bioRxivCell Biology
Cockayne syndrome proteins CSA and CSB maintain mitochondrial homeostasis through NAD+ signaling
M. Okur, E. F. Fang, E. Fivenson, V. Tiwari, D. Croteau, V. A. Bohr
Abstract
BackgroundCockayne syndrome (CS) is a rare premature aging disease, most commonly caused by mutations of the genes encoding the CSA or CSB proteins. CS patients display cachectic dwarfism and severe neurological manifestations and have an average life expectancy of 12 years. The CS proteins are involved in transcription and DNA repair, with the latter including transcription-coupled nucleotide excision repair (TC-NER). However, there is also evidence for mitochondrial dysfunction in CS, which likely contributes to the severe premature aging phenotype of this disease. While damaged mitochondria and impaired mitophagy were characterized in mice with CSB deficiency, such changes in the CS nematodes and CS patients are not fully known. ResultsOur cross-species transcriptomic analysis in CS postmortem brain tissue, CS mouse and nematode models show that mitochondrial dysfunction is indeed a common feature in CS. Restoration of mitochondrial dysfunction through NAD+ supplementation significantly improved lifespan and healthspan in the CS nematodes, highlighting mitochondrial dysfunction as a major driver of the aging features of CS. In cerebellar samples from CS patients, we found molecular signatures of dysfunctional mitochondrial dynamics and impaired mitophagy/autophagy. In primary cells depleted for CSA or CSB, this dysfunction can be corrected with NAD+ supplementation. ConclusionsOur study provides support for the interconnection between major causative aging theories, DNA damage accumulation, mitochondrial dysfunction, and compromised mitophagy/autophagy. Together these three agents contribute to an accelerated aging program that can be averted by NAD+ supplementation.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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