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Can We Estimate Functionality of Soil Microbial Communities from Structure-Derived Predictions? A Reality Test in Agricultural Soils.

ABSTRACT: Computational approaches that link bacterial 16S rRNA gene amplicon data to functional genes based on prokaryotic reference genomes have emerged. This study aims to validate or refute the applicability of the functional gene prediction tools for assessment and comparison of community functionality among experimental treatments, inducing either fast or slow responses in rhizosphere microbial community composition and function. Rhizosphere samples of wheat and barley were collected in two consecutive years at active and mature growth phases from organic and conventional farming plots with ambient or future-climate treatments of the Global Change Experimental Facility. Bacterial community composition was determined by 16S rRNA gene amplicon sequencing, and the activities of five extracellular enzymes involved in carbon (β-glucosidases, cellobiohydrolase, and xylosidase), nitrogen (N-acetylglucosaminidase), and phosphorus (acid phosphatase) cycles were determined. Structural community data were used to predict functional patterns of the rhizosphere communities using Tax4Fun and PanFP. Subsequently, the predictions were compared with the measured activities. Despite the fact that different treatments mainly drove either community composition (plant growth phase) or measured enzyme activities (farming system), the predictions mirrored patterns in the treatments in a qualitative but not quantitative way. Most of the discrepancies between measured and predicted values resulted from plant growth stages (fast community response), followed by farming management and climate (slower community response). Thus, our results suggest the applicability of the prediction tools for comparative investigations of soil community functionality in less-dynamic environmental systems. IMPORTANCE Linking soil microbial community structure to its functionality, which is important for maintaining health and services of an ecosystem, is still challenging. Besides great advances in structural community analysis, functional equivalents, such as metagenomics and metatranscriptomics, are still time and cost intensive. Recent computational approaches (Tax4Fun and PanFP) aim to predict functions from structural community data based on reference genomes. Although the usability of these tools has been confirmed with metagenomic data, a comparison between predicted and measured functions is so far missing. Thus, this study comprises an expansive reality test on the performance of these tools under different environmental conditions, including relevant global change factors (land use and climate). The work provides a valuable validation of the applicability of the prediction tools for comparison of soil community functions across different sufficiently established soil ecosystems and suggest their usability to unravel the broad spectrum of functions provided by a given community structure.

SUBMITTER: Breitkreuz C

PROVIDER: S-EPMC8552701 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:This study investigated the differences in microbial community abundance, composition and diversity throughout the depth profiles in soils collected from corn and soybean fields in lowa, USA using 16S rRNA amplicon sequencing. The results revealed decreased richness and diversity in microbial communities at increasing soil depth. Soil microbial community composition differed due to crop type only in the top 60 cm and due to location only in the top 90 cm. While the relative abundance of most phyla decreased in deep soils, the relative abundance of the phylum Proteobacteria increased and dominated agricultural soils below the depth of 90 cm. Although soil depth was the most important factor shaping microbial communities, edaphic factors including soil organic matter, soil bulk density and the length of time that deep soils were saturated with water were all significant factors explaining the variation in soil microbial community composition. Soil organic matter showed the highest correlation with the exponential decrease in bacterial abundance with depth. A greater understanding of how soil depth influences the diversity and composition of soil microbial communities is vital for guiding sampling approaches in agricultural soils where plant roots extend beyond the upper soil profile. In the long term a greater knowledge of the influence of depth on microbial communities should contribute to new strategies that enhance the sustainability of soil which is a precious resource for food security.IMPORTANCE Determining how microbial properties change across different soils and within the soil depth profile, will be potentially beneficial to understanding the long-term processes that are involved in the health of agricultural ecosystems. Most literature on soil microbes has been restricted to the easily accessible surface soils. However, deep soils are important in soil formation, carbon sequestration, and in providing nutrients and water for plants. In the most productive agricultural systems in the USA where soybean and corn are grown, crop plant roots extend into the deeper regions of soils (> 100 cm), but little is known about the taxonomic diversity or the factors that shape deep soil microbial communities. The findings reported here highlight the importance of soil depth in shaping microbial communities, provide new information about edaphic factors that influence the deep soil communities and reveal more detailed information on taxa that exist in deep agricultural soils.

Project description:The milieu in soil in which microorganisms dwell is never constant. Conditions such as temperature, water availability, pH and nutrients frequently change, impacting the overall functioning of the soil system. To understand the effects of such factors on soil functioning, proxies (indicators) of soil function are needed that, in a sensitive manner, reveal normal amplitude of variation. Thus, the so-called normal operating range (NOR) of soil can be defined. In this study we determined different components of nitrification by analyzing, in eight agricultural soils, how the community structures and sizes of ammonia oxidizing bacteria and archaea (AOB and AOA, respectively), and their activity, fluctuate over spatial and temporal scales. The results indicated that soil pH and soil type are the main factors that influence the size and structure of the AOA and AOB, as well as their function. The nitrification rates varied between 0.11?±?0.03??gN?h(-1)?gdw(-1) and 1.68?±?0.11??gN?h(-1)?gdw(-1), being higher in soils with higher clay content (1.09?±?0.12??gN?h(-1)?gdw(-1)) and lower in soils with lower clay percentages (0.27?±?0.04??gN?h(-1)?gdw(-1)). Nitrifying activity was driven by soil pH, mostly related to its effect on AOA but not on AOB abundance. Regarding the influence of soil parameters, clay content was the main soil factor shaping the structure of both the AOA and AOB communities. Overall, the potential nitrifying activities were higher and more variable over time in the clayey than in the sandy soils. Whereas the structure of AOB fluctuated more (62.7?±?2.10%) the structure of AOA communities showed lower amplitude of variation (53.65?±?3.37%). Similar trends were observed for the sizes of these communities. The present work represents a first step toward defining a NOR for soil nitrification. The sensitivity of the process and organisms to impacts from the milieu support their use as proxies in the NOR of agricultural soils. Moreover, the clear effect of soil texture established here suggests that the NOR should be defined in a soil type-specific manner.

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Can We Estimate Functionality of Soil Microbial Communities from Structure-Derived Predictions? A Reality Test in Agricultural Soils.

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