Spinal muscular atrophy (SMA) is a neuromuscular disorder characterized by the selective degeneration of lower spinal cord motor neurons, which leads to progressive muscle atrophy and death. SMA is caused by mutations of the Survival of Motor Neuron gene, Smn1, and although the genetic bases of SMA have been extensively studied, no effective treatment is available. Unbiased chemical screens using in vivo animal model systems can be a powerful approach to identify potential therapeutic compounds, as well as to elucidate the molecular basis of a disease. In C. elegans a SMA model has been developed by classical genetic approach (allele
cb131) and has been used to screen a library of chemicals, allowing the identification of three compounds (Sleigh et al., Hum. Mol. Gen. 2010). In this genetic mutant, however, no variations in the number of motor neurons have been detected, thus hampering the possibility to identify neuroprotective drugs. In order to find molecules preventing the neurodegeneration caused by
smn-1 loss, we took advantage of a genetic model based on neuron-specific silencing of
smn-1. Our new SMA model was generated by using a modification of the RNA-interference (RNAi) technique (Esposito et al., Gene, 2007), which is transgene driven and is based on the expression, under a cell-specific promoter, of sense and antisense RNA molecules corresponding to fragments of a gene of interest. This RNAi strategy enabled us to efficiently reduce the function of
smn-1 gene specifically in GABAergic motor neurons. Transgenic strains in which
smn-1 is knocked down, present an age-dependent and progressive degeneration of the GABAergic motor neurons that results in altered backward movement and in neuronal cell death. By using this genetic model we screened a small panel of chemicals and found that valproic acid, a compound successfully used in other neurodegenerative models (Kautu BB et al., Neurosci. Lett. 2013), can partially prevent neuronal death. Interestingly, two out of three molecules, identified by the previous screening on the genetic mutant, were not active on our model, thus confirming the power of our alternative approach. Moreover, we have screened 15 natural extracts from various plants and from various part of them, which were cultivated under different growth conditions, and we have observed interesting results with 5 of them, which are capable of preventing up to half of neuronal deaths observed in our model. Finally, we are setting up the conditions to carry out an automated high-content screening of a chemical library, consisting of FDA-approved compounds.