Data1_Matlab

Part 1 of sample data for the Matlab code

Code_Matlab

Code_Matla

Single species can enhance or inhibit their own growth via changing the pH.

<p>The curves show bacterial density over time, and the color shows the pH. (a) <i>C</i>. <i>ammoniagenes</i> increases the pH and also prefers these higher pH values, leading to a minimal viable cell density required for survival. Increasing the buffer concentration from 10 mM (−buffer) to 100 mM (+buffer) phosphate makes it more difficult for <i>C</i>. <i>ammoniagenes</i> to alkalize the environment and therefore increases the minimal viable cell density. (b) <i>P</i>. <i>veronii</i> also increases the pH yet prefers low pH values. Indeed, <i>P</i>. <i>veronii</i> populations can change the environment so drastically that it causes the population to go extinct. Adding buffer tempers the pH change and thus allows for the survival of <i>P</i>. <i>veronii</i>. An Allee effect can also be found in <i>L</i>. <i>plantarum</i> and ecological suicide in <i>S</i>. <i>marcescens</i> (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004248#pbio.2004248.s008" target="_blank">S8 Fig</a>). Note that buffering often just slightly affects the final pH values (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004248#pbio.2004248.s002" target="_blank">S2 Fig</a>) but saves the population by delaying the pH change (as shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004248#pbio.2004248.s004" target="_blank">S4 Fig</a> and discussed in more detail in [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004248#pbio.2004248.ref035" target="_blank">35</a>]). Linlog scale is used for the y-axis. The data for this figure can be found in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004248#pbio.2004248.s022" target="_blank">S1 Data</a>. Ca, <i>Corynebacterium ammoniagenes</i>; CFU, colony-forming unit; Pv, <i>Pseudomonas veronii</i>.</p

Data_C++

Sample data for the C++ code

Data2_Matlab

Part 2 of sample data for the Matlab code

Modifying and reacting to the environmental pH can drive bacterial interactions

<div><p>Microbes usually exist in communities consisting of myriad different but interacting species. These interactions are typically mediated through environmental modifications; microbes change the environment by taking up resources and excreting metabolites, which affects the growth of both themselves and also other microbes. We show here that the way microbes modify their environment and react to it sets the interactions within single-species populations and also between different species. A very common environmental modification is a change of the environmental pH. We find experimentally that these pH changes create feedback loops that can determine the fate of bacterial populations; they can either facilitate or inhibit growth, and in extreme cases will cause extinction of the bacterial population. Understanding how single species change the pH and react to these changes allowed us to estimate their pairwise interaction outcomes. Those interactions lead to a set of generic interaction motifs—bistability, successive growth, extended suicide, and stabilization—that may be independent of which environmental parameter is modified and thus may reoccur in different microbial systems.</p></div

Data3_Matlab

Part 3 of sample data for the Matlab code

Code_C++

Code_C+

The presence of cheaters makes a population unable to survive rapidly deteriorating environments.

<p>Our model predicts that the phase diagram is different for different dilution factors. Here we consider three environments: a “benign” environment, characterized by a low dilution factor; a “harsh” environment, characterized by a high dilution factor, and an “intermediate” environment with a moderate dilution factor. (A) We present the expected shifts in our phase diagram and equilibrium points as a result of a sudden environmental deterioration, as predicted by the model. In a benign environment, the mixed equilibrium point d_eq,1 is located at the bottom right side of the phase diagram (red dot). The basin of attraction for d_eq,1 (i.e., the survival zone) is shaded in gray. A sudden transition to a harsh environment (characterized by a jump in the dilution factor) causes a sudden change in the phase diagram, and leads to both a new survival zone and a new mixed equilibrium point d_eq,2. The point d_eq,1 is out of the survival zone for the harsh environment phase diagram (open circle, dashed), so we expect that a mixed population that was in equilibrium before the sudden environmental deterioration (and was therefore at d_eq,1) should go extinct. The pure-cooperator equilibrium point c_eq,1 in the benign environment phase diagram is also presented (blue dot). A sudden change in the environment would not lead to the extinction of the pure cooperator population, since c_eq,1 is within the basin of attraction of c_eq,2 in the harsh environment phase diagram. (B) We present the expected shifts in the phase diagram if we introduce an intermediate step in the environmental deterioration. All of the phase diagrams were calculated from the model. We note that d_eq,1 is within the survival zone of the intermediate phase diagram (where d_eq,1 is represented as an open dot, red dashed stroke). In addition, d_eq,i (the mixed equilibrium point of the intermediate phase diagram) is within the survival zone of the harsh phase diagram (where d_eq,i is represented as an open dot, red dashed stroke). Therefore, a sudden transition from benign to intermediate environments does not lead to population extinction. For the same reason, a later transition from intermediate to harsh does not lead to extinction either. (C) These predictions were tested experimentally by bringing to equilibrium six pure cooperator populations and six mixed cooperator/cheater populations (all at a low dilution of 667×, characterizing a “benign” environment). The dilution factor was suddenly changed to 1,739× (characterizing a “harsh” environment) on day 3. All six pure cooperator populations tested (lower panel, blue) were able to withstand the rapid deterioration. However, only one out of six mixed populations (lower panel, red) were able to survive the rapid environmental deterioration. (D) A slow environmental deterioration was applied by increasing the dilution factor from 667× to 1,739× in two steps (upper panel); a first jump in dilution factor (to 1,333×, an “intermediate” environment) was imposed at day 2, and a second jump in dilution factor (to 1,739×) was imposed on day 12. In this case, all six mixed populations (red) were able to survive the deterioration (as did all six pure cooperator populations [blue]).</p

Stochastic assembly produces heterogeneous communities in the <i>Caenorhabditis elegans</i> intestine

<div><p>Host-associated bacterial communities vary extensively between individuals, but it can be very difficult to determine the sources of this heterogeneity. Here, we demonstrate that stochastic bacterial community assembly in the <i>Caenorhabditis elegans</i> intestine is sufficient to produce strong interworm heterogeneity in community composition. When worms are fed with two neutrally competing, fluorescently labeled bacterial strains, we observe stochastically driven bimodality in community composition, in which approximately half of the worms are dominated by each bacterial strain. A simple model incorporating stochastic colonization suggests that heterogeneity between worms is driven by the low rate at which bacteria successfully establish new intestinal colonies. We can increase this rate experimentally by feeding worms at high bacterial density; in these conditions, the bimodality disappears. These results demonstrate that demographic noise is a potentially important driver of diversity in bacterial community formation and suggest a role for <i>C</i>. <i>elegans</i> as a model system for ecology of host-associated communities.</p></div