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Ferrero, E.

Publications and source records attributed to Ferrero, E..

2 recordsLinked to original sources

Mice harboring a SCA28 patient mutation in AFG3L2 develop late-onset ataxia associated with enhanced mitochondrial proteotoxicity.

Spinocerebellar ataxia 28 is an autosomal dominant neurodegenerative disorder caused by missense mutations affecting the proteolytic domain of AFG3L2, a major component of the mitochondrial m-AAA protease. However, little is known of the underlying pathogenetic mechanisms or how to treat patients with SCA28. Currently available Afg3l2 mutant mice harbour deletions that lead to severe, early-onset neurological phenotypes that do not faithfully reproduce the late-onset and slowly progressing SCA28 phenotype. Here we describe production and detailed analysis of a new knock-in murine model harbouring an Afg3l2 allele carrying the p.Met665Arg patient-derived mutation. Heterozygous mutant mice developed normally but signs of ataxia were detectable by beam test at 18 months. Cerebellar pathology was negative; electrophysiological analysis showed increased spontaneous firing in Purkinje cells from heterozygous mutants with respect to wild-type controls, although not statistically significant. As homozygous mutants died perinatally with evidence of cardiac atrophy, for each genotype we generated mouse embryonic fibroblasts (MEFs) to investigate mitochondrial function. MEFs from mutant mice showed altered mitochondrial bioenergetics, with decreased basal oxygen consumption rate, ATP synthesis and mitochondrial membrane potential. Mitochondrial network formation and morphology was also altered, in line with greatly reduced expression of Opa1 fusogenic protein L-isoforms. The mitochondrial alterations observed in MEFs were also detected in cerebella of 18-month-old heterozygous mutants, suggesting they may be a hallmark of disease. Pharmacological inhibition of de novo mitochondrial protein translation with chloramphenicol caused reversal of mitochondrial morphology in homozygous mutant MEFs, supporting the relevance of mitochondrial proteotoxicity for SCA28 pathogenesis and therapy development.

genetics

Opportunities And Obstacles For Deep Learning In Biology And Medicine

Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We examine applications of deep learning to a variety of biomedical problems--patient classification, fundamental biological processes, and treatment of patients--and discuss whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges. We find that deep learning has yet to revolutionize or definitively resolve any of these problems, but promising advances have been made on the prior state of the art. Even when improvement over a previous baseline has been modest, we have seen signs that deep learning methods may speed or aid human investigation. More work is needed to address concerns related to interpretability and how to best model each problem. Furthermore, the limited amount of labeled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning powering changes at both bench and bedside with the potential to transform several areas of biology and medicine.

bioinformatics