Tombusvirus p19 Captures RNase III-Cleaved Double-Stranded RNAs Formed by Overlapping Sense and Antisense Transcripts in Escherichia coli

Two methods for expressing p19 proteins in bacteria were designed (Fig. 1a). We previously engineered a pcDNA3.1 plasmid (pcDNA3.1-p19-FLAG) (33) to express p19, driven by the cytomegalovirus (CMV) promoter, which could efficiently initiate RNA transcription in E. coli (36) (Fig. 1a, method 1). To characterize the dsRNAs captured by p19 pulldown, we compared RNAs isolated after overnight culture from cell lysates of two E. coli strains with wild-type (WT) RNase III (DH5α and MG1693) and an RNase III-deficient strain (SK7622; rnc-38 mutant in the MG1693 background), transformed with pcDNA3.1-p19-FLAG. In the rnc-38 strain, the insertion of a kanamycin resistance gene within a 40-bp fragment in the rnc gene abrogates RNase activity (37). p19 protein expression was not affected by rnc mutation (37). dsRNAs bound to p19 were isolated using affinity chromatography, cloned, and deep sequenced. Sequencing reads were reduced ∼10-fold in the RNase III mutant strain (see Table S1a in the supplemental material for a summary of all deep-sequencing data sets) when the same amount of input material and the same cloning procedure were used, consistent with our previous finding that pro-siRNAs are produced by RNase III (33). Sequencing reads were mainly 21 to 22 nt long from WT E. coli, suggesting p19 enriched ∼21-nt dsRNAs produced by RNase III (Fig. 1b). The aligned reads in WT E. coli strains mapped to both the E. coli genome and plasmid, but most of the aligned reads (51% to 78%) mapped to the plasmid (Fig. 1c).

The plasmid reads were unevenly distributed across the entire plasmid but were concentrated in hot spots (Fig. 1d), as previously found in cells expressing exogenous hairpin RNAs (33). The differences in the abundance of sense and antisense reads at the hot spots were shown to likely be due to cloning bias (33). The pcDNA3.1-p19-FLAG plasmid is comprised of a pUC bacterial plasmid backbone but includes additional sequences supporting functions in eukaryotic cells, since pcDNA3.1 was designed for use in mammalian cells (Fig. 1d). p19-captured dsRNA hot spots were most abundant in the sequences of bacterial plasmid origin and distributed along it. Nonbacterial sequences were largely devoid of dsRNAs, except for dsRNAs within the CMV promoter region (dashed box in Fig. 1d), which we speculate is due to the inefficient transcription of eukaryotic transcripts that might not be adapted to initiate transcription in bacteria. Thus, the bacterial plasmid produces multiple overlapping sense and antisense RNAs, consistent with a previous study (38).

The hot spot pattern, observed in the plasmid p19-captured dsRNAs, raised a concern that p19 capture might have sequence bias. To examine whether p19 capture introduces any substantial sequence bias that could skew the distribution of short dsRNAs, two exogenous long dsRNAs were generated by annealing T7 RNA polymerase-transcribed complementary sense and antisense sequences of LMNA or eGFP, gel purified, and incubated in vitro with E. coli RNase III (NEB) (E1-3) or human Dicer (Genlantis) (E4) (Fig. 2a). Two reaction conditions for E. coli RNase III were compared, one that contained Mg²⁺ (E1) and one that contained Mn²⁺ (E2) as the divalent cation. p19 pulldown was also used to capture ∼21-nt dsRNAs produced in the Mn²⁺ reaction (E3). The in vitro RNase III and Dicer cleavage products, with and without p19 capture, were cloned and sequenced. Because of the known cloning bias of different strands of dsRNAs (33), the sense and antisense reads after in vitro digestion were combined to plot the total reads along each sequence. As expected (39), RNase III digestion in the presence of Mg²⁺ mostly produced small RNAs of ∼14 bp, while digestion in the presence of Mn²⁺ or Dicer digestion produced predominantly ∼21-bp dsRNAs (Fig. 2b).

All short RNA products generated in vitro by bacterial RNase III under both conditions or human Dicer showed hot spots (Fig. 2c). Although the hot spot patterns all were somewhat different, many of the peaks coincided between the samples. Short dsRNAs pulled down by p19 from RNase III Mn²⁺ reaction products (E3) displayed a distribution pattern similar but not identical to those of all short dsRNAs generated in the Mn²⁺ reaction (E2). E2 and E3 profiles were highly correlated (Pearson’s correlation coefficient [r] = 0.74 for LMNA sequence, P < 0.0001; r = 0.34 for eGFP sequence, P < 0.0001), but sequences generated under different conditions or by RNase III versus Dicer were less similar. These data suggest that p19 capture can be used to identify RNase III class enzyme sequence preferences. The imperfect correlation between E2 and E3 samples, however, suggested that p19 capture introduces some sequence bias. However, these experiments did not contain enough sequences to characterize possible biases in p19 binding (see below).

To confirm that dsRNAs captured by p19 were generated by RNase III, we extracted total RNAs from WT and rnc mutant strains (Fig. 3a) and evaluated the abundance of dsRNA by blotting for dsRNA using anti-dsRNA antibody (J2 [40]) immunoblotting (Fig. 3b). rnc-14 (41) and rnc-38 (37) are both RNase III null mutants. Long dsRNAs of various lengths were detected in rnc mutant strains but not in WT bacteria (Fig. 3b), confirming that long dsRNAs are processed by RNase III.

To compare short dsRNAs produced by RNase III with those captured by p19, total RNAs purified from WT and rnc mutant strains were incubated with RNase III digestion (NEB ShortCut RNase III kit) (Fig. 3a), and half of the RNase III digestion products were subjected to in vitro RNA pulldown by purified p19 protein (NEB). The total and p19-captured RNase III-digested small RNAs from WT and rnc mutant strains were cloned, deep sequenced, and compared (Table S1a).

In WT E. coli, sequencing reads showed a broad length distribution between 10 and 34 nt, but there was no enrichment of ∼21-nt reads after p19 pulldown (Fig. 3c). In both rnc mutants, sequencing reads contained a distinctive peak centered at ∼22 nt (Fig. 3c). These data, taken together, suggest that RNase III can produce ∼21-nt dsRNAs from long dsRNAs, which are stabilized in rnc mutant strains (Fig. 3b). The ∼22-nt peak was further enriched in p19 pulldown samples, confirming that p19 indeed selects specifically sized dsRNAs. Examples of genomic loci with abundant RNase III-digested sense and asRNA sequencing reads (aligned to the E. coli MG1655 genome and displayed in the UCSC genome browser) in WT or rnc mutants are shown in Fig. 3d with and without p19 capture. The overall location of in vitro RNase III-digested peaks was similar between WT and rnc mutants. Moreover, p19 capture did not appear to grossly modify the overall peak distribution, as we found in Fig. 2c, but somewhat changed the relative abundance of those hot spots, suggesting that p19 has some sequence bias. Some of the differences also could be due to RNase III cleavage products from single-stranded RNAs with stem-loops that are not perfectly paired and, therefore, are not captured by p19 pulldown. The RNase III-digested sequencing reads at those loci overlapped dsRNA fragments, discovered by a previous study (Lybecker et al. [25]), which identified sense-antisense paired transcripts by immunoprecipitation with J2 anti-dsRNA from total RNAs extracted from rnc-105 mutant E. coli (which has an rnc missense mutation encoding a protein with <1% WT RNase III activity [42]). Hot spots were also present at those loci in samples without p19 pulldown, suggesting that RNase III indeed has sequence specificity.

To look at possible biases in RNase III sequence selection and whether p19 pulldown introduces any sequence bias, we next analyzed the %GC at each position of all 22-nt sequencing reads obtained using RNase III-treated total RNA under the 6 conditions (WT, rnc-14, or rnc-38 strains; with or without p19 capture) together with their 10-nt upstream and downstream sequences (Fig. 3e). When the putative RNase III double-cleavage sites and 2-nt overhangs were modeled onto the sequence, we found a characteristic AU and GC enrichment pattern (Fig. 3e). The GC content of the E. coli genome is 50.8%. In sequencing reads from rnc mutant samples, GC content was enriched (∼60%) in the 2-nt overhang region in all samples, and AU content was enriched (∼60%) adjacent to the overhangs on both sides (Fig. 3e). This pattern is unlikely to be due to cloning and sequencing bias, because half of the AU-rich regions are outside the short dsRNAs. This enrichment pattern likely is mostly due to RNase III sequence preference rather than p19 sequence bias, because the overall pattern was not changed in p19 pulldown samples. However, the body (residues 4 to 17) of 22-nt dsRNA pulled down by p19 was significantly higher (P < 0.001) in GC content (on average, 52.7 to 55.9%) than the initial RNase III digestion products (on average, 47.7 to 48.6%) in samples from all three E.coli strains (Fig. 3e), suggesting that p19 protein prefers GC-rich sequences in the middle section of small dsRNAs. This subtle GC bias in p19 binding was unexpected, since previous studies concluded that p19 protein has no sequence bias (32, 43, 44). However, the high concordance (Fig. 2c and 3d and e) in small dsRNA sequencing profiles before and after p19 pulldown suggests that p19 capture does not strongly skew the detection of RNase III digestion products in bacterial cells.

To focus on genome-encoded dsRNAs, His-tagged p19 was integrated into the lambda phage attachment site of the MG1655 Δlac genome (45) (Fig. 1a, method 2), and its expression was driven by an isopropyl-β-d-thiogalactopyranoside (IPTG)-inducible tac promoter. p19-bound short dsRNAs were isolated after IPTG induction in both exponential (sample E) and stationary (S) phases (Fig. S1b), cloned, and sequenced using a SOLiD deep sequencer (Table S1a). Additional repeat experiments were performed using an Illumina sequencer (E-R1, E-R2, S-R1, and S-R2; Table S1a). Approximately 20 million reads that aligned to the E. coli genome were obtained from E and S samples (Table S1b). dsRNAs were generated from most E. coli genes in both samples, and the abundance of reads for each gene in the two samples was highly correlated (r = 0.846), suggesting that bacterial growth stage does not affect dsRNA production globally (Fig. 4a). The abundance of sense and antisense p19-captured RNA reads from each gene were roughly equal, as expected for dsRNAs (r = 0.705) (Fig. 4b). In contrast, sense reads were much more abundant than antisense reads in total RNA, analyzed by deep sequencing of total RNA after rRNA depletion (transcriptome sequencing [RNA-seq]) (r = 0.059) (Table S1c) (Fig. 4c). The level of p19-captured dsRNAs varied greatly among E. coli genes. In the S data set, 87% of genes produced at least 1 dsRNA read per million reads (RPM) and 13.5% produced at least 100 RPM (Fig. 4d), indicating that most (87%) bacterial genes have at least some partially overlapping antisense transcripts. Comparing the level of p19-captured dsRNA sequencing reads with RNA-seq sense or antisense reads, p19-captured dsRNA reads correlated significantly (P < 0.0001) better with antisense RNA-seq reads for all exponential and stationary-phase data sets than with sense RNA-seq reads (Fig. 4e). Thus, p19-captured dsRNAs are generated widely across the E. coli genome, and their abundance is related to asRNA transcription.

To study the dsRNAs that most likely originated from longer dsRNAs formed by overlapping transcription from opposite DNA strands, we focused on clusters of p19-captured dsRNAs with the most sequencing reads. We arbitrarily defined a p19-captured dsRNA cluster as a genomic region that contains at least 2,000 reads (23.5 and 22.9 reads per million in E and S, respectively) within a 200-bp region. A total of 301 dsRNA clusters were identified in the E sample, while 383 were identified in the S sample, and most clusters (248) were found in both samples (Fig. 4f and Table S1d and e). The abundance of p19-captured dsRNA reads in those clusters was equal to that in the top 5% of all genes, and the abundance of sense and antisense p19-captured RNA reads from each cluster were also roughly equal (r = 0.451 for E clusters and r = 0.639 for S clusters) (Fig. 4g), suggesting dsRNAs are formed at those sites. Because the exponential- and stationary-phase clusters highly overlapped, we focused on clusters identified in the S (stationary-phase) data set in the subsequent analysis.

About a third of the p19-captured dsRNA clusters identified in either the E or S sample overlapped the dsRNA forming loci identified by Lybecker et al. (Fig. 4f). These 128 overlapping transcripts (for S) were concentrated in the clusters with the highest number of reads. The p19-captured dsRNA clusters were assigned to 10 groups based on the abundance of reads in each cluster. Eighty-four percent of clusters in the most abundant group (top 10%) were identified as dsRNA loci by Lybecker et al. (25) (Fig. 4h). This high degree of concordance suggests that the most abundant clusters are unlikely to be transcriptional noise.

If p19-captured dsRNA clusters are formed by overlapping sense and antisense transcripts, as we hypothesize, they should contain known antisense transcription start sites (asTSSs). Two recent studies, Dornenburg et al. (28) and Thomason et al. (46), used deep sequencing to identify asTSSs. Both E and S dsRNA clusters contained a number of those identified asTSSs localized between 50 nt downstream and 50 nt downstream of the cluster region (Fig. 3f). When we ranked the S clusters by the abundance of reads, the most abundant clusters overlapped more strongly with the predicted asTSSs (Fig. 4h). Within the top 10% most abundant S clusters, 45% and 68% contained asTSSs identified by Dornenburg et al. and Thomason et al., respectively.

Recently, Conway et al. used high-resolution strand-specific RNA deep sequencing, promoter mapping, and bioinformatics to predict operons (47). The full-length operons they defined included some operons that overlapped at their 5′ ends (divergent operons) or 3′ ends (convergent operons). In total, they identified ∼500 overlapping transcripts, including 89 novel antisense transcripts and 18 coding transcripts that completely overlapped operons on the opposite strand. Ninety-five of the 383 S clusters overlapped the overlapping operons identified by Conway et al. (Fig. 4f). Again, more abundant p19-captured dsRNA clusters overlapped more with the Conway data set (Fig. 4h). The read density (RPKM, or reads per kilobase million) of p19-captured dsRNAs within the overlapping regions of Conway’s divergent operons (5′ overlapping regions) was, on average, 37.9-fold greater (P < 0.001) than the read density of p19-captured dsRNAs within the overlapping regions of Conway et al.’s convergent operons (3′ overlapping regions) and was also significantly greater (P < 0.05) than that of control data sets (con) of genomic regions of similar size in all data sets (Fig. 4i). These results suggest that RNase III-produced short dsRNAs captured by p19 more often are generated from the 5′ overlapping regions of divergent transcripts.

p19-captured dsRNA clusters contained both coding and noncoding genes. The top 15 p19-captured dsRNA stationary-phase clusters involving known small RNA genes, which had 1,575 to 19,505 RPM, are listed in Table 1, and the top 20 S clusters involving only protein-coding genes, which had 4,478 to 17,960 RPM, are listed in Table 2. To further understand potential mechanisms for generating those small dsRNA reads, the p19-captured dsRNA-seq and RNA-seq reads of protein-coding gene loci were mapped onto the annotated genome for E. coli MG1655 in the UCSC genome browser, together with the published dsRNA (25) and TSS (46) predictions (Fig. 5).

Bacterial small RNAs are typically 50 to 300 nt in length, can code for small peptides or be noncoding, and include important gene regulators (4). Fourteen of the 301 E clusters and 18 of the 383 S clusters overlapped known small RNA genes (Table S1d). A schematic of dsRNAs overlapping small RNAs is shown in Fig. 5a. Most of the 15 most abundant dsRNA clusters (11 of 15) were also identified as dsRNAs by Lybecker et al. (25), and 8 of 15 were identified as overlapping transcripts by Conway et al. (47) (Table 1). ryeA-ryeB (also known as sraC-sdsR) was the top stationary-phase p19-captured dsRNA cluster (19,505 RPM) and may play a role in bacterial responses to environmental stress (48, 49) (Fig. 5a). A previous study showed that ryeB (104 nt) regulates the level of ryeA (249 nt) in a growth- and RNase III-dependent manner (50). The finding of this locus again proved that the p19-capture method can identify RNase III-regulated overlapping sense and antisense RNAs. Abundant dsRNA reads were also identified within spf, micA, arcZ, rydC, mgrR, ryjB, and gadY, suggesting that there are overlapping antisense transcripts and RNase III cleavage at those loci (Fig. S2a).

Three tRNA loci (metY, serU, and metZ-metW-metV) were also among the top 15 small RNA p19-captured dsRNA clusters (Table 1), and the metY locus is shown in Fig. 5b. At these tRNA loci, dsRNA reads were not restricted to the region of the mature tRNAs but also occurred in the surrounding regions, suggesting readthrough transcripts are involved in forming dsRNAs at those loci (Fig. 5b and Fig. S2b).

For coding genes, all p19-captured dsRNA clusters were classified according to whether the sense and antisense transcripts were divergent (5′ overlap) or convergent (3′ overlap) or the coding sequence (CDS) overlapped entirely or almost entirely the predicted antisense transcript (Fig. 5c, Table 2, and Table S1d and e). A fourth category was defined by abundant dsRNA clusters that did not overlap previously annotated antisense transcripts. The Conway et al. data set was used to mark full-length transcripts, when available. Some clusters contained more than one type of predicted dsRNA.

Within the top 20 coding gene p19-captured dsRNA clusters, the most common category (13 of 20) involved divergent mRNA transcripts of adjacent genes on opposite strands, which overlap in their 5′ regions. In some cases, dsRNAs formed only within the 5′ untranslated regions (UTRs) but in others included some of the 5′-ends of the coding sequence. An example of this category is the S-298 locus, which contains overlapping 5′ sequences of the asd and yhgN genes on opposite strands (Fig. 5c). p19-captured dsRNAs in this cluster were produced only in the overlapping regions of the RNA transcripts that were predicted by Conway et al. (47) and were supported by our RNA-seq data. At this locus, the dsRNA identified by Lybecker et al. (25) coincided with the region where we sequenced p19-captured dsRNAs. Other examples are shown in Fig. S2c. All 13 of the predicted dsRNAs for these abundant divergent clusters at least partially overlapped dsRNAs pulled down with dsRNA antibody (25), although often they were not identical in position or length.

In the top 20 coding gene clusters, only one cluster arose from convergent transcripts of adjacent genes on opposite strands, which overlap in the 3′ region: the S-332 locus involving fre and fadA genes (Fig. 5c). At this locus, the 3′UTR of fadA mRNA, or possibly a transcript initiated from within the 3′ region of the fadA gene or 3′ to it (as suggested by previously identified asTSSs and the fadA operon mapping by Conway et al. [47]), overlapped the fre transcript. This cluster was not identified by dsRNA pulldown by Lybecker et al. (25).

Another category, full overlap, contains coding genes that substantially overlap another RNA transcript (>50% of the CDS was contained in p19-captured reads). Four of the 20 most abundant clusters fell into this category. An example of this class is the tpx gene in S-116 (Fig. 5c). p19-captured dsRNAs were detected across the entire tpx CDS. Based on the RNA-seq data, the antisense transcript of tpx could come from the 3′ UTR of tyrR mRNA, the 5′ UTR of ycjG mRNA, a read-through transcript containing both tyrR and ycjG (as annotated by Conway et al. [47]), or even a new antisense transcript unrelated to tyrR and ycjG. This cluster, and other examples showing a putative overlapping transcript across the entire CDS of yjjY in S-383 (Fig. S2c) and cspD in S-77, were also identified by dsRNA pulldown by Lybecker et al. (25). This category is also similar to a recently described type of bacterial operon that contains a fully overlapped gene in the opposite direction, found in S. aureus (51).

The last type of dsRNA involves dsRNAs arising from unannotated asRNAs. Some of the p19-captured dsRNA loci could not be assigned to divergent gene transcripts or known asRNAs, suggesting they arise from uncharacterized asRNAs. One example is the S-43 locus, which contains both yajO and dxs genes on one strand (Fig. 5c). RNA-seq reads, corroborated by an asTSS and Conway operon, suggest that there is an antisense transcript (opposite to yajO and dxs) that begins downstream of the 3′ end of dxs. p19-captured dsRNA reads in this cluster were adjacent to the beginning of the RNA-seq overlap, suggesting that RNase III cleavage helped to form the end of this asRNA. This asRNA potentially pairs with the 5′ UTR of yajO or the 3′ UTR of dxs. Other examples include S-169, in which the p19-captured dsRNA profile suggests an antisense transcript within the CDS of galF, and S-283, which predicts an antisense transcript in secY (Fig. S2d).

To investigate whether and how RNase III regulates transcripts overlapping the dsRNA clusters, Northern blots of total RNAs, extracted from WT and rnc mutant strains (rnc-14 and rnc-38 strains), were probed for sense and antisense transcripts of some of the abundant p19-captured dsRNA cluster genes. To test whether sense or antisense RNA stability is affected by RNase III, RNA half-lives also were examined for some clusters by comparing Northern blot sense and antisense signals in WT and rnc mutant bacteria harvested at various times after adding rifampin to block de novo transcription.

Three families of cis-acting TA I loci, ldr-rdl, mok-sok, and ibs-sib, are within the top 15 small RNA p19-captured dsRNA loci. The ldrD-rdlD locus (5,553 RPM) was characterized previously (15) but not identified as forming dsRNA by Lybecker et al. (25). However, an ∼21-nt dsRNA peak is present within the overlapping region of rdlD and ldrD (Fig. 6a, left). The expression level and half-life of the full-length transcript of ldrD (ldrD long), which supposedly encodes a toxic peptide, and rdlD, the antitoxin small RNA, both were slightly increased in rnc mutant strains (Fig. 6a, middle). A stable, smaller fragment of the ldrD transcript (ldrD short), which accumulated during bacterial growth, was detectable only in the rnc mutant (Fig. 6a, right). In two other E. coli type I TA loci with overlapping sense and antisense RNAs, mokC-sokC and ibsD-sibD (52), the stability of the toxin transcripts increased in rnc mutants, and stable, smaller sense RNA fragments were also detected only in the rnc mutants (Fig. S3). The smaller sense RNA fragments could either be alternative transcripts or degradation products of the full-length transcript of the antitoxin small RNA, and their degradation requires RNase III. Thus, p19-capture can identify expected RNase III-regulated small RNA loci, like the ldrD-rdlD locus, which was missed by the anti-dsRNA antibody pulldown approach used by Lybecker et al. (25).

To verify the presence of overlapping antisense transcripts at p19-captured dsRNA clusters of coding genes, we chose the rsd gene, which was among the 3 most abundant coding gene p19-captured dsRNA clusters in both E and S, for Northern blot analysis (Fig. 6b). Antisense reads, which overlapped the 5′ end of the rsd transcript by RNA-seq, may have originated from divergently oriented antisense transcripts that could be the transcript of an adjacent gene, nudC. dsRNA could have been formed between the 5′ UTR of a nudC transcript and the 5′ end of an rsd transcript. This locus resembles the excludon in Listeria, where two operons on opposite strands overlap at 5′ ends (30).

A faint and smeary ∼500-nt signal for rsd sense RNA (coding sequence is 477 nt) was detected in both the WT and rnc mutant at similar levels (Fig. 6b, middle). Two more abundant shorter rsd sense transcripts and similarly sized antisense transcripts between 150 and 300 nt in length were detected only in the rnc mutant, suggesting that the sense and antisense RNAs formed dsRNAs that were degraded by RNase III (Fig. 6b, middle). The rsd asRNA was less abundant in bacteria deficient in both RNase III and rpoS, which encodes a general stress response sigma factor that induces gene expression in stationary phase, suggesting that the transcription of the asRNA was induced by RpoS (Fig. 6b, right).

Another coding gene with an abundant asRNA was cspD, a cold shock protein (CSP) family gene, which actually is not induced by cold shock in E. coli. CspD binds to DNA and can inhibit DNA replication (53). CSP proteins in Salmonella bind RNA and are involved in bacterial virulence (54). Although p19-captured dsRNA reads covered the entire CDS of cspD (225 nt), asRNA were detected by Northern blotting only in the rnc mutant (Fig. 7a), suggesting that the cspD asRNA is not stable in WT cells. Both the level and half-life of the full-length (∼300 nt) cspD RNA increased in the rnc mutant, suggesting that cspD is a direct target of asRNA and that regulation depends on RNase III (Fig. 7b and c). A slightly shortened cspD sense RNA of the same size as the cspD asRNA was detected only in the rnc mutant. The length of the short sense RNA and asRNA were roughly equal to the length of the region covered by dsRNAs. These data suggest that dsRNAs containing the overlapping region of the sense and antisense RNAs accumulated in the rnc mutant. A cspD dsRNA was also identified by dsRNA antibody pulldown at approximately the same location (25). Quantitative proteomics also found increased CspD in the rnc-14 mutant (Fig. 7d and detailed proteomics data in Table S2). These data suggest that cspD mRNA and protein expression are reduced by asRNA in an RNase III-dependent manner.

To investigate the effects of RNase III on protein levels, exponential and stationary phases of WT and rnc-14 and rnc-38 cell lysates were analyzed by quantitative proteomics using the tandem mass tag method (55). Approximately 400 proteins were identified with high confidence in both phases (Fig. S4 and Table S2). Several proteins were consistently upregulated (YjhC, GabD AceA, and AceB) or downregulated (SodA) in both rnc mutants in exponential-phase samples (Fig. S4a). These proteins are involved in glycolysis and antioxidant responses. For stationary-phase samples, CarB, the large subunit of carbamoyl-phosphate synthetase, was consistently downregulated in both rnc mutants compared to the WT (Fig. S4b). However, those genes did not produce abundant p19-captured dsRNA reads and are not known targets of RNase III. We could not find a clear relationship between RNase III cleavage and the regulation of protein abundance from those proteomic data sets.

Many p19-captured dsRNA hot spots contained an ∼21-bp dsRNA bearing 3′ 2-nt overhangs at both ends, consistent with the expected RNase III cleavage signature. Examples include the dsRNA hot spots detected in the E data set for ryeA-ryeB and spf loci (zoomed-in profiles of those loci are shown in Fig. 8a). These data suggest p19-captured dsRNAs contain bona fide RNase III cleavage sites generated in vivo.

We performed GC content analysis on all the unique 21- or 22-nt p19-captured sequences of >1-RPM abundance in all exponential and stationary-phase data sets. The most abundant 21- or 22-nt sequences for which we captured both the sense and antisense strands with at least 1-RPM reads and that were predicted to form perfectly paired RNase III-generated dsRNAs with characteristic 2-nt overhangs at both ends are listed in Table S3. There were 1,225 (21 bp) and 1,643 (22 bp) such duplexes in E and 1,999 (21 bp) and 1,921 (22 bp) duplexes in S (Table S3). AU-rich three-nucleotide sequences were enriched at both sides of the putative RNase III cleavage sites, and the 2-nt overhangs were GC rich (Fig. 8b), consistent with the pattern found in the in vitro RNase III digestion sequencing data sets (Fig. 3e). The body of the p19-captured dsRNAs (residues 4 to 17 for 22-nt dsRNA and 4 to 16 for 21-nt dsRNA) was also GC-rich (Fig. 8b), consistent with the p19 sequence bias identified in RNase III in vitro digestion samples (Fig. 3e). The SOLiD sequencing data sets showed a higher GC enrichment (∼69% compared to ∼53%) in the body of p19-captured dsRNAs than the Illumina data sets, which may have resulted from different GC biases of sequencing platforms or unknown variations in our experiments.

E. coli strain MG1693 and its derivative, SK7622 (rnc-38 mutant), were utilized in the experiments with pcDNA3.1-p19-FLAG plasmid. MG1655 and MG1655 ΔlacZYA strains (also referred to as the MG1655 Δlac strain), and derivatives with mutations in rnc or rnc and rpoS, and the chromosomal His-tagged p19 expression construct were used. E. coli strain FW102 was used to construct the single-copy rsd antisense promoter-lacZ fusions. Detailed information about plasmids and bacterial strains are included in Table S5 in the supplemental material. Unless indicated, strains were cultured in LB (Lennox, BD) at 37°C with shaking at 250 rpm, and antibiotics, when required, were used at the following concentrations: carbenicillin (100 μg/ml), kanamycin (10 or 25 μg/ml), and tetracycline (12.5 μg/ml).

p19 capture of dsRNAs was performed on WT E. coli (DH5α and MG1693) and the rnc-38 mutant (SK7622) transformed with pcDNA3.1-p19-FLAG after overnight culture. For the E. coli strain with the genome-integrated p19 gene, an overnight culture of E. coli was diluted 200 times to inoculate fresh broth. In the case of the exponential-phase samples (E, E-R1, and E-R2), when the culture reached an optical density at 600 nm (OD₆₀₀) of 0.4, isopropyl-β-d-thiogalactoside (IPTG) was added at 0.5 mM for 1 h (final OD₆₀₀ of the culture was 1.2). In the case of the stationary-phase samples (S, S-R1, and S-R2), when the culture reached an OD₆₀₀ of 1.4, IPTG was added at 0.5 mM for 1 h (final OD₆₀₀ of the culture was 2.0). Total RNAs were extracted as described in the supplemental materials and methods (Text S1) (72–77), and p19 magnetic beads (from the p19 miRNA detection kit; E3312; NEB) were used to pull down small RNAs from total RNAs (isolated from 20 ml of bacterial culture) as previously described (33).

Bacterial total RNAs were extracted from overnight cultures of WT and rnc mutant (rnc-14 and rnc-38) E. coli strains. DNA contamination was removed by DNase I digestion (M0303L; NEB), followed by phenol-chloroform extraction. To obtain short dsRNA, 5 μg of purified total RNAs was digested by ShortCut RNase III (M0245; NEB) for 20 min at 37°C, and the products were purified by phenol-chloroform extraction. Half of each product was used in p19 pulldown experiments with p19-chitin magnetic beads (M0310 and E8036; NEB) according to the manufacturer’s protocol.

p19-captured small dsRNAs isolated from E. coli cells expressing p19 from a plasmid were cloned and sequenced according to Huang et al. (33). p19-captured small dsRNAs (for E and S samples), isolated from E. coli cells with integrated His-tagged p19, were cloned using the NEBNext small RNA library prep set for SOLiD (E6160; NEB) according to the manufacturer’s protocol and sequenced on a SOLiD sequencer at NEB. E-R1, E-R2, S-R1, and S-R2 samples were cloned using the NEBNext multiplex small RNA library prep set for Illumina (E7300S; NEB) according to the manufacturer’s protocol and sequenced on an Illumina NextSeq500 sequencer. Small RNAs from RNase III and Dicer digestion assays were cloned using the NEBNext small RNA library prep set for Illumina (E7330L; NEB) according to the manufacturer’s protocol and sequenced on an Illumina GAII sequencer at NEB or on an Illumina NextSeq 500 sequencer.

Cutadapt (https://cutadapt.readthedocs.io/en/stable/index.html) was used to trim cloning adapter sequences. Novocraft (www.novocraft.com) was used for sequence alignment using E. coli K-12 MG1655 genome sequence (GenBank accession no. U00096.2) and pcDNA3.1+ plasmid sequence (GenBank accession no. EF550208.1) as references. A summary of sequence alignment results is included in Table S1a. SAMtools (http://samtools.sourceforge.net) was used to calculate sequencing reads for each gene and for generating sequencing profiles for both plasmid and genome. Sense and antisense reads were defined according to RefSeq annotation of the E. coli genome (NCBI). The UCSC genome browser (E. coli K-12 assembly eschColi_K12; http://microbes.ucsc.edu) was used to view sequencing data and other published data sets. The p19-captured dsRNA, RNase III digestion, and total RNA sequencing data sets were formatted into bedGraph files, which can be downloaded from http://www.pro-sirna.com/lab/data/ and viewed directly using the UCSC genome browser. Customized Perl scripts were created for small dsRNA sequence and cluster analysis and for formatting the data sets. All Perl scripts are available upon request.

Detailed protocols for plasmid extraction and quantification, total RNA extraction, total RNA deep sequencing, Northern blotting, RNA immunoblotting, RNA half-life assay, proteomics, and statistics are included in the supplemental material (Text S1).

All small RNA deep-sequencing data are available under BioProject PRJNA512059 at the NCBI Sequence Read Archive database (https://www.ncbi.nlm.nih.gov/).