Soil ecosystems

Biocrust communities
Climate change will affect sensitive microbial communities such as biocrust in deserts. Biocrusts are top soil microbial communities responsible for soil fertility and stability in arid and semi-arid regions.

Biocrust field site, Moab, UT. Image credit: ?

By combining multiple approaches (metabolomics, metagenomics, metatranscriptomics and activity probing) our group is aiming at assembling a synthetic biocrust microbiome in the lab which will enable a predictive understanding of biocrusts communities.

Recent highlights:

  • Wetting induces a drastic shift in microbial community structure and metabolites composition (Swenson et al, 2018; Karaoz et al. 2017)
  • Biocrust isolates prefer a unique subset of the exometabolite pool (Baran et al, 2015) and these findings are recapitulated in the environment (Swenson et al, 2018)

This research enables the development of further technologies linking metagenomes and community metabolism to our understanding of the exometabolic webs that links keystone cyanobacteria to the larger biocrust community. It may also lead to a better understanding of the specific dependencies within biocrust communities, which will help predict how a changing climate will impact the stability of these sensitive ecosystems. Furthermore, new restoration strategies for biocrusts can be developed.

Ecosystems in agricultural soils

Agricultural soils are rich sources of dissolved and particulate organic matter (DOC and POC) but can become depleted under unsustainable practices. Little is known regarding the composition of DOC and POC and especially how these compounds are cycled by resident microbes. Understanding the interactions between microbes, metabolites and minerals may ultimately lead to better terrestrial carbon cycling models and improvements in crop production and soil health.

Image credit: ?

To understand the metabolite composition of soils, we perform aqueous soil extractions followed by LC-MS/MS or GC-MS. Exometabolomics with cultivated soil microbes allows us to examine environmentally-relevant metabolite consumption and release.

Recent highlights:

  • A simple aqueous extraction and GC-MS analysis allows for the detection of polar metabolites from a range of biochemical classes in soil (Swenson et al, 2015a).
  • Metabolites tend to be highly sorptive on inorganic matter, affecting their bioavailability (Swenson et al, 2015b, Swenson et al, 2017).

This approach can further our understanding of the complex interactions between organic and inorganic components in soil, and their impact on plants and microbes. These insights may lead to better carbon cycling models to predict how farmland soils will respond to various climate change scenarios and could improve crop productivity.

Ecosystems in sediment

Microbial communities play a major role in subsurface nutrient cycling and contaminant immobilization. 

Image credit: ?

However, the links between these processes are poorly understood, especially at the molecular level.

We use exometabolomics to link environmental metabolites to the activities of sediment microbes. Thus obtaining important insights into the chemistry of these microbiomes. Recent efforts have focused on extending our approaches within the ENIGMA SFA to analyze metabolites present in sediment samples collected at various locations, times of the year and depths.

Image credit: ?

We are linking these field observations to laboratory experiments where we to understand the interspecies interactions that mediate the changes in metabolites in environmental samples. A better understanding of the microbial coupling of exometabolites and activities within Earth’s sediments will help researchers improve bioremediation efforts.

 

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Recent highlights:

  • We recently extended the BONCAT technique to determine which microbes are active within ENIGMA communities, thus building testable hypotheses for metabolic changes

References:

1. De Raad M, Northen TR, Bowen BP (2018) Analysis and interpretation of mass spectrometry imaging datasets. Book chapter in CAC Vol 82: Data Analysis for Omic Sciences: Methods and Applications. doi.org/10.1016/bs.coac.2018.06.006

2. Kosina, S.M.; A. Greiner (NERSC), R.K. Lau, S. Jenkins, R. Baran,, Bowen, P.; T,R. Northen (2018) Web of Microbes: an online exometabolomics database and visualization tool for microbial metabolic interactions. BioMed Central: Microbiology. 18:115. doi.org/10.1186/s12866-018-1256-y

3. de Raad, M; T. de Rond, O. Rübel, J.D. Keasling, T.R. Northen, B.P. Bowen. (2017) OpenMSI Arrayed Analysis Toolkit: Analyzing spatially defined samples using mass spectrometry imaging. Analytical Chemistry. doi.org/10.1021/acs.analchem.6b05004

4. Erbilgin, O.; B.P. Bowen, S. Jenkins, S.M. Kosina, R.K. Lau, T.R. Northen (2017) Dynamic substrate preferences and predicted metabolic properties of a simple microbial consortium. BMC Bioinformatics. doi.org/10.1186/s12859-017-1478-2.{PMID}:28114881

5. Jenkins, S., T.L. Swenson, R. Lau, A. Rocha, A. Aaring, T.C. Hazen, R. Chakraborty, and T.R. Northen. (2017) Construction of soil defined media using quantitative exometabolomic analysis of soil metabolites. Frontiers in Microbiology. doi.org/10.1101/151282

6. Louie, K.; B.P. Bowen, R.K. Lau, T.R. Northen (2016) Localizing metabolic synthesis in microbial cultures with kinetic mass spectrometry imaging (kMSI) bioRxiv. doi.org/10.1101/050658