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Soil metaproteome and pH effects

ABSTRACT: Soil microorganisms act as gatekeepers for soil-atmosphere carbon exchange by balancing the accumulation and release of soil organic matter. However, poor understanding of the mechanisms responsible hinders the development of effective land management strategies to enhance soil carbon storage. Here we empirically test the link between microbial ecophysiological traits and topsoil carbon content across geographically distributed soils and land use contrasts. We discovered distinct pH-controls on microbial mechanisms of carbon accumulation. Land use intensification in low-pH soils that increased pH above a threshold (~ 6.2) lead to carbon loss through increased decomposition following alleviation of acid-retardation of microbial growth. However, loss of carbon with intensification in near neutral-pH soils was linked to decreased microbial biomass and reduced growth efficiency that was, in turn, related to tradeoffs with stress alleviation and resource acquisition. Thus, less intensive management practices in near neutral-pH soils have more potential for carbon storage through increased microbial growth efficiency; whereas, in acidic soils microbial growth is a bigger constraint on decomposition rates.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Escherichia Coli

SUBMITTER: Nico Jehmlich

LAB HEAD: Nico Jehmlich

PROVIDER: PXD010526 | Pride | 2018-07-26

REPOSITORIES: Pride

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			Action	DRS
	160805-Ashish_10_KA3_T0_3.msf	Msf
	160805-Ashish_10_KA3_T0_3.raw	Raw
	160805-Ashish_11_KA2_T0_2.msf	Msf
	160805-Ashish_11_KA2_T0_2.raw	Raw
	160805-Ashish_12_KG3_T0_6.msf	Msf

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Project description:It has long been recognized that species occupy a specific ecological niche within their ecosystem. The ecological niche is defined as the number of conditions and resources that limit species distribution. Within their ecological niche, species do not exist in a single physiological state but in a number of states we call the Natural Operating Range. In this paper we link ecological niche theory to physiological ecology by measuring gene expression levels of collembolans exposed to various natural conditions. The soil-dwelling collembolan Folsomia candida was exposed to 26 natural soils with different soil characteristics (soil type, land use, practice, etc). The animals were exposed for two days and gene expression levels were measured. The main factor found to regulate gene expression was the soil type (sand or clay), in which 18.5% of the measured genes were differentially expressed. Gene Ontology analysis showed animals exposed to sandy soils experience general stress, affecting cell homeostasis and replication. Multivariate analysis linking soil chemical data to gene expression data revealed that soil fertility influences gene expression. Land-use and practice had less influence on gene expression; only forest soils showed a different expression pattern. A variation in gene expression variation analysis showed overall low variance in gene expression. The large difference in response to soil type was caused by the soil physicochemical properties where F. candida experiences clay soils and sandy soils as very different from each other. This collembolan prefers fertile soils with high organic matter content, as soil fertility was found to correlate with gene expression and animals exposed to sandy soils (which, in general, have lower organic matter content) experience more general stress. Finally, we conclude that there is no such thing as a fixed physiological state for animals in their ecological niche and the boundary between the ecological niche and a stressed state depends on the genes/pathways investigated. Test animals were exposed to 26 natural soils + 2 control soils. 4 biological replicates per soil containing 25 grams of soil and 30 23-day-old animals per replicate, RNA was isolated after two days of exposure. for the micro-array hybridization design we made use of an interwoven loop design. from the four replicates per soil two were labeled with Cy3 and 2 with Cy5. It was made sure that now two replicates of the same soil were ever hybridized against the same soil.

Project description:Soils are a huge reservoir of organic C, and the efflux of CO2 from soils is one of the largest fluxes in the global C cycle. Out of all natural environments, soils probably contain the greatest microbial biomass and diversity, which classifies them as one of the most challenging habitats for microbiologists (Mocali and Benedetti, 2010). Until today, it is not well understood how soil microorganisms will respond to a warmer climate. Warming may give competitive advantage to species adapted to higher temperatures (Rinnan et al., 2009). The mechanisms behind temperature adaptations of soil microbes could be shifts within the microbial community. How microbial communities will ultimately respond to climate change, however, is still a matter of speculation. As a post-genomic approach in nature, metaproteomics allows the simultaneous examination of various protein functions and responses, and therefore is perfectly suited to investigate the complex interplay between respiration dynamics, microbial community architecture, and ecosystem functioning in a changing environment (Bastida et al., 2012). Thereby we will gain new insights into responses to climate change from a microbial perspective. Our study site was located at 910 m a.s.l. in the North Tyrolean Limestone Alps, near Achenkirch, Austria The 130 year-old mountain forests consist of Norway spruce (Picea abies) with inter-spread of European beech (Fagus sylvatica) and silver fir (Abies alba). Three experimental plots with 2 × 2 m warmed- and control- subplots were installed in 2004. The temperature difference between control and warmed plots was set to 4 °C at 5 cm soil depth. Soil was warmed during snow-free seasons. In order to extract proteins from forest soil samples, the SDS–phenol method was adopted as previously described by Keiblinger et al. (2012). Protein extractions were performed from each subplot soil samples. The abundance of protein-assigned microbial phylogenetic and functional groups, were calculated based on the normalized spectral abundance factor (NSAF, Zybailov et al., 2006).

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Soil metaproteome and pH effects

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