Biology subjects

Pitceathly, R. D.

Publications and source records attributed to Pitceathly, R. D..

3 recordsLinked to original sources

bioRxiv2024

Metabolic remodelling in hiPSC-derived myofibres carrying the m.3243A>G mutation

Mutations in mitochondrial DNA cause severe multisystem disease, frequently associated with muscle weakness. The m.3243A>G mutation is the major cause of Mitochondrial Encephalomyopathy Lactic Acidosis and Stroke Like episodes (MELAS). Experimental models that recapitulate the disease phenotype in vitro for disease modelling or drug screening are very limited. We have therefore generated hiPSC-derived muscle fibres with variable heteroplasmic mtDNA mutation load without significantly affecting muscle differentiation potential. The cells are excitable and show physiological characteristics of muscle fibres and show well organised myofibrillar structure. In cells carrying the m.3243A>G, the mitochondrial membrane potential and oxygen consumption were reduced in relation to the mutant load. We have shown through proteomic, phosphoproteomic, and metabolomic analyses that the m.3243A>G mutation variably affects the cell phenotype in relation to the mutant load. This variation is reflected by an increase in the NADH/NAD+ ratio, which in turns influences key nutrient-sensing pathways in the myofibres. This model enables detailed study of the impact of the mutation on cellular bioenergetics and on muscle physiology with the potential to provide a platform for drug screening.

cell biology↗

bioRxiv2024

The specific AMPK activator A-769662 ameliorates pathological phenotypes following mitochondrial DNA depletion

AMP-activated protein kinase (AMPK) is a master regulator of cellular energy homeostasis that also plays a role in preserving mitochondrial function and integrity. Upon a disturbance in the cellular energy state that increases AMP levels, AMPK activity promotes a switch from anabolic to catabolic metabolism to restore energy homeostasis. However, it is currently unclear how severe of a mitochondrial dysfunction is required to trigger AMPK activation, and whether stimulation of AMPK using specific agonists can improve the cellular phenotype following mitochondrial dysfunction. Using a cell model of mitochondrial disease characterized by progressive mitochondrial DNA (mtDNA) depletion and deteriorating mitochondrial metabolism, we show that mitochondria-associated AMPK becomes activated early in the course of the advancing mitochondrial dysfunction, before any quantifiable decrease in the ATP/(AMP+ADP) ratio or respiratory chain activity. Moreover, stimulation of AMPK activity using the specific small-molecule agonist A-769662 alleviated the mitochondrial phenotypes caused by the mtDNA depletion and restored normal mitochondrial membrane potential. Notably, the agonist treatment was able to partially restore mtDNA levels in cells with severe mtDNA depletion, while it had no impact on mtDNA levels of control cells. The beneficial impact of the agonist was also observed in cells from patients suffering from mtDNA depletion. However, the positive effects of A-769662 in the two experimental cell models appeared to involve at least partially different mechanisms. These findings improve our understanding of the effects of specific small-molecule activators of AMPK on mitochondrial and cellular function, and suggest a potential utility for these compounds in disease states involving mtDNA depletion.

molecular biology↗

bioRxiv2021

Detection of pathogenic splicing events from RNA-sequencing data using dasper

Although next-generation sequencing technologies have accelerated the discovery of novel gene-to-disease associations, many patients with suspected Mendelian diseases still leave the clinic without a genetic diagnosis. An estimated one third of these patients will have disorders caused by mutations impacting splicing. RNA-sequencing has been shown to be a promising diagnostic tool, however few methods have been developed to integrate RNA-sequencing data into the diagnostic pipeline. Here, we introduce dasper, an R/Bioconductor package that improves upon existing tools for detecting aberrant splicing by using machine learning to incorporate disruptions in exon-exon junction counts as well as coverage. dasper is designed for diagnostics, providing a rank-based report of how aberrant each splicing event looks, as well as including visualization functionality to facilitate interpretation. We validate dasper using 16 patient-derived fibroblast cell lines harbouring pathogenic variants known to impact splicing. We find that dasper is able to detect pathogenic splicing events with greater accuracy than existing LeafCutterMD or z-score approaches. Furthermore, by only applying a broad OMIM gene filter (without any variant-level filters), dasper is able to detect pathogenic splicing events within the top 10 most aberrant identified for each patient. Since using publicly available control data minimises costs associated with incorporating RNA-sequencing into diagnostic pipelines, we also investigate the use of 504 GTEx fibroblast samples as controls. We find that dasper leverages publicly available data effectively, ranking pathogenic splicing events in the top 25. Thus, we believe dasper can increase diagnostic yield for a pathogenic splicing variants and enable the efficient implementation of RNA-sequencing for diagnostics in clinical laboratories.

bioinformatics↗