Design and deep learning of synthetic B-cell-specific promoters.
Zong-Heng FuSi-Zhe HeYi WuGuang-Rong ZhaoPublished in: Nucleic acids research (2023)
Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synthetic elements is important for B-cell engineering. Here, we designed and pooled synthesized 23 640 B-cell-specific promoters that exhibit larger sequence space, B-cell-specific expression, and enable diverse transcriptional patterns in B-cells. By MPRA (Massively parallel reporter assays), we deciphered the sequence features that regulate promoter transcriptional, including motifs and motif syntax (their combination and distance). Finally, we built and trained a deep learning model capable of predicting the transcriptional strength of the immunoglobulin V gene promoter directly from sequence. Prediction of thousands of promoter variants identified in the global human population shows that polymorphisms in promoters influence the transcription of immunoglobulin V genes, which may contribute to individual differences in adaptive humoral immune responses. Our work helps to decipher the transcription mechanism in immunoglobulin genes and offers thousands of non-similar promoters for B-cell engineering.
Keyphrases
- transcription factor
- deep learning
- gene expression
- immune response
- dna methylation
- genome wide
- genome wide identification
- endothelial cells
- machine learning
- randomized controlled trial
- heat shock
- single molecule
- convolutional neural network
- binding protein
- long non coding rna
- poor prognosis
- circulating tumor
- cell free
- body composition
- inflammatory response
- open label
- high intensity
- pluripotent stem cells