Ogier, A. | Search bioRxiv

bioRxiv2022

Cyclosporin A delays the terminal disease stage in Tfam KO mice without improving mitochondrial energy production

AO_SCPLOWBSTRACTC_SCPLOWMitochondrial myopathies are rare genetic disorders characterized by muscle weakness and exercise intolerance. Currently, no effective treatment exists for these myopathies. Interestingly, the pharmacological cyclophilin inhibitor cyclosporine A (CsA) extended lifespan and prevented loss of force and mitochondrial Ca2+ overload in muscle fibers in the skeletal muscle-specific Tfam knockout mouse model of lethal mitochondrial myopathy (Tfam KO). The unaffected expression of proteins involved in mitochondrial energy metabolism suggests that these improvements occurred without improvement in metabolism. In this study, we aimed at investigating the effects of four weeks of CsA administration on in vivo contractile function and mitochondrial energy production in Tfam KO mice. The treatment started before the terminal phase with severe muscle weakness and weight loss. Our results show that CsA treatment delayed progression into the terminal disease phase. This occurred without any obvious positive effects on mitochondrial energy production at rest or during fatigue induced by repeated contractions. In conclusion, cyclophilin inhibitors may have the potential of counteracting devastating muscle weakness in patients with mitochondrial myopathies most probably by preventing deleterious effects triggered by excessive mitochondrial Ca2+ uptake rather than by improving mitochondrial energy production.

pathology↗

bioRxiv2022

Machine learning-aided multidimensional phenotyping of Parkinson's disease patient stem cell-derived midbrain dopaminergic neurons

Combining multiple Parkinsons disease (PD) relevant cellular phenotypes might increase the accuracy of midbrain dopaminergic (mDA) in vitro models. We differentiated patient-derived induced pluripotent stem cells (iPSCs) with a LRRK2 G2019S mutation, isogenic control and genetically unrelated iPSCs into mDA neurons. Using automated fluorescence microscopy in 384-well plate format, we identified elevated levels of -synuclein and Serine 129 phosphorylation (pS129), reduced dendritic complexity, and mitochondrial dysfunction. Next, we measured additional image-based phenotypes and used machine learning (ML) to accurately classify mDA neurons according to their genotype. Additionally, we show that chemical compound treatments, targeting LRRK2 kinase activity or -synuclein levels, are detectable when using ML classification based on multiple image-based phenotypes. We validated our approach using a second isogenic patient derived SNCA gene triplication mDA neuronal model. This phenotyping and classification strategy improves the exploitability of mDA neurons for disease modelling and the identification of novel PD drug targets.

neuroscience↗