An NMR-based metabolomics approach was used to investigate the differentiation between subjects consuming cheese or milk and to elucidate the potential link to an effect on blood cholesterol level. Fifteen healthy young men participated in a full crossover study during which they consumed three isocaloric diets with similar fat contents that were either (i) high in milk, (ii) high in cheese with equal amounts of dairy calcium, or (iii) a control diet for 14 days. Urine and feces samples were collected and analyzed by NMR-based metabolomics. Cheese and milk consumption decreased urinary choline and TMAO levels and increased fecal excretion of acetate, propionate, and lipid. Compared with milk intake, cheese consumption significantly reduced urinary citrate, creatine, and creatinine levels and significantly increased the microbiota-related metabolites butyrate, hippurate, and malonate. Correlation analyses indicated that microbial and lipid metabolism could be involved in the dairy-induced effects on blood cholesterol level.

Type 2 diabetes has been associated with cognitive decline, but its metabolic mechanism remains unclear. In the present study, we attempted to investigate brain region-specific metabolic changes in db/db mice with cognitive decline and explore the potential metabolic mechanism linking type 2 diabetes and cognitive decline. We analyzed the metabolic changes in seven brain regions of two types of mice (wild-type mice and db/db mice with cognitive decline) using a H NMR-based metabolomic approach. Then, a mixed-model analysis was used to evaluate the effects of mice type, brain region, and their interaction on metabolic changes. Compared with the wild-type mice, the db/db mice with cognitive decline had significant increases in lactate, glutamine (Gln) and taurine as well as significant decreases in alanine, aspartate, choline, succinate, γ-Aminobutyric acid (GABA), glutamate (Glu), glycine, N-acetylaspartate, inosine monophosphate, adenosine monophosphate, adenosine diphosphate, and nicotinamide adenine dinucleotide. Brain region-specific metabolic differences were also observed between these two mouse types. In addition, we found significant interaction effects of mice type and brain region on creatine/phosphocreatine, lactate, aspartate, GABA, N-acetylaspartate and taurine. Based on metabolic pathway analysis, the present study suggests that cognitive decline in db/db mice might be linked to a series of brain region-specific metabolic changes, involving an increase in anaerobic glycolysis, a decrease in tricarboxylic acid (TCA) and Gln-Glu/GABA cycles as well as a disturbance in lactate-alanine shuttle and membrane metabolism.

Herpes simplex virus-1 immediate-early protein ICP0 activates viral genes during early stages of infection, affects cellular levels of multiple host proteins and is crucial for effective lytic infection. Being a RING-type E3 ligase prone to auto-ubiquitination, ICP0 relies on human deubiquitinating enzyme USP7 for protection against 26S proteasomal mediated degradation. USP7 is involved in apoptosis, epigenetics, cell proliferation and is targeted by several herpesviruses. Several USP7 partners, including ICP0, GMPS, and UHRF1, interact through its C-terminal domain (CTD), which contains five ubiquitin-like (Ubl) structures. Despite the fact that USP7 has emerged as a drug target for cancer therapy, structural details of USP7 regulation and the molecular mechanism of interaction at its CTD have remained elusive. Here, we mapped the binding site between an ICP0 peptide and USP7 and determined the crystal structure of the first three Ubl domains bound to the ICP0 peptide, which showed that ICP0 binds to a loop on Ubl2. Sequences similar to the USP7-binding site in ICP0 were identified in GMPS and UHRF1 and shown to bind USP7-CTD through Ubl2. In addition, co-immunoprecipitation assays in human cells comparing binding to USP7 with and without a Ubl2 mutation, confirmed the importance of the Ubl2 binding pocket for binding ICP0, GMPS and UHRF1. Therefore we have identified a novel mechanism of USP7 recognition that is used by both viral and cellular proteins. Our structural information was used to generate a model of near full-length USP7, showing the relative position of the ICP0/GMPS/UHRF1 binding pocket and the structural basis by which it could regulate enzymatic activity.

Alzheimer's disease (AD) is an amyloid-related neurodegenerative disorder and is also considered to be a metabolic disease. Thus, investigation of metabolic mechanisms of amyloid pathology progression is of substantial importance for the diagnosis, prevention and treatment of AD. In the present study, cognitive function and brain metabolism were explored in the transgenic APP/PS1 mouse model of amyloid pathology at different ages. Using an NMR-based metabolomic approach, we examined metabolic changes in six different brain regions of wild-type and APP/PS1 mice at 1, 5 and 10months of age. Learning and memory performance in mice was evaluated using the Morris water maze test. Furthermore, a generalized linear mixed model was employed to analyze the interaction effect between the mouse-type and brain region (or age) on metabolic alterations. Brain region-specific changes in energy metabolism occurred prior to a very early-stage of amyloid pathology (1month of age) in APP/PS1 mice. A hypermetabolic state was identified in the brains of APP/PS1 mice at 5months of age, and the hypothalamus was identified as the main brain region that underwent significant metabolic alterations. The cognitive function of APP/PS1 mice was impaired at 10months of age; moreover, the hypermetabolic state identified in various brain regions at 5months of age was also significantly decreased. In conclusion, our results suggest that a hypothalamic metabolism abnormality may comprise a potential indicator for the early-diagnosis and monitoring of amyloid pathology progression.

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Untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics studies are usually carried out in both positive and negative ion modes; however, it is frequently ignored that the optimal conditions in positive ion mode and negative ion mode are often not the same. We carried out a systematic investigation on urine samples to evaluate the additive effects in negative ion mode. It was found that the widely used conditions, 0.1% formic acid (FA) and NH(4)Ac at different pH, are far from the optimum for untargeted urine metabolomics studies. Compared to 0.1% FA, the use of 1 mM acetic acid (HAc) resulted in almost three times as many detected peaks (401 vs 148) and around five times the size of the peak area (33.55 × 10(6) vs 6.47 × 10(6)). The remarkable improvement can be explained by two factors: (i) a significantly enhanced ionization efficiency due to the combination of an appropriate pH at around 4.0-4.5, the reducibility of H(+), and the high gas-phase basicity of Ac(-) and (ii) a reproducible LC separation due to an acceptable buffering capacity. Our study revealed the importance and necessity of additive optimization, which can be of benefit in related metabolomics studies.

BackgroundDiabetes-associated cognition decline is one of central nervous system complications in diabetic mellitus, while its pathogenic mechanism remains unclear. In this study, 1H nuclear magnetic resonance-based metabonomics and immunohistochemistry was used to explore key metabolic alterations in hippocampus of type 2 diabetic db/db mice with cognition decline in order to advance understanding of mechanisms underlying the pathogenesis of the disease.ResultsMetabonomics reveals that lactate level was significantly increased in hippocampus of db/db mice with cognition decline compared with age-matched wild-type mice. Several tricarboxylic acid cycle intermediates including succinate and citrate were reduced in hippocampus of db/db mice with cognition decline. Moreover, an increase in glutamine level and a decrease in glutamate and γ-aminobutyric acid levels were observed in db/db mice. Results from immunohistochemistry analysis show that glutamine synthetase was increased and glutaminase and glutamate decarboxylase were decreased in db/db mice.ConclusionsOur results suggest that the development of diabetes-associated cognition decline in db/db mice is most likely implicated in a reduction in energy metabolism and a disturbance of glutamate-glutamine shuttling between neurons and astrocytes in hippocampus.Electronic supplementary materialThe online version of this article (doi:10.1186/s13041-016-0223-5) contains supplementary material, which is available to authorized users.

Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability of the model, we analyzed an independent cohort of 22 subjects. We then used our method to evaluate changes in the metabolic patterns of 23 RCC patients before and after nephrectomy. A biomarker cluster of 7 metabolites (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) was identified for the early diagnosis of RCC. The trained SOM model using a biomarker cluster was able to classify 22 test subjects into the appropriate categories. Following nephrectomy, all RCC patients were classified as healthy, which was indicative of metabolic recovery. But using a diagnostic criterion of 0.80, only 3 of the 23 subjects could not be confidently assessed as metabolically recovered after nephrectomy. We successfully followed-up 17 RCC patients for 8 years post-nephrectomy. Eleven of these patients who diagnosed as metabolic recovery remained healthy after 8 years. Our data suggest that a SOM model using a biomarker cluster from serum metabolome can accurately predict early RCC diagnosis and can be used to evaluate postoperative metabolic recovery.