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.

Epigenetic and transcriptomic consequences of excess X-chromosome material in 47,XXX syndrome-A comparison with Turner syndrome and 46,XX females.

47,XXX (triple X) and Turner syndrome (45,X) are sex chromosomal abnormalities with detrimental effects on health with increased mortality and morbidity. In karyotypical normal females, X-chromosome inactivation balances gene expression between sexes and upregulation of the X chromosome in both sexes maintain stoichiometry with the autosomes. In 47,XXX and Turner syndrome a gene dosage imbalance may ensue from increased or decreased expression from the genes that escape X inactivation, as well as from incomplete X chromosome inactivation in 47,XXX. We aim to study genome-wide DNA-methylation and RNA-expression changes can explain phenotypic traits in 47,XXX syndrome. We compare DNA-methylation and RNA-expression data derived from white blood cells of seven women with 47,XXX syndrome, with data from seven female controls, as well as with seven women with Turner syndrome (45,X). To address these questions, we explored genome-wide DNA-methylation and transcriptome data in blood from seven females with 47,XXX syndrome, seven females with Turner syndrome, and seven karyotypically normal females (46,XX). Based on promoter methylation, we describe a demethylation of six X-chromosomal genes (AMOT, HTR2C, IL1RAPL2, STAG2, TCEANC, ZNF673), increased methylation for GEMIN8, and four differentially methylated autosomal regions related to four genes (SPEG, MUC4, SP6, and ZNF492). We illustrate how these changes seem compensated at the transcriptome level although several genes show differential exon usage. In conclusion, our results suggest an impact of the supernumerary X chromosome in 47,XXX syndrome on the methylation status of selected genes despite an overall comparable expression profile.

A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data.

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.

Pairwise comparisons of ten porcine tissues identify differential transcriptional regulation at the gene, isoform, promoter and transcription start site level.

The transcriptome is the absolute set of transcripts in a tissue or cell at the time of sampling. In this study RNA-Seq is employed to enable the differential analysis of the transcriptome profile for ten porcine tissues in order to evaluate differences between the tissues at the gene and isoform expression level, together with an analysis of variation in transcription start sites, promoter usage, and splicing. Totally, 223 million RNA fragments were sequenced leading to the identification of 59,930 transcribed gene locations and 290,936 transcript variants using Cufflinks with similarity to approximately 13,899 annotated human genes. Pairwise analysis of tissues for differential expression at the gene level showed that the smallest differences were between tissues originating from the porcine brain. Interestingly, the relative level of differential expression at the isoform level did generally not vary between tissue contrasts. Furthermore, analysis of differential promoter usage between tissues, revealed a proportionally higher variation between cerebellum (CBE) versus frontal cortex and cerebellum versus hypothalamus (HYP) than in the remaining comparisons. In addition, the comparison of differential transcription start sites showed that the number of these sites is generally increased in comparisons including hypothalamus in contrast to other pairwise assessments. A comprehensive analysis of one of the tissue contrasts, i.e. cerebellum versus heart for differential variation at the gene, isoform, and transcription start site (TSS), and promoter level showed that several of the genes differed at all four levels. Interestingly, these genes were mainly annotated to the "electron transport chain" and neuronal differentiation, emphasizing that "tissue important" genes are regulated at several levels. Furthermore, our analysis shows that the "across tissue approach" has a promising potential when screening for possible explanations for variations, such as those observed at the gene expression levels.

Gene expression profiling of porcine skeletal muscle in the early recovery phase following acute physical activity.

Acute physical activity elicits changes in gene expression in skeletal muscles to promote metabolic changes and to repair exercise-induced muscle injuries. In the present time-course study, pigs were submitted to an acute bout of treadmill running until near exhaustion to determine the impact of unaccustomed exercise on global transcriptional profiles in porcine skeletal muscles. Using a combined microarray and candidate gene approach, we identified a suite of genes that are differentially expressed in muscles during postexercise recovery. Several members of the heat shock protein family and proteins associated with proteolytic events, such as the muscle-specific E3 ubiquitin ligase atrogin-1, were significantly upregulated, suggesting that protein breakdown, prevention of protein aggregation and stabilization of unfolded proteins are important processes for restoration of cellular homeostasis. We also detected an upregulation of genes that are associated with muscle cell proliferation and differentiation, including MUSTN1, ASB5 and CSRP3, possibly reflecting activation, differentiation and fusion of satellite cells to facilitate repair of muscle damage. In addition, exercise increased expression of the orphan nuclear hormone receptor NR4A3, which regulates metabolic functions associated with lipid, carbohydrate and energy homeostasis. Finally, we observed an unanticipated induction of the long non-coding RNA transcript NEAT1, which has been implicated in RNA processing and nuclear retention of adenosine-to-inosine edited mRNAs in the ribonucleoprotein bodies called paraspeckles. These findings expand the complexity of pathways affected by acute contractile activity of skeletal muscle, contributing to a better understanding of the molecular processes that occur in muscle tissue in the recovery phase.

Transcriptomic and proteomic profiling of two porcine tissues using high-throughput technologies.

BACKGROUND: The recent development within high-throughput technologies for expression profiling has allowed for parallel analysis of transcriptomes and proteomes in biological systems such as comparative analysis of transcript and protein levels of tissue regulated genes. Until now, such studies of have only included microarray or short length sequence tags for transcript profiling. Furthermore, most comparisons of transcript and protein levels have been based on absolute expression values from within the same tissue and not relative expression values based on tissue ratios. RESULTS: Presented here is a novel study of two porcine tissues based on integrative analysis of data from expression profiling of identical samples using cDNA microarray, 454-sequencing and iTRAQ-based proteomics. Sequence homology identified 2.541 unique transcripts that are detectable by both microarray hybridizations and 454-sequencing of 1.2 million cDNA tags. Both transcript-based technologies showed high reproducibility between sample replicates of the same tissue, but the correlation across these two technologies was modest. Thousands of genes being differentially expressed were identified with microarray. Out of the 306 differentially expressed genes, identified by 454-sequencing, 198 (65%) were also found by microarray. The relationship between the regulation of transcript and protein levels was analyzed by integrating iTRAQ-based proteomics data. Protein expression ratios were determined for 354 genes, of which 148 could be mapped to both microarray and 454-sequencing data. A comparison of the expression ratios from the three technologies revealed that differences in transcript and protein levels across heart and muscle tissues are positively correlated. CONCLUSION: We show that the reproducibility within cDNA microarray and 454-sequencing is high, but that the agreement across these two technologies is modest. We demonstrate that the regulation of transcript and protein levels across identical tissue samples is positively correlated when the tissue expression ratios are used for comparison. The results presented are of interest in systems biology research in terms of integration and analysis of high-throughput expression data from mammalian tissues.

A robust linkage map of the porcine autosomes based on gene-associated SNPs.

BACKGROUND: Genetic linkage maps are necessary for mapping of mendelian traits and quantitative trait loci (QTLs). To identify the actual genes, which control these traits, a map based on gene-associated single nucleotide polymorphism (SNP) markers is highly valuable. In this study, the SNPs were genotyped in a large family material comprising more than 5,000 piglets derived from 12 Duroc boars crossed with 236 Danish Landrace/Danish Large White sows. The SNPs were identified in sequence alignments of 4,600 different amplicons obtained from the 12 boars and containing coding regions of genes derived from expressed sequence tags (ESTs) and genomic shotgun sequences. RESULTS: Linkage maps of all 18 porcine autosomes were constructed based on 456 gene-associated and six porcine EST-based SNPs. The total length of the averaged-sex whole porcine autosome was estimated to 1,711.8 cM resulting in an average SNP spacing of 3.94 cM. The female and male maps were estimated to 2,336.1 and 1,441.5 cM, respectively. The gene order was validated through comparisons to the cytogenetic and/or physical location of 203 genes, linkage to evenly spaced microsatellite markers as well as previously reported conserved synteny. A total of 330 previously unmapped genes and ESTs were mapped to the porcine autosome while ten genes were mapped to unexpected locations. CONCLUSION: The linkage map presented here shows high accuracy in gene order. The pedigree family network as well as the large amount of meiotic events provide good reliability and make this map suitable for QTL and association studies. In addition, the linkage to the RH-map of microsatellites makes it suitable for comparison to other QTL studies.

SNP mining porcine ESTs with MAVIANT, a novel tool for SNP evaluation and annotation.

MOTIVATION: Single nucleotide polymorphisms (SNPs) analysis is an important means to study genetic variation. A fast and cost-efficient approach to identify large numbers of novel candidates is the SNP mining of large scale sequencing projects. The increasing availability of sequence trace data in public repositories makes it feasible to evaluate SNP predictions on the DNA chromatogram level. MAVIANT, a platform-independent Multipurpose Alignment VIewing and Annotation Tool, provides DNA chromatogram and alignment views and facilitates evaluation of predictions. In addition, it supports direct manual annotation, which is immediately accessible and can be easily shared with external collaborators. RESULTS: Large-scale SNP mining of polymorphisms bases on porcine EST sequences yielded more than 7900 candidate SNPs in coding regions (cSNPs), which were annotated relative to the human genome. Non-synonymous SNPs were analyzed for their potential effect on the protein structure/function using the PolyPhen and SIFT prediction programs. Predicted SNPs and annotations are stored in a web-based database. Using MAVIANT SNPs can visually be verified based on the DNA sequencing traces. A subset of candidate SNPs was selected for experimental validation by resequencing and genotyping. This study provides a web-based DNA chromatogram and contig browser that facilitates the evaluation and selection of candidate SNPs, which can be applied as genetic markers for genome wide genetic studies. AVAILABILITY: The stand-alone version of MAVIANT program for local use is freely available under GPL license terms at http://snp.agrsci.dk/maviant. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene expression profiles in liver of pigs with extreme high and low levels of androstenone.

BACKGROUND: Boar taint is the unpleasant odour and flavour of the meat of uncastrated male pigs that is primarily caused by high levels of androstenone and skatole in adipose tissue. Androstenone is a steroid and its levels are mainly genetically determined. Studies on androstenone metabolism have, however, focused on a limited number of genes. Identification of additional genes influencing levels of androstenone may facilitate implementation of marker assisted breeding practices. In this study, microarrays were used to identify differentially expressed genes and pathways related to androstenone metabolism in the liver from boars with extreme levels of androstenone in adipose tissue. RESULTS: Liver tissue samples from 58 boars of the two breeds Duroc and Norwegian Landrace, 29 with extreme high and 29 with extreme low levels of androstenone, were selected from more than 2500 individuals. The samples were hybridised to porcine cDNA microarrays and the 1% most significant differentially expressed genes were considered significant. Among the differentially expressed genes were metabolic phase I related genes belonging to the cytochrome P450 family and the flavin-containing monooxygenase FMO1. Additionally, phase II conjugation genes including UDP-glucuronosyltransferases UGT1A5, UGT2A1 and UGT2B15, sulfotransferase STE, N-acetyltransferase NAT12 and glutathione S-transferase were identified. Phase I and phase II metabolic reactions increase the water solubility of steroids and play a key role in their elimination. Differential expression was also found for genes encoding 17beta-hydroxysteroid dehydrogenases (HSD17B2, HSD17B4, HSD17B11 and HSD17B13) and plasma proteins alpha-1-acid glycoprotein (AGP) and orosomucoid (ORM1). 17beta-hydroxysteroid dehydrogenases and plasma proteins regulate the availability of steroids by controlling the amount of active steroids accessible to receptors and available for metabolism. Differences in the expression of FMO1, NAT12, HSD17B2 and HSD17B13 were verified by quantitative real competitive PCR. CONCLUSION: A number of genes and pathways related to metabolism of androstenone in liver were identified, including new candidate genes involved in phase I oxidation metabolism, phase II conjugation metabolism, and regulation of steroid availability. The study is a first step towards a deeper understanding of enzymes and regulators involved in pathways of androstenone metabolism and may ultimately lead to the discovery of markers to reduce boar taint.

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival.

Cancer develops by accumulation of somatic driver mutations, which impact cellular function. Mutations in non-coding regulatory regions can now be studied genome-wide and further characterized by correlation with gene expression and clinical outcome to identify driver candidates. Using a new two-stage procedure, called ncDriver, we first screened 507 ICGC whole-genomes from 10 cancer types for non-coding elements, in which mutations are both recurrent and have elevated conservation or cancer specificity. This identified 160 significant non-coding elements, including the TERT promoter, a well-known non-coding driver element, as well as elements associated with known cancer genes and regulatory genes (e.g., PAX5, TOX3, PCF11, MAPRE3). However, in some significant elements, mutations appear to stem from localized mutational processes rather than recurrent positive selection in some cases. To further characterize the driver potential of the identified elements and shortlist candidates, we identified elements where presence of mutations correlated significantly with expression levels (e.g., TERT and CDH10) and survival (e.g., CDH9 and CDH10) in an independent set of 505 TCGA whole-genome samples. In a larger pan-cancer set of 4128 TCGA exomes with expression profiling, we identified mutational correlation with expression for additional elements (e.g., near GATA3, CDC6, ZNF217, and CTCF transcription factor binding sites). Survival analysis further pointed to MIR122, a known marker of poor prognosis in liver cancer. In conclusion, the screen for significant mutation patterns coupled with correlative mutational analysis identified new individual driver candidates and suggest that some non-coding mutations recurrently affect expression and play a role in cancer development.

DNA hypermethylation and differential gene expression associated with Klinefelter syndrome.

Klinefelter syndrome (KS) has a prevalence ranging from 85 to 250 per 100.000 newborn boys making it the most frequent sex chromosome aneuploidy in the general population. The molecular basis for the phenotypic traits and morbidity in KS are not clarified. We performed genome-wide DNA methylation profiling of leucocytes from peripheral blood samples from 67 KS patients, 67 male controls and 33 female controls, in addition to genome-wide RNA-sequencing profiling in a subset of 9 KS patients, 9 control males and 13 female controls. Characterization of the methylome as well as the transcriptome of both coding and non-coding genes identified a unique epigenetic and genetic landscape of both autosomal chromosomes as well as the X chromosome in KS. A subset of genes show significant correlation between methylation values and expression values. Gene set enrichment analysis of differentially methylated positions yielded terms associated with well-known comorbidities seen in KS. In addition, differentially expressed genes revealed enrichment for genes involved in the immune system, wnt-signaling pathway and neuron development. Based on our data we point towards new candidate genes, which may be implicated in the phenotype and further point towards non-coding genes, which may be involved in X chromosome inactivation in KS.

Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate.

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.

Comparative analysis of protein coding sequences from human, mouse and the domesticated pig.

BACKGROUND: The availability of abundant sequence data from key model organisms has made large scale studies of molecular evolution an exciting possibility. Here we use full length cDNA alignments comprising more than 700,000 nucleotides from human, mouse, pig and the Japanese pufferfish Fugu rubrices in order to investigate 1) the relationships between three major lineages of mammals: rodents, artiodactyls and primates, and 2) the rate of evolution and the occurrence of positive Darwinian selection using codon based models of sequence evolution. RESULTS: We provide evidence that the evolutionary splits among primates, rodents and artiodactyls happened shortly after each other, with most gene trees favouring a topology with rodents as outgroup to primates and artiodactyls. Using an unrooted topology of the three mammalian species we show that since their diversification, the pig and mouse lineages have on average experienced 1.44 and 2.86 times as many synonymous substitutions as humans, respectively, whereas the rates of non-synonymous substitutions are more similar. The analysis shows the highest average dN/dS ratio in the human lineage, followed by the pig and then the mouse lineages. Using codon based models we detect signals of positive Darwinian selection in approximately 5.3%, 4.9% and 6.0% of the genes on the human, pig and mouse lineages respectively. Approximately 16.8% of all the genes studied here are not currently annotated as functional genes in humans. Our analyses indicate that a large fraction of these genes may have lost their function quite recently or may still be functional genes in some or all of the three mammalian species. CONCLUSIONS: We present a comparative analysis of protein coding genes from three major mammalian lineages. Our study demonstrates the usefulness of codon-based likelihood models in detecting selection and it illustrates the value of sequencing organisms at different phylogenetic distances for comparative studies.

Pigs in sequence space: a 0.66X coverage pig genome survey based on shotgun sequencing.

BACKGROUND: Comparative whole genome analysis of Mammalia can benefit from the addition of more species. The pig is an obvious choice due to its economic and medical importance as well as its evolutionary position in the artiodactyls. RESULTS: We have generated approximately 3.84 million shotgun sequences (0.66X coverage) from the pig genome. The data are hereby released (NCBI Trace repository with center name "SDJVP", and project name "Sino-Danish Pig Genome Project") together with an initial evolutionary analysis. The non-repetitive fraction of the sequences was aligned to the UCSC human-mouse alignment and the resulting three-species alignments were annotated using the human genome annotation. Ultra-conserved elements and miRNAs were identified. The results show that for each of these types of orthologous data, pig is much closer to human than mouse is. Purifying selection has been more efficient in pig compared to human, but not as efficient as in mouse, and pig seems to have an isochore structure most similar to the structure in human. CONCLUSION: The addition of the pig to the set of species sequenced at low coverage adds to the understanding of selective pressures that have acted on the human genome by bisecting the evolutionary branch between human and mouse with the mouse branch being approximately 3 times as long as the human branch. Additionally, the joint alignment of the shot-gun sequences to the human-mouse alignment offers the investigator a rapid way to defining specific regions for analysis and resequencing.

Mutational context and diverse clonal development in early and late bladder cancer.

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.

A genome-wide association scan in pig identifies novel regions associated with feed efficiency trait.

Feed conversion ratio (FCR) is an economically important trait in pigs, and feed accounts for a significant proportion of the costs involved in pig production. In this study we used a high-density SNP chip panel, Porcine SNP60 BeadChip, to identify the association between FCR and SNP markers and to study the genetic architecture of the trait. After quality control, a total of 30,847 SNP that could be mapped to the 18 porcine autosomes (SSC) using the pig genome assembly 10.2 were used in the analyses. Deregressed estimated breeding value was used as the response variable. A total of 3,071 Duroc pigs had both FCR data and genotype data. The linkage disequilibrium (r(2)) between adjacent markers was 0.56. Two association mapping approaches were used: a linear mixed model (LMM) based on single-locus regression analysis and a Bayesian variable selection approach (BVS). A total of 79 significant (P < 0.0001) SNP associations on 6 chromosomes were identified by LMM analyses. Out of these, 10 SNP crossed the genome-wide significance threshold. These 10 SNP were all located on SSC 4 and 14. In the BVS analysis, a total of 44 SNP located on 12 chromosomes had posterior probability more than or equal to 0.05 (i.e., Bayes factor ≥ 10). Thirteen SNP were identified by both LMM and BVS. These 13 SNP were located on 4 chromosomes: SSC 4, 7, 8, and 14. Hypoxia inducible factor 1, alpha subunit inhibitor (HIF1AN) and ladybird homeobox 1 (LBX1) are 2 possible candidate genes affecting FCR on SSC 4 and 14, respectively. The study provides a list of SNP associated with FCR and also offers valuable information on the genetic architecture and candidate genes for this trait.

Regulation of basal resistance by a powdery mildew-induced cysteine-rich receptor-like protein kinase in barley.

The receptor-like protein kinases (RLKs) constitute a large and diverse group of proteins controlling numerous plant physiological processes, including development, hormone perception and stress responses. The cysteine-rich RLKs (CRKs) represent a prominent subfamily of transmembrane-anchored RLKs. We have identified a putative barley (Hordeum vulgare) CRK gene family member, designated HvCRK1. The mature putative protein comprises 645 amino acids, and includes a putative receptor domain containing two characteristic 'domain 26 of unknown function' (duf26) domains in the N-terminal region, followed by a rather short 17-amino-acid transmembrane domain, which includes an AAA motif, two features characteristic of endoplasmic reticulum (ER)-targeted proteins and, finally, a characteristic putative protein kinase domain in the C-terminus. The HvCRK1 transcript was isolated from leaves inoculated with the biotrophic fungal pathogen Blumeria graminis f.sp. hordei (Bgh). HvCRK1 transcripts were observed to accumulate transiently following Bgh inoculation of susceptible barley. Transient silencing of HvCRK1 expression in bombarded epidermal cells led to enhanced resistance to Bgh, but did not affect R-gene-mediated resistance. Silencing of HvCRK1 phenocopied the effective penetration resistance found in mlo-resistant barley plants, and the possible link between HvCRK1 and MLO was substantiated by the fact that HvCRK1 induction on Bgh inoculation was dependent on Mlo. Finally, using both experimental and in silico approaches, we demonstrated that HvCRK1 localizes to the ER of barley cells. The negative effect on basal resistance against Bgh and the functional aspects of MLO- and ER-localized HvCRK1 signalling on Bgh inoculation are discussed.

Using microarrays to identify positional candidate genes for QTL: the case study of ACTH response in pigs.

BACKGROUND: Microarray studies can supplement QTL studies by suggesting potential candidate genes in the QTL regions, which by themselves are too large to provide a limited selection of candidate genes. Here we provide a case study where we explore ways to integrate QTL data and microarray data for the pig, which has only a partial genome sequence. We outline various procedures to localize differentially expressed genes on the pig genome and link this with information on published QTL. The starting point is a set of 237 differentially expressed cDNA clones in adrenal tissue from two pig breeds, before and after treatment with adrenocorticotropic hormone (ACTH). RESULTS: Different approaches to localize the differentially expressed (DE) genes to the pig genome showed different levels of success and a clear lack of concordance for some genes between the various approaches. For a focused analysis on 12 genes, overlapping QTL from the public domain were presented. Also, differentially expressed genes underlying QTL for ACTH response were described. Using the latest version of the draft sequence, the differentially expressed genes were mapped to the pig genome. This enabled co-location of DE genes and previously studied QTL regions, but the draft genome sequence is still incomplete and will contain many errors. A further step to explore links between DE genes and QTL at the pathway level was largely unsuccessful due to the lack of annotation of the pig genome. This could be improved by further comparative mapping analyses but this would be time consuming. CONCLUSION: This paper provides a case study for the integration of QTL data and microarray data for a species with limited genome sequence information and annotation. The results illustrate the challenges that must be addressed but also provide a roadmap for future work that is applicable to other non-model species.

Methods for interpreting lists of affected genes obtained in a DNA microarray experiment.

BACKGROUND: The aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. RESULTS: Several conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached. CONCLUSION: It is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experiment.