Combined Soil Inoculation with Mycorrhizae and Trichoderma Alleviates Nematode-Induced Decline in Mycorrhizal Diversity

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper explored the interaction of Arbuscular mycorrhizal fungi (AMF), Trichoderma (T), and root-knot nematodes of the genus Meloidogyne. Based on a laboratory incubation experiment for 63 days, combining the PCR-Single-Strand Conformation Polymorphism (SSCP), the fingerprinting of D1 variable domain of the 28S-rDNA conducted by single-strand conformation polymorphism strategy, the authors found that single and co-inoculation with AMF and T reduced the number of galls and eggs of tomato roots, nematode larvae in the soil-substrate and improved plant growth. Meloidogyne javanica depressed AMF diversity (Glomeraceae family), but the plants received 27 T+AMF; no reduction in AMF diversity was observed. Furthermore, M. javanica reduced root mycorrhizal colonization but did not affect Trichoderma abundance in the substrate. These results suggested that combining Trichoderma with AMF may enhance the resilience of biocontrol strategies, mitigating the impact of M. javanica on AMF d diversity and promoting more effective nematode control. This research is interesting. The authors provided detailed descriptions of the Introduction and Discussion sections, which helped me understand the study and its main results. Despite my overall positive opinion of this paper, I have several suggestions and concerns about the Methods, Data analysis, and Results.

Specific comments:

Methods:

1. line100. What is the sampling depth of AMF inoculum from five agricultural sites in the southeastern Argentinean Pampas?
2. line129-134. In-plant experiment, which is coated on tomato seeds, Trichoderma T363, Trichoderma TJ15, or other Trichoderma?
3. line135-144. Did you inoculate two germinated plantlets again with Trichoderma T363 or Trichoderma TJ15?

 

Data analysis

In this article, the data analysis methods mainly rely on traditional statistical analyses (such as Analysis of Variance, ANOVA, and Tukey's test) and genetic diversity analysis based on Single-Strand Conformation Polymorphism (SSCP). Here are some possible suggestions for improving the data analysis methods:

1. Multivariate Statistical Analysis

Multivariate statistical methods, such as Principal Component Analysis (PCA), are employed to analyze the relationships among multiple variables. For instance, they can be used to explore the associations between Arbuscular Mycorrhizal Fungi (AMF) diversity, root-knot nematode infection levels, and plant growth parameters. PCA can reduce the dimensionality of the data while retaining the most critical information, helping to identify the key variables that drive the relationships among these variables. Other multivariate techniques like Canonical Correlation Analysis (CCA) can also be used to understand the complex interrelationships between these variables further.

2. Machine Learning Methods

Machine learning algorithms, such as Random Forest and Support Vector Machines (SVM), are applied to predict the effects of different inoculation treatments on AMF diversity and nematode control efficacy. These algorithms can handle complex, non-linear relationships in the data. For example, Random Forest can make accurate predictions and identify the key influencing factors by ranking the importance of input variables. Conversely, SVM effectively finds optimal decision boundaries in high-dimensional spaces, which can help classify different treatment effects on AMF and nematodes.

3. Bayesian Statistics

Bayesian statistical methods are utilized to estimate the uncertainty associated with the effects of inoculation treatments on AMF diversity and nematode control efficacy. Bayesian approaches provide a more comprehensive probabilistic estimate by incorporating prior knowledge and updating it with observed data. This will allow for a more nuanced understanding of the potential outcomes and the associated uncertainties, which is crucial in making informed decisions in research and practical applications.

4. Ecological Network Analysis

Ecological network models are constructed to analyze the interactions among AMF, Trichoderma, and root-knot nematodes. These models can visualize the complex web of relationships within the microbial community, including mutualistic, antagonistic, and competitive interactions. By analyzing the network structure, properties such as centrality, clustering coefficient, and modularity can be calculated to understand the key players and the overall stability of the microbial community. This approach provides insights into the complex dynamics of the microbial ecosystem and how different organisms interact.

Results:

In the results section of this article, although some critical findings have been presented, there are still several aspects that could be further improved to enhance its completeness and persuasiveness:

1. Explanation of Statistical Significance

The results section should provide a more detailed account of each statistical test's significance level (p-value) and effect size. This would help readers better understand the reliability and importance of the results.

2. Interaction Effect Analysis

The article could further analyze the interaction effect between AMF and Trichoderma, especially their impact on AMF diversity and nematode control efficacy under different inoculation combinations.

3. Sensitivity Analysis

Conduct a sensitivity analysis to evaluate the impact of different experimental conditions (such as soil type and plant variety) on the results. This would help understand the generality and robustness of the research findings.

Author Response

Comment 1:

Methods: line100. What is the sampling depth of AMF inoculum from five agricultural sites in the southeastern Argentinean Pampas?

Response: Thank you for your question, and we apologize for overlooking the inclusion of this detail in the manuscript. The sampling depth of the AMF inoculum from the five agricultural sites in the southeastern Argentinean Pampas was 20 cm. We have revised the manuscript to include this information for better clarity (Lines 101-103 of the corrected manuscript).

 

Comment 2: Methods: line129-134. In-plant experiment, which is coated on tomato seeds, Trichoderma T363, Trichoderma TJ15, or other Trichoderma?

Response: Thank you for your valuable question. We apologize for the lack of clarity in the description. In the in-plant experiment, tomato seeds were coated with either Trichoderma strain T363 or TJ15, separately, according to the treatment. We have revised the manuscript to clarify this point and avoid any confusion (Lines 134-136 of the corrected manuscript).

 

Comment 3: Methods: line135-144. Did you inoculate two germinated plantlets again with Trichoderma T363 or Trichoderma TJ15?

Response: Thank you for your remark. The germinated plantlets were not re-inoculated with Trichoderma T363 or Trichoderma TJ15. Only the seeds were coated with the respective strains at the beginning of the experiment. We have revised the manuscript to clarify this point and avoid any confusion (Line 141 of the corrected manuscript).

 

Comment 4: Data analysis: In this article, the data analysis methods mainly rely on traditional statistical analyses (such as Analysis of Variance, ANOVA, and Tukey's test) and genetic diversity analysis based on Single-Strand Conformation Polymorphism (SSCP). Here are some possible suggestions for improving the data analysis methods:

Multivariate Statistical Analysis: Multivariate statistical methods, such as Principal Component Analysis (PCA), are employed to analyze the relationships among multiple variables. For instance, they can be used to explore the associations between Arbuscular Mycorrhizal Fungi (AMF) diversity, root-knot nematode infection levels, and plant growth parameters. PCA can reduce the dimensionality of the data while retaining the most critical information, helping to identify the key variables that drive the relationships among these variables. Other multivariate techniques like Canonical Correlation Analysis (CCA) can also be used to understand the complex interrelationships between these variables further.

Machine Learning Methods: Machine learning algorithms, such as Random Forest and Support Vector Machines (SVM), are applied to predict the effects of different inoculation treatments on AMF diversity and nematode control efficacy. These algorithms can handle complex, non-linear relationships in the data. For example, Random Forest can make accurate predictions and identify the key influencing factors by ranking the importance of input variables. Conversely, SVM effectively finds optimal decision boundaries in high-dimensional spaces, which can help classify different treatment effects on AMF and nematodes.

Bayesian Statistics: Bayesian statistical methods are utilized to estimate the uncertainty associated with the effects of inoculation treatments on AMF diversity and nematode control efficacy. Bayesian approaches provide a more comprehensive probabilistic estimate by incorporating prior knowledge and updating it with observed data. This will allow for a more nuanced understanding of the potential outcomes and the associated uncertainties, which is crucial in making informed decisions in research and practical applications.

Ecological Network Analysis: Ecological network models are constructed to analyze the interactions among AMF, Trichoderma, and root-knot nematodes. These models can visualize the complex web of relationships within the microbial community, including mutualistic, antagonistic, and competitive interactions. By analyzing the network structure, properties such as centrality, clustering coefficient, and modularity can be calculated to understand the key players and the overall stability of the microbial community. This approach provides insights into the complex dynamics of the microbial ecosystem and how different organisms interact.

Response: We sincerely thank the reviewer for this helpful suggestion. While we acknowledge the potential of advanced methods such as machine learning, Bayesian statistics, or ecological network analysis, given the small sample size of our experimental design, we agree that PCA was the most suitable multivariate method to explore patterns and relationships without overfitting or compromising statistical robustness. The results of the PCA are now presented in the Results section, including Figure 5 and Figure S2, and discussed accordingly. Details of the analysis have also been added to the Methods section under "Statistical Analysis."

However, it is important to mention that the PCA was performed by exploring the associations between mycorrhizal colonization (AMC), rather than AMF diversity, and the remaining parameters (nematode infestation, plant growth, etc.), as suggested by the reviewer. This decision was based on the lack of replicates for each treatment (since the tomato roots from each treatment were homogenized into a single sample, as indicated in lines 181-183 of the corrected manuscript) in the SSCP study. This limitation arose from both cost and logistical constraints: the vertical electrophoresis vat used could not accommodate all samples with their replicates in the same run (which would require n=24, plus the run controls). In addition to this limitation, we believe analyzing AMC in the PCA was appropriate, as both AMC and AMF diversity were quantified on the tomato roots.

 

Comment 5: Results: In the results section of this article, although some critical findings have been presented, there are still several aspects that could be further improved to enhance its completeness and persuasiveness:

• Explanation of Statistical Significance: The results section should provide a more detailed account of each statistical test's significance level (p-value) and effect size. This would help readers better understand the reliability and importance of the results.

Response: Thank you for this valuable suggestion. We agree with the reviewer and have now provided a more detailed account of the p-values and effect sizes for each statistical test conducted. The Results section has been revised accordingly to reflect these changes. We hope that the added details now offer a clearer understanding of the reliability and significance of our results.

• Interaction Effect Analysis: The article could further analyze the interaction effect between AMF and Trichoderma, especially their impact on AMF diversity and nematode control efficacy under different inoculation combinations.

Response: We sincerely appreciate your valuable suggestion. In response to your comment, we have incorporated a multivariate analysis (Figure 5 and Figure S2) to assess the correlations between AMC and the variables in our study. This has allowed us to explore the interactions between the different factors in greater detail. Additionally, we have included a more thorough discussion regarding the potential synergy or antagonism between AMF and Trichoderma, particularly in relation to their role in nematode control.

Furthermore, we conducted a correlation analysis between the abundance of Trichoderma and the presence of larvae in the soil-substrate in which the plants were grown. However, as the relationship was not significant, the corresponding figure has been included in the supplementary material (Figure S1).

We would like to kindly remind to the reviewer that the primary focus of our study was not the evaluation of biocontrol per se, but rather the effect of nematode infestation on the diversity of AMF in the root-soil environment. While we have considered nematode control in our analysis, the central hypothesis focuses on how nematodes influence AMF colonization and diversity.

 

• Sensitivity Analysis: Conduct a sensitivity analysis to evaluate the impact of different experimental conditions (such as soil type and plant variety) on the results. This would help understand the generality and robustness of the research findings.

Response: Thank you for your valuable suggestion. We agree with the reviewer on the importance of evaluating the impact of different experimental conditions, such as soil type and plant variety, on the robustness and generality of our findings. While a sensitivity analysis was not included in the current study, we plan to incorporate it in future research to assess how these factors might influence the interactions between nematodes, AMF, and Trichoderma. Your feedback will help guide our future work in this area (Lines 457-460).

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The article presented is interesting and the results will contribute to a better understanding of the relationship between nematodes, host plants and fungi. I recommend that the authors increase the basis for the role of mycorrhizal fungi as biological control agents, given that these fungi predominantly affect plant nutrition and their effect as a control agent, although mentioned in many studies, has not yet been demonstrated conclusively. The SSCP approach has many limitations for assessing the diversity of microorganisms and should be analyzed carefully. The text should be revised to improve the English and standardize terms, such as aerial part or shoot, etc.

Comments on the Quality of English Language

The text should be revised to improve the English and standardize terms, such as aerial part or shoot, etc.

Author Response

The article presented is interesting and the results will contribute to a better understanding of the relationship between nematodes, host plants and fungi. I recommend that the authors increase the basis for the role of mycorrhizal fungi as biological control agents, given that these fungi predominantly affect plant nutrition and their effect as a control agent, although mentioned in many studies, has not yet been demonstrated conclusively. The SSCP approach has many limitations for assessing the diversity of microorganisms and should be analyzed carefully. The text should be revised to improve the English and standardize terms, such as aerial part or shoot, etc.

Response: We thank the reviewer for these suggestions. in the revised version we have expanded the evidence base for biocontrol (particularly evidenced for AMF in this study) on nematodes.

In the revised version we included a correlation between the abundance of Trichoderma and the presence of nematode larvae in the substrate (which was shown to be negative but not significant). This was in addition to the correlation presented in the previous version between mycorrhizal colonization and galls and nematode larvae (which was shown to be negative and significant). This, together with the other results suggest that the biocontrol effect was lower for Trichoderma than for AMF

Furthermore and in response to the reviewer's suggestion, we decided to change the order of presentation of the results: we first present the biocontrol and growth-promoting effects of the fungi used as inoculum, and then we discuss the impact of nematodes on AMF colonization and root diversity, as well as the mitigation of the nematode-induced depressive effects in the presence of Trichoderma. This change can be seen in the corrected version of the manuscript.

On the other hand, we acknowledge with reviewer that the SSCP strategy has limitations in understanding microbial diversity. Therefore, we have included a comment in the Discussion (Lines 394-395, with two additional references [40, 42] in the corrected manuscript) suggesting that future studies should confirm these results using state-of-the-art molecular strategies.

 

Comment 1: The text should be revised to improve the English and standardize terms, such as aerial part or shoot, etc.

Reviewer: Thank you for your comment. We appreciate your attention to these details, which have helped improve the overall quality of the manuscript. We have carefully revised the manuscript to improve the English and ensure consistency in the use of terms, thereby enhancing clarity and precision throughout the text. The manuscript was revised and corrected by a professional in scientific translations from Spanish to English. We hope that the revised version now meets the necessary scientific standards.

Author Response File: Author Response.pdf