Optimal Microbiome Networks: Macroecology and Criticality

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.

Emerging Priorities for Microbiome Research

“…These patterns are the foundation of metacommunity modeling. This theoretical and computational modeling philosophy, applicable also in microbiome research for predicting macroecological patterns (Li and Convertino, 2019), is in striking contrast with statistical and mechanistic process-oriented models that are anchored to traditional probability theory or reductionist modeling approaches aiming to mimic precisely the assumed mechanisms, and to preserve the full set of data (thought as uncertainty free) as they are. These mechanistic models typically rely on completely hypothesized processes about the functioning of complex ecosystems such as the microbiome (Hubbell, 2006); mechanisms that are hard to verify if prediction accuracy is the only endpoint to consider.…”

“…In the context of a microbiome, diversity is a key factor in determining the stability of the microbiome and the microbiome-related health of a population (Costello et al, 2012;Coyte et al, 2015). Functional diversity rather than taxonomic diversity is a much more fundamental and meaningful feature highlighting the state of the microbiome (Li and Convertino, 2019). However, functional diversity is difficult to measure and taxonomic diversity, if properly accounting for collective endemic interspecies abundance distribution, can be meaningful of microbiome states and configurations (Woloszynek et al, 2019).…”

“…Each node of the community can contain a detailed characterization of the microbiome interaction network or graph (see Figure 4). Systemic inter-community networks can also be inferred from information theoretic models (Convertino and Valverde, 2019;Li and Convertino, 2019) or statistical models based on interdependence of microbial patterns.…”

“…In this perspective, the theory of multiplex networks (see Servadio and Convertino, 2018) and Li and Convertino (2019) for an example of these methods considering a portfolio of health outcomes and microbial species interactions biodiversity patterns) works well in representing co-evolving nonlinear species, or metacommunities, subjected to stochastic environmental dynamics. Assumption-free pattern-oriented models developed in a metacommunity perspective can detect main local drivers of microbial diversity (Convertino et al, 2013), fundamental dispersal corridors (Martí et al, 2017), alternative stable and transitory states (Rees et al, 2017), stressordependent variability and resilient mechanisms associated to natural stationary conditions or specific population outcomes (Shade et al, 2012;Gonze et al, 2017;Zaneveld et al, 2017;Li and Convertino, 2019; Figure 1). Certainly, models are just a component in future microbiome research, but these data-and theory-based models should also guide field data collection and in vitro experiments (Widder et al, 2016) in order to have an optimal environment-microbiome nexus exploration.…”

“…These mechanistic models typically rely on completely hypothesized processes about the functioning of complex ecosystems such as the microbiome (Hubbell, 2006); mechanisms that are hard to verify if prediction accuracy is the only endpoint to consider. Here we focus our attention on information and network theoretic models (Servadio and Convertino, 2018;Li and Convertino, 2019) that draw their foundational concepts and analytics on statistical physics (or complexity science at large) and define the most relevant input data to characterize macroecological patterns (Li and Convertino, 2019). Information theory is mostly useful when predicting microbiome patterns by simplifying the complexity of the problems and linking driving factors to outcomes considering their whole probability distributions, which can explore the complexity of the microbiome.…”