Temporal assessment of N-cycle microbial functions in a tropical agricultural soil using gene co-occurrence networks

Authors: SCHAEDEL, MARIE - University Of Minnesota; ISHII, SATOSHI - University Of Minnesota; WANG, HA - University Of Minnesota; Venterea, Rodney - Rod; PAUL, BIRTHE - International Center For Tropical Agriculture (CIAT); MUTIMURA, MUPENZI - Rwanda Agriculture Board (RAB); GROSSMAN, JULIE - University Of Minnesota

Submitted to: ISME Communications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/24/2023
Publication Date: 2/14/2023
Citation: Schaedel, M., Ishii, S., Wang, H., Venterea, R.T., Paul, B., Mutimura, M., Grossman, J. 2023. Temporal assessment of N-cycle microbial functions in a tropical agricultural soil using gene co-occurrence networks. ISME Communications. 18(2). Article e0281442. https://doi.org/10.1371/journal.pone.0281442.
DOI: https://doi.org/10.1371/journal.pone.0281442

Interpretive Summary: Soil microbial processes are critical for producing forms of nitrogen that are available for plant uptake or potentially emitted as gases to the atmosphere or leached in soluble forms to ground and surface waters. The dynamics and network relationships of the many microbial genes responsible for these processes are poorly understood. In this study, we quantified the temporal dynamics of a wide range of functional nitrogen-cycling microbial genes in two tropical agricultural systems in Rwanda between September 2020 and March 2021. We then used network analysis to identify keystone genes that connected multiple nitrogen cycle functions and to detect gene co-occurrences that remained stable over time. We found that gene relationships and network properties were driven more by sampling time point than by location. Two gene targets, hydroxylamine oxidoreductase and nitrite oxidoreductase, which regulate the nitrification process, co-occurred across all time points, indicating that they may be ideal year-round targets to limit nitrification in rainfed agricultural soils. We also found that keystone genes varied across time, suggesting that management practices to enhance N-cycle functions, such as the application of nitrification inhibitors, could be adapted to seasonal conditions. Our results mark an important first step in employing gene networks to infer function in soil biogeochemical cycles, using a tropical seasonal gradient as a model system. The methods deployed and results found here will be useful to efforts of scientists and land managers to develop more efficient nutrient management practices.

Technical Abstract: Microbial nitrogen (N) cycling pathways are largely responsible for producing forms of N that are available for plant uptake or lost from the system as gas or leachate. The temporal dynamics of microbial N pathways in tropical agroecosystems are not well defined, even though they are critical to understanding the potential impact of soil conservation strategies. We aimed to 1) characterize temporal changes in functional gene associations across a seasonal gradient, 2) identify keystone genes that play a central role in connecting N cycle functions, and 3) detect gene co-occurrences that remained stable over time. Soil samples (n=335) were collected from two replicated field trials in Rwanda between September 2020 and March 2021. We found high variability among N-cycle gene relationships and network properties that was driven more by sampling timepoint than by location. Two nitrification gene targets, hydroxylamine oxidoreductase and nitrite oxidoreductase, co-occurred across all timepoints, indicating that they may be ideal year-round targets to limit nitrification in rainfed agricultural soils. We also found that gene keystoneness varied across time, suggesting that management practices to enhance N-cycle functions such as the application of nitrification inhibitors could be adapted to seasonal conditions. Our results mark an important first step in employing gene networks to infer function in soil biogeochemical cycles, using a tropical seasonal gradient as a model system.