title: MMinte: an application for predicting metabolic interactions among the microbial species in a community.
abstract: BACKGROUND
  The explosive growth of microbiome research has yielded great quantities of data. These data provide us with many answers, but raise just as many questions. 16S rDNA-the backbone of microbiome analyses-allows us to assess α-diversity, β-diversity, and microbe-microbe associations, which characterize the overall properties of an ecosystem. However, we are still unable to use 16S rDNA data to directly assess the microbe-microbe and microbe-environment interactions that determine the broader ecology of that system. Thus, properties such as competition, cooperation, and nutrient conditions remain insufficiently analyzed. Here, we apply predictive community metabolic models of microbes identified with 16S rDNA data to probe the ecology of microbial communities.
  
  
  RESULTS
  We developed a methodology for the large-scale assessment of microbial metabolic interactions (MMinte) from 16S rDNA data. MMinte assesses the relative growth rates of interacting pairs of organisms within a community metabolic network and whether that interaction has a positive or negative effect. Moreover, MMinte's simulations take into account the nutritional environment, which plays a strong role in determining the metabolism of individual microbes. We present two case studies that demonstrate the utility of this software. In the first, we show how diet influences the nature of the microbe-microbe interactions. In the second, we use MMinte's modular feature set to better understand how the growth of Desulfovibrio piger is affected by, and affects the growth of, other members in a simplified gut community under metabolic conditions suggested to be determinant for their dynamics.
  
  
  CONCLUSIONS
  By applying metabolic models to commonly available sequence data, MMinte grants the user insight into the metabolic relationships between microbes, highlighting important features that may relate to ecological stability, susceptibility, and cross-feeding. These relationships are at the foundation of a wide range of ecological questions that impact our ability to understand problems such as microbially-derived toxicity in colon cancer.
authors: Microbiome Program, Center for Individualized Medicine, Mayo Clinic, 200 First St. SW, Rochester, 55905, MN, USA. soares.maria@mayo.edu.|Department of Surgery, Mayo Clinic, Rochester, MN, USA. soares.maria@mayo.edu.
authors: Helena
authors: http://orcid.org/0000-0002-6727-4327
authors: H
authors: Mendes-Soares
authors: Microbiome Program, Center for Individualized Medicine, Mayo Clinic, 200 First St. SW, Rochester, 55905, MN, USA.
authors: Michael
authors: M
authors: Mundy
authors: Harvard Medical School, Boston, MA, USA.
authors: Luis Mendes
authors: LM
authors: Soares
authors: Microbiome Program, Center for Individualized Medicine, Mayo Clinic, 200 First St. SW, Rochester, 55905, MN, USA. chia.nicholas@mayo.edu.|Department of Surgery, Mayo Clinic, Rochester, MN, USA. chia.nicholas@mayo.edu.|Department of Physiology and Biomedical Engineering, Mayo College, Rochester, MN, USA. chia.nicholas@mayo.edu.
authors: Nicholas
authors: N
authors: Chia
mesh_terms: D001419:Bacteria / Q000378:metabolism; D053858:Metabolic Networks and Pathways; D008660:Metabolism; D064307:Microbiota; D008954:Models, Biological; D012984:Software; D013045:Species Specificity
keywords: 16S rDNA; Metabolic network reconstruction; Microbiome; Network; Predictive community modeling