Our work revealed a long pause  after synthesis of a 6-mer RNA; such pauses can serve as regulatory checkpoints. Region sigma 3.2, which contains a loop blocking the RNA exit channel, was a major pausing determinant. We also obtained evidence for RNA backtracking during abortive initial transcription and for additional pausing prior to escape. We summarized our work in a model for initial transcription, in which pausing is controlled by a complex set of determinants that modulate the transition from a 6- to a 7-nt RNA.In bacteria, RNA polymerase (RNAP) initiates transcription by synthesizing short transcripts that are either released or extended to allow RNAP to escape from the promoter. The mechanism of initial transcription is unclear due to the presence of transient intermediates and molecular heterogeneity. Here, we studied initial transcription on a   •E. coli RNA polymerase pauses during initial transcription at lac promoters•Initiation pausing lasts for ∼20 s and occurs at the transition from 6- to 7-nt RNA•70 is the main protein element controlling pausingRegion 3.2 of σ•Pausing is likely to be controlled further by a complex set of determinants Initial transcription involves synthesis of short RNAs that are either released or extended to allow promoter escape; such mechanisms are unclear due to heterogeneity. Duchi et al. used single-molecule fluorescence to visualize transcription in real time and discovered a long pause after synthesis of a 6-mer RNA. This conformational change leads to the formation of the RNAP-promoter open complex, RPo , which to . In the o . In the ynthesis . The balynthesis .Despite this progress, which has been aided by structures of ITCs , our und70 region 3.2 (σ3.2) in initial transcription, since it is a major determinant of abortive initiation  structural snapshots of transcription complexes. An early smFRET work on immobilized complexes  was alsoitiation .Here, we use an optimized smFRET strategy to monitor de novo RNA synthesis in real time by monitoring DNA scrunching, which occurs concomitantly with each nucleotide incorporation in initial transcription A. Surprilac promoter (lacCONS), a promoter rate-limited in initial transcription (o was expected to be low: as RNAP synthesizes short RNAs (2- to 7-mer), the downstream DNA flanking the acceptor should rotate and approach the donor, leading to a FRET increase -coated surface and imaged them via total internal reflection fluorescence (TIRF) microscopy (o in the presence of dinucleotide ApA (RPITC2) formed the same stable FRET state as DNA alone . Addition of 80 μM UTP and GTP . After the initial increase (completed in ∼1 s), the FRET signal was stable, indicating that the E∗∼0.37 state is stable for >10 s.To observe initial transcription in real time, we provided immobilized RPr escape . Specifiat ∼1 s; C indeed at ∼1 s; C, top; to to a higher FRET state , which was occupied for >20 s. To test whether the increase was due to transcription, we performed controls wherein we added UTP and GTP to immobilized RPITC2 in the presence of rifampicin, an inhibitor that blocks synthesis of RNA of >3 nt in length , we superimposed their FRET traces on a “transcription heat map” D. The man length . Our resn length E, likelyITC≤4 and RPITC≤5 complexes, generated their heatmaps, and compared them to RPITC≤7 with regards to the magnitude of FRET increase and the stability of the highest FRET state  was reached in ∼2 s after NTP addition , the distance decrease for the transition from RPITC≤5 to RPITC≤7 was much smaller than expected , raising the possibility that the main abortive RNAs in RPITC≤7 were shorter than a 7-mer.To monitor scrunching in different ITCs, we followed FRET during the first few nucleotide additions: we formed RPET state A. For RPaddition A, bottomaddition B, bottomistances A; while ITC≤7, we performed in vitro transcription , an RNA one nucleotide shorter than expected for this complex. At short incubations , the 6-nt RNA was the main product and was slowly extended (t1/2 ∼20 s) to a 7-mer (5′-AAUUGUG-3′); the 7-mer became the main product in 60 s  showed complexes reaching the long-lived state of E∗∼0.37 . We also saw that in ∼15% of the traces (n = 39), an E∗∼0.45 state is reached, which we assigned to RPITC7, a complex containing a 7-mer RNA (the longest RNA synthesized with the nucleotide subset used); in ∼65% of these traces (n = 25), the E∗∼0.45 state was reached after a pause at E∗∼0.37 for several seconds  with a duration of 24 ± 2 s  by analyzing complexes retaining their FRET pair for >10 min  points to the presence of additional pausing determinants.To test our hypotheses, we studied complexes formed using a mutant RNAP lacking part of σ3.2 ; this is despite the fact that Δ3.2 complexes with E∗>0.3 are less stable and dissociate quickly, broadening the FRET distribution after NTP addition . Second, the vast majority of Δ3.2 complexes that did reach the E∗∼0.45 state (90% of 47 molecules) did so without an apparent pause at E∗∼0.37 . To avoid any potential interference with the re-annealing of the upstream region of the transcription bubble during escape, we used homoduplex DNA , something not observed in our traces. The remainder 50% showed no clear pause, but part of this population almost certainly includes pauses too short to capture given our temporal resolution (200 ms). Third, once the E∗∼0.6–0.8 FRET state was reached, the complexes remained at that state for ∼8 s  in the maximum FRET state corresponds to a state occupied just before the point where RNAP breaks its promoter interactions during escape; we refer to this pause as the “escape pause.”The long dwell at EITC≤7 (Most molecules reaching the maximum FRET state (65%) do not go through cycling involving synthesis of >4-nt RNAs E. NotablITC≤7 B.lac promoter is not a continuous process, but is interrupted by a long pause (“initiation pause”) after RNAP synthesizes a 6-nt RNA. The observation of high levels of a 6-nt RNA  in the reaction with all NTPs agrees with early observations on lacUV5 , the initiation pause can be rate-limiting for initial transcription. The pause is substantially longer than open-complex formation at NS ∼3 s; , and comlac, many other promoters are likely to display initiation pausing. For example, Tn5 promoter, also rate-limited in initial transcription, showed accumulation of a 6-nt RNA ; such promoters should exhibit less pausing, whereas promoters limited in initial transcription should exhibit significant initiation pausing. This promoter dependence also implies that although σ3.2 is a major pausing determinant, there are additional, DNA-sequence-dependent determinants that modulate the transition from 6- to 7-nt RNA; this is supported by the fact that σ3.2 removal did not eliminate 6-nt RNA accumulation on lacCONS  and blocks the RNA exit path by clashing with the 5′ end of nascent RNA ; σ3.2 haITC6; we suggest that the presence of σ3.2 along the path of growing RNA provides an initial time window (linked to σ3.2 repositioning) that allows RNAP to enter paused states, the stability of which is governed by a complex landscape of determinants, including DNA sequence. In short, σ3.2 is the RNAP structural element that enables initiation pausing  at the 6-to-7 transition.Our results show σ3.2 indeed acts as the protein element that sets the stage for pausing at RPITC6, with stabilization seen first when RNA reaches 5 nt in length. One possibility for the stabilization is that the 5′ end of RNA interacts with σ3.2, as suggested by ITC structures .Our results also suggest that σ3.2 stabilizes the scrunched conformation in RPructures ; since tructures ; consistITC7 revealed transitions consistent with scrunching relaxation by RNA backtracking, since the relaxed state matches the FRET signature of the paused state in RPITC6, which is likely to be in its pre-translocated state. In the backtracked RPITC7 state, RNAP is inactive, since its active site is blocked by the 3′ end of RNA; this state also leads to RNA loss. These series of transitions suggest that the backtracked state is an intermediate on the path to RNA release. RNA backtracking in initiation is supported by reports showing that transcript-cleavage factor GreA (which cleaves the 3′ end of RNA in backtracked elongation complexes to generate new extensible 3′ ends) alters the abortive products on T7A1 and T5N25anti , where the maximum scrunching is expected to be reached; this pause may reflect destabilization of contacts between σ region 4 (σ4) and the −35 element  or the lOur results showed that active complexes exhibit heterogeneity, since ITCs imaged under identical conditions displayed varying tendencies for abortive cycling A. The helac, many features should apply to most bacterial promoters.Based on our findings and existing literature, we present a working model for initial transcription that includes initiation pausing as a regulatory checkpoint controlled by structural, sequence, and environmental factors . While tITC5, most likely at its post-translocated state. This frees the i+1 site at the active center for binding the next complementary NTP, which is incorporated quickly to form a pre-translocated RPITC6  also identifies the secondary channel as the likely RNA release route.At tracking . The for70 bacterial promoters  were purified as described . NTPs and ApA were added at 80 and 500 μM, respectively. Reactions were supplemented with [α32P]UTP , incubated for 10–60 s at 21°C, stopped by 7.5 μL of 1 M HCl, and neutralized with Tris/EDTA  and incubated with 10 μL Ni2+ agarose beads; samples were centrifuged and 6 μL TB was discarded. One microliter lacUV5 DNA was added and incubated for 10 min at 37°C. Transcription was initiated by 1 μL of 5 mM ApA; 2 μL of 250 μM GTP, UTP , and 0.6 μCi [32P]-UTP per reaction; and incubation for 20 s at 37°C. Reactions were stopped by washing the complexes; the supernatant was discarded and Ni beads were supplemented with stop solution. Samples were incubated for 2 min at 65°C before being loaded on a PAGE denaturing gel.For transcription on beads, RNAP was assembled in 10 μL transcription buffer (TB)  and an oxygen scavenging system  were added.TIRF experiments with alternating-laser excitation  were perITC synthesizing RNAs up to N nt in length (RPITC≤N), NTP reaction mixtures were added manually during acquisition; unless stated otherwise, the final NTP concentration was 80 μM. For RPITC≤4, the NTP mixture consisted of imaging buffer plus UTP. For RPITC≤5, 3′ dGTP (TriLink BioTechnologies) was added to RPITC≤4 mixture. For RPITC≤7, GTP was added to RPITC≤4 mixture. For promoter escape, the imaging buffer was supplemented with ATP at 200 μM, and UTP, GTP, and CTP at 100 μM.To form RPtwoTone (∗) was calculated as described (Fluorescence intensities were extracted using twoTone , and theescribed . To seleescribed . The dweescribed  and fittFor extended protocols, see A.N.K. conceived and supervised the project. A.N.K., D.D., D.L.V.B., and K.B. designed experiments. D.D., L.F., N.R., and P.Z. performed microscopy measurements. L.C.H. and K.G. performed preliminary microscopy experiments. D.D., D.L.V.B., G.E., K.G., N.R., P.Z., and A.N.K. performed data analysis. D.L.V.B., N.R., A.T., and Z.M. performed biochemical assays. D.D., D.L.V.B., and A.N.K. wrote the manuscript.


O-methylations. This method also gives excellent results in the quantitative analysis of rRNA modifications in different species and under varying growth conditions. However, until now, RiboMethSeq was only employed for rRNA, and the whole sequencing and analysis pipeline was only adapted to this long and rather conserved RNA species. A deep understanding of RNA modification functions requires large and global analysis datasets for other important RNA species, namely for transfer RNAs (tRNAs), which are well known to contain a great variety of functionally-important modified residues. Here, we evaluated the application of the RiboMethSeq protocol for the analysis of tRNA 2′-O-methylation in Escherichia coli and in Saccharomyces cerevisiae. After a careful optimization of the bioinformatic pipeline, RiboMethSeq proved to be suitable for relative quantification of methylation rates for known modified positions in different tRNA species.Analysis of RNA modifications by traditional physico-chemical approaches is labor intensive, requires substantial amounts of input material and only allows site-by-site measurements. The recent development of qualitative and quantitative approaches based on next-generation sequencing (NGS) opens new perspectives for the analysis of various cellular RNA species. The Illumina sequencing-based RiboMethSeq protocol was initially developed and successfully applied for mapping of ribosomal RNA (rRNA) 2′- Among these various chemical reactions, the most frequent is the transfer of a methyl . This type of methylations may occur in all four canonical nucleotides in RNA and even many base-modified residues, the most frequent probably being 2′-O-methylated pseudouridine (ψm). One very common methylation in many RNA species is 2′-Escherichia coli demonstrated the presence of three possible positions for 2′-O-methylation , while two others are located in the anticodon loop at positions 32 and 34 (wobble) where Cm and Um/cmnm5Um residues are frequently present  or cmnm5Um34 (M. capricolum) at the wobble position of the anticodon. Early on, the analysis of tRNA species from the model bacterial organism hylation . A major present . E. coligmk-rpoZ-spoT-spoU-recG operon in E. coli [Thermus thermophilus [The formation of Gm18 in bacterial tRNA is catalyzed by specific tRNA:Gm18-methyltransferases (MTases) from the SpoU (now renamed to TrmH) family, which had already been predicted to be an RNA:MTase ,17,18, p E. coli  and was mophilus . Aquifex aeolicus has been crystallized and its structure studied at high resolution [Recognition of tRNA substrates by TrmH was extensively investigated and the enzymatic mechanism elucidated by a structure-based site-directed mutagenesis study . The Trmsolution . O-methylation in E. coli tRNA are located in the anticodon loop, at positions 32 and 34. The formation of Cm32 and Um32 tRNASer1 and tRNAGln2 is catalyzed by YfhQ (renamed now to TrmJ or TrMet(Xm32)) [Two other important sites of 2′-t(Xm32)) , which bt(Xm32)) .Leu(CmAA) and tRNALeu(cmnm5UmAA) [O-methylation depends on prior N6-isopentenyladenosine modification at position 37.Finally, the enzyme encoded by yibK, named TrmL, is a part of a more complex pathway  designednm5UmAA) . TrmL mePseudomonas aeruginosa PA14 was found to be involved in resistance to oxidative stress. TrmJ catalyzes the formation of Cm34, Um34 and, in contrast to E. coli, an additional Am34 in tRNAPro(GGG) [In other bacteria, enzymatic tRNA modification systems targeting the ribose-2′-OH are only partially characterized. Recently, a tRNA MTase TrmJ in Pro(GGG) . O-methylation is only partially characterized up to now. In contrast to Bacteria, where only proteinaceous enzymes modify tRNAs, archaeal organisms use both stand-alone protein enzymes and C/D-box small nucleolar RNA (C/D snoRNA) guided machinery  machinery. Methylation occurs sequentially with the C/D-box small RNA (sRNA) guide in trans, which would also eliminate the obligatory refolding of the pre-tRNA before splicing [Trp genes require a box C/D guide RNA within their intron for specifying methylation at these two sites [In contrast to Eubacteria, the methylation of the wobble residue C34 of recursor . 2′-O-mesplicing . Most euwo sites . S-adenosylmethionine (AdoMet)-dependent MTase aTrm56, which is found only in Archaea [Pyrobaculum aerophilum in which the tRNA Cm56 2′-O-methylation is provided by a C/D sRNP [The archaeal-specific modification Cm56 is formed by the SPOUT  Archaea ,33. aTrmC/D sRNP . O-methylations occurs at five rather conserved positions and two minor sites, the best conserved locations being Nm4, Gm18, Cm32/Nm34 and Um44 . Genetic depletion of YDL112w resulted in the complete absence of Gm18 in all tRNAs that naturally contain this modification. Other tRNA ribose methylations and the complex pattern of ribosomal RNA (rRNA) ribose methylations were not affected [The formation of the highly conserved Gm18 in affected .O-modifications both at positions 32 (Cm32) and 34 (Nm34) in yeast tRNAs require a unique MTase Trm7 (encoded by YBR061C) [O-methylation of C32 and with Rtt10 (named Trm734) for 2′-O-methylation of N34 in substrate tRNAs [1G at position 37, in the biosynthesis pathway leading to yW (wybutosine) [Anticodon 2′-YBR061C) , which ate tRNAs . Moreoveutosine) ,37. Actiutosine) .O-methylated Um44 is frequent among eukaryotic cytoplasmic tRNAs bearing a long variable loop. Yeast ORF YPL030w (now renamed to Trm44) was identified by screening for the Um44:MTase activity of yeast genomic library of affinity-purified proteins. Consistent with the conservation of Um44 in eukaryotic tRNAs, Trm44 is found in metazoans and fungi, but surprisingly absent in plants [2′-n plants .O-methylation site in yeast tRNAs was found at position 4, and it is the only 2′-O-methylation that occurs in the middle of a duplex region in tRNA. It was shown that S. cerevisiae ORF YOL125w (renamed to Trm13) is required and sufficient for this modification [The last 2′-fication . O-methylation profile in vertebrates is somehow similar to the one observed for lower eukaryotes, like yeasts, with conserved sites at position Cm/Um4, Gm18, Cm/Um/ψm32, Cm/Gm34 and Um44. One notable exception is the presence of Tm(m5Um)54 in the Tψ-loop; this nucleotide occasionally replaces the universally conserved T54 [The tRNA 2′-rved T54 . AnotherO-methylated residues, tRNAs from higher eukaryotes contain other more complex modified residues, where base modification is coupled with ribose methylation. For example, 2′-O-methyl-5-formyl modification of cytidine (f5Cm) was found at position 34 in bovine tRNALeu and confirmed for rabbit and lamb. Thus, f5Cm could be a general feature of cytoplasmic tRNAsLeu in mammals [In addition to common simple 2′- mammals . O-methylation enzymes have a long-standing history [Phe was detected using microinjection into Xenopus laevis oocytes [Studies of higher eukaryotic RNA 2′- history . Already oocytes . HoweverO-MTase characterized up to now is a human homolog of yeast Trm7. Mutations in human FTSJ1, the Trm7 homolog, are present in two genetically-independent cell lines of patients and cause non-syndromic X-linked intellectual disability (NSXLID). Cells with loss-of-function FTSJ1 mutations nearly completely lack Cm32, as well as Gm34; moreover, an NSXLID patient with a novel FTSJ1-p.A26P missense allele specifically lacked Gm34, while levels of Cm32 and o2yW37 remained normal [The only higher eukaryotic tRNA-specific 2′-d normal .O-MTases [A homology search using yeast tRNA:MTases as probes revealed the presence of predicted homologs for all four known yeast tRNA:2′-O-MTases   primarily due to the steric repulsion among the 2-carbonyl group, the 2′-O-methyl group and the 3′-phosphate group in the C2′-endo form [Ribose 2′-ndo form .4-acetyl-2′-O-methylcytidine (ac4Cm), 5-methyl-2′-O-methylcytidine (m5Cm) and N2-dimethyl-2′-O-methylguanosine (m22Gm) were found to stabilize the C3′-endo form and therefore cause “conformational rigidity” as attested by proton Nuclear Magnetic Resonance spectroscopy. In particular, the ac4Cm was found to be extremely rigid due to additive effects of the N4-acetylation and 2′-O-methylation [Archaea-specific hypermodified residues Nhylation .O-methylation has an important functional role in fine-tuning of bacterial co-habitations with human  organisms. It was noticed a long time ago that preparations of Salmonella typhimurium tRNA and rRNA provide RNA-dependent immunogenic activity [In addition to purely structural roles and general protection of RNA molecules from alkaline hydrolysis, tRNA 2′-activity . More reactivity ,49. Thisactivity ,48. E. coli tRNA was observed for human and murine innate immune receptors TLR7/8, whereas the mouse-specific receptor TLR13 was insensitive to tRNA Gm18 [6-methylation of rRNA (A2085 in Staphylococcus aureus/A2058 E. coli) [In addition, Gm18 also potently inhibited TLR7-independent human monocyte activation by RNA derived from a variety of bacterial strains. Antagonizing effects of Gm18-modified RNA are due to competition with stimulatory RNA for receptor binding. The effect of immunostimulation/suppression mediated by Gm18 in RNA Gm18 , but inhE. coli) .O-methylated residues, no rapid and high-throughput analytical technologies have been applied to tRNAs, mostly due to their small size, very stable secondary and tertiary structure and an important proportion of other modified RNA residues. Reverse-transcription (RT) at low desoxynucleotide triphosphates (dNTP) concentrations is not very suitable for tRNAs, since natural RT-pauses are frequent, and RT-arresting modified residues prevent the generation of long primer extension products [Despite the growing interest in the detection and quantification of 2′-products ,53. Highproducts ,55,56. Oproducts . 6A, m1A, pseudouridine and m5C [O-Me group [O-modification.Recently, high-throughput techniques have been developed for the analysis of a small subset of RNA modifications, namely m and m5C ,60,61,62Me group ,64,65. TO-methylation analysis of tRNAs. The main difficulties are related to the short length of most tRNA species, their stable 2D and 3D RNA structures, the existence of very similar, closely-related tRNA isoacceptors and the presence of numerous other modified nucleotides affecting both the efficiency of RNA cleavage, as well as primer extension by RT. The aim of the present study was to adapt the previously-published RiboMethSeq protocol  to 2′-O-E. coli and S. cerevisiae. The RiboMethSeq protocol [O-methylated 5′-adjacent riboses. However, spontaneous structure- or sequence-dependent resistance to alkaline hydrolysis may generate false-positive hits in the analysis. We first evaluated the results from the application of the standard RiboMethSeq protocol (previously adapted to rRNA) for comparatively short tRNA sequences from protocol  is basedE. coli tRNAs, while a somewhat longer treatment of 8–12 min was required for the S. cerevisiae tRNA fraction  are comparatively resistant to alkaline hydrolysis, and their fragmentation conditions have to be optimized to get sufficient amounts of short 20–40-nt fragments. Preliminary tests indicated that hydrolysis times of 6–10 min were appropriate for fraction . These tWith standard treatment parameters, the RiboMethSeq procedure uses reads of a minimum of 17 nt in length, which are aligned to the reference sequence with the end-to-end option. Only 5′-end extremities are used for analysis with calculation interval of six neighboring nucleotides. The application of these parameters for tRNA 2′-methylation analysis demonstrated, in principle, the detection of modified nucleotides, but also showed the necessity for a careful optimization of all treatment steps of the analysis pipeline. The necessity for optimization is related to short and quite similar tRNA genes  sequences used for alignment, to the very irregular alkaline cleavage profile of these small and strongly-structured RNA molecules and certainly to the presence of numerous other modified nucleotides with RT-arresting properties, decreasing even further the number of available sequencing reads at certain tDNA regions.Taking into account all of these difficulties, we proceeded with the optimization of all treatment steps , the mosInitial tDNA datasets for alignment were constructed from sequences of modified tRNA species deposited in tRNA database (tRNAdb) , convertIn order to identify closely-related tDNA sequences, the Levenshtein distance was calculated for the complete tDNA dataset for known modified tRNA species, obtained from tRNAdb. E. coli and S. cerevisiae tDNA datasets. Details of the adaptation of both tRNA datasets are given in Distances between tDNA sequences are given in For alignment to optimize the tDNA dataset, raw read trimming was performed to keep all sequences >8 nt. Of note, this length, while unsuitable for large genomic alignments, is still appropriate for a limited size tDNA “genome”. In order to gain additional information on both the 5′- and the 3′-end positions for every aligned read, only trimmed (with adapter removed) sequences were retained for subsequent alignment. This reduced the number of usable reads, but still provided some gain in information, since both 5′- and 3′-end locations were used.Bowtie2 alignment parameters were adjusted for short input sequences (L = 10) in end-to-end mode. In order to avoid contamination by ambiguously (multiply) mapped sequencing reads, only uniquely-aligned sequences were taken for further analysis. Details for the number of retained reads at every step are given in The positions of the 5′- and 3′-ends were cumulated together to obtain the global end-coverage profile, and these data were used to calculate RiboMethSeq scores (ScoreMean (derived from ScoreMax) and ScoresA, B and MethScore(C)) as described previously ,65. SincE. coli and one in S. cerevisiae) received sufficient coverage, and further analysis of 2′-O-methylation could be performed.As a result of this optimization, over 95% of trimmed reads were unambiguously mapped to tDNA reference sequences, and a better homogeneity of the coverage was obtained. All tRNAs  strain and for a strain with the deletion of the nonessential trmH gene responsible for Gm18 formation in bacterial tRNAs. E. coli tRNAs were reported to contain 2′-O-methylated residues at three different locations . As expected, RiboMethSeq efficiently detected alkali-resistant positions, which mostly correspond to 2′-O-methylations, to other modified RNA residues affecting cleavage or phosphorylation/ligation efficiency for the adapter or to naturally insensitive non-modified residues. The present goal was to provide a set of potentially modified positions to be verified by independent approaches and also to gain relative quantification of the methylation level for known 2′-O-methylated nucleotides.Analysis of O-methylated sites are shown in O-methylated sites gave rather high values of ScoreMean, while ScoreA [O-methylations (Gm18), but remained very stable for all 2′-O-methylated residues at positions 32 and 34, catalyzed by TrmJ and TrmL , tdbR00000276 (highlighted in grey see E. coli tRNAMet(ac4CAU). Following up on our previous studies on the immunostimulatory effect of a Gm18 modification in E. coli tRNA [oli tRNA , we testO-Me in yeast gave strong and consistent values of ScoreMean  0.077), as well as MethScore . All scores for Gm18 demonstrated a considerable decrease (average ScoreMean −0.037 and MethScore −0.1) in the ΔTrm3 yeast strain compared to the WT cells. In contrast, the calculated values of the scores for other known 2′-O-methylated positions  were not at all affected in the ΔTrm3 strain , but poor selectivity, while the opposite is true for ScoreA. Other proposed scoring schemes are even less discriminative and were not used here.The results obtained in this study demonstrate that RiboMethSeq can be successfully applied not only to rRNA sequences in different species, but also to tRNAs, including also relative quantification of 2′-The use of cell lines or strains devoid of selected RNA-modification enzymes provided considerable support in the distinction between truly ribose-methylated positions from false-positive hits, since only signals corresponding to real modified nucleotides are affected, while signals for false-positives and other unaffected modified nucleotides remain generally stable. For a more precise quantification of the RNA modification level, the calibration of RiboMethSeq signals with unmodified RNA species extracted from the deleted or knockout strain is strongly recommended.S. cerevisiae parental strain  and MTase mutant  deficient for Trm3 (Gm18-2′-O-MTase) were obtained from Euroscarf, Johann Wolfgang Goethe-University Frankfurt and grown at 30 °C in yeast extract-peptone-dextrose (YPD) medium  to mid-log phase.The E. coli parental strain 567, ΔlacZ4787(::rrnB-3), λ, rph-1, Δ(rhaD-rhaB)568, hsdR514) and trmH (Gm18-2′-O-MTase) deficient mutant 567, ΔlacZ4787(::rrnB-3), λ-, rph-1, ΔtrmH755::kan, Δ(rhaD-rhaB)568, hsdR514) were purchased from E. coli Genetic Stock Center CGSC, Yale University, and grown to mid-log phase at 37 °C in Luria-Bertani (LB) medium . Knockout strains were grown with 200 μg/mL geneticin (G418) (Carl Roth) and 50 μg/mL kanamycin (AppliChem), respectively.The For optimal amounts of RNA, bacteria and yeast were pelleted and frozen at −80 °C overnight and thereafter lysed in 40 mg/mL lysozyme  or 50 U/mL lyticase , respectively. Total yeast and bacterial RNA were isolated using TRIzol reagent  according to the manufacturer’s instructions. Total RNA preparations were separated by denaturating PAGE ; tRNA-containing bands were cut out and shaken in 0.5 M sodium acetate overnight. tRNA was precipitated with ice cold ethanol and resuspended in nuclease-free water. The concentration of RNA was controlled via UV absorbance.E. coli or 8–12 min for S. cerevisiae) at 95 °C. The reaction was stopped by ethanol precipitation using 3 M Na-acetate, pH 5.2 and glycoblue as a carrier in liquid nitrogen. After centrifugation, the pellet was washed with 80% ethanol and resuspended in nuclease-free water. The sizes of generated RNA fragments were assessed by capillary electrophoresis using a Small RNA chip on the Bioanalyzer 2100 (Agilent).tRNA (about 100 ng) hydrolysis was performed in 50 mM bicarbonate buffer pH 9.2 for different times  for 30 min at 37 °C. After the inactivation of the phosphatase for 5 min at 70 °C, RNA fragments were phosphorylated at the 5′-end using T4 polynucleotide kinase (PNK) and 1 mM ATP for 1 h at 37 °C. End-repaired RNA fragments were then purified using the RNeasy MinElute Cleanup kit  according to the manufacturer’s recommendations, except that the volume of 96% ethanol was adjusted for RNA binding. Elution was performed in 10 µL of nuclease-free water. RNA fragments were converted to the library using the NEBNext Small RNA Library kit  following the manufacturer’s instructions. DNA library quality was assessed using a High Sensitivity DNA chip on a Bioanalyzer 2100. Library quantification was done using a fluorometer .The library mix for sequencing was adjusted to obtain ~10–15 millions of raw reads for each library; this number was found to be sufficient for a 5000–8000 nt length reference sequence (end-coverage ~1000×). Library sequencing was performed using the Illumina HiSeq 1000 sequencer in a single read mode for 50 nt with SBS kit v3. Primary analysis of sequencing quality was done with Illumina RTA 2.12 software, to insure a >Q30 quality score for >95% of the obtained sequences. Following the SR50 sequencing run, demultiplexing was done with Illumina BclToFastq v2.4; reads not passing the quality filter were removed.Raw reads after demultiplexing were trimmed to remove the sequence of the Illumina 3′ adapter. Trimming was performed with Trimmomatic v0.32  with theO-methylations and their dynamics or variations under different conditions. Quantification of the modification level is possible with careful calibration, obtained either with unmodified tRNAs extracted from a relevant KO strain or, if not available, with a synthetic RNA transcript with an identical sequence. The candidates for novel modified positions revealed by this high-throughput technique should be further validated by orthogonal approaches, either by physico-chemical methods, like HPLC-MS/MS analysis or by primer extension at low dNTP concentrations, an approach specific for RNA 2′-O-methylations. In the case of Gm18 in E. coli tRNAMet(ac4CAU), where we have exercised such a validation, the RiboMethSeq method proved accurate and led to the correction of the data from the literature. As this example has demonstrated, RiboMethSeq may be used for the correct prediction of the immunosilencing properties of RNA molecules based on the functional effect of ribose methylation on the recognition by components of the innate immune system. The alkaline hydrolysis-based RiboMethSeq method  can be s


The evolution of cancer cells is believed to be dependent on genetic or epigenetic alterations. However, this concept has recently been challenged by another mode of nucleotide alteration, RNA editing, which is frequently up-regulated in cancer. RNA editing is a biochemical process in which either Adenosine or Cytosine is deaminated by a group of RNA editing enzymes including ADAR  or APOBEC3B . The result of RNA editing is usually adenosine to inosine (A-to-I) or cytidine to uridine (C-to-U) transition, which can affect protein coding, RNA stability, splicing and microRNA-target interactions. The functional impact of these alterations is largely unclear and is a subject of extensive research. In the present review, we will specifically focus on the influence of ADARs on carcinogenesis via the regulation of microRNA processing and functioning. This follows a brief review of the current knowledge of properties of ADAR enzyme, RNA editing, and microRNA processing. Well-known post-transcriptional modifications of eukaryotes include 5′-capping, 3′ poly-adenylation, alternative splicing, and RNA editing, as has been highlighted by recent studies ,2. RNA ehttp://rnaedit.com, DARNED: http://darned.ucc.ie) [Alu elements [Alu regions, which provides functional roles related to regulation of alternative 3′ untranslated region (UTR) usage and primary miRNA processing in the nucleus of ADAR [Initial discoveries of A-to-I editing events were rarely, and serendipitously made when comparing cDNA sequences with their counterpart genomic DNA in coding regions. Several editing events in protein coding regions were found to alter protein levels and thus affect change in cell function. Thanks to recent advance in sequencing technology , several databases are available to predict editing of RNAs using NGS (Next Generation Sequencing) data (RADAR: .ucc.ie) ,6,7,8. Relements ,9,10. Th of ADAR .There are three members of known ADAR family of enzymes  in vertebrates . All ADAGRIA2 gene. However, the ADAR2 KO lethality can be rescued by the introduction of homozygous GRIA2 Q/R knock-in allele [Regarding the genetically engineered mouse (GEM) model of ADAR, the ADAR1 knockout mice are known to be embryonic lethal ,18, and n allele . On the n allele . For ADAn allele ,22.ADAR gene, located on chromosome 1q21, and is ubiquitously expressed in mammals. The gene encodes proteins of two sizes with separate promoters; p110 isoform (~110 kDa) and p150 isoform (~150 kDa) [ADAR gene cause Aicardi-Goutières syndrome, which is characterized by childhood encephalopathy and massive interferon production [ADAR1 is encoded by 150 kDa) . It is w150 kDa) . In addi150 kDa) . Mutatiooduction . In addioduction ,27. It hoduction . Relatiooduction . Howeveroduction .In addition to three dsRBD domains as well as the deaminase domain in the C-terminus, both ADAR1 isoforms contain Zβ domain, a Z-DNA binding site. However, only p150 isoform contains the Zα domain, which contains a nuclear export signal (NES) . SubcellADARB1 gene, located on chromosome 1q21 [ADAR2 is encoded by ome 1q21 . Althougome 1q21 . ADAR2 iome 1q21 . ADAR2 kome 1q21 .ADARB2 gene, located on chromosome 10p15, and is known to be expressed at detectable levels only in the brain [ADAR3 is encoded by the he brain ,36. The he brain . AlthougCancer is increasingly recognized as a constellation of heterogeneous clones, the development of which follows the Mendelian trait . This heRecently, Paz-Yaacov et al. performed a transcriptome-wide analysis of RNA editing events in the coding region using the cancer genome atlas (TCGA) data, and showed that elevated events of A-to-I editing and level of ADAR1 in most cancer types, which also was found to be associated with patient survival . The autAZIN1 (Antizyme Inhibitor 1) at S367G, COPA  at I164V, COG3 (Conserved oligomeric Golgi complex subunit 3) at I635V, and GRIA (glutamate ionotropic receptor AMPA type subunit 1) at R764G. Interestingly, among the RNA editing events in the coding sequence, AZIN1 S367G, COG3 I635V, and GRIA R764G significantly increased cell viability. In addition, alteration in the response to cancer therapeutics was found: AZIN1 S367G for IGF-1R inhibitor, GRIA R764G for MEK inhibitor and COG3 I635V for MEK inhibitor and trametinib.In addition, Han et al. also underwent bioinformatic assay to investigate RNA editing profiles across 17 cancer types . The autChan et al. reported that ADAR1 over-expression and ADAR2 down-regulation in tumors demonstrated an increased risk of liver cirrhosis and postoperative recurrence as well as poorer prognosis in hepatocellular carcinoma . The autPODXL , the authors demonstrated that the ADAR2-regulated recoding editing at codon 241 (His to Arg) confers a loss-of-function phenotype that neutralizes the tumorigenic ability of the unedited PODXL. Qin et al. reported the overexpression of ADAR1 in primary esophageal cancer cell line due to gene amplification [FLNB (Filamin B) and AZIN1 mRNA, the latter candidate shows a gain-of-function phenotype, leading to aggressive tumor behavior [In gastric cancer, a similar role of ADAR1 and ADAR2 as oncogene and tumor suppressor was reported . Using afication . The phebehavior . Howeverbehavior . These rMicroRNAs are small RNAs of ~22 nucleotides can target mRNAs and thereby function as post-transcriptional regulators . Mature The easiest postulation on how RNA editing affects tumorigenesis would be through the editing of nucleotide sequencing in the coding region, microRNA binding site or miRNA itself. The alteration of miRNA binding site is of interest since the majority of the editing sites are in non-coding regions. Comparing the sequence of transcriptome before and after knock-down in a cancer cell line may provide firm evidence on the role of ADAR1 in RNA editing, since knockdown of the enzyme will directly decrease the frequency of editing. We have recently knocked down ADAR with lentiviral transfection of shRNA, and compared the frequency of editing events before and after the experiment. Most of the editing events were A-to-I, and occurred in the 3′ UTR region, which decreased upon ADAR1 knockdown . Likewise, a number of previous reports have demonstrated editing-dependent ADAR effects either on miRNA itself or its binding target sequence. Choudhury et al. found that RNA editing of miR-376 cluster, which is induced by ADAR2, is reduced in human gliomas, with accumulation of the unedited form of miR-376a-3p . The uneARHGAP26 (Rho GTPase activating protein 26), encoding the Rho GTPase activating protein 26. The authors revealed that both miR-30b-3p and miR-573 can target unedited ARHGAP26 UTR, which in edited form becomes resistant to repression by these two miRNAs. In addition, Nakano et al. recently reported another evidence for the editing of target UTR in hepatocellular carcinoma [AHR 3′-UTR was observed after ADAR1 knockdown increased the aryl hydrocarbon receptor (AhR) protein levels and induced the downstream target gene CYP1A1 , without affecting AHR mRNA level. Thus, the authors suggested that the RNA-editing-dependent down-regulation of AhR by miR-378 contributes to the variability in the constitutive hepatic expression of AhR. Interestingly, despite large differences in ADAR1 expression in human liver samples, the inter-individual differences in the RNA editing levels within the AHR 3′-UTR were relatively small, suggesting that there may be a threshold of ADAR level to induce sufficient editing rate.On the other hand, Wang et al. provided another mechanism of ADAR1 activity to regulate miRNA-mediated modulation of UTR site of target gene and its expression . A-to-I arcinoma . CreatioIn addition to A-to-I sequence changes on miRNAs or their targets, the evidences of ADAR function via an editing-independent mechanism was also revealed. Nemlich et al. reported that ADAR1 regulates Dicer expression via let-7 in metastatic melanoma. In addition, they reported that ADAR1 formed a complex with DGCR8 that was mutually exclusive with the DGCR8-Drosha complex that processes pri-miRNAs in the nucleus . Heale eApart from ADAR’s role in altering miRNA function and/or its level, it could also be postulated that ADAR expression is regulated by miRNA. Indeed, Lim et al. demonstrated that miR-1 interacts with 3′ UTR of ADAR1 and subsequently decreases the mRNA expression of ADAR1 . In addiOne of the unique and important features of the RNA editing is transientness. As most of the RNA have a short lifetime, the RNA editing event by ADAR1 can be robust but does not last as long as the edited RNA decays. Considering the cancer cells face many challenges, including immune cell attack, nutrient limitation, hypoxia as well as therapeutic agents, the RNA editing can be a useful tool for the cancer to overcome these abrupt challenges. Among the editing targets, microRNA is relatively less studied but a promising target to understand the editing-mediated cancer cell plasticity. Further studies in this area will reveal the list of miRNAs edited by RNA editing in certain conditions and address how the miRNA editing changes target gene regulations. Moreover, more research will be needed to understand how the editing-independent mechanism cooperates to change the level of specific miRNAs and its target genes.


Recent experimental evidence indicates that lncRNAs can act as regulatory molecules in the context of development and disease. Xist, the master regulator of X chromosome inactivation, is a classic example of how lncRNAs can exert multi-layered and fine-tuned regulatory functions, by acting as a molecular scaffold for recruitment of distinct protein factors. In this review, we discuss the methodologies employed to define Xist RNA structures and the tight interplay between structural clues and functionality of lncRNAs. This model of modular function dictated by structure, can be also generalized to other lncRNAs, beyond the field of X chromosome inactivation, to explain common features of similarly folded RNAs. Xist is the master regulator of X chromosome inactivation (XCI) and it is both necessary and sufficient for establishing this process, which results in the stable and efficient silencing of one X chromosome of somatic cells of female mammals early in development   Table . Data frs) Table . As withs) Table  the basis) Table , 2D. This) Table . InteresAll aforementioned studies support the notion that the structure of lncRNAs, is conserved during evolution and defines biological function. However, a recent study, based on statistical and phylogenetic analysis, suggests that the function of lncRNA is mostly sequence dependent, as there is no clear indication for structural conservation. One of the lncRNAs analyzed in this study was Xist  secondary structure of Xist RNA .Using a variant of SHAPE technology, called SHAPE-MaP, Smola and colleagues obtained an Finally, a new high-throughput algorithm was implemented to profile RNA structure. This algorithm called CROSS has been trained on existing SHAPE, PARS and NMR datasets . Among these are double stem loop motifs, broadly associated with chromatin remodeling; cloverleaf-like architectures, originally described for tRNAs, and generally found in lncRNAs involved in 3′ end processing, such as MALAT1 and NEAT1; and pseudoknots, mostly found in catalytic RNAs, such as the RNA components of telomerases. Interestingly, it appears that RNA secondary structures within the same lncRNA, tend to form modular platforms  or enzymatic activity. Similarly, RNA can exert its biological function by adopting discrete 3D-folding. However, differently from proteins, the tertiary folding of RNA is thought to be based on the initial formation of stable secondary structures, building hierarchical blocks   complex  and Xist RNA Figure . In factde novo recruitment mechanism for Polycomb on the Xi. Notably, the fact that H3K27me3 accumulation on the Xi is entirely Xist dependent in fully differentiated cells , and is dependent on their structure (Mellin and Cossart, In this context, the convergent evolution case of Xist/Rsx becomes relevant. Rsx RNA is the metatherian analog of Xist (Grant et al., AC: conceived this project. AC, GP and AY designed the figures and wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer EGS and handling Editor declared their shared affiliation.


Caenorhabditis elegans, the feeding organ called the pharynx ingests food by pumping driven by the pharyngeal muscles. Here we report that optical silencing of the body wall muscles, which drive the locomotory movement of worms, affects pumping. In worms expressing the Arch proton pump or the ACR2 anion channel in the body wall muscle cells, the pumping rate decreases after activation of Arch or ACR2 with light illumination, and recovers gradually after terminating illumination. Pumping was similarly inhibited by illumination in locomotion-defective mutants carrying Arch, suggesting that perturbation of locomotory movement is not critical for pumping inhibition. Analysis of mutants and cell ablation experiments showed that the signals mediating the pumping inhibition response triggered by activation of Arch with weak light are transferred mainly through two pathways: one involving gap junction-dependent mechanisms through pharyngeal I1 neurons, which mediate fast signals, and the other involving dense-core vesicle-dependent mechanisms, which mediate slow signals. Activation of Arch with strong light inhibited pumping strongly in a manner that does not rely on either gap junction-dependent or dense-core vesicle-dependent mechanisms. Our study revealed a new aspect of the neural and neuroendocrine controls of pumping initiated from the body wall muscles.Feeding, a vital behavior in animals, is modulated depending on internal and external factors. In the nematode  Caenorhabditis elegans, which is composed of a small number of identifiable cells, provides a unique system. In C. elegans, the pumping movement of a feeding organ called the pharynx has been subjected to intensive genetic studies. Compared to the factors promoting pumping, however, the inhibitory mechanisms of pumping are less well understood. In this paper, we report that optogenetic silencing of the body wall muscles, which drive the locomotory movement of worms, inhibits pumping in the pharynx. Signals emanating from muscles are likely to trigger pumping inhibition, raising an interesting possibility that the proprioceptive sense detecting the relaxation of body wall muscles might be involved. When the Arch proton pump was activated with weak light, signals for pumping inhibition are transferred into the pharynx mainly through two pathways: one involving gap junction-dependent mechanisms through pharyngeal I1 neurons, which mediate fast signals, and the other involving dense-core vesicle-dependent mechanisms, which mediate slow signals. Strong activation of Arch inhibits pumping very strongly via other mechanisms. Thus, we have revealed a new link between pumping and the body wall muscles, and confirmed the important cooperation of neural and neuroendocrine circuits in the regulation of feeding behaviors.Since feeding is an essential behavior for the survival of animals, it is modulated by a variety of neural and neuroendocrine signals that are generated depending on internal and external conditions. To elucidate the cellular and molecular mechanisms underlying the regulation of feeding, the nematode  Caenorhabditis elegans, which is amenable to a variety of experimental manipulations, provides a unique system.Feeding is essential for the survival, growth, and proliferation of animals. Accordingly, feeding behaviors change depending on both the internal physiological state and external environmental conditions . For stuC. elegans takes food in through a hollow organ called the pharynx that is located in the head 18] [18] 17][ [18] 17] mutants , which a mutants . A neuro mutants . Althougin vivo. Archaerhodopsin-3(Arch) is a light-driven outward proton pump 24].[24].in v.[24].in myo-3 promoter carried by an extrachromosomal array, ncEx3031. The transgenic animal, ST300, ceased forward and backward movement immediately after starting illumination with green light (550 nm) . Cer. Cerunc-2) has a strong and quick inhibitory effect on pumping, which does not rely on the UNC-7/innexin-dependent and UNC-31/CADPS/CAPS-dependent pathways. Strong activation of Arch might lead to changes in certain physiological conditions in the body, such as pH, which could directly affect the pharynx. The possible involvement of factors other than body wall muscle relaxation in the Arch-mediated pumping inhibition remains to be examined in future studies. Further analyses of pumping inhibition using the ACR2 anion channel would also be helpful to clarify the issue.Although our result using the ACR2 anion channel strongly suggests that body wall muscle relaxation causes pumping inhibition, it does not exclude the possibility that other factors are involved in pumping inhibition caused by activation of the Arch proton pump. We found that activation of Arch with illumination at a high intensity  agar [trans-retinal (ATR)  were kept in the dark.GM) agar . Animalsrig-3 promoter, which drives gene expression in a subset of neurons including I1 neurons, pDEST-rig-3p, was constructed by inserting the polymerase chain reaction (PCR)-amplified genomic fragment into the SphI site of pDEST-PL (a gift from Hidehito Kuroyanagi) using the following primers: 5’aaGCATGCggaaaaatgtgagatcttcgctgaaa3’ and 5’aaGCATGCgaatgaagttcttctgcaaggaatga3’. pGW-rig-3p::mCherry was generated by recombination between pDEST-rig-3p and pENTR-mCherry using the Gateway system . pMT001: myo-3p::ACR2::gfp was generated by recombination between pDEST-myo-3p and pENTR-ACR2::gfp [myo-3p::Arch::gfp was described previously [A destination vector containing the CR2::gfp . pOKA049eviously .myo-3p::Arch::gfp, 100 ng/μl) and pGW-rig-3p::mCherry (50 ng/μl) were injected together into N2 worms to create strain ST357 carrying ncEx9112. pOKA049 (75 ng/μl), pRF4 , and pCFJ90  were injected together to create strain ST326 carrying ncEx9198. Strain ST371 carrying ncEx3941 was generated by the injection of pMT001  and pRF4  into N2 worms. Strain ST300 carrying ncEx3031 [Transgenic animals were generated by microinjection of DNA into the gonad of N2 hermaphrodites . pOKA049 rol-6d)  was crosunc-31(e928) IV;ncEx3031, ST303 snt-1(n2665) II;ncEx3031, ST304 egl-3(n729) V;ncEx3031, ST305 tph-1(n4622) II;ncEx3031, ST306 unc-29(e193) I;ncEx3031, ST308 unc-49(e382) III;ncEx3031, ST309 unc-25(e156) III;ncEx3031, ST310 unc-17(e245) IV;ncEx3031, ST311 unc-54(e190) I;ncEx3031, ST312 eat-4(ad572) III;ncEx3031, ST313 cat-1(e1111) X;cnEx3031, ST314 unc-7(e5) X;ncEx3031, ST315 inx-4(e1128) V;ncEx3031, ST319 tdc-1(n3420) II;ncEx3031, ST320 flp-13(tm2427) IV;ncEx3031.ST302 ncIs53 was outcrossed seven times, and then crossed with unc-7 and unc-31 mutants to generate the following strains:For chromosomal integration, ST357 L4 larvae were gamma-irradiated with cobalt 60 . The intunc-31(e928) IV; ncIs53, ST324 unc-7(e5) X; ncIs53, ST325 unc-31(e928) IV; unc-7(e5) X; ncIs53, ST361 unc-31(e169) IV; ncIs53, ST362 unc-7(e139) X; ncIs53, ST363 unc-15 (e73) I; ncIs53.ST323 Ex3031 transgene, animals strongly expressing Arch::GFP were used for behavioral assays. Animals were moved onto experimental plates seeded with a solution of OP50 containing 500 μM ATR immediately before transfer of the animals, and incubated for at least 8 h before the assay was started.In the strains carrying the myo-3p:ACR2::gfp transgene were illuminated with a blue light (460–480 nm) through a filter set  at the intensity of 1.5 mW/mm2. At this intensity, the blue light by itself did not affect the pumping rate, as described previously [The behavioral analyses were performed under an upright stereo-microscope  with an objective lens . Movies were acquired at a rate of 30 frames/s with a digital video camera  mounted on the microscope. For activation of Arch, animals were illuminated with a fluorescent light source  through a filter set . Animals carrying the eviously . The dureviously .Animals that pumped constantly were chosen for experiments. The pharyngeal pumping was counted manually by visual inspection of movies played at slow speed. One movement sequence consisting of an opening and a closing of the TB was counted as one pumping movement.The basal pumping rate sometimes differed among mutants. In order to compare the responses to green light for different mutant backgrounds, the number of pumps during the 10 s period before green light illumination of the respective mutants was set as a standard for normalization of the pumping rate.unc-31, unc-7, and unc-31;unc-7 double mutant animals, we excluded trials in which the pumping rate was less than 25 pumps per 10 s during the 30 s before light stimulation was initiated.In assays of N2, rig-3 promoter. The cell body of I1s was irradiated with an 860 nm laser . Ablation of I1s was confirmed by the absence of fluorescence in the cell body position. Behavioral assays were conducted on adult animals following a recovery period of 1 to 3 days after ablation. After the behavioral assay, the pharyngeal neurons in the animal were observed using confocal microscopy  to ensure cell ablation.Laser ablations were performed using two-photon microscopy . Animals were mounted on 4% agar pads and anesthetized with 10 mM levamisole. I1 neurons were identified in transgenic L1/L2 larvae expressing mCherry under the control of the unc-54; ncEx3031 was measured using the ImageJ public domain software and compared with that at 0.5 s before and after applying light illumination.The method used to measure animal body length was described previously . The bodThe pumping rate in a 10 s period after illumination relative to that before starting illumination was calculated, and the scores were used for statistical analysis. The Steel's many-one rank sum test, Kruskal-Wallis test with post-hoc Steel-Dwass multiple comparison test and Mann-Whitney U Test was performed for data shown in Figs S1 Figmyo-3 promoter in an adult ST300 worm carrying Ex(myo-3p::Arch::gfp). The scale bar is 100 μm.(A) Expression of Arch::GFP driven by the (B) A high magnification image of the white square frame in (A). Arch::GFP was expressed in the body wall muscles. The scale bar represents 20 μm.Ex. mCherry was expressed in the pharyngeal muscles in which Arch::GFP was not expressed. The scale bar represents 50 μm.(C) A fluorescent micrograph of an ST326 animal carrying (EPS)Click here for additional data file.S2 FigThe upper panel shows a worm 0.5 s before the light was turned on, and the bottom panel shows the same worm 0.5 s after the light was turned on. The left cross indicates the anterior tip of the head, and the right cross indicates the posterior tip of the tail of the animal before illumination was initiated. The scale bar is 100 μm.(EPS)Click here for additional data file.S3 Fig2, n = 12 worms; B: WT, 10 mW/mm2, n = 27 worms; C: WT, 40 mW/mm2, n = 14 worms; D: unc-7(e5), 2.5 mW/mm2, n = 12 worms; E: unc-7(e5), 10 mW/mm2, n = 19 worms; F: unc-7(e5), 40 mW/mm2; G: unc-31(e928), 2.5 mW/mm2, n = 14 worms; H: unc-31(e928), 10 mW/mm2, n = 21 worms; I: unc-31(e928), 40 mW/mm2, n = 12 worms; J: unc-31(e928); unc-7(e5), 2.5 mW/mm2, n = 13 worms; K: unc-31(e928); unc-7(e5), 10 mW/mm2, n = 13 worms; L: unc-31(e928); unc-7(e5), 40 mW/mm2, n = 13 worms.The pumping rate during the 10 s period immediately before illumination was set as the standard. The boxes include 50% of the data. The inner line marks the median value, and whisker lines extending from the box represent the minimum and maximum values. A: WT, 2.5 mW/mm(EPS)Click here for additional data file.S4 Fig2) in (A) ST362 unc-7(e139) ncIs53 and (B) ST361 unc-31(e169) ncIs53 animals.Modulation of the pumping rate by green light illumination (10 mW/mmncIs53 and ST324 unc-7(e139); ncIs53 animals (A), and in ST322 ncIs53 and ST361 unc-31(e169); ncIs53 animals (B) is also shown.For comparison, modulation of the pumping rate in ST322 (EPS)Click here for additional data file.S5 FigncIs53 animal at the L2 stage. White arrowheads indicate bilateral I1 neurons. The scale bar represents 20 μm.(A1) Expression pattern of mCherry in an ST322 (A2) Expression pattern of mCherry after laser ablation of both I1 neurons in the worm shown in (A1). No mCherry signal was observed at the position indicated by the white arrowheads. The scale bar represents 20 μm.(B) A fluorescent micrograph of the I1-ablated animal shown in (A1) and (A2) at the adult stage. The scale bar represents 50 μm.(EPS)Click here for additional data file.S6 Fig(EPS)Click here for additional data file.S1 TableThe data were analyzed using the Steel's many-one rank sum test.(XLSX)Click here for additional data file.S2 TableThe data were analyzed using the Kruskal-Wallis test with post-hoc Steel-Dwass multiple comparison test(XLSX)Click here for additional data file.S3 TableThe data were analyzed using the Mann-Whiteny test.(XLSX)Click here for additional data file.


Saccharomyces cerevisiae. Most EFs crosslink preferentially to mRNAs, rather than unstable non-coding RNAs. RNA contributes to chromatin association of many EFs, including the Pol II serine 2 kinases Ctk1 and Bur1 and the histone H3 methyltransferases Set1 and Set2. The Ctk1 kinase complex binds RNA in vitro, consistent with direct EF-RNA interaction. Set1 recruitment to genes in vivo depends on its RNA recognition motifs (RRMs). These results strongly suggest that nascent RNA contributes to EF recruitment to transcribing Pol II. We propose that EF-RNA interactions facilitate assembly of the elongation complex on transcribed genes when RNA emerges from Pol II, and that loss of EF-RNA interactions upon RNA cleavage at the polyadenylation site triggers disassembly of the elongation complex.For transcription through chromatin, RNA polymerase (Pol) II associates with elongation factors (EFs). Here we show that many EFs crosslink to RNA emerging from transcribing Pol II in the yeast DOI:http://dx.doi.org/10.7554/eLife.25637.001 For productive transcription through chromatin, RNA polymerase (Pol) II associates with general elongation factors (EFs)  that areDespite extensive research, it remains unclear for several EFs how they are recruited to active genes. EFs may be recruited by interactions with the body of transcribing Pol II, or by contacts with the tail-like C-terminal repeat domain (CTD) of Pol II, or they may bind via other Pol II-associated EFs. Spt5 binds the body of the Pol II elongation complex , whereasHowever, other recruitment mechanisms exist because mutations in EFs that prevent their polymerase interactions do not abolish gene occupancy of such factors, including Bur1, Paf1C subunits, Spt6, and Set2 . FurtherAn alternative mechanism of EF recruitment would involve interactions with the nascent pre-mRNA. Such RNA interactions are well established for RNA processing factors that are recruited during Pol II elongation  for co-tHere we report that most EFs in yeast, including, most notably, CTD Ser2 kinases and histone H3 methyltransferases, directly crosslink to nascent pre-mRNA in vivo. We find that crosslinking preferences can differ for coding RNAs and non-coding (nc) RNAs. We further show that chromatin association of many EFs depends on RNA. We also directly tested one prominent EF for RNA binding in vitro, and found that recombinant, purified Ctk1-containing kinase complex CTDK-I strongly binds RNA in the absence of other components. Moreover, we show that the N-terminal region of Set1 that contains two RNA recognition motifs (RRMs) is required for full Set1 recruitment to genes in vivo. Based on these results we suggest a model where nascent RNA contributes to the assembly and stability of the Pol II elongation complex. RNA-EF interactions provide a missing link for understanding the coordination of the transcription cycle.Saccharomyces cerevisiae  , a methoFor 12 of these 14 EFs we obtained PAR-CLIP signals that were more than two-fold above background, showing that these EFs interact with RNA in vivo . We obtaWe then classified EFs into factors with moderate and high PAR-CLIP signals, based on their fold enrichments  over background TFIIB signals . Spt5, SWe have previously noted the importance of normalizing the raw PAR-CLIP signal, as measured by the number of U-to-C transitions per U site, to account for differences in RNA abundance . Briefly′ part of the transcripts, due to the slightly higher RNA-Seq signal in this region  versus stable mRNAs using the different normalization methods , we obseTo localize EFs on transcripts, we mapped the Pol II normalized PAR-CLIP occupancies onto transcripts in different classes . We then calculated factor occupancies for 2532 mRNA transcripts that were filtered to reduce ambiguous signals from overlapping transcripts. We calculated heat maps with occupancies averaged around the transcript 5′-end, which corresponds to the transcription start site (TSS), and around the polyadenylation (pA) site . The obt′-peak for Bur1-Bur2 slightly preceded that of Ctk1-Ctk2. The three Paf1C subunits Cdc73, Ctr9 and Rtf1 showed similar occupancy profiles as the kinases but with a focused peak at the 5′-end. Fourth, Spt5 and Spt6 showed high PAR-CLIP occupancy at the 5′-end of mRNAs and decreased occupancy towards the pA site. This analysis revealed specific differences in EF localization on mRNAs, and additionally suggested that EFs bind nascent RNA during transcription.Generally, PAR-CLIP occupancies were high at the 5′-end of mRNAs and decreased shortly before the pA site, with few exceptions . First, To test whether EFs interact with nascent pre-mRNA or with spliced, mature mRNA, we measured factor occupancies at introns, which are co-transcriptionally spliced out and subsequently degraded . All EFsWe next investigated where EFs localize on RNAs in relation to previously mapped mRNA biogenesis factors . We deteWe next analyzed our PAR-CLIP data for EF binding to non-coding Pol II transcripts including short-lived cryptic unstable transcripts (CUTs), which often arise from upstream antisense transcription of bidirectional promoters . We seleWe then analyzed PAR-CLIP signals at bidirectional promoters, which produce mRNA in one direction and a CUT in the divergent direction . We obseHow can some EFs distinguish between CUTs and mRNAs? We carried out motif analysis around the strongest PAR-CLIP sites for each EF using XXmotif  and coulWe next investigated whether RNA binding of EFs contributes to their association with chromatin. Yeast cells were lysed and incubated with buffer containing RNases or with buffer only. Chromatin was isolated and associated protein factors were detected by Western blotting . We found that RNase treatment strongly decreased the levels of chromatin-associated enzymes Set1, Set2, Dot1, Bur1, Ctk1, and the cyclins Bur2 and Ctk2 . Thus, RAs a negative control, we subjected TFIIB to the RNase assay. We observed no differences in chromatin binding after RNase treatment , consistThe observed RNA-EF crosslinking in vivo and the RNA-dependent chromatin association data strongly suggested that EFs can directly bind RNA. To investigate this in vitro, we prepared one EF complex in recombinant form. We chose the prominent Ser2 kinase complex CTDK-I that comprises Ctk1, Ctk2, and the small subunit Ctk3 . CTDK-I S. cerevisiae Pol II . Both the GST-CTD and the Rpb1 subunit of Pol II were readily phosphorylated by CTDK-I at the Ser2 position in vitro  RNA oligonucleotides with 45% or 24% GC content and bearing a 5′ FAM label . CTDK-I ar range . All expar range . These eWe also measured gene occupancies of EFs using ChIP-Seq and compared them with our PAR-CLIP occupancies . The obtComparison of our histone methyltransferase PAR-CLIP data sets with ChIP-Seq data of the corresponding methylation marks , left. TTo test the model of RNA-based recruitment for a particular factor, we investigated whether Set1 gene occupancy depends on the N-terminal region of the protein that contains two RNA recognition motifs  that bind RNA in vitro . We perfHere we present a large set of system-wide occupancy data for yeast transcription elongation factors on RNA (PAR-CLIP) and DNA (ChIP-Seq), and complementary biochemical data. The remarkable finding from our work is that many EFs interact with nascent RNA in vivo. Additional in vitro results support these findings and indicate that RNA can contribute to EF recruitment and the stability of the transcription elongation complex. For Set1 we further demonstrate that the two RNA recognition motifs are required for Set1 recruitment to genes in vivo. These results extend our understanding of how the transcription elongation complex is assembled and maintained on active genes. The emerging view from our data is that nascent RNA contributes to EF recruitment and elongation complex stability to different extents for different EFs. We note that our results do not reveal whether all EFs studied here are initially recruited by RNA, and which EFs establish RNA interactions only after they have been recruited by alternative interactions, although EF binding in the very 5′-region of transcripts argues for a RNA-based recruitment model.Our results also elucidate the long-standing question how the yeast CTD Ser2 kinases Ctk1 and Bur1, which are essential for transcription elongation, are recruited to transcribing Pol II. The Pol II Ser2 kinases give rise to strong PAR-CLIP signals and their chromatin association is strongly dependent on RNA. In addition, we show that purified CTDK-I complex strongly binds to RNA in vitro. This all indicates that nascent RNA plays an important role in recruiting Ser2 kinases to transcribing Pol II. Binding of the Ser2 kinases near the RNA 5′-end is consistent with stabilization of these kinases on the elongation complex by the cap-binding complex . A modelHow can some EFs bind both RNA and Pol II? EFs are generally modular and contain multiple domains that can be involved in RNA or protein interactions. However, the same domain can mediate both RNA and protein interactions, as documented for the RNA export factor Yra1, which contains a RNA recognition motif (RRM) domain that binds both RNA and the phosphorylated CTD . Set1 coFinally, we predict that RNA-based recruitment of EFs provides a missing link in our understanding of how the transcription cycle is coordinated. When the initiation complex assembles at the promoter, TFIIH phosphorylates Ser5 residues in the CTD and this enables recruitment of the capping enzyme . The nasSaccharomyces cerevisiae (Sc) BY4741 strains containing C-terminally TAP-tagged genes  were tested for expression of the correctly tagged protein using the Peroxidase Anti-Peroxidase  antibody. To obtain the ΔPaf1 Set1-TAP strain, a ΔPaf1-KanMX6 cassette, amplified from the pFA6a-KanMX6 vector  containing 10 mg/L uracil, 100 µM 4-thiouracil (4tU) and 2% glucose. 4-Thiouracil was added to a final concentration of 1 mM and cells were grown further for 4 hr. Following RNA labeling, cells were harvested, resuspended in 1× PBS and UV-irradiated on ice with an energy dose of 12 J/cm2 at 365 nm under continuous shaking. Cells were harvested, resuspended in lysis buffer , and disrupted by bead beating  in the presence of 1 mL of silica-zirconium beads  for 40 s at 4 m/s, followed by an incubation of the sample for 1 min on ice. This was repeated eight times. The success of the cell lysis was monitored by photometric measurements and the cell lysis efficiency was usually >80%. Samples were solubilized for 1 min via sonication with a Covaris S220 instrument  using following parameters: Peak Incident Power (W): 140; Duty Factor: 5%; Cycles per Burst: 200. The lysate was cleared by centrifugation. Immunoprecipitation was performed on a rotating wheel overnight at 4°C with rabbit IgG-conjugated Protein G magnetic beads . Beads were washed twice in wash buffer  and once in T1 buffer . Immunoprecipitated and crosslinked RNA was partially digested with 50 U of RNase T1 per mL for 20 min at 25°C and 400 rpm. Beads were washed twice in T1 buffer and phosphatase reaction buffer . For dephosphorylation, 1× antarctic phosphatase reaction buffer  with 1 U/µL of antarctic phosphatase and 1 U/µL of RNase OUT (Invitrogen) were added and the suspension was incubated at 37°C for 30 min and 800 rpm. Beads were washed once in phosphatase wash buffer  and twice in polynucleotide kinase (PNK) buffer . Beads were resuspended in 1 × T4 PNK reaction buffer A  with a final concentration of 1 U/µL T4 PNK and 1 U/µL RNase OUT. Phosphorylation of PAR-CLIP samples was performed using either 1 mM ATP per mL (cold-labeling) or 0.5 μCi of gamma-32-P-ATP per mL (radioactive labeling). The bead suspension was incubated for 1 hr at 37°C and 800 rpm and washed in PNK buffer. For visualization of protein-RNA interactions, the radioactively labeled samples were subjected to SDS-PAGE analysis. Radioactive RNA-protein bands were detected with the Typhoon FLA 9500 instrument .PAR-CLIP was performed as described , with mo)), 1 U/µL RNase OUT, and 15% (w/v) PEG 8000. The bead suspension was incubated for 18 hr at 16°C and 600 rpm. Beads were washed in PNK buffer to remove unligated adapters. For 5′ adaptor ligation, beads were resuspended in 1 × T4 RNA ligase buffer (NEB) containing 6 U/µL T4 RNA ligase 1 (NEB), 10 μM 5′ adaptor , 1 mM ATP, 1 U/µL RNase OUT, 5% (v/v) DMSO, and 10% (w/v) PEG 8000. The suspension was incubated for 4 hr at 24°C and 600 rpm. Beads were washed twice in PNK buffer, and twice in proteinase K buffer . Beads were boiled twice at 95°C for 5 min in proteinase K buffer with 1% SDS and eluted RNA-protein complexes were treated with 1.5 mg/mL proteinase K (NEB) for 2 hr at 55°C. RNA was recovered by acidic phenol/chloroform extraction followed by ethanol precipitation supported by addition of 0.5 µL GlycoBlue (Invitrogen) and 100 μM RT primer (5′ CCTTGGCACCCGAGAATTCCA 3′). Reverse transcription was performed for 1 hr at 44°C using SuperScript III RTase. For PCR amplification, NEXTflex barcode primer and universal primer and Phusion HF master mix (NEB) were added. After PCR amplification, cDNA was purified and size-selected on a precast 4% E-Gel EX Agarose Gel (Invitrogen), quantified on an Agilent 2200 TapeStation instrument , and sequenced on an Illumina HiSeq 1500 sequencer . We performed three independent biological replicates for Bur1, Cdc73, Leo1 and Rtf1 and two independent biological replicates for TFIIB, Spt6, Set2, Dot1, Set1, Paf1, Ctr9, Ctk2, Ctk1 and Bur2.For 3′ adaptor ligation, beads were resuspended in 1 × T4 RNA ligase buffer (NEB) containing 10 U/µL T4 RNA ligase 2 (KQ) , 10 μM 3′ adaptor  using the short read aligner STAR . The reWe calculated the P-values for true crosslinking sites as described . BrieflyFor transcript annotation, we used the recent TIF-Seq data from  to derivTo generate transcript class-averaged heat maps and profiles, transcripts were aligned at their 5′-end (‘TSS’) and pA sites and either scaled to the same length (median) or cut around the TSS and pA sites before taking the average RNA-binding occupancy at each genomic position. Average occupancies were smoothed  and for each factor individually re-scaled between 0  and 1  for all figures but For generation of non-averaged heat maps of filtered mRNAs  transcriPAR-CLIP processing indices (PIs)  were calTranscriptome wide averages of Finally the processing index is given byb of factor B is defined as the median of all occupancies of factor B. The colocalization is defined as Colocalization analysis  was doneustering .ChIP was performed as described , with moChromatin was washed with lysis buffer and solubilized via sonication with a Covaris S220 instrument (Covaris) to yield an average DNA fragment size of 200 bp as determined on an Agilent 2200 TapeStation instrument (see below). This was achieved by sonicating the sample for 18 min using the following parameters: Peak Incident Power (W): 140; Duty Factor: 5%; Cycles per Burst: 200. 30 µL of the washed and fragmented chromatin samples were saved as input material and for control of the average chromatin fragment size. The remaining chromatin sample was immunoprecipitated with 100 μL antibody-coated and prewashed magnetic Dynabeads Protein G  at 4°C for 3 hr (ChIP of TAP-tagged proteins) or overnight (ChIP with protein-specific antibodies) on a turning wheel. Immunoprecipitated chromatin was washed five times with ChIP wash buffer  and one time with TE buffer . Immunoprecipitated chromatin was eluted for 10 min at 70°C in the presence of ChIP elution buffer . Eluted immunoprecipitated chromatin as well as input material were incubated with 10 µL RNase A (10 mg/mL) at 37°C for 30 min and subsequently subjected to Proteinase K  digestion at 37°C for 2 hr and reversal of crosslinks (at 65°C overnight).2Odd to the columns with a 5 min incubation time in between. The average chromatin fragment size for each experiment was verified using 1 µL of the purified input samples on an Agilent 2200 TapeStation instrument using a D1000 ScreenTape (Agilent Technologies). DNA concentration of the IP and Input samples was determined with Qubit 1.0, dsDNA HS (Invitrogen). Before preparing libraries for Illumina sequencing, IP and input samples were analyzed via qRT-PCR on four housekeeping genes to assess quality of sample DNA (see below). For Illumina sequencing of ChIP samples, 1–10 ng of IP or Input DNA were used for library preparation according to the manufacturer's recommendations using the ThruPLEX DNA-Seq Kit . Libraries were qualified on an Agilent 2200 TapeStation instrument and quantified with Qubit 1.0. Libraries were pooled and sequenced on an Illumina HiSeq 1500 sequencer.IP DNA and input samples were purified with the QIAquick MinElute PCR Purification Kit  according to the manufacturer’s instructions. Elution was performed adding three times 15 μL HPMA1, PDC1, MUP1, ILV5, ADH1 and ALD5 as well as against a heterochromatic control region of chromosome V (chrV) were designed and the corresponding PCR efficiencies determined. All primer pairs used in this study had PCR efficiencies in the range of 95‐100%. PCR reactions contained 1 µL DNA template, 1.6 µL of 10 μM primer pairs and 10 µL iQ SYBR Green Supermix . Quantitative PCR was performed on a qTOWER 2.2 Real‐Time System  using a 3 min denaturing step at 95°C, followed by 40 cycles of 15 s at 95°C, 30 s at 61°C and 15 s at 72°C. Threshold cycle (Ct) values were determined using the corresponding qPCRsoft 3.1 software. Percent input was determined for each IP sample: 100*2∧(Adjusted input - Ct (IP); Adjusted input: Raw Ct Input - log2 of Adjusted input to 100%. Sequence information of primer pairs is available in For ChIP experiments, input and immunoprecipitated (IP) samples were analyzed by qPCR to assess the extent of protein occupancy at different genomic regions. Primer pairs directed against promoter, coding and terminator regions of the housekeeping genes S. cerevisiae genome  using the short read aligner Bowtie (version 2.2.3)  . SAMTooln 2.2.3) . Alignmen 2.2.3)  was usedn 2.2.3)  with optThe same transcript annotations as for PAR-CLIP data analysis (see above) were used for ChIP-Seq data analysis, except that filtering criteria had to be more stringent due to the lack of strand-specificity and lower resolution of ChIP-Seq data. Thus, for 600, 0.8). Subsequent steps were performed at 4°C with precooled buffers and in the presence of a fresh protease-inhibitor mix. Cells were collected by centrifugation, washed with 1× TBS buffer and with lysis buffer . Cell pellets were flash frozen in liquid nitrogen and stored at −80°C. Pellets were thawed, resuspended in 1 mL lysis buffer, and disrupted via beat beating . The lysate was divided into two samples. One half was treated with 7.5 U of RNase A and 300 U of RNase T1 ; the other half was treated with the same volume of the RNase storage buffer . After 30 min incubation at room temperature, chromatin was isolated by centrifugation at 15,000 rpm for 15 min. Chromatin was solubilized in 1 mL lysis buffer via sonication with a Covaris S220 instrument . Chromatin solutions were then analyzed by SDS-PAGE and Western blotting against the C-terminal TAP tag of the analyzed factor and against H3, which served as loading control. We performed three independent biological replicates for TFIIB, Rpb1, Rtf1, Paf1, Ctk2, Bur1, Set2 and Spt6 and two independent biological replicates for Leo1, Ctk1, Bur1, Set1, Dot1 and Spt5. Band intensities were quantified using ImageJ software (1.49v). For statistical analysis, multiple group comparisons were done by one-way ANOVA with Dunnett post-hoc test. Data are presented as mean ± SD. Differences were considered significant when p<0.05 .Yeast cultures were grown in 200 mL of YPD medium at 30°C to mid-log phase  ). Ctk2 bears an N-terminal 6x His MBP tag followed by a tobacco etch virus (TEV) protease cleavage site. Individual subunits were combined into a single plasmid by successive rounds of ligation independent cloning. Each subunit is preceded by a PolH promoter and followed by an SV40 termination site. Purified plasmid DNA (0.5 µg) was electroporated into DH10EMBacY cells to generate bacmids . BacmidsProtein purification steps were performed at 4°C. Frozen cell pellets were thawed and lysed by sonication. Lysates were clarified by centrifugation in an A27 rotor (ThermoFisher) , followed by ultracentrifugation in a Type 45 Ti rotor  . Clarified lysates were filtered through 0.8 mm syringe filters  and applied to a 5 mL HisTrap column  equilibrated in lysis buffer. HisTrap columns were washed with 10CV of lysis buffer followed by 5CV of high salt wash buffer , 1 mM DTT, 30 mM imidazole pH 8.0, 0.284 µg/ml leupeptin, 1.37 µg/ml pepstatin A, 0.17 mg/ml PMSF, 0.33 mg/ml benzamidine) and 5CV of lysis buffer. An amylose column (NEB) equilibrated in lysis buffer was directly coupled to the HisTrap column. Protein was eluted from the HisTrap column by a gradient from 0–100% nickel elution buffer , 1 mM DTT, 500 mM imidazole pH 8.0, 0.284 µg/mL leupeptin, 1.37 µg/mL pepstatin A, 0.17 mg/mL PMSF, 0.33 mg/ml benzamidine), after which the HisTrap and amylose column were decoupled. The amylose column was washed with 5CV of lysis buffer and protein was eluted with amylose elution buffer , 1 mM DTT, 30 mM imidazole pH 8.0, 10 g maltose, 0.284 µg/mL leupeptin, 1.37 µg/mL pepstatin A, 0.17 mg/mL PMSF, 0.33 mg/ml benzamidine). Peak fractions were analyzed by SDS-PAGE. Amylose column fractions containing CTDK-I were combined with 1.5 mg of His6-TEV protease and dialyzed overnight at 4°C in a Slide-A-Lyzer (3–12 ml 10 kDa MWCO) (ThermoFisher) against 1 L of lysis buffer. Protein was removed from the Slide-A-Lyzer cassette and applied to a 5 mL HisTrap column to remove uncleaved protein and TEV protease. Protein was concentrated in an Amicon 15 mL centrifugal 30 MWCO concentrator (Millipore) to 500 µL. The protein was applied to a Superdex 200 Increase 10/300 column  equilibrated in 400 mM NaCl, 20 mM Na•HEPES pH 7.4, 10% (v/v) glycerol, and 1 mM DTT. Peak fractions were analyzed by SDS-PAGE. Pure fractions were concentrated as described above to 500 µL, aliquoted, flash frozen, and stored at −80°C. Protein preparation yielded ~4 mg of full-length CTDK-I from 3.8 L of insect cell culture.S. cerevisiae Pol II . The reactions were stopped with LDS-loading buffer and heated at 95°C for 5 min before electrophoresis on 4–12% SDS polyacrylamide gel. Standard methods were used for Western blotting. 1:14 and 1:3000 dilutions were used for anti-Ser2-P primary antibody (3E10) (Purified human GST-CTD (see below) and e Pol II  were usey (3E10)  and anti′-FAM labeled ssRNA and dsDNA were obtained from Integrated DNA Technologies and dissolved in water to 100 µM. Sequences for ssRNA were 24% GC, A-rich (AAUAUUCAAGACGAUUUAGACGAUAAUAUCAUA), 24% GC, U-rich (AUGUUGUAUGAUAUCUUGCUAACUUAAUUUGAU), 45% GC, A-rich (AAGCAGCCAAACAAGCAGUCAACAUCAAGUCGU) and 45% GC, U-rich (UUCGUCGGUUUGUGCGUCAGUUGUAGUUCAUCA). The dsDNA sequence corresponds to the 45% GC, A-rich ssRNA sequence. 24% GC, A-rich, 24% GC, U-rich and 45% GC, A-rich sequences correspond to natural coding sequences in S. cerevisiae. RNA oligos were unfolded by incubating the RNA at 95°C for 1 min and transferring to ice for 10 min. The oligonucleotide sequences were diluted in water for all experiments. Purified CTDK-I was serially diluted in two fold steps in dilution buffer . Nucleic acids  were added on ice and the reaction was incubated for 10 min. The assay was brought to a final volume of 30 µL and incubated for 20 min at RT in the dark  as a competitor for non-specific binding). 18 µL of each reaction were transferred to a Greiner 384 Flat Bottom Black Small volume plate. Fluorescence anisotropy was measured at 30°C with an Infinite M1000Pro reader  with an excitation wavelength of 470 nm (±5 nm), an emission wavelength of 518 nm (±20 nm) and a gain of 63. All experiments were done in triplicate and analyzed with GraphPad Prism Version 7. Binding curves were fitted with a single site quadratic binding equation:5max is the maximum specific binding, L is the concentration of nucleic acid, x is the concentration of CTDK-I, Kd,app is the apparent disassociation constant for CTDK-I and nucleic acid. Error bars represent the standard deviation from the mean of three experimental replicates.where BE. coli cells were used. Cells were grown in 2xYT to OD600 of 0.5 , changed to 18°C, and induced overnight with 0.5 mM IPTG. Protein purification steps were performed at 4°C. 4L Bl21(DE3)pLysS) cells were thawed and lysed by sonication. Lysates were clarified by centrifugation in an A27 rotor (ThermoFisher) . Clarified lysate was applied to a 5 mL HisTrap nickel column  equilibrated in lysis buffer . The column was then washed in high salt wash buffer  followed by lysis buffer. A 5 mL HiTrap Q column was equilibrated in lysis buffer and added in line after the nickel column. Protein was eluted with a gradient of 0–100% nickel elution buffer . Following imidazole elution, the nickel column was removed and the Q column washed in 150 mM NaCl, 20 mM Na•HEPES pH 7.4, 1 mM DTT, 10% glycerol, 0.284 µg/ml leupeptin, 1.37 µg/ml pepstatin A, 0.17 mg/ml PMSF, 0.33 mg/ml benzamidine. Gradient elution was performed with 1000 mM NaCl, 20 mM Na•HEPES pH 7.4, 1 mM DTT, 10% glycerol, 0.284 µg/ml leupeptin, 1.37 µg/ml pepstatin A, 0.17 mg/ml PMSF, 0.33 mg/ml benzamidine. Peak fractions from the Q column elution were concentrated in a 5 mL 30 kDa MWCO concentrator to 500 µL. Protein was centrifuged for 10 min at 15k prior loading to a Superdex 200 Increase 10/300 column , equilibrated in in SE buffer . Peak fractions were analyzed by SDS-PAGE, pure fractions concentrated in 5 mL 30 MWCO Amicon Millipore concentrator to 40 µM, aliquoted, flash frozen, and stored at −80°C.For expression, Bl21(DE3)pLysS Data have been deposited in NCBI's Gene Expression Omnibus  and are  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by two peer reviewers and a member of the Board of Reviewing Editors (BRE), and the evaluation has been overseen by the BRE and Dr. James Manley as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "RNA-dependent chromatin association of transcription elongation factors and Pol II CTD kinases" for consideration by The reviewers and the BRE have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife. In addition to several more minor suggestions and textual improvements (included in the reviewers), the reviewers have requested two major additions to the manuscript:Overall, the reviewers find the study very important and think that a revised version should be a great contribution to 1) The reviewers would like for you and colleagues to test the role of the RRM domain of Set1 in Set1 recruitment.2) The reviewers also requested that you test the link between Paf1C subunits and Set1 association with chromatin/Pol II. It was previously demonstrated that Paf1C plays a role as a platform for Set1 association with elongating Pol II, can Set1 still associate with Pol II in the absence of Paf1C?Also testing the role of CTD of Pol II Vs. RNA in Set2 recruitment to the elongating Pol II could be highly informative, however, not a requirement, but we think its addition will improve the manuscript.Reviewer #1:Overall this study is both informative and provides compelling evidence for the role of nascent transcription in the recruitment of EFs to genes. It remains a possibility that RNA:EF crosslinking occurs because of close proximity in the elongation complex. However, the RNAse treatment control  providesSubsection “Comparisons of PAR-CLIP data require normalization”, first paragraph: Authors normalize data to RNA-Seq or Pol II PAR-CLIP. Is there any chromatin RNA-seq data available? Given that we are looking at nascent RNA this would be another interesting normalization method. Another possibility is normalization to NET-Seq signal (Churchman and Weissman 2010).Subsection “Comparisons of PAR-CLIP data require normalization”, second paragraph: The authors point out that the 3' part of the transcripts has higher RNA-Seq signal. Is this generally expected? Is there an explanation for this phenomenon?Subsection “Comparisons of PAR-CLIP data require normalization”, last paragraph: It would be nice to see some unstable profiles with the three normalization methods. Or is there no RNA-Seq signal at all for unstable transcripts? Again, Chr RNA-Seq or NET-Seq would then be a nice alternative.Subsection “Most EFs preferentially interact with coding transcripts”, first paragraph: How well defined is the TTS for unstable transcripts?Subsection “PAR-CLIP data analysis”, second paragraph: What does window half size mean? Does that mean the moving window is 60nt, 30nt to either side of the position? Please clarify.Subsection 2 PAR-CLIP data analysis”, second paragraph 100% occupancy is a misleading terminology.Subsection “PAR-CLIP data analysis”, last paragraph: The authors do not define "b", which I suspect is "background binding".Subsection “Chromatin association of EFs depends on RNA”, end of first paragraph: The authors speak of "discrepancies" between chromatin association and PAR-CLIP. It is not clear what these discrepancies exactly are. Spt6 is not in the PAR-CLIP data, Leo1 and Paf1 had little PAR-CLIP signal and would not be expected to be affected by Rnase – so what are the discrepancies?Figures and supplemental figures are not labelled.Reviewer #2:This manuscript from the Cramer lab represents an interesting study that documents the ability of many transcription elongation factors and histone modifiers to associate with nascent and stable RNA during RNA Pol II transcription in yeast. They show using PAR-CLIP that all elongation factors and modifiers that they tested can crosslink to RNA, and further, that this RNA binding is largely similar to their localization patterns along genes. Among a number of important takeaways, the authors show that the association of several elongation factors to chromatin is dependent on RNA interaction in vitro, thus providing a potential mechanism for how these factors are recruited to genes and RNA Pol II in vivo.This study is sure to be on interest to the broad chromatin and transcription community. One recommendation to improve this study, however, is to include an experiment that would validate the role of RNA binding on in vivo targeting and function on one of these elongation factors, which is currently lacking. For example, Set1 has several RRM motifs that would be ripe to mutate and test in this assay. Would blocking RNA binding prevent Set1 recruitment to genes? I think including an experiment that addresses this obvious and unresolved question is warranted. Overall, the reviewers find the study very important and think that a revised version should be a great contribution to eLife. In addition to several more minor suggestions and textual improvements (included in the reviewers), the reviewers have requested two major additions to the manuscript:1) The reviewers would like for you and colleagues to test the role of the RRM domain of Set1 in Set1 recruitment.S. cerevisiae strain lacking the N-terminal region of Set1 that contains both RRMs; we also introduced a C-terminal TAP-tag on the mutated Set1 for protein detection and immunoprecipitation. We carried out Set1 ChIP-qPCR at four different housekeeping genes and compared gene occupancies between WT and mutant strains. Indeed, the RRM domains of Set1 turned out to be important for gene recruitment of the protein, supporting our model, in which the nascent RNA contributes to the recruitment of elongation factors to chromatin. We have included these interesting results that we feel strengthen the paper.This is a very good suggestion. We generated a 2) The reviewers also requested that you test the link between Paf1C subunits and Set1 association with chromatin/Pol II. It was previously demonstrated that Paf1C plays a role as a platform for Set1 association with elongating Pol II, can Set1 still associate with Pol II in the absence of Paf1C?This is another good suggestion. We deleted the Paf1 gene in a Set1-TAP strain. As above, we carried out Set1 ChIP-qPCR at four different housekeeping genes and compared gene occupancies between WT and mutant strains. As expected and reported in the literature, deletion of Paf1 leads to a decrease in Set1 gene occupancy, providing evidence for Paf1C contributing to recruitment. Taken together, our ChIP-qPCR results suggest that both Paf1C and the RRM domains of Set1 are important for full Set1 recruitment to genes. We present these data in a new figure .Also testing the role of CTD of Pol II Vs. RNA in Set2 recruitment to the elongating Pol II could be highly informative, however, not a requirement, but we think its addition will improve the manuscript.We agree that this experiment will be interesting to further investigate the recruitment of Set2. However, since the RNA-binding region of Set2 is currently not known we were not able to do the suggested experiment. We will, however, keep this in mind for future studies.Reviewer #1:[…] Subsection “Comparisons of PAR-CLIP data require normalization”, first paragraph: Authors normalize data to RNA-Seq or Pol II PAR-CLIP. Is there any chromatin RNA-seq data available? Given that we are looking at nascent RNA this would be another interesting normalization method. Another possibility is normalization to NET-Seq signal (Churchman and Weissman 2010).S. cerevisiae, but we agree with the reviewer that using the yeast NET-Seq data from the Weissman lab represents an interesting alternative to use for normalization. Indeed, we used this data for normalization trials before we generated our Pol II PAR-CLIP data. Similar to our Pol II PAR-CLIP data, NET-Seq data detects unstable transcripts better than total RNA-Seq, and thus NET-Seq normalized and Pol II PAR-CLIP normalized profiles were generally similar. Nevertheless, we wish to keep our Pol II PAR-CLIP data as a reference, since these data were obtained using identical conditions. NET-Seq data was obtained using a different protocol and different library preparation strategy, which might lead to unwanted biases.Unfortunately, we are not aware of any chromatin RNA-Seq data available for Subsection “Comparisons of PAR-CLIP data require normalization”, second paragraph: The authors point out that the 3' part of the transcripts has higher RNA-Seq signal. Is this generally expected? Is there an explanation for this phenomenon?S. cerevisiae are located at the beginning of genes. Moreover, library preparation can introduce biases leading to higher signals towards the 3' end of genes. The latter point highlights the importance of using a reference control sample for normalization obtained using the same protocols and same library preparation strategy.Since we are looking at meta-profiles of total RNA data, which generally show reduced levels of intronic reads, the average signal is expected to be a bit lower in the 5' part of genes since most introns in Subsection “Comparisons of PAR-CLIP data require normalization”, last paragraph: It would be nice to see some unstable profiles with the three normalization methods. Or is there no RNA-Seq signal at all for unstable transcripts? Again, Chr RNA-Seq or NET-Seq would then be a nice alternative.We thank the reviewer for the comment. We now compare meta-profiles over unstable CUTs versus stable mRNAs using all three normalization methods. We added this as S. cerevisiae are located at the beginning of transcripts and splicing occurs very fast (see Oesterreich/Neugebauer Cell 2016). Thus, we would expect slight differences in exon versus intron binding of EFs depending on when these EFs are recruited to elongating Pol II. Set2 and Dot1 are recruited during transcription elongation, later than Spt5 and Set1. Thus, many introns may already be spliced by the time that Set2/Dot1 are recruited, leading to less intron signal. Spt5 and Set1 are recruited immediately after transcription initiation and, therefore, one would expect Pol II normalized signals to be equal over introns and exons. Slightly increased intron signal for Spt5/Set1 may be explained by a longer residence time of these factors on introns than Pol II itself, but for reasons that are presently unknown.Most introns in Subsection “Most EFs preferentially interact with coding transcripts”, first paragraph: How well defined is the TTS for unstable transcripts?In general TTSs for unstable transcripts like CUTs can be quite heterogenous. The CUT TTSs used here represent the most prominent TTSs, i.e. the point where most of the RNA signal drops. These CUT TTSs were identified by Xu et al. using a segmentation algorithm and subsequent manual curation. They used a deletion mutant for the nuclear exosome subunit RRP6 to stabilize CUTs and make their detection easier.We thank the reviewer for this observation. The reviewer is right, the term “Non-averaged heat plots” is misleading. Whereas the PAR-CLIP signal in We thank the reviewer for this observation. We added the appropriate figure legend in the revised article.Subsection “PAR-CLIP data analysis”, second paragraph: What does window half size mean? Does that mean the moving window is 60nt, 30nt to either side of the position? Please clarify.What we mean by window half size 30 is that the window is 61 nt, 30 nt to either side of the current position. We are sorry if this was not clear. We have described it more clearly in the revised Methods.Subsection 2 PAR-CLIP data analysis”, second paragraph 100% occupancy is a misleading terminology.We agree. We have changed “scaled between 0 (0% occupancy) and 1 (100% occupancy)” to “scaled between 0  and 1 ”.We agree. In addition to the average mRNA PAR-CLIP profiles shown for biological replicates in Subsection “PAR-CLIP data analysis”, last paragraph: The authors do not define "b", which I suspect is "background binding".We thank the reviewer for pointing this out. It is correct that background binding is defined by b. It is now correctly defined in the revised Methods.Subsection “Chromatin association of EFs depends on RNA”, end of first paragraph: The authors speak of "discrepancies" between chromatin association and PAR-CLIP. It is not clear what these discrepancies exactly are. Spt6 is not in the PAR-CLIP data, Leo1 and Paf1 had little PAR-CLIP signal and would not be expected to be affected by Rnase – so what are the discrepancies?Generally, the presented chromatin association and PAR-CLIP data are consistent. The few discrepancies we wanted to refer to were Dot1  and Spt6 . We changed the text in the revised article to make this point clear.We adjusted the color and legend of Figures and supplemental figures are not labelled.We fixed this in the revised article.Reviewer #2:[…] This study is sure to be on interest to the broad chromatin and transcription community. One recommendation to improve this study, however, is to include an experiment that would validate the role of RNA binding on in vivo targeting and function on one of these elongation factors, which is currently lacking. For example, Set1 has several RRM motifs that would be ripe to mutate and test in this assay. Would blocking RNA binding prevent Set1 recruitment to genes? I think including an experiment that addresses this obvious and unresolved question is warranted.S. cerevisiae strain lacking the N-terminal region of Set1 that contains both RRMs; we also introduced a C-terminal TAP-tag on the mutated Set1 for protein detection and immunoprecipitation. We carried out Set1 ChIP-qPCR at 4 different housekeeping genes and compared gene occupancies between WT and mutant strains. Indeed, the RRM domains of Set1 are important for gene recruitment of the protein, supporting our model, in which the nascent RNA helps to recruit elongation factors to chromatin. We present these data in a new Figure (We thank the reviewer for this recommendation. As described above, we generated a w Figure .