Lejeune et al. BMC Genomics 2012, 13:91 http://www.biomedcentral.com/1471-2164/13/91 Page 6 of 14 Figure 2 Overview of the screening and data analysis strategy. genes) or suppress (herein referred to as ‘pathogenic process-associated’ or PPA genes) 128Q-neuron dys- function when knocked-down by RNAi. Pathway analy- sis for PA genes identified eight metabolic pathways such as for example those involved in glycolysis and amino acid metabolism (Table 1). Consistent with the notion that mutant htt expression may dysregulate a large number of genes and alter a variety of biological processes [3,10], pathway analysis for PPA genes identi- fied three main pathway categories, including pathways primarily involved in metabolism (glycolysis, amino acid metabolism), cytokine signaling/immune system (inter- feron-gamma signaling, interleukin signaling, Toll recep- tor signaling) and cellular differentiation and homeostasis (IGF pathway, angiogenesis, integrin signal- ing, p53 pathway by glucose deprivation, FGF signaling, EGF signaling, Toll receptor signaling) (Table 2). Inter- estingly, the latter category included the Alzheimer’s dis- ease (AD)-amyloid secretase pathway, pathways previously associated to neuroprotection in HD such as IGF signaling [14] and FGF signaling [42], pathways that could play a role in neurodegenerative disease pathogen- esis such as EGF signaling [43], and pathways having a dual role in neurodegeneration and neuroprotection such as Toll receptor signaling [44]. These observations illustrated the revelance of our RNAi dataset to neuro- degeneration. Given that PPA genes might constitute preferred targets for therapeutic intervention, these results suggested that several of the targets for early- stage neuroprotection in HD may lie in the pathways that are important to neuronal cell differentiation and survival. In contrast to metabolic pathways which may show a rather simple structure, pathways that respond to exter- nal stimuli to regulate gene expression may be highly inter-connected, and they may have highly-branched structures and several biological outcomes. Additionally, RNAi-based screens may generate noise due to off-tar- get effects and variations in the degree of knockdown of target genes. Therefore, to map RNAi activity to func- tional clusters and to identify a more reliable set of genes from the RNAi hits, we used a network-based approach. To this end, we used a Fourier-like analysis [32] based on the reference network Wormnet [26]. To enhance the discriminative power and reliability of this analysis, we developed and applied an optimized version of the Fourier-like analysis (see Materials and Methods). We analyzed the gene set for which a score for activity