Energy Metabolism Heterogeneity-Based Molecular Biomarkers for Ovarian Cancer

Energy metabolism heterogeneity is a hallmark in ovarian cancer; namely, the Warburg and reverse Warburg effects coexist in ovarian cancer. Exploration of energy metabolism heterogeneity benefits the discovery of the effective biomarkers for ovarian cancers. The integrative analysis of transcriptomics (20,115 genes in 419 ovarian cancer samples), proteomics (205 differentially expressed proteins), and mitochondrial proteomics (1198 mitochondrial differentially expressed proteins) revealed (i) the upregulations of rate-limiting enzymes PKM2 in glycolysis, IDH2 in Krebs cycle, and UQCRH in oxidative phosphorylation (OXPHOS) pathways, (ii) the upregulation of PDHB that converts pyruvate from glycolysis into acetyl-CoA in Krebs cycle, and (iii) that miRNA (hsa-miR-186-5p) and RNA-binding protein (EIF4AIII) had target sites in those key proteins in energy metabolism pathways. Furthermore, lncRNA SNHG3 interacted with miRNA (hsa-miR-186-5p) and RNA-binding protein (EIF4AIII). Those results were confirmed in the ovarian cancer cell model and tissues. It clearly concluded that lncRNA SNHG3 regulates energy metabolism through miRNA (hsa-miR-186-5p) and RNA-binding protein (EIF4AIII) to regulate the key proteins in the energy metabolism pathways. SNHG3 inhibitor might interfere with the energy metabolism to treat ovarian cancers. These findings provide more accurate understanding of molecular mechanisms of ovarian cancers and discovery of effective energy-metabolism-heterogeneity therapeutic drug for ovarian cancers

Mitochondrial Proteomic and Molecular Network Alterations in Human Ovarian Cancers

Mitochondrion is a multi-functional organelle, which plays important role in human ovarian cancers. Mitochondrial quantitative proteomics was used to detect, identify, and quantify proteins from mitochondrial samples prepared from ovarian cancer and normal control ovary tissues. A total of 5115 mitochondrial proteins and 1198 mitochondrial differentially expressed proteins (mtDEPs) were identified in human ovarian cancer compared to control tissues. Pathway network analysis revealed multiple pathway network changes to involve those mitochondrial proteins and mtDEPs. These findings provide the scientific data about the role of mitochondria plays in ovarian cancer, and offer the source for discovery of mitochondrial biomarker for ovarian cancers

The Use of Gel Electrophoresis and Mass Spectrometry to Identify Nitroproteins in Nervous System Tumors

Protein tyrosine nitration is an important molecular event in nervous system tumor such as glioma and pituitary adenomas. It is the essential step to identify the protein targets and exact modified sites of tyrosine nitration for addressing the biological roles of protein tyrosine nitration in nervous system tumors and discovering effective biomarkers to understand in-depth molecular mechanisms and determine new diagnosis strategy and novel therapeutic targets. One/two-dimensional gel electrophoresis (1DGE, 2DGE), or nitrotyrosine affinity column (NTAC), coupled with tandem mass spectrometry (MS/MS) have been successfully applied in the analysis of nitroproteins in nervous system tumors. This article address the basic concept of protein tyrosine nitration, nitroproteomics methodology based on gel electrophoresis/immunoaffinity enrichment and tandem mass spectrometry, and the current status of nitroprotein study in nervous system tumors. The established nitroproteomics approach is easily translated to study other diseases

Modeling plant diseases under climate change: evolutionary perspectives

Infectious plant diseases are a major threat to global agricultural productivity, economic development, and ecological integrity. There is widespread concern that these social and natural disasters caused by infectious plant diseases may escalate with climate change and computer modeling offers a unique opportu-nity to address this concern. Here, we analyze the intrinsic problems associated with current modeling strategies and highlight the need to integrate evolutionary principles into polytrophic, eco-evolutionary frameworks to improve predictions. We particularly discuss how evolutionary shifts in functional trade-offs, relative adaptability between plants and pathogens, ecosystems, and climate preferences induced by climate change may feedback to future plant disease epidemics and how technological advances can facilitate the generation and integration of this relevant knowledge for better modeling predictions

Feedback noncausal model predictive control of wave energy converters

In this paper, a novel feedback noncausal model predictive control (MPC) strategy for sea wave energy converters (WECs) is proposed, where the wave prediction information can be explicitly incorporated into the MPC strategy to improve the WEC control performance. The main novelties of the MPC strategy proposed in this paper include: (i) the recursive feasibility and robust constraints satisfaction are guaranteed without a significant increase in the computational burden; (ii) the information of short-term wave prediction is incorporated into the feedback noncausal MPC method to maximise the potential energy output; (iii) the sea condition for the WEC to safely operate in can be explicitly calculated. The proposed feedback noncausal MPC algorithm can also be extended to a wide class of control design problems, especially to the energy maximisation problems with constraints to be satisfied and subject to persistent but predictable disturbances. Numerical simulations are provided to show the efficacy of the proposed feedback noncausal MPC

Ovarian cancer subtypes based on the regulatory genes of RNA modifications: Novel prediction model of prognosis

BackgroundOvarian cancer (OC) is a female reproductive system tumor. RNA modifications play key roles in gene expression regulation. The growing evidence demonstrates that RNA methylation is critical for various biological functions, and that its dysregulation is related to the progression of cancer in human.MethodOC samples were classified into different subtypes (Clusters 1 and 2) based on various RNA-modification regulatory genes (RRGs) in the process of RNA modifications (m1A, m6A, m6Am, m5C, m7G, ac4C, m3C, and Ψ) by nonnegative matrix factorization method (NMF). Based on differently expressed RRGs (DERRGs) between clusters, a pathologically specific RNA-modification regulatory gene signature was constructed with Lasso regression. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic ability of the identified model. The correlations of clinicopathological features, immune subtypes, immune scores, immune cells, and tumor mutation burden (TMB) were also estimated between different NMF clusters and riskscore groups.ResultsIn this study, 59 RRGs in the process of RNA modifications (m1A, m6A, m6Am, m5C, m7G, ac4C, m3C, and Ψ) were obtained from TCGA database. These RRGs were interactional, and sample clusters based on these regulators were significantly correlated with survival rate, clinical characteristics (involving survival status and pathologic stage), drug sensibility, and immune microenvironment. Furthermore, Lasso regression based on these 21 DERRGs between clusters 1 and 2 constructed a four-DERRG signature (ALYREF, ZC3H13, WTAP, and METTL1). Based on this signature, 307 OC patients were classified into high- and low-risk groups based on median value of riskscores from lasso regression. This identified signature was significantly associated with overall survival, radiation therapy, age, clinical stage, cancer status, and immune cells (involving CD4+ memory resting T cells, plasma cells, and Macrophages M1) of ovarian cancer patients. Further, GSEA revealed that multiple biological behaviors were significantly enriched in different groups.ConclusionsOC patients were classified into two subtypes per these RRGs. This study identified four-DERRG signature (ALYREF, ZC3H13, WTAP, and METTL1) in OC, which was an independent prognostic model for patient stratification, prognostic evaluation, and prediction of response to immunotherapy in ovarian cancer by classifying OC patients into high- and low-risk groups

The Anti-Cancer Effects of Anti-Parasite Drug Ivermectin in Ovarian Cancer

Ivermectin is an old, common, and classic anti-parasite drug, which has been found to have a broad-spectrum anti-cancer effect on multiple human cancers. This chapter will focus on the anti-cancer effects of ivermectin on ovarian cancer. First, ivermectin was found to suppress cell proliferation and growth, block cell cycle progression, and promote cell apoptosis in ovarian cancer. Second, drug pathway network, qRT-PCR, and immunoaffinity blot analyses found that ivermectin acts through molecular networks to target the key molecules in energy metabolism pathways, including PFKP in glycolysis, IDH2 and IDH3B in Kreb’s cycle, ND2, ND5, CYTB, and UQCRH in oxidative phosphorylation, and MCT1 and MCT4 in lactate shuttle, to inhibit ovarian cancer growth. Third, the integrative analysis of TCGA transcriptomics and mitochondrial proteomics in ovarian cancer revealed that 16 survival-related lncRNAs were mediated by ivermectin, SILAC quantitative proteomics analysis revealed that ivermectin extensively inhibited the expressions of RNA-binding protein EIF4A3 and 116 EIF4A3-interacted genes including those key molecules in energy metabolism pathways, and also those lncRNAs regulated EIF4A3-mRNA axes. Thus, ivermectin mediated lncRNA-EIF4A3-mRNA axes in ovarian cancer to exert its anticancer capability. Further, lasso regression identified the prognostic model of ivermectin-related three-lncRNA signature (ZNRF3-AS1, SOS1-IT1, and LINC00565), which is significantly associated with overall survival and clinicopathologic characteristics in ovarian cancer patients. These ivermectin-related molecular pattern alterations benefit for prognostic assessment and personalized drug therapy toward 3P medicine practice in ovarian cancer

The Use of Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) to Study Ivermectin-Mediated Molecular Pathway Changes in Human Ovarian Cancer Cells

Stable isotope labeling with amino acids in cell culture (SILAC) was to use isotopic essential amino acids to replace the original amino acids for cell culture and passage for 8–10 generations, followed by mass spectrometry to identify proteins and the isotopic abundance difference to quantify proteins. SILAC can be used to characterize proteomic changes, and analyze protein turnover, protein interactions, and dynamic changes with quantitative accuracy, and high reproducibility. For this study, SILAC “light” (L-Lysine-2HCl [12C6, 14N2], L-Arginine-HCl [12C6, 14N4])- or “heavy” (L-Lysine-2HCl [13C6, 15N2], L-Arginine-HCl [13C6, 15N4])-labeling RPMI 1640 medium was used to culture human ovarian cancer TOV-21G cells for 10 passages, followed by the treatment of 0.1% dimethylsulfoxide for 24 h and 20 µM ivermectin for 24 h, respectively. The light- and heavy-isotope-labeled proteins were equally mixed (1:1) for digestion with trypsin. The tryptic peptide mixture was fractionated with liquid chromatography and analyzed with tandem mass spectrometry. In total, 4,447 proteins were identified in ivermectin-treated TOV-21G cells in relation to controls. Those proteins were enriched in 89 statistically significant signaling pathways and 62 statistically significant biological processes. These findings clearly demonstrated that SILAC quantitative proteomics was a useful and reliable method to study ivermectin-related proteomic changes in cancer cells, which in combination with molecular pathway networks and biological processes enrichments provided more comprehensive insights into molecular mechanisms of ivermectin in inhibiting TOV-21G cells

Selective Immunoproteasome Inhibitors with Non-Peptide Scaffolds

Compounds useful for inhibiting the immunoproteasome have the formula of [image on patent]. Methods and compounds for inhibiting the immunoproteasome, particularly, immunoproteasome inhibitors with non-peptide scaffolds, are described