Global transcription factors analyses reveal hierarchy and synergism of regulatory networks and master virulence regulators in Pseudomonas aeruginosa
- Department of Biomedical Sciences, City University of Hong Kong, China
- Department of Computer Science, The University of Hong Kong, China
- Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, United States
- Shenzhen Research Institute, City University of Hong Kong, China
Figures
Figure 1
Overview of ChIP-seq results.
(A) Density of all transcription factors (TFs, green) and ChIPed TFs (orange) in this study throughout the P. aeruginosa genome. (B) Annotation heatmap of all peak distribution with six 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. (C) Peak distance to the translational start site (TSS) of each DBD family. (D) Treemap of the 172 TFs peak numbers based on DBD family. Each box’s size represents the family’s size (number of peaks), and the explained variance of each DBD type means the colour shades of each box. DBD families of ChIPed TFs are classified into 20 different categories: LuxR, LysR, Two DBDs (two DNA-binding domains), AraC, TetR, ArsR, CRP, OmpR, GntR, MarR, AsnC, Cro/CI, TyrR, Rrf2, MerR, IclR, Fis, RpiR, DeoR, and undetectable. (E) The dot plot shows the top 10 Gene Ontology (GO) terms from the PseudoCAP annotation of ChIPed TFs. The size of the dots indicates the significance of each functional category, quantified by −log10 (p. adjust).
Figure 2 with 1 supplement
Hierarchical networks and network motifs.
(A) Overview of hierarchy regulatory network after removing transcription factors (TFs) with degree O + I <= 10. Nodes indicate TFs, and the colour represents the hierarchical level. The top level was highlighted in red, the middle in yellow, and the bottom highlighted in blue. The edges with arrows indicate the regulatory direction. Grey means downward-pointing, and red means upward-pointing. (B) The auto-regulator motif of nine TFs. (C) Three TF motif occurrences with five basic triangular motifs and eight toggle switch motifs. The circle presents TF, and the arrow indicates the regulatory direction. (D) Alluvial diagram reveals basic triangle motif 3 (n = 1956) depending on DBD types. The colour of splines is highlighted in different DBD families, and the name of DBD families is labelled.
Figure 2—figure supplement 1
Distribution of hierarchy height h.
h = (O − I)/(O + I), O presents the extent they regulate other transcription factors (TFs), and I indicates the level other TFs regulate them. (A) TFs can be divided into almost equal three levels depending on index h: bottom level (–1.0 < h < 0.75), middle level (–0.75 < h < 0.75), and top level (0.75 < h < 1.0). (B) The number of TFs resided in three levels after removing TFs with O + I < 10.
Figure 3 with 2 supplements
Core co-association regulatory networks.
(A) Core clusters of significant co-binding patterns of TFs. Each TF is highlighted in different colours based on DBD types. The co-association score by pair of TFs was calculated by Jaccard statistics, which measures the ratio of the number of base pairs in overlapped binding peaks on both TFs to the number of base pairs in their union. (B) The histogram’s overlapped target genes of TFs in cluster 3 represent the number of target peaks in the individual/overlapped set. (C) The network of co-regulation of TFs in cluster 3 with co-bound targets of more than 4. (D, E). Genome browser view of TFs in cluster 3 binding intensities at the PA2504 and pqsH locus.
Figure 3—figure supplement 1
Circular phylogenetic tree of TF-binding motifs.
(A) Circular phylogenetic tree representing the clustering of TFs based on their motif similarity. The tree was constructed using hierarchical clustering with the Ward.D2 method applied to the pairwise similarity matrix of motifs. The tree was visualised using ggtree (Yu, 2020). Each node represents a TF, colour-coded by its DBD family classification as indicated in the legend on the right. The motif logos are aligned with the respective TFs, with their orientations adjusted to maintain legibility around the circular layout. (B) PA3587 and MetR display similar DNA-binding motifs.
Figure 3—figure supplement 2
Co-association network of TFs.
(A) Heatmap reveals a full co-association pattern of all TFs. (B) The density of co-association score for all TF pairs. Determining co-association score as 0.1 (dashed in red) of significant TF co-associations based on an elbow statistic.
Figure 4 with 2 supplements
Newly identified virulence-related master regulators.
(A) Overview of all identified master regulators related to six pathways, including QS, motility, biofilm, siderophore, pyocyanin, and ROS. Each circle represents one transcription factor (TF), with the height indicating the significance, quantified by −log10 (p.adjust), and the size indicates the number of targets associated with the virulence pathway. (B) Intersection of master regulators in six virulence pathways. The bar chart on the top shows the number of intersections, while the matrix below indicates which TFs are involved in specific biological processes such as siderophore production, pyocyanin production, biofilm formation, ROS response, QS, and motility.
Figure 4—figure supplement 1
Validation and co-regulation of virulence-related master regulators.
(A) The validation of the binding sites of PA0167 on the promoter of fleQ and cdrA by Electrophoretic Mobility Shift Assay (EMSA). Protein concentrations were 0, 0.25, 0.5, and 1 μM, respectively. (B) The validation of the binding sites of PA0815 on the promoter of mvfR and cupA1 by EMSA. Protein concentrations were 0, 0.25, 0.5, and 1 μM, respectively. (C) The validation of the binding sites of PA1380 on the promoter of mvfR and cupB1 by EMSA. Protein concentrations were 0, 0.5, 1, and 2 μM, respectively. (D) The validation of the binding sites of PA3094 on the promoter of lacA and lasR by EMSA. Protein concentrations were 0, 0.25, 0.5, and 1 μM, respectively. (E) The detection of expression of target genes PA1380, cupB1, and cupB3 in WT, ΔPA1380, and complementary strain by reverse transcription quantitative PCR (RT-qPCR). The error bar represents the SD (n = 3). Statistical significance was determined using two-tailed Student’s t-test. (F) The detection of expression of target genes PA3094 and lecA in WT, ΔPA3094, and complementary strain by RT-qPCR. The error bar represents the SD (n = 3). Statistical significance was determined using two-tailed Student’s t-test. (G) Graph diagram of interactions involving target genes of four transcription factors (TFs), including PA1380, PA0815, PA5428, and PA3973. NC indicates negative control. All RT-qPCR experiments were repeated at least twice.
Figure 4—figure supplement 2
Regulators involved in tricarboxylic acid (TCA) and ribosome pathways.
(A) Radar plots show the putative regulator in TCA and ribosome pathways. (B) Graph diagram of interactions involving target genes of 14 TFs, including PA0611, PA5218, PA5431, PA1759, PA0403 (PyrR), PA 1283, PA4381, PA5255 (AlgQ), PA4784, PA0475, PA0893, PA0448, PA2957, and PA5438.
Figure 5
Overview of transcriptional network of TFs in P. aeruginosa.
(A) The interaction network of virulence-related master regulators in P. aeruginosa. The 10 virulence pathways are highlighted in different colours. The size of the circle represents the degree, and the width of the edges indicates the overlapped number of TFs between two pathways. (B) The target annotated using the COG database shows four orthology classification profiling from different DBD types TFs. The size of the rectangle indicates the number of targets.
Figure 6 with 1 supplement
Conservation and variability of TFs in PAO1.
(A) The pie chart shows the proportions of genes categorised 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 categorised 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). (D) The conservation and evolutionary relationship of all 373 TFs in PAO1 among bacteria, archaebacteria, fungi, plants, and animals. The conservation value was normalised after blastp alignment (Coordinators, 2013). The phylogenetic trees were constructed using MEGA11 (Tamura et al., 2021) and plotted via R package ggtree (Yu et al., 2017).
Figure 6—figure supplement 1
Conserved TFs in P. aeruginosa.
(A) The coverage of PhoB and RpoN peak regions over PAO1 and PA14 chromosomes. Each line shows the location and log2 Fold Enrichment of peaks signal in the chromosome. (B) The binding motifs of PhoB and RpoN were analysed via MEME-ChIP. All peaks were used to define the binding motif. (C) Comparison of overlapped targets enriched in PAO1 and PA14 of PhoB and RpoN. The Fisher test made the significance of the overlap ratio. (D) Phylogenetic tree of PA2032 across different domains of life. The phylogenetic tree was constructed using PA2032 sequence from PAO1 as the root.
Additional files
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Supplementary file 1
List of 172 ChIPed transcription factors (TFs).
- https://cdn.elifesciences.org/articles/103346/elife-103346-supp1-v1.xlsx
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Supplementary file 2
Enriched Gene Ontology (GO) terms of 172 ChIPed transcription factors (TFs).
- https://cdn.elifesciences.org/articles/103346/elife-103346-supp2-v1.xlsx
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Supplementary file 3
Hierarchical regulatory network.
- https://cdn.elifesciences.org/articles/103346/elife-103346-supp3-v1.xlsx
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Supplementary file 4
Ternary regulatory motifs.
- https://cdn.elifesciences.org/articles/103346/elife-103346-supp4-v1.xlsx
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Supplementary file 5
PWM pairwise similarity scores.
- https://cdn.elifesciences.org/articles/103346/elife-103346-supp5-v1.xlsx
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Supplementary file 6
Co-association network.
- https://cdn.elifesciences.org/articles/103346/elife-103346-supp6-v1.xlsx
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Supplementary file 7
Virulence-related master regulators.
- https://cdn.elifesciences.org/articles/103346/elife-103346-supp7-v1.xlsx
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Supplementary file 8
Strains and primers used in this study.
- https://cdn.elifesciences.org/articles/103346/elife-103346-supp8-v1.xlsx
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Supplementary file 9
Reference list of strains for pan-genome analysis.
- https://cdn.elifesciences.org/articles/103346/elife-103346-supp9-v1.xlsx
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MDAR checklist
- https://cdn.elifesciences.org/articles/103346/elife-103346-mdarchecklist1-v1.pdf
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Global transcription factors analyses reveal hierarchy and synergism of regulatory networks and master virulence regulators in Pseudomonas aeruginosa
eLife 14:RP103346.
https://doi.org/10.7554/eLife.103346.4
