Priyanka Baloni

Bile Acids Provide More Evidence of the Gut Microbiome’s Effect on Alzheimer’s Disease

ISB researchers and their collaborators are looking beyond the one-drug, one-solution approach that has thus far failed in Alzheimer’s disease research. Instead, they are focusing on other promising research avenues, such as the possible role of the gut microbiome in dementia.

Bacterial tug of war between prevotella and bacteroides -- gut microbiome

It’s ‘Either/Or’ for Two Common Gut Microbiome Genera, and Switching Teams Is Tougher Than Expected

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.

Dr. Christian Diener, postdoc in ISB's Gibbons Lab.

New Modeling Tool Allows Microbiome Researchers to Map Community Ecology to Ecosystem Function

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.

Using Blood to Predict Gut Microbiome Diversity

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.

Seeing the microbiome through a host lens

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…

Correcting Batch Effects in Microbiome Data

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…