Urinary metabolic modulation in human participants residing in Siachen: a 1H NMR metabolomics approach

Paper spray mass spectrometry combined with machine learning as a rapid diagnostic for chronic kidney disease

A new analytical method for chronic kidney disease (CKD) detection utilizing paper spray mass spectrometry (PS-MS) combined with machine learning is presented. The analytical protocol is rapid and simple, based on metabolic profile alterations in urine. Anonymized raw urine samples were deposited (10 μL each) onto pointed PS-MS sample strips. Without waiting for the sample to dry, 75 μL of acetonitrile and high voltage were applied to the strips, using high resolution mass spectrometry measurement (15 s per sample) with polarity switching to detect a wide range of metabolites. Random forest machine learning was used to classify the resulting data. The diagnostic performance for the potential diagnosis of CKD was evaluated for accuracy, sensitivity, and specificity, achieving results >96% for the training data and >91% for validation and test data sets. Metabolites selected by the classification model as up- or down-regulated in healthy or CKD samples were tentatively identified and in agreement with previously reported literature. The potential utilization of this approach to discriminate albuminuria categories (normo, micro, and macroalbuminuria) was also demonstrated. This study indicates that PS-MS combined with machine learning has the potential to be used as a rapid and simple diagnostic tool for CKD.

Do wearing masks and preservatives have a combined effect on skin health?

Chemical exposure and local hypoxia caused by mask-wearing may result in skin physiology changes. The effects of methylparaben (MeP), a commonly used preservative in personal care products, and hypoxia on skin health were investigated by HaCaT cell and ICR mouse experiments. MeP exposure resulted in lipid peroxidation and interfered with cellular glutathione metabolism, while hypoxia treatment disturbed phenylalanine, tyrosine, and tryptophan biosynthesis pathways and energy metabolism to respond to oxidative stress. A hypoxic environment increased the perturbation of MeP on the purine metabolism in HaCaT cells, resulting in increased expression of proinflammatory cytokines. The synergistic effects were further validated in a mouse model with MeP dermal exposure and "mask-wearing" treatment. CAT, PPARG, and MMP2 were identified as possible key gene targets associated with skin health risks posed by MeP and hypoxia. Network toxicity analysis suggested a synergistic effect, indicating the risk of skin inflammation and skin barrier aging.

Mathematical modeling for freshness/spoilage of chicken breast using chemometric analysis

Chicken meat spoilage is a significant concern for food safety and quality, and this study aims to predict the spoilage point of chicken breast meat through various attributes and metabolites. Chicken meat was stored in anaerobic packaging at 4 °C for 13 days, and various meat quality attributes (pH, drip loss, color, volatile basic nitrogen [VBN], total aerobic bacteria [TAB], and metabolites) were examined. First, the spoiled point (VBN >20 mg/100 g and/or TAB >7 log CFU/g) of the chicken breast meat was determined. Using univariate and multivariate analyses, twenty-four candidate metabolites were identified. A receiver operating characteristic (ROC) analysis was used to validate the obtained binary logistic regression model using nine metabolites (proline, methionine, glutamate, threonine, acetate, uridine 5'-monophosphate, hypoxanthine, glycine, and glutamine). The results showed a high area under the ROC curve value (0.992). Thus, this study confirmed the predictability of spoilage points in chicken breast meat through these nine metabolites.

Insight into Nephrotoxicity and Processing Mechanism of Arisaema erubescens (Wall.) Schott by Metabolomics and Network Analysis

Background

Arisaematis Rhizome (AR) has been used as a damp-drying, phlegm-resolving, wind-expelling, pain-alleviating, and swelling-relieving drug for thousands of years. However, the toxicity limits its clinical applications. Therefore, AR is usually processed (Paozhi in Chinese) prior to clinical use. In this study, the integration of ultra-high performance liquid chromatography-quadrupole/ time-of-flight mass spectrometry-based metabolomics and network analysis was adopted to investigate the metabolic shifts induced by AR and explore the processing mechanism.

Materials and Methods

Extracts of crude and processed AR products (1g/kg) were intragastrically administered to rats once daily for four consecutive weeks. The renal function was evaluated by blood urea nitrogen, creatinine, interleukin-1 beta (IL-1β) and tumor necrosis factor-alpha (TNF-α), malondialdehyde (MDA), super oxide dismutase (SOD), the ratio of glutathione/glutathione disulfide (GSH/GSSH), glutathione peroxidase (GSH-Px) and histopathological examination. Furthermore, the chemical composition of AR was clarified by ultra-high performance liquid chromatography-quadrupole/ time-of-flight mass spectrometry, after which the integration of metabolomics and network analysis was adopted to investigate the metabolic shifts induced by AR and explore the processing mechanism.

Results

Crude AR caused renal damage by stimulating inflammation and oxidative stress, as confirmed by the increased production of IL-1β, TNF-α and MDA, and decreased levels of SOD, GSH/GSSH and GSH-Px. Processing with ginger juice, alumen and bile juice alleviated the damage to kidney. Metabolomics results showed that a total of 35 potential biomarkers enriched in amino acid metabolism, glycerophospholipid metabolism, fatty acid-related pathways, etc. were deduced to be responsible for the nephrotoxicity of AR and the toxicity-reducing effect of processing.

Conclusion

This work provided theoretical and data support for the in-depth study of the processing mechanism, showing that processing reduces AR nephrotoxicity through multiple metabolic pathways.

Urine metabolomics analysis of sleep quality in deep-underground miners: A pilot study

Background

In previous questionnaire surveys of miners, sleep disorders were found among underground workers. The influence of the special deep-underground environment and its potential mechanism are still unclear. Therefore, this study intends to utilize LC-MS metabolomics to study the potential differences between different environments and different sleep qualities.

Methods

Twenty-seven miners working at 645-1,500 m deep wells were investigated in this study, and 12 local ground volunteers were recruited as the control group. The Pittsburgh Sleep Quality Index (PSQI) was used to examine and evaluate the sleep status of the subjects in the past month, and valuable basic information about the participants was collected. PSQI scores were obtained according to specific calculation rules, and the corresponding sleep grouping and subsequent analysis were carried out. Through liquid chromatography-mass spectrometry (LC-MS) non-targeted metabolomics analysis, differences in metabolism were found by bioinformatics analysis in different environments.

Results

Between the deep-underground and ground (DUvsG) group, 316 differential metabolites were identified and 125 differential metabolites were identified in the good sleep quality vs. poor sleep quality (GSQvsPSQ) group. The metabolic pathways of Phenylalanine, tyrosine and tryptophan biosynthesis (p = 0.0102) and D-Glutamine and D-glutamate metabolism (p = 0.0241) were significantly enriched in DUvsG. For GSQvsPSQ group, Butanoate metabolism was statistically significant (p = 0.0276). L-Phenylalanine, L-Tyrosine and L-Glutamine were highly expressed in the deep-underground group. Acetoacetic acid was poorly expressed, and 2-hydroxyglutaric acid was highly expressed in good sleep quality.

Conclusions

The influence of the underground environment on the human body is more likely to induce specific amino acid metabolism processes, and regulate the sleep-wake state by promoting the production of excitatory neurotransmitters. The difference in sleep quality may be related to the enhancement of glycolytic metabolism, the increase in excitatory neurotransmitters and the activation of proinflammation. L-phenylalanine, L-tyrosine and L-glutamine, Acetoacetic acid and 2-hydroxyglutaric acid may be potential biomarkers correspondingly.