Novel Methods for Identification of Multi-Dimensional Co-Exclusion Patterns in Oral Microbiomes

University Of Texas Med Br Galveston, Galveston TX

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Abstract

ABSTRACT Oral health plays an important role in overall human health and well-being. The mouth is the most microbiologically diverse environment in the body with 700?800 bacterial species identified from the human oral microbiome to date. As such, the role of human mouth (gum, teeth) associated microbial communities in overall oral health cannot be underestimated. Understanding inter-species relationships among members of microbial communities is key to understand and control the microbiota. Fortunately, significant improvements in High Throughput Sequencing (HTS) technology have led to a dramatic increase in the number of studies focused on microbiomes. This, in turn, has led to ongoing efforts to develop new tools and methods for the analysis of HTS based microbiome data in the hope to advance from simple identification of microorganisms under/over represented in specific groups of samples to detection of complex patterns between abundances of different microorganisms and understanding relationships among members of microbial communities (e.g., commensalism, mutualism and amensalism). Many such efforts have been focused on using correlation to characterize the strength of pairwise co-occurrence patterns. Co-exclusion is arguably one of the most important patterns reflecting microorganisms' intolerance to each other's presence. Knowing these relations opens an opportunity to manipulate microbiota, personalize anti-microbial and probiotic treatments as well as open the possibility of microbiota transplantation in the future. The co-exclusion patterns, however, cannot be appropriately identified by existing methods. The overall goal of the proposed research is to develop a novel way to identify and evaluate the statistical significance of co-exclusion patterns between two, three or more variables describing a microbiota and allow one to extend analysis beyond microorganism abundance by including other microbiome associated measurements such as, pH, temperature, etc., as well as estimate the expected numbers of false positive co-exclusion patterns in a co-exclusion network.