Heat shock disrupts rrnA-rrnD clustering

Gaal and colleagues used FROS to examine pair-wise combinations of 15 of the possible 21 pairs of rrn operons, using distinct parS-ParB partners derived from phage P1 and plasmid pMT1 of Yersinia pestis [6,15]. In this study, we employed the same approach to determine trans-acting factors involved in clustering (Fig 1A). For our analysis we chose rrnA and rrnD, located on two different replicores (see S1 and S2 Figs for their relative location and for loci images, respectively), directing ParB-GFP 361 bp upstream of rrnA and ParB-CFP 282 bp upstream of rrnD by inserting their respective parS sites at these locations; the median rrnA-D distance was estimated to be 134 nm. These cells were then subjected to various well-studied chemical and physical stresses (Fig 1B). Serine hydroxamate (SHX) is a serine analog that inhibits tRNA^Ser aminoacylation, mimicking amino acid starvation and inducing a stringent response with concomitant synthesis of (p)ppGpp [16,17]. Application of this stress failed to produce a notable change (>2-fold) in the median rrnA-D distance (214 nm, compared to a significance cutoff at 230 nm; see figure legend for assignment of significance). Cold shock, which elicits changes in membrane fluidity, protein and nucleic acid folding and ribosome assembly [18], also did not significantly perturb clustering (216 nm). We note that since the resolution of our methods does not enable us to meaningfully distinguish distances below approximately 250 nm, distances below the cutoff are considered the same. Ethanol damages cell wall and membrane integrity, inducing an unfolded protein response in addition [11,19]. This stress increased the rrnA-rrnD distance to 424 nm. The unfolded protein response can also be produced by heat stress, which produced the most pronounced shift of the median distance relative to control (734 nm) (Fig 1B). Representative images from these experiments are shown in Fig 1C and 1D. We note that heat stress appears to localize rrnA, but not rrnD, to the pole, while also reducing the number of rrnA loci to 1 (Fig 1D, green arrow). Since ethanol also induces the unfolded protein response, we infer that it is the unfolded protein response that promotes de-clustering of rrnA and rrnD.

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Fig 1. Distance between rrnA and rrnD operons under various stress conditions.

(A) Scheme for rrn operon tagging. Two different par sites (parSpMT and parSP1) were placed upstream of rrnA and rrnD, respectively, in the parent strain MG1655. These sites were visualized by co-expression of their cognate fluorescent ParB proteins (pMT1 GFP-ParB and P1 CFP-ParB). (B) Violin plots of the distance between rrnA-rrnD under indicated stress conditions (see Methods for details and S1 Table for strains and plasmids). The median distance (nm) between labeled loci is given below each graph. Between 400 and 900 foci are shown for each sample. The solid line indicates the median distance, and the top and bottom dashed lines indicate the third and first quartile, respectively. We note that due to the large number of foci observed, a small change in the median distance is considered statistically significant (p < 0.001) under Mann–Whitney test. The data shown are for one of three biological replicates. We arbitrarily considered a 2.5-fold change of median distance to be significant. We also note that since we have values that are 0, the border tries to encompass these data points, giving a false impression of negative distances. Data used for plotting this, and all other graphs can be found in the Supplemental “S1 Data” file under appropriate figure headings. (C) A representative image of rrnA-rrnD foci without added stress; many such images were used to generate data in (B). GFP and CFP were false-colored and enhanced for better visualization. One cell is outlined, with an arrow pointing to a merged GFP/CFP focus. The foci are edited in post-processing as a perfect circle to provide better contrast and visualization. Most cells appear to contain two copies of the rrn operons, indicating that this region of the chromosome is replicated. (D) Representative image of rrnA-rrnD de-clustering with heat stress. Panel labels as in C. Colored arrows indicate focus from either GFP-field (green) or CFP (red). We note that the number of rrnA-GFP foci is reduced to 1 focus per cell. See Methods for the code used to analyze fluorescent foci in this, and all similar figures. Created in BioRender. Ho, K. (2025) https://BioRender.com/p78s829.

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Since our laboratory has shown that long-range contacts occur less frequently in an hupA (HUα) mutant [8], we also examined a noncoding (nc) RNA known to interact with HU [20]. A single deletion of nc5 showed no difference in the median distance between rrnA and rrnD (S3 Fig). RpoZ (an RNAP subunit) and NusB (a component of the anti-termination complex that interacts with RNAP), reported to contribute to phase-separation of E. coli RNAP [21], were also examined. Deletion of either rpoZ or nusB elicited a small increase (i.e., above our 2-fold cutoff of 230 nm) in the rrnA-D distance (254 nm and 304 nm, respectively) (S3 Fig), but not as drastic as that of heat stress.

In summary, of all the variables tested, heat stress caused the most significant de-clustering of the rrnA-D pair, followed by ethanol stress. These two stresses share the common outcome of producing an unfolded protein response.

Deregulation of GroEL/S disrupts rrnA-rrnD clustering

Since heat shock disrupted the rrnA-D pair, we hypothesized that some protein factor(s) bridging the two loci was displaced as a result. To identify bound proteins, we directed dCas9 (a variant of Cas9 capable of binding but not cleavage [22]), to upstream regions of all seven rrn loci, similar to the location of parS sites (see S2 Table), using the appropriate gRNAs. dCas9 was fused to 3X-FLAG, so anti-FLAG antibodies were used in pulldown experiments (S4A Fig). The efficiency of sgRNA targeting was determined by assessing lethality when Cas9 was provided instead of dCas9 (S5 Fig). Overall, in a host expressing Cas9, sgRNAs directed to all rrn operons caused a reduction in viability relative to a no-PAM control (S5 Fig). The differences in gRNA efficiency may influence the efficiency of dCas9 pulldowns, but since Cas9 targeting was productive, we concluded that the pulldowns would reflect at least the binding of dCas9 to the rDNA.

Mass spectrometry of the proteins identified in pulldowns with the seven rrn samples showed a range of significantly enriched proteins across different sgRNAs (z-score > 2.5, computed from three biological replicates for each sgRNA) using the methods described in [23]. For sgRNA targeting rrnA, rrnB, rrnC, rrnD, rrnE, rrnG, and rrnH, we found 54, 132, 135, 105, 66, 33 and 107 significantly enriched proteins, respectively (S4B and S6 Figs). Common proteins identified across multiple samples are summarized in S7 Fig. The detailed spectral counts of identified proteins for each sgRNA can be found in S3–S9 Tables. No common protein was significantly enriched across all samples, but GroEL was enriched in 4/7 sgRNAs (rrnC, rrnA, rrnB, and rrnD), suggesting that GroEL could be a possible trans-acting factor in clustering. To control background protein abundance, we normalized rrn targeting samples to a gRNA targeting the non-rrn gene lacZ (with PAM) (see Methods). A potential pitfall of this approach is that we could not verify that clustering was preserved after formaldehyde crosslinking, due to loss of resolution of ParB foci after fixation, so it is possible that our data may not have sufficiently captured all the major players in clustering.

GroEL, in complex with GroES, acts as a chaperone for protein folding [24]. To investigate whether GroEL mediated the rrn clustering, we generated a chromosomal groEL deletion in the parS-tagged rrnA-D strain (S4C Fig). Since groEL is essential [25], we provided it in trans from a plasmid under control of pAraBAD and verified induction (with arabinose) and repression (with glucose) by observing corresponding increases and decreases in colony sizes, respectively (S8 Fig). Examination of the degree of rrnA-rrnD clustering under these two conditions showed that clustering was disrupted with arabinose addition (S4 Fig, third plot, red, from the left), even though GroEL levels were sufficient for growth as judged by colony size (S8 Fig); the median distance between rrnA-D increased to 494 nm. A WT ‘control’ carrying both groEL/groES under pAraBAD control increased rrnA-D distance to 216 nm (S4C Fig, second plot, yellow, from left); this slight increase is not significant based on our 2-fold cut-off, but the data nonetheless suggest that ectopic expression of groES/EL affects clustering. Taken together, these results suggest that any perturbation of normal GroES/EL levels destabilizes clustering. Since these two genes are co-transcribed [26], we wondered if the imbalance in their relative levels was responsible for cluster disruption. We therefore placed the entire groES/EL operon on the plasmid vector in the ΔgroEL strain, but that did not restore clustering either (S4C Fig, rightmost plot).

To test if expression of groES/groEL from their native σ^H-promoter (pGroE) would change the results, we compared rrnA-D distance when expression of this operon was controlled by pGroE versus pAra in both WT and ΔgroEL strains (S4D Fig). In WT, pGroE had no discernable effect. In the ΔgroEL strain, however, the rrnA-D distance shifted closer to normal with the pGroE plasmid as indicated by the 25% quartile of 242 nm for pGroE versus 356 nm for pAra (S4D Fig, compare rightmost two plots), suggesting that transcription from the native promoter was better at restoring clustering.

σ^H, also known as σ³² or RpoH, is the major heat shock sigma factor that transcribes the groEL/S operon exclusively [26]. Its levels are kept low through multiple mechanisms, including sequestration at the inner membrane, and direct interaction of GroEL/ES with σ^H [27,28]. Could our inability to restore rrnA-rrnD clustering in the ∆groEL/ES background be attributable to perturbation of σ^H levels? This was tested next.

σ^H disrupts rrnA-rrnD independent of its transcriptional activity

If perturbation of σ^H was the cause of rrnA-D de-clustering, we hypothesized that ectopic expression of σ^H alone should give similar results. We therefore placed rpoH under an inducible Tet promoter (pTc). Consistent with our expectations, rpoH induction increased the median distance between rrnA-D to 566 nm (Fig 2A, third plot from left). However, the same result was seen even in the absence of rpoH induction (Fig 2A, second plot from left). Unlike heat stress, rpoH did not affect the number of rrnA foci (S9 Fig, compare to Fig 1D). To verify that rpoH was being expressed from the plasmid, we constructed a lacZ reporter driven from phtpG, a weak σ^H-responsive promoter [29], and assessed lacZ expression upon induction of rpoH from pTc (Fig 2B). A small but significant increase in β-galactosidase activity was observed after an hour of induction, the same time frame employed for microscopy (Fig 2A), indicating that rpoH was expressed from the Tet promoter. We conclude that even a small increase in σ^H levels promotes de-clustering of rrnA-rrnD.

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Fig 2. σ^H promotes de-clustering of rrnA-rrnD independent of its transcriptional activity.

(A) Distance between rrnA-D with ectopic expression of rpoH from pTc (+). Induction was carried out for one hour prior to microscopy. In the fourth plot from left, rpoD is cloned downstream of TetR, from a constitutively expressed promoter, divergent from pTc. All other descriptions as in Fig 1. (B) Transcriptional activity of ectopically expressed rpoH. Miller assay was carried out as described in Methods. Student t’s test was performed pairwise to determine statistical significance (two-tailed, ns: not statistically significant, *p < 0.05, **p < 0.01, ****p < 0.0001). (C) Distance between rrnA-D with ectopic expression of indicated rpoH mutants, compared to empty vector control. (D) Transcriptional activity of ectopically expressed rpoH mutants, as described in B. Data used for plotting Fig 2 graphs can be found in the Supplemental “S1 Data” file under appropriate figure headings. Created in BioRender. Ho, K. (2025) https://BioRender.com/k54q156.

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To ascertain that the data in Fig 2A are not merely due to perturbation of cellular morphology and nucleoid structure, we measured both, under both heat stress and rpoH induction. Under the heat stress, cell lengths showed a small but statistically significant increase: 2.9 µm on average compared to 2.8 µm for the control (S10A Fig). Under rpoH overexpression, cell lengths decreased on average to 2.5 µm compared to 2.8 µm for the control. To determine the effect of these perturbations on the nucleoid, we performed DAPI staining under these conditions. Curiously, we found that the nucleoid is more condensed, not decondensed, under both conditions (S10B Fig). These results indicate that the de-clustering effect of heat stress or rpoH expression is not a consequence of changes in nucleoid compaction or cellular morphology. As another control to ascertain that the change in rrnA-D distance was specific to these loci and not due to a general increase in distance between any two loci, we tagged lacZ-pMTparS and leuC-P1parS (200 Kbp apart; S10C Fig, left) and measured their distance −/+ rpoH expression (S10C Fig, right). We observed no change in their distance between the two conditions.

E. coli possesses seven experimentally confirmed sigma factors, of which the vegetative σ^D (RpoD or σ⁷⁰) is the most abundant [30]. Since the amount of RNAP is thought to be relatively constant across most growth conditions, competition among the sigma factors for this core RNAP has been proposed to be the mechanism for gene regulation [30,31]. To test if competition between σ^H and σ^D for binding RNAP could be responsible for destabilizing clustering, we inserted rpoD downstream of TetR on the same plasmid, which is driven by the constitutive promoter divergent from pTc (pTetR). Expression of rpoD decreased the median distance between rrnA-D to 213 nm compared to 566 nm with the rpoH vector alone (Fig 2A, compare fourth plot to second plot from the left), supporting the notion that competition between σ^D and σ^H for core RNAP likely suppressed the de-clustering activity of σ^H.

To test this notion further, we examined the following previously characterized mutants of rpoH, reported to have decreased or increased transcription activities: L245P is defective for interaction with RNAP [32], E265A is defective for promoter binding at the −35 region [32], and I54N has increased stability [28,29]. Our expectation was that the L245P and E265A would lose their de-clustering activity, while I54N mutant would not. This expectation was borne out for the L245P and E265A variants (303 nm and 216 nm, respectively), but not for I54N (284 nm) all three variants were defective in destabilizing the rrn cluster, compared to WT RpoH (566 nm) (Fig 2C). We note that in contrast to cells expressing the first two variants, those expressing I54N are elongated (S11 Fig). I54N has been shown to exhibit elevated transcriptional activity due to its inability to localize to the membrane like WT σ^H [27,29], which likely perturbs normal cell physiology. The expected transcriptional activity of all the σ^H mutants was confirmed using the lacZ reporter as before (Fig 2D). We conclude that σ^H -mediated de-clustering is independent of its transcriptional activity.

σ^H does not disrupt all rrn pairs

Our experiments thus far queried the co-localization of the rrnA-D pair as representative of all the rrn loci found in the cluster [6]. To test if heat shock and σ^H were equivalent in their disruptive ability, we decided to query other pairs, including every co-localizing rrn operon at least once, with rrnC as the non-clustering control. Heat shock disrupted clustering of all non-C rrn operons tested, the median distance increasing 1.5–2.5 fold (from 80–120 nm to 450–700 nm) (Fig 3A; summary of the results is diagrammed in Fig 3B). rrnC is reported to not be part of the cluster [6,8], yet its distance from rrnG remained curiously unperturbed by heat, staying at ~400nm median, suggesting perhaps a specificity to clustering that is immune to the global effect of heat stress.

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Fig 3. Disruption of rrn clustering by heat stress and by FecI.

(A) Effect of heat stress on clustering of indicated rrn pairs. Other descriptions as in Fig 1. (B) Schematic summary of data in A. Each of the 7 rrn operons was visited at least once. (C) Response to rpoH induction of tested rrn pairs. +/− symbols are self-explanatory. (D) Effect of induced ectopic expression of five σ factors on rrnA-D distance, all placed under control of pTc with the same RBS. Data used for plotting Fig 3 graphs can be found in the Supplemental “S1 Data” file under appropriate figure headings. Created in BioRender. Ho, K. (2025) https://BioRender.com/g63t231.

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The effect of RpoH on the rrn pairs tested was non-uniform (Fig 3C). Specifically, we observed that two pairs that shared rrnH appeared to be resistant to disruption by σ^H (Fig 3C). Thus, contacts between all the rrn loci in the cluster are not uniform, suggesting that there is likely a sub-organization within this structure.

To determine if σ^H played a unique role in affecting cluster stability, we ectopically expressed the other five sigma factors: RpoE (σ²⁴), RpoF (σ²⁸), RpoN (σ⁵⁴), RpoS (σ³⁸) and FecI (σ¹⁹), under the same promoter as the rpoH-expressing vector, maintaining the same RBS (ribosome binding site). Of these, FecI produced a smaller (compared to RpoH) destabilizing effect (134 nm versus 234 nm; Fig 3D). To test if σ^D would restore clustering to the FecI-expressing strain like it did when co-expressed with σ^H (Fig. 2A), we co-expressed rpoD with fecI; clustering was completely restored to WT levels (Fig 3D, compare last two plots). We note that the shared properties of RpoH and FecI are that they are both normally sequestered at the inner membrane, suggesting perhaps that rrn clustering may have a membrane component.

σ^H disrupts the rrn cluster as tracked by the Mu method

Phage Mu transposition requires direct contact between Mu and its transposition target and displays virtually no sequence specificity in its target choice [33]. Higher or lower frequencies of transposition are therefore interpreted to reflect higher or lower rates of physical contact between the interacting chromosomal regions, analogous to the contact frequencies inferred from normalized Hi-C data [34]. Contacts made by Mu when located next to rrnD showed significantly positive interaction between rrnD and chromosomal regions containing rrnA, B, E, G, and H (but not rrnC), suggesting physical proximity of these regions, as also seen by FROS [6].

Since we did not examine the effect of σ^H on all possible combinations of rrn loci with FROS (Fig 3C), we used the Mu method to examine transposition patterns of Mu located near rrnD in the presence of ectopically expressed RpoH. The pattern of Mu transposition from the rrnD-proximal locus in WT cells was consistent with the earlier report, where Mu could access every region of the chromosome irrespective of its starting location (Fig 4A). In the presence of the vector encoding RpoH, even without induction of RpoH expression, the transposition landscape was altered, with more Mu insertions now occurring proximal to the starting Mu in Bin 73 (Fig 4B). This increase in local versus distal contacts is exacerbated with rpoH induction (Fig 4C). Transposition frequencies across different conditions are summarized in Fig 4D. We observed that with expression of rpoH, Mu predominantly inserts in its starting bin (yellow bar, indicating highest transposition frequency, seen only in the RpoH and +RpoH data), with insertions to distant bins decreased (darker blues in +RpoH and RpoH columns compared to No Vector) (Fig 4D). We conclude that rpoH overexpression disrupts long-range contacts.

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Fig 4. Expression of rpoH disrupts rrn clustering as tracked by Mu transposition.

(A) Frequency of transposition of Mu located in the vicinity of rrnD (Bin 73) after one round of transposition. The number of insertions has been normalized to the read depth of each bin but not normalized to copy number. In the asynchronous population, many cells have partially replicated chromosomes [8]. The initial Mu position is indicated by a red triangle. The Escherichia coli genome was partitioned into 100 equally sized bins, so each bin is ~46 kb. Starting bin number and chromosomal regions are indicated on top. Each vertical bar represents the average normalized transposition frequency of three biological replicates at the indicated bin, expressed as a percentage (with the highest transposition frequency being set to 100%). Gray error bars are the standard deviation. Color of bars correspond to regions of the E. coli chromosome annotated up top [8]. (B) Same as (A) but with the rpoH vector. (C) Same as (B) but with induction of rpoH. (D) Heat map of the data from A through C. In the no-vector control, the highest insertion frequency (yellow bar) occurs in Bin 94 (close to oriC which resides in Bin 90). (E–J) Transposition frequency from mlaF (rrnD) into the indicated rrn operon-containing bin under conditions described in A through C. The individual data points and associated standard deviations are shown. Statistical significance was determined with Student t test (two-tailed), *p < 0.05. ns: not statistically significant. (K) Model for σ^D-mediated rrn clustering. σ^D-bound RNAP (dark green) is responsible for clustering rrns (blue circles) at the membrane either directly or through other unknown factor(s) (gray hexagon). GroEL/ES (red oval) represses the activity of σ^H (orange) preventing it from competing with σ^D for core RNAP. σ^H is typically localized to the inner membrane and degraded. Upon heat stress, σ^H levels rise, and GroEL/ES, being diverted to un-folded substrates (pink oval), is unable to repress σ^H activity. Elevated σ^H levels from a plasmid, phenocopy the heat response. σ^D-bound RNAP complexes are displaced through competition with increased σ^H, and σ^H-bound RNAP now transcribes genes in σ^H regulon (green circles), lowering the total amount of RNAP transcribing rrn operons at the membrane, destabilizing clustering. Data used for plotting Fig 4 graphs can be found in the Supplemental “S1 Data” file under appropriate figure headings. Created in BioRender. Ho, K. (2025) https://BioRender.com/d36h492.

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We next specifically examined the bins containing rrn operons to assess the effect of RpoH on Mu transposition. We observed that contact between rrnD and a majority of the rrn loci decreased (Fig 4E, 4F, 4H, and 4J), with the notable exception of those with rrnC and rrnH, which remained unchanged (Fig 4G and 4I). The rrnC result is consistent with earlier reports [6,8], and the rrnH result is consistent with data in Fig 3C, where RpoH promoted de-clustering of all rrn examined, with the exception of rrnH. Overall, the Mu transposition data support the hypothesis that σ^H promotes de-clustering of rrn operons.

Ability to disrupt its organization validates the existence of the E. coli ‘nucleolus’

In eukaryotic cells, the nucleolus is the primary site of ribosome biogenesis [35]. It is a large structure, where hundreds to thousands (depending on the organism) [36] of rRNA operons localize, a device thought to have evolved to maximize translation efficiency [35]. By contrast, E. coli has only seven operons encoding ribosomal DNA (rrn operons) [37] (S1 Fig), 6/7 of which were observed to come together in some unknown fashion as reported by two completely different methodologies—FROS and Mu transposition [6,8]. Gaal and colleagues (FROS methodology) called this organization a “bacterial nucleolus” and showed that its presence was independent of growth media and cell doubling times [6]. Although the stronger of the two promoters, P1, and it UP element in rrnD, were required, active rrn transcription was ruled out because co-localization of the loci queried was resistant to disruption by rifampicin, which stops RNA chain elongation [38], as well to mutation of a −10 region of the P1 promoter required for initiation of transcription. These properties of the E. coli nucleolus are in contrast to the requirement for Pol I function and rRNA transcription to maintain nucleolar structure and integrity in eukaryotic cells [39]. We will therefore simply call this co-localization a rrn cluster.

To learn more about the nature of the rrn cluster, we attempted to perturb it by exposing cells to several environmental stressors (Fig 1B). Of these, heat and ethanol shock had the largest effect. These two stressors share a common “unfolded protein” response [11]. The important takeaway from this experiment is that by perturbing the cluster, we had not only validated its existence but found a handle to probe its nature.

The heat shock response, GroEL/GroES and σ^H all destabilize the rrn cluster: σ^D counteracts the action of σ^H

Adaptation to heat shock is a universal biological phenomenon [11]. Heat denatures proteins, so organisms adapt by synthesizing protein-folding chaperones. E. coli encodes several chaperones including GroEL/GroES and employs σ^H to transcribe hundreds of genes that enable bacterial survival [13,14]. The ability of heat shock to disrupt the rrn cluster (Fig 1B–1D) suggested to us that proteins must participate in holding the structure together at some level. We therefore directed FLAG-tagged dCas9 immediately upstream of each of the 7 rrn loci, followed by pulldown with FLAG antibodies and mass spectroscopy. No common protein was significantly enriched across all samples, but GroEL was enriched in 4/7 sgRNAs (rrnC, rrnA, rrnB, and rrnD), suggesting that GroEL could be a possible trans-acting factor in clustering (S4B, S6, and S7 Figs). (Although rrnC is not part of the cluster, it is close to the origin of replication ori, and it is conceivable that unrelated events at ori block its incorporation into the cluster).

The groEL/S operon is transcribed exclusively by σ^H, whose levels are kept low through multiple mechanisms, including sequestration at the inner membrane where it is degraded by protease FtsH, as well as direct interaction with GroEL/ES [27,28]. Attempts to query the participation of GroEL by providing it either from regulated promoter or its native promoter resulted in disruption of the rrn cluster (S4C–S4D Fig). To test if our manipulations of GroEL were perturbing σ^H levels, we provided RpoH ectopically from a regulatable promoter. Even in the absence of induction, leaky expression of RpoH was sufficient to disrupt the rrn cluster (Fig 2A).

Why should an apparently slight rise in σ^H levels have such a profound effect on stability of the cluster? Given that the P1 promoter and its UP elements somehow participate in maintaining the cluster, we imagined a scenario where RNAP bound to the housekeeping σ^D, known to transcribe the rrn operons [5], was stationed there, and that σ^H might be competing with it for binding RNAP, displacing it and disrupting the structure. Testing this conjecture by inserting rpoD along with rpoH on the ectopic vector resulted in significant cluster rescue (Fig 2A), supporting our conjecture.

Sigma factors that disrupt the rrn cluster associate with the membrane, suggesting a model for rrn clustering

Competition among sigma factors for the core RNAP has been proposed to be the mechanism for gene regulation [31]. If such a competition model applies to why rise in σ^H disrupts the cluster, then mutants of σ^H that are defective for either RNAP-core or DNA-binding should not disrupt the cluster. RpoH mutants defective for interaction with RNAP (L245P) and for promoter binding at the −35 region (E265A), were both significantly deficient in the de-clustering activity of WT RpoH (Fig 2C), in keeping with the competition model.

σ^H levels are regulated by at least three mechanisms: control through GroEL/GroES, DnaJ/K/GrpE, control of translation efficiency at the mRNA level, and the degradation of σ^H by FtsH, an integral-membrane protease. A third RpoH mutant we tested for its de-clustering activity was I54N, shown to escape FtsH-mediated proteolysis. Our expectation was that this mutant would behave like WT RpoH. Despite its high transcription activity (Fig 2D), however, the effect of the I54N mutant did not align with our expectations (Fig 2C), showing at the very least that high levels of transcription directed by σ^H are not the cause of de-clustering, and that none of the members of the σ^H regulon are involved in de-clustering. So why did the I54N mutant not behave like WT RpoH? Unlike the WT protein, I54N mutant is unable to localize to the membrane, suggesting that access to the membrane is important for the de-clustering effect of RpoH, ergo, the rrn cluster might be anchored in the membrane.

Of the five other sigma factors tested for cluster-disrupting ability, only FecI, an extracytoplasmic function (ECF) σ factor, also disrupted clustering, although not as severely as σ^H (Fig 3D). FecI, along with FecA and FecR, is responsible for the transcription of the ferric citrate transport system, consisting of fecABCDE transport genes [40]. FecA is an outer membrane protein that transports (Fe³⁺-citrate)₂ across the outer membrane. Upon binding of (Fe³⁺-citrate)₂ to FecA, the signal is then transduced through the periplasmic face of FecR, an inner membrane protein, to its cytoplasmic face [41,42]. This conformational change of FecR activates the transcriptional activity of FecI in the cytoplasm and promotes transcription of fecABCDE [43]. The dependence of FecI on FecR, which is localized to the inner-membrane, for efficient transcription by RNAP-FecI of the fec operons, also lends support to a model where the site of rrn clustering is the inner membrane. Why then did overexpression of σ^E, which is also an ECF, fail to disrupt clustering [44,45]? The mode of regulation of σ^E is based on the repression of σ^E by the inner membrane anti-sigma factor RseA, inhibiting the interaction of RNAP core and σ^E [46,47]. Upon membrane stress, such as ethanol or heat, DegS is activated by the presence of unfolded outer-membrane proteins, and, in concert with RseP, degrades RseA, releasing σ^E to promote transcription of downstream genes [48,49]. The negative regulation by RseA may explain why overexpression of σ^E alone is unable to disrupt clustering. In other words, for efficient disruption of the rrn cluster, σ^D-independent transcription needs to occur at the inner membrane.

Taken together, we propose that clustering of rrn is mediated by σ^D at the membrane either through σ^D directly or through other unknown factor(s) (Fig 4K). σ^D has indeed been observed at the membrane [28]. Given that high levels of transcription are not required, but the cis-acting elements responsible for high transcriptional activity of rrn are, and that some σ factors can disrupt clustering implicating σ^D as the clustering factor, we propose that the assembly of RNAP holoenzyme and UP element features of P1 drives rrn clustering. Upon heat stress or elevated σ^H levels, RNAP complexes bound to σ^D are displaced through competition with σ^H, and to a lesser extent FecI, for RNAP core, resulting in de-clustering of rrn operons. The order of RNAP assembly has been shown to be α₂ → α₂β → α₂ββ′ω → α₂ββ′ωσ [50]. The requirement for the P1 promoter may reflect the high affinity of α for the UP element present upstream of the −35 element [51]. The dimerization of the α subunit could drive the bridging interaction between the disparate rrn operons. However, this interaction by itself is unable to explain rrn clustering as demonstrated by de-clustering effect of σ^H and FecI, since σ factor binding and unwinding of the DNA duplex is the last step of RNAP assembly. Therefore, we propose that two elements are responsible for rrn clustering: (1) the high affinity of α for the UP element, and (2) DNA binding driven by σ^D. The affinity of α for the rrn UP element would explain why rrn clustering does not extend to other non-rrn loci. The specificity of σ^D for the rrn promoter would explain the de-clustering activity of σ^H and FecI. We note that σ^H did not affect the position of two pairs that shared rrnH (Fig 3C), suggesting a likely sub-organization within this structure. Finally, why did we recover GroEL at 4/7 rDNA loci (S4A–S4C, S6, and S7 Figs)? One possibility is that GroEL is meant to clear σ^H from the cluster [27]. Alternatively, since GroEL has been shown to restore transcription of heat-treated RNAP in vitro [52], it could either be interacting non-specifically with some component of RNAP or ensuring that the proteins at the cluster do not aggregate.

Relationship of the rrn cluster to RNAP condensates

RNAP has been reported to form distinct clusters on the E. coli nucleoid [53,54]. Since the bulk of cellular transcription is dedicated to rRNA transcription and since RNAP clusters form in fast-growth conditions, it was proposed that these clusters represent high concentrations of RNAP on rrn operons [55]. Subsequent work showed that RNAP clusters colocalize with nascent rRNA, but that their spatial arrangement was not dependent on rRNA synthesis activity and was likely organized by the underlying nucleoid [56]. RNAP clusters have been shown to be biomolecular condensates capable of phase separation, involving known factors associated with RNAP (i.e., ω subunit of RNAP and NusB) [21].

The properties of the rrn cluster are on the one hand reminiscent of RNAP condensates in that high level of transcription of rRNA is not required for their organization, but, on the other hand, are different in that the cluster is immune to transcription inhibitors while the condensates are not [56]. The cluster is unlikely to serve as a precursor for formation of RNAP condensates since these form in a strain where only one rrn operon is present [48]. Antitermination factors such as NusB and ω subunit of RNAP have been shown to contribute to formation of RNAP condensates [23]. However, we showed in this study that neither ∆nusB nor ∆rpoZ strains significantly impact the cluster (S3 Fig). We interpret these results to mean that RNAP condensates represent a feature of highly active transcription that the rrn cluster contributes to by making rDNA readily available through spatial localization.

Media, strains, phages, and plasmids

Unless conditions are specified, all strains are grown in LB at 30 °C with shaking. When appropriate, antibiotics were at the following concentrations: Ampicillin (Amp) at 100 µg/mL, Kanamycin (Kan) at 25 µg/mL, Chloramphenicol (Cam) at 20 µg/mL. Anhydrotetracycline (aTc) was used for induction of Tet promoter at 50 ng/mL. Isopropyl-β-d-Galactoside (IPTG) was used to induce the lac promoter at 1 mM. O-nitrophenyl-β-d-Galactoside (ONPG) was purchased from Sigma. Competent cells for transformation were prepared by washing a growing culture of O.D. 0.4–0.5 in cold 10% glycerol three times. The pellet was resuspended in 1:100 of the original volume in 10% glycerol. Electroporation was performed in E. coli Pulser (Biorad) with 1 mm Electroporation cuvette Plus (Fisher) at 1.8 V. Cells were recovered in SOC (LB supplemented with 10 mM MgCl₂, 10 mM MgSO₄, and 0.2% Glucose) for 1.5 h at 30 °C with shaking prior to plating on the appropriate selection. Mu phage was stored in Mu Buffer (50 mM Tris-HCl pH 8.0, 100 mM NaCl, 5 mM CaCl₂, 5 mM MgCl₂, and 0.1% gelatin). Strains and Phages employed in this study are listed in S1 Table. Primers, purchased from Integrated DNA Technology, used in this study are listed in S2 Table.

General molecular techniques

Routine PCR was performed with Taq DNA Polymerase (NEB), according to manufacturer’s instructions. PCR fragments for cloning were generated with Phusion DNA Polymerase (NEB) according to manufacturer’s instructions. Gibson Master Mix was made according to Gibson and colleagues [57]. Gibson assembly was performed at 50 °C for at least 2 h. Golden Gate Assembly was performed with Esp3I (NEB), T7 DNA Ligase (NEB), and T4 DNA Ligase Buffer (NEB). Primers used to generate gRNAs were used at 10 nM each. Hundred nanograms (100 ng) of the destination vector (pMAZ-SK sfgfp) was used. The program for Golden Gate Assembly was 3 min at 37 °C, 2 min at 16 °C for 35 cycles, followed by 1 cycle of 10 min incubation at 37 °C. T4 DNA Ligase, T4 Polynucleotide kinase (PNK), and T4 DNA Ligase Buffer were purchased from NEB.

Plasmid construction

Plasmids were constructed using Gibson assembly. Typically, 250 µL of the backbone was assembled with a molar equivalent of insert in a 20 µL reaction. The resulting product was purified with PCR clean-up kit (Qiagen) according to the manufacturer’s instructions. Two microliters (2 µL) of the 20 µL eluted product was used for transformation. Colonies were screened by PCR with primers spanning the junction between the backbone and the insert. Positive clones were restruck and checked once more using the same primer pairs. The PCR product was sequenced to confirm the identity of the sequence at either the UT Core Sequencing Facilities or Eton Biosciences. For generating single-point mutants, primers carrying the desired mutation were used to amplify the plasmid of interest. The resulting PCR product was gel-extracted with Qiagen Gel Extraction Kit. The product was then self-ligated overnight with T4 DNA Ligase and T4 DNA PNK in T4 Ligase Buffer. The ligated product was then purified and 2 µL of the 20 µL eluted product was used for transformation. Positive clones carrying the desired mutation were identified by PCR of the target sequence followed by sequencing. The positive clones were then restruck once again and verified with PCR and sequencing.

Strain construction

For insertion of parS sequences, the procedure was essentially as described in [58] with the exception being the template plasmid (pKH3 or pKH4) carrying the appropriate parS linked to antibiotic resistance cassette. Briefly, 0.5 mL of an overnight culture of MG1655 carrying pKD46 was pelleted, washed twice in 1 mL of phosphate-buffered saline (PBS), and diluted 1:100 in fresh LB supplemented with 0.2% arabinose. The culture was grown to an O.D. of approximately 0.4. The cells were made electrocompetent. Cells were then transformed with the appropriate PCR product and let recover in SOC for 3 h at 30 °C. The outgrowth was then plated on the appropriate selection and incubated at 37 °C overnight. Positive clones were identified by PCR with primers amplifying the junction of the expected insertion. Positive clones were struck out on the appropriate selection plate at 37 °C and reconfirmed with PCR followed by sequencing. To remove the antibiotic-encoding cassette for subsequent insertion of additional parS, pCP20 was transformed into the desired host strain. Clones carrying pCP20 were then struck out on LB plate without selection and incubated at 42 °C overnight. Colonies were then checked for the loss of both pCP20 and the antibiotics cassette by streaking on the appropriate selection.

Fluorescent microscopy and post-processing

Overnight cultures used for fluorescent microscopy were grown overnight in EZ-Rich media (Teknova) from a single colony and diluted 1:100 in fresh EZ-Rich media supplemented with IPTG for induction of ParB-fluorescent fusions from pFHC2973. The subculture grew until an O.D. of 0.4. One milliliter of the culture was then pelleted by centrifugation and resuspended in 100 µL of PBS. Six microliters of the suspension was then spotted onto agarose pad (1%) and let dry. The sample was then observed under Olympus-BX 53 microscope, XM10 monochrome camera on the 100× objective with oil immersion. Most images were taken at an exposure of ~100 ms for GFP filter, and ~400 ms for CFP filter, however, some samples required a longer exposure time to obtain acceptable signal for downstream processing; the upper limit of exposure was 3 s. Cellular stressors were applied 20 min prior to imaging, after which they were prepared and imaged as described above. For heat stress, the cells were transferred to a 42 °C water bath. SHX was added to a concentration of 50 µM. Cold stress was induced by incubating the cells in a 4 °C water bath. Ethanol stress was induced by adding ethanol to 0.5%. For image processing, the background was subtracted from the image, and foci were detected with ImageJ using the detect maxima function. The coordinates of the foci were exported and the Cartesian distance between a GFP focus, and its closest CFP focus was determined with custom Python script. The distance was computed by scaling the distance in pixel to nm with 1 pixel = 62 nm. Cell length was measured with ImageJ using the same scale as described above (1 pixel = 62 nm). For each experiment, three biological replicates were examined, and one representative is shown. Code used for analyzing the foci can be found at https://doi.org/10.5281/zenodo.14927315.

DAPI Staining and nucleoid measurements

Cells were grown and subjected to heat stress or rpoH expression in manner identical that used for microscopy. One milliliter of cells were then washed twice in PBS and resuspended in 200 µL of diH₂O. Thirty-seven percent Formaldehyde (Fisher) was added to a final concentration of 3.5% and incubated at 4 °C for 30 min, after which time, the cells were washed three times in 1 mL of PBS and resuspended in DAPI solution (500 nM in diH₂O, 200 µL) for 10 min. The cells were then washed once in 1 mL of PBS, resuspended in 100 µL of PBS, mounted for microscopy, and visualized with DAPI filter. The signal intensity for DAPI was measured by ImageJ.

Pulldown of dCas9 and proteomics

Three independent overnight cultures of MG1655 carrying pKH5 and the corresponding gRNA were pelleted and washed as described above. The pellet was diluted 1:100 into 100 mL fresh LB supplemented with selection and 0.2% arabinose and aTc. The cultures were grown to an O.D. of 0.6 and pelleted and washed three times in 1 mL of PBS. The subsequent pulldown procedure was carried out as described by FLAG Immunoprecipitation kit (Rockland). Every step of the pulldown was conducted at 4 °C. Briefly, the pellet was resuspended in 5 mL of lysis buffer and sonicated (Brason tip, 40% intensity) for 10 min (10 s on, 10 s off cycle). The lysate was clarified by centrifugation at 4 °C. Agarose-αFLAG Ab was washed twice in PBS and once in elution buffer. The washed Agarose-αFLAG Ab was incubated with the lysate overnight. After incubation, the beads were collected and washed 3 times with PBS. The bound proteins were eluted with elution buffer. The proteins were quantified by Mass Spectrometry at the UT Proteomics core. Samples were digested with trypsin, desalted and run on Dionex LC (liquid chromatography) and Orbitrap Fusion 2 (mass spec machine) for 60 min. Raw data were analyzed with PD2.2 and Scaffold 5 software. Downstream analysis was performed as described in [23] with lacZ gRNA pulldown as the negative control. Significantly enriched hits were ranked based on a z-score cut-off of 2.5.

Miller assay

0.5 mL of three overnight cultures of the desired strain carrying the appropriate plasmid was pelleted and washed in 1 mL of PBS twice. The pellet was then resuspended in 0.5 mL of PBS and diluted 1:100 in LB. The cultures were grown to an O.D. of 0.4 and aTc was added. At indicated time points, 20 µL of the culture was withdrawn, its O.D. 600 recorded and added to 80 µL of permeabilization buffer (100 mM NaHPO₄, 20 mM KCl, 2 mM MgSO₄, 0.8 mg/mL CTAB, 0.4 mg/mL sodium deoxycholate, 5.4 µL/mL β-mercaptoethanol). After the final time point, 600 µL of substrate solution (60 mM Na₂HPO₄, 40 mM NaH₂PO₄, 1 mg/mL ONPG, 2.7 µL/mL β-mercaptoethanol) was added to each sample. The samples were incubated at 30 °C for 60 min before 600 µL of 1M Na₂CO₃ was added to stop the reaction. O.D. 420 was recorded. Miller units were calculated as follows:

Mu phage preparation

When required, a Mu prophage strain was grown overnight, diluted 1:100 in fresh LB and grown until an O.D. of 0.5. After which, the culture was shifted to a 42 °C water bath and incubated until lysis is complete. The lysate was clarified by centrifugation at 6000g for 20 min. The supernatant was transferred to a clean flask and NaCl was added to a final concentration of 0.5M followed by the addition of Polyethylene Glycol 8,000 to a final concentration of 10% w/v. The mixture was incubated overnight at 4 °C. The pellet was then collected by centrifugation at 8000g for 20 min. The pellet was then resuspended in 1:100 of the original volume in Mu Buffer. Chloroform was added to the mixture and shaken. The phases were separated by centrifugation at 4000g for 10 min at 4 °C. The aqueous phase was collected (top layer) and phage titer determined prior to use.

Generation of mlaF::Mu and single-transposition experiments

Selection for Mu insertion at mlaF (bin72 as described in [8]) was performed by first introducing sacB at mlaF followed by infection Mu carrying a Cam marker. Briefly, attP was introduced into mlaF by λred recombination, and the Kan marker was removed. KH2 was then introduced into this strain, induced for expression of λ Int with arabinose followed by transformation of KH22. Clones positive for integration of the sacB-kanR cassette was verified by PCR spanning the expected junction between the cassette and mlaF. This strain was then infected with Mu::Cam at an MOI (multiplicity of infection) of 5, and survivors that were resistant to sucrose and Cam were selected for. Three colonies were picked and insertion locations for each were confirmed with PCR. Southern Blot was performed to confirm that only one Mu inserted at the desired location. Of three clones picked, one was confirmed to be a mono-lysogen. This strain was designated KH10. Single hop experiments with KH10 were performed as described in [8] with modifications for plasmid expression of rpoH. For induction of rpoH in KH10, overnight cultures were selected for Tet and subsequently grown in LB absent for Tet. One hour prior to temperature shift for induction of Mu transposition, aTc was added. Genomic DNA was extracted with Wizards Genomic DNA Kit, and sequencing was performed by Novogene on NovaSeq PE150 platform. Partitioning of the genome into 100 bins was described previously [8]. Data was processed using custom script described in [8] without LASSO regression for normalization. Instead, the number of insertions per bin was first corrected for total number of read counts, followed by correcting for replication effect by normalizing to the total number of E. coli reads mapped to that bin. The average of three biological replicates and standard deviation was used to plot data for normalized transposition frequency.