Analyse statistique de données radiomiques et métabolomiques : prédiction des lésions mammaires triple-négatives

International audienceLa caractérisation de l’hétérogénéité tumorale à partir des images médicales (appeléeaussi radiomique) et de l’extraction de données omiques est un enjeu majeur en cancérologie,notamment dans la mise en place de la médecine de précision. Or actuellement, le lien entre lesvariables radiomiques (VR) et les caractéristiques biologiques des lésions est encore mal connu.L’objectif de ce travail est d’étudier la corrélation entre les VR et les variables métabolomiques (VM)dans le cancer du sein, et d’analyser leur capacité à prédire le sous-type immunohistochimique deslésions

Differential binding of poly(ADP-Ribose) polymerase-1 and JunD/Fra2 accounts for RANKL-induced Tcirg1 gene expression during osteoclastogenesis.

We studied Tcirg1 gene expression on RANKL-induced osteoclastic differentiation of the mouse model RAW264.7 cells. We identified a mechanism involving PARP-1 inhibition release and JunD/Fra-2 binding, which is responsible for Tcirg1 gene upregulation. INTRODUCTION: The Tcirg1 gene encodes the a3 isoform of the V-ATPase a subunit, which plays a critical role in the resorption activity of the osteoclast. Using serial deletion constructs of the Tcirg1 gene promoter, we performed a transcriptional study to identify factor(s) involved in the regulation of the RANKL-induced gene expression. MATERIALS AND METHODS: The promoter activity of serial-deletion fragments of the Tcirg1 gene promoter was monitored throughout the RAW264.7 cells differentiation process. We next performed sequence analysis, EMSA, UV cross-linking, qPCR, and gel supershift experiments to identify the factor(s) interacting with the promoter. RESULTS: A deletion of the -1297-1244 region led to the disappearance of the RANKL-induced promoter activity. EMSA experiments showed the binding of two factors that undergo differential binding on RANKL treatment. Supershift experiments led us to identify the dimer JunD/Fra-2 as the binding activity associated with the -1297/-1268 Tcirg1 gene promoter sequence in response to RANKL. Moreover, we observed poly(ADP-ribose) polymerase-1 (PARP-1) binding to an adjacent site (-1270/-1256), and this interaction was disrupted after RANKL treatment. CONCLUSIONS: We provide data that identify junD proto-oncogene (JunD) and Fos-related antigen 2 (Fra-2) as the activator protein-1 (AP-1) factors responsible for the RANKL-induced upregulation of the mouse Tcirg1 gene expression. Moreover, we identified another binding site for PARP-1 that might account for the repression of Tcirg1 gene expression in pre-osteoclastic cells

Effect of cadmium in the clam Ruditapes decussatus assessed by proteomic analysis

Cadmium, an environmental stressor due to its toxicity, persistence and accumulation in biota, is widespread in the aquatic environment. Cadmium accumulation kinetics have revealed that Ruditapes decussatus has a high affinity to this metal. Proteomics is an effective tool to evaluate the toxic effects of contaminants. The aim of this study was to investigate the Cd effects in the gill and digestive gland of the sentinel species R. decussatus. Protein expression profiles (PEPs) in the clam tissues exposed to Cd (40 microg l(-1), 21 days) were compared to unexposed ones. Cd induces major changes in tissue-specific protein expression profiles in gill and digestive gland. This tissue dependent response results mainly from differences in Cd accumulation, protein inhibition and/or autophagy. An overall decrease of protein spots was detected in both treated tissues, being higher in gill. Some of the spots more drastically altered after pollutants exposure were excised and nine were identified by micro liquid chromatography tandem mass spectrometry (LC-MS/MS). Proteins identified by homology search in databases included: three proteins (8-fold) up-regulated, one down-regulated, four suppressed and one induced. Cd induces major changes in proteins involved in cytoskeletal structure maintenance (muscle-type actin, adductor muscle actin and beta-tubulin), cell maintenance (Rab GDP) and metabolism (ALDH and MCAD, both identified by de novo sequencing) suggesting potential energetic change. They provide a valuable knowledge of Cd effects at biochemical and molecular levels in the gill and digestive gland of R. decussatus.Portuguese Foundation for Science and Technologyinfo:eu-repo/semantics/publishedVersio

Osmotically induced synthesis of the dipeptide N-acetylglutaminylglutamine amide is mediated by a new pathway conserved among bacteria

The dipeptide N-acetylglutaminylglutamine amide (NAGGN) was discovered in the bacterium Sinorhizobium meliloti grown at high osmolarity, and subsequently shown to be synthesized and accumulated by a few osmotically challenged bacteria. However, its biosynthetic pathway remained unknown. Recently, two genes, which putatively encode a glutamine amidotransferase and an acetyltransferase and are up-regulated by osmotic stress, were identified in Pseudomonas aeruginosa. In this work, a locus carrying the orthologous genes in S. meliloti, asnO and ngg, was identified, and the genetic and molecular characterization of the NAGGN biosynthetic pathway is reported. By using NMR experiments, it was found that strains inactivated in asnO and ngg were unable to produce the dipeptide. Such inability has a deleterious effect on S. meliloti growth at high osmolarity, demonstrating the key role of NAGGN biosynthesis in cell osmoprotection. β-Glucuronidase activity from transcriptional fusion revealed strong induction of asnO expression in cells grown in increased NaCl concentration, in good agreement with the NAGGN accumulation. The asnO–ngg cluster encodes a unique enzymatic machinery mediating nonribosomal peptide synthesis. This pathway first involves Ngg, a bifunctional enzyme that catalyzes the formation of the intermediate N-acetylglutaminylglutamine, and second AsnO, required for subsequent addition of an amide group and the conversion of N-acetylglutaminylglutamine into NAGGN. Interestingly, a strong conservation of the asnO–ngg cluster is observed in a large number of bacteria with different lifestyles, such as marine, symbiotic, and pathogenic bacteria, highlighting the ecological importance of NAGGN synthesis capability in osmoprotection and also potentially in bacteria host–cell interactions

Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data.

Purpose: Meta-analyses failed to accurately identify patients with non-metastatic breast cancer who are likely to benefit from chemotherapy, and metabolomics could provide new answers. In our previous published work, patients were clustered using five different unsupervised machine learning (ML) methods resulting in the identification of three clusters with distinct clinical and simulated survival data. The objective of this study was to evaluate the survival outcomes, with extended follow-up, using the same 5 different methods of unsupervised machine learning. Experimental design: Forty-nine patients, diagnosed between 2013 and 2016, with non-metastatic BC were included retrospectively. Median follow-up was extended to 85.8 months. 449 metabolites were extracted from tumor resection samples by combined Liquid chromatography-mass spectrometry (LC–MS). Survival analyses were reported grouping together Cluster 1 and 2 versus cluster 3. Bootstrap optimization was applied. Results: PCA k-means, K-sparse and Spectral clustering were the most effective methods to predict 2-year progression-free survival with bootstrap optimization (PFSb); as bootstrap example, with PCA k-means method, PFSb were 94% for cluster 1&2 versus 82% for cluster 3 (p = 0.01). PCA k-means method performed best, with higher reproducibility (mean HR=2 (95%CI [1.4–2.7]); probability of p ≤ 0.05 85%). Cancer-specific survival (CSS) and overall survival (OS) analyses highlighted a discrepancy between the 5 ML unsupervised methods. Conclusion: Our study is a proof-of-principle that it is possible to use unsupervised ML methods on metabolomic data to predict PFS survival outcomes, with the best performance for PCA k-means. A larger population study is needed to draw conclusions from CSS and OS analyses

Ingested Ketone Ester Leads to a Rapid Rise of Acetyl-CoA and Competes with Glucose Metabolism in the Brain of Non-Fasted Mice

International audienceThe role of ketone bodies in the cerebral energy homeostasis of neurological diseases has begun to attract recent attention particularly in acute neurological diseases. In ketogenic therapies, ketosis is achieved by either a ketogenic diet or by the administration of exogenous ketone bodies. The oral ingestion of the ketone ester (KE), (R)-3-hydroxybutyl (R)-3-hydroxybutyrate, is a new method to generate rapid and significant ketosis (i.e., above 6 mmol/L) in humans. KE is hydrolyzed into β-hydroxybutyrate (βHB) and its precursor 1,3-butanediol. Here, we investigate the effect of oral KE administration (3 mg KE/g of body weight) on brain metabolism of non-fasted mice using liquid chromatography in tandem with mass spectrometry. Ketosis (Cmax = 6.83 ± 0.19 mmol/L) was obtained at Tmax = 30 min after oral KE-gavage. We found that βHB uptake into the brain strongly correlated with the plasma βHB concentration and was preferentially distributed in the neocortex. We showed for the first time that oral KE led to an increase of acetyl-CoA and citric cycle intermediates in the brain of non-fasted mice. Furthermore, we found that the increased level of acetyl-CoA inhibited glycolysis by a feedback mechanism and thus competed with glucose under physiological conditions. The brain pharmacodynamics of this oral KE strongly suggest that this agent should be considered for acute neurological diseases

Proteomic Analysis of Iodinated Contrast Agent-Induced Perturbation of Thyroid Iodide Uptake

International audience(1) Background: We recently showed that iodinated contrast media (ICM) reduced thyroid uptake of iodide independently of free iodide through a mechanism different from that of NaI and involving a dramatic and long-lasting decrease in Na/I symporter expression. The present study aimed at comparing the response of the thyroid to ICM and NaI using a quantitative proteomic approach. (2) Methods: Scintiscans were performed on ICM-treated patients. Micro Single-Photon Emission Computed Tomography (microSPECT/CT) imaging was used to assess thyroid uptakes in ICM- or NaI-treated mice and their response to recombinant human thyroid-stimulating hormone. Total thyroid iodide content and proteome was determined in control, NaI-, or ICM-treated animals. (3) Results: The inhibitory effect of ICM in patients was selectively observed on thyroids but not on salivary glands for up to two months after a systemic administration. An elevated level of iodide was observed in thyroids from NaI-treated mice but not in those from ICM animals. Exposure of the thyroid to NaI modulates 15 cellular pathways, most of which are also affected by ICM treatment (including the elF4 and P706SK cell signaling pathway and INSR identified as an upstream activator in both treatments). In addition, ICM modulates 16 distinct pathways and failed to affect thyroid iodide content. Finally, administration of ICM reduces thyroid-stimulating hormone (TSH) receptor expression which results in a loss of TSH-induced iodide uptake by the thyroid. (4) Conclusions: Common intracellular mechanisms are involved in the ICM- and NaI-induced reduction of iodide uptake. However, ICM fails to affect thyroid iodide content which suggests that the modulation of these common pathways is triggered by separate effectors. ICM also modulates numerous distinct pathways which may account for its long-lasting effect on thyroid uptake. These observations may have implications in the management of patients affected by differentiated thyroid carcinomas who have been exposed to ICM. They also provide the basis for the utilization of ICM-based compounds in radioprotection of the thyroid

Identification des cancers mammaires triple-négatifs : analyse statistique de variables radiomiques issues des images TEP et de variables métabolomiques

International audienceObjectif : La caractérisation de l'hétérogénéité tumorale à partir des images TEP connaît un intérêt croissant. Or le lien entre les variables radiomiques (VR) et les caractéristiques biologiques des lésions est encore mal connu. Notre objectif est d'étudier la corrélation entre les VR et les variables métabolomiques (VM) dans le cancer du sein, et d'analyser leur capacité à prédire le sous-type immunohistochimique des lésions. Matériels & Méthodes : 26 patientes ayant un cancer mammaire ont bénéficié d'un examen TEP au 18F-FDG pré-traitement (Biograph TEP/TDM, Siemens). Pour chaque lésion tumorale primitive, 43 VR ont été calculées (logiciel LIFEx). A partir de la pièce opératoire, un spectromètre de masse a été utilisé pour mesurer l'expression de 1500 VM référencées dans la base Human metabolome. Les coefficients de corrélation de Spearman (R) entre chaque VR et chaque VM ont été analysés. Nous avons également étudié séparément la capacité des VR et VM pour identifier les lésions mammaires triple-négatives (TN), en comparant les performances de 5 méthodes statistiques avec celles de SUVmax et du volume métabolique. Cette procédure a été répétée 25 fois avec une sélection aléatoire de 16 patientes pour l'apprentissage et de 10 patientes pour la validation. Les performances de chaque méthode pour l'identification des lésions TN ont été mesurées en utilisant l'index de Youden (sensibilité+spécificité-1). Résultats : Dans notre étude, 7 lésions sont TN. Le coefficient de corrélation moyen, en valeur absolue, entre les VR et VM est égal à 0,20±0,14 (intervalle : [0-0,81], 3% avec |R|≥0,50). Pour l'identification des lésions TN à partir des index conventionnels en TEP, SUVmax conduit aux meilleures performances (Youden=0,29±0,34). En utilisant les différentes méthodes statistiques, l'index de Youden moyen varie entre 0,18 et 0,34 à partir des VM et entre-0,12 et 0,50 pour les VR. Les meilleures performances pour la distinction des lésions TN sont obtenues à partir des VR pour l'analyse discriminante en grande dimension (HDDA) avec Youden=0,50±0,35. Conclusion : Dans le cancer du sein, nous avons montré une corrélation faible à modérée entre les VR issus des images TEP et les VM. Le recrutement de patientes supplémentaires est en cours pour confirmer ces résultats. L'analyse conjointe des VR et VM est à l'étude afin de bénéficier de la complémentarité des informations radiomiques et métabolomiques et ainsi d'améliorer les performances de classification

Statistical analysis of PET radiomic features and metabolomic data: prediction of triple-negative breast cancer

International audienceObjective: The characterization of tumor heterogeneity using radiomic features from PET images is gaining interest in the context of precision medicine. Yet, the relationship between radiomic features and biological characteristics of lesions needs to be clarified. To this end, we studied the relationship between PET radiomic features and metabolomic data in breast cancer and we investigated their ability to predict the immunohistochemical tumor subtypes. Methods: 26 patients with a breast cancer underwent a pre-treatment FDG PET/CT scan on a Biograph PET/CT scanner (Siemens). In each patient, the primary lesion was segmented using a threshold equal to 40% of SUVmax. In each volume of interest, we computed 43 radiomic features using the LIFEx software (absolute discretization: 64 gray-levels between 0 and 20 SUV units) including SUVmax, SUVmean, Metabolic Volume (MV), TLG, 7 histogram indices, 2 shape features and 30 textural indices. Based on the resected tissues, we used a mass spectrometer to analyze the expression of 1500 metabolites listed in the Human metabolome database. Spearman correlation coefficients (R) between each radiomic feature and each metabolite were studied. We investigated the ability of radiomic features and metabolomic data to identify triple-negative breast cancer (TNBC). We compared the performance of logistic regression for SUVmax, MV and SUVmax+MV with those of 5 statistical methods for radiomic features and metabolomic data separately: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), orthogonal partial least squares (OPLS), high dimensional discriminant analysis (HDDA, [1]) and globally sparse HDDA (gsHDDA, [2]). This procedure was repeated 25 times with 16 randomly selected patients for the training set and 10 patients for the validation set. The accuracy of the TNBC identification was measured on the validation set using the Youden index (sensitivity + specificity-1). Results: In our cohort, 7 women had TNBC. The mean correlation coefficient, in absolute value, between radiomic and metabolomic features was equal to 0.20±0.14 (range: [0-0.81]). Only 3% of pairwise correlations were higher than 0.50 (in absolute value). Twenty out of 43 radiomic features were moderately correlated with at least 50 metabolites (|R|≥0.50) including SUVs and MV. With the logistic regression, the best performance for TNBC identification was obtained for SUVmax with a Youden index equal to 0.29±0.34. Using different statistical methods, the Youden indices ranged between 0.18 and 0.34 based on metabolomic data and between-0.12 and 0.50 from radiomic features. The best performance for TNBC identification was obtained for HDDA (Youden = 0.50±0.35) and gsHDDA (Youden = 0.49±0.34) based on radiomic features and these results outperformed those obtained with SUVmax (p-values of Wilcoxon test = 0.03). One of the advantages of gsHDDA compared to other methods is that the model was built based on selected features. The study of these features showed a moderate correlation (|R|=0.21±0.15, range: [0-0.71]) between 10 key radiomic features and 601 key metabolites (features selected by gsHDDA in at least 50% of the tests), suggesting that the combination between these two sources of information could improve the identification of TNBC. Conclusion: In breast lesions, we demonstrated a poor to moderate correlation between PET radiomic features and metabolomic data. However, the two types of data allow the identification of TNBC with similar performances. Additional breast cancer patients are currently being included to validate these results on a large cohort and the joint analysis of radiomic features and metabolomic data will be investigated in order to take advantage of the complementarity of data and enhance classification performance