DNA intercalation has been very useful for engineering DNA-based functional materials. via π–π interactions. The structure is aided by hydrogen bonds between the cationic moieties and the ribose-phosphate backbone, which results in specific selectivity for RNA over DNA. This experimentally observed mechanism is supported by computationally reproducing the fluorescence and CD data. MD simulations confirm the unfolding of RNA due to the intercalation of probes. Elucidation of the mechanism of selective sensing for RNA over DNA would be highly beneficial for dynamical observation of RNA which is essential for studying its biological roles.DNA intercalation has been very useful for engineering DNA-based functional materials. It is generally expected that the intercalation phenomenon in RNA would be similar to that in DNA. Here we note that the neighbor-exclusion principle is violated in RNA by naphthalene-based cationic probes, in contrast to the fact that it is usually valid in DNA. All the intercalation structures are responsible for the fluorescence, where small naphthalene moieties are intercalated in between bases  Small cationic imaging probes are frequently applied in biological research.1H NMR, fluorescence titration, and circular dichroism (CD) experiments were performed to explain the binding stoichiometry and structures. Molecular dynamics (MD) simulations were performed to confirm whether the RNA structure violates the neighbor-exclusion principle within several nanoseconds, which is the time scale for fluorescence. Additionally, density functional theory (DFT) and time-dependent DFT (TD-DFT) methods were used to elucidate the binding and fluorescence mechanisms using stacking nucleobases which exist in the tRNA of baker's yeast: A–A, A–C, A–G, A–U, C–G, C–U, G–G, G–U and U–U pairs . The final concentration of tRNA from baker's yeast and RNA from torula yeast was determined spectrometrically .1–3 display lower fluorescence emissions (λmax = 402 nm) when irradiated at 350 nm  while a quantum yield of 0.05 is observed in the case of probe 3.torula yeast exhibit negligible fluorescence (λmax = 443 nm) is observed in the emission spectrum when the probes were treated with tRNA from baker's yeast  and RNA from torula yeast . The turn-on fluorescence (λmax = 443 nm) is also observed in the emission spectra for the probe 1 treated with tRNA (GCGCGCGCGC) with a quantum yield = 0.22 and tRNA (AUAUAUAUAU) with a quantum yield = 0.21. This indicates that the fluorescence sensing does not depend on the structural skeleton (3 (where the imidazolium group is replaced by a triethyl amino group) gives a similar fluorescence enhancement, indicating that the imidazolium group just gives an electrostatic interaction and has no effect on recognition. Probe 4 exhibits monomer emissions at 398, 421 and 444 nm,5 shows monomer emissions at 379, 398 and 419 nm when irradiated at 350 nm.4 and 5 give decreased fluorescence in the monomer when exposed to tRNA, indicating that the naphthalene moiety is responsible for the fluorescence enhancement and hence for the selective recognition of tRNA  and tRNA (AUAUAUAUAU) is attributed to excimer formation.1–3 showed a broad peak ∼265 nm in the presence of the naphthalene moiety, which became sharper with a distinct blue-shifting of ∼6 nm upon interaction of tRNA with baker's yeast  DNA, single-stranded (ss) DNA, glucose, heme, UTP, TTP, ATP, GTP and CTP ,2. Binding constants4 M–1) and detection limits–6 M) of probes 1–3 were calculated and are summarized in 1–3  de-excitation. The exceptions are C–1–G and C–1–U: oscillator strength <0.01 for the former with delocalized HOMO and localized LUMO, but >0.01 for the latter even with CT excitation. The data could be merged together to reproduce the fluorescence result. Considering both Doppler broadening for the finite width to the spectral lines and the number of each stacking pair, the theoretical fluorescence matches experiment well  studies.torula yeast shows positive ellipticity centred at 290.5 nm  and tRNA (AUAUAUAUAU) were used to investigate which nucleobase is responsible for the interaction with the naphthalene moiety of probe 1  both present in RNA, providing selectivity towards RNA for the cationic moieties of the probes as compared to DNA which only has the phosphate backbone. The computational results support the CD experimental results, suggesting that RNA is stretched by the intercalation of probe molecules. Additionally, we computationally reproduced fluorescence, providing the fluorescence mechanism and supporting the involvement of all intercalation structures in the fluorescence. The breaking of the spatial overlap between HOMO and LUMO at each 1st excited state minimum gives charge transfer driven de-excitations corresponding to fluorescence at 425–450 nm. Overall, we have proposed an effective strategy for RNA recognition, a small fluorophore for facile intercalation with at least one cationic moiety for hydrogen bonding, which is straightforward for both synthesis and further analysis.We have shown that the neighbor-exclusion principle is violated in RNA by naphthalene based cationic probes. The control experiments demonstrate that only the naphthalene moiety is small enough to be inserted into intercalation sites of RNA, unlike pyrene and anthracene. Furthermore, fluorescence titration, CD and 4.4.1.1–5 is described in the ESI.1–5) were fully characterized with standard spectroscopic techniques. Imidazole, 2,6-bis(bromomethyl) naphthalene, 2-bromomethyl naphthalene, 9-bromomethyl anthracene, 1-bromomethyl pyrene and triethyl amine were purchased from Aldrich and were used as such. Sodium salts of ATP, GTP, CTP, TTP and UTP, heme, glucose, dsDNA , RNA (from baker's yeast) and RNA (from torula yeast) were also purchased from Aldrich and used without further purification. Tetrabutyl ammonium salts of F– and I– were also purchased from Aldrich and used without further purification. tRNA (GCGCGCGCGC) and tRNA (AUAUAUAUAU) were purchased from XIDT and used as such.The synthesis of compounds 1, 2, 3, 4, and 5 (1 mM) were prepared at pH 7.4 in 0.01 M HEPES buffer water mixture and used in the preparation of titration solutions by appropriate dilution up to 10 μM. Aliquots of ATP, GTP, CTP, UTP, TTP, heme, glucose, dsDNA, ssDNA, RNA from baker's yeast, RNA from torula yeast, tRNA (GCGCGCGCGC) and tRNA (AUAUAUAUAU) in 0.01 M HEPES buffer water mixture were then injected into the sample solution through a rubber septum in the cap. dsDNA solution was heated to 90 °C and rapidly cooled at 4 °C in order to denature it.26Stock solutions of compounds 1 was also prepared in 0.01 MHEPES buffer (pH 7.4). CD spectra were then recorded with pure RNA and with addition of specific amount of probe 1.The CD spectra were collected at room temperature (25 ± 0.2 °C) using a Jasco made J-815 CD spectropolarimeter. Scans were from 350 to 200 nm with a resolution of 1 nm, with data sampling every 5 s. The 1 cm cell contained 1 mM solution of the RNA in 0.01 M HEPES buffer (pH 7.4).3EPK: tRNA of baker's yeast entangled with eukaryotic dimethylallyltransferase; Fig. S29a1 were constructed using the CHARMM general force field (CGenFF) program after geometry optimization at the M06/6-31G* level.1 molecules was then soaked into TIP3P water box. After minimizing the box for 10 heating from 0 to 295 K for 10 ps, we equilibrated the structure in NPT ensemble for 1 ns using the Nose-Hoover Langevin piston pressure control.The atomic coordinates of tRNA of baker's yeast were taken from the Protein Data Bank (PDB)  for theoretical CD. The M06/6-31G* level of theory was employed with conductor polarizable continuum model (CPCM).The single point TD-DFT computations were performed for a structure with probe 1 was intercalated in between each stacking base due to its smallest size among the probes we synthesized. To deal with the systems more realistically, some water molecules were added into the first solvation shell around bases along with the CPCM. We optimized the ground and 1st excited states and measured the energies and oscillator strengths. For CD calculations, we used M06/6-31G* level of theory for (TD-)DFT computations since it gives a similar UV-visible absorption spectrum (maximum oscillator strength of 0.91 at wavelength of 215.5 nm) of the probe 1 to the experiment (maximum intensity at wavelength of 224.3 nm).51For the in-depth study of the fluorescence mechanism, we performed detailed (TD-)DFT computations further. We acquired some representative base–base stacking fragments including ribose-phosphate backbone from the above mentioned PDB file; we could obtain A–A, A–C, A–G, A–U, C–G, C–U, G–G, G–U and U–U stacking pairs. Then, probe In order to reproduce the fluorescence results, practically it is too time-consuming to compute all the pairs explicitly using the method explained above. Instead, we made approximations; the wavelengths and oscillator strengths of the same stacking nucleobases are the same. Also, one more important point is the Doppler broadening, which brings out a finite width to the spectral lines.Supplementary informationClick here for additional data file.


Plant–viroid interactions represent a valuable model for delineating structure–function relationships of noncoding RNAs. For various functional studies, it is desirable to minimize sample variations by using DNA, RNA, and proteins co-purified from the same samples. Currently, most of the co-purification protocols rely on TRI Reagent (Trizol as a common representative) and require protein precipitation and dissolving steps, which render difficulties in experimental handling and high-throughput analyses. Here, we established a simple and robust method to minimize the precipitation steps and yield ready-to-use RNA and protein in solutions. This method can be applied to samples in small quantities, such as protoplasts. Given the ease and the robustness of this new method, it will have broad applications in virology and other disciplines in molecular biology. The genn levels ,3,4. Comn levels ,6,7 and/n levels ,9. TheseIt is notable that many genes exhibit rapid and sensitive responses to the surrounding environment, which makes collecting experimental materials from the same sample a necessity to ensure the maximum accuracy in subsequent analyses. Simultaneously extracting DNA, RNA, and proteins is possible through a guanidine isothiocyanate-based protocol , and sevHere, we established a simple and robust method to co-purify DNA, RNA, and proteins from the same sample. This method does not employ TRI Reagent, has minimum precipitation steps, and generates ready-to-use RNA and protein solutions with high quality. Our analysis showed that both nuclear and cytosolic proteins with a wide-range of molecular weights can be effectively purified. Of note, this method is compatible with samples of small quantity.Arabidopsis genetic resources to facilitate studies on viroid biogenesis, using the protoplast replication system. Viroids are circular non-coding RNAs that mainly infect crop and ornamental plants [A. thaliana [Arabidopsis genetic resources for viroid research. Nevertheless, multiple viroids can replicate in Arabidopsis transgenic lines expressing their cDNAs, indicating the presence of conserved machinery for viroid biogenesis [Arabidopsis protoplasts and successfully applied our method to co-purify DNA, RNA, and proteins. This experimental platform will significantly enhance our capacity to probe the host machinery and the functional mechanisms for PSTVd nuclear import/export and propagation. Moreover, our new method should have broad applications for various research in virology and other disciplines in molecular biology.We attempted to employ the powerful l plants ,18,19. Pogenesis ,21. HereNicotiana benthamiana plants were grown in a growth chamber at 25 °C and with a 16/8 h light/dark cycle. A. thaliana plants were grown in a growth chamber at 23 °C and with a 16/8 h light/dark cycle. Using 4-week old Arabidopsis plants, protoplasts were isolated following a published protocol [2, 0.1% BSA, 5 mM β-mercaptoethanol, pH 5.7) to digest leaves with the lower epidermis layer removed by tapes. After 1 h digestion, the protoplasts were pelleted using 1 min centrifugation at 150× g. The pelleted protoplasts were then sequentially incubated in the W5 buffer  on ice and MMg solution  at room temperature. About 105 cells in 200 µL MMg buffer were supplemented with 40 μg 35S::GFP plasmid and 5 µg RZ-Int PSTVd RNA [2O, 2 mL 1 M mannitol, 1 mL 1M CaCl2) for 5 min incubation. Finally, protoplasts were washed with W5 solution and then incubated in WI solution  for 2 days before RNA purification. protocol . Brieflyg centrifugation for 2 min and then directly subjected to RNA purification using the MagJET RNA kit.Leaf samples were collected in 1.5 mL microcentrifuge tubes and ground in liquid nitrogen. The GeneJET plant RNA purification kit and the MagJET RNA Kit  were used for RNA purification as instructed in the manuals. Briefly, when using the GeneJET plant RNA purification kit, tissue lysate was loaded into the column for centrifugation. The flow-through (FL) was collected for subsequent protein purification. The column was washed twice using washing buffer from the kit and followed by DNase I treatment as instructed. Nuclease-free water was used to elute the total RNA from the column after the DNase I treatment. When using the MagJET kit, 40 µL of MagJET magnetic beads and 400 µL of ethanol (96–100%) were added to 400 µL tissue lysate free of tissue debris, and the mixture was kept rotating for 5 min at room temperature. The mixture was then placed in the magnetic rack for 1 min. The unbound supernatant fractions (Su) were collected for protein purification. The magnetic beads were then subjected to DNase I treatment and three washes. Finally, 100 µL of nuclease-free water was added to beads to elute total RNA. The flow-through solution collected after RNA binding to the GeneJET column and the supernatant solution collected after RNA binding to the MagJET beads were kept on ice for protein purification. The pellet of tissue debris from both purification kits were saved for genomic DNA extraction. For RNA purification from protoplasts, the cells were pelleted through 1000× w/v) polyacrylamide/8 M urea gels for 1 h at 200 V, transferred to Hybond-XL nylon membranes  using a semi-dry transfer unit , and immobilized by UV cross-linking. The membrane was blocked using Denhardt solution  followed by overnight hybridization with digoxigenin (DIG)-labeled riboprobe at 65 °C. DIG-labeled PSTVd-specific probe was generated using HindIII-linearized pInt(−) plasmid [For RNA analysis, the total RNA extracted from the above methods was subjected to reverse transcription (RT) using Superscript III reverse transcriptase (Thermo Fisher Scientific). We followed the manufacturer’s manual for first strand synthesis. The cDNA products were subjected to PCR using primers specific for GFP mRNA (16C f: 5′-ctcccacaacgtatacatcatggc-3′ and 16C r: 5′-ccatgccatgtgtaatcccagcag-3′). For RNA gel blots, we followed the protocol described previously in Reference . Briefly plasmid  and a T7g for 2 min. The flow-through was discarded. Columns were washed twice using the wash buffer and incubated with the elution buffer at 60 °C for 5 min, followed by centrifugation at 2000× g for 2 min to collect protein solutions. For RIPA buffer (radioimmunoprecipitation assay buffer) purification, leaves were ground to powders in liquid nitrogen and the powders (100 µL in volume) were directly mixed with 100 µL 1× RIPA (Millipore Sigma). The mixtures were incubated at 4 °C for 10 min followed by 14,000× g centrifugation at 4 °C for 3 min. The supernatants were used for analyses.The FL fraction from the GeneJET kit and the Su fraction from the MagJET kit were subjected to protein purification using the Pierce SDS-PAGE Sample Prep Kit (Thermo Fisher Scientific). Briefly, the liquid fractions supplemented with DMSO were directly loaded into a spin column. The samples were subjected to centrifugation at 2000× TM kit (VWR), following instructions in the user manual.The purified proteins were subjected to SDS-PAGE separation and transferred to nitrocellulose membranes using a semidry transfer unit (Bio-Rad). After 10 min incubation with Rapidblock solution (VWR), the following specific primary antibodies were applied for overnight incubation at 4 °C: anti-GFP  1:2000, anti-Histone H3 (Genscript) 1:2000 dilution, anti-beta tubulin (Genscript) 1:2000 dilution, the polyclonal antibodies (MilliporeSigma) against Mitogen-activated protein kinase 3 (MAPK3) 1:1000, the monoclonal antibody  against the largest subunit of RNA polymerase II (NRPB1) 1:500, and the polyclonal antibodies  against ribosomal protein L5 (RPL5) 1:1000. After three washes with 1× TBST , Horseradish peroxidase (HRP)-conjugated secondary antibody against rabbit (Millipore Sigma) was added at 1:3000 dilution for detecting Histone H3, RPL5, and MAPK3, while HRP-conjugated secondary antibody against mouse (Millipore Sigma) was added at 1:8000 dilution for GFP, beta tubulin, and NRPB1. After 1× TBST wash for three times and then incubation with HRP substrates (Li-COR Biosciences), the signals were captured with C-DiGit (Li-COR Biosciences). For silver staining, the gels after electrophoresis were treated with the Silver Bullitg for 10 min, and the supernatant was then transferred to a new centrifuge tube. Then 500 µL 100% ethanol was added to the supernatant and mixed at room temperature for 3 min. Genomic DNA was precipitated at 5000× g centrifugation for 5 min. The DNA pellet was washed twice with 1 mL ice cold 75% ethanol and air dried for 5 min. The pellet was dissolved using 20 µL TE buffer . To clone the transcription factor IIIA (TFIIIA) fragment in Arabidopsis, two primers (AtTFIIIA genome p f: 5′-ggagacctcctgagaagctccagc-3′ and AtTFIIIA genome p r: 5′-gtccttatcacggttgtcattactatg-3′) were used. The PCR product was confirmed using agarose gel electrophoresis.The pellet of cell debris left from RNA purification was subjected to 1 mL DNAzol-ES following instructions from the vendor . The mixture was subjected to centrifugation at 10,000× NcoI and HindIII (New England Biolabs). The digested DNA was subjected to 1% agarose gel separation. After gel running, the agarose gel was subjected to depurination using 0.125 M HCl for 30 min, denatured (0.5 M NaOH and 1.5 M NaCl) for 30 min, and neutralized (0.5 M Tris pH7.5 and 1.5 M NaCl) for 30 min. The treated gel was subjected to transferring to the Hybond-XL membrane  using a vacuum transfer system  and 20× SSC buffer . The membrane was subjected to UV cross-linking, blocking, probe incubation, washing, and signal development using the DIG northern starter kit (MilliporeSigma). The GFP fragment cloned from genomic PCR (16C f: 5′-ctcccacaacgtatacatcatggc-3′ and 16C r: 5′-ccatgccatgtgtaatcccagcag-3′) was ligated into the pCR4 vector (Thermo Fisher Scientific) to generate the pCR4-GFP/probe. The pCR4-GFP/probe plasmid was digested using NotI (New England Biolabs) and served as a template for generating the DIG-labeled probe using the T3 Maxiscript kit (Thermo Fisher Scientific).About 20 µg of genomic DNA purified using our method or directly via DNAzol-ES was subjected to overnight digestion with About 120 ng genomic DNA purified using our method was subjected to bisulfite treatment using the EZ DNA Methylation-Gold kit . The treated DNA was used as templates for PCR amplification using a modified pair of primers for GFP (Bisulfite 16C f: 5′- atgcYtgagggataYgtgcaggagagga-3′ and Bisulfite 16C r: 5′- ggacagggccatcRccaattggagtattttR-3′). The amplified product was ligated into the pCR4 vector (Thermo Fisher Scientific) and subjected to Sanger sequencing. The experiment was repeated twice.N. benthamiana 16C plants with constitutive GFP expression as the test materials. A recent study showed that there is only a single copy insertion of GFP cDNA using the whole genome sequencing approach [We chose transgenic approach . The totBecause proteins normally pass through the column or remain in the supernatant after RNA binding to the magnetic beads, we reason that it is possible to recover proteins from the flow-through fraction (FL) after RNA binding to the GeneJET column or the unbound supernatant fraction (Su) after RNA binding to the MagJET beads. Interestingly, the SDS-PAGE Sample Prep Kit employs a DMSO-denaturing-based principle to enrich proteins and remove undesired chemicals from protein solutions. Therefore, we decided to test whether a combinational use of both RNA and protein purification kits can purify RNA and proteins from the same sample. We added DMSO to the FL and Su fractions and applied the mixtures to the SDS-PAGE Sample Prep Kit to enrich total proteins. After applying the enriched proteins to SDS-PAGE gel electrophoresis and silver staining, we observed effective recovery of total proteins from both FL and Su fractions. To evaluate the purification efficiency of our method, we chose the commonly used RIPA buffer purification for comparison. When comparing the recovering efficiency with proteins directly purified from leaf samples using RIPA buffer, recovery rates from FL and Su were lower A,B. NeveSince the Su fraction contains a high concentration of ethanol that can denature proteins, we tested whether proteins can be effectively recovered using the SDS-PAGE Sample Prep Kit without supplementing DMSO. As shown in Some studies require analysis of genomic DNA. Therefore, it is desirable to purify genomic DNA from the same sample together with the purification of RNA and proteins. We reasoned that tissue debris, leftover from the RNA purification steps, contained a sufficient amount of genomic DNA that could be purified and analyzed. We applied DNAzol-ES to the debris for genomic DNA purification. For Su fractions and their control replicates, six 16C seedlings (~1 mg) were pooled as the initial material for one replicate sample. In this case, genomic DNA was successfully recovered from the debris (157.5 ng and 192.5 ng in two replicates), and the recovery rate is about 5- to 7-fold less as compared with the direct DNAzol-ES purification (850 ng and 1090 ng in two control replicates). For FL fractions and their control replicates, we used ~10 mg leaf powder as the initial material for one replicate sample. In this case, genomic DNA recovery from the debris is a little more efficient (2.18 µg and 2.20 µg in two replicates), as the recovery rate is about 2.5-fold less than the direct DNAzol-ES purification (4.98 µg and 5.30 µg in control replicates). Although the DNA recovery is less in our new method as compared with direct DNA purification, it still provides a reasonable amount of genomic DNA for subsequent analyses. It is notable that if only genomic DNA and RNA are desired for analyses, a combinational use of RNAzol and DNAzol-ES as instructed in manuals will maximize the recovery of genomic DNA and RNA.DNA purified by DNAzol-ES is suitable for multiple downstream analysis, such as cloning, PCR, and southern blots. We can successfully clone the GFP fragment via PCR . Here, wWith the successful recovery of genomic DNA from the same sample, we established an effective method to co-purify genomic DNA, RNA, and proteins from the same sample. As illustrated in 5 protoplast cells were harvested two days post-transfection and were subjected to nucleic acids and protein purification using our method. As shown in We are interested in testing whether this method is efficient for small quantity samples, such as protoplasts. Here, we used RZ-Int PSTVd RNA as the inoculum, together with GFP reporter plasmids (35S::GFP), to co-transfect A. thaliana protoplasts. PSTVd replicates in A. thaliana ,23 but cMany genes exhibit rapid expression dynamics in response to diverse environmental stimuli, so using distinct components from the same sample will overtly enhance the accuracy of functional analyses. In this regard, methods to co-purify various biological components from the same sample are necessary. Currently, most of the available protocols to co-purify DNA, RNA, and proteins from the same sample are largely based on TRI Reagent and involve multiple precipitation steps . These pThis method is simple and straightforward and generates ready-to-use materials for downstream immunoblotting analysis, RNA gel electrophoresis/blots, regular cloning, and bisulfite sequencing. RNA was purified in the first step, which ensures the yield of high-quality RNA with minimum degradation while waiting. Of note, the DNA and RNA purification procedures are based on commonly used commercial kits, which have been routinely used to prepare RNA and DNA samples for RNA-Seq, degradomal RNA-Seq, and genomic sequencing. In this regard, our method is probably compatible for various deep sequencing technologies.Arabidopsis protoplast assays for functional studies.One advantage of our method is that it is compatible with small quantities of starting materials, such as protoplasts. Protoplast assays have been widely used in plant biology to rapidly and efficiently test: (1) The cellular localization of proteins and RNAs ,31; (2) Arabidopsis protoplast replication system and the co-purification method can potentially advance viroid research by opening the door to the powerful Arabidopsis genetic resources [Arabidopsis protoplast assay in combination with our co-purification method, viroid research can now take advantage of the potent genetic resource for future explorations. Our method presented here is also applicable to various other research in virology and molecular biology.Here, we would also like to point out that the combination of the esources . Viroidsesources ,19. As iesources ,39,40, tesources ,41, and esources ,43. Receesources ,54,55,56


It contains the most complete and comprehensive sets of actively transcribed enhancers and promoters in the human and mouse genomes. We determined the transcription activities of these regulatory elements by CAGE  for both steady and dynamic cellular states in all major and some rare cell types, consecutive stages of differentiation and responses to stimuli. We have expanded the resource by employing different assays, such as RNA-seq, short RNA-seq and a paired-end protocol for CAGE (CAGEscan), to provide new angles to study the transcriptome. That yielded additional atlases of long noncoding RNAs, miRNAs and their promoters. We have also expanded the CAGE analysis to cover rat, dog, chicken, and macaque species for a limited number of cell types. The CAGE data obtained from human and mouse were reprocessed to make them available on the latest genome assemblies. Here, we report the recent updates of both data and interfaces in the FANTOM web resource.The FANTOM web resource ( Elements of both of these classes are transcribed  is an international collaborative effort that aims to study genomes from the perspective of the transcriptome . Its fifom mouse , then thom mouse ,13.Macaca mulatta), chicken , dog (Canis lupus familiaris), and rat (Rattus norvegicus) and CAGEscan  and assessed the compatibility of the resulting transcriptome assembly with CAGE-defined transcription start sites (TSSs). Based on the matched CAGE and RNA-seq datasets, we formulated a custom metric, the transcription initiation evidence score (TIEScore), to quantitatively integrate CAGE-defined TSSs and transcriptome assemblies . The raw reads and alignments are available from the FANTOM5 data files repository (http://fantom.gsc.riken.jp/5/datafiles/phase2.5/).The human genome is pervasively transcribed and produces a large number of long noncoding RNAs (lncRNAs) ,16. Howesemblies . We thensemblies , miTranssemblies , and GENsemblies ), to buisemblies . All gensemblies  with ther Figure  allows ihttp://fantom.gsc.riken.jp/5/suppl/De_Rie_et_al_2017/ .In addition to the synthesis of RNA during transcription, gene expression also depends on post-transcriptional processes such as RNA stabilization, translation efficiency, and degradation. Micro RNAs (miRNAs) are a major class of small noncoding RNAs involved in post-transcriptional regulation by repressing target genes via RNA degradation or inhibition of translation. As part of FANTOM5, we created an atlas of miRNA expression by sequencing short RNA libraries produced from a subset of the human and mouse RNA samples that were used for the promoter atlas . The atl/ Figure . A clickn = 56) from the promoter atlas (http://fantom.gsc.riken.jp/5/datafiles/phase2.5/).While the application of CAGE to ∼1800 human samples enabled us to identify nearly 200 000 promoter regions, only half of them were associated with existing gene models  and charer atlas . Alignedhttp://fantom.gsc.riken.jp/5/datafiles/phase2.5/).To complement the promoter atlases created for human and mouse, we obtained CAGE profiles from rhesus macaque, chicken, dog and rat. The macaque samples cover 15 brain regions that match the brain regions samples profiled in human . The chide novo to remedy the absence of converted peaks in the latest genomes due to differences between the two assemblies. The annotations of the peaks, such as names and gene associations, were also updated based on the most recent gene models . ChIP-Atlas integration with transcription initiation activities will facilitate the study of transcriptional regulation. The collected data files with the configuration file (hub.txt) are accessible at http://fantom.gsc.riken.jp/5prim/external/CellLines-CAGE-with-epigenome/.We have updated the configuration of trackHub  to refleWith this latest update of the FANTOM web resource, we focused mainly on diversifying the types of assays rather than increasing the number of samples for the same assay because CAGE technology allows us to identify the 5′-ends of capped RNAs but not the internal structure of the RNAs. We sought to overcome this limitation by integrating data sets targeting different portions of the RNA, and thus to understand the transcriptome from a more comprehensive perspective. We expanded our resource to encompass lncRNAs and miRNAs, whose atlases further our understanding cellular regulatory mechanisms; however, the functional roles of these molecules—in particular lncRNAs—largely remain to be elucidated. The sixth iteration of FANTOM is thus aimed at specifically assessing lncRNA functions in multiple cell types .We have also expanded the number of samples by covering four additional species—rat, dog, chicken and macaque — for a limited amount of cell types. As the comparison of multiple genome sequences enables us to study the evolutionary processes of genetic inheritance, cross-species transcriptome comparisons make it possible in turn to assess the functional consequences of evolution at the molecular level. The expanded data set presented here, and future data releases, will further facilitate these cross-species studies.


Salmonella Typhimurium.Recent methodological advances allowed the identification of an increasing number of RNA-binding proteins (RBPs) and their RNA-binding sites. Most of those methods rely, however, on capturing proteins associated to polyadenylated RNAs which neglects RBPs bound to non-adenylate RNA classes  as well as the vast majority of species that lack poly-A tails in their mRNAs . We have developed the Phenol Toluol extraction (PTex) protocol that does not rely on a specific RNA sequence or motif for isolation of cross-linked ribonucleoproteins (RNPs), but rather purifies them based entirely on their physicochemical properties. PTex captures RBPs that bind to RNA as short as 30 nt, RNPs directly from animal tissue and can be used to simplify complex workflows such as PAR-CLIP. Finally, we provide a global RNA-bound proteome of human HEK293 cells and the bacterium  Salmonella Typhimurium.RNA binding proteins are important regulators of RNA function. Here the authors describe a method for isolation of RNA-protein complexes that does not rely on a specific RNA sequence or motif, and demonstrate the approach by providing the global RNA-bound proteomes of human HEK293 cells and  A powerful tool to study ribonucleoproteins (RNPs) is UV cross-linking: irradiation of cells with short wavelength UV light results in covalent cross-links of proteins in direct contact with the RNA 6. Exploiting the stability of cross-linked RNPs, new methods have been developed to identify and analyse RNPs: (i) RNA interactome capture in which poly-A RNA and its bound proteins are first selected by oligo-dT beads and co-purified proteins subsequently identified by mass spectrometry (MS). This led to the discovery of hundreds of hitherto unknown RBPs8. (ii) Cross-linking and immunoprecipitation (CLIP) and similar methods in which, after UV cross-linking, individual RBPs are immunoprecipitated, and co-precipitated transcripts are identified by RNA-Seq, yielding high resolution data on the RNA binding sites of the RBPs of interest11.RNA-binding proteins are key factors in the post transcriptional regulation of gene expression. Spurred by recent technological advances such as RNA interactome capture (RIC)As RNA interactome capture relies on the purification of cross-linked RNPs based on hybridisation of oligo-dT beads to oligo-A sequences typically found in eukaryotic messenger RNAs, RBPs that exclusively associate with non-adenylate RNA species such as e.g. rRNA, tRNAs, snRNAs, histone mRNAs, or numerous lncRNAs cannot be identified.12 and CARIC13) has been introduced in which labelled RNA along with UV cross-linked interacting proteins was purified in a poly-A-independent fashion. However, the method requires efficient in vivo labelling of RNA, limiting its application to suitable (cell culture) systems. Consequentially, no RNA-bound proteomes of prokaryotes have been determined by biochemical means to date.The same limitations apply to mRNA from bacteria and archaea that lack poly-A tails in general. Recently, RNA interactome using click chemistry  or only RNA (acidic pH) accumulate in the aqueous phase  share a similar chemical structure but toluol is less water soluble due to the lack of the OH group Fig. , we modi2. After irradiation with UV light at 254 nm to induce covalent RNA-protein cross-links, cells were subjected to the PTex procedure, a series of three consecutive organic extractions:Step 1: Phenol:Toluol , pH 7.0Step 2: Phenol, pH 4.8, (chaotropic) detergentsStep 3: Phenol, EtOH, water, pH 4.8The poly-A RNA interactome of human HEK293 cells has been mapped in great depth, providing an ideal reference to establish PTex (Phenol Toluol extraction)-based purification of cross-linked RNPs14. Now, free RNA accumulates in the upper aqueous phase, free proteins in the lower organic phase and clRNPs migrate to the interphase4 , a well established 35 kDa RBP16 , after UV cross-linking. PTex efficiently removes ∼99% of the free spike-in RBP  are accumulating in the upper aqueous phase while DNA and membranes are predominantly found in the interphase  RNA. We used this sample as input for PTex  for which efficient uptake and incorporation of nucleotide analogues can be challenging. Brain tissue can be efficiently UV cross-linked, however, as demonstrated by HITS-CLIP experiments20.To test if PTex can be directly applied to tissues, we UV cross-linked whole mouse brain samples and performed PTex to extract clRNPs directly Fig. . Since b16. After in vivo labelling of cellular RNA using 4-thiouridine (4SU) and UV irradiation at 365 nm, we performed (i) classical PAR-CLIP analysis (PAR-CLIP-classic)11 of HuR, (ii) a PAR-CLIP variant using on-bead ligation of adaptors (PAR-CLIP-on-beads)22, and (iii) a version in which we use phenol extraction (termed pCLIP) for removal of unbound RNA instead of PAGE/membrane excision  has been shown to interact with mRNA and pre-mRNA in several CLIP studies and has a well-documented binding motif (5′- UUUUUU −3′)ion Fig. . We founion Fig. . All thrion Fig. . The cluion Fig. . Althoug3 targets only polyadenylated RNAs suggests that many RBPs that bind non-adenylate RNAs are missed by this experimental approach;13 and (ii) although UV cross-linking has been widely used to research RNA–protein interactions, no systematic study has been performed to determine the optimal irradiation conditions for efficient cross-linking of individual RNPs. Using PTex as an unbiased approach, we set out to explore RNA-protein interactions cell-wide in HEK293 cells. To test the effect of different energy UV-irradiation, we employed besides the most commonly used 0.15 J/cm2 irradiation at 254 nm wavelength also irradiation at two additional energy levels: 0.015 and 1.5 J/cm2; spanning two order of magnitude of UV irradiation dosage. This setup was then used to comprehensively map RNA-protein interactions beyond the established poly-A RNA-bound proteomes8. We independently irradiated HEK293 cells at all 3 energies and performed PTex purification of cross-linked and non-cross-linked RNPs from whole cells  the fact that RNA interactome capturells Fig. . Transcrlls Fig. , and prolls Fig.  were per24. In line with this, we find a similar distribution of RNA classes when comparing RNA from inputs and PTex by RNA-Seq with the vast majority of transcripts being ribosomal RNA . Using these stringent tests, we identify 3188 shared among the three conditions; out of these, 3037 proteins are significantly enriched in a UV-irradiation-dependent fashion in all samples using a cut-off of FDR 0.01  LFQ intensities (from the PTex experiments) were calculated Fig. . P-value.01 Fig.  demonstrion Fig. , indicat3. We therefore expected a gradual increase in enrichment of RNA-interacting proteins after PTex with higher UV dose. However, we only find increased recovery for most proteins when comparing low and medium energies. Surprisingly, irradiation with 1.5 J/cm2 on the other hand leads to a significant decrease in recovery . If this degradation was due to damage of nucleic acids induced by high energy UV light or a result of secondary processes during the extended time of treatment is unclear; in any case extensive shortening of RNAs will cause a loss of RNPs purified by PTex.In principle, extended UV exposure should increase the chance for cross-linking events and thus for subsequent protein recovery by PTex29 and8) and PTex-purified protein patterns differ from the whole proteome in silvers stains  and found in RBPs1. Other enriched domains are: AAA  fold, tetratrico peptide repeat region (TPR) as found in the yeast Clf1p splicing factor33, Ski complex34 and the translation terminator Nro135, and the CH domain which is found among actin-binding proteins3.To test sensitivity and specificity of our approach, we first performed global GO enrichment analysis showing that terms from all aspects of RNA biology are the most enriched among PTex-purified proteins Fig. . At the 36p-value: 1.36 × 10–30), NSUN2 and tRNA synthetases  indicating that our approach indeed captured cellular RNPs in a poly-A-independent fashion. We recover 70% of poly-A RNA-binding proteins found in HEK293 cells by ref. 2 . Importantly, the largest overlap was with RBPs recently found in HeLa cells using the also unbiased RICK technique12  and the CARIC approach (84% overlap)13 13 Fig. . Recent lar Fig. . To rule8 a RNA class which is highly heterogeneous in its sequence but represents only ∼5% of the cellular RNA pool. The differences in between interactome capture (poly-A RNPs) and PTex  is best demonstrated in the case of the eukaryotic RNA exosome41. The core exosome complex consists of 10 protein subunits (Exo-10) from which only one protein (Rrp44) is catalytically acting on RNA as exo- and endoribonuclease  transcription sites48. Hence, none of the proteasome-related RNA activities are approachable via poly-A RNA-mediated purification. The proteasome is a multi-protein complex with structural similarities to the exosome41 and RNAs interacting with Psma5/6 are likely to be UV-cross-linked to other subunits as well.Yet we also enrich for proteins which have no established role in RNA biology such as subunits of the human proteasome , we used the slightly modified PTex protocol (Hot-PTex) in which RNP extraction from Salmonella grown to OD600 3.0 was performed at 65 °C thereby supporting cell lysis 51. One hundred and thirteen of the enriched proteins are so far unknown to interact with RNA. To validate our findings, we picked YihI, a putative GTPase-activating protein which was speculated to play a role in ribosome biogenesis52, the alkyl hydroperoxide reductase c22 protein (AhpC)53 and the cell invasion protein SipA54. Using mutant strains carrying FLAG-tags fused to the C-terminus of PTex candidate RNA-interactors, we performed UV cross-linking, immunoprecipitation and radioactive labelling of co-purified RNA (known as PNK assay as described in ref. 55), validating that YihI, SipA and AhpC are indeed associated to RNA in vivo  such as the nucleic acid binding OB-fold  and domains which were also detected in RBPs when screening eukaryotic cells: the afforementioned AAA ATPase fold in the ATP-dependent protease ATPase subunits HslU, ClpB, and ClpX, or thioredoxin domains as in AhpC, Thiol:disulphide interchange protein (DsbA) and Bacterioferritin comigratory protein (Bcp)3. As in many other species, we find glycolytic enzymes to associate to RNA 57. Using GO terms for functional annotation of the RNA-associated proteins, the most significant terms are “translation” and other terms connected to the ribosome as expected . It is tempting to speculate if the here identified proteins are involved in such a process or are mere bystander proteins.We next used the Hot-PTex fraction of  MS Fig. . Compari MS Fig. , among tivo Fig. , 26. In ted Fig. . To the Salmonella compared to HEK293 cells and we anticipate that additional modifications in UV cross-linking and/or cell lysis could improve sensitivity when applying (Hot-)PTex in bacteria. PTex is the first approach that can purify bacterial RNPs in an unbiased fashion without the necessity of immunoprecipitation or introduction of modifications , rendering it a tool for cell-wide RBP identification and studying bacterial RNA–protein interactions.Overall, we noticed that enrichments even from known RBPs were lower in 4 and yeast59. Importantly: this reflects both cross-linking efficiency and fraction of protein bound to RNA; in other words, how much of the protein is in a steady state associated with the RNA? What is of experimental interest is therefore only a minor fraction of the RBP (the one cross-linked to RNA), while the vast excess of protein stays in a non-cross-linked state.UV cross-linking of RNPs appears rather inefficient. Even after high UV doses only ∼1–10% of any given RBP can be covalently coupled to ribonucleic acids in human cell culture14, and by selecting proteins which were enriched in a UV-dependent fashion. PTex is a fast and simple modular protocol which can be performed in about 3 h. Our approach is independent of the UV wavelength applied for irradiation (254/365 nm) and the type of biomaterial used , and does not rely on presence of a particular RNA sequence such as poly-A tails. Cross-linked proteins of 30–50% could be recovered by PTex compared to the starting material  and subsequent UV irradiation. Using pulse/chase experiments, these methods thus allow to determine newly transcribed RNA species. For PTex, UV irradiation at 254 nm wavelength is sufficient (no labelling required) and we anticipate that this is an advantage when analysing biological material in which uptake of nucleotide analogues is either insufficient or too cost-intensive. It did not escape our attention however that PTex requires fewer input material compared to the other methods  supplemented with 10% bovine serum , penicillin/streptomycin  at 37 °C with 5% CO2. After reaching 80% confluence, cells in monolayer were washed once with cold phosphate buffer saline  and placed on ice. Then, DPBS was removed completely and cells were irradiated with 0.015–1.5 J/cm2 UV light (λ = 254 nm) in a CL-1000 ultraviolet cross-linker device (Ultra-Violet Products Ltd), collected in 15 mL tubes, pelleted by centrifugation , aliquoted in 2 mL tubes and stored at −20/−80 °C (CL). Non-irradiated cells were used as non-cross-link control (-CL). In addition, the potential UV damage on the RNA after exposure to the different radiation energies was assessed: RNA isolated from HEK293 cells before and after exposure UV light by phenol extraction14 were analysed with the RNA 6000 Chip Kit in a Bioanalyzer 2100 .Human embryonic kidney cells  were grown on 148 cmSalmonella Typhimurium SL 1344 Hfq::x3FLAG  was grown on LB medium to stationary phase (OD600 = 3). Aliquots of 20 ml were pelleted  and resuspended in 1/10 of water for UV irradiation. Cells were cross-linked on ice with 5 J/cm2 UV light (λ = 254 nm) in a CL-1000 ultraviolet cross-linker device (Ultra-Violet Products Ltd), snap-frozen and stored at −80 °C. Bacterial suspensions equivalent to 2.5 ml of initial culture were used as input in Fig. Salmonella enterica subsp. enterica Serovar Typhimurium strain SL1344 used for the mapping of RNA-protein interactions was grown in LB medium to OD600 2.0. Half of the cultures were cross-linked in a Vari-X-linker , using UV light (λ = 254 nm) lamps for 90 s. Fractions of 10 ml from each, cross-linked and non-cross-linked cultures, were harvested by filtration64.65. The system is based on two plasmids: pKD46, a temperature-sensitive plasmid that carries gamma, beta and exo genes (the bacteriophage λ red genes) under the control of an Arabinose-inducible promoter, and pSUB11, carrying the x3FLAG::KmR cassette. The cassette in pSUB11 was PCR-amplified with primers (forward: 5′-GAA GCA GGA AGA TAT GAT GCG CCT GCT AAG AGG CGG CAA CGA CTA CAA AGA CCA TGA CG-3′ and reverse: 5′-GGG TTA TAA GCA GGA CGG GCA AGC CCA CGG TGT AAA CCC GCA TAT GAA TAT CCT CCT TAG-3′), the 5′ ends of which were designed to target the 3′ end of the gene of interest, digested with DpnI at 37 °C for 1 h and, upon purification, used for subsequent electroporation. Similarly, AhpC::6xHis-TEV-3xFLAG::TetR and SipA::6xHis-TEV-3xFLAG::TetR constructs were produced by amplification of the plasmid pJet1.2-Hfq-HTF-TetR66 with the primers AhpC-forward (5′-AAA GAA GGC GAA GCG ACT CTG GCT CCA TCC TTA GAC CTG GTC GGT AAA ATC CGC TCT GC TGG ATC CAT GGA G-3′) and AhpC-reverse (5′-GTG AGC AGG CGA CGC CAA CGC AGC TAT GGC GTG AAA GAC GAC GGA AAT TTA CGC GTG AGG GGA TCT TGA AG-3′) or SipA-forward (5′-CCT GGC GTG GAT CGG GTT ATT ACT ACC GTT GAT GGC TTG CAC ATG CAG CGT CGC TCT GCT GGA TCC ATG GAG-3′) and SipA-reverse (5′-TTT GAC TCT TGC TTC AAT ATC CAT ATT CAT CGC ATC TTT CCC GGT TAA TTA CGC GTG AGG GGA TCT TGA AG-3′). Prior to electroporation, PCR products were digested with Exo1 and DpnI during 1 h at 37 °C followed by ethanol precipitation and verification on Agarose gels.Yihi::x3FLAG::KmR was constructed following the procedure based on the Lambda Red system developed by ref. Salmonella Typhimurium SL1344 harbouring the plasmid pKD46 was grown in LB containing Ampicillin (100 μg/ml) and l-Arabinose (100 mM) at 28–30 °C to an OD600 of 0.8. Cells were incubated on ice for 15 min, centrifuged at 3220×g for 5 min at 4 °C and resuspended in ice-cold water. The wash was repeated three times. On the final wash, cells were resuspended in 300 μl water and electroporated with 200 ng of PCR product. Cells were recovered for 1 h in LB at 37 °C on a tabletop thermomixer at 600 rpm, plated on LB agar with Kanamycin (50 μg/ml) or Tetracycline (100 μg/ml) overnight. The following day, 10 colonies per strain were picked, resuspended in PBS and streaked on plates containing Ampicillin or Kanamycin (or Tetracycline) and incubated at 37–40 °C. Colonies that showed resistance to Kanamycin (or Tetracycline) but not to Ampicillin were selected for further analysis, and the correct expression of the epitope tag was verified by western blot.6 cells, ±CL) were mixed with 200 μl of each: neutral phenol , toluol  and 1,3-bromochloropropane (BCP)  for 1 min  and centrifuged 20,000×g 3 min, 4 °C. The upper aqueous phase (Aq1) was carefully removed and transferred to a new 2 ml tube containing 300 μl of solution D ; 31.1 mM sodium citrate ; 25.6 mM N-lauryosyl-sarcosine ; 1% 2-mercaptoethanol (Sigma)). Then, 600 μl neutral phenol and 200 μl BCP were added, mixed and centrifuged as before. After phase separation, the upper 3/4 of Aq2 and the lower 3/4 of Org2 were removed. The resulting interphase (Int2) was kept in the same tube and mixed with 400 μl water, 200 μl ethanol p.a., 400 μl neutral phenol and 200 μl BCP  and centrifuged as previously. Aq3 and Org3 were carefully removed, while Int3 was precipitated with 9 volumes of ethanol . Samples were centrifuged during 30 min at 20,000×g, pellets dried under the hood for max. 10 min and solubilised with 30 μL Laemmli buffer at 95 °C for 5 min; or the indicated buffer/temperature according to the downstream application. For global mapping of RNA-protein interactions in HEK293 cells, 3 replicates from each UV irradiation energy were used. A detailed step-by-step protocol for PTex is available in the HEK293 suspensions in 600 μl of DPBS (5–8 × 102 UV light (λ = 254 nm) in a CL-1000 ultraviolet cross-linker device (Ultra-Violet Products Ltd). Samples (±CL) were resuspended in DPBS on ice. 600 μl aliquots (32.5 mg) were mixed during 5 min at 65 °C in the presence of 0.5 g of low-binding zirconium beads  and the phenol-toluol-BCP mix described above. After centrifuging  Aq1 was carefully removed. Consecutive extractions were done as described above with the solely difference that mixing steps were performed at 65 °C .Mice brain samples were a kind gift from M. Preußner . All animal experiments with C57BL/6 mice were performed in accordance with institutional and governmental recommendations and laws . Mouse-brain tissue (260 mg) was disrupted by cryogenic grinding. Immediately after, 130 mg were irradiated with 0.75 J/cmSalmonella cells from two biological replicates were used. Bacteria attached to filters obtained as described in the section Bacterial cell culture and in vivo cross-linking were collected with 12 ml of DPBS and aliquots of 4 ml were pelleted at 20,000×g, 2 min, 4 °C. A modification of the step 1 was introduced in HOT-PTex in order to improve removal of free proteins, as follows: bacterial pellets (±CL) resuspended in 400 μl of DPBS supplemented with EDTA (5 mM) were mixed with phenol-toluol-BCP (200 μl each), and 0.5 g of zirconium beads during 5 min at 65 °C . After centrifugation at 20,000×g, 3 min, 4 °C, the upper aqueous phase (Aq1) was mixed again with the same volumes of phenol-toluol-BCP (without beads) during 1 min at 65 °C. Then the aqueous phase was carefully transferred to a third tube where steps 2 and 3 of the PTex protocol were performed at 65 °C.For global mapping of RNA–protein interactions in 32P-ATP, 200 ng pUC19 LacZ-containing fragment  or 2–3 × 106 HEK293 cells spiked-in with 0.25 μg of Sxl-RBD4 per tube. PCRs were performed using primers designed to amplify endogenous chromosomal DNA, I l-3 gene , or the LacZ gene present in the pUC19 linear fragment . DNA and RNA samples were electrophoresed in Agarose 1.0–1.5% or TBE-Urea PAGE 12%, respectively. Radioactivity was detected by phosphoimaging, while Sxl-RBD4, endogenous HuR or ACTB in HEK293/Sxl-RBD4 spiked-in proteins were analysed by western blot with specific antibodies as described in the western Blotting section.PTex extractions were carried as described before, using sets of three tubes containing synthetic 30–50 nt RNAs 5′´-labelled d-maltoside]) at 56 °C during 20 min, followed by incubation at 75 °C during 20 min. Samples were mixed with RNaseA (2 ng) and incubated at 37 °C; aliquots of 20 μL were taken at different time points: 0, 1, 5, 10, 30 and 60 min, immediately mixed with 5 μL of 6x Laemmli buffer, heated at 95 °C for 5 min and used for SDS-PAGE.Cross-linked and non-cross-linked HEK293 cell suspensions were subjected to PTex, the resulting pellets were solubilised in 150 μl of buffer TED  were treated with 2000 U/ml benzonase  in the recommended buffer  during 1 h at 37 °C and 1000 rpm . Untreated cells (±CL) were used as controls. PTex extraction was then performed as described above. After ethanol precipitation, pellets were directly solubilised in 40 μl Laemmli buffer (2×). SDS-PAGE and western blots were performed as indicated below.For PTex extractions shown in Fig. 67 by hybridising two complementary sequences containing the T7 RNA polymerase promoter 5′-TAA TAC GAC TCA CTA TAG-3′ and the template sequence 5′-GGT CAT AGG TGT AAA AAA ACT CTC CAT TCC TAT AGT GAG TCG TAT TAA-3′, followed by T7 run-off transcription. T7 RNA polymerase and restriction enzymes were purchased from New England Biolabs. Plasmids were purified using the NucleoBond Xtra Midi kit , DNA fragments by NucleoSpin Gel and PCR clean-up  and RNA by acidic phenol extraction14. RNA 5′-GAG UUU UUU UAC A-3′ (13 nt) was synthesised by Biomers .The T7 promoter and a sxl-target DNA sequence 5′-GAT CCG GTC ATA GGT GTA AAA AAA GTC TCC ATT CCT ATA GTG AGT CGT ATT AA-3′ was cloned into pUC19 using the restriction enzymes BamHI and HindIII. The resulting plasmid was named pUC19-sxl-target. Templates for RNAs of 87 and 191 nt length were generated using DNA restriction fragments from the pUC19-sxl-target plasmid . The 30 nt RNA was synthesised as described beforeSxl-RBD4 (40 μg) in 100 μl cross-linking buffer  were mixed with in vitro-transcribed RNA  harbouring one copy of the target motif 5′-GAG UUU UUU UAC A-3′, incubated at 4 °C for 30 min and cross-linked with 0.25 J/cm2 of UV-254 nm, on ice. Afterwards, 98% of each sample was used for PTex extraction, while 2% of the sample were kept as input control for SDS-PAGE and western blotting to detect Sxl-RBD4.1 to serve as a only-clRNPs starting material. RIC samples from five biological replicates were used for PTex extraction. After ethanol precipitation, PTex samples were washed once with 5 ml of cold ethanol to remove traces of phenol which could interfere with the quantification, and resuspended in 20–50 μl of water. The absorbance of RIC and PTex samples were measured at λ280 nm and λ260/280 nm in a Nanodrop 2000. In addition, 2 and 45% of RIC and PTex samples, respectively, were digested with RNaseA (0.1 μg/μl) at 37 °C during 40 min. Intact and digested samples were loaded in Bis-Tris-MOPS gels 4–12% , transferred to nitrocellulose membranes and blotted to detect HuR (see below). A selection of western blot images generated during this study  samples from +CL HEK293Western blotting was performed following standard techniques. Samples were electrophoresed on SDS-PAGE gradient gels 4–20%  or Bis-Tris 4–12%  and proteins transferred onto nitrocellulose membranes 0.2 μm (BioRad). Membranes were blocked during 30 min with PBST-M , 5% milk) and incubated with 0.1–1.0 μg/ml of the respective antibody overnight at 4 °C (or 2 h room temperature). Primary antibodies targeted the proteins HuR , ABCF2 , CCT7 , FUS , GAPDH , alpha-enolase , PTBP1 , PABPC1 , ACTB , Histone H3 , FLAG-tag , or Sxl-RBD4 . Monoclonal mouse anti-Sxl antibodies (M18 and M114) were developed by P. Schedl and obtained from the Developmental Studies Hybridoma Bank . Antibody binding was detected using anti-mouseHRP , anti-mouse-AlexaFluor680  anti-rabbitHRP , or anti-rabbit-AlexaFluor488  and Clarity ECL Western Blotting Substrate for chemiluminescence in a ChemiDocMP imaging system (BioRad).49 Cell pellets were resuspended in 800 µl NP-T buffer  together with 1 ml glass beads (0.1 mm). Cells were lysed by shaking at 30 Hz for 15 min at 4 °C and centrifuged for 15 min at 16,000×g and 4 °C. Cell lysates were transferred to new tubes and centrifuged for 15 min at 16,000×g and 4 °C. The cleared lysates were mixed with one volume of NP-T buffer with 8 M urea, incubated for 5 min at 65 °C while at 900 rpm and diluted 1:10 in ice-cold NP-T buffer. Anti-FLAG magnetic beads (Sigma) were washed three times in NP-T buffer , added to the lysate, and the mixture was rotated for 1 h at 4 °C. Beads were collected by centrifugation at 800×g, resuspended in 1 ml NP-T buffer, transferred to new tubes, and washed twice with high-salt buffer  and twice with NP-T buffer. Beads were resuspended in 100-µl NP-T buffer containing 1 mM MgCl2 and 2.5 U benzonase nuclease (Sigma) and incubated for 10 min at 37 °C while shaking at 800 rpm, followed by 2 min incubation on ice.Immunoprecipitation of bacterial FLAG-tagged proteins and radioactive labelling of RNA by PNK was performed as described:2), the beads were resuspended in 100 µl CIP buffer with 10 units of calf intestinal alkaline phosphatase (NEB) and incubated for 30 min at 37 °C while shaking at 800 rpm. This was followed by one wash with high-salt buffer and two washes with PNK buffer . Next, beads were resuspended in 100 µl PNK buffer and 1 U T4 PNK  and 5.5 µCi γ 32P-ATP were added and incubated at 37 °C for 30 min. The beads were then washed twice in PNK buffer and resuspended in 50 µl of a 2× denaturing gel loading buffer (Invitrogen). 30 microliter samples were then analysed on a NuPAGE Bis-Tris gel (Invitrogen) and radioactive signal was detected in a Life Science FLA-5100 imaging system (Fujifilm).After one wash with high-salt buffer and two washes with CIP buffer , cells were lysed in a buffer containing 20 mM Tris-HCl pH 7.5, 1 M NaCl, 0.2 mM EDTA, 1 mM DTT, cOmplete Protease Inhibitor Cocktail [Roche] followed by centrifugation for 20 min at 12,000×g. The cleared lysate was then subjected to GSH-affinity chromatography using an ÄKTA FPLC system. Bound protein was eluted in a buffer containing 100 mM HEPES/KOH pH 8.0, 50 mM glutathione, 50 mM KCl and 1 mM DTT. Fractions containing the protein were supplemented with 3 C protease and dialysed overnight against IEX buffer  followed by ion exchange chromatography using a MonoS column. Fractions containing the pure protein were pooled, dialysed against storage buffer  and stored at 80 °C.Recombinant Sxl-RBD4 protein (Sxl amino acids 122–301) was purified essentially as described beforeg at 4 °C. Protein pellets were resuspended in 2 M urea in 50 mM ammonium bicarbonate (ABC) buffer and Benzonase was added for 30 min at 37 °C to digest RNA. Proteins were precipitated with methanol-chloroform extraction69 and resuspended in 8 M urea and 0.1 M Tris pH 8 solution. Proteins were reduced with 10 mM DTT at room temperature for 30 min and alkylated with 55 mM iodoacetamide at room temperature for 30 min in the dark. Proteins were first digested by lysyl endopeptidase (LysC) (Wako) at a LysC-to-protein ratio of 1:50 (w/w) at room temperature for 3 h, diluted to 2 M final concentration of urea with 50 mM ammonium bicarbonate, and further digested with trypsin (Promega) at a trypsin-to-protein ratio of 1:50 (w/w) under constant agitation at room temperature for 16 h. Peptides were desalted with C18 Stage Tips70 prior to LC-MS/MS analysis. Peptides were separated on a 2 m long monolithic column , or on an in-house packed C18 15 cm microcolumns . Peptides were eluted from columns with an increasing acetonitrile concentration gradient of 5–50% at a flow rate of 200 nl/min for 6 h (long column) or 300 nL/min for 2 h (short column). Q Exactive Plus instrument (Thermo Fisher Scientific) was operated in the data dependent mode with a full scan in the Orbitrap followed by top 10 MS/MS scans using higher-energy collision dissociation (HCD). Full scans were performed with a resolution of 70,000, a target value of 3 × 106 ions and a maximum injection time of 20 ms. The MS/MS scans were performed with a 17,500 resolution, a 1 × 106 target value and a 60 ms maximum injection time. Isolation window was set to 2 and normalised collision energy was 26. Ions with an unassigned charge state and singly charged ions were rejected.Human and bacterial cells were cultivated as described above and pellets used in PTex protocol. A minor fraction of initial input material was lyzed and proteins denatured in 1% SDS and 0.1 M DTT Phosphate Buffer Solution (PBS) by boiling for 10 min at 95 °C. After cooling, samples were treated with Benzonase for 30 min at 37 °C, spun down to remove cell debris and supernatant containing cellular proteins transferred to fresh tubes. Remaining input material was used for RBPs enrichment with PTex as described above. After PTex, RBPs were precipitated in 90% ethanol solution at −20 °C and 30 min centrifugation at 20,000×71 using the label-free quantification (LFQ) algorithm72 with default parameters and match between runs option on. Search parameters included minimum peptide length of 7, two missed cleavage sites, protease specificity: trypsin , cysteine carbamidomethyl fixed modification and variable modifications including methionine oxidation and protein N-terminal acetylation. The mass tolerance for precursor ions was 6 ppm and the mass tolerance for fragment ions was 20 ppm. Database search was performed against the human reference proteome  or the Salmonella Typhimurium reference proteome  with common serum and enzyme contaminants sequences included. A minimum peptide count required for protein quantification was set to two. False discovery rate (FDR) was set to 1% at peptide spectrum match (PSM) and protein levels.All raw files were analysed with MaxQuant software (v1.5.1.2)59P-values were calculated from an Ebayes moderated t-test using the limma package73 followed by Benjamini–Hochberg False Discovery Rate (FDR) correction. Only proteins with an adjusted p-value of 0.01 or smaller in all 3 cross-linking intensities were considered being enriched. GO analysis was performed using PANTHER V.1174. Domain enrichment was done using DAVID75 searching the SMART76 database.The complete analysis is available as a supplementary R notebook . Domain and GO terms were analysed using DAVID75.The complete analysis is available as supplementary R notebook (Supplementary Data 2) were proteinase K digested  and the RNA recovered by acidic phenol extraction14 using phase lock gel tubes . Libraries were created according to the TruSeq Stranded Total RNA LT protocol  with the modification that we skipped the rRNA depletion step. We used adaptors AR002,4,5-7,12,13,16,18,19. DNA concentration was determined by Qubit 3.0 Fluorometer (Life Technologies) and the quality of libraries assessed by a Bioanalyzer 2100, DNA 1000 Chip Kit . Sequencing was performed on a Illumina HiSeq4000. Note that we sequenced each condition in triplicates with the exception of input 0.015 J/cm2 for which we have duplicates.Cross-linked PTex samples and non-cross-linked control . We then mapped obtained reads against a single copies of human rRNA and tRNA sequences using bowtie2 (v2.2.6) with the following command line:bowtie2 -no-unal -un $LIBRARY_rRNA_not_aligned_reads.fastq -al $LIBRARY_rRNA_aligned_reads.fastq -x rRNA_db -U, $LIBRARY.fastq > $LIBRARY_rRNA.sam 2», $LIBRARY_rRNA_alignment_stats.txtWe then used the remaining reads and mapped reads to the human genome (GRCh38.p12) and the corresponding comprehensive gene annotation file (gencode.v28.chr_patch_hapl_scaff.annotation.gtf) from GENCODE using the STAR aligner (v020201) with the following command line:STAR -genomeDir PATH/TO/index -readFilesIn $LIBRARY_rRNA_not_aligned_reads.fastq -quantMode GeneCounts25, chromosomal locations of substitutions and deletions were extracted and counted. Only mutations that were unique to the UV-treated samples were considered. To normalise the data for sequencing depth, for each dataset the counts for substitutions and deletions were divided by the total number of mapped nucleotides, which provided an indication of mutation frequencies. To map the distribution of deletions and substitutions around AUG and Stop codons, CDS coordinates from the gencode.v28.chr_patch_hapl_scaff.annotation.gtf annotation files were extracted. Tables containing counts and chromosomal positions for each substitution and deletion were converted into gene transfer format (GTF) files using the GTF2 and NGSFormatWriters classes from the pyCRAC package. Subsequently, pyBinCollector from the pyCRAC package was used to map the distribution of substitutions and deletions around start and stop codons of protein-coding genes. The following command lines were used:After aligning the remaining reads with STAR, samtools was used to annotate each read in the bam files with mutation information. Using the SAM.py parser from the pyCRAC suitepyBinCollector.py -f mutations.gtf -gtf, annotationfile_CDS_coordinates.gtf -s 5end -a protein_coding -normalise -v -o dist_around_AUG.txtpyBinCollector.py -f mutations.gtf -gtf, annotationfile_CDS_coordinates.gtf -s 3end -a protein_coding-normalise-v-o dist_around_STOP.txtFor the feature counts, count tables generated by STAR were used in conjunction with the gencode.v28.chr_patch_hapl_scaff.annotation.gtf annotation file.11 HEK293 cells stably expressing FLAG-tagged HuR (ELAVL1), were grown until 90% confluence. The last 16 h of incubation, 200 mM 4SU was added. Living cells were irradiated with 0.15 J/cm2 365 nm UV light, snap-frozen on dry ice and stored at −80 °C until use. Cells were collected on different days, representing biological replicates. Cells (∼1.2 × 108 cells/replicate) were lysed on ice for 10 min with 3 ml lysis buffer ), 100 mM NaCl (Life Tech. AM9760G), 1% (v/v) Nonidet P40 substitute (Sigma 74385), 0.5% (v/v) Sodium deoxycholate (AppliChem No. A1531) containing 0.04 U/ml RNasin  and 2x Complete Protease Inhibitor  and centrifuged 20,000×g, 10 min, 4 °C. Cleared lysates (1.5 ml/replicate) were digested with 8 U/ml TURBO DNase  and 2 U/μl RNase I  at 37 °C for 4 min (replicate 1) or 3 min 15 sec (replicates 2 and 3). FLAG-tagged HuR was immunoprecipitated with 10 μg of anti-FLAG monoclonal antibody  bound to 40 μl of Protein G Dynabeads . After extensive washes with high-salt buffer  SDS, 1 M NaCl) beads were incubated with 1 U/μl of T4 polynucleotide kinase  and 0.5 μCi/μl of 32P-ATP. After radiolabelling, samples were split into 4 tubes and underwent three versions of CLIP .We performed the PAR-CLIP protocol as described:11.In classic PAR-CLIP, clHuR-RNA complexes were resolved by 4–12% Nu-PAGE MOPS (Invitrogen) transferred to nitrocellulose and excised at a defined size-range (50–60 kDa). Proteins were digested from the membrane with proteinase K and the RNA recovered by acidic phenol/chloroform extraction and ethanol precipitation. Recovered RNA was ligated with 3′ adaptor (5′-App-NNN NTG GAA TTC TCG GGT GCC AAG G-3′InvdT) gel-slice isolated, ligated with the 5′ adaptor (5′-GUU CAG AGU UCU ACA GUC CGA CGA UCN NNN-3′) and purified again from PAGE-Urea gels by elution and ethanol precipitation22. On-beads adaptors ligation was done by incubating the FLAG-clHuR-RNA beads with the 3′ adaptor in the presence of Rnl2(1–249)K227Q ligase and PEG-8000 overnight at 4 °C. After washes, the 5′ adaptor was ligated using the Rnl1 enzyme, 2 h at 37 °C as described in ref. 22. Followed by protein electrophoresis , blotting to nitrocellulose membranes and band excision at the defined size-range (50–60 kDa). Proteins were digested from the membrane with proteinase K. RNA was then recovered by acidic phenol/chloroform extraction and ethanol precipitation.As an alternative, we performed PAR-CLIP on-beads. Here, the ligation of the 3′/5′ adaptors can be achieved directly on the beads used in the affinity capture of the selected clRNPg, 4 °C. Ethanol-precipitated pellets were digested with proteinase K and RNA isolated by phenol/chloroform extraction.pCLIP beads capturing clHuR-RNA complexes were subjected to 3′/ 5′ adaptors ligation as above. Immediately after the 5′ adaptor ligation beads were magnetically captured and resuspended in 600 μl solution D. After a short denaturation (95 °C during 10 min), beads were separated with a magnet and the clHuR-RNA complexes recovered in the supernatant were subjected to the last two steps of the PTex protocol (20 min). PTex recovered interphases were precipitated with 9 volumes of ethanol on dry ice during 30 min, followed by 30 min centrifuging at 20,000×RNAs obtained from the three PAR-CLIP procedures  were reverse-transcribed into cDNA with the reverse transcription primer 5′-GCC TTG GCA CCC GAG AAT TCC A-3′ and the minimal PCR cycles were determined for each case. cDNA libraries were created by PCR using the forward primer 5′- AAT GAT ACG GCG ACC ACC GAG ATC TAC ACG TTC AGA GTT CTA CAG TCC GA-3′ and the Illumina adaptor index RPI 1–6, 8,10–11. Bands obtained at 150 bp were excised from 2% agarose gels and purified using the Zymo-clean Gel Recovery Kit . DNA concentration and library quality was determined by Qubit Fluorometer ds-DNA HS assay  and BioAnalizer DNA HS Kit . Libraries were sequenced in a NextSeq 500.https://github.com/ohlerlab/PARpipe) around the PAR-CLIP data tailored peak caller PARalyzer60, with one modification. In brief, adaptor sequences were trimmed retaining the four randomised adaptor nucleotides on both read ends included during adaptor ligation to serve as unique molecular identifiers during PCR-duplicate removal (read collapsing). Differences in the numbers of uniquely aligning reads were balanced by random subsampling prior to PARalyzer cluster identification. Annotation of identified binding sites (cluster) was simplified by grouping closely related sub-annotation categories  present in GENCODE v19. To select transcripts, we ran RSEM78 and retained transcripts with TPM>3. For each gene, we selected the transcript isoform with the highest isoform percentage or chose one randomly in case of ties (n = 8298). The list of selected transcript isoforms was used to calculate the median 5′UTR, CDS and 3′UTR length proportions  using R Bioconductor packages GenomicFeatures and GenomicRanges79. For regions post annotated transcription ends and splice sites we chose windows of fixed sizes . We generated coverage tracks from the PARalyzer output alignment files and intersected those with the filtered transcripts. Each annotation category was binned according to its relative length and the coverage averaged within each bin. For intronic coverage, we averaged across all introns per gene, given a minimal intron length of 500 nt. All bins were stitched to one continuous track per transcript (n = 6632 intron containing transcripts). Each library bam file was filtered to retain only PARalyzer cluster overlapping alignments. We required transcripts to have a minimal coverage maximum of >2. For each transcript we scaled the binned coverage dividing by its maximal coverage  to emphasise on spatial patterns independent from transcript expression levels. Next, we split transcript coverage in two parts, separating 5′UTR to TES regions and intronic regions. To generate the scaled meta coverage across all targeted transcripts per RBP, we used the heatMeta function from the Genomation package80. For the 5′UTR to TES part, we scaled each RBP meta-coverage track independent of other libraries. For intronic sequences, we scaled each sample relative to all other sample. Finally, we clustered the meta-coverage tracks using ward.D clustering with euclidean distance.For depicting spatial preferences for mRNA binding, we selected genes previously used for RNA classification based on processing and turnover dynamics81.PAR-CLIP alignments were visualised using GvizFurther information on experimental design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Reporting Summary


Uncertainties in Representative Concentration Pathway (RCP) scenarios and Antarctic Ice Sheet (AIS) melt propagate into uncertainties in projected mean sea-level (MSL) changes and extreme sea-level (ESL) events. Here we quantify the impact of RCP scenarios and AIS contributions on 21st-century ESL changes at tide-gauge sites across the globe using extreme-value statistics. We find that even under RCP2.6, almost half of the sites could be exposed annually to a present-day 100-year ESL event by 2050. Most tropical sites face large increases in ESL events earlier and for scenarios with smaller MSL changes than extratropical sites. Strong emission reductions lower the probability of large ESL changes but due to AIS uncertainties, cannot fully eliminate the probability that large increases in frequencies of ESL events will occur. Under RCP8.5 and rapid AIS mass loss, many tropical sites, including low-lying islands face a MSL rise by 2100 that exceeds the present-day 100-year event level. There are significant uncertainties of how large sea level changes due to Antarctic Ice Sheet melting could be. Here, the authors quantify the impact of different greenhouse gas emission scenarios and different Antarctic contributions to changes to extreme sea-level events and find that even under low emissions the occurrence of sea-level extremes could rise significantly due to Antarctic meltwater increase. Any uncertainty in future MSL changes therefore propagates into uncertain ESL projections, which is a major concern for coastal communities, which rely on ESL projections to plan adequate and cost-effective adaptation measures4. In particular, low-lying tropical islands, which often lie only a few meters above sea level, are identified as being sensitive to moderate changes in MSL6.Future mean sea-level (MSL) changes will impact the height and frequency of extreme sea-level (ESL) events—the combination of tides and storm surges which could cause floods9. While in the Intergovernmental Panel on Climate Change (IPCC)’s Fifth Assessment Report (AR5)7, the likely range for the AIS contribution under Representative Concentration Pathway (RCP) 8.5 was estimated to be between −6 to 12 cm for 2081–2100, relative to 1986–2005, recent ice sheet modeling and expert elicitation studies project AIS contributions on the order of multiple decimeters for this century, with high-end projections of AIS contributions exceeding 1 m in 210013. Given the large spread in these projections, and our limited understanding of the underlying physics, the AIS projections are still characterized by a large degree of uncertainty, which hinders the quantification of the full probability density function of its contribution to MSL9. This uncertainty in MSL changes that cannot be fully quantified will lead to an uncertainty in the projected changes in ESL events that is not fully quantifiable either. MSL changes do not translate one-to-one into changes in the height and frequencies of ESL events14, and therefore, the impact of the uncertain MSL changes will result in site-dependent ESL uncertainties.The major factors that cause uncertainty in future MSL changes are the emission scenario and the contribution from the Antarctic Ice Sheet (AIS)15. We estimate ESL changes around mid-century (2046–2065) and end-century (2081–2100) for three RCP scenarios  and the possible range of AIS contributions based on a recent expert elicitation13. Because of the wide range of uncertainty, we do not assign a prescribed probability distribution to the AIS projection, but independently consider multiple scenarios. Therefore, we do not compute a most likely scenario or a single confidence interval for future ESL changes that only depends on the emission scenario. This approach deviates from other recent studies on MSL and ESL changes who assume specific probability distributions for the AIS contribution16. With this approach, we extend previous assessments18 to fully account for the potential range of future ESL events at a global set of coastal sites. Recent developments in risk-assessment methods have highlighted the need to assess the full range of possible MSL and ESL scenarios in order to make robust decisions21. Given the strong possibility that multiple potential AIS distributions may be plausible for quite some time22, such an account provides critical information for risk assessment purposes that consider the full range of possible ESL changes.In this paper, we compute projections of future ESL changes over a range of emission scenarios and possible AIS contributions to determine the impact of this uncertainty on changes in the height and frequency of ESL events. With these projections, we determine how MSL changes for these emission scenarios and the AIS contribution translate into local ESL changes. To do so, we apply extreme-value statistics and regional sea-level projections to a global set of tide-gauge sitesReducing greenhouse gas emissions results in smaller changes in the occurrence and height of ESL events, but some sites, particularly in the tropics will see a large increase in the number of ESL events, even halfway the twenty-first century. Under high-end emission scenarios and AIS contributions, almost all sites see large changes in ESL changes, while for some tropical sites, mean sea level could reach or surpass the height of the present-day 100-year event at the end of the twenty-first century.7 for all components except for the AIS. For the AIS, we assume a possible range of the AIS contribution between 0 and 45 cm in 2046–2065, and a possible range between 0 and 150 cm in 2081–2100, based on the expert elicitation from the ref. 13. All projections are relative to 1986–2005. For clarity, we combine the RCP2.6 and RCP8.5 scenarios with the low-emission and high-emission projections from ref. 13. For each of these scenarios, we select the AIS contribution that corresponds to the upper and lower bound of the likely range , and round the contribution to the nearest 5 cm. All results are available for RCP4.5 and for the full range of AIS contributions from the Supplementary Information.We use the IPCC’s AR5 MSL projections13 is still small. Towards the end of the twenty-first century, for many regions, the spread between the MSL scenarios in Fig. 23) effects due to AIS mass loss become one of the dominant spatial features, which results in above-average MSL changes in the North Atlantic, North Pacific and Indian Oceans 29.Changes in regional MSL will modify the frequencies and heights of ESL events. Under the stationarity assumption , a MSL change results in a vertical shift of the return curve Fig. b. The sh29. The uncertainty in the present-day return curve further enhances this skewness. To account for this uncertainty, we present future return curves as the mean, or best estimate, of this skewed distribution. These mean return periods are shorter than the median return periods relating to the median MSL change. Both uncertainties therefore prescribe that both AF and Al are larger than expected from the median MSL change. For sites where the uncertainty in future MSL changes is large and where small MSL changes result in large amplification factors, this asymmetry causes a larger difference between the allowance and the expected MSL change. The allowance can be used as a guideline for required changes in the design height of coastal infrastructure under an uncertain change of mean sea level.Both future MSL changes and present-day return curves come with an uncertainty. A symmetric uncertainty in the MSL change  and the allowance of the 100-year event (Al100). The impact of MSL change on the allowances and amplification factors depends on a multitude of factors: next to the magnitude of the MSL change, the ESL characteristics and their uncertainties result in a station-specific change in the allowance and the amplification factor. Figure 100, while the same scenarios will induce a smaller AF100 for both Galveston and Saipan. This dependence also allows to qualitatively estimate the impact of MSL changes for places for which no tide-gauge observations are available, but knowledge about typical ESL behavior is available. For places where surges from tropical cyclones or other storms cause large and rare ESL events compared to more common events, MSL changes result in smaller AFs than for places with a flatter return curve.In the remainder of this paper, we will discuss the future changes of ESLs in terms of the AF of the present-day 100-year event , 50% of the sites have a AF100 of 100 or larger, which is not only caused by the above-average MSL changes in the tropics, but also because many of these stations have a relatively flat return curve. For the RCP8.5 scenario and no AIS contribution, these numbers increase modestly: 35% and 63% of the global and tropical sites, respectively will see a AF100 of 100 or larger. Adding a 10 cm AIS contribution, which corresponds to the upper end of the likely range for this scenario, results in a further increase in frequency amplification factors: under RCP2.6, almost half (44%) of all sites, and the majority (72%) of the tropical sites will face an AF100 of 100 or higher. Under the RCP8.5 scenario with a 10 cm AIS contribution, this is the case for more than half (52%) of all sites, and for more than three-quarters (77%) of the tropical sites. The number of stations with an expected AF100 of 100 or larger is higher for RCP2.6 with 10 cm AIS contribution than for RCP8.5 without any AIS contribution, which means that, akin to the MSL scenarios, the AIS uncertainty is on the same level as the spread between scenarios halfway through the century. While many tropical sites will face large AFs halfway through the century, even for the most optimistic scenario, the AFs for large parts of the European and North American coastlines remain relatively modest, even for higher emission scenarios and AIS contributions. However, it must be noted that AIS contributions of multiple decimeters beyond the likely range as depicted in Fig. Halfway through this century, under the scenario with the smallest MSL rise (RCP2.6 and no AIS contribution), 25% of the tide-gauge sites are projected to face a AF100 of 100 or higher rises to 46% globally (73% for the tropics), while for RCP8.5, most sites  will face a AF100 of 100 or more. These numbers indicate that, even without any AIS contribution, many sites will face large increases in the frequency of ESL events at the end of the century, while for the tropics, even under the no-AIS RCP2.6 scenario, 50% of the sites sees such large increases already halfway through this century. For the many extra-tropical sites, notably the coastlines of North America, Europe, and parts of Asia, the expected AF does depend on the emission scenario: in the most optimistic case, 34% of the sites faces an AF100 of 100 or higher, while in the RCP8.5 scenario, or the RCP2.6 scenario with an AIS contribution of 15 cm, more than half of the stations sees such an increase. Under the higher-end AIS contribution, also the vast majority of the extratropical sites faces an AF100 of 100 or more. Again, RCP2.6 combined with the high-end AIS contribution result in larger AFs than RCP8.5 with a low-end AIS contribution.Towards the end of the century the differences between the scenarios become larger: under the no-AIS RCP2.6 scenario, the number of sites with a AF29. Despite the different origin of the spatial patterns, akin to the AF case, the tropics generally see higher allowances than the extratropics, and the spread between the scenarios halfway through the century is limited compared to the end of the twenty-first century. At the end of the century, the allowances also depend strongly on the AIS contribution, as indicated by the Al100 for different end-century scenarios  and 76 cm (tropics). Adding an AIS contribution of 30 cm to the RCP8.5 scenario results in a median Al100 of 1.02  and 1.10 m (tropics), and for a 60 cm AIS contribution, the median Al100 becomes 1.35 and 1.46, with many sites, both in the tropics and along the Eastern US coast, requiring an allowance of 1.5 m or more. The spatial variations in the allowances are primarily caused by the the regional MSL patterns, since the differences between the Al100 and the expected MSL changes are on the order of a decimeter  has been shown to perform well in a global analysis2. Due to these choices, and because of the specific AIS contribution, the estimated future return curves and the resulting amplification factors and allowances could differ from previous assessments . Another assumption in the statistical method is that of stationarity, i.e., the assumption that the local ESL climatology does not change in time. Locally, observations have reported temporal changes in ESL climatology34. In addition, hydrodynamic models coupled to earth-system models suggest that future MSL changes may impact tides and surges in specific regions36, hence modifying local ESL climatology. Changing wind patterns and changes in the strength and probability of tropical cyclones could also occur in a warming climate, and affect local ESL characteristics37. However, both observations39 and model studies18 indicate that changes in MSL are the major contributor to past and future changes in ESLs on a global scale. Besides temporal changes in climatology, we also do not account for inter-annual variability in MSL, which modulates ESL events. As such, variability in present-day ESL events in different sites may be underestimated, which could lead to an overestimation of frequency amplification factors and allowances. In contrast, both frequency amplification factors and allowances may be underestimated since we do not explicitly include an uncertainty related to the AIS contribution, do not consider a full interdependency in the uncertainty of all contributors41, and do not account for the possibility of a heavy-tailed uncertainty for the Greenland Ice Sheet or other individual contributing processes, which could cause larger frequency amplification factors and allowances than reported here. For some locations, AFs in previous work  differ from values presented here due to differences in the Antarctic contribution, GPD parameters, and methods for detrending and declustering.The estimates of future changes in ESL events come with a number of caveats related to the usage of tide-gauge records and the statistical method. Since tide-gauge observations are typically low-pass filtered by stilling wells or other filtering methods, and subsequently sampled at lower frequencies, waves are removed from the observed water levels, and wave setup is poorly represented in the tide-gauge records, although it may contribute significantly to ESLs43, the tropics, which include many low-lying islands, are the first to see large amplification factors, and require above-average allowances. While the high amplification factors are mostly driven by the typical ESL climatology, the high allowances are to a large extent caused by the above-average projected MSL changes for this region. Since many of these islands only lie a few meters or less above present-day MSL, the required allowances under high-end AIS contributions and RCP scenarios are on the same order of magnitude as the height of these islands above present-day mean sea level. Towards the end of the century, for many sites with relatively small present-day return heights, including many tropical islands, the expected MSL change under high-end scenarios will exceed the present-day height with a return period of 100 years or less. This change implies that for these sites and scenarios, a sea level that is now considered an extreme event will become mean sea level.Due to the spatial variations in both the expected local MSL changes and in the local ESL characteristics, the expected amplifications and allowances are dependent on the specific site. While the amplification factors show a strong dependence on the local ESL characteristics, the allowances largely follow the spatial variations in the MSL projections. Similar to the early emergence of climate-driven temperature changesBoth future emissions and the fate of the AIS are poorly constrained, and as a result, allowances typically vary from a few decimeters to one-and-a-half meter by the end of the century within the likely range of AIS contributions, while changes that are substantially larger cannot be ruled out. A reduction of greenhouse gas emissions substantially lowers the required allowances, and limits the likely range of the AIS contribution, although the uncertain AIS contribution could still cause allowances on the order of 50–100 cm. However, a reduction will not only result in smaller allowances, but will also increase the available time for coastal areas to adapt to changing ESL return curves, although for some sites, especially many open-ocean islands, large increases in return frequencies will occur within a few decades, regardless of the Antarctic contribution to MSL and the emission scenario.7, except for the Antarctic contribution. The AR5 projections provide estimates of the processes related to steric expansion and ocean dynamics (ocean), the surface mass balance (SMB) and the dynamic ice-sheet response (Dyn) for the Greenland Ice Sheet (GrIS), the contribution of Glacial Isostatic Adjustment (GIA), and changes in terrestrial water storage (TWS). Since AR5, several studies addressed the dynamic contribution of the Antarctic Ice Sheet (AIS) resulting from marine ice sheet instability12 and its combination with hydro-fracturing of ice shelves and ice cliff instability10. These processes could lead to multiple meters of sea-level rise over the next few centuries. However, ice-sheet dynamics remain a source of debate, and therefore, we have chosen, instead of using a single probability distribution, to compute ESL changes for a range of possible AIS scenarios. We consider possible AIS contributions between 0–45 cm for 2046–2065, and 0–150 cm for 2081–2100, where the upper bound is chosen to match the 95th percentile from13. The figures in the main text show the mean and bounds of the likely range . The projections for all scenarios are available from the Supporting Information.The MSL scenarios depicted in Fig. 44, with rotational feedback included45. We assume that all Antarctic mass loss occurs at Thwaites Glacier basin. This basin currently shows the largest mass-loss rate46 and has the potential of ice-sheet instabilities48. Other nearby regions along the Amundsen Sea Sector are vulnerable as well, but since almost all tide-gauge sites considered in this study are in the far field, small changes in the geometry of the mass loss are unlikely to significantly change the results49. Supplementary Fig. The projections are expressed as changes in regional relative sea level, which are changes in sea surface height relative to the solid earth. The AR5 scenarios account for the regional MSL changes due to GIA and the GRD effects resulting from ice-level and TWS-related sea-level changes. To account for GRD effects for the AIS contribution, we computed the elastic sea-level fingerprint using the pseudo-spectral approachThe AR5 projections come with an estimate of the uncertainties. In the original AR5 projections, the probability distribution for some contributions, especially the Greenland Ice Sheet SMB (GrIS SMB), were considered skewed. We assumed a normal distribution, with standard deviations computed from averaging the upper-bound and lower-bound standard deviation as listed in the AR5 report. We chose this approach, because no full distribution is available. The downside of this approach is that the possible heavy-tailed distribution is not explicitly accounted for, which could result in an underestimation of the allowances. Uncertainties for the individual contributors are assumed to be independent and added in quadrature to obtain the total uncertainty. The exception are the ocean and Greenland SMB contributions which are considered fully correlated, since both are heavily dependent on temperature changes, and hence the sum of the standard deviations of those quantities was added in quadrature to the total uncertainty. Since we use a range of possible AIS scenarios, we do not attach an uncertainty to each AIS scenario. The final expression for the total uncertainty reads:15. Data provided on frequencies higher than hourly are averaged to hourly values. For each station, we use station-years with a data availability of 70%. We limit the extreme-value analysis to stations with at least 20 years of data which are at least 70% complete. When a single site has multiple records in the GESLA2 database, the longest record has been chosen, which results in a total number of 460 stations. From each station-year the annual mean is subtracted to remove the effects of long-term sea-level trends and variability. Since some stations have duplicate entries in the GESLA2 database, for stations that are within a 3 km radius, we select the station with the longest record.The tide-gauge data used in this study originate from the GESLA2 dataset24. Since an event can last for multiple hourly observations, the data has to be de-clustered. We use a declustering time of three days between consecutive events to enforce this requirement. From these observations of sea-level extremes, the return frequency N(i) of the ith-highest ESL can be estimated using the standard Weibull formula24:n the total number of observation-years. To compute return heights of return frequencies that surpass the tide-gauge record length, extreme-value statistics are used. An extreme-value distribution has been fitted to the de-clustered sea-level extremes. Following the recommendations of ref. 2, we use the GPD. The GPD uses all extreme-value information in the record and the shape parameter allows for improved tail modeling, compared to the annual-maximum approach that is often employed when the Gumbel or generalized extreme-value (GEV) distributions are used. For the GPD, the probability density function f(z) for height above MSL z isμ, scale parameter λ and shape parameter ξ. The location parameterμ corresponds to the height of the 99.7th percentile of the hourly observations. f(z) is only defined for z > μ, and we can only determine return levels for heights larger than the location parameter. After de-clustering the extreme events, this threshold coincides to the height of an event that occurs on average six times a year, and thus can be viewed as a commonly-occurring extreme event. This parameter is estimated as the height z that corresponds to the 99.7th percentile. The scale parameter λ and shape parameter ξ are estimated using the method of maximum likelihood. The scale parameter λ determines the slope of the return curve: a large λ corresponds to a large slope. The shape parameter ξ determines the curvature of the tail of the return curve. The return curves in Fig. We define an extreme sea-level (ESL) event as an event for which sea level reaches a height above the annual mean MSL that exceeds the 99.7th percentile of hourly sea-level observations. The choice of the specific percentile is arbitrary, but the chosen value shows stable results in regional and global studiesz0, given z0 > μ isE(z > z0∣z > μ) with ζ, the average number of events per year26:λ and ξ stay constant, but the location parameter μ is augmented with the sea-level change: μf = μh + ΔMSL with μf and μh the future and historical location parameter and ΔMSL the change in mean sea level. From Eq. (28. For the GPD parameters, we obtain a variance-covariance matrix for the uncertainties in the parameters λ and ξ using the observed inverse Fisher information matrix. We generate 104 random pairs of λ and ξ with mean and standard deviation based on the maximum likelihood fit and the accompanying variance–covariance matrix. We also generate 104 Monte-Carlo samples of local MSL change from the mean and standard deviation of each scenario. For each ensemble member, we compute the return curve Eq. (The associated exceedance probability of height Supplementary InformationPeer Review File