Periodic Reporting for period 4 - BactInd (Bacterial cooperation at the individual cell level)
Okres sprawozdawczy: 2021-03-01 do 2021-08-31
Bacteria play a central role for human health. On the one hand, bacteria are beneficial for us because they form complex consortia in or on our bodies, which significantly contribute to our health and well-being. On the other hand, bacteria can also be pathogenic, causing harmful and difficult to treat infections. Research over the last decade has revealed that bacterial social interactions take on an important role for establishing both a healthy microbiome and harmful infections. Social interactions include cell-to-cell communication and the secretion of beneficial metabolites that can be shared between cells at the level of the group.
The aim of this ERC project is to study these social interactions at the individual cell level and assess the complexity of microbial cooperation. This is important because most of our knowledge on social interactions in bacteria is based on group level measures. This contrasts with the fact that bacteria are individuals, interacting with each other at the single cell level. Here we study such single cell interactions and aim to uncover how bacteria communicate with one another to reach optimal decisions. We ask whether all bacteria in a group behave in the same way, or whether they can divide up work and engage in division of labour.
While the primary research objectives are fundamental in nature, we expect the insights gained from this project to have important implications for potential therapeutic applications. For example, it has been proposed that managing social interactions networks between bacteria could be used to strengthen the beneficial microbiome, whereas disrupting social interactions between pathogens could help to manage infections.
During the 60 months of this ERC project, we significantly advanced our understanding of bacterial social interactions. We have developed microscopy and flow cytometry protocols together with automated image analysis pipelines to advance the field of single-cell studies on the technical side. Moreover, we have applied these protocols in our experiments to study the exchange of beneficial compounds between cells, chemical communication between individuals, competition between species, and how individual bacteria colonize hosts. Moreover, we have developed an agent-based modelling platform to simulate social interactions of bacteria in silico.
The aim of this ERC project is to study these social interactions at the individual cell level and assess the complexity of microbial cooperation. This is important because most of our knowledge on social interactions in bacteria is based on group level measures. This contrasts with the fact that bacteria are individuals, interacting with each other at the single cell level. Here we study such single cell interactions and aim to uncover how bacteria communicate with one another to reach optimal decisions. We ask whether all bacteria in a group behave in the same way, or whether they can divide up work and engage in division of labour.
While the primary research objectives are fundamental in nature, we expect the insights gained from this project to have important implications for potential therapeutic applications. For example, it has been proposed that managing social interactions networks between bacteria could be used to strengthen the beneficial microbiome, whereas disrupting social interactions between pathogens could help to manage infections.
During the 60 months of this ERC project, we significantly advanced our understanding of bacterial social interactions. We have developed microscopy and flow cytometry protocols together with automated image analysis pipelines to advance the field of single-cell studies on the technical side. Moreover, we have applied these protocols in our experiments to study the exchange of beneficial compounds between cells, chemical communication between individuals, competition between species, and how individual bacteria colonize hosts. Moreover, we have developed an agent-based modelling platform to simulate social interactions of bacteria in silico.
The main objective of the project is to develop technics and study social interactions between pathogenic bacteria at the single-cell level. We were successful in achieving our aims. On the technical side, we developed: (i) experimental protocols that allow us to follow behavioural interaction and gene expression patterns of individual bacteria over time using fluorescent microscopy and flow cytometry; (ii) a pipeline for automated high-throughput image analysis; and (iii) a individual-based simulation platform, where we can study bacterial interactions in silico and parameterise our mathematical models with data from experiments. In addition, we established a microscopy protocol to study interactions between pathogenic bacteria within a living host (i.e. the nematode Caenorhabditis elegans).
On the scientific side, we applied these techniques and protocols to conduct a number of innovative studies. Below, there is a brief overview of our main achievements.
(a) We used gene-expression reporters combined with single-cell microscopy to study social interactions in Pseudomonas aeruginosa, an important opportunistic pathogen. In particular, we focussed on the sharing of beneficial compounds secreted by bacteria and shared between group members. We examined the physical boundaries of siderophore sharing (Weigert and Kümmerli 2017 Proc B), optimality patterns in compound production and sharing (Schiessl et al. 2019 Evolution), how group-coordinated behaviour can help in competition against other species (Leinweber et al. 2018 Evolution), and how individual bacteria coordinate their actions at the group level (Mridha & Kümmerli 2021 bioRxiv).
(b) We used gene-expression reporters combined with flow cytometry to explore chemical communication between individual bacteria of P. aeruginosa. We discovered that bacteria in a clonal population, albeit coordinating their actions overall, take different communication trajectories over time (Jayakumar et al. 2021 bioRxiv).
(c) We developed an agent-based modelling platform that allows us to study social interactions between bacterial individuals in silico (Wechsler et al. 2019 J Evol Biol). The platform is particularly useful when parameterized with data from experiments. This is what we did in a study on Bacillus subtilis, where we could demonstrate the costs and benefits of division of labor over biofilm matrix production (Dragos et al. 2018 Current Biology).
(d) We used single-time laps microscopy to analyze competitive behaviors and its fitness consequences between the two bacterial pathogens P. aeruginosa and Staphylococcus aureus. This project demonstrates the power of the single-cell perspective to unravel unknown patterns of microbe-microbe interactions (Niggli et al. 2021 Front. Cell. Infect. Microbiol.)
(e) We used the established host-pathogen system to study how social interactions drive virulence evolution (Granato et al. 2018 ISME J), and how bacteria colonize and interact inside the living host (Rezzoagli et al. 2019 ISME J).
(f) Two avenues of applied applications/exploitations arose from the above work. First, we showed that siderophore-based interactions are relevant in the plant rhizosphere and we have identified plant-beneficial bacteria that can protect crop plants from infections due to their siderophore actions (Gu et al. 2020 Nat Microbiol). Second, we showed that treatments that target bacterial social interactions (siderophore sharing and chemical communication) can synergize with antibiotics and even revert selection for antibiotic resistance (Rezzoagli et al. 2020 PLoS Biol).
On the scientific side, we applied these techniques and protocols to conduct a number of innovative studies. Below, there is a brief overview of our main achievements.
(a) We used gene-expression reporters combined with single-cell microscopy to study social interactions in Pseudomonas aeruginosa, an important opportunistic pathogen. In particular, we focussed on the sharing of beneficial compounds secreted by bacteria and shared between group members. We examined the physical boundaries of siderophore sharing (Weigert and Kümmerli 2017 Proc B), optimality patterns in compound production and sharing (Schiessl et al. 2019 Evolution), how group-coordinated behaviour can help in competition against other species (Leinweber et al. 2018 Evolution), and how individual bacteria coordinate their actions at the group level (Mridha & Kümmerli 2021 bioRxiv).
(b) We used gene-expression reporters combined with flow cytometry to explore chemical communication between individual bacteria of P. aeruginosa. We discovered that bacteria in a clonal population, albeit coordinating their actions overall, take different communication trajectories over time (Jayakumar et al. 2021 bioRxiv).
(c) We developed an agent-based modelling platform that allows us to study social interactions between bacterial individuals in silico (Wechsler et al. 2019 J Evol Biol). The platform is particularly useful when parameterized with data from experiments. This is what we did in a study on Bacillus subtilis, where we could demonstrate the costs and benefits of division of labor over biofilm matrix production (Dragos et al. 2018 Current Biology).
(d) We used single-time laps microscopy to analyze competitive behaviors and its fitness consequences between the two bacterial pathogens P. aeruginosa and Staphylococcus aureus. This project demonstrates the power of the single-cell perspective to unravel unknown patterns of microbe-microbe interactions (Niggli et al. 2021 Front. Cell. Infect. Microbiol.)
(e) We used the established host-pathogen system to study how social interactions drive virulence evolution (Granato et al. 2018 ISME J), and how bacteria colonize and interact inside the living host (Rezzoagli et al. 2019 ISME J).
(f) Two avenues of applied applications/exploitations arose from the above work. First, we showed that siderophore-based interactions are relevant in the plant rhizosphere and we have identified plant-beneficial bacteria that can protect crop plants from infections due to their siderophore actions (Gu et al. 2020 Nat Microbiol). Second, we showed that treatments that target bacterial social interactions (siderophore sharing and chemical communication) can synergize with antibiotics and even revert selection for antibiotic resistance (Rezzoagli et al. 2020 PLoS Biol).
Bacteria secrete numerous compounds to cooperate and compete with each other. Compound secretion is involved with virulence and disease. While these aspects are well-studied in batch-culture experiments, we know hardly anything on these interactions at the micrometre scale, thus the level bacteria typically interact with each other. The project aims to close this knowledge gap by delivering results on: (i) the physical scale across which bacteria can exchange secreted compounds; (ii) the scale across which bacteria can sense friendly neighbours or unfriendly competitors and adjust their behaviour accordingly; (iii) whether bacteria in clonal groups all invest in secreted compounds or whether there is the potential for specialization, whereby subgroups invest in different compounds, which are then shared at the level of the group; (iv) how bacteria can coordinate their actions and make meaningful decisions in a speedy way; and (v) whether and how these bacterial community features affect virulence in a host. We believe that the project will push the boundaries of our understanding of sophisticated bacterial interactions, which is of interest and relevance to a broad interdisciplinary audience, including scientists and the general public.