1. Molecular Ecological Network Analyses (MENA)
Ye Deng, Ph.D Postdoctoral Research Associate
Institute of Environmental Genomics The University of Oklahoma
May, 2011

2. From parts to interactions
Microbial species richness
Different species
S =
The number of different species
Species diversity
Species abundance
Shannon index:
Simpson index: …
Species network
Species interactions
Adjacent matrix:
intro
RMT
analysis
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In Ecology, we usually use species richness and diversity to describe a community structure. The richness is the number of different species and the diversity was often calculated by different index.
one of the conventional methods is the Shannon diversity indexes. It considered the species number and abundance of each species.
However, all these method ignore the interactions among different species. In microbial world, these interactions could be the competition or co-operation based on the nutrient, material, information and space. And theoretically the interactions could be mathematically simplified as adjacent matrix and demonstrated ed as network structure.
Conventional methods: Shannon diversity indexes
Species number and abundance of each species
Ignore interactions among different species

3. Molecular Ecological network construction
intro
High throughput sequencing data (e.g. 16S pyrosequencing) or Microarray data
RMT
Standardized relative abundance (SRA) of sequence numbers in a community
analysis
Pair-wise similarity of gene abundance across different samples: e.g. Pearson correlation
Here, I introduce a novel approach to model the microbial community in network structure. First, the network was constructed by a random matrix theory based method.
The major steps of network construction,
concln
Determination of adjacency matrix by RMT-based approach

4. The RMT-based network construction
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Based on random matrix theory, in complex system, two universal extreme distributions are predicted for the nearest neighbor spacing distribution of eigenvalues. One is Gaussian (GOE) statistics, also called Wigner distribution, which reflects the random properties of complex system. The other is Poisson distribution, which is related to the system-specific, nonrandom properties.
Our previous study on gene coexpression networks indicated there is a transition point existed when gradually removing certain elements inside the correlation matrix.
Luo, Yang et al. BMC Bioinformatics, 2007, 8:299.

5. The RMT-based network construction
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After transition point has been detected by RMT approach, the adjacent matrix can be expressed as network graph. Each node represents a OTU or functional gene indicating an individual species. The edge between each two nodes represents positive or negative interactions between those two species. We called it as a Molecular Ecological network,
This graph looks cool, but what is the real meaning behind this structures.
Each node indicates a OTU / species and different colors demonstrate the genus they belong to
The edge indicates the interaction between the linked the OTUs/species

6. The Molecular Ecological Network analyses
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Overall, what is the network topology?
the general network properties and modular structure
For individual species, who are the major players?
the important nodes in the system
How does the network work?
the network individuals/modules’ functions
For multiple networks, what are the differences between them?
the network comparisons
So, we addressed some questions on this network structure and attempted to answer these questions by different network analyses approach.

7. The steps of MENA Network characterization and module detection
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Identification of topological roles of individual nodes
Eigengene network analyses
Network comparison between different conditions
Association of network properties to ecological functional traits

8. Responses of grass ecosystems to elevated CO2
BioCON (Biodiversity, CO2, and Nitrogen) at the University of Minnesota by Peter Reich
Each ring:
20m diameter
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Elevated CO2
There are 296 plots in total.
An example
Ambient CO2
Each plot:
2×2m

9. Modular organization of the MEN under ambient CO2
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The modularity is an inherent character of many large complex systems. In biological systems the modules were usually considered as functional units.
In ecology, a module (also called compartmentalization) is a group of species that interact strongly among themselves, but little with species in other modules. Modularity in an ecological community may reflect habitat heterogeneity, physical contact, functional association, divergent selection and/or phylogenetic clustering of closely related species. Modules with their component species may even be the key units of coevolution.
Based on 454 sequencing data at ambient CO2.
All the MENs examined were modular, with distinct modules .
A module is a group of OTUs/functional genes that are highly correlated among themselves, but have few connections with OTUs/functional genes belonging to other modules.

10. The topological roles of nodes under ambient and elevated CO2
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The next job to identity the key players in the systems
The topological roles of nodes in a modular network can be described in this zP-plot. The x-axis is among-module connectivity. Higher Pi means the nodes had higher connections outer of their own modules. They acted as connectors to communicate with other module units. The y-axis is within-module connectivity. Higher Zi means the nodes had more neighbors within their own modules, indicating they are the leaders within their modules, so called module hubs.
Interestingly, unlike module hubs were separated into different major taxa, four of the five connectors OTUs were Actinobacteria. It may indicate some species in Actinobacteria carry out the task to act as bridges.

11. Effects of eCO2 on the network interactions of Actinobacteria
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eCO2
aCO2
Additionally, different phylogenetic group comparisons also revealed eCO2 had differential impacts on the network structure.
For example, the top 10 Actinobacteria OTUs under eCO2 (Fig. 3A) had more complex interactions than their corresponding OTUs under aCO2 (Fig. 3B).
Top 10 OTUs with the highest connectivity at eCO2 and their corresponding OTUs at aCO2
More complicated network interactions at eCO2 than aCO2

12. Effects of eCO2 on the network interactions of Verrucomicrobia
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aCO2
eCO2
An opposite observation is Verrucomicrobia had much less complex interactions under eCO2 (Fig. S3B) than aCO2 (Fig. S3A),
It appears that eCO2 selected for Actinobacteria but against Verrucomicrobia. Altogether, the above results suggested that eCO2 substantially altered the network interactions in the grassland microbial communities, and that the impacts varied considerably among different microbial groups.
Oppositely, the interaction of Verrucomircrobia was much less under eCO2 than aCO2.

13. Network connectivity vs. the soil geochemical traits
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Phylogeny
aCO2
eCO2
Network size rM p
All detected OTUs
292
0.039
0.169
263
0.368
0.001
Acidobacteria 45
0.054
0.262 34
0.137
0.124
Actinobacteria 81
0.381
0.002 76
0.562
Bacteroidetes 23
-0.084
0.622 24
0.487
0.012
Firmicutes 11
0.023
0.338 8
0.310
0.114
Gemmatimonadetes 10
-0.220
0.958
0.299
0.168
Planctomycetes 4
0.604
0.221 5
-0.651
1.000
α-Proteobacteria
-0.034
0.638 42
0.472
β-Proteobacteria 22
0.029
0.319
0.297
0.044
δ-Proteobacteria 6
0.498
0.138
0.043
0.416
γ-Proteobacteria
-0.159
0.723 9
0.020
0.399
Verrucomicrobia 12
0.184
0.057
0.086
0.253
To discern the relationships between microbial network interactions and soil properties, Mantel tests were performed.
Under eCO2, partial Mantel tests revealed very strong correlations between the connectivity and the OTU significance of the selected soil variables based on all detected OTUs (p = 0.001), or several phylogenetic groups such as Actinobacteria (p = 0.001), Bacteroidetes (p = 0.012), α-Proteobacteria (p = 0.001), and β-Proteobacteria (p = 0.044) under eCO2 (Table 2). Under aCO2, the connectivities for Actinobacteria (p < 0.05) were also significantly correlated to the OTU significance of the selected soil variables. These results suggested that the microbial community network interactions were, at least to some extent, related to soil variables, and that eCO2 could have significant impacts on such relationships.

14. Conclusion
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The high-throughput metagenomic data provide a great opportunity to study the microbial interactions in a large scale
The network analysis can help to identify the key species, compare community structure under different treatments, and link the network topology with environmental traits
Elevated CO2 dramatically altered the network interactions, indicating the switch of microbial community

15. Acknowledgement Jizhong (Joe) Zhou Zhili He Liyou Wu Feng Luo
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Jizhong (Joe) Zhou
Zhili He
Liyou Wu
Feng Luo
Yunfeng Yang
Funding Source:
US Department of Energy
United States Department of Agriculture
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