TY - JOUR
T1 - Interaction variability shapes succession of synthetic microbial ecosystems
AU - Liu, Feng
AU - Mao, Junwen
AU - Kong, Wentao
AU - Hua, Qiang
AU - Feng, Youjun
AU - Bashir, Rashid
AU - Lu, Ting
N1 - We thank Prof. Jon Nissen-Meyer and Prof. Todd Klaenhammer for their generous. pngts of DNA materials. This work was supported by the National Science Foundation (1553649), Office of Naval Research (N000141612525) and Department of Energy (DE-SC0019185). F.L. was supported by the China Scholarship Council. J.M. was supported by the China Scholarship Council and the National Natural Science Foundation of China (11575059 and 11847315). Q.H. was supported by the Research Program of the State Key Laboratory of Bioreactor Engineering and 111 Project (B18022).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - Cellular interactions are a major driver for the assembly and functioning of microbial communities. Their strengths are shown to be highly variable in nature; however, it is unclear how such variations regulate community behaviors. Here we construct synthetic Lactococcus lactis consortia and mathematical models to elucidate the role of interaction variability in ecosystem succession and to further determine if casting variability into modeling empowers bottom-up predictions. For a consortium of bacteriocin-mediated cooperation and competition, we find increasing the variations of cooperation, from either altered labor partition or random sampling, drives the community into distinct structures. When the cooperation and competition are additionally modulated by pH, ecosystem succession becomes jointly controlled by the variations of both interactions and yields more diversified dynamics. Mathematical models incorporating variability successfully capture all of these experimental observations. Our study demonstrates interaction variability as a key regulator of community dynamics, providing insights into bottom-up predictions of microbial ecosystems.
AB - Cellular interactions are a major driver for the assembly and functioning of microbial communities. Their strengths are shown to be highly variable in nature; however, it is unclear how such variations regulate community behaviors. Here we construct synthetic Lactococcus lactis consortia and mathematical models to elucidate the role of interaction variability in ecosystem succession and to further determine if casting variability into modeling empowers bottom-up predictions. For a consortium of bacteriocin-mediated cooperation and competition, we find increasing the variations of cooperation, from either altered labor partition or random sampling, drives the community into distinct structures. When the cooperation and competition are additionally modulated by pH, ecosystem succession becomes jointly controlled by the variations of both interactions and yields more diversified dynamics. Mathematical models incorporating variability successfully capture all of these experimental observations. Our study demonstrates interaction variability as a key regulator of community dynamics, providing insights into bottom-up predictions of microbial ecosystems.
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U2 - 10.1038/s41467-019-13986-6
DO - 10.1038/s41467-019-13986-6
M3 - Article
C2 - 31949154
AN - SCOPUS:85077940879
SN - 2041-1723
VL - 11
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 309
ER -