Single-cell functional pharmacogenomics reveal cell decision information within gene expression noise
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE138832 | GEO | 2020/09/23
REPOSITORIES: GEO
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Project description:Single-cell functional pharmacogenomics reveal cell decision information within gene expression noise
| PRJNA577469 | ENA
Project description:Combining direct anti-tumor effects with targeting of tumor microenvironment is an attractive strategy that may lead to more effective therapies for advanced melanoma. Here, we tested whether association of TRAIL with co-targeting of MEK and PI3K/mTOR, or with MEK blockade, could have synergistic anti-melanoma activity mediated not only by induction of tumor cell death, but also by effects on the tumor microenvironment. Drug interaction analysis by the Chou-Talalay approach in a panel of 21 melanoma cell lines indicated that strong synergism could be achieved by association of the MEK1/2 inhibitor AZD6244, the PI3K/mTOR inhibitor NPV-BEZ235 and TRAIL, as well as by the AZD6244/TRAIL combination. Synergism was observed on most tumors including TRAIL- or inhibitor-resistant melanomas, irrespective of the BRAF, NRAS, p53 and PTEN status, and was explained by enhanced induction of caspase-dependent melanoma apoptosis. The AZD/BEZ/TRAIL and AZD/TRAIL combinatorial treatments induced strong modulation of key apoptosis regulators along the extrinsic and intrinsic cell death pathways, including c-FLIP down-regulation, caspase-8 cleavage, BIMs and BAXa upregulation, clusterin and Mcl-1 inhibition, enhanced mitochondrial depolarization, suppression of inhibitors of apoptosis c-IAP1, c-IAP2, XIAP and Apollon, and caspase 3/7 activation. In an in vivo model, the AZD6244/TRAIL combinatorial treatment induced highly significant growth inhibition of a TRAIL-resistant tumor associated not only with melanoma cell death, but even with suppression of pro-angiogenic molecules HIF1a, VEGFa, IL-8 and TGFb1, and with inhibition of tumor angiogenesis. These results provide a proof of principle supporting the rationale for combinatorial treatments with synergistic anti-melanoma activity based on direct and indirect anti-tumor effects. The human melanoma cell line Me13 was established in our laboratory from a surgical specimen. Cells were routinely maintained in RPMI medium 1640 (Lonza, Basel, Switzerland) supplemented with 10% FBS (Lonza) and 2 mM glutamine (Lonza). Cells were maintained at 37°C in a water-saturated atmosphere of 5% CO2 in air. 3x10^6 cells were seeded in 75 cm2 flasks; after 24 hours, cells were left untreated or treated with Selumetinib (Selleck Chemicals, Houston, TX) at 0.1 micromol/L, NVP-BEZ-235 (Selleck Chemicals, Houston, TX) at 0.1 micromol/L and sTRAIL (AdipoGen) at 25ng/ml as single drugs or in combination for 8 hours. Each treatment or combination was performed in triplicate. At the end of treatment, cells were collected and RNA extracted.
2014-10-08 | E-GEOD-55050 | biostudies-arrayexpress
Project description:Clinical pharmacogenomics (PGx) has the potential to make pharmacotherapy safer and more effective by utilizing genetic patient data for drug dosing and selection. However, widespread adoption of PGx depends on its successful integration into routine clinical care through clinical decision support tools, which is often hampered by insufficient or fragmented infrastructures. This paper describes the setup and implementation of a unique multimodal, multilingual clinical decision support intervention consisting of digital, paper-, and mobile-based tools that are deployed across implementation sites in seven European countries participating in the Ubiquitous PGx (U-PGx) project.
| S-EPMC6016647 | biostudies-literature
Project description:Recent progress in high-throughput genomic technologies has shifted pharmacogenomic research from candidate gene pharmacogenetics to clinical pharmacogenomics (PGx). Many clinical related questions may be asked such as 'what drug should be prescribed for a patient with mutant alleles?' Typically, answers to such questions can be found in publications mentioning the relationships of the gene-drug-disease of interest. In this work, we hypothesize that ClinicalTrials.gov is a comparable source rich in PGx related information. In this regard, we developed a systematic approach to automatically identify PGx relationships between genes, drugs and diseases from trial records in ClinicalTrials.gov. In our evaluation, we found that our extracted relationships overlap significantly with the curated factual knowledge through the literature in a PGx database and that most relationships appear on average 5 years earlier in clinical trials than in their corresponding publications, suggesting that clinical trials may be valuable for both validating known and capturing new PGx related information in a more timely manner. Furthermore, two human reviewers judged a portion of computer-generated relationships and found an overall accuracy of 74% for our text-mining approach. This work has practical implications in enriching our existing knowledge on PGx gene-drug-disease relationships as well as suggesting crosslinks between ClinicalTrials.gov and other PGx knowledge bases.
| S-EPMC3760158 | biostudies-literature
Project description:In the post-genomic era, the rapid evolution of high-throughput genotyping technologies and the increased pace of production of genetic research data are continually prompting the development of appropriate informatics tools, systems and databases as we attempt to cope with the flood of incoming genetic information. Alongside new technologies that serve to enhance data connectivity, emerging information systems should contribute to the creation of a powerful knowledge environment for genotype-to-phenotype information in the context of translational medicine. In the area of pharmacogenomics and personalized medicine, it has become evident that database applications providing important information on the occurrence and consequences of gene variants involved in pharmacokinetics, pharmacodynamics, drug efficacy and drug toxicity will become an integral tool for researchers and medical practitioners alike. At the same time, two fundamental issues are inextricably linked to current developments, namely data sharing and data protection. Here, we discuss high-throughput and next-generation sequencing technology and its impact on pharmacogenomics research. In addition, we present advances and challenges in the field of pharmacogenomics information systems which have in turn triggered the development of an integrated electronic 'pharmacogenomics assistant'. The system is designed to provide personalized drug recommendations based on linked genotype-to-phenotype pharmacogenomics data, as well as to support biomedical researchers in the identification of pharmacogenomics-related gene variants. The provisioned services are tuned in the framework of a single-access pharmacogenomics portal.
| S-EPMC4118603 | biostudies-literature
Project description:The ability to discriminate between similar sensory stimuli relies on the amount of information encoded in sensory neuronal populations. Such information can be substantially reduced by correlated trial-to-trial variability. Noise correlations have been measured across a wide range of areas in the brain, but their origin is still far from clear. Here we show analytically and with simulations that optimal computation on inputs with limited information creates patterns of noise correlations that account for a broad range of experimental observations while at same time causing information to saturate in large neural populations. With the example of a network of V1 neurons extracting orientation from a noisy image, we illustrate to our knowledge the first generative model of noise correlations that is consistent both with neurophysiology and with behavioral thresholds, without invoking suboptimal encoding or decoding or internal sources of variability such as stochastic network dynamics or cortical state fluctuations. We further show that when information is limited at the input, both suboptimal connectivity and internal fluctuations could similarly reduce the asymptotic information, but they have qualitatively different effects on correlations leading to specific experimental predictions. Our study indicates that noise at the sensory periphery could have a major effect on cortical representations in widely studied discrimination tasks. It also provides an analytical framework to understand the functional relevance of different sources of experimentally measured correlations.
| S-EPMC4687541 | biostudies-literature
Project description:One of the challenges that arise from the advent of personal genomics services is to efficiently couple individual data with state of the art Pharmacogenomics (PGx) knowledge. Existing services are limited to either providing static views of PGx variants or applying a simplistic match between individual genotypes and existing PGx variants. Moreover, there is a considerable amount of haplotype variation associated with drug metabolism that is currently insufficiently addressed. Here, we present a web-based electronic Pharmacogenomics Assistant (ePGA; http://www.epga.gr/) that provides personalized genotype-to-phenotype translation, linked to state of the art clinical guidelines. ePGA's translation service matches individual genotype-profiles with PGx gene haplotypes and infers the corresponding diplotype and phenotype profiles, accompanied with summary statistics. Additional features include i) the ability to customize translation based on subsets of variants of clinical interest, and ii) to update the knowledge base with novel PGx findings. We demonstrate ePGA's functionality on genetic variation data from the 1000 Genomes Project.
| S-EPMC5025168 | biostudies-literature
Project description:The technological revolution in biological research, and in particular the use of molecular fluorescent labels, has allowed investigation of heterogeneity of cellular responses to stimuli on the single cell level. Computational, theoretical, and synthetic biology advances have allowed predicting and manipulating this heterogeneity with an exquisite precision previously reserved only for physical sciences. Functionally, this cell-to-cell variability can compromise cellular responses to environmental signals, and it can also enlarge the repertoire of possible cellular responses and hence increase the adaptive nature of cellular behaviors. And yet quantification of the functional importance of this response heterogeneity remained elusive. Recently the mathematical language of information theory has been proposed to address this problem. This opinion reviews the recent advances and discusses the broader implications of using information-theoretic tools to characterize heterogeneity of cellular behaviors.
| S-EPMC5561739 | biostudies-other
Project description:Myasthenia gravis (MG) is an autoimmune disease affecting the neuromuscular junction, whose clinical hallmark is muscle weakness and early fatigability. Azathioprine (AZA) is commonly used in Myasthenia Gravis therapy. AZA is a purine antagonist, which inhibits the cell cycle in the resting and DNA synthesis phases. It is usually used as an immunosuppressant to block T- and B-cell proliferation. AZA, bioconverted to 6-mercaptopurine by glutathione S-transferase (GST), can be metabolized either through the hypoxanthine phosphoribosyl transferase pathway to active 6-thioguanine nucleotides (6-TGN) or through the thiopurine S-methyltransferase (TPMT) pathway to inactive methyl-thiopurine metabolites. The incorporation of active 6-TGNs into DNA, causing breaks in DNA strands resulting in interference with RNA production and thereby protein synthesis, is responsible for the drug effect. The response to AZA is determined by which metabolic pathway is being favored. While balanced use of both HPRT and TPMT pathways results in responsiveness to AZA, hyperactivity of TPMT skews the balance towards the TPMT pathway and results in unresponsiveness to AZA with no pharmacological effect. In contrast, low TPMT activity skews the balance towards the HPRT pathway, resulting in increasing side-effects due to the accumulation of 6-TGNs. Current treatments for MG therapy are often inadequate because less than 40% of patients achieve complete remission with available drugs. This reflects the lack of drugs acting on target sites for which there is strong evidence of pathogenicity and the inability to identify responder patients. No criteria for responsiveness are available, exposing patients to unpredictable failures and unpredictable side effects. These individuals are at particular risk for adverse drug reaction (ADRs) or therapeutic failure. Genetic profiling with the Affymetrix drug metabolizing enzymes and transports genotyping array offers the ability to determine 1,931 variants and 225 genes involved in drug metabolism and disposition. Accordingly, the study of well-known drug-metabolizing genes can be involved in the specific metabolic pathway of a drug, which is more likely to define genotype-phenotype association and thereby genotype profiles relevant to drug response that can be applied as predictive biomarkers for pharmacological treatment.
2016-05-01 | E-MTAB-4489 | biostudies-arrayexpress
Project description:To comprehensively determine which factors are secreted by cancer cells upon TRAIL stimulation, we conducted an unbiased mass spectrometric analysis of TRAIL’s secretome in resistant A549 cells.
2017-12-19 | PXD005664 | Pride