Cell-type-directed design of synthetic enhancers

Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes1. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Here we show that deep learning models2-6, can be used to efficiently design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows detailed tracing of enhancer features at single-nucleotide resolution. We evaluate the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We further exploit enhancer design to create 'dual-code' enhancers that target two cell types and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the state space searches towards local optima, we characterize enhancer codes through the strength, combination and arrangement of transcription factor activator and transcription factor repressor motifs. Finally, we apply the same strategies to successfully design human enhancers, which adhere to enhancer rules similar to those of Drosophila enhancers. Enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

Urea and creatinine levels in saliva of patients with and without periodontitis

Despite the fact that saliva contains measurable concentrations of urea and creatinine, it is not widely used in clinical nephrology. One of the reasons is the high inter- and intra-individual variability in the salivary markers of kidney function. We hypothesized that gingival bleeding in patients with periodontitis could contribute to this variability by increasing the concentration of salivary urea or creatinine. Samples were collected from 25 patients with periodontitis and 29 healthy controls. In addition, saliva samples from five healthy volunteers were artificially contaminated with blood. The concentration of urea, but not that of creatinine, was more than twice as high in patients with periodontitis than in controls. Artificial contamination of saliva with blood did not affect the salivary concentration of creatinine. Salivary urea increased only with very high levels of contamination (≥2.5% blood in saliva), but that did not occur in patients. In conclusion, periodontitis increases the concentration of salivary urea, but this is not likely to be a result of contamination with blood. Future studies should investigate the composition of the oral microbiome, specifically regarding how it affects the concentration of salivary urea. Salivary creatinine seems to be a more robust non-invasive marker of renal functions than salivary urea.