View
216
Download
0
Category
Preview:
Citation preview
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
Recommended