Interaction networks shed light on the ecology and evolution of soil microbiomes. Linda Kinkel Department of Plant Pathology University of Minnesota

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1 Interaction networks shed light on the ecology and evolution of soil microbiomes Linda Kinkel Department of Plant Pathology University of Minnesota

2 Soil Health: Disease suppression How do we measure soil microbiomes to both understand and manage disease suppression? You can t manage what you can t measure

3 Soil microbiomes: *who is present: target populations *diversity/richness *compositional structure (aggregate taxon presence/absence/abundance) Species interactions generate suppressive soil communities.

4 Disease suppressive soils Can we measure interaction structure of communities? Biotic organization

5 Characterizing interaction structure of communities 1. Microbial species co-association networks 2. Inhibitory interaction networks 3. Signaling interaction networks 4. Implications for management

6 Assaying pathogen suppressive activity in soil Streptomyces scabies Fusarium oxysporum Verticillium dahliae Pathogen-inhibitory populations are significantly negatively correlated with diseases Potato: scab, verticillium wilt; Soybean: Pythium, Phytophthora Alfalfa: Pythium Rhizoctonia solani Fusarium graminearum

7 Species co-occurrence networks How do these work? *multiple communities or samples *positive co-association of the abundance of different OTUs/species among samples *Which taxa are consistently together across these communities? *Are co-associated taxa correlated with pathogen suppression?

8 Linking populations (modules) with functional capacity Mean correlation coefficient between OTU abundance and pathogen suppression metrics. Modules co-vary with disease suppressive potential (bacteria) Collections of OTUs are associated with high or low pathogen suppression (Schlatter et al., 2014)

9 Are these OTU combinations important to disease suppression? We tracked individual OTUs across different settings. *are the same OTUs consistently co-associated *are the same OTUs consistently positively associated with disease suppression

10 OTU co-associations vary with plant host. The same OTU `hangs out with different taxa when in the rhizosphere of different plant hosts. Who you interact with varies.. Implications for pathogen suppression? (Bakker et al., 2014)

11 The same collection of OTUs have different functional characteristics in different communities. ******************* Biotic organization matters to function. Mean correlation coefficient between OTU abundance and inhibitor density. Asterisks: significant difference between the mean correlation coefficient in the Andropogon vs. Ss, Lc, or Lp plant hosts. (Bakker et al., 2014)

12 Characterizing patterns of co-association.. understanding functional capacities of soil microbiomes Co-association-/-Interaction

13 From co-association to interaction networks. What are interaction networks?

14 Interaction networks: `Map interactions among coexisting populations *Inhibitory *Niche overlap *Signaling

15 Are there interaction network metrics or structures that: a) Are associated with suppression? b) Shed light on the ecology or evolution of pathogen suppression? Today: 3 communities, sympatric isolates Schlatter, Song, Vaz-Jauri, and Kinkel

16 Streptomyces interaction networks Moderately inhibitory Highly Inhibitory Weakly inhibitory A B C

17 Frequency A Inhibition Network Characteristics B A B C C Out-Degree In-Degree Network Metric Network A Network B Network C Graph Density Mean Shortest Path (L) Clustering Coeff (C)

18 Summary: Inhibition networks 1. Inhibition networks from different locations have distinct structures. 2. Consistent linkages to suppression? Biological interpretations are difficult. Can we capture meaningful network structures at smaller scales? ARE THERE INTERACTION NETWORK STRUCTURES THAT: a) Are associated with suppression? b) Shed light on the ecology or evolution of suppression

19 Triad analysis: simple structural sub-units

20 1. Do triad frequencies differ among communities (suppressive vs. non-suppressive)? 2. Is there evidence that specific triads are selectively enriched? 3. Ecological or evolutionary implications

21 Triad type Triad distributions differ among communities. A B C Triad type 40 A B C 25 %

22 Are triads significantly enriched within communities? Random null models: what are the expected frequencies? Compare triad structures with random networks: 1. Erdős Rényi model: random network has same number of nodes (OTUS) and edges (total # of inhibitory interactions) 2. Rewired model: swap a pair of edges if possible, 100 rewires for each network. This model maintains the phenotypes of each node.

23 Triad analysis: inhibition networks Triads in plots A and B are significantly enriched or depressed. But not in plot C (the least suppressive) Plot C: Inhibition Real Rewired Triad

24 Triads: Stepwise acquisition and loss of inhibition. Increasing inhibition Decreasing inhibition

25 Triads: Stepwise acquisition and loss of inhibition. Increasing inhibition Decreasing inhibition

26 What do these complicated patterns tell us about suppression? 1. Significant enrichment of triads: triadic interactions are important in the suppressive communities (but not non-suppressive?). 2. Non-random accumulation of triads may reflect active and ongoing selection for inhibitory phenotypes and coevolutionary arms race dynamics.

27 Currently mapping networks for a much broader collection of communities..how do interaction structures relate to microbiome suppression? *are suppressive soil communities more connected? *are there `signature triads associated with pathogen suppression? *do coevolved triads offer stable consortia for inoculation? *how do triadic relative abundances shift over time within suppressive or non-suppressive communities?

28 There are multiple types of interaction networks. Among Streptomyces (and lots of other microbes) diffusible signals can alter phenotypes. What are the implications for community-wide function?

29 Antibiotic phenotypes: highly sensitive to signaling Induction networks among distinct Streptomyces communities in soil. sympatric, allopatric.

30 Inhibition Signaling Blue: upregulates inhibitory phenotype Green: suppresses inhibitory phenotypes Biotic organization: complex networks of inhibitory and signaling interactions. Effects on community functions?

31 Does complexity make management impossible? 1. Are there organizing principles? Are there interaction or co-association structures that are consistently associated with suppression? 2. What is it that we DON T yet know that limits our success in managing soil microbiomes?

32 Interaction structure analyses: Biotic organization matters. Metrics of diversity, richness, or composition, while useful, may be inadequate for understanding the factors that generate and predict soil health (pathogen suppression). More complex analyses are needed!!--especially analyses that incorporate the structure of species interactions. *High-throughput approaches (e.g. metatranscriptomics), other omics will help!

33 Acknowledgements The People The Microbes Lindsey Hanson, Dr. Adil Essarioui, Dr. Sarah Castle, Dr. Zewei Song, Dr. Dan Schlatter, Dr. Matt Bakker, Laura Felice, Dr. Patricia Vaz- Jauri, Dr. JP Dundore-Arias, The Money