression of MACC1 (metastasis‐associated in colon cancer‐1) significantly increased the liver metastasis rate.28 SprHere, we identified the mechanism by which circNOLC1 modulates oxPPP‐mediated metabolic reprogramming in human CRC liver metastasis. We discovered that circNOLC1 interacted with AZGP1 to activate mTOR/SREBP1 signaling, or sponged miR‐212‐5p to upregulate c‐Met expression, both of which could further induce G6PD to activate oxPPP in CRC liver metastasis. We also found that circNOLC1 was transcriptionally regulated by YY1 and specifically stabilized HuR‐induced parental gene mRNA expression. The identification of the oxPPP regulated by circNOLC1 interacted with AZGP1 and sponged miR‐212‐5p will help further our understanding of how metabolic reprogramming elicits CRC liver metastasis.22.1FigureTo discover circRNAs specifically functioning in metastatic CRC, we compared three pairs of metastatic CRC tissues and their matched nontumor tissues by using a circRNA microarray. The volcano plot shows the variation in circRNA expression between the CRC tissues and paired adjacent normal tissues  and its parental gene NOLC1, respectively, were one of the most abundant differentially expressed transcripts according to their normalized read counts in our microarray and TPM values in the TCGA‐COADREAD cohort Figure . Consist18 primers compared with the random primers, the relative expression of circNOLC1 was significantly decreased, while NOLC1 mRNA was not , we identify that circNOLC1 is 487 bp in spliced length and consists of 4 exons (exons 2–5) from the NOLC1 gene. Back‐ splicing in the RT‐PCR product of circNOLC1 with the expected size was confirmed by Sanger sequencing Figure . To furtt Figure . Moreovet Figure . qPCR ant Figure . Finally  spanning the back‐splicing junction of circNOLC1 were designed to silence circNOLC1 expression via lentiviral transduction Figure. The CelTo confirm the role of circNOLC1 in CRC liver metastases, we also established a liver capsule injection model. We noted that all mice injected with stable cells expressing circNOLC1 shRNA led to a reduction in the incidence of macroscopic liver metastasis Figure . Surviva2.3 Therefore, we assumed that circNOLC1 may promote CRC liver metastasis by mediating metabolic reprogramming. We performed liquid chromatography coupled with tandem mass spectrometry (LC‐MS/MS) and RNA‐sequencing to investigate the metabolic alterations induced by circNOLC1 knockdown. LC‐MS/MS analysis showed the presence of distinct metabolite clusters in circNOLC1‐depleted HCT116 cells compared with control  and purine metabolism Figure . Functio37 We S Figure . FurtherS Figure . These r Interestingly, circNOLC1‐depleted cells showed a strong reduction (∼55‐65%) in overall G6PD activity compared with control . However, housekeeping genes GAPDH and β‐actin mRNA were no enriched  assay, we found that circNOLC1 was markedly enriched in pull‐downs with antibodies against AZGP1  CLIP data (GSE28865) and found that circNOLC1 could bind to AGO2. AGO2 immunoprecipitation analysis further supported this observation Figure5A. By bTo further validate the promotive effect of circNOLC1 on c‐Met, we detected the expression of c‐Met after silencing circNOLC1 and found that the c‐Met mRNA and protein levels were strikingly downregulated Figure . A simil By using foretinib or siRNA‐specific knockdown of c‐Met, we found that inhibition of c‐Met signaling significantly decreased G6PD expression level in HCT116 cells  contained highly complementary Alu repeats with fourteen short interspersed elements (SINEs) in intron 1 and one SINE (named AluSq) in intron 5 , we assumed that upregulation of circNOLC1 in CRC may be transcriptionally regulated by certain TFs. We used three independent databases to predict TFs that may be involved and found that the promoter of NOLC1 contains some binding motifs of YY1 ,47 and1 Figure . By anal1 Figure , suggestCells were transduced with lentiviral vectors containing short hairpin RNA complementary to the backsplicing junction of circNOLC1. Endogenous circNOLC1, but not NOLC1, could be successfully depleted since the backsplicing junction is the only unique and targetable feature. Interestingly, circNOLC1 knockdown led to substantially reduced mRNA and protein levels of NOLC1 Figure . We spec2.7FigureTo determine the clinical relevance of our observations, we determined whether there are correlations between circNOLC1, miR‐212‐5p, SREBP1, G6PD, and c‐Met expression in primary CRC and liver metastasis  between 2015 and 2019. With approval and support from the ethics committee of the Second Hospital of Dalian Medical University, the diagnosis of colorectal carcinoma was confirmed by pathologic examination. Informed consent was obtained from all patients. The samples used in the circRNA microarray and qPCR analyses were freshly frozen, and the samples used for FISH and IHC staining were FFPE .Total RNA was extracted from three pairs of colon carcinoma and adjacent normal tissues using TRIzol (Invitrogen) and treated with RNase R (Epicentre). Microarray analysis was performed by KangChen Biotech . Briefly, the enriched circular RNAs were amplified and transcribed into fluorescent cRNAs utilizing Arraystar Super RNA Labeling Kit (Arraystar). The labeled cRNAs were purified by RNeasy Mini Kit (Qiagen) and hybridized to Arraystar Human circRNA Array V2 . Subsequently, the arrays were scanned using an Agilent G2505C Microarray Scanner, and acquired array images were analyzed with Agilent Feature Extraction software. Briefly, 2 × 106 CRC cells  transduced with lentiviral vectors were injected into the spleens or liver capsule (Glisson's capsule) of recipient mice. The spleen or liver was returned to the abdominal cavity and the incision was closed. Mice were sacrificed at 3–7 weeks post‐injection and examined microscopically by H&E staining for the development of liver metastatic foci. The dissected tumors were collected for RNA and protein isolation.The animal studies were approved by the Animal Center of Dalian Medical University in accordance with the national guidelines for the care and use of laboratory animals. BALB/c athymic nude mice that were 4–6 weeks of age were purchased from Charles River Laboratories . The liver metastasis model was implemented as the previously described method.31 Bri−1 at 4 °C for 10 min. The supernatant was injected into chromatography columns for LC–MS/MS analysis. The extracts were analyzed using a UPLC–ESI–MS/MS system . Quantification of metabolites was accomplished using multiple reaction monitoring (MRM) analysis of a triple quadrupole‐linear ion trap mass spectrometer (QTRAP 6500+ System). For statistical analysis, the relative abundances of each metabolite were log‐transformed before analysis to meet normality. Differentially expressed metabolites were identified using a combination of the fold change values and the VIP values from the OPLS‐DA model. Metabolites with VIP values ≥1 and fold change ≥ 1.5 or ≤ 0.67 were considered differentially expressed.Stable HCT116 cells expressing control shRNA or circNOLC1 shRNA were seeded in 6 cm plates, and 2 h before metabolite collection, the culture medium was replaced with fresh medium. Widely targeted metabolomics analysis was performed by MetWare Biotechnology . Cells were thawed on ice and dissolved in a cold 70% methanol aqueous solution. Each sample was frozen for 3 min in liquid nitrogen and centrifuged for 2 min, repeating two times. The mixture was centrifuged under 12 000 r min7 cells were applied to reversible formaldehyde crosslinking in situ, followed by hybridization with biotin‐labeled probes. After washing to remove the nonspecifically bound proteins under strong denaturing conditions, the proteins that specifically interacted with circNOLC1 or control were identified and quantitatively analyzed by MS. Proteins with unique peptides ≥ 2 and fold change ≥ 1 were considered differentially expressed.For affinity capture of complexes containing circNOLC1 and chromatin, four probes covering the sequence of circNOLC1 for use in ChIRP were designed. U1 snRNA probes were used as the positive control. ChIRP‐MS analysis was performed by KangChen Bio‐tech . Briefly, 10RNA pull‐down assays were performed using a Magnetic RNA‐Protein Pull‐Down Kit (Thermo Scientific). circNOLC1 antisense and sense probes were synthesized with BiotinTEG at the 3′ end and purchased from Sangon Biotech. The biotinylated probes were incubated with nucleic acid‐compatible streptavidin magnetic beads at 4 °C for 30 min. These complexes were mixed with proteins extracted from CRC cells at 4 °C for 1 h with gentle rotation. After that, the beads were washed three times and eluted with an SDS loading buffer. Proteins were applied to SDS‐PAGE followed by silver staining or western blot analysis.RNA immunoprecipitation assays were performed using an EZ‐Magna RIP Assay Kit (Millipore) according to the manufacturer's protocol. Briefly, cells were lysed in complete RIP lysis buffer, and the extracts were incubated with magnetic beads conjugated to antibodies or nonspecific IgG at 4 °C overnight. The antibody‐protein‐RNA complexes were isolated by immunoprecipitation with protein A/G magnetic beads. After that, the complexes were washed and incubated with proteinase K to remove proteins. Finally, RNAs were eluted and purified for RNA extraction and detection.q value < 0.05) were identified from transcriptomics and integrated with the significantly differentially metabolites  from metabolomics data. The integrated gene‐metabolite profile was analyzed with the Joint Pathway Analysis module of MetaboAnalyst . Pearson correlation coefficients of metabolites and genes were calculated using an R package.The significantly differentially expressed genes  and GraphPad Prism 7. Student's The authors declare no conflict of interest.M.Y., X.Z., F.Y., and F.Z. contributed equally to this work. S.R. and Y.Z. were responsible for the study concept and design. M.Y., F.Y., F.Z., S.J., X.Z., J.L., Z.Z., Y.S., Z.C., H.W., X.L., X.Y., B.W., K.J., and F.L. were responsible for performing the experiments, acquisition and analysis of data, and drafting the manuscript. X.Z. provided and collected the clinical data. Y.Z. and S.R. were responsible for study supervision. S.R., Y.Z., and M.Y. were responsible for critical revision of the manuscript.Supporting InformationClick here for additional data file.


Saccharomyces cerevisiae. Our study reveals pervasive structural transitions during cotranscriptional folding and overall structural similarities between nascent and mature RNAs. Furthermore, a combined analysis with genome-wide R-loop and mutation rate approximations provides quantitative evidence for the antimutator effect of nascent RNA folding through competitive inhibition of the R-loops, known to facilitate transcription-associated mutagenesis. Taken together, we present an experimental evaluation of cotranscriptional folding in eukaryotes and demonstrate the antimutator effect of nascent RNA folding. These results suggest genome-wide coupling between the processing and transmission of genetic information through RNA folding.The transcriptional intermediates of RNAs fold into secondary structures with multiple regulatory roles, yet the details of such cotranscriptional RNA folding are largely unresolved in eukaryotes. Here, we present eSPET-seq , a method to assess the cotranscriptional RNA folding in  Here, the authors present eSPET-seq a method to measure cotranscriptional RNA folding in eukaryotes. Further analysis reveals an antimutagenic effect of nascent RNA folding and contribution to the variability of local mutation rates across the yeast genome. In the cellular environment, the presence of various RNA-binding proteins and helicases can regulate RNA folding beyond thermodynamic equilibrium dictated by the nucleotide sequence of an RNA molecule5. It is therefore not unexpected that different structures might be formed at different stages of the life cycle of RNA molecules6. For example, RNA starts to fold once it has been transcribed7 . The structure of nascent RNA formed by such cotranscriptional folding has been shown to regulate alternative splicing8, as well as the speed of RNA synthesis9.Secondary structure is a significant feature of RNA molecules, as it is a prerequisite of many RNA functions, such as transcription11. Specifically, transcription-associated mutagenesis (TAM) is aggravated by the formation of an R-loop, in which nascent RNA forms a stable RNA-DNA hybrid with the template strand of the DNA, leaving the nontemplate strand of DNA single-stranded/exposed and susceptible to mutagenesis. For a given sequence, RNA–RNA duplexes in the stem of the stem-loop formed by the nascent RNA molecule are thermodynamically more stable than RNA-DNA hybrids with the same base pairing12; therefore, strong nascent RNA folding near the transcription site (3′ end of the nascent RNA) might competitively inhibit or dissolve the R-loop if the loop is not too large in the nascent RNA stem-loop. The dissolution of the R-loop allows the template DNA strand to anneal back with the nontemplate DNA strand, thereby reducing TAM10. Indeed, our previous experiment in Saccharomyces cerevisiae showed that synonymous mutations, which computationally predicted as strengthening for a nascent RNA structure, dissolved the R-loop and lowered the mutation rate of a reporter gene by >80% (ref. 10). Nevertheless, in silico prediction of nascent RNA structure was at best moderately accurate13, thereby leaving the magnitude of the regulatory effect on the mutation rate by the in vivo nascent RNA structure largely unknown, let alone its generality at the genome scale or in other species such as humans9. Additionally, does the antimutator effect of nascent RNA folding contributed to the slower evolution and/or the genetic robustness of highly expressed genes? These questions was never quantitatively answered with genome-wide experimental data despite their broad implications in evolutionary biology11 and cancer genomics14.Among the various potential functions of RNA secondary structures, the regulatory effect on the spontaneous mutation rate of DNA is of particular interest21. Several studies have assessed the structure of nascent RNAs in eukaryotes22, but their lack of information on transcriptional sites hindered the fine-scale resolution of cotranscriptional folding dynamics. To date, the only HTS-based assays assessing the cotranscriptional folding of nascent RNA is structural probing of elongating transcripts (SPET-seq) introduced in bacteria7. However, SPET-seq is based on dimethyl sulfate (DMS), which provides no structural information for guanine (G) or uracil (U), thereby limiting the biological interpretation.Besides the antimutator effect, many detail aspects of cotranscriptional nascent RNA folding remained largely unexplored. For example, is it always temporally coupled with the progression of transcription? Is there nascent RNA-specific structure that is absent in mature RNA? Which segment of nascent RNA does display thermodynamic- or kinetic-dictated in vivo folding? High-throughput assessment of nascent RNA structure should help answering these questions. In this context, recent technological advancements, especially the combination of biochemical probing and high-throughput sequencing (HTS), have facilitated genome-wide investigations of RNA secondary structures and their biological functions3, which modifies all four nucleotides instead of two. Our method, eSPET-seq, simultaneously captures both the NAI-N3 modified single-stranded nucleotides and the transcription site, thus allowing genome-wide assessment of nascent RNA structure during transcription. Experimental data from eSPET-seq in S. cerevisiae recapitulate known nascent RNA structures, revealing the dynamics of cotranscriptional folding, as well as its (dis-)similarity with mature RNA structure. More importantly, we find an anticorrelation between nascent RNA folding and spontaneous mutation rate of DNA, which directly supports the R-loop-dependent antimutator effect of nascent RNA folding10. Further analyses reveals that nascent RNA folding potentially contributes to the slower evolution and higher genetic robustness of highly expressed yeast genes, as well as the gene-specific mutation load in human cancer. Collectively, our results highlight the biological and evolutionary significance of the nascent RNA structure, in particular its potential regulatory effect on the spontaneous mutation rate.To overcome the technical limitations and investigate the TAM-mitigation effect of nascent RNA structures, we adapted the SPET-seq method to eukaryotes and substitute the DMS with NAI-N7 to eukaryotes with several major improvements , U, G, and cytosine (C))23, to probe single-stranded bases in nascent RNA  collectively enabled localization of single-stranded bases (those at NAI-N3-dependent RT stops), which can be further utilized to approximate the secondary structures of nascent RNA. We would like to emphasize that, strictly speaking, the NAI-N3 reactivities captured by eSPET-seq is not a direct measure of the RNA structure, but the flexibility of RNA nucleotides (as compared to NAz), which was previously proposed as a proxy to RNA secondary structure23. Throughout the current study, we followed this practice but will highlight potential deviations from such assumption when necessary.We aimed to capture the nascent RNA structure near the transcription site in yeast. To this end, we adapted the Structural Probing of Elongating Transcripts (SPET-seq) method previously developed in prokaryotesRNA Fig. . The fulHTS Fig. , in whicS. cerevisiae strain BY4741. We found that the number of reads captured for each gene by eSPET-seq was highly repeatable . Note that the 18 nucleotides at the 3′ end were not considered because they were covered by the transcriptional elongation complex7 based on SPET-seq revealed two distinct modes of cotranscriptional folding in bacteria. On the one hand, 71% of local RNA secondary structures are formed as they get transcribed. On the other hand, for the remaining 29% of genomic regions, 5′ halves of long-range helices form transient non-native structures until their 3’ counterparts have been transcribed. Assuming instant folding as the null model, we tried to identify the structural transitions by analyzing the changes in the NAI-N3 reactivity as transcription proceeded.Previous analysesy < 80), the densities of RT stop at nucleotides 30 to 45 were relatively low, a pattern compatible with the existence of an R-loop on 5S rRNA26, in which the nascent RNA is paired with the template DNA. The existence of this R-loop for transcript intermediate <80 nt and its resolution after transcription proceeds to >80 nt in length was further confirmed by DNA:RNA immunoprecipitation(DRIP) followed by reverse transcription quantitative PCR . Note that the stem structure between nucleotides 1–9 and 112–120 as observed in the mature 5S rRNA  should also have similar nascent and mature RNA structures folded in vivo, such as observed for YKL056C .Furthermore, we hypothesized that trans-regulatory elements, such as RNA-binding proteins, should be responsible for the dissimilarities between nascent and mature RNAs, because the two types of RNA molecules are folded in drastically different cellular contexts (nucleus versus cytoplasm). To test our hypothesis, we conducted additional icSHAPE experiments for the in vitro structures of mature RNA (see “Methods”). We reasoned that, as trans-regulatory factors were depleted during in vitro folding, genes whose NAI-N56C Fig. . And vic50C Fig. . Consist27. Using this dataset, we found that the 50% genes with low structural dissimilarities between in vivo and in vitro mature RNA . Similarly, the 50% genes with low structure dissimilarities between nascent and mature structures in vivo . These results suggested that at least some of the unique nascent RNA structures when compared to the mature RNAs can be explained by the unique cellular contexts in which nascent RNAs folded, including the availabilities of RNA-binding proteins.To further assess the molecular mechanism underlying the above observation, we downloaded a list of in vivo interactions between protein and mature RNAs experimentally inferred by gPAR-CLIP  from a previous report10, on one yeast gene (CAN1), has shown that nascent RNA folding alleviates mutagenesis by weakening the R-loop during transcription , the propensity to form R-loops by an “R-loop score” derived from yeast S1 nuclease DNA-RNA immunoprecipitation with deep sequencing29 (see “Methods”), and the prevalence of nascent RNA structure by a single-stranded score estimated from the eSPET-seq data. Here the single-stranded score was first calculated for each nucleotide (see “Methods”), reflecting the relative probability of it being unpaired/single-stranded, and then aggregated for a per-gene metric of prevalence of nascent RNA structure by the negated mean within the gene or by Gini index (a higher Gini index indicates a more structured region5) prevalence of nascent RNA structure, but not thermodynamic stability of the secondary structure of nascent RNA.Our previous studyion Fig. . Neverth10, the genes with higher prevalence of nascent RNA structure indeed had lower R-loop scores  were more than an order of magnitude stronger than previous observations made with computationally predicted nascent RNA structure10 (Spearman’s ρ = −0.014). Then, after confirming the expected relationship between the spontaneous DNA mutation rate and the R-loop score . More importantly, the correlation between the prevalence of nascent RNA structure and R-loop score or mutation rate remained mostly significant if the mature RNA structure was controlled  for the negative single-stranded score and ρ = −0.086 (P < 10−3) for the Gini index of the single-stranded score, which respectively suggested that approximately 34.7% (= 1 − (−0.1772/−0.2192)) and 69.6% (= 1 − (−0.0862/−0.1562)) of the antimutator effects of the nascent RNA folding were mediated via the R-loop  to assess the relationship between the three estimates, such that an excess of sites in support of our hypothesized model . For each of the three ORs, we combined the ORs from all genes using Cochran–Mantel–Haenszel chi-squared test (MH test. See “Methods”) and found all three combined ORs to be significantly greater than 1 .What could be the biological consequence of such antimutator effect of nascent RNA folding? One question, i.e., whether this antimutator effect was correlated with gene expression level, was of particular interest for several reasons. First, TAM is by definition more severe for highly expressed genes compared to lowly expressed genesρ = 0.19–0.56, Fig. 39. Collectively, the above results suggested that highly expressed genes tended to have more prevalent nascent RNA structures, and therefore might have contributed to the slower evolution and genetic robustness of highly expressed genes.To answer the first two questions above, we estimated for yeast protein-coding genes the expression levels, and, as a proxy for functional constraints, the evolutionary conservation of their sequences (see “Methods”). When these two metrics were compared with prevalence of nascent RNA structure, we consistently found positive correlations (Spearman’s −7). This selective advantage was smaller than the inversion of effective population size of yeast (107) (ref. 10), therefore the increased nascent RNA folding would not be selected as an adaptive trait in yeast  can be pervasive, as the fitness cost due to delayed cotranscriptional folding and therefore increased TAM were not strong enough. Collectively, our quantitative analyses of the antimutator effect of nascent RNA folding demonstrated again the potential of eSPET-seq in furthering our biological understanding of nascent RNA structure.As for the third question, we need to estimate the effect size of TAM-mitigation due to nascent RNA folding. Within each gene, we calculated the fold reduction in the spontaneous mutation rate between the one 50-bp segment with the highest prevalence of nascent RNA structure, versus the one 50-bp segment with the lowest prevalence of nascent RNA structure (see “Methods”). Intriguingly, we found that fold reduction of mutation rate appeared greater in highly expressed genes Fig. , suggestIn the current study, we developed eSPET-seq, an experimental assay specifically designed to probe the pairing status of all four RNA nucleotides  near the transcription site, thereby revealing nascent RNA structures. Based on the eSPET-seq data, we found heterogeneous rate of cotranscriptional folding, the (dis-)similarity between nascent and mature RNA structure partially explainable by RNA-binding proteins, and quantitative genomic evidence for the hypothesis that nascent RNA folding mitigates TAM by dissolving the R-loop. eSPET-seq therefore facilitated understandings of nascent RNA folding in eukaryotes with unprecedented resolution, which has broad implications in molecular and evolutionary biology.7. However, SPET-seq can only capture the pairing status of A and C nucleotides, but not U and G nucleotides, and therefore has a limited resolution. In addition, icSHAPE has been applied to chromatin-associated nascent RNAs in human HEK293 cells6. However, since NAI-N3-tagged sites across the whole nascent transcript was captured without enriching those near the transcription site, the generated data, compared to eSPET-seq data, should be less informative for nascent RNA folding ocurring immediately after transcription, a time point that was presumably critical for R-loop dissolution by nascent RNA folding. In other words, eSPET-seq is, to the best of our knowledge, the first experimental technique assessing the nascent RNA structure for all four RNA nucleotides near the transcription site.From a technological perspective, eSPET-seq had several unique advantages over similar techniques previously developed. Specifically, SPET-seq has been used to probe cotranscriptional folding of nascent RNA in bacteriaWith the help of eSPET-seq, several significant features of nascent RNA folding were revealed. For example, an average of 5.4% of nucleotides within mRNA displayed significant structural transition as transcription progressed. Compared to previous observation of 29% in bacteria revealed by SPET-seq, we speculated that the difference was mainly caused by separation of transcription and translation in eukaryotes, such that structural transitions accommodating translation are unnecessary until mRNA had finished its transcription and have been exported to cytoplasm in eukaryotes, whereas similar translation-accommodating structural transitions happened soon after transcription and before translation in prokaryotes. Additionally, our comparison between nascent and mature RNA structure in vivo and in vitro suggested that their differences are at least partially explainable by trans-regulatory elements such as RNA-binding proteins. Collectively, data of eSPET-seq has offered a unique experimental approach for the studies of molecular biology of nascent RNA structure, and shall therefore lay the foundation for further studies.41 (but see ref. 42). To disentangle the antimutator effect of nascent RNA folding and that of TCR, we exploited the different strand biases of the two mechanisms. Specifically, TCR repairs mutations on the template strand43 and nascent RNA folding suppresses mutations on the coding strand10. We collected spontaneous mutation events found in three mutation accumulation experiments32 and classified all C/G to T/A mutations into either template or coding strand by assuming dominate contribution by hydrolytic deamination of cytosine in C/G to T/A mutations44 (see “Methods”). Based on these stranded mutations, we found that the prevalence of nascent RNA structure was anticorrelated with the relative C-to-T mutation rate of the coding strand  but not that of the template strand , thereby suggesting the TCR-independent antimutator effect of nascent RNA folding on the coding strand. In terms of effect size, comparing the top and bottom 20% of genes in terms of prevalence of nascent RNA structure revealed a 25% decrease of C-to-T mutation rate of the coding strand. We further contrast the C-to-T mutation rate of template and coding strand to better isolate the TCR-independent antimutator effect of nascent RNA structure  of approximately 0.1 and then grown at 30°C with shaking at 250 rpm until mid-log phase with an OD660 of 0.6~0.7 at the time of NAI-N3 treatment.3, 50 ml of exponentially growing yeast cells at 30 °C were harvested and resuspended in 475 μl modification buffer , 2.5 μl 10% SDS). Note that for icSHAPE library construction (mature RNA), Actinomycin D was removed from the modification buffer. The samples were then incubated with either 25 μl of 2 M NAI-N3 in DMSO (in vivo modification) or 25 μl of pure DMSO (DMSO control) with moderate shaking (10 rpm) at 30 °C for 10 min, centrifuged and washed twice with 1× PBS to remove traces of NAI-N3. The pellets were stored at −80 °C until further use.For in vivo modification by NAI-NActinomycin D to stop further transcription during NAI-N3 treatment. Although, direct interaction between Actinomycin D and RNA or DNA:RNA hybrid duplex is unlikely47 due to the steric hindrance between the 2-amino group of the phenoxazone ring and the 2′-hydroxyl group of RNA, indirect interference of the nascent RNA structure via competitive bindings with the single-stranded DNA48 is still possible. To show that our result would not be significantly altered by the usage of actinomycin D, we extracted the sequence motifs known to bind to Actinomycin D48 in the genes captured by eSPET-seq data. We found that only 32 genes contain these motifs, such that excluding these genes/regions did not alter the direction or statistical significance of any relevant correlations between nascent folding, R-loop and mutation rate. We therefore concluded that the effect of Actinomycin D is largely negligible across our study.Note that we used 3 modification for 10 min at 30°C by adding 1 μl 1 M NAI-N3. The modified RNA was cleaned up and eluted in RNase-free water by using RNA Clean&Concentrator™−5 kit .RNA was extracted from cells treated with DMSO. One micrograms RNA was resuspended in 5.2 μl RNase-free water, and then denatured at 95 °C for 2 min. It was quickly transferred to ice to cool, refolded in SHAPE reaction buffer at 30 °C for 5 min by adding 3.3 μl 3.3× SHAPE buffer (333 mM HEPES (pH 7.5), 20 mM MgCl2 and 333 mM NaCl) and 0.5 μl RiboLock , followed by NAI-NS. cerevisiae fractionation was performed as previously described49. Specifically, each yeast pellet from a 50 ml culture (OD660~0.7) was homogeneously suspended in 1.5 ml buffer Y1 , with 50 μl lyticase , and the samples were incubated at 30°C with rotation (100 rpm) until >90% of the cells became spheroplasts (~30 min). Spheroplasts were collected by centrifugation at 300 × g for 5 min at 4°C, washed twice with SB buffer (1 M sorbitol and 20 mM Tris-Cl at pH 7.4) and resuspended in 500 μl precooled cytoplasmic lysis buffer , 15 mM β-ME, 50 U/ml SUPERase•In™ RNase Inhibitor  and 1× Complete EDTA-free protease inhibitor ) to lyse the cytoplasmic membrane, and the samples were kept on ice for 10 min with gentle mixing every 3 min. The lysate was layered over 1 ml sucrose buffer  and centrifuged at 13,000 g for 15 min at 4°C. The supernatant contained the cytoplasmic fraction (Cytoplasm). An aliquot (50 μl) was taken for Western analysis and RT-qPCR, and the rest of the supernatant was discarded. The white nuclear pellet was resuspended in 500 μl nuclei lysis buffer  to lyse the nuclear membrane. Samples were kept on ice for 10 min with max speed vortex for 5 s every 3 min and the chromatin fraction was collected by centrifugation at 16,000 × g for 10 min at 4 °C. Chromatin pellets were washed three times with fractionation wash buffer  and suspended in 250 μl 1× PBS. An aliquot (50 μl) was taken for Western analysis and RT-qPCR (Chromatin). The remaining chromatin solution was stored at −80 °C until further use.ADH1 as a molecular indicator to estimate the extent of nascent RNA enrichment. Because nascent RNAs contain the regions after the polyadenylation sites that are present in the nascent transcripts but absent from the mature mRNAs, ADH1 nascent RNA was selectively detected by priming an RT reaction downstream of this site50. Mature ADH1 transcripts were detected by priming an RT reaction at the poly(A)-tail with oligo(dT) primer50. cDNAs were quantified by qPCR to yield the abundance of mRNA and nascent RNA in the samples. Enrichment was calculated by the concentration ratio between the nascent RNA and the mature RNA from the chromatin fractions, normalized to the same ratio from the cytoplasmic fractions.To quantify the nascent RNA enrichment in cell fractionation, RNAs were isolated from the chromatin (nascent RNA) and cytoplasmic (mature RNA) fractions using the miRNeasy Mini Kit Qiagen, 217004) according to the manufacturer’s instructions. One hundred nanograms of RNA was reverse transcribed using SuperScript III reverse transcriptase  with specific primers  and cytoplasmic proteins . Finally, the membrane was probed with horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG  and scanned using a ChemiDoc Touch Imaging System (BioRad).Western blots of different samples from yeast cell fractionation were carried out to assay the success of the fractionation. Western blotting was performed as previously reported with minor modificationsg for 15 min at 4 °C, the aqueous phase was transferred to a 1.5 ml RNase-free tube and purified using the miRNeasy Mini Kit  and on-column DNase I digestion was performed using the RNase-free DNase set  according to the manufacturer’s instructions. For each treatment, three independent biological replicates were performed for the nascent RNA isolation and library construction.The chromatin solution (200 μl) containing nascent RNAs was resuspended in 0.8 ml TRIzol™ LS reagent , thoroughly mixed and incubated at 50 °C for 5 min. Then, 200 μl chloroform was added and shaken vigorously for 15 s. After centrifugation at 12,000 × 23 and Structure-seq52, with some modifications. The whole process can be divided into six steps, namely biotin click reaction of NAI-N3, 3′ adapter ligation, removal of excessive 3′ adapter, cDNA synthesis and isolation of NAI-N3-modified molecules, 5′ adapter ligation, and library amplification by PCR. Each individual step is described in detail below.After nascent RNA extraction, sequencing libraries were prepared similarly to previous protocols used for icSHAPE3. To specifically select NAI-N3-modified RNA, all nascent RNA samples were treated by a biotin-alkyne click reaction . Note that DMSO-treated RNA samples were not capable of ‘click’ reaction due to their lack of the NAI-N3 modification, but this experimental step was still carried out for the DMSO-treated control sample in parallel with the NAI-N3-treated samples. For each reaction, 3 μg of nascent RNA was used in a final volume of 100 μl by adding 100U RiboLock, 37 μM 1.85 mM DIBO-biotin, 50 μl 2× PBS. Biotinylated RNAs were purified using the Zymo RNA Clean & Concentrator-5 column according to the manufacturer’s protocol and eluted in 7 μl of RNase-free water.Step one is the biotin click reaction of NAI-N3-treated samples require a 3′ ddC RNA adapter , 2 μl of RecJ, 4 μl of 10× RecJ buffer and 2 μl of SUPERase•In™ RNase Inhibitor were added to the product of the ligation reaction above. The samples were then incubated at 37 °C for 1 h. Reactions were stopped by purification with the Zymo RNA Clean & Concentrator-5 column according to the manufacturer’s protocol, and the samples were then eluted in 11 μl of RNase-free water.3-modified molecules. RT primer   according to the manufacturer’s protocol and eluted in 6 μl of RNase-free water.Step four is cDNA synthesis and isolation of NAI-NStep five is the ligation of the 5′ adapter. The ligation was performed using T4 RNA ligase 1  which ligated the 3′ end of the cDNA to a single-stranded DNA adapter . The reaction was carried out in a final volume of 20 μl, in the presence of 1× RNA ligase buffer, 20% PEG8000, 1 mM ATP, 100 pmol DNA adapter, and 30 U T4 RNA ligase. The ligation was performed at 25 °C for 16 h and then deactivated at 65 °C for 15 min. The 5′ adapter-ligated cDNA was then purified using Oligo Clean & Concentrator columns according to the manufacturer’s protocol, and eluted in 20 μl of RNase-free water.Step six is the library amplification and purification. PCR amplification was performed on the ligated cDNA using Q5 High Fidelity DNA polymerase  and Illumina TruSeq primers. For each experiment, we took 5 μl of the ligated cDNA and set up an 80 μl reaction for PCR cycle optimization. Reactions (80 μl) were performed with 1× Phusion HF buffer, 0.2 mM dNTPs (each), 0.8125 μM forward primer, 0.5 μM reverse primer . The three nucleotides corresponding to the three “N” in the 5′ adapter (see step five in the eSPET-seq library preparation above) were also removed by cutadapt54 (V 1.18). Any reads shorter than 18 nts were discarded. The S. cerevisiae genome and annotation (R64-1-1) were downloaded from EnsEMBL. The processed forward (corresponding to NAI-N3-modified site) and reverse reads (corresponding to transcription site in eSPET-seq and random DNA fragmentation in icSHAPE) were separately mapped to the genomic sequences (intron retained) of all genes using Bowtie2 with the parameters “—norc” for forward reads, “—nofw” for reverse reads. Any reads with a mismatch on the first nucleotide at the 5′ end or mapping quality score <30 were discarded. Reads with ambiguous origins, or likely PCR duplicates (judged from the mapped position of both reads and the three “N”) were further removed. Biological replicates, which were highly correlated with each other . Such NAI-N3-independent RT stops should be constant for the same nucleotide; therefore, longitudinal comparisons made here for the same nucleotide between transcriptional intermediates intrinsically controlled the probability of NAI-N3-independent RT stops. For all other analyses, we used a single-stranded score, which controlled the probability of NAI-N3-independent RT stops by contrasting the NAI-N3 samples with the DMSO samples (see below).To investigate cotranscriptional folding using eSPET-seq data, we collected all read pairs from whose reverse reads were mapped to the same position, which therefore corresponded to the same transcriptional intermediate. The base immediately preceding the first 5′ end mapping position of the forward read was considered the RT stop site caused by the NAI-Nile Fig.  of RT st3 reactivity reflected by the forward reads, following previous methods55. Briefly, the number of RT stops was normalized to take into account the different sequencing depths between the NAI-N3 and DMSO samples, using the normalization constants kDMSO and kNAI-N3, which are defined as follows:For both eSPET-seq and icSHAPE data, a single-stranded score for each nucleotide was estimated based on the NAI-N3-treated samples, respectively. Then, the normalized number of RT stops for nucleotide i of a gene in the DMSO and in vivo samples were calculated as:i of a gene in the DMSO and NAI-N3 samples, respectively. Finally, the single-stranded score of nucleotide i was calculated as:Note that pseudocounts of 1 were added to avoid a logarithm of or division by zero and made the final score larger than zero. The final single-stranded score is larger than 0 and not larger than 7, with any raw 5.At the gene level, the single-stranded score of a gene was calculated as the arithmetic mean of the single-stranded scores of all the nucleotides within the gene. We also calculated the Gini index (by R package “ineq”) using the single-stranded scores of all nucleotides within a gene to represent the average prevalence of RNA secondary structure of the gene, as it has previously been shown that as the structure unfolds, the single-stranded score becomes more even (low Gini index)ACT1 to yield the relative R-loop signal in this region  and repeated the same experiment of DRIP followed by RT-qPCR  followed by reverse transcription quantitative PCR was conducted to determine whether nucleotides 30–45 of 5S rRNA form an R-loop. Briefly, DRIP was first used to purify R-loops by utilizing the high specificity and affinity of the S9.6 monoclonal antibody. Reverse transcription was conducted using three different RT primers designed for transcription intermediates L1, L2 and L3  were crosslinked for 25 minutes at room temperature using 1% formaldehyde, and quenched for 5 min using 360 mM glycine. The pellets were rinsed with PBS for 5 min, resuspended in 400 μl FA-1 lysis buffer, mixed with 200 μl of glass beads , and vortexed for 15 min at 0 °C in a Thermomixer at full speed. Glass beads were removed after transient centrifugation. By centrifuging at full speed for 15 min at 4 °C, the cross-linked chromatin was recovered, and then resuspended in 800 μl of FA-1 buffer, and sonicated to obtain DNA fragments of approximately 150 bp. Sonicated chromatins were centrifuged at 20,000 × g for 15 min at 4 °C to remove cellular debris, and 5% glycerol  was added to the supernatant. The supernatants were mixed with 25 μg of antibody S9.6  and 100 μl of prewashed Protein A agarose  and rotated overnight at 4 °C. We recovered the agarose beads, washed them successively with FA-1 buffer, FA-2 buffer, FA-3 buffer, and TE at 4 °C, then added 700 μl of QIAzol reagent and mixed them by vortexing. RNA was purified using the miRNeasy mini kit according to the manufacturer’s instructions, including the optional on-column DNase treatment with the RNase-free DNase set . The purified RNA was then used in RT-qPCR to examine the relative R-loop signal. The primers used for RT-qPCR in this study are listed in Supplementary Table In order to detect R-loop in specific regions, we performed DNA:RNA immunoprecipitation followed by reverse transcription quantitative PCR(DRIP-RT-qPCR) as previouslyhttp://varna.lri.fr/). The known structure of tRNA was extracted from RNAcentral  and that of 5S rRNA was extracted from RiboVision (http://apollo.chemistry.gatech.edu/RiboVision).Secondary structure models Figs.  and 2b wSRR3177717. Initial analysis of the sequencing data was carried out in the same manner as previously reported56, after which the “bigwig” files were created from BAM files using bamCoverage of deepTools57 with default parameters. A custom script was used to extract intronic and exonic read counts for all intron-containing genes from the bigwig files according to the annotation of S. cerevisiae genome (R64-1-1). The total number of exonic and intronic reads per gene are normalized by their respective lengths. The ratio of the size-normalized counts is calculated for each gene containing at least one read in both exonic and intronic regions  and the number of synonymous substitutions per synonymous site (dS) detected from one-to-one orthologs between S. cerevisiae and Saccharomyces bayanus following previously described pipelines35.The expression level of the yeast transcriptome was extracted from a previous RNA-seq-based report29 were downloaded from NCBI Sequence Read Archive (SRA) under the accession number SRP071346 specific runs used were: SRR3504389-SRR3504390, SRR3504393-SRR3504394, and SRR3504396 (wild-type data and the corresponding input-chromatin control data). The procedure of the S1-DRIP-seq data analysis was the same as previously reported29. Let the number of reads whose 5′ most nucleotide mapped to a given site be n, and the sum of n over all sites in the genome be m. We calculated x = log2((n + 1)/m), and the R-loop score of a site was defined by x calculated from the wild-type data subtracted by x calculated from the input-chromatin data for the site. Human R-loop score (R-ChIP) data were downloaded from NCBI Gene Expression Omnibus available under the accession number GSE97072. The longest transcript was selected when genes had more than one transcript.The yeast S1 nuclease DNA-RNA immunoprecipitation with deep sequencing (S1-DRIP-seq) dataS. cerevisiae strains, as well as their inferred phylogenetic relationship, were extracted from the original publication28. Based on this dataset, we applied the GAMMA algorithm59 to estimate the (relative) mutation rate for each segregating site, as described below. First, based on the phylogeny and the genotypes of the focal site, GAMMA first inferred its ancestral state on each internal node as the one with the highest (posterior) probability using a distance-based method60. Second, GAMMA estimates the expected number of mutations per unit of time at the focal site using a maximum likelihood (ML) method, which takes into account the possibility of multiple mutations appearing as only one nucleotide change in a long branch. Specifically, let us denote the total branch length of the full phylogeny as L is maximized (i.e. maximum likelihood) when the derivative of B and used as an approximation of per-site mutation rate (relative to other sites within the genome). Note that the unit of time for k is B, which is shared by all sites since they share the same full phylogeny. As a result, k values from different sites can be compared regardless of the actual value of B. Non-segregating sites were assumed to have a zero (negligible) mutation rate. Non-segregating sites were assumed to have a zero (negligible) mutation rate.All single nucleotide variations (SNVs) from previously compiled population genomic data of 190 61, most intraspecific polymorphisms are selectively neutral62, which is indeed empirically supported64. For example, it was previously estimated that only 12% of coding SNPs found in yeast populations are deleterious64. Using Tajima’s D test65, we also independently validated such general neutrality of polymorphisms in the current dataset of 190 strains by confirming that polymorphisms in the whole genome (P = 0.1 by Tajima’s D test) and the vast majority of genes were indeed compatible with the neutral expectation . Such general neutrality of polymorphisms dictates that the majority of variation in polymorphism levels can be attributed to variations in mutation rates rather than natural selection. Indeed, a higher level of polymorphism in a particular genomic region has often been used as an indicator of a higher local mutation rate68. Moreover, we found that there is a positive correlation between our polymorphism-based mutation rates and those estimated from three previously published mutation accumulation datasets32  whether its single-stranded score is higher than the mean single-stranded score within the gene and (ii) whether its R-loop score is higher than the mean R-loop score within the gene. Let the numbers of nucleotides that fall into the four categories be: a (yes to both questions), b (yes to only question i), c (yes to only question ii) and d (no to both questions), respectively. The number of nucleotides in each category was added by 1 as a pseudocount to avoid division by zero. Then, ORS-R = ad/bc. If strong nascent RNA folding reduces the R-loop, ORS-R should be > 1. The function “mantelhaen.test” in R was used to combine the ORS-R values from all genes and perform the MH test (Cochran-Mantel-Haenszel chi-squared test). Similarly, to calculate ORR-M, a 2 × 2 table was constructed for each gene by respectively classifying each nucleotide into one of four groups on the basis of (i) whether its R-loop score is higher than the mean R-loop score within the gene and (ii) whether its mutation rate is higher than the mean mutation rate within the gene. ORR-M should therefore be > 1 if a weak R-loop reduces the mutation rate. To calculate ORS-M, a 2 × 2 table was constructed for each gene by respectively classifying each nucleotide into one of four groups on the basis of (i) whether its single-stranded score is higher than the mean single-stranded score within the gene and (ii) whether its mutation rate is higher than the mean mutation rate within the gene. ORR-M should therefore be > 1 if strong nascent RNA folding reduces the mutation rate.To assess within-gene correspondence among single-stranded scores (S), R-loop scores (R), and mutation rates (M), we calculated three odds ratios, namely, CAN1 gene, which encodes an arginine permease that is commonly used to detect spontaneous mutations. It has been demonstrated10 that a functional CAN1 is lethal in media containing the toxic arginine analog canavanine, whereas a CAN1 null mutant is viable. By measuring the frequency of colonies that were resistant to canavanine, the spontaneous mutation rate in the CAN1 gene can be estimated. Furthermore, S1-DRIP-seq data29 (see above) demonstrated that CAN1 forms R-loops in the first 300 bp of its 5’ end during transcription , we first generated synonymous mutants of CAN1 by shuffling all synonymous codons within this region, so that the protein sequence and the codon usage bias of these mutants remained identical to wild-type CAN1. We then predicted the nascent RNA folding strength of these CAN1 mutants using DrTransformer69. Considering the potential error of in silico structure prediction, we synthesized several mutants with various nascent RNA folding strengths, inserted them into the yeast genome and experimentally measured their nascent RNA structures with eSPET-seq. The eSPET-seq experimental pipeline was same as described above, with the exception of an additional round of 16-cylcle PCR amplification enriching CAN1-derived transcript intermediates , a single colony was grown at 30 °C in 5 ml synthetic complete (SC) medium overnight. The cells were diluted to ~100 cells/ml in 10 separate cultures for each strain and again grown to 1 × 108 cells/ml in SC medium. About ~1.0 × 104 cells were plated onto canavanine-containing SC-arg plates (medium without arginine) to identify forward mutations in CAN1 and onto SC plates to count the number of viable cells. Colonies appearing after 3 or 4 days of growth at 30° were counted. The spontaneous mutation rate of a strain was estimated by the number of CANR colonies divided by the total number of cells after the growth in SC.To measure mutation rates of CAN1 mutants by DRIP-RT-qPCR (see above). Consistent with our model, we found that the R-loop signal was significantly reduced when there was strong nascent RNA folding  conferred by a mutation that increases nascent RNA folding is approximately the reduction in the deleterious mutation rate (Δμd) of its target site71. Let us assume that 70% of mutations at the target site are deleterious and that a mutation alters local nascent RNA folding can affect the mutation rate of a target of 10 sites. If the strengthened nascent RNA folding reduces the mutation rate of the target site by x-fold and the genomic average mutation rate per generation per nucleotide  is y, the fitness advantage can then be calculated as x, y and s is shown in Fig. We estimated the fitness advantage conferred by the TAM-mitigation effect of stronger nascent RNA folding following our previous frameworkx and y of the phase diagram. For x, we used 113 genes with at least 5% sites with mutation rate data and calculated the ratio between the average mutation rate of the 50-bp segment with the highest average single-stranded score, and that of the 50-bp segment with the lowest average single-stranded score. Infinite numbers (there are four of them) caused by division-by-zero were capped at 50. The resulting ratios for each gene appeared positively correlated with the gene expression level , which consists of 37 types of cancer, with 36–9104 samples per type. We calculated the probability of each gene being mutated (regardless of the type of mutation) in all samples within each type of cancer. Then, the probabilities from all types of cancer were averaged and compared with the prevalence of nascent RNA folding. For the density of mutations, we downloaded the Mutation Annotation Files (MAF) for 33 types of cancer from the GDC data portal . Next, the total number of mutations (regardless of cancer type) that appeared in each gene was divided by the gene length to calculate the mutation density.Gene-level somatic mutation data of the TCGA were downloaded from the UCSC Xena website (Further information on research design is available in the Supplementary informationPeer Review FileReporting SummarySource Data


Orthoflavivirus. Some Jingmenvirus group members, namely the Alongshan virus (ALSV) and Jingmen tick virus, are reported to be tick-borne human pathogens that can cause a wide variety of symptoms. The ALSV is widely distributed in Eurasia, yet no reliable assay that can detect it exists. We describe a qPCR system for ALSV detection. Our data showed that this system can detect as little as 104 copies of the ALSV in a sample. The system showed no amplification of the common tick-borne viruses circulating in Eurasia, i.e., the Yanggou tick virus—which is another Jingmenvirus group member—or some known members of the genus Orthoflavivirus. The qPCR system was tested and had no nonspecific signal for the Ixodes ricinus, I. persulcatus, Dermacentor reticulatus, D. marginatus, Haemaphysalis concinna, and H. japonica ticks. The qPCR system had no nonspecific signal for human and sheep serum as well. Overall, the qPCR system described here can be used for reliable and quantitative ALSV detection.The recently discovered Jingmenvirus group includes viruses with a segmented genome, RNA of a positive polarity, and several proteins with distant homology to the proteins of the members of the genus  Orthoflavivirus (family Flaviviridae) are small, enveloped, and have a nonsegmented single-strand RNA genome of a positive polarity. The genome encodes a polyprotein that is co- and post-translationally cleaved by viral and cellular proteases into ten proteins [Orthoflavivirus are well-known human pathogens, such as the West Nile virus [Viruses of the genus proteins . Many orproteins . Some mele virus , dengue le virus , Zika vile virus , and ticle virus .Orthoflavivirus polymerase [Orthoflavivirus envelope protein [Recently, the number of newly described viruses has increased dramatically, with several novel virus groups being discovered ,7,8. Thelymerase . The thilymerase ,10. The  protein ,11. The  protein .The JMV members include the Jingmen tick virus (JMTV) , YanggouRickettsia [The JMTV was discovered in China and was found to be highly prevalent in tick samples from Hubei province. Moreover, the results of a screening of cattle sera that was conducted using an immunofluorescence assay and RT-PCR revealed that samples from Hubei and Zhejiang, China, were JMTV positive . The resckettsia . The repckettsia . Since tckettsia .Ixodes ricinus and I. persulcatus ticks [The ALSV was also discovered in China during surveillance for tick-borne diseases in a patient with a febrile illness . The resus ticks  and is cus ticks ,24.Orthoflavivirus and the JMV group [The ALSV has been detected in Eurasia, including France , GermanyMV group . The cliMV group , which mConsidering all the above-mentioned factors, a tool that can quickly and specifically detect the ALSV is required. The current approaches to ALSV detection in ticks mostly include RT-PCR assays, followed by result confirmation with sequencing ,26,27. PIn two studies, serological screening based on the purified VP2 protein, as well as an RT-qPCR, was used. In both cases, serological assays based on the putative capsid protein VP2 of the ALSV, and SYBR RT-qPCR systems were used ,24. HoweHowever, such assays were never proven to not provide false-positive results with a wide array of co-circulating viruses. With this study, we present a sensitive and specific RT-qPCR assay that can be used to detect viruses both in ticks and human serum.Orthoflavivirus from the institute’s collection were used: the TBEV , LIV strain S1, POWV strain Pow-24, West Nile virus (WNV) strain SHUA-3, Japanese encephalitis virus (JEV) strain Gagar, and dengue-4 strain Cambodia. Kemerovo virus (KEMV) strain 21/10  from the institute’s collection was used. The full virus list, the system where the virus was replicated, and the amount of virus in plaque-forming units per mL (where possible) are presented in The JMV group members, the ALSV strains Miass519 and Miass527 , and the4 copies/μL) from the institute’s collection was used as the internal control for this study (see below).The poliovirus strain Sabin . After that, the water phase (on top) was transferred into the fresh tube with the addition of 250 μL of water; the tube was gently mixed, left at room temperature for 10 min, and centrifuged at 12,000× g for 8 min by using the Eppendorf 5424 centrifuge (Eppendorf). After that, the supernatant was discarded, 1 mL of 80% ethanol was added, and the samples were centrifuged at 7500× g for 5 min. Then, the ethanol was discarded from the mix and the precipitate was dried in the thermostat at 37 °C for 15 min. After the procedure, the RNA was dissolved in the water and was either instantaneously used for the qPCR or, if the porcine embryo kidney cell line’s RNA was being prepared, stored at −70 °C  in accordance with the manufacturer’s instructions. This method is based on the single-step method of phenol-chloroform extraction . Brieflyt −70 °C .The standard RNA samples were prepared as follows. First, a 1381 nt fragment of segment 2 of strain Miass527 was amplified with oligonucleotides , with thC . The RNA molar mass was manually calculated to determine the RNA fragment starting from the BHT7 promoter start base of the BHT7_Miass_VP1a_F3 oligonucleotide , with the cell supernatant being discarded before the procedure. The TRI reagent LS was then used to lyse the PEK cells, and the obtained mixture was used to proceed with the RNA isolation protocol described in The porcine embryo kidney cell line’s total RNA was used to normalise the amount of total RNA in each sample during reverse transcription and qPCR. To isolate the total RNA of the porcine embryo kidney (PEK) cell line, 750 μL of the TRI reagent LS  was added to the one-day PEK cells’ monolayer that was growing on the 25 cmC , and the RNA was subsequently aliquoted to 250 ng/μL and stored at −70 °C.The purified RNA was dissolved in 50 μL of water, the RNA concentration was measured by using NanoDrop One4 copies/μL) was added to each sample as an internal control. The total RNA was isolated from the samples by using the TRI reagent LS  in accordance with the manufacturer’s instructions (see above).Prior to the RNA isolation procedure followed for qPCR, 1 μg of the RNA of the PEK cells see  was adde2, 2 μL of DTT , 2 μL of 10 mM dNTP , and 1 μL of MMLV Revertase  was added to the reaction mix, and the mix was incubated at 42 °C for 1 h. Reverse transcription for the internal control sample was carried out using the same approach, with a specific PVR1 oligonucleotide being used  immediately after RNA isolation. Briefly, the RNA was dissolved in 9 μL of water, and 2 μL of Miass_gly_3R (5 pmol/μL) oligonucleotide was added . The mixing used .2, 2 μL of forward and reverse oligonucleotides, 1 μL of a qPCR fluorescent probe, 0.25 μL of SynTaq polymerase, and 10.25 μL of H2O was prepared . Then, 2 μL of the sample was added and the mixes were placed in C1000 Thermal Cycler . Fluorescence detection was conducted using CFX96 Real-Time System . For ALSV detection, the Miass_gly_3F and Miass_gly_3R oligonucleotides with a Miass_gly3_PROBE fluorescent probe  in accordance with the manufacturer’s instructions. Briefly, a mix containing 2.5 μL of 2.5 mM dNTP, 2.5 μL of the 10× buffer, 2.5 μL of 25 mM MgClnt probe  were usent probe . The exaThe obtained amplification data were analysed using Bio-Rad CFX Manager v.3.1 ; the “Single Threshold” Cq Determination mode, “Baseline subtracted Curve Fit”, and “Apply Fluorescence Drift Correction” baseline settings were used, and the other settings were used as a default.For standard RNA samples, the mean quantification cycle was calculated, using Microsoft Excel 2010’s “AVERAGE“ function. The confidence of the mean value was calculated using a Student’s t distribution with Microsoft Excel 2010 . Results were presented in the “Mean ± confidence” format.7 RNA copies/μL) were added to 100 μL of the serum sample that previously tested negative for the ALSV. Two separate experiments were conducted: one using human serum and another using sheep serum. The obtained spiked samples were then immediately used for RNA isolation followed by the qPCR.To test the ability of the qPCR system to detect the ALSV in sera, serum samples spiked with the ALSV were prepared. To conduct this, 25 μL of the ALSV strain Miass519  to 16.1 (108 RNA copies). The linear dependence between the amount of RNA copies and Cq had an R2 value of 0.997 (The quantification cycle (Cq) for the standard samples ranged from 29.5 . Otherwise, the amplification was considered nonspecific.Because 10We tested the specificity of the qPCR system with several possible targets. All the samples mentioned below were tested to amplify the internal control poliovirus RNA that was added to the sample before isolation. First, we tested the system with two ALSV strains: Miass519 and Miass527. The qPCR system was able to successfully detect both viruses in the samples . To testAdditionally, we used a panel of the tick-borne orthoflaviviruses, including the three most common genotypes of the TBEV, POWV, OHFV, and LIV. We report no detectable amplification of those viruses by our system.I. persulcatus ticks. We report no detectable amplification of those viruses by our system. We detected no amplification of the viruses replicated in the PEK cells, Vero cells, and mouse brain  that can cause coinfection in Russia. Our system also produced no amplification signals in ALSV-free cultures ,14,26, wcultures ,29. We cOverall, the qPCR system that we present can detect ALSV in ticks, the blood serum of humans and animals, and a variety of common cell cultures, which makes it a useful tool for human diagnostics, epidemiological studies of ticks, and laboratory experiments involving the ALSV. One additional benefit of our system is that with an amplicon length of 333 nt, Sanger sequencing can be used to confirm a positive result if necessary. Our system was designed to have a poliovirus internal control qPCR, as well as the RNA of PEK cells to normalise the RNA amount in the samples. The system was designed this way based on the availability of these exact reagents in our lab. However, they can be easily replaced with RNA from other sources if needed because the qPCR assay showed high specificity.For the TBEV, virus loads in the serum were not associated with the patients’ clinical parameters, such as the duration of the first phase of the disease, the duration of the asymptomatic interval, TBE severity, and clinical presentation . HoweverOrthoflavivirus, shares some homology with JMV members [The TBEV, as a member of the genus  members ,10,11, p members . HoweverConsidering the cocirculation and homology of ALSV and TBEV, to obtain accurate epidemiological data on both the ALSV and TBEV, reliable systems that can detect both viruses are needed. We describe a specific system for ALSV detection; however, further research is required to test the specificity of and, if necessary, enhance the existing TBEV-detection systems.