Sox11 promotes neuronal regeneration or death: complexities from heterogeneity
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CitationNorsworthy, Michael. 2018. Sox11 promotes neuronal regeneration or death: complexities from heterogeneity. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractThe objective of achieving central nervous system (CNS) restoration after disease or trauma has bedeviled researchers and practitioners for decades. Some of the more recent advances are promising, but the prospect of restoring function after severe injuries remains elusive.
Here, I began with a screen of candidate genes from embryonic development, hoping to identify factors that could promote regeneration in retinal ganglion cells (RGCs). I found that AAV-delivered overexpression of Sox11 could compel some RGCs to regenerate. Moreover, if Sox11 was combined with mTOR activation, dramatic regeneration of RGC axons could be seen extending past the optic chiasm. Confusingly, Sox11 killed other RGCs, including alpha RGCs. The differing abilities of CNS neurons to regenerate is only beginning to be characterized in the literature, and my findings provide the most extreme example to date. Sox11 may be useful, combined with other regenerative therapies, if its negative effects can be overcome.
Considering the above, many currently promising regenerative approaches could have similar complexities or undetected problems. On the other hand, I argue that more rational engineering, aided by computational simulations, could reduce the search-space for workable therapeutics. I have implemented a basic example of such a simulator, which I name Celletron. By modeling the spatiotemporal environment of cells and tissues, with signaling networks too complex for any single person to understand, we might shave years off of screens and studies.
My findings suggest that future studies will need to detect and account for neuronal heterogeneity in regeneration. Sox11 is a powerful and likely useful axon growth regulator, if its deficits can be addressed. Finally, I outline how developing improved computational tools could accelerate the engineering of CNS-restoring therapies.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:42016096
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