Back

δ-catenin controls layer-specific transcriptional maturation of astrocytes via Zbtb20

Sejourne, G.; Tan, C. X.; Savage, J. T.; Manigault, G.; Richardson, G.; Ding, J.; Hardin, E. J.; Ramirez, J.; Baumert, R.; Sakers, K.; Eroglu, C.

2026-05-13 cell biology
10.64898/2026.05.12.724361 bioRxiv
Show abstract

Coordinated maturation of diverse neural cell types drives mammalian cortical circuit development. Disruption of this coordination is a hallmark of human neurodevelopmental disorders, yet mechanisms that synchronize transcriptional maturation across cell types remain poorly understood. Here, we identify {delta}-catenin (Ctnnd2), a component of adherens junctions, that links cell-cell interactions to transcriptional regulation. Using single-nucleus and spatial transcriptomics, we show that {delta}-catenin loss disrupts transcriptional maturation across neural cell types, particularly in astrocytes. {delta}-catenin loss impairs acquisition of layer-specific astrocyte identities and prolongs ocular dominance plasticity, indicating impaired circuit stabilization. Mechanistically, we identify the BTB/POZ transcription factor Zbtb20, which is enriched in glial cells, as a key regulator of this process. {delta}-catenin loss increases Zbtb20 expression, redistributes its genome-wide binding, and dysregulates its target genes. Together, these findings support a model in which {delta}-catenin regulates Zbtb20-dependent transcriptional programs to establish layer-specific astrocyte identities in coordination with developing cortical circuits. SUMMARYSejourne et al report that loss of the adherens junction protein {delta}-catenin prolongs ocular dominance plasticity and disrupts astrocyte and oligodendrocyte transcriptional identity. The underlying mechanism seems to rely on the glia-enriched transcription factor Zbtb20, which is upregulated and redistributed upon {delta}-catenin loss, resulting in altered expression of its target genes.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.