Adaptation of the functional proteome is essential to counter pathogens during infection, yet precisely timed degradation of these response proteins after pathogen clearance is likewise key to preventing autoimmunity. Interferon regulatory factor 1 (IRF1) plays an essential role as a transcription factor in driving the expression of immune response genes during infection. The striking difference in functional output with other IRFs is that IRF1 also drives the expression of various cell cycle inhibiting factors, making it an important tumor suppressor. Thus, it is critical to regulate the abundance of IRF1 to achieve a ‘Goldilocks’ zone in which there is sufficient IRF1 to prevent tumorigenesis, yet not too much which could drive excessive immune activation. Using genetic screening, we identified the E3 ligase receptor speckle type BTB/POZ protein (SPOP) to mediate IRF1 proteasomal turnover in human and mouse cells. We identified S/T-rich degrons in IRF1 required for its SPOP MATH domain-dependent turnover. In the absence of SPOP, elevated IRF1 protein levels functionally increased IRF1-dependent cellular responses, underpinning the biological significance of SPOP in curtailing IRF1 protein abundance. Adaptation of the functional proteome is essential to counter pathogens during infection, yet precisely timed degradation of these response proteins after pathogen clearance is likewise key to preventing autoimmunity and hyperinflammation .Recognition of pathogen-associated molecular patterns and subsequent secretion of cytokines known as interferons (IFNs) are particularly critical for conferring an anti-pathogen response . Type I Interferon regulatory factors (IRFs) play essential roles as transcription factors in the IFN system. Some IRFs, such as IRF3, are constitutively expressed. Their activity is predominantly regulated through phosphorylation, and they are essential for induction of type I IFNs . In contIRF1 is a key family member for the induction of the innate immune response . The impIn addition to its transcriptional regulation, the other main means of IRF1 regulation is at a protein level through rapid degradation by the proteasome , which sFBXW7) has been reported as a key factor in cMYC degradative ubiquitination in many cell types , IRF1 transactivation IRF-association domain (IAD2), and a C-terminal intrinsically disordered enhancer region (IDR). This C-terminal IDR is an important determinant of IRF1 turnover . On the In addition, the N-terminal DNA binding domain has been reported to determine IRF1 stability . ConsistFBXW7 E3 ligase complex, which drives its degradation.Lastly, the IAD2 domain has been implicated in driving IRF1 ubiquitination and degradation . In partSPOP increased transcriptional output of IRF1-stimulated genes.In this study, we set out to identify novel factors that control IRF1 abundance in an unbiased manner. Genome-wide genetic screens were performed to identify novel IRF1-specific E3 ligases. This identified the Cullin ubiquitin E3 ligase receptor SPOP to functionally mediate IRF1 degradation. Follow-up analyses identified four degrons in, or near the IRF1 IAD2 enhancer domain responsible for SPOP binding, and subsequent IRF1 ubiquitination and degradation. Consequently, loss of The IRF1 protein is ubiquitinated and turned over by the proteasome. However, the cellular E3 ligases contributing to targeting IRF1 for degradation have remained poorly understood. The overarching goal of this study was to identify and characterize novel regulators of IRF1 protein abundance. For the identification of IRF1 stability regulators, we characterized IRF1 degradation properties in the human RKO colon carcinoma cell line, as it is highly amenable for genetic screening.IRF1 is transcriptionally induced upon type I or type II IFN stimulation in a wide variety of cells. Likewise, stimulation of RKO cells with IFNγ strongly induced endogenous IRF1 protein levels . In the Endogenous IRF1 protein levels are influenced by transcriptional, translational, and post-translational effects . TherefoWe reasoned that it would be important to screen in parallel with an unrelated protein that is degraded in a proteasome-dependent manner, in order to distinguish between general players in the ubiquitin proteasome system and factors specifically regulating IRF1 abundance. cMYC is constantly turned over by the proteasome with a 20–30 min half-life . TherefoTo investigate whether the mCherry-tagged IRF1 reporter had comparable degradation characteristics as its endogenous counterpart, cells were subjected to a CHX chase to determine protein stability. In line with endogenous IRF1, mCherry-IRF1 levels rapidly diminished within 4 hr . IRF1 deNext, we set out to determine whether our fluorescent reporters were compatible with fluorescence-activated cell sorting (FACS)-based genetic screening. In particular, we assessed by flow cytometry whether inhibition of protein synthesis or proteasomal degradation would specifically affect the mCherry-tagged unstable proteins, without affecting their stable BFP internal controls. Consistent with our findings by western blot readout, flow cytometry analysis also showed that both mCherry-IRF1 and mCherry-cMYC were degraded in the absence of de novo protein synthesis , while co-expressing iRFP. Subsequently, the ratio between these transduced cells and non-transduced cells in the same cell pool were measured over time by flow cytometry as a proxy for cell fitness.Lastly, we assessed whether our selected monoclonal mCherry-IRF1 and mCherry-cMYC reporter cell lines had retained the ability to drive genome editing. To this end, cells were transduced with a lentiviral vector constitutively expressing an sgRNA targeting the cell-essential factor ribonucleotide reductase catalytic M1  to ensure the targeting of one gene per cell , followeThe two harvesting time points were previously empirically determined to be optimal for capturing essential genes (day 3), and genes that encode proteins whose levels require longer times to deplete (day 6) . Integrahigh cells  substantially increased mCherry-cMYC levels  likewise increased endogenous IRF1 protein levels . Subsequently, wild-type IRF1 or IRF1-SBDmut were expressed in the presence of increasing amounts of a wild-type SPOP expression plasmid  were mutated to A residues  (SPOP interaction with S/T degrons has been previously mapped to its MATH domain . In linemut) . Consistmut) .mut). Upon mutation of the SPOP binding sequences, IRF1 ubiquitination was significantly decreased . IRF1 is in particular important for transcriptional induction of a subset of ISGs, such as members of the guanylate binding protein (GBP) family in response to IFNs . By querut cells .SPOP ablation did not significantly change the mRNA concentration of IRF1 independently transcribed NLRC5  luciferase reporter  reduced fitness by 15%, whereas this was further reduced by nearly twofold in mCherry-IRF1-expressing cells, relative to their respective sgAAVS1 controls  , making ed cells . Over thcontrols . From thSPOP loss on the cellular antiviral state. Pre-treatment of both sgAAVS1 and sgSPOP cells with IFNγ reduced vesicular stomatitis virus (VSV) progeny virus production by approximately fivefold in a multi-cycle infection experiment  and the SPOP-MATHmut binding mutant  were cloned into a modified pLX303 vector. The plasmids and sgRNAs used in this study are listed in The lentiviral human genome-wide sgRNA library consists of six sgRNAs per gene for a whole coding human genome and was described elsewhere . LentiviAll experiments in this study have been reproduced at least twice in independent experiments. RKO-mCherry-IRF1-P2A-eBFP2 or RKO-mCherry-cMYC-P2A-eBFP2 cells were generated by transducing RKO-Dox-Cas9 cells  with SFF5 HEK-293T cells were seeded in six-well clusters in fully supplemented DMEM media. Cells were harvested 36 hr after transfection, washed with ice-cold PBS, and stored at −80°C until further processing. For dual luciferase assays, HEK-293T cells in M24 well clusters were transfected with 170 ng ISRE-Fluc reporter, 30 ng pRL-TK , 320 ng of wtIRF1 or 160 ng of IRF1-SBDmut expression plasmid, and 640 ng of wtSPOP or SPOP-MATHmut expression plasmid. Twenty-four hr after transfection, cells were lysed with Passive Lysis Buffer  and Firefly- and Renilla luciferase activities measured in a dual-luciferase assay using a Synergy H1 plate reader (BioTek). Substrates were prepared as previously reported  in a 1:3 ratio (wt/wt) in DMEM  without supplements. Transfection was performed using 500 ng of total DNA. The day before transfection, 1.5×10reported .6 (mCherrylow and BFPlow) and 2×106 (mCherryhigh and BFPhigh) cells were collected for each time point. Sorted samples were re-analyzed for purity, pelleted and stored at −80°C until further processing. The gating strategy for flow cytometric cell sorting is shown in Lentivirus-like particles were used to transduce RKO-mCherry-IRF1-P2A-eBFP2 or RKO-mCherry-cMYC-P2A-eBFP2 cells at an MOI of less than 0.2 TU/cell, and 500- to 1000-fold library representation. The percentage of library-positive cells was determined after 3 days of transduction by immunostaining of the Thy1.1 surface marker and subsequent flow cytometric analysis. RKO cells with integrated lentiviral vectors were selected with G418  for 7 days. After G418 selection, 120 million cells of unsorted reference sample corresponding to 1000-fold library representation were collected, and stored at −80°C until further processing. Cas9 genome editing was induced with Dox  and after 3 days and 6 days, cells were sorted by FACS. Cells were harvested, washed with PBS, and stained with Fixable Viability Dye eFluor  for 30 min. Subsequently, cells were washed three times with PBS and sorted in supplemented RPMI-1640 using the FACSAria III cell sorter operated by BD FACSDiva software (v8.0). RKO cells were gated for non-debris, singlets, negative for Viability Dye, GFP-positive, mCherry-positive, and BFP-positive. Two to 3% of cells with the lowest and 1–2% of cells with the highest mCherry or BFP signals were sorted into PBS. For both mCherry-IRF1 and mCherry-cMYC genome-wide screens, at least 1×10To estimate endogenous IRF1 and cMYC as well mCherry-IRF1 and mCherry-cMYC protein half-lives, RKO mCherry-IRF1 or mCherry-cMYC-expressing cells were treated with 30 μg/ml of CHX . At indicated time points, total protein extracts were generated using RIPA buffer, analyzed by western blot, quantified, and normalized to ACTIN levels and to time point 0 as indicated. Single exponential decay curves were plotted using GraphPad Prism (v9), from which protein half-lives were calculated.200,000 HeLa cells/35 mm well were seeded onto coverslips  and transfected using polyethylenimine (PEI) transfection reagent  in a 1:6 (wt/wt) DNA/PEI ratio in non-supplemented DMEM with lentiviral plasmids expressing MYC-tagged wtIRF1 and FLAG-tagged wtSPOP. 48 hr after transfection, cells were fixed with 4% paraformaldehyde (PFA) for 15 min. Similarly, 250,000 RKO cells stably expressing HA-tagged wtIRF1 and FLAG-tagged wtSPOP were seeded onto coverslips and after 48 hr stimulated with epoxomicin  followed by fixation. Cells were permeabilized with 0.25% Triton X-100 in PBS for 5 min, followed by blocking of non-specific sites by incubation with 1% BSA for 30 min at RT. Coverslips were incubated for 1 hr at RT with primary anti-IRF1 antibody , anti-HA , or anti-FLAG  antibody in 1% BSA, followed by incubation with anti-rabbit IgG Alexa Fluor 488 , anti-mouse Alexa Fluor 647 , or anti-mouse IgG DyLight 550  secondary antibodies, and incubation for 5 min with 0.4X Hoechst  in PBS. The coverslips were mounted using ProLong Gold Antifade Mountant . Images were collected using a Zeiss LSM 980 confocal microscope at ×40 magnification.g for 60 s at 4°C. 240 µl of supernatants were collected as cytosolic fractions, after which pellets were washed with 500 µl of REAP buffer by centrifugation at 3000 × g for 60 s at 4°C, then resuspended in 240 µl of REAP buffer (nuclear fraction). All fractions were subsequently supplemented with 6x Laemmli sample buffer , and boiled for 10 min. Equal volumes of fractions were loaded on a 10% SDS polyacrylamide gel.Subcellular fractionation was performed as previously described . In brieg for 10 min at 4°C. Supernatants were transferred to new tubes and protein concentrations were determined by Pierce BCA Protein Assay Kit . Between 20 and 50 μg of protein per sample was mixed with Laemmli sample buffer , and boiled for 5 min. Proteins were loaded on SDS polyacrylamide gels, the percentage of which was adjusted based on the molecular weight of the proteins of interest. Proteins were blotted on PVDF or nitrocellulose membranes at 4°C for 1 hr at 300 mA in Towbin buffer . Membranes were blocked in 5% BSA in PBS-T for 1 hr at RT, and subsequently incubated with primary antibodies overnight at 4°C. The next day, the membranes were washed three times for 5 min each with PBS-T and incubated with HRP-coupled secondary antibodies for 1 hr at RT and imaged with the ChemiDoc Imaging System from Bio-Rad. Relative protein levels were quantified using Image Lab (Bio-Rad). Antibodies used in this study are listed in Cells were lysed in RIPA lysis buffer . Cells were rotated for 20 min at 4°C and then centrifuged at 18,500 × 4P2O7, 50 mM NaF, 2 mM EDTA, 1% Triton X-100, 1 mM DTT, 1 mM PMSF, and 1X protease inhibitor cocktail). Cells were incubated on a rotating wheel at 4°C for 30 min and subsequently centrifuged at 20,000 × g at 4°C for 30 min. The supernatant was transferred to a new tube and 10 μl (10% of the lysate used for the immunoprecipitations [IPs]) was collected as input. 300 μg of lysates were incubated overnight at 4°C on a rotating wheel with an IgG Isotype Control , anti-Ollas , or anti-IRF1 antibody . The next day, magnetic beads  used for anti-IRF1 antibody IPs, or Protein G Sepharose beads  used for anti-Ollas antibody IPs, were blocked by rotation in 3% BSA in Frackelton Buffer for 1 hr at 4°C. 25 μl of beads were added to 300 μg of lysates and rotated for 2 hr at 4°C. Then, the beads were washed five times with 1 ml of RIPA buffer, supplemented with 300 mM NaCl. Proteins were eluted by boiling in 2X disruption buffer  for 5 min at 95°C.Cells were lysed in 100 μl of Frackelton lysis buffer  were collected as input. 500 μg of lysates were incubated overnight at 4°C on a rotating wheel with an anti-IRF1 antibody . The next day, magnetic beads  were blocked by rotation in 3% BSA in RIPA Buffer for 1 hr at 4°C. 25 μl of beads were added to 500 μg of lysates and rotated for 2 hr at 4°C. Subsequently, beads were washed five times with 1 ml of RIPA buffer, supplemented with 300 mM NaCl. Proteins were eluted by boiling in 2X disruption buffer  for 5 min at 95°C. Volumes loaded for the IP were adjusted based on the relative IRF1 amount in those samples, quantified by Image Lab.Cells were lysed in 1 ml of RIPA lysis buffer , supplemented with 40 mM HEK-293T cells were transfected with DNA mixes containing lentiviral transfer plasmids, pCRV1-Gag-Pol  and pHCMg. Subsequently, cells were incubated for 10 min at RT in FACS buffer to inhibit non-specific antibody binding. Cells were then incubated with primary antibodies, or left unstained for 1 hr at RT. Following two FACS buffer washes, cells were incubated with secondary antibodies for 15 min at 4°C., washed two times with FACS buffer and resuspended in FACS buffer for flow cytometric analysis on an LSRFortessa (BD Biosciences) operated by BD FACSDiva software (v8.0). FACS data were analyzed in FlowJo (v10.8).For staining of intracellular proteins, cells were collected, washed twice with PBS, and subsequently fixed with 2% PFA for 15 min at RT. After one wash with FACS buffer (PBS supplemented with 1% FCS), cells were permeabilized in ice-cold MeOH for 10 min and pelleted three times for 10 min at 900 × NGS libraries of sorted and unsorted control samples were processed as previously described . In brieThe analysis of the CRISPR-Cas9 screens was carried out as previously described . In brieAAVS1 or SPOP. The multiplicity of transduction was such that 30–60% of cells were iRFP-positive before the start of fitness measurements. Gene editing was induced with Dox and the percentage of iRFP-positive cells monitored for 20 days by flow cytometry at the indicated days. The relative fraction of sgSPOP-positive cells was normalized to sgAAVS1 of the same cell line on day 0. Data were analyzed by two-way ANOVA.Competitive cell fitness assays were performed as described previously . In brieSTAT1 and IRF3 sgRNA expression vectors were additionally transduced with vectors expressing either sgAAVS1 or sgSPOP. Subsequently, sgRNA-positive cells were collected by FACS. After Cas9 induction by Dox, cells were seeded in M24 clusters, and infected with VSV Indiana at an MOI of 0.0005 PFU/cell as previously described  and treated with Turbo DNase . cDNA was prepared using random hexamer primer and RevertAid Reverse Transcriptase . Real-time PCR experiments were run on a Mastercycler (Bio-Rad), using SYBR Green (Thermo Fisher Scientific). Primers for qPCR are listed in Total RNA was extracted from RKO-Dox-Cas9-P2A-GFP cells harboring either All data generated or analyzed during this study are included in the manuscript and supporting files. IRF1 is a key transcription factor with important roles during pathogenic infection and in some cancers. The regulation of IRF1 protein abundance is critical, but the underlying mechanisms are not known. Using a CRISPR-based knockout screen, the authors identify SPOP as an E3 ligase that binds IRF1 and enforces its degradation via the proteasome. The study represents an important advance backed by solid evidence that may have implications in immunology, virology, and cancer fields. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting the paper "SPOP targets the immune transcription factor IRF1 for proteasomal degradation" for consideration by Comments to the Authors:eLife. Should the authors wish to address reviewers' comments, we would be willing to consider a substantially revised manuscript. In particular, a revised study should include experiments to (1) provide more robust evidence of SPOP-mediated ubiquitination of IRF1, (2) provide a deeper understanding of the localization of SPOP-mediated regulation of IRF1 , and (3) assess the biological relevance of SPOP/IRF1 axis in other cells (per reviewers' suggestions below).We are sorry to say that, after consultation with the reviewers, we have decided that this work in its current form will not be considered for publication by Reviewer #1 (Recommendations for the authors):The authors sought to address an important question of how IRF1 is turned over, as aberrant IRF1 expression is linked to several immunological disorders. The authors devised a clever fluorescent protein-based IRF1 degradation assay and used this system to perform a well-controlled genome-wide CRISPR screen for genes that regulated IRF1 expression. They identified SPOP as a hit and then carried out a series of experiments  to validate SPOP as an E3 ligase that mediates the degradation of IRF1. In functional assays, they linked SPOP1-dependent IRF1 regulation to predicted changes in specific downstream IRF1 target genes, and they demonstrated modest effects of this pathway on cell fitness.StrengthsPowerful CRISPR screens to identify SPOP as a regulator of IRF1 protein abundance;Rigorous validation of hit, including binding assays, colocalization, and judicious use of both SPOP and IRF1 mutants as control.WeaknessesCurrent evidence for SPOP directly ubiquitinating IRF1 is incomplete and lacks complementing orthogonal assays;The significance of the SPOP/IRF1 axis in a biologically relevant model system  is lacking.1. The key/new mechanism here is that SPOP directly ubiquitinates IRF1 and enforces its degradation. There is only one Ub assay directly showing this, thus the strength of the evidence is incomplete. The authors need to provide additional data supporting this mechanism. Orthogonal assays (e.g. Ub assays in cells vs in vitro ubiquitination assays using recombinant protein) showing the same result are critical to pin this mechanism down. The use of SPOP mutants that cannot ubiquitinate IRF1 also needs to be included as controls.2. The broader significance of the paper is uncertain in the absence of a more compelling role for the SPOP/IRF1 axis in a biologically relevant setting. The authors claim in the discussion that viral assays would be difficult due to the overlap of IRF1 and ISGF3 target genes. This logic is confusing. There are ample examples in the literature of IRF1 expression conferring antiviral effects to cells, especially in cells that can't signal through IFN (STAT1-deficient). Co-expression of SPOP with IRF1 should reduce IRF1 levels and create a more permissive antiviral state. Alternatively, a more robust cancer-related model could help assign a clearer biological role for SPOP/IRF1.3. The authors should include a new discussion on how their work relates to a recent similar study (PMID: 36481790).Reviewer #2 (Recommendations for the authors):Vunjak and colleagues report the results of a genome-wide CRISPR screen designed to identify genes controlling the post-translational fate of IRF1. IRF1 was the first interferon regulatory factor to be identified, and its activity as a transcription factor has been associated with both successful immune control of intracellular viral and bacterial infections, as well as regulation of cell cycle progression and tumor suppression. Unlike IRFs such as IRF3, which are expressed constitutively and activated via phosphorylation, IRF1 activity is determined primarily by its protein abundance which is regulated transcriptionally, translationally (at the level of protein synthesis), and by the rate of protein degradation. A better understanding of these processes would be important for scientists with interests in innate immune host responses to pathogens as well as the role of IRF1 in protection against cancer.Vunjak et al. focused on host factors controlling IRF1 protein degradation in RKO cells, a poorly differentiated human colonic carcinoma cell line, after confirming that the abundance of IRF1 protein in these cells was stabilized by inhibition of the proteasome when cells were treated with cycloheximide to block new protein synthesis. The authors constructed a cell line with stable expression of an mCherry-IRF1 reporter protein and conditional expression of Cas9, which they then transduced with lentiviruses expressing a genome-wide guide RNA (sgRNA) library. They used flow cytometry to select cells with enhanced mCherry-ORF1 expression, and high throughput sequencing of the selected cells to identify SPOP as a leading candidate for regulation of IRF1 protein abundance, putatively controlling its degradation. The authors went on to show in a series of validation experiments that targeted depletion of SPOP (a 374aa adapter protein that is a component of a cullin-RING E3 ubiquitin ligase complex involved in ubiquitination of a number of other cellular proteins) enhances endogenous IRF1 protein abundance, as anticipated from the screen. They show SPOP forms a complex with IRF1 through interactions with one or more predicted SPOP-interaction domains ('degrons') in IRF1, and provide data suggestive of SPOP-regulated IRF1 ubiquitination.These experiments were generally well-controlled and carried out in a thoughtful manner, and collectively they provide solid evidence that SPOP regulates IRF1 abundance in RKO cells, most likely by mediating its ubiquitination and subsequent proteasome-dependent degradation. These results differ significantly from those of Tawaratsumida et al. (PMID 35857476), who reported earlier this year that the deletion of SPOP decreased, rather than increased, the abundance of IRF1 (and other IRFs) by increasing the activity of the Src kinase family member LYN and subsequent LYN-directed IRF degradation. However, they are consistent with a recent report by Gao et al. (PMID: 36481790), who also demonstrated that SPOP interacts with IRF1 and regulates IRF1 abundance via ubiquitination and proteasome-mediated degradation in endometrial carcinoma cells, thereby suppressing IRF1-mediated expression of the programmed cell death 1 ligand, PD-L1.While providing valuable insight, there are several important limitations to the Vunjak study. As in all such CRISPR screens, genes that are essential for cell proliferation/survival and also involved in IRF1 regulation might be missed due to the global effects of gene knockout. More specific to the authors' screen, however, the half-life of the mCherry-IRF1 reporter protein was approximately twice that of endogenous IRF1 in cycloheximide-treated RKO cells, suggesting that the reporter protein might not be subject to an additional, SPOP-independent degradation pathway acting only on the endogenous protein. Another major limitation of the study is that the fate of IRF1 was investigated only in a single cancer cell line. IRF1 is recognized to have important tumor suppressor activity, and the control of its expression could differ from the norm in cells that have undergone a neoplastic transformation. Thus, while both the Gao report shows SPOP regulates IRF1 abundance directly in other cancer cell types, it would be important to know whether it similarly controls IRF1 abundance in non-neoplastic stromal cells, or in immune cells in the presence or absence of IRF1 activation by pathogen invasion.Another uncertainty is the cellular compartment in which IRF1 degradation is regulated – is it regulated in the cytoplasm, or in the nucleus where most SPOP is expressed, or could it be regulated at the level of nuclear translocation? The latter possibility is particularly intriguing, as Vunjak's CRISPR screen identified multiple components of the nuclear pore complex in addition to SPOP. Also unknown is the impact of IRF1 phosphorylation on its degradation, either at the level of SPOP binding to IRF1, ubiquitination, or nuclear translocation of IRF1. While the authors note these questions, the manuscript leaves them unanswered, to be resolved hopefully in future studies.RKO is a cancer cell line – is IRF1 degradation in these cells representative of IRF1 degradation pathways in normal cells? For example, FBXW7 appears to be not important for IRF1 regulation in these cells but has been reported to be so in other cell types.Line 135-236 – The authors should consider that IRF1 abundance is regulated not only transcriptionally, but also translationally, as IRF1 synthesis is subject to inhibition by PKR independently of transcription .1/2 of the mCherry-IRF1 product is twice as long as that of endogenous IRF1 suggests the possibility that the endogenous degradation pathway may be only partly represented/active in the reporter cell line.Line 172-175 – The fact that the tLine 231 – (a) The authors state that SPOP was "The strongest candidate enriched at both three and six days after Cas9 induction" – however, SPOP is not shown in Figure S2E (the day 3 panel), and it is not the most significantly enriched in Figure 2D. (b) Were each of the 6 sgRNAs targeting SPOP enriched at day 3 and day 7?Line 241-244 – "FBXW7, MDM2, [and] STUB1/CHIP … are unlikely to play a significant role in IRF1 degradation" – or, could these proteins be required for cell survival? The authors should note the limitations of this CRISPR screen.Figure 3A – BFP labeling appears to be increased in the sgSPOP transduced cells. Was the change in MFI from sgAAVS1 significant?Figure S3E (and other bar graphs) – showing symbols for the individual values reflected in the means shown by the bars would be informative and enhance the presentation of the authors' data.Line 260-62 – Is the increase in IRF1 protein primarily cytoplasmic, or nuclear?Lines 298-99, 318 – The data do not show that "the four predicted degrons collectively are required for SPOP-dependent IRF1 degradation" since all four degrons were mutated. It could be that only one of the degrons is functional in the wild-type protein and that the loss of degradation requires mutation of only that specific degron. The authors recognize this limitation in the Discussion on lines 472-473, but ought to change the text in Results to more accurately reflect the data. The Abstract should also be modified to reflect this more accurate interpretation of the data.Figure 4E-H – Were the wt and mutated IRF1 expression products tagged with Myc? It is otherwise not clear why blotting was with antibody to Myc-IRF1 (minor detail).Figure 4J, I – How reproducible were these IRF1 ubiquitylation experiments? There are no error bars in Figure 4I suggesting that the experiment may have been done only once.The authors should cite the recent publication by Gao et al. (PMID: 36481790) in the Discussion, and compare the results of Gao et al., with those reported here.Reviewer #3 (Recommendations for the authors):In this study, Vunjak and colleagues show that the Speckle Type BTB/POZ Protein (SPOP) is responsible for the ubiquitination and turnover of Interferon Regulatory Factor 1 (IRF1). Using a CRISPR genetic screening approach, the authors identified several factors, including the E3 ligase substrate adaptor SPOP as regulators of IRF1 protein levels. The screening approach used good controls (such as Myc instead of IRF1) and showed significant results. Further, the authors identified potential SPOP-binding motifs in IRF1 and showed that they (or at least one of them) are important for the recognition and turnover via SPOP; they used flow cytometry, co-immunoprecipitation, western immunoblotting, and dual luciferase assays. Finally, to determine the response of IRF1 transcriptional activity to the presence and absence of SPOP, the authors performed a qPCR analysis of IRF1-driven transcripts, which revealed a significant increase in SPOP knockout cells.The manuscript is well written. The authors find another substrate of SPOP. One open question is how significant this E3 ligase is for the turnover of IRF1. The difference in ubiquitination in WT and SPOP-binding motif-devoid IRF1 is modest. The differences in transcriptional activity, however, are large. A set of additional experiments are necessary to understand these counterintuitive effects. The authors identify 4 SPOP-binding motifs in IRF1 and suggest that they work synergistically in the recognition of IRF1 by SPOP and its subsequent degradation. However, without testing the contribution of individual SPOP-binding motifs, it is impossible to say whether they all contribute to SPOP turnover.1. The current version of Figure 1E is weak in supporting the idea that IRF1 degradation is independent of autophagy/autolysosomal degradation. Additional controls including time-dependent CHX treatment  and -IFNγ in the presence of Bafilomycin will help to answer this question clearly.2. Is IRF1 ubiquitinated by SPOP also in the absence of inflammation or is this an inflammation-specific response?3. There are no error bars in some histogram plots . How many biological and technical replicates did the authors perform?4. In Figure 4D, it seems that both IRF1 and SPOP localize in the nucleus but the resolution of the microscopy image is not sufficient to clearly understand their subcellular distribution. SPOP is often localized to nuclear bodies such as nuclear speckles. Micrographs with better resolution and concentration on the nucleus are required.5. The decrease in the ubiquitylation level of IRF1 with mutated SB motifs compared to IRF1 WT is modest . SPOP seems to be responsible for the turnover of only a small fraction of IRF1. The authors found other E3 ligase hits in their genetic screen, e.g. HUWE1 . Have they considered testing whether there are other E3 ligases involved in the turnover of IRF1?6. In Figure 5A and 5B, to be able to unambivalently identify whether tested transcripts are only driven by the transcriptional activity of IRF1 but not by other IRF family members or other transcription factors when treated with IFNγ, a control condition like sgIRF1 cells are necessary.7. Experiments in Figure 5C and Figure 5D show a comparison of +/- WT SPOP expression. In Figure 5C, the ratio is about 10fold, but in Figure 5D around 100 fold. What results in this large deviation?8. The authors observe little to no protein turnover effect of SPOP on cMYC. However, it has been previously shown that SPOP recognizes cMYC as a ubiquitination substrate . Could the authors please discuss the potential reasons for this discrepancy?9. The authors mentioned a recent study  that shows that deletion of SPOP results in LYN-dependent (Src kinase family member LYN) degradation of IRF1, and inhibition of its transcriptional activity. The discussion (in lines 452-456) suggests that the differences may arise from the expression of TLRs and associated adapters exclusively in immune cells. However, colon/colorectal cancer cells including RKO cells are known to express most TLRs (e.g. PMID: 33244454). It thus seems that TLR expression is not the critical difference that causes the differing response. Could the authors please discuss what other biological reasons could potentially cause this different response?10. The statement "…the four predicted degrons collectively are required for SPOP-dependent IRF1 degradation." (lines 298-299) cannot be made without testing the response of the SPOP degrons individually, as well as in binary and ternary combinations. It is possible that only one of the motifs is responsible for the observed effect. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Comments to the Authors:We are sorry to say that, after consultation with the reviewers, we have decided that this work in its current form will not be considered for publication by eLife. Should the authors wish to address reviewers' comments, we would be willing to consider a substantially revised manuscript. In particular, a revised study should include experiments to1) Provide more robust evidence of SPOP-mediated ubiquitination of IRF1.New cell-based ubiquitination data were added as figures 4I and 4J. These data address IRF1 ubiquitination by SPOP, using IRF1 mutants that lack SPOP-binding domains, and SPOP mutants lacking an intact substrate recognition domain. These new data show robust wtIRF1 ubiquitination by wtSPOP, which is severely reduced if either the E3 or substrate were mutated in their interaction surfaces. These new data provide evidence for robust IRF1 poly-ubiquitination, supported by quantification of replicates and statistical analysis.2) Provide a deeper understanding of the localization of SPOP-mediated regulation of IRF1 , andNew data from immunofluorescence confocal microscopy and subcellular fractionation experiments were added to the revised manuscript as Figures 4A-B and Figure 4—figure supplement 1A-C. These data show that SPOP localizes in our cell models diffusely in the nucleoplasm and nuclear speckles, whereas IRF1 is exclusively diffusely nucleoplasmic. Additional data show that IRF1 only accumulated as a diffusely nucleoplasmic pool upon proteasome inhibition. In line with these findings, IRF1 and SPOP subcellular fractionation indicated that SPOP targets IRF1 in the nucleus, and that IRF1 is degraded in the nucleus. The fact that IRF1 was undetectable in the cytosol, indicates that IRF1 is likely rapidly imported into the nucleus upon synthesis, and that this is the predominant location where SPOP-dependent IRF1 degradation takes place. These results are in line with the findings from our genetic screen, which showed that knock-out of various nucleopore components resulted in increased cellular IRF1 concentrations .3) Assess the biological relevance of SPOP/IRF1 axis in other cells (per reviewers' suggestions below).New data in Figure 3G demonstrate SPOP-dependent IRF1 degradation in mouse embryonic fibroblasts. These data show that our previous data from human cancer cell lines, are likewise relevant across species in non-cancer-derived cells from mice.Reviewer #1 (Recommendations for the authors):The authors sought to address an important question of how IRF1 is turned over, as aberrant IRF1 expression is linked to several immunological disorders. The authors devised a clever fluorescent protein-based IRF1 degradation assay and used this system to perform a well-controlled genome-wide CRISPR screen for genes that regulated IRF1 expression. They identified SPOP as a hit and then carried out a series of experiments  to validate SPOP as an E3 ligase that mediates the degradation of IRF1. In functional assays, they linked SPOP1-dependent IRF1 regulation to predicted changes in specific downstream IRF1 target genes, and they demonstrated modest effects of this pathway on cell fitness.StrengthsPowerful CRISPR screens to identify SPOP as a regulator of IRF1 protein abundance;Rigorous validation of hit, including binding assays, colocalization, and judicious use of both SPOP and IRF1 mutants as control.WeaknessesCurrent evidence for SPOP directly ubiquitinating IRF1 is incomplete and lacks complementing orthogonal assays;The significance of the SPOP/IRF1 axis in a biologically relevant model system  is lacking.1. The key/new mechanism here is that SPOP directly ubiquitinates IRF1 and enforces its degradation. There is only one Ub assay directly showing this, thus the strength of the evidence is incomplete. The authors need to provide additional data supporting this mechanism. Orthogonal assays (e.g. Ub assays in cells vs in vitro ubiquitination assays using recombinant protein) showing the same result are critical to pin this mechanism down. The use of SPOP mutants that cannot ubiquitinate IRF1 also needs to be included as controls.New data in Figures 4I+J address IRF1 ubiquitination by SPOP, using IRF1 mutants that lack SPOP-binding domains, and SPOP mutants lacking an intact substrate recognition domain. These new data show robust wtIRF1 ubiquitination by wtSPOP, which was severely reduced if either the E3 or substrate were mutated in their interaction surfaces. These new data provide evidence for robust IRF1 poly-ubiquitination, supported by quantification of replicates and statistical analysis.2. The broader significance of the paper is uncertain in the absence of a more compelling role for the SPOP/IRF1 axis in a biologically relevant setting. The authors claim in the discussion that viral assays would be difficult due to the overlap of IRF1 and ISGF3 target genes. This logic is confusing. There are ample examples in the literature of IRF1 expression conferring antiviral effects to cells, especially in cells that can't signal through IFN (STAT1-deficient). Co-expression of SPOP with IRF1 should reduce IRF1 levels and create a more permissive antiviral state. Alternatively, a more robust cancer-related model could help assign a clearer biological role for SPOP/IRF1.STAT1-targeted cells. The data show that exogenous IRF1 expression reduced VSV progeny virus production by ~10 fold, and that this was further reduced by approximately another 10-fold in the absence of SPOP. These results underpin the biological significance of SPOP in controlling the IRF1-driven antiviral state.New data in Figure 5F + Figure 5—figure supplement 1C address the relevance of SPOP for exogenous IRF1-driven antiviral responses in 3. The authors should include a new discussion on how their work relates to a recent similar study (PMID: 36481790).et al.This reference was added to the Discussion section of the revised manuscript, as well as additional text comparing our results to the work of Gao Reviewer #2 (Recommendations for the authors):Vunjak and colleagues report the results of a genome-wide CRISPR screen designed to identify genes controlling the post-translational fate of IRF1. IRF1 was the first interferon regulatory factor to be identified, and its activity as a transcription factor has been associated with both successful immune control of intracellular viral and bacterial infections, as well as regulation of cell cycle progression and tumor suppression. Unlike IRFs such as IRF3, which are expressed constitutively and activated via phosphorylation, IRF1 activity is determined primarily by its protein abundance which is regulated transcriptionally, translationally (at the level of protein synthesis), and by the rate of protein degradation. A better understanding of these processes would be important for scientists with interests in innate immune host responses to pathogens as well as the role of IRF1 in protection against cancer.[…]RKO is a cancer cell line – is IRF1 degradation in these cells representative of IRF1 degradation pathways in normal cells? For example, FBXW7 appears to be not important for IRF1 regulation in these cells but has been reported to be so in other cell types.New data in Figure 3G demonstrate SPOP-dependent IRF1 degradation in mouse embryonic fibroblasts. These data show that our previous data from human cancer cell lines are likewise relevant across species in non-cancer-derived cells from mice.Line 135-236 – The authors should consider that IRF1 abundance is regulated not only transcriptionally, but also translationally, as IRF1 synthesis is subject to inhibition by PKR independently of transcription .Additional text and the indicated reference was added to the Introduction section to reflect that endogenous IRF1 protein concentrations are also controlled at the translational level. It should be noted though that our screens and most other experiments were performed in the absence of infection. We reason that it is thus unlikely that our experiments were influenced by PKR-dependent translational shut-down, as viral RNA needed for PKR activation was absent.1/2 of the mCherry-IRF1 product is twice as long as that of endogenous IRF1 suggests the possibility that the endogenous degradation pathway may be only partly represented/active in the reporter cell line.Line 172-175 – The fact that the tAdditional text was added to the Introduction section to reflect this point.Line 231 – (a) The authors state that SPOP was "The strongest candidate enriched at both three and six days after Cas9 induction" – however, SPOP is not shown in Figure S2E (the day 3 panel), and it is not the most significantly enriched in Figure 2D. (b) Were each of the 6 sgRNAs targeting SPOP enriched at day 3 and day 7?SPOP was marked in green in the original manuscript, but it was by mistake not labeled with its name. This has been rectified in the revised figure panel. The accompanying text was meant to convey that SPOP was the only IRF1-specific candidate enriched at both screening days, thereby making it the strongest IRF1-specific candidate identified by the screens. The relevant text in the Results section was updated to better convey this intended notion.Line 241-244 – "FBXW7, MDM2, [and] STUB1/CHIP … are unlikely to play a significant role in IRF1 degradation" – or, could these proteins be required for cell survival? The authors should note the limitations of this CRISPR screen.FBXW7 is a strong hit in the cMYC screen, but does not score at all in the IRF1 screen. As discussed in the manuscript, this is a reasonably strong indication that the experimental system works, but that FBXW7 targeting does not affect IRF1. For the other two factors, we cannot say this with as much confidence since they do not score in either screen. The text has been adapted to reflect that notion of uncertainty.Science 2023, and Scinicariello et al. eLife 2023), the used screening setup allows for identification of cell-essential genes at the earlier time points post Cas9 induction with Dox (3 days post-Dox). However, by day 6, these cells will be lost. This notion is underpinned by the fact that we readily identify proteasome components, and other highly cell-essential degradation mediators like AKIRIN2 in the screens presented in this manuscript, and the two publications listed above. Moreover, the used screening cell lines containing an sgRNA targeting the highly cell-essential gene RRM1, were only lost upon addition of Dox to induce Cas9, but not without it , further substantiating the great care taken to enable identification of cell-essential genes in our screens, without the risk of out-selection of cells in which essential genes were targeted prior to the screening procedure.As pointed out in the manuscript, and further substantiated by our previous screens using the same screening approach  – showing symbols for the individual values reflected in the means shown by the bars would be informative and enhance the presentation of the authors' data.Based on this comment, data in Figures 3H, 5F, and Figure 5—figure supplement 1B is now presented as individual data points. However, we opted for not displaying the individual data points in other figure panels as it made them overly complex in our opinion, and less clear for that reason.Line 260-62 – Is the increase in IRF1 protein primarily cytoplasmic, or nuclear?SPOP knock-out, indicating that this likely reflects the targeted IRF1 protein population.The revised manuscript includes new data on this . We did not detect cytosolic IRF1. However, we did measure an increase in the nucleoplasmic IRF1 pool upon Lines 298-99, 318 – The data do not show that "the four predicted degrons collectively are required for SPOP-dependent IRF1 degradation" since all four degrons were mutated. It could be that only one of the degrons is functional in the wild-type protein and that the loss of degradation requires mutation of only that specific degron. The authors recognize this limitation in the Discussion on lines 472-473, but ought to change the text in Results to more accurately reflect the data. The Abstract should also be modified to reflect this more accurate interpretation of the data.We agree, and changed the text in the Abstract and Manuscript to reflect this.Figure 4E-H – Were the wt and mutated IRF1 expression products tagged with Myc? It is otherwise not clear why blotting was with antibody to Myc-IRF1 (minor detail).Yes, wtIRF1 and IRF1-SBM were tagged with a N-terminal myc-tag and detected with an anti-myc antibody.Figure 4J, I – How reproducible were these IRF1 ubiquitylation experiments? There are no error bars in Figure 4I suggesting that the experiment may have been done only once.New data in Figure 4I-J address IRF1 ubiquitination by SPOP, using IRF1 mutants that lack SPOP-binding domains, and SPOP mutants lacking an intact substrate recognition domain. These new data show robust wtIRF1 ubiquitination by wtSPOP, which is severely reduced if either the E3 or substrate were mutated in their interaction surfaces. These new data provide evidence for robust IRF1 poly-ubiquitination, supported by quantification of replicates and statistical analysis in Figure 4J.The authors should cite the recent publication by Gao et al. (PMID: 36481790) in the Discussion, and compare the results of Gao et al., with those reported here.et al.This reference was added to the Discussion section of the revised manuscript, as well as additional text comparing our results to the work of Gao Reviewer #3 (Recommendations for the authors):In this study, Vunjak and colleagues show that the Speckle Type BTB/POZ Protein (SPOP) is responsible for the ubiquitination and turnover of Interferon Regulatory Factor 1 (IRF1). Using a CRISPR genetic screening approach, the authors identified several factors, including the E3 ligase substrate adaptor SPOP as regulators of IRF1 protein levels. The screening approach used good controls (such as Myc instead of IRF1) and showed significant results. Further, the authors identified potential SPOP-binding motifs in IRF1 and showed that they (or at least one of them) are important for the recognition and turnover via SPOP; they used flow cytometry, co-immunoprecipitation, western immunoblotting, and dual luciferase assays. Finally, to determine the response of IRF1 transcriptional activity to the presence and absence of SPOP, the authors performed a qPCR analysis of IRF1-driven transcripts, which revealed a significant increase in SPOP knockout cells.The manuscript is well written. The authors find another substrate of SPOP. One open question is how significant this E3 ligase is for the turnover of IRF1. The difference in ubiquitination in WT and SPOP-binding motif-devoid IRF1 is modest. The differences in transcriptional activity, however, are large. A set of additional experiments are necessary to understand these counterintuitive effects. The authors identify 4 SPOP-binding motifs in IRF1 and suggest that they work synergistically in the recognition of IRF1 by SPOP and its subsequent degradation. However, without testing the contribution of individual SPOP-binding motifs, it is impossible to say whether they all contribute to SPOP turnover.1. The current version of Figure 1E is weak in supporting the idea that IRF1 degradation is independent of autophagy/autolysosomal degradation. Additional controls including time-dependent CHX treatment  and -IFNγ in the presence of Bafilomycin will help to answer this question clearly.New data addressing the contribution of autophagy/lysosomal degradation to IRF1 turn-over in a protein stability chase were added as new figures Figure 4—figure supplement 1A-B. IRF1 stability was investigated in the presence of both cycloheximide and Bafilomycin A. From these data, we concluded that IRF1 is predominantly degraded by the proteasome, with little to no contribution by lysosome-dependent mechanisms.2. Is IRF1 ubiquitinated by SPOP also in the absence of inflammation or is this an inflammation-specific response?Exogenously expressed IRF1 is rapidly turned-over in a SPOP-dependent manner in the absence of any cytokine stimulation, indicating that this process does likely not majorly rely on inflammatory signals. Most experiments with exogenously expressed IRF1 presented in the manuscript were performed in the absence of cytokine stimulation, including the genetic screen that identified SPOP.3. There are no error bars in some histogram plots . How many biological and technical replicates did the authors perform?Error bars were added to the respective figures and the number of replicates noted in their figure legends.4. In Figure 4D, it seems that both IRF1 and SPOP localize in the nucleus but the resolution of the microscopy image is not sufficient to clearly understand their subcellular distribution. SPOP is often localized to nuclear bodies such as nuclear speckles. Micrographs with better resolution and concentration on the nucleus are required.et al. was added to the Discussion section.New data in Figures 4A and Figure 4—figure supplement 1A address the subcellular localization of IRF1 and SPOP by confocal immunofluorescence microscopy in two cell models. From these new data, we conclude that SPOP localizes in both of our cell models diffusely in the nucleoplasm and nuclear speckles, whereas IRF1 is exclusively diffusely nucleoplasmic. Additional data show that IRF1 only accumulates as a diffusely nucleoplasmic pool upon proteasome inhibition. Together, these results indicate that IRF1 and SPOP likely interact in the nucleoplasm, and that IRF1 is predominantly degraded in the nucleoplasm, with limited involvement of SPOP in nuclear speckles. Text discussing possible reasons for the discrepancy with data presented by Gao 5. The decrease in the ubiquitylation level of IRF1 with mutated SB motifs compared to IRF1 WT is modest . SPOP seems to be responsible for the turnover of only a small fraction of IRF1. The authors found other E3 ligase hits in their genetic screen, e.g. HUWE1 . Have they considered testing whether there are other E3 ligases involved in the turnover of IRF1?et al., eLife 2023).UBE3C and HUWE1 were two other E3 ligases identified in the IRF1 screen. UBE3C was not further pursued, as the fact that it was also a strong hit in the cMYC screen indicated this may be a more general regulator of proteasomal degradation, consistent with published data . The role of HUWE1 in IRF1 turn-over is being further elucidated outside of this manuscript’s scope, which may be indirect as we recently showed for the inflammation regulator tristetraprolin , the mRNA levels of IRF1 target genes are increased. Importantly, this new data set includes SPOP/IRF1 double knock-out epistasis samples, in which this effect was lost. Together, these data show that SPOP is relevant for curtailing IRF1-dependent IFN-stimulated gene expression.New data presented in Figures 5A/B + Figure 5—figure supplement 1A demonstrate that in 7. Experiments in Figure 5C and Figure 5D show a comparison of +/- WT SPOP expression. In Figure 5C, the ratio is about 10fold, but in Figure 5D around 100 fold. What results in this large deviation?We acknowledge this difference, but would like to point out that irrespective of the effect magnitude, the qualitative behavior is comparable in both panels. Moreover, the internally controlled nature of the dual luciferase setup, makes us confident that both data sets are reliable. Although we cannot identify a particular cause for the difference with certainty, a higher transfection efficiency and higher signal gain settings on the plate reader for the second experiment  may have improved the experimental range of the assay, and consequently may have resulted in larger effect differences upon SPOP expression.8. The authors observe little to no protein turnover effect of SPOP on cMYC. However, it has been previously shown that SPOP recognizes cMYC as a ubiquitination substrate . Could the authors please discuss the potential reasons for this discrepancy?These references and accompanying text addressing this point were added to the Discussion section of the revised manuscript.9. The authors mentioned a recent study  that shows that deletion of SPOP results in LYN-dependent (Src kinase family member LYN) degradation of IRF1, and inhibition of its transcriptional activity. The discussion (in lines 452-456) suggests that the differences may arise from the expression of TLRs and associated adapters exclusively in immune cells. However, colon/colorectal cancer cells including RKO cells are known to express most TLRs (e.g. PMID: 33244454). It thus seems that TLR expression is not the critical difference that causes the differing response. Could the authors please discuss what other biological reasons could potentially cause this different response?This reference, and text discussing this point was added to the Discussion section.10. The statement "…the four predicted degrons collectively are required for SPOP-dependent IRF1 degradation." (lines 298-299) cannot be made without testing the response of the SPOP degrons individually, as well as in binary and ternary combinations. It is possible that only one of the motifs is responsible for the observed effect.We agree, and the text in the Abstract and manuscript are adapted to reflect this notion.


Pseudomonas aeruginosa can express at least 15 different iron uptake pathways and all but one involve a TonB-dependent transporter (TBDT) for the uptake of iron across the outer membrane. Little is known about how bacteria modulate and adapt the expression of their different iron import pathways according to their environment. Here, we have developed fluorescent reporters between the promoter region of genes encoding a TBDT and the fluorescent reporter mCherry. With these constructs, we can follow the expression of TBDTs under different growth conditions. Mathematical modelling of the data obtained showed the transcription and expression of the gene encoding the TBDT PfeA to have a sigmoidal shape, whereas it was logarithmic for the TBDT gene foxA. Maximum transcription for pfeA was reached in the presence of 3 µM enterobactin, the siderophore recognized by PfeA, whereas the maximum was not reached for foxA with 100 µM nocardamine, the siderophore of FoxA.Iron is a key nutrient for bacterial growth. The source can be either heme or siderophore-Fe complexes. Siderophores are small molecules synthesized by bacteria to scavenge iron from the bacterial environment. The pathogen  Paradoxically, this essential nutrient shows extremely low solubility (Ksp = 10–18 M) under aerobic conditions and at neutral pH, which severely limits its bioavailability5. Consequently, iron limitation is a state that microorganisms very often experience and in many ecosystems, the low availability of iron generates fierce competition between them. To overcome iron restriction, many microorganisms synthesize and secrete siderophores, low molecular weight ligands that have a very high affinity for ferric iron (Fe3+)6. The biological function of siderophores is to scavenge iron in the environment of bacteria and bring it into the bacterial. In Gram-negative bacteria, the uptake of ferri-siderophore complexes across the outer membrane involves TonB-dependent transporters (TBDT)7. The energy necessary for the uptake process of ferri-siderophores by these transporters is provided by the inner membrane proton motive force via the inner membrane protein TonB13. TonB forms a molecular motor with two other membrane proteins, ExbB and ExbD, that are able to transfer the energy generated by the proton motive force to the TBDT located in the outer membrane. Many X-ray structures of TBDTs have been published16. All are composed of a β-barrel of 22 β-strands interacting with the lipids of the outer membrane and contain very large extracellular loops. The lumen of the barrel is filled by a globular domain called the plug. The N-terminal end of the transporter is periplasmic and contains a 5 to 8 amino acid conserved domain called the TonB box, which is essential for interaction with the TonB protein19. All TBDTs have a binding site localized on the plug composed of residues of the plug and the extracellular loops7. After binding of the ferri-siderophore complex to its binding site on the plug domain, the TonB box of the TBDT interacts with the periplasmic part of the TonB protein providing the energy necessary to obtain formation of a channel in the transporter and the uptake of ferri-siderophore complexes through the outer membrane19. Ferri-enterobactin TBDTs, PfeA in Pseudomonas aeruginosa and FepA in Escherichia coli, have an additional binding site for ferri-siderophore complexes localized in the extracellular loops of the barrel20. In this case, ferri-enterobactin likely first binds to this binding site before migrating to the binding site on the plug domain.Iron is a key nutrient for almost all microorganisms. It is required for bacterial metabolism, growth, and survivalP. aeruginosa is a ubiquitous microorganism known for its high adaptability to a large range of environmental conditions. It is also an opportunist human pathogen, exhibiting high intrinsic resistance to a broad spectrum of antibiotics. To access iron, P. aeruginosa produces two siderophores, pyoverdine and pyochelin, but is also able to use siderophores from other microorganisms in a piracy strategy21. The genome of P. aeruginosa encodes 35 TBDTs and at least 20 are dedicated to iron import, three for zinc, one for copper, and one for vitamin B1222. The transcription and expression of the genes encoding TBDTs involved in iron acquisition are all regulated by the bacterial intracellular iron concentration. When iron in the cytoplasm of P. aeruginosa cells reaches a threshold concentration, iron binds to the transcriptional regulator Fur and the complex formed represses the transcription of all genes encoding proteins involved in iron homeostasis, including those for TBDTs26. Transcription of some genes encoding TBDTs are also positively regulated, involving sigma ECF/anti-sigma ECF factors, two-component systems, or AraC-like transcriptional regulators32. These three different systems can detect the ability of P. aeruginosa to scavenge and import specific ferric-siderophore complexes present in its environment and activate transcription of the gene encoding the TBDT able to import the ferri-siderophore detected. This results in an increase in the expression of the TBDT of the ferri-siderophore complex present in the bacterial environment to increase the efficiency of its uptake37. This often goes hand in hand with repression of the transcription of fptA, the gene encoding the TBDT of ferric-pyochelin (one of the two siderophores produced by P. aeruginosa).37. How the transcription of such genes changes and varies during bacterial growth and in the presence of different concentrations of siderophores is not precisely known. Here, we aimed to monitor and model the changes in transcription and expression of TBDTs involved in the acquisition of iron across concentration gradients of siderophores. Accordingly, we investigated the transcription of two genes encoding TBDTs, pfeA and foxA, throughout the duration of bacterial culture and in the presence of a wide range of concentrations of siderophores. PfeA imports iron via the tricatechol-type siderophore enterobactin 38. FoxA transports iron complexed to hydroxamate siderophores such as nocardamine , a cyclic trihydroxamate molecule also known as desferrioxamine E35. The affinity for ferric iron of this siderophore is weaker than that of ENT (Ka = 1032 M−1)39. The X-ray structures of these two TBDTs are known and data are available concerning how ferri-ENT and ferri-NOCA interact with their respective binding sites on PfeA and FoxA16. We followed the expression of these two TBDTs using promoter fusions: the promoter region of one of these two genes was fused to the coding sequence of the fluorescent protein mCherry, used as reporter40, and inserted into the genome of P. aeruginosa to have a fluorescent reporter strain for each TBDT. We show these fluorescent reporter strains to be promising tools that make it possible to follow the real-time transcription and expression of pfeA and foxA by fluorescence throughout bacterial growth and in the presence of a wide range of siderophore concentrations. Surprisingly, we did not observe maximum transcription and expression at the same concentrations of siderophores for the two genes. Mathematical modeling was used to precisely characterize the expression levels of these two genes as a function of time and siderophore concentration.Until now, the regulation of transcription and expression of genes encoding TBDTs has been investigated only in the presence of its ligand at one or a few concentrations and at a given time of culture, i.e., stopping the culture and analyzing the levels of transcription or expression using techniques such as RT-qPCR or proteomics16, to create a mathematical model of TBDT expression by P. aeruginosa. To follow the expression of the pfeA and foxA genes, we used promoter fusions in which the promoter sequences of the pfeA or foxA gene were fused to the coding sequence of a reporter that can be easily detected or quantified40. In the promoter fusions, the transcription of the reporter gene is controlled by the promoter sequences of the gene of interest, which in turn controls the quantity of mRNA transcribed and, subsequently, that of the protein synthesized. Here, we used the red fluorescent protein mCherry because of its well-documented stability and efficiency as a reporter43. The promoter regions of pfeA and foxA both contain a Fur box, involved in transcriptional regulation via the regulator Fur and the bacterial intracellular iron concentration 46. The precise mechanisms and interconnection between these three transcriptional regulators involved is not clearly elucidate so far. foxA transcription is positively regulated by a sigma factor (FoxI) and its anti-sigma factor (FoxR)30, and the promoter region of foxA contains the sequences I1 and I2, predicted to be recognized by FoxI  in the presence of increasing concentrations of ENT or NOCA, and the transcription of the mCherry, foxA, and pfeA genes monitored by RT-qPCR  normalized the data  fit this dataset with a single Hill equation pfeA and mCherry).Both the pfeA and mCherry whole data sets and the pfeA + mCherry Hill equation. We found RMSE values of 0.052 and 0.051 for the comparison of the pfeA data set versus that of pfeA + mCherry and the mCherry data set versus that of pfeA + mCherry, respectively. We also calculated the RMSE solely for the transition phase by reducing the range of the dataset on the pfeA and mCherry, respectively, for the first range and 0.070 and 0.071, respectively, for the second. Therefore, the error of our pfeA + mCherry model is approximately 7%. To obtain an idea of what such an error represents, we calculated the dispersion, which intrinsically exists in the data, by calculating the RMSE between the data and the Hill dataset-specific model for both genes. All RMSE values were of the same order of magnitude, regardless of the domain. Thus, modeling both datasets using a unique model did not appear to introduce any additional error to that intrinsically present in the dataset, suggesting that the model is valid. In conclusion, pfeA and mCherry behave in the same manner with respect to the siderophore concentration in qRT-PCR.The normalized and pooled data sets are presented in Fig. foxA-mCherry using the same method and created the pooled data set foxA + mCherry. The logarithmic models of the normalized transcriptional expression data of foxA and mCherry and those of foxA + mCherry all had the same parameters  and the fluorescence of mCherry at 610 nm (excitation wavelength: 570 nm) as a function of time  was first subtracted from the fluorescence of the condition of interest. Then, for each condition, the resulting fluorescence was divided by the OD−1).To characterize all the curves of the fluorescence data shown in Fig. tial Eq.  involvinThe solution of this differential equation is given in 8\documenAccording to Eq. , at steapfeA-mCherry and promfoxA-mCherry are shown in Fig. pfeA-mCherry strain and in a logarithmic manner for the gradient of NOCA in the promfoxA–mCherry strain.Thus, within a factor of one, the gene activity can be deduced from the fluorescence curve after having fitted this fluorescence using Eq. . For cerpfeA-mCherry and promfoxA-mCherry as previously described to compare them to the RT-qPCR data. Again, we calculated the RMSE between the common model and the gene-specific model as a metric to assess the validity of this approach.We treated the fluorescence data for prompfeA-mCherry are represented in Fig. RT-qPCR and Fluo whole data sets and the RT-qPCR + Fluo Hill equation. We found RMSE values of 0.068 and 0.107 for the comparison RT-qPCR/RT-qPCR + Fluo and Fluo/RT-qPCR + Fluo, respectively. We also calculated the RMSE for the transition phase, which involved first using only data between 0.1 and 0.9 and then 0.2 and 0.8 of the normalized fold change. The RMSE values were 0.083 and 0.099 for the data between 0.1 and 0.9 and 0.100 and 0.103 for the data between 0.2 and 0.8 for RT-qPCR and Fluo respectively. Thus, the error of our RT-qPCR + Fluo model is approximately 10%.The normalized and pooled data sets with the fitted models for promRT-qPCR or Fluo data and the RT-qPCR + Fluo model. The RMSE between the data and the model showed a comparable magnitude. The utilization of a single model for both datasets did not introduce any additional errors beyond the inherent variability in the data. Therefore, the validity of the model remains unaltered. Thus, the RT-qPCR and Fluo data, in other words, the transcriptional expression of mCherry and mCherry synthesis, appear to show analogous behavior in relation to the siderophore concentration.Applying a similar methodology as that used for the RT-qPCR data previously, we determined the inherent dispersion between the foxA-mCherry, both the transcriptional expression of mCherry and mCherry synthesis could be normalized and modeled by the logarithmic equation of RT-qPCR + Flu. The normalized models of the transcriptional expression of mCherry and mCherry synthesis and RT-qPCR + Fluo all had the same parameters  in the genome of P. aeruginosa does not significantly modify the transcription of the glmS and PA5548 genes and that the presence of two promoters in the genome (in front of the genes encoding the TBDT and mCherry) of P. aeruginosa does not interfere with the transcription of pfeA or foxA. We also verified that transcription of the gene encoding mCherry follows that of the gene encoding the studied TBDT for both constructs, demonstrating that mCherry transcription and expression are regulated by the promoter region of the TBDT of interest. RT-qPCR data showed parallel mRNA synthesis kinetics for the genes encoding mCherry and the TBDT of interest. However, we observed a higher level of transcription for the genes encoding PfeA or FoxA than mCherry, which was more pronounced for the fusion reporter carrying the pfeA promoter region. One possible reason is that the stability of the mRNA of pfeA or foxA and mCherry may differ, with the mRNA of pfeA or foxA being more stable, resulting in higher mRNA levels than for mCherry.We conducted various tests to verify that the prompfeA-mCherry and promfoxA-mCherry is representative of the expression of the transporters PfeA and FoxA, respectively. The use of these fluorescent reporters was highly successful and allowed us to generate a large amount of data to study the effect of increasing concentrations of siderophores.Overall, the controls we carried out show that the fluorescence of mCherry measured using our fluorescent reporters prompfeA and foxA genes in the two strains, prompfeA-mCherry and promfoxA-mCherry, does not respond in the same way or with the same efficiency to the presence of increasing concentrations of their corresponding siderophores. The transcription of pfeA showed a sigmoidal shape, whereas the transcription of foxA was logarithmic. Maximum transcription for pfeA was reached for 3 µM ENT and higher, whereas the maximum was not reached for foxA with 100 µM NOCA  than NOCA.There are other factors upstream and downstream of these two regulatory cascades that can affect the efficiency of 35. Unlike the NOCA-Fe/FoxA pair, ENT-Fe can interact with two binding sites on PfeA, one located in the extracellular loops and the second on the plug domain of PfeA15. Affinity measurements showed that one of these sites is a high affinity site and the other a lower affinity site (Kd = 60 nm and Kd = 155 µM for the two binding sites on PfeA)15. Currently it is impossible to know which of the two sites identified by crystallography is the low or high affinity site. The differences between the binding of FoxA and PfeA to their siderophore-Fe complexes may affect the regulation of the transcription of the foxA and pfeA genes differently, as this binding step is essential in the regulatory process in both cases. Furthermore, in the cytoplasm, the promoter regions of the pfeA and foxA genes are different and the mechanisms of recognition of PfeR and FoxI are different and may also affect the efficiency of transcription.In addition, when the siderophore-Fe complexes are formed, NOCA-Fe binds to its binding site on FoxA, located on the plug domain of the FoxA structure, with a Kd of 178 nM before being imported through the outer membranefoxA and pfeA genes as a function of the concentration of their respective siderophores. As a next step, it would be informative to test other siderophores  that can be recognized by these two TBDTs to determine whether they affect the expression of these two genes in the same way.The systems and mechanisms of transcriptional regulation of the genes involved in the two iron import pathways are different and highly complex, involving different protein–protein interactions and different interactions between proteins and the siderophore-Fe complexes. The mathematical model described in this paper is more a phenomenological model than a precise description of the biochemical mechanisms involved. The building of such a model would obviously be an asset in understanding regulatory phenomena but would require the parallel measurement of various concentrations of several molecules, which is not straightforward from an experimental point of view. Model refinement techniques could make it possible to generate hypotheses, but these can be very difficult to validate experimentally. Due to such complexity, it is currently impossible to identify which factor(s) is/are responsible for the large differences in the expression kinetics of the It would also be informative to test mixtures of different concentrations of ENT and NOCA and see the effect on the expression of these two genes in a situation in which both siderophores are in competition for iron. The tools that we have developed here can also help improve our understanding of the regulatory mechanisms involved in the expression of TBDTs. One can imagine, for example, testing deletions of genes encoding transcriptional regulators, such as PfeS/PfeR or FoxI/FoxR, or amino acid mutations of the binding sites of PfeA or FoxA and assessing the effect on our model.pfeA-mCherry and promfoxA-mCherry are powerful tools to follow the expression of a TBDT in P. aeruginosa. Mathematical modelling of the data obtained showed that the expression of pfeA displays a sigmoidal shape, whereas it is logarithmic for foxA, with maximum transcription for pfeA at 3 µM ENT, whereas the maximum was not reached with 100 µM NOCA for foxA. These results have important implications for the understanding of how P. aeruginosa adapts the expression of its TBDTs to changing environmental conditions and highlight the value of using mathematical models as a tool to investigate iron homeostasis and, more generally, bacterial physiology. Our results provide a foundation for the development of a larger system for studying the regulation of all iron acquisition pathways of P. aeruginosa.In conclusion, the promoter fusions prom57.Enterobactin (ENT) was purchased from Sigma-Aldrich and Nocardamine (NOCA) was purified as previously describedP. aeruginosa strains used in this study are listed in Table −1 low-iron CAA (Difco), 1.46 g L−1 K2HPO4 3H2O, 0.25 g L−1 MgSO4 7H2O) and grown over night at 30 °C. In order to monitor growth in the presence of siderophores, cells were resuspended again in fresh CAA medium at an optical density at 600 nm (OD600 nm) of 0.01, grown in 96 well plates, in the absence or presence of increasing concentrations of ENT (0.001–100 µM) or NOCA (0.01–100 µM). Plates were incubated at 30 °C, with shaking every 15 min, in a microplate reader . Growth was followed by measuring the OD600 nm and the fluorescence of mCherry (excitation at 570 nm and emission at 610 nm) every 30 min, for 24 h.Escherichia coli TOP10 (Invitrogen) was used as the host strain for the plasmids. The DNA fragments from P. aeruginosa used for cloning were amplified from the genomic DNA of strain PAO1 with Phusion High-Fidelity DNA polymerase (Thermo-Fisher Scientific). The insertion of the sequence was done in the intergenic region between glmS and PA5548 using a pEXG2 plasmid58. The construction of the plasmids was realized with the NEBuilder® HiFi DNA Assembly Master Mix. The primers are listed in Table 37.600 nm of 0.1. The cells were then grown with or without siderophores, under shaking, at 30 °C for 8 h. Afterward, 2.5 × 108 cells were mixed with two volumes of RNAprotect Bacteria Reagent (Qiagen). Samples were lysed in Tris–EDTA at pH 8.0 containing 15 mg mL−1 lysozyme (Sigma-Aldrich) for 15 min at 25 °C. Afterwards, total RNA was extracted using an RNeasy MinElute Spin Columns and gDNA Eliminator Spin Columns from RNeasy Plus Micro Kit (Quiagen). A treatment by DNase  was realized for each sample Reverse transcription of RNA was done using the iScript™ cDNA Synthesis Kit (Bio-Rad) with 1 µg of input RNA. Gene expression was measured with a CFX Opus 96 Real-Time PCR Instrument (Bio-Rad) using iTaq Universal SYBR Green Supermix (Bio-Rad) and the appropriate primers  were tested as reference genes using the Reference Gene Selector Tool based on GeNorm in CFX Maestro™ Software (Bio-Rad). PAO1, prompfeA-mCherry and promfoxA-mCherry strains were grown for 8 h in the absence and presence of siderophores . We have chosen to use clpX and rpoD in our assays because their stability across the different conditions were the highest . Overnight cultures of bacterial cells grown in CAA medium were pelleted, resuspended and diluted in fresh medium to obtain an ODest Fig. . For thehttps://www.python.org/) and packages (https://pypi.org/): Pandas was used to manipulate and manage the data; NumPy and SciPy were used to process the data and create the models; Matplotlib was used for visualizations. The equation parameters were found by using the curvefit function from scipy.optimize on our experimental data.All the data processing and the mathematical modeling was done with Python 3.8 (Supplementary Information.


Neural precursors generate neurons in the embryonic brain and in restricted niches of the adult brain in a process called neurogenesis. The precise control of cell proliferation and differentiation in time and space required for neurogenesis depends on sophisticated orchestration of gene transcription in neural precursor cells. Much progress has been made in understanding the transcriptional regulation of neurogenesis, which relies on dose- and context-dependent expression of specific transcription factors that regulate the maintenance and proliferation of neural progenitors, followed by their differentiation into lineage-specified cells. Here, we review some of the most widely studied neurogenic transcription factors in the embryonic cortex and neurogenic niches in the adult brain. We compare functions of these transcription factors in embryonic and adult neurogenesis, highlighting biochemical, developmental, and cell biological properties. Our goal is to present an overview of transcriptional regulation underlying neurogenesis in the developing cerebral cortex and in the adult brain. Neurogenesis happens primarily during embryonic stages, while the nervous system develops, although some regions in the adult brain retain the capacity to generate new neurons throughout life . In bothThe balance between proliferation and differentiation of neural stem and progenitor cells requires exquisite control at the transcriptional level. Transcriptional control of embryonic and adult neurogenesis relies on shared transcription factors (TFs) that become spatially confined over time, are expressed at specific timepoints, or both. The cell type-specific transcriptional activity of such neurogenic TFs is mediated by epigenetic signatures, chromatin changes, and other protein partners. In this mini-review, we provide a brief overview focusing on the role of some of the best-characterized TFs that control neurogenesis in the embryonic dorsal telencephalon  and in tTranscription factors of the bHLH (basic helix loop helix) superfamily work as dimers and bind DNA through a basic domain at their amino terminal end . SeveralHes genes, Hes1 is the most widely studied in the context of corticogenesis. HES1 levels in cortical neuronal progenitors experience cyclic oscillations, which are essential for the maintenance of neuronal progenitors  family members of the bHLH TF family are effectors of the Notch signaling pathway . During genitors . These o protein . In its  protein , HES1 ac protein . Second, protein . Downstr . Sox2 is part of the SoxB1 subgroup  is lethal during early embryogenesis (SOX2 (SRY-box binding transcription factor 2) is a member of the Sox family of transcription factors, which consists of 9 subfamilies -3; The transcription factor ZEB1 (zinc finger E-box binding homeobox 1) is emerging as a new regulator of self-renewal and fate choice in the CNS. The ZEB family of TFs consists of two members, ansition . Epithelansition . Zeb1-mugulation . StructuZeb1 causes defects in proliferation of embryonic neural progenitors in the ventricular zone of the lateral ventricles and the hypothalamus at E15.5  which promotes differentiation of the stem/progenitor cell pool. Interestingly, Zeb1 loss during embryogenesis promoted asymmetric divisions that prevented expansion of neural progenitors (and therefore caused premature differentiation), whereas in the adult hippocampus Zeb1 KO causes increased symmetric divisions of neural stem/progenitor cells which are necessary for self-renewal, thus promoting their differentiation  cells.  lineage . This isat E14.5 . In bothntiation .Although neurogenic transcription factors are expressed during embryonic and adult neurogenesis their functions often show differences during both processes. These differences include increased spatial confinement and spatial heterogeneity in adult neurogenic niches, different activities in neural progenitor cells at various developmental stages, and/or different effects on downstream progenitor cells. It is important to consider epigenetic modifications, post-translational modifications, and differential expression of interacting partners at different developmental stages to unravel the functions of each neurogenic transcription factor at specific points in time and space. For example, changes in ASCL1 post-translational degradation result in different behavior of adult neural stem cells in juvenile and adult hippocampal neurogenesis . IntegraNo new data was created during this study.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.NS was the recipient of a Biotechnology and Biological Sciences Research Council (BBSRC) PhD studentship (BB/T008741/1). FS was supported by MRC grant MR/S007709/1. IM-G was supported by funding from the BBSRC (BB/S002359/1). Open access publication fees were covered by the Cardiff University’s Open Access Institutional Fund.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The emergence of precision biology has been driven by the development of advanced technologies and techniques in high-resolution biological research systems. Enhancer-mediated transcriptional regulation, a complex network of gene expression and regulation in eukaryotes, has attracted significant attention as a promising avenue for investigating the underlying mechanisms of biological processes and diseases. To address biological problems with precision, large amounts of data, functional information, and research on the mechanisms of action of biological molecules is required to address biological problems with precision. Enhancers, including typical enhancers and super enhancers, play a crucial role in gene expression and regulation within this network. The identification and targeting of disease-associated enhancers hold the potential to advance precision medicine. In this review, we present the concepts, progress, importance, and challenges in precision biology, transcription regulation, and enhancers. Furthermore, we propose a model of transcriptional regulation for multi-enhancers and provide examples of their mechanisms in mammalian cells, thereby enhancing our understanding of how enhancers achieve precise regulation of gene expression in life processes. Precision biology holds promise in providing new tools and platforms for discovering insights into gene expression and disease occurrence, ultimately benefiting individuals and society as a whole. For many years, scientists have faced challenges in solving biological problems with precision and efficiency. Therefore, precision biology has emerged as a field that provides more efficient and accurate means for life science research ,2. PreciTranscriptional regulation, as a determinant in gene expression, is accompanied by a large number of changes in regulatory elements and is particularly important in precision biology . Genes eThere is a large number of non-coding DNA and RNA regulatory elements in the genome, and enhancers are one type of non-coding DNA sequences ,14. TheyHerein, we summarize the research progress, focusing on the importance of enhancer-mediated transcriptional regulation in achieving precision biology. Specifically, we highlight the multi-enhancer transcriptional regulation model, which offers a new approach to studying the function and molecular mechanism of individual genes . We hopePrecision biology has emerged from precision medicine, aiming to gain a more complete and accurate understanding of biological systems through the utilization of advanced technologies and methods. The concept of precision medicine was introduced by the National Institutes of Health (NIH) in 2015, with the goal of providing personalized information to individuals and their families to improve health outcomes . PrecisiIn the previous section, we mentioned that subtle changes in gene expression patterns resulting from interactions between genes and transcriptional elements  play a direct role in precision biology research. To investigate these “minor changes”, a number of 3D genomics techniques have been developed. These include chromosome conformation capture (3C)-based technologies, such as chromosome conformation capture-on-chip (4C) , chromosFor instance, researchers have utilized Hi-C and ChIP-seq to investigate the impact of MED23 mutations on enhancer activation and chromatin conformation. These studies have provided insights into the pathogenic mutations of MED23 and the role of mediator in transcriptional control based on enhancer topology . Single-On the other hand, recent changes in sequencing technology have led to the rapid development of single-cell sequencing , enablinDevelopments in bioinformatics and computational biology have further improved data availability and accuracy, contributing to accurate screening enhancers and enhancer-promoter interactions from large sequencing datasets ,58. FurtThe advancements in precision biology have yielded remarkable progress in medical research, leading to improved healthcare standards and overall public health. In the pursuit of precision biology, to explore the individual differences and mutations that exist in individual genomes and how these differences and mutations affect the physiological and pathological characteristics of cells, tissues, and individuals, transcriptional regulation, especially enhancer-mediated transcriptional regulation, serves as a crucial tool and platform for precision biology.Transcriptional regulation is commonly believed that occurs at two interrelated levels: The first involves transcription factors and the transcriptional apparatus, while the second involves chromatin and its regulators, including enhancers. Researchers are currently striving to elucidate how mammalian gene expression is temporally and spatially constrained by integrating these two aspects ,61. GiveIn the first level, transcription factors and transcription apparatus, since the eukaryotic RNA polymerase itself has no specific affinity for promoters, numerous transcription factors and co-transcription factors are required to form a complex transcription apparatus ,63,64. IFbn2 in mouse embryonic stem cells, revealing a highly dynamic CTCF/cohesin-mediated looping state that illustrates the presence of the TAD most often in a partially extruded state of cohesion [RNA polymerase binds to the promoter to initiate transcription. Enhancer-promoter binding relies on a complex structure of cell type-specific TFs, coregulators, chromatin modifiers, architectural proteins like cohesin, condensin, and CCCTC binding factor (CTCF), other enzymes, and RNA polymerase II (RNAPII). The team of Anders S. Hansen utilized super-resolution live cell imaging to observe chromatin looping within the topologically associating domain (TAD) of cohesion . Frequencohesion . Preinitcohesion ,71, reveIn April 2021, Yanhui Xu et al. solved the complete human mediator-bound preinitiation complex for the first time, demonstrating the dynamic assembly of the PIC-mediator and proposing a molecular model for how mediator regulates the phosphorylation of the Pol II CTD  Figure . This brThe second level of transcriptional regulation involves chromatin and its regulators. Within complex genomes, transcriptional regulatory elements play a critical role in governing gene expression throughout development, maintaining cellular and tissue homeostasis, responding to external stimuli, and contributing to disease . AlthougThe promoter is the most fundamental cis-acting element for transcriptional regulation and serves as the site of enhancer-mediated transcriptional regulation. The length and sequence of the promoter can vary widely depending on the gene, the type or class of RNA polymerase recruited, the transcription product type, and the species of the organism ,76. In vFoxp3 transcription factor play a role in coordinating the development, maintenance, and function of regulatory T cells (Treg) [Although the precise molecular mechanism of enhancer-promoter (E-P) interaction remains poorly understood, it is evident that changes in E-P interactions can modulate the expression of target genes . For exas (Treg) . Howevers (Treg) . Neverths (Treg) . Additios (Treg) ,87.Scientists have made efforts to accurately characterize enhancer-mediated transcriptional regulation in order to determine the precision and accuracy of transcriptional location, level, and timing ,88. The Upon activation of PIC-Mediator, transcription factors can recognize and bind upstream promoter elements and enhancers to exert regulatory effects on gene transcription . In thisCompared to the intricate pathways and diverse transcriptional machinery involved in parsing transcriptional regulation, analyzing the differences in gene expression attributed solely to enhancers can provide insights into the role of transcriptional regulators from a three-dimensional genomics perspective. This approach can help identify transcriptional regulators to some extent.Typical enhancers (TEs) are short genomic DNA sequences. They are usually considered to have the following characteristics, including: Enhancing the expression of the target gene, the function independent of position, being located in the open chromatin region, exhibiting enrichment of transcriptional co-activators with histone modifications, and containing specific DNA sequences that allow for binding TFs . The firTo explain enhancer-mediated transcriptional regulation and understand E-P interactions, scientists need to systematically annotate enhancers. In the genomic era, various methods have been employed for enhancer annotation. For example, histone-modified ChIP combined with microarrays (ChIP-ChIP) and next-generation sequencing (ChIP-seq) to predict enhancers ,95,96. CCells rely on 3D genomic organization for transcriptional control, and they also utilize super enhancers (SEs) to regulate transcription. SEs are a class of cis-regulatory elements with super-transcriptional activation properties. In 2013, Richard A. Young’s lab proposed the concept of SEs  based onSEs play a similar role to Tes in mediating transcriptional regulation. Super enhancers differ from typical enhancers in terms of size, transcription factor density and content, and their ability to activate transcription  Figure . It is gNow ChIP-seq is the most commonly used technique for analyzing super enhancers. This method takes advantage of the enrichment of transcriptionally active markers, such as H3K27ac and H3K4me1, chromatin modification P300, near SEs. Antibodies specific to these active marker molecules are used in ChIP-seq to identify their enrichment patterns in the genome, thereby identifying potential active enhancer sites. The threshold value for SE identification is determined through enhancer stitching and sorting, and the ROSE algorithm is employed to identify super enhancers ,120. AddSEs exhibit diverse target genes within the three-dimensional genome. The functionality and action mechanism of the same SE may vary among different tumor cells. For instance, a recent study identified a novel SE, known as EphA2-super-enhancer (EphA2-SE), which is present in various tumors . EphA2-SRecent studies have proposed a model for the regulation of gene expression by SEs through phase separation . This stBased on the specificity of transcriptional regulation exhibited by both TE and SE, gene regulation often involves multi-enhancers that jointly control transcription ,129 and Sox2 is one of the three core transcription factors  responsible for maintaining the pluripotency of embryonic stem cells (ESCs) [Sox2, but the mechanisms of its transcriptional regulation in ESCs have not been fully resolved, such as whether each enhancer acts individually to affect transcription or in combination to exert its function [Sox2 controls the direction of cell differentiation is of particular interest. Our recent study revealed different transcriptional regulation patterns of the Sox2 gene associated with distinct enhancer-promoter interactions [Sox2 promoter, each playing a unique role [Sox2 enhancer-deficient cells combined with epigenetic data such as ChIP-seq. We found that these three enhancers affect various physiological processes in stem cell differentiation, including differentiation into cardiomyocytes, early differentiation of mESCs into neuronal cells, early embryonic differentiation, and stem cell morphology maintenance. These findings suggest that different enhancers exhibit diverse transcriptional profiles [Sox2 gene function in multiple ways, provides a new paradigm for the precise study of gene function and forms the basis for precision biology research.We emphasize the mediating role of enhancers in transcriptional regulation. In a multi-enhancer regulatory pattern, different enhancers can have diverse biological effects. s (ESCs) ,131. Thes (ESCs) . In mammfunction ,134. Theractions ,26  is downOur limited understanding of the molecular mechanisms of transcriptional regulation suggests that refining genome-wide annotation remains a priority a comprehensive understanding of chromatin structure and transcriptional regulation. Another study earlier this year also pointed out the synergistic and mutually exclusive relationships between super-enhancers of the same locus of action, which has prompted our thinking. Therefore, based on the complexity of molecular composition, we question the existing concept of super enhancers. Is it possible that super enhancers actually belong to a multi-enhancer regulatory model but are simply classified as a cluster of enhancers due to our lack of knowledge and annotation? All of this remains to be discovered. However, we believe that with the development of precision biology, the aforementioned questions will hopefully be gradually solved.In the field of precision biology, the multi-enhancer regulatory model has progressively demonstrated its importance. The complex biological processes are expected to be explained by the resolution of enhancers. Despite the advancements in 3D genomics technology, single-cell technology, and imaging technology, accurately describing the molecular mechanism of enhancer-mediated transcriptional regulation remains challenging ,146. AltThe interaction between enhancers and target genes is predominantly achieved through the three-dimensional structure of the genome . HoweverAlthough we have only summarized the research in transcriptional regulation, especially the involvement of enhancers, in precision biology and three-dimensional genomics here, the technical and analytical methods of precision biology undoubtedly have the potential to unravel the mechanisms of gene regulation, signal transduction, metabolic pathways, and other complex physiological processes at the molecular level. These advancements can greatly contribute to various fields, such as basic biology, medicine, and animal and plant breeding. For example, the comprehensive and accurate analysis of patients using high-throughput techniques and bioinformatics methods can provide value to assist doctors in developing personalized diagnoses and treatment plans ,156. GaiFinally, we hope to explain the concept and principles of precision biology beyond the scope of transcriptional regulation. The development of precision biology requires scientists to improve various foundational theories, such as gene function at the whole genome level, different patterns of gene co-regulation, chromatin conformation, and intracellular biological processes. Additionally, we are pleased to acknowledge that the advancements in three-dimensional genomics can provide a powerful link for studying the dynamic nature of diverse biological processes. Furthermore, integration and collaboration across multiple disciplines, including imaging, genomics, computational science, and physics, are essential. Precision biology instills great optimism in advancing our comprehension of gene regulation and cellular processes. The development of precision biology, grounded in molecular mechanisms, will inevitably drive progress across a wide range of scientific disciplines and industries. Ultimately, this will contribute to a deeper understanding of the natural world and have a positive impact on individuals and society as a whole.