花器官决定的ABC 模型和四因子模型

简要介绍了花器官决定的同源异型基因作用模型——ABC模型的产生和发展过程。从早期ABC模型发展到经典ABC模型,然后到ABCD模型,最后到A-E模型。花器官发育遗传学的创立和发展是ABC模型产生的基础,ABC模型的建立促进了花器官发育遗传学的发展,而后者进一步发展的结果又促使前者更加完善,从而进一步发展为四因子模型。     

花器官发育的分子机制研究进展

经典的ABC模型成功地解释了模式植物拟南芥和金鱼草因同源异型基因突变而引起的植物花器官的变异。随后,大量花器官特征基因和新突变体的研究不断完善和发展了ABC模型。该文综述了近年来花器官发育分子模型及花器官同源基因的调控机理等方面的最新研究成果,并对未来的研究方向进行了展望,以期为深入了解花发育的分子机理和遗传机制奠定基础。     

控制植物花器官发育的分子机理

植物从营养生长向生殖生长的转变和花器官的正常发育是其繁衍的基础.综述了控制植物花器官发育的ABC模型的提出和发展,以及已经克隆的A、B、C、E类基因和其表达调控机理研究的最新进展.     

水稻花器官发育的全新分子机理被揭示

长期以来．植物生殖发育学家们都在寻找控制花器官发育的基因．经过多年研究提出了著名的花发育“ABC”模型。该模型概括了在花的不同部位,不同类型的基因是怎样起作用,从而控制花部器官发育的,但这一模型仍有很多不清楚的地方。     

同源异型基因对花器官发育的调控

以ABC模型为基础,阐述了花器官发育调控的分子机制,对一些相关基因在其中的作用和研究进展作了介绍.     

被子植物花器官发育的模型演变和分子调控

基于模式植物拟南芥(Arabidopsis thaliana)和金鱼草(Antirrhinum majus)花器官突变体研究提出的四聚体模型揭示了花同源异型蛋白的相互作用方式;进一步提出的核小体拟态模型,解释了花同源蛋白四聚体调控目标靶基因的分子机理。被子植物花器官形态多样化与MADS-box基因的表达模式和功能分化密切相关。多年生被子植物花发育的高通量转录组分析表明,多种基因参与调控花器官发育过程。本文重点综述了被子植物花器官发育的模型演变、MADS-box基因结构和基因重复、miRNA调控以及相关转录组分析的最新研究成果,并对花器官发育的研究前景进行了展望。     

水稻花器官特征决定以及数量控制的分子机制

继双子叶模式植物拟南芥之后,单子叶模式植物水稻的生殖发育研究受到广泛的重视。随着水稻正向和反向遗传学研究的不断深入,人们发现了一些调控水稻花器官特征以及花器官数量的重要基因,使得对水稻花器官发育的调控机制有了更多的了解。本文着重概述和讨论水稻花器官特征决定以及花器官数量控制分子机理研究的最新进展。     

水稻花器官特征决定以及数量控制的分子机制

继双子叶模式植物拟南芥之后, 单子叶模式植物水稻的生殖发育研究受到广泛的重视。随着水稻正向和反向遗传学研究的不断深入, 人们发现了一些调控水稻花器官特征以及花器官数量的重要基因, 使得对水稻花器官发育的调控机制有了更多的了解。本文着重概述和讨论水稻花器官特征决定以及花器官数量控制分子机理研究的最新进展。     

植物成花分子机理研究的进展

植物在成花诱导结束后,转变为花分生组织,而后分化产生花器官。文中着重介绍了花器官形成分化ABC模型及基因调控的分子机理。     

植物进化发育生物学的形成与研究进展

植物进化发育生物学是最近十几年来才兴起的一门学科, 它是进化发育生物学的主要分支之一。进化发育生物学的产生经历了进化生物学与胚胎学、遗传学和发育生物学的三次大的综合, 其历史可追溯到19世纪初冯.贝尔所创立的比较胚胎学。相关研究曾沉寂了近一个世纪, 直到20世纪80年代早期, 动物中homeobox基因被发现, 90年代初花发育的 ABC模型被提出, 加之对发育相关基因研究的不断深入, 才使基因型与表型联系了起来, 进而促进了进化发育生物学的飞速发展。目前进化发育生物学已成为21世纪生命科学领域的研究热点之一。本文详细阐述了进化发育生物学产生和发展的历程, 综述了最近十几年来植物进化发育生物学的主要研究进展。文中重点介绍了与植物发育密切相关的MADS-box基因在植物各大类群中的研究现状, 讨论了植物进化发育生物学领域的研究成果对花被演化、花对称性以及叶的进化等重要问题的启示。     

Model choice for phylogeographic inference using a large set of models

Model‐based analyses are common in phylogeographic inference because they parameterize processes such as population division, gene flow and expansion that are of interest to biologists. Approximate Bayesian computation is a model‐based approach that can be customized to any empirical system and used to calculate the relative posterior probability of several models, provided that suitable models can be identified for comparison. The question of how to identify suitable models is explored using data from Plethodon idahoensis, a salamander that inhabits the North American inland northwest temperate rainforest. First, we conduct an ABC analysis using five models suggested by previous research, calculate the relative posterior probabilities and find that a simple model of population isolation has the best fit to the data (PP = 0.70). In contrast to this subjective choice of models to include in the analysis, we also specify models in a more objective manner by simulating prior distributions for 143 models that included panmixia, population isolation, change in effective population size, migration and range expansion. We then identify a smaller subset of models for comparison by generating an expectation of the highest posterior probability that a false model is likely to achieve due to chance and calculate the relative posterior probabilities of only those models that exceed this expected level. A model that parameterized divergence with population expansion and gene flow in one direction offered the best fit to the P. idahoensis data (in contrast to an isolation‐only model from the first analysis). Our investigation demonstrates that the determination of which models to include in ABC model choice experiments is a vital component of model‐based phylogeographic analysis.     

Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation

Summary We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra‐binomial variation in terms of a zero‐one immunity variable, which has a short‐lived presence in the host.     

Inhibition of Sphingosine Kinase-2 in a Murine Model of Lupus Nephritis

Sphingosine-1-phosphate (S1P), a potent bioactive lipid, is emerging as a central mediator in inflammation and immune responses. We have previously implicated S1P and its synthetic enzyme sphingosine kinase (SK) in inflammatory and autoimmune disorders, including inflammatory bowel disease and rheumatoid arthritis. Generation of S1P requires phosphorylation of sphingosine by SK, of which there are two isoforms. Numerous studies have implicated SK1 in immune cell trafficking, inflammation and autoimmune disorders. In this study, we set out to determine the role of SK and S1P in lupus nephritis (LN). To this end, we examined S1P and dihydro-S1P (dh-S1P) levels in serum and kidney tissues from a mouse model of LN. Interestingly dh-S1P was significantly elevated in serum and kidney tissue from LN mice, which is more readily phosphorylated by SK2. Therefore, we employed the use of the specific SK2 inhibitor, ABC294640 in our murine model of LN. Treatment with ABC294640 did not improve vascular or interstitial pathology associated with LN. However, mice treated with the SK2 inhibitor did demonstrate decreases in glomerular pathology and accumulation of B and T cells in the spleen these were not statistically different from lpr mice treated with vehicle. LN mice treated with ABC294640 did not have improved urine thromboxane levels or urine proteinuria measurements. Both S1P and dh-S1P levels in circulation were significantly reduced with ABC294640 treatment; however, dh-S1P was actually elevated in kidneys from LN mice treated with ABC294640. Together these data demonstrate a role for SKs in LN; however, they suggest that inhibition of SK1 or perhaps both SK isoforms would better prevent elevations in S1P and dh-S1P and potentially better protect against LN.     

Test for simultaneous divergence using approximate Bayesian computation

Comparative phylogeographic studies often reveal disparate levels of sequence divergence between lineages spanning a common geographic barrier, leading to the conclusion that isolation was nonsynchronous. However, only rarely do researchers account for the expected variance associated with ancestral coalescence and among-taxon variation in demographic history. We introduce a flexible approximate Bayesian computational (ABC) framework that can test for simultaneous divergence (TSD) using a hierarchical model that incorporates idiosyncratic differences in demographic history across taxon pairs. The method is tested across a range of conditions and is shown to be accurate even with single-locus mitochondrial DNA (mtDNA) data. We apply this method to a landmark dataset of putative simultaneous vicariance, eight geminate echinoid taxon pairs thought to have been split by the Isthmus of Panama 3.1 million years ago. The ABC posterior estimates are not consistent with a history of simultaneous vicariance given these data. Subsequent ABC estimates under a constrained model that assumes two divergence times across the eight taxon pairs suggests simultaneous divergence 3.1 million years ago in seven of the taxon pairs and a more recent divergence in the remaining taxon pair. These ABC estimates on the simultaneous divergence of the seven taxon pairs correspond to a DNA substitution rate of approximately 1.59% per lineage per million years at the mtDNA cytochrome oxidase I gene. This ABC framework can easily be modified to analyze single taxon-pair datasets and/or be expanded to include multiple loci, migration, recombination, and other idiosyncratic demographic histories. The flexible aspect of ABC and its built-in evaluation of estimator bias and statistical power has the potential to greatly enhance statistical rigor in phylogeographic studies.     

MBP-MalFGK2相互作用动力学:ABC转运蛋白的模型研究

本文主要描述了麦芽糖结合蛋白(MBP)和属于ATP结合盒式蛋白(ABC)家族的麦芽糖转运蛋白复合物MalFGK2的相互作用。通过基因、结构和生化分析可知,MBP和MalFGK2以不同构象进行相互作用。在这个转运系统中,MBP与麦芽糖结合,并与MalFGK2发生相互作用,从而将麦芽糖从胞外转运至胞内,但由于MBP和MalFGK2都有多种构象,所以它们的相互作用很复杂。相互作用机理模型最重要的特点是结合配体的MBP,通过稳定MalFGK2的高能量构象来启动依赖ATP的麦芽糖转运过程。麦芽糖转运蛋白机理模型表明,ABC型转运系统利用外周结合蛋白,其转运过程基本上是不可逆的。     

All models are wrong

As the field of phylogeography has continued to move in the model‐based direction, researchers continue struggling to construct useful models for inference. These models must be both simple enough to be tractable yet contain enough of the complexity of the natural world to make meaningful inference. Beyond constructing such models for inference, researchers explore model space and test competing models with the data on hand, with the goal of improving the understanding of the natural world and the processes underlying natural biological communities. Approximate Bayesian computation (ABC) has increased in recent popularity as a tool for evaluating alternative historical demographic models given population genetic samples. As a thorough demonstration, Pelletier & Carstens ( 2014 ) use ABC to test 143 phylogeographic submodels given geographically widespread genetic samples from the salamander species Plethodon idahoensis (Carstens et al. 2004 ) and, in so doing, demonstrate how the results of the ABC model choice procedure are dependent on the model set one chooses to evaluate.     

From Genes to Flower Patterns and Evolution: Dynamic Models of Gene Regulatory Networks

Genes and proteins form complex dynamical systems or gene regulatory networks (GRN) that can reach several steady states (attractors). These may be associated with distinct cell types. In plants, the ABC combinatorial model establishes the necessary gene combinations for floral organ cell specification. We have developed dynamic gene regulatory network (GRN) models to understand how the combinatorial selection of gene activity is established during floral organ primordia specification as a result of the concerted action of ABC and non-ABC genes. Our analyses have shown that the floral organ specification GRN reaches six attractors with gene configurations observed in primordial cell types during early stages of flower development and four that correspond to regions of the inflorescence meristem. This suggests that it is the overall GRN dynamics rather than precise signals that underlie the ABC model. Furthermore, our analyses suggest that the steady states of the GRN are robust to random alterations of the logical functions that define the gene interactions. Here we have updated the GRN model and have systematically altered the outputs of all the logical functions and addressed in which cases the original attractors are recovered. We then reduced the original three-state GRN to a two-state (Boolean) GRN and performed the same systematic perturbation analysis. Interestingly, the Boolean GRN reaches the same number and type of attractors as reached by the three-state GRN, and it responds to perturbations in a qualitatively identical manner as the original GRN. These results suggest that a Boolean model is sufficient to capture the dynamical features of the floral network and provide additional support for the robustness of the floral GRN. These findings further support that the GRN model provides a dynamical explanation for the ABC model and that the floral GRN robustness could be behind the widespread conservation of the floral plan among eudicotyledoneous plants. Other aspects of evolution of flower organ arrangement and ABC gene expression patterns are discussed in the context of the approach proposed here. álvaro Chaos, Max Aldana and Elena Alvarez-Buylla contributed equally to this work.     

Phylodynamic Inference and Model Assessment with Approximate Bayesian Computation: Influenza as a Case Study

A key priority in infectious disease research is to understand the ecological and evolutionary drivers of viral diseases from data on disease incidence as well as viral genetic and antigenic variation. We propose using a simulation-based, Bayesian method known as Approximate Bayesian Computation (ABC) to fit and assess phylodynamic models that simulate pathogen evolution and ecology against summaries of these data. We illustrate the versatility of the method by analyzing two spatial models describing the phylodynamics of interpandemic human influenza virus subtype A(H3N2). The first model captures antigenic drift phenomenologically with continuously waning immunity, and the second epochal evolution model describes the replacement of major, relatively long-lived antigenic clusters. Combining features of long-term surveillance data from the Netherlands with features of influenza A (H3N2) hemagglutinin gene sequences sampled in northern Europe, key phylodynamic parameters can be estimated with ABC. Goodness-of-fit analyses reveal that the irregularity in interannual incidence and H3N2''s ladder-like hemagglutinin phylogeny are quantitatively only reproduced under the epochal evolution model within a spatial context. However, the concomitant incidence dynamics result in a very large reproductive number and are not consistent with empirical estimates of H3N2''s population level attack rate. These results demonstrate that the interactions between the evolutionary and ecological processes impose multiple quantitative constraints on the phylodynamic trajectories of influenza A(H3N2), so that sequence and surveillance data can be used synergistically. ABC, one of several data synthesis approaches, can easily interface a broad class of phylodynamic models with various types of data but requires careful calibration of the summaries and tolerance parameters.