Dynamic resource allocation drives growth under nitrogen starvation in eukaryotes

Coarse-Grained Modeling Elucidates Differential Metabolism of Saccharomyces cerevisiae under Varied Nutrient Limitations

Microorganisms such as Saccharomyces cerevisiae have a native ability to adapt their metabolism to varying nutrient conditions. Understanding their responses to nutrient limitations is critical for decoding cellular physiology and designing strategies for metabolic engineering. While the influence of carbon availability on yeast metabolism has been extensively studied, the role of nitrogen availability remains relatively underexplored. In this study, we utilized a coarse-grained kinetic model to systematically analyze and compare the effects of carbon and nitrogen limitations on yeast metabolism. Our model successfully revealed the differential metabolic characteristics of S. cerevisiae under carbon- and nitrogen-limited chemostat conditions. It also highlighted the significance of protein activity regulation at varying carbon-to-nitrogen ratios, and elucidated distinct strategies employed to maintain ATP homeostasis. This study provides a computational tool for investigating yeast physiology under nutrient limitations and offers quantitative and mechanistic insights into yeast metabolism.

Adaptation to Long-Term Nitrogen Starvation in a Biocrust-Derived Microalga Vischeria sp. WL1: Insights into Cell Wall Features and Desiccation Resistance

In drylands, microalgae dwelling in the biocrust are inevitably confronted with nitrogen deficiency and desiccation stress, despite the protection afforded by the soil biological complex. However, the environmental adaptive features and mechanisms of these microalgae remain largely unknown. In this study, we explored the adaptive changes of a biocrust-derived unicellular microalga, Vischeria sp. WL1 (Eustigmatophyceae), in the face of long-term nitrogen deficiency. Attention was focused on the alterations in cell wall properties and the associated desiccation resistance. After exposure to long-term nitrogen deficiency, the cell walls of Vischeria sp. WL1 thickened substantially, accompanied by enhanced rigidity and an improvement in desiccation resistance. In contrast, Vischeria sp. WL1 cells cultivated under nitrogen-replete conditions were highly vulnerable to desiccation stress. Additional cell wall alterations after nitrogen starvation included distinct surface sculpturing, variations in monosaccharide composition, and changes in functional groups. Collectively, this study provides valuable insights into the survival strategies of biocrust-derived microalgae in nitrogen-deficient dryland environments.

Deciphering metabolic differentiation during Bacillus subtilis sporulation

The bacterium Bacillus subtilis undergoes asymmetric cell division during sporulation, producing a mother cell and a smaller forespore connected by the SpoIIQ-SpoIIIA (or Q-A) channel. The two cells differentiate metabolically, and the forespore becomes dependent on the mother cell for essential building blocks. Here, we investigate the metabolic interactions between mother cell and forespore using genome-scale metabolic and expression models as well as experiments. Our results indicate that nucleotides are synthesized in the mother cell and transported in the form of nucleoside di- or tri-phosphates to the forespore via the Q-A channel. However, if the Q-A channel is inactivated later in sporulation, then glycolytic enzymes can form an ATP and NADH shuttle, providing the forespore with energy and reducing power. Our integrated in silico and in vivo approach sheds light into the intricate metabolic interactions underlying cell differentiation in B. subtilis, and provides a foundation for future studies of metabolic differentiation.

Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae

Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. This study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.

Giving the cells what they need when they need it: Biosensor-based feeding control

"Giving the cells exactly what they need, when they need it" is the core idea behind the proposed bioprocess control strategy: operating bioprocess based on the physiological behavior of the microbial population rather than exclusive monitoring of environmental parameters. We are envisioning to achieve this through the use of genetically encoded biosensors combined with online flow cytometry (FCM) to obtain a time-dependent "physiological fingerprint" of the population. We developed a biosensor based on the glnA promoter (glnAp) and applied it for monitoring the nitrogen-related nutritional state of Escherichia coli. The functionality of the biosensor was demonstrated through multiple cultivation runs performed at various scales-from microplate to 20 L bioreactor. We also developed a fully automated bioreactor-FCM interface for on-line monitoring of the microbial population. Finally, we validated the proposed strategy by performing a fed-batch experiment where the biosensor signal is used as the actuator for a nitrogen feeding feedback control. This new generation of process control, -based on the specific needs of the cells, -opens the possibility of improving process development on a short timescale and therewith, the robustness and performance of fermentation processes.

The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions

The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.

Predicting stress response and improved protein overproduction in Bacillus subtilis

Bacillus subtilis is a well-characterized microorganism and a model for the study of Gram-positive bacteria. The bacterium can produce proteins at high densities and yields, which has made it valuable for industrial bioproduction. Like other cell factories, metabolic modeling of B. subtilis has discovered ways to optimize its metabolism toward various applications. The first genome-scale metabolic model (M-model) of B. subtilis was published more than a decade ago and has been applied extensively to understand metabolism, to predict growth phenotypes, and served as a template to reconstruct models for other Gram-positive bacteria. However, M-models are ill-suited to simulate the production and secretion of proteins as well as their proteomic response to stress. Thus, a new generation of metabolic models, known as metabolism and gene expression models (ME-models), has been initiated. Here, we describe the reconstruction and validation of a ME model of B. subtilis, iJT964-ME. This model achieved higher performance scores on the prediction of gene essentiality as compared to the M-model. We successfully validated the model by integrating physiological and omics data associated with gene expression responses to ethanol and salt stress. The model further identified the mechanism by which tryptophan synthesis is upregulated under ethanol stress. Further, we employed iJT964-ME to predict amylase production rates under two different growth conditions. We analyzed these flux distributions and identified key metabolic pathways that permitted the increase in amylase production. Models like iJT964-ME enable the study of proteomic response to stress and the illustrate the potential for optimizing protein production in bacteria.

Absence of canonical trophic levels in a microbial mat

In modern ecosystems, the carbon stable isotope (δ¹³ C) ratios of consumers generally conform to the principle "you are what you eat, +1‰." However, this metric may not apply to microbial mat systems where diverse communities, using a variety of carbon substrates via multiple assimilation pathways, live in close physical association and phagocytosis is minimal or absent. To interpret the δ¹³ C record of the Proterozoic and early Paleozoic, when mat-based productivity likely was widespread, it is necessary to understand how a microbially driven producer-consumer structure affects the δ¹³ C compositions of biomass and preservable lipids. Protein Stable Isotope Fingerprinting (P-SIF) is a recently developed method that allows measurement of the δ¹³ C values of whole proteins, separated from environmental samples and identified taxonomically via proteomics. Here, we use P-SIF to determine the trophic relationships in a microbial mat sample from Chocolate Pots Hot Springs, Yellowstone National Park (YNP), USA. In this mat, proteins from heterotrophic bacteria are indistinguishable from cyanobacterial proteins, indicating that "you are what you eat, +1‰" is not applicable. To explain this finding, we hypothesize that sugar production and consumption dominate the net ecosystem metabolism, yielding a community in which producers and consumers share primary photosynthate as a common resource. This idea was validated by confirming that glucose moieties in exopolysaccharide were equal in δ¹³ C composition to both cyanobacterial and heterotrophic proteins, and by confirming that highly ¹³ C-depleted fatty acids (FAs) of Cyanobacteria dominate the lipid pool, consistent with flux-balance expectations for systems that overproduce primary photosynthate. Overall, the results confirm that the δ¹³ C composition of microbial biomass and lipids is tied to specific metabolites, rather than to autotrophy versus heterotrophy or to individual trophic levels. Therefore, we suggest that aerobic microbial heterotrophy is simply a case of "you are what you eat."

Regulation strategy for nutrient-dependent carbon and nitrogen stoichiometric homeostasis in freshwater phytoplankton

Phytoplankton carbon (C) and nitrogen (N) stoichiometric homeostasis plays an important role in aquatic ecosystems. Their C:N ratio is a result of cellular metabolic balance, and the relevant regulatory strategy for its plasticity is still unclear. Therefore, a field survey of seven reservoirs in Tianjin, North China, was conducted to understand variations in phytoplankton C:N ratios, and a laboratory culture of Chlamydomonas reinhardtii was performed to understand the relevant regulation strategy for cellular C-N stoichiometric homeostasis under different C and N availability by using transcriptome sequencing and Nano SIMS and C stable isotope analyses. The results indicated that CO₂ limitation had no significant effect on the phytoplankton C:N ratio in either scene, whereas limitation of dissolved inorganic N induced a 35% higher ratio in the field and a 138% higher ratio in the laboratory. Under CO₂ limitation, algal CO₂-concentrating mechanisms were operated to ensure a C supply, and coupled C-N molecular regulation remained the cellular C:N ratio stable. Under nitrate limitation, differentially expressed gene-regulated intensities increase enormously, and their increasing proportion was comparable to that of the algal C:N ratio; cellular metabolism was reorganized to form a "subhealthy" C-N stoichiometric state with high C:N ratios. In addition, the N transport system had a specific role under CO₂ and nitrate limitations. Our study implies that algal stoichiometric homeostasis depends on the involved limitation element and will help to deepen the understanding of C-N stoichiometric homeostasis in freshwater phytoplankton.

Impact of Nitrate and Ammonium Concentrations on Co-Culturing of Tetradesmus obliquus IS2 with Variovorax paradoxus IS1 as Revealed by Phenotypic Responses

Mutual interactions in co-cultures of microalgae and bacteria are well known for establishing consortia and nutrient uptake in aquatic habitats, but the phenotypic changes in terms of morphological, physiological, and biochemical attributes that drive these interactions have not been clearly understood. In this novel study, we demonstrated the phenotypic response in a co-culture involving a microalga, Tetradesmus obliquus IS2, and a bacterium, Variovorax paradoxus IS1, grown with varying concentrations of two inorganic nitrogen sources. Modified Bold's basal medium was supplemented with five ratios (%) of NO₃-N:NH₄-N (100:0, 75:25, 50:50, 25:75, and 0:100), and by maintaining N:P Redfield ratio of 16:1. The observed morphological changes in microalga included an increase in granularity and a broad range of cell sizes under the influence of increased ammonium levels. Co-culturing in presence of NO₃-N alone or combination with NH₄-N up to equimolar concentrations resulted in complete nitrogen uptake, increased growth in both the microbial strains, and enhanced accumulation of carbohydrates, proteins, and lipids. Total chlorophyll content in microalga was also significantly higher when it was grown as a co-culture with NO₃-N and NH₄-N up to a ratio of 50:50. Significant upregulation in the synthesis of amino acids and sugars and downregulation of organic acids were evident with higher ammonium uptake in the co-culture, indicating the regulation of carbon and nitrogen assimilation pathways and energy synthesis. Our data suggest that the co-culture of strains IS1 and IS2 could be exploited for effluent treatment by considering the concentrations of inorganic sources, particularly ammonium, in the wastewaters.

Genetic regulation and fermentation strategy for squalene production in Schizochytrium sp

Squalene, as an important terpenoid, is extensively used in the medicine and health care fields owing to its functions of anti-oxidation, blood lipid regulation and cancer prevention. The marine microalgae, Schizochytrium sp., which acts as an excellent strain with potential of high squalene production was selected as the starting strain. The overexpressed strain with sqs gene got the reduced biomass and lipid, while the squalene titer was increased by 79.6% ± 4.7% to 12.8 ± 0.2 mg/L. In order to further increase squalene production, the recombinant strain (HS strain) with sqs and hmgr gene co-overexpression was further constructed. The biomass and squalene titer of the HS strain were increased by 13.6% ± 1.2% and 88.8% ± 5.3%, respectively, which indicated the carbon flux of the mevalonate pathway was enhanced for squalene accumulation. Regarding the squalene synthesis is completely coupled with cell growth, fermentation strategy to prolong the logarithmic growth phase was conducive to improve squalene production. Under the condition of optimal composition and concentrated medium, the squalene titer of HS strain was 27.0 ± 1.3 mg/L, which was 2.0 times that of the basal medium condition (13.5 ± 0.4 mg/L). This study which combined the metabolic engineering and fermentation strategy provides a new strategy for squalene production in Schizochytrium sp. KEY POINTS: •The overexpression of sqs and hmgr genes promoted carbon metabolism for squalene. •The optimal and concentrated media can increase squalene yield.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Unraveling metabolic alterations in Chlorella vulgaris cultivated on renewable sugars using time resolved multi-omics

The inherent metabolic versatility of Chlorella vulgaris that enables it to metabolize both inorganic and organic carbon under various trophic modes of cultivation makes it a promising candidate for industrial applications. To shed light on the metabolic flexibility of this microalga, time resolved proteomic and metabolomic studies were conducted in three distinct trophic modes (autotrophic, heterotrophic, mixotrophic) at two growth stages (end of linear growth at 6 days and during nutrient deprivation at 10 days). Sweet sorghum bagasse (SSB) hydrolysate was supplied to the cultivation medium as a renewable source of organic carbon mainly in the form of glucose. Integrated multi-omics data showed improved nitrogen assimilation, re-allocation, and recycling and increased levels of photosystem II (PS II) proteins indicating effective cellular quenching of excess electrons during mixotrophy. As external addition of organic carbon (glucose) to the cultivation medium decreases the cell's dependence on photosynthesis, an upregulation in the mitochondrial electron transport chain was recorded that led to increased cellular energy generation and hence higher growth rates under mixotrophy. Moreover, upregulation of the lipid-packaging proteins caleosin and 14_3_3 domain-containing protein resulted in maximum expression during mixotrophy suggesting a strong correlation between lipid synthesis, stabilization, and assembly. Overall, cells cultivated under mixotrophy showed better nutrient stress tolerance and redox balancing leading to higher biomass and lipid production. The study offers a panoramic view of the microalga's metabolic flexibility and contributes to a deeper understanding of the altered biochemical pathways that can be exploited to enhance algal productivity and commercial potential.

Kinetic, metabolic, and statistical analytics: addressing metabolic transport limitations among organelles and microbial communities

Microbial organisms engage in a variety of metabolic interactions. A crucial part of these interactions is the exchange of molecules between different organelles, cells, and the environment. The main forces mediating this metabolic exchange are transporters. This transport can be difficult to measure experimentally because several transport mechanisms remain opaque. However, theoretical calculations about the inputs and outputs of cells via metabolic exchanges have enabled the successful inference of the workings of intra-organismal and inter-organismal systems. Kinetic, metabolic, and statistical modeling approaches in combination with omics data are enhancing our knowledge and understanding about metabolic exchange and mass resource allocation. This model-driven analytics approach can guide effective experimental design and yield new insights into biological function and control.

The sum is greater than the parts: exploiting microbial communities to achieve complex functions

Multi-species microbial communities are ubiquitous in nature. The widespread prevalence of these communities is due to highly elaborated interactions among their members thereby accomplishing metabolic functions that are unattainable by individual members alone. Harnessing these communal capabilities is an emerging field in biotechnology. The rational intervention of microbial communities for the purpose of improved function has been facilitated in part by developments in multi-omics approaches, synthetic biology, and computational methods. Recent studies have demonstrated the benefits of rational interventions to human and animal health as well as agricultural productivity. Emergent technologies, such as in situ modification of complex microbial community and community metabolic modeling, represent an avenue to engineer sustainable microbial communities. In this opinion, we review relevant computational and experimental approaches to study and engineer microbial communities and discuss their potential for biotechnological applications.

Linking metabolic phenotypes to pathogenic traits among "Candidatus Liberibacter asiaticus" and its hosts

Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing, a lethal vector-borne disease affecting citrus crops worldwide. While comparative genomics has provided preliminary insights into the metabolic capabilities of this uncultured microorganism, a comprehensive functional characterization is currently lacking. Here, we reconstructed and manually curated genome-scale metabolic models for the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy, in addition to a model of the closest related culturable microorganism, L. crescens BT-1. Predictions about nutrient requirements and changes in growth phenotypes of CLas were confirmed using in vitro hairy root-based assays, while the L. crescens BT-1 model was validated using cultivation assays. Host-dependent metabolic phenotypes were revealed using expression data obtained from CLas-infected citrus trees and from the CLas-harboring psyllid Diaphorina citri Kuwayama. These results identified conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts, laying the foundation for the development of model-driven Huanglongbing management strategies.

Synthetic microbial communities of heterotrophs and phototrophs facilitate sustainable growth

Microbial communities comprised of phototrophs and heterotrophs hold great promise for sustainable biotechnology. Successful application of these communities relies on the selection of appropriate partners. Here we construct four community metabolic models to guide strain selection, pairing phototrophic, sucrose-secreting Synechococcus elongatus with heterotrophic Escherichia coli K-12, Escherichia coli W, Yarrowia lipolytica, or Bacillus subtilis. Model simulations reveae metabolic exchanges that sustain the heterotrophs in minimal media devoid of any organic carbon source, pointing to S. elongatus-E. coli K-12 as the most active community. Experimental validation of flux predictions for this pair confirms metabolic interactions and potential production capabilities. Synthetic communities bypass member-specific metabolic bottlenecks (e.g. histidine- and transport-related reactions) and compensate for lethal genetic traits, achieving up to 27% recovery from lethal knockouts. The study provides a robust modelling framework for the rational design of synthetic communities with optimized growth sustainability using phototrophic partners.