Recent studies have pointed toward the role of mitochondrial dysfunction in the onset of Parkinson’s disease (PD). Researchers from the Luxembourg Institute of Health (LIH) used a machine-learning approach to identify mitochondria interactions as a novel biomarker to
classify PD patients.

“Since conventional analysis focusing on individual mitochondria has not provided satisfying insights into PD pathogenesis, our pioneering work has gone a step forward by investigating the interaction networks between these organelles,” explained Feng He, PhD, group leader of the Immune Systems Biology Group of the LIH department of infection and immunity.

Their findings, “Mitochondria interaction networks show altered topological patterns in Parkinson’s disease,” were published in the journal Nature Partner Journals Systems Biology and Applications.

“Mitochondrial dysfunction is linked to pathogenesis of Parkinson’s disease. However, individual mitochondria-based analyses do not show a uniform feature in PD patients,” the researchers wrote. Since mitochondria interact with each other, we hypothesize that PD-related features might exist in topological patterns of mitochondria interaction networks (MINs). Here we show that MINs formed nonclassical scale-free supernetworks in colonic ganglia both from healthy controls and PD patients; however, altered network topological patterns were observed in PD patients.”

The researchers analyzed a large 700 Gigabyte dataset of three-dimensional mitochondrial images of colonic neurons, collected from PD patients and healthy controls, and dopaminergic neurons, derived from stem cells. The researchers observed that particular network structure features within MINs were altered in PD patients.

Machine-learning based analysis of mitochondria interaction networks. [LIH]
“These different topological patterns in MINs may mean that energy and information are possibly produced, shared, and distributed less competently in the neuronal mitochondria of PD patients relative to healthy controls, suggesting their connection to mitochondrial damage, deficiencies, and fragmentation typical of neurodegenerative disorders,” explained He.

When applying a machine learning approach to analyze these MIN characteristics, the researchers observed that the use of a combination of those network features alone allowed them to accurately distinguish between PD patients and healthy controls.

“Our findings bring forward the potential of using particular mitochondrial network features as novel biomarkers for the early diagnosis and classification of PD patients, which might help develop a new health index. As a next step, we will explore how our results may offer new perspectives for the understanding of various other neurodegenerative diseases characterized by mitochondrial dysregulation, such as Huntington’s disease and Alzheimer’s, making our work a true instance of translational and transversal research,” added Rejko Krüger, professor and director of transversal translational medicine at LIH and contributing author of the study.

“This publication also constitutes a major step forward in the application of advanced machine-learning techniques to unravel the complex network interactions of cellular organelles for disease stratification. Indeed, data analytics and innovative digital technologies are a core priority area for our department and for LIH as a whole,” concluded Markus Ollert, director of the department of infection and immunity and contributing author of the paper.

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