Macrophage migration inhibitory factor regulates joint capsule fibrosis by promoting TGF-β1 production in fibroblasts

Joint capsule fibrosis caused by excessive inflammation results in post-traumatic joint contracture (PTJC). Transforming growth factor (TGF)-β1 plays a key role in PTJC by regulating fibroblast functions, however, cytokine-induced TGF-β1 expression in specific cell types remains poorly characterized. Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine involved in inflammation- and fibrosis-associated pathophysiology. In this study, we investigated whether MIF can facilitate TGF-β1 production from fibroblasts and regulate joint capsule fibrosis following PTJC. Our data demonstrated that MIF and TGF-β1 significantly increased in fibroblasts of injured rat posterior joint capsules. Treatment the lesion sites with MIF inhibitor 4-Iodo-6-phenylpyrimidine (4-IPP) reduced TGF-β1 production and relieved joint capsule inflammation and fibrosis. In vitro, MIF facilitated TGF-β1 expression in primary joint capsule fibroblasts by activating mitogen-activated protein kinase (MAPK) (P38, ERK) signaling through coupling with membrane surface receptor CD74, which in turn affected fibroblast functions and promoted MIF production. Our results reveal a novel function of trauma-induced MIF in the occurrence and development of joint capsule fibrosis. Further investigation of the underlying mechanism may provide potential therapeutic targets for PTJC.     

The mRNA–miRNA–lncRNA Regulatory Network and Factors Associated with Prognosis Prediction of Hepatocellular Carcinoma

The aim of this study was to identify novel prognostic mRNA and microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) using methods in systems biology. Differentially expressed mRNAs, miRNAs, and long non-coding RNAs (lncRNAs) were compared between HCC tumor tissues and normal liver tissues in The Cancer Genome Atlas (TCGA) database. Subsequently, a prognosis-associated mRNA co-expression network, an mRNA–miRNA regulatory network, and an mRNA–miRNA–lncRNA regulatory network were constructed to identify prognostic biomarkers for HCC through Cox survival analysis. Seven prognosis-associated mRNA co-expression modules were obtained by analyzing these differentially expressed mRNAs. An expression module including 120 mRNAs was significantly correlated with HCC patient survival. Combined with patient survival data, several mRNAs and miRNAs, including CHST4, SLC22A8, STC2, hsa-miR-326, and hsa-miR-21 were identified from the network to predict HCC patient prognosis. Clinical significance was investigated using tissue microarray analysis of samples from 258 patients with HCC. Functional annotation of hsa-miR-326 and hsa-miR-21-5p indicated specific associations with several cancer-related pathways. The present study provides a bioinformatics method for biomarker screening, leading to the identification of an integrated mRNA–miRNA–lncRNA regulatory network and their co-expression patterns in relation to predicting HCC patient survival.     

A general comparison of relaxed molecular clock models

Several models have been proposed to relax the molecular clock in order to estimate divergence times. However, it is unclear which model has the best fit to real data and should therefore be used to perform molecular dating. In particular, we do not know whether rate autocorrelation should be considered or which prior on divergence times should be used. In this work, we propose a general bench mark of alternative relaxed clock models. We have reimplemented most of the already existing models, including the popular lognormal model, as well as various prior choices for divergence times (birth-death, Dirichlet, uniform), in a common Bayesian statistical framework. We also propose a new autocorrelated model, called the "CIR" process, with well-defined stationary properties. We assess the relative fitness of these models and priors, when applied to 3 different protein data sets from eukaryotes, vertebrates, and mammals, by computing Bayes factors using a numerical method called thermodynamic integration. We find that the 2 autocorrelated models, CIR and lognormal, have a similar fit and clearly outperform uncorrelated models on all 3 data sets. In contrast, the optimal choice for the divergence time prior is more dependent on the data investigated. Altogether, our results provide useful guidelines for model choice in the field of molecular dating while opening the way to more extensive model comparisons.     

七姊妹山常绿落叶阔叶混交林物种多样性格局及其环境解释

依托七姊妹山自然保护区6 hm²森林动态监测样地研究平台,基于样地和物种基本信息数据,采用多元回归树和冗余分析研究方法,探讨地形因子对生境的塑造作用及物种分布特征,分析不同群丛类型下物种多样性的变化规律。结果表明：(1)依据“1 SE”规则,4次分割依次以海拔(1 453 m)、坡度(23.13°)、海拔(1 398 m)、凹凸度(4.094)为分界点可将150个样地分为5个群丛。(2)冗余分析表明地形因子对物种分布解释量为0.077 6,解释率为16.36%,各环境因子对物种分布的解释力度依次为：海拔>坡度>凹凸度;坡向与物种的分布无显著相关性。(3)5个群丛中立木密度与胸高截面积最高的均为群丛5(527.4株/400 m²;3.495 cm²/株),立木密度与平均胸高截面积最低为群丛4(225.4株/400 m²;3.057 cm²/株)。(4)5个群丛中Shannon Winener丰富度指数与Simpson优势度指数最高的均为群丛2,最低的为群丛5,物种多样性尺度效应明显;Pielou均匀度指数最高为群丛4,最低为群丛5。(5)两两群丛间Jaccard相似性系数最低为群丛1 群丛2(0.331),最高的为群丛4 群丛5(0.645),海拔对β多样性格局影响较大。研究认为,七姊妹山自然保护区6 hm²样地地形因子对该区域生境的塑造具有一定作用,海拔、坡度、凹凸度组成的“环境筛”影响了该区域的物种分布及多样性格局。     

Hypothesis testing for the genetic background of quantitative traits

The testing of Bayesian point null hypotheses on variance component models have resulted in a tough assignment for which no clear and generally accepted method exists. In this work we present what we believe is a succeeding approach to such a task. It is based on a simple reparameterization of the model in terms of the total variance and the proportion of the additive genetic variance with respect to it, as well as on the explicit inclusion on the prior probability of a discrete component at origin. The reparameterization was used to bypass an arbitrariness related to the impropriety of uninformative priors onto unbounded variables while the discrete component was necessary to overcome the zero probability assigned to sets of null measure by the usual continuous variable models. The method was tested against computer simulations with appealing results.