A simulation-based evaluation of methods for inferring linear barriers to gene flow

Different analytical techniques used on the same data set may lead to different conclusions about the existence and strength of genetic structure. Therefore, reliable interpretation of the results from different methods depends on the efficacy and reliability of different statistical methods. In this paper, we evaluated the performance of multiple analytical methods to detect the presence of a linear barrier dividing populations. We were specifically interested in determining if simulation conditions, such as dispersal ability and genetic equilibrium, affect the power of different analytical methods for detecting barriers. We evaluated two boundary detection methods (Monmonier's algorithm and WOMBLING), two spatial Bayesian clustering methods (TESS and GENELAND), an aspatial clustering approach (STRUCTURE), and two recently developed, non-Bayesian clustering methods [PSMIX and discriminant analysis of principal components (DAPC)]. We found that clustering methods had higher success rates than boundary detection methods and also detected the barrier more quickly. All methods detected the barrier more quickly when dispersal was long distance in comparison to short-distance dispersal scenarios. Bayesian clustering methods performed best overall, both in terms of highest success rates and lowest time to barrier detection, with GENELAND showing the highest power. None of the methods suggested a continuous linear barrier when the data were generated under an isolation-by-distance (IBD) model. However, the clustering methods had higher potential for leading to incorrect barrier inferences under IBD unless strict criteria for successful barrier detection were implemented. Based on our findings and those of previous simulation studies, we discuss the utility of different methods for detecting linear barriers to gene flow.     

REMO: A new protocol to refine full atomic protein models from C‐alpha traces by optimizing hydrogen‐bonding networks

Protein structure prediction approaches usually perform modeling simulations based on reduced representation of protein structures. For biological utilizations, it is an important step to construct full atomic models from the reduced structure decoys. Most of the current full atomic model reconstruction procedures have defects which either could not completely remove the steric clashes among backbone atoms or generate final atomic models with worse topology similarity relative to the native structures than the reduced models. In this work, we develop a new protocol, called REMO, to generate full atomic protein models by optimizing the hydrogen‐bonding network with basic fragments matched from a newly constructed backbone isomer library of solved protein structures. The algorithm is benchmarked on 230 nonhomologous proteins with reduced structure decoys generated by I‐TASSER simulations. The results show that REMO has a significant ability to remove steric clashes, and meanwhile retains good topology of the reduced model. The hydrogen‐bonding network of the final models is dramatically improved during the procedure. The REMO algorithm has been exploited in the recent CASP8 experiment which demonstrated significant improvements of the I‐TASSER models in both atomic‐level structural refinement and hydrogen‐bonding network construction. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

山茱萸种质资源的ISSR遗传多样性分析与初级核心种质库的构建

采用ISSR 标记技术对不同来源地的48份山茱萸种质资源的遗传多样性进行了分析,并依据最小遗传距离逐步抽样法构建了该初级核心种质资源库.结果显示:筛选出的13个多态性引物共扩增出190个位点,多态性位点比率达93.16%,平均Shannon信息指数(I)为0.402 5,平均Nei's基因多样性指数(H)为0.259 4,平均有效等位基因数(NE)为1.425 0.聚类分析表明,种质间的遗传相似系数介于0.65～0.90之间,除个别种质外,48个种质聚类结果与地区来源有较高的一致性.随着抽取种质数目的减少,多态性比率明显降低,但种质库遗传多样性参数变化较小;抽样3构建的初级核心种质库最具代表性,抽样数是抽样前的30%左右,多态位点比率是抽样前的96.0%.     

肉桂高聚原花青素的氢化降解工艺研究

低聚原花青素具有显著的生物活性,高聚体由于空间位阻的影响,活性受到抑制。肉桂在41种食品中原花青素含量位居第一,但高聚体占50％以上。为了充分利用肉桂原花青素资源,采用氢化降解法将肉桂高聚原花青素降解为低聚体。以10％钯碳做催化剂,高压氢气参加反应,考察温度、压强、反应时间和催化剂用量对降解效果的影响。结果表明,平均聚合度为8．2的肉桂高聚原花青素,在100℃,压强3．5MPa,反应200min,催化剂用量0．2g/100mL的条件下,平均聚合度降为2．68。降解产物经HPLC分析表明,此工艺确实实现了肉桂高聚原花青素的降解。     

Fine structure of two Hygropetra species,Hygropetra gelasina sp. nov. and Hygropetra balfouriana (Bacillariophyceae), and the taxonomic position of the genus with special reference to Frankophila

A small diatom, Hygropetra gelasina sp. nov., obtained from wet moss is described. This diatom is similar to Hygropetra balfouriana (Grunow ex Cleve) Krammer & Lange‐Bertalot, which was found in the same moss sample. Fine structural observations revealed that H. gelasina has a reduced raphe slit and depressions along the margin of the axial area, at the proximal ends of the striae. Both species are characterized by multiple rows of areolae in each stria and a hexagonal pattern of sub‐pores similar to that in Pinnularia, but differing in the position of the areola occlusions or hymenes, which are internal in Hygropetra. Comparison with Frankophila, which shares the characteristics of reduced raphe slits and areola structure with Hygropetra, provides a reference for future taxonomic study of these related genera.     

A Novel Bioinformatics Strategy for Function Prediction of Poorly-Characterized Protein Genes Obtained from Metagenome Analyses

As a result of remarkable progresses of DNA sequencing technology, vast quantities of genomic sequences have been decoded. Homology search for amino acid sequences, such as BLAST, has become a basic tool for assigning functions of genes/proteins when genomic sequences are decoded. Although the homology search has clearly been a powerful and irreplaceable method, the functions of only 50% or fewer of genes can be predicted when a novel genome is decoded. A prediction method independent of the homology search is urgently needed. By analyzing oligonucleotide compositions in genomic sequences, we previously developed a modified Self-Organizing Map ‘BLSOM’ that clustered genomic fragments according to phylotype with no advance knowledge of phylotype. Using BLSOM for di-, tri- and tetrapeptide compositions, we developed a system to enable separation (self-organization) of proteins by function. Analyzing oligopeptide frequencies in proteins previously classified into COGs (clusters of orthologous groups of proteins), BLSOMs could faithfully reproduce the COG classifications. This indicated that proteins, whose functions are unknown because of lack of significant sequence similarity with function-known proteins, can be related to function-known proteins based on similarity in oligopeptide composition. BLSOM was applied to predict functions of vast quantities of proteins derived from mixed genomes in environmental samples.     

Structural Basis for the Activation of Muscle Contraction by Troponin and Tropomyosin

The molecular regulation of striated muscle contraction couples the binding and dissociation of Ca²⁺ on troponin (Tn) to the movement of tropomyosin on actin filaments. In turn, this process exposes or blocks myosin binding sites on actin, thereby controlling myosin crossbridge dynamics and consequently muscle contraction. Using 3D electron microscopy, we recently provided structural evidence that a C-terminal extension of TnI is anchored on actin at low Ca²⁺ and competes with tropomyosin for a common site to drive tropomyosin to the B-state location, a constrained, relaxing position on actin that inhibits myosin-crossbridge association. Here, we show that release of this constraint at high Ca²⁺ allows a second segment of troponin, probably representing parts of TnT or the troponin core domain, to promote tropomyosin movement on actin to the Ca²⁺-induced C-state location. With tropomyosin stabilized in this position, myosin binding interactions can begin. Tropomyosin appears to oscillate to a higher degree between respective B- and C-state positions on troponin-free filaments than on fully regulated filaments, suggesting that tropomyosin positioning in both states is troponin-dependent. By biasing tropomyosin to either of these two positions, troponin appears to have two distinct structural functions; in relaxed muscles at low Ca²⁺, troponin operates as an inhibitor, while in activated muscles at high Ca²⁺, it acts as a promoter to initiate contraction.     

Formation of complex AChR aggregates in vitro requires α‐dystrobrevin

Efficient function at the neuromuscular junction requires high‐density aggregates of acetylcholine receptors (AChRs) to be precisely aligned with the motor nerve terminal. A collaborative effort between the motor neuron and muscle intrinsic factors drives the formation and maintenance of these AChR aggregates. α‐Dystrobrevin (αDB), a cytoplasmic protein found at the postsynaptic membrane, has been implicated in the regulation of AChR aggregate density and patterning. To investigate the contribution of αDB to the muscle intrinsic program regulating AChR aggregate development, we analyzed the formation of complex, pretzel‐like AChR aggregates on primary muscle cell cultures derived from αDB knockout (αDB‐KO) mice in the absence of nerve or agrin. In myotubes lacking αDB, complex AChR aggregates failed to form, whereas aggregates formed readily in wildtype myotubes. Five major isoforms of αDB are expressed in skeletal muscle: αDB1, αDB1(?), αDB2, αDB2(?), and αDB3. Expression of αDB1 or αDB1(?) in αDB‐KO myotubes restored formation of complex AChR aggregates similar to those in wildtype myotubes. In contrast, individual expression of αDB2, αDB2(?), αDB3, or an αDB1 phosphorylation mutant resulted in the formation of few, if any, complex AChR aggregates. Collectively, these data suggest that αDB is a significant component of the muscle intrinsic program that mediates the formation of complex AChR aggregates and that αDB's tyrosine phosphorylation sites are of particular functional importance to this program. Although the muscle intrinsic program appears to influence synaptogenesis, the formation of complex mature AChR aggregates in αDB‐KO mice (with the motor neuron present) suggests the motor neuron, not the muscle intrinsic program, is the major stimulus driving the maturation of AChRs from plaque to pretzel in vivo. © 2009 Wiley Periodicals, Inc. Develop Neurobiol 2009     

On estimation of the survivor average causal effect in observational studies when important confounders are missing due to death

Summary .  We focus on estimation of the causal effect of treatment on the functional status of individuals at a fixed point in time t * after they have experienced a catastrophic event, from observational data with the following features: (i) treatment is imposed shortly after the event and is nonrandomized, (ii) individuals who survive to t * are scheduled to be interviewed, (iii) there is interview nonresponse, (iv) individuals who die prior to t * are missing information on preevent confounders, and (v) medical records are abstracted on all individuals to obtain information on postevent, pretreatment confounding factors. To address the issue of survivor bias, we seek to estimate the survivor average causal effect (SACE), the effect of treatment on functional status among the cohort of individuals who would survive to t * regardless of whether or not assigned to treatment. To estimate this effect from observational data, we need to impose untestable assumptions, which depend on the collection of all confounding factors. Because preevent information is missing on those who die prior to t *, it is unlikely that these data are missing at random. We introduce a sensitivity analysis methodology to evaluate the robustness of SACE inferences to deviations from the missing at random assumption. We apply our methodology to the evaluation of the effect of trauma center care on vitality outcomes using data from the National Study on Costs and Outcomes of Trauma Care.