Changes in the metabolic composition of storage solution with prolonged cold ischemia of the uterus.

INTRODUCTION: The development of uterine transplantation (UTx) from deceased donors requires knowledge of the tolerance of the uterus to prolonged cold ischemia (CI). This can be evaluated through the use of biological parameters to assess degradation of the organ between its procurement and transplantation. The objective of this study was to analyze changes in the metabolic composition of the storage solution in cases of prolonged CI in uteri from ewes. METHODS: Eighteen uterine auto-transplantations were performed in ewes. CI time was 1 h (T1) or 24 h (T24). Samples of Celsior® were taken when the explanted uterus was flushed (T0) and at the end of CI. A dual approach to metabolic analyses was followed: targeted biochemical analyses targeting several predefined metabolites and non-targeted metabolomics analyses based on nuclear magnetic resonance (NMR). RESULTS: Metabolic analyses were performed on 16 explanted uteri. Metabolomic profiles differed significantly between T1 and T24 (p = 0.003). Hypoxia-associated degradation of the organ was demonstrated by the significantly higher lactate levels at T24 than at T1 (p < 0.05), accompanied by cell lysis, and significantly higher levels of creatine kinase activity in T24 than in T1 uteri (p < 0.05). Oxidative stress increased over time, with a significantly higher oxidized glutathione/glutathione ratio for T24 than for T1 uteri (p < 0.05). CONCLUSION: The metabolic results indicate a significant degradation of the uterus during 24 h of CI. Metabolic analysis of the storage solution could be used as a non-invasive tool for evaluating uterine degradation during CI before transplantation.

Biomarkers in amyotrophic lateral sclerosis: combining metabolomic and clinical parameters to define disease progression.

BACKGROUND AND PURPOSE: The objectives of this study were to define the metabolomic profile of cerebrospinal fluid in amyotrophic lateral sclerosis (ALS) patients, to model outcome through combined clinical and metabolomic parameters and independently to validate predictive models. METHODS: In all, 74 consecutive newly diagnosed patients were enrolled into training (Tr, n = 49) and test (Te, n = 25) cohorts. Investigators recorded clinical data and the metabalomic profile of cerebrospinal fluid at baseline was analyzed with (1)H nuclear magnetic resonance spectroscopy. Markers of disease progression, collected in 1-year prospective follow-up, included change in ALS Functional Rating Scale (var_ALSFRS), change in weight (var_weight) and survival time. Stepwise multiple regression selected from metabolomic and clinical parameters to model rate of progression in the Tr cohort. Best fit models were validated independently in the Te cohort. RESULTS: The best-fit statistical models, using both metabolomic and clinical covariates, predicted outcome with 70.8% (var_weight), 72% (var_ALSFRS) and 76% (survival) accuracy in the Te cohort. Models that used metabolomics or clinical data alone predicted outcome less well. Highlighted metabolites are involved in pathophysiological pathways previously described in ALS. CONCLUSION: Cerebrospinal fluid metabolomics can aid in predicting the clinical course of ALS and tap into pathophysiological processes. The precision of predictive models, independently reproduced in this study, is enhanced through inclusion of both metabolomic and clinical parameters. The findings bring the field closer to a clinically meaningful disease marker.

Comparative analysis of targeted metabolomics: dominance-based rough set approach versus orthogonal partial least square-discriminant analysis.

BACKGROUND: Metabolomics is an emerging field that includes ascertaining a metabolic profile from a combination of small molecules, and which has health applications. Metabolomic methods are currently applied to discover diagnostic biomarkers and to identify pathophysiological pathways involved in pathology. However, metabolomic data are complex and are usually analyzed by statistical methods. Although the methods have been widely described, most have not been either standardized or validated. Data analysis is the foundation of a robust methodology, so new mathematical methods need to be developed to assess and complement current methods. We therefore applied, for the first time, the dominance-based rough set approach (DRSA) to metabolomics data; we also assessed the complementarity of this method with standard statistical methods. Some attributes were transformed in a way allowing us to discover global and local monotonic relationships between condition and decision attributes. We used previously published metabolomics data (18 variables) for amyotrophic lateral sclerosis (ALS) and non-ALS patients. RESULTS: Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) allowed satisfactory discrimination (72.7%) between ALS and non-ALS patients. Some discriminant metabolites were identified: acetate, acetone, pyruvate and glutamine. The concentrations of acetate and pyruvate were also identified by univariate analysis as significantly different between ALS and non-ALS patients. DRSA correctly classified 68.7% of the cases and established rules involving some of the metabolites highlighted by OPLS-DA (acetate and acetone). Some rules identified potential biomarkers not revealed by OPLS-DA (beta-hydroxybutyrate). We also found a large number of common discriminating metabolites after Bayesian confirmation measures, particularly acetate, pyruvate, acetone and ascorbate, consistent with the pathophysiological pathways involved in ALS. CONCLUSION: DRSA provides a complementary method for improving the predictive performance of the multivariate data analysis usually used in metabolomics. This method could help in the identification of metabolites involved in disease pathogenesis. Interestingly, these different strategies mostly identified the same metabolites as being discriminant. The selection of strong decision rules with high value of Bayesian confirmation provides useful information about relevant condition-decision relationships not otherwise revealed in metabolomics data.

Steroidome and metabolome analysis in gilt saliva to identify potential biomarkers of boar effect receptivity.

Optimal management of gilt reproduction requires oestrus synchronization. Hormonal treatments are used for this purpose, but there is a growing demand for non-hormonal alternatives, especially in organic farms. The boar effect is an important alternative opportunity to induce and synchronize oestrus without hormones. Before puberty, gilts exhibit a 'waiting period' during which boar exposure could induce and synchronize the first ovulation. We searched for salivary biomarkers of this period of boar effect receptivity to improve detection of the gilts to stimulate with the perspective of enhancing the efficacy of the boar effect. Saliva samples were collected from 30 Large-White×Landrace crossbred gilts between 140 and 175 days of age. Gilts were exposed twice a day to a boar and subjected to oestrus detection from 150 to 175 days of age. Among the 30 gilts, 10 were detected in oestrus 4 to 7 days after the first introduction of the boar and were considered receptive to the boar effect, 14 were detected in oestrus more than 8 days after first boar contact, and six did not show oestrus and were considered non-receptive. Saliva samples from six receptive and six non-receptive gilts were analyzed for steroidome and for metabolome using gas chromatography coupled to tandem mass spectrometry and 1H nuclear magnetic resonance spectroscopy, respectively. Four saliva samples per gilt were analyzed: 25 days and 11 days before boar introduction, the day of boar introduction, 3 days later for receptive gilts or 7 days later for non-receptive gilts. Twenty-nine steroids and 31 metabolites were detected in gilt saliva. Salivary concentrations of six steroids and three metabolites were significantly different between receptive and non-receptive gilts: progesterone and glycolate 25 days before boar introduction, 3α5ß20α- and 3ß5α20ß-hexahydroprogesterone, dehydroepiandrosterone, androstenediol, succinate, and butyrate 11 days before boar introduction, and 3ß5α-tetrahydroprogesterone on the day of boar introduction. Thus, nine potential salivary biomarkers of boar effect receptivity were identified in our experimental conditions. Further studies with higher numbers of gilts and salivary sampling points are necessary to ascertain their reliability.

Salivary and urinary metabolome analysis for pre-puberty-related biomarkers identification in porcine.

Estrus synchronization is important for optimal management of gilt reproduction in pig farms. Hormonal treatments, such as synthetic progestogens, are used on a routine basis, but there is a growing demand for non-hormonal alternative breeding tools. Before puberty, gilts exhibit a 'waiting period,' related to the ovarian development and gonadotrophin secretions, during which external stimulations, such as boar exposure, could induce and synchronize first ovulation. Practical non-invasive tools for identification of this period in farms are lacking. During this period, urinary oestrone levels are high, but urine sampling is difficult in group-housed females. The aim of this work was to search for specific biomarkers of the 'waiting period' in saliva and urine. In total, nine 144- to 147-day-old Large White gilts were subjected to trans-abdominal ultrasonography three times a week for 5 weeks until puberty detection (week -5 to week -1 before puberty). Urine and saliva samples were collected for oestrone assay to detect the 'waiting period' and for metabolome analysis using 1H-nuclear magnetic resonance spectroscopy to detect potential biomarkers of the 'waiting period.' Gilts were slaughtered 7 days after puberty detection for puberty confirmation. Results were consistent with ultrasonography data for six gilts. Urine and saliva samples from these six gilts were analyzed. Urinary estrone concentration significantly increased 2 weeks before puberty detection. Metabolome analysis of urine samples allowed the identification of 78 spectral bins, among them, 42 low-molecular-weight metabolites were identified. Metabolome analysis of salivary samples allowed the identification of 59 spectral bins, among them, 23 low-molecular-weight metabolites were detected and 17 were identified. No potential biomarker was identified in urinary samples. In saliva, butyrate and 2HOvalerate, 5.79 ppm (putatively uridine), formate, malonate and propionate could be biomarker candidates to ascertain the pre-puberty period in gilt reproduction. These results confirm that non-invasive salivary samples could allow the identification of the physiological status of the gilts and presumably the optimal time for application of the boar effect. This could contribute to synchronize puberty onset and hence to develop non-hormonal breeding tools.

The combination of four analytical methods to explore skeletal muscle metabolomics: Better coverage of metabolic pathways or a marketing argument?

OBJECTIVES: Metabolomics is an emerging science based on diverse high throughput methods that are rapidly evolving to improve metabolic coverage of biological fluids and tissues. Technical progress has led researchers to combine several analytical methods without reporting the impact on metabolic coverage of such a strategy. The objective of our study was to develop and validate several analytical techniques (mass spectrometry coupled to gas or liquid chromatography and nuclear magnetic resonance) for the metabolomic analysis of small muscle samples and evaluate the impact of combining methods for more exhaustive metabolite covering. DESIGN AND METHODS: We evaluated the muscle metabolome from the same pool of mouse muscle samples after 2 metabolite extraction protocols. Four analytical methods were used: targeted flow injection analysis coupled with mass spectrometry (FIA-MS/MS), gas chromatography coupled with mass spectrometry (GC-MS), liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS), and nuclear magnetic resonance (NMR) analysis. We evaluated the global variability of each compound i.e., analytical (from quality controls) and extraction variability (from muscle extracts). We determined the best extraction method and we reported the common and distinct metabolites identified based on the number and identity of the compounds detected with low analytical variability (variation coefficient<30%) for each method. Finally, we assessed the coverage of muscle metabolic pathways obtained. RESULTS: Methanol/chloroform/water and water/methanol were the best extraction solvent for muscle metabolome analysis by NMR and MS, respectively. We identified 38 metabolites by nuclear magnetic resonance, 37 by FIA-MS/MS, 18 by GC-MS, and 80 by LC-HRMS. The combination led us to identify a total of 132 metabolites with low variability partitioned into 58 metabolic pathways, such as amino acid, nitrogen, purine, and pyrimidine metabolism, and the citric acid cycle. This combination also showed that the contribution of GC-MS was low when used in combination with other mass spectrometry methods and nuclear magnetic resonance to explore muscle samples. CONCLUSION: This study reports the validation of several analytical methods, based on nuclear magnetic resonance and several mass spectrometry methods, to explore the muscle metabolome from a small amount of tissue, comparable to that obtained during a clinical trial. The combination of several techniques may be relevant for the exploration of muscle metabolism, with acceptable analytical variability and overlap between methods However, the difficult and time-consuming data pre-processing, processing, and statistical analysis steps do not justify systematically combining analytical methods.

A Multiplatform Metabolomics Approach to Characterize Plasma Levels of Phenylalanine and Tyrosine in Phenylketonuria.

BACKGROUND: Different pathophysiological mechanisms have been described in phenylketonuria (PKU) but the indirect metabolic consequences of metabolic disorders caused by elevated Phe or low Tyr concentrations remain partially unknown. We used a multiplatform metabolomics approach to evaluate the metabolic signature associated with Phe and Tyr. MATERIAL AND METHODS: We prospectively included 10 PKU adult patients and matched controls. We analysed the metabolome profile using GC-MS (urine), amino-acid analyzer (urine and plasma) and nuclear magnetic resonance spectroscopy (urine). We performed a multivariate analysis from the metabolome (after exclusion of Phe, Tyr and directly derived metabolites) to explain plasma Phe and Tyr concentrations, and the clinical status. Finally, we performed a univariate analysis of the most discriminant metabolites and we identified the associated metabolic pathways. RESULTS: We obtained a metabolic pattern from 118 metabolites and we built excellent multivariate models to explain Phe, Tyr concentrations and PKU diagnosis. Common metabolites of these models were identified: Gln, Arg, succinate and alpha aminobutyric acid. Univariate analysis showed an inverse correlation between Arg, alpha aminobutyric acid and Phe and a positive correlation between Arg, succinate, Gln and Tyr (p < 0.0003). Thus, we highlighted the following pathways: Arg and Pro, Ala, Asp and Glu metabolism. DISCUSSION: We obtain a specific metabolic signature related to Tyr and Phe concentrations. We confirmed the involvement of different pathophysiological mechanisms previously described in PKU such as protein synthesis, energetic metabolism and oxidative stress. The metabolomics approach is relevant to explore PKU pathogenesis.

Quantitative T1rho magnetic resonance imaging of RIF-1 tumors in vivo: detection of early response to cyclophosphamide therapy.

This study compares two potential magnetic resonance imaging (MRI) indices for noninvasive early detection of tumor response to chemotherapy: the spin-lattice relaxation in the rotating frame (T1rho) and the transverse relaxation time (T2). Measurements of these relaxation parameters were performed on a s.c. murine radiation-induced fibrosarcoma (RIF-1) model before and after cyclophosphamide treatment. The number of pixels exhibiting T1rho values longer than controls in viable regions of the tumor increased significantly as early as 18 h after drug administration and remained elevated up to 36 h after treatment (P < 0.005). Although a trend of increasing T2s relative to controls was noted in viable regions of the tumor 36 h after treatment, the changes were not statistically significant. Histological examination indicated a decrease in mitotic index that paralleled the changes in T1rho. We conclude that T1rho measurements may be useful for noninvasive monitoring of early response of tumors to chemotherapy.

Effects of hyperglycemia on oxygenation, radiosensitivity and bioenergetic status of subcutaneous RIF-1 tumors.

Since tissue oxygen tension is a balance between delivery and consumption of oxygen, considerable effort has been directed at increasing the former and/or decreasing the latter. Techniques to decrease the rate of cellular oxygen consumption (increasing the distance oxygen can diffuse into tissues) include increasing glycolysis by administering supra-physiologic levels of glucose. We have examined the effect of hyperglycemia produced by intravenous glucose infusion on the tissue oxygenation and radiation response of subcutaneously implanted murine radiation induced fibrosarcomas (RIF-1). A 0.3 M glucose solution was delivered via tail vein injection according to a protocol that maintained glucose at a plasma concentration of 17+/-1 mM. The effect of this treatment on radiation response (clonogenic and growth delay studies), tumor oxygenation (needle electrode pO2 and 2-[2-nitro-1H-imidazol-1-yl]-N-(2,2,3,3,3-pentafluoropropyl) acetamide (EF5) binding), and tumor bioenergetics and pH (31P NMR spectroscopy) was examined. Systemic measurements included hematocrit and blood glucose and lactate concentrations. The results of these studies suggest that these subcutaneously implanted RIF-1 tumors are both radiobiologically and metabolically hypoxic and that intravenous glucose infusion is not an effective method of modifying this metabolic state.

A newborn piglet study of moderate hypoxic-ischemic brain injury by 1H-MRS and MRI.

Cerebral hypoxia-ischemia (HI) is an important cause of perinatal brain damage in the term newborn. The areas most affected are the parasagittal regions of the cerebral cortex and, in severe situations, the basal ganglia. The aim of this study was to show that the newborn piglet model can be used to produce neuropathology resulting from moderate HI insult and to monitor damage for 7 days. Two acute cerebral HI were induced in newborn Large White piglets by reducing the inspired oxygen fraction to 4% and occluding the carotid arteries. Newborn piglets were resuscitated, extubated and monitored for 7 days. (31)P magnetic resonance spectroscopy (MRS) offers the ability to monitor the severity of the HI insults. Lactate (Lac) was detected in the HI group at 2 h, 3 days and 5 days after insult by (1)H MRS. Lac/n-acetylaspartate and Lac/choline and Lac/creatine ratios increased significantly (p < 0.01) in the HI group 2 h after HI insults and remained high over 7 days. For the HI group, mean T(2) values increased significantly in the parietal white matter (subcortical) for 5 days after HI insult [117.5 (+/-7.4) to 158.5 (+/-19.2) at T+3 days, 167.7 (+/-15.4) at T+5 days and 160.9 (+/-10.1) at T+7 days (p < 0.01)]. This newborn piglet model of moderate HI brain injury with reproducible cerebral damage could be use as reference for the study of neuroprotective strategy for a period of 7 days.

MRI texture analysis on texture test objects, normal brain and intracranial tumors.

Texture analysis was performed in three different MRI units on T1 and T2-weighted MR images from 10 healthy volunteers and 63 patients with histologically confirmed intracranial tumors. The goal of this study was a multicenter evaluation of the usefulness of this quantitative approach for the characterization of healthy and pathologic human brain tissues (white matter, gray matter, cerebrospinal fluid, tumors and edema). Each selected brain region of interest was characterized with both its mean gray level values and several texture parameters. Multivariate statistical analyses were then applied in order to discriminate each brain tissue type represented by its own set of texture parameters. Texture analysis was previously performed on test objects to evaluate the method dependence on acquisition parameters and consequently the interest of a multicenter evaluation. Even obtained on different sites with their own acquisition routine protocol, MR brain images contain textural features that can reveal discriminant factors for tissue classification and image segmentation. It can also offer additional information in case of undetermined diagnosis or to develop a more accurate tumor grading.

Lactate editing and lipid suppression by continuous wavelet transform analysis: application to simulated and (1)H MRS brain tumor time-domain data.

Determination of lactate concentrations in vivo is required in the noninvasive diagnosis, staging, and therapeutic monitoring of diseases such as cancer, heart disease, and stroke. An iterative filtering process based on the continuous wavelet transform (CWT) method in the time domain is proposed to isolate the lactate doublet signal from overlapping lipid resonances and estimate the magnetic resonance spectroscopy (MRS) parameters of the lactate methyl signal (signal amplitude, chemical shift, J-coupling and apparent transverse relaxation time (T*(2))). This method offers a number of advantages over the multiple quantum (MQ) and difference spectroscopy approaches, including: 1) full recovery of the lactate methyl signal, whereas the MQ methods usually detect 50% of the signal intensity; 2) in contrast to MQ methods, the lipid signal is retained together with J-coupling data on the lactate peak; 3) the CWT method is much less sensitive to motion artifacts than difference spectroscopy. Application of the method to simulated and real (1)H MRS data collected from human blood plasma and brain tumors demonstrated that this filter provides accurate estimates of the MRS parameters of the lactate doublet and efficiently removes lipid contributions.