Spatio-temporal metabolic rewiring in the brain of TgF344-AD rat model of Alzheimer's disease.

Brain damage associated with Alzheimer's disease (AD) occurs even decades before the symptomatic onset, raising the need to investigate its progression from prodromal stages. In this context, animal models that progressively display AD pathological hallmarks (e.g. TgF344-AD) become crucial. Translational technologies, such as magnetic resonance spectroscopy (MRS), enable the longitudinal metabolic characterization of this disease. However, an integrative approach is required to unravel the complex metabolic changes underlying AD progression, from early to advanced stages. TgF344-AD and wild-type (WT) rats were studied in vivo on a 7 Tesla MRI scanner, for longitudinal quantitative assessment of brain metabolic profile changes using MRS. Disease progression was investigated at 4 time points, from 9 to 18 months of age, and in 4 regions: cortex, hippocampus, striatum, and thalamus. Compared to WT, TgF344-AD rats replicated common findings in AD patients, including decreased N-acetylaspartate in the cortex, hippocampus and thalamus, and decreased glutamate in the thalamus and striatum. Different longitudinal evolution of metabolic concentration was observed between TgF344-AD and WT groups. Namely, age-dependent trajectories differed between groups for creatine in the cortex and thalamus and for taurine in cortex, with significant decreases in Tg344-AD animals; whereas myo-inositol in the thalamus and striatum showed greater increase along time in the WT group. Additional analysis revealed divergent intra- and inter-regional metabolic coupling in each group. Thus, in cortex, strong couplings of N-acetylaspartate and creatine with myo-inositol in WT, but with taurine in TgF344-AD rats were observed; whereas in the hippocampus, myo-inositol, taurine and choline compounds levels were highly correlated in WT but not in TgF344-AD animals. Furthermore, specific cortex-hippocampus-striatum metabolic crosstalks were found for taurine levels in the WT group but for myo-inositol levels in the TgF344-AD rats. With a systems biology perspective of metabolic changes in AD pathology, our results shed light into the complex spatio-temporal metabolic rewiring in this disease, reported here for the first time. Age- and tissue-dependent imbalances between myo-inositol, taurine and other metabolites, such as creatine, unveil their role in disease progression, while pointing to the inadequacy of the latter as an internal reference for quantification.

The Interplay between Pathophysiological Pathways in Early-Onset Severe Preeclampsia Unveiled by Metabolomics.

INTRODUCTION: Preeclampsia is a multi-system disorder unique to pregnancy responsible for a great part of maternal and perinatal morbidity and mortality. The precise pathogenesis of this complex disorder is still unrevealed. METHODS: We examined the pathophysiological pathways involved in early-onset preeclampsia, a specific subgroup representing its most severe presentation, using LC-MS/MS metabolomic analysis based on multi-level extraction of lipids and small metabolites from maternal blood samples, collected at the time of diagnosis from 14 preeclamptic and six matched healthy pregnancies. Statistical analysis comprised multivariate and univariate approaches with the application of over representation analysis to identify differential pathways. RESULTS: A clear difference between preeclamptic and control pregnancies was observed in principal component analysis. Supervised multivariate analysis using orthogonal partial least square discriminant analysis provided a robust model with goodness of fit (R2X = 0.91, p = 0.002) and predictive ability (Q2Y = 0.72, p < 0.001). Finally, univariate analysis followed by 5% false discovery rate correction indicated 82 metabolites significantly altered, corresponding to six overrepresented pathways: (1) aminoacyl-tRNA biosynthesis; (2) arginine biosynthesis; (3) alanine, aspartate and glutamate metabolism; (4) D-glutamine and D-glutamate metabolism; (5) arginine and proline metabolism; and (6) histidine metabolism. CONCLUSION: Metabolomic analysis focusing specifically on the early-onset severe form of preeclampsia reveals the interplay between pathophysiological pathways involved in this form. Future studies are required to explore new therapeutic approaches targeting these altered metabolic pathways in early-onset preeclampsia.

A new ex vivo method to evaluate the performance of candidate MRI contrast agents: a proof-of-concept study.

BACKGROUND: Magnetic resonance imaging (MRI) plays an important role in tumor detection/diagnosis. The use of exogenous contrast agents (CAs) helps to improve the discrimination between lesion and neighbouring tissue, but most of the currently available CAs are non-specific. Assessing the performance of new, selective CAs requires exhaustive assays and large amounts of material. Accordingly, in a preliminary screening of new CAs, it is important to choose candidate compounds with good potential for in vivo efficiency. This screening method should reproduce as close as possible the in vivo environment. In this sense, a fast and reliable method to select the best candidate CAs for in vivo studies would minimize time and investment cost, and would benefit the development of better CAs. RESULTS: The post-mortem ex vivo relative contrast enhancement (RCE) was evaluated as a method to screen different types of CAs, including paramagnetic and superparamagnetic agents. In detail, sugar/gadolinium-loaded gold nanoparticles (Gd-GNPs) and iron nanoparticles (SPIONs) were tested. Our results indicate that the post-mortem ex vivo RCE of evaluated CAs, did not correlate well with their respective in vitro relaxivities. The results obtained with different Gd-GNPs suggest that the linker length of the sugar conjugate could modulate the interactions with cellular receptors and therefore the relaxivity value. A paramagnetic CA (GNP (E_2)), which performed best among a series of Gd-GNPs, was evaluated both ex vivo and in vivo. The ex vivo RCE was slightly worst than gadoterate meglumine (201.9 ± 9.3% versus 237 ± 14%, respectively), while the in vivo RCE, measured at the time-to-maximum enhancement for both compounds, pointed to GNP E_2 being a better CA in vivo than gadoterate meglumine. This is suggested to be related to the nanoparticule characteristics of the evaluated GNP. CONCLUSION: We have developed a simple, cost-effective relatively high-throughput method for selecting CAs for in vivo experiments. This method requires approximately 800 times less quantity of material than the amount used for in vivo administrations.

Improving the classification of brain tumors in mice with perturbation enhanced (PE)-MRSI.

Classifiers based on statistical pattern recognition analysis of MRSI data are becoming important tools for the non-invasive diagnosis of human brain tumors. Here we investigate the potential interest of perturbation-enhanced MRSI (PE-MRSI), in this case acute hyperglycemia, for improving the discrimination between mouse brain MRS patterns of glioblastoma multiforme (GBM), oligodendroglioma (ODG), and non-tumor brain parenchyma (NT). Six GBM-bearing mice and three ODG-bearing mice were scanned at 7 Tesla by PRESS-MRSI with 12 and 136 ms echo-time, during euglycemia (Eug) and also during induced acute hyperglycemia (Hyp), generating altogether four datasets per animal (echo time + glycemic condition): 12Eug, 136Eug, 12Hyp, and 136Hyp. For classifier development all spectral vectors (spv) selected from the MRSI matrix were unit length normalized (UL2) and used either as a training set (76 GBM spv, four mice; 70 ODG spv, two mice; 54 NT spv) or as an independent testing set (61 GBM spv, two mice; 31 ODG, one mouse; 23 NT spv). All Fisher's LDA classifiers obtained were evaluated as far as their descriptive performance-correctly classified cases of the training set (bootstrapping)-and predictive accuracy-balanced error rate of independent testing set classification. MRSI-based classifiers at 12Hyp were consistently more efficient in separating GBM, ODG, and NT regions, with overall accuracies always >80% and up to 95-96%; remaining classifiers were within the 48-85% range. This was also confirmed by user-independent selection of training and testing sets, using leave-one-out (LOO). This highlights the potential interest of perturbation-enhanced MRSI protocols for improving the non-invasive characterization of preclinical brain tumors.