1. Cell Biology
  2. Chromosomes and Gene Expression
Download icon

Random sub-diffusion and capture of genes by the nuclear pore reduces dynamics and coordinates inter-chromosomal movement

  1. Michael Chas Sumner
  2. Steven B Torrisi
  3. Donna G Brickner
  4. Jason H Brickner  Is a corresponding author
  1. Department of Molecular Biosciences, Northwestern University, United States
  2. Department of Physics, Harvard University, United States
Research Article
  • Cited 0
  • Views 757
  • Annotations
Cite this article as: eLife 2021;10:e66238 doi: 10.7554/eLife.66238

Abstract

Hundreds of genes interact with the yeast nuclear pore complex (NPC), localizing at the nuclear periphery and clustering with co-regulated genes. Dynamic tracking of peripheral genes shows that they cycle on and off the NPC and that interaction with the NPC slows their sub-diffusive movement. Furthermore, NPC-dependent inter-chromosomal clustering leads to coordinated movement of pairs of loci separated by hundreds of nanometers. We developed fractional Brownian motion simulations for chromosomal loci in the nucleoplasm and interacting with NPCs. These simulations predict the rate and nature of random sub-diffusion during repositioning from nucleoplasm to periphery and match measurements from two different experimental models, arguing that recruitment to the nuclear periphery is due to random sub-diffusion and transient capture by NPCs. Finally, the simulations do not lead to inter-chromosomal clustering or coordinated movement, suggesting that interaction with the NPC is necessary, but not sufficient, to cause clustering.

Introduction

In eukaryotes, genomes are spatially organized within the nucleus. Chromosomes occupy distinct subnuclear ‘territories’, heterochromatin is segregated from euchromatin, and individual genes show non-random positioning relative to nuclear structures and other genes (Misteli, 2020). Gene positioning reflects physical interactions of chromosomal loci with nuclear structures like the nuclear lamina, nuclear pore complexes (NPCs), or nuclear bodies, and changes in gene expression are often accompanied by changes in gene positioning (Brickner, 2017). The positioning of genes can impact their transcription, mRNA processing, or chromatin modifications.

One model for such phenomena is the recruitment of genes to the nuclear periphery through interaction with the NPC. Many genes in budding yeast, Caenorhabditis elegans, Drosophila, and mammals physically interact with NPCs, suggesting that the NPC plays an important role in determining the spatial arrangement of eukaryotic genomes (Brown et al., 2008a; Capelson et al., 2010; Casolari et al., 2004; Casolari et al., 2005; Ibarra et al., 2016; Jacinto et al., 2015; Liang et al., 2013; Pascual-Garcia et al., 2017; Rohner et al., 2013; Toda et al., 2017). This is particularly apparent in budding yeast where hundreds of genes interact with the NPC and inducible genes rapidly reposition to the nuclear periphery upon activation (Brickner and Walter, 2004; Casolari et al., 2005; Casolari et al., 2004; Van de Vosse et al., 2013). Interaction with the NPC and localization to the nuclear periphery require specific transcription factors (TFs) and nuclear pore proteins (Brickner et al., 2019; Brickner et al., 2012; Brickner et al., 2007; Cabal et al., 2006; Dieppois et al., 2006; Dilworth et al., 2005; D'Urso et al., 2016; Lapetina et al., 2017; Luthra et al., 2007; Randise-Hinchliff et al., 2016; Texari et al., 2013; Van de Vosse et al., 2013). A majority of yeast TFs can mediate interaction with the NPC (Brickner et al., 2019), suggesting that the yeast genome encodes spatial organization through cis-acting TF binding sites. Such cis-acting DNA zip codes are both necessary and sufficient to mediate interaction with the NPC and positioning to the nuclear periphery (Ahmed et al., 2010; Brickner et al., 2019; Brickner et al., 2012; Light et al., 2010; Randise-Hinchliff et al., 2016). Furthermore, interaction with the NPC frequently leads to inter-chromosomal clustering of co-regulated genes, suggesting that it influences the spatial organization of the yeast genome at multiple levels (Brickner et al., 2016; Brickner et al., 2012; Kim et al., 2019; Kim et al., 2017; Mirkin et al., 2013; Randise-Hinchliff et al., 2016).

Much of the work on gene recruitment to the nuclear periphery has utilized static population measurements such as microscopy, chromatin immunoprecipitation, or HiC. Although these studies have revealed important players necessary for gene positioning to the nuclear periphery, there are questions that cannot be answered using static methods. For example, while some loci interact very stably with the nuclear envelope (e.g., telomeres and centromeres; Heun et al., 2001; Jin et al., 2000), leading to ~85% of cells showing colocalization of these loci with the nuclear envelope, genes that interact with the NPC show lower levels (~50–65%; Brickner and Walter, 2004; Casolari et al., 2004). This has been suggested to reflect transient interaction with the nuclear periphery (Brickner and Walter, 2004), cell-cycle regulation of peripheral localization (Brickner and Brickner, 2010), or, perhaps, two distinct populations, one that stably associates with the NPC and the other that does not (Brickner and Walter, 2004; Cabal et al., 2006). Likewise, the repositioning of inducible genes from the nucleoplasm to the nuclear periphery is not well-understood. Some data – including the involvement of nuclear actin and myosin – has suggested that repositioning to the NPC could involve directed, super-diffusive movement (Guet et al., 2015; Wang et al., 2020). Finally, while inter-chromosomal clustering is a widespread phenomenon (Apostolou and Thanos, 2008; Brickner et al., 2012; Brown et al., 2006; Homouz and Kudlicki, 2013; Kim et al., 2017; Lin et al., 2009; Noma et al., 2006; Schoenfelder et al., 2010; Thompson et al., 2003), relatively few studies have explored the dynamics of clustering over time and it is unclear if clustering reflects a stable physical interaction (Brickner et al., 2016; Dai et al., 2018). High-resolution, quantitative dynamics of chromatin diffusion are required to address each of these questions.

Chromatin is a mobile polymer, and individual loci exhibit constrained or anomalous diffusion (Bystricky et al., 2004; Gasser, 2002; Hajjoul et al., 2013; Heun et al., 2001; Marshall et al., 1997). Chromatin motion can reveal important aspects of the nuclear environment and the biophysical mechanisms that control the spatial organization of the genome. Repositioning to the NPC in budding yeast is an intriguing model for such studies because it is inducible, relatively rapid, controlled by well-understood DNA elements, and induces both a change in position and inter-chromosomal clustering.

Here we show that repositioning to the nuclear periphery is continuous and dynamic but uniform within the population, suggesting that, within each cell, localization to the periphery it is a probabilistic process. Localization at the nuclear periphery correlates with more constrained diffusion, as suggested by previous work (Backlund et al., 2014; Cabal et al., 2006). Using mean-squared displacement (MSD) analysis and molecular genetics, we pinpoint this effect to the interaction with the NPC. The parameters of sub-diffusion derived from MSD of nucleoplasmic loci were used to develop a computational simulation that faithfully recapitulates the behavior of such genes. This simulation was also adapted to model repositioning to the nuclear periphery through random sub-diffusion and transient capture at the nuclear envelope. The repositioning predicted by the simulation was then compared with several rapid repositioning experiments to determine whether it is vectorial or super-diffusive. The simulation matched the observed behavior of loci in cells, suggesting that repositioning from the nucleoplasm to the nuclear periphery does not require directed movement.

Finally, we monitored the dynamics of inter-chromosomal clustering. Unlike pairs of simulated paths, genes that exhibit clustering remain near each other for tens of seconds and show correlated movement. Simulated interaction with the NPC, while sufficient to recapitulate the chromatin dynamics of individual loci, is not sufficient to recapitulate this correlated movement. Therefore, we propose that inter-chromosomal clustering relies on a distinct physical interaction between genes that can extend hundreds of nanometers.

Results

Chromatin positioning to the nuclear periphery is continuous and dynamic

The localization of genes at the nuclear periphery can be followed in live yeast cells by tagging chromosomal loci of interest with an array of 128 Lac operators in a strain expressing GFP-Lac repressor (GFP-LacI) and quantifying its colocalization with mCherry-marked nuclear envelope (Figure 1A; Brickner and Walter, 2004; Egecioglu et al., 2014; Robinett et al., 1996; Straight et al., 1996). In static confocal microscopy experiments, repositioning of inducible genes such as HIS4 or INO1 to the periphery leads to an increase in the fraction of cells in which the locus colocalizes with the nuclear envelope from that expected for a random distribution (~30%) to ~50–65% (Figure 1B,D; Brickner and Walter, 2004; Egecioglu et al., 2014). However, artificially tethering chromatin to the nuclear envelope leads to ~85% colocalization with the nuclear envelope (Brickner and Walter, 2004). This suggests that localization to the nuclear periphery reflects either dynamic or continuous interaction with the NPC or two distinct populations of cells, one that exhibits stable association with the nuclear envelope and the other that does not. To distinguish between these possibilities, we quantified peripheral localization of three LacO-tagged loci over time in individual cells: the inducible genes HIS4 and INO1, as well as the negative control URA3, which localizes in the nucleoplasm (Figure 1B,D; Brickner et al., 2019; Brickner and Walter, 2004; Randise-Hinchliff et al., 2016). To avoid the complication that interaction of many genes with the NPC is lost during S-phase (Brickner and Brickner, 2010), cells were synchronized using nocodazole and released into G1 for 30 min before scoring colocalization with the nuclear envelope every 10 s over 10 min. In complete media (i.e., uninducing conditions), all three genes showed similar patterns: episodic, brief colocalization with the nuclear envelope (Figure 1C, E, and F). However, under inducing conditions (−histidine for HIS4 or −inositol for INO1), the pattern changed. Both HIS4 and INO1 showed longer periods of colocalization with the nuclear envelope (Figure 1C, F, and J), while URA3 was unaffected (Figure 1E). The pattern was consistent across the population, so that the fraction of cells in which HIS4 or INO1 colocalized with the nuclear envelope at each time point (Figure 1H) was in close agreement with the fraction of time spent colocalized with the nuclear envelope in each cell (Figure 1I). This argues against two distinct populations and instead suggests that interaction with the NPC is continuous and dynamic over time, increasing the duration of colocalization with the nuclear envelope.

Continuous and dynamic positioning at the nuclear periphery.

(A) Representative confocal micrographs of cells having the LacO array integrated at a locus of interest, expressing GFP-LacI and Pho88-mCherry (Robinett et al., 1996; Brickner and Walter, 2004; Brickner et al., 2019) and scored as either nucleoplasmic (left) or peripheral (right). (B) Peripheral localization (% of cells ± SEM) of URA3 and HIS4 in cells grown ± histidine. The hatched blue line, here and throughout: peripheral localization predicted by chance. (C, E–G) Kymographs of 10 cells with a LacO array integrated at HIS4 (C), URA3 (E), INO1 (F), or URA3:GRS1 (G) were grown in the indicated medium and scored for peripheral localization every 10 s for 5 min. Yellow: peripheral; purple: nucleoplasmic. (D) Peripheral localization (± SEM) of URA3, INO1, URA3:INO1, and URA3:GRS1 in cells grown ± inositol. (H–J) Summary plots from (C, E–G): (H) mean percentage of cells (± SD) in which the locus is peripheral at each time point (i.e., each dot represents a summary of a single column from kymographs); (I) mean percentage of time (± SD) each locus spent colocalized with the nuclear envelope (i.e., each dot represents a summary of a single row from kymographs); and (J) the distribution and median duration of periods of peripheral localization of each locus.

Interaction with the NPC is mediated by TFs binding to cis-acting elements that function as DNA zip codes (Ahmed et al., 2010; Brickner et al., 2019; Light et al., 2010). For example, the Gene Recruitment Sequence GRS1 from the INO1 promoter binds to the Put3 TF to mediate interaction with the NPC and positioning at the nuclear periphery (Brickner et al., 2012). Likewise, the Gcn4 binding site (GCN4 BS) from the HIS3 promoter is sufficient to mediate interaction with the NPC (Randise-Hinchliff et al., 2016). Inserting zip codes near URA3 is sufficient to reposition URA3 to the nuclear periphery (e.g., URA3:GRS1, Figure 1D; Ahmed et al., 2010; Randise-Hinchliff et al., 2016). The association of URA3:GRS1, which shows unregulated localization to the periphery, with the nuclear envelope over time resembled that of active HIS4 and INO1 (Figure 1G–J). Thus, DNA zip code-mediated interaction with the NPC is sufficient to produce continuous and dynamic association with the nuclear envelope.

Chromatin sub-diffusion is suppressed by interaction with the NPC

We next examined how interaction of genes with the NPC impacts the dynamics of diffusion using MSD analysis. MSD has been used to show that chromosomal loci exhibit constrained sub-diffusion (Marshall et al., 1997). For comparison, we tracked the movement of the less-mobile nuclear envelope-embedded spindle pole body (SPB) and a much more mobile cytoplasmic particle (the µNS viral capsid; Munder et al., 2016). While µNS was highly diffusive, the SPB showed very limited displacement at this timescale, reflecting both slow diffusion within the membrane and movement of the whole nucleus (Figure 2B). The MSD of 11 nucleoplasmic loci (i.e., not associated with the NPC) and two telomeres tethered to the nuclear envelope exhibited a range of intermediate sub-diffusion between these two extremes, with the nucleoplasmic loci showing greater MSD than tethered telomeres and telomeres showing greater MSD than the SPB (Figure 2B; Supplementary file 1). Simultaneously acquiring images of chromosomal loci and the SPB to correct for nuclear movement significantly reduced the time resolution (data not shown). Given that nuclear movement was much less than chromosomal movement at these timescales, it could be ignored. We also determined the MSD of chromosomal loci in 3D. Although this gave very similar results (Supplementary file 1), the quality of the data was lower because of the longer time interval (>1 s). For these reasons, we limited our movies for MSD analysis to 40 s at 210 ms resolution (200 × 0.21 s) in a single focal plane and calculated MSD for time intervals between 210 ms and 4 s (Figure 2B).

Figure 2 with 1 supplement see all
Mean-squared displacement (MSD) of chromatin sub-diffusion.

(A) Schematic of fluorescent foci within the yeast cell. Fluorescently tagged spindle pole body (SPB), cytoplasmic µNS, and chromosomal locus were tracked over 200 × 200 ms. Example micrographs of each particle (left) and overlaid path (right) are shown for each. Scale bar = 1 µm. (B) Average MSD for µNS (orange), SPB (purple), 10 nucleoplasmic loci (gray; listed in Supplementary file 1) and two telomeres (red) at different time intervals (τ). The ribbon around the mean represents standard error. (C) Mean MSD ± standard deviation for τ = 200 ms for each chromosomal locus in (B) vs log10 (base pairs) to the nearest tether point (centromere or telomere). The line is from the fit of the data to a non-linear model for a hyperbolic curve, as described in the text. (D–F) MSD plots of INO1 (D), URA3 (E), or HIS4 (F) in cells grown in the indicated media. In all plots, the dashed line represents the MSD of the SPB. *p<0.05 based on Kolmogorov–Smirnov test comparing MSDs at the indicated times.

The nucleoplasmic loci showed a range of mobility by MSD, perhaps reflecting nearby physical interactions with the nuclear envelope. Tethering to the nuclear envelope has a significant effect on chromatin positioning and the fraction of the nuclear volume explored over distances below 30 kb (Avşaroğlu et al., 2014; Verdaasdonk et al., 2013). Indeed, the initial MSDs (τ = 0.21 s) showed a non-linear relationship to the genomic distance to the nearest nuclear envelope tethering point (either centromeres or telomeres; Figure 2C). Consistent with work from others, we could model this relationship as a hyperbolic curve with a half-maximal MSD observed at ~18 kb (Figure 2C, blue dashed line; Avşaroğlu et al., 2014; Verdaasdonk et al., 2013). Thus, diffusion of chromatin is influenced over relatively short distances by stable interactions with the nuclear envelope (Hediger et al., 2006; Hediger and Gasser, 2002).

To quantify the effect of local interaction with the NPC on chromatin sub-diffusion, we examined genes that show conditional association with the NPC. We compared the MSD of INO1, HIS4, and URA3 under either uninducing or inducing conditions (±histidine and ±inositol). As expected, URA3 showed no change in MSD under these conditions (Figure 2D). However, both HIS4 and INO1 showed significantly reduced mobility upon induction (Figure 2E,F), confirming that repositioning to the nuclear periphery correlates with reduced chromatin sub-diffusion.

To further strengthen this correlation, we exploited the population dynamics illuminated in Figure 1, performing MSD analysis on sub-populations of cells in which the locus was either stably maintained at the nuclear periphery (i.e., those cells in which >50% of the time points were peripheral) or predominantly in the nucleoplasm (<10% peripheral) during the 40 s acquisition (Figure 2—figure supplement 1). When we performed this analysis with repressed INO1, the MSD from predominantly peripheral cells was indistinguishable from the MSD from predominantly nucleoplasmic cells (Figure 2—figure supplement 1C). However, for active INO1, the MSD from predominantly peripheral cells was significantly lower than the MSD from predominantly nucleoplasmic cells (Figure 2—figure supplement 1D), consistent with the decrease in MSD resulting from interaction with the NPC.

If the change in MSD is due to interaction with the NPC, a DNA zip code integrated at an ectopic site should also reduce MSD. Single copies of zip codes from the promoters of INO1 (URA3:GRS1; Figure 3A) or HIS4 (URA3:GCN4BS; Figure 3B) were integrated at the URA3 locus. URA3:GRS1 localizes at the nuclear periphery constitutively (Figures 1D and 3A; Ahmed et al., 2010; Randise-Hinchliff et al., 2016), resulting in a reduced MSD under all conditions. In contrast, URA3:GCN4BS shows conditional localization to the periphery upon amino acid starvation (Figure 3B, inset; Randise-Hinchliff et al., 2016), and a conditional reduction in MSD (Figure 3B). Loss of the NPC protein Nup2 disrupts DNA zip code-mediated localization to the nuclear periphery and resulted in MSD similar to URA3 under all conditions (Figure 3C,D). Thus, DNA zip code-mediated interaction with the NPC is sufficient to suppress chromatin sub-diffusion.

Interaction with the NPC reduces chromatin sub-diffusion.

(A–F) MSD of URA3 (A–D) and INO1 (E, F) in strains grown in the indicated media. Dark line indicates average MSD, ribbon = bootstrapped SEM. Insets: peripheral localization of each locus (mean % of cells ± SEM). The GRS1 zip code from the INO1 promoter (A, C) or the Gcn4 binding site (B, D) was integrated and integrated at URA3 in wild-type (A, B) or nup2∆ (C, D) strains. MSD of INO1 in ino2∆ (E) or opi1∆ (F) strains. *p<0.05 based on Kolmogorov–Smirnov test comparing MSD at the indicated time points.

Transcriptional activation and chromatin remodeling can cause increased chromatin mobility (Gasser et al., 2004; Gu et al., 2018). Therefore, to disentangle the effects of peripheral localization from the effects of transcriptional activity on MSD, we monitored MSD in mutants that lack trans-acting transcriptional regulators of the INO1 gene. Both INO1 transcription and INO1 interaction with the NPC are regulated by the Opi1 repressor, which recruits the Rpd3L histone deacetylase to regulate binding of the Put3 TF to the GRS1 zip code (Randise-Hinchliff et al., 2016). Because Opi1 is recruited to the INO1 promoter by binding to the Ino2 activator (Heyken et al., 2005), loss of either Ino2 or Opi1 leads to constitutive peripheral localization (Figure 3E,F, insets; Randise-Hinchliff et al., 2016). However, these two mutants have opposite effects on INO1 transcription: ino2∆ blocks all expression, while opi1∆ shows unregulated, high-level expression (Greenberg et al., 1982a; Greenberg et al., 1982b). In both mutants, the INO1 MSD resembled that of active INO1 (Figure 3E,F), suggesting that interaction with the NPC is the principal cause of the decrease in sub-diffusion.

Simulating chromatin sub-diffusion and repositioning to the nuclear periphery

Using parameters from the MSD analysis, we developed a simulation of chromatin sub-diffusion (https://github.com/MCnu/YGRW). Sub-diffusion of a segment of chromatin results from forces affecting the chromatin segment both directly (e.g., the viscoelastic potential of the polymer, boundary collision) and indirectly (forces and membrane tethering nearby; Figure 2C). MSD for a Rouse polymer like chromatin reflects a relationship MSD(τ) = Γ(τα) for any time interval τ (Socol et al., 2019). Gamma (Γ) describes the diffusion coefficient, while an α exponent less than one reflects a hallmark for sub-diffusive movement: each step vector is anticorrelated with both the previous and subsequent steps (Lucas et al., 2014). While the exact value for α from different MSD experiments or different loci varies (Backlund et al., 2014), work on multiple loci in yeast (Hajjoul et al., 2013) and our MSD data with nucleoplasmic loci (see Materials and methods) suggests that yeast chromatin has an average α = 0.52.

Chromatin sub-diffusion has been modeled using several approaches (Arbona et al., 2017; Verdaasdonk et al., 2013). Anticorrelated movement cannot be reproduced through either a random walk or a simple process of weighted step sizes derived from our experimental observations (Figure 4—figure supplement 1A,B; uniform and Gaussian, respectively). However, a continuous-time Gaussian process known as fractional Brownian motion (FBM) produces trajectories that approximate chromatin sub-diffusion (Lucas et al., 2014). FBM produces non-independent steps across time, allowing us to impart the anticorrelation between individual steps that is characteristic of yeast chromatin sub-diffusion. For each trajectory, two numeric arrays for the x and y dimensions of movement (Dietrich and Newsam, 1997) were generated based on an expected covariance matrix and α = 0.52. This array produces a stochastic time series of vectors with an anticorrelation structure functionally identical to that observed for chromatin movement. Finally, these vectors were scaled according to the experimentally derived Γ value and Hurst exponent (α/2; Mandelbrot and Van Ness, 1968). Starting from random positions within the nucleus, the resulting array of discrete step lengths describes a single, two-dimensional sub-diffusive particle trajectory. This simple and rapid approach generates trajectories similar to our experimental observations and imparts memory resembling the MSD of chromosomal loci in the nucleoplasm (Figure 4—figure supplement 1A and B).

Paths generated by FBM suffer from one significant shortcoming. In an enclosed volume, FBM will deplete occupancy of particles near the boundary over time, resulting in a biased distribution (Figure 4—figure supplement 1C). This phenomenon has also been reported by others (Vojta et al., 2020) and is not consistent with observations that chromosomal loci, unless associated with the nuclear envelope, localize at the nuclear periphery at a frequency expected from a random distribution (Brickner and Walter, 2004; Hediger et al., 2002). This may reflect a fundamental difference between sub-diffusion of particles and the apparent sub-diffusion of a segment of chromatin. We explored several methods to avoid depletion at the nuclear periphery and found that the following was effective: steps that would have taken the locus beyond the boundary were replaced with steps to the boundary along the same vector and, upon interaction with the boundary, the normalized, correlated noise for future steps was regenerated (Figure 4A, Figure 4—figure supplement 1, FBM + regeneration). This modified simulation produced paths that closely matched the MSD, the distribution of positions within the nucleus, and the peripheral occupancy of nucleoplasmic chromosomal loci (Figure 4B,E–G; loci within 150 nm of the membrane in the simulation were scored as peripheral).

Figure 4 with 2 supplements see all
A fractional Brownian motion simulation of chromatin sub-diffusion.

(A, C) Randomly selected example paths over 5 min at 200 ms time resolution. Color scale represents time. Paths were simulated using parameters (diffusion coefficient and anomalous exponent) extracted from a non-linear regression fit to URA3 MSD (A; simulation) or by also allowing interaction at the nuclear envelope, slowing sub-diffusion to that of the SPB (C; simulation+zip code). (B, D) 150,000 positions visited in 100 simulated 5 min paths at 200 ms time resolution for the simulation (B) or the simulation+zip code (D). (E) Peripheral localization (i.e., positioned ≤150 nm from the edge of the nucleus) every 10 s over 10 min for 100 paths from the simulation (top) and simulation+zip code (bottom). (F) Summary plots for percent of cells in that scored as peripheral at each time (left) or the percent of time each cell scored as peripheral (right) in either the simulation or the simulation+zip code. (G) MSD of the paths from the simulation or the simulation+zip code. Dark line is the mean, and the colored band represents the bootstrapped standard error.

From our model for nucleoplasmic gene movement, we sought to simulate chromatin interaction with NPCs at the nuclear membrane. Based on the height of the NPC basket (Yang et al., 1998; Vallotton et al., 2019), we created a zone 50 nm from the boundary where chromatin could become ‘bound’, causing it to switch to SPB-like sub-diffusion (Figure 2B). The probabilities of binding and unbinding within this zone were varied independently to optimize the agreement with the experimental MSD and peripheral localization (i.e., localization within 150 nm of the nuclear envelope) of URA3:GRS1 (Figure 4—figure supplement 2). Based on this optimization, we found that a binding probability of 0.9 and a probability of remaining bound of 0.95 resulted in a positional distribution (Figure 4D), peripheral occupancy over time (Figure 4E,F), and MSD (Figure 4G) that most closely matched that of URA3:GRS1. We refer to this modified simulation as simulation+zip code. The fit of the simulation to the mean MSD for URA3 and of the simulation+zip code to the mean MSD for URA3:GRS1 was excellent (Pearson’s Χ2 sums of 0.001 and 0.003, respectively, for τ from 0.21 to 4 s). Together, these two relatively simple simulations capture important aspects of chromatin sub-diffusion and gene positioning at the nuclear periphery.

Chromatin repositioning is achieved by random sub-diffusion and capture

Chromosomal loci can undergo long-range, directed movement (Miné-Hattab and Rothstein, 2013), raising the possibility that repositioning from the nucleoplasm to the nuclear periphery could be an active process. Furthermore, actin and the myosin motor Myo3 have been shown to play a role in the localization of INO1 to the nuclear periphery (Wang et al., 2020). We find that deletion of Myo3 leads to a delay in the targeting of URA3:INO1 to the nuclear periphery (Figure 5—figure supplement 1A & B). Importantly, this defect is specific to one (GRS1) of the two DNA zip codes that mediate repositioning of INO1 to the nuclear periphery (Ahmed et al., 2010). When both zip codes (GRS1 and GRS2) are present at the endogenous INO1 gene, loss of Myo3 had no effect (not shown). Furthermore, once positioned at the nuclear periphery, URA3:INO1 localization was unaffected by degradation of Myo3-AID (auxin-inducible degron; Figure 5—figure supplement 1C), suggesting that Myo3 increases the rate or efficiency of repositioning to the nuclear periphery. The MSD of URA3:INO1 in the myo3∆ mutant reflected its localization; under repressing conditions or after only 1 hr of inositol starvation, the MSD was unchanged, whereas after 24 hr of inositol starvation, MSD decreased (Figure 5—figure supplement 1D). These results suggest that Myo3 is impacting either the movement of URA3:INO1 from the nucleoplasm to the nuclear periphery other regulatory steps that are necessary for rapid GRS1-mediated peripheral localization.

To explore whether directed movement is responsible for repositioning of genes from the nucleoplasm to the nuclear periphery, we first determined the behavior of the simulation, which does not possess active, vectorial movement. Initiating either the default simulation of chromatin movement or the simulation+zip code from random positions within the nucleus, we followed the percent of the population showing localization within 150 nm of the nuclear edge over time. For the nucleoplasmic simulation, the peripheral localization remained random over time (~28% peripheral; Figure 5A). However, interaction with the nuclear envelope in the simulation+zip code resulted in stable repositioning to the nuclear periphery within ~2 min (Figure 5A). Therefore, rapid repositioning to the nuclear periphery can occur without any directed, active movement.

Figure 5 with 1 supplement see all
Repositioning from the nucleoplasm to the NPC.

(A) Simulated repositioning. Simulated paths, using either the fractional Brownian simulation or the simulation+zip code, were initiated at random positions within 2 µm diameter nucleus and followed for 20 min (200 ms resolution). Colocalization with the periphery (i.e., ≤150 nm from the edge) was scored for each simulation at each time and smoothed by averaging over 10 s windows. For each time point, three replicates of 33 paths were scored to generate an average (points) ± SEM (error bars). Blue, hatched line: peripheral localization expected for a random distribution. (B) Schematic for repositioning to the nuclear periphery upon release from α-factor arrest. (C) Peripheral localization (% of cells ± SEM) of URA3 or URA3:GRS1 over time after removing α-factor. (D) Schematic for optogenetic light-induced repositioning to the nuclear periphery. (E) Peripheral of URA3:LexABS in strains expressing either LexA-GCN4, LexA-CRY2+mutant PDGCN4-CIB1, or LexA-CRY2+wild-type PDGCN4 at the indicated times after illumination with 488 nm light. (F–I) Summary plots of velocity (F), arrival time (G), and angular deviation from an ideal path (I) from each cell before initial colocalization with nuclear periphery. White circles are the mean values, and error bars represent the standard deviation. For (FI), simulated paths were initiated at random positions within a 1 µm diameter sphere in the center of the 2 µm diameter nucleus and followed for 5 min. Paths that did not make contact with the nuclear periphery were excluded.

Figure 5—source data 1

Comma-separated tables of simulated paths and tracking data used for Figure 5.

https://cdn.elifesciences.org/articles/66238/elife-66238-fig5-data1-v2.zip

To compare these simulations with experimental results, we applied live-cell tracking during repositioning from the nucleoplasm to the periphery. One challenge with such experiments is that the time required for genes to reposition when cells are shifted from uninducing to inducing conditions is gene-specific and can be quite slow (e.g., t1/2 ~ 30min; Brickner et al., 2012, Brickner et al., 2007; Randise-Hinchliff et al., 2016). This suggests that the rate-limiting step for repositioning often reflects the regulation of TFs that mediate repositioning, rather than the rate-limiting step for movement to the periphery (Randise-Hinchliff et al., 2016). To overcome this complication, we developed two approaches to maximize the rate of repositioning from the nucleoplasm to the nuclear periphery. First, we arrested cells bearing URA3:GRS1-LacO with α-factor mating pheromone, which disrupts peripheral localization by inhibiting Cdk, which phosphorylates Nup1 and is required for peripheral localization of URA3:GRS1 (Brickner and Brickner, 2010). Upon release from α-factor arrest, URA3:GRS1 repositioned to the nuclear periphery within ~15 min (Figure 5C).

Tethering of a 27 amino acid ‘positioning domain’ from the Gcn4 TF (PDGCN4) near URA3 using the LexA DNA binding domain (DBD) is sufficient to position URA3:LexABS at the nuclear periphery (Brickner et al., 2019). Therefore, as a complementary approach, we used an optogenetic switch to recruit the PDGCN4 to URA3, resulting in targeting to the nuclear periphery. Cryptochrome 2 (CRY2) and cryptochrome interacting protein CIB1 from Arabidopsis thaliana undergo rapid dimerization when exposed to 488 nm light (Benedetti et al., 2018). In a strain having both the LacO array and the LexA binding site at URA3, CRY2-LexA DBD was co-expressed with CIB1-PDGCN4 to generate a light-induced peripheral localization system (Figure 5D; Brickner et al., 2019). LexA DBD-Gcn4 served as a positive control and a mutant CIB1-pdGCN4 that does not mediate interaction with the NPC served as a negative control (Brickner et al., 2019). Cells were arrested, synchronized in G1, and illuminated with 488 nm light for 1 s pulses every 10 s over 10 min. Illumination resulted in rapid, PDGCN4-dependent repositioning to the nuclear periphery within ~7.5 min (Figure 5E). Thus, both the biological and the optogenetic stimuli led to rapid repositioning to the nuclear periphery with kinetics comparable to the simulation.

Having established that these two approaches lead to rapid peripheral localization, we then used particle tracking to define the nature of the movement during this transition. URA3, URA3:GRS1, or URA3:LexABS were tracked for 5 min at 0.5 s resolution (600 frames) during repositioning. For each movie, the position and time of initial colocalization with the nuclear envelope was recorded (if observed). While peripheral colocalization of URA3:GRS1 and URA3:LexABS+CIB1-PDGCN4 represents – at least some of the time – interaction with the NPC, peripheral colocalization of the negative controls does not. Therefore, we expected that if directed movement brings genes to the nuclear periphery, the positive and negative controls should show differences in the step velocities, time of arrival, or directness of the path preceding arrival at the nuclear periphery. For comparison, we also determined each of these parameters for paths generated by the default simulation and the simulation+zip code, which include no directed movement. The mean velocities for the simulations and experimental controls were statistically indistinguishable, ranging from 0.163 ± 0.10 µm s−1 to 0.207 ± 0.13 µm s−1 (Figure 5F; n = 6077–9724 steps per strain), suggesting that the speed of movement was not increased during peripheral repositioning. We did not observe significantly more large steps in the experimental movies than in the negative control movies (Figure 5F). The mean arrival time prior to initial contact with the nuclear envelope was also similar between the simulations and the experimental controls, ranging from 105 ± 49 s to 133 ± 54 s (Figure 5G; n = 27–40 cells per strain), consistent with the predictions from the simulation. Finally, to assess whether any of the loci underwent processive, vectorial movement during translocation, we measured the radial deviation (θ) of each step from a direct path to the ultimate contact point at the nuclear envelope (Figure 5H). Random sub-diffusion should produce an average θ of ~ π/2 = 1.57 radians, while directed movement would produce an average of ~0. The simulations were close to random, and while the experimental loci appear slightly more directed than random, the positive and negative controls were indistinguishable (Figure 5I). Taken together, these results indicate that repositioning of chromatin from the nucleoplasm to the nuclear periphery is likely due to random sub-diffusion and collision with the NPC.

Dynamics of inter-chromosomal clustering

Genes that interact with the yeast NPC can exhibit inter-allelic or inter-genic clustering with co-regulated genes (Brickner et al., 2015; Brickner et al., 2019; Brickner et al., 2016; Brickner et al., 2012; Kim et al., 2019; Kim et al., 2017; Randise-Hinchliff et al., 2016). Loss of nuclear pore proteins or transcription factors that bind to DNA zip codes disrupts clustering (Brickner et al., 2012). Clustering has been observed using microscopy as a significant shortening of the distances between two loci in the population (Brickner et al., 2012) or using biochemical methods such as 3C/HiC (Kim et al., 2019; Kim et al., 2017). To explore the dynamics of inter-chromosomal clustering, we tracked the positions and inter-genic distances of well-characterized loci over time in live cells (Figure 6A). Both HIS4 and INO1 show inter-allelic clustering in diploids. Furthermore, inserting DNA zip codes at URA3 induces clustering with HIS4 (URA3:GCN4BS; Randise-Hinchliff et al., 2016) and INO1 (URA3:GRS1; Brickner et al., 2012). The URA3 gene, which does not undergo inter-chromosomal clustering (Brickner et al., 2012), and pairs of randomly selected simulated paths served as negative controls.

Dynamics of inter-chromosomal clustering.

(A) Confocal micrographs of diploid cells with two loci marked with LacO arrays, expressing LacI-GFP and Pho88-mCherry. Distance between LacO arrays was measured over 200 × 200 ms time points in 40–50 cells (B–D). (B) Distribution of mean distances between loci for each cell, with the median for each strain or condition indicated with a white dash. p-values<0.05 from the Kolmogorov–Smirnov test are shown. (C) Distribution of lifetimes during which d ≤ 0.55 µm. Dot = mean, error bars = SD. (D) The fraction of all time points that d ≤ 0.55 µm for each strain and media condition. For (B–D), mean distances, the lifetimes, and fraction of timepoints clustered were also determined for pairs of randomly selected simulated paths (with or without zip code; red).

Figure 6—source data 1

Comma-separated tables of simulated paths and tracking data used for Figures 6 and 7.

https://cdn.elifesciences.org/articles/66238/elife-66238-fig6-data1-v2.zip

Similar to snapshots of populations, the distribution of mean distances from each cell over 40 s (200 × 0.21 s) revealed clustering of HIS4 with itself as well as inter-genic clustering of HIS4 with URA3:GCN4BS upon histidine starvation (Figure 6B). Likewise, INO1 inter-allelic clustering was observed upon inositol starvation. Mutations in the upstream open reading frames that negatively regulate Gcn4 expression (uORFmt; Mueller et al., 1987; Mueller and Hinnebusch, 1986), led to high-level, constitutive inter-allelic clustering of HIS4 (Figure 6B; Randise-Hinchliff et al., 2016), while loss of Nup2 disrupted all clustering (Figure 6B). Finally, URA3, the simulated nucleoplasmic paths, and the simulated peripheral paths showed no clustering. Thus, NPC- and TF-dependent clustering can be observed over time, and the simulated interaction with the NPC is not sufficient to produce clustering.

We also assessed the stability of clustering over time. The lifetimes of clustering (i.e., time two loci remain within 550 nm) increased from ~5 s for unclustered loci to 20–40 s upon clustering (Figure 6C). Similarly, the fraction of the total time points in which clustering was observed reflected the strength of clustering (Figure 6D). Because inter-chromosomal clustering persists for relatively long periods of time, it likely reflects a physical interaction.

Finally, we asked if pairs of loci that exhibit clustering show coordinated movement. To quantify the degree of coordination, we determined both the correlation of step sizes by each locus and the average difference in step angles made by each locus over 40 s movies (200 × 0.21 s; Figure 7A,B). Uncorrelated movement would result in a correlation of step sizes ~ 0 and a mean difference of angles of ~ π/2 = 1.57 radians for each movie, while perfectly coordinated movement would show a correlation of step sizes ~ 1 and a mean difference of angles ~ 0 (Figure 7C). Plotting the correlation and the mean difference in angle for many movies against each other gives a scatter plot (Figure 7C–L). As expected, randomly selected pairs of paths generated by the simulation or the simulation+zip code showed no correlated movement (Figure 7D). Likewise, nucleoplasmic URA3 did not show correlated movement with itself (Figure 7—figure supplement 1) or with HIS4 (Figure 7J). However, strains that exhibit clustering (i.e., HIS4 vs HIS4, HIS4 vs URA3:Gcn4BS, or INO1 vs INO1) showed a different pattern (Figure 7E, G, and K). While the movement of loci that were >0.55 µm (orange dots) apart was uncorrelated, the subset of loci that were ≤0.55 µm (purple dots) showed correlated movement, both in terms of step size and angle. We quantified this behavior using the slope and R2 of the scatter plots (Figure 7). Unclustered control loci gave slopes ~ 0 and R2 ≤ 0.1 (e.g., Figure 7D,J). Under inducing conditions (but not under non-inducing conditions), clustered loci gave a slope closer to the ideal slope of −1.57 and R2 ≥ 0.65 (Figure 7E, G, and K). Furthermore, overexpression of Gcn4 (uORFmt) increased coordinated movement (Figure 7H), while loss of Nup2 disrupted coordinated movement (Figure 7F, I, and L). Thus, interaction with the NPC, while not sufficient to cause clustering, is required for clustering and coordinated movement. These results indicate that chromosomal loci separated by hundreds of nanometers physically influence each other at a distance.

Figure 7 with 1 supplement see all
Inter-chromosomal clustering leads to coordinated movement.

(A) Workflow for tracking and analyzing movement of LacO array pairs. For each step from a time series, step distance and step angle are measured (top) and the difference in angles computed (bottom). (B) Each time series produces two values: a Pearson correlation coefficient (cor(d)) for all step sizes and a mean difference in angles (∆θ). (C) Each cell produces a single point on the summary plot (orange). Gray lines highlight cor(d) = 0 and ∆θ = π/2. Uncorrelated movement of two loci would be expected to cluster near cor(d) = 0 and ∆θ = π/2, while perfectly correlated movement would result in cor(d) = 1 and ∆θ = 0. (D–L) Summary plots for correlation analysis of the indicated pairs of loci in the indicated strains grown in the media described in the headers. Cells in which the mean distance between the loci was >0.55 µm appear in orange, while cells in which the mean distance between the loci was ≤0.55 µm appear in purple. For each plot, the slope and R2 for a linear relationship between cor(d) and ∆θ are indicated. Forty to 50 cells were analyzed per strain and condition. Simulations are the 50 pairs of paths generated for Figure 6.

Discussion

Tracking yeast NPC-associated chromatin over time revealed a frequent exchange between the nucleoplasm and periphery (Figure 1), suggesting that the interaction with the NPC is continuously re-established and that the population averages reflect this dynamism, rather than distinct, stable sub-populations. In other words, localization to the nuclear periphery is less well described as tethering than as a change in the steady-state positioning through continuous binding and dissociation. As interaction with the NPC enhances transcription (Ahmed et al., 2010; Brickner et al., 2019; Brickner et al., 2016; Brickner et al., 2012; Capelson et al., 2010; Jacinto et al., 2015; Liang et al., 2013; Pascual-Garcia et al., 2014; Taddei et al., 2006), it is intriguing that the periodic and transient interaction with the NPC is reminiscent of the widespread phenomenon of transcriptional ‘bursting’ (Femino et al., 1998; Rodriguez and Larson, 2020). Transcriptional bursting leads to heterogeneity in the transcription between cells within a population (Zenklusen et al., 2008) and disrupting the interaction of the GAL1-10 promoter with the NPC leads to a decrease in the number of cells expressing these genes without affecting the amount of transcript produced at the site of transcription (Brickner et al., 2016). Perhaps interaction with the yeast NPC functions with other transcriptional regulators to stimulate transcriptional bursts. Exploring this connection will require assessing the dynamics of chromatin positioning and transcription simultaneously in live cells.

Chromatin undergoes anomalous sub-diffusive movement during interphase (Hajjoul et al., 2013; Marshall et al., 1997). The physical interaction between chromatin and the NPC, though transient, reduces chromatin sub-diffusion (Figure 2; Backlund et al., 2014; Cabal et al., 2006), independent of changes in transcription (Figures 2 and 3). Using the parameters derived from MSD, we developed computational simulations for yeast chromatin sub-diffusion in the nucleoplasm and at the nuclear periphery. The anticorrelation between successive steps of chromatin and can be modeled as FBM (a.k.a. overdamped fractional Langevin motion; Lucas et al., 2014). Sub-diffusion of yeast chromosomal loci is determined by the elastic response from the chromatin polymer and the viscous interaction between the polymer and the nucleoplasm. While we do not explicitly simulate the total chromatin polymer or other nuclear occupants, FBM captures their net effects, recapitulating the MSD behavior of a nucleoplasmic locus (Figure 4). However, the FBM model leads to exclusion near boundaries, leading to non-random positioning of loci, a phenomenon that is not consistent with experimental observations. This likely reflects the fact that, while the motion of a segment of chromatin can be modeled as an FBM particle, it is part of a polymer and is not an FBM particle. Our solution to this shortcoming of the FBM model, recalculating the path upon collision with the nuclear boundary (see detailed explanation in Materials and methods), produced localization patterns and MSD behaviors that are consistent with experimental observations. However, additional theoretical and experimental work will help clarify the biological and physical significance of this modification.

To simulate the interaction of chromatin with the NPC, we allowed loci in an area within 50 nm of the nuclear boundary to ‘bind’ to the nuclear periphery, assuming the mobility of the SPB. The width of this annulus is roughly equal to the height of the NPC nuclear basket (Vallotton et al., 2019), whose components are required for chromatin association with the NPC (Ahmed et al., 2010). We independently optimized the probability of binding and of remaining bound by comparing the positioning and MSD of simulated paths with that conferred by a DNA zip code. This simple modification of the simulation was able to reliably recreate the peripheral localization and constraint on chromatin sub-diffusion caused by interaction with the NPC (Figure 4). Thus, the work described here provides a straightforward and powerful theoretical framework for modeling the biophysical nature of gene positioning through association with any stable nuclear structure.

Repositioning of genes to the NPC during transcriptional activation occurs over a wide range of timescales, depending on the stimulus and gene (Randise-Hinchliff et al., 2016), making it difficult to test whether it involves super-diffusive or vectorial movement. Our simulated trajectories offer an important insight; starting from random positions within the center of the yeast nucleus, the population shifted from a random distribution to a peripheral distribution within ~2 min by random sub-diffusion (Figure 5G). This timescale is comparable to the experimental models for peripheral repositioning (Figure 5), arguing that active mechanism(s) are unnecessary to explain the observed rate of repositioning. More importantly, experimental analysis of the speed and vector of individual steps preceding contact with the nuclear envelope showed non-vectorial sub-diffusive movement that was indistinguishable from that captured by the simulation (Figure 5). Furthermore, there was also no difference between experimental cells and negative control cells for these components. These results indicate that zip code-dependent gene localization results from random sub-diffusive chromatin movement, collision with the NPC, leading to dynamic binding. The recently discovered role for actin and Myo3 in localization of INO1 at the nuclear periphery (Wang et al., 2020), raises an important question: how do these factors impact peripheral repositioning through a sub-diffusive mechanism? Our results suggest that loss of Myo3 delays arrival of some loci at the nuclear periphery but does not disrupt localization once it is established (Figure 5—figure supplement 1). Perhaps, like actin (Kapoor et al., 2013), Myo3 impacts the function of chromatin remodeling complexes or histone-modifying enzymes, which regulate binding of transcription factors to DNA zip codes (Randise-Hinchliff et al., 2016). Alternatively, perhaps actin/Myo3 act at the NPC to facilitate capture. A better biochemical and biophysical understanding of these processes will illuminate such possible roles.

Interaction with nuclear pore proteins plays a conserved role in promoting transcription. However, while interaction of yeast genes with nuclear pore proteins occurs at the nuclear periphery in association with the NPC, many genes in mammalian cells and Drosphila interact with soluble nuclear pore proteins in the nucleoplasm (Capelson et al., 2010; Liang et al., 2013; Light et al., 2013). Sub-diffusion for mammalian chromatin (which has been suggested to be less mobile than in yeast; Chubb et al., 2002) in a nucleus with a radius of 5 µm would make it impossible (on a biologically meaningful timescale) for loci in the center of the nucleus to reach the periphery. In larger nuclei, recruitment of nuclear pore proteins to sites of action, regardless of their position, likely overcomes this obstacle.

Inter-chromosomal clustering is a widespread phenomenon in eukaryotes (Bantignies et al., 2011; Brickner et al., 2012; Brown et al., 2008b; Cook and Marenduzzo, 2018; Eskiw et al., 2010; Gehlen et al., 2012; Haeusler et al., 2008; Noma et al., 2006; Ramos et al., 2006; Taddei et al., 2009; Thompson et al., 2003; Xu and Cook, 2008). Genes that interact with the NPC through shared transcription factors exhibit inter-chromosomal clustering (Brickner et al., 2015; Brickner et al., 2016; Brickner et al., 2012; Kim et al., 2019; Kim et al., 2017; Randise-Hinchliff et al., 2016). Such clustering requires transcription factor(s) and nuclear pore proteins (Brickner et al., 2012; Chowdhary et al., 2017; Kim et al., 2019) but is also mechanistically distinguishable from interaction with the NPC (Brickner et al., 2016). Clustering persisted for 20–40 s (Figure 6) and led to correlated movement between pairs of loci that were within 550 nm (Figure 7). Importantly, independently correlating step size and step angle is sensitive to correlations among pairs of loci in a subset of the cells in the population. Such correlated movement, averaged over the entire population, would be more difficult to appreciate. This may explain why previous work tracking movement of pairs of active GAL1-10 alleles in yeast found little correlation in aggregate (Backlund et al., 2014).

Pairs of paths generated by either the simulation or the simulation+zip code do not lead to inter-chromosomal clustering, consistent with the observation that genes that interact with the NPC through different transcription factors do not exhibit clustering (Brickner et al., 2012). Therefore, while clustering requires transcription factors and interaction with the NPC, it represents a distinct physical interaction. Surprisingly, correlated movement was observed between loci separated by hundreds of nanometers, suggesting that it reflects a large molecular complex, or more likely, an environment. Physical interactions that lead to phase separation could encompass groups of genes to create a (perhaps transient) nuclear sub-compartment (Hult et al., 2017). This is reminiscent of superenhancers, which exist within phase-separated droplets (Hnisz et al., 2017; Sabari et al., 2018) and are strongly associated with nuclear pore proteins (Ibarra et al., 2016). It is possible that phase separation is facilitated by multivalent interactions between natively unstructured nuclear pore proteins, which are capable of forming phase-separated droplets in vitro (Frey et al., 2006; Frey and Görlich, 2007). Such conditional phase separation would be regulated and specified by transcription factors, and potentially other transcriptional complexes such as mediator or RNA polymerase II, to functionally compartmentalize the nucleus.

Materials and methods

Chemicals, reagents, and media

Request a detailed protocol

All chemicals were purchased from Sigma-Aldrich unless otherwise noted. Media components were from Sunrise Science Products, and α-factor was from Zymo Research. Yeast and bacteria media and transformations were as described (Burke et al., 2000; Wood et al., 1983).

Yeast strains

Request a detailed protocol

All yeast strains were derived from W303 (ade2-1 ura3-1 trp1-1 his3-11,15 leu2-3,112 can1-100) strains CRY1 (MATa) or CRY2 (MATα; Brickner and Fuller, 1997) and are listed in Supplementary file 2. The μNS cytoplasmic particle was expressed from plasmid pAG415GPD-EGFP-µNS (Munder et al., 2016).

Yeast culturing

Request a detailed protocol

Yeast cultures were inoculated from a YPD agar plate into synthetic dextrose complete (SDC) or drop out media (Burke et al., 2000) and rotated at 30 °C for ≥18 hr, diluting periodically to maintain the cultures at OD600 <0.8. Before MSD tracking microscopy, cultures were diluted to ≤0.1 OD/mL and treated with 2 ng/mL of nocodazole for 2 hr. Cultures were then pelleted, washed, and resuspended in SDC to release from M-phase into G1-phase for 10 min. Cells were then pelleted again, concentrated, applied to a microscope slide, and covered with a glass coverslip for imaging.

For experiments involving mating pheromone, 100 μM α-factor was added to the cultures following release from nocodazole arrest for ≥30 min. To release from pheromone arrest, cells were pelleted, washed into SDC, and mounted for microscopy.

Microscopy

Request a detailed protocol

Confocal microscopy was performed in the Northwestern University Biological Imaging Facility. Tracking microscopy was performed on a Leica Spinning Disk Confocal Microscope (Leica DMI6000 inverted microscope equipped with Yokogawa CSU-X1 spinning disk and Photometrics Evolve Delta512 camera), and static localization experiments (Figures 1B, D, 3, and 5C, E, Figure 5—figure supplement 1A-C) were performed on a Leica TCS SP8 Confocal Microscope.

For both single-locus/particle MSD and multiple loci tracking, the same acquisition protocol was used. GFP-LacI/LacO spots in G1-phase cells were imaged every 210 ms for 200 frames in a single z-plane with a minimum of 40 biological replicates per experimental condition. Cells that did not remain immobilized or whose loci underwent no movement were excluded from our analysis. For peripheral relocalization dynamics experiments (Figure 5F, G, and I), LacI-GFP/LacO128 arrays in G1-phase cells were imaged every 500 ms for 600 frames and Pho88-mCherry was imaged every 10 s to determine the position with respect to the nuclear periphery (D'Urso et al., 2016; Egecioglu et al., 2014).

Static localization experiments (Figures 1B, D, 3, and 5C, E, Figure 5—figure supplement 1A-C) were acquired as z-stacks encompassing the full yeast cell, and 30–50 cells were scored per biological replicate as described (Brickner et al., 2010; Brickner and Walter, 2004; Egecioglu et al., 2014). Each strain and condition included at least three biological replicates. To activate light-induced recruitment, cells imaged in Figure 4C were scanned with the 488 nm laser every 10 s.

Particle tracking and data analysis

Request a detailed protocol

Tracking was performed using the ImageJ plugin MTrackJ. To accommodate clustering experiments (which typically have two or more fluorescent particles per nucleus), MTrackJ’s region of tracking tool was utilized to ensure the signals from individual loci were tracked separately. Tracking data was output as a comma-separated text file and analyzed with R scripts available via GitHub. (https://github.com/MCnu/R_sim_scripts). Repositioning analysis in Figure 4 utilized a lookup table that contained the frame and the position in which the signal from LacI-GFP/LacO128 array of a given cell first colocalized with the Pho88-mCherry nuclear membrane signal. Tracking data for Figures 2, 3, 5, 6, and 7 and simulated paths for Figures 4, 5, and 7 are presented as Source data files associated with each figure.

FBM simulations

Request a detailed protocol

We model the dynamics of chromosomal loci in the cellular nucleus via a discrete-time random walk with continuously varying step sizes. This simulation is governed by FBM, which gives rise to anomalous diffusion of the locus. Anomalous diffusion is distinct from Brownian diffusion due to a non-linear MSD over time, with distinct behaviors for the super-diffusive (α > 1) vs. sub-diffusive (α < 1) regimes. Free fitting our MSD measurements for 23 different loci/conditions, we found an average α = 0.52 (not shown), matching that determined in previous work (Hajjoul et al., 2013). Therefore, for the simulations, we used α = 0.52. Following previous work (Lucas et al., 2014), we present fractional Langevin dynamics simplified by the assumption of overdamping (i.e., no inertial term) and no driving force. In FBM, the statistical noise is a stationary Gaussian process with a mean equal to zero and a nonzero anticorrelation between successive steps (Meyer et al., 1999). This property is exploited to allow random vector generation with a given correlation structure (Dietrich and Newsam, 1997). We draw values for each simulated dimension of movement to generate the entire time series for a trajectory. We re-scale the vectors to an appropriate magnitude for given time units equal to τ using a Γ parameter provided by non-linear regression on experimental MSD data (where MSD (τ) = Γ(τ0.52)). No additional complications in our computational model are required to reproduce experimental MSD (Figure 4—figure supplement 1A,B).

To properly simulate chromatin diffusion within the confines of the nucleus, we added an impassable boundary to serve as a nuclear membrane. Recent work on the behavior of FBM and the fractional Langevin equation in finite volumes of space showed that the presence of boundaries and the handling of those boundary conditions can affect the long-timescale distribution close to the edges of the domain (Guggenberger et al., 2019; Vojta et al., 2020; Vojta et al., 2019; Wada and Vojta, 2018). These studies agree with our findings that in the sub-diffusive regime, depletion occurs at the boundary (Figure 4—figure supplement 1C, D). This depletion at the periphery is rationalized by the fact that because successive steps are anticorrelated, a step that would take the particle over the boundary is likely to be followed by one which would take it away from it. Such depletion is not observed in experimental distributions of control and non-control specimens. It is possible that the physicochemical landscape of the periphery or the region near the periphery involves many interactions which have the effect of attracting the chromatin locus to the periphery, but such effects are not evident in the aforementioned studies (which do not consider transient binding interactions with a hard wall). Because our particle is actually a segment of a much larger polymer, we instead decided to regenerate the underlying noise time series whenever the trajectory collides with the periphery to negate the effects of prior movement. This adaptation succeeded in creating a uniform distribution of positions across the nucleus. However, we acknowledge that our theoretical particle no longer satisfies the fluctuation dissipation theorem inherent to all Brownian motion, including FBM. Additional investigation of the behavior of chromatin at the boundary in silica and in vivo will help clarify the validity of this modification.

Binding of chromatin to NPCs was modeled using a simple two-state Markov model wherein a locus within the peripheral region (an annulus extending 50 nm from the nuclear boundary) can assume a bound state in the next step with a defined probability. Particles bound to the NPC remain bound at a second defined probability for every step until it becomes unbound. A particle bound to the NPC is assumed to be interacting strongly with an NPC, their motion is inhibited, but not entirely arrested. We therefore scaled the step sizes of particles in the bound state with Γ and α parameters derived from non-linear regression of the MSD for the SPB (Figure 2). In this way, we simulate the effective ‘pausing’ of chromatin motion due to NPC interaction.

Source code

Request a detailed protocol

Our simulation data and source code are openly available. Our simulations were implemented in Python, with routine algorithms like random noise generation or the fast Fourier transform from the NumPy library (Harris et al., 2020), and all other codes implemented using custom libraries are available on GitHub (https://github.com/MCnu/YGRW). Analytical pipeline of two-dimensional tracking data is also available. All analyses were implemented in R, and scripts are available on GitHub (https://github.com/MCnu/R_sim_scripts).

Data availability

All tracking data will be included as Source Data. All Scripts are publicly available from Github https://github.com/MCnu/R_sim_scripts copy archived at https://archive.softwareheritage.org/swh:1:rev:6440995193e1245c44d2c9a9e0b21b161d98e788.

References

  1. Book
    1. Burke D
    2. Dawson D
    3. Stearns T
    (2000)
    Methods in Yeast Genetics
    Cold Spring Harbor Laboratory.
  2. Conference
    1. Eskiw CH
    2. Cope NF
    3. Clay I
    4. Schoenfelder S
    5. Nagano T
    6. Fraser P
    (2010)
    Transcription factories and nuclear organization of the genome
    Cold Spring Harbor Symposia on Quantitative Biology.
  3. Conference
    1. Gasser SM
    2. Hediger F
    3. Taddei A
    4. Neumann FR
    5. Gartenberg MR
    (2004)
    The function of telomere clustering in yeast: the Circe effect
    Cold Spring Harbor Symposia on Quantitative Biology. pp. 327–338.
  4. Book
    1. Wood EJ
    2. Maniatis T
    3. Fritsch EF
    4. Sambrook J
    (1983)
    Molecular Cloning: A Laboratory Manual
    New York: Cold Spring Harbor Laboratory.

Decision letter

  1. Megan C King
    Reviewing Editor; Yale School of Medicine, United States
  2. Kevin Struhl
    Senior Editor; Harvard Medical School, United States
  3. Kerry Bloom
    Reviewer; The University of North Carolina at Chapel Hill, United States

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

Acceptance summary:

This manuscript combines computational predictions from the physics of motion and experimental live imaging studies to investigate the process of gene re-localization to the nuclear pore complex (NPC) in yeast. The study reveals gene re-positioning to the NPC can be explained by sub-diffusive Brownian motion of loci followed by capture at the NPC rather than through a directed mechanism. The authors also provide new insights into the dynamics underlying the clustering of co-regulated genes. The data and analysis are well-presented and support the authors' conclusions.

Decision letter after peer review:

Thank you for submitting your article "Random sub-diffusion and capture of genes by the nuclear pore reduces dynamics and coordinates interchromosomal movement" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Kevin Struhl as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Kerry Bloom (Reviewer #3).

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

Essential revisions:

1. The reviewers felt it important to address more explicitly the interplay between the position of the locus within the nucleus and its dynamics. For example, although Figure 2 and 3 show that in activating conditions (-inositol or – histidine), the MSD measurements for INO1 or HIS4 or GCN4 decline (suggesting that nuclear pore contact/tethering leads to reduced movement) this is not further demonstrated more directly by correlating gene position and MSD. Can the authors assess whether loci at the nuclear periphery actually move less than those in the nuclear interior? Although nup2delta mutants in Figure 3 provide further evidence for this, it would be a nice addition to the paper if the relationship between position and movement/MSD were shown more explicitly. This information could likely be gleaned from existing data. An important consideration in this context relates to the resolution of the light microscope and the ability to perceive whether a locus is "at" the periphery. Based on their simulations, can the authors address how this does or does not influence the precision or accuracy of the analysis? The authors decide on 50 nm as their band of "on" and "off" steps in the simulations based on the dimensions of the basket of the NPC, but how are considerations of the microscope resolution accounted for when comparing experiment and theory?

2. On the surface the presented model is somewhat at odds with the recently published report by Wang et al. (Dev Cell 2020, PMID: 31902656), which concludes that peripheral targeting of INO1 depends on a myosin motor and nuclear actin and associated machinery. This should be more directly addressed in the manuscript. Best would be the addition of data in which the authors test the effect of some of these factors in their system and with their measurement. Expansion on this point highlights two other issues for the authors to consider: a) the importance of what can and cannot be gleaned from different approaches (MSD measurements versus population analysis of "on" or "off" the periphery); and b) how a boost in diffusive motion OR directed motion could both influence the ultimate association of a locus with the nuclear periphery. Although the authors present clear evidence against a role for directed motion, the authors also argue against an increase in diffusive motion based on the MSD under conditions that lead to peripheral interactions. However, there does seem to be a trend towards a boost in mobility in an undirected fashion that could still contribute to the kinetics of these processes. Given evidence that transcriptional activation can increase locus mobility from yeast to human cells, it would also extend the study to explore (in simulations) how much of an effect on the kinetics of movement to the periphery would be expected from different extents of such a boost. In other words, could a small (perhaps even insignificant boost based on the errors of the measurements) have a meaningful effect? If not, how much of a boost would be needed? Greater exploration and discussion of these possibilities would be informative for the field.

3. The reviewers questioned whether the data on gene clustering added value to the manuscript, as this aspect of the work was found to be under-developed. The model as applied does not explain the inter-chromosomal clustering (Figure 6). Although informative, how then does binding the NPC dictate clustering? This line of investigation would be made much richer if greater use of modeling could reveal additional parameters that can recapitulate (at least to some extent) the Nup-dependent clustering of genes/alleles. Alternatively, the authors could perhaps further speculate on or test the potential additional forces that drive such clustering. Several studies on clustering chromatin regions indicate these are local interactions (e.g. condensin/transcription factors, Cera et al., 10.1128/mSphere.00063-19 ; Hult et al., doi: 10.1093/nar/gkx741.).

4. The reviewers suggest that the authors expand their discussion and citation of the value for α. Studies from Mikael P. Backlund, Ryan Joyner, Karsten Weis, and W. E. Moerner https://doi.org/10.1091/mbc.e14-06-1127 report the value for α to be 0.6-0.75 in yeast (the same system) and from Weber and Theriot (Weber SC, Spakowitz AJ, Theriot JA (2010) Bacterial chromosomal loci move subdiffusively through a viscoelastic cytoplasm.) to be 0.39 in bacterial systems. This is consistent with the notion that α may realize distinct values at different time scales; the value that they find works well to describe their observations is reasonable but may not always apply to all conditions.

5. Prior work from the Zimmer group argues that there is an effect of the chromosome length in which a locus resides given the impact of the centromeric and telomeric tethers. Do the authors see evidence of this (or in other words, are the loci investigated here on larger chromosomes?). Did the authors consider/explore including such tethers in their simulations?

6. There were several suggestions for greater precision in the language used in the manuscript, including: a) The authors state that zip codes are necessary and sufficient to "cause" peripheral localization, which implies that zip codes drive an active transport mechanism. As the mechanism the authors describe is a random sub-diffusive motion with the zip codes conferring increased dwell time of given loci at the nuclear envelope, stating that zip codes stabilize what would otherwise be very transient interactions would be more appropriate rather than suggesting they direct the localization; b) The reviewers suggest that the authors take greater care with the terms "passive" (p. 14 bottom) and "random". As shown in the Theriot work and many others, these processes are ATP dependent. Motion is severely curtailed upon ATP depletion. It is the case that the motion is random, that does not mean it is passive. In the Introduction the statement that repositioning does not require an active mechanism should also be qualified; active processes contribute to the kinetics and extent of random motion as evidenced by the decrease in MSD in cells depleted for ATP. The authors should clarify that active motion need not equate to a super-diffusive, vectorial mechanism.Reviewer #1:

This manuscript explores the mechanisms that govern the movement of gene loci between the nucleoplasm and the nuclear periphery as well as the clustering of gene loci that share common transcriptional activators. The study provides evidence that association of genes with the nuclear pore complex (NPC) or nuclear periphery occurs through a stochastic, constrained diffusion followed by capture that depresses further dynamics of the locus. Clustering of genes under control of a common transcription factor appears to be a distinct process, likely mediated by physical interactions between clustered loci.The authors address the question of whether the behavior of such loci is ergodic – that is, does variation manifest uniformly across the population or are there distinct states in individual cells that contribute, concluding that there is uniform behavior across the population. The MSD analysis and simulations concur on this point and also argue against directed motion. Using a number of genetic tricks they uncover evidence for a constrained diffusion followed by capture model that is unaffected by transcriptional activity. With regards to gene clustering, the investigators argue that such clustering events, although often occurring coincident with recruitment to the nuclear periphery, instead reflects a distinct, likely physical, mechanism.

Overall this is a careful, mechanistically dissected study and the conclusions are well supported by the data. The combination of experimental approaches and simulations is a strength. There are an important inference from these observations, namely that association with the NPC, rather than driving a specific state, instead can contribute to the constraint of a locus in the (larger volume of) nuclear periphery more generally. Another key question in the field, particularly given the broad observation that chromatin dynamics are influenced by the depletion of ATP, has been whether there is a role for actively-driven, directed motion of chromatin to the nuclear periphery and/or NPC – on this point the data is unequivocal, at least for any type of directed motion. The molecular dissection of the contributions to localization and MSD in response to stimuli is impressive and highlights what can be determined in budding yeast relative to other models. In particular, the kinetics of how long it takes for a locus to diffuse and be captured takes is revealing and is supported by experimental observations using the optogenetic approach.

I have some points for the authors to consider further, listed below.

1. The resolution of the light microscope must enter into the ability to perceive whether a locus is "at" the periphery. Based on the simulations, can the authors address how this does or does not enter into the analysis? The authors decide on 50 nm as their band of "on" and "off" steps in the simulations based on the dimensions of the basket of the NPC, but how are considerations of the microscope resolution accounted for when comparing experiment and theory?

2. In Figure 2C what does the line represent a theoretical relationship or the best fit of the data?

3. The authors comment on page 19 about how their observations could be extended to considerations of chromatin movement in the much larger volume of the mammalian nucleus, but putting numbers on this based on their simulation would be very helpful and would extend the study.

4. Prior work from the Zimmer group argues that there is an effect of the chromosome length in which a locus resides given the impact of the centromeric and telomeric tethers. Do the authors see evidence of this (or in other words, are the loci investigated here on larger chromosomes?). Did the authors consider/explore including such tethers in their simulations?

5. In addition to the clear data against a role for directed motion, the authors also argue against an increase in diffusive motion based on the MSD under conditions that lead to peripheral interactions. However, there does seem to be a trend towards a boost in mobility in an undirected fashion that could still contribute to the kinetics of these processes. Given evidence that transcriptional activation can increase locus mobility from yeast to human cells, it would also extend the study to explore (in simulations) how much of an effect on the kinetics of movement to the periphery would be expected from different extents of such a boost. In other words, could a small (perhaps even insignificant boost based on the errors of the measurements) have a meaningful effect? If not, how much of a boost would be needed?

Reviewer #2:

In the manuscript by Sumner et al., the authors combine live imaging and computational simulation to investigate the process of gene re-localization to the nuclear pore complex (NPC) in yeast. The authors characterize movement dynamics of genes, previously shown to re-position to the NPC from the nuclear interior upon activation, using the lacO tagging system and mean square displacement (MSD) analysis. They find that genes exhibit transient interactions with the NPC in both induced and uninduced conditions, but that such interactions become more lasting upon activation. Importantly, gene sub-diffusion appears to be reduced by interactions with the NPC, in a way that is not explained by just transcriptional activation.

The authors proceed to successfully model such gene movement and re-positioning, and conclude that gene targeting to the NPC can be explained by sub-diffusive Brownian motion of loci followed by capture at the NPC, as opposed to an active directed mechanism of re-localization. This is an interesting and important conclusion, particularly in light of recently published evidence that nuclear actin and myosin are involved in gene movement to the NPC. The authors' approach also suggests that unlike reported movement of damaged loci, which has been shown to occur in a directed nuclear actin-dependent manner, activated genes re-localize primarily through random movement and collision-capture at the NPC.

On the other hand, the authors' modeling was not found to recapitulate the NPC-mediated inter-chromosomal clustering of genes (previously reported for genes or alleles targeted by the same transcription factor). The authors conclude that gene clustering must involve additional forces not accounted for in their modeling.

Overall, the experiments and analysis appear very rigorous, and the conclusions are impactful, introducing new insight into how active genes re-localize within nuclear space. The presented model will be important for future consideration of other contexts of gene and chromosome movement.

Comments for the authors:

1. Figure 2 and 3 show that in activating conditions (-inositol or – histidine), the MSD measurements for INO1 or HIS4 or GCN4:BS decline, suggesting that nuclear pore contact/tethering leads to reduced movement. This is however not demonstrated more directly though correlating gene position of the gene and MSD. Can the authors assess whether loci at the nuclear periphery actually move less than those in the nuclear interior? Although nup2delta mutants in Figure 3 provide further evidence for this, it would be a nice addition to the paper if the relationship between position and movement/MSD were shown more explicitly. This information may already be in the existing live imaging videos.

2. Since the presented model is somewhat at odds with the recently published report by Wang et al. (Dev Cell 2020, PMID: 31902656) that peripheral targeting of INO1 depends on a myosin motor and nuclear actin and associated machinery, can the authors test the effect of some of these factors in their system and with their measurements? It would be informative for the field to know whether perhaps it is the reduced movement of such genes at the NPC or the capture by the NPC that is primarily affected by the myosin/actin machinery.

3. The title states that "…capture of genes by the nuclear pore.…coordinates interchromosomal movement", yet sub-diffusion and NPC interactions do not seem to explain inter-chromosomal clustering in the presented modeling (Figure 6). Although this is likely not trivial, can the authors attempt to add a modeling parameter that can at least partially reflect the Nup-dependent clustering of genes/alleles? This is certainly not a requirement for publication in my opinion, but it would make this portion of the paper more developed (since currently it reads somewhat underdeveloped). Alternatively, the authors could perhaps further speculate on or test the potential additional forces that drive such clustering.

Reviewer #3:

This manuscript follows up work from the investigators' laboratory on the interaction of gene loci with the nuclear envelop. In this manuscript they address the mechanism that drives the interaction and find that it can be explained by sub-diffusive motion, that has been well-characterized at this point for chromosomes in living cells. From the polymer physics perspective, this is entirely consistent with the statistical mechanics of these systems. The work does not preclude findings in the literature that point to active, vectorial processes in specific cases.

The authors state that zip codes are necessary and sufficient to "cause" peripheral localization. This implies the zip codes are part of the transport mechanism. My understanding is that mechanism is a random sub-diffusive motion, and the zip codes increase the dwell time of a given loci to the nuclear envelop. The zip codes don't cause the localization, they stabilize what would otherwise be very transient interactions.

I would caution the authors in citing one value for α. Studies from Mikael P. Backlund, Ryan Joyner, Karsten Weis, and W. E. Moerner https://doi.org/10.1091/mbc.e14-06-1127 report the value for α to be 0.6-0.75 in yeast (the same system) and from Weber and Theriot (Weber SC, Spakowitz AJ, Theriot JA (2010) Bacterial chromosomal loci move subdiffusively through a viscoelastic cytoplasm.) to be 0.39 in bacterial systems.

The authors describe a fractional Brownian model that accounts for the experimental findings. This is not particularly surprising based on several papers in the literature that recapitulate the motion of loci within a chromosome (Verdaasdonk et al., Mol. Cell 2013) and nuclear motion (Arbona et al., Genome Biology, 2017). One needs to be cautious about stating these are "passive" processes (p. 14 bottom). As shown in the Theriot work and many others, these processes are ATP dependent. Motion is severely curtailed upon ATP depletion. It is the case that the motion is random, that does not mean it is passive.

The last figure on clustering raises questions outside the scope of this paper. The authors state that binding the nuclear pore is required for clustering, but it is unclear how binding the pore dictates clustering. Several studies on clustering chromatin regions indicate these are local interactions (e.g. condensin/transcription factors, Cera et al., 10.1128/mSphere.00063-19 ; Hult et al., doi: 10.1093/nar/gkx741.).

I found the data to be very well done and support the authors claims. The suggestions pertain to clarifying several statements and distinguishing between passive processes from random ATP dependent processes.

https://doi.org/10.7554/eLife.66238.sa1

Author response

Essential revisions:

1. The reviewers felt it important to address more explicitly the interplay between the position of the locus within the nucleus and its dynamics. For example, although Figure 2 and 3 show that in activating conditions (-inositol or – histidine), the MSD measurements for INO1 or HIS4 or GCN4 decline (suggesting that nuclear pore contact/tethering leads to reduced movement) this is not further demonstrated more directly by correlating gene position and MSD. Can the authors assess whether loci at the nuclear periphery actually move less than those in the nuclear interior? Although nup2delta mutants in Figure 3 provide further evidence for this, it would be a nice addition to the paper if the relationship between position and movement/MSD were shown more explicitly. This information could likely be gleaned from existing data. An important consideration in this context relates to the resolution of the light microscope and the ability to perceive whether a locus is "at" the periphery. Based on their simulations, can the authors address how this does or does not influence the precision or accuracy of the analysis? The authors decide on 50 nm as their band of "on" and "off" steps in the simulations based on the dimensions of the basket of the NPC, but how are considerations of the microscope resolution accounted for when comparing experiment and theory?

This is an excellent suggestion. First, to clarify our methods, based on our microscopy experiments, we assume that the resolution of our images leads to scoring of a locus as colocalized with the nuclear envelope if it is within 150nm. For our simulations, we assume that the physical interaction with the NPC can only occur if the locus is within 50nm of the nuclear envelope but we score any locus that is within 150nm of the membrane was scored as peripheral. This was described in the manuscript, but we have made it more explicit in multiple locations in the Results section.

Second, we attempted to use our videos to analyze individual steps that occur at the nuclear periphery. This necessitated automating the tracking and gene localization scoring. We were successful in automating the localization scoring and the tracking for these experiments and we validated that the new method produces tracking data very similar to our original method (Figure 2 Supplement 1A). This new tracking method also recapitulated the MSD change upon activation of INO1 (Figure 2 Supplement 1B). We found that analyzing MSD of individual steps was challenging because individual step sizes are very small, making the measurements more error-prone and the differences less obvious than when examining multiple time intervals. Therefore, to address the reviewers’ question, we exploited the heterogeneity within the population (as demonstrated in Figure 1) to separately analyze the MSD (over multiple time intervals) from cells in which the locus was stably at the nuclear periphery and cells in which the locus was mostly in the nucleoplasm. When such analysis was performed for repressed INO1, we observed no difference in MSD between these two populations (Figure 2 Supplement 1C). However, when such analysis was performed for active INO1, the cells in which the locus was stably maintained at the nuclear periphery over most of the 40s video showed significantly lower MSD than cells in which the locus was mostly nucleoplasmic (Figure 2 Supplement 1D). This, combined with our genetic experiments showing that loss of Nup2 leads to an increase in MSD for active genes, supports our model that interaction with the NPC is responsible for the drop in sub-diffusion.

2. On the surface the presented model is somewhat at odds with the recently published report by Wang et al. (Dev Cell 2020, PMID: 31902656), which concludes that peripheral targeting of INO1 depends on a myosin motor and nuclear actin and associated machinery. This should be more directly addressed in the manuscript. Best would be the addition of data in which the authors test the effect of some of these factors in their system and with their measurement.

To understand the role of Myo3, we have performed both gene positioning experiments and chromatin mobility experiments in strains lacking Myo3. Our experiments clarify the nature of the defect of myo3 mutants in repositioning of INO1 to the nuclear periphery. We find that loss of Myo3 due to either deletion of the gene or degradation of Myo3 using the auxin-inducible degron (AID) slows the rate at which the population shifts to the nuclear periphery upon activation of INO1 (Figure 5 Supplement 1A and B). Importantly, this defect is specific to one (GRS1) of the two DNA zip codes that mediate repositioning of INO1 to the nuclear periphery, which we can observe by inserting INO1 with GRS1 but lacking GRS2 at the URA3 locus (URA3:INO1). When both zip codes (GRS1 and GRS2) are present at the endogenous INO1 gene, loss of Myo3 had no effect. GRS1-mediated repositioning of URA3:INO1 to the nuclear periphery occurs after 3-6h, instead of ~1h. Finally, once positioned at the nuclear periphery, URA3:INO1 localization was unaffected by degradation of Myo3-AID (Figure 5 Supplement 1C).

These results suggest that Myo3 is either impacting the GRS1-dependent movement of URA3:INO1 from the nucleoplasm to the nuclear periphery or that it is impacting other regulatory steps that are necessary for GRS1-mediated peripheral localization.

We also measured MSD of URA3:INO1 under repressing, activating (1h) and activating (overnight) conditions. Consistent with delayed repositioning, the drop in MSD observed in the wild type strain after 1h in activating conditions was not observed in the myo3∆ mutant, while both strains showed the drop after 24h (Figure 5 Supplement 1D).

Because we see no evidence of directed or super-diffusive movement during repositioning to the nuclear periphery (Figure 5), we did not subject the myo3∆ mutant to these analyses.

Expansion on this point highlights two other issues for the authors to consider: a) the importance of what can and cannot be gleaned from different approaches (MSD measurements versus population analysis of "on" or "off" the periphery); and b) how a boost in diffusive motion OR directed motion could both influence the ultimate association of a locus with the nuclear periphery. Although the authors present clear evidence against a role for directed motion, the authors also argue against an increase in diffusive motion based on the MSD under conditions that lead to peripheral interactions. However, there does seem to be a trend towards a boost in mobility in an undirected fashion that could still contribute to the kinetics of these processes. Given evidence that transcriptional activation can increase locus mobility from yeast to human cells, it would also extend the study to explore (in simulations) how much of an effect on the kinetics of movement to the periphery would be expected from different extents of such a boost. In other words, could a small (perhaps even insignificant boost based on the errors of the measurements) have a meaningful effect? If not, how much of a boost would be needed? Greater exploration and discussion of these possibilities would be informative for the field.

This is an interesting suggestion. We have tested if our observed MSD behavior is compatible with a small increase in chromatin dynamics using our simulations (Author response image 1). Increasing or decreasing the gamma term of the simulations by 10% gives significantly increased and decreased MSD, respectively. Our MSD data would be sensitive to changes in gamma of > 1%. While it is possible that such a change occurs, they may be more apparent at shorter time scales.

We also asked how a 10% increase or decrease in gamma would impact the time of arrival in our simulations. While arrival time is related to gamma, the differences in arrival time produced by increasing or decreasing gamma by 10% are not statistically significant. Therefore, we prefer to avoid including this complication in our model.

Author response image 1

3. The reviewers questioned whether the data on gene clustering added value to the manuscript, as this aspect of the work was found to be under-developed. The model as applied does not explain the inter-chromosomal clustering (Figure 6). Although informative, how then does binding the NPC dictate clustering? This line of investigation would be made much richer if greater use of modeling could reveal additional parameters that can recapitulate (at least to some extent) the Nup-dependent clustering of genes/alleles. Alternatively, the authors could perhaps further speculate on or test the potential additional forces that drive such clustering. Several studies on clustering chromatin regions indicate these are local interactions (e.g. condensin/transcription factors, Cera et al., 10.1128/mSphere.00063-19 ; Hult et al., doi: 10.1093/nar/gkx741.).

We agree that future work should explicitly model inter-chromosomal clustering. However, we respectfully disagree that the clustering experiments are under-developed. Using rigorous methods and molecular genetics, this work shows for the first time that NPC-dependent interchromosomal clustering is remarkably stable (Figure 6) and that it leads to coordinated chromosomal movement (Figure 7). This is a valuable contribution to the field and is an important step forward in our ability to model the physical forces responsible for clustering. Furthermore, the fact that our simulations do not produce clustering is itself an interesting and important result, highlighting the idea that interaction with the NPC is necessary but not sufficient to produce clustering and that clustering represents a distinct physical phenomenon.

We are currently working to incorporate the appropriate physical interactions to model this. However, this is a significant undertaking and, we believe, not essential to support the conclusions of the present work.

4. The reviewers suggest that the authors expand their discussion and citation of the value for α. Studies from Mikael P. Backlund, Ryan Joyner, Karsten Weis, and W. E. Moerner https://doi.org/10.1091/mbc.e14-06-1127 report the value for α to be 0.6-0.75 in yeast (the same system) and from Weber and Theriot (Weber SC, Spakowitz AJ, Theriot JA (2010) Bacterial chromosomal loci move subdiffusively through a viscoelastic cytoplasm.) to be 0.39 in bacterial systems. This is consistent with the notion that α may realize distinct values at different time scales; the value that they find works well to describe their observations is reasonable but may not always apply to all conditions.

This is an important caveat to any experiments with mean squared displacement. The value of a that we selected (0.52) was based on the excellent agreement between published work examining many loci in budding yeast (Hajjoul et al. 2013) and the mean a from our MSD experiments on nucleoplasmic loci (Figure 2 and Table S1; described in Methods section). However, we agree that a single a for all chromatin is likely an over-simplification and we have made it clear in the text that we are using it as an average exponent for nucleoplasmic yeast chromatin.

5. Prior work from the Zimmer group argues that there is an effect of the chromosome length in which a locus resides given the impact of the centromeric and telomeric tethers. Do the authors see evidence of this (or in other words, are the loci investigated here on larger chromosomes?). Did the authors consider/explore including such tethers in their simulations?

We explored the effect of tethers on experimental MSD behavior in Figure 2C. We observed an effect of nearby tethering on MSD. Previous work showed that strong effects of tethering are observed only for loci within 30kb of the tether (Avşaroğlu et al., 2014; Verdaasdonk et al., 2013). Because only 15% of the yeast genome is within 30kb of a telomere or a centromere, we did not include this in our simulation of the average nucleoplasmic locus.

6. There were several suggestions for greater precision in the language used in the manuscript, including: a) The authors state that zip codes are necessary and sufficient to "cause" peripheral localization, which implies that zip codes drive an active transport mechanism. As the mechanism the authors describe is a random sub-diffusive motion with the zip codes conferring increased dwell time of given loci at the nuclear envelope, stating that zip codes stabilize what would otherwise be very transient interactions would be more appropriate rather than suggesting they direct the localization; b) The reviewers suggest that the authors take greater care with the terms "passive" (p. 14 bottom) and "random". As shown in the Theriot work and many others, these processes are ATP dependent. Motion is severely curtailed upon ATP depletion. It is the case that the motion is random, that does not mean it is passive. In the Introduction the statement that repositioning does not require an active mechanism should also be qualified; active processes contribute to the kinetics and extent of random motion as evidenced by the decrease in MSD in cells depleted for ATP. The authors should clarify that active motion need not equate to a super-diffusive, vectorial mechanism.

We appreciate the reviewers’ advice on the language in the manuscript. While we do not agree that the word “cause” implies an active, vectorial transport mechanism, we agree that terms such as “target” could be interpreted as such and that the term “passive” is not exactly right. Therefore, we have modified the text to replace “passive” with “random” and the use of the word “targeting” has been replaced with either “repositioning”, “relocalization” or “interaction with the NPC.” Finally, we have taken care to avoid any confusion around the word “cause.”

https://doi.org/10.7554/eLife.66238.sa2

Article and author information

Author details

  1. Michael Chas Sumner

    Department of Molecular Biosciences, Northwestern University, Evanston, United States
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  2. Steven B Torrisi

    Department of Physics, Harvard University, Cambridge, United States
    Contribution
    Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4283-8077
  3. Donna G Brickner

    Department of Molecular Biosciences, Northwestern University, Evanston, United States
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Jason H Brickner

    Department of Molecular Biosciences, Northwestern University, Evanston, United States
    Contribution
    Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    j-brickner@northwestern.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8019-3743

Funding

National Institutes of Health (R01 GM118712)

  • Michael Chas Sumner
  • Donna G Brickner
  • Jason H Brickner

National Institutes of Health (R35 GM136419)

  • Michael Chas Sumner
  • Donna G Brickner
  • Jason H Brickner

National Cancer Institute (U54 CA193419)

  • Michael Chas Sumner
  • Jason H Brickner

National Institutes of Health (T32 GM008061)

  • Michael Chas Sumner

Department of Energy, Labor and Economic Growth (DE-FG02-97ER25308)

  • Steven B Torrisi

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

Acknowledgements

The authors would like to thank Dr. Rebecca Menssen and Dr. Madhav Mani for guidance on dynamics analysis; Dr. Reza Vafabakhsh, Dr. Laura Lackner, Dr. Alec Wang, Dr. John Marko, as well as current and former members of the Brickner laboratory for helpful discussions and comments on the manuscript; the Lackner Lab for sharing plasmids, reagents, and guidance with microscopy; the BIF core facility staff at Northwestern University; Dr. Brian Freeman for sharing yeast strains and protocols; Dr. Thomas Vojta for discussions on FBM; and Dr. Yaojun Zhang and Dr. Olga Dudko for access to their MATLAB code used in Lucas et al., 2014. MCS was supported by the Cellular and Molecular Basis of Disease NIH T32 GM008061 and SBT received support from the U.S. Department of Energy through the Computational Science Graduate Fellowship under grant number DE-FG02-97ER25308. This work was funded by NIH grants R01 GM118712 and R35 GM136419 and National Cancer Institute U54 CA193419 (JHB).

Senior Editor

  1. Kevin Struhl, Harvard Medical School, United States

Reviewing Editor

  1. Megan C King, Yale School of Medicine, United States

Reviewer

  1. Kerry Bloom, The University of North Carolina at Chapel Hill, United States

Publication history

  1. Received: January 5, 2021
  2. Accepted: May 17, 2021
  3. Accepted Manuscript published: May 18, 2021 (version 1)
  4. Version of Record published: June 11, 2021 (version 2)

Copyright

© 2021, Sumner 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.

Metrics

  • 757
    Page views
  • 108
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Cell Biology
    Lisa M Strong et al.
    Research Article Updated

    Autophagy is a cellular process that degrades cytoplasmic cargo by engulfing it in a double-membrane vesicle, known as the autophagosome, and delivering it to the lysosome. The ATG12–5–16L1 complex is responsible for conjugating members of the ubiquitin-like ATG8 protein family to phosphatidylethanolamine in the growing autophagosomal membrane, known as the phagophore. ATG12–5–16L1 is recruited to the phagophore by a subset of the phosphatidylinositol 3-phosphate-binding seven-bladedß -propeller WIPI proteins. We determined the crystal structure of WIPI2d in complex with the WIPI2 interacting region (W2IR) of ATG16L1 comprising residues 207–230 at 1.85 Å resolution. The structure shows that the ATG16L1 W2IR adopts an alpha helical conformation and binds in an electropositive and hydrophobic groove between WIPI2 ß-propeller blades 2 and 3. Mutation of residues at the interface reduces or blocks the recruitment of ATG12–5–16 L1 and the conjugation of the ATG8 protein LC3B to synthetic membranes. Interface mutants show a decrease in starvation-induced autophagy. Comparisons across the four human WIPIs suggest that WIPI1 and 2 belong to a W2IR-binding subclass responsible for localizing ATG12–5–16 L1 and driving ATG8 lipidation, whilst WIPI3 and 4 belong to a second W34IR-binding subclass responsible for localizing ATG2, and so directing lipid supply to the nascent phagophore. The structure provides a framework for understanding the regulatory node connecting two central events in autophagy initiation, the action of the autophagic PI 3-kinase complex on the one hand and ATG8 lipidation on the other.

    1. Cell Biology
    Laura Le Pelletier et al.
    Research Article

    Aging is associated with central fat redistribution and insulin resistance. To identify age-related adipose features, we evaluated the senescence and adipogenic potential of adipose-derived-stromal cells (ASCs) from abdominal subcutaneous fat obtained from healthy normal-weight young (<25y) or older women (>60y). Increased cell passages of young-donor ASCs (in vitro aging), resulted in senescence but not oxidative stress. ASC-derived adipocytes presented impaired adipogenesis but no early mitochondrial dysfunction. Conversely, aged-donor ASCs at early passages displayed oxidative stress and mild senescence. ASC-derived adipocytes exhibited oxidative stress, and early mitochondrial dysfunction but adipogenesis was preserved. In vitro aging of aged-donor ASCs resulted in further increased senescence, mitochondrial dysfunction, oxidative stress and severe adipocyte dysfunction. When in vitro aged young-donor ASCs were treated with metformin, no alteration was alleviated. Conversely, metformin treatment of aged-donor ASCs decreased oxidative stress and mitochondrial dysfunction resulting in decreased senescence. Metformin's prevention of oxidative stress and of the resulting senescence improved the cells' adipogenic capacity and insulin sensitivity. This effect was mediated by the activation of AMP-activated-protein-kinase as revealed by its specific inhibition and activation. Overall, aging ASC-derived adipocytes presented impaired adipogenesis and insulin sensitivity. Targeting stress-induced senescence of ASCs with metformin may improve age-related adipose tissue dysfunction.