A unified conceptual framework for prediction and control of microbiomes
Introduction
An intrinsic property of natural biological systems is that they are dynamic and microbiomes are no exception [1]. Understanding the factors that govern microbiome dynamics presents an enormous challenge [2], but new opportunities can emerge by placing studies of temporal dynamics in the context of a unified conceptual framework (Box 1; Figure 1). A unified conceptual framework can help guide the application of developing technologies (e.g. multi-omics) beyond discovery-based exploration towards hypothesis driven investigations [3]. This is essential for maximizing the scientific insights provided by developing technologies, and ultimately enabling knowledge of processes that govern microbiome temporal dynamics.
To predict the function of environmental or human microbiomes under future states and to harness them to achieve beneficial ends requires understanding the processes governing microbiome temporal dynamics [4,5]. For example, environmental microbiomes provide important ecosystem services [6, 7, 8]; a key question is whether they can provision those services in the face of environmental perturbations such as storms, fires, droughts, and contaminant releases. On the human side, microbiomes play essential roles in health and there is significant interest in manipulating microbiomes towards improved health [9] by maintaining beneficial characteristics of microbiomes while suppressing harmful ones. In both systems, predictable outcomes must ultimately be rooted in knowledge of temporal dynamics of the microbiome with respect to community composition, gene expression, and metabolic function [2,10].
While there is a need to ultimately control environmental and human microbiomes for beneficial outcomes to society, there is significant uncertainty regarding the factors governing the temporal dynamics of microbiomes. By advancing a unifying conceptual framework we intend to organize collective thinking [11,12] and investigation of microbiome temporal dynamics. Doing so across environmental and human microbiomes further provides an opportunity to identify major gaps within each of these fields and to promote crosstalk between them. Our goal is to stimulate purposefully coordinated efforts between environmental and human microbiome research targeting a shared conceptual framework focused on transferable knowledge of the processes governing microbiome temporal dynamics.
Section snippets
Conceptual foundations
Our conceptual framework is based on the idea that factors influencing the temporal dynamics of microbiomes can be broken into three broad categories: biotic and abiotic history, internal dynamics, and external forcing factors (Box 1; Figure 1). These three factors are each multifaceted, interact with each other, and feedback with microbiomes. This makes predicting and controlling microbiome dynamics and function an enormous challenge.
There is increasing recognition that biotic and abiotic
Historical contingencies
Historical contingencies are mechanistically influenced by both environmental and biological history. Historical environmental conditions result in physicochemical conditions representing the initial conditions that, in turn, influence how features such as resource availability respond to environmental change. For example, a history of low precipitation may change the physical structure of a soil, thereby changing how accessible organic carbon is to the microbiome [23]. Biologically, the
Internal dynamics
The dynamics of any ecological community can be driven by processes internal to the community itself, namely intra-specific and inter-specific species interactions. The outcome is often temporal transitions in community composition in which early colonizers enhance the ability of other species to succeed, while other species are suppressed. This process is commonly referred to as ‘autogenic succession,’ and is most well-known and best described in plant communities. For example in a classic
External forcing factors
The influence of external (i.e. abiotic) factors over microbiome temporal dynamics can be conceptualized as an environmentally-driven deterministic process [16]. In this case, the prevailing environment determines which taxa have high or low fitness. These differences in fitness lead to a particular assemblage of taxa that are well-adapted to the environment such that temporal changes in environmental conditions drive temporal dynamics in the composition of ecological communities. As discussed
Challenges and opportunities
Across environmental and human microbiome science, the importance of historical contingency is being recognized (e.g. [68]), however key knowledge gaps must be addressed to advance towards an ability to predict and control microbiome dynamics. For example, few studies have explicitly examined how historical conditions impact the dynamics of microbiome composition and expressed metabolic pathways, in addition to system-scale function (e.g. biogeochemical rates, disease progression). The
Conflict of interest statement
Nothing declared.
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as
• of special interest
•• of outstanding interest
Acknowledgements
The research described in this paper was partially supported by the US Department of Energy (DOE), Office of Biological and Environmental Research (BER), as part of Subsurface Biogeochemical Research Program’s Scientific Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL) and partially supported by the Microbiomes in Transition Initiative under the Laboratory Directed Research and Development Program at PNNL. PNNL is a multiprogram national laboratory operated for DOE by
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