Structure and evolution of Streptomyces interaction networks in soil and in silico.
Soil grains harbor an astonishing diversity of Streptomyces strains producing diverse secondary metabolites. However, it is not understood how this genotypic and chemical diversity is ecologically maintained. While secondary metabolites are known to mediate signaling and warfare among strains, no systematic measurement of the resulting interaction networks has been available. We developed a high-throughput platform to measure all pairwise interactions among 64 Streptomyces strains isolated from several individual grains of soil. We acquired more than 10,000 time-lapse movies of colony development of each isolate on media containing compounds produced by each of the other isolates. We observed a rich set of such sender-receiver interactions, including inhibition and promotion of growth and aerial mycelium formation. The probability that two random isolates interact is balanced; it is neither close to zero nor one. The interactions are not random: the distribution of the number of interactions per sender is bimodal and there is enrichment for reciprocity--if strain A inhibits or promotes B, it is likely that B also inhibits or promotes A. Such reciprocity is further enriched in strains derived from the same soil grain, suggesting that it may be a property of coexisting communities. Interactions appear to evolve rapidly: isolates with identical 16S rRNA sequences can have very different interaction patterns. A simple eco-evolutionary model of bacteria interacting through antibiotic production shows how fast evolution of production and resistance can lead to the observed statistical properties of the network. In the model, communities are evolutionarily unstable--they are constantly being invaded by strains with new sets of interactions. This combination of experimental and theoretical observations suggests that diverse Streptomyces communities do not represent a stable ecological state but an intrinsically dynamic eco-evolutionary phenomenon.
Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America.
Kalin
K
Vetsigian
Rishi
R
Jajoo
Roy
R
Kishony
D001431:Bacteriological Techniques / Q000379:methods*; D003198:Computer Simulation; D017753:Ecosystem; D056265:Microbial Interactions; D008954:Models, Biological; D012988:Soil Microbiology; D013302:Streptomyces / Q000254:growth & development / Q000378:metabolism*; D059008:Time-Lapse Imaging