Flower Visitors in a Natural Population of Arabidopsis thaliana

Abstract: Arabidopsis thaliana is commonly regarded as a self-pollinated plant species. One of the many surprises in population genetic studies of the species was the observation of distinct traces of recombination in the DNA sequences that may be the result of rare outcrossing events. We studied flower visitors in a natural population of the species. Solitary bees, diptera and thrips are among the most frequently observed insects among the surprising diversity of insects visiting flowers of A. thaliana. Assuming that every visit equals an outcrossing event, the outcrossing rate was estimated to be 0.84 %. This value falls between estimations of outcrossing rates from molecular data and those of artificial systems. Despite the rather low rate of flower visitation, A. thaliana can no longer be regarded as a completely self-pollinated plant species in the wild. This observation may explain recombination events observed in molecular analyses. Possible pollen transfer between populations due to the mobility of the observed insects should be considered in population genetic analyses.     

Structure of Flower in Arabidopsis thaliana: Spatial Pattern Formation

Morphological analysis of flowers was carried out in Arabidopsis thaliana wild type plants and agamous and apetala2 mutants. No direct substitution of organs takes place in the mutants, since the number and position of organs in them do not correspond to the structure of wild type flower. In order to explain these data, a notion of spatial pattern formation in the meristem was introduced, which preceded the processes of appearance of organ primordia and formation of organs. Zones of acropetal and basipetal spatial pattern formation in the flower of wild type plants were postulated. It was shown that the acropetal spatial pattern formation alone took place in agamous mutants and basipetal spatial pattern formation alone, in apetala2 mutants. Different variants of flower structure are interpreted as a result of changes in the volume of meristem (space) and order of spatial pattern formation (time).     

Molecular Modelling of Protein-Nucleic Acid Interactions

Abstract

Computer modeling techniques to study the interaction of proteins with nucleic acids are presented. The methods utilize information from genetic and chemical modification experiments and macromolecular structural constraints. These techniques, in addition to computer model building procedures and theoretical energy calculations, are illustrated for the study of the lac and cro repressor-operator systems. Our predicted interactions between lac and its operator agree with those recently reported for lac based upon sequence alignment with the cro repressor. Several molecular models of the putative helical segment of cro interacting with its OR3 operator are presented. These models are reflective of intermediate conformations experienced by the repressor in recognition of the operator sequence. The results of our studies are further discussed in terms of the design of short peptides interacting with nucleic acid sequences and the evolutionary requirements in establishing these repressor interactions.     

Interactions between selenium and sulphur nutrition in Arabidopsis thaliana

Selenium (Se) is an essential plant micronutrient, but is toxic at high tissue concentrations. It is chemically similar to sulphur (S), an essential plant macronutrient. The interactions between Se and S nutrition were investigated in the model plant Arabidopsis thaliana (L.) Heynh. Arabidopsis plants were grown on agar containing a complete mineral complement and various concentrations of selenate and sulphate. The Se/S concentration ratio in the shoot ([Se](shoot)/[S](shoot)) showed a complex dependence on the ratio of selenate to sulphate concentration in the agar ([Se](agar)/[S](agar)). Increasing [S](agar) increased shoot fresh weight (FW) and [S](shoot), but decreased [Se](shoot). Increasing [Se](agar) increased both [Se](shoot) and [S](shoot), but reduced shoot FW. The reduction in shoot FW in the presence of Se was linearly related to the shoot Se/S concentration ratio. These data suggest (i) that Se and S enter Arabidopsis through multiple transport pathways with contrasting sulphate/selenate selectivities, whose activities vary between plants of contrasting nutritional status, (ii) that rhizosphere sulphate inhibits selenate uptake, (iii) that rhizosphere selenate promotes sulphate uptake, possibly by preventing the reduction in the abundance and/or activity of sulphate transporters by sulphate and/or its metabolites, and (iv) that Se toxicity occurs because Se and S compete for a biochemical process, such as assimilation into amino acids of essential proteins.     

Molecular characterisation of RecQ homologues in Arabidopsis thaliana

Members of the RecQ family of DNA helicases are involved in processes linked to DNA replication, DNA recombination and gene silencing. RecQ homologues of various animals have been described recently. Here, for the first time for plants, we characterised cDNAs of all in all six different RecQ-like proteins that are expressed to different extents in Arabidopsis thaliana. Surprisingly, three of these proteins are small in size [AtRecQl1, AtRecQl2, AtRecQl3—606, 705 and 713 amino acids (aa), respectively], whereas the two bigger proteins result from a duplication event during plant evolution [AtRecQl4A and AtRecQl4B—1150 and 1182 aa, respectively]. Another homologue (AtRecQsim, 858 aa) most probably arose by insertion of an unrelated sequence within its helicase domain. The presence of these homologues demonstrates the conservation of RecQ family functions in higher eukaryotes. We also detected a small gene (AtWRNexo) encoding 285 aa which, being devoid of any RecQ-like helicase domain, reveals a striking homology to the exonuclease domain of human Werner protein, a prominent RecQ helicase of larger size. By means of the two-hybrid assay we were able to detect an interaction between AtWRNexo and AtRecQl2, indicating that activities that reside in a single protein chain in mammals might in plants be complemented in trans.     

拟南芥根系发育的分子机制研究进展

拟南芥初生根和次生根的发育受不同遗传通路所调控,其中内源激素途径尤其是生长素途径在拟南芥主根、侧根以及根毛的发育过程中均发挥着重要作用.同时也存在一些不依赖于激素通路的遗传途径,如UPB1能通过调节根尖分生区和伸长区活性氧种类的平衡来调控根系顶端分生组织活性,进而影响根系的生长.本文对近年来国内外有关模式植物拟南芥根系发育的分子机制研究进展分别从初生根发育、侧根发育和根毛发育3个方面进行综述.     

Molecular analysis of the myosin gene family in Arabidopsis thaliana

Myosin is believed to act as the molecular motor for many actin-based motility processes in eukaryotes. It is becoming apparent that a single species may possess multiple myosin isoforms, and at least seven distinct classes of myosin have been identified from studies of animals, fungi, and protozoans. The complexity of the myosin heavy-chain gene family in higher plants was investigated by isolating and characterizing myosin genomic and cDNA clones from Arabidopsis thaliana. Six myosin-like genes were identified from three polymerase chain reaction (PCR) products (PCR1, PCR11, PCR43) and three cDNA clones (ATM2, MYA2, MYA3). Sequence comparisons of the deduced head domains suggest that these myosins are members of two major classes. Analysis of the overall structure of the ATM2 and MYA2 myosins shows that they are similar to the previously-identified ATM1 and MYA1 myosins, respectively. The MYA3 appears to possess a novel tail domain, with five IQ repeats, a six-member imperfect repeat, and a segment of unique sequence. Northern blot analyses indicate that some of the Arabidopsis myosin genes are preferentially expressed in different plant organs. Combined with previous studies, these results show that the Arabidopsis genome contains at least eight myosin-like genes representing two distinct classes.     

高表达miR396小分子导致拟南芥花柱头弯曲

MicroRNAs（miRNAs）是大小约21个碱基、内源、非编码的小分子RNA。以拟南芥（Arabidopsis thaliana）miR396小分子为研究对象,分别克隆到了miR396小分子的两个前体（MIR396a,MIR396b）,得到了转基因植株。通过转基因植株的遗传学研究发现,高表达miR396小分子导致转基因拟南芥的花柱头弯曲。花柱头的弯曲影响了角果的正常发育。另外,Northern杂交结果表明转基因拟南芥花部位的miR396及其前体的表达量与对照相比显著增加。这些结果表明高表达miR396小分子可以导致拟南芥花柱头弯曲。     

Defining the Functional Network of Epigenetic Regulators in Arabidopsis thaliana

Development of ChiP-chip and ChlP-seq technologies has allowed genome-wide high-resolution profiling of chromatin-associated marks and binding sites for epigenetic regulators. However, signals for directing epigenetic modifiers to their target sites are not understood. In this paper, we tested the hypothesis that genome location can affect the involvement of epigenetic regulators using Chromatin Charting （CC） Lines, which have an identical transgene construct inserted at different locations in the Arabidopsis genome. Four CC lines that showed evidence for epigenetic silencing of the luciferase reporter gene were transformed with RNAi vectors individually targeting epigenetic regulators LHP1, MOM1, CMT3, DRD1, DRM2, SUVH2, CLF, and HD1. Involvement of a particular epigenetic regulator in silencing the transgene locus in a CC line was determined by significant alterations in luciferase expression after suppression of the regulator＇s expression. Our results suggest that the targeting of epigenetic regulators can be influenced by genome location as well as sequence context. In addition, the relative importance of an epigenetic regulator can be influenced by tissue identity. We also report a novel approach to predict interactions between epigenetic regulators through clustering analysis of the regulators using alterations in gene expression of putative downstream targets, including endogenous loci and transgenes, in epigenetic mutants or RNAi lines. Our data support the existence of a complex and dynamic network of epigenetic regulators that serves to coordinate and control global gene expression in higher plants.     

基于MADS-box诱饵与蛋白质相互作用的拟南芥花瓣发育分子网络拓展

阐明花器官发育调控机理具重要的进化、发育和生态学意义。该文以拟南芥(Arabidopsis thaliana)花瓣发育为例, 整合蛋白质互作、亚细胞定位、基因芯片和基因功能注释等数据库, 通过组建蛋白质互作可信预测模型, 获得拟南芥花瓣蛋白质互作网络, 以含有MADS-box结构域蛋白为诱饵在网络中进行一级拓展, 得到含38个蛋白质和67对互作的拓展网络。基于拓展网络, DAVID基因功能注释表明, 多数蛋白质涉及的生物学过程与花发育调控相关; 提取到19个候选四元互作, 涉及ABCDE模型基因之外的8个基因, 其中含MADS-box结构域的AGL16可能是B类基因新成员或其冗余; SEU、LUH、CHR4、CHR11、CHR17和AT3G04960为拟南芥花瓣AP1-AP3-PI-SEP四聚体的候选靶标基因。研究结果为深入解析拟南芥花瓣发育分子调控网络奠定了基础。     

Developmental defects in mutants of the PsbP domain protein 5 in Arabidopsis thaliana

Plants contain an extensive family of PsbP-related proteins termed PsbP-like (PPL) and PsbP domain (PPD) proteins, which are localized to the thylakoid lumen. The founding member of this family, PsbP, is an established component of the Photosystem II (PS II) enzyme, and the PPL proteins have also been functionally linked to other photosynthetic processes. However, the functions of the remaining seven PPD proteins are unknown. To elucidate the function of the PPD5 protein (At5g11450) in Arabidopsis, we have characterized a mutant T-DNA insertion line (SALK_061118) as well as several RNAi lines designed to suppress the expression of this gene. The functions of the photosynthetic electron transfer reactions are largely unaltered in the ppd5 mutants, except for a modest though significant decrease in NADPH dehydrogenase (NDH) activity. Interestingly, these mutants show striking plant developmental and morphological defects. Relative to the wild-type Col-0 plants, the ppd5 mutants exhibit both increased lateral root branching and defects associated with axillary bud formation. These defects include the formation of additional rosettes originating from axils at the base of the plant as well as aerial rosettes formed at the axils of the first few nodes of the shoot. The root-branching phenotype is chemically complemented by treatment with the synthetic strigolactone, GR24. We propose that the developmental defects observed in the ppd5 mutants are related to a deficiency in strigolactone biosynthesis.     

Computational Identification of Potential Molecular Interactions in Arabidopsis

Knowledge of the protein interaction network is useful to assist molecular mechanism studies. Several major repositories have been established to collect and organize reported protein interactions. Many interactions have been reported in several model organisms, yet a very limited number of plant interactions can thus far be found in these major databases. Computational identification of potential plant interactions, therefore, is desired to facilitate relevant research. In this work, we constructed a support vector machine model to predict potential Arabidopsis (Arabidopsis thaliana) protein interactions based on a variety of indirect evidence. In a 100-iteration bootstrap evaluation, the confidence of our predicted interactions was estimated to be 48.67%, and these interactions were expected to cover 29.02% of the entire interactome. The sensitivity of our model was validated with an independent evaluation data set consisting of newly reported interactions that did not overlap with the examples used in model training and testing. Results showed that our model successfully recognized 28.91% of the new interactions, similar to its expected sensitivity (29.02%). Applying this model to all possible Arabidopsis protein pairs resulted in 224,206 potential interactions, which is the largest and most accurate set of predicted Arabidopsis interactions at present. In order to facilitate the use of our results, we present the Predicted Arabidopsis Interactome Resource, with detailed annotations and more specific per interaction confidence measurements. This database and related documents are freely accessible at http://www.cls.zju.edu.cn/pair/.The complex cellular functions of an organism rely on physical interactions between proteins. Deciphering the protein-protein interaction network to understand higher level phenotypes and their regulations is always a major focus of both experimental biologists and computational biologists. A number of high-throughput (HTP) assays have been developed to identify in vitro protein interactions from several model organisms (Uetz et al., 2000; Giot et al., 2003; Li et al., 2004). A number of initiatives, such as IntAct (Kerrien et al., 2006), Molecular INTeraction database (Chatr-aryamontri et al., 2007), the Database of Interacting Proteins (Salwinski et al., 2004), Biomolecular Interaction Network Database (BIND; Alfarano et al., 2005), and BioGRID (Stark et al., 2006), have been established to systematically collect and organize the interaction data reported by both proteome-scale HTP experiments and traditional low-throughput studies focusing on individual proteins or pathways.Arabidopsis (Arabidopsis thaliana) has long been studied as a model organism to investigate the physiology, biochemistry, growth, development, and metabolism of a flowering plant at the molecular level. The molecular mechanism studies of various phenotypes and their regulations in Arabidopsis may be facilitated by a comprehensive reference protein interaction network, based on which working hypotheses could be invented with more guidance and confidence. However, due to technological limitations, most experimentally reported protein interactions in available databases were from other organisms. A very limited number of plant interactions could be found in these databases. Therefore, an accurate prediction of the Arabidopsis interactome would be valuable to assist relevant research.Studies on the computational identification of potential interactions started along with the advent of HTP interaction-detection technologies, which often produced a large number of false positives (Deane et al., 2002). Indirect evidence of protein interaction (e.g. protein colocalization and relevance in function) were hence introduced to boost the confidence of HTP results (Jansen et al., 2003). Further investigations demonstrated that direct inference of protein interactions from such indirect evidence alone was possible (Scott and Barton, 2007). The accuracy and effectiveness of using indirect evidence to predict interactions have also been thoroughly assessed (Qi et al., 2006; Suthram et al., 2006). These works offered precious insights into how protein interactions may be predicted accurately on a proteomic scale. In other organisms such as Homo sapiens, the prediction of an entire interactome has already been proven applicable and useful (Rhodes et al., 2005).On the other side, several efforts have been made to collect and organize a comprehensive map of Arabidopsis molecular interactions. For instances, around 20,000 interactions were inferred by homology to known interactions in other organisms (Geisler-Lee et al., 2007). Another work predicted 23,396 interactions based on multiple indirect data and curated 4,666 interactions from the literature and enzyme complexes (Cui et al., 2008). The Arabidopsis reactome database was established describing the functions of 2,195 proteins with 8,269 reactions in 318 superpathways (Tsesmetzis et al., 2008). And a general interaction database, IntAct (Kerrien et al., 2006), had allocated a special unit actively curating all plant protein interactions from literature and submitted data sets, which now contains 2,649 Arabidopsis interactions. However, in yeast, approximately 18,000 protein-protein interactions had been estimated for approximately 6,000 genes (Yu et al., 2008). Assuming the same rate of interaction, approximately 200,000 protein interactions would be expected for approximately 20,000 Arabidopsis genes. Therefore, the current collection of Arabidopsis interactions is still significantly limited. Moreover, most previous prediction works did not provide rigorous confidence measurements for their predicted interactions, which further limited their scope of applications.Recent advances in statistical learning presented a powerful algorithm, support vector machine (SVM), which may be used to predict interactions based on multiple indirect data. Although the basis of SVM had been laid in the 1960s, the idea of SVM was only officially proposed in the 1990s by Vapnik (1998, 2000). Then, research on its theoretical and application aspects thrived. It has been applied in a wide range of problems, including text categorization (de Vel et al., 2001; Kim et al., 2001), image classification and object detection (Ben-Yacoub et al., 1999; Karlsen et al., 2000), flood stage forecasting (Liong and Sivapragasam, 2002), microarray gene expression data analysis (Brown et al., 2000), drug design (Zhao et al., 2006a, 2006b), protein solvent accessibility prediction (Yuan et al., 2002), and protein fold prediction (Ding and Dubchak, 2001; Hua and Sun, 2001). Many studies have demonstrated that SVM was consistently superior to other supervised learning methods (Brown et al., 2000; Burbidge et al., 2001; Cai et al., 2003).In this work, with careful preparation of example data and selection of indirect evidence, we constructed an SVM model to predict potential Arabidopsis interactions. False positives were tightly controlled. With the high-confidence model, we identified altogether 224,206 potential interactions, which were expected to be 48.67% accurate and to cover 29.02% of the entire Arabidopsis interactome. More specific confidence measurements were also assigned on a per interaction basis. To facilitate the use of our results, we present the Predicted Arabidopsis Interactome Resource (PAIR; http://www.cls.zju.edu.cn/pair/), featuring detailed annotations and a friendly user interface.