Uridylation is a widespread modification destabilizing eukaryotic mRNAs. Yet, molecular mechanisms underlying TUTase-mediated mRNA degradation remain mostly unresolved. Here, we report that the Arabidopsis TUTase URT1 participates in a molecular network connecting several translational repressors/decapping activators. URT1 directly interacts with DECAPPING 5 (DCP5), the Arabidopsis ortholog of human LSM14 and yeast Scd6, and this interaction connects URT1 to additional decay factors like DDX6/Dhh1-like RNA helicases. Nanopore direct RNA sequencing reveals a global role of URT1 in shaping poly(A) tail length, notably by preventing the accumulation of excessively deadenylated mRNAs. Based on in vitro and in planta data, we propose a model that explains how URT1 could reduce the accumulation of oligo(A)-tailed mRNAs both by favoring their degradation and because 3’ terminal uridines intrinsically hinder deadenylation. Importantly, preventing the accumulation of excessively deadenylated mRNAs avoids the biogenesis of illegitimate siRNAs that silence endogenous mRNAs and perturb Arabidopsis growth and development. TUTase mediated uridylation of mRNA promotes degradation. Here, Scheer, de Almeida et al. show that Arabidopsis TUTase URT1 interacts directly with the translation inhibitor and decay factor DECAPPING5 and suppresses siRNA biogenesis by preventing accumulation of deadenylated mRNAs Uridylation of ncRNAs can promote maturation, control stability, or abrogate activity, depending on the type of ncRNAs and its cellular context4. For mRNAs, the prevalent role of uridylation is to trigger 5′-3′ and 3′-5′ degradation8.Uridylation targets most classes of eukaryotic RNAs, from small and large noncoding RNAs (ncRNAs) to mRNAs12, which is mostly achieved by the multifunctional Carbon Catabolite Repression-Negative On TATA-less (CCR4-NOT) complex13. Two of its core subunits are the deadenylases CCR4 and CCR4-associated factor 1 (CAF1)14. CCR4 is proposed to shorten poly(A) tails of all mRNAs while CAF1 deadenylates mRNAs with lower rates of translation elongation and poor poly(A) binding protein (PABP) occupancy15. Importantly, specificity factors bridge target mRNAs to the CCR4-NOT complex, thereby leading to translational repression and decay of specific mRNAs. Those specificity factors include the RNA-induced silencing complex (RISC) and several RNA binding proteins (RBPs), such as Tristetraprolin (TTP), Pumilio/fem-3 mRNA binding factor (PUF) proteins, and the YT521-B homology (YTH) domain-containing family proteins YTHDF2 and Meiotic mRNA interception protein 1 (Mmi1)19. In addition, the CCR4-NOT complex interacts with central regulators of translation and decapping, such as the GRB10-interacting GYF  proteins (GIGYF)21 and the DExD/H-box RNA helicases DDX6, Dhh1, or Maternal expression at 31B (Me31B) in humans, yeast, and Drosophila, respectively13. DDX6 also interacts with the decapping activators Enhancer of mRNA-decapping protein 3 (EDC3), DNA topoisomerase 2-associated protein (PAT1), and Like SM homolog 14 (LSM14), also called Suppressor of clathrin deficiency (Scd6) in yeast. The binding of DDX6 to CNOT1 or to decapping activators is proposed to be mutually exclusive. A possible scenario is that a succession of interactions allows DDX6 to hand over deadenylated mRNAs to the decapping machinery22.mRNA uridylation is preceded by deadenylationArabidopsis thaliana (hereafter Arabidopsis)11. URT1-mediated uridylation can restore a binding site for a PABP, but its impact on mRNA stability is yet unsolved12. In fission yeast and human cultured cells, uridylation of short oligo(A) tails is proposed to favor the binding of the LSm1-7 complex, which recruits the decapping complex through the interaction with PAT1, and ultimately results in the degradation of the decapped mRNA by the 5′-3′ exoribonuclease 1 XRN1 (XRN4 in plants)23. Alternatively, U-tails can directly attract Dis3-like protein 2 (Dis3L2) or the RNA exosome to trigger 3′-5′ exoribonucleolytic decay of mRNAs24.Once the CCR4-NOT complex has been recruited and the poly(A) tail has been shortened, oligo(A) tails of less than ca 25 As are frequently uridylated by terminal uridylyltransferases (TUTases) such as TUT4/7 in mammals or UTP:RNA URIDYLYLTRANSFERASE (URT1) in In this study, we show that the Arabidopsis TUTase URT1 is integrated in an interaction network comprising the deadenylation complex CCR4-NOT and other translation repressors/decapping activators, including DCP5, the plant ortholog of the translational inhibitor/decapping activator Scd6 or LSM14A. Our interactomic and functional analysis data support a model explaining how URT1-mediated uridylation prevents the accumulation of excessively deadenylated mRNAs. We also show that in absence of URT1-mediated uridylation, excessively deadenylated mRNAs can become a source of spurious siRNAs that silence endogenous mRNAs, with a negative impact on plant fitness.AT2G45620 can be divided in two regions discriminated by compositional biases and the presence of known domains  followed by a PAP-associated domain or PAP/OAS1 substrate-binding domain   compared to the Arabidopsis proteome  Fig. . By contome Fig. . Moreoveome Fig. .Fig. 1Sh1. These URT1 sequences originate from 73 species representing major groups of Archaeplastida: glaucophytes, rhodophytes , chlorophyte and streptophyte algae, bryophytes , lycophytes and pteridophytes , gymnosperms , and angiosperms (flowering plants)1. In all groups, we identified URT1 orthologs with a predicted N-terminal IDR  . Both categories are consistent with the known involvement of URT1 in mRNA metabolism12.The conservation of a large IDR containing SLiMs supports the possibility that URT1 interacts with one or several partners. To identify this interaction network, proteins co-purifying with URT1 (tagged with myc or YFP) expressed in IP) Fig. . The comIP) Fig. . The mosAT5G42950 and also named GYN4, PSIG1, and MUSE1128. The two closest EXA1 homologs (AT1G27430 and AT1G24300) are also significantly enriched in URT1 IPs , the 4EHP ortholog called new cap-binding protein (nCBP) (AT5G18110), PAT1 (AT1G79090) and the DDX6-like RNA helicases RH6, RH8, and RH12 . These results raise the possibility that the chain of interactions described for GIGYF is conserved for EXA1 in Arabidopsis and that URT1 is connected to these factors, including the CCR4-NOT complex.The most significantly enriched protein co-purifying with URT1 is ESSENTIAL FOR POTEXVIRUS ACCUMULATION 1 (EXA1) encoded by AT1G26110 , ortholog to EDC4 and encoded by AT3G13300, and the aforementioned DDX6-like RNA helicases  are enriched in URT1 IPs. Of note, DECAPPING 5-LIKE  and VARICOSE-RELATED , homologs of DCP5 and VCS, respectively, are also significantly enriched in URT1 IPs  encoded by 110 Fig. . The kno30, which are also present in DCP5 and LSM14A  among the most enriched proteins Fig. . A DCP5-14A Fig. .Altogether, our IP results indicate that URT1 is integrated into interaction networks connecting translational repressors and decapping activators, including DCP5.31, which resemble URT1’s M1 motif  is sufficient to pull down 6His-MBP-DCP5LSm . As expected, this interaction is abolished by mutating the M1 motif  or with either one of two catalytically impaired versions of URT1, URT1D491/3A, or URT1P618L   tail and eventually non-A nucleotides) was PCR-amplified and GFP amplicons from six independent biological replicates were sequenced in two MiSeq runs  tails  tails hereafter and in all figures). Indeed, non-uridylated poly(A) tails in samples expressing wild-type URT1 showed a clear decrease of the 16–20 peak and an accumulation of larger poly(A) tails as compared to control samples Fig. . This ac/3A Fig. , despite/3A Fig. .D491/3A resulted in increased GFP mRNA uridylation as compared to control samples  tails for both uridylated and non-uridylated GFP mRNAs  involved in the turnover of uridylated oligoadenylated mRNAs through an interaction involving the M1 motif. Indeed, the comparison of MS data for seven URT1D491/3A versus eight m1-URT1D491/3A IPs revealed that URT1D491/3A co-purifies with N. benthamiana decay factors including DCP5 homologs and that this interaction requires the M1 motif  tails interspersed with non-A ribonucleotides, subsequently called A-rich tails, accumulated in URT1/3A Fig. . Of noteGFP reporter mRNAs toward larger sizes.Taken altogether, those data confirm that URT1 connects decay factors through the M1 motif and, more importantly, reveal that the ectopic overexpression of URT1 remodels the poly(A) tails of GFP reporter mRNA because URT1 is a distributive TUTase11 and tethering would likely entail the synthesis of longer poly(U) tails as compared to the wild-type situation. In our experimental design, the number of uridines added to GFP mRNAs is similar between control and URT1 samples  tails, and led to the accumulation of PR2 mRNAs with longer poly(A) tails as compared to the control samples  tail profiles between three biological replicates of wild-type and urt1 plants using nanopore direct RNA sequencing (DRS). Although DRS does not yet allow to detect mRNA uridylation, it is well suited for measuring poly(A) tail sizes, including long ones. The nanopore DRS analysis revealed the impact of URT1 on poly(A) tail size distributions with a clear shift of the distribution toward short oligo(A) tails in urt1 samples  tail sizes induced by URT1 overexpression reflects bona fide molecular functions of this TUTase, the opposite effect should be observed in les Fig. . This acurt1 plants. Those mRNAs were selected because they have various uridylation levels ranging from 1 to 24 %  tails of 10–25 As for most of the analyzed mRNAs. This effect is particularly obvious for AT4G38770 mRNAs, whose poly(A) tail profile is strongly impacted by loss of URT1, despite its apparently low uridylation frequency in wild type . First, uridylation by URT1 could impede deadenylation, thereby slowing down the production of oligoadenylated mRNAs. Second, URT1 could trigger the degradation of oligoadenylated mRNAs, thereby shifting the distribution toward longer poly(A) tails. Importantly, those possibilities are not mutually exclusive.Two hypotheses could explain that poly(A) tails are shortened in 33. CAF1 from group A (CAF1a and CAF1b) and group C (CAF1h–k) have been proposed to interact with NOT1, the scaffold protein of the CCR4-NOT complex33. We purified recombinant CCR4s and the CAF1 proteins from group A and C and tested their deadenylase activity in vitro. For unknown reasons, only CAF1b had a robust deadenylase activity under the various biochemical conditions tested. Therefore, CAF1b was used to test the intrinsic influence of uridylation on its deadenylation activity. A catalytic mutant CAF1bD42A was used as a negative control. CAF1b and CAF1bD42A were incubated with radiolabeled RNA substrates containing either 14 3′ terminal As, 13 As and 1 U, 12 As and 2 Us, or 14 Us. As expected, CAF1b fastly degraded the oligo(A) tail whereas it was markedly inhibited by 14 Us  tails  tail profiles in ife Fig. . This anEven though a causal relationship between uridylation and mRNA decay has yet to be demonstrated in Arabidopsis and many molecular details remain to be solved to fully understand how URT1-mediated uridylation influences mRNA decay, the data presented here unequivocally show that URT1 shapes the global poly(A) tail profile in Arabidopsis and prevents the accumulation of excessively deadenylated mRNAs.urt1 mutants has no major effect on Arabidopsis growth and development, at least when plants are grown in standard conditions. Yet, introgressing the urt1 mutation into an xrn4 background, lacking the main cytosolic 5′-3′ exoribonuclease, had a detrimental impact on development  conditions  complex, which degrades a variety of structured ncRNAs in the cytoplasm49. In this study, we show that Arabidopsis URT1 co-purifies with several translational repressors/decapping activators, orthologs of which are known to form an intricate and dynamic interaction network in animals51. However, whether this network also comprises a TUTase in animals is not yet known.The composite domain organization of most TUTases is proposed to be key for the recruitment of factors that assist TUTases for the recognition of specific RNA substrates or channel the downstream molecular effects of uridylation21. GIGYF are translation repressors and decapping activators, that interact with different translation repressors via multiple interfaces, among them the GYF domain21. GIGYF interactants include the RNA helicase DDX6/Me31B, PAT1, the 5′ cap-binding protein 4EHP, and the CCR4-NOT complex52. Interestingly, orthologs of all these factors are detected in URT1 IPs, supporting the hypothesis that such an interaction network is conserved in Arabidopsis. In line with our data, a two-hybrid screen using Arabidopsis EXA1 as a bait  retrieved CNOT4 subunits26, providing independent support for a physical association between EXA1 and the CCR4-NOT complex. Interestingly, the GYF domain of EXA1 recognizes a PPGF sequence26, and such a motif is conserved in URT1 of flowering plants. A CCR4-NOT/EXA1/URT1 network may explain, at least in part, how URT1 selects its targets and why deadenylated mRNAs are preferentially uridylated. The 3′ extremity of long poly(A) tails that are either protected by PABPs or being shortened by CCR4-NOT would be poorly accessible to URT1, even though URT1 and CCR4-NOT are connected. However, once poly(A) tails get short enough to loosen their association with the last remaining PABP and when CCR4-NOT’s activity is more distributive, the tethering of URT1 to CCR4-NOT via EXA1 could facilitate the uridylation of oligo(A)-tailed mRNAs.A key factor at the heart of this dynamic network connecting translational repressors and decapping activators is the CCR4-NOT complex. Although not all components of CCR4-NOT were enriched in URT1 IPs, the detection of the CCR4-NOT complex scaffold subunit NOT1 alongside the other CCR4-NOT subunits CNOT10 and CNOT11 strongly supports a connection between the CCR4-NOT complex and URT1. The prime candidate for connecting URT1 to the CCR4-NOT complex is EXA1, a GYF domain containing protein, homologous to the human and fly GIGYF proteinsS. pombe and mammalian cells, uridylation is proposed to favor decapping of deadenylated mRNA by promoting the binding of the LSm1-7 complex53, which recruits the decapping complex through a connection with Pat157. The LSm1-7 complex preferentially binds short oligo(A) tails of <10 As23. Our previous TAIL-seq analyses12 and the 3′RACE-seq data presented here show that the majority of uridylated oligo(A) tails are longer than 10 As and comprise mostly 10–25 As. Such oligo(A) tails are likely suboptimal targets for the LSm1-7 complex. Interestingly, the direct recruitment of decapping activators by URT1 could bypass the requirement for LSm1-7 binding. We demonstrated here that the conserved M1 motif of URT1 directly binds to the decapping activator DCP5. DCP5 interacts with additional decapping factors, like DCP1 and DCP258. For yet unknown reasons, neither DCP1 nor DCP2 were significantly enriched in URT1 IPs. By contrast, we detected VCS, an ortholog of human EDC4, which interacts with LSM14 via a FFD motif29, perfectly conserved in Arabidopsis DCP5. Moreover, a LSM14-DDX6 interaction via LSM14’s FDF and TFG motifs (both conserved in DCP5) is required to expose the FFD motif for EDC4 recruitment29. Our URT1 IP data support the idea that a DCP5/RH6,8,12/VCS connection also exists in Arabidopsis. In addition, the most straightforward interpretation of the poly(A) profiles observed upon overexpression of different URT1 versions in N. benthamiana is that the interaction of URT1’s M1 motif with DCP5 promotes the degradation of oligoadenylated mRNAs (and possibly mRNAs with heteropolymeric A-rich tails). We therefore propose that the conserved M1 motif in the N-terminal IDR of URT1 contacts DCP5, which then recruits the RH6,8,12-VCS decapping activators. In addition, URT1 contacts the GIGYF-like EXA1, likely via URT1’s PPGF motif. EXA1 recruits further translational repressors and decapping activators like nCBP and PAT1. Altogether these interaction networks would facilitate decapping of mRNAs with oligo(A) tails larger than those typically required for LSm1-7 recruitment.In 59. We therefore propose that a key role for URT1-mediated uridylation is to avoid the accumulation of excessively deadenylated mRNAs that otherwise erroneously enter the RNA silencing pathway. In line with this hypothesis, a connection between URT1 and RNA silencing was recently suggested by the identification of URT1 as a silencing suppressor of transgenes60. Our results indicate two additive modes of action to explain how URT1 limits the accumulation of excessively deadenylated mRNAs  tails influences deadenylation or translation, binding of a PABP may also protect mRNA 3′ extremity from being accessible to RDR6. Altogether, these data illustrate the complexity of uridylation-mediated processes. Although facilitating degradation emerges as the prototypical function of mRNA uridylation, the underlying molecular mechanisms are complex and may differ across eukaryotes. This diversity is yet to be fully explored.But why has a bypass of the LSm1-7 recruitment been selected during plant evolution? A likely reason is that in plants, excessive deadenylation could trigger the biogenesis of spurious siRNAs targeting endogenous mRNAs to PTGS. Indeed, the RNA-dependent RNA polymerase RDR6, the key enzyme converting “aberrant” RNA into double stranded RNA that will be diced into siRNAs, intrinsically favors fully deadenylated mRNAs over polyadenylated mRNAs as templatesArabidopsis thaliana plants used in this study are of Columbia (Col-0) accession. T-DNA mutants were described previously: urt1-1 11, urt1-2 (WISCDSLOXHS208_08D)11, xrn4-3 (SALK_014209)61, xrn4-5 (SAIL_681E01)62, dcl2-1 (SALK_064627)63, dcl4-2 (GABI_160G05)64, and dcp5-1 (SALK_008881)58. The plant material used for RNA-seq and small RNA-seq corresponds to Arabidopsis plantlets grown for 24 days in vitro on Murashige & Skoog media with 0.8% agar and 12-h light/12-h darkness cycles (22/18 °C). For other analyses using Arabidopsis, flowers were harvested from plants grown on soil with 16-h light/8-h darkness cycles (21/18 °C). Agroinfiltration experiments were performed in leaves of Nicotiana benthamiana plants grown during 4 weeks on soil with 16-h light/8-h darkness cycles (22/18 °C).All The characterization of URT1 sequences and phylogenetic analyses are detailed in Supplementary Methods. All protein sequences used for Fig. Oligonucleotides and expression plasmids used for cloning are listed in Supplementary Data Details about samples and replicates of co-immunopurification (IP) experiments are provided in Supplementary Data g, 4 °C), supernatants were incubated for 45 min with 50 µl of magnetic microbeads coupled to anti-c-myc antibodies or anti-GFP antibodies (Miltenyi). Beads magnetic capture and washing steps were done according to the manufacturer’s instructions, except that washes were performed with 50 mM Tris-HCl pH 7.5, 50 mM NaCl, 0.1% Triton X-100, protease inhibitors . Samples were eluted in 100 µl of pre-warmed elution buffer . Negative control IPs were performed with beads coupled to anti-c-myc and anti-GFP antibodies in Col-0 plants or in plants expressing the GFP alone65. To improve the stringency of the analysis and improve the identification of contaminant proteins, additional negative control IPs were performed using GFP-BPM6 expressing plants66. BPM6, encoded by AT3G43700, acts as an adaptor for Cullin3-based E3 ubiquitin ligase and has no known function related to RNA metabolism.For each IP with crosslinking step on Arabidopsis samples, 150 mg of Arabidopsis flower buds were ground during 10 min in 1.125 ml of ice-cold lysis buffer supplemented with 0.375% formaldehyde (Thermo Fisher Scientific). The crosslinking reaction was quenched by adding glycine at a final concentration of 200 mM for 5 min. After cell debris removal by centrifugation . Beads magnetic capture and washing steps were performed with the same protocol as the one described above for Arabidopsis samples. Each IP sample was performed in 2–4 biological replicates and 1–3 technical affinity replicates  or the Sol Genomics Niben101 database (for N. benthamiana samples) with a decoy strategy. Peptides were identified with Mascot algorithm  and data were imported into Proline 1.4 software (http://proline.profiproteomics.fr/). Proteins were validated on Mascot pretty rank equal to 1, and 1% FDR on both peptide spectrum matches (PSM score) and protein sets (Protein Set score). The total number of MS/MS fragmentation spectra was used to quantify each protein from at least four independent IPs and two independent biological replicates 68. P value was adjusted using the Benjamini–Hochberg method from stats R package. The gene ontology analysis for URT1 co-purifying proteins was performed using the Functional Annotation tool implemented in DAVID (v6.8)69. For N. benthamiana IPs, XIC (Extracted Ion Chromatograms)-based abundances were also calculated for peptides that map DCP5 isoforms. XIC-based quantification and statistics were performed using the Proteome Discoverer software  with the following parameters: Sequest and MS-Amanda algorithms with a FDR at 1%, “Top 3 Average” method, no imputation.Eluted proteins were digested with sequencing-grade trypsin (Promega) and analyzed by nanoLC-MS/MS. For IPs with crosslinking Fig. , digesteIPs Fig. , the difIPs Fig.  or the d1–40, 6His-GST-m1URT11–40, 6His-MBP-DCP5, 6His-MBP-ΔLSmDCP5, and 6His-MBP-DCP5-LSM were expressed into Escherichia coli BL21 DE3 using plasmids listed in Supplementary g for 5 min. Eluted proteins were separated by SDS-PAGE and stained using SYPRO Ruby dye (Bio-Rad). An Attan DIGE imager (Amersham Biosciences) or Fusion FX camera system (Vilber) was used for visualization.Recombinant 6His-GST, 6His-GST-URT1, 6His-GST-m1URT1, 6His-GST-URT1D42A proteins were expressed in E. coli BL21 using plasmids listed in Supplementary 2, 50 mM KCl, 7% glycerol, and 0.1% Tween 20. The oligoribonucleotides CACCAACCACU-A14, CACCAACCACU-A13U1, CACCAACCACU-A12U2, and CACCAACCACA-U14 were used as substrates. Purified 6His-GST-CAF1b and 6His-GST-CAF1bD42A proteins (30 nM) were incubated with 17.5 nM of radiolabelled substrate for 1 h at 25 °C. Aliquots were taken at different time points and separated on a 17% polyacrylamide/7 M urea gel before autoradiography. The amount of intact substrate RNA was estimated as detailed in Supplementary Methods.Recombinant 6His-GST-CAF1b and 6His-GST-CAF1bAgrobacterium tumefaciens GV3101 (pMP90) were transformed with plant expression plasmids listed in Supplementary g for 15 min. The pellets were resuspended at an OD600 of 1 in 5 ml of agroinfiltration buffer ). The cell suspensions containing the P19, URT1, and GFP constructs were mixed to a 1:1:1 ratio and infiltrated into N. benthamiana leaves using needleless syringes. The plant material was harvested 4 days after infiltration for RNA and protein extraction. Pictures of the infiltrated leaves were taken under UV illumination at 365 nm using a UVP Blak-Ray B-100Y UV lamp (Thermo Fisher Scientific) to detect the expression of the GFP reporter. The intensity of the GFP fluorescence was quantified using ImageJ (see details in Supplementary Methods).N. benthamiana infiltrated leaf patches (four different leaves per sample) were ground in SDS-urea extraction buffer . The samples were separated by SDS-PAGE and electrotransferred to a 0.45 μm Immobilon-P PVDF membrane (Millipore). The membrane was incubated overnight at 4 °C with DCP5 antibodies , URT1 antibodies , MBP antibodies , monoclonal c-myc antibody . Polyclonal and monoclonal antibodies were detected by goat anti-rabbit or anti-mouse IgG coupled to peroxidase , respectively, and using Lumi-Light Western Blotting Substrate (Roche). Pictures were taken with a Fusion FX camera system. The PVDF membranes were stained with 0.1% Coomassie Brilliant Blue R-250, 7% acetic acid, 50% methanol) to monitor loading.N. benthamiana four days after agroinfiltration. Total RNA was extracted using Tri-Reagent (Molecular Research Center), followed by extraction with acid phenol:chloroform:isoamyl alcohol and RNA precipitation with ethanol. Five micrograms of RNAs were then separated on a 1.5% agarose gel containing 0.2-M MOPS pH 7.0, 20 mM sodium acetate, 10 mM EDTA, 5.55% formaldehyde and transferred onto a nylon membrane . After transfer, RNAs were UV crosslinked at 120 mJ/cm2 for 30 s with a Stratagene Stratalinker. The membrane was stained with methylene blue and a picture was taken for illustrating equal loading between samples. After destaining, membranes were incubated with PerfectHyb hybridization buffer (Sigma-Aldrich) for 30 min at 65 °C and hybridized overnight at 65 °C with radiolabeled probes to detect PR2 (Niben101Scf04869g03002.1) or GFP mRNAs. PR2 or GFP PCR amplicons of about 500 pb .For each sample, four infiltrated patches pooled from different leaves were harvested from N. benthamiana infiltrated leaves or Arabidopsis flowers. Ten pmoles of a 5′-riboadenylated DNA oligonucleotide  as detailed in Supplementary Methods. Similar differences in poly(A) tail size distributions were observed using standard Illumina base-calling software and the Tailseeker algorithm, designed to limit poly(A) tail length overestimation  from As Figs. , poly(A)urt1-1 flowers using Tri-Reagent (Molecular Research Center) following the manufacturer’s recommendations. Capped mRNAs were enriched using GST-eIF4E-K119A protein70. Nanopore direct-RNA libraries were prepared from 5 µg of Arabidopsis cap-enriched mRNA with 150 ng of yeast poly(A)-enriched RNA using Direct RNA Sequencing Kit 71. Sequencing was performed with MinION device . Sequencing reads were basecalled using Guppy 4.0.11 (Oxford Nanopore Technologies) and mapped to the TAIR10 reference transcriptome  using MiniMap 2.17. Poly(A) lengths were estimated using Nanopolish 0.13.2. The numbers of sequences obtained for each nanopore library are provided in Supplementary Data Total RNA was extracted from wild-type and 12 and is detailed in Supplementary Methods. Uridylation percentages were calculated for 3340 mRNAs detected with at least 20 reads using the pooled dataset from the three biological replicates. The number of sequences obtained at each processing step of TAIL-seq libraries and the calculated uridylation percentages are provided in Supplementary Data TAIL-seq libraries were generated from three biological replicates of Arabidopsis Col-0 flower buds. Total RNA was extracted using Tri-Reagent (Sigma-Aldrich), treated with DNase I (Thermo Fisher Scientific), and purified using the RNeasy MinElute Clean-up (Qiagen). Per sample, three individual ribodepletion on 10 μg of RNA each in a final volume of 10 μl were performed using the RiboMinus Plant kit (Thermo Fisher scientific) following the manufacturer’s instructions. Ribodepleted RNA were then ligated to 10 pmol of a biotinylated 3′ adapter, . Small RNA libraries were prepared and sequenced at Fasteris (http://www.fasteris.com). Libraries were generated from 3 µg of DNase-treated RNA using the Illumina TruSeq Small RNA protocol after size selection of 18–30-nt RNA fragments on a denaturing polyacrylamide gel. Libraries were sequenced on a HiSeq 2500 . The base calls were acquired from HiSeq 2500 after processing by Illumina RTA 1.18.61.0 and CASAVA pipeline v.1.8.2. Details about further data processing are provided in Supplementary Methods. The numbers of sequences obtained for small RNA-seq libraries and the list of mRNA loci that show differential small RNA accumulation are provided in Supplementary Data Total RNA was extracted using Tri-Reagent (Molecular Research Center) from WT, p value of 0.05 was defined as the threshold of significance. For each figure, the exact value of n and the test used for the statistical analysis are indicated in the figure or in the corresponding legend. Fold change and p values in Fig. 67. Statistical significance shown in Fig. p values were adjusted using the Benjamini–Hochberg method.Statistical analyses were performed using R 3.6.1, Rstudio 1.2, and the following R packages: edgeR 3.26.5, stats 3.6.1, multcompView 0.1-8. For all analyses, a For each graph, the number of independent replicates is indicated directly on the figures. For Figs. Further information on research design is available in the Supplementary InformationPeer Review FileDescriptions of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting Summary


The close synergy between peptides and nucleic acids in current biology is suggestive of a functional co-evolution between the two polymers. Here we show that cationic proto-peptides (depsipeptides and polyesters), either produced as mixtures from plausibly prebiotic dry-down reactions or synthetically prepared in pure form, can engage in direct interactions with RNA resulting in mutual stabilization. Cationic proto-peptides significantly increase the thermal stability of folded RNA structures. In turn, RNA increases the lifetime of a depsipeptide by >30-fold. Proto-peptides containing the proteinaceous amino acids Lys, Arg, or His adjacent to backbone ester bonds generally promote RNA duplex thermal stability to a greater magnitude than do analogous sequences containing non-proteinaceous residues. Our findings support a model in which tightly-intertwined biological dependencies of RNA and protein reflect a long co-evolutionary history that began with rudimentary, mutually-stabilizing interactions at early stages of polypeptide and nucleic acid co-existence. Cooperative relationships are widespread among different classes of biopolymers and are predicted to have existed during emergence of life. This study shows that proto-peptides engage in mutually stabilizing interactions with RNA, providing support for the co-evolution of these molecules. Rudimentary forerunners of such interactions must have influenced prebiotic chemical evolution. Interactions between diverse classes of molecules might have attenuated rates of degradation, promoted folding or solubility, supported ligand binding, or promoted catalysis. Cooperative interactions between molecules also could have expanded the possible mechanisms for the buildup and persistence of certain molecules in a prebiotic environment2. For example, in an environment of periodic, random (non-coded) oligomer synthesis via condensation processes fueled by condensing agents or wet–dry cycles, intermolecular interactions that imparted reduced rates of hydrolysis for one or more of the oligomers involved could naturally lead to a buildup of those sequences. Understanding the nature and genesis of productive molecular interactions among prebiotic molecules is a central issue in exploring the origin of life.Cooperative relationships between different classes of biopolymers are a hallmark of biology. RNA makes protein in the ribosome and protein makes RNA in polymerases8. Over time the pure RNA World incrementally gave rise to proteins and DNA on the path to the current RNA/DNA/protein biological system. An alternative theory is that the evolution of nucleic acids and polypeptides was intimately connected from the beginning 17. In either scenario, it appears that productive relationships between nucleic acids and peptides (or between proto-nucleic acids and proto-peptides) would be required during maturation of the extant RNA/DNA/protein system. A few studies have investigated how peptides could promote folding or functions of RNA22, or vice versa24, but there are few examples of interactions that mutually benefit both partners25. Given the importance that molecular cooperation may have played in the origins of peptides and RNA, and recent descriptions of prebiotic pathways by which amino acids, peptides, and nucleotides can simultaneously be produced28, we aimed to evaluate the possibility of mutually stabilizing interactions between RNA and proto-peptides (polyesters and depsipeptides).A prevailing idea in origins-of-life research is that there was once an RNA World in which RNA served dual roles as genetic polymer and catalyst30, but these proto-peptides also form more readily than pure peptides under mild dry-heat conditions36. Ester linkages enable the formation of amide bonds through a process of ester-amide exchange34. Hydroxy acids are produced together with amino acids in model prebiotic reactions37, are found together in some meteorites38, and can combine to form oligomers >20 residues in length in mild dry-down reaction conditions36. We recently demonstrated that cationic depsipeptides form robustly in dry-down reactions of mixtures of α-hydroxy acids and cationic α-amino acids39.Polyesters and depsipeptides, which contain backbone ester linkages in place of some or all amide bonds, have been proposed as the chemical ancestors of present-day proteins . Polyesters and depsipeptides are prebiotically plausible. Not only can α-hydroxy acids be incorporated ribosomally during translation to generate depsipeptide and polyesterGiven that electrostatics are key elements of protein–nucleic acid interactions in extant life, we hypothesized that cationic proto-peptides might functionally interact with nucleic acids. Here we show that cationic polyesters and depsipeptides, generated either as heterogeneous mixtures from dry-down reactions of α-hydroxy acid monomers alone or in combination with α-amino acids, or synthetically prepared in homogeneous pure form, can interact directly with RNA in mutually stabilizing partnerships. These interactions prolong the lifetimes of proto-peptides due to lower rates of backbone ester bond hydrolysis, and render RNA duplexes more stable against thermal denaturation. Proto-peptides containing Arg, His, or Lys adjacent to backbone ester bonds generally increase RNA duplex thermal stability to a greater extent than do analogous sequences containing non-proteinaceous ornithine (Orn), 2,4-diaminobutyric acid (Dab), or 2,3-diaminopropionic acid (Dpr). Thus, mutually stabilizing interactions appear to be a natural outcome when cationic proto-peptides and RNA co-exist in mixtures. The results of our study support the idea that the intermolecular interactions between RNA and peptides in extant biology have ancient origins and reflect a long co-evolutionary history.39. Based on those results, we hypothesized that dry-down reactions involving only a cationic α-hydroxy acid containing a side chain amino group should yield cationic polyesters, whereas drying them in the presence of an amino acid would yield cationic depsipeptide mixtures. We chose isoserine (isr), 2-hydroxy-4-aminobutyric acid , or 2-hydroxy-6-aminohexanoic acid   Fig.  and a cays) Fig. . Indeed,ys) Fig. –8. Analyres Fig. , NMR anares Fig. .Fig. 1CaTm) of a short 10mer RNA duplex in the presence or absence of individual proto-peptide samples  and mixed with the RNA duplex (2.5 μM each strand). After measuring the thermal denaturation curve for each condition .Under these conditions, the presence of depsipeptide product mixtures that contained the amino acids Arg, His, and Lys, generally caused increases in the  Tm Fig. . The Ornres Fig. ; this maTm of the RNA duplex due to their higher charge. To confirm that the observed effects on RNA Tm stemmed from the oligomeric products, we dialyzed the lac- or glc-containing mixtures  served as a negative control for the dialysis experiments. Indeed, the non-cationic glc + Ala depsipeptides did not affect the Tm of the RNA duplex , Arg, and Lys significantly increased the duplex Tm, whereas Orn and Dab did not 4-NH2, further supporting direct association between the cationic oligomers and RNA , we attribute the changes in the spectra to alterations in RNA duplex conformation upon binding of the cationic depsipeptide/peptides. Fitting of the CD data using a one-site binding model yielded a Kd value of ~3 μM  spectroscopy. Using 5-FAM-labeled peptide 13 Table , we obse 20 Fig. . The obs 20 Fig. . In agreRNA Fig. . Since t μM Fig. .Fig. 4Ca9 in the absence or presence of varying concentrations of RNA duplex 1 or single-stranded RNA at 37 °C in pH 7.3 buffer , the observed kinetics of depsipeptide hydrolysis were essentially identical  was ~34-fold lower than for the free depsipeptide (khyd-free). Given the fixed value of kassoc and the calculated value of kdissoc, the predicted dissociation constant of the depsipeptide 9-RNA complex is 0.1 μM, somewhat lower than the Kd of ~3 μM measured by CD for depsipeptide sequence 14 binding to the same RNA duplex.Data from the nine independent depsipeptide hydrolysis reactions carried out with different concentrations of RNA x 1 Fig.  were simduplex 1 (25 μM each complementary strand), one containing only depsipeptide 9 (25 μM), and the last sample containing both duplex 1 and depsipeptide 9  and intact depsipeptide 9 (due to reduced backbone ester hydrolysis)  Fig. . Thus, n39 and polyesters can form robustly via simple dry-down reactions in the absence of condensing agents  interacted with proto-nucleic acids. Here, we have studied the potential for cationic proto-peptide mixtures generated in plausibly prebiotic dry-down reactions to engage in direct interactions with RNA. We found that such interactions can increase the lifetimes of the proto-peptides by decreasing their rate of degradation, and can stabilize the RNA by increasing the melting temperature of folds and assemblies. Cationic depsipeptidesnts Fig. . We alsonts Fig. . Proto-pnts Fig. . In turnnts Fig. . Thus, R46, efficiencies of non-enzymatic incorporation into growing oligomers, and the relative abilities of different side chains to impart functions within the context of the oligomer47. It is generally believed that the cationic amino acids found in proteins  were not abundant on prebiotic Earth, although Lys has been observed in meteorites49, and potentially-prebiotic routes for both Arg and His have been proposed50. Amino acids with shorter cationic side chains, such as Orn, Dab, and Dpr, have also been observed in meteorites and model prebiotic reactions, and are thought to have been more abundant on prebiotic Earth55. It is intriguing that proto-peptide oligomers containing Arg, His, and Lys generally promoted greater increases in RNA duplex thermal stability than those containing Orn, Dab, or Dpr, both as product mixtures from dry-down reactions  condensation processes, mutually stabilizing interactions could naturally lead to a buildup of those oligomers that possessed increased lifetimes due to the interactions. Shielding RNA against denaturation could increase its robustness and the range of environments in which it was folded and functional. Additional mechanisms have been put forth by which interactions between peptides and RNA could promote synthesis, stabilization, or localization of prebiotic oligomers. In one example, cationic poly(Leu-Lys) peptides were reported to encourage the oligomerization of activated mononucleotide diphosphates, and were especially effective in increasing the abundance of the longer RNA oligomers65 or cause RNA aggregation66. For instance, Brack and colleagues studied a series of (Leu-Lys)n or (Leu-Lys-Lys-Leu)n peptides of varying lengths and degrees of stereochemical purity, and found that they exhibited RNA hydrolysis activities that correlated with the degree of β-sheet or α-helical character in the peptides. Considering that depsipeptides and polyesters should have less β-sheet or α-helical character compared to pure peptides (due to missing hydrogen bond donors and weakened hydrogen bond acceptors), it is possible that catalysis of RNA hydrolysis by proto-peptides would be attenuated compared to peptides. Another consideration is that the formation of complexes between proto-peptides and RNA could impair chemical processes that required conformational changes or unfolding of RNA structures, such as RNA catalysis or replication. An important caveat when considering interactions based primarily on electrostatics is that the strength of the molecular interactions would be modulated by changes in the ionic strength of the solution.In addition to the vast span of known beneficial interactions between RNA and cationic peptides, it is worth mentioning that certain interactions between cationic oligomers and RNA could have a reverse effect and could have negatively impacted the lifetimes or functions of molecules involved in the interaction. For example, it is known that interactions between cationic peptides and RNA can accelerate RNA degradation69. In principle, primordial molecular partnerships could have increased the lifetimes of certain molecules by numerous mechanisms. Our findings demonstrate that proto-peptides and early nucleic acids could have interacted in mutually stabilizing ways, but the processes demonstrated here involving increased thermal stability of noncovalent assemblies and increased backbone ester hydrolytic stability are only two possibilities out of many. Mutually stabilizing interactions provide a potential avenue for chemical selection—in mixtures containing multiple proto-peptide and RNA sequences, molecules with higher binding affinities could selectively associate in solution, persist, and adopt better-folded or more stable structures. We are currently investigating this possibility. Further studies of chemical systems in which productive interactions between different types of molecules could occur will likely lead to important insights in prebiotic chemistry and will be necessary for solving some of the enduring problems in the origin of life.Investigations of biopolymer origins from a co-evolutionary perspective might afford valuable insights into early chemical evolution. The origins and evolution of biopolymers would have occurred amongst significant molecular heterogeneity, and productive cooperative interactions between molecules were almost certainly involved in the emergence, selection, and persistence of certain sets of molecules out of the clutter40. Chain elongations were carried out using 1,3-diisopropylcarbodiimide (DIC) and ethyl 2‐cyano‐2‐(hydroxyimino)acetate (oxyma) in N-methylpyrrolidin-2-one (NMP) with 75-min couplings. Fmoc deprotection was achieved using 2 × 8 min treatments with 25% 4-methylpiperidine in dimethylformamide (DMF). Washing steps involved 6 × 1 min treatments with DMF. Sequences were cleaved from the resin with concomitant side chain deprotection by agitation in a solution of 95:2.5:2.5 TFA:triisopropylsilane (TIS):water for 3 h. The crude products were precipitated with diethyl ether, centrifuged, and washed three additional times with ether. The crude peptides/depsipeptides were purified by preparative reverse-phase (RP)-HPLC on a Vydac 218TP C18 or Thermo BioBasic C18 column. Purity was confirmed by analytical RP-HPLC. Purified peptides were characterized by analytical HPLC and LC–MS. Analytical RP-HPLC was performed using a Zorbax 300-SB C-18 column connected to a Hitachi D-7000 HPLC system. Binary gradients of solvent A  and solvent B  were employed for HPLC.Peptides and depsipeptides were synthesized by using standard Fmoc chemistry with an Advanced Chemtech Apex 396 peptide synthesizer. A typical synthesis was performed on 0.09-mmol scale using Rink amide MBHA resin (~0.6 mmol/g). Standard side chain protecting groups included Lys(Boc), Orn(Boc), diaminopropionic acid(Boc), diaminobutyric acid(Boc), His(Trt), Arg(Pbf), Tyr(tBu), Glu(OtBu), and Asp(OtBu). Ester bonds were incorporated into the depsipeptides by coupling separately synthesized Fmoc-didepsipeptide building blocks that were appropriately protected on the side chainDab-Ala-lac-Dab…) was incorporated as a Bsmoc-protected amino acid rather than being Fmoc-protected, to enable its deprotection with lower concentrations of base70. After coupling Bsmoc-Dab(Boc)-OH to the growing depsipeptide, the Bsmoc group was removed using 3 × 1 min treatment with 2% 4-methylpiperidine/DMF. The subsequent DMF washing steps were limited to only 2 × 30 s to reduce the time that the free amine was present at N-terminus prior to the following coupling step70. These modified procedures eliminated observation of deletion/truncation products during the synthesis.In certain depsipeptide sequence contexts, such as…Dab-Ala-lac-Dab…, extensive truncation/termination was observed during the solid-phase synthesis, presumably due to context-dependent DKP formation. Accordingly, in these problematic sequences, the Dab residue located at the second position following incorporation of the ester bond  or Tyr . RNA strands were purchased from Integrated DNA Technologies with standard desalting or HPLC-purification, and were dissolved in deionized water at 200 μM for stock solutions based on UV absorbance using the manufacturer-provided extinction coefficient.Stock solutions of peptides and depsipeptides were prepared in deionized water at 2 mM based on UV absorbance of Aba  using a BioRad CFX Connect rtPCR instrument with a heating ramp of 1 °C/min and 20 μL final sample volumes. For analyses involving pure, synthetic compounds, the Tm of the RNA was determined by monitoring UV hyperchromicity at 260 nm using a Varian Cary Bio-100 spectrophotometer, with a heating ramp of 1 °C/min and 0.5- or 1-cm pathlength cells. The following RNA sequences were used: RNA duplex 1 = 5′-rCrGrCrUrArArArUrCrG-3′ and 5′-rCrGrArUrUrUrArGrCrG-3′; RNA duplex 2 = 5′-rArArArArUrUrUrArUrArUrUrArUrUrA-3′ and 5′-rUrArArUrArArUrArUrArArArUrUrUrU-3′; RNA duplex 3 = 5′-rArArCrGrUrArUrArCrGrUrU-3′ ; and H26a RNA = 5′-rArUrGrArGrUrArArCrCrGrUrArArGrGrUrGrArArArUrU-3′. RNA was present in each assay at a final concentration of 2.5 μM of the folded structure . Tm values were determined by nonlinear fitting of the melting curves using GraphPad Prism 8.2.1 for duplex 1 and H26a, or by finding the maximum of the first derivative of the curve using the Varian Cary Bio-100 software for duplex 2 and duplex 3. Both methods of determining the Tm gave similar values for a given denaturation curve.Prior to 39. Aqueous solutions of either glc, lac, or isr with a single amino acid at a 5:1 molar ratio (in favor of the hydroxy acid) were allowed to dry at 85 °C under unbuffered, mildly acidic conditions  for 1 week. The amino acids were all used in their HCl form, and no additional salt was added to the reactions. Control reactions contained either a hydroxy acid alone or an amino acid alone. For formation of cationic polyesters and depsipeptides from cationic hydroxy acid monomers, aqueous solutions of either isr, hab or hah (100 μmol) were allowed to dry at 85 °C under unbuffered, mildly acidic conditions  for 1 week, either alone or in a binary mixture with Gly, Ala, lac, or glc. Before analysis, dry-down reaction mixtures were resuspended in ultrapure water to 100 mM concentration , vortexed, sonicated in ice, and centrifuged at 15,294 × g for 5 min. The supernatant was collected and diluted to the specified concentration. For dialysis, the lac- or glc-containing dry-down reaction mixtures were placed in a 500–1000 Da cut-off membrane  and dialyzed against water.Cationic depsipeptides were prepared by drying mixtures of hydroxy acids with cationic amino acids1H-NMR resonance. The extent of amidation at side-chain amines was quantified by integration of the resonance corresponding to methylene protons adjacent to the side-chain amine.NMR spectra were recorded on a Bruker Avance II-500. To ensure quantitative integration of the resonances, relaxation delay of 15 s was used for dry-down reaction mixtures. Data were processed and spectra were plotted with MestReNova software package. The overall conversion of monomers into products was estimated from integration of the free, non-reacted α-proton 39.LC–MS data were collected on an Agilent 1260 HPLC coupled to an Agilent 6130 single quadrupole mass spectrometer and an inline Agilent UV absorbance detector (210 nm) using a 3.0- or 3.5-kV electrospray ionization (ESI) capillary voltage. Samples were either directly infused into the mass spectrometer or separated using a Zorbax C18 column with a binary solvent system of water/acetonitrile/formic acid. Processing of MS data were conducted using a suite of macros using Igor Pro-8.0For band shift analyses, 2.5 µL samples were prepared containing 40 µM of a 5′FAM-labeled 20-mer of polyuridylic acid (U20), a variable concentration of peptide or depsipeptide, and 100 mM MES-TEA, pH 6. An addition of 1.25 µL of glycerol brought the sample volume to a total of 3.75 µL. Samples were loaded onto a 12% native polyacrylamide gel, run at 100 V for 90 min, and imaged using Azure Biosystems c430.The CD spectra were collected at 4 °C using a Jasco J-815 CD instrument. Scans were from 320 nm to 220 nm with a bandwidth of 5 nm, data pitch of 0.2 nm, and scan rate of 50 nm/min with three accumulations. The 1 cm cuvette contained 5 μM RNA duplex (5′-rCrGrArUrUrUrArGrCrG-3′ and 3′-rGrCrUrArArArUrCrGrC-5′) and varying concentrations of the peptides/depsipeptides (0–10 μM) in a final volume of 600 μL in 60 mM MES-TEA buffer. The peptides/depsipeptides were added to the RNA sample and incubated for 10 min in ice prior to collection of the CD spectra. The consecutive addition of the peptides/depsipeptides did not result in any significant change in volume . Data were collected in independent triplicates and the data were averaged. Finally, data were smoothed and plotted using Matlab.duplex 1 (5′-rCrGrArUrUrUrArGrCrG-3′ and 3′-rGrCrUrArArArUrCrGrC-5′) at various concentrations, or with single-stranded RNA , for up to 100 h. Prior to adding depsipeptide to the reaction from its stock solution, the RNA was annealed by heating to 75 °C for 5 min, and then slowly cooling to room temperature. Reaction aliquots were quenched using 3% TFA and frozen or immediately analyzed by HPLC (270 nm). The relative concentrations of intact and hydrolyzed depsipeptide were quantified by integrating the corresponding peaks in HPLC traces. The kinetic profiles of depsipeptide hydrolysis were fit using SimFit-32 (provided by G. von Kiedrowski) according to the reaction scheme shown in Fig. To determine the extent of depsipeptide hydrolysis, the depsipeptide (25 μM) was incubated at 37 °C in buffer  in the presence or absence of 10mer RNA khyd-free. Binding of the depsipeptide to RNA according to an equilibrium determined by kassoc/kdissoc yields a depsipeptide-RNA complex, for which the pseudo-first order rate of ester hydrolysis is described by khyd-complex. During fitting, we fixed kassoc = 1 × 105 M−1 s−1, on the lower end of rates observed and expected for protein–RNA association45. Therefore, three parameters were variable during the fitting, and the final rate constants determined by the fitting were: khyd-free = 1.1 × 10−4 s−1, khyd-complex = 3.2 × 10−6 s−1, and kdissoc = 8.3 × 10−3 s−1. It should be noted that the fixed value used for kassoc did not affect the final values calculated for khyd-free or khyd-complex as long as kassoc was greater than 1 × 103 M−1 s−1, because the kdissoc value adjusted accordingly during the data fitting to compensate for different kassoc values. When kassoc was fixed at values below 1 × 103 M−1 s−1, the root mean square error values for the fit began to increase, indicating that the actual kassoc > 1 × 103 M−1 s−1. Our model assumes a 1:1 stoichiometry for depsipeptide:RNA in the complex, and assumes the RNA is present only as a duplex (ignores steps involving RNA strand hybridization to form the duplex). The final RMS error of the fit was 5.4%. The output file obtained from the fitting is provided in the Data from the nine independent depsipeptide hydrolysis reactions carried out with different concentrations of RNA duplex were simultaneously fit using SimFit to the reaction model shown in Fig. The experiments described in this paper were repeated independently at least two times. All attempts at experimental replication were successful. Graphical data are shown as scatter plots with all data points included. No data were excluded from the analyses.Further information on research design is available in the Supplementary InformationReporting Summary


Subsequently NHS Digital, the NHS coding organisation, released a more extensive set of SNOMED CT concepts named ‘2019 nCoV (novel coronavirus)’ because the use of ‘Wuhan’ had been deprecated; these codes were in turn replaced by ‘SARS –CoV-2 (severe acute respiratory syndrome coronavirus 2)’2We have now been through three iterations of clinical codes in the UK since the end of January. Five temporary codes were added to all the primary care CMR systems using the ‘Suspected Coronavirus infection: 1JX’, and ‘Coronavirus infection: A795’).The situation has been further complicated by the fact that this last release is only now starting to become available in CMRs , and bec3 We have previously classified the incorrect use of codes as miscoding, misclassification, or misdiagnosis.4 In the cases of COVID-19, we are seeing1 both Miscoding ;2 and Misclassification (use of non-specific coronavirus codes), which should stop. https://clininf.eu/index.php/cov-19/This creates challenges for the surveillance system and others monitoring the pandemic.All UK primary care clinicians and coders are recommended to continue to use the temporary codes until the new ones are available, then switch. Accurate data is a key to understanding and monitoring the course of this pandemic.Appendix: Examples of codes not to useExposure to coronavirus infectionSuspected coronavirus infectionCoronavirus infectionDisease due to CoronaviridaeCoronavirus contact


The novel coronavirus, COVID-19, has taken the world by storm, causing widespread quarantines and lockdowns of entire nations, actions not seen in over a century since the 1918 Spanish Influenza. The natural defense against an infectious pathogen is to avoid contracting the pathogen altogether through quarantine, a practice employed since antiquity  have demonstrated effective control of infection with COVID-19 in vitro . Hydroxyphylaxis . ImpressCOVID-19 .in vitro, against avian influenza A H5N1 in mice, and against human coronavirus OC43 in newborn mice , its previous utility as a prophylactic agent against malaria infections, and promising in vitro results  and QT prolongation. Hydroxychloroquine is contraindicated in people with a history of hypersensitivity to 4-aminoquinoline compounds and should be used with caution in people with glucose-6-phosphate dehydrogenase deficiency . Recomme results , 11. DesVP completed all work for this manuscript.VP was employed by Vanda Pharmaceuticals.


In the past few years, thorough investigation of chemical modifications operated in the cells on ribonucleic acid (RNA) molecules is gaining momentum. This new field of research has been dubbed “epitranscriptomics”, in analogy to best-known epigenomics, to stress the potential of ensembles of RNA modifications to constitute a post-transcriptional regulatory layer of gene expression orchestrated by writer, reader, and eraser RNA-binding proteins (RBPs). In fact, epitranscriptomics aims at identifying and characterizing all functionally relevant changes involving both non-substitutional chemical modifications and editing events made to the transcriptome. Indeed, several types of RNA modifications that impact gene expression have been reported so far in different species of cellular RNAs, including ribosomal RNAs, transfer RNAs, small nuclear RNAs, messenger RNAs, and long non-coding RNAs. Supporting functional relevance of this largely unknown regulatory mechanism, several human diseases have been associated directly to RNA modifications or to RBPs that may play as effectors of epitranscriptomic marks. However, an exhaustive epitranscriptome’s characterization, aimed to systematically classify all RNA modifications and clarify rules, actors, and outcomes of this promising regulatory code, is currently not available, mainly hampered by lack of suitable detecting technologies. This is an unfortunate limitation that, thanks to an unprecedented pace of technological advancements especially in the sequencing technology field, is likely to be overcome soon. Here, we review the current knowledge on epitranscriptomic marks and propose a categorization method based on the reference ribonucleotide and its rounds of modifications (“stages”) until reaching the given modified form. We believe that this classification scheme can be useful to coherently organize the expanding number of discovered RNA modifications. Pseudouridine (C5-glycoside isomer of uridine), the first post-transcriptional ribonucleic acid (RNA) modification identified, was discovered in 1957 . InteresAbout 160 distinct RNA modifications have been identified so far , spread In particular, leveraging on the analogy with epigenomics, epitranscriptomic marks may offer a prevalent layer of gene-expression regulation by constituting a substrate for differential binding with RNA-interacting partners performing as effectors of this code. In fact, the epigenetic paradigm of specific sets of regulatory marks made operational by an ensemble of writers, readers, and erasers can also be applied to epitranscriptomics ,14. The n (OMIM)  databaseThe case of m6A is also useful to illustrate the close link between the field of epitranscriptomics and the high-throughput sequencing (HTS) technologies. In fact, in addition to traditional experimental protocols for RNA modification detection, including thin-layer chromatography (TLC), high-performance liquid chromatography (HPLC), and liquid chromatography-mass spectrometry (LC-MS(MS)) , availab map m1A , m5C . De. De80]. ynthesis . Furtherynthesis . Formatiynthesis . Moreoveynthesis  and the ynthesis . In a reynthesis . Importaynthesis .5-hydroxymethylcytidine (hm5C) is a derivative of m5C that has been found in polyadenylated RNA and in rRNA. Its occurrence in all domains of life suggests that hm5C may play important regulatory roles . In the Cm modifications, similarly to all other Nm modifications , are found in many types of RNA, such as snRNA, snoRNA, and lncRNA . In tRNAe moiety . Functioe moiety . In mRNAe moiety .N4-acetylcytidine (ac4C) is a modification found in all domains of life in mRNA, tRNA, and rRNA. In mRNA, acetylation of cytidine to ac4C is enriched in the coding region near the translation start-site, and in the 5’ UTR , where iAgmatidine (C+) modification in archaea and 2-lysidine (k2C) modification in bacteria are known to cause mispairing at least in codon recognition. Both C+ and k2C are found in the wobble position 34 of tRNA b, in arcIn the landscape of epitranscriptomic marks, G-derived modifications vary the most in their complexity, ranging from very simple base methylations  to very complex derivatives (requiring up to 9 stages) b. Among m1G at position 37 in tRNA (m1G37) c is the m2G and its derivative m2,2G are both found in tRNAs at positions 10 and 26 c, where m7G modification occurs both in mRNA and tRNA. In tRNA, it is found at positions 34, 36, and frequently at position 46 c, where Q modifications are irreversible post-transcriptional RNA modifications that occur at the GUN-anticodon sequence of tRNA , where the modified G lies at the wobble position ,107,108 Uridine-derived modifications occur mostly in tRNA. Among RNA bases, uridine exhibits the largest variety of derivatives. Among them, 2-thiouridine (s2U) and 5-carbamoylmethyluridine (ncm5U)  in turn Ψ and D , also cofication . This cofication ,116. Formodified ,118. In he D-arm . Additiodon stem d [83,96,tructure ,123. In RT block . Of noteRT block .1-methylpseudouridine  , and humition 37 , where tis (m5C) . In geneis (m5C) . Dynamicis (m5C) ,148,149.is (m5C) . Among ois (m5C)  and inacis (m5C) . Notablyis (m5C) , includiis (m5C) , NSUN2 [is (m5C) , and ELPis (m5C) . Of themis (m5C) , the Noois (m5C) , and to is (m5C) , while Eis (m5C) . Moreoveis (m5C) . Finallyectively .Over a dozen different epitranscriptomic marks have been annotated in eukaryotic mRNAs  Figure . Of notelity (Ψ) ,145. In lity (Ψ) . In genelity (Ψ) . Howeverlity (Ψ) ). Finalllity (Ψ) ).https://gtexportal.org/), recently expanded to include editing events across different mouse brain tissues reported in a recent study [Arabidopsis thaliana, Pseudomonas aeruginosa, and Escherichia coli) [Arabidopsis thaliana) from 26 independent studies [Many different databases currently provide systematic information about known RNA modifications and editing events or their transcriptome-wide annotated sites reported in different species a. In parnt study . The RNAia coli) . Transcr studies . WHISTLE studies . REDIdb  studies , while t studies . The tRN studies . Most re studies , an up-t studies . The sma studies  provides studies .http://lowelab.ucsc.edu/snoscan/) that predicts occurrence of Nm modification sites in rRNAs, as well as occurrence of guide snoRNA genes, that are responsible for Nm modification in rRNAs, in yeast, humans, and archaea [http://lowelab.ucsc.edu/snoGPS/) the snoGPS algorithm [Many tools have been developed to computationally predict sites of modification for best characterized epitranscriptomic marks and are available as webtools or standalone software b. Among equences  Similarlequences  webtool equences . The Snoequences  is provi archaea . The Lowlgorithm  to predilgorithm . Finallylgorithm ,201.More and more, we appreciate contribution of post-transcriptional regulation to the complexity of gene-expression regulation. Different regulatory layers work complexly and dynamically across different types of cells to ensure a gene expression outcome finely tuned and consonant with cell state. Best characterized examples, such as m6A (6-methyladenosine), uncover key contributions of epitranscriptome in this complex interplay regulating cellular, developmental, and disease processes. Although we already know that RNA modification and editing events are involved in maintaining the functionality of many living systems by impacting on many essential RNA-processing events, such as splicing, transport, localization, translation, and stability, research on functions and mechanisms acted by the epitranscriptome is still in an early phase. Moreover, we cannot rule out different biological roles even for the same RNA modification occurring at the same site in different species. Accordingly, the list of human diseases linked to altered RNA modifications or caused by misfunction of related RBPs is growing and so is the awareness of medicine and pharmaceutics towards epitranscriptomics . Recent


Fragility fracture of the hip is associated with reduced functional status and mortality. Poor self-rated health (SRH) might be such an indicator. Our aim was to study if SRH was associated with hip fractures and all-cause mortality within the next 10 years in community-dwelling older women. A population-based sample of 350 women aged between 69 and 79 years (median 72.4) assessed their SRH by answering the question “How would you rate your health right now” by putting a mark on a visual-analogue scale (0–100 mm). Information on hip fracture and mortality over the next 10 years was retrieved from health care registers. The association between SRH and hip fracture and all-cause mortality was tested with a Cox proportional hazards regression model. SRH was divided into low, intermediate, and high (reference) assessed SRH. During the study, 40 hip fractures and 72 deaths occurred. The median value of SRH was 62 mm (IQR 50–81 mm). The age-adjusted hazard ratio (HR) for hip fracture was significantly higher in the group with low and intermediate SRH; HR: 3.17 (95% CI 1.25–8.01), and HR: 2.75 (95% CI 1.08–7.04), compared with high SRH. Adding bone mineral density  gave even greater risk. We did not find the hypothesized association between SRH and mortality. In our study, SRH indicated a higher risk of future hip fracture in older women. SRH might be a marker that could add information about the risk of hip fracture independently of bone mineral density. The life-time risk over the age of 50 in Sweden to suffer a hip fracture is 22.9% in women and 10.7% in men –8. The aQoL is a subjective measure and might be explained as a person´s evaluation of their state of well-being in a general way. It is considered to add complementary information that medical and epidemiological data might not detect , 14–16. The association between SRH and mortality has been described , 26. ThaWe hypothesized that SRH might predict future hip fractures and mortality in our pre-existing cohort of elderly and community-dwelling women. This hypothesis is based on the suggestion that there are associations between QoL and bone density/osteoporosis even prior to hip fractures. The aim of the study was to explore how SRH is associated to a) hip fracture or b) death by any cause, independently of each other, during the next ten years. This would also add new knowledge about SRH prior to hip fracture.This was a longitudinal cohort study with about 10 years of follow up. The participants were part of a cohort originally gathered as part of the Primary Health Care and Osteoporosis (PRIMOS) project, a series of studies on elderly and community-dwelling women concerning different aspects of osteoporosis. Ethical approval was obtained from the Regional Ethical Review Board in Stockholm  and the Radiation Protection Committee at Karolinska University Hospital. All participants gave their written informed consent at baseline. The first woman was recruited in March 1999 and the last in February 2001 and SRH were assessed at enrolment . The maiIn 1999, 937 women born between 1920 and 1930 were living in Bagarmossen  were eligible for the study . The selAll women were examined at baseline by the same physician.Age: This was measured in years.Body mass index (BMI): This was calculated as kg/m2.One-leg standing time (OLST): This was measured in seconds for the best of four attempts, two on each leg, to stand on one leg for up to 30 seconds with eyes open, barefoot, and arms held alongside the body. This variable was dichotomized to whether or not they managed to stand for 10 seconds or not as earlier studies indicated that this is a relevant cut-off regarding future risk of hip fracture [fracture , 40, 41.Gait speed: The time it took to walk back and forth  as quickly as possible with shoes on in a well-lit corridor on an even floor was measured [measured .Ability to get up from a chair without support: Dichotomous variable where participants were asked to stand up from a sitting position without supporting themselves on the armrests of the chair [he chair , 42.Unable to rise from chair and slow gait speed: Dichotomous variable combining gait speed less than 0.8 mps and inability to get up from a chair without support [ support .Self-rated health (SRH): At baseline the participants answered the question “How would you rate your health right now” by putting a mark somewhere along a visual -analogue scale (VAS) ranging from “worst imaginable” to “best imaginable”. The distance between “worst imaginable” to the mark was measured in millimetres . This variable was divided into tertiles: low SRH, intermediate SRH, and high SRH.Data were also collected via self-reported questionnaires or extracted from medical records on:Smoking status: yes or no.Actual medication at baseline: more than three drugs or not.Bisphosphonate therapy at baseline: yes or no.Calcium and vitamin D supplement at baseline: yes or no.Comorbidity: more than two diagnosis or not.Self-reported fractures: previous fractures in life before inclusion, at all and after the age of 50.Bone mineral densitometry (BMD): Measurements were conducted using Hologic QDR 4500 DXA equipment  between 1999 and 2001. The measurements were expressed as T-scores calculated according to the NHANES-III reference population [pulation .2 as a continuous variable.The variables used in this study were the T-score of the femoral neck, dichotomized to ≤−2.5 or not (indicating osteoporosis or not), and the total BMD of the femoral neck in g/cmFracture risk assessment tool (FRAX®): A widely used algorithm giving the 10-year probability of fragility fractures [ractures –47. The Only participants with data for SRH were included in this analysis (n = 350). In studies dealing with SRH, there are different strategies for how to present the data , 22. Our2 test was used if the variables were dichotomous. If the frequency in one of the groups was lower than five, we used Fisher´s exact test. For comparisons between the three groups of SRH in BMI and gait speed, we used one-way ANOVA and Bartlett´s test for variance testing, assuming that the sample was normally distributed and the variance were homogenous. The relationships between SRH and hip fractures and mortality were tested with the Cox proportional hazards regression model in order to take time to event into account. Since our material is numerically limited analyses with many covariates at the same time are likely to be senseless so we chose to examine simple associations and add one variable at a time. We defined possible confounders as variables that altered the age-adjusted HR ≥10%. Mortality as a competing risk was tested with Fine and Gray cumulative incidence function using the STATA package “stcrreg”. We tested that the proportional hazard assumption was not violated and we tested for multicollinearity using the STATA packages “collin” and “estat phtest”. P-values less than 0.05 were considered significant. All analyses were performed with STATA statistical software version 14.2 .Furthermore, SRH was skewed to the right. When looking at the variable in a histogram, the distribution visually seemed to be three-modal. We therefore chose to divide the participants into three groups: low, intermediate, and high according to their assessed SRH. Low SRH ranged between 5–51 mm (n = 113), intermediate SRH ranged between 52–73 (n = 118) and high SRH ranged between 74–99 mm (n = 119). High SRH was used as the reference. All variables were tested for normality (with Q-Q-plots), and if skewed they are presented as the median and as inter-quartile range (IQR). Otherwise they are presented as mean values and standard deviations (SD). The Kruskal–Wallis test was used for comparison between the three groups of SRH for skewed variables, and the ChiThe women participating were all community-dwelling and aged between 69 and 79 years at baseline. The Non-participants questionnaire revealed that the participants were slightly younger (mean age 72.9 years vs. 73.9 years), reported previous hip fracture less often (1.4% vs. 4.6%p = 0.053), and reported previous wrist fracture more often . The proportion of subjects who considered their health to be as good as others of the same age was 35% in both participants and non-participants, but more women among the participants considered their health to be better than others of the same age (46.1% vs. 19.4%). Their total time at risk was 3,094.026 years with a median follow-up time of 9.8 years (IQR: 9.0–10.4 years). The median age at inclusion was 72.4 years (IQR 71.1–73.8). The median value of SRH was 62 mm (IQR 50–81 mm) .The baseline characteristics are presented in ® result including T-score or T-score alone. There were differences in co-morbidity and having more than two diseases and more than three drugs were more common in the lower tertiles. The women who had assessed their health higher performed better in terms of gait speed, OLST, and the ability to get up from a chair without using the armrests and more often spent more than 30 minutes per day outdoors. There were only a few participants who were treated with bisphosphonates (n = 8) or calcium plus vitamin D (n = 26) at baseline, and for these variables there were no statistically significant differences between the tertiles. There were also no differences between the tertiles regarding self-reported fractures over age of 50, T-score less than −2.5 at the femoral neck, or bone mineral density at the femoral neck. Smoking was equally common in all tertiles. A total of 40 hip fractures occurred during follow-up, and the numbers differed significantly between the tertiles of SRH. There was a clear gradient across the SRH tertiles with most of the fractures among those who rated their health the worst. Out of 40 persons with hip fractures, 15 died over the study period, and their distribution did not differ significantly between the tertiles of SRH, nor did the deaths not caused by hip fracture or mortality in total (There were differences between the tertiles of SRH at baseline in BMI (decreased as SRH increased). The tertiles did not differ significantly in age, BMD, FRAXin total .The probability of hip fracture differed between the three groups of SRH as illustrated in a Kaplan–Meier failure estimate graph. The increase of probability of hip fracture in the group with low SRH also occurred earlier compared to those who assessed their SRH as intermediate or high .The age-adjusted hazard ratio (HR) of suffering a hip fracture was 3.17 in the low tertile and 2.75 in the intermediate tertile compared with the high tertile.The difference was even larger, HR 4.08 and 3.10 respectively, when we also adjusted for bone mineral density at the femoral neck as a continuous variable. Using a dichotomous variable with bone mineral density at the femoral neck (T-score) assessed as below −2.5 or not, HR was 4.04 and 3.18, respectively. Adjusting for smoking and BMI did not affect the significance of the original model see . We obtaWe found no association between SRH and mortality .We found that the age-adjusted risk of suffering a hip fracture in our cohort of older and predominantly white women seemed to be three times higher if the participants assessed their health as low or intermediate compared to high at baseline . This was in line with our hypothesis that low assessed SRH could predict hip fracture in our cohort. In contrast to our second hypothesis, we found no association between SRH and mortality.Earlier studies have also addressed SRH with different approaches and varying results. A prospective cohort with more than 9,000 white women over 65 years in the US explored risk factors for hip fracture and found an association between SRH at baseline and hip fracture during about four years of follow-up with HR: 1.7 (95% CI 1.3–2.2) towards poorer assessed health for each step between three SRH categories. The study subjects were very similar to ours . In our Several studies have also aimed at developing algorithms combining several risk factors for fracture prediction. In a prospective study with older women in the UK, they developed a risk score for predicting hip fracture risk for the next three years. Poor versus good SRH was associated with hip fracture with an OR of 4.1 (95% CI 1.14–14.72). SRH was a candidate for the final algorithm but was not included because the authors chose to replace it with variables they considered more specific . A threeThere are also studies where no significant independent association was found between SRH and hip fracture. Risk factors for hip fractures described by Cummings et al. 1995) were applied for hip fracture prediction in a prospective cohort of older women in Norway 95 were a. The conThe varying results regarding SRH and fragility fractures between different study populations might indicate that SRH is not a robust risk factor. Other studies have concluded that ratings of self-assessed health may vary between different demographic groups for example ethnicity, gender and age . SRH is There has been extensive research on prediction of hip fracture risk, and the state of knowledge today suggests that clinical risk factors together with BMD might be the best way to go.When we adjusted for BMD in our study, this made the association between SRH and hip fracture stronger. This might indicate that SRH is not a proxy for BMD. Another study explored whether SRH could predict total hip low bone mineral density but could not find an association except in white non-obese men . These fInitially, we were a bit concerned that BMD did not differ between SRH tertiles at baseline but affected the association between SRH and hip fracture in the final analysis. One possible explanation for this is that time to hip fracture is taken into account using Cox proportional hazards regression model. Fragility fractures may have occurred earlier in persons with low BMD compared to those with normal BMD. Another explanation might be that not all persons with low BMD suffered a hip fracture but most persons with hip fracture had low BMD.We did not find an association between SRH and mortality nor any significant difference in mortality in the hip fracture patients between the tertiles of SRH (even though there seemed to be a trend). This finding was unexpected because association between SRH and mortality is widely accepted , 26. SolThe strengths with our study were that it had a longitudinal design, a long follow-up time (median 9.8 years), and that we had SRH assessments on all study subjects except one. Comprehensive registers in Sweden made it possible to retrieve outcome data on all our subjects so we had no loss to follow-up , 39. LimFinally we analysed BMD values and T-score of the femoral neck solely. We did not consider BMD of other locations or microstructure of the skeleton.Because hip fracture is such a devastating complication of osteoporosis, it is urgent to identify those persons at risk, preferably before they suffer a fracture. SRH appears to be an interesting variable to explore further in this context, particularly because it seems to add information on fractur risk independently of bone mineral density. In the future, it would be interesting to measure SRH more than once to observe the natural trajectory of SRH relative to the risk of hip fracture. This is also interesting because short-time fracture prediction might be more relevant in the oldest old. It is also worth keeping in mind that SRH might vary between ethnic groups, and this should be explored further.S1 Appendix(XLSX)Click here for additional data file.


The increasing number of transcriptomic datasets has allowed for meta-analyses, which can be valuable due to their increased statistical power. However, meta-analyses can be confounded by so-called “batch effects,” where technical variation across different batches of RNA-seq experiments can clearly produce spurious signals of differential expression and reduce our power to detect true differences. While batch effects can sometimes be accounted for, albeit with caveats, a better strategy is to understand their sources to better avoid them. In this study, we examined the effects of RNA isolation method as a possible source of batch effects in RNA-seq design.Saccharomyces cerevisiae heat shock response as a well-validated environmental response. Comparing technical replicates that only differed in RNA isolation method, we found over one thousand transcripts that appeared “differentially” expressed when comparing hot phenol extraction with the two kits. Strikingly, transcripts with higher abundance in the phenol-extracted samples were enriched for membrane proteins, suggesting that indeed the chemistry of hot phenol extraction better solubilizes those species of mRNA.Based on the different chemistries of “classic” hot phenol extraction of RNA compared to common commercial RNA isolation kits, we hypothesized that specific mRNAs may be preferentially extracted depending upon method, which could masquerade as differential expression in downstream RNA-seq analyses. We tested this hypothesis using the Within a self-contained experimental batch (e.g. control versus treatment), the method of RNA isolation had little effect on the ability to identify differentially expressed transcripts. However, we suggest that researchers performing meta-analyses across different experimental batches strongly consider the RNA isolation methods for each experiment. The decreasing cost of massively parallel sequencing has led to an explosion of transcriptomic datasets. This large number of datasets has allowed for meta-analyses, which can be valuable due to their increase in statistical power. However, researchers performing meta-analyses on transcriptomic datasets need to be cautious in their use and aware of so-called “batch effects,” where technical differences between experimental batches can clearly produce spurious signals of differential expression and reduce our power to detect true differences.In some cases the sources of batch effects are known and can be avoided. Some well-known batch effects include sequencing lane effects, library construction protocol, and RNA quality –3. OtherIn this study, we examined the effects of RNA isolation method as a possible source of batch effects in RNA-seq design. It is well known that the RNA distribution within cells is not uniform. Newly synthesized pre-mRNAs are processed in the nucleus before being exported. Once exported, mRNAs are frequently trafficked to specific subcellular sites as a mechanism for spatially controlling protein synthesis. Indeed, perhaps the most widespread example of mRNA localization is that used for spatial control of protein synthesis, where mRNAs encoding secreted and membrane proteins are translated at the ER membrane allowing for proper protein localization and folding .Despite the widespread acknowledgement that mRNAs are differentially localized within the cell, there has been a paucity of studies examining whether “common” RNA extraction methods are equivalent in their abilities to extract differentially localized RNA species, and whether the method of RNA isolation affects our ability to detect differentially expressed transcripts. Sultan and colleagues compared two RNA isolation methods (Qiagen RNeasy kit and guanidinium-phenol (TRIzol) extraction) and two library selection schemes (poly-A enrichment and rRNA depletion) on downstream transcript abundance estimates, and found that rRNA depletion was particularly sensitive to the RNA extraction method . HoweverSaccharomyces cerevisiae heat shock response. The different RNA isolation methods were the classic “hot acid phenol” method, and the two most commonly-used types of kits [Saccharomyces cerevisiae before and after a 20-min heat shock. Importantly, each biological sample was split into three identical technical replicates that differed only in their mode of RNA isolation. This allowed us to systematically test whether the RNA isolation method affects relative transcript abundance between technical replicates, and whether that matters for differential expression analysis.Thus, we sought to systematically examine whether three common RNA isolation methods led to differences in transcript abundance and/or our ability to detect differential expression between two experimental conditions in the form of the  of kits —a silica of kits , 9, we hOur analysis found a striking number of transcripts (nearly 1/3 of the genome) that appeared “differentially” expressed when comparing the Phenol method to either Kit method, and a small number of differences when comparing the Kit methods to each other. Transcripts over-represented by Phenol extraction compared to either Kit were enriched for membrane proteins, suggesting that indeed the combination of SDS plus phenol better extracts those species of mRNA. Importantly, there were virtually no differences when comparing differential expression for the heat shock response within samples where RNA was isolated via same method. Based on these results, we strongly recommend that meta-analyses be performed on groups of experiments with common RNA isolation methods.To test whether RNA extraction methods impact between-sample comparisons and the power to identify differentially expressed genes, we used the well-characterized yeast heat shock response as an environmental perturbation. We collected four biological replicates for comparison. For each biological replicate, three “technical replicate” samples were collected to understand the impact of RNA extraction method. The only difference was that each technical replicate had their RNA extracted by one of three methods: classic hot acid phenol (Phenol Method), a silica-based column kit (RNeasy Method) and a guanidinium-phenol (TRIzol)-based kit (Direct-zol Method) Fig. . RNA isoP = 2 × 10− 6, t-test) or the RNeasy kit   Fig. . Not surP = 1 × 10− 520, Fisher’s exact test)  Fig. c. In conP < 4 × 10− 68), establishment of localization (P < 9 × 10− 54), lipid metabolism (P < 1 × 10− 27), and cell wall organization (P < 1 × 10− 18). Looking more closely at the cellular component GO enrichments, transcripts with higher abundance in the phenol samples were strongly enriched for those encoding intrinsic membrane proteins (P < 4 × 10− 191), as well as proteins localized to the endoplasmic reticulum (P < 6 × 10− 84), cell periphery (P < 3 × 10− 80), and the vacuole (P < 3 × 10− 53). In contrast, mRNAs with lower relative abundance in the phenol samples were enriched for nuclear in localization (P < 3 × 10− 60), and included those encoding functions related to nucleic acid metabolism (P < 1 × 10− 38), RNA metabolism (P < 6 × 10− 28), chromosome organization (P < 4 × 10− 17), and gene expression (P < 8 × 10− 17). Notably, the transcripts that appeared “repressed” in the Phenol samples compared to both Kits also had significantly lower expression relative to the genomic average . We hypothesize that lowly expressed transcripts are more sensitive to appearing spuriously “repressed” (see Discussion and conclusions).To better visualize these differences, we performed hierarchical clustering on all 3127 transcripts with significantly differential abundance (FDR < 0.01) in any pairwise comparison of RNA isolation method  with log2 fold changes, depending upon the comparison group, while estimated in vivo half-life weakly correlated with log2 fold changes in either comparison . Moreover, because of the substantial overlap between genes called as differentially expressed in the Phenol vs. RNeasy and Phenol vs. Direct-zol comparisons, we hypothesize that the differing chemistries in the extraction are responsible for the batch effect, and not RNA degradation (see Discussion and conclusions).That Phenol-isolated samples have higher transcript abundance for mRNAs encoding membrane proteins fits with the hypothesis that the Phenol method better solubilizes that species of mRNA. Another possibility is that differences in transcript degradation rates are responsible for the spurious patterns of differential expression. Because GC content and transcript length correlate with in vivo mRNA degradation rates , we examThe striking differences in transcript abundance depending on RNA isolation could conceivably affect the ability to detect differential expression. To test this, we examined our ability to detect differential expression in cells shifted from 30 °C to 37 °C for 20 min—the classic yeast heat shock response. We identified ~ 3800 differentially expressed transcripts for all three RNA isolation methods, with substantial overlap for all three Fig. . HierarcOne possibility is that the transcripts encoding the processes most affected by differences in the extraction methods (i.e. membrane-associated proteins) are lowly represented during the heat shock response, and thus the method of RNA isolation could affect differential expression under different conditions. To test this, we examined whether genes with the GO component term of “membrane” were less likely to be differentially expressed during heat shock. Genes encoding membrane proteins comprise 28.7% of the yeast genome, which turned out to be statistically equivalent  to their proportion within the heat shock response . Thus, we hypothesize that at sufficient sequencing depth, the ability to detect differential expression is robust to the modest differences in transcript counts caused by differences in RNA isolation method.In this study, we tested whether differences in RNA isolation method affect relative transcript abundance between samples, and whether the RNA isolation method impacts our ability to detect differential expression. Our results suggest that differences in RNA isolation method can substantially affect relative transcript abundance, and we saw thousands of differences in transcript abundance when comparing hot acid phenol extraction with an RNeasy or Direct-zol kit. It is well established that mRNAs encoding membrane and secreted proteins are anchored to the membrane during translation . That trr2 = 0.02). And while transcripts with higher relative abundance in the phenol-extracted samples versus the kits had higher GC content and gene length, which both correlate with higher in vivo degradation rates [We disfavor the alternative hypothesis that we are capturing differences in transcript degradation rates for a number of reasons. First, while we do see differences in RIN values across the different RNA isolation methods, the differences are relatively small, and our RIN values are all much higher than the points where other studies identified them as confounding RNA-seq analysis , 12. Secon rates , the coron rates , 15. FinRegardless of the cause of these differences between hot-phenol extracted samples and kits, it clear that this can represent a large source of batch-effect variation between samples whose RNA has been isolated via different methods. Within an individual lab, we are largely agnostic. The method of RNA isolation had little effect on the ability to identify differentially expressed transcripts in our heat shock test case. Thus, experiments within a single lab are unlikely to be affected by the choice of RNA isolation method as long as the same method is used throughout an experiment. For meta-analyses however, we recommend that researchers avoid comparing experiments where the RNA isolation methods differ.his3∆1 leu2∆0 met15∆0 ura3∆0), obtained from Open Biosystems. To compare RNA isolation methods, we collected three identical 10-ml ‘technical’ replicates for each biological replicate . Cells were grown > 8 generations in 100-ml synthetic complete medium (SC) [600 of 0.3–0.6), and 10-ml samples were removed representing the unstressed control. For heat shock treatment, one volume of 55 °C medium was added to the remaining culture, immediately bringing the final temperature to 37 °C, and the culture was incubated at 37 °C for another 20 min before removing 10-ml samples. Both unstressed and heat shocked cells were collected by centrifugation at 1500 x g for 3 min, and cell pellets were flash frozen in liquid nitrogen and stored at − 80 °C until processing.All experiments were performed using yeast strain BY4741   at 30 °Cdx.doi.org/10.17504/protocols.io.inwcdfe. Briefly, 1 volume of acid saturated phenol and 1 volume of lysis buffer  were added to frozen cell pellets, vortexed, and then placed in a 65 °C preheated Multi-Therm incubated vortexer (Benchmark Scientific) at 1500 rpm for 45 min. Samples were centrifuged for 10 min at 4 °C at maximum speed in a microcentrifuge, extracted once more with phenol, once with chloroform, and then precipitated overnight at − 20 °C with 0.1 volumes of sodium acetate (pH 5.2) and 2.5 volumes of 100% ethanol. Precipitated RNA was washed once with 70% ethanol and then resuspended in TE . The phenol extracted RNA was then ‘cleaned’ using an RNeasy Miniprep Kit with optional on-column DNase treatment according to the manufacturer’s instructions.Cells were lysed and RNA was isolated using a standard hot phenol method as described , and a d7 cells from both manufacturers (ranging from 2.5 × 107–4.5 × 107 cells). For both kits, we mechanically lysed cells with a Beadbeater-24 . Mechanical lysis was performed in 2-ml screw-capped tubes containing an equal volume (600 μl) of lysis buffer (RLT for RNeasy or TRI reagent for Direct-zol) and acid-washed glass beads .RNA was extracted using two different kits: the Qiagen RNeasy Mini Kit  and the Zymo Research Direct-zol RNA Miniprep Kit (Cat. R2050). Cell concentrations were all below the maximum recommendation of 5 × 10RNA was then purified according to each manufacturer’s protocol for yeast, including the optional on-column DNase digestion. For all samples, RNA was quantitated using a Qubit RNA HS Assay kit and Qubit fluorometer according to the manufacturer’s instructions. The RNA integrity number (RIN) for each sample was measured using an Agilent 2200 TapeStation. RNA concentrations and RIN values for each sample can be found in Supplementary Table dx.doi.org/10.17504/protocols.io.uueewte. cDNA libraries were sequenced on a HiSeq4000 at the University of Chicago Genomics Facility, generating single-end 50-bp reads.RNA-seq libraries were prepared from polyA-enriched RNA using the KAPA Biosystems mRNA HyperPrep Kit (KK8581) and KAPA Single-Indexed Adapter Set A + B (KK8700), according to manufacturer’s instructions. We started with 500 ng total RNA, fragmentation time (6 min) was optimized to generate 200–300-nt RNA fragments, and the libraries were amplified with 9 cycles of PCR. All libraries were constructed in a single batch through an automated Eppendorf epMotion 5075 liquid handling robot, and a detailed a protocol can be found on protocols.io under DOI Reads were trimmed of low-quality reads and adapter sequence (KAPA v1 indices) using Trimmomatic (version 0.32) , with thDifferential expression analysis was conducted using the Bioconductor package edgeR version 3.22.3) using the quasi-likelihood (QL) framework. For the QL model, sample type  and biological replicate were used as factors. To account for differences in RIN across samples, we also performed a separate analysis that included sample type, replicate, and RIN as factors in the model. To control for differences in sequencing depth across samples, the edgeR function thincounts was used to randomly subsample counts across all samples to be equal to the sample with the lowest number of total counts . Only genes with at least 1 count per million (CPM) in at least one condition were included for TMM normalization and differential expression analysis. All RNA-seq data are available through the National Institutes of Health Gene Expression Omnibus (GEO) database under accession no. GSE135430, and the edgeR outputs can be found in Additional File .22.3 usihttp://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm) using uncentered Pearson correlation and centroid linkage as the metric [https://go.princeton.edu/cgi-bin/GOTermFinder) [P-values < 0.01 taken as significant. Complete lists of enriched categories can be found in Additional File Principal component analysis (PCA) was performed using ClustVis  on ln-tre metric . RNA-seqmFinder) , with BoLAS17, SED1, PRY3) or lower  abundance in the Phenol vs. Kit RNA-seq data were validated by real-time quantitative (q) PCR using the Maxima SYBR-qPCR Master Mix (Thermo Fisher Scientific) and a Bio-Rad CFX96 Touch Real-Time PCR Detection System as described [ERV25 as control gene whose expression is unaffected by various stresses [dx.doi.org/10.17504/protocols.io.bbgpijvn.Six genes with either significantly higher  values. Table S2. Summary of mapping statistics. Table S3. Correlation coefficient (r) for log2 fold changes versus each factor. Table 4. Log2 fold-abundance changes versus each factor.Additional file 2. RSEM Output.Additional file 3. EdgeR Output.Additional file 4. GO enrichments for clusters in Fig.