Quantifying fecal and plasma short-chain fatty acids in healthy Thai individuals.

Short-chain fatty acids (SCFAs) are involved in important physiological processes such as gut health and immune response, and changes in SCFA levels can be indicative of disease. Despite the importance of SCFAs in human health and disease, reference values for fecal and plasma SCFA concentrations in healthy individuals are scarce. To address this gap in current knowledge, we developed a simple and reliable derivatization-free GC-TOFMS method for quantifying fecal and plasma SCFAs in healthy individuals. We targeted six linear- and seven branched-SCFAs, obtaining method recoveries of 73-88% and 83-134% in fecal and plasma matrices, respectively. The developed methods are simpler, faster, and more sensitive than previously published methods and are well suited for large-scale studies. Analysis of samples from 157 medically confirmed healthy individuals showed that the total SCFAs in the feces and plasma were 34.1 ± 15.3 µmol/g and 60.0 ± 45.9 µM, respectively. In fecal samples, acetic acid (Ace), propionic acid (Pro), and butanoic acid (But) were all significant, collectively accounting for 89% of the total SCFAs, whereas the only major SCFA in plasma samples was Ace, constituting of 93% of the total plasma SCFAs. There were no statistically significant differences in the total fecal and plasma SCFA concentrations between sexes or among age groups. The data revealed, however, a positive correlation for several nutrients, such as carbohydrate, fat, iron from vegetables, and water, to most of the targeted SCFAs. This is the first large-scale study to report SCFA reference intervals in the plasma and feces of healthy individuals, and thereby delivers valuable data for microbiome, metabolomics, and biomarker research.

Prolonged Egg Supplement Advances Growing Child's Growth and Gut Microbiota.

Protein-energy malnutrition still impacts children's growth and development. We investigated the prolonged effects of egg supplementation on growth and microbiota in primary school children. For this study, 8-14-year-old students (51.5% F) in six rural schools in Thailand were randomly assigned into three groups: (1) whole egg (WE), consuming 10 additional eggs/week (n = 238) (n = 238); (2) protein substitute (PS), consuming yolk-free egg substitutes equivalent to 10 eggs/week (n = 200); and (3) control group (C, (n = 197)). The outcomes were measured at week 0, 14, and 35. At the baseline, 17% of the students were underweight, 18% were stunted, and 13% were wasted. At week 35, compared to the C group the weight and height difference increased significantly in the WE group (3.6 ± 23.5 kg, p < 0.001; 5.1 ± 23.2 cm, p < 0.001). No significant differences in weight or height were observed between the PS and C groups. Significant decreases in atherogenic lipoproteins were observed in the WE, but not in PS group. HDL-cholesterol tended to increase in the WE group (0.02 ± 0.59 mmol/L, ns). The bacterial diversity was similar among the groups. The relative abundance of Bifidobacterium increased by 1.28-fold in the WE group compared to the baseline and differential abundance analysis which indicated that Lachnospira increased and Varibaculum decreased significantly. In conclusion, prolonged whole egg supplementation is an effective intervention to improve growth, nutritional biomarkers, and gut microbiota with unaltered adverse effects on blood lipoproteins.

Metabolic profiles alteration of Southern Thailand traditional sweet pickled mango during the production process.

Sweet pickled mango named Ma-Muang Bao Chae-Im (MBC), a delicacy from the Southern part of Thailand, has a unique aroma and taste. The employed immersion processes (brining 1, brining 2, and immersion in a hypertonic sugar solution, sequentially) in the MBC production process bring changes to the unripe mango, which indicate the occurrence of metabolic profiles alteration during the production process. This occurrence was never been explored. Thus, this study investigated metabolic profile alteration during the MBC production process. The untargeted metabolomics profiling method was used to reveal the changes in volatile and non-volatile metabolites. Headspace solid-phase micro-extraction tandem with gas chromatography quadrupole time of flight (GC/QTOF) was employed for the volatile analysis, while metabolites derivatization for non-volatile analysis. In conclusion, a total of 82 volatile and 41 non-volatile metabolites were identified during the production process. Terpenes, terpenoids, several non-volatile organic acids, and sugars were the major mango metabolites that presented throughout the process. Gamma-aminobutyric acid (GABA) was only observed during the brining processes, which suggested the microorganism's stress response mechanism to an acidic environment and high chloride ions in brine. Esters and alcohols were abundant during the last immersion process, which had an important role in MBC flavor characteristics. The knowledge of metabolites development during the MBC production process would be beneficial for product development and optimization.

Predicting lupus membranous nephritis using reduced picolinic acid to tryptophan ratio as a urinary biomarker.

The current gold standard for classifying lupus nephritis (LN) progression is a renal biopsy, which is an invasive procedure. Undergoing a series of biopsies for monitoring disease progression and treatments is unlikely suitable for patients with LN. Thus, there is an urgent need for non-invasive alternative biomarkers that can facilitate LN class diagnosis. Such biomarkers will be very useful in guiding intervention strategies to mitigate or treat patients with LN. Urine samples were collected from two independent cohorts. Patients with LN were classified into proliferative (class III/IV) and membranous (class V) by kidney histopathology. Metabolomics was performed to identify potential metabolites, which could be specific for the classification of membranous LN. The ratio of picolinic acid (Pic) to tryptophan (Trp) ([Pic/Trp] ratio) was found to be a promising candidate for LN diagnostic and membranous classification. It has high potential as an alternative biomarker for the non-invasive diagnosis of LN.

Deep metabolome: Applications of deep learning in metabolomics.

In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes.