Dynamic telomerase gene suppression via network effects of GSK3 inhibition

<b>Background</b>: Telomerase controls telomere homeostasis and cell immortality and is a promising anti-cancer target, but few small molecule telomerase inhibitors have been developed. Reactivated transcription of the catalytic subunit hTERT in cancer cells controls telomerase expression. Better understanding of upstream pathways is critical for effective anti-telomerase therapeutics and may reveal new targets to inhibit hTERT expression. <b>Methodology/Principal Findings</b>: In a focused promoter screen, several GSK3 inhibitors suppressed hTERT reporter activity. GSK3 inhibition using 6-bromoindirubin-3′-oxime suppressed hTERT expression, telomerase activity and telomere length in several cancer cell lines and growth and hTERT expression in ovarian cancer xenografts. Microarray analysis, network modelling and oligonucleotide binding assays suggested that multiple transcription factors were affected. Extensive remodelling involving Sp1, STAT3, c-Myc, NFκB, and p53 occurred at the endogenous hTERT promoter. RNAi screening of the hTERT promoter revealed multiple kinase genes which affect the hTERT promoter, potentially acting through these factors. Prolonged inhibitor treatments caused dynamic expression both of hTERT and of c-Jun, p53, STAT3, AR and c-Myc. <b>Conclusions/Significance</b>: Our results indicate that GSK3 activates hTERT expression in cancer cells and contributes to telomere length homeostasis. GSK3 inhibition is a clinical strategy for several chronic diseases. These results imply that it may also be useful in cancer therapy. However, the complex network effects we show here have implications for either setting

Probabilistic models of information retrieval based on measuring the divergence from randomness

We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach. We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a random process. Among the random processes we study the binomial distribution and Bose--Einstein statistics. We define two types of term frequency normalization for tuning term weights in the document--query matching process. The first normalization assumes that documents have the same length and measures the information gain with the observed term once it has been accepted as a good descriptor of the observed document. The second normalization is related to the document length and to other statistics. These two normalization methods are applied to the basic models in succession to obtain weighting formulae. Results show that our framework produces different nonparametric models forming baseline alternatives to the standard tf-idf model

A Spectral Line Survey of Selected 3 mm Bands Toward Sagittarius B2(N-LMH) Using the NRAO 12 Meter Radio Telescope and the BIMA Array I. The Observational Data

We have initiated a spectral line survey, at a wavelength of 3 millimeters, toward the hot molecular core Sagittarius B2(N-LMH). This is the first spectral line survey of the Sgr B2(N) region utilizing data from both an interferometer (BIMA Array) and a single-element radio telescope (NRAO 12 meter). In this survey, covering 3.6 GHz in bandwidth, we detected 218 lines (97 identified molecular transitions, 1 recombination line, and 120 unidentified transitions). This yields a spectral line density (lines per 100 MHz) of 6.06, which is much larger than any previous 3 mm line survey. We also present maps from the BIMA Array that indicate that most highly saturated species (3 or more H atoms) are products of grain chemistry or warm gas phase chemistry. Due to the nature of this survey we are able to probe each spectral line on multiple spatial scales, yielding information that could not be obtained by either instrument alone.Comment: 35 pages, 15 figures, to be published in The Astrophysical Journa

Whole blood angiopoietin-1 and -2 levels discriminate cerebral and severe (non-cerebral) malaria from uncomplicated malaria

<p>Abstract</p> <p>Background</p> <p>Severe and cerebral malaria are associated with endothelial activation. Angiopoietin-1 (ANG-1) and angiopoietin-2 (ANG-2) are major regulators of endothelial activation and integrity. The aim of this study was to investigate the clinical utility of whole blood angiopoietin (ANG) levels as biomarkers of disease severity in <it>Plasmodium falciparum </it>malaria.</p> <p>Methods</p> <p>The utility of whole blood ANG levels was examined in Thai patients to distinguish cerebral (CM; n = 87) and severe (non-cerebral) malaria (SM; n = 36) from uncomplicated malaria (UM; n = 70). Comparative statistics are reported using a non-parametric univariate analysis (Kruskal-Wallis test or Chi-squared test, as appropriate). Multivariate binary logistic regression was used to examine differences in whole blood protein levels between groups (UM, SM, CM), adjusting for differences due to ethnicity, age, parasitaemia and sex. Receiver operating characteristic curve analysis was used to assess the diagnostic accuracy of the ANGs in their ability to distinguish between UM, SM and CM. Cumulative organ injury scores were obtained for patients with severe disease based on the presence of acute renal failure, jaundice, severe anaemia, circulatory collapse or coma.</p> <p>Results</p> <p>ANG-1 and ANG-2 were readily detectable in whole blood. Compared to UM there were significant decreases in ANG-1 (p < 0.001) and significant increases in ANG-2 (p < 0.001) levels and the ratio of ANG-2: ANG-1 (p < 0.001) observed in patients with SM and CM. This effect was independent of covariates (ethnicity, age, parasitaemia, sex). Further, there was a significant decrease in ANG-1 levels in patients with SM (non-cerebral) versus CM (p < 0.001). In participants with severe disease, ANG-2, but not ANG-1, levels correlated with cumulative organ injury scores; however, ANG-1 correlated with the presence of renal dysfunction and coma. Receiver operating characteristic curve analysis demonstrated that the level of ANG-1, the level of ANG-2 or the ratio of ANG-2: ANG-1 discriminated between individuals with UM and SM (area under the curve, p-value: ANG-2, 0.763, p < 0.001; ANG-1, 0.884, p < 0.001; Ratio, 0.857, p < 0.001) or UM and CM (area under the curve, p-value: ANG-2, 0.772, p < 0.001; ANG-1, 0.778, p < 0.001; Ratio, 0.820, p < 0.001).</p> <p>Conclusions</p> <p>These results suggest that whole blood ANG-1/2 levels are promising clinically informative biomarkers of disease severity in malarial syndromes.</p

Pion-Muon Asymmetry Revisited

Long ago an unexpected and unexplainable phenomena was observed. The distribution of muons from positive pion decay at rest was anisotropic with an excess in the backward direction relative to the direction of the proton beam from which the pions were created. Although this effect was observed by several different groups with pions produced by different means, the result was not accepted by the physics community, because it is in direct conflict with a large set of other experiments indicating that the pion is a pseudoscalar particle. It is possible to satisfy both sets of experiments if helicity-zero vector particles exist and the pion is such a particle. Helicity-zero vector particles have direction but no net spin. For the neutral pion to be a vector particle requires an additional modification to conventional theory as discussed herein. An experiment is proposed which can prove that the asymmetry in the distribution of muons from pion decay is a genuine physical effect because the asymmetry can be modified in a controllable manner. A positive result will also prove that the pion is NOT a pseudoscalar particle.Comment: 9 pages, 3 figure

ρ(770)0\rho(770)^0, K∗(892)0^*(892)^0 and f0(980)_{0}(980) Production in Au-Au and pp Collisions at sNN\sqrt{s_{NN}} = 200 GeV

Preliminary results on $\rho(770)^0 \to \pi^{+}\pi^{-}$, K$^{*}(892)^{0} \to \pi$K and $f_{0}(980) \to \pi^{+}\pi^{-}$ production using the mixed-event technique are presented. The measurements are performed at mid-rapidity by the STAR detector in $\sqrt{s_{NN}}$= 200 GeV Au-Au and pp interactions at RHIC. The results are compared to different measurements at various energies.Comment: 4 pages, 6 figures. Talk presented at Quark Matter 2002, Nantes, France, July 18-24, 2002. To appear in the proceedings (Nucl. Phys. A

Lambda Polarization in Polarized Proton-Proton Collisions at RHIC

We discuss Lambda polarization in semi-inclusive proton-proton collisions, with one of the protons longitudinally polarized. The hyperfine interaction responsible for the $\Delta$-$N$ and $\Sigma$-$\Lambda$ mass splittings gives rise to flavor asymmetric fragmentation functions and to sizable polarized non-strange fragmentation functions. We predict large positive Lambda polarization in polarized proton-proton collisions at large rapidities of the produced Lambda, while other models, based on SU(3) flavor symmetric fragmentation functions, predict zero or negative Lambda polarization. The effect of $\Sigma^0$ and $\Sigma^*$ decays is also discussed. Forthcoming experiments at RHIC will be able to differentiate between these predictions.Comment: 18 pages, 5 figure

Production of pizero and eta mesons at large transverse momenta in pi-p and pi-Be interactions at 515 GeV/c

We present results on the production of high transverse momentum pizero and eta mesons in pi-p and pi-Be interactions at 515 GeV/c. The data span the kinematic ranges 1 < p_T < 11 GeV/c in transverse momentum and -0.75 < y < 0.75 in rapidity. The inclusive pizero cross sections are compared with next-to-leading order QCD calculations and to expectations based on a phenomenological parton-k_T model.Comment: RevTeX4, 15 pages, 15 figures, to be submitted to Phys. Rev.

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Mathematical model of a telomerase transcriptional regulatory network developed by cell-based screening: analysis of inhibitor effects and telomerase expression mechanisms

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability