PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 3733717

Oxygen exchange between nitrate molecules during nitrite oxidation by Nitrobacter.

A A DiSpirito·A B Hooper
View original source ↗Export metadata ↓

Abstract

During oxidation of nitrite, cells of Nitrobacter winogradskyi are shown to catalyze the active exchange of oxygen atoms between exogenous nitrate molecules (production of 15N16/18O3- during incubation of 14N16/18O3-, 15N16O3-, and 15N16O2- in H216O). Little, if any, exchange of oxygens between nitrate and water also occurs (production of 15N16/18O3- during incubation of 15N16O3- and 14N16O2- in H218O). 15N species of nitrate were assayed by 18O-isotope shift in 15N NMR. Taking into account the O-exchange reactions which occur during nitrite oxidation, H2O is seen to be the source of O in nitrate produced by oxidation of nitrite by N. winogradskyi. The data do not establish whether the nitrate-nitrate O exchange is catalyzed by nitrite oxidase (H2O + HNO2----HNO3 + 2H+ + 2e-) or nitrate reductase (HNO3 + 2H+ + 2e-----HNO2 + H2O) or both enzymes in consort. The nitrate-nitrate exchange reaction suggests the existence of an oxygen derivative of a H2O-utilizing oxidoreductase.

Explore related subjects

KineticsNitratesNitritesNitrobacterNitrogen IsotopesOxygen IsotopesJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

A A DiSpirito, A B Hooper. 1986-08-15. Oxygen exchange between nitrate molecules during nitrite oxidation by Nitrobacter.. https://pubmed.ncbi.nlm.nih.gov/3733717/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Enzyme kinetics shapes the growth response of metabolic networks.

Microbes adjust their metabolism to environmental challenges by changing protein expression levels, metabolite concentrations, and reaction rates. Average expression levels in large proteome sectors change coherently, while individual proteins show divergent shifts even within the same pathway. Here, we establish a metabolic model that integrates local enzyme kinetics and global network architecture to predict the joint growth response of proteins and metabolites. Under nutrient limitation, we predict a remarkably simple pattern of proteome reallocation with growth rate: protein expression levels change linearly but heterogeneously. For a given enzyme, the direction of change is determined by its local kinetic constants - catalytic rate and substrate affinity - and by the degree of nutrient restriction affecting its embedding pathway. This double-graded growth response of the proteome is mediated by restriction-dependent metabolite levels, which are predicted to decrease with growth rate in a nonlinear way. The model establishes three specific growth laws: protein expression changes of individual enzymes are negatively correlated with their expression and with their substrate saturation at high growth; average changes of pathways and larger functional sectors are correlated with their internal variance. These predictions are in quantitative agreement with measured system-wide proteomics and metabolomics data of E. coli. Enzyme-specific response patterns are a starting point for model-guided interventions into bacterial metabolism.

Kinetics↗
PubMed2026

Recovering membrane interaction kinetics of single molecules from 3D tracking data.

Interactions between cytosolic biomolecules and the bacterial inner membrane are fundamental to many cellular processes, yet directly measuring their binding kinetics in living cells remains challenging. Conventional 2D single-molecule tracking analyses can be insufficient, particularly when membrane association does not markedly alter the diffusion rate. Here, we present a method to recover membrane interaction kinetics from 3D single-molecule trajectories in rod-shaped bacteria. Using simulated 3D tracking data, we identify membrane-associated motion by quantifying how well short trajectory segments follow the circular curvature of the cell membrane. The resulting measure is further analyzed using a hidden Markov modeling framework, enabling robust discrimination between cytosolic and membrane-bound states and capturing the dynamics of state transitions without requiring diffusion-rate changes or direct colocalization with membrane markers. This work establishes a general framework for extracting membrane interaction kinetics from 3D single-molecule tracking data in live bacteria and highlights the value of realistic microscopy simulations for quantitative interpretation and systematic bias assessment.

Kinetics↗
PubMed2026

Kinetic model of E-P condensates dynamics reveals transcriptional speed, noise, and energy trade-offs.

Gene regulation emerges from the interplay between chromatin architecture and the molecular interactions that connect enhancers to promoters. To study how these interactions shape transcriptional dynamics, we developed a kinetic model that incorporates multivalent enhancer-promoter binding, transcription factor competition, steric constraints, and chromatin accessibility. The model shows that competition among regulatory factors can generate bistable promoter states at the expense of increased transcriptional noise, revealing a direct relationship between bistability and noise levels. It further predicts that promoter response times are fastest in parameter regimes where bistability appears, suggesting that regulatory dynamics supporting two promoter states may intrinsically enable rapid activation. Extending this analysis, we find that intermediate chromatin accessibility and competition between activators and repressors both promote bistable enhancer-bound promoter clusters and fast switching at the cost of higher noise, whereas very high or low accessibility and noncompetitive transcription factors result in monostable expression states. The model also offers a quantitative framework to compare the energetic costs of different regulatory strategies, indicating that, under energy constraints, cells may favor adjusting transcription factor concentrations rather than altering chromatin accessibility, thereby linking energy expenditure to regulatory flexibility and robustness.

Kinetics↗