Padwal Mahesh Kumar, Sarma Uddipan, Saha Bhaskar
Lab-5, National Center for Cell Science, Pune, Maharashtra, India.
PLoS One. 2014 Apr 3;9(4):e92481. doi: 10.1371/journal.pone.0092481. eCollection 2014.
Among the 13 TLRs in the vertebrate systems, only TLR4 utilizes both Myeloid differentiation factor 88 (MyD88) and Toll/Interleukin-1 receptor (TIR)-domain-containing adapter interferon-β-inducing Factor (TRIF) adaptors to transduce signals triggering host-protective immune responses. Earlier studies on the pathway combined various experimental data in the form of one comprehensive map of TLR signaling. But in the absence of adequate kinetic parameters quantitative mathematical models that reveal emerging systems level properties and dynamic inter-regulation among the kinases/phosphatases of the TLR4 network are not yet available. So, here we used reaction stoichiometry-based and parameter independent logical modeling formalism to build the TLR4 signaling network model that captured the feedback regulations, interdependencies between signaling kinases and phosphatases and the outcome of simulated infections. The analyses of the TLR4 signaling network revealed 360 feedback loops, 157 negative and 203 positive; of which, 334 loops had the phosphatase PP1 as an essential component. The network elements' interdependency (positive or negative dependencies) in perturbation conditions such as the phosphatase knockout conditions revealed interdependencies between the dual-specific phosphatases MKP-1 and MKP-3 and the kinases in MAPK modules and the role of PP2A in the auto-regulation of Calmodulin kinase-II. Our simulations under the specific kinase or phosphatase gene-deficiency or inhibition conditions corroborated with several previously reported experimental data. The simulations to mimic Yersinia pestis and E. coli infections identified the key perturbation in the network and potential drug targets. Thus, our analyses of TLR4 signaling highlights the role of phosphatases as key regulatory factors in determining the global interdependencies among the network elements; uncovers novel signaling connections; identifies potential drug targets for infections.
在脊椎动物系统的13种Toll样受体(TLR)中,只有TLR4同时利用髓样分化因子88(MyD88)和含Toll/白细胞介素-1受体(TIR)结构域的衔接蛋白干扰素-β诱导因子(TRIF)来转导触发宿主保护性免疫反应的信号。早期关于该信号通路的研究将各种实验数据整合为TLR信号传导的一张综合图谱。但由于缺乏足够的动力学参数,尚未有能揭示TLR4网络中激酶/磷酸酶之间新出现的系统水平特性和动态相互调节的定量数学模型。因此,我们在此使用基于反应化学计量学且与参数无关的逻辑建模形式,构建了TLR4信号网络模型,该模型捕捉了反馈调节、信号激酶和磷酸酶之间的相互依赖性以及模拟感染的结果。对TLR4信号网络的分析揭示了360个反馈回路,其中157个为负反馈回路,203个为正反馈回路;其中,334个回路以磷酸酶PP1作为关键组成部分。在诸如磷酸酶敲除等扰动条件下,网络元件的相互依赖性(正依赖性或负依赖性)揭示了双特异性磷酸酶MKP-1和MKP-3与丝裂原活化蛋白激酶(MAPK)模块中的激酶之间的相互依赖性,以及PP2A在钙调蛋白激酶-II自调节中的作用。我们在特定激酶或磷酸酶基因缺陷或抑制条件下的模拟结果与先前报道的多项实验数据相符。模拟鼠疫耶尔森菌和大肠杆菌感染的结果确定了网络中的关键扰动和潜在药物靶点。因此,我们对TLR4信号的分析突出了磷酸酶作为决定网络元件之间全局相互依赖性的关键调节因子的作用;揭示了新的信号连接;确定了感染的潜在药物靶点。