Gut bacterial aggregates as living gels
- Department of Physics, Institute of Molecular Biology, and Materials Science Institute, University of Oregon, United States
- Department of Physics and Department of Molecular and Cell Biology, University of California, Berkeley, United States
Figures
Figure 1
Overview of experimental methods.
Larval zebrafish were derived germ-free and then monoassociated with single bacterial species (left). After 24 hr of colonization, images spanning the entire gut were acquired with light sheet …
Figure 2 with 2 supplements
Different bacterial species exhibit similar cluster size distributions.
Reverse cumulative distributions, the probability that the cluster size is greater than as a function of , for eight bacterial strains in larval zebrafish intestines. Small circles connected by …
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Figure 2—source data 1
Spreadsheet with all cluster sizes by strain.
- https://cdn.elifesciences.org/articles/71105/elife-71105-fig2-data1-v2.xlsx
Figure 2—figure supplement 1
Cluster size distributions as probability densities.
Different bacterial species exhibit similar cluster size distributions. Probability densities for eight bacterial strains monoassociated in larval zebrafish intestines. Small circles connected by …
Figure 2—figure supplement 2
Images of individual -slices showing mild heterogeneity of fluorescence intensity within aggregates.
Fluorescence intensity is mostly homogeneous within clusters, although small dark regions do occur. Three individual z slices of a fish colonized with Enterobacter are shown. The approximate gut …
Figure 3
A minimal model inspired by evolutionary dynamics generates power law distributions.
(A) Fragmentation is analogous to mutation and we can construct a genealogy that mirrors the physical structure of the clusters. (B) Summary of a growth/fragmentation process that includes the …
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Figure 3—source data 1
Results of power-law fits to simulated distributions.
- https://cdn.elifesciences.org/articles/71105/elife-71105-fig3-data1-v2.xlsx
Figure 4 with 3 supplements
Size-dependent aggregation introduces a plateau in the size distribution.
(A) Schematic of the generalized model. Parameters summarized in Table 3. (B) Reverse cumulative distributions obtained from simulations for different values of (left, middle, right) and …
Figure 4—figure supplement 1
Distributions of growth/fragmentation process at short times.
Mild curvature appears in the minimal growth/fragmentation process distribution at finite time, but the result is inconsistent with experimental data. Circles are the result of stochastic …
Figure 4—figure supplement 2
Distributions for a process with density-dependent growth, fragmentation, and expulsion.
A modified process with carrying capacity and expulsion does not produce a plateau in the stationary size distribution. Reverse cumulative distributions computed from stochastic simulations with …
Figure 4—figure supplement 3
Distributions for a model with only aggregation and .
Plateaus arise at the gelation transition of purely aggregating systems. Reverse cumulative distributions computed from stochastic simulations are shown. Different curves represent different …
Tables
Table 1
Summary of cluster data by bacterial strain.
Each row corresponds to one of the bacterial strains included in this study. Entries include strain name, total number of fish colonized with that strain, total number of clusters identified across …
| Bacterial strain | Number of fish | Number of clusters | Source publication |
|---|---|---|---|
| Aeromonas ZOR0001 | 6 | 445 | Schlomann et al., 2018 |
| Aeromonas ZOR0002 | 6 | 1901 | Schlomann et al., 2018 |
| Enterobacter ZOR0014 | 18 | 3597 | Schlomann et al., 2018; Schlomann et al., 2019 |
| Plesiomonas ZOR0011 | 3 | 223 | Schlomann et al., 2018 |
| Pseudomonas ZWU0006 | 6 | 133 | Schlomann et al., 2018 |
| Vibrio ZOR0036 | 6 | 2430 | Schlomann et al., 2018 |
| Vibrio ZWU0020 Δmot | 11 | 5888 | Wiles et al., 2020 |
| Vibrio ZWU0020 Δche | 11 | 3551 | Wiles et al., 2020 |
Table 2
Analytic results for the minimal growth-fragmentation process.
Distribution exponent, μ, as a function of fragmentation exponent, , fragmentation rate, β, and growth rate, r, as plotted in Figure 3D. Results are expected to be valid for long times (), large …
| distribution exponent, μ |
Table 3
Summary of model variables and parameters.
| Variable/parameter | Description |
|---|---|
| Cluster size (number of cells) | |
| Probability of cluster size, | |
| Cumulative probability; probability of size being larger than | |
| μ | Exponent of power law; , |
| Cell division rate | |
| Carrying capacity; maximum number of cells | |
| Fragmentation rate | |
| Fragmentation exponent; clusters of size fragment with rate | |
| Aggregation rate | |
| Aggregation exponent; clusters of sizes and aggregate with rate | |
| Expulsion rate | |
| Expulsion exponent; clusters of size are expelled with rate |
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Software, algorithm | Analysis code | This study | see Materials and methods, Simulations | |
| Other | Cluster size data | Schlomann and moments, 2018 | ||
| Other | Cluster size data | Schlomann et al., 2019 | ||
| Other | Cluster size data | Wiles et al., 2020 |
Additional files
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Supplementary file 1
Cumulative distribution exponents of cluster size distributions by strain, with analysis of sensitivity to single-cell detection.
The small size regime of the cluster size distributions were fit to a power-law model for sizes up to 100 cells using two methods: a linear fit to and maximum likelihood estimation (Materials and methods). The fits were done for each animal and the resulting mean ± std. dev of the exponents (corresponding to , as defined in the text) are given for each strain. For each method, the fits were done twice, once including single cells, and once considering only cells of size two or greater. As discussed in the main text, the largest uncertainty in cluster size enumeration from the images occurs at small sizes. Ignoring single cells in the fit only mildly changes the average exponent, and all changes are within uncertainties. Most exponents are consistent with .
- https://cdn.elifesciences.org/articles/71105/elife-71105-supp1-v2.xlsx
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Transparent reporting form
- https://cdn.elifesciences.org/articles/71105/elife-71105-transrepform-v2.docx
