Yamamoto et al. BMC Genomics 2012, 13:335 http://www.biomedcentral.com/1471-2164/13/335 in intestinal homeostasis, which are driven by the intes- tinal bacterial burden [7-9] Many epidemiological studies suggest that there is an inverse relationship between infections in early childhood and the subsequent development of allergic diseases [10,11]. Therapy with broad-spectrum antibio- tics is frequently performed in pediatric practice and children receiving this therapy within their first year of life are particularly prone to develop allergic diseases later in life [12-14]. Studies in animal models, such as GF animals, have also suggested that microbiota play a critical role in normal development of the immune system [15]. Oyama et al. [16] reported that antibiotic use during infancy in mice promotes a shift in the Th1/Th2 balance toward Th2-dominant immunity. Further, they demonstrated that GF mice do not de- velop oral tolerance, which was restored by microbial reconstitution in neonatal (3-week-old) mice but not in older mice [17]. Impairment of immune tolerance has been shown to augment disease in various models of allergy and/or autoimmunity, including diabetes onset in NOD mice [18,19], collagen-induced arthritis [20,21], and experimental colitis [22]. Taken together, these findings provide new perspectives on the patho- genesis and recurrence of these diseases [15,23,24] In the present study, to elucidate the impact of microbes on the immune system during the neonatal period, we performed microarray analysis of LI, SI, LIV, and SPL of mice with or without enteric micro- biota, and of GF mice reconstituted with microbiota at different ages. In order to extract useful information from the massive amount of gene expression data obtained by microarray assay, we employed a gene set enrichment analysis approach in the present study. This type of ana- lysis uses predetermined aggregations of genes (alterna- tively called gene sets, metagenes, gene modules, etc.) rather than individual genes to assess for coordinated expression in the samples. Single gene analysis may miss important effects on signaling because cellular processes often affect sets of genes acting in concert, with moder- ate effects on the strength of expression. Subtle but orchestrated changes of internally-related genes have often been found to be more important than a dramatic increase/decrease of a single gene. Therefore, it may be important to evaluate the statistical significance of changes in a gene aggregate or gene set, rather than in an individual gene. Since Subramanian et al.. [25] ini- tially proposed “Gene Set Enrichment Analysis (GSEA)”, a number of algorithms optimized for this type of ana- lysis have been developed [26-33], and in the present study, we employed “MetaGene Profiler (MGP)” devel- oped by Gupta et al. [34]. Unlike most existing methods, the main characteristic of MGP is that it evaluates Page 2 of 15 statistical data for a set of genes independently from data of other gene sets. Because of the advantage of the ana- lysis, it is logical to compare the results of tests for the same set of genes observed under different conditions, such as case–control experiments with multiple cases and time-course experiments, because the statistical evi- dence is evaluated using the same standard. MGP was therefore suitable for the present study, which required multiple cross comparisons of the same samples [35]. This study is part of a research project using GF and SPF mice of the IQI strain, which has been established as an inbred strain from ICR mice at the Central Insti- tute for Experimental Animals (Kawasaki, Japan). We previously reported some of the results of microarray analyses conducted for the project. These studies indi- cated that 1) activation of the IFN-α system in LI differs significantly between GF and SPF mice [36], and 2) there is a striking commonality in transcriptome profiles be- tween GF LI and SI, while the profiles of SPF LI and SI share almost nothing in common [37]. The present study, despite the use of different cohorts of animals, different versions of GeneChip arrays, and completely different strategies and algorithms for bioinformatics analysis from previous studies, has given support to our earlier findings. Furthermore, we demonstrated that the lack of neonatal encounter with commensal microor- ganisms may result in profound alteration of certain signaling pathways including TLRs, Rac 1, and type-1 IFN, which cannot be restored by later exposure to microbiota. These findings may provide important insights into the molecular events underlying the inter- action between neonatal immune systems and com- mensal microbiota. Results Generation of gnotobiotic mice and targets of microarray analysis In the present study we prepared 4 groups of mice with different status of enteric flora: GF, SPF, 0WexGF, and 5WexGF (Figure 1). Bacterial reconstitution was exe- cuted not by oral administration of fecal suspension but by cohabitation of pregnant GF mice with female SPF mice of the same age, because 1) contact with microor- ganisms via a wide variety of parenteral routes such as birth canal, skin, eyes, ears, nostrils, airway, urinary tract, and vagina are also critically important for the es- tablishment of relevant microbial flora, which differ pro- foundly among these anatomical sites, and 2) it is impossible to administer bacterial suspension orally to neonatal mice immediately after delivery. Using GF and SPF mice of the same strain, we have analyzed the gene expression profiles of LI and SI in pre- vious studies [36,37]. In the present study, we have extended the target of transcriptome analyses to the SPL