Dynamics in the mixed microbial concourse

Abstract

Isolated, clonal populations of cells are rarely found in nature. The emergent properties of microbial consortia present a challenge for the systems approach to biology, as chances for competition, communication, or collaboration multiply with the number of interacting agents. This review focuses on recent work on intercourse within biofilms, among quorum-sensing populations, and between cross-feeding metabolic cooperators. New tools from synthetic biology allow microbial interactions to be designed and tightly controlled, creating valuable model systems. We address both natural and synthetic partnerships, with an emphasis on how system behaviors derive from the properties of their components. Essential features of microbial biology arose in the context of a very mixed culture and cannot be understood without unscrambling it.

Figure 1.

Examples of natural metabolic cooperation. Most natural syntrophic relationships involve the exchange of waste or metabolic by-products. The right column notes an example pair of partners for each class of interaction.

Figure 2.

Synthetic metabolic cooperation. Designed microbial partnerships exhibit a range of interactive modes, many not known in nature. Synthetic interactions allow researchers to isolate and test key functional components of interaction dynamics.

Figure 3.

Byproduct cooperation stabilizes a designed yeast population. (A) The synthetic yeast interaction produced by Gore et al. (2009). The cooperator strain secretes Suc2p, which breaks down sucrose into glucose and fructose for uptake. Suc2p production entails a fitness cost c, which is not paid by the SUC2Δ cheater strain. Cooperators are able to capture the sugars produced by their own activity with capture efficiency ɛ before they are lost to diffusion. (B) Cooperator and cheater strains show density-dependant fitness P. Sugars lost to diffusion contribute equally to the fitness of both strains through the term 1 − ɛ. Only the cooperator pays the cost c, and only the cooperator benefits from retained sugars through the term ɛ. Critically, fitness is not linear with sugar availability, but is sublinear with exponent a < 1. The a term was estimated to be 0.15 in this system. (C) Cooperators and cheaters can coexist for certain values of a. Regions indicate parameter values for which the frequency of cooperators is between 1% and 99% of the population in the steady state. For a = 1, either cooperators or cheaters must eventually dominate the population and the mixed region shrinks to 0. For a < 1, frequency-dependent selection stabilizes population heterogeneity.

Figure 4.

Quorum sensing. (A) Example of communication molecules of three classes. AHLs are found in Gram-negative bacteria such as V. fischeri. AIPs are produced by Gram-positive strains like Staphylococcus. AI-2 is an intermediate of methionine metabolism, found among both Gram-positive and Gram-negative bacteria. (B) QS bacteria produce signal molecules at a basal level that accumulate locally. Signal levels reflect the local population density and trigger a collective response at high densities. Responding cells are indicated in orange. (C) The QS response is often nonlinear with signal concentration. Typically, response levels increase sharply above a critical signal threshold.

Figure 5.

Natural and synthetic feedback loops in QS. (A) A simple topology for a functional communication network. The AHL signal is produced constitutively by LuxI and is received by LuxR, which may activate downstream genes. LuxR activity shows a simple saturating increase with time, as cells proliferate and AHL accumulates. This design was used successfully in early synthetic QS networks (You et al. 2004; Basu et al. 2005). (B) Positive feedback slows response time and improves response fidelity. With LuxI expression under the control of LuxR, the increase in LuxR activity with time becomes slower and more concave. Note the relatively flat response for early time points with low AHL levels, reflecting insensitivity to transient induction. The dotted line indicates the time required for half-maximal LuxR activity, which is extended in this case. Natural QS systems widely employ this kind of positive feedback, and it has been useful in synthetic systems as well (Brenner et al. 2007). (C) Negative feedback and temporal control. By introducing AiiA, which degrades AHL, under the control of LuxR, Danino et al. (2010) produced cells that respond to a quorum by destroying the QS signal. The result was synchronized, population-level oscillations in QS activity. Negative feedback is also a feature in many natural QS systems, where it may provide fine temporal response control.