Linking transcriptional dynamics of CH4-cycling grassland soil microbiomes to seasonal gas fluxes

Täumer, Jana; Marhan, Sven; Groß, Verena; Jensen, C. J.; Kuß, Andreas W.; Kolb, Steffen; Urich, Tim

doi:10.1038/s41396-022-01229-4

Cited by 22 publications

(11 citation statements)

References 84 publications

(105 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent study, for instance, found that CH 4 fluxes were more strongly correlated with methanogenesis mRNA transcript abundances, but not with methanogen rRNA transcript abundances in grassland soils. 37 Supporting this, more studies showed stronger mRNA functional responses than rRNA taxonomic responses to environmental changes. 29,69,70 These results also support our previous finding that mRNA is the best proxy in revealing prokaryotic microbiome dynamics.…”

Section: ■ Discussionmentioning

confidence: 84%

See 1 more Smart Citation

Linking Transcriptional Dynamics of Peat Microbiomes to Methane Fluxes during a Summer Drought in Two Rewetted Fens

Wang

Jurasinski

Täumer

et al. 2023

Environ. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Rewetted peatlands are reestablished hot spots for CH4 emissions, which are subject to increased drought events in the course of climate change. However, the dynamics of soil methane-cycling microbiomes in rewetted peatlands during summer drought are still poorly characterized. Using a quantitative metatranscriptomic approach, we investigated the changes in the transcript abundances of methanogen and methanotroph rRNA, as well as mcrA and pmoA mRNA before, during, and after the 2018 summer drought in a coastal and a percolation fen in northern Germany. Drought changed the community structure of methane-cycling microbiomes and decreased the CH4 fluxes as well as the rRNA and mRNA transcript abundances of methanogens and methanotrophs, but they showed no recovery or increase after the drought ended. The rRNA transcript abundance of methanogens was not correlated with CH4 fluxes in both fens. In the percolation fen, however, the mcrA transcript abundance showed a positive and significant correlation with CH4 fluxes. Importantly, when integrating pmoA abundance, a stronger correlation was observed between CH4 fluxes and mcrA/pmoA, suggesting that relationships between methanogens and methanotrophs are the key determinant of CH4 turnover. Our study provides a comprehensive understanding of the methane-cycling microbiome feedbacks to drought events in rewetted peatlands.

show abstract

Section: ■ Discussionmentioning

confidence: 84%

“…36 Recently, a new type of quantitative metatranscriptomics has been developed and validated, allowing us to infer absolute abundances based on total RNA content. 37,38 This approach, therefore, provides quantitative information and, thus, allows for accurately linking microbial dynamics to environmental processes.…”

Section: ■ Introductionmentioning

confidence: 99%

Linking Transcriptional Dynamics of Peat Microbiomes to Methane Fluxes during a Summer Drought in Two Rewetted Fens

Wang

Jurasinski

Täumer

et al. 2023

Environ. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Overall, even though DNA- and RNA-based meta-genomic techniques have provided insight into the microbiome compositions of soils, they may not relate well to ecosystem processes or functions. The relationship between the abundances of rRNA and mRNA of CO 2 -cycling microbes needs further study [ 69 ].…”

Section: Discussionmentioning

confidence: 99%

Asynchronous responses of microbial CAZymes genes and the net CO2 exchange in alpine peatland following 5 years of continuous extreme drought events

Yan

Kang

Zhang

et al. 2022

ISME Communications

View full text Add to dashboard Cite

Peatlands act as an important sink of carbon dioxide (CO2). Yet, they are highly sensitive to climate change, especially to extreme drought. The changes in the net ecosystem CO2 exchange (NEE) under extreme drought events, and the driving function of microbial enzymatic genes involved in soil organic matter (SOM) decomposition, are still unclear. Herein we investigated the effects of extreme drought events in different periods of plant growth season at Zoige peatland on NEE and microbial enzymatic genes of SOM decomposition after 5 years. The results showed that the NEE of peatland decreased significantly by 48% and 26% on average (n = 12, P < 0.05) under the early and midterm extreme drought, respectively. The microbial enzymatic genes abundance of SOM decomposition showed the same decreasing trend under early and midterm extreme drought, but an increasing trend under late extreme drought. The microbial community that contributes to these degradation genes mainly derives from Proteobacteria and Actinobacteria. NEE was mainly affected by soil hydrothermal factors and gross primary productivity but weakly correlated with SOM enzymatic decomposition genes. Soil microbial respiration showed a positive correlation with microbial enzymatic genes involved in the decomposition of labile carbon (n = 18, P < 0.05). This study provided new insights into the responses of the microbial decomposition potential of SOM and ecosystem CO2 sink function to extreme drought events in the alpine peatland.

show abstract

“…There is a great general need to build a predictive understanding of the ecological and evolutionary dynamics of microbial communities in the context of soil structure formation and mechanical stabilization (Rillig et al., 2017). The main challenges are the coupling of soil development or management‐induced soil changes with changes in the microbial habitat (soil microbiome) and the adaption of soil microorganisms at different time scales (e.g., Täumer et al., 2022). The evolution of microbial communities has not been considered, despite that these communities react within a few years to these changes in conditions (Udiković‐Kolić et al., 2012).…”

Section: Mechanistic Modelingmentioning

confidence: 99%

3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase

Gerke

Vogel

Weber

et al. 2022

J. Plant Nutr. Soil Sci.

View full text Add to dashboard Cite

A 3-4D soil model represents a logical step forward from one-dimensional soil columns (1D), two-dimensional soil maps (2D), and three-dimensional soil volumes (3D) toward dynamic soil models (4D), with time as the fourth dimension. The challenge is to develop modeling tools that account for the states of soil properties, including the spatial structure of solids and pores, as well as their dynamics, including soil mass and solute transfers in landscapes. Our envisioned 3-4D soil model approach aims at improving the capability to predict fundamental soil functions (e.g., plant growth, storage, matter fluxes) that provide ecosystem services in the socioeconomic context.This study provides a structured overview on current soil models, challenges, open questions, and urgent research needs for developing a 3-4D soil model. A 3-4D soil model should provide an inventory of spatially distributed and temporally variable soil properties. As basis for this, we propose a mass balance model for the solid phase, which needs to be supplemented by a model describing its structure. This should eventually provide adequate 3D parameter sets for the numerical modeling of soil functions (e.g., flow and transport). The target resolution is decameters in the horizontal plane and centimeters to decimeters in the vertical direction to represent characteristic soil properties and soil horizons. The actual state of soils and their properties can be estimated from spatial data that represent the soil forming factors, with the use of machine learning tools. Improved modeling of the dynamics of soil bulk density, biological processes, and the pore structure are required to relate the solid mass balance to matter fluxes.A 3-4D soil model can be built from several types of modeling approaches. We distinguish between (1) process models that simulate mass balances, fluxes and soil structure dynamics, (2) statistical pedometric models using machine learning and geostatistics to estimate the soil inventory within landscapes, and (3) pedotransfer functions to link observable attributes to specific model parameters required to simulate soil functions including water and matter fluxes. This should provide the prerequisites to predict the spatial distribution of soil functions and their changes in response to external forcing. This endeavor can draw upon many already established models and techniques, yet combining them into a newly created 3-4D soil model is a truly an ambitious, but promisingThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Linking transcriptional dynamics of CH4-cycling grassland soil microbiomes to seasonal gas fluxes

Cited by 22 publications

References 84 publications

Linking Transcriptional Dynamics of Peat Microbiomes to Methane Fluxes during a Summer Drought in Two Rewetted Fens

Linking Transcriptional Dynamics of Peat Microbiomes to Methane Fluxes during a Summer Drought in Two Rewetted Fens

Asynchronous responses of microbial CAZymes genes and the net CO2 exchange in alpine peatland following 5 years of continuous extreme drought events

3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase

Contact Info

Product

Resources

About