Modeling dynamics of cell-to-cell variability in TRAIL-induced apoptosis explains fractional killing and predicts reversible resistance.
Modeling dynamics of cell-to-cell variability in TRAIL-induced apoptosis explains fractional killing and predicts reversible resistance.
复制标题
对 TRAIL 诱导的细胞凋亡中细胞间变异的动态建模解释了部分杀伤并预测了可逆耐药性。
DOI：
10.1371/journal.pcbi.1003893
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发表时间：
2014-10
影响因子：
4.3
通讯作者：
Batt G
中科院分区：
文献类型：
--
作者：
Bertaux F;Stoma S;Drasdo D;Batt G
感受到相同外部信号的同基因细胞可以做出明显不同的决定。这些决定通常与预先存在的细胞间蛋白质水平差异相关。当在信号转导模型中不被忽略时，通过假设随机分布的初始蛋白质水平，以静态方式考虑这些差异。然而，这种方法忽略了信号转导和这种细胞间变异性的来源之间的先验非平凡的相互作用：由噪声合成和降解驱动的单个细胞中蛋白质水平的时间波动。因此，蛋白质波动的建模，而不是其对初始群体异质性的影响，将建立在更坚实的基础上的信号转导的定量分析。采用这种关于细胞间差异的动力学观点，相当于将外在的变异性重新塑造成内在的噪音。在这里，我们提出了一个通用的方法合并，在一个系统的和原则性的方式，信号转导模型与随机蛋白质周转模型。当应用到一个已建立的动力学模型的TRAIL诱导的细胞凋亡，我们的方法显着提高模型的预测能力。人们获得了一个机制的解释，尚未解释的分数杀伤和非平凡的强大的预测细胞的时间演变的细胞耐TRAIL在HeLa细胞的观察。我们的研究结果提供了一个替代的解释，通过诱导生存途径，因为没有TRAIL诱导的法规是必要的，并表明，短暂的抗凋亡蛋白Mcl 1表现出大的和罕见的波动。更一般地说，我们的研究结果强调了占随机蛋白质周转的重要性，以定量地了解信号转导在较长的持续时间，并意味着短期蛋白质的波动值得特别注意。TRAIL选择性地诱导癌细胞凋亡，目前正在临床上进行测试。对TRAIL抗性的机械理解有助于限制其出现。一些观察结果表明，蛋白质水平波动在TRAIL抗性及其获得中起重要作用。然而，缺乏定量的，系统级的方法来研究它们在细胞决策过程中的作用。我们提出了一个通用的和原则性的方法来扩展信号转导模型与蛋白质波动模型的途径中的所有蛋白质。关键的方面是使用标准的蛋白质波动模型长寿命的蛋白质。我们表明，它的应用TRAIL诱导的细胞凋亡提供了一个定量的，机械的解释，以前发表的，但尚未解释的关键观察。
Isogenic cells sensing identical external signals can take markedly different decisions. Such decisions often correlate with pre-existing cell-to-cell differences in protein levels. When not neglected in signal transduction models, these differences are accounted for in a static manner, by assuming randomly distributed initial protein levels. However, this approach ignores the a priori non-trivial interplay between signal transduction and the source of this cell-to-cell variability: temporal fluctuations of protein levels in individual cells, driven by noisy synthesis and degradation. Thus, modeling protein fluctuations, rather than their consequences on the initial population heterogeneity, would set the quantitative analysis of signal transduction on firmer grounds. Adopting this dynamical view on cell-to-cell differences amounts to recast extrinsic variability into intrinsic noise. Here, we propose a generic approach to merge, in a systematic and principled manner, signal transduction models with stochastic protein turnover models. When applied to an established kinetic model of TRAIL-induced apoptosis, our approach markedly increased model prediction capabilities. One obtains a mechanistic explanation of yet-unexplained observations on fractional killing and non-trivial robust predictions of the temporal evolution of cell resistance to TRAIL in HeLa cells. Our results provide an alternative explanation to survival via induction of survival pathways since no TRAIL-induced regulations are needed and suggest that short-lived anti-apoptotic protein Mcl1 exhibit large and rare fluctuations. More generally, our results highlight the importance of accounting for stochastic protein turnover to quantitatively understand signal transduction over extended durations, and imply that fluctuations of short-lived proteins deserve particular attention. TRAIL induces apoptosis selectively in cancer cells and is currently tested in clinics. Having a mechanistic understanding of TRAIL resistance could help to limit its apparition. Several observations suggested that protein level fluctuations play an important role in TRAIL resistance and its acquisition. However, quantitative, systems-level approaches to investigate their role in cellular decision-making processes are lacking. We propose a generic and principled approach to extend signal transduction models with protein fluctuation models for all proteins in the pathway. The key aspect is to use standard protein fluctuation models for long-lived proteins. We show that its application to TRAIL-induced apoptosis provide a quantitative, mechanistic explanation to previously published but yet unexplained critical observations.
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影响因子：
32.4
作者：
Chaudhary, PM;Eby, M;Hood, L
通讯作者：
Hood, L
影响因子：
8
作者：
Dimberg LY;Anderson CK;Camidge R;Behbakht K;Thorburn A;Ford HL
通讯作者：
Ford HL
影响因子：
9.8
作者：
Albeck JG;Burke JM;Spencer SL;Lauffenburger DA;Sorger PK
通讯作者：
Sorger PK
影响因子：
2
作者：
Flusberg, Deborah A.;Sorger, Peter K.
通讯作者：
Sorger, Peter K.
影响因子：
3.3
作者：
Flusberg DA;Roux J;Spencer SL;Sorger PK
通讯作者：
Sorger PK