ISB researchers have shown which blood metabolites are associated with the gut microbiome, genetics, or the interplay between both. Their findings, published in the journal Nature Metabolism, have promising implications for guiding targeted therapies designed to alter the composition of the blood metabolome to improve human health.
New ISB research shows that different patient responses to statins can be explained by the variation in the human microbiome. The findings were published in the journal Med, and suggest that microbiome monitoring could be used to help optimize personalized statin treatments.
The strongest associations with weight loss success or failure – independent of BMI – are found in the genetic capacity of the gut microbiome. These new findings open the door to diagnostic tests that can identify people likely to lose weight with healthy lifestyle changes and those who might need more drastic interventions.
The gut microbiome is an integral component of the body, but its importance in the human aging process is unclear. ISB researchers and their collaborators have identified distinct signatures in the gut microbiome that are associated with either healthy or unhealthy aging trajectories, which in turn predict survival in a population of older individuals.
There is a dichotomy between Bacteroides- and Prevotella-dominated guts — two common gut bacterial genera — and there is a significant barrier when it comes to transitioning from one to the other.
A promising new open-source metabolic modeling tool provides microbiome researchers a path forward in predicting ecosystem function from community structure. News of the software package, called MICOM, was developed in part by researchers in ISB’s Gibbons Lab, and its uses were published in the journal mSystems.
Predicting the alpha diversity of an individual’s gut microbiome is possible by examining metabolites in the blood. The robust relationship between host metabolome and gut microbiome diversity opens the door for a fast, cheap and reliable blood test to identify individuals with low gut diversity.
Sean recently published a commentary in the journal mSystems that outlines a vision of defining ‘microbiome health’ through a host lens: i.e. determining what exact components of the variation in the microboita influence host phenotypes. Much of the variation in the microbiome likely has nothing to do with the health state of the host, but loss/gain of critical diversity and/or functionality can have a major impact on host health. To…
Batch Effects in 16S Datasets Complicate Cross-Study Comparisons High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in…
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