A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription.
A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription.
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DOI：
10.1371/journal.pcbi.1010152
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发表时间：
2022-09
影响因子：
4.3
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中科院分区：
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响应环境线索而激活基因表达会导致细胞之间显着的表型异质性，从而影响多种结果，包括分化、病毒激活和耐药性。基因表达噪音的一个重要来源是转录爆发，或者是在启动子活性不频繁爆发期间产生转录本的过程。染色质可及性通过调节启动子上转录因子和聚合酶复合物的组装来影响转录爆发，这表明激活信号对转录噪音的影响将取决于启动子处的初始染色质状态。为了探索这种可能性，我们使用转录循环模型模拟转录激活，该模型具有代表染色质重塑、聚合酶结合和暂停释放的三种启动子状态。我们在代表具有不同染色质环境的目标基因的大参数范围内启动了该模型，并发现，在增加聚合酶暂停释放速率以激活转录时，基因表达噪声的变化在初始启动子状态之间存在显着差异。该模型捕获了整合在不同染色质位置并由转录因子 NF-κB 介导的潜伏 HIV 病毒激活的表型差异。通过增加一种或多种转录物产生速率（如 NF-κB 激活后发生的情况）来激活模型中的转录，再现了四种不同潜伏 HIV 病毒的实验测量转录物分布，以及导致重新激活病毒子集的 HIV 蛋白表达的双峰模式。重要的是，参数“激活路径”对基因表达噪音和最终病毒激活有不同的影响，这与实验观察结果一致。这项工作展示了上游信号通路如何与转录爆发背后的生物过程连接起来，从而在刺激单个上游通路后产生目标基因特异性的噪声谱。许多基因通过称为转录爆发的过程以不频繁的 mRNA 产生爆发的方式进行转录，这导致了细胞之间反应的变异性。细胞反应的异质性可能会产生重要的生物学影响，例如细胞是否支持病毒复制或对药物产生反应，因此人们正在努力用数学模型来描述这一过程以预测生物学结果。以前的模型将爆发描述为“关闭”状态或“开启”状态之间的转变，这是复杂分子机制的一种优雅而简单的数学表示，但未能捕获上游激活信号如何影响爆发。为了解决这个问题，我们添加了一个额外的启动子状态，以更好地反映爆发背后的生物机制。通过将该模型与 T 细胞中静态 HIV 感染的可变激活进行拟合，我们发现我们的模型更准确地描述了响应上游刺激的细胞间病毒表达变异性。我们的工作强调了如何进一步开发数学模型来理解复杂的生物机制，并提出了将转录爆发与上游激活途径联系起来的方法。
Activation of gene expression in response to environmental cues results in substantial phenotypic heterogeneity between cells that can impact a wide range of outcomes including differentiation, viral activation, and drug resistance. An important source of gene expression noise is transcriptional bursting, or the process by which transcripts are produced during infrequent bursts of promoter activity. Chromatin accessibility impacts transcriptional bursting by regulating the assembly of transcription factor and polymerase complexes on promoters, suggesting that the effect of an activating signal on transcriptional noise will depend on the initial chromatin state at the promoter. To explore this possibility, we simulated transcriptional activation using a transcriptional cycling model with three promoter states that represent chromatin remodeling, polymerase binding and pause release. We initiated this model over a large parameter range representing target genes with different chromatin environments, and found that, upon increasing the polymerase pause release rate to activate transcription, changes in gene expression noise varied significantly across initial promoter states. This model captured phenotypic differences in activation of latent HIV viruses integrated at different chromatin locations and mediated by the transcription factor NF-κB. Activating transcription in the model via increasing one or more of the transcript production rates, as occurs following NF-κB activation, reproduced experimentally measured transcript distributions for four different latent HIV viruses, as well as the bimodal pattern of HIV protein expression that leads to a subset of reactivated virus. Importantly, the parameter ‘activation path’ differentially affected gene expression noise, and ultimately viral activation, in line with experimental observations. This work demonstrates how upstream signaling pathways can be connected to biological processes that underlie transcriptional bursting, resulting in target gene-specific noise profiles following stimulation of a single upstream pathway. Many genes are transcribed in infrequent bursts of mRNA production through a process called transcriptional bursting, which contributes to variability in responses between cells. Heterogeneity in cell responses can have important biological impacts, such as whether a cell supports viral replication or responds to a drug, and thus there is an effort to describe this process with mathematical models to predict biological outcomes. Previous models described bursting as a transition between an “OFF” state or an “ON” state, an elegant and simple mathematical representation of complex molecular mechanisms, but one which failed to capture how upstream activation signals affected bursting. To address this, we added an additional promoter state to better reflect biological mechanisms underlying bursting. By fitting this model to variable activation of quiescent HIV infections in T cells, we showed that our model more accurately described viral expression variability across cells in response to an upstream stimulus. Our work highlights how mathematical models can be further developed to understand complex biological mechanisms and suggests ways to connect transcriptional bursting to upstream activation pathways.
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影响因子：
9.2
作者：
Corrigan, Adam M.;Chubb, Jonathan R.
通讯作者：
Chubb, Jonathan R.
影响因子：
4.1
作者：
Chan JK;Greene WC
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Greene WC
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16
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Barboric, M;Nissen, RM;Peterlin, BM
通讯作者：
Peterlin, BM
影响因子：
8.8
作者：
Baudrimont, Antoine;Jaquet, Vincent;Becskei, Attila
通讯作者：
Becskei, Attila
影响因子：
14.9
作者：
Bryan LC;Weilandt DR;Bachmann AL;Kilic S;Lechner CC;Odermatt PD;Fantner GE;Georgeon S;Hantschel O;Hatzimanikatis V;Fierz B
通讯作者：
Fierz B