O-GlcNAcylation is a dynamic post-translational modification that takes place on ser/thr residues of nucleocytoplasmic proteins. O-GlcNAcylation regulates almost all cellular events as a nutrient sensor, a transcriptional and translational regulator, and a disease-related factor. Although the role of O-GlcNAcylation in insulin signaling and metabolism are well established, the relationship between O-GlcNAcylation and autophagy is largely unknown. Here, we manipulated O-GlcNAcylation in Drosophila and found that it regulates autophagy through Akt/dFOXO signaling. We demonstrate that O-GlcNAcylation and the levels of O-GlcNAc transferase (OGT) are increased during starvation. Furthermore, Atg proteins and autolysosomes are increased in OGT-reduced flies without fasting. Atg proteins and autophagosomes are reduced in OGT-overexpressing flies. Our results suggest that not only autophagy gene expression but also autophagic structures are regulated by OGT through Akt and dFOXO. These data imply that O-GlcNAcylation is important in modulating autophagy as well as insulin signaling in Drosophila.

Extensive progress in understanding the molecular mechanisms of cancer growth and proliferation has led to the remarkable development of drugs that target cancer-driving molecules. Most target molecules are proteins such as kinases and kinase-associated receptors, which have enzymatic activities needed for the signaling cascades of cells. The small molecule inhibitors for these target molecules greatly improved therapeutic efficacy and lowered the systemic toxicity in cancer therapies. However, long-term and high-dosage treatment of small inhibitors for cancer has produced other obstacles, such as resistance to inhibitors. Among recent approaches to overcoming drug resistance to cancers, targeted protein degradation (TPD) such as proteolysis-targeting chimera (PROTAC) technology adopts a distinct mechanism of action by which a target protein is destroyed through the cellular proteolytic system, such as the ubiquitin–proteasome system or autophagy. Here, we review the currently developed PROTACs as the representative TPD molecules for cancer therapy and the N-degrons of the N-degron pathways as the potential TPD ligands.

Recently, Japan and EU have made an effort to reflect external costs into power generation costs. Korea which is planed to introduce a emission trading market is also needed to reflect the external costs. In the light of this, we conducted a portfolio analysis to make an optimal power mix for new power generation facilities in Korea with external costs. As results, the generation cost increased by 5.96-18.48 Won/kWh when applying Japanese data and by 18.02-38.07 Won/kWh when applying EU data. The scenario analysis applying the decreases of the cost of renewable power sources was also conducted. The result indicated that the generation cost decreases by 5.89-15.07 Won/kWh in 2030. The analysis results considering emission trading prices showed the cost will be increased by 7.08-7.55 Won/kWh although there was negative correlation between emission trading price and unit balance price.

Cell type identification is a key step to downstream analysis of single cell RNA-seq experiments. Indispensible information for this is gene expression, which is used to cluster cells, train the model and set rejection thresholds. Problem is they are subject to batch effect arising from different platforms and preprocessing. We present MarkerCount, which uses the number of markers expressed regardless of their expression level to initially identify cell types and, then, reassign cell type in cluster-basis. MarkerCount works both in reference and marker-based mode, where the latter utilizes only the existing lists of markers, while the former required pre-annotated dataset to train the model. The performance was evaluated and compared with the existing identifiers, both marker and reference-based, that can be customized with publicly available datasets and marker DB. The results show that MarkerCount provides a stable performance when comparing with other reference-based and marker-based cell type identifiers.

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