Collaborative Research: Oxygenase Enzymes and Microbial Metabolic Limitation by Hypoxia

Microbial life consumes oxygen when organic matter is degraded, causing many environments to reach low oxygen concentrations that exclude most animal life. For example, in the oceans, deoxygenation has been linked to large economic losses in marine fisheries. This project follows up on the recent discovery that biochemical processes that consume oxygen have two rate-limiting steps, and that the degradation of some types of organic matter requires both steps, and higher oxygen concentrations, but other types of organic matter are degraded efficiently at lower oxygen concentrations. Experiments will determine how oxygen loss depends on the type of organic matter supplied, and how microbial life has evolved to adapt to the stepwise inhibition of metabolism during oxygen declines. This basic science project uses artificial intelligence to predict general relationships between oxygen and organic matter that can be used to optimize biotechnology applications in low oxygen systems. Controlled laboratory experiments with cultured cell lines and specific organic compounds are used to validate hypotheses that emerged from studying the behavior of complex natural ecosystems. This information will enable engineers and scientists to make more accurate predictions of oxygen loss, and the stability of molecules, including drugs and pollutants, at low oxygen concentrations. This study will improve capacity to predict and control oxygen declines in natural ecosystems and industrial processes such as wastewater treatment and will prepare graduate and undergraduate students to enter the STEM workforce. This research is focused on marine systems, where the initial discovery was made, but the conceptual framework being developed applies broadly to cellular metabolism across environments where oxygen availability fluctuates, from coastal upwelling zones to sediments, biofilms, and even host-associated microbiomes. This project aims to 1) identify hypoxia-sensitive microorganisms and metabolic pathways, 2) identify microbial adaptations that compensate for oxygenase inhibition by hypoxia, for example up-regulation or alternative pathway switching, 3) identify a new class of oxygenase-dependent compounds that accumulate in hypoxic microbial ecosystems. Cultures of microorganisms that were observed to respond positively or negatively to hypoxia are exposed to controlled oxygen conditions, gene transcription is measured, and the findings extrapolated across genomes of marine and non-marine environments to identify regulatory strategies involving catabolic oxygenase enzymes. Observations of senescing phytoplankton blooms in dark, oxygen-controlled mesocosms are monitored for microbial gene expression, microbial community composition, and biogeochemical activity. To identify potential signatures of the newly proposed class of oxygenase-dependent dissolved organic matter, compounds impacted by hypoxia are identified by applying machine learning to mass spectrometry (LC-MS/MS) data and validated with the KEGG database of biochemical pathways. Oxygenase-dependent dissolved organic matter is classified in all published environmental LC-MS/MS data sets and mapped across microbial ecosystems. In field samples from Oregon coastal waters, the oxygen concentration dependence of microbial oxygen utilization across gradients of productivity and respiration history is measured to test predictions. This investigation could explain, at least in part, why modelers have empirically found that assuming micromolar Michaelis constants provides the best fit in simulations of ocean oxygen volume relationships, when respiratory Michaelis constants are orders of magnitude lower. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.