RESUMO
Nuclear receptors (NR) are ligand-modulated transcription factors that regulate multiple cell functions and thus represent excellent drug targets. However, due to a considerable NR structural homology, NR ligands often interact with multiple receptors. Here, we describe a multiplex reporter assay (the FACTORIAL NR) that enables parallel assessment of NR ligand activity across all 48 human NRs. The assay comprises one-hybrid GAL4-NR reporter modules transiently transfected into test cells. To evaluate the reporter activity, we assessed their RNA transcripts. We used a homogeneous RNA detection approach that afforded equal detection efficacy and permitted the multiplex detection in a single-well format. For validation, we examined a panel of selective NR ligands and polypharmacological agonists and antagonists of the progestin, estrogen, PPAR, ERR, and ROR receptors. The assay produced highly reproducible NR activity profiles (r > 0.96) permitting quantitative assessment of individual NR responses. The inferred EC50 values agreed with the published data. The assay showed excellent quality (
Assuntos
Ligantes , Polifarmacologia/métodos , Receptores Citoplasmáticos e Nucleares/fisiologia , Bioensaio/métodos , Genes Reporter/efeitos dos fármacos , Humanos , Programas de Rastreamento/métodos , Ligação Proteica , Receptores Citoplasmáticos e Nucleares/efeitos dos fármacos , Receptores Citoplasmáticos e Nucleares/metabolismoRESUMO
Assessing the biological activity of compounds is an essential objective of biomedical research. We show that one can infer the bioactivity of compounds by assessing the activity of transcription factors (TFs) that regulate gene expression. Using a multiplex reporter system, the FACTORIAL, we characterized cell response to a compound by a quantitative signature, the TF activity profile (TFAP). We found that perturbagens of biological pathways elicited distinct TFAP signatures in human cells. Unexpectedly, perturbagens of the same pathway all produced identical TFAPs, regardless of where or how they interfered. We found invariant TFAPs for mitochondrial, histone deacetylase, and ubiquitin/proteasome pathway inhibitors; cytoskeleton disruptors; and DNA-damaging agents. Using these invariant signatures permitted straightforward identification of compounds with specified bioactivities among uncharacterized chemicals. Furthermore, this approach allowed us to assess the multiple bioactivities of polypharmacological drugs. Thus, TF activity profiling affords straightforward assessment of the bioactivity of compounds through the identification of perturbed biological pathways.
Assuntos
Biomarcadores Tumorais/genética , Carcinoma Hepatocelular/patologia , Drogas em Investigação/farmacologia , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Neoplasias Hepáticas/patologia , Fatores de Transcrição/genética , Transcriptoma/efeitos dos fármacos , Carcinoma Hepatocelular/tratamento farmacológico , Carcinoma Hepatocelular/genética , Proliferação de Células , Biologia Computacional , Relação Dose-Resposta a Droga , Perfilação da Expressão Gênica , Humanos , Neoplasias Hepáticas/tratamento farmacológico , Neoplasias Hepáticas/genética , Células Tumorais CultivadasRESUMO
We developed a high-content reporter system that allows quantitative assessment of activities of multiple transcription factors (TFs) in a eukaryotic cell. The system comprises a library of reporter constructs that are evaluated according to their transcription rates. All reporters produce essentially identical messages that are subjected to 'processing', which generates a spectrum of distinguishable fragments that are analyzed quantitatively. The homogeneity of the reporter library afforded inherently uniform detection conditions for all reporters and provided repeatability, accuracy and robustness of assessment. We showed that this technology can be used to identify pathways transmitting cell responses to inducers, and that the profile of TF activities generated using this system represents a stable and sustained cell signature that clearly distinguishes different cell types and pathological conditions. This technology provides a framework for functional characterization of signal transduction networks through profiling activities of multiple TFs.