Lejeune et al. BMC Genomics 2012, 13:91 http://www.biomedcentral.com/1471-2164/13/91 the genes associated to these pathways may interact within modules. Furthermore, network-boosted analysis identified pathways not detected by gene set enrichment analysis. As indicated by the pathways embedded in the modules resulting from network-boosted analysis (see the on-line database at http://www.broca.inserm.fr/ EHDN2/RNAiscreen), a role for neurodegenerative dis- ease pathways and neurodevelopmental/cell survival pathways in the modulation of expanded-polyQ cyto- toxicity was emphasized, which may provide guidance to the selection of candidate targets for neuroprotection in HD. The annotation of the modules resulting from net- work-boosted analysis identified clusters enriched in genes of interest such as druggable genes, htt partners and genes involved in autophagy, mitochondrial function and synaptic activity (all gene classes which can be quer- ied using the on-line database), providing additional gui- dance to neuroprotective target discovery in HD. Here, it is important to note that the hypothesis underlying network-based approaches is that highly-interconnected genes might show similar biological activities. In this respect, the network-boosted analysis of our data is pre- dictive of the genes that might decrease or increase 128Q-neuron dysfunction when knocked-down by RNAi, even though they showed no effect or borderline effects in the RNAi screen. While this may increase the number of candidate targets to be considered for neuro- protective intervention, it decreases the risk for noise associated with RNAi screens and favors the inclusion of potentially-interesting information for multi-model target discovery. Multi-model target discovery is strongly expected to enhance the drug discovery process for neuroprotection in HD. A standard method for the validation of modi- fiers of neuronal dysfunction as instructed by simple models of HD pathogenesis relies on target validation in the mouse. Because it is not yet feasible to test in the mouse all genes supported as modifiers of neuronal decline in simple models, the outcome of our functional screen and other screens will be analyzed using data integration procedures in which the selection of the best targets for validation will rely on target expression levels and activity in multiple models of HD. One of the objectives of data integration is to assess the relevance of simple-model data to mouse models, and more lar- gely to human neurodegenerative disease. In this respect, it is notable that several modifiers of 128Q-neu- ron dysfunction found in our screen were previously associated with the pathogenesis of neurodegenerative diseases such as HD or AD, suggesting that our C. ele- gans dataset is relevant to HD pathogenesis in higher order models of the disease. This is for example the case for four genes retained by network-boosted analysis including (i) dnj-1/DNAJ, a gene that encodes a DNAJ Page 12 of 14 chaperone [51], aggravates neuron dysfunction when knocked-down by RNAi and is down-regulated in the CHL2 mice, (ii) sel-8, a C. elegans gene that encodes a nuclear protein required for lin-12/NOTCH signaling, which may have neuron survival activities [52], and that aggravates 128Q-neuron dysfunction when knocked- down by RNAi, (iii) jnk-1/JNK, a gene that promote HD pathology in a rat model of the disease [53] and sup- presses 128Q-neuron dysfunction when knocked-down by RNAi, and (iv) pnk-1/PanK4, a gene that encodes a panthotenate kinase and suppresses 128Q-neuron dys- function when knocked-down by RNAi, which is consis- tent with the pathological role of panthotenate kinases in neurodegeneration [45]. Additionally, the comparison of our data to gene dysregulation in the striatum of CHL2 knock-in mice versus that of R6/2 transgenic mice further indicated that our dataset contains infor- mation relevant to HD target selection as illustrated by the proposed inhibition of phenylalanine-4-hydroxylase as a candidate strategy to reduce early HD phenotypes that may be associated to a transient excess of dopamine [49]. Conclusions In conclusion, our results provide new insights into the genes and pathways that might regulate the dysfunction and death of neurons expressing mutant polyQs, emphasizing a role for specific metabolic, developmental and pro-survival pathways. Several models of HD are being used to screen for modifiers of mutant polyQ toxicity and for genes dysregulated by mutant htt. In this context, the use of cross-species data integration for the analysis of our RNAi dataset and other datasets is anticipated to boost the prediction and prioritisation of neuroprotective targets in HD, and this will be investi- gated in additional studies. Additional material Additional file 1: Figure S1. Distribution of S scores for the primary screen in 128Q;rrf-3 nematodes. The graph shows the S scores for 4017 RNAi clones that did not elicit lethality or developmental abnormalities. The S score was calculated as [(Percent response - mean baseline)/mean baseline]. The maximally-achievable score is 4.55 (100% response to touch) and the smallest score is -1 (complete loss of touch response). The blue line indicate the mean baseline (S = 0). Dooted blue lines show the mean baseline ± SD*2.6. Clones with S scores falling outside the mean baseline ± SD*2.6 interval were retained for the secondary screen. Additional file 2: Table S1. List of the 2211 genes that caused lethality and developmental abnormalities when knocked-down by RNAi. Additional file 3: Table S2. List of the 3823 genes that either showed no effect or modified 128Q-neuron dysfunction when knocked-down by RNAi in the primary screen. Additional file 4: Table S3. List of the 662 genes that modified 128Q- neuron dysfunction when knocked-down by RNAi in the secondary screen.