Two-way hierarchical clustering of gene expression ratios.

<p>Heatmaps displays the log2(M) on a color scale from green indicating lower expression to red indicating a higher expression, interpolated over black for log2(M) = 0. A. Overview of entire hierarchical clustering showing 26.877 cDNAs (row-wise) and 23 tissues (column-wise). B. Enlarged view of selected cDNA cluster. BFE, Biceps femoris; CBE, Cerebellum; FAT, fat; FCO, Frontal cortex; HEA, heart; ISP, Infraspinatus; KID, kidney; LDO, Longissimus dorsi; LIN, large intestine; LIV, liver; LUN, lung; PAN, pancreas; PGL, pituitary gland; SIN, small intestine; SKI, skin; SME, Semimembranosus; SPL, spleen; SSP, Supraspinatus; STE, Semitendinosus; STO, stomach; TBR, Triceps brachii; THG, thyroid gland; VIN, Vastus intermedius.</p

Expression profiles of uncharacterized genes and co-expressed genes with known function.

<p>Each histogram represents the expression profiles of a single cDNA across all 23 tissues for two uncharacterized protein coding genes (PC_207218, PC_207224) and four co-expressed genes with known function (PC_207826: ABCA10, PC_214110: UBE2D2). Each bar represents the gene expression ratio (M) between the tissue sample and the common reference sample on the logarithmic scale. M values below zero, indicating lower gene expression level, are shown by green bars. M values above zero, indicating higher expression, are shown by red bars.</p

KEGG pathways for cDNAs representing differently expressed genes across tissues.

<p>Size of dots corresponds to the number of cDNAs (minimum = 10 and maximum = 253) that were tested to have a positive influence on the expression levels and color codes indicate tissue type. Only significant (P≤0.05) KEGG pathways represented by 50 or more cDNAs on the array were included.</p

Correlation in gene expression between common tissues of pig and human.

<p>Pearson's correlation coefficients computed for pairs of common tissues from pig and human. Color scale from white to dark blue represents correlation coefficients from −0.10 to 0.31.</p

Enriched GO BP terms for cDNAs representing positively regulated genes.

<p>Size of dots corresponds to the number of cDNAs that were tested (minimum = 6 and maximum = 1025) and color codes indicate tissue type. Only significant (P≤0.01) GO-BP terms represented by 50 or more cDNAs on the array were included.</p

Effects of Bacterial Colonization on the Porcine Intestinal Proteome

The gastrointestinal tract harbors a complex community of bacteria, of which many may be beneficial. Studies of germ-free animal models have shown that the gastrointestinal microbiota not only assists in making nutrients available for the host but also contributes to intestinal health and development. We studied small intestinal protein expression patterns in gnotobiotic pigs maintained germ-free, or monoassociated with either Lactobacillus fermentum or non-pathogenic Escherichia coli. A common reference design in combination with labeling with stable isobaric tags allowed the individual comparison of 12 animals. Our results showed that bacterial colonization differentially affected mechanisms such as proteolysis, epithelial proliferation, and lipid metabolism, which is in good agreement with previous studies of other germ-free animal models. We have also found that E. coli has a profound effect on actin remodeling and intestinal proliferation, which may be related to stimulated migration and turnover of enterocytes. Regulations related to L. fermentum colonization involved individual markers for immunoregulatory mechanisms. Keywords: iTRAQ • gnotobiotic • germ-free • quantitative proteomics • LC−MS/MS • Escherichia coli • Lactobacillus fermentum • gut • small intestine • pi

Effects of Bacterial Colonization on the Porcine Intestinal Proteome

The gastrointestinal tract harbors a complex community of bacteria, of which many may be beneficial. Studies of germ-free animal models have shown that the gastrointestinal microbiota not only assists in making nutrients available for the host but also contributes to intestinal health and development. We studied small intestinal protein expression patterns in gnotobiotic pigs maintained germ-free, or monoassociated with either Lactobacillus fermentum or non-pathogenic Escherichia coli. A common reference design in combination with labeling with stable isobaric tags allowed the individual comparison of 12 animals. Our results showed that bacterial colonization differentially affected mechanisms such as proteolysis, epithelial proliferation, and lipid metabolism, which is in good agreement with previous studies of other germ-free animal models. We have also found that E. coli has a profound effect on actin remodeling and intestinal proliferation, which may be related to stimulated migration and turnover of enterocytes. Regulations related to L. fermentum colonization involved individual markers for immunoregulatory mechanisms. Keywords: iTRAQ • gnotobiotic • germ-free • quantitative proteomics • LC−MS/MS • Escherichia coli • Lactobacillus fermentum • gut • small intestine • pi

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available

The repertoire of mutational signatures in human cancer

Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature 1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium 2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses 3–15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated—but distinct—DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer

Integrative pathway enrichment analysis of multivariate omics data

Multi-omics datasets represent distinct aspects of the central dogma of molecular biology. Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we integrated genes with coding and non-coding mutations and revealed frequently mutated pathways and additional cancer genes with infrequent mutations. We also analyzed prognostic molecular pathways by integrating genomic and transcriptomic features of 1780 breast cancers and highlighted associations with immune response and anti-apoptotic signaling. Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway across normal human tissues identified processes of tissue regeneration and stem cell regulation. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations