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Machine learning algorithms reveal the secrets of mitochondrial dynamics

Lookup NU author(s): Jack Collier, Professor Robert Taylor

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Abstract

© 2021 The Authors. Published under the terms of the CC BY 4.0 licenseMitochondria exist as dynamic networks whose morphology is driven by the complex interplay between fission and fusion events. Failure to modulate these processes can be detrimental to human health as evidenced by dominantly inherited, pathogenic variants in OPA1, an effector enzyme of mitochondrial fusion, that lead to network fragmentation, cristae dysmorphology and impaired oxidative respiration, manifesting typically as isolated optic atrophy. However, a significant number of patients develop more severe, systemic phenotypes, although no genetic modifiers of OPA1-related disease have been identified to date. In this issue of EMBO Molecular Medicine, supervised machine learning algorithms underlie a novel tool that enables automated, high throughput and unbiased screening of changes in mitochondrial morphology measured using confocal microscopy. By coupling this approach with a bespoke siRNA library targeting the entire mitochondrial proteome, the work described by Cretin and colleagues yielded significant insight into mitochondrial biology, discovering 91 candidate genes whose endogenous depletion can remedy impaired mitochondrial dynamics caused by OPA1 deficiency.


Publication metadata

Author(s): Collier JJ, Taylor RW

Publication type: Article

Publication status: Published

Journal: EMBO Molecular Medicine

Year: 2021

Volume: 13

Online publication date: 27/05/2021

Acceptance date: 13/04/2021

ISSN (print): 1757-4676

ISSN (electronic): 1757-4684

Publisher: Blackwell Publishing Ltd

URL: https://doi.org/10.15252/emmm.202114316

DOI: 10.15252/emmm.202114316


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