One of the burning questions in the study of the human microbiome is whether and how it is possible to design specific strategies for rebalancing the taxonomic and functional properties of human-associated microbial communities, triggering the transition from ?disease states? to ?healthy states?. While empirical studies provide strong support for the idea that we may be able to cure, or at least treat, a number of diseases by simply transplanting microbiomes, or inducing changes through taxonomic or environmental perturbations, to date little mechanistic understanding exists on how microbial communities work, and on how to extend microbiome research from an empirical science to a systematic, quantitative field of biomedicine. We propose here to establish a computational platform-- a database (Aim 1) with fully integrated analytical software (Aims 2 and 3) --- developed for and with the cooperation of the scientific community. The resource goes beyond cataloguing microbial abundances under different condition;
its aim i s to enable an understanding of networks of interacting species and their condition-dependence, with the goal of eventually facilitating disease diagnosis and prognosis, and designing therapeutic strategies for microbiome intervention. Our project is centered around three key aims: 1. The creation of a Microbial Interaction Network Database (MIND), a public resource that will collect data on inter-species interactions from metagenomic sequencing projects, computer simulations and direct experiments. This database will be accessed through a web-based platform complemented with tools for microbial interaction network analysis and visualization, akin to highly fruitful tools previously developed for the study of genetic networks; the database will also serve as the public repository of microbial networks associated with human diseases; 2. The implementation of an integrated tool for simulation of interspecies interactions under different environments, based on genomic data and whole-cell models of metabolism; 3. The implementation of new algorithms for microbial community analysis and engineering. These algorithms, including stoichiometric, machine-learning and statistical approaches will facilitate a ?synthetic ecology? approach to help design strategies (e.g. microbial transplants or probiotic mixtures) for preventing and targeting microbiome-associated diseases. Our work will fill a major gap in current microbiome research, creating the first platform for global microbial interaction data integration, mining and computation.

Public Health Relevance

Among the major developments of the genomic revolution has been the ability to identify thousands of microbial species and strains living in communities in 5 major habitats in the human body, and the recognition that the relative abundances of these populations is strongly correlated with environment: disease state, diet, treatment protocol and so on. A major challenge in utilizing the deluge of health relevant data is structuring it into a database that facilitates understanding inter-microbial interactions in these communities. The aim of this proposal is to create a database and integrated computational platform, open to and contributed to by the research community, which will greatly accelerate the conversion of data into health related actionable knowledge.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM121950-03
Application #
9638561
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Ravichandran, Veerasamy
Project Start
2017-02-01
Project End
2020-01-31
Budget Start
2019-02-01
Budget End
2020-01-31
Support Year
3
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Boston University
Department
Other Basic Sciences
Type
Graduate Schools
DUNS #
049435266
City
Boston
State
MA
Country
United States
Zip Code
Goldford, Joshua E; Lu, Nanxi; Baji?, Djordje et al. (2018) Emergent simplicity in microbial community assembly. Science 361:469-474
DiMucci, Demetrius; Kon, Mark; Segrè, Daniel (2018) Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks. mSystems 3:
Reznik, Ed; Christodoulou, Dimitris; Goldford, Joshua E et al. (2017) Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity. Cell Rep 20:2666-2677
Zomorrodi, Ali R; Segrè, Daniel (2017) Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities. Nat Commun 8:1563
Zomorrodi, Ali R; Segrè, Daniel (2016) Synthetic Ecology of Microbes: Mathematical Models and Applications. J Mol Biol 428:837-61