Compositional tensor factorization that allows control for individuality across time or space will enable significant advancements in the field of microbiome research
Dr. Rob Knight, Faculty Director for the Center for Microbiome Innovation (CMI), and a team of researchers at UC San Diego, UCLA, and the Flatiron Center for Computational Biology unveiled in a paper published today in Nature Biotechnology, a new tool developed to identify patterns driving differences in microbial composition and allow control in research studies across time or space.
Genetically speaking, humans are 99% the same, but the trillions of microbes that live in, on, and around a person make each of us microbially unique. Microbiomes play a significant role in who we are as humans, in sickness, and in health. Research has shown that microbes help us digest and process nutrients, and also constantly interact with — and help shape — our immune systems. To date, the makeup of our gut microbiomes has been associated with diseases and conditions such as food allergies, obesity, inflammatory bowel disease, and colon cancer.
Not only do the community of trillions of microbes vary dramatically from person to person, but they can significantly change over the course of an individual’s lifetime as well. This microbial variation can be on the scale of hours — from eating a meal to over the course of a night’s sleep — to over a number of years due to aging or other changes in one’s health.
While a great deal of research has advanced our understanding of microbiomes and how they interact with and affect one’s overall health, to date there has been a lack of available research tools to help account for the significant amount of microbial variation from person to person that is reproducible across data sets to help enable broader research advancements in the field.
In “Context-aware dimensionality reduction deconvolutes gut microbial community dynamics,” the new method is compositional tensor factorization (CTF), built specifically to control for individuality in studies with repeated measures of the same subject across time or space. The tool incorporates information from the same host across multiple samples to reveal patterns driving differences in microbial composition.
Research findings using CTF included reproducible longitudinal microbial “signatures” for infant birth mode (i.e. vaginal v. cesarean section). Using tools such as CTF will enable researchers to understand how the combination of microbial “signatures” developed across an individual’s lifetime results in a unique set of microbes.
Tools such as CTF will widely enable and advance the field of microbiome research as the number of repeated measurable microbiome datasets will greatly increase with the goal to bring researchers one step closer to individualized precision medicine for the microbiome.
Additional co-authors include: Cameron Martino, Liat Shenhav, Clarisse A. Marotz, George Armstrong, Daniel McDonald, Yoshiki Vázquez-Baeza, James T. Morton, Lingjing Jiang, Maria Gloria Dominguez-Bello, Austin D. Swafford, and Eran Halperin.
The full paper is available in Nature Biotechnology here.
About Center for Microbiome Innovation at University of California San Diego:
The UC San Diego Center for Microbiome Innovation leverages the university’s strengths in clinical medicine, bioengineering, computer science, the biological and physical sciences, data sciences, and more to coordinate and accelerate microbiome research. We also develop methods for manipulating microbiomes for the benefit of human and environmental health. Learn more at cmi.ucsd.edu/ and follow @CMIDigest.
