Inflammatory bowel disease (IBD) is a chronic intestinal disorder, with two main types: Crohn’s disease (CD) and ulcerative colitis (UC), whose molecular pathology is not well understood. The majority of IBD-associated SNPs are located in non-coding regions and are hard to characterize since regulatory regions in IBD are not known. Here we profile transcription start sites (TSSs) and enhancers in the descending colon of 94 IBD patients and controls. IBD-upregulated promoters and enhancers are highly enriched for IBD-associated SNPs and are bound by the same transcription factors. IBD-specific TSSs are associated to genes with roles in both inflammatory cascades and gut epithelia while TSSs distinguishing UC and CD are associated to gut epithelia functions. We find that as few as 35 TSSs can distinguish active CD, UC, and controls with 85% accuracy in an independent cohort. Our data constitute a foundation for understanding the molecular pathology, gene regulation, and genetics of IBD. Many SNPs associated with inflammatory bowel disease are located in non-coding genomic regions. Here, the authors perform CAGE-sequencing on descending colon biopsies of Crohn’s disease and ulcerative colitis patients to map transcription start sites and enhancer activity for analysis of regulatory regions. UC is characterized by mucosal inflammation of the colon, whereas CD may affect all layers of the intestine throughout the gastrointestinal tract  have identified >200 loci containing IBD-associated variants8, but IBD-associated single-nucleotide polymorphisms (SNPs) can only explain 15–20% of the disease phenotype9. Moreover, ~70% of IBD-associated SNPs are non-coding10 and might affect gene regulation. However, a comprehensive map of active promoters and enhancers is lacking for IBD patients.Inflammatory bowel disease (IBD) is an umbrella term for a range of chronic idiopathic disorders, of which Crohn’s disease (CD) and ulcerative colitis (UC) constitute the two major entities11. Distal enhancer regions can also be detected by CAGE, because active enhancers transcribe enhancer RNAs (eRNAs)13. eRNA expression is a powerful proxy for cell-specific enhancer activity14, and CAGE-identified enhancers are two to three times more likely to validate in vitro than non-transcribed enhancers detected by chromatin-based methods13. An advantage of CAGE is that it can be easily applied on small biological samples, such as colonic biopsies that are routinely taken when diagnosing IBD.Genome-wide 5′-RNA sequencing of capped RNAs  can detect transcription start sites (TSSs) and thereby promoter regionsHere we present CAGE analysis on biopsies from the descending colon from 94 IBD patients and controls. These data enabled annotation of IBD-regulated enhancers and TSSs, and characterization of IBD-associated SNPs in such regions. Furthermore, we define a small subset of TSSs that allow for accurate classification between UC, CD, and control subjects.15. UC patients were graded by the Mayo score: a score ≤2 with endoscopic sub-score of 0 (no macroscopic inflammation) as quiescent UC and >2 as active UC. CD patients were graded by the Harvey–Bradshaw score: a score <5 as quiescent CD and ≥5 as active CD. For controls, an endoscopy was performed due to gastrointestinal symptoms but all clinical investigations returned normal. We extracted RNA from pinch biopsies from the descending colon of 94 subjects (cohort 1): 25 active UC (UCa), 20 active CD (CDa), 17 UC patients in remission (UCi), 3 CD patients in remission (CDi), and 29 control subjects (Ctrl)  to annotated alternative TSSs and 40.7%  TSSs were putative novel alternative TSSs within known gene loci, although the majority of these were lowly expressed  showed a separation between active IBD  and other groups , and a weaker separation between CDa and UCa Fig. . Surpris18. As observed above, CDa and UCa samples were characterized by a shared response compared to Ctrl . To identify TSSs distinguishing CDa and UCa, we identified TSSs significantly upregulated in CDa vs. UCa, defined as CDspec, and TSSs significantly upregulated in UCa vs. CDa, defined as UCspec. The differential expression analysis recapitulated the PCA results: the number of TSSs within IBDup (4376) and IBDdown (2536) was much larger than in CDspec (337) and UCspec (71)  Fig. . We also71) Fig. : 2068 geIBDdown 236 was muup genes showed a strong overrepresentation of GO terms related to inflammatory response and cytokines. IBDup genes were also enriched for colon-specific processes previously related to IBD pathogenesis, for example remodeling of the extracellular matrix (FDR = 2.66e−09) and antibacterial peptide secretion (FDR = 1.02e−14). Because small networks of biologically linked genes are challenging to identify through GO analysis, we supplemented this analysis with STRING network analysis19. This identified smaller sets of upregulated genes with known functions in gut epithelia barrier integrity, including gap junctions  and tight junctions   and c-lectins REG1A, REG1B, and REG3A  were highly upregulated in IBD and at the same time showed extreme variance across UC and CD subjects  and intestinal epithelial cells, while the UCspec TSSs were mostly enriched in epithelial cells and mesenchymal cells. Although T-helper cells have previously suggested to define the difference between CDa and UCa (reviewed in ref. 22), they were not enriched in our analysis, consistent with previous results23.Colonic biopsies consist of multiple cell types, whose composition may change in IBD. We assessed this in two ways. First, we took advantage of the FANTOM5 TSS atlas, where individual TSSs were annotated as being preferentially expressed in one or more cell types sampled across the human body based on CAGE expressionpes Fig. . IBDup T24 and monocytes, which were stimulated with TNF , one of the most prominent inflammatory molecules associated with flaring disease in IBD patients25. Cells were stimulated with TNF for 4 and 24 h , and these enhancers were significantly more conserved across mammals than non-transcribed DHSs from gut tissue27 and randomly selected genomic regions , upregulated in CDa vs. UCa (CDspec) and vice versa (UCspec)  and Ctrl, but only minor separation between UCa and CDa Fig. . We usedets Fig. .31 to identify shared predicted TF binding sites around IBDup and IBDdown enhancers . The same analysis was used for promoter regions of corresponding TSS groups (defined as −500/+100 bp around TSSs)  Fig. . IBDup p TF Fig. . We obse13, enhancer-TSS pairs can be predicted based on CAGE co-expression across samples. Following the same approach, we identified 21,502 enhancer-TSS pairs within 500 kb with positive and significant Pearson correlation   were linked with at least one enhancer, compared to smaller fractions for CDspec, UCspec (40.9% and 18.3%) and remaining TSSs , and two receptors for these chemokines (CXCR1 and CXCR2), represent examples of enhancers linked with neighboring TSSs of genes associated with IBD pathogenesis   Fig. . Consistup TSS-linked enhancers from enhancer clusters with >6 members with (ii) IBDup TSS-linked singleton enhancers (not belonging to any cluster). Singleton enhancers had a greater overlap with ENCODE ChIP-seq peaks corresponding to TFs associated with inflammation   IBDF1) Fig. , while eup enhancer located between cytokine receptors CXCR1 and CXCR2  into a single block, using the 1000 genomes CEU reference population39. If LD blocks overlapped, they were merged into an LD clump.First, we obtained all SNPs significantly associated with a disease/trait from the GWAS catalogup promoters and enhancers were enriched for LD clumps associated with several immune-related diseases, but only IBD, CD, and UC LD clumps were enriched in both promoters and enhancers  and enhancer regions (±300 bp around enhancer midpoints) by using empirical Bayes to shrink fraction of overlapping LD clumps toward the average of all GWAS diseases. Only IBD, CD and UC LD clumps had a high degree of overlap with both the promoter and enhancer sets Fig. . Next, wers Fig. . Enhancease Fig. .Fig. 6Re40 to investigate if TSSs and enhancers were enriched for SNPs explaining the heritability of IBD. Briefly, this method measures SNP heritability enrichment in a set of genomic regions (e.g. enhancers) as the proportion of SNP heritability 40. To extend this analysis with our enhancer and TSS sets in an IBD setting, we obtained GWAS summary statistics for ~12 million SNPs from the International IBD Genetics Consortium8. We then analyzed the partitioned heritability of IBD in the 24 annotated functional regions from40 but added our enhancer (±300 bp around enhancer midpoints) and promoter (−500/+100 bp around TSS peaks) regions, and the subsets of these belonging to the IBDup or IBDdown groups, to the original model. We found that our enhancers and promoter sets were significantly enriched for IBD SNP heritability. This was even more evident in the IBDup enhancers and promoters, while IBDdown was not significantly enriched  than coding exons.The above analysis only assessed enrichments of genetic variants reaching genome-wide significance. However, many genetic variants might collectively make important contributions to IBD pathogenesis without individually reaching genome-wide significance. To this end, we performed partitioning of heritability analysis using the stratified LD-score regression methodOverall, these results suggest that there is an association between IBD-associated SNPs and the regulatory regions we define. While the genotypes of the samples investigated here would be necessary to establish the causal effect of individual SNPs, our analysis indicate that the enhancer and promoter resources we provide are useful starting points for future investigations of non-coding IBD-associated SNPs, since they allow for a more detailed interpretation of otherwise uncharacterized intergenic regions.The statistical analyses above could identify TSSs and enhancers that were up- or downregulated between subject groups, but cannot assess whether expression data for a single biopsy contain enough information to accurately classify the subject as active UC, active CD, or control. This is a relevant question as the diagnosis between active UC and CD is often clinically difficult, and expression data might be useful to increase overall diagnosis accuracy. We reasoned that CAGE-based analysis is not realistic in a clinical setting; instead, we sought to define a small set of CAGE-based biomarkers for active UC, active CD and controls that were amendable to robust targeted methods such as qPCR.To achieve this, we used successive steps of biomarker selection and testing Fig. . First, 41, training on the same 35 biomarkers cohort 1 and predicted the diagnosis of the patients in cohort 2. This resulted in an increase in CDa classification sensitivity by 13% points with no overall classification performance decrease  using FAIRE-seq, a method identifying accessible DNA, while H3K27ac ChIP-seq has been performed in gut tissue for even fewer individuals, and not in an IBD context43. With our data, we could establish that promoters and enhancers that are upregulated in IBD share the same DNA-binding patterns enrichments, and that enhancer clusters upregulated in IBD are distinct from corresponding singleton enhancers.This is a large-scale analysis of enhancer activities across a total of 94 CD, UC, and control subjects: ref. The enhancer component of IBD reported here gives new possibilities for understanding the disease and its genetics. We show a clear overrepresentation of IBD-associated SNPs in both IBD-upregulated enhancer and promoter regions, and these regions had the largest IBD heritability enrichment compared to a large set of other genomic regions. Thus, the IBD TSS and enhancer sets reported here are unique resources for interpretation of the functional impact of noncoding genetic variants.44), with varying degrees of success ranging from high separation in small cohorts to little or no separation: a meta-study analyzing multiple microarray experiments indicated that UC-CD differences are not substantial23, and the accuracy of one of the reportedly best-performing methods could not be verified in an independent laboratory45. These variations in accuracy may be due to small patient cohorts, and that different standards may have been applied in the initial patient diagnosis and inclusion criteria. For comparison, we tested the classifying power of gene expression markers previously selected by two previous studies44 in our set, using CAGE data in cohort 1. These markers performed consistently worse than ours  was used for CAGE assay and subsequent microfluidic real-time quantitative reverse transcription polymerase chain reaction (qPCR) validation. This cohort included 25 patients with active UC (UCa), 17 patients with UC in remission (UCi), 20 patients with active CD (CDa), 3 patients with CD in remission (CDi) and 29 healthy controls (Ctrl). Cohort 2 (N = 101) was used for independent qPCR validation of selected target genes. The cohort included 37 UCa, 18 CDa, and 46 Ctrl subjects. All included subjects underwent a routine sigmoidoscopy or colonoscopy as part of their clinical evaluation at the Department of Gastroenterology, Medical Section, Herlev Hospital, Denmark or Department of Gastroenterology, Medical Section, Hvidovre Hospital, Denmark due to their clinical symptoms. They were included into the study as UC or CD patients undergoing lower endoscopy to assess possible disease activity, extension of disease or surveillance for dysplasia, or as controls . IBD patients were diagnosed established using well-defined criteria15. UC patients were graded by Mayo score: a score ≤2 with endoscopic sub-score of 0  as quiescent (in remission) UC and >2 as active UC, and CD patients were graded in accordance with the Harvey-Bradshaw score: a score <5 as quiescent (in remission) CD and ≥ 5 as active CD. Exclusion criteria were as follows: age >80 or <18, clinical evidence of infection, use of antibiotics or probiotics within 14 days, severe mental illness and pregnancy. Additional data for each cohort is available in Supplementary Table Two independent cohorts, including IBD patients and controls, were analyzed. Cohort 1 . All samples were obtained from patients who provided informed consent before surgery (for gut organoids) or blood sampling (monocytes).48. Quantity and purity of the RNA was determined for all the samples on a Nanodrop ND-1000 spectrophotometer (Thermo Scientific). The purity was measured as the A260/280 ratio and was between 1.9 and 2.1. RNA quality (integrity) was determined using the 2100 Bioanalyzer Instrument (Agilent Technologies) with the Agilent RNA 6000 Pico Kit (Agilent Technologies) as recommended by manufacturer. In cohort 1, the average RNA integrity number (RIN) was 7.6 and no samples were below 5.3. For cohort 2, the average RIN value was 8.0 and no samples were below 6.2.Mucosal pinch biopsies (weight on average 15 mg) were obtained from the descending colon using endoscopic forceps. We choose the descending colon in order to avoid intersegmental variation. The endoscopic diagnosis of active or inactive disease was confirmed by histopathology conducted on parallel biopsies taken within an inch of the 1st biopsy. The biopsies were immediately placed in RNAlater® Stabilization Solution  and kept at 4 °C for 24 h before long-term storage at −80 °C. The RNA was extracted using PureLink® RNA Mini Kit  with freshly made lysis buffer containing 1% 2-mercaptoethanol (Sigma). The biopsies were homogenized directly in the lysis buffer using an ULTRA-TURRAX® . The purification was performed following the manufacturer’s instructions including an on-column DNase treatment with the PureLink® DNase Set . For 16 samples RNA extraction was performed as previously described in ref. 6 monocytes were plated in each well of 24-well plates  in 1 ml growing medium  at 37 °C in a cell culture incubator. Cells were cultured overnight and stimulated with 10 ng/ml TNFα the next day and harvested at indicated time. Control cells were not treated with TNFα but otherwise treated in the same way. Cells were pelleted, snap frozen on dry ice and stored at −80 °C until use. RNA was purified using Qiagen RNeasy Micro Kit as recommended by manufacturer with lysis buffer containing 1% 2-mercaptoethanol (Sigma) and on column DNase digestion. The average yield was 700 ng, with RIN values between 8 and 9.2. TSS expression analysis of 36 TSS targets (as defined below) was done using the microfluidics Fluidigm platform in parallel with biopsy qPCR, as described below.Blood was freshly collected from IBD patients with informed written consent. First, freshly drawn blood was diluted at a ratio of 1:1 with PBS. Peripheral blood mononuclear cells (PBMCs) were isolated from diluted blood by Ficoll-Paque density gradient centrifugation according to the manufacturer’s instructions . Harvested PBMCs were used for monocytes isolation. CD14+ monocytes were isolated from PBMCs by negative immunomagnetic bead separation using Monocytes Isolation Kit II . After monocyte isolation, 1 × 10d-sorbitol, 44 mM sucrose, 10 mM EDTA, 8 mM KH2PO4, 5.6 mM Na2HPO4, 1.6 mM KCl, 0.5 mM DTT) for 45 min with agitation on ice. After replacing chelation buffer with PBS, biopsies were manually shaken 3 × 10 s in order to detach colonic crypts. Filtered crypts were suspended in ice cold Matrigel, plated in 48-well plates and maintained in organoid culture medium: Advanced DMEM/F12 (Life Technologies), 1× penicillin/streptomycin (Life Technologies), 10 mM HEPES (Life Technologies), 2 mM GlutaMAX (Life Technologies), 10 mM Nicotamide (Sigma), 1× N2 (Life Technologies), 1× B27 (Life Technologies), 1  mM N-acetylcysteine (Sigma), 10 μM Y-27632, 0.5 μM A-83-01 (Tocris), 10 μM SB202190 (Sigma), 100 ng/ml mWnt3a (Cell Guidance Systems), 500 ng/ml mRspondin-1 (R&D systems), 100 ng/ml mNoggin (R&D systems), 50 ng/ml hEGF (Peprotech), 2.5 μM PGE-2 (Sigma). For TNFα stimulation experiments, primary organoid lines from different patients (passage number < 3) were run in technical triplicates and maintained in organoid culture medium supplemented with 10 nM hTNFα (R&D systems) for 0, 4, or 24 h in duplicate wells. Duplicate wells were pooled together and RNA was isolated using Trizol (Invitrogen) in combination with Purelink minicolumns (Life Technologies). The Purelink minikit was used as recommended by manufacturer including the on column DNase step. The average yield was 5540 ng with a RIN between 6.8 and 9.8. TSS expression analysis of 36 TSS targets (as defined below) was done using the microfluidics Fluidigm platform in parallel with biopsy qPCR, as described below.Biopsies of morphologically normal epithelium from patients undergoing surgery for colorectal cancer were aseptically collected. After washing thoroughly with PBS, biopsies were cut into ca. 4 mm pieces and incubated in freshly prepared chelation buffer . The following four barcodes were used: no. 2 (CTT), no. 3 (GAT), no. 6 (ACG), and no. 8 (ATC). All used primers and adaptors were purchased from Integrated DNA technologies (IDT). A HiSeq2000 instrument from Illumina at the National High-throughput DNA sequencing Centre, University of Copenhagen was used; 30% Phi-X spike-ins were added to each sequencing lane. CAGE reads were matched to their originating samples if barcodes matched identicaly. Linker sequences were removed and reads were filtered, requiring at least 30 in 50% of the bases usingthe FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) with the length of 25 bp. CAGE tags were mapped using Bowtie49 (version 0.12.7) to the hg19 assembly using v = 2 and otherwise standard settings but allowing for multiple alignments. Subsequently, only uniquely mapping reads were retained. Reads that mapped to chrM were discarded. Supplementary Data CAGE libraries were prepared according to ref. 50. In brief: for initial cluster definition, for each library, CTSSs supported only by one CAGE tag 5′ end were ignored. After Tags Per Million (TPM) normalization , remaining CTSSs from all libraries were summed into a joint CTSS profile. CTSSs within 20 bp of each other on the same strand were merged into TCs. We trimmed weakly TC expressed tails around TCs using a queue-based trimming algorithm: base pairs were removed iteratively from the edge of TCs, always choosing the most lowly expressed edge, until 10% of the total TPM of the TC had been removed. Trimmed TCs were then quantified in all samples by counting the total number of CAGE tags in each TC and each sample . Unless otherwise mentioned, only TCs having ≥1 TPM in at least three libraries were retained for further analysis. For simplicity, we refer to TCs as “CAGE-defined TSSs”. A summit, or ‘TC peak’, was identified in each TC, defined as the single base-pair position within the TC with highest total TPM coverage across all samples.To generate a genome wide map of transcription start sites, 5′ ends of the CAGE tags (CTSSs) that mapped close to each other on the same strand were grouped into tag clusters (TCs) which were used for all the post analysis as in ref. 17. Canonical TSSs were defined as the most upstream annotated TSS in the GENCODE gene model. CAGE-defined TSSs within ±100 bp of the most upstream annotated TSS for a GENCODE-annotated gene were thus labeled as canonical TSSs. CAGE-defined TSSs ±100 bp from all other GENCODE-annotated TSS were defined as “known alternative TSS”. CAGE-defined TSSs within gene bodies more than 100 bp from an GENCODE-annotated TSS were annotated as “novel alternative TSSs”, CAGE-defined TSSs > 100 bp distant from gene annotation were annotated as novel intergenic TSSs and finally all TSSs located on the antisense strand to annotated genes were defined as novel antisense TSSs. Some analyses were performed at the level of annotated genes, rather than on CAGE-defined TSS level. For these analyses, based GENCODE v19 annotation17, we summed up all CAGE-defined TSSs overlapping the gene on the same strand, including intronic TSSs. In case of a TSS overlapping different genes (not different transcripts of the same gene), we used an annotation ranking so that the TSS was associated with a single gene, based on the type of annotation overlap with the following priority order: canonical TSSs > known alternative TSS > novel alternative TSSs.CAGE-defined TSSs were annotated using GENCODE v19 annotation13 with the following modifications: only enhancer regions candidates whose bidirectionality score |D| < 0.6  were used. The permissive set was defined by requiring at least two CAGE tags in at least one sample within the enhancer region (as in ref. 13), while the strict set was defined by regions having at least two tags in at least eight samples  was calculated using the intersectbed command from BEDTools package51.Enhancer regions were identified from CAGE-derived bidirectionally transcribed loci, as in ref. 52 (http://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=cons100way). Enhancers from the permissive set were analyzed in the same way. For comparison, we defined a set of DHS peaks from gut tissue from ENCODE data (https://www.encodeproject.org/). These included transverse colon  and sigmoid colon . We excluded the DHS peaks overlapping any gene TCs or enhancers defined in either this study or FANTOM513. We further discarded DHS regions overlapping ±200 bp around any annotated exons or ±500 bp around any annotated TSSs (the same criterion used to define CAGE enhancers). The middle points of remaining DHS peaks were extended 2000 bp and used in the analysis in the same way as the strict enhancer set. Similarly, to assess background levels of conservation, we sampled 500,000 regions of 4001 bp from intergenic regions in canonical chromosomes  excluding assembly gaps. Expected background per bp was calculated as mean per position across all random regions.Enhancer regions from the strict set were aligned by their midpoint. Enhancers whose ±2000 bp flanks from their midpoint overlapped were discarded, because otherwise the same regions would contribute to the analysis more than once. For each nucleotide in the ±2000 bp region, we calculated the average PhyloP evolutionary conservation score based on 100 vertebrate genomes27. Heat maps were ordered by the increasing width of enhancers from the strict set and centered at enhancer middle points. ChIP-Seq signals falling in ±2 kb around enhancer middle points were visualized in 401 bins as averages. Binned values were then broken into permilles and assigned to a color gradient ranging from red  to blue .ChIP heatmaps for transcription factor peaks were derived from ChIP-Seq peaks in the ENCODE consortium . Number of peaks were aggregated for coverage per bp and used as TFBS signals in the heatmap. H3K27ac and H3K4me1 ChIP-Seq signals used for equivalently arranged heat maps were downloaded from the Roadmap Epigenetics Consortium2 transformed with a pseudocount of 0.25  and cpm(log = TRUE)). The R-function cor.test was used to test for significant Pearson correlations between pairs of TSSs and enhancers where the TSS peak and enhancer midpoint were within ±500 kb of each other. Resulting P-values were FDR corrected using the Benjamini-Hochberg method. TSS-enhancer correlations with FDR < 0.05 and a positive correlation were considered significant for the following analyses.TSS-enhancer linkage prediction was assessed by CAGE expression correlation across subjects. Prior to correlation calculation, the TSS and enhancer expression matrices, for all samples including CDi and UCi, were TPM-normalized using edgeR’s RLE-method and logConsecutive enhancers from the strict set were chained together if their distance was no longer than 15 kb, producing enhancer clusters with varying number of enhancer members. An enhancer cluster was defined as linked with a TSS if at least one of its enhancer members had a significant and positive correlation with the TSS, as defined above.53 (with blind = TRUE) to improve linear modeling of the data by dampening the mean-variance trend inherent in count-type data. Principal Component Analysis (PCA) was used to explore major expression patterns in the dataset using the prcomp function from base R . This revealed four major groups which were not related to any specific part of the study design, but was correlated with the date, on which the individual samples were prepared  from the sva R-package54, after first performing a variance stabilizing transformation with blind = FALSE  was used as input to the PLS-DA with components set to two (comps = 2). 95% confidence ellipses were added using stat_ellipse from ggplot2 (with type = ”t”).We defined an expression matrix consisting of CAGE-defined TSSs (TCs as defined above) and their summed number of CAGE tags for each subject. Raw counts were normalized to log-TPM values and subjected to a variance stabilizing transformation using the varianceStabilizingTransformation function from the DESeq2 packageedgeR package was used to test for differential expression at both TSS- and gene-level using the GLM quasi-likelihood framework . As described in the main text, only CDa, UCa, and Ctrl samples were included in this analysis. The four major batches were included as blocking factors . To obtain the final sets of differentially expressed TSSs (or genes) used in the main text, we filtered TSSs (or genes) with |log2 fold change| > 1 and FDR < 0.05. The IBDup/IBDdown sets correspond to TSSs (or genes) with a positive/negative log2 fold change in contrast i) and the CDspec/UCspec sets correspond to TSSs (or genes) with a positive/negative log2 fold change in contrast ii), where a positive log2 fold change corresponds to higher expression in CDa compared to UCa (and vice versa). The diffSpliceDGE function was used to identify differential TSS usage within annotated genes. The Simes method was used to aggregate TSS P-values to gene level. To decide whether including additional covariates would improve modeling of the data, we added effects of various medical labels, such as medication, smoking, gender, etc. to the design matrix described above, and used limma-voom to test for differential expression in a similar manner (due to the higher speed of limma compared to edgeR). Most extra covariates did not have any significantly differentially expressed genes, except minor effects for gender and SASA treatment. Since these were far smaller than the effects of either inflammation or batch, we chose not to include them in the main analysis shown in the main text  was used for the identification of differentially expressed enhancers based on the normalized data30. EBSeq assumes that any given data row  can follow one of a number of possible expression patterns, corresponding to different plausible hypotheses about that specific enhancer. For the three conditions considered here, there are a total of five possible patternsBecause edgeR analysis has limited power for globally lowly expressed entities like enhancer RNAs, we used an alternative approach to assess differential expression between conditions for these data. The enhancer RNA data were normalized using the median-based method implemented in the DESeq package (default)Pattern 1: Enhancers expressed at the same level in CDa, UCa, and Ctrl .Pattern 2: Enhancers with the same expression level in CDa and UCa, and a different expression level in Ctrl .Pattern 3: Enhancers having similar expression levels in CDa and Ctrl, while being expressed differently in UCa (enhancers specific for UCa).Pattern 4: Enhancers having similar expression levels in UCa and Ctrl, while being expressed differently in CDa (enhancers specific for CDa).Pattern 5: Enhancers expressed differently in all three conditions.r- and q-parameters are inferred from the data. For any given enhancer, these parameters may be identical across conditions  or different across conditions . Different enhancers each have their own set of parameters, but all q-parameters are assumed to be related, in that they are drawn from a higher-level beta distribution whose (hyper) parameters are also estimated from data. This multi-level modeling approach results in “borrowing of strength” (inference from data on one enhancer will help inform inference for other enhancers), and “shrinkage” . Since the majority of all enhancers are not differentially expressed the approach is conservative  and thereby helps avoiding issues with multiple testing.For each enhancer in the dataset, EBSeq computes the posterior probabilities that it follows each of these five patterns. EBSeq also produces posterior estimates of the fold change in expression levels between each pair of the three conditions for any given enhancer. Another feature of the EBSeq method is that it uses hierarchical (or multi-level) modeling: the expression level (read count) of individual enhancers is assumed to follow negative binomial distributions whose EBMultiTest function in the EBSeq package with maxround set to 100 iterations. Convergence was checked by inspecting plots of the hyper-parameters Alpha and Beta and the mixture parameter P and ensuring their values had settled at stable values at the end of the iterations. The function QQP was used to generate QQ-plots confirming that the beta prior used in the analysis was appropriate (data not shown). We used a posterior probability cutoff of 0.9 for deciding when to label an enhancer as differentially expressed, corresponding to a false discovery rate of 0.1. Labeled enhancers were then reconciled with the classification of differential expression made for TSSs as follows (P = posterior probability):Specifically, the analysis was performed using the up: P(Pattern 2) > 0.9 and (CDa and UCa positive log fold change vs. Ctrl)IBDdown: P(Pattern 2) > 0.9 and (CDa and UCa negative log fold change vs. Ctrl)IBDspec: P(Pattern 4) > 0.9CDaspec: P(Pattern 3) > 0.9UCaThere is no simple way of accounting for batch effects in the software used here, and we therefore performed the analysis independently for the two batches. Results reported in this paper are for the larger batch (58 subjects), but we found very similar results for the smaller batch  using the gProfiler function. Default settings were used, except the background set  was set to all expressed genes. The results of the enrichment analysis on each of the four sets can be found in Supplementary Data For differential expression analysis performed at the gene-level, we performed enrichment tests for GO-terms using the gProfiler online toolhttp://fantom.gsc.riken.jp/5/datafiles/phase1.1/extra/Sample_ontology_enrichment_of_CAGE_peaks/. For each TSS, we noted whether it was differentially expressed and if it overlapped (based on genomic coordinates) with a FANTOM5 cell-specific tag cluster. We used the table function from base R to create a 2 × 2 contingency table for differential expression statistics and cell specificity, and tested for independence between rows and columns of this table using a Fisher’s Exact test (via R’s fisher.test). P-values for each cell category were corrected for multiple testing using the Benjamini–Hochberg method. The complete table of statistics for all cell type terms can be found in Supplementary Data For TSSs that were differentially expressed, we performed enrichment tests for FANTOM5 cell specificity of TSSs. We downloaded the table of cell type specificity of CAGE tag clusters from up, IBDdown, as defined above), we extracted the genomic positions corresponding to −500 and +100 bp around each such TSS peak. Background promoters were defined as corresponding regions from TSSs, which were not part of any of the four differential expression sets . For enhancer regions, we selected enhancer regions that were differentially expressed in the respective sets . We extracted the genomic positions corresponding to ±300 bp around the midpoint of each such enhancer region. Background enhancers were defined as corresponding regions from enhancers not part of any differentially expressed set. Next, we used the HOMER tool31 to score enrichment of sites corresponding to known motifs in the regions and background described in the previous paragraph . Sequence logos for these motifs were acquired from http://homer.salk.edu/homer/motif/HomerMotifDB/. HOMER motifs were manually paired with CAGE-defined transcription factor expression (ComBat-corrected gene-level expression).Promoter regions were defined as follows: for each TSS defined as differentially expressed in the respective sets (IBDup enhancers) by constructing a contingency table as defined in Supplementary Table ChIP-Seq peaks were downloaded from the ENCODE consortium . We analyzed the enrichment of each set of TF peaks in a given set of enhancer regions  and the background regions as singleton enhancers (not part of any cluster).Enrichment of a given TF in the region of interest was defined as entclass1pt{minimaIn order to create an initial candidate list for machine learning-based selection and classification, we reasoned that any single prioritization method has its own disadvantages. Therefore, we performed an ensemble approach where we integrated the results of multiple analysis methods each aimed to extract TSSs with high power to distinguish the subject groups. As our main goal was classification by machine learning, we will refer to these TSS regions as “features” in this section (instead of biomarkers as in the main text). Because the shared inflammatory response in UCa and CDa vs. Ctrl was strong, we divided our analysis into an IBD set of features (shared CDa and UCa up- or downregulation vs. Ctrl), and a set of features corresponding to features differentially expressed between UCa and CDa.i.18. In a similar fashion to the quasi-likelihood implementation explained above, standard edgeR (fitGLM and glmLRT) was used to test for effects of CDa vs. Ctrl, UCa vs. Ctrl, CDa vs. UCa, and CDa&UCa vs. Ctrl (shared inflammatory response), while controlling for batch effects. Extracted TSSs had a Benjamini–Hochberg FDR < 0.05 and a |log2 fold change| >1, for a total of four sets.edgeRii.plsDA function from the R-package DiscriMiner (https://cran.r-project.org/package=DiscriMiner) was used to perform PLSDA with two components on the variance stabilized expression values from above, using all samples, including quiescent patients, in three groups: CDa+ CDi, UCa+ UCi, and Ctrl. The 500 TSSs with the highest and lowest loadings for the component separating Ctrl from the remaining samples were extracted for a total of 1000 TSSs.Partial Least Squares Discriminant Analysis (PLSDA): The iii.54 followed by PCA: ComBat corrected values were used for PCA analysis. We identified the component that contributed the most to the difference between the two states and sampled 1000 TSSs from each side with the highest rotation values. We focused on the PCAs that gave the most explanations for each of the comparisons , producing two lists for IBD (from each side one list) and one list for CDspec and UCspec (from each side one list).ComBativ.57: Combat normalized data (as described above) was used for differential expression analysis using the limma package and the lmFit function. TSSs that passed the threshold of log fold change > |1| and FDR < 0.05 were taken into account making two lists for IBD (overrepresented in UCa vs. Ctrl and overrepresented in CDa vs. Ctrl) and one list for CDa vs. UCa.ComBat followed by limmav.randomForest function from the randomForest R package (https://CRAN.R-project.org/package=randomForest) on Combat-normalized TC expression data . To circumvent the “Large p, small N” problem with our data , which makes difficult to find the most predictive features, we used an iterative feature selection approach where we in each step remove a subset of the least important features. More specifically: in each iteration we first evenly divided all the data into subsets each containing max 500 features. For each of these data subsets a RF was trained to classify the patient labels based only on the data in that particular subset. Next, for each of the data subsets, we removed the 5% of features that had the lowest classification power  resulting in a list of features that is analyzed in the next iteration of the procedure. To make sure an important feature was not lost due to a challenging subset of data, in each iteration we randomly divided all our data into subsets 10 times. In other words, features had several chances of showing its importance. This iterative procedure was terminated when less than 200 features were left. Each RF was made with default parameters except specifying that 501 trees per forest should be generated and specifying the number of variables randomly sampled as candidates at each split by setting the “mtry” parameter to (sqrt(x) −1) * 2, where x was the number of features supplied to the individual RF. Using this approach we generated four candidate lists: two that enabled classification of IBD patients (CDa and UCa) from healthy patients (Ctrl) and two lists that aimed at differentiating between CDa and UCa. For the two lists in each category, one was based on the full data set  and the other was based on a high confidence dataset (N = 9699 TCs). The high-confidence data set was obtained by requiring that (i) the tag cluster was annotated as “5_prime_UTR”, “intergenic”, “known_alternative_cds”, “known_alternative_tss”, “proximal” or “tss”. (ii) the width of the TC was in the range of 2–100 nucleotides (both included). (iii) the TC was expressed more than 10 TPM in at least 3 subjects.Random Forest analysis. We utilized the inherent ability for Random Forests (RFs) to rank features by their importance for classification accuracy. To do this we used the The ensemble analysis consisted of the following components:As a summary, the ensemble produced 10 candidate sets for separating CDa and UCa from Ctrl  and 5 candidate sets for separating CDa and UCa .Next, we overlapped the lists from each comparison to find a consensus set of classification candidates. For the IBD vs. Ctrl comparison, we selected features that appeared in at least 9 out of 10 lists, resulting in 63 features. From the UC/CD set we selected the 169 features that appeared in at least 2 out of 5 lists. The reasoning behind this unbalanced selection was to have a higher number of features for the more difficult task of distinguishing CDa from UCa, compared to the easier task of distinguishing Ctrl from UCa or CDa.The two lists were merged and a short list of expert-curated known and novel biomarkers (21 TSSs and 10 enhancers) was added. The combined list was then further pruned by manual curation, taking into account overall expression strength and genomic loci complexity. In total, the initial list comprised 263 features, which was used for initial classification using CAGE data (see below).For qPCR analysis, the 263 features identified above were assessed for qPCR primer targeting suitability. 181 candidates were taken into the primer design stage, where genomic complexity and primer design feasibility was evaluated manually.58 aiming for an amplicon length of 70–110 bp and a primer melting temperature of 60 °C calculated by Breslauer thermodynamics. Primers were designed to be intron-spanning when possible, and each designed primer-set was analyzed with the UCSC browser in-silico PCR tool in order to ensure a unique region would be amplified. Primers were synthesized by Integrated DNA Technologies (IDT). Primer sequences are shown in Supplementary Data Primers were designed using Primer3 (v.0.4.0)59. Total RNA  was converted into cDNA using QuantiTECT Reverse Transcription kit (Qiagen), using a mix of random and oligo-dT primers, as per the manufacturer’s instructions. We performed two separate cDNA reactions for each RNA sample. Preamplification was performed using TaqMan PreAmp Master Mix (Applied Biosystems). A 500 μl primer mix (200 nM) combining all primers to be used on the Fluidigm plate was prepared by pooling 5 μl of all primer pairs (20 μM) and filling up the remaining volume with low EDTA TE-buffer . TaqMan PreAmp Master Mix (5 μl) was mixed with 2.5 μl of the 200 nM primer mix, 2.5 μl diluted cDNA, and incubated at 95 °C for 10 min, followed by 15–21 cycles of 95 °C for 15 s and 60 °C for 4 min (the number of cycles depended on the expression measured by CAGE). 16 U of Exonuclease I (New England BioLabs) was added to the preamplified cDNA, and incubated 30 min at 37 °C, 80 °C for 15 min. The preamplified cDNA was diluted with low EDTA TE-buffer  either 1:5 if the cDNA was preamplified for 21 cycles or 1:10 if the cDNA was preamplified for 19 or 15 cycles.The cDNA synthesis and preamplification for cohort 1 was performed as described previously™ 96.96 Dynamic Array (Fluidigm) following the protocol described previously59. The following cycle parameter was used: Thermal Mix with 2 min at 50 °C, 30 min at 70 °C, 10 min at 25 °C, UNG and Hot start with 2 min at 50 °C, 10 min at 95 °C, followed by 35 cycles with denaturing for 15 s at 95 °C and annealing/elongation for 1 min at 60 °C. Melting curves were generated after each run to confirm a single PCR product . We performed reactions in duplicates. Non-template controls were used to indicate problems with sample contaminations or non-specific amplification. To assess potential DNA contamination, non-reverse transcriptase controls were used.RNA expression was analyzed by real-time qRT-PCR in the microfluidics system BioMark60 and NormFinder61, we identified the most stable reference genes of the 10 reference genes included on the plates. This was done separately for all plates run with the same number of preamplification cycles. The used reference genes for each preamplification batch are specified in Supplementary Data We used the Fluidigm Real-Time PCR Analysis software 3.0.2 (Fluidigm) to aquire Cq values. These were exported to GenEx (MultiD) for data pre-processing including normalization to reference genes, interplate calibration, individual PCR efficiency correction for each primer assay, and averaging of cDNA technical replicates. Using GeNorm62. The optimal value for the mtry parameter was found using 100 five-fold cross-validations using the caret package (https://cran.r-project.org/web/packages/caret/index.html) train-function and ntree = 1000. Feature importance for both classification tasks were extracted using the importance function with type = 2 (mean decrease in node impurity or gini index). Primer pairs with a high importance in both complete and one-vs.-rest classification were selected as the most predictive. In addition to these RF predictive features, six gene-based features were selected to validate pathology-based findings in the first cohort.The 161 features were reduced to 36 features by ranking the features for classification power. Briefly, a RF was trained to predict subject groups, both using all groups and one-vs.-rest specification (i.e. UCa vs. non-UCa) using the randomForest R package™ 192.24 Dynamic Array (Fluidigm) following the same protocol as used for the cohort 1 runs, with volumes adjusted to the 192.24 format, as described by manufacturer. Expression data  were acquired and treated similar to cohort 1 (see above).cDNA synthesis and preamplification for cohort 2 was performed as described above for cohort 1 with an adjustment of the number of pre-amplification cycles. In this cohort cDNA was either preamplified 15 or 20 cycles, depending on expression level of the primer targets. The preamplified cDNA was diluted with low EDTA TE-buffer  either 1:5 if the cDNA was preamplified for 20 cycles, or 1:10 if the cDNA was preamplified for 15 cycles. RNA expression was analyzed by real-time qRT-PCR in the microfluidics system BioMark™ 192.24 Dynamic Array (Fluidigm) as described for cohort 2. Monocyte samples and organoid samples were placed on two independent plates.cDNA synthesis and pre-amplification was performed as described for cohort 1 and 2, but with an input of 300 μg (organoids) or 440 μg (monocytes) of RNA in the cDNA synthesis due to limited amounts of RNA. RNA expression was analyzed by real-time qRT-PCR in the microfluidics system BioMarkremoveBatchEffect function from limma, giving the subject/group labels  as covariates and cohort and hospital of origin as batch effects and specifying the ‘robust’ method should be used for the linear models. To make organoid and monocyte data comparable with the batch corrected data a batch correction factor, obtained as the mean of the batch correction factors for the two hospitals, was applied to the organoid and monocyte data. Afterwards the batch corrected ∆∆Cq values were calculated in a two-step process: (1) x = 2(−1* ∆Cq). (2), ∆∆Cq = log2(x/min(x)).For each primer all ∆Cq values from cohort 1 and cohort 2 were batch corrected using the N = 16, 0.26% of total) were imputed via bagged regression trees using the preprocess function from the “caret” R package (https://CRAN.R-project.org/package=caret) specifying methods to the bagImpute function. Experimental practice and reporting were performed according to MIQE guidelines.For classification analysis of cohorts 1 and 2 (see below), only Ctrl, UCa, and CDa subjects were considered. We removed one subject and one primer pair from the analysis which both had >10% “NA” values from the ∆∆Cq Fluidigm data )−1).Inflammatory response in UCa or CDa as logii.2)−1)4 or 24 h TNFα response in gut epithelia organoid as logiii.2)−1)4 or 24 h TNFα response in monocyte as logOnly measurable ∆∆Cq Fluidigm data were analyzed . For each qPCR primer we defined the following quantifications:Median of responses across replicates or subject group  was then used to indicate the IBD or TNFα response of the group. Specifically, if the median IBD or TNFα response of a qPCR primer in a sample group was >0, it was labeled upregulated in the sample group, and vice versa. qPCR primers with missing median in any of the sample groups were discarded, resulting in analysis of 30 qPCR primers in Fig. For specific enhancer RNA expression validations Fig. , we usedrandomForest function from the randomForest R package (https://CRAN.R-project.org/package=randomForest). In this approach, the first step was a RF trained to distinguish IBD (CDa and UCa) from Ctrl and the second step was a RF trained to distinguish CDa from UCa. The two-step approach was chosen over an all-in-one approach because, based on five-fold cross-validation in the CAGE data, resulted in better CDa sensitivities (data not shown).For all three classification analyses in Fig. m indicating the number of variables randomly sampled as candidates at each split and n indicating the minimum size of terminal nodes (corresponding to the mtry and nodesize options in the R RandomForest package), we used a grid search approach testing each pairwise combination where x is the number of features supplied to the RF and y={1/2, 3/4, 1, 6/4, 2}. For each of the three classifications analyses performed, the final m and n parameters were chosen as the combination giving the highest overall accuracy in the two-step RF approach based on the average result of five-fold cross-validation in cohort 1. Apart from this all RFs were created with default parameters except specifying that each RF should have 1001 trees.To optimize the parameters most important for a RF model, To assess the accuracy of the classification model we performed 101 RF iterations of 5-fold cross validation. The only exception to this was the classification of subject labels on cohort 2, where 101 RFs were created based on data from cohort 1 and used for prediction in cohort 2. For each of the 101 iterations, the overall accuracy and CDa, UCa, and Ctrl-group-specific accuracy, sensitivity and specificity were calculated. For each of these, we reported the average and 95% confidence interval across the 101 iterations. The CDa, UCa and Ctrl-group specific performance measures were calculated by for each label recasting the problem in a binary setting (e.g. for the “Ctrl” label converting both CDa and UCa to “non-Ctrl”) and then assessing accuracy, sensitivity, and specificity. The overall accuracy was assessed on all three categories.To assess the observed vs. expected performance in classification, we trained 1001 RF models on randomly shuffled labels (again only based on cohort 1 data). These models were then used to predict the patient labels of cohort 2 and these predictions were compared to the true labels. Performance measures were calculated as described above.i, we randomly selected i features from the total set of features and performed five-fold cross-validation with the same parameters as identified by the grid search above. This procedure was repeated 11 times for each selected i. The average accuracy and 95% confidence interval of these repeated analyses were reported for each i.To analyze the relation between number of features in the model and classification accuracy, for a selected number of features referred to as 41 to perform the same classification as done with Random Forests in Fig. max_depth, min_child_weight and subsample), the final classifier was used on the cohort 2.We used the XGboost frameworkgwascat R-package (http://bioconductor.org/packages/gwascat/). To calculate linkage disequilibrium (LD), we used genotype data from whole-genome sequencing of Europeans. This was obtained from the VCF files from the 1000 Genomes CEU population release 20130502. We use only diallelic SNPs with <5% missing genotypes that were polymorphic in the CEU population39. SNPs from the GWAS catalog were merged with 1000G SNPs by genomic position, and PLINK63 was used to calculate LD between all SNPs within 500 kb (using the function plink -r2 -ld-window-kb 500 -ld-window 99999 -ld-window-r2 0.5). Pairs of SNPs were considered to be in LD if R2 > 0.75. LD clumps were found by expanding all SNPs with their associated LD-SNPs, and then reducing the set by merging overlapping regions.A recent version of the GWAS catalog was downloaded (2016-12-15) and lifted to hg19 using the https://github.com/dgrtwo/ebbr) package using the ebb_fit_prior and augment functions (with default arguments). Prior means were calculated as alphaz1− and added to the plot of shrunken proportions. LD-clump (one-sided) enrichment was calculated using the kegga function from the limma R-package57, but using user-supplied sets: universe was set to all TSSs/enhancers, de the respective differentially expressed sets and gene.pathway set to all promoters overlapping LD clumps for a GWAS disease. No trend based on an additional covariate was used.To assess LD clump enrichment, Empirical Bayes shrinkage was performed with the ebbr R-package (https://github.com/bulik/ldsc)64. The two analyses below were done based on recommended practice by the creators of the tool: https://github.com/bulik/ldsc/wiki/LD-Score-Estimation-Tutorial, https://github.com/bulik/ldsc/wiki/Partitioned-Heritability). LD-scores were calculated for TSSs, enhancers, IBDup and IBDdown sets (including both promoter (−500/+100 bp around TSS peaks) and enhancers (±300 bp around enhancer midpoints)). IBD SNP summary statistics were obtained from the International IBD Genomics Consortium, https://www.ibdgenetics.org/downloads.html (downloaded 2016-12-22). SNP summary statistics were processed using munge_sumstats.py (with settings -N-cas 12882 -N-con 21770 for sample sizes (as described in ref. 8). Base model enrichment statistics for this set of GWAS summary statistics can be seen in Supplementary Fig. 40. Proportions, enrichments and coefficients were obtained from each model and compared (Supplementary Fig. Partitioned heritability analysis was performed using the ldsc tool from (GSE95437. Microfluidics qPCR data are available in Supplementary Data CAGE data from this study has been deposited in GEO database under accession number Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18


The data presented in this article are related to the research article entitled “The expression profiles of circRNAs in lung tissues from rats with lipopolysaccharide-induced acute respiratory distress syndrome (ARDS): A microarray study.”   Specialization TableValue of the data•We first performed microarray detection on the expression profiles of circRNAs in lung tissues from rats with lipopolysaccharide-induced ARDS. The data are important and can be consulted by the other researchers in future research in this research area.•Using circRNA microarray data, the other researchers can compare the circRNA expression profiles in ARDS lung tissues with others methods.•This data allows the other researchers and research students to extend the statistical analyses.1https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102523.The dataset of this article provides information on the expression profiles of circRNAs in lung tissues from rats with lipopolysaccharide-induced ARDS. Microarray and sample annotation data were deposited in Gene Expression Omnibus under accession number GSE102523. Direct link to deposited data is 22.1Lung tissue samples were collected from Sprague Dawley rats undergoing lipopolysaccharide-induced ARDS and normal rats 2.2Sample labelling and microarray hybridization were performed according to the Arraystar's standard protocols  It is worthy of note that part of the probes in rat chip were designed by using mouse and human orthology sequence, hoping to find similar circRNAs in rat through the junction site on account of High Gene homology.2.3P-value less than 0.05 and fold change greater than 2.0 were considered as significantly differentially expressed circRNAs.The acquired scanned images were imported into Agilent Feature Extraction software (version 11.0.1.1) for raw data extraction. The R software package limma package (R version 3.1.2) was used for quantile normalization and subsequent data processing. After quantile normalization of the raw data, low intensity filtering was performed, and the circRNAs that at least 2 out of 6 samples have flags in “P” or “M”  were retained for further analyses. CircRNAs exhibiting The experiment workflow is listed in


Nucleotide-binding oligomerization domain-containing protein 2 (NOD2) may play an important role in the outcome of kidney cancer patients. To explore the relationship between NOD2 and the prognosis of kidney cancer patients, a databank-based reanalysis was conducted.Data related to kidney cancer patients at least with survival information, was obtained mainly from The Cancer Genome Atlas (TCGA). Some clinical data, not available online, was collected by personal email to the author. Then, we reanalyzed all the data in order to make a conclusion about the relationship between NOD2 gene and the prognosis of kidney cancer patients.p-value of the log-rank testing equality of survival curves , p = 0.001005) , while a box plot across risk groups, including the p-value testing for difference using t-test (or f-test for more than two groups) was shown. There was a statistical significance for the p value of the result (p < 0.01 ). The similar results could be seen in KIRC and the fourth data (including 468 samples).A total of 1953 samples with NOD2 information from four databanks of The Cancer Genome Atlas (TCGA) were enrolled in this study. The results of KIPAN showed the Kaplan-Meier curve for risk groups, concordance index, and The status of NOD2 gene maybe a biomarker for the survival of kidney cancer patients. The nucleotide-binding oligomerization domain 2 (NOD2)-like receptors (NLRs) belong to evolution-conserved pattern recognition receptors (PRRs) family locating in cytoplasm, closely related to responses, innate immunity and adaptive immunity . SchrodeFormerly, it was mainly considered to be a pathogenic factor for immune inflammations –8. With A total of 1953 samples with NOD2 information from four databanks of TCGA were enrolled in this study. Of 1953 subjects with survival status, 468 cases were along with data about clinical grade and stage. The overall characteristic of them can be seen in Table p-value of the log-rank testing equality of survival curves , p = 0.001005) of KIPAN, as recommended by Bovelstad HM [p-value testing for difference using t-test (or f-test for more than two groups) was shown in Figure P value of the result (P < 0.01 ). From the above analysis, we found that the expression level of NOD2 gene might be a bad signal for the prognosis of kidney cancer patients. Then,we analyzed another two data from TCGA, named KIRC and KIRP, but the similar results were only observed in KIRC , p = 0.4142), the p value of KIRP is of no statistical significance.We analyzed the data only with survival information first. Among all the results, green color represents low-risk groups, and red color means high-risk groups. Figure C Figure . As is sp = 0.001002), which p value was of notable significance. Then,the stratification analysis of 468 samples was made according to grade, stage, pathology, and death of the tumor data.. The Log−Rank Equal Curves were obviously separated from each other in Figure To further analyze the data about NOD2 gene expressive level, we tested the NOD2 in the fourth database from TCGA that contains 468 samples with survival, grade and stage data using the SurvExpress stratification functionality. The overall results of the fourth data were summarized in Figure There are nearly 270,000 patients suffering kidney cancer, which leads to over 115,000 deaths each year . As knowThough, seven FDA-supported agents, targeting the VHL pathway, had been admitted for the therapy of patients with advanced kidney cancer, further genomic studies, such as whole genome sequencing, gene expression patterns, and so on, will still be needed to get a complete understanding of the genetic basic mechanism of kidney cancer and the kidney cancer gene pathways and, most importantly, to provide the foundation for the development of effective forms of therapy for patients with the disease . More anThe NOD2-like receptors (NLRs) belong to evolution-conserved pattern recognition receptors (PRRs) family locating in cytoplasm, closely related to responses, innate immunity and adaptive immunity . NLRs arWith the development of whole genome sequencing, plenty of data about NOD2 gene in cancers can be acquired online, such as TCGA, Gene Expression Omnibus (GEO) and so on. Previously, most of us paid our attention to the relationship between NOD2 and common cancers except for kidney cancers. Some databases contained some information about the NOD2 gene, but we ignored its existence. Therefore, we tried to reanalyze the data online to make sure the role of NOD2 in kidney cancer patients.p-value of the log-rank testing equality of survival curves, were shown in corresponding Figures The data used for reanalysis represented a wide variety of sample sizes. To achieve the results that would be representative of the greatest number of study parameters possible, all reanalysis included as much data information as possible. The analysis results of four TCGA data, including the Kaplan-Meier curve for risk groups, concordance index (CI), and p-value of the log-rank test was of statistically significant. The concordance index (CI) is one of the most commonly used performance measures of survival models. It can be interpreted as the fraction of all pairs of subjects whose predicted survival times are correctly ordered among all subjects that can actually be ordered. In other words, it is the probability of concordance between the predicted and the observed survival. The concordance index (CI = 56.57 and 56.48) of KIPAN and KIRC suggest that the the fraction of all pairs of samples whose predicted survival times are correctly ordered.The NOD2 expressive level seemed to be an adverse event for the survival of kidney cancer patients in KIPAN and KIRC database, which is obvious in Figure p = 0.4129, R^2 = 0/0.756,Risk Groups Hazard Ratio = 0.77 (conf. int. 0.41 ~ 1.44), p = 0.4142). After checking up the primary data of KIRP, which is listed in the supplementary material, we could not find out the definite reason for it. In order to collect more information about the NOD2 gene in kidney cancers and find out the reason for the result of KIRP, we use SurvExpress to put stratification of 468 samples into practice according to death, grade, stage, and pathology. The detail of the reanalysis that was similar to KIPAN and KIRC ,was arranged in the form of graphs in Figure p value was not statistically significant. I deduced that the emergence of such results may be related to limited samples assigned to different groups.The deeper analysis results of the fourth database also showed the inclination that the NOD2 expressive level was an unfavorable signal for the survival of kidney cancer patients.The reanalysis results of KIRP, shown in Figure A lot of researches revealed that the NOD2 gene may be relevant to caners –24, espehttp://bioinformatica.mty.itesm.mx/SurvExpress). It includes a tutorial that describes the analysis options, plots, tables, key concepts related to survival analysis, and representative methods to identify biomarker from gene expression data. The Characteristics of all the data was shown in Table We got all the data mainly from TCGA using keywords related to NOD2, kidney cancer, survival, and gene expression technologies. From TCGA, all data were obtained at the gene level (level 3). RNA-Seq counts data were log2 transformed. Then, we analyzed all the data with the help of online tools as ITTACA, KMPlot, Recurrence Online, bc-GeneExMiner, GOBO, PrognoScan and SurvExpress. We found that SurvExpress was most convenient online tool for this analysis, which is available in  using the survival package. Second, we can make sure a weight for each gene but using the values from the Cox fitting.During the process of this analysis, we mainly paid our attention to the prognostic index (PI), also named as the risk score sometimes, usually taken to generate risk groups and the linear component of the Cox model . PI = β12 or Fisher's exact test, when appropriate. A value of P < 0.01 was considered to be significant.The concordance index (CI) is one of the most commonly used performance measures of survival models. It can be interpreted as the fraction of all pairs of subjects whose predicted survival times are correctly ordered among all subjects that can actually be ordered . In othep-value is chosen as the split point for the algorithm. This procedure is generalized for more than two groups repeatedly optimizing one risk group at the time until no changes are found. All the process of risk estimation can be easily finished by the online analysis tools [We take the first method to generate the risk groups splitting the ordered PI  and then leave equal number of samples in each group to mark the number of risk groups. If there are two risk groups, we will split the PI by the median. The second method producing risk groups takes an optimization algorithm for the ordered PI. In a word, for two groups, the log-rank test will be implemented along all values of the arranged PI. Then,the minimum is tools . In this


NSD1 and NSD2, with findings confirmed in an independent validation cohort consisting of 63 laryngeal cancer patients. Intriguingly, NSD1/2 mutations are not prognostic for nonlaryngeal SCCHN. These results provide an immediately useful clinical metric for patient stratification and prognostication.Squamous cell carcinomas of the head and neck (SCCHN) affect anatomical sites including the oral cavity, nasal cavity, pharynx, and larynx. Laryngeal cancers are characterized by high recurrence and poor overall survival, and currently lack robust molecular prognostic biomarkers for treatment stratification. Using an algorithm for integrative clustering that simultaneously assesses gene expression, somatic mutation, copy number variation, and methylation, we for the first time identify laryngeal cancer subtypes with distinct prognostic outcomes, and differing from the non-prognostic laryngeal subclasses reported by The Cancer Genome Atlas (TCGA). Although most common laryngeal gene mutations are found in both subclasses, better prognosis is strongly associated with damaging mutations of the methyltransferases  The authors use an integrative clustering approach to identify two laryngeal cancer clusters with distinct prognosis and show that mutations damaging the NSD1 and NSD2 methyltransferases segregate to the cluster with favorable prognosis, and independently predict longer survival in patients with laryngeal, but not other head and neck cancers. Squamous cell carcinomas of the head and neck (SCCHN) are highly heterogeneous, arise from multiple anatomical sites, and are characterized by aggressive local invasion and overall poor prognosis. While oropharyngeal SCCHN is subclassified into human papillomavirus-initiated (HPV+) and HPV-negative (HPV−) disease, most HPV– cases arise from the oral cavity and larynx, with a smaller number of cases originating from the hypopharynx and oropharynx. Tobacco use and alcohol consumption are major risk factors for laryngeal carcinomas, which account for nearly 25% of SCCHN and are associated with high recurrence after treatment and limited overall survival (OS). Patients who are candidates for organ preservation are typically treated with a combination of chemotherapy and radiation, whereas those with high T stages often undergo surgery followed by adjuvant treatment.2, using mutation4, epigenomic6, and expression profiling7, including The Cancer Genome Atlas (TCGA)8, has focused on tumors of the oral cavity and oropharynx, and the biology of HPV+ vs. HPV– disease10. The TCGA analysis identified laryngeal cancer subclasses, of atypical, classic, basal, and mesenchymal types based on mRNA profiling. However, these subclasses were not prognostic. Other studies using TCGA data have shown prognostic differences in SCCHN subtypes, including larynx, based on differences in intratumor heterogeneity10. However, no comprehensive analyses have identified prognostic molecular differences informative for the mechanism.Because treatments can be associated with significant morbidity, there is intense interest in identifying prognostic biomarkers and defining tumor subtypes that more accurately identify high-risk patients who may benefit from intensified therapy or novel targeted therapy trials. Molecular characterization of SCCHN to identify tumor subclasses associated with clinical outcomes11 that simultaneously assesses DNA mutation, copy number variation (CNV), methylation, and gene expression, for 256 cases of SCCHN for which complete data for these parameters and clinical prognosis are available in the TCGA. This analysis identified two distinct clusters of laryngeal cancer, associated with strong prognostic value, and showed that mutations in the genes NSD1 and NSD2 entirely segregated to the cluster with favorable prognosis. Subsequent validation in an independent set of 63 laryngeal cancer samples again demonstrated that mutations in NSD1 or NSD2 are independent favorable prognostic biomarkers for laryngeal cancer.In this study, we analyzed data for SCCHN using a recently developed integrative clustering approach11, we performed integrative analysis for 256 cases of SCCHN for which complete data on DNA mutation, CNV, methylation, and gene expression were available in the TCGA. This distinguished 5 clusters that segregate tumors by disease site and HPV status  and 2 (37%), with the remaining 24% distributed throughout clusters 3, 4, and 5. While TP53 mutations were predominant in oral and laryngeal clusters, mutation of TP53 was low in cluster 5 (27%), reflecting the high incidence of HPV + oropharyngeal tumors, where TP53 is inactivated posttranslationally by the virally encoded E6 oncoprotein2. The recently described prognostic 3pdel/TP53 subclass12  and 4 (39%), with the remaining 36% distributed evenly across the other clusters. SCCHN tumors in cluster 3 were associated with significantly higher OS vs. those in cluster 4 (2-year OS: 81.6% (95% CI 63.3–91.3%) vs. 54.6% (95% CI 37.9–68.5%); log-rank test: p = 0.010), and OS probability of 77.5% (95% CI 50.1–91%) vs. 53%  , but not copy number changes. Among the genes reported as most commonly mutated in laryngeal cancer , most of them were mutated at similar frequencies in L1 and L2   Fig. . No sign47) Fig.  and 9 an47) Fig.  and 10,  L2 Fig. . However01) Fig. , and 15 NSD1 in the complete TCGA set of 116 laryngeal tumors for which at least mutational data were available among a total of 526 SCCHN cases  was seen for high-impact NSD1 mutations  in comparison to low-impact or no NSD1 mutations  , vs. 32.5 months for those with both genes of wild type  . The results were specific to mutation rather than altered gene expression, as among cases with NSD1 and NSD2 wild-type alleles in oropharynx and hypopharynx tumors  Fig. . We furt02) Fig. . In cont OS Fig. . No signors Fig.  and laryors Fig. , NSD1 mRNSD1 (21/63)   vs. the 39 with benign or no mutations  (log-rank test: p < 0.001)  and gender (Fisher’s exact test: p = 0.022).To validate these results, we compiled a set of 63 previously unstudied laryngeal tumors from surgical resections obtained before any treatment, including chemotherapy or irradiation Table . Using F63) Fig.  or NSD2 63) Fig.  in 24 of01) Fig. . In exteNSD1 depletion, DNA hypomethylation, and differential expression of some miRNAs8, including enriched expression of miR-200a/b, which is involved in epithelial–mesenchymal transition (EMT)14. However, that work analyzed a combined set of oral and laryngeal tumors. Considering only the laryngeal tumor set, we find several miRNAs to be differentially expressed between L1 and L2  are urgently needed. Using integrative clustering of four different genomic data of SCCHN including mutation, copy number, gene expression, and methylation obtained from TCGA, we identified a novel laryngeal cluster with a significant difference in OS where the prognostic laryngeal cancer subtype is associated with mutations in NSD1 cause Sotos syndrome18, and are implicated in a number of cancers19, as is mutation of NSD220. As lysine methyltransferases, NSD1 and NSD2 influence gene expression by methylating specific histone sites  and nonhistone targets such as NF-κB22. Mutations in NSD1 have been associated with extensive demethylation of DNA in CpG islands at promoters, as well as intergenic regions23, compatible with the significantly lower levels of methylation observed in cluster L1  in cases of SCCHN was associated with damaging NSD1 mutations29, although this study did not identify a prognostic role for NSD1 mutations, consider a possible role for NSD2, or assign prognostic value specifically to tumors of the larynx. While they have been reported as tumor suppressive in neuroblastoma and glioma30, amplification and overexpression of NSD1  have been reported as oncogenic in some settings, suggesting context-dependent action31. While other studies8 have cataloged distinct prognostic molecular features for HPV + and oral tumors, this work for the first time distinguishes molecular subclasses of laryngeal tumors with markedly different prognosis, with a positive outcome specifically associated with mutation of NSD1 and NSD2. This classification markedly differs from earlier TCGA analyses, which segregated laryngeal tumors into non-prognostic “classical” and “atypical” subclasses lacking the separation of NSD18  and decitabine (5-aza-CdR) as promising agents in clinical trialsNSD mutation status may play an important role in the efficacy of such inhibitors in patients with laryngeal cancer. Finally, although no significant association between clinical parameters  and NSD1/2 mutation status was observed in either the discovery or validation sets, further evaluation in additional independent cohorts is required. In sum, this work nominates NSD1 and NSD2 as rapidly exploitable prognostic biomarkers and targets for functional scrutiny in laryngeal cancers.Our study suggests that https://tcga-data.nci.nih.gov/tcga/) (August 2014 data freeze) and The Cancer Genome Atlas Genome Data Analysis Center (GDAC) Firehose website (https://confluence.broadinstitute.org/display/GDAC/). To maintain coherency of analysis across different data layers and cancer types, we used normalized data classified as levels 2 and 3 in the TCGA data hierarchy37 as the starting point of our analysis. Of the 528 samples that are available from TCGA consortium, 256 samples had profiling data for all 4 genomic data sets , and were used for integrative clustering.Molecular data sets describing somatic mutations, CNV, gene expression based on RNA-seq, and methylation for 256 SCCHN cases were obtained from the TCGA data portal 39, Polyphen, and a Grantham score available through SeattleSeq annotation server (http://snp.gs.washington.edu/SeattleSeqAnnotation138/). MutationMapper40 was used to display mutations graphically. For analysis of OS associated with NSD1 mutation in larynx cancer, the full set of 116 laryngeal tumors present in the 526-specimen SCCHN TCGA collection containing mutational data was analyzed.For somatic mutation data, all genes with nonsynonymous or nonsense variants from among these 256 cases were identified and used to construct a binary matrix after applying a 5% mutation frequency cutoff to remove infrequently mutated genes. For copy number variation data, we applied the strategy described in the supplement of Mo et al.2-transformed and subjected to a variation filter: the 5000 genes with the highest median absolute deviation (MAD) across all 256 SCCHN specimens were retained. Similarly, level 3 methylation data  were filtered to retain 2000 probes with the highest MAD across samples. These β values were then logit-transformed into M-values41.For gene expression data, gene-level RPKM values (reads per kilobase per million mapped reads) from the TCGA data portal were log11. A plot relating the percentage of explained variation to the number of clusters suggested five clusters as an appropriate choice for clustering the 256 SCCHN samples  values between chromosome 3p25 and 3p13 was calculated. Cases with MSMs < −0.342 are identified as cases with 3p loss. These samples were further segregated into two classes based on the mutant and wild-type status of TP53 gene.The resulting four matrices were used as input to integrative clustering analysis using iClusterPlusWe also applied the methods described above to cluster the 69 laryngeal samples among the 256 fully characterized SCCHN samples. The processing of the data described above for expression, CNV, and methylation was applied to larynx samples. A submatrix of the SCCHN mutation matrix representing the larynx samples was extracted. The resulting data matrices were analyzed using iClusterPlus; given the relatively small sample size, a two-cluster solution was selected  and were used for enrichment analysis with Gene Set Enrichment Analysis (GSEA)16, which was run using default parameters and ‘classic’ as the enrichment statistic. For NSD1 wild-type SCCHN tumors, to evaluate whether NSD1 gene expression was associated with survival differences in laryngeal or oral cancer cases, we used Voom-normalized45 RSEM (RNA-Seq by Expectation Maximization) expression data. NSD1 expression was trichotomized into low (<or = 15% quantile), intermediate (>15% and <85% quantile), or high (> or = 85% quantile), and survival distributions were estimated and compared using Kaplan–Meier method and log-rank test, respectively, on the resulting groups.We identified differentially expressed genes between clusters L1 and L2 using DESeq2p-values were calculated with Benjamini–Hochberg method, and a 5% FDR cutoff was applied to select differentially expressed miRNAs. To visualize the differentially expressed miRNAs, we applied hierarchical clustering on miRNA expression data using average linkage clustering with Pearson’s uncentered correlation as a distance measure. The resulting clustered data table was used as input to Java TreeView (http://jtreeview.sourceforge.net) to generate a heatmap. In order to assess the enrichment of epithelial–mesenchymal transition signatures in L1 and L2 clusters, we applied single-sample GSEA (ssGSEA)46 using the EMT signatures curated from Byers et al.15 and the available gene sets from MSigdb resource. The resulting enrichment scores were depicted as waterfall plots.We evaluated the differential miRNA expression between L1 and L2 clusters based on miRNA quantification reported as reads per million miRNA mapped, establishing significance by applying Wilcoxon rank sum tests. FDR-adjusted 47 ,  columns indicate TCGA mRNA classification  and laryngeal L1 and L2 tumors. The bands between these two groups indicate the intersection of tumor samples between two groups. The width of the band shows the level of intersection.The functional significance of genes that contributed to segregation of five SCCHN and two laryngeal clusters was assessed using enrichment of gene ontology terms (GO)  present in the DAVID bioinformatics resourcesn = 68) and FCCC (n = 31) biosample repositories for evaluation of NSD1/2 mutation status. Based on exclusion criteria, we excluded 15 and 7 samples from JH and FCCC cohort from the analysis, respectively. Samples with low DNA yield or quality were eliminated . After accounting for exclusion and inclusion criteria listed above, we obtained 63  samples from both institutions. All samples were reviewed by a pathologist to reconfirm the diagnosis prior to DNA extraction. Frozen tissues were cut into 5-μm sections, stained with H&E, and examined by light microscopy. Lesions with a low neoplastic cellularity (<70%) were additionally microdissected to remove contaminating normal cells before DNA extraction. Paraffin-embedded slides were microdissected to obtain > 70% neoplastic cells. Neoplastic cellularity was estimated from the sequential slides, which highly reflect the cellularity of the section used for DNA sequencing.A validation set for laryngeal tumors was compiled from the biosample registries of Sidney Kimmel Cancer Center at Johns Hopkins School of Medicine (JH) and Fox Chase Cancer Center (FCCC) .Genomic DNA was isolated from fresh frozen samples by the QIAamp DNA Kit (Qiagen). DNA extractions from FFPE-derived material were performed using standard protocols as previously describedPrimers pairs (91 for NSD1 and 49 for NSD2) were designed by Fluidigm  to cover all exons of the NSD1/NSD2 genes and exon–intron boundaries. A total of 100 ng of genomic DNA from neoplastic lesions were used for PCR amplification of target genes using an Access Array™ microfluidic chip according to the manufacturer’s instructions. Each sample was combined with primer pairs in a microfluidic chip, and thermal cycling on a Fluidigm FC1 Cycler was performed. PCR products were then collected using the integrated fluidic circuit controller and transferred to a 96-well plate. In a separate PCR, Illumina sequence-specific adaptors and barcodes were attached. Prior to sequencing, all amplified samples were checked on the Agilent 2100 BioAnalyzer to determine if the PCR product in the DNA reaction has the expected size. All fresh frozen samples passed the quality control (QC). Twelve FFPE-derived samples did not pass the QC and were excluded from sequencing. A total of 50 fresh frozen and 13 FFPE cases were considered for subsequent downstream analysis.50. The resulting BAM files were processed using the GATK best-practices pipeline51 with no mark duplicate step. We applied the Mutect2 algorithm (based on Mutect52) with default parameters using the tumor samples-only mode. Variants were filtered at an 8% variant allele fraction53. The resulting VCF files were processed using ANNOVAR54 for functional annotation of identified variants, and with independent variant calling using HaplotypeCaller (GATK) and VarScan55 for comparison. Most of the NSD1/2 damaging variants were identified by all three variant callers. In assessing the functional impact of missense variants, we applied a rule that at least 3 functional impact prediction algorithms  predict functionally damaging variants. For predicting overall survival differences among NSD1/2 loss-of-function mutations, we combined benign- and wild-type alleles into one group.Pooled and indexed PCR products were sequenced on the Illumina MiSeq instrument following standard protocols with the following modifications: Illumina-specific sequencing primers were substituted with a mixture of two Fluidigm-specific primer pairs (FL1 and FL2). Quality and coverage metrics for MiSeq sequencing data indicated a mean coverage depth of 2000 reads. The sequencing reads were aligned to human genome (Hg19) using BWA60. To test the association between clinical parameters (and the presence of mutations) and cluster membership, Fisher’s exact or Wilcoxon rank sum tests were used. These tests were two-sided with 5% type 1 error. Wherever necessary, significance levels of multiple comparisons were corrected for type 1 error rates using Benjamini–Hochberg false-discovery rate (FDR) method44. To test the association between clinical parameters (and the presence of mutations) and cluster membership, or the presence/absence of damaging mutations of NSD1 or NSD2 genes, two-sided Fisher’s exact and Wilcoxon rank sum tests were used, and p-values < 0.05 were considered significant.Clinical information including follow-up and new tumor event (NTE) data for 526 SCCHN samples (including the 256 samples chosen for clustering) were obtained from the TCGA website (July 2015). OS and RFS distributions were estimated using Kaplan–Meier methods. OS was based on vital status and “days to death” (CDE_ID:3165475) from initial pathologic diagnosis. Individuals who were still alive at the time of the last follow-up were censored. The RFS outcome included time to first recurrence or death . Recurrence was defined as the appearance of either “locoregional recurrence” or “distant metastasis”, and time to first recurrence was estimated from “days to new tumor event after initial treatment” (CDE_ID: 3392464). As this item starts at the end of initial treatment rather than the date of pathologic diagnosis, the recurrence-free interval may be underestimated; data on the interval from diagnosis to initial treatment were not available in this data freeze. Survival curves were compared with log-rank tests, and these calculations were done using the R ‘survival’ packageDe-identified patient variant data that support the findings of this study are available from the corresponding authors upon request, and are submitted to the database of Genotypes and Phenotypes (dbGaP). Primary results from data analyses were done using standard R and Bioconductor packages including iClusterPlus and Survival. R scripts calling routines from these packages will be available from the corresponding author upon request.Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16