The retinol-retinoic acid metabolic pathway is impaired in the lumbar spine of a rat model of congenital kyphoscoliosis

Although congenital scoliosis is defined as a genetic disease characterized by a congenital and abnormal curvature of the spinal vertebrae, our knowledge of the genetic underpinnings of the disease is insufficient. We herein show that the downregulation of the retinol-retinoic acid metabolism pathway is involved in the pathogenesis of congenital scoliosis. By analyzing DNA microarray data, we found that the expression levels of genes associated with the retinol metabolism pathway were decreased in the lumbar spine of Ishibashi rats (IS), a rat model of congenital kyphoscoliosis. The expression of Adh1 and Aldh1a2 (alcohol dehydrogenase), two enzymes that convert retinol to retinoic acid in this pathway, were decreased at both the gene and protein levels. Rarα, a receptor of retinoic acid and bone morphogenetic protein 2, which play a central role in bone formation and are located downstream of this pathway, were also downregulated. Interestingly, the serum retinol levels of IS rats were higher than those of wild-type control rats. These results indicate that the adequate conversion from retinol to retinoic acid is extremely important in the regulation of normal bone formation and it may also be a key factor for understanding the pathogenesis of congenital scoliosis.     

The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA-lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA-lncRNA signature to improve the ability to predict HCC patients’ survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high-risk group and low-risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648-6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well-performed nomogram integrating the prognostic signature and other clinical information to predict 3- and 5-year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA-lncRNA may be an efficient classification tool to evaluate the prognosis of patients’ with HCC.     

NetLogoR: a package to build and run spatially explicit agent‐based models in R

NetLogoR is an R package to build and run spatially explicit agent‐based models (SE‐ABMs) using the R language. SE‐ABMs are models that simulate the fate of entities at the individual level within a spatial context and where patterns emerge at the population level. NetLogoR follows the same framework as the NetLogo software (Wilensky 1999). Rather than a call function to use the NetLogo software, NetLogoR is a translation into the R language of the structure and functions of NetLogo. Models built with NetLogoR are written in R language and are run on the R platform; no other software or language has to be involved. NetLogoR provides new R classes to define model agent objects and functions to implement spatially explicit agent‐based models in the R environment. Users of this package benefit from the fast and easy coding provided by the highly developed NetLogo framework, coupled with the versatility, power and massive resources of the R language.     

Impact of apolipoprotein A1- or lecithin:cholesterol acyltransferase-deficiency on white adipose tissue metabolic activity and glucose homeostasis in mice

High density lipoprotein (HDL) has attracted the attention of biomedical community due to its well-documented role in atheroprotection. HDL has also been recently implicated in the regulation of islets of Langerhans secretory function and in the etiology of peripheral insulin sensitivity. Indeed, data from numerous studies strongly indicate that the functions of pancreatic β-cells, skeletal muscles and adipose tissue could benefit from improved HDL functionality. To better understand how changes in HDL structure may affect diet-induced obesity and type 2 diabetes we aimed at investigating the impact of Apoa1 or Lcat deficiency, two key proteins of peripheral HDL metabolic pathway, on these pathological conditions in mouse models. We report that universal deletion of apoa1 or lcat expression in mice fed western-type diet results in increased sensitivity to body-weight gain compared to control C57BL/6 group. These changes in mouse genome correlate with discrete effects on white adipose tissue (WAT) metabolic activation and plasma glucose homeostasis. Apoa1-deficiency results in reduced WAT mitochondrial non-shivering thermogenesis. Lcat-deficiency causes a concerted reduction in both WAT oxidative phosphorylation and non-shivering thermogenesis, rendering lcat^?/? mice the most sensitive to weight gain out of the three strains tested, followed by apoa1^?/? mice. Nevertheless, only apoa1^?/? mice show disturbed plasma glucose homeostasis due to dysfunctional glucose-stimulated insulin secretion in pancreatic β-islets and insulin resistant skeletal muscles. Our analyses show that both apoa1^?/? and lcat^?/? mice fed high-fat diet have no measurable Apoa1 levels in their plasma, suggesting no direct involvement of Apoa1 in the observed phenotypic differences among groups.     

基于鼠脑海马位置细胞与Q学习面向目标导航

生理学实验表明在鼠脑海马结构中存在的一种具有特异性放电特征的细胞,它在大鼠空间导航和环境认知中起着关键性作用,该特异性神经元被称之为位置细胞。本文将基于位置细胞、运动神经元来构建一种前馈神经网络模型,采用Q学习算法实现大鼠面向目标导航任务。实验结果表明该前馈神经网络模型能快速实现大鼠面向目标导航任务。     

基于肿瘤免疫微环境鉴定IDH突变型弥漫性胶质瘤的预后标志物

异柠檬酸脱氢酶(IDH)突变存在于大多数低级别胶质细胞瘤中,免疫逃逸是肿瘤标志性特征之一,免疫治疗在胶质瘤的治疗中的作用越来越重要。利用生信手段分析TCGA、CGGA、GEO数据集中IDH突变胶质瘤的770个免疫相关基因及其临床相关数据,从而获得每个患者的免疫风险评分(IMRS);结合IMRS和临床信息,筛选出6个差异表达基因(TRAF3、ATG10、BID、TAB1、MAP3K1、RPS6)组成IMRS模型并生成诺莫图对患者预后进行评估,发现低风险组患者的总生存期(OS)较高风险组均明显延长。此外,签名相关免疫细胞浸润分析发现肿瘤相关巨噬细胞浸润评分(TAM)与肿瘤相关T细胞浸润评分(TIS)呈明显的负相关,表明高IMRS富集了促肿瘤免疫浸润,而低IMRS则富集了相对较多的抗肿瘤免疫浸润。     

基于HP模型的蛋白质折叠问题的研究

基于蛋白质二维HP模型提出改进的遗传算法对真实蛋白质进行计算机折叠模拟。结果显示疏水能量函数最小值的蛋白质构象对应含疏水核心的稳定结构,疏水作用在蛋白质折叠中起主要作用。研究表明二维HP模型在蛋白质折叠研究中是可行的和有效的并为进一步揭示蛋白质折叠机理提供重要参考信息。     

黑龙江省2010～2020年猩红热发病与气象因素的关联性研究

分析黑龙江省气象因素与猩红热发病的关系,建立时间序列模型,为今后制定更科学有效的猩红热防控策略提供参考依据。收集黑龙江省2010～2020年猩红热月发病数据以及同期气温、气压等气象资料,应用广义相加模型分析气象因素与猩红热发病之间的关联程度和形式。结果发现: 猩红热全年均有发病而且呈现出较为典型的双峰型特征,在春季的4～5月份和冬季的11～12月份发病数达到高峰;月平均气压、月平均相对湿度、月日照时数和月平均风速的P值均小于0.05,表明具有统计学意义。同时,RR(相对危险度Risk Ratio)值均小于1,即猩红热发病与四个气象因素呈负相关。黑龙江省猩红热发病每年存在两个流行高峰,主要以冬季为主,发病数随着月平均相对湿度、月日照时数、月平均风速与月平均气压的升高而降低。     

赤水河外来鱼类尖头(鱥)和董氏须鳅的引种溯源及生态适应性分析

历史上,赤水河流域干流及支流的鱼类物种组成中未曾出现过尖头(鱥)(Rhynchocypris oxycephalus)和董氏须鳅(Barbatula toni).但是,近年来在赤水河支流白沙河的鱼类资源调查中发现了这两个物种的分布,其来源及未来的生存可能性受到关注.本研究比较了尖头(鱥)赤水河野外种群与养殖种群形态上的...     

Pyroptosis in glioblastoma: A crucial regulator of the tumour immune microenvironment and a predictor of prognosis

Recent studies have shown that pyroptosis, an inflammatory form of cell death, has a dual role in tumorigenesis and tumour progression and affects the prognosis of patients; however, the role of pyroptosis in glioblastoma (GBM) is still unclear. In this study, based on GBM patients'' data from two independent cohorts, we performed a comprehensive analysis of the expression and prognostic value of 33 pyroptosis‐associated genes (PAGs) in GBM, as well as their role in the tumour immune microenvironment (TIME) of GBM. We identified 29 PAGs that were differentially expressed between GBM and normal brain tissue, 18 of which were upregulated in GBM tissue. Most of the 33 PAGs were strongly correlated with the levels of immune cell infiltration. Based on the 33 PAGs, the GBM samples can be divided into two clusters (C1‐C2), with C1 having a ‘hot’ but immunosuppressive TIME and C2 having a ‘cold’ TIME, suggesting different immunotherapeutic responses in the two clusters. In addition, we identified four PAGs that were strongly associated with GBM prognosis and constructed a risk model based on these four PAGs. This risk model is an independent prognostic factor for GBM patients, and there is a different immune status between high‐ and low‐risk groups. In conclusion, this study demonstrates that pyroptosis is closely associated with the prognosis and TIME of GBM and provides an important basis for further studies on the relationship between pyroptosis and GBM.