Claus-Dieter Mayer

Environmentally-acquired bacteria influence microbial diversity and natural innate immune responses at gut surfaces

BackgroundEarly microbial colonization of the gut reduces the incidence of infectious, inflammatory and autoimmune diseases. Recent population studies reveal that childhood hygiene is a significant risk factor for development of inflammatory bowel disease, thereby reinforcing the hygiene hypothesis and the potential importance of microbial colonization during early life. The extent to which early-life environment impacts on microbial diversity of the adult gut and subsequent immune processes has not been comprehensively investigated thus far. We addressed this important question using the pig as a model to evaluate the impact of early-life environment on microbe/host gut interactions during development.ResultsGenetically-related piglets were housed in either indoor or outdoor environments or in experimental isolators. Analysis of over 3,000 16S rRNA sequences revealed major differences in mucosa-adherent microbial diversity in the ileum of adult pigs attributable to differences in early-life environment. Pigs housed in a natural outdoor environment showed a dominance of Firmicutes, in particular Lactobacillus, whereas animals housed in a hygienic indoor environment had reduced Lactobacillus and higher numbers of potentially pathogenic phylotypes. Our analysis revealed a strong negative correlation between the abundance of Firmicutes and pathogenic bacterial populations in the gut. These differences were exaggerated in animals housed in experimental isolators. Affymetrix microarray technology and Real-time Polymerase Chain Reaction revealed significant gut-specific gene responses also related to early-life environment. Significantly, indoor-housed pigs displayed increased expression of Type 1 interferon genes, Major Histocompatibility Complex class I and several chemokines. Gene Ontology and pathway analysis further confirmed these results.ConclusionEarly-life environment significantly affects both microbial composition of the adult gut and mucosal innate immune function. We observed that a microbiota dominated by lactobacilli may function to maintain mucosal immune homeostasis and limit pathogen colonization.

Restricting microbial exposure in early life negates the immune benefits associated with gut colonization in environments of high microbial diversity.

Background Acquisition of the intestinal microbiota in early life corresponds with the development of the mucosal immune system. Recent work on caesarean-delivered infants revealed that early microbial composition is influenced by birthing method and environment. Furthermore, we have confirmed that early-life environment strongly influences both the adult gut microbiota and development of the gut immune system. Here, we address the impact of limiting microbial exposure after initial colonization on the development of adult gut immunity. Methodology/Principal Findings Piglets were born in indoor or outdoor rearing units, allowing natural colonization in the immediate period after birth, prior to transfer to high-health status isolators. Strikingly, gut closure and morphological development were strongly affected by isolator-rearing, independent of indoor or outdoor origins of piglets. Isolator-reared animals showed extensive vacuolation and disorganization of the gut epithelium, inferring that normal gut closure requires maturation factors present in maternal milk. Although morphological maturation and gut closure were delayed in isolator-reared animals, these hard-wired events occurred later in development. Type I IFN, IL-22, IL-23 and Th17 pathways were increased in indoor-isolator compared to outdoor-isolator animals during early life, indicating greater immune activation in pigs originating from indoor environments reflecting differences in the early microbiota. This difference was less apparent later in development due to enhanced immune activation and convergence of the microbiota in all isolator-reared animals. This correlated with elevation of Type I IFN pathways in both groups, although T cell pathways were still more affected in indoor-reared animals. Conclusions/Significance Environmental factors, in particular microbial exposure, influence expression of a large number of immune-related genes. However, the homeostatic effects of microbial colonization in outdoor environments require sustained microbial exposure throughout development. Gut development in high-hygiene environments negatively impacts on normal succession of the gut microbiota and promotes innate immune activation which may impair immune homeostasis.

Identification of genes involved in cell wall biogenesis in grasses by differential gene expression profiling of elongating and non-elongating maize internodes

Despite the economic importance of grasses as food, feed, and energy crops, little is known about the genes that control their cell wall synthesis, assembly, and remodelling. Here a detailed transcriptome analysis that allowed the identification of genes involved in grass cell wall biogenesis is provided. Differential gene expression profiling, using maize oligonucleotide arrays, was used to identify genes differentially expressed between an elongating internode, containing cells exhibiting primary cell wall synthesis, and an internode that had just ceased elongation and in which many cells were depositing secondary cell wall material. This is one of only a few studies specifically aimed at the identification of cell wall-related genes in grasses. Analysis identified new candidate genes for a role in primary and secondary cell wall biogenesis in grasses. The results suggest that many proteins involved in cell wall processes during normal development are also recruited during defence-related cell wall remodelling events. This work provides a platform for studies in which candidate genes will be functionally tested for involvement in cell wall-related processes, increasing our knowledge of cell wall biogenesis and its regulation in grasses. Since several grasses are currently being developed as lignocellulosic feedstocks for biofuel production, this improved understanding of grass cell wall biogenesis is timely, as it will facilitate the manipulation of traits favourable for sustainable food and biofuel production.

Substrate-driven gene expression in Roseburia inulinivorans: Importance of inducible enzymes in the utilization of inulin and starch

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.

Moderated effect size and P-value combinations for microarray meta-analyses

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.

Custom Design of a GeXP Multiplexed Assay Used to Assess Expression Profiles of Inflammatory Gene Targets in Normal Colon, Polyp, and Tumor Tissue

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.

NuGO contributions to GenePattern

NuGO, the European Nutrigenomics Organization, utilizes 31 powerful computers for, e.g., data storage and analysis. These so-called black boxes (NBXses) are located at the sites of different partners. NuGO decided to use GenePattern as the preferred genomic analysis tool on each NBX. To handle the custom made Affymetrix NuGO arrays, new NuGO modules are added to GenePattern. These NuGO modules execute the latest Bioconductor version ensuring up-to-date annotations and access to the latest scientific developments. The following GenePattern modules are provided by NuGO: NuGOArrayQualityAnalysis for comprehensive quality control, NuGOExpressionFileCreator for import and normalization of data, LimmaAnalysis for identification of differentially expressed genes, TopGoAnalysis for calculation of GO enrichment, and GetResultForGo for retrieval of information on genes associated with specific GO terms. All together, these NuGO modules allow comprehensive, up-to-date, and user friendly analysis of Affymetrix data. A special feature of the NuGO modules is that for analysis they allow the use of either the standard Affymetrix or the MBNI custom CDF-files, which remap probes based on current knowledge. In both cases a .chip-file is created to enable GSEA analysis. The NuGO GenePattern installations are distributed as binary Ubuntu (.deb) packages via the NuGO repository.

Using Gene Expression to Predict Differences in the Secretome of Human Omental vs. Subcutaneous Adipose Tissue

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.

A Thyroid Hormone Challenge in Hypothyroid Rats Identifies T3 Regulated Genes in the Hypothalamus and in Models with Altered Energy Balance and Glucose Homeostasis

BACKGROUND The thyroid hormone triiodothyronine (T3) is known to affect energy balance. Recent evidence points to an action of T3 in the hypothalamus, a key area of the brain involved in energy homeostasis, but the components and mechanisms are far from understood. The aim of this study was to identify components in the hypothalamus that may be involved in the action of T3 on energy balance regulatory mechanisms. METHODS Sprague Dawley rats were made hypothyroid by giving 0.025% methimazole (MMI) in their drinking water for 22 days. On day 21, half the MMI-treated rats received a saline injection, whereas the others were injected with T3. Food intake and body weight measurements were taken daily. Body composition was determined by magnetic resonance imaging, gene expression was analyzed by in situ hybridization, and T3-induced gene expression was determined by microarray analysis of MMI-treated compared to MMI-T3-injected hypothalamic RNA. RESULTS Post mortem serum thyroid hormone levels showed that MMI treatment decreased circulating thyroid hormones and increased thyrotropin (TSH). MMI treatment decreased food intake and body weight. Body composition analysis revealed reduced lean and fat mass in thyroidectomized rats from day 14 of the experiment. MMI treatment caused a decrease in circulating triglyceride concentrations, an increase in nonesterified fatty acids, and decreased insulin levels. A glucose tolerance test showed impaired glucose clearance in the thyroidectomized animals. In the brain, in situ hybridization revealed marked changes in gene expression, including genes such as Mct8, a thyroid hormone transporter, and Agrp, a key component in energy balance regulation. Microarray analysis revealed 110 genes to be up- or downregulated with T3 treatment (± 1.3-fold change, p<0.05). Three genes chosen from the differentially expressed genes were verified by in situ hybridization to be activated by T3 in cells located at or close to the hypothalamic ventricular ependymal layer and differentially expressed in animal models of long- and short-term body weight regulation. CONCLUSION This study identified genes regulated by T3 in the hypothalamus, a key area of the brain involved in homeostasis and neuroendocrine functions. These include genes hitherto not known to be regulated by thyroid status.

Sequential Analysis for Microarray Data Based on Sensitivity and Meta-Analysis

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.