In silico discovery and anti-tumor bioactivities validation of an algal lectin from Kappaphycus alvarezii genome.

Lectins are proteins that bind specifically and reversibly to carbohydrates, and some of them have significant anti-tumor activities. Compared to those of lectins from land plants, there are far fewer studies on algal lectins, despite of the high biodiversity of algae. However, canonical strategies based on chromatographic feature-oriented screening cannot satisfy the requirement for algal lectin discovery. In this study, prospecting for novel OAAH family lectins throughout 358 genomes of red algae and cyanobacteria was conducted. Then 35 candidate lectins and 1843 of their simulated mutated forms were virtually screened based on predicted binding specificities to characteristic carbohydrates on cancer cells inferred by a deep learning model. A new lectin, named Siye, was discovered in Kappaphycus alvarezii genome and further verified on different cancer cells. Without causing agglutination of erythrocytes, Siye showed significant cytotoxicity to four human cancer cell lines (IC50 values ranging from 0.11 to 3.95 µg/mL), including breast adenocarcinoma HCC1937, lung carcinoma A549, liver cancer HepG2 and romyelocytic leukemia HL60. And the cytotoxicity was induced through promoting apoptosis by regulating the caspase and the p53 pathway within 24 h. This study testifies the feasibility and efficiency of the genome mining guided by evolutionary theory and artificial intelligence in the discovery of algal lectins.

Bridging Nickel-MOF and Copper Single Atoms/Clusters with H-Substituted Graphdiyne for the Tandem Catalysis of Nitrate to Ammonia.

Interfacial engineering of synergistic catalysts is one of the keys to achieving multiple proton-coupled electron transfer processes in nitrate-to-ammonia conversion. Herein, by joining ultrathin nickel-based metal-organic framework (denoted Ni-MOF) nanosheets with few-layered hydrogen-substituted graphdiyne-supported copper single atoms and clusters (denoted HsGDY@Cu), a tandem catalyst of Ni-MOFs@HsGDY@Cu with dual-active interfaces was developed for the concerted catalysis of nitrate-to-ammonia. In such a system, the sandwiched HsGDY layer could serve as a bridge to connect the coordinated unsaturated Ni2+ sites with Cu single atoms/clusters in a limited range of 0 to 3.6 nm. From Ni2+ to Cu, via the hydrogen spillover process, the hydrogen radicals (H⋅) generated at the unsaturated Ni2+ sites could migrate across HsGDY to the Cu sites to participate in the transformation of *HNO3 to NH3. From Cu to Ni2+, bypassing the higher reaction energy for *HNO3 formation on the Ni2+ sites, the NO2 - detached from the Cu sites could diffuse onto the unsaturated Ni2+ sites to form NH3 as well. The combined results make this hybrid a tandem catalyst with dual active sites for the catalysis of nitrate-to-ammonia conversion with improved Faradaic efficiency at lower overpotentials.

A systematic and comprehensive analysis of T cell exhaustion related to therapy in lung adenocarcinoma tumor microenvironment.

Background: T cell exhaustion (TEX) is an important immune escape mechanism, and an in-depth understanding of it can help improve cancer immunotherapy. However, the prognostic role of TEX in malignant lung adenocarcinoma (LUAD) remains unclear. Methods: Through TCGA and GEO datasets, we enrolled a total of 498 LUAD patients. The patients in TCGA-LUAD were unsupervised clustered into four clusters according to TEX signaling pathway. WGCNA analysis, survival random forest analysis and lasso regression analysis were used to select five differentially expressed genes among different clusters to construct a TEX risk model. The risk model was subsequently validated with GEO31210. By analyzing signaling pathways, immune cells and immune checkpoints using GSEA, GSVA and Cibersortx, the relationship between TEX risk score and these variables was evaluated. In addition, we further analyzed the expression of CCL20 at the level of single-cell RNA-seq and verified it in cell experiments. Results: According to TEX signaling pathway, people with better prognosis can be distinguished. The risk model constructed by CD109, CCL20, DKK1, TNS4, and TRIM29 genes could further accurately identify the population with poor prognosis. Subsequently, it was found that dendritic cells, CD44 and risk score were closely related. The final single-cell sequencing suggested that CCL2O is a potential therapeutic target of TEX, and the interaction between TEX and CD8 + T is closely related. Conclusion: The classification of T cell depletion plays a crucial role in the clinical decision-making of lung adenocarcinoma and needs to be further deepened.

ER stress-related mRNA-lncRNA co-expression gene signature predicts the prognosis and immune implications of esophageal cancer.

BACKGROUND: Esophageal cancer (EC) is one of the most common malignant cancers in the world. Endoplasmic reticulum (ER) stress is an adaptive response to various stress conditions and has been implicated in the development of various types of cancer. Long noncoding RNAs (lncRNAs) refer to a group of noncoding RNAs (ncRNAs), which regulate gene expression by interacting with DNA, RNA and proteins. Accumulating evidence suggests that lncRNAs are critical regulators of gene expression in development, differentiation, and human diseases, such as cancers and heart diseases. However, the prognostic model of EC based on ER stress-related mRNA and lncRNA has not been reported. METHODS: Firstly, we downloaded RNA expression profiles from The Cancer Genome Atlas (TCGA) and obtained ER stress-related genes from the Molecular Signature Database (MSigDB). Next, Weighted Correlation Network Analysis (WGCNA) co-expression analysis was used to identify survival-related ER stress-related modules. Prognostic models were developed using univariate and Least absolute shrinkage and selection operator (LASSO) regression analyses on the training set and validated on the test set. Afterwards, The Receiver Operating Characteristic (ROC) curve and nomogram were used to evaluate the performance of risk prediction models. Differentially expressed gene (DEG) and enrichment analysis were performed between different groups in order to identify the biological processes correlated with the risk score. Finally, the fraction of immune cell infiltration and the difference of tumor microenvironment were identified in high-risk and low-risk groups. RESULTS: The WGCNA co-expression analysis identified 49 ER genes that are highly associated with EC prognosis. Using univariate Cox regression and LASSO regression analysis, we developed prognostic risk models based on nine signature genes (four mRNAs and five lncRNAs). Both in the training and in the test sets, the overall survival (OS) of EC patients in the high-risk group was significantly lower than that in the low-risk group. The Kaplan-Meier curve and the ROC curve demonstrate the prognostic model we built can precisely predict the survival with more than 70% accuracy. The correlation analysis between the risk score and the infiltration of immune cells showed that the model can indicate the state of the immune microenvironment in EC. CONCLUSION: In this study, we developed a novel prognostic model for esophageal cancer based on ER stress-related mRNA-lncRNA co-expression profiles that could predict the prognosis, immune cell infiltration, and immunotherapy response in patients with EC. Our results also may provide clinicians with a quantitative tool to predict the survival time of patients and help them individualize treatment strategies for the patients with EC.

System analysis based on the ER stress-related genes identifies WFS1 as a novel therapy target for colon cancer.

BACKGROUND: Colon cancer (COAD) is the third-largest common malignant tumor and the fourth major cause of cancer death in the world. Endoplasmic reticulum (ER) stress has a great influence on cell growth, migration, proliferation, invasion, angiogenesis, and chemoresistance of massive tumors. Although ER stress is known to play an important role in various types of cancer, the prognostic model based on ER stress-related genes (ERSRGs) in colon cancer has not been constructed yet. In this study, we established an ERSRGs prognostic risk model to assess the survival of COAD patients. METHODS: The COAD gene expression profile and clinical information data of the training set were obtained from the GEO database (GSE40967) and the test set COAD gene expression profile and clinical informative data were downloaded from the TCGA database. The endoplasmic reticulum stress-related genes (ERSRGs) were obtained from Gene Set Enrichment Analysis (GSEA) website. Differentially expressed ERSRGs between normal samples and COAD samples were identified by R "limma" package. Based on the univariate, lasso, and multivariate Cox regression analysis, we developed an ERSRGs prognostic risk model to predict survival in COAD patients. Finally, we verified the function of WFS1 in COAD through in vitro experiments. RESULTS: We built a 9-gene prognostic risk model based on the univariate, lasso, and multivariate Cox regression analysis. Kaplan-Meier survival analysis and Receiver operating characteristic (ROC) curve revealed that the prognostic risk model has good predictive performance. Subsequently, we screened 60 compounds with significant differences in the estimated half-maximal inhibitory concentration (IC50) between high-risk and low-risk groups. In addition, we found that the ERSRGs prognostic risk model was related to immune cell infiltration and the expression of immune checkpoint molecules. Finally, we determined that knockdown of the expression of WFS1 inhibits the proliferation of colon cancer cells. CONCLUSIONS: The prognostic risk model we built may help clinicians accurately predict the survival of patients with COAD. Our findings provide valuable insights into the role of ERSRGs in COAD and may provide new targets for COAD therapy.

System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma.

BACKGROUND: Liver hepatocellular carcinoma (LIHC) ranks sixth among the most common types of cancer with a high mortality rate. Cuproptosis is a newly discovered type of cell death in tumor, which is characterized by accumulation of intracellular copper leading to the aggregation of mitochondrial lipoproteins and destabilization of proteins. Thus, understanding the exact effects of cuproptosis-related genes in LIHC and determining their prognosticvalue is critical. However, the prognostic model of LIHC based on cuproptosis-related genes has not been reported. METHODS: Firstly, we downloaded transcriptome data and clinical information of LIHC patients from TCGA and GEO (GSE76427), respectively. We then extracted the expression of cuproptosis-related genes and established a prognostic model by lasso cox regression analysis. Afterwards, the prediction performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic curve (ROC). Then, the prognostic model and the expression levels of the three genes were validated using the dataset from GEO. Subsequently, we divided LIHC patients into two subtypes by non-negative matrix factorization (NMF) classification and performed survival analysis. We constructed a Sankey plot linking different subtypes and prognostic models. Next, we calculate the drug sensitivity of each sample from patients in the high-risk group and low-risk group by the R package pRRophetic. Finally, we verified the function of LIPT1 in LIHC. RESULTS: Using lasso cox regression analysis, we developed a prognostic risk model based on three cuproptosis-related genes (GCSH, LIPT1 and CDKN2A). Both in the training and in the test sets, the overall survival (OS) of LIHC patients in the low-risk group was significantly longer than that in the high-risk group. By performing NMF cluster, we identified two molecular subtypes of LIHC (C1 and C2), with C1 subtype having significantly longer OS and PFS than C2 subtype. The ROC analysis indicated that our model had a precisely predictive capacity for patients with LIHC. The multivariate Cox regression analysis indicated that the risk score is an independent predictor. Subsequently, we identified 71 compounds with IC50 values that differed between the high-risk and low-risk groups. Finally, we determined that knockdown of LIPT1 gene expression inhibited proliferation and invasion of hepatoma cells. CONCLUSION: In this study, we developed a novel prognostic model for hepatocellular carcinoma based on cuproptosis-related genes that can effectively predict the prognosis of LIHC patients. The model may be helpful for clinicians to make clinical decisions for patients with LIHC and provide valuable insights for individualized treatment. Two distinct subtypes of LIHC were identified based on cuproptosis-related genes, with different prognosis and immune characteristics. In addition, we verified that LIPT1 may promote proliferation, invasion and migration of LIHC cells. LIPT1 might be a new potential target for therapy of LIHC.

Robust, Multiresponsive, Superhydrophobic, and Oleophobic Nanocomposites via a Highly Efficient Multifluorination Strategy.

Artificial superhydrophobic surfaces are garnering constant attention due to their wide applications. However, it is a great challenge for superhydrophobic materials to simultaneously achieve good oil repellency, mechanochemical robustness, adhesion, thermomechanical properties, and multiresponsive ability. Herein, we propose a highly efficient multifluorination strategy to prepare superhydrophobic nanocomposites with the above features, which can be used as monoliths or coatings on various substrates. In this strategy, long-chain perfluorinated epoxy (PFEP) provides outstanding water/oil repellency, tetrafluorophenyl-based epoxy (FEP) possesses good thermodynamic compatibility with PFEP and increases the mechanical performance of the matrix, and carbon nanotubes grafted with perfluorinated segments and flexible spacers (FCNTs) tailor the surface roughness as well as impart multiple functions and ensure good binding interfaces. Notedly, all of the applications of constrained long-chain perfluorinated compounds are achieved via thiol-ene click chemistry, following the ethos of atom economy. The resultant PFEP30/FCNTs40 exhibits superhydrophobicity and oleophobicity, thermal conductivity (1.33 W·m-1·K-1), electronic conductivity (232 S m-1), and electromagnetic interference shielding properties (∼30 dB at 8.2-12.4 GHz, 200 µm). Importantly, after different extreme physical/chemical tests, the PFEP30/FCNTs40 coating still shows outstanding water/oil repellency. In addition, the coating exhibits good photo/electrothermal conversion ability and shows the potential for sensor application. Moreover, the novel strategy provides an efficient guideline for large-scale preparation of robust, multiresponsive, superhydrophobic, and oleophobic materials.