Direct monitoring of Exogenous γ-Hydroxybutyric Acid in body fluids by NMR spectroscopy

γ-Hydroxybutyric acid (GHB) is a popular drug increasingly associated to cases of drug-facilitated sexual assault (DFSA). Currently, expanding procedures of analysis and having forensic evidence of GHB intake at a long term are mandatory. Up to now, most studies have been performed using GC-MS and LC-MS as analytical platforms, which involve significant manipulation of the sample and, often, indirect measurements. In this work, procedures used in NMR-based metabolomics were applied to a GHB clinical trial on urine and serum. Detection, identification and quantification of the drug by NMR methods were surveyed, as well as the use of NMR-based metabolomics for the search of potential surrogate biomarkers of GHB consumption. Results demonstrated the suitability of NMR spectroscopy, as a robust nondestructive technique, to monitor (detect, identify and quantify) fast and directly exogenous GHB in almost intact body fluids, and its high potential in the search for metabolites associated to GHB intake.</p

Effects of Butyrate Supplementation on Inflammation and Kidney Parameters in Type 1 Diabetes: A Randomized, Double-Blind, Placebo-Controlled Trial

Type 1 diabetes is associated with increased intestinal inflammation and decreased abundance of butyrate-producing bacteria. We investigated the effect of butyrate on inflammation, kidney parameters, HbA1c, serum metabolites and gastrointestinal symptoms in persons with type 1 diabetes, albuminuria and intestinal inflammation. We conducted a randomized placebo-controlled, double-blind, parallel clinical study involving 53 participants randomized to 3.6 g sodium butyrate daily or placebo for 12 weeks. The primary endpoint was the change in fecal calprotectin. Additional endpoints were the change in fecal short chain fatty acids, intestinal alkaline phosphatase activity and immunoglobulins, serum lipopolysaccharide, CRP, albuminuria, kidney function, HbA1c, metabolites and gastrointestinal symptoms. The mean age was 54 ± 13 years, and the median [Q1:Q3] urinary albumin excretion was 46 [14:121] mg/g. The median fecal calprotectin in the butyrate group was 48 [26:100] μg/g at baseline, and the change was −1.0 [−20:10] μg/g; the median in the placebo group was 61 [25:139] μg/g at baseline, and the change was −12 [−95:1] μg/g. The difference between the groups was not significant (p = 0.24); neither did we find an effect of butyrate compared to placebo on the other inflammatory markers, kidney parameters, HbA1c, metabolites nor gastrointestinal symptoms. Twelve weeks of butyrate supplementation did not reduce intestinal inflammation in persons with type 1 diabetes, albuminuria and intestinal inflammation

Effects of Butyrate Supplementation on Inflammation and Kidney Parameters in Type 1 Diabetes: A Randomized, Double-Blind, Placebo-Controlled Trial

Type 1 diabetes is associated with increased intestinal inflammation and decreased abundance of butyrate-producing bacteria. We investigated the effect of butyrate on inflammation, kidney parameters, HbA1c, serum metabolites and gastrointestinal symptoms in persons with type 1 diabetes, albuminuria and intestinal inflammation. We conducted a randomized placebo-controlled, double-blind, parallel clinical study involving 53 participants randomized to 3.6 g sodium butyrate daily or placebo for 12 weeks. The primary endpoint was the change in fecal calprotectin. Additional endpoints were the change in fecal short chain fatty acids, intestinal alkaline phosphatase activity and immunoglobulins, serum lipopolysaccharide, CRP, albuminuria, kidney function, HbA1c, metabolites and gastrointestinal symptoms. The mean age was 54 ± 13 years, and the median [Q1:Q3] urinary albumin excretion was 46 [14:121] mg/g. The median fecal calprotectin in the butyrate group was 48 [26:100] μg/g at baseline, and the change was −1.0 [−20:10] μg/g; the median in the placebo group was 61 [25:139] μg/g at baseline, and the change was −12 [−95:1] μg/g. The difference between the groups was not significant (p = 0.24); neither did we find an effect of butyrate compared to placebo on the other inflammatory markers, kidney parameters, HbA1c, metabolites nor gastrointestinal symptoms. Twelve weeks of butyrate supplementation did not reduce intestinal inflammation in persons with type 1 diabetes, albuminuria and intestinal inflammation

Dysregulation of multiple metabolic networks related to brain transmethylation and polyamine pathways in Alzheimer disease: A targeted metabolomic and transcriptomic study.

BACKGROUND: There is growing evidence that Alzheimer disease (AD) is a pervasive metabolic disorder with dysregulation in multiple biochemical pathways underlying its pathogenesis. Understanding how perturbations in metabolism are related to AD is critical to identifying novel targets for disease-modifying therapies. In this study, we test whether AD pathogenesis is associated with dysregulation in brain transmethylation and polyamine pathways. METHODS AND FINDINGS: We first performed targeted and quantitative metabolomics assays using capillary electrophoresis-mass spectrometry (CE-MS) on brain samples from three groups in the Baltimore Longitudinal Study of Aging (BLSA) (AD: n = 17; Asymptomatic AD [ASY]: n = 13; Control [CN]: n = 13) (overall 37.2% female; mean age at death 86.118 ± 9.842 years) in regions both vulnerable and resistant to AD pathology. Using linear mixed-effects models within two primary brain regions (inferior temporal gyrus [ITG] and middle frontal gyrus [MFG]), we tested associations between brain tissue concentrations of 26 metabolites and the following primary outcomes: group differences, Consortium to Establish a Registry for Alzheimer's Disease (CERAD) (neuritic plaque burden), and Braak (neurofibrillary pathology) scores. We found significant alterations in concentrations of metabolites in AD relative to CN samples, as well as associations with severity of both CERAD and Braak, mainly in the ITG. These metabolites represented biochemical reactions in the (1) methionine cycle (choline: lower in AD, p = 0.003; S-adenosyl methionine: higher in AD, p = 0.005); (2) transsulfuration and glutathione synthesis (cysteine: higher in AD, p < 0.001; reduced glutathione [GSH]: higher in AD, p < 0.001); (3) polyamine synthesis/catabolism (spermidine: higher in AD, p = 0.004); (4) urea cycle (N-acetyl glutamate: lower in AD, p < 0.001); (5) glutamate-aspartate metabolism (N-acetyl aspartate: lower in AD, p = 0.002); and (6) neurotransmitter metabolism (gamma-amino-butyric acid: lower in AD, p < 0.001). Utilizing three Gene Expression Omnibus (GEO) datasets, we then examined mRNA expression levels of 71 genes encoding enzymes regulating key reactions within these pathways in the entorhinal cortex (ERC; AD: n = 25; CN: n = 52) and hippocampus (AD: n = 29; CN: n = 56). Complementing our metabolomics results, our transcriptomics analyses also revealed significant alterations in gene expression levels of key enzymatic regulators of biochemical reactions linked to transmethylation and polyamine metabolism. Our study has limitations: our metabolomics assays measured only a small proportion of all metabolites participating in the pathways we examined. Our study is also cross-sectional, limiting our ability to directly test how AD progression may impact changes in metabolite concentrations or differential-gene expression. Additionally, the relatively small number of brain tissue samples may have limited our power to detect alterations in all pathway-specific metabolites and their genetic regulators. CONCLUSIONS: In this study, we observed broad dysregulation of transmethylation and polyamine synthesis/catabolism, including abnormalities in neurotransmitter signaling, urea cycle, aspartate-glutamate metabolism, and glutathione synthesis. Our results implicate alterations in cellular methylation potential and increased flux in the transmethylation pathways, increased demand on antioxidant defense mechanisms, perturbations in intermediate metabolism in the urea cycle and aspartate-glutamate pathways disrupting mitochondrial bioenergetics, increased polyamine biosynthesis and breakdown, as well as abnormalities in neurotransmitter metabolism that are related to AD

Effects of Butyrate Supplementation on Inflammation and Kidney Parameters in Type 1 Diabetes : A Randomized, Double-Blind, Placebo-Controlled Trial

Type 1 diabetes is associated with increased intestinal inflammation and decreased abundance of butyrate-producing bacteria. We investigated the effect of butyrate on inflammation, kidney parameters, HbA1c, serum metabolites and gastrointestinal symptoms in persons with type 1 diabetes, albuminuria and intestinal inflammation. We conducted a randomized placebo-controlled, double-blind, parallel clinical study involving 53 participants randomized to 3.6 g sodium butyrate daily or placebo for 12 weeks. The primary endpoint was the change in fecal calprotectin. Additional endpoints were the change in fecal short chain fatty acids, intestinal alkaline phosphatase activity and immunoglobulins, serum lipopolysaccharide, CRP, albuminuria, kidney function, HbA1c, metabolites and gastrointestinal symptoms. The mean age was 54 +/- 13 years, and the median [Q1:Q3] urinary albumin excretion was 46 [14:121] mg/g. The median fecal calprotectin in the butyrate group was 48 [26:100] mu g/g at baseline, and the change was -1.0 [-20:10] mu g/g; the median in the placebo group was 61 [25:139] mu g/g at baseline, and the change was -12 [-95:1] mu g/g. The difference between the groups was not significant (p = 0.24); neither did we find an effect of butyrate compared to placebo on the other inflammatory markers, kidney parameters, HbA1c, metabolites nor gastrointestinal symptoms. Twelve weeks of butyrate supplementation did not reduce intestinal inflammation in persons with type 1 diabetes, albuminuria and intestinal inflammation.Peer reviewe

APOE ε4 alters docosahexaenoic acid associations with preclinical markers of Alzheimer disease

Docosahexaenoic acid (DHA) is the main long chain omega-3 polyunsaturated fatty acids in the brain and accounts for 30% to 40% of fatty acids in the grey matter of the human cortex. Although the influence of DHA on memory function is widely researched, its association with brain volumes is under investigated and its association with spatial navigation is virtually unknown. This is despite the fact that spatial navigation deficits are a new cognitive fingerprint for symptomatic and asymptomatic Alzheimer’s disease (AD). We investigated the relationship between DHA levels and the major structural and cognitive markers of preclinical AD, namely hippocampal volume, entorhinal volume, and spatial navigation ability. Fifty-three cognitively normal adults underwent volumetric magnetic resonance imaging, measurements of serum DHA (including serum lysophosphatidylcholine DHA (LPC DHA)) and APOE ε4 genotyping. Relative regional brain volumes were calculated and linear regression models were fitted to examine DHA associations with brain volume. APOE genotype modulated serum DHA associations with entorhinal cortex volume and hippocampal volume. Linear models showed that greater serum DHA was associated with increased entorhinal cortex volume, but not hippocampal volume, in APOΕ ε4 non-carriers. APOE also interacted with serum LPC DHA to predict hippocampal volume. After testing interactions between DHA and APOE ε4 on brain volume, we investigated whether DHA and APOE interact to predict spatial navigation performance on a novel virtual reality diagnostic test for AD in an independent population of APOE genotyped adults (n = 46). Crucially, the APOE genotype modulated DHA associations with spatial navigation performance, showing that DHA was inversely associated with path integration in APOE ε4 carriers only. Interventions aiming to increase DHA status to protect against cognitive decline must consider APOE ε4 carrier status, and focus on higher doses of supplementary DHA to ensure adequate brain delivery

Coping with iron limitation : a metabolomic study of Synechocystis sp. PCC 6803

Iron (Fe) is a key element for all living systems, especially for photosynthetic organisms because of its important role in the photosynthetic electron transport chain. Fe limitation in cyanobacteria leads to several physiological and morphological changes. However, the overall metabolic responses to Fe limitation are still poorly understood. In this study, we integrated elemental, stoichiometric, macromolecular, and metabolomic data to shed light on the responses of Synechocystis sp. PCC 6803, a non-N2-fixing freshwater cyanobacterium, to Fe limitation. Compared to Synechocystis growing at nutrient replete conditions, Fe-limited cultures had lower growth rates and amounts of chlorophyll a, RNA, RNA:DNA, C, N, and P, and higher ratios of protein:RNA, C:N, C:P, and N:P, in accordance with the growth rate hypothesis which predicts faster growing organisms will have decreased biomass RNA contents and C:P and N:P ratios. Fe-limited Synechocystis had lower amounts Fe, Mn, and Mo, and higher amount of Cu. Several changes in amino acids of cultures growing under Fe limitation suggest nitrogen limitation. In addition, we found substantial increases in stress-related metabolites in Fe-limited cyanobacteria such antioxidants. This study represents an advance in understanding the stoichiometric, macromolecular, and metabolic strategies that cyanobacteria use to cope with Fe limitation. This information, moreover, may further understanding of changes in cyanobacterial functions under scenarios of Fe limitation in aquatic ecosystems

Predicting AT(N) pathologies in Alzheimer’s disease from blood-based proteomic data using neural networks

Background and objective: Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD. Methods: We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis. Results: Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway. Conclusion: Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size. Keywords: Alzheimer’s disease; amyloid β; artificial neural networks; machine learning; neurodegeneration; plasma proteomics; ta

The EMIF-AD Multimodal Biomarker Discovery study: design, methods and cohort characteristics.

There is an urgent need for novel, noninvasive biomarkers to diagnose Alzheimer's disease (AD) in the predementia stages and to predict the rate of decline. Therefore, we set up the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study. In this report we describe the design of the study, the methods used and the characteristics of the participants. Participants were selected from existing prospective multicenter and single-center European studies. Inclusion criteria were having normal cognition (NC) or a diagnosis of mild cognitive impairment (MCI) or AD-type dementia at baseline, age above 50 years, known amyloid-beta (Aβ) status, availability of cognitive test results and at least two of the following materials: plasma, DNA, magnetic resonance imaging (MRI) or cerebrospinal fluid (CSF). Targeted and untargeted metabolomic and proteomic analyses were performed in plasma, and targeted and untargeted proteomics were performed in CSF. Genome-wide SNP genotyping, next-generation sequencing and methylation profiling were conducted in DNA. Visual rating and volumetric measures were assessed on MRI. Baseline characteristics were analyzed using ANOVA or chi-square, rate of decline analyzed by linear mixed modeling. We included 1221 individuals (NC n = 492, MCI n = 527, AD-type dementia n = 202) with a mean age of 67.9 (SD 8.3) years. The percentage Aβ+ was 26% in the NC, 58% in the MCI, and 87% in the AD-type dementia groups. Plasma samples were available for 1189 (97%) subjects, DNA samples for 929 (76%) subjects, MRI scans for 862 (71%) subjects and CSF samples for 767 (63%) subjects. For 759 (62%) individuals, clinical follow-up data were available. In each diagnostic group, the APOE ε4 allele was more frequent amongst Aβ+ individuals (p &lt; 0.001). Only in MCI was there a difference in baseline Mini Mental State Examination (MMSE) score between the A groups (p &lt; 0.001). Aβ+ had a faster rate of decline on the MMSE during follow-up in the NC (p &lt; 0.001) and MCI (p &lt; 0.001) groups. The characteristics of this large cohort of elderly subjects at various cognitive stages confirm the central roles of Aβ and APOE ε4 in AD pathogenesis. The results of the multimodal analyses will provide new insights into underlying mechanisms and facilitate the discovery of new diagnostic and prognostic AD biomarkers. All researchers can apply for access to the EMIF-AD MBD data by submitting a research proposal via the EMIF-AD Catalog