Nup98-dependent transcriptional memory is established independently of transcription

  1. Pau Pascual-Garcia
  2. Shawn C Little
  3. Maya Capelson  Is a corresponding author
  1. Department of Cell and Developmental Biology, Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, United States

Abstract

Cellular ability to mount an enhanced transcriptional response upon repeated exposure to external cues is termed transcriptional memory, which can be maintained epigenetically through cell divisions and can depend on a nuclear pore component Nup98. The majority of mechanistic knowledge on transcriptional memory has been derived from bulk molecular assays. To gain additional perspective on the mechanism and contribution of Nup98 to memory, we used single-molecule RNA FISH (smFISH) to examine the dynamics of transcription in Drosophila cells upon repeated exposure to the steroid hormone ecdysone. We combined smFISH with mathematical modeling and found that upon hormone exposure, cells rapidly activate a low-level transcriptional response, but simultaneously begin a slow transition into a specialized memory state characterized by a high rate of expression. Strikingly, our modeling predicted that this transition between non-memory and memory states is independent of the transcription stemming from initial activation. We confirmed this prediction experimentally by showing that inhibiting transcription during initial ecdysone exposure did not interfere with memory establishment. Together, our findings reveal that Nup98’s role in transcriptional memory is to stabilize the forward rate of conversion from low to high expressing state, and that induced genes engage in two separate behaviors – transcription itself and the establishment of epigenetically propagated transcriptional memory.

Editor's evaluation

The authors quantitatively assess transcriptional memory in the context of mathematical modeling and testing of the models through single cell approaches. They extend their work to show how single cell data relates to population-level transcription outcomes. The models produced make predictions that the authors successfully test to demonstrate that transcription initiation is not necessary for establishment of memory.

https://doi.org/10.7554/eLife.63404.sa0

Introduction

Organisms continuously adapt to environmental changes, with transcriptional responses to varying stimuli playing critical roles in development and survival. One mechanism of cellular adaptation relies on the cells’ ability to mount increasingly robust transcriptional responses upon repeated exposure to signals they have previously encountered. This phenomenon by which cells increase their re-activation kinetics after a period of repression is called transcriptional memory (Avramova, 2015; Bonifer and Cockerill, 2017; D’Urso and Brickner, 2017; Fabrizio et al., 2019). The ability of cells to increase transcriptional responsiveness upon subsequent exposure (i.e. ‘remember’ their previous exposure) can be propagated through cell division, and is thus considered epigenetic. Transcriptional memory is well-characterized for genes that respond to environmental changes: for example, in budding yeast, rounds of inositol starvation trigger the transcriptional memory response for INO1, which encodes the enzyme catalyzing the limiting step in the biosynthesis of inositol (Ahmed et al., 2010; Brickner et al., 2007; Light et al., 2010); or in plants, where pre-exposure to various abiotic stresses such as drought or changes in salinity intensifies their resistance by elevating transcript levels from a subset of stress-response genes (Ding et al., 2013; Ding et al., 2012; Lämke et al., 2016; Liu et al., 2016; Sani et al., 2013). In both cases, transcriptional memory provides an advantage to survive environmental challenges by mounting a more robust transcriptional response that facilitates adjustment to new conditions.

Importantly, the primed state that is induced after initial exposure is not unique to environmental signals and can be found in different cellular processes. In mammalian cells, interferon-induced transcriptional memory is thought to confer an enhanced immune response to infectious agents (Gialitakis et al., 2010; Kamada et al., 2018; Light et al., 2013), and in Drosophila, transcriptional memory has been reported in the context of signaling by the steroid hormone ecdysone. Ecdysone or 20-hydroxyecdysone (20E) and its nuclear hormone receptor complex EcR/USP coordinate a transcriptional response that is essential for multiple events during fly development (Hill et al., 2013). A defined set of early ecdysone-induced genes, such as ecdysone-induced protein 74EF (E74) and early gene at 23 (E23), are activated by the hormone receptor and remain primed for enhanced expression upon a second exposure for at least 24 hr (Pascual-Garcia et al., 2017). Further evidence of roles for transcriptional memory during organismal development comes from a study of re-activation dynamics during Drosophila embryogenesis, where transgenes were shown to re-activate more frequently and more rapidly after cell division if they were transcribing in the previous cell cycle (Ferraro et al., 2016).

Mechanistically, several models have been put forward to explain how transcriptional memory may work. One such model proposed that memory is based primarily on the cytoplasmic inheritance of a key regulatory factor, which is build up during the initial exposure and remains at high levels through the period of memory (Kundu and Peterson, 2009). Such a mode of inheritance regulates the long-term transcriptional memory of the yeast GAL1 gene, which is repressed in normal glucose-containing media, but can be activated by switching cells to galactose (Brickner et al., 2007; Kundu et al., 2007; Zacharioudakis et al., 2007). In this case, the high expression of GAL1 regulators during initial activation and their subsequent persistence are thought to underlie the higher re-activation dynamics upon second exposure to galactose (Kundu and Peterson, 2010; Sood et al., 2017; Zacharioudakis et al., 2007). Another set of models focuses on a nuclear mode of inheritance of a transcriptional state, with chromatin and architectural features of the gene as the functional memory marks (D’Urso and Brickner, 2017; Kundu and Peterson, 2009; Randise-Hinchliff and Brickner, 2018). This model of transcriptional memory centers on the binding of gene-specific transcriptional factors to cis-acting DNA elements and on contacts of the gene with nuclear pore complex (NPC) components (or Nups). Significant efforts have been made to understand how transcriptional memory operates for the yeast INO1 gene, where recruitment of transcriptional factor Sfl1 at INO1 promoter and contact with Nup100 (homolog of metazoan Nup98) have been shown to promote the incorporation of histone variant H2A.Z and H3K4me2, both of which have been linked to memory establishment in multiple systems (Bevington et al., 2016; Brickner et al., 2007; D’Urso et al., 2016; Gialitakis et al., 2010; Lämke et al., 2016; Light et al., 2013; Light et al., 2010; Muramoto et al., 2010; Petter et al., 2011).

Studies in multiple organisms have revealed that Nup98 is an evolutionarily conserved factor necessary for transcriptional memory. In addition to INO1 regulation described above, interferon-induced genes require Nup98 for memory in human cells (Light et al., 2013), where Nup98 similarly promotes deposition of the H3K4Me2 mark. However, this memory event occurs at genes in the nuclear interior, which reflects the reported ability of Nup98 and other Nups to move on and off the pore and occupy intranuclear locations in metazoan cells (Capelson et al., 2010; Kalverda et al., 2010). Nup98 is also necessary for transcriptional memory in ecdysone-induced signaling. In Drosophila embryonic cells, loss of Nup98 does not affect transcription of ecdysone-induced genes during initial ecdysone exposure, but results in a poor memory response during re-induction (Pascual-Garcia et al., 2017). In fly cells, Nup98 has been implicated in the maintenance of ecdysone-induced enhancer-promoter loops of genes like E74 and E23, providing evidence that Nups can influence transcriptional memory through changes in chromatin contacts (Pascual-Garcia et al., 2017). Thus, the roles of NPC components in transcriptional memory appear complex and may involve a network of coordinated events. Investigation of this conserved function of Nup98 provides an opportunity to build further understanding of the memory phenomenon.

Previous work on transcriptional memory has primarily relied upon population-level analyses, using bulk biochemical and molecular assays to investigate its mechanisms (Brickner et al., 2007; Ding et al., 2012; Gialitakis et al., 2010; Pascual-Garcia et al., 2017). Here, we aimed to understand how transcriptional memory could work at the level of single cells and how single-cell behaviors can give rise to population-level transcriptional outcomes. We combined precise quantification of ecdysone-induced E74 mRNAs by single-molecule RNA FISH (smFISH) with mathematical modeling to describe transcriptional memory and the role of Nup98 from a single-cell perspective. By comparing predicted population distributions based on our modeling approaches to actual distributions of nascent transcriptional states obtained by smFISH, we have defined the transcriptional parameters that change in the reinduced vs. induced states and shed light on the contribution of Nup98 to the transcriptional process. Strikingly, we found that transition into the memory state is independent of the extent of transcriptional activity during initial induction. Our results introduce a possible model that accounts for cell- and population-level dynamics during transcriptional memory response, allow us to rule out some previously proposed models for ecdysone-mediated memory, and suggest a functional role of Nup98 in driving the formation of a memory state that is separate from ongoing transcription.

Results

Assessing Nup98-dependent transcriptional memory in absolute units

We have previously shown that ecdysone-inducible genes exhibit transcriptional memory, and that Nup98 is required for the proper establishment and/or maintenance of the primed memory state (Pascual-Garcia et al., 2017). To further investigate the role of Nup98 in modulating transcription, we examined the transcription dynamics of the ecdysone-responsive gene E74 during repeated hormone exposure in more detail. Drosophila S2 cells were exposed to synthetic 20E for a 4 hr initial induction, then washed and allowed to recover for 24 hr before re-exposure to 20E for an additional 4 hr (Figure 1A). We assayed E74 expression dynamics during both exposures by collecting cells every 30 min, isolating mRNA, and performing RT-qPCR on cells treated with dsRNA against the control white gene (dsWhite) or against Nup98 (dsNup98). To aid in quantitative analysis, we adapted a previously described protocol (Petkova et al., 2014) to estimate the absolute number of E74 mRNA molecules per cell. We performed RT-qPCR using known numbers of in vitro transcribed RNAs as template spanning six orders of magnitude. This allowed us to construct a calibration curve relating the qPCR threshold cycle Ct to the number of input molecules (Figure 1—figure supplement 1A; see Materials and methods). We measured the number of cells from which we extracted mRNA for each experimental condition and time point. We also estimated the mRNA recovery efficiency of the mRNA extraction step. Together, these measurements allowed us to convert experimental Ct values into absolute numbers of E74 mRNA molecules per cell, generating a detailed description of E74 expression dynamics during hormone exposure in control and Nup98 knockdown cells (Figure 1B).

Figure 1 with 2 supplements see all
Absolute quantification of E74 induction and transcriptional memory.

(A) Overview of ecdysone/20E treatment. Cells were treated with 5 μM 20E, then washed with fresh media and recovered for 24 hr. Memory response was assessed by incubating cells with 5 μM 20E after recovery period. (B). Absolute number of E74 mRNAs per cell as a function of time after the addition of 20E during either the first or second induction in control (dsWhite) and Nup98 knockdown (dsNup98) S2 cells. Samples were collected every 30 min. Error bars represent standard deviation of the mean of three experiments. (C). Schematic of smFISH labeling of sites of nascent transcription and spliced transcripts in either the nucleus or cytoplasm. (D). Representative images of E74 smFISH labeling in single cells during the first and second induction, displayed with the ImageJ ‘red fire’ look-up table. (E). Violin plots of E74 puncta per cell. Mean and median indicated by black and red horizontal lines.

Using this approach, we found as expected that the number of E74 mRNA molecules per cell reaches considerably higher amounts during re-induction than during initial induction in control dsWhite-treated cells (Figure 1B). E74 transcripts accumulate slowly during the first 2 hr of initial hormone exposure before increasing. Moreover, the accumulation trajectory in Nup98-depleted cells is not different from control during the first induction. In contrast, accumulation is rapidly onset during the second induction in control cells, whereas in Nup98 knockdown conditions, the second induction is more similar to the first induction (Figure 1B). This supports the proposed requirement for Nup98 in transcriptional memory, in agreement with previous work from our and other laboratories (Light et al., 2013; Pascual-Garcia et al., 2017). The efficiency of Nup98 depletion was similar during both the first and second inductions (Figure 1—figure supplement 1B), supporting our conclusion that the response of E74 to initial exposure does not require normal levels of Nup98; instead, cells require Nup98 to rapidly express E74 upon repeated exposure.

In the simplest model of hormone-induced transcription, all loci would be rapidly activated upon hormone treatment and then begin producing mRNAs at a constant rate. If true, then mRNA content should be relatively uniform between cells, particularly at later times after induction, when cells will have ample opportunity for their expression levels to closely approach the mean level. To ask whether E74 expression is uniform between cells, and to gain insight into the nature of hormone-induced memory response, we employed smFISH to measure E74 expression in single cells. E74 mRNAs were labeled with a set of 67 probes complimentary to exon sequences (Figure 1C and Supplementary file 1) and imaged by confocal microscopy. smFISH reveals two classes of labeled objects: relatively dim, diffraction-limited puncta enriched in the cytoplasm representing mature mRNAs, and one or more bright nuclear-localized puncta corresponding to sites of nascent transcription (Figure 1C–D). To estimate E74 mRNA levels, we counted the number of mRNA puncta at 0, 1, 2, and 4 hr post-induction and observed that the average number of puncta per cell accumulated with dynamics that mirrored those obtained by absolute qPCR (Figure 1D–E and Figure 1—figure supplement 1C-D). mRNA content of puncta also increased in a manner consistent with qPCR (Figure 1—figure supplement 1E-F). As expected, during the first induction, dsWhite-treated and Nup98-depleted cells accumulated puncta at similar rates, while during the second induction, control cells accumulated mRNA puncta more rapidly and to higher levels than Nup98-depleted cells or cells undergoing the initial induction (Figure 1E). Such accumulation of mRNA puncta was not seen in smFISH of control probes against genes that do not respond to ecdysone (Shlyueva et al., 2014), which exhibited similar counts before and after ecdysone addition (Figure 1—figure supplement 2A-C). The validity of these smFISH counts is also supported by our demonstration that the detected objects are diffraction limited (Figure 1—figure supplement 2D-F, Petkova et al., 2014; Little et al., 2015). Importantly, we observed that E74 expression is heterogeneous between cells, with mRNA amounts exhibiting broad distribution at all times and under all induction conditions. Even under the most highly expressed condition at 4 hr after the second induction, 20% of cells possess 20 puncta or fewer, whereas <5% contain 100 or greater. These smFISH results validate our quantitative RT-qPCR data, but do not support the simple model of E74 activation or production occurring at a uniform rate. The observed heterogeneous mRNA levels across cells in all conditions also suggest that the role of Nup98 is to increase average expression levels without increasing the uniformity of the hormone response during the second induction. Consistently, analysis of transcriptional noise in the four conditions reveals that Nup98 knockdown does not alter variability in mRNA counts between cells (Figure 1—figure supplement 2G).

Nup98 modulates E74 mRNA production without affecting export or degradation

To further explore the role of Nup98 in the memory response, we undertook additional quantitative measurements of E74 mRNA dynamics. Our preceding results (Figure 1) confirmed a Nup98-dependent increase in the rate of E74 mRNA accumulation during the second induction. Generally, the rate of mRNA accumulation depends on both mRNA production and degradation. Degradation of mRNA may be affected by the process of mRNA export, which interfaces with NPCs (Rodríguez-Navarro and Hurt, 2011; Tutucci and Stutz, 2011). Certain NPC components, including Nup98, have been previously implicated in mRNA export through interactions with the mRNA export machinery (Blevins et al., 2003; Chakraborty et al., 2006; Powers et al., 1997), and mRNA export could in turn affect mRNA stability either directly or indirectly. Since prior work has not addressed whether Nup98 affects E74 mRNA levels through these processes, we first asked whether Nup98 knockdown induces a change in E74 mRNA trafficking out of the nucleus or in the rate of degradation of E74.

To answer these questions, we examined the smFISH images for evidence of altered nucleo-cytoplasmic transport by assessing the fraction of mRNA puncta that are found outside of the Hoechst-based mask out of the total amount of mRNA puncta per cell (Figure 2—figure supplement 1A). We found that this fraction does not change in control/dsWhite versus dsNup98 cells significantly in either induced condition, with >85% of mRNAs found in the cytoplasm across conditions (Figure 2A). Next, to determine whether E74 mRNA stability is altered upon Nup98 knockdown, we monitored E74 mRNA levels following transcriptional arrest. We inhibited transcriptional elongation by treating cells with flavopiridol (FP), a potent inhibitor of p-TEFb, which phosphorylates Ser2 of the C-terminal domain of RNA Pol II (Chao and Price, 2001). E74 mRNA synthesis was stimulated with 20E for 4 hr during the first or the second inductions, after which the hormone was washed out and transcription blocked with FP. As the mRNA levels of E74 declined, we monitored them for 240 min, with time points collected every 30 min, during the first or the second inductions in control or Nup98-depleted conditions (Figure 2B). We observed that the degradation rates were largely unchanged between Nup98-depleted and control cells during either induction, with no significant difference in E74 mRNA lifetimes, which were determined by fitting to a first-order reaction (Figure 2B and Figure 2—figure supplement 1B). We concluded that neither mRNA export nor mRNA degradation are major contributors to the phenotype of Nup98 in transcriptional memory. Instead, our data suggests that the role of Nup98 in transcriptional memory is mediated through regulation of transcription, which is supported by the previously identified binding of Nup98 to the promoters and enhancers of ecdysone-inducible genes (Pascual-Garcia et al., 2017).

Figure 2 with 1 supplement see all
Altering Nup98 levels modulates transcription, not mRNA export or degradation.

(A) Fraction of E74 mRNA found in cytoplasm, as assessed by smFISH, with Nup98 knockdown (+) or control knockdown (-) prior to the first induction (unind), after 4 hr (1st ind), 24 hr after hormone removal (recov), and 4 hr after 2nd induction. Error bars represent standard deviation of the mean. Welch’s t-test was used to calculate p-values. (B). Degradation rates of E74 mRNA assessed by qPCR. Cells from 1st induction or 2nd induction (as depicted in Figure 1A) were washed and collected every 30 min after the addition of 1 μM flavopiridol at zero minutes. Experiment was repeated three times and mRNA lifetimes were determined by fit to exponential curves. p-Values, determined by Welch’s t-test, have values > 0.2 for all pairs of fits. (C–D). E74 transcription rates increase during 20E exposure. Data points with error bars obtained by qPCR as shown in Figure 1B. Shaded bands indicate 95% confidence intervals for the fit rates P for the models indicated. Top row: first induction; second row: second induction. (C) Best fit of data to model of constant transcription rate P. (D): Best fit of data to model of time-dependent increasing E74 transcription rate. (E). Comparison of the rates of mRNA accumulation (left) and underlying transcription rates (right) between first and second induction in control and dsNup98 cells.

Our combined knowledge of the degradation rate with the accumulation dynamics in absolute units allowed us to test quantitative models describing the effect of Nup98 on transcription. In the simplest model, Nup98 would act to boost the average transcription rate from a low constant mRNA production rate during the first induction to a large constant rate during the second induction. To determine whether a model of constant production correctly describes the data, we found the best-fitting values for the production rates under each induction and knockdown combination (Figure 2C). In this straight forward model, the change in mRNA concentration per time equals mRNA production rate P minus the degradation rate, which was determined experimentally (Figure 2B). The estimate of the degradation rate permitted us to obtain best-fit values for P (Figure 2C). As expected, the value for production rate for the second induction in control cells was higher than during the first induction by a factor of 2.4 x, whereas the production rates are similar in first induction control and Nup98 knockdown conditions under both exposures. However, constant production rates provide a poor description of the data (Figure 2C and Figure 2—figure supplement 1C). In all cases, the constant production model predicts the fastest accumulation during the first 2 hr of expression, with gradual attenuation of the accumulation as transcript levels reach steady-state. The data instead exhibits a gradual and slow increase in the rate of accumulation during the majority of interval for hormone exposure. This is most obvious for the first induction in both control and Nup98 knockdown cells. The results indicate that the simplest explanation for Nup98 activity, boosting a constant production rate, is incompatible with observation.

Instead of constant production rate, the data suggested that the production of E74 mRNA increases with time. We therefore fit a model in which production of new mRNAs increases linearly with time, and found that this model provided an excellent fit (Figure 2D and Figure 2—figure supplement 1C). We observed that the production rates increase at similar rates in three scenarios: first induction in control, and both inductions in Nup98 knockdown conditions. In contrast, during the second induction in control cells, the production rate increases at more than twice the rate of the first induction. This can be clearly visualized by taking the derivative of the fit accumulation curves to reveal the accumulation rate, that is the change in the average number of mRNAs per cell as a function of time (Figure 2E). The accumulation rates are similar in control first induction and dsNup98 first and second induction, but become increasingly fast in control second induction. The accumulation rates begin to stabilize at around 4 mRNAs per minute during the first induction but reach greater than 10 mRNAs per minute during the second induction in control cells (Figure 2E, left). During hormone exposure, the transcription rate climbs continuously, reaching only about 2 mRNAs per minute per locus during the first induction but climbing to 5 mRNA per minute per locus during the second induction in control cells (Figure 2E, right). By the end of the fourth hour of the second exposure, this corresponds to an average production rate per locus similar to that observed for the most highly expressed genes in Drosophila embryos (Little et al., 2013; Zoller et al., 2018). Overall, these results show that Nup98 changes transcription of E74 without affecting mRNA trafficking or stability. Moreover, normal levels of Nup98 are required not simply to promote a higher level of transcription, but to stimulate a continuous increase in transcription rates upon additional hormone exposure.

A two-state model for the mechanism of Nup98-dependent transcription rate increase

We next sought to explore models describing the Nup98-dependent increase in the rate of mRNA production. We began with a two-state model in which loci switch between active and inactive states (Figure 3A–B). During the active state, new RNA Pol II molecules enter productive elongation at a rate given by kPol. For simplicity, we assumed that while hormone is present, the rate switching into the active state kA is much greater than the rate of switching into the inactive state k-A. This ensures that once active, loci are in essence irreversibly committed to the active state as long as the hormone is present. These assumptions are reasonable given the large but slow increase in transcript levels observed over the duration of the hormone exposure. In this model, the average production rate per locus is given by the loading rate kPol multiplied by the fraction of active loci (Figure 3B).

Figure 3 with 2 supplements see all
The two-state promoter model does not describe single-cell measurements of transcription.

(A) A model utilizing two promoter states (active and inactive) contains two mechanisms that can account for the increase in the transcription rates observed by qPCR. Model 1: loci are slowly recruited into a transcriptionally active state upon first induction and more rapidly upon second induction. Once active, loci produce new mRNAs at a constant rate equivalent to the rate at which new RNA Pol II molecules enter productive elongation. Model 2: all cells are rapidly recruited into an active state, and the rate of transcription increases over time, slowly upon first induction and quickly upon second induction. (B). Model describing the rate of recruitment into the active state kA, into the inactive state k-A, and the rate of recruiting RNA Pol II molecules into productive elongation kPol. The presence of 20E permits kA to be larger than k-A. The production rate as a function of time is given by the RNA Pol II recruitment rate multiplied by the fraction of active loci. In Model 1, kA increases and kPol is constant during 20E exposure, whereas in Model 2, kPol increases and kA is constant. (C–N). Histograms (cyan) show distribution of measured total instantaneous transcriptional activity in normalized units (C.U.), obtained from smFISH of E74 as shown in Figure 1. Lines represent predicted values generated by simulation using best-fitting parameters for Model 1 (green) and Model 2 (magenta) under conditions of control (C–H) or Nup98 knockdown conditions (I–N) during the first (C,F,I,L), second (D,G,J,M), or fourth (E,H,K,N) hour of the first (C–E, I–K) or second (F–H, L–N) inductions.

There are then two basic ways in which the mRNA production rate can increase over time in a two-state model. The most straightforward is by recruitment of loci into the transcriptionally active state, while the loading rate kPol is held constant (Figure 3A, Model 1 and Figure 3B). In this scenario, what would change over time is the fraction of cells in a transcriptionally active state (Figure 3A, Model 1). Once a locus enters an active state, mRNA production would occur at a constant rate. The increased transcriptional output during re-induction would thus stem from a higher fraction of cells being active in post-memory conditions. This scenario requires a kA that is small such that any given locus is unlikely to enter the active state in any given minute. The gradual increase in the fraction of active loci would thus be proportional to the continuous increase in the average transcription rate (Figure 2E, right). Under Model 1, the role of Nup98 is to increase kA such that a larger fraction of loci become active sooner during the second induction.

We fit this model to the qPCR data. For each of the four scenarios (control/dsWhite or dsNup98, first or second induction), we found values and confidence intervals that described the accumulation curves (Figure 3—figure supplement 1A). Fitting each scenario independently, we found that kPol was roughly constant at 2.0±0.2 RNA Pol II per minute for all scenarios (average and standard deviation calculated across all four scenarios) (Figure 3—figure supplement 1B). Moreover, fit values of kA are very similar for the three low expressing scenarios, dsWhite first induction and both inductions in dsNup98, at an average of 5.1±1.6 x 10–3 per minute (Figure 3—figure supplement 1B, E). In contrast, kA for the second induction in control is 33±15 × 10–3 per minute, a sixfold increase (Figure 3—figure supplement 1B, E). In this model, Nup98 is required for this dramatic upregulation in rate of entry into the active state during the second induction.

Although Model 1 can explain increasing transcription rate at the population level, we can also envision an alternative scenario, where all cells enter an active state rapidly upon hormone stimulation, and what increases over time is the transcriptional rate at individual loci (Figure 3A, Model 2). The two-state model provides an additional means of regulation, in which kPol is not constant but instead increases with time (Figure 3A, Model 2, and Figure 3B). In the most extreme version of this scenario, kA is very large such that all loci switch into the active state immediately upon hormone exposure. This would be consistent with the known rapid rate of nuclear import of hormone receptors, on the order of minutes (Nieva et al., 2007). In this scenario, the increasing transcription rate would result from increasing kPol, and the increased rate of mRNA production during second induction would be explained by a faster increase in transcriptional rate at individual loci. Under Model 2, the role of Nup98 would be to ensure a more rapid increase in kPol upon repeated hormone treatment.

To determine whether this scenario could explain our observations, we fit our qPCR data to a model in which (1) kA is very fast (1000 per minute) to ensure that nearly all loci become active within a minute of hormone exposure, and (2) the number of attempts that RNA Pol II makes to engage in transcription increases linearly with time. The fraction of attempts that are successful is limited by process(es) that prohibit Pol II from loading at an indeterminately high density, thereby imposing a minimum interval between transcribing Pol IIs. In implementing the model, the minimum interval is determined by fitting the qPCR data with a constant elongation rate of 1500 nt/min (Izban and Luse, 1992, Ardehali and Lis, 2009; Buckley et al., 2014; Yao et al., 2007) and introducing a free parameter representing the minimum interval in units of nucleotides. The model is agnostic with regard to the molecular mechanism(s) underlying either the rate increase or the minimum Pol II interval. With these constraints we were able to fit the qPCR data from each scenario to a model of increasing RNA Pol II attempt rate that described the accumulation trajectories (Figure 3—figure supplement 1C-D). As before, three conditions (dsWhite first and dsNup98 first and second inductions) have similar acceleration constants, with an average of 11.7±2.2 × 10–3 Pol II min–2 (Figure 3—figure supplement 1E). In contrast, control second induction has an acceleration constant that is about six-fold larger than the first at 65.7 × 10–3 Pol II min–2 (Figure 3—figure supplement 1D-E). In this model, the role of Nup98 is to increase the acceleration of the attempt rate kPol.

Overall, both versions of the two-state model perform equally well at describing the qPCR data (Figure 3—figure supplement 1F). Additionally, the two versions are not mutually exclusive; our qPCR data would be equally well fit by many possible combinations of values for kA and kPol acceleration constants. To discern between these possibilities requires single-cell measurements of transcription provided by smFISH.

Single-cell analysis does not support the two-state model

To discern between possible versions of the model outlined above, we proceeded to determine whether they could account for the distribution of transcriptional activity observed in our smFISH analysis of E74 (Figure 1). We determined the instantaneous transcriptional activity in individual cells by summing the fluorescence intensity of all nascent transcription sites in each cell and normalizing to the intensity of single mRNAs (also see Materials and methods). The resulting values are the absolute amount of nascent RNA present at transcribing loci in units of the equivalent number of mature mRNAs (Little et al., 2013; Zoller et al., 2018). As mRNAs tend to be localized to the cytoplasm, the unit intensity is referred to as the ‘cytoplasmic unit’ intensity (C.U.). The number of C.U.s associated with each transcribing site depends on the number of probe binding sites present, and thus on how many RNA Pol II molecules are present and how much of the transcript has been synthesized by each Pol II. The normalization is possible because our probes target only exon sequences and thus the fluorescence intensity is not affected by mRNA splicing or the presence of introns (Figure 1C).

To determine if either version could account for the actual transcriptional behavior in ecdysone-induced responses, we used the values for kA and kPol from the fit to qPCR to simulate the distribution of C.U.s for both models at three time points (1, 2, and 4 hr post-induction) under all four induction conditions. Monte Carlo simulations were used to generate RNA Pol II positions for 100,000 cells under each condition and time point. The RNA Pol II positions were used in combination with the probe positions along the transcribed RNA to obtain a sum of probe binding sites present in the simulated nascent mRNA. This sum was then normalized to the number of probes present in a single mRNA to generate simulated distributions in units of C.U.s. Histograms of measured and simulated C.U. distributions were then compared (Figure 3C–N; see Materials and methods).

We examined the distribution of transcriptional activity across hormone treatments and time points (cyan bars, Figure 3C–N). Notable features in the observed distribution of transcriptional activity include a gradual shift in the activity levels as a function of time, as expected from the RT-qPCR data, meaning that an increasing fraction of cells shift to transcribing at higher amounts. This shift occurs more rapidly for the control/dsWhite second induction (Figure 3F–H) than for other conditions. The observed distribution begins to resemble a bimodal distribution in control cells at 4 hr (Figure 3H), demonstrating that a subset of nuclei have begun transcribing E74 at rates much faster than earlier in the exposure, as measured in C.U.s. This is again consistent with the prediction from qPCR that the transcription rates in general are increasing. Notably, this bimodal behavior is less evident in dsNup98 conditions (compare Figure 3L–N), where the highly transcribing fraction of cells is much less pronounced at 4 hr of second induction (Figure 3N). This suggests that Nup98 could be involved in a shift from low to high expression rates. Additionally, the change in the estimated fractions of cells with 0–4 active loci over time appears similar among the four conditions (Figure 3—figure supplement 2A-E, see Materials and methods for description of how active transcription sites were designated), supporting the notion that the rate of locus-associated transcriptional activity is the point of regulation.

We compared the distribution of the data to those predicted by each of the two-state models. Overall, the simulated distributions from either of the two two-state models provide a poor fit to data (Figure 3C–N, compare simulated green and violet curves to cyan bars of observed data, and Figure 4—figure supplement 1D). Model 1, with changing kA and constant kPol, consistently overestimates the fraction of cells with small numbers of transcripts at early times (green curves, Figure 3C,I, notice the early green ‘spike’). Model 2, with rapid kA and increasing kPol, suffers less from an overestimate of inactive cells. However, both models predict a narrowly distributed peak of highly active cells at 4-hr time point in all four conditions. In contrast, measurements reveal broadly distributed expression levels, with many cells showing less activity than predicted (Figure 3E, H, K and N). Furthermore, hybrid models combining both more rapid kA with more slowly increasing kPol suffer from both shortcomings in combination (data not shown). This stems from the failure of the two-state model to account for the large fraction of cells containing mRNAs in the first hour combined with an overestimate of narrowly distributed but highly active cells at late times. From the simulation results, we concluded that no two-state model correctly captures the transcription dynamics of the hormone response in either the first or second induction.

A four-state ‘Memory Switch’ model explains single-cell data of transcriptional memory

Given the distribution of transcriptional states we observed in our single-cell smFISH analysis (Figure 3), we reasoned that loci may express E74 at two different rates, such that loci are either in the low- or high-expressing state in the presence of ecdysone. Both the low- and high-expressing rates would be larger than the very slow basal expression rate observed in the absence of ecdysone. The slow emergence of the high-expressing state during exposure to ecdysone would be consistent with the gradual increase in transcription rate we have observed (Figure 2E). Moreover, the rapid accumulation of transcripts upon hormone re-exposure suggests a mechanism for memory: loci that previously converted into the high-expressing state bypass the low-expressing state upon hormone re-exposure, and immediately enter the high-expressing state. We termed such converted state the induced memory (IM) state, whereas loci that have not converted remain in a default low-expressing state, which we called induced default (ID) state (Figure 4A). Importantly, the model also suggests that the high expression observed during the second induction results from the continuous accumulation of loci into the memory state even after the withdrawal of hormone. In this case, the role of Nup98 may be both to ensure that converted loci retain information about their conversion state and to enact the continuous conversion of loci after hormone withdrawal (Figure 4A, right). This would explain the strong resemblance of the first induction in controls cells to both inductions when Nup98 is depleted: in the absence of hormone, all loci reconvert to the default, non-memory state without Nup98 activity.

Figure 4 with 1 supplement see all
The memory switch model describes the distribution of transcriptional activity.

(A) Promoters can occupy one of four states: Uninduced and Induced (U and I), related to current presence of hormone; and Default and Memory (D and M), associated with prior hormone exposure. The Uninduced state is associated with a basal RNA Pol II rate kPolB, whereas the Induced state shows two independent RNA Pol II rates kPolL and kPolH associated with Default and Memory states, respectively. 20E has two roles: one, to activate transcription by ensuring kA >> k-A, as in earlier models; and two, to increase the rate of conversion from Default to Memory by ensuring kC >> k-C. The role of Nup98 is to maintain kC >> k-C upon withdrawal of 20E. (B–M). Histograms (cyan) show distribution of measured total instantaneous transcriptional activity in normalized units (C.U.), obtained from smFISH of E74 as shown in Figure 1. Lines represent predicted values generated by simulation using best-fitting parameters under the memory switch model.

Based on this reasoning, we constructed a ‘memory switch’ model containing four states: two un-induced states in the absence of hormone, termed Un-induced Default (UD) and Un-induced Memory (UM) (both characterized by very low basal levels of transcription), in addition to the two induced states, IM and ID (Figure 4A). In this model, the basal level of expression in the absence of ecdysone is described by the transcription rate constant kpolB, while hormone exposure has two independent effects. First, similarly to the two-state model, hormone switches non-expressing loci into an expressing state at rate kA. As before, hormone ensures that the conversion to expression is irreversible as long as hormone is present. Second, hormone treatment converts loci from the default state (either UD or ID) into the memory state (either UM or IM). This conversion occurs at rate kC and is necessarily slow. The two induced states each have an associated rate of RNA Pol II loading, low kPolL for ID and high kPolH for IM. To account for the immediate entry of loci into the high expressing IM state upon second induction, the memory state is established irreversibly, with kC much greater than k-C in normal conditions. Moreover, the model implies that once the hormone is applied, the conversion of loci to the memory state continues even after the hormone has been withdrawn, so that after the 24 hr recovery period in our hormone treatment regimen, most loci are found in the memory state. Under this model, the primary role of Nup98 would be to maintain a high kC, or the probability that cells remain in the memory or high-expressing state, such that depletion of Nup98 abrogates the maintenance of the memory state upon removal of hormone. In the absence of Nup98, k-C becomes greater than kC, and loci do not remember their prior exposure once hormone is withdrawn (Figure 4A, right), which explains why the second induction strongly resembles the first in Nup98-depleted cells.

If this model is correct, then a single set of values for the parameters kC, kA, kPolL, and kPolH should describe both the qPCR and smFISH results from all four conditions. To test this, we started by fitting the model to the qPCR data for each individual condition. We used the functions describing the average transcription rate over time (Figure 2E, right) as a constraint to narrow the range of allowable parameter values capable of recapitulating the functions. We then performed Monte Carlo simulations, searching a wide range of parameter values to find sets that reproduced the smFISH data. The resulting values captured the rise in the transcription rate and the resulting accumulation trajectories observed by qPCR under all four conditions (Figure 4—figure supplement 1A-C). As required by the model, the fraction of cells in either expressing state increased in a similar manner over time between all conditions (Figure 4—figure supplement 1B). This conversion into either expressing state ID or IM occurs at the same rate (on average, kA = 17 +/- 1 × 10–3 per min, Figure 4—figure supplement 1C; see also Figure 4A). In contrast, the conversion into either memory state UM or IM is very slow (kC = 0.8 +/- 0.2 × 10–3 per min) (Figure 4—figure supplement 1C; see also Figure 4A), meaning the characteristic decay time out of the default state is very long ( > 1200 min or about 20 hr). This means that the more time that passes after the first induction, the larger the fraction of loci that convert to the memory state, even after the withdrawal of hormone. In this manner, after the 24 hr recovery, the majority of loci (74%) are in the memory state (Figure 4—figure supplement 1E), ready to enter the high-expressing state immediately upon re-exposure.

To verify the goodness of the model, we again performed Monte Carlo simulations and compared the resulting simulated distributions to smFISH data for E74. The simulation closely matches observation (Figure 4B–M) and provides a vastly improved fit relative to either of the two-state models (Figure 4—figure supplement 1D). The model thus successfully captures the emergence of the most highly-expressing cells during the first induction as a consequence of the slow conversion from non-memory to memory states. Notably, the rate of RNA Pol II loading is 15 times greater for the high-expressing IM state (kpolH 4.5 versus kpolL 0.3 RNA Pol II per minute, Figure 4—figure supplement 1C) and approaches the maximum observed value found in Drosophila embryos (Little et al., 2013). The vastly increased expression rate during the second induction again requires Nup98 function; upon depletion, loci fail to re-enter the high-expressing state upon re-exposure and are found in the default state upon re-induction (Figure 4—figure supplement 1E).

In summary, the four-state memory model encapsulates all the essential features of our observations. Our analysis puts forth a mathematical model that accurately describes the transcriptional memory behavior of ecdysone-inducible genes. The analysis predicts that in addition to activating default transcription, hormone exposure leads to a response event that drives a switch between non-memory and memory states.

The establishment of transcriptional memory is independent of transcriptional activity during the initial induction

The memory switch model, described above, reveals novel mechanistic insights into how transcriptional memory is established and maintained, and allows us to make predictions that can be tested experimentally. Thus, to further validate our model, we proceeded to test two of its main predictions. The most striking and surprising prediction of the model is the independence of kC (the rate of conversion from the default state to the memory state) from kA and kPol, which are rate constants that describe the transcriptional process itself. Otherwise stated, the model suggests that the ability of cells to establish the transcriptional memory response is independent from the process or the extent of transcription itself. The model predicts that at individual loci, addition of ecdysone sets off the conversion into the high-expressing memory state, but the parameters governing the rate of the conversion should be independent from the transcription that takes place during the first induction.

We tested this prediction by two different approaches. First, we varied the amount of transcription by varying the length of exposure to 20E during the initial induction. We assessed the transcriptional memory response of the E74 gene via RT-qPCR, using diminishing initial incubation times of 20E ranging from 4 hr to a minimum of 10 min, after which cells were recovered for 24 hr and re-induced as usual (Figure 5A). In agreement with the model, we observed that cells responded with a similarly robust increase of transcription during the second induction after all initial hormone incubation times. No large changes were found in the E74 transcriptional memory response between cells that were induced for 10 min versus 4 hr (Figure 5A), demonstrating that the memory state is established independently of the length of 20E incubation times and thus of the length of time these loci were engaged in active transcription.

Figure 5 with 1 supplement see all
Induction of the memory state requires neither transcription nor prolonged 20E exposure.

(A) E74 mRNAs measured by qPCR and normalized relative to rp49 with varying duration of 20E exposure during first induction. The data represent the mean of three independent experiments and the error bars the standard deviation of the mean. (B). Flavopiridol or Triptolide inhibitors were added 30 min prior 20E first induction at 1 μM and 5 μM, respectively. After 4 hr of induction, both, the hormone and the transcriptional inhibitors were washed-out and cells recovered for 24 hr. 20E re-induced cells were collected and, E74 expression was measured by qPCR from three independent experiments. Fold change values were normalized using rp49 and error bars represent the standard deviation of the mean. (C). Cells were treated with Flavopiridol or Triptolide as described in B and E74 mRNAs were monitored by smFISH. Error bars represent standard deviation of the mean. (D). E74 mRNAs levels were measured by qPCR and normalized against rp49 using two different recovery times (6 hr and 24 hr). The data represent the mean of three independent experiments ± standard deviation. (E). Calibrated Memory Index (CMI) of E74, E23, and E75 genes.

Second, to address this prediction, we utilized the transcriptional inhibitor FP to prevent transcriptional elongation altogether. Cells were treated with FP for 30 min before as well as during 20E induction. We monitored E74 expression and observed no transcriptional activity in cells treated with FP during ecdysone induction, revealing the efficacy of FP blockage (Figure 5B). Cells were then washed, and we measured the transcriptional memory response after the usual 24 hr of recovery. Strikingly, we observed that blocking transcriptional elongation during the first induction does not substantially affect the ability of the cells to generate a robust memory response, indicating that the transition into the memory state is independent of mRNA production during the first induction (Figure 5B). Levels of E74 were normalized to transcript levels of the housekeeping gene rp49, expression of which has been shown to be unaffected by ecdysone exposure (Shlyueva et al., 2014; Figure 5—figure supplement 1A). In addition to FP, we tested another transcriptional inhibitor Triptolide (TPL), which is known to block transcription at an earlier, initiation-associated point by inhibiting TFIIH (Titov et al., 2011). Treatment with TPL during initial induction similarly resulted in the majority of transcriptional memory being preserved (Figure 5B), although at somewhat lower levels than FP, suggesting that transcriptional initiation may partially contribute to memory, but transcription itself does not. We further validated our RT-qPCR findings by smFISH analysis, which likewise yielded similar counts of E74 mRNA spots and transcriptional output during second induction, in control or after treatment with either FP or TPL in the initial induction (Figure 5C and Figure 5—figure supplement 1B-C). Importantly, we obtained very similar conclusions with two other ecdysone-inducible genes E23 and E75, which we previously found to depend on Nup98 for transcriptional memory (Pascual-Garcia et al., 2017) and which also retained most of their transcriptional memory after transcriptional inhibition (Figure 5—figure supplement 1D-E). Taken together, our experimental data support the prediction proposed by our modeling studies and reveal a previously unreported feature of transcriptional memory: that its establishment is independent from the extent of transcriptional activity during initial stimulation.

The second prediction of the model that we aimed to test was the slow conversion from non-memory to memory state, on the order of 20 hr (for a conversion of 62% of loci) based on the obtained parameters. We tested this timescale by shortening the recovery times after ecdysone induction. Cells were induced with 20E for 1 hr and recovered for six or 24 before testing transcriptional memory responses. The model anticipates that cells that have been recovered for 6 hr should have a lowered memory response than cells that have been recovered for 24 hr. Consistently with this prediction, we found a reduced memory response for cells recovered for 6 hr (Figure 5D), underpinning the notion that transition into the memory state relies on mechanisms that (1) do not require the continuous presence of ecdysone and (2) have a relatively long timescale.

In order to compare the differences in transcriptional memory responses caused by distinct factors or treatments, we represented the observed differences in transcriptional memory responses as Calibrated Memory index (CMI), derived from the ratios of slopes in mRNA accumulation during second inductions and calibrated to control and Nup98-disrupted responses, as measured by RT-qPCR for E74, E23, or E75 (Figure 5E; see also Materials and methods). Comparing CMI among different analyzed treatments demonstrates that while depletion of Nup98 has a substantial impact on transcriptional memory, inhibition of transcription by FP or TPL affects transcriptional memory to a much lesser extent, reducing the memory index on average by only 20–30% for any of the three genes (Figure 5E). Together, these results support our computationally derived model of transcriptional memory and reinforce the notion that ecdysone-driven induction initiates two independent events at a given locus: (1) the rapid switch and transition into active transcription, and (2) the slow Nup98-dependent conversion into the memory state (Figure 6A–B). The population dynamics of the transcriptional responses to ecdysone would thus reflect the changing mixture of the low-expressing and high-expressing/memory cells (Figure 6A), with Nup98 functioning as a key determinant for the prevailing fraction of high-expressing cells post-memory establishment. Our approach suggests that the main role of Nup98 in transcriptional regulation lies not with influencing transcriptional entry, but instead, with stabilizing and maintaining the specialized state of high transcriptional output, conversion to which is determined by a separate rate constant kC (Figure 6B).

Memory switch model.

(A) Two events are triggered independently by exposure to 20E: loci rapidly enter a low-expressing state and at the same time slowly switch from the default state to memory. Memory is characterized by high transcription rate in the presence of hormone, and importantly, cells continue to accumulate loci in the memory state after the hormone is withdrawn. When cells are exposed to 20E a second time, the converted memory loci transit into the high-expressing state, resulting in a more robust second induction. (B). Implication of the memory switch model: upon hormone exposure, loci engage in two separate activities, controlled by independent rate constants – entry into the active state (controlled by kA) and transition into the memory state (controlled by kC). In the memory switch model, normal levels of Nup98 are required for loci to accumulate and remain in the memory state (kC>>k-C), such that depletion of Nup98 abrogates the maintenance of the memory state upon removal of hormone.

Discussion

The different models by which the primed state of transcription is established and maintained have been primarily drawn from bulk cell population studies. Using the E74 gene as a model, we aimed to understand the gain of transcriptional priming in single cells within a population. Together, our data show that the acquired E74 transcriptional memory is characterized by a high transcriptional output from a sub-population of cells transitioning into a memory state. Our modeling and experimental approaches suggest the following model (Figure 6A–B): upon ecdysone stimulation, two independent but simultaneous processes are initiated: (1) cells rapidly activate the ecdysone-dependent transcriptional response, characterized during this initial induction by a low rate of transcriptional output, and (2) concurrently, cells slowly progress into a specialized memory state defined by a high transcriptional rate, which is detected in subsequent ecdysone inductions. Importantly, we found that transition into the memory state is independent of the transcriptional process stemming from ecdysone activation since blocking transcription does not compromise the memory response significantly. Our transcriptional inhibition experiments suggest that memory establishment likely relies on ecdysone-triggered events that precede TPL-targeted TFIIH activity, such as pre-initiation complex (PIC) assembly, which was shown to remain intact upon TPL treatment (Krebs et al., 2017), or chromatin and architectural changes that accompany it. Another interesting conclusion from our model is that, if such a separation of transcription and memory occurs generally at inducible genes, then phenotypes of certain epigenetic regulators may not show up until much later on and would not be detected within a short time window, which is often used in Auxin-induced degradation system of rapid protein removal (Morawska and Ulrich, 2013; Nabet et al., 2018; Nishimura et al., 2009). As our findings reveal, on-going transcription and transcriptional memory can be controlled by separate mechanisms and would manifest their effects at different time scales.

A complex interplay of transcriptional factors, changes in histone modifications and the incorporation of histone variants have been linked to the transcriptional memory response of multiple genes. Additionally, transcriptional memory in certain systems involves the cytoplasmic inheritance of a regulatory protein produced during the initial process of transcription, such that the transcriptional memory response is dependent on the protein’s level, which gets diluted in each cell division. This type of memory process has been found in yeast genes that respond to oxidative stress or changes in nutrients (Guan et al., 2012; Kundu and Peterson, 2010; Zacharioudakis et al., 2007). For example, in the case of the yeast GAL1 gene, a key factor controlling the memory response is the passive inheritance of the trans-acting Gal1 protein itself, produced during transcriptional activation (Kundu and Peterson, 2010; Zacharioudakis et al., 2007). For the ecdysone-induced E74 gene, this type of regulation seems unlikely since blocking all transcription still results in a robust memory response, demonstrating that the model of cytoplasmic inheritance described above does not extend to all memory systems.

Nup98 has emerged as an evolutionarily conserved factor required for transcriptional memory (Light et al., 2013; Pascual-Garcia et al., 2017). Our smFISH analysis, combined with mathematical modeling, revealed that active loci in a memory state initiate new transcription about 15 times faster than active non-memory loci. It also shed light on Nup98’s role in this process, demonstrating that Nup98 promotes the rate of conversion between non-memory to memory state, once the activating agent has been removed. Thus, Nup98 does not affect transcription during initial induction, yet cells are unable to remain in or transition into the memory state upon hormone withdrawal, causing a defect in the amplified transcriptional response upon secondary ecdysone stimulation. How does Nup98 promote the memory state in molecular terms? In line with Nup98’s role in securing the memory state, we have previously reported that Nup98 depletion disrupts the enhancer-promoter loops induced by ecdysone’s initial induction (Pascual-Garcia et al., 2017). Our current results support a model where the stabilization of enhancer-promoter contacts by Nup98 might form part of the memory state, reinforcing our previously proposed notion that changes in enhancer-promoter contacts are functionally separate from initial transcriptional activity. Consistently, we have also reported that Nup98 gains physical interactions with EcR and architectural proteins upon ecdysone stimulation, suggesting that they might also form part of the memory state (Pascual-Garcia et al., 2017). In agreement with the present data, the memory complex formed by interactions of Nup98, architectural proteins, and enhancer-promoter contacts were similarly found to persist during transcriptional shut-off. Overall, our data are consistent with the idea that such architectural role of Nup98, as well as architectural functions suggested for other Nups (Ibarra and Hetzer, 2015; Kuhn and Capelson, 2018; Kuhn and Capelson, 2019; Pascual-Garcia and Capelson, 2019; Sun et al., 2019), may play a role in memory maintenance. It is worth noting that at the galactose-induce yeast gene HXK1, where memory is associated with gene loop interactions between the promoter and 3’-end of the gene, the maintenance of these loops is mediated by an NPC component Mlp1 and is thought to promote faster recruitment of RNA Pol II due to retention of transcription factors in the loop scaffold (Tan-Wong et al., 2009). A similar mechanism may be executed by Nup98 in transcriptional memory of ecdysone-inducible genes, where a large complex, consisting of the looped gene, architectural proteins and key transcription factors, is ‘locked in’ by Nup98 to create a high-expressing memory state. In this manner, it is tempting to speculate that phase-separating properties of Nup98 and other Nups may be involved in creating a complex specialized for high transcriptional outputs (Pascual-Garcia and Capelson, 2019; Schmidt and Görlich, 2016; Schmidt and Görlich, 2015).

Another critical pathway implicated in transcriptional memory and in epigenetic memory in general is the deposition of histone modifications. H3K4 methylation is functionally linked to the transcriptional priming of a variety of genes (Ding et al., 2012; D’Urso et al., 2016; Jaskiewicz et al., 2011; Light et al., 2013; Light et al., 2010; Muramoto et al., 2010; Sood et al., 2017), and more recently, histone H3K36me3 was correlated with the acquisition of memory upon IFNβ stimulation in mouse fibroblasts (Kamada et al., 2018). Although the interplay between transcriptional memory and deposition of histone modifications has not been fully explored in the Drosophila system, multiple connections between H3K4 histone methyltransferases (HMTs) and factors that regulate ecdysone-induced transcriptional kinetics have been reported. For example, HMT Trithorax-related (Trr), responsible for the deposition of H3K4Me1, has been found to regulate the transcriptional activation of ecdysone-inducible genes through interactions with EcR (Herz et al., 2012; Sedkov et al., 2003). Additionally, Nup98 has been found to interact with the related HMT Trithorax (Trx) physically and genetically (Pascual-Garcia et al., 2014; Xu et al., 2016), and to regulate some of Trx target genes during fly development (Pascual-Garcia et al., 2014). Trx is a well-known regulator of epigenetic memory during development, and is critical for maintaining the active transcriptional state of homeotic genes, which define tissue identity (Kingston and Tamkun, 2014). Whether the discovered role of Nup98 in maintaining a specialized active state plays a role in the epigenetic memory of Trx targets remains to be determined, but our findings open an intriguing possibility that the transcriptional memory dynamics described here apply to the broader Trx-mediated memory.

Our modeling study indicates that the transition between non-memory to memory state is relatively long, lasting around 20 hr to convert 62% of loci (or 48 hr to convert 90% of loci) in an asynchronous cell population. In agreement with the model, our expression data show that reducing the time interval between ecdysone stimulations deteriorates the memory response. One possible explanation for such long-time scales of the kC rate constant is that the formation of a memory complex requires stochastic or biochemical events that involve these time scales. Another explanation may be a communication between cell-cycle completion and E74 memory. The transmission of transcriptional states from mother to daughter cells has been studied in multiple organisms from human cells to developing fly embryos and is thought to be critical for the maintenance of differentiation programs (Ferraro et al., 2016; Zhao et al., 2011). In Drosophila embryos, the use of live imaging to visualize transcription uncovered a fourfold higher probability for rapid re-activation after mitosis when the mother cell experienced transcription (Ferraro et al., 2016). In mammalian cell culture, BRD4, a member of the Trx group (TrxG) proteins, has been implicated in faster re-activation kinetics of a previously activated transgene in post-mitotic cells (Zhao et al., 2011). In both cases, the authors proposed that transcription of the DNA template might render it more susceptible to rapid reactivation after mitosis, which may be achieved through heritable changes of DNA-bound transcriptional factors or histone modifications (Ferraro et al., 2016; Zhao et al., 2011). Additionally, it has been suggested that the process of mitosis itself is needed to help re-activate transcription to levels higher than those observed in the previous cell cycle, possibly via enhanced recruitment of regulatory factors to post-mitotic decondensing chromatin (Zhao et al., 2011). In this manner, it is plausible that the memory complex coordinated by Nup98 interplays with events immediately following mitosis to accelerate the dynamics of RNA synthesis, which could also explain why the enhanced transcriptional response depends on the length of time between ecdysone inductions.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Gene (Drosophila melanogaster)Early gene 23 (E23)FlybaseFBgn0020445
Gene (Drosophila melanogaster)Ecdysone-induced protein 74EF(E74)FlybaseFBgn0000567
Gene (Drosophila melanogaster)Ecdysone-induced protein 75B(E75)FlybaseFBgn0000568
Cell line (D. melanogaster)S2-DRSCDrosophila Genomics Resource Center (DGRC)RRID: CVCL_Z992
Chemical compound, drug20-hydroxyecdysone (20E)Sigma-AldrichCat#: H5142
Chemical compoundFlavopiridol hydrochlorideTocris BioscienceCat#: 3,094
Chemical compoundTriptolideSigma-AldrichCat#: T3652
Chemical compoundFugene HDPromegaCat#: E2311
Chemical compoundTrizolAmbionCat#: 15596018
Commercial kitMegascript T7 kitAmbionCat#: AM1334
Commercial kitPurelink RNA mini kit columns (; 12183018 A)AmbionCat#: 12183018 A
Commercial kitQuantiTect RT-PCRQiagenCat#: 205,311
Commercial kitPowerSYBR Green PCR Master MixApplied BiosystemsCat#: 4367659
Chemical compoundAtto-565Sigma-AldrichCat#: 72,464
Chemical compoundProLong Gold mountant mediaThermoFisherCat#: P36930
MATLABMathWorkshttps://www.mathworks.com/

Cell culture and chemicals

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We obtained Drosophila embryonic S2-DRSC cells from Drosophila Genomics Resource Center (DGRC) - this cell line is authenticated by DGRC and is one of the primary lines used by ModEncode and RNAi screens at DRSC (https://dgrc.bio.indiana.edu/product/View?product=181). We have confirmed mycoplasma negative status by PCR testing. Cells were grown at 25 °C in Schneider’s medium (Gibco; 21720), supplemented with 10% (v/v) heat inactivated fetal bovine serum (Gibco; 10438034) and 1% (v/v) of penicillin-streptomycin antibiotics (10,000 U/mL) (Gibco; 15140163). For ecdysone induction experiments, 20-hydroxyecdysone (Sigma-Aldrich; H5142) was dissolved in 100% ethanol and used at 5 μM. Flavopiridol hydrochloride (Tocris Bioscience; 30-941-0) and Triptolide (Sigma-Aldrich; T3652) were prepared in 100% DMSO and added at 1 μM and 5 μM, respectively.

dsRNA synthesis and transfection conditions

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Double-stranded RNAs (dsRNA) fragments against Nup98 or white genes were synthesized with Megascript T7 kit (Ambion; AM1334) using a PCR-ed DNA template from fly genomic DNA (primers listed in Supplementary file 1). The integrity of the RNA was assessed by running the denatured product in a 1.2% agarose gel. To knock-down cells we mixed 10 μg of dsRNA per million cells with 7.5 μl of Fugene HD (Promega; E2311) in serum-free media and we incubated the dsRNA cocktail with exponentially growing cells for at least 3 days.

RNA extraction and quantitative PCR

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Total RNA was isolated in 1 ml Trizol (Ambion; 15596018) and purified using Purelink RNA mini kit columns (Ambion; 12183018 A) following manufactured instructions. RNA concentration was determined by measuring absorbance at 260 nm with a NanoDrop 2000 (ThermoFisher; ND-2000). cDNAs were synthesized using one-step RT-PCR kit (Qiagen; 205311). To amplify specific cDNAs, primers were designed to span an exon-exon junction except for nascent rp49 qPCR experiment, where primers were designed to amplify the first intron-exon boundary (primers listed in Supplementary file 1). In this case, the RNA was pre-treated with 1 U of DNAseI (ThermoFisher; EN0525) and incubated at 37 C for 30 min before reverse transcription reaction. We verified the efficiency of this digestion by running control qPCRs with the DNAseI-treated RNA samples, and checked for the presence of insufficiently digested genomic DNA.

For absolute quantification analysis, the template for E74 synthetic RNA standard was generated from a cDNA library and amplifying with specific primers covering the first two exons of Eip74EF-RA. ssRNA was in vitro transcribed using the Megascript T7 kit. The concentration in grams per volume of the synthesized reference RNA was determined by fluorometric quantification using the RNA HS assay kit (ThermoFisher; Q32852) and Qubit 2.0 (ThermoFisher; Q32866). RNA dilution series were converted to copy numbers per volume using the molecular weight of the RNA standard (MW = 126060 g/mole). For the RT reactions of the RNA standard dilutions, we added an additional Eip74EF-RA primer in addition to the random primers included in the reaction kit. To minimize our experimental error, we also constructed a DNA standard using E74 specific primers. We used this standard to calculate the amplification efficiency of the E74 primers by fitting a linear curve to log2(Ct) as a function of E74 DNA template copy number; the resulting line contains a slope of –3.566 and R2 of 0.9985. We constructed the RNA standard curve by single-parameter fit of a line of –3.566 slope to the previously retrotranscribed RNA dilution series. We also factored the losses of mRNA extraction and purification by adding a known amount of in vitro transcribed E74 RNA to Trizol followed by purification and comparing the obtained amount of RNA; this approach resulted in 56% of RNA losses.

In all qPCR experiments, we used PowerSYBR Green PCR Master Mix (Applied Biosystems; 4367659) and QuantStudio 7 Flex thermal cycler (ThermoFisher; 4485701). Each qRT-PCR was repeated at least three times, the values were normalized to the rp49 transcript unless otherwise stated, and the error bars represent the standard deviation of the mean.

To compare the impact of inhibiting transcription during the 20E reinduction between different ecdysone-responsive genes, we define the calibrated memory index (CMI) as the ratio of the Nup98-dependent memory in inhibitor-treated cells to untreated cells:

CMI=F*MIR
F=1-MN/MC
R=MC-MN

where M is the fit slope of the second induction response either in Nup98-depleted cells (MN), in control cells (MC), or in the presence of transcriptional inhibitors (MI). F represents the Nup-dependent fraction of the second induction response, whereas R is the absolute value of the Nup98-dependent fit slope upon second induction and is the value to which F * MI must be normalized for comparison among genes. The maximum and minimum of the responses are specific to each gene because each gene exhibits a different value for the increased slope in untreated cells and the extent of reliance on Nup98. For convenience, CMI is zero under Nup98-depletion and the maximum is 1. CMI therefore represents the fraction of memory that remains during the second induction upon transcriptional inhibition during the first induction.

Single-molecule RNA fluorescence in situ hybridization (SmRNA FISH)

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S2 cells were cytospun in poly-L-lysine treated coverslips and fixed with 4% para-formaldehyde for 10 min. Coverslips were rinsed 3 X in PBS and submerge in cold 70% EtOH for at least 24 hr. Complementary probes to the reading frame of eip74ef-RA were designed using Stellaris Probe Designer (https://www.biosearchtech.com/stellaris-designer), ordered from Biosearch and conjugated to Atto-565 (Sigma-Aldrich; 72464). Cells were washed twice with wash buffer [2 X SSC, 10% formamide, 0.01% Tween-20] and equilibrated with hybridization buffer [2 X SSC, 10% formamide, 10% dextran sulfate,1 μg/ml BSA]. The hybridization to probes was performed overnight at 37°C in a moisturized chamber at a concentration of about 1 nM. After hybridization, samples were washed 3 X in pre-warmed wash buffer and incubated at 37°C for 30 min. We performed a final wash with 2 X SSC, stained with Hoechst and mounted in ProLong gold (ThermoFisher; P36930). Imaging was performed by laser-scanning confocal microscopy on a Leica SP8 with a 63 x oil immersion objective using identical scanning parameters and laser power for all samples. Voxel dimensions are 76 × 76 × 250 nm.

Image Analysis

mRNA detection and normalization

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By scanning confocal imaging, RNA puncta corresponding to single mRNAs, RNPs, and sites of nascent transcription all appear as diffraction-limited objects whose fluorescence intensities correlate with mRNA content (Little et al., 2015; Little et al., 2013; Little et al., 2011). Puncta were separated from imaging noise and centroids of true diffraction-limited objects were found with difference-of-Gaussian (DoG) thresholding using custom MATLAB scripts (Little et al., 2013) on deconvolved images followed by fluorescence intensity measurements using raw images as described (Little et al., 2015). Objects were classified as mRNA puncta or nascent transcription sites on the basis of DoG intensities as described (Zoller et al., 2018). Both the numbers and fluorescence intensities of non-nascent puncta increase during hormone induction. To obtain measurements of mRNA content of each individual puncta in absolute units by smFISH, we adapted a prior normalization technique (Little et al., 2015; Little et al., 2013; Little et al., 2011; Zoller et al., 2018). Briefly, we divide the fluorescence intensity of all puncta by the mean intensity of the non-nascent site puncta found during uninduced conditions, since, in the absence of hormone, the mean number of mRNAs detected by smFISH corresponds to the mean number measured by qPCR. Individual puncta are thus assigned a value corresponding to the equivalent number of finished, mature mRNAs they each contain. Because non-nascent puncta are mostly found in the cytoplasm, we term the units of this measurement ‘cytoplasmic units’ or C.U.s, the unit intensity of single mRNAs (Little et al., 2013). These measurements are thus in absolute units. When reporting transcriptional activity, we sum the fluorescence of all nascent site puncta assigned to each cell (Little et al., 2013; Zoller et al., 2018). This is advantageous because of the phenomenon of chromosome pairing, prevalent in Drosophila, in which homologous chromosomes are found in close physical association (Joyce et al., 2016). Pairing prevents unambiguous assignment of fluorescence to individual nascent sites, but does not preclude us from accurately assessing transcriptional activity in single cells. Objects were designated as sites of nascent transcription using a threshold of three times the intensity of single mRNAs. For nuclei that contain fewer than three objects that are greater than 3 x the average intensity of cytoplasmic spots, 1 or two spots are selected at random and assigned the status of transcription sites; the random assignment yields a more accurate assessment of transcription than an assertion that no transcription occurs, as previously described (Zoller et al., 2018). All transcriptional activity reported in Figures 3 and 4 is in units of C.U. per individual cell. In contrast, all parameter values derived from fitting are for individual loci, as described below.

Nuclear-cytoplasm segmentation

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Hoechst stain was used to determine pixels corresponding to nuclear volumes as described (Petrovic et al., 2019), and the same approach was applied to the low-level nonspecific cytoplasmic fluorescence in the RNA channel to delineate total cell volumes. All RNA puncta were assigned to the nearest nucleus using on the basis of nearest-neighbor comparisons of the positions in 3D space of the centroids of puncta and nuclei, as described (Petrovic et al., 2019).

Modeling

mRNA degradation

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mRNA stability was assessed for the first and second inductions by collecting cells for qPCR at 30 min intervals. After 4 hr of hormone treatment, cells were washed-out and treated with 1 μM Flavopiridol to disrupt transcription. qPCR was used to measure E74 levels relative to rp49 as a function of time following the start of transcription inhibition. qPCR was performed in triplicate on cells treated for 72 hr with dsRNA against Nup98 or white genes. The data were fit to a model of exponential decay using nonlinear regression to obtain mRNA lifetimes and 95% confidence reported in Figure 2B and Figure 2—figure supplement 1B.

mRNA export

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Image segmentation of cells into nuclear and total cell volume described above was used to assign all non-nascent RNA puncta to either the nucleus or cytoplasm based on the centroid positions of puncta in three dimensions. Puncta densities in both volumes were calculated as the number of puncta per cubic micron, and the fraction of puncta found in cytoplasm calculated for individual cells. Under an assumption of unchanging mRNA degradation, the ratio of cytoplasmic to nuclear densities is constant regardless of expression level as long as RNA processing and transport are rapid compared to mRNA degradation (Aleman et al., 2021). Since degradation rates are slow and indistinguishable between Nup98 knockdown and control, we conclude that mRNA transport rates are unaffected by Nup98 depletion.

mRNA accumulation and production

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Fitting of qPCR data was performed using nonlinear regression and the measured mRNA degradation rate to obtain 95% confidence intervals for parameter values. Fits were performed for each experimental condition individually to produce the values displayed in the panels. S2 cells are tetraploid with a division time of 24 hr, spending similar amounts of time in G1 and G2 (Cherbas and Gong, 2014). We therefore performed all qPCR fits using the assumption that the average number of E74 loci per cell is six. All fit parameter values are reported in terms of individual loci. Curves shown in Figure 1B are piecewise polynomials. Models in Figure 2 each contain a single free parameter representing either the rate of constant mRNA production or the rate of linear increase in the mRNA production rate as a function of time. Both models in Figure 3 contain two free parameters. For the model of constant RNA Pol II loading, these are the first-order rate of conversion to the active state kA and the rate of RNA Pol II loading while active kPol. For the model of accelerating kPol, kA is fixed so as to virtually guarantee that all loci convert to the active state in 1 min (as noted in the text, Figure 3—figure supplement 1D is plotted with a much smaller kA for illustrative purposes). We introduced an additional parameter, the RNA Pol II footprint on the DNA template, to provide a natural limit on the maximum attainable kPol.

To compare the fitting of the qPCR results to the measurements of transcriptional activity obtained by smFISH, the rates from fitting were utilized in Monte Carlo simulations of transcription (Gillespie, 1976). We simulated the time-dependent evolution of RNA Pol II numbers and positions on the E74 gene for 100,000 cells for each of the four conditions (Nup98 knockdown or dsWhite/control, with or without 20E). The time scale of the simulation was set by the previously measured RNA Pol II elongation rate in S2 cells of 1500 nt/min (Izban and Luse, 1992; Ardehali and Lis, 2009; Buckley et al., 2014; Yao et al., 2007). The simulation was updated every 1/1500 min, the interval needed to transcribe one nucleotide. For the model of increasing production rate, the production rate was also updated based on the acceleration parameter multiplied by the elapsed time. RNA Pol II molecules and newly completed mRNA molecules were assumed to be evicted as soon as transcription was finished. For simplicity, we assumed that half of simulated cells are in G1 and half in G2, therefore containing either four or 8 E74 loci. We therefore simulated 600,000 single loci and combined them randomly into 50,000 sets of 4 and 50,000 sets of 8 to represent 100,000 individual cells. We converted RNA Pol II positions into C.U.s by noting that the RNA Pol II position in the gene body determined how many probe binding sites are present in the nascent RNA. The total C.U.s per cell was attained by summing the number of probe binding sites present across all four or eight simulated loci. Because all probes bind exonic sequences, neither the simulations nor observations are affected by splicing. With this procedure, we deduced parameter values in terms of single loci, rather than single cells. Starting conditions assigned to each cell were chosen at random from a Poisson distribution using the mean number of detected puncta under uninduced conditions. Simulated transcriptional activity values were taken at appropriate times after the start of the simulation (60, 120, and 240 min) to compare the simulated distribution to that observed by smFISH. Goodness-of-fit scores were calculated by finding the intersection of the areas under the normalized histograms of simulated and observed cells, using histograms generated with bins of identical width. For convenience, we compared log(C.U.) values due to the long tail of observed transcriptional activity, identical to a previously utilized approach for describing the distribution of mRNAs in ribonuclear protein complexes (Little et al., 2015). The areas of intersection between simulated and observed histograms for each of the three time points corresponding to each of four conditions were summed and then divided by the area of the union, generating an effective Jaccard index (JI) that was used as a goodness-of-fit score. A score of 1 indicates perfect overlap between observation and simulation. We note that the absolute value of the goodness-of-fit score for any individual model is not by itself informative and is heavily reliant on the positions of bin edges; in contrast, the comparison of scores between identically prepared distributions informs on the extent of difference in overlap between model and observation.

For the four-parameter model of transcriptional memory (Figure 4), we searched parameter space across eight orders of magnitude: For the first-order activation and conversion rates kA and kC, between 10–8 and 1 min–1, and for the kPol rates, between 10–7 and 10 min–1. Fitting the curves derived from qPCR was uninformative, as a vast volume of parameter space can describe population-averaged data. We therefore performed Monte Carlo simulations of transcription in order to approximate the observed distributions of transcriptional activity at all six time points in control conditions. In principle, this entails a search through 4D space across cells simulated for 32 hr (4 hr 1st induction, 24 hr recovery, and 4 hr 2nd induction). However, the combination of the four parameter values is constrained to match the trend of the average mRNA production rate determined by the prior fit of the qPCR results. This effectively reduced the search space to three dimensions, since we could choose a value for one parameter and simulate across combinations of the remaining three. To reduce computational burden, we simulated 10,000 cells for each parameter set over the four hours of the first induction. We then inferred the fraction of loci that would be in the memory state with a given kC following a 24 hr recovery period, and randomly assigned memory status to that fraction of loci, in order to avoid simulating the recovery period. We then simulated the 4 hr period of the second induction. The histograms of simulated and observed C.U.s were then scored by JI. This rapidly narrowed the possible range of parameter values to within approximately one order of magnitude. Subsequent fine-grained search of parameter space employed 100,000 cells per parameter set to find the maximum JI. The 95% confidence interval was assigned by bootstrap, taking 1000 random subsets of single cells from the data, redeploying the search of the narrow parameter window, and finding the mean and two standard deviations of the parameter values producing the maximum JI.

We note that this procedure requires that the transcriptionally active state has a lifetime significantly shorter than 24 hr after hormone withdrawal. We did not attempt to measure the lifetime of the active state. However, we note there is minimal transcription following 24 hr recovery, supporting an assumption of short active lifetime without hormone. Likewise, we did not attempt to measure the lifetime of the converted state, which would only become apparent in Nup98 knockdown cells upon hormone withdrawal. However, the fit parameters from control data provide a reasonable explanation of both control and Nup98 knockdown cells. This supports the hypothesis that the memory state lifetime is significantly less than 24 hrs in the absence of Nup98, whereas memory is maintained indefinitely in normal cells and their progeny.

Data availability

No sequencing or structural datasets have been generated by this study. All of the data analyzed during this study is included in the manuscript.

References

    1. Izban MG
    2. Luse DS
    (1992)
    Factor-stimulated RNA polymerase II transcribes at physiological elongation rates on naked DNA but very poorly on chromatin templates
    The Journal of Biological Chemistry 267:13647–13655.
    1. Kuhn T
    2. Capelson M
    (2018) Nuclear pore and genome organization and gene expression in Drosophila
    Nuclear Pore Complexes in Genome Organization, Function and Maintenance 10:111–135.
    https://doi.org/10.1007/978-3-319-71614-5_5

Decision letter

  1. Robert H Singer
    Reviewing Editor; Albert Einstein College of Medicine, United States
  2. Kevin Struhl
    Senior Editor; Harvard Medical School, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "Nup98-dependent transcriptional memory is established independently of transcription" for consideration by eLife. Your article has been reviewed by 4 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Kevin Struhl as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.

Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in eLife, either of which would be linked to the original paper.

If it is possible to carry out the suggested experiment on transcriptional inhibition, we urge you to do so.

In this manuscript by Pascual-Garcia et al., the authors use single-cell gene expression analysis to dissect transcriptional memory in the ecdysone response. They explore several different models and the surrounding parameter space in order to fit the data and then arrive at a plausible multi-state model for how Nup98 might be enabling transcriptional re-activation. The overall conclusion is that ecdysone stimulation in the first round 'primes' the re-activation state through a Nup98-dependent process. This priming does not depend on transcriptional activation because treatment with the elongation inhibitor flavopiridol doesn't interfere with the memory response.

All the reviewers find the work interesting and potentially important. However before it can be published, they had several concerns in common. One of them deals with the clarity of the manuscript, in particular the explanation of the modeling and why certain states were chosen, and why so much text deals with the non-working model.. A second concern is that the quality of the FISH on which the approach is based is not optimal. A third concern, as expressed by all reviewers (esp #4) is that the use of the drug flavopiridol does not test the hypothesis that transcription is not necessary for memory because it acts primarily on elongation and not initiation, where chromatin modifications are made. Further data is requested using a drug that acts earlier in the transcription. Finally reviewer #1 questions the conclusion that RNA turnover is not a factor given the time for memory to occur.

The full reviews are below. We feel that the essential experiment would be to determine which component of transcriptional inhibition (initiation, escape) is essential or not for memory.

Reviewer #1:

This is a combined modelling and experimental study probing the nature of the ecdysone induced memory response in S2 cells. This is a very interesting biological problem, and detailed mechanisms- beyond data indicating that protein X or modification Y are required, have proved elusive outside the yeast GAL system. As such, studies probing the causality underlying transcription memory phenomena are welcome.

In the study, the simple models of transcriptional dynamics are discarded, with reference to qPCR and smFISH data and measurements of export and RNA turnover. The researchers fix on a 4 state model that fits the data well, then test 2 predictions of the model- based on the requirement for transcription and the timescale of the memory phenomenon.

The work appears well performed, and for the most part clearly documented, and should certainly be part of the scientific record in close to its current form.

My major reservation is the state of the field. This paper seems largely a model optimisation paper, and the work appears very well carried out. However, there are many different multi-state models around these days, all specific to different genes and systems. From the perspective of understanding transcription dynamics- does this carry us further? From the perspective of memory, it is interesting the transcriptional elongation at the locus is not initially required- but the state change of the cell, or gene, that brings about the memory is no clearer beyond this.

One issue that needs to be addressed before publication relates to the RNA turnover aspect. In the experiment in Figure 2B this analysis is conducted at 4h not 24h (when the initial memory effect was detailed in Figure 1), so is your conclusion about no role for RNA turnover relevant to the memory phenomenon? Especially in the light of Figure 7, where both the experiment and model indicate a longer timescale is required to get the memory effect.

Reviewer #2:

Transcriptional memory is a phenomenon whereby certain inducible genes show stronger/faster induction if they have previously been expressed. This behavior can persist through multiple cell divisions. Such phenomena have been explored in yeast, plants, flies and mammalian cells and several different types of memory have been discovered. In the cases where we have some mechanistic understanding, there are two classes of memory. The regulation of some genes is impacted by the persistence of transcriptional regulators that were previously produced and cytoplasmically inherited (e.g. yeast GAL1). The second class of genes (e.g. yeast INO1 and interferon γ-induced genes in HeLa cells) there are cis-acting changes in transcription factor binding and/or chromatin structure that impact future expression. This latter class of genes shows a physical interaction with the nuclear pore protein Nup98 and memory requires Nup98.

This manuscript explores the transcriptional memory of ecdysone-regulated genes in Drosophila S2 cells, a phenomenon that is Nup98-dependent and correlates with a Nup98-modulated enhancer-promoter loop. The focus of the study is a thorough and quantitative anlalysis of the population dynamics of transcription during the initial induction and memory phases using single molecule RNA FISH. These data are then used to assess three different quantitative models for memory: 1. A two-state model in which memory increases the number of cells that respond to ecdysone, 2. A two-state model in which memory increases the rate of RNA polymerase loading onto the promoter and 3. A four-state model in which these genes exist in an equilibrium between two different expression states (low and high) and this equilibrium is regulated by memory. The authors find that model 3 is the best fit for the observations and, importantly, this model makes two predictions that they could test. First, this model predicts that transcription should not be required for memory. Second, memory should "develop" following the initial induction over some time scale. The authors then test both of these predictions and find that they are true. The former is interesting and rules out that the production of a transcriptional regulator during the first induction is key to this process. The latter is very interesting and surprising and will stimulate additional work to discover its meaning. Overall, the paper is interesting and impactful and worthy of publication in eLife. I have some suggestions for clarifying changes to the manuscript and, if possible, additional experiments that would strengthen the story.

1. The four state model is not well described in the manuscript and the schematic in Figure 5A is confusing. This schematic suggests that there are equilibria between non-memory and memory (which is understandable) and between low-expressing and high-expressing states (also understandable) but that there is also an equilibrium between non-memory and low-expressing and between memory and high-expressing, which does not make sense to me. I think what the authors are trying to convey is that the equilibrium between low-expressing and high-expressing states is influenced by Nup98-dependent memory. If so, then the model should include the following states: non-memory, low-expressing, non-memory high-expressing, memory low-expressing and memory high-expressing. Between each of these states would be an equilibrium and the equilibrium constants for the equilibrium between low and high expressing states would be different for non-memory and memory. I assume that this is how they generated their model. If not, please spend more space to clarify the model and provide an explanation of how the non-memory state is in equilibrium with the low-expressing state, etc.

2. The technical perfection of smRNA FISH is unclear. I doubt it is perfect. Yet, the modeling is highly leveraged on the goal of matching the smRNA FISH data. Some additional controls would strengthen the interpretation. For example, the untreated S2 cells (0h) should be included for the smRNA FISH and modeling. Likewise, knowing how control genes that are not up/down-regulated by ecdysone behave in smRNA FISH would improve the rigor of this approach.

3. Some molecular information about transcription factor occupancy or chromatin changes at the enhancer that is involved in the memory-enhancing loop would greatly strengthen the mechanistic insights of this paper.

Reviewer #3:

In this manuscript by Pascual-Garcia et al., the authors use single-cell gene expression analysis to dissect transcriptional memory in the ecdysone response. They explore several different models and the surrounding parameter space in order to fit the data and then arrive at a plausible multi-state model for how Nup98 might be enabling transcriptional re-activation. The overall conclusion is that ecdysone stimulation in the first round 'primes' the re-activation state through a Nup98-dependent process. This priming does not depend on transcriptional activation because treatment with the elongation inhibitor flavopiridol doesn't interfere with the memory response. Although there is not much biochemical insight, I find this conclusion interesting. Overall, the analysis is fairly rigorous, and I applaud the effort to apply more quantitative models to the field of transcriptional memory.

1. The modeling is a mix of ordinary differential equation analysis and stochastic simulations with the Gillespie algorithm. It is a little unwieldy at times, and I feel the manuscript spends quite a bit of time on a model which doesn't work. Since this non-working model is the authors' invention (and not a standard in the field per se), it seems a bit awkward to present the story this way. Ideally, all the models would be stochastic models from the beginning since the single-cell analysis is the main thrust at the end. Although I find their approach a little ad hoc given the current state of the field, I don't feel strongly enough about it to demand a more rigorous treatment.

2. The persistence of memory in the presence of flavopiridol is a striking result. Would the authors predict a similar result for an inhibitor working upstream of transcriptional pause release? Specifically, the authors mention in the discussion that enhancer-promoter loops may be implicated. One experiment which the authors might consider (which I am not suggesting as necessary for a revision) is whether antagonists or partial agonists can uncouple these two activities of ecdysone. In other words, they have a negative result which emerges as a model prediction but not a positive result.

Reviewer #4:

The authors use a combination of RT-qPCR, smFISH and mathematical modeling to probe the mechanism and contribution of NUP98 to transcriptional memory upon hormone stimulation in Drosophila cells. The authors reveal that the transcriptional memory response is independent of transcription levels immediately after hormone treatment. Furthermore NUP98's role in transcriptional memory relates to its ability to stabilize a slow transition (~20 hrs) of the gene from low-expression to high-expression state. While the results are quite striking and the interpretations are timely in some regards, a few controls and clarifications in the text are necessary before recommending publication in eLife.

1. The authors rely heavily on smFISH to validate their computational modeling. However, the accuracy of quantitation of the smFISH is not evident. The authors should show a histogram of intensities of single diffraction limited spots along with a representative figure of multiple zoomed in panels of the single RNA spots. This is important as the majority of spots, particularly in the transcriptional memory response, are not diffraction limited which complicates both the presentation and analysis of their data.

2. Is there any evidence of E74 being alternatively spliced which may differentially affect FISH probe binding to the mRNAs producing large heterogeneously sized diffuse spots seen in the figure 1D?

3. While the differences are readily apparent in most of the data, the authors do not show any statistical testing in many if not most plots (e.g. violin plots Figure 1E). This should be corrected in a revised manuscript.

4. S2 cells are considered aneuploid. For nascent transcription site analysis, it would be helpful for the readers to see a simple diagram showing the percentage of cells showing 1, 2, 3, 4 etc active TS sites throughout the time course as it was not apparent whether a certain percentage of cells in the population never expressed E74.

5. Figure 7 is one of the most key figures that the authors use to conclude that NUP98 is stabilizing a memory state independent of transcription. Yet they have chosen to use a less informative assay (e.g. RT-qPCR) rather than the most sensitive assay (e.g smFISH) to support their model and conclusions. The authors would make a stronger case for their findings if they employed smFISH to monitor transcript abundance and TS activity after inhibiting transcription.

6. FP primarily only prevents Pol II pause escape and not promoter escape of Pol II. So transcription initiation and Pol II loading at the promoter still occurs in flavopiridol treated cells. This is important because chromatin marks associated with active transcription are likely still being placed in the promoter and enhancer regions of E74 even though a complete transcript is not being produced. The authors should repeat their assays and treat the cells with inhibitors such as triptolide or THZ1 that act earlier in the transcription cycle or better yet use an ecdysone receptor mutant that prevents Pol II recruitment or chromatin modification to the gene in the first place.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for submitting your article "Nup98-dependent transcriptional memory is established independently of transcription" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Kevin Struhl as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Jonathan R Chubb (Reviewer #1); Jason H Brickner (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

All reviewers find that the manuscript is novel and exciting. However reviewer #4 has additional comments that need to be addressed, in particular the observation 1 that triptolide is not required for PIC formation in Drosophila. This issue needs to be addressed in the Discussion along with other issues raised by this reviewer. Please send a response to these comments with the revised manuscript.

Reviewer #1:

This is a much improved manuscript, and deals extensively with the comments raised by myself and the other reviewers. The additional experiments, particularly the more comprehensive RNA turnover work, and the transcription initiation blockade, are a great addition. I think that the overall message is much clearer in the revised paper, with the condensed version of the modelling much more straightforward to read. The overall message, that the transcriptional process is not required for the sensitisation, is now much more clear than I found in the initial submission, and is an important addition to the cellular memory literature.

Reviewer #2:

The authors have responded to my suggestions and, in some cases, added additional data or clarification to the paper. The manuscript is now stronger and publishable in eLife.

Reviewer #4:

The authors take an important step in quantitatively assessing transcriptional memory in the context of mathematical modeling and testing of the models through single cell approaches. Furthermore, the authors extend their work to show how single cell data relates to population-level transcription outcomes. This second approach is highly relevant since transcriptional memory has been traditionally studied at the population-level allowing interpretation of previous studies in the context of single cell data. More importantly, the models produced can make predictions that the authors successfully test to show that transcription initiation is not necessary for establishment of memory. The authors do a good job of addressing the reviewer's critiques. While a clear mechanism for Nup98 establishment of memory is still lacking, I think that this manuscript represents an important step in integrating single cell data into studying memory. There are a few critiques that should still be addressed before final acceptance for publication.

1. While the authors indeed show that transcription initiation is not required for memory, Triptolide treatment does not prevent PIC formation in Drosophila (Krebs et al., 2017, Mol Cell, which the authors cite in the manuscript). PIC assembly actually increases during Triptolide treatment suggesting that key factors and histone marks related to the assembly of the PIC (e.g. H3K4 methylation due to its presumed role in recruiting TFIID) are likely still present and capable of establishing memory in the absence of initiation. The authors should be very clear of this possibility in the discussion.

2. Lines 175-177 the authors are essentially making a statement about transcriptional noise being independent of Nup98. It would be highly beneficial to quantitatively measure transcriptional noise (CV2 or std2/mean2) under the different conditions to validate this statement.

3. I realize that Models 1 and 2 are not viable in the end. However, the wording in Lines 301-306 is misleading creating a potential flaw in these models that seems to be carried throughout the manuscript. The authors invocation of a quantitative assessment of the PIC (e.g. Author's should use the term PIC since RNA Pol II holoenzyme can be confusing) footprint into the model is naive and underestimates the role of a stable TFIID scaffold and TFIIB in re-initiation rates (Zhang et al. Genes and Dev 2016, Yudkovsky et al., Nature 2000). It is highly likely that core promoter accessibility plays a major role in going from an inactive to active state. But core promoter accessibility would likely be a binary effect and not scaleable as the authors seem to intimate in lines 302-304 and let freely fluctuate in their modeling. Model 1 and 2 also assumes that transitions to the inactive states are negligible suggesting that the gene is stuck in the ON state and therefore Nup98 is enhancing Pol II's attempted loading onto a stable PIC scaffold of defined size (e.g. footprint).

4. It would be interesting to see whether the FP and TL treatments change the distributions of transcriptional output during the second induction in single cells compared to those shown in Figure 4.

https://doi.org/10.7554/eLife.63404.sa1

Author response

The full reviews are below. We feel that the essential experiment would be to determine which component of transcriptional inhibition (initiation, escape) is essential or not for memory.

We thank the Editors for a positive assessment of our work, and for outlining the main suggestions by the reviewers and the key experiment needed for revisions. As we detail further below, we have addressed all 4 main points, either experimentally or via further explanation, and have added the essential experiment utilizing an additional transcriptional inhibitor that acts earlier in the transcriptional process.

Reviewer #1:

This is a combined modelling and experimental study probing the nature of the ecdysone induced memory response in S2 cells. This is a very interesting biological problem, and detailed mechanisms- beyond data indicating that protein X or modification Y are required, have proved elusive outside the yeast GAL system. As such, studies probing the causality underlying transcription memory phenomena are welcome.

In the study, the simple models of transcriptional dynamics are discarded, with reference to qPCR and smFISH data and measurements of export and RNA turnover. The researchers fix on a 4 state model that fits the data well, then test 2 predictions of the model- based on the requirement for transcription and the timescale of the memory phenomenon.

The work appears well performed, and for the most part clearly documented, and should certainly be part of the scientific record in close to its current form.

My major reservation is the state of the field. This paper seems largely a model optimisation paper, and the work appears very well carried out. However, there are many different multi-state models around these days, all specific to different genes and systems. From the perspective of understanding transcription dynamics- does this carry us further? From the perspective of memory, it is interesting the transcriptional elongation at the locus is not initially required- but the state change of the cell, or gene, that brings about the memory is no clearer beyond this.

We thank the reviewer for the overall positive assessment of our manuscript and for considering it a welcome addition to the field. While it is certainly the case that many models of transcription have been put forth, the memory field has suffered from a lack of quantitative approaches. We believe the simplicity of our model provides a general framework against which to test the behavior of genes in the diverse systems that exhibit the memory phenomenon. We also reemphasize our use of modeling to generate novel predictions, which we have tested, unlike many papers focused only on model optimization.

Although we agree that our findings do not pinpoint specific state changes or molecular factors that define transcriptional memory, we believe they do offer important conceptual insight into how memory works and into how we interpret effects of transcriptional regulators. Our key conclusion that transcriptional elongation and memory establishment appear to be separate pathways will direct and restrict future inquiries into possible players of transcriptional memory. For instance, our studies allow to discount a number of previously suggested players such as transcription-coupled histone modifications or synthesized mRNA and guide the field to focus on a much more targeted set of events surrounding or preceding transcriptional initiation. Similarly, our model predicts that loss of function phenotypes of many epigenetic regulators may not manifest their transcriptional and cellular effects until much later, if such regulators function in the memory pathway as opposed to transcription pathway. This is an important consideration for the entire epigenetics field, especially in our current age of acute degradation approaches, and it will encourage better understanding of epigenetic pathways in general.

One issue that needs to be addressed before publication relates to the RNA turnover aspect. In the experiment in Figure 2B this analysis is conducted at 4h not 24h (when the initial memory effect was detailed in Figure 1), so is your conclusion about no role for RNA turnover relevant to the memory phenomenon? Especially in the light of Figure 7, where both the experiment and model indicate a longer timescale is required to get the memory effect.

We agree with the reviewer that this was an important issue to address, and have now included an additional RNA degradation experiment conducted after the 24h recovery process, during the 2nd induction (Figures 2B, Figure 2-supplement 1B). One possibility that we wanted to address in this manuscript is whether lower E74 mRNA levels in Nup98-depleted cells in memory conditions can be explained by higher degradation of the mRNA upon Nup98 KD. As can be seen in the control vs. Nup98 KD conditions, the degradation rate of E74 mRNA during the 2nd induction in either condition is still quite similar to the control 1st induction, allowing us to make the conclusion that changes in RNA turnover do no contribute significantly to the observed memory defects in Nup98-depleted cells. If anything, the E74 mRNA might possess a mildly increased lifetime during the 2nd induction in Nup98 KD relative to WT conditions (mRNA degrades at a slightly lower rate). Although the difference is not large, a potentially lower degradation rate suggests that the transcriptional output in Nup98-depleted conditions during the 2nd induction may be even lower than we report. Furthermore, this minor difference is not reflected in our mRNA export assessments in Figure 2A – the nuclear/cytoplasmic ratio of E74 mRNA remains unchanged by Nup98 KD in either 1st or 2nd inductions (mRNA export can influence mRNA degradation dynamics). Based on these combined results, we conclude that the memory defect (lower levels of E74) upon Nup98 KD cannot be explained by an increase in mRNA turnover.

Reviewer #2:

Transcriptional memory is a phenomenon whereby certain inducible genes show stronger/faster induction if they have previously been expressed. This behavior can persist through multiple cell divisions. Such phenomena have been explored in yeast, plants, flies and mammalian cells and several different types of memory have been discovered. In the cases where we have some mechanistic understanding, there are two classes of memory. The regulation of some genes is impacted by the persistence of transcriptional regulators that were previously produced and cytoplasmically inherited (e.g. yeast GAL1). The second class of genes (e.g. yeast INO1 and interferon γ-induced genes in HeLa cells) there are cis-acting changes in transcription factor binding and/or chromatin structure that impact future expression. This latter class of genes shows a physical interaction with the nuclear pore protein Nup98 and memory requires Nup98.

This manuscript explores the transcriptional memory of ecdysone-regulated genes in Drosophila S2 cells, a phenomenon that is Nup98-dependent and correlates with a Nup98-modulated enhancer-promoter loop. The focus of the study is a thorough and quantitative anlalysis of the population dynamics of transcription during the initial induction and memory phases using single molecule RNA FISH. These data are then used to assess three different quantitative models for memory: 1. A two-state model in which memory increases the number of cells that respond to ecdysone, 2. A two-state model in which memory increases the rate of RNA polymerase loading onto the promoter and 3. A four-state model in which these genes exist in an equilibrium between two different expression states (low and high) and this equilibrium is regulated by memory. The authors find that model 3 is the best fit for the observations and, importantly, this model makes two predictions that they could test. First, this model predicts that transcription should not be required for memory. Second, memory should "develop" following the initial induction over some time scale. The authors then test both of these predictions and find that they are true. The former is interesting and rules out that the production of a transcriptional regulator during the first induction is key to this process. The latter is very interesting and surprising and will stimulate additional work to discover its meaning. Overall, the paper is interesting and impactful and worthy of publication in eLife. I have some suggestions for clarifying changes to the manuscript and, if possible, additional experiments that would strengthen the story.

1. The four state model is not well described in the manuscript and the schematic in Figure 5A is confusing. This schematic suggests that there are equilibria between non-memory and memory (which is understandable) and between low-expressing and high-expressing states (also understandable) but that there is also an equilibrium between non-memory and low-expressing and between memory and high-expressing, which does not make sense to me. I think what the authors are trying to convey is that the equilibrium between low-expressing and high-expressing states is influenced by Nup98-dependent memory. If so, then the model should include the following states: non-memory, low-expressing, non-memory high-expressing, memory low-expressing and memory high-expressing. Between each of these states would be an equilibrium and the equilibrium constants for the equilibrium between low and high expressing states would be different for non-memory and memory. I assume that this is how they generated their model. If not, please spend more space to clarify the model and provide an explanation of how the non-memory state is in equilibrium with the low-expressing state, etc.

We apologize for the confusion surrounding our final model and have now substantially reworked the explanations and the terminology, as well as provided a new schematic for it (now in Figure 4A and accompanying text). Generally, our model is based on envisioning 4 states that an ecdysone-responsive gene can be in:

1) a state without ecdysone stimulation and that is not transcribing substantially or is at basal levels (this is the top left state in the diagram, which we now renamed “Uninduced Default” or “UD”) – this state is the default state in non-stimulated conditions (we have added kpolB to describe this very low basal rate of transcription in the absence of ecdysone);

2) a state in conditions of ecdysone stimulation and that is characterized by default transcriptional response, which is relatively low compared to the high states we observe – we renamed this state “Induced Default” or “ID” (this transcriptional response is described by kpolL);

3) a state in conditions without ecdysone stimulation, that is not transcribing above basal levels, but that has established memory – we renamed this state “Uninduced Memory” or “UM” (this is the state that would express highly if exposed to ecdysone);

4) a state in conditions of ecdysone stimulation and that has established memory, which together produce a high transcriptional response – we renamed this state “Induced Memory” or “IM”; (this transcriptional response is described by kpolH).

In this model, a “memory high-expressing” state is now termed IM (used to be termed “Highexpressing”), and there would be no “non-memory high-expressing” state – the high-expressing state is the equivalent of the memory state, in the presence of ecdysone (see Figure 4A). The IM state is the product of a conversion governed by the rate constant kC, combined with ecdysone stimulation (the way to get to this state would be either via kA, then kC process, or via kC, then kA process). Similarly, there would be no “memory low-expressing” state, only a “nonmemory low-expressing” state (“ID”), according to our model.

In basic terms, the right half of the diagram means ecdysone-induced transcription, and the left half means no ecdysone-induced transcription. The un-induced states (or those states with only basal transcription in the absence of hormone) are either non-memory or memory, and this intro-conversion is defined by kC. The induced states (in the presence of hormone) are also either non-memory (low-expressing) or memory (high-expressing), and this intro-conversion is similarly defined by kC. The switch into hormone-induced transcription is governed by kA. We believe that this separation of the kA and kC governed processes is what explains the observed independence of the re-induction response from first induction transcription.

Although we agree that models with additional states and additional rate constants could also work well, we generated our model as the simplest/smallest set of states and parameters that can describe our in vivo data and that can generate testable predictions (that we have tested and provided evidence for).

2. The technical perfection of smRNA FISH is unclear. I doubt it is perfect. Yet, the modeling is highly leveraged on the goal of matching the smRNA FISH data. Some additional controls would strengthen the interpretation. For example, the untreated S2 cells (0h) should be included for the smRNA FISH and modeling. Likewise, knowing how control genes that are not up/down-regulated by ecdysone behave in smRNA FISH would improve the rigor of this approach.

To address the reviewer’s concern, we have now included smRNA FISH with control probes against genes that do not change expression upon ecdysone addition (according to Shlyueva et al. Mol. Cell. 2014) – as shown in Figure 1-supplement 2A-C, the observed distribution of smRNA FISH signal for the mRNAs of such genes Pp4-19C and Rpt6 does not increase or change considerably with addition of ecdysone, unlike that for E74 (Figure 1D-E).

In prior publications, we have shown that our single molecule counting has a maximum error rate of roughly 12% (Little SC et al., 2013, 2015), whereas the error is approximately 4-5% in the quantification of instantaneous transcriptional activity (Zoller B et al., 2018). To further demonstrate the technical accuracy of our smRNA FISH quantification, we now also included further analysis of single diffraction limited spots of our smRNA FISH, as well as representation of average spot intensities for all conditions (Figure 1-supplement 2D-F), providing evidence that the vast majority of smRNA FISH objects are diffraction-limited.

For cells at time 0h, our quantitative RT-qPCR measurements indicate an average production rate of 0.02 mRNAs per minute. Thus, the distribution of CUs at 0h is heavily skewed toward zero and appears to be uninformative in a model of ecdysone-induced activity. For this reason we do not include it in the histograms shown in Figures 3-4, although the production rate at 0h has been included into the fitting of the RT-qPCR data.

3. Some molecular information about transcription factor occupancy or chromatin changes at the enhancer that is involved in the memory-enhancing loop would greatly strengthen the mechanistic insights of this paper.

We very much agree with the reviewer that these questions are very interesting and important in the context of memory-associated enhancer loop. We are currently conducting these types of experiments, as well as testing actual looping in various conditions. We hope to compile this data as a separate manuscript in the future, and believe that given the complexity of the question, this information currently lies outside the scope of this manuscript.

Reviewer #3:

In this manuscript by Pascual-Garcia et al., the authors use single-cell gene expression analysis to dissect transcriptional memory in the ecdysone response. They explore several different models and the surrounding parameter space in order to fit the data and then arrive at a plausible multi-state model for how Nup98 might be enabling transcriptional re-activation. The overall conclusion is that ecdysone stimulation in the first round 'primes' the re-activation state through a Nup98-dependent process. This priming does not depend on transcriptional activation because treatment with the elongation inhibitor flavopiridol doesn't interfere with the memory response. Although there is not much biochemical insight, I find this conclusion interesting. Overall, the analysis is fairly rigorous, and I applaud the effort to apply more quantitative models to the field of transcriptional memory.

We are grateful for the reviewer’s positive assessment of our manuscript and for appreciating our effort to describe transcriptional memory with a quantitative model.

1. The modeling is a mix of ordinary differential equation analysis and stochastic simulations with the Gillespie algorithm. It is a little unwieldy at times, and I feel the manuscript spends quite a bit of time on a model which doesn't work. Since this non-working model is the authors' invention (and not a standard in the field per se), it seems a bit awkward to present the story this way. Ideally, all the models would be stochastic models from the beginning since the single-cell analysis is the main thrust at the end. Although I find their approach a little ad hoc given the current state of the field, I don't feel strongly enough about it to demand a more rigorous treatment.

We agree with the reviewer that in the original version, too much of the manuscript was devoted to the models that did not match the in vivo data. We thus restructured our Figures and combined Figures 3-6 into Figures 3 and 4, with the rest of the parameter and modeling description put into Supplementary figures. The revised manuscript offers more focus on the model that did work and we hope this helps address the reviewer’s comment.

2. The persistence of memory in the presence of flavopiridol is a striking result. Would the authors predict a similar result for an inhibitor working upstream of transcriptional pause release? Specifically, the authors mention in the discussion that enhancer-promoter loops may be implicated. One experiment which the authors might consider (which I am not suggesting as necessary for a revision) is whether antagonists or partial agonists can uncouple these two activities of ecdysone. In other words, they have a negative result which emerges as a model prediction but not a positive result.

The question of earlier transcriptional inhibitors is very interesting, and we have addressed it in our revised manuscript. We have now carried out this experiment with an additional inhibitor Triptolide, which is known to inhibit transcription at an earlier, initiation-associated point (Titov et al., Nat. Chem. Bio. 2011), and observed that treatment with Triptolide similarly allows for the majority of the transcriptional memory response to occur (Figure 5B and 5E). Interestingly, we found that Triptolide treatment affected memory formation to a greater extent than Flavopiridol, but still produced around 60-70% of memory remaining (Figure 5E). We have also performed these transcriptional inhibition experiments on additional ecdysone-inducible genes E75 and E23 and detected similar effects in both treatments (Figures 5E and Figure 5-supplement 1C-D). Furthermore, using smRNA FISH, we observed similarly high re-induction levels in Flavopiridol and Triptolide treated conditions by mRNA spot counts (Figure 5C and Figure5supplement 1B). Based on these combined results, we concluded that inhibiting transcription upstream of transcriptional pause release during initial induction similarly allows for transcriptional memory, although at somewhat lower levels than inhibiting transcriptional elongation.

The implication of the enhancer-promoter loop is also interesting, and we thank the reviewer for the suggestion. It would indeed be very informative to find antagonists that can influence the enhancer-promoter loop or influence the two activities of ecdysone separately, and we plan to explore these approaches in our future work.

Reviewer #4:

The authors use a combination of RT-qPCR, smFISH and mathematical modeling to probe the mechanism and contribution of NUP98 to transcriptional memory upon hormone stimulation in Drosophila cells. The authors reveal that the transcriptional memory response is independent of transcription levels immediately after hormone treatment. Furthermore NUP98's role in transcriptional memory relates to its ability to stabilize a slow transition (~20 hrs) of the gene from low-expression to high-expression state. While the results are quite striking and the interpretations are timely in some regards, a few controls and clarifications in the text are necessary before recommending publication in eLife.

1. The authors rely heavily on smFISH to validate their computational modeling. However, the accuracy of quantitation of the smFISH is not evident. The authors should show a histogram of intensities of single diffraction limited spots along with a representative figure of multiple zoomed in panels of the single RNA spots. This is important as the majority of spots, particularly in the transcriptional memory response, are not diffraction limited which complicates both the presentation and analysis of their data.

To address the reviewer’s concern, we now include panels of average single spot intensities for all conditions and further analysis of single diffraction limited spots of our smRNA FISH (Figure 1-supplement 2D-F). Our prior work has shown that the vast majority of smFISH objects in our RNA FISH approach are diffraction-limited, including actively transcribing loci, regardless of mRNA content, and that such objects can contain the equivalent of hundreds of mature mRNAs (Little SC et al. 2013, Petkova MD et al. 2014, Little SC et al. 2015, Zoller B et al. 2018). The same is true for the objects detected in this study, where the full width at half-maximum (FWHM) of the detected objects is identical regardless of their intensities, as we now show in Figure 1supplement 2D-F. The FWHM (110 nm) is more or less the same as the radius expected from the Abbe limit (emission wavelength / numeric aperture = 105 nm) regardless of how bright the spots become. So the spots are all the same apparent size and their size is that expected for diffraction-limited objects.

2. Is there any evidence of E74 being alternatively spliced which may differentially affect FISH probe binding to the mRNAs producing large heterogeneously sized diffuse spots seen in the figure 1D?

The E74 locus does produce two transcript isoforms – the long isoform, transcribed from its main promoter, and the short isoform, transcribed from an alternative promoter within the gene. The two mRNA isoforms share 5 exons (Author response image 1A). Our previous work (Pascual-Garcia et al. Mol Cell 2017) and current RT-qPCR analysis found that it is the long isoform that changes in response to ecdysone and exhibits transcriptional memory in S2 cells (Author response image 1B), thus we designed our smRNA FISH probe to favor the long probe: the smRNA FISH probes were designed to the 5 exons, excluding the short isoform-specific exon (Author response image 1A). With this design, if RNA pol II (green) distributes uniformly along the length of the gene (Author response image 1C), the number of probes bound to a nascent mRNAs attached to a transcribing RNA pol II increases with total transcribed distance. Therefore probes binding 5' sequences are represented more frequently than those binding 3' sequences, as illustrated by the cyan probes in the diagram (6 sites for the 5' cyan probe but only 3 for the 3' cyan probe, Author response image 1C). Thus, there is a strong bias towards the 5’ long form-specific exons in detection by these probes, as shown in Author response image 1D – provided uniform distribution, we can estimate that the long isoform would account for 95% of the probe signal.

Author response image 1
Utilized smRNA FISH probe detects mainly the long isoform of E74.

RNA pol II does not have to distribute uniformly along the gene, but our single-cell FISH data supports the predominance of the long isoform in our detection – if we compare dynamics of the FISH-detected transcription sites with the RT-qPCR-detected dynamics of the long isoform vs. a mixture of the long and short isoforms, we observe a much better correlation to the long isoform only (Author response image 1E).

Furthermore, even if we assume that the rate of transcription from the alternative/short promoter is 10X that of the main/long promoter, given the 5’ bias in detection described above (Author response image 1C-D), we can estimate that the fraction of the short isoform in the total mRNA is still negligible for our analysis (Author response image 1F). In such an estimation based on our isoform-specific RT-qPCR, we see that the only times when the amount of short isoform approaches that of the long isoform are at Uninduced and Recovery time points (Author response image 1F). At these time points, we did not observe easily detectible nascent transcription sites by smRNA FISH, and these time points were not used in our modeling for this reason (more precisely, the uncertainty of assigning nuclear FISH dots to being a transcription site is too high at these uninduced time points.) At the time points that were used in our modeling, the fraction of the short isoform is so small (for example, Author response image 1F, 2nd induction – less than 1%) that we can ignore it. The approximately 10% additional signal during the 1st induction is less than the typical error of signal intensity (Little et al., Cell 2013). Thus at worst, we are overestimating the output during the first induction by about 10%, but this still falls well within the confidence intervals we report (Figures 2-4) and thus does not change the conclusions or the numbers that we present in the paper.

We did not include this analysis into the manuscript, but can do so if the reviewer feels that we should.

3. While the differences are readily apparent in most of the data, the authors do not show any statistical testing in many if not most plots (e.g. violin plots Figure 1E). This should be corrected in a revised manuscript.

Per reviewer’s suggestions, we have now added statistical testing and resulting p-values for Figure 1E (now in Figure 1-supplement 1D, F) and have also included statistical assessments into Figure 2. For model fittings in Figures 2C-E, 3 and 4, we also provide 95% confidence intervals that describe the probable range of the fitted rates (Figure supplements for Figures 3, 4).

4. S2 cells are considered aneuploid. For nascent transcription site analysis, it would be helpful for the readers to see a simple diagram showing the percentage of cells showing 1, 2, 3, 4 etc active TS sites throughout the time course as it was not apparent whether a certain percentage of cells in the population never expressed E74.

In response to reviewer’s concern, we would like to mention several points. First, S2 cells, like most Drosophila cell types, have a strong tendency to align/cluster their homologous chromosomes in interphase. As a result, by direct observation we most often observe a tight cluster of diffraction-limited spots by DNA or RNA FISH, although we can also observe a fraction of cells with multiple physically separate sites by either DNA or RNA FISH (FISH studies by Eric Joyce’s lab have suggested that chromosome 3L, which contains the E74 locus, is hexaploid in S2 cells).

Second, the way we designate active transcription sites and measure transcription in our imaging analysis makes it difficult to directly assess the fraction of cells that do not transcribe E74, but allows us to estimate it – this estimate is always very small, as detailed below:

We designate active sites using a threshold of three times the intensity of single mRNAs. This allows us to estimate the fraction of loci that have not transcribed sufficient immature RNA to allow detection. This fraction is naturally an underestimate of the true fraction of loci that have not been activated. To address this underestimate, we developed a method in prior studies (Little et al., Cell 2013, Zoller et al., Cell 2018), where we have examined the consequences of this underestimate by first identifying cells with fewer than the expected number of detected loci, then assigning to those cells additional candidate loci by selecting intra-nuclear spots either at random or by choosing the brightest available intra-nuclear object. In our current studies, we have verified that the objects chosen by either method are relatively dim. The relative uniformity of the dim intensities means that neither method outperforms the other when used in the modeling. We therefore conclude that our estimates of the activation rate kA = 0.017 +/- 0.001 is accurate. We can therefore calculate the fraction of inactive loci as a function of time as I = exp(-t*kA). At 1, 2, and 4 hrs we find that 36%, 13%, and 1.7% of loci remain inactive. Because of the presence of multiple loci per cell, we find that the fraction of cells with zero active loci is always very small: 0.2%, 5x10-6, and 2x10-11.

5. Figure 7 is one of the most key figures that the authors use to conclude that NUP98 is stabilizing a memory state independent of transcription. Yet they have chosen to use a less informative assay (e.g. RT-qPCR) rather than the most sensitive assay (e.g smFISH) to support their model and conclusions. The authors would make a stronger case for their findings if they employed smFISH to monitor transcript abundance and TS activity after inhibiting transcription.

To address the reviewer’s point, we have now carried out smRNA FISH assay against E74 in conditions of transcriptional inhibition by either Flavopiridol or Triptolide (now in Figure 5C and Figure 5-supplement 1B), and similarly observed high levels of transcriptional reinduction/transcriptional memory by mRNA spot counts. We thank the reviewer for suggesting this experiment, as it does provide for a stronger case for our conclusions.

6. FP primarily only prevents Pol II pause escape and not promoter escape of Pol II. So transcription initiation and Pol II loading at the promoter still occurs in flavopiridol treated cells. This is important because chromatin marks associated with active transcription are likely still being placed in the promoter and enhancer regions of E74 even though a complete transcript is not being produced. The authors should repeat their assays and treat the cells with inhibitors such as triptolide or THZ1 that act earlier in the transcription cycle or better yet use an ecdysone receptor mutant that prevents Pol II recruitment or chromatin modification to the gene in the first place.

We thank the reviewer for the excellent suggestion, and have now included an additional experiment, which used Triprolide to inhibit transcription during the initial treatment with ecdysone (new Figure 5D-E). As the reviewer points out, Triptolide acts earlier in the transcription cycle, as an inhibitor of TFIIH (Titov et al., Nat. Chem. Bio. 2011). We observed that treatment with Triptolide similarly allows for the majority of the transcriptional memory response to occur (Figure 5B and 5E). Interestingly, we found that Triptolide treatment affected memory formation to a greater extent than Flavopiridol, but still produced around 60-70% of memory remaining (especially as compared to much stronger effects of Nup98 KD and shorter recovery – Figure 5E). We have also performed these transcriptional inhibition experiments on additional ecdysone-inducible genes E75 and E23 and detected similar effects in both treatments (Figures 5E and Figure 5-supplement 1C-D). Furthermore, using smRNA FISH, we similarly observed high re-induction levels in Flavopiridol and Triptolide treated conditions by mRNA spot counts (Figure 5C and Figure 5-supplement 1B).

Based on these combined results, we concluded that inhibiting transcriptional initiation during initial induction similarly allows for transcriptional memory, although at somewhat lower levels than inhibiting transcriptional elongation. We have added a discussion of these points into the main text.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

All reviewers find that the manuscript is novel and exciting. However reviewer #4 has additional comments that need to be addressed, in particular the observation 1 that triptolide is not required for PIC formation in Drosophila. This issue needs to be addressed in the Discussion along with other issues raised by this reviewer. Please send a response to these comments with the revised manuscript.

We sincerely thank the reviewers and the reviewing and senior editors for the input on the manuscript and for the positive assessment of our work. As outlined below, we have now addressed the remaining comments from reviewer #4 and included all the needed changes in the re-revised manuscript.

Reviewer #4:

The authors take an important step in quantitatively assessing transcriptional memory in the context of mathematical modeling and testing of the models through single cell approaches. Furthermore, the authors extend their work to show how single cell data relates to population-level transcription outcomes. This second approach is highly relevant since transcriptional memory has been traditionally studied at the population-level allowing interpretation of previous studies in the context of single cell data. More importantly, the models produced can make predictions that the authors successfully test to show that transcription initiation is not necessary for establishment of memory. The authors do a good job of addressing the reviewer's critiques. While a clear mechanism for Nup98 establishment of memory is still lacking, I think that this manuscript represents an important step in integrating single cell data into studying memory. There are a few critiques that should still be addressed before final acceptance for publication.

We thank the reviewer for the overall positive assessment of our revisions and suggestions to improve the manuscript. As detailed below, we have incorporated all of the latest suggestions and resolved the remaining issues raised by the reviewer.

1. While the authors indeed show that transcription initiation is not required for memory, Triptolide treatment does not prevent PIC formation in Drosophila (Krebs et al., 2017, Mol Cell, which the authors cite in the manuscript). PIC assembly actually increases during Triptolide treatment suggesting that key factors and histone marks related to the assembly of the PIC (e.g. H3K4 methylation due to its presumed role in recruiting TFIID) are likely still present and capable of establishing memory in the absence of initiation. The authors should be very clear of this possibility in the discussion.

We thank the reviewer for pointing out this possibility and agree that this is an important point to add. As the reviewer states, while our experiments demonstrate that transcriptional memory can still occur in Triptolide-inhibited conditions, they do not delve into which of the ecdysone-triggered initiation events that still occur in the presence of Triptolide are required for this preservation. PIC assembly or setting up permissive chromatin structure are indeed likely candidates for establishing memory. We have now included this point into the revised discussion (first paragraph of Discussion section).

2. Lines 175-177 the authors are essentially making a statement about transcriptional noise being independent of Nup98. It would be highly beneficial to quantitatively measure transcriptional noise (CV2 or std2/mean2) under the different conditions to validate this statement.

Per reviewer’s suggestion, we now provide CV2 measurements of transcriptional noise under different conditions in Figure 1-supplement 2G, plotted as noise (CV2) as a function of mean number of mRNA molecules per cell. These plots show similar trends between the different conditions, suggesting that Nup98 does not contribute substantially to transcriptional noise.

3. I realize that Models 1 and 2 are not viable in the end. However, the wording in Lines 301-306 is misleading creating a potential flaw in these models that seems to be carried throughout the manuscript. The authors invocation of a quantitative assessment of the PIC (e.g. Author's should use the term PIC since RNA Pol II holoenzyme can be confusing) footprint into the model is naive and underestimates the role of a stable TFIID scaffold and TFIIB in re-initiation rates (Zhang et al. Genes and Dev 2016, Yudkovsky et al., Nature 2000). It is highly likely that core promoter accessibility plays a major role in going from an inactive to active state. But core promoter accessibility would likely be a binary effect and not scaleable as the authors seem to intimate in lines 302-304 and let freely fluctuate in their modeling. Model 1 and 2 also assumes that transitions to the inactive states are negligible suggesting that the gene is stuck in the ON state and therefore Nup98 is enhancing Pol II's attempted loading onto a stable PIC scaffold of defined size (e.g. footprint).

We thank the reviewer for altering us to the overly simplistic and potentially misleading presentation of Model 2. As previously written, the model (1) ignores "bursts" of activity resulting from promoter state (e.g. accessibility) switching, and (2) appears to use a "naive" estimate of the Pol II footprint on DNA. We have now rewritten the presentation of Model 2 (second to last paragraph of section 3 of Results) to emphasize that the quantity that matters is not the footprint per se, but is instead the interval between sequential Pol IIs transcribing a given nucleotide.

It was most straightforward to implement this model by allowing the Pol II footprint to vary as a free parameter, as we noted in the prior version of the main text, combined with a constant elongation rate of 1500 nt/min, a value supported by multiple studies in Drosophila. Use of this parameter, paired with the "loading acceleration" parameter, permitted us to fit the qPCR data. The model is thus agnostic about the nature of the process(es) that determine the minimum interval. We note that the maximum average loading rate kPol is ~3 mRNA per locus per minute in model 2. This is less than half of the maximum that has been observed in Drosophila (e.g. Zoller et al. 2018).

We believe it is likely that promoter switching in this context prohibits achievement of the maximum rate observed in embryos. We also note that the promoter switching rates would need to be rapid in order to support this rate of mRNA production. While such switching could be assessed using multi-color FISH (as in Zoller et al. 2018), we have not as yet produced such data.

4. It would be interesting to see whether the FP and TL treatments change the distributions of transcriptional output during the second induction in single cells compared to those shown in Figure 4.

To address the reviewer’s point, we have compared the population distributions of transcriptional output in FP or TPL treated conditions of 2nd induction, relative to control 2nd induction, using the smRNA FISH data in Figures 5 and 4 (the comparison is now in Figure 5 supplement 1C). The distributions do exhibit some minor differences, the extent of which and the mechanism behind which could be subjects of future studies.

https://doi.org/10.7554/eLife.63404.sa2

Article and author information

Author details

  1. Pau Pascual-Garcia

    Department of Cell and Developmental Biology, Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Present address
    Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  2. Shawn C Little

    Department of Cell and Developmental Biology, Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Maya Capelson

    Department of Cell and Developmental Biology, Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Investigation, Supervision, Writing – original draft, Writing – review and editing
    For correspondence
    capelson@pennmedicine.upenn.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1695-316X

Funding

National Institutes of Health (R01GM124143)

  • Maya Capelson

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

Acknowledgements

We are thankful to members of the Capelson, Little, DiNardo and Bashaw laboratories for insightful comments and suggestions. We are also grateful to Jack M Gallup (Newport Laboratories) for helping with qPCR analysis, Jessica Talamas (Dana Farber Cancer Institute) and Terra Kuhn (European Molecular Biology Laboratory) for critical reading of the manuscript. We acknowledge the Drosophila Genomics Resource Center, supported by National Institutes of Health grant 2P40OD010949, and the Cell and Developmental Biology Microscopy Core Facility for confocal microscope use. M C is supported by the National Institutes of Health grant R01GM124143.

Senior Editor

  1. Kevin Struhl, Harvard Medical School, United States

Reviewing Editor

  1. Robert H Singer, Albert Einstein College of Medicine, United States

Publication history

  1. Received: September 24, 2020
  2. Preprint posted: October 12, 2020 (view preprint)
  3. Accepted: February 26, 2022
  4. Version of Record published: March 15, 2022 (version 1)

Copyright

© 2022, Pascual-Garcia 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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  1. Pau Pascual-Garcia
  2. Shawn C Little
  3. Maya Capelson
(2022)
Nup98-dependent transcriptional memory is established independently of transcription
eLife 11:e63404.
https://doi.org/10.7554/eLife.63404

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    Bretta Hixson et al.
    Tools and Resources Updated

    Mosquitoes transmit numerous pathogens, but large gaps remain in our understanding of their physiology. To facilitate explorations of mosquito biology, we have created Aegypti-Atlas (http://aegyptiatlas.buchonlab.com/), an online resource hosting RNAseq profiles of Ae. aegypti body parts (head, thorax, abdomen, gut, Malpighian tubules, ovaries), gut regions (crop, proventriculus, anterior and posterior midgut, hindgut), and a gut time course of blood meal digestion. Using Aegypti-Atlas, we provide insights into regionalization of gut function, blood feeding response, and immune defenses. We find that the anterior and posterior midgut possess digestive specializations which are preserved in the blood-fed state. Blood feeding initiates the sequential induction and repression/depletion of multiple cohorts of peptidases. With respect to defense, immune signaling components, but not recognition or effector molecules, show enrichment in ovaries. Basal expression of antimicrobial peptides is dominated by holotricin and gambicin, which are expressed in carcass and digestive tissues, respectively, in a mutually exclusive manner. In the midgut, gambicin and other effectors are almost exclusively expressed in the anterior regions, while the posterior midgut exhibits hallmarks of immune tolerance. Finally, in a cross-species comparison between Ae. aegypti and Anopheles gambiae midguts, we observe that regional digestive and immune specializations are conserved, indicating that our dataset may be broadly relevant to multiple mosquito species. We demonstrate that the expression of orthologous genes is highly correlated, with the exception of a ‘species signature’ comprising a few highly/disparately expressed genes. With this work, we show the potential of Aegypti-Atlas to unlock a more complete understanding of mosquito biology.

    1. Chromosomes and Gene Expression
    2. Genetics and Genomics
    Bethany Sump et al.
    Research Article

    For some inducible genes, the rate and molecular mechanism of transcriptional activation depends on the prior experiences of the cell. This phenomenon, called epigenetic transcriptional memory, accelerates reactivation and requires both changes in chromatin structure and recruitment of poised RNA Polymerase II (RNAPII) to the promoter. Memory of inositol starvation in budding yeast involves a positive feedback loop between transcription factor-dependent interaction with the nuclear pore complex and histone H3 lysine 4 dimethylation (H3K4me2). While H3K4me2 is essential for recruitment of RNAPII and faster reactivation, RNAPII is not required for H3K4me2. Unlike RNAPII-dependent H3K4me2 associated with transcription, RNAPII-independent H3K4me2 requires Nup100, SET3C, the Leo1 subunit of the Paf1 complex and, upon degradation of an essential transcription factor, is inherited through multiple cell cycles. The writer of this mark (COMPASS) physically interacts with the potential reader (SET3C), suggesting a molecular mechanism for the spreading and re-incorporation of H3K4me2 following DNA replication.