Functional Predictions of Microbial Communities in Soil as Affected by Long‐term Tillage Practices

Abstract

Core Ideas Microbial function is important but difficult to assess in soil. An omics‐driven tool, PICRUSt, was used to characterize functions of soil microbial communities. No‐tillage compared with plow tillage was functionally enriched for most nutrient cycles. Many other functions integral to soil health can be explored by the PICRUSt omics approach. Soil microbial communities affect the soil's biological, chemical, and physical properties, but there is still a knowledge gap regarding the long‐term impact of tillage practices on soil microbial dynamics. Additionally, the accurate identification of belowground microbial functions is a topic of active interest. In this study, microbial community profiles and functions in soil from a 50‐plus‐year‐old experiment in Ohio, representing one of the world's longest running comparisons of a plow‐tillage system and a continuous no‐tillage system, were compared. The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) algorithm was used to predict associated functional traits from 16S rRNA gene sequences. Analysis of the sequences revealed a large number of unidentified operational taxonomic units (67%), which is consistent with expectations of the soil ecosystem. Next, we investigated gene and enzyme predictions for nitrogen, sulfur, and methane metabolism and hydrocarbon degradation in soil. Results indicated that no‐tillage was functionally enriched for most nutrient cycles. This study has allowed us to predict distinct functional profiles as a result of legacy land uses. It serves as an example of improved analysis of the functional differences in soil managed by long‐term tillage versus no‐till.

Bacterial community dissimilarity in soils is driven by long‐term land‐use practices

“…PICRUSt predicted NSTI (Nearest Sequenced Taxon Index) values between 0.17 and 0.20 for the soil samples, a range similar to soil test datasets in Langille et al. (Langille et al., 2013) and the NSTI values obtained in a previous study by our group (0.17–0.19) (Hariharan, Sengupta, Grewal, & Dick, 2017). The FOR soil had the lowest NSTI, whereas NTCS, PTCS, and GRA had the highest values.…”

“…As noted in Hariharan et al (Hariharan et al, 2017), a large proportion of the PICRUSt functional predictions were found to be unclassified (around 13% of the reference OTUs were not mapped to any protein/function). Given that a high percentage of OTUs remained unclassified at the Family and Genus level, which in turn were dropped from PICRUSt evaluations, it is difficult to accurately estimate bacterial functional capacity of the soils.…”

Differences in Bacterial Diversity, Composition and Function due to Long-Term Agriculture in Soils in the Eastern Free State of South Africa

“…However, the reliability of such tools needs to be understood in assessing the validity of such predictions. Computing NSTI scores as a quality control measure has been widely used to validate metagenomic predictions from PICRUSt [72,73]. Although functional predictions from PICRUSt showed lower NSTI scores in agricultural soils (0.12 ± 0.031 s.d.)…”

Structure, Diversity, and Composition of Bacterial Communities in Rhizospheric Soil of Coptis chinensis Franch under Continuously Cropped Fields

“…Recently, PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states) has been used to predict the functional characteristics of the bacterial community based on the 16s rRNA homology and conserved feature of functional contribution [22][23][24][25]. This cost-effective bioinformatics tool can use only a few hundred sequences to grasp most of the variations in gene content.…”