A Gene Selection Method Considering Measurement Errors.

The analysis of gene expression data has made significant contributions to understanding disease mechanisms and developing new drugs and therapies. In such analysis, gene selection is often required for identifying informative and relevant genes and removing redundant and irrelevant ones. However, this is not an easy task as gene expression data have inherent challenges such as ultra-high dimensionality, biological noise, and measurement errors. This study focuses on the measurement errors in gene selection problems. Typically, high-throughput experiments have their own intrinsic measurement errors, which can result in an increase of falsely discovered genes. To alleviate this problem, this study proposes a gene selection method that takes into account measurement errors using generalized liner measurement error models. The method consists of iterative filtering and selection steps until convergence, leading to fewer false positives and providing stable results under measurement errors. The performance of the proposed method is demonstrated through simulation studies and applied to a lung cancer data set.

Estimation of Ordinary Differential Equation Models for Gene Regulatory Networks Through Data Cloning.

Ordinary differential equations (ODEs) are widely used for elucidating dynamic processes in various fields. One of the applications of ODEs is to describe dynamics of gene regulatory networks (GRNs), which is a critical step in understanding disease mechanisms. However, estimation of ODE models for GRNs is challenging because of inflexibility of the model and noisy data with complex error structures such as heteroscedasticity, correlations between genes, and time dependency. In addition, either a likelihood or Bayesian approach is commonly used for estimation of ODE models, but both approaches have benefits and drawbacks in their own right. Data cloning is a maximum likelihood (ML) estimation method through the Bayesian framework. Since it works in the Bayesian framework, it is free from local optimum problems that are common drawbacks of ML methods. Also, its inference is invariant for the selection of prior distributions, which is a major issue in Bayesian methods. This study proposes an estimation method of ODE models for GRNs through data cloning. The proposed method is demonstrated through simulation and it is applied to real gene expression time-course data.

Identification of New Clusters from Labeled Data Using Mixture Models.

Nowadays attempts to segment classes or groups are often found in various fields. Especially, one of emerging issues in biological and medical areas is identification of new subtypes of biological samples or patients. For the identification, we often need to find new subtypes from known classes. In such cases, we usually use clustering techniques. However, usual clustering methods could mix up the labels of the known classes in clustering outcomes and it might lead to wrong interpretation for the identified clusters. Also, they do not use the information about known classes. Thus, this study proposes a Gaussian mixture model-based approach for identifying new clusters from known classes while it maintains them. The performance of the proposed model is verified through simulations and it is applied to a breast cancer data set.

Phosphodiesterase 7B/microRNA-200c relationship regulates triple-negative breast cancer cell growth.

Members of microRNA-200 (miRNA-200) family have a regulatory role in epithelial to mesenchymal transition (EMT) by suppressing Zeb1 and Zeb2 expression. Consistent with its role in suppressing EMT, Hsa-miR-200c-3p (miR-200c), a member of miR-200 family is poorly expressed in mesenchymal-like triple-negative breast cancer (TNBC) cells and ectopic miR-200c expression suppresses cell migration. In this study, we demonstrated that miR-200c potently inhibited TNBC cell growth and tumor development in a mechanism distinct from its ability to downregulate Zeb1 and Zeb2 expression, because silencing them only marginally affected TNBC cell growth. We identified phosphodiesterase 7B (PDE7B) as a bona fide miR-200c target. Importantly, miR-200c-led inhibition in cell growth and tumor development was prevented by forcing PDE7B transgene expression, while knockdown of PDE7B effectively inhibited cell growth. These results suggest that miR-200c inhibits cell growth by targeting PDE7B mRNA. To elucidate mechanism underlying miR-200c/PDE7B regulation of TNBC cell growth, we showed that cAMP concentration was lower in TNBC cells compared with estrogen receptor-positive (ER + ) cells, and that both miR-200c and PDE7B siRNAs were able to increase cAMP concentration in TNBC cells. High level of cellular cAMP has been shown to induce cell cycle arrest and apoptosis in TNBC cells. Our observation that ectopic expression of miR-200c triggered apoptosis indicates that it does so by elevating level of cellular cAMP. Analysis of breast tumor gene expression datasets revealed an inverse association between miR-200c and PDE7B expression. Especially, both low miR-200c and high PDE7B expression were correlated with poor survival of breast cancer patients. Our study supports a critical role of miR-200c/PDE7B relationship in TNBC tumorigenesis.

Bootstrapping Time-Course Gene Expression Data for Gene Networks: Application to Gene Relevance Networks.

Identification of gene regulatory networks (GRNs) is a fundamental step to understand the molecular role of each gene and it helps to develop treatment and cure of a disease. To identify GRNs, time-course gene expression data are widely used. However, the identification is hampered by intrinsic attributes of the data such as small sample size, a large number of variables, and complex error structures with high variation. Under this situation, most GRN inference methods utilize point estimators or make numerous assumptions that are often incompatible with the experimental data. Moreover, different inference methods often provide inconsistent results. An alternative to alleviate this problem can be the bootstrap method because it provides more reliable outcomes by integrating results from multiple bootstrap samples without any distributional assumptions. In this study, we propose a bootstrap method for dependent time-course gene expression data and we mainly focus on its application to gene relevance networks. The proposed method is applied to gene networks for zebrafish retina.

Estimation of Dynamic Systems for Gene Regulatory Networks from Dependent Time-Course Data.

Dynamic system consisting of ordinary differential equations (ODEs) is a well-known tool for describing dynamic nature of gene regulatory networks (GRNs), and the dynamic features of GRNs are usually captured through time-course gene expression data. Owing to high-throughput technologies, time-course gene expression data have complex structures such as heteroscedasticity, correlations between genes, and time dependence. Since gene experiments typically yield highly noisy data with small sample size, for a more accurate prediction of the dynamics, the complex structures should be taken into account in ODE models. Hence, this study proposes an ODE model considering such data structures and a fast and stable estimation method for the ODE parameters based on the generalized profiling approach with data smoothing techniques. The proposed method also provides statistical inference for the ODE estimator and it is applied to a zebrafish retina cell network.

Promoter Methylation Modulates Indoleamine 2,3-Dioxygenase 1 Induction by Activated T Cells in Human Breast Cancers.

Triple-negative breast cancer (TNBC) cells are modulated in reaction to tumor-infiltrating lymphocytes. However, their specific responses to this immune pressure are unknown. In order to address this question, we first used mRNA sequencing to compare the immunophenotype of the TNBC cell line MDA-MB-231 and the luminal breast cancer cell line MCF7 after both were cocultured with activated human T cells. Despite similarities in the cytokine-induced immune signatures of the two cell lines, MDA-MD-231 cells were able to transcribe more IDO1 than MCF7 cells. The two cell lines had similar upstream JAK/STAT1 signaling and IDO1 mRNA stability. However, using a series of breast cancer cell lines, IFNγ stimulated IDO1 protein expression and enzymatic activity only in ER-, not ER+, cell lines. Treatment with 5-aza-deoxycytidine reversed the suppression of IDO1 expression in MCF7 cells, suggesting that DNA methylation was potentially involved in IDO1 induction. By analyzing several breast cancer datasets, we discovered subtype-specific mRNA and promoter methylation differences in IDO1, with TNBC/basal subtypes exhibiting lower methylation/higher expression and ER+/luminal subtypes exhibiting higher methylation/lower expression. We confirmed this trend of IDO1 methylation by bisulfite pyrosequencing breast cancer cell lines and an independent cohort of primary breast tumors. Taken together, these findings suggest that IDO1 promoter methylation regulates anti-immune responses in breast cancer subtypes and could be used as a predictive biomarker for IDO1 inhibitor-based immunotherapy. Cancer Immunol Res; 5(4); 330-44. ©2017 AACR.

HOXC8 promotes breast tumorigenesis by transcriptionally facilitating cadherin-11 expression.

Cell-cell adhesion molecule cadherin-11(CDH11) is preferentially expressed in basal-like breast cancer cells and facilitates breast cancer cell migration by promoting small GTPase Rac activity. However, how the expression of CDH11 is regulated in breast cancer cells is not understood. Here, we show that CDH11 is transcriptionally controlled by homeobox C8 (HOXC8) in human breast cancer cells. HOXC8 serves as a CDH11-specific transcription factor and binds to the site of nucleotides -196 to -191 in the CDH11 promoter. Depletion of HOXC8 leads to the decrease in anchorage-independent cell growth, cell migration/invasion and spontaneous metastasis of breast cancer cells; however, suppressed tumorigenic events were fully rescued by ectopic CDH11 expression in HOXC8-knockdown cells. These results indicate that HOXC8 impacts breast tumorigenesis through CDH11. The analysis of publically available human breast tumor microarray gene expression database demonstrates a strong positive linear association between HOXC8 and CDH11 expression ( = 0.801, p < 0.001). Survival analysis (Kaplan-Meier method, log-rank test) show that both high HOXC8 and CDH11 expression correlate with poor recurrence-free survival rate of patients. Together, our study suggests that HOXC8 promotes breast tumorigenesis by maintaining high level of CDH11 expression in breast cancer cells.

SHOX2 is a direct miR-375 target and a novel epithelial-to-mesenchymal transition inducer in breast cancer cells.

MicroRNAs have added a new dimension to our understanding of tumorigenesis and associated processes like epithelial-to-mesenchymal transition (EMT). Here, we show that miR-375 is elevated in epithelial-like breast cancer cells, and ectopic miR-375 expression suppresses EMT in mesenchymal-like breast cancer cells. We identified short stature homeobox 2 (SHOX2) as a miR-375 target, and miR-375-mediated suppression in EMT was reversed by forced SHOX2 expression. Ectopic SHOX2 expression can induce EMT in epithelial-like breast cancer cells, whereas SHOX2 knockdown diminishes EMT traits in mesenchymal-like breast cancer cells, demonstrating SHOX2 as an EMT inducer. We show that SHOX2 acts as a transcription factor to upregulate transforming growth factor ß receptor I (TßR-I) expression, and TßR-I inhibitor LY364947 abolishes EMT elicited by ectopic SHOX2 expression, suggesting that transforming growth factor ß signaling is essential for SHOX2-induced EMT. Manipulating SHOX2 abundance in breast cancer cells impact in vitro invasion and in vivo dissemination. Analysis of breast tumor microarray database revealed that high SHOX2 expression significantly correlates with poor patient survival. Our study supports a critical role of SHOX2 in breast tumorigenicity.

COP1 and GSK3ß cooperate to promote c-Jun degradation and inhibit breast cancer cell tumorigenesis.

High abundance of c-Jun is detected in invasive breast cancer cells and aggressive breast tumor malignancies. Here, we demonstrate that a major cause of high c-Jun abundance in invasive breast cancer cells is prolonged c-Jun protein stability owing to poor poly-ubiquitination of c-Jun. Among the known c-Jun-targeting E3 ligases, we identified constitutive photomorphogenesis protein 1 (COP1) as an E3 ligase responsible for c-Jun degradation in less invasive breast cancer cells because depletion of COP1 reduced c-Jun poly-ubiquitination leading to the stabilization of c-Jun protein. In a panel of breast cancer cell lines, we observed an inverse association between the levels of COP1 and c-Jun. However, overexpressing COP1 alone was unable to decrease c-Jun level in invasive breast cancer cells, indicating that efficient c-Jun protein degradation necessitates an additional event. Indeed, we found that glycogen synthase kinase 3 (GSK3) inhibitors elevated c-Jun abundance in less invasive breast cancer cells and that GSK3ß nonphosphorylable c-Jun-T239A mutant displayed greater protein stability and poorer poly-ubiquitination compared to the wild-type c-Jun. The ability of simultaneously enforced expression of COP1 and constitutively active GSK3ß to decrease c-Jun abundance in invasive breast cancer cells allowed us to conclude that c-Jun is negatively regulated through the coordinated action of COP1 and GSK3ß. Importantly, co-expressing COP1 and active GSK3ß blocked in vitro cell growth/migration and in vivo metastasis of invasive breast cancer cells. Gene expression profiling of breast tumor specimens further revealed that higher COP1 expression correlated with better recurrence-free survival. Our study supports the notion that COP1 is a suppressor of breast cancer progression.

Model discrimination in dynamic molecular systems: application to parotid de-differentiation network.

In modern systems biology the modeling of longitudinal data, such as changes in mRNA concentrations, is often of interest. Fully parametric, ordinary differential equations (ODE)-based models are typically developed for the purpose, but their lack of fit in some examples indicates that more flexible Bayesian models may be beneficial, particularly when there are relatively few data points available. However, under such sparse data scenarios it is often difficult to identify the most suitable model. The process of falsifying inappropriate candidate models is called model discrimination. We propose here a formal method of discrimination between competing Bayesian mixture-type longitudinal models that is both sensitive and sufficiently flexible to account for the complex variability of the longitudinal molecular data. The ideas from the field of Bayesian analysis of computer model validation are applied, along with modern Markov Chain Monte Carlo (MCMC) algorithms, in order to derive an appropriate Bayes discriminant rule. We restrict attention to the two-model comparison problem and present the application of the proposed rule to the mRNA data in the de-differentiation network of three mRNA concentrations in mammalian salivary glands as well as to a large synthetic dataset derived from the model used in the recent DREAM6 competition.

Statistical Model for Biochemical Network Inference.

We describe a statistical method for predicting most likely reactions in a biochemical reaction network from the longitudinal data on species concentrations. Such data is relatively easily available in biochemical laboratories, for instance, via the popular RT-PCR technology. Under the assumed kinetics of the law of mass action, we also propose the data-based algorithms for estimating the prediction errors and for network dimension reduction. The second algorithm allows in particular for the application of the original algebraic inferential procedure described in [4] without the unnecessary restrictions on the dimension of the network stoichiometric space. Simulated examples of biochemical networks are analyzed, in order to assess the proposed methods' performance.

Release of bisphenol A from resin composite used to bond orthodontic lingual retainers.

INTRODUCTION: In this study, we assessed the changes in bisphenol A (BPA) levels in saliva and urine after placing lingual bonded retainers. METHODS: Liquid chromatography/mass spectrometry was used to examine the BPA levels in the saliva and urine samples collected from 22 volunteers who received a lingual bonded retainer on their mandibular dentition. Samples were collected immediately before placement and 30 minutes, 1 day, 1 week, and 1 month after placement. The time elapsed after placement, type of resin composite (nanohybrid filled flowable resin or conventional hybrid resin), surface prophylaxis, age, and sex were evaluated for their effects on the BPA levels. RESULTS: The only significant high level of BPA was observed in the saliva collected just after placement of the lingual bonded retainer. Age and sex did not affect the BPA levels. Subjects in the flowable resin group had lower BPA levels than those in the conventional hybrid resin group; pumice prophylaxis decreased the level of BPA released from the conventional hybrid resin at the immediate time point. The salivary BPA level (maximum, 20.889 ng/mL) detected in the samples collected just after placement was far lower than the reference daily intake dose. CONCLUSIONS: Accordingly, the potential toxicity of BPA from placing lingual bonded retainer might be negligible. On the other hand, because the health-effective amount of BPA is controversial, BPA release should be minimized.