Bacteria: To biofilm or not to biofilm
A new model helps to predict under which conditions a species of bacteria will switch to a static lifestyle.
- Department of Earth and Environmental Sciences, University of Pennsylvania, United States
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, United States
A trip to the dentist is seldom fun, but it is often necessary to remove the sticky, slimy deposits (or biofilms) that adhere to our teeth and gums. These structures are formed by bacteria that have adopted a static lifestyle in the moist and warm environment of our mouths. In fact, biofilms are common in a range of natural, clinical, and industrial settings, where they can be dangerous for our health or contaminate equipments (Davey and O’Toole, 2000; Hall-Stoodley et al., 2004).
In general, bacteria can either exist in a mobile, ‘planktonic’ state where they freely disperse and explore their environment for nutrients, or stay statically as ‘biofilms’, a communal state where the cells share resources and are protected from harmful conditions (Adler, 1966). What triggers bacteria to transition from a mobile state to a biofilm lifestyle depends on how each species responds to certain environmental conditions. The factors include nutrient availability, production of certain chemical triggers as well as cellular parameters - such as bacterial concentration, proliferation rate, or diffusing behavior (Berg, 2018).
Overall, however, the switch to (immobile) biofilm formation is controlled by bacterial dispersion (which is dependent on nutrient levels), and it occurs when the concentration of bacterial molecules known as autoinducers goes above a certain threshold (Davies et al., 1998; Waters and Bassler, 2005). These signals, which are produced by bacteria, serve as a proxy for the level of other bacterial cells in the environment and trigger intracellular signals which impact the genes a cell expresses, and the lifestyle it will adopt. Once the biofilm is created, it is maintained by the autoinducer molecules produced by the immobilized bacteria (Figure 1).
Figure 1
Visual representation of the model for biofilm formation.
Bacteria can exist in two different states: a motile state in which they can disperse freely around their environment (top), and an immobile state in which they live together in static as a biofilm (bottom). The red gradient in the biofilm box indicates to which extent bacterial density is increasing in the biofilm from left to right alongside rising nutrient concentrations (grey gradient). The motile bacteria move towards increasing nutrient concentration to the right. The concentration of autodiffusers (molecules produced by bacteria which trigger biofilm formation; blue gradient), is highest close to the biofilm and decreases further away.
Yet, how biofilms emerge and the exact conditions that trigger their formation remain a topic of intense research. In general, motile and biofilm lifestyles are studied separately, making it difficult to predict with certainty whether a species of bacteria will form a biofilm under certain conditions. Now, in eLife, Sujit Datta and colleagues at Princeton University – including Jenna Moore-Ott as first author – report having developed a unified framework that can examine both states simultaneously (Moore-Ott et al., 2022).
The team developed a series of equations that describe the transition from planktonic state to biofilm under a range of parameters covering all possible conditions. The resulting model, which describes the behavior of the cells, is governed by two main factors: nutrient consumption and bacterial dispersion in the motile state. Both parameters focus on the competition between bacterial dispersion and the production of autoinducer molecules.
Based on the model, Moore-Ott et al. predict two conditions where the concentration of autoinducers remains under the threshold required for biofilm formation. In the first case, nutrients are consumed at such a high rate that the autoinducers are produced (by bacteria) in limited quantities; there is simply not enough autoinducer ‘production’ time. In the second case, bacteria diffuse and therefore disperse at increased levels (possibly because of environmental conditions), limiting the accumulation of the autoinducers in one location.
In addition, Moore-Ott et al. also pinpointed a third factor the ratio between the time it takes for nutrients to be consumed and for autoinducer to be produced, which affects how fast the biofilm forms and how large they become. For instance, a larger ratio between these two timescales results in the biofilm proliferating, while a smaller ratio slows down the formation of the biofilm. Overall, the combination of these three parameters – nutrient consumption, bacterial dispersion, and ratio of consumption to production time scale – determine which lifestyle a specific species adopts, and at what concentration.
While nutrient consumption and bacterial dispersion vary between different species of bacteria and across environments, they are quantifiable through experiments. This means that the model provides a unique general framework that can be used to predict which state a given bacterial species will adopt under specific circumstances.
Further work should aim to refine the model so it can become closer to real life conditions. For example, the framework assumes that biofilm formation and the production of autoinducers in a nutrient-dependent fashion are irreversible, two assumptions which can be relaxed for certain species of bacteria (Bridges and Bassler, 2019). In addition, more complex elements could be added to tailor the framework to a specific system, such as incorporating how the biofilm is spatially organized, inputting the role of secondary signaling molecules which fine-tune the impact of autoinducers, or acknowledging how individual cells may respond differently to signals (Bhattacharjee et al., 2022; Jenal et al., 2017; Nadezhdin et al., 2020). Nevertheless, this work represents an important step forward in our quantitative understanding of biofilm formation, which in turn will help us in both fighting and harnessing biofilms, which can be useful in wound healing, bioremediation, or functional materials production.
References
-
Microbial biofilms: from ecology to molecular geneticsMicrobiology and Molecular Biology Reviews 64:847–867.https://doi.org/10.1128/MMBR.64.4.847-867.2000
-
Bacterial biofilms: from the natural environment to infectious diseasesNature Reviews Microbiology 2:95–108.https://doi.org/10.1038/nrmicro821
-
Cyclic di-GMP: second messenger extraordinaireNature Reviews. Microbiology 15:271–284.https://doi.org/10.1038/nrmicro.2016.190
-
Quorum sensing: cell-to-cell communication in bacteriaAnnual Review of Cell and Developmental Biology 21:319–346.https://doi.org/10.1146/annurev.cellbio.21.012704.131001
Article and author information
Author details
Publication history
Copyright
© 2022, Pradeep and Arratia
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,227
- views
-
- 301
- downloads
-
- 2
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Bacteria: To biofilm or not to biofilm
eLife 11:e80891.
https://doi.org/10.7554/eLife.80891
Further reading
-
- Computational and Systems Biology
To help maximize the impact of scientific journal articles, authors must ensure that article figures are accessible to people with color-vision deficiencies (CVDs), which affect up to 8% of males and 0.5% of females. We evaluated images published in biology- and medicine-oriented research articles between 2012 and 2022. Most included at least one color contrast that could be problematic for people with deuteranopia (‘deuteranopes’), the most common form of CVD. However, spatial distances and within-image labels frequently mitigated potential problems. Initially, we reviewed 4964 images from eLife, comparing each against a simulated version that approximated how it might appear to deuteranopes. We identified 636 (12.8%) images that we determined would be difficult for deuteranopes to interpret. Our findings suggest that the frequency of this problem has decreased over time and that articles from cell-oriented disciplines were most often problematic. We used machine learning to automate the identification of problematic images. For a hold-out test set from eLife (n=879), a convolutional neural network classified the images with an area under the precision-recall curve of 0.75. The same network classified images from PubMed Central (n=1191) with an area under the precision-recall curve of 0.39. We created a Web application (https://bioapps.byu.edu/colorblind_image_tester); users can upload images, view simulated versions, and obtain predictions. Our findings shed new light on the frequency and nature of scientific images that may be problematic for deuteranopes and motivate additional efforts to increase accessibility.
-
- Computational and Systems Biology
The force developed by actively lengthened muscle depends on different structures across different scales of lengthening. For small perturbations, the active response of muscle is well captured by a linear-time-invariant (LTI) system: a stiff spring in parallel with a light damper. The force response of muscle to longer stretches is better represented by a compliant spring that can fix its end when activated. Experimental work has shown that the stiffness and damping (impedance) of muscle in response to small perturbations is of fundamental importance to motor learning and mechanical stability, while the huge forces developed during long active stretches are critical for simulating and predicting injury. Outside of motor learning and injury, muscle is actively lengthened as a part of nearly all terrestrial locomotion. Despite the functional importance of impedance and active lengthening, no single muscle model has all these mechanical properties. In this work, we present the viscoelastic-crossbridge active-titin (VEXAT) model that can replicate the response of muscle to length changes great and small. To evaluate the VEXAT model, we compare its response to biological muscle by simulating experiments that measure the impedance of muscle, and the forces developed during long active stretches. In addition, we have also compared the responses of the VEXAT model to a popular Hill-type muscle model. The VEXAT model more accurately captures the impedance of biological muscle and its responses to long active stretches than a Hill-type model and can still reproduce the force-velocity and force-length relations of muscle. While the comparison between the VEXAT model and biological muscle is favorable, there are some phenomena that can be improved: the low frequency phase response of the model, and a mechanism to support passive force enhancement.
