Using fluorescence recovery after photobleaching (FRAP), we have previously shown that the GFP-tagged glucocorticoid receptor is bound at a specific promoter for at most 60 seconds, even though transcription persists for several hours. Similar results have now been observed for a number of other transcription factors and for a variety of other nuclear proteins. These in vivo measurements are in many cases very different from measurements made in the test tube which typically have indicated that nuclear proteins, including transcription factors, are much more stably bound. Thus to understand how these proteins function in live cells it is critically important to measure their residence times on chromatin and see how this relates to their functions on chromatin. For the case of transcription factors, this question translates to how does the transcription factor residence time relate to the amount of transcript produced from genes to which the transcription factor binds. In order to address these questions we have developed three different methods to measure residence times of transcription factors on chromatin within live cells. We initially used the data from fluorescence recovery after photobleaching data (FRAP) combined with mathematical models of this experiment to obtain estimates of transcription factor residence times on chromatin. However, other groups using similar analysis procedures obtained very different estimates, and so we investigated the source of this discrepancy. We found that many different mathematical models could fit the same FRAP data, and so yield very different residence times. By evaluating these different modeling approaches, we showed how false assumptions in some of the models led to significant errors in the estimation of residence times. This led us to propose a more robust approach to make these measurements by FRAP analysis. To validate our FRAP protocol, we developed an alternative approach using fluorescence correlation spectroscopy (FCS), which we showed is also capable of measuring binding of a transcription factor to chromatin. By comparing the FCS analysis with the FRAP analysis we identified errors in the FCS analysis that we were able to correct and so achieve good agreement between the estimates of residence times by FRAP and FCS. A limitation of both FRAP and FCS is that they rely on mathematical models to describe changes in fluorescence intensity that arise due to at least two underlying processes, diffusion and binding. Neither process can be directly visualized by FRAP or FCS, so an incorrect assumption about how diffusion occurs can lead to an error in the estimates of binding. To evaluate more directly how diffusion and binding occur in the nucleus we have developed methods for single molecule tracking of transcription factors in live cell nuclei. This approach makes it easier to distinguish diffusion from binding since single molecules bound to chromatin move much less than molecules that diffuse through the nucleoplasm. Using this approach, we have shown that the measured residence times by single molecule tracking are close to those measured by FRAP and FCS for the transcription factor p53. This reasonable agreement among three different methods for the measurement of live cell binding suggests that we can now make these measurements reasonably accurately. This has now allowed us to direct our attention to how transcription factor residence times affect transcription. We are taking a multi-pronged approach to this question using primarily single molecule tracking to analyze transcription factor binding for several different transcription factors and in several different types of cells. We complement as necessary the single molecule measurements with FRAP and FCS measurements. We are currently performing these measurements on the glucocorticoid receptor and p53 in mammalian cells, the heat shock factor in yeast cells, and artificial transcription factors in mammaliancells. Consistent with all of our previous data we find that the glucocorticoid receptor, p53 and the heat shock factor all exhibit transient binding on the order of a few seconds. To understand how much of this transient binding reflects non-specific interactions with chromatin which are involved in the search process to locate a target site vs. how much of the binding reflects specific interactions at target sites, we are performing single molecule tracking of transcription factors with a second label tagging the location of RNA polymerase. The RNA polymerase stain reveals numerous foci throughout the nucleus that correspond to sites of active transcription. We use this polymerase stain to perform single molecule tracking at and away from these sites of active transcription. We find a striking difference in the residence times of transcription factors at these two locations. Residence times at transcription sites are much longer than at non-transcription sites suggesting that longer transcription factor residence times on chromatin are required for transcription. Interestingly, these long residence time events are infrequent. Less than 10% of the transcription factor molecules in the nucleus exhibit these long residence times. This suggests that for any given transcription factor, only a small fraction of its molecules are engaged in the process of transcription. We are also performing these measurements on artificial transcription factors. These are molecules that have been designed to bind specific target sequences by using an engineered combination of zinc fingers which bind to different three-base pair regions. A number of other groups are working with these kinds of designer transcription factors to up or down regulate specific target genes for either medical or research purposes. Surprisingly, we find that artificial transcription factors exhibit much longer residence times than observed for natural transcription factors. This suggests that the artificial transcription factors will exhibit functional differences from natural transcription factors, and our current work is focused on identifying these differences. Our working hypothesis is that these factors bind tightly to many non-specific sequences around the genome, and we are working to test this by performing ChIP Seq on these factors.
| Mazza, Davide; Mueller, Florian; Stasevich, Timothy J et al. (2013) Convergence of chromatin binding estimates in live cells. Nat Methods 10:691-2 |
| Lickwar, Colin R; Mueller, Florian; Hanlon, Sean E et al. (2012) Genome-wide protein-DNA binding dynamics suggest a molecular clutch for transcription factor function. Nature 484:251-5 |
| Mazza, Davide; Abernathy, Alice; Golob, Nicole et al. (2012) A benchmark for chromatin binding measurements in live cells. Nucleic Acids Res 40:e119 |
| Rieder, Dietmar; Trajanoski, Zlatko; McNally, James G (2012) Transcription factories. Front Genet 3:221 |
| Mazza, Davide; Stasevich, Timothy J; Karpova, Tatiana S et al. (2012) Monitoring dynamic binding of chromatin proteins in vivo by fluorescence correlation spectroscopy and temporal image correlation spectroscopy. Methods Mol Biol 833:177-200 |
| Quénet, Delphine; McNally, James G; Dalal, Yamini (2012) Through thick and thin: the conundrum of chromatin fibre folding in vivo. EMBO Rep 13:943-4 |
| Mueller, Florian; Morisaki, Tatsuya; Mazza, Davide et al. (2012) Minimizing the impact of photoswitching of fluorescent proteins on FRAP analysis. Biophys J 102:1656-65 |
| Mueller, Florian; Karpova, Tatiana S; Mazza, Davide et al. (2012) Monitoring dynamic binding of chromatin proteins in vivo by fluorescence recovery after photobleaching. Methods Mol Biol 833:153-76 |
| Stasevich, Timothy J; McNally, James G (2011) Assembly of the transcription machinery: ordered and stable, random and dynamic, or both? Chromosoma 120:533-45 |
| McNally, James G (2011) Foreword. Biophysics in chromatin structure and nuclear dynamics. Chromosome Res 19:1-3 |
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