Project description:We present metaproteome data from maize rhizosphere from sodic soil. Isolation of proteome from maize rhizosphere collected from Experimental Farm, ICAR-IISS, Mau, India was done with the standardized protocol at our laboratory and metaproteome analysis was done with the standardized pipepline. In total 696 proteins with different functions representing 245 genus and 395 species were identified. The proteome data provides direct evidence on the biological processes in soil ecosystem and is the first reported reference data from maize rhizosphere.
Project description:Microbial communities in the rhizosphere make significant contributions to crop health and nutrient cycling. However, their ability to perform important biogeochemical processes remains uncharacterized. Important functional genes, which characterize the rhizosphere microbial community, were identified to understand metabolic capabilities in the maize rhizosphere using GeoChip 3.0-based functional gene array method. Triplicate samples were taken for both rhizosphere and bulk soil, in which each individual sample was a pool of four plants or soil cores. To determine the abundance of functional genes in the rhizosphere and bulk soils, GeoChip 3.0 was used.
Project description:Microbial communities in the rhizosphere make significant contributions to crop health and nutrient cycling. However, their ability to perform important biogeochemical processes remains uncharacterized. Important functional genes, which characterize the rhizosphere microbial community, were identified to understand metabolic capabilities in the maize rhizosphere using GeoChip 3.0-based functional gene array method. Triplicate samples were taken for both rhizosphere and bulk soil, in which each individual sample was a pool of four plants or soil cores. To determine the abundance of functional genes in the rhizosphere and bulk soils, GeoChip 3.0 was used.
Project description:Understanding the environmental factors that shape microbial communities is crucial, especially in extreme environments, like Antarctica. Two main forces were reported to influence Antarctic soil microbes: birds and plants. Both birds and plants are currently undergoing unprecedented changes in their distribution and abundance due to global warming. However, we need to clearly understand the relationship between plants, birds and soil microorganisms. We therefore collected rhizosphere and bulk soils from six different sampling sites subjected to different levels of bird influence and colonized by Colobanthus quitensis and Deschampsia antarctica in the Admiralty Bay, King George Island, Maritime Antarctic. Microarray and qPCR assays targeting 16S rRNA genes of specific taxa were used to assess microbial community structure, composition and abundance and analyzed with a range of soil physico-chemical parameters. The results indicated significant rhizosphere effects in four out of the six sites, including areas with different levels of bird influence. Acidobacteria were significantly more abundant in soils with little bird influence (low nitrogen) and in bulk soil. In contrast, Actinobacteria were significantly more abundant in the rhizosphere of both plant species. At two of the sampling sites under strong bird influence (penguin colonies), Firmicutes were significantly more abundant in D. antarctica rhizosphere but not in C. quitensis rhizosphere. The Firmicutes were also positively and significantly correlated to the nitrogen concentrations in the soil. We conclude that the microbial communities in Antarctic soils are driven both by bird and plants, and that the effect is taxa-specific.
Project description:Cover cropping is an effective method to protect agricultural soils from erosion, promote nutrient and moisture retention, encourage beneficial microbial activity, and maintain soil structure. Reusing winter cover crop root channels with the maize roots during the summer allows the cash crop to extract resources from farther niches in the soil horizon. In this study, we investigate how reusing winter cover crop root channels to grow maize (Zea mays L.) affects the composition and function of the bacterial communities in the rhizosphere using 16S rRNA gene amplicon sequencing and metaproteomics. We discovered that the bacterial community significantly differed among cover crop variations, soil profile depths, and maize growth stages. Re-usage of the root channels increased bacterial abundance, and it further increases as we elevate the complexity from monocultures to mixtures. Upon mixing legumes with brassicas and grasses, the overall expression of several steps of the carbon cycle (C) and the nitrogen cycle (N) improved. The deeper root channels of legumes and brassicas compared to grasses correlated with higher bacterial 16S rRNA gene copy numbers and community roles in the respective variations in the subsoil regimes due to the increased availability of root exudates secreted by maize roots. In conclusion, root channel re-use (monocultures and mixtures) improved the expression of metabolic pathways of the important C and N cycles, and the bacterial communities, which is beneficial to the soil rhizosphere as well as to the growing crops.
Project description:In the soil the stability of urea is affected by the presence of urease, a ubiquitous enzyme released in the rhizosphere by microbial population and by decomposition of organic matter. To reduce the impact on farmer economies and environmental pollution, a common agronomical practice consists of applying urease inhibitors which delays the hydrolysis of urea and, in turn, ammonia is slowly release in the soil. General aim of the present work was the description of changes in maize root transcriptome occurring in response to treatment with the urease inhibitor NBPT.
Project description:Intercropping is a vital technology in resource-limited agricultural systems with low inputs. Peanut/maize intercropping enhances iron (Fe) nutrition in calcareous soil. Proteomic studies of the differences in peanut leaves, maize leaves and maize roots between intercropping and monocropping systems indicated that peanut/maize intercropping not only improves Fe availability in the rhizosphere but also influences the levels of proteins related to carbon and nitrogen metabolism. Moreover, intercropping may enhance stress resistance in the peanut plant (Xiong et al. 2013b). Although the mechanism and molecular ecological significance of peanut/maize intercropping have been investigated, little is known about the genes and/or gene products in peanut and maize roots that mediate the benefits of intercropping. In the present study, we investigated the transcriptomes of maize roots grown in intercropping and monocropping systems by microarray analysis. The results enabled exploration differentially expressed genes in intercropped maize. Peanut (Arachis hypogaea L. cv. Luhua14) and maize (Zea mays L. cv. Nongda108) seeds were grown in calcareous sandy soil in a greenhouse. The soil was enhanced with basal fertilizers [composition (mg·kg−1 soil): N, 100 (Ca (NO3)2·4H2O); P, 150 (KH2PO4); K, 100 (KCl); Mg, 50 (MgSO4·7H2O); Cu, 5 (CuSO4·5H2O); and Zn, 5 (ZnSO4·7H2O)]. The experiment consisted of three cropping treatments: peanut monocropping, maize monocropping and intercropping of peanut and maize. After germination of peanut for 10 days, maize was sown. Maize samples were harvested after 63 days of growth of peanut plants based on the degree of Fe chlorosis in the leaves of monocropped peanut. The leaves of monocropped peanut plants exhibited symptoms of Fe-deficiency chlorosis at 63 days, while the leaves of peanut plants intercropped with maize maintained a green color.
Project description:Microbial communities in the rhizosphere make significant contributions to crop health and nutrient cycling. However, their ability to perform important biogeochemical processes remains uncharacterized. Important functional genes, which characterize the rhizosphere microbial community, were identified to understand metabolic capabilities in the maize rhizosphere using GeoChip 3.0-based functional gene array method.