Several genes in the RAS/MAPK signalling pathway were significantly down-regulated in pax3a/pax3b double mutant zebrafish. Progression of rhabdomyosarcoma tumours was also delayed in the pax3a/pax3b double mutant zebrafish indicating that Pax3 transcription factors have an unappreciated role in mediating malignancy in fusion negative rhabdomyosarcoma.Rhabdomyosarcoma is a soft tissue cancer that arises in skeletal muscle due to mutations in myogenic progenitors that lead to ineffective differentiation and malignant transformation. The transcription factors Pax3 and Pax7 and their downstream target genes are tightly linked with the fusion positive alveolar subtype, whereas the RAS pathway is usually involved in the embryonal, fusion negative variant. Here, we analyse the role of Pax3 in a fusion negative context, by linking alterations in gene expression in  Based on histological features, RMS tumours can be classified into two major subtypes, embryonal RMS (ERMS) and alveolar RMS (ARMS)3. ERMS is associated with a variety of mutational events, typically involving components in the Rat sarcoma virus (RAS) pathway4, whereas ARMS is characterized by chromosomal translocation resulting in PAX3-FOXO1 or PAX7-FOXO1 fusion proteins, which in humans has led to the replacement of histological annotations ERMS and ARMS with ‘Fusion negative’ and ‘Fusion positive’ RMS5. RMS tumours are generally caused by a genetic perturbation associated with muscle progenitor cell proliferation versus cell differentiation. Dysregulation of RAS and its downstream signalling cascade, RAF-MEK-ERK, alters cell cycle progression6, transcription of myogenic regulatory factors and ultimately influences myogenic differentiation9.Rhabdomyosarcoma (RMS) is a rare soft tissue sarcoma with molecular and histological features that resemble undifferentiated skeletal muscle9. The paired homeobox transcription factors (Pax3 and Pax7) and sine oculis related homeobox (Six1 and Six4) are also important for early lineage specification10. Pax3 plays a critical role in cell proliferation, differentiation, and migration during embryonic development of cells11, whereas Pax7 is mainly involved in progression of muscle regeneration via muscle stem cells11. Myogenic factors regulate normal myogenesis and regenerative processes, but they may also play a role in pathogenic myogenic differentiation, as dysregulation may lead to the development of RMS12. In fusion positive RMS, the DNA binding domains of either PAX3 or PAX7 are usually translocated to form a chimeric fusion with the transactive domain of FOXO1, which leads to altered expression of the PAX3/PAX7 downstream targets15. Less is known on the possible roles of the PAX3/PAX7 genes in fusion negative RMS. Regardless of fusion status, altered levels of transcription factors that regulate the myogenic process are characteristics for RMS cells19.During early development, skeletal muscle progenitors are formed from cells within the axial mesoderm. Myogenesis is regulated by myogenic regulatory factors, such as myogenin, MyoD, Myf5, and MRF420. However, it remains less clear what factors cause these myogenic regulatory factors to depart from their canonical roles as drivers of muscle differentiation to instead maintain RMS cells as less differentiated muscle progenitors. The gene coding for the transcription factor SIX1, co-expressed with PAX3 and PAX7 in myogenic precursors, is highly expressed in human RMS tumours and has been reported to be a regulator of metastasis in mice models for RMS21. Being an important developmental factor, the expression of pax3 gradually decreases as the tissue differentiates. The molecular mechanism of loss of function of pax3 in RMS formation and progression is not yet clear. However, re-expression of pax3 in differentiated adult tissues, that require Pax3 function during embryonic development, leads to tumour formation23.Several studies to date have discovered distinct activities of these myogenic transcription factors in the context of normal muscle development and RMS24, including RMS, and several studies have shown similarities between human fusion negative RMS and zebrafish RMS (zRMS)25. However, as a consequence of an ancestral genome duplication event in the evolution of teleost fish, the zebrafish genome harbours two separate pax3 genes, pax3a and pax3b27 .The zebrafish pax3 genes have over-lapping expression patterns and their functions have been shown to be partially redundant during development29. In zebrafish, fusion negative RMS can be induced by overactivation of the RAS pathway and has been shown to mimic human fusion negative RMS. Similarly, a human PAX3/FOXO1 fusion construct drives fusion positive RMS formation in zebrafish30. Given these similarities to human disease, and the lack of knowledge regarding the role of Pax3 in fusion negative RMS, we performed transcriptome analysis of pax3a/pax3b double mutant embryos to characterize the downstream targets and possible effectors of Pax3 transcriptional regulation. We also investigated the onset and progression of induced zRMS in pax3a/pax3b single and double mutants. This study demonstrates that progression of tumour is delayed in pax3a/pax3b mutant embryos, and that several genes in the RAS/MAPK signalling pathway are down-regulated in the mutants, linking Pax3 transcriptional regulation to one of the main mediators of malignancy in zRMS. This points to a yet unappreciated role of Pax3 also in fusion negative RMS.The zebrafish has emerged as an efficient model for studying molecular and genetic aspects of cancerAll experiments were performed in compliance with national and institutional laws and guidelines and the study is reported in accordance with ARRIVE guidelines. Zebrafish (Danio rerio) used in this study were maintained in compliance with the standard procedures at the Umeå University Zebrafish Facility. All animal experiments were approved by the Regional Ethics Committee at the Court of Appeal of Northern Norrland, Dnr A 6 2020.rag2-kRASG12D plasmid  was linearized with XhoI  as described previously25 and purified with NucleoSpin Gel and PCR clean-up mini kit for gel extraction and PCR clean up . 30 ng/µl of the linearized DNA was injected into the 1-cell stage of transgenic TgBAC(pax3a:EGFP)i15031 zebrafish crossed with the pax3a−/−(umu5) and pax3b−/−(umu6) knock out lines29 to generate single and double pax3a−/− and pax3b−/− mutants also carrying the pax3a:EGFP transgenic insertion. Zebrafish larvae were monitored daily and assessed in vivo for tumour onset using dissecting fluorescence microscope. Tumour onset was identified by abnormal accumulation of pax3a:EGFP in the muscle tissue. zRMS zebrafish larvae were kept separately, examined and photographed every four to five days to evaluate zRMS progression. zRMS tumour area was quantified using ImageJ.The zRMS were collected at 29 days post fertilization (dpf) to 31 dpf and tumour tissue was extracted using micro incision blade from each fish and used for RNA isolation. Total RNA extraction was performed from each extracted zRMS tumour using RNeasy mini kit (Qiagen) and reverse transcribed to cDNA using SuperScript III reverse transcriptase (Invitrogen), following the manufacturer’s protocol. qRT-PCR was performed on Applied Biosystems VIIA 7 Real-Time PCR System (ThermoFisher Scientific) using FastStart universal SYBR green master mix (Roche). The primer sequences are listed in supplemental Table pax3a−/−(umu5); pax3b−/−(umu6) homozygous double mutants were collected at 42 h post fertilization (hpf) for RNA preparation. 9–10 zebrafish larvae of each type were collected and pooled for a sufficient amount of RNA per sample. A total of six samples, two triplicates each, were included in RNA sequencing. Total RNA was extracted using TRizole and treated with TURBO DNase for 15 min at 37 °C to remove genomic DNA. Purity and integrity of the RNA samples was confirmed using Agilent 2100 bioanalyzer.Zebrafish embryos from wild type and https://www.scilifelab.se/). Raw reads were aligned to the zebrafish genome  using STAR 32. Genes with a minimum of 10 reads were kept for further analysis. Normalization and differential expression analyses were performed using DESeq233. Genes were considered differentially expressed with p-value < 0.01 and FDR < 0.01. Clustering of differentially expressed genes was obtained using pheatmap package. Gene ontology, KEGG and Disease ontology enrichment analysis were performed using clusterProfiler software34.Poly-A selection libraries were prepared, pooled and sequenced on a 1/4th NovaSeq600 S4 lane, with 2 × 150 bp reads , disease ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed separately on genes either up- or down-regulated in the mutants. These analyses revealed that DEGs are enriched for many biological processes and signalling pathways in which Pax3 has an established role, including ncRNA processing, ribosome biogenesis, axonogenesis and neuron development , pax3a−/−;pax3a:EGFP, pax3b−/−;pax3a:EGFP and pax3a−/−;pax3b−/−;pax3a:EGFP zebrafish lines  fish  common to RAS and MAPK signalling among the genes that were down-regulated in our RNA-seq analysis  respectively, no differences were observed , in zRMS from wildtype and pax3a−/−;pax3b−/− mutants and found that they were expressed significantly lower in double mutant zRMS than in wildtype zRMS  are one of the most dysregulated in human cancer45. This pathway regulates multiple critical cellular functions including proliferation, growth and apoptosis46. RAS proteins function as binary molecular switches that control intracellular signalling pathways involved in fundamental cellular processes such as cell polarity, proliferation, differentiation, adhesion, migration and apoptosis48. RAS and RAS related proteins are often dysregulated in cancers by activating mutation of RAS isoforms or its effectors in nearly one third of human cancers49.In this study, we presented transcriptional profile of 50. Previous studies demonstrated that overexpression of pak1 in cancer cells increases cell migration potential51. Comparably, our RNA-seq and qRT-PCR results showed that pak1 was down-regulated in pax3a−/−;pax3b−/− double mutants, in particular in the slow progressing tumours, which makes pak1 a plausible driver of fusion negative RMS progression in our model. It has been shown that inhibition of MAPK signalling pathway reduces the size and tumourigenicity of stem-like cell populations52. In the same study, the potential advantage of using MAPK signalling pathway inhibitors to target embryonal RMS was suggested. Similarly, both our transcriptome and qRT-PCR analyses indicated that pak1, mapk10, akt3b and rac3b were significantly reduced in pax3a−/−;pax3b−/− double mutants and thereby putatively contributing to a delay in zRMS development by inhibition of MAPK signalling activity. The expression of pak1, mapk10, akt3b and rac3b was also slightly lower in healthy muscle tissue from pax3a−/−;pax3b−/− double mutants in comparison with wildtype controls, but only akt3b was significantly reduced , that previously have been shown to be down-regulated when MAPK is inhibited54. As predicted, all these genes were significantly reduced in the zRMS from pax3a−/−;pax3b−/− double mutants compared to wildtype zRMS (Fig. The p21-activated PAK1 is one of the critical effectors of the small GTPase Rac1 and Cdc42pax3a−/−;pax3b−/− mutants at 10 and 15 dpf, suggesting an important role of pax3 genes in tumour progression. The delay in pax3a−/−;pax3b−/− mutants was less prominent after 20 dpf, likely due to an increased variability in tumour size in all genotypes over time. Due to a putative genetic redundancy in zebrafish, the delayed tumour progression was not observed in single pax3a−/− or pax3b−/− mutants. We did not observe any delay in tumour initiation in any of the mutant genotypes compared with wildtypes, but a significant delay was found in pax3a−/−;pax3b−/− when compared with pax3b−/− mutants indicating loss of both pax3 genes affects zRMS biology and behaviour to a higher extent than single mutations. This was also confirmed by the qRT-PCR analyses (Fig. pax3a and pax3b genes. A recent study reported that six1b knockout alone, without mutation of six1a, inhibits zebrafish RMS growth and progression55, which shows that redundancy between paralogues not always is the case in this context. Mutation of six1b resulted in a change in Myod cis-element occupancy and thereby altered myogenic progression55. Hence, even though Pax3 and Six1 are linked to similar genetic programmes in myogenesis and that mutation of Pax3 and Six1 both lead to inhibition of zRMS in zebrafish, the downstream effects of the mutations appear to differ. However, similar to Six1, Pax3 acts upstream of Myf5 in myogenesis56 and Myf5 can impart tumour propagating potential to zRMS cells in the zebrafish model57. In addition, Myf5 or Myod knockdown suppress RMS proliferation in vitro57. The Pax3/Foxo1 gene product has also been shown to dirceclty regulate the myod and myog gene promoters in fusion positive RMS13. The myogenic factors myod and myf5 were however not significantly altered in our transcriptome analysis, possibly due to the analysis being performed on whole embryos rather than muscle tissue, but would unlikely be the only cause of delayed tumour progression in the pax3a−/−;pax3b−/− mutants. In addition to myod, previous studies in myoblast and rhabdomyosarcoma cells have shown that pax3 also is involved in supressing apoptosis and regulation of tumour suppressor genes60. Together with our data, this indicates that there are parallel pathways involved in RMS progression among the downstream targets of pax3, which include MAPK signalling.Interestingly, our results showed a significant delay in tumour growth in In conclusion, Pax3a and Pax3b redundantly regulate genes linked to RAS/MAPK signalling both in the context of development and in zRMS progression. This provides new insights into investigating a molecular mechanism of fusion negative RMS progression and a new role for Pax3 in this context.Supplementary Information.Supplementary Table 2.


Pseudomonas aeruginosa. Here, with a library of dual-color transcriptional reporters, we estimated the NTR for over 90% of the promoters in P. aeruginosa. Most promoters exhibit low NTR, while 42 and 115 promoters with high NTR were screened out in the exponential and the stationary growth phases, respectively. Specifically, a rearrangement of NTR was found in promoters involved in amino acid metabolism when bacteria enter the exponential phase. In addition, during the stationary phase, high NTR was found in a wide range of iron-related promoters involving siderophore synthesis and heme uptake, ExsA-regulated promoters involving bacterial virulence, and FleQ-regulated promoters involving biofilm development. We also found a large-scale negative dependence of transcriptional regulation between high-NTR promoters belonging to different functional categories. Our findings offer a global view of transcriptional heterogeneity in P. aeruginosa.The part of expression noise that is brought about by transcriptional regulation (represented here as NTR) is an important criterion for estimating the regulatory mode of a gene. However, characterization of NTR is an under-explored area, and there is little knowledge regarding the genome-wide NTR in the model pathogen IMPORTANCE The phenotypic diversity of Pseudomonas aeruginosa is frequently observed in research, suggesting that bacteria adopt strategies such as bet-hedging to survive ever-changing environments. Gene expression noise (GEN) is the major source of phenotypic diversity. Large GEN from transcriptional regulation (represented as NTR) represent an evolutionary necessity to maintain the copy number diversity of certain proteins in the population. Here, we provide a system-wide view of NTR in P. aeruginosa under nutrient-rich and stressed conditions. High NTR was found in genes involved in flagella biosynthesis and amino acid metabolism under both conditions. Specially, iron acquisition genes exhibited high NTR in the stressed condition, suggesting a great diversity of iron physiology in P. aeruginosa. We further revealed a global negative dependence of transcriptional regulation between those high-NTR genes under the stressed condition, suggesting a mutually exclusive relationship between different bacterial survival strategies. While noise in gene expression is inherent, it can also be modulated by transcriptional regulation. For example, bacteria can increase their gene expression noise via transcriptional regulation to adopt a bet-hedging strategy in ever-changing environments , 2. Tran3–7–Pseudomonas aeruginosa. We screened out multiple promoters with high NTR in both the exponential and stationary growth phases, with functional categories containing amino acid metabolism, virulence, biofilm formation, efflux pumps, iron acquisition, etc. The high-NTR promoters found in this study provide a wealth of new clues for further study of transcriptional regulation in P. aeruginosa.The fluorescent protein-based transcriptional reporter is a commonly used tool to characterize the expression variability for a large number of genes –14. Expr–12–sfGFP as a quantifiable marker for the transcription of each candidate promoter and used constitutively expressed cyOFP1 as an internal standard; both modules were cloned into the PUCP20 backbone . Thus, we finally obtained qualified expression data for promoters of 3,025 ORFs, accounting for 90.6% of all ORFs in PAO1. The reporter strains were grown in LB medium. The single-cell fluorescence of SfGFP and CyOFP1 for each reporter in both exponential and stationary growth phases was evaluated by microscopy. Fluorescent intensity was transformed to SfGFP or CyOFP1 numbers in single cells . To assess the repeatability of our methods, we selected 100 reporters and repeated the data-collection procedure on two different days. R-squares for mean SfGFP expression during both growth phases reached 0.99, while R-squares for coefficients of variations  for SfGFP expression in the cell population during the exponential and stationary phases were 0.732 and 0.896, respectively  Reproducibility of experimental measurements of mean SfGFP expression  and SfGFP expression variation  in the exponential and stationary growth phases. EXP, exponential phase; STA, stationary phase. Download Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.00963-22.3FIG S3Pseudomonas aeruginosa when cells were treated or untreated with 1 μg/mL ciprofloxacin. (c) Different cell-to-cell expression heterogeneity of LexA-regulated genes  and PrtR-PrtN-regulated genes . (d and e) Distribution of single-cell expression of acsA and aceA reporters when acetate or succinate was used as the single carbon source in fastidious anaerobe broth (FAB) medium. The products of both acsA and aceA are key enzymes for P. aeruginosa to metabolize acetate. (f and g) SfGFP images of cupA1 reporter in wild-type (f) and mvaT mutant (g) P. aeruginosa cells. The same contrast parameters were used for both images. (h) Distribution of single-cell expression of cupA1 in the wild-type and mvaT mutant strain. Coefficients of variations are displayed in the map. Download FIG S3, file, MB.Validation of the method. (a and b) Mean level (a) and cell-cell variation (b) of SOS gene expression in Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P. aeruginosa, we plotted the total noise  represent the population average of the number of fluorescent proteins in single cells. b + 1)/<n>) and (ii) expression noise arising from fluctuations in both the expression level and the promoter-binding state of upstream transcription factors which are specific to the target promoter (represented here as NTR).To obtain an overview of gene expression noise for eporters  and d. Aeporters –14, ηtotescribed :(1)ηtotb + 1)/<n> to fit data   . In additer2 map , indicat10.1128/msystems.00963-22.4FIG S4FIG S4, file, MB.Contingency test of the number difference of large NTR values between data from the exponential and the stationary phases. Download Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the b + 1)/<n>, Due to the dependence of n as the mean expression level. Genes within the same operon were assumed to have the same transcriptional divergence that is dependent on their shared promoter and therefore have the same NTR. Mean expression level, https://figshare.com/articles/dataset/Figures/21266778), although there are some locally large NTRs distributed around the chromosomal positions of 2.7, 3.8, and 4.6 Mbp in the stationary phase data , accounting for 5.61% of all genes and 5.87% of all promoters. Except for the PA1156 (nrdA) promoter, which has a moderate NTR (0.21) in the stationary phase  . Differesum test . The teswn in LB . We mappry phase  and c, pry phase  and c, whia coli . Essentihia coli . On the E. coli  in relationship to NTR in the exponential phase (a) or NTR in the stationary phase (b), or the difference in NTR (delta NTR) between two growth phases (NTRSTA − NTREXP) (c). EXP, exponential phase; STA, stationary phase. Download FIG S5, file, MB.Fold change of normalized promoter expression . The fixed NTR threshold of 0.15 is an empirical value. It was chosen for the following reasons. (i) According to our repeated experiments for 100 reporters, the experimental error for NTR measurement is less than 0.1 .We further used a fixed threshold (NTR = 0.15) to search for promoters with very high NTR, and a total of 140 promoters (287 genes) were screened out (than 0.1 . Therefothan 0.1 . (iii) Wthan 0.1  at NTR =10.1128/msystems.00963-22.6FIG S6FIG S6, file, MB.Venn diagrams representing the number of reporters with high residual of intrinsic noise (NTR) in two growth phases. (a) Number of promoters; (b) number of genes controlled by the corresponding promoters in panel a. Download Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.00963-22.7FIG S7FIG S7, file, MB.Elaboration of the fixed NTR threshold. (a and b) NTR measurement results of two repeated experiments (100 reporters) in the exponential (a) and the stationary (b) growth phase. (c and d) promoter-specific intrinsic noise map for data from the exponential (c) and the stationary phase (d). (e) distribution of NTR of all reporters in two growth phases. Download Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Pseudomonas database and previous studies  showed high NTRs during both growth phases, including promoters of virulence genes involved in type III secretion system, type IVb pilus synthesis, and lectin production, and promoters of genes involved in transcriptional regulation (TR) and flagellum biogenesis. Notably, FlgM is an anti-sigma factor of FliA which directly controls the transcription of promoter . NTRs ofPA5471.1, which encodes a leader peptide that plays an inhibitory role in ArmZ activation, thereby inhibiting the expression of the downstream efflux pump MexXY-OprM  showed high NTRs specific to the exponential phase. Among the proteins expressed by these promoters, BetI and PhhR are two transcriptional regulators controlling the expression of genes involved in glycine, phenylalanine, and tyrosine biosynthesis, and SpeC is an ornithine decarboxylase involved in arginine degradation –40. Thes–xXY-OprM . MexXY-OphhR and betI promoters are greatly reduced. In contrast, the NTRs of promoters of genes involved in glycine, lysine, serine, methionine, and histidine metabolism are increased, indicating a rearrangement of amino acid metabolism heterogeneity among the bacterial population. Moreover, large NTRs are also found in promoters of pctA and pctB, both of which encode methyl-accepting enzymes that are involved in bacterial chemotaxis toward multiple amino acids  showed high NTRs specific to the stationary phase. The corresponding genes have a wide range of functions, including chemotaxis, swimming motility, iron acquisition, biofilm formation, carbon metabolism, amino acid metabolism, and bacterial virulence. Interestingly, when bacterial cells enter the stationary phase, the NTRs of no acids . NotablyP. aeruginosa, Fur is the central regulator for iron homeostasis that represses the transcription of iron uptake genes when bound to its corepressor, Fe2+  are directly regulated by Fur  with high NTRs are all within the direct regulon of sigma factor PvdS  are under the direct regulation of a c-di-GMP-dependent regulator, FleQ , 30P. aeor, FleQ  34, 35), 35P. aeP. aeruginosa and compared the data with the transcription levels of pyoverdine biogenesis genes. Pyoverdine efflux pump (pvdR-pvdT-opmQ) genes were deleted so that pyoverdine production could be estimated by measuring pyoverdine’s characteristic fluorescence in single cells. As expected, there is great heterogeneity in pyoverdine production among P. aeruginosa cells  exhibit good positive correlations with cellular pyoverdine concentration (pvdI shows the best correspondence with pyoverdine production (correlation coefficient ≈ 0.8) under both iron-deficient and iron-sufficient conditions  within bacteria, heterogeneity at the transcription level does not necessarily correspond to the heterogeneity of protein expression and bacterial phenotype. To address this issue, we examined pyoverdine production at the single-cell level in sa cells  to c. Montration  to c, innditions . pvdI isSfGFP and CyOFP1. Fifteen promoters belonging to different functional categories were selected from the high-NTR promoters in the stationary phase, generating 105 (https://figshare.com/articles/figure/Supplementary_figures/21275937/2). For example, a negative correlation was observed between the expression of flp promoter and PA5127 promoter, in which cells with lower levels of flp transcription generally had higher levels of PA5127 transcription , indicating a global negative correlation of transcriptional regulation between heterogeneously expressed genes. It is unclear what the underlying mechanics are. Bacteria may maintain subpopulations which employ various survival strategies while under stress.In general, the κp1,p2 are seen in flp-PA5127 (−0.82) and flp-PA0208 (−0.69) reporters . Of the few reporters with positive κp1,p2, the pvdS-pvdA reporter exhibits the most positive κp1,p2, which is expected because PvdS controls pvdA transcription. In addition, the mexG-pctB, mexG-pchE, and mexG-pvdS reporters exhibit relatively large positive κp1,p2, suggesting that there is some functional synergy between the MexGHI-OpmD efflux pump and chemotaxis system and pyochelin/pyoverdine biosynthesis in the stationary phase. Notably, transcriptional regulation of fliC promoter exhibits negative correlations  with all other 14 promoters  with gamma distribution  with the relationship Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the pvdP, prrH, and PA5127 exhibit bimodal expression patterns , exsC (0.69), PA4580 (0.67), and flp (0.66) in the exponential phase and in promoters of pvdA (0.79), PA4073 (0.78), pvdF (0.75), and PA4570 (0.73) in the stationary phase . The bimodal expression patterns of these promoters indicate probable phenotypic separation in the expression of these genes.We found two distinct expression patterns of high-NTR reporters. Promoters of patterns  and b, wodal one  and d. Todal one  of SfGFPP. aeruginosa promoters. Separation of the promoter-specific intrinsic noise from the total noise with a dual-color reporter facilitates the characterization of gene expression variability brought about by transcriptional regulation, as measured by NTR. Interestingly, we found high NTRs in a wide range of promoters driving the expression of iron-acquisition genes during the stationary growth phase, and a large-scale negative dependence of transcriptional regulation was also found between high-NTR promoters belonging to different functional categories. Genome-wide studies of NTR under other usual growth circumstances, such as host infection or antibiotic stress, are necessary to acquire more knowledge for clinical use. Additionally, while some putative explanations for the high NTRs of the promoters we tested have been postulated, the underlying molecular pathways remain unknown. To obtain further insights into issues such as how transcription noise propagates through the iron-Fur-PvdS-pvdAFGHPRI regulation cascade and why the pvdA promoter exhibits significantly higher NTR than promoters of other pyoverdine synthesis genes will likely require the clarification of detailed binding dynamics of transcription factors, with a combination of biophysical modeling.We have conducted a genome-wide analysis of NTR for P. aeruginosa. Thus, the mean expression levels of the promoters we measured were higher than the corresponding chromosomal promoters, which enhances our system’s detection limit and enables measurement of low-expressed promoters. Variation in the plasmid copy number contributes to the extrinsic noise of gene expression, whereas multiple identical copies of promoter-gene pairs in a cell reduces the pure intrinsic noise N is the plasmid copy number  –51 can a–P. aeruginosa are soil, water, vegetation, and animal hosts. The primary concerns for this bacterium are how to deal with its infection in human wounds and lungs and how to clear its biofilms, due to the rising challenge of antibiotic failure. On the other hand, with changes in growth conditions involving carbon sources, nitrogen sources, oxygen concentration, temperature, and antibiotic stress, both the expression level and activation state of transcription factors change in P. aeruginosa. Therefore, our results in LB represent only two examples; the NTR of bacteria may change substantially under different growth conditions. To gain further insights into NTR regarding bacterial physiology in a biofilm, under antibiotic stress, and in infection, experiments using minimal media with single controllable variables are needed.In this study, we measured the NTR of transcriptional reporters in LB broth, which is the most commonly used medium in the laboratory. However, the natural habitats of in vivo ferrous ion also mediates the amplification of reactive oxygen species attack through the Fenton reaction . Unless otherwise stated, 30 μg/mL gentamicin was added to all culture media to prevent plasmid loss. All bacterial culture experiments were conducted at 37°C. For transcriptional noise measurements, each cryopreserved (−80°C) reporter strain was recovered on an LB agar plate and incubated for 12 h. Next, bacterial colonies were scraped into a 5.5-mL polystyrene tube containing 1 mL LB broth medium and cultured in a shaker for 12 h to reach stationary growth phase. The bacterial culture was further diluted 500-fold in 1 mL of fresh LB medium and allowed to grow to mid-exponential phase  for another 3.5 h and shaken at 220 rpm. Cells in the stationary and exponential phases were diluted 100-fold and 10-fold in transparent fastidious anaerobe broth (FAB) medium  (All strains used in this study are based on the osition) , respectlexA, dinB, PA2288, pyoS5, pys2, and PA3866 were cultured into the exponential growth phase in FAB medium containing 30 mM glutamate. One μg/mL of ciprofloxacin was added to the bacterial cultures, and they were grown for 4 h. Bacteria were harvested for microscope observation. For the carbon-source treatment experiment, reporter strains of aceA and acsA were grown overnight in FAB medium containing 30 mM acetate or 30 mM succinate as the sole carbon source. The cultures were then diluted 100 times in the same medium and grown to the exponential phase for the observation of cupA1 reporter in the wild-type and mvaT mutant strains. Bacterial strains were grown in LB overnight and diluted 100 times until grown into the exponential phase. FAB minimal medium was composed as follows: ammonium sulfate , disodium hydrogen phosphate dodecahydrate , potassium dihydrogen phosphate , sodium chloride , magnesium chloride , calcium chloride , ferric chloride , and trace metals solution (1 mL/L). The trace metals solution was composed as follows: calcium sulfate dihydrate , manganese sulfate monohydrate , copper sulfate pentahydrate , zinc sulfate heptahydrate , cobalt sulfate heptahydrate , sodium molybdate monohydrate , and boracic acid .For all culture media, 30 μg/mL gentamicin was added. In the ciprofloxacin treatment experiment, reporter strains of sfGFP, cyOFP1, and terminator fragments were amplified and cloned into the PUCP20 backbone via Gibson assembly. The resultant genetic organization was RNAseIII-RBS2-sfGFP-T0T1-J23102-RBS2-cyOFP1-T-PUCP20, in which J23102 is a constitutive promoter (http://parts.igem.org/Part:BBa_J23102). Promoter fragments of all ORFs in PAO1 were amplified using primers designed by a self-written code based on MATLAB Bioinformatics Toolbox. The captured promoter sequences consisted of the whole intergenic region as well as 0 to 300 bp upstream genes and 0 to 100 bp downstream genes. The promoter fragments were separately cloned into PUCPgfp immediately in front of the RNAseIII site, generating the transcriptional reporter plasmid library. Those plasmids were extracted from the top 10 strains and introduced by chemical transformation into the P. aeruginosa PAO1 strain. High-efficiency PAO1 competent cells were prepared using an Ultra-Competent Cell Preps kit , according to the manufacturer’s instructions. To construct dual-color reporter plasmids for the purpose of studying the cross-correlation of two distinct promoters, the J23102 promoter from the original exsC  reporter plasmid was substituted with the desired promoter. Plasmids and strains are available on request.A general template plasmid, PUCPgfp, was constructed, in which https://figshare.com/articles/figure/Supplementary_figures/21275937/2). These binary images were called mask images because the connected regions in them indicate the occupied areas of bacterial cells in CyOFP1 images. Single-cell intensities of SfGFP and CyOFP1 were determined from mask images using a built-in function (regionprops). A fixed pixel threshold of the bacterial occupied area was used so that only the intensities of separate single cells were extracted for data collection.Fluorescent images of SfGFP and CyOFP1 were acquired simultaneously using two Zyla 4.2 sCMOS cameras (Andor) on a fluorescence microscope  equipped with a 100× oil objective. The fluorescence of both SfGFP and CyOFP1 was excited at 488 nm using a solid-state light source (Lumencor Spectra X), and collected with emission filters of 520/28 nm and 583/22 nm, respectively. For exponential-phase and stationary-phase bacterial samples, 5 × 5 and 4 × 4 montage images were collected, respectively. Both cameras have a visual field of 2,048 × 2,048 pixels, equivalent to 133 × 133 μm when the 100× oil objective was used. Each field contained 200 to 400 single cells for the exponential-phase samples and 800 to 1,000 single cells for stationary-phase samples. Thus, for each sample, gene expression data of approximately 10,000 single cells were obtained. Image processing was conducted using MATLAB with a self-written code. SfGFP and CyOFP1 images were aligned using a built-in function (imwarp) and CyOFP1 images were used for bacterial cell identification. To identify cell masks, CyOFP1 images were subsequently filtered with a gaussian filter, edge filter, and gaussian filter using a built-in function (imfilter). The images were then transformed into binary images using a built-in function (im2bw) with a fixed intensity threshold . PAO1 strains expressing only SfGFP  or CyOFP  were constructed first. Bacterial cells were cultured in LB, resuspended in FAB medium, and sandwiched between a thin 2% agarose pad and a glass coverslip on the bottom. SfGFP and CyOFP images were captured for both the PA_gfp and PA_ofp strains. Then, single-cell intensities of SfGFP and CyOFP for PA_gfp and PA_ofp were calculated following the procedure described above . βGO and βOG were obtained according to the linear regression of the scatter diagram of SfGFP-to-CyOFP1 intensity for PA_gfp and PA_ofp .Then, the expected SfGFP and CyOFP1 intensities can be calculated as follows:The protein concentrations of SfGFP and CyOFP1 in single cells were determined according to the concentration-intensity relationship of purified fluorescent proteins. Briefly, SfGFP and CyOFP1 proteins were purified using the Ni-chelating affinity chromatography method. Purified SfGFP and CyOFP1 protein solutions were diluted to a fixed concentration, and silica microspheres of different sizes (0.5 to 10 μm) were added. Then, each solution was added in between two 18 mm × 18 mm cover glasses. Cover glasses were firmly pressed to squeeze out excess solution, and the edges were sealed with jelly petroleum. The solution layer thickness of each sample was based on the size of added silica microspheres. Then, the fluorescence of these samples was measured and the working curve of fluorescence against solution layer thickness was obtained. A solution layer thickness of 0.5 μm was picked as the thickness of a bacterial cell lying flat. The working curve of fluorescence against different protein concentrations was obtained using the same method. Then, the intracellular SfGFP and CyOFP1 fluorescence intensities of single cells were transformed into protein concentrations according to these two work curves. Protein numbers in single cells were calculated by multiplying protein concentration with the estimated cell volume (2/3 × cell occupied area × cell width).Pseudomonas Community Annotation Project (PseudoCAP) data from the Pseudomonas database (https://www.pseudomonas.com/). Differences in intrinsic noise, NTR, and mean expression level of genes belonging to different functional categories were estimated using a single-sided Wilcoxon rank-sum test . Essential genes of the PAO1 strain grown in LB medium were determined according to the methods of a previous study .Functional classification of genes was based on the us study , and genpvdR-pvdT-opmQ operon. Markerless gene knockout was performed according to a previous protocol .Original gene expression data for all transcriptional reporters are included in the supplemental material. MATLAB codes for promoter design and fluorescent image processing have been uploaded in GitHub (


Saccharomyces cerevisiae, Rtt106 specifically localises to PDR network gene promoters dependent on transcription factor Pdr3, but not Pdr1, and is essential for Pdr3-mediated basal expression of the PDR network genes, while SWI/SNF is essential for both basal and drug-induced expression. Also in the pathogenic fungus Candida glabrata, Rtt106 and SWI/SNF regulate drug-induced PDR gene expression. Consistently, loss of Rtt106 or SWI/SNF sensitises drug-resistant S. cerevisiae mutants and C. glabrata to antifungal drugs. Since they cooperatively drive PDR network gene expression, Rtt106 and SWI/SNF represent potential therapeutic targets to combat antifungal resistance.The Pleiotropic Drug Resistance (PDR) network is central to the drug response in fungi, and its overactivation is associated with drug resistance. However, gene regulation of the PDR network is not well understood. Here, we show that the histone chaperone Rtt106 and the chromatin remodeller SWI/SNF control expression of the PDR network genes and confer drug resistance. In  The Pleiotropic Drug Resistance (PDR) network is central to drug response in fungi, and its overactivation is associated with drug resistance. Here the authors show that the chromatin remodeller SWI/SNF and the histone chaperone Rtt106 are important for PDR gene expression in fungi and therefore mediate resistance to antifungal drugs. Candida infections stands at around 400,000 cases annually, with the emergence of multidrug-resistant strains of Candida species being a major global health concern3. Undermining the utility of antifungal drugs, the Pleiotropic Drug Resistance (PDR) network of fungi works as the first line of defence against a broad range of xenobiotics5, and is central to the fungal drug response7. Among the PDR network gene products, Pdr5, a major ATP-binding cassette (ABC) multidrug transporter, is critical for drug resistance in Saccharomyces cerevisiae8, a fungus closely related to the pathogenic fungus Candida glabrata. Pdr5 pumps a variety of drugs out of cells at the expense of ATP hydrolysis, and Pdr5 overexpression is one of the main causes of drug resistance in S. cerevisiae8. Drug resistance to azole antifungals in clinical isolates of C. glabrata is almost exclusively associated with overexpression of multidrug transporter genes CgCDR1 (homolog of ScPDR5) and CgCDR29.The global incidence of life-threatening PDR5 and the other PDR network genes in S. cerevisiae is regulated by Pdr1 and Pdr3, key transcription factors whose functions are partially redundant12. Pdr1 and Pdr3 form a heterodimer or homodimers, which through their Zn(2)-Cys(6) finger DNA-binding domains13 recognise the PDR responsive element (PDRE: 5′-TCCG(C/T)GGA-3′) present in promoters of the PDR network genes. Pdr1 and Pdr3 together sustain basal gene expression, and Pdr1 activates increased gene expression in response to direct binding to the azole antifungal ketoconazole7. While Pdr3 also can directly bind ketoconazole7, it has not been shown whether its activity is stimulated by ketoconazole binding. Hyperactivation of Pdr1 or Pdr3 is the main cause of overexpression of PDR5 leading to drug resistance. Such hyperactivation can be caused by gain-of-function (GOF) mutations in PDR1 or PDR315 or mitochondrial dysfunction , the latter of which only stimulates Pdr3-dependent gene expression16. In C. glabrata also, overexpression of the drug transporter genes is caused by GOF mutations in the transcription factor Pdr19. Given the importance of multidrug transporter levels for drug resistance, it is vital to understand the regulation of their expression.The expression of 18. Recruitment of transcription machinery to promoters is restricted by nucleosomes19. Histone chaperones and ATP-dependent chromatin-remodelling complexes are accordingly important for gene regulation given they regulate nucleosome dynamics at promoters, restricting or increasing promoter accessibility for recruitment of transcription factors and RNA polymerase II (Pol II)23.Accessibility of gene promoters is a key factor in gene regulationS. cerevisiae, and preferentially binds H3K56-acetylated histone H3-H426. Loss of Rtt106 causes aberrant ‘cryptic’ transcription initiation within gene bodies26 and compromised silencing at the mating loci and telomeres28, likely due to defective nucleosome assembly29.Rtt106, a fungal-specific histone chaperone, functions in histone deposition during DNA replication and transcription elongation in S. cerevisiae30. Rtt106 localises at the promoters of three out of four histone gene pairs , and restricts their transcription to S phase, when new histone supply is required to package newly synthesised DNA32. Outside S phase, Rtt106 forms a repressive complex together with other histone chaperones HIR and Asf1 to recruit RSC (Remodel the Structure of Chromatin), an essential ATP-dependent chromatin-remodelling complex, to the histone promoters33. In contrast, in the late G1 and early S phase Rtt106 recruits SWI/SNF, another ATP-dependent chromatin-remodelling complex, which activates transcription of the histone genes by releasing HIR- and RSC-mediated repression34. Rtt106 recruitment to the histone promoters almost completely depends on Asf1, HIR and acetylation of histone H3 at K56 (H3K56Ac), and is limited by Yta7, a bromodomain- and AAA-ATPase domain-containing boundary protein35. Although this role of Rtt106 at the histone promoters is relatively well-studied, whether Rtt106 plays a role at promoters other than those of histone genes remains unknown.Apart from its role in histone deposition during replication and transcription elongation, Rtt106 regulates the activity of the histone gene promoters during the cell cycle in 36. SWI/SNF modulates nucleosome position to regulate the expression of a subset of genes including stress-responsive genes40. SWI/SNF localises to nucleosome-depleted regions (NDRs) in promoters and regulates the position of the transcription start site (TSS)-associated (or ‘+1’) nucleosome critical for transcription initiation20. Recruitment of SWI/SNF to promoters can be mediated through sequence-specific transcription factors including Gcn4, Pho4, Swi5 and Hap442. However, at the histone promoters, SWI/SNF is recruited by Rtt106, probably through direct physical interactions33.The yeast SWI/SNF complex is an ATP-dependent chromatin-remodelling complex, and it is composed of 12 subunits, 5 of which are fungal-specificS. cerevisiae Rtt106 localises at subsets of specific promoters, binding particularly strongly to the promoters of the PDR network genes where it upregulates their expression exclusively through Pdr3. Consistently, loss of Rtt106 sensitised wild-type S. cerevisiae and a drug-resistant mutant (a GOF mutation in PDR3) to azole antifungal drugs. We further show that SWI/SNF acts as an essential activator of the PDR network genes for both basal and azole-induced expression, and therefore is essential for drug resistance of wild-type S. cerevisiae, and of a pdr3-GOF mutant. Lastly, we show that C. glabrata Rtt106 and SWI/SNF also localise at the promoter of the multidrug transporter gene CgCDR1, regulating its expression in response to an antifungal drug and conferring resistance to azole antifungals. Therefore, Rtt106 and SWI/SNF represent potential therapeutic targets for combating antifungal resistance of the pathogenic fungus C. glabrata.In this study we show that S. cerevisiae, we performed ChIP-seq analysis. As expected32, Rtt106 localised at the promoter of the histone gene pair HTA1-HTB1 , indicating that recruitment of Rtt106 to the PDRE-containing promoters depends on Pdr1 or Pdr3 or both. Rtt106 binding to the HTA1-HTB1 promoter was not affected by the loss of Pdr1 and Pdr3, as expected since the HTA1-HTB1 promoter does not contain a PDRE .Since PDRE is recognised by transcription factors Pdr1 and Pdr3HTA1-HTB1 promoter32 , contributes to Rtt106 recruitment to PDRE-containing promoters, since the loss of the HIR subunit Hir1 caused a reduction of Rtt106 binding . We further observed that Yta7, a boundary protein that restricts Rtt106 binding at the histone promoters32 , limits Rtt106 binding also at the PDRE-containing promoters, as loss of Yta7 increased Rtt106 binding . However, the yta7Δ mutation did not cause Rtt106 binding to spread from PDRE-containing promoters, unlike at the histone promoters where Rtt106 was observed to spread into regions normally occupied by Yta7 . Yta7 may therefore limit Rtt106 binding through different mechanisms at the PDRE-containing and histone promoter sites. Taken together, our results show that HIR and Yta7 contribute to Rtt106 recruitment at the promoters of both the PDR network genes and the histone genes, but their specific effects differ somewhat in the two classes of promoters.To further understand the mechanism of Rtt106 recruitment to PDRE-containing promoters, we tested for regulation by other factors involved in Rtt106 recruitment to histone gene promoters. We observed that the HIR histone chaperone, a crucial factor for recruitment of Rtt106 to the r32 Fig. , hir1Δ, ing Fig. , hir1Δ. s32 Fig. , yta7Δ, ing Fig. , yta7Δ. pdr1Δ pdr3Δ, hir1Δ and yta7Δ mutations  based on Rtt106 binding in WT and the effects of ons Fig. , indicatons Fig. .HTA2-HTB2 where Rtt106 does not bind strongly), as well as AGP1 and ECL1  that do not depend on Pdr1, Pdr3 or HIR, and are not limited by the loss of Yta7  show regulated expression, either in response to environmental changes or during the cell cycle .We examined the relationship between Rtt106 binding and nucleosome organisation at promoters, by analysing nucleosome positioning and occupancy in MNase-seq datasets published by Rine and colleaguesDRs Fig. , possibl32, so we tested if it also regulates expression at these newly identified binding sites. We performed RNA-seq analysis of total RNA prepared from WT and rtt106Δ cells in log phase growth in rich medium (YPD). Loss of Rtt106 caused a significant reduction of mRNA levels of the majority of the PDR network genes whose promoters it binds (16 out of 20 genes in Type A), with a particular reduction in PDR5, CIS1 and RSB1 expression . Almost all PDR network genes whose mRNA levels were reduced in rtt106Δ showed a similar reduction in pdr3Δ; however, no reduction was observed in the mRNA for most such genes in pdr1Δ  were transiently treated with ketoconazole, causing rapid induction of PDR5 expression  as a ~50% reduction of Pdr3 binding at the PDR5 promoter in rtt106Δ cannot alone explain why rtt106Δ and pdr3Δ mutants have similar effects on PDR5 expression.Conversely, Rtt106 is not essential for Pdr3 recruitment to the E. coli expression system was not co-precipitated with GST-Pdr3  also contribute to Rtt106 recruitment to the PDR5 promoter, although their contributions at PDR5 are less than at the HTA1-HTB1 histone promoter  mutant, but not in a pdr1-GOF (pdr1-3) mutant  by immunoprecipitating FLAG-tagged Lac Repressor (LacI) , compared to a control minichromosome (Empty). We successfully identified PDR5 promoter-binding proteins including transcription factors Pdr1 and Pdr3, and the RNA polymerase II Mediator complex that binds Pdr1 and Pdr347, validating this approach for capturing promoter-binding proteins in a near-native context  Fig. . ProteinPDR5 promoter is dependent mainly on Pdr3  and its binding partners were also identified in this fraction  and sensitised it to ketoconazole  grown in YPD, but combining pdr1-GOF and snf6Δ caused a major growth defect even in the absence of antifungal drugs  caused reduced induction of mRNA of the multidrug transporter genes CgCDR1, CgCDR2 and of other PDR network genes, in cells transiently treated with ketoconazole  also contribute to its recruitment to the PHO5 promoter for its activation in yeast23. In human, histone chaperones HIRA and ASF1a localise at active promoters where BRG1, a human SWI/SNF complex subunit, also localises52. Interestingly, at promoters, human HIRA and ASF1a essentially localise to nucleosome-free TSSs52, reminiscent of our findings that yeast Rtt106 is enriched at wider nucleosome-free regions upstream of TSSs  and regulates CgPdr1-mediated, drug-induced expression. We detected no obvious contribution of CgRtt106 nor CgSWI/SNF to the basal expression of the PDR network genes in C. glabrata, which might reflect the fact that without drugs PDR gene expression is very low, compared to expression upon ketoconazole treatment  background was constructed by replacing pdr1Δ::natNT2 in the genome of strain VNY70 with a PCR product containing the entire pdr1-3 locus amplified from the original pdr1-3 strain (congenic to W303-1A)15 following DNA break induction at natNT2 using CRISPR-Cas960. Hyperactive pdr3-2 in the S288C (BY4741) background was constructed by the same approach, but with pdr3Δ::natMX (VNY45) and the original pdr3-2 mutant15. Sequencing of the inserted pdr1-3 and pdr3-2 genes in the S288C backgrounds confirmed that pdr1-3 contains the following mutations compared to the S288C reference genome: Q411K, F815S, T820A, T921I and loss of 5Ns at 1011 aa, while pdr3-2 contains Q56R, H110Q, G834S, A885T, N916S and E941G. Strains for minichromosome isolation were constructed from the SILAC compatible strain SHY20161 by replacing the TRP1 gene with the fragment containing LacI-3FLAG and URA3 (amplified from pRS406-CMV-LacI-3FLAG46 by PCR), followed by transformation with minichromosome plasmids pUC-TALO8 or pUC-TALO8-PDR5pr. Plasmid pUC-TALO8-PDR5pr (PDR5pro) was constructed by inserting the fragment of the PDR5 promoter  into the NdeI site of plasmid pUC-TALO8 (Empty)46, using NEBuilder HiFi Assembly Master Mix . The inserted sequences were confirmed by sequencing. For luciferase reporter assay, YCplac22-based plasmid containing the luciferase gene fused to the PDR5 promoter (from −1000 bp to −1 bp upstream of ATG) was used. E. coli expression vectors, pGEX-6P-1-ScPDR1, pGEX-6P-1-ScPDR3, pGEX-6P-1-ScRTT106, and pET28a-ScRTT106 were constructed using NEBuilder HiFi Assembly Master Mix .62 using mouse monoclonal anti-HA antibody  or mouse monoclonal anti-myc antibody  with modifications: cells were disrupted using a FastPreP-24 bead beater , and sonication of DNA was performed using a Bioruptor (Diagnode). For ChIP-seq analysis, libraries of input and ChIP DNA were generated by NEBNext Ultra II DNA Library Prep Kit for Illumina  and sequenced on NextSeq 500 to generate single-end 75 bp reads. For ChIP-qPCR analysis, input and ChIP DNA were analysed by LightCycler 480 II (Roche) using Light cycler SYBR Green master reagent  or Takyon No ROX SYBR MasterMix . ChIP efficiency (ChIP/Input) at each locus was calculated as the mean of three biological replicates, each one of which has three technical replicates.Cells were fixed with 1% formaldehyde at room temperature for 30 min and then at 4 °C overnight with gently shaking. The fixed cells were washed in ice-cold PBS three times and used for ChIP. ChIP was performed essentially as describedhttps://usegalaxy.org) was used for bioinformatics analysis of ChIP-seq data. Sequence reads from fastq files were mapped against the latest version of the yeast genome sacCer3 using BWA (version 0.7.17.4). Non-uniquely mapped reads were removed by filtering out all reads with mapping quality less than 20 using SAMtools Filter SAM or BAM. To view read coverage distribution across the genome, BAM files generated with BWA were first converted into bigWig format using DeepTools bamCoverage (version 3.1.2.0.0), with the following settings: Bin size, 25 bp; Scaling/Normalisation method, normalise coverage to 1×; and Extend reads to the given average fragment size, 300 bp. BigWig datasets of Input and ChIP DNA were then displayed separately using Integrated Genome Browser .The Galaxy web interface  and plotHeatmap (version 3.1.2.0.1). Finally, the data table underlying the plots was exported and utilised for calculating Rtt106 enrichment in a region spanning 500 bp upstream of the TSS of each gene. To calculate the fold change of Rtt106 signals in pdr1Δ pdr3Δ, hir1Δ or yta7Δ over WT at each promoter, enrichment of ChIP sequence reads in those mutants over WT was calculated.To calculate Rtt106 enrichment or occupancy at each promoter region, enrichment of sequence reads in ChIP samples over corresponding Input samples was first calculated using DeepTools bamCompare (version 3.1.2.0.0), with bin size 50 bp. To extract values around the TSS of each gene, heatmap was then plotted using annotation of TSSpdr1Δ pdr3Δ, hir1Δ and yta7Δ. The thresholds were determined to classify PDRE-containing promoters into cluster 1. All the promoters bound by Rtt106 were inspected manually to find features within promoters.In clustering analysis, promoters bound by Rtt106 were classified into nine clusters based on Rtt106 enrichment at promoters in WT and its change in 7 with minor modifications. Briefly, cells grown in YPD  at 30 °C overnight until stationary phase were pelleted and washed with sterilised MilliQ purified water twice, resuspended in YEP  to an optical density (OD600) of 0.6, and further incubated for 24 h at 30 °C. Cells were treated with 40 μM ketoconazole  or 130 μM fluconazole  for 15 min for RNA preparation and ChIP-qPCR. For sampling cells in the glucose-starved condition (YEP), cells were collected before the addition of azole antifungal drugs to the cultures.Cells were treated with azole antifungal drugs as describedTotal RNA was prepared from cells using Macherey-Nagel NucleoSpin RNA Columns  according to the manufacturer’s protocol. ERCC  spike-in controls were added to the RNA samples to be used as internal controls. Libraries were prepared with the Illumina TruSeq Stranded mRNA kit and sequenced using the High Output 1 × 75 kit on the Illumina NextSeq 500 platform producing 75 bp single-end reads.64. The cleaned reads were aligned to the S. cerevisiae reference genome using HISAT2 (version 2.1.0)64. SAMtools (version 1.9)65 was used to process the alignments and reads were counted at gene locations using featureCounts (part of the sub read version 1.6.2 package)66 utilising the parameter to split multi-mapped reads as a fraction across all genes that they align to and the parameter for stranded analysis. edgeR (version 3.26.8)67 was used normalise reads counts, retaining genes that had a CPM (count per million) value of more than one in three or more samples, and to detect which of the genes had a significant differential change in expression (FDR < 0.05). Gene Ontology Term Finder  was used for gene ontology analysis.Raw reads were filtered using TrimGalore! (version 0.6.4) with a phred quality score threshold of 30. ERCC spike-in reads were removed by alignment to the ERCC reference genome using HISAT2 (version 2.1.0)68. Briefly, total RNA was separated on the HT (HEPES-Triethanolamine) gel and then transferred onto Hybond N + membrane  by capillary transfer. RNA was crosslinked with the membrane by UV irradiation. PDR5 mRNA and a loading control ARF1 mRNA69 were detected using biotin-labelled oligo DNA probes (Merck) and North2South Chemiluminescent Hybridisation and Detection Kit  according to the manufacturer’s protocol. rRNAs were detected as loading controls, by SYBR Gold staining . The images were captured using ChemiDoc system (Bio-Rad).Northern blot analysis was performed as describedg for 10 min at 4 °C. Luciferase activity was measured using Bright-Glo Luciferase Assay System  and Omega Plate Reader (BMG Labtech) and calculated as relative luminescence units per mg of total soluble protein.Cells carrying luciferase reporter plasmids were grown in SC-Trp (Kaiser synthetic complete media lacking tryptophan (Formedium), supplemented with 2% glucose) at 30 °C until log phase and harvested. Cells were washed with sterilised MilliQ purified water, and resuspended in Lysis buffer A . Cells were disrupted using a FastPreP-24 bead beater . The cleared lysate was prepared by centrifugation at 15,000 × g at 4 °C to precipitate proteins. The precipitated proteins were resuspended in the protein sample buffer  and incubated at 37 °C for 15 min. Total proteins for detecting epitope-tagged Pdr3 were prepared by alkaline method: cells were washed once with water, incubated in 0.1 M NaOH for 5 min at room temperature, spun down, resuspended in SDS sample buffer  and incubated at 85 °C for 5 min. Total proteins prepared were separated by SDS-PAGE using 4–20% TGX stain-free gel , and transferred onto the PVDF membrane using the Trans-Blot Turbo Transfer system (Bio-Rad). Tagged proteins were detected using anti-HA antibody , anti-FLAG M2 antibody  or anti-myc antibody  and Clarity Western ECL substrate . As a loading control, total protein was detected using Stain-Free technology (Bio-Rad).To detect Pdr5-3HA, total protein was prepared from cells by the TCA method as follows. Cells grown in 7 ml of YPD at 30 °C until log phase were washed in ice-cold water, then resuspended in 150 μl of ice-cold YEX lysis buffer  and incubated on ice for 10 min on ice. After adding 150 μl of ice-cold 50% (w/v) TCA to the cell suspension, the samples were incubated on ice for 10 min, and then span for 5 min at 10,000 × g at 4 °C for 20 min. The cleared lysate was mixed with anti-FLAG M2 antibody  bound to Dynabeads Protein G  and incubated at 4 °C for 2 h. The beads were washed five times in 1 ml of Lysis buffer B. Bound proteins were eluted with 200 μl of Lysis buffer B containing 1 mg/ml 3× FLAG peptide (Merck) by incubating at room temperature for 30 min, with shaking. The elution step was repeated twice, yielding 400 μl of eluate. The SWI/SNF complex was further purified and concentrated using Amicon Ultra centrifugal filter unit , removing smaller proteins including 3× FLAG peptide and exchanging buffer to PBS containing 0.02% Tween 20 and 50% glycerol. The purified proteins were analysed by 4–15% TGX gel  followed by SYPRO Ruby staining .The SWI/SNF complex was purified from a protease-deficient yeast strain containing 3× Flag tag at the C-terminus of Snf6 (Snf6-3Flag). The tagged yeast strain was grown in 2 L of YPD overnight at 30 °C. Cells were collected by centrifugation and washed with 40 ml of cold PBS. Cells were resuspended in 15 ml of Lysis buffer B ) and lysed in a FastPreP-24 bead beater . Cell debris was removed by centrifugation at 16,000 × E. coli using Ni-NTA resin . E. coli cells NiCo21(DE3)  carrying pET28a-ScRTT106 were grown in 100 ml LB-kanamycin at 37 °C to OD600 0.6. Expression of His-Rtt106 was induced by incubating with 0.4 mM IPTG at 37 °C for 3 h. Cells were then collected by centrifugation, resuspended in 2 ml of Lysis buffer C , and lysed by sonicating on ice (Soniprep 150), with four cycles of 10 sec on then 10 sec off, at an amplitude of 8 μm. Cell debris was removed by centrifugation at 16,000 × g at 4 °C for 20 min. The cleared lysate was mixed with 250 μl of Ni-NTA resin and incubated at 4 °C for 60 min. After washing the resin with Wash buffer , His-Rtt106 was eluted with Elution buffer .His-Rtt106 was purified from E. coli cells NiCo21(DE3) carrying pGEX-6P-1, pGEX-6P-1-ScPDR1, pGEX-6P-1-ScPDR3, and pGEX-6P-1-ScRTT106 were grown in LB-ampicillin at 30 °C until OD600 reaches 0.5. The expression of GST and GST-fusion proteins was induced by incubating with 0.1 mM IPTG at 16 °C for 18 h. Cells were then collected by centrifugation, resuspended in cold Lysis buffer D , and lysed by sonicating on ice (Soniprep 150), with four cycles of 10 sec on, 10 sec off, at an amplitude of 8 μm. Cell debris was removed by centrifugation at 16,000 × g at 4 °C for 20 min. The cleared lysate was mixed with 20 μl of glutathione-Sepharose beads  and incubated at 4 °C for 30 min. After washing four times with Lysis buffer D, the beads bound to GST or GST fusions were incubated with purified SWI/SNF, purified His-Rtt106 or yeast cell lysate containing HA-tagged Rtt106 in Binding buffer  at 4 °C for 2 h. After washing four times with binding buffer, bound proteins were eluted in SDS sample buffer, and analysed by SDS-PAGE and western blot. GST and GST fusions were detected using Stain-Free technology (Bio-Rad) or by Amido black staining . Snf6-3FLAG, His-Rtt106 and Rtt106-6HA were detected using anti-FLAG M2-HRP , anti-His , and anti-HA  antibodies, respectively.Yeast cultures grown in YPD at 30 °C overnight were diluted and then spotted on YPD plates containing fluconazole  or ketoconazole . The plates were then incubated at 30 °C for 2–4 days.46. Cells carrying minichromosome plasmids were grown in 1 L of SILAC media70 (Kaiser SC-Arg-Lys-Trp (Formedium), supplemented with 20 mg/L heavy arginine-HCl  and 30 mg/L lysine-2HCl  or, for a control, 20 mg/L light arginine-HCl and 30 mg/L light lysine-2HCl) at 30 °C until log phase and harvested. After washing cells in ice-cold water supplemented with 2 mM PMSF, cells were resuspended in buffer H150 , PhosSTOP , 5 mM nicotinamide) and disrupted using a FastPreP-24 bead beater . Clarified lysates were prepared by centrifugation at 20,000 × g for 20 min three times. lacO-containing minichromosomes were isolated by immunoprecipitating LacI-3FLAG with anti-FLAG M2 antibody  crosslinked to Dynabeads protein G  by dimethyl pimelimidate. Proteins on purified minichromosomes were eluted in Elution buffer , 1 mM IPTG). The eluted proteins were analysed by SYPRO Ruby staining and western blot analysis.Minichromosomes were isolated essentially as describedn = 1 biological sample) were analysed using an Orbitrap Q Exactive Plus mass spectrometer (Thermo Scientific) equipped with a Dionex U3000 RSLCnano liquid chromatography system configured for pre-concentration on C18 PepMap 100 (300 µm i.d. × 5 mm) then peptide separation on EASY-Spray PepMap RSLC C18 (75 µm i.d. × 50 cm) over a 245 min elution gradient as described previously71 with minor modifications. Two solvents were formed, solvent A was made from water/formic acid (1000:0.1) and solvent B from water/acetonitrile/formic acid (20:80:0.1). An increasing proportion of solvent B was used to separate peptides along a gradient: 3–40% from 5 to 205 min; 40–90% from 205 to 206 min; 90% from 206 to 216 min; 90–3% from 216 to 217 min, followed by re-equilibration of the column (3% Solvent B from 217–245 min). Mass spectra were acquired using a Top 10 data-dependent method starting at 25 min and lasting 205 min. Full MS scans were conducted between 375 and 1750 m/z at a resolution 70,000 (m/z 200), automatic gain control 3E + 6 and a maximum injection time 50 ms. Following each survey scan the 10 most intense ions of charge state 2–5 were sequentially selected (isolation window 1.6 m/z) and fragmented in the higher-energy collisional dissociation (HCD) cell at a normalized collision energy of 28%. MS/MS scans were conducted at a resolution 17,500, automatic gain control 5E + 4 and a maximum injection time 100 ms. Dynamic exclusion of 40 s prevented ions from triggering subsequent data-dependent scans during that time.Peptides digested by trypsin in solution (72 using the standard settings for Orbitrap against a S. cerevisiae protein database (UP000002311_559292). Carbamidomethylation of cysteines was set as a fixed modification and oxidation of methionines, deamidation of asparagines and glutamines, and protein N-terminal acetylation as variable modifications. Minimal peptide length was set to seven amino acids and a maximum of two missed Trypsin/P cleavages was allowed. SILAC quantification was performed using standard settings and a Label min ratio count of 5. RAW data obtained from the SWI/SNF-enriched fraction was processed with Proteome Discoverer  incorporating Mascot Server (version 2.5) using the standard settings for Orbitrap, against a S. cerevisiae protein database (UP000002311_559292). Carbamidomethylation of cysteines was set as a fixed modification and oxidation of methionines, and phosphorylation of serines, threonines and tyrosines as variable modifications. A maximum of three missed Trypsin cleavages was allowed. A strict target FDR of 0.01 for the decoy database search was used.RAW data obtained from SILAC samples were processed with MaxQuant (version 1.6.5.0)t-test, one-way ANOVA with post-hoc Tukey HSD test, or two-sided Mann–Whitney test as described in each figure legend. For Northern blot and GST pull-down analyses and testing protein level of Pdr5  was subsequently confirmed by ChIP-qPCR analysis with three biological replicates with similar results. Enrichment of the SWI/SNF complex by immunopurification was assessed by comparing the band pattern on the gel with that in published data48 , and subsequently confirmed by mass spectrometry (n = 1).Statistical analysis was performed by two-tailed unpaired Student’s dr5 Fig. , each exdr5 Fig.  were perdr5 Fig.  were repFurther information on research design is available in the Supplementary informationPeer Review FileDescription of additional Supplementary FileSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6Supplementary Dataset 7Reporting Summary


Pseudomonas putida KT2440 and it has been shown that one, K1-T6SS, is used to outcompete a wide range of phytopathogens, protecting plants from pathogen infections. Given the relevance of this system as a powerful and innovative mechanism of biological control, it is critical to understand the processes that govern its expression. Here, we experimentally defined two transcriptional units in the K1-T6SS cluster. One encodes the structural components of the system and is transcribed from two adjacent promoters. The other encodes two hypothetical proteins, the tip of the system and the associated adapters, and effectors and cognate immunity proteins, and it is also transcribed from two adjacent promoters. The four identified promoters contain the typical features of σ70-dependent promoters. We have studied the expression of the system under different conditions and in a number of mutants lacking global regulators. P. putida K1-T6SS expression is induced in the stationary phase, but its transcription does not depend on the stationary σ factor RpoS. In fact, the expression of the system is indirectly repressed by RpoS. Furthermore, it is also repressed by RpoN and the transcriptional regulator FleQ, an enhancer-binding protein typically acting in conjunction with RpoN. Importantly, expression of the K1-T6SS gene cluster is positively regulated by the GacS–GacA two-component regulatory system (TCS) and repressed by the RetS sensor kinase, which inhibits this TCS. Our findings identified a complex regulatory network that governs T6SS expression in general and P. putida K1-T6SS in particular, with implications for controlling and manipulating a bacterial agent that is highly relevant in biological control.The type VI secretion system (T6SS) is an antimicrobial molecular weapon that is widespread in Proteobacteria and offers competitive advantages to T6SS-positive micro-organisms. Three T6SSs have recently been described in  Pseudomonas aeruginosa, Vibrio cholerae and enteropathogenic Escherichia coli strains, and it was initially assumed that T6SS’s main role was in delivering effectors inside eukaryotic cells ATP (6000 mCi mmol−1), and 1 U phage T4 polynucleotide kinase. The reaction mixture was incubated for 1 h at 37 °C and 10 min at 70 °C to inactivate the kinase, and the labelled oligonucleotide was filtered through a Bio-Rad Micro Bio-Spin column to eliminate unbound nucleotide. Labelled primers were annealed to total RNA isolated as described above in a 10 µl annealing mixture that contained 2 µl 5× annealing buffer, 105 c.p.m. of 5′-end-labelled primer and 50 µg total RNA template. The mixture was heated at 95 °C for 3 min, incubated at 65 °C for 5 min, and then slowly cooled to 44 °C. cDNA was synthesized by the addition of 40 µl of reverse transcriptase buffer, 1 mM of dNTPs, 0.4 U µl−1 of RNase inhibitor and 8 U of AMV reverse transcriptase. The mixture was incubated for 1 h at 44 °C and the reaction was terminated by adding 5 µl 3 M sodium acetate and 150 µl ethanol. The product of reverse transcription was analysed in urea–polyacrylamide sequencing gels. The gel was exposed to a GS-525 Molecular Imager (Bio-Rad).Primer extension analyses were performed as previously described by Pacheco-Sánchez et al. . An oligtagB1 and PP3104 genes with the SMARTer RACE cDNA Amplification kit (cat. no. 634924) from Clontech. Following the manufacturer’s instructions, total RNA was ligated to an adapter at the 5′ end, and cDNA synthesis was initiated from an oligo(dT) primer containing a 3′ adapter sequence. Gene-specific primers  were used in conjunction with the adapter primers provided to amplify 5′ and 3′ cDNA ends. The PCR products were extracted from a 1.5 % agarose gel and purified with the QIAquick PCR purification kit following the manufacturer’s instructions (Qiagen). The purified PCR products resulting from RACE were cloned into the pCR 2.1-TOPO vector (Invitrogen) as recommended. RACE products were sequenced by GATC Biotech services.To identify the transcription start site (TSS) of the K1-T6SS promoters, 5′ rapid amplification of cDNA ends (RACE)  was perft-test in which two conditions are compared independently. P-values from raw data were calculated by two-tailed t-test and from ratio data to the control by one-sample t-test using GraphPad Prism version 8.3.0 and are represented in the graphs by ns, non-significant; *P<0.05; **P<0.01; and ***P<0.001.Statistical analyses are based on the P. putida KT2440 encoding structural and accessory components comprises two predicted operons that we previously named structural and vgrG operon, and an intermediate region [vgrG operon encompasses genes encoding proteins with unknown function (PP3104 and PP3105), the VgrG1 protein and associated adapters EagR1a and EagR1b, the Tke2 nuclease toxin and its cognate immunity protein Tki2, and an additional putative effector–immunity pair (Tke3–Tki3) , which suggests that these genes are also arranged in a single transcriptional unit.The 44 kb K1-T6SS gene cluster of e region . The stre region . The vgre3–Tki3) . The int . When using mRNA, cDNA fragments of the expected sizes were obtained with all pairs of primers in both the structural  and mRNA isolated from ructural , P11–28 al  operons, total RNA of P. putida cells in the stationary phase was analysed using RACE and primers complementary to tagB1 and PP3104 genes (Table S3). A single PCR band placed the transcriptional start site (+1) of the S operon in an adenine (A) located 121 bp upstream of the predicted translational start site of tagB1. This promoter was named PS  and the s region . The lowart site . These t and PS2 .In silico analyses showed that the four identified promoter regions contain σ70 consensus sequences located at positions −35 and −10 from the +1 (70-loaded RNA polymerase (RNAP).m the +1 . This suP. putida cells grown until the exponential or stationary phase. Representative genes from both K1-T6SS operons were used in this analysis, i.e. hcp1 and tssB1 for the S operon, and PP3104 from the V operon.The K1-T6SS has previously been described as an active system under routine laboratory conditions and in the stationary phase of growth , a condihcp1 is located at the end of the S operon and tssB1 at the beginning, mRNA levels of hcp1 were higher than those of tssB1 .Expression of the three genes was induced considerably in the stationary phase compared to the exponential phase  . Interesof tssB1 . This suS, PV and the putative Ph promoters to a promoterless lacZ gene using the pMP220 plasmid. The resulting plasmids, pMPS, pMPV and pMPh were introduced into the P. putida wild-type KT2440 strain and the β-galactosidase activity was determined in the exponential and stationary phases of growth. pMPS contains the 345 bp upstream of the tagB1 translational start site that includes the previously identified PS1 and PS2 sequential promoters  and the stationary phase (~9000 MU)  and notably induced by more than 10-fold in the stationary phase (724 MU) , and similar to that of the empty vector control  . We first focused on the Gac signalling cascade and the retS mutant. This mutant has been used extensively in P. aeruginosa to study the activity of the H1-T6SS because this system is not produced in the wild-type strain containing RetS [hcp1, tssB1 and PP3104 genes in the P. putida wild-type versus the retS mutant in the exponential phase of growth by qRT-PCR. The three genes were significantly upregulated in the retS mutant, with fold-changes >3 for hcp1 and >1.5 for tssB1 and PP3104  independently of the growth phase  levels. To identify the main transcriptional regulators involved in the control of the K1-T6SS cluster, we used a battery of ing RetS . First, d PP3104 . InductiZ fusion . This efZ fusion , which sf growth . Howeverth phase . This inA mutant , indicatA mutant , the acttor RpoS  and S2.  regions . Strikinand FleQ , especiaS promoter. RpoN recognizes and binds the 5′-TGGCAC-N5-TTGCW-3′ (W is A/T) consensus sequence located at −12 and −24 bp from the transcriptional start site [10-GC [We next aimed to analyse the effect of RpoN on the expression of the K1-T6SS S operon. This σ factor could either promote the transcription of an unknown T6SS repressor or directly repress the transcription from the Part site . The mine [10-GC .S2 promoter that overlaps with the σ70 binding site  factor (PP3086), a transmembrane anti-σ factor (PP3085) that sequesters the σECF factor in absence of the CSS inducing signal, and a TonB-dependent outer-membrane receptor (PP3084)  . CSS sysσ factor . Upon acreceptor .ECF factor in the KT2440 wild-type strain bearing the PS or PV::lacZ fusions. Overproduction of σECF factors is known to promote the expression of the σECF regulated genes in absence of the CSS inducing signal [ECF factor did not promote expression of the K1-T6SS cluster, since the activity of the PS and PV promoters was similar in the strain not overproducing (−) and overproducing (+) this regulatory protein . However, PpkA/PppA (or similar kinases/phosphatases), FHA and TagF regulatory components are not always present in T6SS systems, and any combination of those elements is possible   . Each trfectors) , which sependent . This isSS genes .hcp1. These possibilities need to be further explored.In addition to the main promoter region, it is expected that lengthy operons, such as S and V, contain internal promoters to secure the expression of all components. In this regard, our semiquantitative analysis of the transcription of K1-T6SS genes by RT-PCR showed that some genes that are distant from the main identified promoter regions are highly expressed . This obP. putida cells in the rhizosphere have this physiological state [Further, our studies showed that expression of both operons of the K1-T6SS cluster was induced in the stationary phase of growth . This isal state  and the Paraburkholderia phymatum T6SS-3 [P. aeruginosa H2- and H3-T6SS [Other T6SSs have been observed to be induced in the stationary phase of growth, including the  T6SS-3  and the  H3-T6SS . In all  H3-T6SS .S and PV K1-T6SS promoters was not reduced but increased in the absence of RpoS (70 but no RpoS consensus sequences in these promoter regions (Intriguingly, despite the fact that K1-T6SS expression is induced in the stationary phase of growth , the act of RpoS  and S2.  regions  and simiP. putida K1-T6SS (rpoN mutant [P. putida cells that assemble a K1-T6SS machinery visualized by fluorescence microscopy increases from 1 % of the population in the wild-type to 10 % of the population in a rpoN mutant [P. aeruginosa, RpoN downregulates the expression of the gene encoding the T6SS activator GacA [P. putida, RpoN could be repressing expression of the K1-T6SS cluster by downregulating the expression of the GacA–GacS cascade. Hence, the FleQ bEBP that interacts with RpoN to drive an open complex formation domain is also involved in the regulation of the K1-T6SS. This transcription factor has previously been described to activate several genes, importantly flagellar genes, and by binding to c-di-GMP, derepresses the expression of biofilm component genes [rpoN mutant background (S promoter, inhibiting the transcription of the K1-T6SS structural operon in response to low levels of c-di-GMP [Likewise, we found that RpoN and FleQ factors repress the transcription of the  K1-T6SS . This isN mutant . ConsistN mutant . Our anator GacA . If thisnt genes . As expeckground . This coc-di-GMP .Pseudomonas [P. aeruginosa H1-T6SS [P. syringae [P. putida could be coordinating different biocontrol strategies, such as the synthesis of classical antimicrobial compounds, the production of siderophores to sequester iron from phytopathogens and the expression and assembly of a potent molecular weapon, the T6SS.In contrast, the two-component system GacS–GacA was found to activate the expression of the two K1-T6SS promoters, while the sensor protein RetS, which is known to repress the GacS–GacA cascade in omonas , repress H1-T6SS  and P.ringae . Among tringae . Thus, tP. putida K2-T6SS [P. aeruginosa MvaT protein, which belongs to the same family, silences the expression of the H2- and H3-T6SS loci [P. putida is controlled, at least partially, by different global regulators. This would allow the strain to selectively activate T6SSs with different behaviours, e.g. the assembly and firing speed and the secreted effectors, according to the bacterium’s needs.A previous study showed that the histone-like protein TurA represses the transcription of the  K2-T6SS . Similar6SS loci . HowevervgrG operon are, in general, similarly regulated by the same actors (V promoter is 100- and 15-fold higher than the basal expression from the PS promoter in the exponential and stationary phases, respectively (V promoter could secure the expression of the immunity genes independently of the expression of the rest of the apparatus. Differentially regulating the promoter that controls the expression of the immunity genes could be an efficient mechanism of self-protection against T6SS intoxication by sister cells.As expected, the structural and the e actors , guarante actors . Nonetheectively . Since tIt is important to emphasize that of the seven transcriptional regulators that have been tested in this study, five are involved in the regulation of the system . This prP. putida K1-T6SS established in this study will lay the ground for the development of an improved biocontrol agent with an optimized antimicrobial weapon to outcompete deleterious plant pathogens.The deeper understanding of the transcriptional regulation of the Click here for additional data file.