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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.
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.
Biocides, including disinfectants and antimicrobial surfaces (AMCs), are important to prevent the spread of pathogens and antimicrobial resistant bacteria via surfaces. However, concerns have been raised about the evolution and selection of resistance against disinfectants and AMCs. In turn, resistance against disinfectants and AMCs can be associated to antibiotic resistance due to cross-resistance and co-resistance. We need to understand the mechanisms and risks of disinfectants and AMCs for resistance and cross-resistance evolution to optimize their application and safeguard their long-term efficacy. We used adaptive laboratory evolution (ALE) experiments based on repeated exposure of bacteria to disinfectants. Our results show that repeated disinfection of E. coli with benzalkonium chloride in suspension results in a 2000-fold increase in survival within 5 exposure cycles. Adaption is linked to the initial presence of persister cells highly tolerant to benzalkonium chloride. We used the same approach to develop standardizable ALE experiments to determine resistance evolution to AMCs. The results highlight rapid adaptation of E. coli and P. aeruginosa towards copper surfaces. Moreover, there are multiple situations in the clinic or in the environment in which biocides and antibiotics co-occur and in which combination effects can shape their antimicrobial activity or their selective effects. Our work with P. aeruginosa shows prevalent combination effects of biocides and antibiotics, ranging from synergy to antagonism and resulting in the selection for or against antibiotic resistant strains. The combination effects are dependent on the biofilm mode-of-growth, manifesting in apparent differences in the structural arrangement of antibiotic sensitive and resistant strains in biofilms exposed to combinations. Furthermore, biocides affect rates of mutation and horizontal gene transfer, thereby having a potential facilitating effect on resistance evolution. Taken together, our work shows that the role of biocides as potential drivers of resistance evolution and selection deserves further study and regulative action.
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).
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).