Further details about data processing and quality control selleck steps can be found in Supplemental Methods. Taxonomic classification was made using the Ribosomal Database Project na?ve Bayesian classifier (Wang et al., 2007). Rarefaction curves, alpha diversity metrics and UniFrac distances (Lozupone and Knight, 2005) calculated using QIIME (Caporaso et al., 2010) employed re-sampling (bootstrapping and jackknifing: 1000 re-samples) at below the size of the smallest library to avoid sample size-based artifacts (Lozupone et al., 2011). Relative abundance correlation between pairs of phylotypes detected in at least two samples was performed in MATLAB (MathWorks, Natick, MA, USA) by calculating the Pearson correlation coefficient using amplicon libraries produced from lumen DNA.

This analysis was also conducted with phylotypes divided into taxonomic families based on the results of the Ribosomal Database Project na?ve Bayesian classifier. Non-parametric permutational multivariate analysis of variance (perMANOVA) was conducted using the ��vegan’ package (Oksanen et al., 2010) was performed using the ��indicspecies’ package in R (De C��ceres and Legendre, 2009). The indicator species analysis determines the strength of the association between a phylotype and a condition and considers the relative frequency and abundance of phylotypes in target versus non-target conditions (De C��ceres and Legendre, 2009). To focus on dominant indicators, indicator phylotypes were selected that (1) were significantly associated with DSS-treated or healthy mice using indicator species analysis or correlation analysis (P<0.

05), and (2) had an arithmetic average difference of >0.5% relative abundance between healthy and DSS-treated mice. Genotype-insensitive as well as genotype-sensitive (that is, specific to either wt or STAT1?/?) indicator phylotypes were identified in separate analyses using data from DNA and cDNA templates. Representative indicator phylotype sequences were added to a bootstrapped RAxML phylogenetic tree as described in Supplemental Methods. Metatranscriptomic data analysis Metatranscriptomic sequencing data were analyzed following the double RNA analysis pipeline described by Urich et al. (2008). Briefly, rRNA tags present in non-rRNA depleted samples (IDs 1, 6, 8, 9 Supplementalry Table S1) were taxonomically binned using MEGAN (Huson et al.

, 2007) and a custom reference database of small subunit rRNA sequences (Urich et al., 2008). mRNA tags were compared against the NCBI non-redundant database using BlastX and taxonomically classified using MEGAN. Multiple mRNA libraries from the same condition were combined (wt control: IDs Carfilzomib 1�C5, wt DSS: IDs 6 and 7) (Supplementary Table S1 and S5). SEED categories were assigned using the MG-RAST Server (Version 2, significance threshold: E-value 10?5) (Meyer et al., 2008).