The organization of chromatin into higher order structures is essential for chromosome segregation, the repair of DNA-damage, and the regulation of gene expression. Using Micro-C XL to detect chromosomal interactions, we observed the pervasive presence of cohesin-dependent loops with defined positions throughout the genome of budding yeast, as seen in mammalian cells. In early S phase, cohesin stably binds to cohesin associated regions (CARs) genome-wide. Subsequently, positioned loops accumulate with CARs at the bases of the loops. Cohesin regulators Wpl1 and Pds5 alter the levels and distribution of cohesin at CARs, changing the pattern of positioned loops. From these observations, we propose that cohesin with loop extrusion activity is stopped by preexisting CAR-bound cohesins, generating positioned loops. The patterns of loops observed in a population of wild-type and mutant cells can be explained by this mechanism, coupled with a heterogeneous residency of cohesin at CARs in individual cells. The genome is organized into three-dimensional structures that are essential for the correct segregation of chromosomes, the efficient repair of DNA damage, and the regulation of gene transcription. The initial existence of higher order chromosome structures was discovered in the early 1900s by the dramatic cytological appearance of pairs of condensed sister chromatids in mitosis. Moreover, electron microscopy studies showed that mitotic and meiotic chromatin is organized in loops with a protein scaffold .A high-resolution view of chromosome folding became possible with the development of new methods based on chromosome conformation capture (3C) technology . EssentiA major advance came with the discovery that loops formation depended upon the evolutionarily conserved SMC  complexes . SMC comHowever, five observations paint a more complicated picture of the regulation and function of cohesin-dependent looping. First, robust loops still form when the orientation preference of CTCF sites was eliminated through depletion of a cohesin regulator . ClearlyA deep understanding of complex cellular processes often resulted from their study in diverse organisms. Indeed loop formation in yeast was analyzed with a high-throughput 3C-method called Hi-C. Interestingly, a seminal study showed that cohesin mediates a change in chromosomal interactions during mitosis that, through polymer simulations, is compatible with the presence of chromatin loops . AnotherWe reasoned that the formation of positioned loops in yeast could be cell cycle-regulated. Moreover, given the small size of the yeast genome relative to mammals, positioned loops in yeast could be smaller and their detection might require a high-resolution methodology. Micro-C and its evolution Micro-C XL can detect chromatin interactions from the scale of nucleosomes to the whole genome . Indeed,To maximize the detection of loops, we arrested cells in mitosis, using the inhibitor of microtubule polymerization nocodazole (Nz), where chromosomes are highly structured and analyzed them by high-resolution Micro-C XL analysis with improved short-range interactions detection . In brieThree primary 3D features of chromosomes can be found on contact maps. First, local interactions between neighboring nucleosomes are likely, giving rise to a robust signal along the diagonal on the contact map , loop. TMicro-C XL analysis of mitotically arrested cells revealed many off-diagonal spots on the contact map, indicative of the presence of positioned loops. These positioned loops became easily visible when zooming-in on smaller regions of the chromosome with increasing resolution , top rowWe proceeded to identify the protein/s that are responsible to form the positioned loops in yeast mitotic chromosomes. In mammals, cohesin is required for loop formation and is present at loop anchors . In buddTo gather more information on the portion of the CAR that functions as a loop anchor, we compared the signals from the contact map with the cohesin ChIP-seq. For this analysis, we piled-up the contact map signal of the five kilobases regions centered at the intersection of the coordinates of each pair of Mcd1p ChIP-seq peaks . The porThis correlation prompted us to examine the causal role of cohesin in positioned loop formation. To test this possibility, we depleted the cohesin subunit Mcd1p using an auxin-induced degron system  and analCondensin, another member of the SMC family, was known to promote mitotic chromosome structure by condensing chromosomes in vertebrate and yeast cells . PreviouContact density decay analysis showed a drop in contacts starting around the 10 kilobases region when cohesin was depleted . This drTo evaluate the effect of cohesin depletion on chromatin loops, we compared the changes in contact probability in Micro-C XL and previous Hi-C datasets . ContactThe ribosomal DNA locus (RDN) has 75 to 100 tandem repeats of the 9.1 kilobases rDNA unit, with two transcribed loci (RDN37 and RDN5) spaced by two non-transcribed regions (NTS1 and NTS2). In interphase, the RDN locus forms a diffused puff adjacent to the rest of the chromosomal DNA, while in mitosis the RDN condenses into a thin line apart from the rest of the genome . Both coWe analyzed by ChIP-seq where cohesin (Mcd1p) and condensin (Brn1p) accumulate on one rDNA repeat . When weThese results are consistent with the cytology of the RDN in interphase and mitosis. In interphase, the diffused RDN puff allows for many contacts within the RDN. The shared boundary of the RDN and the rest of the genome promotes contacts between them. In mitosis, the condensation of the RDN to a thin line away from the rest of the genome limits contacts both between RDN and the rest of the genome, and within the RDN. The ability of condensin and cohesin to restrict local contacts has not been reported previously. The restriction of local interactions could alter the accessibility of gene regulatory regions. This mechanism for cohesin and condensin regulation of gene expression is distinct from their proposed roles as regulators of long-range interactions between enhancers and promoters.To better understand how the cohesin-rich regions organized the mitotic genome into positioned loops, we performed a more detailed comparison of the cohesin ChIP-seq and the Micro-C XL contact map. This analysis revealed three features of CARs in positioned loop organizations that were present throughout the yeast genome. First, each CAR formed at least two positioned loops, one by pairing with the CAR that precedes it on the chromosome, and one by pairing with the CAR that follows it .We confirmed and elaborated on these observations through genome-wide analyses. The distribution of the distance between adjacent Mcd1p peaks overlapped with the distribution of the distance between two anchors of the called positioned loop, consistent with most positioned loops being called between adjacent CARs . These pWe noted that the cohesin-rich regions in ChIP-seq presented a wide-range of peak heights. We ordered CARs based on the height of their peaks of cohesin binding from lower to higher, and we defined the top 10% as high-residency CARs. We also noted that the dots on the contact map presented different levels of intensity. We correlated the intensity of the dots/positioned loop in the contact map with the ChIP-seq peak height of the corresponding CAR . We founWe showed that chromosomes from mitotically arrested cells were organized into positioned loops, but when these positioned loops formed during the cell cycle and how long they persist remained unanswered. To address these questions, we arrested cells in G1 with alpha-factor. We then released cells from the arrest and monitored loop formation and cohesin binding to chromosomes every 15 min for one entire cell cycle at two different temperatures 23°C and 30°C. We used two different temperatures to assess whether loop formation was affected by the rate of progression through the cell cycle. In budding yeast, the Mcd1p subunit of cohesin is synthesized at the beginning of S phase, allowing the assembly of a functional cohesin complex . We deteThe number of positioned loops that were detected at different time intervals was smaller than the loops detected in mitotically arrested cells. . The 75 A key observation from our time course was that most of the cohesin deposition at CARs occurred 15 min before the appearance of the majority of positioned loop signal  and insights into the molecular determinants of loop formation.One feature of our study is the remarkable correlation between the anchors of positioned loops and cohesin associated regions (CARs). First, the genomic position of positioned loop anchors correlates almost perfectly with the position of CARs throughout the genome. The pairing of two anchors in a positioned loop always occurred between a CAR and the adjacent CAR on either side , but many CARs could also partner with more distal CARs (loop expansion). On average the portion between the two anchors for a positioned loop (~8 Kb) mirrors the average distance between adjacent CARs. Much larger loops were also observed (20–40 Kb), reflecting the distance between distal CARs. These data suggest that CARs determine the size and position of chromatin loops in mitotic budding yeast. Similarly, CARs have been suggested to determine the size and position of chromatin loops in meiosis . The patThe pervasive presence of CARs and their respective positioned loops along the chromosomes suggest that almost all, if not all, regions in the yeast genome can form a positioned loop. A similar omnipresence of positioned loops is also observed in yeast meiosis, and in mammalian cells . This shThe data from this and other studies in yeast revealed two differences between loop determinants in yeast and mammalian cells . First, Indeed, from our study of loop anchors, we observed a positive correlation between the level of cohesin binding to a CAR and its probability of acting as a loop anchor in a positioned loop. A key to understanding this correlation comes from two observations. In yeast and mammals, cohesin binding to chromosomes is split between a stably bound pool and a pool that is more dynamic . The ChIUsing these observations, we postulated a model to explain the correlation of the level of cohesin binding at CARs with the formation of loops and loop barriers . In S-phThis model can also explain the signal of CAR domains and their boundaries. Our model postulates that positioned loops of different sizes being formed between a certain number of CARs. The sequences within each loop are more likely to interact with each other resulting in a ‘loop domain’ square signal on the contact map with the spot signal for the positioned loops at its vertices. The ‘loop domains’ square signal of different sizes coming from individual cells will be piled-up in the contact map, resulting in the observed CAR domain .This model can also explain the pattern of positioned loops in Wpl1p- and Pds5p-depleted cells. Pds5p helps cohesin to establish cohesion but also forms a complex with Wpl1p to remove cohesin from chromosomes. It has been proposed that depletion of Wpl1p or Pds5p stabilizes the binding of dynamic cohesin, increasing the processivity of its looping activity and loop size . HoweverIn cells depleted for Wpl1p, the number of loops and loop expansion increased genome-wide compared to wild type. We can explain these changes in looping through our cohesin occupancy model and the fact that in budding yeast Wpl1p depletion reduced cohesin binding at CARs about 50% genome-wide  This reIn Pds5-depleted cells, the number of loops decreased and more pronounced loop expansion (100's of kilobases) occurred only from the centromere compared to wild-type or Wpl1p-depleted cells. These differences can also be explained by our occupancy model and the dramatic differences in cohesin binding to different CARs in Pds5p-depleted cells. In ChIP-seq, we observed a prominent cohesin binding at the centromeres, greatly reduced binding in the centromere-proximal regions, and almost no binding at centromere-distal regions. We suggest that the high occupancy of cohesin at the centromere will always stop the looping of the dynamic cohesin. On the other side, the looping will proceed down the arm for long distances before being stopped by the occasional CAR site occupied by stably bound cohesin. This process will result in a series of loops emanating from the centromeres to the arms in a cross pattern, as observed in our contact map. The low occupancy of the other CARs with stable cohesin is so low that dynamic cohesin is stopped rarely between these CARs, and therefore rarely generates loops on chromosome arms. Thus, the two very different looping patterns of Wpl1p- and Pds5p-depletion can be explained by our occupancy model. In summary, the patterns of cohesin binding to chromosomes and position of loops in wild-type and mutants suggest that loop expansion is restrained not only by decreasing the level of dynamic cohesin with looping activity but also by increasing the level of stably bound cohesin to CARs. In mammalian cells looping by dynamic cohesin may also be blocked by cohesin that is stably bound to chromosomes because of Sororin or CTCF.The analysis of our results presents a novel interpretation of how yeast cells organize their genomes into loops. A previous study analyzed the chromosome contacts density produced with Hi-C using in silico modeling . They prAll strains used in this study are derived from the A364A background and their genotypes are listed in To assess protein level, cells were collected, pelleted, washed with 1 × PBS and frozen at −80°C. Frozen pellets were resuspended in 200 µL of 20% TCA and broken for 40 s using a FastPrep-24 5G instrument (6.0 m/s each) (MP Bio). Lysates were diluted with 1 mL of 5%TCA and spun at 14,000 rpm for 10 min at 4° C. Pellets were resuspended in 2 × Laemmli buffer, boiled for 7 min, and spun. Cleared lysates were used for Western blotting on SDS-PAGE gels. Primary antibodies used were a mouse monoclonal anti-V5 used at a 1:5000 dilution , mouse monoclonal anti-Tub1 used at 1:20,000 dilution, Anti-Mcd1p antibody (V. Guacci (via Covance)) at 1:10000 dilution, Secondary antibody used were an HRP-conjugated goat anti-mouse or rabbit at 1:25,000 .Fixed cells were washed twice in 50 mM sodium citrate (pH 7.2), then treated with RNase A  overnight at 37°C. Proteinase K was then added to a final concentration of 0.2 mg/ml and samples were incubated at 50°C for 2 hr. Samples were sonicated for 30 s or until cells were adequately disaggregated. SYBR Green DNA I dye  was then added at 1:20,000 dilution and samples were run on a Guava easyCyte flow cytometer . 20,000 events were captured for each time point. Quantification was performed using FlowJo analysis software.10 cells of exponentially growing cells (in 0.5 L of YEP+ 2% glucose) were fixed with paraformaldehyde  for 1 hr. Glycine was added to stop the reaction at 0.125M for 10’. Cells were collected by centrifugation and washed once with PBS 1X buffer, once with TE 1X buffer and stored at −80C. Frozen cell pellets were resuspended in FA buffer  and disrupted three times using a FastPrep-24 5G instrument for 40 s (6.0 m/s each) (MP Bio). Chromatin was sheared 10 × 30 s on/30 s off with a Bioruptor Pico (Diagenode). Supernatant with chromatin extract was isolated after a 10 min centrifugation at 14,000 g at 4°C. Immunoprecipitation of DNA-protein was performed using 4 µL of the polyclonal rabbit anti-Mcd1p (Covance Biosciences) overnight at 4°C or anti-V5 for Brn1p-V5 (Invitrogen). Antibody-bound lysates were incubated with 120 µL Protein A dynabeads (Invitrogen 10001D) for 1 hr. Beads were then washed and the DNA eluted according to standard ChIP protocols: five successive washes with 1X FA, 1X FA with 0.5M NaCl, Lithium chloride detergent , and finally twice with TE buffer. DNA was then eluted from the beads with 300 ml of Elution buffer . To ensure antibody is fully removed samples are treated with 1 ml proteinase K (20 mg/ml) for 1 hr at 42 while shaking at 1000 rpm, and the ssDNA was then isolated using QIAGEN PCR purification kit (28104 QIAGEN) and eluted in 20 ml low EDTA TE. The library was prepared using the Accel-NGS 1S Plus DNA Library Kit (Swift Bioscience) following the manufacturer protocol. Libraries were sequenced using Illumina Hiseq 4000. The sequencing files were aligned to the SacCer3 yeast genome using Bowtie2 tool and bigwig files were normalized for number of reads. Identified regions of enrichment were called using MACS, with default settings.Cells were grown and arrested at 23°C as described in the previous paragraph. ChIP-seq was performed as previously described . Briefly, 1 × 10ChIP-seq data are accessible from the GEO repository accession number GSE151416.We used the Micro-C XL protocol reported in Crosslinked cells were permeabilized with MB1  on ice for 5 min. Chromatin was fragmented into ~90% mononucleosome and ~10% dinucleosome with Micrococcal Nuclease (MNase) (Worthington Biochem #LS004798). We recommend titrating the MNase digesting condition for each sample. Typically, we treat cells with various pre-titrated MNase concentrations and then incubate them at 37°C for 10 min with vigorously mixing. MNase reaction was inactivated by adding 2.5 mM EGTA and incubated at 65°C for 10 min. Chromatin was pelleted by centrifugation at 16,000 g at 4°C for 5 min and washed twice with ice-cold MB2 . We note that MNase titration to yield 90% monomer/10% dimers reduces un-digested (un-ligated) dimers contamination in Micro-C data.MNase-digested fragments were then subject to the customized three-step end-repair protocol. First, chromatin was resuspended in the end-repair buffer  and phosphorylated the 5’-ends with 25 units of T4 Polynucleotide Kinase (NEB #M0201) at 37°C for 15 min. Second, treating chromatin with 25 units of DNA Polymerase I, Large (Klenow) Fragment (NEB #M0210) in the absence of dNTPs at 37°C for 15 min allows the Klenow enzyme using its 3’-to-5’ exonuclease activity to convert the mixed types of nucleosomal ends to the cohesive ends. Tight binding and chemical crosslinking between DNA and nucleosome are supposed to prevent the Klenow enzyme’s 3’-to-5’ exonuclease activity from chewing nucleosomal DNA all the way through. Third, Klenow enzyme’s polymerase activity repairs the nucleosomal DNA ends to the blunt and ligatible ends upon the supplement of dNTPs including 66 µM dTTP, dGTP (NEB #N0446), biotin-dATP (Jena Bioscience #NU-835-BIO14), and biotin-dCTP (Jena Bioscience #NU-809-BIOX) in 1X T4 DNA ligase reaction buffer (NEB #B0202). The reaction was incubated at 25°C for 45 min with interval mixing and then inactivated with 30 mM EDTA at 65°C for 20 min. Chromatin was pelleted by centrifugation at 16,000 g at 4°C for 5 min and washed once with ice-cold MB3 .Biotinylated-nucleosomes were then subjected to the proximity ligation reaction with 5000 cohesive end units (CEU) of T4 DNA ligase (NEB #M0202) in 1X T4 DNA ligase reaction buffer (NEB #B0202) at room temperature for at least 1.5 hr with slowly shaking. After ligation, biotin-dNTPs at the DNA termini were removed with 500 units of Exonuclease III (NEB #M0206) in 1X NEBuffer 1 (NEB #B7001) at 37°C for 15 min. Samples were then reverse crosslinked with 1X proteinase K solution (500 ug/uL Proteinase K (ThermoFisher #AM2542), 1% SDS, 0.1 µg/µL RNaseA) at 65°C overnight. DNA was extracted by the standard Phenol:Chloroform:Isoamyl Alcohol (25:24:1) and ethanol precipitation procedure. DNA was then loaded and purified on ZymoClean column. Purified DNA was separated on a 3% NuSieve GTG agarose gel (Lonza #50081). The gel band corresponding to the size of dinucleosomal DNA (~250 to 350 bp) was cut and purified with Zymoclean Gel DNA Recovery Kit (Zymo #D4008). We note that the size-selection for DNA larger than 200 bp greatly reduces the ratio of unligated monomer in Micro-C data.Micro-C sequencing library was generated by using NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB #E7645) with some modifications. Purified DNA was end-repaired using End-it from Lucigen. 17.5 µl DNA was added to 2.5 ul 10X end-it buffer, 2.5 ul 10x ATP and 2.5 µl dNTP 10x, and finally 0.5 end-it enzyme was added. The mix was incubated at 25°C for 45 min and at 70°C fro 10 min. Then the Biotinylated-DNA was captured by Dynabeads MyOne Streptavidin C1 beads (ThermoFisher #65001) in 1X BW buffer  at room temperature for 20 min with gentle rotation. DNA-conjugated beads were washed twice with 1X TBW buffer  at 55°C for 5 min with vigorously shaking, rinsed once with Tris buffer (10 mM Tris-HCl pH 7.5), and then resuspended in Tris buffer. We then performed ‘on-bead’ end-repair/A tailing and adapter ligation following the NEB protocol. Adapter-ligated DNA was washed once with 1X TBW and rinsed once with Tris buffer before PCR amplification. Micro-C library was generated by using KAPA HiFi HotStart ReadyMix (Roche #KK2601) or Q5 High-Fidelity 2X Master Mix (NEB #E7645) with commercial recommended conditions. We recommend using a minimal PCR cycle to reduce PCR duplicates, typically ranging from 8 to 12 cycles that can generate high-quality data for yeast samples. Prior to sequencing, purifying the library twice with 0.85X AMPure XP beads (Beckman #A63880) can eliminate primer dimers and adapters. We used Illumina 100 bp paired-end sequencing (PE100) to obtain ~50M reads for each replicate in this study.Micro-C data are accessible from the GEO repository accession number GSE151553.https://github.com/nservant/HiC-Pro. We recommend checking these quality control statistics before moving forward to the downstream analysis: (1) bowtie mapping rate; (2) reads pairing percentage; (3) ratio of sequencing artifacts; (4) ratio of cis/trans contacts; (5) unligated monomer percentage. If any of the above statistics is not in the optimal range, one might consider checking mapping and filtering parameters or further optimize the Micro-C experiment.Each fastq mate was mapped to the yeast sacCer3 genome independently using Bowtie 2.3.0  with ‘vehttps://github.com/mirnylab/cooler; https://github.com/aidenlab/juicer; https://www.4dnucleome.org/). Contact matrices were then normalized by using iterative correction (IC) in COOL files ,' ‘IN-OUT’ (+/+),' ‘OUT-IN’ (-/-),' and ‘OUT-OUT’ (-/+)' according to the readouts of Illumina sequencing . ‘UNI’ pSlopes of contact decay curves were obtained by measuring slopes in a fixed-width window. After the window searching through the entire range of curves, the slopes obtained from each window were connected and plotted against the genomic distance.--pearson 0.3 --min-dist 5000 --max-dist 1000000 --min-separation 1000 --perc-undetected 10). Hi-C data from Loops  in this study were identified by using the HICCUPS algorithm  in the JGenome-wide loop intensity was assessed by the aggregate peak analysis (APA) . We usedWe used the same approach to analyze the genome-wide target-centered loop intensity. Instead of aggregating at the intersection of loop anchors, the matrix is centered at the paired MCD1 ChIP-seq peaks. The pairs are selected for the adjacent peaks at distances shorter than 100 kb or the indicated peak intervals. The pile-up matrix was then normalized by KR, ICE, or distance, as indicated in the figure legend.https://github.com/mirnylab/cooltools) (https://github.com/deeptools/HiCExplorer)   or the ‘xplorer)  to identi,j=((Ci-Dstart)/(Dend-Dstart), (Cj-Dstart)/(Dend-Dstart)), where Ci,j is a pair of contact loci within domain D that is flanked by Dstart and Dend, and Ni,j is a pair of the rescaled coordinates. The rescaled domains can be aggregated at the center of the plot with ICE or distance normalization.For aggregate domain analysis (ADA), each domain was rescaled to a pseudo-size by N In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This study examines the structure of budding yeast chromosomes in mitosis using the high resolution chromosome capture technique, Micro-C XL. By analysis of specific mutants, high quality datasets are presented to unequivocally show that yeast chromosomes are organised into loops that are defined by cohesin. A model is presented for cohesin-dependent loop formation.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, including Adèle L Marston as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and Jessica Tyler as the Senior Editor.Thank you for submitting your article "Cohesin residency determines chromatin loop patterns" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as Summary:S. cerevisiae, using a high-resolution method termed micro-C (an evolution of Hi-C) that was recently developed by one of the authors .The manuscript describes the generation and analysis of chromosome conformation changes in synchronised populations of budding yeast, The authors describe a punctate interaction pattern that arises in metaphase-arrested cells, is dependent on the cohesin subunit Mcd1, and is altered upon depletion of the cohesin regulators Wpl1 and Pds5. The authors propose a modification of prior models of loop extrusion with barrier elements to interpret the microC patterns, specifically proposing that the stable binding of cohesin acts as a barrier to loop extrusion by dynamic cohesin.The datasets themselves are informative, and will be of great use to the research community. It should be noted that some of the key findings were described in earlier work by Schalbetter et al., (2019) and . The paper is technically impressive, the beautiful interaction patterns are compelling and the increased resolution of the micro-C both reinforces and extends conclusions from earlier work. However, there are a number of concerns related to how the analysis was done and how the findings are interpreted. The general perception of this manuscript would also be boosted by a small amount of additional analysis.Essential revisions:1) Though the data quality in the current study is unmatched, others have convincingly shown the presence of loops in budding yeast by Hi-C  and these studies need to be appropriately acknowledged throughout. Credit should be given to Dauban et al., 2020 for cohesin-dependent domains in G1, demonstrating cohesion-independent chromosome looping.2) The authors must substantially rephrase and rewrite the text to avoid confusing the presence/absence of the punctate/grid-like focal interactions detected by Hi-C/micro-C with the presence/absence of "loops". The importance of this point cannot be overstated.Critically, whilst it is correct to interpret focal matrix signals to represent the higher-order polymer interactions that can arise at loop bases, it is incorrect to infer that the lack of such focal interactions in a matrix indicates the lack of loops. Rather, the latter simply means that there are no "well-positioned" loops within the population of cells assayed.Fundamentally, looped chromatin can be inferred from the relationship between the decay rate of interactions over distance (P(s)) without any explicit need to observe focal interactions-be this either because the dataset being studied lacks resolution to resolve such interactions , or because loops may not arise at preferred locations (across the population). Indeed, prior mitotically-arrested yeast datasets were able to estimate both average loop length, and average loop number (per cell), despite the lower spatial resolution of the data analysed .3) A brief sentence subsection “Chromosome domains in mitosis” states that the boundaries of CAR domains are enriched at terminators , where CARs are preferentially located. This point may be far from trivial. I indeed wonder whether the anchors of the loops presented in this paper overall correspond with terminators. A recent bioRxiv piece by the Cees Dekker and Uhlmann labs raises serious doubt as to whether yeast cohesin (in contrast to human cohesin) can in fact extrude loops. Yeast cohesin may still act as a topological anchor that when pushed along DNA enables the enlargement of a loop, but cohesin itself might not act as the motor. Of course the current manuscript does not need to fully address this point, but it would be relatively straightforward to assess whether the loop anchors genome-wide appear to correspond with terminators in general and/or with sites of convergent transcription in particular. Such an analysis could for sure add to the impact and the novelty of the current paper.4) Do those CARs that act as boundaries then maybe correspond with convergent genes? If cohesin were pushed to these sites by transcription from two sides, this would also explain why these CARs have higher cohesin peaks by ChIP. It would be good to then also place these findings in the context of a recent paper by Paldi et al., on how convergent transcription shapes pericentromeres.5) In Figure 2D, depletion of Mcd1 leads to the loss of focal loops. Yet, one can still observe domains. Also, here, it would be good to link these structures to the presence of genes. I would actually recommend that the authors throughout plot the genes above the matrices. A comparison of these domains with the Mcd1-independent domains observed in the previously published Micro-C work from asynchronous cells would also be useful here.6) Figure 5—figure supplement 1: The plots in panels B and F suggest that loss of Wapl or Pds5 yields similar results, while e.g. panels A and E suggest that loss of these two factors yields very different results. Please explain.7) The authors appear to conclude that it is non-random cohesin binding that defines the non-random location of loops (last sentence of Abstract for example). How do they exclude that it is not the non-random position of loops (generated by SMCs at their bases) that creates the non-random cohesin pattern?8) Please specify in the text  for how long cells were arrested, and for the depletion experiments, how long after auxin was added were specific samples harvested? Have the authors confirmed depletion of AID-tagged proteins by western blotting in each experiment?9) Critically, it is far from clear what genotypes and conditions are being compared. For example, is the wild type nocodazole sample also treated with auxin (as a control) when compared to the mcd1-AID, pds5-AID, and wpl1-AID mutants? Or are the AID strains processed {plus minus}auxin? Were durations of nocodazole arrest identical in the comparisons presented?10) Please provide flow cytometry profiles to help demonstrate the homogeneity (or otherwise) of the samples/timepoints being analysed for each dataset in every figure. This is relevant for all data samples – especially when auxin may have been added for an unspecified time after the initial nocodazole arrest. Moreover, this is especially important for the S-phase timecourse presented in Figure 4. For these experiments, please explain why t = 0 minutes is not included as the starting state.eLife. Your revised article has been evaluated by Jessica Tyler (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Cohesin residency determines chromatin loop patterns" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:Although it is now clearer what had been shown in previous studies and how the current study advances the field, there are still instances where more context is required and there are some points that need revision to improve clarity.Subsection “Micro-C XL reveals prevalent chromatin loops with a defined position in mitotic chromosomes”: The sentence should read "The pervasive presence of positions loops shown…."Subsection “Micro-C XL reveals prevalent chromatin loops with a defined position in mitotic chromosomes”: "These positioned loops were mostly absent in contact maps using datasets from previously published Hi-C maps of mitotic cells ". This is not completely accurate – though there is no doubt that the Micro-C XL offers improved resolution, the positioned loops can be seen in the Schalbetter et al., data and are clearly present in the Paldi, 2020 data, just less distinct. This statement should be revised to "" These positioned loops were less obvious in contact maps using datasets from previously published Hi-C maps of mitotic cells ".Subsection “Cohesin complex mediates loop formation”. As a general comment about the role of cohesin in loop formation in budding yeast, this should include a reference to Schalbetter, 2019. "To assess if cohesin was also needed for the formation of positioned loops in budding yeast…". The yeast meiotic observations (referenced in the Introduction) clearly demonstrate cohesin-dependence for loop positioning in yeast. To avoid confusion, it is suggested to write "whilst cohesin-dependent positioned loops have been clearly demonstrated in yeast meiosis, whether or not the same is true in mitotically-dividing yeast cells is unclear."Subsection “Cohesin complex mediates loop formation”. These two paragraphs confirm conclusions already reported by others and this should be made clear. Schalbetter et al., 2017 already demonstrated that looping was mostly cohesin rather than condensin-dependent – this was the main conclusion of that study. Similarly, this point is confirmed in Daubain et al., 2020. Please preface these two paragraphs as confirming the previous findings in these studies, rather than presenting this as a new finding.Subsection “Cohesin complex mediates loop formation”. A comparison to the meiotic result would provide better context and make it clear that this general concept has already been clearly demonstrated in the same organism albeit under different growth conditions. e.g. "Thus, we conclude, as in yeast meiosis , that cohesin is required for positioned loop formation genome wide.".Figure 2B. What are the units of the scale bar in B? How is this calculated? Is it a ratio? Is it a linear scale? Please specify in the legend.Figure 2—figure supplement 2 and subsection “Cohesin complex mediates loop formation”. The main conclusion from this figure is that the increased resolution of the microC-XL allows the authors to draw the conclusion that the short distance interactions are also dependent on cohesin. Otherwise the datasets broadly similar and this point should be explicitly stated.Subsection “Cohesin complex mediates loop formation”. It is not clear why this sentence was changed from the original. The revised text is now incorrect. Whilst it is true that cohesin is responsible for chromosome individualisation, cohesin activity leads to a *reduction* in interchromosomal contacts.Subsection “Cohesin and condensin shape the rDNA locus in mitosis”: should be a single sentence "……, while…" Also, please remove the comma within the text: "…loci (RDN37 and RDN5), spaced by two non-transcribed regions (NTS1 and NTS2)."Subsection “Cohesin and condensin shape the rDNA locus in mitosis”: Schalbetter et al., 2017 also looked at the rDNA by Hi-C in the absence of cohesin and condensin with similar conclusions and should be cited here.Subsection “Cohesin organizes chromosomal loops genome-wide”. For clarity, it is important to make it clear that the analysis of inter-anchor distances described here is only considering adjacent (+1) distances.Subsection “Cohesin organizes chromosomal loops genome-wide” "While we detected…". As written, this sentence doesn't make sense grammatically. Please clarify what is meant.Subsection “Cohesin organizes chromosomal loops genome-wide”. Without a direct test of causality, "confirming" is an overstatement. Please revise to "suggesting", or "supporting a model where.".Subsection “Chromosome loops form during S-phase”. Please explain int the main text why two different temperatures were performed. The data are nice, but it is entirely unclear what conclusion the reader is expected to draw from the two-temperatures without further guidance.Subsection “Chromosome loops form during S-phase”. "wild type cells" is stated, but do the authors mean 'wild-type cells arrested at mitosis with nocodazole'?Subsection “Chromosome loops form during S-phase”. An alternative explanation is that there could be an increase in cohesin occupancy on chromosomes in mitotic arrested cells?Subsection “Chromosome loops form during S-phase”: To make clear that this is speculation: "Loop extrusion may be mediated by a different pool of cohesin that is activated later in S phase.Subsection “Cohesin regulators affect the size and location of chromosomal loops”. To improve readability, please add commas around the central clause as indicated here: "This paradox can be explained if Wpl1p, by dissociating randomly bound cohesin from chromosomes, allows efficient accumulation of cohesin at CARs ."Subsection “Cohesin regulators affect the size and location of chromosomal loops”. What is the analysis that lead to the conclusion that the amount of cohesin detected at CARs was reduced 50%. The ChIP-seq data themselves are not calibrated. If analysis has not been done to determine this point, please revise the text accordingly. This also applies to the description for the data in pds5 mutant.Subsection “Chromosome domains in mitosis”. To aid the reader, please provide a reference to Figure 7—figure supplement 1A here. However…the presented figure does not allow the reader to reach the conclusion that: "A visual inspection pointed out that most cohesin-depleted domains have a roughly similar boundary distribution to the ones detected in asynchronous cell populations." The few patterns that can be seen at this scale scale look to be quite different when comparing the mcd1-AID to the asynchronous Hsieh dataset.Subsection “Chromosome domains in mitosis”. Where do these numbers come from? Is there a relevant graph that has been omitted?Subsection “Chromosome domains in mitosis”. The authors should be wary of their conclusions drawn in this analysis. It is very likely that the domains detected by the algorithms in the wild type are almost entirely driven by the loop signals at the apex (and edge). . Thus, obviously, if loops disappear, so will the ability to detect these domains bioinformatically.Discussion section. Since this point has already clearly been demonstrated for preferred Rec8 sites in meiosis, Schalbetter, 2019 should be both mentioned and referenced here to avoid overstating the novelty of the findings.Discussion section. Similarly, this finding was also demonstrated, clearly, by the meiotic yeast data, and thus should be described and cited.Discussion section: As above, the conclusion that cohesin defines the loops is not entirely novel and previous work showing this should be cited here.Discussion section. The model that is described is entirely congruent with the finding of the simulations employed by Schalbetter, 2019, which employed a stochastic model of loop expansion driven by extruders that could be blocked by barriers at preferred cohesin-binding sites (Rec8 in this case). As such, it would be entirely relevant to mention the congruence of the author's data (and model) with the findings of the polymer simulations developed by others. Summary:S. cerevisiae, using a high-resolution method termed micro-C (an evolution of Hi-C) that was recently developed by one of the authors .The manuscript describes the generation and analysis of chromosome conformation changes in synchronised populations of budding yeast, The authors describe a punctate interaction pattern that arises in metaphase-arrested cells, is dependent on the cohesin subunit Mcd1, and is altered upon depletion of the cohesin regulators Wpl1 and Pds5. The authors propose a modification of prior models of loop extrusion with barrier elements to interpret the microC patterns, specifically proposing that the stable binding of cohesin acts as a barrier to loop extrusion by dynamic cohesin.The datasets themselves are informative, and will be of great use to the research community. It should be noted that some of the key findings were described in earlier work by Schalbetter et al., (2019) and . The paper is technically impressive, the beautiful interaction patterns are compelling and the increased resolution of the micro-C both reinforces and extends conclusions from earlier work. However, there are a number of concerns related to how the analysis was done and how the findings are interpreted. The general perception of this manuscript would also be boosted by a small amount of additional analysis.Essential revisions:1) Though the data quality in the current study is unmatched, others have convincingly shown the presence of loops in budding yeast by Hi-C  and these studies need to be appropriately acknowledged throughout. Credit should be given to Dauban et al., 2020 for cohesin-dependent domains in G1, demonstrating cohesion-independent chromosome looping.We cited Schalbetter et al., 2017, 2019; Dauban et al., 2020 as indicated. We would like to point out that the Dauban reference was already referenced in the text.2) The authors must substantially rephrase and rewrite the text to avoid confusing the presence/absence of the punctate/grid-like focal interactions detected by Hi-C/micro-C with the presence/absence of "loops". The importance of this point cannot be overstated.Critically, whilst it is correct to interpret focal matrix signals to represent the higher-order polymer interactions that can arise at loop bases, it is incorrect to infer that the lack of such focal interactions in a matrix indicates the lack of loops. Rather, the latter simply means that there are no "well-positioned" loops within the population of cells assayed.Fundamentally, looped chromatin can be inferred from the relationship between the decay rate of interactions over distance (P(s)) without any explicit need to observe focal interactions-be this either because the dataset being studied lacks resolution to resolve such interactions , or because loops may not arise at preferred locations (across the population). Indeed, prior mitotically-arrested yeast datasets were able to estimate both average loop length, and average loop number (per cell), despite the lower spatial resolution of the data analysed .We changed loops to “positioned loops” through the paper. To avoid any confusion to the scope of our paper, we defined the positioned loops initially and stated that we were going to focus only on those loops. We also indicated that random loops might be present and referenced the papers that analyzed those.3) A brief sentence subsection “Chromosome domains in mitosis” states that the boundaries of CAR domains are enriched at terminators , where CARs are preferentially located. This point may be far from trivial. I indeed wonder whether the anchors of the loops presented in this paper overall correspond with terminators. A recent bioRxiv piece by the Cees Dekker and Uhlmann labs raises serious doubt as to whether yeast cohesin (in contrast to human cohesin) can in fact extrude loops. Yeast cohesin may still act as a topological anchor that when pushed along DNA enables the enlargement of a loop, but cohesin itself might not act as the motor. Of course the current manuscript does not need to fully address this point, but it would be relatively straightforward to assess whether the loop anchors genome-wide appear to correspond with terminators in general and/or with sites of convergent transcription in particular. Such an analysis could for sure add to the impact and the novelty of the current paper.Many papers demonstrated that the vast majority of CARs in yeast are preferentially located at terminators of convergent genes in budding yeast (80-85%), suggesting that cohesin is pushed to CAR sites by transcription . We now reference that in the text. However, transcription as a motor to push cohesin as a way to form positioned loops is extremely unlikely because a positioned loop contains multiple genes with different directions of transcription in between the two anchors.4) Do those CARs that act as boundaries then maybe correspond with convergent genes? If cohesin were pushed to these sites by transcription from two sides, this would also explain why these CARs have higher cohesin peaks by ChIP. It would be good to then also place these findings in the context of a recent paper by Paldi et al., on how convergent transcription shapes pericentromeres.Similar to the comment on point 3, being positioned at the terminators of convergent genes is not only a feature of the boundaries of CAR-domain (that correspond to high cohesin peak by ChIP-seq), but is a feature of almost all CARs in yeast.5) In Figure 2D, depletion of Mcd1 leads to the loss of focal loops. Yet, one can still observe domains. Also, here, it would be good to link these structures to the presence of genes. I would actually recommend that the authors throughout plot the genes above the matrices. A comparison of these domains with the Mcd1-independent domains observed in the previously published Micro-C work from asynchronous cells would also be useful here.We described in the text the presence of Cohesin dependent domain (CAR domain) and cohesin independent domain whose presence is revealed upon depletion of Mcd1p. This is similar to the human domains observed in the referenced papers. We have an analysis that shows the preferential location of the 2 different domain families with respect to the genes (CAR domains on terminators and cohesin-independent domains on promoters). We added the suggested comparison with previously published asynchronous cells and show that the majority of cohesin-independent domains overlap with the domains registered on asynchronous cells. In the revised manuscript, we extensively tested multiple methods  to identify domains and found that the results from hicexplorer agree with our visual inspection more consistently. We updated the related figures and text with the new analysis. Given the packed distribution of genes and the size of chromosomes shown, a plot for genes is not really informative .6) Figure 5—figure supplement 1: The plots in panels B and F suggest that loss of Wapl or Pds5 yields similar results, while e.g. panels A and E suggest that loss of these two factors yields very different results. Please explain.WPL1-AID and PDS5-AID are significantly different. To make this difference more apparent to the reader, we plotted them together. The range/distance of slower decaying interactions reflect the range of size of the foci pattern on the contact map. WPL1-AID presents an enrichment of contacts slightly larger than WT, indicating an enrichment for bigger loops. PDS5-AID shows contact enrichment around 40 to 100kb and does not appear to have the enrichment in the same range as in the WT, suggesting the existence of extra wide-ranging loops is likely in the mutant .The decay curves for 7) The authors appear to conclude that it is non-random cohesin binding that defines the non-random location of loops (last sentence of Abstract for example). How do they exclude that it is not the non-random position of loops (generated by SMCs at their bases) that creates the non-random cohesin pattern?In the time course experiment, the bulk of cohesin binding to CAR by ChIP-seq appears 15minutes before the bulk of “well positioned” loops. Hence our conclusion that cohesin binding defines the position of loops.8) Please specify in the text  for how long cells were arrested, and for the depletion experiments, how long after auxin was added were specific samples harvested? Have the authors confirmed depletion of AID-tagged proteins by western blotting in each experiment?We thank the reviewer for pointing this out. We had included this information in the original Material and methods section but it was unclearly presented. We added further description of the cultures and the strain used in the Material and methods section to avoid any confusion. Briefly, all the strains were treated with the same exact regimen. The same amount of Auxin was added (both in WT and AID strains) and treated with alpha-factor and nocodazole for the same period of time. In fact, most of the experiments were done on the same day side by side. The depletion was checked on every experiment, and we added the western blot to show that every AID tagged protein is not detected upon auxin. The strains were analyzed extensively in . We added the reference for the strains in the strain table.9) Critically, it is far from clear what genotypes and conditions are being compared. For example, is the wild type nocodazole sample also treated with auxin (as a control) when compared to the mcd1-AID, pds5-AID, and wpl1-AID mutants? Or are the AID strains processed {plus minus}auxin? Were durations of nocodazole arrest identical in the comparisons presented?Please refer to the comment on point 8. We clarify further the strains used in the material and methods. Basically, the strains: WT, MCD1-AID, BRN1-AID, MCD1-AID/BRN1-AID, WPL1-AID and PDS5-AID were treated with auxin and nocodazole for the same time.10) Please provide flow cytometry profiles to help demonstrate the homogeneity (or otherwise) of the samples/timepoints being analysed for each dataset in every figure. This is relevant for all data samples – especially when auxin may have been added for an unspecified time after the initial nocodazole arrest. Moreover, this is especially important for the S-phase timecourse presented in Figure 4. For these experiments, please explain why t = 0 minutes is not included as the starting state.All the mitotically arrested cells were treated with the same amount of drug (auxin and nocodazole) for the same length of time, as indicated in the original Materials and methods section. We checked their cell-cycle arrest by flow cytometry and all the strains were arrested in G2/M and are indistinguishable from one another. We agree that flow cytometry profiles are important in the time course and we added the flow cytometry profile below each panel.Since cohesin-subunit Mcd1p is not present on alpha-factor arrested cells, but is needed to form loops (defined and undefined), we decided to start the analysis a few minutes after the release from alpha-factor. Furthermore, in our experience alpha-factor arrested cells (t=0) are slightly different from G1 cells. Therefore, we allowed the cells to recover from alpha-factor for a few minutes before beginning our analysis. We start seeing foci on the contact map at a later time point at both temperatures used, showing that we did not lose the initiation nor the range of loop formation.The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:Although it is now clearer what had been shown in previous studies and how the current study advances the field, there are still instances where more context is required and there are some points that need revision to improve clarity.Subsection “Micro-C XL reveals prevalent chromatin loops with a defined position in mitotic chromosomes”: The sentence should read "The pervasive presence of positions loops shown…."We changed to “shown”.Subsection “Micro-C XL reveals prevalent chromatin loops with a defined position in mitotic chromosomes”: "These positioned loops were mostly absent in contact maps using datasets from previously published Hi-C maps of mitotic cells ". This is not completely accurate – though there is no doubt that the Micro-C XL offers improved resolution, the positioned loops can be seen in the Schalbetter et al. data and are clearly present in the Paldi 2020 data, just less distinct. This statement should be revised to "" These positioned loops were less obvious in contact maps using datasets from previously published Hi-C maps of mitotic cells ".We changed to: “These positioned loops were less frequent and less defined in contact maps using datasets from previously published Hi-C maps of mitotic cells.” This qualitative statement was subsequently validated by quantitative analysis that showed around 8 times more spots being called in our dataset.Subsection “Cohesin complex mediates loop formation”. As a general comment about the role of cohesin in loop formation in budding yeast, this should include a reference to Schalbetter, 2019. "To assess if cohesin was also needed for the formation of positioned loops in budding yeast…". The yeast meiotic observations (referenced in the Introduction) clearly demonstrate cohesin-dependence for loop positioning in yeast. To avoid confusion, it is suggested to write "whilst cohesin-dependent positioned loops have been clearly demonstrated in yeast meiosis, whether or not the same is true in mitotically-dividing yeast cells is unclear."In the Introduction we changed the sentence to underscore that cohesin-dependent loops in meiosis were previously detected in the referenced paper: “A recent study revealed a robust signal for cohesin-dependent positioned loops during meiosis , revealing that yeast has indeed the capability to efficiently form positioned loops.”In the Results section we focus our attention on mitotic yeast.Subsection “Cohesin complex mediates loop formation”. These two paragraphs confirm conclusions already reported by others and this should be made clear. Schalbetter et al., 2017 already demonstrated that looping was mostly cohesin rather than condensin-dependent – this was the main conclusion of that study. Similarly, this point is confirmed in Daubain et al., 2020. Please preface these two paragraphs as confirming the previous findings in these studies, rather than presenting this as a new finding.Subsection “Cohesin complex mediates loop formation” we clearly credited the mentioned papers: ‘In budding yeast, cohesin is needed for loops genome-wide .’We added “Previous studies have shown that genome-wide loops are cohesin- not condensin-dependent . We tested whether the newly detected mitotic positioned loop in our study were also condensin-independent, by depleting Brn1p, a subunit of condensin .”Subsection “Cohesin complex mediates loop formation”. A comparison to the meiotic result would provide better context and make it clear that this general concept has already been clearly demonstrated in the same organism albeit under different growth conditions. e.g. "Thus, we conclude, as in yeast meiosis , that cohesin is required for positioned loop formation genome wide.".The Results section focuses on the mitotic yeast. The idea that chromosome loops in mitosis and meiosis should be similar because they come from the same organism under different growth conditions ignores the dramatic structural  and functional differences between meiotic and meiosis chromosomes. Therefore, we think it is more appropriate to compare with the meiotic results in the introduction and discussion, as we now extensively do.Figure 2B. What are the units of the scale bar in B? How is this calculated? Is it a ratio? Is it a linear scale? Please specify in the legend.It is simply the number of reads from the ChIP-seq file. We specified that now in the legend.Figure 2—figure supplement 2 and subsection “Cohesin complex mediates loop formation”. The main conclusion from this figure is that the increased resolution of the microC-XL allows the authors to draw the conclusion that the short distance interactions are also dependent on cohesin. Otherwise the datasets broadly similar and this point should be explicitly stated.We are confused by this comment. We stated: “Previous studies have characterized that the contact enrichment at ~10 – 20kb appears to be cohesin dependent. In addition to those contacts, the higher resolution Micro-C XL data revealed a finer and more pronounced separation between the curves of wild-type and cohesin depletion, showing that the cohesin-dependent structures overrepresent at the length of ~3 – 15kb .” This statement acknowledges the similarity in the big range and then focus on the differences between the datasets, as asked by the previous round of reviews.Subsection “Cohesin complex mediates loop formation”. It is not clear why this sentence was changed from the original. The revised text is now incorrect. Whilst it is true that cohesin is responsible for chromosome individualisation, cohesin activity leads to a *reduction* in interchromosomal contacts.We changed to: “Therefore, cohesin is also responsible for chromosome individualization and decreased inter-chromosome contacts in mitosis, as previously shown by Hi-C studies .”Subsection “Cohesin and condensin shape the rDNA locus in mitosis”: should be a single sentence "……, while…" Also, please remove the comma within the text: "…loci (RDN37 and RDN5), spaced by two non-transcribed regions (NTS1 and NTS2)."RDN37 and RDN5) spaced by two non-transcribed regions (NTS1 and NTS2). In interphase, the RDN locus forms a diffused puff adjacent to the rest of the chromosomal DNA, while in mitosis the RDN condenses into a thin line apart from the rest of the genome  .”We changed to: “The ribosomal DNA locus (RDN) has 75 to 100 tandem repeats of the 9.1 kilobases rDNA unit, with two transcribed loci . However, their impact on chromosomal contacts within the RDN had not been reported.”Subsection “Cohesin organizes chromosomal loops genome-wide”. For clarity, it is important to make it clear that the analysis of inter-anchor distances described here is only considering adjacent (+1) distances.The inter-anchor distances described here is for every single positioned loop being called by the loop calling program, not just the +1.Subsection “Cohesin organizes chromosomal loops genome-wide” "While we detected…". As written, this sentence doesn't make sense grammatically. Please clarify what is meant.We changed to: “We detected a slow decrease in the loop signal as a function of the distance till the +5 CAR . This data showed that the biggest positioned loops reached sizes of tens of kilobases.”Subsection “Cohesin organizes chromosomal loops genome-wide”. Without a direct test of causality, "confirming" is an overstatement. Please revise to "suggesting", or "supporting a model where.".We changed to “suggesting”.Subsection “Chromosome loops form during S-phase”. Please explain int the main text why two different temperatures were performed. The data are nice, but it is entirely unclear what conclusion the reader is expected to draw from the two-temperatures without further guidance.We added: We used two different temperatures to assess whether loop formation was affected by the rate of progression through the cell cycle.Subsection “Chromosome loops form during S-phase”. "wild type cells" is stated, but do the authors mean 'wild-type cells arrested at mitosis with nocodazole'?We changed to: “Interestingly, the contact enrichment in the cycling cells appeared to be slightly shorter than in the mitotically arrested wild-type cells.”Subsection “Chromosome loops form during S-phase”. An alternative explanation is that there could be an increase in cohesin occupancy on chromosomes in mitotic arrested cells?We changed to: “This decreased detection of positioned loops in the time course might reflect increased noise due to imperfect synchronization, an increase of cohesin binding to chromosome during the mitotic arrest, or the stabilization of loops and loop expansion during the mitotic arrest.”Subsection “Chromosome loops form during S-phase”: To make clear that this is speculation: "Loop extrusion may be mediated by a different pool of cohesin that is activated later in S phase.We changed as suggested.Subsection “Cohesin regulators affect the size and location of chromosomal loops”. To improve readability, please add commas around the central clause as indicated here: "This paradox can be explained if Wpl1p, by dissociating randomly bound cohesin from chromosomes, allows efficient accumulation of cohesin at CARs ."We changed as suggested.Subsection “Cohesin regulators affect the size and location of chromosomal loops”. What is the analysis that lead to the conclusion that the amount of cohesin detected at CARs was reduced 50%. The ChIP-seq data themselves are not calibrated. If analysis has not been done to determine this point, please revise the text accordingly. This also applies to the description for the data in pds5 mutant.We changed the text to explicitly indicate to the reader that the drop in signal in the 2 strains was assessed by simple visual inspection. The reduction presented in our dataset, reflects the magnitude of cohesin binding drop observed previously in other studies (referenced in the text).We changed that to: “Depletion of Wpl1p reduced the amount of cohesin being detected at CARs  as previously reported for yeast .”“As published previously, Pds5p depletion caused a marked loss of cohesin all along the chromosome arms from visual inspection  .”Subsection “Chromosome domains in mitosis”. To aid the reader, please provide a reference to Figure 7—figure supplement 1A here. However…the presented figure does not allow the reader to reach the conclusion that: "A visual inspection pointed out that most cohesin-depleted domains have a roughly similar boundary distribution to the ones detected in asynchronous cell populations." The few patterns that can be seen at this scale scale look to be quite different when comparing the mcd1-AID to the asynchronous Hsieh dataset.We added the figure reference. We changed the text to reflect that (some instead of most): A visual inspection pointed out that some cohesin-depleted domains have a roughly similar boundary distribution to the ones detected in asynchronous cell populations . We quantified this overlap by genome-wide analysis confirming that around 64% of the boundaries in the cohesin-depleted cells were located at the same positions as the boundaries detected in an asynchronous population, while only 14% overlapped with the boundaries of wild-type domains.Subsection “Chromosome domains in mitosis”. Where do these numbers come from? Is there a relevant graph that has been omitted?We presented the overlap between the datasets just in the text, since it is just 2 numbers.Subsection “Chromosome domains in mitosis”. The authors should be wary of their conclusions drawn in this analysis. It is very likely that the domains detected by the algorithms in the wild type are almost entirely driven by the loop signals at the apex (and edge). . Thus, obviously, if loops disappear, so will the ability to detect these domains bioinformatically.We used a sliding window algorithm to detect the boundary-delimited domains. In our test, the method is minimally impacted by the present of spots at the apex. Over 90% of boundaries/domains were recovered in the data after removing the focal signals at the corner spots. Furthermore, the majority of the domains are called by the presence of a distinct square signal that does not present a strong spot signal at their apexes.Discussion section. Since this point has already clearly been demonstrated for preferred Rec8 sites in meiosis, Schalbetter, 2019 should be both mentioned and referenced here to avoid overstating the novelty of the findings.We added: These data suggest that CARs determine the size and position of chromatin loops in mitotic budding yeast. Similarly, CARs have been suggested to determine the size and position of chromatin loops in meiosis . The patterns of positioned loops in mitosis and meiosis are different, likely reflecting differences in the chromosomal structure and function in these two different yeast cell types.Discussion section. Similarly, this finding was also demonstrated, clearly, by the meiotic yeast data, and thus should be described and cited.We added: A similar omnipresence of positioned loops is also observed in yeast meiosis, and in mammalian cells .Discussion section: As above, the conclusion that cohesin defines the loops is not entirely novel and previous work showing this should be cited here.We added: The data from this and other studies in yeast revealed two differences between loop determinants in yeast and mammalian cells .Discussion section. The model that is described is entirely congruent with the finding of the simulations employed by Schalbetter, 2019, which employed a stochastic model of loop expansion driven by extruders that could be blocked by barriers at preferred cohesin-binding sites (Rec8 in this case). As such, it would be entirely relevant to mention the congruence of the author's data (and model) with the findings of the polymer simulations developed by others.We added: A similar model has been suggested for yeast meiotic chromosomes through polymer simulation modeling, and for mammalian mitotic chromosomes through an ingenious approach that allows differentiating contacts within and between sister chromatids .


Promoter choice is an essential consideration for transgene expression in gene therapy. The expression of multiple genes requires ribosomal entry or skip sites, or the use of multiple promoters. Promoter systems comprised of two separate, divergent promoters may significantly increase the size of genetic cassettes intended for use in gene therapy. However, an alternative approach is to use a single, compact, bidirectional promoter. We identified strong and stable bidirectional activity of the RPBSA synthetic promoter comprised of a fragment of the human Rpl13a promoter, together with additional intron/exon structures. The Rpl13a-based promoter drove long-term bidirectional activity of fluorescent proteins. Similar results were obtained for the EF1-α and LMP2/TAP1 promoters. However, in a lentiviral vector, the divergent bidirectional systems failed to produce sufficient titres to translate into an expression system for dual chimeric antigen receptor (CAR) expression. Although bidirectional promoters show excellent applicability to drive short RNA in Sleeping Beauty transposon systems, their possible use in the lentiviral applications requiring longer and more complex RNA, such as dual-CAR cassettes, is limited.  Bidirectional promoters allow transcription from both the sense and antisense direction within a region defined as <1 kb apart . In the Surprisingly, the use of naturally occurring bidirectional promoters in synthetic biology has largely been overlooked and separate, divergently placed dual promoters are commonly used in available vectors . The enhCompact bidirectional promoters would be useful for synthetic biology with potential use in gene therapy . GeneticOur findings highlight the potential of bidirectional promoters for driving genome-integrated gene expression in the Sleeping Beauty system. However, our findings suggest that the investigated bidirectional promoters are sub-optimal for the lentiviral production of longer RNA encoding longer dual-CAR constructs.Dual fluorescent constructs containing multiple promoters  were traTo determine if the gene expression by the promoters could be maintained for extended periods, dual fluorescent promoter constructs  were tra7–1.34 × 108; mean 1.1 × 108 ± 2.4 × 107 SD; n = 2) and 40–80% transduction frequencies, concentrated LV preparations of all dual-CAR constructs yielded low titres  and did not result in detectable GFP expression in transduced primary T cells  in T cells. CAR T cell therapy can lead to the clonal expansion of malignant cells with lost CD19 expression ,18. A so T cells . In this study, we compared bidirectional promoters for the optimal expression of two genetic cassettes in HEK293T and Jurkat T cell lines. To determine the strength of the selected bidirectional promoters, we utilized a dual fluorescent vector and measured the bidirectional activity using fluorescent light microscopy, flow cytometry and qPCR using HEK293T and Jurkat cells. In HEK293T, RPBSA was the strongest bidirectional promoter. The deletion of the intron resulted in a reduction in expression, but surprisingly, when both the intron and exon were deleted, expression from the truncated RPBSA promoter became similar to full-length in HEK293T cells. This suggests that, in the case of RPBSA in HEK293T cells, the activation effects of the intron are negated by an inhibitory effect of the exon on the promoter function. In contrast, deletion of the intron or intron plus exon 1 and exon 2 resulted in a drop in bidirectional gene expression in Jurkat cells. EF1α was also shown to be a strong bidirectional promoter, especially in the Jurkat T cell line. EF1α is around twice the size of RPBSA and contains an abundance of TF sites together with an enhancer sequence ,13,14,15While there is no consensus to determine whether a promoter is bidirectional, structural core elements may exist amongst bidirectional promoters. These include a collection of elements such as the TATA box, CCAAT box, B recognition element (BRE), initiator element (INR) and downstream promoter element (DPE). The TATA box exists in both unidirectional and bidirectional promoters; however, bidirectional promoters tend to have a higher ratio of CCAAT boxes and the BRE element than unidirectional promoters, while the ratio of DPE and INR remains largely unchanged . AlthougGene therapy almost invariably requires gene expression for extended periods of time, either to maintain therapeutic gene expression to correct inborn errors of cellular function or to clear residual malignant cells . In JurkFor anti-cancer CAR T cell therapy, the expression of two gene products separated by an internal ribosome entry site (IRES) or 2A ribosomal skip site is possible, along with the use of dual separate promoters, or double-transduction procedures ,22. HoweWhile the three promoters showed strong bidirectional activity driving short RNA encoding fluorescent markers in the Sleeping Beauty system, we were not able to produce sufficient titres of lentiviral constructs incorporating bidirectional promoters to allow primary T cell transduction. Therefore, there is little clinical utility in using these bidirectional promoters to drive a dual-CAR T cell phenotype. Our recent comparison of single promoter constructs in driving short or long RNA did reveal major differences in the genomic titres or transduction efficiencies of EF1α, CMV, RPBSA or hPGK. Therefore, the reasons for the poor titres of the dual-CAR constructs could be: (i) the additional genetic elements in the context of the bidirectional promoters interfering with the production of genomic LV RNA ; (ii) exDespite the disappointing performance of the dual-CAR constructs in the lentivirus system, the bidirectional constructs developed here offer a compact and convenient method to express two transcripts of at least 700 bp using the Sleeping Beauty transposon system.The dual-reporter tdTomato (RFP)/enhanced green fluorescent protein (GFP) plasmid named pSBbiRG in this study was constructed by amplifying the RFP and GFP genes from pSBbiRP and pSBtet-GP ,9. The R2. HEK293T (ATCC CRL-1573) and Jurkat E6.1 (ATCC TIB-152) cell lines were cultured in high glucose-DMEM and RPMI-1640, respectively, supplemented with 10% foetal calf serum , penicillin (100 U/mL) and streptomycin (100 µg/mL) (Gibco). HEK293T cells at 4 × 105 cells/mL in DMEM without antibiotics were added into a 24-well plate and incubated overnight at 37 °C and 5% CO2. Lipofectamine 3000 reagent diluted in OptiMEM  was added to 100 ng of pCMV(CAT)T7-SB100 (transposase containing plasmid) and 400 ng of Sleeping Beauty plasmid OptiMEM and incubated at room temperature before being added dropwise to HEK293T cells. The cells were then incubated overnight before the medium was replaced with fresh DMEM without antibiotics. Jurkat cells were washed once in PBS, resuspended in Buffer R (Invitrogen #MPK10025) at 2 × 107 cells/mL and electroporated in 100 µL Neon gold tips with 1 µg of pCMV(CAT)T7-SB100, plus 4 µg of the plasmid of interest. The cell:plasmid mix was added to the electroporation tube holder containing 3 mL of electrolyte buffer. Cells were electroporated with three pulses at 1350 V for 10 ms and then added to pre-warmed RPMI-1640 with 10% FCS without antibiotics in a 6-well plate. Lentiviral production and titration on HEK293 with 8 µg/mL of polybrene  was carried out using LV-Max Viral production system  as previously described [Cell lines were cultured in a humidified atmosphere at 37 °C with 5% COBrightfield, GFP and RFP images were captured using an Olympus IX-71 inverted microscope. Flow cytometric data were acquired using a BD LSRFortessa with BD FACSDiva software. Data were analysed with FlowJo v10.6.2 software. Events were filtered using FSc/SSc doublet discrimination.The bidirectional promoters characterized in this study will be useful for synthetic biology applications, with a potential role in gene therapy using Sleeping Beauty vectors.


It has been intensely studied to increase rifamycin yield from Amycolatopsis mediterranei, and nitrate is found to provide a stable and remarkable stimulating effect on the rifamycin production, a phenomenon known as “nitrate-stimulating effect (NSE)”. Although the NSE has been widely used for the industrial production of rifamycin, its detailed molecular mechanism remains ill-defined. And our previous study has established that the global nitrogen regulator GlnR may participate in the NSE, but the underlying mechanism is still enigmatic. Here, we demonstrate that GlnR directly controls rifamycin biosynthesis in A. mediterranei and thus plays an essential role in the NSE. Firstly, GlnR specifically binds to the upstream region of rifZ, which leads us to uncover that rifZ has its own promoter. As RifZ is a pathway-specific activator for the whole rif cluster, GlnR indirectly upregulates the whole rif cluster transcription by directly activating the rifZ expression. Secondly, GlnR specifically binds to the upstream region of rifK, which is also characterized to have its own promoter. It is well-known that RifK is a 3-amino-5-hydroxybenzoic acid  synthase, thus GlnR can promote the supply of the rifamycin precursor by directly activating the rifK transcription. Notably, GlnR and RifZ independently activate the rifK transcription through binding to different sites in rifK promoter region, which suggests that the cells have a sophisticated regulatory mechanism to control the AHBA biosynthesis. Collectively, this study reveals that GlnR activates the rif cluster transcription in both direct (for rifZ and rifK) and indirect (for the whole rif cluster) manners, which well interprets the phenomenon that the NSE doesn’t occur in the glnR null mutant. Furthermore, this study deepens our understanding about the molecular mechanism of the NSE.Because of the remarkable efficacy in treating  Amycolatopsis mediterranei (a rare actinomycete)   and optiomycete) . Among tomycete) . This phrif cluster) and the complete genome of A. mediterranei were sequenced  are also profoundly activated by nitrate supplementation to furnish more precursors for rifamycin biosynthesis  are involved in the assimilation and utilization of a variety of nitrogen sources, such as ammonium, nitrate and urea  was constructed using double crossover recombination (glnR complemented strain (glnR+), the glnR gene (with its native promoter) was integrated into the chromosome of ΔglnR, employing the integrative plasmid pDZL803 as the vector  by 120 h  were remarkably reduced compared to those in the wild type, especially at 24 h, when their ratio was only ∼1%  to determine the potential regulatory influence of GlnR on the transcription of  operons    . Subsequp region . In thisp region . Here borifZ using the primer extension assay. In detail, two specific primers were designed to be complementary to either the rifZ coding DNA sequence (CDS) or the rifZ promoter region (rifZ began from an adenine (A) site, which was located at the −328th nucleotide (nt) position relative to the translational start site of rifZ  of the gene r region , and bot of rifZ . In addim region , which frifZ undoubtedly has its own promoter. Although the DNase I footprinting-confirmed GlnR binding sequences are positioned downstream of the rifZ TIS, we have already established that GlnR positively regulates the transcription of rifZ on the basis of the genetic experimental results above , but the binding affinity to them was much reduced relative to the promoters of both rifK and rifZ.Since the two promoters of affinity . FurtherrifK promoter (To verify these (possibly) positive EMSA results in promoter  but protpromoter . ThereforifK promoter, GlnR protected a region from the −280th nt to the −255th nt relative to the rifK translational start site  when cultured in liquid Bennet medium with 80 mM nitrate. Interestingly, it was found that the rifK’s transcriptional level in ΔrifZ was a little higher than in ΔglnR at 24 h, while its level in ΔrifZ was much lower than in ΔglnR at 48 and 72 h  site which is located at the −79th nt position relative to the rifK translational start site .The phenotypic analyses  showed tptomyces , the celentation . Here werif genes in the wild type U32 stayed high till 72 h under nitrate condition (rif cluster (a key character/mechanism of NSE) , which demonstrates that one reason for GlnR being necessary for NSE is its remarkable up-regulation on the rif cluster transcription. Interestingly, the rif cluster transcription under no nitrate condition , indicating that NSE needs other factors besides the GlnR-mediated regulation on the rif cluster transcription. Furthermore, the magnitude of the differences in transcriptional levels between the glnR– and glnR+ strains decreased significantly over time  may be controlled by other unknown mechanisms as well, which will be the focus of our future research.During the transcriptional assays, we noticed that the transcriptional levels of ondition  but had ondition . These r of NSE) . And frover time , indicatin vivo evidence in this study have clearly demonstrated that GlnR is an activator for the transcription of rifZ and rif cluster. However, the in vitro experimental results show that the GlnR binding regions are located downstream of the rifZ TIS, which is different from the binding locations of the typical transcriptional activators. In fact, some previous reports have shown that transcriptional regulators can have binding sites downstream of the TIS but still function as activators, e.g., Rns  to enhance the glutamine biosynthesis  was obtained by double crossover recombination (glnR complemented strain (glnR+) was constructed by integration of the glnR gene with its native promoter into ΔglnR, using the integrative plasmid pDZL803  medium  at 30°C  medium . A. medi at 30°C . If neceA. mediterranei strains were transferred into 50 ml liquid Bennet medium containing ceramic beads (diameter: 2.5 mm) to avoid mycelia aggregation. After incubation in a shaker at 220 rpm for about 36 h, they were inoculated into 200 ml fresh liquid Bennet medium containing ceramic beads and 0/80 mM KNO3 with the final OD600 = 0.2. The growth curves of different strains were determined by measuring the OD600 values with NanoDrop 2000C (Thermo Fisher Scientific) every 12 h.After streak cultivation on Bennet plates for 4–5 days, 3 for 24 h, 1 ml bacterial suspensions were taken from the fermentation broth at 12 h intervals. For determining the rifamycin yield, the samples were centrifuged and the supernatant was collected. Then the rifamycin yield was measured using the spectrophotometric method as previously described  and SV Total RNA Isolation System (Promega), following the manufacturers’ instruction. Reverse transcription was performed with random primers using 1 μg total RNA as the template in a volume of 20 μl by the PrimeScript 1st Strand cDNA synthesis kit (TaKaRa), and the qRT-PCR was carried out with SYBR Premix Ex Taq II (Tli RNaseH Plus) (TaKaRa). At least three independent biological samples were tested in this experiment, and the relative transcriptional level of the tested genes was presented as means ± standard deviations (SD), using the rpoB gene as the internal control.When rif cluster were PCR amplified with Phanta Max super-fidelity DNA polymerase (Vazyme), employing primers listed in HincII site of pUC18H (Tolo Biotech.). After verification through DNA sequencing, these recombinant plasmids were used as the PCR templates to prepare FAM-labeled probes using primers of HincII-F and HincII-R (6-carboxyfluorescein [FAM] labeled). Finally, the FAM-labeled probes were purified by the Wizard SV gel and the PCR Clean-Up system (Promega), and quantified with NanoDrop 2000C (Thermo Fisher Scientific).Promoter regions in A. mediterranei U32 were performed as previously described (The heterogenous expression and purification of the His-tagged GlnR of escribed . EMSA waescribed . DNase Iescribed . The samA. mediterranei U32 was inoculated into liquid Bennet medium with 80 mM KNO3 and incubated in a shaker at 30°C for about 36 h. Then, the mycelia were harvested by centrifugation, followed by extraction of the total RNA using a protocol the same as described above. For each assay, about 60 μg total RNA was used to characterize the TIS, employing FAM-labeled primers listed in Primer extension assay was carried out by Tolo Biotech . To prepAll datasets generated for this study are included in the article/XL performed most of the experiments and prepared the draft. YL performed the primer extension assay. YL and XL performed the DNase I footprinting experiment. CL prepared the FAM-labeled probes. JW and GZ designed the study. JW revised the manuscript and supervised the whole project.YL was employed by the company Shanghai Tolo Biotechnology Company Limited. The remaining 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.


The synthesis of natural products in yeast has gained remarkable achievements with intensive metabolic engineering efforts. In particular, transcription factor (TF)-based biosensors for dynamic control of gene circuits could facilitate strain evaluation, high-throughput screening (HTS), and adaptive laboratory evolution (ALE) for natural product synthesis. In this review, we summarized recent developments of several TF-based biosensors for core intermediates in natural product synthesis through three important pathways, i.e., fatty acid synthesis pathway, shikimate pathway, and methylerythritol-4-phosphate (MEP)/mevalonate (MVA) pathway. Moreover, we have shown how these biosensors are implemented in synthetic circuits for dynamic control of natural product synthesis and also discussed the design/evaluation principles for improved biosensor performance. Microbial synthesis of natural products not only ensures sustainable production but also enables synthesis of novel derivatives of interest. Yeast has been exploited as microbial cell factories to produce natural products that belong to several families, including fatty acids, isoprenoids, flavonoids, and alkaloids . With goIn a TF-based biosensor, TF undergoes an allosteric conformational change induced by metabolite binding, which affects its binding at its operator and thereof regulates gene transcription . Among aHere we reviewed several TF-based biosensors developed in yeast and recent applications for production of natural products, including biosensors for malonyl-CoA, fatty acyl-CoA, isopentenyl pyrophosphate (IPP), naringenin, and aromatic amino acids (AAAs), which are core metabolites for synthesis of several families . ThroughThe fatty acid synthesis pathway from malonyl-CoA via a cyclic elongation manner could be engineered and utilized for production of natural products like cocoa butter equivalents and jojoba-like wax esters . BiosensfapO from Bacillus subtilis have been extensively studied and exploited for malonyl-CoA sensors in E. coli, S. cerevisiae, and mammalian cells .In yeast, FapR was usually expressed under a strong constitutive promoter to regulate reporter gene expression under a al range , includiTEF1 promoter and the other harboring tdTomato regulated under a hybrid fapO-GPM1 promoter, fapO was identified as a better design with a broad dynamic range, up to 4-fold fluorescence increase in response to 8 mg/L cerulenin, which was supplemented as a fatty acid synthase inhibitor to increase the cellular malonyl-CoA level (TEF1 promoter and a green fluorescent protein (GFP) under a hybrid fapO-TEF1 promoter. The optimized sensor with three fapO sites could respond to 13.5 μM cerulenin with up to 1.9-fold increase in the GFP fluorescence. The biosensor was then utilized for dynamic control for 3-hydroxypropionic acid (3-HP) production with fatty acid synthase expressed under a glucose sensitive HXT1 promoter to control the malonyl-CoA availability. The hierarchical dynamic control improved 3-HP titer from 0.4 to 1.0 g/L, which could be applied for production of other malonyl-CoA-derived natural products. TEF1 promoter and yeGFP (yeast-enhanced GFP) under a hybrid TEF1-fapO-GAL1 promoter and showed a better sensitivity, up to 8-fold yeGFP increase in response to 10 μM cerulenin  of an activator, it activated gene transcription under a promoter  instead fadO from E. coli have been evaluated for fatty acyl-CoA sensors in yeast  were fused to a synthetic GAL1 core promoter containing three fadO sites. In the AND-gate controllers, both fatty acids and copper presence/phosphate starvation were required to switch the AND-gate ON , respectively. With Idi-DBD bound to the GAL10 promoter, Idi dimerization triggered by IPP could bring Idi-AD close enough to activate transcription of a fluorescent protein yEcitrine  is an essential intermediate for isoprenoid synthesis, and its supply may be limited to its high toxicity. Therefore, dynamic control through the IPP node will benefit synthesis pathways for isoprenoids. However, no natural TF has been identified responsive to IPP. In yeast, a synthetic TF was repurposed for sensing IPP by fusing IPP isomerase Idi to the Ecitrine . The IPPEcitrine  and showde novo biosynthesis of various aromatic products, like flavonoids and alkaloids  and 48 h (r = 0.96) between GFP intensities and metabolite concentrations  including tryptophan (Trp), tyrosine (Tyr), and phenylalanine (Phe) are synthesized from the intermediates of the shikimate pathway . Endogen of AAAs , which cUASaro element dissected from the Aro9 promoter with Aro80 binding site and fused it with a core promoter to form a hybrid promoter, which could induce the expression of yellow fluorescent protein (YFP) in response to exogenous tryptophan. With an Aro80 variant expressed under a strong GAL1 promoter and five UASaro sites inserted in a minimal core promoter, a AAA biosensor was constructed, which could respond to 1 g/L tryptophan and 1 g/L galactose with 6- and 12-fold induced expression, respectively. In the presence of tryptophan and galactose, the biosensor resulted in 14-fold induced GFP expression. This work demonstrated the potential of native transcription factors for biosensor construction.UASaro was then used in ALE for production of AAAs and muconic acid , which was expressed under a weak REV1 promoter. With six copies of TrpO inserted into a GAL1 core promoter, the constructed biosensor could sense 2–200 mg/L tryptophan with a 5-fold dynamic range. The biosensor was used for HTS of a combinatorial library based on a platform strain with increased AAA accumulation and facilitated mechanistic and machine learning models with the recommendations, which improved tryptophan titer and productivity by up to 74 and 43%, respectively -based biosensors have become powerful tools in strain evaluation, HTS, and ALE for yeast synthesis of natural products. With more natural TFs identified and characterized both experimentally and bioinformatically, TF-based biosensors for more metabolites could be established to facilitate natural product synthesis. However, one big challenge for TF biosensors might be for their applicability in natural product synthesis where a larger dynamic range is needed. The biosensor design may need to be adjusted or evolved as the titers become higher or be matched to the production kinetics of the individual strain or library of biocatalysts.E. coli, which resulted in a balanced metabolism between cell growth and product formation (Biosensor engineering on TFs and the hybrid operator-promoters have enabled improved sensing sensitivity, dynamic range, operational range, and ligand specificity, as well as inversion of function between activated and repressive regulation . Evolutiormation .It is also interesting to notice the construction of logic gates in TF-based biosensors , which cYZ and SS outlined this manuscript. YZ drafted the manuscript. SS revised the manuscript. Both authors contributed to the article and approved the submitted version.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.


The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis 3.0) recommended defining sepsis as a life-threatening organ dysfunction caused by the host's uncontrolled response to infection. The bromodomain and extra-terminal (BET) protein family , an epigenetic regulator of gene transcription, has recently been recognized as a significant septic regulator of inflammation and immune response, including cytokine and chemokine production. Mechanistically, the two N-terminal conserved tandem bromodomains (namely the first bromodomain [BD1] and the second bromodomain [BD2]) favor the binding of BETs to acetylated histones or transcription factors, thereby initiating gene transcription machinery after CycT1 and CDK9  are recruited to gene promoters to phosphorylate RNA pol II. Notably, BD1 and BD2 are not functionally redundant because they have different target genes in innate immune cells. Small-molecule BET inhibitors (BETis) for different BDs, such as I-BET, JQ1, I-BET151, apabetalone, RVX-297, and dBET1 have shown promising therapeutic effects in experimental sepsis models. This mini-review summarizes the emerging roles of BETs and the applications of BETis in sepsis, discusses the existing shortcomings of BETis, and introduces possible future research directions in this area. Sepsis is a medical condition driven by an unrestricted host response to infection and subsequent multiple organ dysfunction or failure . While bThe pathophysiology of sepsis is complex and involves multiple steps . CytokinThe epigenome describes all heritable chemical modifications added to DNA and histone proteins, which regulates the transcription of genes in the genome without affecting the DNA sequence . The maiBromodomain is an evolutionarily conserved protein-protein interaction module, which comprises approximately 110 amino acids that recognize acetylated lysine residues within histones and other proteins. 61 bromodomain modules, including bromodomain and extra-terminal domain (BET) family , have beIn this mini-review, we not only outline the structure and function of BET family in innate immunity , but als+ T cells during virus infection . Under pathological conditions, the expression of BETs may be further changed (upregulation or downregulation) to meet the requirements for orchestrating a genetic regulatory response . In the nfection . The expnfection , the traAlthough the amino acid length of each BET is different, they have a similar protein secondary structure: two N-terminal conserved tandem bromodomains (namely the first bromodomain [BD1] and the second bromodomain [BD2]) and a unique C-terminal extraterminal (ET) domain. These BDs contain four alpha helices, which are separated by a variable loop region to form a central hydrophobic cavity. Acetylated lysine residues in histone tails and other proteins  are recognized through this central hydrophobic pocket by anchoring to a conserved asparagine residue . Unlike As for the C-terminal ET domain in each BET, they contain a helical architecture, including an acidic surface, shaped in a continuous ridge. Because the ET domain is responsible for the interaction between proteins, BETs act as scaffold proteins for the recruitment of transcription factors and coactivators. Other domains, such as motif B and Ser/Glu/Asp-rich region (SEED), are conserved in the C-terminal moiety of each BET, whereas the C-terminal domain (CTD) and motif A are not present in every BET . This maBETs not only function as scaffolds to recruit different transcription factors  and transcription elongation complexes , but also serves as switches to initiate gene transcription machinery upon the interaction of BDs with acetylated chromatin either at gene promoters or in long range cis regulatory elements (namely “enhancers”) . ConsequOne of the key events of infection-related innate immunity is the recognition of evolutionary conserved structures on pathogens (namely pathogen-associated molecular patterns [PAMPs]) through different pattern recognition receptors (PRRs) expressed in immune and non-immune cells. Main PRRs include transmembrane  and intracellular -like receptors [NLRs]) PRRs . InfectiTLRs are central PRRs responsible for recognizing PAMP to trigger the expression of immune mediators by activating transcription factors, such as nuclear factor kappa B (NFKB) and interferon regulatory factors (IRFs). 10 TLRs (TLR1-10) in humans and 13 TLRs (TLR1-13) in mice have been identified, which show different subcellular localizations and PAMP recognition preferences. TLR-1, -2, -4, -5, and -6 are located at the extracellular surface and bind to the components of microbial cell walls and membranes of pathogens . TLR-3, -7, -8, and -9 are mainly expressed in the endoplasmic reticulum and endosome, and detect microbial nucleic acids, such as double or single-stranded RNA from RNA viruses and DNA presented in bacteria and viruses. BET affects the signal pathway of TLRs through two potential mechanisms. On one hand, BETs directly mediate the transcription upregulation of TLR-2, -4, and -6 genes, thereby activating TLR pathway . On the The inflammasome machinery is important in the innate immune system, which not only mediates the maturation and release of the interleukin-1 (IL1) family , but also promotes the activation of caspase-1 (CASP1) or CASP4/5/11 to trigger gasdermin D (GSDMD)-dependent pyroptosis to release DAMPs . One of the largest subfamilies of inflammasomes is called NLR inflammasomes. According to the structure of N-terminal domain, NLRs are further divided into four subfamilies  to recognize PAMPs or DAMPs. Among them, the NLR family pyrin domain containing 3 (NLRP3) inflammasome is best characterized and shows sustained activation in sepsis through canonical CASP1-dependent or non-canonical CASP4/5/11-mediated pathway. Interestingly, BETs confer opposite effects on NRLP3 activation depending on cell types. For example, the inhibition of BRD4 alleviates the inflammatory response by blocking TNF-related NRLP3 activation in rat nucleus pulposus cells . HoweverAnother multifunctional regulator of innate immunity is the DNA sensor. In addition to TLR9, a receptor essential for identifying unmethylated CpG DNA, cyclic GMP-AMP synthase (CGAS) plays a broad role in the recognition of various types of DNA and their metabolites produced by microorganisms and hosts. After the endogenous second messenger cyclic GMP-AMP (cGAMP) is synthesized by CGAS, cGAMP binds to stimulator of interferon response cGAMP interactor 1 , resulting in the transcriptional activation of NFKB1 and IRF3, thereby increasing the production of type I interferon and pro-inflammatory cytokines . The actAccording to the structure of BETs, some chemical compounds have been developed to disrupt or compete the binding of BD1 and BD2 to acetylated histones and transcription factors. The first-generation BETis are also considered pan-BETis because they have no selectivity for BD1 and BD2. Since the BDs between different BETs have a high degree of homology, these BETis cannot distinguish individual member of the BETs. While selective BD1-BETis and BD2-BETis show different inhibition activity of BET, BD1-BETis seems to be as effective as pan-BETis in some cases . HoweverBrd4 conditional knockout mice (termed Brd4 CKO) are used to further investigate the pathological effects of BRD4 in sepsis. Surprisingly, Brd4 CKO mice are resistant to endotoxemia, but are more susceptible to intraperitoneal injection of group B Streptococcus-induced infection  was discovered in 2010 . It bindnfection , which mL. monocytogenes, JQ1 also inhibits the expression of cytokines, such as nitric oxide synthase 2 (NOS2), IL6, and interleukin 1 receptor antagonist (IL1RN) , fungi , and parasites  (JQ1 (best known as (+)-JQ1), the most widely used BETi reported in 2010, is a thienotriazolodiazepine which competitively binds to both BD1 and BD2 of BETs with acetylated lysine. It exhibits prominent anti-inflammatory and immunoregulatory activity in endotoxemic mice by reducing the levels of IL6 and TNF, and rescues mice from LPS-induced death . In BMDM (IL1RN) . Moreove (IL1RN) . In addiponicum) . DespiteI-BET151 , which belongs to the quinoline isoxazole BET family bromodomain inhibitors, was developed in 2012 and has good oral bioavailability with a similar effect as I-BET . The admApabetalone , an oral BETi selective for BD2 within BETs, is currently used in phase 3 clinical trials for the treatment of coronary artery disease, diabetes, and chronic kidney failure. In a mouse model of lethal endotoxemia, apabetalone prevents liver damage by inhibiting the expression of alpha-2-macroglobulin and serum amyloid P. In patients with cardiovascular disease, apabetalone provides benefit for limiting chronic cytokine signaling . Thus, tin vitro and in vivo. dBET1 effectively inhibits LPS-induced the expression of proinflammatory factors  in the microglia by degrading BRD2 and BRD4 in a time- and dose-dependent manner  has shown excellent targeting ability and activity. The advantage of BETs degradation rather than inhibition is that it may lead to selective suppression of individual BET-dependent genes. Indeed, PROTAC-based BETis  exhibit promising immunoregulatory activities in various disease models . DBET1 it manner . These aSepsis and septic shock cause more than 11 million deaths each year , which i