Tenascin-W is a specific marker of glioma-associated blood vessels and stimulates angiogenesis in vitro.

The microenvironment hosting a tumor actively participates in regulating tumor cell proliferation, migration, and invasion. Among the extracellular matrix proteins enriched in the stroma of carcinomas are the tenascin family members tenascin-C and tenascin-W. Whereas tenascin-C overexpression in gliomas is known to correlate with poor prognosis, the status of tenascin-W in brain tumors has not been investigated so far. In the present study, we analyzed protein levels of tenascin-W in 38 human gliomas and found expression of tenascin-W in 80% of the tumor samples, whereas no tenascin-W could be detected in control, nontumoral brain tissues. Double immunohistochemical staining of tenascin-W and von Willebrand factor revealed that tenascin-W is localized around blood vessels, exclusively in tumor samples. In vitro, the presence of tenascin-W increased the proportion of elongated human umbilical vein endothelial cells (HUVECs) and augmented the mean speed of cell migration. Furthermore, tenascin-W triggered sprouting of HUVEC spheroids to a similar extent as the proangiogenic factor tenascin-C. In conclusion, our study identifies tenascin-W as a candidate biomarker for brain tumor angiogenesis that could be used as a molecular target for therapy irrespective of the glioma subtype.-Martina, E., Degen, M., Rüegg, C., Merlo, A., Lino, M. M., Chiquet-Ehrismann, R., Brellier, F. Tenascin-W is a specific marker of glioma-associated blood vessels and stimulates angiogenesis in vitro.

In vivo metabolic profiling of glioma-initiating cells using proton magnetic resonance spectroscopy at 14.1 Tesla.

In the last decade, evidence has emerged indicating that the growth of a vast majority of tumors including gliomas is sustained by a subpopulation of cancer cells with stem cell properties called cancer initiating cells. These cells are able to initiate and propagate tumors and constitute only a fraction of all tumor cells. In the present study, we showed that intracerebral injection of cultured glioma-initiating cells into nude mice produced fast growing tumors showing necrosis and gadolinium enhancement in MR images, whereas gliomas produced by injecting freshly purified glioma-initiating cells grew slowly and showed no necrosis and very little gadolinium enhancement. Using proton localized spectroscopy at 14.1 Tesla, decreasing trends of N-acetylaspartate, glutamate and glucose concentrations and an increasing trend of glycine concentration were observed near the injection site after injecting cultured glioma-initiating cells. In contrast to the spectra of tumors grown from fresh cells, those from cultured cells showed intense peaks of lipids, increased absolute concentrations of glycine and choline-containing compounds, and decreased concentrations of glutamine, taurine and total creatine, when compared with a contralateral non-tumor-bearing brain tissue. A decrease in concentrations of N-acetylaspartate and γ-aminobutyrate was found in both tumor phenotypes after solid tumor formation. Further investigation is needed to determine the cause of the dissimilarities between the tumors grown from cultured glioma-initiating cells and those from freshly purified glioma-initiating cells, both derived from human glioblastomas.

Genetic and genomic analysis modeling of germline c-MYC overexpression and cancer susceptibility

Background: Germline genetic variation is associated with the differential expression of many human genes. The phenotypic effects of this type of variation may be important when considering susceptibility to common genetic diseases. Three regions at 8q24 have recently been identified to independently confer risk of prostate cancer. Variation at 8q24 has also recently been associated with risk of breast and colorectal cancer. However, none of the risk variants map at or relatively close to known genes, with c-MYC mapping a few hundred kilobases distally. Results: This study identifies cis-regulators of germline c-MYC expression in immortalized lymphocytes of HapMap individuals. Quantitative analysis of c-MYC expression in normal prostate tissues suggests an association between overexpression and variants in Region 1 of prostate cancer risk. Somatic c-MYC overexpression correlates with prostate cancer progression and more aggressive tumor forms, which was also a pathological variable associated with Region 1. Expression profiling analysis and modeling of transcriptional regulatory networks predicts a functional association between MYC and the prostate tumor suppressor KLF6. Analysis of MYC/Myc-driven cell transformation and tumorigenesis substantiates a model in which MYC overexpression promotes transformation by down-regulating KLF6. In this model, a feedback loop through E-cadherin down-regulation causes further transactivation of c-MYC.Conclusion: This study proposes that variation at putative 8q24 cis-regulator(s) of transcription can significantly alter germline c-MYC expression levels and, thus, contribute to prostate cancer susceptibility by down-regulating the prostate tumor suppressor KLF6 gene.

Physiologically based pharmacokinetic modeling of arsenic in the mouse

A remarkable feature of the carcinogenicity of inorganic arsenic is that while human exposures to high concentrations of inorganic arsenic in drinking water are associated with increases in skin, lung, and bladder cancer, inorganic arsenic has not typically caused tumors in standard laboratory animal test protocols. Inorganic arsenic administered for periods of up to 2 yr to various strains of laboratory mice, including the Swiss CD-1, Swiss CR:NIH(S), C57Bl/6p53(+/-), and C57Bl/6p53(+/+), has not resulted in significant increases in tumor incidence. However, Ng et al. (1999) have reported a 40% tumor incidence in C57Bl/6J mice exposed to arsenic in their drinking water throughout their lifetime, with no tumors reported in controls. In order to investigate the potential role of tissue dosimetry in differential susceptibility to arsenic carcinogenicity, a physiologically based pharmacokinetic (PBPK) model for inorganic arsenic in the rat, hamster, monkey, and human (Mann et al., 1996a, 1996b) was extended to describe the kinetics in the mouse. The PBPK model was parameterized in the mouse using published data from acute exposures of B6C3F1 mice to arsenate, arsenite, monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA) and validated using data from acute exposures of C57Black mice. Predictions of the acute model were then compared with data from chronic exposures. There was no evidence of changes in the apparent volume of distribution or in the tissue-plasma concentration ratios between acute and chronic exposure that might support the possibility of inducible arsenite efflux. The PBPK model was also used to project tissue dosimetry in the C57Bl/6J study, in comparison with tissue levels in studies having shorter duration but higher arsenic treatment concentrations. The model evaluation indicates that pharmacokinetic factors do not provide an explanation for the difference in outcomes across the various mouse bioassays. Other possible explanations may relate to strain-specific differences, or to the different durations of dosing in each of the mouse studies, given the evidence that inorganic arsenic is likely to be active in the later stages of the carcinogenic process. [Authors]

Model-based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Computed Tomography (CT) represents the standard imaging modality for tumor volume delineation for radiotherapy treatment planning of retinoblastoma despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a highly accurate definition of the tumor volume thanks to its high spatial resolution but it is not currently integrated in the treatment planning but used only for diagnosis and follow-up. Our ultimate goal is an automatic segmentation of gross tumor volume (GTV) in the 3D US, the segmentation of the organs at risk (OAR) in the CT and the registration of both modalities. In this paper, we present some preliminary results in this direction. We present 3D active contour-based segmentation of the eye ball and the lens in CT images; the presented approach incorporates the prior knowledge of the anatomy by using a 3D geometrical eye model. The automated segmentation results are validated by comparing with manual segmentations. Then, we present two approaches for the fusion of 3D CT and US images: (i) landmark-based transformation, and (ii) object-based transformation that makes use of eye ball contour information on CT and US images.

DNA fingerprinting of glioma cell lines and considerations on similarity measurements.

Glioma cell lines are an important tool for research in basic and translational neuro-oncology. Documentation of their genetic identity has become a requirement for scientific journals and grant applications to exclude cross-contamination and misidentification that lead to misinterpretation of results. Here, we report the standard 16 marker short tandem repeat (STR) DNA fingerprints for a panel of 39 widely used glioma cell lines as reference. Comparison of the fingerprints among themselves and with the large DSMZ database comprising 9 marker STRs for 2278 cell lines uncovered 3 misidentified cell lines and confirmed previously known cross-contaminations. Furthermore, 2 glioma cell lines exhibited identity scores of 0.8, which is proposed as the cutoff for detecting cross-contamination. Additional characteristics, comprising lack of a B-raf mutation in one line and a similarity score of 1 with the original tumor tissue in the other, excluded a cross-contamination. Subsequent simulation procedures suggested that, when using DNA fingerprints comprising only 9 STR markers, the commonly used similarity score of 0.8 is not sufficiently stringent to unambiguously differentiate the origin. DNA fingerprints are confounded by frequent genetic alterations in cancer cell lines, particularly loss of heterozygosity, that reduce the informativeness of STR markers and, thereby, the overall power for distinction. The similarity score depends on the number of markers measured; thus, more markers or additional cell line characteristics, such as information on specific mutations, may be necessary to clarify the origin.

Marker-independent identification of glioma-initiating cells.

Tumor-initiating cells with stem cell properties are believed to sustain the growth of gliomas, but proposed markers such as CD133 cannot be used to identify these cells with sufficient specificity. We report an alternative isolation method purely based on phenotypic qualities of glioma-initiating cells (GICs), avoiding the use of molecular markers. We exploited intrinsic autofluorescence properties and a distinctive morphology to isolate a subpopulation of cells (FL1(+)) from human glioma or glioma cultures. FL1(+) cells are capable of self-renewal in vitro, tumorigenesis in vivo and preferentially express stem cell genes. The FL1(+) phenotype did not correlate with the expression of proposed GIC markers. Our data propose an alternative approach to investigate tumor-initiating potential in gliomas and to advance the development of new therapies and diagnostics.

Molecular modeling of peptide-MHC class I complexes : applications to peptide based immunotherapy

Summary The specific CD8+ T cell immune response against tumors relies on the recognition by the T cell receptor (TCR) on cytotoxic T lymphocytes (CTL) of antigenic peptides bound to the class I major histocompatibility complex (MHC) molecule. Such tumor associated antigenic peptides are the focus of tumor immunotherapy with peptide vaccines. The strategy for obtaining an improved immune response often involves the design of modified tumor associated antigenic peptides. Such modifications aim at creating higher affinity and/or degradation resistant peptides and require precise structures of the peptide-MHC class I complex. In addition, the modified peptide must be cross-recognized by CTLs specific for the parental peptide, i.e. preserve the structure of the epitope. Detailed structural information on the modified peptide in complex with MHC is necessary for such predictions. In this thesis, the main focus is the development of theoretical in silico methods for prediction of both structure and cross-reactivity of peptide-MHC class I complexes. Applications of these methods in the context of immunotherapy are also presented. First, a theoretical method for structure prediction of peptide-MHC class I complexes is developed and validated. The approach is based on a molecular dynamics protocol to sample the conformational space of the peptide in its MHC environment. The sampled conformers are evaluated using conformational free energy calculations. The method, which is evaluated for its ability to reproduce 41 X-ray crystallographic structures of different peptide-MHC class I complexes, shows an overall prediction success of 83%. Importantly, in the clinically highly relevant subset of peptide-HLAA*0201 complexes, the prediction success is 100%. Based on these structure predictions, a theoretical approach for prediction of cross-reactivity is developed and validated. This method involves the generation of quantitative structure-activity relationships using three-dimensional molecular descriptors and a genetic neural network. The generated relationships are highly predictive as proved by high cross-validated correlation coefficients (0.78-0.79). Together, the here developed theoretical methods open the door for efficient rational design of improved peptides to be used in immunotherapy. Résumé La réponse immunitaire spécifique contre des tumeurs dépend de la reconnaissance par les récepteurs des cellules T CD8+ de peptides antigéniques présentés par les complexes majeurs d'histocompatibilité (CMH) de classe I. Ces peptides sont utilisés comme cible dans l'immunothérapie par vaccins peptidiques. Afin d'augmenter la réponse immunitaire, les peptides sont modifiés de façon à améliorer l'affinité et/ou la résistance à la dégradation. Ceci nécessite de connaître la structure tridimensionnelle des complexes peptide-CMH. De plus, les peptides modifiés doivent être reconnus par des cellules T spécifiques du peptide natif. La structure de l'épitope doit donc être préservée et des structures détaillées des complexes peptide-CMH sont nécessaires. Dans cette thèse, le thème central est le développement des méthodes computationnelles de prédiction des structures des complexes peptide-CMH classe I et de la reconnaissance croisée. Des applications de ces méthodes de prédiction à l'immunothérapie sont également présentées. Premièrement, une méthode théorique de prédiction des structures des complexes peptide-CMH classe I est développée et validée. Cette méthode est basée sur un échantillonnage de l'espace conformationnel du peptide dans le contexte du récepteur CMH classe I par dynamique moléculaire. Les conformations sont évaluées par leurs énergies libres conformationnelles. La méthode est validée par sa capacité à reproduire 41 structures des complexes peptide-CMH classe I obtenues par cristallographie aux rayons X. Le succès prédictif général est de 83%. Pour le sous-groupe HLA-A*0201 de complexes de grande importance pour l'immunothérapie, ce succès est de 100%. Deuxièmement, à partir de ces structures prédites in silico, une méthode théorique de prédiction de la reconnaissance croisée est développée et validée. Celle-ci consiste à générer des relations structure-activité quantitatives en utilisant des descripteurs moléculaires tridimensionnels et un réseau de neurones couplé à un algorithme génétique. Les relations générées montrent une capacité de prédiction remarquable avec des valeurs de coefficients de corrélation de validation croisée élevées (0.78-0.79). Les méthodes théoriques développées dans le cadre de cette thèse ouvrent la voie du design de vaccins peptidiques améliorés.

Comparative localization of glioma-reactive monoclonal antibodies in vivo in an athymic mouse human glioma xenograft model.

Radioiodinated murine monoclonal antibodies (Mabs) 81C6, Me 1-14, C12, D12, and E9, made against or reactive with human gliomas but not normal brain, and Mab UJ13A, a pan-neuroectodermal Mab reactive with normal human glial and neural cells, were evaluated in paired label studies in the D-54 MG subcutaneous human glioma xenograft model system in nude mice. Following intravenous injection in the tail vein of mice bearing 200-400 mm3 tumors, specific localization of Mabs to tumor over time (6 h-9 days) was evaluated by tissue counting; each Mab demonstrated a unique localization profile. The comparison of localization indices (LI), determined as a ratio of tissue level of Mab to control immunoglobulin with simultaneous correction for blood levels of each, showed Mabs 81C6 and Me 1-14 to steadily accumulate in glioma xenografts, maintaining LI from 5-20 at 7-9 days after Mab injection. Mab UJ13A peaked at day 1, maintaining this level through day 2, and declining thereafter. Mabs D12 and C12 peaked at days 3 and 4, respectively, and E9 maintained an LI of greater than 3 from days 3-9. Percent injected dose localized/g of tumor varied from a peak high of 16% (81C6) to a low of 5% (Me 1-14 and UJ13A). Immunoperoxidase histochemistry, performed with each Mab on a battery of primary human brain neoplasms, revealed that Mabs 81C6 and E9, which demonstrated the highest levels of percent injected dose localized/g of tumor over time, reacted with antigens expressed in the extracellular matrix. This finding suggests that extracellular matrix localization of antigen represents a biologically significant factor affecting localization and/or binding in the xenograft model used. The demonstration of significant localization, varied kinetics and patterns of localization of this localizing Mab panel warrants their continued investigation as potential imaging and therapeutic agents for human trials.

Model-based Segmentation and Image Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

For radiotherapy treatment planning of retinoblastoma inchildhood, Computed Tomography (CT) represents thestandard method for tumor volume delineation, despitesome inherent limitations. CT scan is very useful inproviding information on physical density for dosecalculation and morphological volumetric information butpresents a low sensitivity in assessing the tumorviability. On the other hand, 3D ultrasound (US) allows ahigh accurate definition of the tumor volume thanks toits high spatial resolution but it is not currentlyintegrated in the treatment planning but used only fordiagnosis and follow-up. Our ultimate goal is anautomatic segmentation of gross tumor volume (GTV) in the3D US, the segmentation of the organs at risk (OAR) inthe CT and the registration of both. In this paper, wepresent some preliminary results in this direction. Wepresent 3D active contour-based segmentation of the eyeball and the lens in CT images; the presented approachincorporates the prior knowledge of the anatomy by usinga 3D geometrical eye model. The automated segmentationresults are validated by comparing with manualsegmentations. Then, for the fusion of 3D CT and USimages, we present two approaches: (i) landmark-basedtransformation, and (ii) object-based transformation thatmakes use of eye ball contour information on CT and USimages.

Regulation of peptidase activity in a three-dimensional aggregate model of brain tumor vasculature.

The abnormal vascular system of brain cancers inappropriately expresses membrane proteins, including proteolytic enzymes, ultimately resulting in blood extravasation. The production of inflammatory mediators, such as cytokines and nitric oxide, and tumor hypoxia have been implicated in these effects. We have previously shown that the activity of aminopeptidase A is increased in the abnormal vascular system of human and rat brain tumors. To study the mechanisms regulating the activities of peptidases in cerebral vasculature in brain tumors, we have developed a three-dimensional model of differentiated rat brain cells in aggregate cultures in which rat brain microvessels were incorporated. The secretion of interleukin-6 (IL-6) in the culture medium of aggregates was used as an indicator of inflammatory activation. Addition to these aggregates of C6 glioma cell medium (C6-CM) conditioned under hypoxic or normoxic conditions or serum mimicked tumor-dependent hypoxia or conditions of dysfunction of brain tumor vasculature. Hypoxic and normoxic C6-CM, but not serum, regulated peptidase activity in aggregates, and in particular it increased the activity of aminopeptidase A determined using histoenzymography. Serum, but not C6-CM, increased IL-6 production, but did not increase aminopeptidase A activity in aggregates. Thus soluble glioma-derived factors, but not serum-derived factors, induce dysfunctions of cerebral vasculature by directly regulating the activity of peptidases, not involving inflammatory activation. Tumor hypoxia is not necessary to modulate peptidase activity.

Pore-scale modeling of two-phase flow instabilities in porous media

Les problèmes d'écoulements multiphasiques en média poreux sont d'un grand intérêt pour de nombreuses applications scientifiques et techniques ; comme la séquestration de C02, l'extraction de pétrole et la dépollution des aquifères. La complexité intrinsèque des systèmes multiphasiques et l'hétérogénéité des formations géologiques sur des échelles multiples représentent un challenge majeur pour comprendre et modéliser les déplacements immiscibles dans les milieux poreux. Les descriptions à l'échelle supérieure basées sur la généralisation de l'équation de Darcy sont largement utilisées, mais ces méthodes sont sujettes à limitations pour les écoulements présentant de l'hystérèse. Les avancées récentes en terme de performances computationnelles et le développement de méthodes précises pour caractériser l'espace interstitiel ainsi que la distribution des phases ont favorisé l'utilisation de modèles qui permettent une résolution fine à l'échelle du pore. Ces modèles offrent un aperçu des caractéristiques de l'écoulement qui ne peuvent pas être facilement observées en laboratoire et peuvent être utilisé pour expliquer la différence entre les processus physiques et les modèles à l'échelle macroscopique existants. L'objet premier de la thèse se porte sur la simulation numérique directe : les équations de Navier-Stokes sont résolues dans l'espace interstitiel et la méthode du volume de fluide (VOF) est employée pour suivre l'évolution de l'interface. Dans VOF, la distribution des phases est décrite par une fonction fluide pour l'ensemble du domaine et des conditions aux bords particulières permettent la prise en compte des propriétés de mouillage du milieu poreux. Dans la première partie de la thèse, nous simulons le drainage dans une cellule Hele-Shaw 2D avec des obstacles cylindriques. Nous montrons que l'approche proposée est applicable même pour des ratios de densité et de viscosité très importants et permet de modéliser la transition entre déplacement stable et digitation visqueuse. Nous intéressons ensuite à l'interprétation de la pression capillaire à l'échelle macroscopique. Nous montrons que les techniques basées sur la moyenne spatiale de la pression présentent plusieurs limitations et sont imprécises en présence d'effets visqueux et de piégeage. Au contraire, une définition basée sur l'énergie permet de séparer les contributions capillaires des effets visqueux. La seconde partie de la thèse est consacrée à l'investigation des effets d'inertie associés aux reconfigurations irréversibles du ménisque causé par l'interface des instabilités. Comme prototype pour ces phénomènes, nous étudions d'abord la dynamique d'un ménisque dans un pore angulaire. Nous montrons que, dans un réseau de pores cubiques, les sauts et reconfigurations sont si fréquents que les effets d'inertie mènent à différentes configurations des fluides. A cause de la non-linéarité du problème, la distribution des fluides influence le travail des forces de pression, qui, à son tour, provoque une chute de pression dans la loi de Darcy. Cela suggère que ces phénomènes devraient être pris en compte lorsque que l'on décrit l'écoulement multiphasique en média poreux à l'échelle macroscopique. La dernière partie de la thèse s'attache à démontrer la validité de notre approche par une comparaison avec des expériences en laboratoire : un drainage instable dans un milieu poreux quasi 2D (une cellule Hele-Shaw avec des obstacles cylindriques). Plusieurs simulations sont tournées sous différentes conditions aux bords et en utilisant différents modèles (modèle intégré 2D et modèle 3D) afin de comparer certaines quantités macroscopiques avec les observations au laboratoire correspondantes. Malgré le challenge de modéliser des déplacements instables, où, par définition, de petites perturbations peuvent grandir sans fin, notre approche numérique apporte de résultats satisfaisants pour tous les cas étudiés. - Problems involving multiphase flow in porous media are of great interest in many scientific and engineering applications including Carbon Capture and Storage, oil recovery and groundwater remediation. The intrinsic complexity of multiphase systems and the multi scale heterogeneity of geological formations represent the major challenges to understand and model immiscible displacement in porous media. Upscaled descriptions based on generalization of Darcy's law are widely used, but they are subject to several limitations for flow that exhibit hysteric and history- dependent behaviors. Recent advances in high performance computing and the development of accurate methods to characterize pore space and phase distribution have fostered the use of models that allow sub-pore resolution. These models provide an insight on flow characteristics that cannot be easily achieved by laboratory experiments and can be used to explain the gap between physical processes and existing macro-scale models. We focus on direct numerical simulations: we solve the Navier-Stokes equations for mass and momentum conservation in the pore space and employ the Volume Of Fluid (VOF) method to track the evolution of the interface. In the VOF the distribution of the phases is described by a fluid function (whole-domain formulation) and special boundary conditions account for the wetting properties of the porous medium. In the first part of this thesis we simulate drainage in a 2-D Hele-Shaw cell filled with cylindrical obstacles. We show that the proposed approach can handle very large density and viscosity ratios and it is able to model the transition from stable displacement to viscous fingering. We then focus on the interpretation of the macroscopic capillary pressure showing that pressure average techniques are subject to several limitations and they are not accurate in presence of viscous effects and trapping. On the contrary an energy-based definition allows separating viscous and capillary contributions. In the second part of the thesis we investigate inertia effects associated with abrupt and irreversible reconfigurations of the menisci caused by interface instabilities. As a prototype of these phenomena we first consider the dynamics of a meniscus in an angular pore. We show that in a network of cubic pores, jumps and reconfigurations are so frequent that inertia effects lead to different fluid configurations. Due to the non-linearity of the problem, the distribution of the fluids influences the work done by pressure forces, which is in turn related to the pressure drop in Darcy's law. This suggests that these phenomena should be taken into account when upscaling multiphase flow in porous media. The last part of the thesis is devoted to proving the accuracy of the numerical approach by validation with experiments of unstable primary drainage in a quasi-2D porous medium (i.e., Hele-Shaw cell filled with cylindrical obstacles). We perform simulations under different boundary conditions and using different models (2-D integrated and full 3-D) and we compare several macroscopic quantities with the corresponding experiment. Despite the intrinsic challenges of modeling unstable displacement, where by definition small perturbations can grow without bounds, the numerical method gives satisfactory results for all the cases studied.

TP53 induced glycolysis and apoptosis regulator (TIGAR) knockdown results in radiosensitization of glioma cells

Background and purpose: The TP53 induced glycolysis and apoptosis regulator (TIGAR) functions to lower fructose-2,6-bisphosphate (Fru-2,6-P2) levels in cells, consequently decreasing glycolysis and leading to the scavenging of reactive oxygen species (ROS), which correlate with a higher resistance to cell death. The decrease in intracellular ROS levels in response to TIGAR may also play a role in the ability of p53 to protect from the accumulation of genomic lesions. Given these good prospects of TIGAR for metabolic regulation and p53-response modulation, we analyzed the effects of TIGAR knockdown in U87MG and T98G glioblastoma-derived cell lines. Methods/results: After TIGAR-knockdown in glioblastoma cell lines, different metabolic parameters were assayed, showing an increase in Fru-2,6-P2, lactate and ROS levels, with a concomitant decrease in reduced glutathione (GSH) levels. In addition, cell growth was inhibited without evidence of apoptotic or autophagic cell death. In contrast, a clear senescent phenotype was observed. We also found that TIGAR protein levels were increased shortly after irradiation. In addition, avoiding radiotherapy-triggered TIGAR induction by gene silencing resulted in the loss of capacity of glioblastoma cells to form colonies in culture and the delay of DNA repair mechanisms, based in c-H2AX foci, leading cells to undergo morphological changes compatible with a senescent phenotype. Thus, the results obtained raised the possibility to consider TIGAR as a therapeutic target to increase radiotherapy effects. Conclusion: TIGAR abrogation provides a novel adjunctive therapeutic strategy against glial tumors by increasing radiation-induced cell impairment, thus allowing the use of lower radiotherapeutic doses.

Modeling of allergen proteins found in sea food products

Shellfish are a source of food allergens, and their consumption is the cause of severe allergic reactions in humans. Tropomyosins, a family of muscle proteins, have been identified as the major allergens in shellfish and mollusks species. Nevertheless, few experimentally determined three-dimensional structures are available in the Protein Data Base (PDB). In this study, 3D models of several homologous of tropomyosins present in marine shellfish and mollusk species (Chaf 1, Met e1, Hom a1, Per v1, and Pen a1) were constructed, validated, and their immunoglobulin E binding epitopes were identified using bioinformatics tools. All protein models for these allergens consisted of long alpha-helices. Chaf 1, Met e1, and Hom a1 had six conserved regions with sequence similarities to known epitopes, whereas Per v1 and Pen a1 contained only one. Lipophilic potentials of identified epitopes revealed a high propensity of hydrophobic amino acids in the immunoglobulin E binding site. This information could be useful to design tropomyosin-specific immunotherapy for sea food allergies.

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing