VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data

Delmar, Paul; Robin, Stéphane; Daudin, Jean Jacques

doi:10.1093/bioinformatics/bti023

Cited by 80 publications

(84 citation statements)

References 12 publications

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“…1A and B). Using a novel statistical method for the differential analysis of gene expression (16) and cluster analysis, we defined six mutually exclusive groups of genes (Supplementary Fig. 1; Supplementary Table 2).…”

Section: Resultsmentioning

confidence: 99%

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The Transcriptional Coactivator Peroxisome Proliferator–Activated Receptor (PPAR)γ Coactivator-1α and the Nuclear Receptor PPARα Control the Expression of Glycerol Kinase and Metabolism Genes Independently of PPARγ Activation in Human White Adipocytes

et al. 2007

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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.

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Section: Resultsmentioning

confidence: 99%

“…Between 30,313 and 31,453 spots were recovered from each array slide. To assess differentially expressed genes, we used the Varmixt method that allows a control of the heterogeneity of variance (16).…”

Section: Methodsmentioning

confidence: 99%

The Transcriptional Coactivator Peroxisome Proliferator–Activated Receptor (PPAR)γ Coactivator-1α and the Nuclear Receptor PPARα Control the Expression of Glycerol Kinase and Metabolism Genes Independently of PPARγ Activation in Human White Adipocytes

et al. 2007

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“…html). Differential analysis was carried out separately for each comparison between two cell samples, using the VM method [VarMixt package (Delmar et al, 2005)], together with the Benjamini and Yekutieli (Reiner et al, 2003) P-value adjustment method. Only genes with significant (P,0.05) fold changes in expression were taken into consideration.…”

Section: Methodsmentioning

confidence: 99%

Transcriptional profiling of Legionella pneumophila biofilm cells and the influence of iron on biofilm formation

et al. 2008

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In aquatic environments, biofilms constitute an ecological niche where Legionella pneumophila persists as sessile cells. However, very little information on the sessile mode of life of L. pneumophila is currently available. We report here the development of a model biofilm of L. pneumophila strain Lens and the first transcriptome analysis of L. pneumophila biofilm cells. Global gene expression analysis of sessile cells as compared to two distinct populations of planktonic cells revealed that a substantial proportion of L. pneumophila genes is differentially expressed, as 2.3 % of the 2932 predicted genes exhibited at least a twofold change in gene expression. Comparison with previous results defining the gene expression profile of replicative-and transmissive-phase Legionella suggests that sessile cells resemble bacteria in the replicative phase. Further analysis of the most strongly regulated genes in sessile cells identified two induced gene clusters. One contains genes that encode alkyl hydroperoxide reductases known to act against oxidative stress. The second encodes proteins similar to PvcA and PvcB that are involved in siderophore biosynthesis in Pseudomonas aeruginosa. Since iron has been reported to modify biofilm formation in other species, we further focused on iron control of gene expression and biofilm formation. Among the genes showing the greatest differences in expression between planktonic cells and biofilm, only pvcA and pvcB were regulated by iron concentration. A DpvcA L. pneumophila mutant showed no changes in biofilm formation compared to the wild-type, suggesting that the pvcA product is not mandatory for biofilm formation. However, biofilm formation by L. pneumophila wild-type and a DpvcA strain was clearly inhibited in iron-rich conditions.

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“…An estimation of the variance for each protein observation was performed using the mixed model of Delmar et al (2005aDelmar et al ( , 2005b implemented in the anapuce R package and consisting in the estimation of several groups of proteins with the same variance. Finally, a classical FDR procedure was applied to correct for multiple comparison tests according to the procedure of Benjamini and Hachberg (1995) implemented in the anapuce R package.…”

Section: Normalization and Differential Abundance Analysismentioning

confidence: 99%

Proteomic Analysis of Chloroplast-to-Chromoplast Transition in Tomato Reveals Metabolic Shifts Coupled with Disrupted Thylakoid Biogenesis Machinery and Elevated Energy-Production Components

et al. 2012

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A comparative proteomic approach was performed to identify differentially expressed proteins in plastids at three stages of tomato (Solanum lycopersicum) fruit ripening (mature-green, breaker, red). Stringent curation and processing of the data from three independent replicates identified 1,932 proteins among which 1,529 were quantified by spectral counting. The quantification procedures have been subsequently validated by immunoblot analysis of six proteins representative of distinct metabolic or regulatory pathways. Among the main features of the chloroplast-to-chromoplast transition revealed by the study, chromoplastogenesis appears to be associated with major metabolic shifts: (1) strong decrease in abundance of proteins of light reactions (photosynthesis, Calvin cycle, photorespiration) and carbohydrate metabolism (starch synthesis/degradation), mostly between breaker and red stages and (2) increase in terpenoid biosynthesis (including carotenoids) and stress-response proteins (ascorbate-glutathione cycle, abiotic stress, redox, heat shock). These metabolic shifts are preceded by the accumulation of plastid-encoded acetyl Coenzyme A carboxylase D proteins accounting for the generation of a storage matrix that will accumulate carotenoids. Of particular note is the high abundance of proteins involved in providing energy and in metabolites import. Structural differentiation of the chromoplast is characterized by a sharp and continuous decrease of thylakoid proteins whereas envelope and stroma proteins remain remarkably stable. This is coincident with the disruption of the machinery for thylakoids and photosystem biogenesis (vesicular trafficking, provision of material for thylakoid biosynthesis, photosystems assembly) and the loss of the plastid division machinery. Altogether, the data provide new insights on the chromoplast differentiation process while enriching our knowledge of the plant plastid proteome.

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VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data

Abstract: http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique/outil.html.

Cited by 80 publications

References 12 publications

The Transcriptional Coactivator Peroxisome Proliferator–Activated Receptor (PPAR)γ Coactivator-1α and the Nuclear Receptor PPARα Control the Expression of Glycerol Kinase and Metabolism Genes Independently of PPARγ Activation in Human White Adipocytes

The Transcriptional Coactivator Peroxisome Proliferator–Activated Receptor (PPAR)γ Coactivator-1α and the Nuclear Receptor PPARα Control the Expression of Glycerol Kinase and Metabolism Genes Independently of PPARγ Activation in Human White Adipocytes

Transcriptional profiling of Legionella pneumophila biofilm cells and the influence of iron on biofilm formation

Proteomic Analysis of Chloroplast-to-Chromoplast Transition in Tomato Reveals Metabolic Shifts Coupled with Disrupted Thylakoid Biogenesis Machinery and Elevated Energy-Production Components

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