N-acylhomoserine lactone dynamics in continuous cultures

of Pseudomonas putida IsoF

Core Principles of Bacterial Autoinducer Systems

Michael Rothballer (AMP) and Burkhard Hense (ICB)

Helena Lecture Day – Environmental Sciences

Introduction

Quorum sensing (QS) plays a fundamental role in adapting to environmental changes via small diffusible signal molecules called autoinducer (AI)

Various chemically different AI types

Gram-negative bacteria mainly communicate via N-acylhomoserine lactones (AHLs)

Pseudomonas putida IsoF contains only one known QS system (PpuI/PpuR)

Therefore P. putida IsoF serves as a model organism for understanding AHL regulation

In a previous work with P. putida IsoF, the production and degradation of AHLs were analysed in batch cultures

Introduction

Fekete et al. (2010) FEMS Microbiol. Ecol. 72: 22-34

Experimental data and numerical simulations of the continuous culture

Buddrus-Schiemann et al. (2014) Anal. Bioanal. Chem. 406: 6373-6383

Main result: stable QS system characterized by a putatively QS regulated lactonase

Changing environmental con-ditions in batch cultures e.g. nutrients, waste products

Continuous culture with a chemostat enabled time resolved AHL concentrations measurements

All data was subjected to mathematical modelling

Lactonase activity implies flexibility of QS system

Buddrus-Schiemann et al. (2014) Anal. Bioanal. Chem. 406: 6373-6383

What does this mean for QS?

Bacterial cells are exposed to AHL concentrations which accumulate over time →

information about the past

Information about change of conditions more relevant than actual state

QS system might work in an oscillating manner →

periodical reset

What cells sense depends on their environment

Cell density

Mass transfer

Spatial distribution

(aggregation)

Non-induced Induced

Efficiency sensing – A broader view on QS:

Unifies aspects of QS and diffusion sensing (DS) incl. information about cell distri-bution (aggregation)

Autoinducer as proxies for environmental changes

Efficiency testing strategy before synthesizing costlier macro-molecules

Not limited to released effectors but also intracellular enzymes (e.g. oxidative stress or acidi-fication reduction)

Hense et al. (2007) Nat. Rev. Microbiol. 5: 230-239

Homeostasis of cooperative behaviour

Production costs increase linearly with growing cell desities

Fitness benefits increases disproportionally strongly

Above a certain cell density threshold counteracting and saturation effects become eminent

Population density range where cooperation pays off → homeostasis

Variation in costs Variation in benefits AI control of costs and benefits

Hense and Schuster (2015) MMBR 79(1): 153-169

Fine tuning of QS for maximum fitness

Hense and Schuster (2015) MMBR 79(1): 153-169

Many counteracting “tuning” mechanisms:

enzymes (e.g. lactonase)

transcriptional (e.g. RsaL)

post-transcriptional (e.g. QteE, QscR)

Limitation of cooperative cells either by community heterogeneity or release of cells from biofilms

Hybrid Push-Pull Model

QS linked to metabolism

Starvation usually induces QS, i.e. joint production of exoenzymes to acquire nutrients

Severe starvation stops cooperation

Hense and Schuster (2015) MMBR 79(1): 153-169

Summary and conclusions

AI systems generally characterized by release of diffusible substances into the environment, measurement of their concentration by the releasing cells, and targeted response via changes in gene expression

AI systems predominantly regulate cooperative coordinated behaviors

AI systems allow estimation of efficiency of regulated target activity in context of specific cell conditions

AI system integrates push (demand) and pull (strength) information as a mechanism of homeostatic control

QS team

AMP: M. Rothballer, M. Rieger., K. Buddrus-Schiemann …

ICB: B. Hense

TUM: C. Kuttler

Oregon State University: M. Schuster