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  • br The murine KP lung derived gd T


    The murine KP-lung derived gd T gene expression signature (jzj > 3 genes) was translated to human symbols (homology information from MGI, and was used to score individual TCGA tumor expression profiles using ssGSEA (Barbie et al., 2009). Patients were stratified based on standardized ssGSEA scores and Kaplan-Meier survival analyses were conducted to compare high-scoring patients (top quartile) with low-scoring patients (bottom quartile) and significance was assessed using the log-rank test. Additionally, the TCGA LUAD patients were also scored by the ESTIMATE immune signature to evaluate their global immune cell infiltration (Yoshihara et al., 2013). Pearson correlation analysis was performed between patient scores from the two signatures. For Kaplan-Meier survival analysis based on IL22RA1 expression, TCGA LUAD cohort patients were similarly stratified based on standardized expression values of IL22RA1 and the top 30% of patients were compared with the rest of the cohort. All survival analyses were conducted using the survival package in R.
    16S sequencing analysis
    16S sequencing reads were processed using the QIIME analysis pipeline. OTUs were clustered at 97% identity using QIIME pick_ with the Greengenes 13.8 reference database (Caporaso et al., 2010). OTUs that did not appear in at least 3 samples and have at least 50 sequence counts were removed. On average, BALF samples had 50,000 sequencing reads and FP samples had 10,000 sequencing reads. A minimum threshold of 500 reads was used to retain samples after filtering. For alpha di-versity, we calculated the Shannon index of the 97% identity OTUs using, with samples rarefied to 1000 reads. Beta diversity measurements were calculated using to determine the Bray-Curtis and weighted UniFrac metrics. PCoA plots were made using To test for differences in the Methoxy-X04 composition between sample groups, we performed Permutational Multivariate Analysis of Variance (PERMANOVA) based on the Bray-Curtis and weighted UniFrac distance matrices using QIIME The LEfSe method (Segata et al., 2011) first utilizes the non-parametric Krus-kal-Wallis rank-sum test to compare relative abundances of all bacterial taxa between tumor-bearing and healthy mice (at a = 0.05). Subsequently, linear discriminant analysis (LDA) is used to estimate the effect size of each differentially abundant taxa. Taxa with LDA score > 2.0 were included in the plot.
    Microbial Detection in RNA Sequencing Datasets
    The PathSeq (Kostic et al., 2011) algorithm was used to perform computational subtraction of human reads, followed by alignments of residual reads to human reference genomes / transcriptomes and microbial reference genomes (which include bacterial, viral, archaeal, and fungal sequences - downloaded from NCBI in October, 2015). These alignments resulted in taxonomical classification of reads into bacterial, viral, archaeal, and fungal sequences in RNA sequencing (RNA-Seq) data. The human reference genome/tran-scriptome sequences were downloaded from the Ensembl database ( Homo_sapiens.GRCh37.74.cdna.all.fa.gz AND, female human reference genome (, human genome plus transcriptome database ( and the human reference genome from NCBI (ftp://ftp. Bacterial, fungal and archaeal reference sequences were downloaded from NCBI reference sequence database ( Viral and phage sequences were downloaded from NCBI nucleotide database ( using query strings ‘‘Viruses[Organism] AND srcdb_refseq[PROP] NOT wgs[prop] NOT cellular organisms[ORGN] NOT AC_000001:AC_999999[pacc]’’ and ‘‘Viruses[Organ-ism] NOT srcdb_refseq[PROP] NOT cellular organisms[ORGN] AND nuccore genome samespecies[Filter] NOT nuccore genome [filter] NOT gbdiv syn[prop].’’ The curated databases in FASTA format are available at Downloads.html webpage.