Dataset Information

Structure and function of the topsoil metaproteome across global biomes: a survey of the topsoil metaproteome across global biomes

ABSTRACT: Herein, we evaluated the changes in biological functions in soils across global biomes through the identification and quantification of proteins. This knowledge is essential to provide one stepforward in soil microbial ecology in order to decipher the cellular and molecular mechanisms employed by soil microbial communities to adapt to their environment and to explain the potential responses of microbial communities, and their microbially-driven ecosystem services, to global change and land use. Our study aims to provide the most comprehensive assessment on the structure and function of the topsoil metaproteome across global biomes, and hence provide direct identification of the most domimant protein-encoded functions in terrestrial ecosystems.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Escherichia Coli

SUBMITTER: Nico Jehmlich

LAB HEAD: Nico Jehmlich

PROVIDER: PXD018448 | Pride | 2023-02-24

REPOSITORIES: Pride

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Publications

Functional soil mycobiome across ecosystems.

Fernandes Maysa Lima Parente MLP Bastida Felipe F Jehmlich Nico N Martinović Tijana T Větrovský Tomáš T Baldrian Petr P Delgado-Baquerizo Manuel M Starke Robert R

Journal of proteomics 20211121

Fungi support a wide range of ecosystem processes such as decomposition of organic matter and plant-soil relationships. Yet, our understanding of the factors driving the metaproteome of fungal communities is still scarce. Here, we conducted a field survey including data on fungal biomass (by phospholipid fatty acids, PLFA), community composition (by metabarcoding of the 18S rRNA gene from extracted DNA) and functional profile (by metaproteomics) to investigate soil fungi and their relation to ed ...[more]

PMID: 34818587

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Project description:Microarrays have become established tools for describing microbial systems, however the assessment of expression profiles for environmental microbial communities still presents unique challenges. Notably, the concentration of particular transcripts are likely very dilute relative to the pool of total RNA, and PCR-based amplification strategies are vulnerable to amplification biases and the appropriate primer selection. Thus, we apply a signal amplification approach, rather than template amplification, to analyze the expression of selected lignin-degrading enzymes in soil. Controls in the form of known amplicons and cDNA from Phanerochaete chrysosporium were included and mixed with the soil cDNA both before and after the signal amplification in order to assess the dynamic range of the microarray. We demonstrate that restored prairie soil expresses a diverse range of lignin-degrading enzymes following incubation with lignin substrate, while farmed agricultural soil does not. The mixed additions of control cDNA with soil cDNA indicate that the mixed biomass in the soil does interfere with low abundance transcript changes, nevertheless our microarray approach consistently reports the most robust signals. Keywords: comparative analysis, microbial ecology, soil microbial communities We used lignin degradation as a model process to demonstrate the use of an oligonucleotide microarray for directly detecting gene expression in soil communities using signal amplification instead of template amplification to avoid the introduction of PCR bias. In the current study, we analyzed mRNA isolated from two distinct soil microbial communities and demonstrate our ability to detect the expression of a small subset of lignin degrading genes following exposure to a lignitic substrate. We also included purified control amplicons and mixed target experiments with pure P. chrysosporium genomic cDNA to determine the level of interference from soil biomass on target hybridization.

Dataset Information

Structure and function of the topsoil metaproteome across global biomes: a survey of the topsoil metaproteome across global biomes

Publications

Functional soil mycobiome across ecosystems.

Similar Datasets

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