Myogenin regulates a distinct genetic program in adult muscle stem cells

In contrast to the detailed understanding we have for the regulation of skeletal muscle gene expression in embryos, similar insights into postnatal muscle growth and regeneration are largely inferential or do not directly address gene regulatory mechanisms. Muscle stem cells (satellite cells) are chiefly responsible for providing new muscle during postnatal and adult life. The purpose of this study was to determine the role that the myogenic basic helix-loop-helix regulatory factor myogenin has in postnatal muscle growth and adult muscle stem cell gene expression. We found that myogenin is absolutely required for skeletal muscle development and survival until birth, but it is dispensable for postnatal life. However, Myog deletion after birth led to reduced body size implying a role for myogenin in regulating body homeostasis. Despite a lack of skeletal muscle defects in Myog-deleted mice during postnatal life and the efficient differentiation of cultured Myog-deleted adult muscle stem cells, the loss of myogenin profoundly altered the pattern of gene expression in cultured muscle stem cells and adult skeletal muscle. Remarkably, these changes in gene expression were distinct from those found in Myog-null embryonic skeletal muscle, indicating that myogenin has separate functions during postnatal life.     

A robust correlation estimator and nonlinear recurrent model to infer genetic interactions in Saccharomyces cerevisiae and pathways of pulmonary disease in Homo sapiens

In order to identify genes involved in complex diseases, it is crucial to study the genetic interactions at the systems biology level. By utilizing modern high throughput microarray technology, it has become feasible to obtain gene expressions data and turn it into knowledge that explains the regulatory behavior of genes. In this study, an unsupervised nonlinear model was proposed to infer gene regulatory networks on a genome-wide scale. The proposed model consists of two components, a robust correlation estimator and a nonlinear recurrent model. The robust correlation estimator was used to initialize the parameters of the nonlinear recurrent curve-fitting model. Then the initialized model was used to fit the microarray data. The model was used to simulate the underlying nonlinear regulatory mechanisms in biological organisms. The proposed algorithm was applied to infer the regulatory mechanisms of the general network in Saccharomyces cerevisiae and the pulmonary disease pathways in Homo sapiens. The proposed algorithm requires no prior biological knowledge to predict linkages between genes. The prediction results were checked against true positive links obtained from the YEASTRACT database, the TRANSFAC database, and the KEGG database. By checking the results with known interactions, we showed that the proposed algorithm could determine some meaningful pathways, many of which are supported by the existing literature.     

转录因子与microRNA在基因表达调控中的功能联系及差异

转录因子和微RNA(microRNA)是最大的两类反式作用因子,它们是基因表达调控的重要调控因子.它们协调发挥调控作用,精细调控基因的表达,在细胞分化和动物生长发育过程中发挥重要的作用.随着对转录因子和microRNA研究的深入,人们发现转录因子和microRNA在基因表达调控网络中关系紧密,它们的分子作用机制有许多相似之处,两者都通过各自的顺式作用元件调控基因表达,且作用的方式类似.但转录因子和microRNA也存在不同之处,转录因子既可以激活基因表达,也可抑制基因表达,而microRNA主要是抑制基因表达.另外,转录因子调控区的复杂性一般高于microRNA的调控区域.本文综述了转录因子和microRNA的异同点,并提出了未来转录因子和microRNA的研究方向.     

乳腺癌致病microRNA调控网络的识别与生物信息学分析

目的:构建并解析乳腺癌致病microRNA(miRNA)调控网络,探究其在乳腺癌发生发展中的调控机制。方法:整合TCGA、ENCODE、Fantom等公共数据库资源,得到miRNA、转录因子和基因候选调控关系数据,结合差异表达、变异系数与PCA,构建乳腺癌miRNA调控网络,解析调控网络的度中心性与聚类系数,使用DAVID进行功能富集分析,构建Cox回归模型作生存曲线。结果:共识别miRNA调控网络262个,其中包含5个显著差异表达miRNA,8个转录因子和130个基因。通过功能富集分析发现这些miRNA靶基因显著参与细胞周期、细胞分化、细胞生长、转移等转录后调控的肿瘤生物进程,并与FoxO信号通路、p53信号通路、基因监测通路等信号通路高度相关。通过分析生存曲线发现hsa-mir-144与hsa-mir-133a-2显著与乳腺癌患者生存相关。结论:识别的乳腺癌致病miRNA调控网络中miRNA之间有相互作用,且网络整体功能不仅受hub网络影响,也受元件自身特性影响,这些miRNA靶基因显著富集于肿瘤相关生物学进程与信号通路中。     

Network modelling of gene regulation

Gene regulatory network (GRN) modelling has gained increasing attention in the past decade. Many computational modelling techniques have been proposed to facilitate the inference and analysis of GRN. However, there is often confusion about the aim of GRN modelling, and how a gene network model can be fully utilised as a tool for systems biology. The aim of the present article is to provide an overview of this rapidly expanding subject. In particular, we review some fundamental concepts of systems biology and discuss the role of network modelling in understanding complex biological systems. Several commonly used network modelling paradigms are surveyed with emphasis on their practical use in systems biology research.     

Robustness and state-space structure of Boolean gene regulatory models

Robustness to perturbation is an important characteristic of genetic regulatory systems, but the relationship between robustness and model dynamics has not been clearly quantified. We propose a method for quantifying both robustness and dynamics in terms of state-space structures, for Boolean models of genetic regulatory systems. By investigating existing models of the Drosophila melanogaster segment polarity network and the Saccharomyces cerevisiae cell-cycle network, we show that the structure of attractor basins can yield insight into the underlying decision making required of the system, and also the way in which the system maximises its robustness. In particular, gene networks implementing decisions based on a few genes have simple state-space structures, and their attractors are robust by virtue of their simplicity. Gene networks with decisions that involve many interacting genes have correspondingly more complicated state-space structures, and robustness cannot be achieved through the structure of the attractor basins, but is achieved by larger attractor basins that dominate the state space. These different types of robustness are demonstrated by the two models: the D. melanogaster segment polarity network is robust due to simple attractor basins that implement decisions based on spatial signals; the S. cerevisiae cell-cycle network has a complicated state-space structure, and is robust only due to a giant attractor basin that dominates the state space.     

On the evolution of scale-free topologies with a gene regulatory network model

A novel approach to generating scale-free network topologies is introduced, based on an existing artificial gene regulatory network model. From this model, different interaction networks can be extracted, based on an activation threshold. By using an evolutionary computation approach, the model is allowed to evolve, in order to reach specific network statistical measures. The results obtained show that, when the model uses a duplication and divergence initialisation, such as seen in nature, the resulting regulation networks not only are closer in topology to scale-free networks, but also require only a few evolutionary cycles to achieve a satisfactory error value.     

Sampling-rate-dependent probabilistic Boolean networks

External control of a genetic regulatory network is used for the purpose of avoiding undesirable states, such as those associated with a disease. To date, intervention has mainly focused on the external control of probabilistic Boolean networks via the associated discrete-time discrete-space Markov processes. Implementation of an intervention policy derived for probabilistic Boolean networks requires nearly continuous observation of the underlying biological system since precise application requires the observation of all transitions. In medical applications, as in many engineering problems, the process is sampled at discrete time intervals and a decision to intervene or not must be made at each sample point. In this work, sampling-rate-dependent probabilistic Boolean network is proposed as an extension of probabilistic Boolean network. The proposed framework is capable of capturing the sampling rate of the underlying system.     

来源于tRNA的小分子RNA——降解碎片还是新的调控分子

具有经典三叶草结构的tRNA作为细胞蛋白质合成机器的重要元件,已经拥有几十年深入细致的研究历史.但是,对于其功能的认识远没有止境,尤其在其作为潜在的基因表达调控分子前体的功能目前正逐渐被人们认识.最新的多项研究结果表明,在多种细胞系中通过高通量测序发现某种来源于tRNA的小片段RNA,这些剪切产物被认为与多种microRNA加工体系关键分子(如Dicer、Ago家族中的蛋白质)具有相互作用的能力.同时,报告基因检测系统的研究结果也暗示,这些小片段RNA具有类似microRNA的潜在调控功能,可能在细胞应对外界环境刺激时发挥重要的调节作用.如其具体的作用机制能够被更多的实验结果阐明,将极大地扩展我们对于非编码RNA调控功能的认识.     

The endoderm gene regulatory network in sea urchin embryos up to mid-blastula stage

As the result of early specification processes, sea urchin embryos eventually form various mesodermal cell lineages and a gut consisting of fore-, mid- and hindgut. The progression of specification as well as the overall spatial organization of the organism is encoded in its gene regulatory networks (GRNs). We have analyzed the GRN driving endoderm specification up to the onset of gastrulation and present in this paper the mechanisms which determine this process up to mid-blastula stage. At this stage, the embryo consists of two separate lineages of endoderm precursor cells with distinct regulatory states. One of these lineages, the veg2 cell lineage, gives rise to endoderm and mesoderm cell types. The separation of these cell fates is initiated by the spatially confined activation of the mesoderm GRN superimposed on a generally activated endoderm GRN within veg2 descendants. Here we integrate the architecture of regulatory interactions with the spatial restriction of regulatory gene expression to model the logic control of endoderm development.