Wastewater monitoring of COVID-19: a perspective from Scotland.

Scotland introduced wastewater monitoring for COVID-19 early in the pandemic. From May 2020, samples have been taken and analysed using quantitative polymerase chain reaction (qPCR). The programme was expanded to over 100 sites accounting for around 80% of the population. Data are presented publicly via a dashboard and regular reports are produced for both the public and health professionals. Wastewater-based epidemiology (WBE) offers opportunities and challenges. It offers an objective means of measuring COVID-19 prevalence and can be more practical or timely than other methods of mass testing. However, it also has substantial variability impacted by multiple environmental factors. Methods for data collection and analysis have developed significantly through the pandemic, reflecting the evolving situation and policy direction. We discuss the Scottish experience of wastewater monitoring for COVID-19, with a focus on the analysis of data. This includes our approach to flow normalisation, our experience of variability in measurements and anomalous values, and the visualisation and presentation of data to stakeholders. Summarising the Scottish methodology as of March 2022, we also discuss how wastewater data were used for informing policy and public health actions. We draw lessons from our experience and consider future directions for WBE in Scotland.

SARS-CoV-2 RNA levels in Scotland's wastewater.

Nationwide, wastewater-based monitoring was newly established in Scotland to track the levels of SARS-CoV-2 viral RNA shed into the sewage network, during the COVID-19 pandemic. We present a curated, reference dataset produced by this national programme, from May 2020 to February 2022. Viral levels were analysed by RT-qPCR assays of the N1 gene, on RNA extracted from wastewater sampled at 162 locations. Locations were sampled up to four times per week, typically once or twice per week, and in response to local needs. We report sampling site locations with geographical coordinates, the total population in the catchment for each site, and the information necessary for data normalisation, such as the incoming wastewater flow values and ammonia concentration, when these were available. The methodology for viral quantification and data analysis is briefly described, with links to detailed protocols online. These wastewater data are contributing to estimates of disease prevalence and the viral reproduction number (R) in Scotland and in the UK.

EORNA, a barley gene and transcript abundance database.

A high-quality, barley gene reference transcript dataset (BaRTv1.0), was used to quantify gene and transcript abundances from 22 RNA-seq experiments, covering 843 separate samples. Using the abundance data we developed a Barley Expression Database (EORNA*) to underpin a visualisation tool that displays comparative gene and transcript abundance data on demand as transcripts per million (TPM) across all samples and all the genes. EORNA provides gene and transcript models for all of the transcripts contained in BaRTV1.0, and these can be conveniently identified through either BaRT or HORVU gene names, or by direct BLAST of query sequences. Browsing the quantification data reveals cultivar, tissue and condition specific gene expression and shows changes in the proportions of individual transcripts that have arisen via alternative splicing. TPM values can be easily extracted to allow users to determine the statistical significance of observed transcript abundance variation among samples or perform meta analyses on multiple RNA-seq experiments. * Eòrna is the Scottish Gaelic word for Barley.

Diverse LEF/TCF Expression in Human Colorectal Cancer Correlates with Altered Wnt-Regulated Transcriptome in a Meta-Analysis of Patient Biopsies.

Aberrantly activated Wnt signaling causes cellular transformation that can lead to human colorectal cancer. Wnt signaling is mediated by Lymphoid Enhancer Factor/T-Cell Factor (LEF/TCF) DNA-binding factors. Here we investigate whether altered LEF/TCF expression is conserved in human colorectal tumor sample and may potentially be correlated with indicators of cancer progression. We carried out a meta-analysis of carefully selected publicly available gene expression data sets with paired tumor biopsy and adjacent matched normal tissues from colorectal cancer patients. Our meta-analysis confirms that among the four human LEF/TCF genes, LEF1 and TCF7 are preferentially expressed in tumor biopsies, while TCF7L2 and TCF7L1 in normal control tissue. We also confirm positive correlation of LEF1 and TCF7 expression with hallmarks of active Wnt signaling (i.e., AXIN2 and LGR5). We are able to correlate differential LEF/TCF gene expression with distinct transcriptomes associated with cell adhesion, extracellular matrix organization, and Wnt receptor feedback regulation. We demonstrate here in human colorectal tumor sample correlation of altered LEF/TCF gene expression with quantitatively and qualitatively different transcriptomes, suggesting LEF/TCF-specific transcriptional regulation of Wnt target genes relevant for cancer progression and survival. This bioinformatics analysis provides a foundation for future more detailed, functional, and molecular analyses aimed at dissecting such functional differences.

BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq.

BACKGROUND: The time required to analyse RNA-seq data varies considerably, due to discrete steps for computational assembly, quantification of gene expression and splicing analysis. Recent fast non-alignment tools such as Kallisto and Salmon overcome these problems, but these tools require a high quality, comprehensive reference transcripts dataset (RTD), which are rarely available in plants. RESULTS: A high-quality, non-redundant barley gene RTD and database (Barley Reference Transcripts - BaRTv1.0) has been generated. BaRTv1.0, was constructed from a range of tissues, cultivars and abiotic treatments and transcripts assembled and aligned to the barley cv. Morex reference genome (Mascher et al. Nature; 544: 427-433, 2017). Full-length cDNAs from the barley variety Haruna nijo (Matsumoto et al. Plant Physiol; 156: 20-28, 2011) determined transcript coverage, and high-resolution RT-PCR validated alternatively spliced (AS) transcripts of 86 genes in five different organs and tissue. These methods were used as benchmarks to select an optimal barley RTD. BaRTv1.0-Quantification of Alternatively Spliced Isoforms (QUASI) was also made to overcome inaccurate quantification due to variation in 5' and 3' UTR ends of transcripts. BaRTv1.0-QUASI was used for accurate transcript quantification of RNA-seq data of five barley organs/tissues. This analysis identified 20,972 significant differentially expressed genes, 2791 differentially alternatively spliced genes and 2768 transcripts with differential transcript usage. CONCLUSION: A high confidence barley reference transcript dataset consisting of 60,444 genes with 177,240 transcripts has been generated. Compared to current barley transcripts, BaRTv1.0 transcripts are generally longer, have less fragmentation and improved gene models that are well supported by splice junction reads. Precise transcript quantification using BaRTv1.0 allows routine analysis of gene expression and AS.

High-Resolution RT-PCR Analysis of Alternative Barley Transcripts.

Assembly of the barley genome and extensive use of RNA-seq has resulted in an abundance of gene expression data and the recognition of wide-scale production of alternatively spliced transcripts. Here, we describe in detail a high-resolution reverse transcription-PCR based panel (HR RT-PCR) that confirms the accuracy of alternatively spliced transcripts from RNA-seq and allows quantification of changes in the proportion of splice isoforms between different experimental conditions, time points, tissues, genotypes, ecotypes, and treatments. By validating a selection of barley genes, use of the panel gives confidence or otherwise to the genome-wide global changes in alternatively spliced transcripts reported by RNA-seq. This simple assay can readily be applied to perform detailed transcript isoform analysis for any gene in any species.

SerpinA3N is a novel hypothalamic gene upregulated by a high-fat diet and leptin in mice.

BACKGROUND: Energy homeostasis is regulated by the hypothalamus but fails when animals are fed a high-fat diet (HFD), and leptin insensitivity and obesity develops. To elucidate the possible mechanisms underlying these effects, a microarray-based transcriptomics approach was used to identify novel genes regulated by HFD and leptin in the mouse hypothalamus. RESULTS: Mouse global array data identified serpinA3N as a novel gene highly upregulated by both a HFD and leptin challenge. In situ hybridisation showed serpinA3N expression upregulation by HFD and leptin in all major hypothalamic nuclei in agreement with transcriptomic gene expression data. Immunohistochemistry and studies in the hypothalamic clonal neuronal cell line, mHypoE-N42 (N42), confirmed that alpha 1-antichymotrypsin (α1AC), the protein encoded by serpinA3, is localised to neurons and revealed that it is secreted into the media. SerpinA3N expression in N42 neurons is upregulated by palmitic acid and by leptin, together with IL-6 and TNFα, and all three genes are downregulated by the anti-inflammatory monounsaturated fat, oleic acid. Additionally, palmitate upregulation of serpinA3 in N42 neurons is blocked by the NFκB inhibitor, BAY11, and the upregulation of serpinA3N expression in the hypothalamus by HFD is blunted in IL-1 receptor 1 knockout (IL-1R1 -/- ) mice. CONCLUSIONS: These data demonstrate that serpinA3 expression is implicated in nutritionally mediated hypothalamic inflammation.

Dietary fibers inhibit obesity in mice, but host responses in the cecum and liver appear unrelated to fiber-specific changes in cecal bacterial taxonomic composition.

Dietary fibers (DF) can prevent obesity in rodents fed a high-fat diet (HFD). Their mode of action is not fully elucidated, but the gut microbiota have been implicated. This study aimed to identify the effects of seven dietary fibers (barley beta-glucan, apple pectin, inulin, inulin acetate ester, inulin propionate ester, inulin butyrate ester or a combination of inulin propionate ester and inulin butyrate ester) effective in preventing diet-induced obesity and links to differences in cecal bacteria and host gene expression. Mice (n = 12) were fed either a low-fat diet (LFD), HFD or a HFD supplemented with the DFs, barley beta-glucan, apple pectin, inulin, inulin acetate ester, inulin propionate ester, inulin butyrate ester or a combination of inulin propionate ester and inulin butyrate ester for 8 weeks. Cecal bacteria were determined by Illumina MiSeq sequencing of 16S rRNA gene amplicons. Host responses, body composition, metabolic markers and gene transcription (cecum and liver) were assessed post intervention. HFD mice showed increased adiposity, while all of the DFs prevented weight gain. DF specific differences in cecal bacteria were observed. Results indicate that diverse DFs prevent weight gain on a HFD, despite giving rise to different cecal bacteria profiles. Conversely, common host responses to dietary fiber observed are predicted to be important in improving barrier function and genome stability in the gut, maintaining energy homeostasis and reducing HFD induced inflammatory responses in the liver.

Predictive gene signatures: molecular markers distinguishing colon adenomatous polyp and carcinoma.

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.

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.

Plasma zinc's alter ego is a low-molecular-weight humoral factor.

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 of >2500 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.

An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies.

BACKGROUND: The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate. RESULTS: We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures. CONCLUSION: T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially.

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.

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.

Exploratory analysis of multiple omics datasets using the adjusted RV coefficient.

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 multidimensional scaling. We show that this reveals biological meaningful subgroups of data sets in our study.

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.

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 ß-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 ß-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 ß-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.

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.

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

BACKGROUND: Early 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. RESULTS: Genetically-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. CONCLUSION: Early-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.

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.