Modeling of miRNA and drug action in the EGFR signaling pathway

Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine

Interindividual variability in drug metabolism can significantly affect drug concentrations in the body and subsequent drug response. Understanding an individual's drug metabolism capacity is important for predicting drug exposure and developing precision medicine strategies. The goal of precision medicine is to individualize drug treatment for patients to maximize efficacy and minimize drug toxicity. While advances in pharmacogenomics have improved our understanding of how genetic variations in drug-metabolizing enzymes (DMEs) affect drug response, nongenetic factors are also known to influence drug metabolism phenotypes. This minireview discusses approaches beyond pharmacogenetic testing to phenotype DMEs-particularly the cytochrome P450 enzymes-in clinical settings. Several phenotyping approaches have been proposed: traditional approaches include phenotyping with exogenous probe substrates and the use of endogenous biomarkers; newer approaches include evaluating circulating noncoding RNAs and liquid biopsy-derived markers relevant to DME expression and function. The goals of this minireview are to 1) provide a high-level overview of traditional and novel approaches to phenotype individual drug metabolism capacity, 2) describe how these approaches are being applied or can be applied to pharmacokinetic studies, and 3) discuss perspectives on future opportunities to advance precision medicine in diverse populations. SIGNIFICANCE STATEMENT: This minireview provides an overview of recent advances in approaches to characterize individual drug metabolism phenotypes in clinical settings. It highlights the integration of existing pharmacokinetic biomarkers with novel approaches; also discussed are current challenges and existing knowledge gaps. The article concludes with perspectives on the future deployment of a liquid biopsy-informed physiologically based pharmacokinetic strategy for patient characterization and precision dosing.

Verbascoside inhibits progression of glioblastoma cells by promoting Let-7g-5p and down-regulating HMGA2 via Wnt/beta-catenin signalling blockade

Glioblastoma (GBM) continues to show a poor prognosis despite advances in diagnostic and therapeutic approaches. The discovery of reliable prognostic indicators may significantly improve treatment outcome of GBM. In this study, we aimed to explore the function of verbascoside (VB) in GBM and its effects on GBM cell biological processes via let‐7g‐5p and HMGA2. Differentially expressed GBM‐related microRNAs (miRNAs) were initially screened. Different concentrations of VB were applied to U87 and U251 GBM cells, and 50 µmol/L of VB was selected for subsequent experiments. Cells were transfected with let‐7g‐5p inhibitor or mimic, and overexpression of HMGA2 or siRNA against HMGA2 was induced, followed by treatment with VB. The regulatory relationships between VB, let‐7g‐5p, HMGA2 and Wnt/β‐catenin signalling pathway were determined. The results showed that HMGA2 was a direct target gene of let‐7g‐5p. VB treatment or let‐7g‐5p overexpression inhibited HMGA2 expression and the activation of Wnt/β‐catenin signalling pathway, which further inhibited cell viability, invasion, migration, tumour growth and promoted GBM cell apoptosis and autophagy. On the contrary, HMGA2 overexpression promoted cell viability, invasion, migration, tumour growth while inhibiting GBM cell apoptosis and autophagy. We demonstrated that VB inhibits cell viability and promotes cell autophagy in GBM cells by up‐regulating let‐7g‐5p and down‐regulating HMGA2 via Wnt/β‐catenin signalling blockade.

Expression and Localization of Huntingtin-Associated Protein 1 (HAP1) in the Human Digestive System

BACKGROUND

Huntingtin-associated protein 1 (HAP1) is a neuronal protein that is predominantly expressed in neurons in the brain. HAP1 is critical for maintenance of neuronal survival as well as regulation of food intake and body weight in animals. In addition to the critical
role of HAP1 in the central nervous system, HAP1 is also found in endocrine cells, raising an interesting issue of whether HAP1 is expressed in the digestive system.

AIMS

To examine the expression and localization of HAP1 in the human gastrointestinal tract and to compare the differences of the HAP1 expression between benign and malignant tissues in the digestive system.

METHODS

We used Western blot and immunohistochemistry to examine the expression and distribution of HAP1 in the human gastrointestinal tract tissues.

RESULTS

We observed that the presence of HAP1-positive cells in the gastrointestinal tract was not uniform with immunohistochemistry staining. Western blot revealed that only one isoform (75KD) HAP1 was present in the human gastrointestinal system. Interestingly, the expression of HAP1 was higher in the stomach than other regions of the gastrointestinal tract and was at the lowest level in the intestine. We also found that HAP1 was unlikely altered in benign gastric polyps, but was downregulated in pancreatic cancer.

CONCLUSIONS

This is the first study showing the differential expression and location of HAP1 in the human digestive system. These findings suggested that HAP1 may have cell-type-dependent function in the gastrointestinal tract and may serve as a diagnostic marker for pancreatic cancer.

Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases

MicroRNAs (miRs) are endogenous non-coding RNA molecules that play important roles in human health and disease by regulating gene expression and cellular processes. In recent years, with the increasing scientific knowledge and new discovery of miRs and their gene targets, as well as the plentiful experimental evidence that shows dysregulation of miRs in a wide variety of human diseases, the computational modeling approach has emerged as an effective tool to help researchers identify novel functional associations between differential miR expression and diseases, dissect the phenotypic expression patterns of miRs in gene regulatory networks, and elucidate the critical roles of miRs in the modulation of disease pathways from mechanistic and quantitative perspectives. Here we will review the recent systems biology studies that employed different kinetic modeling techniques to provide mechanistic insights relating to the regulatory function and therapeutic potential of miRs in human diseases. Some of the key computational aspects to be discussed in detail in this review include (i) models of miR-mediated network motifs in the regulation of gene expression, (ii) models of miR biogenesis and miR–target interactions, and (iii) the incorporation of such models into complex disease pathways in order to generate mechanistic, molecular- and systems-level understanding of pathophysiology. Other related bioinformatics tools such as computational platforms that predict miR-disease associations will also be discussed, and we will provide perspectives on the challenges and opportunities in the future development and translational application of data-driven systems biology models that involve miRs and their regulatory pathways in human diseases.

A microRNA molecular modeling extension for prediction of colorectal cancer treatment

Background

Several studies show that the regulatory impact of microRNAs (miRNAs) is an essential contribution to the pathogenesis of colorectal cancer (CRC). The expression levels of diverse miRNAs are associated with specific clinical diagnoses and prognoses of CRC. However, this association reveals very little actionable information with regard to how or whether to treat a CRC patient. To address this problem, we use miRNA expression data along with other molecular information to predict individual response of CRC cell lines and CRC patients.

Methods

A strategy has been developed to join four types of information: molecular, kinetic, genetic and treatment data for prediction of individual treatment response of CRC.

Results

Information on miRNA regulation, including miRNA target regulation and transcriptional regulation of miRNA, in integrated into an in silico molecular model for colon cancer. This molecular model is applied to study responses of seven CRC cell lines from NCI-60 to ten agents targeting signaling pathways. Predictive results of models without and with implemented miRNA information are compared and advantages are shown for the extended model. Finally, the extended model was applied to the data of 22 CRC patients to predict response to treatments of sirolimus and LY294002. The in silico results can also replicate the oncogenic and tumor suppression roles of miRNA on the therapeutic response as reported in the literature.

Conclusions

In summary, the results reveal that detailed molecular events can be combined with individual genetic data, including gene/miRNA expression data, to enhance in silico prediction of therapeutic response of individual CRC tumors. The study demonstrates that miRNA information can be applied as actionable information regarding individual therapeutic response.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1437-0) contains supplementary material, which is available to authorized users.

Functional aspects of the EGF-induced MAP kinase cascade: a complex self-organizing system approach

The EGF-induced MAP kinase cascade is one of the most important and best characterized networks in intracellular signalling. It has a vital role in the development and maturation of living organisms. However, when deregulated, it is involved in the onset of a number of diseases. Based on a computational model describing a "surface" and an "internalized" parallel route, we use systems biology techniques to characterize aspects of the network's functional organization. We examine the re-organization of protein groups from low to high external stimulation, define functional groups of proteins within the network, determine the parameter best encoding for input intensity and predict the effect of protein removal to the system's output response. Extensive functional re-organization of proteins is observed in the lower end of stimulus concentrations. As we move to higher concentrations the variability is less pronounced. 6 functional groups have emerged from a consensus clustering approach, reflecting different dynamical aspects of the network. Mutual information investigation revealed that the maximum activation rate of the two output proteins best encodes for stimulus intensity. Removal of each protein of the network resulted in a range of graded effects, from complete silencing to intense activation. Our results provide a new "vista" of the EGF-induced MAP kinase cascade, from the perspective of complex self-organizing systems. Functional grouping of the proteins reveals an organizational scheme contrasting the current understanding of modular topology. The six identified groups may provide the means to experimentally follow the dynamics of this complex network. Also, the vulnerability analysis approach may be used for the development of novel therapeutic targets in the context of personalized medicine.

Modeling of non-steroidal anti-inflammatory drug effect within signaling pathways and miRNA-regulation pathways

To date, it is widely recognized that Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) can exert considerable anti-tumor effects regarding many types of cancers. The prolonged use of NSAIDs is highly associated with diverse side effects. Therefore, tailoring down the NSAID application onto individual patients has become a necessary and relevant step towards personalized medicine. This study conducts the systemsbiological approach to construct a molecular model (NSAID model) containing a cyclooxygenase (COX)-pathway and its related signaling pathways. Four cancer hallmarks are integrated into the model to reflect different developmental aspects of tumorigenesis. In addition, a Flux-Comparative-Analysis (FCA) based on Petri net is developed to transfer the dynamic properties (including drug responsiveness) of individual cellular system into the model. The gene expression profiles of different tumor-types with available drug-response information are applied to validate the predictive ability of the NSAID model. Moreover, two therapeutic developmental strategies, synthetic lethality and microRNA (miRNA) biomarker discovery, are investigated based on the COX-pathway. In conclusion, the result of this study demonstrates that the NSAID model involving gene expression, gene regulation, signal transduction, protein interaction and other cellular processes, is able to predict the individual cellular responses for different therapeutic interventions (such as NS-398 and COX-2 specific siRNA inhibition). This strongly indicates that this type of model is able to reflect the physiological, developmental and pathological processes of an individual. The approach of miRNA biomarker discovery is demonstrated for identifying miRNAs with oncogenic and tumor suppressive functions for individual cell lines of breast-, colon- and lung-tumor. The achieved results are in line with different independent studies that investigated miRNA biomarker related to diagnostics of cancer treatments, therefore it might shed light on the development of biomarker discovery at individual level. Particular results of this study might contribute to step further towards personalized medicine with the systemsbiological approach.

High-throughput miRNA and mRNA sequencing of paired colorectal normal, tumor and metastasis tissues and bioinformatic modeling of miRNA-1 therapeutic applications

MiRNAs are discussed as diagnostic and therapeutic molecules. However, effective miRNA drug treatments with miRNAs are, so far, hampered by the complexity of the miRNA networks. To identify potential miRNA drugs in colorectal cancer, we profiled miRNA and mRNA expression in matching normal, tumor and metastasis tissues of eight patients by Illumina sequencing. We validated six miRNAs in a large tissue screen containing 16 additional tumor entities and identified miRNA-1, miRNA-129, miRNA-497 and miRNA-215 as constantly de-regulated within the majority of cancers. Of these, we investigated miRNA-1 as representative in a systems-biology simulation of cellular cancer models implemented in PyBioS and assessed the effects of depletion as well as overexpression in terms of miRNA-1 as a potential treatment option. In this system, miRNA-1 treatment reverted the disease phenotype with different effectiveness among the patients. Scoring the gene expression changes obtained through mRNA-Seq from the same patients we show that the combination of deep sequencing and systems biological modeling can help to identify patient-specific responses to miRNA treatments. We present this data as guideline for future pre-clinical assessments of new and personalized therapeutic options.

Huntingtin-associated protein 1: a potential biomarker of breast cancer

It is reported that patients with Huntington's disease (HD) have a low incidence of cancer. In this study, we investigated the expression of huntingtin-associated protein 1 (HAP1), the ligand of HD's production, in breast tumor and normal tissues. We found that HAP1 expression was significantly lower in tumor compared to normal tissues. We then transfected the HAP1 gene into the breast cancer lines MCF-7 and MDA-MB-231, and results showed that the overexpression of HAP1 reduced the growth of the two cell lines. In addition, we observed that HAP1 also reduced invasion and migration, and upregulated apoptosis in MCF-7 cells; however, these changes were not observed in MDA-MB-231 cells. We also demonstrated that the expression of EGFR and apoptosis-related genes might be involved in cell proliferation and apoptosis. In conclusion, overexpression of HAP1 reduced in vitro cell growth in breast cancer cell lines, suppressed the migration and invasion, and promoted the apoptosis of certain cell lines. Therefore, HAP1 is a potential molecular target for the diagnosis and treatment of breast cancer.