Search

Modulation of the cytokine milieu is one approach for vaccine development. However, therapy with pro-inflammatory cytokines, such as IL-12, is limited in practice due to adverse systemic effects. Spatially-restricted gene expression circumvents this problem by enabling localized amplification. Intracellular co-delivery of gold nanorods (AuNR) and a heat shock protein 70 (HSP70) promoter-driven expression vector enables gene expression in response to near infrared (NIR) light. AuNRs absorb the light, convert it into heat and thereby stimulate photothermal expression of the cytokine. As proof-of-concept, human HeLa and murine B16 cancer cells were transfected with a HSP70-Enhanced Green Fluorescent Protein (EGFP) plasmid and polyethylenimine (PEI)-conjugated AuNRs. Exposure to either 42 °C heat-shock or NIR light induced significant expression of the reporter gene. In vivo NIR driven expression of the reporter gene was confirmed at 6 and 24 h in mice bearing B16 melanoma tumors using in vivo imaging and flow-cytometric analysis. Overall, we demonstrate a novel opportunity for site-directed, heat-inducible expression of a gene based upon the NIR-absorbing properties of AuNRs and a HSP70 promoter-driven expression vector.

S-glutathionylation, the reversible formation of mixed disulfides between glutathione(GSH) and cysteine residues in proteins, is a specific form of post-translational modification that plays important roles in various biological processes, including signal transduction, redox homeostasis, and metabolism inside cells. Experimentally identifying S-glutathionylation sites is labor-intensive and time consuming, whereas bioinformatics methods provide an alternative way to this problem by predicting S-glutathionylation sites in silico. The bioinformatics approaches give not only candidate sites for further experimental verification but also bio-chemical insights into the mechanism of S-glutathionylation. In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data. Promising results are obtained by our method with an AUC (area under ROC curve) score of 0.879 in 5-fold cross-validation, which demonstrates the predictive power of our proposed method. The datasets used in this work are available at http://csb.shu.edu.cn/SGDB.