The Effects of Soil Depth on the Structure of Microbial Communities in Agricultural Soils in Iowa, USA.

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.

Unexpected synergistic HIV neutralization by a triple microbicide produced in rice endosperm.

The transmission of HIV can be prevented by the application of neutralizing monoclonal antibodies and lectins. Traditional recombinant protein manufacturing platforms lack sufficient capacity and are too expensive for developing countries, which suffer the greatest disease burden. Plants offer an inexpensive and scalable alternative manufacturing platform that can produce multiple components in a single plant, which is important because multiple components are required to avoid the rapid emergence of HIV-1 strains resistant to single microbicides. Furthermore, crude extracts can be used directly for prophylaxis to avoid the massive costs of downstream processing and purification. We investigated whether rice could simultaneously produce three functional HIV-neutralizing proteins (the monoclonal antibody 2G12, and the lectins griffithsin and cyanovirin-N). Preliminary in vitro tests showed that the cocktail of three proteins bound to gp120 and achieved HIV-1 neutralization. Remarkably, when we mixed the components with crude extracts of wild-type rice endosperm, we observed enhanced binding to gp120 in vitro and synergistic neutralization when all three components were present. Extracts of transgenic plants expressing all three proteins also showed enhanced in vitro binding to gp120 and synergistic HIV-1 neutralization. Fractionation of the rice extracts suggested that the enhanced gp120 binding was dependent on rice proteins, primarily the globulin fraction. Therefore, the production of HIV-1 microbicides in rice may not only reduce costs compared to traditional platforms but may also provide functional benefits in terms of microbicidal potency.

Evaluating maize and soybean grain dry-down in the field with predictive algorithms and genotype-by-environment analysis.

A delayed harvest of maize and soybean crops is associated with yield or revenue losses, whereas a premature harvest requires additional costs for artificial grain drying. Accurately predicting the ideal harvest date can increase profitability of US Midwest farms, but today's predictive capacity is low. To fill this gap, we collected and analyzed time-series grain moisture datasets from field experiments in Iowa, Minnesota and North Dakota, US with various maize (n = 102) and soybean (n = 36) genotype-by-environment treatments. Our goal was to examine factors driving the post-maturity grain drying process, and develop scalable algorithms for decision-making. The algorithms evaluated are driven by changes in the grain equilibrium moisture content (function of air relative humidity and temperature) and require three input parameters: moisture content at physiological maturity, a drying coefficient and a power constant. Across independent genotypes and environments, the calibrated algorithms accurately predicted grain dry-down of maize (r2 = 0.79; root mean square error, RMSE = 1.8% grain moisture) and soybean field crops (r2 = 0.72; RMSE = 6.7% grain moisture). Evaluation of variance components and treatment effects revealed that genotypes, weather-years, and planting dates had little influence on the post-maturity drying coefficient, but significantly influenced grain moisture content at physiological maturity. Therefore, accurate implementation of the algorithms across environments would require estimating the initial grain moisture content, via modeling approaches or in-field measurements. Our work contributes new insights to understand the post-maturity grain dry-down and provides a robust and scalable predictive algorithm to forecast grain dry-down and ideal harvest dates across environments in the US Corn Belt.