Sébastien Déjean

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.

Novel aspects of PPARα-mediated regulation of lipid and xenobiotic metabolism revealed through a nutrigenomic study

Peroxisome proliferator‐activated receptor‐α (PPARα) is a major transcriptional regulator of lipid metabolism. It is activated by diverse chemicals such as fatty acids (FAs) and regulates the expression of numerous genes in organs displaying high FA catabolic rates, including the liver. The role of this nuclear receptor as a sensor of whole dietary fat intake has been inferred, mostly from high‐fat diet studies. To delineate its function under low fat intake conditions (4.8% w/w), we studied the effects of five regimens with contrasted FA compositions on liver lipids and hepatic gene expression in wild‐type and PPARα‐deficient mice. Diets containing polyunsaturated FAs reduced hepatic fat stores in wild‐type mice. Only sunflower, linseed, and fish oil diets lowered hepatic lipid stores in PPARα−/− mice, a model of progressive hepatic triglyceride accumulation. These beneficial effects were associated, in particular, with dietary regulation of Δ9‐desaturase in both genotypes, and with a newly identified PPARα‐dependent regulation of lipin. Furthermore, hepatic levels of 18‐carbon essential FAs (C18:2ω6 and C18:3ω3) were elevated in PPARα−/− mice, possibly due to the observed reduction in expression of the Δ6‐desaturase and of enoyl‐coenzyme A isomerases. Effects of diet and genotype were also observed on the xenobiotic metabolism‐related genes Cyp3a11 and CAR. Conclusion: Together, our results suggest that dietary FAs represent—even under low fat intake conditions—a beneficial strategy to reduce hepatic steatosis. Under such conditions, we established the role of PPARα as a dietary FA sensor and highlighted its importance in regulating hepatic FA content and composition. (HEPATOLOGY 2007;45:767–7777.)

StatFingerprints: a friendly graphical interface program for processing and analysis of microbial fingerprint profiles

Molecular fingerprint methods are widely used to compare microbial communities in various habitats. The free program StatFingerprints can import, process, and display fingerprint profiles and perform numerous statistical analyses on them, and also estimate diversity indexes. StatFingerprints works with the free program R, providing an environment for statistical computing and graphics. No programming knowledge is required to use StatFingerprints, thanks to its friendly graphical user interface. StatFingerprints is useful for analysing the effect of a controlled factor on the microbial community and for establishing the relationships between the microbial community and the parameters of its environment. Multivariate analyses include ordination, clustering methods and hypothesis‐driven tests like 50–50 multivariate analysis of variance, analysis of similarity or similarity percentage procedure and the program offers the possibility of plotting ordinations as a three‐dimensional display.

Visualising associations between paired ‘omics’ data sets

BackgroundEach omics platform is now able to generate a large amount of data. Genomics, proteomics, metabolomics, interactomics are compiled at an ever increasing pace and now form a core part of the fundamental systems biology framework. Recently, several integrative approaches have been proposed to extract meaningful information. However, these approaches lack of visualisation outputs to fully unravel the complex associations between different biological entities.ResultsThe multivariate statistical approaches ‘regularized Canonical Correlation Analysis’ and ‘sparse Partial Least Squares regression’ were recently developed to integrate two types of highly dimensional ‘omics’ data and to select relevant information. Using the results of these methods, we propose to revisit few graphical outputs to better understand the relationships between two ‘omics’ data and to better visualise the correlation structure between the different biological entities. These graphical outputs include Correlation Circle plots, Relevance Networks and Clustered Image Maps. We demonstrate the usefulness of such graphical outputs on several biological data sets and further assess their biological relevance using gene ontology analysis.ConclusionsSuch graphical outputs are undoubtedly useful to aid the interpretation of these promising integrative analysis tools and will certainly help in addressing fundamental biological questions and understanding systems as a whole.AvailabilityThe graphical tools described in this paper are implemented in the freely available R package mixOmics and in its associated web application.

The transcriptional coactivator peroxisome proliferator activated receptor (PPAR)gamma coactivator-1 alpha and the nuclear receptor PPAR alpha control the expression of glycerol kinase and metabolism genes independently of PPAR gamma activation in human white adipocytes.

OBJECTIVE—The purpose of this work was to determine the pattern of genes regulated by peroxisome proliferator–activated receptor (PPAR) γ coactivator 1α (PGC-1α) in human adipocytes and the involvement of PPARα and PPARγ in PGC-1α transcriptional action. RESEARCH DESIGN AND METHODS—Primary cultures of human adipocytes were transduced with a PGC-1α adenovirus and treated with PPARγ and PPARα agonists. Variation in gene expression was assessed using pangenomic microarrays and quantitative RT-PCR. To investigate glycerol kinase (GyK), a target of PGC-1α, we measured enzymatic activity and glycerol incorporation into triglycerides. In vivo studies were performed on wild-type and PPARα−/− mice. The GyK promoter was studied using chromatin immunoprecipitation and promoter reporter gene assays. RESULTS—Among the large number of genes regulated by PGC-1α independently of PPARγ, new targets involved in metabolism included the gene encoding GyK. The induction of GyK by PGC-1α was observed at the levels of mRNA, enzymatic activity, and glycerol incorporation into triglycerides. PPARα was also upregulated by PGC-1α. Its activation led to an increase in GyK expression and activity. PPARα was shown to bind and activate the GyK promoter. Experiments in mice confirmed the role of PGC-1α and PPARα in the regulation of GyK in vivo. CONCLUSIONS—This work uncovers novel pathways regulated by PGC-1α and reveals that PPARα controls gene expression in human white adipocytes. The induction of GyK by PGC-1α and PPARα may promote a futile cycle of triglyceride hydrolysis and fatty acid reesterification.

Contribution of Energy Restriction and Macronutrient Composition to Changes in Adipose Tissue Gene Expression during Dietary Weight-Loss Programs in Obese Women

CONTEXT Hypoenergetic diets are used to reduce body fat mass and metabolic risk factors in obese subjects. The molecular changes in adipose tissue associated with weight loss and specifically related to the dietary composition are poorly understood. OBJECTIVE We investigated adipose tissue gene expression from human obese women according to energy deficit and the fat and carbohydrate content of the diet. DESIGN AND SETTING Obese subjects recruited among eight European clinical centers were followed up 10 wk of either a low-fat (high carbohydrate) or a moderate-fat (low carbohydrate) hypoenergetic diet. SUBJECTS Two sets of 47 women in each dietary arm were selected among 648 subjects matched for anthropometric and biological parameters. MAIN OUTCOME MEASURE We measured adipose tissue gene expression changes in one set using a candidate gene approach. The other set was used to survey 24,469 transcripts using DNA microarrays. Results were analyzed using dedicated statistical methods. Diet-sensitive regulations were confirmed on the other set of subjects. RESULTS The two diets induced similar weight loss and similar changes for most of the biological variables except for components of the blood lipid profile. One thousand genes were regulated by energy restriction. We validated an effect of the fat to carbohydrate ratio for five genes (FABP4, NR3C1, SIRT3, FNTA, and GABARAPL2) with increased expression during the moderate-fat diet. CONCLUSIONS Energy restriction had a more pronounced impact on variations in human adipose tissue gene expression than macronutrient composition. The macronutrient-sensitive regulation of a subset of genes may influence adipose tissue function and metabolic response.

Size control of the Drosophila hematopoietic niche by bone morphogenetic protein signaling reveals parallels with mammals

The Drosophila melanogaster larval hematopoietic organ, the lymph gland, is a model to study in vivo the function of the hematopoietic niche. A small cluster of cells in the lymph gland, the posterior signaling center (PSC), maintains the balance between hematopoietic progenitors (prohemocytes) and their differentiation into specialized blood cells (hemocytes). Here, we show that Decapentaplegic/bone morphogenetic protein (Dpp/BMP) signaling activity in PSC cells controls niche size. In the absence of BMP signaling, the number of PSC cells increases. Correlatively, no hemocytes differentiate. Controlling PSC size is, thus, essential for normal blood cell homeostasis. Activation of BMP signaling in the PSC requires expression of the Dally-like heparan-sulfate proteoglycan, under the control of the Collier/early B-cell factor (EBF) transcription factor. A Dpp > dpp autoregulatory loop maintains BMP signaling, which limits PSC cell proliferation by repressing the protooncogene dmyc. Dpp antagonizes activity of wingless (Wg)/Wnt signaling, which positively regulates the number of PSC cells via the control of Dmyc expression. Together, our data show that Collier controls hemocyte homeostasis via coordinate regulation of PSC cell number and PSC signaling to prohemocytes. In mouse, EBF2, BMP, and Wnt signaling in osteoblasts is required for the proper number of niche and hematopoietic stem cells. Our findings bring insights to niche size control and draw parallels between Drosophila and mammalian hematopoiesis.

Nonconvex Regularizations for Feature Selection in Ranking With Sparse SVM

Feature selection in learning to rank has recently emerged as a crucial issue. Whereas several preprocessing approaches have been proposed, only a few have focused on integrating feature selection into the learning process. In this paper, we propose a general framework for feature selection in learning to rank using support vector machines with a sparse regularization term. We investigate both classical convex regularizations, such as ℓ1 or weighted ℓ1, and nonconvex regularization terms, such as log penalty, minimax concave penalty, or ℓp pseudo-norm with p<;1. Two algorithms are proposed: the first, an accelerated proximal approach for solving the convex problems, and, the second, a reweighted ℓ1 scheme to address nonconvex regularizations. We conduct intensive experiments on nine datasets from Letor 3.0 and Letor 4.0 corpora. Numerical results show that the use of nonconvex regularizations we propose leads to more sparsity in the resulting models while preserving the prediction performance. The number of features is decreased by up to a factor of 6 compared to the ℓ1 regularization. In addition, the software is publicly available on the web.

Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives

Microarray data acquired during time-course experiments allow the temporal variations in gene expression to be monitored. An original postprandial fasting experiment was conducted in the mouse and the expression of 200 genes was monitored with a dedicated macroarray at 11 time points between 0 and 72 hours of fasting. The aim of this study was to provide a relevant clustering of gene expression temporal profiles. This was achieved by focusing on the shapes of the curves rather than on the absolute level of expression. Actually, we combined spline smoothing and first derivative computation with hierarchical and partitioning clustering. A heuristic approach was proposed to tune the spline smoothing parameter using both statistical and biological considerations. Clusters are illustrated a posteriori through principal component analysis and heatmap visualization. Most results were found to be in agreement with the literature on the effects of fasting on the mouse liver and provide promising directions for future biological investigations.

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.