bioRxivMicrobiology
The evolution of multiply substituted isotopologues of methane during microbial aerobic oxidation
J. Li, B. K. Chiu, A. M. Piasecki, X. Feng, J. D. Landis, S. Marcum, E. D. Young, W. D. Leavitt
Abstract
Aerobic methane oxidation (AeOM) is an important biological sink of methane on Earth. Stable isotopes are critical tools in tracking the sources and sinks of Earths surface methane budget. However, the major factors that influence the two multiply-substituted (clumped) isotope signatures of AeOM, {Delta}13CH3D and {Delta}12CH2D2, are not well known. Here we quantify the influence of kinetics as a function of temperature, and different methane monooxygenase (MMO) enzymes as a function of copper, on the isotopologue concentrations of residual methane by the obligate aerobic methanotroph, Methylococcus capsulatus (Bath). We observe deviations from traditional closed-system distillation (Rayleigh) fractionation during exponential growth at high oxidation rates. We model this as a reservoir effect controlled by the ratio of oxidation rate in the cells to transport rate of methane into the cells, where environmental temperature affects both rates. We also test whether clumped isotope fractionation values vary for the particulate versus soluble MMOs, but the results show minimal differences. We further determine that the back reaction (re-equilibration) of methane with medium water is unlikely. Together, the observations and model demonstrate that at low oxidation-to-transport ratios, the clumped isotope signatures follow canonical Rayleigh fractionation, whereas at high ratios, more positive {Delta}12CH2D2 values result, deviating from simple Rayleigh-like trajectories. This study shows that the methane oxidation-to-transport ratio is a critical influence on clumped isotope signatures of AeOM that should be considered when interpreting the isotopic data of natural methane samples in both open and closed systems.
- organism: bacteria (other)
- assay: metabolomics
- research focus: host-pathogen & immune response
- approach: statistical model
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