Bozich Emily R, Guo Xiaolu, Wilson Jennifer L, Hoffmann Alexander
Department of Bioengineering, University of California, Los Angeles, California, United States of America.
Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California, United States of America.
PLoS Comput Biol. 2025 Aug 21;21(8):e1013344. doi: 10.1371/journal.pcbi.1013344. eCollection 2025 Aug.
Single-cell studies of signal transduction have revealed complex temporal dynamics that determine downstream biological function. For example, the stimulus-specific dynamics of the transcription factor NFκB specify stimulus-specific gene expression programs, and loss of specificity leads to disease. Thus, it is intriguing to consider drugs that may restore signaling specificity in disease contexts, or reduce activity but maintain signaling specificity to avoid unwanted side effects. However, while steady-state dose-response relationships have been the focus of pharmacological studies, there are no established methods for quantifying drug impact on stimulus-response signaling dynamics. Here we evaluated how drug treatments affect the stimulus-specificity of NFκB activation dynamics and its ability to accurately code ligand identity and dose. Specifically, we simulated the dynamic NFκB trajectories in response to 15 stimuli representing various immune threats under treatment of 10 representative drugs across 20 dosage levels. To quantify the effects on coding capacity, we introduced a Stimulus Response Specificity (SRS) score and a stimulus confusion score. We constructed stimulus confusion maps by employing epsilon network clustering in the trajectory space and in various dimensionally reduced spaces: canonical polyadic decomposition (CPD), functional principal component analysis (fPCA), and NFκB signaling codons (i.e., established, informative dynamic features). Our results indicated that the SRS score and the stimulus confusion map based on signaling codons are best-suited to quantify stimulus-specific NFκB dynamics confusion under pharmacological perturbations. Using these tools we found that temporal coding capacity of the NFκB signaling network is generally robust to a variety of pharmacological perturbations, thereby enabling the targeting of stimulus-specific dynamics without causing broad side-effects.
信号转导的单细胞研究揭示了决定下游生物学功能的复杂时间动态。例如，转录因子NFκB的刺激特异性动态决定了刺激特异性基因表达程序，而特异性丧失会导致疾病。因此，考虑在疾病背景下可能恢复信号特异性，或降低活性但保持信号特异性以避免不良副作用的药物是很有趣的。然而，虽然稳态剂量反应关系一直是药理学研究的重点，但目前还没有确定的方法来量化药物对刺激反应信号动态的影响。在这里，我们评估了药物治疗如何影响NFκB激活动态的刺激特异性及其准确编码配体身份和剂量的能力。具体来说，我们模拟了在20种剂量水平下10种代表性药物治疗下，NFκB对代表各种免疫威胁的15种刺激的动态反应轨迹。为了量化对编码能力的影响，我们引入了刺激反应特异性（SRS）评分和刺激混淆评分。我们通过在轨迹空间和各种降维空间中采用ε网络聚类来构建刺激混淆图：规范多adic分解（CPD）、功能主成分分析（fPCA）和NFκB信号密码子（即已建立的、信息丰富的动态特征）。我们的结果表明，基于信号密码子的SRS评分和刺激混淆图最适合量化药理学扰动下刺激特异性NFκB动态的混淆。使用这些工具，我们发现NFκB信号网络的时间编码能力通常对各种药理学扰动具有鲁棒性，从而能够靶向刺激特异性动态而不会引起广泛的副作用。
