Abstract
Amyotrophic Lateral Sclerosis is a progressive, incurable amyloid aggregating neurodegenerative disease involving the motor neurons. Identifying potential biomarkers and therapeutic targets can assist in the better management of the disease. We used an integrative approach encompassing analysis of transcriptomic datasets of human and mice from the GEO database. Our analysis of ALS patient datasets showed deregulation in Non-alcoholic fatty acid liver disease and oxidative phosphorylation. Transgenic mice datasets of SOD1, FUS and TDP-43 showed deregulation in oxidative phosphorylation and ribosome-associated pathways. Commonality analysis between the human and mice datasets showed oxidative phosphorylation as a major deregulated pathway. Further, protein-protein and protein-drug interaction network analysis of mitochondrial electron transport chain showed enrichment of proteins and inhibitors of mitochondrial Complex III and IV. The results were further validated using the yeast model system. Inhibitor studies using metformin (Complex-I inhibitor) and malonate (Complex-II inhibitor) did not show any effect in mitigating the amyloids, while antimycin (Complex-III inhibitor) and azide (Complex-IV inhibitor) reduced amyloidogenesis. Knock-out of QCR8 (Complex-III) or COX8 (Complex–IV) cleared the amyloids. Taken together, our results show a critical role for mitochondrial oxidative phosphorylation in amyloidogenesis and as a potential therapeutic target in ALS.
Communicated by Ramaswamy H. Sarma
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Acknowledgement
A sincere thanks to Sri Sathya Sai institute of higher learning for providing free and valuable education. We acknowledge our Central Research Instruments Facility (CRIF), Prasanthi Nilayam and the Institute of Bioinformatics and Applied Biotechnology, Bengaluru. We also acknowledge Agilent technologies for providing “Genespring Software”. We thank Dr. Andrew Bubak, Assistant Research Professor and Professor. Ravi Mahalingam at University of Colorado School of Medicine for giving valuable feedback on this manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.
Author’s contribution
The study was equally contributed by S.S.R and A.P.S. S.S.P and B.P helped in standardizing and processing several bio-informatic pipelines used for the study. B.P also contributed by providing “Agilent Genespring software “used for the study. R.R.K and M.M performed the transcriptomic analysis and assisted in analysis of the results. B.C helped in analysis and interpretation of transcriptomic yeast datasets. S.V conceptualized the entire idea, interpreted the results and played a major role in the preparation of manuscript.