Biology subjects

Sterrett, J. D.

Publications and source records attributed to Sterrett, J. D..

2 recordsLinked to original sources

bioRxiv2024

Appearance of green tea compounds in plasma following green tea consumption is modulated by the gut microbiome in mice

Studies have suggested that phytochemicals in green tea have systemic anti-inflammatory and neuroprotective effects. However, the mechanisms behind these effects are poorly understood, possibly due to differential metabolism of phytochemicals resulting from variation in gut microbiome composition. To unravel this complex relationship, our team utilized a novel combined microbiome analysis and metabolomics approach applied to low complexity microbiome (LCM) and human colonized (HU) gnotobiotic mice treated with an acute dose of powdered matcha green tea. A total of 20 LCM mice received 10 distinct human fecal slurries for an n=2 mice per human gut microbiome; 9 LCM mice remained un-colonized with human slurries throughout the experiment. We performed untargeted metabolomics on green tea and plasma to identify green tea compounds that were found in plasma of LCM and HU mice that had consumed green tea. 16S ribosomal RNA gene sequencing was performed on feces of all mice at study end to assess microbiome composition. We found multiple green tea compounds in plasma associated with microbiome presence and diversity (including acetylagmatine, lactiflorin, and aspartic acid negatively associated with diversity). Additionally, we detected strong associations between bioactive green tea compounds in plasma and specific gut bacteria, including associations between spiramycin and Gemmiger, and between wildforlide and Anaerorhabdus. Additionally, some of the physiologically relevant green tea compounds are likely derived from plant-associated microbes, highlighting the importance of considering foods and food products as meta-organisms. Overall, we describe a novel workflow for discovering relationships between individual food compounds and composition of the gut microbiome. ImportanceFoods contain thousands of unique and biologically important compounds beyond the macro- and micro-nutrients listed on nutrition facts labels. In mammals, many of these compounds are metabolized by the community of microbes in the colon. These microbes may impact the thousands of biologically important compounds we consume; therefore, understanding microbial metabolism of food compounds will be important for understanding how foods impact health. We used metabolomics to track green tea compounds in plasma of mice with and without complex microbiomes. From this, we can start to recognize certain groups of green tea-derived compounds that are impacted by mammalian microbiomes. This research presents a novel technique for understanding microbial metabolism of food-derived compounds in the gut, which can be applied to other foods.

systems biology↗

bioRxiv2022

Poly-omic risk scores predict inflammatory bowel disease diagnosis

Inflammatory Bowel Disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD Multi-omics Database, with 1,785 repeated samples from 131 individuals (103 cases, 27 controls). After splitting the participants into training and testing groups, we used mixed effects least absolute shrinkage and selection operator (LASSO) regression to select features for each -omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual -omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between datasets. Individually, metabolomics and viromics scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerkes R2 of 0.46 and an AUC of 0.80 [95% CI: 0.63, 0.98]. Our work suggests that some single-omic models for complex traits are more predictive than others, that incorporating multiple -omics datasets may improve prediction, and that each -omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic datasets. ImportanceComplex traits are characterized by many biological and environmental factors, such that multi-omics datasets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple -omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each -omic dataset in single-omic versus multi-omics models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these -omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple -omic data types to disentangle complex disease etiologies and biological signatures.

bioinformatics↗