New Research In
Physical Sciences
Social Sciences
Featured Portals
Articles by Topic
Biological Sciences
Featured Portals
Articles by Topic
- Agricultural Sciences
- Anthropology
- Applied Biological Sciences
- Biochemistry
- Biophysics and Computational Biology
- Cell Biology
- Developmental Biology
- Ecology
- Environmental Sciences
- Evolution
- Genetics
- Immunology and Inflammation
- Medical Sciences
- Microbiology
- Neuroscience
- Pharmacology
- Physiology
- Plant Biology
- Population Biology
- Psychological and Cognitive Sciences
- Sustainability Science
- Systems Biology
Escape band in Escherichia coli chemotaxis in opposing attractant and nutrient gradients
Edited by Howard C. Berg, Harvard University, Cambridge, MA, and approved December 21, 2018 (received for review May 11, 2018)

Significance
Not all nutrient molecules act as attractant signals, and not all attractants are good nutrients. Here, we study behaviors of bacterial cells in an environment with competing sources: One has a strong attractant but a poor nutrient, and the other has a rich nutrient but a weak attractant. We find that, although initially cells are attracted toward the strong attractant, the opposing nutrient gradient generated by consumption eventually becomes dominant when the cell density reaches a critical value. The cells then form a band escaping the strong attractant but weak nutrient “trap” and migrating toward the rich nutrient. Our study shows that combination of growth and chemotaxis allows cells to find nutrient source in complex environments with conflicting sensory cues.
Abstract
It is commonly believed that bacterial chemotaxis helps cells find food. However, not all attractants are nutrients, and not all nutrients are strong attractants. Here, by using microfluidic experiments, we studied Escherichia coli chemotaxis behavior in the presence of a strong chemoattractant (e.g., aspartate or methylaspartate) gradient and an opposing gradient of diluted tryptone broth (TB) growth medium. Our experiments showed that cells initially accumulate near the strong attractant source. However, after the peak cell density (h) reaches a critical value
Chemotactic bacteria are capable of sensing and migrating toward favorable locations for survival and growth in complex environments. As one of the most studied model systems in biology, Escherichia coli chemotaxis is relatively well understood (1⇓⇓⇓–5). E. coli can sense different external chemical signals by using five different types of chemoreceptors: two abundant receptors (Tar and Tsr) and three minor receptors (Trg, Tap, and Aer). These receptors can bind to different ligands either directly or indirectly through periplasmic binding proteins (6). Binding of a ligand molecule triggers conformational changes of receptors and affects the autophosphorylation activity of the histidine kinase CheA that forms the core signaling complex with the receptors and the adapter protein CheW. The signal is then transmitted to the flagellar motor by a response regulator CheY to control the flagellar motor’s tumbling frequency.
While the molecular mechanism of bacterial chemotaxis has been investigated extensively, the relationship between chemotaxis and metabolism remains unclear. Early pioneering work by Adler and colleagues (7, 8) showed that not all nutrient molecules (amino acids and sugars) are attractants and not all attractants are good nutrients. In the classical swarm plate experiments (9, 10), the attractant gradient and the nutrient gradient are in the same direction. Thus, by following the attractant gradient, cells in the swarm rings migrate to locations with higher nutrient concentrations. However, in a more complex environment, these two gradients may not be in the same direction. For example, there can be two (or more) spatially separated sources: One source has a high concentration of a strong attractant, which may be only weakly or not consumable; the other has a high concentration of nutrient, which contains only weak attractants. Between the two sources, the attractant gradient is opposite to that of the nutrient. In complex environments, either geometrically (11⇓–13) or chemically with multiple conflicting cues, the questions on whether and how chemotaxis guides cells toward locations with favorable conditions remain unanswered.
To address some of the questions raised above, we study bacterial chemotaxis behaviors in an attractant gradient and an opposing nutrient field that changes with time due to consumption, by combining microfluidic experiments and quantitative modeling. We show that the interplay among bacterial chemotaxis in opposing gradients, nutrient consumption, and cell growth leads to traveling shape-preserving cell density bands. These traveling bands allow cells to escape from traps near strong attractant but poor nutrient sources.
Results
The Traveling Escape Band in Opposing Gradients.
We study bacterial population dynamics in opposing attractant and nutrient gradients in a microfluidic channel. As shown in Fig. 1A, a linear attractant gradient is set up by maintaining a constant attractant concentration in reservoir 1 at one end of the channel (14, 15). For simplicity, we used MeAsp, a nonmetabolizable attractant, to maintain a fixed attractant gradient. Cells and then diluted tryptone broth (TB) growth medium were added in reservoir 2 in sequence. Once TB was added at time
The traveling escape band in a microfluidic channel with opposing gradients. (A) Schematic diagram of the microfluidic device (15). Reservoir 1 (green) contains 2 mM MeAsp. Reservoir 2 (red) contains
The cell density is determined by measuring the fluorescence intensity of the GFP-labeled cells in the observation window of the channel. Fig. 1 A, Left shows a series of cell-density images at different times. Initially, E. coli cells, attracted by MeAsp, move across the channel to accumulate near reservoir 1, where the cell population grows as a small amount of nutrient (TB) reaches them through diffusion from reservoir 2. However, after the bacteria population grows to a critical density
The spatial cell-density profiles
From our experimental data (Fig. 1 A and B), we found that the density profile
The Onset of Escape Band Depends on the Initial Cell Density and the Nutrient Concentration.
What determines the onset of the escape band? In Fig. 2, the observed spatiotemporal profiles of cell density are shown for different initial cell densities characterized by their optical density (OD) and for different TB concentrations in reservoir 2. For a small TB concentration (1%), there is no escape band during our experiments (5 h) as shown in Fig. 2i. For larger TB concentrations (
The kymographs of RP437 for different initial loading densities given by their OD and different TB concentrations. Each column has a fixed TB concentration
Sensing of dipeptide in TB by Tap is crucial for the escape band.
Which chemoreceptors are responsible for this complex behavior? In TB, aspartate and serine are the main known consumable attractants contributing to the formation of swarm rings observed in TB agar plate (9). Surprisingly, as shown in Fig. 3 A, i, the escape band still exists in the Tsr receptor mutant strain UU2599 (
Spatiotemporal cell density profile (kymograph) for mutants and WT under different nutrient conditions. (A) Kymograph for different mutant strains. (A, i)
To determine which minor receptor is responsible for the escape band, we carried out a series of experiments with different E. coli mutants. Our experiments showed that the strains RP3544 (
Our comprehensive mutant experiments identified Tap as the relevant chemosensor for the escape band. But what does Tap sense in TB? Tap is known to sense dipeptide through a dipeptide transport protein DppA (6, 16) and also pyrimidine (17). To find the responsible chemoeffector, we used the mutant strain DYM1 (
The Population Model Based on the Molecular Mechanism of the Chemotaxis Pathway.
To understand the mechanism for the escape-band formation, we studied the coupled spatiotemporal dynamics of the cell density
As shown in our previous work (18, 19), when environment changes are slower than the intracellular adaption dynamics, chemotaxis motility is controlled by an effective chemotaxis potential
Model Results Agree with Experiments Quantitatively.
We used the pathway based population model, Eqs. 1–3, to simulate the experimental system with the boundary conditions:
As shown in Fig. 4A, there are three phases (S1–S3) in the cell-distribution dynamics. Cells move toward the MeAsp side
Simulation results from the modified K-S model based on chemotaxis signaling pathway dynamics (parameters given in SI Appendix, Table S1). (A) Kymograph shows the escape band (white arrow) and the initial wave (yellow arrow) in agreement with the experiments (Fig. 1A). (B) The collapsed cell-density profile after rescaling. The dynamics of
The model also allows us to study dynamics of the nutrient concentration
The Mechanism and Conditions for the Escape-Band Formation.
Given that cell growth is much slower than the cell motility time scale:
The compact traveling band structure in the S2 and S3 phases depends on the balance between the two attractant gradients, which results in a minimum of the effective chemotaxis potential
The mechanism for the band formation is evident from the balance equation, Eq. 4. The growth-driven consumption of nutrient (c) amplifies chemotaxis toward the nutrient by reducing c and increasing
Conditions for the escape-band formation. (A) Phase diagram spanned by the dissociation constants
The mechanism of the escape band can be understood by studying dynamics of the effective chemotaxis potential
Scaling Properties of the Escape Band: Theory Predictions and Experimental Verification.
In our microfluidic experiments with a relatively short channel, the consumption of nutrient is much slower than the supply of nutrient by diffusion across the channel. Therefore, nutrient concentration field can be approximated by a piece-wise linear form at the leading order:
Next, we tested the scaling properties of the escape band predicted by Eq. 5. For six separate experiments with different initial cell densities (OD) and growth rates (
Scaling behaviors of the traveling escape band. (A) In six experiments with different initial cell density (OD) and different growth rate, both
Besides the scaling relationships among
Summary and Discussion
In this work, we studied E. coli chemotaxis behavior in an environment with two competing gradients: a strong but nonconsumable attractant at
Illustration of the formation and dynamics of the escape band during the three phases (S1–S2–S3) of population behaviors in competing gradients: a strong attractant source at
The mechanism for this rich set of spatiotemporal phenomena is due to the interplay between cell metabolism/growth and chemotaxis in competing gradients. The sharp (“soliton”-like) structure of the escape band, especially the trailing edge (Figs. 2B and 4B), is determined (shaped) by the two competing gradients. It is qualitatively different from the cell-density profile in the classic swarm-ring experiments, which has a wider width and a more diffused trailing tail (28, 29). The two opposing gradients also explain the much slower migrating speed of the escape band (
The escape band is a general phenomenon not limited by specific chemoattractants or receptors, and it should be physiologically relevant, as it enhances the fitness of the population by avoiding being trapped by nonmetabolizable or weakly metabolizable attractants, such as AI-2 in the biofilm (32, 33) and DHMA for commensal bacteria in the gut microbiota (34, 35). Indeed, our model showed that the overall population growth rate increases as the system changes from the no-band-forming regime to the band-forming regime (SI Appendix, Fig. S11). Besides growth, another condition for the escape band is the cell’s ability to sense some components of the nutrient. Here, we have identified Tap as the key receptor, which senses dipeptide in TB. The combined effects of growth and multiple chemosensors—E. coli has five different types of chemoreceptors that can sense a diverse set of different amino acids, sugars, oxygen, and other nutrient molecules—allow cells to find favorable conditions in complex environments with multiple nutrient sources and conflicting sensory cues.
Materials and Methods
All E. coli strains used in our experiments were from the Parkinson laboratory except DYM1 (
Acknowledgments
We thank Prof. Sandy Parkinson (University of Utah) for the gift of strains used in this study and Dr. Yihao Zhang (Peking University) for the GFP plasmid. We thank one of the reviewers for helping us to make the paper more accessible for readers focused on the biology. This work is partially supported by National Science Foundation of China Grants 11434001, 11674010, and 11774011; and NIH Grant R01-GM081747 (to Y.T.).
Footnotes
- ↵1To whom correspondence may be addressed. Email: yuhai{at}us.ibm.com or pkuluocx{at}pku.edu.cn.
Author contributions: X.Z., Q.O., C.L., and Y.T. designed research; X.Z., G.S., Y.D., K.C., C.L., and Y.T. performed research; X.Z., C.L., and Y.T. analyzed data; and X.Z., Q.O., C.L., and Y.T. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1808200116/-/DCSupplemental.
Published under the PNAS license.
References
- ↵
- ↵
- ↵
- Li M,
- Hazelbauer GL
- ↵
- Berg HC
- ↵
- ↵
- Neumann S,
- Hansen CH,
- Wingreen NS,
- Sourjik V
- ↵
- Mesibov R,
- Adler J
- ↵
- Adler J,
- Hazelbauer GL,
- Dahl M
- ↵
- Adler J
- ↵
- Wolfe A,
- Berg H
- ↵
- Park S, et al.
- ↵
- Park S, et al.
- ↵
- ↵
- Zhang X,
- Li L,
- Luo C
- ↵
- ↵
- ↵
- Liu X,
- Parales RE
- ↵
- ↵
- ↵
- ↵
- ↵
- Maddock J,
- Shapiro L
- ↵
- ↵
- Ames P,
- Studdert CA,
- Reiser RH,
- Parkinson JS
- ↵
- Mello B,
- Tu Y
- ↵
- ↵
- Li Z, et al.
- ↵
- Saragosti J, et al.
- ↵
- Fu X, et al.
- ↵
- ↵
- Kalinin Y,
- Neumann S,
- Sourjik V,
- Wu M
- ↵
- ↵
- ↵
- Sule N, et al.
- ↵
- Pasupuleti S,
- Sule N,
- Manson MD,
- Jayaraman A
Citation Manager Formats
Sign up for Article Alerts
Article Classifications
- Biological Sciences
- Microbiology
- Physical Sciences
- Biophysics and Computational Biology