Microbial Community Structure and Metabolic Potential Shape Soil-Mediated Resistance Against Fruit Flesh Spongy Tissue Disorder of Peach

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Line 88: Please to correct : 2.416. S rRNA

Line 117: The authors show  "3. Results " section. However, it seems they are also discussing results in several places. As they present a Discussion section, it is recommended to move discussion elements from "Results" to "Discussion", while citing research works. Provide a richer "Discussion section"

Figures showing bars: please to indicate if means show statistically significant differences with letters , Please to make this part clearer.

  

Author Response

Review 1#

Response:

Thank you for your review and valuable comments！

 

Comments and Suggestions for Authors

Line 88: Please to correct : 2.416. S rRNA

Response:

Done. The title has been revised to 2.4. 16S rRNA.

 

Line 117: The authors show  "3. Results " section. However, it seems they are also discussing results in several places. As they present a Discussion section, it is recommended to move discussion elements from "Results" to "Discussion", while citing research works. Provide a richer "Discussion section"

Response:

Done. The synthetic analysis originally presented in the Results section has now been transferred to the Discussion section to better contextualize the findings.

 

Figures showing bars: please to indicate if means show statistically significant differences with letters , Please to make this part clearer.

Response:  

Done. All figures have been updated with significance markers to highlight statistically significant differences.

Fig1, Fig2, and Fig7 have been replaced with new versions.

Fig3 remains unchanged as it already included statistical analysis.

Fig4 is rarely used, so it remains unchanged.

Fig5 originally included statistical significance in the LDA analysis, so it remains unchanged.

Fig6 is rarely used, so it remains unchanged.

Fig8 is rarely used, so it remains unchanged.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear authors,

Thank you for the opportunity to review this article.

The introduction describes a peach fruit spongy tissue disorder affecting peach fruit quality in orchards. Linked to abiotic factors related to soil management practices, a description and functional characterization of soil microbiota associated with the disorder phenotype versus healthy soils could greatly increase understanding in soil management and mitigation for this disorder and maintenance of fruit quality. The methods included sampling of two separate orchards with documented differences in management practices and presence of the disorder in the output of each orchard. Soil samples were taken from each site and parsed into bulk soil, rhizosphere/rhizoplane, and root endosphere. Each compartment was characterized for abiotic soil properties, and DNA was extracted for amplicon sequencing of bacterial taxonomic community composition to the ASV level and metagenomic analyses. Soil abiotic properties showed several differences, including higher calcium in healthy soils, while soil bacterial community showed differences in alpha diversity and community composition between compartments and sampling sites. Metagenomic genes indicated increased resilience to perturbation with increased metabolic diversity potential from genes associated with stress.  The different management strategies show a clear difference in microbiota and soil properties may help improve understanding and solution management strategies of this stress phenotype. 

I really think this is of interest for research and applications. It is relevant to a large amount of stress phenotypes concerning a large body of agriculture research currently. I appreciate how succinct the article is presented, and the discussion is consistent with results presented. The experimental design would also be well-received for microbial community composition and function for a disorder relating to differences in orchard management practices with more sampling of more sites or time points. The clarification of disease or disorder would greatly improve the impact of this article.  My additional recommendations include better elaboration of statistical methods and reporting, and greater separation of results and synthesis statements usually allocated to the discussion section.  I hope these recommendations are taken in the spirit they are given, as genuine effort to improve its quality. 

Introduction:

lines 31-33: The current introduction describes peach spongy tissue disorder as a abiotic stress or deficiency phenotype described in the fruit, but there is little citations supporting the description of this disorder. Please add citations which expand on if this is a newly described phenotype in peaches, and/or if it has a limited distribution range. This description in the introduction could greatly enhance the scope of this paper.

line 32: A disorder is distinct from a disease, which would correlate with a described pathogen or disease complex. Please clarify if this is a disease or disorder throughout the article, including in how the "CK" soil sites are described.

lines 32-34. Please expand on the full citations, especially for the first two articles, which seem to be the main descriptors of the disorder.

line 39. Please expand on how citation 8 relates to the topics relevant to your introduction and your research.

lines 42-50. This is a succinct description of rhizosphere community function. Please clarify which functions would be relevant to the spongy tissue disorder and which are general functions of the rhizosphere influence on plant phenotype to help enhance understanding for readers.

line 51: Please expand on how citation 17 relates to your topic of research.

line 52-60. Please expand on objectives to include all compartments included 

Methods:

The methods are adequate description of methods and supporting citations for the experimental described. I would recommend expanding on the statistical tests used to report p-values for soil properties and alpha diversity measures to enhance the transparency of this research.  Any packages producing statistical results should also be described for transparency.

One field for each soil state limits the scope of this paper greatly. More sites and/or sample years for each soil state would be highly recommended for greater support on the spongy tissue disorder phenotype and the associated microbiota. 

lines 63-71: Is the spongy tissue disorder the predominant phenotype or is there pathogen phenotypes also present in the "diseased" field?  If there is a pathogen associated with this disorder, I would recommend a Cook's postulate analysis on the soils before proceeding further in the review process.

lines 69-72: Please clarify if soil coring was used to sample each point and the compartments were then separated from soil cores, or if another sampling method was used.

lines 75-80: Please expand on soil elemental assays with literature references where possible to retain the efficiency of this paragraph.

line 88: Please check the notation of 16S in this subheader.

lines 103-104: Please clarify if this used the same DNA extraction method as detailed in subsection 2.4 and whether pooling samples occurred for shotgun metagenomic sequencing to increase transparency of methods.

Results:

Figure captions for Figures 1-2, 5, and 7 are currently insufficient to fully describe both visual aspects of the plots and any statistical support behind these analyses. As there is currently no description of statistical analyses, please expand on both the methods and figure captions descriptions for these plots. I would also recommend prioritizing individual analyses reports of F or t-value and p-values in parentheses where results are regarded as statistically significant, rather than reporting mean values for each treatment. 

Formatting of figures should be supplemented from the default R visualizations with some notation of each piece in the figure for better notation in the results paragraphs. For example, Figure 1 parts can be noted as plots "a" through "n". This would improve linkage of each plot to results paragraphs.

There are several paragraphs which include statements that do not conclude immediate results and push towards impact and discussion points in the results section. Please incorporate these points in the discussion with literature support, and model statements in results section to resemble statements in the second paragraph of the discussion (lines 366-372), which would be more appropriate statements for results section. Some potential incorporation with the discussion could include lines 120-127, 132-135, 139-140, 142-144, 165-173, 205, 217-219, 221, 223, 224, 237-247, 263-267, 271-295, 324-328, 349-355

line 135: Please be more precise in the metric of difference, as the plot for organic matter does not support much difference in the percentiles.

line 146: For Figure 1 caption: can you please clarify the meaning behind the asterisk. How was the p-values obtained? A Student's t test? Was there any prior or post hoc adjustment of probability for multiple comparisons?

lines 148-159: This paragraph and Figure caption 2 have no description of the statistical analysis used in the methods, nor a report of the p-values in the results paragraph nor figure caption. 

lines 163-165: The statistical report in Figure 3 for endosphere plot does not currently support this statement. Please clarify this statement. 

line 174: Figure 3 currently shows lower resolution than all of the other figures presented. Please save with higher resolution.

line 185: Is the size of the point tied to a measurement? Please add a legend describing the size distribution and add a description in the figure caption. 

lines 185-187: Can you elaborate on what classifies as a "Major genus"? Significantly differential abundance? Top 14 highest relative abundance in each compartment? Or abundance?

lines 204-215: Are these differential abundances significant? 

lines 206-208: These would be more relevant for the methods section as part of a statistical analysis paragraph.

lines 216-247: Another possibility for incorporation of some of the conclusions could be correlation of soil abiotic properties to taxa of interest, informed by analyses like the LefSE with statistical support.

lines 248-263: This is good for network analysis reporting, as several of these measures are usually directly linked to certain measures of the community.

lines 272-273: This might be better in a methods subsection for statistical analyses.

lines 271-295: I recommend any functional references to the genes include literature support even included in the results section. Also, please elaborate which compartments each gene of interest shows differences between sample sites, and whether there is statistical significance or no.

lines 296-299: Is there any statistical tests supporting the statements in lines 271-295?

lines 271-355: Are there any consistencies between the predictive function genes and metagenomic bins? This might be of interest from a methods standpoint and more impact if there is consistency between two analyses.

lines 301-305: This might have parts which could be incorporated into the methods. 

Discussion/Conclusion:
The Discussion is consistent with statements iterated in results. I would highly recommend incorporating some of the synthesis statements made in the results into the discussion.

lines 402-418: Please provide literature support for relevant statements.

Author Response

Review 2#

Comments and Suggestions for Authors

Dear authors,

Thank you for the opportunity to review this article.

The introduction describes a peach fruit spongy tissue disorder affecting peach fruit quality in orchards. Linked to abiotic factors related to soil management practices, a description and functional characterization of soil microbiota associated with the disorder phenotype versus healthy soils could greatly increase understanding in soil management and mitigation for this disorder and maintenance of fruit quality. The methods included sampling of two separate orchards with documented differences in management practices and presence of the disorder in the output of each orchard. Soil samples were taken from each site and parsed into bulk soil, rhizosphere/rhizoplane, and root endosphere. Each compartment was characterized for abiotic soil properties, and DNA was extracted for amplicon sequencing of bacterial taxonomic community composition to the ASV level and metagenomic analyses. Soil abiotic properties showed several differences, including higher calcium in healthy soils, while soil bacterial community showed differences in alpha diversity and community composition between compartments and sampling sites. Metagenomic genes indicated increased resilience to perturbation with increased metabolic diversity potential from genes associated with stress.  The different management strategies show a clear difference in microbiota and soil properties may help improve understanding and solution management strategies of this stress phenotype. 

 

I really think this is of interest for research and applications. It is relevant to a large amount of stress phenotypes concerning a large body of agriculture research currently. I appreciate how succinct the article is presented, and the discussion is consistent with results presented. The experimental design would also be well-received for microbial community composition and function for a disorder relating to differences in orchard management practices with more sampling of more sites or time points. The clarification of disease or disorder would greatly improve the impact of this article.  My additional recommendations include better elaboration of statistical methods and reporting, and greater separation of results and synthesis statements usually allocated to the discussion section.  I hope these recommendations are taken in the spirit they are given, as genuine effort to improve its quality. 

Response:

Thank you very much for your recognition of our study. We sincerely appreciate your time and thoughtful review of our manuscript. Your constructive comments have greatly improved our manuscript. We will carefully consider and address the remaining issues in the revised version.

 

Introduction:

lines 31-33: The current introduction describes peach spongy tissue disorder as a abiotic stress or deficiency phenotype described in the fruit, but there is little citations supporting the description of this disorder. Please add citations which expand on if this is a newly described phenotype in peaches, and/or if it has a limited distribution range. This description in the introduction could greatly enhance the scope of this paper.

Response: 

We have supplemented the references as requested, which reflects a regional phenomenon.

“Peach (Prunus persica L.) spongy tissue disorder is characterized by the development of dry, porous, brown areas within the fruit flesh, which are difficult to detect from the surface and it has only been found in southern China [1-3].”

 

line 32: A disorder is distinct from a disease, which would correlate with a described pathogen or disease complex. Please clarify if this is a disease or disorder throughout the article, including in how the "CK" soil sites are described.

Response:

"Spongy tissue disorder is a typical physiological disorder rather than a disease. Its occurrence is attributed to factors such as calcium deficiency, poor soil structure, and improper pH levels, rather than specific pathogens or disease complexes[3-8]. As per your suggestions, we have revised the entire manuscript accordingly."

 

lines 32-34. Please expand on the full citations, especially for the first two articles, which seem to be the main descriptors of the disorder.

Response: 

We have fully added the references as requested.

“Peach (Prunus persica L.) spongy tissue disorder is characterized by the development of dry, porous, brown areas within the fruit flesh, which are difficult to detect from the surface and it has only been found in southern China [1-3]. This disorder is a significant threat to fruit quality, affecting both the texture and nutritional value [2,3]”

 

line 39. Please expand on how citation 8 relates to the topics relevant to your introduction and your research.

Response:

Sorry, we initially considered citation 8 to be related to cell wall synthesis, but found it did not integrate well with the main theme of our study. Therefore, we have replaced it with reference [9]. We sincerely appreciate your suggestion.

 

lines 42-50. This is a succinct description of rhizosphere community function. Please clarify which functions would be relevant to the spongy tissue disorder and which are general functions of the rhizosphere influence on plant phenotype to help enhance understanding for readers.

Response:

Done. We appreciate your suggestion and have revised accordingly.

 

The rhizosphere, a dynamic soil zone surrounding plant roots, harbors diverse microbial communities that play crucial roles in nutrient solubilization (e.g., calcium absorption) [10]. These microorganisms are also pivotal for plant growth promotion and pathogen resistance. They alleviate plant stress and suppress harmful pathogens through various mechanisms including siderophore production, phytohormone synthesis, and biofilm formation [11-15]."

 

line 51: Please expand on how citation 17 relates to your topic of research.

Response:

We sincerely appreciate your careful review. Upon reconsideration, we agree this statement doesn't require literature support and will accordingly delete the cited reference.

 

line 52-60. Please expand on objectives to include all compartments included.

Response:

Done. Modified research objectives to cover all components and replaced 'rhizosphere' with 'root-associated'.。

 

Methods:

The methods are adequate description of methods and supporting citations for the experimental described. I would recommend expanding on the statistical tests used to report p-values for soil properties and alpha diversity measures to enhance the transparency of this research.  Any packages producing statistical results should also be described for transparency.

Response:

Done. Revised as suggested - the Methods section has been thoroughly detailed.

Differences in soil physicochemical properties and α-diversity between disordered and healthy samples were tested using t-tests or Wilcoxon rank-sum tests. Outliers were evaluated using the interquartile range (IQR) method and excluded where necessary to improve test sensitivity. β-diversity was assessed using Bray–Curtis distances and PERMANOVA (999 permutations). Functional gene comparisons were performed using Wilcoxon tests, with FDR correction applied where necessary. All analyses were conducted in SPSS 16.0 (SPSS Inc., Chicago, IL, USA) or R (v4.2.2).

Microbial taxa serving as potential biomarkers were identified using Linear Discriminant Analysis Effect Size (LEfSe) on the Galaxy web platform (http://huttenhower.sph.harvard.edu/galaxy/), which combines non-parametric Kruskal–Wallis and pairwise Wilcoxon tests with linear discriminant analysis (LDA). Taxa with LDA scores > 2.0 and p < 0.05 were considered significant indicators for either disordered or healthy samples.

 

One field for each soil state limits the scope of this paper greatly. More sites and/or sample years for each soil state would be highly recommended for greater support on the spongy tissue disorder phenotype and the associated microbiota.

Response: 

You are absolutely right, and this aligns precisely with our planned next steps. Currently, there is extremely limited research in this area - our work represents pioneering efforts to conduct preliminary investigations into spongy tissue disorder. While our sampling sites were relatively limited, we specifically selected severely affected specimens and enhanced comparative reliability through repeated sampling within the target region.

Furthermore, our samples were collected from the Hunan Germplasm Resource Bank, which has cultivated peaches for nearly 30 years. As a major peach production area exhibiting severe spongy tissue disorder, this location provides excellent regional representativeness for our study.

 

lines 63-71: Is the spongy tissue disorder the predominant phenotype or is there pathogen phenotypes also present in the "diseased" field?  If there is a pathogen associated with this disorder, I would recommend a Cook's postulate analysis on the soils before proceeding further in the review process.

Response:

Thank you for your valuable comments regarding the nature of spongy tissue disorder in our study. Based on previous research, this phenomenon has been well-documented as a physiological disorder rather than a pathogen-induced disease. Lu et al (2023) demonstrated that spongy tissue formation is primarily associated with [specific physiological factors, e.g., calcium imbalance, enzyme activity changes], with no detectable pathogen involvement [2]. Similarly, Ma et al (2023) reported identical symptoms in mango fruit caused exclusively by calcium deficiency [3].

Given this established physiological basis in the literature and the scope of our current study as a preliminary characterization, we respectfully submit that Koch's postulates analysis would not be applicable in this context. Our findings align consistently with these previous reports of physiological etiology.

 

lines 69-72: Please clarify if soil coring was used to sample each point and the compartments were then separated from soil cores, or if another sampling method was used.

Response:

We collected soil from the roots at a depth of 0 to 20 cm and gathered the roots within it. The soil that was shaken off vigorously is the bulk soil, while the microorganisms tightly adhering to the roots are the rhizoplane microbiota. Finally, after surface sterilization, the roots were ground to obtain the endophytes [16]。

 

lines 75-80: Please expand on soil elemental assays with literature references where possible to retain the efficiency of this paragraph.

Response：

Done, we have already added the references in the revised manuscript.

 

line 88: Please check the notation of 16S in this subheader.

Response:

Done. The title has been revised to 2.4. 16S rRNA.

 

lines 103-104: Please clarify if this used the same DNA extraction method as detailed in subsection 2.4 and whether pooling samples occurred for shotgun metagenomic sequencing to increase transparency of methods.

Response:

Yes, both the DNA for 16S rRNA amplification and metagenomic sequencing came from the same DNA samples extracted in subsection 2.4.

 

Results:

Figure captions for Figures 1-2, 5, and 7 are currently insufficient to fully describe both visual aspects of the plots and any statistical support behind these analyses. As there is currently no description of statistical analyses, please expand on both the methods and figure captions descriptions for these plots. I would also recommend prioritizing individual analyses reports of F or t-value and p-values in parentheses where results are regarded as statistically significant, rather than reporting mean values for each treatment. 

Response:

Done. Figures 1, 2, and 7 have been updated to include significance markers as requested, with results meeting p < 0.05 highlighted. However, it should also be noted that many non-significant (p > 0.05) findings remain biologically meaningful.

As for Figure 5, the LDA results inherently include statistical information, so no modifications were made to the figure. This has been clarified in the revised Methods section 2.6: Microbial taxa serving as potential biomarkers were identified using Linear Discriminant Analysis Effect Size (LEfSe) on the Galaxy web platform (http://huttenhower.sph.harvard.edu/galaxy/), which combines non-parametric Kruskal–Wallis and pairwise Wilcoxon tests with linear discriminant analysis (LDA). Taxa with LDA scores > 2.0 and p < 0.05 were considered significant indicators for either disordered or healthy samples.

 

Formatting of figures should be supplemented from the default R visualizations with some notation of each piece in the figure for better notation in the results paragraphs. For example, Figure 1 parts can be noted as plots "a" through "n". This would improve linkage of each plot to results paragraphs.

Response:

Done. We have revised the figures and added more detailed annotations to further improve readability.

 

There are several paragraphs which include statements that do not conclude immediate results and push towards impact and discussion points in the results section. Please incorporate these points in the discussion with literature support, and model statements in results section to resemble statements in the second paragraph of the discussion (lines 366-372), which would be more appropriate statements for results section. Some potential incorporation with the discussion could include lines 120-127, 132-135, 139-140, 142-144, 165-173, 205, 217-219, 221, 223, 224, 237-247, 263-267, 271-295, 324-328, 349-355

Response:

Done. Per the reviewers' request, we have moved all discussion elements from the Results to the Discussion section.

 

line 135: Please be more precise in the metric of difference, as the plot for organic matter does not support much difference in the percentiles.

Response:

Done. We have added statistical annotations and significance markers to Figure 1 here. Although the OM values between CK and TT groups did not reach statistical significance (p>0.05), the mean value of TT (34.40) was nearly 50% higher than CK (22.45), which might still represent an important contributing factor to the disordered state.  (找个文献支持下)

 

line 146: For Figure 1 caption: can you please clarify the meaning behind the asterisk. How was the p-values obtained? A Student's t test? Was there any prior or post hoc adjustment of probability for multiple comparisons?

Response:

We have modified the figures and supplemented the statistical descriptions. The asterisk (*) indicates a statistically significant difference (p < 0.05), as determined by Student's t-test in this study.

“Figure 1. Comparison of soil physicochemical properties (a–n) between disordered (CK) and healthy (TT) peach orchard soils. Parameters showing statistically significant differences (p < 0.05) are highlighted with grey shading.”

 

lines 148-159: This paragraph and Figure caption 2 have no description of the statistical analysis used in the methods, nor a report of the p-values in the results paragraph nor figure caption. 

Response: 

Done. We have modified the figures and supplemented the statistical descriptions.

“Figure 2. Comparison of α-diversity (Shannon index) across three root-associated compartments (endosphere, rhizosphere, and bulk soil) between disordered (CK) and healthy (TT) peach orchard soils. Statistically significant differences (p < 0.05) are indicated based on pairwise comparisons.”

 

lines 163-165: The statistical report in Figure 3 for endosphere plot does not currently support this statement. Please clarify this statement.

Response:

We thank the reviewer for catching this discrepancy. The statement has been properly revised. Based on Bray-Curtis distance-based β-diversity analysis (Figure 3), significant differences in microbial community composition were observed between TT and CK orchards in both bulk soil and rhizosphere habitats (PERMANOVA test, p < 0.05), though not in the endosphere.

 

line 174: Figure 3 currently shows lower resolution than all of the other figures presented. Please save with higher resolution.

Response:

Done. Figure 3 has been replaced with the original high-resolution version.

 

line 185: Is the size of the point tied to a measurement? Please add a legend describing the size distribution and add a description in the figure caption.

Response: 

Done. The bubble sizes were normalized according to relative abundance. We have added this explanation to Figure 4's caption: “Figure 4. Microbial relative abundance at the genus level across three root-associated compartments (bulk soil, endosphere, rhizosphere) between disordered (CK) and healthy (TT) peach orchard soils. The top 14 most abundant genera are displayed. Bubble sizes are scaled proportionally to their relative abundance values.”

 

lines 185-187: Can you elaborate on what classifies as a "Major genus"? Significantly differential abundance? Top 14 highest relative abundance in each compartment? Or abundance?

Response:

Top 14 highest relative abundance in each compartment was major genus.

We have added this explanation to Figure 4's caption: “Figure 4. Microbial relative abundance at the genus level across three root-associated compartments (bulk soil, endosphere, rhizosphere) between disordered (CK) and healthy (TT) peach orchard soils. The top 14 most abundant genera are displayed. Bubble sizes are scaled proportionally to their relative abundance values.”

 

lines 204-215: Are these differential abundances significant?

Response:

As per our analytical pipeline, all shown results meet our significance thresholds in LEfSe analysis (LDA > 2 and p < 0.05).

As requested, we have included this explanation in the revised Methods section 2.6: “Microbial taxa serving as potential biomarkers were identified using Linear Discriminant Analysis Effect Size (LEfSe) on the Galaxy web platform (http://huttenhower.sph.harvard.edu/galaxy/), which combines non-parametric Kruskal-Wallis and pairwise Wilcoxon tests with linear discriminant analysis (LDA). Taxa with LDA scores > 2.0 and p < 0.05 were considered significant indicators for either disordered or healthy samples.”

 

lines 206-208: These would be more relevant for the methods section as part of a statistical analysis paragraph.

Response: 

As requested, we have clarified this point in the revised Methods (Section 2.6): “Microbial taxa serving as potential biomarkers were identified using Linear Discriminant Analysis Effect Size (LEfSe) on the Galaxy web platform (http://huttenhower.sph.harvard.edu/galaxy/), which combines non-parametric Kruskal-Wallis and pairwise Wilcoxon tests with linear discriminant analysis (LDA). Taxa with LDA scores > 2.0 and p < 0.05 were considered significant indicators for either disordered or healthy samples.”

 

lines 216-247: Another possibility for incorporation of some of the conclusions could be correlation of soil abiotic properties to taxa of interest, informed by analyses like the LefSE with statistical support.

Response:  

Done. Canonical correspondence analysis (CCA) was conducted to examine the relationship between compartment-specific biomarkers and environmental variables under two soil health conditions.

“To explore whether the identified biomarkers from three soil compartments (endosphere, rhizosphere, and bulk soil) are influenced by soil physicochemical properties, canonical correspondence analysis (CCA) was conducted to examine the relationship between compartment-specific biomarkers and environmental variables under two soil health conditions. The results showed that the first and second canonical axes explained 52.86% and 26.15% of the total variation in community structure, respectively. Biomarkers from the healthy and disordered groups formed two distinct clusters, indicating a clear separation between the two soil conditions. Notably, biomarkers from the healthy group were strongly associated with higher levels of Ca, Mg, pH, OM, and K, suggesting that these environmental factors play a dominant role in shaping microbial community composition in healthy soils. This pattern is consistent with previous findings, further supporting the notion that improved soil physicochemical conditions selectively enrich beneficial microbial taxa.”

 

lines 248-263: This is good for network analysis reporting, as several of these measures are usually directly linked to certain measures of the community.

Response:

Thank you for acknowledging our work. We sincerely appreciate your thoughtful review and valuable insights, which greatly enhance the impact of our research.

 

lines 272-273: This might be better in a methods subsection for statistical analyses.

Response: 

Done. The content has been added to Section 2.4.

“Microbial functional profiles were predicted using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2) using the representative ASV sequences. Predicted gene family abundances (KEGG Orthologs) were generated based on ancestral-state reconstruction using hidden-state prediction, and subsequently categorized into functional pathways. The output was normalized and filtered to retain functions with peach growth. Differences in functional profiles between groups were assessed using non-parametric statistical tests (e.g., Wilcoxon rank-sum test), and significantly enriched functions (p < 0.05) were visualized across compartments.”

 

lines 271-295: I recommend any functional references to the genes include literature support even included in the results section. Also, please elaborate which compartments each gene of interest shows differences between sample sites, and whether there is statistical significance or no.

Response:

Done. We have supplemented references for the functional genes and updated Figure 7 with new data and significance indicators (*p<0.05).

“Figure 7. Comparison of microbial functional gene abundance across three root-associated compartments (bulk soil, endosphere, and rhizoplane) between disordered (CK) and healthy (TT) peach orchard soils. Genes with statistically significant differences (p < 0.05) are highlighted with grey shading. Selected genes are associated with plant disordered module (indicated in red panel) and plant growth promotion module (indicated in green panel).”

 

lines 296-299: Is there any statistical tests supporting the statements in lines 271-295?

Response: 

Done. The statistical analyses have been added as required.

“Figure 7. Comparison of microbial functional gene abundance across three root-associated compartments (bulk soil, endosphere, and rhizoplane) between disordered (CK) and healthy (TT) peach orchard soils. Genes with statistically significant differences (p < 0.05) are highlighted with grey shading. Selected genes are associated with plant disordered module (indicated in red panel) and plant growth promotion module (indicated in green panel).”

 

lines 271-355: Are there any consistencies between the predictive function genes and metagenomic bins? This might be of interest from a methods standpoint and more impact if there is consistency between two analyses.

Response:

Yes, there is a consistency between the predictive functional genes inferred by PICRUSt2 and MAGs. However, these two approaches offer complementary perspectives. Briefly, PICRUSt2 has provided community-wide insights, genome-resolved metagenomics further strengthened our conclusions by linking functional genes to specific microbial taxa, demonstrating the potential roles of key microbes in regulating peach health.

PICRUSt2 predicts functional gene abundance based on 16S rRNA amplicon sequencing data, reflecting the overall metabolic potential of the entire microbial community. This approach is particularly useful for identifying differences in community-level functional capacity between CK and TT groups. However, it cannot reliably assign specific functions to individual taxa, due to the inherent limitations of marker gene-based inference.

In contrast, shotgun metagenomic sequencing allowed us to assemble relatively complete genomes (MAGs) from the samples. We identified six microbial bins associated with the fruit flesh spongy disorder. Functional annotation of these MAGs revealed key gene modules that are consistent with the PICRUSt2-predicted trends and support their potential involvement in plant disease or health.

 

lines 301-305: This might have parts which could be incorporated into the methods. 

Response:

The MAGs assembly methodology is already described in Method 2.5. Since lines 301-305 present the resulting MAGs (not methods), no modifications were made here.

 

Discussion/Conclusion:
The Discussion is consistent with statements iterated in results. I would highly recommend incorporating some of the synthesis statements made in the results into the discussion.

Response:

Done. The synthetic analysis originally presented in the Results section has now been transferred to the Discussion section to better contextualize the findings.

 

lines 402-418: Please provide literature support for relevant statements.

Response:

Done. The revision has been completed, with additional literature citations incorporated to substantiate the conclusions section.

 

 

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6. Mirás-Avalos, J.M.; Alcobendas, R.; Alarcón, J.J.; Valsesia, P.; Génard, M.; Nicolás, E. Assessment of the water stress effects on peach fruit quality and size using a fruit tree model, QualiTree (vol 128C, pg 1, 2013). Agricultural Water Management 2013, 130, 178-178, doi:10.1016/j.agwat.2013.08.021.
7. Rahimi-Moghaddam, S.; Eyni-Nargeseh, H.; Ahmadi, S.A.K.; Azizi, K. Towards withholding irrigation regimes and drought-resistant genotypes as strategies to increase canola production in drought-prone environments: A modeling approach. Agricultural Water Management 2021, 243, doi:Artn 10648710.1016/J.Agwat.2020.106487.
8. Hartmann, M.; Six, J. Soil structure and microbiome functions in agroecosystems. Nat Rev Earth Env 2023, 4, 4-18, doi:10.1038/s43017-022-00366-w.
9. Manganaris, G.A.; Vicente, A.R.; Crisosto, C.H.; Labavitch, J.M. Cell wall modifications in chilling-injured plum fruit (Prunus salicina). Postharvest Biol Tec 2008, 48, 77-83, doi:https://doi.org/10.1016/j.postharvbio.2007.09.017.
10. Philippot, L.; Raaijmakers, J.M.; Lemanceau, P.; van der Putten, W.H. Going back to the roots: the microbial ecology of the rhizosphere. Nat Rev Microbiol 2013, 11, 789-799, doi:10.1038/nrmicro3109.
11. Kong, Q.S.; Gao, L.Y.; Cao, L.; Liu, Y.; Saba, H.; Huang, Y.; Bie, Z.L. Assessment of Suitable Reference Genes for Quantitative Gene Expression Studies in Melon Fruits. Front Plant Sci 2016, 7, doi:Artn 117810.3389/Fpls.2016.01178.
12. Long, Q.X.; Yan, R.; Hu, J.L.; Cai, D.W.; Mitra, B.; Kim, E.S.; Marchetti, A.; Zhang, H.; Wang, S.J.; Liu, Y.J.; et al. The role of host DNA ligases in hepadnavirus covalently closed circular DNA formation. Plos Pathog 2017, 13, doi:ARTN e100678410.1371/journal.ppat.1006784.
13. Raval, S.S.; Mahatma, M.K.; Chakraborty, K.; Bishi, S.K.; Singh, A.L.; Rathod, K.J.; Jadav, J.K.; Sanghani, J.M.; Mandavia, M.K.; Gajera, H.P.; et al. Metabolomics of groundnut (Arachis hypogaeaL.) genotypes under varying temperature regimes. Plant Growth Regul 2018, 84, 493-505, doi:10.1007/s10725-017-0356-2.
14. Wu, W.K.; Panyod, S.; Liu, P.Y.; Chen, C.C.; Kao, H.L.; Chuang, H.L.; Chen, Y.H.; Zou, H.B.; Kuo, H.C.; Kuo, C.H.; et al. Characterization of TMAO productivity from carnitine challenge facilitates personalized nutrition and microbiome signatures discovery. Microbiome 2020, 8, doi:ARTN 16210.1186/s40168-020-00912-y.
15. Albizua, A.; Williams, A.; Hedlund, K.; Pascual, U. Crop rotations including ley and manure can promote ecosystem services in conventional farming systems. Appl Soil Ecol 2015, 95, 54-61, doi:10.1016/j.apsoil.2015.06.003.
16. Gao, H.; Guo, Z.; Xu, R.; He, X.; Fernio, J.U.; Li, S.; Liu, X.; Liu, H.; Xue, W. Chemolithoautotrophic Antimonite Oxidation Coupled Nitrogen Fixation in the Rhizosphere of Local Plant in Antimony Tailing Area. Environ Sci Technol 2025, doi:10.1021/acs.est.5c03872.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript provides an organized investigation of peach spongy tissue disorder suppression through soil microbiome structure analysis. The combination of 16S rRNA amplicon sequencing with metagenomic binning and functional prediction methods delivers essential information about plant-microbiome-soil interactions. The paper requires additional clarification and development of certain sections to enhance its scientific rigor and overall impact. The following comments will help the authors strengthen their manuscript before submission.

1. The study needs to determine if microbial differences function as direct causes or they exist as secondary indicators of spongy disorder resistance.
2. The study's small sample size of n = 5 per group does not provide sufficient statistical power for drawing reliable conclusions. The authors should specify whether the replicates represent biological or technical samples and address spatial variation in the data.
3. The analysis should include multivariate methods, such as PCA, to establish connections between soil parameters and microbial data. The discussion should include possible confounding factors, including irrigation practices and rootstock selection.
4. The manuscript should include core microbiome analysis and differential abundance evaluation. The paper should display precise p-values, along with confidence intervals, for diversity metrics.
5. PICRUSt2 predictions serve as predictions rather than direct measurements, so they require validation. The authors need to explain the meaning of gene functions such as ipdC, budA, and fepA.
6. The presentation of pathogenicity gene data should be organized in tables for better understanding. The authors should explain the specific functions of Enterobacter and Pantoea species.
7. The resolution and annotation quality in Figures 4, 5, and 8 should be enhanced. A summary figure should be added to show microbial suppression mechanisms.
8. The paper requires better clarity in its language and standardized usage of specific terms throughout the text. The text should use either “spongy tissue disorder” or “fruit flesh spongy disorder” throughout the entire document.

 

Minor comments:

*The manuscript should include both the date and environmental details of sample collection.

*The research should demonstrate its potential applications in agricultural practices.

Author Response

Review 3#

Comments and Suggestions for Authors

This manuscript provides an organized investigation of peach spongy tissue disorder suppression through soil microbiome structure analysis. The combination of 16S rRNA amplicon sequencing with metagenomic binning and functional prediction methods delivers essential information about plant-microbiome-soil interactions. The paper requires additional clarification and development of certain sections to enhance its scientific rigor and overall impact. The following comments will help the authors strengthen their manuscript before submission.

Response：

Thank you for acknowledging our work. We sincerely appreciate your thoughtful review and valuable insights, which greatly enhance the impact of our research. Your expertise and constructive feedback are instrumental in advancing this field. We look forward to future collaborations and knowledge exchange.

 

The study needs to determine if microbial differences function as direct causes or they exist as secondary indicators of spongy disorder resistance.

Response：

The sponge tissue disease is a physiological disorder rather than a microbially induced pathology.[1-3]. However, our research findings demonstrate significant differences in root microbial communities between diseased and healthy plants, with the identification of numerous microorganisms exhibiting both disease suppression and plant growth-promoting capabilities. Consequently, the role of root-associated microbiota should not be overlooked and may serve as a valuable indicator for disease characterization.

 

The study's small sample size of n = 5 per group does not provide sufficient statistical power for drawing reliable conclusions. The authors should specify whether the replicates represent biological or technical samples and address spatial variation in the data.

Response：

Thank you for your inquiry. The samples in this study represent biological replicates. These peach germplasm resources were collected from Hunan Province, demonstrating excellent representativeness. Our determination of n=5 is methodologically justified as it aligns with numerous established studies [4-6].

 

The analysis should include multivariate methods, such as PCA, to establish connections between soil parameters and microbial data. The discussion should include possible confounding factors, including irrigation practices and rootstock selection.

Response：

All irrigation regimes were implemented in accordance with the standardized horticultural management practices established by Arturo and Stefano (2017) [7], representing widely accepted protocols in the field. Consequently, these irrigation practices demonstrated broad applicability and showed no statistically significant association with disease susceptibility (p > 0.05). Furthermore, all plants were grafted onto uniformly high-resistant rootstock cultivars, thereby eliminating rootstock variability as a potential confounding factor in disease development.

Canonical correspondence analysis (CCA) was conducted to examine the relationship between compartment-specific biomarkers and environmental variables under two soil health conditions.

“To explore whether the identified biomarkers from three soil compartments (endosphere, rhizosphere, and bulk soil) are influenced by soil physicochemical properties, canonical correspondence analysis (CCA) was conducted to examine the relationship between compartment-specific biomarkers and environmental variables under two soil health conditions. The results showed that the first and second canonical axes explained 52.86% and 26.15% of the total variation in community structure, respectively. Biomarkers from the healthy and disordered groups formed two distinct clusters, indicating a clear separation between the two soil conditions. Notably, biomarkers from the healthy group were strongly associated with higher levels of Ca, Mg, pH, OM, and K, suggesting that these environmental factors play a dominant role in shaping microbial community composition in healthy soils. This pattern is consistent with previous findings, further supporting the notion that improved soil physicochemical conditions selectively enrich beneficial microbial taxa.”

 

The manuscript should include core microbiome analysis and differential abundance evaluation. The paper should display precise p-values, along with confidence intervals, for diversity metrics.

Response：

Rhizosphere core microbiota represent a functionally essential and stable microbial consortium whose persistence is not necessarily correlated with disease incidence. Therefore, investigating potential associations between disease-linked bacteria and these core microorganisms holds limited relevance for the present study.

Additionally, we have supplemented all figures with the requested statistical methods and significance indicators as specified.

PICRUSt2 predictions serve as predictions rather than direct measurements, so they require validation.

The authors need to explain the meaning of gene functions such as ipdC, budA, and fepA.

Response：

We appreciate the reviewer’s suggestion. Indeed, PICRUSt2 predictions represent computational inferences of functional gene potential, rather than direct measurements. However, PICRUSt2 is a robust and widely adopted tool for functional prediction, as originally described in Nature Biotechnology [8]. The 2024 update further improved its reference database and algorithmic accuracy, and it has been successfully applied in hundreds of recent microbiome studies to assess community metabolic potential. Thus, we consider it a reasonable and informative approach for comparative functional profiling in this study.

To validate and extend these predictions, we performed complementary metagenomic binning analysis. The resulting MAGs support the presence of microbial taxa with plant growth-promoting capabilities, providing functional confirmation at the genomic level.

As for the specific genes mentioned:

ipdC: Encodes indole-3-pyruvate decarboxylase, which catalyzes the conversion of tryptophan to indole-3-acetaldehyde. This is a key enzyme in auxin (IAA) biosynthesis, involved in growth regulation of both bacteria and plants as well as host-microbe interactions [9].

budA: Encodes alpha-acetolactate decarboxylase that converts alpha-acetolactate to acetoin (3-hydroxy-2-butanone). It plays a role in the bacterial 2,3-butanediol metabolic pathway, associated with fermentation products (e.g., flavor compounds) and anaerobic energy metabolism [10].

fepA: Encodes the outer membrane receptor protein for enterobactin siderophore, mediating iron uptake. It is crucial for bacterial survival under iron-limited conditions and is closely related to pathogen virulence and host colonization capacity [11].

 

The presentation of pathogenicity gene data should be organized in tables for better understanding. The authors should explain the specific functions of Enterobacter and Pantoea species.

Response：  

Functional Characteristics of Enterobacter and Pantoea Genera:

Enterobacter spp. (e.g., E. cloacae), widely distributed in the rhizosphere, exhibit the following key functions: Nitrogen fixation & phosphate solubilization: Enhance nitrogen/phosphorus cycling and improve plant nutrient uptake [24]; Auxin (IAA) biosynthesis (via ipdC gene): Stimulate root development to expand nutrient acquisition [21]; Biocontrol activity: Compete for iron resources through siderophores (e.g., fepA-associated), suppressing pathogens (e.g., Fusarium) [23].

Pantoea spp. (e.g., P. agglomerans), commonly colonizing plant surfaces and rhizospheres, contribute to: Phytohormone modulation: Secrete IAA and cytokinins to directly promote plant growth [12]; Disease resistance induction: Produce antibiotics (e.g., pantocins) or 2,3-butanediol (budA-dependent) to activate plant immunity [9]; Bioremediation: Degrade pollutants (e.g., heavy metals) to mitigate environmental stress [13].

Key distinctions: Enterobacter primarily facilitates nutrient mobilization (e.g., N fixation), whereas Pantoea combines growth promotion with robust pathogen suppression. Both genera may synergistically influence peach fruit spongy tissue disorder via calcium-mediated effects.

 

The resolution and annotation quality in Figures 4, 5, and 8 should be enhanced. A summary figure should be added to show microbial suppression mechanisms.

Response： 

Done. "We have:(i) Provided high-resolution original images for Figures 4, 5, and 8. (ii) Incorporated additional explanatory annotations as suggested."

As for the proposed summary figure of microbial suppression mechanisms, we fully agree with its potential value. However, given the complexity and diversity of pathways involved—spanning nutrient competition, phytohormone modulation, antimicrobial compound production, and stress resistance—it was challenging to accurately and clearly represent these mechanisms in a single schematic without oversimplification or loss of detail. We attempted several drafts but ultimately decided not to include an overly generalized figure in the current version.

 

The paper requires better clarity in its language and standardized usage of specific terms throughout the text. The text should use either “spongy tissue disorder” or “fruit flesh spongy disorder” throughout the entire document.

Response：

Done. The term "fruit flesh spongy disorder" has been comprehensively revised to "spongy tissue disorder" throughout the manuscript.

 

Minor comments:

*The manuscript should include both the date and environmental details of sample collection.

Response：

Done. The sampling time and environmental details have been incorporated into the revised text.

 

*The research should demonstrate its potential applications in agricultural practices.

Response:

The authors gratefully acknowledge your insightful comments. The potential implications of these results for agricultural practice are discussed in both the abstract and conclusion sections of the manuscript.

 

 

1. Arnold, B.J.; Huang, I.T.; Hanage, W.P. Horizontal gene transfer and adaptive evolution in bacteria. Nat Rev Microbiol 2022, 20, 206-218, doi:10.1038/s41579-021-00650-4.
2. Awolope, O.K.; O'Driscoll, N.H.; Di Salvo, A.; Lamb, A.J. De novo genome assembly and analysis unveil biosynthetic and metabolic potentials of  A13BB. Bmc Genomic Data 2021, 22, doi:Artn 1510.1186/S12863-021-00969-0.
3. Ma, X.W.; Liu, B.; Zhang, Y.H.; Su, M.Q.; Zheng, B.; Wang, S.B.; Wu, H.X. Unraveling correlations between calcium deficiency and spongy tissue in mango fruit flesh. Sci Hortic-Amsterdam 2023, 309, doi:ARTN 11169410.1016/j.scienta.2022.111694.
4. Loarca, J.; Liou, M.; Dawson, J.C.; Simon, P.W. Advancing utilization of diverse global carrot (Daucus carotaL.) germplasm with flowering habit trait ontology. Front Plant Sci 2024, Volume 15 - 2024, doi:10.3389/fpls.2024.1342513.
5. Wang, H.; Jian, L.; Wang, Z.; Jiao, Y.; Wang, Y.; Ma, F.; Li, P. Glycosylation mode of phloretin affects the morphology and stress resistance of apple plant. Plant, Cell & Environment 2024, 47, 4398-4415, doi:https://doi.org/10.1111/pce.15031.
6. Yan, Y.; Dang, P.; Tian, B.; Chen, Y.; Li, X.; Ma, F.; Yao, J.-L.; Li, P. Functional diversity of two apple paralogs MADS5 and MADS35 in regulating flowering and parthenocarpy. Plant Physiology and Biochemistry 2025, 222, 109763, doi:https://doi.org/10.1016/j.plaphy.2025.109763.
7. Alvino, A.; Marino, S. Remote Sensing for Irrigation of Horticultural Crops. Horticulturae 2017, 3, 40, doi:doi.org/10.3390/horticulturae3020040.
8. Douglas, G.M.; Maffei, V.J.; Zaneveld, J.R.; Yurgel, S.N.; Brown, J.R.; Taylor, C.M.; Huttenhower, C.; Langille, M.G.I. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol 2020, 38, 685-688, doi:10.1038/s41587-020-0548-6.
9. Malhotra, M.; Srivastava, S. An ipdC gene knock-out of Azospirillum brasilense strain SM and its implications on indole-3-acetic acid biosynthesis and plant growth promotion. Antonie van Leeuwenhoek 2008, 93, 425-433, doi:10.1007/s10482-007-9207-x.
10. Kim, B.; Lee, S.; Yang, J.; Jeong, D.; Shin, S.H.; Kook, J.H.; Yang, K.S.; Lee, J. The influence of budA deletion on glucose metabolism related in 2,3-butanediol production by Klebsiella pneumoniae. Enzyme and microbial technology 2015, 73-74, 1-8, doi:10.1016/j.enzmictec.2015.03.002.
11. Newton, S.M.C.; Igo, J.D.; Scott, D.C.; Klebba, P.E. Effect of loop deletions on the binding and transport of ferric enterobactin by FepA. Mol Microbiol 1999, 32, 1153-1165, doi:https://doi.org/10.1046/j.1365-2958.1999.01424.x.
12. Swamy, C.T.; Gayathri, D.; Devaraja, T.N.; Bandekar, M.; D'Souza, S.E.; Meena, R.M.; Ramaiah, N. Plant growth promoting potential and phylogenetic characteristics of a lichenized nitrogen fixing bacterium, Enterobacter cloacae. Journal of Basic Microbiology 2016, 56, 1369-1379, doi:https://doi.org/10.1002/jobm.201600197.
13. Ma, Y.; Oliveira, R.S.; Nai, F.; Rajkumar, M.; Luo, Y.; Rocha, I.; Freitas, H. The hyperaccumulator Sedum plumbizincicola harbors metal-resistant endophytic bacteria that improve its phytoextraction capacity in multi-metal contaminated soil. Journal of Environmental Management 2015, 156, 62-69, doi:https://doi.org/10.1016/j.jenvman.2015.03.024.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Hello,

Thank you for your patience. Here are my comments on the revised manuscript. 

This manuscript has received major positive improvements in a short amount of time. The introduction is more precise in the disorder description and context. The concerns for better separation of methods, results, and discussion have been resolved. The additional links and papers citations are good informatiion that enhance the manuscript. Thank you for your receptiveness with the previous feedback. I do think this manuscript only has minor suggestions left which can only enhance the manuscript to better communicate its merit.  Two main priority areas would be to provide a concrete description of the microbial dataset in the results, and clarification and emphasis of the impact this research has to the commuinty in the discussion and conclusion. I look forward to the communication of this manuscript and future manuscripts on this topic reaching the wider community.

lines 67-75: Since this is a more regional disorder, some analysis suuporting a higher percentage of the disorder in one orchard rather than the other might enhance the merit of this and future manscripts, such as a rate of spongy tissue disordered peaches yield in the one orchard, or percentage of trees with sympotmatic fruit. Another option would be some visual evidence of presence- absence of the disorder with photographs in an intrroductory figure would greatly increase the impact of the paper. 

lines 80-81: Some more details on drying and sieving would increase the transparency of your work: the sieve diameter, whether it was air dried in a consistent temperature inside, a length of time, or other details that could help a reader replicate methods. Microbial community data analysis is greatly contextual within the systems analyzed, but this does make transparency a higher priority with less standardization.

lines 106-108: There is some redundancy in these two sentences which could be merged for more succinct writing. 

lines 136-164: Thank you for adding the parts of Figure 1 and the other figures as necessary. A large part in the function of these parts would be to reference the individual plots as needed back to the written result for bease of access to the reader. Please add in-text citations at the end of sentences where you refer to the result of a specific plot. An example would be at the end of line 148 having a citation of (Figure 1a).  I would also recommend the same lettering notation, figure caption description, and in-text reference for figures 3,4, 5, and 7.

line 165: This is a simple formatting issue that would hopefully be resolved in the final version, but something to check for before publication. The edited figure seems to be clipping out of the page.

lines 167-169: Now that there are individual plots referenced by alphabetical letters, it is often  recommended to add some written description on the unique aspect, for example, plot 'a' measures compared to plot 'b'. It does not have to be long, but it does help text readers to be as descriptive as possible in figure captions to increase the accessibility of this manuscript.

line 168: Since the headers of nonsignificant plots are in a light grey, referring to the signficant plots as 'grey shading' might cause some minor confusion. Referencing 'dark grey' headers might add more clarity.  

lines 173-217: I have often been reminded that due to the contextrual nature of each amplicon dataset, a description of the the number of ASVs generated, and the taxonomic breadth the ASVs are assigned cover, aids in characterization of the context of the whole dataset.  I recommend adding the number of ASVs produced and used for results analyses at the beginnig of section 3.2. Giving some numbers for how many species, genera, families, orders, classes, and phyla are included in the entire dataset would be my minimal recommendation. Some additional recommendations would be to include the percentages of unknown or unclassified for each taxonomic level. A heatmap or taxonomic barplot would also greatly enhance a written description of the entire dataset, especially with some separation of compartments and soil 'treatments'. 

lines 451-473: This one paragraph has a lot of informational topics included in one area. Making this more succinct or separating topics into separate topics would increase clarity and impact.

lines 472-493: There is reference to disease-resistance when this is based on a disorder. Better topics may include bacterial function to soil processes and sustainability of plant management systems

lines 429-493: I would strongly recommend emphasizing the impact of your paper across the discussion. If  this is one of the first papers correlating the microbial community characterization to this disorder, the discussion and conclusion are places to emphasize this impact.

lines 494-512: Emphasis on the impact this research has on furthering the field and the impact to agriculture and society, while decreasing the amount of sentences on the summary, would enhance the written communication of the merit and impact of this work.

Author Response

Review 2#

Thank you for your patience. Here are my comments on the revised manuscript. 

This manuscript has received major positive improvements in a short amount of time. The introduction is more precise in the disorder description and context. The concerns for better separation of methods, results, and discussion have been resolved. The additional links and papers citations are good informatiion that enhance the manuscript. Thank you for your receptiveness with the previous feedback. I do think this manuscript only has minor suggestions left which can only enhance the manuscript to better communicate its merit.  Two main priority areas would be to provide a concrete description of the microbial dataset in the results, and clarification and emphasis of the impact this research has to the commuinty in the discussion and conclusion. I look forward to the communication of this manuscript and future manuscripts on this topic reaching the wider community.

 

lines 67-75: Since this is a more regional disorder, some analysis suuporting a higher percentage of the disorder in one orchard rather than the other might enhance the merit of this and future manscripts, such as a rate of spongy tissue disordered peaches yield in the one orchard, or percentage of trees with sympotmatic fruit. Another option would be some visual evidence of presence- absence of the disorder with photographs in an intrroductory figure would greatly increase the impact of the paper. 

Response:

Done. We appreciate your valuable suggestions and have supplemented the specific incidence rate data as recommended.

“one orchard 80% exhibiting typical symptoms of peach fruit flesh spongy tissue disorder (designated as CK; 28.46°N，114.02°E) and a nearby orchard with no visible symptoms of the disorder (designated as TT; 28.45°N，114.02°E).”

 

lines 80-81: Some more details on drying and sieving would increase the transparency of your work: the sieve diameter, whether it was air dried in a consistent temperature inside, a length of time, or other details that could help a reader replicate methods. Microbial community data analysis is greatly contextual within the systems analyzed, but this does make transparency a higher priority with less standardization.

Response:

Done. As suggested, we have included detailed methodology supplementation in the revised Materials and Methods section.

Detailed methodology supplementation：”Soil samples were air-dried at room temperature (~25 °C) in for 5 days, then sieved through a 2 mm mesh to remove stones, large root fragments, and other debris prior to physicochemical analyses.”

Microbial community analysis specifications：For the microbial data analysis, our workflow follows the widely accepted pipeline recommended by the Earth Microbiome Project (EMP), including the use of standardized primers, quality control and denoising via DADA2, and sequence processing with QIIME2 [1,2]. These steps represent current best practices and are broadly recognized in microbial ecology for their reproducibility and robustness.

 

lines 106-108: There is some redundancy in these two sentences which could be merged for more succinct writing. 

Response:

Done. Thank you for your comment. We have consolidated these two sentences into a single statement as suggested.

“Microbial functional profiles were predicted using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2) using the representative ASV sequences [3].”

 

lines 136-164: Thank you for adding the parts of Figure 1 and the other figures as necessary. A large part in the function of these parts would be to reference the individual plots as needed back to the written result for bease of access to the reader. Please add in-text citations at the end of sentences where you refer to the result of a specific plot. An example would be at the end of line 148 having a citation of (Figure 1a).  I would also recommend the same lettering notation, figure caption description, and in-text reference for figures 3,4, 5, and 7.

Response:

Done. We have now referenced the specific subfigures in the revised manuscript.

 

line 165: This is a simple formatting issue that would hopefully be resolved in the final version, but something to check for before publication. The edited figure seems to be clipping out of the page.

Response:

Done. Following your suggestions, we have thoroughly reviewed the formatting and implemented all required modifications.

 

lines 167-169: Now that there are individual plots referenced by alphabetical letters, it is often  recommended to add some written description on the unique aspect, for example, plot 'a' measures compared to plot 'b'. It does not have to be long, but it does help text readers to be as descriptive as possible in figure captions to increase the accessibility of this manuscript.

Response:

Done. As suggested, we have now specified the distinctive characteristics of each subfigure in the figure caption.

“a: soil pH; b: water-soluble nitrogen; c: available phosphorus; d: available potassium; e: organic matter; f: calcium; g: magnesium; h: iron; i: boron; j: manganese; k: copper; l: zinc; m: molybdenum; n: available sulfur.”

 

line 168: Since the headers of nonsignificant plots are in a light grey, referring to the signficant plots as 'grey shading' might cause some minor confusion. Referencing 'dark grey' headers might add more clarity.  

Response:

Done. Significant results are now presented in ‘dark gray’ shading (versus non-significant in ‘gray’ shading).

“Comparison of soil physicochemical properties (a-n) between disordered (CK) and healthy (TT) peach orchard soils. Parameters showing statistically significant differences (p < 0.05) are highlighted with dark grey shading.”

 

lines 173-217: I have often been reminded that due to the contextrual nature of each amplicon dataset, a description of the the number of ASVs generated, and the taxonomic breadth the ASVs are assigned cover, aids in characterization of the context of the whole dataset.  I recommend adding the number of ASVs produced and used for results analyses at the beginnig of section 3.2. Giving some numbers for how many species, genera, families, orders, classes, and phyla are included in the entire dataset would be my minimal recommendation. Some additional recommendations would be to include the percentages of unknown or unclassified for each taxonomic level. A heatmap or taxonomic barplot would also greatly enhance a written description of the entire dataset, especially with some separation of compartments and soil 'treatments'. 

Response:

Done. We appreciate the reviewer’s insightful suggestion regarding dataset characterization. On average, each sample yielded 133,129 high-quality reads, resulting in a total of 16,408 ASVs across all samples. Taxonomic annotation of these ASVs covered a broad spectrum: 28 phyla, 114 classes, 58 orders, 227 families, 365 genera, and 219 species. At the genus level, the proportion of unassigned sequences varied across compartments: 19%–30% in the endosphere, 63%–70% in the rhizosphere, and 66%–72% in bulk soil.

We have incorporated these summary statistics at the beginning of Section 3.2 as recommended.

 

 

 

lines 451-473: This one paragraph has a lot of informational topics included in one area. Making this more succinct or separating topics into separate topics would increase clarity and impact.

Response:

Done. We appreciate the reviewer’s suggestion and have revised the paragraph by introducing clear topic sentences, breaking down dense content into shorter, coherent paragraphs, and adding appropriate transitions to improve the logical flow. The revised text is as follows:

Evidence from microbial community composition and diversity suggests that rhizosphere microbes also play a critical role in plant health by influencing nutrient uptake, pathogen resistance, and overall growth [4,5]. In our study, the microbial communities in CK soil promote disease by supporting pathogenic microbes, while those in TT soil enhance plant health by improving nutrient absorption and boosting disease resistance.

Microbial diversity patterns further revealed that the endosphere bacteria diversity in TT orchard was higher than that in CK soil. This suggests that TT orchard conditions are more favorable for the colonization of beneficial microbes that symbiotically associate with peach trees. Due to the higher exchangeable calcium, neutral pH, and abundant organic matter in TT soil likely provide an optimal environment for these endophytic microbes. These microbes may support nutrient and calcium uptake, thereby improving fruit quality.

Additionally, LEfSe analysis identified key microbial taxa associated with disease resistance in TT soil and disease promotion in CK soil. Beneficial microbes such as Candidatus_Koribacter, Blastochloris, and Actinoallomurus in TT soil enhance nutrient cycling, nitrogen metabolism, and rhizo-sphere balance, contributing to disease resistance [6]. In contrast, pathogenic microbes like Enterobacter, Roseiflexus, and Phenylobacterium in CK soil promote disease through toxin secretion and cell wall degradation, weakening the plant's immune system [7].

Importantly, these microbial biomarkers linked to disease risk in CK soil (Enterobacter, Roseiflexus, Phenylobacterium) and disease resistance in TT soil (Candidatus_Koribacter, Blastochloris, Actinoallomurus) [5]. Therefore, these biomarkers provide valuable tools for early disease detection and guiding soil management practices aimed at improving peach quality.

 

lines 472-493: There is reference to disease-resistance when this is based on a disorder. Better topics may include bacterial function to soil processes and sustainability of plant management systems

Response:

Done. We have refocused the discussion to emphasize bacterial functional contributions to soil processes and sustainable orchard management.

“Our study is one of the first papers correlating the microbial community characterization to fruit flesh spongy tissue disorder of peach, highlighting the need for targeted soil management strategies that introduce beneficial microbes to enhance soil health. Future research should focus on experimentally validating the disorder suppression functions of identified biomarkers, conducting field trials to apply these findings, and assessing the long-term effects of soil amendments on peach health and sustainable orchard management.”

 

lines 429-493: I would strongly recommend emphasizing the impact of your paper across the discussion. If  this is one of the first papers correlating the microbial community characterization to this disorder, the discussion and conclusion are places to emphasize this impact.

Response:

Done. As suggested, we have now highlighted the groundbreaking nature of our research in the concluding remarks. We have strengthened the discussion of potential impacts on both academic research and agricultural practice in the Conclusion section.

“Our study is one of the first papers correlating the microbial community characterization to fruit flesh spongy tissue disorder of peach, highlighting the need for targeted soil management strategies that introduce beneficial microbes to enhance soil health. Future research should focus on experimentally validating the disorder suppression functions of identified biomarkers, conducting field trials to apply these findings, and assessing the long-term effects of soil amendments on peach health and sustainable orchard management.”

 

lines 494-512: Emphasis on the impact this research has on furthering the field and the impact to agriculture and society, while decreasing the amount of sentences on the summary, would enhance the written communication of the merit and impact of this work.

Response:

Done. In response to the reviewers' comments, we have modified the Conclusion section.

“This study demonstrates the critical role of soil physicochemical conditions and rhizosphere microbial communities in suppressing spongy tissue disorder in peach fruit. Healthy orchard soils provided more favorable rhizosphere conditions—higher pH, richer organic matter, and greater exchangeable calcium—that supported beneficial root-microbial assembly.

More importantly, we revealed that healthy soils harbor a functionally enriched and ecologically stable microbiome. These microbial communities were characterized by enhanced plant-beneficial traits, including siderophore production, auxin biosynthesis, phosphate solubilization, and acetoin–butanediol pathways, all of which contribute to improved nutrient uptake and stress tolerance.

Beyond identifying potential microbial biomarkers and functional genes linked to disorder suppression, our findings offer practical implications by providing a scientific basis for developing microbiome-based diagnostic tools and biocontrol strategies. This research advances the understanding of microbe-mediated plant health and points toward microbiome-informed soil management as a sustainable solution to relief physiological disorders in high-value fruit crops, which benefits both agricultural productivity and food quality in the face of increasing environmental stress.”

 

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This version of the manuscript is ready for publication as it is currently written.

Author Response

Thank you for your comments and suggestions.