Hyperglycemia regulates thioredoxin-ROS activity through induction of thioredoxin-interacting protein (TXNIP) in metastatic breast cancer-derived cells MDA-MB-231

<p>Abstract</p> <p>Background</p> <p>We studied the RNA expression of the genes in response to glucose from 5 mM (condition of normoglycemia) to 20 mM (condition of hyperglycemia/diabetes) by microarray analysis in breast cancer derived cell line MDA-MB-231. We identified the thioredoxin-interacting protein (TXNIP), whose RNA level increased as a gene product particularly sensitive to the variation of the level of glucose in culture media. We investigated the kinesis of the TXNIP RNA and protein in response to glucose and the relationship between this protein and the related thioredoxin (TRX) in regulating the level of reactive oxygen species (ROS) in MDA-MB-231 cells.</p> <p>Methods</p> <p>MDA-MB-231 cells were grown either in 5 or 20 mM glucose chronically prior to plating. For glucose shift (5/20), cells were plated in 5 mM glucose and shifted to 20 mM at time 0. Cells were analyzed with Affymetrix Human U133A microarray chip and gene expression profile was obtained. Semi-quantitative RT-PCR and Western blot was used to validate the expression of TXNIP RNA and protein in response to glucose, respectively. ROS were detected by CM-H2DCFDA (5–6-chloromethyl-2',7'-dichlorodihydrofluorescein diacetate) and measured for mean fluorescence intensity with flow cytometry. TRX activity was assayed by the insulin disulfide reducing assay.</p> <p>Results</p> <p>We found that the regulation of TXNIP gene expression by glucose in MDA-MB-231 cells occurs rapidly within 6 h of its increased level (20 mM glucose) and persists through the duration of the conditions of hyperglycemia. The increased level of TXNIP RNA is followed by increased level of protein that is associated with increasing levels of ROS and reduced TRX activity. The inhibition of the glucose transporter GLUT1 by phloretin notably reduces TXNIP RNA level and the inhibition of the p38 MAP kinase activity by SB203580 reverses the effects of TXNIP on ROS-TRX activity.</p> <p>Conclusion</p> <p>In this study we show that TXNIP is an oxidative stress responsive gene and its expression is exquisitely regulated by glucose level in highly metastatic MDA-MB-231 cells.</p

Quantitative techniques in 18FDG PET scanning in oncology

The clinical applications of 18F-fluoro-2-deoxyglucose (18FDG) positron emission tomography (PET) in oncology are becoming established. While simple static scanning techniques are used for the majority of routine clinical examinations, increasing use of PET in clinical trials to monitor treatment response with 18FDG and novel tracers reflecting different pharmacodynamic end points, often necessitates a more complex and quantitative analysis of radiopharmaceutical kinetics. A wide range of PET analysis techniques exist, ranging from simple visual analysis and semiquantitative methods to full dynamic studies with kinetic analysis. These methods are discussed, focusing particularly on the available methodologies that can be utilised in clinical trials

Effects of sample size on robustness and prediction accuracy of a prognostic gene signature

<p>Abstract</p> <p>Background</p> <p>Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature.</p> <p>Results</p> <p>A data set of 1,372 samples was generated by combining eight breast cancer gene expression data sets produced using the same microarray platform and, using the data set, effects of varying samples sizes on a few performances of a prognostic gene signature were investigated. The overlap between independently developed gene signatures was increased linearly with more samples, attaining an average overlap of 16.56% with 600 samples. The concordance between predicted outcomes by different gene signatures also was increased with more samples up to 94.61% with 300 samples. The accuracy of outcome prediction also increased with more samples. Finally, analysis using only Estrogen Receptor-positive (ER+) patients attained higher prediction accuracy than using both patients, suggesting that sub-type specific analysis can lead to the development of better prognostic gene signatures</p> <p>Conclusion</p> <p>Increasing sample sizes generated a gene signature with better stability, better concordance in outcome prediction, and better prediction accuracy. However, the degree of performance improvement by the increased sample size was different between the degree of overlap and the degree of concordance in outcome prediction, suggesting that the sample size required for a study should be determined according to the specific aims of the study.</p

Meta-Analysis and Gene Set Enrichment Relative to ER Status Reveal Elevated Activity of MYC and E2F in the “Basal” Breast Cancer Subgroup

10.1371/journal.pone.0004710PLoS ONE43

AMP-Activated Protein Kinase (AMPK) Mediates Nutrient Regulation of Thioredoxin-Interacting Protein (TXNIP) in Pancreatic Beta-Cells

Thioredoxin-interacting protein (TXNIP) regulates critical biological processes including inflammation, stress and apoptosis. TXNIP is upregulated by glucose and is a critical mediator of hyperglycemia-induced beta-cell apoptosis in diabetes. In contrast, the saturated long-chain fatty acid palmitate, although toxic to the beta-cell, inhibits TXNIP expression. The mechanisms involved in the opposing effects of glucose and fatty acids on TXNIP expression are unknown. We found that both palmitate and oleate inhibited TXNIP in a rat beta-cell line and islets. Palmitate inhibition of TXNIP was independent of fatty acid beta-oxidation or esterification. AMP-activated protein kinase (AMPK) has an important role in cellular energy sensing and control of metabolic homeostasis; therefore we investigated its involvement in nutrient regulation of TXNIP. As expected, glucose inhibited whereas palmitate stimulated AMPK. Pharmacologic activators of AMPK mimicked fatty acids by inhibiting TXNIP. AMPK knockdown increased TXNIP expression in presence of high glucose with and without palmitate, indicating that nutrient (glucose and fatty acids) effects on TXNIP are mediated in part via modulation of AMPK activity. TXNIP is transcriptionally regulated by carbohydrate response element-binding protein (ChREBP). Palmitate inhibited glucose-stimulated ChREBP nuclear entry and recruitment to the Txnip promoter, thereby inhibiting Txnip transcription. We conclude that AMPK is an important regulator of Txnip transcription via modulation of ChREBP activity. The divergent effects of glucose and fatty acids on TXNIP expression result in part from their opposing effects on AMPK activity. In light of the important role of TXNIP in beta-cell apoptosis, its inhibition by fatty acids can be regarded as an adaptive/protective response to glucolipotoxicity. The finding that AMPK mediates nutrient regulation of TXNIP may have important implications for the pathophysiology and treatment of diabetes

Matrix Metalloproteinase 1: Role in Sarcoma Biology

In carcinomas stromal cells participate in cancer progression by producing proteases such as MMPs. The expression MMP1 is a prognostic factor in human chondrosarcoma, however the role in tumor progression is unknown. Laser capture microdissection and In Situ hybridization were used to determine cellular origin of MMP1 in human sarcomas. A xenogenic model of tumor progression was then used and mice were divided in two groups: each harboring either the control or a stably MMP1 silenced cell line. Animals were sacrificed; the neovascularization, primary tumor volumes, and metastatic burden were assessed. LCM and RNA-ISH analysis revealed MMP1 expression was predominantly localized to the tumor cells in all samples of sarcoma (p = 0.05). The percentage lung metastatic volume at 5 weeks (p = 0.08) and number of spontaneous deaths secondary to systemic tumor burden were lower in MMP1 silenced cell bearing mice. Interestingly, this group also demonstrated a larger primary tumor size (p<0.04) and increased angiogenesis (p<0.01). These findings were found to be consistent when experiment was repeated using a second independent MMP1 silencing sequence. Prior clinical trials employing MMP1 inhibitors failed because of a poor understanding of the role of MMPs in tumor progression. The current findings indicating tumor cell production of MMP1 by sarcoma cells is novel and highlights the fundamental differences in MMP biology between carcinomas and sarcomas. The results also emphasize the complex roles of MMP in tumor progression of sarcomas. Not only does metastasis seem to be affected by MMP1 silencing, but also local tumor growth and angiogenesis are affected inversely

High Glucose Suppresses Human Islet Insulin Biosynthesis by Inducing miR-133a Leading to Decreased Polypyrimidine Tract Binding Protein-Expression

BACKGROUND: Prolonged periods of high glucose exposure results in human islet dysfunction in vitro. The underlying mechanisms behind this effect of high glucose are, however, unknown. The polypyrimidine tract binding protein (PTB) is required for stabilization of insulin mRNA and the PTB mRNA 3'-UTR contains binding sites for the microRNA molecules miR-133a, miR-124a and miR-146. The aim of this study was therefore to investigate whether high glucose increased the levels of these three miRNAs in association with lower PTB levels and lower insulin biosynthesis rates. METHODOLOGY/PRINCIPAL FINDINGS: Human islets were cultured for 24 hours in the presence of low (5.6 mM) or high glucose (20 mM). Islets were also exposed to sodium palmitate or the proinflammatory cytokines IL-1beta and IFN-gamma, since saturated free fatty acids and cytokines also cause islet dysfunction. RNA was then isolated for real-time RT-PCR analysis of miR-133a, miR-124a, miR-146, insulin mRNA and PTB mRNA contents. Insulin biosynthesis rates were determined by radioactive labeling and immunoprecipitation. Synthetic miR-133a precursor and inhibitor were delivered to dispersed islet cells by lipofection, and PTB was analyzed by immunoblotting following culture at low or high glucose. Culture in high glucose resulted in increased islet contents of miR-133a and reduced contents of miR-146. Cytokines increased the contents of miR-146. The insulin and PTB mRNA contents were unaffected by high glucose. However, both PTB protein levels and insulin biosynthesis rates were decreased in response to high glucose. The miR-133a inhibitor prevented the high glucose-induced decrease in PTB and insulin biosynthesis, and the miR-133a precursor decreased PTB levels and insulin biosynthesis similarly to high glucose. CONCLUSION: Prolonged high-glucose exposure down-regulates PTB levels and insulin biosynthesis rates in human islets by increasing miR-133a levels. We propose that this mechanism contributes to hyperglycemia-induced beta-cell dysfunction

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

BACKGROUND: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses. RESULTS: A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics. CONCLUSION: Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power

Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

<p>Abstract</p> <p>Background</p> <p>Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes.</p> <p>Methods</p> <p>Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach.</p> <p>Results</p> <p>We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population.</p> <p>Conclusions</p> <p>Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.</p