Pseudomonas aeruginosa, which resulted in the robust enrichment of 199 unique sequence motifs describing the binding specificities of 182 TFs. By scanning the genome, we predicted in total 33,709 significant interactions between TFs and their target loci, which were more than 11-fold enriched in the intergenic regions but depleted in the gene body regions. To further explore and delineate the physiological and pathogenic roles of TFs in P. aeruginosa, we constructed regulatory networks for nine major virulence-associated pathways and found that 51 TFs were potentially significantly associated with these virulence pathways, 32 of which had not been characterized before, and some were even involved in multiple pathways. These results will significantly facilitate future studies on transcriptional regulation in P. aeruginosa and other relevant pathogens, and accelerate to discover effective treatment and prevention strategies for the associated infectious diseases.A high-throughput systematic evolution of ligands by exponential enrichment assay was applied to 371 putative TFs in  Pseudomonas aeruginosa is an opportunistic human pathogen of considerable medical importance, as it causes pneumonia that is associated with high morbidity and mortality rates in immunocompromised patients, burn victims, and cystic fibrosis patients  assay  models describing the DNA-binding specificities of 182 TFs. With the obtained PWM models, we scanned the PAO1 genome and reported 33,709 putative TFBSs that were highly enriched in the intergenic regions encompassing regulatory elements compared with gene body regions (odds ratio = 11.75). TF–target interactions were established for nine virulence-associated pathways, which enabled global decoding of the pathogenic regulatory networks of this microorganism. We validated some binding sites through a series of biochemical and genetic experiments and, interestingly, identified novel TFs potentially implicated in pathogenesis. This study provides an unprecedented scale of high-quality data on TF-binding specificity in a single bacterium and a global view of transcriptional regulatory relationships in P. aeruginosa, which constitute a major step toward deciphering the regulatory mechanisms of virulence. The findings are expected to substantially facilitate the development of effective therapies for the associated infectious disease.Although nearly 400 transcriptional regulators were previously predicted, only approximately 30 regulators have been characterized as virulence-associated TFs in  decades . To dateX) assay  to all phttps://www.pseudomonas.com/)  belong to six families: the LysR family, AraC family, LuxR family, OmpR family, TetR family, and GntR family  , the P. families . The vasR family . To deteR family  for all R family  with prestem see , which fstem see , each restem see . Twelve stem see . In the stem see . Taken truginosa .P. aeruginosa   and crosP. aeruginosa, with the most prevalent length being 16 bp   . Early sto-tail) . For exato-tail) , supportto-tail) . Consistto-tail) , RsaL (Kto-tail) , PsrA (Kto-tail) , FleQ (Sto-tail) , and Qscto-tail) . Interesto-tail) . The heato-tail) .P. aeruginosa PAO1 reference genome and identified 33,709 significant putative TFBSs (p<1.0e-5). For individual TFs, the number of TFBSs differed substantially, with more than 36% (65/182) of TFs predicted to bind to over 100 sites in the 6.3 Mb genome . Consequently, we managed to associate 51 unique TFs with the nine virulence-associated pathways, 32 TFs of which had not been functionally characterized before   , supportoduction . Our netl operon .psl operon, we found that FleQ and three uncharacterized factors, PA0225 (ErfA)  cells. The depleted pelA expression could be completely restored by ectopic overexpression of PA0225 in the mutant cells  , PA0191,lABCDEFG . The binus study . We aimeing EMSA . Next, wnt cells . These f of PelA , and theoduction .P. aeruginosa  but activating that of H1-T6SS (tssA1) and H3-T6SS (vgrG3)  . Here, wd PA0099 , which id PA0099 . In addipathways .amrZ promoter was supported by ChIP-seq data and confirmed in vitro by EMSA   ; the bin by EMSA .P. aeruginosa converts its biofilm lifestyle to a planktonic lifestyle by regulating bacterial motility, which enhances its infectivity of the host , the QS pathway (rhlB), T6SS (tss), antibiotic resistance (pstB), and the persistence pathway (dnaJ). Our results provide a valuable resource on an unprecedented scale to dissect the intricate transcriptional regulatory networks in different important biological processes in the pathogenic bacterium P. aeruginosa and explicitly illustrate the inter-connectivity among these pathways.Likewise, some TFs were putatively associated with multiple virulence-associated pathways. For example, PhoB and its target TF gene aTFs exert their functions by binding to DNA and driving transcription of the target genes. To potentially decode the transcriptional regulatory function of all putative TFs, we performed a gene ontology (GO) enrichment analysis for the targets of each TF . The tarP. aeruginosa is a model strain for studying pathogens as its virulence is controlled by many pathways. In this study, we successfully generated 199 unique PWM models to describe the DNA-binding specificities of 182 distinct TFs in P. aeruginosa, which covered nearly 50% of all TFs (including putative TFs) in its genome. We used all of the PWM models to scan the P. aeruginosa PAO1 genome and deduced 33,709 direct TF–DNA interactions, which enabled the construction of transcriptional regulatory networks for nine virulence-associated pathways, providing a valuable resource for deciphering the pathogenicity of P. aeruginosa.Microbes appeared on earth nearly 2 billion years before the evolutionary arrival of human beings. Today, the population of microbes is estimated to be approximately 1 trillion, far exceeding the population of every other species in number and diversity . High abP. aeruginosa genome  owing to method-specific differences, e.g. interference by protein–protein interactions, the number of TFBSs used to derived the motifs . To furta genome  and evala genome . ChIP-sea genome . For exaChIP-seq . This suP. aeruginosa TFs, including AlgR, CdpR, RsaL, VqsR, AnvM, and VqsM (P. aeruginosa genome-wide regulatory network (PAGnet), which illustrates the regulatory relationships of 20 key virulence-related TFs with their target genes as profiled by ChIP-seq and RNA-seq  agar plates statically or LB broth with shaking at 220 rpm.The bacterial strains used in this study are listed in E. coli and its derivatives were used at the following concentrations: for E. coli with pET28a, 50 μg/ml kanamycin; for E. coli with pEX18AP, final concentration of 60 μg/ml ampicillin LB; for E. coli with pEX18AP-Gm plasmid, using final concentration of 15 μg/ml gentamycin in LB. For P. aeruginosa PAO1 with pEX18AP-Gm plasmid in LB media, antibiotic with final concentration 60 μg/ml gentamycin. For P. aeruginosa PAO1 with pAK1900 plasmid in LB media, antibiotic with final concentration of 100 μg/ml carbenicillin in LB. Antibiotics for P. aeruginosa PAO1 mutants, 60 μg/ml gentamycin.The plasmids and primers in this study are listed in P. aeruginosa PAO1 reference genome to obtain the coding regions of full-length proteins. Each forward PCR primer carried a 20 bp sequence identical to the linearized plasmid sequence at the 5’- and 3’-end of the cutting site followed by the gene-specific sequence. The homologous match between these two 20 bp recombination fragments determined the direction of the target gene in the expression vector. Then the BamHI-digested pET28a vector and individual TF PCR products  were mixed in the molar ratio of 1:2, and then incubated with recombinase for 30 min at 37°C. Each successfully constructed vector  was then transformed into E. coli BL21 (DE3) strain and cultured in the LB agar plate, respectively. Then, a single colony of each strain was picked and cultured into 3 ml LB overnight, which was transferred into 300 ml LB containing 50 μg/ml kanamycin for protein extraction. After bacterial OD600 was near 0.6. 0.5 mM IPTG  was added into the cell culture with 16°C for 16 h. Then cell pellet was collected by centrifuging 7000 rpm for 5 min, at 4°C. The pellet was suspended in 15 ml buffer A ) and lysed by sonication for 30 min , and protein supernatant was obtained by centrifuging 12,000 rpm, 30 min, at 4°C. After filtering protein supernatant with a 0.45 μm filter, each filtrate was injected into a Ni-NTA column (Bio-Rad) to start a fast protein liquid chromatography (FPLC) system, respectively. The Ni-NTA column was washed with 30 ml gradient from 60 to 500 mM imidazole in buffer A gradually. Each eluted fraction was pooled, collected, and verified by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) based on molecular weight of each target TF plus 6× His-tag. HT-SELEX pipeline was exercised for all 371 TF proteins.Oligonucleotides and vectors used for cloning of His-tagged proteins in this study are listed in 2, 4% glycerol, 0.5 mM EDTA, 5 µg/ml poly-dIdC [Sigma P4929]) to make 25 µl of total volume. Protein–DNA complexes were incubated with 150 µl of Promega binding buffer (without poly-dIdC) containing 10 µl Ni Sepharose 6 Fast Flow resin  equilibrated in binding buffer for 60 min with a gentle shaking after 30 min of reaction at room temperature. Subsequently, the unbound ligands were separated from the bound beads through washing with a gentle shaking for 12 times with 200 μl of Promega binding buffer (without poly-dIdC). After the washes, the beads were suspended by using 200 µl double-distilled water. Finally, the DNA libraries were built by 18 cycles of PCR amplification using the 20 µL bound DNA as template and the primers in The HT-SELEX experimental pipeline was adapted from our previous study . BrieflyRaw sequencing data were binned according to barcodes for each sample. Sequences from the 40-nt random region were further analyzed, the low quality reads with bases annotated as ‘N’ being filtered out. PWM models were generated using initial seeds identified using Autoseed  that werWe calculated the similarities of all pairs of 199 PWMs using SSTAT  paramet.P. aeruginosa PAO1 genome with all PWMs using FIMO (p<1.0e-5)  . The p-v<1.0e-5)  was usede v3.7.2 .P. aeruginosa PAO1 genome. The 30 ng probe was mixed with varying amounts of TFs in binding buffer  with the final volume of 20 μl. Meanwhile, DNA probes of the negative controls in each group of reaction were chosen randomly and ensured without the corresponding TFBSs. After 30 min incubation at room temperature, the reactions were loaded and run by 6% polyacrylamide gel electrophoresis at 100 V for 1 hr. Then, the gels were subjected to nucleic acid dye for 5 min, and visualized and photographed using the gel imaging system (Bio-Rad). The assay was repeated at least twice with similar results.EMSA is conducted in vitro using recombinant proteins and synthesized DNA ligands. DNA probes were PCR-amplified using primers listed in P. aeruginosa PAO1 genome and digested with XbaI . The ligated DNA products were inserted into the EcoRI and HindIII digested pEX18AP plasmid using ClonExpress MultiS One Step Cloning Kit  to yield the pEX18AP-Up-Down plasmid. Then, pEX18AP-Up-Down of each TF was digested by XbaI and ligated with a 0.9 kb XbaI-digested gentamicin resistance cassette, generating pEX18AP-Up-Down-Gm plasmid, which was transformed into P. aeruginosa PAO1 WT competent cells with electroporation and cultured on the agar plate. Colonies were selected for gentamicin resistance and then transferred to LB agar plates containing 5% sucrose, which typically happens a double-cross-over event and thus gene replacement. Each TF mutant was further confirmed by PCR to detect the DNA and RT-qPCR to detect the mRNA level.Gene deletions were constructed as previously described . The priP. aeruginosa PAO1 WT cells and its derivatives were cultured until OD600 to 0.6. To harvest the bacterial cells, the cultures were centrifuged at 6000 rpm for 3 min. RNA extraction and purification were performed using RNeasy minikit (Qiagen) following the manufacturer’s instruction. RNA concentration was measured by Nanodrop 2000 spectrophotometer (ThermoFisher). The synthesis of cDNA was carried out using the FastKing RT Kit (Tiangen Biotech). RT-qPCR was performed by SuperReal Premix Plus Kit  following the manufacturer’s instruction. Each reaction was performed in triplicates in 20 μl reaction volume with 20 ng cDNA and 16S rRNA as an internal control. For each reaction, 100 nM primers (-(ΔΔCt). All the reactions were conducted with two biological repeats. primers  were useP. aeruginosa PAO1 WT and TF mutants were transferred to a 10 ml borosilicate tube containing 1 ml LB medium (without antibiotics) with the original concentration OD600 = 0.1. Then, the cultures grow statically at 37°C for 12–24 hr. Then, 0.1% crystal violet was used to stain the biofilm adhered to the tube tightly for 30 min and other components bound to tube loosely was washed off with distilled deionized water (ddH2O). Borosilicate tubes were washed for more than three times with ddH2O gently, and the remaining crystal violet was fully dissolved in 1 ml 95% ethanol with constantly shaking after photograph. 100 µl of this eluate was transferred to a transparent 96-well plate to measure its optical density at 590 nm using Biotek microplate reader. The experiment was repeated using three independent bacterial cultures.Biofilm production was detected as previously reported in minor modifications . In briehttps://david-d.ncifcrf.gov/). The GO term with p-value < 0.05 was defined as significantly enriched term.GO enrichment analysis of TFs target genes was catalogued using DAVID version 6.7 (128).Two-tailed Student’s t-tests were performed using Microsoft Office Excel 2010. *p < 0.05, **p < 0.01, and ***p < 0.001 and error bars represent means ± standard deviation (SD). All experiments were repeated for at least twice. Statistical graphs were drawn using the GraphPad Prism 8, R ggplot2, and Python Matplotlib packages. All motif logos are drawn using R package ggseqlogo. All network was visualized by using Cytoscape software v3.7.2 .Thank you for submitting your article "The Binding Specificity Atlas of The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:Pseudomonasaeruginosa using a high-throughput DNA enrichment method. The consensus binding motifs of 182  TFs were obtained and genomic loci were predicted to be targets of these TFs, some of which were experimentally validated. The work provides a useful resource for studying the transcription regulation network in Pseudomonasaeruginosa, and also provides DNA motif information for TFs in other bacteria. The reviewers all agree the work performed is generally of good quality, however, specific concerns raised by the reviewers regarding how the data are obtained, classified, and validated, need to be addressed.This manuscript analyzes the DNA binding sequence specificity of transcription factors (TFs) in the human pathogen Essential revisions:1. One of the major conclusions is that most TFs bind as homodimers because their PWMs contain 'head-to-head' duplications of the same sequence. However, it could also be that two monomers bind a single double stranded DNA molecule in the HT-SELEX assays. It would be helpful if the authors could investigate whether homodimeric protein-protein interactions are known to occur for relevant TFs, or whether homodimers have been observed in structural studies.2. The comparison of their HT-SELEX-derived PWMs with those obtained by ChIP should be expanded. After predicting binding events based on their PWM data, precision/recall analysis should be done with the ChIP data that compares individual binding events rather than the overall composite PWM binding motif. This will address the question of how many predicted binding events were also reported by ChIP and how many binding events detected by ChIP were also predicted. This is a key point.Pseudomonas genome is not sufficiently described: how was thresholding determined to call binding sites for each TF?3. The computational scanning for TF binding sites in the 4. How a TF is associated with a biological pathway is not well described. How many binding events are required to call an association? Is this done one TF at a time, or one pathway at a time? How is a pathway defined?5. The biological validation experiments appear ad hoc. It is not clear what this says about the entire dataset. For instance, only one new TF was validated experimentally out of 57 biofilm-associated TFs. The crystal violet assay can be easily performed in a 96-well plate format to validate 57 TFs that affect biofilm formation. PAO1 mutants can be obtained from Manoil lab (PAO1 transposon insertion library (https://www.gs.washington.edu/labs/manoil/libraryindex.htm)6. It should be noted that the gel shift assays are similar to SELEX.7. How many (predicted) binding events are functional? Analysis of (available) RNA-seq data would be key for this. For instance, Figure 1B. shows that the well-studied GacA and AmrZ have similar DNA binding specificity. Transcriptomic data are available for both these TFs (PMID: 31270321) and should be compared to the physical binding predictions.Pseudomonas.com/feature/show/?id=102905&view=operons). The figure and the text imply that there are 6 independent TF binding sites located in 6 different promoters. However, there is a single promoter, and ShpR and PA4008 share only one target. Operon genes that share a single promoter are represented in the figures as independent genes. This is a major problem throughout the paper and figures and needs to be resolved.8. Page 11. Lines 266-268, also Figure 5A. The authors state that ShpR shares six targets with PA4008 implying a cooperative (or redundant?) function in regulating T6SS. From the figure 5A, these 6 targets are: tagJ1, tssE1, tssF1, tssG1, clpV1, vgrG1. However these six genes are in the same operon and share a single promoter  in regulating motility-related genes" is an overstatement.10. Throughout the manuscript, the authors should be careful not to overstate their findings and be precise when biological function is predicted vs demonstrated experimentally.11. The manuscript would benefit from thorough proofreading and editing.12. Overall, 371 TFs entered in the pipeline and a result could be generated for 182 TFs (app. 50%). From an experimental design point of view, only 3 TFs have been used to benchmark the reproducibility of the binding site motifs which represents <1% of the input. This should be improved to provide a resource for the field. Since it is an in vitro assay, I would have expected to see the results validated systematically in duplicate. The author claim that the three replicates obtained "virtually identical binding specificity": but what does this mean statistically?13. How did the authors select 182 TFs out of 371 TFs? Were there issues with protein purification for the others?Pseudomonas virulence pathways. How is it possible to predict the role of 365 TFs while they obtained PWM for only 182 TFs?14. Line 167/Figure 2E: using the binding motifs, the authors predicted the involvement of 365 TFs in 15. Figure 3B/C: AmrZ binding site: it is not clear for the link between Figure 3B and 3C. Where is the motif in the psl operon?16. The authors have already published a TF regulatory network paper (https://doi.org/10.1038/s41467-019-10778-w) covering 20 TFs : a supplementary figure that compares side-by-side the motifs found in the both papers could be interesting.17. The author should explain why 201 sequence motifs (PWMs) were obtained for 182 TFs in the main text. For example, in the Data S1, the Module 37 of PA1241 has two sequence motifs containing almost the same consensus sequence. Why have two consensus sequence motifs have to be assigned?18. The sequence motif for sig54 (rpoN) is conserved across bacteria and the core consensus sequence of GGN(10)GC has been confirmed in various studies. The sequence motif for sig54 by SELEX  is inconsistent with previous literatures and should be reanalyzed and discussed.19. The manuscript also describes interesting monomeric sequence motifs for 19 TFs. The discussion of these 19 TFs should be expanded.Pseudomonasaeruginosa are published, it would strengthen the report to integrate the TSS information into the analysis herein.20. The location of TF-binding sites relative to transcription start site (TSS) on the promoter DNA could help predict the activity of the corresponding TFs. For example, TF binding sites overlapping with the core promoter region (-35 to +1) and the proximal downstream region of a TSS may suggest transcriptional repression. As TSS profiles in Pseudomonasaeruginosa Transcription Factors Reveals Novel Regulators in Virulence" for further consideration by eLife. Your revised article has been evaluated by Bavesh Kana (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "The Binding Specificity Atlas of Summary:Pseudomonasaeruginosa and represents an important resource for the research community. With the use of novel assays, the work has uncovered transcription factor binding specificities for nearly half of the annotated transcription factors and forms a good foundation for future studies. The study will be of interest to microbiologists and those interested in bacterial metabolism.This manuscript provides a global view of transcription factor interactions in Although the manuscript has been improved, there are some remaining issues that need to be addressed, as outlined below:1. There are still concerns about the interpretations of RpoN binding data. It is well established that RpoN doesn't function as a dimer to facilitate transcription initiation. It binds RNAP core enzyme in a 1:1 molar ratio to form an RNAP holoenzyme. Second, the sequence motif itself is not a direct repeat of two half-sites. The 5' 'GG' and 3' 'GC' consensuses of the GGN(10)GC motif are representative sequences of the '-24' and '-12' elements of a typical sigma 54 (encoded by rpoN)-initiated promoter. The two elements are recognized by two separate domains of RpoN, both in the presence and absence of RNAP holoenzyme. This issue of data interpretation and representation needs to be addressed. It is also recommended that the authors use a different example of head to tail binding.2. The authors associate a TF with a pathway when a single predicted binding event occurs with any gene in that pathway. However, statistical analysis must be performed to determine the false discovery rate and whether their findings could occur via chance.3. There are still points that the authors equate binding with function in the manuscript, please revise throughout to not make this overstatement. Essential revisions:1. One of the major conclusions is that most TFs bind as homodimers because their PWMs contain 'head-to-head' duplications of the same sequence. However, it could also be that two monomers bind a single double stranded DNA molecule in the HT-SELEX assays. It would be helpful if the authors could investigate whether homodimeric protein-protein interactions are known to occur for relevant TFs, or whether homodimers have been observed in structural studies.1, 2), RsaL (3), PsrA , FleQ (6), and QscR (7). We have now cited these studies in our manuscript.Thanks for the constructive comment. Many of the TF homodimers have been reported before by independent works, such as LasR . If the “head-to-head” motif had been indeed caused by “two monomers bind a single double stranded DNA molecule in the HT-SELEX assays”, the spacing between the monomer motif would have not been specific. In other words, two independent monomers could bind to a double stranded DNA in ANY orientation or spacing. Here in our study, we have shown that the TF homodimers displayed strong enrichment for particular orientation and spacing between two monomers. We have added the discussion to the revised manuscript. Please see Lines 154159.Furthermore, it should also be noted that unlike other cytosolic proteins, many TF dimer formations are mediated by sequence specific DNA molecules which they bound to and therefore may not be appropriately detected by protein-protein interaction assays. This has been shown before for both TF homodimers and heterodimers  and HT-SELEX, including AlgR, CdpR, ExsA, FleQ, GacA, MexT, PchR, PhoB, RsaL, and SphR. We used their PWM models to predict the binding events for each TF in the Pseudomonas aeruginosa genome and assessed the performance with precision-recall analysis suggested by the reviewer. We found that most of TFs showed AUC  over 0.5, demonstrating that our PWMs could well predict most of the in vivo binding . We also observed that prediction for some TFs is below 0.5, which is likely due to the fact that the binding is affected by chromatin accessibility, protein-protein interaction or other unknown mechanisms, which were quite common in higher species. The results are consistent with our direct comparison between motifs generated with ChIP-seq and HT-SELEX. We have discussed the comparison between ChIP-seq and PWM in the revised manuscript, please see Lines 419-429.Thanks for the suggestion. We agree that this is a very good way to evaluate the quality of our data. There are 10 TFs that were analyzed with both ChIP-seq . When we set the p value cutoff of 1.0E-5, we obtained 22 binding sites, which is highly consistent with ChIP-seq result. Therefore, we determined to use p<1.0E-5 as a standard cutoff for FIMO prediction throughout our manuscript.(p<1.0E-5) to the manuscript, and other parameters take default values. This p value is the most commonly used cutoff when applying FIMO to scan TF binding sites in the genome. When we used the FIMO default p value cutoff (p<1.0E-4), an excessive number of peaks were predicted far beyond what we observed from the ChIP-seq data. For example, the highquality PhoB ChIP-seq detected 64 (34 of them were located upstream of a gene) binding peaks in vivo  to scan genome-wide TF-binding sites (TFBSs) for the 182 TFs with available PWMs in the P. aeruginosa PAO1 reference genome (p<1.0E-5). Since the vast majority of binding within the gene body appeared to have little or no effect on transcription levels in prokaryotes according to previous studies (12), we primarily focused on predicted TFBSs in the intergenic regions in the subsequent analyses. In total, 33,709 significant TFBSs (p<1.0e-5) enriched in the intergenic regions were predicted. We are particularly interested in the transcriptional regulatory program in relation to the 9 virulence pathways of P. aeruginosa. Therefore, we comprehensively summarized 9 virulence-involved gene lists based on the last decades of studies, respectively. Then bedtools (v2.25.0) (87) was introduced to annotate all predicted TFBSs, which were intersected with the promoter regions of genes involved in these 9 virulence pathways. According to our analyses, TFs whose predicted binding sites were located in these genes are recognized as the corresponding pathway-associated TFs. We have revised the manuscript to include the explicit detail, please see Lines 189-195.We apologize for the unclear description. We defined “association” as TF binding to at least one gene involved in a pathway or the TF itself was previously known to be implicated in the pathway. To predict direct interactions between TFs and DNA sequences, we first used the FIMO software P. aeruginosa recently (13).Thanks for suggesting the resource to evaluate our finding. We have obtained mutants and carried out the advised experiments. Among the 57 biofilm-associated TFs, 53 TF mutants were obtained from the Manoil lab except 4 mutants . PA0225 has already been analyzed in our study while the mutant strains of PA2121, PA2118, and PA2028 were not available in their collection. A recent study had revealed that TF PA2121 functioned as a novel biofilm synthesis repressor in The crystal violet assay demonstrated that the biofilm production of the 10 TFs  had significant difference compared to the wild-type strain (P<0.05) , also consistent with the previous studies:P. aeruginosa (14-16). Our crystal violet assay showed that FleQ mutant produced less biofilm compared to the wild-type, consistent to the previous study;(1) FleQ, as c-di-GMP effector, positively regulates the expression of biofilm component- Pel exopolysaccharide in psl operon in P. aeruginosa . Consistently, our crystal violet assay confirmed the point and the accurate binding site on the psl operon was successfully identified by AmrZ PWM generated from HT-SELEX  ;(2) AmrZ represses biofilm architecture by directly repressing transcription of the P. aeruginosa biofilm formation. A gacA mutant contains a deficit biofilm development revealed by scanning electron microscopy (19), consistent to our crystal violet result;(3) GacA is a regulatory element in P. aeruginosa by controlling the Pho regulon (20), consistent to our crystal violet assay result that the phoB mutant strain produced more biofilm production compared to the wild-type strain.(4) PhoB negatively modulates the biofilm production of 21), CprR (22), AlgB , CdpR (25), BqsR (Synonym: CarR) , and PA3782 (28) had been reported to influence biofilm production in previous studies.Although the altered biofilm production of the remaining 43 TFs mutant strains were not significant in our assay, 6 TFs, including RsaL (rsaL mutant is impaired in biofilm formation (21);(1) The 22);(2) CprR is involved to biofilm formation on indwelling medical devices such as endotracheal tubes (ETTs) ;(3) AlgB is required for the repression of colony biofilm formation by repressing biofilmrelated genes, while the cdpR mutant results in increased production of biofilm (25);(4) A ∆2+ signaling to influence biofilm production ; (6) The inactivation of PA3782 caused defects in biofilm formation in static and flowing systems (28).(5) BqsR (or named CarR) modulates CaWe reasoned that unlike the clear deletion, the transposon insertion mutants could influence the expression of more than one genes because of the polar effects on the expression of downstream genes, or that mutating one TF gene could not change the biofilm phenotype owing to the complexity of regulation in vivo, e.g. the case of AlgB mentioned above.6. It should be noted that the gel shift assays are similar to SELEX.Thanks for the advice. We have noted that EMSA and HT-SELEX are similar in that they are both conducted in vitro using recombinant proteins and synthesized DNA ligands in the revised manuscript. However, we would like also to highlight the fundamental difference between these two experiments: 1) HT-SELEX introduces a large pool of DNA sequences for competition and selection, while EMSA can only analyzed one sequence at a time; 2) HTSELEX is based on exponential enrichment of binding sequences along the repeated cycles while EMSA is less quantitative in terms of binding affinity; 3) HT-SELEX can be easily adapted for high-throughput study, investigating binding of multiple TFs with multiple DNA sequences, while EMSA is difficult to be carried out in a high-throughput manner. Therefore, EMSA could be used as an orthogonal method to validate the individual binding events. In addition to EMSA, we also used other experiments, such as ChIP-seq to validate our findings.7. How many (predicted) binding events are functional? Analysis of (available) RNA-seq data would be key for this. For instance, Figure 1B. shows that the well-studied GacA and AmrZ have similar DNA binding specificity. Transcriptomic data are available for both these TFs (PMID: 31270321) and should be compared to the physical binding predictions.10) RNA-seq data to compare the overlapping of the changed expression upon deletion. The list of GacA and AmrZ manipulation caused differentially expressed genes (defined as > 2 times fold change of the expression) was intersected with the list of GacA and AmrZ target genes predicted in this paper respectively (see Author response image 1). The results showed that only a small fraction of predicted target genes were dysregulated upon knockdown of the TF in cells. This could be due to several factors: 1) many TFs co-bound to the promoter region and therefore loss of one TF binding was not sufficient to significantly change the expression of the following gene by twofold; 2) Huang et al. did RNA-seq at the log phase (OD=0.6) only, whereas other growth phases could show a very different list of DEGs; 3) complex regulatory mechanism existed for the transcription, involving protein-protein interactions, chromatin accessibility, so that the changed transcription did not reflect cellular response to a simple one-to-one relationship between TF and target gene. Predicting transcription output is still a very challenging task in the field, even if we integrate the information of chromatin epigenetic information, protein abundance, and 3D genome folding, etc. Therefore, we avoided using the TF depletion followed by RNA-seq to assess the PWM quality in our manuscript. We have included the discussion in the revision.We appreciate the reviewer for suggesting multiple ways of validating the quality of our HTSELEX data. Accordingly, we have downloaded Huang et al.  function in regulating T6SS. From the figure 5A, these 6 targets are: tagJ1, tssE1, tssF1, tssG1, clpV1, vgrG1. However these six genes are in the same operon and share a single promoter (https://Pseudomonas.com/feature/show/?id=102905&view=operons). The figure and the text imply that there are 6 independent TF binding sites located in 6 different promoters. However, there is a single promoter, and ShpR and PA4008 share only one target. Operon genes that share a single promoter are represented in the figures as independent genes. This is a major problem throughout the paper and figures and needs to be resolved.Thanks a lot for pointing out this issue and the constructive suggestion. To avoid confusion, we have revised the sentence to emphasize the point in manuscript, please see Lines 327 to 333. We also have merged all the genes sharing the same operon into one box in the network illustration to avoid the confusion. Please see revised Figure 3, 4 and 5 and Figure 2—figure supplement 4, 5, 6, 7, 8, 9, 10.9. Page 12. This paragraph is speculative as no functional motility assays were performed and no target genes were functionally validated. Thus the statement "in sum, 37 TFs were found involved (..) in regulating motility-related genes" is an overstatement.Thanks a lot for pointing out this issue. To avoid overstatement and confusion, we revised the statement from “In sum, 37 TFs were found involved in regulating motility-related genes.” to “In sum, 37 TFs were predicted to regulate motility-related genes.” Please see Line 358-359.We have also gone through the manuscript and tuned down the overstatement of our findings.10. Throughout the manuscript, the authors should be careful not to overstate their findings and be precise when biological function is predicted vs demonstrated experimentally.Thanks a lot for pointing out the overstatement issue in this manuscript. We have carefully checked the manuscript and clearly differentiated predicted and experimentally verified TFBS to avoid confusion.11. The manuscript would benefit from thorough proofreading and editing.Thanks a lot. A scientist who is a native speaker of English has carefully checked the grammatical issues and edited the text throughout the manuscript.12. Overall, 371 TFs entered in the pipeline and a result could be generated for 182 TFs (app. 50%). From an experimental design point of view, only 3 TFs have been used to benchmark the reproducibility of the binding site motifs which represents <1% of the input. This should be improved to provide a resource for the field. Since it is an in vitro assay, I would have expected to see the results validated systematically in duplicate. The author claim that the three replicates obtained "virtually identical binding specificity": but what does this mean statistically?8), we have systematically compared the technical replicates (replicates using the same protein but different DNA input libraries) and biological replicates  and demonstrated that HT-SELEX resulted in virtually the same results. Therefore, we only picked up three TFs as QC controls when we carried out HT-SELEX in different batches and expectedly these replicates turned out identical motifs.According to our previous experience, HT-SELEX is a highly quantitative and reproducible method. In our previous publication , we did not recover any motif either. We then checked the protein expression and found that except QscR, the other TF proteins were very abundant, suggesting that failure of motif enriched was unlikely to be caused by lack of protein in the reaction see . The sam13. How did the authors select 182 TFs out of 371 TFs? Were there issues with protein purification for the others?We apologize for confusing the reviewer due to our poor wording. We actually did not “select” any TFs. We have performed HT-SELEX for all 371 TFs, out of which 182 TF showed DNA binding specificities. We don’t think the failure was caused by protein purification issue as we carefully inspected protein amount for each HT-SELEX experiment. We ran SDS-PAGE gel for all TF proteins used in our paper (371 TFs) and measured the concentrations carefully. We used 100-200 ng per TF in each round of SELEX. The enrichment of specific motifs is not related to the protein abundance. For example, PA3122, PA3260 and PA3932, have a clear and bright protein band in SDS-PAGE gel, but HTSELEX did not detect any specific DNA sequence motifs see .14. Line 167/Figure 2E: using the binding motifs, the authors predicted the involvement of 365 TFs in Pseudomonas virulence pathways. How is it possible to predict the role of 365 TFs while they obtained PWM for only 182 TFs?As stated above, we apologize for the poor wording that caused confusion. The 365 TFs included redundant counting for TFs involved in multiple different pathways. Of these 365 TFs, only 127 TFs are unique, among which 92 TFs had never been characterized before. We have revised the manuscript, and noted the number clearly. Please see Lines 33-34, and 199200 in the revised manuscript.15. Figure 3B/C: AmrZ binding site: it is not clear for the link between Figure 3B and 3C. Where is the motif in the psl operon?Pseudomonas aeruginosa PAO1 genome with the PWM model of AmrZ generated in this study (FIMO score 8.75E-06). To clarify, we have now merged the former Figure 3 C with Figure 3 Band highlighted the predicted AmrZ-binding site.The binding site “TAGCTATCACAAAGCCACTAT” was identified by scanning the 16. The authors have already published a TF regulatory network paper (https://doi.org/10.1038/s41467-019-10778-w) covering 20 TFs : a supplementary figure that compares side-by-side the motifs found in the both papers could be interesting.29). We have added the comparison data and a brief discussion to the revised manuscript. See Lines 419438.Thank you. This is a similar question to Point 2 above. We have provided a supplementary table  to compare the ChIP-seq motif and HT-SELEX motif side-by-side, in total 13 TFs with both datasets available, as suggested by this reviewer see . By comp17. The author should explain why 201 sequence motifs (PWMs) were obtained for 182 TFs in the main text. For example, in the Data S1, the Module 37 of PA1241 has two sequence motifs containing almost the same consensus sequence. Why have two consensus sequence motifs have to be assigned?8). Therefore, multiple PWMs were assigned to one TF to respectively describe its different spacing and orientation. This has been discussed before in our previous studies . To avoid confusion, we have noted this in the revised manuscript. Please see Lines 91-96.Thanks for the advice. Some TFs bind to the DNA in a homodimeric mode with different spacing and monomer orientation (18. The sequence motif for sig54 (rpoN) is conserved across bacteria and the core consensus sequence of GGN(10)GC has been confirmed in various studies. The sequence motif for sig54 by SELEX  is inconsistent with previous literatures and should be reanalyzed and discussed.Vibrio cholerae, Salmonella enterica Serovar Typhimurium, Yersinia pseudotuberculosis, Agrobacterium tumefaciens, Geobacter sulfurreducens, etc. Then, we repeated the HT-SELEX experiment for RpoN using independently purified protein lysates and input DNA oligos for 4 times, we have now detected the GGN(10)GC conventional motif as the most enriched one  on the promoter DNA could help predict the activity of the corresponding TFs. For example, TF binding sites overlapping with the core promoter region (-35 to +1) and the proximal downstream region of a TSS may suggest transcriptional repression. As TSS profiles in Pseudomonas aeruginosa corresponded to the UCBPP-PA14 strain (31) which was different from the Pseudomonas aeruginosa PAO1 strain used in this study. We could not find a proper tool to liftover the two reference genomes and therefore to avoid confusing future users, we chose not to include such information. Researchers may have their own TFBS prediction in the PA14 strain (using PWM from the current study) to associate the TFs with corresponding target genes if interested.Thanks for your advice. We also agree that it would be very useful information to annotate the genome with the location of TF-binding sites relative to transcription start site (TSS) on the promoter. Unfortunately, the published TSS information of 31) of pel operon .1. M. J. Bottomley, E. Muraglia, R. Bazzo, A. Carfi, Molecular insights into quorum sensing in the human pathogen et al., QsIA disrupts LasR dimerization in antiactivation of bacterial quorum sensing. Proc Natl Acad Sci U S A 110, 20765-20770 (2013).2. H. Fan et al., Crystal structure of Pseudomonasaeruginosa RsaL bound to promoter DNA reaffirms its role as a global regulator involved in quorum-sensing. Nucleic Acids Res 45, 699-710 (2017).3. H. Kang et al., The long-chain fatty acid sensor, PsrA, modulates the expression of rpoS and the type III secretion exsCEBA operon in Pseudomonas aeruginosa. Mol Microbiol 73, 120-136 (2009).4. Y. Kang Pseudomonas rpoS and psrA promoters. J Bacteriol 184, 2324-2330 (2002).5. M. Kojic, C. Aguilar, V. Venturi, TetR family member psrA directly binds the et al., The REC domain mediated dimerization is critical for FleQ from Pseudomonasaeruginosa to function as a c-di-GMP receptor and flagella gene regulator. J Struct Biol 192, 1-13 (2015).6. T. Su et al., Mechanism of agonism and antagonism of the Pseudomonasaeruginosa quorum sensing regulator QscR with non-native ligands. Mol Microbiol 108, 240-257 (2018).7. C. L. Wysoczynski-Horita et al., DNA-binding specificities of human transcription factors. Cell 152, 327-339 (2013).8. A. Jolma et al., DNA-dependent formation of transcription factor pairs alters their binding specificity. Nature 527, 384-388 (2015).9. A. Jolma et al., An integrated genomic regulatory network of virulence-related transcriptional factors in Pseudomonas aeruginosa. Nat Commun 10, 2931 (2019).10. H. Huang Bioinformatics 27, 1017-1018 (2011).11. C. E. Grant, T. L. Bailey, W. S. Noble, FIMO: scanning for occurrences of a given motif. Escherichia coli RutR transcription factor binds at targets within genes as well as intergenic regions. Nucleic Acids Res 36, 3950-3955 (2008).12. T. Shimada, A. Ishihama, S. J. Busby, D. C. Grainger, The et al., A putative LysR-type transcriptional regulator inhibits biofilm synthesis in Pseudomonas aeruginosa. Biofouling 35, 541-550 (2019).13. X. Yang Proc Natl Acad Sci U S A 110, 18478-18483 (2013).14. C. Baraquet, C. S. Harwood, Cyclic diguanosine monophosphate represses bacterial flagella synthesis by interacting with the Walker A motif of the enhancer-binding protein FleQ. Pseudomonasaeruginosa as a c-di-GMP-responsive transcription factor. Mol Microbiol 69, 376-389 (2008).15. J. W. Hickman, C. S. Harwood, Identification of FleQ from Pseudomonasaeruginosa functions as both a repressor and an activator to control gene expression from the pel operon promoter in response to c-di-GMP. Nucleic Acids Res 40, 7207-7218 (2012).16. C. Baraquet, K. Murakami, M. R. Parsek, C. S. Harwood, The FleQ protein from et al., ChIP-seq and RNA-Seq reveal an AmrZ-mediated mechanism for cyclic di-GMP synthesis and biofilm development by Pseudomonas aeruginosa. PLoS Pathog 10, e1003984 (2014).17. C. J. Jones Pseudomonasaeruginosa biofilm architecture by directly repressing transcription of the psl operon.18. C. J. Jones, C. R. Ryder, E. E. Mann, D. J. Wozniak, AmrZ modulates J Bacteriol 195, 1637-1644 (2013).Pseudomonasaeruginosa GacA, a factor in multihost virulence, is also essential for biofilm formation. Mol Microbiol 40, 12151226 (2001).19. M. D. Parkins, H. Ceri, D. G. Storey, Pseudomonas aureofaciens PA147-2. Mol Microbiol 42, 415-426 (2001).20. R. D. Monds, M. W. Silby, H. K. Mahanty, Expression of the Pho regulon negatively regulates biofilm formation by Pseudomonasaeruginosa virulence and biofilm formation. FEMS Microbiol Lett 301, 210-217 (2009).21. G. Rampioni, M. Schuster, E. P. Greenberg, E. Zennaro, L. Leoni, Contribution of the RsaL global regulator to Pseudomonasaeruginosa biofilm formation on endotracheal tubes requires multiple two-component systems. J Med Microbiol 69, 906-919 (2020).22. D. Badal, A. V. Jayarani, M. A. Kollaran, A. Kumar, V. Singh, Pseudomonasaeruginosa Virulence. J Bacteriol 194, 65376547 (2012).23. N. S. Chand, A. E. Clatworthy, D. T. Hung, The two-component sensor KinB acts as a phosphatase to regulate Pseudomonasaeruginosa collective behaviors. PLoS Biol 17, e3000579 (2019).24. S. Mukherjee, M. Jemielita, V. Stergioula, M. Tikhonov, B. L. Bassler, Photosensing and quorum sensing are integrated to control et al., Structural and Molecular Mechanism of CdpR Involved in QuorumSensing and Bacterial Virulence in Pseudomonasaeruginosa. PLoS Biol 14, e1002449 (2016).25. J. Zhao et al., The Pseudomonasaeruginosa PAO1 Two-Component Regulator CarSR Regulates Calcium Homeostasis and Calcium-Induced Virulence Factor Production through Its Regulatory Targets CarO and CarP. J Bacteriol 198, 951-963 (2016).26. M. Guragain Pseudomonasaeruginosa biofilms. J Bacteriol 187, 4327-4337 (2005).27. S. Sarkisova, M. A. Patrauchan, D. Berglund, D. E. Nivens, M. J. Franklin, Calciuminduced virulence factors associated with the extracellular matrix of mucoid Pseudomonasaeruginosa biofilm development. J Bacteriol 185, 2700-2710 (2003).28. A. Finelli, C. V. Gallant, K. Jarvi, L. L. Burrows, Use of in-biofilm expression technology to identify genes involved in et al., Transcription factor binding in human cells occurs in dense clusters formed around cohesin anchor sites. Cell 154, 801-813 (2013).29. J. Yan et al., Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science 356, (2017).30. Y. Yin et al., The single-nucleotide resolution transcriptome of Pseudomonasaeruginosa grown in body temperature. PLoS Pathog 8, e1002945 (2012).31. O. Wurtzel et al., Expression of the multidrug resistance operon mexA-mexB-oprM in Pseudomonasaeruginosa: mexR encodes a regulator of operon expression. Antimicrob Agents Chemother 40, 2021-2028 (1996).32. K. Poole Pseudomonasaeruginosa. Antimicrob Agents Chemother, (2019).33. A. Kawalek, M. Modrzejewska, B. Zieniuk, A. A. Bartosik, G. Jagura-Burdzy, Interaction of ArmZ with the DNA-binding domain of MexZ induces expression of mexXY multidrug efflux pump genes and antimicrobial resistance in et al., An in vivo inducible gene of Pseudomonasaeruginosa encodes an anti-ExsA to suppress the type III secretion system. Mol Microbiol 54, 307-320 (2004).34. U. H. Ha Pseudomonasaeruginosa suppresses the type III secretion system under the stress of DNA damage. J Bacteriol 187, 6058-6068 (2005).35. W. Wu, S. Jin, PtrB of Pseudomonasaeruginosa. J Bacteriol 193, 399-410 (2011).36. Y. Jin, H. Yang, M. Qiao, S. Jin, MexT regulates the type III secretion system through MexS and PtrC in Pseudomonasaeruginosa. Environ Microbiol 19, 3721-3733 (2017).37. A. Daddaoua, A. Corral-Lugo, J. L. Ramos, T. Krell, Identification of GntR as regulator of the glucose metabolism in et al., QscR, a modulator of quorum-sensing signal synthesis and virulence in Pseudomonasaeruginosa. Proc Natl Acad Sci U S A 98, 2752-2757 (2001).38. S. A. Chugani et al., Systems-level analysis of NalD mutation, a recurrent driver of rapid drug resistance in acute Pseudomonasaeruginosa infection. PLoS Comput Biol 15, e1007562 (2019).39. J. Yan Pseudomonasaeruginosa. PLoS One 7, e38148 (2012).40. G. Dieppois, V. Ducret, O. Caille, K. Perron, The transcriptional regulator CzcR modulates antibiotic resistance and quorum sensing in Pseudomonasaeruginosa biofilm development. PLoS Pathog 5, e1000668 (2009).41. O. E. Petrova, K. Sauer, A novel signaling network essential for regulating et al., Biofilm formation in Pseudomonasaeruginosa: fimbrial cup gene clusters are controlled by the transcriptional regulator MvaT. J Bacteriol 186, 28802890 (2004).42. I. Vallet et al., Specific lipopolysaccharide found in cystic fibrosis airway Pseudomonasaeruginosa. Science 286, 1561-1565 (1999).43. R. K. Ernst Pseudomonasaeruginosa regulate expression of the outer-membrane protein OprH and polymyxin B resistance. Mol Microbiol 34, 305-316 (1999).44. E. L. Macfarlane, A. Kwasnicka, M. M. Ochs, R. E. Hancock, PhoP-PhoQ homologues in Pseudomonasaeruginosa attachment and biofilm development in dynamic environments. Mol Microbiol 53, 1075-1087 (2004).45. M. M. Ramsey, M. Whiteley, et al., Contribution of the PhoP-PhoQ and PmrA-PmrB two-component regulatory systems to Mg2+-induced gene regulation in Pseudomonasaeruginosa. J Bacteriol 188, 3995-4006 (2006).46. J. B. McPhee Pseudomonasaeruginosa quinolone signal system and pyocyanin production. J Bacteriol 190, 6217-6227 (2008).47. H. Liang, L. Li, Z. Dong, M. G. Surette, K. Duan, The YebC family protein PA0964 negatively regulates the 48. G. G. Nicastro, A. L. Boechat, C. M. Abe, G. H. Kaihami, R. L. Baldini,Pseudomonasaeruginosa PA14 cupD transcription is activated by the RcsB response regulator, but repressed by its putative cognate sensor RcsC. FEMS Microbiol Lett 301, 115-123 (2009).Pseudomonasaeruginosa CupD fimbrial genes is antagonistically controlled by RcsB and the EAL-containing PvrR response regulators. PLoS One 4, e6018 (2009).49. H. Mikkelsen, G. Ball, C. Giraud, A. Filloux, Expression of Pseudomonasaeruginosa pathogenesis and biofilm development independently of its canonical homoserine lactone autoinducer. PLoS Pathog 13, e1006504 (2017).50. S. Mukherjee, D. Moustafa, C. D. Smith, J. B. Goldberg, B. L. Bassler, The RhlR quorum-sensing receptor controls Pseudomonasaeruginosa sensor kinase KinB negatively controls alginate production through AlgW-dependent MucA proteolysis. J Bacteriol 191, 2285-2295 (2009).51. F. H. Damron, D. Qiu, H. D. Yu, The et al., ChIP-seq reveals the global regulator AlgR mediating cyclic di-GMP synthesis in Pseudomonasaeruginosa. Nucleic Acids Res 43, 8268-8282 (2015).52. W. Kong Pseudomonasaeruginosa. J Bacteriol 172, 5544-5554 (1990).53. V. Deretic, W. M. Konyecsni, A procaryotic regulatory factor with a histone H1-like carboxy-terminal domain: clonal variation of repeats within algP, a gene involved in regulation of mucoidy in et al., The regulatory repertoire of Pseudomonasaeruginosa AmpC ss-lactamase regulator AmpR includes virulence genes. PLoS One 7, e34067 (2012).54. D. Balasubramanian Pseudomonasaeruginosa Biofilms. J Bacteriol 197, 2810-2820 (2015).55. J. H. Hammond, E. F. Dolben, T. J. Smith, S. Bhuju, D. A. Hogan, Links between Anr and Quorum Sensing in J Bacteriol 192, 5275-5288 (2010).56. O. E. Petrova, K. Sauer, The novel two-component regulatory system BfiSR regulates biofilm development by controlling the small RNA rsmZ through CafA. Pseudomonasaeruginosa two-component regulator BfmR controls bacteriophage-mediated lysis and DNA release during biofilm development through PhdA. Mol Microbiol 81, 767-783 (2011).57. O. E. Petrova, J. R. Schurr, M. J. Schurr, K. Sauer, The novel Pseudomonasaeruginosa. J Bacteriol 193, 918-931 (2011).58. A. T. Yeung, M. Bains, R. E. Hancock, The sensor kinase CbrA is a global regulator that modulates metabolism, virulence, and antibiotic resistance in Pseudomonasaeruginosa quinolone signal via repression of the FadE homolog, PA0506. PLoS One 12, e0189331 (2017).59. G. Wells, S. Palethorpe, E. C. Pesci, PsrA controls the synthesis of the Pseudomonasaeruginosa. PLoS One 8, e74444 (2013).60. I. Blus-Kadosh, A. Zilka, G. Yerushalmi, E. Banin, The effect of pstS and phoB on quorum sensing and swarming motility in […] Although the manuscript has been improved, there are some remaining issues that need to be addressed, as outlined below:1. There are still concerns about the interpretations of RpoN binding data. It is well established that RpoN doesn't function as a dimer to facilitate transcription initiation. It binds RNAP core enzyme in a 1:1 molar ratio to form an RNAP holoenzyme. Second, the sequence motif itself is not a direct repeat of two half-sites. The 5' 'GG' and 3' 'GC' consensuses of the GGN(10)GC motif are representative sequences of the '-24' and '-12' elements of a typical sigma 54 (encoded by rpoN)-initiated promoter. The two elements are recognized by two separate domains of RpoN, both in the presence and absence of RNAP holoenzyme. This issue of data interpretation and representation needs to be addressed. It is also recommended that the authors use a different example of head to tail binding.1) and by others (2), PhoB binds to DNA as a homodimer in a head-to-tail consecutive orientation, with a “GTCA(C/T)” monomer sequence preference spaced by a stretch of 6-bp ATrich nucleotides . We have now added the text to the manuscript in Line 139-142.We deeply appreciate the reviewers for pointing out the potential issue of the data interpretation and representation of RpoN motif we included in previous submissions. We apologize for not carefully comparing the two similar motifs of RpoN identified in HT-SELEX. Putting the so-called “degenerated dimer” motif of RpoN in a reverse complementary order, we found that the motif was also composed of a “GG” and a “GC” half site. The difference lies on the fact that “GC” core sequence is on the 5’ position relative to the 3’ “GG” core sequence in the unknown motif. However, this previously uncharacterized motif, as explained by the reviewers, was not reproduced by any in vivo ChIP-seq data and may result from a possible topology in the in vitro biochemical condition (without interaction with other proteins) and therefore, we decided not to overstate the finding. Suggested by the reviewers, we replaced Figure 1C illustration of RpoN with another confident example of head-to-tail binding TF, PhoB, to avoid confusing readers. Agreed by the data from HT-SELEX and ChIP-seq by us . Then we associated a TF with a pathway when its binding sites were significantly enriched in promoters of genes in that pathway (FDR < 0.05). Finally, we counted the number of TFs significantly involved in each virulence pathway, and updated the numbers in the new Figure 2E. We have also modified the relevant description in the text, as shown in Lines 183-186. In addition, to highlight and better illustrate the major TFs involved in each pathway, we visualized the enrichment analysis results for each pathway using radar plots, as shown in "Figure 2—figure supplement 12" . In the plot, the radius represents the minus logarithm-transformed FDR, meaning that the longer the radius is, the more significantly the TF is associated with the pathway.Thanks for the reviewers’ suggestion. We have now carried out enrichment analysis of TF binding sites located in promoter of genes involved in various functional pathways by the hypergeometric test. The 3. There are still points that the authors equate binding with function in the manuscript, please revise throughout to not make this overstatement.Thanks a lot for pointing out this issue. We have now thoroughly gone through the text and revised the overstatements mixing the biological function with the putative TF binding. Please see changes highlighted in red font in the revised manuscript file.References:et al., An integrated genomic regulatory network of virulence-related transcriptional factors in Pseudomonasaeruginosa. Nat Commun 10, 2931 (2019).1. H. Huang et al., Cross talk between the response regulators PhoB and TctD allows for the integration of diverse environmental signals in Pseudomonasaeruginosa. Nucleic Acids Res 43, 6413-6425 (2015).2. P. Bielecki


SPRY2 were evaluated for 5mC using Combined Bisulfite Restriction Analysis (COBRA). Previously sequenced 5hmC nano-hmC seal data within SPRY2 promoter and gene body were evaluated in CRC. Combined bioinformatics analyses of SPRY2 CRC transcripts by RNA-seq/microarray and 450K methyl-array data archived in The Cancer Genome Atlas (TCGA) and GEO database were performed. SPRY2 protein in CRC tumors and cells was measured by Western blotting. Increased SPRY2 mRNA was observed across several CRC datasets and increased protein expression was observed among CRC patient samples. For the first time, SPRY2 hypomethylation was identified in adenocarcinomas in the promoter and gene body. We also revealed, for the first time, increases of 5hmC deposition in the promoter region of SPRY2 in CRC. SPRY2 promoter hypomethylation and increased 5hmC may play an influential role in upregulating SPRY2 in CRC.Conventional wisdom is that Sprouty2 (SPRY2), a suppressor of Receptor Tyrosine Kinase (RTK) signaling, functions as a tumor suppressor and is downregulated in many solid tumors. We reported, for the first time, that increased expression of SPRY2 augments cancer phenotype and Epithelial-Mesenchymal-Transition (EMT) in colorectal cancer (CRC). In this report, we assessed epigenetic DNA modifications that regulate SPRY2 expression in CRC. A total of 4 loci within  Sprouty (SPRY) mammalian proteins consist of 4 evolutionarily conserved family members SPRY 1–4) that exhibit tissue-specific expression patterns, apart from the most conserved sprouty isoform SPRY2 that is ubiquitously expressed –4 that e, plateleReduced expression levels of SPRY proteins are observed in many cancers and are believed to contribute to tumorigenesis and metastasis. In many instances, SPRY2 isoform is downregulated, suggesting a possible tumor suppressor function in chronic lymphocytic leukemia CLL , non-smaSPRY2 promoter sequence  provided adenocarcinomas and adjacent normal-appearing colonic mucosa samples. Care was taken to ensure that the underlying muscle and necrosis tissue was excluded from resected tumor and adjacent normal-appearing colonic mucosa. Colonocytes were isolated as previously described and Western blotting was performed to check sample purity assessed by cytokeratin20 (colonocyte marker) . PatientColonocytes were isolated following previously published methods ,26,27. B2 and 5% CO2 and 10% fetal calf serum I . Authentication of mycoplasma-free cell lines was confirmed by short tandem repeats by . HCT116 cells were grown in high glucose DMEM medium . RKO, Caco2, CCD-841, CCD-18Co cells were grown in ATCC-formulated Eagle’s Minimum Essential Medium . FHs74 cells were grown in an ATCC-formulated Hybri-Care medium .All colon cancer and normal colon cell lines were obtained from ATCC and cultured at 37 °C in a humified atmosphere of 95% OProteins were extracted in SDS-containing Laemmli buffer, quantified by RC-DC protein assay, and subjected to Western blotting. Sample lysates and pre-stained molecular weight markers were separated by SDS-PAGE on 4–20% resolving polyacrylamide gradient gels and electroblotted to PVDF membranes. Blots were incubated overnight at 4 °C with specific primary rabbit polyclonal antibody at 1:500 dilution    followed by 1 h incubation with appropriate peroxidase-coupled secondary antibodies that were detected by enhanced chemiluminescence using X-OMAT film . Xerograms were digitized using Epson Perfection V600I scanner .Genomic DNA (gDNA) was extracted and eluted in water from human tissue samples, normal colon cell lines , and colorectal cancer cell lines  using the DNeasy Blood and Tissue Kit . For the assays, gDNA (~200 ng) was treated with sodium bisulfite using the EZ DNA Methylation-Gold Kit  per the manufacturer’s protocol. Bisulfite-treated DNA was eluted in water. Bisulfite converted DNA (~200 ng) was used as a template under standard thermal conditions  followed by 72 °C final extension for 10 min) in a 25 μL reaction using the PyroMark PCR kit  according to the manufacturer’s instructions. The bisulfite-specific primers with sequences designed by MethPrimer software are shown in Region #1: Promoter: putative CTCF binding site [2 CpGs] HpyCH4IVForward primer: (5′→3′)TTAAAGTTTGGGTGTGTAAGGTTAG;Reverse primer: (5′→3′)AAAACCAACAAAACACAAATATCTC;Region #2: Promoter: upstream of the TSS [1 CpG] HpyCH4IVForward primer: (5′→3′)TTAGAGATGGTAAGGGAAGTGGTAT;Reverse primer: (5′→3′)AACCAAAAAAACCCAACTATTTTAC;Region #3: Promoter: closest to the TSS [3 CpGs] BstUIForward primer: (5′→3′)GTAGGAGGTATTTAAAAGGGTAAT;Reverse primer: (5′→3′)AAAAAAAATTCCCTAAATCAACTC;Region #4: Intragenic CpG island: putative CTCF binding site [4 CpGs] HpyCH4IVForward primer: (5′→3′)TTGTTAGGTTTTTTGTAAAGTTTTT;Reverse primer: (5′→3′)CAAATTACAAACAATACCCCCTTC;SPRY2 regions #1 and #4 in CRC patients can be found in PCR products (~7 μL) were digested in a 25 μL reaction containing either BstUI (60 °C) or HpyCH4IV (37 °C) restriction enzyme for 4 h. Restriction digests were then resolved on a 2.5% agarose gel with ethidium bromide. BstUI: cuts 5′CGCG’3 sequence and HpyCH4IV: cuts 5′ACGT’3 sequence . DensitoSPRY2 promoter and gene body were different between tumors and adjacent tissues .5hmC-Seal data were downloaded for 12 pairs of tumor (TU) and adjacent tissue (TI) samples from patients with colorectal cancer (GSE89570) . In shorSPRY2 copy number variant score: Diploid (0) and copy number gain (1) for analysis. A python statistical data visualization library (Seaborn) was used to generate boxplots displaying methylation and mRNA expression distributions [x-axis is gene-level copy number variation groups, which are categorized as +1 and 0, representing copy number amplification and diploid normal copy. The y-axis represents FPKM (mRNA gene expression) values from RNA-seq data.High-throughput data from tumors from CRC patients were extracted from The Cancer Genome Atlas (TCGA) and GEO datasets (GSE8671) (GSE166427) (GSE24514), then utilized for dissecting relationships between 5mC methylation  and mRNA gene expression  ,32. CRC ibutions . DNA metibutions . TCGA paibutions . The genibutions  and provibutions . An R stSPRY2 had an effect on mRNA expression in CRC patients archived in TCGA  database (GSE8671) . An over in TCGA f. We foul tissue g. OveralSPRY2 gene, a known regulator of gene expression, and to correlate our findings with mRNA and protein abundance. According to the DNA Methylation Interactive Visualization Database (DNMIVD), SPRY2 promoter methylation is decreased in adenocarcinomas compared to control mucosa  that are displayed as signal peaks in CRC cell lines Caco2 and HCT116 (SPRY2 promoter regions near the TSS (regions #2 and #3), and one small intragenic CpG island (region #4)  . Given tSPRY2 in human colon cancers, DNA was extracted from 10 matched tumor and adjacent normal-appearing colonocytes and evaluated by COBRA  exhibited hypermethylation in region #1. Differential methylation was not observed in promoter regions #2 or #3. Taken together, this is the first study to our knowledge to demonstrate that putative CTCF binding regulatory regions #1 and #4 are mostly hypomethylated in colorectal tumors, which correlates with overall increased SPRY2 protein expression.In order to investigate the 5mC status of by COBRA a. As shoSPRY2 in n = 12 tumors compared to normal colonic mucosa  and 5′-hydroxymethylcytosine (5hmC), and assessed whether these might correlate with changes in SPRY2 protein expression in CRC. We have demonstrated that loss in 5mC content (hypomethylation) in CTCF binding regulatory regions #1 and #4, with exception of one CRC patient that exhibited increased 5mC (hypermethylation) in region #1. Furthermore, we demonstrate that losses of 5mC within the SPRY2 promoter inversely correlate with increased promoter 5hmC. Indeed, the phenomenon of global epigenetic dysregulation, linking decreased levels of 5mC with increased levels of 5hmC, has been observed in pontine gliomas and hematopoietic cells [SPRY2 promoter, along with increased 5hmC within SPRY2 promoter, might enhance SPRY2 expression in colon tumors that could promote oncogenic signals. In this regard, analysis of GEO datasets showed that SPRY2 transcripts are increased in adenomas compared to normal mucosa. We speculate that similar epigenetic aberrations observed in our adenocarcinoma samples might be occurring in the precancerous adenomas.Concerning effects on tumor growth, the precise role of SPRY2 in CRC remains controversial as one study has reported SPRY2 functions as a tumor suppressor , whereasic cells ,45. In oic cells . We therSPRY2 might inhibit CTCF binding, thereby leading to decreased SPRY2 protein. This is supported by the observation that in HCT116 cells SPRY2 is methylated in all 4 regions and concomitantly SPRY2 is virtually not expressed. In contrast in RKO cells, only 1/4 regulatory regions were methylated and SPRY2 is moderately expressed, whereas in Caco2 cells, none of the 4 regions were methylated; thus, SPRY2 is highly expressed.As a trans-regulatory element protein, CTCF may serve as a positive regulator of gene expression by promoting chromatin accessibility, enabling transcriptional activation of genes through distal enhancer-promoter interactions and/or direct binding of the promoter ,49,50,51miR-125b1 promoter inhibiting CTCF binding and gene activation.Several studies have identified irregular CTCF binding linked to oncogenic transcriptional misregulation in various cancer cell types. In respect to our study, one report observed that the majority of altered CTCF binding is likely due to DNA methylation modifications as opposed to sequence mutations within or around CTCF binding motifs . Other iSPRY2 promoter regions #2 and #3 in any colon tumors tested in this study [SPRY2 that were associated with increased tumor sizes in CRC. As previously described in a prostate cancer model, the higher GC-rich dinucleotide percentage near the TSS of SPRY2 renders neighboring CpGs prone to oxidative degradation by sodium bisulfite treatment, in which CG#11207507 resides [SPRY2. Thus, oxidation by sodium bisulfite-associated DNA methylation techniques like COBRA and many others prevented the evaluation of 5mC in the CpG site CG#11207507. While Feng et al. reported an inverse relationship between SPRY2 (down) and miR-21 (up) gene expression in later stages of CRC, increases in SPRY2 promoter 5mC levels as shown in HCT116 cells and potentially some CRCs could be a consequence of miR-21 signals or could be up-stream of miR-21 gene activation or perhaps an unrelated and indirect consequence. Further studies are clearly needed to delineate the complexities of SPRY2 epigenetic regulation in CRC, especially in regard to the potential role of 5mC and 5hmC modifications of enhancer–promoter activity in SPRY2 expression. Further, in-depth analysis of SPRY2’s many enhancer interactions and its effects on SPRY2 gene expression is therefore warranted and are currently a focus in our laboratory.In agreement with Feng et al., we found no evidence of 5mC or differential methylation within is study . However resides . This liSPRY2 KO mice model and found that in response to acute colitis, deletion/suppression of SPRY2 played a protective role in maintaining the integrity of the colon epithelium against inflammation. This signaling mechanism may therefore be lost in CRC patients with constitutively expressed SPRY2, which may in part be regulated by promoter hypomethylation and increased 5hmC as discussed in our study. Furthermore, the changes in SPRY2 5mC and 5hmC in adenocarcinomas observed in CRC patients from this study may have clinical implications as it pertains to identifying potential adenocarcinomas that may originate from precancerous adenomas and therefore warrants further studies. In regard to previously established DNA methylation-based assays, the SEPT9 gene promoter methylation assay was the first FDA-approved blood assay for CRC screening and early detection, displaying both high sensitivity and specificity [The clinical relevance of SPRY2 research in CRC patients has just started appearing. A recent study identified a differential mRNA expression panel consisting of nine genes, in which SPRY2 was upregulated in patients with advanced rectal cancer who did not respond to preoperative chemoradiotherapy (PCRT) . The autcificity .SPRY2 in CRC.In summary, apart from malignant gliomas, CRC is the second example where SPRY2 expression is increased and is responsible for augmenting cancer phenotype. Identified, for the first time, SPRY2 transcript and protein upregulation is in concordance with promoter hypomethylation and increased 5hmC in colon cancers. In the context of diagnostic strategies, this might help detect precancerous colonocytes by identifying altered SPRY2 expression or 5mC methylation patterns in region #1 and or region #4 of


GAL promoter system for engineered yeast (Saccharomyces cerevisiae) suffers from several drawbacks; specifically, unintended induction during laboratory development, and unintended repression in industrial production applications, which decreases overall production capacity. Eukaryotic synthetic circuits have not been well examined to address these problems. Here, we explore a modularised engineering method to deploy new genetic circuits applicable for expanding the control of GAL promoter-driven heterologous pathways in S. cerevisiae. Trans- and cis- modules, including eukaryotic trans-activating-and-repressing mechanisms, were characterised to provide new and better tools for circuit design. A eukaryote-like tetracycline-mediated circuit that delivers stringent repression was engineered to minimise metabolic burden during strain development and maintenance. This was combined with a novel 37 °C induction circuit to relief glucose-mediated repression on the GAL promoter during the bioprocess. This delivered a 44% increase in production of the terpenoid nerolidol, to 2.54 g L−1 in flask cultivation. These negative/positive transcriptional regulatory circuits expand global strategies of metabolic control to facilitate laboratory maintenance and for industry applications.Temporal control of heterologous pathway expression is critical to achieve optimal efficiency in microbial metabolic engineering. The broadly-used  trans-regulatory mechanisms are exploited to synthesize a stringent tetracycline-repressible circuit and a heat-inducible circuit for negative/positive metabolic controls using the GAL system in Saccharomyces cerevisiae.Eukaryotic  This delays laboratory strain development via the design-build-test-learn cycle and impedes industrial processes. It is critical to have robust control mechanisms that provide tight repression when optimal growth is required as well as boosted induction when maximal production is needed6. Importantly, these tools should not increase the cost for industrial processes.Metabolic burden from expression of heterologous pathways may dramatically inhibit normal cell proliferation in microbial cell factoriesSaccharomyces cerevisiae is a model Eukaryotic organism for understanding biological principles and a primary chassis organism used in manufacturing a variety of bio-products8. The endogenous yeast galactose-inducible (GAL) expression system has been engineered to respond to glucose starvation for automatic induction of heterologous pathway10. In the presence of glucose, the glucose-dependent repressor Mig1p represses expression of alternative carbon source catabolic genes . When the GAL repressor gene GAL80 is disrupted, GAL promoters are automatically induced upon glucose depletion in batch cultivation11. The low expression from GAL promoters in exponential growth phase on glucose prevents metabolic burden from causing growth inhibition. This engineered gal80Δ GAL induction circuit is now broadly used in metabolic engineering, as exemplified by high level production of terpenoids12 and flavanones14. However, the system has limitations: (i) unintended auto-induction during routine strain maintenance and development is problematic when induced pathways result in metabolic burden or cellular toxicity; (ii) in prolonged pulse-feeding high-cell-density glucose processes, Mig1-mediated repression on GAL promoters may disrupt expression patterns; (iii) an increase in promoter strength is highly desirable to improve expression of bottleneck enzymes. The current work aims to address these limitations by expanding control range of GAL promoters for better application in metabolic engineering.The yeast 17. Many synthetic transcriptional circuits have been reported for S. cerevisiae20. They include VP16 trans-activating sequence-mediated activation circuits21 and prokaryote-like bacterial repressor-mediated circuits15. A variety sensing mechanisms have been used to trigger the OFF/ON states of these circuits, i.e., small molecules22, light25, and cold -shock26. However, each circuit has specific down sides, which may make them non-ideal in application. For example, adding expansive small molecules is not economical for large-scale cultivation. In addition, cold-shock induction may not be possible in tropical regions, including Mackay, Queensland, one of Australia’s major cane sugar feedstock processing regions. Current prokaryote-like circuits require high-level expression of bacterial repressors and intensive optimisation of promoters to achieve ideal on/off response ratio27, which do not reach to the efficiency of natural eukaryotic regulatory mechanisms28.One of the applications of synthetic biology is in development of artificial regulatory circuits for application in metabolic engineering34. Consequently, significant work has been devoted to development of robust, predictable genetic componentry in yeast36. Comprehensive characterisation of individual modular components in different growth states is required to improve design parameterisation for these circuits. In this study, we employed a modularised engineering strategy for synthesis of synthetic regulatory circuits to expand control mechanisms on GAL promoters for metabolic engineering applications. We investigated eukaryote-like mechanisms to deploy trans-activation modules and trans-repression modules. We integrated new control mechanisms to render non-natural regulatory properties to the GAL expression system, including using tetracycline to trigger repression on GAL promoters and elevated temperature to trigger de-repression of GAL promoters. We also investigated possibility of applying artificial trans-activators to enhance GAL promoter strength. The expanded GAL system was then validated for repression or upregulation of heterologous sesquiterpene production in yeast.The complexity and relative unpredictability of biological systems still hinders rational design of genetic circuitry37. However, in natural eukaryotic systems, TFs are generally not expressed at high levels38. We wanted to explore whether we could achieve a better outcome by exploiting native TF promoters to mimic natural expression systems. We therefore characterised fourteen TF and regulatory protein promoters.A strong promoter, commonly used in overexpression of metabolic enzymes or primary cellular constitutive proteins, is often used to control the expression of artificial transcription factors (TFs) in synthetic circuitsvia single-copy genome integration  expression cassette under the control of promoter of interest ion Fig. . FluoresTEF1 promoter to identify the best one to use for promoter comparison . Insertion of a PGK1 terminator improved yEGFP fluorescence by >5-fold , TOR1 (encoding a primary protein kinase in Target Of Rapamycin regulatory network), SNF1 (encoding a primary protein kinase in glucose repression regulatory networks), NRG1 (encoding a repressor mediating glucose repression), ROX1 (encoding a repressor in oxygen regulation), HAP4 , and UPC2 (encoding a ergosterol-sensing activator). This shows that many primary components in natural regulatory networks are expressed at the levels much lower than the levels achievable from strong constitutive promoters.Compared to the ter Fig. . The proYPK2 (encoding a protein kinase required for cell growth), ADR1 , and HAC1  showed a moderate strength, around 10-fold lower than the TEF1 promoter. The promoter of DAL80 (encoding a negative repressor responsive to nitrogen levels) showed induced activities on ethanol  binds to transcriptional co-regulator proteins to refine and determine the magnitude of the response. Ottoz and colleagues19 used a chimeric construct approach to characterise four TADs in yeast: VP16  from the alphaherpesviruses gene UL48 promoter), B112, B42, and the Gal4 activation domain. VP16A showed the strongest activation capacity19. To expand the pool of available elements, we characterised two more TAD modules, a ten-WD-repeat sequence (10*WD)40 and a Gcn4p trans-activating domain . Mediators are evolutionarily conserved and form the coactivation complex generally required for eukaryotic RNA polymerase II-dependent transcription41. Recruiting mediators to upstream activation sequences in promoters may allow transcription, even under inhibitory conditions42. To better understanding transactivation effects of mediators, we included the tail mediator 3 (Med3)42 and the tail mediator 15 (Med15)42 in the comparison.The 43, and TAD (or mediator). VP16A was included as a reference. We selected the HAC1 promoter, which delivers moderate and stable expression during both the exponential and ethanol growth phases  DNA binding domain (Zif268), a linker harbouring a SV40 nuclear localisation sequence (NLS)ses Fig. , to contses Fig. . This prcis-regulatory reporter cassettes were constructed using yEGFP as the reporter gene and introduced into yeast via genome integration at the URA3 locus  including four Zif268 binding elements . Reference promoters were constructed by inserting the synthetic sequence with Z268 elements removed (PCYC1(−) and PTEF1(−)).Synthetic cus Fig. . For a bcis-regulatory reporter cassettes and the trans-activator-expressing cassettes were sequentially transformed into the CEN.PK2-1C strain. The yEGFP fluorescence from cells in exponential and in ethanol growth phases was measured.The CYC1 core promoter, PCYC1(−), showed a very low basal activity  increased basal expression mildly in the absence of an artificial TF. A variety of responses were observed in the presence of synthetic TFs with different TADs. Gcn4A showed insignificant activation effects in the exponential growth phase, and a weak effect in the ethanol growth phase. VP16A, 10*WD, Med3, and Med15 showed >3-fold activation in the cells at the exponential phase. Med3 and Med15 drove significantly higher expression on ethanol than VP16A.The ity Fig. . InsertiTEF1 promoter to provide a landing pad for additional cis-acting elements ; Fig. PTEF1+4×[Z268] promoter responses positively correlated with the effects observed in the PCYC1+4×[Z268] promoter  and S. kudriavzevii (Sk.GAL2)9. As shown above, promoter hybrids with cis-regulatory elements can be boosted by synthetic trans-activators  and one TALE domain, TALPZ4 (recognising ‘TA GAG TGA GAC GTT’) based on the dHax3 scaffold47. These DNA-binding domains were separately cloned into the N-terminal of the Med3 or Med15 modules to generate synthetic trans-activators. Only ZifPZ42 and ZifPZ43 , with the ZifPZ43-containing trans-activators being stronger. However, by observing multi-well plate cultures, we saw that the strains expressing ZifPZ43-Med3 and ZifPZ43-Med15 grew slower compared to the reference. Strains expressing ZifPZ42-Med15 and ZifPZ43-Med15 also exhibited a non-homogeneous population  was used as a reference. Expression of a yEGFP reporter cassette was examined in the presence and absence of a synthetic trans-activator using a gal80Δ background strain in early exponential phase on glucose or ethanol . This may indicate that there is a non-specific trans-interaction between Zif268 and the sequences inserted into Sk.GAL2 4×PZ4 promoter and Sk.GAL2 5×[PZ4] promoter. The Sk.GAL2 5×[PZ4] promoter in the presence of ZifPZ43-Med3 showed ~3-fold stronger expression output, in comparison with the commonly-used TEF1 promoter with the same yEGFP-reporter construct  with Zif268-Med3 and Zif268-Med15 grew slower compared to the reference, and the Zif268-Med15-expressing strain did not grow on ethanol. Such effects were not seen in the TEF1/CYC1 + [Z268] promoter-testing strains 53, Tup154, Cyc854, and Mig1 C-terminal domain 55. Sin3C mediates repression through its interactions with histone deacetylase complexes53, Tup1/Cyc8 through recruiting histone deacetylase complexes and masking binding of co-activators54, and Mig1C is thought to recruit the Tup1/Cyc8 complex55. Sin3C, Tup1, and Cyc8 have been previously tested in various studies by fusing them to a DNA-binding domain, but Mig1C has not54.We characterised the NLS with the C-terminal Sin3C, Tup1, or Mig1C. The HAC1 promoter was used to control the fusion expression. yEGFP controlled by PTEF1+4×[Z268] was used as the reporter  was observed  grew normally on plates and showed strong repression on the TEF1 + 4×[TetO] promoter in the absence of tetracycline. TetR-Tup1 was most efficient, showing >100-fold repression and eliminating yEGFP fluorescence in > 90% cell population  would therefore make a convenient tool for regulatory control and may make the bioprocess more economical. To exploit this potential, we designed a heat-inducible circuit to modulate expression from GAL promoters in strains growing on glucose. Our model employs a heat-inducible degron59 to regulate the stability of the glucose-dependent repressor Mig1p  that is cleaved off by ubiquitin C-terminal hydrolase after translation, an ‘N-end rule’ residue Arg, a linker ‘HGSGTMV’, and mouse dihydrofolate reductase P66L temperature sensitive mutant (DHFR*). The heat-inducible degron Ubi4-RHGSGTMV-DHFR* was fused to the N-terminus of yEGFP, and the fusion (UBI4-RHGSGTMV-DHFR*-yEGFP) was expressed under the control of TEF1 promoter and the PGK1 terminator tor Fig. . yEGFP w °C Fig. , indicatTagging Ubi4-RHGSGTMV-DHFR* decreased fluorescence at 30 °C by 4.5-fold compared to the control yEGFP Fig. , suggestGAL promoters, it will be important to overcome these two limitations, otherwise a sharp temperature-induced boundary effect cannot be achieved.In summary, the basal degradation (or impaired expression) at 30 °C caused by Ubi4-RHGSGTMV-DHFR* (H.Degron) is significant; moreover, heat-inducible degradation by this degron at 37 °C, although dramatically decreasing yEGFP abundance, did not result in a full depletion of target yEGFP Fig. . IncomplGAL promoters, we used a gal80Δ strain expressing yEGFP under the control of the GAL1 promoter and the URA3 terminator. In this strain, we modified the MIG1 locus by fusing the H.Degron to the N-terminus of Mig1p and introducing an alternative promoter to control the expression strength. We tested three promoters to provide variable expression strength with the aim of titrating the transcription of H.Degron-MIG1 to obtain a sharp boundary effect: the TEF2 promoter (strong)39, the HAC1 promoter , in which the synthetic genetic modules for tetracycline-mediated repression and 37 °C-mediated de-repression of GAL promoters were also established  and Actinidia chinensis (kiwifruit) nerolidol synthase (AcNES1) were expressed through a 2A sequence-mediated bicistronic expression mechanism (YFAST-2A-AcNES1)17. The YFAST-2A-AcNES1 was under the control of the GAL2 promoter. This construct not only contributed to an improved nerolidol production, but also allowed us to monitor the GAL2 promoter activities by measuring YFAST fluorescence throughout the whole cultivation as a proxy for GAL2 promoter-driven expression. We evaluated the effects of tetracycline and elevated temperature on nerolidol production and the GAL promoter activities in this strain via two-phase dodecane-overlayed flask cultivation.Previously, we developed yeast strains for high-level sesquiterpene production through metabolic engineeringhed Fig. . In thismax) of 0.3 h−1 and a maximum cell density at OD600 of ~14 at 72 hr. N9R5HIRBU produced ~1.76 g L−1 nerolidol at 72 hr 17. These data show that the ‘OFF’ state of tetracycline-mediated repression on GAL promoters is tight, producing a gal80Δ-like genotype and phenotype.In flask cultivation at 30 °C and the absence of tetracycline, strain N9R5HIRBU  threshold effects exist for trans-activating module activity in engineered promoters. In support of this, while there was generally a positive correlation between the number of cis-acting elements inserted in engineered promoters, additional trans-activation modules did not increase the maximal activities of the Sk.GAL2 promoter, which can already be activated to very high levels  in the gal80Δ background. Similarly, Zif268-Mig1C, but not Zif268-Tup1, could be transformed into yeast, although Mig1C recruits the Tup1-Cyc8 repression complex55. These results suggest that adding a layer of protein-protein interaction may provide an equilibrium to the system, resulting in attenuation of off-target toxicity. On the other hand, such a multi-layer regulatory mechanism may contribute to the specificity of transcription factor regulation67.Moreover, some synthetic TFs like Zif268-Tup1 and Zif268-Sin3trans-repressing modules, similarly to the approach used for the trans-activating modules (cis-element engineering on promoters with concurrent expression of trans-repressing transcription factors). This system was established using the TEF1 promoter; the established module was then applied to control the GAL80 repressor, providing a further level of control to the ‘on’ and ‘off’ switch and decrease leakiness of the GAL promoter.Adding to the component toolbox, we went on to establish repression circuits based on eukaryotic GAL promoters and the circuit for 37 °C-mediated induction of GAL promoters. Improving on previous tetracycline regulatory circuits33, the TetR-derivative circuit developed in this study fully represses the TEF1 + [TetO] promoter in absence of tetracycline  was used to prepare the media with ammonium as the nitrogen source. When urea was used as the nitrogen source, 1.7 g L−1 yeast nitrogen base without amino acids and without ammonium sulphate (Sigma Aldrich) and 1 g L−1 urea were used to prepare the base media, and sterilised by filtration. Amino acids  were supplemented in YNB media to grow auxotrophic strains. For the YNB media without additional buffer, pH was adjusted to 6.0 using sodium hydroxide solution; for the YNB media with 2-(N-morpholino)ethanesulfonic acid (MES) buffer, 19.5 g L−1 MES was used, and pH was adjusted to 6.0 with ammonia hydroxide solution. Glucose (20 g L−1) or ethanol (2% v/v) was used as the carbon source.Yeast nitrogen base without amino acids  was inoculated into 100 μl MES-buffered YNB-glucose medium to prepare Culture 1. Culture 1 (2 μl) was inoculated into 100 μl MES-buffered YNB-glucose medium to prepare Culture 2. Culture 2 was incubated in a 350 rpm 30 °C incubator overnight for analysis of yEGFP fluorescent in the cells grown to the exponential growth phase, and Culture 1 for ~36 h for analysis in the cells grown to the ethanol growth phase. Seed culture (2 μl) was inoculated into 100 μl YNB-ethanol medium, and the culture was incubated in a 350 rpm 30 °C incubator for ~48 h for analysis in the cells grown grow on ethanol. For the growth in 10 ml test tubes, yeast cells were inoculated into 500 μl MES-buffered YNB-glucose medium and grown overnight in a 200 rpm 30 °C or 37 °C incubator for analysis of yEGFP fluorescent in the cells grown to the exponential growth phase. Stock tetracycline solutions (125 mM) was prepared in DMSO-ethanol (1:1) solution and supplemented to prepare the medium with 125 μM or 250 μM tetracycline, and 25 mM stock solution for the medium with 50 μM tetracycline, and 2.5 mM stock solution for the medium with 5 μM tetracycline.600 between 1 to 4) and collected by centrifugation. Collected cells were then resuspended in fresh fermentation medium. To initiate the cultivation, appropriate volumes of pre-cultured cells were transferred to MES-buffered YNB medium with 20 g L−1 glucose to an initial OD600 of 0.2 in a total volume of 23 mL medium in a 250 mL flask, and 2 mL sterile dodecane was added after inoculation. Seed culture and test culture were grown in a 200 rpm 30 °C or 37 °C incubator. In the first 12 h of cultivation, 3 ml culture was sampled for growth curve measurement. Dodecane was sampled and stored at −80 °C for terpene analysis.For characterisation of nerolidol-producing strains, dodecane-overlayed two-phase flask cultivation was used. Yeast cells from glycerol stocks were streaked on YNB -glucose agar containing 125 μM tetracycline. Before initiating the two-phase flask cultivation, cells were pre-cultured in MES-buffered YNB-glucose to exponential phase , with 10,000 events recorded per sample. Mean values of FSC.A, SSC.A, and FL1.A for all detected events were extracted using a BD Csampler software (BD Accuri C6 software version 1.0.264.21). GFP or YFAST fluorescence level was expressed as the percentage of the average background auto-fluorescence from the exponential-phase cells of GFP-negative reference strain GH4 as described previously39.Fluorescence in single cells was analysed using a BD Accuri™ C6 flow cytometer 62. Dodecane samples  were diluted in 40-fold volume of ethanol. The ethanol-diluted samples (20 µL) were injected. A Zorbax Extend C18 column  equipped with a guard column  was used. Analytes were eluted at 35 °C at 0.9 mL/min using the mixture of solvent A (water) and solvent B , with a linear gradient of 5–100% solvent B from 0–24 min, then 100% from 24–30 min, and finally 5% from 30.1–35 min. Analytes of interest were monitored using a diode array detector  at 202 nm wavelength.The Metabolomics Australia Queensland Node analysed extracellular metabolites. Nerolidol in dodecane samples were analysed as previously describedt-test was used for comparing two groups. Data generated from two-to-four biological replicates are presented. Construction-in-processing strains and strain construction procedures are not replicated.Two-tailed Welch’s Further information on research design is available in the Transparent Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary