Browsing by Subject "Sequence Analysis, RNA"

Now showing 1 - 3 of 3
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    Changes in mRNA/protein expression and signaling pathways in in vivo passaged mouse ovarian cancer cells
    (Public Library of Science, 2018-06-21) Cai, Qingchun; Fan, Qipeng; Buechlein, Aaron; Miller, David; Nephew, Kenneth P.; Liu, Sheng; Wan, Jun; Xu, Yan; Obstetrics and Gynecology, School of Medicine
    The cure rate for late stage epithelial ovarian cancer (EOC) has not significantly improved over several decades. New and more effective targets and treatment modalities are urgently needed. RNA-seq analyses of a syngeneic EOC cell pair, representing more and less aggressive tumor cells in vivo were conducted. Bioinformatics analyses of the RNA-seq data and biological signaling and function studies have identified new targets, such as ZIP4 in EOC. Many up-regulated tumor promoting signaling pathways have been identified which are mainly grouped into three cellular activities: 1) cell proliferation and apoptosis resistance; 2) cell skeleton and adhesion changes; and 3) carbohydrate metabolic reprograming. Unexpectedly, lipid metabolism has been the major down-regulated signaling pathway in the more aggressive EOC cells. In addition, we found that hypoxic responsive genes were at the center stage of regulation and detected functional changes were related to cancer stem cell-like activities. Moreover, our genetic, cellular, biochemical, and lipidomic analyses indicated that cells grown in 2D vs. 3D, or attached vs. suspended had dramatic changes. The important clinical implications of peritoneal cavity floating tumor cells are supported by the data proved in this work. Overall, the RNA-seq data provide a landscape of gene expression alterations during tumor progression.
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    LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data
    (Oxford University Press, 2019-10-10) Wan, Changlin; Chang, Wennan; Zhang, Yu; Shah, Fenil; Lu, Xiaoyu; Zang, Yong; Zhang, Anru; Cao, Sha; Fishel, Melissa L.; Ma, Qin; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
    A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.
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    A snapshot of the hepatic transcriptome: ad libitum alcohol intake suppresses expression of cholesterol synthesis genes in alcohol-preferring (P) rats
    (PLoS, 2014-12-26) Klein, Jonathon D.; Sherrill, Jeremy B.; Morello, Gabriella M.; San Miguel, Phillip J.; Ding, Zhenming; Liangpunsakul, Suthat; Liang, Tiebing; Muir, William M.; Lumeng, Lawrence; Lossie, Amy C.; Department of Psychiatry, IU School of Medicine
    Research is uncovering the genetic and biochemical effects of consuming large quantities of alcohol. One prime example is the J- or U-shaped relationship between the levels of alcohol consumption and the risk of atherosclerotic cardiovascular disease. Moderate alcohol consumption in humans (about 30 g ethanol/d) is associated with reduced risk of coronary heart disease, while abstinence and heavier alcohol intake is linked to increased risk. However, the hepatic consequences of moderate alcohol drinking are largely unknown. Previous data from alcohol-preferring (P) rats showed that chronic consumption does not produce significant hepatic steatosis in this well-established model. Therefore, free-choice alcohol drinking in P rats may mimic low risk or nonhazardous drinking in humans, and chronic exposure in P animals can illuminate the molecular underpinnings of free-choice drinking in the liver. To address this gap, we captured the global, steady-state liver transcriptome following a 23 week free-choice, moderate alcohol consumption regimen (∼ 7.43 g ethanol/kg/day) in inbred alcohol-preferring (iP10a) rats. Chronic consumption led to down-regulation of nine genes in the cholesterol biosynthesis pathway, including HMG-CoA reductase, the rate-limiting step for cholesterol synthesis. These findings corroborate our phenotypic analyses, which indicate that this paradigm produced animals whose hepatic triglyceride levels, cholesterol levels and liver histology were indistinguishable from controls. These findings explain, at least in part, the J- or U-shaped relationship between cardiovascular risk and alcohol intake, and provide outstanding candidates for future studies aimed at understanding the mechanisms that underlie the salutary cardiovascular benefits of chronic low risk and nonhazardous alcohol intake.