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.
In ISB’s first-ever Research Roundtable event, Assistant Professor Dr. Sean Gibbons delivered a presentation titled “Gut-Check: Personalized Nutrition and Your Microbiome.” His talk covered a lot of ground, including recently published research showing how the health of our microbiomes can predict longevity, and how we can build and maintain a healthy gut microbiome.
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.
ISB researchers examined the associations between the gut microbiomes of about 3,400 people and roughly 150 host characteristics. The team looked at diet, medication use, clinical blood markers, and other lifestyle and clinical factors, and found evidence that variations of the gut microbiome are associated with health and disease.
Everybody pees and poops. What if there was a way to use the byproducts of our everyday bodily functions to understand the general health of a population? That is exactly what MIT’s Dr. Eric Alm is pursuing. In an ISB-Town Hall Seattle live stream, Alm discussed the promise of this novel form of public health tracking.
ISB’s virtual course and symposium focusing on the microbiome and its future role in precision medicine will take place on October 15 and 16. The event’s website went live earlier this week. The virtual course will be taught by Sean Gibbons, Christian Diener, Tomasz Wilmanski, Noa Rappaport, Alex Carr, Priyanka Baloni and Nathan Price. Symposium speakers are Jason Papin (University of Virginia), Ines Thiele (National University of Ireland, Galway), Thomas…
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.
Freaked out about a “germy” bathroom? You don’t need to be. ISB Assistant Professor and microbiome researcher Dr. Sean Gibbons was featured prominently in an article, headlined “The Germiest Place in your Bathroom Isn’t Your Toilet,” published online by TIME.
The human microbiome is a relatively new area of research, and there are numerous questions surrounding it. What is the human microbiome? Can we change it? Does it make us sick? Keep us well? ISB Assistant Professor and microbiome researcher Dr. Sean Gibbons answers these questions — and many more.
“This new organ that we’re coming to recognize as the microbiome is part and parcel to the functionality of the whole system, and if it breaks down, if it starts to fall apart, we start to get sick,” said Dr. Sean Gibbons, ISB’s newest faculty member, in a WGBH Forum Network presentation.
Dr. Sean Gibbons has joined ISB as our newest faculty member. Gibbons’ new position brings a number of changes, including relocating to the Pacific Northwest from the Northeast. Read on for a Q&A with Gibbons that sheds light on his research career to date, areas of study and even a hidden talent.
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…