A prognostic biomarker NRG1 promotes U-87 MG glioblastoma cell malignancy by inhibiting autophagy via ERBB2/AKT/mTOR pathway.

Glioblastoma (GBM) is the most common and aggressive primary brain malignancy. Studies have shown that autophagy-related (ATG) genes play important roles in regulating GBM malignancy. However, the mechanism still needs to be fully elucidated. Based on clinical and gene expression information of GBM patients downloaded from The The Cancer Genome Atlas database, Kaplan-Meier, univariate Cox regression, least absolute shrinkage and selection operator regression and multivariate Cox regression were applied to construct a risk signature for GBM prognosis, followed by validation using receiver operating characteristic analysis. Next, Cell Counting Kit-8, wound healing assay, flow cytometry, monodansyl cadaverine autophagy staining assay, immunofluorescence staining and western blot, either in the absence or presence of ERBB2/AKT/mTOR inhibitors, were carried out in GBM U87 cell line to explore molecular pathway underlying GBM malignancy. A three-ATG-gene signature (HIF1A, ITGA3, and NGR1) was constructed for GBM prognosis with the greatest contribution from NRG1. In vitro experiments showed that NRG1 promoted U87 cell migration and proliferation by inhibiting autophagy, and ERBB2/AKT/mTOR is a downstream pathway that mediates the autophagy-inhibitory effects of NRG1. We constructed an ATG gene prognostic model for GBM and demonstrated that NRG1 inhibited autophagy by activating ERBB2/AKT/mTOR, promoting GBM malignancy, thus providing new insights into the molecular contribution of autophagy in GBM malignancy.

An Oxidative Stress-Related Gene Signature in Granulosa Cells Is Associated with Ovarian Aging.

Ovarian aging is associated with a decrease in fecundity. Increased oxidative stress of granulosa cells (GCs) is an important contributor. We thus asked whether there is an oxidative stress-related gene signature in GCs associated with ovarian aging. Public nonhuman primate (NHP) single-cell transcriptome was processed to identify GC cluster. Then, a GC signature for ovarian aging was established based on six oxidative stress-related differentially expressed genes (MAPK1, STK24, AREG, ATG7, ANXA1, and PON2). Receiver operating characteristic (ROC) analysis confirmed good discriminating capacity in both NHP single-cell and human bulk transcriptome datasets. Gene expression levels were investigated using qPCR in the human ovarian granulosa-like tumor cell line (KGN) and mouse GCs. In an oxidative stress model, KGN cells were treated with menadione (7.5 µM, 24 h) to induce oxidative stress, after which upregulation of MAPK1, STK24, ATG7, ANXA1, and PON2 and downregulation of AREG were observed (p < 0.05). In an aging model, KGN cells were continuously cultured for 3 months, leading to increased expressions of all genes (p < 0.05). In GCs of reproductively aged (8-month-old) Kunming mice, upregulated expression of Mapk1, Stk24, Atg7, and Pon2 and downregulated expression of Anxa1 and Areg were observed (p < 0.01). We therefore here identify a six-gene GC signature associated with oxidative stress and ovarian aging.

Three Oxidative Stress-Related Genes That Associate Endometrial Immune Cells Are Considered as Potential Biomarkers for the Prediction of Unexplained Recurrent Implantation Failure.

Recurrent implantation failure (RIF) represents a new challenge in the field of assisted reproductive technology (ART). Considering the known effects of immune cell regulation on embryo implantation process, as well as our gene set variation analysis (GSVA) results that suggested the association between RIF and pathways of oxidative stress and immune responses, we hypothesized that oxidative stress- related genes (OSGs) associated with aberrant immunological factor may represent novel biomarkers for unexplained RIF. We therefore screened out the immune cell coexpressed OSGs by performing CIBERSORT, LM22 matrix and Pearson correlation, followed by constructing an OSG signature by least absolute shrinkage and selection operator (LASSO) regression. Three OSGs (AXL, SLC7A11 and UBQLN1) were then identified to establish a RIF risk signature, which showed high ability to discriminating RIF from fertile control. A nomogram was established, with a free online calculator for easier clinical application. Finally, Chilibot, protein-protein interaction analysis and BioGPS were sequentially applied for the investigation of functional relationships of these three genes with RIF and other OSGs, as well as their expression abundance across different human tissues. In conclusion, we identified an OSG signature that are relevant novel markers for the occurrence of unexplained RIF.

Identification of long non-coding RNA biomarkers and signature scoring, with competing endogenous RNA networks- targeted drug candidates for recurrent implantation failure.

Recurrent implantation failure (RIF) remains a source of frustration and presents challenges to clinicians in the practice of assisted reproductive technology (ART). Long non-coding RNAs (lncRNAs) are increasingly recognised as potential biomarkers in various diseases. In this study, eight differentially expressed lncRNAs (LINC00645, LINC00844, LINC02349, AC010975.1, AC022034.1, AC096719.1, AC104072.1 and DLGAP1-AS3) to distinguish RIF from fertile women were identified by RobustRankAggreg (RRA). A two-lncRNA signature for predicting RIF was established by least absolute shrinkage and selection operator (LASSO) regression, with accuracy confirmed by receiver operating characteristic (ROC) curves. After lncRNA-microRNA-mRNA regulatory networks were established by Cytoscape 3.7.2, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were performed, suggesting that the lncRNA-miRNA-mRNA regulatory networks were associated with biological processes involved in endometrial receptivity. Finally, three putative drugs (miconazole, terfenadine and STOCK1N-35215) for RIF were predicted by a Connectivity Map. In conclusion, we identified eight lncRNA biomarkers and a two-lncRNA signature for predicting RIF, as well as proposing three candidate drugs against RIF by targeting the ceRNA networks.

Identification and validation of a five-lncRNA signature for predicting survival with targeted drug candidates in ovarian cancer.

The dysregulation of long non-coding RNAs (lncRNAs) plays a crucial role in ovarian cancer (OC). In this study, we screened out five differentially expressed lncRNAs (AC092718.4, AC138035.1, BMPR1B-DT, RNF157-AS1, and TPT1-AS1) between OC and normal ovarian based on TCGA and GTEx RNA-seq databases by using Kaplan-Meier analysis and univariate Cox, LASSO, and multivariate Cox regression. Then, a risk signature was constructed, with 1, 3, 5-year survival prediction accuracy confirmed by ROC curves, and an online survival calculator for easier clinical use. With lncRNA-microRNA-mRNA regulatory networks established, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, suggesting the involvement of a variety of cancer-related functions and pathways. Finally, five candidate small-molecule drugs (thioridazine, trifluoperazine, loperamide, LY294002, and puromycin) were predicted by Connectivity Map. In conclusion, we identified a 5-lncRNA signature of prognostic value with its ceRNA networks, and five candidate drugs against OC.[Figure: see text].

The emerging role of the chondroitin sulfate proteoglycan family in neurodegenerative diseases.

Chondroitin sulfate (CS) is a kind of linear polysaccharide that is covalently linked to proteins to form proteoglycans. Chondroitin sulfate proteoglycans (CSPGs) consist of a core protein, with one or more CS chains covalently attached. CSPGs are precisely regulated and they exert a variety of physiological functions by binding to adhesion molecules and growth factors. Widely distributed in the nervous system in human body, CSPGs contribute to the major component of extracellular matrix (ECM), where they play an important role in the development and maturation of the nervous system, as well as in the pathophysiological response to damage to the central nervous system (CNS). While there are more than 30 types of CSPGs, this review covers the roles of the most important ones, including versican, aggrecan, neurocan and NG2 in the pathogenesis of neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and multiple sclerosis. The updated reports of the treatment of neurodegenerative diseases are involving CSPGs.

A risk signature of three autophagy-related genes for predicting lower grade glioma survival is associated with tumor immune microenvironment.

Treatment for lower-grade gliomas (LGG) has been challenging. Though emerging approaches such as immunotherapy is promising, it is still faced with immune tolerance, an obstacle that may be overcome by targeting autophagy-related (ATG) genes. After identifying three differentially expressed ATG genes (RIPK2, MUL1 and CXCR4), we constructed an ATG gene risk signature by Kaplan-Meier, univariate Cox regression, least absolute shrinkage and selection operator regression and multivariate Cox regression, followed by internal and external validation using K-M and ROC analysis. Since gene set enrichment analysis (GSEA) suggested that the signature was strongly associated with immune cell functions, CIBERSORT, LM22 matrix and Pearson correlation were further performed, showing that the risk signature was significantly correlated with immune cell infiltration and immune checkpoint genes. In conclusion, we identified and independently validated an ATG gene risk signature for LGG patients, as well as discovering its significant association with LGG immune microenvironment.

Bifurcation analysis of an age-structured SIRI epidemic model.

In this paper, an SIRI epidemic model with age of infection and the proliferation of susceptible individuals with logistic growth is investigated. By using the theory of integral semigroup and Hopf bifurcation theory for semilinear equations with non-dense domain, it is shown that if the threshold parameter is greater than unity, sufficient condition is derived for the occurrence of the Hopf bifurcation. Numerical simulations are carried out to illustrate the theoretical results.

Synchronization of a novel model for memristive neural networks via sliding mode control.

In this paper, a novel memristive neural networks model is developed. In the new model, the states of memristors are related to the initial resistance of the memristors and the amount of charge flowing through them in a specific direction, which embodies the memory characteristic of memristors. As a consequence, parameters in the model vary continuously and cannot be determined by the states of neurons. Existing results on synchronization of memristive neural networks are useless to this model. To investigate the synchronization of the new model, the main difficulty is how to deal with the time-varying parameter mismatches between the drive and response networks. Since the error is unbounded and only utilizing output feedback control is not enough, a sliding mode controller is designed. An integral sliding surface is designed for the desired sliding motion, and a feasible control law is proposed. Moreover, an example is given to demonstrate the novelty of our model and to illustrate the effectiveness of the sliding mode controller.

Neuregulin 1 enhances cell adhesion molecule L1 like expression levels and promotes malignancy in human glioma.

Neural cell adhesion molecular L1-like protein (CHL1) is a member of the cell adhesion molecule L1 family and serves an important role in the development and progression of tumors. The cytokine neuregulin 1 (NRG1) has been indicated in the tumorigenesis and promotion of metastasis through the modulation of L1. However, the roles of NRG1 in regulating CHL1 in glioma have not been elucidated. The present study investigated the protein expression levels and roles of CHL1 and the possible correlation between NRG1 and CHL1 protein expression levels in human gliomas, both in vivo and in vitro. Using immunohistochemistry coupled with a human glioma tissue microarray, it was demonstrated that the percentage of CHL1-positive areas was the highest in grade II glioma tissues. Using immunofluorescence staining, a positive correlation was identified between the expression levels of CHL1 and proliferating cell nuclear antigen. In addition, CHL1 downregulation also resulted in increased senescence of U-87 MG human glioblastoma cells. In vitro, administration of NRG1α induced a significant increase in CHL1 protein expression levels in human glioma SHG-44 and U251 cells and in human glioblastoma U-87 MG cells, whereas NRG1ß failed to increase CHL1 expression levels in U251 cells. These findings were further confirmed by the downregulation of NRG1 expression levels using small interfering RNA treatment, which resulted in the reduction of CHL1 protein expression levels in U-87 MG cells. These data indicate that NRG1 can regulate CHL1 protein expression levels in gliomas, that it is correlated with malignancy, and that NRG1 may contribute to malignancy by upregulating CHL1 protein expression levels in glioma/glioblastoma cells.

Identification and validation of a six-lncRNA prognostic signature with its ceRNA networks and candidate drugs in lower-grade gliomas.

Gliomas account for 75% of the primary malignant brain tumors and a majority of lower-grade gliomas (LGG) inevitably develop into glioblastoma. The dysregulation of lncRNAs play a crucial role in LGG. In the present study, we first screened out six differentially expressed lncRNAs (AC021739.2, AL031722.1, AL354740.1, FGD5-AS1, LINC00844, and NEAT1) based on TCGA and GTEx RNA-seq databases. LncRNA prognostic signature was then established by Kaplan-Meier and multivariate Cox proportional hazards regression, with its predictive value validated by time-dependent receiver operating characteristic (ROC) curves. After lncRNA-miRNA-mRNA regulatory networks were established by Cytoscape 3.7.2, Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, with results enriched in various malignancy-related functions and pathways. Finally, six putative drugs (irinotecan, camptothecin, mitoxantrone, azacitidine, mestranol, and enilconazole) were predicted by Connectivity Map. In conclusion, we identified a 6-lncRNA prognostic signature with its ceRNA networks, and six candidate drugs against LGG.

Global dynamics of a tuberculosis model with fast and slow progression and age-dependent latency and infection.

In this paper, a mathematical model describing tuberculosis transmission with fast and slow progression and age-dependent latency and infection is investigated. It is assumed in the model that infected individuals can develop tuberculosis by either of two pathogenic mechanisms: direct progression or endogenous reactivation. It is shown that the transmission dynamics of the disease is fully determined by the basic reproduction number. By analyzing corresponding characteristic equations, the local stability of a disease-free steady state and an endemic steady state of the model is established. By using the persistence theory for infinite dimensional system, it is proved that the system is uniformly persistent when the basic reproduction number is greater than unity. By constructing suitable Lyapunov functionals and using LaSalle's invariance principle, it is verified that the global dynamics of the system is completely determined by the basic reproduction number.

Global dynamics of an age-structured cholera model with multiple transmissions, saturation incidence and imperfect vaccination.

In this paper, an age-structured cholera model with multiple transmissions, saturation incidence and imperfect vaccination is proposed. In the model, we consider both the infection age of infected individuals and the biological age of Vibrio cholerae in the aquatic environment. Asymptotic smoothness is verified as a necessary argument. By analysing the characteristic equations, the local stability of disease-free and endemic steady states is established. By using Lyapunov functionals and LaSalle's invariance principle, it is proved that the global dynamics of the model can be completely determined by basic reproduction number. The study of optimal control helps us seek cost-effective solutions of time-dependent vaccination strategy against cholera outbreaks. Numerical simulations are carried out to illustrate the corresponding theoretical results.

Threshold dynamics of an HIV-1 model with both viral and cellular infections, cell-mediated and humoral immune responses.

Human specific immunity consists of two branches: humoral immunity and cellular immunity. To protect us from pathogens, cell-mediated and humoral immune responses work together to provide the strongest degree of efficacy. In this paper, we propose an HIV-1model with cell-mediated and humoral immune responses, in which both virus-to-cell infection and cell-to-cell transmission are considered. Five reproduction ratios, namely, immunity-inactivated reproduction ratio, cell-mediated immunity-activated reproduction ratio, humoral immunity-activated reproduction ratio, cell-mediated immunity-competed reproduction ratio and humoral immunity-competed reproduction ratio, are calculated and verified to be sharp thresholds determining the local and global properties of the virus model. Numerical simulations are carried out to illustrate the corresponding theoretical results and reveal the effects of some key parameters on viral dynamics.