Background: Microbial interactions shape the structure and function of microbial communities; microbial co-occurrence networks in specific environments have been widely developed to explore these complex systems, but their interconnection pattern across microbiomes in various environments at the global scale remains unexplored. Here, we have inferred an Earth microbial co-occurrence network from a communal catalog with 23,595 samples and 12,646 exact sequence variants from 14 environments in the Earth Microbiome Project dataset.
Results: This non-random scale-free Earth microbial co-occurrence network consisted of 8 taxonomy distinct modules linked with different environments, which featured environment specific microbial co-occurrence relationships. Different topological features of subnetworks inferred from datasets trimmed into uniform size indicate distinct co-occurrence patterns in the microbiomes of various environments. The high number of specialist edges highlights that environmental specific co-occurrence relationships are essential features across microbiomes. The microbiomes of various environments were clustered into two groups, which were mainly bridged by the microbiomes of plant and animal surface. Acidobacteria Gp2 and Nisaea were identified as hubs in most of subnetworks. Negative edges proportions ranged from 1.9% in the soil subnetwork to 48.9% the non-saline surface subnetwork, suggesting various environments experience distinct intensities of competition or niche differentiation.
Conclusion: This investigation highlights the interconnection patterns across microbiomes in various environments and emphasizes the importance of understanding co-occurrence feature of microbiomes from a network perspective.
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The study of microbial interactions tends to happen at one of two scales – the very small and the very large. This study uses the latter approach. We took a large dataset of microbial genetic data from a well curated source and used a bunch of math to figure out which microbes tend to occur in the same places.
What do the results mean?
The results show a ‘scale free network’ of co-occurrences. This means that a small number of microbes have the bulk of the connections and the rest have very few connections. You could compared it to the 1%/99% economic disparity. Microbial networks are inherently biased.
Environment is an important determinant of both the presence of certain microbes and how they interact with others. Much like your local neighbourhood, different kinds of people live in different towns/suburbs/apartment blocks and the type of environment you live in affects how you interact with your neighbours. It’s perfectly normal for masses of people to congregate every night in my Chinese apartment complex’s plaza until quite late. It would be strange to do that in suburban Australia.
More microbes tend to have special interactions with their neighbours than general interactions. This means that rather than a one size fits all approach, microbes are cultivating special relationships with their connected neighbours.
Aside from just being pretty cool, this kind of analysis is an important technique for classifying microbial data. We have so much genetic data but so little taxonomic data and it’s useful to have different techniques to sort out who’s talking to who.