IIT Madras Researchers find Energy Deficiency in some Brain Cells to be a major cause for Parkinson’s Disease

IIT Madras Researchers find Energy Deficiency in some Brain Cells to be a major cause for Parkinson’s Disease

Hummingbird News Desk

CHENNAI, 11 MARCH: Indian Institute of Technology Madras researchers have found that energy deficiency in certain cells in the human brain to be a major cause for Parkinson’s Disease (PD), a neurodegenerative disorder. This research could lead to scientists and other stakeholders focusing on improving the efficiency of energy delivery to these cells, which might finally lead to a cure for PD.

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PD is the second most prominent neurodegenerative disease around the world after Alzheimer’s disease. More than 200 years after it was first described by Dr. James Parkinson as “shaking palsy,” the world is still searching for a cure. Currently, the medical community is mostly focused only on the management of the disease. The innovative methodology and novel findings of this IIT Madras research will go a long way forward in understanding the pathophysiology of PD.

Although it is known that PD is caused by the loss of dopaminergic cells in substantia nigra pars compacta (SNc), the decisive cause of this inexorable cell loss has not clearly been elucidated before. The IIT Madras researchers developed a computational model that showed that energy deficiency might be a major cause of SNc cell loss in Parkinson’s Disease.

This computational modelling was developed by Dr. Vignayanandam Ravindernath Muddapu, who completed his Ph.D. recently at IIT Madras, under the guidance of Prof. V. Srinivasa Chakravarthy, Department of Biotechnology, IIT Madras. Dr. Vignayanandam Ravindernath Muddapu has now joined the Blue Brain Project, an EPFL-linked research center for postdoctoral research. The findings of this research have been published recently in the prestigious peer-reviewed International Journal Nature Scientific Reports.

Elaborating on the important findings of this research, Prof. V. Srinivasa Chakravarthy, Department of Biotechnology, IIT Madras, said, “While existing treatments manage PD symptoms – sometimes with great effect – a cure demands an understanding of the root cause of SNc cell loss. This is the main question addressed in our work: What is the major underlying cause of SNc cell loss in PD?”

Prof. V. Srinivasa Chakravarthy, Department of Biotechnology, IIT Madras (Image courtesy: IIT Madras)

Further, Prof. V. Srinivasa Chakravarthy said, “It is quite remarkable that loss of neurons in a small nucleus like SNc can have wide-ranging, devastating effects in all the four major domains of brain function – sensory-motor, cognitive, affective, and autonomous. The sequence of the three computational studies suggests that metabolic deficiency within the basal ganglia circuit is the common underlying factor at the subcellular, cellular, and network level in PD. Thus, we have a reasonably comprehensive theory of the pathogenesis of Parkinson’s disease.”

This research was undertaken at IIT Madras Computational Neuroscience Laboratory, which aims to build a simplified model of the whole brain and use it to develop applications in medicine and engineering. Prof. V. Srinivasa Chakravarthy is the head of Computational Neuroscience Laboratory.

The computational model showed that: At the subcellular level, metabolic deficiency leads to changes like including alpha-synuclein aggregation, reactive oxygen species production, calcium elevation, and dopamine dysfunction, which are characteristic subcellular changes in Parkinson’s disease.

Building on the platform of this research, Prof. V. Srinivasa Chakravarthy and his team plan to develop a therapeutic computational testbench for PD, wherein the proposed model of SNc will be the center of a larger framework. This will link cellular-level dysfunctions to behavioural-level abnormalities.

Moving forward in the next five years, this type of framework will help in providing personalized medicine for PD patients rather than the currently employed trial and error approach.

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