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Mutations in the DYNC1H1 gene encoding for dynein heavy chain cause two closely related human motor neuropathies, dominant spinal muscular atrophy with lower extremity predominance (SMA–LED) and axonal Charcot–Marie–Tooth (CMT) disease, and lead to sensory neuropathy and striatal atrophy in mutant mice. Dynein is the molecular motor carrying mitochondria retrogradely on microtubules, yet the consequences of dynein mutations on mitochondrial physiology have not been explored. Here, we show that mouse fibroblasts bearing heterozygous or homozygous point mutation in Dync1h1, similar to human mutations, show profoundly abnormal mitochondrial morphology associated with the loss of mitofusin 1. Furthermore, heterozygous Dync1h1 mutant mice display progressive mitochondrial dysfunction in muscle and mitochondria progressively increase in size and invade sarcomeres. As a likely consequence of systemic mitochondrial dysfunction, Dync1h1 mutant mice develop hyperinsulinemia and hyperglycemia and progress to glucose intolerance with age. Similar defects in mitochondrial morphology and mitofusin levels are observed in fibroblasts from patients with SMA–LED. Last, we show that Dync1h1 mutant fibroblasts show impaired perinuclear clustering of mitochondria in response to mitochondrial uncoupling. Our results show that dynein function is required for the maintenance of mitochondrial morphology and function with aging and suggest that mitochondrial dysfunction contributes to dynein-dependent neurological diseases, such as SMA–LED.

We investigated the mechanisms underlying the online-processing of phonological constraints using oddball fast-periodic visual stimulation coupled with EEG. We focused on the Sonority Sequencing Principle and examined whether steady-state visual evoked potentials (SSVEPs) are sensitive to the sonority constraint on syllable onsets. Native French speakers were presented with streams of CCVC non-words (C: consonant, V: vowel) at a fixed 6-Hz base rate. We manipulated the phonological well-formedness and lexical attestedness of CC onsets in two conditions. SSVPs were observed at the base rate associated to visual stimuli. As expected, they did not differ between conditions. Oddball SSVEPs were observed at 1.2 Hz (and its harmonics) and differed in the two conditions. These results showed that SSVEPs are sensitive to sublexical features. They also suggest that the processing of phonological constraints rely on mechanisms which could be dissociated from those underlying the processing of statistical properties of the lexicon., (Copyright © 2022. Published by Elsevier Inc.)

In natural listening situations, understanding spoken sentences requires interactions between several multisensory to linguistic levels of information. In two electroencephalographical studies, we examined the neuronal oscillations of linguistic prediction produced by unimodal and bimodal sentence listening to observe how these brain correlates were affected by the sensory streams delivering linguistic information. Sentence contexts which were strongly predictive of a particular word were ended by a possessive adjective matching or not the gender of the predicted word. Alpha, beta and gamma oscillations were investigated as they were considered to play a crucial role in the predictive process. During the audiovisual or auditory-only listening to sentences, no evidence of word prediction was observed. In contrast, in a more challenging listening situation during which bimodal audiovisual streams switched to unimodal auditory stream, gamma power was sensitive to word prediction based on prior sentence context. Results suggest that prediction spreading from higher sentence levels to lower word levels is optional during unimodal and bimodal sentence listening and is observed when the listening situation is more challenging. Alpha and beta oscillations were found to decrease when semantically constraining sentences were delivered in the audiovisual modality in comparison with the auditory-only modality. Altogether, our findings bear major implications for our understanding of the neural mechanisms that support predictive processing in multimodal language comprehension., (Copyright © 2022 Elsevier B.V. All rights reserved.)