Hydrophilic interaction chromatography-mass spectrometry for anionic metabolic profiling of urine from antibiotic-treated rats

Spermidine improves the antioxidant capacity and morphology of intestinal tissues and regulates intestinal microorganisms in Sichuan white geese

Introduction

Intestinal health is very important to the health of livestock and poultry, and is even a major determining factor in the performance of livestock and poultry production. Spermidine is a type of polyamine that is commonly found in a variety of foods, and can resist oxidative stress, promote cell proliferation and regulate intestinal flora.

Methods

In this study, we explored the effects of spermidine on intestinal health under physiological states or oxidative stress conditions by irrigation with spermidine and intraperitoneal injection of 3-Nitropropionic acid (3-NPA) in Sichuan white goose.

Results and discussion

Our results showed that spermidine could increase the ratio of intestinal villus to crypt and improve intestinal morphology. In addition, spermidine can also reduce malondialdehyde (MDA) accumulation caused by 3-NPA by increasing superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPX) enzyme activity, thus alleviating intestinal damage. Furthermore, spermidine can regulate intestinal digestive enzyme activities and affect intestinal digestion and absorption ability. Spermidine can also promote an increase in intestinal microbial diversity and abundance and alleviate the change of microflora structure caused by 3-NPA. In conclusion, spermidine promotes the production of beneficial intestinal metabolites such as Wikstromol, Alpha-bisabolol and AS 1-5, thus improving the level of intestinal health. Taken together, these results indicate that spermidine can improve intestinal health by improving intestinal morphology, increasing antioxidant capacity and regulating intestinal flora structure.

Quantitative Metabolite Profiling of an Amino Group Containing Pharmaceutical in Human Plasma via Precolumn Derivatization and High-Performance Liquid Chromatography-Inductively Coupled Plasma Mass Spectrometry

Quantitative determination of the candidate drug molecule and its metabolites in biofluids and tissues is an inevitable step in the development of new pharmaceuticals. Because of the time-consuming and expensive nature of the current standard technique for quantitative metabolite profiling, i.e., radiolabeling followed by high-performance liquid chromatography (HPLC) with radiodetection, the development of alternative methodologies is of great interest. In this work, a simple, fast, sensitive, and accurate method for the quantitative metabolite profiling of an amino group containing drug (levothyroxine) and its metabolites in human plasma, based on precolumn derivatization followed by HPLC-inductively coupled plasma mass spectrometry (ICPMS), was developed and validated. To introduce a suitable "heteroelement" (defined here as an element that is detectable with ICPMS), an inexpensive and commercially available reagent, tetrabromophthalic anhydride (TBPA) was used for the derivatization of free NH₂-groups. The presence of a known number of I atoms in both the drug molecule and its metabolites enabled a cross-validation of the newly developed derivatization procedure and quantification based on monitoring of the introduced Br. The formation of the derivatives was quantitative, providing a 4:1 stoichiometric Br/NH₂ ratio. The derivatives were separated via reversed-phase HPLC with gradient elution. Bromine was determined via ICPMS at a mass-to-charge ratio of 79 using H₂ as a reaction gas to ensure interference-free detection, and iodine was determined at a mass-to-charge ratio of 127 for cross-validation purposes. The method developed shows a fit-for-purpose accuracy (recovery between 85% and 115%) and precision (repeatability <15% RSD). The limit of quantification (LoQ) for Br was approximately 100 μg/L.

Metabolic network modeling of microbial communities

Genome-scale metabolic network reconstructions and constraint-based analyses are powerful methods that have the potential to make functional predictions about microbial communities. Genome-scale metabolic networks are used to characterize the metabolic functions of microbial communities via several techniques including species compartmentalization, separating species-level and community-level objectives, dynamic analysis, the 'enzyme-soup' approach, multiscale modeling, and others. There are many challenges in the field, including a need for tools that accurately assign high-level omics signals to individual community members, the need for improved automated network reconstruction methods, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be corresponding advances in the fields of ecology, health science, and microbial community engineering.