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Abstract Methanogenesis and iron reduction play major roles in determining global fluxes of greenhouse gases. Despite their importance, environmental factors that influence their interactions are poorly known. Here, we present evidence that pH significantly influences the balance between each reaction in anoxic environments that contain ferric (oxyhydr)oxide minerals. In sediment bioreactors that contained goethite as a source of ferric iron, both iron reduction and methanogenesis occurred but the balance between them varied significantly with pH. Compared to bioreactors receiving acidic media (pH 6), electron donor oxidation was 85% lower for iron reduction and 61% higher for methanogenesis in bioreactors receiving alkaline media (pH 7.5). Thus, methanogenesis displaced iron reduction considerably at alkaline pH. Geochemistry data collected from U.S. aquifers demonstrate that a similar pattern also exists on a broad spatial scale in natural settings. In contrast, in bioreactors that were not augmented with goethite, clay minerals served as the source of ferric iron and the balance between each reaction did not vary significantly with pH. We therefore conclude that pH can regulate the relative contributions of microbial iron reduction and methanogenesis to carbon fluxes from terrestrial environments. We further propose that the availability of ferric (oxyhydr)oxide minerals influences the extent to which the balance between each reaction is sensitive to pH. The results of this study advance our understanding of environmental controls on microbial methane generation and provide a basis for using pH and the occurrence of ferric minerals to refine predictions of greenhouse gas fluxes. 

Chemotrophic microorganisms face the steep challenge of limited energy resources in natural environments. This observation has important implications for interpreting and modeling the kinetics and thermodynamics of microbial reactions. Current modeling frameworks treat microbes as autocatalysts, and simulate microbial energy conservation and growth with fixed kinetic and thermodynamic parameters. However, microbes are capable of acclimating to the environment and modulating their parameters in order to gain competitive fitness. Here we constructed an optimization model and described microbes as self-adapting catalysts by linking microbial parameters to intracellular metabolic resources. From the optimization results, we related microbial parameters to the substrate concentration and the energy available in the environment, and simplified the relationship between the kinetics and the thermodynamics of microbial reactions.We took as examples Methanosarcina and Methanosaeta – the methanogens that produce methane from acetate – and showed how the acclimation model extrapolated laboratory observations to natural environments and improved the simulation of methanogenesis and the dominance of Methanosaeta over Methanosarcina in lake sediments. These results highlight the importance of physiological acclimation in shaping the kinetics and thermodynamics of microbial reactions and in determining the outcome of microbial interactions. 

The Monodequationhasbeenwidelyappliedasthegeneralratelaw of microbialgrowth,butitsapplicationsarenotalwayssuccessful.Bydrawingon the frameworksofkineticandstoichiometricmetabolicmodelsandmetaboliccon- trol analysis,themodelingreportedheresimulatedthegrowthkineticsofametha- nogenic microorganismandillustratedthatdifferentenzymesandmetabolitescon- trol growthratetovariousextentsandthattheircontrolspeakateitherverylow, intermediate, orveryhighsubstrateconcentrations.Incomparison,withasingle term andtwoparameters,theMonodequationonlyapproximatelyaccountsforthe controls ofrate-determiningenzymesandmetabolitesatveryhighandverylow substrate concentrations,butneglectstheenzymesandmetaboliteswhosecontrols are mostnotableatintermediateconcentrations.These findings supportalimited link betweentheMonodequationandmethanogengrowth,andunifythecompet- ing viewsregardingenzymerolesinshapinggrowthkinetics.Theresultsalsopre- clude amechanisticderivationoftheMonodequationfrommethanogenmetabolic networks andhighlightafundamentalchallengeinmicrobiology:single-termexpres- sions maynotbesufficient foraccuratepredictionofmicrobialgrowth. 

The Q10 coefficient is the ratio of reaction rates at two temperatures 10°C apart, and has been widely applied to quantify the temperature sensitivity of organic matter decomposition. However, biogeochemists and ecologists have long recognized that a constant Q10 coefficient does not describe the temperature sensitivity of organic matter decomposition accurately. To examine the consequences of the constant Q10 assumption, we built a biogeochemical reaction model to simulate anaerobic organic matter decomposition in peatlands in the Upper Peninsula of Michigan, USA, and compared the simulation results to the predictions with Q10 coefficients. By accounting for the reactions of extracellular enzymes, mesophilic fermenting and methanogenic microbes, and their temperature responses, the biogeochemical reaction model reproduces the observations of previous laboratory incubation experiments, including the temporal variations in the concentrations of dissolved organic carbon, acetate, dihydrogen, carbon dioxide, and methane, and confirms that fermentation limits the progress of anaerobic organic matter decomposition. The modeling results illustrate the oversimplification inherent in the constant Q10 assumption and how the assumption undermines the kinetic prediction of anaerobic organic matter decomposition. In particular, the model predicts that between 5°C and 30°C, the decomposition rate increases almost linearly with increasing temperature, which stands in sharp contrast to the exponential relationship given by the Q10 coefficient. As a result, the constant Q10 approach tends to underestimate the rates of organic matter decomposition within the temperature ranges where Q10 values are determined, and overestimate the rates outside the temperature ranges. The results also show how biogeochemical reaction modeling, combined with laboratory experiments, can help uncover the temperature sensitivity of organic matter decomposition arising from underlying catalytic mechanisms.