Lysyl-glycine

Oral epithelial adhesion to the lamina propria underlies the physiologic function of the oral mucosa and contributes to resisting bacterial invasion, preventing body fluid loss, and maintaining routine chewing; thus, understanding the factors that positively influence oral epithelial adhesion is a research topic of great interest. Rete pegs contribute to oral epithelial adhesion by enlarging the contact areas, whereas integrins are the major molecules that mediate epithelial cell adhesion to the basement membrane. Keratinocyte growth factor (KGF) can promote both rete peg elongation in the skin and the expression of integrins in various cell types. Herein, we tested the effects of submucosal injection of KGF in the ventral surfaces of rat tongues on oral epithelial adhesion. The data confirmed that topical injection of KGF elevated the adhesive forces, elongated the rete pegs, and increased the abundance of integrins, KGF, and KGF receptor on the rat tongue ventral surface. However, HYD-1 (Lys-Ile-Lys-Met-Val-Ile-Ser-Trp-Lys-Gly), an integrin antagonist, inhibited the KGF-enhanced epithelial adhesion and rete peg elongation. Moreover, KGF promoted the expression of integrin subunits α6, β4, α3, and β1 in human immortalized oral epithelial cells in 2- and 3-dimensional culture systems. In vitro cell attachment assays demonstrated that HYD-1 inhibited the adhesion of human immortalized oral epithelial cells on Matrigel. Strikingly, the expression of integrins, KGF, and KGFR in human masticatory mucosae with longer rete pegs was more abundant than that in the lining mucosae with shorter rete pegs. In addition, rete peg lengths were positively correlated with the expression levels of integrins, KGF, and KGF receptor. These findings indicate that KGF strengthens oral epithelial adhesion and rete peg elongation via integrins.

Introduction: Renal localization of high radioactivity levels during targeted imaging compromises tissue visualization in the kidney region and limits diagnostic accuracy. Radioiodinated antibody fragments with a renal enzyme-cleavable N(ε)-maleoyl-L-lysyl-glycine (MAL) linkage demonstrated low renal radioactivity levels in mice, from early postinjection times. This study tested the hypothesis whether a (64)Cu-labeled NODAGA-exendin-4 peptide with a MAL linkage ([(64)Cu]NODAGA-MAL-exendin-4) could decrease kidney radioactivity levels in rats, compared to a [(64)Cu]NODAGA-exendin-4 reference, without impairing the radioactivity levels in the target tissue. Methods: NODAGA-MAL-exendin-4 was synthesized in a two-phase approach using solid support to prepare maleoyl-derivatized NODAGA followed by Michael addition to cysteine-derivatized exendin-4 in solution. Radiolabeling was performed in buffered aqua with [(64)Cu]CuCl2, which was produced via the (64)Ni(p,n)(64)Cu nuclear reaction. The in vitro and in vivo stability, lipophilicity, and distribution kinetics in major rat organs for [(64)Cu]NODAGA-MAL-exendin-4 were studied and compared to [(64)Cu]NODAGA-exendin-4. Labeling of pancreatic islets was assessed using autoradiography. Results: NODAGA-MAL-exendin-4 was synthesized, with an overall yield of 9%, and radiolabeled with (64)Cu with high specific radioactivity. Serum incubation studies showed high stability for [(64)Cu]NODAGA-MAL-exendin-4. Similar tissue distribution kinetics was observed for [(64)Cu]NODAGA-MAL-exendin-4 and [(64)Cu]NODAGA-exendin-4, with high kidney radioactivity levels. Conclusions: The incorporated MAL linkage in [(64)Cu]NODAGA-MAL-exendin-4 was unable to reduce kidney radioactivity levels, compared to [(64)Cu]NODAGA-exendin-4. The applicability of metabolizable linkages in the design of kidney-saving exendin-4 analogs requires further investigation.

Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a R2 value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease.