Decoding molecular interactions in microbial communities - PubMed (original) (raw)

Review

Decoding molecular interactions in microbial communities

Nicole A Abreu et al. FEMS Microbiol Rev. 2016 Sep.

Abstract

Microbial communities govern numerous fundamental processes on earth. Discovering and tracking molecular interactions among microbes is critical for understanding how single species and complex communities impact their associated host or natural environment. While recent technological developments in DNA sequencing and functional imaging have led to new and deeper levels of understanding, we are limited now by our inability to predict and interpret the intricate relationships and interspecies dependencies within these communities. In this review, we highlight the multifaceted approaches investigators have taken within their areas of research to decode interspecies molecular interactions that occur between microbes. Understanding these principles can give us greater insight into ecological interactions in natural environments and within synthetic consortia.

Keywords: corrinoids; metagenomics; microbial community; microbiome; molecular interactions; synthetic biology.

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Figures

Graphical Abstract Figure.

Graphical Abstract Figure.

Understanding how microbes interact is key to deciphering microbial community assembly and stability; approaches that span whole communities to single isolate analyses, as well as sequence-function and synthetic approaches, lead to a deeper understanding of microbial interactions.

Figure 1.

Figure 1.

Integrated, multilayered approaches needed to decode microbiomes. Whole communities can be studied by culture-independent approaches performed directly on environmental samples or on communities cultivated in the laboratory. Culture-dependent approaches, in which defined consortia, cocultures or single microbes are examined by a variety of methods in the laboratory, enable more detailed studies about molecular interactions in a subset of the community.

Figure 2.

Figure 2.

Simplified model of carbon flow in the human gut. Complex carbohydrates obtained from the diet or host mucin are broken down by saccharolytic primary fermenters, usually Bacteroides and Bifidobacterium spp (Willis et al. 1996). Some end products of this fermentation (acetate, butyrate and propionate) are absorbed by the host. Other products such as glucose (not shown) and lactate are fermented to butyrate by bacteria such as Clostridium, Eubacterium and Fusobacterium spp. (Bourriaud et al. ; Belenguer et al. 2006). Consumption of formate, CO2 and H2 by acetogens (like Blautia hydrogenotrophica and Marvinbryantia formatexigens) prevents their buildup and provides additional acetate. Methanogens (such as M. smithii) and SRBs (such as D. piger), which are present in 30% and 50% of individuals (Hansen et al. ; Rey et al. 2013), also consume fermentation products to generate methane and hydrogen sulfide, respectively (Loubinoux et al. ; Samuel and Gordon 2006).

Figure 3.

Figure 3.

Localization of oral microbial taxa in dental plaque. (A) CLASI-FISH imaging of plaque microbial communities. (B) Schematic of the spatial distribution of bacteria based on CLASI-FISH images. (Reprinted with permission from Mark Welch et al. .)

Figure 4.

Figure 4.

Model of A. actinomycetemcomitans (Aa) and S. gordonii (Sg) interaction in a mouse infection model. Sg adheres to the tooth pellicle surface and produces lactate, the preferred carbon source for Aa. Sg also produces hydrogen peroxide, which kills Aa at higher concentrations and induces production of biofilm dispersant by Aa (‘Flight’ zone). In the ‘Fight’ zone, catalase secreted by Aa detoxifies hydrogen peroxide effectively, and dispersant production is downregulated, allowing biofilm formation. In this zone, Aa has access to lactate. In the starvation zone, the lactate concentration is insufficient to promote growth of Aa (Stacy et al. 2014).

Figure 5.

Figure 5.

Imaging and metabolic profiling of ANME/SRB aggregates treated with 15N2. (A–C) ANME/SRB consortium with sulfate. (D–F) ANME/SRB consortium with AQDS as the sole electron acceptor. (A and D) FISH images highlighting AMNE in red and Desulfobacteriaceae in green. (B and E) NanoSIMS detection of 12C14N, indicating total cellular biomass. (C and F) NanoSIMS detection of 15N. (Reprinted with permission from Scheller et al. .)

Figure 6.

Figure 6.

Corrinoid structure. (A) The structure of cobalamin is shown with the lower ligand, DMB, boxed. R represents the upper ligand (see Box 3). (B) Structures of commonly detected lower ligand bases.

Figure 7.

Figure 7.

Decoding corrinoid specificity. (A) In corrinoid-requiring organisms, specificity for particular corrinoid structures can be encoded in (I) corrinoid transporters, (II) corrinoid adenosyltransferases (Ado represents 5′-deoxyadenosine) and (III) corrinoid remodeling enzymes. (B) In corrinoid-producing organisms, specificity can be encoded in (I) the regulation of corrinoid-related genes (e.g. in corrinoid-binding riboswitches) and (II) the corrinoid biosynthetic pathway. (C) Corrinoid-dependent enzymes may also be specific for particular corrinoids.

Figure 8.

Figure 8.

Theoretical model and experimental evidence for spatial heterogeneity of cooperators and non-cooperators. (A) Modeling simulation and (B) growth experiment of two ‘cooperator’ strains (labeled with red and green fluorescent proteins, resulting in yellow color) indicate a high degree of intermixing. (C) Modeling simulation and (D) growth experiment of the two ‘cooperator’ strains (red) and a ‘non-cooperator’ strain (green) indicate spatial segregation of non-cooperating bacteria. MALDI-TOF MS analysis of histidine and tryptophan concentrations in colonies illustrates the exclusion of these molecules from non-cooperator regions (reprinted with permission from Pande et al. 2016).

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