How Difficult Is Inference of Mammalian Causal Gene Regulatory Networks?

Gene regulatory networks (GRNs) play a central role in systems biology, especially in the study of mammalian organ development. One key question remains largely unanswered: Is it possible to infer mammalian causal GRNs using observable gene co-expression patterns alone? We assembled two mouse GRN datasets (embryonic tooth and heart) and matching microarray gene expression profiles to systematically investigate the difficulties of mammalian causal GRN inference. The GRNs were assembled based on pieces of experimental genetic perturbation evidence from manually reading primary research articles. Each piece of perturbation evidence records the qualitative change of the expression of one gene following knock-down or over-expression of another gene. Our data have thorough annotation of tissue types and embryonic stages, as well as the type of regulation (activation, inhibition and no effect), which uniquely allows us to estimate both sensitivity and specificity of the inference of tissue specific causal GRN edges. Using these unprecedented datasets, we found that gene co-expression does not reliably distinguish true positive from false positive interactions, making inference of GRN in mammalian development very difficult. Nonetheless, if we have expression profiling data from genetic or molecular perturbation experiments, such as gene knock-out or signalling stimulation, it is possible to use the set of differentially expressed genes to recover causal regulatory relationships with good sensitivity and specificity. Our result supports the importance of using perturbation experimental data in causal network reconstruction. Furthermore, we showed that causal gene regulatory relationship can be highly cell type or developmental stage specific, suggesting the importance of employing expression profiles from homogeneous cell populations. This study provides essential datasets and empirical evidence to guide the development of new GRN inference methods for mammalian organ development.基因调控网络（Gene Regulatory Networks, GRNs）在系统生物学中占据核心地位，尤其在哺乳动物器官发育研究领域。目前仍有一个核心问题尚未得到充分解答：仅通过可观测的基因共表达模式，能否推断出哺乳动物的因果型基因调控网络？我们构建了两套小鼠GRN数据集（分别对应胚胎牙齿与心脏组织），并匹配了对应的微阵列基因表达谱，以系统性探究哺乳动物因果型GRN推断过程中的难点。本研究所用的GRNs基于人工研读原始研究论文所获取的实验性遗传扰动证据构建而成：每一条扰动证据均记录了当某一基因被敲低或过表达后，另一基因的表达量发生的定性变化。我们的数据集对组织类型、胚胎阶段以及调控类型（激活、抑制与无效应）均进行了详尽注释，这使得我们得以精准估算组织特异性因果型GRN边推断的灵敏度与特异度。借助这套前所未有的数据集，我们发现基因共表达无法可靠地区分交互作用的真阳性与假阳性结果，这使得哺乳动物发育过程中的GRN推断极具挑战。尽管如此，若我们拥有来自遗传或分子扰动实验的表达谱数据——例如基因敲除或信号通路刺激实验数据——则可借助差异表达基因集，以优异的灵敏度与特异度恢复因果调控关系。本研究结果证实了在因果网络重构中使用扰动实验数据的重要性。此外，我们证实因果基因调控关系具有极强的细胞类型或发育阶段特异性，这提示使用均质细胞群体的表达谱数据进行研究的必要性。本研究为面向哺乳动物器官发育的新型GRN推断方法的开发，提供了至关重要的数据集与实验依据。