A conserved fungal Knr4/Smi1 protein is vital for maintaining cell wall integrity and host plant pathogenesis

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   Aug 23, 2024

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   Reply to the reviewers

   Thank you very much for your editorial handling of our manuscript entitled 'A conserved fungal Knr4/Smi1 protein is vital for maintaining cell wall integrity and host plant pathogenesis'. We have taken on board the reviewers' comments and thank them for their diligence and time in improving our manuscript.

   Please find our responses to each of the comments below.

   Reviewer(s)' comments

   Reviewer #1

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   Major comments:

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   __1.1. As a more critical comment, I find the presentation of the figures somewhat confusing, especially with the mixing of main figures, supplements to the main figures, and actual supplemental data. On top of that, the figures are …

   Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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   Reply to the reviewers

   Thank you very much for your editorial handling of our manuscript entitled 'A conserved fungal Knr4/Smi1 protein is vital for maintaining cell wall integrity and host plant pathogenesis'. We have taken on board the reviewers' comments and thank them for their diligence and time in improving our manuscript.

   Please find our responses to each of the comments below.

   Reviewer(s)' comments

   Reviewer #1

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   Major comments:

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   __1.1. As a more critical comment, I find the presentation of the figures somewhat confusing, especially with the mixing of main figures, supplements to the main figures, and actual supplemental data. On top of that, the figures are not called up in the right order (e.g. Figure 4 follows 2D, while 3 comes after 4; Figure 6 comes before 5...), and some are never called up (I think) (e.g. Figure 1B, Figure 2B). __

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   __Response: __The figure order has been revised according to the reviewer's suggestion, while still following eLife's formatting guidelines for naming supplementals. Thank you.

   1.2. I agree that there should be more CWI-related genes in the wheat module linked to the FgKnr4 fungal module, or, vice-versa, CW-manipulating genes in the fungal module. It would at least be good if the authors could comment further on if they find such genes, and if not, how this fits their model.

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   Response: Thank you for your insightful suggestion regarding the inclusion of more CWI-related genes in the wheat module linked to the FgKnr4 fungal module F16, or vice versa. We did observe a co-regulated response between the wheat module W05 which is correlated to the FgKnr4 module F16. Namely, we observed an enrichment of oxidative stress genes including respiratory burst oxidases and two catalases (lines 304 - 313) in the correlated wheat module (W05). Early expression of these oxidative stress inducing genes likely induces the CWI pathway in the fungus, which is regulated by FgKnr4. Knr4 functions as both a regulatory protein in the CWI pathway and as a scaffolding protein across multiple pathways in S. cerevisiae (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ). Scaffolding protein-encoding genes are typically expressed earlier than the genes they regulate to enable pre-assembly with their interacting partners, ensuring that signaling pathways are ready to activate when needed. In this context, the CWI integrity MAPKs Bck1 and Mkk1 are part of module F05, which includes two chitin synthases and a glucan synthase. This module is highly expressed during the late symptomless phase. The MAPK Mgv1, found in module F13, is expressed consistently throughout the infection process, which aligns with the expectation that MAPKs are mainly post-transcriptionally regulated. Thank you for bringing our attention to this, this is now included in the discussion (lines 427 - 443) along with eigengene expression plots of all modules added to the supplementary (Figure 3 - figure supplement 1).

   To explore potential shared functions of FgKnr4 with other genes in its module, we re-analyzed the high module membership genes within module F16, which includes FgKnr4, using Knetminer (Hassani-Pak et al., 2021; https://onlinelibrary.wiley.com/doi/10.1111/pbi.13583 ). This analysis revealed that 8 out of 15 of these genes are associated with cell division and ATP binding. Four of the candidate genes are also part of a predicted protein-protein interaction subnetwork of genes within module F16, which relate to cell cycle and ATP binding. In S. cerevisiae, the absence of Knr4 results in cell division dysfunction (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ). Accordingly, we tested sensitivity of ΔFgknr4 to microtubule inhibitor benomyl (a compound commonly used to identify mutants with cell division defects; Hoyt et al., 1991 https://www.cell.com/cell/pdf/0092-8674(81)90014-3.pdf). We found that the ΔFgknr4 mutant was more susceptible to benomyl, both when grown on solid agar and in liquid culture. This data has now been added Figure 7, and referred to in lines 338-348.

   __Specific issues: __

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   1.3. In the case of figure 5, I generally find it hard to follow. In the text (line 262/263), the authors state that 5C shows "eye-shaped lesions" caused by ΔFgknr4 and ΔFgtri5, but I can't see neither (5C appears to be a ΔFgknr4 complementation experiment). The figure legend also states nothing in this regard.

   __Response: __Thank you for your suggestion. We have amended the manuscript to include an additional panel that shows the dissected spikelet without its outer glumes, making the eye shaped diseased regions more visible in Figure 5.

   __1.4. Figure 5D supposedly shows 'visibly reduced fungal burden' in ΔFgknr4-infected plants, but I can't really see the fungal burden in this picture, but the infected section looks a lot thinner and more damaged than the control stem, so in a way more diseased. __

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   __Response: __Thank you for your insight. We have revised our conclusions based on this image to state that while* ΔFgknr4* can colonise host tissue, it does so less effectively compared to the wild-type strain as we are unable to quantitatively evaluate fungal burden using image-colour thresholding due to the overlapping colours of the fungal cells and wheat tissues. Decreased host colonisation is evidenced by (i) reduced fungal hyphae proliferation, particularly in the thicker adaxial cell layer, (ii) collapsed air spaces in wheat cells, and (iii) increased polymer deposition at the wheat cell walls, indicating an enhanced defence response. Figure 5 has been amended to include these observations in the corresponding figure legend and the resin images now include insets with detailed annotation.

   __1.5. The authors then go on to state (lines 272-273) that they analyzed the amounts of DON mycotoxin in infected tissues, but don't seem to show any data for this experiment. __

   __Response: __We have amended this to now include the data in Figure 5 - figure supplement 2B, thank you.

   Reviewer #2

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   __Major issues: __

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   2.1 If Knf4 is involved in the CWI pathway, what other genes involved in the CWI pathway are in this fungal module? one of the reasons for developing modules or sub-networks is to assign common function and identify new genes contributing to the function. since FgKnr4 is noted to play a role in the CWI pathways, then genes in that module should have similar functions. If WGCN does not do that, what is the purpose of this exercise?

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   __Response: __Thank you for raising this point regarding the role of FgKnr4 in the CWI pathway and the expectations for genes of shared function within the *FgKnr4 *module F16. We did observe that the module containing *FgKnr4 *(F16) was also correlated to a wheat module (W05) which was significantly enriched for oxidative stress genes. This pathogen-host correlated pattern led us to study module F16, which otherwise lacks significant gene ontology term enrichment, unique gene set enrichments, and contains few characterised genes. This is now highlighted in lines 233-246. This underscores the strength of the WGCNA. By using high-resolution RNA-seq data to map modules to specific infection stages, we identified an important gene that would have otherwise been overlooked. This approach contrasts with other network analyses that often rely on the guilt-by-association principle to identify novel virulence-related genes within modules containing known virulence factors, potentially overlooking significant pathways outside the scope of prior studies. Therefore, our analysis has already benefited from several advantages of WGCNA, including the identification of key genes with high module membership that may be critical for biological processes, as well as generating a high-resolution, stage-specific co-expression map of the F. graminearum infection process in wheat. This point is now emphasised in lines 233-252. As discussed in response to reviewer 1, Knr4 functions as both a regulatory protein in the CWI pathway and as a scaffolding protein across multiple pathways in S. cerevisiae (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ) which would explain its clustering separate from the CWI pathway genes. The high module membership genes within module F16 containing FgKnr4 were re-analysed using Knetminer (Hassani-Pak et al., 2021; https://onlinelibrary.wiley.com/doi/10.1111/pbi.13583 ), which found that 8/15 of these genes were related to cell division and ATP binding. Four of the candidate genes are also part of a predicted protein-protein interaction subnetwork of genes within module F16, which relate to cell cycle and ATP binding. In *S. cerevisiae, *the absence Knr4 leads to dysfunction in cell division. Accordingly, we tested sensitivity of ΔFgknr4 to the microtubule inhibitor benomyl (a compound commonly used to identify mutants with cell division defects; Hoyt et al., 1991 https://www.cell.com/cell/pdf/0092-8674(81)90014-3.pdf). We found that the ΔFgknr4 mutant was more susceptible to benomyl, both when grown on solid agar and in liquid culture. This data has now been added as Figure 7 and referred to in lines 338-348.

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   2.2. Due to development defects in the Fgknr1 mutant, I would not equate to as virulence factor or an effector gene.

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   __Response: __We are in complete agreement with the reviewer and are not suggesting that FgKnr4 is an effector or virulence factor, we have been careful with our wording to indicate that FgKnr4 is simply necessary for full virulence and its disruption results in reduced virulence and have outlined how we believe FgKnr4 participates in a fungal signaling pathway required for infection of wheat.

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   2.3. What new information is provided with WGCN modules compared with other GCN network in Fusarium (examples of GCN in Fusarium is below) ____https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069591/ https://doi.org/10.1186/s12864-020-6596-y____ DOI: 10.1371/journal.pone.0013021. The GCN networks from Fusarium have already identified modules necessary/involved in pathogenesis.

   __Response: __The 2016 New Phytologist gene regulatory network (GRN) by Guo et al. is large and comprehensive. However, only three of the eleven datasets are in planta, with just one dataset focusing on F. graminearum infection on wheat spikes. The other two in planta datasets involve barley infection and Fusarium crown rot. By combining numerous in planta and in vitro datasets, the previous GRNs lack the fine resolution needed to identify genetic relationships under specific conditions, such as the various stages of symptomatic and symptomless F. graminearum infection of mature flowering wheat plants. This limitation is highlighted in the 2016 paper itself. This network is expanded in the Guo et al., 2020 BMC genomics paper where it includes one additional *in planta *and nine *in vitro *datasets. However, the in planta dataset involves juvenile wheat coleoptile infection, which serves as an artificial model for wheat infection but is not on mature flowering wheat plants reminiscent of Fusarium Head Blight of cereals in the field. This model differs significantly in the mode of action of F. graminearum, notably DON mycotoxin is not essential for virulence in this context (Armer et al. 2024, https://pubmed.ncbi.nlm.nih.gov/38877764/ ). The Guo et al., 2020 paper still faces the same issues in terms of resolution and the inability to draw conclusions specific to the different stages of *F. graminearum *infection. Additionally, these GRNs use Affymetrix data, which miss over 400 genes (~ 3 % of the genome) from newer gene models. In contrast, our study addresses these limitations by analysing a meticulously sampled, stage- and tissue-specific in planta RNA-seq dataset using the latest reference annotation. Our approach provides higher resolution and insights into host transcriptomic responses during the infection process. The importance of our study in the context of these GRNs is now addressed in the introduction (lines 85-92).

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   2.4. Ideally, the WGCN should have been used identify plant targets of Fusarium pathogenicity genes. This would have provided credibility and usefulness of the WGCN. Many bioinformatic tools are available to identify virulence factors and the utility of WGCN in this regard is not viable. However, if the authors had overlapped the known virulence factors in a fungal module to a particular wheat module, the impact of the WGCN would be great. The module W12 has genes from numerous traits represented and WGCN could have been used to show novel links between Fg and wheat. For example, does tri5 mutant affect genes in other traits?

   __Response: __Thank you for your suggestions. In this study we have shown the association between the main fungal virulence factor of *F. graminearum, *DON mycotoxin, with wheat detoxification responses. Through this we have identified a set of *tri5 *responsive genes and validated this correlation in two genes belonging to the phenylalanine pathway and one transmembrane detoxification gene. Although we could validate more genes in this tri5 responsive wheat module, our paper aimed to investigate previously unstudied aspects of the F. graminearum infection process and how the fungus responded to changing conditions within the host environment. We accomplished this by characterising a gene within a fungal module that had limited annotation enrichment and few characterised genes. Tri5 on the other hand is the most extensively studied gene in F. graminearum and while the network we generated may offer new insights into tri5 responsive genes, this is beyond the scope of our current study. In addition to the tri5 co-regulated response, we have also demonstrated the coordinated response between the fungal module F16, which contains FgKnr4 that is necessary for tolerance to oxidative stress, and the wheat module W05, which is enriched for oxidative stress genes.

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   While our co-expression network approach can be used to explore and validate other early downstream signaling and defense components in wheat cells, several challenges must be considered: (a) the poor quality of wheat gene calls, (b) genetic redundancy due to both homoeologous genes and large gene families, and (c) the presence of DON, which can inhibit translation and prevent many transcriptional changes from being realised within the host responses. Additionally, most plant host receptors are not transcriptionally upregulated in response to pathogen infection (most R gene studies for the NBS-LRR and exLRR-kinase classes), making their discovery through a transcriptomics approach unlikely. These points will be included in our discussion (lines 408-413), thank you.

   Specific issues

   2.5. Since tri5 mutant was used a proof of concept to link wheat/Fg modules, it would have been useful to show that TRI14, which is not involved DON biosynthesis, but involved in virulence ( https://doi.org/10.3390/applmicrobiol4020058____) impact the wheat module genes.

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   __Response: __Our goal was to show that wheat genes respond to the whole *TRI *cluster, not just individual TRI genes. Therefore, the tri5 mutant serves as a solid proof-of-concept, because TRI5 is essential for DON biosynthesis, the primary function of the *TRI gene cluster, thereby representing the function of the cluster as a whole. This is now clarified in lines 217-219. *Additionally, the uncertainties surrounding other *TRI *mutants would complicate the question we were addressing-namely, whether a wheat module enriched in detoxification genes is responding to DON mycotoxin, as implied by shared co-expression patterns with the *TRI *cluster. For instance, the referenced TRI14 paper indicates that DON is produced in the same amount *in vitro *in a single media. Although the difference is not significant, the average DON produced is lower for the two Δtri14 transformants tested. Therefore, we cannot definitively rule out that TRI14 is involved in DON biosynthesis and extrapolate this to DON production in planta. Despite this, the suggestion is interesting, and would make a nice experiment but we believe it does not contribute to the overall aim of this study.

   2.6. Moreover, prior RNAseq studies with *tri5 *mutant strain on wheat would have revealed the expression of PAL and other phenylpropanoid pathway genes?

   __Response: __We agree that this would be an interesting comparison to make but unfortunately no dataset comparing in planta expression of the tri5 mutant within wheat spikes exists.

   2.7. Table S1 lists 15 candidate genes of the F16 module; however, supplementary File 1 indicates 74 genes in the same module. The basis of exclusion should be explained. The author has indicated genes with high MM was used as representative of the module. The 59 remaining genes of this module did not meet this criteria? Give examples.

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   __Response: __The 15 genes with the highest module membership were selected as initial candidates for further shortlisting from the 74 genes within module F16. In WGCNA, genes with high module membership (MM) (i.e. intramodular connectivity) are predicted to be central to the biological functions of the module (Langfelder and Horvath, 2008; https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559 ) and continues to be a metric to identify biologically significant genes within WGCN analyses (https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-024-05366-0 Tominello-Ramirez et al., 2024; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9151341/ ;Zheng et al., 2022; https://www.nature.com/articles/s41598-020-80945-3 Panahi and Hejazi et al 2021). Following methods by Mateus et al. (2019) (https://academic.oup.com/ismej/article/13/5/1226/7475138 ) key genes were defined as those exhibiting elevated MM within the module, which were also strongly correlated (R > |0.70|) with modules of the partner organism (wheat). We have clarified this point in the manuscript. Thank you for the suggestion. (Lines 253-263).

   2.____8. A list from every module that pass this criteria will be useful resource for functional characterization studies.

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   __Response: __A supplementary spreadsheet has been generated which includes full lists of the top 15 genes with the highest module membership within the five fungal modules correlated to wheat modules and a summary of shared attributes among them. Thank you for this suggestion.

   2.9. Figure 3 indicates TRI genes in the module F12; your PHI base in Supp File S2 lists only TRI14. Why other TRI genes such as TRI5 not present in this File?

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   Response: For clarity, the TRI genes in module F12 are TRI3, TRI4, TRI11, TRI12, and *TRI14 *which was stated in Table 1. TRI5 clusters with its neighboring regulatory gene TRI6 in module F11, which exhibits a similar but reduced expression pattern compared to module F12. To improve clarity on this the TRI genes in module F12 are also listed in-text in line 168 and added to Figure 4. The enrichment and correlated relationship of W12 to a cluster's expression still imply a correlated response of the wheat gene to the TRI cluster's biosynthetic product (DON), which is absent in the Δtri5 mutant.

   *TRI14 *and TRI12 are listed in PHI-base. TRI12 was mistakenly excluded due to an unmapped Uniprot ID, which were added separately in the spreadsheet. We will recheck all unmapped ID lists to ensure all PHI-base entries are included in the final output. Thank you for pointing out this error.

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   2.10. What is purpose of listing the same gene multiple times? Example, osp24 (a single gene in Fg) is listed 13 times in F01 module.

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   __Response: __This is a consequence of each entry having a separate PHI ID, which represents different interactions including inoculations on different cultivar. Cultivar and various experimental details were omitted from the spreadsheet to reduce information density, however the multiple PHI base ID's will be kept separate to make the data more user friendly when working with the PHI-base database. An explanation for this is now provided in the file's explanatory worksheet, thank you.

   Reviewer #3:

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   3.1. Why only use of high confidence transcripts maize to map the reads and not the full genome like Fusarium graminearum? I have never analyzed plant transcriptome.

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   __Response: __ In the wheat genome, only high-confidence gene calls are used by the global community (Choulet et al., 2023; https://link.springer.com/chapter/10.1007/978-3-031-38294-9_4 ) until a suitable and stable wheat pan-genome becomes available.

   3.2. The regular output of DESeq are TPMs, how did the authors obtain the FPKM used in the analysis?

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   Response: FPKM was calculated using the GenomicFeatures package and included on GitHub to enhance accessibility for other users. However, the input for WGCNA and this study as a whole was normalised counts rather than FPKM. The FPKM analysis was done to improve interoperability of the data for future users and made available on Github. To complement this, the information regarding FPKM calculation is now included in the methods section of the revised manuscript (line 491).

   3.3. Do the authors have a Southern blot to prove the location of the insertion and number of insertions in Zymoseptoria tritici mutant and complemented strains?

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   __Response: __No, but the phenotype is attributed to the presence or absence of ZtKnr4, as the mutant was successfully complemented in multiple phenotypic aspects. This satisfies Koch's postulates which is the gold standard for reverse genetics experimentation (Falkow 1988; https://www.jstor.org/stable/4454582 ).

   __3.4. Boxplots and bar graphs should have the same format. In Figures 5 B and F and supplementary figure 6.3 the authors showed the distribution of samples but it is lacking in figure 3 B and all bar graphs. __

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   __Response: __Graphs have been modified to display the distribution of all samples, thank you.

   * *

   * *

   3.5. Line 247 FGRAMPH1_0T23707 should be FGRAMPH1_01T23707

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   __Response: __Thank you this has now been amended.

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   Aug 23, 2024

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   Referee #3

   Evidence, reproducibility and clarity

   The authors of the manuscript entitled "A conserved fungal Knr4/Smi1 protein is vital for maintaining cell wall integrity and host plant pathogenesis" used a weighted gene co-expression network to identify Fusarium graminearum genes highly expressed during early symptomless infection of wheat. Based on its sequence and previous studies, authors selected FgKnr4 from the early symptomless Fusarium modules. The characterization of knockout strains revealed a role in morphogenesis, growth, cell wall stress tolerance, and virulence in F. graminearum and the phylogenetically distant fungus Zymoseptoria tritici.

   The methods are properly …

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   Referee #3

   Evidence, reproducibility and clarity

   The authors of the manuscript entitled "A conserved fungal Knr4/Smi1 protein is vital for maintaining cell wall integrity and host plant pathogenesis" used a weighted gene co-expression network to identify Fusarium graminearum genes highly expressed during early symptomless infection of wheat. Based on its sequence and previous studies, authors selected FgKnr4 from the early symptomless Fusarium modules. The characterization of knockout strains revealed a role in morphogenesis, growth, cell wall stress tolerance, and virulence in F. graminearum and the phylogenetically distant fungus Zymoseptoria tritici.

   The methods are properly described and statistical analysis are reasonable so reproducibility is possible. The RNA-seq dataset is already published and the authors provided a repository with the code used to create the co-expression network. However, I have the following questions:

   • Why only use of high confidence transcripts maize to map the reads and not the full genome like Fusarium graminearum? I have never analyzed plant transcriptome.
   • The regular output of DESeq are TPMs, how did the authors obtain the FPKM used in the analysis?
   • Do the authors have a southern blot to prove the location of the insertion and number of insertions in Zymoseptoria tritici mutant and complemented strains?
   • Boxplots and bar graphs should have the same format. In Figures 5 B and F and supplementary figure 6.3 the authors showed the distribution of samples but it is lacking in figure 3 B and all bar graphs.
   • Line 247 FGRAMPH1_0T23707 should be FGRAMPH1_01T23707

   Referees cross-commenting

   I agree with reviewer 1, the order in which the figures are called in the text is confusing. Regardless of figures 5C-D I am no expert in the field therefore I can only say they look like they have not been edited.

   I agree with reviewer 1, data of DON mycotoxin production in infected issues is need it to support statement in line 272-273.

   I agree with Reviewer 2, the criteria to exclude genes from the final selection list should be explained.

   Significance

   The study showed, once again, that a weighted gene co-expression network is a great method to identify new genes of interest regardless of the organism or condition even if not very popular in the fungal pathogen field yet. The study proved that functions identified in a WGCN module from a pathogen have their opposite in the host module. The authors go beyond the theory and demonstrate the effect of the highest expressed gene during the early symptomless stage of infection in maize and wheat fungal pathogens.

   Fungal pathogen, RNA-seq, metabolic models, metabolism, comparative genomics

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   Referee #2

   Evidence, reproducibility and clarity

   Summary: The authors in this manuscript use "dual weighting" to identify clusters or modules of genes from the fungus F. graminearum (Fg) with coordinated expression patterns with genes in wheat modules - potentially uncover key regulators or pathways linking Fg genes with plant traits, including plant pathogenesis. As proof of concept, the authors use one of the fungal genes FgKnr4 identified in a fungal module that has strong link with the wheat module. They were able to show that this gene is likely involved in CWI pathway and affects virulence properties of the fungus

   Major comments:

   Does the WGCN provide useful framework to …

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   Referee #2

   Evidence, reproducibility and clarity

   Summary: The authors in this manuscript use "dual weighting" to identify clusters or modules of genes from the fungus F. graminearum (Fg) with coordinated expression patterns with genes in wheat modules - potentially uncover key regulators or pathways linking Fg genes with plant traits, including plant pathogenesis. As proof of concept, the authors use one of the fungal genes FgKnr4 identified in a fungal module that has strong link with the wheat module. They were able to show that this gene is likely involved in CWI pathway and affects virulence properties of the fungus

   Major comments:

   Does the WGCN provide useful framework to link fungal genes affecting plant traits? If Knf4 is involved in the CWI pathway, what other genes involved in the CWI pathway are in this fungal module? This is not forthcoming. Due to development defects in the Fgknr1 mutant, I would not equate to as virulence factor or an effector gene.

   Since tri5 mutant was used a proof of concept to link wheat/Fg modules, it would have been useful to show that TRI14, which is not involved DON biosynthesis, but involved in virulence ( https://doi.org/10.3390/applmicrobiol4020058) impact the wheat module genes. Moreover, prior RNAseq studies with tri5 mutant strain on wheat would have revealed the expression of PAL and other phenylpropanoid pathway genes?

   Table S1 lists 15 candidate genes of the F16 module; however, supplementary File 1 indicates 74 genes in the same module.

   The basis of exclusion should be explained. The author has indicated genes with high MM was used as representative of the module. The 59 remaining genes of this module did not meet this criteria? Give examples. Did similar exclusion criteria used for other modules and if so, how many genes in each module pass the criteria? For example, Did TRI5 in module F12 pass this criteria. A list from every module that pass this criteria will be useful resource for functional characterization studies.

   Minor comments:

   Figure 3 indicates TRI genes in the module F12; your PHI base in Supp File S2 lists only TRI14. Why other TRI genes such as TRI5 not present in this File? What is purpose of listing the same gene multiple times? Example, osp24 (a single gene in Fg) is listed 13 times in F01 module.

   Referees cross-commenting

   agree with both reviewers regarding clarification of Figures.

   one of the reasons for developing modules or sub-networks is to assign common function and identify new genes contributing to the function. since FgKnr4 is noted to play a role in the CWI pathways, then genes in that module should have similar functions. If WGCN does not do that, what is the purpose of this exercise?

   Significance

   What new information is provided with WGCN modules compared with other GCN network in Fusarium (examples of GCN in Fusarium is below)

   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069591/ https://doi.org/10.1186/s12864-020-6596-y DOI: 10.1371/journal.pone.0013021

   The GCN networks from Fusarium have already identified modules necessary/involved in pathogenesis. Ideally, the WGCN should have been used identify plant targets of Fusarium pathogenicity genes. This would have provided credibility and usefulness of the WGCN.

   Many bioinformatic tools are available to Identify virulence factors and the utility of WGCN in this regard is not viable. However, if the authors had overlapped the known virulence factors in a fungal module to a particular wheat module, the impact of the WGCN would be great. The module W12 has genes from numerous traits represented and WGCN could have been used to show novel links between Fg and wheat. For example, does tri5 mutant affect genes in other traits?

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   Referee #1

   Evidence, reproducibility and clarity

   Summary:

   A public mRNA-seq dataset from Dilks et al. (2019) for wheat spikelets infected by Fusarium graminearum was used to generate a dual weighted gene co-expression network (WGCN). Since colonization of the spike by F. graminearum progresses from spikelet to spikelet, thereby forming an infection-gradient from early to late stages, quasi spatio-temporal resolution for the transcriptomic dataset can be achieved by cutting the spike into equal pieces along this gradient (in this case cuts were done at rachis internodes 1-2, 3-4, 5-6, and 7-8. The authors created co-expression networks for both, fungal and plant genes, and …

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   Referee #1

   Evidence, reproducibility and clarity

   Summary:

   A public mRNA-seq dataset from Dilks et al. (2019) for wheat spikelets infected by Fusarium graminearum was used to generate a dual weighted gene co-expression network (WGCN). Since colonization of the spike by F. graminearum progresses from spikelet to spikelet, thereby forming an infection-gradient from early to late stages, quasi spatio-temporal resolution for the transcriptomic dataset can be achieved by cutting the spike into equal pieces along this gradient (in this case cuts were done at rachis internodes 1-2, 3-4, 5-6, and 7-8. The authors created co-expression networks for both, fungal and plant genes, and cross-correlated them. They identify several modules specific for each infection stage. For further analysis, the authors focus on two module pairs. (1) the wheat module 12 (W12), which correlates to Fusarium module 12 (F12), and (2) the Fusarium module 16 (F16) and the correlated wheat modules 1 and 5 (W01/W05). The W12/F12 modules were deemed of interest because they were specific to the transition from symptomless to symptomatic infection stage. Here, the authors find genes related to mycotoxin production to be upregulated in the F12 module, while the W12 is enriched in genes involved in detoxification. F16 and W01/W05 are specific to the earliest stages of infection, and thus most likely involved in fungal virulence. Here, one of the key genes identified is FgKnr4, which the authors show to be important for fungal virulence, as gene knockout leads to a premature stop of disease progression. As the authors show that FgKnr4 is involved in activating cell wall-integrity mechanisms, and may function in oxidative stress-resistance, this reduced virulence may be the result a reduced ability of the fungus to withstand plant defense mechanisms. Interestingly, knocking out an orthologue of FgKnr4 in Zymoseptoria tritici led to similarly reduced virulence of this pathogenic fungus on wheat plant.

   Comments:

   Overall, I find the WGCN analysis to be very interesting and informative, especially because of the different stages of infection. As the dataset is made public (I believe), I think that this will be a really important resource for the community. The exemplary functional analysis of the F16/W01/W05 modules via FgKnr4 is very interesting and demonstrates that novel genes involved in virulence can be identified via this approach. A similar more detailed analysis of the W12/F12 modules with a focus on detoxification mechanisms in the plant (i.e. the W12 module) would be a very interesting bonus, but as much as I would be interested in reading about it, functional gene analyses in wheat are obviously time-consuming, and it is not essential to this manuscript. As a more critical comment, I find the presentation of the figures somewhat confusing, especially with the mixing of main figures, supplements to the main figures, and actual supplemental data. On top of that, the figures are not called up in the right order (e.g. Figure 4 follows 2D, while 3 comes after 4; Figure 6 comes before 5...), and some are never called up (I think) (e.g. Figure 1B, Figure 2B). In the case of figure 5, I generally find it hard to follow. In the text (line 262/263), the authors state that 5C shows "eye-shaped lesions" caused by ΔFgknr4 and ΔFgtri5, but I can't see neither (5C appears to be a ΔFgknr4 complementation experiment). The figure legend also states nothing in this regard. Figure 5D supposedly shows 'visibly reduced fungal burden' in ΔFgknr4-infected plants, but I can't really see the fungal burden in this picture, but the infected section looks a lot thinner and more damaged than the control stem, so in a way more diseased. The authors then go on to state (lines 272-273) that they analyzed the amounts of DON mycotoxin in infected tissues, but don't seem to show any data for this experiment. In contrast to the sometimes confusing data presentation, I find the table of correlated modules (table 1) very helpful, and obviously am happy to see that all data is available in the first author's GitHub account.

   Referees cross-commenting

   just to clarify in regards to my comment on Figures 5C-D, and Reviewer #3's comment "Regardless of figures 5C-D I am no expert in the field therefore I can only say they look like they have not been edited." - I didn't want to insinuate that the images have been edited. Based on the images provided, I just can't see what the authors state is shown. So this is not about editing/manipulation - just about image quality/choice. The phenotypic descriptions by the authors are quite detailed ("eye-shaped lesions", 'visibly reduced fungal burden'...), but at least for me, the images aren't good enough to illustrate and underpin their statements. Maybe better images are needed, maybe magnifications of the exact regions showing the phenotypes? But this is simply a matter of presentation, not of editing/manipulation.

   Second, I agree that there should be more CWI-related genes in the wheat module linked to the FgKnr4 fungal module, or, vice-versa, CW-manipulating genes in the fungal module. It would at least be good if the authors could comment further on if they find such genes, and if not, how this fits their model.

   Significance

   In summary, I think that the presented WGCN analysis of mRNA-seq data with quasi-spatio-temporal resolution is a very helpful approach to identify novel fungal virulence and plant immunity genes, and with the created datasets made public, this will be an interesting and valuable resource for the community. The identification and functional analysis of FgKnr4 works as proof-of-principle. If the data presentation is improved, I believe that this will be an interesting publication.

   Read the original source
5. 

   Version published to 10.1101/2024.05.31.596832v1 on bioRxiv

   Jun 3, 2024