Allicin inhibits the growth of HONE-1 and HNE1 human nasopharyngeal carcinoma cells by inducing ferroptosis.

Allicin (AL) is one of garlic-derived organosulfides and has a variety of pharmacological effects. Studies have reported that AL has notable inhibitory effects on liver cancer, gastric cancer, breast cancer, and other cancers. However, there are no relevant reports about its role in human nasopharyngeal carcinoma. Ferroptosis is an iron-dependent form of non-apoptotic regulated cell death. Increasing evidence indicates that induction of ferroptosis can inhibit the proliferation, migration, invasion, and survival of various cancer cells, which act as a tumor suppressor in cancer. In this study, we confirmed that AL can inhibit cell proliferation, migration, invasion, and survival in human nasopharyngeal carcinoma cells. Our finding shows that AL can induce the ferroptosis axis by decreasing the level of GSH and GPX4 and promoting the induction of toxic LPO and ROS. AL-mediated cytotoxicity in human nasopharyngeal carcinoma cells is dependent on ferroptosis. Therefore, AL has good anti-cancer properties and is expected to be a potential drug for the treatment of nasopharyngeal carcinoma.

A Novel Putative Role of TNK1 in Atherosclerotic Inflammation Implicating the Tyk2/STAT1 Pathway.

OBJECTIVE: Atherosclerosis is a chronic inflammatory disease which is responsible for many clinical manifestations. The present study was to investigate the anti-inflammatory functions and mechanisms of TNK1 in atherosclerosis. METHODS: The ApoE(-/-) mice and human carotid endarterectomy (CEA) atherosclerotic plaques were used to investigate the differential expression of TNK1. The ApoE(-/-) mice were fed with high-fat diet (HFD) or normal-fat diet (NFD) for 8 weeks; the aorta was separated and stained with oil red O to evaluate the formation of atherosclerosis. TNK1 in mice aorta was measured by qPCR. The human CEA were obtained and identified as ruptured and stable plaques. The level of TNK1 was measured by qPCR and Western-blot staining. Further studies were conducted in THP-1 cells to explore the anti-inflammatory effects of TNK1. We induced the formation of macrophages by incubating THP-1 cells with PMA (phorbol 12-myristate 13-acetate). Afterwards, oxidized low-density lipoprotein (oxLDL) was used to stimulate the inflammation, and the secretion of inflammatory factors was measured by ELISA and qPCR. The levels of TNK1, total STAT1 and Tyk2, and the phosphorylation of STAT1 and Tyk2 were measured by western blot to uncover the mechanisms of TNK1. RESULTS: The oil red O staining indicated obvious deposition of lipid on the aorta of ApoE(-/-) mice after 8-week HFD treatment. The TNK1 level was much higher in both the HFD-fed ApoE(-/-) mice aorta arch and the ruptured human CEA plaques. We found that TNK1 was highly expressed in THP-1 cells, compared to other atherosclerotic related cells (HUVEC, HBMEC, and HA-VSMC), indicating TNK1 might be involved in the inflammation. Suppressing the expression of TNK1 by shTNK1 inhibited the oxLDL-induced secretion of inflammatory factors, such as IL-12, IL-6, and TNF-α. ShTNK1 also inhibited the uptake of lipid and decreased the cellular cholesterol content in THP-1 cells. Furthermore, the shTNK1 suppressed the oxLDL-induced phosphorylation of Tyk2 and STAT1. CONCLUSION: TNK1 participated in the inflammation in atherosclerosis. shTNK1 suppressed the oxLDL-induced inflammation and lipid deposition in THP-1 cells. The mechanism might be related to the Tyk2/STAT signal pathway.

Transcriptome analysis unveils the mechanisms of lipid metabolism response to grayanotoxin I stress in Spodoptera litura.

Background: Spodoptera litura (tobacco caterpillar, S. litura) is a pest of great economic importance due to being a polyphagous and world-distributed agricultural pest. However, agricultural practices involving chemical pesticides have caused resistance, resurgence, and residue problems, highlighting the need for new, environmentally friendly methods to control the spread of S. litura. Aim: This study aimed to investigate the gut poisoning of grayanotoxin I, an active compound found in Pieris japonica, on S. litura, and to explore the underlying mechanisms of these effects. Methods: S. litura was cultivated in a laboratory setting, and their survival rate, growth and development, and pupation time were recorded after grayanotoxin I treatment. RNA-Seq was utilized to screen for differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to determine the functions of these DEGs. ELISA was employed to analyze the levels of lipase, 3-hydroxyacyl-CoA dehydrogenase (HOAD), and acetyl-CoA carboxylase (ACC). Hematoxylin and Eosin (H & E) staining was used to detect the development of the fat body. Results: Grayanotoxin I treatment significantly suppressed the survival rate, growth and development, and pupation of S. litura. RNA-Seq analysis revealed 285 DEGs after grayanotoxin I exposure, with over 16 genes related to lipid metabolism. These 285 DEGs were enriched in the categories of cuticle development, larvae longevity, fat digestion and absorption. Grayanotoxin I treatment also inhibited the levels of FFA, lipase, and HOAD in the hemolymph of S. litura. Conclusion: The results of this study demonstrated that grayanotoxin I inhibited the growth and development of S. litura. The mechanisms might, at least partly, be related to the interference of lipid synthesis, lipolysis, and fat body development. These findings provide valuable insights into a new, environmentally-friendly plant-derived insecticide, grayanotoxin I, to control the spread of S. litura.

Linc00312 Single Nucleotide Polymorphism as Biomarker for Chemoradiotherapy Induced Hematotoxicity in Nasopharyngeal Carcinoma Patients.

Background: Linc00312 is downregulated in nasopharyngeal carcinoma (NPC) and associates with poor treatment efficacy. Genetic variations are the main cause of individual differences in treatment response. The objective of this study is to explore the relationship between genetic variations of linc00312 and the risk of chemoradiotherapy induced toxic reactions in NPC patients. Methods: We used a bioinformatics approach to select 3 single nucleotide polymorphisms (SNPs) with regulatory feature in linc00312 (rs12497104, rs15734, and rs164966). 505 NPC patients receiving chemoradiotherapy with complete follow-up data were recruited. Genotyping was carried out by MassARRAY iPLEX platform. Univariate logistic and multivariate logistic regression were used to analyze the risk factors responsible for toxic reactions of NPC patients. Results: Our result demonstrated that linc00312 rs15734 (G > A) was significantly associated with severe leukopenia in NPC patients underwent chemoradiotherapy (AA vs. GG, OR = 3.145, P = 0.029). In addition, the risk of severe leukopenia was remarkably increased to 5.635 times (P = 0.034) in female with rs15734 AA genotype compared to male with rs15734 GG genotype. Moreover, patients with rs12497104 (G > A) AA genotype showed a 67.5% lower risk of thrombocytopenia than those with GG genotype (P = 0.030). Remarkably, the younger patients (age < 47) with rs12497104 AA genotype displayed a 90% decreased risk of thrombocytopenia compared with older patients (age ≥ 47) carrying rs12497104 GG genotype (P = 0.030). Conclusions: Genetic variations of linc00312 affect the risk of chemoradiotherapy induced hematotoxicity in nasopharyngeal carcinoma patients and may serve as biomarkers for personalized medicine.

Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity.

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer-associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer-associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein-protein interaction (PPI) network. We found that the breast cancer-associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer-associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer-associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer-associated genes, and the top predicted genes are better enriched on known breast cancer-associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer-associated genes, which could be used for further in vitro and in vivo experimental validation.

Genetic Polymorphisms of Long Non-coding RNA Linc00312 Are Associated With Susceptibility and Predict Poor Survival of Nasopharyngeal Carcinoma.

BACKGROUND: Linc00312 is dysregulated in nasopharyngeal carcinoma (NPC) and participates in the initiation and progression of NPC. Our previous studies suggested that linc00312 was able to enhance the sensitivity of NPC cells to irradiation and NPC patients with higher expression of linc00312 was associated with better short-term curative effect and overall survival. The single nucleotide polymorphisms (SNPs) of lncRNAs may influence the disease course and outcome by affecting the expression, secondary structure or function of lncRNAs. However, the role of SNPs in linc00312 on the occurrence and survival of NPC remains unknown. METHODS: We recruited 684 NPC patients and 823 healthy controls to evaluate the association between linc00312 SNPs and NPC susceptibility by using multivariate logistic regression analysis. Kaplan-Meier analysis and Cox proportional hazards regression were applied to assess the effect of linc00312 SNPs on the survival of NPC patients. The relative expression of linc00312 in NPC tissues was determined by real-time PCR. The interaction between linc00312 and mir-411-3p was explored by luciferase reporter assay. In silico prediction of the changes on linc00312 folding structure was conducted by RNAfold WebServer. RESULT: We demonstrated that rs12497104 (G > A) GA genotype carriers had a higher risk than others for suffering from NPC (GA vs GG, OR = 1.437, P = 0.003). Besides, patients with rs12497104 AA genotype showed a poorer overall survival in contrast to GG genotype (AA vs GG, HR = 2.117, P = 0.011). In addition, the heterozygous carriers of rs15734 (G > A) and rs164966 (A > G) were correlated with decreased risk of NPC (GA vs GG, OR = 0.778, P = 0.031; GA vs AA, OR = 0.781, P = 0.033, respectively). We found that the three SNPs might influence the expression of linc00312 in a genotype specific feature. The local centroid secondary structure as well as the minimum free energy of linc00312 were changed following the candidate SNPs alterations. Besides, we revealed that the G to A alteration at rs12497104 disrupted the binding between mir-411-3p and linc00312. CONCLUSION: Our results indicated genetic polymorphisms of linc00312 might serve as potential biomarkers for NPC carcinogenesis and prognosis.

Identification of key genes in ruptured atherosclerotic plaques by weighted gene correlation network analysis.

The rupture of atherosclerotic plaques is essential for cardiovascular and cerebrovascular events. Identification of the key genes related to plaque rupture is an important approach to predict the status of plaque and to prevent the clinical events. In the present study, we downloaded two expression profiles related to the rupture of atherosclerotic plaques (GSE41571 and GSE120521) from GEO database. 11 samples in GSE41571 were used to identify the differentially expressed genes (DEGs) and to construct the weighted gene correlation network analysis (WGCNA) by R software. The gene oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment tool in DAVID website, and the Protein-protein interactions in STRING website were used to predict the functions and mechanisms of genes. Furthermore, we mapped the hub genes extracted from WGCNA to DEGs, and constructed a sub-network using Cytoscape 3.7.2. The key genes were identified by the molecular complex detection (MCODE) in Cytoscape. Further validation was conducted using dataset GSE120521 and human carotid endarterectomy (CEA) plaques. Results: In our study, 868 DEGs were identified in GSE41571. Six modules with 236 hub genes were identified through WGCNA analysis. Among these six modules, blue and brown modules were of the highest correlations with ruptured plaques (with a correlation of 0.82 and -0.9 respectively). 72 hub genes were identified from blue and brown modules. These 72 genes were the most likely ones being related to cell adhesion, extracellular matrix organization, cell growth, cell migration, leukocyte migration, PI3K-Akt signaling, focal adhesion, and ECM-receptor interaction. Among the 72 hub genes, 45 were mapped to the DEGs (logFC > 1.0, p-value < 0.05). The sub-network of these 45 hub genes and MCODE analysis indicated 3 clusters (13 genes) as key genes. They were LOXL1, FBLN5, FMOD, ELN, EFEMP1 in cluster 1, RILP, HLA-DRA, HLA-DMB, HLA-DMA in cluster 2, and SFRP4, FZD6, DKK3 in cluster 3. Further expression detection indicated EFEMP1, BGN, ELN, FMOD, DKK3, FBLN5, FZD6, HLA-DRA, HLA-DMB, HLA-DMA, and RILP might have potential diagnostic value.

Prediction of potential disease-associated microRNAs by composite network based inference.

MicroRNAs (miRNAs) act a significant role in multiple biological processes and their associations with the development of all kinds of complex diseases are much close. In the research area of biology, medicine, and bioinformatics, prediction of potential miRNA-disease associations (MDAs) on the base of a variety of heterogeneous biological datasets in a short time is an important subject. Therefore, we proposed the model of Composite Network based inference for MiRNA-Disease Association prediction (CNMDA) through applying random walk to a multi-level composite network constructed by heterogeneous dataset of disease, long noncoding RNA (lncRNA) and miRNA. The results showed that CNMDA achieved an AUC of 0.8547 in leave-one-out cross validation and an AUC of 0.8533+/-0.0009 in 5-fold cross validation. In addition, we employed CNMDA to infer novel miRNAs for kidney neoplasms, breast neoplasms and lung neoplasms on the base of HMDD v2.0. Also, we employed the approach for lung neoplasms on the base of HMDD v1.0 and for breast neoplasms that have no known related miRNAs. It was found that CNMDA could be seen as an applicable tool for potential MDAs prediction.

Identifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix Factorization.

With the rapid development of biological research, microRNAs (miRNA) have become an attractive topic because lots of experimental studies have revealed the significant associations between miRNAs and diseases. However, considering that experiments are expensive and time-consuming, computational methods for predicting associations between miRNAs and diseases have become increasingly crucial. In this study, we proposed a neighborhood regularized logistic matrix factorization method for miRNA-disease association prediction (NRLMFMDA) by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally validation of disease-miRNA association. We used Gaussian interaction profile kernel similarity to cover the shortage of the traditional similarity to make it more reasonable and complete. Furthermore, NRLMFMDA also considered the important influences of the neighborhood information and took full advantage of them to improve the accuracy of the miRNA-disease association prediction. We also improved the accuracy by giving higher weights to the known association data in the process of calculating the potential association probabilities. In the global and the local leave-one-out cross validation, NRLMFMDA got the AUCs of 0.9068 and 0.8239, respectively. Moreover, the average AUC of NRLMFMDA in 5-fold cross validation was 0.8976 ± 0.0034. All the three kinds of cross validations have shown significant advantages to a number of previous models. In the case studies of breast neoplasms, esophageal neoplasms and lymphoma according to known miRNA-disease associations in the recent version of HMDD database, there were 78, 80, and 74% of top 50 predicted related miRNAs verified to have associations with these three diseases, respectively. In the further case studies for new disease without any known related miRNAs and the previous version of HMDD database, there were also high proportions of the predicted miRNAs verified by experimental reports. All the validation experiment results have demonstrated the effectiveness and practicability of NRLFMDA to predict the potential miRNA-disease associations.

Human Microbe-Disease Association Prediction With Graph Regularized Non-Negative Matrix Factorization.

A microbe is a microscopic organism which may exists in its single-celled form or in a colony of cells. In recent years, accumulating researchers have been engaged in the field of uncovering microbe-disease associations since microbes are found to be closely related to the prevention, diagnosis, and treatment of many complex human diseases. As an effective supplement to the traditional experiment, more and more computational models based on various algorithms have been proposed for microbe-disease association prediction to improve efficiency and cost savings. In this work, we developed a novel predictive model of Graph Regularized Non-negative Matrix Factorization for Human Microbe-Disease Association prediction (GRNMFHMDA). Initially, microbe similarity and disease similarity were constructed on the basis of the symptom-based disease similarity and Gaussian interaction profile kernel similarity for microbes and diseases. Subsequently, it is worth noting that we utilized a preprocessing step in which unknown microbe-disease pairs were assigned associated likelihood scores to avoid the possible negative impact on the prediction performance. Finally, we implemented a graph regularized non-negative matrix factorization framework to identify potential associations for all diseases simultaneously. To assess the performance of our model, cross validations including global leave-one-out cross validation (LOOCV) and local LOOCV were implemented. The AUCs of 0.8715 (global LOOCV) and 0.7898 (local LOOCV) proved the reliable performance of our computational model. In addition, we carried out two types of case studies on three different human diseases to further analyze the prediction performance of GRNMFHMDA, in which most of the top 10 predicted disease-related microbes were verified by database HMDAD or experimental literatures.