Laurent Bréhélin

Chemical inhibitors of monogalactosyldiacylglycerol synthases in Arabidopsis thaliana.

Monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG) are the main lipids in photosynthetic membranes in plant cells. They are synthesized in the envelope surrounding plastids by MGD and DGD galactosyltransferases. These galactolipids are critical for the biogenesis of photosynthetic membranes, and they act as a source of polyunsaturated fatty acids for the whole cell and as phospholipid surrogates in phosphate shortage. Based on a high-throughput chemical screen, we have characterized a new compound, galvestine-1, that inhibits MGDs in vitro by competing with diacylglycerol binding. Consistent effects of galvestine-1 on Arabidopsis thaliana include root uptake, circulation in the xylem and mesophyll, inhibition of MGDs in vivo causing a reduction of MGDG content and impairment of chloroplast development. The effects on pollen germination shed light on the contribution of galactolipids to pollen-tube elongation. The whole-genome transcriptional response of Arabidopsis points to the potential benefits of galvestine-1 as a unique tool to study lipid homeostasis in plants.

Galvestine-1, a novel chemical probe for the study of the glycerolipid homeostasis system in plant cells.

Plant cells are characterized by the presence of chloroplasts, membrane lipids of which contain up to ∼80% mono- and digalactosyldiacylglycerol (MGDG and DGDG). The synthesis of MGDG in the chloroplast envelope is essential for the biogenesis and function of photosynthetic membranes, is coordinated with lipid metabolism in other cell compartments and is regulated in response to environmental factors. Phenotypic analyses of Arabidopsis using the recently developed specific inhibitor called galvestine-1 complete previous analyses performed using various approaches, from enzymology, cell biology to genetics. This review details how this probe could be beneficial to study the lipid homeostasis system at the whole cell level and highlights connections between MGDG synthesis and Arabidopsis flower development.

PlasmoDraft: a database of Plasmodium falciparum gene function predictions based on postgenomic data

BackgroundOf the 5 484 predicted proteins of Plasmodium falciparum, the main causative agent of malaria, about 60% do not have sufficient sequence similarity with proteins in other organisms to warrant provision of functional assignments. Non-homology methods are thus needed to obtain functional clues for these uncharacterized genes.ResultsWe present PlasmoDraft http://atgc.lirmm.fr/PlasmoDraft/, a database of Gene Ontology (GO) annotation predictions for P. falciparum genes based on postgenomic data. Predictions of PlasmoDraft are achieved with a Guilt By Association method named Gonna. This involves (1) a predictor that proposes GO annotations for a gene based on the similarity of its profile (measured with transcriptome, proteome or interactome data) with genes already annotated by GeneDB; (2) a procedure that estimates the confidence of the predictions achieved with each data source; (3) a procedure that combines all data sources to provide a global summary and confidence estimate of the predictions. Gonna has been applied to all P. falciparum genes using most publicly available transcriptome, proteome and interactome data sources. Gonna provides predictions for numerous genes without any annotations. For example, 2 434 genes without any annotations in the Biological Process ontology are associated with specific GO terms (e.g. Rosetting, Antigenic variation), and among these, 841 have confidence values above 50%. In the Cellular Component and Molecular Function ontologies, 1 905 and 1 540 uncharacterized genes are associated with specific GO terms, respectively (740 and 329 with confidence value above 50%).ConclusionAll predictions along with their confidence values have been compiled in PlasmoDraft, which thus provides an extensive database of GO annotation predictions that can be achieved with these data sources. The database can be accessed in different ways. A global view allows for a quick inspection of the GO terms that are predicted with high confidence, depending on the various data sources. A gene view and a GO term view allow for the search of potential GO terms attached to a given gene, and genes that potentially belong to a given GO term.

Assessing functional annotation transfers with inter-species conserved coexpression: application to Plasmodium falciparum

BackgroundPlasmodium falciparum is the main causative agent of malaria. Of the 5 484 predicted genes of P. falciparum, about 57% do not have sufficient sequence similarity to characterized genes in other species to warrant functional assignments. Non-homology methods are thus needed to obtain functional clues for these uncharacterized genes. Gene expression data have been widely used in the recent years to help functional annotation in an intra-species way via the so-called Guilt By Association (GBA) principle.ResultsWe propose a new method that uses gene expression data to assess inter-species annotation transfers. Our approach starts from a set of likely orthologs between a reference species (here S. cerevisiae and D. melanogaster) and a query species (P. falciparum). It aims at identifying clusters of coexpressed genes in the query species whose coexpression has been conserved in the reference species. These conserved clusters of coexpressed genes are then used to assess annotation transfers between genes with low sequence similarity, enabling reliable transfers of annotations from the reference to the query species. The approach was used with transcriptomic data sets of P. falciparum, S. cerevisiae and D. melanogaster, and enabled us to propose with high confidence new/refined annotations for several dozens hypothetical/putative P. falciparum genes. Notably, we revised the annotation of genes involved in ribosomal proteins and ribosome biogenesis and assembly, thus highlighting several potential drug targets.ConclusionsOur approach uses both sequence similarity and gene expression data to help inter-species gene annotation transfers. Experiments show that this strategy improves the accuracy achieved when using solely sequence similarity and outperforms the accuracy of the GBA approach. In addition, our experiments with P. falciparum show that it can infer a function for numerous hypothetical genes.

Using repeated measurements to validate hierarchical gene clusters

MOTIVATION Hierarchical clustering is a common approach to study protein and gene expression data. This unsupervised technique is used to find clusters of genes or proteins which are expressed in a coordinated manner across a set of conditions. Because of both the biological and technical variability, experimental repetitions are generally performed. In this work, we propose an approach to evaluate the stability of clusters derived from hierarchical clustering by taking repeated measurements into account. RESULTS The method is based on the bootstrap technique that is used to obtain pseudo-hierarchies of genes from resampled datasets. Based on a fast dynamic programming algorithm, we compare the original hierarchy to the pseudo-hierarchies and assess the stability of the original gene clusters. Then a shuffling procedure can be used to assess the significance of the cluster stabilities. Our approach is illustrated on simulated data and on two microarray datasets. Compared to the standard hierarchical clustering methodology, it allows to point out the dubious and stable clusters, and thus avoids misleading interpretations. AVAILABILITY The programs were developed in C and R languages.

Fitting hidden Markov models of protein domains to a target species: application to Plasmodium falciparum.

BackgroundHidden Markov Models (HMMs) are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as P. falciparum, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains.ResultsUsing P. falciparum as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD) procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values.ConclusionWe show that the new approaches allow identification of several domain families previously absent in the P. falciparum proteome and the Apicomplexa phylum, and identify many domains that are not detected by previous approaches. In terms of the number of new discovered domains, the new approaches outperform the previous ones when no close species are available or when they are used to identify likely occurrences among potential domains with high E-values. All predictions on P. falciparum have been integrated into a dedicated website which pools all known/new annotations of protein domains and functions for this organism. A software implementing the two proposed approaches is available at the same address: http://www.lirmm.fr/~terrapon/HMMfit/

Hidden Markov models with patterns to learn Boolean vector sequences and applications to the built-in self-test for integrated circuits

We present a new model, derived from the hidden Markov model (HMM), to learn Boolean vector sequences. Our HMM with patterns (HMMP) is a simple, hybrid, and interpretable model that uses Boolean patterns to define emission probability distributions attached to states. Vectors consistent with a given pattern are equally probable, while inconsistent ones have probability zero to be emitted. We define an efficient learning algorithm for this model, which relies on the maximum likelihood principle, and proceeds by iteratively simplifying the structure and updating the parameters of an initial specific HMMP that represents the learning sequences. HMMPs and our learning algorithm are applied to the built-in self-test (BIST) for integrated circuits, which is one of the key microelectronic problems. An HMMP is learned from a test sequence set that covers most of the potential faults of the circuit at hand. Then, this HMMP is used as test sequence generator. The experiments carried out show that learned HMMPs have a very high fault coverage.

EuPathDomains: The divergent domain database for eukaryotic pathogens

Eukaryotic pathogens (e.g. Plasmodium, Leishmania, Trypanosomes, etc.) are a major source of morbidity and mortality worldwide. In Africa, one of the most impacted continents, they cause millions of deaths and constitute an immense economic burden. While the genome sequence of several of these organisms is now available, the biological functions of more than half of their proteins are still unknown. This is a serious issue for bringing to the foreground the expected new therapeutic targets. In this context, the identification of protein domains is a key step to improve the functional annotation of the proteins. However, several domains are missed in eukaryotic pathogens because of the high phylogenetic distance of these organisms from the classical eukaryote models. We recently proposed a method, co-occurrence domain detection (CODD), that improves the sensitivity of Pfam domain detection by exploiting the tendency of domains to appear preferentially with a few other favorite domains in a protein. In this paper, we present EuPathDomains (http://www.atgc-montpellier.fr/EuPathDomains/), an extended database of protein domains belonging to ten major eukaryotic human pathogens. EuPathDomains gathers known and new domains detected by CODD, along with the associated confidence measurements and the GO annotations that can be deduced from the new domains. This database significantly extends the Pfam domain coverage of all selected genomes, by proposing new occurrences of domains as well as new domain families that have never been reported before. For example, with a false discovery rate lower than 20%, EuPathDomains increases the number of detected domains by 13% in Toxoplasma gondii genome and up to 28% in Cryptospordium parvum, and the total number of domain families by 10% in Plasmodium falciparum and up to 16% in C. parvum genome. The database can be queried by protein names, domain identifiers, Pfam or Interpro identifiers, or organisms, and should become a valuable resource to decipher the protein functions of eukaryotic pathogens.

Hidden Markov and independence models with patterns for sequential BIST

We propose a novel BIST technique for non-scan sequential circuits which does not modify the circuit under test. It uses a learning algorithm to build a hardware test sequence generator capable of reproducing the essential features of a set of precomputed deterministic test sequences. We use for this purpose two new models called hidden Markov model with patterns and independence model with patterns. Compared to existing methods, the proposed technique exhibits a very high fault coverage, including performance testing, at the expense of a low silicon area overhead.

Computational discovery of regulatory elements in a continuous expression space

Approaches for regulatory element discovery from gene expression data usually rely on clustering algorithms to partition the data into clusters of co-expressed genes. Gene regulatory sequences are then mined to find overrepresented motifs in each cluster. However, this ad hoc partition rarely fits the biological reality. We propose a novel method called RED2 that avoids data clustering by estimating motif densities locally around each gene. We show that RED2 detects numerous motifs not detected by clustering-based approaches, and that most of these correspond to characterized motifs. RED2 can be accessed online through a user-friendly interface.