Mechanisms governing target search and binding dynamics of hypoxia-inducible factors

  1. Yu Chen
  2. Claudia Cattoglio
  3. Gina M Dailey
  4. Qiulin Zhu
  5. Robert Tjian
  6. Xavier Darzacq

  • Curated by eLife

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    Evaluation Summary:

    The notion of transcription factors as composed of interchangeable parts where DNA binding activity can be separated from activation activity has been a dominant paradigm in molecular biology for decades. However, recent evidence suggests that activation domains may contribute to binding specificity as well. This paper describes the use of single-molecule imaging of endogenously tagged transcription factors to dissect how transcription factors move in the nucleus and how these dynamics are related to functional protein domains. These results will be of interest to the transcription and gene regulation fields, but the conclusions require additional experimental support.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

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Abstract

Transcription factors (TFs) are classically attributed a modular construction, containing well-structured sequence-specific DNA-binding domains (DBDs) paired with disordered activation domains (ADs) responsible for protein-protein interactions targeting co-factors or the core transcription initiation machinery. However, this simple division of labor model struggles to explain why TFs with identical DNA-binding sequence specificity determined in vitro exhibit distinct binding profiles in vivo. The family of hypoxia-inducible factors (HIFs) offer a stark example: aberrantly expressed in several cancer types, HIF-1α and HIF-2α subunit isoforms recognize the same DNA motif in vitro – the hypoxia response element (HRE) – but only share a subset of their target genes in vivo, while eliciting contrasting effects on cancer development and progression under certain circumstances. To probe the mechanisms mediating isoform-specific gene regulation, we used live-cell single particle tracking (SPT) to investigate HIF nuclear dynamics and how they change upon genetic perturbation or drug treatment. We found that HIF-α subunits and their dimerization partner HIF-1β exhibit distinct diffusion and binding characteristics that are exquisitely sensitive to concentration and subunit stoichiometry. Using domain-swap variants, mutations, and a HIF-2α specific inhibitor, we found that although the DBD and dimerization domains are important, another main determinant of chromatin binding and diffusion behavior is the AD-containing intrinsically disordered region (IDR). Using Cut&Run and RNA-seq as orthogonal genomic approaches, we also confirmed IDR-dependent binding and activation of a specific subset of HIF target genes. These findings reveal a previously unappreciated role of IDRs in regulating the TF search and binding process that contribute to functional target site selectivity on chromatin.

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  1. Version published to 10.7554/elife.75064 on eLife
  2. eLife

    Evaluation Summary:

    The notion of transcription factors as composed of interchangeable parts where DNA binding activity can be separated from activation activity has been a dominant paradigm in molecular biology for decades. However, recent evidence suggests that activation domains may contribute to binding specificity as well. This paper describes the use of single-molecule imaging of endogenously tagged transcription factors to dissect how transcription factors move in the nucleus and how these dynamics are related to functional protein domains. These results will be of interest to the transcription and gene regulation fields, but the conclusions require additional experimental support.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

  3. eLife

    Reviewer #1 (Public Review):

    In this manuscript by Chen et al., the authors use live-cell single-molecule imaging to dissect the role of DNA binding domains (DBD) and activation domains (AD) in transcription factor mobility in the nucleus. They focus on the family of Hypoxia-Inducible factors isoforms, which dimerize and bind chromatin to induce a transcriptional response. The main finding is that activation domains can be involved in DNA binding as indicated by careful observations of the diffusion/reaction kinetics of transcription factors in the nucleus. For example, different bound fractions of HIF-1beta and HIF-2alpha are observed in the presence of different binding partners and chimeras. The paradigm of interchangeable parts of transcription factors has been eroded over the years (the recent work of Naama Barkai comes to mind, cited herein), so the present observations are not unexpected per se. Yet, the measurements are rigorous and well-performed and have the important benefit of being in living cells. Enthusiasm is also dampened by the exclusive use of one technique and one analysis to reach conclusions.

  4. eLife

    Reviewer #2 (Public Review):

    The authors raise the very important question how different transcription factors with similar in vitro DNA sequence specificity are able to achieve distinct binding profiles associated with distinct functions. They use hypoxia inducible factors (HIF) as model system and combine live cell single-particle tracking with comprehensive genetic and chemical perturbations to study the mechanisms underlying isoform-specific gene regulation. Their main experimental readout is the distribution of diffusion coefficients of a molecular species, extracted from a population of single-particle trajectories. From this distribution, the authors extract the fractions of immobile and mobile molecules as well as the peak diffusion coefficient of the mobile fraction. They find that in addition to the structured DNA binding domain and the dimerization interface of HIF-1a and HIF-2a, the C-terminus of those factors, which includes intrinsically disordered regions and an activation domain, contributes to modulating the bound fraction of HIF-1b and the HIF-a isoforms. In particular, the C-terminus of HIF-2a mediates a higher bound fraction than the one of HIF-1a. This finding is important as it demonstrates that separating HIF into distinct domains that each have clearly defined functions is an oversimplification. Rather, a more holistic view seems suitable, in which all parts of HIF contribute to nuclear diffusion and binding.

    The conclusions drawn on the bound fractions and the nuclear dynamics of HIF isoforms are mostly backed up by data and proper controls. However, some controls are missing and some aspects of data analysis need to be clarified and extended. Moreover, the authors fail to answer their initial question, as the experimental readout does not contain information on the DNA sequences involved in the binding events.

    Experimental controls:
    For some imaging experiments, the authors use cell lines where endogenous HIF-1b or HIF-2a was fused to a N-terminal HaloTag by CRISPR/Cas editing. These cell lines are comprehensively controlled for proper functionality of the edited transcription factors, including expression levels, cellular localization and DNA binding. However, differential expression compared to unedited levels is not quantified and only Halo-HIF-2a is tested for functional gene transcription.
    Other experiments include overexpression of exogenously expressed factors. For those, the authors give statements such as "expressed from a relatively strong ... promoter" and "weakly expressed", but do not provide any control of the amount of overexpression. Quantifying the expression levels will be important, as some of the author's experiments demonstrate a strong dependency of results on expression level. Moreover, the authors do not provide any control for proper functionality of domain-swap mutants.
    The authors further state that they use a high illumination power of 1100 mW. Such high laser power might be detrimental to cells and the authors should control whether this laser power induces any artifacts.

    Data analysis:
    Distributions of diffusion coefficients greatly vary between individual cells (e.g. Fig. 2A and B, Fig. S3A and C, Fig. S4E). Unfortunately, the authors do not explain whether this variation is a real cell-to-cell variation, or rather reflects variation of their analysis method, potentially due to a low number of single particle tracks per cell. Moreover, the bound fraction of HIF-1b differs between two independent measurements including three biological replicates each (Fig. 5 C and F). This raises the concern that not enough data enter each biological replicate, or not enough replicates are considered.
    The authors compare the bound fractions among various mutants and experimental conditions. However, the peak diffusion is not, or only descriptively, evaluated. Thus, it is not clear whether the main effect of a mutation or chemical treatment is to change the bound fraction, or rather the diffusion coefficient of the mobile fraction.

    Conclusions:
    The authors provide data that highlight a potential role of the intrinsically disordered domain of HIF in modulating the bound fraction of these transcription factors. They further claim that the intrinsically disordered domains have a main contribution to this bound fraction. However, the autors do not quantify how this contribution relates to those of the DNA binding domain or the dimerisation interface. Changes in bound fraction estimated from the data in e.g. Fig. 3C, Fig. 4C, Fig. 5C and F rather hint to a dominant effect of dimerisation, followed by DNA binding and a smaller contribution of the intrinsically disordered domain. The authors should quantify the relative changes of the bound fraction for all mutants and experimental conditions, to clarify the importance of the contribution of the intrinsically disordered domain.
    The authors state that the intrinsically disordered domains of HIF determine their differential binding specificity to chromatin. However, the experiments provided do not allow for such a conclusion. In particular, measuring changes in the bound fractions is not sufficient. Such a conclusion requires a method that is able to inform about the DNA sequences involved in HIF binding, for example chromatin immunoprecipitation.

  5. eLife

    Reviewer #3 (Public Review):

    In this work, Chen et al. measured the DNA binding dynamics of HIF transcription factors using single-particle tracking. In particular, they examined the impact of heterodimerization between the alpha and beta subunits, the integrity of the DNA binding domain and the nature of the transactivation domain in DNA binding. As expected, they found that the stoichiometry between the heterodimerization partners directly impacts the bound fraction of the beta subunit which is devoid of a DNA binding domain. More interestingly, using domain swaps between HIF-1alpha and HIF2-alpha they found that the transactivation domain of the alpha subunit plays a major role in determining the bound fraction of the beta subunit (and thus the heterodimer). This work is important because it increases our understanding of how TF search the genome, beyond the traditional conception of the "addressing tag" provided solely by the DNA binding domain. This work is thus of interest to the broad audience of scientists studying gene regulation.

  6. eLife

    Author Response

    Reviewer #1 (Public Review):

    In this manuscript by Chen et al., the authors use live-cell single-molecule imaging to dissect the role of DNA binding domains (DBD) and activation domains (AD) in transcription factor mobility in the nucleus. They focus on the family of HypoxiaInducible factors isoforms, which dimerize and bind chromatin to induce a transcriptional response. The main finding is that activation domains can be involved in DNA binding as indicated by careful observations of the diffusion/reaction kinetics of transcription factors in the nucleus. For example, different bound fractions of HIF-1beta and HIF2alpha are observed in the presence of different binding partners and chimeras. The paradigm of interchangeable parts of transcription factors has been eroded over the years (the recent work of Naama Barkai comes to mind, cited herein), so the present observations are not unexpected per se. Yet, the measurements are rigorous and wellperformed and have the important benefit of being in living cells. Enthusiasm is also dampened by the exclusive use of one technique and one analysis to reach conclusions.

    In the revised manuscript we complement the single molecule imaging experiments with genomic approaches, including Cut&Run and RNA-seq, that largely confirm our main conclusions derived from the SPT results.

    Reviewer #2 (Public Review):

    The authors raise the very important question how different transcription factors with similar in vitro DNA sequence specificity are able to achieve distinct binding profiles associated with distinct functions. They use hypoxia inducible factors (HIF) as model system and combine live cell single-particle tracking with comprehensive genetic and chemical perturbations to study the mechanisms underlying isoform-specific gene regulation. Their main experimental readout is the distribution of diffusion coefficients of a molecular species, extracted from a population of single-particle trajectories. From this distribution, the authors extract the fractions of immobile and mobile molecules as well as the peak diffusion coefficient of the mobile fraction. They find that in addition to the structured DNA binding domain and the dimerization interface of HIF-1a and HIF-2a, the C-terminus of those factors, which includes intrinsically disordered regions and an activation domain, contributes to modulating the bound fraction of HIF-1b and the HIF-a isoforms. In particular, the C-terminus of HIF-2a mediates a higher bound fraction than the one of HIF-1a. This finding is important as it demonstrates that separating HIF into distinct domains that each have clearly defined functions is an oversimplification. Rather, a more holistic view seems suitable, in which all parts of HIF contribute to nuclear diffusion and binding.

    The conclusions drawn on the bound fractions and the nuclear dynamics of HIF isoforms are mostly backed up by data and proper controls. However, some controls are missing and some aspects of data analysis need to be clarified and extended. Moreover, the authors fail to answer their initial question, as the experimental readout does not contain information on the DNA sequences involved in the binding events.

    Experimental controls:

    For some imaging experiments, the authors use cell lines where endogenous HIF-1b or HIF-2a was fused to a N-terminal HaloTag by CRISPR/Cas editing. These cell lines are comprehensively controlled for proper functionality of the edited transcription factors, including expression levels, cellular localization and DNA binding. However, differential expression compared to unedited levels is not quantified and only Halo-HIF-2a is tested for functional gene transcription.

    To confirm that the tagged proteins still maintain normal function in driving target gene expression, we performed RNA-seq on WT cells, HaloTag-HIF-2α KIN and Halo-HIF-1β KIN cells, and show that gene expression on these edited cells do not differ significantly from unedited WT cells (Figure 1—figure supplement 3B, C).

    Other experiments include overexpression of exogenously expressed factors. For those, the authors give statements such as "expressed from a relatively strong ... promoter" and "weakly expressed", but do not provide any control of the amount of overexpression. Quantifying the expression levels will be important, as some of the author's experiments demonstrate a strong dependency of results on expression level.

    We have now included Western Blot results showing L30-driven expression of all HIF variants in comparison with KIN levels (Fig 4—Figure Supplement 1). However, we note that cells stably expressing the HIF variants are polyclonal and Western Blotting is a bulk assay only able to assess the population average. As such, Western blot analysis may not reflect the actual expression level in the individual cells used in the imaging experiments. To properly control HIF expression at the individual cell level, we instead monitored the protein concentration in each cell and only chose to image cells with similar fluorescence level, as measured by localization density (Fig 4—Figure Supplement 1 and see detailed discussion in Appendix 2).

    Moreover, the authors do not provide any control for proper functionality of domainswap mutants.

    We now include RNA-seq results demonstrating that WT cells over-expressing HIF-α

    WT and domain swap variants (Halo-HIF-1α, Halo-HIF-1α/2α, Halo-HIF-2α, Halo-HIF2α/1α) can activate their specific target genes, confirming that all these variants are also transcriptionally active. (See Figure 6A, B, Figure 6—figure supplement 2 - increased binding of wild type or domain-swapped HIF to several gene loci or neighboring regions coincide with increased transcription levels of these genes, and Figure 7 - HIF expressing cells with same HIF-IDR co-cluster in their mRNA transcription profile).

    The authors further state that they use a high illumination power of 1100 mW. Such high laser power might be detrimental to cells and the authors should control whether this laser power induces any artifacts.

    We agree that a high illumination power (indispensable to achieve high signal-to-noise ratio and detect single molecules) may be detrimental to cells in the long run. However, we only took 1 movie with < 2000 frames for each cell. With a 5-ms frame rate, the total imaging duration per cell was under 10 seconds. Cells are unlikely to respond to any stimulus/damage in such a short time. Moreover, we used stroboscopic illumination instead of continuous illumination, with only 1-ms laser exposure for each 5-ms frame. The total integrated laser exposure is thus only 2 seconds. In addition, all imaging was done with a red laser (633 nm), which has a relatively low phototoxicity. Finally, the 1100 mW is the output from the laser box, but the actual laser power density used for imaging were measured to approximately 2.3 kW/cm2 at 633 nm (Graham et al., 2021). Such an imaging scheme is very unlikely to generate phototoxicity artifacts within the short time window of our measurements. Lastly, we are comparing results across all conditions with the exact same imaging set-up, so any artifact should be accounted and controlled for. We do consider fast SPT a terminal, end-point experiment, where each cell is only imaged once and never re-used.

    Data analysis:

    Distributions of diffusion coefficients greatly vary between individual cells (e.g. Fig. 2A and B, Fig. S3A and C, Fig. S4E). Unfortunately, the authors do not explain whether this variation is a real cell-to-cell variation, or rather reflects variation of their analysis method, potentially due to a low number of single particle tracks per cell.

    We agree with the reviewer that the cell-to-cell variation we observed could be due to a low number of trajectories collected for each cell. In fact, sampling small numbers of trajectories allows us to identify protein species with unique diffusion coefficients, which might be lost if we just looked at a large population. Also, the fact that the diffusion coefficient distribution varies between cells does not mean that a particular cell only contains the more prevalent species that was detected. Here we are not trying to determine whether proteins in each cell indeed behave differently or whether the observed variation in the diffusion coefficient distribution is simply an effect of the limited trajectories collected in each cell. We instead analyzed data collected from many cells combined to get a better estimation of the population behavior. We have modified our text to make this important point clear to the readers.

    Moreover, the bound fraction of HIF-1b differs between two independent measurements including three biological replicates each (Fig. 5 C and F). This raises the concern that not enough data enter each biological replicate, or not enough replicates are considered.

    Unfortunately, the number of cells that could be measured in our current setup is limited. It takes approximately 1 hour to collect 20 cells per sample, including staining, washing, looking for cells with desired expression level, and acquiring movies. For experiments with multiple conditions (>12), 20 cells per sample is the upper limit that can fit into a single day.

    To address the question of what is the minimum number of cells/replicates needed we included in Figure 2—figure supplement 3 - the result of a bootstrapping analysis. We used data collected from a total of 243 cells of the same cell line, from over 11 replicates as the “population” and performed a bootstrapping analysis to identify the source of variation. We have also included appendix 1 with a detailed discussion. Our results showed that cell-to-cell variation contributes most to the total variation of the data, followed by day-to-day (replicate-to-replicate) variation. However, sampling over 800 trajectories, and from over 60 cells, imaged in 3 replicates well approximates the “population value” (bound fraction calculated from 243 cells from over 11 replicates). As a result, in each figure we always used over 60 cells from 3 replicates to generate the reported parameters. Although this approach still gives variable numbers from figure to figure, the variations seen for the same cell line are much smaller compared to the differences observed between different cell lines/conditions.

    The authors compare the bound fractions among various mutants and experimental conditions. However, the peak diffusion is not, or only descriptively, evaluated. Thus, it is not clear whether the main effect of a mutation or chemical treatment is to change the bound fraction, or rather the diffusion coefficient of the mobile fraction.

    Since there might be multiple mobile populations (defined as the fraction with a diffusion coefficient > 0.5 μm2/sec), the mean diffusion coefficient can change while the mode (peak) diffusion coefficient stays the same and vice versa. Because of such complexity in the mobile population, we prefer to use descriptive words to report the trend for the change instead of reporting exact values. However, as requested, we have added peak diffusion coefficient information to relevant figures as bar plots. We have also included in Table 1 a summary of mean and mode diffusion coefficient estimated for moving molecules in all relevant figures for reader’s reference. Note that the diffusion coefficient estimation is on a log scale, and the larger the diffusion coefficient, the lower the resolution (e.g, there is 1-grid of difference both between 2.63 and 2.75, and between 9.55 and 10).

    Conclusions:

    The authors provide data that highlight a potential role of the intrinsically disordered domain of HIF in modulating the bound fraction of these transcription factors. They further claim that the intrinsically disordered domains have a main contribution to this bound fraction. However, the autors do not quantify how this contribution relates to those of the DNA binding domain or the dimerisation interface. Changes in bound fraction estimated from the data in e.g. Fig. 3C, Fig. 4C, Fig. 5C and F rather hint to a dominant effect of dimerisation, followed by DNA binding and a smaller contribution of the intrinsically disordered domain. The authors should quantify the relative changes of the bound fraction for all mutants and experimental conditions, to clarify the importance of the contribution of the intrinsically disordered domain.

    It would be ideal if we could quantify what percent of the bound fraction is contributed by dimerization interface, DBD and IDR, respectively. However, it is very likely that these different domains do not act independently of each other in terms of binding to chromatin fibers. In practice, it is very difficult to dissect and quantify these effects independently. For example, we did try to express HIF-1α and 2α with their IDR completely deleted; however, because the protein-degradation signals are within the IDRs, these deletions caused massive stabilization of these proteins, making it impossible to find cells that express these forms at similar levels as the full-length counterpart. As a result, although these IDR-deleted HIF-α show greatly reduced binding, we did not include the results in the paper because the loss of binding could also be due to the overall higher protein expression levels, leading to large unbound fractions. Regarding the DBD mutants, they only have 1 mutation, so it is hard to tell whether the remaining binding in Figure 5B is due to some residual binding affinity of HIF-α (HIF-α only partially lost its binding affinity), or is due to binding through its partner HIF-1β (HIF-α completely lost binding affinity, but can still bind through dimerization with HIF-1β). All we can safely conclude from Figure 5B is that HIF-α DBD is required for optimal binding, but we cannot determine how much exactly it contributes to binding. We thus argue that, given the interdependence of the different protein domains, the reviewer’s request is not experimentally feasible.

    The authors state that the intrinsically disordered domains of HIF determine their differential binding specificity to chromatin. However, the experiments provided do not allow for such a conclusion. In particular, measuring changes in the bound fractions is not sufficient. Such a conclusion requires a method that is able to inform about the DNA sequences involved in HIF binding, for example chromatin immunoprecipitation.

    As requested, we have included new Cut&Run and RNA-seq results in the revised manuscript showing HIF-α-IDR-specific binding and gene activation.

  7. Version published to 10.1101/2021.10.27.466110v3 on bioRxiv
  8. Version published to 10.1101/2021.10.27.466110v2 on bioRxiv
  9. Version published to 10.1101/2021.10.27.466110v1 on bioRxiv