, Wenzhong Xiao1
, Lyle L Moldawer4
, Ronald W Davis1
and Markus W Covert3,*
1 Stanford Genome Technology Center, Palo Alto, California, USA
2 Department of Electrical Engineering, University, Stanford, California, USA
3 Department of Bioengineering, Stanford University, Stanford, California, USA
4 Department of Surgery, University of Florida, Gainesville, Florida, USA
The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1752-0509/3/78
Received: 27 October 2008
Accepted: 28 July 2009
Published: 28 July 2009
© 2009 Seok et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Understanding the transcriptional regulatory networks that map out the coordinated dynamic responses of signaling proteins, transcription factors and target genes over time would represent a significant advance in the application of genome wide expression analysis. The primary challenge is monitoring transcription factor activities over time, which is not yet available at the large scale. Instead, there have been several developments to estimate activities computationally. For example, Network Component Analysis (NCA) is an approach that can predict transcription factor activities over time as well as the relative regulatory influence of factors on each target gene.
In this study, we analyzed a gene expression data set in blood leukocytes from human subjects administered with lipopolysaccharide (LPS), a prototypical inflammatory challenge, in the context of a reconstructed regulatory network including 10 transcription factors, 99 target genes and 149 regulatory interactions. We found that the computationally estimated activities were well correlated to their coordinated action. Furthermore, we found that clustering the genes in the context of regulatory influences greatly facilitated interpretation of the expression data, as clusters of gene expression corresponded to the activity of specific factors or more interestingly, factor combinations which suggest coordinated regulation of gene expression. The resulting clusters were therefore more biologically meaningful, and also led to identification of additional genes under the same regulation.
Using NCA, we were able to build a network that accounted for between 8–11% genes in the known transcriptional response to LPS in humans. The dynamic network illustrated changes of transcription factor activities and gene expressions as well as interactions of signaling proteins, transcription factors and target genes.