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Sean M Gibbons

Publications and source records attributed to Sean M Gibbons.

2 recordsLinked to original sources
PubMed2026

Microbial Interactions with Protein Intake and Preterm Infant Body Composition: Secondary Analysis of a Randomized Trial.

BACKGROUND: Enteral protein supplementation improves preterm infant growth and may impact body composition and the gut microbiota. OBJECTIVES: This study aimed to identify the effects of additional enteral protein supplementation on the gut microbiota and microbial and clinical drivers of body composition. METHODS: Secondary analysis of a masked randomized trial of additional enteral protein vs. standard fortification in preterm infants born at 25 to 28 weeks of gestation (NCT03586102) was conducted. Stool samples at weeks 4 and 8 underwent 16S rRNA sequencing; functional potential was predicted by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2). Body composition was measured by air-displacement plethysmography at 36 wk postmenstrual age (PMA). Least absolute shrinkage and selection operator (LASSO) regression with multivariable linear regression identified body composition predictors. RESULTS: Among 46 infants, gestational age (P = 0.16) and sex (P = 0.55) did not differ between groups. The protein group had higher week 4 Shannon diversity than standard fortification (median 1.2 vs. 0.87, P = 0.049). Week 4 Shannon diversity was positively correlated with fat-free mass z-score at 36 wk PMA (r2 = 0.34, P = 0.02). Adjusting for covariates, the protein group had higher Peptoniphilus (&#x3b2; = 1.6, Padj = 0.10) and lower Vibrio centered log-ratio abundance (&#x3b2; = -0.98, Padj = 0.10); 62 predicted metabolic pathways were lower in the protein group (false discovery rate < 0.20). In combined LASSO models, Bacillus abundance at week 4 was the strongest predictor of fat-free mass z-score (&#x3b2; = -0.17, P < 0.001; R2 = 0.80) and fat mass z-score (&#x3b2; = -0.31, P < 0.001; R2 = 0.66). CONCLUSIONS: Additional protein supplementation is associated with fat-free mass z-score and alterations to the gut microbiota. Clinical variables and microbial variables are key predictors of body composition, suggesting that nutrition, clinical factors, and the gut microbiota jointly contribute to body composition in extremely preterm infants. This study was registered at clinicaltrials.gov as NCT03586102 https://clinicaltrials.gov/study/NCT03586102 (registered in March 2020).

Humans↗
PubMed2025

Understanding disease-associated metabolic changes in human colonic epithelial cells using the iColonEpithelium metabolic reconstruction.

The colonic epithelium plays a key role in the host-microbiome interactions, allowing uptake of various nutrients and driving important metabolic processes. To unravel detailed metabolic activities in the human colonic epithelium, our present study focuses on the generation of the first cell-type-specific genome-scale metabolic model (GEM) of human colonic epithelial cells, named iColonEpithelium. GEMs are powerful tools for exploring reactions and metabolites at the systems level and predicting the flux distributions at steady state. Our cell-type-specific iColonEpithelium metabolic reconstruction captures genes specifically expressed in the human colonic epithelial cells. iColonEpithelium is also capable of performing metabolic tasks specific to the colonic epithelium. A unique transport reaction compartment has been included to allow for the simulation of metabolic interactions with the gut microbiome. We used iColonEpithelium to identify metabolic signatures associated with inflammatory bowel disease. We used single-cell RNA sequencing data from Crohn's Diseases (CD) and ulcerative colitis (UC) samples to build disease-specific iColonEpithelium metabolic networks in order to predict metabolic signatures of colonocytes in both healthy and disease states. We identified reactions in nucleotide interconversion, fatty acid synthesis and tryptophan metabolism were differentially regulated in CD and UC conditions, relative to healthy control, which were in accordance with experimental results. The iColonEpithelium metabolic network can be used to identify mechanisms at the cellular level, and we show an initial proof-of-concept for how our tool can be leveraged to explore the metabolic interactions between host and gut microbiota.

Humans↗