Background

Harnessing beneficial microbes presents a promising strategy to optimize plant growth and agricultural sustainability. Little is known to which extent and how specifically soil and plant microbiomes can be manipulated through different cropping practices. Here, we investigated soil and wheat root microbial communities in a cropping system experiment consisting of conventional and organic managements, both with different tillage intensities.

Results

While microbial richness was marginally affected, we found pronounced cropping effects on community composition, which were specific for the respective microbiomes. Soil bacterial communities were primarily structured by tillage, whereas soil fungal communities responded mainly to management type with additional effects by tillage. In roots, management type was also the driving factor for bacteria but not for fungi, which were generally determined by changes in tillage intensity. To quantify an "effect size" for microbiota manipulation, we found that about 10% of variation in microbial communities was explained by the tested cropping practices. Cropping sensitive microbes were taxonomically diverse, and they responded in guilds of taxa to the specific practices. These microbes also included frequent community members or members co-occurring with many other microbes in the community, suggesting that cropping practices may allow manipulation of influential community members.

Conclusions

Understanding the abundance patterns of cropping sensitive microbes presents the basis towards developing microbiota management strategies for smart farming. For future targeted microbiota management-e.g., to foster certain microbes with specific agricultural practices-a next step will be to identify the functional traits of the cropping sensitive microbes.

Background

Diverse assemblages of microbes colonize plant roots and collectively function as a microbiome. Earlier work has characterized the root microbiomes of numerous plant species, but little information is available for legumes despite their key role in numerous ecosystems including agricultural systems. Legumes form a root nodule symbiosis with nitrogen-fixing Rhizobia bacteria and thereby account for large, natural nitrogen inputs into soils. Here, we describe the root bacteria microbiome of the legume Trifolium pratense combining culture-dependent and independent methods. For a functional understanding of individual microbiome members and their impact on plant growth, we began to inoculate root microbiome members alone or in combination to Trifolium roots.

Results

At a whole-root scale, Rhizobia bacteria accounted for ~70% of the root microbiome. Other enriched members included bacteria from the genera Pantoea, Sphingomonas, Novosphingobium, and Pelomonas. We built a reference stock of 200 bacteria isolates, and we found that they corresponded to ~20% of the abundant root microbiome members. We developed a microcosm system to conduct simplified microbiota inoculation experiments with plants. We observed that while an abundant root microbiome member reduced plant growth when inoculated alone, this negative effect was alleviated if this Flavobacterium was co-inoculated with other root microbiome members.

Conclusions

The Trifolium root microbiome was dominated by nutrient-providing Rhizobia bacteria and enriched for bacteria from genera that may provide disease protection. First microbiota inoculation experiments indicated that individual community members can have plant growth compromising activities without being apparently pathogenic, and a more diverse root community can alleviate plant growth compromising activities of its individual members. A trait-based characterization of the reference stock bacteria will permit future microbiota manipulation experiments to decipher overall microbiome functioning and elucidate the biological mechanisms and interactions driving the observed effects. The presented reductionist experimental approach offers countless opportunities for future systematic and functional examinations of the plant root microbiome.

Root-microbiome interactions are of central importance for plant performance and yield. A distinctive feature of legumes is that they engage in symbiosis with N₂-fixing rhizobia. If and how the rhizobial symbiotic capacity modulates root-associated microbiomes are still not yet well understood. We determined root-associated microbiomes of soybean inoculated with wild type (WT) or a noeI mutant of Bradyrhizobium diazoefficiens USDA 110 by amplicon sequencing. UPLC-MS/MS was used to analyze root exudates. The noeI gene is responsible for fucose-methylation of Nod factor secreted by USDA 110 WT strain. Soybean roots inoculated with the noeI mutant showed a significant decrease in nodulation and root-flavonoid exudation compared to roots inoculated with WT strain. The noeI mutant-inoculated roots exhibited strong changes in microbiome assembly in the rhizosphere and rhizoplane, including reduced diversity, changed co-occurrence interactions and a substantial depletion of root microbes. Root exudates and soil physiochemical properties were significantly correlated with microbial community shift in the rhizosphere between different rhizobial treatments. These results illustrate that rhizobial symbiotic capacity dramatically alters root-associated microbiomes, in which root exudation and edaphic patterns play a vital role. This study has important implications for understanding the evolution of plant-microbiome interactions.

The emergence of high-throughput DNA sequencing methods provides unprecedented opportunities to further unravel bacterial biodiversity and its worldwide role from human health to ecosystem functioning. However, despite the abundance of sequencing studies, combining data from multiple individual studies to address macroecological questions of bacterial diversity remains methodically challenging and plagued with biases. Here, using a machine-learning approach that accounts for differences among studies and complex interactions among taxa, we merge 30 independent bacterial data sets comprising 1,998 soil samples from 21 countries. Whereas previous meta-analysis efforts have focused on bacterial diversity measures or abundances of major taxa, we show that disparate amplicon sequence data can be combined at the taxonomy-based level to assess bacterial community structure. We find that rarer taxa are more important for structuring soil communities than abundant taxa, and that these rarer taxa are better predictors of community structure than environmental factors, which are often confounded across studies. We conclude that combining data from independent studies can be used to explore bacterial community dynamics, identify potential 'indicator' taxa with an important role in structuring communities, and propose hypotheses on the factors that shape bacterial biogeography that have been overlooked in the past.