1. Chromosomes and Gene Expression
  2. Structural Biology and Molecular Biophysics
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Evidence for DNA-mediated nuclear compartmentalization distinct from phase separation

  1. David Trombley McSwiggen
  2. Anders S Hansen
  3. Sheila S Teves
  4. Hervé Marie-Nelly
  5. Yvonne Hao
  6. Alec Basil Heckert
  7. Kayla K Umemoto
  8. Claire Dugast-Darzacq
  9. Robert Tjian  Is a corresponding author
  10. Xavier Darzacq  Is a corresponding author
  1. University of California, Berkeley, United States
  2. University of British Columbia, Canada
  3. Howard Hughes Medical Institute, University of California, Berkeley, United States
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Cite this article as: eLife 2019;8:e47098 doi: 10.7554/eLife.47098

Abstract

RNA Polymerase II (Pol II) and transcription factors form concentrated hubs in cells via multivalent protein-protein interactions, often mediated by proteins with intrinsically disordered regions. During Herpes Simplex Virus infection, viral replication compartments (RCs) efficiently enrich host Pol II into membraneless domains, reminiscent of liquid-liquid phase separation. Despite sharing several properties with phase-separated condensates, we show that RCs operate via a distinct mechanism wherein unrestricted nonspecific protein-DNA interactions efficiently outcompete host chromatin, profoundly influencing the way DNA-binding proteins explore RCs. We find that the viral genome remains largely nucleosome-free, and this increase in accessibility allows Pol II and other DNA-binding proteins to repeatedly visit nearby DNA binding sites. This anisotropic behavior creates local accumulations of protein factors despite their unrestricted diffusion across RC boundaries. Our results reveal underappreciated consequences of nonspecific DNA binding in shaping gene activity, and suggest additional roles for chromatin in modulating nuclear function and organization.

https://doi.org/10.7554/eLife.47098.001

Introduction

Controlling the local concentration of molecules within cells is fundamental to living organisms, with membrane-bound organelles serving as the prototypic mechanism. In recent years, our understanding of the forces driving the formation of sub-nuclear compartments has undergone a paradigm shift. A number of studies suggest that many proteins have the ability to spontaneously form separated liquid phases in vitro (Banani et al., 2017), and recent work highlights the possibility that similar liquid compartments may occur in vivo (Courchaine et al., 2016Bracha et al., 2018). Such liquid-liquid demixing (liquid-liquid phase separation, LLPS) has been proposed to be a common mechanism in sequestering specific macromolecules within a compartment, or in increasing their local concentration and thereby facilitating molecular interactions. Formation of these structures is thought to be predominantly driven by multivalent interactions mediated through intrinsically disordered regions (IDRs), or via modular binding motifs, RNA, or DNA (Banani et al., 2017).

These observations have generated a deeper appreciation for the diversity of mechanisms that a cell may deploy so as to locally concentrate select molecular constituents. The list of proteins—particularly nuclear proteins—that can undergo phase separation in vitro continues to grow (Courchaine et al., 2016). For example, recent studies of RNA Polymerase II (Pol II) and its regulators have shown that Pol II forms dynamic hubs whose sizes depend on the number of intrinsically disordered heptad peptide repeats contained within the C-terminal domain (CTD) (Boehning et al., 2018), and that various CTD interacting factors may form phase-separated droplets in vitro (Lu et al., 2018) as well as local concentration hubs in vivo (Chong et al., 2018). We do not, however, fully understand the nature of the molecular forces that drive compartmentalization, and we lack compelling evidence of the functional consequences of these compartments.

Herpes Simplex Virus type 1 (HSV1) lytic infection provides an attractive model system because of its ability to form nuclear compartments de novo. HSV1 hijacks its host’s transcription machinery during lytic infection (Rice et al., 1994), transcribing its genome in three waves: immediate early, early, and late, with the latter strictly occurring only after the onset of viral DNA replication (Knipe and Cliffe, 2008). Viral replication generates subcellular structures called replication compartments (RCs) where both viral and host factors congregate to direct replication of the viral genome, continue viral transcription, and assemble new virions (Knipe and Cliffe, 2008). Recent reports highlight the ability of HSV1 to hijack host Pol II such that, once late gene transcription commences, the host chromatin is largely devoid of productively transcribing Pol II, and the majority of newly synthesized mRNAs are viral in origin (Abrisch et al., 2015; Rutkowski et al., 2015). Concomitantly, RCs show a dramatic enrichment of Pol II and other nuclear factors (Rice et al., 1994).

Given this shift in both the sub-nuclear localization of Pol II upon infection, and its effect on the transcriptional output of an infected cell, we chose to examine the mechanism of Pol II recruitment to HSV1 RCs as a model case for the generation of new subcellular compartments. We employed a combination of imaging approaches, and complemented these with genetic, genomic, and chemical perturbation experiments while measuring Pol II behavior in infected and uninfected cells. Despite initial indications that RCs exhibit many of the macroscopic hallmarks of LLPS, we find that recruitment of Pol II and other DNA-binding proteins to RCs is achieved through a distinct compartmentalization mechanism. Pol II recruitment occurs predominantly through transient, nonspecific binding of Pol II to viral DNA. These interactions are independent of transcription initiation, relying instead on the unusual feature that the HSV1 genome is largely free of nucleosomes, and therefore hyper-accessible to DNA-binding proteins relative to host chromatin. Our findings reveal that nonspecific binding can play a key role in the recruitment and retention of Pol II during infection, and more generally in the repertoire of distinct mechanisms a cell might employ to generate membraneless compartments.

Results

Pol II recruitment to RCs exhibit hallmarks of liquid-liquid demixing

HSV1 replication compartments form de novo following lytic infection, making them an attractive system to dissect compartment formation at the molecular level. To determine the mechanisms leading to the hijacking of Pol II, we used a U2OS cell line in which the catalytic subunit of Pol II has been fused to HaloTag (Boehning et al., 2018). HSV1 infection occurs rapidly, with large RCs forming within a few hours (Figure 1A). Because we were most interested in the early stages of lytic infection when Pol II is actively recruited to the RC, we focused our experiments on the period between 3 hours post infection (hpi), when RCs begin to emerge, and 6 hpi when infected cells begin to display significant cytopathic effects (Figure 1—video 1 and 2).

Figure 1 with 3 supplements see all
Pol II recruitment to Replication Compartments exhibits hallmarks of liquid-liquid demixing.

(A) Representative fluorescence and phase images in uninfected and infected cells. RCs shows a different phase value compared with the surrounding nucleoplasm. Red arrows show matched examples of RCs in the two channels. (B) Time-lapse images of Pol II recruitment to RCs. Zoom in shows RC fusion events. See also Figure 1—video 1 and 2. (C) Aspect ratios (max diameter/min diameter) of RCs from 817 RCs in 134 cells, 3 to 6 hpi. Red ellipses provided a guide to the eye of different aspect ratios. (D) IUPred scores for two Immediate Early viral proteins, ICP0 and UL54, as a function of residue position. Green boxes are predicted IDRs. (E) The fraction of each protein in the viral proteome that is unstructured, separated by kinetic class. HSV1 proteins are compared to a curated list of proteins containing IDRs known to drive phase separation (Cited IDRs). (F) FRAP curves of Pol II in RCs from 3 to 4 hpi, 4–5 hpi, and 5–6 hpi (n = 24, 33, and 33), compared with uninfected cells (n = 31). Shown is the mean flanked by SEM. (G) Infected HaloTag-RPB1 cell lines with a C-terminal domain containing different numbers of heptad repeats. (H) Pol II localization 1, 5 and 10 min after 10% 1,6-hexanediol treatment. All scale bars are 10 µm. Source data for of the list of IDRs in the HSV genome as well as previously cited IDRs can be found in Figure 1—source datas 1 and 2, respectively.

https://doi.org/10.7554/eLife.47098.002

In addition to Pol II, many other viral and nuclear factors re-localize to RCs (Dembowski and DeLuca, 2015). This redistribution of proteins is so dramatic that it can be seen as a change in the refractive index of RCs (Figure 1A). RCs grow and move over the course of infection (Figure 1B), and RCs exhibit other behaviors characteristic of liquid droplets, such as fusion (Figure 1B; Figure 1—video 1 and 2) and a spherical shape with an aspect ratio close to one (Figure 1C), reminiscent of interfaces subject to surface tension (Brangwynne et al., 2011).

Another hallmark of LLPS compartments is that they are commonly associated with enrichment in proteins with IDRs. Across all HSV1 proteins, we identified predicted IDRs based on the protein sequence (Figure 1D). When categorized by temporal class, the immediate early (IE) and viral tegument proteins—the two groups that are first available to the cell upon infection—had the highest fraction of predicted intrinsic disorder. Compared to a list of proteins known to undergo LLPS in vitro, the IE and tegument proteins are even slightly more disordered (Figure 1EFigure 1—source data 1). Under the working hypothesis that interactions between IDRs drive phase separation, the similarity in predicted disorder profiles between our curated list and the IE and tegument proteins suggests that IDRs in viral proteins may be as likely to undergo LLPS as experimentally validated proteins.

Based on the above descriptive observations, we hypothesized that Pol II should be recruited to RCs through interactions between its CTD and other IDR-containing proteins within the RC. To test this, we measured the Fluorescence Recovery After Photobleaching (FRAP) dynamics of Pol II in RCs. We saw a consistent slowing of recovery as infection progressed (Figure 1F), which could be interpreted as evidence that Pol II is incorporated and sequestered within the RC, an ‘ageing’ phenotype that others have described (Shin et al., 2017). Subsequent experiments to directly test this hypothesis, however, cast doubt on this interpretation.

Hub formation by Pol II in uninfected cells occurs in a manner dependent on the length of the Pol II CTD, a prominent IDR (Boehning et al., 2018). To test whether the Pol II CTD likewise mediates interaction with RCs, we compared Pol II accumulation in RCs using the cells generated by Boehning and colleagues: wild-type Pol II CTD (with 52 heptad repeats), and with truncated (25 repeats) or extended (70 repeats) CTDs. Despite a strong effect in uninfected cells on the distribution of Pol II (Boehning et al., 2018), the length of the CTD had no detectable effect on Pol II incorporation into RCs (Figure 1G), suggesting that Pol II recruitment in not sensitive to CTD length.

As a further test of the role of IDR interactions in Pol II accumulation within RCs, we treated cells with 1,6-hexanediol, which disrupts weak hydrophobic interactions between IDRs that drive LLPS (Lin et al., 2016). We infected cells for 5 hr, and then subjected them to treatment with a high concentration (10% w/v) of 1,6-hexanediol. Despite significant morphological changes in the nucleus after treatment, consistent with widespread disruption of cellular organization (Lin et al., 2016), Pol II remained highly enriched in RCs (Figure 1H). Furthermore, other IDRs with LLPS capabilities and which are known to interact with the CTD (Chong et al., 2018) are not enriched in RCs (Figure 1—figure supplement 1), suggesting that formation of RCs does not require interactions between the IDRs of Pol II and other host or viral proteins.

Unrestricted Pol II diffusion across RC boundaries is inconsistent with an LLPS model

The data outlined in Figure 1 present a potential contradiction, as RCs exhibit several properties commonly associated with phase separation in vitro, yet Pol II recruitment to RCs is clearly not dominated by homo- or heterotypic interactions through its IDR. We sought to better understand the mechanism driving the enrichment of Pol II in RCs by measuring the behavior of individual Pol II molecules. To accurately capture both immobile and freely diffusing Pol II molecules, we used stroboscopic photo-activatable single particle tracking (spaSPT) to visualize and track molecules (Figure 2A) (Hansen et al., 2017; Hansen et al., 2018). We labeled Halo-Pol II with equal amounts of JF549 and PA-JF646 (Grimm et al., 2015; Grimm et al., 2016), allowing us to accurately generate masks to then sort trajectories as either ‘inside’ or ‘outside’ of RCs (Figure 2B, Figure 2—video 1 and 2). A qualitative comparison of trajectories of single Pol II molecules in RCs shows enrichment in short, constrained jumps compared to uninfected cells (Figure 2C, red arrows).

Figure 2 with 5 supplements see all
spaSPT of Pol II in infected cells shows no change in diffusion but an increase in binding.

(A) Example frames from spaSTP localization and tracking. Scale bar is 1 µm. (B) spaSPT experiments in infected cells at different times post infection. RCs are identified using Pol II fluorescence and used to make masks for sorting trajectories (green inside RCs; gray outside). (C) Zoom-in of trajectories in infected and uninfected cells. Red arrows show examples of traces with restricted movement. (D) Jump length distributions between consecutive frames of spaSPT trajectories. Histograms pooled from uninfected cells (n = 27), or HSV1 infected cells between 4 and 6 hpi (n = 96). Each distribution is fit with a two-state model. Inset shows depiction of two-state model where Pol II can either be freely diffusing or DNA-bound. (E) Mean apparent diffusion coefficient from the two-state fit in (D). Error bars are the standard deviation of the mean, calculated as described in Materials and methods. (F) FLIP curves comparing the rate of fluorescence loss after photobleaching Pol II in uninfected and HSV1 infected cells. Schematic shows location of bleaching laser (red crosshairs) and the region measured (black crosshairs). (G) Cumulative distribution function of the mean flanked by the SEM for jump lengths of molecules entering (left) or exiting (right) RCs. The distribution for HSV1-infected cells is compared to the distribution of jump lengths when RC annotations have been shuffled randomly. (H) Mean fraction of bound molecules from the two-state fit in (D). Error bars are the standard deviation of the mean, calculated as described in Materials and methods.

https://doi.org/10.7554/eLife.47098.008

Quantitative measurements can be made by building histograms of all the displacement distances from the trajectories, and fitting to a two-state model in which Pol II can either be freely diffusing (‘free’), or immobile and hence presumably bound to DNA (‘bound’) (Figure 2D, inset). Such a two-state model gives two important pieces of information: the fraction of ‘bound’ and ‘free’ molecules, and the apparent diffusion coefficient of each population (Hansen et al., 2018). It is important to note that, because this modeling approach takes the aggregate of many thousands of traces, these data cannot measure how long a particular molecule remains bound in a given binding event. Therefore, ‘bound’ refers to both specific DNA binding events—for example molecules assembled at a promoter or engaged in mRNA elongation—as well as transient, non-specific binding interactions.

The difference in the behavior of Pol II inside RCs compared with the rest of the nucleoplasm is immediately apparent from examining the lengths of jumps between consecutive frames (Figure 2C,D). Surprisingly, the mean apparent diffusion coefficient of the free population was unchanged between trajectories inside of RCs compared with those outside RCs or in uninfected cells (Figure 2E; Figure 2—figure supplement 1A–C). If RCs were a bona fide separate phase, one would expect differences in molecular crowding or intermolecular interactions to predominantly affect free diffusion, resulting in substantially different diffusion coefficients.

To confirm this result, we performed a fluorescence loss in photobleaching (FLIP) experiment, in which a strong bleaching laser targets the inside of an RC and loss of fluorescence elsewhere in the nucleus is measured to quantify exchange of Pol II between the nucleoplasm and the RC. Consistent with the spaSPT data, we see that Pol II molecules exchange between RCs and the rest of the nucleoplasm as fast as Pol II in uninfected cells (Figure 2F). Similar results were obtained by using Pol II tagged with the photo-convertible fluorescent protein Dendra2 (Cisse et al., 2013) and photo-converting, rather than bleaching, molecules in the RC (Figure 2—figure supplement 2A). Unlike the FRAP data, the rate of photobleaching does not change as a function of time after infection (Figure 2—figure supplement 2B–C). Thus, Pol II molecules freely diffuse out of the RC, rather than remain sequestered within RCs.

An LLPS model predicts that a diffusing Pol II molecule within an RC should be more likely to remain within the RC than to exit when it reaches the compartment boundary. We tested this prediction by examining all trajectories for events in which a molecule crosses from inside RCs to outside, or vice versa, to look for evidence of such a boundary constraint. Comparing the distribution of displacements for a particle going from inside the RC to outside, we see no difference in the distribution of displacements, either entering or leaving RCs, when compared to uninfected cells in which mock RC annotations were randomly imposed in silico (Figure 2G; Figure 2—figure supplement 3). Indeed, we cannot detect any evidence of a boundary for molecules entering or leaving RCs, further arguing that RCs do not consist of a distinct liquid phase.

While the two-state model shows no change in diffusion coefficient of Pol II, the fraction of molecules in the ‘bound’ state doubles inside RCs, reaching ~70% (Figure 2H). We verified that this was not an artifact of the masking process by randomly shuffling RC annotations around in silico (Figure 2—figure supplement 3C,D), and that diffusion coefficients of the bound population are consistent with those of chromatin (Hansen et al., 2018), and thus reflect DNA binding (Figure 2—figure supplement 1D). The increase in the fraction of bound molecules is further supported by slowed recovery in the FRAP data (Figure 1F). The striking shift in the fraction of DNA-bound molecules, even while the FLIP decay rates remain unchanged, argues that this is due to an increase in the rate of Pol II binding rather than a decrease in the rate of Pol II unbinding. Thus, the mechanism driving Pol II recruitment to RCs is dominated by DNA binding rather than unbinding, which argues against the ‘aging’ phenomenon that others have observed (Shin et al., 2017).

Pol II recruitment to RCs occurs independent of transcription initiation

One possible explanation for the increased fraction of bound Pol II in RCs would be a high level of active transcription in these compartments. Multiple lines of evidence suggest that transcription derived from the viral genome is activated to a much greater extent than transcription of even the most highly transcribed host mRNAs (Rutkowski et al., 2015), and this may be sufficient to explain the increase in DNA-bound Pol II.

To test whether active transcription is driving Pol II recruitment to RCs, we treated infected cells with either Triptolide or Flavopiridol, small molecules that selectively inhibit stable Pol II promoter binding or transcription initiation, respectively (Figure 3A) (Bensaude, 2011). HSV1 requires the expression of immediate-early and early genes to generate its DNA replication machinery, so we allowed the infection to progress for four hours before treating with either compound. Cells at this time point have well-formed RCs, and Pol II binding is already greatly increased (Figure 2H). We treated these cells with either drug for 15, 30, or 45 min to inhibit de novo transcription and allow any elongating polymerases to finish transcribing (Figure 3B). RNA fluorescence in situ hybridization (FISH) against an intronic region showed significantly reduced nascent transcripts after 30 min of drug treatment (Figure 3C,D). Remarkably, even after 45 min of treatment, ~80% of the Pol II signal remains within RCs (Figure 3E,F). These data suggest that the recruitment of Pol II to RCs occurs largely independently of transcription, and without stable engagement with gene promoters.

Figure 3 with 1 supplement see all
Pol II recruitment to RCs occurs independent of active transcription.

(A) Schematic of Pol II-mediated transcription inhibition. (B) Schematic of the experiment regimen for imaging infected cells after transcription inhibition. (C) RNA FISH against the ICP0 intron to measure nascent transcription after Flavopiridol or Triptolide treatment. ICP8 marks viral RCs. (D) Quantification of the ICP0 intron signal in untreated cells (n = 170 RCs) those treated with TRP(n = 192, 171, 191 RCs, respectively) and FLV(n = 158, 238, 153 RCs, respectively). Error bars are standard error of the mean. (E) Halo-Pol II distribution after 45 min of Triptolide or Flavopiridol treatment. All scale bars are 10 µm. F) Quantification of the total fraction of Pol II recruited to RCs in untreated cells (n = 29) with TRP(n = 33, 24, 33, respectively) and FLV(n = 36, 24, 38, respectively). Error bars represent standard error of the mean. (G) Mean fraction bound measured from spaSPT of Halo-Pol II, after transcription inhibition. Error bars are the standard deviation of the mean, calculated as described in STAR methods. (H) FRAP recovery curves of Pol II with (hashed) and without (solid) Triptolide treatment, for uninfected cells (n = 31, nine respectively) and cells infected with HSV1, 5hpi (n = 32, 12 respectively).

https://doi.org/10.7554/eLife.47098.014

By spaSPT, in uninfected cells, Triptolide or Flavopiridol treatment both reduce the fraction of bound Pol II by half, to ~15% (Figure 3G), similar to what others have reported (Boehning et al., 2018; Teves et al., 2018). Nevertheless, inhibition of transcription with Flavopiridol reduced the bound fraction inside of RCs by only ~5% (Figure 3G). Even treatment with Triptolide, which prevents stable engagement with TSS-proximal DNA, only reduced the fraction bound by ~12% (Figure 3G). Given this result, we conclude that the majority of binding events we measure are independent of viral transcription.

HSV1 infection appears also to confer some resistance to the effects of these drugs on Pol II binding to host chromatin, despite the fact that these inhibitors are sufficient to abrogate transcription (Figure 3C–F). Given the inherent limitation of spaSPT for inferring the length of binding events, we wanted to confirm that drug treatment prevented stable Pol II binding. Indeed, FRAP experiments in cells treated with Triptolide show a dramatically faster recovery rate for both uninfected and infected cells (Figure 3H). For the infected samples, this means that the ‘bound’ molecules measured by SPT do not remain bound for long times, as one would expect from high affinity protein-protein or protein-DNA interactions at cognate sites. Instead, the majority of the bound fraction is comprised of transient binding events independent of transcription. The fact that infected cells show increased DNA binding outside of RCs after drug treatment may be a result of other viral mechanisms that occur during infection, such as aberrant Pol II CTD phosphorylation (Rice et al., 1994) or termination defects (Rutkowski et al., 2015). Still, our results suggest that viral DNA and/or DNA-associated proteins mediate rapid and predominantly nonspecific interactions with Pol II in RCs.

It has been reported that the viral protein ICP8 interacts with the CTD of Pol II through a bridging interaction by the viral protein ICP27 (Zhou and Knipe, 2002). Others have used ICP27 truncation mutants to suggest that this ICP27-mediated mechanism is responsible for Pol II recruitment into RCs (Dai-Ju et al., 2006). Thus, we tested HSV1 mutant strains n504 and n406, which carry nonsense mutations in ICP27 that weaken or abrogate (respectively) the Pol II-ICP8 interaction, and should be defective for Pol II recruitment to RCs (Rice and Knipe, 1990; Zhou and Knipe, 2002). While these mutant strains generally show a deficiency in forming RCs and producing virus, we found that in cells where RCs do form, Pol II is recruited as efficiently as in cells infected with a WT virus (Figure 3—figure supplement 1A), and the FRAP recovery dynamics are indistinguishable from WT virus-infected cells (Figure 3—figure supplement 1B) suggesting it is unlikely that this specific viral complex is the major player in recruiting Pol II to RCs.

HSV1 DNA is much more accessible than host chromatin to Pol II

The finding that Pol II molecules remain bound—however transiently—to the viral DNA, even in the absence of transcription or other interactions involving viral proteins, suggests that the DNA itself could plays a dominant role in Pol II enrichment in RCs. Knowing the amount of viral DNA contained in any one RC may be crucial to understand the role viral DNA may play in RC formation and function, but to our knowledge, this has not been determined. We therefore sought to measure the amount of DNA in RCs using DNA FISH by targeting fluorescent probes to two specific regions of the viral genome (Figure 4A). Fluorescence intensities from infected samples were compared at different times post infection to samples that were infected in the presence of phosphonoacetic acid (PAA), an inhibitor of viral DNA replication that ensures there is only one copy of the viral genome per punctum (Figure 4B; Figure 4—figure supplement 1A) (Eriksson and Schinazi, 1989).

Figure 4 with 1 supplement see all
ATAC-seq reveals HSV1 DNA is much more accessible than chromatin.

(A) Schematic of the Oligopaint targets for DNA FISH. Separate probe sets target regions in the Unique Long (UL) arm and the Unique Short (US) arm. B) Representative images of DNA FISH of cells four hpi, infected in the presence (PAA, left) or absence (4hpi, right) of the replication inhibitor PAA. Pixel intensity values are the same for the two images. Scale bars are 10 µm. (C) Fluorescence intensity of DNA FISH signal in RCs after infection. 5–95% intervals are shown, with inner quartiles and median. Data are normalized to the median intensity value of PAA-treated infected cells. Medians: PAA = 1.0, 3 hpi = 0.8, 4 hpi = 4.8, 5 hpi = 31.1, 6 hpi = 47.0. (D) Mean fraction bound for Pol II in infected cells with and without PAA. Error bars are the standard deviation of the mean, calculated as described in Materials and methods. (E) H2B-Halo cells show histone H2B is not incorporated into RCs. Innumofluorescence against ICP4 marks RCs. (F) Fragment length distribution of ATAC-seq data for cells 4 hpi. Lengths corresponding to intra-nucleosomal DNA (50–100 bp) and mononucleosomal DNA (180–250 bp) are marked as a reference. (G) ATAC-seq read density plotted across HSV1 genomic coordinates. (H) ATAC-seq analysis of intra-nucleosomal DNA (50–100 bp) and mononucleosomal DNA (180–250 bp). Global analysis of all human Pol II-transcribed genes, centered at the transcription start site (TSS). (I) The same analysis as in (G), but centered at the TSS of HSV1 genes.

https://doi.org/10.7554/eLife.47098.016

The number of genomes within an RC correlates well with the time post infection (Figure 4C), and there is also a strong correlation between RC size and genome copy number (Figure 4—figure supplement 1B). Based on these data, we calculate that the average RC at 6 hpi has a DNA concentration of 3.9 × 104 bp/µm3, approximately 240 times less concentrated than average host chromatin (Monier et al., 2000). The totality of viral DNA in an average cell after 6 hr of infection corresponds to just ~0.2% of total DNA in karyotypically normal human nuclei (Table 1). Yet, despite its 100-fold lower DNA concentration, inhibition of viral DNA replication with PAA caused the fraction of bound Pol II molecules inside the pre-replication foci to decrease to ~50% (Figure 4D).

Table 1
Quantitative measurements of HSV1 DNA inside of RCs.

Related to Figure 4. Using the values obtained through DNA FISH and ATAC-seq, we can make estimates of the copy number, concentrations, and relative enrichment of the viral DNA compared to the host. All values are calculated based on measurements of cells 6 hpi.

https://doi.org/10.7554/eLife.47098.018
Table 1Genome Size (bp)Genome Copy numberTotal DNA (bp)Percent of Total DNAConcentration (bp/μm3 )§ATAC-seq read percentageFold enrichment over expected**
Host Genome*3.2 × 10926.4 × 10999.8 (±0.2)9.4 (±1.6)
x106
75.8 (±10.4)0.8 (±0.1)
Viral DNA1.5 × 10582 (±105)1.3 (±1.6)
x107
0.2 (±0.2)3.9 (±5.8)
x104
24.2 (±10.4)130 (±170)
Rel. Diff.2.1 × 104513 (±658)240 (±369)
  1. All values are the Mean (±S.D.).

    *. Assuming karyotypically normal human cell; †. relative difference = Human/HSV1; ‡. Under experimental conditions of MOI = 1; §. Concentration assuming nucleus volume taken from Monier et al. (2000); ¶. based on total reads mapped from each organism, n = 3; ** Fold enrichment = ATAC seq read percentage/Percent of Total DNA.

Since most of the observed Pol II binding events that we observe inside of RCs appear to be unrelated to transcription, but are clearly dependent on viral DNA replication, we wondered what might be different about the viral genome relative to host chromosomes. A likely candidate is the chromatin state of the viral DNA. There is presently no consensus about the organization of viral DNA during lytic infection, but mass spectrometry studies have failed to detect histones associated with viral DNA (Dembowski and DeLuca, 2015). Moreover, infection of a cell line constitutively expressing Histone H2B fused to HaloTag is not incorporated into RCs (Figure 4E).

To measure histone occupancy on HSV1 DNA, and get a measure of its accessibility, we turned to ATAC-seq, which gives signal proportional to the accessibility of the DNA at a given locus (Buenrostro et al., 2013). Based on the amount of viral DNA present in an infected cell, we calculated the fraction of reads one would expect to map to the virus relative to the host. At 6 hpi, by DNA FISH the viral DNA represents an average 0.2% of total nuclear DNA content. Yet under the same conditions at this time point, 24.2% of reads mapped to the virus on average, showing that viral DNA is at least 100-fold more accessible (Table 1).

The ATAC-seq fragment length distributions (Figure 4F; Figure 4—figure supplement 1C) showed a much faster decay for reads mapping to the virus at all times post infection, and with no evidence of nucleosomal laddering, in stark contrast to reads that map to the host genome. When we visualized the position of all HSV1-mapped reads along the viral genome, the profiles were strikingly flat and featureless (Figure 4G). An average of all annotated human mRNA genes, centered at the TSS, shows a characteristic peak of accessibility at the TSS for reads with a length corresponding to inter-nucleosomal distances (<100 bp), and a characteristic trough of mono-nucleosome sized fragments (180–250 bp) (Figure 4H). By contrast, TSS averages mapped to the viral genome for either short or mono-nucleosome fragments show no changes in accessibility. Even averaging over all viral transcripts, it is clear that the entire viral DNA remains equally accessible (Figure 4I). Taken together, these data indicate that the HSV genome is maintained in a largely nucleosome-free state, and thus highly accessible to DNA binding proteins like Pol II.

Transient DNA-protein interactions drive Pol II hub formation through repetitive exploration of the replication compartment

Knowing that the DNA inside RCs is vastly more accessible to nuclear factors than host chromatin, we next asked what emergent properties of this accessible DNA might help explain Pol II recruitment. Using an HSV1 strain that allows incorporation of nucleotide analogs, (Dembowski and DeLuca, 2015), we fluorescently labeled DNA, imaged it at super-resolution, and found that, within a given RC, viral DNA shows variability in local density of nearly three orders of magnitude (Figure 5A).

DNA-binding alters Pol II exploration of RCs.

(A) STORM image of fluorescently labeled HSV1 DNA. Zoom-in shows one RC, and the heatmap shows the number of fluorophore localizations in each rendered pixel. (B) Schematic of Pol II exploring an RC and randomly sampling the viral DNA. (C) Example spaSPT trace, marking the angles between consecutive steps. (D) Angular distribution histograms extracted from Halo-Pol II in uninfected cells, and HSV1 infected cells 4–6 hpi, inside and outside of RCs. (E) Quantification of the relative probability of moving backward compared to forward (180°±30°/0°±30°). Error bars are the standard deviation of the mean, calculated as described in Materials and methods. (F) Same as in (D), except that cells were treated with Triptolide at least 30 min prior to imaging. Quantification of this data is also show in (E). (G) Representative PALM image of Halo-Pol II. ICP4 marks viral RCs. Heatmap corresponds to the number of detections per rendered pixel. (H) L-modified Ripley Curve (L(r)-r) for Halo-Pol II inside of RCs in cells five hpi (n = 13 cells). Graph shows the mean flanked by the SEM. All scale bars are 10 µm. Also see Figure 2—figure supplement 3E and F.

https://doi.org/10.7554/eLife.47098.019

The greater accessibility and higher variability in local density of viral DNA lend themselves to a possible mechanism by which Pol II becomes enriched. Recent theoretical work has shown that a polymer like DNA, which has many binding sites in close proximity, can induce an interacting protein to revisit the same or adjacent sites repetitively during its exploration of the nucleus (Amitai, 2018) (Figure 5B). In such a case, we should be able to see signatures in our spaSPT dataset of Pol II continually revisiting adjacent sites on the viral DNA. To check, we calculated the angle formed by consecutive displacements and compiled these angles into a histogram (Figure 5C) (Izeddin et al., 2014). For particles experiencing ideal Brownian motion, the angular histogram will be isotropic. Anisotropy can arise through a variety of mechanisms, such as adding the aforementioned ‘traps,’ thereby giving the particle a greater probability of revisiting proximal sites before diffusing away (Amitai, 2018).

In uninfected cells, and in infected cells outside of RCs, Pol II displays diffusion that is largely isotropic. In stark contrast, inside RCs Pol II diffusion is highly anisotropic, particularly around 180° (Figure 5D; Figure 2—figure supplement 3E; Figure 2—figure supplement 3F). To compare across samples, we computed the likelihood of a backward translocation (180°±30°) relative to a forward translocation (0°±30°). Analyzed this way, Pol II inside RCs has a 1.7-fold greater chance of making a backward step for every forward step it takes (Figure 5E). In cells treated with Triptolide, where stable binding is inhibited, the effect created by transient binding events is further amplified (Figure 5E,F), which helps explain the dramatic retention of Pol II inside RCs, even 45 min after inhibition of transcription (Figure 3E). These data are most consistent with a model in which Pol II repetitively visits the highly accessible viral genome via multiple weak, transient binding events which likely result in Pol II hopping or sliding along the DNA. The sharp anisotropy of the molecular exploration within the compartment means that a given Pol II molecule within an RC is more likely to visit the same or proximal sites multiple times before either finding a stable binding site or diffusing away.

The heterogeneous distribution of viral DNA within RCs, and the anisotropic way Pol II explores RCs, is also borne out in the distribution of Pol II molecules. Similar to the viral DNA, super-resolution photo-activated localization microscopy (PALM) renderings of infected nuclei revealed a heterogeneous Pol II distribution within RCs (Figure 5G). A key prediction of the formation of phase condensates is that LLPS compartments should form at a characteristic critical concentration, and that molecules within the high concentration phase should return to homogeneity within the phase (Freeman Rosenzweig et al., 2017). The highly heterogeneous nature of Pol II within the RCs provides yet further evidence that these compartments are not derived through an LLPS process. We used Ripley’s L-function to measure how the Pol II distribution deviates from spatial randomness, with values greater than zero indicating a concentration higher than predicted for complete randomness at that given radius (Figure 5H) (Ripley, 1977). We find that the curve remains well above zero, and increases, for all radii up to one micron. This suggests that Pol II forms hubs within RCs at multiple length scales, consistent with the behavior of Pol II in uninfected cells (Boehning et al., 2018), and inconsistent LLPS driving the constitution of RCs.

Nonspecific interactions with viral DNA license recruitment of other proteins

Seeing that Pol II is recruited to RCs via transient and nonspecific binding to the viral genome made us wonder whether this effect was specific to Pol II, or whether DNA accessibility can generally drive the recruitment of any DNA-binding proteins to RCs. Certainly, many other DNA-binding proteins are recruited to RCs (Dembowski and DeLuca, 2015). To assess whether nonspecific DNA binding could be responsible for their accumulation as well, we looked to an extreme example: The tetracycline repressor (TetR), and the Lac repressor (LacI). Both proteins are sequence-specific bacterial transcriptions factor, the consensus sites for which are absent in both human and HSV1 genomes. If proteins like TetR and LacI can be recruited to RCs despite lacking cognate binding sites, this is strong evidence that nonspecific DNA association is the driving mechanism for recruitment.

Expression of TetR-Halo and LacI-Halo shows enrichment within RCs (Figure 6), in stark contrast to Halo-NLS or HaloTag-fused IDRs (Figure 1—figure supplement 1). Furthermore, a comparison of the jump lengths measured in single particle tracking of TetR-Halo also reveals an enrichment in short translocations inside of RCs, consistent with higher fraction of bound TetR-Halo molecules (Figure 6—figure supplement 1). Thus, while IDR-based interactions alone are unable to generate strong enrichment in the RCs (Figure 1—figure supplement 1), even modest nonspecific DNA-binding affinity appears sufficient to do so.

Figure 6 with 1 supplement see all
Nonspecific DNA binding drives accumulation of other factors in RCs.

(A and C) Two representative cells from SNAPtag-RPB1 cells expressing TetR-Halo (A) and LacI-Halo (C), showing that both bacterial transcription factors are enriched in RCs. (B and D) Pixel line scans of images in (A) and (C). Red arrows give the direction of the x-axis. Left y-axis is the intensity of TetR-Halo or LacI-Halo fluorescence, right y-axis is the intensity of SNAPtag--Pol II fluorescence. All scale bars are 10 µm. Also see Figure 1—figure supplement 1.

https://doi.org/10.7554/eLife.47098.020

These data suggest a model in which viral Pol II recruitment consists of transient, nonspecific binding/scanning events of the highly exposed viral genome (Figure 7A). A DNA-binding protein exploring the nucleus (uninfected, or infected but outside of RCs) may encounter some occasions for nonspecific interaction with duplex DNA, but because of the nucleosome-bound nature of the host chromatin, these binding/scanning events are necessarily spatially dispersed and infrequent (Figure 7B). Within RCs, many copies of the unprotected HSV1 DNA are present, allowing nonspecific events to happen much more frequently, with fewer and shorter 3D excursions between DNA contacts (Figure 7C). Thus, transient protein-DNA interactions drive enrichment of DNA-binding proteins within RCs.

Model for Pol II exploration of RCs.

(A) A Pol II molecule encounters the accessible viral DNA multiple times along one potential route to eventually bind at a promoter. 3D diffusion through the RC is interrupted by binding interactions with the viral DNA (gray circles). (B) Hypothetical comparison of nuclear exploration outside RCs as a function of time and binding energy. A DNA-binding protein in the chromatinized nucleus will encounter nucleosome-free DNA sporadically, making multiple low-affinity interactions before eventually finding a high-affinity site. (C) Inside an RC, the high DNA accessibility might shorten the length of 3D excursions before a DNA-binding protein encounters another region of viral DNA in a low-affinity, nonspecific interaction. This, in turn, may reduce the distance a molecule might diffuse before its next binding event, and increases both the chances of that molecule remaining in close proximity and the chances that it will find a high binding energy interaction.

https://doi.org/10.7554/eLife.47098.022

Discussion

Multiple routes to create high local concentrations

Here, we have demonstrated that Herpes Simplex Virus type one accumulates Pol II in replication compartments because the virus’ unusually accessible DNA genome provides many potential nonspecific binding sites, acting as a molecular sink which causes a net accumulation of Pol II even in the absence of transcription. Such a mechanism for locally concentrating proteins is revealing, as it neither requires the formation of stable macromolecular structures nor produces any behaviors at the single-molecule level suggesting a separate liquid phase. Instead, by virtue of the fact that the viral genome appear to act as a single polymer globule (Figure 5A), from the macroscopic view Pol II recruitment to RCs appears to share many of the behaviors commonly attributed to liquid-liquid phase separation, and yet RCs are clearly a distinct class of membraneless compartment that operate on principles very different from an LLPS model.

We cannot completely rule out the possibility that some form of LLPS-like mechanism contributes to our observations in Figure 1. However, our data demonstrate that even if this is the case, it does not contribute to the enrichment of Pol II or the other proteins that we have tested. It is also difficult to rationalize how RCs could exist as a phase condensate without having any measurable impact on the free diffusion (Figure 2E), distribution (Figure 5G,H) or exchange of molecules that diffuse within and between compartments (Figure 2F,G; Figure 6—figure supplement 1). Our results prompt the need for a better characterization of bona fide phase separation, with a focus on its functional consequences in vivo, and suggest that caution should be exercised before assigning LLPS as the primary assembly mechanism based on criteria such as those applied in Figure 1. Likewise, significant caution should be exercised before interpreting the functional role of an LLPS-like system solely based on macroscopic behaviors.

We recently showed that the CTD of Pol II and other Pol II interacting partners can undergo LLPS in vitro and can form hubs in vivo (Boehning et al., 2018; Lu et al., 2018). Given the data presented above, there appears a contradiction between this and our previous findings. We emphasize that our current results do not mean that interactions between IDRs are not important. Rather, our results suggest an ‘upper limit’ for the potency Pol II CTD-mediated interactions to facilitate recruitment to RCs. While ectopic over-expression or in vitro preparations of IDRs may spontaneously create droplet-like structures (Figure 1—figure supplement 1E), these condensates do not become enriched in RCs either through heterotypic interactions with the Pol II CTD, or with other viral IDRs.

Multiple viral proteins are known to interact with Pol II or other preinitiation complex components. While we tested the most prominent of these interactions, and found that Pol II remains recruited to the viral DNA in the absence of interactions with the viral protein ICP27 (Figure 3—figure supplement 1), we cannot—nor do we wish to—rule out the possibility that other viral proteins may help facilitate this process. Importantly, our results do not contradict any of these unique mechanisms, but rather they provide a unifying rationalization for how they may work. As we demonstrated in Figure 6, even proteins that would never have been exposed to HSV1 over evolutionary time can still be recruited to RCs, provided they have some nonspecific affinity for DNA. In this way, any protein complex, be it solely viral or host or a composition of both, should be recruited to RCs provided it contains a DNA-binding domain.

Nonspecific DNA binding is an important feature for nuclear exploration

Our data also reveal a previously underappreciated aspect of how a DNA-binding protein finds its target site within the nucleus. It has long been recognized that nonspecific binding to DNA could accelerate the target search process by sliding in 1D; reducing the search space and empowering faster-than-diffusion association kinetics (Berg et al., 1981). The data we present here offer a new perspective on the importance of nonspecific low-affinity binding. When HSV1 replicates its genome, the newly synthesized viral DNA representing just 0.2% of the host chromosome load, is nevertheless, much more accessible to DNA-binding proteins than the totality of host chromatin (Table 1).

The finding that Pol II recruitment to RCs is independent of its CTD is reminiscent of RNA Polymerase I (Pol I) transcription of rDNA in the nucleolus. Pol I, lacking the long unstructured CTD that its homolog Pol II contains, is nevertheless robustly recruited to the nucleolus and transcribes rDNA into ribosomal precursors at prodigious rates. While there are certainly differences in the structure and stability of nucleoli and RCs, it has been shown that nucleolar components indeed exchange with the rest of the nucleoplasm rapidly (Chen and Huang, 2001). It is tempting to speculate that recruitment of some nucleolar proteins may benefit from the same mechanism of non-specific DNA binding that drives recruitment of Pol II and other DNA-binding proteins to viral RCs. We speculate that nonspecific protein:nucleic-acid interactions could also be a general mechanism used in other contexts. In particular, many RNA-binding proteins have been reported to undergo apparent LLPS (Courchaine et al., 2016), and it will be interesting to explore if these RNA-binding proteins share a similarities to what we observe here.

Mechanism of Pol II recruitment may explain robust transcription of late genes

An unresolved question in the study of herpesviruses is how genes with seemingly weak promoter elements can sustain such robust transcription (Rutkowski et al., 2015). While it is clear that other regulatory components also play a role in regulating late gene transcription (Davis et al., 2015), our data may at least help shed light on how the virus robustly transcribes these late genes. After replication onset, when there are many copies of the viral genome present in a single RC, the compartmentalization of Pol II (and the other general transcription factors) mediated through nonspecific binding could greatly favor assembly of PICs at otherwise weak late gene promoters. In this way, the virus can conserve precious sequence space in its genome to encode other important features, relying on fundamental mechanisms of nuclear exploration for Pol II and other components of the transcription machinery while still providing sufficiently robust gene expression for these essential late genes.

Revisiting insights into chromatin function

DNA accessibility in eukaryotes has long been recognized as a critical parameter for gene regulation (Paranjape et al., 1994; Weintraub and Groudine, 1976), and many chromatin remodelers have been shown to play a role in modulating nucleosome occupancy at promoters and enhancers. In vivo experiments using sequence-specific eukaryotic transcription factors find that a given factor will spend approximately half its search time undergoing 3D diffusion, and the other half bound nonspecifically, presumably scanning in 1D (Normanno et al., 2015); that it may visit as many as 105 non-cognate sites during its search. These experiments highlight the challenge a cell faces ensuring that endogenous regulatory sequences are able to effectively compete for cognate DNA-binding factors without becoming adversely influenced by non-target DNA sites. In this context, our results suggest that a less obvious—but critical—function of nucleosomes may involve the passivation of genomic DNA to minimize nonspecific interactions so as to maintain an active pool of freely diffusing nuclear factors, less hindered by their intrinsic propensity for nonspecific binding.

We postulate that a fine balance between the total amount of DNA-binding proteins and the degree of accessible DNA content in the cell is critically important. Nucleosomes, in addition to their obvious structural role in DNA compaction and cis-repression, could serve to uncouple cellular DNA content from the expression level of binding proteins. This mechanism of DNA passivation may be necessary in eukaryotes where the gene density and coding capacity is sparse, but total genomic load is very high; an essential step enabling the evolution of large genomes concomitant with the appearance of chromatin.

This may also point to a less obvious function for the observed increase in accessibility around promoters and enhancers, as a mechanism for effectively funneling DNA-binding proteins into the correct sites. The data presented above suggest that maintaining enhancers and promoters depleted of nucleosomes and accessible to DNA-binding proteins may contribute critically to facilitating the local accumulation of Pol II and other PIC components for transcription activation, without the need to invoke LLPS. In the case of RCs and the recruitment of Poll II, even well-established interactions between IDRs seem to be dispensable, underscoring the diversity of mechanisms driving local hub formation and functional compartments.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
Cell line
(Homo sapiens)
Halo-TAF15This paperU2OS SNAPtag-RPB1,
HaloTag-TAF15
U2OS (15 y/o female
osteosarcoma,
RRID: CVCL_0042) expressing
HaloTag-RPB1(N792D) selected for using alpha-amanitin, further expressing
HaloTag-TAF15
(AA 2–205)-NLS and selected for with Hygromycin
Cell line
(Homo sapiens)
H2B-SNAP-HaloHansen et al., 2018U2OS Histone
H2B-SNAPtag-HaloTag
U2OS
(15 y/o female
osteosarcoma,
RRID: CVCL_0042)
expressing Histone H2B-SNAPtag-HaloTag
and maintained in
selection with G418
Cell line (Cercopithecus aethiops)VeroATCCATCC CCL-81;
RRID:CVCL_0059
Cell line (Cercopithecus aethiops)V27Rice and Knipe, 1990V27Vero cells stable expressing ICP27 under selection of G418. A generous gift from Septhen Rice.
Sequence-based reagentCommon DNA
FISH forward
primer: 5’-GACACGTGATCCGCGATACGATGAAAGCGCGACGTCAGGTCGGCC-3’
Integrated DNA TechnologiesN/A
Sequence-based reagentCommon DNA
FISH forward
primer:
5’-GACACGTGATCCGCGATACGATGAAAGCGCGACGTCAGGTCGGCC-3’
Integrated DNA TechnologiesN/A
Sequence-based reagentCommon DNA FISH reverse primer: 5’- CTCGCTAATACGACTCACTATAGCCGGCTCCAGCGG −3’Integrated DNA TechnologiesN/A
Sequence-based reagentAlexa Fluor 647-labeled RT primer: 5’- TCGCGCTTTCATCGTATCGCGGATCACGTGTC-Alexa647-3’Integrated DNA TechnologiesN/A
Sequence-based reagentAlexa Fluor 555-labeled RT primer: 5’- TCGCGCTTTCATCGTATCGCGGATCACGTGTC-Alexa555-3’Integrated DNA TechnologiesN/A
Recombinant
DNA reagent
pSNAP-RPB1(N792D) (plasmid)This paperRPB1 carrying
N792D mutation for alpha-amanitin resistence
inserted
downstream
of SNAPtag with
the TEV protease sequence as a
linker reagion.
Recombinant DNA reagentpHalo-TetR (plasmid)This paperThe Tet repressor inserted downstream of HaloTag with
the TEV proease
site as a short linker.
Recombinant DNA reagentpHalo-LacI (plasmid)This paperThe Lac repressor inserted downstream of HaloTag with the TEV proease site as a short linker and a single SV40 NLS at the c-terminus.
Recombinant DNA reagentpHaloTag-3xNLS (plasmid)Hansen et al., 2017
Recombinant DNA reagentpHalo-TEV-EWS LC-NLS (plasmid)Chong et al., 2018
Recombinant DNA reagentpHalo-TEV-FUS LC-NLS (plasmid)Chong et al., 2018
Recombinant DNA reagentpHalo-TEV-Taf15 LC-NLS (plasmid)Chong et al., 2018
Software, algorithmCustom implementation of Spot-On and graphical analysisHansen et al., 2018; this paperSpot-OnThe source code is freely available at https://gitlab.com/dmcswiggen/mcswiggen_et_al_2019
Software, algorithmMatlab versions2014b, 2017aMathworks2014b, 2017a
Software, algorithmIUPred 2ADosztányi et al., 2005a; Dosztányi et al., 2005bIUPredThis tool is available at: https://iupred2a.elte.hu/download
Software, algorithmBowtie2Langmead and Salzberg, 2012BowtieThis tool is availabe at:http://bowtie-bio.
sourceforge.net/bowtie2/index.shtml
Software, algorithmSamToolsLi et al., 2009SamToolsThis tool is available at: http://samtools.sourceforge.net
Software, algorithmdeepTools2Li et al., 2009deepToolsThis tool is available at: https://deeptools.readthedocs.io/en/develop/
Software, algorithmIntegrative Genomics
Viewer 2.4.4
Robinson et al., 2011IGVThis tool is available at: https://software.broadinstitute.org/software/igv/ReleaseNotes/2.4.x
Software, algorithmR version 3.5.1R projectR
Software, algorithmADS R packagePélissier and Goreaud, 2015ADS R packageThis tool is available at:https://cran.r-project.org/web/packages/ads/index.html
Software, algorithmvbSPTPersson et al., 2013vbSPTThis tool is available at http://vbspt.sourceforge.net
Software, algorithmAdobe Illustrator CC2017Adobe Inc
Software, algorithmPrism 7GraphPad

Tissue culture

Human U2OS cells (female, 15 year old, osteosarcoma; STR verified) were cultured at 37°C and 5% CO2 in 1 g/L glucose DMEM supplemented with 10% Fetal Bovine Serum and 10 U/mL Penicillin-Streptomycin, and we subcultivated at a ratio of 1:3 – 1:6 every 2 to 4 days. Stable cell lines expressing the exogenous gene product α-amanitin resistant HaloTag-RPB1(N792D), SNAPf-RPB1(N792D) or Dendra2-RPB1(N792D) were generated using Fugene 6 (Promega) following the manufacturer’s protocol, and selection with 2 µg/mL α-amanitin. Stable colonies were pooled and maintained under selection with 1 µg/mL α-amanitin to ensure complete replacement of the endogenous RPB1 pool, as described previously (Boehning et al., 2018; Cisse et al., 2013). Cells co-expressing SNAPf-RPB1 and Halo-TetR were generated using the previously described SNAP-RPB1 cell line, and transfecting with TetR-HaloTag and a linearized Hygromycin resistance marker using Fugene six following the manufacturer’s protocol. Cells were selected and maintained with 100 µg/mL Hygromycin B. Fluorescent cells were selected by labeling the TetR-Halo with 500 nM JF549 and using Fluorescence Activated Cell Sorting to identify and keep the fluorescent clones.

Vero cells (Cercopithecus aethiops kidney cells; STR verified), were cultured for the growth and propagation of HSV1. Vero cells were cultured at 337°C and 5% CO2 in 4.5 g/L glucose DMEM supplemented with 10% Fetal Bovine Serum and 10 U/mL Penicillin-Streptomycin. Cells were subcultivated at a ratio of 1:3 – 1:8 every 2 to 4 days.

Virus infection

HSV1 Strain KOS was a generous gift from James Goodrich and Jennifer Kugel (Abrisch et al., 2015). UL2/50 was a generous gift from Neal DeLuca (Dembowski and DeLuca, 2015). All virus strains were propagated in Vero cells as previously described (Blaho et al., 2005). Briefly, cells were infected by incubation at an MOI ~ 0.01 in Medium 199 (Thermo) for 1 hr. 36-48 hpi, cells were harvested by freeze-thawing, pelleted, and sonicated briefly, and then centrifuged to clear large cellular debris. Because we were interested in the early events in infection, approximate titers were first determined by plaque formation assay in Vero cells (Blaho et al., 2005). More accurate MOI were determined by infecting U2OS cells plated on coverslips with the same protocol as would be using for imaging experiments. Cells were washed once with PBS, and then 100 µL of complete medium containing 1:10 – 1:105 dilutions of harvested virus were added dropwise onto the coverslip to form a single meniscus on the coverslip. Infection was allowed to proceed for 15 min at 37 °C. Samples were then washed once with PBS and returned to culturing medium and incubated for 8 hours before fixation. To measure the MOI, immunofluorescence for the expression of ICP4 using an anti-ICP4 primary antibody (Abcam), and counting the number of infected versus uninfected cells. MOI was then calculated, assuming a Poisson distribution of infection events, as P(kinf)=MOIkinfeMOIkinf!, where kinf is the number of infection events per cell. When counting the uninfected cells, this simplifies to MOI=ln(funinfected). All experiments were performed from the same initial viral stock, with care taken so that each experiment was done with virus experiencing the same total number of freeze/thaw cycles to ensure as much consistency as possible.

Transient transfection

For experiments where transiently transfected cells were also infected with HSV1, nucleofection was used to achieve more consistent infection across the coverslip. 1 × 106 cells were trypsinized and resuspended in Kit V buffer plus supplement (Lonza) with 500 ng plasmid, and nucleofected using program X-001, per the manufacturer’s instructions. Cells were plated on coverslips and allowed to recover for 48 hr prior to HSV1 infection.

Live cell imaging

Cells were plated on plasma-cleaned 25 mm circular No. 1.5H cover glasses (Marienfeld High-Precision 0117650) and allowed to adhere overnight. For experiments with HaloTag-expressign cells, cells were incubated with 5–500 nM fluorescent dye (e.g. JF549) conjugated with the HaloTag ligand for 15 min in complete medium. Cells were washed once with PBS, and the media replaced with imaging media (Fluorobrite media (Invitrogen) supplemented with 10% FBS and 10 U/mL Penicillin-Streptomycin). For experiments with cells expressing SNAP-RPB1, cells were labeled with 250 nM fluorescent dye (e.g. JF549) conjugated with the cpSNAP ligand for 30 min. After labeling, cells were washed for 30 min in complete medium. Prior to imaging, coverslips were mounted in an Attofluor Cell Chamber filled with 1 mL of imaging medium. Cells were maintained at 37°C and 5% CO2 for the duration of the experiment. For long-term time course imaging experiments, cells were plated in 35 mm No. 1.5 glass-bottomed imaging dishes (MatTek), infected with HSV1 at an MOI of ~1, and labeled with JF549, and finally the media exchanged for imaging media before placing in a pre-warmed Biostation (Nikon). At 3 hr post infection, infected cells were identified and imaged were taken every 30 s for 5 hr. For phase images, cells were plated and labeled as above, and imaged on a custom-built widefield microscope with a SLIM optics module (PhiOptics) placed in the light path directly before the camera.

Fluorescence recovery after photobleaching (FRAP)

FRAP experiments were performed as previously described, with modifications. HaloTag-RPB1 cells labeled with 500 nM JF549 were imaged on an inverted Zeiss LSM 710 AxioObserver confocal microscope with an environment chamber to allow incubation at 37°C and 5% CO2. JF549 was excited with a 561 nm laser, and the microscope was controlled with Zeiss Zen software. Images were acquired with a 63x Oil immersion objective with a 3x optical zoom. 1200 total frames were acquired at a rate of 250 msec per frame (4 Hz). Between frames 15 and 16, an 11-pixel (0.956 µm) circle was bleached, either in the center of a RC, or in a region of the nucleus far from the nuclear periphery or nucleoli.

FRAP movies were analyzed as previously described (Hansen et al., 2017). Briefly, the center of the bleach spot was identified manually, and the nuclear periphery segmented using intensity thresholding that decays exponentially to account for photobleaching across the time of acquisition. We measured the intensity in the bleach spot using a circle with a 10 pixel diameter, to make the measurement more robust to cell movement. The normalized FRAP values were calculated by first internally normalizing the signal to the intensity of the whole nucleus to account for photobleaching, then normalizing to the mean value of the spot in the first 15 frames. We corrected for drift by manually updating a drift-correction vector with the stop drift every ~40 frames. FRAP values from individual cells were averaged across replicates to generate a mean recovery curve, and the error displayed is the standard error of the mean.

Fluorescence loss in photobleaching (FLIP)

FLIP experiments were performed on the same microscope described above for FRAP. Rather than bleach an 11-pixel spot a single time, in FLIP the spot is bleached with a 561 nm laser (or in the case of Dendra2, photoconverted with a 405 nm laser) between each acquisition frame. Movies were collected for 1000 frames at 250 msec per frame (4 Hz), or one frame per second (1 Hz) for Dendra2.

FLIP movies were analyzed using the same core Matlab code as the FRAP data, except that fluorescence intensities from another 10-pixel circle were recorded to measure the loss of fluorescence elsewhere in the nucleus. This analysis spot was chosen to be well away from the bleach spot, either at a neighboring RC in infected samples or somewhere else in the nucleoplasm far away from both the nuclear periphery and nucleoli. Instead of internally correcting for photobleaching, photobleaching correction was based on an exponential decay function empirically determined to be at a rate of e-0.09 per frame. FLIP data from multiple cells were averaged together to determine the mean and standard error for a given condition.

RNA fluorescence in situ hybridization (FISH) and immunofluorescence (IF)

RNA FISH was used to measure the transcription output for a given RC. To ensure we were measuring nascent transcription, we chose to tile the intronic region of RL2, one of the few HSV1 transcripts with an intron. The 25 oligonucleotide probes were synthesized conjugated with a Cal Fluor 610 dye (Biosearch Technologies; for a full list of oligo sequences see Supplementary file 1). FISH was performed based on the manufacturer’s protocol. Briefly, cells were plated on 18 mm No. 1.5 coverslips (Marienfield) and infected. At the desired time point, cells were fixed in 4% Paraformaldehyde diluted in PBS for 10 min. After two washes with PBS, coverslips were covered with 70% v/v ethanol and incubated at −20°C for 1 hr up to 1 week.

For hybridizations, coverslips were removed from ethanol and washed in freshly-prepared Wash Buffer A (2 volumes 5x Wash Buffer A, 1 vol formamide, seven volumes H2O) (Bioseach Technologies). Hybridization buffer (10% v/v Dextran Sulfate, 300 mM Sodium Chloride, 30 mM Sodium Citrate, 400, 10% Formamide v/v, and 12.5 nM pooled fluorescent probes) was prepared freshly before each hybridization. A hybridization chamber was prepared with moistened paper towels laid in a 15 cm tissue culture plate. A single sheet of Parafilm was laid over the moistened paper towel. 50 µL of hybridization buffer was pipetted onto the parafilm, and a coverslip inverted into the hybridization buffer. The chamber was sealed with parafilm and placed in a dry 37°C oven for 4–16 hr. After hybridization, coverslips were placed back into a 12-well plate containing 1 mL Wash Buffer A and incubated twice for 20 min in a dry oven at 37°C, with the second wash containing 300 nM DAPI. In a final wash step, cells were washed in Wash Buffer B (Biosearch Technologies). Coverslips were mounted on glass microscope slides in Vectashield mounting medium (Vector Laboratories) and the edges sealed with clear nail polish (Electron Microscopy Sciences). For experiments with combined immunofluorescence and FISH, primary antibody was added to the hybridization buffer at a concentration of 2 µg/mL. An additional wash step with Wash Buffer A containing 1 µg/mL anti-mouse polyclonal antibody conjugated to AlexaFluor 647 was performed before DAPI staining and incubated at 37°C for 20 min.

Samples were imaged on a custom-built epifluorescence Nikon Eclipse microscope equipped with piezoelectric stage control and EMCCD camera (Andor), as well as custom-built filter sets corresponding to the wavelength of dye used. All samples were imaged the same day after hybridaztion and/or incubation with secondary antibody, and all samples to be quantitatively compared across coverslips were imaged on the same day using exactly the same illumination and acquisition settings to minimize coverslip-to-coverslip variation.

Single particle tracking (spaSPT)

Single particle tracking experiments were carried out as previously described (Hansen et al., 2017), but are described here in brief. After overnight growth, U2OS cells expressing Halo-RPB1 were labeled with 50 nM each of JF549 and PA-JF646. Single molecules imaging was performed on a custom-built Nikon Ti microscope fitted with a 100x/NA 1.49 oil-immersion TIRF objective, motorized mirror are to allow HiLo illumination of the sample, Perfect Focus System, and two aligned EM-CCD cameras. Samples were illuminated using 405 nm (140 mW, OBIS coherent), 561 nm (1 W, genesis coherent), and 633 nm (1 W, genesis coherent) lasers, which were focused onto the back pupil plane of the objective via fiber and multi-notch dichromatic mirror (405 nm/488 nm/561 nm/633 nm quad-band; Semrock, NF03-405/488/532/635E-25). Excitation intensity and pulse width were controlled through an acousto-optic transmission filter (AOTF nC-VIS-TN, AA Opto-Electronic) triggered using the camera’s TTL exposure output signal. Fluorescence emissions were filtered with a single bandpass filter in front of the camera (Semrock 676/37 nm bandpass filter). All the components of the microscope, camera, and other hardware were controlled through NIS-Elements software (Nikon).

For all spaSPT experiments, frames were acquired at a rate of 7.5 ms per frame (7 ms integration time plus 0.447 ms dead time). In order to obtain both the population-level distribution of the molecules for masking and the single trajectories, we used the following illumination scheme: First 100 frames with 561 nm light and continuous illumination were collected; then 20,000 frames with 633 nm light at 1–2 ms pulses per frame and 0.4 msec pulses of 405 nm light during the camera dead time; then 100 frames with 561 nm light and continuous illumination were collected. 405 nm illumination was optimized to achieve a mean density of ~0.5 localizations per camera frame, a density sufficiently low to unambiguously identify trajectories, even in dense regions like RCs. Data were collected over multiple courses of infection and 2 to 4 separate days for each condition in order to ensure a sufficiently large sample size.

ATAC-seq sample preparation

ATAC-seq experiments were performed as previously described (Buenrostro et al., 2013). Briefly, 100,000 U2OS cells stably expressing HaloTag-RPB1 were plated and allowed to grow overnight. The following day, cells were infected as described above, and incubated either in complete medium, or complete medium supplemented with 300 µg/mL phosphonoacetic acid (PAA). Infections were timed such that all cells were harvested at once. All the infected cell lines were then trypsinized, and 100,000 cells were transferred to separate eppendorff tubes. Cells were briefly centrifuged at 500 xg for 5 min at 4°C, and the supernatant discarded. After one wash with ice-cold PBS and another 5 min spin at 500 xg and 4°C, cells were resuspended directly in tagmentation buffer (25 µL 2x Buffer TD, 22.5 µL nuclease-free water, 2.5 µL Tn5 (Illumina)) and incubated for 30 min at 37°C. DNA extraction and amplification with barcodes were performed as previously described, with 10–16 total cycles amplification. Barcoded samples were pooled in equimolar amounts and sequenced using a full flow-cell of an Illumina Hi-Seq 2500 per replicate. Three replicates were performed, although the first replicate was deemed to have been over-amplified during the PCR step, and thus was omitted from the analysis.

Oligopaint on infected cells

For DNA FISH experiments, custom pools of fluorescently labeled DNA oligos were generated using previously published protocols (Boettiger et al., 2016). Briefly, oligo sequences tiling a 10,016 bp region in the Unique Long arm (JQ673480 position 56,985 to 66,999) and a 7703 bp region in the Unique Short arm (JQ673480 position 133,305 to 141,007) were manually curated using oligo BLAST (NCBI) against the HSV1 and human genomes with the following settings, following guidelines for Tm, GC-content, and length from previous Oligopaint protocols (Boettiger et al., 2016). Individual oligos were purchased commercially (the sequences for these oligos can be found in Supplementary file 2 and pooled. PCR was used to introduce a common T7 promoter on the 3’ end of the final probe sequence, then the PCR products were gel purified before in vitro transcription to generate ssRNA complimentary to the hybridization sequence. Finally, the entire RNA pool was reverse transcribed in a single reaction using Maxima RT (ThermoFisher) using either AlexaFluor-647 or AlexaFluor-555 5’-labeled oligos as the reverse transcription primer. After acid hydrolysis to remove the RNA, oligos were purified using high binding capacity oligo cleanup columns (Zymo) and resuspended in TE.

Cells were plated on 18 mm coverslips and infected as described above. Infected was allowed to progress for between 3 and 8 hr in the presence or absence of phosphonoacetic acid, then fixed with 4% paraformaldehyde for 15 min. Coverslips were washed twice with PBS, then incubated with 100 mM Glycine in PBS for 10 min. Samples were permeabilized for 15 min with 0.5% Triton-X100 in PBS, then washed twice with PBS. After permeabilization, samples were treated with 100 mM HCl for 5 min, then washed twice with PBS. Prior to hybridization, samples were washed twice with 2X SSC (300 mM NaCl, 30 mM Sodium Citrate), and then incubated at 42°C for 45 min in 2X SSC with 50% v/v Formamide. Coverslips were inverted onto a slide containing 25 µL hybridization buffer (300 mM NaCl, 30 mM Sodium Citrate, 20% w/v Dextran Sulfate, 50% v/v Formamide, and 75 pmol of fluorescently labeled oligos) and sealed with rubber cement. Samples were denatured at 78°C on an inverted heat block for 3 min, then incubated in a humidified chamber at 42°C for 16 hr. Samples were then removed from the glass slides and washed twice to 60°C with pre-warmed 2x SSC for 15 min, then washed twice with 0.4x SSC at room temperature for 15 min. Finally, coverslips were mounted on glass slides with Vectashield mounting medium.

DNA FISH samples were imaged on the same microscope as described above for immunofluorescence and RNA FISH. Z-stack images were collected from all the way below the focal plane to all the way above the focal plane, with a step size of 100 nm. All samples were imaged on the same day using the same illumination and acquisition settings to minimize coverslip to coverslip differences.

PALM of Pol II in RCs

For PALM experiments to precisely localize Pol II molecules within RCs, cells were labeled with 500 nM PA-JF549, and then infected as described above. Cells were fixed in 4% Paraformaldehyde in PBS, washed twice with PBS. Fluorescent 100 nm and 200 nmTetraspek beads were mixed in a 9:1 ratio then diluted 1000-fold in PBS. 100 µL was added to each coverslip and allowed to settle for 5 min, followed by 5 min of washing while rocking. Coverslips were mounted in Attofluor Cell Chambers and covered with PALM imaging buffer (50 mM NaCl, 50 mM Tris pH 7.9, 2 mM Trolox) to reduce triplet-state blinking.

Samples were imaged on a custom-built Nikon Ti microscope equipped similarly to the microscope for single particle tracking, with some differences described here. An Adaptive Optics module (MicAO) and a removable cylindrical lens were placed in the light path ahead of the EM-CCD (Andor iXon Ultra 897) cameras in the left and right camera ports (respectively) of the microscope. Astigmatism for precise 3D localization was introduced using the Adaptive Optics system. The Adaptive Optics system was controlled through the MicAO software and calibrated on 200 nM Tetraspek beads based on the total photon yield and point spread function shape after iterative tuning of the deformable mirror. After optimization, a slight astigmatism in the vertical Zernike mode (Astigmatism 90°=0.060) was added, and several z-stacks of 100 nM Tetraspek beads with 10 nm between slices to calibrate the PSF shape with the Z-position. 30,000 frames were acquired with the 561 nm laser line and increasing amounts of 405 nm illumination in order to keep the number of single molecules consistent across the duration of acquisition.

STORM on infected cells

For STORM experiments to visualize both RNA Polymerase II and the viral DNA, U2OS cells stably expressing Halo-RPB1 were plated on coverslips, labeled with 300 nM JF549, and infected with the UL2/50 virus strain (Dembowski and DeLuca, 2015) as described above. After infection incubation with virus, cells were transferred into complete medium containing 300 µg/mL PAA for two hours to prevent replication. After two hours, cells were released from inhibition by exchanging the culture medium with complete medium containing 2.5 µM 5-Ethynyldeoxyuridine for 4 hr. Cells were fixed with 4% Paraformaldehyde in PBS for 10 min, then permeabilized with 0.5% Triton X100 in PBS for 10 min. Copper(1)-catalyzed alkyne-azide cycloaddition was performed with the ClickIT imaging kit following the manufacturer’s protocol (Thermo). Coverslips were mounted in Attofluor Cell Chambers and covered with freshly-made STORM buffer (50 mM NaCl, 50 mM Tris pH 7.9, 10% D-glucose, 10 mM DTT, 700 µg/mL Glucose Oxidase (Sigma), and 4 µg/mL catalase). STORM experiments were performed on the same microscope described for PALM.

IUPred disorder prediction

Disorder predictions were preformed using a custom built python script to implement the IUPred intrinsic disorder prediction program (Dosztányi et al., 2005a; Dosztányi et al., 2005b). Specific protein sequences were placed in a table and this was fed into the script. All protein sequences were downloaded from the reference organism at uniport.org. The resulting traces were smoothed by a rolling mean of 8 residues to remove noise and prevent single low-energy residues from splitting single large IDRs into multiple apparent IDRs. Contiguous substrings of residues with centered-mean IUPred disorder likelihood greater than 0.55 were annotated as ‘disordered regions’ (Figure 1E), and those contiguous regions larger than 10 amino acids were included in the calculation of ‘fraction IDR’.

spaSPT data processing

SPT data sets were processed in four general steps using a custom-written Matlab (Mathworks): 1) Masks for RCs were annotated manually, 2) the masks were corrected for drift throughout the sample acquisition, 3) particles were localized and trajectories constructed, and 4) trajectories were sorted as ‘inside’ compartments or ‘outside’.

First, the 100 frames at the beginning and the end of each movie were separately extracted and a maximum-intensity projection used to generate ‘before’ and ‘after’ images of the cell or cells in the field of view. These images would be used to correct for movement of the cell as well as the individual RCs. For each cell, the nucleus was annotated in the ‘before’ image, and then again in the ‘after’ image. We assumed that the cell movement over the ~4 min of acquisition was approximately linear and calculated the drift-corrected nuclear boundary for every frame in the stack of SPT images. The same procedure was applied to each of the replication compartments. Particle localization and tracking were implemented based on an adapted version of the Multiple Target Tracking (MTT) algorithm, available at https://gitlab.com/tjian-darzacq-lab/SPT_LocAndTrack(Hansen, 2019; copy archived at https://github.com/elifesciences-publications/SPT_LocAndTrack). In the first step, particles were identified with the following input parameters: Window = 9 px; Error Rate = 10-6.25; Deflation Loops = 0. Following detection, a mask generated from the drift-corrected nuclear boundary was applied to discard any detections not within the nucleus. Trajectories were reconstructed with the following parameters: Dmax = 10 µm2/sec; Search exponent factor = 1.2; Max number of competitors = 3; Number of gaps allowed = 1.

Finally, after trajectories have been reconstructed, they were sorted as ‘inside’ RCs or ‘outside’. To minimize the potential for bias in calling trajectories inside of compartments, we only required a single localization in a trajectory to fall within a compartment for that trajectory to be labeled as ‘inside’. As is discussed in the main text, we tested this sorting strategy for implicit bias by computationally generating mock RCs in uninfected or infected samples (Figure 2—figure supplement 3). To do this, all the annotations for RCs from the infected samples (n = 817), as well as the distribution of number of RCs per infected cell, were saved in a separate library. We then took the uninfected cells and, in a similar process as described above, annotated the nuclear boundary and nucleoli. We then randomly sampled from distribution of RCs per cell a number of RCs to place in the nucleus, and then from the library of annotations randomly chose these RCs and placed them in the nucleus by trial-and-error until all of the chosen RCs could be placed in the nucleus without overlapping with each other, a nucleolus, or the nuclear boundary (Figure 2—figure supplement 3A). The SPT data were then analyzed as above—drift-correction, followed by localization, building of trajectories, and sorting into compartments—using the exact same parameters. We also followed this same procedure of randomly choosing and placing artificial RCs in infected cells, this time avoiding previously annotated RCs instead of nucleoli Figure 2—figure supplement 3B.

Two-state kinetic modeling using Spot-On

We employed the Matlab version of Spot-On (available at https://spoton.berkeley.edu) in our analysis and embedded this code into a custom-written Matlab routine. All data for a given condition were merged, and histograms of displacements were generated for between 1 and 7 Δt. These histograms were fitted to a two-state kinetic model which assumes one immobile population and one freely diffusing population: Localization Error = 45 nm; Dfree = [0.5 µm2/s, 25 µm2/s]; Dbound = [0.0001 µm2/s, 0.08 µm2/s]; Fraction Bound = [0, 1]; UseWeights = 1; UseAllTraj = 0; JumpsToConsider = 4; TimePoints = 7; dZ = 0.700. Trajectory CDF data were fit to a two-state model as first outlined by Mazza and colleagues, then expanded with implementation in Hansen and colleagues.

Spot-On has been shown to robustly estimate allthe fitted parameters, provided there is sufficient data—at a minimum 1000 trajectories for a 2-state fit of a model protein with diffusion characteristics similar to Pol II (50% bound, Dfree = 3.5 µm2/sec) (Figure 2—figure supplement 1A) (Hansen et al., 2018). Because of the sparsity of the data we collected per cell, we found that we could not reliably generate single-cell statistics, particularly within RCs where the total number of trajectories per cell fell well below the 1000-trajectory threshold (Figure 2—figure supplement 1B). In order to robustly fit our data and simultaneously estimate its variability, we first calculated the number of cells we would need to confidently fit all compartments and found 15 cells to optimal (Figure 2—figure supplement 1B). We then implemented a random subsampling approach where 15 cells from a particular condition were randomly chosen and analyzed. The Dfree, Dbound, and Fraction Bound were calculated iteratively for trajectories inside and outside of RCs. This random resampling was repeated 100 times, and the median values and standard deviations calculated and reported. When compared to the values that would have been obtained for taking the mean and standard deviation of the individual biological replicates, our subsampling approach agreed with these means within the measurement error (Figure 2—figure supplement 1C).

Analysis of angular distribution

Angular distribution calculations were performed using a custom written routine in Matlab, implementing a previous version of this analysis (available at https://gitlab.com/anders.sejr.hansen/anisotropyHansen, 2018, copy archived at https://github.com/elifesciences-publications/anisotropy/). To analyze the angular distribution of trajectories in different conditions, we started with the list of trajectories generated above, annotated as either ‘inside’ or ‘outside’ of RCs. A trajectory of length N will have N-2 three-localization sets that form an angle, and so we built a matrix consisting of all consecutive three-localization sets. It is crucially important that only diffusing molecules be considered in the analysis, as localization error of bound molecules would skew all of the data to be highly anisotropic. To address this, we used two criteria. First, we only applied a Hidden-Markov Model based trajectory classification approach to classify trajectories as either diffusing or bound (Persson et al., 2013), and kept only the trajectories that were annotated as diffusing. Second, we applied a hard threshold that both translocations (1 to 2, 2 to 3) had to be a minimum of 150 nm, which ensured that we could accurately compute the angle between them. Because a particle may diffuse into or outside of the annotated region, we counted a trajectory as ‘inside’ only if the vertex of the angle occurred within an annotated region.

ATAC-seq analysis

Sequenced reads were mapped separately to hg19 genome using Bowtie2 (Langmead and Salzberg, 2012) with the following parameters: --no-unal --local --very-sensitive-local --no-discordant --no-mixed --contain --overlap --dovetail --phred33. Reads were separately mapped to the HSV1 genome, JQ673480, using Bowtie2 with the following parameters: --no-unal --no-discordant --no-mixed --contain --overlap --dovetail --phred33. The bam files were converted to bigwig files and visualized using IGV (Robinson et al., 2011). TSS plots were generated using Deeptools suite (bamCoverage, computeMatrix, plotHeatmap tools) using UCSC TSS annotations for hg19 genome (Ramírez et al., 2016), and using a highly refined map of the gene starts in HSV1 kindly provided by Lars Dölken (University of Cambridge, to be published separately).

Analysis of immunofluorescence, RNA, and DNA FISH

All cells were analyzed using a custom Matlab script. First, a single image for each color channel was generated by automatically identifying the focal plane of the stack, and then integrating the pixel intensity for all pixels 1 µm above and below the focal plane. Nuclei were automatically segmented, but replication compartments could not reliable by detected using simple thresholding, and so each was manually annotated. A region of the image was selected to represent the black background, and the mean pixel value of this region was subtracted from every pixel in the image. After segmentation, the pixel values for each nucleus were recorded, as well as every RC within a given nucleus, and these were used to measure the signal within the RC, as well as the fraction of signal within compared to the rest of the nucleus (immunofluorescence only).

Quantification of DNA content within RCs

DNA FISH data were compared with ATAC-seq data for the six hpi timepoint. Despite the fact that U2OS are hypertriploid, we based all the calculations on the DNA content of a diploid cell. As such, the values presented here likely represent an upper bound on the relative concentrations of host and HSV1 gDNA for our experiments. Volume estimates for nuclei were based on data from Monier et al. (2000); volumetric measurements for RCs were taken directly from the annotations of the DNA FISH data.

PALM spatial statistics

Spatial statistics were collected on cells using previously published methods (Boehning et al., 2018). First, cell boundaries and replication compartments were annotated as for spaSPT experiments (above). Particularly for small objects like RCs, edge correction is crucial for accurate spatial point pattern statistics. Given a set of detections P, we used the estimator f to correct for biases generated by points near the RC boundary:

f(i,j,r)= {0, if d(i,j)>r2π d(i,j)Cin, otherwise

where d(i,j) is the distance between points i and j for i,j∈P, and Cin is arclength of the part of the circle of d(i,j) centered on i which is inside the annotated region (Goreaud and Pélissier, 1999). We then calculated N(r), the local neighborhood density:

N(r)=1NpiPijf(i,j,r)

where Np is the total number of detections within the region (Goreaud and Pélissier, 1999).

The modified L-function is compared to complete spatial randomness (CSR), a homogenous Poisson process with intensity λ, equal to the density of detections in the region of interest A. The K-Ripley function is defined as:

K(r)=N(r)λ

(Ripley, 1977). We estimated the modified L-function given by:

L(r)r= K(r)πr

(Goreaud and Pélissier, 1999). For the modified L-function, a spatial distribution with CSR remains at 0 for all radii. To implement this analysis, we used a previously published python script and the ADS R package to estimate the spatial statistics (Boehning et al., 2018; Pélissier and Goreaud, 2015). In order to estimate the error in our measurements, for each cell we performed random subsampling of the data, before annotation, to randomly select 25,000 detections 100 times, and fed these subsampled data to the R script computing the statistic. For very small radii, a high L(r)-r value is likely due to blinking and other photo-physical artifacts (Annibale et al., 2011), but at length scales larger than localization error the method becomes robust.

Data and software availability

The GEO accession number for the ATAC-seq data is: GSE117335. The SPT trajectory data are available via Zenodo at DOI:10.5281/zenodo.1313872. The software used to generate these data is available at https://gitlab.com/tjian-darzacq-lab.

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Decision letter

  1. Jessica K Tyler
    Reviewing Editor; Weill Cornell Medicine, United States
  2. Kevin Struhl
    Senior Editor; Harvard Medical School, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "DNA-mediated Nuclear Compartmentalization Distinct From Phase Separation" for consideration by eLife. Your article has been evaluated by Kevin Struhl (Senior Editor) and Jessica Tyler (Reviewing Editor). Dr Tyler's review is appended below, and the content of this decision letter also reflects input that she obtained from a leading expert in the liquid:liquid phase separation field and a leading expert on HSV-1 genome function.

Summary:

Both editors and external experts agreed that the work is timely, important and of high quality, thoughtfully addressing a variety of different hypotheses for why RNA polymerase II is enriched in membraneless compartments that contain the lytic Herpes Simplex Virus -1 (HSV-1) genome. We all agree that you fairly interpret your results and do a good job of explaining how your data goes against the simple predictions of liquid-liquid phase separated states. Furthermore, this work is congruent with, and adds important insights into, our understanding of the biology of HSV-1 and function of DNA binding proteins.

All four of the consulted experts/editors had access to the reviews from a previous journal that you provided with your submission, and your responses to the concerns raised in these previous reviews. The eLife editors and consulted experts all felt that your responses were appropriate and adequately addressed the previous reviewer comments. The problem (as you also argue in your responses to the previous journal's reviews) is that the field does not have good definitions for how to define the criteria for what an LLPS compartment should behave like in vivo. We think this is exactly the type of work that is needed to catalyze a deeper discussion in the field.

Taking this into account, the reviewers agreed that the work should be published largely as is, with the following suggestion: please consider softening the title to either "Evidence for DNA-mediated Nuclear Compartmentalization Distinct From Phase Separation", or "Is there DNA-mediated Nuclear Compartmentalization Distinct From Phase Separation?"

Our detailed comments are appended below for reference:

The compartmentalization of proteins into non-membrane bound, nuclear substructures is an important phenomenon that contributes to a multitude of molecular processes. An understanding of the mechanisms underlying various nuclear compartments is a burgeoning field. In particular, there is a growing and strong appreciation in liquid-liquid phase separation (LLPS) as an important feature of some nuclear compartments. In this report, McSwiggen et al. test whether LLPS is involved in the compartmentalization of replication centers (RCs), which are large nuclear compartments established upon the infection of Herpes Simplex Virus. RCs are composed of viral DNA that sequester host proteins important for the transcription of the viral genome, notably RNA polymerase II (RNAP II). The authors find that RC display characteristics distinct from traditional LLPS. To this end, the authors use live imaging analyses to track RNAP II movement within both the RCs and the nucleoplasm. They find that RNAP II enters and leaves the RC with similar kinetics, suggesting that RNAP II is not beholden to restricted diffusion characteristic of LLPS. Although the findings of this paper do not entirely rule out the possibility of a role for LLPS in nucleating the RC, the authors provide evidence of an alternative mechanism that concentrates RNAP II to the RC based on non-specific binding of RNAP II to the nucleosome-free viral genome. They show that the viral genome is predisposed for non-specific binding due to its high accessibility. Moreover, they demonstrate that other non-specific DNA binders, such as TetR and LacI, can similarly concentrate within the RCs. This latter experiment is particularly powerful in showing that non-nucleosomal DNA is sufficient for sequestering DNA-binding proteins and creating a unique biochemical environment in the cell. In sum, the findings of this paper are important to furthering the understanding of the dynamics of non-membrane bound nuclear substructures. Furthermore, this work has important implications for DNA binding factor localization, chromatin function and HSV-1 biology.

https://doi.org/10.7554/eLife.47098.033

Author response

[Editors' note: we include below the reviews obtained from another journal, along with the authors’ responses.]

The reviewers’ comments from our initial submission were largely quite positive. Reviewer 1 had many criticisms, though many of these stemmed from a misunderstanding of the experiments or of the model we are proposing. Reviewer 2 was much more optimistic and suggested some additional experiments for clarity. Reviewer 3 was also quite positive, though they telegraph a significant bias favoring phase separation as a mechanism in their review.

Reviewer #1:

In this manuscript by McSwiggen et al., the authors use an array of cutting-edge imaging and ATAC-seq approaches to study Pol II exploration of herpesvirus genomes that reside in replication compartments (RCs). While the approaches are impressive and the concept interesting, there are many sweeping conclusions drawn largely from drug experiments that lack proper controls. In addition, there is little regard for the complexity of RCs or the underlying biology of infection that offer alternatives, which seem to be brushed aside without rigorous testing. In addition, there is little to no mechanistic insight into the events being imaged. The choice of cell line is also a concern.

Major Concerns:

As the authors state, they use a "battery" of live cell imaging approaches. However, each approach seems to draw a sweeping conclusion that dismisses other possibilities in order to move to the next, rather than solidifying the basis for each conclusion, becoming more of a "bewildering battery".

We respectfully disagree with the statement that our conclusions are sweeping, or that they are made at the expense of other parsimonious explanations. For the few instances where Reviewer 1 has offered an alternative explanation, they are addressed below. We also are open to other specific suggestions that might challenge our current model, if such suggestions arise. We appreciate that explanations for our conclusions may have been couched too much in the parlance of particular imaging techniques, and we have tried to improve the clarity for a more general audience.

In addition, in each case a drug is used but not properly controlled, and in many experiments critical controls for probe specificity are missing. A good example is the PAA data presented in Figure 4. The authors assume PAA treatment worked, but they need to perform some kind of late gene expression analysis to confirm this [sic.].

We believe that we have performed all of the pertinent and reasonable controls with respect to each of our chemical perturbation experiments, and have included these data in the manuscript in as transparent a manner as possible. In the case of phosphonoacetic acid (PAA) treatment, the concentration of 300 µg/mL is widely used to prevent HSV1 replication, even in U2OS cells (Boissière et al., 2004; Crumpacker, 1992; Hancock et al., 2010; Lee et al., 2016; Rutkowski et al., 2015; Scott et al., 2001; Wyler et al., 2017). Also, we have direct experimental evidence showing that our protocol for treating infected cells with HSV1 indeed prevents viral replication, not the least of which is the fact that cells infected for 6 hours in the presence of PAA show two orders of magnitude lower fluorescence signal for DNA than cells infected for the same amount of time without addition of PAA (Figure 4C). As far as an assay goes, this is much stronger evidence than indirect assays such as qPCR of Late genes.

Indeed, a major problem with the paper is that it is almost entirely imaging with no real validation.

We reject the notion that data and conclusions generated via microscopy are any less valid than bulk biochemistry techniques, so long as one has the proper controls—controls that we have carefully performed and included. One could argue that the live cell and fixed cell imaging experiments are the best controls for the multitude of biochemical experiments so widely employed in the virus field and certainly should serve as a good complement to the more conventional strategies which have been informative but also necessarily limited in some cases. As is often the case, when new advanced technologies are applied to old problems, they may reveal entirely new mechanisms that have been invisible and this is especially true with single molecule, single cell quantitative imaging strategies. We hope it is evident that science moves forward best when orthologous methods are applied and new information interpreted without prejudice.

In addition, early in paper the authors state that low MOI is used, although in the methods an MOI of 1 is mentioned, yet both cells in the field of view in Figure 4B are infected and both contain multiple puncta, so they must be infected at quite high MOI if PAA blocked DNA replication as assumed

The term “low” is used in our manuscript to contrast our experiments to those by previous groups which were performed at MOI of 10 or higher (Chang et al., 2011; Taylor et al., 2003), but we concede that this is a relative term that is irrelevant to the remainder of the manuscript. As such, we’ve removed this specific language.

Two points are essential to consider here: First, MOI is a statistic of the mean number of infectious particles (as measured, most commonly, by plaque assay or similar) the average cell in a culture will receive upon addition of a virion-containing solution. The number of infectious particles a given cell receives is best modeled by a Poisson Random process (P(k) = e-mmk/k!, where k is the number of infectious viral particles per cell, and m is the MOI), and for an MOI of 1 it should follow the theoretical distribution shown in Author Response Image 1. Thus, approximately 1/3 of the cells will be uninfected, 1/3 will receive 1 infectious particle, 1/6 will receive two, and so on. This makes it highly probable that more than one infected cell will appear in a single field of view.

Author response image 1

Second, almost all measures of MOI, especially ones such as a plaque assay, measure only the number of fully-infectious particles, as they assay new virion production as the end-point. As the reviewer points out later in their comments, not every viral particle that enters a cell will cause productive lytic infection. In our case, where we are infecting with an MOI of 1—calculated using the fraction of uninfected cells as measured by ICP4+ immunofluorescence at 4 hours post infection—we find that the infected cells have multiple RCs. Regardless, these defective virions still have genomes that replicate and that are transcribed by Pol II, as we have measured and demonstrated using time lapse imaging and RNA FISH respectively, and so we feel it is perfectly reasonable to conduct our analyses under these conditions.

However, there is no uninfected control to show these FISH puncta are even specific. As such, all of the analysis stemming from this is flawed.

Uninfected cells were not shown in the main figure to conserve figure space. We have now included full fields of view in Figure 6—figure supplement 1 where uninfected cells can be clearly seen.

There are similar problems with many other figures. For example, the major conclusion that IDRs in either host or viral proteins are not involved is based on the assumption that viral RCs are accessible to the drugs used and that the drugs work against viral proteins. Is there evidence for this?

In Figure 3 we directly measured the nascent transcription of the ICP0 transcript as a means of confirming that both Flavopiridol and Triptolide inhibit transcription. Given that these drugs are quite low in molecular weight (~480 and 360 Da, respectively), and that they are permeable to the cell membrane, it would be difficult to imagine a scenario where they were unable to access Pol II inside of the RCs. We make no claims that these drugs work against any viral proteins; rather we show that they are very effective at preventing new Pol II-mediated transcription within RCs.

Confounding points about drug activity (e.g. outside RCs in Figure 3D) are ignored as "some resistance" when it is essentially complete resistance.

Host gene transcription is severely reduced and modified due to the viral infection and we therefore do not discuss drug activity on host genes. Once again, we have proper controls to show that the transcriptional inhibition is robust and efficient on viral genes, which is essential and sufficient for our conclusion. The fact that the fraction of bound Pol II outside of RCs hovers around ~30%, even after transcription inhibition, is an unexpected result that we have insufficient data to fully explain. However, we have some ideas based on our observations as well as on previously published work. It is known that although the majority of transcription in the host chromatin is redirected to the virus, some host transcription remains. For these transcripts, the polymerase experiences a defect in proper termination, and so can run on hundreds of kilobases past its normal termination site (Rutkowski et al., 2015). In addition, we can see from the ATAC-seq data in our own experiments that regions around the TSS become more accessible. We see a broader peak of accessibility in intra-nucleosome sized fragments, and smaller peaks corresponding to well-positioned nucleosomes in the mononucleosome sized fragments in Figure 4G at 4 and 6 hours post infection. This increase in accessibility could lead to more transient Pol II–DNA binding in much a similar manner as it does in the RC.

We have no data to say whether this 30% bound fraction is bound stably to host chromatin or whether it is transiently bound as we observe inside of RCs. While interesting, this phenomenon is outside the scope of our claims for this paper, and lacking any significant evidence that it is caused by a given mechanism, we acknowledge it in the manuscript but do not wish to speculate any further so as to not over interpret our data.

It is notable that the drugs in question do not affect ICP8 localization to RCs, and ICP8 has been shown to bind PolII. Points like this are ignored but could easily explain many of the observations; are the IDR drugs ineffective in disrupting ICP8-PolII interaction? Some form of pulldown and greater exploration of this is needed, beyond just imaging with assumptions.

We thank the reviewer for this comment. We had no intention to suggest that Pol II only interacts with the viral DNA: It is well known that a number of viral proteins interact with Pol II or other members of the core Pre-Initiation Complex. This includes the viral proteins VP16, ICP4, ICP8, ICP27, and ICP22 among others. It is certainly the case that each of the above proteins serve important, often indispensable functions in the biology of the virus. Both ICP8 and ICP27 have been shown to Co-IP with each other and with RNA Polymerase II in an ICP27-dependent manner (Zhou and Knipe, 2002). If fact, other reports have suggested what reviewer 1 is suggesting: That ICP27 is directly responsible for recruiting Pol II to RCs (Dai-Ju et al., 2006). This paper reports viral mutants of ICP27 (called n504 and n406) which they claim form RCs but do not recruit Pol II. Naturally, we were interested in these viral strains, as this would offer the simplest explanation for Pol II recruitment.

Unfortunately, we found that we were completely unable to replicate these authors’ data regarding Pol II recruitment. These mutants abrogate the interaction between ICP8 and Pol II (Zhou and Knipe, 2002), but what we found is that despite a deficiency in activating replication, once RCs are formed, Pol II was recruited to RCs as robustly as with the WT virus (Figure 3—figure supplement 1). Furthermore, FRAP of cells infected with either n406 or n504 have recoveries indistinguishable from WT virus. We had not included this data in the manuscript because it might distract from our key findings, and because we did not wish to create animosity with other groups where avoidable, but we now see that this is an important distinction with relevance given previous literature and have since included it.

Other evidence that alternative proteins are not generally responsible for the recruitment of Pol II can be seen in our other data. Specifically, Pol II does not show a different diffusion coefficient in RCs, as one might predict if there were changes in the size of the complex. Further, the FLIP data presented in Figure 2F shows that the rate of unbinding of Pol II is actually slightly faster inside of RCs, which argues against the formation of a stable complex as one might expect if Pol II were stably interacting with and predominantly recruited via ICP8, ICP27, or an as-yet-unknown viral factor. As the reviewer points out, we cannot say for sure that 1,6-HD disrupts ICP27-Pol II interactions, but taken together with the above arguments, we believe that we have sufficient evidence to disfavor ICP8/ICP27 interactions as a major contributor to bulk recruitment of Pol II to RCs.

Why are so many tegument proteins included in the IDR analysis in Figure 1? They are structural proteins and may skew this analysis in favor of the theory. The analysis should be limited to proteins that function in transcription.

It is incorrect to say that all tegument proteins are structural. Tegument proteins are part of the payload delivered to the cell upon viral entry past the cell membrane. Important viral transcriptional activators—particularly the viral protein VP16 that is known to be a strong driver of Pol II-mediated transcription for the Immediate Early and Early genes—are among the list of tegument proteins. We group these together with the Immediate Early genes, as we say in the paper, to represent a category of proteins that include the collective set of polypeptides available to the virus to initiate the formation of RCs.

This leads to a broader point. While the Pol II behaviors are interesting, they could also be explained by the highly crowded and complex nature of RCs that is quite different to the host nucleus. There is DNA replication and packing into virions going on here too, and although the authors mention it they do not address the question of Pol II modification by the virus or viral proteins that recruit Pol II in any meaningful way.

This criticism is a little perplexing, as our goal in this manuscript was to highlight just how the environment of RCs differ from the rest of the nucleoplasm, and how that affects Pol II recruitment. Namely, we show that the high accessibility of the viral genome leads to a number of different behaviors that we elaborate on in the text. We have strong and convincing evidence that molecular crowding is not of particular importance in these behaviors, as it does not change Pol II’s ability to diffuse within or exchange between RCs, shown using multiple different techniques. It is well known that RNA Polymerase II is aberrantly phosphorylated after infection (Fraser and Rice, 2005; Rice et al., 1995), but previous work has shown that this feature does not affect Pol II recruitment to RCs, nor is it absolutely required for productive infection in a tissue culture system. As stated above, we believe we have sufficient evidence that the proteins that are known to interact with Pol II are not primarily involved in its bulk recruitment to RCs, and have attempted to clarify this point in the manuscript.

In parts of the text they mention that there are behaviors of Pol II in the nucleus outside of RCs that are like but not exactly like a normal uninfected cell, which may hint that Pol II modification is involved. This should be explored by testing Pol II mutants in some manner.

We were unable to decipher which claims this reviewer would like to see investigated further, nor what sorts of Pol II mutants they believe would address the question. One possibility is that this comment was based on our description of Pol II CTD truncation mutants used in this paper and more fully described in our recently published report (Boehning et al., 2018). We have referenced our previous report and tried to clarify our use of these mutants in this paper.

The authors do everything in U20S cells. Not only are they transformed cell lines, they are "rogues" in the herpesvirus field; one critical factor in this whole process is ICP0. Yet U20S cells express an as yet unidentified protein that complements the phenotype of an ICP0 mutant and as the only cells known to have this function, they are widely used to grow ICP0 mutant viruses. It would be important to show that this behavior is not an oddity of the unusual choice of cell line in these studies.

It is true that U2OS cells are able to complement ICP0-null HSV1 mutants. Recent work has shown this is a deficiency in the U2OS cGAS/STING innate immune response to foreign DNA (Deschamps and Kalamvoki, 2017), a silencing step that the virus must evade prior to the induction of viral replication and formation of RCs. Given that we focus our study to the events occurring after RC formation, we do not anticipate that our choice of cell line greatly affects our results. Since these cells still produce RCs and recruit RNA Pol II in a manner indistinguishable from what is observed in other published cell lines, and given that we are ultimately interesting in using HSV1 infection as a model system to demonstrate a broader underlying phenomenon of DNA driven compartment formation, we do not see further insight being gained by repeating these experiments in a different cell type since RCs have been demonstrated by many labs to be similar in many cell systems.

There are also issues with the imaging approaches used:

1) Representative tracking data (raw images next to overlays with tracks) should be provided for all spaSPT datasets in the paper (infection time course, PAA, pol II inhibitors), movies and figures, in both whole nuclei and enlarged to within RCs. This is necessary for visual confirmation that tracking is accurate within the RCs.

We appreciate the reviewer’s point, as the devil is in the details regarding how masks are generated and further analysis performed. With this said, there are over 3000 individual image files (either or SPT or FISH) which have been annotated, representing multiple terabytes of data, and inclusion of these movies/figure would come at the expense of other supplemental information, as we have reached the maximum allowable supplemental figures. However, the raw data is all available on a data-sharing server with a dedicated DOI number, and we have now added more examples of the celllevel trajectories, including an expanded panel in Figure 2.

Accuracy of tracking in crowded areas should be highlighted specifically (especially if mean localizations per frame increases above 1).

For our spaSPT tracking technique, individual movies average ~0.5 particles per frame to avoid tracking errors, although more recent work from our lab suggests that accurate tracking is possible with might higher densities provided the probability for very long displacements (>800 nm) is sufficiently low.

2) What value ranges are used for DBOUND and DFREE in each dataset? Methods indicate that these ranges overlap, is this correct/appropriate?

We thank the reviewer for their careful reading of this section, which contains a typographical error. The correct values for the bounds used in all conditions are Dfree = [0.5, 25] µm2/sec, Dbound = [0.0001, 0.08] µm2/sec, FractionBound = [0, 1]. We apologize for any confusion this may have caused, and have corrected this error. For a comprehensive explanation of our methodology and parameter selection process, we refer the reviewer to our recent publication describing SpotOn (Hansen et al. 2018). All methods here adhere to the standards described there. We will stress this point in the manuscript and include relevant quality controls performed on our datasets demonstrating the statistical value of our results.

Why does the DBOUND limit exceed 0.08 µm2/sec? Does this indicate viral genomes are unusually dynamic?

This error has been corrected, and the upper limit of the Dbound for fitting is indeed 0.08 µm2/sec.

Are these ranges changed between samples within each dataset?

The same ranges are used for all data sets.

If not, why does mean DBOUND change so significantly in Figure S4?

The diffusion of chromatin between consecutive frames is much smaller than the localization accuracy of a single molecule, adding some error to the exact value of Dbound. It is important to keep in mind that a difference of 0.05 µm2/sec is not a particularly large difference, and that it only looks large in the graph because of the scale used. We do not interpret these values farther than to say that for all conditions tested they fall well below the diffusion rate of anything aside from chromatin. We have tried to clarify this in the manuscript.

3) If any image acquisition or SPT data modelling values are changed between different samples in each dataset, how comparable are the resulting models? It is necessary to address this question of two-state model interoperability specifically.

No acquisition or modeling parameters are changed between any of the data sets, and as such they are immediately comparable between treatment conditions and time points.

Further, it is important to perform true biological replicates (not just model resampling) to see the real error in the estimation of the mean from these measurements.

All data sets were collected over at least two, but typically four, different imaging sessions, on as many different days. We disagree with the assumption that the day-to-day variability assayed by grouping into “biological replicates” is necessarily more informative or more useful than the cell-to-cell variability that our random resampling approach provides, although we are happy to repeat the analysis treating each of the biological replicates separately. Grouping many independent recordings from different cells into “biological replicates” often gives a false impression of stability and accuracy in the assay since it fails to represent accurately the cell to cell variability. The other important factor to consider is that Spot-On model fitting is more robust as the amount of data is increased. Because of the sparsity of data in RCs, particularly at early times in infection, we sought to use the bootstrapping approach in order to minimize the error in the estimate that is derived from the fitting. Previous work has shown that the mean estimate converges quite quickly, but any individual iteration may be somewhat divergent. We chose to perform bootstrap resampling with 15 cells because this number of cells guaranteed a minimum of 1000 trajectories in the fitting, but would allow us to iterate many times.

Still we appreciate the reviewer’s concern, and so we compared the results of grouping cells into biological replicates as opposed to pooling and randomly subsampling them, and have included this data as a supplemental figure. In summary, either approach yielded results and conclusions that are indistinguishable, within the error measurement of the assay. These results confirm that there are indeed cell-to-cell sources of variability, as well as infection-to-infection, a result that is probably not particularly surprising. We appreciate the reviewer’s comment, and have chosen to include these data as an example of the many different potential sources of error that can arise within single-cell data.

4) Extending this model of non-specific DNA binding to other DNA binding proteins based solely on the data provided in Figure 6 is a stretch. To make such claims it would be important to determine if this holds within RCs for TetR-Halo using spaSPT, and this should be determined for several DNA binding proteins and in contexts outside of HSV1 RCs.

We appreciated the reviewer’s point regarding the figure, and have performed additional experiments to support this conclusion, as shown in the revised Figure 6 and related supplemental figures. We find that the same result when looking at cells expressing the Lac repressor, another bacterial transcription factor which lacks binding canonical binding sites in either virus or human genome. We also established a cell line stably expressing TetR-HaloTag in order to perform SPT on TetR in the context of infection, and find that the same principles we observed with Pol II hold with TetR.

All in all, while the imaging is impressive the conclusions are poorly supported and casually dismiss many alternatives, there is a lack of mechanism here and the advances over prior studies (even if they used more conventional methods) in terms of our understanding of viral genome chromatin state are limited.

Minor Points:

It would be helpful if the authors referred to viral proteins in a consistent manner. A key protein, ICP0 is referred to as RL2 in the text and figures but ICP0 in the table. This gets confusing.

We have made this change.

If Pol II is proposed to randomly explore "naked" viral DNA, it would be helpful if the authors discussed how the virus might accomplish such a carefully coordinated kinetic gene expression program under such circumstances.

Our discussion centers on the general recruitment of Pol II to RCs, rather than the specific regulation of different kinetic classes of HSV1 genes. We do not find any reason to doubt or modify much of the current consensus regarding the activation of Immediate Early and Early genes, and the process that licenses Late gene transcription. We do briefly comment in the Discussion on the fact that the increase in promiscuous Pol II binding may help explain how so much Pol II loading and transcription occurs at such weak promoter sequences. However, because our focus is to understand the mechanism of general recruitment to RCs rather than gene-specific phenomena, we believe that further commentary would go beyond the scope of our study.

While immunofluorescence is used to determine viral titer in the methods, production of ICP4 does not mean that each of these cells is actually productively infected as some infections can abort. It would be important to show how the titer obtained from determining plaque forming units compares with this measurement, or additionally confirm that the ICP4 positive cells go on to make late proteins also.

Indeed, virus titers were determined using both the standard plaque assay, and additionally using IF. We found that the plaque assay underestimates the number of infected cells by ~20% for wild-type KOS and therefore very much agrees with the reviewer’s comment.

Overall the paper has many typos, "cit" where references should be and is sloppily formatted.

We have made these corrections, where relevant.

Reviewer #2:

In this manuscript by McSwiggen et al., the authors investigate the compartmentalization of HSV1 in the nucleus during lytic infection. They specifically test the hypothesis that the local concentration of viral DNA and transcription machinery constitutes a liquid-liquid phase separated (LLPS) region in the cell as described in a number of recent publications. They carry out the standard LLPS experiments and characterizations such as determination of the aspect ratio, diffusion in the interior and boundary, identification of intrinsically disordered regions in the protein, and disruption via hexanediol. All of these experiments indicate the macroscopic blob visible in the microscope is indeed not a LLPS as previously defined but rather due to an enrichment of non-specific interactions between RNAP2 and the highly abundant nucleosome-free DNA. Thus, their conclusion is that the immense amount of viral DNA concentrates RNAP2 through weak binding interactions. In biophysical terms, they are describing the difference between avidity and affinity. Given the frenzy surrounding this topic of phase separation, I think this paper which takes a rigorous, unbiased approach to the topic is timely and will likely engage the broad readership of the journal. I recommend publication after a few revisions.

Major comments:

1) My primary concern is on the issue of nucleosome-free regions. There is a debate in the chromatin field about what 'open' chromatin means, but there are a number of papers which seem to indicate that open chromatin (i.e. by ATAC or DNAse hypersensitivity) does not equal nucleosome free (here is one example: PMID 29126175). I suggest the authors supplement their ATAC-seq studies with an IF assay as described in the cited publications but done in their samples under their conditions. I think it is critical to determine what the histone proteins look like in the RC.

We thank the reviewer for this suggestion, and have now included this experiment as a panel in Figure 4.

2) Discussion of phase condensates or liquid-liquid phase separation has reached a fever pitch, and there are likely differences between p-podies, stress granules, heterochromatin, etc. However, the closest analogy I see to this work on transcriptional activation is the nucleolus, which consists of many copies of the ribosomal genes but is transcribed by Pol1, which doesn't have a CTD. I suggest the authors make a direct, detailed comparison of their data to the phase separation work on the nucleolus. For example, I didn't notice 'fusion' of these HSV1 RCs, and this assay has always been one of the most direct measures of LLPS.

Overall, this is an interesting point. To be clear, we do observe some fusion events (see Figure 1B for two examples; and see the related supplemental movies). The comparison with nucleolar structures is an interesting one, especially given some of the pioneering nuclear dynamics work showing that nucleolar factors exchange between nucleoli and the nucleoplasm rapidly, similar to what we see with Pol II. Given the richness of the literature surrounding nucleoli, both in their role as LLPS domains and in the dynamics of nucleolar compartments, the length limitations of the manuscript prevent the rich discussion that this topic deserves. Still, we have attempted to highlight the comparison in the updated Discussion section.

3) Related to this point, it could be that phase separation as postulated in other instances of transcriptional activation is due to the presence of nucleosomes and disordered histone tails, which would provide the crowding agent. If one waits long enough in the infection cycle, does the viral genome become chromatinized, and do the diffusion properties of RNAP2 change?

The reviewer raises an interesting question, which is unfortunately quite difficult to answer since our cells lyse after a few hours releasing their viral load. To our knowledge, there is no way to force chromatinization of the viral genome after lytic infection has begun as RCs are formed. Latency is typically established in neuronal cell types, and while these latent genomes are chromatinized, they exist as single episomes so it is not clear that they would share any of the behaviors of lytic, replicating viral DNA.

Minor comment:

1) Include the positive control for drug efficacy in Figure 3.

We have made this change in the new version of the manuscript.

Reviewer #3:

McSwiggen et al. the authors have attempted here to access potential mechanisms by which the Herpes Simplex Virus 1 generates so called replication compartments (RC) within the nuclei of cells in which RNA polymerase II (Pol II) and a number of other proteins are recruited in order to hijack transcriptional machinery to drive expression of viral genes. Following on recent arguments that Pol II may form transcriptional foci via liquid-liquid phase separation (LLPS) and that this phase separation is driven by weak interactions with a Pol II subunit C-terminal domain (CTD) intrinsically disordered heptad repeats, the authors set out to test the hypothesis that Pol II is recruited to RCs through interactions between its CTD and other IDR-containing proteins within the RC. Importantly, the key experiment the authors performed was to sequentially delete heptad repeats and test whether Pol II is no longer recruited to RCs, as one would expect if the CTD heptad repeats are essential to LLPS. They clearly demonstrate that this is not the case. Furthermore, they demonstrate that, although the kinetic exchange of Pol II and fusion of RC are consistent with LLPS, other behavior, for example sensitivity of Pol II focus formation to 1,6hexanediol is not consistent with LLPS. Some further analyses of Pol II dynamics including measurements of jump-length frequencies and sizes and FISH analyses of the viral RNA DNA the authors argue that the formation of RCs is in fact driven by non-specific interactions of Pol II with the viral DNA, which is more open than other chromatin and therefore acts as a sort of sink for diffusion of other transcriptional proteins to enhance expression of viral genes.

The study is a very well performed and sophisticated analysis of the problem and based on the assumptions of the central hypothesis, I'd have to conclude that they have proven their point.

We greatly thank the reviewer for this comment.

There is, however, a problem and it's easy to address. First of all, I don't dispute anything that the authors claim about Pol II binding to the viral DNA, but I do not agree that they've excluded LLPS as essential to instigating the Pol II recruitment. The problem rests here: the authors assume LLPS is important to partitioning of Pol II to RCs, it must be driven by interactions of the Pol II CTD with the intrinsically disordered domains of viral proteins. It is possible that LLPS of the viral proteins precedes and is essential to Pol II recruitment; albeit for reasons that are not necessarily clear. The fact that some of the experiments to test for LLPS of the RCs seem negative, I would argue that this is an over-interpretation of these experiments.

Indeed, our initial expectation regarding the recruitment of Pol II to RCs would have been a CTD-mediated process, since we had observed such CTD interactions both in vitro and in uninfected cells as reported in our recent paper (Boehning et al., 2018). However, the results we obtained with HSV1 RCs clearly show that even in very early stages of RC formation Pol II is not being recruited via a CTD mediated mechanism but rather by DNA avidity as succinctly articulated by reviewer #2. So it seems evident that a transition occurs early in the infection cycle. However, we cannot and do not claim to rule out the possibility that the CTD of RNA polymerase II is important during, for example, transcription of IE and Early genes prior to RC formation. And certainly, we are not challenging the notion that the Pol II CTD likely plays a role in hub formation in uninfected cells and perhaps also in the very early stages of viral gene transcription. However, late gene expression, after onset of DNA replication and during formation of RCs is a different story which we believe these studies reveal an elegant and parsimonious mechanism for usurping Pol II from the host chromosome to the naked viral genome.

Our conclusion that Pol II is not accumulating in RCs due to an LLPS process is based on our dynamic measurements of Pol II. In particular, we find that Pol II freely exchanges between RCs (Figure 2F), experiences no barrier to enter or exit RCs (Figure 2G), displays no change in diffusion coefficient (Figure 2E), and shows more than two orders of magnitude in variance of its local density within RCs. None of these observed features would compel one to postulate that the Pol II CTD would mediate these behaviors.

New experiments using other IDRs support our previous findings, showing that these IDRs do not become enriched in RCs despite their propensity to interact with the CTD (Figure 1—figure supplement 1) (Chong et al., 2018). It is unfortunate that 1,6-hexanediol treatment has become such an “acid test” in the burgeoning LLPS field, as it is a very harsh and nonspecific way to address “weak hydrophobic interactions”. Our goal in using 1,6-hexanediol was primarily to demonstrate that we have followed all of the “standard” tests for LLPS, and to show where our system deviates from the current literature. Our unexpected findings also offer a rationale as to why we might suspect that some mechanism other than phase separation could be occurring, but we refrain from interpreting further than this. We have emphasized this better in the text.

In fact the time dependent decreases in mobile fraction of Pol II measured in FRAP experiments are perfectly consistent with the "aging" that has been described for other bodies demonstrated to form by LLPS where the configurations of the network of interactions within a condensate go from loosely associated, relatively low-valency to extended hydrogen bind networks interactions. This change of state is, in fact, common to LLPS-generated bodies, not an exception.

We agree that “aging” can be a common property of LLPS-bodies, but believe that our data clearly rule out aging in this case. Suppose Pol II aging took place in viral RCs. Then one would expect the “apparent koff” to decrease or get slower as a function of time (hpi). The clearest measurement of the koff comes from the FLIP measurements (Figure 2F; Figure 2—figure supplement 2). The FLIP data show no difference between infected and uninfected cells and no change in “apparent koff” with increasing hpi. Therefore, since the “apparent koff” does not change as would be expected for LLPS aging, we feel confident in ruling out LLPS aging for Pol2 RCs.

Regarding FRAP, the recovery time is a complicated function of the chromatin associated fraction, the kon, the koff, whether diffusion is Brownian and more. Our data clearly show non-Brownian diffusion (Figure 5D-F), and a large change in the chromatin associated fraction (Figure 3D). When taken together, our FRAP and FLIP data show that it is these factors and NOT a change in koff that is responsible for the lower FRAP recovery in Figure 1F and 3H.

So here's what the authors need to do. If you want to exclude LLPS as essential to formation of RCs, start with the hypothesis that the initial step of RC for is LLPS by the viral proteins that they identified, including UL49, RL2 and UL54.

As stated before, we cannot exclude CTD interactions as a potential mechanism very early in the viral infection process, since none of our experiments were directed at analyzing these immediate early or early gene events. We have rephrased the text to clarify this point, and not distract the reader from the main findings that are squarely aimed at the massive recruitment and enrichment of Pol II in RCs following their formation.

Then, do the following experiments:

1) Delete the low complexity domains of all combinations of the low complexity domains of these proteins, from individual to pairs to all of the domains, and test whether the viral proteins AND Pol II partition to RCs.

2) Next: To exclude the possibility that the low complexity domains are simply forming essential proteinprotein interactions, swap the low complexity domains between the three viral proteins, excluding the individual domains and repeat the experiments in 1.

3) Repeat 2, but replace individual domains with another low complexity domain of similar composition and length but completely different origin and test whether RCs form.

4) Finally, in each of these cases, test for expression of the viral genes to determine whether transcription is normal.

Reviewer #3 suggests an extensive set of experiments that are logical extensions if we wished to entirely rule out the possibility of LLPS in all steps leading up to the formation of RCs. We’ve addressed these comments above; but in addition, the experiments that are proposed are technically unfeasible for multiple reasons. The first is that many viral proteins perform multiple roles throughout infection. The viral protein ICP27 (gene UL54), for example, has documented roles in gene-specific transactivation/repression, mRNA export, 3’ end processing, and global repression of splicing. While many mutant viruses have been generated by deleting regions of ICP27, it can often be very challenging to identify what is that direct cause of the mutant phenotype. As an example, see our response to reviewer 1 regarding the n504 and n406 mutants. The second major issue is that the HSV genome is very densely packed, often with multiple overlapping ORFs occupying the same sequence space. Again, this makes large deletions and domain swapping nearly impossible because it then becomes unclear what are primary and what are secondary effects of the mutation. Lastly, HSV1 is not a genome that can be readily edited, and successfully incorporating a single edit into a viral mutant can take months. The scope of the experiments proposed would take years to complete, and at the end still may not accomplish the goal of determining what role these low complexity domains play. Indeed, the existence of IDRs in a number of HSV1 genes very likely indicates that some IDR-driven protein:protein interactions are undoubtedly taking place. But clearly, whatever these are doing, they are not affecting Pol II recruitment to RCs.

Nevertheless, we appreciate the importance of this point, and have performed a set of experiments that we hope at least obliquely address the spirit of the proposed experiments. Specifically, we took the low complexity IDRs from the FET family proteins FUS, EWS, and Taf15 because these have been shown to interact with the Pol II CTD in a phase-separation/hub formation mechanism (Chong et al., 2018). We transfected cells with HaloTag-fused to these IDRs, and subsequently infected these cells. Despite their tendency for homo- and heterotypic protein:protein interactions with themselves and with the Pol II CTD, these proteins are not enriched in RCs, nor do these interactions appear to “outcompete” whatever interactions are driving Pol II into RCs. Meanwhile, we have evidence now for two nontargeting DNA-binding proteins (LacI and TetR) that are enriched. We believe that this is very strong evidence that protein:DNA interactions rather than protein:protein interactions are the drivers for Pol II recruitment to the domain.

If none of these make any difference than I will concede that the authors original hypothesis is likely supported. If not, the authors can merely embrace the notion that LLPS is happening and is essential. You can't lose one way of the other.

We thank this reviewer for their general support of the manuscript. However, phrased this way, the reviewer has erected a false dilemma. As we mention above, we cannot conclusively say that LLPS does not occur in the lifecycle of HSV1 or during early events leading to the formation of RCs. However, we dispute the suggestion that LLPS, even if it is occurring between other HSV1 proteins, is necessarily essential to their function. To our knowledge, no group has yet demonstrated a case where LLPS is essential to the functioning of transcriptional regulatory systems. Instead, many IDRs play a role in the formation of transient local high concentration hubs, but there is little or no evidence that these functional hubs actually form LLPS condensates. Indeed, this is a major point of discussion in the field at large.

In either case, we do not see how conceding that some LLPS may occur between some viral proteins during some stages of the viral life cycle changes the interpretation of our data, which is that these forces are not what govern Pol II recruitment into RCs, and that Pol II does not “experience” the defining constraints of phase separation even if it does occur. We have updated the manuscript to emphasize this point more forcefully.

[Editors' note: the authors’ responses to the re-review process at another journal follow.]

Summary:

After implementing the changes and suggestions from the reviewers’ first round of comments, the responses were generally even more positive. All three reviewers accept the quality of the data we presented, with the newly incorporated changes, and the remaining comments largely center on interpretation of the data. Reviewer #2 suggests the manuscript should be published as revised, and even goes on the recognize the bias that Reviewer #3 shows in their response. Reviewer #1’s criticisms to the revised manuscript focus on the fact that HSV1 expresses proteins involved in transcription activation. As is discussed in more detail below, we contend that even if these proteins are involved in Pol II recruitment, we show that the mechanism we propose applies broadly not just to Pol II, but to any other viral complex that has the capability to bind DNA. Reviewer 3 again telegraphs their own biases, ignoring key data in the manuscript as well as their own previous comments in order to maintain their assertion that HSV1 RC formation is yet another form of phase separation.

Reviewer #1:

There is no disputing that the authors use elegant approaches that demonstrate non-specific binding or random exploration as a means of PolII retention at RCs, making this an important report. But serious concerns remain about the overinterpretation of data, weakly supported at points and with particular disregard to the underlying biology of infection, along with one-sided views that should at the very least be addressed up front in the manuscript to improve fairness and overall readability – unless I am completely missing something, in which case I am happy to be corrected.

Major Points:

The rebuttal often seems at odds with what is in the actual paper:

A key point of contention is the idea that viral proteins are simply resistant to 1,6-HD and mediate recruitment of PolII to viral DNA. I actually don't have much of a problem with the novelty of random exploration mediated by viral proteins, it is still novel I think? Yet the authors stubbornly refuse to do any experiments beyond imaging to test this idea and refuse to discuss the notion.

The reviewer’s initial comments highlight an important aspect of our model that we have clearly not communicated well enough, and will try harder to improve in future versions of the text. Imagine that rather than doing these SPT experiments with RPB1, the Pol II catalytic subunit, we had instead used an accessory subunit such as RPB3. We have fairly strong evidence to suggest that in this hypothetical scenario we would obtain the same values and behaviors by spaSPT and FRAP. Assuming this were the case, we would propose the same model: That nonspecific binding of Pol II, as a protein complex, to the viral DNA drives RC accumulation. The fact that the particular subunit we are labeling may or may not itself have ability to bind DNA is immaterial, because we are considering Pol II and its behavior as a complex. We know of no experimental technique that can show in cells that it is specifically the RPB1 catalytic subunit that is making nonspecific contact with the DNA, and so in our model we don’t make so specific of a statement, but appreciate that this could be more clearly specified.

We know from previous literature that many viral proteins interact with many host proteins, including Pol II. We cannot fully exclude the possibility that a viral protein binds Pol II forming a new complex, and that this is the interaction that drives nonspecific binding to the viral DNA. We disfavor this model for the reasons that are detailed below. More importantly, our model is that any DNA-binding protein, viral or human in origin, will be subjected to the same mechanism of nonspecific binding and that this is occurring contemporaneously with all of the other protein-specific mechanisms that have previously been elucidated.

We will make this point more clearly and explicitly in the manuscript going forward.

For example, the rebuttal states:

"As the reviewer points out, we cannot say for sure that 1,6-HD disrupts ICP27-Pol II interactions, but taken together with the above arguments, we believe that we have sufficient evidence to disfavor ICP8/ICP27 interactions as a major contributor to bulk recruitment of Pol II to RCs". Cases of viral proteins mimicking the function of cellular proteins yet being both structurally dissimilar and resistant to conventional inhibitors of their host counterpart abound. HSV-1 actually encodes a classic example, the Us3 kinase that mimics Akt but is resistant to Akt inhibitors.

We thank the reviewer for this perspective, and would offer a few points in response. Firstly, the reviewer should think of the use of 1,6-hexanediol as less comparable to inhibitor compounds like Akt inhibitors, or the drugs we use later in the manuscript Flavopiridol and Triptolide, and more like treatment with a hypotonic buffer. That is to say, 1,6-hexanediol is supposed to inhibit the weak van der Waals forces that are thought to drive interactions between just about every unstructured protein domain. At a concentration of 10%, this treatment is strong enough to melt nuclear speckles, PML bodies, Cajal bodies, transcription “factories”, and transcription factor-Pol II interactions, to name a few. We note in the manuscript that you can see the results of such harsh treatment in the complete disruption of nuclear morphology in the treated cells. As harsh as this treatment is, we use it in Figure 1 because it has become something of a “standard” assay when looking at phase separation in cells.

With the above comments in mind, we do not know whether 1,6-HD specifically impacts ICP27-Pol II interactions. If the interactions are predominantly the result of weak and hydrophobic interactions, it very well might, but if the binding is due to ionic interactions then this may not be the case. In either case, probing this specific interaction wasn’t the goal of the 1,6-HD experiments. Rather, we were applying a commonly used assay in the field of phase separation to test whether RCs exhibit the same behavior (i.e. being driven by weak hydrophobic interactions) as other phase-separated compartments—which they do not.

The latter point disfavoring IC8/ICP27 is only supported by data in Figure S5 that not only contradicts findings in the field, according to the authors interpretation, but to me the n406 mutant that is fully defective in Pol II binding seems to exhibit pretty severe defects. Only one cell is shown and only 10 are used for FRAP analysis, while 30 or more are used for WT. While I understand the point is that these small RCs recruit Pol II and exhibit similar FRAP profiles, there is limited analysis here to truly show what is going on particularly given how it is viewed as refuting findings from well-respected virologists. How much viral DNA is in these cells (is this limited exploration of large amounts of viral DNA for example, which would be counter to their model), and what is happening in the majority of cells that they mention are defective? Quantitation is limited: What is the proportion of the total population that exhibit these behaviors, and are the ones shown simply "escapees" that struggle to form RCs?

It is absolutely the case that these ICP27 mutants are replication deficient. Given this protein’s central role in not only activation of immediate early and early gene transcription, but also RNA processing and export, it shouldn’t be surprising that even small modulations of this protein’s potency would affect the virus’ ability to replicate. However retarded in their growth they are, RCs do form (seen both by presence of ICP4 and decrease in DAPI staining) and, more importantly, they still recruit Pol II. We have not measured the DNA content of n406 RCs, as this is not a trivial undertaking. Still, it is telling that the RCs that form are larger than the pre-replicative compartments seen in PAA-inhibited WT infections (Figure 4 and S6), and have FRAP recovery profiles more consistent with WT infections than they do with PAA-inhibited samples (this data will be included in Figure 3—figure supplement 1 going forward).

Regarding the numbers of cells measured by FRAP, 30 cells is overkill for the type of analysis we are doing (i.e. qualitative comparison of recovery profile). We had started making many more measurements because we had initially planned to fit the data to binding/diffusion models, and only after collecting all of the data found that RCs do not satisfy all of the assumptions to use FRAP data in this quantitative manner. With this in mind, we collected data for a more reasonable number of cells for all of the mutants and drug treatments.

Regarding our finding contradicting published literature, we would invite the reviewer, if they have not already done so, to read the paper in question (Dai-Ju, J.Q., Li, L., Johnson, L.A., and Sandri-Goldin, R.M. (2006). ICP27 Interacts with the C-Terminal Domain of RNA Polymerase II and Facilitates Its Recruitment to Herpes Simplex Virus 1 Transcription Sites, Where It Undergoes Proteasomal Degradation during Infection. J. Virol. 80, 3567– 3581. DOI: 10.1128/JVI.80.7.3567-3581.2006) to judge whether they agree with the authors’ interpretation of the data presented in that paper. A close rereading, specifically of Figure 4D and the associated text, suggest that the authors actually see something very similar to what we observe here – that Pol II is indeed enriched in RCs. Thus our data are mostly in agreement with the data they show in this figure, however the addition of FRAP experiments have caused us to come to a different conclusion than what they suggest.

In addition, other viral proteins could be mediating this. As the authors themselves state in the rebuttal, "a number of viral proteins interact with Pol II or other members of the core Pre-Initiation Complex. This includes the viral proteins VP16, ICP4, ICP8, ICP27, and ICP22 among others". Claims that PolII diffusion is not affected don't really support there being no role for viral proteins – if they are small or transiently interact there might not be a discernable change in PolII behavior. It seems inappropriate to simply ignore these possibilities to claim they cannot reproduce others’ findings and one-sidedly push their model of "naked" DNA.

The reviewer is correct that a sufficiently small or transient interaction with Pol II may go undetected by SPT. It is possible that some other viral protein besides the ICP27/ICP8 complex might interact directly with Pol II and thereby facilitate recruitment, though there is little biochemical evidence to support this. In a hypothetical scenario where ICP27/ICP8 or another viral protein is involved, this doesn’t really solve the question of how Pol II (or any other protein) is specifically enriched in RCs, but rather only abstracts it one level. How, then, does viral factor X get to RCs? Especially given that most HSV1 proteins, including ICP27 and ICP8, localize to RCs while lacking sequence-specific DNA binding motifs that would direct them to the viral genome over the host genome, and where we have convincingly demonstrated there is no border enclosing RCs to constrain host or viral proteins within RCs, invoking interaction with viral proteins doesn’t improve our understanding of the underlying mechanism. What we rather hope we have demonstrated is that the virus is capitalizing on its highly accessible genome (more on this point below) to affect the recruitment of many proteins – both host and viral in origin. We favor a model where Pol II is directly interacting with the viral DNA because of the experiments outlined above and because it doesn’t require invoking additional host or viral factors, but we will make sure to clarify in the text what our assumptions are in this model, and what are its limitations.

Indeed, key section statements like viral DNA being far more accessible than host DNA require clarification as it implies naked DNA, and the broader inference throughout is that the DNA is naked even when not explicitly stated.

Oddly, the rebuttal says "We make no claims that these drugs work against any viral proteins" and "We had no intention to suggest that Pol II only interacts with the viral DNA". Yet throughout the paper statements to that effect are made e.g.; "Pol II recruitment occurs predominantly through transient, nonspecific binding of Pol II to naked viral DNA." Or: "Within RCs, many copies of the unprotected HSV1 DNA are present" etc. The reality is HSV-1 encodes many non-specific DNA binding proteins, transcription factors etc. and DNA is engaged in replication, transcription and packaging and is unlikely to be "naked". Perhaps regions are exposed, but the authors should be specific. They also claim "competition" with host chromatin? The virus affects host transcription, so it's not a simple competition as far as I am aware. Again, I don't have a problem with the data, this is all about statements that don't consider the biology of infection, which should be discussed. While this paper has interesting implications for cell biology, it lacks context for what is actually happening during infection.

ATAC-seq is a measurement of DNA accessibility (Klemm et al., 2019), of which it has been shown that the absence of nucleosomes is of primary importance relative to any other class of DNA binding protein. While naked is a colloquial term, it is one we feel is very fitting for this situation. A person wearing 100-fold less clothes is by any metric going to be considered naked with respect to their peers; and just as with people, it is quite shocking to see any DNA in a eukaryotic nucleus so devoid of its nucleosomal armor. With this said, the reviewer is correct that it would be more accurate to replace “naked” with “non-nucleosomal”, or something to this effect.

As stated above, we believe the most parsimonious model given all of the data that we’ve presented is one in which Pol II is predominantly recruited to RCs through nonspecific interactions with the unprotected viral DNA, and so it seems appropriate for that to be a sentence we use to summarize our results without adding qualifying statements.

Regarding the discussion of competition between host and viral chromatin, while it is absolutely true that HSV1 disrupts transcription of the host genome through a wide variety of mechanisms (phosphorylation defects affecting promoter binding and escape, transcription termination, etc.), they all appear to occur at steps that happen after a polymerase has bound to a PIC. For the binding itself, all the DNA occupying the nucleus is in potential competition for Pol II binding, and based on our model it is clear that the HSV1 DNA is outcompeting the host chromatin for this initial step of DNA binding. We believe our experiments with TetR (and now also LacI) are crucial experiments because they demonstrate that the mechanism we are describing for recruitment of Pol II and other DNA-binding proteins is occurring regardless of all the other potential interesting mechanisms that HSV1 employs, which may be occurring contemporaneously.

Regarding the point about the crowded RC, and rebuttal "This criticism is a little perplexing, as our goal in this manuscript was to highlight just how the environment of RCs differ from the rest of the nucleoplasm, and how that affects Pol II recruitment", the point is that the authors don't consider the nature of this crowded environment where DNA replication and packaging may affect the apparent behavior of Pol II, as it may be continuously kicked off DNA that is being used for purposes other than transcription. The claim is high accessibility of the viral genome leads to a number of different behaviors but is it really accessible, or is Pol II simply struggling to compete for DNA binding here? While histones might be absent, they haven't ruled out other proteins occupying the DNA.

We approached this project expecting the environment of the RC to be different from the rest of the nucleus, and in some respects the reviewer is correct in saying that the environment is different. However, it is not clear exactly how different and how much this contributes to the behavior of macromolecules in the infected nucleus. For example, for all of the factors that we’ve measured by spaSPT, RCs have no effect on the mean diffusion coefficient of any of them. Moreover, the quantitative phase imaging suggests that RCs are, if anything, less densely packed with DNA than the surrounding nucleoplasm. We do not discuss this in the text of the paper because we were unable to get satisfactory standards to convert gray values into absolute dry mass, but the fact that RCs appear as dark/black spots in the image instead of bright/white spots like the nucleolus does suggest that there is generally lower dry mass inside of the RC than the surrounding nucleoplasm. If this is truly the case, however, it does not appear to have an effect on how molecules diffuse through the space, which generally underscores how much the field still has to learn or relearn about what diffusion looks like in low Reynolds Number environments like the cell.

The reviewer is quite correct that DNA devoid of nucleosomes is not devoid of protein, but there is a large chasm of difference between the interaction of DNA-binding proteins like replication and transcription factors, and nucleosomes. Measurements taken on the stability of nucleosomes bound to DNA suggest that the average nucleosome will stay bound to DNA for hours after deposition, whereas even the strongest and most stable DNA binders like TATA-binding protein and Cohesin have residence times of minutes (Rhodes et al., 2017; Teves et al., 2018). The viral DNA is likely decorated with many proteins, including replication complexes and transcription complexes, but it is very unlikely that there is anything binding so stably to the viral DNA to appreciably affect Pol II binding.

While it might be incorrect to state tegument proteins are structural, they are components of the virion but many of them are not involved in transcriptional control. If you focus only on transcriptional regulators, which are known, how does this affect the IDR distribution? It is possible many unrelated proteins in the virion have IDRs that skew this analysis.

As with the 1,6-hexanediol experiments, these data are included in the manuscript because this is another “standard practice” in the phase-separation field. The logic appears to be something like: proteins with high intrinsic disorder implies proteins likely to interact through multivalent/hydrophobic interactions which imply phase separation. One of the goals of this paper is to offer a refutation to this particular chain of logic: that the existence of IDRs does not necessarily imply phase separation. To this end, we could just have plotted all viral proteins against the proteins known to undergo phase separation, or subdivided the virus into any number of other categories arbitrarily, and it would not change the conclusion.

With that said, we’re happy to adjust the plot to keep the proteins categorized strictly by their kinetic class. Either way, it doesn’t change our conclusion from the figure (Figure 1E).

The use of LacI and TetR (mislabeled TatR in Figure 6) is a nice approach and supports the notion that random exploration likely happens. However, only a couple of cells are shown and everything is done in infected cells. Do these proteins form aggregates that generate PolII concentrations in uninfected cells? Viral proteins are also highly promiscuous protein binders and could again be involved. Although unlikely, at least show uninfected cells to show specificity and indicate the frequency at which these structures form; it was not clear in figure legends or methods.

Neither LacI or TetR form aggregates in uninfected cells (Chong et al., 2018; Normanno et al., 2015), but we’re happy to include images from uninfected cells as well. It seems to us very unlikely that the virus would be capable of binding and recruiting both proteins, bacterial transcription factors which the virus would never have encountered over the course of its evolution. Additionally, Figure S2 shows that RCs do not accumulate the activation domains of FUS, EWS, or TAF15, nor HaloTag alone. We will also include an estimate of how often TetR and LacI are recruited to RCs.

Why is it that "SPT data for TetR-Halo were not well fit by the two state model in Spot-On, however a qualitative assessment can be made from the CDF curves".

There are a number of potential reasons that certain proteins, for one reason or another, are not well fitted by Spot-On. One of the assumptions that Spot-On makes in fitting is that state transitions (bound -> free, or free-> bound) are assumed not to occur on a fast enough timescale that they appear in the trajectories. Hansen et al., 2018, Figure 3-supplemental figure 10 shows that the model fails and fits the data poorly if state transitions occur fast enough to be non-negligible. Given what we know about how TetR binds DNA, it is quite possible that this is the case. Despite this, it is the cumulative distribution function that Spot-On is using to fit the model, so gross changes in the shape of the curve like those we see for TetR can still be interpreted to indicate a shift towards binding within the RCs, even if we can’t precisely measure what fraction of molecules are bound using existing tools.

To be clear, I am not arguing against the novelty of the findings or the paper overall, but I don't understand why the possible role of viral proteins is not more rigorously addressed and the insistence on implying naked DNA is so important here. There seems to be a grossly one-sided interpretation of data that needs to be more balanced, and a fairer acknowledgment of what has not been ruled out in this study. Again, maybe I am completely missing the point here and I'm happy to be schooled otherwise.

We thank the reviewer for their careful rereading of our revised manuscript, and hope that the above comments address their lingering concerns. Ultimately, we are not trying to subvert decades of research on HSV1 in favor of our findings, but rather are highlighting a new mechanism that we believe helps support previous work. This mechanism is not unique to Pol II (or TetR and LacI, for that matter), but applies generally.

Reviewer 2:

The authors have satisfactorily addressed my concerns.

I would also like to concur with their statement to Reviewer #3: "To our knowledge, no group has yet demonstrated a case where LLPS is essential to the functioning of transcriptional regulatory systems. Instead, many IDRs play a role the formation of transient local high concentration hubs, but there is little or no evidence that these functional hubs actually form LLPS condensates. Indeed, this is a major point of discussion in the field at large." Indeed. One of the main reasons it that active genes appear as diffractionlimited spots in most cases, ruling out interrogation of internal composition which is essential to a definitive demonstration of LLPS. It is for this reason that studies are being carried out on superenhancers and viral replication compartments: the diffusion across the boundary is a key experiment in the opinion of this referee and can only be done on macroscopic blobs. Moreover, the fact that the authors demonstrate that the phenomena can be explained with a kinetic description, independent of phase separation, is a strong argument.

Reviewer 3:

McSwiggen et al. have made a valiant attempt to respond to my criticisms. I remain, however, unconvinced that the RCs are the result of some alternative type of organization to phase-separated condensates. I do understand their claim that it is not the Pol II CTD interactions with low complexity domain-containing viral proteins that leads to Pol II sequestration within RCs, the anisotropic dynamics of Pol II that occurs inside of them. The question is, why would non-specific interactions of Pol III with DNA be any different in other regions of the chromatin with similar nucleosome occupancy? This is not at all clear to me.

As we demonstrated in Figures 4 and 5A, there is a significant difference between the viral DNA and any other site in host chromatin. To our knowledge, there exists no region of the host genome that even comes close to being as depleted of nucleosomes as the viral DNA is in RCs. Because of this, a DNA-binding protein has much less restricted access to DNA once in the RC, thereby facilitating many more nonspecific interactions.

Further, I can appreciate the technical difficulties that the authors describe to performing the experiments that I suggested and I also appreciate them performing alternative experiments of overexpressing proteins that bind to the CTD of Pol II to test whether they affect Pol II sequestration in RCs, but for reasons I describe below, I think that a different interpretation of their results is possible.

So how do we get out of this mess? First of all, the authors do say that they cannot rule out that LLPS is occurring early in the process. So why not simply hypothesize that indeed is what's happening? One could argue that all evidence points to this possibility except for the problem of Pol II CTD interactions not being important.

We have provided in the manuscript, and highlighted in the text, multiple lines of evidence to suggest that LLPS is not occurring. First, the RCs are not dissipating even when exposed to high concentrations of 1,6-hexanediol (Figure 1H). Secondly, SPT analysis of Pol II shows that there is no change in diffusion coefficient upon entering the RC, suggesting no change in viscosity as LLPS would predict (Figure 2E), and the Tet repressor shows no penalty for crossing into or out of RCs (Figures 2F and G, Figure S7C). Third, the addition of a DNA-binding domain (Lac repressor or Tet repressor, Figure 6) is sufficient to drive recruitment of a protein to RCs, whereas addition of protein domains known to undergo LLPS is not (Figure S1). Fourth, PALM data from both the viral DNA (Figure 5A) and RNA Pol II (Figure 5G andH) show that within the RC these molecules are not randomly distributed as one would predict from an LLPS model, but rather show clustering below the scale of the RC. Taken together we believe this strongly disfavors any potential model relying on LLPS. Furthermore, it is somewhat problematic that invoking LLPS has now mysteriously, in the mind of this reviewer, become the null hypothesis rather than a model that requires a higher bar for assignment particularly in vivo.

This also explains why expressing FUS, etc., have no effect. So what? Let's assume that RCs are condensates in which Pol II can enter and exit the chromatin-protein meshwork quite freely, making very weak interactions with all molecular species in the condensate.

Let us assume for the moment, as the reviewer suggests, that RCs do indeed represent a new type of condensate that recruits Pol II (and other DNA-binding proteins) selectively. This model would be just as problematic, if not more so, for anyone hoping to understand the functional role of phase condensates. As stated above, we have found through a battery of assays that being in the RC gives no better understanding of how a given molecule will behave.

An important consideration, one that authors don't account for, is phase separation of chromatin itself. It has long been appreciated that chromatin phase separates, euchromatin from heterochromatin and even different regions of either of these types of chromatin from each other. Importantly, there is very recent compelling evidence that chromatin phase separates dependent on histone complex composition and posttranslational modifications of histones and other protein binding to chromatin (Gibson, et al., http://dx.doi.org/10.1101/523662; Sanulli, et al., http://dx.doi.org/10.1101/473132).

The reviewer offers another red herring regarding the question at hand, both because we show conclusively that there are no histones incorporated into the viral DNA and because neither of the studies referenced demonstrate that the LLPS tendencies shown in vitro translate directly to LLPS inside the cell. In fact, while there is certainly evidence for the role of HP1 and related proteins in the formation of heterochromatin domains in cells in certain contexts, those are a far cry from a system that is marked by absence of the ingredients required to build a liquid heterochromatin domain.

In light of these results, it is interesting to note that the authors observe a paucity of nucleosomes in the viral DNA that is in RCs, consistent with modifications that could increase nucleosome dynamics and resulting in changes in the properties of the viral DNA, changes that might contribute to its phase separation. All of the diagnostic work the authors have done point to RC being a viscoelastic condensate that must be formed in order to sequester Pol II.

We would again remind the reviewer that whatever recruitment mechanisms are at work, they must be acting not only on Pol II, but on many other nuclear factors, including foreign transgenes products like the Tet and Lac repressors. Additionally, Pol II is not sequestered, but remains free to enter and leave the compartment, as well as diffuse within the compartment, without penalty or change in diffusion coefficient. It is truly puzzling that the reviewer chooses to favor a model of RCs as condensates with so much evidence to the contrary.

I will grant you that the field of biomolecular condensates hasn't come up with a strict definition of what these things are and why they wouldn't be simply mistaken for a network of protein-protein interactions.

This single sentence perhaps most clearly embodies our disagreement with the reviewer, as well as a major fault in the field at large. We completely agree that biomolecular condensates remain poorly defined in most contexts, especially when identifying them in vivo. While Reviewer 3 appears to be of the opinion that the label of “viscoelastic condensate” can and should be applied to a system in spite of evidence showing that it differs in some key aspects, we believe that our data underscore the weakness of such an argument. LLPS has very specific physical interpretations and makes key predictions (as Reviewer 3 succinctly lays out below), only some of which are satisfied by HSV1 RCs, and some of which are clearly violated.

At a recent meeting, Rohit Pappu, certainly the leading theorist of the biomolecular condensate field, posed the following set of definitions: "To be a condensate that arises from phase separation, there has to be a saturation concentration threshold above which condensates form and below which condensates dissolve. And because phase separation is a collective phenomenon defined by infinite cooperativity, the interactions that stabilize condensates will be quantifiably non-stoichiometric in nature. These two conditions are necessary for stipulating that a condensate is a phase separation. In addition, a diluent can dissolve the condensate by preferentially interacting with the key regions that are required for forming the condensate.”

While we believe there is still some debate, as the reviewer mentioned in the line above, regarding the exact definition of LLPS condensates, this definition seems as good of a jumping-off point as any. By this definition, we can show that RCs cannot be LLPS condensates simply by examining the Pol II PALM data (Figure 5G). Within each RC, the concentration of Pol II can vary by more than two orders of magnitude, and Pol II shows clustering at all length scales less then 1 µm (Figure 5H). If RCs were undergoing LLPS, then in the concentrated phase (the RC) we would expect Pol II to reach but not exceed the critical concentration. In a system like this, the L(r)-r curve should remain at zero for all length scales because all molecules in the concentrated phase should be at the same local concentration (and thus spatially randomly distributed). Instead we see a range of concentrations of Pol II that dip below the concentration of the dilute phase, and soar way above the mean concentration of the concentrated phase, clearly violating the one tenant that the reviewer has set forth as a key requirement for invoking LLPS.

It would be difficult for the authors to prove the first condition but if RCs are composed of phase separated DNA, it is at least arguable that it forms non-stoichiometric complexes and they do see dissolution by 1,6-hexanediol. A further and simple experiment that the authors could do with 1,6hexanediol is first to titrate the RCs with it and see if they observe a sharp, all or none transition, in which the RCs disperse. They should also show that this is simply reversible by removing the 1,6-hexanediol. If they see a sharp transition instead of a linear degradation of RC structure, it would suggest an infinitely cooperative transition as would be expected for a phase -separated condensate. They should also do a control of the same experiment but using 1,2,3-hexanetriol, which has not been shown to dissolve biomolecular condensates.

We are perplexed by this proposed experiment, as we do not see dissolution by 1,6-hexanediol, even at a very high concentration. It is unclear how much higher one can even titrate the compound before proteins begin to denature.

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https://doi.org/10.7554/eLife.47098.034

Article and author information

Author details

  1. David Trombley McSwiggen

    1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    2. California Institute of Regenerative Medicine Center of Excellence, University of California, Berkeley, Berkeley, United States
    Contribution
    Conceptualization, Resources, Software, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3844-7433
  2. Anders S Hansen

    1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    2. California Institute of Regenerative Medicine Center of Excellence, University of California, Berkeley, Berkeley, United States
    Contribution
    Conceptualization, Software, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7540-7858
  3. Sheila S Teves

    1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    2. Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, Canada
    Contribution
    Conceptualization, Software, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1220-2414
  4. Hervé Marie-Nelly

    1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    2. California Institute of Regenerative Medicine Center of Excellence, University of California, Berkeley, Berkeley, United States
    Contribution
    Resources, Software, Writing—review and editing
    Competing interests
    No competing interests declared
  5. Yvonne Hao

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Contribution
    Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  6. Alec Basil Heckert

    1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    2. California Institute of Regenerative Medicine Center of Excellence, University of California, Berkeley, Berkeley, United States
    Contribution
    Software, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8748-6645
  7. Kayla K Umemoto

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Contribution
    Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  8. Claire Dugast-Darzacq

    1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    2. California Institute of Regenerative Medicine Center of Excellence, University of California, Berkeley, Berkeley, United States
    Contribution
    Resources, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8602-3534
  9. Robert Tjian

    1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    2. Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing—review and editing
    For correspondence
    jmlim@berkeley.edu
    Competing interests
    is one of the three founding funders of eLife, and a member of eLife's Board of Directors
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0539-8217
  10. Xavier Darzacq

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing—review and editing
    For correspondence
    darzacq@berkeley.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2537-8395

Funding

National Institutes of Health (UO1- 497 EB021236)

  • David Trombley McSwiggen
  • Anders S Hansen
  • Yvonne Hao
  • Alec Basil Heckert
  • Kayla K Umemoto
  • Claire Dugast-Darzacq
  • Xavier Darzacq

National Institutes of Health (U54-DK107980)

  • David Trombley McSwiggen
  • Anders S Hansen
  • Yvonne Hao
  • Alec Basil Heckert
  • Kayla K Umemoto
  • Claire Dugast-Darzacq
  • Xavier Darzacq

Howard Hughes Medical Institute (003061)

  • David Trombley McSwiggen
  • Anders S Hansen
  • Sheila S Teves

California Institute for Regenerative Medicine (LA1-08013)

  • Anders S Hansen
  • Alec Basil Heckert
  • Xavier Darzacq

National Institutes of Health (K99GM130896)

  • Anders S Hansen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank James Goodrich, Jennifer Kugel, and Robert Abrisch for providing the HSV1 strain KOS that began this project, and for helpful discussions. Thank you also to Stephen Rice for the n504 and n406 HSV1 strains, and Neal DeLuca for the UL2/50 HSV1 strain. Thank you to Luke Lavis for generously providing all of the Janelia Fluor dyes that enabled these experiments. Thank you to Ana Robles, Mustafa Mir, and Astou Tangara for their tireless work keeping the microscopes in working order. Thank you to all of the individuals who provided reagents, comments, and critical insight for this manuscript, including Claudia Cattoglio, Shasha Chong, Thomas Graham, Britt Glaunsinger, Ella Hartenian, Matthew Parker, James McSwiggen, and the Tjian and Darzacq Lab members. This work was supported by NIH grants UO1-EB021236 and U54-DK107980 (XD), the California Institute of Regenerative Medicine grant LA1-08013 (XD), by the Howard Hughes Medical Institute (003061, RT). A.B.H. is supported by the NIH predoctoral fellowship T32 GM098218. Portions of this work were performed on shared instrumentation at the CRL Molecular Imaging Center, supported by The Gordon and Betty Moore Foundation. We would like to thank Holly Aaron and Jen-Yi Lee for their assistance. DNA sequencing in this work used the Vincent J Coates Genomics Sequencing Laboratory at UC Berkeley, supported by NIH 669 S10 Instrumentation Grants S10RR029668 and S10RR027303.

Senior Editor

  1. Kevin Struhl, Harvard Medical School, United States

Reviewing Editor

  1. Jessica K Tyler, Weill Cornell Medicine, United States

Publication history

  1. Received: March 26, 2019
  2. Accepted: April 29, 2019
  3. Accepted Manuscript published: April 30, 2019 (version 1)
  4. Accepted Manuscript updated: May 7, 2019 (version 2)
  5. Version of Record published: May 16, 2019 (version 3)

Copyright

© 2019, McSwiggen et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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