Design and deep learning of synthetic B- cell-specific promoters

Flexible regulation of gene expression is essential and highly sought for synthetic biology and biotechnology. Designing regulators with specific functions remains a challenge due to the limited understanding of specific regulatory mechanisms. We design and synthesize 23,640 B-cell-specific promoters, following the design-build-test-learn pipeline in synthetic biology. Synthetic promoters exhibit B-cell-specific expression and lead to diverse expression patterns in B-cells. By conducting MPRA testing, we uncovered the factors that influence promoter strength, including core motifs and motif syntax, which shape B-cell-specific promoter strength. Finally, we developed a deep leaning model capable of predicting promoter activity directly from the sequence, and to predict promoter activity for 26,193 variants identified in the global population, indicating that polymorphisms in IgV gene promoters can influence gene expression. Our work helps to decipher the regulatory code in immunoglobulin genes and offers thousands of non-repetitive promoter elements for B-cell engineering. The synthetic promoters were designed to drive the expression of a barcoded green fluorescent protein (GFP) reporter gene, every synthetic promoter drives a reporter gene carrying a unique DNA barcode in its 5’, which allows investigators to quantify the activity of each synthetic promoter by the ratio of its barcode abundances in the output RNA and input DNA.