Adaptive Thresholding for Reconstructing Regulatory Networks from Time-Course Gene Expression Data

Discovering regulatory interactions from time-course gene expression data constitutes a canonical problem in functional genomics and systems biology. The framework of graphical Granger causality allows one to estimate such causal relationships from these data. In this study, we propose an adaptively thresholding estimates of Granger causal effects obtained from the lasso penalization method. We establish the asymptotic properties of the proposed technique, and discuss the advantages it offers over competing methods, such as the truncating lasso. Its performance and that of its competitors is assessed on a number of simulated settings and it is applied on a data set that captures the activation of T-cells.     

神经系统特异表达基因调控机理的研究进展

神经系统特异性基因正确的时空表达受细胞内外信号的调控,信号传导途径最终的靶位点是能结合特异转录因子的DNA序列.目前发现的决定神经系统基因特异性表达的顺式作用元件既有增强子,也有沉默子.它们可以特异性地增强基因在神经系统的表达,或特异性抑制基因在非神经系统的表达. 顺式元件要发挥这些作用,依赖于与其结合的反式因子,而这些反式因子又能与其他蛋白质或DNA序列发生互动, 通过协调作用,共同决定基因的时空表达顺序.     

HIV-1基因表达的转录调控

高效抗逆转录病毒治疗（HAART）可以有效地抑制人类免疫缺陷病毒Ⅰ型（HIV-1）的复制及血浆病毒载量,延缓发病进程,改善、提高患者的生活质量和存活时间。但是,一旦停止治疗就会导致血浆病毒血症迅速反弹,HIV-1以原病毒的形式在静息记忆CD4⁺T等细胞中的持续存在是清除HIV-1的一个障碍。HIV-1基因转录的激活与阻抑决定了受感染细胞进入产毒性感染或潜伏感染。本文从原病毒整合位置与转录干扰、细胞转录因子与HIV-1启动子相互作用招募RNA聚合酶起始转录、转录的表观遗传调控和反式激活因子Tat及其相关蛋白促进转录延伸等方面探讨了HIV-1原病毒转录调控机制。     

Integration of Steady-State and Temporal Gene Expression Data for the Inference of Gene Regulatory Networks

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.     

Indian-Ink Perfusion Based Method for Reconstructing Continuous Vascular Networks in Whole Mouse Brain

The topology of the cerebral vasculature, which is the energy transport corridor of the brain, can be used to study cerebral circulatory pathways. Limited by the restrictions of the vascular markers and imaging methods, studies on cerebral vascular structure now mainly focus on either observation of the macro vessels in a whole brain or imaging of the micro vessels in a small region. Simultaneous vascular studies of arteries, veins and capillaries have not been achieved in the whole brain of mammals. Here, we have combined the improved gelatin-Indian ink vessel perfusion process with Micro-Optical Sectioning Tomography for imaging the vessel network of an entire mouse brain. With 17 days of work, an integral dataset for the entire cerebral vessels was acquired. The voxel resolution is 0.35×0.4×2.0 µm³ for the whole brain. Besides the observations of fine and complex vascular networks in the reconstructed slices and entire brain views, a representative continuous vascular tracking has been demonstrated in the deep thalamus. This study provided an effective method for studying the entire macro and micro vascular networks of mouse brain simultaneously.     

数据噪音构建基因布尔网络模型的方法

在分析基因数据时,往往有噪音出现,因此借用基因表达谱数据中的噪音来建立卡诺图,可以得到布尔网络逻辑函数。而且利用此方法确定蛋白质与蛋白质之间的逻辑关系,建立蛋白质的逻辑网络。通过该方法可以寻找直系同源簇蛋白质数据的逻辑关系。     

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

A system-level understanding of the regulation and coordination mechanisms of gene expression is essential for studying the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The scLink R package is available at https://github.com/Vivianstats/scLink.     

Developmental Regulation of Microtubule-Associated Protein 2 Expression in Regions of Mouse Brain

The relative levels of microtubule-associated protein 2(MAP2) were determined during postnatal development of the mouse in six different discrete brain regions: cerebellum, cortex, hippocampus, olfactory bulb, brainstem, and hypothalamus. Brain homogenates were electrophoresed on sodium dodecyl sulfate-containing gels and analyzed by immunoblotting with MAP2-specific antibodies. The levels of MAP2 in each region were determined using radiolabeled secondary antibodies and densitometric quantification of the autoradiograms over a range that was determined to have a linear response. The results indicated that in all regions and at all ages there was only one high-molecular-weight polypeptide of MAP2, which did not change in electrophoretic mobility after dephosphorylation. In most regions, the levels of MAP2 increased during the first 2 postnatal weeks. However, there were differences in the time course and relative levels of MAP2 between regions. In addition, all regions of the brain expressed the low-molecular-weight form of MAP2 (MAP2c) that was present at birth as a heterogeneous group of polypeptides with an apparent molecular weight of 70K. Most of the heterogeneity of MAP2c, however, was eliminated after dephosphorylation. The levels of MAP2c decreased dramatically after 2 weeks postnatally, except for the olfactory bulb, where the levels of MAP2c remained relatively high even in adults.     

胰岛素基因的转录调控

胰岛素基因的转录是一个十分复杂的网络式调控过程,主要通过该基因上游调控序列的启动子、增强子与转录因子复合物的相互作用来实现;多种证据表明胰岛因子1可能参与其中,对其具体机制的研究将是该领域的研究方向之一。     

G-box和G-box结合蛋白在植物基因诱导表达中的转录调控作用

文章就G-box与其它顺式作用元件的组合调控作用,以及G-box结合蛋白通过二聚体化、磷酸化、亚细胞定位等调控方式在植物基因诱导表达中的调控作用作了评述。     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]