长链非编码RNA的免疫调节机制研究进展

真核生物的基因表达受多个层面调控,包括染色体水平、DNA水平、转录水平和转录后水平的调控等.长链非编码RNA(lnc RNA)是一类转录本超过200 nt的非编码RNA,其对基因表达的调控涉及上述各个层面,如组蛋白修饰、DNA甲基化的调控、转录的促进和抑制、m RNA的剪辑及对转录因子的调控等.其作用方式复杂多样,可与DNA、mRNA和蛋白质等相互作用而发挥调节作用.LncRNA保守性较差,但其表达却有较高的细胞、组织和分化阶段特异性.免疫系统的发育和分化受到精密的调控,且具有较高的阶段性和特异性.因此研究lnc RNA的功能及作用机制,免疫系统是较好的选择,这能促进我们对免疫调控的理解,为免疫性疾病的治疗提供新的思路和方法.本文主要介绍lnc RNA的分类和lnc RNA作用的一般分子机制,及其对T细胞、B细胞、固有免疫细胞和炎症因子的分子调控机制及其进展.     

Steady-state expression of self-regulated genes

MOTIVATION: Regulatory gene networks contain generic modules such as feedback loops that are essential for the regulation of many biological functions. The study of the stochastic mechanisms of gene regulation is instrumental for the understanding of how cells maintain their expression at levels commensurate with their biological role, as well as to engineer gene expression switches of appropriate behavior. The lack of precise knowledge on the steady-state distribution of gene expression requires the use of Gillespie algorithms and Monte-Carlo approximations. Methodology: In this study, we provide new exact formulas and efficient numerical algorithms for computing/modeling the steady-state of a class of self-regulated genes, and we use it to model/compute the stochastic expression of a gene of interest in an engineered network introduced in mammalian cells. The behavior of the genetic network is then analyzed experimentally in living cells. RESULTS: Stochastic models often reveal counter-intuitive experimental behaviors, and we find that this genetic architecture displays a unimodal behavior in mammalian cells, which was unexpected given its known bimodal response in unicellular organisms. We provide a molecular rationale for this behavior, and we implement it in the mathematical picture to explain the experimental results obtained from this network.     

INSISTC: Incorporating network structure information for single-cell type classification

Uncovering gene regulatory mechanisms in individual cells can provide insight into cell heterogeneity and function. Recent accumulated Single-Cell RNA-Seq data have made it possible to analyze gene regulation at single-cell resolution. Understanding cell-type-specific gene regulation can assist in more accurate cell type and state identification. Computational approaches utilizing such relationships are under development. Methods pioneering in integrating gene regulatory mechanism discovery with cell-type classification encounter challenges such as determine gene regulatory relationships and incorporate gene regulatory network structure. To fill this gap, we developed INSISTC, a computational method to incorporate gene regulatory network structure information for single-cell type classification. INSISTC is capable of identifying cell-type-specific gene regulatory mechanisms while performing single-cell type classification. INSISTC demonstrated its accuracy in cell type classification and its potential for providing insight into molecular mechanisms specific to individual cells. In comparison with the alternative methods, INSISTC demonstrated its complementary performance for gene regulation interpretation.     

Towards more biological mutation operators in gene regulation studies

Genetic regulation is often viewed as a complex system whose properties emerge from the interaction of regulatory genes. One major paradigm for studying the complex dynamics of gene regulation uses directed graphs to explore structure, behaviour and evolvability. Mutation operators used in such studies typically involve the insertion and deletion of nodes, and the insertion, deletion and rewiring of links at the network level. These network-level mutational operators are sufficient to allow the statistical analysis of network structure, but impose limitations on the way networks are evolved. There are a wide variety of mutations in DNA sequences that have yet to be analysed for their network-level effects. By modelling an artificial genome at the level of nucleotide sequences and mapping it to a regulatory network, biologically grounded mutation operators can be mapped to network-level mutations. This paper analyses five such sequence level mutations (single-point mutation, transposition, inversion, deletion and gene duplication) for their effects at the network level. Using analytic and simulation techniques, we show that it is rarely the case that nodes and links are cleanly added or deleted, with even the simplest point mutation causing a wide variety of network-level modifications. As expected, the vast majority of simple (single-point) mutations are neutral, resulting in a neutral plateau from which a range of functional behaviours can be reached. By analysing the effects of sequence-level mutations at the network level of gene regulation, we aim to stimulate more careful consideration of mutation operators in gene regulation models than has previously been given.     

Modelling in molecular biology: describing transcription regulatory networks at different scales

Approaches to describe gene regulation networks can be categorized by increasing detail, as network parts lists, network topology models, network control logic models or dynamic models. We discuss the current state of the art for each of these approaches. We study the relationship between different topology models, and give examples how they can be used to infer functional annotations for genes of unknown function. We introduce a new simple way of describing dynamic models called finite state linear model (FSLM). We discuss the gap between the parts list and topology models on one hand, and network logic and dynamic models, on the other hand. The first two classes of models have reached a genome-wide scale, while for the other model classes high-throughput technologies are yet to make a major impact.     

基于双聚类挖掘癌症共享的基因功能模块

基因多效性是癌症遗传机制中的普遍现象, 但罕见系统性的分析。文章提出采用双聚类挖掘基因功能模块的新思路探索癌症的共享分子机制和不同癌症间的关系。获取20种癌症的基因表达数据, 应用改良t检验和倍数法筛选出至少在两种癌症中差异表达的基因, 得到10417×20的数据矩阵; 采用双聚类方法获得22个癌症共享的基因簇; 进一步富集分析得到17个基因功能模块(Bonferroni校正后P<0.05), 主要参与有丝分裂染色单体分离的调控、细胞分化、免疫和炎症反应、胶原纤维组织等生物过程; 主要执行ATP结合和微管活动、MHCⅡ类受体活性、肽链内切酶抑制活性等分子功能; 活动区域主要在细胞骨架、染色体、MHCⅡ蛋白质复合体、中间丝蛋白、胶原纤维等。基于模块构建癌症相关网络, 显示胃癌、卵巢腺癌、宫颈鳞癌和间皮瘤等之间相关程度较高, 而两种血液系统癌症(急性髓细胞性白血病与多发性骨髓瘤)分子机制与其他癌症存在较大差异。可见癌症共享的基因功能模块与多种生物机制有关, 癌症之间相似性可能与组织起源、共同的致癌机制等有关。文章提出的基因多效性分析方法有助于解释人类复杂性疾病的共享分子机制。     

Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data

We performed computational reconstruction of the in silico gene regulatory networks in the DREAM3 Challenges. Our task was to learn the networks from two types of data, namely gene expression profiles in deletion strains (the ‘deletion data’) and time series trajectories of gene expression after some initial perturbation (the ‘perturbation data’). In the course of developing the prediction method, we observed that the two types of data contained different and complementary information about the underlying network. In particular, deletion data allow for the detection of direct regulatory activities with strong responses upon the deletion of the regulator while perturbation data provide richer information for the identification of weaker and more complex types of regulation. We applied different techniques to learn the regulation from the two types of data. For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method. For perturbation data, we used differential equations to model the change of expression levels of a gene along the trajectories due to the regulation of other genes. We tried different models, and combined their predictions. The final predictions were obtained by merging the results from the two types of data. A comparison with the actual regulatory networks suggests that our approach is effective for networks with a range of different sizes. The success of the approach demonstrates the importance of integrating heterogeneous data in network reconstruction.     

Kinetic Mechanisms of Biological Regulation in Photosynthetic Organisms

Principles of regulation on different levels of photosynthetic apparatus are discussed. Mathematical models of isolated photosynthetic reaction centers and general system of energy transduction in chloroplast are developed. A general approach to model these complex metabolic systems is suggested. Regulatory mechanisms in plant cell are correlated with the different patterns of fluorescence induction curve at different internal physiological states of the cells and external (environmental) conditions. Light regulation inside photosynthetic reaction centers, diffusion processes in thylakoid membrane, generation of transmembrane electrochemical potential, coupling with processes of CO₂ fixation in Calvin Cycle are considered as stages of control of energy transformation in chloroplasts in their connection with kinetic patterns of fluorescence induction curves and other spectrophotometric data.     

Respiratory burst oxidases: the engines of ROS signaling

Reactive oxygen species (ROS) play a key signal transduction role in cells. They are involved in the regulation of growth, development, responses to environmental stimuli and cell death. The level of ROS in cells is determined by interplay between ROS producing pathways and ROS scavenging mechanisms, part of the ROS gene network of plants. Recent studies identified respiratory burst oxidase homologues (RBOHs) as key signaling nodes in the ROS gene network of plants integrating a multitude of signal transduction pathways with ROS signaling. The ability of RBOHs to integrate calcium signaling and protein phosphorylation with ROS production, coupled with genetic studies demonstrating their involvement in many different biological processes in cells, places RBOHs at the center of the ROS network of cells and demonstrate their important function in plants.