Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. "General network analysis" identifies candidate taxa for maintaining an existing microbial community. "Host-focused analysis" includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. "Pathogen-focused analysis" identifies taxa with direct or indirect associations with taxa known a priori as pathogens. "Disease-focused analysis" identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

Next generation fungal amplicon sequencing is being used with increasing frequency to study fungal diversity in various ecosystems; however, the influence of sample preparation on the characterization of fungal community is poorly understood. We investigated the effects of four procedural modifications to library preparation for high-throughput sequencing (HTS). The following treatments were considered: 1) the amount of soil used in DNA extraction, 2) the inclusion of additional steps (freeze/thaw cycles, sonication, or hot water bath incubation) in the extraction procedure, 3) the amount of DNA template used in PCR, and 4) the effect of sample pooling, either physically or computationally. Soils from two different ecosystems in Minnesota, USA, one prairie and one forest site, were used to assess the generality of our results. The first three treatments did not significantly influence observed fungal OTU richness or community structure at either site. Physical pooling captured more OTU richness compared to individual samples, but total OTU richness at each site was highest when individual samples were computationally combined. We conclude that standard extraction kit protocols are well optimized for fungal HTS surveys, but because sample pooling can significantly influence OTU richness estimates, it is important to carefully consider the study aims when planning sampling procedures.

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The routine use of high-throughput sequencing to profile microbial communities necessitates improved protocols for detecting and adjusting for variation among sequencing runs for marker gene analysis. Although mock communities are widely used as a control among runs, the composition and diversity of mock communities, in most cases, are orders of magnitude lower than the actual samples. We demonstrated that replicated biological samples (“technical replicates”) are superior to a mock community in detecting variation and potential bias among sequencing runs. We present a case in which technical replicates of three soil samples were sequenced in three MiSeq runs containing samples from multiple experiments. The technical replicate samples revealed a potentially biased, outlier sequencing run, from which several Ascomycota taxa were substantially underestimated. Similar bias was seen in the other samples sequenced but was not detected using the mock community. Our study demonstrates that using technical replicates along with traditional mock communities provide additional quality control information and aid in detecting outlier sequencing runs.

Onions are highly responsive to arbuscular mycorrhizal fungi (AMF), but little is known about AMF communities in onion crops (∼10,000 ha) in the semiarid, irrigated region of the Columbia Basin of Washington and Oregon. AMF communities and root colonization were compared in organic and conventional onion fields, and between paired conventional fields that were fumigated or not with metam sodium. AMF were detected in all fields at all sampling times, with no differences in root colonization of onions used to bait soil from organic versus conventional and fumigated versus nonfumigated fields. However, AMF colonization of roots of onion plants sampled midsummer was greater in organic versus conventional fields (67 versus 51%) and less in fumigated versus nonfumigated conventional fields (45 versus 67%). Pyrosequencing identified four AMF genera (Glomus, Claroideoglomus, Paraglomus, and Diversispora) and four dominant operational taxonomic units (OTUs) (Glomus mosseae (Funneliformis mosseae), Glomus Whitfield type 17, Claroideoglomus lamellosum, and Glomus MO_G17). AMF community structure in roots of onion plants collected from crops midsummer was different in organic versus conventional crops, with greater AMF diversity and richness in organic than conventional crops. There was no effect of organic versus conventional crops on dominant OTUs, but several low-abundance OTUs in organic fields were not detected in conventional fields. There was no consistent effect of metam sodium chemigation on AMF communities in onion crops. Overall, cropping practices in organic versus conventional onion production, and the use of metam sodium soil fumigation by center-pivot chemigation do not appear to be major drivers of AMF communities.