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Link to original content: http://pubmed.ncbi.nlm.nih.gov/38997772/
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. 2024 Jul 12;19(1):46.
doi: 10.1186/s40793-024-00590-5.

Fine-scale characterization of the soybean rhizosphere microbiome via synthetic long reads and avidity sequencing

Brett Hale  1   2   3 , Caitlin Watts  4   5 , Matthew Conatser  4 , Edward Brown  4 , Asela J Wijeratne  6   7
Affiliations

Fine-scale characterization of the soybean rhizosphere microbiome via synthetic long reads and avidity sequencing

Brett Hale et al. Environ Microbiome. .

Abstract

Background: The rhizosphere microbiome displays structural and functional dynamism driven by plant, microbial, and environmental factors. While such plasticity is a well-evidenced determinant of host health, individual and community-level microbial activity within the rhizosphere remain poorly understood, due in part to the insufficient taxonomic resolution achieved through traditional marker gene amplicon sequencing. This limitation necessitates more advanced approaches (e.g., long-read sequencing) to derive ecological inferences with practical application. To this end, the present study coupled synthetic long-read technology with avidity sequencing to investigate eukaryotic and prokaryotic microbiome dynamics within the soybean (Glycine max) rhizosphere under field conditions.

Results: Synthetic long-read sequencing permitted de novo reconstruction of the entire 18S-ITS1-ITS2 region of the eukaryotic rRNA operon as well as all nine hypervariable regions of the 16S rRNA gene. All full-length, mapped eukaryotic amplicon sequence variants displayed genus-level classification, and 44.77% achieved species-level classification. The resultant eukaryotic microbiome encompassed five kingdoms (19 genera) of protists in addition to fungi - a depth unattainable with conventional short-read methods. In the prokaryotic fraction, every full-length, mapped amplicon sequence variant was resolved at the species level, and 23.13% at the strain level. Thirteen species of Bradyrhizobium were thereby distinguished in the prokaryotic microbiome, with strain-level identification of the two Bradyrhizobium species most reported to nodulate soybean. Moreover, the applied methodology delineated structural and compositional dynamism in response to experimental parameters (i.e., growth stage, cultivar, and biostimulant application), unveiled a saprotroph-rich core microbiome, provided empirical evidence for host selection of mutualistic taxa, and identified key microbial co-occurrence network members likely associated with edaphic and agronomic properties.

Conclusions: This study is the first to combine synthetic long-read technology and avidity sequencing to profile both eukaryotic and prokaryotic fractions of a plant-associated microbiome. Findings herein provide an unparalleled taxonomic resolution of the soybean rhizosphere microbiota and represent significant biological and technological advancements in crop microbiome research.

Keywords: Amplicon sequencing; Microbiome; Rhizosphere; Soil biology; Soybean; Synthetic long reads.

PubMed Disclaimer

Conflict of interest statement

BH is employed by AgriGro, Inc.

Figures

Fig. 1
Fig. 1
Schematic representation of the experimental design. This graphic was created using BioRender (Biorender.com)
Fig. 2
Fig. 2
α diversity estimation. A Boxplots of the Chao1 index, Shannon diversity, Simpson diversity, and Pielou's evenness for eukaryotic and prokaryotic datasets. B Rank-based association between eukaryotic and prokaryotic Chao1 (top left), eukaryotic and prokaryotic Shannon diversity (top right), and Shannon diversity and Chao1 (bottom left). C Mean estimates (coefficients) for explanatory variables in α diversity GLMMs. Following removal of the baseline timepoint (V1), GLMMs were implemented to determine the effect of treatment, cultivar, and growth stage (fixed effects) on each response variable. Point size corresponds to hierarchically partitioned R2 values..p ≤ 0.1, *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001
Fig. 3
Fig. 3
Microbiome composition and β diversity. A Relative abundance of eukaryotic phyla for each experimental condition. B PCoA (top row) and NMDS (bottom row) ordinations of eukaryotic Bray–Curtis dissimilarity. Compositional dissimilarity was calculated independently at ASV level using Bray–Curtis, Euclidean, and Jaccard distances, each of which yielded results consistent with those presented. Point size reflects Shannon diversity. C Relative abundance of prokaryotic phyla for each sample and experimental condition. D PCoA (top row) and NMDS (bottom row) ordinations of prokaryotic Bray–Curtis dissimilarity. Point size reflects Shannon diversity. E Variance explained by treatment, cultivar, growth stage, and interactions thereof on eukaryotic community composition as determined by PERMANOVA with Bray–Curtis dissimilarity. F Variance explained for prokaryotic community composition. G Heatmap of ASVs most influential for pairwise dissimilarity between fixed effect levels. The lowest taxonomic classification for each ASV is displayed below the corresponding column
Fig. 4
Fig. 4
Community membership analyses. Community membership was determined at genus and species levels for eukaryotic and prokaryotic communities, respectively, which were the lowest taxonomic classifications to which 100% ASVs mapped. A Core taxa demonstrating a prevalence ≥ 0.5 across all samples. Left heatmap annotations are taxon metadata, and bottom annotations are sample metadata. B The number of shared and unique taxa by treatment (left), cultivar (middle), and growth stage (right) for eukaryotic (top) and prokaryotic (bottom) communities. The corresponding heatmap displays presence/absence of unique taxa across fixed effect levels (summarized collectively and by domain in left bar plots) in addition to taxon metadata (top annotation). C Differentially abundant taxa between experimental conditions. Left heatmap annotations are taxon metadata, and top annotation is the number of differentially abundant taxa (summarized collectively and by domain) between fixed effect levels (bottom). Eukaryotes are purple and prokaryotes are green for all bar plot annotations
Fig. 5
Fig. 5
Global and condition-specific co-occurrence network analysis. A Global genus-level co-occurrence network constructed by obtaining significant positive and negative pairwise Spearman associations (Rho >  ± 0.6, q-value ≤ 0.05). B Global genus-level co-occurrence network constructed by obtaining significant positive and negative Pearson associations. C Venn diagram of unique and overlapping co-occurrences between Spearman and Pearson global networks. D Condition-specific networks constructed with significant Spearman associations. E Condition-specific networks constructed with significant Pearson associations. F Spearman associations between network density (edge count and node count) and topological features for each set of condition-specific networks. Both x and y axes represent log10 values. G Venn diagram of unique and overlapping co-occurrences between Spearman and Pearson condition-specific networks. (H) Heatmap of Pearson/Spearman condition-specific network nodes. Node color represents Kleinberg hub centrality, with blue reflecting a network member (hub score < 0.2) and tan/red reflecting a network hub (hub score ≥ 0.2). The top annotation represents the number of networks in which a node is a network member (blue) or hub (red). The right annotation shows the number of genera in each condition-specific network and is partitioned by domain (eukaryotes are purple and prokaryotes are green)
Fig. 6
Fig. 6
Phenotype-taxon networks for edaphic parameters. A Mean estimates for edaphic measure GLMMs. Point size corresponds to hierarchically partitioned R2 values. B Pairwise Spearman associations for edaphic measures. C Genus-level phenotype-taxon networks constructed by coupling lasso regression, reduced GLMs, and co-occurrences (all significant Spearman and Pearson associations). D Heatmap of node composite score (calculated with normalized modularity measures and Kleinberg's hub centrality) for each phenotype-taxon network. The top annotation represents mean composite score across all networks. The right annotation shows the number of nodes in each phenotype-taxon network and is partitioned by domain (eukaryotes are purple and prokaryotes are green). E Relative abundance of nodes in phenotype-taxon networks. The top annotation represents the mean composite score. F Pairwise Spearman associations for the top 20 nodes with respect to mean composite score. G Pairwise Spearman associations for the top 20 nodes and edaphic measures..p ≤ 0.1, *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001
Fig. 7
Fig. 7
Phenotype-taxon networks for agronomic parameters. A Agronomic measures across treatments and cultivars. Note that biomass measurements reflect dry weight, and 100-seed weight and theoretical yield were determined at 13% moisture. B Mean estimates for agronomic measure GLMMs. (C) Genus-level phenotype-taxon networks. D Heatmap of node composite score for each phenotype-taxon network. The right annotation shows the mean composite score across all networks for each node. The top annotation shows the number of nodes in each phenotype-taxon network and is partitioned by domain (eukaryotes are purple and prokaryotes are green). E Relative abundance of nodes in phenotype-taxon networks. The top annotation shows the mean composite score. F Pairwise Spearman associations for the top 20 nodes with respect to composite score. G Pairwise Spearman associations for the top 20 nodes and agronomic measures..p ≤ 0.1, *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001
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References

    1. Turner TR, James EK, Poole PS. The plant microbiome. Genome Biol. 2013;14(6):1. doi: 10.1186/gb-2013-14-6-209. - DOI - PMC - PubMed
    1. Hirsch AM. Plant-microbe symbioses: a continuum from commensalism to parasitism. Symbiosis. 2004;37(1–3):345–363.
    1. Kiers ET, Heijden MG. Mutualistic stability in the arbuscular mycorrhizal symbiosis: exploring hypotheses of evolutionary cooperation. Ecology. 2006;87(7):1627–1636. doi: 10.1890/0012-9658(2006)87[1627:MSITAM]2.0.CO;2. - DOI - PubMed
    1. Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18(11):607–621. doi: 10.1038/s41579-020-0412-1. - DOI - PubMed
    1. Bai B, Liu W, Qiu X, Zhang J, Zhang J, Bai Y. The root microbiome: Community assembly and its contributions to plant fitness. J Integr Plant Biol. 2022;64(2):230–243. doi: 10.1111/jipb.13226. - DOI - PubMed

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