Biology subjects

Chung, C. H.

Publications and source records attributed to Chung, C. H..

3 recordsLinked to original sources

bioRxiv2018

Meteorin-like (Metrnl) adipomyokine improves glucose tolerance in type 2 diabetes via AMPK pathway

Meteorin-like (metrnl) is a recently identified adipomyokine that has beneficial effects on glucose metabolism. However, its underlying mechanism of action is not completely understood. In this study, we have shown that a level of metrnl increase in vitro under electrical-pulse-stimulation (EPS) and in vivo in exercise mice, suggesting that metrnl is an exercise-induced myokine. In addition, metrnl increases glucose uptake through the calcium-dependent AMPK pathway. Metrnl also increases the phosphorylation of HDAC5, a transcriptional repressor of GLUT4, in an AMPK-dependent manner. Phosphorylated HDAC5 interacts with 14-3-3 proteins and sequesters them in the cytoplasm, resulting in the activation of GLUT4 transcription. The intraperitoneal injection of recombinant metrnl improves glucose tolerance in mice with high fat-induced obesity or type 2 diabetes (db/db), but this is not seen in AMPK {beta}1{beta}2 muscle-specific null mice (AMPK {beta}1{beta}2 MKO). In conclusion, we have demonstrated that metrnl induces beneficial effects on glucose metabolism via AMPK and is a promising therapeutic candidate for glucose-related diseases such as type 2 diabetes.

biochemistry↗

bioRxiv2018

lncDIFF: a novel distribution-free method for differential expression analysis of long non-coding RNA

MotivationLong non-coding RNA expression data has been increasingly used in finding diagnostic and prognostic biomarkers in cancer studies. Existing differential analysis tools for RNA sequencing does not effectively accommodate low abundant genes, as commonly observed in lncRNA. We propose a novel and robust statistical method lncDIFF to detect differential expressed (DE) genes without assuming the true density on normalized counts.\n\nResultslncDIFF adopts the generalized linear model with zero-inflated exponential quasi likelihood to estimate group effect on normalized counts, and employs the likelihood ratio test to detect differential expressed genes. The proposed method and tool is suitable for data processed with standard RNA-Seq preprocessing and normalization pipelines. Simulation results illustrate that lncDIFF detects DE genes with more power and lower false discovery rate regardless of the data pattern. The analysis on a head and neck squamous cell carcinomas study also confirms that lncDIFF has better sensitivity in identifying novel lncRNA genes with relatively large fold change and prognostic value.\n\nAvailability and ImplementationlncDIFF is an R package available at https://github.com/qianli10000/lncDIFF.\n\nSupplementary InformationSupplementary Data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Untangling The Gene-Epigenome Networks: Timing Of Epigenetic Regulation Of Gene Expression In Acquired Cetuximab Resistance

BACKGROUNDTargeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model.\n\nRESULTSAn unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically.\n\nCONCLUSIONSGenes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

cancer biology↗