HAC1 mRNA during the unfolded protein response (UPR). The UPR regulates the protein folding capacity of the endoplasmic reticulum (ER). ER stress activates Ire1, an ER-resident kinase/RNase, which excises an intron from HAC1 mRNA followed by exon-exon ligation by Trl1. The spliced product encodes for a potent transcription factor that drives the UPR. Here we report the crystal structure of Trl1 RNA ligase domain from Chaetomium thermophilum at 1.9 Å resolution. Structure-based mutational analyses uncovered kinetic competition between RNA ligation and degradation during HAC1 mRNA splicing. Incompletely processed HAC1 mRNA is degraded by Xrn1 and the Ski/exosome complex. We establish cleaved HAC1 mRNA as endogenous substrate for ribosome-associated quality control. We conclude that mRNA decay and surveillance mechanisms collaborate in achieving fidelity of non-conventional mRNA splicing during the UPR.Yeast tRNA ligase (Trl1) is an essential trifunctional enzyme that catalyzes exon-exon ligation during tRNA biogenesis and the non-conventional splicing of  RNA ligases are found in all domains of life. They catalyze the ligation of RNA molecules via phosphodiester bonds during different RNA processing events, such as repair, editing and splicing . The funHAC1 mRNA in fungi or XBP1 mRNA in metazoans at the splice sites  complex . In addice sites . Next, ace sites , which ace sites  or the Rce sites . The splectively .Chaetomium thermophilum. Guided by our structural data, we provide evidence that the non-conventional splicing of HAC1 mRNA competes with RNA decay in the cell. We further establish cleaved HAC1 mRNA as an endogenous substrate for ribosome-associated mRNA quality control.Here, we report the crystal structure of the Trl1 ligase domain from Saccharomyces cerevisiae (sc) revealed a single His(148)Tyr point mutation lying within Trl1-LIG that in vivo abolished the UPR signaling yet did not impair tRNA splicing. This mutant allele  paved the way to discovery of Trl1 as the RNA ligase during Ire1-mediated splicing of HAC1 mRNA , a thermophilic fungus that previously proved invaluable for structural studies , an inhibitor of N-linked glycosylation , after its endonucleolytic cleavage by Ire1 completing the splicing reaction in vitro  bound to the α,β-non-hydrolyzable ATP analog α,β-methyleneadenosine 5′-triphosphate (AMPcPP). The structure was solved by SeMet-SAD phasing to 1.9 Å resolution  of the LIG-C subdomain coordinate a single well-ordered sulfate ion via salt bridges near the active site pocket. In addition, His227 is within distance to form a third salt bridge, and Asn150 forms a hydrogen bond with the sulfate ion . NotablyS. cerevisiae and C. thermophilum Trl1  is surface-exposed and located on the periphery of the active-site groove in the vicinity of other essential residues  to yield an appropriate substrate for Trl1-LIG . Equivalent concentrations of the WT enzyme yielded more spliced (i.e. ligated) product of the HAC1U-508 RNA substrate compared to scTrl1-LIG-H148Y  of scTrl1-LIG-WT and scTrl1-LIG-H148Y towards HAC1-derived 5’ and 3’ exon RNA oligonucleotides using fluorescence titration experiments. Despite the reduced ligation efficiency of the H148Y mutant, we only observed a small impact on binding affinity towards the RNA substrates  to an unlabeled 3’ exon RNA oligonucleotide . The isolated ligase domain (scTrl1-LIG-WT or -H148Y) was pre-incubated with the 3’ exon fragment to improve the reaction efficiency. The pre-formed LIG/3’ exon complex was used in excess over the FAM-labeled 5’ exon fragment and the time-course of ligation was analyzed by denaturing urea-PAGE (1/2 = 9.2 min) compared to WT ligase (t1/2 = 4.1 min). These kinetic data confirmed the impeding effect of the His-to-Tyr mutation on HAC1 exon-exon ligation. Interestingly, the mutant ligase did not only display slower ligation kinetics but also plateaued at a lower amount of ligation product (8%) compared to wild-type enzyme (50%).Based on the strong homology of lum Trl1 , we founTrl1-LIG , we founTrl1-LIG . By contsplicing . This coIG-H148Y . These rbstrates . To obtarea-PAGE . The resrea-PAGE  and reveHAC1 mRNA splicing so severely in cells as to entirely block UPR signaling, we surmised that, in the presence of RNA decay machinery in vivo, the balance between exon ligation and RNA decay might be tipped towards decay. To test this notion directly, we expressed Trl1-H148Y from the strong constitutive alcohol dehydrogenase 1 (ADH1) promoter. Both scTrl1-H148Y and ctTrl1-H182Y allowed growth on Tm-containing medium, as did expression of the corresponding LIG only construct (trl1-H148Y cells.To explain how the reduced ligase activity of Trl1-H148Y would impair HAC1 mRNA are substrates for RNA decay pathways using yeast growth assays (XRN1 and SKI2 (encoding for an RNA helicase in the exosome-assisting Ski complex). Indeed, individual deletion of either XRN1 or SKI2 rescued growth of trl1-H148Y cells upon ER stress PCR to monitor HAC1 mRNA splicing in these cells. To this end, we used oligonucleotide primers in both exons flanking the intron to obtain differently sized amplification products for unspliced and spliced HAC1 mRNA based on the presence or absence of the 252 nt intron. As expected, HAC1 mRNA was spliced upon Tm-induced ER stress in the WT strain and not affected by deletion of XRN1 or SKI2  is a fundamentally still functional transcription factor that can induce the UPR . This ribosome recycling pathway hinges on the splitting of ribosomal subunits by the non-canonical release factors Dom34  and Hbs1. We generated DOM34 deletion strains to test for the possible involvement of NGD in translation of the severed HAC1 5’ exon. Growth analysis of these strains showed that deletion of DOM34 re-sensitized ski2Δ trl1-H148Y cells to ER stress (trl1-H148Y by depletion of Ski2 (i.e. cytosolic RNA exosome activity) is dependent on successful ribosome rescue at the 5’ non-conventional splice site of the cleaved HAC1 mRNA.Previous studies showed that a truncated form of Hac1 produced from an mRNA bearing a stop codon immediately after the 5’ exon and hence lacking the entire C-terminal portion encoded by the 3’ exon  represses Hac1 protein production  complex . The absence of human homologs of Trl1-LIG could therefore be explored as a promising new drug target in antifungal therapy, whose development has largely stalled in recent years. Together with the recently published structure of Trl1-KIN , our stuctTrl1  was cloned from C. thermophilum cDNA into pET15b  as described previously  was generated by PCR-based introduction of a stop codon (oligo: TAGCGTGACATCATTC-3’5’-GCTAGCGTG) into the pET15b vector using site-directed mutagenesis. The expression construct for ctTrl1-CPD  was generated by cloning the target sequence into pET15b using PCR amplification, followed by restriction digestion and DNA ligation. Similarly, the expression constructs for WT (pPW3209) and H148Y (pPW3210) scTrl1-LIG (residues 1–388) were cloned into pET47b using the restriction enzymes SmaI and BamHI. The expression construct for E. coli RtcA (pPW3420) was generated using the In-Fusion HD Cloning Plus kit (Takara Bio). For the In-Fusion reaction, pET28a (EMD Millipore) was linearized by NdeI (New England BioLabs) and the RtcA insert was amplified from E. coli DNA by PCR. All described constructs were confirmed by DNA sequencing. See Full-length eviously . The expE. coli BL21-CodonPlus (DE3)-RIL (Agilent Technologies). The cells were grown in Luria broth medium including the appropriate antibiotic at 37°C, expression was induced by 1 mM isopropyl-1-thio-β-D-galactopyranoside (IPTG), cells were harvested by centrifugation and lysed using an EmulsiFlex-C3 (Avestin) high-pressure homogenizer (exceptions to the above as noted).All recombinant proteins in this study were expressed in 6-tagged ctTrl1 was purified by Ni2+ affinity chromatography using a HisTrap FF column , followed by size-exclusion chromatography using a HiLoad 16/60 Superdex200 pg column  in 20 mM Tris/HCl pH 7.1, 300 mM NaCl, and 1 mM MgCl2  was expressed using feedback inhibition of methionine biosynthesis by adding selected amino acids to the E. coli culture prior to induction. In detail, cells were grown in standard M9 minimal medium supplemented with thiamine (0.5% w/v) and trace elements. When the OD600 reached 0.6, the temperature was lowered to 28°C and the feedback-inhibition amino acids mix -SeMet) was added and, after 30 min, the cells were induced with IPTG. After 16 hr expression, cells were harvested and disrupted in lysis buffer  plus protease inhibitor . The cleared supernatant was applied to a Ni2+ affinity chromatography column  and ctTrl1-LIG eluted with a 25–500 mM imidazole gradient (same as lysis buffer). LIG-containing fractions were dialysed against AEX buffer  and further purified by anion exchange chromatography  using a 10–1000 mM NaCl gradient in AEX buffer. Finally, the protein was applied to size-exclusion chromatography using a HiLoad 16/60 Superdex200 pg column  in SEC buffer , concentrated and flash frozen in N2 (l).Selenomethionine (SeMet)-substituted His6-tagged ctTrl1-CPD (residues 576–846) was expressed at 30°C for 5 hr. Cells were harvested and disrupted in lysis buffer  plus protease inhibitor . The cleared supernatant was applied to a Ni2+ affinity chromatography column  and ctTrl1-CPD eluted with a 20–500 mM imidazole gradient (same as lysis buffer). CPD-containing fractions were further purified by size-exclusion chromatography using a HiLoad 16/60 Superdex200 pg column  in SEC buffer phosphine hydrochloride [TCEP]), concentrated and flash frozen in N2 (l).His6-tagged scTrl1-LIG-WT and -H148Y (residues 1–388) were expressed at 20°C for 16 hr. Cells were harvested and disrupted in lysis buffer  plus protease inhibitor . The cleared supernatant was applied to a Ni2+ affinity chromatography column  and scTrl1-LIG eluted with a 25–500 mM imidazole gradient (same as lysis buffer). LIG-containing fractions were dialysed against 3C cleavage buffer  followed by proteolytic removal of the His6-tag by incubation with recombinant His10-tagged human rhinovirus 3C (HRV3C) protease. To separate the cleaved target protein from the uncleaved fraction and HRV3C protease, another round of Ni2+ affinity chromatography was performed. The flow-through was dialysed against AEX buffer  and further purified by anion exchange chromatography  using a 25–1000 mM NaCl gradient in AEX buffer. Finally, the protein was applied to size-exclusion chromatography using a HiLoad 16/60 Superdex200 pg column  in SEC buffer , concentrated and flash frozen in N2 (l).HisE. coli as glutathione S-transferase (GST) fusion protein and purified as described previously (The cytosolic kinase/RNase portion of yeast Ire1 including 32 residues of the linker region (Ire1-KR32) was recombinantly expressed in eviously .6-tagged E. coli RtcA was expressed at 37°C for 4 hr. Cells were harvested and disrupted in lysis buffer  plus protease inhibitor . The cleared supernatant was applied to a Ni2+ affinity chromatography column  and RtcA eluted with a 20–500 mM imidazole gradient (same as lysis buffer). Protein-containing fractions were diluted to 50 mM NaCl and further purified by anion exchange chromatography  using a 50–1000 mM NaCl gradient. Finally, the protein was applied to size-exclusion chromatography using a HiLoad 16/60 Superdex75 pg column  in SEC buffer , concentrated and flash frozen in N2 (l).HisHAC1U-508 RNA . For splicing, ligation of the Ire1-cleaved HAC1U-508 RNA was initiated with 500 nM ctTrl1  or the indicated amounts of scTrl1-LIG. To allow ligation by Trl1-LIG, the ends of the cleaved RNA were first modified by 1 μM ctTrl1-CPD and 667 units/ml T4 polynucleotide kinase  plus 4 mM ATP. Stop solution  was added at five-fold excess to stop the reactions. Samples were then denatured by heating at 80°C for 3 min and analyzed by 6% TBE–urea gels (Thermo Fisher Scientific) stained with SYBR Gold nucleic acid stain (Thermo Fisher Scientific).For endonucleolytic cleavage, in vitro transcribed, polyacrylamide gel electrophoresis (PAGE)-purified, refolded -508 RNA  was incuHAC1 in vitro splicing. The assay was performed similarly as described previously . Samples were heated to 75°C, incubated at 43°C for 10 min to allow hybridization, then slowly cooled to 37°C and incubated with 5 U (addition of 1 μL) of RNase H  for 30 min. After proteinase K treatment, the samples were analyzed by PAGE as described in the previous section.Oligonucleotide-guided RNase H digestion was used to identify RNA products of eviously . After cHAC1-derived RNA oligonucleotides were purchased from Integrated DNA Technologies. The 5’ exon and 3’ exon RNA substrates were synthesized with 3’ and 5’ phosphate groups at their respective splice site ends. RNA ligation kinetics were measured using RNA oligonucleotides based on the exon sequences closest to the splice sites. We used a 10 nt long 5’ exon fragment with a terminal fluorescein (FAM) fluorophore (5’-FAM-CGUAAUCCAG-3’-PO4) and a 20 nt long 3’ exon fragment (5’-PO4-AAGCGCAGUCAGGUUUGAAU-3’). The 5’exon fragment was modified by RtcA and ctTrl1-CPD in the presence of ATP to yield 2’ phosphate/3’ hydroxyl ends. Both RNA substrates were PAGE-purified prior to the assay. 250 nM scTrl1-LIG (WT or H148Y) were incubated with 500 nM of 3’ exon oligonucleotide and 0.5 mM ATP in assay buffer  for 10 min at 30°C. The ligation kinetics were initiated by addition of 50 nM FAM-labeled 5’ exon fragment. Aliquots were taken at the indicated timepoints and mixed with a 3-fold excess of Stop solution. The ligation reaction was analyzed by 15% TBE–urea gels (Thermo Fisher Scientific) and imaged for FAM fluorescence using a Typhoon 9400 Variable Mode Imager (GE Lifesciences). The relative intensity of the bands was determined by densitometry using ImageJ . The 5’exon fragment was modified by RtcA and ctTrl1-CPD in the presence of ATP to yield 2’ phosphate/3’ hydroxyl ends. Both RNA substrates were PAGE-purified prior to the binding assay. Binding was measured by changes in fluorescence using a Monolith NT.115 (NanoTemper). The RNA substrate concentration was held constant at 10 nM and the concentration of scTrl1-LIG. RNA binding data were fitted to a Hill model using Origin .Affinity measurements were performed using RNA oligonucleotides based on the ctTrl1-LIG were grown at 20°C by hanging drop vapor diffusion. Two volumes of SeMet-ctTrl1-LIG (12.5 mg/ml), 2 mM AMPcPP  and 2 mM MgCl2 were mixed with one volume of reservoir solution containing 1.7 M ammonium sulfate, 200 mM NaCl, 100 mM HEPES (pH 7.3) and 5 mM DTT. Obtained crystals were harvested directly from the crystallization drops, cryoprotected by soaking in reservoir solution including 25% glycerol and frozen in liquid nitrogen. X-ray diffraction data were collected at the Advanced Photon Source beamline 23ID-D and processed using MOSFLM  yeast expression vector  or YPD plates containing the indicated concentration of tunicamycin  dissolved in DMSO (+Tm). All growth tests were performed as three biological replicates; representative images are shown in the respective figures.HAC1 was N-terminally tagged with a 3x hemagglutinin (HA) tag (n-YPYDVPDYAGYPYDVPDYAGSYPYDVPDYA-c) in the strains of interest using the Delitto perfetto method  from 1 μg of total RNA. The cDNAs of interest were amplified from the reverse transcription reactions (1:100 dilution) with Phusion polymerase (Thermo Scientific) using the appropriate primer sets and optimal cycle numbers  with oligo-(dT)Extraction of yeast proteins was performed as described elsewhere . ProteinctTrl1-LIG structure have been deposited in the Protein Data Bank under accession code 6N67.Structural coordinates for the  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by Nahum Sonenberg, the Reviewing Editor and Cynthia Wolberger as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Javier Martinez (Reviewer #2).Thank you for submitting your article "tRNA ligase structure reveals kinetic competition between non-conventional mRNA splicing and mRNA decay" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:HAC1 pre-mRNA is spliced in the cytoplasm of yeast cells, which is triggered by UPR. To this end, the authors solved the crystal structure of the Saccharomyces cerevisiae Trl1 homolog from Chaetomium thermophilum. They exploited in particular the His-to-Tyr mutation at Trl1 position 148 (within the ligase domain), which abolishes UPR signaling but not splicing, gaining important insight into the HAC1 pre-mRNA splicing mechanism by demonstrating kinetic competition between non-conventional mRNA splicing and RNA degradation, which effects the outcome of Ire1-initiated mRNA processing. Thus, the mRNA decay and surveillance mechanisms interact to ensure fidelity of non-conventional mRNA splicing during the UPR.The authors studied the mechanism by which Essential revisions:eLife if amended in response to the reviewers' critiques. The Reviewing Editor concurs with the reviewers' opinions. As you can see in the comments below both reviewers believe that the key results are shown in Figure 3, but they argue that some issues related to these results should be experimentally addressed.Both reviewers agree that the paper presents an interesting study, which would warrant publication in 1) What is the basis of the reduction in splicing efficiency in the trl1-H148Y enzyme? Does the enzyme have a lower affinity for specific RNA fragments? An answer to this question might provide a link to the understanding of the enhanced sensitivity to Xrn1 or the exosome. This experiment could be readily done.2) What is the band marked with an asterisk in Figure 1D, lane 3?3) The results in Figure 4 should be quantified.4) Discussion: Where is it shown that Xrn1 degrades the 3' exon? Essential revisions:Both reviewers agree that the paper presents an interesting study, which would warrant publication in eLife if amended in response to the reviewers' critiques. The Reviewing Editor concurs with the reviewer' opinions. As you can see in the comments below both reviewers believe that the key results are shown in Figure 3, but they argue that some issues related to these results should be experimentally addressed.1) What is the basis of the reduction in splicing efficiency in the trl1-H148Y enzyme? Does the enzyme have a lower affinity for specific RNA fragments? An answer to this question might provide a link to the understanding of the enhanced sensitivity to Xrn1 or the exosome. This experiment could be readily done.scTrl1-LIG. Unexpectedly, these experiments yielded very similar affinities for WT and mutant Trl1-LIG towards 5’ and 3’ exon fragment , respectively). We included these fluorescence binding measurements as new Figure 4—figure supplement 1.We agree with the reviewers’ assessment that a detailed mechanistic understanding of the reduced splicing (ligation) efficiency of the H148Y mutant will add to our understanding of the Trl1 ligation mechanism. We tested several different in vitro approaches to determine binding affinities for both exons. We measured affinities by monitoring fluorescence changes of labeled RNA oligonucleotides upon binding to We conclude from this work that, if binding affinities are not significantly impeded, the reduced activity of the mutant enzyme must result from a changed catalytic activity. To confirm the reduced ligation kinetics (efficiency) of the Trl1-H148Y enzyme , we performed time-resolved in vitro ligation experiments with labeled RNA oligonucleotides. These results confirmed the effect of the His-to-Tyr mutation, causing a ~2-fold reduced apparent rate constants. Interestingly, the mutant ligase is not only a 2x slower enzyme but also plateaued at a >5-fold lower amount of ligation product compared to the wild-type enzyme. These results are depicted as new Figures 4C (PAGE gel) and 4D (quantification and fitting). Taken together, these results strongly support our kinetic competition model.2) What is the band marked with an asterisk in Figure 1D, lane 3?HAC1 intron. We now added experimental evidence that this is indeed the case. To this end, we used antisense DNA oligos complementary to either the presumptive ligation (circularization) site or the middle of the intron. After annealing to the RNA products of the in vitro splicing reaction and treatment with RNase H, which specifically hydrolyzes the phosphodiester bonds of RNA in RNA/DNA hybrids, the band in question disappeared and a band at the exact position of the severed intron (252 nt) re-appeared. We conclude that * represents two cleaved introns that were circularized by two intermolecular ligation events at their 5’ and 3’ ends. The results are depicted in Figure 2—figure supplement 1. We added the RNase H assay to the Materials and methods.The * demarks a circularized tandem 3) The results in Figure 4 should be quantified.HAC1 splicing results and added the results to Figure 5 .We quantified the 4) Discussion: Where is it shown that Xrn1 degrades the 3' exon?HAC1 mRNA component left in the non-conventional splicing reaction. Our RT-PCR data demonstrate that deletion of XRN1 restores splicing, which requires a fully intact 3’ exon as substrate.We softened the statement by changing “Our data show…” to “Our data suggest…”. Based on our RT-PCR results , we could show that levels of the 5’ exon are not affected by the presence or absence of Xrn1. Although we did not directly “show” the degradation of the 3’ exon by Xrn1, it is the only


This study aimed to optimise techniques for whole transcriptome and small RNA analyses on clinical tissue samples from patients with cardiovascular disease. Clinical samples often represent a particular challenge to extracting RNA of sufficient quality for robust RNA sequencing analysis, and due to availability, it is rarely possible to optimise techniques on the samples themselves. Therefore, we have used equivalent samples from pigs undergoing cardiopulmonary bypass surgery to test different protocols for optimal RNA extraction, and then validated the protocols in human samples. Here we present an assessment of the quality and quantity of RNA obtained using a variety of commercially-available RNA extraction kits on both left ventricular biopsies and blood plasma. RNA extraction from these samples presents different difficulties; left ventricular biopsies are small and fibrous, while blood plasma has a low RNA content. We have validated our optimised extraction techniques on human clinical samples collected as part of the ARCADIA (Association of non-coding RNAs with Coronary Artery Disease and type 2 Diabetes) cohort study, resulting in successful whole transcriptome and small RNA sequencing of human left ventricular tissue. Despite improvements in coronary revascularisation techniques, ischaemic heart disease (IHD) remains a leading cause of morbidity and mortality in more economically developed countries, and patiTranslational research provides the unique possibility to understand transcriptomic changes in the diseased human heart and how this is reflected in factors released into the circulation, facilitating identification of new targets for therapeutic intervention and as non-invasive biomarkers of cardiac conditions. The increasing sophistication of sequencing technology and big data analysis is starting to reveal the dynamic transcriptomic changes underlying disease processes. Non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs), have received much attention for their potential as therapeutic targets, due to their role as regulators of gene expression, and notably the ability of individual ncRNA to act as a network hub, regulating multiple transcripts. In particular, the discovery that miRNAs and other ncRNAs can be detected in the circulation, protected from degradation by RNases due to their encapsulation in extracellular vesicles (EVs),–6 or as et al, who retracted their 2011 Molecular Cell paper after discovering that their TRIzol-based RNA extraction method selectively lost miRNAs with low GC-content when isolating RNA from low cell numbers, leading them to draw incorrect conclusions.[Understanding the systemic nature of cardiometabolic diseases requires investigation of multiple affected tissues, while the discovery and development of novel biomarkers is best done in peripheral accessible biofluids. Given the diverse sample types, one challenge of this type of study is to optimise analysis methods for each sample type while ensuring the methods themselves aren’t introducing bias. A cautionary example of the effects of methodology on discovery is given by Kim clusions.In recent years several groups have published excellent systematic analyses of different RNA extraction protocols on plasma and serum, although as yet there is no consensus as to the optimal protocol.–28 It isHere we present methodological work with findings that may be useful for others working on similar clinical samples. Before beginning work with human samples we have tested 6 commercially-available RNA extraction kits on left ventricular biopsies and blood plasma from 3 pigs undergoing cardiopulmonary bypass, assessing yield, quality, and RT-qPCR performance. The results from this were validated on equivalent human surgical samples, which were successfully used for RNAseq and miRNA RT-qPCR screening. We highlight the importance of comprehensive validation of basic methods used when preparing samples for high-throughput experiments to ensure data are representative and biologically valid.ARCADIA is an observational prospective cohort study developed between two-centres, the Bristol Royal Infirmary  and the Hammersmith Hospital . Written informed consent was obtained from all patients. All human samples were obtained in accordance with the principles of the Declaration of Helsinki. The study was reviewed and approved by the National Research Ethics Service Committee London–Fulham .Further details of the ARCADIA study are provided in the supplemental methods.Sus scrofa domesticus) undergoing non-recovery cardiopulmonary bypass surgery. These pigs were enrolled in a separate study, and the samples obtained were surplus tissue and biofluids taken immediately prior to termination. All animal experiments were performed in accordance with the UK Animals (Scientific Procedures) Act 1986, amended 2012, conducted under the UK Home Office licence 70/8975 and were approved by the University of Bristol Animal Welfare and Ethical Review Body.Samples were obtained from 18–24 kg female White Landrace pigs  and incubated overnight at 4°C. The RNAlater was then removed and the sample frozen at -80°C.After establishment of cardio-pulmonary bypass, left ventricular (LV) biopsies were taken from the apex of the heart using a biopsy needle; biopsies were 14 gauge in core diameter and 3–5 mm in length. After collection, the LV biopsies were placed immediately into RNAg for 10 minutes at room temperature. The plasma supernatant was immediately removed to a fresh tube without disturbing the buffy coat, then centrifuged again at 2240 g for 10 minutes at room temperature. The supernatant was removed to a fresh tube and stored at -80°C.Peripheral blood was obtained from the arterial line, collected into a 5 mL sodium citrate vacutainer. Whole blood was centrifuged at 2240 mirVana ; C) miRCURY tissue ; D) miRNeasy micro (Qiagen 217084). Biopsies were weighed, then homogenised at 4°C in the recommended volume of lysis buffer using a Bertin minilys with ceramic beads, 4 x 30 second bursts at maximum speed with 5 minutes incubation on ice between each burst. Following homogenization, lysates were centrifuged at 300 g, 5 minutes, 4°C to minimize foam, then removed to a fresh tube. Thereafter, the manufacturer’s protocol was followed exactly in one set of extractions, while in a parallel set of extractions the protocol was modified to include a proteinase K digestion step; samples were incubated with 200 μg/mL proteinase K (Invitrogen AM2546) for 15 minutes at 55°C. For the miRCURY tissue kit the manufacturer’s proteinase K digestion protocol was followed. All products were eluted in the recommended volume of RNase-free water and stored at -80°C in low-bind tubes.RNA isolation from pig LV biopsies was performed using four commercially available kits: A) RNAqueous-micro ; B) mirVana kit (AM1560) following the manufacturer’s protocol, with the exception that samples were eluted in 50 μL nuclease-free water.RNA extraction from human LV biopsies was performed using the mirVana ; C) miRCURY biofluids ; D) Plasma/serum RNA purification mini kit (Norgen Biotek 217084). In one set of extractions the manufacturer’s protocols were followed, while in a parallel set the protocol was modified to include glycogen (Thermo Fisher RNA grade glycogen R0551) as a co-precipitant at a final concentration of 0.4 μg/μL. All plasma RNA extractions included 10 μL of 5 fmol/μL synthetic cel-miR-39-3p (Qiagen 219610) spiked in to the relevant lysis buffer as an exogenous control for isolation efficiency.[RNA isolation from 200 μL pig plasma (frozen at -80°C and thawed once) was performed using four commercially available kits: A) miRNeasy serum/plasma (Qiagen 217184); B) vide infra).RNA isolation from 200 μL human plasma (frozen at -80°C and thawed once) was performed using the Qiagen miRNeasy serum/plasma kit (217184), following the manufacturer’s protocol, with the addition of glycogen as a co-precipitant to a final concentration of 0.4 μg/μL. Instead of using the cel-miR-39-3p spike-in, we used the Exiqon UniSp2/UniSp4/UniSp5 spike-ins required for subsequent analysis of the RNA using the miRCURY LNA Universal RT microRNA PCR System human panels I and II . RNA integrity was assessed using a Bioanalyzer 2100 (Agilent) Nano chip and Small RNA chip.Flavobacterium heparinum, Sigma-Aldrich H2519) digestion, since residual heparin administered during surgery may interfere with downstream reactions.[Stem-loop reverse transcription primers and PCR primers/hydrolysis probes were used for data on pig plasma . Reverse transcription on pig plasma RNA was performed using the TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems 4366596) with a modified protocol to include heparinase I  master mix, using a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems) with the manufacturer’s thermocycling protocol. The manufacturer’s instructions were scaled to a final PCR volume of 12 μL. Raw Cq values were calibrated using the UniSp3 inter-plate calibrator, with an upper threshold of Cq = 37 applied.RT-qPCR on human plasma was performed using the miRCURY LNA Universal RT microRNA PCR System human panels I and II v4.0 (Exiqon 203600), with the Universal cDNA Synthesis Kit II (Exiqon 203301) including the UniSp6 and cel-miR-39-3p (Exiqon 203203) spike-ins, and ExiLENT SYBR Green Master Mix (Exiqon 203403). Cfree DNA removal kit AM1906). 20 ng digested RNA was depleted of rRNA (NEBNext rRNA depletion kit E6350L) and libraries were constructed using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (E7760S). The libraries were sequenced on an Illumina HiSeq 2500 using paired-end 100 bp reads and at a depth of ~60 million reads per sample.RNA extracted from human left ventricular tissue was digested with DNase I  to 280 nm and 230 nm . A 260/280 of ~2.0, and 260/230 of ~2.0–2.2 indicate good RNA purity. All the protocols tested had acceptable 260/280 values , while tmirVana and miRCURY tissue kits  and RNA concentration, while the Small RNA chip permits examination of RNA in the range 6–150 nt. LV biopsy RNA samples were analysed using both Nano and Small RNA chips. Consistently high (>8) RIN values were obtained using the sue kits , howeversue kits  that is sue kits , while tsue kits . RNA consue kits .mirVana kits more reliably recovered small RNAs, however it is worth noting that a high RIN score from the Nano chip does not necessarily correspond to good small RNA recovery, therefore if small RNAs are of interest it is essential to run both assays during validation , although this was only significant for the mirVana kit . Glycogen did not appear to affect Cq values for RNA extracted with the other two methods.Biofluids typically contain very low concentrations of RNA, below the current detection limit of spectrophotometry or electrophoresis., 40 TherTo test recovery of endogenous miRNAs, we screened a panel of 7 functionally important miRNAs that are predicted to be conserved between humans and pigs, known to be present in human heart and circulation and with relevance to cardiovascular function and disease. Briefly, miR-15b-5p and miR-16-5p are upregulated in cardiomyocytes during cardiac ischaemia and are involved in angiogenesis, miR-21-5mirVana kits  producing a more concentrated sample, rather than superior total recovery. Therefore, we compared the miRNeasy serum/plasma kit with the mirVana kit, using elution volumes of 30 and 50 μL. The mirVana kit was chosen for this comparison because i) this kit performed best with the LV biopsy RNA (vide supra) and ii) this kit has the most comparable protocol to the miRNeasy serum/plasma. q values for the tested miRNAs .In all cases, the miRNeasy serum/plasma kit with added glycogen had the lowest CmirVana kit produced the best RNA isolation for this sample type, with consistent yield, high RIN and reasonable recovery of small RNAs, although the low 260/230 ratios remain a factor to consider. One additional consideration is that this kit recommends elution in 100 μL water; with small tissue samples this can result in a sample too dilute for some downstream applications. Although precipitation protocols can allow concentration of RNA samples, we were reluctant to introduce further processing steps that may introduce additional variability. Instead, reducing the elution volume to 30 or 50 μL produced a more concentrated preparation without appreciable loss of RNA  collected as part of the ARCADIA study. 92/96 biopsies had a RIN>7, and 71/96 had RIN>8, which is considered high enough quality for demanding applications such as RNAseq . All the miRNAs ranked very high among the most expressed miRNAs .Recovery of miRNAs from pig plasma was consistently highest using the Qiagen miRNeasy micro kit with additional glycogen co-precipitation, therefore this protocol was used for further biofluids RNA extractions from human clinical samples. RNA extracted from pre-operative peripheral plasma, intra-operative coronary sinus and ascending aorta plasma from patients enrolled in the ARCADIA study was screened for 752 miRNAs using human panels I and II (Exiqon). To verify the success of this method, the expression of the miRNAs screened for in the pig plasma was evaluated in the human pre-operative peripheral plasma samples . All miRet al compared an organic extraction method with a solid-phase extraction method on archived right atrial appendage samples,[Our results indicate that for technically demanding and costly applications such as RNA sequencing or other high-throughput screening methods, it is critical to validate the RNA extraction method for each sample type, as no single method is optimal for all. To the best of our knowledge, few similar systematic analyses have been performed on other cardiovascular clinical tissue sample types. Ip  samples, however mirVana protocol. RNA yield per mg tissue was consistent in the range 1–6 mg . While this may indicate that the protocol performs more efficiently on smaller masses of tissue, it is more likely that this reflects the proportionately higher contribution of contaminants to the measured yield. No saturation effect on yield was observed at the upper bound. This is not surprising for this protocol, given the manufacturer’s stated upper limit of 250 mg, however it is one factor that should be considered when evaluating a protocol. No strong relationship between biopsy mass and RIN was observed, although it is notable that those few biopsies that yielded poor-quality RNA were <1 mg . The best-performing kit in our hands is also the costliest, therefore users may need to factor this in to their choice of reagents, although we note that this kit is also available at a reduced cost if the user supplies the phenol. The biofluids-specific kits are more expensive, ranging from £7.62 (miRNeasy serum/plasma) to £8.08 (Norgen Biotek plasma/serum) per sample. While this manuscript was being prepared, Exiqon became part of the Qiagen portfolio, therefore a direct cost-comparison of the miRCURY kits was not possible.For large-scale clinical studies, the cost of reagents used to process samples is inevitably an additional consideration. Based on the list price in British pound sterling (£) at the time of writing, for the LV biopsies the kit cost per sample ranges from £6.08 (RNAqueous micro), to £6.94 (miRNeasy micro), to £7.88 Click here for additional data file.S1 FigN.B. Exiqon miRCURY tissue protocol requires proteinase K, therefore no–proteinase K condition was done with this kit. Numbers in brackets indicate the number of samples in each group.Bioanalyzer Nano chip quantification of LV biopsy RNA was similar to the spectrophotometry quantification presented in (TIF)Click here for additional data file.S2 FigmirVana protocol recommends elution in 100 μL water; with small tissue samples this can result in a sample too dilute for some downstream applications. Reducing the elution volume to 50 or 30 μL produced a more concentrated preparation without appreciable loss of RNA yield, measured by spectrophotometry .The (TIF)Click here for additional data file.S3 FigTwo randomly selected pig plasma RNA samples were screened for the presence of 7 candidate circulating miRNAs predicted to be conserved between humans and pigs.(TIF)Click here for additional data file.S4 FigThe expression levels of the top 35 miRNAs in LV biopsies are plotted as log2 normalised counts. For each plotted miRNA, the box indicates the upper (75%) and lower (25%) quartiles of the counts with a straight black line within the box indicating the median. The whiskers of the box indicate counts that lie outside of this. Circles represent outliers. All candidate circulating miRNAs from pig plasma are also expressed in human LV; only hsa-miR-15b-5p lies outside the top 35.(TIF)Click here for additional data file.S5 Figq values are expressed after inter-plate calibration.The expression levels in human plasma of the 7 candidate circulating miRNAs from pig plasma. Mean C(TIF)Click here for additional data file.S6 Figy = −0.0161x + 1.326 (B) For biopsies weighing <1 mg, the yield is no longer constant; non-linear regression, least-squares fit y = 1.503x−0.4715 (C) The RIN remains constant across the range of biopsy masses.(A) RNA yield is constant over the range of biopsy masses from 1–6 μg. Linear regression (TIF)Click here for additional data file.S1 Data(ZIP)Click here for additional data file.


N1-methyladenosine (m1A) was proposed to be a highly prevalent modification in mRNA 5’UTRs based on mapping studies using an m1A-binding antibody. We developed a bioinformatic approach to discover m1A and other modifications in mRNA throughout the transcriptome by analyzing preexisting ultra-deep RNA-Seq data for modification-induced misincorporations. Using this approach, we detected appreciable levels of m1A only in one mRNA: the mitochondrial MT-ND5 transcript. As an alternative approach, we also developed an antibody-based m1A-mapping approach to detect m1A at single-nucleotide resolution, and confirmed that the commonly used m1A antibody maps sites to the transcription-start site in mRNA 5’UTRs. However, further analysis revealed that these were false-positives caused by binding of the antibody to the m7G-cap. A different m1A antibody that lacks cap-binding cross-reactivity does not show enriched binding in 5’UTRs. These results demonstrate that high-stoichiometry m1A sites are exceedingly rare in mRNAs and that previous mappings of m1A to 5’UTRs were the result of antibody cross-reactivity to the 5’ cap. N1-methyladenosine (m1A) was recently reported as a new mRNA modification but its prevalence has been controversial. Here the authors showed that m1A, if present in mRNA, is at very low stoichiometry, with the notable exception of MT-ND5. Further, they show that the previously reported enrichment of m1A near the start of transcripts are false-positive identifications due to cross-reactivity of the commonly used m1A antibody with mRNA caps. N6-methyladenosine (m6A) residues in the transcriptome2. Two studies later identified N1-methyladenosine (m1A) as another abundant epitranscriptomic modification4. Both studies mapped m1A in thousands of mRNAs by sequencing mRNA fragments immunoprecipitated with a monoclonal antibody (clone AMA-2) commercially distributed by MBL Bioscience, which was originally raised against KLH-conjugated 1-methyladenosine5. This antibody was previously shown to recognize m1A-containing RNAs6. One study estimated the average stoichiometry of mapped m1A sites at 20%3. Notably, most m1A sites were located near start codons and proposed to provide a novel form of translational regulation3.The initial concept of the epitranscriptome was born with the transcriptome-wide mapping of thousands of internally located modified nucleotide 1A, one arguing that m1A was exceptionally rare in mRNA7. In that study, the antibody-bound RNA was reverse transcribed with an enzyme that efficiently introduces misincorporations at m1A. Using this approach, m1A was rarely observed in the RNA immunoprecipitated with m1A antibodies7. Although mRNA fragments from 5′UTRs and start codon-proximal regions were immunoprecipitated, these fragments did not generate misincorporations. Thus it was concluded that mRNA fragments from the 5′UTR may be nonspecifically enriched during immunoprecipitation7. Ultimately it was concluded that only two mRNAs contained high-confidence m1A sites: C9orf100 and MT-ND5, a cytosolic and a mitochondrial mRNA, respectively7. Twelve other sites were detected at very low stoichiometry.Subsequent work reported different distributions for m1A to 740 sites, 473 of which were in mRNA and lncRNA8. In mRNAs, the majority of sites were found in the 5′UTR; 22 of which the authors localized to the first nucleotide of the transcript. Based on this location, it was proposed that m1A forms a novel cap structure in which m1A immediately follows the 7-methylguanosine (m7G) cap of mRNA (m7G-ppp-m1A). A re-analysis of these data showed that many of the sites that were mapped internally within the 5′UTR were actually transcription-start sites9.The second study mapped m1A maps, and if m1A exists at transcription-start sites or start codons, or neither, and why these particular sites are so prominent in m1A-mapping studies. Additionally, whether m1A sites are present with high stoichiometry as initially reported3, or low stoichiometry and rare7 also remained to be resolved.It remained unclear why those studies produced divergent m1A in the transcriptome, we used both a high-resolution m1A-mapping method as well as a bioinformatic approach, termed “misincorporation mapping”. Misincorporation mapping takes advantage of the ability of m1A and numerous other modified nucleotides to induce misincorporations during the reverse transcription step common to most RNA-Seq protocols. By probing several ultra-deep RNA-Seq datasets for such misincorporations, we discovered that very few mRNAs contain misincorporations. Only the MT-ND5 mitochondrial transcript and the MALAT1 noncoding RNA generated statistically significant misincorporations, demonstrating the rarity of high stoichiometry m1A sites. To understand why misincorporation mapping identified only a few m1A sites while m1A antibody-based mapping detects many, we mapped m1A at high resolution using the same m1A-directed antibody used in all previous studies. This mapping recapitulated the selective binding of the AMA-2 m1A antibody to transcription-start nucleotides in mRNA. However, we also found that this m1A antibody recognizes the m7G cap structure, and that m1A-independent binding explains why previous maps showed m1A in mRNA 5′UTR regions. To further confirm this observation, we demonstrate that a different m1A antibody, which we show does not bind the m7G cap, produces an m1A map that no longer enriches for the 5′ end of mRNAs. Overall, our data demonstrate that (1) m1A and other hard stop nucleotides are rare in mRNA; (2) that—with the exception of MT-ND5—m1A sites have very low stoichiometry; and (3) that cross-reactivity of the AMA-2 m1A antibody with 5′ caps leads to false-positive localization of m1A to transcription-start nucleotides and start codons.Here, to address the question of the prevalence and location of m1A-mapping methods , 3-methyluridine, N2,N2-dimethylguanosine, and N6,N6-dimethyladenosine since these also produce misincorporations10. Therefore, misincorporations can directly localize m1A and other hard-stop nucleotides in sequencing data11.Given the inconsistency in the different antibody-dependent m1A since m1A typically induces a misincorporation in only approximately 20–30% of the cDNAs generated by SuperScript III (refs. 11). The misincorporations would therefore be particularly difficult to detect for low stoichiometry m1A residues. Second, misincorporations cannot be readily distinguished from stochastic errors originating during PCR amplification or during sequencing. Thus, m1A cannot be definitively identified in standard RNA-seq experiments.Misincorporations are difficult to distinguish from sequencing errors using standard next-generation sequencing for two major reasons. First, substantial read depth is required to detect m10 to distinguish modified nucleotides from sequencing errors . Importantly, this method only reveals misincorporations, not the identity of the modification; the identity would have to be determined by biochemical methods.To overcome these problems, we developed a bioinformatic approach similar to high-throughput annotation of modified ribonucleotides (HAMR)ors Fig. . We used1A sites. After aligning reads to rRNA, we readily detected the known 28S rRNA m1A at position 1322. pseudouridine and m1A detection could be impaired because m1A can convert to m6A through the Dimroth rearrangement, a heat and base-catalyzed reaction13 , a short tRNA-like ncRNA that is derived from endonucleolytic processing of MALAT1 (ref. 16) . Of these, 12 occurred at adenosine residues (Supplementary Data 16) Fig. . Notably16) Fig. , a conse7 m1A-containing MT-ND5 mitochondrial mRNA also contained a modified adenosine . The misincorporation rates in poly(A) RNA-Seq for these mRNAs were very low . However, misincorporations could be detected when m1A-containing mRNAs were enriched using the m1A antibody, suggesting that m1A-containing transcripts are indeed present in cells, but are so rare that they require enrichment to be detected. We therefore leveraged the exceptional mitochondrial read depth in our ultra-deep RNA-seq samples (average ~1 million reads/nucleotide). Here we found that with the exception of MT-ND5 which had a misincorporation rate of 13.5%, the misincorporation rates for all other putative m1A sites was less than 0.4%, which is close to the background rate  such as CCDC71, DLST, and STK16, each lacked A→T transitions based on misincorporation mapping   Fig. . We initRNA Fig. . UV cros1A-miCLIP differs from earlier methods4 by preserving the cDNA 3′ ends. Any m1A antibody-binding site at the transcription-start site would produce a peak that is displaced in the 3′ direction  RNA from HEK293T cells and examined termination signatures at known m1A sites in rRNA and tRNA, which typically co-purify to some extent with poly(A) RNA23. The majority of reads truncated at the +1 position relative to the m1A at position 1322 of the 28S rRNA  since the first encoded nucleotide of mRNAs is typically subjected to 2′-O-methylation28. To biochemically validate this, we used mass spectrometry to detect m1A or m1Am in “cap dinucleotides,” i.e., m7G-ppp-m1Am. We treated cellular RNA with P1 nuclease, which digests internal nucleotides to mononucleotides, but leaves the cap dinucleotide intact (see Methods).Because the AMA-2 mN1-methylated adenosine in a cap dinucleotide can be detected, we used synthetic RNA standards (see Methods). Using these standards, we readily detected m7G-ppp-m1A as well as other cap dinucleotides, such as m7G-ppp-Am, and m7G-ppp-m6Am . To confirm that the 6Am Fig. .Fig. 3Th7G-ppp-m6Am (m/z = 815.1) and also m7G-ppp-Am (m/z = 801.1), though to a lower degree. m7G-ppp-Cm (m/z = 777.1), m7G-ppp-Gm (m/z = 817.1), and m7G-ppp-Um (m/z = 778.1) were also detected  and the base comprising the first nucleotide  within the extended cap.We next examined endogenous cap dinucleotides prepared by digesting HEK293T poly(A) RNA. We readily detected mted Fig. . The ide7G-ppp-m1A or m7G-ppp-m1Am is present in mRNA. Their masses  are identical to cap dinucleotides containing Am or m6Am. Moreover, the mass of the fragment produced by the N1-methylated adenine base (m/z = 150.1) would be identical to that produced by the m6Am cap. However, m7G-ppp-m1A and m7G-ppp-m1Am exhibit very distinct retention times from the Am and m6Am cap dinucleotides based on our synthetic RNA standards  was undetectable.Next, we asked whether either msee Fig. . This alRNA Fig. . Thus, m1A at the transcription-start nucleotide, we wanted to directly and sensitively determine the transcription-start nucleotide that is enriched by the m1A antibody. We used two-dimensional thin layer chromatography (2D-TLC), which can identify and quantify the first encoded nucleotide29. In this approach, mRNA is decapped, and the exposed 5′ RNA end is radiolabeled, permitting sensitive detection of the first transcribed nucleotide. The radiolabeled nucleotide species are then resolved using 2D-TLC29. Based in the mobility of each species, the transcription-start nucleotide can be determined.Since mass spectrometry analysis could not validate m1A antibody by 2D-TLC. We also used synthetic RNA containing m7G-ppp-m1A and m7G-ppp-m1Am extended cap structures as standards (see Methods). We first optimized the solvent conditions so that m1A and m1Am migrated to distinct positions by 2D-TLC  mRNA with the AMA-2 m1A antibody before 2D-TLC. Here, we again did not see m1A or m1Am as the transcription-start nucleotide  RNA and poly(A) RNA enriched with the mTLC Fig. . In polydes Fig. . Since tide Fig. .1A and m1Am are not readily detectable at the transcription-start nucleotide, and m7G-ppp-m1Am does not constitute a novel and prevalent mRNA cap structure as proposed8.Taken together, the mass spectrometry and the TLC data suggest that m1A-miCLIP suggested that m1A is at the transcription-start nucleotide, but we did not observe m1A or m1Am at this site by either mass spectrometry or TLC. Therefore, we wondered if the AMA-2 antibody binds the transcription-start region in an m1A-independent manner. When we originally characterized the specificity of the antibody, we performed classic competition studies using nucleosides or nucleotides. However, based on the binding properties of the antibody revealed by mapping studies, we considered the possibility that the AMA-2 m1A antibody could recognize an epitope comprising the mRNA extended cap.At this juncture, we had contradictory results: m1A antibody to an m1A-containing oligonucleotide in the presence of various competitors. We considered performing the dot blot assay using m7G-ppp-A immobilized on the membrane. However, this approach could be misleading since we do not know if m7G-ppp-A interacts with the membrane in a way that would prevent antibody binding. We therefore used the classic competition approach. In this approach, a m1A-containing RNA is immobilized on the membrane and different competitors are added in solution. Competitors that bind the m1A antibody will prevent the antibody from binding to m1A on the membrane.To test this, we used a dot blot assay to measure binding of the AMA-2 m1A inhibited antibody binding, while related nucleotides, including adenosine, m6A, ethenoadenosine, and N1-substituted nucleotides, like N1,6-dimethyladenosine , did not compete with binding   Fig. . The hig7G-ppp, but not m7G or ATP . Unlike the AMA-2 m1A monoclonal antibody available from MBL, the Abcam antibody did not bind the m7G-ppp-A cap analog  RNA that both ab208196 and AMA-2 antibodies can detect authentic m1A sites. In each case, we observed a robust peak at the m1A sites in MT-ND5 and MT-RNR2, the mitochondrially encoded 16S RNA  with a fraction in mitochondrial mRNA8, while other studies suggest even fewer sites, with only two mRNAs having an m1A at a stoichiometry above 5%7. To address these discrepancies, we developed two m1A mapping approaches: (1) misincorporation mapping, a computational approach to discover m1A-induced misincorporations in ultra-deep RNA-Seq datasets; and (2) m1A-miCLIP, a high-resolution method for mapping m1A antibody-binding sites in the transcriptome using two different m1A-binding antibodies. Misincorporation mapping shows that m1A is present at detectable stoichiometries only in the MT-ND5 transcript, with no m1A in other mitochondrial mRNAs or 5′UTRs of mRNAs as reported previously. Using m1A-miCLIP, we find that the previously observed binding of the AMA-2 m1A antibody to transcription-start nucleotides and the vicinity of start codons is due to a previously unrecognized cross-reactivity of the AMA-2 m1A antibody to cap structures. We confirm this using a separate m1A antibody that lacks this cap-binding cross-reactivity. We further show that m1A is not detectable at transcription-start nucleotides, as previously proposed8, based on mass spectrometry and TLC. Overall, these data show that the divergent m1A mapping data and large number of 5′UTR-mapped m1A sites largely reflect cross-reactivity of the m1A antibody with mRNA caps.Considerable attention has revolved around m1A and m7G are positively charged purines. This common structural feature may be recognized by the AMA-2 antibody. Since the Ab208196 binds m1A but not cap structures, this antibody does not generate 5′UTR false-positive signals. m1A-miCLIP carried out using the Abcam antibody shows an m1A signature in MT-ND5 but no transcriptome-wide enrichment in 5′UTRs. These data, along with mass spectrometry, TLC, and misincorporation mapping data, support the idea that the m1A localization to 5′UTR sites is specific to the AMA-2 antibody rather than a reflection of bona fide m1A nucleotides at these sites.Both m1A is a rare and low stoichiometry modification except in the case of MT-ND5 mRNA, as seen in another study7. We were not able to detect m1A in any other cytosolic mRNAs or the mitochondrial mRNAs that were reported to contain m1A8. This could reflect the inability of our method to detect very low stoichiometry m1A modifications.Our results thus support the idea that m1A mapping studies found that m1A is highly prevalent based on mass spectrometry analysis of mRNA4. However, more recent experiments showed that poly(A) preparations used for mass spectrometry are usually contaminated with tRNA and rRNA, and when these contaminants are meticulously removed, the m1A signal in poly(A) preparations is absent33. Thus, the newer mass spectrometry data also support the idea that m1A is rare.The initial m34. It is therefore notable that the original m1A mapping studies localized many m1A sites to coding sequences, which contributed to skepticism about these maps. If hard-stop nucleotides occur in mRNA, they would likely be transient and act to induce mRNA degradation though this surveillance pathway.A pressing question is whether there are more yet-to-be-discovered modified nucleotides in mRNA. Misincorporation mapping suggests that this is not the case, at least for hard-stop nucleotides. The relative paucity of hard-stop nucleotides in mRNA may reflect the incompatibility of these nucleotides with translation. The ribosome mRNA surveillance pathway induces degradation of mRNAs when tRNAs cannot basepair with codons12. For m1A-miCLIP, HEK293T cells  or whole mouse brain  was used. HEK293T cells were purchased directly from ATCC but not further validated for identity or tested for mycoplasma contamination. Experiments involving the use of animals were approved by the Institutional Animal Care and Use Committee at Weill Cornell Medicine.For misincorporation mapping, an ultra-deep RNA-seq dataset that profiled RNA expression in blood mononucleocytes was used1A antibody is a mouse monoclonal antibody . This antibody is documented to react with both m1A within RNA and the N1-methylated adenine base, as documented in MBL’s product specifications for AMA-2  and prior studies4. The specificity was validated previously4 and in the current study. The Abcam m1A antibody (ab208196) is a rabbit monoclonal generated against m1A. Its specificity for m1A was validated in this study.The AMA-2 m12 were downloaded from GEO (accession code: GSE33029). This RNA-seq dataset was prepared using standard reverse transcription with SuperScript III, an enzyme expected to produce misincorporations at m1A positions11. Variants identified in the genomic DNA corresponding to this dataset were acquired from http://snyderome.stanford.edu. Coordinates of other SNPs that may be present in the DNA sequence were downloaded from the SNP database dbSNP . Read alignment of forward and reverse read mates was performed using STAR (version 2.5.3a) and the hg19 genome build. Alignment incorporated removal of PCR duplicates, and clipping of 10 bases on either end of each read, since the ends of Illumina reads are prone to sequencing error35. Only reads that mapped to a single location in the genome were used for downstream analysis. A maximum of one mismatch per read was permitted for alignment.Raw reads from the ultra-deep RNA-seq dataset used for this study12. For this reason, analysis of cytosolic tRNAs, which are ~75 nt-long RNAs that contain known conserved m1A residues, could not be performed. However, short RNAs generated from polycistronic transcripts, like mitochondrial tRNAs36, were represented in the analyzed library. To identify sites of modification throughout the transcriptome, we initially filtered for all nucleotide positions that were covered by at least 500 mapped reads and contained a 1% misincorporation rate, and that were present in at least half of biological replicates . This program was used to determine the frequency of each of the four nucleotides present in mapped reads at every genomic position with read coverage. We limited our analysis to nucleotide positions with a minimum combined read depth of 500 unique reads across the 20 biological replicates to maximize sensitivity of detecting modified nucleotides. To prevent calling genomic variants and SNPs as modification-induced misincorporations, we did not analyze nucleotide positions containing variants discovered in the genomic DNA corresponding to the RNA-seq dataset, or SNPs annotated in dbSNP. Importantly, our analysis could only be performed on transcripts longer than the library insert size of ~250 bases.27.16. T1A-miCLIP was performed as previously described37, briefly described below along with any modifications: C57BL/6 mice (8 weeks) were sacrificed by CO2 inhalation and cervical dislocation as approved by the Weill Cornell Medicine Institutional Animal Care and Use Committee (IACUC). Total RNA from HEK293T cells  or whole mouse brain  was extracted using TRIzol (ThermoFisher) and treated with RNase-free DNase I (Promega). Poly(A) RNA was isolated using one round of selection with oligo(dT)25 magnetic beads (New England Biolabs). This resulted in approximately 10 μg of poly(A) RNA for each replicate used in this study. Poly(A) RNA was subjected to fragmentation using RNA Fragmentation Reagents (ThermoFisher) for exactly 12 min at 75 °C. This fragmentation protocol is identical to the one in m1A-seq, and has been reported not to facilitate substantial m1A to m6A rearrangement.3 Fragmented RNA was then incubated with 10–15 μg of m1A antibody per replicate and the antibody-RNA complexes were processed for crosslinking, immunoprecipitation, RNA 3′ linker ligation, purification, and reverse transcription37. Following reverse transcription of purified peptide-RNA complexes, first-strand cDNA was circularized using CircLigase II ssDNA Ligase (EpiBio) to preserve the 3′ end of the cDNA, and thus, sites of m1A-induced terminations of reverse transcription. To generate priming sites for library amplification, the cDNA was cut in the middle of the cDNA primer sequence using a single-stranded DNA oligo complementary to this sequence and FastDigest BamHI (ThermoFisher)37. This generated priming sites for the Illumina P5 and P3 primers on either side of the first-strand cDNA, eliminating the need for a second-strand synthesis step. For library amplification, Accuprime Supermix I (ThermoFisher) and Illumina P5 and P3 primers were used (see Supplementary Data m25 magnetic beads (New England Biolabs). RNA was then subjected to fragmentation using RNA Fragmentation Reagents (ThermoFisher) for exactly 12 min at 75 °C. Fragmented RNA was then subjected to RNA 3′ linker ligation using T4 RNA Ligase I (New England Biolabs) and reverse transcription using a primer complementary to the linker sequence and SuperScript III (ThermoFisher) , treated with RNase-free DNase I (Promega), and poly(A) RNA was isolated using one round of selection with oligo(dT)1A-miCLIP or RNA-seq libraries were trimmed of the 3′ linker sequence and barcoded reverse transcription primer sequences using Flexbar (version 2.5) , the tag2profile.pl, tag2cluster.pl and extractPeak.pl scripts of the CIMS software package were used. A custom awk script was then used to filter for clusters of a minimum score .m1A-miCLIP dataset, we focused on potential motifs present in m1A-miCLIP clusters in the 5′UTR, the region of predominant m1A-miCLIP cluster enrichment. The genomic sequences of these clusters were retrieved using bedtools and subjected to motif discovery using the MEME suite (version 4.11.4).To search for a possible common sequence motif present in our HEK293T cell m1A-miCLIP clusters on mRNAs, MetaPlotR was used40, with in-house modifications. The density of m1A-miCLIP coverage was normalized to that of RNA-seq coverage to reveal any enrichments using a custom R script (available upon request). For the HEK293T cell metagene, the in-house HEK293T cell RNA-seq dataset described above was used for normalization. For the mouse brain metagene, a published whole-brain RNA-seq dataset was used41 (accession code: GSE52564). To plot the coverage of transcription-start sites by m1A-miCLIP at higher resolution, the plotProfile tool of the Deeptools suite was used.To analyze the metagene distribution of m1A-miCLIP and RNA-seq. Terminations were counted at positions ranging from the transcription-start site to position +4 relative to the transcription-start site. Then, transcription-start sites were filtered for those that contained a minimum coverage of five unique reads at the transcription-start site position in both m1A-miCLIP and RNA-seq. This filtered set of transcription-start sites was then used to compare the distributions of read terminations in m1A-miCLIP and RNA-seq. We focused on terminations rather than misincorporations in m1A-miCLIP is because the misincorporation profile of m1A is sequence dependent, with both upstream and downstream nucleotides contributing to misincorporation variability11. Thus, we used the presence of terminations as a signature of antibody crosslinking events in our dataset. Additionally, while rare types of reverse transcriptases that read through m1A have been described42, standard reverse transcriptases, like the SuperScript III used in m1A-miCLIP, produce frequent terminations at m1A residues43.For analysis of antibody crosslinks at the transcription-start sites of mRNAs, we analyzed terminations of reverse transcription (i.e. 5′ ends of reads) around these sites. To do so, the number of terminations was measured around RefSeq-annotated transcription-start sites that had coverage in both m1A-miCLIP clusters, we used a collection of transcription-start sites that included RefSeq transcription-start sites as well as recently-mapped transcription-start site regions containing the m6Am mRNA extended cap24. The frequencies of all transcription-start site types or those overlapping m1A-miCLIP clusters were thus determined using this collective set.To determine the types of transcription-start sites overlapping m7G-ppp-Am, m7G-ppp-m6A, or m7G-ppp-m6Am were synthesized chemically44. Oligonucleotides containing m7G-ppp-m1A or m7G-ppp-m1Am were synthesized enzymatically using an oligonucleotide initiating with ppp-m1A (Trilink). This oligonucleotide was capped using ScriptCap Cap 1 Capping System (CellScript) to generate the m7G cap and, in the case of m7G-ppp-m1Am, 2′-O-methylation of m1A.For biochemical analysis of various modifications present within the extended caps of mRNAs, synthetic oligonucleotides were generated as standards for mass spectrometry and/or thin layer chromatography  RNA was prepared for mass spectrometry as follows. Total RNA from HEK293T cells was treated with TURBO DNase (ThermoFisher) according to the manufacturer’s instructions, followed by two rounds of poly(A) selection using oligo(dT) magnetic beads (NEB). Small RNAs shorter than 200 nt were then removed from the poly(A) RNA using the RNeasy kit (Qiagen). This size selection was performed to prevent detection of extended cap structures that are known to be present in certain small RNAs, like small nuclear RNAs (snRNAs). Approximately 5 μg of DNase-treated, poly(A)-selected, and size-selected RNA was thus generated for each sample for mass spectrometry analysis. To release extended cap structures from the nucleotides comprising the internal portion of the RNA, RNA was digested with 2–4 units of Nuclease P1 (Sigma Aldrich) in a final buffer concentration of 30 mM sodium acetate (pH 5.5) for 3 h at 37 °C. Following digestion, the nuclease was removed from the samples using molecular weight cutoff centrifugal filters (VWR). The digested and purified RNA was finally dried using an Eppendorf Vacufuge and reconstituted with 70% acetonitrile  to a final concentration of 0.5 µg/µl. Two microliters of the resulting solution were subjected to MS analysis.Samples were injected into an LC-MS/MS system comprising an Agilent 1260 HPLC and an Agilent 6460 triple quadrupole mass spectrometer equipped with a JetStream electrospray ionization source. Positive ion monitoring and multiple reaction monitoring was used for detection of extended caps. The caps were resolved on an aqueous normal phase column . To achieve chromatographic separation of the cap structures from mononucleotides, the following gradient was used. The aqueous mobile phase (Buffer A) was 50% isopropanol with 0.025% acetic acid, and the organic mobile phase (Buffer B) was 90% acetonitrile containing 5 mM ammonium acetate. EDTA was added to the mobile phase in a final concentration of 6 µM. The final gradient applied was 0–1.0 min 99% B, 1.0–7.0 min to 80% B, 7.0–18.0 min to 50% B, 18.0–19.0 min to 0% B, and 19.1–29.0 min 99% B. The flow rate was 0.4 mL/min during data acquisition and 0.6 mL/min during column re-equilibration. Data were saved in centroid mode using MassHunter workstation acquisition software (Agilent). Data files were processed with MassHunter Qualitative Analysis Software (Agilent).Exact operating source parameters for the LC-MS analysis are available upon request.1A antibody, we utilized 2D-TLC analysis of mRNA extended caps24. For analysis of cellular RNA, we used HEK293T cell poly(A) RNA or poly(A) RNA enriched using the m1A antibody4. Oligonucleotide standards, input (antibody-unbound) poly(A) RNA, and m1A antibody-enriched poly(A) RNA were then subjected to 2D-TLC24. To enhance resolution of m1A and m1Am from other nucleotide species, the first dimension of 2D-TLC was resolved using 66% isobutyric acid and 1% NH4OH in water, and the second dimension was resolved using 60% ammonium sulfate in 100 mM sodium phosphate buffer of pH 6.8 (w/v) with a final concentration of 2% n-propanol. Both dimensions were resolved overnight.To identify the initiating nucleotide structure in mRNAs bound by the mN1,6-methyladenosine, N6-methyladenosine (Selleckchem) was dissolved in dry DMF and followed with addition of iodomethane . The mixture was stirred overnight at room temperature. The product was purified by flash chromatography on silica gel (EMD), eluting with methanol and dichloromethane . This resulted in a product yield of 46.3% N1,6-methyladenosine. Product identity was confirmed by nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HR-MS).To synthesize 1H NMR  δ 8.11 , 8.04 , 5.76 , 5.15 , 4.43 , 4.15 , 3.92 , 3.63 , 3.56 , 3.50 , 3.45 , 1.23 .NMR spectra were recorded using a 500-MHz Bruker DMX-500 instrument at room temperature, and chemical shifts were referenced to the residual solvent peak. Shifts were as follows: N1,6-methyladenosine, or C12H18N5O4+, of 296.1353, the mass found was 296.1361.HR-MS data were recorded with Waters LCT-Premier XE at room temperature. For a predicted mass for 8 m1A sites for nuclear-encoded genes was obtained from supplementary Table The Li et al.1A antibody for various nucleosides, nucleotides, or cap structures was determined as follows. To determine the specificity of the m1A antibody for various nucleosides, two approaches were performed. For testing the specificity of the antibody for various nucleosides in the context of m1A-miCLIP, the antibody was crosslinked to total cellular RNA in the presence of various competitor nucleotides. Antibody binding, crosslinking, and detection of crosslinked antibody-RNA complexes was performed exactly as in miCLIP, except with the inclusion of the competitor nucleotide during the antibody-binding reaction.The specificity or affinity of the mN1-methylated adenine, a dot blot assay was performed wherein the competing molecule is added during antibody binding1. Competition assays are usually used to measure binding, rather than spotting the nucleotides to the membrane, since the manner of interaction of each nucleotide to the membrane is not known and can affect antibody binding. For measuring the affinity, the IC50 of the various nucleotides and cap dinucleotides was measured. In these experiments, a series of the dot blot assays were performed, where serial dilutions of each competitor molecule (ranging from 10 µM to 1 nM) were used in parallel during antibody binding reactions. The dot blots were performed as follows: 250 ng of m1A-containing synthetic oligonucleotide , and then incubated in a dilution of 1:2500 of secondary antibody  in 0.5% milk in PBST for 1 h at room temperature. Finally, the membrane was washed three times in PBST (5 min per wash), and developed using ECL Prime (GE). Membranes corresponding to a dilution series of a specific competitor molecule were imaged together using a ChemiDoc Imager (Bio-Rad).For testing the specificity of the antibody for modified adenines, especially those resembling Further information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1-7Source Data


We found these circRNAs also functioned in cell adhesion, cell activation and the immune response. These results show that the crosstalk between circRNAs and their competing mRNAs might play crucial roles in the development of atherosclerosis.Atherosclerosis is a chronic and multifactorial inflammatory disease and is closely associated with cardiovascular and cerebrovascular diseases. circRNAs can act as competing endogenous RNAs to mRNAs and function in various diseases. However, there is little known about the function of circRNAs in atherosclerosis. In this study, three rabbits in the case group were fed a high-fat diet to induce atherosclerosis and another three rabbits were fed a normal diet. To explore the biological functions of circRNAs in atherosclerosis, we analyzed the circRNA, miRNA and mRNA expression profiles using RNA-seq. Many miRNAs, mRNAs and circRNAs were identified as significantly changed in atherosclerosis. We next predicted miRNA-target interactions with the miRanda tool and constructed a differentially expressed circRNA-miRNA-mRNA triple network. A gene ontology enrichment analysis showed that genes in the network were involved in cell adhesion, cell activation and the immune response. Furthermore, we generated a dysregulated circRNA-related ceRNAs network and found seven circRNAs ( Atherosclerosis (AS) is a chronic and multifactorial inflammatory disease with high morbidity and mortality worldwide . Moreoveet al. discovered cANRIL, an antisense circRNA from the INK4/ARF locus, whose expression correlated with INK4/ARF transcription and atherosclerotic vascular disease risk . The rabbits in the control group (n=3) were fed with a standard diet. The high-fat diet in the AS group was initiated immediately after balloon injury and continued for 8 weeks. Endothelial injury caused by a 2 F Fogarty balloon catheter  was performed to accelerate the formation of atherosclerotic plaque. Rabbits were anaesthetized by intramuscularly injected xylazine (5 mg/kg), ketamine (35 mg/kg) and acepromazine (0.75 mg/kg). Anesthesia was maintained with isoflurane inhalation through a mask during the procedure. As previously reported , the balThe progression of atherosclerotic plaques was monitored by 2D-ultrasound weekly. After several weeks, the atherosclerotic plaques of the right common carotid arteries were obvious. Following euthanasia, the rabbits were killed with overdose of pentobarbital sodium. Then, we intercepted 1 cm of the right common carotid artery of each rabbit in the case group with ultrasound guidance where the atherosclerotic plaques were most obvious. Similarly, we intercepted 1cm of the right common carotid artery of each rabbit in the control group. These blood vessels were immediately transferred to liquid nitrogen for preservation. The right common carotid artery adjacent to the incision of each rabbit was swiftly removed. Each specimen was fixed with 4% paraformaldehyde fixative and embedded in paraffin for subsequent hematoxylin and eosin (H&E) staining. Serial cross-sections with a thickness of 3 μm were stained with H&E and observed by light microscopy .Total RNA was isolated from the right common carotid arteries from six samples, including three atherosclerotic sample and three control samples, using TRIzol reagent  according to the manufacturer’s recommended protocol. The RNA purity was verified with a NanoPhotometer® spectrophotometer . The RNA integrity was assessed using the RNA Nano 6000 Assay Kit for the Bioanalyzer 2100 system .Approximately 8 μg of RNA per sample was used in the protocol to deplete ribosomal RNA according to the instructions in the Epicentre Ribo -zero™ rRNA Removal Kit . The rRNA-depleted RNAs were used as input material for preparing the libraries following the manufacturer’s recommendations in the NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina . The products were purified (AMPure XP system), and the library quality was assessed on the Agilent Bioanalyzer 2100 system. Sample clustering was performed on a cBot Cluster Generation System using the TruSeq PE Cluster Kit v3-cBot-HS  according to the manufacturer’s instructions. Paired-end sequencing with 125-bp reads was performed on the Illumina HiSeq 2500 platform.A total of 3 μg of RNA per sample was used as input material for the small RNA library. Sequencing libraries were generated using the NEBNextR Multiplex Small RNA Library Prep Set for IlluminaR  following the manufacturer’s recommendations. Barcodes were added to map the sequences to each sample. The library quality was assessed on the Agilent Bioanalyzer 2100 system using DNA High Sensitivity Chips. Clustering of the barcoded samples was performed on a cBot Cluster Generation System using the TruSeq SR Cluster Kit v3-cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the libraries were sequenced on an Illumina HiSeq 2500 platform and 50-bp single-end reads were generated.http://www.bioinformatics.babraham.ac.uk/projects/fastqc/), which included measures of base quality distribution, average quality of reads and adaptor check. Before alignment, we used Trimmomatic [The quality of the raw sequencing reads was estimated by the FastQC program (mmomatic  to removftp://ftp.ensembl.org/pub/release-92/fasta/oryctolagus_cuniculus/). The clean mRNA reads were aligned to the rabbit genome using the HISAT2 program [The reference genome (OryCun2.0) and gene annotation files were downloaded from the Ensembl genome browser  , which pWe next extracted the miRNAs that were predicted to interact with both circRNAs and mRNAs. These miRNAs and their targets were used to construct a DEcircRNA-DEmiRNA-DEmRNA triple network. To identify competing circRNA-mRNA interactions, we used a hypergeometric test which enabled the evaluation of the significance of the shared miRNAs between each circRNA and mRNA. We considered a p-value < 0.05 as statistically significant. The p-value was calculated as follows:m is the number of miRNAs in the genome, and r represents the number of miRNAs shared between mRNAs and circRNAs. The number of miRNAs interacting with mRNAs and circRNAs is represented by t and n, respectively.where We used Cytoscape software (version 3.4.0)  to consthttps://david.ncifcrf.gov/summary.jsp, version 6.8) [Functional enrichment analysis at the GO level was performed using the DAVID bioinformatics resource (ion 6.8) ,54. In oion 6.8) , a plugiSupplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Figure S1Supplementary Figure S2


Thus, our study highlights the ability of viruses, such as HEV, to maneuver host systems to create favorable cellular environments for virus propagation. Studying the host-virus interactions can facilitate the identification of antiviral therapeutic strategies and novel targets.Host-pathogen interactions are crucial for the successful propagation of pathogens inside the host cell. Knowledge of interactions between host proteins and viral proteins or viral RNA may provide clues for developing novel antiviral strategies. Hepatitis E virus (HEV), a water-borne pathogen that causes acute hepatitis in humans, is responsible for epidemics in developing countries. HEV pathology and molecular biology have been poorly explored due to the lack of efficient culture systems. A contemporary approach, to better understand the viral infection cycle at the molecular level, is the use of system biology tools depicting virus-host interactions. To determine the host proteins which participate in the regulation of HEV replication, we indentified liver cell proteins interacting with HEV RNA at its putative promoter region and those interacting with HEV polymerase (RdRp) protein. We employed affinity chromatography followed by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) to identify the interacting host proteins. Protein-protein interaction networks (PPI) were plotted and analyzed using web-based tools. Topological analysis of the network revealed that the constructed network is potentially significant and relevant for viral replication. Gene ontology and pathway enrichment analysis revealed that HEV RNA promoter- and polymerase-interacting host proteins belong to different cellular pathways such as RNA splicing, RNA metabolism, protein processing in endoplasmic reticulum, unfolded protein response, innate immune pathways, secretory vesicle pathway, and glucose metabolism. We showed that hnRNPK and hnRNPA2B1 interact with both HEV putative promoters and HEV RdRp, which suggest that they may have crucial roles in HEV replication. We demonstrated  Hepatitis E virus (HEV) is a hepevirus that is transmitted via contaminated drinking water to cause acute hepatitis in humans. Although prevalence of HEV has been mainly observed in developing countries, its spread has been reported in many industrialized countries across the globe in recent years. In infected adults, mortality rate due to HEV is up to ∼2% while in infected pregnant women, it increases up to 30% . Due to Hepatitis E virus belongs to the Orthohepevirus genus, and its genome consists of positive sense, single-stranded RNA comprised of three open reading frames (ORFs) . First sViruses bear relatively compact genomes, encoding a limited number of proteins and, therefore, rely on host factors to establish replication in the infected cell. Being obligatory intracellular parasites, viruses have to subvert the biosynthetic pathways of the host cell. Constant interactions between the virus and its host during the process of co-evolution have shaped the anti-viral immune system of the host and, in turn, the capability of viruses to manipulate host control mechanisms to facilitate their propagation . ClassicStudies on several viruses reveal that viral proteins or viral RNA interact with host proteins to regulate viral replication. Previous studies on the HEV ORF1 and ORF2 interactome showed the involvement of factors associated with different biological processes, such as ubiquitin proteasome system, innate immunity and RNA metabolism . In anotin vitro binding of HEV promoters and HEV RdRp with HNRNPK and HNRNPA2B1, confirming the validity of interactions obtained by mass spectrometry.In order to better understand the HEV-host interface, we identified liver cell proteins interacting with the HEV polymerase and HEV putative promoters and generated a protein-protein interaction network. We further utilized a bioinformatics approach to analyze these interaction networks and assess their significance. Our study identified host proteins related to cellular processes like RNA metabolism, unfolded protein response, stress granules, secretory vesicles, endoplasmic reticulum protein processing, and innate immune pathways. HNRNPK and HNRNPA2B1 proteins were found to be interacting with both HEV promoters and HEV RdRp. We demonstrated the Infectious replicon of Sar55 strain of genotype 1 of HEV (pSK-HEV2) and a subclone of a human hepatoma cell line Huh7 S10-3 which is permissive for the replication of HEV infectious clone was obtained from Dr. Suzanne U. Emerson, NIH, Bethesda, MD, United States. Cells were maintained in DMEM GlutaMAX (Invitrogen) medium supplemented with 10% fetal bovine serum (FBS) (Invitrogen) and 100 U/ml penicillin and 100 mg/ml streptomycin (Sigma).Coding sequence of HEV RNA dependent RNA polymerase (RdRp) was amplified from pSK-HEV2 replicon. RdRp coding sequence was cloned in pcDNA 3.1/myc-His (-) mammalian expression vector in such a way that it will be expressed as FLAG tagged RdRp at its N terminal. This clone has been designated as pcDNA_FLAG-RdRp. Primers used for the amplification have been listed in To confirm interaction of HEV RdRp with hnRNPK/hnRNPA2B1, FLAG tagged RdRp and c-Myc tagged hnRNPK or hnRNPA2B1 encoding sequence was cloned in pTandem vector (Clontech) under CMV promoter and IRES, respectively. The constructs have been designated as pTandem_FLAG-RdRp_Myc-hnRNPK or pTandem_FLAG-RdRp_Myc-hnRNPA2B1. Primers used for the cloning are mentioned in the In vitro RNA was synthesized by using MEGAscript kit (Ambion) following the manufacturer’s instructions. Biotinylated in vitro transcribed RNAs were prepared using 5 mM rATP, 5 mM rGTP, 5 mM rUTP, 4.5 mM rCTP, and 0.5 mM of biotin-14 CTP (Invitrogen) in the rNTP mix for the in vitro transcription reaction. For synthesizing non-biotinylated RNAs of respective regions, total 5 mM rCTP was added instead of biotin-14-CTP. Unincorporated nucleotides were removed by purifying the RNA using phenol-chloroform precipitation method. Purified RNAs were visualized on 2% agarose gel.Sequences coding for the putative genomic promoter (G promoter: nt 1 to 139 on positive sense RNA) and putative sub-genomic promoter (Sg promoter: nt 5051 to 5200 on positive sense RNA) of HEV genotype 1 were PCR amplified from pSK-HEV2 replicon. The primers for the amplification of template were designed with T7 promoter sequence in such a way that the RNA of anti-sense orientation is generated. Primers used for the amplification have been listed in 2, 0.5% Tritin X-100 with protease inhibitor cocktail). The lysate was prepared by centrifugation at 12000 rpm at 4°C for 20 min. The bound RNA-beads complexes were incubated with Huh7 S10-3 cell lysate pre-cleared with 20 μl beads for 1 h at 4°C. Cell lysate and RNA-beads complexes were mixed and incubated together at 4°C on a rotator for 2 h. Bound complexes were washed with B&W buffer and proteins bound to RNA were eluted in 100 μl elution buffer . Eluted proteins were loaded on 12% SDS PAGE followed by silver staining for visualization of protein bands using ProteoSilver staining kit (Sigma). Eluates from three independent RNA affinity chromatography experiments were pooled together and subjected to protein identification by mass spectrometry.A total of 2 μg of each of biotinylated RNA corresponding to either HEV putative genomic or sub-genomic promoter were coupled with M280 streptavidin dynabeads (Invitrogen) in the presence of nucleic acid binding and washing buffer  for 15 min at room temperature on a rotator. Before RNA binding step, beads were washed with solution A  followed by solution B (DEPC treated 0.1 M NaCl) to remove RNase. Huh7 S10-3 cells were harvested at ∼80% confluency in the lysis buffer  transfection reagent. After 48 h post transfection, cells were harvested and lysed in IP lysis buffer . Protein G dynabeads  were used for each immunoprecipitation experiment. Next, 4 μg of either rabbit anti FLAG antibody or a non-specific isotype IgG was incubated with washed dynabeads for 30 min at room temperature on a rotator. The cell lysate was incubated with antibody plus dynabeads complex for 2 h at 4°C on a rotator. Three washes with IP lysis buffer were given, followed by a final wash with PBS. Interacting proteins were eluted in 50 μl of elution buffer . Eluted proteins were loaded on 12% SDS PAGE followed by silver staining for visualization of protein bands using ProteoSilver staining kit (Sigma). Eluates from three independent immunoprecipitation experiments were pooled together and subjected to protein identification by mass spectrometry.Hepatitis E virus RNA-dependent RNA polymerase and RNA interacting proteins isolated by immunoprecipitation and RNA affinity chromatography, respectively, were subjected to liquid chromatography-mass spectrometry (LC-MS) analysis. Total 30 μg of each of the protein samples was acetone precipitated, and the protein pellets were dissolved by adding 10 μl of 8 M urea, and the volume was brought to 15 μl with water. Samples were then reduced by the addition of 1.5 μl of 100 mM DTT and heated at 90°C for 15 min. The sample was cooled and alkylated by adding 1.5 μl of 200 mM IAA and incubated in the dark at RT for 15 min. 82 μl of ABC was added, and proteins were digested by adding 1 μl of 1 mg/ml trypsin protease and incubating at 37°C for 16 h. The reaction was stopped by the addition of 1–2 μl of concentrated TFA. Then peptides were dissolved in 0.1% TFA, 5% ACN in water for MS-analysis. Agilent 1260 infinity HPLC-Chip/MS system is a microfluidic chip-based technology was used for peptide enrichment and separation. Charged peptides from HPLC-Chip system were directly infused into mass-spectrometer for detection. Agilent Mass Hunter software was used for data acquisition and analysis of total ion chromatograms. Protein searches were carried out using Morpheus software. Protein identification was performed with the following criteria: (a) Trypsin digested peptides with 2 missed cleavages allowed, (b) peptide tolerance <10 ppm, (c) >2 unique peptides, (d) FDR <1%. Fasta files for Human Proteome database were downloaded from the UniProt was used for protein searches. Proteins found in respective negative control sample were eliminated from the dataset to remove non-specific interactions.All the experimentally derived data sets were used to generate HEV-host proteins interaction network by using “Cytoscape version 3.6.1” . To analt-test. To control false discovery rate (FDR) Benjamini and Hochberg multiple test correction was used; p-value ≤ 0.05 was considered significant. The corrected p-value for each GO term has been given in the section “Results.”Gene ontology “GO” analysis was carried out by using web-based tools like Panther (Gene Ontology Consortium’s web tool), Gprofiler, STRING, and Enricher. To analyze the enrichment of specific pathway, KEGG annotation database was used. Gene set and pathway enrichment analysis was validated by Fisher’s exact Huh7 S10-3 cells expressing FLAG-RdRp and Myc-hnRNPK/hnRNPA2B1 were harvested at 48 h post transfection and resuspended in IP lysis buffer  for half an hour at 4°C. Cell lysate was prepared by centrifugation at 12000 rpm for 30 min. Clear supernatant was mixed with anti Myc antibody or isotype IgG antibody attached to protein G dynabeads (Invitrogen) for 2 h. Three washes of IP lysis buffer were given to remove non-specific interactions. Interacting RdRp-host cellular protein complexes were eluted by using elution buffer . Eluted proteins were subjected to western blot to confirm RdRp-host protein interaction using anti-FLAG antibody.2, 0.5% Tritin X-100 with protease inhibitor cocktail) for 20 min at 4°C followed by centrifugation at 12000 rpm at 4°C for 10 min. Clear supernatant was incubated with in vitro transcribed RNAs of either putative genomic or sub-genomic promoters of HEV for 2 h at 4°C on rotator. 25 μl of protein G dynabeads were incubated with 4 μg of anti-hnRNPK antibody produced in rabbit (Gene Tex) or anti-hnRNPA2B1 antibody produced in rabbit (Gene Tex) or a non-specific isotype IgG antibody in antibody binding buffer (PBS + 0.0.2% Tween 20) for 35 min at room temperature. Beads bound antibody complexes were added to previously incubated lysate plus RNA complex for 1 h at 4°C on rotator. Three washes of lysis buffer were given followed by a final wash of PBS. Complexes were eluted with 80 μl of elution buffer. Elutes were processed for total RNA isolation using phenol-chloroform method. The RNA was transcribed into cDNA using reverse transcriptase polymerase chain reaction (RT PCR) with Superscript III first strand synthesis kit followed by PCR amplification using Platinum taq DNA polymerase (Invitrogen) using specific primers for the detection of promoter regions. The PCR product was visualized on 2% Agarose gel.Huh7 S10-3 cells were harvested at ∼80% confluency in cold DPBS. Cells were lysed in lysis buffer  of the data using various web-based tools. The GO annotation resulted in classification of the proteins based on their functional clusters or GO categories. The enrichment analysis performed using Panther (Gene Ontology Consortium’s web tool) , Gprofilthe FDRs .p-value: 3.5 × 10–3), response to unfolded proteins , nucleic acid metabolic process , and regulation of protein processing   . A vital × 10–3) . We obse × 10–3) .p-value: 3.3 × 10–2) in our data. Stress granules are dynamic structures that are formed when cellular translational rates decline after external stresses are applied to cells. Viral infection into the host cell has been shown to induce cytoplasmic stress granule formation in the host cells due to the upregulation of stress granule proteins, such as helicase DDX3X and DHX9 , RNA binding  and RNA stem loop binding .Virus replication alters the normal metabolic processes of the host by interacting with components of different molecular pathways. Enrichment of GO terms in molecular function category also resulted in enrichment of proteins involved in RNA metabolism processes, which again highlights the need of the virus to interact with proteins of host RNA turnover. The enriched proteins in molecular function category belong to Translation elongation factor activity  have also been observed to be enriched in the molecular function category , secretory granule lumen  and endocytic vesicle lumen   . HEV has × 10–2) . In agre × 10–2) . A recen × 10–2) . Previou × 10–2) .Escherichia coli infection, protein processing in endoplasmic reticulum, legionellosis, spliceosome, antigen presentation and processing, gap junction, citrate cycle, IL17 signaling pathways, apoptosis, and phagosome . Seven proteins of this pathway, namely, CKAP4, HSPA5, HSP90B1, PDIs, HSP70, HSP90, and VCP were found to interact with HEV RNA and polymerase (Protein processing in the endoplasmic reticulum pathway was enriched (lymerase . These fProteins belonging to the heat shock protein (HSP) family are well known for their roles as chaperons in protein folding. These proteins have been observed to play similar roles in the maintenance of proper viral protein folding and stabilization . In manyp-value of 5.9 × 10–4). Different heterogeneous nuclear ribonucleoproteins, such as HNRNPK, HNRNPA2B1, HNRNPH, HNRNPE1, HNRNPE2, HNRNPC and HNRNPU, and other spliceosomal accessory complex proteins, such as HSP73 and SR, interact with HEV (In our analyses, the spliceosome pathway was found to be enriched by with HEV . Spliceowith HEV . Previouwith HEV . HNRNPK with HEV . HNRNPK with HEV . In anotwith HEV . HNRNPK with HEV . HNRNPH with HEV . A quantwith HEV . In anotwith HEV . Our stuin vitro validated the interactions between HNRNPK and HNRNPA2B1 with that of HEV promoters and RdRp. In order to confirm the interaction of RdRp with host proteins, coimmunoprecipitation technique was employed. HEV RdRp encoding sequence and the coding sequence of selected host protein were cloned in pTandem vector. pTandem vector was chosen for its additional feature which enables cloning of two different genes in the same construct to increase the co-transfection efficiency. Huh7 S10-3 cells transfected with pTandem_Flag-RdRp_Myc-HNRNPK or pTandem_Flag-RdRp_Myc-HNRNPA2B1 plasmids. Post 48 h of transfection co-immunoprecipitation was performed by using anti myc antibody or isotype IgG antibody. After co-immunoprecipitation, interaction of host factors with RdRp was confirmed by western blot using anti FLAG antibody. We observed that both HNRNPK and HNRPA2B1 specific immunoprecipitation could pull down HEV RdRp (Analysis of HEV-host protein interactions suggested that HNRNPK and HNRNPA2B1 are common factors associating with HEV RNA promoters and polymerase . To demoHEV RdRp . Our resin vitro binding of HNRNPK and HNRNPA2B1 with HEV RdRp and HEV promoters as obtained from the mass spectrometry data, further increasing the confidence level of the obtained interactions. Thus, we believe that HNRNPK and HNRNPA2B1 play crucial roles in HEV replication and their functional significance in the context of HEV replication can be further assessed. Taken as a whole, our study reveals the importance of host cellular machinery in HEV lifecycle regulation. Studying host pathways targeted by HEV can facilitate the hunt for putative anti-viral candidates for therapeutic purposes. In conclusion, our study shows that analyzing host-virus interactions through system biology approach can be beneficial in understanding the molecular regulation of viral lifecycle and can put forth set testable hypotheses for future experimental validation.To further confirm the binding of HNRNPK and HNRNPA2B1 with HEV putative promoters; we employed RNA immunoprecipitation followed by RT PCR. Huh7 S10-3 cell lysate was incubated with G or Sg promoter RNA followed by pull down by anti-HNRNPK or anti-HNRNPA2B1 or isotype IgG antibody. From the eluted complexes, total RNA was isolated and presence of promoter region RNA sequence was checked by RT PCR. We observed that both HNRNPK and HNRNPA2B1 could successfully pull down HEV genomic and sub-genomic promoter RNA as evident by the specific band on agarose gel . TherefoAll datasets generated/analyzed for this study are included in the article/GK, KP, and YK conceived and designed the experiments, analyzed the data, and wrote the manuscript. GK and KP performed the experiments.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.