My PhD Research

Introduction

The microbial world operates under complex social dynamics that we’re only beginning to understand. Bacteria, far from being simple single-celled organisms acting independently, engage in sophisticated social behaviors including cooperation and “cheating.” These social interactions profoundly influence how bacterial communities respond to environmental stressors, particularly antibiotics. This blog post explores the fascinating research into how bacterial cooperative traits respond differently in stable versus fluctuating environments, with special attention to the emergence and spread of antibiotic resistance.

Bacterial populations often include both “cooperators” (individuals that produce beneficial compounds that help the entire community) and “cheaters” (individuals that benefit from these compounds without contributing to their production). This dynamic creates a microbial version of social dilemmas that have been studied extensively in various scientific fields (Kümmerli et al., 2009).

The cooperative behaviors bacteria engage in include:

Production of extracellular enzymes like β-lactamase, which break down antibiotics
Formation of biofilms that protect the community
Secretion of iron-scavenging molecules like pyoverdin
Quorum sensing mechanisms that coordinate population-wide behaviors

Each of these carries costs to individual cells but potential benefits to the population as a whole.

Key Findings: Stable vs. Fluctuating Environments

Behavior in Stable Environments

When environmental conditions remain constant, research has revealed several consistent patterns in bacterial cooperative behaviors:

Quorum sensing-controlled public goods can actually delay population growth when nutrient acquisition depends on public goods (Mellbye & Schuster, 2011).
β-lactamase production provides protection to susceptible cells, particularly at low antibiotic concentrations, creating a “shield” effect for the bacterial community (Medaney et al., 2015).
Persister cell formation allows non-resistant bacteria to survive near resistant colonies, exploiting protection without paying the metabolic cost of resistance.
Pyoverdin production tends to decrease at higher cell densities, suggesting regulation based on need (Kümmerli et al., 2009).

Behavior in Fluctuating Environments

When environments fluctuate, bacterial cooperation changes in fascinating ways:

Biofilm formation exhibits a non-linear response to disturbance, peaking at intermediate disturbance levels while decreasing under frequent disturbance (Brockhurst et al., 2007).
Pyoverdin production increases under iron limitation and, interestingly, in the presence of cheater strains – possibly a compensatory mechanism (Kümmerli et al., 2009).
β-lactamase production scales with both antibiotic concentration and cell density, resulting in an equilibrium fraction of resistant cells proportional to these factors (Yurtsev et al., 2013).
Spatial structure enhances the protective effects of cooperative traits, with structured environments promoting the persistence of cooperation (Frost et al., 2018).

One particularly striking finding is that daily dilutions of antibiotic exposure lead to oscillations in population composition. Yurtsev et al. (2016) demonstrated that cross-protection through antibiotic degradation produces a form of mutualism between resistant and susceptible strains that is highly sensitive to antibiotic levels – stable at modest concentrations but collapsing at higher doses.

Understanding antibiotic resistance as a social phenomenon offers new insights into its emergence and spread. When resistant bacteria produce enzymes that degrade antibiotics (like β-lactamase), they create a “common good” that benefits the entire population, including susceptible strains. This protection allows susceptible strains to persist without paying the metabolic cost of resistance (Yurtsev et al., 2013).

Several key mechanisms emerge from the research:

Equilibrium dynamics: The fraction of resistant cells in a population often reaches an equilibrium proportional to both antibiotic concentration and cell density.
Spatial effects: Resistance is sometimes less favored in colonies due to cooperative effects, as structured environments allow susceptible strains to benefit from resistant strains.
Population oscillations: Under fluctuating antibiotic exposure, population composition may oscillate rather than reach a stable state.
Persister phenomenon: Bacterial dormancy facilitates the social exploitation of resistance mechanisms, allowing susceptible cells to “cheat” by benefiting from antibiotic degradation (Medaney et al., 2015).

Experimental Approaches

Researchers have employed diverse experimental strategies to investigate these phenomena:

Laboratory co-culture models with varying antibiotic concentrations (Evans et al., 2018)
In vitro competition experiments coupled with in silico simulations (Frost et al., 2018)
Experimental tracking of plasmid spread in bacterial populations (Yurtsev et al., 2013)
Competition experiments under varying conditions (Vasse et al., 2017)

The most commonly studied species include Pseudomonas aeruginosa (appearing in four of the studies reviewed) and Escherichia coli (in two studies), although other organisms like Pseudomonas fluorescens, Vibrionaceae bacteria, and mixed populations have also been examined.

Implications and Applications

These findings have significant implications for:

Clinical antibiotic treatments: Understanding how bacterial populations respond to antibiotic pulses versus continuous administration could improve treatment protocols and help combat resistance.
Evolutionary biology: The research illuminates how cooperative traits evolve and persist despite the potential for exploitation by cheaters.
Environmental microbiology: Insights into how bacterial communities respond to fluctuating conditions have applications in bioremediation and ecosystem management.
Synthetic biology: Knowledge of bacterial social dynamics could inform the design of engineered microbial communities for biotechnology applications.
Public health strategies: A deeper understanding of resistance mechanisms might lead to novel approaches for preventing or reversing antibiotic resistance.

Conclusion

The research summarized here reveals bacterial communities as complex social systems whose responses to environmental fluctuations—particularly antibiotic stress—involve sophisticated cooperative and competitive dynamics. These findings challenge us to move beyond viewing bacteria as simple individual cells and instead recognize them as participants in intricate social networks that profoundly influence their survival strategies.

As antibiotic resistance continues to pose a global health challenge, understanding the social dimensions of bacterial resistance may open new avenues for intervention. By considering how environmental fluctuations affect the balance between cooperators and cheaters, researchers and clinicians might develop more effective strategies for managing bacterial infections and limiting the spread of resistance.

References

Brockhurst, M., Buckling, A., & Gardner, A. (2007). Cooperation peaks at intermediate disturbance. Current Biology.

Cordero, O., Wildschutte, H., Kirkup, B., Proehl, S., Ngo, L., Hussain, F., Le Roux, F., Mincer, T., & Polz, M. (2012). Ecological populations of bacteria act as socially cohesive units of antibiotic production and resistance. Science.

Evans, K., Benomar, S., Camuy-Vélez, L. A., Nasseri, E. B., Wang, X., Neuenswander, B., & Chandler, J. R. (2018). Quorum-sensing control of antibiotic resistance stabilizes cooperation in Chromobacterium violaceum. The ISME Journal.

Frost, I., Smith, W. P. J., Mitri, S., Millán, Á. S., Davit, Y., Osborne, J., Pitt-Francis, J., MacLean, R., & Foster, K. (2018). Cooperation, competition and antibiotic resistance in bacterial colonies. The ISME Journal.

Kümmerli, R., Jiricny, N., Clarke, L. S., West, S., & Griffin, A. (2009). Phenotypic plasticity of a cooperative behaviour in bacteria. Journal of Evolutionary Biology.

Medaney, F., Dimitriu, T., Ellis, R., & Raymond, B. (2015). Live to cheat another day: Bacterial dormancy facilitates the social exploitation of β-lactamases. The ISME Journal.

Mellbye, B. L., & Schuster, M. (2011). The sociomicrobiology of antivirulence drug resistance: A proof of concept. mBio.

Vasse, M., Noble, R., Akhmetzhanov, A., Torres-Barceló, C., Gurney, J., Bénateau, S., Gougat-Barberá, C., Kaltz, O., & Hochberg, M. (2017). Antibiotic stress selects against cooperation in the pathogenic bacterium Pseudomonas aeruginosa. Proceedings of the National Academy of Sciences of the United States of America.

Yurtsev, E., Chao, H., Datta, M., Artemova, T., & Gore, J. (2013). Bacterial cheating drives the population dynamics of cooperative antibiotic resistance plasmids. Molecular Systems Biology.

Yurtsev, E., Conwill, A., & Gore, J. (2016). Oscillatory dynamics in a bacterial cross-protection mutualism. Proceedings of the National Academy of Sciences of the United States of America.

Bacterial Cooperation and Antibiotic Resistance: Understanding Microbial Social Dynamics in Changing Environments