Submitted to: Frontiers in Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/15/2017
Publication Date: 4/3/2017
Citation: Chang, H., Haudenshield, J.S., Bowen, C.R., Hartman, G.L. 2017. Metagenome-wide association study and machine learning prediction of bulk soil microbiome and crop productivity. Frontiers in Microbiology. doi: 10.3389/fmicb.2017.00519.
Interpretive Summary: Areas within an agricultural field in the same season often differ in crop productivity despite having the same cropping history, crop variety, and management practices. These differences in productivity could be caused by variation in microbial communities or the soil physical properties within the fields. In this study, soil samples were collected from a high and a low productivity areas from within six Illinois crop fields that for the preceding years had been planted in a corn-soybean rotation in Illinois. Microbial communities and soil properties were characterized for each sample. To assess the microbial communities, DNA was extracted for the soil samples, sequenced, and analyzed various statistical programs to determine if there was an association between microbial communities and ares on the fields with high and low productivity. The analyses showed that crop productivity was associated with differences in microbial communities. Microbes related to nitrogen utilization were especially associated with highly productive soils. Different groups of microbes were associated with low productivity soils. This research is significant because it shows microbes are associated and may be responsible for differences in crop productivity. This is important to other scientists including pathologist, agronomists, soil scientists, and microbiologists that have interest in studying crop productivity relationships with soil microbes.
Technical Abstract: Areas within an agricultural field in the same season often differ in crop productivity despite having the same cropping history, crop genotype, and management practices. One hypothesis is that abiotic or biotic factors in the soils differ between areas resulting in these productivity differences. In this study, bulk soil samples collected from a high and a low productivity area from within six agronomic fields in Illinois were quantified for abiotic and biotic characteristics. Extracted DNA from these bulk soil samples were shotgun sequenced. While logistic regression analyses resulted in no significant association between crop productivity and the 26 soil characteristics, principal coordinate analysis and constrained correspondence analysis showed crop productivity explained a major proportion of the taxa variance in the bulk soil microbiome. Metagenome-wide association studies (MWAS) identified more Bradyrhizodium and Gammaproteobacteria in higher productivity areas and more Actinobacteria, Ascomycota, Planctomycetales, and Streptophyta in lower productivity areas. Machine learning using a random forest method successfully predicted productivity based on the microbiome composition with the best accuracy of 0.79 at the order level. Our study showed that crop productivity differences were associated with bulk soil microbiome composition and highlighted several nitrogen utility-related taxa. We demonstrated the merit of MWAS and machine learning for the first time in plant-microbiome study.