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13 February 2026

Transcriptome Analysis Reveals the Potential Mechanism of MAP34-B Targeting Pasteurella multocida

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1
Anhui Key Laboratory of Eco-Engineering and Bio-Technique, School of Life Sciences, Anhui University, Hefei 230601, China
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Center for Evolution and Conservation Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
*
Authors to whom correspondence should be addressed.
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These authors contributed equally to this work.
Microorganisms2026, 14(2), 458;https://doi.org/10.3390/microorganisms14020458
This article belongs to the Section Molecular Microbiology and Immunology
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Abstract

Pasteurella multocida is a widespread zoonotic pathogen responsible for substantial economic losses in the poultry industry. The antimicrobial peptide MAP34-B has been shown to exhibit potent antibacterial activity against Pasteurella multocida, while the mechanism of action remains unclear. To elucidate the antibacterial mechanism of MAP34-B, we performed transcriptomic profiling via RNA sequencing (RNA-seq) on clinical strain HB03 treated with or without 47.4 µM MAP34-B for one hour. The results showed that, after treatment with MAP34-B, 281 differentially expressed genes were identified, including 161 upregulated genes and 120 downregulated genes. KEGG pathway enrichment analysis revealed that the Ribosome pathway had the highest proportion of affected genes. After treatment with MAP34-B, the gene expressions of rps2, rps3, rps9, rps16, rpl3, rpl9, rpl22, and rpl23 were upregulated, which may affect bacterial protein synthesis. Additionally, the expression levels of membrane-associated genes, such as SecE, SecG, lolB, and ompR, were also altered, indicating disruption of bacterial membrane integrity. Thus, the antibacterial activity of MAP34-B against Pasteurella multocida primarily involves impairment of cell membrane integrity and inhibition of protein synthesis, providing a theoretical foundation for its potential application in treating bacterial infections.

1. Introduction

Pasteurella multocida, a Gram-negative, facultative anaerobic bacterium, is the causative agent of numerous significant animal diseases, including avian cholera, porcine atrophic rhinitis, rabbit septicemia, and bovine pneumonia. Additionally, it is a zoonotic pathogen capable of human transmission via animal bites or scratches, resulting in wound infections and meningitis. According to the specificity of its capsular antigens, Pasteurella multocida can be classified into five serotypes (A, B, D, E, F), each of which exhibits a strong association with distinct animal diseases [1,2]. Serotypes A and F are frequently linked to avian cholera, whereas serotypes B and E are known to induce pneumonia and septicemia in ruminants, including cattle. Serotype D is predominantly associated with porcine atrophic rhinitis [3]. Infection by Pasteurella multocida typically initiates following exposure through the oral cavity, nasal passages, or respiratory tract. Upon entry into the host, the pathogen can circumvent innate immune defenses, invade mucosal tissues, and compromise epithelial barrier integrity, thereby inducing a systemic inflammatory response [4]. In animals, the high morbidity and mortality rates associated with Pasteurella multocida result in significant economic losses to the livestock industry. The limited cross-protection between serotypes makes prevention more challenging [1]. Due to the lack of an effective multiserotype vaccine, antibiotic treatment remains the primary method for controlling Pasteurella multocida infections. However, the overuse of antibiotics over an extended period has led to an increasing issue of bacterial resistance, severely affecting the efficacy of antibacterial drugs and raising treatment costs. Therefore, the development of novel antimicrobial agents has become an urgent necessity [5].
Antimicrobial peptides (AMPs) are considered one of the most promising alternatives to antibiotics due to their high cell selectivity, low pathogen resistance, and broad-spectrum antibacterial activity. They have demonstrated rapid bactericidal effects, particularly against multidrug-resistant bacterial infections [6]. The antimicrobial peptide MAP34-B was isolated from the submandibular gland of goats. Preliminary studies revealed that peptide MAP34-B is a cationic polypeptide with a high arginine content and exhibits direct antibacterial activity against both Gram-negative and Gram-positive bacteria [7]. The main sterilization mechanism of MAP34-B is increasing bacterial cell membrane permeation, and disrupting the integrity of the cell membrane.
Transcriptome sequencing enables the comprehensive analysis of all RNA transcripts within specific cells or tissues under defined conditions [8]. Through high-throughput sequencing, nearly all transcripts in the cell or tissue can be obtained comprehensively. Transcriptome sequencing (RNA-seq) is widely employed to detect alterations in gene expression levels and serves as a foundational tool for investigating gene function and structure. This approach will contribute to understanding the mechanisms underlying the bactericidal activity of antimicrobial peptides [9]. To investigate the bactericidal mechanism of MAP34-B against Pasteurella multocida, the HB03 strain of Pasteurella multocida, serotype A, was isolated from pigs for this study [1,10]. First, the strain was treated with peptide MAP34-B, followed by transcriptome analysis using RNA-seq on the HB03 strain samples treated or untreated with 47.4 µM of peptide MAP34-B for 1 h. This provided new insights into the antibacterial activity of MAP34-B against Pasteurella multocida. Through this approach, we aim to delineate the key genes modulated by MAP34-B, thereby providing deeper insight into its mechanism of action.

2. Materials and Methods

2.1. Peptide Synthesis

Antimicrobial peptide MAP34-B (GLFGRLRDSLRRGGQKILEKVERIGDRIKDIFRG) was synthesized by GL Biochem (Shanghai) Ltd. It was synthesized using solid-phase chemical synthesis with a purity of >97% (HPLC) and purified through high-performance liquid chromatography. The peptide’s quality was assessed using reverse-phase high-performance liquid chromatography (RP-HPLC) and mass spectrometry. The peptide powder was dissolved in distilled water to prepare a 2 mM stock solution and stored at −20 °C for future use.

2.2. Bacterial Culture

Strain HB03 is a pneumonic-type Pasteurella multocida, isolated from pig lung tissue and classified as serotype A. This strain was generously provided by Professor Jianhua Zhang of South China Agricultural University. The strain was streaked on blood agar plates to isolate single colonies. Single colonies were then picked using a sterile inoculation loop and cultured in BHI liquid medium (containing 10% FBS) at 37 °C, 220 rpm, until the logarithmic growth phase was reached. In the treatment group, MAP34-B was added to achieve a final concentration of 47.4 µM in a total volume of 1 mL containing 5 × 107 CFU/mL bacteria, which was confirmed in our previous study. The control group received an equal volume of sterile physiological saline. Three biological replicates were set for each sample. After incubating at 37 °C for 1 h, bacterial pellets were collected by centrifugation (3500 rpm, 4 °C, 10 min), washed three times with sterile physiological saline, rapidly frozen in liquid nitrogen for 3 min, and stored at −80 °C until further use.

2.3. Total RNA Extraction

Bacterial total RNA was extracted using an RNA extraction kit (Novizan, Nanjing, China) as recommended by the manufacturer: 500 μL of Buffer RL was added to the bacterial pellet and vortexed. The lysed bacterial solution was transferred to a FastPure gDNA-Filter Column III, followed by centrifugation at 12,000 rpm for 30 s. The FastPure gDNA-Filter Column III was discarded, and the filtrate was collected. To the filtrate, 0.5 volumes of anhydrous ethanol were added and mixed thoroughly. The mixed solution was transferred to a FastPure RNA Column III and centrifuged at 12,000 rpm for 30 s, after which the filtrate was discarded.
In total, 700 μL of Buffer RW1 was added to the FastPure RNA Column III and centrifuged at 12,000 rpm for 30 s, with the filtrate discarded.
In total, 700 μL of Buffer RW2 was added to the FastPure RNA Column III and centrifuged at 12,000 rpm for 30 s, with the filtrate discarded.
In total, 500 μL of Buffer RW2 was added to the FastPure RNA Column III and centrifuged at 12,000 rpm for 2 min. The adsorption column was then removed from the collection tube and transferred to a nuclease-free 1.5 mL centrifuge tube. In total, 60 μL of RNase-free ddH2O was added to the center of the adsorption column, and the mixture was left to stand at room temperature for 1 min, followed by centrifugation at 12,000 rpm for 1 min to elute the RNA.
The extracted RNA was stored at −80 °C and used as soon as possible.

2.4. RNA Quality Testing and Library Construction

Six RNA samples (three treatment groups and three control groups) were subjected to agarose gel electrophoresis to assess RNA degradation and protein contamination. The gel concentration was 1.0%, the voltage was set to 180 V, and the electrophoresis time was 16 min. Additionally, 1 μL of RNA sample was used to measure RNA concentration and purity (OD260/OD280, OD260/OD230) using a NanoDrop 2000 UV-Vis spectrophotometer (Thermo Scientific, Waltham, MA, USA). The RNA integrity was further assessed using an Agilent 2100 (Agilent Technologies, USA), which precisely determined the RNA Integrity Number (RIN) and the rRNA ratio (23S/16S).
The qualified samples were processed using the Ribo-zero kit to remove rRNA and enrich for mRNA. Subsequently, mRNA was fragmented into short pieces by adding fragmentation buffer. Using the fragmented mRNA as a template and random oligonucleotides as primers, the first cDNA strand was synthesized. The second cDNA strand was synthesized by adding buffer, dNTPs (with dUTP replacing dTTP), DNA polymerase I, and RNase H. The resulting double-stranded cDNA was then purified using Agencourt AMPure XP Beads (1.8 ×) (PCR purification kit). The purified double-stranded cDNA underwent end repair, A-tailing, and adapter ligation. USER enzyme was used to degrade the second cDNA strand containing U, followed by selection of cDNA fragments of approximately 370–420 bp using AMPure XP Beads. Finally, PCR amplification was performed, and the PCR products were purified again using AMPure XP Beads (1.0 ×), resulting in the final gene library.

2.5. Library Quality Control and Transcriptome Sequencing

After the library construction is completed, the insertion fragment length is initially assessed using an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Subsequently, the effective concentration of the library is accurately quantified via qRT-PCR to ensure the quality of the library. Samples that meet the quality criteria are then subjected to sequencing on the Illumina NovaSeq X Plus platform (Illumina, San Diego, CA, USA).

2.6. Sequencing and the Translation Following the Three-Step Method

Raw data from the platform were filtered using FAST (version 0.20.0) to remove sequences containing adapters, sequences with ‘N’ (representing undetermined base calls), and low-quality sequences (where more than 50% of bases have a quality score ≤ 20). Clean reads were retained for further analysis. These clean reads were subsequently aligned to the reference genome using Bowtie2 software (version 2.2) [11]. This version maintains the original meaning, improves clarity, and conforms to standard academic writing conventions, with data quality assessment and sequence comparison.

2.7. Transcriptome Bioinformatics Analysis

Gene expression levels were calculated using RSEM (version 1.2.19) software, with FPKM (Fragments Per Kilobase of transcript per Million mapped reads) method [12]. Differential expression analysis between the control and treatment groups was performed using the DESeq2 R package (version 1.20.0), with the selection criteria of padj < 0.05 and |log2 fold change| > 1. Subsequently, Gene Ontology (GO) functional enrichment (http://www.geneontology.org) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment (https://www.kegg.jp/, accessed on 1 February 2026) analyses were conducted on the differentially expressed genes (DEGs). A p-value < 0.05 was considered as significant enrichment for the differentially expressed genes.

2.8. PPI Network and Module Analysis

The differentially expressed genes (DEGs) were imported into the STRING database for protein name conversion, generating a protein–protein interaction (PPI) network and interaction information, which were then imported into Cytoscape (version 3.10.4) software for visualization. Subsequently, the nodes, degree, betweenness centrality, closeness centrality, and edges in the visualized network were analyzed. The MCODE plugin was used to filter and analyze modules, with parameters set as follows: node score cutoff = 0.2, degree cutoff = 2, K-core ≥ 2, and depth = 100 [13].

2.9. Real-Time Fluorescence Quantitative PCR Validation (RT-qPCR)

Based on the method described by Li et al. [14], with slight modifications, six key DEGs (skp, RPL3, RPS2, glpR, ompR, lolB) were selected for qPCR. Their expression levels were compared with the corresponding gene transcriptomic data to statistically validate the RNA-seq results. 16S rRNA was chosen as the internal reference gene [15]. Primers were designed using Primer-Blast (http://www.ncbi.nlm.nih.gov/tools/primer-blast/, accessed on 15 September 2025) based on the genome sequence of HB03 in GenBank and synthesized by Shanghai Shenggong Biological Engineering Co., Ltd. The primer sequences are shown in Table 1.
Table 1. Sequences of RT-qPCR primers.

3. Results

3.1. Fundamental Properties of MAP34-B

The sequence information, charge count, isoelectric point, and related physicochemical properties of peptide MAP34-B are shown in Table 2.
Table 2. Physicochemical parameters of MAP34-B.

3.2. Sequencing Data Statistics

This study conducted RNA sequencing on the HB03 control and treatment groups, constructing cDNA libraries for six samples. The raw reads and clean reads from sequencing are presented in Table 3. To ensure the quality and reliability of the data analysis, raw data filtration was performed. Fastp quality control software (version 0.23.2) was used to assess all samples, resulting in at least 6,727,388 clean reads per sample. The Q20 value for all samples was above 97.97%, and the Q30 value exceeded 93.9% for each sample.
Table 3. Sequencing data quality.

3.3. Sequencing Sequence Comparison Results

Filtered clean reads were aligned to the HB03 genome using the transcriptomic alignment software Bowtie2 (Table 4). The results showed that more than 90.80% of reads from all samples had a unique alignment position in the reference genome, while the number of reads with multiple alignment positions did not exceed 2.19%.
Table 4. Reference Genome Comparison.

3.4. Sample Correlation Analysis

The correlation of gene expression levels between samples in diversity analysis is an important indicator for assessing the reliability of the experiment. The values in the grid represent the Pearson correlation coefficient between two samples, with different colors indicating varying levels of correlation. Higher correlations correspond to values closer to 1, and the red color in the figure becomes darker as the correlation increases. The correlation heatmap (Figure 1A) shows that, among the three replicate samples in the same treatment group, the correlation coefficient between any two samples is greater than 0.957, indicating excellent parallelism among the samples in the same group. The data quality meets the analysis requirements, and the experimental results are reliable. This indicates that treatment MAP34-B has a significant effect on the gene expression of HB03. The PCA score plot (Figure 1B) also demonstrates the same result: the treatment group and the control group are separated, reflecting the differences in gene expression levels between the two groups.
Figure 1. Relationship between MAP34-B-treated and control samples. Pearson correlation between samples (A). FPKM-based principal component analysis (PCA) (B).

3.5. Differential Expression Gene Screening Analysis

A total of 2091 genes were detected in all samples from the B treatment and control groups, accounting for 98.73% of the total genes in the reference strain H (which has 2118 annotated genes in GenBank). Differentially expressed genes (DEGs) between the B treatment and control groups were analyzed using DESeq2, with the selection criteria of |log2 Fold change| > 1 and p-value < 0.05 [16]. Bioinformatics analysis identified 281 DEGs between the control and treatment groups, including 161 upregulated genes and 120 downregulated genes (Figure 2A). Clustering analysis was used to assess the gene expression patterns of DEGs across different treatment groups. Genes with similar expression patterns are likely to have similar functions or to be involved in the same metabolic processes or biological pathways. The heatmap clustering analysis of DEGs (Figure 2C) revealed that gene expression trends were highly consistent within replicate groups, with significant differences observed between groups.
Figure 2. Histogram of differentially expressed gene statistics between groups (A); volcano plot of the distribution of differentially expressed genes between groups (padj < 0.05) (B), red: genes with up-regulated expression, orange: genes with down-regulated expression, grey: genes with no significant differentially expressed genes; cluster analysis plot of differentially expressed genes (C), After normalisation, blue gene expression is down-regulated, while red indicates up-regulation of gene expression.

3.6. GO Enrichment Analysis of Differentially Expressed Genes

GO enrichment analysis utilizes a comprehensive database that categorizes gene functions into three main categories: Biological Process, Cellular Component, and Molecular Function. For GO functional enrichment analysis, a significance threshold of Padj < 0.05 was applied. The biological processes included 908 differentially expressed genes, the molecular functions included 527 differentially expressed genes, and the cellular components included 322 differentially expressed genes (Figure 3). Among the biological processes, 583 genes were downregulated, while 325 genes were upregulated. The upregulated genes were predominantly involved in cellular processes, metabolic processes, and biological regulation, while the downregulated genes were mainly associated with cellular processes, metabolic processes, and localization. In terms of molecular function, 325 genes were upregulated and 202 genes were downregulated, with the primary molecular functions being binding activity, catalytic activity, and transport activity. For cellular components, 216 genes were upregulated, and 106 genes were downregulated, with the major components of the cellular structure being cellular anatomical entities and protein complexes.
Figure 3. GO enrichment analysis of HB03 differentially expressed genes after treatment with MAP34-B. Orange is up-regulated genes and blue is down-regulated genes.
Based on the GO enrichment analysis results, it can be inferred that the mechanism by which MAP34-B inhibits HB03 may be related to biological processes such as intracellular nitrogen metabolism, amino acid metabolism, and cellular degradation.

3.7. KEGG Enrichment Analysis of Differentially Expressed Genes

In the KEGG pathway analysis, a total of 64 enriched pathways were identified. The top 10 significant KEGG pathways were selected and visualized in a bubble chart (Figure 4), with a threshold of p value < 0.05 used to define significant enrichment. These pathways primarily include the following: in Genetic Information Processing, Ribosome and RNA degradation; in Metabolism, Photosynthesis, Oxidative phosphorylation, Nitrogen metabolism, Glycolysis/Gluconeogenesis, and Fructose and Mannose metabolism; and in Environmental Information Processing, the Bacterial Secretion System and Phosphotransferase System (PTS). Among these, the Ribosome pathway showed the highest gene ratio, with significant enrichment observed in this pathway.
Figure 4. KEGG bubble plots of HB03 differentially expressed genes after treatment with MAP34-B (A); The up-down normalization of genetic pathways (B). The size of the bubbles represents the number of enriched genes, and the colour of the bubbles represents the enriched padj value.

3.8. Protein Interaction Network Analysis

The STRING online database was used to analyze the interaction relationships between proteins encoded by differentially expressed genes. A confidence score threshold of 0.4 was applied to define potential interactions. After filtering, the resulting PPI network consisted of 41 nodes, with 189 functional interactions between them (Figure 5A). Visualization was performed in Cytoscape software, and the MCODE plugin was used for module filtering and analysis. The parameters were set as follows: node score cutoff = 0.2, degree cutoff = 2, K-core ≥ 2, and depth = 100 (Figure 5B). Core genes meeting the selection criteria were primarily ribosomal proteins, including rps3, rps2, rps6, rps9, rps16, and rps19. These RPS proteins are involved in the structure and function of the small ribosomal subunit and are key components of protein synthesis. rps3 is considered to be associated with additional functions, such as DNA repair and apoptosis, while rps6 plays a critical role in regulating protein synthesis. rps6 is typically regulated in the mTOR signaling pathway, and its phosphorylation status can influence the rate of protein synthesis, thus affecting cell growth and proliferation. RPS9 and RPS16 are likely closely related to the translation initiation process and have a direct impact on translation efficiency. Ribosomal large subunit proteins, including rpl3, rpl17, rpl22, and rpl23, play critical roles in the structure and function of the large ribosomal subunit. rpl3 and rpl23 are directly involved in the peptidyl transferase reaction, ensuring the accurate synthesis of the amino acid sequence encoded by mRNA. Under certain conditions, rpl22 can transform into a transcription factor, participating in the transcriptional regulation of the cell, thus displaying additional functions outside the ribosome. Both rpl17 and rpl23 exhibit regulatory activity under various stress conditions, playing important roles in cellular adaptability and survival. The targeted ribosomal proteins depicted in the figure are essential for the translation process in cells, not only maintaining cellular physiological functions but also potentially playing key regulatory roles in disease states.
Figure 5. Protein interaction network diagram (A): each node represents a protein produced by a protein-coding gene locus, and the edges between nodes indicate protein interactions, Edge colors indicate different interaction evidence types as defined by the STRING database; MCODE core genes (B): The deeper the color, the stronger the interaction.

3.9. Real-Time Fluorescence Quantitative PCR

To validate the reliability of the transcriptomic data, the same method used for transcript sequencing was applied to treat HB03. Six differentially expressed genes (three upregulated and three downregulated) were randomly selected for RT-qPCR verification. The results showed that the expression trends of these genes were consistent with the sequencing data (Figure 6). After treatment with MAP34-B, the expression of the skp, rps3, and rps2 genes was upregulated, while the expression of the glpR, ompR, and lolB genes was downregulated, confirming that the RNA-seq transcriptomic sequencing results in this study are reliable.
Figure 6. qPCR to validate differentially expressed genes. *** means p < 0.001.

4. Discussion

Transcriptomic sequencing, a technology used for screening DEGs and elucidating gene functions, offers advantages including rapid processing, cost-effectiveness, high data throughput, and exceptional accuracy. MAP34-B is a cationic peptide that effectively inhibits the growth of Pasteurella multocida. Previous studies have shown that AMPs induce varying levels of disruption to bacterial cell membranes [17,18]. Earlier work from our laboratory demonstrated that the treatment with MAP34-B increases the permeability and aggravates structural damage in HB03 cells—an observation corroborated in the present investigation. Consequently, this study aimed to elucidate, from a molecular biology standpoint, the impact of MAP34-B on membrane integrity and energy metabolism pathways in Pasteurella multocida. Our analyses revealed that MAP34-B treatment elicits differential expression of multiple genes in Pasteurella multocida, indicating its potential role in modulating transcriptional activity in strain HB03.
The bacterial cell wall, a structure primarily consisting of peptidoglycan and teichoic acid, confers mechanical stability and defines the characteristic morphology of the cell. MAP34-B acts by suppressing the transcription of peptidoglycan biosynthesis enzyme (murQ), which results in compromised bacterial cell wall architecture, a loss of structural integrity, and a diminished supportive function, culminating in bacterial cell lysis [19]. Furthermore, the cell membrane consists primarily of a phospholipid bilayer that protects the cell from external stresses. The transport and membrane integration of peptides are carried out by specific protein complexes present in all living cell membranes. Among these, the Sec transport pathway represents an essential and universal mechanism for protein translocation [20]. SecE and SecG are key components of the secretion system and have been shown to influence bacterial sensitivity to antimicrobial peptides [21]. Upon exposure to the antimicrobial peptide MAP34-B, the expression levels of SecE and SecG are upregulated, suggesting that HB03 may modulate the activity of secretion systems such as SecE and SecG. This modulation could alter the protein composition of both the outer and inner membranes, thereby affecting membrane permeability. This further confirms that MAP34-B may act on the bacterial cell membrane, affecting bacterial permeability and exerting a bactericidal effect.
Numerous studies have demonstrated that lipoprotein binding, membrane targeting, and lipoprotein–membrane interaction are intrinsic functions of the outer membrane lipoprotein receptor LolB. Regulation of lolB expression enhances lipoprotein transport and is therefore essential for maintaining cell membrane integrity [22,23]. After treatment with MAP34-B, the expression of the outer membrane-associated lolB is markedly downregulated, resulting in compromised membrane integrity and subsequent cell membrane damage.
ompR, a component of the two-component signal transduction system, is capable of activating the transcription of genes involved in outer membrane porins and lipopolysaccharide modification [24]. Specifically, ompR regulates the expression of outer membrane proteins ompF and ompC, thereby controlling the permeability of the outer membrane. Alterations in these proteins can influence bacterial susceptibility to antimicrobial peptides. When ompR activity is suppressed or absent, the outer membrane becomes more permeable, facilitating the penetration of antimicrobial peptides and ultimately leading to disruption of membrane integrity [25]. AS shown in the results, MAP34-B reduces the expression of the ompR gene, disrupting the integrity of the cell membrane and increasing sensitivity to antimicrobial peptides.
Furthermore, the ribosome functions as the essential cellular machinery that catalyzes protein synthesis, comprising the small 30S subunit and the large 50S subunit, with ribosomal proteins playing integral roles throughout the biosynthetic process. [26]. Proline-rich antimicrobial peptides (PrAMPs) penetrate bacterial membranes and inhibit protein synthesis through disrupting bacterial growth and proliferation, ultimately leading to bacterial death [27]. In this study, after being treated with MAP34-B, multiple ribosomal protein genes, including rps2, rps3, rps9, rps16, rpl3, rpl9, rpl22, and rpl23, were induced in the HB03 strain. The expression of these genes was significantly upregulated, which may disrupt the production of specific proteins by ribosomal genes within the cell. This disruption impaired bacterial protein synthesis, resulting in a bactericidal effect.
Among these differential expressed genes, some originate from ribosomal RNA, with ribosomes likely being another target for peptidesor a secondary effect after membrane disruption. Ribosome-targeting antimicrobial peptides generally exert bactericidal effects by traversing the bacterial cytoplasm and binding to the 70S ribosome, specifically within the peptide exit tunnel of the 50S subunit. Structural analyses indicate that proline-rich antimicrobial peptides—including Oncocin and Bac7—inhibit protein synthesis by impeding peptide elongation, rather than causing ribosomal RNA degradation [28,29]. This distinct mechanism explains stable rRNA transcription during ribosome inhibition. In this study, whether MAP34-B’s disruption of the cell membrane further affects ribosomal transcription or is merely a secondary effect still requires further verification.
The KEGG result showed that HB03 also responds and self-repairs to attacks on MAP34-B: the most significant ribosomal metabolic pathway significantly upregulates protein synthesis to replace peptide-damaged proteins, which is a typical stress repair mechanism, and a high proportion of ABC transporter genes. It can pump harmful substances like peptides entering the cell out of the cell, directly reducing peptide damage to cells, making it one of the core protective measures against peptide attacks by bacteria. Simultaneously, genes for Glycolysis/Glycogenetic and Oxidative phosphorylation, which are core energy metabolic pathways, exhibit differential expression, indicating that bacteria are reprogramming energy metabolism. This is to prioritize meeting the energy requirements of defensive mechanisms such as efflux pumps and protein synthesis, while reducing energy consumption for non-essential metabolism.
In summary, this study presents the analysis of the antimicrobial mechanism of MAP34-B against HB03 using transcriptome data. The results revealed that MAP34-B treatment significantly altered the expression of genes involved in membrane transport, amino acid metabolism, carbohydrate metabolism, and energy metabolism. These changes disrupted the cell wall, cell membrane, and protein synthesis of HB03, mediating its antimicrobial effect.

5. Conclusions

Overall, MAP34-B exhibited significant growth inhibition against HB03. Transcriptional analysis revealed that MAP34-B disrupted the cell wall, cell membrane, and protein synthesis of HB03. These disruptions resulted in structural damage to the cell envelope and inhibition of protein production, thereby compromising bacterial physiological functions and conferring an antimicrobial effect.

Author Contributions

J.D.: Writing—original draft, Investigation, Formal analysis, Data curation; Y.P.: Writing—review & editing, scrub data and maintain research data, Data curation; F.Z.: Writing—review & editing; Q.P.: project administration; Z.C.: visualization. B.Z.: Design experiments and guide research topics. A.W.: Design topics, schedule experiments. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by National Natural Science Foundation of China (32300416), Key-Area Research and Development Program of Guangdong Province (2023B1111050008). The transcriptome data were processed by Novogene Co., Ltd.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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