MicroRNA-203a regulates pancreatic β cell proliferation and apoptosis by targeting IRS2

Molecular Biology Reports - The main pathogenesis of type 1 diabetes mellitus (T1DM) is autoimmune-mediated apoptosis of pancreatic islet β cells. We sought to characterize the function of...     

The AKR1B1 inhibitor epalrestat suppresses the progression of cervical cancer

Molecular Biology Reports - Cervical cancer is the leading cause of cancer-related death among women worldwide. Identifying an effective treatment with fewer side effects is imperative, because all...     

Runx3 regulates chondrocyte phenotype by controlling multiple genes involved in chondrocyte proliferation and differentiation

Molecular Biology Reports - Chondrocytes are the sole cell type present within cartilage, and play pivotal roles in controlling the formation and composition of health cartilage. Chondrocytes...     

Protective Mechanism and Treatment of Neurogenesis in Cerebral Ischemia

Neurochemical Research - Stroke is the fifth leading cause of death worldwide and is a main cause of disability in adults. Neither currently marketed drugs nor commonly used treatments can promote...     

A single-nucleotide polymorphism in lnc-LAMC2-1:1 interferes with its interaction with miR-128 to alter the expression of deleted in colorectal cancer and its effect on the survival rate of subjects with ovarian cancer

This study aimed to identify the association between lnc-LAMC2-1:1 polymorphism rs2147578 and the recurrence of ovary cancer, as well as to study the underlying mechanism of rs2147578 in ovary cancer. Real-time polymerase chain reaction, Western blot analysis, immunohistochemistry, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, Logrank test, and Kaplan-Meier analysis were carried out to explore the role of rs2147578 in ovary cancer. No obvious difference was observed concerning all clinical characteristics among 90 patients genotyped as CC (N = 28), CG (N = 38), and GG (N = 24) in their rs2147578 polymorphism. In addition, the subjects carrying the CC genotype had longer recurrence-free survival time and showed a lower level of malignancy compared with those carrying CG and GG genotypes. Lnc-LAMC2-1:1 and miR-128 were lowly expressed in the CC group, while deleted in colorectal cancer (DCC) was highly expressed in the CC group. Furthermore, DCC was identified as a target gene of miR-128, and miR-128 mimics decreased the luciferase activity of cells cotransfected with wild-type DCC 3′-untranslated region. Lnc-LAMC2:1-1 directly targeted and affected miR-128 expression, and the G allele in lnc-LAMC2-1:1 rs2147578 upregulated miR-128 expression. Transfection with a miR-128 precursor evidently downregulated the expression of lnc-LAMC2-1:1, miR-128, and DCC expression, but did not affect the expression of ABCC5 and body mass index. Finally, miR-128 precursor promoted cell proliferation and inhibited cell apoptosis. Compared with lnc-LAMC2-1:1 rs2147578C allele, the G allele increases the risk of ovarian cancer by reducing the binding between lnc-LAMC2-1:1 and miR-128-3p, which in turn further decreases the expression of DCC and inhibits cell apoptosis.     

Transcriptomic Analyses of Chilling Stress Responsiveness in Leaves of Tobacco (Nicotiana tabacum) Seedlings

Plant Molecular Biology Reporter - Low temperature is among the most significant abiotic stresses restricting geographical distribution of plants and reducing crop productivity. However, the...     

Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysis

Experiments that longitudinally collect RNA sequencing (RNA-seq) data can provide transformative insights in biology research by revealing the dynamic patterns of genes. Such experiments create a great demand for new analytic approaches to identify differentially expressed (DE) genes based on large-scale time-course count data. Existing methods, however, are suboptimal with respect to power and may lack theoretical justification. Furthermore, most existing tests are designed to distinguish among conditions based on overall differential patterns across time, though in practice, a variety of composite hypotheses are of more scientific interest. Finally, some current methods may fail to control the false discovery rate. In this paper, we propose a new model and testing procedure to address the above issues simultaneously. Specifically, conditional on a latent Gaussian mixture with evolving means, we model the data by negative binomial distributions. Motivated by Storey (2007) and Hwang and Liu (2010), we introduce a general testing framework based on the proposed model and show that the proposed test enjoys the optimality property of maximum average power. The test allows not only identification of traditional DE genes but also testing of a variety of composite hypotheses of biological interest. We establish the identifiability of the proposed model, implement the proposed method via efficient algorithms, and demonstrate its good performance via simulation studies. The procedure reveals interesting biological insights, when applied to data from an experiment that examines the effect of varying light environments on the fundamental physiology of the marine diatom Phaeodactylum tricornutum.     

Variable selection in joint frailty models of recurrent and terminal events

Recurrent event data are commonly encountered in biomedical studies. In many situations, they are subject to an informative terminal event, for example, death. Joint modeling of recurrent and terminal events has attracted substantial recent research interests. On the other hand, there may exist a large number of covariates in such data. How to conduct variable selection for joint frailty proportional hazards models has become a challenge in practical data analysis. We tackle this issue on the basis of the “minimum approximated information criterion” method. The proposed method can be conveniently implemented in SAS Proc NLMIXED for commonly used frailty distributions. Its finite-sample behavior is evaluated through simulation studies. We apply the proposed method to model recurrent opportunistic diseases in the presence of death in an AIDS study.