SEPS1 protects RAW264.7 cells from pharmacological ER stress agent-induced apoptosis

Selenoprotein S (SEPS1) is a novel endoplasmic reticulum (ER) resident protein and it is known to play an important role in production of inflammatory cytokines. Here, we show evidence that SEPS1 is stimulated by pharmacological ER stress agents in RAW264.7 macrophages as well as other cell types. Overexpression studies reveal a protective action of SEPS1 in macrophages against ER stress-induced cytotoxicity and apoptosis, resulting in promoting cell survival during ER stress. The protective action of SEPS1 is largely dependent on ER stress-mediated cell death signal with less effect on non-ER stress component cell death signals. Conversely, suppression of SEPS1 in macrophages results in sensitization of cells to ER stress-induced cell death. These findings suggest that SEPS1 could be a new ER stress-dependent survival factor that protects macrophage against ER stress-induced cellular dysfunction.

Hub-based reliable gene expression algorithm to classify ER+ and ER- breast cancer subtypes

Identifying gene signatures that are associatedwith the estrogen receptor based breast cancer samples is achallenging problem that has significant implications in breastcancer diagnosis and treatment. Various existing approaches foridentifying gene signatures have been developed but are not ableto achieve the satisfactory results because of their severallimitations. Subnetwork-based approaches have shown to be arobust classification method that uses interaction datasets suchas protein-protein interaction datasets. It has been reported thatthese interaction datasets contain many irrelevant interactionsthat have no biological meaning associated with them, and thusit is essential to filter out those interactions which can improvethe classification results. In this paper, we therefore, proposed ahub-based reliable gene expression algorithm (HRGE) thateffectively extracts the significant biologically-relevantinteractions and uses hub-gene topology to generate thesubnetwork based gene signatures for ER+ and ER- breastcancer subtypes. The proposed approach shows the superiorclassification accuracy amongst the other existing classifiers, inthe validation dataset.

Multimodal iron oxide (Fe₃O₄)-saturated lactoferrin nanocapsules as nanotheranostics for real-time imaging and breast cancer therapy of claudin-low, triple-negative (ER-/PR-/HER2-)

AIM: To unravel the multimodal nanotheranostic ability of Fe3O4-saturated bovine lactoferrin nanocapsules (FebLf NCs) in claudin-low, triple-negative breast cancer model. MATERIALS & METHODS: Xenograft study was performed to examine biocompatibility, antitumor efficacy and multimodal nanotheranostic action in combination with near-infrared live mice imaging. RESULTS: FebLf NCs exhibited a size range of 80 nm ± 5 nm with observed superparamagnetism. FebLf NCs successfully internalized into breast cancer cells through receptor-mediated endocytosis and induced apoptosis through the downregulation of inhibitor of apoptosis survivin and livin proteins. Investigations revealed a remarkable biocompatibility, anticancer efficacy of the FebLf NCs. Near-infrared imaging observations confirmed selective localization of multimodal FebLf NCs at the tumor site and lead to time-dependent reduction of tumor growth. CONCLUSION: FebLf NCs can be safe, biocompatible nanotheranostic approach for real-time imaging and monitoring the effect of drugs in real time and have potentials in future clinical trials.