Purification and characterization of three lowmolecular- weight acid phosphatases from peanut (Arachis hypogaea) seedlings

The maximum acid phosphatasic activity was detected in peanut seedlings the 5th day of germination. At least, three acid phosphatases were identified and purified by successive chromatography separations on DEAE-Sepharose CL-6B, CM Sepharose CL-6B, Sephacryl S-200 HR, and Phenyl- Sepharose HP to apparent homogeneity from developing five days old peanut seedlings. These enzymes designated acid phosphatase PI, PIIa and PIIb had native molecular weights of approximately 25.3, 22.4 and 24 kDa, respectively by gel permeation. SDS-PAGE of the purified acid phosphatase PI resolved two closely protein bands that migrated to approximately 14 and 12 kDa. Thus, this acid phosphatase likely functions as a heterodimer. Acid phosphatases PIIa and PIIb migrated as single band (each) with a similar molecular weight estimated to 21 kDa. The three enzymes had a similar optima pH (5.0) and temperature (55°C), and appeared to be stable in the presence of non-ionic detergents such as Triton X-100, Nonidet P 40 as well as Na+ and K+. Substrate specificity indicated that the three acid phosphatases hydrolyzed a broad range of phosphorylated substrates. However, natural substrates such as ADP and ATP were the compounds with highest rate of hydrolysis for acid phosphatase PI, while acid phosphatase PIIa exhibited phytasic activity. These results indicate that each purified acid phosphatase from peanut seedlings played a peculiar role during germination.Keywords: acid phosphatase; seedling; peanut; arachis hypogaea; germination; low-molecular-weigh

Classification of spectra and search for biomarkers in prostate tumours from proton nuclear magnetic resonance spectroscopy

Le cancer de la prostate est le cancer le plus fréquent chez l'homme de plus de 50 ans. Actuellement, les méthodes de dépistage manquent soit de sensibilité, soit de spécificité ou sont désagréables pour le patient. La spectroscopie de résonance magnétique permet l'étude du métabolisme in vivo. L'utilisation d'appareil haut champ (≥3T) permet dorénavant d'analyser la prostate sans antenne endorectale. L’objectif de cette thèse est de créer un système automatique de dépistage de ce cancer en mettant au point une méthode de classification automatique permettant de traiter les données obtenues grâce à la spectroscopie de résonance magnétique. La spectroscopie de résonance magnétique est un phénomène complexe, très sensible aux conditions d'acquisition. Nous avons donc étudié comment améliorer l’acquisition de ce signal. Cependant, même avec une acquisition de très bonne qualité, le signal de résonance magnétique doit subir quelques traitements pour être analysable automatiquement par une méthode de classification. La suite du travail a donc consisté à rechercher les traitements à appliquer pour optimiser les spectres en vue d'une classification. Nous avons alors recherché la méthode de classification optimale pour ce problème. Cet ensemble d’étapes (acquisition du signal, traitement des spectres puis classification des données obtenues) nous permet de mettre en évidence la présence de tumeurs de la prostate avec un taux d'erreur global de moins de 12%. Dans un second temps, nous avons cherché de nouveaux biomarqueurs dans les spectres. Ces biomarqueurs pouvaient être un métabolite précis ou une plage de fréquence correspondant à plusieurs métabolites. Nous n'avons pas trouvé d'attributs plus significatifs que la choline ou le citrate, cependant quelques bandes de fréquence semblent participer à l'amélioration des taux d'erreurs. Enfin, nous avons élargi notre champ d’investigation en tentant d’appliquer ces techniques chez le rat. Des contraintes liées à l'acquisition ne nous ont pas permis d'obtenir suffisamment de spectres dans le cas pré-clinique. Nous avons cependant pu valider la faisabilité de la SRM chez le rongeur et sa pertinence dans le cerveau. La technique doit cependant être améliorée pour pouvoir être validée dans le cas du cancer de la prostate chez le rat.Prostate cancer is the most common cancer in men over 50 years. Current detection methods either lack sensitivity or specificity or are unpleasant for the patient. Magnetic resonance spectroscopy allows the study of metabolism in vivo. The use of a high field machine (≥3T) has allowed us to dispense with the use of an endorectal coil, which is particularly uncomfortable for the patient. The objective of this thesis is to create an automatic method to detect cancer by processing data obtained through magnetic resonance spectroscopy MRS is a complex phenomenon, very sensitive to acquisition conditions. Firstly, we have studied how to improve and optimise signal acquisition. However, even with a very good quality signal, it must still undergo further post-processing to be analysed automatically by a classification method. Further work was therefore needed to investigate which postprocessing steps were required in order to optimize the spectra for classification. We then investigated the optimal classification method for this problem. A particular set of steps (signal acquisition, processing and spectral classification data) allows us to highlight the presence of prostate tumors with an overall error rate of less than 12%. In a second step, we searched for new biomarkers within the spectra. These biomarkers could be a metabolite or a specific frequency range corresponding to several metabolites. We did not find any additional significant attributes other than choline and citrate, however, some frequency bands seem to participate in improving the error rate. Finally, we expanded our investigation by attempting to apply these techniques to the rat. Technical constraints related to acquisition did not allow us to obtain a sufficient number of spectra in the pre-clinical cases. Nonetheless, we have validated the feasibility of MRS in rodents and its relevance in the brain. The technique, however, must be improved in order to be validated in the case of prostate cancer in rats

Classification de spectres et recherche de biomarqueurs en spectroscopie par résonance magnétique nucléaire du proton dans les tumeurs prostatiques

Prostate cancer is the most common cancer in men over 50 years. Current detection methods either lack sensitivity or specificity or are unpleasant for the patient. Magnetic resonance spectroscopy allows the study of metabolism in vivo. The use of a high field machine (≥3T) has allowed us to dispense with the use of an endorectal coil, which is particularly uncomfortable for the patient. The objective of this thesis is to create an automatic method to detect cancer by processing data obtained through magnetic resonance spectroscopy MRS is a complex phenomenon, very sensitive to acquisition conditions. Firstly, we have studied how to improve and optimise signal acquisition. However, even with a very good quality signal, it must still undergo further post-processing to be analysed automatically by a classification method. Further work was therefore needed to investigate which postprocessing steps were required in order to optimize the spectra for classification. We then investigated the optimal classification method for this problem. A particular set of steps (signal acquisition, processing and spectral classification data) allows us to highlight the presence of prostate tumors with an overall error rate of less than 12%. In a second step, we searched for new biomarkers within the spectra. These biomarkers could be a metabolite or a specific frequency range corresponding to several metabolites. We did not find any additional significant attributes other than choline and citrate, however, some frequency bands seem to participate in improving the error rate. Finally, we expanded our investigation by attempting to apply these techniques to the rat. Technical constraints related to acquisition did not allow us to obtain a sufficient number of spectra in the pre-clinical cases. Nonetheless, we have validated the feasibility of MRS in rodents and its relevance in the brain. The technique, however, must be improved in order to be validated in the case of prostate cancer in rats.Le cancer de la prostate est le cancer le plus fréquent chez l'homme de plus de 50 ans. Actuellement, les méthodes de dépistage manquent soit de sensibilité, soit de spécificité ou sont désagréables pour le patient. La spectroscopie de résonance magnétique permet l'étude du métabolisme in vivo. L'utilisation d'appareil haut champ (≥3T) permet dorénavant d'analyser la prostate sans antenne endorectale. L’objectif de cette thèse est de créer un système automatique de dépistage de ce cancer en mettant au point une méthode de classification automatique permettant de traiter les données obtenues grâce à la spectroscopie de résonance magnétique. La spectroscopie de résonance magnétique est un phénomène complexe, très sensible aux conditions d'acquisition. Nous avons donc étudié comment améliorer l’acquisition de ce signal. Cependant, même avec une acquisition de très bonne qualité, le signal de résonance magnétique doit subir quelques traitements pour être analysable automatiquement par une méthode de classification. La suite du travail a donc consisté à rechercher les traitements à appliquer pour optimiser les spectres en vue d'une classification. Nous avons alors recherché la méthode de classification optimale pour ce problème. Cet ensemble d’étapes (acquisition du signal, traitement des spectres puis classification des données obtenues) nous permet de mettre en évidence la présence de tumeurs de la prostate avec un taux d'erreur global de moins de 12%. Dans un second temps, nous avons cherché de nouveaux biomarqueurs dans les spectres. Ces biomarqueurs pouvaient être un métabolite précis ou une plage de fréquence correspondant à plusieurs métabolites. Nous n'avons pas trouvé d'attributs plus significatifs que la choline ou le citrate, cependant quelques bandes de fréquence semblent participer à l'amélioration des taux d'erreurs. Enfin, nous avons élargi notre champ d’investigation en tentant d’appliquer ces techniques chez le rat. Des contraintes liées à l'acquisition ne nous ont pas permis d'obtenir suffisamment de spectres dans le cas pré-clinique. Nous avons cependant pu valider la faisabilité de la SRM chez le rongeur et sa pertinence dans le cerveau. La technique doit cependant être améliorée pour pouvoir être validée dans le cas du cancer de la prostate chez le rat

Classification de spectres et recherche de biomarqueurs en spectroscopie par résonance magnétique nucléaire du proton dans les tumeurs prostatiques

Le cancer de la prostate est le cancer le plus fréquent chez l'homme de plus de 50 ans. Actuellement, les méthodes de dépistage manquent soit de sensibilité, soit de spécificité ou sont désagréables pour le patient. La spectroscopie de résonance magnétique permet l'étude du métabolisme in vivo. L'utilisation d'appareil haut champ (>=3T) permet dorénavant d'analyser la prostate sans antenne endorectale. L objectif de cette thèse est de créer un système automatique de dépistage de ce cancer en mettant au point une méthode de classification automatique permettant de traiter les données obtenues grâce à la spectroscopie de résonance magnétique. La spectroscopie de résonance magnétique est un phénomène complexe, très sensible aux conditions d'acquisition. Nous avons donc étudié comment améliorer l acquisition de ce signal. Cependant, même avec une acquisition de très bonne qualité, le signal de résonance magnétique doit subir quelques traitements pour être analysable automatiquement par une méthode de classification. La suite du travail a donc consisté à rechercher les traitements à appliquer pour optimiser les spectres en vue d'une classification. Nous avons alors recherché la méthode de classification optimale pour ce problème. Cet ensemble d étapes (acquisition du signal, traitement des spectres puis classification des données obtenues) nous permet de mettre en évidence la présence de tumeurs de la prostate avec un taux d'erreur global de moins de 12%. Dans un second temps, nous avons cherché de nouveaux biomarqueurs dans les spectres. Ces biomarqueurs pouvaient être un métabolite précis ou une plage de fréquence correspondant à plusieurs métabolites. Nous n'avons pas trouvé d'attributs plus significatifs que la choline ou le citrate, cependant quelques bandes de fréquence semblent participer à l'amélioration des taux d'erreurs. Enfin, nous avons élargi notre champ d investigation en tentant d appliquer ces techniques chez le rat. Des contraintes liées à l'acquisition ne nous ont pas permis d'obtenir suffisamment de spectres dans le cas pré-clinique. Nous avons cependant pu valider la faisabilité de la SRM chez le rongeur et sa pertinence dans le cerveau. La technique doit cependant être améliorée pour pouvoir être validée dans le cas du cancer de la prostate chez le rat.Prostate cancer is the most common cancer in men over 50 years. Current detection methods either lack sensitivity or specificity or are unpleasant for the patient. Magnetic resonance spectroscopy allows the study of metabolism in vivo. The use of a high field machine (>=3T) has allowed us to dispense with the use of an endorectal coil, which is particularly uncomfortable for the patient. The objective of this thesis is to create an automatic method to detect cancer by processing data obtained through magnetic resonance spectroscopy MRS is a complex phenomenon, very sensitive to acquisition conditions. Firstly, we have studied how to improve and optimise signal acquisition. However, even with a very good quality signal, it must still undergo further post-processing to be analysed automatically by a classification method. Further work was therefore needed to investigate which postprocessing steps were required in order to optimize the spectra for classification. We then investigated the optimal classification method for this problem. A particular set of steps (signal acquisition, processing and spectral classification data) allows us to highlight the presence of prostate tumors with an overall error rate of less than 12%. In a second step, we searched for new biomarkers within the spectra. These biomarkers could be a metabolite or a specific frequency range corresponding to several metabolites. We did not find any additional significant attributes other than choline and citrate, however, some frequency bands seem to participate in improving the error rate. Finally, we expanded our investigation by attempting to apply these techniques to the rat. Technical constraints related to acquisition did not allow us to obtain a sufficient number of spectra in the pre-clinical cases. Nonetheless, we have validated the feasibility of MRS in rodents and its relevance in the brain. The technique, however, must be improved in order to be validated in the case of prostate cancer in rats.DIJON-BU Doc.électronique (212319901) / SudocSudocFranceF

Classification of prostate magnetic resonance spectra using Support Vector Machine

International audienceProstate cancer is the most common cancer in men over 50 years of age and it has been shown that nuclear magnetic resonance spectra are sensitive enough to distinguish normal and cancer tissues. In this paper, we propose a classification technique of spectra from magnetic resonance spectroscopy. We studied automatic classification with and without quantification of metabolite signals. The dataset is composed of 22 patient datasets with a biopsy-proven cancer, from which we extracted 2464 spectra from the whole prostate and of which 1062 were localised in the peripheral zone. The spectra were manually classed into 3 different categories by a spectroscopist with 4 years experience in clinical spectroscopy of prostate cancer: undetermined, healthy and pathologic. We used different preprocessing methods (module, phase correction only, phase correction and baseline correction) as input for Support Vector Machine and for Multilayer Perceptron, and we compared the results with those from the expert. If we class only healthy and pathologic spectra we reach a total error rate of 4.51%. However, if we class all spectra (undetermined, healthy and pathologic) the total error rate rises to 11.49%. We have shown in this paper that the best results are obtained using the pre-processed spectra without quantification as input for the classifiers and we confirm that Support Vector Machine are more efficient than Multilayer Perceptron in processing high dimensional data

Effect of some chemicals on the accuracy of protein estimation by the Lowry method

The influence of 57 chemicals (mineral and organic acids, organic solvents, phenolic compounds, mineral and organic salts) on the efficiency of protein determination by the Lowry method was assessed. The study revealed the Lowry method to be unreliable in an acidic and increasing vitamin C-rich medium. For organic solvents, it is advisable to evaporate these compounds when they are used to extract proteins, prior to proteins measurement the Lowry method. The influence of phenolic compounds on the Lowry method was found to be governed by the structure of their molecules (presence of double bounds, number of OH groups, presence of methyl groups, etc.), while ammonium sulfate, which is a major agent used in the enzyme purification process, was found to result in an overestimation of the protein content. Therefore, its use for enzyme purification should be done with caution. Keywords: Lowry method, protein determination, method validation, chemical interference Biokemistri Vol. 17(2) 2005: 73-8

Purification and characterization of three lowmolecular-weight acid phosphatases from peanut (Arachis hypogaea) seedlings

The maximum acid phosphatasic activity was detected in peanut seedlings the 5th day of germination. At least, three acid phosphatases were identified and purified by successive chromatography separations on DEAE-Sepharose CL-6B, CM-Sepharose CL-6B, Sephacryl S-200 HR, and Phenyl-Sepharose HP to apparent homogeneity from developing five days old peanut seedlings. These enzymes designated acid phosphatase PI, PIIa and PIIb had native molecular weights of approximately 25.3, 22.4 and 24 kDa, respectively by gel permeation. SDS-PAGE of the purified acid phosphatase PI resolved two closely protein bands that migrated to approximately 14 and 12 kDa. Thus, this acid phosphatase likely functions as a heterodimer. Acid phosphatases PIIa and PIIb migrated as single band (each) with a similar molecular weight estimated to 21 kDa. The three enzymes had a similar optima pH (5.0) and temperature (55°C), and appeared to be stable in the presence of non-ionic detergents such as Triton X-100, Nonidet P 40 as well as Na+ and K+. Substrate specificity indicated that the three acid phosphatases hydrolyzed a broad range of phosphorylated substrates. However, natural substrates such as ADP and ATP were the compounds with highest rate of hydrolysis for acid phosphatase PI, while acid phosphatase PIIa exhibited phytasic activity. These results indicate that each purified acid phosphatase from peanut seedlings played a peculiar role during germination

MR-based biomarkers in the diagnosis and the evaluation of the therapeutic response to radiotherapy (ETRR) in prostatic carcinoma (PCa): Implementation of clinical and experimental approaches

International audienc

EARLY CHOLINE LEVELS FROM 3-TESLA MR SPECTROSCOPY AFTER EXCLUSIVE RADIATION THERAPY IN PATIENTS WITH CLINICALLY LOCALIZED PROSTATE CANCER ARE PREDICTIVE OF PLASMATIC LEVELS OF PSA AT 1 YEAR

International audienceAbstract: Purpose: To investigate the time course response of prostate metabolism to irradiation using magnetic resonance spectroscopy (MRS) at 3-month intervals and its impact on biochemical control. Methods and Materials: Between January 2008 and April 2010, 24 patients with localized prostate cancer were prospectively enrolled in the Evaluation of the Response to Irradiation with MR Spectroscopy (ERIS) trial. All the patients had been treated with intensity-modulated radiation therapy with or without long-term adjuvant hormonal therapy (LTHT) and underwent 3-T MRS and prostate-specific antigen (PSA) assays at baseline and every 3 months thereafter up to 12 months. Results: After radiation, the mean normalized citrate level (citrate/water) decreased significantly over time, both in the peripheral zone (PZ) (p = 0.0034) and in the entire prostate (p = 0.0008), whereas no significant change was observed in mean normalized choline levels (choline/water) in the PZ (p = 0.84) and in the entire prostate (p = 0.95). At 6 months after radiation, the mean choline level was significantly lower in the PZ for patients with a PSAvalue of <= 0.5 ng/mL at 12 months (4.9 +/- 1.7 vs. 7.1 +/- 1.5, p = 0.0378). Similar results were observed at 12 months in the PZ (6.2 +/- 2.3 vs. 11.4 +/- 4.1, p = 0.0117 for choline level and 3.4 +/- 0.7 vs. 16.1 +/- 6.1, p = 0.0054 for citrate level) and also in the entire prostate (6.2 +/- 1.9 vs. 10.4 +/- 3.2, p = 0.014 for choline level and 3.0 +/- 0.8 vs. 13.3 +/- 4.7, p = 0.0054 for citrate level). For patients receiving LTHT, there was no correlation between choline or citrate levels and PSAvalue, either at baseline or at follow-up. Conclusions: Low normalized choline in the PZ, 6 months after radiation, predicts which patients attained a PSA <= 0.5 ng/mL at 1 year. Further analyses with longer follow-up times are warranted to determine whether or not these new biomarkers can conclusively predict the early radiation response and the clinical outcome for patients with or without LTHT

Temozolomide and radiotherapy antitumor efficacy evaluation with magnetic resonance imaging and proton magnetic resonance spectroscopy in human glioma models in nude rats

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