PubMed · 9182793
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J Williams. 1997. How does a vesicle know it is full?. https://doi.org/10.1016/s0896-6273(00)80308-6
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Intronic GGGGCC repeat expansions in C9orf72 cause amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). This expansion supports a non-canonical form of translational initiation known as repeat-associated non-AUG (RAN) translation to produce toxic dipeptide repeat proteins that contribute to neurodegeneration. Here, we find that the efficiency of RAN translation and its dependency on the 5' 7-methylguanosine mRNA cap are variable across cell types, with both rodent neurons and human iNeurons favoring cap-independent RAN translation from two distinct repeats (CGG and GGGGCC) across multiple reading frames. Treatment with an eIF4E inhibitor that blocks cap-dependent translation enhances RAN translation specifically in neurons. Intriguingly, cap-independent RAN translation exhibits less reliance on near-cognate codons for initiation than cap-dependent RAN translation. This finding led us to identify a surprising global alteration in neuronal start codon stringency as a contributor to the relatively higher cap-independent RAN translation in this cell type. This effect correlates with cytoplasmic redistribution of eIF1 in neurons and is reversed with overexpression of the eukaryotic initiation factor eIF5, which relaxes start codon stringency and preferentially enhances cap-dependent RAN translation. Together, these findings reveal several neuron-specific features of translational regulation that favor cap-independent RAN translation with implications for nucleotide repeat expansion disorder pathogenesis.
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The stochastic mechanism of synchronous firing in a population of neurons is studied from the point of view of information geometry. Higher-order interactions of neurons, which cannot be reduced to pairwise correlations, are proved to exist in synchronous firing. In a neuron pool where each neuron fires stochastically, the probability distribution q(r) of the activity r, which is the fraction of firing neurons in the pool, is studied. When q(r) has a widespread distribution, in particular, when q(r) has two peaks, the neurons fire synchronously at one time and are quiescent at other times. The mechanism of generating such a probability distribution is interesting because the activity r is concentrated on its mean value when each neuron fires independently, because of the law of large numbers. Even when pairwise interactions, or third-order interactions, exist, the concentration is not resolved. This shows that higher-order interactions are necessary to generate widespread activity distributions. We analyze a simple model in which neurons receive common overlapping inputs and prove that such a model can have a widespread distribution of activity, generating higher-order stochastic interactions.