Biotransformation pathway maps in WikiPathways enable direct visualization of drug metabolism related expression changes.

In recent decades, our knowledge of the genetics and functional genomics of drug-metabolizing enzymes has increased and a wealth of data on drug-related 'omics' has become available. Despite the availability of large amounts of biological information on xenobiotic biotransformation, the number of available biotransformation pathway maps that can easily be used for visualization of multiple omics data is limited. Here, we created integrated biotransformation pathway maps suitable for multiple omics analysis using PathVisio. The ease of visualizing data on these maps was demonstrated by using published microarray data from human hepatocyte-like cell models, exemplifying - where a sufficient capacity for metabolizing chemicals is a prerequisite for a suited model - how the biotransformation pathway maps can be used for model selection

Chicken fatness: from QTL to candidate gene

Excessive fat is a well known problem in poultry. It depresses feed efficiency, has no commercial value, and is less appreciated by consumers. Therefore, considerable research effort has been applied around the world to study factors associated with fat deposition and methods of decreasing it.his dissertation concerns the identification of genes controlling fat deposition in broilers. The strategy used to identify these genes is the so-called positional candidate gene approach. This approach moves from mapping quantitative trait loci (QTL) towards the identification of candidate genes. A total genome scan of a large three generation broiler population has resulted in the localization of QTL for fat deposition. Several of these QTL are confirmed as well as new QTL are identified in an advanced intercross line produced from this broiler population. The construction of high resolution comparative maps has helped to identify potential candidate genes for fat deposition mapped in the QTL regions. These genes and the polymorphisms within these genes will be of interest for future research. Identified SNPs can be used to refine the map position of the QTL by analysing linkage and linkage disequilibrium.Finally, a causal link between the genetic trait and the candidate genes can be established from functional evidence

From multi-omics integration towards novel genomic interaction networks to identify key cancer cell line characteristics

Cancer is a complex disease where cancer cells express epigenetic and transcriptomic mechanisms to promote tumor initiation, progression, and survival. To extract relevant features from the 2019 Cancer Cell Line Encyclopedia (CCLE), a multi-layer nonnegative matrix factorization approach is used. We used relevant feature genes and DNA promoter regions to construct genomic interaction network to study gene-gene and gene-DNA promoter methylation relationships. Here, we identified a set of gene transcripts and methylated DNA promoter regions for different clusters, including one homogeneous lymphoid neoplasms cluster. In this cluster, we found different methylated transcription factors that affect transcriptional activation of EGFR and downstream interactions. Furthermore, the hippo-signaling pathway might not function properly because of DNA hypermethylation and low gene expression of both LATS2 and YAP1. Finally, we could identify a potential dysregulation of the CD28-CD86-CTLA4 axis. Characterizing the interaction of the epigenome and the transcriptome is vital for our understanding of cancer cell line behavior, not only for deepening insights into cancer-related processes but also for future disease treatment and drug development. Here we have identified potential candidates that characterize cancer cell lines, which give insight into the development and progression of cancers

Gene expression profiling in primary mouse hepatocytes discriminates true from false-positive genotoxic compounds.

Well-established in vitro methods for testing the genotoxic potency of chemicals-such as the Ames/Salmonella test, the mouse lymphoma assay, the micronucleus test and the chromosomal aberration test-show a high false-positive rate for predicting in vivo genotoxicity and carcinogenicity. Thus, there is a need for more reliable in vitro assays. We investigated whether gene expression profiling in metabolically competent primary mouse hepatocytes is capable of discriminating true genotoxic (GTX) compounds from false-positive genotoxic (FP-GTX) compounds. Sandwich-cultured primary hepatocytes from male C57Bl6 mice were treated for 24 and 48 h with five true GTX and five FP-GTX compounds. Whole genome gene expression modifications were analysed by means of Affymetrix mouse genome 430 2.0 microarrays. Filtered genes were used for hierarchical clustering and class prediction methods. Classifiers were generated by prediction analysis of microarray using a leave-one-compound-out method and selecting the genes that were common to the 10 training sets. For the training compounds, all but one were correctly classified. Validation of the classification model with five new compounds resulted in a 100% correct classification at 24 h and 80% at 48 h. The generated classifiers were mostly involved in metabolic and biosynthetic processes, immune responses and apoptosis. Applying genes whose expression change correlates with gammaH2AX foci, a measure for DNA damage, did not improve the classification. The present study shows that gene expression profiling in primary mouse hepatocytes is capable of discriminating between true GTX and FP-GTX compounds

Comparative map between chicken chromosome 15 and human chromosomal region 12q24 and 22q11-q12

The physical and comparative map of GGA15 was improved by the construction of 9 BAC contigs around loci previously mapped on GGA15 by linkage analysis. In total, 240 BAC clones were isolated, covering 30-35% of GGA15, and 120 STS were developed (104 STS derived from BAC end sequences and 18 STS derived within genes). Seventeen chicken orthologues of human genes located on human Chr 22q11-q12 were directly mapped within BAC contigs of GGA15. Furthermore, the partial sequences of the chicken BAC clones were compared with sequences present in the EMBL/GenBank databases and revealed matches to 26 genes, ESTs, and genomic clones located on HSA22q11-q12 and HSA12q24. These results provide a better alignment of GGA15 with the corresponding regions in human and mouse, and improve our knowledge of the evolution and dynamics of the vertebrate genome

Comparative map between chicken chromosome 15 and human chromosomal region 12q24 and 22q11-q12

The physical and comparative map of GGA15 was improved by the construction of 9 BAC contigs around loci previously mapped on GGA15 by linkage analysis. In total, 240 BAC clones were isolated, covering 30-35% of GGA15, and 120 STS were developed (104 STS derived from BAC end sequences and 18 STS derived within genes). Seventeen chicken orthologues of human genes located on human Chr 22q11-q12 were directly mapped within BAC contigs of GGA15. Furthermore, the partial sequences of the chicken BAC clones were compared with sequences present in the EMBL/GenBank databases and revealed matches to 26 genes, ESTs, and genomic clones located on HSA22q11-q12 and HSA12q24. These results provide a better alignment of GGA15 with the corresponding regions in human and mouse, and improve our knowledge of the evolution and dynamics of the vertebrate genome

Integrating SNPs-based genetic risk factor with blood epigenomic response of differentially arsenic-exposed rural subjects reveals disease-associated signaling pathways

Arsenic (As) contamination in groundwater is responsible for numerous adverse health outcomes among millions of people. Epigenetic alterations are among the most widely studied mechanisms of As toxicity. To understand how As exposure alters gene expression through epigenetic modifications, a systematic genome-wide study was designed to address the impact of multiple important single nucleotide polymorphisms (SNPs) related to As exposure on the methylome of drinking water As-exposed rural subjects from Pakistan. Urinary As levels were used to stratify subjects into low, medium and high exposure groups. Genome-wide DNA methylation was investigated using MeDIP in combination with NimbleGen 2.1 M Deluxe Promotor arrays. Transcriptome levels were measured using Agilent 8 x 60 K expression arrays. Genotyping of selected SNPs (As3MT, DNMT1a, ERCC2, EGFR and MTHFR) was measured and an integrated genetic risk factor for each respondent was calculated by assigning a specific value to the measured genotypes based on known risk allele numbers. To select a representative model related to As exposure we compared 9 linear mixed models comprising of model 1 (including the genetic risk factor), model 2 (without the genetic risk factor) and models with individual SNPs incorporated into the methylome data. Pathway analysis was performed using ConsensusPathDB. Model 1 comprising the integrated genetic risk factor disclosed biochemical pathways including muscle contraction, cardio-vascular diseases, ATR signaling, GPCR signaling, methionine metabolism and chromatin modification in association with hypoand hyper-methylated gene targets. A unique pathway (direct P53 effector) was found associated with the individual DNMT1a polymorphism due to hyper-methylation of CSE1L and TRRAP. Most importantly, we provide here the first evidence of As-associated DNA methylation in relation with gene expression of ATR, ATF7IP, TPM3, UBE2J2. We report the first evidence that integrating SNPs data with methylome data generates a more representative epigenome profile and discloses a better insight in disease risks of As-exposed individuals

Integrating SNPs-based genetic risk factor with blood epigenomic response of differentially arsenic-exposed rural subjects reveals disease-associated signaling pathways

Arsenic (As) contamination in groundwater is responsible for numerous adverse health outcomes among millions of people. Epigenetic alterations are among the most widely studied mechanisms of As toxicity. To understand how As exposure alters gene expression through epigenetic modifications, a systematic genome-wide study was designed to address the impact of multiple important single nucleotide polymorphisms (SNPs) related to As exposure on the methylome of drinking water As-exposed rural subjects from Pakistan. Urinary As levels were used to stratify subjects into low, medium and high exposure groups. Genome-wide DNA methylation was investigated using MeDIP in combination with NimbleGen 2.1 M Deluxe Promotor arrays. Transcriptome levels were measured using Agilent 8 x 60 K expression arrays. Genotyping of selected SNPs (As3MT, DNMT1a, ERCC2, EGFR and MTHFR) was measured and an integrated genetic risk factor for each respondent was calculated by assigning a specific value to the measured genotypes based on known risk allele numbers. To select a representative model related to As exposure we compared 9 linear mixed models comprising of model 1 (including the genetic risk factor), model 2 (without the genetic risk factor) and models with individual SNPs incorporated into the methylome data. Pathway analysis was performed using ConsensusPathDB. Model 1 comprising the integrated genetic risk factor disclosed biochemical pathways including muscle contraction, cardio-vascular diseases, ATR signaling, GPCR signaling, methionine metabolism and chromatin modification in association with hypoand hyper-methylated gene targets. A unique pathway (direct P53 effector) was found associated with the individual DNMT1a polymorphism due to hyper-methylation of CSE1L and TRRAP. Most importantly, we provide here the first evidence of As-associated DNA methylation in relation with gene expression of ATR, ATF7IP, TPM3, UBE2J2. We report the first evidence that integrating SNPs data with methylome data generates a more representative epigenome profile and discloses a better insight in disease risks of As-exposed individuals