Characterization of fission and fusion mitochondrial dynamics in HD fibroblasts according to patient's severity
Saja Gharaba1, Uri Sprecher1, Adam Baransi1
1Laboratory for Personalized Medicine and Neurodegenerative Diseases, The Shmunis School of Biomedicine and Cancer Research, The George S. Wise Faculty for Life Sciences, Sagol School of Neurosciences, Tel Aviv University, Ramat Aviv, 69978 Tel Aviv, Israel.
Insights
Huntington's Disease (HD) patient cells show altered mitochondrial dynamics. A new machine learning tool identifies these HD cell changes, even before disease onset, aiding diagnostics and drug evaluation.
Area of Science:
- Neurodegenerative diseases
- Cell biology
- Genetics
Background:
- Huntington's Disease (HD) is an inherited neurodegenerative disorder.
- It is caused by CAG trinucleotide repeats in the HTT gene, with repeat length correlating to disease severity and earlier onset.
- Previous studies identified unique cellular phenotypes in HD patient fibroblasts, including nuclear morphology and actin cytoskeleton alterations linked to disease severity.
Purpose of the Study:
- To investigate mitochondrial morphology and dynamics in Huntington's Disease (HD) patient fibroblasts.
- To develop and validate a machine learning tool for classifying mitochondrial phenotypes.
- To assess the correlation between mitochondrial dynamics and HD disease severity, including premanifest stages.
Main Methods:
- Utilized primary skin fibroblasts from HD patients and healthy controls.
- Employed a custom image-based high-content analysis (HCA) machine learning tool to classify mitochondrial fission-fusion morphology.
- Quantified levels of key mitochondrial fission-fusion biomarkers (Drp1, MFN1, MFN2, VAT1).
Main Results:
- Mitochondrial fission-fusion balance dynamics were significantly altered in HD patient fibroblasts.
- The HCA machine learning tool demonstrated an improved correlation with HD patient disease severity status.
- Changes in mitochondrial morphology were supported by altered expression levels of fission-fusion biomarkers.
- The methodology successfully identified HD phenotypes in premanifest individuals.
Conclusions:
- HD patient fibroblasts exhibit dysregulated mitochondrial dynamics, with potential disruption in both fission and fusion processes.
- The developed HCA tool offers a novel method for identifying HD phenotypes, including in premanifest stages.
- This approach has potential for improving HD diagnostics, monitoring disease progression, and evaluating drug therapies.
Abstract:
Huntington's Disease (HD) is an inheritable neurodegenerative condition caused by an expanded CAG trinucleotide repeat in the HTT gene with a direct correlation between CAG repeats expansion and disease severity with earlier onset-of- disease. Previously we have shown that primary skin fibroblasts from HD patients exhibit unique phenotype disease features, including distinct nuclear morphology and perturbed actin cap linked with cell motility, that are correlated with the HD patient disease severity. Here we provide further evidence that mitochondrial fission-fusion morphology balance dynamics, classified using a custom image-based high-content analysis (HCA) machine learning tool, that improved correlation with HD severity status. This mitochondrial phenotype is supported by appropriate changes in fission-fusion biomarkers (Drp1, MFN1, MFN2, VAT1) levels in the HD patients' fibroblasts. These findings collectively point towards a dysregulation in mitochondrial dynamics, where both fission and fusion processes may be disrupted in HD cells compared to healthy controls. This study shows for the first time a methodology that enables identification of HD phenotype before patient's disease onset (Premanifest). Therefore, we believe that this tool holds a potential for improving precision in HD patient's diagnostics bearing the potential to evaluate alterations in mitochondrial dynamics throughout the progression of HD, offering valuable insights into the molecular mechanisms and drug therapy evaluation underlying biological differences in any disease stage.
