Impact of spatial heterogeneity for selection regimes in multispecies biofilms
Bibliographic record
Abstract
Bacteria live in sprawling communities forming complex dynamic biofilm structures. The spatial heterogeneity found in biofilms may be a driver for the selection for optimized biofilm variants. Bacterial species that increase their matrix production can position favourably in the exterior biofilm regions in order to compete for valuable substrates. Here, we study both the spatial organisation and growth of bacterial cells in different bacterial communities over time to determine links between the structure of interspecies biofilms and selection for phenotypes adapted to growth and persistence in biofilms. This leads to the identification of driving mechanisms behind the dynamic spatial organisation combined with the performance of individual species over time in the spatial biofilm structure. Four species, previously co-isolated from soil, were used in different combinations. We examined the formation and spatial organisation of biofilms in distinct experimental model systems, as we hypothesized that their interaction would change dependent on the specific environment. The biofilm models used included the static Calgary biofilm device assay and two flow systems: the microfluidic BioFlux model (liquid bulk flow), and the drip flow reactor (liquid-air interphase). Both chromosomal fluorescent markers and FISH were used to visualize the organisation within biofilms by confocal laser scanning microscopy. We reveal how the changes in biofilm structure affect the overall performance of the biofilm community as well as the individual species in the biofilm. Our data indicates that a favourable localization of the individual species in a multispecies biofilm reduces selection for competitive phenotypes. Furthermore, we also observed that changes in matrix production could serve to stabilise the interspecific interaction between bacteria. This highlights the specific structural composition of a biofilm community as important for explaining biofilm dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".