Istituto per le Applicazioni del Calcolo Mauro Picone, Consiglio Nazionale delle Ricerche, Rome, Italy.
PLoS Comput Biol. 2010 Dec 16;6(12):e1001032. doi: 10.1371/journal.pcbi.1001032.
Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease.
两种辅助性 T 细胞（Th）亚群，即 Th1 和 Th2 细胞，在炎症性疾病中发挥重要作用。这两个亚群被认为相互拮抗，它们之间的平衡改变会导致不同的疾病。这一范式受到了最近临床和实验数据的挑战。由于涉及调节 Th1 和 Th2 细胞的基因数量众多，通过建模或实验来评估这一范式是困难的。基于形式方法的新算法现在允许分析大型基因调控网络。通过将这些算法与计算性敲除和来自人类 T 细胞的基因表达微阵列数据相结合，我们研究了这些结果是否与 Th1 和 Th2 细胞的拮抗作用一致。我们通过文本挖掘和手动注释构建了一个调节 Th1 和 Th2 细胞的基因有向网络模型。我们在网络中识别出四个吸引子，其中三个吸引子包含与 Th0、Th1 和 Th2 细胞相对应的基因。第四个吸引子包含 Th1 和 Th2 基因的混合物。我们发现，无论是计算性敲除 Th1 和 Th2 吸引子基因，还是免疫性疾病患者和健康受试者的基因表达微阵列数据，都不支持 Th1 和 Th2 细胞的拮抗作用。通过将网络建模与转录组数据分析和计算性敲除相结合，我们设计了一种实用的方法来帮助揭示复杂的调控网络拓扑结构，并增加我们对网络行为在健康和疾病中可能存在差异的理解。
