Fluoride contamination is a long-standing persistent environmental issue, and the silkworm (Bombyx mori) has been recognized as superior as a model organism for toxicological studies. When exposed to fluoride, silkworms can accumulate twice the fluoride content of ingested mulberry leaves. Growth and development are severely slowed. The mechanisms of fluoride toxicity have been studied to a small but unclear extent. In this study, we performed transcriptome and proteome sequencing analysis of the silk glands of silkworms under fluoride exposure and regular feeding. We identified 67 differentially expressed genes (DEGs) and 671 differentially expressed proteins (DEPs). By GO and KEGG analysis, we identified three critical pathways, Ribosome biogenesis in eukaryotes, Protein export, and Protein processing in endoplasmic reticulum. In these three pathways, BmSPCS2, BmSPCS3, and BmHsp20.8 were significantly down-regulated and BmHEATR1 was significantly up-regulated. We found that fluoride exposure severely affected protein translation and folding. Our findings provide speculative insights into the molecular mechanism of fluoride toxicity in silkworm silk glands at the transcriptomic and proteomic levels. It allows further expansion of the study on the toxicity mechanism of fluoride in silkworms. It also provides essential gene/protein sequence information for insect toxicology studies.
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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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.