ena-STUDY-UCONNMISEQ-14-04-2017-22:20:10:517-732
The Effect of Diet on the Microbiota of the termite Reticulitermes flavipes
As the importance of beneficial bacteria becomes more apparent, understanding the dynamics of the symbiosis are a sought-after field. In many gut symbioses, it is essential to understand whether changes in the host diet plays a role in the bacterial community persisting in the gut.  Although many advances have been made in genomic sequencing for analyzing the composition of microbiomes, very few studies have attempted to learn and model their dynamics.  The challenge in learning and modeling microbiome dynamics is due to the complex, interdependent, and large number of highly non-linear interactions among members of a microbiome, as well as environmental factors. In this study, six diet sources were used to understand the effect of diet change on the termite hindgut microbiota. Termites were fed a mulch mixture, cardboard, spruce, oak, maple, and birch and hindguts were sampled at various time points over a 49-day period. DNA was extracted from each hindgut and 16S rRNA gene sequencing on the V4 hypervariable region was performed to determine the bacterial community at each time point. It was found that the sixteen core taxa [1] remained stable regardless of diet, and that many of the changes seen were in non-core taxa. The hindgut microbiota shifted on an OTU (operational taxonomic unit) level from the original day 0 samples when the termites fed on different wood sources.  We also present a computationally tractable strategy using machine learning methods and stochastic optimization to characterize a microbiome. A deep backpropagation artificial neural network (ANN) is utilized to learn how the six different lignocellulose food sources affect the temporal composition of the hindgut microbiome of Reticulitermes flavipes, the eastern subterranean termite.  These dynamics are then explored using a sensitivity analysis of the ANN to determine strength of taxon-taxon and taxon-substrate interactions.  The findings of the ANN are compared with 16S rRNA amplicon sequencing analysis.
As the importance of beneficial bacteria becomes more apparent, understanding the dynamics of the symbiosis are a sought-after field. In many gut symbioses, it is essential to understand whether changes in the host diet plays a role in the bacterial community persisting in the gut.  Although many advances have been made in genomic sequencing for analyzing the composition of microbiomes, very few studies have attempted to learn and model their dynamics.  The challenge in learning and modeling microbiome dynamics is due to the complex, interdependent, and large number of highly non-linear interactions among members of a microbiome, as well as environmental factors. In this study, six diet sources were used to understand the effect of diet change on the termite hindgut microbiota. Termites were fed a mulch mixture, cardboard, spruce, oak, maple, and birch and hindguts were sampled at various time points over a 49-day period. DNA was extracted from each hindgut and 16S rRNA gene sequencing on the V4 hypervariable region was performed to determine the bacterial community at each time point. It was found that the sixteen core taxa [1] remained stable regardless of diet, and that many of the changes seen were in non-core taxa. The hindgut microbiota shifted on an OTU (operational taxonomic unit) level from the original day 0 samples when the termites fed on different wood sources.  We also present a computationally tractable strategy using machine learning methods and stochastic optimization to characterize a microbiome. A deep backpropagation artificial neural network (ANN) is utilized to learn how the six different lignocellulose food sources affect the temporal composition of the hindgut microbiome of Reticulitermes flavipes, the eastern subterranean termite.  These dynamics are then explored using a sensitivity analysis of the ANN to determine strength of taxon-taxon and taxon-substrate interactions.  The findings of the ANN are compared with 16S rRNA amplicon sequencing analysis.