A first truly systems level mechanistic model unravelling the gene regulation of Th2 differentiation [IRF4]. Homo sapiens

Recent and ongoing revolutions in measurement technologies imply completely new possibilities for genome research: today, time-resolved, quantitative, and systems-level data are available. Nevertheless, without a corresponding revolution in methods for data analysis, these new data tend to drown researchers and doctors, rather than provide clear and useful insights. Such new methods are developed within the field of systems biology. Systems biology has two main approaches: mechanistically detailed and well-determined simulation models for small subsystems, and more approximative statistical models for the entire genome. However, there are few, if any, methods that combine the strengths of these two approaches. Herein, we present LASSIM, a new simulation-based approach, which can be applied to systems of the size of the entire genome. The superior performance of LASSIM is demonstrated in three examples: i) an example with simulated data shows that unlike traditional large-scale methods, LASSIM correctly identifies the true behavior between measured data-points, ii) LASSIM outperforms the winner of a previous DREAM challenge, the most competitive benchmarking approach available, iii) based on new data from TH2 differentiation, LASSIM identifies a first mechanistic model for the entire genome. The key predictions of this model are typically enriched for DNA bindings, which suggests that most predicted interactions are direct. Moreover, in silico knockdowns were experimentally validated. In summary, LASSIM opens the door to a new type of model-based data analysis: to models that combine the strengths of reliable mechanistic models with truly systems-level data. Overall design: Human naive CD4+ T cells were isolated from fresh buffy coats with Miltenyis Naïve CD4+ T Cell Isolation Kit II according to the manufacturers instructions. 1x106 cells were either transfected in a cuvette with 600nM human on target plus SMART pool against IRF4 (Dharmacon, USA), non-targeting siRNA (Dharmacon) or with transfection buffer using the Amaxa transfection program U-014. Six hours after the nucleofection cells were washed, activated and polarized towards Th2 for 12 hours. The cells were activated with platebound anti-CD3 (500 ng/ml), 500 ng/ml soluble anti-CD28, 5 ug/ml anti-IL-12, 10 ng/ml IL-4 and 17 ng/ml IL-2 (R&D Systems). For microarray experiments the cells were harvested at 12 hours of polarization and lysed in 600 ll Qiazol. For the gene expression microarray analysis 200 ng total RNA was Cy3-labeled using the Agilent Quick Amp Labeling Kit, one color. The labeled cRNA was purified with the RNeasy Mini Kit from Qiagen. After checking the labeling efficiency using the NanoDrop ND-1000 UVVIS Spectrophotometer, 1.65 ug Cy3 labeled RNA from each sample was hybridized to Agilent Sure-Print G3 Human GE 4 x 44 K slides (Agilent Gene Expression Hybridization Kit). The microarray slides were incubated for 17 h at 65 C in a rotating hybridization oven. After washing according to the manufacturers protocol the slides were analyzed using the Agilent Microarray scanner (2505C) with default settings for all parameters. Microarray expression data were obtained by use of Agilent feature extraction software (version 10.7.3.1).

近年来方兴未艾且仍在推进的测量技术革命，为基因组研究带来了全新的可能性：如今我们已可获取时间分辨、定量且系统级别的各类数据。然而，若缺乏与之匹配的数据分析方法革新，这些海量新数据非但无法为研究者与临床医师提供清晰实用的洞见，反而会令其陷入数据过载的困境。这类新型分析方法正于系统生物学（systems biology）领域中被逐步开发。系统生物学主要包含两类研究路径：其一为针对小型子系统的精细化机制建模与确定性仿真模型，其二为针对全基因组的近似统计模型。但目前几乎鲜有方法能够同时兼顾这两种路径的优势。

本文中我们提出了LASSIM这一基于仿真的新型分析方法，其可应用于全基因组规模的系统研究。我们通过三个实例验证了LASSIM的优异性能：其一，基于模拟数据的测试表明，与传统大规模分析方法不同，LASSIM可准确识别实测数据点间的真实关联行为；其二，LASSIM的表现优于此前DREAM挑战赛（DREAM challenge）的冠军方案——当前最具竞争力的基准测试方法；其三，基于Th2细胞分化（TH2 differentiation）的全新实验数据，LASSIM首次构建了针对全基因组的机制模型。该模型的核心预测结果显著富集于DNA结合位点，这意味着绝大多数预测得到的分子互作均为直接互作。此外，计算机模拟基因敲降（in silico knockdowns）实验也已通过湿实验得到验证。

综上，LASSIM为新型基于模型的数据分析开辟了道路：即可构建兼具可靠机制模型优势与真正系统级数据价值的分析模型。

总体实验设计：采用美天旎（Miltenyi）Naïve CD4+ T细胞分离试剂盒II，按照制造商说明书从新鲜血沉棕黄层（buffy coats）中分离人类初始CD4+ T细胞。将1×10^6个细胞分别通过比色杯转染：使用600nM靶向IRF4的人类ON-TARGETplus SMARTpool小干扰RNA（siRNA，Dharmacon，美国）、非靶向siRNA（Dharmacon），或仅使用转染缓冲液（采用Amaxa转染程序U-014）。核转染6小时后，洗涤细胞并进行活化与极化，使其向Th2细胞分化12小时。活化所用试剂为：固相包被抗CD3抗体（500 ng/ml）、可溶性抗CD28抗体（500 ng/ml）、抗IL-12抗体（5 μg/ml）、IL-4（10 ng/ml）与IL-2（17 ng/ml，R&D Systems）。

对于微阵列实验，在细胞极化12小时后收集样本，并用600 μl Qiazol裂解液裂解。基因表达微阵列分析步骤如下：取200 ng总RNA，使用安捷伦Quick Amp单标标记试剂盒进行Cy3荧光标记。标记后的互补RNA（cRNA）通过Qiagen的RNeasy迷你试剂盒进行纯化。使用NanoDrop ND-1000紫外-可见分光光度计检测标记效率后，将每个样本的1.65 μg Cy3标记RNA与安捷伦Sure-Print G3人类基因表达4×44K芯片（Agilent Gene Expression Hybridization Kit配套芯片）进行杂交。杂交过程在旋转杂交炉中于65℃孵育17小时。按照制造商的洗涤流程完成芯片洗涤后，使用安捷伦微阵列扫描仪（2505C）以默认参数扫描芯片。微阵列表达数据通过安捷伦特征提取软件（版本10.7.3.1）获取。