Colorimetric aptasensor based on temporally controllable light-stimulated oxidase-mimicking fluorescein for the sensitive detection of exosomes in mild conditions.

Analysis of exosomes provides important information for rapid and non-invasive screening of tumors. However, sensitive and convenient detection of exosomes remains technically challenging to date. Herein, a colorimetric aptasensor based on the light-stimulated oxidase-mimicking activity of FITC was constructed for detecting ovarian cancer (OC) exosomes. The aptasensor contained an EpCAM aptamer to capture OC exosomes. Cholesterol and fluorescein (FITC) were used to modify either end of the DNA (DNA anchor). The DNA anchor could combine with exosomes through a hydrophobic reaction between cholesterol and the lipid membrane. FITC oxidized 3,3',5,5'-tetramethylbenzidine (TMB) under a 365 nm LED light source in a temporally controllable manner under mild conditions, causing the solution to change from colorless to blue, and the corresponding UV-vis absorbance increased. Based on this principle, the exosomes were qualitatively analyzed by observing the color change with the naked eye. In parallel, the exosome concentration was also detected using UV-vis spectrophotometry. The linear range was from 2 × 105 to 100 × 105 particles per mL with a limit of detection of 1.77 × 105 particles per mL. The developed aptasensor also exhibited favorable selectivity and could discriminate the exosomes from OC cells and normal cells. Besides, the receiver operating characteristic (ROC) curve demonstrates that it is possible to distinguish between patients with OC and healthy donors (HDs) using exosomes as the biomarker. Our technology may expand the applications of DNA-based detection method-enabled OC diagnostic tools.

MicroRNA-211 Regulates Proliferation, Expansion, and Immune Inhibitory Function of Myeloid-Derived Suppressor Cells via Mediation of CHOP Expression.

BACKGROUND: Myeloid-derived suppressor cells (MDSCs) are capable of effectively repressing immune responses against tumors and orchestrating the tumor microenvironment, which can promote tumor angiogenesis and metastasis. The pathway networks used to modulate tumor-expanded MDSC accumulation and function remain unclear. This study identified microRNA-211 (miR-211), whose expression was significantly decreased by factors derived from tumors. METHODS: miR-211 was assumed to be critical in modulating the accumulation and activity of MDSCs isolated from ovarian cancer (OC)-bearing mice by targeting C/EBP homologous protein (CHOP). RESULTS: The upregulation of miR-211 repressed MDSC proliferation, inhibited MDSC immunosuppressive functions, and increased the number of co-incubated CD4+ and CD8+ cells. Furthermore, overexpression of miR-211 led to decreased activities of the NF-κB, PI3K/Akt, and STAT3 pathways and the subsequent downregulation of matrix metalloproteinases to promote tumor cell invasion and metastasis. CHOP overexpression counteracted the effects of miR-211 elevation on these phenotypic changes. Upregulation of miR-211 also dramatically impaired the activity of MDSCs and suppressed OC tumor growth in vivo. CONCLUSION: These results indicated that the miR-211-CHOP axis in MDSCs plays an essential role in the metastasis and proliferation of tumor-expanded MDSCs and might represent a promising cancer treatment target.

AND-Gated Nanosensor for Imaging of Glutathione and Apyrimidinic Endonuclease 1 in Cells, Animals, and Organoids.

The development of a strategy for imaging of glutathione (GSH) and apurinic/apyrimidinic endonuclease 1 (APE1) in an organism remains challenging despite their significance in elaborating the correlated pathophysiological processes. Therefore, in this study, we propose a DNA-based AND-gated nanosensor for fluorescence imaging of the GSH as well as APE1 in living cells, animals, and organoids. The DNA probe is composed of a G-strand and A-strand. The disulfide bond in the G-strand is cleaved through a GSH redox reaction, and the hybridization stability between the G-strand and A-strand is decreased, leading to a conformational change of the A-strand. In the presence of APE1, the apurinic/apyrimidinic (AP) site in the A-strand is digested, producing a fluorescence signal for the correlated imaging of GSH and APE1. This nanosensor enables monitoring of the expression level change of GSH and APE1 in cells. Additionally, we illustrate the capability of this "dual-keys-and-locked" conceptual methodology in achieving specific tumor imaging when GSH and APE1 are present simultaneously (overexpressed GSH and APE1 in tumor cells) with improving tumor-to-normal tissue ratio in vivo. Furthermore, using this nanosensor, the GSH and APE1 also are visualized in organoids that recapitulate the phenotypic and functional traits of the original biological specimens. Overall, this study demonstrates the potential of our proposed biosensing technology in investigating the roles of various biological molecules involved in specific diseases.

Machine Learning Generation of Dynamic Protein Conformational Ensembles.

Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.

DLC1 Is a Prognosis-Related Biomarker Correlated With Tumor Microenvironment Remodeling in Endometrial Carcinoma.

BACKGROUND AND AIM: Deleted in liver cancer 1 (DLC1) is confirmed as a metastasis suppressor gene in endometrial carcinoma (EC). However, its functional mechanisms remain unclear. This study aimed to explore the relationship between DLC1 expression and EC. METHODS: The Cancer Genome Atlas database was used for evaluating the expression of DLC1 in pan-cancer. CIBERSORT was used to assess the relationship between DLC1 and tumor immune infiltration. We applied real-time quantitative polymerase chain reaction to determine the expression of DLC1 in EC and adjacent normal tissue samples. The targeting endogenous protein levels were assessed using the dataset from the cBioPortal database. RESULTS: DLC1 expression negatively correlated with the clinical characteristics (clinical stage, histologic grade) and positively correlated with the survival of patients with uterine corpus EC (UCEC). The gene set enrichment analysis displayed that the low-expression DLC1 group was enriched in metabolic pathways. Concomitantly, the high-expression DLC1 group was enriched in tumor immune-related activities. The CIBERSORT analysis showed that the number of resting memory CD4 T cells and resting mast cells positively correlated with DLC1 expression, while the number of macrophages M2 had a negative correlation, indicating that DLC1 played a key role in mediating immune cell infiltration. The target gene validation confirmed that DLC1 expression was downregulated in tumor samples. The target protein level was consistently downregulated in tumor samples. CONCLUSIONS: DLC1 levels might be useful in predicting the prognosis of patients with UCEC and especially governing the status of tumor microenvironment transition from immune-dominant to metabolic-dominant. The findings shed a different light on the immune therapeutics of UCEC.

The diagnosis and pathological value of combined detection of HE4 and CA125 for patients with ovarian cancer.

OBJECTIVE: To evaluate the value of individual and combined measurement of human epididymis protein 4 (HE4) and cancer antigen 125 (CA-125) in the diagnosis of ovarian cancer. METHODS: A clinical case-control study was performed in which the levels of serum HE4 and CA-125 of subjects with malignant, borderline, benign ovarian tumors and healthy women were measured before surgery. An immunohistochemistry method was used to measure the expression of HE4 in different tissues. Statistical analysis was performed to determine the relationship between the level of HE4 and the pathologic type as well as the stage of the ovarian tumors. RESULTS: The level of HE4 in the serum was significantly elevated in the malignant ovarian cancer group compared with other groups. Women with benign ovarian tumors and non-neoplastic lesions, and healthy women were designated as references. When the level of HE4 in the serum was 58.66 pmol/L, the sensitivity and specificity of HE4 in diagnosing malignant ovarian tumors was 82.35% and 96.03%, respectively. The level of HE4 was negatively correlated with the differentiation extent of the tumors whereas positively correlated to the clinical staging. In the groups of malignant and borderline tumors, the levels of HE4 were higher than the other groups. The expression of HE4 was significant higher in the serous types of ovarian tumors than that of the mucous types (P<0.05). The level of HE4 in the serum and tissues were positively correlated with each other. CONCLUSION: HE4 can be used as a novel clinical biomarker for predicting malignant ovarian tumors and its expression was closely related with the clinical pathological features of malignant ovarian tumors.