Innovative DendrisChips® Technology for a Syndromic Approach of In Vitro Diagnosis: Application to the Respiratory Infectious Diseases.

Clinical microbiology is experiencing the emergence of the syndromic approach of diagnosis. This paradigm shift will require innovative technologies to detect rapidly, and in a single sample, multiple pathogens associated with an infectious disease. Here, we report on a multiplex technology based on DNA-microarray that allows detecting and discriminating 11 bacteria implicated in respiratory tract infection. The process requires a PCR amplification of bacterial 16S rDNA, a 30 min hybridization step on species-specific oligoprobes covalently linked on dendrimers coated glass slides (DendriChips®) and a reading of the slides by a dedicated laser scanner. A diagnostic result is delivered in about 4 h as a predictive value of presence/absence of pathogens using a decision algorithm based on machine-learning method, which was constructed from hybridization profiles of known bacterial and clinical isolated samples and which can be regularly enriched with hybridization profiles from clinical samples. We demonstrated that our technology converged in more than 95% of cases with the microbiological culture for bacteria detection and identification.

Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework.

BACKGROUND: In omics data integration studies, it is common, for a variety of reasons, for some individuals to not be present in all data tables. Missing row values are challenging to deal with because most statistical methods cannot be directly applied to incomplete datasets. To overcome this issue, we propose a multiple imputation (MI) approach in a multivariate framework. In this study, we focus on multiple factor analysis (MFA) as a tool to compare and integrate multiple layers of information. MI involves filling the missing rows with plausible values, resulting in M completed datasets. MFA is then applied to each completed dataset to produce M different configurations (the matrices of coordinates of individuals). Finally, the M configurations are combined to yield a single consensus solution. RESULTS: We assessed the performance of our method, named MI-MFA, on two real omics datasets. Incomplete artificial datasets with different patterns of missingness were created from these data. The MI-MFA results were compared with two other approaches i.e., regularized iterative MFA (RI-MFA) and mean variable imputation (MVI-MFA). For each configuration resulting from these three strategies, the suitability of the solution was determined against the true MFA configuration obtained from the original data and a comprehensive graphical comparison showing how the MI-, RI- or MVI-MFA configurations diverge from the true configuration was produced. Two approaches i.e., confidence ellipses and convex hulls, to visualize and assess the uncertainty due to missing values were also described. We showed how the areas of ellipses and convex hulls increased with the number of missing individuals. A free and easy-to-use code was proposed to implement the MI-MFA method in the R statistical environment. CONCLUSIONS: We believe that MI-MFA provides a useful and attractive method for estimating the coordinates of individuals on the first MFA components despite missing rows. MI-MFA configurations were close to the true configuration even when many individuals were missing in several data tables. This method takes into account the uncertainty of MI-MFA configurations induced by the missing rows, thereby allowing the reliability of the results to be evaluated.

Engineering of a Synthetic Metabolic Pathway for the Assimilation of (d)-Xylose into Value-Added Chemicals.

A synthetic pathway for (d)-xylose assimilation was stoichiometrically evaluated and implemented in Escherichia coli strains. The pathway proceeds via isomerization of (d)-xylose to (d)-xylulose, phosphorylation of (d)-xylulose to obtain (d)-xylulose-1-phosphate (X1P), and aldolytic cleavage of the latter to yield glycolaldehyde and DHAP. Stoichiometric analyses showed that this pathway provides access to ethylene glycol with a theoretical molar yield of 1. Alternatively, both glycolaldehyde and DHAP can be converted to glycolic acid with a theoretical yield that is 20% higher than for the exclusive production of this acid via the glyoxylate shunt. Simultaneous expression of xylulose-1 kinase and X1P aldolase activities, provided by human ketohexokinase-C and human aldolase-B, respectively, restored growth of a (d)-xylulose-5-kinase mutant on xylose. This strain produced ethylene glycol as the major metabolic endproduct. Metabolic engineering provided strains that assimilated the entire C2 fraction into the central metabolism or that produced 4.3 g/L glycolic acid at a molar yield of 0.9 in shake flasks.

Optimization of ethylene glycol production from (D)-xylose via a synthetic pathway implemented in Escherichia coli.

BACKGROUND: Ethylene glycol (EG) is a bulk chemical that is mainly used as an anti-freezing agent and a raw material in the synthesis of plastics. Production of commercial EG currently exclusively relies on chemical synthesis using fossil resources. Biochemical production of ethylene glycol from renewable resources may be more sustainable. RESULTS: Herein, a synthetic pathway is described that produces EG in Escherichia coli through the action of (D)-xylose isomerase, (D)-xylulose-1-kinase, (D)-xylulose-1-phosphate aldolase, and glycolaldehyde reductase. These reactions were successively catalyzed by the endogenous xylose isomerase (XylA), the heterologously expressed human hexokinase (Khk-C) and aldolase (Aldo-B), and an endogenous glycolaldehyde reductase activity, respectively, which we showed to be encoded by yqhD. The production strain was optimized by deleting the genes encoding for (D)-xylulose-5 kinase (xylB) and glycolaldehyde dehydrogenase (aldA), and by overexpressing the candidate glycolaldehyde reductases YqhD, GldA, and FucO. The strain overproducing FucO was the best EG producer reaching a molar yield of 0.94 in shake flasks, and accumulating 20 g/L EG with a molar yield and productivity of 0.91 and 0.37 g/(L.h), respectively, in a controlled bioreactor under aerobic conditions. CONCLUSIONS: We have demonstrated the feasibility to produce EG from (D)-xylose via a synthetic pathway in E. coli at approximately 90 % of the theoretical yield.

Genome-wide analyses of Shavenbaby target genes reveals distinct features of enhancer organization.

BACKGROUND: Developmental programs are implemented by regulatory interactions between Transcription Factors (TFs) and their target genes, which remain poorly understood. While recent studies have focused on regulatory cascades of TFs that govern early development, little is known about how the ultimate effectors of cell differentiation are selected and controlled. We addressed this question during late Drosophila embryogenesis, when the finely tuned expression of the TF Ovo/Shavenbaby (Svb) triggers the morphological differentiation of epidermal trichomes. RESULTS: We defined a sizeable set of genes downstream of Svb and used in vivo assays to delineate 14 enhancers driving their specific expression in trichome cells. Coupling computational modeling to functional dissection, we investigated the regulatory logic of these enhancers. Extending the repertoire of epidermal effectors using genome-wide approaches showed that the regulatory models learned from this first sample are representative of the whole set of trichome enhancers. These enhancers harbor remarkable features with respect to their functional architectures, including a weak or non-existent clustering of Svb binding sites. The in vivo function of each site relies on its intimate context, notably the flanking nucleotides. Two additional cis-regulatory motifs, present in a broad diversity of composition and positioning among trichome enhancers, critically contribute to enhancer activity. CONCLUSIONS: Our results show that Svb directly regulates a large set of terminal effectors of the remodeling of epidermal cells. Further, these data reveal that trichome formation is underpinned by unexpectedly diverse modes of regulation, providing fresh insights into the functional architecture of enhancers governing a terminal differentiation program.

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.

BACKGROUND: Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits. RESULTS: A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework. CONCLUSIONS: sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets.

Linear covariance models to examine the determinants of protein levels in Lactococcus lactis.

When confronted to environmental changes, microorganisms adjust protein levels in order to adapt their growth and metabolic performances. Biological mechanisms involved in protein regulation are extremely complex and still poorly understood. This study aims at the identification, by statistical modelling, of the major determinants of protein concentrations in a bacterial model Lactococcus lactis. Protein concentrations were predicted by covariance models taking into account various quantitative and qualitative parameters. Best models were selected thanks to Akaïke Information Criterion. For protein estimation, we found that the sequence-related feature Codon Adaptative Index was a more influential parameter than the transcript amount, suggesting the control by genetic determinism is stronger than by metabolic adaptation. In addition, protein length, aromaticity but also their biological functions, were proved to have unexpected influences on protein concentrations. These protein determinants were for the first time demonstrated to be not constant and depended on the adaptation process, the main difference between permanent and transient adaptations being detected for regulatory protein concentrations. With the growing accumulation of omics data this statistical method appears to be a valuable tool to understand biological networks and their regulations. This approach was applied to study the translation of proteins but can be extended to other metabolic processes and is also adaptable to other microorganisms.

Sparse canonical methods for biological data integration: application to a cross-platform study.

BACKGROUND: In the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. In this high dimensional setting, variable selection is crucial to give interpretable results. We focus on a sparse Partial Least Squares approach (sPLS) to handle two-block data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been developed either for a regression or a canonical correlation framework and includes a built-in procedure to select variables while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips) were used to study the transcriptome of sixty cancer cell lines. RESULTS: We compare the results obtained with two other sparse or related canonical correlation approaches: CCA with Elastic Net penalization (CCA-EN) and Co-Inertia Analysis (CIA). The latter does not include a built-in procedure for variable selection and requires a two-step analysis. We stress the lack of statistical criteria to evaluate canonical correlation methods, which makes biological interpretation absolutely necessary to compare the different gene selections. We also propose comprehensive graphical representations of both samples and variables to facilitate the interpretation of the results. CONCLUSION: sPLS and CCA-EN selected highly relevant genes and complementary findings from the two data sets, which enabled a detailed understanding of the molecular characteristics of several groups of cell lines. These two approaches were found to bring similar results, although they highlighted the same phenomenons with a different priority. They outperformed CIA that tended to select redundant information.

A sparse PLS for variable selection when integrating omics data.

Recent biotechnology advances allow for multiple types of omics data, such as transcriptomic, proteomic or metabolomic data sets to be integrated. The problem of feature selection has been addressed several times in the context of classification, but needs to be handled in a specific manner when integrating data. In this study, we focus on the integration of two-block data that are measured on the same samples. Our goal is to combine integration and simultaneous variable selection of the two data sets in a one-step procedure using a Partial Least Squares regression (PLS) variant to facilitate the biologists' interpretation. A novel computational methodology called ;;sparse PLS" is introduced for a predictive analysis to deal with these newly arisen problems. The sparsity of our approach is achieved with a Lasso penalization of the PLS loading vectors when computing the Singular Value Decomposition. Sparse PLS is shown to be effective and biologically meaningful. Comparisons with classical PLS are performed on a simulated data set and on real data sets. On one data set, a thorough biological interpretation of the obtained results is provided. We show that sparse PLS provides a valuable variable selection tool for highly dimensional data sets.

Growth rate regulated genes and their wide involvement in the Lactococcus lactis stress responses.

BACKGROUND: The development of transcriptomic tools has allowed exhaustive description of stress responses. These responses always superimpose a general response associated to growth rate decrease and a specific one corresponding to the stress. The exclusive growth rate response can be achieved through chemostat cultivation, enabling all parameters to remain constant except the growth rate. RESULTS: We analysed metabolic and transcriptomic responses of Lactococcus lactis in continuous cultures at different growth rates ranging from 0.09 to 0.47 h-1. Growth rate was conditioned by isoleucine supply. Although carbon metabolism was constant and homolactic, a widespread transcriptomic response involving 30% of the genome was observed. The expression of genes encoding physiological functions associated with biogenesis increased with growth rate (transcription, translation, fatty acid and phospholipids metabolism). Many phages, prophages and transposon related genes were down regulated as growth rate increased. The growth rate response was compared to carbon and amino-acid starvation transcriptomic responses, revealing constant and significant involvement of growth rate regulations in these two stressful conditions (overlap 27%). Two regulators potentially involved in the growth rate regulations, llrE and yabB, have been identified. Moreover it was established that genes positively regulated by growth rate are preferentially located in the vicinity of replication origin while those negatively regulated are mainly encountered at the opposite, thus indicating the relationship between genes expression and their location on chromosome. Although stringent response mechanism is considered as the one governing growth deceleration in bacteria, the rigorous comparison of the two transcriptomic responses clearly indicated the mechanisms are distinct. CONCLUSION: This work of integrative biology was performed at the global level using transcriptomic analysis obtained in various growth conditions. It raised the importance of growth rate regulations in bacteria but also participated to the elucidation of the involved mechanism. Though the mechanism controlling growth rate is not yet fully understood in L. lactis, one expected regulatory mechanism has been ruled out, two potential regulators have been pointed out and the involvement of gene location on the chromosome has also been found to be involved in the expression regulation of these growth related genes.

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.

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

UNLABELLED: Peroxisome proliferator-activated receptor-alpha (PPARalpha) 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 PPARalpha-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 PPARalpha-/- mice, a model of progressive hepatic triglyceride accumulation. These beneficial effects were associated, in particular, with dietary regulation of Delta9-desaturase in both genotypes, and with a newly identified PPARalpha-dependent regulation of lipin. Furthermore, hepatic levels of 18-carbon essential FAs (C18:2omega6 and C18:3omega3) were elevated in PPARalpha-/- mice, possibly due to the observed reduction in expression of the Delta6-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 PPARalpha as a dietary FA sensor and highlighted its importance in regulating hepatic FA content and composition.

Identification of biomarkers of human pancreatic adenocarcinomas by expression profiling and validation with gene expression analysis in endoscopic ultrasound-guided fine needle aspiration samples.

AIM: To compare gene expression profiles of pancreatic adenocarcinoma tissue specimens, human pancreatic and colon adenocarcinoma and leukemia cell lines and normal pancreas samples in order to distinguish differentially expressed genes and to validate the differential expression of a subset of genes by quantitative real-time RT-PCR (RT-QPCR) in endoscopic ultrasound-guided fine needle aspiration (EUS-guided FNA) specimens. METHODS: Commercially dedicated cancer cDNA macroarrays (Atlas Human Cancer 1.2) containing 1176 genes were used. Different statistical approaches (hierarchical clustering, principal component analysis (PCA) and SAM) were used to analyze the expression data. RT-QPCR and immunohistochemical studies were used for validation of results. RESULTS: RT-QPCR validated the increased expression of LCN2 (lipocalin 2) and for the first time PLAT (tissue-type plasminogen activator or tPA) in malignant pancreas as compared with normal pancreas. Immunohistochemical analysis confirmed the increased expression of LCN2 protein localized in epithelial cells of ducts invaded by carcinoma. The analysis of PLAT and LCN2 transcripts in 12 samples obtained through EUS-guided FNA from patients with pancreatic adenocarcinoma showed significantly increased expression levels in comparison with those found in normal tissues, indicating that a sufficient amount of high quality RNA can be obtained with this technique. CONCLUSION: Expression profiling is a useful method to identify biomarkers and potential target genes. Molecular analysis of EUS-guided FNA samples in pancreatic cancer appears as a valuable strategy for the diagnosis of pancreatic adenocarcinomas.