抑郁症易感基因和抗抑郁药物靶点相关[]神经细胞类型及相互作用网络分析

基于抑郁症的全基因组关联分析研究(GWAS),对于获得的单核苷酸多态性位点(SNP)使用Haploreg软件进行基因注释,得到SNP注释的102个易感基因.。使用MAGMA软件对GWAS的汇总统计数据做基因水平的分析,获得了270个校正之后显著的基因,两者合并共得到320个抑郁症易感基因。通过药物数据库Drugbank获取133个抗抑郁药物靶点基因。使用EWCE包对抑郁症易感基因和抗抑郁药物靶点在三套脑组织单细胞测序数据中,分别进行神经细胞类型富集分析。结果发现大脑皮质的GABA神经元(抑制性神经元)和谷氨酸能神经元(兴奋性神经元)是抑郁症易感基因和抗抑郁药物靶点共同的神经元。这两种类型的神经细胞可能是抗抑郁药物与抑郁症易感基因相互作用的神经细胞,另外少突胶质前体细胞可能是抑郁症特有的易感神经细胞。使用Network Calculator软件构建网络并进行进行网络拓扑学参数分析。结果表明抑郁症易感基因与抗抑郁药物靶点组成了一个具有显著的相互连接的网络。本研究从单细胞层面揭示抑郁症的遗传机制,在网络层面为寻找新的抗抑郁药物靶点提供了一定的启示。     

DLGCN:基于图卷积网络的药物-lncRNA[]关联预测

为实现高通量识别新的药物-长链非编码RNA(Long non-coding RNA, lncRNA)关联,本文提出了一种基于图卷积网络模型来识别潜在药物-lncRNA关联的方法DLGCN(Drug-LncRNA graph convolution network)。首先,基于药物的结构信息和lncRNA的序列信息分别构建了药物-药物和lncRNA-lncRNA相似性网络,并整合实验证实的药物-lncRNA关联构建了药物-lncRNA异质性网络。然后,将注意力机制和图卷积运算应用于该网络中,学习药物和lncRNA的低维特征,基于整合的低维特征预测新的药物-lncRNA关联。通过效能评估,DLGCN的受试者工作特性曲线下面积(Area under receiver operating characteristic, AUROC)达到0.843 1,优于经典的机器学习方法和常见的深度学习方法。此外,DLGCN预测到姜黄素能够调控lncRNA MALAT1的表达,已被最近的研究证实。DLGCN能够有效预测药物-lncRNA关联,为肿瘤治疗新靶点的识别和抗癌药物的筛选提供了重要参考。     

基于小波神经网络的麦蚜发生程度预测模型

【目的】建立基于小波神经网络病虫害预测预报模型,对提前采取防病防虫措施、减少农作物病虫害损失、提高农作物产量与质量具有重要意义。【方法】本研究以山西省运城市芮城县1980-2014年麦蚜发生程度和气象因子数据为基础,采用主成分分析法从40个基础气象因子中整合形成9个新的自变量输入模型,采用试凑法筛选隐含层节点数,用1980-2009年的数据进行网络训练,对2010-2014年麦蚜发生程度进行回测,建立了以Morlet小波函数为传递函数的小波神经网络模型,并与以Sigmoid函数为传递函数的BP神经网络模型进行了比较。【结果】小波和BP神经网络两种模型对训练样本的平均拟合精度均有10年以上超过80%,两者MAPE 值分别为 89.83% 和83.07%,MSE 值分别为0.0578和0.6192。【结论】两个模型都能较好地描述麦蚜发生程度;从预测精度和模型的稳定性来看,小波神经网络好于BP神经网络。     

2-D DIGE analysis of UV-C radiation-responsive proteins in globe artichoke leaves

Plants respond to ultraviolet stress inducing a self-defence through the regulation of specific gene family members. The UV acclimation is the result of biochemical and physiological processes, such as enhancement of the antioxidant enzymatic system and accumulation of UV-absorbing phenolic compounds (e.g. flavonoids). Globe artichoke is an attractive species for studying the protein network involved in UV stress response, being characterized by remarkable levels of inducible antioxidants. Proteomic tools can assist the evaluation of the expression patterns of UV-responsive proteins and we applied the difference in-gel electrophoresis (DIGE) technology for monitoring the globe artichoke proteome variation at four time points following an acute UV-C exposure. A total of 145 UV-C-modulated proteins were observed and 119 were identified by LC-MS/MS using a ～144,000 customized Compositae protein database, which included about 19,000 globe artichoke unigenes. Proteins were Gene Ontology (GO) categorized, visualized on their pathways and their behaviour was discussed. A predicted protein interaction network was produced and highly connected hub-like proteins were highlighted. Most of the proteins differentially modulated were chloroplast located, involved in photosynthesis, sugar metabolisms, protein folding and abiotic stress. The identification of UV-C-responsive proteins may contribute to shed light on the molecular mechanisms underlying plant responses to UV stress.     

Morphological and molecular divergence of Indian hill trout,Barilius bendelisis (Hamilton, 1822) stocks from different Rivers in Indo‐Burma biodiversity hotspot: Does river altitude and Dam play a role?

The present study explains the intraspecific variation in Indian Hill trout (Barilius bendelisis) on the basis of image based truss network system and D‐loop region of mtDNA. A total of 210 samples were collected from three different rivers (Teesta, Kameng and Myntudu River) of North East India in Indo‐Burma Biodiversity Hotspot. By using the software applications (tpsDig version 2.1 and PAST), a total of 25 morphometric measurements were generated from 10 landmarks. The Analysis of Variance (ANOVA), Factor Analysis (FA) and Discriminate Function Analysis (DFA) showed, out of the total variations, factor 1 explained 46.74% while factor 2 and factor 3 explained 27.14% and 11.92%, respectively. Using these variables 83.33% of the cross‐validated specimens were classified into distinct groups. Analysis of Molecular Variance (AMOVA) and pairwise Fst value for D‐loop region of mtDNA also showed high to medium level of genetic variation among the stocks and within the stocks. We conclude that the observed discrete stocks might be the result of changing environmental conditions in different rivers of the hotspot as the rivers are present at different altitudinal labels. It is also believed that the variation might be due to the construction of barrages across the river which hinder the mixing among the stocks.     

Chaotic gene regulatory networks can be robust against mutations and noise

Robustness to mutations and noise has been shown to evolve through stabilizing selection for optimal phenotypes in model gene regulatory networks. The ability to evolve robust mutants is known to depend on the network architecture. How do the dynamical properties and state-space structures of networks with high and low robustness differ? Does selection operate on the global dynamical behavior of the networks? What kind of state-space structures are favored by selection? We provide damage propagation analysis and an extensive statistical analysis of state spaces of these model networks to show that the change in their dynamical properties due to stabilizing selection for optimal phenotypes is minor. Most notably, the networks that are most robust to both mutations and noise are highly chaotic. Certain properties of chaotic networks, such as being able to produce large attractor basins, can be useful for maintaining a stable gene-expression pattern. Our findings indicate that conventional measures of stability, such as damage propagation, do not provide much information about robustness to mutations or noise in model gene regulatory networks.     

Estimating co-extinction threats in terrestrial ecosystems

The biosphere is changing rapidly due to human endeavour. Because ecological communities underlie networks of interacting species, changes that directly affect some species can have indirect effects on others. Accurate tools to predict these direct and indirect effects are therefore required to guide conservation strategies. However, most extinction-risk studies only consider the direct effects of global change—such as predicting which species will breach their thermal limits under different warming scenarios—with predictions of trophic cascades and co-extinction risks remaining mostly speculative. To predict the potential indirect effects of primary extinctions, data describing community interactions and network modelling can estimate how extinctions cascade through communities. While theoretical studies have demonstrated the usefulness of models in predicting how communities react to threats like climate change, few have applied such methods to real-world communities. This gap partly reflects challenges in constructing trophic network models of real-world food webs, highlighting the need to develop approaches for quantifying co-extinction risk more accurately. We propose a framework for constructing ecological network models representing real-world food webs in terrestrial ecosystems and subjecting these models to co-extinction scenarios triggered by probable future environmental perturbations. Adopting our framework will improve estimates of how environmental perturbations affect whole ecological communities. Identifying species at risk of co-extinction (or those that might trigger co-extinctions) will also guide conservation interventions aiming to reduce the probability of co-extinction cascades and additional species losses.     

Association of a GPI-anchored protein with detergent-resistant membranes facilitates its trafficking through the early secretory pathway

Membrane microdomains are implicated in the trafficking and sorting of several membrane proteins. In particular GPI-anchored proteins cluster into Triton X-100 resistant, cholesterol- and sphingolipid-rich membrane microdomains and are sorted to the apical membrane. A growing body of evidence has pointed to the existence of other types of microdomains that are insoluble in detergents, such as Lubrol WX and Tween-20. Here, we report on the role of detergent-resistant membranes formed at early stages in the biosynthesis of membrane dipeptidase (MDP), a GPI-anchored protein, on its trafficking and sorting. Pulse-chase experiments revealed a retarded maturation rate of the GPI-anchor deficient mutant (MDPΔGPI) as compared to the wild type protein (wtMDP). However, Golgi to cell surface delivery rate did not show a significant difference between the two variants. On the other hand, early biosynthetic forms of wtMDP were partially insoluble in Tween-20, while MDPΔGPI was completely soluble. The lack of association of MDPΔGPI with detergent-resistant membranes prior to maturation in the Golgi and the reduction in its trafficking rate strongly suggest the existence of an early trafficking control mechanisms for membrane proteins operating at a level between the endoplasmic reticulum and the cis-Golgi.