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Most microorganisms live in environments where nutrients are limited and fluctuate over time. Cells respond to nutrient fluctuations by sensing and adapting their physiological state. Recent studies suggest phenotypic heterogeneity in isogenic populations as an alternative strategy in fluctuating environments, where a subpopulation of cells express a function that allows growth under conditions that might arise in the future. It is unknown how environmental factors such as nutrient limitation shape phenotypic heterogeneity in metabolism and whether this allows cells to respond to nutrient fluctuations. Here, we show that substrate limitation increases phenotypic heterogeneity in metabolism, and this heterogeneity allows cells to cope with substrate fluctuations. We subjected the N2-fixing bacterium Klebsiella oxytoca to different levels of substrate limitation and substrate shifts, and obtained time-resolved single-cell measurements of metabolic activities using nanometre-scale secondary ion mass spectrometry (NanoSIMS). We found that the level of NH4+ limitation shapes phenotypic heterogeneity in N2 fixation. In turn, the N2 fixation rate of single cells during NH4+ limitation correlates positively with their growth rate after a shift to NH4+ depletion, experimentally demonstrating the benefit of heterogeneity. The results indicate that phenotypic heterogeneity is a general solution to two important ecological challenges - nutrient limitation and fluctuations - that many microorganisms face. Currently, we use NanoSIMS to develop a new approach that defines functionally-relevant, phenotypic biodiversity in microbial systems. In the last part of my presentation, I will highlight why the concept of phenotypic diversity is relevant for the understanding of antimicrobial resistance.
A fundamental question in ecology is how biodiversity affects ecosystem function. Biodiversity is commonly estimated based on genetic variation. We investigated a new approach that defines and measures biodiversity in complex microbial communities. We used the variation in multiple functionally-relevant, phenotypic traits measured in parallel in single cells as a metric for microbial phenotypic diversity. We studied phenotypic diversity and ecosystem functioning throughout different photosynthetic layers dominated by divergent microbial communities in the gradient of Lago di Cadagno. We determined genetic diversity by 16S and 18S amplicon sequencing and bulk ecosystem functioning (photosynthesis). In addition, we determined phenotypic diversity using single-cell technologies such as nanometer-scale secondary ion mass spectrometry (NanoSIMS) correlated with confocal laser scanning microscopy (CLSM) and scanning flow-cytometry. We measured functional trait variation between individuals in 13CO2 fixation, 15NH4+ uptake, and variation in physio-morphological cell traits, such as cell size, shape, and auto-fluorescence for various pigments related to photosynthesis. We used the distances between individuals in a multidimensional trait space to derive phenotypic trait-based diversity indices, such as trait richness, trait evenness, and trait divergence. We find that phenotypic trait divergence associates with ecosystem functioning, whereas genetic diversity does not. Including activity-based, single-cell phenotypic measurements with NanoSIMS provided an additional accuracy to the trait-based diversity assessment and allowed us to formulate hypotheses on the mechanisms that shape the correlation between phenotypic diversity and eco-system function. Together, our results show that phenotypic diversity is a meaningful concept to measure microbial biodiversity and associate it with ecosystem functioning.
A fundamental question in ecology is how biodiversity affects ecosystem function. Biodiversity is commonly estimated based on genetic variation. We investigated a new approach that defines and measures biodiversity in complex microbial communities. We used the variation in multiple functionally-relevant, phenotypic traits measured in parallel in single cells as a metric for microbial phenotypic diversity. We studied phenotypic diversity and ecosystem functioning throughout different photosynthetic layers dominated by divergent microbial communities in the gradient of Lago di Cadagno. We determined genetic diversity by 16S and 18S amplicon sequencing and bulk ecosystem functioning (photosynthesis). In addition, we determined phenotypic diversity using single-cell technologies such as nanometer-scale secondary ion mass spectrometry (NanoSIMS) correlated with confocal laser scanning microscopy (CLSM) and scanning flow-cytometry. We measured functional trait variation between individuals in 13CO2 fixation, 15NH4+ uptake, and variation in physio-morphological cell traits, such as cell size, shape, and auto-fluorescence for various pigments related to photosynthesis. We used the distances between individuals in a multidimensional trait space to derive phenotypic trait-based diversity indices, such as trait richness, trait evenness, and trait divergence. We find that phenotypic trait divergence associates with ecosystem functioning, whereas genetic diversity does not. Including activity-based, single-cell phenotypic measurements with NanoSIMS provided an additional accuracy to the trait-based diversity assessment and allowed us to formulate hypotheses on the mechanisms that shape the correlation between phenotypic diversity and eco-system function. Together, our results show that phenotypic diversity is a meaningful concept to measure microbial biodiversity and associate it with ecosystem functioning.
Antimicrobial surfaces have broad use in multiple settings including touch surfaces in hospitals, implanted devices, or consumer products. Their aim is to support existing hygiene procedures, and to help combat the increasing threat of antimicrobial resistance. However, concerns have been raised over the potential selection pressure exerted by such surfaces, which might drive the evolution and spread of antimicrobial resistance. In my presentation, I will highlight the risks and knowledge gaps associated with resistance on antimicrobial surfaces by different processes including evolution by de novo mutations and horizontal gene transfer, and species sorting of inherently resistant bacteria dispersed onto antimicrobial surfaces. The latter process has the potential to select for antibiotic resistance via cross-resistance between traits that confer resistance to both the antimicrobial surface coating and antibiotics. Conditions in which antibiotics and antimicrobial coatings are present simultaneously (e.g. implants) will lead to more complex interactions that can either result in the selection for or against antibiotic resistance. We mapped these interactions between several antimicrobials and antibiotics on growth and selection of Pseudomonas aeruginosa. We find prevalent physiological (i.e. synergy and antagonism) and evolutionary (i.e. cross-resistance and collateral sensitivity) combination effects. Understanding these interactions opens the door to tailor therapeutic interventions to select against resistance. In additions, we need new methods and translational studies that investigate resistance development to antimicrobial surfaces under realistic conditions. Therefore, I will present recent developments in our lab on the development of such a method based on existing efficacy standards.
Antimicrobials can exert specific physiological effects when used in combination that are different from those when applied alone. These effects include physiological effects (i.e. synergy, antagonism and suppression) as well as evolutionary effects on the selection of resistant strains (i.e. cross-resistance and collateral sensitivity). While combination effects have been extensively mapped for antibiotic-antibiotic combinations, the combination effects of antibiotics with antimicrobials used as biocides or antiseptics have not been systematically investigated.
Here, we investigated the physiological and evolutionary consequences of combinations of antibiotics (meropenem, gentamicin and ciprofloxacin) and substances used as biocides or antiseptics (octenidine, benzalkonium chloride, cetrimonium bromide, chlorhexidine, povidone-iodine, silver) on growth and selection of Pseudomonas aeruginosa. We find prevalent physiological combination effects with synergy occurring 6 times and antagonism occurring 10 times. The effects are specific to the antibiotic-biocide combination with meropenem showing a tendency for antagonism with biocides (6 of 7), while gentamicin has a tendency for synergy (5 of 7). A particular strong antagonism is apparent for the meropenem-chlorhexidine combination, for which we conducted an in-depth study on the underlying molecular mechanism using RNASeq. Moreover, we find widespread effects of the biocide-antibiotic combinations on selection of P. aeruginosa strains resistant to the antibiotics, including cross-resistance and collateral sensitivity.
In conclusion, antibiotics and biocides or antiseptics exert physiological and evolutionary combination effects on the pathogen P. aeruginosa. These effects have consequences for the efficacy of both types of substances and for the selection of antimicrobial resistant strains in clinical applications with combined exposure (e.g. wound care, coated biomaterials).
Development of a laboratory method to assess resistance development of microorganisms to biocides
(2021)
Bacteria are exposed to biocides through surface disinfection or by antimicrobial surfaces. These stressful environments provide a strong selective pressure for bacteria to adapt. Here, we describe the development of a laboratory method to assess adaption of bacteria by resistance development in response to surface disinfection and antimicrobial surfaces.
Development of a laboratory method to assess resistance development of microorganisms to biocides
(2021)
Bacteria are exposed to biocides through surface disinfection or by antimicrobial surfaces. These stressful environments provide a strong selective pressure for bacteria to adapt. Here, we describe the development of a laboratory method to assess adaption of bacteria by resistance development in response to surface disinfection and antimicrobial surfaces.