Computational Identification of Transcriptional Regulators in Human Endotoxemia

One of the great challenges in the post-genomic era is to decipher the underlying principles governing the dynamics of biological responses. As modulating gene expression levels is among the key regulatory responses of an organism to changes in its environment, identifying biologically relevant transcriptional regulators and their putative regulatory interactions with target genes is an essential step towards studying the complex dynamics of transcriptional regulation. We present an analysis that integrates various computational and biological aspects to explore the transcriptional regulation of systemic inflammatory responses through a human endotoxemia model. Given a high-dimensional transcriptional profiling dataset from human blood leukocytes, an elementary set of temporal dynamic responses which capture the essence of a pro-inflammatory phase, a counter-regulatory response and a dysregulation in leukocyte bioenergetics has been extracted. Upon identification of these expression patterns, fourteen inflammation-specific gene batteries that represent groups of hypothetically ‘coregulated’ genes are proposed. Subsequently, statistically significant cis-regulatory modules (CRMs) are identified and decomposed into a list of critical transcription factors (34) that are validated largely on primary literature. Finally, our analysis further allows for the construction of a dynamic representation of the temporal transcriptional regulatory program across the host, deciphering possible combinatorial interactions among factors under which they might be active. Although much remains to be explored, this study has computationally identified key transcription factors and proposed a putative time-dependent transcriptional regulatory program associated with critical transcriptional inflammatory responses. These results provide a solid foundation for future investigations to elucidate the underlying transcriptional regulatory mechanisms under the host inflammatory response. Also, the assumption that coexpressed genes that are functionally relevant are more likely to share some common transcriptional regulatory mechanism seems to be promising, making the proposed framework become essential in unravelling context-specific transcriptional regulatory interactions underlying diverse mammalian biological processes.后基因组时代的重大挑战之一，在于解析调控生物应答动态过程的内在原理。由于调控基因表达水平是生物体响应环境变化的关键调控应答之一，鉴定具有生物学意义的转录调控因子及其与靶基因的潜在调控互作，是研究转录调控复杂动态过程的必要步骤。本研究整合多种计算与生物学分析手段，基于人类内毒素血症模型探究系统性炎症应答的转录调控机制。我们从人类血液白细胞的高维转录谱数据中，提取出一组核心时间动态应答模式，涵盖促炎阶段、负调控应答阶段以及白细胞生物能学失调的核心特征。在鉴定出这些表达模式后，本研究提出了14个炎症特异性基因簇，代表了一组假设的"共调控"基因集合。随后，我们鉴定出统计学显著的顺式调控模块（cis-regulatory modules, CRMs），并将其拆解为34个关键转录因子的列表，这些因子的有效性大多得到了原始文献的验证。最后，本研究进一步构建了宿主全身时间维度的转录调控程序动态图谱，解析了转录因子间可能发挥活性的组合调控互作模式。尽管仍有诸多问题有待探索，但本研究已通过计算手段鉴定出关键转录因子，并提出了与宿主关键转录性炎症应答相关的推测性时间依赖性转录调控程序。本研究结果为未来解析宿主炎症应答背后的转录调控机制提供了坚实的研究基础。此外，"功能相关的共表达基因更有可能共享共同的转录调控机制"这一假设颇具前景，使得本研究提出的分析框架成为解析多样哺乳动物生物学过程中特定情境下转录调控互作的关键手段。