Elucidating functional context within microarray data by integrated transcription factor-focused gene-interaction and regulatory network analysis.

Microarrays do not yield direct evidence for functional connections between genes. However, transcription factors (TFs) and their binding sites (TFBSs) in promoters are important for inducing and coordinating changes in RNA levels, and thus represent the first layer of functional interaction. Similar to genes, TFs act only in context, which is why a TF/TFBS-based promoter analysis of genes needs to be done in the form of gene(TF)-gene networks, not individual TFs or TFBSs. In addition, integration of the literature and various databases (e.g. GO, MeSH, etc) allows the adding of genes relevant for the functional context of the data even if they were initially missed by the microarray as their RNA levels did not change significantly. Here, we outline a TF-TFBSs network-based strategy to assess the involvement of transcription factors in agonist signaling and demonstrate its utility in deciphering the response of human microvascular endothelial cells (HMEC-1) to leukemia inhibitory factor (LIF). Our strategy identified a central core of eight TFs, of which only STAT3 had previously been definitively linked to LIF in endothelial cells. We also found potential molecular mechanisms of gene regulation in HMEC-1 upon stimulation with LIF that allow for the prediction of changes of genes not used in the analysis. Our approach, which is readily applicable to a wide variety of expression microarray and next generation sequencing RNA-seq results, illustrates the power of a TF-gene networking approach for elucidation of the underlying biology.

[1]  Warren S Alexander,et al.  Absence of Suppressor of Cytokine Signalling 3 Reduces Self‐Renewal and Promotes Differentiation in Murine Embryonic Stem Cells , 2006, Stem cells.

[2]  T. Werner Bioinformatics applications for pathway analysis of microarray data. , 2008, Current opinion in biotechnology.

[3]  R. Altman,et al.  Whole-genome expression analysis: challenges beyond clustering. , 2001, Current opinion in structural biology.

[4]  C. Stewart,et al.  Leukemia inhibitory factor regulates microvessel density by modulating oxygen-dependent VEGF expression in mice. , 2008, The Journal of clinical investigation.

[5]  G. Booz,et al.  JAK redux: a second look at the regulation and role of JAKs in the heart. , 2009, American journal of physiology. Heart and circulatory physiology.

[6]  Thomas Werner,et al.  MatInspector and beyond: promoter analysis based on transcription factor binding sites , 2005, Bioinform..

[7]  T. Werner,et al.  Computer modeling of promoter organization as a tool to study transcriptional coregulation , 2003, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[8]  Leah Barrera,et al.  The transcriptional regulatory code of eukaryotic cells--insights from genome-wide analysis of chromatin organization and transcription factor binding. , 2006, Current opinion in cell biology.

[9]  Gordon K. Smyth,et al.  limmaGUI: A graphical user interface for linear modeling of microarray data , 2004, Bioinform..

[10]  Hang Zhang,et al.  TF-Cluster: A pipeline for identifying functionally coordinated transcription factors via network decomposition of the shared coexpression connectivity matrix (SCCM) , 2011, BMC Systems Biology.

[11]  Thomas Werner,et al.  The next generation of literature analysis: Integration of genomic analysis into text mining , 2005, Briefings Bioinform..

[12]  G. Church,et al.  Computational identification of transcription factor binding sites via a transcription-factor-centric clustering (TFCC) algorithm. , 2002, Journal of molecular biology.