A theoretical reassessment of microbial maintenance and implications for microbial ecology modeling

Sub-inhibitory antibiotic treatment selects for enhanced metabolic efficiency

Bacterial growth and metabolic rates are often closely related. However, under antibiotic selection, a paradox in this relationship arises: antibiotic efficacy decreases when bacteria are metabolically dormant, yet antibiotics select for resistant cells that grow fastest during treatment. That is, antibiotic selection counterintuitively favors bacteria with fast growth but slow metabolism. Despite this apparent contradiction, antibiotic resistant cells have historically been characterized primarily in the context of growth, whereas the extent of analogous changes in metabolism is comparatively unknown. Here, we observed that previously evolved antibiotic-resistant strains exhibited a unique relationship between growth and metabolism whereby nutrient utilization became more efficient, regardless of the growth rate. To better understand this unexpected phenomenon, we used a simplified model to simulate bacterial populations adapting to sub-inhibitory antibiotic selection through successive bottlenecking events. Simulations predicted that sub-inhibitory bactericidal antibiotic concentrations could select for enhanced metabolic efficiency, defined based on nutrient utilization: drug-adapted cells are able to achieve the same biomass while utilizing less substrate, even in the absence of treatment. Moreover, simulations predicted that restoring metabolic efficiency would re-sensitize resistant bacteria exhibiting metabolic-dependent resistance; we confirmed this result using adaptive laboratory evolutions of Escherichia coli under carbenicillin treatment. Overall, these results indicate that metabolic efficiency is under direct selective pressure during antibiotic treatment and that differences in evolutionary context may determine both the efficacy of different antibiotics and corresponding re-sensitization approaches.IMPORTANCEThe sustained emergence of antibiotic-resistant pathogens combined with the stalled drug discovery pipelines highlights the critical need to better understand the underlying evolution mechanisms of antibiotic resistance. To this end, bacterial growth and metabolic rates are often closely related, and resistant cells have historically been characterized exclusively in the context of growth. However, under antibiotic selection, antibiotics counterintuitively favor cells with fast growth, and slow metabolism. Through an integrated approach of mathematical modeling and experiments, this study thereby addresses the significant knowledge gap of whether antibiotic selection drives changes in metabolism that complement, and/or act independently, of antibiotic resistance phenotypes.

Microbial Growth under Limiting Conditions-Future Perspectives

Microorganisms rule the functioning of our planet and each one of the individual macroscopic living creature. Nevertheless, microbial activity and growth status have always been challenging tasks to determine both in situ and in vivo. Microbial activity is generally related to growth, and the growth rate is a result of the availability of nutrients under adequate or adverse conditions faced by microbial cells in a changing environment. Most studies on microorganisms have been carried out under optimum or near-optimum growth conditions, but scarce information is available about microorganisms at slow-growing states (i.e., near-zero growth and maintenance metabolism). This study aims to better understand microorganisms under growth-limiting conditions. This is expected to provide new perspectives on the functions and relevance of the microbial world. This is because (i) microorganisms in nature frequently face conditions of severe growth limitation, (ii) microorganisms activate singular pathways (mostly genes remaining to be functionally annotated), resulting in a broad range of secondary metabolites, and (iii) the response of microorganisms to slow-growth conditions remains to be understood, including persistence strategies, gene expression, and cell differentiation both within clonal populations and due to the complexity of the environment.

Microbial maintenance energy quantified and modeled with microcalorimetry

Refining the energetic costs of cellular maintenance is essential for predicting microbial growth and survival in the environment. Here, we evaluate a simple batch culture method to quantify energy partitioning between growth and maintenance using microcalorimetry and thermodynamic modeling. The constants derived from the batch culture system were comparable to those that have been reported from meta-analyses of data derived from chemostat studies. The model accurately predicted temperature-dependent biomass yield and the upper temperature limit of growth for Desulfovibrio alaskensis G20, suggesting the method may have broad application. An Arrhenius temperature dependence for the specific energy consumption rate, inferred from substrate consumption and heat evolution, was observed over the entire viable temperature range. By combining this relationship for specific energy consumption rates and observed specific growth rates, the model describes an increase in nongrowth associated maintenance at higher temperatures and the corresponding decrease in energy available for growth. This analytical and thermodynamic formulation suggests that simply monitoring heat evolution in batch culture could be a useful complement to the recognized limitations of estimating maintenance using extrapolation to zero growth in chemostats.

Consideration of Maintenance in Wine Fermentation Modeling

We show that a simple model with a maintenance term can satisfactorily reproduce the simulations of several existing models of wine fermentation from the literature, as well as experimental data. The maintenance describes a consumption of the nitrogen that is not entirely converted into biomass. We show also that considering a maintenance term in the model is equivalent to writing a model with a variable yield that can be estimated from data.

Pre-incubation conditions determine the fermentation pattern and microbial community structure in fermenters at mild hydrostatic pressure

Fermentation at elevated hydrostatic pressure is a novel strategy targeting product selectivity. However, the role of inoculum history and cross-resistance, that is, acquired tolerance from incubation under distinctive environmental stress, remains unclear in high-pressure operation. In our here presented work, we studied fermentation and microbial community responses of halotolerant marine sediment inoculum (MSI) and anaerobic digester inoculum (ADI), pre-incubated in serum bottles at different temperatures and subsequently exposed to mild hydrostatic pressure (MHP; < 10 MPa) in stainless steel reactors. Results showed that MHP effects on microbial growth, activity, and community structure were strongly temperature-dependent. At moderate temperature (20°C), biomass yield and fermentation were not limited by MHP; suggesting a cross-resistance effect from incubation temperature and halotolerance. Low temperatures (10°C) and MHP imposed kinetic and bioenergetic limitations, constraining growth and product formation. Fermentation remained favorable in MSI at 28°C and ADI at 37°C, despite reduced biomass yield resulting from maintenance and decay proportionally increasing with temperature. Microbial community structure was modified by temperature during the enrichment, and slight differences observed after MHP-exposure did not compromise functionality. Results showed that the relation incubation temperature-halotolerance proved to be a modifier of microbial responses to MHP and could be potentially exploited in fermentations to modulate product/biomass ratio.

Maintenance power requirements of anammox bacteria "Candidatus Brocadia sinica" and "Candidatus Scalindua sp."

Little is known about the cell physiology of anammox bacteria growing at extremely low growth rates. Here, "Candidatus Brocadia sinica" and "Candidatus Scalindua sp." were grown in continuous anaerobic membrane bioreactors (MBRs) with complete biomass retention to determine maintenance energy (i.e., power) requirements at near-zero growth rates. After prolonged retentostat cultivations, the specific growth rates (μ) of "Ca. B. sinica" and "Ca. Scalindua sp." decreased to 0.000023 h^-1 (doubling time of 1255 days) and 0.000157 h^-1 (184 days), respectively. Under these near-zero growth conditions, substrate was continuously utilized to meet maintenance energy demands (m_e) of 6.7 ± 0.7 and 4.3 ± 0.7 kJ mole of biomass-C^-1 h^-1 for "Ca. B. sinica" and "Ca. Scalindua sp.", which accorded with the theoretically predicted values of all anaerobic microorganisms (9.7 and 4.4 kJ mole of biomass-C^-1 h^-1at 37 °C and 28 °C, respectively). These m_e values correspond to 13.4 × 10^-15 and 8.6 × 10^-15 watts cell^-1 for "Ca. B. sinica" and "Ca. Scalindua sp.", which were five orders of magnitude higher than the basal power limit for natural settings (1.9 × 10^-19 watts cells^-1). Furthermore, the minimum substrate concentrations required for growth (S_min) were calculated to be 3.69 ± 0.21 and 0.09 ± 0.05 μM NO₂^- for "Ca. B. sinica" and "Ca. Scalindua sp.", respectively. These results match the evidence that "Ca. Scalindua sp." with lower maintenance power requirement and S_min are better adapted to energy-limited natural environments than "Ca. B. sinica", suggesting the importance of these parameters on ecological niche differentiation in natural environments.

High carbon losses from oxygen-limited soils challenge biogeochemical theory and model assumptions

Oxygen (O₂ ) limitation contributes to persistence of large carbon (C) stocks in saturated soils. However, many soils experience spatiotemporal O₂  fluctuations impacted by climate and land-use change, and O₂ -mediated climate feedbacks from soil greenhouse gas emissions remain poorly constrained. Current theory and models posit that anoxia uniformly suppresses carbon (C) decomposition. Here we show that periodic anoxia may sustain or even stimulate decomposition over weeks to months in two disparate soils by increasing turnover and/or size of fast-cycling C pools relative to static oxic conditions, and by sustaining decomposition of reduced organic molecules. Cumulative C losses did not decrease consistently as cumulative O₂ exposure decreased. After >1 year, soils anoxic for 75% of the time had similar C losses as the oxic control but nearly threefold greater climate impact on a CO₂ -equivalent basis (20-year timescale) due to high methane (CH₄ ) emission. A mechanistic model incorporating current theory closely reproduced oxic control results but systematically underestimated C losses under O₂  fluctuations. Using a model-experiment integration (ModEx) approach, we found that models were improved by varying microbial maintenance respiration and the fraction of CH₄ production in total C mineralization as a function of O₂ availability. Consistent with thermodynamic expectations, the calibrated models predicted lower microbial C-use efficiency with increasing anoxic duration in one soil; in the other soil, dynamic organo-mineral interactions implied by our empirical data but not represented in the model may have obscured this relationship. In both soils, the updated model was better able to capture transient spikes in C mineralization that occurred following anoxic-oxic transitions, where decomposition from the fluctuating-O₂ treatments greatly exceeded the control. Overall, our data-model comparison indicates that incorporating emergent biogeochemical properties of soil O₂ variability will be critical for effectively modeling C-climate feedbacks in humid ecosystems.

Energetic scaling in microbial growth

Microbial growth is a clear example of organization and structure arising in nonequilibrium conditions. Due to the complexity of the microbial metabolic network, elucidating the fundamental principles governing microbial growth remains a challenge. Here, we present a systematic analysis of microbial growth thermodynamics, leveraging an extensive dataset on energy-limited monoculture growth. A consistent thermodynamic framework based on reaction stoichiometry allows us to quantify how much of the available energy microbes can efficiently convert into new biomass while dissipating the remaining energy into the environment and producing entropy. We show that dissipation mechanisms can be linked to the electron donor uptake rate, a fact leading to the central result that the thermodynamic efficiency is related to the electron donor uptake rate by the scaling law [Formula: see text] and to the growth yield by [Formula: see text] These findings allow us to rederive the Pirt equation from a thermodynamic perspective, providing a means to compute its coefficients, as well as a deeper understanding of the relationship between growth rate and yield. Our results provide rather general insights into the relation between mass and energy conversion in microbial growth with potentially wide application, especially in ecology and biotechnology.

An Improved Robust Adaptive Controller for a Fed-Batch Bioreactor with Input Saturation and Unknown Varying Control Gain via Dead-Zone Quadratic Forms

In this work, a new adaptive controller is designed for substrate control of a fed-batch bioreactor in the presence of input saturation and unknown varying control gain with unknown upper and lower bounds. The output measurement noise and the unknown varying nature of reaction rate and biomass concentration and water volume are also handled. The design is based on dead zone quadratic forms. The designed controller ensures the convergence of the modified tracking error and the boundedness of the updated parameters. As the first distinctive feature, a new robust adaptive auxiliary system is proposed in order to tackle input saturation and control gain uncertainty. As the second distinctive feature, the modified tracking error converges to a compact region whose bound is user-defined, in contrast to related studies where the convergence region depends on upper bounds of either external disturbances, system states, model parameters or terms and model parameter values. Simulations confirm the properties of the closed loop behavior.

Systematic variation in the temperature dependence of bacterial carbon use efficiency

Carbon use efficiency (CUE) is a key characteristic of microbial physiology and underlies community-level responses to changing environments. Yet, we currently lack general empirical insights into variation in microbial CUE at the level of individual taxa. Here, through experiments with 29 strains of environmentally isolated bacteria, we find that bacterial CUE typically responds either positively to temperature, or has no discernible response, within biologically meaningful temperature ranges. Using a global data synthesis, we show that these results are generalisable across most culturable groups of bacteria. This variation in the thermal responses of bacterial CUE is taxonomically structured, and stems from the fact that relative to respiration rates, bacterial population growth rates typically respond more strongly to temperature, and are also subject to weaker evolutionary constraints. Our results provide new insights into microbial physiology, and a basis for more accurately modelling the effects of thermal fluctuations on complex microbial communities.

A review of mathematical models for composting

Composting is a valuable method to treat and valorize organic waste. However, the process is defined by its dynamic nature and governed by a multitude of operating parameters. As such, mathematical modelling of the process offers a powerful tool to simulate and predict the variable outcomes of the process, allowing for its optimization. This can include improving efficiency, lowering costs and reducing environmental impact. To aid with the development of future models, we provide an up to date review and assessment on the state of the art of composting modelling. By reviewing 40 years of literature, this review paints the most complete picture of the field to date. This includes an analysis of trends in composting modelling: looking at the type of systems that are targeted, the aim of the models and the approaches to kinetics and mass and heat transfer. Regarding modelling approaches, we explore the fractionation of both substrates and microorganisms, the biological processes that can be included (disintegration, hydrolysis, uptake and death) and their kinetics (first-order, Monod-type), energy balances (biological generation, convection, conduction) and mass balances. We also provide a review of the results of sensitivity analyses performed on composting models, finding that models are most sensitive to microbial growth and death rates, as well as consumption rates and product yields. In the final portion of the review, we identify, explore, and provide guiding recommendations for work on emerging areas and areas requiring development in composting modelling (volume change, pH, maturation, artificial intelligence, etc.).

About biomass overyielding of mixed cultures in batch processes

We study mechanisms that can produce an increase of biomass production in batch processes when considering mixed cultures, compared to pure cultures. We show that growth thresholds or variable yields can produce 'overyielding', while this is not possible in the classical batch model with multiple species. We give sufficient conditions on the characteristics of the species to obtain overyielding, and illustrate these theoretical results with numerical simulations. This work provides new insights on species complementary in models of mixed cultures, without having to consider direct interactions terms between species as, for instance in the well known Generalized Lotka-Volterra model.

Reduced carbon use efficiency and increased microbial turnover with soil warming

Global soil carbon (C) stocks are expected to decline with warming, and changes in microbial processes are key to this projection. However, warming responses of critical microbial parameters such as carbon use efficiency (CUE) and biomass turnover (rB) are not well understood. Here, we determine these parameters using a probabilistic inversion approach that integrates a microbial-enzyme model with 22 years of carbon cycling measurements at Harvard Forest. We find that increasing temperature reduces CUE but increases rB, and that two decades of soil warming increases the temperature sensitivities of CUE and rB. These temperature sensitivities, which are derived from decades-long field observations, contrast with values obtained from short-term laboratory experiments. We also show that long-term soil C flux and pool changes in response to warming are more dependent on the temperature sensitivity of CUE than that of rB. Using the inversion-derived parameters, we project that chronic soil warming at Harvard Forest over six decades will result in soil C gain of <1.0% on average (1st and 3rd quartiles: 3.0% loss and 10.5% gain) in the surface mineral horizon. Our results demonstrate that estimates of temperature sensitivity of microbial CUE and rB can be obtained and evaluated rigorously by integrating multidecadal datasets. This approach can potentially be applied in broader spatiotemporal scales to improve long-term projections of soil C feedbacks to climate warming.

Determination of Microbial Maintenance in Acetogenesis and Methanogenesis by Experimental and Modeling Techniques

For biogas-producing continuous stirred tank reactors, an increase in dilution rate increases the methane production rate as long as substrate input can be converted fully. However, higher dilution rates necessitate higher specific microbial growth rates, which are assumed to have a strong impact on the apparent microbial biomass yield due to cellular maintenance. To test this, we operated two reactors at 37°C in parallel at dilution rates of 0.18 and 0.07 days-1 (hydraulic retention times of 5.5 and 14 days, doubling times of 3.9 and 9.9 days in steady state) with identical inoculum and a mixture of volatile fatty acids as sole carbon sources. We evaluated the performance of the Anaerobic Digestion Model No. 1 (ADM1), a thermodynamic black box approach (TBA), and dynamic flux balance analysis (dFBA), to describe the experimental observations. All models overestimated the impact of dilution rate on the apparent microbial biomass yield when using default parameter values. Based on our analysis, a maintenance coefficient value below 0.2 kJ per carbon mole of microbial biomass per hour should be used for the TBA, corresponding to 0.12 mmol ATP per gram dry weight per hour for dFBA, which strongly deviates from the value of 9.8 kJ Cmol h-1 that has been suggested to apply to all anaerobic microorganisms at 37°C. We hypothesized that a decrease in dilution rate might select taxa with minimized maintenance expenditure. However, no major differences in the dominating taxa between the reactors were observed based on amplicon sequencing of 16S rRNA genes and terminal restriction fragment length polymorphism analysis of mcrA genes. Surprisingly, Methanosaeta dominated over Methanosarcina even at a dilution rate of 0.18 days-1, which contradicts previous model expectations. Furthermore, only 23-49% of the bacterial reads could be assigned to known syntrophic fatty acid oxidizers, indicating that unknown members of this functional group remain to be discovered. In conclusion, microbial maintenance was found to be much lower for acetogenesis and methanogenesis than previously assumed, likely due to the exceptionally low growth rates in anaerobic digestion. This finding might also be relevant for other microbial systems operating at similarly low growth rates.

Survival of the fewest: Microbial dormancy and maintenance in marine sediments through deep time

Microorganisms buried in marine sediments are known to endure starvation over geologic timescales. However, the mechanisms of how these microorganisms cope with prolonged energy limitation is unknown and therefore yet to be captured in a quantitative framework. Here, we present a novel mathematical model that considers (a) the physiological transitions between the active and dormant states of microorganisms, (b) the varying requirement for maintenance power between these phases, and (c) flexibility in the provenance (i.e., source) of energy from exogenous and endogenous catabolism. The model is applied to sediments underlying the oligotrophic South Pacific Gyre where microorganisms endure ultra-low fluxes of energy for tens of millions of years. Good fits between model simulations and measurements of cellular carbon and organic carbon concentrations are obtained and are interpreted as follows: (a) the unfavourable microbial habitat in South Pacific Gyre sediments triggers rapid mortality and a transition to dormancy; (b) there is minimal biomass growth, and organic carbon consumption is dominated by catabolism to support maintenance activities rather than new biomass synthesis; (c) the amount of organic carbon that microorganisms consume for maintenance activities is equivalent to approximately 2% of their carbon biomass per year; and (d) microorganisms must rely solely on exogenous rather than endogenous catabolism to persist in South Pacific Gyre sediments over long timescales. This leads us to the conclusion that under oligotrophic conditions, the fitness of an organism is determined by its ability to simply stay alive, rather than to grow. This modelling framework is designed to be flexible for application to other sites and habitats, and thus serves as a new quantitative tool for determining the habitability of and an ultimate limit for life in any environment.

Cinética de crecimiento de Gluconacetobacter diazotrophicus usando melaza de caña y sacarosa: evaluación de modelos

Gluconacetobacter diazotrophicus es una bacteria endófita promotora del crecimiento vegetal utilizada como inoculante microbiano en diferentes cultivos agrícolas. El objetivo del presente trabajo fue aplicar diferentes modelos matemáticos para representar su crecimiento en un cultivo sumergido por lotes empleando un biorreactor de 3 L y usando melazas de caña y sacarosa como fuente de energía. Se obtuvo el perfil temporal de pH, biomasa celular y azúcares totales. Se compararon los modelos estudiados por calidad de ajuste y complejidad y se realizó un análisis de sensibilidad paramétrica. Se consideraron modelos de cuatro y cinco parámetros con expresiones que incluyen efectos de inhibición por sustrato y por biomasa. El modelo con mayor calidad de ajuste fue el de Herbert-Pirt-Contois con coeficientes de determinación para biomasa y sustrato de 0,888 y 0,425 respectivamente. Estos valores indican una mayor correspondencia de los datos experimentales de biomasa con los datos calculados por el modelo, en comparación con los resultados obtenidos para azúcares totales para los que esta correspondencia fue menor. Este modelo generó la mejor combinación de calidad de ajuste y complejidad según el criterio de información de Akaike. El estudio cinético desarrollado permitió observar un comportamiento bifásico en la etapa de crecimiento de la bacteria cuando se cultiva en melaza y un efecto de limitación de su crecimiento por la biomasa. Los resultados obtenidos proporcionan una descripción matemática útil para el diseño, escalamiento y operación de un futuro proceso de producción de un inoculante microbiano a base de la bacteria G. diazotrophicus.

Oscillatory behaviour control in a continuous culture under double-substrate limitation

The paper discusses the possibility of using a mixture of two growth limiting substrates to induce or eliminate self-sustained oscillations in a continuous culture process. The proportion of both substrates in the mixture is treated as a new control variable. The presented approach is based on the assumption that the oscillatory behaviour occurs for selected substrates in some range of dilution rates. Because a double-substrate limitation may occur, the analysis is performed for two fundamental substrate utilization patterns: simultaneous consumption and diauxic growth. By using model simulations and bifurcation analysis, we show that an appropriate proportion of two substrates in the mixture allows for the control of the oscillatory behaviour.

Bioenergetic Controls on Microbial Ecophysiology in Marine Sediments

Marine sediments constitute one of the most energy-limited habitats on Earth, in which microorganisms persist over extraordinarily long timescales with very slow metabolisms. This habitat provides an ideal environment in which to study the energetic limits of life. However, the bioenergetic factors that can determine whether microorganisms will grow, lie dormant, or die, as well as the selective environmental pressures that determine energetic trade-offs between growth and maintenance activities, are not well understood. Numerical models will be pivotal in addressing these knowledge gaps. However, models rarely account for the variable physiological states of microorganisms and their demand for energy. Here, we review established modeling constructs for microbial growth rate, yield, maintenance, and physiological state, and then provide a new model that incorporates all of these factors. We discuss this new model in context with its future application to the marine subsurface. Understanding the factors that regulate cell death, physiological state changes, and the provenance of maintenance energy (i.e., endogenous versus exogenous metabolism), is crucial to the design of this model. Further, measurements of growth rate, growth yield, and basal metabolic activity will enable bioenergetic parameters to be better constrained. Last, biomass and biogeochemical rate measurements will enable model simulations to be validated. The insight provided from the development and application of new microbial modeling tools for marine sediments will undoubtedly advance the understanding of the minimum power required to support life, and the ecophysiological strategies that organisms utilize to cope under extreme energy limitation for extended periods of time.

Effect of different cover crops on C and N cycling in sorghum NT systems

In many no-till (NT) systems, residue input is low and fallow periods excessive, for which reasons soil degradation occurs. Cover crops could improve organic matter, biological activity, and soil structure. In order to study changes in soil carbon, nitrogen and microbial biomass a field experiment (2010-2012) was set up with sorghum (Sorghum bicolor Moench.) monoculture and with cover crops. Treatments were control (NT with bare fallow), rye (Secale cereale L.) (R), rye with nitrogen fertilization (R+N), vetch (Vicia villosa Roth.) (V), and rye-vetch mixture (VR) cover crops. A completely randomized block design with 4 replicates was used. Soil was sampled once a year at 0.06 and 0.12m depth for total C, microbial biomass carbon (MBC) and-nitrogen (MBN) determinations. Shoot and root biomass of sorghum and cover crops, litter biomass, and their respective carbon and nitrogen contents were determined. Soil temperatures at 0.06 and 0.12m depth, volumetric water contents and nitrate concentrations were determined at sowing, and harvest of each crop, and during sorghum's vegetative phase. NT led to a small increase in MBC and MBN, despite low litter and root biomass residue. Cover crops increased litter, root biomass, total C, MBC, and MBN. Relationships between MBC, MBN, and root-C and -N adjusted to logistic models (R(2)=0.61 and 0.43 for C and N respectively). Litter cover improved soil moisture to 45-50% water filled pore space and soil temperatures not exceeding 25°C during the warmest month. Microbial biomass stabilized at 20.1gCm(-2) and 1.9gNm(-2) in the upper 0.06m. Soil litter disappearance was a good indicator of mineral N availability. These findings support the view that cover crops, specifically legumes in NT systems can increase soil ecosystem services related to water and carbon storage, habitat for biodiversity, and nutrient availability.

Integrated analysis of gene expression and metabolic fluxes in PHA-producing Pseudomonas putida grown on glycerol

Background

Given its high surplus and low cost, glycerol has emerged as interesting carbon substrate for the synthesis of value-added chemicals. The soil bacterium Pseudomonas putida KT2440 can use glycerol to synthesize medium-chain-length poly(3-hydroxyalkanoates) (mcl-PHA), a class of biopolymers of industrial interest. Here, glycerol metabolism in P. putida KT2440 was studied on the level of gene expression (transcriptome) and metabolic fluxes (fluxome), using precisely adjusted chemostat cultures, growth kinetics and stoichiometry, to gain a systematic understanding of the underlying metabolic and regulatory network.

Results

Glycerol-grown P. putida KT2440 has a maintenance energy requirement [0.039 (mmol_glycerol (g_CDW h)⁻¹)] that is about sixteen times lower than that of other bacteria, such as Escherichia coli, which provides a great advantage to use this substrate commercially. The shift from carbon (glycerol) to nitrogen (ammonium) limitation drives the modulation of specific genes involved in glycerol metabolism, transport electron chain, sensors to assess the energy level of the cell, and PHA synthesis, as well as changes in flux distribution to increase the precursor availability for PHA synthesis (Entner–Doudoroff pathway and pyruvate metabolism) and to reduce respiration (glyoxylate shunt). Under PHA-producing conditions (N-limitation), a higher PHA yield was achieved at low dilution rate (29.7 wt% of CDW) as compared to a high rate (12.8 wt% of CDW). By-product formation (succinate, malate) was specifically modulated under these regimes. On top of experimental data, elementary flux mode analysis revealed the metabolic potential of P. putida KT2440 to synthesize PHA and identified metabolic engineering targets towards improved production performance on glycerol.

Conclusion

This study revealed the complex interplay of gene expression levels and metabolic fluxes under PHA- and non-PHA producing conditions using the attractive raw material glycerol as carbon substrate. This knowledge will form the basis for the development of future metabolically engineered hyper-PHA-producing strains derived from the versatile bacterium P. putida KT2440.

Carbon and nitrogen additions induce distinct priming effects along an organic-matter decay continuum

Decomposition of organic matter (OM) in soil, affecting carbon (C) cycling and climate feedbacks, depends on microbial activities driven by C and nitrogen (N) availability. However, it remains unknown how decomposition of various OMs vary across global supplies and ratios of C and N inputs. We examined OM decomposition by incubating four types of OM (leaf litter, wood, organic matter from organic and mineral horizons) from a decay continuum in a subtropical forest at Ailao Mountain, China with labile C and N additions. Decomposition of wood with high C:N decreased for 3.9 to 29% with these additions, while leaf decomposition was accelerated only within a narrow C:N range of added C and N. Decomposition of OM from organic horizon was accelerated by high C:N and suppressed by low C:N, but mineral soil was almost entirely controlled by high C:N. These divergent responses to C and N inputs show that mechanisms for priming (i.e. acceleration or retardation of OM decomposition by labile inputs) vary along this decay continuum. We conclude that besides C:N ratios of OM, those of labile inputs control the OM decay in the litter horizons, while energy (labile C) regulates decomposition in mineral soil. This suggests that OM decomposition can be predicted from its intrinsic C:N ratios and those of labile inputs.

Social dynamics within decomposer communities lead to nitrogen retention and organic matter build-up in soils

The chemical structure of organic matter has been shown to be only marginally important for its decomposability by microorganisms. The question of why organic matter does accumulate in the face of powerful microbial degraders is thus key for understanding terrestrial carbon and nitrogen cycling. Here we demonstrate, based on an individual-based microbial community model, that social dynamics among microbes producing extracellular enzymes (‘decomposers') and microbes exploiting the catalytic activities of others (‘cheaters') regulate organic matter turnover. We show that the presence of cheaters increases nitrogen retention and organic matter build-up by downregulating the ratio of extracellular enzymes to total microbial biomass, allowing nitrogen-rich microbial necromass to accumulate. Moreover, increasing catalytic efficiencies of enzymes are outbalanced by a strong negative feedback on enzyme producers, leading to less enzymes being produced at the community level. Our results thus reveal a possible control mechanism that may buffer soil CO₂ emissions in a future climate.

Microbial decomposers in soil provide the largest ecosystem flux of CO₂ to the atmosphere, but interactions at the microscale are poorly understood. Here, the authors use a computer modelling approach to show that social interactions among microbes buffer changing environmental conditions.

Modeling evolution of spatially distributed bacterial communities: a simulation with the haploid evolutionary constructor

Background

Multiscale approaches for integrating submodels of various levels of biological organization into a single model became the major tool of systems biology. In this paper, we have constructed and simulated a set of multiscale models of spatially distributed microbial communities and study an influence of unevenly distributed environmental factors on the genetic diversity and evolution of the community members.

Results

Haploid Evolutionary Constructor software http://evol-constructor.bionet.nsc.ru/ was expanded by adding the tool for the spatial modeling of a microbial community (1D, 2D and 3D versions). A set of the models of spatially distributed communities was built to demonstrate that the spatial distribution of cells affects both intensity of selection and evolution rate.

Conclusion

In spatially heterogeneous communities, the change in the direction of the environmental flow might be reflected in local irregular population dynamics, while the genetic structure of populations (frequencies of the alleles) remains stable. Furthermore, in spatially heterogeneous communities, the chemotaxis might dramatically affect the evolution of community members.

Representation of dormant and active microbial dynamics for ecosystem modeling

Dormancy is an essential strategy for microorganisms to cope with environmental stress. However, global ecosystem models typically ignore microbial dormancy, resulting in notable model uncertainties. To facilitate the consideration of dormancy in these large-scale models, we propose a new microbial physiology component that works for a wide range of substrate availabilities. This new model is based on microbial physiological states and the major parameters are the maximum specific growth and maintenance rates of active microbes and the ratio of dormant to active maintenance rates. A major improvement of our model over extant models is that it can explain the low active microbial fractions commonly observed in undisturbed soils. Our new model shows that the exponentially-increasing respiration from substrate-induced respiration experiments can only be used to determine the maximum specific growth rate and initial active microbial biomass, while the respiration data representing both exponentially-increasing and non-exponentially-increasing phases can robustly determine a range of key parameters including the initial total live biomass, initial active fraction, the maximum specific growth and maintenance rates, and the half-saturation constant. Our new model can be incorporated into existing ecosystem models to account for dormancy in microbially-driven processes and to provide improved estimates of microbial activities.

Dynamic mathematical models for biodegradation of formaldehyde by Ralstonia eutropha in a batch bioreactor

Degradation of formaldehyde by Ralstonia eutropha was studied in a batch bioreactor operated in recycling mode (30 °C, initial pH of 6.5, aeration rate 0.5 vvm, and a recycling flow rate of 6 mL min(-1)). Growth kinetics equations were described using four substrate inhibition models, and the initial formaldehyde concentration ranged from 54.5 to 993.0 mg L(-1). In each case, model parameters were estimated interactively using nonlinear regression analysis and on the basis of the goodness of fit, the fitness of the model to the experimental data was obtained (i.e., the coefficient of determination and the percent of standard deviation). The estimated parameters according to the Luong equation were μmax = 0.101 h(-1), KS = 54.1 mg L(-1), Sm = 1329 mg L(-1), and n = 2.07. According to the maintenance energies explained by Pirt, cell maintenance was quantified with q = Aμ + B; where A and B are the associated and non-associated growth parts of substrate consumption, respectively. The importance of these terms was verified using the developed models, which would efficiently describe the dynamic nature of growth and formaldehyde biodegradation.

Dynamic relationships between microbial biomass, respiration, inorganic nutrients and enzyme activities: informing enzyme-based decomposition models

We re-examined data from a recent litter decay study to determine if additional insights could be gained to inform decomposition modeling. Rinkes et al. (2013) conducted 14-day laboratory incubations of sugar maple (Acer saccharum) or white oak (Quercus alba) leaves, mixed with sand (0.4% organic C content) or loam (4.1% organic C). They measured microbial biomass C, carbon dioxide efflux, soil ammonium, nitrate, and phosphate concentrations, and β-glucosidase (BG), β-N-acetyl-glucosaminidase (NAG), and acid phosphatase (AP) activities on days 1, 3, and 14. Analyses of relationships among variables yielded different insights than original analyses of individual variables. For example, although respiration rates per g soil were higher for loam than sand, rates per g soil C were actually higher for sand than loam, and rates per g microbial C showed little difference between treatments. Microbial biomass C peaked on day 3 when biomass-specific activities of enzymes were lowest, suggesting uptake of litter C without extracellular hydrolysis. This result refuted a common model assumption that all enzyme production is constitutive and thus proportional to biomass, and/or indicated that part of litter decay is independent of enzyme activity. The length and angle of vectors defined by ratios of enzyme activities (BG/NAG vs. BG/AP) represent relative microbial investments in C (length), and N and P (angle) acquiring enzymes. Shorter lengths on day 3 suggested low C limitation, whereas greater lengths on day 14 suggested an increase in C limitation with decay. The soils and litter in this study generally had stronger P limitation (angles >45°). Reductions in vector angles to <45° for sand by day 14 suggested a shift to N limitation. These relational variables inform enzyme-based models, and are usually much less ambiguous when obtained from a single study in which measurements were made on the same samples than when extrapolated from separate studies.

Microbial responses to multi-factor climate change: effects on soil enzymes

The activities of extracellular enzymes, the proximate agents of decomposition in soils, are known to depend strongly on temperature, but less is known about how they respond to changes in precipitation patterns, and the interaction of these two components of climate change. Both enzyme production and turnover can be affected by changes in temperature and soil moisture, thus it is difficult to predict how enzyme pool size may respond to altered climate. Soils from the Boston-Area Climate Experiment (BACE), which is located in an old field (on abandoned farmland), were used to examine how climate variables affect enzyme activities and microbial biomass carbon (MBC) in different seasons and in soils exposed to a combination of three levels of precipitation treatments (ambient, 150% of ambient during growing season, and 50% of ambient year-round) and four levels of warming treatments (unwarmed to ~4°C above ambient) over the course of a year. Warming, precipitation and season had very little effect on potential enzyme activity. Most models assume that enzyme dynamics follow microbial biomass, because enzyme production should be directly controlled by the size and activity of microbial biomass. We observed differences among seasons and treatments in mass-specific potential enzyme activity, suggesting that this assumption is invalid. In June 2009, mass-specific potential enzyme activity, using chloroform fumigation-extraction MBC, increased with temperature, peaking under medium warming and then declining under the highest warming. This finding suggests that either enzyme production increased with temperature or turnover rates decreased. Increased maintenance costs associated with warming may have resulted in increased mass-specific enzyme activities due to increased nutrient demand. Our research suggests that allocation of resources to enzyme production could be affected by climate-induced changes in microbial efficiency and maintenance costs.

Carbon use efficiency of microbial communities: stoichiometry, methodology and modelling

Carbon use efficiency (CUE) is a fundamental parameter for ecological models based on the physiology of microorganisms. CUE determines energy and material flows to higher trophic levels, conversion of plant-produced carbon into microbial products and rates of ecosystem carbon storage. Thermodynamic calculations support a maximum CUE value of ~ 0.60 (CUE max). Kinetic and stoichiometric constraints on microbial growth suggest that CUE in multi-resource limited natural systems should approach ~ 0.3 (CUE max /2). However, the mean CUE values reported for aquatic and terrestrial ecosystems differ by twofold (~ 0.26 vs. ~ 0.55) because the methods used to estimate CUE in aquatic and terrestrial systems generally differ and soil estimates are less likely to capture the full maintenance costs of community metabolism given the difficulty of measurements in water-limited environments. Moreover, many simulation models lack adequate representation of energy spilling pathways and stoichiometric constraints on metabolism, which can also lead to overestimates of CUE. We recommend that broad-scale models use a CUE value of 0.30, unless there is evidence for lower values as a result of pervasive nutrient limitations. Ecosystem models operating at finer scales should consider resource composition, stoichiometric constraints and biomass composition, as well as environmental drivers, to predict the CUE of microbial communities.

Activation energy of extracellular enzymes in soils from different biomes

Enzyme dynamics are being incorporated into soil carbon cycling models and accurate representation of enzyme kinetics is an important step in predicting belowground nutrient dynamics. A scarce number of studies have measured activation energy (E_a) in soils and fewer studies have measured E_a in arctic and tropical soils, or in subsurface soils. We determined the E_a for four typical lignocellulose degrading enzymes in the A and B horizons of seven soils covering six different soil orders. We also elucidated which soil properties predicted any measurable differences in E_a. β-glucosidase, cellobiohydrolase, phenol oxidase and peroxidase activities were measured at five temperatures, 4, 21, 30, 40, and 60°C. E_a was calculated using the Arrhenius equation. β-glucosidase and cellobiohydrolase E_a values for both A and B horizons in this study were similar to previously reported values, however we could not make a direct comparison for B horizon soils because of the lack of data. There was no consistent relationship between hydrolase enzyme E_a and the environmental variables we measured. Phenol oxidase was the only enzyme that had a consistent positive relationship between E_a and pH in both horizons. The E_a in the arctic and subarctic zones for peroxidase was lower than the hydrolases and phenol oxidase values, indicating peroxidase may be a rate limited enzyme in environments under warming conditions. By including these six soil types we have increased the number of soil oxidative enzyme E_a values reported in the literature by 50%. This study is a step towards better quantifying enzyme kinetics in different climate zones.

Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses

We developed a microbial-enzyme-mediated decomposition (MEND) model, based on the Michaelis-Menten kinetics, that describes the dynamics of physically defined pools of soil organic matter (SOC). These include particulate, mineral-associated, dissolved organic matter (POC, MOC, and DOC, respectively), microbial biomass, and associated exoenzymes. The ranges and/or distributions of parameters were determined by both analytical steady-state and dynamic analyses with SOC data from the literature. We used an improved multi-objective parameter sensitivity analysis (MOPSA) to identify the most important parameters for the full model: maintenance of microbial biomass, turnover and synthesis of enzymes, and carbon use efficiency (CUE). The model predicted that an increase of 2 degrees C (baseline temperature 12 degrees C) caused the pools of POC-cellulose, MOC, and total SOC to increase with dynamic CUE and decrease with constant CUE, as indicated by the 50% confidence intervals. Regardless of dynamic or constant CUE, the changes in pool size of POC, MOC, and total SOC varied from -8% to 8% under +2 degrees C. The scenario analysis using a single parameter set indicates that higher temperature with dynamic CUE might result in greater net increases in both POC-cellulose and MOC pools. Different dynamics of various SOC pools reflected the catalytic functions of specific enzymes targeting specific substrates and the interactions between microbes, enzymes, and SOC. With the feasible parameter values estimated in this study, models incorporating fundamental principles of microbial-enzyme dynamics can lead to simulation results qualitatively different from traditional models with fast/slow/passive pools.