It remains unclear why acute depletion of CTCF (CCCTC-binding factor) and cohesin only marginally affects expression of most genes despite substantially perturbing three-dimensional (3D) genome folding at the level of domains and structural loops. To address this conundrum, we used high-resolution Micro-C and nascent transcript profiling in mouse embryonic stem cells. We find that enhancer–promoter (E–P) interactions are largely insensitive to acute (3-h) depletion of CTCF, cohesin or WAPL. YY1 has been proposed as a structural regulator of E–P loops, but acute YY1 depletion also had minimal effects on E–P loops, transcription and 3D genome folding. Strikingly, live-cell, single-molecule imaging revealed that cohesin depletion reduced transcription factor (TF) binding to chromatin. Thus, although CTCF, cohesin, WAPL or YY1 is not required for the short-term maintenance of most E–P interactions and gene expression, our results suggest that cohesin may facilitate TFs to search for and bind their targets more efficiently. Micro-C and nascent transcript profiling in mouse embryonic stem cells shows that enhancer–promoter interactions are robust to acute depletion of CTCF, cohesin, WAPL or YY1. Live-cell, single-molecule imaging shows that cohesin depletion reduces transcription factor binding to chromatin. Based on such studies, we can distinguish at least three levels of 3D genome folding. First, the genome is segregated into A and B compartments, which largely correspond to active and inactive chromatin segments, respectively, and appear as a plaid-like pattern in Hi-C contact maps3. Second, the proteins CTCF and cohesin help fold the genome into topologically associating domains (TADs)5 and structural chromatin loops6, probably through DNA loop extrusion8. Third, at a much finer scale, transcriptional elements engage in long-range chromatin interactions such as E–P and promoter–promoter (P–P) interactions to form local domains11.High-throughput chromosomal conformation capture (Hi-C)-based assays have transformed our understanding of 3D genome folding16. However, Hi-C is ineffective for capturing the third level of 3D genome folding: the fine-scale transcriptionally important E–P/P–P interactions18. Our understanding of the role of CTCF and cohesin in regulating gene expression has mainly come from genetic experiments focusing on a few developmental loci21. Thus, it remained unclear whether, when, where and how CTCF/cohesin regulates E–P/P–P interactions and gene expression.Elegant experiments combining acute protein depletion of CTCF, cohesin and cohesin-regulatory proteins with Hi-C or imaging approaches have revealed the role of CTCF and cohesin in regulating the first two levels: TADs and compartments23, including E–P/P–P interactions17. In the present study, we used Micro-C, chromatin immunoprecipitation sequencing (ChIP-seq), total RNA-sequencing (RNA-seq) and nascent RNA-seq24 to systematically investigate how acutely depleting CTCF, RAD21 (cohesin subunit), WAPL (cohesin unloader) or YY1  affects gene regulatory chromatin interactions and transcription in mouse embryonic stem cells (mESCs). Finally, focusing on the dynamics of YY1 uncovered an unexpected role for cohesin in facilitating TF binding.We recently reported that Micro-C can effectively resolve ultra-fine 3D genome folding at nucleosome resolution9. In the present study, we identified over 75,000 statistically significant loops in mESCs using the newly developed loop caller Mustache26 ops Fig. . Newly tops Fig. . Specifiops Fig. . Thus, b27, as illustrated by the region around the Klf2 gene , which may confound the primary molecular response with potential secondary effects40. To minimize indirect effects, we used a shorter degradation time and achieved almost-complete degradation of AID-tagged proteins after 3 h of iodoacetamide (IAA) treatment, confirmed by western blotting  with an auxin-inducible degron (AID) by clustered regularly repeating interspaced short palindromic repeats (CRISPR)–Cas9-mediated genome editing in mESC lines expressing the F-box protein OsTir1 Fig. 38. Despiing Fig.  and biocing Fig. , red box41, both CTCF and cohesin lose their occupancy after CTCF depletion ) or IAA to degrade the tagged protein   being called significantly changed by differential peak analysis42   Fig. . Taken t49 (designated YN11 and YN31 clones) for live-cell, single-molecule imaging using CRISPR–Cas9-mediated genome editing  Hz Fig. . YY1 molnts Fig. . We note57. To estimate the overall residence time of the bound fraction of YY1, we used fluorescence recovery after photobleaching (FRAP) to measure in vivo protein-binding kinetics by fitting the fluorescence recovery curve to a kinetic model59. Using a reaction-dominant FRAP model, we estimated a residence time of ~13 s for most of the YY1 molecules  further revealed a subpopulation of YY1 that binds to chromatin for 40–60 s  is bound to chromatin and that YY1 binding is more dynamic (average residence time of ~13 to 60 s) than CTCF (~50% bound for ~1 to 4 min) and cohesin (~40 to 50% bound for ~20 to 25 min), which may help explain why YY1 protein depletion has a much weaker effect on looping and 3D genome folding.62. To test whether CTCF and cohesin may similarly affect YY1’s target search, we endogenously fused an AID to CTCF or RAD21 in the HaloTag-YY1 parental line and confirmed >90% depletion after 3 h of IAA treatment  in the bound fraction of YY1  or slower target search (kon). To distinguish between these possibilities, we analyzed the FRAP data and found YY1 residence times (koff) to be only weakly affected by CTCF and cohesin depletion . Specifically, we estimated a ~54% decrease in kon after cohesin depletion, resulting in a ~2.2-fold longer YY1 search time , the time it takes YY1 on average to find and bind a cognate binding site after dissociating from DNA.We next examined YY1 nuclear target search efficiency in the absence of CTCF and cohesin using spaSPT. Although CTCF depletion had no major effect, cohesin depletion resulted in a modest but reproducible decrease from ~33% to 22% ) were lost after RAD21 degradation and >82% of these loci were associated with promoter regions Fig. . In contTaken together, our results reveal a role for cohesin in accelerating the target search of TFs, resulting in increased YY1 chromatin binding as measured by SPT, FRAP and ChIP-seq. Cohesin or cohesin-mediated genome structure is likely to facilitate transcriptional establishment via more efficient target sampling of TFs Fig. . These f67. In the present study we applied high-resolution Micro-C to overcome this limitation. Surprisingly, we found that CTCF, cohesin, WAPL or YY1 is not required for the maintenance of most E–P/P–P loops or transcription at least within a 3-h depletion in mESCs. When affected, the altered E–P/P–P interactions only result in moderate expression changes of the underlying genes. Our findings, together with other evidence68, allow us to distinguish and/or eliminate several models of E–P interactions previously assigned to these ubiquitous structural proteins  only a very small handful of genes showed altered expression levels after CTCF, cohesin or WAPL depletion  CTCF and cohesin loops are both rare (~5% of the time) and dynamic (median lifetime ~10–30 min)34; (4) most of the E–P/P–P loops persist after depletion of these structural proteins  CTCF/cohesin generally does not colocalize with transcription loci67; and (6) E–P loops and transcription can be established before CTCF/cohesin interactions on mitotic exit71, in some cases even with no CTCF/cohesin expression66. Second, YY1 was proposed to be a master structural regulator of E–P interactions25  may be sufficient to maintain E–P interactions and gene expression for several hours without the contribution of these architectural factors. We propose that this time-buffering model and its variants90 reconcile our observations with the unambiguous genetic evidence that CTCF and cohesin regulate some E–P interactions. An alternative, more conservative, interpretation of our data and the evidence cited above is that CTCF and cohesin only regulate a very small, unique set of genes in specific biological processes and cell types and their effect on a handful of loci simply cannot be generalized as a universal rule.The evidence that CTCF and cohesin can directly or indirectly regulate E–P interactions and affect gene expression in many cases is overwhelmingdel Fig. , Model 391. Furthermore, a study using high-throughput ChIP-seq analysis suggested that cohesin is critical to promote TF rebinding after mitosis92. We therefore propose that cohesin could facilitate TF binding to chromatin in general  or TFs bridge long-range genomic loci together, forming a ‘loop-shaped’ structure in vitro and in vivo. Unlike cohesin, which is likely to form loops through loop extrusion, E–P or P–P interactions may occur by a variety of mechanisms without looping. Their interactions are typically detected as ‘dots’ in contact matrices. We agree that the term ‘dot’ is ideal for describing these enhanced focal contacts without making any assumptions about their folding mechanisms or actual 3D structures. However, we chose to use ‘loop’ over ‘dots’  to make the manuscript more accessible to the general audience and to match the terms that are commonly used in the field.95 , Davis) were used for all experiments. Cells were cultured on plates precoated with 0.1% gelatin  in knock-out Dulbecco’s modified Eagle’s medium  supplemented with 15% fetal bovine serum (HyClone FBS SH30910.03 lot no. AXJ47554), 0.1 mM minimal essential medium nonessential amino acids , 2 mM GlutaMAX , 0.1 mM 2-mercaptoethanol , 1% penicillin–streptomycin  and 1,000 units of leukemia inhibitory factor (Millipore). Medium was replaced daily and cells were passaged every 2 d by trypsinization. Cells were grown at 37 °C and 5.5% CO2 in a Sanyo copper alloy IncuSafe humidified incubator (MCO-18AIC(UV)). For imaging, the medium was identical except that knock-out DMEM lacking phenol red  was used to minimize background fluorescence.JM8.N4 mESCsCell lines stably expressing 3× FLAG-HaloTag-YY1 and YY1-HaloTag-3×FLAG were generated using PiggyBac transposition and drug selection. Full details are given in 3.For PALM experiments, cells were grown overnight on Matrigel-coated , 25-mm circular no. 1.5H cover glasses . Before all experiments, the cover glasses were plasma cleaned and then stored in isopropanol until use. Cells were labeled with 500 nM PA-JFX549 HaloTag ligand for 30 min, washed twice with fresh medium for 5 min and then washed once with phosphate-buffered saline (PBS), pH 7.4. Labeled cells were fixed with 4% paraformaldehyde and 2% glutaraldehyde in PBS for 20 min at 37 °C, washed once with PBS and imaged in PBS with 0.01% (w:v) NaNFor FRAP experiments, cells were grown overnight on Matrigel-coated glass-bottomed 35-mm dishes (MatTek P35G-1.5-14C). Cells were labeled with 500 nM HaloTag tetramethylrhodamine (TMR) ligand  for 30 min and washed twice with PBS.96 with modifications. Full details are given in Endogenously tagged mESC lines were generated by CRISPR–Cas9-mediated genome editing as previously describedSee 97(see ChIP was performed as described with a few modificationsChIP-seq libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs (NEB), catalog no. E7645) according to the manufacturer’s instructions with a few modifications  and freshly added 0.1% Triton X-100, v:v, and protease inhibitors) and rocked for 8 min at 4 °C. Nuclei were pelleted at 3,000g for 3 min at 4 °C and the supernatant containing the cytoplasmic fraction was saved. Nuclei were resuspended in 350 μl of buffer B with 75 mM NaCl )tetra-acetate, 1 mM DTT, freshly added 0.1% Triton X-100, v:v, and protease inhibitors) and rocked at 4 °C for 15 min. Nuclei were pelleted again as above (supernatant saved as the 75 mM wash fraction) and washed with 350 μl of buffer B with increasing NaCl concentrations  with some minor modifications https://github.com/mirnylab/cooler)100 or in HIC file format using the JUICER package (v.1.22.01) (https://github.com/aidenlab/juicer)101. Contact matrices were then normalized by using iterative correction in COOL files102 or Knight–Ruiz in HIC files103. Regions with low mappability and high noise were blocked before matrix normalization. We expect that matrix-balancing normalization corrects systematic biases such as nucleosome occupancy, sequence uniqueness, GC content or crosslinking effects102. We notice that both normalization methods produce qualitatively equal contact maps. To visualize the contact matrices, we generated a compilation of COOL files with multiple resolutions  that can be browsed on the HiGlass 3D genome server (http://higlass.io)104. In the present study, all snapshots of Micro-C or Hi-C contact maps and the one-dimensional (1D) browser tracks  were generated by the HiGlass browser (v.1.11.7) unless otherwise stated.Valid Micro-C contacts were assigned to the corresponding ‘pseudo’ nucleosome bin. The bin file was pregenerated from the mouse mm10 genome by a 100-bp window that virtually resembles the nucleosome resolution. The binned matrix can be stored in HDF5 format as a COOL file using the COOLER package (v.0.8.10) (https://github.com/kundajelab/3DChromatin_ReplicateQC)105 ’, ‘IN-OUT (+/+)’, ‘OUT-IN (−/−)’ and ‘OUT-OUT (−/+)’ according to the readouts of Illumina sequencing23. ‘UNI’ pairs combine ‘IN-OUT’ and ‘OUT-IN’ because both orientations are theoretically interchangeable. In the present study, we plotted the contact decaying curves with the ‘UNI’ pairs and then normalized to the total number of valid contact pairs. Slopes of contact decay curves were obtained by measuring slopes in a fixed-width window searching across the entire range of decaying curves. We then plotted the derivative slope in each window against the corresponding genomic distance.To analyze the genome-wide, contact-decaying 3. The genome-wide compartment strength analysis shown as a saddle plot represents the rearrangement and aggregation of the genome-wide, distance-normalized contact matrix with the order of increasing eigenvector values. The chromosome arm is first divided into quantiles based on the compartment score. All combinations of quantile bins are averaged and rearranged in the saddle plot. The Cooltools package  has implemented the ‘call-compartments’ and ‘compute-saddle’ functions with the COOL files.To identify chromosome compartments, we first transformed the observed:expected Micro-C matrices at the 200-kb resolution to Pearson’s correlation matrices and then obtained the eigenvector of the first principal component of Pearson’s matrix by principal component analysis. The sign of the eigenvector was corrected using active histone marks (H3K27ac and H3K4me3), because positive values are the A compartment (gene-rich or active chromatin) and negative values are the B compartment (gene-poor or inactive chromatin). The detailed description can be found in Lieberman-Aiden et al.https://github.com/mirnylab/cooltools) or arrowhead transformation analysis from the JUICER package 101 (see To identify chromatin domains (TADs) along the diagonal, we used insulation score analysis from the Cooltools package 107 to define the chromatin states in mESCs, which subclassifies chromatin into 12 states including: (1) CTCF/insulator, (2) active promoter (designated as ‘P’), (3) strong enhancer, (4) medium enhancer, (5) weak enhancer, (6) mix of promoter and enhancer, (7) bivalent promoter, (8) gene body, (9) polycomb repressor, (10) intergenic regions, (11) heterochromatin and (12) repeats. To simplify the analysis, we further combined the groups of strong, medium and weak enhancers and mix of promoter and enhancer into ‘enhancer’ (designated as ‘E’). In the present study, we use the terms that are widely accepted in the field to describe the chromatin structures in Micro-C contact maps as well as avoid any ambiguous description that implicates their biological functions if they have not been well characterized, including: (1) TAD: squares along matrix diagonal enriched with self-interactions, which are defined as genomic intervals demarcated by the boundaries characterized by the insulation score analysis or the arrowhead transformation analysis; (2) cohesin loops: focal enrichment of contacts in contact maps with the coenrichment of CTCF/cohesin ChIP-seq peaks at loop anchors, which is thought to be formed by active loop extrusion halted by CTCF; and (3) E–P/P–P dots: focal enrichment of contacts in contact maps with the coenrichment of chromatin states for ‘active promoter (P)’ or ‘enhancer (E)’ at loop anchors. Although not all cohesin loops and E–P/P–P loops are formed through ‘looping’, and some studies suggest using ‘dots’ instead of ‘loops’, to simplify and be consistent with most of the findings, we chose to use ‘loops’ for cohesin-mediated focal contacts and ‘dots’ for other categories of enhanced focal contacts in this manuscript.We first used the published ChromHMM (7 mESCs (~70% confluent P10 dish) using the standard TRIzol RNA extraction protocol. The abundant rRNAs were depleted from the sample using the NEBNext rRNA Depletion Kit . The rRNA-depleted RNAs were then subjected to RNA-seq library construction using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina . The final RNA-seq libraries were amplified with seven to eight PCR cycles.Total RNA was extracted from ~1 × 10108 to quantify the number of transcripts and performed DEseq2 (v.1.30.1)109 analysis for DEG identification according to the recommended settings in the walkthrough (http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) with Padj < 0.01 and fold-change >2. Full lists of DEGs are available in Supplementary Table For RNA-seq analysis, we used Kallisto (v.0.46.2)110 with minor changes, detailed in We used the nascent RNA-seq (mNET-seq) protocol described in Nojima et al.RNA libraries were prepared according to the protocol of the NEBNext Small RNA Library Prep Kit . The mNET-seq library was obtained by PCR for 12–14 cycles.https://github.com/FelixKrueger/TrimGalore) to remove sequencing adapters ‘AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC’ and ‘GATCGTCGGACTGTAGAACTCTGAAC’ at each side of the reads; (2) mapping: trimmed reads were mapped to the mouse mm10 reference genome with STAR RNA-seq aligner (v.2.7.10a)111; (3) identifying the last nucleotide incorporated by Pol II: we used the Python script mNET_snr (https://github.com/tomasgomes/mNET_snr) to locate the 3′-nucleotide of the second read and the strand sign of the first read. The bigWig files were generated using Deeptools (v.3.5.0) as described in 112 package or FeatureCounts (v.1.22.2)113 and DEseq2 (v.1.30.1)109 to statistically quantify differential changes of the mNET-seq signal at the gene body between UT- and IAA-treated cells (with Padj < 0.01 and fold-change >2). Full lists of DEGs are available in Supplementary Table For mNET-seq analysis, we wrote a customized pipeline to process raw data as follows: (1) adapter trimming: we used TrimGalore (v.0.6.7)  and the photoactivation laser (405 nm). The intensity of the 405-nm laser was gradually increased over the course of the illumination sequence to image all molecules and avoid too many molecules being activated at any given frame. The camera was set for 25-ms exposure time, frame transfer mode and vertical shift speed at 0.9 μs. In total, 40,000–60,000 frames were recorded for each cell (~20–25 min), which was sufficient to image and bleach all labeled molecules.https://github.com/alecheckert/quot) to generate trajectories from raw spaSPT videos with the steps of spot detection, subpixel localization and tracking. All localization and tracking for this manuscript were performed with the following settings: (1) detection: generalized log(likelihood ratio test) with a 2D Gaussian kernel  = 15 and a log(ratio threshold (t)) = 26.0). (2) Subpixel localization: Levenberg–Marquardt fitting of a 2D integrated Gaussian point spread function model . (3) Tracking: we chose to use a conservative tracking algorithm with a 1.3-μm search radius . This setting makes the algorithm search for spot reconnections unambiguously, meaning that no other reconnections are possible within the specified search radius. Jumps were discarded if other reconnection possibilities given the search radius existed.For analysis of spaSPT experiments, we used the QUOT package 54 to estimate the likelihood of diffusion coefficients for each trajectory. The detailed discussion is available in Heckert et al.114 and described in We next used the SASPT package k = 1.0, w = 15, t = 18; (2) subpixel localization: ‘ls_int_gaussian’ with w = 9, sigma = 1.0, ridge = 0.001, maximal iteration = 20 and damping = 0.3; (3) tracking: ‘euclidean’ with search radius = 0.5, maximal blinks = 1 and maximal diffusion constant (μm2 s−1) = 0.08.For analysis of slow-SPT experiments, we used the following tracking settings for this manuscript: (1) detection: ‘llr’ with Details on how we extracted residence times from slow-SPT are in 2, a heated stage and an X-Cite 120 illumination source, as well various laser lines. Images were acquired on a ×40 Plan NeoFluar, numerical aperture 1.3, oil-immersion objective at a zoom corresponding to a 76 nm × 76 nm pixel size. The microscope was controlled using the Zeiss Zen imaging software.FRAP was performed on an inverted Zeiss LSM 900 Axio Observer confocal microscope equipped with Airyscan 2 detector, a motorized stage, a full incubation chamber maintaining 37 °C/5% COr = 6 pixels) was chosen in a region of homogeneous fluorescence at a position at least 1 μm from nuclear or nucleolar boundaries. Alternatively, we bleached a square at one corner of the nucleus, which reduces noise while introducing some uncertainty for our downstream fitting analysis. The spot was bleached using maximal laser intensity and pixel dwell time corresponding to a total bleach time of ~1 s. We note that, because the bleach duration was relatively long compared with the timescale of molecular diffusion, it is not possible to accurately estimate the bound and free fractions from our FRAP curves.In this manuscript, we recorded 60 s of videos for YY1-HaloTag at 1 frame per 250 ms, corresponding to a total of 240 frames. The first 20 frames were acquired before the bleach pulse, allowing us to accurately measure baseline fluorescence. A circular bleach spot 115 with the following setting for this manuscript: (1) image filtering: ‘Wavelet filter (B-Spline)’ with B-Spline order = 3 and B-Spline scale = 2.0; (2) approximate localization: ‘Local maximum’ with peak intensity threshold = 1.5 × std(Wave.F1) and 8-neighbourhood connectivity; (3) subpixel localization: ‘Integrated Gaussian’ with fitting radius = 3 pixels, fitting method = maximum likelihood, initial sigma = 1.6, multi-emitter analysis disabled; and (4) image reconstruction: ‘Averaged shifted histogram’. After tracking, we further filtered ambiguous emitters with the following setting: (1) filtering: frame > 100 & intensity > 100 & sigma < 220 & uncertainty_xy < 50; (2) merge: Max distance = 10 & Max frame off = 1 & Max frames = 0; and (3) remove duplicates enabled. This setting combines the blinking molecules into one and removes the multiple localizations in a frame.For analysis of PALM experiments, we used the publicly available ThunderSTORM package (v.1.3; See Supplementary Table No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment. Western blotting, biochemical fractionation and flow cytometry experiments were repeated and confirmed at least twice.Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01223-8.Supplementary InformationSupplementary Notes and Methods.Reporting SummaryPeer Review FileSupplementary Table 1-10Supplementary Table 1: Plasmids, primers and antibodies used in the present study. Supplementary Table 2: Summary of Micro-C interactions. Supplementary Table 3: TAD arrowhead. Supplementary Table 4: TAD insulation score. Supplementary Table 5: Loop-calling options and filtering parameters for Chromosight. Supplementary Table 6: Mustache loops. Supplementary Table 7: Chromosight loops. Supplementary Table 8: Cohesin loops called by Chromosight using Micro-C data at 4-kb resolution. Supplementary Table 9: E–P loops called by Chromosight using Micro-C data at 4-kb resolution. Supplementary Table 10: P–P loops called by Chromosight using Micro-C data at 4-kb resolution.Supplementary Table 11Supplementary Table 11 RNA-seq–DEseq2.Supplementary Table 12Supplementary Table 12 mNET-seq–DEseq2.Supplementary Data 1Plasmid sequence.


This review discusses our current understanding of chromatin biology and bioinformatics under the unifying concept of “chromatin hubs.” The first part reviews the biology of chromatin hubs, including chromatin–chromatin interaction hubs, chromatin hubs at the nuclear periphery, hubs around macromolecules such as RNA polymerase or lncRNAs, and hubs around nuclear bodies such as the nucleolus or nuclear speckles. The second part reviews existing computational methods, including enhancer–promoter interaction prediction, network analysis, chromatin domain callers, transcription factory predictors, and multi-way interaction analysis. We introduce an integrated model that makes sense of the existing evidence. Understanding chromatin hubs may allow us (i) to explain long-unsolved biological questions such as interaction specificity and redundancy of mechanisms, (ii) to develop more realistic kinetic and functional predictions, and (iii) to explain the etiology of genomic disease. The study of such hubs has offered a new understanding of chromatin organization that is not limited to the chromosomes but involves the nuclear periphery, nuclear bodies, and small compartments created around large macromolecules. In this picture, hubs are not random agglomerations of interactions but are compartments with liquid-like properties that possess different functions. Such compartments promote intra-compartmental and discourage extra-compartmental interactions, and help to explain complex phenomena ranging from organelle biogenesis to gene co-regulation and disease. Studying such chromatin hubs is important because most hubs are involved in either the transcription of genes or repression of transcription, which are vital processes to maintaining a cell’s identity.Consequently, this review extends our previous discussion of single chromatin interactions to hubs of chromatin . As in the previous review, we will also present the current challenges and perspectives.22.1Biologically, a chromatin hub can be defined as a group of chromatin segments that interact directly or mediated by certain proteins, complexes, lncRNAs, or nuclear bodies. Some chromatin hubs are built of chromatin-chromatin interactions only, which are characterized mainly as short loops, with ∼2% of the Hi-C peaks corresponding to loops more than two megabase pairs (Mb) apart While the biological definition assumes that chromatin hubs physically exist as such at a given time, the computational definition does not consider whether the loops in the hub occur simultaneously or at different times/in different cells. In fact, the chromatin hubs that appear in bulk Hi-C experiments could correspond to an ensemble of all the alternatives in a population of cells, not happening at the same time in any given cell. As an example, MYC, the most central node in most gene-based chromatin interaction networks, interacts with its enhancers in an exclusive and probably stochastic manner. Under the conditions of one study, a given enhancer interacts with MYC in no more than 10% of the cells at any given time e.g., lncRNAs or PML proteins) could accumulate at specific chromatin loci and undergo phase separation after reaching a certain saturation concentration, which might be followed by detachment or coalescence of the newly formed condensates From a physical point of view, a new chromatin organization model is gaining acceptance, one in which chromatin hubs form liquid–liquid “phase-separated condensates” that behave as liquid compartments. This image is similar to the current description of membrane-less nuclear bodies, such as the nucleolus. Therefore, the model offers a unified view of nuclear compartmentalization as a series of dynamic “bubbles” able of generation, growth, fusion, fission, and decay. It is known that nuclear bodies are not enclosed by lipid bilayers but, instead, exist in a stable, round-shaped, liquid-like state, similar to oil drops in water, which is also called a “biomolecular condensate” or a “phase-separated” state Our definition of chromatin hubs emphasizes that chromatin interactions can be mediated by certain macromolecules or nuclear bodies. Therefore, we start this review by discussing chromatin interaction hubs in the context of the structures that mediate their interactions. To do so, we have classified chromatin hubs into 18 categories , Fig. 1.2.2TADs are defined as megabase-sized chromatin interaction domains, easy to identify as interaction clusters in a chromatin interaction heatmap . Their boundaries are enriched for CTCF and cohesin binding sites, as well as some other genes, and, in bulk studies, they have been shown to be highly conserved across species and stable across different cell types i.e., sub-TADs within TADs), with sub-TADs being more variable across cell types in situ hybridization imaging showed “TAD-like” regions in single-cells, though these regions have variable borders and do not disappear after cohesin depletion While TADs have been the most studied and reviewed of all chromatin hubs in the last decade, single-cell HiC (sc-HiC) studies have shown that TADs do not appear in single cells as clear as they do in bulk studies. Such findings sparked a debate regarding the existence of TADs in individual cells, particularly whether the absence of TADs is a limitation of current single-cell technologies or TADs from bulk studies are an artificial ensemble of mutually exclusive alternatives from different cells Fbn2 TAD in mESCs using super-resolution live-cell imaging. The Fbn2 TAD appears fully un-looped ∼6% of the time, while it is fully looped ∼3% of the time (with a median lifetime of ∼10–30 min). Interestingly, it is in a “partially extruded” state for ∼92% of the time. In such a state, ∼57–61% of the chromatin exists in ∼1–3 cohesin loops while the rest remain un-extruded TAD structure has also shown to be highly dynamic. A recent paper has examined the dynamics of the HP1) is a fundamental unit of heterochromatin, especially enriched at pericentromeric and telomeric heterochromatin. It has been shown that HP1α in Drosophila can nucleate into foci that have liquid-like properties. This suggests that the formation of heterochromatin domains is not mediated by chromatin compaction (an explanation that accounts for the domain isolation but not for domain interactions) but a phase separation mechanism instead Heterochromatin protein 1 . Among other features, they include most gene deserts  and a subset of telomeric regions; they are enriched for H3K9me2 and H3K9me3, which are characteristic histone marks of heterochromatin, and their position overlaps with the so-called B compartments derived from Hi-C studies. Similar to TADs, LAD borders are enriched with the CTCF protein. TADs and LADs can overlap in multiple cases, but LADs are best correlated to B compartments It is generally accepted that most active parts of the chromosomes face the nuclear interior, while most inactive parts face the nuclear periphery. in situ hybridization (FISH) has been used to determine LADs localization LADs have been mapped by DNA adenine methyltransferase identification (DamID) and Chromatin immunoprecipitation sequencing (ChIP-seq) of either laminA or laminB1. Both methods allow identification of LAD sequences. Additionally, fluorescence 2.4Transcription factories are chromatin hubs around RNAPol2 and other transcription-related molecules that are postulated to play a central role in transcription.et al. observed that transcription in HeLa cells occurred at 300–500 specific sites and not across the nucleus, while Iborra et al. found two populations of RNAPol2 molecules, one scattered and one organized in clusters that co-localized with transcription In the early 1990 s, Jackson Factories are currently depicted as polymorphic compartments with a “diameter” of 60–200 nm where RNAPol2 molecules are stationary on the surface and the core is rich in proteins, including TFs, co-activators, chromatin remodelers, histone modifiers, RNA helicases, and splicing factors, among others et al. showed that the number of transcription factories remains constant over time. Mitchell and Fraser showed that transcription factories exist even if transcription is inhibited et al. found that most long-range interactions related to the β -globin locus were maintained even though the polymerase was absent The consensus has moved towards the idea that factories are stable structures and not only assemble after transcriptional requirements. Iborra et al. found the TF Klf1 at ∼40 discrete foci, mostly co-localized with transcription factories, with 59–72% of Klf1-regulated genes  being transcribed in the same factories et al. showed that groups of co-expressing genes also co-localize by 3C et al.Transcription factories can also be seen as specialized compartments enriched with a given TF, where genes regulated by this TF move to the factory to be transcribed et al. used RNAPol2-associated ChIA-PET data to show the existence of chromatin hubs of both protein- and miRNA-coding genes. Genes in such hubs share functionality and are coordinately expressed when they co-localize et al.RNAPol2 transcribes both protein- and miRNA-coding genes Although RNAPol2 factories are the best studied, the existence of RNAPol1 and RNAPol3 transcription factories has also been confirmed Polycomb bodies are foci of polycomb group (PcG) proteins that reportedly repress chromatin hubs Trx chromatin domains” enriched in H3K4me3. One model suggests that chromatin is bistable, switching from repressive PcG to active Trx domains. If transcription activation signals are absent or low, PRC1 and PRC2 can form PcG domains, whereas if they are high, Trx and RNAPol2 antagonize PRC1 and PRC2 and form transcription-permissive Trx domains It has also been suggested that the trithorax group (Trx) chromatin-modifying complexes (which include proteins such as MLL1/MLL2 and SETD1A/SETD1B) can interact with RNAPol2 to inhibit PRC1 and PRC2 and generate “lncRNAs act as miRNA sponges, transcriptional activators or inhibitors, and scaffolds for nuclear organelles, but they can also serve as foci for chromatin hubs. It has been suggested that lncRNA foci also resemble membrane-less organelles. One example is XIST, which mediates X chromosome inactivation and can spread along the condensed X chromosome et al. have shown that around 60% of all annotated lncRNAs are chromatin-associated, near active protein-coding regions, and tethered by RNAPol2 Nascent RNAs have been reported to form clouds over regulatory DNA elements, which link distant promoters and enhancers and generate “nascent-RNA-associated transcription hubs” 2.5Several membrane-less organelles or “nuclear bodies” interact with chromatin, and therefore, may also serve as foci for chromatin hubs. These include the nucleolus , nuclear speckles and paraspeckles (for pre-mRNAs from diverse genes), Cajal bodies (for snRNAs), histone locus bodies (for histone mRNAs), and PML bodies (for both euchromatic and heterochromatic DNA).nucleolus, rather than only RNAPol1 factories, can be considered a chromatin hub, as multiple segments of chromatin interact with the nucleolar periphery. Such chromatin regions are enriched on repeats of the ribosomal gene (rDNA) and have been called “perinucleolar chromatin,” “nucleolar organizing regions,” or “nucleolus-associated chromatin domains” (NADs) The entire Nuclear speckles  were previously considered to be just a place for the storage of splicing factors. Modern techniques have elucidated their role in gene expression, since they contain numerous proteins related to epigenetic regulation, chromatin organization, TFs, and ncRNAs. The existence of chromatin-nuclear speckle hubs has been proposed, in which such hubs might coordinate all the gene expression regulation steps in a way similar to a transcription factory et al. reported the repositioning of the HSP70 gene to nuclear speckles after heat shock, followed by their transcription Paraspeckles are formed near nuclear speckles Cajal bodies are nuclear compartments located at the interface between chromosome territories, near chromatin domains enriched on small nuclear RNA (snRNA) genes or small nucleolar RNA (snoRNA) genes Histone locus bodies are similar to Cajal bodies, but they have been differentiated because they interact with chromatin domains enriched on the genes that code for histones, and they contain factors needed to process histone pre-mRNAs PML bodies, also known as ND10) are compartments with a diameter of approximately 0.1–1 μm that are involved in H3.3 chromatin assembly, SUMOylation, sequestration of TFs, senescence, and antiviral defense. Around 5–30 PML bodies exist in a cell, depending on cell type and cell cycle phase, forming at regions of high transcriptional activity or near telomeric DNA. They are mainly characterized by the PML protein, which is located at their periphery, enclosing a core filled with both constitutive and transient proteins, especially chromatin-related factors, such as histone H3.3 and histone modifiers  Finally, promyelocytic leukemia nuclear bodies . It has been reported that DNA of the HSV-1 virus can form a replication compartment around RNAPol2 foci Novel types of chromatin hubs are being constantly described. For example, an senescence-associated heterochromatin foci (SAHF)In senescent cells and cells with “laminopathies” (diseases due to lamin protein mutations), it has also been reported that the lamina is disrupted and LADs re-localize away from the nuclear periphery or the nucleolus, accompanied by the formation of G-quadruplexes (G4s) could be considered either chromatin hubs or a part of a chromatin hub. G4s are DNA secondary structures consisting of stacks of guanine tetrads (four guanine bases associated in a planar structure) in Guanine-rich regions. Although they were initially reported as transcriptional repressors at oncogene promoters, current evidence suggests that G4s are mainly transcriptional enhancers located at the promoters of transcriptionally active genes Additionally, DNA domains belonging to 2.7In recent years, novel experimental technologies have revolutionized the study of chromatin interactions, the most popular development being “single-cell HiC” in situ MARGI” (iMARGI), and “RNA and DNA interacting complexes ligated sequencing” (RADICL-seq) Several methodologies to identify RNA–chromatin interactions have also appeared. Some of them are valid for one RNA at a time, such as “chromatin isolation by RNA purification” (ChIRP), “capture hybridization analysis of RNA targets” (CHART), and RNA antisense purification (RAP). A few others can be applied to evaluate RNA–chromatin interactions across all RNAs, such as “mapping RNA–genome interactions” (MARGI), “global RNA interaction with DNA sequencing” (GRID-seq), “chromatin-associated RNA sequencing” (ChAR-seq), “e.g., to identify interactions between chromatin regions around a nuclear body); therefore, ligation-free methods have been developed. Other authors see a problem in that 3C-based protocols generate large concatemers that are trimmed in order to be sequenced; therefore, they focus on keeping the full concatemers and using long-read sequencing to detect multi-way interactions. In summary, current multi-way interaction detection methods include ligation-based methods . These methods are further reviewed in We want to highlight recent experimental techniques that go beyond pairwise interactions and detect multiple-loci or multi-way interactions. Some authors argue that the “proximity ligation” approach is not useful for regions too far apart to be directly ligated  prediction of enhancer–promoter interactions, using either machine learning models based on different genomic features or natural language processing methods applied to the DNA sequence; (ii) both traditional and novel network analyses of chromatin interaction networks; (iii) development of different types of domain callers to call TADs, LADs, and other domains from genomic data; (iv) various chromatin hub and, specifically, transcription factory inference methods based on statistical comparisons; and (v) novel methods of multi-way interaction data analysis ; Fig. 2.3.1Enhancer–promoter interaction prediction is different to the computational prediction of chromatin hubs; however, predicted interactions can be aggregated into networks and subjected to clustering, as a way of predicting enhancer–promoter hubs.In our previous review TargetFinder is a method that applies ensembles of boosted decision trees to hundreds of genomic features in six ENCODE cell lines. The method is as follows: enhancers and promoters are identified using segmentation data from ENCODE, while enhancer–promoter interactions are extracted from Hi-C data , and ZNF384], open chromatin (DNase-seq), and DNA methylation. Some highly–ranked proteins, which are not commonly reported as predictors, included CUX1; SRF; SUPT20H; EBF1; MAX; TFAP2C; chromatin looping related proteins SP1, SPI1 (PU.1), HCFC1, and TBP; and histone modifiers KDM1A and RCOR1. CAGE (e-RNA) was consistently the best predictor in both multivariate  and univariate (with one feature) models, suggesting that, in case we can only obtain data from one feature, e-RNA should be the chosen one. SUMOylation, a post-translational modification not included in ENCODE or Roadmap Epigenomics at the date of the publication, was found to be another good predictor. RNAPol2, commonly included in software for enhancer–promoter prediction, was not predictive on its own; however, elongation-associated histone marks proved to be more indicative of promoter activity. Additionally, PRC2 and heterochromatin marks were associated with non-interacting pairs According to TargetFinder results, the models and ranks of features are different for different cell lines; however, some predictors performed consistently well across cell lines, including CAGE (e-RNA) data, activation-associated  and elongation-associated marks , structural proteins  have already been reviewed 3.4et al. published a statistical test to determine the existence of transcription factories. They examined whether co-regulated genes were clustered in space significantly more than their clustering along the genome, which proved to be true for 64 out of 117 TFs, i.e., 64 types of specialized transcription factories, in S.cerevisiaeMultiple methods predicting chromatin hubs and transcription factories have emerged in recent years. In 2013, Ben-Elazar et al. introduced the concept of “variable chromatin modules” (VCMs). After comparing expression, three histone marks, the TF PU.1, and RNAPol2 levels in lymphoblastoid cell lines from 47 individuals, they found that inter-individual variation was concentrated on chromatin modules that they termed VCMs and that TADs were enriched in such VCMs. They described VCMs as sub-TAD transcription-related spatial structures, similar to transcription factories et al. used 3D structures of both deconvoluted Hi-C data and sc-HiC data to identify a series of 3D chromatin clusters that occurred frequently across the population. They called these clusters “regulatory communities,” as a large number of them were enriched on regulatory factors, with the two major factors being “centromere clustering” and “transcription factor binding” Waszak et al. superposed ChIP-seq data to a chromatin interaction network to detect 3D regions for which the ChIP-seq peak height was significantly high, which they called “functional 3D hotspots” and suggested that transcription factories could be one example of such hotspots et al. introduced “EpiTensor,” a tensor modeling method to identify 3D spatial associations from 1D epigenomic information of histone marks, DNA accessibility, and gene expression. Using this method, the authors identified a group of “interaction hotspots” enriched on TFs and ncRNAs, which could be transcription factories Capurso et al. introduced a method to locate co-localized and co-regulated chromatin regions et al. performed a similar analysis for sc-HiC data Belyaeva Chromatin Module Inference on Trees (CMINT) is a related method that finds the dynamic changes of chromatin modules 3.5et al. introduced “Statistical inference of co-segregation” (SLICE), the algorithm to process data obtained from GAM The advent of multi-way interaction detection methods and datasets see  has inspet al. introduced “multiplex interaction analysis by signal processing algorithms” (MIA-Sig) Kim de novo predictions. For SPRITE data, MATCHA enhanced the data quality by calling 700 k 3-way, 800 k 4-way, and 700 k 5-way interactions. Regarding prediction, the authors used their own hypergraph representation learning model, which uses labeled data and node features as input. MATCHA is used to distinguish multi-way interaction data from cliques of pairwise interaction data in a cell population.“Multiway-interacting chromatin analysis” (MATCHA) is an example of a method that can be used to process multiple types of multi-way data et al. presented a method for multi-way interaction prediction based on pairwise interaction frequencies from bulk Hi-C data Finally, Liu 3.6Network analysis and machine learning are arguably the most common computational approaches to study chromatin hubs. To build a network or machine learning analysis, several aspects have to be considered. In 4etc.), and their similarities and differences to other compartments, (iv) the chromatin domain’s potential for stochastically joining different transcriptional or repressive environments/compartments, and (v) the potential of some chromatin domains for moving from transcriptional to repressive compartments (bi-stability).We are starting to find the pieces of the puzzle to build a unified theory able to explain all of the different types of chromatin hubs . Such pi4.1The liquid–liquid phase separation model has quickly become the strongest contestant to explain chromatin hubs et al. put forward a model for splicing in nuclear speckles in which exons are transported into the speckles by SR proteins, while introns are barred entry by nucleoplasmic hnRNP proteins, leaving splicing sites at the nuclear speckle interface, where interface-located spliceosomes will perform the splicing reaction. The authors suggested that a similar organization of reactions could exist in other nuclear bodies, such as RNAPol1 factories/nucleoli It has also been proposed that the interfaces of membrane-less compartments could have functional roles. Liao e.g., the processes involved in the genesis of the condensates. Recently, Qi et al. built a molecular dynamics simulation model where chromatin was represented as coarse-grained particles  and suggested that there are two ways in which nucleolar particles associate with NADs and each other: In the absence of the chromatin network, all nucleolar particles condense into a single droplet, while, in the presence of the chromatin network, they condense into multiple droplets The new model also brings to light new questions, 4.2There is evidence of similarity in composition between many phase-separated compartments containing chromatin hubs, as well as evidence of protein exchange between them. For example, nuclear speckles share proteins with other types of nuclear bodies, such as (i) spliceosomal snRNPs with Cajal bodies, (ii) PSF and PSP2 with paraspeckles, and (iii) Pat1b with PML bodies et al. observed that co-expressed genes tend to be located in a “nuclear neighborhood” more often than by chance. Such nuclear neighborhoods are ∼ 1 μm in diameter and can be associated with either a factory or a speckle. The authors explained such organization with a mathematical model that reproduces the frequencies of speckles and factories sharing co-expressed genes as a stochastic selection process of a nuclear body (either factory or speckle) within a volume defined by the global organization existing before gene expression An early computational model of chromatin hubs was built for transcription factories and nuclear speckles. An important feature of nuclear speckles is that they form in the vicinity of RNAPol2 transcription sites. They contain several RNAPol2 subunits, are rich in proteins related to transcription elongation, and are poor in proteins related to transcription initiation et al. went one step further and introduced a model in which some chromatin regions (LADs) were linked to the nuclear lamina while others (inter-LADs) were linked to either transcription factories or nuclear speckles. The authors postulated that, similar to factories and speckles, some LADs can stochastically interact with the nuclear lamina, nucleolus, or centromeres, which correspond to three distinct repressive environments. As previously mentioned, LADs partially overlap with NADs; moreover, some NADs have been found near the nuclear lamina by FISH, while some LADs have been detected close to pericentromeric heterochromatin by 4C. Consequently, the model suggests that large heterochromatin domains are “LADs” if they interact with the nuclear lamina, “NADs” if they interact with the nucleolus, or “PADs” if they interact with centromeres; however, it is important to clarify that the three types of domains are similar but not identical, and some genomic regions show a preference for one over the others Steensel The authors also introduced a model where a subset of LADs can oscillate between the nuclear lamina and either a transcription factory or a splicing speckle, alternating between being transcriptionally repressive and transcriptionally active. They suggest that the contact between the LADs and the nuclear lamina constitutes a separate 3D compartment, keeping them isolated from transcription factories or speckles. Therefore, a possible mechanism for LADs to move away from the lamina starts with active promoters at the LAD borders interacting with transcription factories or nuclear speckles and, as a consequence, the factories/speckles pull the edges of the LAD away from the nuclear lamina, bringing the entire domain with them et al. have shown that during daytime, circadian genes are repressed through a CTCF- and PARP1- mediated recruitment to the nuclear membrane, which changes (activates) during night-time. Consequently, an oscillation of circadian genes occurs between active circadian loci and repressive LADs More examples of such bi-stability have been reported. Zhao e.g., factories, nuclear speckles, Trx domains, MALAT1 lncRNA, nascent RNAs, R-loops, G4s, and transcriptional histone marks), (ii) the network of interactions and pathways for most repression-related hubs , and (iii) the network of interactions and pathways for hubs with the potential of being bistable . Such networks might have a stochastic nature .None of the previous models considers all of the known chromatin hubs. We suggest that a unified model should include three components: (i) the network of interactions and pathways for most transcription-related hubs  the development of more complex and specific signatures, (ii) the use of multiplexed imaging techniques, and (iii) the development of new experimental methods to detect chromatin in their specific hub/compartment.First, we must take into account the technology that will be used to generate the chromatin interaction datasets. Proximity-ligation methods are more conservative and only register hubs in close proximity, while ligation-free methods can detect hubs around nuclear bodies but might also introduce new artifacts. Thus, we must clarify which technologies are suitable to predict specific types of hubs. As an example, Arrastia A few technical problems must also be addressed. For example, many of the computational methods here reviewed still use hg19 data (see JEME’s and SEPT’s articles), a reference genome version that does not include centromere and other interesting heterochromatic data 5In this review, we have built a comprehensive and unified model of the current progress in chromatin biology under the concept of a “chromatin hub.” The biological review involved the study of chromatin hubs made of chromatin–chromatin interactions only; hubs at the nuclear periphery; hubs around macromolecules, such as RNA polymerase, lncRNAs, and nascent RNAs; and hubs around nuclear bodies, such as the nucleolus or nuclear speckles . The com(i)Gene regulatory networks and the redundancy of transcriptional regulators(ii)The stochastic switch of interaction partners, and thus mechanisms, without changing the genome structure(iii)DNA–DNA, protein–DNA, and lncRNA–DNA interaction specificity(iv)versus models assuming diffusion towards the DNA molecule) or chromatin hub-aware gene set analysisNew quantitative predictions, such as kinetic models in bubbles ((v)Understanding disease: A hub-centric view could allow us to see why some perturbations are not dangerous (redundant mechanisms) while others disrupt the whole hub and lead to disease.There are multiple reasons to study chromatin hubs. We believe that chromatin hubs may help explain:et al. positioned enhancers at different distances of a promoter and observed that the contact probabilities between enhancer and promoter decay with increasing genomic distance, falling significantly when approaching the TAD boundaries and falling even further across the TAD boundary. A similar pattern is followed by the promoter's transcriptional output, where transcriptional levels decrease with increasing genomic distance and fall to promoter-only levels when the enhancer is located outside of the TAD and is not able to activate the promoter. Therefore, the authors built a mathematical model to explain transcriptional output in terms of contact probabilities: Transcription is described by a two-state (on–off) model where the frequency of transcriptional bursts depends on the enhancer-promoter contact probability through a Hill function There are recent examples of mathematical modeling having into account chromatin hubs. Zuin in vivo loss-of-function mouse model have revealed that CTCF depletion is enough to induce heart failure. Also, heart failure genomes display a decreased stability of chromatin interactions around cardiac disease genes Previously, we have reviewed how diseases attributed to a given gene may be related to distant genes thanks to long-range interactions The study of chromatin hubs is a new challenge for chromatin biology and bioinformatics, and it is a path to explore the answers to new interesting questions, including: (i) Where in the nucleus are chromatin interactions occurring? (ii) What are the functional consequences of this? (iii) What are the processes leading to chromatin hub disruption? (iv) What are the processes occurring after chromatin hub disruption? (v) Why are chromatin hubs occurring in some individual cells but not in others? (vi) How can this knowledge be used for disease understanding and treatment? Answering such questions might become a path to a more realistic view of the genome, its regulatory processes, and their disruption.AM contributed to sections 1, 2, 3, 4, 5, and Supp.Mat. XWH and SJ contributed to section 3 and Supp.Mat. QJ and XL contributed to sections 2, 4, and 5. All authors have read and agreed to the final version of the manuscript.AM was funded by Guangzhou Medical University, high-level talent fund; SJ was funded by China Postdoctoral Science Foundation grant 2019M652847; QJ is supported by National Natural Science Foundation of China (NSFC) grants 82070983 and 81870679; XL is supported by the State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou Scientific Research Plan 202102010179 and NSFC grant 82150710555.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Using a comparative genomic approach, we previously identified a novel DNA-binding transcription factor (named HisR) that controls the histidine metabolism genes in diverse Gram-positive bacteria from the Firmicutes phylum.The histidine metabolism and transport (Ruminococcus gnavus. To fill a knowledge gap in the regulation of histidine metabolism genes in Firmicutes genomes that lack a hisR repressor gene, we systematically searched their upstream regions for potential RNA regulatory elements. As result, we identified 158 T-box riboswitches preceding the histidine biosynthesis and/or transport genes in 129 Firmicutes genomes. Finally, novel candidate RNA attenuators were identified upstream of the histidine biosynthesis operons in six species from the Bacillus cereus group, as well as in five Eubacteriales and six Erysipelotrichales species.Here we report the identification of HisR-binding sites within the regulatory regions of the histidine metabolism and transport genes in 395 genomes representing the Bacilli, Clostridia, Negativicutes, and Tissierellia classes of Firmicutes, as well as in 97 other HisR-encoding genomes from the Actinobacteria, Proteobacteria, and Synergistetes phyla. HisR belongs to the TrpR family of transcription factors, and their predicted DNA binding motifs have a similar 20-bp palindromic structure but distinct lineage-specific consensus sequences. The predicted HisR-binding motif was validated in vitro using DNA binding assays with purified protein from the human gut bacterium The obtained distribution of the HisR transcription factor and two RNA-mediated regulatory mechanisms for histidine metabolism genes across over 600 species of Firmicutes is discussed from functional and evolutionary points of view.The online version contains supplementary material available at 10.1186/s12864-022-08796-y. The structure and organization of the his operons were extensively studied in Proteobacteria [his operon in bacteria from three major phyla, namely Proteobacteria, Actinobacteria, and Firmicutes, suggested that this operon is indeed ancient, however, a few other lineages of bacteria such as Cyanobacteria possess stand-alone his genes that are not clustered into operons [his genes are scattered on the chromosomes in the majority of Euryarchaeota, in contrast to the Crenarchaeota and Thaumarchaeota, where his genes are arranged into the his operons of various structures [his genes underwent fusion, thus affecting the number of genes in the his operon [hisF-hisI-hisE, hisZ-hisG, hisJ-hisB) have undergone various single or multiple fusions in a variety of genomes [L-histidine (hereafter histidine) is a positively charged proteinogenic amino acid containing an imidazole side chain . Biosyntbacteria , Archaeabacteria , and othbacteria . Broad p operons . Among Aructures . In the s operon . Among e genomes , 9.TableE. coli and other g-proteobacteria, the his operon is regulated by an attenuation mechanism, which is based on coupling between transcription and translation of the leader peptide containing tandem histidine codons [his operon. In some attenuators, the transcriptional terminator is replaced with another regulatory structure that sequesters the ribosomal-binding site (RBS) of the first gene in the regulated operon, thus functioning at the translation, not transcription level [Histidine biosynthesis and transport genes can be regulated by a variety of mechanisms including a repressor protein and various RNA structures, such as transcriptional attenuators and T-box riboswitches. In e codons , 11. AttCorynebacterium glutamicum and Lactococcus lactis, the his operon is regulated via the T-box transcriptional attenuation mechanism [his biosynthesis operon and the candidate histidine ABC-transporter operon hisXYZ* was found among the Lactobacillales order, as well as in Listeria monocytogenes  and Clostridioides difficile  [yerC in B. subtilis and its ortholog in Staphylococcus aureus [his operon and the putative histidine uptake permease yuiF. The predicted histidine repressor HisR belongs to the TrpR family of tryptophan-sensing repressors [his operon in a Gram-negative organism, Xanthomonas oryzae [In echanism , 13. Theechanism . T-box rechanism . This speriales) . Howevers aureus , 17. Thepressors , 17. A ps oryzae , howeverhisR orthologs. We identified DNA binding sites of HisR regulators and reconstructed their regulons in the majority of Firmicutes and other taxonomic lineages. To evaluate the accuracy of the genomic reconstructions of the HisR regulon, we performed experimental testing of the predicted regulatory site upstream of the his operon in Ruminococcus gnavus using in vitro binding assay with purified HisR protein. As result, we confirmed that HisR binds to its DNA binding site, and that L-histidine is required for HisR-DNA binding. Comparison of HisR proteins and their predicted DNA motifs with that of the previously characterized tryptophan repressor TrpR in E. coli provided insights into the mechanism of HisR action. Analysis of the distribution of RNA regulatory elements for histidine metabolism genes in taxonomic lineages without HisR allowed us to propose a likely evolutionary scenario for histidine regulons of Firmicutes.In this study, we report a detailed analysis of the histidine biosynthesis and uptake genes and their regulation by the HisR regulator and RNA regulatory mechanisms in many available genomes of Firmicutes, as well as other taxonomic groups of bacteria possessing Bacillus subtilis (encoded by the yerC gene) [Bacillus cereus group (six closely related species), Gemella spp., and Macrococcus caseolyticus. Among the Eubacteriales order (195 genomes) HisR is absent from 21 genomes, most of which belong to the Peptostreptococcaceae family, e.g. Clostridioides difficile . As discussed below, the reconstruction of regulons for these rare members of the HisR regulator family in Lactobacillales suggests that their function is unrelated to the histidine biosynthesis or uptake. Finally, all 20 genomes from the Erysipelotrichales order of Firmicutes lack HisR orthologs. Thus, although HisR regulator is broadly distributed among Firmicutes, it was apparently lost in several independent taxonomic groups and in multiple individual species. It should be noted that among 231 genomes that lack a hisR regulator, there are both histidine auxotrophs missing histidine biosynthesis and prototrophs that possess the his genes. In this study, we analyzed the HisR binding sites and reconstructed corresponding regulons in the genomes possessing this transcription factor and also identified RNA regulatory mechanisms for his genes in the majority of remaining genomes that lack HisR orthologs.We selected a set of 626 representative genomes of Firmicutes and classified them into 11 taxonomic groups, with three major groups being the Bacillales, Lactobacillales, and Eubacteriales orders. Using this genome set, we identified orthologs of the HisR regulator from hisR genes in Firmicutes and specifically checked for cases of co-localization of hisR with genes involved in histidine biosynthesis or transport , in six genomes from the Syntrophomonadaceae and Thermoanaerobacteraceae families, as well as in two Salinicoccus species .Co-localization of a transcription factor (TF) gene with its cognate regulated genes is common for local TF regulons involved in regulation of specific metabolic pathways in bacteria –21. We aLegionella, Francisella, and Wohlfahrtiimonas) and plant pathogens from the Xanthomonadales order. HisR was also found in 11 genomes from the Rhodobacterales and Caulobacterales orders of a-proteobacteria, many of which were isolated from marine or extremely cold environments. In the majority of Proteobacteria, hisR is the first gene of his operon, suggesting its local character of regulation. Additional hisR orthologs were found in 22 genomes of Actinobacteria from the orders of Coriobacteriales and Eggerthellales, where they were never found in chromosomal clusters with the histidine biosynthesis and transport genes. In six out of nine genomes from the Synergistetes phylum, hisR forms a putative operon with hisC, while in Pyramidobacter piscolens it belongs to the his operon. Finally, a hisR ortholog was identified in a single species of Mollicutes, Acholeplasma laidlawii (forming an operon with the hisXYZ* transporter), two Dictyoglomus species from the phylum of Dictyoglomi , and Elusimicrobium minutum from the phylum of Elusimicrobia (as the first gene of the his operon).In addition to Firmicutes, HisR orthologs were identified in a small subset of genomes representing other bacterial phyla  suggests that these HisR2 paralogs could have acquired a new function in the control of tyrosine/tyramine metabolism. Finally, one of two hisR paralogs in Oscillibacter spp. is located within a conserved gene cluster with histidine utilization genes, namely histidine ammonia-lyase hutH and histidine permease hisT, suggesting another potential case of functional specialization of HisR regulators.Similar to other bacterial TFs controlling the central metabolic pathways, such as ArgR, TrpR, MetJ, and TyrR repressors in g-proteobacteria , HisR rensporter . ChromoshisR repressors  and/or standalone genes/operons encoding histidine transporters from two major families  and Elusimicrobium minutum (Elusimicrobia). In both Dictyoglomus species, a HisR-binding site was identified upstream the his operon, whereas hisR is not autoregulated. Acholeplasma laidlawii is the only hisR-containing species among the Mollicutes phylum. The genome of this histidine auxotrophic bacterium contains a single HisR-binding site located upstream of the hisXYZ*-hisR operon.In Proteobacteria, HisR almost exclusively controls the isR Fig. . In a siBacillus subtilis and related bacteria control gene expression via a unique transcription antitermination mechanism. T-box serves as a riboswitch that binds directly to a specific uncharged tRNA and thus measures the amino acid availability in the cell.The T-box leader is a highly conserved RNA regulatory element that is involved in regulation of various genes involved in amino acid metabolism in Gram-positive bacteria, mostly in the Firmicutes phylum . T-box eEnterococcus species  also developed the T-box regulation. Interestingly, both HisR and T-box regulatory mechanisms are combined in Listeria spp., where the his operon is under HisR control, whereas the hisYZ*X transporter is regulated by His-specific T-box. This configuration can be explained by possible horizontal transfer of the T-box-regulated hisYZ*X transporter from the Lactobacillales order. Indeed, each of the three components of this ABC transporter from Listeria spp. clusters with their respective orthologs from Lactobacillus spp. demonstrating 55–60% amino acid identity.Within the Lactobacillales lineage, T-boxes were found in upstreams regions of histidine biosynthesis and/or transport operons Fig. . For exahisXYZ* transporter in Alkaliphilus oremlandii, which lacks HisR regulator. The closest hits for the components of this T-box-regulated transporter belong to the HisR-regulated transporter operon from Clostridium perfringens (60–65% identity), suggesting that the replacement of its regulatory region by a T-box leader is the most plausible evolutionary scenario. Among the Peptostreptococcaceae family, T-boxes exclusively control the hisXYZ* transporter operon, suggesting that histidine-specific T-box riboswitches have been acquired in the common ancestor of these species. It should be noted that the his biosynthetic operons that are present in some Peptostreptococcaceae genomes are regulated by a different RNA-based mechanism, attenuation (see below). In the Natranaerobiales class, there is only one genome in the reference set, Natranaerobius thermophilus, which has a T-box upstream of a single gene hisC, whereas other histidine biosynthesis genes are controlled by the HisR repressor . Additional T-box regulatory elements were identified upstream of the hisXYZ* transporter in two histidine auxotrophic Holdemania species, as well as in Clostridium innocuum, which has two T-box-regulated operons  operons in E. coli and related g-proteobacteria and is involved in the amino acid-dependent attenuation of their transcription [The histidine operon leader is an RNA element that has been found upstream of histidine biosynthesis  a group of six closely related Bacillus species from the B. cereus group; (ii) a group of four related species from the Peptostreptococcaceae family  and Lutispora thermophila from the Eubacteriales order; and (iii) six different species from the Erysipelotrichales order  and E. coli TrpR holorepressor in complex with tryptophan (PDB ID code 1ZT9). Pairwise structure alignment confirmed similar positioning of a-helixes in these proteins and overall structural similarity with root mean square deviation (rmsd) between the aligned pairs of the backbone Ca atoms of 2.7 Å , which is a part of a larger clan (CL0123) of DNA-binding proteins containing the helix-turn-helix (HTH) domain. HisR and TrpR proteins are characterized by relatively short lengths (< 100 a.a.) and low pairwise sequence similarity. For example, a BLAST-generated alignment of the TrpR protein from To assess the conservation of key structural features in HisR proteins from diverse taxonomic lineages we analyzed multiple alignment of ~ 500 proteins that were previously used for the HisR phylogenetic tree construction . Bacteria from two distinct lineages lacking a HisR regulon, the Erysipelotrichaceae and Peptostreptococcaceae families, have acquired (or evolved) two different RNA regulatory motifs, either T-box (in 15 genomes) and/or a novel potential RNA attenuator (in 10 genomes) that control the his operon and/or hisZ*YX transporter. Finally, the Bacillus cereus group of the Bacillales order has independently acquired a novel RNA attenuator controlling the his operon.Hence, the most parsimonious evolutionary scenario for regulation of histidine metabolism in Firmicutes is the following. HisR was present in the last common ancestor of all Firmicutes, and it was lost in the Lactobacillales, and Erysipelotrichales orders, as well as several narrow taxonomic groups from the Bacillales and Eubacteriales orders Fig. . In LacthisYZ*X operon, potentially from an unknown Lactobacillus spp. Another potential horizontal transfer of a target operon together with its RNA control elements is the his operon preceded by an RNA attenuator in Lutispora thermophila (a bacterium from the Clostridiaceae family), which is the best hit to the RNA attenuator-controlled his operons from C. difficile and other Peptostreptococcaceae genomes. In a few cases, a target operon, which is commonly regulated by HisR among the Eubacteriales genomes, has obtained RNA-dependent regulation in a single genome (as compared to closely-related species), such as the acquisition of a T-box upstream element by the hisXYZ* operon in Alkaliphilus oremlandii and the hisC gene in Natranaerobius thermophilus. Thus, the latter bacterium from the Natranerobiales order is another example (in addition to Listeria spp.) that implicates both HisR and T-box in the regulation of the histidine metabolism.Some other potential evolutionary events can be deduced from our analysis. The Listeriaceae genomes have acquired the T-box-regulated Ruminococcus gnavus. This anaerobic clostridium is a dominant community member in the human gut. Our genomic reconstruction suggests that R. gnavus contains three candidate HisR-binding sites located upstream of the hisZGDB(FIE) operon, hisXYZ* transporter and hisR regulator gene. A synthetic fluorescence-labeled DNA fragment containing the predicted HisR-binding site upstream of the hisX (Rumgna_0302) gene in R. gnavus was assessed for its specific interaction with the purified HisR protein using the fluorescence polarization assay. This synthetic 30-bp DNA fragment contained a 20-bp HisR site sequence flanked by poly-guanine stretches on each side. In the presence of an excess of histidine (0.75 mM), HisR specifically binds to the synthetic DNA fragment containing a HisR-binding site from R. gnavus. Exclusion of histidine from the incubation mixture led to the disruption of HisR-DNA binding  was included to the alignment and tree as an outgroup. The tree was exported in.nwk format and downloaded in the iTOL visualization system (https://itol.embl.de) [The comparative genomic analysis of histidine biosynthesis, transport and regulatory genes was performed using the subsystems approach implemented in the SEED database and analysis tool, which combines protein similarity search, positional gene clustering, and phylogenetic gene profiling . The Hisg MUSCLE  from theg MUSCLE  with theg MUSCLE , using 1embl.de) . For vishisR-containing genomes we used the training sets of non-coding regulatory regions of the histidine subsystem genes. For training sets, we collected up to 300 nucleotides upstream of the translation start site and excluded the coding regions of any upstream gene if the intergenic region was less than 300 nt. The obtained training sets for each HisR-containing bacterial lineage were used for a search of lineage-specific conserved DNA motifs possessing a palindromic structure using the SignalX software [http://bioinf.fbb.msu.ru/SignalX/), resulting in the construction of respective PWMs. The obtained lineage-specific PWMs were further used to scan the studied genomes and identify additional HisR-binding sites using the z-scan script and the GenomeExplorer software [hisR genes, and reported all candidate binding sites with scores above a PWM-specific threshold . The search parameters were selected allowing identification of potential sites between 350 nt upstream and 50 nt downstream of a gene start codon. Cross-species comparisons of the predicted sets of potentially regulated genes allowed us to tentatively define regulon composition for each analyzed lineage. Candidate HisR binding sites with scores below threshold were validated by phylogenetic footprinting using multiple sequence alignment of upstream gene regions for each group of orthologs, as previously described in [For the identification of DNA binding sites and reconstruction of HisR regulons, we applied the integrative comparative genomics approach as previously described . Brieflysoftware   and His leader (RF00514), were taken from Rfam database . The 100ge 1.1.3 . OutputsTo construct a phylogenetic tree of the analyzed Firmicutes species, we used two groups of the following ribosomal proteins: L5, L6, L9, L10, L15, L20 and S2, S4, S5, S6, S8, which represent small (S) and large (L) ribosomal subunits, respectively. These eleven ribosomal proteins were identified in the analyzed 626 genomes using subsystems in the SEED database . MultiplhisR gene from Ruminococcus gnavus ATCC 29,149  was synthesized by GenScript Inc. with optimized codons for expression in Escherichia coli by IDT tool (https://www.idtdna.com/CodonOpt). The cloning of the synthesized hisR gene fragment into the pODC vector was performed using the SalI and NcoI restriction enzymes. The PCR fragment was purified and subjected to the second round of PCR using primers HisR-NcoIF-GATTATCCATGGCTAAGAAAATCCGTACGG, and HisR-SalIR-CTAATTGTCGACTTATTTTTCCATGCGCTCAAAC, to append a six histidine codons and tobacco Etch Virus protease cleavage sequence. The fragment was digested with NcoI/SalI and ligated to the same sites of pODC. The resulting plasmid was introduced into E. coli DH5α to inoculate onto LB agar plates containing ampicillin. The colonies were isolated and checked by sequencing using verification primers. The HisR-pODC plasmid was introduced into E. coli BL21/DE3 strain used for overproduction of HisR protein.The 6 protein containing an N-terminal 6 × His tag was overproduced by growing the BL21/DE3 strain carrying the plasmid in LB at 37 °C until mid-exponential phase (OD600 ~ 0.6), and 0.8 mM isopropyl-b-D-thiogalactopyranoside was added. The cultures were incubated at 24 °C overnight with continuous shaking, and cells were collected by centrifugation. The recombinant protein was purified by Ni-chelation chromatography from the soluble fraction as previously described [6 protein is 11.79 kilidaltons, with total length 100 amino acids. Protein concentrations were measured using the Bradford assay kit (Bio-Rad).The recombinant HisR-Hisescribed –44. The R. gnavus was assessed using fluorescence polarization assay (FPA), as previously described [hisX gene (Rumgna_00302), CAGTTTAGTATAGTAAAGT, that was linked to the GGGGG sequences at both ends to improve annealing of single stranded DNA fragments. The single stranded labeled and unlabeled DNA oligos were synthesized by Integrated DNA Technologies. The double-stranded DNA fragments were obtained by annealing synthesized oligonucleotides at a 1:10 ratio of 5’-labeled with 6-carboxyfluorescein to unlabeled complementary oligonucleotides. The obtained 30-bp DNA fragments (1 mM) were incubated with the increasing concentrations of HisR for 20 min. The HisR binding assay mixture (0.1 ml) contained Tris buffer, pH 7.5, 0.1 M NaCl, 0.5 mM EDTA, 10 mM MgSO4, 2 mM DTT, 5 mg/ml herring sperm DNA. Thereffect of histidine (0.75 mM) was tested by its addition to the incubation mixture.Interaction of the purified recombinant HisR repressor protein with its cognate DNA-binding sites in escribed . We seleAdditional file 1. Maximum-likelihood phylogenetic tree for the HisR proteins in studied genomes.Additional file 2. Distribution of histidine biosynthesis genes, transporters and regulons in Firmicutes genomes.Additional file 3. (A) Predicted HisR-binding sites and reconstructed regulons in other lineages; (B) Distribution of histidine biosynthesis genes and transporters in other lineages.Additional file 4. (A) Predicted HisR-binding sites (A) and Histidine-specific T-boxes (B) and the reconstructed regulons in Firmicutes genomes.Additional file 5. Phylogenetic footprinting of upstream regions of candidate HisR-regulated genes.Additional file 6. Phylogenetic footprinting of upstream regions of genes controlled by candidate Histidine-dependent translational attenuators.Additional file 7. Distribution of various regulatory mechanisms for histidine metabolism genes among analyzed Firmicutes species.Additional file 8. Outputs of cmsearch  with alignments of candidate histidine-specific T-boxes in Firmicutes.


Activation of gene expression in response to environmental cues results in substantial phenotypic heterogeneity between cells that can impact a wide range of outcomes including differentiation, viral activation, and drug resistance. An important source of gene expression noise is transcriptional bursting, or the process by which transcripts are produced during infrequent bursts of promoter activity. Chromatin accessibility impacts transcriptional bursting by regulating the assembly of transcription factor and polymerase complexes on promoters, suggesting that the effect of an activating signal on transcriptional noise will depend on the initial chromatin state at the promoter. To explore this possibility, we simulated transcriptional activation using a transcriptional cycling model with three promoter states that represent chromatin remodeling, polymerase binding and pause release. We initiated this model over a large parameter range representing target genes with different chromatin environments, and found that, upon increasing the polymerase pause release rate to activate transcription, changes in gene expression noise varied significantly across initial promoter states. This model captured phenotypic differences in activation of latent HIV viruses integrated at different chromatin locations and mediated by the transcription factor NF-κB. Activating transcription in the model via increasing one or more of the transcript production rates, as occurs following NF-κB activation, reproduced experimentally measured transcript distributions for four different latent HIV viruses, as well as the bimodal pattern of HIV protein expression that leads to a subset of reactivated virus. Importantly, the parameter ‘activation path’ differentially affected gene expression noise, and ultimately viral activation, in line with experimental observations. This work demonstrates how upstream signaling pathways can be connected to biological processes that underlie transcriptional bursting, resulting in target gene-specific noise profiles following stimulation of a single upstream pathway. Many genes are transcribed in infrequent bursts of mRNA production through a process called transcriptional bursting, which contributes to variability in responses between cells. Heterogeneity in cell responses can have important biological impacts, such as whether a cell supports viral replication or responds to a drug, and thus there is an effort to describe this process with mathematical models to predict biological outcomes. Previous models described bursting as a transition between an “OFF” state or an “ON” state, an elegant and simple mathematical representation of complex molecular mechanisms, but one which failed to capture how upstream activation signals affected bursting. To address this, we added an additional promoter state to better reflect biological mechanisms underlying bursting. By fitting this model to variable activation of quiescent HIV infections in T cells, we showed that our model more accurately described viral expression variability across cells in response to an upstream stimulus. Our work highlights how mathematical models can be further developed to understand complex biological mechanisms and suggests ways to connect transcriptional bursting to upstream activation pathways. Heterogeneity in gene expression between cells impacts a wide range of phenotypic outcomes, including differentiation ,2, viralSeveral possible points of control have been proposed for transcriptional bursting, including transcription factor (TF) regulation ,14, polyOne biological context in which the two-state promoter model lacks descriptive detail is in recapitulating the role of TF activation in inducible gene expression. While several studies have assessed how TFs modulate inducible gene expression noise ,28, theyA recent model of transcriptional bursting that includes an additional promoter state to decouple RNAPII pausing from RNAPII recruitment more accurately describes steady-state transcriptional bursting ,19. HereTo explicitly consider the effect of promoter accessibility and RNAPII pausing on transcriptional noise, we explored a previously published three-state promoter model . In thisThis transcriptional cycling model was shown to accurately reflect the biological regulatory mechanisms of burst initiation and pause release for steady-state gene expression . At highTo explore this, we used HIV as a model system. As a retrovirus, HIV integrates into the infected host cell’s genome . AlthougWe first sought to understand the parameter space of the three-state transcriptional cycling model  by examiTo support these qualitative observations, we derived quantitative relationships between parameter ratios and the ratios of promoter states by examining the five ODEs at steady-state. Overall, we found thatBP state , consistBP state . With inUnder steady-state conditions, the fractional distribution of the initial promoter states affects the amount of mRNA produced. We visualized how the promoter accessibility ratio (i.e. BIR:BTR) and the transcriptional cycling ratio (i.e. PPRR:PBR) differentially affect steady-state mRNA production using different heatmap configurations. We first examined the effect of the promoter accessibility ratio  by varyiTo observe the distribution of initial promoter states varied with these ratios, we zoomed in on one inner heat map. First, we fixed PPRR and PBR at values of 10 , and then visually explored how the distribution of initial promoter states varied for a range of promoter accessibility ratios , box. FoF = σ2/μ, where σ2 is the variance and μ is the mean) varied for the same “zoomed in” heatmaps. We observed that the promoter accessibility ratio had a modest influence on the Fano factor, with the highest values generally observed for promoter accessibility ratios < 1  for the same fixed PBR and PPRR as in Next, we examined how varying the transcriptional cycling ratio  contributes to noise and heterogeneity upon PPRR activation. As noted previously, under basal conditions, as the ratio of transcriptional cycling decreases, the fraction of BP increases, while the fraction of UP remains constant , left. UTo generalize our observations, we classified sections of the parameter space as “always on,” “always off,” and “bimodal” , with the lowest promoter accessibility ratio exhibiting the largest increases by 24 hours . As expeFeedback also variably affected activation trajectories initialized across different transcriptional cycling ratios , demonstrating that the chosen feedback parameters can reproduce Tat amplification.We then added Tat positive feedback into the transcriptional cycling model and compared simulation to experimental measurements of transcript distributions in the presence of feedback without further fitting . We founWhen we analyzed the initial promoter states for the selected parameter set for the four integrations, we noted that viral integrations with higher ratios of AcH3:H3 were fit with parameters that exhibited higher ratios of (AP+BP):UP  but thatFinally, we compared our simulations of TNF-activated HIV Tat protein expression over 24 hours to our experimental measurements  hr-1, BIR = [0.005 0.01 0.05 0.1 0.5 1 5 10 50] hr-1, and BTR = [0.005 0.01 0.05 0.1 0.5 1 5 10 50] hr-1. These ranges are up to 10-fold higher and/or lower than the parameter ranges used for our analysis of inducible transcription  as described previously . TrajectTo visualize the differences along the promoter accessibility and the transcriptional cycling ratios, six different parameter combinations were selected. Three represented variations in promoter accessibility , while the other three represented variations in transcriptional cycling  .We fit our model to a previously published experimental data set measured in four Jurkat T cell lines each containing a latent HIV virus integrated at a unique location in the genome . This daWe first limited the parameter space to parameters that meet conditions for bursting transcription, characterized by infrequent transitions from low levels of mRNA production to high levels of mRNA production. Based on our steady-state analysis, this would result in higher levels of BP as compared to AP and BP, and is met with the following condition:The second condition is imposed to reproduce the experimentally observed differences in our T cell populations with latent HIV integrations that are low activating (integrations 8.4 and 4.4) and high activation (integrations 6.6 and 10.6). The high-activating viral integrations have higher levels of histone acetylation at the promoter under basal conditions, while the low-activating viral integrations have higher levels of histone occupancy. To correlate this to our three-state transcriptional cycling model, the low-activating viral integrations should have higher levels of UP as compared to the high-activating viral integrations. We know that the amount of UP is controlled by the BIR:BTR ratio, so we can represent the parameter space as follows:Our third condition is imposed to reproduce the observation that high-activating viral integrations have higher levels of open chromatin, without significant changes to the amount of transcriptional machinery priming as compared to the low-activation viral integrations. We can capture this in our model if AP is greater for high-activating vs. low-activating viral integrations, whereas the amount of BP remains the same. Based on our steady-state analysis of PBR to PPRR, we can capture with the following condition:With these conditions, we then sought to find the parameter space that best matched experimental results. Stochastic simulations were run over a biologically plausible parameter space  Deterministic solution of mRNA counts representing steady state values plotted as a log scale heatmap. Parameter ranges are represented low to high via arrow directionality and correspond to the following sets: PBR = [0.1 0.5 1 5 10 50 100 500] hr-1, PPRR = [0.1 0.5 1 5 10 50 100] hr-1, BIR = [0.005 0.01 0.05 0.1 0.5 1 5 10 50] hr-1, and BTR = [0.005 0.01 0.05 0.1 0.5 1 5 10 50] hr-1. Square inset corresponds to the following parameter set: PBR = 10 hr-1, BIR = 0.1 hr-1, PPRR = [0.5 1 5 10 50] hr-1, and BTR = [0.01 0.05 0.1 0.5 1] hr-1. (E-F) Pie charts of the fractional promoter-state probability (E) and Fano factor (F) at each parameter combination. Values determined by stochastic simulation under basal conditions out to 10 days for 1,000 cells for each parameter combination. Promoter states denoted as UP (blue), AP , and BP (yellow).(A-C) Heat maps of the deterministic steady state solutions for UP, AP, and BP fractional probabilities. Parameter ranges are represented low to high via arrow directionality and correspond to the following sets: PBR = [0.1 0.5 1 5 10 50 100 500] hr(PDF)Click here for additional data file.S2 Fig-1. (B) Average Protein counts at 24 hours. Protein counts were generated through stochastic simulation for 1,000 cells for each parameter combination. Phenotypes of “Always On,” “Bimodal,” and “Always Off” were established by separating out the bimodal population  Fold Change mRNA trajectories for PPRR increases of 0.5 (green), 2 (cerulean), 5 (red), 10 (blue), and 50 (purple). Shaded areas are 95% confidence intervals. PPRR and PBR were set at 10 hr(PDF)Click here for additional data file.S3 Fig-1, BTR = 1 hr-1, PBR = PPRR = 10 hr-1. Data was generated by stochastic simulation for 1,000 cells for each parameter combination. The feedback terms K  and A (amplification factor) were varied over 5 orders of magnitude. (C) Feedback strength calculated for varied K values and plotted versus protein.(A-B) Heatmap of average mRNA at 24 hours (A) and fractional promoter-state probabilities in the initial state (B) for a range of feedback strengths. All other parameters are set to BIR = 0.1 hr(PDF)Click here for additional data file.S4 Fig(A-C) Scatterplots of results from simulation of the three-state transcriptional cycling model without feedback compared to experimental measurements of mRNA distributions from populations of T cells harboring latent HIV integrations and stimulated with TNF with Tat feedback blocked (from ref. 4). Correlation between simulation and experimental data is shown for mRNA average (A), Fano factor (B), and CV (C). Error bars represent 95% bootstrapped confidence intervals.(PDF)Click here for additional data file.S5 Fig(A-C) Scatterplots of results from simulation of the three-state transcriptional cycling model without feedback compared to experimental measurements of mRNA distributions from populations of T cells harboring latent HIV integrations and stimulated with TNF with Tat feedback blocked (from ref. 4). Correlation between simulation and experimental data is shown for mRNA average (A), Fano factor (B), and CV (C). Error bars represent 95% bootstrapped confidence intervals.(PDF)Click here for additional data file.S6 Fig(A-B) Average mRNA counts (A) and Fano factor (B) for the four activation paths across the four HIV integrations for timepoints of 0, 1, 2, 4 and 24 hours. mRNA counts were generated through stochastic simulation for 1,000 cells for each parameter combination. Error bars represent 95% bootstrapped confidence intervals. (C) Percentage of cells above ‘ON’ threshold (set at 250 proteins per cell) at 24 hours.(PDF)Click here for additional data file.