Like other plant compartments, the seed harbors a microbiome. Seed microbiome members are the first to colonize a germinating seedling, and they may initiate the trajectory of microbiome assembly for the next plant generation. Therefore, the members of the seed microbiome are important for the dynamics of plant microbiome assembly and the vertical transmission of potentially beneficial symbionts. However, it remains challenging to assess the microbiome at the individual seed level (and, therefore, for the future individual plants) due to low endophytic microbial biomass, seed exudates that can select for particular members, and high plant and plastid contamination of resulting reads. Here, we report a protocol for extracting microbial DNA from an individual seed (common bean, Phaseolus vulgaris L.) with minimal disruption of host tissue, which we expect to be generalizable to other medium- and large-seed plant species. We applied this protocol to determine the 16S rRNA V4 and rRNA ITS2 amplicon composition and examine the variability of individual seeds harvested from replicate common bean plants grown under standard, controlled conditions to maintain health. Using DNA extractions from individual seeds, we compared seed-to-seed, pod-to-pod, and plant-to-plant microbiomes, and found highest microbiome variability at the plant level. This suggests that several seeds from the same plant could be pooled for microbiome assessment, given experimental designs that apply treatments at the parent plant level. This study adds protocols and insights to the growing toolkit of approaches to understand the plant-microbiome engagements that support the health of agricultural and environmental ecosystems.

Microbiomes provide critical functions that support animals, plants, and ecosystems. High‐throughput sequencing (HTS) has become an essential tool for the cultivation‐independent study of microbiomes found in diverse environments, but requires effective and meaningful controls. One such critical control is a mock microbial community, which is used as a positive control for nucleic acid extraction, marker gene amplification, and sequencing. While mock community standards can be purchased, they can be costly and often include only medically relevant microbial strains that are not expected to be major players in non‐human microbiomes. As an alternative, it is possible to design and construct a do‐it‐yourself (DIY) mock community, which can then be used as a positive control that is specifically customized to the protocol needs of a particular study system. In this article, we describe protocols to select appropriate microbial strains for the construction of a mock community. We first describe the steps to verify the identity of community members via Sanger sequencing. Then, we provide guidance on assembling and storing the DIY mock community as viable whole cells. This includes steps to create standard growth curves referenced to plate counts for each member, so that the community members can be quantified and later compared in terms of their “expected versus returned” relative contributions after sequencing. We also describe appropriate methods for the cryostorage of the fully assembled mock community as viable whole cells, so that they can be used as a unit in a microbiome analysis, from the lysis and nucleic acid extraction steps onwards. Finally, we provide an example of returned data and interpretation of DIY mock community sequences, discussing how to assess possible contamination and identify protocol biases for particular members. Overall, DIY mock communities serve to determine success and possible bias in a cultivation‐independent microbiome analysis. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Strain identification and verification using Sanger sequencing Basic Protocol 2: Creation of glycerol stocks of each mock community strain for long‐term cryostorage Basic Protocol 3: Assessment of strain freezer viability without cryoprotectant Basic Protocol 4: Creation of standard curve to determine CFU/ml of a liquid culture as a function of optical density Basic Protocol 5: Full mock community assembly using community concentration calculations and standard curves

Seed microbiome members initiate the assembly of plant-associated microbial communities, but the environmental drivers of endophytic seed microbiome composition are unclear. Here, we exposed plants to short-term drought and fertilizer treatments during early vegetative growth and quantified the microbiome composition of the seeds that were ultimately produced.

Soil is a highly heterogeneous environment with many physical and chemical factors that are expected to vary within and across fruit orchards, and many of these factors also drive changes in the soil microbiome. To understand how biogeography influences apple root microbiomes, we characterized the bacterial and archaeal, fungal, nematode, oligochaete, and mycorrhizal communities of the root zone soil (soil adjacent to the tree trunk and expected to be influenced by the plant) across 20 sites that represent the main Michigan apple-producing region. Amplicon sequencing of the 16S rRNA and internal transcribed spacer genes were performed, as well as direct quantification of nematodes, oligochaetes, and mycorrhizal fungi with microscopy. The microbiome community structures were affected by site and rootstock, but not by scion. Microbiomes had taxa typical of soil, including an archaeal taxon affiliated with family Nitrososphaeraceae, bacterial phyla Proteobacteria and Acidobacteria, and fungal phyla Ascomycota and Basidiomycota. While many taxa were detected in all samples and collectively composed 41.55% of the relative abundances, they had average relative abundances each of less than 1%, with no notable dominance. We used network analysis to understand potential for intertrophic interactions, but detected few cross-kingdom associations. Together, these results show the complexity of the apple root zone microbiome and did not identify obvious biotargets that may universally associate with tree health. This suggests that the key attributes of the apple root zone community may be in the community-level functional traits that are shared and distributed across the membership, rather than by its composition.