Fluxomic Analysis Reveals Central Carbon Metabolism Adaptation for Diazotroph Azotobacter vinelandii Ammonium Excretion

Regulatory response to a hybrid ancestral nitrogenase in Azotobacter vinelandii

Biological nitrogen fixation, the microbial reduction of atmospheric nitrogen to bioavailable ammonia, represents both a major limitation on biological productivity and a highly desirable engineering target for synthetic biology. However, the engineering of nitrogen fixation requires an integrated understanding of how the gene regulatory dynamics of host diazotrophs respond across sequence-function space of its central catalytic metalloenzyme, nitrogenase. Here, we interrogate this relationship by analyzing the transcriptome of Azotobacter vinelandii engineered with a phylogenetically inferred ancestral nitrogenase protein variant. The engineered strain exhibits reduced cellular nitrogenase activity but recovers wild-type growth rates following an extended lag period. We find that expression of genes within the immediate nitrogen fixation network is resilient to the introduced nitrogenase sequence-level perturbations. Rather the sustained physiological compatibility with the ancestral nitrogenase variant is accompanied by reduced expression of genes that support trace metal and electron resource allocation to nitrogenase. Our results spotlight gene expression changes in cellular processes adjacent to nitrogen fixation as productive engineering considerations to improve compatibility between remodeled nitrogenase proteins and engineered host diazotrophs. IMPORTANCE Azotobacter vinelandii is a key model bacterium for the study of biological nitrogen fixation, an important metabolic process catalyzed by nitrogenase enzymes. Here, we demonstrate that compatibilities between engineered A. vinelandii strains and nitrogenase variants can be modulated at the regulatory level. The engineered strain studied here responds by adjusting the expression of proteins involved in cellular processes adjacent to nitrogen fixation, rather than that of nitrogenase proteins themselves. These insights can inform future strategies to transfer nitrogenase variants to non-native hosts.

Bulk and Spatially Resolved Extracellular Metabolome of Free-Living Nitrogen Fixation

Soil nitrogen (N) transformations constrain terrestrial net primary productivity and are driven by the activity of soil microorganisms. Free-living N fixation (FLNF) is an important soil N transformation and key N input to terrestrial systems, but the forms of N contributed to soil by FLNF are poorly understood. To address this knowledge gap, a focus on microorganisms and microbial scale processes is needed that links N-fixing bacteria and their contributed N sources to FLNF process rates. However, studying the activity of soil microorganisms in situ poses inherent challenges, including differences in sampling scale between microorganism and process rates, which can be addressed with culture-based studies and an emphasis on microbial-scale measurements. Culture conditions can differ significantly from soil conditions, so it also important that such studies include multiple culture conditions like liquid and solid media as proxies for soil environments like soil pore water and soil aggregate surfaces. Here we characterized extracellular N-containing metabolites produced by two common, diazotrophic soil bacteria in liquid and solid media, with or without N, across two sampling scales (bulk via GC-MS and spatially resolved via MALDI mass spec imaging). We found extracellular production of inorganic and organic N during FLNF, indicating terrestrial N contributions from FLNF occur in multiple forms not only as ammonium as previously thought. Extracellular metabolite profiles differed between liquid and solid media supporting previous work indicating environmental structure influences microbial function. Metabolite profiles also differed between sampling scales underscoring the need to quantify microbial scale conditions to accurately interpret microbial function.

IMPORTANCE Free-living nitrogen-fixing bacteria contribute significantly to terrestrial nitrogen availability; however, the forms of nitrogen contributed by this process are poorly understood. This is in part because of inherent challenges to studying soil microorganisms in situ, such as vast differences in scale between microorganism and ecosystem and complexities of the soil system (e.g., opacity, chemical complexity). Thus, upscaling important ecosystem processes driven by soil microorganisms, like free-living nitrogen fixation, requires microbial-scale measurements in controlled systems. Our work generated bulk and spatially resolved measurements of nitrogen released during free-living nitrogen fixation under two contrasting growth conditions analogous to soil pores and aggregates. This work allowed us to determine that diverse forms of nitrogen are likely contributed to terrestrial systems by free-living nitrogen bacteria. We also demonstrated that microbial habitat (e.g., liquid versus solid media) alters microbial activity and that measurement of microbial activity is altered by sampling scale (e.g., bulk versus spatially resolved) highlighting the critical importance of quantifying microbial-scale processes to upscaling of ecosystem function.

Computational Framework for Machine-Learning-Enabled 13C Fluxomics

¹³C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for ¹³C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical ¹³C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.

Metabolic Model of the Nitrogen-Fixing Obligate Aerobe Azotobacter vinelandii Predicts Its Adaptation to Oxygen Concentration and Metal Availability

There is considerable interest in promoting biological nitrogen fixation (BNF) as a mechanism to reduce the inputs of nitrogenous fertilizers in agriculture, but considerable fundamental knowledge gaps still need to be addressed. BNF is catalyzed by nitrogenase, which requires a large input of energy in the form of ATP and low potential electrons. Diazotrophs that respire aerobically have an advantage in meeting the ATP demands of BNF but face challenges in protecting nitrogenase from inactivation by oxygen. Here, we constructed a genome-scale metabolic model of the nitrogen-fixing bacterium Azotobacter vinelandii, which uses a complex respiratory protection mechanism to consume oxygen at a high rate to keep intracellular conditions microaerobic. Our model accurately predicts growth rate under high oxygen and substrate concentrations, consistent with a large electron flux directed to the respiratory protection mechanism. While a partially decoupled electron transport chain compensates for some of the energy imbalance under high-oxygen conditions, it does not account for all substrate intake, leading to increased maintenance rates. Interestingly, the respiratory protection mechanism is required for accurate predictions even when ammonia is supplemented during growth, suggesting that the respiratory protection mechanism might be a core principle of metabolism and not just used for nitrogenase protection. We have also shown that rearrangement of flux through the electron transport system allows A. vinelandii to adapt to different oxygen concentrations, metal availability, and genetic disruption, which cause an ammonia excretion phenotype. Accurately determining the energy balance in an aerobic nitrogen-fixing metabolic model is required for future engineering approaches.

Evaluation of temperature, pH and nutrient conditions in bacterial growth and extracellular hydrolytic activities of two Alicyclobacillus spp. strains

Extremophile bacteria have developed the metabolic machinery for living in extreme temperatures, pH, and high-salt content. Two novel bacterium strains Alicyclobacillus sp. PA1 and Alicyclobacillus sp. PA2, were isolated from crater lake El Chichon in Chiapas, Mexico. Phylogenetic tree analysis based on the 16SrRNA gene sequence revealed that the strain Alicyclobacillus sp. PA1 and Alicyclobacillus sp. PA2 were closely related to Alicyclobacillus species (98% identity and 94.73% identity, respectively). Both strains were Gram variable, and colonies were circular, smooth and creamy. Electron microscopy showed than Alicyclobacillus sp. PA1 has a daisy-like form and Alicyclobacillus sp. PA2 is a regular rod. Both strains can use diverse carbohydrates and triglycerides as carbon source and they also can use organic and inorganic nitrogen source. But, the two strains can grow without any carbon or nitrogen sources in the culture medium. Temperature, pH and nutrition condition affect bacterial growth. Maximum growth was produced at 65 °C for Alicyclobacillus sp. PA1 (0.732 DO₆₀₀) at pH 3 and Alicyclobacillus sp. PA2 (0.725 DO₆₀₀) at pH 5. Inducible extracellular extremozyme activities were determined for β-galactosidase (Alicyclobacillus sp. PA1: 88.07 ± 0.252 U/mg, Alicyclobacillus sp. PA2: 51.57 ± 0.308 U/mg), cellulose (Alicyclobacillus sp. PA1: 141.20 ± 0.585 U/mg, Alicyclobacillus sp. PA2: 51.57 ± 0.308 U/mg), lipase (Alicyclobacillus sp. PA1: 138.25 ± 0.600 U/mg, Alicyclobacillus sp. PA2: 175.75 ± 1.387 U/mg), xylanase (Alicyclobacillus sp. PA1: 174.72 ± 1.746 U/mg, Alicyclobacillus sp. PA2: 172.69 ± 0.855U/mg), and protease (Alicyclobacillus sp. PA1: 15.12 ± 0.121 U/mg, Alicyclobacillus sp. PA2: 15.33 ± 0.284 U/mg). These results provide new insights on extreme enzymatic production on Alicyclobacillus species.

Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph Azotobacter vinelandii DJ

Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH₃). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date.

Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.

•

Genome-scale metabolic model of Azotobacter vinelandii DJ achives over 90% accuracy.

•

iDT1278 is the most comprehensive model to simulate diazotrophy.

•

Determining the most suitable culture conditions to produce polymers A. vinelandii.

•

Constraint-based modeling unravels links among nitrogen fixation and production of organic nitrogen.