Center for Biological Physics, Department of Physics, Arizona State University, Tempe, Arizona.
Department of Mathematics, University of Tennessee, Knoxville, Tennessee.
Biophys J. 2021 May 4;120(9):1665-1679. doi: 10.1016/j.bpj.2021.02.045. Epub 2021 Mar 9.
The time spent by a single RNA polymerase (RNAP) at specific locations along the DNA, termed "residence time," reports on the initiation, elongation, and termination stages of transcription. At the single-molecule level, this information can be obtained from dual ultrastable optical trapping experiments, revealing a transcriptional elongation of RNAP interspersed with residence times of variable duration. Successfully discriminating between long and short residence times was used by previous approaches to learn about RNAP's transcription elongation dynamics. Here, we propose an approach based on the Bayesian sticky hidden Markov model that treats all residence times for an Escherichia coli RNAP on an equal footing without a priori discriminating between long and short residence times. Furthermore, our method has two additional advantages: we provide full distributions around key point statistics and directly treat the sequence dependence of RNAP's elongation rate. By applying our approach to experimental data, we find assigned relative probabilities on long versus short residence times, force-dependent average residence time transcription elongation dynamics, ∼10% drop in the average backtracking durations in the presence of GreB, and ∼20% drop in the average residence time as a function of applied force in the presence of RNaseA.
单个 RNA 聚合酶 (RNAP) 在 DNA 上特定位置的停留时间，称为“停留时间”，报告了转录的起始、延伸和终止阶段。在单分子水平上，该信息可以通过双超稳定光学捕获实验获得，揭示了 RNAP 的转录延伸穿插着不同持续时间的停留时间。以前的方法成功地区分了长停留时间和短停留时间，从而了解了 RNAP 的转录延伸动力学。在这里，我们提出了一种基于贝叶斯粘性隐马尔可夫模型的方法，该方法平等对待所有的 RNAP 停留时间，而无需先验地区分长停留时间和短停留时间。此外，我们的方法还有两个额外的优势：我们提供了关键统计数据周围的完整分布，并直接处理 RNAP 延伸率的序列依赖性。通过将我们的方法应用于实验数据，我们发现了长停留时间与短停留时间的相对概率分配、力依赖性的平均停留时间转录延伸动力学、在 GreB 存在下平均回溯持续时间下降约 10%、以及在 RNaseA 存在下平均停留时间下降约 20%。
