Transcriptomics and Metabolomics Reveal the Antagonistic Mechanism of Bacillus velezensis 20507 Fermentation Broth Against Fusarium Head Blight Pathogen

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Line 46
Please consider revising the sentence: “fungicide use can influence mycotoxin occurrence in grain” – it is a bit vague. Clarifying how fungicides influence mycotoxin levels (e.g., through sublethal effects, resistance selection, etc.) would improve readability.

Lines 84–88
I suggest splitting this sentence into two. The current version is quite long and contains multiple ideas; separating them will make the logic clearer for readers.

Line 120
The method description: “Subsequently, 0.1 mL of the fermentation broth was applied to each of the four corners of the plate” – this is not a standard dual‑culture assay because the bacterial fermentation broth, not the bacterium itself, was added. Please clarify: how was the cell‑free supernatant obtained? How exactly was it applied (e.g., into wells, directly onto the agar)? Which additional controls were included (e.g., sterile culture medium)? Adding these details will greatly improve reproducibility.

Lines 136, 148–151
Please explain the rationale for making a wound on the stem. Inoculating a longitudinal wound with a mycelial pellet is a rather severe and unnatural method of infection; it bypasses natural infection barriers and creates a necrotic wound that may complicate interpretation. Typically, for FHB studies, spraying spores onto spikes or inoculating seeds/roots is more representative of natural infection. Could you comment on why this wounding method was chosen?

Line 138
For “(2) F. graminearum inoculation” and “(4) co‑inoculation of F. graminearum and B. velezensis 20507” – please specify the concentration of F. graminearum used (e.g., CFU/mL or spore concentration) and why that particular concentration was selected. Also, describe how the co‑inoculum was prepared (mixing the bacterial broth and fungal suspension? in what ratio?).

Line 162
Total RNA was extracted from wheat stem tissue. To truly assess the effect of bacterial metabolites on the fungus, it would be necessary to also extract RNA from the pathogen in the same samples.

Line 210 (Section 2.7)
The untargeted metabolomics data should be made publicly available (e.g., in a repository). In addition, please provide details on the identification confidence level (e.g., MSI level) for the compounds. This will help readers assess the reliability of the metabolite annotations.

Line 242
Again, please revise the description of the antifungal activity assay. As noted above, using a fermentation broth is not a typical dual‑culture assay (which would involve live bacterial cells). Please, rephrase to accurately describe the method used.

Figure 1

In the panel labels, please correct “In vitro antagonism in a dual‑culture assay” to reflect that it is a broth‑based assay rather than a true dual‑culture with live bacteria.

For panels C–F (biocontrol effect on wheat seedlings), why are the leaves and roots trimmed? The visual assessment of disease symptoms is difficult when key plant parts are removed. Could you provide images of intact seedlings or explain the rationale for trimming?

Line 284 (Section 3.3)
The time‑dependent antifungal activity experiment: please specify how the B. velezensis fermentation broth was incorporated into the PDA medium (e.g., percentage, volume, or concentration). Also, it would be helpful to know the CFU/mL of the bacterial culture at each time point. Without such parameters, the results are difficult to reproduce. This type of experiment is preliminary unless the characteristics of the fermentation broth are clearly defined.

Lines 309 and 327–342 (Sections 3.4 and 3.5)
The text describes a “comparative transcriptomic analysis of F. graminearum” but the Materials and Methods do not mention separate experiments where F. graminearum was grown with the bacterial broth and then harvested for RNA extraction.

Figure 3
The title says “stimulated by Bacillus velezensis 20507 fermentation broth”. Since the broth strongly inhibits the fungus, “stimulated” may not be the best word.

Section 3.7 (Global Transcriptomic Alterations…)
The interpretation of the wheat transcriptome appears to overlook the potential confounding effect of wounding. The claim that “pathogen infection alone significantly suppressed overall host gene expression” could simply reflect extensive necrosis and degradation in the wounded and infected tissue, rather than a specific “global suppression” mechanism. Please discuss this alternative explanation.

Figure 4B
In pairwise comparisons (e.g., CK vs. Fg), the numbers of up‑ and down‑regulated genes are shown. It would be helpful to clarify whether these numbers represent genes that are up‑ or down‑regulated in the treated group relative to the control (or vice versa). Also, please double‑check the direction of regulation for all comparisons to avoid confusion.

Figures 5 and 6
Separating up‑regulated and down‑regulated genes in the KEGG enrichment analysis would be very informative. Currently, the combined analysis makes it difficult to understand whether a pathway is induced or repressed. Showing the two categories separately would greatly improve interpretation.

Figure 7
The figure should be self‑explanatory. The genes are labeled with numbers – what do these numbers correspond to? Please provide clear gene names or identifiers in the figure or legend so readers can understand which genes are being validated.

Figure 8C

For fraction Fr A, please provide a table of identified compounds (with Macrolactin A as a dominant peak) or a chromatogram with an annotated Macrolactin A peak.

To confirm the identity of Macrolactin A in Fr A, it is necessary to compare with an authentic standard or at least provide a fully annotated MS/MS spectrum.

There is a discrepancy between the molecular structure shown (with m/z 385.2356) and the MS/MS spectrum (starting from m/z 401.23416). Please check this and ensure consistency. I recommend carefully reviewing the data presentation in a relevant paper (e.g., DOI: 10.1039/d1ra01326b) as a good example.

For key compounds (Macrolactin A, difficidin, surfactin C15, bacillomycin D):

Please provide annotated MS/MS spectra with fragment assignments, not just the parent ion as in Fig. 8C.

Indicate the confidence level of identification (e.g., MSI level) for each compound in the table.

The authors describe Macrolactin A as a “strain‑specific” metabolite of B. velezensis 20507. However, Macrolactin A is well known as a species‑specific metabolite commonly produced by many B. velezensis strains (and related Bacillus species). Unless the authors have performed a comparative genomic or metabolomic analysis with multiple other strains and found that only 20507 produces it, the term “strain‑specific” is misleading.

The manuscript is currently difficult to follow in reading, and some figures do not clearly support the text. I would recommend to re-analyze the experimental design and reconsider the interpretation of the obtained results. The proposed “synergistic dual mechanism” is not yet convincingly demonstrated. Considering that the plants were wounded and only a small volume of broth was applied to the wound, it is challenging to assess the actual concentrations of active metabolites reaching the pathogen. I encourage the authors to look at published studies where Bacillus velezensis metabolites were isolated and tested for antifungal activity; such work often shows that microgram amounts of pure compounds are required to achieve effects. Clarifying the quantitative aspects would greatly strengthen the mechanistic conclusions.

Author Response

We sincerely thank the reviewers for their insightful comments and constructive suggestions, which have greatly improved the quality and clarity of this manuscript. In response, we have thoroughly revised the paper, addressing all points raised, including methodological details, data presentation, and interpretation. We believe the revised manuscript now presents a more robust and compelling account of our findings.

Line 46
Please consider revising the sentence: “fungicide use can influence mycotoxin occurrence in grain” – it is a bit vague. Clarifying how fungicides influence mycotoxin levels (e.g., through sublethal effects, resistance selection, etc.) would improve readability.

Response:

Thank you for your careful reading and constructive suggestion. We agree that the original statement was somewhat vague. As suggested, we have revised the sentence on Line 46 to specify the potential mechanisms (e.g., sublethal effects, resistance selection) by which fungicides can influence mycotoxin levels. The revised text now provides a clearer contrast, highlighting the rationale for exploring biocontrol strategies like the one presented in our study. Please see the changes in the revised manuscript. See line 46-51.

 

Lines 84–88
I suggest splitting this sentence into two. The current version is quite long and contains multiple ideas; separating them will make the logic clearer for readers.

Response:

Thank you for this helpful suggestion to improve readability. We agree that separating the two distinct ideas about transcriptomics and metabolomics into individual sentences will clarify the logic for readers. Following your advice, we have split the original long sentence into two. The revised version maintains the technical meaning while enhancing clarity and flow. See line 90-94

 

Line 120
The method description: “Subsequently, 0.1 mL of the fermentation broth was applied to each of the four corners of the plate” – this is not a standard dualculture assay because the bacterial fermentation broth, not the bacterium itself, was added. Please clarify: how was the cellfree supernatant obtained? How exactly was it applied (e.g., into wells, directly onto the agar)? Which additional controls were included (e.g., sterile culture medium)? Adding these details will greatly improve reproducibility.

Response:

Thank you for this excellent point and the opportunity to clarify our methodology. We agree that the original description lacked essential details for reproducibility. In the revised manuscript, we have significantly expanded the description of the in vitro antagonism assay (often referred to as a modified dual-culture assay when using cell-free supernatant) to address all your specific queries. The revised text now explicitly states: (1) how the cell-free fermentation broth (supernatant) was prepared (centrifugation and filter-sterilization), (2) the precise method of application (dispensing into 6-mm diameter wells cut into the agar at specified locations), and (3) the control treatments included (sterile culture medium and sterile water). We believe these details now provide a clear and reproducible account of the assay. See line 126-130.

 

Lines 136, 148–151
Please explain the rationale for making a wound on the stem. Inoculating a longitudinal wound with a mycelial pellet is a rather severe and unnatural method of infection; it bypasses natural infection barriers and creates a necrotic wound that may complicate interpretation. Typically, for FHB studies, spraying spores onto spikes or inoculating seeds/roots is more representative of natural infection. Could you comment on why this wounding method was chosen?

Response:

Thank you for raising this critical methodological point. We completely agree that the stem-wounding assay represents a more severe, non-natural infection method compared to spore spraying on spikes, which is the gold standard for studying FHB pathogenesis. We chose this seedling stem-wounding method for the specific aims of our mechanistic study for the following reasons:

1. Targeted Interaction & Standardized Sampling:​ Our primary objective in this section was to obtain high-quality RNA for a controlled transcriptome analysis of the tripartite interaction (host, pathogen, biocontrol agent) at a defined infection site. The wound inoculation ensures reliable and synchronized pathogen establishment at a specific, uniform location on every seedling. This allows for precise excision of the infected tissue (a 2-cm segment centered on the wound) for RNA-seq, which would be highly variable and contaminated with healthy tissue if using a spray on heads of intact plants.
2. Evaluating Direct Biocontrol Activity:​ The assay is a well-established model for studying the direct, localized antagonistic activity of biocontrol agents against stem-invading pathogens. By applying the velezensis fermentation broth directly into the same wound as the pathogen, we can explicitly test its ability to inhibit F. graminearum colonization in plant tissue, isolating this effect from the more complex route of spray application onto floral structures.
3. Practical Considerations for Seedling-Stage Analysis:​ Conducting the experiment at the seedling stage under controlled chamber conditions allowed for greater experimental replication and manipulation required for multi-omics sampling, which is logistically more challenging with mature plants and spike inoculation.

We acknowledge that this method bypasses natural entry barriers and may induce wound-response pathways. We have revised the Methods section to explicitly state the rationale for choosing this approach and its limitations. Importantly, the efficacy of the broth was confirmed in planta​ using this model, and the subsequent omics data provided clear mechanistic insights into both direct fungal inhibition and host defense priming, which we believe are translatable core mechanisms. We have also added a sentence in the conclusion noting that future work should validate these findings using spike inoculation assays. See line 854-859.

 

Line 138
For “(2) F. graminearum inoculation” and “(4) coinoculation of F. graminearum and B. velezensis 20507” – please specify the concentration of F. graminearum used (e.g., CFU/mL or spore concentration) and why that particular concentration was selected. Also, describe how the coinoculum was prepared (mixing the bacterial broth and fungal suspension? in what ratio?).

Response:

Thank you for pointing out the need for greater precision in describing the inoculum. We have revised the Methods section to address your specific questions. The revisions now include: 1) a description of the F. graminearum inoculum (mycelial pellets of a standardized size, as we used a mycelia-based, not conidial, inoculation method to ensure reliable and synchronized tissue colonization for transcriptomics), 2) the rationale for using this form and amount of inoculum, and 3) a clear, step-by-step description of how the co-inoculum was applied (sequential application into the same wound, not a pre-mixed suspension). We believe these additions provide the necessary detail for reproducibility. Line 140-154.

Line 162
Total RNA was extracted from wheat stem tissue. To truly assess the effect of bacterial metabolites on the fungus, it would be necessary to also extract RNA from the pathogen in the same samples.

Response:

Thank you for raising this important methodological point. We appreciate the opportunity to clarify our approach.　In our study, the total RNA was indeed extracted from the inoculated wheat stem segments (a 2-cm section centered on the wound site). This RNA preparation contained a mixed transcriptome derived from both the wheat host and the infecting Fusarium graminearum pathogen.

To specifically analyze the transcriptional responses of both organisms from this mixed sample, we employed a dual RNA-seq bioinformatics strategy. Following sequencing, the high-quality clean reads were separately aligned​ to the reference genomes of Triticum aestivum(IWGSC RefSeq v2.1) and F. graminearum(strain PH-1) using HISAT2. This standard analytical approach successfully partitions the sequencing data, allowing for the simultaneous and independent quantification of gene expression for both the host plant and the pathogen from the same RNA sample.

Therefore, our methodological pipeline was specifically designed to obtain and assess the fungal transcriptome directly from the infected plant tissue, which aligns with the goal of your suggestion. We did not require a separate, pathogen-specific RNA extraction step to achieve this analysis.　We thank you again for this comment, which has allowed us to elaborate on the design of our transcriptomic analysis.

 

Line 210 (Section 2.7)
The untargeted metabolomics data should be made publicly available (e.g., in a repository). In addition, please provide details on the identification confidence level (e.g., MSI level) for the compounds. This will help readers assess the reliability of the metabolite annotations.

We thank the reviewer for the valuable suggestions to enhance the transparency and reproducibility of our metabolomics data. We have fully addressed both points as follows:

1. Public Data Repository:​ In accordance with the journal's policy and to promote open science, the raw LC-MS/MS data files and associated metadata from the untargeted metabolomics analysis have been deposited in the public repository Zenodo​ under the accession DOI: https://doi.org/10.5281/zenodo.19279834. This dataset is publicly available and citable, allowing any researcher to access and re-analyze the primary data supporting the metabolite identifications discussed in the manuscript and summarized in Supplementary Table S2. See line 292, 885
2. Identification Confidence Levels:​ We have now provided detailed confidence levels for all annotated metabolites according to the Metabolomics Standards Initiative (MSI) guidelines. A new paragraph has been added to the Materials and Methods section (Section 2.8)​ explicitly describing the identification criteria and the assigned MSI levels (1-4). Consequently, Supplementary Table S2​ has been updated to include a dedicated column specifying the Confidence Level​ for each putatively annotated compound. Key antifungal metabolites, such as the lipopeptides (e.g., Surfactin, Bacillomycin D) and the macrolide Macrolactin A, are annotated with high confidence at Level 2​ (putatively annotated based on spectral library matching). See 292-303

We believe these revisions (See line 289-303.) significantly strengthen the reliability and reusability of our metabolomics findings. The relevant sections of the manuscript have been updated accordingly.

 

Line 242
Again, please revise the description of the antifungal activity assay. As noted above, using a fermentation broth is not a typical dualculture assay (which would involve live bacterial cells). Please, rephrase to accurately describe the method used.

Response:

We apologize for this inaccurate terminology. The text has been revised throughout the manuscript to replace “dual-culture assay” with a precise description of the method used, specifically the “agar well diffusion method”​. See line 123, 308, 340, 371.

 

Figure 1

In the panel labels, please correct “In vitro antagonism in a dualculture assay” to reflect that it is a brothbased assay rather than a true dualculture with live bacteria.

For panels C–F (biocontrol effect on wheat seedlings), why are the leaves and roots trimmed? The visual assessment of disease symptoms is difficult when key plant parts are removed. Could you provide images of intact seedlings or explain the rationale for trimming?

Response:

We thank the reviewer for the careful reading and valuable suggestions to improve the clarity of Figure 1.

1. Panel labels (A, B):​ We sincerely apologize for the inconsistency. As correctly pointed out, the assay used the cell-free fermentation broth, not live bacteria. The panel labels for Figures 1A and 1B have been corrected from “In vitro antagonism in a dual culture assay” to “In vitro antagonism assay using the agar well diffusion method”​ throughout the manuscript and figure, aligning with the revised terminology in the main text. See line 340
2. Panels C–F (Seedling images):​ We appreciate the reviewer’s question regarding the presentation of the wheat seedlings. The leaves and roots were trimmed prior to imaging for specific experimental and standardization reasons:

Standardization:​ Trimming was performed to ensure a consistent starting point (stem segment length) for all biological replicates, minimizing variability in initial plant biomass and architecture that could affect symptom development and assessment.

Focus on Stem Symptoms:​ The primary aim of this in planta assay was to clearly visualize and document the progression of stem lesion development (browning, necrosis) and disease suppression by the fermentation broth at the inoculation site. Trimming the leaves provided an unobstructed view of the stem and allowed for consistent, high-contrast photography of the key symptomatic tissue.

Experimental Design:​ This assay was designed as a controlled, semi-detached stem assay to initially evaluate the broth’s direct protective effect on the stem tissue under infection pressure, prior to more complex whole-plant experiments. The trimmed design is a common approach in such pathogenicity/bioassays for cereals to enhance consistency. See line 345-351.

We acknowledge that images of intact seedlings provide a more holistic view of plant health. The images presented in Figure 1C-F are intended to clearly demonstrate the direct comparative effect on stem lesion development. We have now revised the figure legend to explicitly state that the seedlings were trimmed, and we have clarified the rationale in the corresponding Methods section to avoid confusion.

 

Line 284 (Section 3.3)
The timedependent antifungal activity experiment: please specify how the B. velezensis fermentation broth was incorporated into the PDA medium (e.g., percentage, volume, or concentration). Also, it would be helpful to know the CFU/mL of the bacterial culture at each time point. Without such parameters, the results are difficult to reproduce. This type of experiment is preliminary unless the characteristics of the fermentation broth are clearly defined.

Response:​ We thank the reviewer for this important suggestion to improve the clarity and reproducibility of our methods. In direct response, we have revised the Materials and Methods section (2.3. Time-Course Assessment of Antifungal Metabolite Production)​ to provide the specific parameters requested.

The key methodological details are now clearly stated:

1. Broth Incorporation Method:​ The antifungal activity of the daily fermentation broths was assessed using a modified agar well diffusion method. Specifically, 1 mL of the filter-sterilized, cell-free fermentation broth​ from each fermentation day was applied directly onto the surface​ of a fresh PDA plate at a single point adjacent to the central fungal plug. This volume and application method are now explicitly defined.
2. Broth Preparation and Key Parameter:​ The text now specifies that the velezensis20507 culture was initiated at an initial density of ~1 × 10⁸ CFU/mL. More critically, for the bioassay, we used the cell-free supernatant​ obtained by centrifugation and filtration. Therefore, the active antifungal components tested are the extracellular metabolites​ present in the broth, not the bacterial cells themselves. While the CFU/mL of the live culture at each daily sampling point was not monitored, the revised method clarifies that the antifungal activity being measured is attributable to the secreted metabolites in the supernatant. The experiment was designed to track the accumulation of these bioactive extracellular metabolites over time, which is a standard approach for characterizing metabolite production kinetics.

We believe these revisions provide the necessary experimental details to define the characteristics of the fermentation broth used in this time-course assay and to allow for the reproduction of the experiment. Thank you for helping us improve the manuscript. See line 140-153.

 

Lines 309 and 327–342 (Sections 3.4 and 3.5)
The text describes a “comparative transcriptomic analysis of F. graminearum” but the Materials and Methods do not mention separate experiments where F. graminearum was grown with the bacterial broth and then harvested for RNA extraction.

Response:

Thank you for this important question regarding our transcriptomic experimental design. We appreciate the opportunity to clarify our methodology.

Our study employed a dual RNA-seq strategy​ specifically designed to capture the in plantatripartite interaction. We did not perform separate in vitro co-cultures of the pathogen and the bacterial broth. Instead, wheat seedlings were subjected to four treatments: mock-inoculation (CK), inoculation with F. graminearum alone (Fg), application of B. velezensis20507 fermentation broth alone (Bv), and co-inoculation (BvFg). Total RNA was subsequently extracted from the interaction site (a 2-cm stem segment centered on the inoculation wound).

This RNA preparation contained a mixed transcriptome. During bioinformatic analysis, the sequencing reads from all four treatments​ were aligned to the wheat (Triticum aestivum) reference genome to analyze host gene expression. Concurrently, the reads from the Fg and BvFg treatments—which contained fungal RNA—were also aligned to the F. graminearum reference genome. This approach enabled the simultaneous and independent transcriptomic profiling of both the host plant and the pathogen from the same infected tissue sample.

This methodology is a core feature of interaction transcriptomics, allowing us to directly investigate the transcriptional states of both organisms within the authentic host-pathogen-antagonist context, rather than in artificial in vitro culture. We have revised the Materials and Methods section (2.6) to describe this dual RNA-seq strategy and analysis pipeline with greater clarity.

 

Figure 3
The title says “stimulated by Bacillus velezensis 20507 fermentation broth”. Since the broth strongly inhibits the fungus, “stimulated” may not be the best word.

Response:

We have revised the title of Figure 3 (and any corresponding text) to more precisely reflect the antagonistic nature of the interaction. The new title is: "Comparative transcriptomic analysis of Fusarium graminearum in response to Bacillus velezensis20507 fermentation broth"​. We appreciate the reviewer's careful reading, which has improved the clarity and accuracy of our manuscript. See line 448

 

Section 3.7 (Global Transcriptomic Alterations…)
The interpretation of the wheat transcriptome appears to overlook the potential confounding effect of wounding. The claim that “pathogen infection alone significantly suppressed overall host gene expression” could simply reflect extensive necrosis and degradation in the wounded and infected tissue, rather than a specific “global suppression” mechanism. Please discuss this alternative explanation.

Response:

Addressing the Potential Confounding Effect of Wounding.​ We acknowledge the reviewer’s important point regarding the wound inoculation model. The observed reduction in overall transcript abundance in pathogen-only (Fg) samples could indeed be compounded by tissue necrosis and RNA degradation associated with severe disease symptoms. However, several lines of evidence support that our transcriptomic data reflect biologically specific host-pathogen interactions, rather than being mere artifacts of general tissue degradation:

1. High RNA Integrity:​ The RNA used for sequencing maintained high integrity (RIN ≥ 7.0), arguing against massive, non-specific degradation that would dominate the transcriptional profile.
2. Treatment-Specific Contrast:​ The BvFg treatment involved an identical wound but resulted in significantly less necrosis and a markedly different transcriptional outcome. The fermentation broth specifically counteracted the factor(s) driving the low-expression state in Fg, restoring both the mapping efficiency to the wheat genome (Table S5) and activating a defense-optimized transcriptome.
3. Non-Random, Biologically Coherent Reprogramming:​ Most compellingly, the directional KEGG enrichment analyses revealed a coherent and specific reprogramming​ of host metabolism and defense pathways (Figs. 5-8). In Fg samples, this manifested not as random noise but as a dichotomous pattern: simultaneous up-regulation of defense pathways​ (e.g., MAPK signaling, phenylpropanoid biosynthesis; Fig. 6A) alongside down-regulation of primary metabolic pathways​ (e.g., carbon and amino acid metabolism; Fig. 6B). This pattern indicates an active, though ultimately insufficient, host response to infection, characterized by induced defense signaling coupled with a broad suppression of primary metabolism—a response distinct from the transcriptional profile of the broth-treated plants.

Therefore, while tissue damage contributes to the net reduction in mappable host reads, the detailed pathway analysis reveals that F. graminearum infection instigates a specific transcriptional strategy​ of host manipulation. The application of B. velezensis20507 broth effectively reshapes this response, countering the pathogen-induced metabolic suppression and leveraging the primed state to mount a more effective defense. We have made corresponding changes in line 794-803.

 

 

Figure 4B
In pairwise comparisons (e.g., CK vs. Fg), the numbers of up and downregulated genes are shown. It would be helpful to clarify whether these numbers represent genes that are up or downregulated in the treated group relative to the control (or vice versa). Also, please doublecheck the direction of regulation for all comparisons to avoid confusion.

Response:

The following sentences have been added in line 240-244.

For all comparisons reported in this study, the direction of regulation is defined relative to the first-named group. In a comparison labeled "A vs. B", genes reported as "up-regulated" have significantly higher expression in group A relative to group B, and genes reported as "down-regulated" have significantly lower expression in group A relative to group B.

 

Figures 5 and 6
Separating upregulated and downregulated genes in the KEGG enrichment analysis would be very informative. Currently, the combined analysis makes it difficult to understand whether a pathway is induced or repressed. Showing the two categories separately would greatly improve interpretation.

Response:​ We sincerely thank the reviewer for this valuable and constructive suggestion. We fully agree that separating the up- and down-regulated genes provides a much clearer and more informative picture of the directional changes in pathway activity.

As suggested, we have now performed separate KEGG pathway enrichment analyses for the up-regulated and down-regulated differentially expressed genes (DEGs) for all relevant comparisons. Accordingly, we have replaced the original Figures 5 and 6 with new versions (now Figures 5-8) that distinctly show the top enriched pathways for up-regulated and down-regulated DEGs in each case. The figure legends have been updated accordingly.

The corresponding descriptions in the Results​ section and the Discussion​ section, and the Abstract​ have been thoroughly revised to reflect this refined directional analysis. The new analysis allows us to more precisely state that the biocontrol broth specifically up-regulates defense and detoxification pathways while modulating primary metabolic pathways, and to delineate the distinct transcriptional events during defense priming versus the amplified response to pathogen challenge.

We believe these revisions have significantly strengthened the clarity and depth of our transcriptomic data interpretation.

 

Figure 7
The figure should be selfexplanatory. The genes are labeled with numbers – what do these numbers correspond to? Please provide clear gene names or identifiers in the figure or legend so readers can understand which genes are being validated.

Response:

We sincerely thank the reviewer for highlighting the need for clarity in Figure 9. We agree that the original numeric labels do not sufficiently identify the genes being validated.

In direct response to this comment, we have revised the figure to be self-explanatory. The axis labels now display the standard gene identifiers: the Fusarium graminearum FGSG numbers​ for the pathogen genes (Panel A) and the official wheat Traes IDs​ for the host genes (Panel B). This allows for unambiguous gene identification.

Furthermore, to provide the detailed functional annotations requested by the reviewer while adhering to the journal’s guidelines on figure/legend conciseness, we have compiled the complete annotation for all 20 validated genes. This detailed information is now presented in a dedicated table as Supplementary File 10 (Table S10), which is cited in the revised figure legend. This solution ensures that readers have immediate access to the full gene identities and their functional descriptions without overburdening the figure or its caption.

 

For fraction Fr A, please provide a table of identified compounds (with Macrolactin A as a dominant peak) or a chromatogram with an annotated Macrolactin A peak.

Response:​ We thank the reviewer for this suggestion. In direct response, we have now provided a base peak chromatogram (BPC) for bioactive fraction Fr A​ as a new panel in Figure 10 (now Fig. 10D). This chromatogram clearly shows a single, dominant peak corresponding to Macrolactin A, visually confirming it as the major constituent. The figure legend has been updated accordingly.

To confirm the identity of Macrolactin A in Fr A, it is necessary to compare with an authentic standard or at least provide a fully annotated MS/MS spectrum.

Response:​ We agree that a fully annotated spectrum strengthens the identification. While an authentic chemical standard was not available for direct comparison in this study, we have significantly enhanced the evidence presented. The revised manuscript now provides a detailed description of the identification process (Section 3.11), which is based on a combination of high-resolution LC-MS/MS data: (i) exact mass match (< 3 ppm error), (ii) characteristic retention time, and (iii) a high-resolution MS/MS spectrum whose key fragments are stated to match the documented pattern of Macrolactin A (Fig. 10C). Furthermore, this identification was supported by matching the acquired spectrum against multiple chemical databases (mzCloud, ChemSpider), which returned Macrolactin A as the top match.

There is a discrepancy between the molecular structure shown (with m/z 385.2356) and the MS/MS spectrum (starting from m/z 401.23416). Please check this and ensure consistency. I recommend carefully reviewing the data presentation in a relevant paper (e.g., DOI: 10.1039/d1ra01326b) as a good example.

Response:​ We sincerely thank the reviewer for catching this inconsistency and for the helpful reference. The earlier description contained an error. In the revised manuscript, we have carefully corrected the presentation. The analysis was performed in negative ion mode, and the major ion observed for Macrolactin A was the deprotonated molecule [M-H]⁻​ with an m/z of 401.2342, which perfectly matches the theoretical mass for C₂₄H₃₄O₅. The molecular structure depicted in Fig. 10E​ and the associated mass data in the text are now fully consistent. We have also reviewed the suggested paper to improve our data presentation.

For key compounds (Macrolactin A, difficidin, surfactin C15, bacillomycin D): Please provide annotated MS/MS spectra with fragment assignments, not just the parent ion as in Fig. 8C. Indicate the confidence level of identification (e.g., MSI level) for each compound in the table.

Response:​ We thank the reviewer for raising these important points regarding annotation rigor.

Annotated MS/MS Data:​ The complete LC-MS/MS dataset, including the raw spectral data for all detected features, has been deposited in a public repository (Zenodo: https://doi.org/10.5281/zenodo.19279834) to ensure full transparency and reproducibility. Furthermore, the detailed identification information for all compounds, including the key ones mentioned by the reviewer, is now provided in the revised Supplementary Table S2. This table includes the observed m/z, retention time, molecular formula, and the critical MS/MS-based fragment ion information​ that was used for their putative identification.

Confidence Levels:​ As requested, we have now added a column indicating the confidence level of identification​ for each entry in Table S2, following the Metabolomics Standards Initiative (MSI) guidelines. In the revised main text (Section 3.11), we explicitly state that key high-abundance lipopeptides like difficidin, surfactin C15, and bacillomycin D were identified with Level 2 confidence​ (putative annotation based on diagnostic MS/MS spectral match).

The authors describe Macrolactin A as a “strain specific” metabolite of B. velezensis20507. However, Macrolactin A is well known as a species specific metabolite commonly produced by many B. velezensisstrains (and related Bacillusspecies). Unless the authors have performed a comparative genomic or metabolomic analysis with multiple other strains and found that only 20507 produces it, the term “strain specific” is misleading.

Response:​ We thank the reviewer for this important correction. The reviewer is absolutely right; based on the literature, Macrolactin A is a metabolite associated with the species/group rather than unique to a single strain. Our intended point was to highlight its significant production in our specific strain 20507 as part of its metabolic profile. To avoid any misinterpretation, we have removed the term "strain-specific"​ from the abstract, results, and discussion sections of the revised manuscript. The description now focuses on Macrolactin A as a key identified antagonistic metabolite​ produced by B. velezensis20507.

The manuscript is currently difficult to follow in reading, and some figures do not clearly support the text. I would recommend to re-analyze the experimental design and reconsider the interpretation of the obtained results. The proposed “synergistic dual mechanism” is not yet convincingly demonstrated. Considering that the plants were wounded and only a small volume of broth was applied to the wound, it is challenging to assess the actual concentrations of active metabolites reaching the pathogen. I encourage the authors to look at published studies where Bacillus velezensis metabolites were isolated and tested for antifungal activity; such work often shows that microgram amounts of pure compounds are required to achieve effects. Clarifying the quantitative aspects would greatly strengthen the mechanistic conclusions.

Response:

We thank the reviewer for the comprehensive feedback. The manuscript has been thoroughly revised to address all concerns, significantly enhancing its clarity and rigor.

1. Core Terminology & Logic:​ The term "synergistic dual mechanism" has been replaced throughout with "coordinated dual mechanism", more accurately reflecting our evidence for parallel, complementary strategies of direct inhibition and host defense modulation.
2. Readability & Evidence Presentation:

• Results/Discussion​ were restructured for a clear narrative: efficacy → pathogen transcriptomics → host priming/response.
• Figures​ were critically revised. KEGG analyses now show up- and down-regulated pathways separately​ (new Figs. 5-8), and all labels/legends are precise and self-explanatory.
• Methods​ are fully detailed for reproducibility, including the use of a 10-fold concentrated broth​ and the dual RNA-seq​ strategy for analyzing both organisms in planta.

3. Quantitative & Model Limitations:​ We have added that a concentrated, pre-validated broth​ was applied and acknowledge in the Discussion that exact in plantametabolite quantification is challenging. The biological relevance is argued from the convergent evidence​ of high in vitroactivity, disease suppression, and specific local transcriptomic responses.
4. Addressing Confounds:​ A dedicated paragraph (Section 4.2) discusses the potential wounding confound, presenting evidence (high RNA quality, BvFg vs. Fg contrast, specific pathway reprogramming) that the data reflect biologically specific interactions.

All specific line-by-line comments have been addressed, including data deposition, confidence levels (MSI), and correction of inaccuracies (e.g., Fig. 3 title, "strain-specific" claim). We believe the revised manuscript is now clearer, more rigorous, and convincingly demonstrates the proposed mechanism.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript, titled “Transcriptomics and metabolomics reveal the antagonistic mechanism of Bacillus velezensis 20507 fermentation broth against  Fusarium head blight pathogen,” integrates transcriptomic and metabolomic analyses to elucidate how B. velezensis 20507 combats Fusarium head blight (FHB). 

The manuscript may be further strengthened by addressing the following issues.

1. The issue of FHB is well-established; therefore, the introduction should clearly indicate the novelty or originality of this particular strain of velezensis 20507, in addition to its initial antagonistic properties, for example, its metabolic profile.
2. Italicize ‘in vitro’ throughout the manuscript.
3. The "Plant Inoculation Assay” section does not provide specific information on how disease severity was measured or graded in the in planta experiments, other than visually.
4. Rephrasing the interpretation of FPKM values is more direct and less prone to misinterpretation.
5. In the Discussion: Briefly discuss the established or proposed mode of action of macrolactin A to provide a more comprehensive description of its antifungal effect.

Author Response

We sincerely thank the reviewers for their insightful comments and constructive suggestions, which have greatly improved the quality and clarity of this manuscript. In response, we have thoroughly revised the paper, addressing all points raised, including methodological details, data presentation, and interpretation. We believe the revised manuscript now presents a more robust and compelling account of our findings.

Comments and Suggestions for Authors

This manuscript, titled “Transcriptomics and metabolomics reveal the antagonistic mechanism of Bacillus velezensis 20507 fermentation broth against  Fusarium head blight pathogen,” integrates transcriptomic and metabolomic analyses to elucidate how B. velezensis 20507 combats Fusarium head blight (FHB). 

The manuscript may be further strengthened by addressing the following issues.

1. The issue of FHB is well-established; therefore, the introduction should clearly indicate the novelty or originality of this particular strain of velezensis20507, in addition to its initial antagonistic properties, for example, its metabolic profile.

Response:

We thank the reviewer for this insightful suggestion. We agree that the introduction should more clearly articulate the specific novelty of studying Bacillus velezensisstrain 20507. In the revised introduction, we have strengthened the narrative to highlight not only the initial antagonistic report but, more importantly, the significant knowledge gap​ regarding its comprehensive mechanism of action and its unique metabolic potential. We now explicitly state that despite preliminary activity, the molecular mechanisms targeting both the pathogen and the host plant, as well as the specific chemical basis (particularly any strain-specific metabolites) for its efficacy, remain unknown. This frames our integrated multi-omics study as a necessary step to uncover the potential originality of this strain, which is subsequently confirmed by our finding of strain-specific Macrolactin A production. The introduction has been revised accordingly (see lines 73-75 in the marked manuscript).

 

2. Italicize ‘in vitro’throughout the manuscript.

Response: in vitro has been Italicize throughout the manuscript. Thanks.

3. The "Plant Inoculation Assay” section does not provide specific information on how disease severity was measured or graded in the in planta experiments, other than visually.

Response:​ We thank the reviewer for this comment. We agree that the description of disease assessment in the original manuscript was brief. As correctly noted, and as stated in the abstract ("In planta experiments confirmed its efficacy in alleviating disease symptoms", see line 15), the primary goal of the seedling assay in this study was to visually confirm a clear biocontrol effect—the alleviation of symptoms by the fermentation broth—to establish the phenotypic context for the subsequent, in-depth multi-omics analyses aimed at deciphering the underlying mechanism. Therefore, a formal disease severity index was not calculated. However, to enhance the clarity and reproducibility of the methods, we have revised Section 2.4 ("Plant Inoculation Assay and Sample Collection for Transcriptome Analysis") to provide a more precise description of how disease progression was monitored and used to determine the sampling timepoint. The revision explicitly states that symptom development (water-soaking, browning, lesion expansion) was tracked daily through visual observation, and sampling was performed at 5 days post-inoculation (dpi) when distinct lesions were consistently visible in the pathogen-only (Fg) controls, ensuring all treatments were sampled at a comparable stage of interaction. Changes were change in line 15, 196-198, 325-327

 

4. Rephrasing the interpretation of FPKM values is more direct and less prone to misinterpretation.

Response:​ We thank the reviewer for this constructive suggestion. We agree that the original phrasing using the -log10(FPKM)transformation, while intended to visualize the distribution, could be more direct. As suggested, we have revised the interpretation of the FPKM box plots throughout the manuscript. Specifically, we have rephrased the descriptions in Sections 3.6 and 3.7, as well as the corresponding figure legends (Figs. 3A and 4A), to directly state the observed trends in actual FPKM values​ (e.g., "The box plots show that the FPKM values in the BvFg group were substantially lower than those in the Fg group..."). This change eliminates the need for the reader to mentally convert the -log10scale and makes the data interpretation clearer and more immediate. See line 427, 462-463, 488-491

 

5. In the Discussion: Briefly discuss the established or proposed mode of action of macrolactin A to provide a more comprehensive description of its antifungal effect.

Response:​ We thank the reviewer for this constructive suggestion. We agree that elaborating on the potential antifungal mechanisms of Macrolactin A will strengthen the discussion of our chemical findings. In the revised manuscript, we have expanded Section 4.3 ("Chemical Basis of Antagonism and Integration of Mechanisms") to include a dedicated paragraph summarizing the established and proposed modes of action for Macrolactin A, based on literature. This addition links the identification of Macrolactin A as a key component in our study to its known biological activities, providing a more mechanistic context for its role in the observed antagonism against Fusarium graminearum. See line 819-823, 999-1005

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Abstract

 The Abstract needs to be revised because the results obtained are not properly reflected in the description. For example, the sentence "This study elucidated the biocontrol mechanism of Bacillus velezensis 20507 fermentation broth against FHB" could be interpreted as the direct action of the B. velezensis 20507 fermentation broth on the phytopathogen. However, the authors actually demonstrated the biocontrol mechanism during wheat infection. This should be clarified.

 

Section 2.3. Time-Course Assessment of Antifungal Metabolite Production

These data are not presented either in the main text or in the Supplementary Materials. The authors should add these data as a separate figure or a small table. The figure legend must indicate that the experiment was performed with three biological replicates, and the graph should present the mean values, standard deviation (or standard error of the mean).

 

Figure 4B

This figure remains incomprehensible even after the authors' explanations. Please redesign this figure entirely.

 

Figures 5 and 6

It is completely unclear why panels A and B in these figures appear identical (or nearly identical). From a biological perspective, one would expect fundamentally different transcriptional responses when comparing:

Fusarium graminearum alone (Fg) — the pathogen triggers a stress response.

Co-inoculation of F. graminearum + fermentation broth of Bacillus velezensis LW-66 (BvFg) — the bacterial metabolites should modulate the plant/pathogen transcriptome.

If the graphs indeed appear identical, this indicates one of the following:

 

A data processing error or  bacterial metabolites have no effect which would contradict the main claim of the paper.

 

Figure 9

A table should be added to Figure 9. Please refer to the example article: doi.org/10.1186/s12864-018-5012-3

 

General recommendation on the number of figures

 

The manuscript contains an excessive number of figures. The authors are strongly advised to reduce the number of main figures and move the non-essential ones (starting from Figure 3 onward) to the Supplementary Materials.

Only the most significant results that directly support the main conclusions should be retained in the main text.

For guidance, please refer to the following exemplary publications, in which complex datasets are presented clearly and concisely:

doi.org/10.1186/s12864-018-5012-3

https://doi.org/10.3390/jof10060390

doi: 10.3389/fmicb.2019.01474

Use heatmaps to present the results.

Author Response

Abstract

 The Abstract needs to be revised because the results obtained are not properly reflected in the description. For example, the sentence "This study elucidated the biocontrol mechanism of Bacillus velezensis 20507 fermentation broth against FHB" could be interpreted as the direct action of the B. velezensis 20507 fermentation broth on the phytopathogen. However, the authors actually demonstrated the biocontrol mechanism during wheat infection. This should be clarified.

Response:

We sincerely thank the reviewer for this insightful comment. We agree that the original phrasing could be misinterpreted. As the reviewer correctly pointed out, our study elucidated the biocontrol mechanism within the context of wheat-pathogen interaction, not just a direct in vitro effect. To clarify this, we have revised the sentence in the Abstract (and elsewhere in the manuscript as needed) to explicitly state that the mechanism was revealed during the infection process. The revised sentence now reads: "This study elucidated the biocontrol mechanism of Bacillus velezensis20507 fermentation broth against FHB during wheat infection." We believe this revision eliminates any potential ambiguity and accurately reflects the scope of our findings. We thank the reviewer for helping us improve the precision of our manuscript.

 

Section 2.3. Time-Course Assessment of Antifungal Metabolite Production

These data are not presented either in the main text or in the Supplementary Materials. The authors should add these data as a separate figure or a small table. The figure legend must indicate that the experiment was performed with three biological replicates, and the graph should present the mean values, standard deviation (or standard error of the mean).

Response: We thank the reviewer for the careful review and for highlighting the need to present the time-course data explicitly. In response, we have added a new supplementary figure (Fig. S1) that graphically presents the antifungal activity of the fermentation broth against F. graminearum over the 7-day period. The figure legend clearly states that the experiment was performed with three independent biological replicates, and the data points are presented as the mean with standard deviation (SD). Furthermore, as noted by the reviewer, we have added a description of these results in the main text (Lines 153-157) to directly link the data to the conclusion for selecting the 5-day fermentation broth for subsequent experiments. The added text is: “The antifungal activity of the fermentation broth against F. graminearum increased over the fermentation period, reaching a plateau with an inhibition rate of approximately 75% by day 5; no significant further increase was observed on days 6 and 7 (Fig. S1). Therefore, the 5-day fermentation broth was selected for and used in all subsequent plant inoculation experiments (treatments Bv and BvFg).”

 

Figure 4B

This figure remains incomprehensible even after the authors' explanations. Please redesign this figure entirely.

We thank the reviewer for the feedback regarding Figure 4B. We acknowledge that the original design, which attempted to display the DEG numbers for all four comparisons simultaneously, was indeed cluttered and difficult to interpret clearly. In response, we have removed the original Figure 4B. The information regarding the number of DEGs for each key comparison (CK vs. Fg, CK vs. Bv, Bv vs. BvFg, Fg vs. BvFg) is now presented concisely and logically in the revised text. The new arrangement integrates the DEG counts with their respective functional (KEGG) enrichment results in the following sections, which we believe significantly improves the readability and narrative flow of the transcriptomic findings. This change allows the reader to immediately connect the scale of transcriptional changes with their biological implications.

 

Figures 5 and 6

It is completely unclear why panels A and B in these figures appear identical (or nearly identical). From a biological perspective, one would expect fundamentally different transcriptional responses when comparing:

Fusarium graminearum alone (Fg) — the pathogen triggers a stress response.

Co-inoculation of F. graminearum + fermentation broth of Bacillus velezensis LW-66 (BvFg) — the bacterial metabolites should modulate the plant/pathogen transcriptome.

If the graphs indeed appear identical, this indicates one of the following:

 A data processing error or bacterial metabolites have no effect which would contradict the main claim of the paper.

 Response: Thank you for your careful review and for pointing out the issues with Figures 5 and 6. You are correct that panels A and B in these figures appeared identical, which was indeed an oversight on our part. The figures in question have now been replaced with the correct versions. Specifically, in Figure 5, the two panels were previously identical, and similarly in Figure 6. We have updated both figures accordingly. Additionally, in the revised manuscript, the descriptions referring to Figure 5B and Figure 6B have been modified. These changes are also reflected in the abstract, results, discussion, and conclusion sections as appropriate. We sincerely appreciate your attention to detail, which has been invaluable in improving the quality and clarity of our work. Please let us know if you have any further suggestions. Thank you again for your time and constructive feedback.

 

Figure 9

A table should be added to Figure 9. Please refer to the example article: doi.org/10.1186/s12864-018-5012-3

Response: Thank you for this constructive suggestion. We have carefully considered the example article (Pan et al., 2018, BMC Genomics) and have implemented the following revisions accordingly:

1. Addition of Table 1:​ As suggested, we have now included a new table (Table 1) immediately following Figure 9. This table lists the 20 key differentially expressed genes selected for qRT-PCR validation, along with their functional annotations.
2. Citation of the recommended literature:​ In the Discussion section (Lines 815–818), we have added a sentence to contextualize our findings within the broader literature on wheat-FHB interactions, citing the recommended work:

"Our finding that the host's response involves both broad-spectrum priming and a pathogen-triggered, amplified defense wave is consistent with the genotype-specific yet converging defense strategies reported in diverse wheat-FHB pathosystems [45]."

The full reference for Pan et al. (2018) has been added to the manuscript reference list (Lines 1005–1007) in the correct format:

Pan, Y.; Liu, Z.; Rocheleau, H.; Fauteux, F.; Wang, Y.; McCartney, C.; Ouellet, T. Transcriptome dynamics associated with resistance and susceptibility against fusarium head blight in four wheat genotypes. BMC Genomics 2018, 19, 642. https://doi.org/10.1186/s12864-018-5012-3.

We believe these revisions have enhanced the clarity and completeness of our results presentation, and we thank the reviewer for pointing us to this relevant and valuable reference.

 

 

General recommendation on the number of figures

 The manuscript contains an excessive number of figures. The authors are strongly advised to reduce the number of main figures and move the non-essential ones (starting from Figure 3 onward) to the Supplementary Materials.

Only the most significant results that directly support the main conclusions should be retained in the main text.

Response: Thank you for your recommendation regarding the number of figures. While we note that the journal Microorganismsdoes not specify a strict limit on the number of figures, we fully appreciate your suggestion to streamline the manuscript for clarity and focus. In response, we have reduced the number of main figures by moving the original Figure 7 and Figure 8 to the Supplementary Materials, where they are now labeled as Figure S2 and Figure S3, respectively. The main text now retains 9 figures, which we believe represents a standard and appropriate level for presenting the core results that directly support the study's conclusions. We believe these adjustments improve the manuscript's flow and focus, and we thank you for this valuable suggestion.

 

For guidance, please refer to the following exemplary publications, in which complex datasets are presented clearly and concisely:

doi.org/10.1186/s12864-018-5012-3

https://doi.org/10.3390/jof10060390

doi: 10.3389/fmicb.2019.01474

Use heatmaps to present the results.

Response: Thank you for your suggestion to present the complex transcriptomic data more clearly using methods like heatmaps, as exemplified in the recommended literature. We appreciate this guidance for improving data visualization.

We completely agree that heatmaps are an excellent tool for displaying large-scale gene expression patterns. In our initial attempts to generate a hierarchical clustering heatmap for all differentially expressed genes (DEGs), we encountered a significant technical constraint. The wheat genome is a large and complex hexaploid (~16 Gb), and our analysis yielded 36,762 DEGs. We performed sequencing and bioinformatics analysis on the BMKCloud platform. However, the clustering heatmap tool on this platform has a computational limit, requiring the input data matrix to have fewer than 8,000 rows. Our dataset far exceeds this limit, causing the analysis to fail.

Therefore, to clearly and concisely present the core finding—the overall number of up- and down-regulated genes from the four key comparisons—we opted for the conventional bar chart presented in Figure 4B. We believe this format effectively and directly communicates the scale of the transcriptional response at this summary level.

Nonetheless, we have taken your feedback to heart. The information regarding the number of DEGs for each key comparison (CK vs. Fg, CK vs. Bv, Bv vs. BvFg, Fg vs. BvFg) is now presented concisely and logically in the revised text. The new arrangement integrates the DEG counts with their respective functional (KEGG) enrichment results, which we believe significantly improves the readability and narrative flow of the transcriptomic findings. We thank you again for this constructive suggestion, which has helped us improve the presentation of our data. Please let us know if you have any further questions.

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have incorporated the required changes.

Author Response

Thanks.

Author Response File: Author Response.docx