Modeling Dynamics of Cell-to-Cell Variability in TRAIL-Induced Apoptosis Explains Fractional Killing and Predicts Reversible Resistance

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.

同基因细胞在感知到完全相同的外部信号时，可做出截然不同的细胞命运决策。这类决策通常与细胞间预先存在的蛋白质水平差异密切相关。在信号转导（signal transduction）模型中，若未忽略此类差异，现有研究通常通过假设初始蛋白质水平呈随机分布，以静态方式对其进行表征。然而，该方法忽视了信号转导与细胞间变异来源之间先验非平凡的相互作用：即由噪声性合成与降解过程驱动的单个细胞内蛋白质水平的时间波动。因此，相较于仅针对初始群体异质性的后果进行建模，对蛋白质波动本身进行建模，能够为信号转导的定量分析奠定更坚实的基础。将细胞间差异纳入动态视角，等价于将外在变异性重塑为内在噪声。本文提出一种通用方法，可通过系统且规范的方式，将信号转导模型与随机蛋白质周转（protein turnover）模型相融合。当将该方法应用于已确立的TRAIL诱导细胞凋亡动力学模型时，其显著提升了模型的预测性能。本研究不仅为尚未得到合理解释的分数杀伤现象提供了机制层面的阐释，还对海拉细胞中细胞对TRAIL的抵抗性的时间演化做出了非平凡的稳健预测。我们的研究结果无需依赖TRAIL诱导的调控过程，即可为通过激活存活通路实现细胞存活提供一种替代解释，并提示短寿命抗凋亡蛋白Mcl1存在大振幅且罕见的波动。总体而言，本研究结果强调了在定量理解长期信号转导过程时，考虑随机蛋白质周转的重要性，并指出短寿命蛋白质的波动值得予以特别关注。