Lejeune et al. BMC Genomics 2012, 13:91 http://www.biomedcentral.com/1471-2164/13/91 Page 11 of 14 Figure 4 Overlap between 239 conserved modifiers of 128Q-neuron dysfunction in C-elegans and genes dysregulated in the striatum of either CHL2 knock-in mice or R6/2 transgenic mice. (A) Overlap between dysregulated genes in the mouse striatum and suppressors of 128Q-neuron dysfunction that have mouse orthologs. Numbers of genes are indicated. The lists of genes at the intersection of the nematode and mouse datasets are indicated below the graphs. (B) Overlap between dysregulated genes in the mouse striatum and enhancers of 128Q- neuron dysfunction that have mouse orthologs. The numbers of genes is indicated. The lists of genes at the intersection of the nematode and mouse datasets are indicated below the graphs. Some genes are oppositely dysregulated in the CHL2 mice compared to R6/2 mice (yellow tags). In all panels, red circles indicate C. elegans genes that modify 128Q-neuron toxicity. *Druggable genes. previously identified showed no effect on normal polyQ cytotoxicity [14,18]. Additionally, we observed that greater than 90% of the modifiers of 128Q-neuron dys- function showed no effect in 19Q nematodes using a subset of 243 genes identified by RNAi screening in 128Q nematodes. Collectively, these observations sug- gest that the mechanisms of neuron dysfunction in 19Q nematodes [14] are different from those underlying the strong level of neuron dysfunction in 128Q nematodes. While specific modifiers of expanded-polyQ cytotoxicity might constitute preferred HD targets, one cannot exclude the possibility that non-specific modifiers of neuron dysfunction in polyQ nematodes may belong to the core machinery for neuron differentiation and survi- val, thus being of potential interest, at least from a screening perspective. This was for example illustrated by two N-acetylgalactosaminyltransferases (gly-9 and gly-3), a family of enzymes involved in neuron differen- tiation and survival [50]. A major feature of network-boosted analysis is to reduce data complexicity by highlighting individual pathways and possibly inter-connected pathways that are embedded into modules seeded with gene activity. In this respect, the network-boosted data analysis of our RNAi dataset was more instructive compared to the sole use of GO annotations or gene set enrichment analysis. While a role for a variety of pathways was suggested by gene set enrichment analysis, network-boosted analysis indeed emphasized a role for these pathways in a more comprehensive fashion, providing new insights of how