Nitrogen Availability Regulates Carbon Allocation and Protein Biosynthesis in Yarrowia lipolytica Grown on Acetate

1. Introduction

The increasing global demand for sustainable protein sources has stimulated growing interest in microbial protein production as an alternative to conventional agriculture[1]. Compared with plant- and animal-derived proteins, microbial protein offers advantages such as rapid growth, high productivity, and reduced dependence on arable land and climate[2]. Among various carbon substrates, acetate has emerged as a promising feedstock due to its availability from CO₂-derived processes and industrial waste streams, enabling environmentally friendly and carbon-efficient biomanufacturing[3,4]. However, compared with sugar-based substrates, acetate metabolism provides limited energy and reducing power, which constrains cell growth and biosynthesis. Under such conditions, cellular metabolism becomes highly sensitive to nutrient availability, particularly nitrogen[5,6]. Therefore, a deeper understanding of carbon-nitrogen metabolic coordination is essential to enable precise regulation of carbon flux distribution between protein and other biomass components under acetate-based cultivation.

The oleaginous yeast Yarrowia lipolytica represents an attractive microbial chassis owing to its Generally Recognized As Safe (GRAS) status, metabolic versatility, and capacity to efficiently utilize acetate[7,8]. To date, most studies on Y. lipolytica have focused on lipid production, since nitrogen limitation typically redirects carbon flux toward storage lipid accumulation[9,10]. In contrast, its potential for microbial protein production remains underexplored. From a metabolic perspective, nitrogen availability not only influences biomass composition but also regulates intracellular carbon allocation and metabolic network activity[11,12]. Microorganisms assimilate ammonium primarily via the glutamate dehydrogenase (GDH) pathway or the glutamine synthetase/glutamate synthase (GS-GOGAT) pathway, which differ in energy requirements and substrate affinity[13,14]. In oleaginous yeasts, nitrogen limitation is known to induce lipid accumulation while repressing protein synthesis, whereas sufficient nitrogen availability promotes protein biosynthesis[15]. However, under acetate-based cultivation—where metabolic resources are inherently constrained—how nitrogen availability modulates carbon partitioning between lipid and protein biosynthesis, and how distinct nitrogen assimilation pathways are coordinately regulated, remain poorly understood. This knowledge gap hinders the rational design of strategies to enhance protein production in Y. lipolytica.

In this study, we systematically investigated the effect of nitrogen availability on protein accumulation in Y. lipolytica cultivated on acetate. By constructing a series of nitrogen concentration gradients, we first evaluated changes in biomass formation, protein content, lipid content, and carbon utilization efficiency. Transcriptomic analysis was subsequently performed to elucidate global metabolic responses and to identify key pathways involved in nitrogen assimilation and amino acid biosynthesis. Particular attention was paid to the differential regulation of the GDH pathway and the GS-GOGAT pathway under nitrogen-limited and nitrogen-replete conditions. Furthermore, metabolic engineering strategies were applied to validate the roles of key enzymes in nitrogen assimilation and to assess their effects on intracellular protein accumulation and amino acid composition. This work provides mechanistic insights into carbon-nitrogen metabolic coordination and offers both theoretical and technical support for the industrial production of single-cell protein using Y. lipolytica.

2. Materials and Methods

Genetic Manipulation for the Construction of Plasmids and Strains

All strains used here are listed in Supplementary Table 1. All the vectors described in this paper were constructed via Gibson assembly, are listed in Supplementary Table 2. All the fragments used for overexpression of genes were amplified by PCR using primers as described in Supplementary Table 3. Nucleotide sequences of functional genes used to construct engineering strains are listed in Supplementary Table 4. Cloning was performed with chemically competent Escherichia coli strain DMT or DH5α.

The strain used in this experiment was derived from the previously engineered strain HR9, which had been modified by knockout of the lipid biosynthesis pathway and enhancement of acetate utilization. Genome integrations were carried out using a CRISPR/Cas9-based strategy that employed a single gRNA plasmid and an integrative donor plasmid. All genes used in this study are endogenous to Yarrowia lipolytica strain W29: GDH (YALI1_F23664g), GS1 (YALI1_F00821g), GS2 (YALI1_D16151g), and GOGAT (YALI1_B26112g).

Media and Growth Conditions

Peptone, tryptone, yeast extract, (NH₄)₂SO₄, MgSO₄·7H₂O, KH₂PO₄, K₂HPO₄, NaCl, MgCl₂, trace metals and vitamins in MM medium, sodium acetate, glucose, LiAc and ssDNA were purchased from Solarbio. Kanamycin and ampicillin were purchased from Sigma-Aldrich, USA. Nourseothricin was purchased from Mei5 Biotechnology. Transformed Y. lipolytica cells were grown on YPD plates with 250 mg L^⁻1 nourseothricin. YPD medium was used for activation, supplemented with 20 g L^⁻1 glucose as the sole carbon source. The MM medium containing acetate (200 mM) as the carbon source and supplemented with different nitrogen concentrations (0.1 g L^⁻1, 1 g L^⁻1, 7.5 g L^⁻1, 15 g L^⁻1) was used for shake flask cultivation.

Shake Flask Culture and Determination of Cell Content

Y. lipolytica cells pre-cultured in activation medium were inoculated into 250 mL shake flasks containing 50 mL MM medium supplemented with 200 mM sodium acetate and cultivated at 30 °C and 220 rpm until the OD₆₀₀ reached 6-8 to prepare the seed culture. The resulting seed culture was then transferred into fresh 250 mL flasks containing the same MM medium with an initial OD₆₀₀ of 0.05. Cells were harvested by centrifugation at 3000×g for 30 min, washed twice with deionized water, and lyophilized to constant weight using a freeze-dryer for further analysis.

Crude lipid content determination: The lyophilized cell biomass was ground in a mortar for 15 min, and the weight of the cell powder was recorded (m₁). The powder was resuspended in 3 mL of 4 mol L^⁻1 hydrochloric acid in a glass tube, followed by gentle shaking for 1.5 h. The mixture was then boiled in a water bath for 8 min and frozen at −20 °C for 30 min. Subsequently, 6 mL of chloroform:methanol (1:1, v/v) was added, and the mixture was centrifuged at 2000×g for 10 min. The supernatant was transferred to a clean test tube, mixed with 3 mL of 0.15% NaCl solution, and centrifuged again. Finally, the supernatant was transferred to a pre-weighed clean test tube and evaporated to dryness using a nitrogen blow-down concentrator. The mass of crude lipid was determined using an analytical balance (m₂). The crude lipid content was calculated as:

Crude lipid content (%) = m₂/m₁ × 100%

Crude protein content determination: The crude protein content was quantified by elemental analysis using a FlashSmart CHNS/O analyzer. Approximately 3.0-4.0 mg of freeze-dried biomass was combusted in pure oxygen at 950 °C. The resulting total nitrogen was detected by a thermal conductivity detector and subsequently converted to crude protein using a nitrogen-to-protein conversion factor of 6.25.

Protein (%) = Nitrogen (%) × 6.25

Amino acid content analysis: Accurately weighed freeze-dried cell powder was resuspended in an extraction buffer, and the cells were disrupted by ultrasonication or repeated freeze-thaw cycles. After centrifugation, the supernatant was collected, and proteins were precipitated by adding trichloroacetic acid or sulfosalicylic acid. Following another centrifugation, the resulting supernatant was filtered through a 0.22 μm membrane filter and analyzed using an automatic amino acid analyzer based on cation-exchange chromatography with post-column ninhydrin derivatization. Amino acids were detected at 570 nm and 440 nm, and quantified by external or internal standard methods.

Analytical Methods

Cell density, as the optical density at 600 nm, was determined using a spectrophotometer. Dry cell weight (DCW) was determined by taking 1 mL of the mixed bacterial culture, centrifuging to collect the cells, freeze drying to constant weight, and weighing the dried material on a precision balance to obtain the DCW (g L^⁻1).

The specific growth rate ($\mathsf{\mu }$) was calculated during the exponential phase using the following equation:

$$\mathsf{\mu }={\displaystyle \frac{\ln ({\mathrm{X}}_2)-\ln \left({\mathrm{X}}_1\right)}{{\mathrm{t}}_2-{\mathrm{t}}_1}}$$

where ${\mathrm{X}}_1$ and ${\mathrm{X}}_2$ are the biomass (OD₆₀₀ value in this work) at the time points ${\mathrm{t}}_1$ and ${\mathrm{t}}_2$, respectively.

Transcriptome Sequencing and Analysis

The transcriptome samples were prepared by culturing the strain in MM medium containing different concentrations of (NH₄)₂SO₄ (0.1 g L^⁻1, 1.0 g L^⁻1, 7.5 g L^⁻1, 15 g L^⁻1) for 48 hours. Three biological replicates were performed for each group. Cell pellets from 50 mL of culture were collected by centrifugation at 2000×g and washed twice with PBS.

For transcriptomic analysis, RNA extraction, library construction, and sequencing were conducted by GENEWIZ. Clean reads were aligned to the reference genome (ASM176148v1) using HISAT2 software, and gene expression levels were quantified using the featureCounts tool, resulting in FPKM values for each sample. Principal component analysis (PCA) of gene expression (FPKM) was performed to assess inter-group differences and reproducibility. Differentially expressed genes between groups ( 7.5 g L^⁻1 (NH₄)₂SO₄ vs. 0.1 g L^⁻1 (NH₄)₂SO₄) were identified using DESeq2 with thresholds of |log₂(FoldChange)| ≥1 and padj ≤0.05. Visualization of differentially expressed genes was achieved using volcano plots, and hierarchical clustering of FPKM values was performed with R software. Functional enrichment analysis, including Gene Ontology (GO) and KEGG pathway enrichment, was conducted using the clusterProfiler package.

3. Results

Effects of Nitrogen Availability on Growth and Protein Accumulation

To investigate the effects of nitrogen availability on cell growth and protein synthesis, four culture conditions with distinct C/N ratios (high, medium, and nitrogen-limited levels) were established based on a modified minimal medium. Due to significant differences in growth kinetics among the tested conditions, the exponential growth phase for each group was individually determined according to the linear relationship between ln(OD₆₀₀) and cultivation time. Under nitrogen-limited conditions, a pronounced delay in the onset of exponential growth was observed, with the exponential phase occurring approximately between 20 and 50 h (Figure 1a). The specific growth rate was calculated to be 0.06 h^⁻1, which was markedly lower than that observed under the other three nitrogen conditions. In contrast, cells cultivated under high and medium nitrogen concentrations exhibited comparable growth kinetics, with similar specific growth rates of approximately 0.17 h^⁻1 (Figure 1a and Supplementary Table 5). Consistent with the reduced growth rate, biomass accumulation under nitrogen-limited conditions was significantly decreased, reaching only about one-third of that obtained under nitrogen-replete conditions (Figure 1b). In parallel, both the protein content and protein fraction were substantially reduced, with protein accounting for only 19.56% of total biomass under nitrogen limitation (Figure 1c). These results indicate that nitrogen deficiency severely restricts not only cell growth but also protein biosynthesis. Further analysis of acetate consumption revealed that carbon utilization was markedly suppressed under nitrogen-limited conditions (Figure 1d). The reduced acetate uptake suggests that carbon metabolism was strongly constrained, which likely represents the primary factor limiting biomass formation and protein synthesis under nitrogen deficiency. In contrast, no significant differences in growth trends or protein accumulation were observed between high and medium nitrogen conditions (Figure 1c and Figure 1d). Notably, increasing nitrogen concentration beyond a certain threshold did not further enhance protein synthesis, implying that intracellular nitrogen metabolism is subject to more complex regulatory mechanisms rather than simple substrate-driven effects. The decrease in protein fraction was accompanied by a reduction in absolute protein content, indicating that the observed trend was not merely due to changes in biomass composition but reflected a genuine limitation in protein biosynthesis.

Nitrogen Availability Regulates Carbon Partitioning Between Lipid and Protein Biosynthesis

To further elucidate how nitrogen availability influences intracellular carbon allocation, lipid accumulation was quantified under different C/N conditions in terms of lipid content and lipid fraction. As shown in Figure 2a, lipid content exhibited a distinct inverse trend relative to protein accumulation. Under nitrogen-limited conditions, lipid content reached 34.16% of DCW, which was significantly higher than that observed under nitrogen-replete conditions (11.73-17.50%). However, this increase in lipid proportion did not translate into a corresponding increase in lipid titer. Due to the significantly reduced biomass accumulation under nitrogen limitation (Figure 1), the overall lipid content remained relatively low (0.55 g L^⁻1), indicating that enhanced lipid accumulation at the cellular level was offset by limited cell growth. In contrast, the highest lipid titer was observed at a moderately low nitrogen concentration (Figure 2a), where a balanced metabolic state was achieved. Under this condition, cells maintained relatively high biomass while still allocating a substantial fraction of carbon toward lipid synthesis, resulting in maximal lipid production (0.82 g L^⁻1). Further increases in nitrogen availability led to no significant differences in either lipid content or lipid fraction (0.52 g L^⁻1-0.55 g L^⁻1 and 11.73%-11.82%), suggesting that lipid biosynthesis was suppressed under nitrogen-replete conditions and that excess nitrogen did not further influence carbon allocation toward storage compounds. To better visualize the relationship between these two major biomass components, the relative proportions of protein and lipid were plotted across different nitrogen conditions (Figure 2b). A clear negative correlation between protein and lipid fractions was observed, suggesting a competitive allocation of carbon resources. As nitrogen availability increased from limiting to sufficient levels, carbon flux was progressively redirected from lipid accumulation toward protein synthesis, resulting in an increase in protein yield (0.068 g g^⁻1 to 0.18 g g^⁻1) and a concomitant decrease in lipid yield (0.12 g g^⁻1 to 0.048 g g^⁻1). To further assess carbon utilization efficiency, the distribution of assimilated carbon into major biomass components was analyzed (Figure 2c). Under nitrogen-limited conditions, a larger fraction of carbon was diverted toward lipid biosynthesis, while a smaller proportion was incorporated into protein. In contrast, nitrogen-replete conditions favored the incorporation of carbon into proteinaceous biomass. These results indicate that nitrogen availability plays a central role in modulating intracellular carbon partitioning.

Importantly, the observed increase in protein proportion under higher nitrogen conditions was accompanied by a corresponding increase in absolute protein content (0.31 g L^⁻1 to 2.0 g L^⁻1), rather than being solely attributable to reduced lipid accumulation (Figure 1c). This finding confirms that nitrogen availability actively promotes protein biosynthesis rather than passively altering biomass composition. Collectively, these results demonstrate that nitrogen availability governs carbon flux distribution between storage metabolism (lipid) and biosynthetic metabolism (protein), thereby determining the overall biomass composition in acetate-grown cells.

Transcriptomic Analysis Reveals Nitrogen-Dependent Regulation of Carbo–Nitrogen Metabolism

To elucidate the molecular mechanisms underlying nitrogen-regulated protein accumulation and carbon partitioning, transcriptomic analysis was performed to compare gene expression profiles under nitrogen-limited (0.1 g L^⁻1) and nitrogen-replete(7.5 g L^⁻1) conditions. A total of 2702 differentially expressed genes (DEGs) were identified, including 1399 upregulated and 1303 downregulated genes in the nitrogen-limited group versus the nitrogen-replete group (Figure 3a), indicating extensive transcriptional reprogramming in response to nitrogen availability. To further resolve functional metabolic shifts, pathways related to amino acid biosynthesis and fatty acid metabolism were manually curated from KEGG annotations and comparatively analyzed (Figure 3b and Supplementary Figure 1). The number of upregulated and downregulated genes within each pathway was quantified to evaluate pathway-level responses. Notably, under nitrogen-limited conditions, the majority of pathways involved in both amino acid biosynthesis and lipid metabolism exhibited a predominance of downregulated genes, with the number of downregulated genes exceeding that of upregulated genes across most pathways. This global downregulation pattern suggests that nitrogen limitation imposes a broad suppression on cellular anabolic metabolism. Specifically, multiple amino acid biosynthesis pathways, including (β-alanine, alanine, aspartate, glutamate, tyrosine, valine, leucine, isoleucine and tryptophan), showed an overall decrease in transcriptional activity, with 5-14 downregulated genes compared to 4-8 upregulated genes per pathway. Similarly, pathways associated with fatty acid biosynthesis and lipid metabolism, such as (fatty acid, unsaturated fatty acids, fatty acid degradation), also displayed a general trend toward downregulation. These results indicate that nitrogen limitation leads to a systemic reduction in biosynthetic capacity, affecting both protein and lipid synthesis at the transcriptional level.

To further identify key responsive genes under nitrogen limitation beyond pathway-level analysis, the top 10 most strongly upregulated genes were extracted based on log2foldchange and manually annotated using NCBI and UniProt databases (Figure 3c). Notably, these genes were not enriched in KEGG or GO functional categories but were predominantly predicted to encode membrane-associated transport proteins or transport-related regulatory factors. Functional annotation revealed that the majority of these genes are involved in ammonium transport, amino acid permeases, and transport regulation, indicating a pronounced enhancement of transmembrane nitrogen uptake and intracellular redistribution capacity under nitrogen-limited conditions. The consistent upregulation of these transport-related genes suggests that cells respond to nitrogen scarcity not only by adjusting intracellular metabolic pathways but also by actively strengthening nutrient acquisition systems at the membrane level. This observation highlights a multi-layered adaptive response to nitrogen limitation, in which transport processes play a critical role in maintaining intracellular nitrogen availability when external supply is restricted. Such reinforcement of nitrogen uptake capacity may represent a primary compensatory mechanism that precedes or complements downstream metabolic reprogramming.

In addition to enhanced transport capacity, differential regulation was also observed in key intracellular nitrogen assimilation pathways. To further elucidate how assimilated nitrogen is incorporated into central metabolism, the expression patterns of core ammonium assimilation enzymes were examined. The expression of GDH was significantly downregulated under nitrogen limitation, whereas genes encoding GS and GOGAT were markedly upregulated (Figure 3d). This contrasting expression pattern suggests a shift in nitrogen assimilation strategy from the GDH-dependent pathway to the high-affinity GS-GOGAT pathway under nitrogen-limited conditions. Given that both pathways converge on glutamate production, a central nitrogen donor for amino acid biosynthesis, this transcriptional reprogramming likely reflects an adaptive mechanism to optimize nitrogen utilization under nutrient-limited conditions. When integrated with the carbon partitioning results (Figure 2), these findings suggest that nitrogen availability regulates biomass composition through coordinated control of nitrogen assimilation and carbon flux distribution. Under nitrogen limitation, although high-affinity nitrogen assimilation pathways are activated, insufficient nitrogen supply and restricted carbon metabolism ultimately limit protein biosynthesis and favor lipid accumulation. In contrast, under nitrogen-replete conditions, efficient nitrogen assimilation supports amino acid and protein synthesis, enabling carbon flux to be preferentially directed toward protein production.

Functional Validation of Nitrogen Assimilation Pathways Through Targeted Gene Overexpression

To further validate the role of nitrogen assimilation in regulating protein biosynthesis, a stepwise metabolic engineering strategy was implemented based on an acetate-utilization-enhanced strain (HR9). Key genes involved in nitrogen assimilation, including GDH, GS, and GOGAT, were sequentially overexpressed to evaluate their cumulative effects on protein accumulation and cellular metabolism (Figure 4a). Introduction of GDH into the strain HR9 resulted in a noticeable increase in protein content, reaching 52.44% compared to 48.52% in the control strain. Subsequent co-expression of GS further enhanced protein accumulation to 55.77%, indicating a synergistic effect between GDH and GS in promoting nitrogen incorporation into biomass. This stepwise improvement suggests that reinforcing both direct ammonium assimilation (via GDH) and high-affinity nitrogen assimilation (via GS) effectively increases intracellular nitrogen flux toward amino acid and protein biosynthesis. However, upon further introduction of GOGAT into the GDH-GS co-expression strain, the protein content decreased to 50.35%, accompanied by reduced biomass. This result indicates that, unlike GDH and GS, overexpression of GOGAT does not contribute to enhanced protein accumulation and may instead impose a detrimental effect on cellular metabolism. To further characterize the protein quality of the engineered strain, the GDH-GS co-expression strain was selected for amino acid composition analysis (Figure 4b). Compared with the control strain, the relative abundance of nearly all amino acids was significantly increased, with the exception of cysteine. Notably, glutamate exhibited the most pronounced increase, consistent with its central role as a key nitrogen donor in amino acid biosynthesis. In addition, all eight essential amino acids showed varying degrees of enhancement, including histidine, isoleucine, leucine, lysine, methionine, phenylalanine, tyrosine and valine, highlighting the improved nutritional quality of the microbial protein produced by the engineered strain (Figure 4c). The observed enhancement in amino acid content can be attributed to improved nitrogen assimilation and redistribution toward biosynthetic pathways. GDH directly converts ammonium into glutamate using reducing equivalents, while GS enhances ammonium assimilation efficiency through ATP-dependent glutamine formation. The combined action of these two pathways likely increases the intracellular pools of glutamate and glutamine, thereby supporting the synthesis of a broad range of amino acids. In contrast, the negative effect observed upon GOGAT overexpression can be explained by metabolic and energetic imbalance. The GS-GOGAT cycle is highly demanding in terms of both ATP (GS) and reducing equivalents (GOGAT). Excessive GOGAT activity may lead to overconsumption of NAD(P)H and accelerated depletion of α-ketoglutarate, a key intermediate linking carbon and nitrogen metabolism. This can constrain flux through the tricarboxylic acid (TCA) cycle, thereby limiting energy generation and precursor supply required for biomass and protein synthesis. Furthermore, disproportionate expression of GOGAT relative to GS may disrupt the balance of the nitrogen assimilation cycle, resulting in inefficient flux distribution and reduced overall metabolic efficiency.

Taken together, these results demonstrate that coordinated enhancement of GDH and GS effectively promotes nitrogen assimilation and protein biosynthesis, while further overexpression of the GS–GOGAT pathway without balanced regulation leads to metabolic burden and diminished performance. Importantly, the improved amino acid profile, particularly the increased levels of essential amino acids, highlights the potential of the engineered strain as a high-quality microbial protein source for feed applications.

Based on the combined results of carbon partitioning analysis, transcriptomic profiling, and metabolic engineering validation, an integrated model was proposed to illustrate how nitrogen assimilation regulates carbon allocation and protein biosynthesis in the acetate-utilizing system (Figure 4d). As shown in Figure. 4d, acetate is assimilated into central metabolism via acetyl-CoA and subsequently enters the tricarboxylic acid (TCA) cycle, where key intermediates such as α-ketoglutarate serve as critical nodes linking carbon and nitrogen metabolism. At this branch point, carbon flux can be directed either toward lipid accumulation or toward amino acid and protein biosynthesis, depending on nitrogen availability and assimilation capacity.

4. Discussion

In this study, we systematically investigated the regulatory role of nitrogen assimilation in controlling carbon partitioning and protein biosynthesis in an acetate-utilizing yeast. By integrating transcriptomic analysis with stepwise metabolic engineering, we revealed that nitrogen availability reshapes intracellular carbon allocation through coordinated regulation of GDH and the GS-GOGAT pathway. Importantly, we demonstrated that simultaneous enhancement of GDH and GS effectively promotes protein accumulation and improves amino acid composition, whereas further amplification of GOGAT disrupts metabolic balance and impairs protein synthesis. These findings not only clarify the functional roles of distinct nitrogen assimilation routes but also establish an efficient engineering strategy to enhance microbial protein production.

Beyond mechanistic insights, this work has important implications for sustainable protein manufacturing based on renewable resources. Acetate represents a promising intermediate carbon source, as it can be efficiently produced from CO₂ via electrochemical reduction powered by renewable electricity[16,17]. Compared with gaseous substrates, acetate is readily soluble, easy to store and transport, and compatible with existing fermentation infrastructure, making it particularly suitable for scalable bioprocesses[18,19]. The integration of photovoltaic electricity, electrochemical CO2 conversion, and microbial fermentation offers a highly efficient route for protein production[20,21,22]. Compared with traditional agriculture, this approach has the potential to achieve substantially higher solar-to-biomass energy conversion efficiency and significantly reduced land use, thereby alleviating pressure on arable land and enabling decentralized production[23,24]. In this context, oleaginous yeasts such as Y. lipolytica provide an attractive host due to their robustness, metabolic versatility, and inherent capacity to efficiently utilize acetate and accumulate biomass[1].

Overall, this study provides both mechanistic understanding and practical strategies for improving nitrogen utilization and protein biosynthesis in acetate-based systems. By advancing the efficient conversion of CO₂-derived substrates into high-quality microbial protein, our work contributes to the development of a sustainable and scalable platform for future food and feed production.

Authors should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.