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, 10 (4), e0122221

Differential Assemblage of Functional Units in Paddy Soil Microbiomes


Differential Assemblage of Functional Units in Paddy Soil Microbiomes

Yongkyu Kim et al. PLoS One.


Flooded rice fields are not only a global food source but also a major biogenic source of atmospheric methane. Using metatranscriptomics, we comparatively explored structural and functional succession of paddy soil microbiomes in the oxic surface layer and anoxic bulk soil. Cyanobacteria, Fungi, Xanthomonadales, Myxococcales, and Methylococcales were the most abundant and metabolically active groups in the oxic zone, while Clostridia, Actinobacteria, Geobacter, Anaeromyxobacter, Anaerolineae, and methanogenic archaea dominated the anoxic zone. The protein synthesis potential of these groups was about 75% and 50% of the entire community capacity, respectively. Their structure-function relationships in microbiome succession were revealed by classifying the protein-coding transcripts into core, non-core, and taxon-specific transcripts based on homologous gene distribution. The differential expression of core transcripts between the two microbiomes indicated that structural succession is primarily governed by the cellular ability to adapt to the given oxygen condition, involving oxidative stress, nitrogen/phosphorus metabolism, and fermentation. By contrast, the non-core transcripts were expressed from genes involved in the metabolism of various carbon sources. Among those, taxon-specific transcripts revealed highly specialized roles of the dominant groups in community-wide functioning. For instance, taxon-specific transcripts involved in photosynthesis and methane oxidation were a characteristic of the oxic zone, while those related to methane production and aromatic compound degradation were specific to the anoxic zone. Degradation of organic matters, antibiotics resistance, and secondary metabolite production were detected to be expressed in both the oxic and anoxic zones, but by different taxonomic groups. Cross-feeding of methanol between members of the Methylococcales and Xanthomonadales was suggested by the observation that in the oxic zone, they both exclusively expressed homologous genes encoding methanol dehydrogenase. Our metatranscriptomic analysis suggests that paddy soil microbiomes act as complex, functionally coordinated assemblages whose taxonomic composition is governed by the prevailing habitat factors and their hierarchical importance for community succession.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Fig 1
Fig 1. Phylum-level changes in microbiome composition in the oxic and anoxic zones over incubation period (A) and principal coordinate analysis based on the weighted UniFrac distance matrix (B).
Fig 2
Fig 2. Temporal changes in the relative abundance of the dominant taxonomic groups in the SSU rRNA-tags over incubation period.
Each of the dominant groups persistently contributed > 5% to total SSU rRNA-tags or showed significant fold changes in their relative abundance during incubation, while no other taxonomic group accounted for greater than 3% at order level. The abundance cutoff was applied to the lowest taxonomic rank possible, explaining why the taxonomic ranks vary from genus to phylum levels.
Fig 3
Fig 3. Discrepancies between the taxonomic patterns derived from SSU rRNA-tags and mRNA-tags.
Bars indicate the measures of Bray-Curtis dissimilarity in pairwise comparison of taxonomic composition. Minimum value (0) means the identical composition and maximum value (1) shows there is no shared taxonomy. Filling circles indicate the range of Bray-Curtis indices by quarter.
Fig 4
Fig 4. The relative contribution of the dominant taxonomic groups to the community composition (x-axis: SSU rRNA-tags) and microbiome function (y-axis: mRNA-tags).
(A) The top hit (filled symbols) and LCA (unfilled symbols) of BLAST hits were taken for conventional taxonomic assignment of total mRNA-tags. (B) The relative abundance of the taxon-specific mRNA-tags for each of the dominant groups was normalized to that of the entire microbiome by taking into account the proportion of the dominant group-derived mRNA-tags on total mRNA-tags (see Fig 5). Linear regression curves were generated with intercept value of 0 for the oxic zone (red line) and anoxic zone (blue line).
Fig 5
Fig 5. The hierarchical classification profile of putative mRNA-tags retrieved from the oxic (A) and anoxic (B) zones.
Novel mRNA-tags had no homologous proteins in NCBI nr protein database. Among annotated mRNAs, those having no homologs with any of the dominant groups were indicated to be derived from minor groups. They were searched against NCBI nr protein database to infer taxonomic origin.
Fig 6
Fig 6. Principal component analysis of the core mRNA-tags (circle) and non-core mRNA-tags (triangle) based on the functional expression profiles using SEED subsystems.
Samples indicated in red and blue relate, respectively, to the oxic and the anoxic zone. Differential expression of subsystems between two samples was calculated using two-sided Chi-square test with Yate's correction. Level 1 subsystems which were most significantly overrepresented in either core or non-core mRNA-tags (P-value < 0.01) are shown in a horizontal comparison. Level 2 subsystems showing differential expression between the oxic and anoxic zones are displayed by vertical comparison.
Fig 7
Fig 7. Heat map of KEEG pathways belonging to the carbohydrate metabolism represented by core and non-core mRNA-tags of the oxic and anoxic zones.
Each column represents an enzyme responsible for catalytic reactions of the individual pathways. The first two rows show the expression patterns of the non-core mRNA-tags in the order oxic vs. anoxic zone. The next two rows represent the core mRNA-tags in the same order.
Fig 8
Fig 8. The expression of key functions in each dominant group, as derived from the analysis of the taxon-specific mRNA-tags.
The relative expression level of key functions within each group was calculated by dividing the number of taxon-specific mRNA-tags involved in the respective function by the total number of taxon-specific mRNA-tags determined for the respective group.

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This research was funded by the Deutsche Forschungsgemeinschaft (collaborative research center SFB 987). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.