Low-abundant bacteria drive compositional changes in the gut microbiota after dietary alteration

• Main Authors: Jacquelynn Benjamino, Stephen Lincoln, Ranjan Srivastava, Joerg Graf
• Format: Article
• Language: English
• Published: BMC 2018-05-01
• Series: Microbiome
• Subjects: Termite microbiota; 16S rRNA gene sequencing; Artificial neural network; Deep learning; Low-abundant drivers
• Online Access: http://link.springer.com/article/10.1186/s40168-018-0469-5

Abstract Background As the importance of beneficial bacteria is better recognized, understanding the dynamics of symbioses becomes increasingly crucial. In many gut symbioses, it is essential to understand whether changes in host diet play a role in the persistence of the bacterial gut community. In this study, termites were fed six dietary sources and the microbial community was monitored over a 49-day period using 16S rRNA gene sequencing. A deep backpropagation artificial neural network (ANN) was used to learn how the six different lignocellulose food sources affected the temporal composition of the hindgut microbiota of the termite as well as taxon-taxon and taxon-substrate interactions. Results Shifts in the termite gut microbiota after diet change in each colony were observed using 16S rRNA gene sequencing and beta diversity analyses. The artificial neural network accurately predicted the relative abundances of taxa at random points in the temporal study and showed that low-abundant taxa maintain community driving correlations in the hindgut. Conclusions This combinatorial approach utilizing 16S rRNA gene sequencing and deep learning revealed that low-abundant bacteria that often do not belong to the core community are drivers of the termite hindgut bacterial community composition.