Microbial metabolic exchange--the chemotype-to-phenotype link

Abstract

The function of microbial interactions is to enable microorganisms to survive by establishing a homeostasis between microbial neighbors and local environments. A microorganism can respond to environmental stimuli using metabolic exchange-the transfer of molecular factors, including small molecules and proteins. Microbial interactions not only influence the survival of the microbes but also have roles in morphological and developmental processes of the organisms themselves and their neighbors. This, in turn, shapes the entire habitat of these organisms. Here we highlight our current understanding of metabolic exchange as well as the emergence of new technologies that are allowing us to eavesdrop on microbial conversations comprising dozens to hundreds of secreted metabolites that control the behavior, survival and differentiation of members of the community. The goal of the rapidly advancing field studying multifactorial metabolic exchange is to devise a microbial 'Rosetta stone' in order to understand the language by which microbial interactions are negotiated and, ultimately, to control the outcome of these conversations.

Figure 1. Microbial interactions

Microbial interactions may be parasitic, such that one organism benefits at the cost of another; mutualistic, such that both organisms benefit; or commensal, such that one organism benefits at no cost or benefit to the other. all of these interactions, regardless of the outcome, occur through a diverse set of mechanisms by which genetic and molecular information is transferred. The most widely studied mechanisms of microbial interaction, some of which remain controversial, are shown. These include pili (1)^,, nanotubes (2)^–, secretion systems (3)^–, cell surface recognition (4)^,, vesicles (5)^–, aerosols (6)^–, small molecules (7)^– transported via efflux pumps or diffusion (8), phages or viruses (9)^– and biofilms (10). Each of these types of interaction plays a vital part in microbial metabolic exchange and provides the basis for microbial survival. Although some of these interactions are dependent on cell-to-cell contact, many do not occur through physical contact. Contact-independent metabolic exchange is advantageous because the signals are dispersed, enabling them to reach many neighboring cells and communities as opposed to only one cell at a time. The dispersion of metabolic exchange factors allows them to serve as nutrients or cues to neighboring microbes, thereby controlling the behavior of the larger microbial community and, in effect, leading to behavior as a multicellular entity.

Figure 2. Percentages of the predicted ORFs used in microbial interactions

On the basis of BLAST analysis, the predicted ORFs of S. aureus subsp. aureus USA300 FPR3757, P. aeruginosa str. PAO1 and B. subtilis subsp. subtilis str. 168 were categorized by function into four groups: hypothetical or unassigned ORFs (gray), ORFs involved in primary metabolism (light blue), ORFs for which homologs exist but whose role in metabolic exchange is unclear (purple), and ORFs involved in microbial interactions and metabolic exchange (dark blue). The number above each group corresponds to the number of ORFs in that category. The roles of these ORFs were putatively assigned on the basis of BLAST analysis or inferred from clustering within the genome. For example, hypothetical or primary genes that clustered within gene clusters involved in the production of secondary metabolites were assigned to metabolic exchange, even though their roles in biosynthesis remain unknown. It should be noted that nutrition-based sensing and signaling were not included in this assessment.

Figure 3. Chemical diversity of quorum-sensing molecules

The diversity of quorum-sensing molecules described in the literature is shown. The chemical scaffolds of quorum-sensing factors range in structural complexity from simple isoprenoids and cyclic nucleotides to quinolones to complex peptide scaffolds.

Figure 4. Chemical diversity of metabolic exchange factors

The metabolites involved in metabolic exchange have diverse structural scaffolds, ranging from small molecules and peptides to proteins (hydrolases, chitinases, protease and so on).

Figure 5. Cell differentiation of Bacillus subtilis at the colony and cellular levels

Monospecies bacterial communities are in fact multicellular communities with various subpopulations. (a) using an overlay of fluorescence and transmitted light micrographs, distinct populations of a B. subtilis biofilm can be observed where motile cells, sporulating cells and matrix-producing cells are false-colored. Scale bars, 50 μm. (b) using an overlay of fluorescence and transmitted light microscopy, distinct populations of surfactin-producing cells (expressing surfactin synthase subunit 1 (SrfAA)-YFP; artificially colored green) and matrix-producing cells (expressing YqxM-CFP; YqxM is a protein involved in anchoring cells together in B. subtilis biofilms; artificially colored red) can be observed. Although just 1 in 1,000–3,000 cells in a colony produces surfactin, this allows production of up to 1 g l⁻¹ in liquid culture (4b)^,. Scale bar, 3 μm. Figure adapted from ref.26 with permission.

Figure 6. Ecological roles of microbial metabolic exchange

Microbial metabolic exchange has important roles in ecology and the survival of higher organisms. (a) Symbiotic bacteria of the brine shrimp produce the antifungal compound istatin, thereby protecting shrimp embryos from pathogenic fungi. (b)Actinomyces spp. symbionts of leaf-cutting ants produce metabolites that protect the fungus farmed by the ants from a pathogenic fungus.

Figure 7. MALDI-IMS links chemistry to bacterial phenotypes

MALDI-IMS can be used to visualize the spatial distribution of metabolic exchange factors and to aid in their identification. To prepare the sample for MALDI-IMS, microbial colonies are cultured on an agar plate, excised, transferred to a MALDI target surface, covered with matrix, dried and subjected to rastering MALDI-MS. A MALDI-MS image is generated by directing a laser at different x,y positions of a sample in a predefined manner, creating a two-dimensional molecular profile of the molecules that are present in the top layer of the sample. Any ion observed in these spectra can be spatially visualized with a false-color that reflects the intensity of the MS signal.