Back

Keystone species determine the productivity of synthetic microbial biofilm communities

Sun, X.; Xie, J.; Zheng, D.; Wang, W.; Xia, R.; Zhang, R.; Kovacs, A. T.; Xu, Z.; Shen, Q.

2022-01-23 microbiology
10.1101/2022.01.23.477386 bioRxiv
Show abstract

Microbes typically reside in multi-species communities, whose interactions have considerable impacts on the robustness and functionality of such communities. To manage microbial communities, it is essential to understand the factors driving their assemblage and maintenance. Even though the community composition could be easily assessed, interspecies interactions during community establishment remain poorly understood. Here, we combined co-occurrence network analysis with quantitative PCR to examine the importance of each species within synthetic communities (SynComs) of pellicle biofilms. Genome-scale metabolic models and in vitro experiments indicated that the biomass of SynComs was primarily affected by keystone species that are acting either as metabolic facilitators or as competitors. Our study sets an example of how to construct a model SynCom and investigate interspecies interactions.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
The ISME Journal
◐ 228 papers in training set
Top 0.3%
12.5%
Similar papers in this journal
2
mSystems
● 394 papers in training set
Top 0.5%
11.5%
Similar papers in this journal
3
Frontiers in Microbiology
○ 427 papers in training set
Top 0.6%
11.5%
Similar papers in this journal
4
npj Biofilms and Microbiomes
○ 58 papers in training set
Top 0.1%
7.0%
Similar papers in this journal
5
Environmental Microbiology
○ 133 papers in training set
Top 0.6%
5.3%
Similar papers in this journal
6
FEMS Microbiology Ecology
◐ 54 papers in training set
Top 0.2%
4.7%
50% of probability mass above
7
eLife
● 5828 papers in training set
Top 27%
4.7%
8
ISME Communications
◐ 120 papers in training set
Top 0.6%
4.2%
9
Environmental Microbiology Reports
○ 31 papers in training set
Top 0.1%
3.9%
10
Microbiology Spectrum
● 469 papers in training set
Top 5%
2.7%
11
Water Research
○ 79 papers in training set
Top 0.5%
2.6%
12
Applied and Environmental Microbiology
● 339 papers in training set
Top 3%
2.6%
13
mBio
● 833 papers in training set
Top 6%
2.6%
14
Microbial Biotechnology
○ 34 papers in training set
Top 0.3%
2.3%
15
Microbiological Research
○ 22 papers in training set
Top 0.3%
1.3%
16
Nature Communications
○ 5641 papers in training set
Top 49%
1.3%
17
mSphere
● 302 papers in training set
Top 6%
1.1%
18
Communications Biology
○ 993 papers in training set
Top 27%
1.0%
19
Phytobiomes Journal
● 27 papers in training set
Top 0.5%
0.8%
20
Microbial Ecology
○ 29 papers in training set
Top 0.8%
0.8%
21
Microbiome
○ 154 papers in training set
Top 2%
0.8%
22
Scientific Reports
○ 3612 papers in training set
Top 74%
0.8%
23
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 46%
0.6%
24
microLife
◐ 22 papers in training set
Top 0.7%
0.6%
25
iScience
○ 1154 papers in training set
Top 43%
0.6%
26
Soil Biology and Biochemistry
○ 35 papers in training set
Top 0.4%
0.6%
27
Environmental Microbiome
○ 29 papers in training set
Top 0.9%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.