Improved Core Genes Prediction for Constructing well-supported Phylogenetic Trees in large sets of Plant Species

The way to infer well-supported phylogenetic trees that precisely reflect the evolutionary process is a challenging task that completely depends on the way the related core genes have been found. In previous computational biology studies, many similarity based algorithms, mainly dependent on calculating sequence alignment matrices, have been proposed to find them. In these kinds of approaches, a significantly high similarity score between two coding sequences extracted from a given annotation tool means that one has the same genes. In a previous work article, we presented a quality test approach (QTA) that improves the core genes quality by combining two annotation tools (namely NCBI, a partially human-curated database, and DOGMA, an efficient annotation algorithm for chloroplasts). This method takes the advantages from both sequence similarity and gene features to guarantee that the core genome contains correct and well-clustered coding sequences (\emph{i.e.}, genes). We then show in this article how useful are such well-defined core genes for biomolecular phylogenetic reconstructions, by investigating various subsets of core genes at various family or genus levels, leading to subtrees with strong bootstraps that are finally merged in a well-supported supertree.Comment: 12 pages, 7 figures, IWBBIO 2015 (3rd International Work-Conference on Bioinformatics and Biomedical Engineering

Development of a PNA Probe for Fluorescence In Situ Hybridization Detection of Prorocentrum donghaiense

Prorocentrum donghaiense is a common but dominant harmful algal bloom (HAB) species, which is widely distributed along the China Sea coast. Development of methods for rapid and precise identification and quantification is prerequisite for early-stage warning and monitoring of blooms due to P. donghaiense. In this study, sequences representing the partial large subunit rDNA (D1–D2), small subunit rDNA and internal transcribed spacer region (ITS-1, 5.8S rDNA and ITS-2) of P. donghaiense were firstly obtained, and then seven candidate DNA probes were designed for performing fluorescence in situ hybridization (FISH) tests on P. donghaiense. Based on the fluorescent intensity of P. donghaiense cells labeled by the DNA probes, the probe DP0443A displayed the best hybridization performance. Therefore, a PNA probe (PP0443A) analogous to DP0443A was used in the further study. The cells labeled with the PNA probe displayed more intensive green fluorescence than that labeled with its DNA analog. The PNA probe was used to hybridize with thirteen microalgae belonging to five families, i.e., Dinophyceae, Prymnesiophyceae, Raphidophyceae, Chlorophyceae and Bacillariophyceae, and showed no visible cross-reaction. Finally, FISH with the probes PP0443A and DP0443A and light microscopy (LM) analysis aiming at enumerating P. donghaiense cells were performed on the field samples. Statistical comparisons of the cell densities (cells/L) of P. donghaiense in the natural samples determined by FISH and LM were performed using one-way ANOVA and Duncan's multiple comparisons of the means. The P. donghaiense cell densities determined by LM and the PNA probe are remarkably higher than (p<0.05) that determined by the DNA probe, while no significant difference is observed between LM and the PNA probe. All results suggest that the PNA probe is more sensitive that its DNA analog, and therefore is promising for the monitoring of harmful algal blooms of P. donghaiense in the future

EEF2 Analysis Challenges the Monophyly of Archaeplastida and Chromalveolata

BACKGROUND: Classification of eukaryotes provides a fundamental phylogenetic framework for ecological, medical, and industrial research. In recent years eukaryotes have been classified into six major supergroups: Amoebozoa, Archaeplastida, Chromalveolata, Excavata, Opisthokonta, and Rhizaria. According to this supergroup classification, Archaeplastida and Chromalveolata each arose from a single plastid-generating endosymbiotic event involving a cyanobacterium (Archaeplastida) or red alga (Chromalveolata). Although the plastids within members of the Archaeplastida and Chromalveolata share some features, no nucleocytoplasmic synapomorphies supporting these supergroups are currently known. METHODOLOGY/PRINCIPAL FINDINGS: This study was designed to test the validity of the Archaeplastida and Chromalveolata through the analysis of nucleus-encoded eukaryotic translation elongation factor 2 (EEF2) and cytosolic heat-shock protein of 70 kDa (HSP70) sequences generated from the glaucophyte Cyanophora paradoxa, the cryptophytes Goniomonas truncata and Guillardia theta, the katablepharid Leucocryptos marina, the rhizarian Thaumatomonas sp. and the green alga Mesostigma viride. The HSP70 phylogeny was largely unresolved except for certain well-established groups. In contrast, EEF2 phylogeny recovered many well-established eukaryotic groups and, most interestingly, revealed a well-supported clade composed of cryptophytes, katablepharids, haptophytes, rhodophytes, and Viridiplantae (green algae and land plants). This clade is further supported by the presence of a two amino acid signature within EEF2, which appears to have arisen from amino acid replacement before the common origin of these eukaryotic groups. CONCLUSIONS/SIGNIFICANCE: Our EEF2 analysis strongly refutes the monophyly of the Archaeplastida and the Chromalveolata, adding to a growing body of evidence that limits the utility of these supergroups. In view of EEF2 phylogeny and other morphological evidence, we discuss the possibility of an alternative eukaryotic supergroup

The role of interactions between Prorocentrum minimum and Heterosigma akashiwo in bloom formation

We examined the growth and interactions between the bloom-forming flagellates Prorocentrum minimum and Heterosigma akashiwo using bi-algal culture experiments. When both species were inoculated at high cell densities, growth of H. akashiwo was inhibited by P. minimum. In other combinations of inoculation densities, the species first reaching the stationary phase substantially suppressed maximum cell densities of the other species, but the growth inhibition effect of P. minimum was stronger than that of H. akashiwo. We used a mathematical model to simulate growth and interactions of P. minimum and H. akashiwo in bi-algal cultures. The model indicated that P. minimum always out-competed H. akashiwo over time. Additional experiments showed that crude extracts from P. minimum and H. akashiwo cultures did not affect the growth of either species, but both strongly inhibited the growth of the bloom-forming diatom Skeletonema costatum. Further experiments showed that it was unlikely that reactive oxygen species produced by H. akashiwo were responsible for the inhibition of P. minimum growth

PHYTOPK28-D1D2: A curated database of 28S rRNA gene D1-D2 domains from eukaryotic organisms dedicated to metabarcoding analyses of marine phytoplankton samples

The PHYTOPK28-D1D2 database comprises accession numbers, taxonomic classification and 28S rDNA (D1-D2 domains) sequences that are available in public DNA databases. The sequences, listed in FASTA format, are identified by the accession number and the hierarchical taxonomy information. The PHYTOPK28-D1D2 database was built for the taxonomic annotation of DNA metabarcodes generated from water samples collected in six French Mediterranean lagoons, once a month between May and September/October 2012, and fractionated by size (three size ranges: 0.7-5 µm, 5-20 µm and 20-100 µm). This metabarcode dataset was deposited in the European Nucleotide Archive under the accession number PRJEB18757. The PHYTOPK28-D1D2 database was started with an initial dataset that was retrieved on the April 19, 2013 from the ribosomal DNA database SILVA. Further sequences were added by extensive BLAST searches in the NCBI/GenBank nucleotide database by targeting the main taxonomic divisions among eukaryotic, marine or freshwater, algal and plankton lineages, and excluding environmental sequences. The hereby first version of the database assembled by the end of June 2015, PHYTOPK28-D1D2_v1, reached 8,753 reference sequences, including more than 3,600 from algal/phytoplanktonic lineages (Chlorophyta, Cryptophyta, Dinophyceae, Haptophyceae, Stramenopiles, Rhodophyta, Euglenozoa, Rhizaria, Glaucocystophyceae) and ~700 from microzooplankton (including ciliates, rotifers, copepods) when it was used for computing the annotation of the metabarcode library. It is not claimed that this PHYTOPK28-D1D2 database is exhaustive with respect to its purpose. It is not warranted that the database does not contain overlooked identification errors from undetected errors originating from the deposition in public databases or from missed literature reporting taxonomic changes. The database can also lack recently released data at the time of use in June 2015. It is intended to further enrich the database by adding new–mostly recently released–sequence accessions and to make a new database version available from time to time. Anyone interested in receiving a recently updated database can contact the first author (DG). Any information reporting errors, omissions or recently released sequences would also be welcome to help in this updating effort. It would be interesting to make this database become richer by adding more information on reference sequences, for example by linking the accession numbers to GenBank database information, by adding and linking to the article reference related to the sequence submission (an information that is not always updated in the public DNA databases) and eventually, the subsequent literature references leading to changes in the taxonomic name or in the classification of organisms

Modèles statistiques appliqués à l'épidémiologie neuro-comportementale

L'épidémiologie neuro-comportementale a recours à des tests psychométriques qui génèrent des données particulières comprenant des multi-liaisons entre variables. Ce document fait la synthèse d'une étude qui visait à mettre au point une méthode d'analyse statistique adaptée aux problèmes multi-liaisons. Après une revue de la littérature des méthodes statistiques disponibles pour traiter cette configuration des données, a été finalement retenu le modèle linéaire à variables latentes. Il a d'abord été testé et validé sur des données simulées puis appliqué à deux jeux de données réelles, l'un étant issu d'une étude transversale sur la neurotoxicité du toluène réalisée par l'INRS et l'autre étant issu de la cohorte PAQUID de l'Unité 330 de l'INSERM. PAQUID est destinée à estimer l'incidence de la démence chez les personnes agées et d'en étudier les facteurs de risque ; les données provenant de tests psychométriques, présentent également un modèle de multi-liaisons. Ces différentes mises enoeuvre du modèle à variables latentes ont permis de cerner les conditions de son application et d'en apprécier sa pertinence et ses limite