Genetic diversity and population structure of Swertia tetraptera (Gentianaceae), an endemic species of Qinghai-Tibetan Plateau

Swertia tetraptera Maxim is an annual alpine herb endemic to the Qinghai-Tibetan Plateau (QTP). Its populations are locally scattered as isolated patches throughout this region. Genetic variation within and among thirty-four populations of this species was assessed using ISSR fingerprinting with 10 primers. High levels of genetic diversity exist within species (P = 98.9%, I = 0.3475; He = 0.2227), while the within-population diversity is low (P = 32.7%, I = 0.177; He = 0.12). High levels of genetic differentiation were detected among populations based on various statistics, including Nei’s genetic diversity analysis (G_ST = 0.4608), Bayesian analysis (θ^B = 0.476) and AMOVA (F_ST = 0.57). That is, populations shared low levels of genetic identity (I = 0.2622–0.0966). This genetic structure was probably due to severe genetic drift, breeding system and limited gene flow. The observed genetic structure of the populations implies that different populations across the distribution range of the species should be sampled to maintain high genetic diversity when a conservation strategy is implemented.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.     

CircRNA has_circ_0006427 suppresses the progression of lung adenocarcinoma by regulating miR-6783–3p/DKK1 axis and inactivating Wnt/β-catenin signaling pathway

Recently, circular RNAs (circRNAs) are identified as a novel class of noncoding RNAs playing important roles in human malignant tumors. However, the regulatory function of circRNA in lung adenocarcinoma (LUAD) is still largely unknown. Present study aimed to explore the role of circ_0006427 in LUAD progression. Firstly, the downregulation of circ_0006427 in LUAD tissues and cell lines was revealed by microarray analysis and qRT-PCR analysis. And we also confirmed the circ_0006427 as a prognostic target in LUAD patients. Functionally, overexpression of circ_0006427 effectively suppressed cell proliferation, migration and invasion. Mechanistically, circ_0006427 was found to be predominantly located in the cytoplasm of LUCA cell, and was further revealed to positively regulate DKK1 in LUAD by sponging miR-6783–3p. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and western blot analysis revealed that circ_0006427 inactivated Wnt/β-catenin signaling pathway by upregulating DKK1. At last, rescue assays proved the function of circ_0006427/miR-6783–3p/DKK1 axis in LUAD progression. In conclusion, our study revealed that circ_0006427 suppressed lung adenocarcinoma progression through regulating miR-6783–3p/DKK1 axis.