A new homeobox-leucine zipper gene from Arabidopsis thaliana

We have isolated a homeobox-containing gene from Arabidopsis thaliana using a degenerate oligonucleotide probe corresponding to the most conserved region of the homeodomain. This strategy has been used previously to isolate homeobox-containing genes from Caenorhabditis, and recently from A. thaliana. The Arabidopsis genes have an unusual structure in that they have a leucine zipper motif adjacent to the carboxy terminal region of the homeo domain, a feature not found in homeobox-containing genes isolated from animals. We report the isolation and primary structure of a new member of this Arabidopsis homeobox-leucine zipper gene family. This new member has the homeodomain and leucine-zipper motif similar to the two genes previously identified, but differs from these genes in the part corresponding to the carboxy terminus of the polypeptide, as well as in size and isoelectric point of the protein.     

A modular framework for gene set analysis integrating multilevel omics data

Modern high-throughput methods allow the investigation of biological functions across multiple ‘omics’ levels. Levels include mRNA and protein expression profiling as well as additional knowledge on, for example, DNA methylation and microRNA regulation. The reason for this interest in multi-omics is that actual cellular responses to different conditions are best explained mechanistically when taking all omics levels into account. To map gene products to their biological functions, public ontologies like Gene Ontology are commonly used. Many methods have been developed to identify terms in an ontology, overrepresented within a set of genes. However, these methods are not able to appropriately deal with any combination of several data types. Here, we propose a new method to analyse integrated data across multiple omics-levels to simultaneously assess their biological meaning. We developed a model-based Bayesian method for inferring interpretable term probabilities in a modular framework. Our Multi-level ONtology Analysis (MONA) algorithm performed significantly better than conventional analyses of individual levels and yields best results even for sophisticated models including mRNA fine-tuning by microRNAs. The MONA framework is flexible enough to allow for different underlying regulatory motifs or ontologies. It is ready-to-use for applied researchers and is available as a standalone application from http://icb.helmholtz-muenchen.de/mona.     

DASMiner: discovering and integrating data from DAS sources

Background  

DAS is a widely adopted protocol for providing syntactic interoperability among biological databases. The popularity of DAS is due to a simplified and elegant mechanism for data exchange that consists of sources exposing their RESTful interfaces for data access. As a growing number of DAS services are available for molecular biology resources, there is an incentive to explore this protocol in order to advance data discovery and integration among these resources.     

In silico gene prioritization by integrating multiple data sources

Identifying disease genes is crucial to the understanding of disease pathogenesis, and to the improvement of disease diagnosis and treatment. In recent years, many researchers have proposed approaches to prioritize candidate genes by considering the relationship of candidate genes and existing known disease genes, reflected in other data sources. In this paper, we propose an expandable framework for gene prioritization that can integrate multiple heterogeneous data sources by taking advantage of a unified graphic representation. Gene-gene relationships and gene-disease relationships are then defined based on the overall topology of each network using a diffusion kernel measure. These relationship measures are in turn normalized to derive an overall measure across all networks, which is utilized to rank all candidate genes. Based on the informativeness of available data sources with respect to each specific disease, we also propose an adaptive threshold score to select a small subset of candidate genes for further validation studies. We performed large scale cross-validation analysis on 110 disease families using three data sources. Results have shown that our approach consistently outperforms other two state of the art programs. A case study using Parkinson disease (PD) has identified four candidate genes (UBB, SEPT5, GPR37 and TH) that ranked higher than our adaptive threshold, all of which are involved in the PD pathway. In particular, a very recent study has observed a deletion of TH in a patient with PD, which supports the importance of the TH gene in PD pathogenesis. A web tool has been implemented to assist scientists in their genetic studies.     

A genetic framework for fruit patterning in Arabidopsis thaliana

In the model plant Arabidopsis thaliana, the establishment of organ polarity leads to the expression of FILAMENTOUS FLOWER (FIL) and YABBY3 (YAB3) on one side of an organ. One important question that has remained unanswered is how does this positional information lead to the correct spatial activation of genes controlling tissue identity? We provide the first functional link between polarity establishment and the regulation of tissue identity by showing that FIL and YAB3 control the non-overlapping expression patterns of FRUITFULL (FUL) and SHATTERPROOF (SHP), genes necessary to form stripes of valve margin tissue that allow the fruit to shatter along two defined borders and disperse the seeds. FIL and YAB3 activate FUL and SHP redundantly with JAGGED (JAG), a gene that also promotes growth in organs, indicating that several pathways converge to regulate these genes. These activities are negatively regulated by REPLUMLESS (RPL), which divides FIL/JAG activity, creating two distinct stripes of valve margin.     

Mapping from heterogeneous biodiversity monitoring data sources

Field monitoring can vary from simple volunteer opportunistic observations to professional standardised monitoring surveys, leading to a trade-off between data quality and data collection costs. Such variability in data quality may result in biased predictions obtained from species distribution models (SDMs). We aimed to identify the limitations of different monitoring data sources for developing species distribution maps and to evaluate their potential for spatial data integration in a conservation context. Using Maxent, SDMs were generated from three different bird data sources in Catalonia, which differ in the degree of standardisation and available sample size. In addition, an alternative approach for modelling species distributions was applied, which combined the three data sources at a large spatial scale, but then downscaling to the required resolution. Finally, SDM predictions were used to identify species richness and high quality areas (hotspots) from different treatments. Models were evaluated by using high quality Atlas information. We show that both sample size and survey methodology used to collect the data are important in delivering robust information on species distributions. Models based on standardized monitoring provided higher accuracy with a lower sample size, especially when modelling common species. Accuracy of models from opportunistic observations substantially increased when modelling uncommon species, giving similar accuracy to a more standardized survey. Although downscaling data through a SDM approach appears to be a useful tool in cases of data shortage or low data quality and heterogeneity, it will tend to overestimate species distributions. In order to identify distributions of species, data with different quality may be appropriate. However, to identify biodiversity hotspots high quality information is needed.     

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.     

Identification of a tRNA isopentenyltransferase gene from Arabidopsis thaliana

The tRNA of most organisms contain modified adenines called cytokinins. Situated next to the anticodon, they have been shown to influence translational fidelity and efficiency. The enzyme that synthesizes cytokinins on pre-tRNA, tRNA isopentenyltransferase (EC 2.5.1.8), has been studied in micro-organisms like Escherichia coli and Saccharomyces cerevisiae, and the corresponding genes have been cloned. We here report the first cloning and functional characterization of a homologous gene from a plant, Arabidopsis thaliana. Expression in S. cerevisiae showed that the gene can complement the anti-suppressor phenotype of a mutant that lacks MOD5, the intrinsic tRNA isopentenyltransferase gene. This was accompanied by the reintroduction of isopentenyladenosine in the tRNA. The Arabidopsis gene is constitutively expressed in seedling tissues.     

Characterization of an acyl-CoA synthetase from Arabidopsis thaliana

One of the major goals of modern plant biotechnology is to manipulate lipid metabolism in oilseed crops to produce new and improved edible and industrial vegetable oils. Lipids constitute the structural components of cellular membranes and act as sources of energy for the germinating seed and are therefore essential to plant cell function. Both de novo synthesis and modification of existing lipids are dependent on the activity of acyl-CoA synthetases (ACSs). To date, ACSs have been recalcitrant to traditional methods of purification due to their association with membranes. In our laboratory, several isoforms of ACSs have been identified in Arabidopsis thaliana. Reverse genetics allowed us to identify a mutant containing a transfer DNA-interrupted ACS gene. Results will be presented that describe the isolation and characterization of this mutant. The elucidation of the specific roles of ACSs will lead to a greater understanding of plant lipid metabolism.