Variation In Metabolite Concentrations Research Articles

Association of Whole Blood Amino Acid and Acylcarnitine Metabolome with Anthropometry and IGF-I Serum Levels in Healthy Children and Adolescents in Germany.

Physiological changes of blood amino acids and acylcarnitines during healthy child development are poorly studied. The LIFE (Leipziger Forschungszentrum für Zivilisationserkrankungen) Child study offers a platform with a large cohort of healthy children to investigate these dynamics. We aimed to assess the intra-person variability of 28 blood metabolites and their associations with anthropometric parameters related to growth and excess body fat. Concentrations of 22 amino acids (AA), 5 acylcarnitines (AC) and free carnitine of 2213 children aged between 3 months and 19 years were analyzed using liquid chromatography/tandem mass spectrometry. Values were transformed into standard deviation scores (SDS) to account for sex- and age-related variations. The stability of metabolites was assessed through the coefficient of determination. Associations with parameters for body composition and insulin-like growth factor-I (IGF-I) SDS were determined by the Pearson correlation and linear regression. Our research revealed substantial within-person variation in metabolite concentrations during childhood and adolescence. Most metabolites showed a positive correlation with body composition parameters, with a notable influence of sex, pubertal status and weight group. Glycine exhibited negative associations with parameters of body fat distribution, especially in normal weight girls, overweight/obese boys and during puberty. Blood AA and AC measurements may contribute to elucidating pathogenesis pathways of adiposity-related comorbidities, but the specific timings and conditions of development during childhood and adolescence need to be taken into consideration.

Identifying critical age and gender-based metabolomic shifts in a Japanese population of the Tohoku Medical Megabank cohort

Understanding the physiological changes associated with aging and the associated disease risks is essential to establish biomarkers as indicators of biological aging. This study used the NMR-measured plasma metabolome to calculate age-specific metabolite indices. In doing so, the scope of the study was deliberately simplified to capture general trends and insights into age-related changes in metabolic patterns. In addition, changes in metabolite concentrations with age were examined in detail, with the period from 55–59 to 60–64 years being a period of significant metabolic change, particularly in men, and from 45–49 to 50–54 years in females. These results illustrate the different variations in metabolite concentrations by sex and provide new insights into the relationship between age and metabolic diseases.

Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy.

Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful. This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system. The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson's correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content. In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.

Bioconversion of mangosteen pericarp extract juice with bacterial cellulose production and probiotic integration: A promising approach for functional food supplement development

This study optimized bioconversion process of mangosteen pericarp extract juice (MPEj) with a single-culture of Komagataeibacter xylinus TISTR 1061, a bacterial cellulose-producing strain and a co-culture with the probiotic Lactiplantibacillus plantarum. K. xylinus TISTR 1061 was identified as the optimal bacterial cellulose-producing strain for fermenting MPEj. Optimal conditions were determined to be 20% (v/v) MPEj supplemented with 7% (w/v) sucrose and 0.2% (w/v) ammonium dihydrogen phosphate as carbon and nitrogen sources, respectively. The growth profile of single K. xylinus was not significantly different from those of the co-culture. They showed good adaptability and cohabited during fermentation without antagonistic effects. Dynamic changes in total titratable acidity (TTA), total soluble solids (TSS), and pH during fermentation were relatively similar for both single- and co-cultures. Post-fermented MPEj with co-culture exhibited significantly higher bioactive activities, particularly antioxidant properties. Significant variations in metabolite concentrations, including 2-aminobutyrate, valine, xylitol, propionate, isoleucine, glucose, and glycerol, were observed. These compounds exhibited significantly higher average concentrations after fermentation, highlighting their potential as biomarkers distinguishing pre- and post-fermentation MPEj. Bacterial cellulose (BC) from single culture exhibited greater thickness and weight, with a more complex and branched structure, while that from co-culture was infused with probiotic cells, showing less complexity allowing the BC from co-culture demonstrated low hardness and chewiness values. The properties of both bioconverted MPEj and BC obtained as observed offer potential for innovative functional drinks and/or dietary supplements with enhanced health benefits.

GWAS and genetic and phenotypic correlations of plasma metabolites with complete blood count traits in healthy young pigs reveal implications for pig immune response.

Introduction: In this study estimated genetic and phenotypic correlations between fifteen complete blood count (CBC) traits and thirty-three heritable plasma metabolites in young healthy nursery pigs. In addition, it provided an opportunity to identify candidate genes associated with variation in metabolite concentration and their potential association with immune response, disease resilience, and production traits. Methods: The blood samples were collected from healthy young pigs and Nuclear Magnetic Resonance (NMR) was used to quantify plasma metabolites. CBC was determined using the ADVIA® 2120i Hematology System. Genetic correlations of metabolite with CBC traits and single step genome-wide association study (ssGWAS) were estimated using the BLUPF90 programs. Results: Results showed low phenotypic correlation estimates between plasma metabolites and CBC traits. The highest phenotypic correlation was observed between lactic acid and plasma basophil concentration (0.36 ± 0.04; p < 0.05). Several significant genetic correlations were found between metabolites and CBC traits. The plasma concentration of proline was genetically positively correlated with hemoglobin concentration (0.94 ± 0.03; p < 0.05) and L-tyrosine was negatively correlated with mean corpuscular hemoglobin (MCH; -0.92 ± 0.74; p < 0.05). The genomic regions identified in this study only explained a small percentage of the genetic variance of metabolites levels that were genetically correlated with CBC, resilience, and production traits. Discussion: The results of this systems approach suggest that several plasma metabolite phenotypes are phenotypically and genetically correlated with CBC traits, suggesting that they may be potential genetic indicators of immune response following disease challenge. Genomic analysis revealed genes and pathways that might interact to modulate CBC, resilience, and production traits.

Sources of Variation in Food-Related Metabolites during Pregnancy.

The extent to which variation in food-related metabolites are attributable to non-dietary factors remains unclear, which may explain inconsistent food-metabolite associations observed in population studies. This study examined the association between non-dietary factors and the serum concentrations of food-related biomarkers and quantified the amount of variability in metabolite concentrations explained by non-dietary factors. Pregnant women (n = 600) from two Canadian birth cohorts completed a validated semi-quantitative food frequency questionnaire, and serum metabolites were measured by multisegment injection-capillary electrophoresis-mass spectrometry. Hierarchical linear modelling and principal component partial R-square (PC-PR2) were used for data analysis. For proline betaine and DHA (mainly exogenous), citrus foods and fish/fish oil intake, respectively, explained the highest proportion of variability relative to non-dietary factors. The unique contribution of dietary factors was similar (15:0, 17:0, hippuric acid, TMAO) or lower (14:0, tryptophan betaine, 3-methylhistidine, carnitine) compared to non-dietary factors (i.e., ethnicity, maternal age, gestational age, pre-pregnancy BMI, physical activity, and smoking) for metabolites that can either be produced endogenously, biotransformed by gut microbiota, and/or derived from multiple food sources. The results emphasize the importance of adjusting for non-dietary factors in future analyses to improve the accuracy and precision of the measures of food intake and their associations with health and disease.

Hadamard-encoded dual-voxel SPECIAL: Short-TE MRS acquired in two brain regions simultaneously using Hadamard encoding.

The spin-echo, full-intensity acquired localized (SPECIAL) sequence is a method for single-voxel, localized MRS in vivo with short TEs. In this study we modified the SPECIAL sequence to simultaneously record spectra from two volumes of interest. This new technique is called Hadamard-encoded dual-voxel SPECIAL (HD-SPECIAL). The SPECIAL sequence consists of a spin echo localized to a column of tissue, preceded by a slice-selective inversion pulse in alternating scans to invert a section of the column. Full localization is achieved by subtraction of the inversion-on scans from the inversion-off scans. In HD-SPECIAL, the two-step inversion scheme is replaced by a four-step Hadamard-encoded scheme involving single-band and dual-band inversion pulses to select two regions of the spin-echo column. By appropriate Hadamard combination of the four acquired shots, spectra can be reconstructed from both desired regions. This approach does not rely on parallel imaging reconstruction. Using a 3T scanner, HD-SPECIAL localization is demonstrated both in phantoms and in the human brain in vivo, and the performance of HD-SPECIAL is assessed by comparing with the conventional SPECIAL sequence. Phantom and in vivo measurements show excellent agreement between measures from HD-SPECIAL and SPECIAL sequences. Relative to consecutive SPECIAL measurements from two regions, HD-SPECIAL reduces the total scan time 2-fold with minimal penalty in terms of spectral quality or SNR. The HD-SPECIAL sequence enables reliable acquisition of MR spectra simultaneously from two regions at 3 T, offering the potential to study interregional variations in metabolite concentrations.

Single‐Cell Metabolomics by Mass Spectrometry: Opportunities and Challenges

AbstractCellular heterogeneity is an inherent property of cell populations with a wide spectrum of biological manifestations, ranging from barely observable variations that enhance organismal adaptation, to life‐threatening differences. Single‐cell metabolomics can reveal molecular information and variations in metabolite concentrations between cells that are masked in cell‐population studies. These differences are quantitatively captured by the abundance distributions for the population and their statistical analysis can reveal the presence of latent subpopulations. In recent years, mass spectrometry (MS), combined with novel sampling and ionization techniques, has become an important tool for single‐cell metabolomics. These new techniques must contend with significant challenges in the form of small cell sizes and volumes, ultratrace metabolite amounts per cell, and potentially interfering high turnover rates and rapid diffusion. Owing to the ultrasmall sample volume, low abundance of some metabolites, and poor ionization efficiencies, metabolite detection, identification, and accurate quantitation remain a major challenge. The ability of some techniques to analyze tissue‐embedded cells opens the door for spatial metabolomics, potentially revealing cellular synergism in organ level metabolism. In this Concept article, we present a bird's eye view of these major themes in single cell metabolomics and some of the relevant MS‐based methods.

Kiwifruit Metabolomics-An Investigation of within Orchard Metabolite Variability of Two Cultivars of Actinidia chinensis.

Plant metabolomics within field-based food production systems is challenging owing to environmental variability and the complex architecture and metabolic growth cycles of plants. Kiwifruit cultivars of Actinidia chinensis are vigorous perennial vines grown as clones in highly structured orchard environments, intensively managed to maximize fruit yield and quality. To understand the metabolic responses of vines to orchard management practices, we needed to better understand the various sources of metabolic variability encountered in the orchard. Triplicate composite leaf, internode and fruit (mature and immature) samples were collected from each of six Actinidia chinensis var. deliciosa ‘Hayward’ and A. chinensis var. chinensis ‘Zesy002’ kiwifruit vines at three times during the growing season and measured by LC-MS. In general, there was more variation in metabolite concentrations within vines than between vines, with ‘Hayward’ showing a greater percentage of within-vine variability than ‘Zesy002’ (c. 90 vs. 70% respectively). In specific tissues, the sampler, infection by Pseudomonas syringae var. actinidiae and the rootstock also influenced metabolite variability. A similar pattern of metabolic variability was observed from quantitative analysis of specific carbohydrates and phytohormones. High within-vine metabolic variability indicates that it is more important to obtain sufficient replicate samples than to sample from multiple vines. These data provide an objective basis for optimizing metabolite sampling strategies within kiwifruit orchards.

Metabolomics and metabolites in ischemic stroke.

Stroke is a major reason for disability and the second highest cause of death in the world. When a patient is admitted to a hospital, it is necessary to identify the type of stroke, and the likelihood for development of a recurrent stroke, vascular dementia, and depression. These factors could be determined using different biomarkers. Metabolomics is a very promising strategy for identification of biomarkers. The advantage of metabolomics, in contrast to other analytical techniques, resides in providing low molecular weight metabolite profiles, rather than individual molecule profiles. Technically, this approach is based on mass spectrometry and nuclear magnetic resonance. Furthermore, variations in metabolite concentrations during brain ischemia could alter the principal neuronal functions. Different markers associated with ischemic stroke in the brain have been identified including those contributing to risk, acute onset, and severity of this pathology. In the brain, experimental studies using the ischemia/reperfusion model (IRI) have shown an impaired energy and amino acid metabolism and confirmed their principal roles. Literature data provide a good basis for identifying markers of ischemic stroke and hemorrhagic stroke and understanding metabolic mechanisms of these diseases. This opens an avenue for the successful use of identified markers along with metabolomics technologies to develop fast and reliable diagnostic tools for ischemic and hemorrhagic stroke.

Quantitative analysis of nutrient metabolite compositions of retail cow’s milk and milk alternatives in Thailand using GC-MS

To provide comprehensive metabolite profiles and their concentrations, this study characterized retail cow’s milk and plant-based milk (soymilk and almond milk) for amino acids (AA), fatty acids (FA), and sugars. The cow’s milk samples included conventional milk with different heating processes and lactose-free cow’s milk. Multivariate analysis was applied to identify pattern associated with different types of milk. Principal component analysis (PCA) indicated that the plant-based milk alternatives were markedly different from cow’s milk samples. This was attributed mainly to FA, particularly C18:2n-6 and C18:1 (cis-9) which were highest in soymilk and almond milk, respectively. Unlike the cow’s milk samples, the plant-based milk samples exhibited greater brand-to-brand variation in metabolite concentrations. The mean concentration of total AA in soymilk samples was comparable to that in cow’s milk samples, but the cow’s milk samples were 1.4–6.3 times more enriched in branched-chain AA. There was no significant difference in AA and FA concentrations between conventional and lactose-free cow’s milk. Heating processes of cow’s milk did not result in distinct metabolite patterns. This study further showed that FA profiling could be applied to evaluating soymilk adulteration with 1 %–2 % cow’s milk.

Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism.

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

Variability of salivary metabolite levels in patients with Sjögren's syndrome.

To investigate inter- and intra-individual variation in the levels and outputs (concentration multiplied by salivary flow rate) of salivary metabolites in patients with primary Sjögren's syndrome (pSS). A total of 56 samples of stimulated saliva were collected from 14 female pSS patients during four laboratory visits within 20 weeks and analyzed using proton nuclear magnetic resonance spectroscopy. Single saliva samples from each of 15 controls were also analyzed. Among 21 quantified metabolites, choline was significantly elevated in the pSS patients at each time point (P ≤ 0.015), taurine at the last three time points (P ≤ 0.013), alanine at the last two time points (P ≤ 0.007) and glycine at the last time point (P = 0.005). Inter-individual variation in metabolite concentrations was generally larger among the patients than among the controls, and significantly large variations were observed for glycine (P ≤ 0.007, all time points), choline (P ≤ 0.033, three last time points) and alanine (P = 0.028, baseline). Metabolite output analysis showed that choline had the lowest intra-patient variation. In spite of considerable intra- and inter-individual variation, levels and outputs of specific metabolites in patients with pSS differ from those in controls, and may be potentially applicable as new biological markers for monitoring of the response to treatment.

ApcMin/+ tumours and normal mouse small intestines show linear metabolite concentration and DNA cytosine hydroxymethylation gradients from pylorus to colon

Topographical variations of metabolite concentrations have been reported in the duodenum, jejunum and ileum of the small intestine, and in human intestinal tumours from those regions, but there are no published metabolite concentrations measurements correlated with linear position in the mouse small intestine or intestinal tumours. Since DNA methylation dynamics are influenced by metabolite concentrations, they too could show linear anatomical variation. We measured metabolites by HR-MAS 1H NMR spectroscopy and DNA cytosine modifications by LC/MS, in normal small intestines of C57BL/6J wild-type mice, and in normal and tumour samples from ApcMin/+ mice. Wild-type mouse intestines showed approximately linear, negative concentration gradations from the pylorus (i.e. the junction with the stomach) of alanine, choline compounds, creatine, leucine and valine. ApcMin/+ mouse tumours showed negative choline and valine gradients, but a positive glycine gradient. 5-Hydroxymethylcytosine showed a positive gradient in the tumours. The linear gradients we found along the length of the mouse small intestine and in tumours contrast with previous reports of discrete concentration changes in the duodenum, jejunum and ileum. To our knowledge, this is also the first report of a systematic measurement of global levels of DNA cytosine modification in wild-type and ApcMin/+ mouse small intestine.

Spatially-resolved metabolic profiling of living Drosophila in neurodegenerative conditions using 1H magic angle spinning NMR

Drosophila flies are versatile animal models for the study of gene mutations in neuronal pathologies. Their small size allows performing in vivo Magic Angle Spinning (MAS) experiments to obtain high-resolution 1H nuclear magnetic resonance (NMR) spectra. Here, we use spatially-resolved 1H high-resolution MAS NMR to investigate in vivo metabolite contents in different segments of the fly body. A comparative study of metabolic changes was performed for three neurodegenerative disorders: two cell-specific neuronal and glial models of Huntington disease (HD) and a model of glutamate excitotoxicity. It is shown that these pathologies are characterized by specific and sometimes anatomically localized variations in metabolite concentrations. In two cases, the modifications of 1H MAS NMR spectra localized in fly heads were significant enough to allow the creation of a predictive model.

Intracellular Metabolites in Marine Microorganisms during an Experiment Evaluating Microbial Mortality.

Metabolomics is a tool with immense potential for providing insight into the impact of biological processes on the environment. Here, we used metabolomics methods to characterize intracellular metabolites within marine microorganisms during a manipulation experiment that was designed to test the impact of two sources of microbial mortality, protozoan grazing and viral lysis. Intracellular metabolites were analyzed with targeted and untargeted mass spectrometry methods. The treatment with reduced viral mortality showed the largest changes in metabolite concentrations, although there were organic compounds that shifted when the impact of protozoan grazers was reduced. Intracellular concentrations of guanine, phenylalanine, glutamic acid, and ectoine presented significant responses to changes in the source of mortality. Unexpectedly, variability in metabolite concentrations were not accompanied by increases in microbial abundance which indicates that marine microorganisms altered their internal organic carbon stores without changes in biomass or microbial growth. We used Weighted Correlation Network Analysis (WGCNA) to identify correlations between the targeted and untargeted mass spectrometry data. This analysis revealed multiple unknown organic compounds were correlated with compatible solutes, also called osmolytes or chemical chaperones, which emphasizes the dominant role of compatible solutes in marine microorganisms.

Aerobic Glycolysis in the Brain: Warburg and Crabtree Contra Pasteur.

Information processing is onerous. Curiously, active brain tissue does not fully oxidize glucose and instead generates a local surplus of lactate, a phenomenon termed aerobic glycolysis. Why engage in inefficient ATP production by glycolysis when energy demand is highest and oxygen is plentiful? Aerobic glycolysis is associated to classic biochemical effects known by the names of Pasteur, Warburg and Crabtree. Here we discuss these three interdependent phenomena in brain cells, in light of high-resolution data of neuronal and astrocytic metabolism in culture, tissue slices and in vivo, acquired with genetically-encoded fluorescent sensors. These sensors are synthetic proteins that can be targeted to specific cell types and subcellular compartments, which change their fluorescence in response to variations in metabolite concentration. A major site of acute aerobic glycolysis is the astrocyte. In this cell, a Crabtree effect triggered by K+ coincides with a Warburg effect mediated by NO, superimposed on a slower longer-lasting Warburg effect caused by glutamate and possibly by NH4+. The compounded outcome is that more fuel (lactate) and more oxygen are made available to neurons, on demand. Meanwhile neurons consume both glucose and lactate, maintaining a strict balance between glycolysis and respiration, commanded by the Na+ pump. We conclude that activity-dependent Warburg and Crabtree effects in brain tissue, and the resulting aerobic glycolysis, do not reflect inefficient energy generation but the marshalling of astrocytes for the purpose of neuronal ATP generation. It remains to be seen whether neurons contribute to aerobic glycolysis under physiological conditions.

Crosstalk between metabolism and epigenetic modifications in autoimmune diseases: a comprehensive overview.

Little information is available regarding mechanistic links between epigenetic modifications and autoimmune diseases. It seems plausible to surmise that aberrant gene expression and energy metabolism would disrupt immune tolerance, which could ultimately result in autoimmune responses. Metaboloepigenetics is an emerging paradigm that defines the interrelationships between metabolism and epigenetics. Epigenetic modifications, such as the methylation/demethylation of DNA and histone proteins and histone acetylation/deacetylation can be dynamically produced and eliminated by a group of enzymes that consume several metabolites derived from various physiological pathways. Recent insights into cellular metabolism have demonstrated that environmental stimuli such as dietary exposure and nutritional status act through the variation in concentration of metabolites to affect epigenetic regulation and breakdown biochemical homeostasis. Metabolites, including S-adenosylmethionine, acetyl-CoA, nicotinamide adenine dinucleotide, α-ketoglutarate, and ATP serve as cofactors for chromatin-modifying enzymes, such as methyltransferases, deacetylases and kinases, which are responsible for chromatin remodelling. The concentration of crucial nutrients, such as glucose, glutamine, and oxygen, spatially and temporally modulate epigenetic modifications to regulate gene expression and the reaction to stressful microenvironments in disease pathology. In this review, we focus on the interaction between metabolic intermediates and epigenetic modifications, integrating environmental signals with programmes through modification of the epigenome-metabolome to speculate as to how this may influence autoimmune diseases.

Astrocytic and neuronal oxidative metabolism are coupled to the rate of glutamate-glutamine cycle in the tree shrew visual cortex.

Astrocytes play an important role in glutamatergic neurotransmission, namely by clearing synaptic glutamate and converting it into glutamine that is transferred back to neurons. The rate of this glutamate-glutamine cycle (VNT ) has been proposed to couple to that of glucose utilization and of neuronal tricarboxylic acid (TCA) cycle. In this study, we tested the hypothesis that glutamatergic neurotransmission is also coupled to the TCA cycle rate in astrocytes. For that we investigated energy metabolism by means of magnetic resonance spectroscopy (MRS) in the primary visual cortex of tree shrews (Tupaia belangeri) under light isoflurane anesthesia at rest and during continuous visual stimulation. After identifying the activated cortical volume by blood oxygenation level-dependent functional magnetic resonance imaging, 1 H MRS was performed to measure stimulation-induced variations in metabolite concentrations. Relative to baseline, stimulation of cortical activity for 20min caused a reduction of glucose concentration by -0.34±0.09µmol/g (p<0.001), as well as a -9%±1% decrease of the ratio of phosphocreatine-to-creatine (p<0.05). Then 13 C MRS during [1,6-13 C]glucose infusion was employed to measure fluxes of energy metabolism. Stimulation of glutamatergic activity, as indicated by a 20% increase of VNT , resulted in increased TCA cycle rates in neurons by 12% ( VTCAn, p<0.001) and in astrocytes by 24% ( VTCAg, p=0.007). We further observed linear relationships between VNT and both VTCAn and VTCAg. Altogether, these results suggest that in the tree shrew primary visual cortex glutamatergic neurotransmission is linked to overall glucose oxidation and to mitochondrial metabolism in both neurons and astrocytes.

Infant Maturity at Birth Reveals Minor Differences in the Maternal Milk Metabolome in the First Month of Lactation1–3

Background: Human milk is the gold standard of nutrition for infants, providing both protective and essential nutrients. Although much is known about milk from mothers giving birth to term infants, less is known about milk from mothers giving birth to premature infants. In addition, little is known about the composition and diversity of small molecules in these milks and how they change over the first month of lactation.Objective: The objective was to understand how milk metabolites vary over the first month of lactation in mothers giving birth to term and preterm infants.Methods:1H nuclear magnetic resonance (NMR) metabolomics was used to characterize metabolites that were present in micromolar to molar concentrations in colostrum (day 0–5 postpartum), transition milk (day 14), and mature milk (day 28) from mothers who delivered term (n = 15) and preterm (n = 13) infants. Principal components analysis, linear mixed-effects models (LMMs), and linear models (LMs) were used to explore the relation between infant maturity and the postpartum day of collection of milk samples.Results: By using a standard NMR metabolite library, 69 metabolites were identified in the milks, including 15 sugars, 23 amino acids and derivatives, 11 energy-related metabolites, 10 fatty acid–associated metabolites, 3 nucleotides and derivatives, 2 vitamins, and 5 bacteria-associated metabolites. Many metabolite concentrations followed a similar progression over time in both term and preterm milks, with more biological variation in metabolite concentrations in preterm milk. However, although lacto-N-neotetraose (LMM, P = 4.0 × 10−5) and lysine (LM, P = 1.5 × 10−4) significantly decreased in concentration in term milk over time, they did not significantly change in preterm milk.Conclusion: Overall, the metabolic profile of human milk is dynamic throughout the first month of lactation, with more variability in preterm than in term milk and subtle differences in some metabolite concentrations. This trial was registered at clinicaltrials.gov as NCT01841268.