TY - JOUR
T1 - TF-centered downstream gene set enrichment analysis
T2 - Inference of causal regulators by integrating TF-DNA interactions and protein post-translational modifications information
AU - Liu, Qi
AU - Tan, Yejun
AU - Huang, Tao
AU - Ding, Guohui
AU - Tu, Zhidong
AU - Liu, Lei
AU - Li, Yixue
AU - Dai, Hongyue
AU - Xie, Lu
N1 - Funding Information:
Special acknowledgements go to Merck Research Laboratories, USA, for funding a joint postdoctoral program. We acknowledge Yvonne Poindexter from the Vanderbilt-Ingram Cancer Center for her editing and the anonymous reviewers for their valuable comments and suggestions. Other funding for this work include: the National Basic Research Program of China (Grant No. 2009CB918404, No. 2006CB910700), the National Natural Science Foundation of China (Grant No. 30700154, No. 31070746, No. 60874105, No. 31070752), and Key Infectious Disease Project (Grant No. zx10002-021). This article has been published as part of BMC Bioinformatics Volume 11 Supplement 11, 2010: Proceedings of the 21st International Conference on Genome Informatics (GIW2010). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/11?issue=S11.
PY - 2010/12/14
Y1 - 2010/12/14
N2 - Background: Inference of causal regulators responsible for gene expression changes under different conditions is of great importance but remains rather challenging. To date, most approaches use direct binding targets of transcription factors (TFs) to associate TFs with expression profiles. However, the low overlap between binding targets of a TF and the affected genes of the TF knockout limits the power of those methods.Results: We developed a TF-centered downstream gene set enrichment analysis approach to identify potential causal regulators responsible for expression changes. We constructed hierarchical and multi-layer regulation models to derive possible downstream gene sets of a TF using not only TF-DNA interactions, but also, for the first time, post-translational modifications (PTM) information. We verified our method in one expression dataset of large-scale TF knockout and another dataset involving both TF knockout and TF overexpression. Compared with the flat model using TF-DNA interactions alone, our method correctly identified five more actual perturbed TFs in large-scale TF knockout data and six more perturbed TFs in overexpression data. Potential regulatory pathways downstream of three perturbed regulators- SNF1, AFT1 and SUT1 -were given to demonstrate the power of multilayer regulation models integrating TF-DNA interactions and PTM information. Additionally, our method successfully identified known important TFs and inferred some novel potential TFs involved in the transition from fermentative to glycerol-based respiratory growth and in the pheromone response. Downstream regulation pathways of SUT1 and AFT1 were also supported by the mRNA and/or phosphorylation changes of their mediating TFs and/or " modulator" proteins.Conclusions: The results suggest that in addition to direct transcription, indirect transcription and post-translational regulation are also responsible for the effects of TFs perturbation, especially for TFs overexpression. Many TFs inferred by our method are supported by literature. Multiple TF regulation models could lead to new hypotheses for future experiments. Our method provides a valuable framework for analyzing gene expression data to identify causal regulators in the context of TF-DNA interactions and PTM information.
AB - Background: Inference of causal regulators responsible for gene expression changes under different conditions is of great importance but remains rather challenging. To date, most approaches use direct binding targets of transcription factors (TFs) to associate TFs with expression profiles. However, the low overlap between binding targets of a TF and the affected genes of the TF knockout limits the power of those methods.Results: We developed a TF-centered downstream gene set enrichment analysis approach to identify potential causal regulators responsible for expression changes. We constructed hierarchical and multi-layer regulation models to derive possible downstream gene sets of a TF using not only TF-DNA interactions, but also, for the first time, post-translational modifications (PTM) information. We verified our method in one expression dataset of large-scale TF knockout and another dataset involving both TF knockout and TF overexpression. Compared with the flat model using TF-DNA interactions alone, our method correctly identified five more actual perturbed TFs in large-scale TF knockout data and six more perturbed TFs in overexpression data. Potential regulatory pathways downstream of three perturbed regulators- SNF1, AFT1 and SUT1 -were given to demonstrate the power of multilayer regulation models integrating TF-DNA interactions and PTM information. Additionally, our method successfully identified known important TFs and inferred some novel potential TFs involved in the transition from fermentative to glycerol-based respiratory growth and in the pheromone response. Downstream regulation pathways of SUT1 and AFT1 were also supported by the mRNA and/or phosphorylation changes of their mediating TFs and/or " modulator" proteins.Conclusions: The results suggest that in addition to direct transcription, indirect transcription and post-translational regulation are also responsible for the effects of TFs perturbation, especially for TFs overexpression. Many TFs inferred by our method are supported by literature. Multiple TF regulation models could lead to new hypotheses for future experiments. Our method provides a valuable framework for analyzing gene expression data to identify causal regulators in the context of TF-DNA interactions and PTM information.
UR - http://www.scopus.com/inward/record.url?scp=78650839537&partnerID=8YFLogxK
U2 - 10.1186/1471-2105-11-S11-S5
DO - 10.1186/1471-2105-11-S11-S5
M3 - Article
C2 - 21172055
AN - SCOPUS:78650839537
SN - 1471-2105
VL - 11
JO - BMC Bioinformatics
JF - BMC Bioinformatics
IS - SUPPL. 11
M1 - S5
ER -