The Massachusetts Lab for Artificial Intelligence/Deep Learning for the Microbiome

The Massachusetts Lab for Artificial Intelligence/Deep Learning for the Microbiome

Through a $3.3M grant from the Massachusetts Life Science Center and in-kind support from Brigham and Women’s Hospital and Mass General Brigham, the BWH Massachusetts Host-Microbiome Center (MHMC) and Division of Computational Pathology will establish a new lab to develop and apply advanced AI/deep learning technologies to microbiome research. Dr. Georg Gerber, Chief of BWH Computational Pathology and co-director of the MHMC will head the new lab.

The microbiome is inherently complex and dynamic. Multi-omic data characterizing microbes in culture systems, animal models, and human populations can provide unique and complementary insights into these rich host-microbial ecosystems. However, to fully realize the potential of these data, sophisticated computational approaches are needed.

Artificial Intelligence (AI), and in particular Deep Learning (DL), are revolutionizing many fields, such as speech and image recognition. These technologies are also increasingly impacting the biomedical sciences.

The Lab aims to unleash the power of AI and DL technologies for the microbiome field.

Anchored by a dedicated large GPU with Tesla A100 nodes and CPU compute clusters, the Lab will develop custom AI/DL applications for the microbiome, deploy existing software in a managed and easy-to-use environment, and provide outreach and education to the microbiome community. The Lab will be staffed by principal investigators in the Division of Computational Pathology, as well as an application scientist and network engineers.

A joint initiative between the Brigham and Women’s Hospital (BWH) Division of Computational Pathology and the Massachusetts Host-Microbiome Center (MHMC), the Lab is funded by the Massachusetts Life Sciences Center and Brigham and Women’s Hospital/Mass General Brigham. Industry and academic users will be able to access the Lab through the MHMC’s existing core services model and through collaborations.

Gerber lab study showing gut metabolites predict C. diff recurrence

Gerber lab study showing gut metabolites predict C. diff recurrence

Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the USA, with recurrence rates > 15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Using broad metabolomics data and statistics and machine learning models, Jen Dawkins, a HST PhD student and member of the Gerber lab, showed the metabolites in the gut can accurately predict C. difficile recurrence. These findings have implications for development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence, by providing candidate metabolic biomarkers for diagnostics development, as well as offering insights into the complex microbial and metabolic alterations that are protective or permissive for recurrence.

Dawkins JJ, Allegretti JR, Gibson TE, McClure E, Delaney M, Bry L, Gerber GK. Gut metabolites predict Clostridioides difficile recurrence. Microbiome. 2022 Jun 9;10(1):87. doi: 10.1186/s40168-022-01284-1. PMID: 35681218; PMCID: PMC9178838.