PGENETICS-D-26-00159

Gene network analysis predicts the primary regulators of ABA-dependent transcriptional activation and repression in Populus roots

PLOS Genetics

Dear Dr. Cohen,

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Arun Sampathkumar

Academic Editor

PLOS Genetics

Angela Hancock

Section Editor

PLOS Genetics

Aimée Dudley

Editor-in-Chief

PLOS Genetics

Anne Goriely

Editor-in-Chief

PLOS Genetics

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: This manuscript assayed the effect of extraneous ABA on non-stressed plant roots as a method to estimate the regulatory cascade in response to ABA without the confounding effects of other stress sensors. The authors posit that this work fills an important knowledge gap in understanding root responses to ABA, particularly in Poplar. The study was well-designed and the authors have done meticulous work in analyzing the transcriptomic data to identify the individual genes and broad trends involved in ABA response. I particularly appreciate that they compared the ABA response in this study to the osmotic stress response which helps place the pure ABA-triggered response within the broader osmotic stress faced often by plants. However, there are a few shortcomings in the way the paper is conceived and presented.

1. The authors correctly identify that there is a knowledge gap in our understanding of ABA response in roots. However, beyond the transcriptome data and the derived functional analyses the authors do not address the key question of what knowledge was gained by the design and execution of this experiment. An important component of plant response to drought stress is the change to the root system architecture, which is presumably within the purview of ABA signaling. This work would benefit from a systematic comparison of the ABA response seen here to the previously described studies on root development to define the effect of ABA responsive modules on root traits such as meristem activity, lateral root initiation and expansion etc.

2. This study was performed on roots after the removal of shoot tissue to hone in specifically on the root ABA response. The question then is what was learnt from this approach beyond what was already known from shoot/whole plant studies? Much of the results and discussion sections describe how the known regulators of ABA response (ABFs etc.) were rediscovered here. While that proves the validity of the approach it does not provide any new insights into the root response. The authors should compare and contrast the root response to shoot responses (e.g., from previous studies) to specify what portion of the root response is broadly true across all organs and which portion is specific to roots. If a set of genes are uniquely ABA responsive in roots, then what are the potential regulators of these genes that are identified by the iDREM or WGCNA approaches.

3. The results section includes multiple subsections that discuss the individual classes of genes or processes in ABA response. It is a bit tedious to discuss the effect of ABA on each process and component gene separately. The manuscript would benefit from a discussion of the significance of these changes. For example, what is the significance of repressing Tetrapyrrole and chlorophyll metabolism in roots?

Further specific comments:

1. The design of the study with experiment1 and experiment 2 is difficult to track, especially since there is no direct statement or illustration of the time points sampled. Please include in the first section discussing transcriptomic data (Lines 200-210), an explicit definition of the time points sampled. The embedded timepoint information in Fig. 3B is both too late and too difficult to decipher for the reader.

2. In lines 205-210 the authors place the point of path divergence as the earliest point before the divergence as visualized in Fig 2. It would be fairer to say that the paths diverged sometime before the next timepoint. For example, line 205 should state “Path A and B separated within 15 minutes of the start of treatment….” Again on line 209 “divergence between path D and F occurred later (between 15 and 30 min), …”

3. On lines 240 and 241, the exact sampling timepoints for ABA+ and ABA+/- should be defined. Another suggestion for clarity it to relabel the ABA+/- samples as +60 minutes and +960 minutes or some other way to differentiate them from the 60 and 960 minutes sampled in the ABA+ treatment.

4. Fig. 2C uses the red-white gradient to denote two different contexts, once for module identity and second time for significance which makes it hard to interpret this panel. Please switch to an alternate color gradient for the -log10(p-val) such as green or purple gradient.

5. Fig. 3C preserves the module number for arranging columns, which is not biologically meaningful. The panel would be much easier to interpret is the columns are grouped by the induction/repressed axis used in panel B (i.e., M1,10,4,16 followed by M2,5,3)

Reviewer #2: This paper reports a detailed time‑course analysis of the transcriptomic response that occurs in poplar root tips upon ABA treatment. Using iDREM, the authors classified the early ABA response into six groups, and through WGCNA they identified 17 modules (M1–M17). The discussion focuses on the M3 and M4 modules: M4 forms a large‑scale network centered on ABF/ABI5, which are directly regulated by the ABA core signaling pathway. The ABA‑activated network represents the core of the drought‑stress response, whereas the ABA‑repressed module (M3) integratively suppresses secondary metabolism, senescence‑related pathways, and strigolactone biosynthesis.

As the authors note, the root transcriptome is well suited for dissecting short‑term transcriptional responses to ABA. Although several studies in various plant species have reported time‑course transcriptome analyses of ABA‑induced responses, this work is unique in that it collects appropriately fine‑scaled time points and analyzes in detail how transcriptional networks are constructed from the very earliest primary responses. Conducting ABA treatments in complete darkness may also be considered relatively unique. Overall, the experiments are carefully designed, and the resulting transcriptome data appear to be very clean. Given the large volume of high‑quality transcriptomic data, the authors’ decision to focus the paper specifically on transcriptional regulation is logical and appropriate.

Below are several minor corrections:

The authors need to deposit raw transcriptome data to the public site.

Discussion (line 553): “Abscisic acid” → “ABA”

Reference list: The formatting lacks consistency and contains errors.

Reviewer #3: In this study, Cohen et al. generate time-resolved, high-resolution RNA-seq datasets from Populus roots following ABA treatment. Using these datasets, the authors identify early regulators of this process, pinpoint a potential core gene set involved in ABA-dependent stress responses, and propose a comprehensive working model for transcriptional ABA action in Populus roots. While this study provides a valuable resource to the research community, the manuscript requires substantial revision and structural improvement before it is suitable for publication.

Although the authors have generated a comprehensive co-expression network (Figures 3 and 4) and characterized the dynamics of the ABA response (Figure 2), the current analysis remains overly descriptive. Applying a more quantitative approach would greatly improve the holistic understanding of this physiological response and its major regulators. The following analytical and structural issues should be addressed:

Regarding Figure 2, the authors should clarify whether any specific transcription factor (TF) families were overrepresented. Furthermore, it is unclear what proportion of the TFs and potential target genes identified in Figure 2 are included in the co-expression network (Figure 4), and to which modules they belong. Integrating this information into a unified gene regulatory network would make these findings significantly more informative.

A similar issue occurs in the analysis of the public osmotic stress dataset. The authors generate a second co-expression network and compute the correlation between the osmotic and ABA networks (Figure 5). While these data are interesting, the current text and visualizations make it difficult to determine which modules are the most statistically significant in ABA-mediated osmotic regulation, as well as the direction of regulation for the differentially expressed genes (DEGs). The text should be revised to clearly identify the key TFs and their target genes in this context.

The Results subsection titled "Identifying the hallmarks of ABA-dependent transcriptional network" discusses the functions of numerous genes that are either activated or repressed by ABA, relying heavily on heatmaps (Figure 6 and supporting figures). If these genes were derived from the broader network, displaying them as targeted sub-networks would be much more effective. Additionally, this section lacks quantitative context (e.g., noting what percentage of genes involved in ABA feedback loops were actually recovered in the analysis). As currently written, this section reads like a Discussion; it is excessively lengthy and requires substantial reorganization and streamlining.

Finally, there are a few minor methodological and visual issues that should be corrected to improve the manuscript's clarity and rigor:

The authors need to specify in the methods whether any batch correction was performed on Experiments 1 and 2, assuming these sample sets were collected at different times.

The data presented in Figures 3A and 3B appear to be redundant and should be consolidated or clearly differentiated.

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: None

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Reviewer #1: Yes: Kranthi Varala

Reviewer #2: Yes: Eiji Nambara

Reviewer #3: No

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Dear Dr Cohen,

We are pleased to inform you that your manuscript entitled "Gene network analysis predicts the primary regulators of ABA-dependent transcriptional activation and repression in Populus roots" has been editorially accepted for publication in PLOS Genetics. Congratulations!

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Arun Sampathkumar

Academic Editor

PLOS Genetics

Angela Hancock

Section Editor

PLOS Genetics

Aimée Dudley

Editor-in-Chief

PLOS Genetics

Anne Goriely

Editor-in-Chief

PLOS Genetics

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Comments from the reviewers (if applicable):

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Thanks to the authors for revising the updated and improved manuscript. The new version addresses all my concerns and I am happy to recommend it for publication.

Reviewer #2: The authors have revised the manuscript correctly. I have no further comments.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics  data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes: Kranthi Varala

Reviewer #2: No

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