Revealing the mechanism of the lutein protective function of epicatechin-fructan glycosylated soybean protein isolate.

Lutein possesses various physiological activities but is susceptible to light degradation, thermal degradation, and oxidative degradation. As such, protecting the activity of lutein-based products using natural extracts has become a current research. In this study, lutein was protected by complexing inulin-type fructan (ITF), soybean protein isolate (SPI), and epicatechin (EC), and the protection mechanism of epicatechin-fructan glycosylated soybean protein isolate (EC-GSPI) toward lutein was elucidated comprehensively. The results showed that the addition of EC delayed the degradation of lutein. The results of light stability experiments showed that increased EC significantly enhanced the storage time of the GSPI-Lutein system from 4 to 13 days. Additionally, the effect of EC on glycosylated soybean 7S globulin (G7S) and glycosylated soybean 11S globulin (G11S) was assessed. The light stability of G11S-Lutein and G7S-Lutein after the addition of EC was from G11S > G7S → G7S > G11S. Furthermore, the proteins purified from SPI interacted differently with EC and ITF, with soybean 7S globulin (7S) mainly interacting with EC and soybean 11S globulin (11S) mainly interacting with ITF. EC-GSPI-Lutein exhibited a good protective effect, probably due to the occurrence of hygrothermal Maillard between ITF and 11S, providing a porous structure for lutein storage. At the same time, the binding of EC to 7S significantly enhanced the antioxidant property of the solution and the stability of the protein secondary structure, thereby prolonging the storage time of lutein.

Identification of different subtypes of ovarian cancer and construction of prognostic models based on glutamine-metabolism associated genes.

Ovarian cancer (OC) is common malignant tumor of female reproductive system. Glutamine metabolism-related genes (GMRGs) play a key role in ovarian cancer. Here, available database-- The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases were applied in our research. OC samples from TCGA were divided into different clusters based on Cox analysis, which filtering GMRGs with survival information. Then, differentially expressed genes (DEGs) between these clusters were intersected with DEGs between normal ovary samples and OC samples, and GMRGs in order to obtain GMRGs-related DEGs. Next, a risk model of OC was constructed and enrichment analysis of risk model was performed based on hallmark gene set. Besides, the immune cells ratio in OC samples were detected via Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT). Finally, we explored a series of potential biomarkers of OC. In this research, 9 GMRGs-related DEGs were obtained. GMRGs-related DEGs were enriched to canonical Wnt signaling pathway.NKD2, C2orf88, and KLHDC8A, which were significantly associated with prognosis, were retained for risk model construction. Based on the risk model, 18 hallmark pathways with significant difference were enriched. Fifteen types of immune cells (such as iDC, NK CD56dim cells, and neutrophils) enjoying significant difference between these 2 risk groups (high risk group vs. low risk group) were detected, which indicates possible disparate TME in different metabolic subtypes of ovarian cancer.