A gene regulatory network controlling the embryonic specification of endoderm

Specification of endoderm is the prerequisite for gut formation in the embryogenesis of bilaterian organisms. Modern lineage labelling studies have shown that in the sea urchin embryo model system, descendants of the veg1 and veg2 cell lineages produce the endoderm, and that the veg2 lineage also gives rise to mesodermal cell types. It is known that Wnt/β-catenin signalling is required for endoderm specification and Delta/Notch signalling is required for mesoderm specification. Some direct cis-regulatory targets of these signals have been found and various phenomenological patterns of gene expression have been observed in the pre-gastrular endomesoderm. However, no comprehensive, causal explanation of endoderm specification has been conceived for sea urchins, nor for any other deuterostome. Here we propose a model, on the basis of the underlying genomic control system, that provides such an explanation, built at several levels of biological organization. The hardwired core of the control system consists of the cis-regulatory apparatus of endodermal regulatory genes, which determine the relationship between the inputs to which these genes are exposed and their outputs. The architecture of the network circuitry controlling the dynamic process of endoderm specification then explains, at the system level, a sequence of developmental logic operations, which generate the biological process. The control system initiates non-interacting endodermal and mesodermal gene regulatory networks in veg2-derived cells and extinguishes the endodermal gene regulatory network in mesodermal precursors. It also generates a cross-regulatory network that specifies future anterior endoderm in veg2 descendants and institutes a distinct network specifying posterior endoderm in veg1-derived cells. The network model provides an explanatory framework that relates endoderm specification to the genomic regulatory code.     

Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast

Under laboratory conditions 80% of yeast genes seem not to be essential for viability. This raises the question of what the mechanistic basis for dispensability is, and whether it is the result of selection for buffering or an incidental side product. Here we analyse these issues using an in silico flux model of the yeast metabolic network. The model correctly predicts the knockout fitness effects in 88% of the genes studied and in vivo fluxes. Dispensable genes might be important, but under conditions not yet examined in the laboratory. Our model indicates that this is the dominant explanation for apparent dispensability, accounting for 37-68% of dispensable genes, whereas 15-28% of them are compensated by a duplicate, and only 4-17% are buffered by metabolic network flux reorganization. For over one-half of those not important under nutrient-rich conditions, we can predict conditions when they will be important. As expected, such condition-specific genes have a more restricted phylogenetic distribution. Gene duplicates catalysing the same reaction are not more common for indispensable reactions, suggesting that the reason for their retention is not to provide compensation. Instead their presence is better explained by selection for high enzymatic flux.     

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth

Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.     

FluxExplorer： A general platform for modeling and analyses of metabolic networks based on stoichiometry

An increasing number of genomic and biochemical data make it possible to reconstruct biochemical net-works, especially metabolic networks, of an organelle or even a whole cell. Some methods for metabolism modeling and analyses in this field have been deve…     

基于酵母基因组高通量数据的基因-基因相互作用预测

后基因组时代的显著特点是大规模基因组和蛋白质组实验平台所产生的大量高通量数据,整合并利用基因组和蛋白组信息成为这一时代的主要挑战之一. 因此,基因-基因相互作用将有助于理解细胞内基因之间的相互作用以及信号传导通路研究提供有价值的参考. 为预测酵母基因组中基因-基因相互作用,我们利用高通量数据中的蛋白-蛋白相互作用、遗传表型数据、基因微阵列表达数据以及功能基因注释数据等来分析酵母中的基因-基因相互作用. 本文建立的预测方法为在系统水平上理解酵母基因组中的基因功能提供了依据,也为揭示酵母基因组中的基因-基因相互作用网络奠定理论基础.     

基因调控网络的父节点筛选贝叶斯建模方法

在构建基因调控网络的方法中,贝叶斯网络模型可以直观地表达基因间的调控关系,但在结构学习时的复杂度极高,使得网络建模效率较低且规模有限.因此,本文提出一种基于父节点筛选的贝叶斯网络(parent node screening based Bayesian network, PS-BN)建模方法.PS-BN方法将关联模型与贝叶斯网络模型相结合,在充分利用贝叶斯网络模型结构学习搜索策略的前提下,先基于父节点筛选方法去除部分冗余信息,以达到缩减搜索空间的目的.实验结果表明,与传统的贝叶斯网络模型方法相比,PS-BN方法极大提升了基因调控网络构建效率,同时准确率有所提高.     

基因调控网络研究进展

基因调控网络是一种连续而复杂多变的动态网络系统,是细胞内各种信号因子之间相互作用关系的整体表现.近些年来,很多物种全基因组测序的完成、高通量实验技术的发展以及高性能分子生物学工具的应用,使得构建一个复杂和相对完整的基因调控网络成为可能,从而使绘制整个活细胞内各种基因表达的调控网络成为当前研究的热点.作为系统生物学的核心领域,构建和分析基因调控网络将有利于我们更系统地剖析细胞的功能,更深刻地洞见生命的本质.综述关于基因调控网络研究的基本原理和方法,为今后进行更深入的研究和探讨打下良好的基础.     

一种改进的多元回归估计基因调控网络的方法

针对用多元回归分析估计基因调控网络时遇到的计算量过大的问题,提出了先用线性模型构建基因类间的调控网络,再用多元回归模型构建基因间调控网络的改进方案,利用类间网络提供的参考信息可以大大减少多元回归分析的计算量.将该方案应用到人类基因调控网络的估计中,并通过现有文献验证了部分调控关系,从而初步证明了该方法的有效性.     

果蝇体极性基因网络的稳定性分析

果蝇的全基因组的序列测定早已完成,但是,对于基因之间是如何相互调控以实现复杂的生物功能,还需要深入的研究.认识并解析复杂的基因调控网络的构成和动力学机制,已成为现代生命科学中的前沿课题之一.在本文中,我们重点研究了果蝇的胚胎发育过程中图式形成的体极性基因网络调控.这一调控是通过相邻细胞中的基因网络的相互影响而达成的.本文主要考察这种基因网络调控对于初始条件的稳定性,以此说明生物胚胎发育对于初始条件的相对稳定性.我们发现该调控系统对于特定位置的干扰有极好的稳定性,对于整个系统的小干扰有良好的稳定性.     

A unique regulatory phase of DNA methylation in the early mammalian embryo

DNA methylation is highly dynamic during mammalian embryogenesis. It is broadly accepted that the paternal genome is actively depleted of 5-methylcytosine at fertilization, followed by passive loss that reaches a minimum at the blastocyst stage. However, this model is based on limited data, and so far no base-resolution maps exist to support and refine it. Here we generate genome-scale DNA methylation maps in mouse gametes and from the zygote through post-implantation. We find that the oocyte already exhibits global hypomethylation, particularly at specific families of long interspersed element 1 and long terminal repeat retroelements, which are disparately methylated between gametes and have lower methylation values in the zygote than in sperm. Surprisingly, the oocyte contributes a unique set of differentially methylated regions (DMRs)--including many CpG island promoters--that are maintained in the early embryo but are lost upon specification and absent from somatic cells. In contrast, sperm-contributed DMRs are largely intergenic and become hypermethylated after the blastocyst stage. Our data provide a genome-scale, base-resolution timeline of DNA methylation in the pre-specified embryo, when this epigenetic modification is most dynamic, before returning to the canonical somatic pattern.     

鹿茸草全长转录组测序与次生代谢产物生物合成相关基因的挖掘

为获得鹿茸草的全长转录组信息,挖掘鹿茸草次生代谢化合物生物合成途径相关酶的基因,该文基于单分子测序技术,利用Pacbio高通量测序平台,对鹿茸草进行全长转录组测序,共获得48 005条去冗余的高质量转录本,与NR、Swiss-Prot、GO、KEGG等8个数据库进行BLAST比对,共有45 362个转录本被成功注释,注释率为94.50%.其中有389条转录本被注释到KEGG的10条标准次生代谢生物合成通路中.对转录组数据进一步分析发现：参与鹿茸草苯丙素类生物合成的转录本有194条,参与生物碱类生物合成的转录本有115条,参与类黄酮化合物生物合成的转录本有23条,参与其他次生代谢产物的转录本有57条,参与次生代谢后氧化与糖基化修饰的转录本有204条.鹿茸草全长转录组的获得极大地丰富了鹿茸草的遗传信息,初步揭示了参与鹿茸草次生代谢产物合成相关的基因通路,为深入研究鹿茸草次生代谢产物合成途径关键酶的功能及其调控机制奠定了基础.     

Chance and necessity in the evolution of minimal metabolic networks

It is possible to infer aspects of an organism's lifestyle from its gene content. Can the reverse also be done? Here we consider this issue by modelling evolution of the reduced genomes of endosymbiotic bacteria. The diversity of gene content in these bacteria may reflect both variation in selective forces and contingency-dependent loss of alternative pathways. Using an in silico representation of the metabolic network of Escherichia coli, we examine the role of contingency by repeatedly simulating the successive loss of genes while controlling for the environment. The minimal networks that result are variable in both gene content and number. Partially different metabolisms can thus evolve owing to contingency alone. The simulation outcomes do preserve a core metabolism, however, which is over-represented in strict intracellular bacteria. Moreover, differences between minimal networks based on lifestyle are predictable: by simulating their respective environmental conditions, we can model evolution of the gene content in Buchnera aphidicola and Wigglesworthia glossinidia with over 80% accuracy. We conclude that, at least for the particular cases considered here, gene content of an organism can be predicted with knowledge of its distant ancestors and its current lifestyle.     

乳酸菌基因组代谢网络模型的研究进展

基因组代谢网络模型(genome-scale metabolic models,GEMs/GSMM)是基因组规模的细胞生化反应网络,可以定量描述基因与表型的关系,广泛应用于系统生物学、代谢工程、环境科学等领域。微生物基因组代谢网络模型是基于微生物基因组构建的生理生化知识库,通过数学模型实现对目标微生物生理生化反应的模拟,已成为研究微生物代谢调控的重要工具。基因组代谢网络模型的构建步骤通常包括构建模型草图、人工精炼、模型转换、验证评估4个阶段。在介绍以模式微生物(大肠杆菌、枯草芽孢杆菌、酿酒酵母)为代表的微生物基因组代谢网络模型发展过程基础上,重点聚焦乳酸菌基因组代谢网络模型的研究现状,系统介绍了已构建的乳酸菌基因组代谢网络模型及其应用。对乳酸菌基因组代谢网络模型的发展方向进行了展望,提出未来乳酸菌基因组代谢网络模型的研究应在现有模型的基础上构建更高质量的代谢与大分子表达模型和泛基因组模型,为乳酸菌的研究提供更高效的工具。     

Metabolic P systems for biochemical dynamics

Metabolic P systems are a special class of P systems which seem to be adequate for expressing biological phenomena, especially, metabolism and signaling transduction. Basic motivations for their introduction are given and their main aspects are explained by means of an example of biological modeling. A new kind of regulation mechanism is outlined, which could be the basis for a more efficient construction of computational models from experimental data of specific metabolic processes.     

分子分型与分子病理指导下的脑胶质瘤诊疗进展

 脑胶质瘤是颅内最常见的原发性恶性肿瘤,具有多克隆起源、高度异质性及放化疗耐药等特性,导致患者预后极差。经典组织病理学分型往往忽视恶性肿瘤的高度异质性,难以满足肿瘤精准医疗的要求。因此,建立以经典组织病理学为基础、肿瘤标志物检测为核心的分子病理体系迫在眉睫。本文介绍了高通量技术的发展及分子生物学技术的进步,以及多维组学的脑胶质瘤分子分型及标志物的研究进展,和分子病理指导下的胶质瘤个体化治疗。     

MiRdetector：一个预测与搜寻miRNA基因的计算工具

MicroRNA（miRNA）是一类内源性的长约21～24 nucleotide(nt)的非编码小分子RNA,能够调节其它基因的表达活性,在生物体的发育过程中发挥重要的作用.每种生物体中miRNA的基因总数还未知,生物信息学分析手段为发现新的miRNA基因提供了有效的方法.作者开发了一个预测与搜寻miRNA基因的完全自动化系统“MiRdetector”.MiRdetector系统是基于计算机比对和miRNA前体茎环结构判断的.用水稻miRNA基因对系统的预测精度进行了检验,系统正确识别了155个样本中的140个,预测精度达到90.32％,并且搜寻到了95个可能的水稻miRNA基因.由于系统的假阳性率较低,因此能够为进一步证实miRNA基因的实验研究提供高质量的数据集.