Nitrogen Transformation Survival Strategies of Ammonia-Oxidizing Bacterium N.eA1 Under High Nitrite Stress

Abstract

Ammonia-oxidizing bacteria (AOB) are key to the nitrogen cycle, but their resistance to nitrite (NO₂⁻-N) accumulation is unclear. This study examined N.eA1, an AOB from the completely autotrophic nitrogen removal over nitrite (CANON) process, assessing its adaptive responses to NO₂⁻-N. The ammonia oxidation and N₂O emission were evaluated at varying NO₂⁻-N levels, and 3D fluorescence, extracellular polymeric substances (EPS), and soluble microbial products (SMP) analysis were used to probe stress responses. Cellular respiration and key enzyme activities were measured, and proteomics was applied to study protein expression changes. Results showed that higher NO₂⁻-N levels boosted N₂O production, inhibited nitrification, and stimulated denitrification in N.eA1. At 100 mg·L⁻¹ NO₂⁻-N, EPS rose and SMP fell, with ammonia monooxygenase (AMO) suppressed and nitrite reductase (NIR) as well as nitric oxide reductase (NOR) enhanced. Gene expression analysis revealed decreased AMO, hydroxylamine oxidoreductase (HAO), and energy transport-related enzymes, but increased NIR and NOR genes. The downregulation of electron transport complex genes offered insights into molecular adaptation to nitrite stress of N.eA1, highlighting the interplay between metabolic and genetic responses, which is essential for developing sustainable and efficient nitrogen management strategies.

1. Introduction

N₂O, a major greenhouse gas, is recognized as significantly contributing to global warming. Research has indicated that nitrification and denitrification, which were driven by AOB, mainly controlled N₂O production in terrestrial and aquatic ecosystems (Tian et al., 2020; Soliman et al., 2018) [1,2]. These processes are influenced by NO₂⁻-N concentrations, which in turn affected both nitrification and denitrification. Such as the AOB N.eA1 strain, can be impaired by high NO₂⁻-N levels through hindering their survival, energy acquisition, and gene expression regulation (Chen et al., 2018; Bi et al., 2023; Yuan et al., 2023) [3,4,5]. Therefore, it is deemed crucial to understand the resistance mechanisms of AOB under stress to mitigate the negative impacts of NO₂⁻-N accumulation on N₂O production and advance sustainable nitrogen cycle management.

A wealth of research has delved into the effects of NO₂⁻-N concentrations on N₂O production, conclusively demonstrating that NO₂⁻-N presence markedly amplified N₂O emissions (Peng et al., 2015; Castro-Barros et al., 2016) [6,7]. However, there is still a gap in research on this aspect, particularly regarding the resistance mechanisms of various AOB strains in the face of NO₂⁻-N accumulation and the associated nitrogen transformation processes. Moreover, the understanding of enzyme activities and proteomic profiles at high concentrations (40–100 mg·L⁻¹) of NO₂⁻-N is found to be wanting (Chandran et al., 2011; Peng et al., 2015) [6,8]. From a biochemical standpoint, elevated NO₂⁻-N levels are known to induce oxidative stress, causing damage to critical cellular components such as membranes, proteins, and nucleic acids (Ridnour et al., 2004) [9], thereby impairing key metabolic pathways. This encompassed enzymes like AMO and HAO, which play an essential role in nitrification (Su et al., 2021) [10]. While AOB are equipped with genes such as nirK and NIR, which facilitate the reduction of NO₂⁻ to N₂O (Zhao et al., 2022) [11], a more thorough investigation is required to fully elucidate their proteomic and genetic responses to sustained NO₂⁻-N stress.

Recent studies have revealed a range of biochemical and proteomic responses in AOB that could help alleviate stress induced by NO₂⁻-N. For instance, elevated NO₂⁻-N levels have triggered differential gene expression linked to stress resistance, metabolic adaptation, and detoxification (Brotto et al., 2018) [12]. In particular, genes involved in the denitrification pathway, such as those encoding NIR and NOR, have facilitated the reduction of NO₂⁻ to less toxic nitrogenous gases. This process has served not only to mitigate toxicity but also to enhance nitrogen removal (Singh et al., 2021) [13]. These metabolic adjustments had highlighted a potential resilience mechanism in AOB to counteract the effects of NO₂⁻-N accumulation. However, the long-term adaptations and molecular interactions between NO₂⁻-N and cellular components, such as proteins and DNA, have remained poorly understood (Arp et al., 2007; Kumar et al., 2020; Ren et al., 2020) [14,15,16]. Further investigation into these biochemical pathways is considered crucial for gaining a deeper understanding of the resistance mechanisms of AOB.

Extracellular polymeric substances (EPS) and soluble microbial products (SMP) are critical for understanding microbial responses to environmental stress. EPS forms a protective matrix around cells that can shield against high nitrite concentrations and enhance biofilm formation while also participating in nitrite adsorption and transformation processes (Salama, et al., 2016) [17]. Similarly, SMP reflects the metabolic state of bacteria under stress conditions, with changes in composition indicating altered metabolic pathways (Kunacheva, et al., 2014) [18]. Protein content analysis complements these investigations by indirectly reflecting changes in nitrogen metabolism enzyme quantities. Together, these components provide crucial insights into how AOB like N.eA1 adapt physiologically and metabolically to nitrite stress, yet their specific roles in nitrite resistance mechanisms remain insufficiently explored.

In this study, the resistance mechanisms of the N.eA1 strain under varying N₂O⁻-N concentrations were thoroughly examined. A comprehensive analysis was conducted on the growth patterns, enzyme activities, gene expression profiles, and metabolic responses of N.eA1, aiming to elucidate the pivotal role of this AOB strain in alleviating nitrite stress during the processes of nitrification and denitrification. Concurrently, innovative perspectives on microbial adaptation and resilience within the nitrogen cycle were gleaned through the application of biochemical assays, proteomic analyses, and fluorescence spectroscopy. It is anticipated that the findings from these investigations will provide valuable guidance for strategies aimed at enhancing nitrogen removal efficiency and effectively managing nitrite accumulation.

2. Materials and Methods

2.1. Strain and Culture Medium

The N.eA1 strain was obtained from a stable CANON bioreactor in the laboratory (Guizhou, China). Its influent water ammonia nitrogen was maintained at 450 mg·L⁻¹ and nitrite at 75–100 mg·L⁻¹ for a long time, which was characterized by high ammonia nitrogen and high nitrite. The strain underwent 16S rRNA gene sequencing and BLAST alignment, revealing a 95.33% homology with Nitrosomonas europaea H1 AOB3 (Liu, et al., 2025) [19].

The culture medium for this strain was a liquid enrichment medium. The liquid medium was prepared by mixing 1000 mL of nutrient storage solution and 1 mL of trace element solution, then sterilized at 121 °C for 20 min and prepared into a liquid medium with a volume of 100 mL/part. Among them, the element content of nutrient storage liquid is 0.30 g·L⁻¹ of NaCl, 0.14 g·L⁻¹ of MgSO₄·7H₂O, 0.03 g·L⁻¹ of FeSO₄·7H₂O, 0.02 g·L⁻¹ of KH₂PO₄, 0.282 g·L⁻¹ of NH₄HCO₃, and 1.60 g·L⁻¹ of NaHCO₃. Element content of trace element solution: 0.075 g·L⁻¹ of CuSO₄·5H₂O, 0.3 g·L⁻¹ of ZnSO₄·7H₂O, 0.375 g·L⁻¹ of CoCl₂·6H₂O, 0.3 g·L⁻¹ of MnCl₂·2H₂O, 0.014 g·L⁻¹ of H₃BO₃, 0.5 g·L⁻¹ of EDTA, and 0.22 g·L⁻¹ of Na₂MoO₄·2H₂O.

2.2. Nitrogen Transformation Experiment

The N.eA1 bacterial cultures were inoculated into 50 mL of culture medium with different NO₂⁻-N concentrations (0, 10, 20, 40, and 100 mg·L⁻¹) at an inoculation volume ratio of 10% (v/v), pH was adjusted to 8.0 using 1 mol∙L⁻¹ hydrochloric acid (HCl) and sodium hydroxide (NaOH) solutions. Three parallel groups were set for each concentration gradient. The culture bottles were red-capped perforated bottles with sealing gaskets on the bottle mouths. The upper space was air. Before collecting the gas, the bottles were shaken thoroughly. Every 6 h, 20 mL of gas was collected from the serum bottle using a gas sampling needle and placed in a gas collection bag. After gas collection, the changes in NH₄⁺-N and NO₂⁻-N contents were detected, and this process was continued for 24 h. NH₄⁺-N and NO₂⁻-N concentrations were measured using a UV-visible spectrophotometer (Shimadzu, Japan). N₂O was measured using a gas sampler and analyzed using a GC9790⁺ gas chromatograph (Fuli, China). High-purity N₂ (99.999%) was used as the carrier gas at a flow rate of 21 mL·min⁻¹. The temperatures of the ECD detector and column oven were set at 250 °C and 70 °C, respectively. Data analyses and visualizations were performed using Python (Version 3.8.5, Python Software Foundation). The ammonia oxidation rate (AOR) was determined through linear regression analysis of the natural logarithm of changes in NH₄⁺-N and NO₂^--N using the SciPy Stats module.

The N₂O conversion percentage was calculated based on the ratio of NO₂⁻-N (the sum of N₂O in both liquid and gas phases) to the total nitrogen change (ΔN). The specific calculation for the N₂O conversion percentage is as follows (for detailed information, refer to the Supporting Information):

$$\mathsf{\eta } ={\displaystyle \frac{\left({\mathrm{m}}_{{\mathrm{N}}_2\mathrm{O},\mathrm{l}}+{ \mathrm{m}}_{{\mathrm{N}}_2\mathrm{O},\mathrm{g}}\right)}{{\mathsf{\Delta }\mathrm{T}}_{\mathrm{N}} }}\times 100\%$$

(1)

where ${\mathrm{m}}_{{\mathrm{N}}_2\mathrm{O},\mathrm{l}}$ and ${\mathrm{m}}_{{\mathrm{N}}_2\mathrm{O},\mathrm{g}}$ represent the amount of N₂O released in the liquid phase (mg) and gas phase (mg), respectively, and ${\mathsf{\Delta }\mathrm{T}}_{\mathrm{N}}$ represents the change in total nitrogen (mg); η represents N₂O (both liquid and gas phases) as a percentage of total nitrogen change (%).

2.3. Extraction and Determination of EPS and SMP

Excitation-Emission Matrix (EEM) fluorescence spectroscopy was employed to assess the impact of different NO₂⁻-N concentrations (0, 10, 20, 40, and 100 mg·L⁻¹) on the composition and content of EPS and SMP in the N.eA1 strain. EPS and SMP were extracted from the bacterial culture using the heat extraction method and physical centrifugation (Kunacheva and Stuckey, 2014) [18]. The EPS and SMP were analyzed using an RF-6000 3D fluorescence spectrophotometer (SHIMADZU, Kyoto, Japan). The SMP was further analyzed for total organic carbon (TOC) content using a Vario TOC analyzer (SHIMADZU, Japan), and the protein content of EPS was quantified using a BCA protein assay kit (Solarbio, Beijing, China). The three-dimensional EEM fluorescence spectra were corrected for regions affected by first-order Rayleigh scattering, second-order Rayleigh scattering, and Raman scattering (Bahram et al. 2006) [20]. Fluorescence regional integration (FRI) was then used for quantitative analysis of the EEM spectra (Chen et al. 2003) [21]. The EEM spectra were divided into five regions: Region I (E_x/E_m = 200–250 nm/250–330 nm), Region II (E_x/E_m = 200–250 nm/330–380 nm), Region III (E_x/E_m = 200–250 nm/>380 nm), Region IV (E_x/E_m = 250–450 nm/250–380 nm), and Region V (E_x/E_m = 250–450 nm/>380 nm). PARAFAC was used to analyze EEM in the tested samples to obtain the information of the main fluorescence components and the fluorescence intensity (F_max) of the main fluorescence components.

2.4. Measurement of Cellular Respiration Rate and Key Enzyme Activities

2.4.1. Respiration Rate Measurement

The N.eA1 bacterial cultures were inoculated into 50 mL of culture medium at an inoculation volume ratio of 10% (v/v), sealed with PTFE septa, and incubated at 30 °C in the dark on a constant temperature shaker for 24 h (Bellucci et al., 2011) [22]. Gas samples were collected using medical syringes, and the CO₂ content in the headspace was measured using a GC9790⁺ gas chromatograph (Fuli, China) equipped with an FID detector.

2.4.2. Preparation of Enzyme Activity Extracts

N.eA1 flocs were collected, centrifuged to discard the supernatant, and washed with 0.1 M phosphate buffer (pH of 7.4). The cell pellet was then resuspended in the same buffer and disrupted using an XO-150 ultrasonic cell disruptor at low temperatures. After centrifugation, the supernatant, serving as the crude enzyme extract, was collected.

2.4.3. Enzyme Activity Assays

AMO Activity Measurement: The AMO activity assay was initiated by adding 0.5 mL of crude enzyme extract to growth media containing gradient concentrations of NO₂⁻-N (0, 20, 40, and 100 mg·L⁻¹). The mixtures were then incubated for 48 h in a shaking incubator (VRERA, Changzhou, China) at 30 °C in the dark. The NO₂⁻-N concentration in the supernatant was measured, and AMO activity was quantified as the amount of NO₂⁻-N produced per unit of protein, which was determined using a BCA protein assay kit (Bennett, K et al., 2016) [23].

NIR and NOR Activity Measurement: To measure NIR and NOR activities, we added 0.5 mL of crude enzyme extract to NH₄⁺-N-free growth media containing different NO₂⁻-N concentrations (20, 40, and 100 mg·L⁻¹). The mixtures were incubated for 48 h in a shaking incubator at 30 °C in the dark. Subsequently, we quantified N₂O content in the headspace using a GC9790⁺ gas chromatograph (Fuli, Taizhou, China) and analyzed NO₂⁻-N reduction in the supernatant. NIR activity was calculated as the amount of NO₂⁻-N reduced per milligram of protein, while NOR activity was determined as N₂O produced per milligram of protein (Franceschini et al., 2004) [24].

2.5. Protein Extraction and Proteomics Analysis

Samples of the N.eA1 culture were taken from 1. the initial culture and 2. the culture exposed to 100 mg·L⁻¹ NO₂⁻-N for 24 h. Centrifuged at 4500 rpm for 5 min at 4 °C. The resulting pellets were washed three times with deionized water, with centrifugation following each wash. The washed pellets were then flash-frozen in liquid nitrogen and stored at −80 °C for subsequent analysis.

Total protein extraction and enzymatic digestion were conducted by Sangon Biotech (Shanghai, China). The digested protein samples were analyzed using one-dimensional Liquid Chromatography with Tandem Mass Spectrometry (Agilent, Lexington, MA, USA). The mass spectrometry data were processed using MaxQuant software, and the identified proteins were annotated by comparison with the Kyoto Encyclopedia of Genes and Genomes (KEGG) protein database. The samples were analyzed using a nano-LC-MS/MS system. The raw data were processed with MaxQuant software (version 1.6.10.43) using the N.eA1 protein database from UniProt. Label-free quantification (LFQ) was performed using the MaxLFQ algorithm. The iBAQ (intensity-based absolute protein quantification) method was employed to compare the protein molar ratios within the samples.

3. Results and Discussions

3.1. Nitrogen Transformation at Different NO₂⁻-N Concentrations

The strain produced and transformed N₂O compounds under different initial NO₂⁻-N concentrations (0, 10, 20, 40, and 100 mg·L⁻¹) (Figure 1a,b). The NH₄⁺-N concentrations (Figure 1c) steadily decreased over time. The total conversion rates of NH₄⁺-N within 24 h were 90.9%, 95.9%, 91.8%, 86.2%, and 74%, respectively. Specifically, at 40 and 100 mg·L⁻¹, the NH₄⁺-N conversion rates were significantly lower in the 100 mg·L⁻¹ group, where conversion efficiency decreased by 16.9% compared to the control group. These results were consistent with previous works (Zhao et al., 2023) [25], which showed that NO₂⁻-N accumulation in nitrogen treatment systems suppressed AOB activity; the ammonia oxidation capacity of the strain was significantly inhibited by high nitrite concentration.

Furthermore, under lower NO₂⁻-N concentrations (0–40 mg·L⁻¹), N.eA1 demonstrated stable ammonia oxidation activity throughout the 24 h incubation period. However, when the NO₂⁻-N concentration was 100 mg·L⁻¹, the ammonia oxidation rate significantly decreased, indicating that high NO₂⁻-N concentrations exerted oxidative stress on the strain, inhibiting its metabolic processes. Between 6 and 12 h, the ammonia oxidation rate in the 10 mg·L⁻¹ NO₂⁻-N group peaked at 4.158 mg·L⁻¹·h⁻¹, while that of the 100 mg·L⁻¹ NO₂⁻-N group was 1.57 mg·L⁻¹·h⁻¹. This suggests that moderate NO₂⁻-N concentrations enhance AOB activity, whereas higher concentrations suppress it. These findings corroborate reports that AOB tolerate moderate NO₂⁻-N levels but undergo metabolic stress at elevated concentrations (Zhao et al., 2023) [26].

As NO₂⁻-N concentration rose, N₂O production significantly increased (Figure 1a). In the 100 mg·L⁻¹ NO₂⁻-N group, the N₂O-N conversion rate peaked at 25.9% after 18 h (Figure 1b), with cumulative N₂O production reaching 435 µg·L⁻¹ after the 24 h mark—markedly higher than in lower concentration groups. This indicates that high NO₂⁻-N concentrations activate the denitrification pathway, boosting N₂O production (Fux et al., 2004) [27]. The N.eA1 strain likely uses enzymes like NIR to convert NO₂⁻-N to N₂O, underscoring the dual impact of high NO₂⁻-N levels: inhibiting ammonia oxidation while promoting partial denitrification and N₂O production (Kampschreur et al., 2008; Fang et al., 2020) [28,29].

The trend of NO₂⁻-N accumulation further illustrates the metabolic dynamics. With an initial NO₂⁻-N concentration of 20 mg·L⁻¹, nitrite accumulation peaked at 41.88 mg·L⁻¹. In contrast, the 100 mg·L⁻¹ NO₂⁻-N group showed significantly lower nitrite accumulation (Figure 1d), likely due to the increased metabolic burden on AOB at high NO₂⁻-N concentrations, resulting in reduced conversion rates and nitrite accumulation.

These results offer crucial insights for optimizing nitrogen treatment processes in CANON bioreactors. It is recommended to maintain NO₂⁻-N concentrations below 40 mg·L⁻¹ in practical applications to ensure optimal AOB ammonia oxidation activity and minimize N₂O emissions. At higher NO₂⁻-N concentrations, AOB activity declines significantly, and the denitrification pathway is activated, leading to increased N₂O production. A concentration of approximately 20 mg·L⁻¹ NO₂⁻-N is suggested to balance high ammonia oxidation rates with low N₂O emissions. Future research could explore strategies to mitigate N₂O production at high NO₂⁻-N concentrations, such as introducing N₂O-reducing bacteria or optimizing the composition of the denitrifying microbial community, thereby enhancing the environmental benefits of CANON bioreactors.

3.2. Effect of NO₂⁻-N on EPS and SMP

3.2.1. EPS Analysis

The EEM analysis of EPS (Figure 2a–e) revealed two primary fluorescence peaks: Region IV (295–320 nm/345 nm, excitation/emission wavelength) and Region V (350–375 nm/450 nm, excitation/emission wavelength). These peaks corresponded to microbial metabolic byproducts and humic acid-like substances, respectively. As the NO₂⁻-N concentration increased, the fluorescence intensity in both regions rose sharply. Further parallel factor analysis (PARAFAC) of the three-dimensional fluorescence spectra (Figures S2 and S3) confirmed three main fluorescent components: two humic-like substances (EPS-C1 at Ex/Em = 375/440 nm and EPS-C2 at Ex/Em = 325/370 nm) and one soluble microbial byproduct (EPS-C3 at Ex/Em = 305/340 nm). The fluorescence intensity (F_max) of component C2 dramatically increased from 5.52 to 25.79 as nitrite concentration rose from 0 to 100 mg·L⁻¹, while component C3 intensity increased from 8.02 to 17.54, further confirming the significant accumulation of protective compounds under stress conditions.

These results showed that higher nitrite stress prompted N.eA1 to boost EPS production as a defensive strategy, accompanied by an accumulation of humic-like substances to fortify biofilm resilience (Zhao et al., 2016) [30]. The substantial increase in humic-like substances suggests their critical role in the protective mechanism, potentially through chelation effects or formation of a physical barrier that reduces nitrite toxicity to the cells. In addition, the protein content in EPS increased, reaching a maximum of 1.26 mg·L⁻¹ in the 100 mg·L⁻¹ group, compared to 0.79, 0.93, 0.89, and 0.67 mg·L⁻¹ in the other groups (Figure 2f). This indicated that elevated NO₂⁻-N concentrations stimulated bacteria to secrete protein-rich EPS, forming a comprehensive defense system that enhances the stability of the extracellular matrix and defense mechanisms.

3.2.2. SMP Analysis

The fluorescence spectra of SMP (Figure 3a–e) exhibited distinct patterns across different groups. In the control group (0 mg·L⁻¹ of NO₂⁻-N), a strong peak was observed in Region IV, indicative of microbial metabolic products (such as protein components in extracellular polymers, nitrogen-containing intermediate metabolites, enzyme-related products from nitrification and denitrification, etc.). In contrast, in the other groups, peaks were predominantly found in Region V, suggesting humic acid-like substances. Further PARAFAC analysis identified two main fluorescent components in SMP (Figures S4 and S5): a humic-like component (SMP-C1 at Ex/Em = 365/448 nm) and a soluble microbial byproduct (SMP-C2 at Ex/Em = 320/363 nm). While the fluorescence intensity of the humic-like component showed only minor changes from 6.5 to 5.78 as nitrite increased to 100 mg·L⁻¹, the microbial byproduct component exhibited a dramatic reduction of over 90%, decreasing from 15.35 in the control group to between 0.57 and 1.85 in nitrite-treated groups.

This implies that these substances in SMP may be byproducts of bacterial metabolism under nitrite stress. In the 100 mg·L⁻¹ group, the fluorescence intensity in Region V was slightly lower than in the lower concentration groups, indicating that severe stress conditions prompted bacteria to adjust their metabolic pathways (Zhao et al., 2016; Schreiber et al., 2012) [30,31], leading to reduced SMP secretion and a focus on producing more EPS to enhance biofilm stability. This substantial reduction in microbial byproducts in SMP strongly suggests a strategic metabolic shift in N.eA1, where cellular resources are redirected from SMP production toward enhanced EPS synthesis as a survival adaptation mechanism. The increase in humic acid-like substances within EPS may help stabilize the biofilm structure, thereby enhancing stress resistance (Flemming et al., 2023) [32]. Furthermore, the TOC content of SMP generally decreased with increasing NO₂⁻-N concentrations, dropping from 28.7 mg·L⁻¹ in the 0 mg·L⁻¹ group, demonstrating that high NO₂⁻-N conditions redirect metabolic resources towards EPS production.

3.2.3. Proportion Analysis in EEM Spectra

Figure 4a,b illustrates the distribution of standard volume proportions (Pᵢ,ₙ) in the EEM spectra of EPS and SMP across different groups. In EPS (Figure 4a), the proportions of microbial metabolic byproducts (P_IV,ₙ) and recalcitrant organic matter (P_V,ₙ) had significantly increased with rising NO₂⁻-N concentration. In the 100 mg·L⁻¹ group, microbial byproducts (P_IV,ₙ) had reached 61%. Meanwhile, protein-like substances (P_I,ₙ and P_II,ₙ) and fulvic acid-like substances (P_III,ₙ) had shown slight increases in other groups but had remained low in protein proportion.

In SMP (Figure 4b), Region V had accounted for 41% in the 10 mg·L⁻¹ group but had decreased to 34% and 31% in the 40 mg·L⁻¹ and 100 mg·L⁻¹ groups, respectively, indicating an altered SMP composition and a decrease in humic substance production under high stress. Region IV (microbial metabolic products) had dominated in the 0 mg·L⁻¹ group at 66%, but as NO₂⁻-N levels had increased, the proportions of fulvic acid-like and protein-like substances had risen, supporting the notion of adaptive metabolic adjustments under nitrite stress (Li et al., 2018; Ye et al., 2018) [33].

The above results demonstrated that the compositions of EPS and SMP significantly changed under NO₂⁻-N stress, with increased humic acid-like substances and protein-rich EPS acting as key defense components. The transition from SMP to structural EPS indicated that N.eA1 reallocated resources to stabilize the biofilm under extreme conditions, which is consistent with previous research (Zhao et al., 2016; Flemming et al., 2023) [30,32] and highlights the role of EPS in protecting cells and maintaining biofilm resilience. Other environmental stressors that affect biofilm components should also be explored in the future, particularly the balance between EPS and SMP, to better understand survival strategies in nitrite-rich environments.

3.3. Cellular Respiration Rate and Key Enzyme Activity

The variations in key enzyme activities (AMO, NIR, and NOR) and the respiration rate of the N.eA1 strain under different NO₂⁻-N concentration gradients (0, 20, 40, and 100 mg·L⁻¹) were further explored.

As shown in Figure 5a, the activity of AMO was highest at 0 mg·L⁻¹ NO₂⁻-N, achieving 0.66 mg·L⁻¹ NO₂⁻-N·(mg protein)⁻¹·h⁻¹. However, AMO activity significantly decreased as the NO₂⁻-N concentration increased, dropping to 0.12 mg·L⁻¹ NO₂⁻-N·(mg protein)⁻¹·h⁻¹ at 100 mg·L⁻¹. AMO is crucial for the oxidation of NH₃ to NO₂⁻, and the sharp decline in activity implies that NO₂⁻-N inhibits AMO, thereby restricting its ability to oxidize NH₃ to NO₂⁻. This inhibition prevents excessive nitrite accumulation, a critical metabolic regulation mechanism for avoiding toxic intermediates within the cell (Kimura et al., 2010) [34]. The above results highlight the delicate balance required in engineered systems like wastewater treatment plants, where excessive nitrite could impair ammonia oxidation and result in inefficient nitrogen removal. Figure 5b shows that NIR activity gradually increased from 0.9 mg·L⁻¹ NO₂⁻-N·(mg protein)⁻¹·h⁻¹ with no NO₂⁻-N to 6.38 mg·L⁻¹ NO₂⁻-N·(mg protein)⁻¹·h⁻¹ at 100 mg·L⁻¹. As a key enzyme in denitrification, the increased NIR activity suggests that under high NO₂⁻-N conditions, the strain enhances denitrification pathways to remove excess NO₂⁻-N, thereby mitigating toxic intermediate accumulation. This adaptive response highlights the capability of the strain to manage nitrite toxicity and maintain cellular homeostasis under stress. In Figure 5c, NOR activity peaked at 2.09 mg·L⁻¹ N₂O·(mg protein)⁻¹·h⁻¹ under 100 mg·L⁻¹ NO₂⁻-N. NOR catalyzes the conversion of NO to N₂O, and its increased activity indicates that N.eA1 counters high NO₂⁻-N concentrations by boosting N₂O production, thereby alleviating toxic stress and ensuring strain survival and stability (Schreiber et al., 2012) [31]. Figure 5d presents the CO₂ release of the strain, indicating that the respiration rate decreased from 223.8 to 113.3 mg·h⁻¹ as NO₂⁻-N concentration increased from 0 to 40 mg·L⁻¹. At 100 mg·L⁻¹, the respiration rate rebounded to 153.4 mg·h⁻¹, reflecting increased metabolic activity under these conditions. This partial recovery might be linked to the upregulation of NIR and NOR activities, which help mitigate nitrite and its toxic intermediates (Maia and Moura, 2014) [35]. Despite some recovery, the ongoing stress at high nitrite levels underscores a constraint on overall metabolic efficiency.

Under varying NO₂⁻-N concentrations, N.eA1 dynamically balances nitrite accumulation and detoxification through metabolic regulation. At 20 mg·L⁻¹ NO₂⁻-N, sustained high ammonia monooxygenase (AMO) activity promotes NH₄⁺ oxidation, but insufficient denitrification (low NIR/NOR activity) leads to NO₂⁻-N accumulation (Figure 1d). Conversely, under 100 mg·L⁻¹ NO₂⁻-N, N.eA1 suppresses AMO activity (Figure 5a) to limit further nitrite production while upregulating nitrite reductase (NIR) and nitric oxide reductase (NOR) activities (Figure 5b,c), accelerating NO₂⁻-N conversion to N₂O. This metabolic prioritization mitigates nitrite toxicity but elevates N₂O emissions (Figure 1b), highlighting a trade-off between stress adaptation and greenhouse gas release. The strain’s ability to reallocate electron flux (via cytochrome c-independent pathways) and enhance extracellular polymeric substance (EPS) synthesis (Figure 2f) further stabilizes enzyme functionality under stress. These adaptive strategies underscore the need for optimized nitrogen removal systems that balance microbial resilience with environmental impact.

3.4. Proteomics of N.eA1

3.4.1. Protein Identification and Quantification

Proteomic analysis was performed on N.eA1 samples cultured for 24 h under 0 mg·L⁻¹ and 100 mg·L⁻¹ NO₂⁻-N conditions. In the 0 mg·L⁻¹ group, 1429 proteins and 7296 peptides were identified, with 1297 proteins and 6839 specific peptides quantified. In the 100 mg·L⁻¹ group, 1409 proteins and 6873 peptides were identified, with 1285 proteins and 6414 specific peptides quantified.

3.4.2. Major Protein Expression and Functions

Figure 6 illustrates the distribution of genes with an iBAQ value greater than 0.5% in samples under conditions of 0 and 100 mg·L⁻¹ NO₂⁻-N. The key genes that were expressed included amoB1, amoC2, NE0084, NE2057, tuf1, NE2563, atpD, groEL, cspD2, rplL, atpA, NE2465, cycA1, hao1, cbbG, hppA, and NE1879. Among these, the AMO (amo) genes had the highest representation. Specifically, amoB1 and amoC2 accounted for 2.32% and 1.9% in the 0 mg·L⁻¹ group and 2.54% and 2.52% in the 100 mg·L⁻¹ group, respectively.

The N.eA1 genes with high iBAQ values highlighted key metabolic functions. Notably, the amo gene, which encodes ammonia monooxygenase, played a critical role in AOB by catalyzing the oxidation of ammonia to hydroxylamine (NH₂OH), indicating strong ammonia oxidation activity (Zorz et al., 2018) [36]. The prevalence of amo in proteomic samples suggested robust ammonia oxidation in N.eA1. Additionally, NE0084 encoded a hydrogen carrier protein involved in reduction reactions, while NE2563 encoded a gram-negative porin system, facilitating transport across the outer membrane. The tuf gene encoded elongation factor Tu, which was essential for bacterial protein synthesis, and groL encoded chaperonins involved in protein folding and stress responses (Zhang et al., 2020) [37]. The groEL gene similarly encoded chaperonins that assisted in macromolecular folding under stress. The hao gene encoded hydroxylamine oxidoreductase, which oxidized hydroxylamine to NO₂⁻ during ammonia oxidation. The atpD gene encoded the ATP synthase beta subunit, which was essential for cellular energy production. NE0961 encoded a hydroxylamine oxidase protein involved in oxidizing ammonium to hydroxylamine. The car gene encoded carbamoyl phosphate synthetase, which was responsible for forming carbamoyl phosphate from NH₃, CO₂, and ATP (Martin and Russell, 2007) [38]. Lastly, the cbb gene encoded ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO) (Xiao et al., 2014) [39] and higher RubisCO activity, indicated by cbbG gene expression, suggested enhanced nitrogen fixation capability in autotrophic microorganisms (Yoshizawa et al., 2004) [40].

3.4.3. Key Enzymes in Nitrification and Denitrification

LFQ (label-free quantification) intensity was utilized for a comparative analysis between samples to indicate gene expression levels. Figure 7a,b illustrated the expression intensities of genes encoding key enzymes and complexes involved in nitrification and denitrification processes. In Figure 7a, Regions I, II, III, IV, and V represented genes encoding AMO, HAO, enzymes involved in energy and material transport, NIR, and NOR, respectively. Noteworthy genes included amoB1, amoC2, and petC in Region I; hao1 in Region II; and aniA in Region IV. The SAMN06296273_1336 gene, which encoded NOR, exhibited an expression intensity of 0 in the 0 mg·L⁻¹ sample, which increased to 1.6 × 10⁸ in the 100 mg·L⁻¹ sample. This indicated a significant upregulation of NIR and NOR genes under high NO₂⁻-N conditions, while genes related to AMO, HAO, and energy transport enzymes were downregulated.

Figure 7b showed the expression intensities of genes encoding three respiratory complexes and cytochrome c. Complex I-related genes included nuoX, nqrx, C8R28_100414, and NE2205; Complex II included argGX, sdhX, purA, and sucX; and Complex III included coq7. Cytochrome c-related genes included ccX and coX. Under high NO₂⁻-N conditions, the expression of these genes decreased, indicating reduced cellular activity, consistent with the observed decrease in respiration rates at 100 mg·L⁻¹ NO₂⁻-N.

The expression of genes encoding AMO, HAO, and enzymes involved in energy and material transport significantly decreased in samples exposed to 100 mg·L⁻¹ NO₂⁻-N (Figure 7b). This suggested that high NO₂⁻-N concentrations inhibited the ammonia oxidation process of N.eA1 and overall cellular activity (Law et al., 2013) [41]. In contrast, genes encoding NIR and NOR were significantly upregulated, indicating increased denitrification activity under high NO₂⁻-N conditions (Zhao et al., 2013) [42], which led to a rise in N₂O production. This finding aligned with experimental results showing substantial N₂O release at high NO₂⁻-N concentrations. These results suggested that under nitrite stress, N.eA1 shifted towards denitrification, reducing NO₂⁻ to N₂O to avoid nitrite toxicity.

High NO₂⁻-N concentrations also significantly reduced the expression of genes encoding Complexes I, II, III, and cytochrome c. Ubiquinone (UQ), a lipid-soluble molecule, and its reduced form, ubiquinol (UQH₂), played a key role in electron transport. Complex I (NADH dehydrogenase) catalyzed electron transfer from NADH to UQ, forming UQH₂ (Wang and Hekimi, 2016) [43]. Complex II (succinate dehydrogenase) was membrane-bound and contained FAD, catalyzing key reactions in the TCA cycle (Pathania et al., 2009) [44]. Complex III (cytochrome bc₁) facilitated electron transfer from UQH₂ to cytochrome c, which ultimately transferred electrons to denitrification enzymes NIR, NOR, and NOS (Richardson et al., 2001) [45].

NIR catalyzed the reduction of NO₂⁻ with electrons derived from cytochrome c (Zeng et al., 2018) [46]. First, NADH dehydrogenase converted NADH to NAD, transferring electrons to NAR via the UQ/UQH₂ cycle, reducing NO₃⁻ to NO₂⁻. Simultaneously, electrons were transferred to cytochrome c via the UQ/UQH₂ cycle (Gille et al.,2004) [47]. Complex II (succinate dehydrogenase) also generated electrons through the TCA cycle, which were transferred to cytochrome c via the UQ/UQH₂ cycle (Gnaiger, 2024) [48]. Cytochrome bc₁ catalyzed electron transfer from UQH₂ to cytochrome c, subsequently passing them to NIR, NOR, and NOS enzymes to catalyze the reduction of NO₂ to NO, NO to N₂O, and N₂O to N₂, respectively (Wan et al., 2011) [49]. Under high nitrite stress, N.eA1 compensates for the downregulation of cytochrome c-related genes (ccX and ctaG) by activating alternative electron transfer pathways (cox and cyx), enabling sustained electron supply to nitrite reductase (NIR) despite suppressed ammonia oxidation (Figure 5a–c and Figure 7a,b). This metabolic reallocation prioritizes nitrite detoxification over energy-intensive processes, evidenced by the upregulation of aniA (NIR) and SAMN06296273_1336 (NOR) alongside increased CO₂ release (Figure 5d).

N.eA1 exhibited multiple adaptive responses to nitrite stress, including inhibited nitrification and enhanced denitrification pathways. Its ability to produce protective EPS, regulate key enzyme activities, and modify gene expression profiles in response to NO₂⁻-N accumulation demonstrated resilience. However, these adaptations came with trade-offs in metabolic efficiency.

4. Conclusions

In this study, we found that high concentrations of nitrite were found to inhibit nitrification and ammonia oxidation in N.eA1 while enhancing denitrification processes and thereby increasing N₂O emissions. N.eA1 utilized NO₂⁻ as a substrate for denitrification and to secrete EPS. Furthermore, high nitrite levels were found to trigger a metabolic shift, as evidenced by variations in respiration rates and enzyme activities, particularly the inhibition of AMO and the enhancement of NIR functions. Nitrite stress was found to suppress nitrification and energy transport genes while enhancing denitrification genes, thereby increasing resilience to NO₂⁻-N toxicity. Despite these adaptations, nitrite stress induced metabolic limitations, affecting the electron transport chain and reducing overall cellular activity. Our work provides a new insight into understanding the microbial adaptation and resilience in ammonia-oxidizing bacterial systems under nitrite stress.