Oxygen-consumption based quantification of chemogenetic H2O2 production in live human cells.

Abstract

Reactive Oxygen Species (ROS) in the form of H2O2 can act both as physiological signaling molecules as well as damaging agents, depending on their concentration and localization. The downstream biological effects of H2O2 were often studied making use of exogenously added H2O2, generally as a bolus and at supraphysiological levels. But this does not mimic the continuous, low levels of intracellular H2O2 production by for instance mitochondrial respiration. The enzyme d-Amino Acid Oxidase (DAAO) catalyzes H2O2 formation using d-amino acids, which are absent from culture media, as a substrate. Ectopic expression of DAAO has recently been used in several studies to produce inducible and titratable intracellular H2O2. However, a method to directly quantify the amount of H2O2 produced by DAAO has been lacking, making it difficult to assess whether observed phenotypes are the result of physiological or artificially high levels of H2O2. Here we describe a simple assay to directly quantify DAAO activity by measuring the oxygen consumed during H2O2 production. The oxygen consumption rate (OCR) of DAAO can directly be compared to the basal mitochondrial respiration in the same assay, allowing to estimate whether the ensuing level of H2O2 production is within the range of physiological mitochondrial ROS production. In the tested monoclonal RPE1-hTert cells, addition of 5 mM D-Ala to the culture media amounts to a DAAO-dependent OCR that surpasses ∼5% of the OCR that stems from basal mitochondrial respiration and hence produces supra-physiological levels of H2O2. We show that the assay can also be used to select clones that express differentially localized DAAO with the same absolute level of H2O2 production to be able to discriminate the effects of H2O2 production at different subcellular locations from differences in total oxidative burden. This method therefore greatly improves the interpretation and applicability of DAAO-based models, thereby moving the redox biology field forward.