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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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21 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Utilisation of flavonoids from Lycium barbarum L. leaves cross‐linked with whey protein for the stabilisation and delivery of β ‐carotene emulsions

Summary Although β ‐carotene is an essential nutrient for the maintenance of human health, it is insoluble in water and extremely unstable, which limits its application. Here, we prepared a stable emulsification system of whey protein (WP)‐ Lycium barbarum L. leaves flavonoids (LBLF) complex as a carrier to load β ‐carotene. The rheological properties, antioxidant properties and stability of emulsion were studied, and the effects of emulsion on the biological retention of β ‐carotene and stability were investigated. The results indicate that, compared with WP, the WP‐LBLF emulsion has smaller particles, a smaller creaming index, and significantly enhanced chemical stability under light and heat treatment conditions. The thiobarbituric acid value of the WP‐LBLF emulsion (0.031 ± 0.016 mg/kg) was dramatically lower than that of the WP emulsion (0.045 ± 0.012 mg/kg) ( P < 0.05). In the simulated gastrointestinal digestion experiment, the retention rate of β ‐carotene was higher in WP‐LBLF emulsion. Additionally, the WP‐LBLF emulsion loaded with β ‐carotene has good salt tolerance and heat stability. In our study, WP‐LBLF was used as an emulsifier to load β ‐carotene emulsion to improve its bioavailability and stability, which provides a new preparation and application method for a complex functional emulsifier.

Fan, Yanli↗
DOE OSTI2024

Metalloproteomics Reveals Multi-Level Stress Response in Escherichia coli When Exposed to Arsenite

The arsRBC operon encodes a three-protein arsenic resistance system. ArsR regulates the transcription of the operon, while ArsB and ArsC are involved in exporting trivalent arsenic and reducing pentavalent arsenic, respectively. Previous research into Agrobacterium tumefaciens 5A has demonstrated that ArsR has regulatory control over a wide range of metal-related proteins and metabolic pathways. We hypothesized that ArsR has broad regulatory control in other Gram-negative bacteria and set out to test this. Here, we use differential proteomics to investigate changes caused by the presence of the arsR gene in human microbiome-relevant Escherichia coli during arsenite (AsIII) exposure. We show that ArsR has broad-ranging impacts such as the expression of TCA cycle enzymes during AsIII stress. Additionally, we found that the Isc [Fe-S] cluster and molybdenum cofactor assembly proteins are upregulated regardless of the presence of ArsR under these same conditions. An important finding from this differential proteomics analysis was the identification of response mechanisms that were strain-, ArsR-, and arsenic-specific, providing new clarity to this complex regulon. Given the widespread occurrence of the arsRBC operon, these findings should have broad applicability across microbial genera, including sensitive environments such as the human gastrointestinal tract.

Biochemistry & Molecular Biology↗
DOE OSTI2026

Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies

Multiple studies have highlighted the interaction of the human microbiome with physiological systems such as the gut, immune, liver, and skin, via key axes. Advances in sequencing technologies and high-performance computing have enabled the analysis of large-scale metagenomic data, facilitating the use of machine learning to predict disease likelihood from microbiome profiles. However, challenges such as compositionality, high dimensionality, sparsity, and limited sample sizes have hindered the development of actionable models. One strategy to improve these models is by incorporating key metadata from both the human host and sample collection/processing protocols. This remains challenging due to sparsity and inconsistency in metadata annotation and availability. In this paper, we introduce a machine learning-based pipeline for predicting human disease states by integrating host and protocol metadata with microbiome abundance profiles from 68 different studies, processed through a consistent pipeline. Our findings indicate that metadata can enhance machine learning predictions, particularly at higher taxonomic ranks like Kingdom and Phylum, though this effect diminishes at lower ranks. Our study leverages a large collection of microbiome datasets comprising 11,208 samples, therefore enhancing the robustness and statistical confidence of our findings. This work is a critical step toward utilizing microbiome and metadata for predicting diseases such as gastrointestinal infections, diabetes, cancer, and neurological disorders.

Mathematics and Computing↗