TGF-β stimulation in human and murine cells reveals commonly affected biological processes and pathways at transcription level

Abstract

Background: The TGF-β signaling pathway is a fundamental pathway in the living cell, which plays a key role in many central cellular processes. The complex and sometimes contradicting mechanisms by which TGF-β yields phenotypic effects are not yet completely understood. In this study we investigated and compared the transcriptional response profile of TGF-β1 stimulation in different cell types. For this purpose, extensive experiments are performed and time-course microarray data are generated in human and mouse parenchymal liver cells, human mesenchymal stromal cells and mouse hematopoietic progenitor cells at different time points. We applied a panel of bioinformatics methods on our data to uncover common patterns in the dynamic gene expression response in respective cells.

Results: Our analysis revealed a quite variable and multifaceted transcriptional response profile of TGF-β1 stimulation, which goes far beyond the well-characterized classical TGF-β1 signaling pathway. Nonetheless, we could identify several commonly affected processes and signaling pathways across cell types and species. In addition our analysis suggested an important role of the transcription factor EGR1, which appeared to have a conserved influence across cell-types and species. Validation via an independent dataset on A549 lung adenocarcinoma cells largely confirmed our findings. Network analysis suggested explanations, how TGF-β1 stimulation could lead to the observed effects.

Conclusions: The analysis of dynamical transcriptional response to TGF-β treatment experiments in different human and murine cell systems revealed commonly affected biological processes and pathways, which could be linked to TGF-β1 via network analysis. This helps to gain insights about TGF-β pathway activities in these cell systems and its conserved interactions between the species and tissue types.

Figure 1

Log2 fold-changes of 17 genes, which are DE in at least one cell type and are known to play a role in the TGF-β pathway (according to KEGG annotation).

Figure 2

Cluster mean-curves of log2 fold changes for the different cell types. For mouse HPC no clusters could be identified and hence all DE genes treated as one group.

Figure 3

Log2 fold changes of two groups of functionally similar clusters detected in different cell types. Genes appearing in more than one cluster are depicted in color, gray curves are cluster-specific genes. Upper group: two similar clusters MPP and CDP. Lower group: two similar clusters in human HPC and MSC.

Figure 4

Clustered heatmaps of (a) the 6 common KEGG pathways and (b) 11 GO terms in different cell types. The color code indicates the degree of association (−log(FDR)) of a KEGG pathway and GO term to each cell type, respectively.

Figure 5

Network of eight overrepresented TFBS and DE genes containing these binding sites. For the sake of better visualization only the set of genes being DE in both, HPC and MSC as well as both, MPP and CDP, are shown. Red genes are known to play role in the TGF-β pathway. The width of the blue lines is chosen to be proportional to the average –log E-value, which resulted from the XXmotif analysis.

Figure 6

Human protein-protein interaction network connecting TGFB1, TGFBR1, SMAD7, SKIL, EGR1, PPARG with genes involved into commonly identified biological processes and pathways. The dashed green line indicates the putative transcriptional regulation of SMAD7 by transcription factor EGR1. The darker the red color of a node the higher the average probability for differential time course expression.

Figure 7

Murine protein-protein interaction network connecting Tgbfr1, Smad7, Skil, Egr1, Pparg with genes involved into commonly identified biological processes and pathways. The dashed green line indicates the putative transcriptional regulation of Smad7 and Tgfbr1 by transcription factor Egr1. The darker the red color of a node the higher the average probability for differential time course expression.

Figure 8

Percentages of KEGG pathways (left) and GO terms (right) enriched commonly in our cell types that could be reproducibly identified in GSE17708. The numbers in tip of the bars are the p-value for the null-hypothesis to see the corresponding overlap just by chance (hyper-geometric test).