Quantitative analysis and modeling of glucocorticoid-controlled gene expression

The long winding road to the safer glucocorticoid receptor (GR) targeting therapies

Glucocorticoids (Gcs) are widely used to treat inflammatory diseases and hematological malignancies, and despite the introduction of novel anti-inflammatory and anti-cancer biologics, the use of inexpensive and effective Gcs is expected to grow. Unfortunately, chronic treatment with Gcs results in multiple atrophic and metabolic side effects. Thus, the search for safer glucocorticoid receptor (GR)-targeted therapies that preserve therapeutic potential of Gcs but result in fewer adverse effects remains highly relevant. Development of selective GR agonists/modulators (SEGRAM) with reduced side effects, based on the concept of dissociation of GR transactivation and transrepression functions, resulted in limited success, and currently focus has shifted towards partial GR agonists. Additional approach is the identification and inhibition of genes associated with Gcs specific side effects. Others and we recently identified GR target genes REDD1 and FKBP51 as key mediators of Gcs-induced atrophy, and selected and validated candidate molecules for REDD1 blockage including PI3K/Akt/mTOR inhibitors. In this review, we summarized classic and contemporary approaches to safer GR-mediated therapies including unique concept of Gcs combination with REDD1 inhibitors. We discussed protective effects of REDD1 inhibitors against Gcs–induced atrophy in skin and bone and underlined the translational potential of this combination for further development of safer and effective Gcs-based therapies.

A systematic review of modeling and simulation approaches in designing targeted treatment technologies for Leukemia Cancer in low and middle income countries

Virtual experimentation is a widely used approach for predicting systems behaviour especially in situations where resources for physical experiments are very limited. For example, targeted treatment inside the human body is particularly challenging, and as such, modeling and simulation is utilised to aid planning before a specific treatment is administered. In such approaches, precise treatment, as it is the case in radiotherapy, is used to administer a maximum dose to the infected regions while minimizing the effect on normal tissue. Complicated cancers such as leukemia present even greater challenges due to their presentation in liquid form and not being localised in one area. As such, science has led to the development of targeted drug delivery, where the infected cells can be specifically targeted anywhere in the body. Despite the great prospects and advances of these modeling and simulation tools in the design and delivery of targeted drugs, their use by Low and Middle Income Countries (LMICs) researchers and clinicians is still very limited. This paper therefore reviews the modeling and simulation approaches for leukemia treatment using nanoparticles as an example for virtual experimentation. A systematic review from various databases was carried out for studies that involved cancer treatment approaches through modeling and simulation with emphasis to data collected from LMICs. Results indicated that whereas there is an increasing trend in the use of modeling and simulation approaches, their uptake in LMICs is still limited. According to the review data collected, there is a clear need to employ these tools as key approaches for the planning of targeted drug treatment approaches.

Reversal of glucocorticoid resistance in paediatric acute lymphoblastic leukaemia is dependent on restoring BIM expression

BACKGROUND

Acute lymphoblastic leukaemia (ALL) is the most common paediatric malignancy. Glucocorticoids form a critical component of chemotherapy regimens and resistance to glucocorticoid therapy is predictive of poor outcome. We have previously shown that glucocorticoid resistance is associated with upregulation of the oncogene C-MYC and failure to induce the proapoptotic gene BIM.

METHODS

A high-throughput screening (HTS) campaign was carried out to identify glucocorticoid sensitisers against an ALL xenograft derived from a glucocorticoid-resistant paediatric patient. Gene expression analysis was carried out using Illumina microarrays. Efficacy, messenger RNA and protein analysis were carried out by Resazurin assay, reverse transcription-PCR and immunoblotting, respectively.

RESULTS

A novel glucocorticoid sensitiser, 2-((4,5-dihydro-1H-imidazol-2-yl)thio)-N-isopropyl-N-phenylacetamide (GCS-3), was identified from the HTS campaign. The sensitising effect was specific to glucocorticoids and synergy was observed in a range of dexamethasone-resistant and dexamethasone-sensitive xenografts representative of B-ALL, T-ALL and Philadelphia chromosome-positive ALL. GCS-3 in combination with dexamethasone downregulated C-MYC and significantly upregulated BIM expression in a glucocorticoid-resistant ALL xenograft. The GCS-3/dexamethasone combination significantly increased binding of the glucocorticoid receptor to a novel BIM enhancer, which is associated with glucocorticoid sensitivity.

CONCLUSIONS

This study describes the potential of the novel glucocorticoid sensitiser, GCS-3, as a biological tool to interrogate glucocorticoid action and resistance.

Insight into glucocorticoid receptor signalling through interactome model analysis

Glucocorticoid hormones (GCs) are used to treat a variety of diseases because of their potent anti-inflammatory effect and their ability to induce apoptosis in lymphoid malignancies through the glucocorticoid receptor (GR). Despite ongoing research, high glucocorticoid efficacy and widespread usage in medicine, resistance, disease relapse and toxicity remain factors that need addressing. Understanding the mechanisms of glucocorticoid signalling and how resistance may arise is highly important towards improving therapy. To gain insight into this we undertook a systems biology approach, aiming to generate a Boolean model of the glucocorticoid receptor protein interaction network that encapsulates functional relationships between the GR, its target genes or genes that target GR, and the interactions between the genes that interact with the GR. This model named GEB052 consists of 52 nodes representing genes or proteins, the model input (GC) and model outputs (cell death and inflammation), connected by 241 logical interactions of activation or inhibition. 323 changes in the relationships between model constituents following in silico knockouts were uncovered, and steady-state analysis followed by cell-based microarray genome-wide model validation led to an average of 57% correct predictions, which was taken further by assessment of model predictions against patient microarray data. Lastly, semi-quantitative model analysis via microarray data superimposed onto the model with a score flow algorithm has also been performed, which demonstrated significantly higher correct prediction ratios (average of 80%), and the model has been assessed as a predictive clinical tool using published patient microarray data. In summary we present an in silico simulation of the glucocorticoid receptor interaction network, linked to downstream biological processes that can be analysed to uncover relationships between GR and its interactants. Ultimately the model provides a platform for future development both by directing laboratory research and allowing for incorporation of further components, encapsulating more interactions/genes involved in glucocorticoid receptor signalling.

Here we present modelling of the glucocorticoid receptor (GR) signalling network. The GR is the effector for a class of drugs known as corticosteroids, which are widely used in medicine for their anti-inflammatory effects and ability to induce apoptosis in leukaemic cells. However, side effects, treatment-related toxicity and glucocorticoid resistance remain and therefore increased understanding of the glucocorticoid receptor mechanism of action may improve therapeutic outcomes. The GEB052 model presented herein has been used to generate predictions for how the network is altered between glucocorticoid-sensitive and glucocorticoid-resistant scenarios, and these predictions have been verified using published gene expression data from established cell lines (for both qualitative and semi-quantitative analysis). The model has also been preliminarily assessed as a predictive clinical tool by correlating model predictions with clinical outcomes of thirteen leukaemia patients. Thus, the GEB052 model demonstrates successful modelling to understand GR function. GEB052 provides accurate predictions and has indicated potential routes through which glucocorticoid resistance may arise. The work presented herein thus demonstrates a proof-of-principle of this modelling approach to furthering GR research, and provides insight into potential mechanisms of corticosteroids resistance.

Differential regulation of cell death pathways by the microenvironment correlates with chemoresistance and survival in leukaemia

Glucocorticoids (GCs) and topoisomerase II inhibitors are used to treat acute lymphoblastic leukaemia (ALL) as they induce death in lymphoid cells through the glucocorticoid receptor (GR) and p53 respectively. Mechanisms underlying ALL cell death and the contribution of the bone marrow microenvironment to drug response/resistance remain unclear. The role of the microenvironment and the identification of chemoresistance determinants were studied by transcriptomic analysis in ALL cells treated with Dexamethasone (Dex), and Etoposide (Etop) grown in the presence or absence of bone marrow conditioned media (CM). The necroptotic (RIPK1) and the apoptotic (caspase-8/3) markers were downregulated by CM, whereas the inhibitory effects of chemotherapy on the autophagy marker Beclin-1 (BECN1) were reduced suggesting CM exerts cytoprotective effects. GCs upregulated the RIPK1 ubiquitinating factor BIRC3 (cIAP2), in GC-sensitive (CEM-C7-14) but not in resistant (CEM-C1-15) cells. In addition, CM selectively affected GR phosphorylation in a site and cell-specific manner. GR is recruited to RIPK1, BECN1 and BIRC3 promoters in the sensitive but not in the resistant cells with phosphorylated GR forms being generally less recruited in the presence of hormone. FACS analysis and caspase-8 assays demonstrated that CM promoted a pro-survival trend. High molecular weight proteins reacting with the RIPK1 antibody were modified upon incubation with the BIRC3 inhibitor AT406 in CEM-C7-14 cells suggesting that they represent ubiquitinated forms of RIPK1. Our data suggest that there is a correlation between microenvironment-induced ALL proliferation and altered response to chemotherapy.

Glucocorticoid-Induced Leucine Zipper in Central Nervous System Health and Disease

The central nervous system (CNS) is a large network of intercommunicating cells that function to maintain tissue health and homeostasis. Considerable evidence suggests that glucocorticoids exert both neuroprotective and neurodegenerative effects on the CNS. Glucocorticoids act by binding two related receptors in the cytoplasm, the mineralocorticoid receptor (MR) and the glucocorticoid receptor (GR). The glucocorticoid receptor complex mediates cellular responses by transactivating target genes and by protein: protein interactions. The paradoxical effects of glucocorticoids on neuronal survival and death have been attributed to the concentration and the ratio of mineralocorticoid to glucocorticoid receptor activation. Glucocorticoid-induced leucine zipper (GILZ) is a recently identified protein transcriptionally upregulated by glucocorticoids. Constitutively, expressed in many tissues including brain, GILZ mediates many of the actions of glucocorticoids. It mimics the anti-inflammatory and anti-proliferative effects of glucocorticoids but exerts differential effects on stem cell differentiation and lineage development. Recent experimental data on the effects of GILZ following induced stress or trauma suggest potential roles in CNS diseases. Here, we provide a short overview of the role of GILZ in CNS health and discuss three potential rationales for the role of GILZ in Alzheimer's disease pathogenesis.

The effects of dexamethasone on the proliferation and apoptosis of human ovarian cancer cells induced by paclitaxel

BACKGROUND

Dexamethasone (DEX) has been routinely used as a pre-treatment in the clinical application of paclitaxel (PTX) to treat ovarian cancer. However, PTX-induced apoptosis might be inhibited by DEX. This study was undertaken to investigate the effects of DEX on the apoptosis induced by PTX.

METHODS

Both of SKOV-3 and HO-8910 human ovarian cancer cells were divided into four groups: (1) untreated (Con); (2) treated with DEX (0.1 μM) alone; (3) treated with PTX (50 nM); and (4) pre-treated with DEX (0.1 μM), and 24 h later, treated with PTX (DEX + PTX). Cell proliferation was determined by the 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di- phenytetrazoliumromide (MTT) dye uptake method, while cell apoptosis was analyzed by propidium iodide (PI) staining and flow cytometry. Then, reverse transcription polymerase chain reactions (RT-PCRs) were applied to semi-quantitative analysis, followed by western blot analysis. Statistical analysis was performed, with Fisher's least significant difference test.

RESULTS

Our results demonstrated that DEX can differentially inhibit SKOV-3 and HO-8910 cell proliferation induced by PTX and decrease the apoptosis rates in cancer cells. Pre-treatment with DEX could up-regulate the expressions of members of anti-apoptotic Bcl-2 family (Bcl-2 and Bcl-XL) and members of IAP family (survivin). The expression of cleaved caspase-3 was down-regulated by DEX, shown by semi-quantitative RT-PCRs and western blot analysis.

CONCLUSIONS

Our data gained invaluable insights of the antagonistic mechanisms of DEX on PTX-induced cancer cell death and may provide new methods of using DEX as antineoplastic drugs or agents in the clinical treatment for ovarian cancer patients.

Roadmap to personalized medicine

Abstract

Standard clinical protocols and the concept “one drug fits all” that are currently used to treat illness in many cases are not effective, and strikingly so in the treatment of cancer, where 75% of therapeutic schemes are ineffective. The concept of personalized medicine is that the treatment of the disease is designed on the basis of the individual needs of each patient and the factors that influence their response to different drugs. Individualization of patient care has the potential to generate novel effective therapies, limit the adverse drug effects, create optimal treatments for individual patients, and decrease the cost associated with chronic illness and complications of drug usage. However, to achieve the goals of personalized medicine many challenges must be addressed. Here we discuss possible ways to increase the consistency of data generated by basic research and their suitability for application in medicine. New technologies employing systems biology and computer based approaches will facilitate overcoming many of the scientific challenges in the field. Changes in the education of researchers, health professionals, and the public are also required to successfully implement personalized medicine as a routine in the clinic. Finally, shift of the focus away from the development of blockbuster drugs in the biopharmaceutical industry, and modifications in the legal system to accommodate novel advancements need to be considered. The joint effort of all interested parties is needed to generate an efficient roadmap that will take us rapidly and safely to effective individual treatment, which will eliminate diseases and create better health care for all.

Modeling the Mechanism of GR/c-Jun/Erg Crosstalk in Apoptosis of Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is one of the most common forms of malignancy that occurs in lymphoid progenitor cells, particularly in children. Synthetic steroid hormones glucocorticoids (GCs) are widely used as part of the ALL treatment regimens due to their apoptotic function, but their use also brings about various side effects and drug resistance. The identification of the molecular differences between the GCs responsive and resistant cells therefore are essential to decipher such complexity and can be used to improve therapy. However, the emerging picture is complicated as the activities of genes and proteins involved are controlled by multiple factors. By adopting the systems biology framework to address this issue, we here integrated the available knowledge together with experimental data by building a series of mathematical models. This rationale enabled us to unravel molecular interactions involving c-Jun in GC induced apoptosis and identify Ets-related gene (Erg) as potential biomarker of GC resistance. The results revealed an alternative possible mechanism where c-Jun may be an indirect GR target that is controlled via an upstream repressor protein. The models also highlight the importance of Erg for GR function, particularly in GC sensitive C7 cells where Erg directly regulates GR in agreement with our previous experimental results. Our models describe potential GR-controlled molecular mechanisms of c-Jun/Bim and Erg regulation. We also demonstrate the importance of using a systematic approach to translate human disease processes into computational models in order to derive information-driven new hypotheses.

Erg and AP-1 as determinants of glucocorticoid response in acute lymphoblastic leukemia

Glucocorticoids (GCs) are among the most widely prescribed medications in clinical practice. The beneficial effects of GCs in acute lymphoblastic leukemia (ALL) are based on their ability to induce apoptosis, but the underlying transcriptional mechanisms remain poorly defined. Computational modeling has enormous potential in the understanding of biological processes such as apoptosis and the discovery of novel regulatory mechanisms. We here present an integrated analysis of gene expression kinetic profiles using microarrays from GC sensitive and resistant ALL cell lines and patients, including newly generated and previously published data sets available from the Gene Expression Omnibus. By applying time-series clustering analysis in the sensitive ALL CEM-C7-14 cells, we identified 358 differentially regulated genes that we classified into 15 kinetic profiles. We identified GC response element (GRE) sequences in 33 of the upregulated known or potential GC receptor (GR) targets. Comparative study of sensitive and resistant ALL showed distinct gene expression patterns and indicated unexpected similarities between sensitivity-restored and resistant ALL. We found that activator protein 1 (AP-1), Ets related gene (Erg) and GR pathways were differentially regulated in sensitive and resistant ALL. Erg protein levels were substantially higher in CEM-C1-15-resistant cells, c-Jun was significantly induced in sensitive cells, whereas c-Fos was expressed at low levels in both. c-Jun was recruited on the AP-1 site on the Bim promoter, whereas a transient Erg occupancy on the GR promoter was detected. Inhibition of Erg and activation of GR lead to increased apoptosis in both sensitive and resistant ALL. These novel findings significantly advance our understanding of GC sensitivity and can be used to improve therapy of leukemia.