Bioactive exometabolites drive maintenance competition in simple bacterial communities
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Abstract
During prolonged resource limitation, bacterial cells can persist in metabolically active states of non-growth. These maintenance periods, such as those experienced in stationary phase, can include upregulation of secondary metabolism and release of exometabolites into the local environment. As resource limitation is common in many environmental microbial habitats, we hypothesized that neighboring bacterial populations employ exometabolites to compete or cooperate during maintenance and that these exometabolite-facilitated interactions can drive community outcomes. Here, we evaluated the consequences of exometabolite interactions over the stationary phase among three environmental strains: Burkholderia thailandensis E264, Chromobacterium subtsugae ATCC 31532 , and Pseudomonas syringae pv. tomato DC3000. We assembled them into synthetic communities that only permitted chemical interactions. We compared the responses (transcripts) and outputs (exometabolites) of each member with and without neighbors. We found that transcriptional dynamics were changed with different neighbors and that some of these changes were coordinated between members. The dominant competitor B. thailandensis consistently upregulated biosynthetic gene clusters to produce bioactive exometabolites for both exploitative and interference competition. These results demonstrate that competition strategies during maintenance can contribute to community-level outcomes. It also suggests that the traditional concept of defining competitiveness by growth outcomes may be narrow and that maintenance competition could be an additional or alternative measure.
IMPORTANCE
Free-living microbial populations often persist and engage in environments that offer few or inconsistently available resources. Thus, it is important to investigate microbial interactions in this common and ecologically relevant condition of non-growth. This work investigates the consequences of resource limitation for community metabolic output and for population interactions in simple synthetic bacterial communities. Despite non-growth, we observed active, exometabolite-mediated competition among the bacterial populations. Many of these interactions and produced exometabolites were dependent on the community composition but we also observed that one dominant competitor consistently produced interfering exometabolites regardless. These results are important for predicting and understanding microbial interactions in resource-limited environments.
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Numerous transcripts and exometabolite were non-additive in the 3-member community, suggesting that predictions of members outcomes in more complex communities will not always be a simple summation of pairwise outcomes.
Very nice example of non additivity of interactions. Thanks!
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We observed relatively unchanged viability in B. thailandensis
I feel like the information of species viability in the different conditions could be mentioned at the beginning of the results and be an actual figure (not only a supplementary figure). I think it is an important level of information about interaction and gives more material and information to understand and interpret the RNASeq and Metabolomic data.
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Because member populations are physically separated in the SynCom transwell system but allowed to interact chemically, observed transcript responses in different community memberships are inferred to result from exometabolite interactions.
This is a very neat and clever system.
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When included in the community, B. thailandensis strongly determined the transcript profiles of the other two members
When multiple species are together, are their stationary phase synchronized? I am wondering whether one species would be in early stationary phase or still growth phase while the other species are already in stationary phase and how this would affect interpretation of the results.
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Our aim was for each member to achieve stationary phase at similar times across all conditions to compare transcripts and exometabolites under similar growth trajectories.
Does that mean that each species would reach stationary phase after the same time independently of the presence of another species so the time point for RNASeq would match? In other, were the glucose concentrations set so that each species keep a similar growth rate in each condition?
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in stationary phase
The question of stationary phase is very interesting here. I have a couple of questions about how the authors define/characterize stationary phase in their system ? While stationary phase usually means no observation of active growth, do the authors know whether this is thanks to cell maintenance or a balanced ratio between cell death and cell division? - would one or the other explanation modify the interpretation of their results, as each scenario could be driven by very different metabolic states?
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