Review Reports - Multi-Level Data Integration Decodes the Metabolic Strategies of Kawisari Coffee Hybrid Resistance to Hemileia vastatrix

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors conducted Polar untargeted metabolomics (GC-TOF-MS) analysis on four time points of disease resistance in three coffee hybrid cultivars, revealing significant differences in carbohydrates, amino acids, polyamines, and phenolic compounds. Combined with starch content determination, semi-high-throughput enzyme activity assays, and RT-qPCR gene expression profiling, the study demonstrated strategic carbon redistribution during resistance responses.

The reviewers provided the following comments:

1) The primary data focus on GC-TOF-MS results with limited molecular experiments, leading to overly generalized conclusions without identifying key differential genes in specific pathways. It is recommended to perform transcriptome analysis on the time points showing maximum inter-sample variation among the three cultivars to substantiate the conclusions.

2) The abstract should explicitly summarize the findings - the current statement "resistance was associated with the activation of the pentose phosphate pathway" is too vague. Key genes involved in the identified pathways and their specific responses to coffee rust pathogen (Hemileia vastatrix) should be highlighted in the abstract.

3) Materials and Methods section lacks critical details about experimental replication: while five biological replicates were mentioned, the number of plants used per replicate remains unspecified and should be clarified.

4) Figure 2 should be moved to supplementary materials.

5) The rationale for selecting specific genes in Figure 6 for RT-qPCR validation requires clearer justification.

6) The discussion section appears oversimplified compared to the results; a more comprehensive interpretation integrating the experimental findings is recommended.

Author Response

Dear Editor and Reviewer,

We would like to express our sincere gratitude for the thoughtful evaluation, constructive feedback, and valuable suggestions provided for our manuscript “Multi-Level Data Integration Decodes the Metabolic Strategies of Kawisari Coffee Hybrid Resistance to Hemileia vastatrix” (ID: agronomy-4624683)

We have carefully considered all comments and revised the manuscript accordingly. We believe these revisions have significantly improved the quality, clarity, and focus of our work.

Below, we provide a point-by-point response to each comment. For ease of review, all responses in this document and the corresponding revised text in the manuscript have been highlighted in blue.

Top of Form

Comments and Suggestions for Authors

The reviewers provided the following comments:

The primary data focus on GC-TOF-MS results with limited molecular experiments, leading to overly generalized conclusions without identifying key differential genes in specific pathways. It is recommended to perform transcriptome analysis on the time points showing maximum inter-sample variation among the three cultivars to substantiate the conclusions.

Response 1: We thank the reviewer for this important and insightful comment. We would like to clarify that the conceptual design and target gene selection for this study were directly guided by comprehensive RNA-Seq transcriptomic (Illumina NovaSeq) and cytological profiling previously performed by our group on the exact same plant material and time points (Diniz et al., 2021 [Ref 26]; Azevedo et al., 2023 [Ref 27]).

Importantly, the transcriptomic data have also informed the selection of candidate genes for targeted RT-qPCR validation in the present manuscript, including RPE, TK, PPDK, S6PDH, and FBPase. These genes were selected because of their relevance to key regulatory steps in primary metabolism and carbon allocation. Thus, the molecular analyses presented here were designed to complement, rather than reproduce, the comprehensive transcriptomic characterization, by examining selected transcriptional responses alongside metabolomic profiles, carbohydrate dynamics, and enzymatic activities. We believe this integrated approach provides a distinct and valuable contribution to understanding the metabolic processes associated with resistance.

We have revised the Introduction (Lines 90–97), Materials and Methods (Lines 221-227), and Results (Lines 368-373) sections to make this connection explicit. We have ensured that the conclusions are framed in accordance with the scope and evidence provided by the current study, and we acknowledge that the identification of broader transcriptional regulatory networks falls beyond the primary scope of this manuscript.

Response 2: We have revised the Abstract (Lines 34–40) to explicitly detail the specific key genes (RPE, TK), their temporal expression patterns at 4 DAI, and how they coordinate with specific enzymatic hubs (PGM, cytInv, vacInv) and metabolic markers (phenolics, asparagine, ornithine, spermine).

Response 3: We thank the reviewer for this observation. To clarify, a total of five independent, large clonal plants were used per experimental condition (). Each individual plant served as one biological replicate, from which one pair of leaves was sampled at each time point (1, 4, 7, and 16 DAI).

We have updated the Materials and Methods section to make this replication scheme explicit (Lines 125–128):

"For each condition and time point, five biological replicates were analyzed (), with each replicate corresponding to one pair of leaves collected from five individual clonal plants maintained throughout the experiment."

4) Figure 2 should be moved to supplementary materials.

Response 4: We have followed the reviewer’s suggestion. Figure 2 has been moved to the Supplementary Materials and renamed Supplementary Figure S1. All figure callouts and references in the main text have been updated accordingly.

5) The rationale for selecting specific genes in Figure 6 for RT-qPCR validation requires clearer justification.

Response 5: We thank the reviewer for this request for clarification. We would like to emphasize that the primary core of this study relies on non-targeted polar metabolomics (GC-TOF-MS) and functional enzyme activity profiling.

To support and gain a deeper insight into the underlying regulation of the primary metabolic shifts observed in our metabolomic dataset, we performed RT-qPCR targeted validation. The eight candidate genes (RPE, TK, FBA, FBPase, PRK, PPDK, S6PDH, and HPR) were selected based on prior transcriptomic profiling of the Kawisari hybrid–H. vastatrix interaction (Azevedo et al., 2023 [Ref 27]), as they encode rate-limiting or branch-point enzymes that connect central carbon fixation and partitioning (Calvin cycle, glycolysis, gluconeogenesis) to the pentose phosphate pathway (PPP) and secondary phenolic metabolism.

Rather than replacing or overshadowing the metabolomic findings, the main objective of this targeted RT-qPCR analysis was to verify whether key transcriptomic switches aligned with and helped explain the dynamic changes in metabolite levels and enzyme activity signatures measured across the infection time points.

We have updated the Materials and Methods (Lines 221–227) and Results (Lines 368–373) sections to explicitly frame this rationale, as well as updated the Figure 6 legend.

6) The discussion section appears oversimplified compared to the results; a more comprehensive interpretation integrating the experimental findings is recommended.

Response 6: While our original Discussion addressed the main metabolic, enzymatic, and transcriptional findings, we appreciate the reviewer’s suggestion to enhance the integration across datasets.

We have refined the Discussion section (Lines 515-527) by adding a concise synthesis that explicitly highlights the temporal progression connecting all three analytical layers:

Early stage (1–4 DAI): Transcriptional priming (RPE, TK) and early metabolic shifts toward the pentose phosphate pathway to supply precursors for phenolic defenses.
Intermediate stage (4–7 DAI): Active phenolic accumulation alongside nitrogen remobilization (L-asparagine, L-ornithine) and polyamine signaling.
Late stage (7–16 DAI): Strategic carbon sequestration into starch reserves (driven by PGM, cytInv, and vacInv activities) to restrict sugar availability to the pathogen while preserving host homeostasis.

This addition clarifies the functional crosstalk between datasets without altering the core scope of our study.

We also thank the reviewer for the general recommendation regarding the figures and legends. To improve clarity, self-explainability, and scientific rigor, we have thoroughly updated all figure legends throughout the manuscript. Specifically, we have:

Explicitly indicated sample sizes (), biological vs. technical replication, and statistical test parameters across all figures.
Clarified abbreviations, data scaling procedures (e.g., Z-scores, -transformation), and color coding in heatmaps and score plots.
Added functional and biological context to each legend so that figures can be interpreted independently of the main text.
Moved Figure 2 (PCA) to Supplementary Material (Supplementary Figure S1), as requested in Comment 4, and updated all callouts in the text."

Reviewer 2 Report

Comments and Suggestions for Authors

The main aim of the study was to elucidate mechanisms underlying resistance of Kawisari coffee hybrid to fungal disease (coffee leaf rust) caused by Hemileia vastatrix with the use of multi-level analysis involving untargeted metabolomics, enzymatic activity and gene expression profiling. This research approach leaded to better understanding of the biochemical shifts during fungal infection, the strategic reallocation of carbon in the resistant response (how primary metabolic networks sustain resistance against coffee leaf rust).

The topic is original and relevant to the field. The study is specifically devoted to the fungal disease economically important for coffee production. Apart from widening knowledge on the metabolic consequences on fungal resistance, it revealed some data on potential targets for breeding or biotechnological interventions, that might in the future support sustainable coffee production in disease-prone regions.

The methods were well selected, and they were properly described in methodological section. The conclusions are clear and consisted with the obtained results. The cited references are appropriate. The tables, figures and supplementary material are well designed and suitable for illustrating the manuscript and presenting the results.

I have only a few comments:

Line 310. Please correct typographical error (change Clavin to Calvin)

Figure 4. (“Heatmap analysis of phenolic-like metabolites”…). The term “phenolic-like” is rather awkward in this case (usually it is used to artificial compounds, e.g. industrial antioxidants). All compounds mentioned in the Figure are actually natural phenols, so it would be better to refer them just as phenolic metabolites.

Author Response

Dear Editor and Reviewer 2

We would like to express our sincere gratitude for the positive evaluation of our work “Multi-Level Data Integration Decodes the Metabolic Strategies of Kawisari Coffee Hybrid Resistance to Hemileia vastatrix” (ID: agronomy-4624683), as well as for highlighting its originality, relevance, and contribution to understanding coffee resistance against Hemileia vastatrix. We appreciate the reviewer’s helpful feedback regarding minor text and terminology corrections (the typographical error "Clavin" to "Calvin" at Line 324, and replacing the term "phenolic-like" with "phenolic metabolites" in Figure 4 and throughout the main text). All corrections have been fully performed in the revised manuscript as suggested and are highlighted in blue.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have satisfactorily responded to all comments. The revised manuscript meets the journal’s requirements for publication.