Global transcription factors analyses reveal hierarchy and synergism of regulatory networks and master virulence regulators in Pseudomonas aeruginosa

Reviewer #1 (Public review):

Summary:

In this work, Huang et al. revealed the complex regulatory functions and transcription network of 172 unknown transcriptional factors (TFs) in Pseudomonas aeruginosa PAO1. They have built a global TF-DNA binding landscape and elucidated binding preferences and functional roles of these TFs. More specifically, the authors established a hierarchical regulatory network and identified ternary regulatory motifs, and co-association modules. Since P. aeruginosa is a well known pathogen, the authors thus identified key TFs associated with virulence pathways (e.g., quorum sensing [QS], motility, biofilm formation), which could be potential drug targets for future development. The authors also explored the TF conservation and functional evolution through pan-genome and phylogenetic analyses. For the easy searching by other researchers, the authors developed a publicly accessible database (PATF_Net) integrating ChIP-seq and HT-SELEX data.

Strengths:

(1) The authors performed ChIP-seq analysis of 172 TFs (nearly half of the 373 predicted TFs in P. aeruginosa) and identified 81,009 significant binding peaks, representing one of the largest TF-DNA interaction studies in the field. Also, The integration of HT-SELEX, pan-genome, and phylogenetic analyses provided multi-dimensional insights into TF conservation and function.

(2) The authors provided informative analytical Framework for presenting the TFs, where a hierarchical network model based on the "hierarchy index (h)" classified TFs into top, middle, and bottom levels. They identified 13 ternary regulatory motifs and co-association clusters, which deepened our understanding of complex regulatory interactions.

(3) The PATF_Net database provides TF-target network visualization and data-sharing capabilities, offering practical utility for researchers especially for the P. aeruginosa field.

Weaknesses:

(1) There is very limited experimental validation for this study. Although 24 virulence-related master regulators (e.g., PA0815 regulating motility, biofilm, and QS) were identified, functional validation (e.g., gene knockout or phenotypic assays) is lacking, leaving some conclusions reliant on bioinformatic predictions. Another approach for validation is checking the mutations of these TFs from clinical strains of P. aeruginosa, where chronically adapted isolates often gain mutations in virulence regulators.

(2) ChIP-seq in bacteria may suffer from low-abundance TF signals and off-target effects. The functional implications of non-promoter binding peaks (e.g., coding regions) were not discussed.

(3) PATF_Net currently supports basic queries but lacks advanced tools (e.g., dynamic network modeling or cross-species comparisons). User experience and accessibility remain underevaluated. But this could be improved in the future.

Achievement of Aims and Support for Conclusions

(1) The authors successfully mapped global P. aeruginosa TF binding sites, constructed hierarchical networks and co-association modules, and identified virulence-related TFs, fulfilling the primary objectives. The database and pan-genome analysis provide foundational resources for future studies.

(2) The hierarchical model aligns with known virulence mechanisms (e.g., LasR and ExsA at the bottom level directly regulating virulence genes). Co-association findings (e.g., PA2417 and PA2718 co-regulating pqsH) resonate with prior studies, though experimental confirmation of synergy is needed.

Impact on the Field and Utility of Data/Methods

(1) This study fills critical gaps in TF functional annotation in P. aeruginosa, offering new insights into pathogenicity mechanisms (e.g., antibiotic resistance, host adaptation). The hierarchical and co-association frameworks are transferable to other pathogens, advancing comparative studies of bacterial regulatory networks.

(2) PATF_Net enables rapid exploration of TF-target interactions, accelerating candidate regulator discovery.

Reviewer #3 (Public review):

Summary:

The authors utilized ChIP-seq on strains containing tagged transcription factor (TF)-overexpression plasmids to identify binding sites for 172 transcription factors in P. aeruginosa. High-quality binding site data provides a rich resource for understanding regulation in this critical pathogen. These TFs were selected to fill gaps in prior studies measuring TF binding sites in P. aeruginosa. The authors further perform a structured analysis of the resulting transcriptional regulatory network, focusing on regulators of virulence and metabolism, in addition to performing a pangenomic analysis of the TFs. The resulting dataset has been made available through an online database. While the implemented approach to determining functional TF binding sites has limitations, the resulting dataset still has substantial value to P. aeruginosa research.

Strengths:

The generated TF binding site database fills an important gap in regulatory data in the key pathogen P. aeruginosa. Key analyses of this dataset presented include an analysis of TF interactions and regulators of virulence and metabolism, which should provide important context for future studies into these processes. The online database containing this data is well organized and easy to access. As a data resource, this work should be of significant value to the infectious disease community.

Weaknesses:

Drawbacks of the study include 1) challenges interpreting binding site data obtained from TF overexpression due to unknown activity state of the TFs on the measured conditions, 2) limited practical value of the presented TRN topological analysis, and 3) lack of independent experimental validation of the proposed master regulators of virulence and metabolism.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This work done by Huang et.al. revealed the complex regulatory functions and transcription network of 172 unknown transcription factors of Pseudomonas aeruginosa PAO1. The authors utilized ChIP-seq to profile TFs binding site information across the genome, demonstrating diverse regulatory relationships among them via hierarchical networks with three levels. They further constructed thirteen ternary regulatory motifs in small subs and co-association atlas with 7 core associated clusters. The study also uncovered 24 virulence-related master regulators. The pan-genome analysis uncovered both the conservation and evolution of TFs with P. aeruginosa complex and related species. Furthermore, they established a web-based database combining both existing and novel data from HT-SELEX and ChIP-seq to provide TF binding site information. This study offered valuable insights into studying transcription regulatory networks in P. aeruginosa and other microbes.

Strengths:

The results are presented with clarity, supported by well-organized figures and tables that not only illustrate the study's findings but also enhance the understanding of complex data patterns.

Thank you for your valuable feedback on our paper exploring the transcription regulatory networks in P. aeruginosa.

Weaknesses:

The results of this manuscript are mainly presented in systematic figures and tables. Some of the results need to be discussed as an illustration how readers can utilize these datasets.

We appreciate the valuable suggestion about enhancing the practical aspects of our manuscript. We have expanded the discussion section to include more detailed explanations of how these datasets can be utilized in practical applications.

Reviewer #2 (Public review):

In this work, the authors comprehensively describe the transcriptional regulatory network of Pseudomonas aeruginosa through the analysis of transcription factor binding characteristics. They reveal the hierarchical structure of the network through ChIP-seq, categorizing transcription factors into top-, middle-, and bottom-level, and reveal a diverse set of relationships among the transcription factors. Additionally, the authors conduct a pangenome analysis across the Pseudomonas aeruginosa species complex as well as other species to study the evolution of transcription factors. Moreover, the authors present a database with new and existing data to enable the storage and search of transcription factor binding sites. The findings of this study broaden our knowledge on the transcriptome of P. aeruginosa. This study sheds light on the complex interconnections between various cellular functions that contribute to the pathogenicity of P. aeruginosa, along with the associated regulatory mechanisms. Certain findings, such as the regulatory tendencies of DNA-binding domain-types, provides valuable insights on the possible functions of uncharacterized transcription factors and new functions of those that have already been characterized. The techniques used hold great potential for discovery of transcription factor functions in understudied organisms as well.

The study would benefit from a more clear discussion on the implications of various findings, such as binding preferences, regulatory preferences, and the link between regulatory crosstalk and virulence. Additionally, the pangenome analysis would be furthered through a discussion of the divergence of the transcription factors of P. aeruginosa PAO1 across species in relation to the findings on the hierarchical structure of the transcriptional regulatory network.

Thank you for your positive feedback and suggestions.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Major:

(1) It appears that many TFs are conserved among bacteria, archaebacteria, fungi, plants, and animals. Does this mean these TFs in bacterial could be the ancestors of TFs in fungi, plants, and animals? If we fetch these TFs out and build an evolutionary tree, can we visual the three kingdoms as well?

Thank you for this comment. While many TFs are conserved across bacteria, archaea, fungi, plants, and animals, this conservation does not necessarily imply a direct ancestral relationship. Instead, it may reflect the fundamental importance of certain domains and regulatory mechanisms, which could have arisen from a common ancestral system or through convergent evolution. If we fetch TF PA2032 out to build an evolutionary tree by setting PAO1 as the root, we can visualize these kingdoms in a tree. We added this content in the revised manuscript. Please see Figure S7D and Lines 404-411.

“The phylogenetic tree of PA2032 across bacteria, archaea, fungi, plants, and animals, with PAO1 as the root revealed that the bacterial TFs (purple) indicates a high degree of conservation within prokaryotes, suggesting a fundamental role in core regulatory processes. In contrast, eukaryotic TFs (fungi, plants, and animals) form distinct clades with longer branch lengths, indicating significant divergence and specialization during eukaryotic evolution. These findings suggest that while TF is conserved across domains of life, its functional roles and regulatory mechanisms have undergone substantial diversification in eukaryotes.”

(2) Can the authors give an indication how could we employ the findings of this study in designing next generation of antimicrobial agents?

Thank you for this important suggestion. We have provided this content in the discussion part. Please see Lines 481-492.

“The extensive datasets generated in this study offer valuable insights into understanding and targeting P. aeruginosa pathogenicity. The genome-wide binding profiles can be systematically analyzed through our hierarchical regulatory network framework to decode complex virulence mechanisms. The virulence-related master regulators and core regulatory clusters identified in this study highlighted key nodes of transcriptional control. Understanding these regulatory relationships is particularly valuable for identifying targets whose modulation would significantly impact virulence while accounting for potential compensatory mechanisms. This knowledge base thus provides a foundation for developing targeted approaches to combat P. aeruginosa infections, moving beyond traditional antibiotic strategies toward more sophisticated interventions based on regulatory network manipulation.”

Minor：

(1) Lines 178-180: It would strengthen the discussion to include a few additional references that support the claims made in this section, providing a more comprehensive context for the readers.

Yes. We have added more citations(1-5) (No. 1-5 in the references at the end of the rebuttal) to support the claims. Please see Line 182.

(2) Line 198: You mention 'seven' motifs containing toggle switches, but Fig.3 actually displays eight motifs. Please revise this discrepancy to ensure consistency between the text and the figure.

Yes. We have revised the wording to “eight”. Please see Line 200.

(3) Figure 3A: Consider adding a diagram or legend that represents the colors associated with each DNA-binding domain (DBD) family.

Thank you for your suggestion. The colors of DBD were aligned with the legend in Figure S3. We have added it in Figure 3A.

Reviewer #2 (Recommendations for the authors):

Line 21: The use of the abbreviation 'TF' should be done at the first instance of 'transcription factor'.

Yes. We have revised it. Please see Line 21.

Line 74: The purpose of this paragraph is slightly unclear. It is recommended that appropriate modifications are made.

We are sorry for the confusion. The purpose of this paragraph was to introduce the major virulence pathways in P. aeruginosa and mention the important role of TRN in these pathways. We have modified it to make it clearer. Please see Lines 74-75.

“P. aeruginosa employs diverse virulence pathways to establish successful infection, with QS being one of the major mechanisms involving the expression of many virulence genes.”

Line 113: How were these 172 TFs selected?

Thank you for indicating this question. In a previous study, we performed HT-SELEX to characterize the DNA-binding motifs of all TFs in P. aeruginosa PAO1, successfully identifying binding sequences for 182 TFs. To further elucidate the binding landscapes of the rest, we performed ChIP-seq on the remaining TFs (172 TFs in total with high-quality ChIP-seq libraries). Please see Lines 100-101 in the revised manuscript.

Line 119: Defining other features, namely downstream and include Feature, would be helpful.

Thank you for your suggestion. We have added the definition for all peak annotation in the legend. Please see Lines 569-574.

“Annotation heatmap of all peak distribution with 6 locations: Upstream, where the peak is located entirely upstream of the gene; Downstream, where the peak is positioned completely downstream of the gene; Inside, where the peak is entirely contained within the gene body; OverlapStart, where the peak overlaps with the 5' end of the gene; OverlapEnd, where the peak overlaps with the 3' end of the gene; and IncludeFeature, where the peak completely encompasses the gene.”

Line 129: The distribution type of AraC-type TFs is unclear - it is mentioned that AraC has a 'broad distribution', but it is later stated that it has a 'narrow distribution'.

We are sorry for this mistake, and we have revised the example for “broad distribution”, which is Cor_CI instead of AraC. Please see Lines 132-135.

Line 161: 'h value' here may need to be modified to 'absolute h value'.

Yes. We have revised it. Please see Line 164.

Line 502: "s The DNA" needs to be corrected.

Yes. We have revised it. Please see Line 514.

Line 515: It would be helpful to readers if the reference used for these pathways was cited.

Yes. We have added the review reference (Shao et al, 2023) related to these pathways(6) (the 6th reference at the end of the rebuttal). Please see Line 527.

Line 558: "Translation start site" needs to be corrected to "Transcription start site"

The “TSS” here exactly indicated “Translation start site”.

Line 593. "Virulent" pathways needs to be corrected to "virulence" pathways.

Yes. We have revised it. Please see Line 609.

Line 604: The type of categorization based on which the proportion of genes is displayed needs to be mentioned.

Yes, we agree. We have added the type of categorization in the legend. Please see Lines 621-627.

“Figure 6. Conservation and variability of TFs in PAO1. (A). The pie chart shows the proportions of genes categorized by their presence across P. aeruginosa strains for all genes. (B). The pie chart shows the distribution of TFs identified from PAO1 across different conservation categories. (C). The bar plot of the proportion for non-core TFs. Genes are categorized based on their presence frequency across P. aeruginosa strains: Core genes (present in 99% ~ 100% strains), Soft core genes (present in 95% ~ 99% strains), Shell genes (present in 15% ~ 95% strains), and Cloud genes (present in 0% ~ 15% strains).”

Reference:

(1) Liang H, Deng X, Li X, Ye Y, Wu M. 2014. Molecular mechanisms of master regulator VqsM mediating quorum-sensing and antibiotic resistance in Pseudomonas aeruginosa. Nucleic acids research 42:10307-10320.

(2) Jones CJ, Ryder CR, Mann EE, Wozniak DJ. 2013. AmrZ modulates Pseudomonas aeruginosa biofilm architecture by directly repressing transcription of the psl operon. Journal of bacteriology 195:1637-1644.

(3) Hickman JW, Harwood CS. 2008. Identification of FleQ from Pseudomonas aeruginosa as ac‐di‐GMP‐responsive transcription factor. Molecular microbiology 69:376-389.

(4) Déziel E, Gopalan S, Tampakaki AP, Lépine F, Padfield KE, Saucier M, Xiao G, Rahme LG. 2005. The contribution of MvfR to Pseudomonas aeruginosa pathogenesis and quorum sensing circuitry regulation: multiple quorum sensing‐regulated genes are modulated without affecting lasRI, rhlRI or the production of N‐acyl‐L‐homoserine lactones. Molecular microbiology 55:998-1014.

(5) Lizewski SE, Lundberg DS, Schurr MJ. 2002. The transcriptional regulator AlgR is essential for Pseudomonas aeruginosa pathogenesis. Infection and immunity 70:6083-6093.

(6) Shao X, Yao C, Ding Y, Hu H, Qian G, He M, Deng X. 2023. The transcriptional regulators of virulence for Pseudomonas aeruginosa: Therapeutic opportunity and preventive potential of its clinical infections. Genes & Diseases 10:2049-2063.