MPTP's pathway of toxicity indicates central role of transcription factor SP1

Abstract

Deriving a Pathway of Toxicity from transcriptomic data remains a challenging task. We explore the use of weighted gene correlation network analysis (WGCNA) to extract an initial network from a small microarray study of MPTP toxicity in mice. Five modules were statistically significant; each module was analyzed for gene signatures in the Chemical and Genetic Perturbation subset of the Molecular Signatures Database as well as for over-represented transcription factor binding sites and WGCNA clustered probes by function and captured pathways relevant to neurodegenerative disorders. The resulting network was analyzed for transcription factor candidates, which were narrowed down via text-mining for relevance to the disease model, and then combined with the large-scale interaction FANTOM4 database to generate a genetic regulatory network. Modules were enriched for transcription factors relevant to Parkinson's disease. Transcription factors significantly improved the number of genes that could be connected in a given component. For each module, the transcription factor that had, by far, the highest number of interactions was SP1, and it also had substantial experimental evidence of interactions. This analysis both captures much of the known biology of MPTP toxicity and suggests several candidates for further study. Furthermore, the analysis strongly suggests that SP1 plays a central role in coordinating the cellular response to MPTP toxicity.

Fig. 1

Network generated by WGCNA, colored by module, using spring-embedded bio-layout based on edge strength

Fig. 2

Brown module, identified transcription factors in grey. Legend the Brown module formed a dense of network of regulatory interactions centered on SP1. Self-loops indicate a gene interacts with itself

Fig. 3

SP1, JUN and STAT1 subnetwork from the Brown module. Legend green indicates ChIP data; red indicates perturbation experiment; yellow, published protein–DNA interactions, and purple indicates protein–protein interaction. Node size is proportional to predicted dynamics of the gene, and darker nodes indicate higher scaled expression levels. Because the FANTOM4 database gives an estimate of the dynamics of gene expression, the resulting gene regulatory network can be used as the foundation for building a dynamic model

Fig. 4

Cyan module with TFs identified as indicated in grey; SP1 is in the middle. Legend edges represent experimentally verified interactions; straight lines are ChIP experiments or protein-protein interactions, wavy lines are siRNA preturbation experiments. All nodes were connected when predicted interactions were included (data not shown)

Fig. 5

Midnight blue module; SP1 interactions verified with 4 ChIP experiments

Fig. 6

Genetic regulatory network based on published interactions. Legend node label is proportionate to hub status as determined by edge count. Self-interactions were deleted for visual clarity

Fig. 7

HDAC1 subnetwork from FANTOM4; all genes within one or two degrees of HDAC1 in the FANTOM4 network (not including SP1); single leaves deleted for visual clarity. In the WGCNA network, all the above were linked to HDAC