Sahin Naqvi

Gene expression control is fundamental to all biological systems and is primarily mediated by transcription factors (TFs) that bind specific DNA sequences. This control is quantitative—approximately 50% reductions in TF levels cause rare disorders, while smaller changes drive common disease associations in genome-wide studies. Despite this known dosage sensitivity, studying it has been challenging due to limited quantitative tools and conceptual frameworks. The Naqvi lab addresses transcriptional dosage sensitivity through three interconnected approaches:

1) Tools to quantitatively manipulate dosage. We have developed a chemical genetic approach to precisely modulate TF dosage in human pluripotent stem cell (hPSC)-derived cell types and are working to enable both bidirectional tuning and massively increased throughput of this approach.

2) TF dosage in human neural crest development. We are applying and developing increasingly realistic hPSC-derived models to study human-specific aspects of TF dosage sensitivity in neural crest cells, a multipotent embryonic population that underlies numerous developmental disorders ranging from craniofacial malformations to enteric and peripheral neuropathies.

3) Deep learning to decode regulatory mechanisms. We combine machine learning approaches with quantitative perturbations and functional genomic data to reveal hidden layers of the cis-regulatory code that predict enhancer and gene responsiveness to TF dosage.