O-GlcNAcylation Facilitates the Interaction between Keratin 18 and Isocitrate Dehydrogenases and Potentially Influencing Cholangiocarcinoma Progression.

Glycosylation plays a pivotal role in the intricate landscape of human cholangiocarcinoma (CCA), actively participating in key pathophysiological processes driving tumor progression. Among the various glycosylation modifications, O-linked ß-N-acetyl-glucosamine modification (O-GlcNAcylation) emerges as a dynamic regulator influencing diverse tumor-associated biological activities. In this study, we employed a state-of-the-art chemical proteomic approach to analyze intact glycopeptides, unveiling the critical role of O-GlcNAcylation in orchestrating Keratin 18 (K18) and its interplay with tricarboxylic acid (TCA) cycle enzymes, specifically isocitrate dehydrogenases (IDHs), to propel CCA progression. Our findings shed light on the mechanistic intricacies of O-GlcNAcylation, revealing that site-specific modification of K18 on Ser 30 serves as a stabilizing factor, amplifying the expression of cell cycle checkpoints. This molecular event intricately fosters cell cycle progression and augments cellular growth in CCA. Notably, the interaction between O-GlcNAcylated K18 and IDHs orchestrates metabolic reprogramming by down-regulating citrate and isocitrate levels while elevating α-ketoglutarate (α-KG). These metabolic shifts further contribute to the overall tumorigenic potential of CCA. Our study thus expands the current understanding of protein O-GlcNAcylation and introduces a new layer of complexity to post-translational control over metabolism and tumorigenesis.

Chemoproteomics reveals berberine directly binds to PKM2 to inhibit the progression of colorectal cancer.

Colorectal cancer is one of the most serious tumors and berberine can inhibit the recurrence and transformation of colorectal adenoma into colorectal cancer. However, the direct binding target proteins of berberine in inhibiting colorectal cancer remain unclear. In this study, the chemical proteomics method was used and demonstrated that berberine is directly bound to pyruvate kinase isozyme type M2 (PKM2) in colorectal cancer cells. The triangular N-O-O triangular structure of berberine contributed to hydrophobic interaction with I119 amino acid residues and π-π interaction with F244 amino acid residues of PKM2 protein. Moreover, berberine was shown to inhibit the reprogramming of glucose metabolism and the phosphorylation of STAT3, down regulate the expression of Bcl-2 and Cyclin D1 genes, ultimately inhibiting the progression of colorectal cancer. This study uncovered the direct binding target protein and mechanism of berberine to improve metabolic reprogramming in colorectal cancer, which is helpful to guide the optimization of berberine.

Shear viscosity prediction of alcohols, hydrocarbons, halogenated, carbonyl, nitrogen-containing, and sulfur compounds using the variable force fields.

Viscosity of organic liquids is an important physical property in applications of printing, pharmaceuticals, oil extracting, engineering, and chemical processes. Experimental measurement is a direct but time-consuming process. Accurately predicting the viscosity with a broad range of chemical diversity is still a great challenge. In this work, a protocol named Variable Force Field (VaFF) was implemented to efficiently vary the force field parameters, especially λvdW, for the van der Waals term for the shear viscosity prediction of 75 organic liquid molecules with viscosity ranging from -9 to 0 in their nature logarithm and containing diverse chemical functional groups, such as alcoholic hydroxyl, carbonyl, and halogenated groups. Feature learning was applied for the viscosity prediction, and the selected features indicated that the hydrogen bonding interactions and the number of atoms and rings play important roles in the property of viscosity. The shear viscosity prediction of alcohols is very difficult owing to the existence of relative strong intermolecular hydrogen bonding interaction as reflected by density functional theory binding energies. From radial and spatial distribution functions of methanol, we found that the van der Waals related parameters λvdW are more crucial to the viscosity prediction than the rotation related parameters, λtor. With the variable λvdW-based all-atom optimized potentials for liquid simulations force field, a great improvement was observed in the viscosity prediction for alcohols. The simplicity and uniformity of VaFF make it an efficient tool for the prediction of viscosity and other related properties in the rational design of materials with the specific properties.