Mapping pseudouridines by Pseudo-Seq

Pseudouridine detection can be enabled by chemical treatment that generates a modification-dependent signature in sequencing datasets, even though Ψ by itself cannot be detected through reverse transcription and sequencing. One such reagent is CMC (N-cyclohexyl-N′-β-(4-methylmorpholinium)ethylcarbodiimide p-tosylate), a bulky carbodiimide that derivatizes the NH groups in Ψ and causes robust termination during reverse transcription [30]. To detect pseudouridines in the E. coli transcriptome, we used the Pseudo-Seq method [1], which combines CMC treatment with size-selection steps that enrich truncated reverse transcription products, followed by high-throughput sequencing (Fig 1B). This method generates a CMC-dependent pileup of reads whose 5′ ends are immediately downstream of the modified site (Fig 1C). A mock-treated sample is prepared in parallel to remove potential false positives from other types of events that can generate reverse transcription stops, such as strong secondary structure, other RNA modifications, or transcript ends. These events appear as CMC-independent peaks, so CMC dependence is a requirement for calling a peak as a pseudouridine.

We applied Pseudo-Seq to wild-type E. coli grown to exponential phase (OD₆₀₀ ∼ 0.10–0.15). This approach successfully reproduced a known site in the tRNA-like domain of tmRNA (Fig 1C) [31], as well as several known sites in tRNAs and rRNAs (S1 Fig). Each of these sites exhibits the expected CMC-dependent peak at the annotated position, indicating that known sites are reliably captured in our dataset.

E. coli mRNAs are abundantly modified with pseudouridine

To identify pseudouridines in E. coli mRNAs along with the responsible pseudouridine synthases, we generated Pseudo-Seq datasets for wild-type E. coli and for individual knockout strains of all 11 pseudouridine synthases. In the wild-type dataset, we observed strong CMC-dependent peaks in some mRNAs, indicating the presence of pseudouridines in protein-coding genes (Fig 1D and 1E). In order to systematically call Ψ sites transcriptome-wide while accounting for biases arising from nearby modifications or transcript ends, we implemented a modified Z-score that includes 95% winsorization and background window adjustment (S2 Fig, Methods). We identified candidate pseudouridine sites as strong peaks in the CMC-treated library that were absent from the mock-treated library. Based on the distribution of Z-scores among uridines in mRNAs, we defined these strong peaks as those that have a Z-score greater than 15 (Fig 2A) and at least 3-fold greater than the Z-score in the mock-treated library (Fig 2B). Using these stringent criteria, we were able to validate 8 out of the 10 ribosomal RNA sites, with no false positives (S2C Fig).

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Fig 2. Systematically calling pseudouridine sites transcriptome-wide.

(a) Z-score distribution for all uridines in mRNA in wild-type libraries, with CMC-treated in orange and mock-treated in gray. Distribution is visualized as a complementary cumulative density function, indicating the fraction of positions that have a Z-score greater than a given value. The cutoff for calling a peak in the CMC-treated library, Z ≥ 15, is indicated by the dashed line. (b) Joint distribution of mRNA Z-scores in CMC-treated (y-axis) and mock-treated (x-axis) libraries. Cutoffs for calling potential pseudouridine sites are indicated by the dashed lines: sites must have a Z-score ≥ 15 in the CMC-treated library, and must be at least 3-fold greater than the corresponding peak in the mock-treated library. Sites that meet both cutoffs are considered potential pseudouridine sites and are shown in blue. (c) Number of CMC-dependent peaks called at increasing mRNA read coverage. Reads from multiple wild-type samples were pooled to achieve the top coverage shown, and reads were subsampled from this pool to read depths ranging from 300,000 to 36 million mRNA-mapping reads. Top panel shows the number of peaks called in each subsample, bottom panel shows the cumulative number of unique peaks.

https://doi.org/10.1371/journal.pgen.1011100.g002

We then applied the modified Z-score metric to reads mapped to the entire E. coli transcriptome (Fig 2B, Methods). Peak height calculations are very noisy at lower sequencing coverage (S2D Fig), necessitating strict coverage cutoffs in the background region surrounding each putative site. Applying these cutoffs limits site calling to genes with sufficient expression, and so the number of sites called in a library will vary depending on sequencing depth. To address this issue, we generated a high-depth wild-type Pseudo-Seq library, which spans 24.2% of uridines in protein-coding genes, to call a high-confidence list of sites. The profiled uridines encompass 1532 protein-coding genes, out of the 4099 genes detected in our sequencing data. The genes sequenced to sufficient depth for pseudouridine detection were expressed to a median RPKM of 201, while the overall median expression level was 44.4 (S2E Fig).

This top-tier list of targets consisted of 44 mRNA sites (S1 Table). 43 of these were within coding-sequences, where they could potentially impact the fidelity and rate of translation. Modifications outside of the coding sequence were comparatively rare, as expected from the relative sizes of these regions in E. coli, with only one modification in the 5′ UTR, and none in the 3′ UTR.

The genes modified with pseudouridine do not appear to form a coherent regulon—rather than targeting genes that are part of the same processes or pathways, the modification is present in mRNAs encoding components of many different biological processes. Among these, there is modification of genes that encode proteins involved in translation, including three ribosomal proteins (RpsA, RpsH, RpsQ), two translation elongation factors (TufA, FusA), and one tRNA biogenesis factor (Fmt) (S1 Table).

Sequence coverage is the limiting factor in calling new sites. Thus, most called sites are in highly expressed genes. To determine whether pseudouridylation also occurs in mRNAs with lower expression levels, we simulated higher coverage by pooling reads from different Pseudo-Seq libraries, and subsampling the pooled libraries to depths ranging from 1 million to 67.5 million mRNA reads. Increasing coverage in this manner identified many new CMC-dependent peaks, with up to 100 sites detected when coverage exceeds 65 million mRNA-mapping reads, indicating that pseudouridine is also present in genes with lower expression levels. (Fig 2C and S2 Table).

RluA is the predominant mRNA pseudouridylation enzyme

Pseudouridine is installed in E. coli tRNAs and rRNAs by a universally conserved class of pseudouridine synthases. These enzymes are derived from an ancestral pseudouridine synthase, identified by a common fold, and belong to five different families (with an additional sixth family in eukaryotes) [15,32]. Each of these five families is represented among the E. coli pseudouridine synthases, with two of these families having diversified further, for a total of 11 pseudouridine synthases. To determine which of these enzymes carry out mRNA pseudouridylation, we obtained knockout strains of each of these synthases and carried out Pseudo-Seq on cultures grown to exponential phase (OD₆₀₀ ∼ 0.10–0.15).

Enzyme-dependent sites are expected to show a CMC-dependent peak across all libraries except the relevant knockout (Fig 3A and 3B, Methods). In this manner, 31 out of the 44 high-confidence sites are unambiguously assigned to RluA. There is additional modification carried out by the enzymes RluC and RluD (Fig 3C); RluD unambiguously modifies at least three mRNA sites, while RluC modifies a single site in the atpG mRNA. Each of these three enzymes is known to modify multiple sites among tRNAs and rRNAs. RluA modifies four tRNAs and a single position in rRNA; the RluD enzyme was previously known to modify three sites in the 23S rRNA, all in the loop of helix 69; and RluC modifies three sites in separate regions of the 23S rRNA. Meanwhile, the remaining synthases appear to modify only a single rRNA site, or a specific position in tRNAs.

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Fig 3. RluA is the predominant mRNA pseudouridine synthase in E. coli.

(a) Pseudo-Seq signal for a sample mRNA site in 13 libraries: wild-type, all 11 pseudouridine synthase knockouts, and a mock-treated control for the wild-type. The site appears as an RluA- and CMC-dependent peak in read 5′ ends. (b) Pseudo-Seq signal for a sample RluD-dependent site in an mRNA. The site appears as a RluD- and CMC-dependent peak in read 5′ ends. (c) Enzyme assignment for the highest confidence sites. In order to assign enzymes, we required sufficient coverage in both the wild-type and knockout libraries, and for the peak to be both CMC- and synthase-dependent. (d-f) Targeted bisulfite sequencing of select Ψ sites identified by Pseudo-Seq. (d) Deletion rate at Ψ position at 10 selected sites in WT and ΔrluA strains. “Ψ-oligo” is a pool of synthetic RNA installed with 100% Ψ within variable sequence contexts, as a reference for the magnitude and spread of deletion signal given complete modification. (e-f) Trace of deletion signature at candidate sites in WT and ΔrluA strains. (e) is an RluA-dependent site in iscS, and (f) is an unassigned RluA-independent site in acnB.

https://doi.org/10.1371/journal.pgen.1011100.g003

To orthogonally validate high-confidence sites and estimate modification stoichiometry, we performed bisulfite sequencing [33,34] using targeted PCR amplification of selected candidate sites. Instead of forming modification-dependent cDNA truncations, bisulfite sequencing produces a 1–2 nucleotide deletion signature (Fig 3E and 3F), with a frequency roughly proportional to absolute modification rate [33]. All 10 sites selected for validation showed clear deletion signatures at modification positions identified by Pseudo-Seq (Figs 3D and S4). Comparison to synthetic RNA oligos containing 100% Ψ (Ψ-oligo) indicated several of these sites (iscS, rpsQ) had nearly total modification occupancy, while others (sslE, glf) were only sparsely modified (Fig 3D). Of these 10 sites, 8 were identified as RluA-dependent by Pseudo-Seq. Indeed, all RluA-assigned sites showed almost no deletion signature in ΔrluA RNA, while unassigned sites showed comparable signal to between ΔrluA and WT, indicating accurate and reproducible enzyme assignment.

Nine of the high-confidence pseudouridine sites could not be conclusively assigned to an enzyme. In these cases, low sequencing coverage of the target site in a subset of the libraries prevented unambiguous assignment. Sites remain unassigned (Methods) when they exhibit a strong peak in all libraries where they have high enough coverage to call a CMC-dependent peak in the wild-type library, but insufficient coverage in one or more knockouts prevents the absence of a peak from being confidently called. Meanwhile, sites can be ambiguously assigned when the peak is absent in two or more knockout libraries. This ambiguity could be biological, a consequence of partially redundant modification by more than one enzyme, or technical, resulting from low coverage.

We performed RNA sequencing on wild-type and ΔrluA to examine the effects of RluA on expression. We identified few changes overall. A single mRNA target of RluA, fimA/the fim operon, was substantially affected. The 5′ UTR of the fim operon contains an RluA-dependent pseudouridine site (Fig 4A). The fim operon encodes a polycistronic transcript with the components of the Type 1 pilus, which are rod-like structures on the outer membrane of E. coli that promote surface adhesion. fimA, which encodes the major subunit of the pilus, experienced the greatest fold-change in expression, though all genes in the operon were upregulated. This upregulation was highly specific for the fim operon, as the rluA knockout did not result in global changes in gene expression (Fig 4A). One additional gene, pdeL, was substantially upregulated in ΔrluA, but its sequence coverage in wild-type was insufficient to determine whether it is pseudouridylated.

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Fig 4. Loss of RluA alters gene expression.

(a) Gene expression data in wild-type (x-axis) and ΔrluA (y-axis). Genes are colored by their fold-change in expression relative to wild-type. RPM: reads per million. (b) Pseudo-Seq read 5′ end counts for CMC treated libraries (orange) in every pseudouridine synthase knockout background, and in a mock treated library (gray). Counts are expressed as a fraction of reads mapped to the transcript. Read coverage contributing to rluA’s RPM originates from portions of the transcript left behind by the deletion, although the rluA protein-coding sequence is fully removed. (c) fimS ON conformation frequency, measured through qPCR using primers that amplify only the ON conformation. Two sets of primers were used, targeting the 5′ and 3′ junctions of fimS.

https://doi.org/10.1371/journal.pgen.1011100.g004

To determine whether the fimA upregulation in ΔrluA is due to transcriptional or post-transcriptional effects, we constructed a reporter of the fimA RNA driven by an exogenous promoter (S5A Fig). There was no substantial difference in the reporter expression levels between wild-type and ΔrluA, suggesting that RluA’s effect on fimA expression occurs during transcriptional regulation of the gene, rather than through post-transcriptional processes (S5B Fig). Expression of the fim operon can be controlled by the invertible genetic element fimS, which is located upstream of fimA [35]. fimS can be in an ON conformation that allows high fim expression, or it can be inverted into its OFF conformation. RT-qPCR measurements show that the wild-type samples were in the ON conformation with an approximate frequency of 2.5%, and the frequency increased to up to 8.5% in ΔrluA (Fig 4C), consistent with the higher expression observed by RNA-Seq.

RluA modifies positions in mRNA, tRNA, and rRNA that share a sequence and structure motif

Our results illustrate the diversity of pseudouridine synthase targets, which span multiple classes of RNA. Strikingly, the enzyme RluA is capable of specifically modifying sites in tRNA, rRNA, and mRNA, despite the drastic differences in structure and life cycle among these classes. By contrast, most of the other E. coli pseudouridine synthases modify sites in a single region or position. We next show that RluA modification across these distinct sites are likely guided by a common sequence and structure motif.

RluA’s canonical targets, which consist of one site in the large subunit rRNA and position 32 in the anticodon stem-loop for four different tRNAs [18,36], occur within a ΨURAA motif (where R indicates an A or a G), in which positions U₁ and A₄ are particularly important for recognition (Fig 5A) [36]. We observe that the high-confidence targets of RluA occur in a similar context as the canonical sites, with highly constrained U₁ and A₄ positions, and a strong preference for purines at position +2 and for A at +3 (Fig 5B). This sequence motif is also enriched among the ambiguous and unassigned sites (Fig 5B), indicating that RluA is likely to modify more sites than those we have identified under our stringent hit-calling criteria.

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Fig 5. RluA targets tRNAs, rRNA, and mRNAs with a common sequence and structure motif.

(a) Sequence and structure of all four canonical tRNA targets of RluA. (b) Sequence logos illustrating the sequence motif for the high confidence RluA-dependent mRNA sites (left, N = 31) and for ambiguous and unassigned sites (right, N = 9). (c) Predicted pairing probabilities for the mRNA targets of RluA. DMS-seq reactivities in the 100-nt surrounding each site were obtained from Burkhardt et al. (2017) [37] and used to inform secondary structure prediction. Background set consists of 94 highly expressed sites containing the UURAA motif, but lacking a CMC-dependent peak. (d) Sample structures of RluA target mRNAs. Shades of green indicate DMS reactivity, and the pseudouridine site is highlighted in blue.

https://doi.org/10.1371/journal.pgen.1011100.g005

The UURAA motif is ubiquitous in the transcriptome, with over 8000 instances sequenced to sufficient depth in this dataset, but the majority of these instances did not exhibit a CMC-dependent peak (S6 Fig). Therefore, this simple motif cannot be sufficient for modification, and additional features beyond the presence of a sequence motif must be required for recognition. The tRNA and rRNA targets of RluA share a similar structure, with the pseudouridines located in the loops of short hairpins, within one or two nucleotides of the last 5′-paired base [36]. Therefore, we sought to determine whether the mRNA targets of RluA occur in a similar structure context as the canonical sites in tRNA and rRNA. To determine the structural context of mRNA sites, we carried out dimethyl sulfate (DMS)-informed secondary structure prediction, using published RNA structure probing data from [37]. These data provide DMS reactivity scores for each A and C nucleotide, with higher reactivity indicating more accessible nucleotides. Visualizing the distribution of predicted pairing probabilities for each position relative to the Ψ, we observe that the RluA mRNA sites follow a very similar pattern as the canonical tRNA and rRNA sites, suggesting a common structural motif (Fig 5C). Inspection of the predicted structures confirms that this is the case (Figs 5D and S7).

Strains

Pseudouridine synthase knockout strains were derived from the Keio collection [47]. Each knockout was transduced into an MG1655 background using P1 phage transduction, as described in [48]. Plate lysates were generated by growing each strain to mid-log phase in LB+0.1% glucose, adding CaCl₂ to a final concentration of 5 mM, and mixing 3 mL of this culture with 50 μL of an existing P1_vir stock. This mix was allowed to adsorb for 20 minutes, then mixed with 5 mL of melted top agar and poured onto 2 plates (LB with 0.1% glucose, 2.5 mM CaCl₂, 1.5% agar). The plates were incubated overnight, then top agar was scraped into 50-mL tubes and vortexed with 5 drops of chloroform. To store the new P1_vir stock, the tube was centrifuged for 10 minutes, and the supernatant transferred to a new tube to be stored at 4°C. To perform the P1 transduction, 1.5 mL of overnight cultures of E. coli MG1655 in LB were pelleted and resuspended in 0.75 mL P1 salts (10 mM CaCl₂, 5mM MgSO₄). 100 μL of the resuspended cells were mixed with 1–100 μL of the P1 lysate and left to adsorb at room temperature for 30 minutes. 1 mL of LB and 200 μL of 1 M sodium citrate were added to the mixture, then the cells were grown for 1 hour at 37°C. The cells were pelleted and resuspended in 100 μL LB, then spread on LB plates supplemented with 50 ng/mL of kanamycin.

Growth conditions

Starter cultures were grown overnight in 5 mL EZ Rich MOPS complete media at 37°C. To start the experiment cultures, 1–2 μL of the overnight starter cultures were inoculated into 15 mL pre-warmed MOPS complete media, to OD₆₀₀ < 0.001. These were incubated at 37°C shaking in glass flasks at 220 rpm until they reached a target OD₆₀₀ between 0.10 and 0.15. This setup aimed to obtain exponential-phase E. coli cultures that had undergone 8 or more doublings since back-dilution.

RNA extraction for RT-qPCR and BID-seq

For RT-qPCR and BID-seq, 7 mL culture was combined with 7 mL ice-cold methanol in 15 mL tubes, and pelleted and flash frozen. Pellets were resuspended in 100 μL Tris-EDTA pH 8.0 buffer with 10 mg/mL lysozyme and incubated at 37°C for 5 minutes to lyse cells. RNA was then depleted of gDNA and extracted using an RNEasy Mini Plus kit (Qiagen) according to manufacturer instructions.

RT-qPCR

RNA was extracted as described above, then treated with TURBO DNase (Thermo Fisher) to remove residual genomic and plasmid DNA by combining 10 μg of RNA with 5 μL TURBO DNase and 1 μL buffer and bringing to a final volume of 50 μL. DNase reactions were incubated at 37°C for 30 minutes, then subsequently inactivated by adding 8.8 μL 100 mM EDTA and incubating for an additional 10 minutes at 75°C, and cleaned up with an RNA Clean and Concentrate-25 kit (Zymo) according to manufacturer instructions. 2 μg RNA was reverse transcribed with SuperScript III (Thermo Fisher) and random hexamer priming (5 μM final concentration) for 1 hour at 50°C. RNA was then degraded by adding 10 μL 1M NaOH, heating at 95°C for 5 min, and neutralizing with 10 μL 1 M HCl, after which samples were brought to 200 μL. qPCR was performed with 10 μL KAPA SYBR qPCR Master Mix, 4 μL cDNA, and 3 μL of each primer on a Light Cycler 480 II Real-Time qPCR Machine. fimA-gfp levels were measured with primers in GFP (oHL205/206) and normalized to expression of hcaT (oJD070/071) [49]. Uncertainty was calculated as propagated standard error of the mean. Control measurements from wild type E. coli not carrying the reporter plasmid were 7 or more Ct values greater than measurements in reporter-carrying strains.

RNA extraction for Pseudo-Seq and Rend-Seq

RNA samples to be used for Pseudo-Seq and Rend-Seq were extracted following the RNA snap protocol [50]. Briefly, cultures were transferred to 15-mL tubes and pelleted by centrifugation at 3220 ×g, at 4°C. The media was decanted, then the tubes were spun down for an additional minute. The remaining media was aspirated and the pellets were flash frozen in liquid nitrogen. For RNA extraction, 200 μL of RNA extraction solution (consisting of 18 mM EDTA, 0.025% SDS, 1% 2-mercaptoethanol, 95% formamide) were added to each pellet, followed by vortexing for 20 seconds at top speed. The mixture was transferred to 1.5-mL tubes and incubated at 95°C for 7 minutes in a ThermoMixer. To remove cell debris, tubes were centrifuged at >18000 ×g at 4°C for 5 minutes. Extracted RNA was precipitated in ethanol by mixing 50 μL of the supernatant with 200 μL of 10 mM Tris (pH 7.0), 75 μL of 3 M sodium acetate (pH 5.3) and 825 μL of ethanol.

RNA purification by size selection

During library preparation, several steps will call for the purification of RNA by size selection using excision from polyacrylamide gels. RNA pellets are resuspended in 6 μL of 10 mM Tris pH 7, or similarly small volume appropriate for loading into gel wells. A 10% TBE-Urea gel is pre-run at 200V (or appropriate voltage) for at least 30 minutes. The samples are prepared by adding 1 volume of 2X TBE-Urea Loading Dye, denaturing at 80°C for 10 minutes, and finally placing back on ice. Samples are loaded into the gel wells, and the gel is run at 200V (or appropriate voltage) until the dye fronts indicate that the desired separation has been achieved, which will vary depending on the size of fragment to be excised. To visualize the RNA bands, the gel was soaked in SYBR gold diluted 1:10000 in 10 mM Tris pH 7, then lit on a SafeImager. Gel fragments were cut out and transferred to a 1.5-mL tube with 400 μL 10 mM Tris pH 7 and eluted overnight at 4°C. Alternatively, the gel fragments may be crushed and eluted by incubating at 80°C, shaking for 10 minutes. The solvent is transferred to a new tube and precipitated.

Pseudo-Seq library preparation

Pseudo-Seq protocol was based on [1,16], with some adjustments for handling of the shorter transcripts in E. coli. Total RNA (see RNA Extraction section above) was purified using the QIAGEN RNEasy Plus Mini Kit, which depletes RNAs shorter than 200 nt and removes genomic DNA. rRNA was depleted using either RiboZero or MicrobExpress kits. RNA was fragmented by adding 4.4 μL of 10X RNA fragmentation buffer (ThermoFisher), incubating at 95°C for 22 seconds, then quenched with 5uM of stop solution (ThermoFisher). CMC treatment was performed by incubating RNA in 40 μL of 5 mM EDTA at 80°C for 3 minutes, then adding 100 μL of 0.5 M CMC in BEU buffer (7 M urea, 4 mM EDTA, 50mM bicine, then incubating at 40°C for 45 minutes, shaking at 1000 rpm. Mock treatment was performed by adding 100 μL BEU without CMC before incubating. RNAs were precipitated in isopropanol and resuspended in 30 μL of pH 10.4 buffer (50 mM Na₂CO₃, 2mM EDTA, adjusted to pH 10.4), then incubated at 50°C for 2 hours to reverse CMC adducts from guanidines and uridines. RNA fragments were precipitated, then run on a 10% TBE-Urea gel, and fragments 100–115 nt long were excised and purified. RNA 3′ ends were dephosphorylated with T4 polynucleotide kinase (PNK), at 37°C for 1 hour. Enzyme was inactivated at 75°C for 10 minutes. 3′ adapters were ligated onto RNA fragments using T4 RNA ligase 2 truncated (20 μL reaction with ∼3 pmols input RNA, 25% PEG, 5 μM Linker-1 [5′ App/CTGTAGGCACCATCAAT/3ddC]) for 2.5 hours at 25°C. Ligated RNAs were purified by size selection. Reverse transcription (RT) was carried out with SuperScriptIII: adapter-ligated RNAs were annealed to the RT primer (oCSB76, CAAGCAGAAGACGGCATACGAGATATTGATGGTGCCTACAG) in 11 μL of water with 25 pmol of the RT primer, incubated at 65°C for 5 minutes. Reverse transcription proceeded at 50°C for 30 minutes, with 0.5 mM dNTPs, 5 mM DTT, 10 U/μL SuperScriptIII and 1 U/μL SuperaseIn. NaOH was added to a final concentration of 0.1M, and the mixture was incubated at 95°C for 20 minutes to hydrolyze template RNA. This solution was mixed with 20 μL of 2X RNA loading dye, and loaded onto a 10% TBE-Urea gel. Products 66–116 nt long, corresponding to insert sizes of 25–75 nt, were excised and purified. An adapter (oCSB77, /5Phos/NNNNNNNNNNAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT/3SpC3/) was ligated to the 3′ ends of cDNAs as follows: cDNA was incubated for 2 minutes at 75°C with 8pmols of the adapter and 1 μL of DMSO in a reaction volume of 10.8 μL. Then the rest of the ligation components were added (final reaction volume of 23 μL, 0.9 mM ATP, 20% PEG-8000, 1 μL T4 RNA ligase, high concentration), and the reaction proceeded overnight at 25°C. Partway through the incubation, the reactions were supplemented with an additional 0.5 μL of ligase. The reactions were precipitated, and ligated cDNAs were purified by size selection. After PCR amplification, libraries were sequenced on an Illumina NextSeq, generated 75-nt single-end libraries.

REnd-Seq library preparation

REnd-Seq was performed as described in [51]. rRNA was depleted from 10 μg total RNA (see RNA Extraction section above) with MICROBexpress, using two reaction volumes. Reaction was precipitated in isopropanol. RNA was fragmented using RNA Fragmentation reagents, by first incubating RNA in 40 μL water at 95°C for 2 minutes, then adding Fragmentation Buffer to 1X and incubating for 25 seconds at 95°C and finally quenching with Stop Buffer. Samples were precipitated in isopropanol and resuspended in 5 μL of 10 mM Tris pH 7. To size-select, RNA was mixed with one volume of 2X TBE-Urea sample loading buffer and run on a 15% TBE-Urea gel. RNA fragments 15–45 long were excised and purified, then precipitated. RNA was dephosphorylated with T4 polynucleotide kinase (PNK) in a 20-μL reaction, incubated at 37°C for 1 hour, then inactivated at 75°C for 10 minutes. RNA was precipitated in isopropanol. Linker-1 was ligated to 3 moles RNA 3′ ends using T4 RNA ligase 2 (truncated), in a 20-μL reaction containing 25% PEG-8000, 1X T4 RNA ligase buffer, and 100 pmol of Linker-1. Reactions were precipitated in isopropanol and resuspended in 5 μL 10 mM Tris pH 7. To purify ligated RNA, samples were mixed with one volume of 2X loading dye and ran on a 10% TBE-Urea gel. Ligated fragments were excised, purified, and precipitated. Reverse transcriptions were carried out with SuperScript III. First, samples were incubated with 25 pmol of RT primer (oCJ485) in 12 μL water at 65°C for 5 minutes, then placed on ice. Remaining reaction reagents were added to the sample for a final volume of 20 μL (1X First Strand Buffer, 0.5 mM dNTPs, 5 mM DTT, 20U SuperaseIn and 1 μL SuperScriptIII), and incubated at 50°C for 45 minutes. NaOH was added to a final concentration of 0.1M, and the mixture was incubated at 95°C for 20 minutes to hydrolyze template RNA. This solution was mixed with 20 μL of 2X RNA loading dye, and loaded onto a 10% TBE-Urea gel. cDNAs were excised, purified, and precipitated.

Pseudo-Seq data analysis

Adapter sequences were trimmed from read 3′ ends using bbduk (parameters: ktrim = r k = 10 hdist = 1 mink = 1). 10-nt unique molecular identifiers (UMI) were removed from the 5′ end of the reads and added to the record headers using a custom script. Reads were mapped to the E. coli MG1655 genome (NC_000913_3) using bbmap (slow = t perfectmode = true k = 11 ambiguous = all). Alignments were converted to bam files and sorted with samtools, then filtered to keep only uniquely mapping reads. Multi-mapping reads were parsed into a separate bam file. PCR duplicates were collapsed by means of the UMI, using a custom script. Read 5′ ends were quantified on each strand using bedtools (bedtools genomecov -d -5 -strand +), and were then saved as wig files.

Peak calling

We calculate a Z-score for each U position relative to the 100-nt window surrounding it, requiring that at least 100 reads map to the window. Before the Z-score calculation, we modify the background window in two ways. First, we determine whether the window spans a strong transcript end. If it does, we shift the background window to avoid the peaks to achieve the following criteria: the 5′ boundary of a window must be at least 40-nucleotides downstream of the nearest transcript 5′ end, and at least 5 nucleotides downstream of the nearest transcript 3′ end. On the other side, the 3′ boundary of a window must be at least 25 nt upstream of the nearest downstream 3′ end, and at least 5 nt upstream of the nearest downstream transcript 5′ end. These adjustments are implemented to minimize the impact of read coverage biases close to transcript ends, and from differences in coverage between overlapping transcript isoforms. Transcript end positions were obtained from wild-type REnd-Seq data in the same growth conditions. To reduce the impact of nearby outliers (such as peaks from other modifications and secondary structure) on Z-score calculation, we apply 95% winsorization to the window surrounding the target site, in which all positions whose normalized read count was greater than the 95^th percentile for the window were adjusted to be equal to the 95^th percentile. The value of the target site itself is not changed.

Z-scores are calculated relative to the adjusted 100-nt background window. To call CMC-dependent peaks, we require a Z-score ≥ 15 in the CMC-treated library, and for the CMC-treated Z-score to be at least 3-fold greater than the corresponding Z-score in the mock-treated library (Fig 2B). A CMC-dependent site is assigned as dependent on a given pseudouridine synthase if it meets three criteria: (1) it has sufficient coverage in the knockout library, (2) the knockout peak Z-score < 10, and (3) the WT CMC Z-score is at least 4-fold greater than the knockout peak.

Targeted Bisulfite Sequencing (BID-Seq)

Total RNA was prepared for bisulfite sequencing using a method adapted from [33]. Specifically, 8.5 μL of RNA (1 μg total) was combined with 45 μL 2.4M Na₂SO₃, 0.36M NaHSO₃, and heated at 70°C for 3 hours. RNA was brought to 100 μL with RNAse-free water and desalted on a PD MiniTrap G-25 spin column. RNA was desulfonated by adding 100 μL 2M Tris-HCl (pH 9) and heated at 37°C for 2 hours. RNA was recovered with ethanol precipitation and resuspended in 5 μL RNase-free water. For reverse-transcription, random-hexamer primers were annealed to RNA by combining 5 μL RNA, 1 μL 50 mM random-hexamers, 10 mM dNTPs, 6 μL water and heating at 65°C for 5 minutes, then snap-chilling on ice for 1 minute. Primer-annealed RNA was combined with 4 μL 5x SuperScript IV buffer, 100 mM DTT, 1 μL RNaseIn Plus, 1 μL SuperScript IV, and incubated at 23°C for 10 minutes, 55°C 10 minutes, 80°C 10 minutes. RNA was hydrolyzed by addition of 5 μL 1M NaOH and heating at 95°C for 5 minutes, then quenching with 5 μL 1M HCl. Targeted primers were used to PCR amplify each target site, which were purified using Ampure-XP beads and pooled at equal concentration before submission for amplicon sequencing (Genewiz). Raw fastq files were aligned to a reference of target genes using bbmap, and the resulting bam files sorted and indexed using samtools. Deletion rate at each position was quantified using the samtools mpileup command and custom python scripts.

Structure analysis

In-cell DMS-Seq reactivities for wild-type E. coli were obtained from [37]. Coordinates were converted to NC_000913_3 using LiftOver. For each site of interest, reactivities for the surrounding 100-nt window were parsed from this dataset into individual files (since only As and Cs react with DMS, other bases had a placeholder value of -999). These were provided as input to RNAfold for DMS-informed secondary structure prediction, using the ‐‐shape option (with parameters -p, -T 37, ‐‐shapeMethod = Z). Pairing probability matrices were parsed from the RNAfold output files, and columns were summed to summarize pairing probability. Pairing probabilities were visualized by calculating relative positions relative to the site of interest and visualizing the pairing probability distributions for groups of sites at each relative position.

Background site selection

A set of background sites was selected to control for expression level and for the influence of the RluA motif. First, we used our REnd-Seq data set to obtain WT expression levels for each gene that contains a high-confidence Ψ site. We consider only uridine positions within genes with RPKM ≥ 105 in our wild-type dataset, which was the lowest expression level among the genes containing high-confidence sites. Within these genes, we search for instances of the UURAA motif, and remove any sites that are within 100-nt of a Ψ site.

Secondary structure diagrams were generated using VARNA (v3.93)