Complex genetic mechanisms both endow developing neuronal subtypes with distinct molecular identities and translate those identities into the signatures of cell surface axon guidance molecules that direct neural circuit assembly. The final steps of this process, where axon guidance molecules instruct circuit outcomes, are well-understood. However, the upstream identity molecules that define guidance molecule signatures, and the molecular mechanisms by which cell type identity is transformed into these signatures, remain enigmatic.
The murine olfactory system contains nearly 1,5000 olfactory sensory neuron (OSN) subtypes which are intermixed in the olfactory epithelium (OE). Each OSN subtype expresses a unique olfactory receptor (OR) protein which both tunes its response properties to odorants in the environment and acts as an identity molecule that ensures all axons of a given OSN type converge to a single set of target glomeruli in the olfactory bulb (OB). Using a combination of bioinformatic and mouse genetic approaches, we have discovered an unanticipated role for endoplasmic reticulum stress (ER stress) and the unfolded protein response (UPR) in the translation of OR identity to OSN axon guidance molecule expression and glomerular targeting.
We find that slight differences in OR amino acid sequences lead to differential activation of the ER stress sensor PERK in different OSN subtypes. Graded patterns of the UPR are then interpreted through a master regulator transcription factor, Ddit3, which controls a set of stress-responsive axon guidance molecules that orchestrate the process of glomerular segregation in the OB. Our results define a novel paradigm for axon guidance in which graded activation of a canonical stress response pathway is leveraged towards the conversion of discrete neuronal identities into discrete circuit formation outcomes. These findings may be widely relevant for the formation of neural circuits across a variety of systems.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/mftn-qc71 |
| Date | January 2023 |
| Creators | Shayya, Hani |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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