Eric Bonnet

The Ectocarpus genome and the independent evolution of multicellularity in brown algae

Brown algae (Phaeophyceae) are complex photosynthetic organisms with a very different evolutionary history to green plants, to which they are only distantly related. These seaweeds are the dominant species in rocky coastal ecosystems and they exhibit many interesting adaptations to these, often harsh, environments. Brown algae are also one of only a small number of eukaryotic lineages that have evolved complex multicellularity (Fig. 1). We report the 214 million base pair (Mbp) genome sequence of the filamentous seaweed Ectocarpus siliculosus (Dillwyn) Lyngbye, a model organism for brown algae, closely related to the kelps (Fig. 1). Genome features such as the presence of an extended set of light-harvesting and pigment biosynthesis genes and new metabolic processes such as halide metabolism help explain the ability of this organism to cope with the highly variable tidal environment. The evolution of multicellularity in this lineage is correlated with the presence of a rich array of signal transduction genes. Of particular interest is the presence of a family of receptor kinases, as the independent evolution of related molecules has been linked with the emergence of multicellularity in both the animal and green plant lineages. The Ectocarpus genome sequence represents an important step towards developing this organism as a model species, providing the possibility to combine genomic and genetic approaches to explore these and other aspects of brown algal biology further.

Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences

MOTIVATION Most non-coding RNAs are characterized by a specific secondary and tertiary structure that determines their function. Here, we investigate the folding energy of the secondary structure of non-coding RNA sequences, such as microRNA precursors, transfer RNAs and ribosomal RNAs in several eukaryotic taxa. Statistical biases are assessed by a randomization test, in which the predicted minimum free energy of folding is compared with values obtained for structures inferred from randomly shuffling the original sequences. RESULTS In contrast with transfer RNAs and ribosomal RNAs, the majority of the microRNA sequences clearly exhibit a folding free energy that is considerably lower than that for shuffled sequences, indicating a high tendency in the sequence towards a stable secondary structure. A possible usage of this statistical test in the framework of the detection of genuine miRNA sequences is discussed.

The genome of Tetranychus urticae reveals herbivorous pest adaptations

The spider mite Tetranychus urticae is a cosmopolitan agricultural pest with an extensive host plant range and an extreme record of pesticide resistance. Here we present the completely sequenced and annotated spider mite genome, representing the first complete chelicerate genome. At 90 megabases T. urticae has the smallest sequenced arthropod genome. Compared with other arthropods, the spider mite genome shows unique changes in the hormonal environment and organization of the Hox complex, and also reveals evolutionary innovation of silk production. We find strong signatures of polyphagy and detoxification in gene families associated with feeding on different hosts and in new gene families acquired by lateral gene transfer. Deep transcriptome analysis of mites feeding on different plants shows how this pest responds to a changing host environment. The T. urticae genome thus offers new insights into arthropod evolution and plant–herbivore interactions, and provides unique opportunities for developing novel plant protection strategies.

A Tandem Affinity Purification-based Technology Platform to Study the Cell Cycle Interactome in Arabidopsis thaliana

Defining protein complexes is critical to virtually all aspects of cell biology because many cellular processes are regulated by stable protein complexes, and their identification often provides insights into their function. We describe the development and application of a high throughput tandem affinity purification/mass spectrometry platform for cell suspension cultures to analyze cell cycle-related protein complexes in Arabidopsis thaliana. Elucidation of this protein-protein interaction network is essential to fully understand the functional differences between the highly redundant cyclin-dependent kinase/cyclin modules, which are generally accepted to play a central role in cell cycle control, in all eukaryotes. Cell suspension cultures were chosen because they provide an unlimited supply of protein extracts of actively dividing and undifferentiated cells, which is crucial for a systematic study of the cell cycle interactome in the absence of plant development. Here we report the mapping of a protein interaction network around six known core cell cycle proteins by an integrated approach comprising generic Gateway-based vectors with high cloning flexibility, the fast generation of transgenic suspension cultures, tandem affinity purification adapted for plant cells, matrix-assisted laser desorption ionization tandem mass spectrometry, data analysis, and functional assays. We identified 28 new molecular associations and confirmed 14 previously described interactions. This systemic approach provides new insights into the basic cell cycle control mechanisms and is generally applicable to other pathways in plants.

TAPIR, a web server for the prediction of plant microRNA targets, including target mimics

UNLABELLED We present a new web server called TAPIR, designed for the prediction of plant microRNA targets. The server offers the possibility to search for plant miRNA targets using a fast and a precise algorithm. The precise option is much slower but guarantees to find less perfectly paired miRNA-target duplexes. Furthermore, the precise option allows the prediction of target mimics, which are characterized by a miRNA-target duplex having a large loop, making them undetectable by traditional tools. AVAILABILITY The TAPIR web server can be accessed at: http://bioinformatics.psb.ugent.be/webtools/tapir. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Module network inference from a cancer gene expression data set identifies microRNA regulated modules

Background MicroRNAs (miRNAs) are small RNAs that recognize and regulate mRNA target genes. Multiple lines of evidence indicate that they are key regulators of numerous critical functions in development and disease, including cancer. However, defining the place and function of miRNAs in complex regulatory networks is not straightforward. Systems approaches, like the inference of a module network from expression data, can help to achieve this goal. Methodology/Principal Findings During the last decade, much progress has been made in the development of robust and powerful module network inference algorithms. In this study, we analyze and assess experimentally a module network inferred from both miRNA and mRNA expression data, using our recently developed module network inference algorithm based on probabilistic optimization techniques. We show that several miRNAs are predicted as statistically significant regulators for various modules of tightly co-expressed genes. A detailed analysis of three of those modules demonstrates that the specific assignment of miRNAs is functionally coherent and supported by literature. We further designed a set of experiments to test the assignment of miR-200a as the top regulator of a small module of nine genes. The results strongly suggest that miR-200a is regulating the module genes via the transcription factor ZEB1. Interestingly, this module is most likely involved in epithelial homeostasis and its dysregulation might contribute to the malignant process in cancer cells. Conclusions/Significance Our results show that a robust module network analysis of expression data can provide novel insights of miRNA function in important cellular processes. Such a computational approach, starting from expression data alone, can be helpful in the process of identifying the function of miRNAs by suggesting modules of co-expressed genes in which they play a regulatory role. As shown in this study, those modules can then be tested experimentally to further investigate and refine the function of the miRNA in the regulatory network.

Validating module network learning algorithms using simulated data

BackgroundIn recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance.ResultsOverall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.ConclusionWe show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network algorithms. We used SynTReN data to develop and test an alternative module network learning strategy, which is incorporated in the software package LeMoNe, and we provide evidence that this alternative strategy has several advantages with respect to existing methods.

Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data

Motivation: Cancer is a complex disease, triggered by mutations in multiple genes and pathways. There is a growing interest in the application of systems biology approaches to analyze various types of cancer-related data to understand the overwhelming complexity of changes induced by the disease. Results: We reconstructed a regulatory module network using gene expression, microRNA expression and a clinical parameter, all measured in lymphoblastoid cell lines derived from patients having aggressive or non-aggressive forms of prostate cancer. Our analysis identified several modules enriched in cell cycle-related genes as well as novel functional categories that might be linked to prostate cancer. Almost one-third of the regulators predicted to control the expression levels of the modules are microRNAs. Several of them have already been characterized as causal in various diseases, including cancer. We also predicted novel microRNAs that have never been associated to this type of tumor. Furthermore, the condition-dependent expression of several modules could be linked to the value of a clinical parameter characterizing the aggressiveness of the prostate cancer. Taken together, our results help to shed light on the consequences of aggressive and non-aggressive forms of prostate cancer. Availability: The complete regulatory network is available as an interactive supplementary web site at the following URL: http://bioinformatics.psb.ugent.be/webtools/pronet/ Contact: yves.vandepeer@psb.vib-ugent.be

Scalable hardware accelerator for comparing DNA and protein sequences

Comparing genetic sequences is a well-known problem in bioinformatics. Newly determined sequences are being compared to known sequences stored in databases in order to investigate biological functions. In recent years the number of available sequences has increased exponentially. Because of this explosion a speedup in the comparison process is highly required. To meet this demand we implemented a dynamic programming algorithm for sequence alignment on reconfigurable hardware. The algorithm we implemented, Smith-Waterman-Gotoh (SWG) has not been implemented in hardware before. We show a speedup factor of 40 in a design that scales well with the size of the available hardware. We also demonstrate the limits of larger hardware for small problems, and project our design on the largest Field Programmable Gate Array (FPGA) available today.

Decision Making for Food Choice by Grasshoppers (Orthoptera: Acrididae): Comparison Between a Specialist Species on a Shrubby Legume and Three Graminivorous Species

Abstract Dwarf gorse bush (Ulex minor) heathlands in Limousin, France, are ecological islands often separated by tens of kilometers of grasslands and hedges, where several species of grasshoppers belonging to the genus Chorthippus (Acrididae: Gomphocerinae) coexist. Chorthippus binotatus (Charpentier) feeds only on Ulex minor; nymphs feed exclusively on leaves whereas adults become florivorous at the end of the season. The other species studied (C. biguttulus (L.), C. albomarginatus (De Geer), and C. parallelus (Zetterstedt)) are all graminivorous. The importance of sugars, nitrogen content, sparteine (a quinolizidine alkaloid), and plant architecture in food selection was investigated. Chorthippus binotatus is sensitive to sucrose and fructose, consistent with the high sugar content of Ulex minor flowers. Experiments with grass coated with sparteine showed that this molecule associated with sucrose is a phagostimulant for this grasshopper. Different behavioral responses of graminivorous species are observed with sparteine alone, but never phagostimulation. We compared the response times corresponding to decision-making between the different species toward several components involved in food selection. The food choice toward host plant and sugars is as quick for C. binotatus as for the two graminivorous species (C. parallelus and C. albomarginatus), whereas C. biguttulus is slower and exhibits atypical reactions. Chorthippus binotatus can feed on Poaceae, but with more time spent, leading to an increasing predation risk. This situation is a limitation toward dispersal between different heathlands (patchy habitats).