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Optimal Microbiome Networks: Macroecology and Criticality

1,2 and 1,2,*
1
Nexus Group, Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan
2
GI-CORE Global Station for Big Data and Cybersecurity, Hokkaido University, Sapporo 060-0814, Japan
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(5), 506; https://doi.org/10.3390/e21050506
Received: 25 March 2019 / Revised: 4 May 2019 / Accepted: 13 May 2019 / Published: 17 May 2019
(This article belongs to the Special Issue Information Theory in Complex Systems)
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Abstract

The human microbiome is an extremely complex ecosystem considering the number of bacterial species, their interactions, and its variability over space and time. Here, we untangle the complexity of the human microbiome for the Irritable Bowel Syndrome (IBS) that is the most prevalent functional gastrointestinal disorder in human populations. Based on a novel information theoretic network inference model, we detected potential species interaction networks that are functionally and structurally different for healthy and unhealthy individuals. Healthy networks are characterized by a neutral symmetrical pattern of species interactions and scale-free topology versus random unhealthy networks. We detected an inverse scaling relationship between species total outgoing information flow, meaningful of node interactivity, and relative species abundance (RSA). The top ten interacting species are also the least relatively abundant for the healthy microbiome and the most detrimental. These findings support the idea about the diminishing role of network hubs and how these should be defined considering the total outgoing information flow rather than the node degree. Macroecologically, the healthy microbiome is characterized by the highest Pareto total species diversity growth rate, the lowest species turnover, and the smallest variability of RSA for all species. This result challenges current views that posit a universal association between healthy states and the highest absolute species diversity in ecosystems. Additionally, we show how the transitory microbiome is unstable and microbiome criticality is not necessarily at the phase transition between healthy and unhealthy states. We stress the importance of considering portfolios of interacting pairs versus single node dynamics when characterizing the microbiome and of ranking these pairs in terms of their interactions (i.e., species collective behavior) that shape transition from healthy to unhealthy states. The macroecological characterization of the microbiome is useful for public health and disease diagnosis and etiognosis, while species-specific analyses can detect beneficial species leading to personalized design of pre- and probiotic treatments and microbiome engineering. View Full-Text
Keywords: microbiome; complex networks; species diversity; criticality; RSA; information flow; transitions microbiome; complex networks; species diversity; criticality; RSA; information flow; transitions
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Li, J.; Convertino, M. Optimal Microbiome Networks: Macroecology and Criticality. Entropy 2019, 21, 506.

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