Transcription factor mobility

使用光漂白后荧光恢复（FRAP），我们之前已经证明，即使转录持续数小时，gfp标记的糖皮质激素受体在特定启动子上的结合时间最多为60秒。类似的结果现在已经在许多其他转录因子和各种其他核蛋白中被观察到。在许多情况下，这些体内的测量结果与试管中的测量结果非常不同，试管中的测量结果通常表明核蛋白（包括转录因子）的结合要稳定得多。因此，为了了解这些蛋白质在活细胞中的功能，测量它们在染色质上的停留时间并了解这与它们在染色质上的功能之间的关系是至关重要的。对于转录因子而言，这个问题转化为转录因子的停留时间与转录因子结合的基因产生的转录物数量之间的关系。为了解决这些问题，我们开发了三种不同的方法来测量转录因子在活细胞内染色质上的停留时间。我们最初使用光漂白后荧光恢复数据（FRAP）结合本实验的数学模型来估计转录因子在染色质上的停留时间。然而，其他小组使用类似的分析程序得到了非常不同的估计，因此我们调查了这种差异的来源。我们发现许多不同的数学模型可以拟合相同的FRAP数据，因此产生非常不同的停留时间。通过评估这些不同的建模方法，我们展示了一些模型中的错误假设如何导致在估计停留时间时出现重大错误。这使我们提出了一种更可靠的方法，通过FRAP分析来进行这些测量。为了验证我们的FRAP方案，我们开发了一种使用荧光相关光谱（FCS）的替代方法，我们证明该方法也能够测量转录因子与染色质的结合。通过比较FCS分析和FRAP分析，我们确定了FCS分析中的错误，我们能够纠正这些错误，从而在FRAP和FCS对停留时间的估计之间取得良好的一致性。FRAP和FCS的一个局限性是，它们依赖于数学模型来描述荧光强度的变化，这些变化是由至少两个潜在的过程引起的，即扩散和结合。这两个过程都不能通过FRAP或FCS直接可视化，因此关于扩散如何发生的错误假设可能导致结合估计的错误。为了更直接地评估扩散和结合是如何在细胞核中发生的，我们开发了活细胞核中转录因子单分子跟踪的方法。这种方法更容易区分扩散和结合，因为与染色质结合的单个分子比通过核质扩散的分子移动得少得多。使用这种方法，我们已经证明单分子跟踪测量的停留时间与FRAP和FCS测量的转录因子p53的停留时间接近。测量活细胞结合的三种不同方法之间的这种合理的一致性表明，我们现在可以使这些测量相当准确。现在我们可以把注意力转向转录因子停留时间是如何影响转录的。我们正在采取多管齐下的方法来解决这个问题，主要使用单分子跟踪来分析几种不同转录因子和几种不同类型细胞中的转录因子结合。我们根据需要用FRAP和FCS测量来补充单分子测量。我们目前正在对哺乳动物细胞中的糖皮质激素受体和p53、酵母细胞中的热休克因子和哺乳动物细胞中的人工转录因子进行这些测量。与我们之前的所有数据一致，我们发现糖皮质激素受体、p53和热休克因子都表现出几秒钟的短暂结合。为了了解这种瞬时结合有多少反映了与染色质的非特异性相互作用，这些相互作用涉及到寻找目标位点的过程中，而有多少结合反映了目标位点的特异性相互作用，我们正在使用标记RNA聚合酶位置的第二个标签对转录因子进行单分子跟踪。RNA聚合酶染色显示整个细胞核中有许多与活性转录位点相对应的病灶。我们使用这种聚合酶染色在这些活性转录位点进行单分子跟踪。我们发现转录因子在这两个位置的停留时间有显著差异。在转录位点的停留时间比在非转录位点的停留时间长得多，这表明转录需要更长的转录因子在染色质上的停留时间。有趣的是，这些长时间停留的事件并不常见。只有不到10%的转录因子分子在细胞核中表现出如此长的停留时间。这表明，对于任何给定的转录因子，只有一小部分分子参与转录过程。我们还对人工转录因子进行了这些测量。这些分子被设计用来结合特定的目标序列，通过使用锌指的工程组合来结合不同的三碱基对区域。许多其他的研究小组正在研究这些类型的设计转录因子，以提高或降低特定的目标基因，用于医学或研究目的。令人惊讶的是，我们发现人工转录因子比自然转录因子表现出更长的停留时间。这表明人工转录因子与天然转录因子在功能上存在差异，我们目前的工作重点是识别这些差异。我们的工作假设是，这些因子与基因组周围的许多非特异性序列紧密结合，我们正在努力通过对这些因子进行ChIP Seq来验证这一点。

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.