[37] Samples were analysed in duplicate in at least two independ

[37]. Samples were analysed in duplicate in at least two independent runs. Statistical and data analyses Statistical analysis

of both qPCR and HITChip data was carried out with log-transformed data. In qPCR data, non-detected values were imputed with the half of the theoretical detection limit. For HITChip data, linear models with factors for treatment, health status, time point and breast-feeding with subsequent ANOVA and contrast tests were used to determine the statistical differences between groups. In microarray data, cut-off values for positive responding probes were calculated as described before [28]. In HITChip data the analysed values were summary values on phylum-like and genus-like GSK2126458 order level, find more obtained by summing the intensities from

all the probes assigned to the respective phylum-like or genus-like phylogetic groups. Totally 19 phylum-like and 78 genus-like level groups reached the detection threshold and were thus used in statistical analysis. The data is presented as mean with standard deviation values. Redundancy analysis (RDA) was performed by using the multivariate statistical analysis package Canoco [38]. RDA plot shows bacterial groups principally contributing to the difference between the groups of subjects. The significance of separation in RDA was assessed by Monte Carlo Permutation Procedure (MCPP [39]). The diversity of the microbial selleck chemical community assessed by HITChip was expressed as Simpson’s reciprocal index of diversity much (1/D) as described before [28, 40]. Results Temporal development of microbiota The faecal microbiota of 34 children at age of 6 and 18 months was analysed using the HITChip phylogenetic microarray. The diversity of total microbiota increased significantly with age, as the Simpson’s the reciprocal diversity index has changed from 78 ± 24 to 111 ± 27 at age

of 6 and 18 months, respectively (p < .001). At the phylum-like level, significant changes in the relative abundances of major bacterial groups were detected (Figure 1). The most prominent decline in abundance was observed for Actinobacteria that contributed 24.2% and 14.1% to the total signal at 6 and 18 months of age, respectively (p= 0.01). Signal intensities for Actinobacteria were almost entirely obtained from bifidobacteria (22.9% of the total microbiota at 6 months and 12.6% at 18 months, p= 0.01). This finding was consistent with quantitative PCR analysis, where total bifidobacteria counts decreased significantly with age (p= 0.03, Additional file 3). At the species level, the amounts of B. longum/infantis group, B. breve, B. bifidum, B. catenulatum group and B. adolescentis decreased over time as assessed by qPCR. In addition to Actinobacteria, the relative abundance of Bacilli decreased with age (from 11.8% to 7.1%, p= 0.03). All genus-like groups belonging to Bacilli decreased, most of which not significantly as individual groups, but the sum effect at the phylum-like level was significant (Figure 1).

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