Emerging Priorities for Microbiome Research

Abstract

Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology. From June 19-23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. This review is inspired by some of the topics that arose as priority areas from this unique, interactive workshop. The goal of this review is to summarize the Innovation Lab's findings by introducing the reader to emerging challenges, exciting potential, and current directions in microbiome research. The review is broken into five key topic areas: (1) interactions between microbes and the human body, (2) evolution and ecology of microbes, including the role played by the environment and microbe-microbe interactions, (3) analytical and mathematical methods currently used in microbiome research, (4) leveraging knowledge of microbial composition and interactions to develop engineering solutions, and (5) interventional approaches and engineered microbiota that may be enabled by selectively altering microbial composition. As such, this review seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today and provide inspiration for future investigation and multi-disciplinary collaboration.

Keywords: gut microbiome; microbial forensics; microbiome ecology; microbiome evolution; microbiome interactions; prebiotics; probiotics; skin microbiome.

FIGURE 1

Metacommunity approach for studying the ecology and evolution of the microbiome. The ecosystem is discretized in communities (nodes) connected via environmental and human links representing relevant connection determining the spread of species and/or hosts such as river networks and human mobility networks (Convertino et al., 2009; Coyte et al., 2015; Bashan et al., 2016). Local/nodal environmental and human features constitute the likely niche of species to exist in a community. The human-environmental microbiome nexus (HEM), that is the multiplex network between functionally relevant microbiome networks in the human population and the environment, determines some population outcomes of interest (such as diseases in human populations, and other ecological outcomes such as collective population abundance and functional diversity in animal populations). 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.

FIGURE 2

State-of the–science-Gut-Brain-Bidirectional Axis (GBM). Three ways microbes communicate with GBM: neurobiochemical, neuroendocrinal, and neuroimmune mechanisms. Microbial sps can modulate hypothalamus-pituitary-adrenal gland (HPA) axis, by affecting corticotrophin releasing factor (CRF), and cortisone levels which can subsequently affect intestinal permeability and cause hypersensitivity. Neuroactive molecules like γ-aminobutyric acid (GABA), 5-HT, norepinephrine, and dopamine are produced independently by bacteria or through digestion of other food sources. Lactobacillus subspecies, Candida, Streptococcus, E. coli, and Enterococcus can make 5HT which affects sleep, appetite, mood, and cognition (Liu and Zhu, 2018). Clostridiales regulate synthesis and release of 5-HT by making tryptophan available (Martin et al., 2018) for its synthesis. Vagus nerve is the major connection between microbiome and gut, is imperative for GBM-axis. Microbial metabolites like short chain fatty acids, bile acids, and tryptophan can communicate between gut and brain directly or through vagal/spinal highways. Stress, dietary changes and microbiome can lead to cytokines imbalance and increases the risk of intestinal inflammation, IBD, and allergies, etc. Gut microbiota made metabolites like butyrate have epigenetic effect on FOXP3 (forkhead box P3) promoter of T-regs (Furusawa et al., 2013). Prebiotics like fructo-oligosaccharides and galacto-oligosaccharides increase BDNF, serotonin, GABAb receptor levels while reducing cortisone and L-Trp, hence have anti-anxiety and anti-depressant effect. Prebiotics and probiotics regulate the capacity of intestinal microbiota, preserve the integrity of the intestinal barrier (enteroendocrine cells), prevent bacterial translocation and regulate local inflammatory reaction through the intestinal related immune system. BDNF, Brain-derived Neurotrophic Factor; 5-HT, Serotonin; Trp, Tryptophan; SCFA, Short-chain fatty acid. (A) Showing human brain with detailed picture of hypothalamus and pituitary gland (B). Showing the gastric mucosa lined by epithelial, goblet, and enterochromaffin cells (EEC), gastric mucosa is also showing bicarbonate buffer and lumen which has most of the microbiota (from Wikipedia) (C). Showing the epigenetic effect of butyrate on FOXP3 promoter.

FIGURE 3

Conceptual model complexity-uncertainty-scale manifold and desirable model outputs. (A) According to general computational complexity principles, it is expected that microbiome uncertainty (information) grows with the spatio-temporal scale of analysis and the complexity of the system (data) analyzed. These principles are independent of model and microbial systems. The scale is the biological, spatial and/or temporal level of analysis and defines the sensitivity (variability) of the model. For instance, at micro-, meso-, and macro-scales the analysis can be at the individual, population and multi-population level. The scale also defines information complexity that may be related to potentially causal networks for the microbiome such as natural and human spreading networks (in blue and red). Each node of the community details a microbiome interaction network or graph. (B) Three outputs of general interest in microbiome research for assessing systemic risk and resilience that have an increasing focus on systemic properties, from left to right: microbiome feature value over time (e.g., function), microbiome feature state-space over a gradient of drivers, and systemic probability distribution of microbiome features under different scenarios.

FIGURE 4

Association graphs demonstrating microbial co-occurrence networks and microbial composition changes over time. These association graphs can be included into metacommunity models such as the one in Figure 1. (A) Schematic diagram of an association graph. (B) Schematic microbial co-occurrence network based on the association graph shown in panel A. (C) Schematic showing the changes in gut microbiota associations within an individual in response to the introduction of food; other external stressors can be considered equivalently. (D) Variation of the schematic shown in panel C with color-coding to show the degree of change at each node.