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2015 ; 79
(1
): 153-69
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Core principles of bacterial autoinducer systems
#MMPMID25694124
Hense BA
; Schuster M
Microbiol Mol Biol Rev
2015[Mar]; 79
(1
): 153-69
PMID25694124
show ga
Autoinduction (AI), the response to self-produced chemical signals, is widespread
in the bacterial world. This process controls vastly different target functions,
such as luminescence, nutrient acquisition, and biofilm formation, in different
ways and integrates additional environmental and physiological cues. This
diversity raises questions about unifying principles that underlie all AI
systems. Here, we suggest that such core principles exist. We argue that the
general purpose of AI systems is the homeostatic control of costly cooperative
behaviors, including, but not limited to, secreted public goods. First, costly
behaviors require preassessment of their efficiency by cheaper AI signals, which
we encapsulate in a hybrid "push-pull" model. The "push" factors cell density,
diffusion, and spatial clustering determine when a behavior becomes effective.
The relative importance of each factor depends on each species' individual
ecological context and life history. In turn, "pull" factors, often stress cues
that reduce the activation threshold, determine the cellular demand for the
target behavior. Second, control is homeostatic because AI systems, either
themselves or through accessory mechanisms, not only initiate but also maintain
the efficiency of target behaviors. Third, AI-controlled behaviors, even
seemingly noncooperative ones, are generally cooperative in nature, when
interpreted in the appropriate ecological context. The escape of individual cells
from biofilms, for example, may be viewed as an altruistic behavior that
increases the fitness of the resident population by reducing starvation stress.
The framework proposed here helps appropriately categorize AI-controlled
behaviors and allows for a deeper understanding of their ecological and
evolutionary functions.
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