Transcriptomic and Metabolomic Responses to Carbon and Nitrogen Sources in Methylomicrobium album BG8

A comparative analysis of soil physicochemical properties and microbial community structure among four shelterbelt species in the northeast China plain

Conducting studies that focus on the alterations occurring in the soil microbiome within protection forests in the northeast plain is of utmost importance in evaluating the ecological rehabilitation of agricultural lands in the Mollisols region. Nevertheless, the presence of geographic factors contributes to substantial disparities in the microbiomes, and thus, addressing this aspect of influence becomes pivotal in ensuring the credibility of the collected data. Consequently, the objective is to compare the variations in soil physicochemical properties and microbial community structure within the understory of diverse shelterbelt species. In this study, we analyzed the understory soils of Juglans mandshurica (Jm), Fraxinus mandschurica (Fm), Acer mono (Am), and Betula platyphylla (Bp) from the same locality. We employed high-throughput sequencing technology and soil physicochemical data to investigate the impact of these different tree species on soil microbial communities, chemical properties, and enzyme activities in Mollisols areas. Significant variations in soil nutrients and enzyme activities were observed among tree species, with soil organic matter content ranging from 49.1 to 67.7 g/kg and cellulase content ranging from 5.3 to 524.0 μg/d/g. The impact of tree species on microbial diversities was found to be more pronounced in the bacterial community (Adnoism: R = 0.605) compared to the fungal community (Adnoism: R = 0.433). The linear discriminant analysis effect size (LEfSe) analysis revealed a total of 5 (Jm), 3 (Bp), and 6 (Am) bacterial biomarkers, as well as 2 (Jm), 6 (Fm), 4 (Bp), and 1 (Am) fungal biomarker at the genus level (LDA3). The presence of various tree species was observed to significantly alter the relative abundance of specific microbial community structures, specifically in Gammaproteobacteria, Ascomycota, and Basidiomycota. Furthermore, environmental factors, such as pH, total potassium, and available phosphorus were important factors influencing changes in bacterial communities. We propose that Fm be utilized as the primary tree species for establishing farmland protection forests in the northeastern region, owing to its superior impact on enhancing soil quality.

IMPORTANCE

The focal point of this study lies in the implementation of a controlled experiment conducted under field conditions. In this experiment, we deliberately selected four shelterbelts within the same field, characterized by identical planting density, and planting year. This deliberate selection effectively mitigated the potential impact of extraneous factors on the three microbiomes, thereby enhancing the reliability and validity of our findings.

Leveraging genome-scale metabolic models to understand aerobic methanotrophs

Genome-scale metabolic models (GEMs) are valuable tools serving systems biology and metabolic engineering. However, GEMs are still an underestimated tool in informing microbial ecology. Since their first application for aerobic gammaproteobacterial methane oxidizers less than a decade ago, GEMs have substantially increased our understanding of the metabolism of methanotrophs, a microbial guild of high relevance for the natural and biotechnological mitigation of methane efflux to the atmosphere. Particularly, GEMs helped to elucidate critical metabolic and regulatory pathways of several methanotrophic strains, predicted microbial responses to environmental perturbations, and were used to model metabolic interactions in cocultures. Here, we conducted a systematic review of GEMs exploring aerobic methanotrophy, summarizing recent advances, pointing out weaknesses, and drawing out probable future uses of GEMs to improve our understanding of the ecology of methane oxidizers. We also focus on their potential to unravel causes and consequences when studying interactions of methane-oxidizing bacteria with other methanotrophs or members of microbial communities in general. This review aims to bridge the gap between applied sciences and microbial ecology research on methane oxidizers as model organisms and to provide an outlook for future studies.

State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs

Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.

Transcriptional and metabolomic responses of Methylococcus capsulatus Bath to nitrogen source and temperature downshift

Methanotrophs play a significant role in methane oxidation, because they are the only biological methane sink present in nature. The methane monooxygenase enzyme oxidizes methane or ammonia into methanol or hydroxylamine, respectively. While much is known about central carbon metabolism in methanotrophs, far less is known about nitrogen metabolism. In this study, we investigated how Methylococcus capsulatus Bath, a methane-oxidizing bacterium, responds to nitrogen source and temperature. Batch culture experiments were conducted using nitrate or ammonium as nitrogen sources at both 37°C and 42°C. While growth rates with nitrate and ammonium were comparable at 42°C, a significant growth advantage was observed with ammonium at 37°C. Utilization of nitrate was higher at 42°C than at 37°C, especially in the first 24 h. Use of ammonium remained constant between 42°C and 37°C; however, nitrite buildup and conversion to ammonia were found to be temperature-dependent processes. We performed RNA-seq to understand the underlying molecular mechanisms, and the results revealed complex transcriptional changes in response to varying conditions. Different gene expression patterns connected to respiration, nitrate and ammonia metabolism, methane oxidation, and amino acid biosynthesis were identified using gene ontology analysis. Notably, key pathways with variable expression profiles included oxidative phosphorylation and methane and methanol oxidation. Additionally, there were transcription levels that varied for genes related to nitrogen metabolism, particularly for ammonia oxidation, nitrate reduction, and transporters. Quantitative PCR was used to validate these transcriptional changes. Analyses of intracellular metabolites revealed changes in fatty acids, amino acids, central carbon intermediates, and nitrogen bases in response to various nitrogen sources and temperatures. Overall, our results offer improved understanding of the intricate interactions between nitrogen availability, temperature, and gene expression in M. capsulatus Bath. This study enhances our understanding of microbial adaptation strategies, offering potential applications in biotechnological and environmental contexts.

Methanol bioconversion in Methylotuvimicrobium buryatense 5GB1C through self-cycling fermentation

Methanol is an abundant and low-cost next-generation carbon source. While many species of methanotrophic bacteria can convert methanol into valuable bioproducts in bioreactors, Methylotuvimicrobium buryatense 5GB1C stands out as one of the most promising strains for industrialization. It has a short doubling time compared to most methanotrophs, remarkable resilience against contamination, and a suite of tools enabling genetic engineering. When approaching industrial applications, growing M. buryatense 5GB1C on methanol using common batch reactor operation has important limitations; for example methanol toxicity leads to mediocre biomass productivity. Advanced bioreactor operation strategies, such as fed-batch and self-cycling fermentation, have the potential to greatly improve the industrial prospects of methanotrophs growing on methanol. Herein, implementation of fed-batch operation led to a 26-fold increase in biomass density, while two different self-cycling fermentation (SCF) strategies led to 3-fold and 10-fold increases in volumetric biomass productivity. Interestingly, while synchronization is a typical trait of microbial populations undergoing SCF, M. buryatense 5GB1C cultures growing under this mode of operation led to stable, reproducible cycles but no significant synchronization.

Current advances for omics-guided process optimization of microbial manufacturing

Currently, microbial manufacturing is widely used in various fields, such as food, medicine and energy, for its advantages of greenness and sustainable development. Process optimization is the committed step enabling the commercialization of microbial manufacturing products. However, the present optimization processes mainly rely on experience or trial-and-error method ignoring the intrinsic connection between cellular physiological requirement and production performance, so in many cases the productivity of microbial manufacturing could not been fully exploited at economically feasible cost. Recently, the rapid development of omics technologies facilitates the comprehensive analysis of microbial metabolism and fermentation performance from multi-levels of molecules, cells and microenvironment. The use of omics technologies makes the process optimization more explicit, boosting microbial manufacturing performance and bringing significant economic benefits and social value. In this paper, the traditional and omics technologies-guided process optimization of microbial manufacturing are systematically reviewed, and the future trend of process optimization is prospected.

Integrative Genome-Scale Metabolic Modeling Reveals Versatile Metabolic Strategies for Methane Utilization in Methylomicrobium album BG8

Methylomicrobium album BG8 is an aerobic methanotrophic bacterium with promising features as a microbial cell factory for the conversion of methane to value-added chemicals. However, the lack of a genome-scale metabolic model (GEM) of M. album BG8 has hindered the development of systems biology and metabolic engineering of this methanotroph. To fill this gap, a high-quality GEM was constructed to facilitate a system-level understanding of the biochemistry of M. album BG8. Flux balance analysis, constrained with time-series data derived from experiments with various levels of methane, oxygen, and biomass, was used to investigate the metabolic states that promote the production of biomass and the excretion of carbon dioxide, formate, and acetate. The experimental and modeling results indicated that M. album BG8 requires a ratio of ∼1.5:1 between the oxygen- and methane-specific uptake rates for optimal growth. Integrative modeling revealed that at ratios of  >2:1 oxygen-to-methane uptake flux, carbon dioxide and formate were the preferred excreted compounds, while at ratios of <1.5:1 acetate accounted for a larger fraction of the total excreted flux. Our results showed a coupling between biomass production and the excretion of carbon dioxide that was linked to the ratio between the oxygen- and methane-specific uptake rates. In contrast, acetate excretion was experimentally detected during exponential growth only when the initial biomass concentration was increased. A relatively lower growth rate was also observed when acetate was produced in the exponential phase, suggesting a trade-off between biomass and acetate production.

IMPORTANCE A genome-scale metabolic model (GEM) is an integrative platform that enables the incorporation of a wide range of experimental data. It is used to reveal system-level metabolism and, thus, clarify the link between the genotype and phenotype. The lack of a GEM for Methylomicrobium album BG8, an aerobic methane-oxidizing bacterium, has hindered its use in environmental and industrial biotechnology applications. The diverse metabolic states indicated by the GEM developed in this study demonstrate the versatility in the methane metabolic processes used by this strain. The integrative GEM presented here will aid the implementation of the design-build-test-learn paradigm in the metabolic engineering of M. album BG8. This advance will facilitate the development of a robust methane bioconversion platform and help to mitigate methane emissions from environmental systems.