Abstract The circadian clock enables organisms to align internal daily rhythms with environmental cues, with major consequences for survival and fitness. Although the molecular framework of this system in Drosophila melanogaster is well characterized through transcription translation feedback loops involving ten core clock genes, the genetic basis of natural variation in their expression remains poorly understood. Here, we used natural expression variation to identify expression quantitative trait loci (eQTLs) through genome-wide association mapping. Using the Drosophila Genetic Reference Panel, we measured relative expression of all core clock genes at a single time point two hours after light onset. We identified 109 significant SNPs and 28 indels associated with expression variation across the clock network. Expression levels varied widely, with Pdp1ε showing the greatest variation (an 86-fold difference between extreme lines) and cyc the least (11.3-fold). Only three significant SNPs were located within clock genes themselves, all in Clk , whereas most associations represented trans-eQTLs in genes with diverse molecular functions. Candidate regulators included transcription factors such as Abd-B , tai , and E5 ; RNA binding proteins including Pum , Bru-3 , and Mbl ; and several long noncoding and antisense RNAs. Variants were also detected in gbb and the BMP pathway transcription factor Mad . Consistent with this, Mad knockdown reduced vri expression. Together, these results reveal a complex regulatory architecture underlying natural variation in circadian gene expression.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.