Biology subjects

Hepkema, J.

Publications and source records attributed to Hepkema, J..

2 recordsLinked to original sources

bioRxiv2021

Intronization enhances expression of S-protein and other transgenes challenged by cryptic splicing

The natural habitat of SARS-CoV-2 is the cytoplasm of a mammalian cell where it replicates its genome and expresses its proteins. While SARS-CoV-2 genes and hence its codons are presumably well optimized for mammalian protein translation, they have not been sequence optimized for nuclear expression. The cDNA of the Spike protein harbors over a hundred predicted splice sites and produces mostly aberrant mRNA transcripts when expressed in the nucleus. While different codon optimization strategies increase the proportion of full-length mRNA, they do not directly address the underlying splicing issue with commonly detected cryptic splicing events hindering the full expression potential. Similar splicing characteristics were also observed in other transgenes. By inserting multiple short introns throughout different transgenes, significant improvement in expression was achieved, including >7-fold increase for Spike transgene. Provision of a more natural genomic landscape offers a novel way to achieve multi-fold improvement in transgene expression.

genetics↗

bioRxiv2020

Predicting the impact of sequence motifs on gene regulation using single-cell data

BackgroundBinding of transcription factors (TFs) at proximal promoters and distal enhancers is central to gene regulation. Yet, identification of TF binding sites, also known as regulatory motifs, and quantification of their impact on gene expression remains challenging. ResultsHere we infer putative regulatory motifs along with their cell type-specific importance using a convolutional neural network trained on single-cell data. Comparison of the importance score to expression levels across cells allows us to identify the TFs most likely to be binding at a given motif. Using multiple mouse tissues we obtain a model with cell type resolution which explains 29% of the variance in gene expression. Finally, by applying scover to distal enhancers identified using scATAC-seq from the mouse cerebral cortex we characterize changes in distal regulatory motifs during development. ConclusionsIt is possible to identify regulatory motifs as well as their importance from single-cell data using a neural network model where all of the parameters and outputs are easily interpretable to the user.

bioinformatics↗