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Colon cancers are characterized by aberrant gene expression signatures associated with disease initiation and progression. Identification of aberrant gene expression associated with colon carcinogenesis has increased significantly with application of gene array technologies. Downstream processing of these data has been hindered by the lack of robust multiplexed gene quantitative technologies facilitating study of the identified multiple gene targets. The GenomeLab Genetic Analysis System presents a novel technology platform for quantitative multiplexed gene expression analysis. This report describes the custom design of a GeXP multiplexed assay used to assess expression profiles of 14 inflammatory gene targets in normal, polyp, and tumor tissue. Characteristic normal, polyp, and tumor tissue gene expression profiles were obtained. Statistical analysis confirmed comparable relative quantitation of gene expression using the GeXP, macroarray, and single-plex real-time polymerase chain reaction assays. GeXP assays may be usefully applied in clinical and regulatory studies of multiple gene targets. This system permits custom-design options for relative quantification of multiple gene target expression, simultaneously in a single reaction, using nanogram quantities of total RNA template. The system provides an approach to advance the study of multiple targets identified from gene array analysis with potential for characterizing gene expression signatures in clinical diagnostics.

“ Roseburia inulinivorans ” is an anaerobic polysaccharide-utilizing firmicute bacterium from the human colon that was identified as a producer of butyric acid during growth on glucose, starch, or inulin. R. inulinivorans A2-194 is also able to grow on the host-derived sugar fucose, following a lag period, producing propionate and propanol as additional fermentation products. A shotgun genomic microarray was constructed and used to investigate the switch in gene expression that is involved in changing from glucose to fucose utilization. This revealed a set of genes coding for fucose utilization, propanediol utilization, and the formation of propionate and propanol that are up-regulated during growth on fucose. These include homologues of genes that are implicated in polyhedral body formation in Salmonella enterica . Dehydration of the intermediate 1,2-propanediol involves an enzyme belonging to the new B 12 -independent glycerol dehydratase family, in contrast to S. enterica , which relies on a B 12 -dependent enzyme. A typical gram-positive agr -type quorum-sensing system was also up-regulated in R. inulinivorans during growth on fucose. Despite the lack of genome sequence information for this commensal bacterium, microarray analysis has provided a powerful tool for obtaining new information on its metabolic capabilities.

SUMMARY Many small mammals respond to seasonal changes in photoperiod by altering body mass and adiposity. These animals may provide valuable models for understanding the regulation of energy balance. Here, we present data on the field vole (Microtus agrestis) – a previously uncharacterised example of photoperiod-induced changes in body mass. We examined the effect of increased day length on body mass, food intake, apparent digestive efficiency,body composition, de novo lipogenesis and fatty acid composition of adipose tissue in cold-acclimated (8°C) male field voles by transferring them from a short (SD, 8 h:16 h L:D) to long day photoperiod (LD, 16 h:8 h L:D). During the first 4 weeks of exposure to LD, voles underwent a substantial increase in body mass, after which the average difference between body masses of LD and SD voles stabilized at 7.5 g. This 24.8% increase in body mass reflected significant increases in absolute amounts of all body components, including dry fat mass, dry lean mass and body water mass. After correcting body composition and organ morphology data for the differences in body mass, only gonads (testes and seminal vesicles) were enlarged due to photoperiod treatment. To meet energetic demands of deposition and maintenance of extra tissue, voles adjusted their food intake to an increasing body mass and improved their apparent digestive efficiency. Consequently, although mass-corrected food intake did not differ between the photoperiod groups, the LD voles undergoing body mass increase assimilated on average 8.4 kJ day-1 more than animals maintained in SD. The majority(73–77%) of the fat accumulated as adipose tissue had dietary origin. The rate of de novo lipogenesis and fatty acid composition of adipose tissue were not affected by photoperiod. The most important characteristics of the photoperiodic regulation of energy balance in the field vole are the clear delineation between phases where animals regulate body mass at two different levels and the rate at which animals are able to switch between different levels of energy homeostasis. Our data indicate that the field vole may provide an attractive novel animal model for investigation of the regulation of body mass and energy homeostasis at both organism and molecular levels.

Mild dietary zinc deprivation in humans and rodents has little effect on blood plasma zinc levels, and yet cellular consequences of zinc depletion can be detected in vascular and other tissues. We proposed that a zinc-regulated humoral factor might mediate the effects of zinc deprivation. Using a novel approach, primary rat vascular smooth muscle cells (VSMCs) were treated with plasma from zinc-deficient (1 mg Zn/kg) or zinc-adequate (35 mg Zn/kg, pair-fed) adult male rats, and zinc levels were manipulated to distinguish direct and indirect effects of plasma zinc. Gene expression changes were analyzed by microarray and qPCR, and incubation of VSMCs with blood plasma from zinc-deficient rats strongly changed the expression of2500 genes, compared to incubation of cells with zinc-adequate rat plasma. We demonstrated that this effect was caused by a low-molecular-weight (∼2-kDa) zinc-regulated humoral factor but that changes in gene expression were mostly reversed by adding zinc back to zinc-deficient plasma. Strongly regulated genes were overrepresented in pathways associated with immune function and development. We conclude that zinc deficiency induces the production of a low-molecular-weight humoral factor whose influence on VSMC gene expression is blocked by plasma zinc. This factor is therefore under dual control by zinc.

The objective of this study was to characterize differences in the secretome of human omental compared with subcutaneous adipose tissue using global gene expression profiling. Gene expression was measured using Affymetrix microarrays (Affymetrix, Santa Clara, CA) in subcutaneous and omental adipose tissue in two independent experiments (n = 5 and n = 3 independent subjects; n = 16 arrays in total, 2 for each subject). Predictive bioinformatic algorithms were employed to identify secreted proteins. Microarray analysis identified 22 gene probe sets whose expression was significantly different with a fold change (FC) greater than 5 in expression in both experiments between omental and subcutaneous adipose tissue. Using bioinformatic predictive programs 11 of these 22 probe sets potentially coded for secreted proteins. Pathway network analysis of the secreted proteins showed that three of the proteins are part of a common pathway network. These proteins gremlin 1 (GREM1), pleiotrophin (PTN), and secretory leukocyte peptidase inhibitor (SLPI) are expressed respectively 43×, 23×, and 5× in omental adipose tissue relative to subcutaneous adipose tissue as determined by real-time PCR. The presence of GREM1, PTN, and SLPI protein in human adipose tissue was confirmed by western blotting. All three proteins are expressed in the human Simpson-Golabi-Behmel syndrome (SGBS) preadipocyte cell line. The expression of GREM1, PTN, and SLPI changed with the differentiation of the preadipocytes into mature adipocytes. Gene expression coupled with predictive bioinformatic algorithms have identified several genes coding for secreted proteins which are expressed differently in omental adipose tissue compared to subcutaneous adipose tissue proving a valid alternative approach to help further define the adipocyte secretome.

The integration of multiple high-dimensional data sets (omics data) has been a very active but challenging area of bioinformatics research in recent years. Various adaptations of non-standard multivariate statistical tools have been suggested that allow to analyze and visualize such data sets simultaneously. However, these methods typically can deal with two data sets only, whereas systems biology experiments often generate larger numbers of high-dimensional data sets. For this reason, we suggest an explorative analysis of similarity between data sets as an initial analysis steps. This analysis is based on the RV coefficient, a matrix correlation, that can be interpreted as a generalization of the squared correlation from two single variables to two sets of variables. It has been shown before however that the high-dimensionality of the data introduces substantial bias to the RV.We therefore introduce an alternative version, the adjusted RV, which is unbiased in the case of independent data sets. We can also show that in many situations, particularly for very high-dimensional data sets, the adjusted RV is a better estimator than previously RV versions in terms of the mean square error and the power of the independence test based on it.We demonstrate the usefulness of the adjusted RV by applying it to data set of 19 different multivariate data sets from a systems biology experiment. The pairwise RV values between the data sets define a similarity matrix that we can use as an input to a hierarchical clustering or a multi-dimensional scaling. We show that this reveals biological meaningful subgroups of data sets in our study.

Roseburia inulinivorans is a recently identified motile representative of the Firmicutes that contributes to butyrate formation from a variety of dietary polysaccharide substrates in the human large intestine. Microarray analysis was used here to investigate substrate-driven gene-expression changes in R. inulinivorans A2-194. A cluster of fructo-oligosaccharide/inulin utilization genes induced during growth on inulin included one encoding a β-fructofuranosidase protein that was prominent in the proteome of inulin-grown cells. This cluster also included a 6-phosphofructokinase and an ABC transport system, whereas a distinct inulin-induced 1-phosphofructokinase was linked to a fructose-specific phosphoenolpyruvate-dependent sugar phosphotransferase system (PTS II transport enzyme). Real-time PCR analysis showed that the β-fructofuranosidase and adjacent ABC transport protein showed greatest induction during growth on inulin, whereas the 1-phosphofructokinase enzyme and linked sugar phosphotransferase transport system were most strongly up-regulated during growth on fructose, indicating that these two clusters play distinct roles in the use of inulin. The R. inulinivorans β-fructofuranosidase was overexpressed in Escherichia coli and shown to hydrolyze fructans ranging from inulin down to sucrose, with greatest activity on fructo-oligosaccharides. Genes induced on starch included the major extracellular α-amylase and two distinct α-glucanotransferases together with a gene encoding a flagellin protein. The latter response may be concerned with improving bacterial access to insoluble starch particles.

MOTIVATION Transcriptomic studies using microarray technology have become a standard tool in life sciences in the last decade. Nevertheless the cost of these experiments remains high and forces scientists to work with small sample sizes at the expense of statistical power. In many cases, little or no prior knowledge on the underlying variability is available, which would allow an accurate estimation of the number of samples (microarrays) required to answer a particular biological question of interest. We investigate sequential methods, also called group sequential or adaptive designs in the context of clinical trials, for microarray analysis. Through interim analyses at different stages of the experiment and application of a stopping rule a decision can be made as to whether more samples should be studied or whether the experiment has yielded enough information already. RESULTS The high dimensionality of microarray data facilitates the sequential approach. Since thousands of genes simultaneously contribute to the stopping decision, the marginal distribution of any single gene is nearly independent of the global stopping rule. For this reason, the interim analysis does not seriously bias the final p-values. We propose a meta-analysis approach to combining the results of the interim analyses at different stages. We consider stopping rules that are either based on the estimated number of true positives or on a sensitivity estimate and particularly discuss the difficulty of estimating the latter. We study this sequential method in an extensive simulation study and also apply it to several real data sets. The results show that applying sequential methods can reduce the number of microarrays without substantial loss of power. An R-package SequentialMA implementing the approach is available from the authors.

Motivation: With the proliferation of microarray experiments and their availability in the public domain, the use of meta-analysis methods to combine results from different studies increases. In microarray experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably increase the statistical power and give more accurate results. Results: A moderated effect size combination method was proposed and compared with other meta-analysis approaches. All methods were applied to real publicly available datasets on prostate cancer, and were compared in an extensive simulation study for various amounts of inter-study variability. Although the proposed moderated effect size combination improved already existing effect size approaches, the P-value combination was found to provide a better sensitivity and a better gene ranking than the other meta-analysis methods, while effect size methods were more conservative. Availability: An R package metaMA is available on the CRAN. Contact: guillemette.marot@jouy.inra.fr

Cancers exhibit abnormal molecular signatures associated with disease initiation and progression. Molecular signatures could improve cancer screening, detection, drug development and selection of appropriate drug therapies for individual patients. Typically only very small amounts of tissue are available from patients for analysis and biopsy samples exhibit broad heterogeneity that cannot be captured using a single marker. This report details application of an in-house custom designed GenomeLab System multiplex gene expression assay, the hCellMarkerPlex, to assess predictive gene signatures of normal, adenomatous polyp and carcinoma colon tissue using archived tissue bank material. The hCellMarkerPlex incorporates twenty-one gene markers: epithelial (EZR, KRT18, NOX1, SLC9A2), proliferation (PCNA, CCND1, MS4A12), differentiation (B4GANLT2, CDX1, CDX2), apoptotic (CASP3, NOX1, NTN1), fibroblast (FSP1, COL1A1), structural (ACTG2, CNN1, DES), gene transcription (HDAC1), stem cell (LGR5), endothelial (VWF) and mucin production (MUC2). Gene signatures distinguished normal, adenomatous polyp and carcinoma. Individual gene targets significantly contributing to molecular tissue types, classifier genes, were further characterised using real-time PCR, in-situ hybridisation and immunohistochemistry revealing aberrant epithelial expression of MS4A12, LGR5 CDX2, NOX1 and SLC9A2 prior to development of carcinoma. Identified gene signatures identify aberrant epithelial expression of genes prior to cancer development using in-house custom designed gene expression multiplex assays. This approach may be used to assist in objective classification of disease initiation, staging, progression and therapeutic responses using biopsy material.