Gamma-Normal-Gamma Mixture Model for Detecting Differentially Methylated Loci in Three Breast Cancer Cell Lines

With state-of-the-art microarray technologies now available for whole genome CpG island (CGI) methylation profiling, there is a need to develop statistical models that are specifically geared toward the analysis of such data. In this article, we propose a Gamma-Normal-Gamma (GNG) mixture model for describing three groups of CGI loci: hypomethylated, undifferentiated, and hypermethylated, from a single methylation microarray. This model was applied to study the methylation signatures of three breast cancer cell lines: MCF7, T47D, and MDAMB361. Biologically interesting and interpretable results are obtained, which highlights the heterogeneity nature of the three cell lines. This underlies the premise for the need of analyzing each of the microarray slides individually as opposed to pooling them together for a single analysis. Our comparisons with the fitted densities from the Normal-Uniform (NU) mixture model in the literature proposed for gene expression analysis show an improved goodness of fit of the GNG model over the NU model. Although the GNG model was proposed in the context of single-slide methylation analysis, it can be readily adapted to analyze multi-slide methylation data as well as other types of microarray data

Lifetime Prediction of DC-link Capacitors in Multiple Drives System Based on Simplified Analytical Modeling

Lifetime prediction of dc-link capacitors in a single drive has been discussed before, which indicates that the capacitor in a standard drive meets serious reliability challenges and in a slim drive does not. However, in most of the applications, drives are connected in parallel with the power grid. The large amount of harmonic distortion produced by nonlinearity drives may transmit and couple between grid and drives, which changes the stresses of devices as well as the dc-link filters. Therefore, the estimated results in a single drive cannot be extended to multiple drives any more. This article investigates the lifetime of dc-link capacitors in multiple drives system. First, by decoupling the interactions among grid-connected drives, a simplified equivalent circuit model and its analytical model to obtain the dc-link continuous current in multiple drives is proposed, which releases the designers from configuring the large simulation for multiple drives. Then, applying the lifetime prediction method, the lifetime of dc-link capacitors in multiple drives is investigated, in terms of types of drives, numbers of drives, and grid conditions. The results show that the lifetime of the standard drives extends in the multidrive systems and the lifetime of the slim drives decreases in the multidrive systems, which break the previous mind. Finally, based on the proposed analytical model and lifetime estimation method, the capacitor sizing from reliability aspect for multiple slim drives is given. The outcomes of the lifetime investigation could be a guideline for the design of the capacitive dc link in multidrive systems

Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data

BACKGROUND: In order to recapitulate tumor progression pathways using epigenetic data, we developed novel clustering and pathway reconstruction algorithms, collectively referred to as heritable clustering. This approach generates a progression model of altered DNA methylation from tumor tissues diagnosed at different developmental stages. The samples act as surrogates for natural progression in breast cancer and allow the algorithm to uncover distinct epigenotypes that describe the molecular events underlying this process. Furthermore, our likelihood-based clustering algorithm has great flexibility, allowing for incomplete epigenotype or clinical phenotype data and also permitting dependencies among variables. RESULTS: Using this heritable clustering approach, we analyzed methylation data obtained from 86 primary breast cancers to recapitulate pathways of breast tumor progression. Detailed annotation and interpretation are provided to the optimal pathway recapitulated. The result confirms the previous observation that aggressive tumors tend to exhibit higher levels of promoter hypermethylation. CONCLUSION: Our results indicate that the proposed heritable clustering algorithms are a useful tool for stratifying both methylation and clinical variables of breast cancer. The application to the breast tumor data illustrates that this approach can select meaningful progression models which may aid the interpretation of pathways having biological and clinical significance. Furthermore, the framework allows for other types of biological data, such as microarray gene expression or array CGH data, to be integrated

Vehicle emission and atmospheric pollution in China: problems, progress, and prospects

China has been the largest vehicle market in the world since 2009. The stalemate between the rapid development of the vehicle industry and delayed vehicle emission control has become increasingly prominent. Vehicle emission has become a significant source of air pollution in China’s cities. Understanding the current barriers in the vehicle industry is necessary for the development of effective and sustainable measures and policy to manage vehicle-induced air pollution. This review provides insight into the circumstances and causes of vehicle-induced air pollution and outlines recent progress in policy-makers’ long-term strategies and regulations. The development of an integrated mechanism of social participation, technical revolution, and regulatory innovation in vehicles, fuel, and roads is suggested to break the stalemate between air pollution and the automobile boom in China; the implications of this review extend to other countries facing the similar atmospheric pollution problems

Chronic IL-1β-induced inflammation regulates epithelial-to-mesenchymal transition memory phenotypes via epigenetic modifications in non-small cell lung cancer.

Chronic inflammation facilitates tumor progression. We discovered that a subset of non-small cell lung cancer cells underwent a gradually progressing epithelial-to-mesenchymal (EMT) phenotype following a 21-day exposure to IL-1β, an abundant proinflammatory cytokine in the at-risk for lung cancer pulmonary and the lung tumor microenvironments. Pathway analysis of the gene expression profile and in vitro functional studies revealed that the EMT and EMT-associated phenotypes, including enhanced cell invasion, PD-L1 upregulation, and chemoresistance, were sustained in the absence of continuous IL-1β exposure. We referred to this phenomenon as EMT memory. Utilizing a doxycycline-controlled SLUG expression system, we found that high expression of the transcription factor SLUG was indispensable for the establishment of EMT memory. High SLUG expression in tumors of lung cancer patients was associated with poor survival. Chemical or genetic inhibition of SLUG upregulation prevented EMT following the acute IL-1β exposure but did not reverse EMT memory. Chromatin immunoprecipitation and methylation-specific PCR further revealed a SLUG-mediated temporal regulation of epigenetic modifications, including accumulation of H3K27, H3K9, and DNA methylation, in the CDH1 (E-cadherin) promoter following the chronic IL-1β exposure. Chemical inhibition of DNA methylation not only restored E-cadherin expression in EMT memory, but also primed cells for chemotherapy-induced apoptosis

Optimization of brewing conditions in epigallocatechin-3-gallate (EGCG) extraction from Jinxuan summer green tea by response surface methodology

The extraction conditions of epigallocatechin-3-gallate (EGCG) from Jinxuan summer green tea and antitumor activity against human gastric cancer SGC-7901 cells of the green tea extracts were investigated. On the basis of a single factor experiment, Box-Behnken design and response surface methodology were employed to optimize the hot water extraction conditions. The optimal extraction conditions for EGCG were determined as: extraction temperature of 85 °C, extraction time of 34 min, water-tea ratio of 41 mL/g, a solution of pH 6, and extraction twice. Under these conditions, the experimental extraction yield value of EGCG was 33.82 mg/g, which was not significantly different in comparison to predicted values. The results indicated that the regression models were suitable for the EGCG extraction from Jinxuan summer green tea. The summer green tea extract prepared under the optimal conditions had a higher antitumor activity against human gastric cancer SGC-7901 cells than the green tea extract made with traditional tea brewing method

Identifying hypermethylated CpG islands using a quantile regression model

<p>Abstract</p> <p>Background</p> <p>DNA methylation has been shown to play an important role in the silencing of tumor suppressor genes in various tumor types. In order to have a system-wide understanding of the methylation changes that occur in tumors, we have developed a differential methylation hybridization (DMH) protocol that can simultaneously assay the methylation status of all known CpG islands (CGIs) using microarray technologies. A large percentage of signals obtained from microarrays can be attributed to various measurable and unmeasurable confounding factors unrelated to the biological question at hand. In order to correct the bias due to noise, we first implemented a quantile regression model, with a quantile level equal to 75%, to identify hypermethylated CGIs in an earlier work. As a proof of concept, we applied this model to methylation microarray data generated from breast cancer cell lines. However, we were unsure whether 75% was the best quantile level for identifying hypermethylated CGIs. In this paper, we attempt to determine which quantile level should be used to identify hypermethylated CGIs and their associated genes.</p> <p>Results</p> <p>We introduce three statistical measurements to compare the performance of the proposed quantile regression model at different quantile levels (95%, 90%, 85%, 80%, 75%, 70%, 65%, 60%), using known methylated genes and unmethylated housekeeping genes reported in breast cancer cell lines and ovarian cancer patients. Our results show that the quantile levels ranging from 80% to 90% are better at identifying known methylated and unmethylated genes.</p> <p>Conclusions</p> <p>In this paper, we propose to use a quantile regression model to identify hypermethylated CGIs by incorporating probe effects to account for noise due to unmeasurable factors. Our model can efficiently identify hypermethylated CGIs in both breast and ovarian cancer data.</p

Author Correction: Chronic IL-1β-induced inflammation regulates epithelial-to-mesenchymal transition memory phenotypes via epigenetic modifications in non-small cell lung cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Integrated analysis identifies a class of androgen-responsive genes regulated by short combinatorial long-range mechanism facilitated by CTCF

Recently, much attention has been given to elucidate how long-range gene regulation comes into play and how histone modifications and distal transcription factor binding contribute toward this mechanism. Androgen receptor (AR), a key regulator of prostate cancer, has been shown to regulate its target genes via distal enhancers, leading to the hypothesis of global long-range gene regulation. However, despite numerous flows of newly generated data, the precise mechanism with respect to AR-mediated long-range gene regulation is still largely unknown. In this study, we carried out an integrated analysis combining several types of high-throughput data, including genome-wide distribution data of H3K4 di-methylation (H3K4me2), CCCTC binding factor (CTCF), AR and FoxA1 cistrome data as well as androgen-regulated gene expression data. We found that a subset of androgen-responsive genes was significantly enriched near AR/H3K4me2 overlapping regions and FoxA1 binding sites within the same CTCF block. Importantly, genes in this class were enriched in cancer-related pathways and were downregulated in clinical metastatic versus localized prostate cancer. Our results suggest a relatively short combinatorial long-range regulation mechanism facilitated by CTCF blocking. Under such a mechanism, H3K4me2, AR and FoxA1 within the same CTCF block combinatorially regulate a subset of distally located androgen-responsive genes involved in prostate carcinogenesis