Identifying and Quantifying the Intermediate Processes during Nitrate-Dependent Iron(II) Oxidation.
James JamiesonHenning PrommerAnna H KaksonenJing SunAdam J SiadeAnna YusovBenjamín C BostickPublished in: Environmental science & technology (2018)
Microbially driven nitrate-dependent iron (Fe) oxidation (NDFO) in subsurface environments has been intensively studied. However, the extent to which Fe(II) oxidation is biologically catalyzed remains unclear because no neutrophilic iron-oxidizing and nitrate reducing autotroph has been isolated to confirm the existence of an enzymatic pathway. While mixotrophic NDFO bacteria have been isolated, understanding the process is complicated by simultaneous abiotic oxidation due to nitrite produced during denitrification. In this study, the relative contributions of biotic and abiotic processes during NDFO were quantified through the compilation and model-based interpretation of previously published experimental data. The kinetics of chemical denitrification by Fe(II) (chemodenitrification) were assessed, and compelling evidence was found for the importance of organic ligands, specifically exopolymeric substances secreted by bacteria, in enhancing abiotic oxidation of Fe(II). However, nitrite alone could not explain the observed magnitude of Fe(II) oxidation, with 60-75% of overall Fe(II) oxidation attributed to an enzymatic pathway for investigated strains: Acidovorax ( A.) strain BoFeN1, 2AN, A. ebreus strain TPSY, Paracoccus denitrificans Pd 1222, and Pseudogulbenkiania sp. strain 2002. By rigorously quantifying the intermediate processes, this study eliminated the potential for abiotic Fe(II) oxidation to be exclusively responsible for NDFO and verified the key contribution from an additional, biological Fe(II) oxidation process catalyzed by NDFO bacteria.
Keyphrases
- hydrogen peroxide
- visible light
- nitric oxide
- metal organic framework
- drinking water
- electron transfer
- microbial community
- randomized controlled trial
- machine learning
- risk assessment
- electronic health record
- room temperature
- deep learning
- climate change
- transcription factor
- artificial intelligence
- data analysis
- arabidopsis thaliana
- high speed
- meta analyses