Compendium Of Gene Expression Profiles Research Articles

Single cell atlas for 11 non-model mammals, reptiles and birds

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs.

070 Comprehensive single-cell analysis of Sézary syndrome reveals novel expression and therapeutic biomarkers

Cutaneous T cell lymphomas (CTCL) are a group of malignancies thought to derive from a clonal proliferation of skin-tropic T cells. Diagnosis is often delayed several years due to variations in presentation and lack of definitive biomarkers, which result in missed early intervention opportunities that may prevent disease progression. In the setting of more aggressive forms of CTCL, most treatments can initially decrease the tumor burden, but often ultimately fail to prevent the selective outgrowth of a resistant population. The lack of effective therapies is due in part to an incomplete understanding of tumor biology. Using single-cell mRNA and adaptive immune receptor sequencing of peripheral blood immune cells in SS, transcriptomic variations were quantified of almost 50,000 malignant and nonmalignant T cells across six SS patients. Within the population of single cells, we identified both quiescent and proliferative populations across multiple patients, and were able to newly identify a large number of genes associated with malignancy. Furthermore, in a single patient, we characterized differences in cell populations comparing malignant T cells after disease progression in the setting of treatment with histone deacetylase inhibition (HDACi) and photopheresis. HDACi therapy led to new transcriptional profiles in a subset of SS cells, characterized by the increased expression of the transcriptional factor FOXP3. These data are critical in defining a compendium of gene expression profiles and intratumoral heterogeneity in SS and CTCL, which are pivotal in our understanding of CTCL biology and in the development of targeted treatments.

Computational repositioning of dimethyl fumarate for treating alcoholic liver disease

Alcoholic liver disease (ALD) is a chronic alcohol-induced disorder of the liver for which there are few effective therapies for severe forms of ALD and for those who do not achieve alcohol abstinence. In this study, we used a systematic drug-repositioning bioinformatics approach querying a large compendium of gene-expression profiles to identify candidate U.S. Food and Drug Administration (FDA)–approved drugs to treat ALD. One of the top compounds predicted to be therapeutic for ALD by our approach was dimethyl fumarate (DMF), an nuclear factor erythroid 2-related factor 2 (NRF2) inducer. We experimentally validated DMF in liver cells and in vivo. Our work demonstrates that DMF is able to significantly upregulate the NRF2 protein level, increase NRF2 phosphorylation, and promote NRF2 nuclear localization in liver cells. DMF also reduced the reactive oxygen species (ROS) level, lipid peroxidation, and ferroptosis. Furthermore, DMF treatment could prevent ethanol-induced liver injury in ALD mice. Our results provide evidence that DMF might serve as a therapeutic option for ALD in humans, and support the use of computational repositioning to discover therapeutic options for ALD.

A reverse-engineering approach to dissect post-translational modulators of transcription factor's activity from transcriptional data.

BackgroundTranscription factors (TFs) act downstream of the major signalling pathways functioning as master regulators of cell fate. Their activity is tightly regulated at the transcriptional, post-transcriptional and post-translational level. Proteins modifying TF activity are not easily identified by experimental high-throughput methods.ResultsWe developed a computational strategy, called Differential Multi-Information (DMI), to infer post-translational modulators of a transcription factor from a compendium of gene expression profiles (GEPs). DMI is built on the hypothesis that the modulator of a TF (i.e. kinase/phosphatases), when expressed in the cell, will cause the TF target genes to be co-expressed. On the contrary, when the modulator is not expressed, the TF will be inactive resulting in a loss of co-regulation across its target genes. DMI detects the occurrence of changes in target gene co-regulation for each candidate modulator, using a measure called Multi-Information. We validated the DMI approach on a compendium of 5,372 GEPs showing its predictive ability in correctly identifying kinases regulating the activity of 14 different transcription factors.ConclusionsDMI can be used in combination with experimental approaches as high-throughput screening to efficiently improve both pathway and target discovery. An on-line web-tool enabling the user to use DMI to identify post-transcriptional modulators of a transcription factor of interest che be found at http://dmi.tigem.it.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0700-3) contains supplementary material, which is available to authorized users.

Abstract 4610: A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors

Abstract Lung cancer is the leading cause of cancer deaths worldwide, with more than 1.3 million deaths annually. SCLC is a subtype of lung cancer representing ∼15% of all lung cancer cases, and is characterized by the rapid expansion and metastasis of small cells with neuroendocrine features. Patients are typically treated with 4-6 rounds of etoposide and a platinum-based agent, to which there is a 30-40% complete response rate. However, patients almost invariably relapse with disease that is resistant to their primary therapy, and rapidly succumb to their malignancy due to this chemoresistance. No targeted therapy has ever been approved despite numerous attempts. Consequently, 5-year survival for patients has hovered near 5% for the last 30 years. Drug repositioning, which is the discovery of new indications for existing drugs, is an increasingly attractive mode of therapeutic discovery. Recent advances in computing, concomitant with the increasing availability of large datasets, have enabled the development of in silico approaches to drug discovery. Here we applied a systematic drug-repositioning bioinformatics approach querying a large compendium of gene expression profiles to identify candidate FDA-approved drugs to treat small cell lung cancer (SCLC). We found that tricyclic antidepressants (TCAs) and related molecules, such as imipramine and promethazine, potently induce apoptosis in both chemonaïve and chemoresistant SCLC cells in culture, in mouse and human SCLC tumors transplanted into immunocompromised mice, and in endogenous tumors from a mouse model for human SCLC. The candidate drugs activate stress pathways and induce cell death in SCLC cells, at least in part by disrupting autocrine/paracrine survival signals involving neurotransmitters and their receptors. In particular, the mechanism of action of these drugs in inhibiting SCLC tumor growth is through antagonizing G- protein coupled receptors (GPCRs) and blocking signaling downstream, leading to a decrease in cAMP levels and PKA activity, subsequently resulting in activation of the c-Jun/JNK pathway. Moreover, TCAs induce rapid cell death in other neuroendocrine tumors, including carcinoid tumors and Merkel cell carcinoma. These experiments identify novel targeted strategies that can be rapidly evaluated in patients with neuroendocrine tumors through the repurposing of approved drugs. Since these drugs have already been vetted in terms of safety, dosage and toxicity, they can rapidly enter clinical trials. Citation Format: Nadine S. Jahchan, Joel T. Dudley, Pawel K. Mazur, Natasha Flores, Dian Yang, Alec Palmerton, Anne-Flore Zmoos, Dedeepya Vaka, Kim Q.t. Tran, Margaret Zhou, Karolina Krasinska, Jonathan W. Riess, Joel W. Neal, Purvesh Khatri, Kwon S. Park, Atul J. Butte, Julien Sage. A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4610. doi:10.1158/1538-7445.AM2014-4610

Modulation of Gene Expression Regulated by the Transcription Factor NF-κB/RelA

Modulators (Ms) are proteins that modify the activity of transcription factors (TFs) and influence expression of their target genes (TGs). To discover modulators of NF-κB/RelA, we first identified 365 NF-κB/RelA-binding proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We used a probabilistic model to infer 8349 (M, NF-κB/RelA, TG) triplets and their modes of modulatory action from our combined LC-MS/MS and ChIP-Seq (ChIP followed by next generation sequencing) data, published RelA modulators and TGs, and a compendium of gene expression profiles. Hierarchical clustering of the derived modulatory network revealed functional subnetworks and suggested new pathways modulating RelA transcriptional activity. The modulators with the highest number of TGs and most non-random distribution of action modes (measured by Shannon entropy) are consistent with published reports. Our results provide a repertoire of testable hypotheses for experimental validation. One of the NF-κB/RelA modulators we identified is STAT1. The inferred (STAT1, NF-κB/RelA, TG) triplets were validated by LC-selected reaction monitoring-MS and the results of STAT1 deletion in human fibrosarcoma cells. Overall, we have identified 562 NF-κB/RelA modulators, which are potential drug targets, and clarified mechanisms of achieving NF-κB/RelA multiple functions through modulators. Our approach can be readily applied to other TFs.

A Drug Repositioning Approach Identifies Tricyclic Antidepressants as Inhibitors of Small Cell Lung Cancer and Other Neuroendocrine Tumors

Small cell lung cancer (SCLC) is an aggressive neuroendocrine subtype of lung cancer with high mortality. We used a systematic drug repositioning bioinformatics approach querying a large compendium of gene expression profiles to identify candidate U.S. Food and Drug Administration (FDA)-approved drugs to treat SCLC. We found that tricyclic antidepressants and related molecules potently induce apoptosis in both chemonaïve and chemoresistant SCLC cells in culture, in mouse and human SCLC tumors transplanted into immunocompromised mice, and in endogenous tumors from a mouse model for human SCLC. The candidate drugs activate stress pathways and induce cell death in SCLC cells, at least in part by disrupting autocrine survival signals involving neurotransmitters and their G protein-coupled receptors. The candidate drugs inhibit the growth of other neuroendocrine tumors, including pancreatic neuroendocrine tumors and Merkel cell carcinoma. These experiments identify novel targeted strategies that can be rapidly evaluated in patients with neuroendocrine tumors through the repurposing of approved drugs. Our work shows the power of bioinformatics-based drug approaches to rapidly repurpose FDA-approved drugs and identifies a novel class of molecules to treat patients with SCLC, a cancer for which no effective novel systemic treatments have been identified in several decades. In addition, our experiments highlight the importance of novel autocrine mechanisms in promoting the growth of neuroendocrine tumor cells.

Suppressed Expression of T-Box Transcription Factors Is Involved in Senescence in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a major global health problem. The etiology of COPD has been associated with apoptosis, oxidative stress, and inflammation. However, understanding of the molecular interactions that modulate COPD pathogenesis remains only partly resolved. We conducted an exploratory study on COPD etiology to identify the key molecular participants. We used information-theoretic algorithms including Context Likelihood of Relatedness (CLR), Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Inferelator. We captured direct functional associations among genes, given a compendium of gene expression profiles of human lung epithelial cells. A set of genes differentially expressed in COPD, as reported in a previous study were superposed with the resulting transcriptional regulatory networks. After factoring in the properties of the networks, an established COPD susceptibility locus and domain-domain interactions involving protein products of genes in the generated networks, several molecular candidates were predicted to be involved in the etiology of COPD. These include COL4A3, CFLAR, GULP1, PDCD1, CASP10, PAX3, BOK, HSPD1, PITX2, and PML. Furthermore, T-box (TBX) genes and cyclin-dependent kinase inhibitor 2A (CDKN2A), which are in a direct transcriptional regulatory relationship, emerged as preeminent participants in the etiology of COPD by means of senescence. Contrary to observations in neoplasms, our study reveals that the expression of genes and proteins in the lung samples from patients with COPD indicate an increased tendency towards cellular senescence. The expression of the anti-senescence mediators TBX transcription factors, chromatin modifiers histone deacetylases, and sirtuins was suppressed; while the expression of TBX-regulated cellular senescence markers such as CDKN2A, CDKN1A, and CAV1 was elevated in the peripheral lung tissue samples from patients with COPD. The critical balance between senescence and anti-senescence factors is disrupted towards senescence in COPD lungs.

Abstract LB-351: Identification of new drugs for treatment of renal cell cancer using genomics and the Connectivity Map Database

Abstract Clear cell renal cell cancer (RCC) is the most lethal of urological malignancies. Despite numerous recent advances in diagnostic imaging, surgical therapy, and basic molecular understanding, many patients still present or develop metastatic disease. More recently, anti-angiogenic therapies such as the VEGF receptor antagonist sunitinib have shown significant benefit in delaying Progression Free Survival (PFS); however, resistance to sunitinib invariably develops. There are few if any complete or durable responses to these agents and median survival for patients remains less than 2 years with less than 10% of patients surviving 5 years. There is therefore a great unmet need for more efficacious therapies for RCC. The Connectivity Map (C-Map) database is a compendium of gene expression profiles of cancer cell lines treated with a spectrum of bioactive small molecules. One potential use of the C-Map is to find drugs that induce a desired gene expression pattern. As proof-of-concept for identifying drugs for RCC, we used an early release of the C-Map database (453 gene expression profiles of 164 drugs) to search for drugs that induced gene expression changes correlated with normal kidney and anti-correlated with a malignant RCC gene expression pattern. We selected the highest scoring FDA approved drugs for further analysis and demonstrated that 6 of them induced apoptosis in vitro in three different RCC cell lines. We used a xenograft model of RCC (786-0 cells) to evaluate 3 of these agents, and found that one of the drugs, a commonly used FDA-approved antiparasitic drug with very little previous evidence for anti-cancer activity, had significant anti-tumor efficacy in vivo. To identify drugs that may delay or prevent resistance to sunitinib we identified a gene signature for 786-O human RCC xenografts resistant to sunitinib in comparison to the same xenografts during the time of response to sunitinib and applied this sunitinib response gene signature to C-Map analysis (7000 gene expression profiles of 1309 drugs) to search for drugs that induced gene expression changes anti-correlated with the sunitinib resistance gene signature. We selected 9 high scoring FDA approved drugs for further analysis and demonstrated that 6 of them induced apoptosis in vitro in RCC cells. We demonstrated that 3 of these 6 drugs reversed RCC as well as sunitinib resistance gene signatures. We are currently testing the efficacy of these 3 drugs alone and in combination with sunitinib in RCC xenograft models, and positive results could be rapidly tested in patients with RCC. The results establish proof-of-concept for using the C-Map database to identify novel treatments for RCC. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-351. doi:1538-7445.AM2012-LB-351

Functional characterization of bacterial sRNAs using a network biology approach

Small RNAs (sRNAs) are important components of posttranscriptional regulation. These molecules are prevalent in bacterial and eukaryotic organisms, and involved in a variety of responses to environmental stresses. The functional characterization of sRNAs is challenging and requires highly focused and extensive experimental procedures. Here, using a network biology approach and a compendium of gene expression profiles, we predict functional roles and regulatory interactions for sRNAs in Escherichia coli. We experimentally validate predictions for three sRNAs in our inferred network: IsrA, GlmZ, and GcvB. Specifically, we validate a predicted role for IsrA and GlmZ in the SOS response, and we expand on current knowledge of the GcvB sRNA, demonstrating its broad role in the regulation of amino acid metabolism and transport. We also show, using the inferred network coupled with experiments, that GcvB and Lrp, a transcription factor, repress each other in a mutually inhibitory network. This work shows that a network-based approach can be used to identify the cellular function of sRNAs and characterize the relationship between sRNAs and transcription factors.

Genome-wide Analysis of Simultaneous GATA1/2, RUNX1, FLI1, and SCL Binding in Megakaryocytes Identifies Hematopoietic Regulators

SummaryHematopoietic differentiation critically depends on combinations of transcriptional regulators controlling the development of individual lineages. Here, we report the genome-wide binding sites for the five key hematopoietic transcription factors—GATA1, GATA2, RUNX1, FLI1, and TAL1/SCL—in primary human megakaryocytes. Statistical analysis of the 17,263 regions bound by at least one factor demonstrated that simultaneous binding by all five factors was the most enriched pattern and often occurred near known hematopoietic regulators. Eight genes not previously appreciated to function in hematopoiesis that were bound by all five factors were shown to be essential for thrombocyte and/or erythroid development in zebrafish. Moreover, one of these genes encoding the PDZK1IP1 protein shared transcriptional enhancer elements with the blood stem cell regulator TAL1/SCL. Multifactor ChIP-Seq analysis in primary human cells coupled with a high-throughput in vivo perturbation screen therefore offers a powerful strategy to identify essential regulators of complex mammalian differentiation processes.

Gene Set-Based Module Discovery Decodes cis-Regulatory Codes Governing Diverse Gene Expression across Human Multiple Tissues

Decoding transcriptional programs governing transcriptomic diversity across human multiple tissues is a major challenge in bioinformatics. To address this problem, a number of computational methods have focused on cis-regulatory codes driving overexpression or underexpression in a single tissue as compared to others. On the other hand, we recently proposed a different approach to mine cis-regulatory codes: starting from gene sets sharing common cis-regulatory motifs, the method screens for expression modules based on expression coherence. However, both approaches seem to be insufficient to capture transcriptional programs that control gene expression in a subset of all samples. Especially, this limitation would be serious when analyzing multiple tissue data. To overcome this limitation, we developed a new module discovery method termed BEEM (Biclusering-based Extraction of Expression Modules) in order to discover expression modules that are functional in a subset of tissues. We showed that, when applied to expression profiles of human multiple tissues, BEEM finds expression modules missed by two existing approaches that are based on the coherent expression and the single tissue-specific differential expression. From the BEEM results, we obtained new insights into transcriptional programs controlling transcriptomic diversity across various types of tissues. This study introduces BEEM as a powerful tool for decoding regulatory programs from a compendium of gene expression profiles.

Investigating the Mechanistic Basis for Hepatic Toxicity Induced by an Experimental Chemokine Receptor 5 (CCR5) Antagonist Using a Compendium of Gene Expression Profiles

A compendium of hepatic gene expression signatures was used to identify a mechanistic basis for the hepatic toxicity of an experimental CCR5 antagonist (MrkA). Development of MrkA, a potential HIV therapeutic, was discontinued due to hepatotoxicity in preclinical studies. Rats were treated with MrkA at 3 dose levels (50, 250, and 500 mg/kg) for 1, 3, or 7 days. Hepatic toxicity (vacuolation, consistent with steatosis, and elevated serum transaminase levels) was observed at 250 and 500 mg/kg, but not at 50 mg/kg. Hepatic gene expression profiles were compared to a compendium of hepatic expression profiles. MrkA was similar to 3 beta-oxidation inhibitors (valproate, cyclopropane carboxylate, pivalate), 8 PPARalpha agonists (fenofibrate, bezafibrate and 6 fibrate analogues), and 3 other diverse compounds (diethylnitrosamine, microcystin LR & actinomycin D). These data indicate MrkA to be a mitochondrial inhibitor, and activation of PPARalpha-regulated transcription was thought to be due to an accumulation of endogenous ligands. While mitochondrial inhibition was likely responsible for steatosis, canonical pathway analysis revealed that progression to liver injury may be mediated by activation of the innate immune system primarily through NF-kB pathways. These results demonstrate the utility of a gene expression response compendium in developing transcriptional biomarkers and identifying the mechanistic basis for toxicity.

PROFILING THE HEPATIC EFFECTS OF FLUTAMIDE IN RATS: A MICROARRAY COMPARISON WITH CLASSICAL ARYL HYDROCARBON RECEPTOR LIGANDS AND ATYPICAL CYP1A INDUCERS

The antiandrogen flutamide (FLU) is used primarily for prostate cancer and is an idiosyncratic hepatotoxicant that sometimes causes severe liver problems. To investigate FLU's overt hepatic effects, especially on inducible drug clearance-related gene networks, FLU's hepatic gene expression profile was examined in female Sprague-Dawley rats using approximately 22,500 oligonucleotide microarrays. Rats were dosed daily for 3 days with FLU at 500, 250, 62.5, 31.3, and 15.6 mg/kg/day, and hepatic RNA was isolated. FLU resulted in the dose-dependent regulation of approximately 350 genes. Employing a gene-response compendium, FLU was compared with three classical aryl hydrocarbon receptor (AhR) ligands, 3-methylcholanthrene, benzo[a]pyrene, and beta-naphthoflavone, and four atypical CYP1A inducers, indole-3-carbinol (I3C), omeprazole (OME), chlorpromazine (CPZ), and clotrimazole (CLO). The FLU gene response was comparable with classical AhR ligands across a signature AhR ligand gene set that included CYP1A1 and other members of the AhR gene battery. Dose-related responses of CYP1 genes established a maximum response ceiling and discerned potency differences in atypical inducers. FLU had a sharp down-regulation of c-fos that was comparable with all the compounds except CPZ and CLO. FLU absorption, distribution, metabolism, and excretion (ADME) gene expression analysis revealed that FLU, as well as I3C and OME, induced CYP2B and CYP3A, distinguishing them from the classical AhR ligands. By using a compendium of gene expression profiles, FLU was shown to signal in rats similar to an AhR activator with additional CYP2B and CYP3A effects that most resembled the ADME gene expression pattern of the atypical CYP1A inducers I3C and OME.

An Integrated Strategy for Analyzing the Unique Developmental Programs of Different Myoblast Subtypes

An important but largely unmet challenge in understanding the mechanisms that govern the formation of specific organs is to decipher the complex and dynamic genetic programs exhibited by the diversity of cell types within the tissue of interest. Here, we use an integrated genetic, genomic, and computational strategy to comprehensively determine the molecular identities of distinct myoblast subpopulations within the Drosophila embryonic mesoderm at the time that cell fates are initially specified. A compendium of gene expression profiles was generated for primary mesodermal cells purified by flow cytometry from appropriately staged wild-type embryos and from 12 genotypes in which myogenesis was selectively and predictably perturbed. A statistical meta-analysis of these pooled datasets—based on expected trends in gene expression and on the relative contribution of each genotype to the detection of known muscle genes—provisionally assigned hundreds of differentially expressed genes to particular myoblast subtypes. Whole embryo in situ hybridizations were then used to validate the majority of these predictions, thereby enabling true-positive detection rates to be estimated for the microarray data. This combined analysis reveals that myoblasts exhibit much greater gene expression heterogeneity and overall complexity than was previously appreciated. Moreover, it implicates the involvement of large numbers of uncharacterized, differentially expressed genes in myogenic specification and subsequent morphogenesis. These findings also underscore a requirement for considerable regulatory specificity for generating diverse myoblast identities. Finally, to illustrate how the developmental functions of newly identified myoblast genes can be efficiently surveyed, a rapid RNA interference assay that can be scored in living embryos was developed and applied to selected genes. This integrated strategy for examining embryonic gene expression and function provides a substantially expanded framework for further studies of this model developmental system.

Microarray-based compendium of hepatic gene expression profiles for prototypical ADME gene-inducing compounds in rats and mice in vivo

To examine species-specific aspects of the induction of absorption, distribution, metabolism and excretion (ADME)-related genes, we used 25 000 gene oligonucleotide microarrays to construct a rodent gene-response compendium that compared hepatic gene expression profiles and developed consensus aryl hydrocarbon receptor (AhR), constitutive androstane receptor (CAR) and pregnane X-receptor (PXR) ligand signatures relevant to drug clearance. Twenty-six inducer compounds were chosen from the literature. Rats and mice received one of six dose levels (log2 dose escalation, 32-fold dose range) of each compound daily for 3 days. Animals were necropsied 6–9 h after the last dose, and tissues were collected for RNA analysis. Hepatic gene expression profiles were obtained using Rosetta Resolver® expression analysis system, and ADME-related genes were extracted. Cross-talk among nuclear receptors or hepatoxicity at high dose levels resulted in large signatures (usually >1000 genes at p < 0.01) for most compounds. After ADME gene transcript enrichment, agglomerative clustering separated AhR ligands from CAR/PXR ligands, but it was difficult to distinguish CAR from PXR ligands. Consensus signatures were derived from groups of AhR, CAR and PXR ligands; and cross-talk among responding genes was determined. Many compounds had distinct log dose–response profiles, and relative potencies for ligands were established. Robust responses by CYP1A1, CYP2B10 (CAR responsive in mice) and CYP2B15 (CAR responsive in rats) and CYP3A1 (PXR responsive in rats) were used to benchmark the relative potency of different ligands and to determine the relative selectivity for AhR, CAR or PXR. By using a compendium of gene expression profiles, we defined species-specific induction patterns across the ADME transcriptome.

Compendium of gene expression profiles comprising a baseline model of the human liver drug metabolism transcriptome

Oligonucleotide microarrays were used to study the variability of pharmacokinetics and drug metabolism (PKDM)-related gene expression in 75 normal human livers. The objective was to define and use absorption, distribution, metabolism and excretion (ADME) gene expression variability to discern co-regulated genes and potential surrogate biomarkers of inducible gene expression. RNA was prepared from donor tissue and hybridized on Agilent microarrays against an RNA mass balanced pool from all donors. Clustering of PKDM gene sets revealed donors with distinct patterns of gene expression that grouped genes known to be regulated by the nuclear receptor, pregnane X-receptor (PXR). Fold range metrics and frequency distributions from the heterogeneous human population were used to define the variability of individual PKDM genes in the 75 human livers and were placed in context by comparing expression data with basal ADME gene expression variability in an inbred and diet/environment controlled population of 27 Rhesus livers. The most variable genes in the hepatic transcriptome were mainly related to drug metabolism, intermediary metabolism, inflammation and cell cycle control. Unique patterns of expression across 75 individuals of inducible ADME gene expression allowed their expression to be correlated with the expression of many other genes. Correlated genes for AhR, CAR and PXR responsive genes (CYP1A2, CYP2B6 and CYP3A4) were identified that may be co-regulated and, therefore, provide clues to the identity of surrogate gene or protein markers for CYP induction. In conclusion, microarrays were used to define the variable expression of hepatic ADME genes in a diverse human population, the expression variability of ADME genes was compared with the expression variability in an inbred population of Rhesus monkeys, and genes were defined that may be co-regulated with important inducible CYP genes.