GO‐At : in silico prediction of gene function in Arabidopsis thaliana by combining heterogeneous data

Despite recent advances, accurate gene function prediction remains an elusive goal, with very few methods directly applicable to the plant Arabidopsis thaliana. In this study, we present GO‐At (gene ontology prediction in A. thaliana), a method that combines five data types (co‐expression, sequence, phylogenetic profile, interaction and gene neighbourhood) to predict gene function in Arabidopsis. Using a simple, yet powerful two‐step approach, GO‐At first generates a list of genes ranked in descending order of probability of functional association with the query gene. Next, a prediction score is automatically assigned to each function in this list based on the assumption that functions appearing most frequently at the top of the list are most likely to represent the function of the query gene. In this way, the second step provides an effective alternative to simply taking the ‘best hit’ from the first list, and achieves success rates of up to 79%. GO‐At is applicable across all three GO categories: molecular function, biological process and cellular component, and can assign functions at multiple levels of annotation detail. Furthermore, we demonstrate GO‐At’s ability to predict functions of uncharacterized genes by identifying ten putative golgins/Golgi‐associated proteins amongst 8219 genes of previously unknown cellular component and present independent evidence to support our predictions. A web‐based implementation of GO‐At ( http://www.bioinformatics.leeds.ac.uk/goat ) is available, providing a unique resource for plant researchers to make predictions for uncharacterized genes and predict novel functions in Arabidopsis.     

Towards Arabidopsis genome analysis: monitoring expression profiles of 1400 genes using cDNA microarrays

cDNA microarrays containing 1443 Arabidopsis thaliana genes were analyzed for expression profiles in major organs of Arabidopsis plants. Novel expression profiles were identified for many coding sequences with putative gene identifications. Expression patterns of novel sequences provided clues to their possible functions. The results demonstrate how microarrays containing a large number of Arabidopsis genes can provide a powerful tool for plant gene discovery, functional analysis and elucidation of genetic regulatory networks.     

Using gene knockouts to investigate plant metabolism

Arabidopsis functional genomics resources now make the isolation of knockout mutants in any gene of choice both realistic and increasingly straightforward. Coupled with the completion of the genome sequence, this reverse genetics approach provides a platform facilitating dramatic progress in our understanding of fundamental aspects of plant metabolism. Recent experience shows that knockouts of genes encoding enzymes of primary metabolism can produce mutants with clear and sometimes unexpected phenotypes. They can provide new information about old pathways. Specific functions for individual members of multigene families can be revealed. Knockouts of enzymes of undefined function can lead to the discovery of those functions, and the analysis of enzymes which have previously never been studied at the biochemical level offers the potential to reveal new pathways of plant metabolism. Furthermore, the mutants isolated provide the starting point for genetic modification experiments to determine exactly how metabolism fuels growth and development, so providing a rational basis for the future modification of plant productivity.     

Analysis of sequence, map position, and gene expression reveals conserved essential genes for iron uptake in Arabidopsis and tomato

Arabidopsis (Arabidopsis thaliana) and tomato (Lycopersicon esculentum) show similar physiological responses to iron deficiency, suggesting that homologous genes are involved. Essential gene functions are generally considered to be carried out by orthologs that have remained conserved in sequence and map position in evolutionarily related species. This assumption has not yet been proven for plant genomes that underwent large genome rearrangements. We addressed this question in an attempt to deduce functional gene pairs for iron reduction, iron transport, and iron regulation between Arabidopsis and tomato. Iron uptake processes are essential for plant growth. We investigated iron uptake gene pairs from tomato and Arabidopsis, namely sequence, conserved gene content of the regions containing iron uptake homologs based on conserved orthologous set marker analysis, gene expression patterns, and, in two cases, genetic data. Compared to tomato, the Arabidopsis genome revealed more and larger gene families coding for the iron uptake functions. The number of possible homologous pairs was reduced if functional expression data were taken into account in addition to sequence and map position. We predict novel homologous as well as partially redundant functions of ferric reductase-like and iron-regulated transporter-like genes in Arabidopsis and tomato. Arabidopsis nicotianamine synthase genes encode a partially redundant family. In this study, Arabidopsis gene redundancy generally reflected the presumed genome duplication structure. In some cases, statistical analysis of conserved gene regions between tomato and Arabidopsis suggested a common evolutionary origin. Although involvement of conserved genes in iron uptake was found, these essential genes seem to be of paralogous rather than orthologous origin in tomato and Arabidopsis.     

The small, the large and the wild: the value of comparison in plant genomics.

Studies of the model plant Arabidopsis are providing knowledge about the function of plant genes with an unprecedented clarity and quantity. A major challenge now is to apply this new information to the improvement of crop plants in a systematic manner. Sequence comparisons between Arabidopsis and rice can define potential functional relationships, and conserved gene order among cereals can then be used to ascribe functions to genes in many cereals.     

麻风树LEC1基因的生物信息学分析

LEC1是调控植物种子发育过程和油脂产量的重要基因。本文选择功能已知的拟南芥LEC1基因为参考序列,对麻风树LEC1基因开展了蛋白组分和理化性质分析,蛋白结构和功能预测,系统进化分析等生物信息学研究,为进一步研究麻风树LEC1基因的生物学功能奠定了基础。     

Arabidopsis gene knockout: phenotypes wanted

Gene knockout is considered to be a major component of the functional genomics toolbox, and is aimed at revealing the function of genes discovered through large-scale sequencing programs. In the past few years, several Arabidopsis populations mutagenized with insertion elements, such as the T-DNA of Agrobacterium or transposons, have been produced. These large populations are routinely screened for insertions into specific genes, allowing mass-isolation of knockout lines. Although many Arabidopsis knockouts have already been obtained, few of them have been reported to present informative phenotypes that provide a direct clue to gene function. Although functional redundancy explains the lack of phenotypical alterations in some cases, it also appears that many mutations are conditional and/or do not alter plant morphology even in the presence of severe physiological defects. Consequently, gene knockout per se is not sufficient to assess gene function and must be integrated into a more global approach for determining biological functions.     

Functional genomics in Arabidopsis: large-scale insertional mutagenesis complements the genome sequencing project

The ultimate goal of genome research on the model flowering plant Arabidopsis thaliana is the identification of all of the genes and understanding their functions. A major step towards this goal, the genome sequencing project, is nearing completion; however, functional studies of newly discovered genes have not yet kept up to this pace. Recent progress in large-scale insertional mutagenesis opens new possibilities for functional genomics in Arabidopsis. The number of T-DNA and transposon insertion lines from different laboratories will soon represent insertions into most Arabidopsis genes. Vast resources of gene knockouts are becoming available that can be subjected to different types of reverse genetics screens to deduce the functions of the sequenced genes.     

Approaches for extracting practical information from gene co-expression networks in plant biology

Gene co-expression, in many cases, implies the presence of a functional linkage between genes. Co-expression analysis has uncovered gene regulatory mechanisms in model organisms such as Escherichia coli and yeast. Recently, accumulation of Arabidopsis microarray data has facilitated a genome-wide inspection of gene co-expression profiles in this model plant. An approach using network analysis has provided an intuitive way to represent complex co-expression patterns between many genes. Co-expression network analysis has enabled us to extract modules, or groups of tightly co-expressed genes, associated with biological processes. Furthermore, integrated analysis of gene expression and metabolite accumulation has allowed us to hypothesize the functions of genes associated with specific metabolic processes. Co-expression network analysis is a powerful approach for data-driven hypothesis construction and gene prioritization, and provides novel insights into the system-level understanding of plant cellular processes.     

The Arabidopsis SeedGenes Project

The SeedGenes database (http://www.seedgenes.org) presents molecular and phenotypic information on essential, non-redundant genes of Arabidopsis that give a seed phenotype when disrupted by mutation. Experimental details are synthesized for efficient use by the community and organized into two major sections in the database, one dealing with genes and the other with mutant alleles. The database can be queried for detailed information on a single gene to create a SeedGenes Profile. Queries can also generate lists of genes or mutants that fit specified criteria. The long-term goal is to establish a complete collection of Arabidopsis genes that give a knockout phenotype. This information is needed to focus attention on genes with important cellular functions in a model plant and to assess from a genetic perspective the extent of functional redundancy in the Arabidopsis genome.     

Systematic approaches to using the FOX hunting system to identify useful rice genes

Ectopic gene expression, or the gain-of-function approach, has the advantage that once the function of a gene is known the gene can be transferred to many different plants by transformation. We previously reported a method, called FOX hunting, that involves ectopic expression of Arabidopsis full-length cDNAs in Arabidopsis to systematically generate gain-of-function mutants. This technology is most beneficial for generating a heterologous gene resource for analysis of useful plant gene functions. As an initial model we generated more than 23 000 independent Arabidopsis transgenic lines that expressed rice fl-cDNAs (Rice FOX Arabidopsis lines). The short generation time and rapid and efficient transformation frequency of Arabidopsis enabled the functions of the rice genes to be analyzed rapidly. We screened rice FOX Arabidopsis lines for alterations in morphology, photosynthesis, element accumulation, pigment accumulation, hormone profiles, secondary metabolites, pathogen resistance, salt tolerance, UV signaling, high light tolerance, and heat stress tolerance. Some of the mutant phenotypes displayed by rice FOX Arabidopsis lines resulted from the expression of rice genes that had no homologs in Arabidopsis . This result demonstrated that rice fl-cDNAs could be used to introduce new gene functions in Arabidopsis. Furthermore, these findings showed that rice gene function could be analyzed by employing Arabidopsis as a heterologous host. This technology provides a framework for the analysis of plant gene function in a heterologous host and of plant improvement by using heterologous gene resources.     

根据GO分类体系选择与实验条件相关的差异表达功能结点进行基因功能深化预测

利用有限个实验条件下的基因表达谱数据,只能对与实验条件相关的基因功能类进行有效预测,所以有必要限定可预测的基因功能类范围。据此,首先基于GeneOntology(GO)选择富集差异表达基因与实验条件相关的功能类。再通过支持向量机分类器,深化预测迄今只注释到实验条件相关功能类的父结点的基因是否属于该实验条件相关功能类。应用于一套酵母基因表达谱数据,结果显示,在剔除了高度不平衡的训练集合后,平均真阳性率(precision)与平均覆盖率(recall)都分别达到了71%与47%以上。     

Computational prediction and molecular characterization of an oomycete effector and the cognate Arabidopsis resistance gene

Hyaloperonospora arabidopsidis (Hpa) is an obligate biotroph oomycete pathogen of the model plant Arabidopsis thaliana and contains a large set of effector proteins that are translocated to the host to exert virulence functions or trigger immune responses. These effectors are characterized by conserved amino-terminal translocation sequences and highly divergent carboxyl-terminal functional domains. The availability of the Hpa genome sequence allowed the computational prediction of effectors and the development of effector delivery systems enabled validation of the predicted effectors in Arabidopsis. In this study, we identified a novel effector ATR39-1 by computational methods, which was found to trigger a resistance response in the Arabidopsis ecotype Weiningen (Wei-0). The allelic variant of this effector, ATR39-2, is not recognized, and two amino acid residues were identified and shown to be critical for this loss of recognition. The resistance protein responsible for recognition of the ATR39-1 effector in Arabidopsis is RPP39 and was identified by map-based cloning. RPP39 is a member of the CC-NBS-LRR family of resistance proteins and requires the signaling gene NDR1 for full activity. Recognition of ATR39-1 in Wei-0 does not inhibit growth of Hpa strains expressing the effector, suggesting complex mechanisms of pathogen evasion of recognition, and is similar to what has been shown in several other cases of plant-oomycete interactions. Identification of this resistance gene/effector pair adds to our knowledge of plant resistance mechanisms and provides the basis for further functional analyses.     

Genomic basis for cell-wall diversity in plants. A comparative approach to gene families in rice and Arabidopsis

Monocotyledons and dicotyledons are distinct, not only in their body plans and developmental patterns, but also in the structural features of their cell walls. The recent completion of the rice (Oryza sativa) genomic sequence and publication of the sequence data, together with the completed database of the Arabidopsis thaliana genome, provide the first opportunity to compare the full complement of cell-wall-related genes from the two distinct classes of flowering plants. We made this comparison by exploiting the fact that Arabidopsis and rice have type I and type II walls, respectively, and therefore represent the two extremes in terms of the structural features of plant cell walls. In this review article, we classify all cell-wall-related genes into 32 gene families, and generate their phylogenetic trees. Using these data, we can phylogenetically compare individual genes of particular interest between Arabidopsis and rice. This comparative genome approach shows that the differences in wall architecture in the two plant groups actually mirror the diversity of the individual gene families involved in the cell-wall dynamics of the respective plant species. This study also identifies putative rice orthologs of genes with well-defined functions in Arabidopsis and other plant species.