Browsing by Author "Liu, Xiaoqi"

Now showing 1 - 5 of 5
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    Cholesteryl Ester Accumulation Induced by PTEN Loss and PI3K/AKT Activation Underlies Human Prostate Cancer Aggressiveness
    (Elsevier, 2014-03-04) Yue, Shuhua; Li, Junjie; Lee, Seung-Young; Lee, Hyeon Jeong; Shao, Tian; Song, Bing; Cheng, Liang; Masterson, Timothy A.; Liu, Xiaoqi; Ratliff, Timothy L.; Cheng, Ji-Xin; Department of Pathology & Laboratory Medicine, IU School of Medicine
    Altered lipid metabolism is increasingly recognized as a signature of cancer cells. Enabled by label-free Raman spectromicroscopy, we performed quantitative analysis of lipogenesis at single cell level in human patient cancerous tissues. Our imaging data revealed an unexpected, aberrant accumulation of esterified cholesterol in lipid droplets of high-grade prostate cancer and metastases. Biochemical study showed that such cholesteryl ester accumulation was a consequence of loss of tumor suppressor PTEN and subsequent activation of PI3K/AKT pathway in prostate cancer cells. Furthermore, we found that such accumulation arose from significantly enhanced uptake of exogenous lipoproteins and required cholesterol esterification. Depletion of cholesteryl ester storage significantly reduced cancer proliferation, impaired cancer invasion capability, and suppressed tumor growth in mouse xenograft models with negligible toxicity. These findings open opportunities for diagnosing and treating prostate cancer by targeting the altered cholesterol metabolism.
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    Inhibition of polo-like kinase 1 (Plk1) enhances the antineoplastic activity of metformin in prostate cancer
    (2015-01) Shao, Chen; Ahmad, Nihal; Hodges, Kurt; Kuang, Shihuan; Ratliff, Tim; Liu, Xiaoqi; Department of Pathology and Laboratory Medicine, IU School of Medicine
    The widely used anti-diabetic drug metformin has been shown to exert strong antineoplastic actions in numerous tumor types, including prostate cancer (PCa). In this study, we show that BI2536, a specific Plk1 inhibitor, acted synergistically with metformin in inhibiting PCa cell proliferation. Furthermore, we also provide evidence that Plk1 inhibition makes PCa cells carrying WT p53 much more sensitive to low-dose metformin treatment. Mechanistically, we found that co-treatment with BI2536 and metformin induced p53-dependent apoptosis and further activated the p53/Redd-1 pathway. Moreover, we also show that BI2536 treatment inhibited metformin-induced glycolysis and glutamine anaplerosis, both of which are survival responses of cells against mitochondrial poisons. Finally, we confirmed the cell-based observations using both cultured cell-derived and patient-derived xenograft studies. Collectively, our findings support another promising therapeutic strategy by combining two well tolerated drugs against PCa proliferation and the progression of androgen-dependent PCa to the castration-resistant stage.
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    Notch signaling regulates adipose browning and energy metabolism
    (Nature Publishing Group, 2014-08) Bi, Pengpeng; Shan, Tizhong; Liu, Weiyi; Yue, Feng; Yang, Xin; Liang, Xin-Rong; Wang, Jinghua; Li, Jie; Carlesso, Nadia; Liu, Xiaoqi; Kuang, Shihuan; Department of Pediatrics, IU School of Medicine
    Beige adipocytes in white adipose tissue (WAT) are similar to classical brown adipocytes in that they can burn lipids to produce heat. Thus, an increase in beige adipocyte content in WAT browning would raise energy expenditure and reduce adiposity. Here we report that adipose-specific inactivation of Notch1 or its signaling mediator Rbpj in mice results in browning of WAT and elevated expression of uncoupling protein 1 (Ucp1), a key regulator of thermogenesis. Consequently, as compared to wild-type mice, Notch mutants exhibit elevated energy expenditure, better glucose tolerance and improved insulin sensitivity and are more resistant to high fat diet-induced obesity. By contrast, adipose-specific activation of Notch1 leads to the opposite phenotypes. At the molecular level, constitutive activation of Notch signaling inhibits, whereas Notch inhibition induces, Ppargc1a and Prdm16 transcription in white adipocytes. Notably, pharmacological inhibition of Notch signaling in obese mice ameliorates obesity, reduces blood glucose and increases Ucp1 expression in white fat. Therefore, Notch signaling may be therapeutically targeted to treat obesity and type 2 diabetes.
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    Polo-like kinase 1 (Plk1) overexpression enhances ionizing radiation-induced cancer formation in mice
    (American Society for Biochemistry and Molecular Biology, 2017-10-20) Li, Zhiguo; Liu, Jinghui; Li, Jie; Kong, Yifan; Sandusky, George; Rao, Xi; Liu, Yunlong; Wan, Jun; Liu, Xiaoqi; Biochemistry and Molecular Biology, School of Medicine
    Polo-like kinase 1 (Plk1), a serine/threonine protein kinase normally expressed in mitosis, is frequently up-regulated in multiple types of human tumors regardless of the cell cycle stage. However, the causal relationship between Plk1 up-regulation and tumorigenesis is incompletely investigated. To this end, using a conditional expression system, here we generated Plk1 transgenic mouse lines to examine the role of Plk1 in tumorigenesis. Plk1 overexpression in mouse embryonic fibroblasts prepared from the transgenic mice led to aberrant mitosis followed by aneuploidy and apoptosis. Surprisingly, Plk1 overexpression had no apparent phenotypes in the mice. Given that no malignant tumor formation was observed even after a long period of Plk1 overexpression, we reasoned that additional factors are required for tumorigenesis in Plk1-overexpressing mice. Because Plk1 can directly participate in the regulation of the DNA damage response (DDR) pathway, we challenged Plk1-overexpressing mice with ionizing radiation (IR) and found that Plk1-overexpressing mice are much more sensitive to IR than their wild-type littermates. Analysis of tumor development in the Plk1-overexpressing mice indicated a marked decrease in the time required for tumor emergence after IR. At the molecular level, Plk1 overexpression led to reduced phosphorylation of the serine/threonine kinases ATM and Chk2 and of histone H2AX after IR treatment both in vivo and in vitro Furthermore, RNA-Seq analysis suggested that Plk1 elevation decreases the expression of several DDR genes. We conclude that Plk1 overexpression may contribute to tumor formation by both inducing chromosomal instability and suppressing the DDR pathway.
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    SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma
    (BMC, 2020) Liu, Enze; Zhang, Zhuang Zhuang; Cheng, Xiaolin; Liu, Xiaoqi; Cheng, Lijun; BioHealth Informatics, School of Informatics and Computing
    Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic malignancy. Due to its wide heterogeneity, PDAC acts aggressively and responds poorly to most chemotherapies, causing an urgent need for the development of new therapeutic strategies. Cell lines have been used as the foundation for drug development and disease modeling. CRISPR-Cas9 plays a key role in every step-in drug discovery: from target identification and validation to preclinical cancer cell testing. Using cell-line models and CRISPR-Cas9 technology together make drug target prediction feasible. However, there is still a large gap between predicted results and actionable targets in real tumors. Biological network models provide great modus to mimic genetic interactions in real biological systems, which can benefit gene perturbation studies and potential target identification for treating PDAC. Nevertheless, building a network model that takes cell-line data and CRISPR-Cas9 data as input to accurately predict potential targets that will respond well on real tissue remains unsolved. Methods: We developed a novel algorithm 'Spectral Clustering for Network-based target Ranking' (SCNrank) that systematically integrates three types of data: expression profiles from tumor tissue, normal tissue and cell-line PDAC; protein-protein interaction network (PPI); and CRISPR-Cas9 data to prioritize potential drug targets for PDAC. The whole algorithm can be classified into three steps: 1. using STRING PPI network skeleton, SCNrank constructs tissue-specific networks with PDAC tumor and normal pancreas tissues from expression profiles; 2. With the same network skeleton, SCNrank constructs cell-line-specific networks using the cell-line PDAC expression profiles and CRISPR-Cas 9 data from pancreatic cancer cell-lines; 3. SCNrank applies a novel spectral clustering approach to reduce data dimension and generate gene clusters that carry common features from both networks. Finally, SCNrank applies a scoring scheme called 'Target Influence score' (TI), which estimates a given target's influence towards the cluster it belongs to, for scoring and ranking each drug target. Results: We applied SCNrank to analyze 263 expression profiles, CRPSPR-Cas9 data from 22 different pancreatic cancer cell-lines and the STRING protein-protein interaction (PPI) network. With SCNrank, we successfully constructed an integrated tissue PDAC network and an integrated cell-line PDAC network, both of which contain 4414 selected genes that are overexpressed in tumor tissue samples. After clustering, 4414 genes are distributed into 198 clusters, which include 367 targets of FDA approved drugs. These drug targets are all scored and ranked by their TI scores, which we defined to measure their influence towards the network. We validated top-ranked targets in three aspects: Firstly, mapping them onto the existing clinical drug targets of PDAC to measure the concordance. Secondly, we performed enrichment analysis to these drug targets and the clusters there are within, to reveal functional associations between clusters and PDAC; Thirdly, we performed survival analysis for the top-ranked targets to connect targets with clinical outcomes. Survival analysis reveals that overexpression of three top-ranked genes, PGK1, HMMR and POLE2, significantly increases the risk of death in PDAC patients. SCNrank is an unbiased algorithm that systematically integrates multiple types of omics data to do potential drug target selection and ranking. SCNrank shows great capability in predicting drug targets for PDAC. Pancreatic cancer-associated gene candidates predicted by our SCNrank approach have the potential to guide genetics-based anti-pancreatic drug discovery.