Assessing the clinical value of faecal bile acid profiling to predict recurrence in primary Clostridioides difficile infection

The Progression of Microbiome Therapeutics for the Management of Gastrointestinal Diseases and Beyond

There has been an increased ability to investigate human microbiota through next-generation sequencing and functional assessment. This advancement has rapidly expanded our ability to study and manipulate the gastrointestinal microbiome to mitigate disease. Fecal microbiota transplantation, a therapy that broadly transfers the entire intestinal ecosystem, has been explored as a potential therapeutic in a variety of gastrointestinal, hepatic, and extraintestinal conditions. The field, however, continues to evolve, with a movement toward precision microbiome therapeutics individualizing care for various disorders. This review will describe the use of fecal microbiota transplantation, microbiota restoration, and precision microbiome therapeutics focusing on gastrointestinal and hepatic diseases.

Repurposing dried blood spot device technology to examine bile acid profiles in human dried fecal spot samples

Dried blood spot (DBS) analysis has existed for >50 years, but application of this technique to fecal analysis remains limited. To address whether dried fecal spots (DFS) could be used to measure fecal bile acids, we collected feces from five subjects for each of the following cohorts: 1) healthy individuals, 2) individuals with diarrhea, and 3) Clostridioides difficile-infected patients. Homogenized fecal extracts were loaded onto quantitative DBS (qDBS) devices, dried overnight, and shipped to the bioanalytical lab at ambient temperature. For comparison, source fecal extracts were shipped on dry ice and stored frozen. After 4 mo, frozen fecal extracts and ambient DFS samples were processed and subjected to targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics with stable isotope-labeled standards. We observed no differences in the bile acid levels measured between the traditional extraction and the qDBS-based DFS methods. This pilot data demonstrates that DFS-based analysis is feasible and warrants further development for fecal compounds and microbiome applications.NEW & NOTEWORTHY Stool analysis in remote settings can be challenging, as the samples must be stored at -80°C and transported on dry ice for downstream processing. Our work indicates that dried fecal spots (DFS) on Capitainer quantitative DBS (qDBS) devices can be stored and shipped at ambient temperature and yields the same bile acid profiles as traditional samples. This approach has broad applications for patient home testing and sample collection in rural communities or resource-limited countries.

Untargeted plasma metabolome identifies biomarkers in patients with extracranial arteriovenous malformations

Objective: This study aimed to investigate the plasma metabolic profile of patients with extracranial arteriovenous malformations (AVM). Method: Plasma samples were collected from 32 AVM patients and 30 healthy controls (HC). Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to analyze the metabolic profiles of both groups. Metabolic pathway enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst. Additionally, machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO) and random forest (RF) were conducted to screen characteristic metabolites. The effectiveness of the serum biomarkers for AVM was evaluated using a receiver-operating characteristics (ROC) curve. Result: In total, 184 differential metabolites were screened in this study, with 110 metabolites in positive ion mode and 74 metabolites in negative mode. Lipids and lipid-like molecules were the predominant metabolites detected in both positive and negative ion modes. Several significant metabolic pathways were enriched in AVMs, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and protein translation. Through machine learning algorithms, nine metabolites were identify as characteristic metabolites, including hydroxy-proline, L-2-Amino-4-methylenepentanedioic acid, piperettine, 20-hydroxy-PGF2a, 2,2,4,4-tetramethyl-6-(1-oxobutyl)-1,3,5-cyclohexanetrione, DL-tryptophan, 9-oxoODE, alpha-Linolenic acid, and dihydrojasmonic acid. Conclusion: Patients with extracranial AVMs exhibited significantly altered metabolic patterns compared to healthy controls, which could be identified using plasma metabolomics. These findings suggest that metabolomic profiling can aid in the understanding of AVM pathophysiology and potentially inform clinical diagnosis and treatment.