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Article

Decoding Virulence Mechanisms of Bacillus anthracis Using a Galleria mellonella Infection Model: Differential Host Response Profiles Elicited by AtxA and PlcR

by
Pengyao Wang
1,2,†,
Dongshu Wang
2,†,
Xiaojing Wang
2,
Yufei Lyu
2,
Sicheng Shen
2,
Ruilin Hu
1,
Li Zhu
2,
Xiankai Liu
2,* and
Hengliang Wang
1,3,*
1
College of Food, Shanghai Ocean University, 999 Hucheng Huan Road, Lingang New City, Shanghai 201306, China
2
National Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, 20 Dongdajie Street, Fengtai District, Beijing 100071, China
3
State Key Laboratory of Pathogens and Biosecurity, Academy of Military Medical Sciences Beijing, 20 Dongdajie Street, Fengtai District, Beijing 100071, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work and share first authorship.
Microorganisms 2026, 14(2), 505; https://doi.org/10.3390/microorganisms14020505
Submission received: 14 January 2026 / Revised: 12 February 2026 / Accepted: 13 February 2026 / Published: 20 February 2026
(This article belongs to the Section Molecular Microbiology and Immunology)
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Abstract

A thorough understanding of the functions of virulence regulators in Bacillus anthracis evolution and host adaptation, particularly the systematic host responses they trigger, requires an efficient infection model capable of resolving subtle mechanisms. This study constructed a high-resolution host immune response decoder based on Galleria mellonella to analyze the specific response profiles elicited by different virulence regulators in a capsule-deficient background. By integrating transcriptomic, histopathological, and bacterial colonization analyses, the research delineated distinct host immune stress profiles regulated by AtxA and PlcR. The results showed that the AtxA-deficient strain failed to elicit significant host responses; wild-type infection activated broad systemic immune recognition pathways, while the PlcR-activated strain induced a unique response profile characterized by perturbations in oxidative stress pathways. Its enhanced virulence was associated with the expression of downstream hydrolases and occurred without strong systemic immune activation. This work successfully advanced the G. mellonella model from a phenotypic screener to a mechanistic resolver, providing a new methodological framework for distinguishing B. anthracis virulence regulatory mechanisms at the host response level. This approach not only deciphers pathogen-specific immune signatures but also offers a practical platform for rapid anti-virulence compound screening and guides the design of targeted validation in mammalian systems, thereby accelerating therapeutic strategy development against anthrax.

1. Introduction

Anthrax is a severe zoonotic infectious disease caused by Bacillus anthracis [1,2]. This bacterium is a Gram-positive, spore-forming, aerobic bacillus. Its classic virulence factors are primarily encoded by two large plasmids, pXO1 Plasmid (pXO1) and pXO2 Plasmid (pXO2) [3]. The pXO1 plasmid encodes a lethal toxin complex composed of protective antigen (PA) [4], lethal factor (LF) [5,6], and edema factor (EF) [7]. Here, PA serves as the binding and transport component, while LF (a zinc metalloprotease) and EF (a calmodulin-dependent adenylate cyclase) are the effector components [8]. They act synergistically to disrupt host cell signaling, leading to cell death, edema, and systemic shock. The pXO2 plasmid encodes the gene cluster responsible for synthesizing the anti-phagocytic poly-γ-D-glutamic acid capsule [9]. This capsule is crucial for the bacterium to evade clearance by the host’s innate immune system.
The evolution of virulence regulatory networks within the Bacillus cereus group is integral to its host adaptation and pathogenic diversification [10]. A hallmark of this group is the conserved chromosomal locus encoding the global transcriptional regulator PlcR, a pleiotropic master regulator descended from a common ancestor [11]. PlcR orchestrates the expression of a suite of tissue-destructive effector genes—including those for phospholipases and hemolysins—in response to the extracellular concentration of the quorum-sensing peptide PapR [11,12,13,14,15,16]. This regulatory circuit facilitates bacterial survival in diverse environmental niches and during opportunistic infections. Bacillus anthracis, the causative agent of anthrax, presents a notable evolutionary deviation. Despite retaining the plcR gene locus, it harbors a nonsense mutation that abrogates the production of a functional protein [17]. Consistent with its inactive state, genetic deletion of this pseudogene (including its truncated remnant) does not attenuate virulence in murine infection models, underscoring its functional irrelevance in this pathogen [18]. In contrast, our prior work demonstrated that genetic restoration of a functional plcR allele in B. anthracis reactivates the ancestral virulence program, leading to the reappearance of hallmark phenotypes such as phospholipase and hemolysin activity [19]. Conversely, B. anthracis has evolved a specialized, plasmid-encoded virulence regulon governed by the master regulator AtxA, located on pXO1 [20]. The activity of AtxA is exquisitely tuned to the host milieu, being specifically induced by host body temperature and elevated CO2 concentrations. This central regulator exerts dual control over the two primary virulence determinants of anthrax: it directly activates the synthesis of the anti-phagocytic poly-γ-glutamate capsule (encoded by the cap operon on plasmid pXO2) and coordinates the expression of the tripartite lethal toxins—protective antigen (PA), lethal factor (LF), and edema factor (EF) [2,21]. This coordinated regulation is crucial for the obligate mammalian parasitic lifecycle of B. anthracis; the capsule enables immune evasion, while the toxins disrupt host cell signaling and induce pathology. Loss-of-function studies robustly validate AtxA’s central role, as atxA deletion mutants exhibit severe attenuation of virulence, including a drastic reduction in mortality in murine anthrax models [22,23,24]. To deeply analyze the potential functions of these regulators in virulence evolution and cross-host adaptation, especially to delineate the systemic host responses they trigger, a suitable and efficient infection model is essential. Traditional mammalian models, such as mice, guinea pigs, rabbits, and non-human primates, have played pivotal roles in anthrax research, but each has significant advantages and disadvantages. Mouse models are low-cost and have well-defined genetic backgrounds, but their resistance to B. anthracis spores varies by strain, leading to substantial differences in susceptibility [25]. Guinea pigs are highly sensitive to B. anthracis and have been used as a classic model for vaccine efficacy evaluation, but their immunological and genetic tools are relatively limited [26]. Rabbits show high similarity to humans in simulating the pathological process of inhalational anthrax, but the disease process is highly focused on systemic sepsis, with relatively inconspicuous local inflammatory responses [27]. Non-human primates (e.g., rhesus macaques) are closest to humans in physiology and immune response, serving as the “gold standard” for the preclinical evaluation of vaccines and therapeutics, but they are extremely costly and face stringent ethical constraints. The limitations of these mammalian models in terms of cost, throughput, and ethical issues have prompted the scientific community to seek effective complementary research systems.
In this context, Galleria mellonella has emerged as a promising invertebrate infection model, showing significant potential in recent years for research on pathogenic microorganisms [28,29,30]. This model has been successfully applied to virulence assessment, host–pathogen interaction studies, and drug screening for various human pathogens (e.g., Listeria [31], Staphylococcus, aureus [29], Pseudomonasaeruginosa [32,33], and Burkholderia [34]). In studies investigating the pathogenic mechanisms of B. anthracis, the Galleria mellonella model has emerged as a valuable alternative host system due to its unique balance of immunological relevance, experimental practicality, and ethical feasibility. A key advantage is its ability to be maintained at 37 °C [35], coupled with the evolutionary conservation of its innate immune pathways with those of mammals [36], which provides necessary immunological resolution for studying early infection events and innate immune responses. Compared to mammalian models, it offers significantly greater experimental throughput, scalability, and cost-effectiveness while aligning with the principles of the 3Rs (Replacement, Reduction, Refinement) in animal research [35]. These attributes establish it as an efficient, intermediate-throughput platform suitable for the initial screening of virulence factors and the exploration of core host–pathogen interactions. However, current applications largely remain confined to phenotypic-level virulence screening, and the model’s potential for systematically dissecting the molecular mechanisms underlying these interactions has not yet been fully realized [37,38]. Advancing the model from a “phenotypic screening tool” to a “mechanistic discovery platform” is therefore a central objective for enhancing its utility. In this context, the absence of an adaptive immune system in G. mellonella is not merely a limitation but a defining feature that creates a simplified, focused system. This allows researchers to isolate and interrogate the fundamental dialogue between bacterial virulence factors and the conserved core of innate host defense—without the confounding complexity of antibody- or T-cell-mediated immunity [34]. This focused scope enables clearer mechanistic insights into the initial stages of infection. Consequently, findings generated in this model provide a purified readout of innate immune pathogenesis, which then establishes a robust and logically prior foundation for targeted investigation in more complex mammalian systems that include adaptive immunity and microbiome interactions. Ultimately, strategic integration of the G. mellonella model into a multi-tiered research framework will contribute to a more efficient and comprehensive understanding of B. anthracis pathogenesis.
This study aims to overcome this limitation. We adopted a multi-dimensional comparative analysis strategy. Using capsule-deficient (pXO1+, pXO2−) B. anthracis as the background, we systematically studied a set of genetically well-defined, functionally distinct anthrax mutants (including an AtxA knockout strain, a plcR-activated strain, and the wild-type). By synchronously integrating whole-transcriptome analysis of the infected host, quantitative histopathological assessment, and in vivo bacterial kinetics, we systematically delineated, for the first time, the specific host response profiles triggered by different virulence regulatory circuits. This work not only deepens the understanding of the in vivo functions of AtxA and PlcR but, more importantly, establishes an analytical framework capable of distinguishing between different virulence paradigms: early clearance due to immune evasion defects, typical infection and pathology due to homeostasis disruption, and molecular functional toxicity mediated by toxins. We demonstrate here that the G. mellonella model, combined with multi-dimensional readouts, can effectively decode the types of virulence mechanisms, providing a new methodological paradigm for rapidly inferring the functions of unknown virulence genes in the future.

2. Materials and Methods

2.1. Bacterial Strains

Bacillus anthracis A16D2 (pXO1+, pXO2−) produces anthrax toxins but does not express capsules. A16D2ΔatxA (pXO1+, pXO2−) is a capsule-deficient strain with the atxA gene deleted [39]. A16D2(plcRG): The nonsense mutation TAA at position 640 in the chromosomal plcR gene was repaired to GAA using CRISPR/Cas9 technology to obtain a PlcR function-activated strain. [19]. The strains pBE2-atxA-His/A16D2 and pBE2-atxA-His/A16D2(plcRG) were laboratory stocks (National Key Laboratory of Advanced Biotechnology, Academy of Military Medical Sciences, Beijing, China).

2.2. Spore Preparation

A single colony was picked and inoculated into 3 mL of LB liquid medium, cultured at 37 °C, 220 rpm for 12–13 h. Then, 2 mL of the bacterial culture was transferred to a Roux bottle containing 200 mL of sporulation medium (the sporulation medium contained (per liter): 10 g NaCl, 10 g tryptone, 5 g yeast extract, and 20 g agar;), spread evenly, and incubated upright at 37 °C overnight, followed by inverted incubation at 30 °C for approximately 15 days to form spores. Under sterile conditions, 25 mL of pre-cooled 10% glycerol solution was added to the bottle, and spores were scraped off. The suspension was incubated in a water bath at 48 °C for 16–18 h to break vegetative chains. After filtration, the suspension was incubated in a water bath at 70 °C for 30 min to inactivate vegetative cells, cooled, and centrifuged at 4 °C, 8000 rpm for 10 min. The supernatant was discarded. The pellet was washed with 15 mL of spore washing solution (containing 1 g sodium diatrizoate and 6.6 g meglumine diatrizoate) and centrifuged to remove vegetative cells, followed by three washes with pre-cooled 10% glycerol. Finally, spores were resuspended in 30% glycerol, aliquoted, and stored at 4 °C.

2.3. J774A.1 Cell Virulence Assay

J774A.1 cells (Procell Life Science & Technology Co., Ltd., Shanghai, China) were revived in DMEM containing 10% fetal bovine serum, passaged three times, and counted after mixing with trypan blue. Cell density was adjusted to 2 × 105/mL and seeded into a 96-well plate (100 µL/well) with blank and negative control groups set up (three replicates per group). After incubation at 37 °C for 24 h for cell adhesion, cells were infected with spores at a multiplicity of infection (MOI) of 0.1. After 8 h of infection, the medium was replaced with medium containing gentamicin. Cells were washed 2–3 times with PBS, and cell viability was detected according to the instructions of the CCK-8 kit (Dojindo Laboratories, Kumamoto, Japan). After incubation at 37 °C in the dark for 1 h, the absorbance at 450 nm was measured. Cell viability (%) = [(Experimental group OD − Blank group OD)/(Control group OD − Blank group OD)] × 100%.

2.4. Galleria mellonella Virulence Assay

Spore concentration was determined by the gradient dilution plate counting method. Spores were serially diluted from 1 × 109 CFU/mL to 1 × 107 CFU/mL. Healthy larvae with clean surfaces and no gray-black spots (purchased from Beijing Anxinkang Technology Co., Ltd., Beijing, China, weight approximately 250 mg) were selected and randomly grouped, with 30 larvae per group. Before challenge, larvae were fasted for 24 h. The larval abdomen was disinfected with an alcohol swab, and 10 µL of spore dilution was injected from the second leftmost proleg using an insulin syringe (KRUUSE, Odense, Denmark, Cat. No 112416C). Larvae were then placed in a clean Petri dish and incubated at 37 °C overnight. Larval death was observed and recorded continuously.

2.5. Histopathology and Bacterial Load Analysis

For larval samples infected for 48 h, histopathological observation and quantitative bacterial load analysis were performed separately. For histopathological analysis, larval samples were fixed with 4% paraformaldehyde. Subsequent processes including paraffin embedding, sectioning (5 µm thickness), and staining (hematoxylin-eosin staining and Gram staining) were completed by a professional service provider (Mango Biotechnology Co., Ltd., Yancheng, Jiangsu, China). Tissue pathological changes were observed under an optical microscope. A semi-quantitative histopathological grading system was utilized, with the detailed criteria provided in Table S2. In parallel, another group of larvae infected for 48 h was processed under sterile conditions to prepare tissue homogenates. After gradient dilution, homogenates were plated on B. cereus chromogenic agar plates (Hopebio, Qingdao, China), incubated at 37 °C for 24 h, colonies were counted, and results were calculated as colony-forming units per larva (CFU/larva).

2.6. RNA Extraction from Galleria mellonella

RNA extraction from Galleria mellonella tissues was performed using TRIzol® Reagent (Thermo Fisher Scientific, Waltham, MA, USA). Briefly, tissue, previously snap-frozen in liquid nitrogen and stored in sterile aluminum foil, was ground into a fine powder under liquid nitrogen. The powder was promptly transferred to a 1.5 mL microcentrifuge tube containing 1 mL of TRIzol, vortexed vigorously, and incubated at room temperature for 2 min. For phase separation, 0.2 mL of chloroform (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) was added, followed by vigorous shaking and a 5-min incubation at room temperature. The mixture was then centrifuged at 12,000× g for 15 min at 4 °C. The upper aqueous phase was carefully transferred to a new tube. RNA was precipitated by adding 0.5 mL of isopropanol (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China), incubating for 10 min at room temperature, and centrifuging at 12,000× g for 10 min at 4 °C. The resulting RNA pellet was washed twice with 1 mL of ice-cold 75% ethanol, with centrifugation at 12,000× g for 2 min at 4 °C between washes. After air-drying, the purified RNA was stored at −20 °C for subsequent analysis.

2.7. Transcriptome Sequencing and Bioinformatics Analysis

After passing quality control, the samples were entrusted to Shanghai Majorbio Bio-pharm Technology Co., Ltd., Shanghai, China. for cDNA library construction and sequencing analysis. The specific workflow was as follows: To obtain full-length cDNA sequences from limited RNA samples, cDNA libraries were constructed using the SMART-Seq™ v4 RNA Kit (standard input), followed by paired-end sequencing of the Galleria mellonella transcriptome on an Illumina platform. After quality control of the raw sequencing data, differential expression analysis was performed using the DESeq2 (Version 1.56.1) software, with a screening threshold set at a False Discovery Rate (FDR) < 0.05 and |log2FoldChange| ≥ 1. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted on the obtained differentially expressed genes (DEGs) to systematically decipher the host’s transcriptional response characteristics.

2.8. Real-Time Quantitative PCR (RT-qPCR) Validation

To validate the transcriptome sequencing results, a subset of differentially expressed genes (DEGs) was randomly selected, and their expression levels were verified using RT-qPCR. First, cDNA was synthesized using EasyQuick RT MasterMix purchased from Yisheng Biotechnology Co., Ltd., Beijing, China. Subsequently, using the G. mellonella Ef-1α gene as an internal reference, amplification was performed using 2× Hieff UNICON® Universal Blue qPCR SYBR Green Master Mix (Yisheng Biotechnology Co., Ltd.). The reaction system included cDNA template, specific primers (sequences in Table 1), and the aforementioned master mix. qPCR reactions were run on a CFX Connect™ Real-Time System (Bio-Rad, Hercules, CA, USA). The reaction program was set according to the reagent instructions, including pre-denaturation, cyclic amplification, and melt curve analysis stages. Relative gene expression was calculated using the 2−ΔΔCt method.

2.9. Determination of Reactive Oxygen Species Levels in Galleria mellonella Larvae

Intact Galleria mellonella larvae, collected 24 h post-infection with the appropriate strain at a dose of 1 × 104 spores per larva, were surface-rinsed three times with ice-cold phosphate-buffered saline (PBS) to remove external contaminants. After brief blotting on sterile filter paper, whole larvae were weighed and transferred into sterile 2 mL microcentrifuge tubes. Ice-cold tissue homogenization buffer was added at a ratio of approximately 450 µL per 100 mg of larval mass. Homogenization was performed on ice using a SWE-EP tissue homogenizer (Wuhan Saiwei Biotechnology Co., Ltd., Wuhan, China) until no visible particles remained. The homogenate was then centrifuged at 10,000 rpm for 10 min at 4 °C. The supernatant was carefully collected, and the pellet was discarded. The resulting clarified whole-larva homogenate was either used immediately for reactive oxygen species (ROS) detection or aliquoted. All procedures followed the instructions of the commercial ROS assay kit (Wuhan Saiwei Biotechnology Co., Ltd., Wuhan, China).

2.10. Western Blot

PA antibody and LF antibody were used to detect the expression levels of protective antigen (PA) and lethal factor (LF) in A16D2 and A16D2(plcRG). The expression of the atxA-His fusion protein was analyzed by Western blotting using an anti-His tag antibody in the Bacillus anthracis strains pBE2-atxA-HisA16D2 and pBE2-atxA-His/A16D2(plcRG), respectively. Strains were inoculated into BHI medium containing 0.8% NaHCO3 and cultured to the T3 phase. Culture supernatants were collected by centrifugation, filtered to sterilize, and protein samples were obtained via acetone precipitation. Proteins were mixed with an equal volume of 2× SDS loading buffer, boiled for 10 min, separated by 10% polyacrylamide gel electrophoresis, and transferred onto a membrane. After blocking with skim milk, the membrane was sequentially incubated with His antibody, LF antibody, PA antibody, and horseradish peroxidase-labeled goat anti-mouse IgG secondary antibody. Finally, imaging was performed using chemiluminescence.

2.11. Construction of Promoter-lacZ Reporter Strains

The integrative plasmid pHT304-lacZ was linearized with HindIII and BamHI. Promoter regions of the atxA, lef, and pagA genes were PCR-amplified from B. anthracis A16 genomic DNA. The PCR primers used in this study were synthesized by Tianyi Huiyuan Biotechnology Co., Ltd., Beijing, China and the detailed sequence information is listed in Table S1. Following digestion, the fragments were ligated into the linearized vector and transformed into E. coli DH5α. Positive clones, confirmed by colony PCR, were passaged through E. coli JM110 to obtain unmethylated plasmid DNA. The plasmids were introduced into B. anthracis strains A16D2 and A16D2(plcRG) by electroporation, as described in our previous study [19]. Transformants were selected on BHI agar with erythromycin (5 µg/mL) and verified by sequencing, yielding the reporter strains: A16D2/pHT-PpagA-lacZ, A16D2(plcRG)/pHT-PpagA-lacZ, A16D2/pHT-Plef-lacZ, A16D2/pHT-PatxA-lacZ, A16D2(plcRG)/pHT-Plef-lacZ, A16D2(plcRG)/pHT-PatxA-lacZ.

2.12. β-Galactosidase Assay

Cultures of reporter strains were grown in defined sporulation medium (DSM; per liter: 8 g Difco nutrient broth, 1 g KCl, 0.25 g MgSO4·7H2O, autoclaved and supplemented with filter-sterilized CaCl2, MnCl2, and FeSO4). Samples were harvested at indicated time points. Cells were lysed in Z-buffer (0.06 M Na2HPO4·2H2O, 0.04 M NaH2PO4·H2O, 0.01 M KCl, 0.001 M MgSO4·7H2O, 0.001 M DTT, pH 7.0) using a Precellys 24 homogenizer (Bertin Technologies, Montigny-le-Bretonneux, France).
β-Galactosidase activity was measured by incubating the lysate supernatant with ONPG in Z-buffer at 37 °C. Reactions were stopped with Na2CO3, and the OD420 was measured (Molecular Devices microplate reader). Protein concentration was determined by the Bradford assay. Specific activity (Miller units) was calculated as (OD420 × 1500)/(T × V × C), where T is the reaction time (min), V is the supernatant volume (mL), and C is the protein concentration (mg/mL). Assays were performed in triplicate. Data are the mean ± SD; significance was assessed by the Student’s t-test.

2.13. Statistical Analysis

Graph plotting and statistical analysis were performed using GraphPad Prism 10.1.2 software. Survival curves were analyzed using the Log-rank test. Other inter-group comparisons were performed using the Student’s t-test or one-way analysis of variance (ANOVA), with p < 0.05 considered statistically significant.

3. Results and Discussion

3.1. Infection Model Outcomes: Macrophage Cytotoxicity and G. mellonella Survival, Melanization, and Bacterial Load

To systematically evaluate the pathogenic capacity of B. anthracis strains with distinct virulence regulatory mechanisms, we conducted parallel infection experiments at both cellular and whole-animal levels. In the J774A.1 macrophage infection model, the PlcR-activated strain A16D2(plcRG) exhibited the strongest cytotoxicity, with a cell survival rate of only 30%, significantly lower than that of the wild-type strain A16D2 (60%). In contrast, the AtxA-deficient strain A16D2ΔatxA exhibited virtually no cytotoxicity against macrophages (Figure 1A). These results clearly underscore the essential role of AtxA in virulence and highlight the enhanced cytotoxicity conferred by PlcR activation. Further validation in a G. mellonella larval infection model revealed that the PlcR-activated strain caused the highest lethality, followed by the wild-type strain, while the AtxA-deficient strain was completely avirulent (Figure 1B). This indicates that the hypervirulent phenotype mediated by PlcR is particularly pronounced in an insect host, suggesting that its mechanism may be more effective or linked to specific factors in this host context.
The observed differences in virulence were further reflected in distinct host pathological phenotypes. Infected larvae exhibited a graded melanization response, as assessed by visual inspection. Melanization—a rapid, visible immune reaction in insects mediated by the prophenoloxidase (PPO) cascade [40]—results in melanin deposition at infection sites, contributing to pathogen encapsulation and limiting microbial dissemination. Visual evaluation revealed that the PlcR-activated strain triggered rapid and intense melanization, the wild-type strain induced a weaker and slower response, and the AtxA-deficient strain caused no detectable melanization (Figure 1C), indicating a clear gradient in the activation of the insect innate immune system.
Histopathological analysis at 48 h post infection showed that both the PlcR-activated and wild-type strains caused adipose tissue necrosis and bacterial colonization (Figure 1D). Semi-quantitative scoring based on the established grading system (Table S2) indicated that necrosis grades were similar between these two groups. Quantitative bacterial load analysis demonstrated that larvae infected with the PlcR-activated strain carried a significantly higher bacterial burden than those infected with the wild-type strain (Figure 1E). In contrast, the AtxA-deficient strain fell below the detection limit in colony counts, and no bacteria were observed in tissue sections. Consistent with this, tissue sections from this group showed no observable necrosis (necrosis grade 0), with adipose architecture largely preserved.
Together, the integrated phenotypic data across cellular and whole-organism models define a clear virulence gradient and uncover differential host-response patterns linked to the regulatory functions of AtxA and PlcR.

3.2. Comparison of PA, LF, and atxA-His Expression Levels in B. anthracis A16D2 and the plcR-Activated Strain

To further confirm whether plcR activation influences the promoter activities of these genes, promoter-lacZ reporter strains (pHT304-PpagA-lacZ, pHT304-Plef-lacZ, and pHT304-PatxA-lacZ) were constructed (Figure S1). β-Galactosidase activity assays revealed that plcR activation had no significant effect on the promoter activities of pagA, lef, or atxA (Figure 2A). Western blot detection showed that plcR activation did not affect the expression of virulence genes pagA, lef, or the regulatory gene atxA (Figure 2B), indicating no antagonistic effect between PlcR and AtxA on AtxA expression or the regulation of virulence genes. This result suggests that the enhanced virulence of the PlcR-activated strain is not achieved by upregulating classical virulence factors (PA, LF) or affecting AtxA expression. Therefore, the high virulence of the PlcR-activated strain likely stems from other virulence factors it regulates, such as phospholipases, hemolysins, and other hydrolases, which may play key roles in host tissue destruction and immune evasion.

3.3. Global Host Transcriptomic Response and Differential Expression Gene Analysis

Whole-transcriptome sequencing and analysis were performed on infected G. mellonella larvae. Principal component analysis (PCA) results showed that samples from the control group, AtxA-deficient strain infection group, wild-type infection group, and PlcR-activated strain infection group formed four independent and well-separated clusters in the transcriptomic space (Figure 3A), indicating that each infection state induced a distinct overall gene expression profile. The distinct clustering of samples was further confirmed by an unbiased hierarchical clustering analysis of global gene expression patterns (Supplementary Figure S2).
Differentially expressed gene (DEG) analysis showed that using the wild-type infection group as the baseline, the AtxA-deficient strain infection group had 4363 DEGs (2204 upregulated, 2159 downregulated); the PlcR-activated strain infection group had 4255 DEGs (2383 upregulated, 1872 downregulated) (Figure 3C,D). Venn diagram analysis showed that the two sets of DEGs shared a large number of overlaps but also had unique portions (Figure 3B).
Global transcriptomic analysis molecularly confirmed the differences in virulence phenotypes among strains, demonstrating that the transcriptional response of the G. mellonella host can distinguish with high resolution between “avirulent”, “classically virulent”, and “non-classically hypervirulent” infection states. This provides core evidence for using the G. mellonella model as a “host response decoder”.

3.4. Analysis of Differential Host Response Patterns Based on Functional and Pathway Enrichment

To elucidate the distinct host response features triggered by different virulence programs in the G. mellonella model, this study performed Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses on the differentially expressed genes (DEGs).
GO enrichment analysis revealed that compared to the AtxA-deficient strain, DEGs induced by wild-type infection were significantly enriched in biological process terms such as “defense response” and “response to biotic stimulus” (Figure 4A). This directly corresponds to the conserved core innate immune programs of G. mellonella, including the activation of downstream signaling via pattern recognition receptors, induction of antimicrobial peptide (AMP) synthesis, and initiation of the melanization cascade (prophenoloxidase system). It indicates that the host mobilizes a systematic immune recognition and effector system in response to a classical virulence attack.
In sharp contrast, when comparing the wild-type and the PlcR-activated strain, although the DEGs showed significant changes in molecular function terms like “oxidoreductase activity” and “hydrolase activity”, they were not comparably enriched in the aforementioned systemic immune response terms (Figure 4B). This suggests that the host response elicited by the PlcR-activated strain is fundamentally different from the typical immune activation pattern induced by wild-type infection at the overall biological process level.
KEGG pathway enrichment analysis further delineated the differences among the three infection groups from a signaling network perspective (Figure 4C,D). Infection by the PlcR-activated strain specifically and significantly enriched the “Glutathione metabolism” pathway, with key antioxidant enzyme genes (e.g., glutathione S-transferase, glutathione peroxidase) being markedly upregulated (Figure 4E). In G. mellonella, the glutathione system is the core defense against oxidative damage [41]. The pronounced perturbation of this pathway indicates that the PlcR-activated strain primarily triggers severe host cellular oxidative stress, likely caused by bacterial hydrolases, rather than activating typical immune pathways.
Conversely, wild-type infection significantly enriched and activated the “Toll and Imd signaling pathways” (Figure 4F). These two pathways are the central hubs of antibacterial immunity in G. mellonella. Their activation directly leads to the nuclear translocation of NF-κB family transcription factors (e.g., Dorsal, Dif) [42,43], thereby driving the high expression of various antimicrobial peptides (e.g., Gallerimycin) [44]. This result clearly delineates the classical immune defense landscape provoked by wild-type infection.
Particularly noteworthy is that the PlcR-activated strain also exhibited the significant upregulation of genes in the “Lysosome” pathway, whereas the expression levels in the wild-type and AtxA-deficient strain groups were similar and relatively low (Figure 4G). In G. mellonella, the upregulation of the lysosome pathway may have dual implications: on the one hand, it could reflect an enhanced damage clearance and autophagy process by host cells in response to the widespread cellular membrane and organelle damage caused by PlcR-regulated hydrolases; on the other hand, it might also be associated with pathogen attempts to interfere with host cellular degradation pathways for its own survival. Across all of these key pathways, the gene expression levels in the AtxA-deficient strain infection group were the lowest among the three groups (Figure 4E–G), showing no significant global transcriptional reprogramming. This aligns with the phenotype of this strain, which, due to its virulence defect, is effectively cleared by the host innate immune system early in infection [45,46].
Together, the GO and KEGG analysis results revealed three distinct host molecular response landscapes in G. mellonella: a Silent Landscape for the AtxA-deficient strain, which failed to induce significant transcriptional reprogramming due to its virulence defect; an Immune Activation Landscape for wild-type infection, characterized by the activation of a systematic immune recognition and defense program; and a unique Metabolic/Oxidative Stress-Dominant Landscape mode for the PlcR-activated strain. The core feature of this mode is the intense perturbation of oxidative stress pathways, rather than further activation of classical immune pathways. The concurrent upregulation of lysosomal pathway genes is likely related to the cellular damage and subsequent clearance processes instigated by PlcR-regulated hydrolases. This metabolic disruption-dominated response pattern is consistent with the pathogenic hypothesis that PlcR, via hydrolases, directly damages host cells and induces internal environmental disturbance.
This study successfully links specific bacterial virulence genotypes with characteristic host molecular response profiles at the functional and pathway levels within the G. mellonella model, providing refined mechanistic insights into the host-intrinsic effects of different pathogenic strategies.

3.5. RT-qPCR Validation

To verify the reliability of the RNA-Seq data, we selected key differentially expressed genes for RT-qPCR confirmation. These genes were chosen from pathways central to the observed phenotypes: glutathione metabolism, the Toll/Imd signaling pathway, and lysosome function. The results were highly concordant with the transcriptomic data (Figure 5). Specifically, expression of the oxidative stress-related genes LOC113515697 and LOC113521642 was significantly upregulated in larvae infected with the PlcR-activated strain compared to the wild-type infection group. Conversely, the immune pathway gene LOC113520181 and the anti-apoptotic factor gene LOC113511724 (associated with Toll/Imd signaling) showed markedly higher expression in the wild-type infection group than in both the AtxA-deficient and PlcR-activated strain groups. Furthermore, the expression of lysosome-related genes LOC113519915 and LOC113514771 was specifically elevated in response to the PlcR-activated strain, consistent with the pathway enrichment analysis. This orthogonal validation strongly supports the accuracy of our RNA-Seq findings and the proposed host response patterns.
To further functionally validate the oxidative-stress phenotype predicted for the PlcR-activated strain, intracellular reactive oxygen species (ROS) levels were measured. The ROS assay confirmed a significant increase in oxidative stress in larvae infected with the PlcR-activated strain compared to the wild-type and AtxA-deficient groups (Figure 5C), providing direct functional support for the transcriptomic prediction.
Although differences from the transcriptomic trend were observed for individual genes with extremely low expression levels (possibly due to technical fluctuations in quantifying low-abundance transcripts), the key genes directly linked to the core phenotypes (oxidative stress and immune activation), which were the focus of this validation, were accurately confirmed. Based on the RT-qPCR validation and reactive oxygen species (ROS) assays conducted above, this study delineates, at the molecular level, the distinct host-response profiles elicited by the three bacterial strains during infection: the AtxA-deficient strain failed to induce significant transcriptional reprogramming due to its virulence defect; the wild-type strain (A16D2) activated a systemic innate immune response typified by the Toll/Imd signaling pathway; whereas the PlcR-activated strain specifically triggered a host reaction characterized by the dysregulation of oxidative stress pathways. These results indicate that the virulence mechanisms regulated by PlcR and AtxA differ fundamentally in their modes of action. Furthermore, this study demonstrates that a multi-dimensional analytical strategy integrating transcriptomics, targeted molecular validation, and functional assays can provide key experimental support for the systematic dissection of the specific host-response signatures associated with different virulence regulatory networks in the Galleria mellonella model.

4. Conclusions

This study systematically delineated the distinct host immune response patterns triggered by the Bacillus anthracis virulence regulators AtxA and PlcR, utilizing a Galleria mellonella infection model combined with analysis. Within this insect model system, AtxA was confirmed to be essential for full bacterial pathogenicity, as its absence resulted in an Immune Clearance state in the host—a finding consistent with its established role as a master regulator in mammalian infections. In contrast, activation of PlcR significantly enhanced bacterial virulence through a mechanism independent of the canonical toxin pathway, potentially involving direct tissue damage mediated by hydrolases. This observation extends our previous work on PlcR functional reconstruction reviving ancestral virulence phenotypes by providing new insights at the host-pathogen interface.
Our analysis further defined three characteristic host response signatures: a silent profile associated with AtxA deficiency, a systemic immune profile induced by wild-type infection, and a unique oxidative stress profile specific to PlcR activation. This latter signature suggests that PlcR may exert its pathogenic effect by disrupting host metabolic homeostasis and inducing oxidative damage. It is noteworthy that subversion of host metabolism is a recognized virulence strategy, exemplified by the Edema Factor (EF) of B. anthracis, which directly hijacks cellular energy metabolism. Our findings indicate that PlcR may drive a novel dimension of this strategy: a mechanism distinct from the EF-cAMP canonical pathway, likely involving hydrolase-mediated tissue injury and oxidative stress that collectively contribute to an indirect reprogramming of host metabolism. Thus, the PlcR-associated response illustrates a new facet of metabolic interference, revealing an added layer of complexity in the pathogen’s armamentarium and offering a fresh explanatory perspective for its pathogenic versatility.
By linking specific host transcriptional signatures to bacterial virulence mechanisms, this work elevates the G. mellonella model from a traditional phenotypic screening tool to a host innate immune response decoder capable of resolving mechanistic nuances. This approach provides a novel methodological framework for the rapid functional inference of uncharacterized virulence genes within an innate immunity context and offers a refined perspective for understanding the evolutionary logic of pathogen–host interactions.
It is important to emphasize that the conclusions of this study are derived from the Galleria mellonella invertebrate model, which possesses an innate immune system but lacks adaptive immunity. While this model serves as a powerful and tractable system for revealing conserved principles of host–pathogen interaction and initial immune activation, the translational relevance of the specific virulence strategies identified—particularly the PlcR-associated metabolic interference phenotype—to mammalian hosts with greater physiological and immunological complexity requires careful validation. Therefore, future work should prioritize validating the functions of AtxA and PlcR in established mammalian infection models, investigating their interplay with adaptive immune responses, and exploring their roles in more complex infection scenarios such as polymicrobial co-infections. Furthermore, as a direct mechanistic extension of the present work, future studies will investigate the virulence of an isogenic, fully encapsulated B. anthracis strain in the G. mellonella model. This experiment is crucial to definitively decouple the contributions of the canonical poly-γ-D-glutamate capsule from the novel AtxA/PlcR-mediated, capsule-independent pathogenic potential revealed herein, and to further clarify the evolutionary context of these virulence strategies. Collectively, these directions will be essential for bridging the findings from the invertebrate model to mammalian infection biology and for defining the boundaries and generalizability of the proposed virulence mechanisms.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms14020505/s1, Figure S1. PCR verification of promoter-lacZ reporter constructs in Bacillus anthracis; Figure S2. Clustered heatmap of sample correlations based on whole-transcriptome profiles; Table S1. Primers used for amplification and verification of the promoter-lacZ transcriptional fusion constructs for pagA, lef, and atxA; Table S2. Semi-quantitative histopathological grading system for G. mellonella infection. This four-level (0–3) grading system was applied to transform morphological observations into statistically comparable ordinal data for objective comparison across infection conditions.

Author Contributions

P.W., D.W. designed the experiments. P.W. and X.W. performed the experiments. P.W. and S.S. performed data collation and investigations. D.W., Y.L., and X.L. performed the theoretical analyses. P.W., D.W., Y.L., and X.L., prepared the original draft, reviewed, and edited the manuscript. H.W., X.L., and D.W. supervised the work, undertook project administration, and acquired funding. R.H. and L.Z. contributed equally to this work. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (grant numbers: 82172317) and the State Key Laboratory of Pathogen and Biosecurity (Academy of Military Medical Science, SKLPBS2419).

Institutional Review Board Statement

The invertebrate model, Galleria mellonella, used in this study is not covered by mandatory ethical approval requirements under either EU Directive 2010/63/EU or Chinese regulations (State Council Order No. 676, 2017), which apply primarily to vertebrate animals. All experimental protocols involving G. mellonella fall under the governance and were conducted in accordance with the guidelines established by our Institutional Animal Care and Use Committee (IACUC-SWGCYJS). The study was designed and conducted in strict accordance with the principles of Replacement, Reduction, and Refinement (the 3Rs) to ensure responsible and ethical use of organisms in research. We confirm that this work complies with all applicable ethical standards and institutional guidelines.

Data Availability Statement

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

Acknowledgments

The authors are solely responsible for the conception, execution, analysis, and interpretation of the research presented in this manuscript. All original data, core arguments, and scientific conclusions are the product of the authors’ independent work. During the preparation of this manuscript, artificial intelligence (Deepseek V3.2) was used strictly for the purpose of post-writing language polishing and proofreading and to assist with grammar, sentence structure, and clarity. The authors have critically reviewed and edited all AI-assisted text and assume full responsibility for the final content.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Pathogenic characterization of B. anthracis regulatory mutants. (A) Cytotoxicity against J774A.1 macrophages. (B) Survival of infected G. mellonella larvae. (C) Larval melanization phenotype, The red arrow points to larvae exhibiting melanization. (D) Histopathology (H&E) and bacterial colonization (Gram stain) in larval tissues. (E) Bacterial burdens in infected larvae. Data are the mean ± SD. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 (ANOVA with Tukey’s test for (A,E); log-rank test for (B)).
Figure 1. Pathogenic characterization of B. anthracis regulatory mutants. (A) Cytotoxicity against J774A.1 macrophages. (B) Survival of infected G. mellonella larvae. (C) Larval melanization phenotype, The red arrow points to larvae exhibiting melanization. (D) Histopathology (H&E) and bacterial colonization (Gram stain) in larval tissues. (E) Bacterial burdens in infected larvae. Data are the mean ± SD. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 (ANOVA with Tukey’s test for (A,E); log-rank test for (B)).
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Figure 2. Expression analysis of virulence determinants in Bacillus anthracis strains under different genetic backgrounds. (A) Transcriptional activity of virulence gene promoters. β-galactosidase activity was measured from lacZ fusions to the promoters of pagA (PpagA-lacZ, left), lef (Plef-lacZ, center), and atxA (PatxA-lacZ, right) in strains A16D2 (wild-type) and A16D2(plcRG) (plcR-overexpressing). T(n) represents the time point at n hours after the onset of the plateau phase. Values are mean Miller Units ± SD (n = 3). (B) Protein-level detection of key virulence components. A sequential, multi-panel analysis is shown: 1. SDS-PAGE (Coomassie Blue-stained) of total protein, displaying the relative abundance of protective antigen (PA) and lethal factor (LF). 2. Western blot for specific immunodetection of PA. 3. Western blot for specific immunodetection of LF. 4. SDS-PAGE (Coomassie Blue-stained) of total protein, showing the relative abundance of the recombinant AtxA-His protein. 5. Western blot for specific immunodetection of the AtxA-His fusion protein via its His-tag. Transcriptional activity of virulence gene promoters. β-galactosidase activity was measured from lacZ fusions to the promoters of pagA (PpagA-lacZ, left), lef (Plef-lacZ, center), and atxA (PatxA-lacZ, right) in strains A16D2 (wild-type) and A16D2(plcRG) (plcR-overexpressing). Activity was assessed under the indicated conditions (e.g., time or inducer gradient; specific labels defined in Methods). Values are mean Miller Units ± SD (n = 3).
Figure 2. Expression analysis of virulence determinants in Bacillus anthracis strains under different genetic backgrounds. (A) Transcriptional activity of virulence gene promoters. β-galactosidase activity was measured from lacZ fusions to the promoters of pagA (PpagA-lacZ, left), lef (Plef-lacZ, center), and atxA (PatxA-lacZ, right) in strains A16D2 (wild-type) and A16D2(plcRG) (plcR-overexpressing). T(n) represents the time point at n hours after the onset of the plateau phase. Values are mean Miller Units ± SD (n = 3). (B) Protein-level detection of key virulence components. A sequential, multi-panel analysis is shown: 1. SDS-PAGE (Coomassie Blue-stained) of total protein, displaying the relative abundance of protective antigen (PA) and lethal factor (LF). 2. Western blot for specific immunodetection of PA. 3. Western blot for specific immunodetection of LF. 4. SDS-PAGE (Coomassie Blue-stained) of total protein, showing the relative abundance of the recombinant AtxA-His protein. 5. Western blot for specific immunodetection of the AtxA-His fusion protein via its His-tag. Transcriptional activity of virulence gene promoters. β-galactosidase activity was measured from lacZ fusions to the promoters of pagA (PpagA-lacZ, left), lef (Plef-lacZ, center), and atxA (PatxA-lacZ, right) in strains A16D2 (wild-type) and A16D2(plcRG) (plcR-overexpressing). Activity was assessed under the indicated conditions (e.g., time or inducer gradient; specific labels defined in Methods). Values are mean Miller Units ± SD (n = 3).
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Figure 3. Statistical analysis of differentially expressed genes in G. mellonella. (A) Transcriptome PCA analysis of samples from the infected and control groups of G. mellonella. (B) Venn diagram of differentially expressed genes between the two groups. (C) Volcano plot of differentially expressed genes between A16D2(ΔatxA) and A16D2. (D) Volcano plot of differentially expressed genes between A16D2(plcRG) and A16D2.
Figure 3. Statistical analysis of differentially expressed genes in G. mellonella. (A) Transcriptome PCA analysis of samples from the infected and control groups of G. mellonella. (B) Venn diagram of differentially expressed genes between the two groups. (C) Volcano plot of differentially expressed genes between A16D2(ΔatxA) and A16D2. (D) Volcano plot of differentially expressed genes between A16D2(plcRG) and A16D2.
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Figure 4. Differential gene expression and pathway enrichment analysis in G. mellonella after infection with B. anthracis A16D2 and its mutants. (A) GO enrichment analysis of DEGs in G. mellonella (comparison A16D2 vs. A16D2(ΔatxA)); (B) GO enrichment analysis of DEGs in G. mellonella (comparison A16D2 vs. A16D2(plcRG)); (C) KEGG enrichment analysis of DEGs in G. mellonella after infection with different strains (enriched pathways for A16D2 vs. A16D2(ΔatxA)); (D) KEGG enrichment analysis of DEGs in G. mellonella after infection with different strains (enriched pathways for A16D2 vs. A16D2(plcRG)); (E) Gene expression patterns of key KEGG pathways in G. mellonella after infection with different strains (heatmap of key genes in the glutathione metabolism pathway); (F) Gene expression patterns of key KEGG pathways in G. mellonella after infection with different strains (heatmap of key genes in the Toll and IMD signaling pathways); (G) Gene expression patterns of key KEGG pathways in G. mellonella after infection with different strains (heatmap of key genes in the lysosome pathway).
Figure 4. Differential gene expression and pathway enrichment analysis in G. mellonella after infection with B. anthracis A16D2 and its mutants. (A) GO enrichment analysis of DEGs in G. mellonella (comparison A16D2 vs. A16D2(ΔatxA)); (B) GO enrichment analysis of DEGs in G. mellonella (comparison A16D2 vs. A16D2(plcRG)); (C) KEGG enrichment analysis of DEGs in G. mellonella after infection with different strains (enriched pathways for A16D2 vs. A16D2(ΔatxA)); (D) KEGG enrichment analysis of DEGs in G. mellonella after infection with different strains (enriched pathways for A16D2 vs. A16D2(plcRG)); (E) Gene expression patterns of key KEGG pathways in G. mellonella after infection with different strains (heatmap of key genes in the glutathione metabolism pathway); (F) Gene expression patterns of key KEGG pathways in G. mellonella after infection with different strains (heatmap of key genes in the Toll and IMD signaling pathways); (G) Gene expression patterns of key KEGG pathways in G. mellonella after infection with different strains (heatmap of key genes in the lysosome pathway).
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Figure 5. RT-qPCR confirmation of RNA-Seq findings. (A) Confirmation of differential expression for selected genes using RT-qPCR. (B) Corresponding original RNA-Seq data for the analyzed genes. (C) Quantification of reactive oxygen species (ROS) in infected larval tissues. Data are shown as mean ± SD. ** p < 0.01, *** p < 0.001, **** p < 0.0001.
Figure 5. RT-qPCR confirmation of RNA-Seq findings. (A) Confirmation of differential expression for selected genes using RT-qPCR. (B) Corresponding original RNA-Seq data for the analyzed genes. (C) Quantification of reactive oxygen species (ROS) in infected larval tissues. Data are shown as mean ± SD. ** p < 0.01, *** p < 0.001, **** p < 0.0001.
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Table 1. Primer sequences for RT-qPCR.
Table 1. Primer sequences for RT-qPCR.
Gene NameForward Primer (5′ → 3′)Reverse Primer (5′ → 3′)
Ef-1α (Reference)ATGTTATCTCCGTCCCAGAACCTCCTTACAGTGAATCC
LOC113521642CTGCAACATGTCACAGCCTCCGTCAATCCTGGTTCATTGGC
LOC113515697TGCATACCTCGTGTCCCAGATTCTGTCCTGATGTTCCAGAGCA
LOC113520181CGCTGGAGGGAAGAATCGATTCCGCTCCTTGGTCATACC
LOC113511724GGCGTGATCTAGCCAGAAACTCTCTTCGTCGTGCACGTTCT
LOC113519915ATGAGTGGCAAGAGCTCCACACAAAGTCTCCCTCTGCACG
LOC113514771AGGTTGGTTAGCCCATCACGGCGGTTCCCCTTGGAAACTA
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MDPI and ACS Style

Wang, P.; Wang, D.; Wang, X.; Lyu, Y.; Shen, S.; Hu, R.; Zhu, L.; Liu, X.; Wang, H. Decoding Virulence Mechanisms of Bacillus anthracis Using a Galleria mellonella Infection Model: Differential Host Response Profiles Elicited by AtxA and PlcR. Microorganisms 2026, 14, 505. https://doi.org/10.3390/microorganisms14020505

AMA Style

Wang P, Wang D, Wang X, Lyu Y, Shen S, Hu R, Zhu L, Liu X, Wang H. Decoding Virulence Mechanisms of Bacillus anthracis Using a Galleria mellonella Infection Model: Differential Host Response Profiles Elicited by AtxA and PlcR. Microorganisms. 2026; 14(2):505. https://doi.org/10.3390/microorganisms14020505

Chicago/Turabian Style

Wang, Pengyao, Dongshu Wang, Xiaojing Wang, Yufei Lyu, Sicheng Shen, Ruilin Hu, Li Zhu, Xiankai Liu, and Hengliang Wang. 2026. "Decoding Virulence Mechanisms of Bacillus anthracis Using a Galleria mellonella Infection Model: Differential Host Response Profiles Elicited by AtxA and PlcR" Microorganisms 14, no. 2: 505. https://doi.org/10.3390/microorganisms14020505

APA Style

Wang, P., Wang, D., Wang, X., Lyu, Y., Shen, S., Hu, R., Zhu, L., Liu, X., & Wang, H. (2026). Decoding Virulence Mechanisms of Bacillus anthracis Using a Galleria mellonella Infection Model: Differential Host Response Profiles Elicited by AtxA and PlcR. Microorganisms, 14(2), 505. https://doi.org/10.3390/microorganisms14020505

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