Abstract

   Root rot diseases caused by Ganoderma pseudoferreum and Pyrrhoderma
   noxium inflict substantial economic losses in rubber tree (Hevea
   brasiliensis) cultivation, while conventional control methods face
   environmental and resistance challenges. This study aimed to
   specifically investigate the molecular mechanisms by which ammonium
   sulfate enhances the biocontrol efficacy of Bacillus subtilis Czk1.
   Using label-free quantitative proteomics (LC-MS/MS), we characterized
   ammonium sulfate-induced alterations in the secretory proteome of Czk1.
   A total of 351 differentially expressed proteins (DEPs) were
   identified, with 329 significantly up-regulated and 22 down-regulated.
   GO functional enrichment analysis indicated that up-regulated DEPs were
   associated with metabolic pathways (glyoxylate/dicarboxylate,
   arginine/proline, cofactor biosynthesis) and extracellular localization
   (13 proteins), while down-regulated DEPs were linked to small molecule
   catabolism. KEGG pathway annotation identified DEP involvement in 124
   pathways, including secondary metabolite biosynthesis and membrane
   transport. These findings demonstrate that ammonium sulfate remodels
   the Czk1 secretome to enhance the expression of key antagonistic
   proteins, thereby providing crucial molecular targets and a scientific
   foundation for developing effective biofungicides against rubber root
   rot, with clear practical implications for sustainable disease
   management.

   Keywords: rubber tree root rot, label-free quantitative proteomics,
   Bacillus subtilis Czk1, proteomics

1. Introduction

   The rubber tree (Hevea brasiliensis Muell. Arg.), a perennial tropical
   crop, is a key source of high-quality natural rubber. It serves as a
   vital economic pillar for natural rubber production and significantly
   contributes to the livelihoods of smallholder farmers in developing
   countries [[38]1,[39]2,[40]3]. Root rot disease is one of the three
   most devastating diseases in natural rubber production and is serious
   and widespread in rubber-growing areas around the world. It is also a
   major biological constraint limiting rubber yield growth in China
   [[41]4]. A total of seven distinct types of rubber root diseases have
   been documented in China, including red root rot disease (Ganoderma
   pseudoferreum (Wakef.) Over et Steinm.), brown root rot disease
   (Pyrrhoderma noxium (Corner) L.W. Zhou & Y.C. Dai), purple root rot
   disease (Helicobasidium compactum Boed), stink root rot disease
   (Sphaerostilbe repens Berk. et Br.), white root rot disease (Rigidoprus
   lignosus (Klotzsch) Imaz), black root rot disease (Poria hypobrunnea
   Petch.) and ustulina root rot disease (Ustulina deusta (Hoffmm et Fr.)
   Petrak), of which red root rot and brown root rot are the most harmful
   [[42]5]. Among these, red root rot and brown root rot are prevalent in
   various rubber plantation areas in China. These two diseases are
   prevalent across Chinese rubber plantations, with severe infection
   prevalence exceeding 4.0%. Without timely intervention, mortality rates
   can reach 100% [[43]6,[44]7,[45]8,[46]9].

   The diagnosis of rubber tree root disease remains heavily
   labor-dependent. Early detection systems are often inadequate,
   resulting in inefficient monitoring and delayed responses. By the time
   symptoms become visible in the aboveground parts of the tree, the
   infection is usually already severe, leading to high control costs and
   limited effectiveness. At present, the primary management strategies
   include agricultural and chemical controls. Agricultural methods mainly
   consist of trench isolation, which serves only as a preventive measure
   and is both time-consuming and labor-intensive. Chemical control has
   long relied on the application of thirteen morpholine for root
   irrigation [[47]10,[48]11,[49]12,[50]13]. However, this approach poses
   significant risks such as pathogen resistance, pesticide residues, and
   environmental pollution.

   Biological control is an eco-friendly strategy that employs naturally
   occurring microorganisms and their metabolites to manage plant diseases
   [[51]14,[52]15]. The metabolites produced by biocontrol bacteria are
   relatively stable in the environment and less influenced by external
   conditions, making them among the most successfully commercialized
   biocontrol products to date. The use of antimicrobial compounds derived
   from microorganisms thus represents a pioneering green approach to
   plant disease management. Numerous microorganisms have been exploited
   for the development of biopesticides on a commercial scale. Among
   these, Bacillus spp. have proven to be a highly effective source of
   antifungal agents and are widely employed as biocontrol organisms
   [[53]16,[54]17,[55]18]. A key advantage of Bacillus spp. lies in their
   ability to produce diverse bioactive compounds with agricultural
   applications, including antimicrobial metabolites, surfactants, and
   plant defense stimulators [[56]19,[57]20,[58]21].

   Proteomics, an integral component of functional genomics, enables the
   isolation of protein complexes and functional characterization,
   offering critical insights into biological systems [[59]22,[60]23].
   Among its advanced methodologies, label-free quantitative proteomics
   stands out as a cost-effective and scalable approach. This technique
   leverages modern high-resolution mass spectrometry to analyze
   enzymatically digested peptide mixtures without isotope labeling,
   thereby facilitating large-scale comparative studies of proteome
   dynamics [[61]24,[62]25]. This technique has been widely applied to
   dissect molecular mechanisms across disciplines. In plant–pathogen
   interactions, for instance, Yu et al. (2019) demonstrated that nitrogen
   starvation in Phytophthora infestans—the pathogen responsible for
   potato late blight—upregulated virulence-related effector proteins,
   thereby enhancing pathogenicity [[63]26]. In abiotic stress studies,
   Liu et al. (2021) identified N-glycans as critical modulators of salt
   stress tolerance in Arabidopsis through regulation of stress-responsive
   proteins [[64]27]. Similarly, Song et al. (2022) revealed proteomic
   adaptations in Dendrobium huoshanense under salt stress, highlighting
   key proteins involved in osmotic adjustment and antioxidant defense
   [[65]28]. Beyond plant biology, label-free proteomics has advanced
   applications in food science: Zhang et al. (2024) systematically linked
   freezing–thawing methods (e.g., liquid nitrogen, immersion, and air
   freezing) to protein denaturation and quality deterioration in perch
   filets [[66]29], whereas Menezes et al. (2024) demonstrated that
   sourdough fermentation significantly reduces allergenic wheat
   components, such as glutenins and α-amylase inhibitors [[67]30].
   Collectively, these applications highlight the versatility and impact
   of label-free proteomics in advancing molecular understanding and
   supporting innovation across biological and industrial fields.

   The Bacillus subtilis Czk1 strain, isolated from the roots of the
   rubber tree in our laboratory, exhibits potent antifungal activity
   against several major pathogens, including G. pseudoferreum, P. noxium,
   H. compactum, R. lignosus, S. repens and C. gloeosporioides
   [[68]31,[69]32]. To characterize the protein composition of its
   fermentation broth and investigate the molecular basis of its
   biocontrol activity, we employed liquid chromatography–tandem mass
   spectrometry (LC-MS/MS) coupled with label-free quantitative
   proteomics. This approach allowed us to identify differentially
   expressed proteins (DEPs) in response to ammonium sulfate treatment
   compared to an untreated control. Subsequent bioinformatic analyses
   focused on characterizing secreted proteins and those associated with
   biocontrol functions. The primary objective of this study was to
   investigate how ammonium sulfate modulates the synthesis and secretion
   of key effector proteins in Czk1, thereby providing crucial
   proteome-level insights that can guide the rational design of more
   effective biocontrol formulations against rubber tree root diseases.

2. Materials and Methods

2.1. Extraction and Activity Detection of Antifungal Protein Crude Extract

   The single colony of B. subtilis Czk1 was inoculated into sterile LB
   liquid medium and cultured at 28 °C with 180 rpm agitation for 12 h to
   prepare a seed culture. The seed culture was then transferred to fresh
   LB medium at 7% (v/v) inoculum density and incubated under identical
   conditions (28 °C, 180 rpm) for 60 h. Bacterial cells were subsequently
   removed by centrifugation at 12,000× g for 30 min at 4 °C. To
   specifically analyze the existing secreted proteins without further
   stimulating live bacteria, solid ammonium sulfate was directly added to
   the cell-free supernatant with continuous stirring until saturation.
   This approach allows efficient precipitation of already secreted
   proteins for proteomic analysis while avoiding potential changes in
   bacterial gene expression that could occur if ammonium sulfate were
   introduced during active fermentation. The mixture was maintained at 4
   °C for 24 h to allow complete precipitation, followed by centrifugation
   at 12,000× g for 30 min at 4 °C. The resulting pellet containing crude
   proteins was resuspended in a minimal volume of phosphate buffer (pH
   7.2) and subjected to dialysis for desalting. This crude protein
   extract was used for proteomic analysis.

2.2. Protein Extraction

   Frozen samples were thawed on ice and transferred to sterile tubes. An
   appropriate amount of protein lysate (8 M urea, 1% SDS, containing
   protease inhibitors) was added. The samples were homogenized using a
   high-throughput tissue mill with three cycles of 40 s each time,
   followed by lysis on ice for 30 min with periodic vortexing (5–10 s
   every 5 min). The lysates were then centrifuged at 12,000× g for 30 min
   at 4 °C. The supernatant was collected, and its protein concentration
   was determined using the bicinchoninic acid (BCA) assay kit (Pierce,
   Thermo Scientific, Waltham, MA, USA) according to the manufacturer’s
   instructions. Briefly, a standard curve was prepared using bovine serum
   albumin (BSA) at concentrations ranging from 0 to 2.0 mg/mL. Each
   sample (2 µL) was diluted with 18 µL distilled water, mixed with 200 µL
   BCA working reagent, and incubated at 37 °C for 30 min with shaking.
   Absorbance was measured at 562 nm using a SPECTRA MAX microplate
   reader. Protein extraction quality was assessed by sodium dodecyl
   sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) [[70]33,[71]34].

2.3. Protein Digestion and Peptide Desalting

   A 100 μg protein aliquot was first denatured in 100 mM triethylammonium
   bicarbonate (TEAB) buffer. Reduction was performed with 10 mM
   tris(2-carboxyethyl)phosphine (TCEP) at 37 °C for 60 min, followed by
   alkylation using 40 mM iodoacetamide (IAM) for 40 min at 25 °C in the
   dark. Proteins were then precipitated with six volumes of pre-chilled
   acetone and incubated at −20 °C for 4 h. The pelleted material were
   collected by centrifugation at 10,000× g for 20 min at 4 °C. and
   subsequently resuspended in 100 μL of 100 mM TEAB. Trypsin was added at
   an enzyme-to-protein ratio of 1:50 (w/w) and digestion proceeded at 37
   °C for 16 h.

   The resulting peptides were dried under vacuum, reconstituted in 0.1%
   (v/v) trifluoroacetic acid (TFA), and desalted using HLB solid-phase
   extraction cartridges. After vacuum drying again, peptide concentration
   was determined with a quantitative peptide assay kit (Thermo
   Scientific, Cat. No. 23275). Finally, samples were prepared for
   LC–MS/MS analysis by dissolving dried peptides in loading buffer to a
   final concentration of 0.25 μg/μL [[72]35].

2.4. LC-MS/MS Analysis

   Tryptic peptides were analyzed using an EASY nLC-1200 system (Thermo
   Scientific, Waltham, MA, USA) coupled online to a Q Exactive HF-X
   quadrupole Orbitrap mass spectrometer (Thermo Scientific, Waltham, MA,
   USA) at Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). After
   being solubilized in mass spectrometry-compatible buffer, the peptides
   were loaded onto a C18 reversed-phase analytical column (75 μm × 25 cm,
   Thermo Scientific) equilibrated with solvent A (2% acetonitrile, 0.1%
   formic acid). Separation was performed using a linear gradient with
   solvent B (80% acetonitrile, 0.1% formic acid) at a flow rate of 300
   nL/min under the following conditions: 0–45 min, 0% to 3% B; 45–50 min,
   3% to 28% B; 50–55 min, 28% to 44% B; 55–60 min, 44% to 90% B.

   Mass spectrometry acquisition was conducted in data-dependent mode.
   Full-scan MS spectra (m/z 350–1500) were acquired in the Orbitrap at a
   resolution of 60,000 with an automatic gain control (AGC) target of 3 ×
   10^6 and a maximum injection time of 20 ms. The top 20 most intense
   precursor ions were selected for fragmentation via higher-energy
   collisional dissociation (HCD) with a normalized collision energy
   setting of 28%. MS/MS spectra were acquired at a resolution of 15,000
   (at m/z 100), with an AGC target of 1 × 10^5, a maximum injection time
   of 50 ms, and a dynamic exclusion duration of 18 s [[73]36].

2.5. Protein Discovery Results

   Raw MS data were processed using ProteomeDiscoverer™ Software (v 2.2,
   Thermo Scientific). Database searching was performed using MaxQuant
   [[74]37] (v 2.0.3.1) with the following parameters:
   carbamidomethylation was designated as a fixed modification, while
   methionine oxidation was designated as a variable modification; trypsin
   was designated as the protease; the precursor mass tolerance was set to
   ±10 ppm; and the false discovery rate (FDR) for peptide identification
   was set to 1% (FDR ≤ 0.01). Only proteins containing at least one
   unique peptide were considered confidently identified.

   To evaluate global sample relationships, principal component analysis
   (PCA) [[75]38] was performed on the protein abundance matrix using the
   R programming environment (v 4.2.1). Abundance values were
   log[2]-transformed and scaled to unit variance prior to PCA. The
   analysis was conducted using the prcomp function, and the resulting
   scores were visualized in a two-dimensional plot to assess overall
   sample distribution and group clustering.

   The protein abundance information obtained from the library search was
   used to analyze the statistical test for differential proteins.
   Student’s t-test was employed to calculate the significance of the
   difference between samples, and proteins exhibiting a fold change (FC)
   ≥ 1.2 or ≤0.83 and a p-value < 0.05 were designated as differentially
   expressed proteins (DEPs) [[76]27,[77]28].

2.6. Bioinformatics Analysis

2.6.1. Functional Annotation and Enrichment Analysis

   Functional annotation of the target proteins was performed using a
   combination of bioinformatics tools and databases [[78]39]. KEGG
   [[79]40] pathway annotations were assigned with KOBAS (v 2.1.1).
   Homologous gene clustering and functional classification were carried
   out using EggNOG (v 2020.06) [[80]41]. Gene Ontology (GO) annotation
   was conducted with Blast2GO (v 2.5.0) [[81]42] based on the Gene
   Ontology database (v 2022.0915). Protein domain analyses were performed
   using the Pfam database (v 35.0) [[82]43]. Subcellular localization of
   differentially expressed proteins (DEPs) was predicted using ngloc
   (2023 version) by comparison against reference subcellular localization
   databases.

   To identify functionally enriched terms and pathways, GO enrichment
   analysis was carried out with goatools (v 0.6.5) across the three major
   GO categories: biological process, cellular component, and molecular
   function. KEGG pathway enrichment analysis was performed using custom
   Python scripts (v 3.8.10).

2.6.2. Metabolic Pathway Visualization

   Global metabolic pathway mapping and visualization were conducted using
   iPath3.0 [[83]44] to illustrate systemic metabolic networks and
   functional interactions among the annotated proteins.

3. Results

3.1. Extraction and Identification of Secreted Protein

   The culture medium of B. subtilis Czk1 was debuffered, concentrated,
   and separated by SDS-PAGE ([84]Figure 1). Electrophoretic analysis
   revealed that Czk1 secretes a diverse array of proteins.

Figure 1.

   [85]Figure 1
   [86]Open in a new tab

   SDS-PAGE analysis of secreted proteins from B. subtilis Czk1 culture
   supernatant. (Lane M, protein marker (10–180 kDa); Lane 1–2: Czk1
   sample protein, Lane 3–4: CK control sample protein).

3.2. Peptide Segment Information

   Post-quality control statistical analysis of identified peptides
   included visualization of peptide count and length distributions.
   [87]Figure 2 illustrates the number of peptides identified per protein,
   indicating robust proteome coverage.

Figure 2.

   [88]Figure 2
   [89]Open in a new tab

   Characterization of the identified peptides. (a) Distribution of the
   number of peptides per protein. (b) Distribution of peptide lengths.

3.3. Distribution of Protein Identified by B. subtilis Czk1

   Venn diagram analysis ([90]Figure 3) revealed distinct proteomic
   profiles between ammonium sulfate-treated B. subtilis Czk1 and the
   untreated control (CK). The Czk1 group expressed a total of 394
   proteins, while the CK group expressed 106 proteins. Among these, 106
   proteins were common to both groups. Notably, 288 proteins were
   exclusively identified in the Czk1 treatment group, suggesting a
   substantial alteration in the proteome in response to ammonium sulfate
   stress.

Figure 3.

   [91]Figure 3
   [92]Open in a new tab

   Venn diagram of proteins identified in the B. subtilis Czk1 group
   versus the control (CK) group. Note: The overlapping part of the figure
   represents the number of proteins shared between the groups, the
   non-overlapping part represents the number of proteins unique to the
   group, and the number represents the number of corresponding proteins.

3.4. Sample Correlation Heat Map

   Correlation analysis was performed to evaluate the similarity in
   protein composition among the biological replicates of the ammonium
   sulfate-treated B. subtilis Czk1 and the untreated control (CK) groups.
   As illustrated in [93]Figure 4, high correlation coefficients (ranging
   from 0.996 to 0.997) were observed within the Czk1 replicates,
   indicating excellent reproducibility. Similarly, strong correlations
   (0.998–0.999) were detected within the CK group replicates. In
   contrast, the correlation coefficients between the Czk1 and CK groups
   were substantially lower (0.337–0.340), demonstrating pronounced
   divergence in their proteomic profiles and a marked treatment effect.

Figure 4.

   [94]Figure 4
   [95]Open in a new tab

   Heatmap of sample correlation coefficients for the Czk1 group and CK
   group. Note: The right and lower sides of the figure are the sample
   names. Each grid in the graph represents the correlation between two
   samples, and different colors represent the size of the correlation
   coefficient between samples.

3.5. Principal Component Analysis

   Principal Component Analysis (PCA) was performed to identify overall
   differences between sample groups, despite the limited sample size, as
   it effectively visualizes group segregation. Distinct sample clustering
   was observed in the PCA score plot ([96]Figure 5), with clear
   separation between Czk1 and CK groups. All samples resided within the
   95% confidence interval, confirming significant group segregation.

Figure 5.

   [97]Figure 5
   [98]Open in a new tab

   PCA score scatter plot of Czk1 group and CK group. Note: Following the
   completion of the dimension reduction analysis, the relative coordinate
   points on the principal component plane are identified. The distance
   between each sample point represents the degree of similarity between
   the samples. A shorter distance indicates a higher degree of similarity
   between the samples.

3.6. Analysis of Differentially Expressed Proteins

   Differential expression analysis of proteins was conducted based on
   spectral processing and database searching. Differentially expressed
   proteins (DEPs) between B. subtilis Czk1 and the control (CK) group
   were identified using thresholds of |fold change| > 1.2 (up-regulated >
   1.2, down-regulated < 0.83) and a significance level of p < 0.05. As
   shown in [99]Figure 6, a total of 351 DEPs were identified, among which
   329 were significantly up-regulated and 22 were down-regulated in the
   Czk1 group compared to CK. The marked predominance of up-regulated
   proteins suggests a robust proteomic response to ammonium sulfate
   treatment.

Figure 6.

   [100]Figure 6
   [101]Open in a new tab

   Summary of differentially expressed proteins (DEPs). Note: The abscissa
   represents the difference between the comparison groups. The default
   red color represents the protein that has been shown to be up-regulated
   in the different comparison group, while the blue color represents the
   protein that has been demonstrated to be down-regulated.

3.7. GO Function Annotation of Differentially Expressed Proteins

   GO annotation analysis was performed to categorize the functional roles
   of DEPs identified in B. subtilis Czk1 under ammonium sulfate
   treatment, using a significance threshold of p < 0.05. GO annotation
   categorized DEPs into Biological Process (BP), Cellular Component (CC),
   and Molecular Function (MF) ontologies ([102]Figure 7). In the BP
   category, Up-regulated DEPs were significantly enriched in a wide range
   of processes including biological regulation, metabolic process,
   reproduction, cellular process, developmental process, localization,
   signaling, response to stimulus. Conversely, down-regulated DEPs
   exhibited expression solely in the metabolic process and cellular
   process. Within the CC category, both up- and down-regulated DEPs were
   represented in protein-containing complexes and cellular anatomical
   entities. Within MF, down-regulated DEPs were exclusively associated
   with binding and catalytic activity. Up-regulated DEPs were expressed
   in a number of other activities, including translation regulator
   activity, transcription regulator activity, structural molecule
   activity, ATP-dependent activity, protein folding chaperone,
   antioxidant activity, transporter activity, small molecule sensor
   activity, and binding and catalytic activity.

Figure 7.

   [103]Figure 7
   [104]Open in a new tab

   GO functional annotation analysis of DEPs. Note: Each column in the
   figure represents a multi-level classification of GO, and the higher
   the column, the more proteins in this multi-level classification; the
   abscissa represents the multi-level classification term of GO, and the
   ordinate represents the number of differential proteins annotated to
   the multi-level classification.

   These results indicate that ammonium sulfate treatment triggers
   extensive functional responses in B. subtilis Czk1, particularly
   enhancing regulatory, transport, antioxidant, and transcriptional
   activities.

3.8. KEGG Metabolic Pathway of Differentially Expressed Proteins

   To elucidate the functional pathways involved in the response of B.
   subtilis Czk1 to ammonium sulfate treatment, KEGG pathway enrichment
   analysis was performed on DEPs. The DEPs were categorized into five
   classes: Cellular Processes, Human Diseases, Genetic Information
   Processing, Environmental Information Processing, and Metabolism
   ([105]Figure 8). Within Cellular Processes, DEPs were associated with
   Cell motility and Cellular community—prokaryotes. Human Diseases has
   metabolic pathways was Drug resistance: antimicrobial. Genetic
   Information Processing comprised folding, sorting and degradation;
   replication and repair; and translation. Similarly, the field of
   Environmental Information Processing is characterized by two metabolic
   pathways: signal transduction and membrane transport. Metabolism
   encompasses 12 distinct metabolic pathways, including Global and
   overview maps, Carbohydrate metabolism, Amino acid metabolism,
   Metabolism of cofactors and vitamins, Lipid metabolism, Energy
   metabolism, Metabolism of other amino acids, Nucleotide metabolism,
   Biosynthesis of other secondary metabolites, Glycan biosynthesis and
   metabolism, Metabolism of terpenoids and polyketides, Xenobiotics
   biodegradation and metabolism.

Figure 8.

   [106]Figure 8
   [107]Open in a new tab

   KEGG pathway annotation analysis of DEPs. Note: The ordinate in the
   figure is the name of the KEGG metabolic pathway, and the abscissa is
   the number of proteins annotated to the pathway. Paths of different
   colors represent different categories to which they belong.

   The up-regulated DEPs were expressed in the aforementioned pathways,
   whereas the down-regulated DEPs only detected in a limited number of
   pathways, including folding, sorting and degradation (under Genetic
   Information Processing), signal transduction (under Environmental
   Information Processing), and amino acid metabolism, carbohydrate
   metabolism, energy metabolism, global and overview maps, glycan
   biosynthesis and metabolism, lipid metabolism, metabolism of cofactors
   and vitamins and metabolism of terpenoids and polyketides (under
   Metabolism).

   These findings suggest that ammonium sulfate treatment extensively
   reprograms cellular metabolic and regulatory activities in B. subtilis
   Czk1, with a pronounced up-regulation of proteins involved in diverse
   biosynthetic, energy-yielding, and stress-responsive pathways.

3.9. EggNOG Annotation

   EggNOG functional annotation revealed distinct patterns between up- and
   down-regulated differentially expressed proteins (DEPs). Among the 224
   up-regulated DEPs, annotations spanned 17 functional categories
   ([108]Figure 9), covering processes such as energy production and
   conversion (C), amino acid transport and metabolism (E), nucleotide
   transport and metabolism (F), carbohydrate transport and metabolism
   (G), coenzyme transport and metabolism (H), lipid transport and
   metabolism (I), translation, ribosomal structure and biogenesis (J),
   transcription (K), replication, recombination and repair (L), cell
   wall/membrane/envelope biogenesis (M), cell motility (N),
   posttranslational modification, protein turnover, and chaperones (O),
   inorganic ion transport and metabolism (P), secondary metabolites
   biosynthesis, transport and catabolism (Q), signal transduction
   mechanisms (T), intracellular trafficking, secretion, and vesicular
   transport (U), and defense mechanisms (V).

Figure 9.

   [109]Figure 9
   [110]Open in a new tab

   EggNOG functional classification of DEPs. Note: The abscissa represents
   the functional classification of EggNOG (represented by capital letters
   C–V), and the ordinate represents the number of proteins with this
   function. (C) Energy production and conversion; (E) Amino acid
   transport and metabolism; (F) Nucleotide transport and metabolism; (G)
   Carbohydrate transport and metabolism; (H) Coenzyme transport and
   metabolism; (I) Lipid transport and metabolism; (J) Translation,
   ribosomal structure and biogenesis; (K) Transcription; (L) Replication,
   recombination and repair; (M) Cell wall/membrane/envelope biogenesis;
   (N) Cell motility; (O) Posttranslational modification, protein
   turnover, chaperones; (P) Inorganic ion transport and metabolism; (Q)
   Secondary metabolites biosynthesis, transport and catabolism; (T)
   Signal transduction mechanisms; (U) Intracellular trafficking,
   secretion, and vesicular transport; (V) Defense mechanisms.

   The most enriched categories included amino acid transport and
   metabolism (E, 38 proteins), energy production and conversion (C, 32
   proteins), carbohydrate transport and metabolism (G, 29 proteins), and
   cell wall/membrane/envelope biogenesis (M, 23 proteins). This
   enrichment pattern suggests enhanced biosynthesis of protein
   precursors, upregulated energy metabolism, altered glycolytic pathways,
   and active cellular remodeling, potentially related to adaptation or
   defense.

   In contrast, the 15 down-regulated DEPs were mapped to only six
   functional categories, which were predominantly associated with core
   metabolic processes: energy production and conversion (C), amino acid
   transport and metabolism (E), nucleotide transport and metabolism (F),
   carbohydrate transport and metabolism (G), coenzyme transport and
   metabolism (H), and lipid transport and metabolism (I).

3.10. Pfam Classification

   The DEPs were analyzed against the Pfam database to characterize their
   functional domains ([111]Figure 10). Among the up-regulated proteins,
   82 were annotated to 20 distinct functional domains, including
   amidohydro_1, amidase_2, alpha-amylase, aldo-ket-red, aldedh,
   AP-endonus, peptidase_M20, peptidase_M42, lactamase_B, Glu_dehyd C,
   Aminotran-1-2, ADH-zinc-N-2, adh-short-C2, peptidase-S8,
   nitroreductase, KR, ADH-zinc-N, ADH-N, glyoxalase, and adh-short. In
   contrast, only two down-regulated proteins were annotated,
   corresponding to two functional domains: peptidase_M20 and Glyoxalase.

Figure 10.

   [112]Figure 10
   [113]Open in a new tab

   Pfam domain classification of DEPs. Note: The ordinate is the
   abbreviation of the functional domain (domain), and the abscissa is the
   number of proteins annotated to each domain.

   The identified domains are predominantly categorized as
   oxidoreductases, which catalyze essential redox reactions, and
   hydrolases, which play a pivotal role in cleaving peptide, glycosidic,
   and amide bonds. Furthermore, key biosynthetic and metabolic functions
   are underscored by domains such as aminotransferases, beta-ketoacyl
   synthases, and the CoA-ligase/AMP-binding enzyme family. Collectively,
   these functional capabilities underpin primary metabolic processes,
   maintain cell envelope integrity, and facilitate critical environmental
   interactions.

3.11. Subloc Annotation

   The subcellular localization information of DEPs were obtained by
   comparing the subcellular localization database, and its annotations in
   the database were statistically analyzed. As shown in [114]Figure 11,
   the Czk1 DEPs were divided into three subcellular compartments:
   Extracellular (EXT), plasma membrane (PLA), and cytoplasm (CYT). The
   results of this analysis revealed that there were 13 up-regulated
   proteins in the EXT, 10 up-regulated proteins in the PLA, and 306
   up-regulated proteins in the CYT. Conversely, 22 down-regulated DEPs
   were localized exclusively to PLA.

Figure 11.

   [115]Figure 11
   [116]Open in a new tab

   Subcellular localization prediction of DEPs. Note: The abscissa is the
   name of different subcellular organelles, and the ordinate is the
   number of proteins annotated to the subcellular organelles.
   Localization class and corresponding code are CYT: Cytoplasm, PLA:
   Plasma Membrane, EXC: Extracellular.

   Quantitative analysis revealed a distinct distribution pattern among
   up-regulated DEPs. The majority (306 proteins) were localized to the
   CYT, suggesting a pronounced activation of intracellular metabolic or
   signaling processes. Additionally, 13 and 10 up-regulated proteins were
   identified in the EXT and PLA compartments, respectively, indicating
   potential modifications in secretory functions or membrane-related
   activities. In contrast, down-regulated DEPs exhibited a strikingly
   specific localization pattern, with all 22 significantly decreased
   proteins residing exclusively in the PLA. This strong bias implies a
   targeted suppression of pathways associated with membrane integrity,
   nutrient transport, cell adhesion, or signal transduction.

3.12. GO Enrichment

   The GO function enrichment analysis of the DEPs were performed to
   clarify the biological processes involved in the protein, the cell
   components, and the molecular functions achieved at the functional
   level, and to clarify the main GO functions of the protein ([117]Figure
   12). For the up-regulated DEPs, molecular function terms were
   prominently enriched. Specifically, 36 proteins were associated with
   transferase activity, and 23 proteins were implicated in nucleic acid
   binding. Among the down-regulated DEPs, notable enrichment was observed
   in specific biological processes and molecular functions. In biological
   processes, three proteins were assigned to small molecule catabolic
   process and two to organic acid catabolic process. In molecular
   functions, 2 proteins each were enriched in the following terms:
   oxidoreductase activity, acting on the CH-CH group of donors, with a
   flavin as acceptor, acyl-CoA dehydrogenase activity,4 iron, 4 sulfur
   cluster binding and oxidoreductase activity, acting on the CH-CH group
   of donors. Additionally, 3 proteins were enriched each in iron-sulfur
   cluster binding, metal cluster binding, hydro-lyase activity and
   carbon-oxygen lyase activity.

Figure 12.

   [118]Figure 12

   [119]Figure 12
   [120]Open in a new tab

   GO enrichment analysis of DEPs. Note: The abscissa represents the GO
   term, and the ordinate represents the enrichment rate (calculated as
   the number of DEPs enriched in the term divided by the number of
   background proteins annotated to that term). A higher value indicates
   greater enrichment. The column color gradient represents the
   significance of the enrichment, where p or FDR < 0.05 is marked as *.

   Significant enrichment was identified for eight functional terms: small
   molecule catabolic process, oxidoreductase activity, acting on the
   CH-CH group of donors, with a flavin as acceptor, acyl-CoA
   dehydrogenase activity, iron-sulfur cluster binding, metal cluster
   binding, hydro-lyase activity,4 iron, 4 sulfur cluster binding,
   carbon-oxygen lyase activity.

3.13. KEGG Pathway Enrichment

   KEGG pathway enrichment analysis revealed distinct metabolic signatures
   for DEPs ([121]Figure 13). The up-regulated DEPs were mainly enriched
   in Glyoxylate and dicarboxylate metabolism, Arginine and proline
   metabolism, Biosynthesis of cofactors, Biosynthesis of nucleotide
   sugars, Biotin metabolism, Butanoate metabolism, Chloroalkane and
   chloroalkene degradation, Cysteine and methionine metabolism, Fatty
   acid biosynthesis, Fatty acid degradation, Fatty acid metabolism,
   Glycine, serine and threonine metabolism, Pantothenate and CoA
   biosynthesis, Porphyrin metabolism, Riboflavin metabolism, Sulfur
   metabolism, Tyrosine metabolism, Ubiquinone and other terpenoid-quinone
   biosynthesis, Vitamin B6 metabolism and beta-Alanine metabolism. The
   down-regulated DEPs showed significant enrichment in Thiamine
   metabolism, Microbial metabolism in diverse environments, Lysine
   biosynthesis, 2-Oxocarboxylic acid metabolism, Metabolic pathways,
   Biosynthesis of amino acids, Monobactam biosynthesis, Sulfur relay
   system, Glutathione metabolism, Peptidoglycan biosynthesis, Terpenoid
   backbone biosynthesis, Pentose phosphate pathway, C5-Branched dibasic
   acid metabolism, Citrate cycle (TCA cycle), Arginine biosynthesis,
   Histidine metabolism, Two-component system, Glyoxylate and
   dicarboxylate metabolism, Biosynthesis of secondary metabolites and
   Carbon metabolism.

Figure 13.

   [122]Figure 13

   [123]Figure 13
   [124]Open in a new tab

   KEGG pathway enrichment analysis of DEPs.

   These results indicate a broad rewiring of metabolic activity, with
   up-regulated DEPs enhancing energy production and anabolic processes,
   while down-regulated DEPs reflect a reduction in basal metabolic and
   biosynthetic functions.

4. Discussion

   The future trajectory of sustainable agriculture will be increasingly
   shaped by the adoption of biological control strategies for plant
   disease management. B. subtilis is a prominent biocontrol bacterium,
   valued for its capacity to produce diverse antimicrobial proteins
   (AMPs). These metabolites offer advantages such as chemical stability,
   sustained efficacy, and broad-spectrum activity, conferring significant
   application potential. A deeper understanding of the antimicrobial
   proteome of B. subtilis is essential for elucidating its biocontrol
   mechanisms, improving its efficacy, and informing novel disease
   management strategies.

   Recent advances in proteomics technologies, synergized with expanding
   genomic databases, have precipitated proteomics analysis to the
   forefront of scientific inquiry as a highly efficacious means to
   explore protein expression levels, protein modifications, and
   protein–protein interactions in diverse biological systems [[125]45].
   Among emerging techniques, label-free proteomics has emerged as a
   robust LC-MS/MS methodology that enables comparative protein
   quantification through spectral analysis of enzymatically digested
   peptides, eliminating the need for isotopic [[126]46,[127]47]. This
   technique provides an unbiased platform for systematic protein
   profiling under diverse physiological conditions. Previous studies have
   successfully applied label-free proteomics in diverse contexts: Wang et
   al. (2018) employed this approach to detect alterations in protein
   levels in B. cereus spores treated with HPP-SAEW [[128]48]. Panda et
   al. (2020) conducted label-free quantitative proteomic analysis on
   shoots of Haloxylon salicornicum to unravel functional protein networks
   associated with salt tolerance [[129]49]. Yang et al. (2022) employed
   label-free proteomics to elucidate the impact of harvest time on
   photosynthetic apparatus quality in the red alga Porphyra dentata
   [[130]50]. Zhao et al. (2023) conducted comparative label-free
   quantitative analysis of the milk fat globule membrane profiles in
   porcine colostrum versus mature milk [[131]51]. In this study, we
   implemented label-free quantitative proteomics to investigate ammonium
   sulfate-induced dynamics of the secretory protein in B. subtilis Czk1.
   A total of 351 DEPs were identified between treated and control groups.
   Bioinformatic analyses (KEGG and GO enrichment) revealed significant
   reprogramming of metabolic pathways closely linked to biocontrol
   functions. Crucially, ammonium sulfate induction enhanced the
   expression of proteins involved in antagonistic activities, including
   secondary metabolite synthesis, amino acid metabolism, and cofactor
   biosynthesis, while repressing pathways related to primary catabolism.
   This shift suggests a metabolic reallocation from growth to
   defense-related protein secretion, providing a proteomic basis for the
   enhanced antifungal effects observed.

   Proteins in living systems operate within coordinated networks of
   biochemical pathways to execute biological functions. Pathway analysis
   is therefore essential for a holistic and systematic interpretation of
   cellular processes. Recent proteomic studies underscore its utility
   across biological models. For example, Jia et al. (2023) used
   data-independent acquisition (DIA) proteomics to show that energy
   transduction pathways were downregulated in Bacillus cereus spores
   under combined stress, suggesting a metabolic slowdown that promotes
   survival [[132]52]. Wang et al. (2024) integrated HPLC-MS/MS with
   label-free proteomics to identify DEPs in Lactobacillus plantarum after
   bacteriocin exposure, noting enrichment in peptidoglycan catabolism,
   carbohydrate metabolism, and nucleotide biosynthesis [[133]53]. In
   Czk1, we observed notable enrichment in oxidoreductase activities and
   small molecule catabolism, which are known to contribute to plant
   resistance and pathogen inhibition.

   KEGG analysis revealed that the up-regulated DEPs participated in 95
   pathways, while down-regulated DEPs participated in 29. Key enriched
   pathways included amino acid metabolism (glyoxylate/dicarboxylate,
   arginine/proline, cysteine/methionine, glycine/serine/threonine),
   cofactor biosynthesis (biotin, riboflavin, pantothenate/CoA, porphyrin,
   ubiquinone, vitamin B6), carbohydrate/fatty acid metabolism, and
   secondary metabolite biosynthesis. This reprogramming suggests that
   ammonium sulfate induction shifts central carbon and nitrogen
   metabolism in Czk1 toward the synthesis and secretion of antimicrobial
   compounds, such as secondary metabolites, amino acid derivatives, and
   enzyme cofactors. Concurrent down-regulation of core catabolic
   pathways—including the TCA cycle, amino acid biosynthesis, and
   carbohydrate metabolism—further indicates a metabolic transition from
   primary growth functions toward specialized metabolite production.

   In conclusion, our integrated proteomic and bioinformatic analyses
   reveal that ammonium sulfate induction promotes a metabolic
   reconfiguration in B. subtilis Czk1, favoring the production and
   secretion of antimicrobial compounds over growth-associated processes.
   These insights not only clarify the mechanistic basis for improved
   biocontrol performance but also offer actionable molecular targets for
   designing effective Bacillus-based biofungicides. Future work should
   validate the functional roles of key DEPs and assess their efficacy in
   field applications to translate these proteomic discoveries into
   practical disease management solutions.

5. Conclusions

   This study employed label-free quantitative proteomics to analyze
   ammonium sulfate-induced alterations in the secretory proteome of the
   biocontrol agent Bacillus subtilis Czk1. A total of 351 differentially
   expressed proteins (DEPs) were identified, including 329 up-regulated
   and 22 down-regulated proteins. GO enrichment analysis highlighted
   eight significantly enriched functional terms, largely associated with
   metabolic enzymes and catabolic processes. KEGG analysis assigned the
   DEPs to 124 distinct pathways, with significant enrichment in pathways
   central to amino acid metabolism, cofactor biosynthesis,
   glyoxylate/dicarboxylate metabolism, and secondary metabolite
   production. Crucially, 13 up-regulated proteins were predicted to be
   extracellular, directly implicating them in the secreted biocontrol
   arsenal. This work establishes a robust label-free quantitative-based
   workflow for secretome profiling in biocontrol bacteria. Our results
   demonstrate that ammonium sulfate induction markedly reprograms the
   secretome of strain Czk1, promoting the expression of key proteins
   involved in antimicrobial metabolic pathways. These findings provide
   crucial molecular targets for understanding and enhancing the
   biocontrol mechanisms of B. subtilis Czk1, facilitating the development
   of more effective biofungicides against economically devastating rubber
   tree root rot diseases caused by Ganoderma pseudoferreum and
   Pyrrhoderma noxium. Future work should focus on validating the
   antifungal activity of the identified secretory proteins and optimizing
   fermentation conditions to maximize their production for large-scale
   biocontrol applications.

Author Contributions

   Conceptualization, Y.L. (Yanqiong Liang), W.W. and C.H.; resources
   S.T., Y.L. (Ying Lu), H.C.; formal analysis, X.H. and Y.L. (Ying Lu);
   data curation, H.C. and X.H.; writing—original draft, Y.L. (Yanqiong
   Liang); writing—review and editing, Y.L. (Yanqiong Liang), W.W. and
   C.H.; supervision, S.T., C.H. and K.Y.; project administration, S.T.
   and W.W.; funding acquisition, Y.L. (Yanqiong Liang), W.W. and K.Y. All
   authors have read and agreed to the published version of the
   manuscript.

Institutional Review Board Statement

   Not applicable.

Informed Consent Statement

   Not applicable.

Data Availability Statement

   The datasets presented in this article are not readily available due to
   privacy reasons. Requests to access the datasets should be directed to
   corresponding authors.

Conflicts of Interest

   The authors declare no conflict of interest.

Funding Statement

   This research was funded by Innovational Fund for Scientific and
   Technological Personnel of Hainan Province (KJRC2023B18), Hainan
   provincial Natural Science Foundation of China (322QN360, 324MS108) and
   the Earmarked Fund for China Agriculture Research System, grant number
   (CARS-33-BC1).

Footnotes

   Disclaimer/Publisher’s Note: The statements, opinions and data
   contained in all publications are solely those of the individual
   author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI
   and/or the editor(s) disclaim responsibility for any injury to people
   or property resulting from any ideas, methods, instructions or products
   referred to in the content.

References