Abstract
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.