medRxivGenetic & Genomic MedicinePreprint
Non-coding structural variants identify a commonly affected regulatory region steering FOXG1 transcription in early neurodevelopment
Lisa HamerlinckiD, Eva D’haeneiD, Nore Van LooniD, Michael VaughaniD, María del Rocío Pérez BacaiD, Sebastian LeimbacheriD, Lara ColomboiD, Lies Vantomme, Esperanza Daal, Annelies DheedeneiD, Himanshu GoeliD, Björn MenteniD, Bert Louis CallewaertiD, Sarah VergultiD
Abstract
The FOXG1 transcription factor is a crucial regulator of embryonic brain development. Pathogenic FOXG1 variants cause FOXG1 syndrome. Although structural variants (SVs) in the non-coding region downstream of FOXG1 have been reported in 38 individuals with similar characteristics, the regulatory pathomechanisms remain unknown. We identified a de novo non-coding deletion in an individual with FOXG1 syndrome-like disorder, allowing us to delineate a [~]124 kb commonly affected regulatory region (CARR). By integrating epigenomics data, 3D chromatin interaction profiles (Hi-C, UMI-4C), and in vivo enhancer assays in zebrafish, we uncovered multiple regulatory elements within this CARR, including a neuronal enhancer cluster and a conserved boundary of the FOXG1-containing topologically associating domain (TAD). Hi-C analysis on case lymphoblastoid cells revealed increased interactions with the adjacent TAD. Moreover, sequential UMI-4C and CUT&RUN assays during neural progenitor cell (NPC) differentiation demonstrated dynamic activation of, and interaction with the enhancer cluster. Finally, CRISPR-Cas9 deletion of the enhancer cluster and TAD boundary in NPCs resulted in decreased FOXG1 transcription. We identified and characterized enhancer and architectural elements essential for proper FOXG1 transcription. Our findings provide new insights into chromatin architecture and gene regulation at the FOXG1 locus, improving SV interpretation in individuals with FOXG1 syndrome-like disorder.
- organism: zebrafish
- assay: Hi-C
- assay: CUT&Tag
- assay: CRISPR screen
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 4
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