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6 February 2026

16 Pages

Comparative Transcriptomics Reveals Metabolic Adaptations of Priestia megaterium BZ-95 to Different Nitrogen Sources

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Key Laboratory of Efficient Utilization of Non-Grain Feed Resources of Ministry of Agriculture and Rural Affairs, Shandong Agricultural University, Taian 271000, China
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Author to whom correspondence should be addressed.
Microorganisms2026, 14(2), 397;https://doi.org/10.3390/microorganisms14020397
This article belongs to the Special Issue Bioinformatics and Omic Data Analysis in Microbial Research, 2nd Edition
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Abstract

While intensive aquaculture has developed rapidly, the consequent buildup of nitrogenous compounds, poses a critical threat to aquatic organisms. Microbial degradation offers an environmentally sustainable solution. We investigated the metabolic regulatory capacity of Priestia megaterium BZ-95 under four nitrogen regimes—ammonium (NH4+-N), nitrite (NO2−-N), nitrate (NO3−-N), and a mixture of them (Mix)—using comparative transcriptomics. We revealed that BZ-95 in NH4+-N activated a direct assimilation program prioritizing branched-chain amino acid biosynthesis. Conversely, under nitrate, BZ-95 enhanced membrane transport and 2-oxocarboxylic acid metabolism to facilitate the rapid incorporation of nitrate-derived ammonium into biomass. Nitrite stress triggered a coordinated response involving the assimilatory nir module (nirC-nirB-nirD) and enhanced energy metabolism to meet the heightened demand for reducing power during its rapid reduction. Under mixed nitrogen sources, BZ-95 established a highly synergistic carbon-nitrogen network, simultaneously processing multiple nitrogen inputs without a hierarchical preference, highlighting its remarkable metabolic plasticity. Intersection analysis defined a refined core of 692 nitrite-specific DEGs and revealed broad transcriptional activation under nitrite stress. Analysis of the NO2−-specific core identified enhanced transmembrane transport capacity, coupled with auxiliary metabolic tuning, as central adaptive strategies for nitrite processing. Collectively, these findings provide crucial insights into the molecular basis of nitrogen coordination in P. megaterium BZ-95.

1. Introduction

The rapid expansion of intensive aquaculture, characterized by high-density culture, has led to the substantial accumulation of nitrogenous substances such as residual feed and feces in water, resulting in excessive levels of ammonia, nitrite, and nitrate [1,2,3]. Nitrite is particularly hazardous as it oxidizes hemoglobin to methemoglobin, impairing oxygen transport in aquatic species and potentially leading to disease and mortality [4,5]. Furthermore, nitrate can disrupt endocrine functions and induce chronic stress responses in aquatic organisms [6,7]. While physical and chemical remediation methods offer short-term nitrogen removal, they are often limited by secondary pollution risks and high costs [8,9,10]. In contrast, microbial removal presents a sustainable and efficient alternative, establishing it as a central strategy for mitigating nitrogen pollution in modern aquaculture [11,12,13].
In recent years, various bacteria with degradation capabilities, such as Bacillus [14], Pseudomonas [15,16], and Bacillus velezensis [17], have been isolated and identified, and their application in nitrogen transformation has been confirmed. However, most studies have focused on validating their removal efficiency, while systematic investigation into the molecular mechanisms underlying nitrate and nitrite degradation remains limited, particularly the identification and expression regulation of key functional genes [18,19]. As a powerful tool for profiling functional gene expression [20,21], transcriptomic analysis can compare the gene expression under different nitrogen source conditions, thereby precisely identifying genes involved in nitrogen metabolic pathways. Transcriptomic analysis provides critical theoretical support for elucidating the degradation mechanisms of microbial strains.
Previously, we isolated and identified a bacterial strain, Priestia megaterium BZ-95, characterized by its efficient nitrite degradation capability. Phenotypic analysis confirmed that Priestia megaterium BZ-95 is a highly efficient, assimilatory nitrate-reducing strain, capable of rapidly depleting nitrite, nitrate and ammonium without ammonium accumulation as a byproduct (unpublished data). To elucidate the molecular basis of its nitrogen degradation, four treatment groups with different nitrogen sources were cultured (NH4Cl, NaNO2, KNO3, or a mixture of NH4Cl + NaNO2 + KNO3). Transcriptomic sequencing was performed to analyze differential gene expression under these nitrogen conditions. Our study aims to identify key functional genes associated with nitrogen degradation, unravel the underlying mechanisms, and provide a theoretical foundation for the application of Priestia megaterium BZ-95 in mitigating nitrogen pollution in aquaculture [22].

2. Materials and Methods

2.1. Test Samples and Medium

Artificial wastewater medium was prepared by dissolving 0.01146 g NH4Cl (Kaitong Tianjin, China), 0.01216 g NaNO2 (Kaitong Tianjin, China), 0.2167 g KNO3 (Kaitong Tianjin, China), 0.02 g K2HPO4 (Kaitong Tianjin, China), 0.03 g KH2PO4 (Kaitong Tianjin, China), 1 g NaCl (Kaitong Tianjin, China), 0.05 g MgSO4·7H2O (Kaitong Tianjin, China), 0.02 g CaCl2 (Kaitong Tianjin, China), and 0.156 g sucrose (Kaitong Tianjin, China) in 1 L of deionized water. The solution was sterilized by UV irradiation for 40 min and stored until use. Similarly, Ammonium Medium (NH4+-M), Nitrate Medium (NO3−-M), Nitrite Medium (NO2−-M), and Mixed-Nitrogen Medium (Mix-M) were prepared with artificial wastewater medium but using different sole nitrogen sources (NH4Cl, KNO3, NaNO2, or a mixture of KNO3 + NH4Cl + NaNO2, respectively), followed by 40 min of UV sterilization. UV irradiation was used only as a pre-inoculation sterilization step. To exclude potential deviations in nitrogen speciation, we verified the inorganic nitrogen profiles of each medium immediately after UV treatment (at time zero, before inoculation). No significant inter-conversion was detected. For all culture experiments, the initial pH was adjusted to 7.2, and the dissolved oxygen (DO) level was maintained at 5.5 ± 0.2 mg/L using continuous aeration with sterile air. Cultures were kept under gentle mixing (120 rpm, orbital shaker (Zhichu ZQLY-180N; Shanghai, China)) to ensure homogeneity without causing excessive shear stress. The initial cell concentration was adjusted to 1.0 × 104 CFU/mL. To minimize growth-stage confounding factors and focus on nitrogen source-dependent responses, we harvested the strain for transcriptome sequencing at a single, consistent time point (24 h post-inoculation) under identical culture conditions for all four nitrogen treatments. All experiments were conducted in six replicates at 28 °C.

2.2. Total RNA Extraction, Library Construction and Sequencing

Total RNA was extracted using the Bacterial Total RNA Extraction Kit (TIANGEN, Beijing, China). The quantity and quality of RNA samples were determined using a Nanodrop 2000 (Thermo Fisher Scientific, Wilmington, DE, USA) and checked using RNase-free agarose gel electrophoresis. The constructed libraries were carried out for sequencing on Illumina NextSeq 500 platform, in paired-end mode.

2.3. Transcriptome Assembly and Analysis

Was conducted by FastQC [23]. Adapters, reads with length < 50 bp, and low-quality reads with a Phred score < 20 were removed. Clean reads were aligned to a ribosomal RNA database using Bowtie2 [24] to identify and remove ribosomal RNA-derived sequences. The remaining reads were retained for subsequent transcriptome assembly and analysis. Differential expression analysis was performed using the DESeq2 package. Genes with an absolute log2 fold change (log2 FC) ≥ 1 and an adjusted p-value < 0.05 were considered significantly differentially expressed. Transcriptional profiles from the four independent parallel cultures were compared pairwise to identify nitrogen source-dependent responses. Associated GO enrichment was considered significant at FDR ≤ 0.05 and default parameters. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database-based metabolic pathways were assigned using the KEGG automatic annotation server.

2.4. Identification of Nitrogen-Specific Gene Sets

To systematically identify the transcriptional adaptations of P. megaterium BZ-95 to various nitrogen sources, a consistent intersection analysis framework was employed. For each nitrogen regime (NH4+, NO2−, NO3−, and Mix), a core set of condition-specific differentially expressed genes (DEGs) was identified by intersecting the results of all pairwise comparisons involving that specific treatment group. This comparative intersection strategy allowed for the isolation of distinct gene sets that represent the unique metabolic response to each specific nitrogen condition, effectively filtering out common stress-response or growth-related transcriptional signals.

2.5. Quantitative Real-Time PCR

The cDNA was synthesized with the Evo M-MLV Reverse Transcription Kit (AG, Beijing, China). According to the manufacturer’s instructions, the amplification was carried out in a reaction system of 20 µL. The reaction mixture was incubated for 50 min at 42 °C, followed by 5 min at 95 °C to deactivate the reverse transcriptase. Quantitative real-time PCR was performed with the SYBR Green Pro Taq HS premixed qPCR kit (AG, Beijing, China). Specific primers were designed in conserved regions and are listed in Table 1. The reaction volume was 20 μL with 10 μL 2× premix, 0.4 μM forward primer, 0.4 μM reverse primer, 1 μL cDNA template, and nuclease-free water to volume. Cycling conditions were 95 °C for 2 min followed by 40 cycles of 95 °C for 10 s and 60 °C for 30 s; a melt curve verified specificity. The reference gene was 16S rRNA. No-template and no-reverse-transcription controls were included.
Table 1. Primer information.

2.6. Statistical Analysis

Data are presented as mean ± standard deviation. Relative gene expression levels were calculated using the 2−ΔΔCt method. Statistical analyses were performed using IBM SPSS Statistics 22. All data met the assumptions for parametric analysis (normality by Shapiro–Wilk test, p > 0.05; homogeneity by Levene’s test, p > 0.05). One-way analysis of variance (ANOVA) was used to assess differences between groups, with p < 0.05 considered significant.

3. Results

3.1. Transcriptome Sequencing Data Statistics

All 24 samples were subjected to paired-end sequencing on the Illumina sequencing platform. The number of raw reads per sample ranged from 6,700,112 to 13,714,422 after quality control (Table 2). The proportion of clean reads exceeded 97.5% for all samples, with Q20 and Q30 values of ≥97.43% and ≥92.12%, respectively. The GC content varied between 42.64% and 50.31%. These metrics confirm the high quality of the sequencing data, making the dataset suitable for subsequent genome alignment and differential expression analysis.
Table 2. Summary of transcriptome data for Priestia megaterium BZ-95.

3.2. Global Gene Expression Profiles and PCA Clustering

Gene expression analysis revealed consistent distribution patterns across the different treatment groups (Figure 1). The density curves of gene expression exhibited similar shapes among all groups, with the majority of genes expressed at low to moderate levels, while highly expressed genes constituted a minor proportion (Figure 1a). This trend was further supported by violin plots (Figure 1b), which showed no substantial differences in the distribution ranges of gene expression across samples. These results indicate that the different nitrogen source treatments had minimal impact on the global distribution of gene expression, and the expression profiles remained relatively uniform across all treatment groups. In contrast, principal component analysis (PCA) revealed distinct intra-group clustering of replicates within the same treatment group, alongside clear separation between different treatment groups (Figure 1c). Collectively, these results demonstrate high reproducibility of gene expression profiles within groups and significant differential expression patterns across groups.
Figure 1. Global gene expression profiling and differential expression analysis under different nitrogen source treatments. (a) Density curves illustrating the distribution of gene expression levels across the four treatment groups. (b) Violin plots displaying the distribution ranges and density of gene expression profiles for individual samples. (c) Principal component analysis (PCA) based on global gene expression, revealing distinct intra-group clustering and inter-group separation. (d) Statistical summary of up-regulated and down-regulated differentially expressed genes (DEGs) identified in pairwise comparisons between treatment groups.

3.3. Identification of Differentially Expressed Genes

To identify differentially expressed genes (DEGs) in Priestia megaterium BZ-95 under different nitrogen source treatments, pairwise comparisons of gene expression profiles across the four groups were performed (Figure 1d). The distribution of DEGs in most comparison groups displayed an asymmetric pattern, indicating a distinct and specific regulatory preference in the transcriptional response of BZ-95 to different nitrogen sources. Differential expression analysis identified substantial transcriptional changes across all comparisons. Specifically, the Mix-M vs NO2−-M comparison identified 1890 DEGs (633 upregulated and 1257 downregulated, Figure 2a); the NH4+-M vs NO2−-M comparison identified 3210 DEGs (1597 upregulated and 1613 downregulated, Figure 2b); the NO2−-M vs NO3−-M comparison identified 3554 DEGs (2425 upregulated and 1129 downregulated, Figure 2c); the Mix-M vs NH4+-M comparison identified 3226 DEGs (1337 upregulated and 1889 downregulated, Figure 2d); NH4+-M vs NO3−-M comparison identified 2032 DEGs (1824 upregulated and 208 downregulated, Figure 2e); the Mix-M vs NO3−-M comparison identified 3338 DEGs (2036 upregulated and 1302 downregulated, Figure 2f).
Figure 2. Volcano plots of DEGs from different comparison groups. (a) NO2−-M vs Mix-M, (b) NO2−-M vs NH4+-M, (c) NO2−-M vs NO3−-M, (d) NH4+-M vs Mix-M, (e) NH4+-M vs NO2−-M, (f) NO3−-M vs Mix-M.

3.4. Genomic Resource Allocation Patterns Dictated by Different Nitrogen Sources

In order to summarize the global transcriptomic divergence of Priestia megaterium BZ-95 under different nitrogen regimes, we quantified the expressed gene repertoire in each condition and defined condition-responsive DEG sets using intersection analyses (Figure 3). Under NH4+-M, 4854 genes showed detectable expression (FPKM > 1), within which 995 genes were identified as the NH4+-responsive core by intersecting three pairwise comparisons (NH4+-M vs Mix-M, NH4+-M vs NO3−-M, and N NH4+-M vs NO2−-M; Figure 3a). Under NO3−-M, 5170 genes were expressed (FPKM > 1), and intersecting (NO3−-M vs Mix-M, NO3−-M vs NH4+-M, and NO3−-M vs NO2−-M) identified 1175 NO3−-responsive core genes (Figure 3b). Under NO2−-M, 4307 genes were expressed (FPKM > 1), and the analogous intersection across (NO2−-M vs Mix-M, NO2−-M vs NH4+-M, and NO2−-M vs NO3−-M) yielded 997 NO2−-responsive core genes (Figure 3c).
Figure 3. Stepwise identification of nitrogen-regime–responsive and nitrite-specific DEGs in Priestia megaterium BZ-95. (a–c) Core DEGs for NH4+-M (995; FPKM >1: 4854), NO3−-M (1175; FPKM >1: 5170), and NO2−-M (997; FPKM >1: 4307), defined as the intersection of DEGs from three pairwise comparisons for each nitrogen regime. (d) Exclusive screening of the three core sets to derive unique DEGs: NH4+ (298), NO3− (478), and NO2− (692).
To extract highly specific signatures, we next intersected the three condition-core sets (NH4+-responsive, NO3−-responsive, and NO2−-responsive) and partitioned them into unique and shared subsets (Figure 3d). This analysis yielded 298 NH4+-unique genes, 478 NO3−-unique genes, and 692 NO2−-unique genes, while the remaining genes were shared between at least two nitrogen regimes.

3.5. Genes and Pathway Associated with Ammonium Transport and Assimilation

Although BZ-95 exhibited a rapid initial uptake rate when cultured with NH4+, the overall nitrogen removal efficiency was markedly lower compared to nitrate- or nitrite-fed systems, resulting in significant residual ammonium accumulation [22]. To elucidate the molecular basis of this distinct phenotype, we focused on the NH4+-responsive core gene set. Based on KEGG mapping, the core genes clustered into several functional blocks, among which transport was prominently represented. In the transport category, the phosphotransferase system (PTS) was represented by the glucose-specific component ptsG (D0441_RS06180). Within the 2-oxocarboxylic acid metabolism pathway, mapped genes included those encoding enzymes for α-keto acid and amino-acid precursor conversion. Specifically, this included the arginine-biosynthesis loci argC (D0441_RS02995) and argJ (D0441_RS03000), along with branched-chain amino acid (BCAA)–associated genes leuC (D0441_RS23615), leuB (D0441_RS23620), ilvC (D0441_RS23630), and ilvN (D0441_RS23635).
Among amino-acid pathways, glycine, serine, and threonine metabolism exhibited the highest enrichment significance. This pathway included lpdA (D0441_RS09970), a gene encoding a lipoamide dehydrogenase component. lpdA was also identified in tryptophan metabolism (alongside kynurenine-route genes kynA and kynB) and in valine, leucine, and isoleucine degradation. In aromatic amino-acid biosynthesis, mapped loci included aroA (D0441_RS15390), the tryptophan operon genes (trpA, trpB, trpC, trpD, trpE), and hisC (D0441_RS21750).
Nucleotide metabolism was also enriched. The purine metabolism pathway contained a contiguous set of genes, comprising the salvage gene hpt (D0441_RS00320), the de novo biosynthesis genes purE (D0441_RS01125), purF, purM, purN, purH, and purD, as well as allC (D0441_RS15435). Additionally, specific genes involved in reductant supply and lipid metabolism were mapped to their respective pathways, including zwf (D0441_RS10200) in the pentose phosphate pathway, acpP (D0441_RS21255) in fatty-acid biosynthesis, and cobA (D0441_RS05470) in porphyrin metabolism. Pathways such as glycerophospholipid metabolism and folate biosynthesis were also enriched but primarily contained unannotated loci.

3.6. Genes and Pathway Associated with Nitrate Transport and Degradation

Reflecting the phenotype of rapid nitrate degradation and high removal efficiency—accompanied by transient ammonium accumulation, the enriched set was heavily concentrated in broad functional categories (Metabolic pathways; Biosynthesis of secondary metabolites) and specific nitrogen-linked modules. Accordingly, genes encoding proteins for nitrate/nitrite acquisition and assimilatory reduction were also identified, including the nitrate/nitrite transporter narT and the assimilatory reduction enzymes nasC (D0441_RS00415), nirB, and nirD. 2-Oxocarboxylic acid metabolism showed nominal enrichment and mapped to α-keto acid–associated loci, including argC (D0441_RS02995), argJ (D0441_RS03000), and leuC (D0441_RS23615). In parallel, purine metabolism was enriched, containing the salvage enzyme gene hpt (D0441_RS00320) and de novo biosynthesis genes such as purM (D0441_RS01165) and purN (D0441_RS01170).
Redox- and sulfur-related processes were represented by glutathione metabolism, which included zwf (D0441_RS10200), and cysteine and methionine metabolism, which mapped to D0441_RS05910 and pgeF (D0441_RS21500). Additional metabolic modules included glycine, serine, and threonine metabolism lpdA (D0441_RS09970) and glycerophospholipid metabolism (D0441_RS07110, D0441_RS11590). Regulatory signaling via the two-component system mapped to D0441_RS00880 and D0441_RS16970. Finally, enrichment signals were observed for secondary metabolism pathways, including monobactam biosynthesis (D0441_RS20840) and carbapenem biosynthesis (proB D0441_RS11835, D0441_RS26250).

3.7. Context-Dependent Transcriptional Landscape of BZ-95 to Nitrite

Consistent with the observed phenotype of rapid nitrite depletion without intermediate accumulation, the NO2− condition showed a pronounced enrichment of membrane transport–associated DEGs (Table 3 and Supplementary Materials), with multiple permease and transporter families represented. Specifically, two induced permease loci (D0441_RS07470 and D0441_RS18155) were accompanied by an MFS transporter (D0441_RS08520) and ABC-associated components including abc-f (D0441_RS14215), indicating that multiple transporter families (permease/MFS/ABC) were simultaneously represented in the NO2−-responsive set. In parallel, a focused pair of nitrogen-linked enzymes was mapped within the arginine node, including putrescine aminotransferase (D0441_RS04060) and agmatinase (D0441_RS04065). In the KEGG-mapped pathway summary, these gene-level signals corresponded to two strongly supported annotations: Arginine and proline metabolism (mapping to D0441_RS04060 and D0441_RS04065) and the broad category Biosynthesis of secondary metabolites, which included mapped purine-related nodes hpt (D0441_RS00320) and purF (D0441_RS01160).
Table 3. DEGs related to nitrite metabolism.

3.8. Genes and Pathway Associated with Mixed Nitrogen Transport and Degradation

To counterbalance the slight intermediate accumulation of ammonium and facilitate its rapid assimilation into biomass, the enriched set encompassed loci involved in carbon flux, reductant supply, and energy generation. This suggests a coordinated metabolic response aimed at providing sufficient ATP to match the nitrogen reduction rate.
Central carbon routing genes were mapped, including aceA (D0441_RS02785), ppc (D0441_RS03780), and acsA (D0441_RS24195). In parallel, lpdA (D0441_RS09970) was identified. Genes involved in transport and nutrient/metal acquisition included abc-f (D0441_RS14215), the nickel-uptake module nikB–E (D0441_RS08470–D0441_RS08485), and nikA (D0441_RS02255). Terminal oxidase components qoxA (D0441_RS16040), qoxD (D0441_RS16025), and cyoD (D0441_RS09910) were also mapped. Additional loci included crtI (D0441_RS10515) for carotenoid biosynthesis, fabF (D0441_RS30065) for fatty-acid metabolism, and glpK (D0441_RS023) for glycerol utilization. Regulatory and physiological markers were represented by the peptide transport/quorum-linked gene opp4C (D0441_RS03130), motility/flagellum genes (hag, flhA, flgD, fliJ, fliF), and cofactor/one-carbon–linked genes (metH, metE, megL).
Pathway enrichment analysis highlighted energy- and carbon-flux modules. Carbon metabolism was significantly enriched, mapping to aceA and ppc. Valine, leucine, and isoleucine degradation was also enriched, featuring lpdA. Other carbon-routing pathways showing enrichment included carbon fixation pathways (ppc, acsA), glyoxylate and dicarboxylate metabolism (aceA, acsA), propanoate metabolism (acsA), and pyruvate metabolism (ppc). Transport and energy-conservation functions were represented by ABC transporters (abc-f, nikB–E) and oxidative phosphorylation (qoxA, qoxD, cyoD). Quorum sensing was also enriched (opp4C, nikA). Additional enriched modules included glycerolipid metabolism (glpK), carotenoid biosynthesis (crtI), fatty acid metabolism/degradation (fabF).

3.9. Validation by qRT-PCR

To validate the RNA-seq results, twelve genes were selected for qRT-PCR analysis (Figure 4). Although the exact fold changes were not identical, the up-regulation or down-regulation patterns of these twelve genes exhibited a consistent trend between the RNA-seq and qRT-PCR results, confirming the reliability of the transcriptome sequencing data.
Figure 4. RT-qPCR validation of RNA-Seq data. The same letters indicate no significant difference, whereas different letters indicate a significant difference.

4. Discussion

The accumulation of nitrogen pollutants, particularly the toxic nitrite, in aquaculture environments poses a severe threat to aquatic organisms [25,26]. Although microbial remediation technology is considered an environmentally friendly solution, its efficiency is highly dependent on the inherent metabolic capabilities of the functional strains. Our study addresses this gap by employing a multi-nitrogen-source comparative transcriptomic analysis of Priestia megaterium BZ-95, revealing its strategic metabolic adaptations in response to various nitrogen source [22].
Our comparative transcriptomic data indicate that P. megaterium BZ-95 maintains metabolic homeostasis under various nitrogen source through the coordinated regulation of transport, energy production, and nitrogen incorporation into core metabolic pathways. These findings highlight the strain’s sophisticated metabolic plasticity, demonstrating its ability to dynamically rebalance its metabolism to optimize utilization efficiency across diverse nitrogen environments. Such systemic transcriptional reprogramming is a conserved survival strategy observed in various microorganisms. For instance, Pseudomonas sp. F2 prioritizes energy-efficient pathways to balance the costs of nitrogen assimilation [27]. Collectively, these examples underscore that the metabolic transformations exhibited by BZ-95 represent a conserved mechanism for maintaining physiological stability in fluctuating nitrogenous environments.
The chemical nature of the nitrogen source critically influences microbial metabolism [28], often triggering strain-specific regulatory adaptations in metabolic networks. Our transcriptomic data provide direct molecular evidence for the active nitrite assimilation capacity of Priestia megaterium BZ-95, revealing distinct regulatory priorities across different nitrogen source environments:
In the Mix-M vs NO2−-M comparison, DEGs were primarily enriched in pathways related to the metabolism and catabolism of organic acids and small molecules. Notably, genes associated with small molecule metabolic processes accounted for 63% of the differentially expressed set, suggesting that relative to the mixed nitrogen condition, cultivation with nitrite as the sole nitrogen source necessitates a broad adjustment in small molecule metabolism, likely to support both nitrite detoxification and the provision of energy and metabolic intermediates [29]. Furthermore, the enrichment of pathways such as valine, leucine, and isoleucine degradation indicates that the strain may adjust its amino acid metabolism as a strategy to maintain intracellular nitrogen balance under nitrite-only conditions [30].
The transcriptomic divergence observed in the NH4+-M vs NO2−-M group highlights the metabolic trade-offs between nitrogen sources of varying utilization efficiency. While ammonium is the most readily utilized nitrogen source with a straightforward metabolic route, nitrite utilization requires a significantly more complex and energetically demanding enzymatic system [31,32,33]. The significant enrichment of amino acid biosynthesis pathways in this group implies that under nitrite-rich conditions, BZ-95 may enhance anabolism to repair cellular damage induced by nitrite stress, while concurrently providing the amino donors necessary for nitrite reduction. This is further supported by the enrichment of cofactor biosynthesis (pantothenate and CoA), reflecting a typical microbial regulation strategy to ensure the stability and activity of key metabolic enzymes under environmental stress.
The comparison between the two oxidized forms, NO2−-M and NO3−-M, reveals a distinctive physiological pattern regarding energy metabolism and growth. The enrichment of aerobic respiration and the tricarboxylic acid (TCA) cycle pathways underscores the disparate energy demands of these two environments [34,35,36]. Since the nitrate reduction process is energy-intensive, BZ-95 may drive aerobic respiration to maximize ATP production efficiency. In contrast, nitrite processing likely involves finer adjustments to the electron transport chain to adapt to the specific redox characteristics of the substrate. Crucially, the upregulation of spore germination-related genes in the nitrite environment suggests that NO2− may inhibit standard growth and reproduction, prompting the strain to activate sporulation mechanisms as a survival trade-off to withstand adverse conditions. Additionally, the enrichment of aromatic compound degradation pathways in this comparison suggests that BZ-95 may possess the metabolic versatility to concurrently remediate both nitrite and aromatic pollutants, providing a theoretical framework for its application in treating complex wastewater.
Enrichment analysis of the 997 nitrite metabolism-associated DEGs revealed that these genes were involved in diverse metabolic processes, indicating a close connection between nitrite degradation and the overall intracellular metabolic network. GO analysis showed significant enrichment in terms related to small molecule catabolism, organic acid metabolism, and amino acid metabolism, suggesting the rapid incorporation of nitrite reduction products into central nitrogen metabolism. KEGG pathway analysis further confirmed the activation of multiple pathways, including 2-oxocarboxylic acid metabolism, isoleucine biosynthesis, and amino acid biosynthesis, which indicates that BZ-95 efficiently utilizes the ammonium generated from nitrite reduction to synthesize various amino acids, including branched-chain amino acids, to meet its growth and biosynthetic demands under stress conditions. Particularly noteworthy was the significant enrichment of the TCA cycle and oxidative phosphorylation pathways, jointly pointing to the high energy demand accompanying nitrite assimilation. Oxidative phosphorylation supplies the necessary ATP for nitrite reduction and subsequent assimilation, while the TCA cycle not only provides carbon skeleton precursors for amino acid synthesis but also couples closely with the energy production process via the electron transport chain. In NH4+-M vs NO2−-M comparison, the expression of nirD, encoding the small subunit of nitrite reductase, was significantly upregulated, strongly suggesting its specific induction under nitrite conditions [37,38]. Within the assimilatory nitrate reduction (ANR) pathway, the Nir enzyme catalyzes the reduction of nitrite to ammonium [39,40,41]. The resulting ammonium is subsequently incorporated into organic nitrogen via the glutamine synthetase (GS) and glutamate synthase (GOGAT) pathways. This finding aligns with the reported response mechanisms of many denitrifying bacteria to nitrite [42]. Notably, the expression pattern of nirD differed across other comparisons. It showed a slight upregulation in the mixed nitrogen source versus nitrite comparison (Mix-M vs NO2−-M) and a significant downregulation in the nitrate versus nitrite comparison (NO2−-M vs NO3−-M). This differential expression pattern reveals a complicated hierarchical regulation of nitrogen source utilization. When a more readily utilizable nitrogen source like NH4+ is present in the environment (in Mix-M), the nitrite reduction pathway is partially repressed to conserve energy. Conversely, when nitrate and nitrite coexist, the strain likely prioritizes activating the nitrate reduction pathway while temporarily suppressing the downstream nitrite reduction step to prevent excessive accumulation of the intermediate nitrite. Our previous study confirmed that the genome of Priestia megaterium BZ-95 lacks the genes of nirS and nirK (The National Genomics Data Center, PRJCA055988). Consequently, no expression signals for these genes were detected in any condition. Instead, transcriptional induction was exclusively observed for the assimilatory nitrite reduction operon (nirB-1–cysG–nirC–nirD), supporting its dedicated role in nitrite reduction. Furthermore, no significant expression changes were observed in genes encoding typical dissimilatory nitrite reductases (nirS or nirK). This further supports the conclusion that BZ-95 primarily employs an assimilatory, rather than a dissimilatory, pathway to process nitrite, aiming to incorporate nitrogen into biomass rather than using it solely as a terminal electron acceptor for the respiratory chain [43,44].
Genes associated with transmembrane transport systems, such as ABC transporters, were generally upregulated across all tested nitrogen conditions, including ammonium (NH4+), nitrate (NO3−), and the mixed regime (Mix), in addition to the nitrite (NO2−) group. For instance, the consistent upregulation of transporter-related genes such as D0441_RS02640 across these nitrogen sources underscores an essential adaptive response. This likely represents a coordinated strategy by the strain to enhance the uptake of limited nutritional substrates from the environment while facilitating the export of biosynthetic products, thereby providing sufficient material and energy support for both the direct assimilation of NH4+ and the highly energy-consuming reduction processes of NO3− and NO2−.
ABC transporters primarily utilize the energy derived from ATP hydrolysis to facilitate the transmembrane transport of a wide array of substances, including amino acids, peptides, ions, metabolites, vitamins, and organic anions [45,46]. In the context of Priestia megaterium BZ-95, the activation of these systems is particularly crucial under microbial cellular stress, such as that induced by nitrite toxicity or the high metabolic demand of multi-nitrogen processing. By effectively regulating the balance of substances inside and outside the cell, these transporters provide the necessary substrates and maintain the energy status required for survival. Ultimately, this enhanced transport capacity enables the microorganism to adapt to and thrive within fluctuating and stressful external nitrogen environments [47].

5. Conclusions

Our study elucidates the molecular response mechanisms of the highly efficient nitrite-degrading bacterium Priestia megaterium BZ-95 through comparative transcriptomic analysis under multiple nitrogen sources. The results indicate that BZ-95 primarily degrades nitrite via the assimilatory nitrite reduction pathway, with the expression of the key gene nirD being finely regulated in a hierarchical manner depending on the nitrogen source type. BZ-95 coordinates the upregulation of pathways involved in small molecule catabolism, amino acid biosynthesis, and energy metabolism to supply carbon skeletons, reducing power, and ATP for nitrite reduction and assimilation. Concurrently, the activation of transmembrane transport systems suggests adaptive adjustments in nutrient uptake. These findings provide new insights into the bacterial adaptation strategies to nitrogen stress and offer a solid theoretical foundation for developing BZ-95 into an environmentally friendly and efficient microbial agent for mitigating nitrogen pollution in aquaculture systems.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms14020397/s1. Table S1: DEGs associated with nitrogen compound transport. Figure S1: KEGG enrichment analysis of nitrogen-specific DEG sets (a–c: NH4+-M, NO3−-M, and Mix-M). Figure S2: GO (a) and KEGG (b) enrichment analyses of DEGs from the NO2−-M vs. Mix-M comparison. Figure S3: GO (a) and KEGG (b) enrichment analyses of DEGs from the NO2−-M vs. NH4+-M comparison. Figure S4: GO (a) and KEGG (b) enrichment analyses of DEGs from the NO2−-M vs. NO3−-M comparison.

Author Contributions

H.C.J.: Writing—original draft & Investigation. Z.Y.J.: Data analysis. Y.Z.: Supervision. Y.Z., X.S.J.: Review & editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grants from Key Research and Development Plan of Shandong Province: 2024LZGC024; earmarked fund for the Modern Agro-industry Technology Research System in Shandong Province: (SDAIT-13).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

We hereby confirm that all raw and processed sequencing data generated in this study have been submitted to the National Genomics Data Center (NGDC, https://ngdc.cncb.ac.cn/?lang=en, accessed on 1 February 2026) under the accession number PRJCA055988.

Acknowledgments

We thank the staff at Shandong Agricultural University for their dedication to aquaculture microbiology and microbial cultivation.

Conflicts of Interest

The authors declare no conflict of interest.

References

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