Olivier Gonçalves

Environmental microarray analyses of Antarctic soil microbial communities

Antarctic ecosystems are fascinating in their limited trophic complexity, with decomposition and nutrient cycling functions being dominated by microbial activities. Not only are Antarctic habitats exposed to extreme environmental conditions, the Antarctic Peninsula is also experiencing unequalled effects of global warming. Owing to their uniqueness and the potential impact of global warming on these pristine systems, there is considerable interest in determining the structure and function of microbial communities in the Antarctic. We therefore utilized a recently designed 16S rRNA gene microarray, the PhyloChip, which targets 8741 bacterial and archaeal taxa, to interrogate microbial communities inhabiting densely vegetated and bare fell-field soils along a latitudinal gradient ranging from 51 °S (Falkland Islands) to 72 °S (Coal Nunatak). Results indicated a clear decrease in diversity with increasing latitude, with the two southernmost sites harboring the most distinct Bacterial and Archaeal communities. The microarray approach proved more sensitive in detecting the breadth of microbial diversity than polymerase chain reaction-based bacterial 16S rRNA gene libraries of modest size (∼190 clones per library). Furthermore, the relative signal intensities summed for phyla and families on the PhyloChip were significantly correlated with the relative occurrence of these taxa in clone libraries. PhyloChip data were also compared with functional gene microarray data obtained earlier, highlighting numerous significant relationships and providing evidence for a strong link between community composition and functional gene distribution in Antarctic soils. Integration of these PhyloChip data with other complementary methods provides an unprecedented understanding of the microbial diversity and community structure of terrestrial Antarctic habitats.

HIGHLIGHTING RELATIONSHIPS BETWEEN HETEROGENEOUS BIOLOGICAL DATA THROUGH GRAPHICAL DISPLAYS BASED ON REGULARIZED CANONICAL CORRELATION ANALYSIS

Biological data produced by high throughput technologies are becoming more and more abundant and are arousing many statistical questions. This paper addresses one of them; when gene expression data are jointly observed with other variables with the purpose of highlighting significant relationships between gene expression and these other variables. One relevant statistical method to explore these relationships is Canonical Correlation Analysis (CCA). Unfortunately, in the context of postgenomic data, the number of variables (gene expressions) is usually greater than the number of units (samples) and CCA cannot be directly performed: a regularized version is required. We applied regularized CCA on data sets from two different studies and show that its interpretation evidences both previously validated relationships and new hypothesis. From the first data sets (nutrigenomic study), we generated interesting hypothesis on the transcription factor pathways potentially linking hepatic fatty acids and gene expression. From the second data sets (pharmacogenomic study on the NCI-60 cancer cell line panel), we identified new ABC transporter candidate substrates which relevancy is illustrated by the concomitant identification of several known substrates. In conclusion, the use of regularized CCA is likely to be relevant to a number and a variety of biological experiments involving the generation of high throughput data. We demonstrated here its ability to enhance the range of relevant conclusions that can be drawn from these relatively expensive experiments.

Qualitative and quantitative assessment of water sorption in natural fibres using ATR-FTIR spectroscopy

In the field of composite materials, natural fibres appear to be a viable replacement for glass fibres. However, in humid conditions, strong hydrophilic behaviour of such materials can lead to their structural modification. Then, understanding moisture sorption mechanisms in these materials is an important issue for their efficient use. In this work, the water sorption on three natural fibres (flax, hemp and sisal) was studied using Fourier transformed infrared spectroscopy. The spectral information allowed both qualitative and quantitative analyses of the moisture absorption mechanisms. The main chemical functions involved in the water sorption phenomenon were identified. The absolute water content of the fibres was also determined by using a partial least square regression (PLS-R) approach. Moreover, typical sorption isotherm curves described by Park model were fitted as well as water diffusion kinetics. These last applications confirmed the validity of the FTIR spectra based predictive models.

Annotation of microsporidian genomes using transcriptional signals

High-quality annotation of microsporidian genomes is essential for understanding the biological processes that govern the development of these parasites. Here we present an improved structural annotation method using transcriptional DNA signals. We apply this method to re-annotate four previously annotated genomes, which allow us to detect annotation errors and identify a significant number of unpredicted genes. We then annotate the newly sequenced genome of Anncaliia algerae. A comparative genomic analysis of A. algerae permits the identification of not only microsporidian core genes, but also potentially highly expressed genes encoding membrane-associated proteins, which represent good candidates involved in the spore architecture, the invasion process and the microsporidian-host relationships. Furthermore, we find that the ten-fold variation in microsporidian genome sizes is not due to gene number, size or complexity, but instead stems from the presence of transposable elements. Such elements, along with kinase regulatory pathways and specific transporters, appear to be key factors in microsporidian adaptive processes.

Rapid geographical differentiation of the European spread brown macroalga Sargassum muticum using HRMAS NMR and Fourier-Transform Infrared spectroscopy

Two recent techniques based on chemical footprinting analysis, HRMAS NMR and FTIR spectroscopy, were tested on a brown macroalgal model. These powerful and easily-to-use techniques allowed us to discriminate Sargassum muticum specimens collected in five different countries along Atlantic coasts, from Portugal to Norway. HRMAS NMR and FTIR permitted the obtaining of an overview of metabolites produced by the alga. Based on spectra analysis, results allowed us to successfully group the samples according to their geographical origin. HRMAS NMR and FTIR spectroscopy respectively point out the relation between the geographical localization and the chemical composition and demonstrated macromolecules variations regarding to environmental stress. Then, our results are discussed in regard of the powerful of these techniques together with the variability of the main molecules produced by Sargassum muticum along the Atlantic coasts.

Selection of biologically relevant genes with a wrapper stochastic algorithm

We investigate an important issue of a meta-algorithm for selecting variables in the framework of microarray data. This wrapper method starts from any classification algorithm and weights each variable (i.e. gene) relative to its efficiency for classification. An optimization procedure is then inferred which exhibits important genes for the studied biological process.Theory and application with the SVM classifier were presented in Gadat and Younes, 2007 and we extend this method with CART. The classification error rates are computed on three famous public databases (Leukemia, Colon and Prostate) and compared with those from other wrapper methods (RFE, lo norm SVM, Random Forests). This allows the assessment of the statistical relevance of the proposed algorithm. Furthermore, a biological interpretation with the Ingenuity Pathway Analysis software outputs clearly shows that the gene selections from the different wrapper methods raise very relevant biological information, compared to a classical filter gene selection with T-test.

How metabolomics can contribute to bio-processes: a proof of concept study for biomarkers discovery in the context of nitrogen-starved microalgae grown in photobioreactors

Microalgae appear to be one of the most promising sustainable resources as alternative crops for the production of renewable transport fuel. The exploitation of this bioresource requires, however, a fine monitoring of the culture conditions, for example by using more relevant control variables than usual macroscopic indicators (biomass or pigment estimation). In this proof of concept study, we propose to search potential biomarkers of progressive nitrogen regime culture conditions using an untargeted metabolomic approach based on LC-HRMS combined to a non-invasive analysis based on FTIR spectroscopy. One microalgae model was investigated i.e. Chlamydomonas reinhardtii to characterize the effect of progressive nitrogen regime in batch culture conditions on its metabolome. FTIR allowed assessing the intracellular macrometabolic perturbations, highlighting the over-accumulation of carbohydrates. LC-HRMS complemented the macromolecular information by revealing the dependence of microalgae metabotypes on nitrogen regime conditions tested for cells culture. Patterns of significantly modulated metabolites were also detected during those slight contrasted nitrogen regimes and interesting features were structurally elucidated. This included metabolites belonging to the pantothenate, branched chain and aromatic amino acids pathways. In the last step of this proof of concept study, amino acid targets proposed by metabolomic investigations were assessed on nitrogen-limited continuous culture on photobioreactors. This was performed to test the validity of proposed targets in real small-scale industrial production conditions. Results were very encouraging and suggested the possibility of using potentially relevant metabolites as intracellular biomarkers only (tryptophan) or as both intra and extracellular biomarkers (e.g. 2-methylbutyric acid and ketoleucine).

Photobioreactor design for isotopic non-stationary 13C-metabolic flux analysis (INST 13C-MFA) under photoautotrophic conditions

Adaptive metabolic behavior of photoautotrophic microorganisms toward genetic and environmental perturbations can be interpreted in a quantitative depiction of carbon flow through a biochemical reaction network using isotopic non‐stationary 13C‐metabolic flux analysis (INST 13C‐MFA). To evaluate 13C‐metabolic flux maps for Chlamydomonas reinhardtii, an original experimental framework was designed allowing rapid, reliable collection of high‐quality isotopomer data against time. It involved (i) a short‐time 13C labeling injection device based on mixing control in a torus‐shaped photobioreactor with plug‐flow hydrodynamics allowing a sudden step‐change in the 13C proportion in the substrate feed and (ii) a rapid sampling procedure using an automatic fast filtration method coupled to a manual rapid liquid nitrogen quenching step. 13C‐substrate labeling enrichment was controlled through the total dissolved inorganic carbon concentration in the pulsed solution. First results were obtained from steady‐state continuous culture measurements allowing the characterization of the kinetics of label incorporation into light‐limited growing cells cultivated in a photobioreactor operating at the maximal biomass productivity for an incident photon flux density of 200 µmol m−2 s−1. 13C label incorporation was measured for 21 intracellular metabolites using IC‐MS/MS in 58 samples collected across a labeling experiment duration of 7 min. The fastest labeling rate was observed for 2/3‐phosphoglycerate with an apparent isotopic stationary state reached after 300 s. The labeling rate was consistent with the optimized mixing time of about 4.9 s inside the reactor and the shortest reliable sampling period assessed at 5 s. Biotechnol. Bioeng. 2012; 109: 3030–3040.

Detecting variants with Metabolic Design, a new software tool to design probes for explorative functional DNA microarray development.

BackgroundMicroorganisms display vast diversity, and each one has its own set of genes, cell components and metabolic reactions. To assess their huge unexploited metabolic potential in different ecosystems, we need high throughput tools, such as functional microarrays, that allow the simultaneous analysis of thousands of genes. However, most classical functional microarrays use specific probes that monitor only known sequences, and so fail to cover the full microbial gene diversity present in complex environments. We have thus developed an algorithm, implemented in the user-friendly program Metabolic Design, to design efficient explorative probes.ResultsFirst we have validated our approach by studying eight enzymes involved in the degradation of polycyclic aromatic hydrocarbons from the model strain Sphingomonas paucimobilis sp. EPA505 using a designed microarray of 8,048 probes. As expected, microarray assays identified the targeted set of genes induced during biodegradation kinetics experiments with various pollutants. We have then confirmed the identity of these new genes by sequencing, and corroborated the quantitative discrimination of our microarray by quantitative real-time PCR. Finally, we have assessed metabolic capacities of microbial communities in soil contaminated with aromatic hydrocarbons. Results show that our probe design (sensitivity and explorative quality) can be used to study a complex environment efficiently.ConclusionsWe successfully use our microarray to detect gene expression encoding enzymes involved in polycyclic aromatic hydrocarbon degradation for the model strain. In addition, DNA microarray experiments performed on soil polluted by organic pollutants without prior sequence assumptions demonstrate high specificity and sensitivity for gene detection. Metabolic Design is thus a powerful, efficient tool that can be used to design explorative probes and monitor metabolic pathways in complex environments, and it may also be used to study any group of genes. The Metabolic Design software is freely available from the authors and can be downloaded and modified under general public license.

REAL TIME INVESTIGATION OF THE FREEZING OF RAW POTATO BY NMR MICROIMAGING

Results are presented of an NMR microimaging study of the freezing of raw potato at 253 K. A fast radial imaging technique was used to obtain profiles along the sample radius. The profiles show that there is not a sharply defined ice front moving through the tissue, but rather a region where various fractions of ice and liquid water co‐exist whose spatial extent progressively increases. The results are compared with predictions of a generalized Plank model and with numerical solutions of the thermal diffusion equation. Although the integrated profile intensities could be fitted, neither model succeeds in accurately reproducing the image profiles. This suggests that more sophisticated models which take explicit account of the compartmentalized cellular and sub‐cellular nature of the tissue may be necessary for understanding the observed imaging data.