Project description:Nuclear magnetic resonance (NMR) spectroscopy is one of the most promising methods for use in metabolomics studies as it is able to perform non targeted measurement of metabolites in a quantitative and non-destructive way. Sample preparation of liquid samples like urine or blood serum is comparatively easy in NMR metabolomics, because mainly buffer and chemical shift reference substance are added. For solid samples like feces suitable extraction protocols need to be defined as initial step, where the exact protocol depends on sample type and features. Focusing on short chain fatty acids (SCFAs) in mice feces, we describe here a set of extraction protocols developed with the aim to suppress changes in metabolite composition within 24 h after extraction. Feces are obtained from mice fed on either standard rodent diet or high fat diet. The protocols presented in this manuscript are straightforward for application, and successfully minimize residual bacterial and enzymatic activities. Additionally, they are able to minimize the lipid background originating from the high fat diet.
Project description:Nuclear magnetic resonance (NMR)-based metabolomics, which comprehensively measures metabolites in biological systems and investigates their response to various perturbations, is widely used in research to identify biomarkers and investigate the pathogenesis of underlying diseases. However, further applications of high-field superconducting NMR for medical purposes and field research are restricted by its high cost and low accessibility. In this study, we applied a low-field, benchtop NMR spectrometer (60 MHz) employing a permanent magnet to characterize the alterations in the metabolic profile of fecal extracts obtained from dextran sodium sulfate (DSS)-induced ulcerative colitis model mice and compared them with the data acquired from high-field NMR (800 MHz). Nineteen metabolites were assigned to the 60 MHz 1H NMR spectra. Non-targeted multivariate analysis successfully discriminated the DSS-induced group from the healthy control group and showed high comparability with high-field NMR. In addition, the concentration of acetate, identified as a metabolite with characteristic behavior, could be accurately quantified using a generalized Lorentzian curve fitting method based on the 60 MHz NMR spectra.
Project description:BackgroundMost of the current bacteriophages (phages) are mostly isolated from environments. However, phages isolated from feces might be more specific to the bacteria that are harmful to the host. Meanwhile, some phages from the environment might affect non-pathogenic bacteria for the host.MethodsHere, bacteriophages isolated from mouse feces were intratracheally (IT) or intravenously (IV) administered in pneumonia mice caused by Pseudomonas aeruginosa at 2 hours post-intratracheal bacterial administration. As such, the mice with phage treatment, using either IT or IV administration, demonstrated less severe pneumonia as indicated by mortality, serum cytokines, bacteremia, bacterial abundance in bronchoalveolar lavage fluid (BALF), and neutrophil extracellular traps (NETs) in lung tissue (immunofluorescence of neutrophil elastase and myeloperoxidase).ResultsInterestingly, the abundance of phages in BALF from the IT and IV injections was similar, supporting a flexible route of phage administration. With the incubation of bacteria with neutrophils, the presence of bacteriophages significantly improved bactericidal activity, but not NETs formation, with the elevated supernatant IL-6 and TNF-α, but not IL-1β. In conclusion, our findings suggest that bacteriophages against Pseudomonas aeruginosa can be discovered from feces of the host.ConclusionsThe phages attenuate pneumonia partly through an enhanced neutrophil bactericidal activity, but not via inducing NETs formation. The isolation of phages from the infected hosts themselves might be practically useful for future treatment. More studies are warranted.
Project description:Since requirements of related applications for time series remotely-sensed images with high spatial resolution have been hard to be satisfied under current observation conditions of satellite sensors, it is key to reconstruct high-resolution images at specified dates. As an effective data reconstruction technique, spatiotemporal fusion can be used to generate time series land surface parameters with a clear geophysical significance. In this study, an improved fusion model based on the Sparse Representation-Based Spatiotemporal Reflectance Fusion Model (SPSTFM) is developed and assessed with reflectance data from Gaofen-2 Multi-Spectral (GF-2 MS) and Gaofen-1 Wide-Field-View (GF-1 WFV). By introducing a spatially enhanced training method to dictionary training and sparse coding processes, the developed fusion framework is expected to promote the description of high-resolution and low-resolution overcomplete dictionaries. Assessment indices including Average Absolute Deviation (AAD), Root-Mean-Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), spectral angle mapper (SAM), structure similarity (SSIM) and Erreur Relative Global Adimensionnelle de Synthèse (ERGAS) are then used to test employed fusion methods for a parallel comparison. The experimental results show that more accurate prediction of GF-2 MS reflectance than that from the SPSTFM can be obtained and furthermore comparable with popular two-pair based reflectance fusion models like the Spatial and Temporal Adaptive Fusion Model (STARFM) and the Enhanced-STARFM (ESTARFM).
Project description:Exosomes secreted by colonic epithelial cells are present in feces and contain valuable epigenetic information, such as miRNAs, proteins, and metabolites. An in-depth study of this information is conducive to the diagnosis or treatment of relevant diseases. A crucial prerequisite of such a study is to establish an efficient isolation method, through which we can obtain a relatively more significant amount of exosomes from feces. This protocol is designed to effectively isolate a large number of exosomes from contaminants and other particles in feces by a combined method with fast filtration and sucrose density gradient ultracentrifugation. Exosomes generated by this method are suitable for further RNA, protein, and lipid analysis.
Project description:A metabolomic investigation of depression and chronic fluoxetine treatment was conducted using a chronic unpredictable mild stress model with C57BL/6N mice. Establishment of the depressive model was confirmed by body weight measurement and behavior tests including the forced swim test and the tail suspension test. Behavioral despair by depression was reversed by four week-treatment with fluoxetine. Hippocampus, serum, and feces samples collected from four groups (control + saline, control + fluoxetine, model + saline, and model + fluoxetine) were subjected to metabolomic profiling based on ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry. Alterations in the metabolic patterns were evident in all sample types. The antidepressant effects of fluoxetine appeared to involve various metabolic pathways including energy metabolism, neurotransmitter synthesis, tryptophan metabolism, fatty acid metabolism, lipid metabolism, and bile acid metabolism. Predictive marker candidates of depression were identified, including β-citryl-L-glutamic acid (BCG) and docosahexaenoic acid (DHA) in serum and chenodeoxycholic acid and oleamide in feces. This study suggests that treatment effects of fluoxetine might be differentiated by altered levels of tyramine and BCG in serum, and that DHA is a potential serum marker for depression with positive association with hippocampal DHA. Collectively, our comprehensive study provides insights into the biochemical perturbations involved in depression and the antidepressant effects of fluoxetine.