İşret, kumar, nisvan belası

Paul de Kock'un Sabah'ta yayımlanan İşret, Kumar, Nisvan Belası adlı romanının ilk ve son tefrikalar

Xenobiotic CAR activators induce Dlk1-Dio3 locus non-coding RNA expression in mouse liver

Predicting the impact of human exposure to chemicals such as pharmaceuticals and agrochemicals requires the development of reliable and predictive biomarkers suitable for the detection of early events potentially leading to adverse outcomes. In particular, drug-induced non-genotoxic carcinogenesis (NGC) during preclinical development of novel therapeutics intended for chronic administration in humans is a major challenge for drug safety. We previously demonstrated Constitutive Androstane Receptor (CAR) and WNT signaling-dependent up-regulation of the pluripotency associated Dlk1-Dio3 imprinted gene cluster non-coding RNAs (ncRNAs) in the liver of mice treated with tumorpromoting doses of phenobarbital (PB). Here, to explore the sensitivity and the specificity of this candidate liver tumor promotion ncRNAs signature we compared phenotypic, transcriptional and proteomic data from wild-type, CAR/PXR double knock-out and CAR/PXR double humanized animals treated with tumor-promoting doses of PB or chlordane, both well-established CAR activators. We further investigated selected transcriptional profiles from mouse liver samples exposed to seven NGC compounds working through different mode of actions, overall suggesting CAR-activation specificity of the Dlk1-Dio3 long ncRNAs activation. We propose that Dlk1-Dio3 long ncRNAs up-regulation is an early CAR-activation dependent transcriptional signature during xenobiotic-induced mouse liver tumor promotion. This signature may further contribute mode of action-based ‘weight of evidence’ cancer risk assessment for xenobiotic-induced rodent liver tumors

Importance of investigating epigenetic alterations for industry and regulators: An appraisal of current efforts by the Health and Environmental Sciences Institute

AbstractRecent technological advances have led to rapid progress in the characterization of epigenetic modifications that control gene expression in a generally heritable way, and are likely involved in defining cellular phenotypes, developmental stages and disease status from one generation to the next. On November 18, 2013, the International Life Sciences Institute (ILSI) Health and Environmental Sciences Institute (HESI) held a symposium entitled “Advances in Assessing Adverse Epigenetic Effects of Drugs and Chemicals” in Washington, D.C. The goal of the symposium was to identify gaps in knowledge and highlight promising areas of progress that represent opportunities to utilize epigenomic profiling for risk assessment of drugs and chemicals. Epigenomic profiling has the potential to provide mechanistic information in toxicological safety assessments; this is especially relevant for the evaluation of carcinogenic or teratogenic potential and also for drugs that directly target epigenetic modifiers, like DNA methyltransferases or histone modifying enzymes. Furthermore, it can serve as an endpoint or marker for hazard characterization in chemical safety assessment. The assessment of epigenetic effects may also be approached with new model systems that could directly assess transgenerational effects or potentially sensitive stem cell populations. These would enhance the range of safety assessment tools for evaluating xenobiotics that perturb the epigenome. Here we provide a brief synopsis of the symposium, update findings since that time and then highlight potential directions for future collaborative efforts to incorporate epigenetic profiling into risk assessment

Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations

Drug-induced liver injury (DILI) cannot be accurately predicted by animal models. In addition, currently available in vitro methods do not allow for the estimation of hepatotoxic doses or the determination of an acceptable daily intake (ADI). To overcome this limitation, an in vitro/in silico method was established that predicts the risk of human DILI in relation to oral doses and blood concentrations. This method can be used to estimate DILI risk if the maximal blood concentration (Cmax) of the test compound is known. Moreover, an ADI can be estimated even for compounds without information on blood concentrations. To systematically optimize the in vitro system, two novel test performance metrics were introduced, the toxicity separation index (TSI) which quantifies how well a test differentiates between hepatotoxic and non-hepatotoxic compounds, and the toxicity estimation index (TEI) which measures how well hepatotoxic blood concentrations in vivo can be estimated. In vitro test performance was optimized for a training set of 28 compounds, based on TSI and TEI, demonstrating that (1) concentrations where cytotoxicity first becomes evident in vitro (EC10) yielded better metrics than higher toxicity thresholds (EC50); (2) compound incubation for 48 h was better than 24 h, with no further improvement of TSI after 7 days incubation; (3) metrics were moderately improved by adding gene expression to the test battery; (4) evaluation of pharmacokinetic parameters demonstrated that total blood compound concentrations and the 95%-population-based percentile of Cmax were best suited to estimate human toxicity. With a support vector machine-based classifier, using EC10 and Cmax as variables, the cross-validated sensitivity, specificity and accuracy for hepatotoxicity prediction were 100, 88 and 93%, respectively. Concentrations in the culture medium allowed extrapolation to blood concentrations in vivo that are associated with a specific probability of hepatotoxicity and the corresponding oral doses were obtained by reverse modeling. Application of this in vitro/in silico method to the rat hepatotoxicant pulegone resulted in an ADI that was similar to values previously established based on animal experiments. In conclusion, the proposed method links oral doses and blood concentrations of test compounds to the probability of hepatotoxicity

Carcinogen-Specific Gene Expression Profiles in Short-term Treated Eker and Wild-type Rats Indicative of Pathways Involved in Renal Tumorigenesis

Eker rats heterozygous for a dominant germline mutation in the tuberous sclerosis 2 (Tsc2) tumor suppressor gene were used as a model to study renal carcinogenesis. Eker and corresponding wild-type rats were exposed to genotoxic aristolochic acid (AA) or non-genotoxic ochratoxin A (OTA) to elucidate early carcinogen-specific gene expression changes and to test whether Eker rats are more sensitive to carcinogen-induced changes in gene expression. Male Eker and wild-type rats were gavaged daily with AA (10 mg/kg body weight) or OTA (210 µg/kg body weight). After 1, 3, 7, and 14 days of exposure, renal histopathology, tubular cell proliferation, and Affymetrix gene expression profiles from renal cortex/outer medulla were analyzed. AA-treated Eker and wild-type rats were qualitatively comparable in all variables assessed, suggesting a Tsc2-independent mechanism of action. OTA treatment resulted in slightly increased cortical pathology and significantly elevated cell proliferation in both strains, although Eker rats were more sensitive. Deregulated genes involved in the phosphatidylinositol 3-kinase-AKT-Tsc2-mammalian target of rapamycin signaling, among other important genes prominent in tumorigenesis, in conjunction with the enhanced cell proliferation and presence of preneoplastic lesions suggested involvement of Tsc2 in OTA-mediated toxicity and carcinogenicity, especially as deregulation of genes involved in this pathway was more prominent in the Tsc2 mutant Eker rat

Evaluation of toxicogenomics approaches for assessing the risk of nongenotoxic carcinogenicity in rat liver.

The current gold-standard method for cancer safety assessment of drugs is a rodent two-year bioassay, which is associated with significant costs and requires testing a high number of animals over lifetime. Due to the absence of a comprehensive set of short-term assays predicting carcinogenicity, new approaches are currently being evaluated. One promising approach is toxicogenomics, which by virtue of genome-wide molecular profiling after compound treatment can lead to an increased mechanistic understanding, and potentially allow for the prediction of a carcinogenic potential via mathematical modeling. The latter typically involves the extraction of informative genes from omics datasets, which can be used to construct generalizable models allowing for the early classification of compounds with unknown carcinogenic potential. Here we formally describe and compare two novel methodologies for the reproducible extraction of characteristic mRNA signatures, which were employed to capture specific gene expression changes observed for nongenotoxic carcinogens. While the first method integrates multiple gene rankings, generated by diverse algorithms applied to data from different subsamplings of the training compounds, the second approach employs a statistical ratio for the identification of informative genes. Both methods were evaluated on a dataset obtained from the toxicogenomics database TG-GATEs to predict the outcome of a two-year bioassay based on profiles from 14-day treatments. Additionally, we applied our methods to datasets from previous studies and showed that the derived prediction models are on average more accurate than those built from the original signatures. The selected genes were mostly related to p53 signaling and to specific changes in anabolic processes or energy metabolism, which are typically observed in tumor cells. Among the genes most frequently incorporated into prediction models were Phlda3, Cdkn1a, Akr7a3, Ccng1 and Abcb4

Glomerulonephritis-induced changes in kidney gene expression in rats

We investigated a glomerulonephritis (GN) model in rats induced by nephrotoxic serum (NTS) which contains antibodies against the glomerular basement membrane (GBM). The anti-GBM GN model in rats is widely used since its biochemical and histopathological characteristics are similar to crescentic nephritis and Goodpasture's disease in humans (Pusey, 2003 [2]). Male Wistar Kyoto (WKY) and Sprague–Dawley (SD) rats were dosed once with 1, 2.5 and 5 ml/kg nephrotoxic serum (NTS) or 1.5 and 5 ml/kg NTS, respectively. GN and tubular damage were observed histopathologically in all treated rats after 14 days. To obtain insight into molecular processes during GN pathogenesis, mRNA expression was investigated in WKY and SD kidneys using Affymetrix's GeneChip Rat genome 230_2.0 arrays (GSE64265). The immunopathological processes during GN are still not fully understood and likely involve both innate and adaptive immunity. In the present study, several hundred mRNAs were found deregulated, which functionally were mostly associated with inflammation and regeneration. The β-chain of the major histocompatibility complex class II RT1.B (Rt1-Bb) and complement component 6 (C6) were identified as two mRNAs differentially expressed between WKY and SD rat strains which could be related to known different susceptibilities to NTS of different rat strains; both were increased in WKY and decreased in SD rats (Pavkovic et al., 2015 [1]). Increased Rt1-Bb expression in WKY rats could indicate a stronger and more persistent cellular reaction of the adaptive immune system in this strain, in line with findings indicating adaptive immune reactions during GN. The complement cascade is also known to be essential for GN development, especially terminal cascade products like C6

Establishment of a protocol for the gene expression analysis of laser microdissected rat kidney samples with affymetrix genechips

Laser microdissection in conjunction with microarray technology allows selective isolation and analysis of specific cell populations, e.g., preneoplastic renal lesions. To date, only limited information is available on sample preparation and preservation techniques that result in both optimal histomorphological preservation of sections and high-quality RNA for microarray analysis. Furthermore, amplification of minute amounts of RNA from microdissected renal samples allowing analysis with genechips has only scantily been addressed to date. The objective of this study was therefore to establish a reliable and reproducible protocol for laser microdissection in conjunction with microarray technology using kidney tissue from Eker rats p.o. treated for 7 days and 6 months with 10 and 1 mg Aristolochic acid/kg bw, respectively. Kidney tissues were preserved in RNAlater or snap frozen. Cryosections were cut and stained with either H&E or cresyl violet for subsequent morphological and RNA quality assessment and laser microdissection. RNA quality was comparable in snap frozen and RNAlater-preserved samples, however, the histomorphological preservation of renal sections was much better following cryopreservation. Moreover, the different staining techniques in combination with sample processing time at room temperature can have an influence on RNA quality. Different RNA amplification protocols were shown to have an impact on gene expression profiles as demonstrated with Affymetrix Rat Genome 230_2.0 arrays. Considering all the parameters analyzed in this study, a protocol for RNA isolation from laser microdissected samples with subsequent Affymetrix chip hybridization was established that was also successfully applied to preneoplastic lesions laser microdissected from Aristolochic acid-treated rats

Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat.

In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens

Separation and classification of compounds based on EFS and SR signature.

<p>(<b>A</b>) The dots correspond to different treatment groups and are colored according to the classes of the compounds used for treatment. Each treatment group was originally represented by a vector composed of the fold-changes of the 54 signature genes measured after 14 days of repeated dosing. In order to inspect the compound-specific expression profiles in a lower-dimensional space, these vectors were transformed to the first and second principal component resulting from PCA. In order to highlight clusters of NGCs and NCs, convex hulls were drawn around the respective compounds. The compounds WY, MP and MCT were considered as undefined, due to ambiguous outcomes of published studies. (<b>B</b>) PCA plot similar to (A), but generated on the basis of the SR signature. (<b>C</b>) The heatmaps depict the confidence of the predictions made by diverse classifiers for assessing the carcinogenic potential of GCs (AAF, DEN) and undefined compounds (MP, WY, MCT). Columns represent compounds and rows correspond to classifiers. The compound classes are indicated by the colorbar on top. The discrimination between carcinogens (blue) and non-carcinogens (green) was done based on the EFS signature. (<b>D</b>) Toxicogenomics-based assessment of the carcinogenic potential of GCs and undefined compounds using diverse classifiers which incorporate the SR signature genes as predictive features.</p