09/21/2016

New Model Integrates Biogeochemistry and Multiomic Sequence Data

Results quantitatively improve previous conceptual models describing microbial metabolic networks in oxygen minimum zones.

The Science

Researchers developed a new, integrated model to explain key microbial metabolic processes that influence greenhouse gas production and consumption in a model marine ecosystem. The new model is based on geochemical and multiomic sequence information (DNA, RNA, and proteins).

The Impact

The model’s simulations could lead to better-informed predictions about the influence of microbial metabolic networks on future patterns of global nutrient and energy cycling in specific areas of the ocean, as well as improve the accuracy of global climate models.

Summary

Oxygen minimum zones are widespread areas in the ocean where oxygen is depleted due to metabolic activity of microbes. Rising temperatures drive expansion of oxygen minimum zones, making these areas especially relevant as model ecosystems for climate science. In turn, microbial metabolic networks in these areas are predicted to have a growing influence on nutrient and energy cycling in the ocean, which will affect atmospheric levels of greenhouse gases such as carbon dioxide, methane, and nitrous oxide. Despite important interactions between microbial activity and global biogeochemical processes, climate models have largely neglected modern molecular sequencing data containing critical information about metabolic networks. Moreover, climate models often do not incorporate sufficient information about biogeochemical processes in the ocean. Researchers from the University of British Columbia, University of Minnesota, Canadian Institute for Advanced Research, and Max Planck Institute for Marine Microbiology worked together to develop a biogeochemical model. The model integrates observational geochemical data with metagenomic, metatranscriptomic, and metaproteomic sequence data on the distribution of DNA, messenger RNA (mRNA), and proteins from waters in the Saanich Inlet, British Columbia, Canada. This site is serving as a model ecosystem for studying key metabolic processes of the oceanic microbial community and their responses to oxygen minimum zone expansion. The team used resources from two U.S. Department of Energy Office of Science user facilities: Joint Genome Institute and Environmental Molecular Sciences Laboratory. The new model reproduced measured biogeochemical reaction rates as well as DNA, mRNA, and protein concentration profiles at the ecosystem scale. Moreover, simulations predicted the role of ubiquitous microorganisms in mediating carbon, nitrogen, and sulfur cycling. These results quantitatively improve previous conceptual models describing microbial metabolic networks in oxygen minimum zones. The integration of real geochemical and multiomic sequence data in a biogeochemical model provides holistic insight into microbial metabolic networks driving nutrient and energy flow at ecosystem scales.

Principal Investigator(s)

Steve Hallam
University of British Columbia
[email protected]

Ljiljana Paša-Tolic
Environmental Molecular Sciences Laboratory
[email protected]

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Funding

This work was supported by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research, including support of the Joint Genome Institute and Environmental Molecular Sciences Laboratory, both DOE Office of Science user facilities; G. Unger Vetlesen and Ambrose Monell foundations; Tula Foundation; Natural Sciences and Engineering Research Council of Canada; Genome British Columbia; Canada Foundation for Innovation; and Canadian Institute for Advanced Research.

References

S. Louca, A. K. Hawley, S. Katsev, M. Torres-Beltran, M. P. Bhatia, S. Kheirandish, C. C. Michiels, D. Capelle, G. Lavik, M. Doebeli, S. A. Crowe, and S. J. Hallam, “Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone.” Proceedings of the National Academy of Sciences (USA) 113(40), E5925-E5933 (2016). [DOI: 10.1073/pnas.1602897113].