Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron...

Area of Science:

• Neuroscience
• Genetics
• Computational Biology

Background:

• Neuronal differentiation, particularly of inhibitory interneurons, relies on complex interactions between transcription factors (TFs) and regulatory elements (REs).
• The precise roles of specific TFs and their target REs in inhibitory interneuron progenitors remain incompletely understood.

Purpose of the Study:

• To develop and apply a deep-learning framework (eMotif-RE) for identifying TF motifs within gene REs.
• To elucidate the functions of TFs and REs in inhibitory interneuron progenitors using epigenetic data and functional assays.

Main Methods:

• Developed a deep-learning framework (eMotif-RE) to identify TF motifs in REs.
• Utilized epigenetic datasets (ATAC-seq, ChIP-seq) to distinguish active and non-active enhancers in interneuron progenitors.
• Performed in vivo enhancer assays to functionally validate predicted REs.

Main Results:

• Identified enriched motifs for ASCL1, SOX4, and SOX11 in active enhancers, suggesting cooperative functions.
• Found enriched ZEB1 and CTCF motifs in non-active REs, with mutations increasing enhancer activity, indicating a repressive role.
• Validated 25% of non-active REs as poised enhancers in vivo.

Conclusions:

• The eMotif-RE framework effectively identifies TF-RE interactions in neuronal progenitors.
• ASCL1, SOX4, and SOX11 play cooperative roles in active enhancers, while ZEB1 and CTCF act repressively on other REs.
• This approach advances understanding of gene regulation in interneuron differentiation and can be applied to other cell types.