Probiotic modulation of symbiotic gut microbial–host metabolic interactions in a humanized microbiome mouse model

The transgenomic metabolic effects of exposure to either Lactobacillus paracasei or Lactobacillus rhamnosus probiotics have been measured and mapped in humanized extended genome mice (germ‐free mice colonized with human baby flora). Statistical analysis of the compartmental fluctuations in diverse metabolic compartments, including biofluids, tissue and cecal short‐chain fatty acids (SCFAs) in relation to microbial population modulation generated a novel top‐down systems biology view of the host response to probiotic intervention. Probiotic exposure exerted microbiome modification and resulted in altered hepatic lipid metabolism coupled with lowered plasma lipoprotein levels and apparent stimulated glycolysis. Probiotic treatments also altered a diverse range of pathways outcomes, including amino‐acid metabolism, methylamines and SCFAs. The novel application of hierarchical‐principal component analysis allowed visualization of multicompartmental transgenomic metabolic interactions that could also be resolved at the compartment and pathway level. These integrated system investigations demonstrate the potential of metabolic profiling as a top‐down systems biology driver for investigating the mechanistic basis of probiotic action and the therapeutic surveillance of the gut microbial activity related to dietary supplementation of probiotics.     

NMR-based metabonomics for detection of Helicobacter pylori infection in gerbils: which is more descriptive

Background:  Helicobacter pylori , the human pathogenic gram-negative microaerophilic bacterium, causes chronic gastric infection in more than half of the human population regardless of race. The infection of microbe is not yet controllable to pose a substantial public health impact and a growing social burden. The management of H. pylori infection primarily necessitates accurate and timely diagnosis at case level, on-demand supervision of pathologic progression, and reliable evaluation of the impact of pharmacologic interventions on the patients' population.
Methods:  The characterization of H. pylori infection on gerbils model was performed by metabolic profiling, employing ¹H NMR spectroscopy compounding multivariate pattern recognition strategies. In the same manner, urine samples were individually collected from 10 gerbils infected with H. pylori GS13, and from 10 uninfected control animals equally accessible to feed and water.
Results:  The resultant metabolic profiles indicate that H. pylori infection disturbs carbohydrate metabolism to elevate the levels of α- and β-glucose, and cis -aconitate (a TCA cycle intermediate). In addition to the energy metabolism alteration, the colonization of H. pylori in gerbil stomach generates a remarkable deviation of amino acid metabolism as indicated by depletion of taurine and arginine, and elevation of proline and glutamine in the animal urine. Moreover, the H. pylori infection modifies the gut microbiota as highlighted by a range of microbial-related metabolites such as indoxyl sulfate and hippurate.
Conclusions:  These findings demonstrate that the ¹H NMR-based urine metabolic profiling is a promising technique capable of providing an accurate, noninvasive, and rapid diagnosis of H. pylori infection.     

Strategies for data analyses in a high resolution 1H NMR based metabolomics study of a mouse model of Batten disease

Using an NMR based approach, employing both solution state and high resolution magic angle spinning (HR MAS) ¹H NMR spectroscopy, in conjunction with an array of statistical methods, we report cerebral metabolic deficits in a mouse model of Batten disease (Cln3 null mutant mice). Batten disease is the most common progressive neurodegenerative disorder of childhood and is caused by mutations in the Cln3 gene. In particular, brain tissue from Cln3 mice was characterised by increased concentrations of glutamine, myo-inositol, scyllo-inositol, aspartate and lactate, alongside decreased concentrations of N-acetyl-l-aspartate (NAA), N-acetyl-l-glutamate (NAG), γ-amino butyric acid (GABA), glutamate and creatine. Accompanying changes in lipid deposition were also detected in intact cortical tissue by HR MAS ¹H NMR spectroscopy. To realise the true potential of metabolomic datasets necessitates a comprehensive analysis of the data, such that useful biological information can be extracted and used to generate hypotheses which can be further tested and refined. We found that using a combination of univariate and multivariate analyses, a maximal number of metabolic deficits were successfully identified. In particular the complementary nature of the statistical approaches allowed the definition of changes which were relative, absolute or simply a change in variance, allowing a greater understanding of the disease processes detected.     

Lipidomic Trajectories Characterize Delayed Mucosal Wound Healing in Quiescent Ulcerative Colitis and Identify Potential Novel Therapeutic Targets

Improving the long-term prognosis of ulcerative colitis (UC) requires sustained deep mucosal colonic healing with histologic remission, making the study of colonic tissue regeneration essential. In experimental colitis models, lipid metabolites are recognized as pivotal components of this process. This study aimed to describe the kinetics of wound healing and lipid metabolites engaged in regeneration in the normal colonic mucosa and how they are affected in UC to reveal new therapeutic targets. Experimental colonic wounds were created endoscopically in quiescent UC (n=21) and controls (n=9), and the healing process was surveilled by serial endoscopies and cross-sectional wound biopsies post-wounding. Biopsies were analyzed by liquid chromatography coupled with mass spectrometry. Endoscopic wound scores were significantly higher in UC at day two (p=0.001) and seven (p<0.0001) post-wounding, demonstrating a prolonged wound healing process. The wound scores were correlated with lipid mediators crucial for normal regeneration and sustained UC-specific changes in key phospholipids and eicosanoids, i.e., lysophosphatidylcholine, phosphatidylcholine, lysophosphatidic acid, phosphatidylglycerol, phosphatidylinositol, prostaglandin D₂, and prostaglandin E₁, were observed. A prolonged wound healing process is identified in quiescent UC with altered disease specific lipidomic trajectories providing potential novel therapeutic avenues for stimulating mucosal regeneration as an add-on to the traditional immune suppression treatment.     

Antitumor effect and metabonomics of niclosamide micelles

Polymer micelles now have promising applications in the treatment of cancer, increasing the water solubility and bioavailability of drugs. Previous studies have found that micelles of niclosamide have good anti‐liver cancer effect. In view of the poor water solubility of niclosamide (NIC), we decided to prepare niclosamide micelles. However, its therapeutic mechanism is not clear, so this paper conducted a preliminary study on its vitro anti‐tumour mechanism and metabonomics to find out its impact. It was found that the drug‐loaded micelles (PEG_2K‐FIbu/NIC) had an inhibitory effect on HepG2 cells. Moreover, it can promote apoptosis of HepG2 cells and block S and G2/M phase of cell cycle. The plasma and liver metabolomics of mice in normal group, model group and administration group were studied by UPLC‐MS and ¹H‐NMR. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS‐DA) were used to process the data and find the relevant metabolites. metaboanalyst 5.0 was used to integrate the relevant metabolites to find the main related metabolic pathways. Thus, the anti‐tumour mechanism of PEG_2K‐FIbu/NIC was analysed. Fifty‐one biomarkers were detected in plasma, and 43 biomarkers were detected in liver. After comprehensive biomarker and metabolic pathway analysis, it was found that PEG_2K‐FIbu/NIC micelles could affect the changes of many metabolites, mainly affecting amino acid metabolism. This article is an in‐depth study based on the published Preparation and pharmacodynamics of niclosamide micelles (DOI: 10.1016/j.jddst.2021.103088).     

Metabolomics of the interaction between PPAR‐α and age in the PPAR‐α‐null mouse

Regulation between the fed and fasted states in mammals is partially controlled by peroxisome proliferator‐activated receptor‐α (PPAR‐α). Expression of the receptor is high in the liver, heart and skeletal muscle, but decreases with age. A combined ¹H nuclear magnetic resonance (NMR) spectroscopy and gas chromatography‐mass spectrometry metabolomic approach has been used to examine metabolism in the liver, heart, skeletal muscle and adipose tissue in PPAR‐α‐null mice and wild‐type controls during ageing between 3 and 13 months. For the PPAR‐α‐null mouse, multivariate statistics highlighted hepatic steatosis, reductions in the concentrations of glucose and glycogen in both the liver and muscle tissue, and profound changes in lipid metabolism in each tissue, reflecting known expression targets of the PPAR‐α receptor. Hepatic glycogen and glucose also decreased with age for both genotypes. These findings indicate the development of age‐related hepatic steatosis in the PPAR‐α‐null mouse, with the normal metabolic changes associated with ageing exacerbating changes associated with genotype. Furthermore, the combined metabolomic and multivariate statistics approach provides a robust method for examining the interaction between age and genotype.     

Distinguishing transgenic from non-transgenic Arabidopsis plants by 1H NMR-based metabolic fingerprinting

We have recently reported the construction of an nuclear magnetic resonance （NMR）-based metabonomics study platform, Automics. To examine the application of Automics in transgenic plants, we performed metabolic fingerprinting analysis, i.e., 1H NMR spectroscopy and multivariate analysis, on wild-type and transgenic Arabidopsis. We found that it was possible to distinguish wild-type from four transgenic plants by PLS-DA following application of orthogonal signal correction （OSC）. Scores plot following OSC clearly demonstrates significant variation between the transgenic and non-transgenic groups, suggesting that the metabolic changes among wild-type and transgenic lines are possibly associated with transgenic event, We also found that the major contributing metabolites were some specific amino acids （i.e., threonine and alanine）, which could correspond to the insertion of the selective marker BAR gene in the transgenic plants. Our data suggests that NMR-based metabonomics is an efficient method to distinguish fingerprinting difference between wild-type and transgenic plants, and can potentially be applied in the bio-safety assessment of transgenic plants.     

Metabolomics Standards Workshop and the development of international standards for reporting metabolomics experimental results

Informatics standards and controlled vocabularies are essentialfor allowing information technology to help exchange, manage,interpret and compare large data collections. In a rapidly evolvingfield, the challenge is to work out how best to describe, butnot prescribe, the use of these technologies and methods. AMetabolomics Standards Workshop was held by the US NationalInstitutes of Health (NIH) to bring together multiple ongoingstandards efforts in metabolomics with the NIH research community.The goals were to discuss metabolomics workflows (methods, technologiesand data treatments) and the needs, challenges and potentialapproaches to developing a Metabolomics Standards Initiativethat will help facilitate this rapidly growing field which hasbeen a focus of the NIH roadmap effort. This report highlightsspecific aspects of what was presented and discussed at the1st and 2nd August 2005 Metabolomics Standards Workshop.      

Metabolomic evaluation of rat liver and testis to characterize the toxicity of triazole fungicides

The effects of two triazole fungicides, myclobutanil and triadimefon, on endogenous rat metabolite profiles in blood serum, liver, and testis was assessed using proton nuclear magnetic resonance (¹H-NMR) spectroscopy. Adult male Sprague-Dawley rats were dosed daily by gavage for 14 days with myclobutanil or triadimefon, at two dose levels for each triazole. Following exposure, serum, liver, and testis were collected and processed for NMR analysis. Principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) of the resulting spectra were used to determine changes in metabolite profiles as a result of exposure. Using this approach, responses common to both triazoles were identified, as well as responses indicative of differences in the toxicity of these two compounds. Although changes were observed in serum metabolites following exposure, none were robust enough to be considered a biomarker of exposure/effect. A number of metabolic changes were, however, observed in the liver with both triazoles, particularly in metabolites related to the methionine cycle. The testes of myclobutanil-exposed animals displayed altered levels of creatine and creatinine, consistent with testicular toxicity. Overall, the results of this study support the possible application of a metabolomics approach to assessing the toxicity of triazole fungicides and identifying biomarkers of exposure and/or effect.RNM supported by EPA/NCSU Cooperative Training Agreement #CT826512010 with the Department of Environmental and Molecular Toxicology, North Caroline State University, Raleigh.The research described in this article has been reviewed by the U.S. Environmental Protection Agency, and approved for publication. Approval does not signify that the contents necessarily reflect the views and the policies of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.     

Statistical significance analysis of nuclear magnetic resonance-based metabonomics data

Use of nuclear magnetic resonance (NMR)-based metabonomics to search for human disease biomarkers is becoming increasingly common. For many researchers, the ultimate goal is translation from biomarker discovery to clinical application. Studies typically involve investigators from diverse educational and training backgrounds, including physicians, academic researchers, and clinical staff. In evaluating potential biomarkers, clinicians routinely use statistical significance testing language, whereas academicians typically use multivariate statistical analysis techniques that do not perform statistical significance evaluation. In this article, we outline an approach to integrate statistical significance testing with conventional principal components analysis data representation. A decision tree algorithm is introduced to select and apply appropriate statistical tests to loadings plot data, which are then heat map color-coded according to P score, enabling direct visual assessment of statistical significance. A multiple comparisons correction must be applied to determine P scores from which reliable inferences can be made. Knowledge of means and standard deviations of statistically significant buckets enabled computation of effect sizes and study sizes for a given statistical power. Methods were demonstrated using data from a previous study. Integrated metabonomics data assessment methodology should facilitate translation of NMR-based metabonomics discovery of human disease biomarkers to clinical use.