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BACKGROUND: Gene expression microarrays have been intensively applied to screen for genes involved in specific biological processes of interest such as diseases or responses to environmental stimuli. For mammalian species, cataloging of the global gene expression profiles in large tissue collections under normal conditions have been focusing on human and mouse genomes but is lacking for the pig genome. METHODOLOGY/PRINCIPAL FINDINGS: Here we present the results from a large-scale porcine study establishing microarray cDNA expression profiles of approximately 20.000 genes across 23 healthy tissues. As expected, a large portion of the genes show tissue specific expression in agreement with mappings to gene descriptions, Gene Ontology terms and KEGG pathways. Two-way hierarchical clustering identified expected tissue clusters in accordance with tissue type and a number of cDNA clusters having similar gene expression patterns across tissues. For one of these cDNA clusters, we demonstrate that possible tissue associated gene function can be inferred for previously uncharacterized genes based on their shared expression patterns with functionally annotated genes. We show that gene expression in common porcine tissues is similar to the expression in homologous tissues of human. CONCLUSIONS/SIGNIFICANCE: The results from this study constitute a valuable and publicly available resource of basic gene expression profiles in normal porcine tissues and will contribute to the identification and functional annotation of porcine genes.

The knowledge of genes and molecular processes controlling stress reactions and involved in the genetic system determining resistance to stress in pigs could be important for the improvement of meat quality. This research aimed to compare the expression profiles of skeletal muscle between physically stressed and not stressed pigs of different breeds immediately before slaughter. DNA microarray analysis showed that different functional categories of genes are up-regulated in stressed compared to not stressed pigs and relevant differences among breeds were found.

Abstract Background Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration. Results To take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures. Conclusion We find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development.

Non-coding mutations may drive cancer development. Statistical detection of non-coding driver regions is challenged by a varying mutation rate and uncertainty of functional impact. Here, we develop a statistically founded non-coding driver-detection method, ncdDetect, which includes sample-specific mutational signatures, long-range mutation rate variation, and position-specific impact measures. Using ncdDetect, we screened non-coding regulatory regions of protein-coding genes across a pan-cancer set of whole-genomes (n = 505), which top-ranked known drivers and identified new candidates. For individual candidates, presence of non-coding mutations associates with altered expression or decreased patient survival across an independent pan-cancer sample set (n = 5454). This includes an antigen-presenting gene (CD1A), where 5’UTR mutations correlate significantly with decreased survival in melanoma. Additionally, mutations in a base-excision-repair gene (SMUG1) correlate with a C-to-T mutational-signature. Overall, we find that a rich model of mutational heterogeneity facilitates non-coding driver identification and integrative analysis points to candidates of potential clinical relevance.

Bladder cancer (or urothelial cell carcinoma [UCC]) is characterized by field disease (malignant alterations in surrounding mucosa) and frequent recurrences. Whole-genome, exome, and transcriptome sequencing of 38 tumors, including four metachronous tumor pairs and 20 superficial tumors, identified an APOBEC mutational signature in one-third. This was biased toward the sense strand, correlated with mean expression level, and clustered near breakpoints. A > G mutations were up to eight times more frequent on the sense strand (p < 0.002) in [ACG]AT contexts. The patient-specific APOBEC signature was negatively correlated to repair-gene expression and was not related to clinicopathological parameters. Mutations in gene families and single genes were related to tumor stage, and expression of chromatin modifiers correlated with survival. Evolutionary and subclonal analyses of early/late tumor pairs showed a unitary origin, and discrete tumor clones contained mutated cancer genes. The ancestral clones contained Pik3ca/Kdm6a mutations and may reflect the field-disease mutations shared among later tumors.