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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.

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BACKGROUND: The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem. RESULTS: Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in biological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a force-directed graph network style, involving multiple network analysis methods such as degree threshold, but more importantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure. Valuable biological information can be highlighted on the nodes in the network, by the integration of various databases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, disease-to-gene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol, gene synonyms and gene type from the NCBI database. CONCLUSIONS: DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can visualize the dynamic perturbations in biological networks and allows the user to investigate the changes over time. The integrated data from various online databases makes it easy to identify the biological relevance of nodes in the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to visualize a network.

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Inflammation is one of the factors that may increase the sensitivity of hepatic cells to acetaminophen (APAP) induced toxicity. To investigate the mechanisms, we exposed 3-dimensional (3D) Human Liver Microtissues, a co-culture of primary human hepatocytes (PHH) and Kupffer cells (KCs), to 0, 0.5 (low), 5 (median) and 10 mM (high dose) APAP for 24 h, with/without lipopolysaccharide (LPS). Microarray-technology was used to evaluate the transcriptome changes. In the presence of LPS, the median-dose of APAP is sufficient to inhibit the expression of respiratory chain- and antioxidant-related genes, suggesting the involvement of reactive oxygen species (ROS) and oxidative stress. Furthermore, the median- and high-dose of APAP inhibited the expression of Fc fragment receptor (FcγR)-coding genes, regardless of the presence of LPS. The toll-like receptor 4 (TLR4) expression, however, was continuously elevated after the LPS/APAP co-exposures, which may result in reduced KC-phagocytosis and unbalanced cytokine patterns. Compared to the treatment with LPS only, LPS/APAP co-exposures induced the production of interleukin (IL)-8, a pro-inflammatory cytokine, but suppressed the secretion of IL-6, a cytokine regulating hepatic regeneration, along with the increase in APAP dosages. In addition to the disrupted mitochondrial functions, the presence of LPS exacerbated APAP toxicity. These findings suggest that 3D Microtissues are a suitable model for the mechanistic exploration of inflammation-associated drug toxicity.

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Beauvericin (BEA), an ionophoric cyclic hexadepsipeptide mycotoxin, is able to increase oxidative stress by altering membrane ion permeability and uncoupling oxidative phosphorylation. A toxicogenomic study was performed to investigate gene expression changes triggered by BEA exposure (1.5, 3 and 5 µM; 24 h) in Jurkat cells through RNA-sequencing and differential gene expression analysis. Perturbed gene expression was observed in a concentration dependent manner, with 43 differentially expressed genes (DEGs) overlapped in the three studied concentrations. Gene ontology (GO) analysis showed several biological processes related to electron transport chain, oxidative phosphorylation, and cellular respiration significantly altered. Molecular functions linked to mitochondrial respiratory chain and oxidoreductase activity were over-represented (q-value < 0.01). Pathway analysis revealed oxidative phosphorylation and electron transport chain as the most significantly altered pathways in all studied doses (z-score > 1.96; adj p-value < 0.05). 77 genes involved in the respiratory chain were significantly down-regulated at least at one dose. Moreover, 21 genes related to apoptosis and programmed cell death, and 12 genes related to caspase activity were significantly altered, mainly affecting initiator caspases 8, 9 and 10. The results demonstrated BEA-induced mitochondrial damage affecting the respiratory chain, and pointing to apoptosis through the caspase cascade in human lymphoblastic T cells.

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The inflammatory stress has been associated with an increase in susceptibility to idiosyncratic drug-induced liver injury (DILI). However, the molecular mechanisms of this inflammation-associated idiosyncratic drug hepatotoxicity remain unknown. We exposed HepG2 cells with high and low doses of three idiosyncratic (I) and three non-idiosyncratic (N) compounds, in the presence (I+ and N+) or absence (I- and N-) of a cytokine mix for 6, 12 and 24 h. To investigate the genome-wide expression patterns, microarray was performed using the Agilent 4×44K Whole Human Genome chips. The data presented in this DIB include the expression of genes participating in the ceramide metabolism, ER stress, apoptosis and cell survival pathways. The functions of these genes were illustrated in our associated article (Jiang et al., 2017) [1]. Raw and normalized gene expression data are available through NCBI GEO (accession number GSE102006).

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The mechanisms of idiosyncratic drug-induced hepatotoxicity remain largely unclear. It has demonstrated that the drug idiosyncrasy is potentiated in the context of inflammation and intracellular ceramides may play a role in this process. To study the mechanisms, HepG2 cells were co-treated with high and low doses of three idiosyncratic (I) and three non-idiosyncratic (N) compounds, with (I+ and N+) or without (I- and N-) a cytokine mix. Microarray, lipidomics and flow cytometry were performed to investigate the genome-wide expression patterns, the intracellular ceramide levels and the induction of apoptosis. We found that all I+ treatments significantly influenced the immune response- and response to stimulus-associated gene ontology (GO) terms, but the induction of apoptotic pathways, which was confirmed by flow cytometry, only appeared to be induced after the high-dose treatment. The ceramide signaling-, ER stress-, NF-kB activation- and mitochondrial activity-related pathways were biologically involved in apoptosis induced by the high-dose I+. Additionally, genes participating in ceramide metabolism were significantly altered resulting in a measurable increase in ceramide levels. The increases in ceramide concentrations may induce ER stress and activate the JNK pathway by affecting the expression of the related genes, and eventually trigger the mitochondria-independent apoptosis in hepatocytes. Overall, our study provides a potential mechanism to explain the role of inflammation in idiosyncratic drug reactions.

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Transcription factors (TFs) are important modulators of the inducible portion of the transcriptome, and therefore relevant in the context of exposure to exogenous compounds. Current approaches to predict the activity of TFs in biological systems are usually restricted to a few entities at a time due to low-throughput techniques targeting a limited fraction of annotated human TFs. Therefore, high-throughput alternatives may help to identify new targets of mechanistic and predictive value in toxicological investigations. In this study, we inferred the activity multiple TFs using publicly available microarray data from primary human hepatocytes exposed to hundreds of chemicals and evaluated these molecular profiles using multiple correspondence analysis. Our results demonstrate that the lowest dose and latest exposure time (24h) in a subset of chemicals generates a signature indicative of carcinogenicity possibly due to DNA-damaging properties. Furthermore, profiles from the earliest exposure time (2h) and highest dose creates clusters of chemicals implicated in the development of diverse forms of drug-induced liver injury (DILI). Both approaches yielded a number of TFs with similar activity across groups of chemicals, including TFs known in toxicological responses such as AhR, NFE2L2 (Nrf2), NF-κB and PPARG. FOXM1, IRF1 and E2F4 were some of the TFs identified that may be relevant in genotoxic carcinogenesis. SMADs (SMAD1, SMAD2, SMAD5) and KLF5 were identified as some of potentially new TFs whose inferred activities were linked to acute and progressive outcomes in DILI. In conclusion this study offers a novel mechanistic approach targeting TF activity during chemical exposure.

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The assessment of the carcinogenic potential of chemicals with alternative, human-based in vitro systems has become a major goal of toxicogenomics. The central read-out of these assays is the transcriptome, and while many studies exist that explored the gene expression responses of such systems, reports on robustness and reproducibility, when testing them independently in different laboratories, are still uncommon. Furthermore, there is limited knowledge about variability induced by the data analysis protocols. We have conducted an inter-laboratory study for testing chemical carcinogenicity evaluating two human in vitro assays: hepatoma-derived cells and hTERT-immortalized renal proximal tubule epithelial cells, representing liver and kidney as major target organs. Cellular systems were initially challenged with thirty compounds, genome-wide gene expression was measured with microarrays, and hazard classifiers were built from this training set. Subsequently, each system was independently established in three different laboratories, and gene expression measurements were conducted using anonymized compounds. Data analysis was performed independently by two separate groups applying different protocols for the assessment of inter-laboratory reproducibility and for the prediction of carcinogenic hazard. As a result, both workflows came to very similar conclusions with respect to (1) identification of experimental outliers, (2) overall assessment of robustness and inter-laboratory reproducibility and (3) re-classification of the unknown compounds to the respective toxicity classes. In summary, the developed bioinformatics workflows deliver accurate measures for inter-laboratory comparison studies, and the study can be used as guidance for validation of future carcinogenicity assays in order to implement testing of human in vitro alternatives to animal testing.

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The liver is a vital organ in vertebrates that can be subject to disease, among others due to exposure to toxic xenobiotic compounds. A group of transcription factors named ligand activated nuclear receptors (LANR) influence and regulate important liver functions, and can be activated by many xenobiotic compounds, which thereby can cause hepatotoxicity. Systematic analysis of the gene pathways regulated by LANR using modern 'omics technologies is important for investigating modes-of-action of hepatotoxicants. So far, these pathways are not publicly available in a format that allows these studies. We used PathVisio to build liver-specific LANR pathways, both for rats and humans. Since many LANR pathways are linked to each other, we also merged them into a meta-pathway. The pathways are in a GPML-format that enables pathway statistics and visualisations, and will be made available to the public through WikiPathways. We demonstrate the performance of these novel pathways in evaluating transcriptomic studies from the Japanese toxicogenomics project database (Open TG-GATEs). We show that the new pathways can be used to accurately analyse and visualize the effects of prototypical hepatotoxicants in important liver processes, and thus to evaluate the possible mode-of-actions of hepatotoxic xenobiotic compounds by assessing which LANRs are possible targets.

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Oxidative stress plays an important role in chemically induced liver injury, however, our insight into molecular responses to different oxygen radicals is fragmentary. Since these cellular responses will differ over time, examining time-dependent changes in gene expression, and correlating these with markers for oxidative stress, may provide new insights into responses to oxidants. We used the human hepatoma cell line HepG2 to investigate the effects of oxidative stress on the transcriptome level by micro-arrays at seven time points (0.5, 1, 2, 4, 6, 8 and 24h) following exposure to the oxidants menadione, hydrogen peroxide and tert-butyl hydroperoxide including the effects on cell cycle and apoptosis by flow cytometry, protein carbonyl formation by spectrophotometry and oxidative DNA damage by FPG-comet. In total, 3429 genes were differentially expressed, including 136 genes that were significantly modified by all oxidants. Time-dependent biological pathway analysis showed that these genes were particularly involved in inflammatory responses, cell cycle processes and glutathione signaling. These responses were confirmed and supported by phenotypic anchoring to the different cellular endpoints. In addition, using an innovative temporal analysis we established an oxidative stress-related gene expression time cluster. Altogether, this study provides new insights in temporal oxidative stress mechanisms and demonstrates sequential cellular responses that may contribute to a better hazard identification and the mechanisms of toxicological responses in the liver induced by oxidative stress.

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The lack of accurate in vitro assays for predicting in vivo toxicity of chemicals together with new legislations demanding replacement and reduction of animal testing has triggered the development of alternative methods. This study aimed at developing a transcriptomics-based in vitro prediction assay for in vivo genotoxicity. Transcriptomics changes induced in the human liver cell line HepG2 by 34 compounds after treatment for 12, 24, and 48 h were used for the selection of gene-sets that are capable of discriminating between in vivo genotoxins (GTX) and in vivo nongenotoxins (NGTX). By combining transcriptomics with publicly available results for these chemicals from standard in vitro genotoxicity studies, we developed several prediction models. These models were validated by using an additional set of 28 chemicals. The best prediction was achieved after stratification of chemicals according to results from the Ames bacterial gene mutation assay prior to transcriptomics evaluation after 24h of treatment. A total of 33 genes were selected for discriminating GTX from NGTX for Ames-positive chemicals and 22 for Ames-negative chemicals. Overall, this method resulted in 89% accuracy and 91% specificity, thereby clearly outperforming the standard in vitro test battery. Transcription factor network analysis revealed HNF3a, HNF4a, HNF6, androgen receptor, and SP1 as main factors regulating the expression of classifiers for Ames-positive chemicals. Thus, the classical bacterial gene mutation assay in combination with in vitro transcriptomics in HepG2 is proposed as an upgraded in vitro approach for predicting in vivo genotoxicity of chemicals holding a great promise for reducing animal experimentations on genotoxicity.

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The conventional in vitro assays for genotoxicity assessment of chemicals are characterised by a high false-positive rate, thus failing to correctly predict their in vivo genotoxic effects. This study aimed to identify the cellular mechanisms induced by the false-positive genotoxins quercetin, 8-Hydroxyquinoline and 17-beta oestradiol in comparison to true genotoxins and non-genotoxins, by combining in vitro phenotypic parameters with transcriptomics data from HepG2 cells. The effects of these compounds on the phosphorylation of H2AX, cell cycle distribution and whole genome gene expression following treatment for 12, 24 and 48 h were compared with the effects of true genotoxins [benzo[a]pyrene and aflatoxin B1] and non-genotoxins (2,3,7,8-tetrachlorodibenzodioxin, cyclosporin A and ampicillin C). Quercetin induced similar phenotypic effects as true genotoxins and to some extent similar gene expression alterations. Different gene expression changes were also observed, including the up-regulation of DNA repair-related genes. 8-Hydroxyquinoline and 17-beta oestradiol showed no similarities to the true genotoxins at both the phenotypic and the transcriptomic level. In a classification approach, classifiers were selected to discriminate between genotoxins and non-genotoxins. Subsequent analysis for the false-positive compounds showed quercetin to be predicted as genotoxic and 8-hydroxyquinoline and 17-beta oestradiol as non-genotoxic. Our results support that transcriptomics analysis of compound effects in HepG2 leads to similar results with phenotypic analysis and provides additional mechanistic information. Therefore, combined evaluation of gene expression alterations and relevant functional end points using HepG2 cells may contribute to the better understanding of modes-of-action of chemicals and the correct evaluation of their genotoxic properties.

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Mechanistically relevant information on responses of humans to xenobiotic exposure in relation to chemically induced biological effects, such as micronuclei (MN) formation can be obtained through large-scale transcriptomics studies. Network analysis may enhance the analysis and visualisation of such data. Therefore, this study aimed to develop a 'MN formation' network based on a priori knowledge, by using the pathway tool MetaCore. The gene network contained 27 genes and three gene complexes that are related to processes involved in MN formation, e.g. spindle assembly checkpoint, cell cycle checkpoint and aneuploidy. The MN-related gene network was tested against a transcriptomics case study associated with MN measurements. In this case study, transcriptomic data from children and adults differentially exposed to ambient air pollution in the Czech Republic were analysed and visualised on the network. Six genes from the network, i.e. BAX, DMNT1, PCNA, HIC1, p21 and CDC20, were retrieved. Based on these six genes and in combination with p53 and IL-6, a dedicated network was created. This dedicated network is possibly suited for the development of a reporter gene assay that could be used to screen populations complementary to the current MN test assay. In conclusion, we have shown that network analysis of transcriptomics data in relation to the formation of MN is possible and provides a novel mechanistic hypothesis by indicating which genes are regulated and influence others.

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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 γH2AX 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.

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The objective of this study was to examine the relationship of the relative abundance of transcripts of myosin heavy chain (MyHC) isoforms and muscling in several diverse pig breeds. The animals used were from 3 pure breeds (Pietrain, Duroc, and Mongcai) and 2 crosses [Duroc x Pietrain (DUPI) and Duroc x Berlin Miniature pigs (DUMI)]. Real-time PCR quantification of MyHC isoforms I, IIa, IIx, and IIb showed that the relative expression of MyHC IIb was greater in pigs with large LM areas in both DUPI (69.6 vs. 53.0%) and DUMI (60.5 vs. 47.5%). In DUPI, similar transcript levels of MyHC I were found in both large and small LM (14.7 and 15.2%), whereas in DUMI animals, these values were 18.4 and 33.5% (P < 0.05). The groups of animals with large and small LM area in the DUPI also tended to differ in MyHC IIa and IIx transcripts. The comparison among different breeds confirmed the trend of high MyHC IIb transcript abundance together with high muscularity. In Pietrain, Duroc, DUPI, and DUMI, MyHC IIb accounted for more than half of the MyHC transcripts (65.4, 59.7, 54.0, and 54.0%). Mongcai showed low MyHC IIb (11.4%) but high type I, IIa, and IIx relative RNA levels (24.1, 28.5, and 35.9%). Frequencies of fibers, determined by muscle fiber staining with ATPase, and relative abundance of MyHC isoforms, determined by quantitative reverse transcription-PCR, of corresponding pairs of type I, IIa, and IIx/ IIb were correlated (r = 0.71, 0.67, and 0.52, respectively). The study demonstrates that MyHC IIb fibers are the most prominent in pigs having large LM area and implies that MyHC IIb is the determining fiber contributing to the differentiation of large and small loin eye muscle area in the pig.

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The amount and distribution of water inside the meat has a considerable influence on its properties. High losses of fluid in the form of drip may affect financial output, nutritional value, consumer appeal and/or technological properties of porcine meat. Therefore, a deeper insight into the traits water-holding capacity (WHC) and drip is favourable on behalf of producers, industry and consumers. Similar to most meat quality traits, WHC and drip loss (DRIP) are moderately heritable. The genetic correlation between these two traits is high. Correlation to other meat quality traits, such as pH value, cooking loss, reflectance, etc. is existent as predictable. Two major genes are known, RYR1 on chromosome 6 and RN on chromosome 15, to influence meat quality in general and WHC in particular. Furthermore, a number of candidate genes exist, e.g. phosphoglycerate mutase 2. Within the variety of quantitative trait loci (QTL) experiments, a number of QTL have been identified. QTL for DRIP and/or WHC have been found on chromosome 1, 2, 4, 5, 6, 11, 13, 14, 15, 18; for cooking loss on 7, 14 and18, and for pH value on nearly all chromosomes. Recently, a QTL study for meat quality and body composition traits in a Duroc-Pietrain (DUPI) resource population has been conducted at the University of Bonn, Germany. Four QTL for DRIP were identified on chromosomes 2, 3, 5 and 18. The QTL regions are in agreement with previously published QTL for this and other related traits. Further research and finemapping has begun and candidate genes located within the QTL regions are currently under investigation. Combination and comparison of results should lead to deeper insights in the genetic background of meat quality and DRIP.

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We performed a genome-wide QTL scan for production traits in a line cross between Duroc and Pietrain breeds of pigs, which included 585 F(2) progeny produced from 31 full-sib families genotyped with 106 informative microsatellites. A linkage map covering all 18 autosomes and spanning 1987 Kosambi cM was constructed. Thirty-five phenotypic traits including body weight, growth, carcass composition and meat quality traits were analysed using least square regression interval mapping. Twenty-four QTL exceeded the genome-wide significance threshold, while 47 QTL reached the suggestive threshold. These QTL were located at 28 genomic regions on 16 autosomal chromosomes and QTL in 11 regions were significant at the genome-wide level. A QTL affecting pH value in loin was detected on SSC1 between marker-interval S0312-S0113 with strong statistical support (P < 3.0 x 10(-14)); this QTL was also associated with meat colour and conductivity. QTL for carcass composition and average daily gain was also found on SSC1, suggesting multiple QTL. Seventeen genomic segments had only a single QTL that reached at least suggestive significance. Forty QTL exhibited additive inheritance whereas 31 QTL showed (over-) dominance effects. Two QTL for trait backfat thickness were detected on SSC2; a significant paternal effect was found for a QTL in the IGF2 region while another QTL in the middle of SSC2 showed Mendelian expression.

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Mannose-binding lectin (MBL) mediates activation of the complement system via the lectin pathway. Two forms of MBL, MBL-A and MBL-C, were characterized in rodents, rabbits, bovine and rhesus monkeys, whereas only one form was identified in humans, chimpanzees and chickens. The two forms are encoded by two distinct genes named MBL1 and MBL2, which have been identified in many species including the pig. In this report, we studied the two porcine genes MBL1 and MBL2. The porcine MBL genes had higher identities to bovine rather than primate and rodent sequences. Both genes were assigned to chromosome 14 by radiation hybrid panel and linkage mapping. Both MBL genes were highly expressed in liver. MBL1 was also found to be expressed in the lung, testis and brain, whereas low expression of MBL2 was detected in the testis and kidney. New single nucleotide polymorphisms of porcine MBL2 gene were found and genotyped in an experimental F2 pig population, together with a previously reported SNP of MBL1. MBL1 genotypes differed in C3c serum concentration, i.e. in vivo complement activity, at P < 0.1. Correspondingly, linkage analysis revealed a quantitative trait locus for C3c serum level close to the position of the MBL genes. The study thus promotes the porcine MBL genes as functional and positional candidate gene for complement activity.

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beta-actin (ACTB) was examined as a direct functional candidate gene for the possible association with sperm concentration, motility (MOT), semen volume per ejaculate, plasma droplet rate, abnormal sperm rate (ASR) and the fertility traits, non-return rate and number of piglets born alive (NBA). Three polymorphisms in intron 3 (T>C) and one polymorphism in exon 4 (T>C) of porcine ACTB gene were identified by comparative sequencing of animals of the breeds Pietrain and Hampshire. Association analysis revealed that haplotypes affected the variation of the traits MOT, ASR and NBA. The beneficial haplotypes may provide considerable improvement of sperm quality and fertility in the tested commercial boar population.

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This study was conducted to determine the effect of Nitric oxide (NO) inhibition in bovine in vitro development and expression analysis of the three Nitric oxide synthase (NOS) isoforms: endothelial (eNOS), neuronal (nNOS) and inducible (iNOS), mRNA and protein in bovine oocytes and embryos. Selective inhibitor of NOS, N-omega-nitro-l-arginine methyl ester (l-NAME) was applied at different doses (0, 0.1, 1 and 10 mm) in maturation (experiment 1A), culture medium (experiment 1B) and in both maturation and culture media (experiment 1C). No significant differences were observed in cleavage and blastocyst rates when oocytes were matured in the presence of l-NAME as long as the inhibitor was omitted during fertilization and culture. However, significantly lower blastocyst rates were observed when l-NAME was present at higher level (10 mm) in culture medium alone and in both maturation and culture media. In experiment 2, mRNA isolated from triplicate pools of oocytes and embryos (n = 15-20) was subjected to quantitative real time reverse transcription polymerase chain reaction to investigate the expression of eNOS, iNOS and nNOS mRNA in normal IVP bovine oocytes and embryos. While eNOS and iNOS transcripts were detected at higher level in oocytes (immature and mature), two-cell and four-cell stage embryos, the nNOS was detected only in immature oocyte, two-cell and morula stages. In experiment 3, eNOS and iNOS protein expression analysis was performed in IVP oocytes and embryos and both proteins were detected in the cytoplasm and the nuclei (weak) of oocytes and embryos. These data provide the first evidence for the role of NO production and the presence of mRNA and protein products of NOS isoforms during bovine embryogenesis.

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