Happy New Year!
To kick off 2025 on the right foot, I read a fascinating paper titled: “Noncanonical function of folate through folate receptor 1 during neural tube formation.”
As you may know, folate is a crucial vitamin, and its supplementation during pregnancy is widely recommended. Folate deficiency is a well-established cause of neural tube defects, leading to severe and often fatal congenital abnormalities. When I was a student (not that long ago), I was taught that the role of folate in pregnancy was thought to be primarily metabolic: in the absence of folate, appropriate metabolism led to cells misbehaving.
This paper challenges that assumption by exploring an alternative mechanism for neural tube closure involving folate through its receptor FOLR1. The authors demonstrate that the folate receptor localizes apically in neural stem cells forming the neural tube in Xenopus embryos and within organoids resembling neural tubes. When the receptor is knocked down using morpholinos, the organoids exhibit tube closure defects. Interestingly, the authors found that providing a folate precursor (which cannot be metabolized into folate) partially rescues these defects. This suggests that the interaction between the folate receptor and folate has a direct, non-metabolic role in neural tube formation.
The paper delves deeply into the mechanisms, concluding that the folate receptor aids in organizing the apical constriction necessary for tube closure. Additionally, the authors present evidence suggesting that the receptor may modulate calcium signaling, though the significance of this modulation remains somewhat unclear.
I particularly enjoyed the innovative use of organoid models to study the mechanics of neural tube closure, and I felt I gained valuable insights from this work.
It’s also intriguing to consider how studies like this could be complemented by foundation models. While an RNA-sequence foundation model may not have immediate applications here, a machine learning model for protein-interaction predictions could have significantly enriched the study. Similarly, I’m curious about how much additional information could be extracted from the authors’ images using advanced imaging AI techniques.