The Ah receptor (AHR) is a ligand-activated transcription factor that mediates a pleiotropic response to environmental contaminants such as benzo(a)pyrene and 2,3,7,8-tetrachlorodibenzo-p-dioxin. In an effort to gain in- sight into the physiological role of the AHR and to develop models useful in risk assessment, gene targeting was used to inactivate the murine Ahr gene by homologous recombination. Ahr 2/2 mice are viable and fertile but show a spectrum of hepatic defects that indicate a role for the AHR in normal liver growth and development. The Ahr 2/2 phenotype is most severe between 0-3 weeks of age and involves slowed early growth and hepatic defects, including reduced liver weight, transient microvesicular fatty metamorphosis, prolonged extramedul- lary hematopoiesis, and portal hypercellularity with thicken- ing and fibrosis.

Characterization of a murine Ahr null allele: Involvement of the Ah receptor in hepatic growth and development (dioxiny2,3,7,8-tetrachlorodibenzo-p-dioxinygene targetingyliver)

The aryl hydrocarbon receptor (AHR) is a protein best known for its role in mediating toxicity. Over 30 years of research has uncovered additional roles for the AHR in xenobiotic metabolism and normal vascular development. Activation of the AHR has long been known to cause immunotoxicity, including thymic involution. Recent data suggesting a role for the AHR in regulatory T-cell (Treg) and T-helper 17 (Th17) cell development have only added to the excitement about this biology. In this review, we will attempt to illustrate what is currently known about AHR biology in the hope that data from fields as diverse as evolutionary biology and pharmacology will help elucidate the mechanism by which AHR modifies immune responses. We also will discuss the complexities of AHR pharmacology and genetics that may influence future studies of AHR in the immune system.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2567.2009.03054.x

The aryl hydrocarbon receptor: a perspective on potential roles in the immune system.

The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor regulating the expression of genes, for instance encoding the monooxygenases cytochrome P450 (CYP) 1A1 and CYP1A2, which are important enzymes in metabolism of xenobiotics. The AHR is activated upon binding of polycyclic aromatic hydrocarbons (PAHs), persistent organic pollutants (POPs), and related ubiquitous environmental chemicals, to mediate their biological and toxic effects. In addition, several endogenous and natural compounds can bind to AHR, thereby modulating a variety of physiological processes. In recent years, ambient particulate matter (PM) associated with traffic related air pollution (TRAP) has been found to contain significant amounts of PAHs. PM containing PAHs are of increasing concern as a class of agonists, which can activate the AHR. Several reports show that PM and AHR-mediated induction of CYP1A1 results in excessive generation of reactive oxygen species (ROS), causing oxidative stress. Furthermore, exposure to PM and PAHs induce inflammatory responses and may lead to chronic inflammatory diseases, including asthma, cardiovascular diseases, and increased cancer risk. In this review, we summarize findings showing the critical role that the AHR plays in mediating effects of environmental pollutants and stressors, which pose a risk of impacting the environment and human health.

The aryl hydrocarbon receptor as a target of environmental stressors - Implications for pollution mediated stress and inflammatory responses.

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct—but often complementary—information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets.

Results: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI’s performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation–chip and protein–protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques—as well as to non-integrative approaches—demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods.

Availability: A Matlab implementation of MDI is available from http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/.

Contact: D.L.Wild@warwick.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

/pdf/bayesian-correlated-clustering-to-integrate-multiple-4xj0jlymhv.pdf

Bayesian correlated clustering to integrate multiple datasets

https://zenodo.org/record/894712/files/article.pdf

An Aryl Hydrocarbon Receptor Odyssey to the Shores of Toxicology: The Deichmann Lecture, International Congress of Toxicology-XI

Expression of genes in the TGF-β signaling pathway is significantly deregulated in smooth muscle cells from aorta of aryl hydrocarbon receptor knockout mice

Benzo[a]pyrene (B[a]P) and 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) are potent ligands for the aryl hydrocarbon receptor (AHR). High-density oligonucleotide microarrays were used to generate global gene expression profiles of wild-type and Ahr
 −/− vascular smooth muscle cells (SMCs) from mouse aorta. To determine whether there are signaling pathways other than the AHR involved in B[a]P metabolism, wild-type and AHR knockout (Ahr
 −/− SMCs were exposed to B[a]P. Two signaling pathways, represented by TGF-β2 and IGF-1, were identified as potential candidates of an AHR alternate pathway for cells to respond to B[a]P. The wild-type SMCs responded similarly to B[a]P and TCDD in the regulation of a small set of common genes known to respond to the activated AHR (e.g., glutamine S-transferase). However, wild-type SMCs responded in a way that involves many additional genes, suggesting that a very divergent cellular response may be involved when SMCs are exposed to the two classic inducers of the AHR. In contrast, many more genes in the Ahr
 −/− cells responded similarly to B[a]P and TCDD, inducluding Cyp1b1, than responded differently, which indicates that eliminating the AHR is effective for investigating potential alternate cellular mechanisms that respond to B[a]P and TCDD.

Different global gene expression profiles in benzo[a]pyrene- and dioxin-treated vascular smooth muscle cells of AHR-knockout and wild-type mice.

Unsupervised identification of patterns in microarray data has been a productive approach to uncovering relationships between genes and the biological process in which they are involved. Traditional model-based clustering approaches as well as some recently developed model-based mining approaches for integrating genomic and functional genomic data rely on one's ability to determine the correct number of clusters or modules in the data. In this paper we demonstrate that the performance of such methods in general can be significantly improved by accounting for uncertainties inherent to the process of identifying the optimal number of clusters in the data. We demonstrate that the Bayesian averaging approach to clustering via infinite mixture model offers a more robust performance than the traditional finite mixture model in which the optimal number of clusters is determined using the Bayesian Information Criterion. This performance improvement is demonstrated through a simulation study and by the analysis of a relatively large microarray dataset. Finally, we describe the novel heuristic modification of the Gibbs sampler used to fit the infinite mixture mode that effectively deals with issues of slow mixing.

Bayesian model-averaging in unsupervised learning from microarray data

Motivation: Identifying groups of co-regulated genes by monitoring their expression over various experimental conditions is complicated by the fact that such co-regulation is condition-specific. Ignoring the context-specific nature of co-regulation significantly reduces the ability of clustering procedures to detect co-expressed genes due to additional 'noise' introduced by non-informative measurements.

Results: We have developed a novel Bayesian hierarchical model and corresponding computational algorithms for clustering gene expression profiles across diverse experimental conditions and studies that accounts for context-specificity of gene expression patterns. The model is based on the Bayesian infinite mixtures framework and does not require a priori specification of the number of clusters. We demonstrate that explicit modeling of context-specificity results in increased accuracy of the cluster analysis by examining the specificity and sensitivity of clusters in microarray data. We also demonstrate that probabilities of co-expression derived from the posterior distribution of clusterings are valid estimates of statistical significance of created clusters.

Availability: The open-source package gimm is available at http://eh3.uc.edu/gimm

Contact: Mario.Medvedovic@uc.edu

Supplementary information: http://eh3.uc.edu/gimm/csimm

/pdf/context-specific-infinite-mixtures-for-clustering-gene-3xrmij523a.pdf

Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset

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Expression of genes in the TGF-β signaling pathway is significantly deregulated in smooth muscle cells from aorta of aryl hydrocarbon receptor knockout mice

Different global gene expression profiles in benzo[a]pyrene- and dioxin-treated vascular smooth muscle cells of AHR-knockout and wild-type mice.

Bayesian model-averaging in unsupervised learning from microarray data

Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset