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Background: Mitigating the effects of climate stress on crops is important for global food security. The microbiome associated with plant roots, henceforth, the rhizobiome, can harbor beneficial microbes that alleviate stress impacts. However, the factors influencing the recruitment of the rhizobiome during stress are unclear. We conducted an experiment to understand bacterial rhizobiome responses to short-term drought for two crop species: switchgrass and common bean. We used 16S rRNA and 16S rRNA gene sequencing to investigate the impact of drought severity on the recruitment of active bacterial rhizobiome members. We included planted and unplanted conditions to distinguish the environment- versus plant mediated drivers of the active rhizobiome. Results: Though each crop had a distinct rhizobiome, there were differences in the active microbiome structure between drought and watered and between planted and unplanted treatments. Despite their different community structures, the drought rhizobiome dynamics were similar across the two crops. However, the presence of a plant more strongly explained the rhizobiome variation in bean (17%) than in switchgrass (3%), with a small effect of plant mediation during drought only observed for the bean rhizobiome. The switchgrass rhizobiome was stable despite differences in the rhizosphere metabolite profiles between planted and unplanted treatments. Specifically, steroidal saponins and diterpennoids were enriched in drought, planted switchgrass soils. Conclusions: We conclude that rhizobiome benefits to resist short-term drought are crop-specific, with the possibility of decoupling of plant exudation and rhizobiome responses, as we observed in switchgrass. We propose bacterial taxa uniquely associated with common bean plants during the short-term drought, which could be further evaluated to determine any plant benefit during drought. 

Plant leaves harbor complex microbial communities that influence plant health and productivity. Nevertheless, a detailed understanding of phyllosphere community assembly and drivers is needed, particularly for phyllosphere fungi. Here, we investigated seasonal dynamics of epiphytic phyllosphere fungal communities in switchgrass (Panicum virgatum L.), a focal bioenergy crop. We also leverage previously published data on switchgrass phyllosphere bacterial communities from the same experimental plants, allowing us to compare fungal and bacterial dynamics and explore interdomain network associations in the switchgrass phyllosphere. Overall, we found a strong impact of sampling date on fungal community composition, with multiple taxonomic levels exhibiting clear temporal patterns in relative abundance. In addition, leaf nitrogen concentration, leaf dry matter content, plant height, and minimum daily air temperature explained significant variation in phyllosphere fungal communities, likely due to their correlation with sampling date. Finally, among the core taxa, fungi–bacteria network associations were much more common than bacteria–bacteria associations, suggesting the importance of interdomain phylogenetic diversity in microbiome assembly. Although our findings highlight the complexity of phyllosphere microbiome assembly, the clear temporal patterns in lineage-specific fungal abundances give promise to the potential for accurately predicting shifts in fungal phyllosphere communities throughout the growing season, a key research priority for sustainable agriculture. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license . 

ABSTRACT At any given time, only a subset of microbial community members are active in their environment. The others are in a state of dormancy, with strongly reduced metabolic rates. It is of interest to distinguish active and inactive microbial cells and taxa to understand their functional contributions to ecosystem processes and to understand shifts in microbial activity in response to change. Of the methods used to assess microbial activity-dormancy dynamics, 16S rRNA/rRNA gene amplicons (16S ratios) and active cell staining with 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) are two of the most common, yet each method has limitations. Given that in situ activity-dormancy dynamics are proxied only by laboratory methods, further study is needed to assess the level of agreement and potential complementarity of these methods. We conducted two experiments investigating microbial activity in plant-associated soils. First, we treated corn field soil with phytohormones to simulate plant soil stress signaling, and second, we used rhizosphere soil from common bean plants exposed to drought or nutrient enrichment. Overall, the 16S ratio and CTC methods exhibited similar patterns of relative activity across treatments when treatment effects were large, and the instances in which they differed could be attributed to changes in community size (e.g., cell death or growth). Therefore, regardless of the method used to assess activity, we recommend quantifying community size to inform ecological interpretation. Our results suggest that the 16S ratio and CTC methods report comparable patterns of activity that can be applied to observe ecological dynamics over time, space, or experimental treatment. IMPORTANCE Although the majority of microorganisms in natural ecosystems are dormant, relatively little is known about the dynamics of the active and dormant microbial pools through both space and time. The limited knowledge of microbial activity-dormancy dynamics is in part due to uncertainty in the methods currently used to quantify active taxa. Here, we directly compared two of the most common methods (16S ratios and active cell staining) for estimating microbial activity in plant-associated soil and found that they were largely in agreement in the overarching patterns. Our results suggest that 16S ratios and active cell staining provide complementary information for measuring and interpreting microbial activity-dormancy dynamics in soils. They also support the idea that 16S rRNA/rRNA gene ratios have comparative value and offer a high-throughput, sequencing-based option for understanding relative changes in microbiome activity, as long as this method is coupled with quantification of community size.